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Posts Tagged ‘Glycolysis’

Pathway Specific Targeting in Anticancer Therapies

Writer and Curator: Larry H. Bernstein, MD, FCAP 

 

7.7 Pathway specific targeting in anticancer therapies

7.7.1 Structural basis for the allosteric inhibitory mechanism of human kidney-type glutaminase (KGA) and its regulation by Raf-Mek-Erk signaling in cancer cell metabolism

7.7.2 Sonic hedgehog (Shh) signaling promotes tumorigenicity and stemness via activation of epithelial-to-mesenchymal transition (EMT) in bladder cancer.

7.7.3 Differential activation of NF-κB signaling is associated with platinum and taxane resistance in MyD88 deficient epithelial ovarian cancer cells

7.7.4 Activation of apoptosis by caspase-3-dependent specific RelB cleavage in anticancer agent-treated cancer cells

7.7.5 Identification of Liver Cancer Progenitors Whose Malignant Progression Depends on Autocrine IL-6 Signaling

7.7.6 Acetylation Stabilizes ATP-Citrate Lyase to Promote Lipid Biosynthesis and Tumor Growth

7.7.7 Monoacylglycerol Lipase Regulates a Fatty Acid Network that Promotes Cancer Pathogenesis

7.7.8 Pirin regulates epithelial to mesenchymal transition and down-regulates EAF/U19 signaling in prostate cancer cells

7.7.9 O-GlcNAcylation at promoters, nutrient sensors, and transcriptional regulation

 

7.7.1 Structural basis for the allosteric inhibitory mechanism of human kidney-type glutaminase (KGA) and its regulation by Raf-Mek-Erk signaling in cancer cell metabolism

Thangavelua, CQ Pana, …, BC Lowa, and J. Sivaramana
Proc Nat Acad Sci 2012; 109(20):7705–7710
http://dx.doi.org:/10.1073/pnas.1116573109

Besides thriving on altered glucose metabolism, cancer cells undergo glutaminolysis to meet their energy demands. As the first enzyme in catalyzing glutaminolysis, human kidney-type glutaminase isoform (KGA) is becoming an attractive target for small molecules such as BPTES [bis-2-(5 phenylacetamido-1, 2, 4-thiadiazol-2-yl) ethyl sulfide], although the regulatory mechanism of KGA remains unknown. On the basis of crystal structures, we reveal that BPTES binds to an allosteric pocket at the dimer interface of KGA, triggering a dramatic conformational change of the key loop (Glu312-Pro329) near the catalytic site and rendering it inactive. The binding mode of BPTES on the hydrophobic pocket explains its specificity to KGA. Interestingly, KGA activity in cells is stimulated by EGF, and KGA associates with all three kinase components of the Raf-1/Mek2/Erk signaling module. However, the enhanced activity is abrogated by kinase-dead, dominant negative mutants of Raf-1 (Raf-1-K375M) and Mek2 (Mek2-K101A), protein phosphatase PP2A, and Mek-inhibitor U0126, indicative of phosphorylation-dependent regulation. Furthermore, treating cells that coexpressed Mek2-K101A and KGA with suboptimal level of BPTES leads to synergistic inhibition on cell proliferation. Consequently, mutating the crucial hydrophobic residues at this key loop abrogates KGA activity and cell proliferation, despite the binding of constitutive active Mek2-S222/226D. These studies therefore offer insights into (i) allosteric inhibition of KGA by BPTES, revealing the dynamic nature of KGA’s active and inhibitory sites, and (ii) cross-talk and regulation of KGA activities by EGF-mediated Raf-Mek-Erk signaling. These findings will help in the design of better inhibitors and strategies for the treatment of cancers addicted with glutamine metabolism.

The Warburg effect in cancer biology describes the tendency of cancer cells to take up more glucose than most normal cells, despite the availability of oxygen (12). In addition to altered glucose metabolism, glutaminolysis (catabolism of glutamine to ATP and lactate) is another hallmark of cancer cells (23). In glutaminolysis, mitochondrial glutaminase catalyzes the conversion of glutamine to glutamate (4), which is further catabolized in the Krebs cycle for the production of ATP, nucleotides, certain amino acids, lipids, and glutathione (25).

Humans express two glutaminase isoforms: KGA (kidney-type) and LGA (liver-type) from two closely related genes (6). Although KGA is important for promoting growth, nothing is known about the precise mechanism of its activation or inhibition and how its functions are regulated under physiological or pathophysiological conditions. Inhibition of rat KGA activity by antisense mRNA results in decreased growth and tumorigenicity of Ehrlich ascites tumor cells (7), reduced level of glutathione, and induced apoptosis (8), whereas Myc, an oncogenic transcription factor, stimulates KGA expression and glutamine metabolism (5). Interestingly, direct suppression of miR23a and miR23b (9) or activation of TGF-β (10) enhances KGA expression. Similarly, Rho GTPase that controls cytoskeleton and cell division also up-regulates KGA expression in an NF-κB–dependent manner (11). In addition, KGA is a substrate for the ubiquitin ligase anaphase-promoting complex/cyclosome (APC/C)-Cdh1, linking glutaminolysis to cell cycle progression (12). In comparison, function and regulation of LGA is not well studied, although it was recently shown to be linked to p53 pathway (1314). Although intense efforts are being made to develop a specific KGA inhibitor such as BPTES [bis-2-(5-phenylacetamido-1, 2, 4-thiadiazol-2-yl) ethyl sulfide] (15), its mechanism of inhibition and selectivity is not yet understood. Equally important is to understand how KGA function is regulated in normal and cancer cells so that a better treatment strategy can be considered.

The previous crystal structures of microbial (Mglu) and Escherichia coli glutaminases show a conserved catalytic domain of KGA (1617). However, detailed structural information and regulation are not available for human glutaminases especially the KGA, and this has hindered our strategies to develop inhibitors. Here we report the crystal structure of the catalytic domain of human apo KGA and its complexes with substrate (L-glutamine), product (L-glutamate), BPTES, and its derived inhibitors. Further, Raf-Mek-Erk module is identified as the regulator of KGA activity. Although BPTES is not recognized in the active site, its binding confers a drastic conformational change of a key loop (Glu312-Pro329), which is essential in stabilizing the catalytic pocket. Significantly, EGF activates KGA activity, which can be abolished by the kinase-dead, dominant negative mutants of Mek2 (Mek2-K101A) or its upstream activator Raf-1 (Raf-1-K375M), which are the kinase components of the growth-promoting Raf-Mek2-Erk signaling node. Furthermore, coexpression of phosphatase PP2A and treatment with Mek-specific inhibitor or alkaline phosphatase all abolished enhanced KGA activity inside the cells and in vitro, indicating that stimulation of KGA is phosphorylation dependent. Our results therefore provide mechanistic insights into KGA inhibition by BPTES and its regulation by EGF-mediated Raf-Mek-Erk module in cell growth and possibly cancer manifestation.

Structures of cKGA and Its Complexes with L-Glutamine and L-Glutamate.
The human KGA consists of 669 amino acids. We refer to Ile221-Leu533 as the catalytic domain of KGA (cKGA) (Fig. 1A). The crystal structures of the apo cKGA and in complex with L-glutamine or L-glutamate were determined (Table S1). The structure of cKGA has two domains with the active site located at the interface. Domain I comprises (Ile221-Pro281 and Cys424 -Leu533) of a five-stranded anti-parallel β-sheet (β2↓β1↑β5↓β4↑β3↓) surrounded by six α-helices and several loops. The domain II (Phe282-Thr423) mainly consists of seven α-helices. L-Glutamine/L-glutamate is bound in the active site cleft (Fig. 1B and Fig. S1B). Overall the active site is highly basic, and the bound ligand makes several hydrogen-bonding contacts to Gln285, Ser286, Asn335, Glu381, Asn388, Tyr414, Tyr466, and Val484 (Fig. 1C and Fig. S1C), and these residues are highly conserved among KGA homologs (Fig. S1D). Notably, the putative serine-lysine catalytic dyad (286-SCVK-289), corresponding to the SXXK motif of class D β-lactamase (17), is located in close proximity to the bound ligand. In the apo structure, two water molecules were located in the active site, one of them being displaced by glutamine in the substrate complex. The substrate side chain is within hydrogen-bonding distance (2.9 Å) to the active site Ser286. Other key residues involved in catalysis, such as Lys289, Tyr414, and Tyr466, are in the vicinity of the active site. Lys289 is within hydrogen-bonding distance to Ser286 (3.1 Å) and acts as a general base for the nucleophilic attack by accepting the proton from Ser286. Tyr466, which is close to Ser286 and in hydrogen-bonding contact (3.2 Å) with glutamine, is involved in proton transfer during catalysis. Moreover, the carbonyl oxygen of the glutamine is hydrogen-bonded with the main chain amino groups of Ser286 and Val484, forming the oxyanion hole. Thus, we propose that in addition to the putative catalytic dyad (Ser286 XX Lys289), Tyr466 could play an important role in the catalysis (Fig. 1Cand Fig. S2).

structure of the cKGA-L-glutamine complex

structure of the cKGA-L-glutamine complex

http://www.pnas.org/content/109/20/7705/F1.medium.gif

Fig. 1.  Schematic view and structure of the cKGA-L-glutamine complex. (A) Human KGA domains and signature motifs (refer to Fig. S1A for details). (B) Structure of the of cKGA and bound substrate (L-glutamine) is shown as a cyan stick. (C) Fourier 2Fo-Fc electron density map (contoured at 1 σ) for L-glutamine, that makes hydrogen bonds with active site residues are shown.

Allosteric Binding Pocket for BPTES. The chemical structure of BPTES has an internal symmetry, with two exactly equivalent parts including a thiadiazole, amide, and a phenyl group (Fig. S3A), and it equally interacts with each monomer. The thiadiazole group and the aliphatic linker are well buried in a hydrophobic cluster that consists of Leu321, Phe322, Leu323, and Tyr394 from both monomers, which forms the allosteric pocket (Fig. 2 B–E). The side chain of Phe322 is found at the bottom of the allosteric pocket. The phenyl-acetamido moiety of BPTES is partially exposed on the loop (Asn324-Glu325), where it interacts with Phe318, Asn324, and the aliphatic part of the Glu325 side chain. On the basis of our observations we synthesized a series of BPTES-derived inhibitors (compounds2–5) (Fig. S3 AF and SI Results) and solved their cocrystal structure of compounds 2–4. Similar to BPTES, compounds 24 all resides within the hydrophobic cluster of the allosteric pocket (Fig. S3 CF).

Fig. 2. Structure of cKGA: BPTES complex and the allosteric binding mode of BPTES.

Allosteric Binding of BPTES Triggers Major Conformational Change in the Key Loop Near the Active Site.  The overall structure of these inhibitor complexes superimposes well with apo cKGA. However, a major conformational change at the Glu312 to Pro329 loop was observed in the BPTES complex (Fig. 2F). The most conformational changes of the backbone atoms that moved away from the active site region are found at the center of the loop (Leu316-Lys320). The backbone of the residues Phe318 and Asn319 is moved ≈9 Å and ≈7 Å, respectively, compared with the apo structure, whereas the side chain of these residues moved ≈14 Å and ≈12 Å, respectively. This loop rearrangement in turn brings Phe318 closer to the phenyl group of the inhibitor and forms the inhibitor binding pocket, whereas in the apo structure the same loop region (Leu316-Lys320) was found to be adjacent to the active site and forms a closed conformation of the active site.

Binding of BPTES Stabilizes the Inactive Tetramers of cKGA.  To understand the role of oligomerization in KGA function, dimers and tetramers of cKGA were generated using the symmetry-related monomers (Fig. 2 A–E and Fig. S4 D and E). The dimer interface in the cKGA: BPTES complex is formed by residues from the helix Asp386-Lys398 of both monomers and involves hydrogen bonding, salt bridges, and hydrophobic interactions (Phe389, Ala390, Tyr393, and Tyr394), besides two sulfate ions located in the interface (Fig. 2E). The dimers are further stabilized by binding of BPTES, where it binds to loop residues (Glu312-Pro329) and Tyr394 from both monomers (Fig. 2 D and E). Similarly, residues from Lys311-Asn319 loop and Arg454, His461, Gln471, and Asn529-Leu533 are involved in the interface with neighboring monomers to form the tetramer in the BPTES complex.

BPTES Induces Allosteric Conformational Changes That Destabilize Catalytic Function of KGA

Fig. 3A shows that 293T cells overexpressing KGA produced higher level of glutamate compared with the vector control cells. Most significantly, all of these mutants, except Phe322Ala, greatly diminished the KGA activity.

Fig. 3. Mutations at allosteric loop and BPTES binding pocket abrogate KGA activity and BPTES sensitivity.

Raf-Mek-Erk Signaling Module Regulates KGA Activity. Because KGA supports cell growth and proliferation, we first validated that treatment of cells with BPTES indeed inhibits KGA activity and cell proliferation (Fig. S5 A–D and SI Results). Next, as cells respond to various physiological stimuli to regulate their metabolism, with many of the metabolic enzymes being the primary targets of modulation (18), we examined whether KGA activity can be regulated by physiological stimuli, in particular EGF, which is important for cell growth and proliferation. Cells overexpressing KGA were made quiescent and then stimulated with EGF for various time points. Fig. 4A shows that the basal KGA activity remained unchanged 30 min after EGF stimulation, but the activity was substantially enhanced after 1 h and then gradually returned to the basal level after 4 h. Because EGF activates the Raf-Mek-Erk signaling module (19), treatment of cells with Mek-specific inhibitor U0126 could block the enhanced KGA activity with parallel inhibition of Erk phosphorylation (Fig. 4A). Interestingly, such Mek-induced KGA activity is specific to EGF and lysophosphatidic acid (LPA) but not with other growth factors, such as PDGF, TGF-β, and basic FGF (bFGF), despite activation of Mek-Erk by bFGF (Fig. S6A).

The results show that KGA could interact equally well with the wild-type or mutant forms of Raf-1 and Mek2 (Fig. 4C). Importantly, endogenous Raf-1 or Erk1/2, including the phosphorylated Erk1/2 (Fig. 4 C and D), could be detected in the KGA complex. Taken together, these results indicate that the activity of KGA is directly regulated by Raf-Mek-Erk downstream of EGF receptor. To further show that Mek2-enhanced KGA activity requires both the kinase activity of Mek2 and the core residues for KGA catalysis, wild-type or triple mutant (Leu321Ala/Phe322Ala/Leu323Ala) of KGA was coexpressed with dominant negative Mek2-KA or the constitutive active Mek2-SD and their KGA activities measured. The result shows that the presence of Mek2-KA blocks KGA activity, whereas the triple mutant still remains inert even in the presence of the constitutively active Mek2 (Fig. 4E), and despite Mek2 binding to the KGA triple mutant (Fig. S7B). Consequently, expressing triple mutant did not support cell proliferation as well as the wild-type control (Fig. S7C).

Fig. 4. EGFR-Raf-Mek-Erk signaling stimulates KGA activity.

When cells expressing both KGA and Mek2-K101A were treated with subthreshold levels of BPTES, there was a synergistic reduction in cell proliferation (Fig. S6C and SI Results). Lastly, to determine whether regulation of KGA by Raf-Mek-Erk depends on its phosphorylation status, cells were transfected with KGA with or without the protein phosphatase PP2A and assayed for the KGA activity. PP2A is a ubiquitous and conserved serine/threonine phosphatase with broad substrate specificity. The results indicate that KGA activity was reduced down to the basal level in the presence of PP2A (Fig. 5A). Coimmunoprecipitation study also revealed that KGA interacts with PP2A (Fig. 5B), suggesting a negative feedback regulation by this protein phosphatase. Furthermore, treatment of immunoprecipitated and purified KGA with calf-intestine alkaline phosphatase (CIAP) almost completely abolished the KGA activity in vitro (Fig. S6D). Taken together, these results indicate that KGA activity is regulated by Raf-Mek2, and KGA activation by EGF could be part of the EGF-stimulated Raf-Mek-Erk signaling program in controlling cell growth and proliferation (Fig. 5C).

KGA activity is regulated by phosphorylation

KGA activity is regulated by phosphorylation

http://www.pnas.org/content/109/20/7705/F5.medium.gif

Fig. 5. KGA activity is regulated by phosphorylation. (C) Schematic model depicting the synergistic cross-talk between KGA-mediated glutaminolysis and EGF-activated Raf-Mek-Erk signaling. Exogenous glutamine can be transported across the membrane and converted to glutamate by glutaminase (KGA), thus feeding the metabolite to the ATP-producing tricarboxylic acid (TCA) cycle. This process can be stimulated by EGF receptor-mediated Raf-Mek-Erk signaling via their phosphorylation-dependent pathway, as evidenced by the inhibition of KGA activity by the kinase-dead and dominant negative mutants of Raf-1 (Raf-1-K375M) and Mek2 (Mek2-K101A), protein phosphatase PP2A, and Mek-specific inhibitor U0126. Consequently, inhibiting KGA with BPTES and blocking Raf-Mek pathway with Mek2-K101A provide a synergistic inhibition on cell proliferation.

Small-molecule inhibitors that target glutaminase activity in cancer cells are under development. Earlier efforts targeting glutaminase using glutamine analogs have been unsuccessful owing to their toxicities (2). BPTES has attracted much attention as a selective, nontoxic inhibitor of KGA (15), and preclinical testing of BPTES toward human cancers has just begun (20). BPTES selectively suppresses the growth of glioma cells (21) and inhibits the growth of lymphoma tumor growth in animal model studies (22). Wang et al. (11) reported a small molecule that targets glutaminase activity and oncogenic transformation. Despite extensive studies, nothing is known about the structural and molecular basis for KGA inhibitory mechanisms and how their function is regulated during normal and cancer cell metabolism. Such limited information impedes our effort in producing better generations of inhibitors for better treatment regimens.

Comparison of the complex structures with apo cKGA structure, which has well-defined electron density for the key loop, we provide the atomic view of an allosteric binding pocket for BPTES and elucidate the inhibitory mechanism of KGA by BPTES. The key residues of the loop (Glu312-Pro329) undergo major conformational changes upon binding of BPTES. In addition, structure-based mutagenesis studies suggest that this loop is essential for stabilizing the active site. Therefore, by binding in an allosteric pocket, BPTES inhibits the enzymatic activity of KGA through (i) triggering a major conformational change on the key residues that would normally be involved in stabilizing the active sites and regulating its enzymatic activity; and (ii) forming a stable inactive tetrameric KGA form. Our findings are further supported by two very recent reports on KGA isoform (GAC) (2324), although these studies lack full details owing to limitation of their electron density maps. BPTES is specific to KGA but not to LGA (15). Sequence comparison of KGA with LGA (Fig. S8A) reveals two unique residues on KGA, Phe318 and Phe322, which upon mutation to LGA counterparts, become resistant to BPTES. Thus, our study provides the molecular basis of BPTES specificity.

7.7.2 Sonic hedgehog (Shh) signaling promotes tumorigenicity and stemness via activation of epithelial-to-mesenchymal transition (EMT) in bladder cancer.

Islam SS, Mokhtari RB, Noman AS, …, van der Kwast T, Yeger H, Farhat WA.
Molec Carcinogenesis mar 2015; 54(5). http://dx.doi.org:/10.1002/mc.22300

shh sonic hedgehog signaling pathway nri2151-f1

shh sonic hedgehog signaling pathway nri2151-f1

Activation of the sonic hedgehog (Shh) signaling pathway controls tumorigenesis in a variety of cancers. Here, we show a role for Shh signaling in the promotion of epithelial-to-mesenchymal transition (EMT), tumorigenicity, and stemness in the bladder cancer. EMT induction was assessed by the decreased expression of E-cadherin and ZO-1 and increased expression of N-cadherin. The induced EMT was associated with increased cell motility, invasiveness, and clonogenicity. These progression relevant behaviors were attenuated by treatment with Hh inhibitors cyclopamine and GDC-0449, and after knockdown by Shh-siRNA, and led to reversal of the EMT phenotype. The results with HTB-9 were confirmed using a second bladder cancer cell line, BFTC905 (DM). In a xenograft mouse model TGF-β1 treated HTB-9 cells exhibited enhanced tumor growth. Although normal bladder epithelial cells could also undergo EMT and upregulate Shh with TGF-β1 they did not exhibit tumorigenicity. The TGF-β1 treated HTB-9 xenografts showed strong evidence for a switch to a more stem cell like phenotype, with functional activation of CD133, Sox2, Nanog, and Oct4. The bladder cancer specific stem cell markers CK5 and CK14 were upregulated in the TGF-β1 treated xenograft tumor samples, while CD44 remained unchanged in both treated and untreated tumors. Immunohistochemical analysis of 22 primary human bladder tumors indicated that Shh expression was positively correlated with tumor grade and stage. Elevated expression of Ki-67, Shh, Gli2, and N-cadherin were observed in the high grade and stage human bladder tumor samples, and conversely, the downregulation of these genes were observed in the low grade and stage tumor samples. Collectively, this study indicates that TGF-β1-induced Shh may regulate EMT and tumorigenicity in bladder cancer. Our studies reveal that the TGF-β1 induction of EMT and Shh is cell type context dependent. Thus, targeting the Shh pathway could be clinically beneficial in the ability to reverse the EMT phenotype of tumor cells and potentially inhibit bladder cancer progression and metastasis

Sonic_hedgehog_pathway

Sonic_hedgehog_pathway

7.7.3 Differential activation of NF-κB signaling is associated with platinum and taxane resistance in MyD88 deficient epithelial ovarian cancer cells

Gaikwad SM, Thakur B, Sakpal A, Singh RK, Ray P.
Int J Biochem Cell Biol. 2015 Apr; 61:90-102
http://dx.doi.org:/10.1016/j.biocel.2015.02.001

Development of chemoresistance is a major impediment to successful treatment of patients suffering from epithelial ovarian carcinoma (EOC). Among various molecular factors, presence of MyD88, a component of TLR-4/MyD88 mediated NF-κB signaling in EOC tumors is reported to cause intrinsic paclitaxel resistance and poor survival. However, 50-60% of EOC patients do not express MyD88 and one-third of these patients finally relapses and dies due to disease burden. The status and role of NF-κB signaling in this chemoresistant MyD88(negative) population has not been investigated so far. Using isogenic cellular matrices of cisplatin, paclitaxel and platinum-taxol resistant MyD88(negative) A2780 ovarian cancer cells expressing a NF-κB reporter sensor, we showed that enhanced NF-κB activity was required for cisplatin but not for paclitaxel resistance. Immunofluorescence and gel mobility shift assay demonstrated enhanced nuclear localization of NF-κB and subsequent binding to NF-κB response element in cisplatin resistant cells. The enhanced NF-κB activity was measurable from in vivo tumor xenografts by dual bioluminescence imaging. In contrast, paclitaxel and the platinum-taxol resistant cells showed down regulation in NF-κB activity. Intriguingly, silencing of MyD88 in cisplatin resistant and MyD88(positive) TOV21G and SKOV3 cells showed enhanced NF-κB activity after cisplatin but not after paclitaxel or platinum-taxol treatments. Our data thus suggest that NF-κB signaling is important for maintenance of cisplatin resistance but not for taxol or platinum-taxol resistance in absence of an active TLR-4/MyD88 receptor mediated cell survival pathway in epithelial ovarian carcinoma.

7.7.4 Activation of apoptosis by caspase-3-dependent specific RelB cleavage in anticancer agent-treated cancer cells

Kuboki MIto ASimizu SUmezawa K.
Biochem Biophys Res Commun. 2015 Jan 16; 456(3):810-4
http://dx.doi.org:/10.1016/j.bbrc.2014.12.024

Activation of caspase 3 and caspase-dependent apoptosis  nrmicro2071-f1

Activation of caspase 3 and caspase-dependent apoptosis nrmicro2071-f1

Highlights

  • We have prepared RelB mutants that are resistant to caspase 3-induced scission.
  • Vinblastine induced caspase 3-dependent site-specific RelB cleavage in cancer cells.
  • Cancer cells expressing cleavage-resistant RelB showed less sensitivity to vinblastine.
  • Caspase 3-induced RelB cleavage may provide positive feedback mechanism in apoptosis.

DTCM-glutarimide (DTCM-G) is a newly found anti-inflammatory agent. In the course of experiments with lymphoma cells, we found that DTCM-G induced specific RelB cleavage. Anticancer agent vinblastine also induced the specific RelB cleavage in human fibrosarcoma HT1080 cells. The site-directed mutagenesis analysis revealed that the Asp205 site in RelB was specifically cleaved possibly by caspase-3 in vinblastine-treated HT1080 cells. Moreover, the cells stably overexpressing RelB Asp205Ala were resistant to vinblastine-induced apoptosis. Thus, the specific Asp205 cleavage of RelB by caspase-3 would be involved in the apoptosis induction by anticancer agents, which would provide the positive feedback mechanism.

apoptotic-caspases-control-microglia-activation-cdd2011107f3

apoptotic-caspases-control-microglia-activation-cdd2011107f3

 

 

7.7.5 Identification of Liver Cancer Progenitors Whose Malignant Progression Depends on Autocrine IL-6 Signaling

He GDhar DNakagawa HFont-Burgada JOgata HJiang Y, et al.
Cell. 2013 Oct 10; 155(2):384-96
http://dx.doi.org/10.1016%2Fj.cell.2013.09.031

Il-6 signaling in cancer cells

Il-6 signaling in cancer cells

Hepatocellular carcinoma (HCC) is a slowly developing malignancy postulated to evolve from pre-malignant lesions in chronically damaged livers. However, it was never established that premalignant lesions actually contain tumor progenitors that give rise to cancer. Here, we describe isolation and characterization of HCC progenitor cells (HcPCs) from different mouse HCC models. Unlike fully malignant HCC, HcPCs give rise to cancer only when introduced into a liver undergoing chronic damage and compensatory proliferation. Although HcPCs exhibit a similar transcriptomic profile to bipotential hepatobiliary progenitors, the latter do not give rise to tumors. Cells resembling HcPCs reside within dysplastic lesions that appear several months before HCC nodules. Unlike early hepatocarcinogenesis, which depends on paracrine IL-6 production by inflammatory cells, due to upregulation of LIN28 expression, HcPCs had acquired autocrine IL-6 signaling that stimulates their in vivo growth and malignant progression. This may be a general mechanism that drives other IL-6-producing malignancies.

Clonal evolution and selective pressure may cause some descendants of the initial progenitor to cross the bridge of no return and form a premalignant lesion. Cancer genome sequencing indicates that most cancers require at least five genetic changes to evolve (Wood et al., 2007). It has been difficult to isolate and propagate cancer progenitors prior to detection of tumor masses. Further, it is not clear whether cancer progenitors are the precursors for the  cancer stem cells (CSCs)isolated from cancers. An answer to these critical questions depends on identification and isolation of cancer progenitors, which may also enable definition of molecular markers and signaling pathways suitable for early detection and treatment.

Hepatocellular carcinoma (HCC), the end product of chronic liver diseases, requires several decades to evolve (El-Serag, 2011). It is the third most deadly and fifth most common cancer worldwide, and in the United States its incidence has doubled in the past two decades. Furthermore, 8% of the world’s population are chronically infected with hepatitis B or C viruses (HBV and HCV) and are at a high risk of new HCC development (El-Serag, 2011). Up to 5% of HCV patients will develop HCC in their lifetime, and the yearly HCC incidence in patients with cirrhosis is 3%–5%. These tumors may arise from premalignant lesions, ranging from dysplastic foci to dysplastic hepatocyte nodules that are often seen in damaged and cirrhotic livers and are more proliferative than the surrounding parenchyma (Hytiroglou et al., 2007). There is no effective treatment for HCC and, upon diagnosis, most patients with advanced disease have a remaining lifespan of 4–6 months. Premalignant lesions, called foci of altered hepatocytes (FAH), were described in chemically induced HCC models (Pitot, 1990), but it was questioned whether these lesions harbor tumor progenitors or result from compensatory proliferation (Sell and Leffert, 2008). The aim of this study was to determine whether HCC progenitor cells (HcPCs) exist and if so, to isolate these cells and identify some of the signaling networks that are involved in their maintenance and progression.

We now describe HcPC isolation from mice treated with the procarcinogen diethyl nitrosamine (DEN), which induces poorly differentiated HCC nodules within 8 to 9 months (Verna et al., 1996). The use of a chemical carcinogen is justified because the finding of up to 121 mutations per HCC genome suggests that carcinogens may be responsible for human HCC induction (Guichard et al., 2012). Furthermore, 20%–30% of HCC, especially in HBV-infected individuals, evolve in noncirrhotic livers (El-Serag, 2011). Nonetheless, we also isolated HcPCs fromTak1Δhep mice, which develop spontaneous HCC as a result of progressive liver damage, inflammation, and fibrosis caused by ablation of TAK1 (Inokuchi et al., 2010). Although the etiology of each model is distinct, both contain HcPCs that express marker genes and signaling pathways previously identified in human HCC stem cells (Marquardt and Thorgeirsson, 2010) long before visible tumors are detected. Furthermore, DEN-induced premalignant lesions and HcPCs exhibit autocrine IL-6 production that is critical for tumorigenic progression. Circulating IL-6 is a risk indicator in several human pathologies and is strongly correlated with adverse prognosis in HCC and cholangiocarcinoma (Porta et al., 2008Soresi et al., 2006). IL-6 produced by in-vitro-induced CSCs was suggested to be important for their maintenance (Iliopoulos et al., 2009). Little is known about the source of IL-6 in HCC.

DEN-Induced Collagenase-Resistant Aggregates of HCC Progenitors

A single intraperitoneal (i.p.) injection of DEN into 15-day-old BL/6 mice induces HCC nodules first detected 8 to 9 months later. However, hepatocytes prepared from macroscopically normal livers 3 months after DEN administration already contain cells that progress to HCC when transplanted into the permissive liver environment of MUP-uPA mice (He et al., 2010), which express urokinase plasminogen activator (uPA) from a mouse liver-specific major urinary protein (MUP) promoter and undergo chronic liver damage and compensatory proliferation (Rhim et al., 1994). HCC markers such as α fetoprotein (AFP), glypican 3 (Gpc3), and Ly6D, whose expression in mouse liver cancer was reported (Meyer et al., 2003), were upregulated in aggregates from DEN-treated livers, but not in nonaggregated hepatocytes or aggregates from control livers (Figure S1A). Using 70 μm and 40 μm sieves, we separated aggregated from nonaggregated hepatocytes (Figure 1A) and tested their tumorigenic potential by transplantation into MUP-uPA mice (Figure 1B). To facilitate transplantation, the aggregates were mechanically dispersed and suspended in Dulbecco’s modified Eagle’s medium (DMEM). Five months after intrasplenic (i.s.) injection of 104 viable cells, mice receiving cells from aggregates developed about 18 liver tumors per mouse, whereas mice receiving nonaggregated hepatocytes developed less than 1 tumor each (Figure 1B). The tumors exhibited typical trabecular HCC morphology and contained cells that abundantly express AFP (Figure S1B).

Only liver tumors were formed by the transplanted cells. Other organs, including the spleen into which the cells were injected, remained tumor free (Figure 1B), suggesting that HcPCs progress to cancer only in the proper microenvironment. Indeed, no tumors appeared after HcPC transplantation into normal BL/6 mice. But, if BL/6 mice were first treated with retrorsine (a chemical that permanently inhibits hepatocyte proliferation [Laconi et al., 1998]), intrasplenically transplanted with HcPC-containing aggregates, and challenged with CCl4 to induce liver injury and compensatory proliferation (Guo et al., 2002), HCCs readily appeared (Figure 1C). CCl4 omission prevented tumor development. Notably, MUP-uPA or CCl4-treated livers are fragile, rendering direct intrahepatic transplantation difficult. CCl4-induced liver damage, especially within a male liver, generates a microenvironment that drives HcPC proliferation and malignant progression. To examine this point, we transplanted GFP-labeled HcPC-containing aggregates into retrorsine-treated BL/6 mice and examined their ability to proliferate with or without subsequent CCl4 treatment. Indeed, the GFP+ cells formed clusters that grew in size only in CCl4-treated host livers (Figure S1E). Omission of CC14 prevented their expansion.

Because CD44 is expressed by HCC stem cells (Yang et al., 2008Zhu et al., 2010), we dispersed the aggregates and separated CD44+ from CD44 cells and transplanted both into MUP-uPA mice. Whereas as few as 103 CD44+ cells gave rise to HCCs in 100% of recipients, no tumors were detected after transplantation of CD44 cells (Figure 1E). Remarkably, 50% of recipients developed at least one HCC after receiving as few as 102 CD44+ cells.

HcPC-Containing Aggregates in Tak1Δhep Mice

We applied the same HcPC isolation protocol to Tak1Δhep mice, which develop HCC of different etiology from DEN-induced HCC. Importantly, Tak1Δhep mice develop HCC as a consequence of chronic liver injury and fibrosis without carcinogen or toxicant exposure (Inokuchi et al., 2010). Indeed, whole-tumor exome sequencing revealed that DEN-induced HCC contained about 24 mutations per 106 bases (Mb) sequenced, with B-RafV637E being the most recurrent, whereas 1.4 mutations per Mb were detected inTak1Δhep HCC’s exome (Table S1). By contrast, Tak1Δhep HCC exhibited gene copy number changes. HCC developed in 75% of MUP-uPA mice that received dispersed Tak1Δhep aggregates, but no tumors appeared in mice receiving nonaggregated Tak1Δhep or totalTak1f/f hepatocytes (Figure 2B). bile duct ligation (BDL) or feeding with 3,5-dicarbethoxy-1,4-dihydrocollidine (DDC), treatments that cause cholestatic liver injuries and oval cell expansion (Dorrell et al., 2011), did increase the number of small hepatocytic cell aggregates (Figure S2A). Nonetheless, no tumors were observed 5 months after injection of such aggregates into MUP-uPA mice (Figure S2B). Thus, not all hepatocytic aggregates contain HcPCs, and HcPCs only appear under tumorigenic conditions.

The HcPC Transcriptome Is Similar to that of HCC and Oval Cells

To determine the relationship between DEN-induced HcPCs, normal hepatocytes, and fully transformed HCC cells, we analyzed the transcriptomes of aggregated and nonaggregated hepatocytes from male littermates 5 months after DEN administration, HCC epithelial cells from DEN-induced tumors, and normal hepatocytes from age- and gender-matched littermate controls. Clustering analysis distinguished the HCC samples from other samples and revealed that the aggregated hepatocyte samples did not cluster with each other but rather with nonaggregated hepatocytes derived from the same mouse (Figure S3A). 57% (583/1,020) of genes differentially expressed in aggregated relative to nonaggregated hepatocytes are also differentially expressed in HCC relative to normal hepatocytes (Figure 3B, top), a value that is highly significant (p < 7.13 × 10−243). More specifically, 85% (494/583) of these genes are overexpressed in both HCC and HcPC-containing aggregates (Figure 3B, bottom table). Thus, hepatocyte aggregates isolated 5 months after DEN injection contain cells that are related in their gene expression profile to HCC cells isolated from fully developed tumor nodules.

Figure 3 Aggregated Hepatocytes Exhibit an Altered Transcriptome Similar to that of HCC Cells

We examined which biological processes or cellular compartments were significantly overrepresented in the induced or repressed genes in both pairwise comparisons (Gene Ontology Analysis). As expected, processes and compartments that were enriched in aggregated hepatocytes relative to nonaggregated hepatocytes were almost identical to those that were enriched in HCC relative to normal hepatocytes (Figure 3C). Several human HCC markers, including AFP, Gpc3 and H19, were upregulated in aggregated hepatocytes (Figures 3D and 3E). Aggregated hepatocytes also expressed more Tetraspanin 8 (Tspan8), a cell-surface glycoprotein that complexes with integrins and is overexpressed in human carcinomas (Zöller, 2009). Another cell-surface molecule highly expressed in aggregated cells is Ly6D (Figures 3D and 3E). Immunofluorescence (IF) analysis revealed that Ly6D was undetectable in normal liver but was elevated in FAH and ubiquitously expressed in most HCC cells (Figure S3C). A fluorescent-labeled Ly6D antibody injected into HCC-bearing mice specifically stained tumor nodules (Figure S3D). Other cell-surface molecules that were upregulated in aggregated cells included syndecan 3 (Sdc3), integrin α 9 (Itga9), claudin 5 (Cldn5), and cadherin 5 (Cdh5) (Figure 3D). Aggregated hepatocytes also exhibited elevated expression of extracellular matrix proteins (TIF3 and Reln1) and a serine protease inhibitor (Spink3). Elevated expression of such proteins may explain aggregate formation. Aggregated hepatocytes also expressed progenitor cell markers, including the epithelial cell adhesion molecule (EpCAM) (Figure 3E) and Dlk1 (Figure 3D). We compared the HcPC and HCC (Figure 3A) to the transcriptome of DDC-induced oval cells (Shin et al., 2011). This analysis revealed a striking similarity between the HCC, HcPC, and the oval cell transcriptomes (Figure S3B). Despite these similarities, some genes that were upregulated in HcPC-containing aggregates and HCC were not upregulated in oval cells. Such genes may account for the tumorigenic properties of HcPC and HCC.

Figure 4  DEN-Induced HcPC Aggregates Express Pathways and Markers Characteristic of HCC and Hepatobiliary Stem Cells

We examined the aggregates for signaling pathways and transcription factors involved in hepatocarcinogenesis. Many aggregated cells were positive for phosphorylated c-Jun and STAT3 (Figure 4A), transcription factors involved in DEN-induced hepatocarcinogenesis (Eferl et al., 2003He et al., 2010). Sox9, a transcription factor that marks hepatobiliary progenitors (Dorrell et al., 2011), was also expressed by many of the aggregated cells, which were also positive for phosphorylated c-Met (Figure 4A), a receptor tyrosine kinase that is activated by hepatocyte growth factor (HGF) and is essential for liver development (Bladt et al., 1995) and hepatocarcinogenesis (Wang et al., 2001). Few of the nonaggregated hepatocytes exhibited activation of these signaling pathways. Despite different etiology, HcPC-containing aggregates from Tak1Δhep mice exhibit upregulation of many of the same markers and pathways that are upregulated in DEN-induced HcPC-containing aggregates. Flow cytometry confirmed enrichment of CD44+ cells as well as CD44+/CD90+ and CD44+/EpCAM+ double-positive cells in the HcPC-containing aggregates from either DEN-treated or Tak1Δhep livers (Figure S4B).

HcPC-Containing Aggregates Originate from Premalignant Dysplastic Lesions

FAH are dysplastic lesions occurring in rodent livers exposed to hepatic carcinogens (Su et al., 1990). Similar lesions are present in premalignant human livers (Su et al., 1997). Yet, it is still debated whether FAH correspond to premalignant lesions or are a reaction to liver injury that does not lead to cancer (Sell and Leffert, 2008). In DEN-treated males, FAH were detected as early as 3 months after DEN administration (Figure 5A), concomitant with the time at which HcPC-containing aggregates were detected. In females, FAH development was delayed. FAH contained cells positive for the same progenitor cell markers and activated signaling pathways present in HcPC-containing aggregates, including AFP, CD44, and EpCAM (Figure 5C). FAH also contained cells positive for activated STAT3, c-Jun, and PCNA (Figure 5C).

HcPCs Exhibit Autocrine IL-6 Expression Necessary for HCC Progression

In situ hybridization (ISH) and immunohistochemistry (IHC) revealed that DEN-induced FAH contained IL-6-expressing cells (Figures 6A, 6B, and S5), and freshly isolated DEN-induced aggregates contained more IL-6 messenger RNA (mRNA) than nonaggregated hepatocytes (Figure 6C). We examined several factors that control IL-6 expression and found that LIN28A and B were significantly upregulated in HcPCs and HCC (Figures 6D and 6E). LIN28-expressing cells were also detected within FAH (Figure 6F). As reported (Iliopoulos et al., 2009), knockdown of LIN28B in cultured HcPC or HCC cell lines decreased IL-6 expression (Figure 6G). LIN28 exerts its effects through downregulation of the microRNA (miRNA) Let-7 (Iliopoulos et al., 2009).

Figure 6  Liver Premalignant Lesions and HcPCs Exhibit Elevated IL-6 and LIN28 Expression

Figure 7  HCC Growth Depends on Autocrine IL-6 Production

The isolation and characterization of cells that can give rise to HCC only after transplantation into an appropriate host liver undergoing chronic injury demonstrates that cancer arises from progenitor cells that are yet to become fully malignant. Importantly, unlike fully malignant HCC cells, the HcPCs we isolated cannot form s.c. tumors or even liver tumors when introduced into a nondamaged liver. Liver damage induced by uPA expression or CCl4 treatment provides HcPCs with the proper cytokine and growth factor milieu needed for their proliferation. Although HcPCs produce IL-6, they may also depend on other cytokines such as TNF, which is produced by macrophages that are recruited to the damaged liver. In addition, uPA expression and CCl4 treatment may enhance HcPC growth and progression through their fibrogenic effect on hepatic stellate cells. Although HCC and other cancers have been suspected to arise from premalignant/dysplastic lesions (Hruban et al., 2007Hytiroglou et al., 2007), a direct demonstration that such lesions progress into malignant tumors has been lacking. Based on expression of common markers—EpCAM, CD44, AFP, activated STAT3, and IL-6—that are not expressed in normal hepatocytes, we postulate that HcPCs originate from FAH or dysplastic foci, which are first observed in male mice within 3 months of DEN exposure.

7.7.6 Acetylation Stabilizes ATP-Citrate Lyase to Promote Lipid Biosynthesis and Tumor Growth

Lin R1Tao RGao XLi TZhou XGuan KLXiong YLei QY.
Mol Cell. 2013 Aug 22; 51(4):506-18
http://dx.doi.org:/10.1016/j.molcel.2013.07.002

Increased fatty acid synthesis is required to meet the demand for membrane expansion of rapidly growing cells. ATP-citrate lyase (ACLY) is upregulated or activated in several types of cancer, and inhibition of ACLY arrests proliferation of cancer cells. Here we show that ACLY is acetylated at lysine residues 540, 546, and 554 (3K). Acetylation at these three lysine residues is stimulated by P300/calcium-binding protein (CBP)-associated factor (PCAF) acetyltransferase under high glucose and increases ACLY stability by blocking its ubiquitylation and degradation. Conversely, the protein deacetylase sirtuin 2 (SIRT2) deacetylates and destabilizes ACLY. Substitution of 3K abolishes ACLY ubiquitylation and promotes de novo lipid synthesis, cell proliferation, and tumor growth. Importantly, 3K acetylation of ACLY is increased in human lung cancers. Our study reveals a crosstalk between acetylation and ubiquitylation by competing for the same lysine residues in the regulation of fatty acid synthesis and cell growth in response to glucose.

Fatty acid synthesis occurs at low rates in most nondividing cells of normal tissues that primarily uptake lipids from circulation. In contrast, increased lipogenesis, especially de novo lipid synthesis, is a key characteristic of cancer cells. Many studies have demonstrated that in cancer cells, fatty acids are preferred to be derived from de novo synthesis instead of extracellular lipid supply (Medes et al., 1953Menendez and Lupu, 2007;Ookhtens et al., 1984Sabine et al., 1967). Fatty acids are key building blocks for membrane biogenesis, and glucose serves as a major carbon source for de novo fatty acid synthesis (Kuhajda, 2000McAndrew, 1986;Swinnen et al., 2006). In rapidly proliferating cells, citrate generated by the tricarboxylic acid (TCA) cycle, either from glucose by glycolysis or glutamine by anaplerosis, is preferentially exported from mitochondria to cytosol and then cleaved by ATP citrate lyase (ACLY) (Icard et al., 2012) to produce cytosolic acetyl coenzyme A (acetyl-CoA), which is the building block for de novo lipid synthesis. As such, ACLY couples energy metabolism with fatty acids synthesis and plays a critical role in supporting cell growth. The function of ACLY in cell growth is supported by the observation that inhibition of ACLY by chemical inhibitors or RNAi dramatically suppresses tumor cell proliferation and induces differentiation in vitro and in vivo (Bauer et al., 2005Hatzivassiliou et al., 2005). In addition, ACLY activity may link metabolic status to histone acetylation by providing acetyl-CoA and, therefore, gene expression (Wellen et al., 2009).

While ACLY is transcriptionally regulated by sterol regulatory element-binding protein 1 (SREBP-1) (Kim et al., 2010), ACLY activity is regulated by the phosphatidylinositol 3-kinase (PI3K)/Akt pathway (Berwick et al., 2002Migita et al., 2008Pierce et al., 1982). Akt can directly phosphorylate and activate ACLY (Bauer et al., 2005Berwick et al., 2002Migita et al., 2008Potapova et al., 2000). Covalent lysine acetylation has recently been found to play a broad and critical role in the regulation of multiple metabolic enzymes (Choudhary et al., 2009Zhao et al., 2010). In this study, we demonstrate that ACLY protein is acetylated on multiple lysine residues in response to high glucose. Acetylation of ACLY blocks its ubiquitinylation and degradation, thus leading to ACLY accumulation and increased fatty acid synthesis. Our observations reveal a crosstalk between protein acetylation and ubiquitylation in the regulation of fatty acid synthesis and cell growth.

Acetylation of ACLY at Lysines 540, 546, and 554

Recent mass spectrometry-based proteomic analyses have potentially identified a large number of acetylated proteins, including ACLY (Figure S1A available online; Choudhary et al., 2009Zhao et al., 2010). We detected the acetylation level of ectopically expressed ACLY followed by western blot using pan-specific anti-acetylated lysine antibody. ACLY was indeed acetylated, and its acetylation was increased by nearly 3-fold after treatment with nicotinamide (NAM), an inhibitor of the SIRT family deacetylases, and trichostatin A (TSA), an inhibitor of histone deacetylase (HDAC) class I and class II (Figure 1A). Experiments with endogenous ACLY also showed that TSA and NAM treatment enhanced ACLY acetylation (Figure 1B).

Figure 1  ACLY Is Acetylated at Lysines 540, 546, and 554

Ten putative acetylation sites were identified by mass spectrometry analyses (Table S1). We singly mutated each lysine to either a glutamine (Q) or an arginine (R) and found that no single mutation resulted in a significant reduction of ACLY acetylation (data not shown), indicating that ACLY may be acetylated at multiple lysine residues. Three lysine residues, K540, K546, and K554, received high scores in the acetylation proteomic screen and are evolutionarily conserved from C. elegans to mammals (Figure S1A). We generated triple Q and R mutants of K540, K546, and K554 (3KQ and 3KR) and found that both 3KQ and 3KR mutations resulted in a significant (~60%) decrease in ACLY acetylation (Figure 1C), indicating that 3K are the major acetylation sites of ACLY.  Further, we found that the acetylation of endogenous ACLY is clearly increased after treatment of cells with NAM and TSA (Figure 1D). These results demonstrate that ACLY is acetylated at K540, K546, and K554.

Glucose Promotes ACLY Acetylation to Stabilize ACLY

In mammalian cells, glucose is the main carbon source for de novo lipid synthesis. We found that ACLY levels increased with increasing glucose concentration, which also correlated with increased ACLY 3K acetylation (Figure 1E). Furthermore, to confirm whether the glucose level affects ACLY protein stability in vivo, we intraperitoneally injected glucose in BALB/c mice and found that high glucose resulted in a significant increase of ACLY protein levels (Figure 1F).

To determine whether ACLY acetylation affects its protein levels, we treated HeLa and Chang liver cells with NAM and TSA and found an increase in ACLY protein levels (Figure S1G, upper panel). ACLY mRNA levels were not significantly changed by the treatment of NAM and TSA (Figure S1G, lower panel), indicating that this upregulation of ACLY is mostly achieved at the posttranscriptional level. Indeed, ACLY protein was also accumulated in cells treated with the proteasome inhibitor MG132, indicating that ACLY stability could be regulated by the ubiquitin-proteasome pathway (Figure 1G). Blocking deacetylase activity stabilized ACLY (Figure S1H). The stabilization of ACLY induced by high glucose was associated with an increase of ACLY acetylation at K540, K546, and K554. Together, these data support a notion that high glucose induces both ACLY acetylation and protein stabilization and prompted us to ask whether acetylation directly regulates ACLY stability. We then generated ACLYWT, ACLY3KQ, and ACLY3KRstable cells after knocking down the endogenous ACLY. We found that the ACLY3KR or ACLY3KQmutant was more stable than the ACLYWT (Figures 1I and S1I). Collectively, our results suggest that glucose induces acetylation at K540, 546, and 554 to stabilize ACLY.

Acetylation Stabilizes ACLY by Inhibiting Ubiquitylation

To determine the mechanism underlying the acetylation and ACLY protein stability, we first examined ACLY ubiquitylation and found that it was actively ubiquitylated (Figure 2A). Previous proteomic analyses have identified K546 in ACLY as a ubiquitylation site (Wagner et al., 2011). In order to identify the ubiquitylation sites, we tested the ubiquitylation levels of double mutants 540R–546R and 546–554R (Figure S2A). We found that the ubiquitylation of the 540R-546R and 546R-554R mutants is partially decreased, while mutation of K540, K546, and K554 (3KR), which changes all three putative acetylation lysine residues of ACLY to arginine residues, dramatically reduced the ACLY ubiquitylation level (Figures 2B and S2A), indicating that 3K lysines might also be the ubiquitylation target residues. Moreover, inhibition of deacetylases by NAM and TSA decreased ubiquitylation of WT but not 3KQ or 3KR mutant ACLY (Figure 2C). These results implicate an antagonizing role of the acetylation towards the ubiquitylation of ACLY at these three lysine residues.

Figure 2  Acetylation Protects ACLY from Proteasome Degradation by Inhibiting Ubiquitylation

We found that ACLY acetylation was only detected in the nonubiquitylated, but not the ubiquitylated (high-molecular-weight), ACLY species. This result indicates that ACLY acetylation and ubiquitylation are mutually exclusive and is consistent with the model that K540, K546, and K554 are the sites of both ubiquitylation and acetylation. Therefore, acetylation of these lysines would block ubiquitylation.

We also found that glucose upregulates ACLY acetylation at 3K and decreases its ubiquitylation (Figure S2B). High glucose (25 mM) effectively decreased ACLY ubiquitylation, while inhibition of deacetylases clearly diminished its ubiquitylation (Figure 2E). We conclude that acetylation and ubiquitylation occur mutually exclusively at K540, K546, and K554 and that high-glucose-induced acetylation at these three sites blocks ACLY ubiquitylation and degradation.

UBR4 Targets ACLY for Degradation

UBR4 was identified as a putative ACLY-interacting protein by affinity purification coupled with mass spectrometry analysis (data not shown). To address if UBR4 is a potential ACLY E3 ligase, we determined the interaction between ACLY and UBR4 and found that ACLY interacted with the E3 ligase domain of UBR4; this interaction was enhanced by MG132 treatment (Figure 3A). UBR4 knockdown in A549 cells resulted in an increase of endogenous ACLY protein level (Figure 3C). Moreover, UBR4 knockdown significantly stabilized ACLY (Figure 3D) and decreased ACLY ubiquitylation (Figure 3E). Taken together, these results indicate that UBR4 is an ACLY E3 ligase that responds to glucose regulation.

Figure 3  UBR4 Is the E3 Ligase of ACLY

PCAF Acetylates ACLY

PCAF knockdown significantly reduced acetylation of 3K, indicating that PCAF is a potential 3K acetyltransferase in vivo (Figure 4C, upper panel). Furthermore, PCAF knockdown decreased the steady-state level of endogenous ACLY, but not ACLY mRNA (Figure 4C, middle and lower panels). Moreover, we found that PCAF knockdown destabilized ACLY (Figure 4D). In addition, overexpression of PCAF decreases ACLY ubiquitylation (Figure 4E), while PCAF inhibition increases the interaction between UBR4 E3 ligase domain and wild-type ACLY, but not 3KR (Figure 4F). Together, our results indicate that PCAF increases ACLY protein level, possibly via acetylating ACLY at 3K.

Figure 4  PCAF Is the Acetylase of ACLY

SIRT2 Deacetylates ACLY

Figure 5  SIRT2 Decreases ACLY Acetylation and Increases Its Protein Levels In Vivo

Acetylation of ACLY Promotes Cell Proliferation and De Novo Lipid Synthesis

The protein levels of ACLY 3KQ and 3KR were accumulated to a level higher than the wild-type cells upon extended culture in low-glucose medium (Figure S6A, right panel), indicating a growth advantage conferred by ACLY stabilization resulting from the disruption of both acetylation and ubiquitylation at K540, K546, and K554. Cellular acetyl-CoA assay showed that cells expressing 3KQ or 3KR mutant ACLY produce more acetyl-CoA than cells expressing the wild-type ACLY under low glucose (Figures 6B and S6B), further supporting the conclusion that 3KQ or 3KR mutation stabilizes ACLY.

Figure 6  Acetylation of ACLY at 3K Promotes Lipogenesis and Tumor Cell Proliferation

ACLY is a key enzyme in de novo lipid synthesis. Silencing ACLY inhibited the proliferation of multiple cancer cell lines, and this inhibition can be partially rescued by adding extra fatty acids or cholesterol into the culture media (Zaidi et al., 2012). This prompted us to measure extracellular lipid incorporation in A549 cells after knockdown and ectopic expression of ACLY. We found that when cultured in low glucose (2.5 mM), cells expressing wild-type ACLY uptake significantly more phospholipids compared to cells expressing 3KQ or 3KR mutant ACLY (Figures 6C, 6D, and S6D). When cultured in the presence of high glucose (25 mM), however, cells expressing either the wild-type, 3KQ, or 3KR mutant ACLY all have reduced, but similar, uptake of extracellular phospholipids (Figures 6C, 6D, and S6D). The above results are consistent with a model that acetylation of ACLY induced by high glucose increases its stability and stimulates de novo lipid synthesis.

3K Acetylation of ACLY Is Increased in Lung Cancer

ACLY is reported to be upregulated in human lung cancer (Migita et al., 2008). Many small chemicals targeting ACLY have been designed for cancer treatment (Zu et al., 2012). The finding that 3KQ or 3KR mutant increased the ability of ACLY to support A549 lung cancer cell proliferation prompted us to examine 3K acetylation in human lung cancers. We collected a total of 54 pairs of primary human lung cancer samples with adjacent normal lung tissues and performed immunoblotting for ACLY protein levels. This analysis revealed that, when compared to the matched normal lung tissues, 29 pairs showed a significant increase of total ACLY protein using b-actin as a loading control (Figures 7A and S7A). The tumor sample analyses demonstrate that ACLY protein levels are elevated in lung cancers, and 3K acetylation positively correlates with the elevated ACLY protein. These data also indicate that ACLY with 3K acetylation may be potential biomarker for lung cancer diagnosis.

Figure 7
  Acetylation of ACLY at 3K Is Upregulated in Human Lung Carcinoma

Dysregulation of cellular metabolism is a hallmark of cancer (Hanahan and Weinberg, 2011Vander Heiden et al., 2009). Besides elevated glycolysis, increased lipogenesis, especially de novo lipid synthesis, also plays an important role in tumor growth. Because most carbon sources for fatty acid synthesis are from glucose in mammalian cells (Wellen et al., 2009), the channeling of carbon into de novo lipid synthesis as building blocks for tumor cell growth is primarily linked to acetyl-CoA production by ACLY. Moreover, the ACLY-catalyzed reaction consumes ATP. Therefore, as the key cellular energy and carbon source, one may expect a role for glucose in ACLY regulation. In the present study, we have uncovered a mechanism of ACLY regulation by glucose that increases ACLY protein level to meet the enhanced demand of lipogenesis in growing cells, such as tumor cells (Figure 7C). Glucose increases ACLY protein levels by stimulating its acetylation.

Upregulation of ACLY is common in many cancers (Kuhajda, 2000Milgraum et al., 1997Swinnen et al., 2004Yahagi et al., 2005). This is in part due to the transcriptional activation by SREBP-1 resulting from the activation of the PI3K/AKT pathway in cancers (Kim et al., 2010Nadler et al., 2001Wang and Dey, 2006). In this study, we report a mechanism of ACLY regulation at the posttranscriptional level. We propose that acetylation modulated by glucose status plays a crucial role in coordinating the intracellular level of ACLY, hence fatty acid synthesis, and glucose availability. When glucose is sufficient, lipogenesis is enhanced. This can be achieved, at least in part, by the glucose-induced stabilization of ACLY. High glucose increases ACLY acetylation, which inhibits its ubiquitylation and degradation, leading to the accumulation of ACLY and enhanced lipogenesis. In contrast, when glucose is limited, ACLY is not acetylated and thus can be ubiquitylated, leading to ACLY degradation and reduced lipogenesis. Moreover, our data indicate that acetylation and ubiquitylation in ACLY may compete with each other by targeting the same lysine residues at K540, K546, and K554. Consistently, previous proteomic analyses have identified K546 in ACLY as a ubiquitylation site (Wagner et al., 2011). Similar models of different modifications on the same lysine residues have been reported in the regulation of other proteins (Grönroos et al., 2002Li et al., 20022012). We propose that acetylation and ubiquitylation have opposing effects in the regulation of ACLY by competitively modifying the same lysine residues. The acetylation-mimetic 3KQ and the acetylation-deficient 3KR mutants behaved indistinguishably in most biochemical and functional assays, mainly due to the fact that these mutations disrupt lysine ubiquitylation that primarily occurs on these three residues.

ACLY is increased in lung cancer tissues compared to adjacent tissues. Consistently, ACLY acetylation at 3K is also significantly increased in lung cancer tissues. These observations not only confirm ACLY acetylation in vivo, but also suggest that ACLY 3K acetylation may play a role in lung cancer development. Our study reveals a mechanism of ACLY regulation in response to glucose signals.

 

7.7.7 Monoacylglycerol Lipase Regulates a Fatty Acid Network that Promotes Cancer Pathogenesis

Nomura DK1Long JZNiessen SHoover HSNg SWCravatt BF.
Cell. 2010 Jan 8; 140(1):49-61
http://dx.doi.org/10.1016.2Fj.cell.2009.11.027

Highlights

  • Monoacylglycerol lipase (MAGL) is elevated in aggressive human cancer cells
  • Loss of MAGL lowers fatty acid levels in cancer cells and impairs pathogenicity
  • MAGL controls a signaling network enriched in protumorigenic lipids
  • A high-fat diet can restore the growth of tumors lacking MAGL in vivo
monoacylglycerol-lipase-magl-is-highly-expressed-in-aggressive-human-cancer-cells-and-primary-tumors

monoacylglycerol-lipase-magl-is-highly-expressed-in-aggressive-human-cancer-cells-and-primary-tumors

http://www.cell.com/cms/attachment/1082768/7977146/fx1.jpg

Tumor cells display progressive changes in metabolism that correlate with malignancy, including development of a lipogenic phenotype. How stored fats are liberated and remodeled to support cancer pathogenesis, however, remains unknown. Here, we show that the enzyme monoacylglycerol lipase (MAGL) is highly expressed in aggressive human cancer cells and primary tumors, where it regulates a fatty acid network enriched in oncogenic signaling lipids that promotes migration, invasion, survival, and in vivo tumor growth. Overexpression of MAGL in nonaggressive cancer cells recapitulates this fatty acid network and increases their pathogenicity—phenotypes that are reversed by an MAGL inhibitor. Impairments in MAGL-dependent tumor growth are rescued by a high-fat diet, indicating that exogenous sources of fatty acids can contribute to malignancy in cancers lacking MAGL activity. Together, these findings reveal how cancer cells can co-opt a lipolytic enzyme to translate their lipogenic state into an array of protumorigenic signals.

We show that the enzyme monoacylglycerol lipase (MAGL) is highly expressed in aggressive human cancer cells and primary tumors, where it regulates a fatty acid network enriched in oncogenic signaling lipids that promotes migration, invasion, survival, and in vivo tumor growth. Overexpression of MAGL in non-aggressive cancer cells recapitulates this fatty acid network and increases their pathogenicity — phenotypes that are reversed by an MAGL inhibitor. Interestingly, impairments in MAGL-dependent tumor growth are rescued by a high-fat diet, indicating that exogenous sources of fatty acids can contribute to malignancy in cancers lacking MAGL activity. Together, these findings reveal how cancer cells can co-opt a lipolytic enzyme to translate their lipogenic state into an array of pro-tumorigenic signals.

The conversion of cells from a normal to cancerous state is accompanied by reprogramming of metabolic pathways (Deberardinis et al., 2008Jones and Thompson, 2009Kroemer and Pouyssegur, 2008), including those that regulate glycolysis (Christofk et al., 2008Gatenby and Gillies, 2004), glutamine-dependent anaplerosis (DeBerardinis et al., 2008DeBerardinis et al., 2007Wise et al., 2008), and the production of lipids (DeBerardinis et al., 2008Menendez and Lupu, 2007). Despite a growing appreciation that dysregulated metabolism is a defining feature of cancer, it remains unclear, in many instances, how such biochemical changes occur and whether they play crucial roles in disease progression and malignancy.

Among dysregulated metabolic pathways, heightened de novo lipid biosynthesis, or the development a “lipogenic” phenotype (Menendez and Lupu, 2007), has been posited to play a major role in cancer. For instance, elevated levels of fatty acid synthase (FAS), the enzyme responsible for fatty acid biosynthesis from acetate and malonyl CoA, are correlated with poor prognosis in breast cancer patients, and inhibition of FAS results in decreased cell proliferation, loss of cell viability, and decreased tumor growth in vivo (Kuhajda et al., 2000Menendez and Lupu, 2007Zhou et al., 2007). FAS may support cancer growth, at least in part, by providing metabolic substrates for energy production (via fatty acid oxidation) (Buzzai et al., 2005Buzzai et al., 2007Liu, 2006). Many other features of lipid biochemistry, however, are also critical for supporting the malignancy of cancer cells, including:

Prominent examples of lipid messengers that contribute to cancer include:

Here, we use functional proteomic methods to discover a lipolytic enzyme, monoacylglycerol lipase (MAGL), that is highly elevated in aggressive cancer cells from multiple tissues of origin. We show that MAGL, through hydrolysis of monoacylglycerols (MAGs), controls free fatty acid (FFA) levels in cancer cells. The resulting MAGL-FFA pathway feeds into a diverse lipid network enriched in pro-tumorigenic signaling molecules and promotes migration, survival, and in vivo tumor growth. Aggressive cancer cells thus pair lipogenesis with high lipolytic activity to generate an array of pro-tumorigenic signals that support their malignant behavior.

Activity-Based Proteomic Analysis of Hydrolytic Enzymes in Human Cancer Cells

To identify enzyme activities that contribute to cancer pathogenesis, we conducted a functional proteomic analysis of a panel of aggressive and non-aggressive human cancer cell lines from multiple tumors of origin, including melanoma [aggressive (C8161, MUM2B), non-aggressive (MUM2C)], ovarian [aggressive (SKOV3), non-aggressive (OVCAR3)], and breast [aggressive (231MFP), non-aggressive (MCF7)] cancer. Aggressive cancer lines were confirmed to display much greater in vitro migration and in vivo tumor-growth rates compared to their non-aggressive counterparts (Figure S1), as previously shown (Jessani et al., 2004;Jessani et al., 2002Seftor et al., 2002Welch et al., 1991). Proteomes from these cancer lines were screened by activity-based protein profiling (ABPP) using serine hydrolase-directed fluorophosphonate (FP) activity-based probes (Jessani et al., 2002Patricelli et al., 2001). Serine hydrolases are one of the largest and most diverse enzyme classes in the human proteome (representing ~ 1–1.5% of all human proteins) and play important roles in many biochemical processes of potential relevance to cancer, such as proteolysis (McMahon and Kwaan, 2008Puustinen et al., 2009), signal transduction (Puustinen et al., 2009), and lipid metabolism (Menendez and Lupu, 2007Zechner et al., 2005). The goal of this study was to identify hydrolytic enzyme activities that were consistently altered in aggressive versus non-aggressive cancer lines, working under the hypothesis that these conserved enzymatic changes would have a high probability of contributing to the pathogenic state of cancer cells.

Among the more than 50 serine hydrolases detected in this analysis (Tables S13), two enzymes, KIAA1363 and MAGL, were found to be consistently elevated in aggressive cancer cells relative to their non-aggressive counterparts, as judged by spectral counting (Jessani et al., 2005Liu et al., 2004). We confirmed elevations in KIAA1363 and MAGL in aggressive cancer cells by gel-based ABPP, where proteomes are treated with a rhodamine-tagged FP probe and resolved by 1D-SDS-PAGE and in-gel fluorescence scanning (Figure 1A). In both cases, two forms of each enzyme were detected (Figure 1A), due to differential glycoslyation for KIAA1363 (Jessani et al., 2002), and possibly alternative splicing for MAGL (Karlsson et al., 2001). We have previously shown that KIAA1363 plays a role in regulating ether lipid signaling pathways in aggressive cancer cells (Chiang et al., 2006). On the other hand, very little was known about the function of MAGL in cancer.

Figure 1  MAGL is elevated in aggressive cancer cells, where the enzyme regulates monoacylgycerol (MAG) and free fatty acid (FFA) levels

The heightened activity of MAGL in aggressive cancer cells was confirmed using the substrate C20:4 MAG (Figure 1B). Since several enzymes have been shown to display MAG hydrolytic activity (Blankman et al., 2007), we confirmed the contribution that MAGL makes to this process in cancer cells using the potent and selective MAGL inhibitor JZL184 (Long et al., 2009a).

MAGL Regulates Free Fatty Acid Levels in Aggressive Cancer Cells

MAGL is perhaps best recognized for its role in degrading the endogenous cannabinoid 2-arachidonoylglycerol (2-AG, C20:4 MAG), as well as other MAGs, in brain and peripheral tissues (Dinh et al., 2002Long et al., 2009aLong et al., 2009bNomura et al., 2008). Consistent with this established function, blockade of MAGL by JZL184 (1 μM, 4 hr) produced significant elevations in the levels of several MAGs, including 2-AG, in each of the aggressive cancer cell lines (Figure 1C and Figure S2). Interestingly, however, MAGL inhibition also caused significant reductions in the levels of FFAs in aggressive cancer cells (Figure 1D and Figure S2). This surprising finding contrasts with the function of MAGL in normal tissues, where the enzyme does not, in general, control the levels of FFAs (Long et al., 2009aLong et al., 2009b;Nomura et al., 2008).

Metabolic labeling studies using the non-natural C17:0-MAG confirmed that MAGs are converted to LPC and LPE by aggressive cancer cells, and that this metabolic transformation is significantly enhanced by treatment with JZL184 (Figure S1). Finally, JZL184 treatment did not affect the levels of MAGs and FFAs in non-aggressive cancer lines (Figure 1C, D), consistent with the negligible expression of MAGL in these cells (Figure 1A, B).

We next stably knocked down MAGL expression by RNA interference technology using two independent shRNA probes (shMAGL1, shMAGL2), both of which reduced MAGL activity by 70–80% in aggressive cancer lines (Figure 2A, D and Figure S2). Other serine hydrolase activities were unaffected by shMAGL probes (Figure 2A, D and Figures S2), confirming the specificity of these reagents. Both shMAGL probes caused significant elevations in MAGs and corresponding reductions in FFAs in aggressive melanoma (Figure 2B, C), ovarian (Figure 2E, F), and breast cancer cells (Figure S2).

Figure 2  Stable shRNA-mediated knockdown of MAGL lowers FFA levels in aggressive cancer cells.

Together, these data demonstrate that both acute (pharmacological) and stable (shRNA) blockade of MAGL cause elevations in MAGs and reductions in FFAs in aggressive cancer cells. These intriguing findings indicate that MAGL is the principal regulator of FFA levels in aggressive cancer cells. Finally, we confirmed that MAGL activity (Figure 3A, B) and FFA levels (Figure 3C) are also elevated in high-grade primary human ovarian tumors compared to benign or low-grade tumors. Thus, heightened expression of the MAGL-FFA pathway is a prominent feature of both aggressive human cancer cell lines and primary tumors.

Figure 3  High-grade primary human ovarian tumors possess elevated MAGL activity and FFAs compared to benign tumors.

Disruption of MAGL Expression and Activity Impairs Cancer Pathogenicity

shMAGL cancer lines were next examined for alterations in pathogenicity using a set of in vitro and in vivo assays. shMAGL-melanoma (C8161), ovarian (SKOV3), and breast (231MFP) cancer cells exhibited significantly reduced in vitro migration (Figure 4A, F and Figure S2), invasion (Figure 4B, G and Figure S2), and cell survival under serum-starvation conditions (Figure 4C, H and Figure S2). Acute pharmacological blockade of MAGL by JZL184 also decreased cancer cell migration (Figure S2), but not survival, possibly indicating that maximal impairments in cancer aggressiveness require sustained inhibition of MAGL.

Figure 4  shRNA-mediated knockdown and pharmacological inhibition of MAGL impair cancer aggressiveness.

MAGL Overexpression Increases FFAs and the Aggressiveness of Cancer Cells

Stable MAGL-overexpressing (MAGL-OE) and control [expressing an empty vector or a catalytically inactive version of MAGL, where the serine nucleophile was mutated to alanine (S122A)] variants of MUM2C and OVCAR3 cells were generated by retroviral infection and evaluated for their respective MAGL activities by ABPP and C20:4 MAG substrate assays. Both assays confirmed that MAGL-OE cells possess greater than 10-fold elevations in MAGL activity compared to control cells (Figure 5A and Figure S4). MAGL-OE cells also showed significant reductions in MAGs (Figure 5B andFigure S4) and elevated FFAs (Figure 5C and Figure S4). This altered metabolic profile was accompanied by increased migration (Figure 5D and Figure S4), invasion (Figure 5E and Figure S4), and survival (Figure S4) in MAGL-OE cells. None of these effects were observed in cancer cells expressing the S122A MAGL mutant, indicating that they require MAGL activity.  MAGL-OE MUM2C cells also showed enhanced tumor growth in vivo compared to control cells (Figure 5F). Notably, the increased tumor growth rate of MAGL-OE MUM2C cells nearly matched that of aggressive C8161 cells (Figure S4). These data indicate that the ectopic expression of MAGL in non-aggressive cancer cells is sufficient to elevate their FFA levels and promote pathogenicity both in vitro and in vivo.

Figure 5 Ectopic expression of MAGL elevates FFA levels and enhances the in vitro and in vivo pathogenicity of MUM2C melanoma cells.

Metabolic Rescue of Impaired Pathogenicity in MAGL-Disrupted Cancer Cells

MAGL could support the aggressiveness of cancer cells by either reducing the levels of its MAG substrates, elevating the levels of its FFA products, or both. Among MAGs, the principal signaling molecule is the endocannabinoid 2-AG, which activates the CB1 and CB2 receptors (Ahn et al., 2008Mackie and Stella, 2006). The endocannabinoid system has been implicated previously in cancer progression and, depending on the specific study, shown to promote (Sarnataro et al., 2006Zhao et al., 2005) or suppress (Endsley et al., 2007Wang et al., 2008) cancer pathogenesis. Neither a CB1 or CB2 antagonist rescued the migratory defects of shMAGL cancer cells (Figure S5). CB1 and CB2 antagonists also did not affect the levels of MAGs or FFAs in cancer cells (Figure S5).

We then determined whether increased FFA delivery could rectify the tumor growth defect observed for shMAGL cells in vivo. Immune-deficient mice were fed either a normal chow or high-fat diet throughout the duration of a xenograft tumor growth experiment. Notably, the impaired tumor growth rate of shMAGL-C8161 cells was completely rescued in mice fed a high-fat diet. In contrast, shControl-C8161 cells showed equivalent tumor growth rates on a normal versus high-fat diet. The recovery in tumor growth for shMAGL-C8161 cells in the high-fat diet group correlated with significantly increases levels of FFAs in excised tumors (Figure 6D). Collectively, these results indicate that MAGL supports the pathogenic properties of cancer cells by maintaining tonically elevated levels of FFAs.

Figure 6  Recovery of the pathogenic properties of shMAGL cancer cells by treatment with exogenous fatty acids.

MAGL Regulates a Fatty Acid Network Enriched in Pro-Tumorigenic Signals

Studies revealed that neither

  • the MAGL-FFA pathway might serve as a means to regenerate NAD+ (via continual fatty acyl glyceride/FFA recycling) to fuel glycolysis, or
  • increased lipolysis could be to generate FFA substrates for β-oxidation, which may serve as an important energy source for cancer cells (Buzzai et al., 2005), or
  • CPT1 blockade (reduced expression of CPT1 in aggressive cancer cells (data not shown) has been reported previously (Deberardinis et al., 2006))

providing evidence against a role for β-oxidation as a downstream mediator of the pathogenic effects of the MAGL-fatty acid pathway.

Considering that FFAs are fundamental building blocks for the production and remodeling of membrane structures and signaling molecules, perturbations in MAGL might be expected to affect several lipid-dependent biochemical networks important for malignancy. To test this hypothesis, we performed lipidomic analyses of cancer cell models with altered MAGL activity, including comparisons of:

  1. MAGL-OE versus control cancer cells (OVCAR3, MUM2C), and
  2. shMAGL versus shControl cancer cells (SKOV3, C8161).

Complementing these global profiles, we also conducted targeted measurements of specific bioactive lipids (e.g., prostaglandins) that are too low in abundance for detection by standard lipidomic methods. The resulting data sets were then mined to identify a common signature of lipid metabolites regulated by MAGL, which we defined as metabolites that were significantly increased or reduced in MAGL–OE cells and showed the opposite change in shMAGL cells relative to their respective control groups (Figure 7A, B and Table S4).

Figure 7  MAGL regulates a lipid network enriched in pro-tumorigenic signaling molecules.

Most of the lipids in the MAGL-fatty acid network, including several lysophospholipids (LPC, LPA, LPE), ether lipids (MAGE, alkyl LPE), phosphatidic acid (PA), and prostaglandin E2 (PGE2), displayed similar profiles to FFAs, being consistently elevated and reduced in MAGL-OE and shMAGL cells, respectively. Only MAGs were found to show the opposite profile (elevated and reduced in shMAGL and MAGL-OE cells, respectively). Interestingly, virtually this entire lipidomic signature was also observed in aggressive cancer cells when compared to their non-aggressive counterparts (e.g., C8161 versus MUM2C and SKOV3 versus OVCAR3, respectively; Table S4). These findings demonstrate that MAGL regulates a lipid network in aggressive cancer cells that consists of not only FFAs and MAGs, but also a host of secondary lipid metabolites. Increases (rather than decreases) in LPCs and LPEs were observed in JZL184-treated cells (Figure S1 and Table S4). These data indicate that acute and chronic blockade of MAGL generate distinct metabolomic effects in cancer cells, likely reflecting the differential outcomes of short- versus long-term depletion of FFAs.

Within the MAGL-fatty acid network are several pro-tumorigenic lipid messengers, including LPA and PGE2, that have been reported to promote the aggressiveness of cancer cells (Gupta et al., 2007Mills and Moolenaar, 2003). Metabolic labeling studies confirmed that aggressive cancer cells can convert both MAGs and FFAs (Figure S1) to LPA and PGE2 and, for MAGs, this conversion was blocked by JZL184 (Figure S1). Interestingly, treatment with either LPA or PGE2 (100 nM, 4 hr) rescued the impaired migration of shMAGL cancer cells at concentrations that did not affect the migration of shControl cells (Figure 7E).

Heightened lipogenesis is an established early hallmark of dysregulated metabolism and pathogenicity in cancer (Menendez and Lupu, 2007). Cancer lipogenesis appears to be driven principally by FAS, which is elevated in most transformed cells and important for survival and proliferation (De Schrijver et al., 2003;Kuhajda et al., 2000Vazquez-Martin et al., 2008). It is not yet clear how FAS supports cancer growth, but most of the proposed mechanisms invoke pro-tumorigenic functions for the enzyme s fatty acid products and their lipid derivatives (Menendez and Lupu, 2007). This creates a conundrum, since the fatty acid molecules produced by FAS are thought to be rapidly incorporated into neutral- and phospho-lipids, pointing to the need for complementary lipolytic pathways in cancer cells to release stored fatty acids for metabolic and signaling purposes (Prentki and Madiraju, 2008Przybytkowski et al., 2007). Consistent with this hypothesis, we found that acute treatment with the FAS inhibitor C75 (40 μM, 4 h) did not reduce FFA levels in cancer cells (data not shown). Furthermore, aggressive and non-aggressive cancer cells exhibited similar levels of FAS (data not shown), indicating that lipogenesis in the absence of paired lipolysis may be insufficient to confer high levels of malignancy.

Here we show that aggressive cancer cells do indeed acquire the ability to liberate FFAs from neutral lipid stores as a consequence of heightened expression of MAGL. MAGL and its FFA products were found to be elevated in aggressive human cancer cell lines from multiple tissues of origin, as well as in high-grade primary human ovarian tumors. These data suggest that the MAGL-FFA pathway may be a conserved feature of advanced forms of many types of cancer. Further evidence in support of this premise originates from gene expression profiling studies, which have identified increased levels of MAGL in primary human ductal breast tumors compared to less malignant medullary breast tumors (Gjerstorff et al., 2006). The key role that MAGL plays in regulating FFA levels in aggressive cancer cells contrasts with the function of this enzyme in normal tissues, where it mainly controls the levels of MAGs, but not FFAs (Long et al., 2009b). These data thus provide a striking example of the co-opting of an enzyme by cancer cells to serve a distinct metabolic purpose that supports their pathogenic behavior.

Taken together, our results indicate that MAGL serves as key metabolic hub in aggressive cancer cells, where the enzyme regulates a fatty acid network that feeds into a number of pro-tumorigenic signaling pathways.

 

7.7.8 Pirin regulates epithelial to mesenchymal transition and down-regulates EAF/U19 signaling in prostate cancer cells

7.7.8.1  Pirin regulates epithelial to mesenchymal transition independently of Bcl3-Slug signaling

Komai K1Niwa Y1Sasazawa Y1Simizu S2.
FEBS Lett. 2015 Mar 12; 589(6):738-43
http://dx.doi.org:/10.1016/j.febslet.2015.01.040

Highlights

  • Pirin decreases E-cadherin expression and induces EMT.
  • The induction of EMT by Pirin is achieved through a Bcl3 independent pathway.
  • Pirin may be a novel target for cancer therapy.

Epithelial to mesenchymal transition (EMT) is an important mechanism for the initial step of metastasis. Proteomic analysis indicates that Pirin is involved in metastasis. However, there are no reports demonstrating its direct contribution. Here we investigated the involvement of Pirin in EMT. In HeLa cells, Pirin suppressed E-cadherin expression and regulated the expression of other EMT markers. Furthermore, cells expressing Pirin exhibited a spindle-like morphology, which is reminiscent of EMT. A Pirin mutant defective for Bcl3 binding decreased E-cadherin expression similar to wild-type, suggesting that Pirin regulates E-cadherin independently of Bcl3-Slug signaling. These data provide direct evidence that Pirin contributes to cancer metastasis.

Pirin regulates the expression of E-cadherin and EMT markers

In melanoma, Pirin enhances NF-jB activity and increases Slug expression by binding Bcl3 [31], and it may also be involved in adenoid cystic tumor metastasis [23]. Since Slug suppresses E-cadherin transcription and is recognized as a major EMT inducer, we hypothesized that Pirin may regulate EMT through inducing Slug expression. To investigate whether Pirin regulates EMT, we measured E-cadherin expression following Pirin knockdown. As shown in Fig. 1A and B, E-cadherin expression was significantly increased following Pirin knockdown indicating that it may promote EMT. To confirm this, we established Pirin-expressing HeLa cells (Fig. 1C), which inhibited the expression of E-cadherin (Fig. 1D). Additionally, the expression of Occludin, an epithelial marker, was decreased, and several mesenchymal markers, including Fibronectin, N-cadherin, and Vimentin, were increased by Pirin expression (Fig. 1D). These data suggest that Pirin promotes EMT.

Pirin induces EMT-associated cell morphological changes

As mentioned above, cells undergo morphological changes during EMT. Therefore, we next analyzed whether Pirin expression affects cell morphology. Quantitative analysis of morphological changes was based on cell circularity, {4p(area)/(perimeter)2}100, which decreases during EMT-associated morphological changes [34–36]. Indeed, TGF-b or TNF-a exposure induced EMTassociated cell morphological changes in HeLa cells (data not shown). Employing this parameter of circularity, we compared the morphology of our established HeLa/Pirin-GFP cells with control HeLa/GFP cells. Although the control HeLa/GFP cells displayed a cobblestone-like morphology, HeLa/Pirin-GFP cells were elongated in shape (Fig. 2A). Indeed, compared with control cells, the circularity of HeLa/Pirin-GFP cells was significantly decreased (Fig. 2B). To confirm that these observations were dependent on Pirin expression, HeLa/Pirin-GFP cells were treated with an siRNA targeting Pirin. HeLa/Pirin-GFP cells recovered a cobblestone-like morphology (Fig. 2C) and circularity (Fig. 2D) when treated with Pirin siRNA indicating that Pirin expression induces EMT.

Pirin induces cell migration

During EMT cells acquire migratory capabilities. Therefore, we analyzed whether Pirin affects cell migration. HeLa cells were treated with an siRNA targeting Pirin and migration was assessed using a wound healing assay. Although Pirin knockdown had no effect on cell proliferation (data not shown), wound repair was inhibited in Pirin-depleted HeLa cells (Fig. 3A and B) suggesting that Pirin promoted cell migration. Furthermore, camptothecin treatment of HeLa/GFP cells caused decreased cell viability in a dose-dependent manner, whereas HeLa/Pirin-GFP cells were more resistantto drugtreatment (datanot shown).These results suggest that Pirin induces EMT-like phenotypes, such as cell migration and anticancer drug resistance.
Pirin regulates EMT independently of Bcl3-Slug signaling

To investigate whether Pirin controls E-cadherin expression at the transcriptional level, we measured E-cadherin promoter activity with a reporter assay. Indeed, the luciferase reporter analysis indicated that Pirin inhibited E-cadherin promoter activity (Fig. 4A and B). To determine if Bcl3 is involved in Pirin-induced EMT, we tested whether a Pirin mutant defective in Bcl3 binding could inhibit E-cadherin expression. We generated a mutation in the metal-binding cavity of Pirin(E103A) and confirmed that it disrupted Bcl3 binding. In vitro GST pull-down analysis using recombinant Pirin and Bcl3/ARD demonstrated that the Pirin mutant was defective for Bcl3 binding compared to wild-type (Fig. 5A). Interestingly, expression of both wild-type Pirin and the mutant defective in Bcl3 binding reduced E-cadherin gene and protein expression (Fig. 5B and C). Taken together these results indicate that Pirin decreases E-cadherin expression without binding Bcl3, and suggest that Pirin regulates EMT independently of Bcl3-Slug signaling.

Discussion

A characteristic feature of EMT is the disruption of epithelial cell–cell contact, which is achieved by reduced E-cadherin expression. Therefore, revealing the regulatory pathways controlling E-cadherin expression may elucidate the mechanisms of EMT. Several transcription factors regulate E-cadherin transcription. For instance,Snail,Slug,Twist,and Zebact as mastertranscriptional regulators that bind the consensus E-box sequence in the E-cadherin gene promoter and decrease the transcriptional activity [38]. Since Pirin regulates the transcription of Slug [31], we hypothesized that Pirin may also regulate EMT. In this study we demonstrated that Pirin decreases E-cadherin expression, and induces EMT and cancer malignant phenotypes. Since EMT is an initial step of metastasis, Pirin may contribute to cancer progression. We next examined whether the regulation of EMT by Pirin is attributed to Bcl3 binding and the induction of Slug. To this end, we generated a Pirin mutant (E103A) defective for Bcl3 binding (Fig. 5A). Single Fe2+ ion chelating is coordinated by His56, His58, His101, and Glu103 of Pirin, and the N-terminal domain containing these residues is highly conserved between mammals, plants, fungi, and prokaryotic organisms [15,27]. Therefore, it has been predicted that this N-terminal domain containing the metal-binding cavity is important for Pirin function [20,26,31]. Indeed, TPh A inserts into the metal-binding cavity and inhibits binding to Bcl3 suggesting that the interaction occurs with the metal-binding cavity of Pirin [31]. In contrast, Hai Pang suggests that a Pirin–Bcl3– (p50)2 complex forms between acidic regions of the N-terminal Pirin domain at residues 77–82, 97–103 and 124–128 with a basic patch of Bcl3 [27]. In this study, we mutated Glutamic acid 103, a residue common between Hai Pang’s model and Pirin’s metalbinding cavity. Pull-down analysis indicated that an E103A mutant is defectiveinfor Bcl3binding(Fig.5A). Thisis the firstexperimental demonstration showing that Glu103 of Pirin is important Bcl3 binding. However, expression of the E103A mutant suppressed Ecadherin gene expression similarly to wild-type Pirin (Fig. 5B and C). Although the Bcl3–(p50)2 complex participates in oncogene addiction in cervical cells [39,40], expression of Pirin in HeLa cells did not increase Slug expression (data not shown). Therefore, we concludethatPirindecreasesE-cadherinexpressionindependently of Bcl3-Slug signaling. To understand how Pirin suppresses E-cadherin gene expression, we analyzed E-cadherin promoter activity (Fig. 4). Since Pirin decreased the activity of the E-cadherin promoter (995+1), we constructed a series of promoter deletion mutants (795+1, 565+1, 365+1, 175+1) to identify a region important for Pirin-mediated regulation. Expression of Pirin decreased the transcriptional activity of all constructs (Supplementary Fig. S1A), suggesting that Pirin may suppress E-cadherin expression through element(s) in region 175+1. Yan-Nan Liu and colleagues proposed that this region contains four Sp1-binding sites and two E-boxes that regulate E-cadherin expression.

Fig. 1. Pirin regulates E-cadherin gene expression. (A, B) HeLa cells were transfected with siRNA targeting Pirin (siPirin#1 or #2) or control siRNA (siCTRL). Forty-eight hours after transfection, cDNA was used for PCR using primer sets specific against Pirin, E-cadherin and GAPDH (A). Forty-eight hours after transfection, HeLa cells were lysed and the lysates were analyzed by Western blot with the indicated antibodies (B). (C) Lysates from HeLa/Pirin-GFP and HeLa/GFP cells were analyzed by Western blot with the indicated antibodies. (D) cDNA from HeLa/GFP or HeLa/Pirin-GFP cells was used for PCR to determine the effect of Pirin on the expression of EMT marker genes.

Fig. 2. Pirin induces cell morphological changes associated with EMT. (A) Phase contrast and fluorescence microscopic images were taken of HeLa/GFP and HeLa/Pirin-GFP cells. (B) Cell circularity was defined as form factor, {4p(area)/(perimeter)2}100 [%], and calculated using Image J software. A random selection of 100 cells from each condition was measured. (C, D) Phase contrast and fluorescence microscopic images were taken of siRNA-treated HeLa/GFP and HeLa/Pirin-GFP cells. Each cell line was transfected with siPirin#2 or siCTRL. Cells were observed by microscopy 48 h after transfection (C) and circularity was measured (D). Data shown are means ± s.d. ⁄P <0.05, bars 100lm.

Fig. 3. Pirin knockdown suppresses cell migration. (A, B) HeLa cells were transfected with siPirin#2 or siCTRL. An artificial wound was created with a tip 24h after transfection and cells were cultured for an additional 12 h. For quantification, the cells were photographed after 12h of incubation (A) and the area covered by cells was measured using Image J and normalized to control cells (B).

Fig. 4. Pirin regulates E-cadherin promoter activity.(A). HeLacells were transfected with siPirin#2 or siGFP (control) and cultured for 24 h. The E-cadherin promoter construct (995+1) and phRL-TK vectorwere transfected and cellswere cultured for an additional 24 h. Luciferase activities were measured and normalized to Renilla luciferase activity. (B) HeLa cells were transfected with the promoter construct (995+1), phRL-TK vector, and a Pirin expression vector. After 24 h, luciferase activities were measured and normalized to Renilla luciferase activity. Data are the mean ± s.d. ⁄P < 0.05.

Fig. 5. Pirin decreases E-cadherin expression in a Bcl3-independent manner. (A) Purified His6-Pirin and His6-Pirin(E103A) were incubated with Glutathione-Sepharose beads conjugated to GST or GST-Bcl3/ARD. The samples were analyzed by Western blot. (B, C) HeLa cells were transfected with vectors encoding GFP, Pirin-GFP, or Pirin(E103A)GFP. Cells were lysed 48 h after transfection and lysates were analyzed by Western blot (B). RNA collected at 48h was used for RT-PCR with the specified primer sets for each gene (C).

7.7.8.2 1324 PIRIN DOWN-REGULATES THE EAF2/U19 SIGNALING AND RETARDS THE GROWTH INHIBITION INDUCED BY EAF2/U19 IN PROSTATE CANCER CELLS

Zhongjie Qiao, Dan Wang, Zhou Wang
The Journal of Urology Apr 2013; 189(4), Supplement: e541
http://dx.doi.org/10.1016/j.juro.2013.02.2678
EAF2/U19, as the tumor suppressor, has been reported to induce apoptosis of LNCaP cells and suppress AT6.1 xenograft prostate tumor growth in vivo, and its expression level is down-regulated in advanced human prostate cancer. EAF2/U19 is also a putative transcription factor with a transactivation domain and capability of sequence-specific DNA binding. Identification and characterization of the binding partners and regulators of EAF2/U19 is essential to understand its function in regulating apoptosis/survival of prostate cancer cells.

7.7.8.3 Pirin Inhibits Cellular Senescence in Melanocytic Cells

Cellular senescence has been widely recognized as a tumor suppressing mechanism that acts as a barrier to cancer development after oncogenic stimuli. A prominent in vivo model of the senescence barrier is represented by nevi, which are composed of melanocytes that, after an initial phase of proliferation induced by activated oncogenes (most commonly BRAF), are blocked in a state of cellular senescence. Transformation to melanoma occurs when genes involved in controlling senescence are mutated or silenced and cells reacquire the capacity to proliferate. Pirin (PIR) is a highly conserved nuclear protein that likely functions as a transcriptional regulator whose expression levels are altered in different types of tumors. We analyzed the expression pattern of PIR in adult human tissues and found that it is expressed in melanocytes and has a complex pattern of regulation in nevi and melanoma: it is rarely detected in mature nevi, but is expressed at high levels in a subset of melanomas. Loss of function and overexpression experiments in normal and transformed melanocytic cells revealed that PIR is involved in the negative control of cellular senescence and that its expression is necessary to overcome the senescence barrier. Our results suggest that PIR may have a relevant role in melanoma progression

Cellular senescence is a physiological process through which normal somatic cells lose their ability to divide and enter an irreversible state of cell cycle arrest, although they remain viable and metabolically active.1,2The specific molecular circuitry underlying the onset of cellular senescence is dependent on the type of stimulus and on the cellular context. A central role is held by the activation of the tumor suppressor proteins p53 and retinoblastoma susceptibility protein (pRB),3–5 which act by interfering with the transcriptional program of the cell and ultimately arresting cell cycle progression.

In the last decade, senescence has been recognized as a major barrier against the development of tumors in mammals.6–8 One of the most prominent in vivo examples is represented by nevi, in which cells proliferate after oncogene activation and then become senescent. Melanoma is a highly aggressive form of neoplasm often observed to derive from nevi, and the transition implies suppression of the mechanisms that sustain the onset and maintenance of senescence.9 In fact, many of the melanoma-associated tumor suppressor genes identified to date are themselves involved in control of senescence, including BRAF (encoding serine/threonine-protein kinase B-raf), CKD4 (cyclin-dependent kinase 4), and CDKN2A (encoding cyclin-dependent kinase inhibitor 2A isoforms p16INK4a and p19ARF).3,10

Nevi frequently harbor oncogenic mutations of the tyrosine kinase BRAF gene, particularly V600E,11 andBRAFV600E is also found in approximately 70% of cutaneous melanomas.12 Expression of BRAFV600E in human melanocytes leads to oncogene-induced senescence,8 which can be considered as a mechanism that protects from malignant progression. In time, some cells may eventually escape senescence, probably through the acquisition of additional genetic abnormalities, thus favoring transformation to melanoma.13

Pirin (PIR) is a highly conserved nuclear protein belonging to the Cupin superfamily14 whose function is, to date, poorly characterized. It has been described as a putative transcriptional regulator on the basis of its physical association with the nuclear I/CCAAT box transcription factor NFI/CTF115 and with the B-cell lymphoma protein, BCL-3, a regulator of NF-κB/Rel activity. A recent report shows that PIR controls melanoma cell migration through the transcriptional regulation of snail homolog 2, SNAI2 (previously SLUG).16 Other reports described quercetinase enzymatic activity,17 and regulation of apoptosis18,19 and stress response, unveiling a high degree of cell-type and species specificity in PIR function.

There is evidence of variations in PIR expression levels in different types of malignancies, but a systematic analysis of PIR expression in human tumors has been lacking. We analyzed PIR expression pattern in a collection of normal and neoplastic human tissues and found that it is expressed in scattered melanocytes, virtually absent in more mature regions of nevi, and present at high levels in a subset of melanomas. Functional studies performed in normal and transformed melanocytic cells revealed that PIR ablation results in cellular senescence, and that PIR levels decrease in response to senescence stimuli. Our results suggest that PIR may be a relevant player in the negative control of cellular senescence in PIR-expressing melanomas.

PIR overexpression in melanoma

Figure 3  PIR overexpression in PIR melanoma cells has no effect on proliferation.
PIR Expression Is Down-Regulated by BRAF Activation and Camptothecin Treatment

BRAF mutations are frequent in nevi, and are directly linked to the induction of oncogene-induced senescence. Variations in PIR expression levels were therefore investigated in an experimental model of senescence induced by oncogenic BRAF. Human diploid fibroblasts (TIG3–hTERT) expressing a conditional form of constitutively activated BRAF fused to the ligand-binding domain of the estrogen receptor (ER) rapidly undergo oncogene-induced senescence on treatment with 4-hydroxytamoxifen (OHT).28,29 PIR protein and mRNA levels were measured in TIG3-BRAF-ER cells at different time points of treatment with 800 nmol/L OHT. PIR expression was significantly repressed both at the mRNA and at the protein level after BRAF activation (Figure 6A), and remained at low levels after 120 hours, suggesting that a significant reduction of PIR expression is associated with the establishment of oncogene-induced senescence in different cell types.

7.7.9 O-GlcNAcylation at promoters, nutrient sensors, and transcriptional regulation

Brian A. Lewis
Biochim et Biophys Acta (BBA) – Gene Regulatory Mechanisms Nov 2013; 1829(11): 1202–1206
http://dx.doi.org/10.1016/j.bbagrm.2013.09.003

Highlights

  • This review article discusses recent advances in the links between O-GlcNAc and transcriptional regulation.
  • Discusses several systems to illustrate O-GlcNAc dynamics: Tet proteins, MLL complexes, circadian clock proteins and RNA pol II.
  • Suggests that promoters are nutrient sensors.

Post-translational modifications play important roles in transcriptional regulation. Among the less understood PTMs is O-GlcNAcylation. Nevertheless, O-GlcNAcylation in the nucleus is found on hundreds of transcription factors and coactivators and is often found in a mutually exclusive ying–yang relationship with phosphorylation. O-GlcNAcylation also links cellular metabolism directly to the proteome, serving as a conduit of metabolic information to the nucleus. This review serves as a brief introduction to O-GlcNAcylation, emphasizing its important thematic roles in transcriptional regulation, and highlights several recent and important additions to the literature that illustrate the connections between O-GlcNAc and transcription.

links between O-GlcNAc and transcriptional regulation.

links between O-GlcNAc and transcriptional regulation.

http://ars.els-cdn.com/content/image/1-s2.0-S1874939913001351-gr1.sml
links between O-GlcNAc and transcriptional regulation.

systems to illustrate O-GlcNAc dynamics

systems to illustrate O-GlcNAc dynamics

http://ars.els-cdn.com/content/image/1-s2.0-S1874939913001351-gr2.sml
systems to illustrate O-GlcNAc dynamics

7.7.10 O-GlcNAcylation in cellular functions and human diseases

Yang YR1Suh PG2.
Adv Biol Regul. 2014 Jan; 54:68-73
http://dx.doi.org:/10.1016/j.jbior.2013.09.007

O-GlcNAcylation is dynamic and a ubiquitous post-translational modification. O-GlcNAcylated proteins influence fundamental functions of proteins such as protein-protein interactions, altering protein stability, and changing protein activity. Thus, aberrant regulation of O-GlcNAcylation contributes to the etiology of chronic diseases of aging, including cancer, cardiovascular disease, metabolic disorders, and Alzheimer’s disease. Diverse cellular signaling systems are involved in pathogenesis of these diseases. O-GlcNAcylated proteins occur in many different tissues and cellular compartments and affect specific cell signaling. This review focuses on the O-GlcNAcylation in basic cellular functions and human diseases.

O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

http://ars.els-cdn.com/content/image/1-s2.0-S2212492613000717-gr2.sml
O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

aberrant regulation of O-GlcNAcylation in disease

aberrant regulation of O-GlcNAcylation in disease

http://ars.els-cdn.com/content/image/1-s2.0-S2212492613000717-gr3.sml
aberrant regulation of O-GlcNAcylation in disease

 Comment:

Body of review in energetic metabolic pathways in malignant T cells

Antigen stimulation of T cell receptor (TCR) signaling to nuclear factor (NF)-B is required for T cell proliferation and differentiation of effector cells.
The TCR-to-NF-B pathway is generally viewed as a linear sequence of events in which TCR engagement triggers a cytoplasmic cascade of protein-protein interactions and post-translational modifications, ultimately culminating in the nuclear translocation of NF-B.
Activation of effect or T cells leads to increased glucose uptake, glycolysis, and lipid synthesis to support growth and proliferation.
Activated T cells were identified with CD7, CD5, CD3, CD2, CD4, CD8 and CD45RO. Simultaneously, the expression of CD95 and its ligand causes apoptotic cells death by paracrine or autocrine mechanism, and during inflammation, IL1-β and interferon-1α. The receptor glucose, Glut 1, is expressed at a low level in naive T cells, and rapidly induced by Myc following T cell receptor (TCR) activation. Glut1 trafficking is also highly regulated, with Glut1 protein remaining in intracellular vesicles until T cell activation.

Dr. Aurel,
Targu Jiu

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Mitochondrial Pyridine Nucleotides and Electron Transport Chain

Larry H Bernstein, MD, FCAP, writer and curator

http://pharmaceuticalinnovation.com/2015/04/03/larryhbern/Mitochondrial_Pyridine_Nucleotides_and_Electron_Transport_Chain

2.1.5 Mitochondrial Pyridine Nucleotides and Electron Transport Chain

2.1.5.1 Mitochondrial function in vivo evaluated by NADH fluorescence

Mayevsky A1, Rogatsky GG.
Am J Physiol Cell Physiol. 2007 Feb; 292(2):C615-40
http://dx.doi.org:/10.1152/ajpcell.00249.2006

Normal mitochondrial function is a critical factor in maintaining cellular homeostasis in various organs of the body. Due to the involvement of mitochondrial dysfunction in many pathological states, the real-time in vivo monitoring of the mitochondrial metabolic state is crucially important. This type of monitoring in animal models as well as in patients provides real-time data that can help interpret experimental results or optimize patient treatment. The goals of the present review are the following: 1) to provide an historical overview of NADH fluorescence monitoring and its physiological significance; 2) to present the solid scientific ground underlying NADH fluorescence measurements based on published materials; 3) to provide the reader with basic information on the methodologies used in the past and the current state of the art fluorometers; and 4) to clarify the various factors affecting monitored signals, including artifacts. The large numbers of publications by different groups testify to the valuable information gathered in various experimental conditions. The monitoring of NADH levels in the tissue provides the most important information on the metabolic state of the mitochondria in terms of energy production and intracellular oxygen levels. Although NADH signals are not calibrated in absolute units, their trend monitoring is important for the interpretation of physiological or pathological situations. To understand tissue function better, the multiparametric approach has been developed where NADH serves as the key parameter. The development of new light sources in UV and visible spectra has led to the development of small compact units applicable in clinical conditions for better diagnosis of patients.

UNDERSTANDING THE MITOCHONDRIAL function has been a challenge for many investigators, including cytologists, biochemists, and physiologists, since its discovery more than 120 years ago. In addition to many books regarding the mitochondria, Ernster and Schatz (79) reviewed the history of mitochondrial structure and function studies. In the past two decades, several studies have reported mitochondrial involvement in pathological processes such as stroke (225) or cytoprotection (77). Most of the information on the mitochondrial function has been accumulated from in vitro studies. A relatively small portion of published papers dealt with the monitoring of mitochondrial function in vivo and in real time. Presently, examination of the involvement of the mitochondrial function in many pathological states, such as sepsis, requires monitoring of patients treated in intensive care units. Unfortunately, real-time monitoring of the mitochondrial function in patients has rarely been performed. The current study presents a review of this issue. To evaluate the activity of the respiratory chain in vivo, it is possible to monitor the mitochondrial NADH, FAD, or the cytochrome oxidase oxidation-reduction state. The interference of blood with the monitoring of FAD and cytochrome oxidase is much higher than with NADH (48); therefore, we invest our effort into the monitoring of the mitochondrial NADH redox state. We do not know of any publication showing clearly that Fp fluorescence could be monitored in vivo in blood-perfused organs. In our preliminary report, we showed that in specific brain areas, one can see the fluorescence of Fp but we were not sure how to validate the results. During the past 33 years, we have published >140 papers in this very significant area, including the largest number of studies using NADH redox state monitoring in patients.

Since the discovery of pyridine nucleotides by Harden and Young (94), >1,000 papers have been published on the use of NADH (Fig. 1A) as a marker for mitochondrial function. In 2000, Schleffler et al. (217) reviewed mitochondrial research methods over the past century. A major aspect of mitochondrial function, namely monitoring the energy state of tissues in vivo, was not discussed in that review. Therefore, the present review will summarize 50 years of research, started in 1955 by Chance and Williams (5657), by defining the mitochondrial metabolic state in vitro. To understand mitochondrial function in vivo and under various pathophysiological conditions, it is important to monitor the redox state of the respiratory chain in real time. The present review will discuss the monitoring principles for one of the electron carriers, namely, nicotinamide adenine dinucleotide (NADH). It is well known that mitochondrial dysfunction is involved in many diseases, such as ischemia, hypoxemia, Parkinson’s disease, Alzheimer’s disease, and in the apoptotic process. Therefore, the possibility of monitoring the mitochondrial NADH redox state in experimental animals and patients is of great importance.

inter-conversion of NAD+ and NADH & difference in the absorption spectra of NAD+ and NADH

inter-conversion of NAD+ and NADH & difference in the absorption spectra of NAD+ and NADH

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Fig. 1. A: molecular structure of NAD+ and the inter-conversion of NAD+ and NADH. B: difference in the absorption spectra of NAD+ and NADH. C: emission spectra of brain NADH excited by 366 nm light (A1, A2, B1, B2, C1) or 324 nm laser light (C2). C1 and C2 show measurements from a dead brain, for comparison of NADH spectra using two different light sources.

To assess the energy demand, it is necessary to measure different organ-specific parameters. In the brain, the energy demand can be evaluated by measuring the extracellular levels of K+ that reflect the activity of the major ATP consumer: Na+-K+-ATPase (152161). In the heart, most of the energy is consumed by the muscle contraction activity. On the other hand, the energy supply mechanism is the same in all tissues: oxygenated blood reaching the capillary bed releases O2that diffuses into the cells. Therefore, it is possible to evaluate tissue energy supply by monitoring the same four different parameters in all tissues.

The main function of the mitochondria is to convert the potential energy stored in various substrates (e.g., glucose) into ATP. The inner membrane of the mitochondria contains 5 complexes of integral membrane proteins, including NADH dehydrogenase (complex 1). Three of those proteins are involved in the respiratory chain activity. The main function of the respiratory chain is to gradually transfer electrons from NADH and FADH2 (originating from the TCA cycle) to O2. With the addition of protons (H+), H2O is generated in complex 4. NADH (Fig. 1Aright side) is a substrate or a coenzyme for the enzymatic activity of dehydrogenases that form part of the respiratory chain and reside in the inner membrane of the mitochondria.

Spectroscopic Monitoring of NADH: An Historical Overview

The discovery of the optical properties of reduced nicotinamide adenine dinucleotide (NADH; previously known as diphosphopyridine nucleotide or pyridine nucleotide) has led to a very intensive research since the early 1950s. The reduced form of this molecule, NADH, absorbs light at 320–380 nm (Fig. 1B) and emits fluorescent light at the 420–480 nm range (Fig. 1C).

Because the oxidized form NAD+ does not absorb light in this range, it was possible to evaluate the redox state of the mitochondria by monitoring the UV absorbance (see Monitoring UV absorbance by NADH) or blue fluorescence of NADH (see Monitoring NADH fluorescence).

Undoubtedly, the pioneering work of Britton Chance of the Johnson Research Foundation at the University of Pennsylvania in Philadelphia led to the establishment and development of the unique measurement technology and theoretical conceptualization of the mitochondrial function based on NADH redox state monitoring in vitro as well as in vivo.

The foundations for future NADH monitoring in vitro and in vivo were established mainly in the 1950s; thus this period will be discussed in this section.

Monitoring of NADH UV absorbance

In 1951, Theorell and Bonnichsen found a shift in the absorption spectrum of DPNH upon addition of alcohol dehydrogenase (238). In the same year, Theorell and Chance described a new spectrophotometric technique for measuring the formation and disappearance of the compound of alcohol dehydrogenase and NADH (239). In 1952, Chance showed the applicability of this new technique to the measurements of pyridine nucleotide enzymes of muscle homogenate or intact cells (25). In 1954, Chance and Williams briefly described new sensitive differential spectrophotometric methods applied to the study of reduced NADH in isolated rat liver mitochondria and the same approach was used by Connelly and Chance (61) in monitoring NADH in stimulated frog nerve and muscle preparations. The oxidation of NADH in the muscle was similar to its oxidation in isolated mitochondria upon addition of ADP. In a comprehensive paper, “Enzyme mechanisms in living cells,” Chance described in detail the measurements of the respiratory enzymes, including NADH (26).

A major milestone in NADH monitoring was the technique presented in 1954 by Chance (27) using a double beam spectrophotometer to determine the appropriate wavelengths in measurements of respiratory enzymes.

The detailed descriptions of the respiratory chain and oxidative phosphorylation in the mitochondria (published in 1955 by Chance and Williams) established our basic knowledge of the mitochondrial function (57). Chance and Williams defined, for the first time, the metabolic states of isolated mitochondria in vitro, depending on the substrate, oxygen, and ADP levels. In addition, they correlated those metabolic states to the oxidation-reduction levels of the respiratory enzymes. The physiological significance of those metabolic states was discussed in 1956 by Chance and Williams (58).

Monitoring NADH fluorescence

The fact that NADH was monitored by the difference in the absorption spectrum of its reduced form, limited the use of that technique to the study of mitochondria in vitro, and in very thin tissue samples (e.g., muscle) or in cell suspension. To provide a method more specific than absorption spectroscopy, fluorescence spectrophotometry in the near-ultraviolet range was applied for NADH measurement. The initial model of fluorescence recorder was described by Theorell and Nygaard in 1954 (240). The first detailed study using fluorescence spectrophotometry of NADH in intact Baker’s yeast cells and algae cells was published in 1957 by Duysens and Amesz (75).

In the next 5 years (1958–1962), the monitoring of NADH fluorescence was significantly expanded, led by Chance and collaborators. In a first preliminary study, Chance et al. (37) performed simultaneous fluorometric and spectrophotometric measurements of the reaction kinetics of bound pyridine nucleotides (PN) in the mitochondria. In the same year (1958), Chance and Baltscheffsky presented preliminary results of measuring the fluorescence of intramitochondrial PN (34). In this study, they proved the connection between the mitochondrial metabolic state and the redox state of NADH as measured by spectral fluorometry in mitochondria isolated from rat liver (57). The correlation between the enzymatic assay of PN and sensitive spectrophotometry was investigated by Klingeberger et al. (120) by using the rat liver, heart, kidney, and brain.

In 1959, Chance and collaborators were able to expand the use of NADH fluorometry to various experimental models, from isolated mitochondria to intact tissue. To monitor NADH localization in intact cells, Chance and Legallais (42) developed a unique differential microfluorimeter with a very high spatial resolution. This approach was used in various cells to identify the intracellular localization of NADH fluorescence signals (54201). The next step was to apply the fluorometric technique to the higher organization level of animal tissues. Together with Jobsis, Chance measured in vitro changes in muscle NADH fluorescence following stimulation (41). In another paper published by Chance and Theorell (55) the authors came to the very significant conclusion that “The oxidation and reduction state of mitochondrial pyridine nucleotide without a measurable change of cytoplasmic fluorescence suggest that compartmentalization of mitochondrial and cytoplasmic pyridine nucleotide occurs in vivo, at least in the grasshopper spermatid.”

An intensive use of the in vivo NADH monitoring approach started in 1962. The “classic” paper on in vivo monitoring of NADH was published in 1962 by Chance et al. (36). They were able to simultaneously monitor the brain and kidney of anesthetized rats using two microfluorometers. In 1962, Chance and collaborators elaborated on this kind of in vivo monitoring and used it in other rat organs (4350).

Scientific Background And Technological Aspects

The absorption and fluorescence spectra of NADH (the reduced form) have been well characterized at different levels of organization, i.e., in solution, mitochondria and cell suspensions, tissue slices, and organs in vitro and in vivo. NADH has an optical absorption band at about 300 to 380 nm and a fluorescence emission band at 420 to 480 nm (Fig. 1B and C). The spectra are considered the same, although there are small differences in the shape and maxima of the spectra for different environments and measurement conditions. However, there is a universal agreement that the intensity of the fluorescence band, independent of the organization level of the environment, is proportional to the concentration of mitochondrial NADH (the reduced form), particularly when measured in vivo from a tissue.

The biochemical and physiological significance of these spectral qualities is also universally accepted, that is, an increase in the fluorescence intensity indicates a more reduced state of NADH and of the rest of the mitochondrial electron transfer chain. Under various circumstances, changes in the redox state of the electron transport chain can be associated with various conditions.

To monitor NADH fluorescence, it is possible to use one of the two principles available. At the early stage, it was necessary to measure and identify the fluorescence spectrum of NADH. Fluorescence spectra were compared in different in vitro and in vivo preparations. In parallel, the second approach was adopted, namely, measuring the total fluorescence signal accumulated and integrated into a single intensity using appropriate filters. This approach was necessary to measure NADH fluorescence continuously. The following parts of this section describe the fluorescence spectra of NADH measured in various in vitro and in vivo models by different investigators. We present this review of the reported spectra to describe the foundations for the second monitoring approach, namely, the continuous monitoring of integrated spectra.

Fluorescence Emission Spectra of NADH

NADH in solution.

Several investigators have measured NADH fluorescence in solution. Very recently, Alfano’s group (62) performed a calibration test of pure β-NADH in solution, compared it to porcine myocutaneous flap, and found a very significant correlation. The NADH solution spectrum and mitochondrial spectrum were also compared by Chance and Baltscheffsky (34).

Similar spectra of NADH in solution were recorded by Schomacker et al. (219) using 337-nm excitation light for colonic tissue diagnosis.

NADH spectra in isolated mitochondria.

The excitation and emission spectra of NADH (PN) and flavoprotein were measured in frozen samples of pigeon heart mitochondria (52). Using rat liver mitochondria, Chance and Baltscheffsky (34) measured the fluorescence spectra in the three metabolic states defined by Chance and Williams (58). The 330-nm light excitation resulted in a fluorescence peak at 440–450 nm. The same kind of spectra was obtained by other investigators using different fluorometers or mitochondria isolated from various organs. Galeotti et al. (87) measured similar spectra from rat liver mitochondria. Using Rhodamine B as an internal standard for system calibration, Koretsky and Balaban (125) found the same spectra emitted from isolated rat liver mitochondria. Koretsky et al. (126) compared the emitted spectrum from heart homogenates (similar to isolated mitochondria) with that of dissolved heart homogenates (126).

Intact cells.

The use of microfluorimetry to study intact cell metabolism was described in several publications by Kohen and collaborators (see, for example, Ref. 123).

The typical NADH fluorescence spectrum was measured in suspension of ascite tumor cells (87). This study demonstrated that the spectrum of intact cells was similar to that of NADH solution.

Using isolated myocytes, Eng et al. (78) compared the spectra measured under various conditions of the mitochondria. They found that cyanide induced an increase in the spectrum difference, whereas FCCP, used as a typical uncoupler of oxidative phosphorylation, produced a marked decrease in the spectrum.

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Principles of NADH monitoring.

As described in the introductory section, NADH can be measured by utilizing its absorption spectrum in the UV range, as well as by the blue fluorescence spectrum under UV illumination. In the early stages, NADH monitoring was based on the difference in the absorption of NADH and NAD+. At the range of 320 to 380 nm, only the reduced form; NADH absorbs light, while NAD+does not (Fig. 1B). Therefore, when a mixture of NADH and NAD+ is illuminated in a cuvette by 320–380 nm, only NADH will affect the absorption spectrum peak at 340 nm. This property of NADH was used in the early 1950s by several investigators, as reviewed in Spectroscopic Monitoring of NADH–Historical Overview. Chance and collaborators utilized this technique to measure NADH in muscle homogenates or intact cells (25) and published many papers concerning the unique absorption spectrum of NADH.

The absorption approach is not practical for measuring NADH in a thick tissue; hence, another property of NADH was used. Since the early 1950s, fluorescence spectrophotometry of NADH has been employed in various in vitro and in vivo models. The emission of NADH fluorescence, under illumination at 320–380 nm, has a very wide spectrum (420–480) with a peak at 450–460 nm (Fig. 1C). NADH fluorescence has been identified by Chance and his collaborators as a good indicator of the intramitochondrial oxidation-reduction state (48).

The review article on in vivo NADH fluorescence monitoring, published in 1992 by Ince et al. (102) included many other technical aspects of the methodology. Nevertheless, here we will elaborate on the historical development of the various models of NADH fluorometers. We recently (155) reported on a new type of NADH fluorometer based on a very small and stable UV light source: a 375-nm light-emitting diode.

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In 1959, Chance and Legallais (42) described a differential fluorometer that heralded a new era in monitoring NADH fluorescence in vivo as an indicator of mitochondrial function. They used a microscope, serving as the fluorometer basis, with two light sources: tungsten and mercury lamps with appropriate filters. In 1959, Chance and Jobsis (41) proved that mechanical muscle activity is associated with NADH oxidation measured in excised muscle. This study was the bridge from the subcellular (mitochondria) and cellular (intact cell) monitoring approaches toward actual in vivo applications.

The first in vivo NADH monitoring device was presented in the early 1960s. At that stage, the effects of scattered light and tissue absorption due to blood were not taken into consideration when monitoring NADH fluorescence. The first detailed results of in vivo NADH fluorescence measurements were published in 1962 (36).

These classic papers described two microfluorometers that were modifications of previous designs (4254). This microfluorimeter type employed Leitz “Ultrapack” illumination, which had been used for many years by various groups until the appearance of UV transmitting optical fibers. To avoid movement artifacts, rats were anesthetized deeply and their heads were fixated in a special holder attached to the operation table. Numerous studies utilized the principles of the “Ultrapack” illumination system. The same instrumentation was used in other in vivo studies, including those of Chance’s group (38434459), Dora and Kovach’s group (7192), Rievich’s group (93), Jobsis and collaborators (108110111213), Gosalvez et al. (89), and Anderson and Sundt (5232). This is only a partial list.

Monitoring NADH fluorescence and reflectance.

The effect of blood on NADH fluorescence was discussed early by Chance et al. (36). To monitor NADH in vivo, Chance’s group had to avoid areas containing large blood vessels, which interfere with the emission and excitation light. The monitoring of a second channel in tissue fluorometry in vivo was reported by Chance and Legallais in 1963 (44). They showed that “changes due to the deoxygenation of oxyhemaglobin do not interfere with measurement of the time course of fluorescence changes in the tissue studies.”

The addition of a second monitoring signal, namely, tissue reflectance at the excitation wavelength was reported in 1968 by Jobsis and Stansby (112). It was based on a previous model described by Jobsis et al. in 1966 (107). In two more papers by Jobsis and collaborators (110,111), the measurement of 366-nm reflectance was used for the correction of the NADH fluorescence signal from the brain. The reflectance signal was subtracted from the fluorescence signal. The same type of instrumentation was used by various groups for the measurement of NADH in single cells (124) or in vitro preparations (1319).

Fiber optic fluorometer/reflectometer.

To enable the monitoring of NADH fluorescence in unanesthetized animals or other in vivo preparations, a flexible means was needed to connect the fluorometer with the tested organ, for example the brain. This was achieved in 1972, when UV transmitting quartz fibers became available (Schott Jena Glass). We have used the light guide-based fluorometer for in vivo monitoring of the brain (48157) subjected to anoxia or cortical spreading depression. The historical development of light guide-based fluorometery-reflectometry is shown in Fig. 2. The original device functioned on the time-sharing principle (Fig. 2A), where four filters were placed in front of a two-arms light guide. Filters 1 and 3 enabled the measurement of NADH fluorescence, while filters 2 and 4 were used to measure tissue reflectance at the excitation wavelength. The reflectance trace was used to correct the NADH signal for hemodynamic artifacts, and to indicate changes in the blood volume of the sampled tissue.

Fig. 2. The three stages in the development of the fiber optic fluorometer/reflectometer (started in the early 1970s).

 

development of the fiber optic fluorometer_reflectometer

development of the fiber optic fluorometer_reflectometer

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Factors Affecting NADH Fluorescence and Reflectance Signals

The excitation and emission spectra of NADH are affected by the redox state of this fluorochrome and by other factors, leading to artifacts in the fluorescence measurements. This section will discuss various NADH-unrelated factors, affecting the measured signal. Since most fluorometers involve the measurement of the total backscattered light at the excitation wavelength (i.e., 366 nm), the discussion will concern changes in NADH fluorescence as well as in tissue reflectance.

The following factors may affect the two measured signals, 366-nm reflectance and 450-nm fluorescence: 1) tissue movement due to mechanical or intracranial pressure changes; 2) extracellular space events, such as volume changes or ion shifts between intra- and extracellular space; 3) vascular and intravascular events, for example, oxy-deoxy Hb changes, and blood volume changes due to autoregulatory vasoconstriction under pathological conditions; and 4) intracellular space factors, such as O2 level, ATP turnover rate, substrate availability, and mitochondrial redox state.

Fig. 3. Comparison between the mitochondrial metabolic state, defined by Chance and Williams (56, 57) and responses of the in vivo brain to changes in O2 supply and brain activation.

mitochondrial metabolic state, defined by Chance and Williams (56, 57) and responses of the in vivo brain to changes in O2 supply

mitochondrial metabolic state, defined by Chance and Williams (56, 57) and responses of the in vivo brain to changes in O2 supply

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Changes in mitochondrial NADH and tissue metabolic state

The pioneering work of Chance and Williams in the 1950s, led to the definition of the metabolic state of isolated mitochondria in vitro. The foundations for the use of NADH fluorescence as a marker of mitochondrial activity have been posited in detail by Chance and Williams (5657). The left portion of Fig. 6 is a modification of a published table, while the right hand segment demonstrates the responses of NADH fluorescence measured in the brain in vivo under various perturbations. The “resting state” of the mitochondria in vitro was defined as state 4, where NADH was 99% in the reduced form, and ADP was the rate limiting substance. If ADP is added to a suspension of mitochondria, ATP synthesis will be stimulated, O2 consumption will increase, and the rate limit will be determined by the activity of the respiratory chain. During this state 3, or the “active state,” the NADH redox state will decrease or become more oxidized (∼50%). When the resting mitochondria are deprived of O2, the activity of the mitochondria will stop and NADH will reach its maximum redox state (state 5).

A definitive description of the mitochondrial metabolic state has never been given for in vivo conditions. Therefore, we described the in vivo mitochondria conditions as recorded by NADH fluorescence in a representative tissue or organ; e.g., the brain. While the range between minimal NADH (∼0) and its maximal level was determined in vitro, it is almost impossible to determine in the intact brain or other organs in vivo. For example, state 2, with a substrate free medium, could not be achieved in vivo since the tissue would die. On the other hand, the maximal level of NADH (state 5) could be monitored in vivo under complete deprivation of O2 by anoxia or complete ischemia.

We used changes in NADH levels monitored in vivo to create a new scale ranging from a maximal definite point to the minimal level recorded in vivo. Details of this approach have been published (152). As shown in Fig. 3, the maximal NADH level is achieved under complete O2 deprivation that can be induced both under in vitro and in vivo conditions. This signifies that this definitive point can be used to determine state 5 in vivo as well. The problem is to determine the metabolic state of a tissue in an in vivo situation. If we adopt the in vitro value of a resting state (state 4), this would signify that the increase in NADH during state 5, induced by anoxia (0% O2), would be only 1%. According to all in vivo studies, this is not the case, and during anoxia the increase in NADH is lager than the decrease under state 4 to 3 transition. Figure 3right, illustrates that the observed level of NADH increase is indeed larger than the decrease. Therefore, we concluded that, under in vivo conditions, the “resting” metabolic state of the brain is found between states 4 and 3 rather than in state 4 as defined in vitro (152). To determine the maximal and minimal levels of NADH in vivo it is almost impossible to use cyanide or uncoupler (FCCP). Nevertheless, we were able to determine the maximal level by anoxia and the “minimal” level by nonfluorescing uncoupler. We injected the uncoupler pentachlorophenol into the ventricles of the rat’s brain while monitoring the NADH responses to anoxia and spreading depression (146). To perform a reliable study with cyanide, the animal would have to die and the results will not be helpful; therefore, we used the anoxia response to measure the maximal level of NADH. Using fiber optic fluorometry, we were able to monitor both anesthetized and awake rats. This figure will be discussed later on in this review. It is important to note that most of the published data on NADH monitoring, have been accumulated in brain studies. Therefore, we will present our data mainly relating to the brain, though results on other organs will be presented as well. Table 1 lists studies published by various investigators as well as our publications. The papers are classified according to the organ monitored and the type of perturbation used. This table does not include rarely studied types of organs or perturbations. Such studies are cited individually in the text.

Table 1. Effect of O2 delivery and consumption on NADH redox state measured in various intact organs by various investigators

Table 2. Historical milestones in monitoring NADH fluorescence in vivo

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Anoxia and Hypoxia.

The responses to hypoxia and anoxia are very similar; therefore, they will be discussed together. According to the definition of Chance and Williams (5657), a shift toward state 5 involves an increase in NADH proportional to a decrease in O2 supply.

It is assumed that the response of NADH fluorescence to hypoxia or anoxia, induced in vivo, should be very similar to the response of isolated mitochondria. As shown in Fig. 4B, when the blood-free brain was exposed to N2, the fluorescence showed a clear increase-decrease cycle depending on the availability of O2. The reflectance trace was not affected at all. In autoregulated blood-perfused organs, it is expected that the lack of O2 will trigger compensation mechanisms that may lead to an increase in the blood flow and volume, or a decrease in thereflectancesignal. We tested, in the same rats, the response to anoxia of the normoxic blood-perfused brain. The results are shown in Fig. 4A. Indeed, reflectance exhibited a large decrease due to the increase in blood volume (vasodilatation of brain vessels). Figure 4C and D, presents the responses to anoxia measured via 2 mm and 1 mm light guides. A small variation can be seen in the reflectance response between the two light guides.

Ischemia, Or Decreased Blood Flow.

Under partial or complete ischemia, blood flow to the monitored organ is decreased and, as a result, O2 delivery is limited or even abolished. The use of ischemia in animal models provides information relevant to critical clinical situations such as brain stroke or heart attack. The primary factor starting the pathological state is the decrease in O2 supply, making the tissue energy balance negative, and preventing the tissue from performing its function. Figure 7 illustrates the effects of ischemia and anoxia on the NADH level in the brain of an anesthetized gerbil. The measurements of NADH in the cerebral hemispheres were correlated to the brain electrical activity (ECoG; electrocorticogram). To test and compare the measurements done in the two hemispheres, we exposed the gerbil to short-term anoxia. As shown, the two responses are very similar and correlate to the depression of the ECoG signal measured in the two hemispheres.

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After the introduction of the light guide-based fluorometry, we were able to expose the awake brain to hyperbaric conditions. A clear decrease in NADH (oxidation) was recorded during the shift from 21% to 100% O2, as well as during compression of up to 10 atmospheres 100% O2 (150,152153167177). A similar oxidation was found upon CO2 addition to the gas mixture (94–99% O2) (149). We also found a correlation between the elevated brain PO2 and the oxidation of NADH in awake rats (151). The oxidation of NADH was also recorded under normobaric hyperoxia (113). Furthermore, we tested the effects of hyperbaric oxygenation on carbon monoxide intoxication (212) or cyanide exposure (235).

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Responses to energy consumption changes

As shown by Chance and Williams (57, 58), the activation of the mitochondria by increased ADP is coupled with oxidation of NADH (decreased NADH levels) and is known as the state 4 to state 3 transition in isolated mitochondria. Most of the investigations in this field of tissue activation were made on neuronal tissue in vivo. However, studies of other organs, such as the heart or skeletal muscle, were conducted as well. The demand for energy (ATP) by various tissues is dependent on the specific tasks of each organ or tissue. Nevertheless, the stimulation of mitochondrial function is common in all tissues in the body. We will describe the effects of tissue activation on NADH fluorescence under normoxic conditions as well as during limitation of O2 supply in the tissue (hypoxia, ischemia).

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Responses to energy consumption changes

As shown by Chance and Williams (5758), the activation of the mitochondria by increased ADP is coupled with oxidation of NADH (decreased NADH levels) and is known as the state 4 to state 3 transition in isolated mitochondria. Most of the investigations in this field of tissue activation were made on neuronal tissue in vivo. However, studies of other organs, such as the heart or skeletal muscle, were conducted as well. The demand for energy (ATP) by various tissues is dependent on the specific tasks of each organ or tissue. Nevertheless, the stimulation of mitochondrial function is common in all tissues in the body. We will describe the effects of tissue activation on NADH fluorescence under normoxic conditions as well as during limitation of O2 supply in the tissue (hypoxia, ischemia).

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The effects of pharmacological agents on NADH redox state in various organs were published as well. Kedem et al. researched the influence of various inotropic agents (1) as well as nitroprusside (2), nitroglycerin (76), and propranolol (86).

Osbakken and collaborators (194195) also monitored NADH under various drug exposures. Baron et al. (17) described the effects of lidocaine on NADH, during ischemia in the dog heart. The effects of blood substitute emulsion on NADH in the kidney were reported (260). The influence of radioprotective chemicals on NADH in rat tissue was described in the 1960s (103). The action of various drugs (e.g., the uncoupler Amytal) was studied in the liver exposed to hyperbaric oxygenation (3140).

Monitoring Human Body Organs

The first attempt to apply NADH fluorometry to human tissues in vivo was made in 1971 by Jobsis et al. (111). Using NADH fluorescence microfluorometry, they monitored the exposed brain of neurosurgical patients undergoing treatments for focal cerebral seizures. They correlated the electrocorticographic data to the NADH redox state under direct cortical stimulation of the monitored area. The clear decrease in the NADH signal was interpreted as a change in oxidation. The recorded changes were very similar to those obtained in analogous procedures in the cat brain (213). A few years later, the collaboration between Austin and Chance (8) led to the recording of NADH in the brain of patients subjected to microanastomosis of the superficial temporal artery to the middle cerebral artery. The same group found an improvement of cerebral oxidative metabolism after the anastomosis, which was correlated to the elevated blood flow and increased tissue PO2 (9).

The next step was taken by Barlow et al. (16), who expanded this technique to monitor the heart and the brain. Using a different type of fluorometer, Van Buren et al. showed a decrease in NADH (oxidation) due to cortical stimulation in epileptic patients (251). In 1979, Fein and Jobsis (81) studied the changes in brain energetics in patients undergoing superficial temporal arterial-middle cerebral artery microanastomosis. Fein and Olinger (8283) monitored patients after transient ischemic attacks. The brain of these patients, who had undergone an extracranial-intracranial bypass, was stimulated, and changes in NADH were recorded.

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The laser-based fluorimeter developed by Renault (207) was used to monitor NADH redox state in the heart muscle during pharmacological treatments (207), as well as in skeletal muscle (91). Attempts to apply NADH fluorometry in clinical practice (reported in a dozen short publications) did not lead to the development of a proper medical device applicable on a daily basis.

In 1990, our team started developing a unique multiparametric monitoring system that included the measurement of NADH fluorescence, using a light guide-based device. This system was initially applied to monitor neurosurgical patients undergoing brain surgery or those treated in the intensive care unit. In the first paper on the subject (published in 1991), we showed the feasibility of our approach. After a transient short occlusion of one common carotid artery, the increase in NADH was correlated to a decrease in cerebral blood flow (164). It took another 5 years to restart organized clinical testing of our monitoring system.

Monitoring Nadh And The Multiparametric Approach

The need for multiparametric monitoring of other parameters, additional to NADH, results from the basic understanding that NADH is affected by two major factors. The redox state of NADH reflects not only the availability of O2 inside the mitochondria but also the turnover rate of the ATP-ADP cycling activity (state 4 to state 3 transition). The interaction between these two factors affects the nature of NADH response to various conditions. For example, an increase in energy consumption (e.g., cortical spreading depression) under O2 restriction will be manifested as an increase in NADH rather than a decrease (oxidation) measured in normal well-oxygenated tissue. According to Chance and Williams, an increase in ATP production is always recorded as a decrease in NADH (5758). Therefore, the “reduction cycle” measured by the NADH signal in response to CSD can be interpreted as an artifact of some kind. This phenomenon and the fact that the mitochondrial NADH signal cannot yet be calibrated in absolute values prompted us to develop a multiparametric monitoring approach and a probe that could be used in various tissues exposed to different pathophysiological conditions. By this approach, two major advantages were gained. First, it provided the possibility of a better interpretation of the recorded results; second, nonphysiological responses could also be more easily detected. To elaborate on these points, we will consider the following typical example. In the early stage of NADH monitoring using a time sharing fluorometer, we found that a few minutes after complete ischemia was induced by decapitation in a rat model, a large increase in the reflectance signal was recorded in parallel to a clear NADH decrease in the dead monitored brain, apparently indicative of NADH oxidation. We termed this event “the Secondary Reflectance Increase-SRI” (147). It was clear to us that this late “oxidation” of NADH in the dead animal was an artifact of the monitoring system. The same response was recorded also when partial ischemia was induced in a gerbil’s brain. The “oxidation” of NADH in a dead or partially ischemic brain did not have any physiological or biochemical interpretation, so we suspected that this “oxidation” is due to the large increase in the reflectance signal, and to a failure of the fluorescence signal’s correction method. We speculated that the large increase in the reflectance trace (SRI) after ischemia or brain death, resulted from a spasm of blood vessels. Such spasms are known to occur in this type of conditions, namely during cortex depolarization. Only when monitoring other parameters, in addition to NADH, such as extracellular K+ and DC steady potential, were we able to give a substantial explanation for the SRI event (85). On the basis of these experiments, we concluded that the SRI phenomenon is always associated with a negative shift in the DC potential and a large increase in extracellular potassium when energy is not available.

….

NADH and electrical activity

The first attempt to combine NADH and electrical measurements was made by Chance and Schoener in 1962 (50). They showed the time relationship between the increase in NADH due to anoxia or hypoxia, and the disappearance of electrical activity (ECoG) in rat cerebral cortex. The same type of correlation was reported later by Jobsis et al. (110) for epileptic activity, and by Rosenthal and Somjen (163) and Mayevsky and Chance (157) for CSD. The accumulated results have made it clear that under limited energy or O2 supply, NADH becomes elevated in the brain, while the spontaneous ECoG activity is depressed. The ECoG begins to decelerate when NADH reaches 70%-80% of its maximal increase upon death (157159) or decapitation (160259). The recovery of ECoG after anoxia is completed much later than NADH oxidation, suggesting that energy availability is a prerequisite condition but not the only condition needed for a complete ECoG recovery. Depression of the ECoG is also recorded when the brain is exposed to depolarization due to CSD; however, it is not caused by a lack of O2. Similar correlations between NADH and ECoG were described in cat cerebral cortex exposed to seizures and hemorrhagic hypotension (100).

NADH and respiratory chain components

Since the activities of various respiratory chain components are strongly coupled, the tissue respiratory rate can be better evaluated by monitoring several such components. Very few attempts have been made to correlate NADH responses in vivo, with other components of the respiratory chain. The main reason for this was the stronger interference of blood with Fp or cytochrome oxidase measurements, compared with NADH. The effects of hypotension and anoxia on NADH and cytochrome aa3, were measured in the brain in vivo (99). LaManna et al. showed the effects of Ethanol on brain NADH and cytochrome aa3 in rats and cats (137). Therefore, almost all correlations between Fp and NADH were studied in blood-free organs (49). In 1976, we presented preliminary results indicating that in certain morphological areas of the brain, containing less blood vessels, a good correlation is recorded between NADH and Fp responses to anoxia in vivo (146). The only practical way to measure these two signals together was to freeze the tissue and then analyze the two parameters in the frozen state (168183). Another approach to correlating NADH and Fp redox state was suggested by Paddle et al. (198). They used a NADH/Fp scanning fluorometer to monitor the muscle (198) or rat diaphragm (197). A few papers have been published on the use of flying spot fluorometer to monitor the two fluorescent signals in the brain and other organs (35). Most of the data published in this field have been acquired in vitro (3349) or in blood-free organs such as the liver (218).

In this review, we tried to summarize the scientific background and technological aspects of in vivo NADH fluometry approach for the monitoring of mitochondrial functions. This technology still has some limitations including the need for better correction technique for hemodynamic artefacts as well as a new approach for quantitative calibration of the signals. During the past decade, the preliminary application of the NADH fluorometry to clinical environment was very promising. This stimulates us to improve the technology to provide a practical medical device that will be used by many clinicians after approval by the regulatory agencies around the world.

2.1.5.2 A microscale mathematical model for metabolic symbiosis: Investigating the effects of metabolic inhibition on ATP turnover in tumors

Colin Phipps, Hamid Molavian, Mohammad Kohandel
J Theoret Biol 2015; 366: 103-114
http://dx.doi.org/10.1016/j.jtbi.2014.11.016

Cancer cells are notorious for their metabolic adaptations to hypoxic and acidic conditions, and especially for highly elevated glycolytic rates in tumor tissues. An end product of glycolysis is lactate, a molecule that cells can utilize instead of glucose to fuel respiration in the presence of oxygen. This could be beneficial to those cells that do not have sufficient oxygen as it conserves glucose for glycolysis. To better quantify this phenomenon we develop a diffusion-reaction mathematical model for nutrient concentrations in cancerous tissue surrounding a single cylindrical microvessel. We use our model to analyze the interdependence between cell populations’ metabolic behaviors on a microscopic scale, specifically the emerging paradigm of metabolic symbiosis that exists between aerobic and glycolytic cells. The ATP turnover rates are calculated as a function of distance from the blood vessel, which exhibit a lactate-consuming population at intermediate distances from the vessel. We also consider the ramifications of the Warburg effect where cells utilize aerobic glycolysis along with this lactate consuming respiration. We also investigate the effect of inhibiting metabolic pathways on cancer cells since insufficient ATP can trigger cell apoptosis. Effects that could be induced by metabolic inhibitors are analyzed by calculating the total ATP turnover in a unit tissue annulus in various parameter regimes that correspond to treatment conditions where specific metabolic pathways are knocked out. We conclude that therapies that target glycolysis, e.g. lactate dehydrogenase inhibitors or glycolytic enzyme inhibition, are the keys to successful metabolic repression.

The extensive metabolic requirements for cancer cell proliferation coupled with the harsh microenvironment in solid tumors culminate in a highly adaptive and complex network for cellular energy production. The genetically altered metabolic behavior of cancer cells has led to a number of emerging metabolic paradigms, in addition to those that are universally exhibited in both cancerous and normal cells. We will investigate this complex metabolic behavior by formulating a minimal mathematical model that includes the essential metabolites of glucose, lactate and oxygen in the tissue surrounding a microvessel. The cylindrical geometry used here has been used in a similar context to consider interactions between metabolites and tumor cells with treatment effects in a simplified setting (e.g. Bertuzzi et al., 2000, 2007a). The model presented here will enable the quantification of various behaviors, such as the symbiotic relationship that exists between lactate producing glycolytic cells and lactate-consuming respiratory cells, and the analysis of metabolic dependence on various physiological conditions such as hypoxia and induced metabolic inhibition. Metabolic inhibition including glycolytic inhibitors among many others targets could be very important for cancer treatment since an ATP deficit can induce apoptosis (Izyumov et al., 2004). The key consideration for addressing this problem with mathematics is the formulation of nutrient consumption rates that encompass the various primary facets of cancer cell metabolism and their corresponding ATP yields. In normal well-oxygenated tissues the primary source of ATP is the process of cellular respiration. The complete conversion of glucose to carbon dioxide and water has an ideal yield of about 29 ATP, although realistically the yield is substantially lower (Brand, 2005). The preliminary stage of cellular respiration is glycolysis, the conversion of glucose to pyruvate; this process directly produces 2 ATP. In hypoxic conditions this pyruvate is preferentially converted into lactate via the enzyme lactate dehydrogenase (LDH) to regenerate the essential cofactor NAD+. In oxygenated conditions this pyruvate is transported across the inner mitochondrial matrix where it is decarboxylated and enters the citric acid cycle; the citric acid cycle directly generates 2 more ATP per glucose. The primary energy payoff is a result of cofactor oxidization that enables the electron transport chain to establish a proton gradient across the inner mitochondrial matrix. ATP synthase utilizes this electrochemical gradient to drive the phosphorylation of approximately 25 additional ATP per glucose molecule.

The aforementioned universal traits that cancer cells and normal cells share include cellular responses to various levels of oxygen, lactate or glucose. Examples include a Crabtree-like effect and a Pasteur-like effect (Casciari et al., 1992a). The Crabtree-like effect is when oxygen consumption decreases as glucose concentration increases. This can be explained by an increasing reliance on glycolysis for ATP when hyperglycemic conditions are encountered. The Pasteur-like effect is decreased glucose consumption as oxygen increases. This is due primarily to the inhibition of various metabolic steps by the presence of elevated ATP and other intermediaries. However, cancer cells are unique in that they preferentially utilize glycolysis, even in the presence of oxygen, coined aerobic glycolysis. This phenomenon is generally referred to as the Warburg effect whereby cells rely primarily on glycolysis even in the presence of sufficient oxygen to perform respiration (Warburg, 1956). There is a perceived inefficiency of this metabolic strategy, namely the dramatically reduced ATP yield, just 2 per glucose instead of 29, however, it has the benefits of faster ATP production and it is likely that much of this glucose is being consumed for proliferative (Vander Heiden et al., 2009) (e.g. by the pentose phosphate pathway) purposes. In addition to the typical glycolytic phenotype exhibited in many cancers, there is also a developing story of a co-operative relationship existing between aerobic and anaerobic  cancer cells. The lactate necessarily produced by glycolytic cells is being pushed back into the respiratory cycle by being converted into pyruvate (summarized in Feron, 2009; Nakajima and Van Houten, 2012); this spatial relationship is shown in Fig. 1. Lactate consumption has been observed in vitro in various models (Bouzier et al.,1998; Katz et al., 1974) as well as in vivo as early as the early 1980s (Sauer et al., 1982). However, a renewed interest in the topic was piqued when Sonveaux et al. (2008) showed that reducing lactate uptake by cancer cells led to hypoxic cell death, a particularly difficult subpopulation to target using traditional methods.

Metabolic phenomena have been studied in great detail by mathematical models, but models of tumor metabolism rarely include the interaction of the transport mechanisms of microvessels with the localized metabolic behavior of cells (with one recent exception McGillen et al., 2013). In the section to follow, we will develop a mathematical model that describes the concentrations of molecules that are important to cellular metabolism in the tissue around a single three-dimensional vessel that exhibits diffusion dominated interstitial transport. We will then use this model to demonstrate how the properties of the tumor cell population, such as glucose, lactate and oxygen consumption rates, affect tumor hypoxia and ATP production around a single vessel. The effects of metabolic inhibitors will be investigated by parameter changes that could be elicited by the application of glycolysis inhibitors, lactate dehydrogenase (LDH) inhibitors or respiratory inhibitors. We are interested in those metabolic inhibitors that could cripple the cells’ ability to produce ATP.

Fig.1. The spatial relationship between the cell populations in the model indicating dominant metabolism as we move away from the vessel. When the glucose and oxygen concentrations are highest near the vessel wall, the cells preferentially utilize glucose-fuelled respiration. When the oxygen supply is depleted far from the vessel, the cells rely on glycolysis. The glycolytic cells produce large quantities of lactate which are consumed by cells at intermediary distances and hypoxic oxygen concentrations. These cells are participating in a behavior that we will refer to as metabolic symbiosis.

A model to describe the concentrations of the major players in the metabolic pathways of respiration and glycolysis, will be outlined here. Its origins lie in a metabolic model developed by Casciari et al. (1992b) that was subsequently applied on the microscale by Molavian et al. (2009). The functional forms for the production rates are similar to those proposed by MendozaJuez et al. (2012) and subsequently extended to a spatial model by McGillen et al. (2013). In hypoxic and anoxic conditions, cells must partially or exclusively rely on metabolic pathways, such as glycolysis, that do not require oxygen for ATP production. In glycolysis, the preliminary stage of respiration, a single glucose molecule (C6H12O6) yields 2 ATP, which we will denote under the reaction arrow with a boxed ATP yield number, with the byproducts of lactate and a proton. Denoting glucose by G and lactate by L C3H5O3, the net reaction is G kG> 2L + 2H+; [2]

where kG (mM/s) is the rate of glucose consumption by glycolysis that results in lactate formation. The accumulation of these hydrogen ions in a solid tumor is a primary contributor to tumor acidosis. In the presence of oxygen (O2), glycolysis is typically followed by the rest of the respiratory process with an ideal energy yield of approximately 29 ATP molecules with carbon dioxide (CO2) and water (H2O) as the only byproducts. The simplified summary reaction is given by G kO à 6O2 6CO2 + 6H2O; [29]

where kO is the rate of glucose consumption that results in cellular respiration. To represent the metabolic symbiosis between cells primarily producing energy via glycolysis and those consuming lactate in well-oxygenated areas, we will link the above two reactions with the lactate-consuming net reaction: L+ H+ + 3O2 kLà  3CO2 + 3H2O; [13.5]

where kL is the rate of lactate consumption. This summarizes the re-entry of lactate, via conversion to pyruvate, into aerobic respiration that yields 13.5 ATP per lactate molecule. The relationships between the summary reactions included in the model are given in Fig. 2.

Fig. 2. The summary reactions included in the metabolism model. Glycolysis proceeds at rate kG and produces 2 ATP from the conversion of glucose to pyruvate. Glucose-fuelled respiration occurs at rate kO in the presence of oxygen, while lactate-fuelled respiration occurs at rate kL (2 kL is present in the diagram to remain consistent with the glycolytic yield of 2 lactate molecules).

Fig. 3. Solution to base case boundary value problem. Nondimensional oxygen o and glucose g concentrations decrease due to metabolic consumption. Lactate  ℓ  increases to almost double its vessel concentration since it is produced by glycolysis at a higher rate than it is consumed by respiration due to a limiting oxygen concentration. This image has been spatially truncated to 300 μm since the concentrations are approximately constant after this point.

Fig. 4. Consumption rates of oxygen, lactate and glucose (QO, QL and QG for the concentrations given in Fig. 3). The glucose and oxygen consumption rates are strictly positive while the consumption rate of lactate is predominantly negative. This indicates that even in regions where lactate is being consumed, it is being produced at a higher rate by glycolysis.

Fig. 5. The base case for ATP turnover (consumption/production) rates corresponding to consumption rates given in Fig. 4. The contributions of the pathways are bounded by the total ATP turnover rate  PATP . Glycolysis dominates in hypoxic/ anoxic regions while glucose-fuelled respiration occurs sparingly near the blood vessel. Lactate-fuelled cells are consuming the byproduct of the glycolytic cells where there is oxygen present.

Warburg effect

In the base case considered above glycolysis is inhibited until the oxygen consumption drops to values that prevent the production of sufficient ATP to maintain cell survival. However, cancer cells will commonly utilize glycolysis as a primary energy source even when there is enough oxygen to ensure cell survival. In the model we characterize the cell’s ability to hold off on utilizing glycolysis in oxygenated areas by the parameter ΛO. Reducing it 400-fold from the base case above (from 4000 to 100) results in spatial ATP turnover rate as given in Fig. 6. Cells near the vessel greedily consume the available resources leaving cells further from the vessel to die from insufficient ATP supply. The ATP production breakdown corresponds to the second bar in Fig. 8 and is slightly higher than the whole tissue considered in the base case above.

Fig. 6. ATP turnover (consumption/production) rates for cells exhibiting the Warburg effect (differs from base case because ΛO = 100 instead of 4000). The contributions of the pathways are bounded by the total ATP turnover rate PATP. Glycolysis is dominant in all regions of the tumor, however, glucose and lactate fueled respiration occur sparingly near the blood vessel where there is oxygen present.

Fig. 7. The optimal metabolic behavior on the microscale given an ATP turnover maximum of X mM=s. This shows glucose-fuelled respiration near the vessel, glycolysis far from the vessel and a lactate-consuming population in between these two

Instead of fixing all of the parameters to the values given in Table 1, we could leave some of the parameters free and optimize the amount of ATP generated from the given metabolites by imposing a maximum constraint on ATP production. For instance, setting all of the parameters initially to those given in Table 1, and then minimizing some function of Z¼PATP θ where θ is the maximum allowed ATP turnover rate would theoretically ensure that the available resources were not being selfishly consumed by oxidative cells near the vessel. Allowing cells to alter their glycolytic parameters: βg, δ and κg along with their lactate–glucose switch parameter λ yields the results shown in Fig. 7. While there was still enough constraint that the system still exhibited a non-constant ATP turnover where it could, this reinforces the suggested optimal strategy of glucose-fuelled respiration near the vessel, glycolysis far from the vessel and a lactate-consuming population in between these two. This optimization procedure most notably resulted in a reduced δ enabling the switch to glycolysis to happen closer to the vessel and a lower κg enabling a later and more drastic shut off of glycolysis; the parameter results of this simulation are presented in Table C1.

The mathematical model presented here can give insight into the effects of blocking various metabolic pathways. The three metabolic pathways that we have considered, namely (i) glucosefuelled respiration, (ii) lactate-fuelled respiration and (iii) glycolysis, could be inhibited by various agents, and the effects on local ATP production will be outlined below. For illustrative purposes we will consider complete inhibition of these pathways, but this will be followed by consideration of the more realistic scenario where these pathways are only partially inhibited. Entirely knocking out lactate metabolism could be achieved by inhibiting lactate dehydrogenase (LDH) which is responsible for the reentry of lactate into respiratory pathways by converting lactate into pyruvate. Successful inhibition would concurrently prevent the conversion of pyruvate to lactate as well, a crucial step for regenerating NAD+ in glycolytic cells. This has been shown to reduce ATP levels and consequently induce cell death in tumors (Le et al., 2010). The complete inhibition of lactate dehydrogenase would eliminate two of the three pathways considered here: lactate-fuelled respiration and glycolysis. Complete inhibition can be reflected in the model by setting BL¼0 and BG¼0, leaving only glucose-fuelled respiration to produce ATP, a physiologically normal condition. However, the hypoxic and hypoglycaemic conditions considered here do not leave enough fuel for cell survival. This scenario corresponds to the third bar in Fig. 8. We could also target glucose transport into the cell, an intermediary of glycolysis or one of the critical enzymes responsible for converting glucose to pyruvate. This is distinct from the strategy noted above of inhibiting LDH which prevents the conversion of lactate to pyruvate and vice versa. This has also been noted as a prime target for cancer therapy (Pelicano et al., 2006; Gatenby and Gillies, 2007) and there are currently many potential targets (Granchi and Minutolo, 2012). Here we will consider the shutdown of glycolysis as preventing both glucose-fuelled respiration and glycolysis since both of these require glucose to be converted into pyruvate. However, it leaves the lactate-fuelled respiratory pathway intact. This could be considered in the model by taking BO¼0 and BG¼0.Similar tothe caseof LDH inhibition this leads to a significant decrease in ATP production as shown in the fourth bar of Fig. 8. The final scenario that we consider corresponds to full inhibition of respiration somewhere along the chain between pyruvate transport into the mitochondria and the electron transport chain. There are numerous potential targets in the mitochondria (Costantini et al., 2000) and we will consider the complete shutdown of respiration by setting BL¼0 and BO¼0. This would result in negligible oxygen consumption and with our base case of ΛO 100-4000 this would lead to repressed glycolysis in the tissue.

Fig. 9. The effects of metabolic repression on total ATP production in a unit annulus of tumor tissue ðΦÞ. Cell metabolism is fully functioning when the relative rate is 1, while the cell metabolism is fully inhibited when the relative rateis 0. Intermediary values correspond to partial inhibition of both affected metabolic rates, e.g. for LDH half-inhibition: BG and BL are half the base case value, and BO remains at its base case value. The legend abbreviations are the same as those used in bars 3–6 in Fig. 8: LDH inhibition (LDH), glycolytic inhibition (Glyc), respiratory inhibition (Resp), and respiratory inhibition with Warburg effect (R-W). The total ATP production begins to significantly decrease for LDH and glycolytic inhibition only once more than half inhibition is reached. For respiratory inhibition, significant decreases are not detected until metabolic rates drop to one-tenth of the base case value.

The functional form for glycolysis given in (8) is similar to that used in McGillen et al. (2013), except where our form uses oxygen as the inhibitory molecule, they use lactate. McGillen et al. (2013) do not include glucose-fuelled respiration at all (as it was deemed to occur at negligible rates), and they use a similar lactate-fuelled respiratory term as used here, that was originally formulated by Mendoza-Juez et al. (2012). Instead of including a Michaelis– Menten oxygen dependence, they introduce a switch parameter that turns oxygen-fuelled metabolism on and off at a threshold oxygen concentration. However, they did enable the cells to use combinations of respiration and glycolysis as opposed to the strict switching between these two pathways modelled by Mendoza Juez et al. (2012). The novel aspects of our model include the introduction of a glycolytic inhibition parameter that can prevent or enable the Warburg effect, an explicit and smoothly defined oxygen dependence for the respiratory pathways, and the inclusion of an accurate ATP yield formula. While our results focus on the energetic consequences of metabolic inhibition, McGillen et al. (2013) focus on the interaction between metabolite consumption and tumor growth.

Conclusions

The mathematical model formulated and analyzed above can give insight into the metabolic behaviors of cancer cells on the microscale. The tumor microenvironment characterized by hypoxia and nutrient deprivation leads to the utilization of highly unregulated glycolytic pathways and the consumption by respiring cells of the lactate produced by these cells. These metabolic scenarios are encompassed by the functional forms proposed for glucose, lactate and oxygen consumption. To consider the effect of altering parameters in the model to the efficiency of energy production we must also consider the rate of ATP turnover in the tissue. To this end a detailed biochemical summary was performed in order to calculate estimates for ATP yields. These energetic landscapes were considered in tissues that utilize anaerobic glycolysis, thus keeping more cells alive, and those that experience the Warburg effect, performing glycolysis in oxygenated areas. The analysis shows that the latter does confer a proliferative advantage by producing more ATP. The effects of metabolic inhibition were taken into account by knocking out the pathways considered in our model. Glycolytic inhibition blocked glycolysis and glucose-fuelled respiration, LDH inhibition blocked glycolysis and lactate-fuelled respiration while respiration inhibition blocked both forms of respiration. Both strategies that block glycolysis lead to appreciable decreases in total ATP production, while those that block respiration are only effective in the base case where the cells are unable to elevate glycolytic rates due to the repressive effect of oxygen in the model. However, when considering a more realistic scenario where cells can adapt to blocked respiratory pathways by upregulating glycolysis via the Warburg effect, we observe that this treatment strategy allows sufficient ATP for cell survival. The work presented here should lead to a reconsideration of the importance of the spatial relationships between cells performing under specific metabolic regimes and provides a minimally parameterized and straightforward basis for future phenomenological metabolite consumption models.

2.1.5.3 Localization and Kinetics of Reduced Pyridine Nucleotide in Living Cells by Microfluorometry J. Biol. Chem.-1959-Chance-3044-50

Britton Chance and Bo Thorell

J Biological Chemistry Nov 1959; 234(11)
On the basis of early studies of the blue fluorescence of living cells and tissues before chemical treatment, l Sjiistrand (1) suggested its association with the mitochondrial bodies. Microspectroscopic observations of prepared tissue sections revealed emission bands of the fluorescent material of axons (1) and acid treated groups of kidney cells; (2) critical evaluations of available spectrograms of purified materials lead to the identification of thiamin and riboflavin, respectively. Although some of the  of the kidney sections, before acid treatment, showed fluorescence bands in the spectrograms that are now regarded as suggestive of reduced pyridine nucleotide, the fluorescence of which was first observed by Warburg (2), insufficient data were available at that time to consider reduced pyridine nucleotide as a possible cause of the tissue fluorescence. Recent studies by Boyer and Theorell (3) and Duysens and Kronenberg (4) on alcohol dehydrogenase show clearly the great enhancement of DPNH fluorescence that is caused by a binding of the coenzyme to the enzyme surface. Furthermore, Duysens and Amesz (5) demonstrate that the intact yeast cell shows a fluorescence characteristic of bound reduced pyridine nucleotide. In more recent experiments, it has been found that intramitochondrial reduced pyridine nucleotide also exhibits the same characteristic fluorescence, calling attention to the possibility of a close relationship between this effect and the blue fluorescence of living cells and tissues (6). The fluorometric result agrees with the spectrophotometrically determined large RPN3 content of mitochondria (7). Furthermore, its binding to a mitochondrial component has been suggested by kinetic studies (7). More recent data show that the fluorescence of intact muscle diminishes upon electrically induced contraction, in agreement with the spectrophotometrically observed oxidation of RPN (8). Thus, there is good evidence that a considerable amount of tissue fluorescence is due to this component. To study the fluorescence of mitochondrial RPN independently of that of the cytoplasm, it has been desirable to develop a microfluorometric method, which, in conjunction with suitable biological materials showing isolation of the mitochondrial bodies, could be used to investigate cytoplasmic-mitochondrial interactions and also to permit the assay of RPN localized in different
1 The term “autofluorescence” is used by Sjostrand and other workers to indicate the fluorescence of a tissue before its treatment with stains, acids, and so forth.
2 F. S. Sjiistrand, unpublished experiments.
* The abbreviation used is: RPN, reduced pyridine nucleotide.

This paper describes such an instrument and its application to the observation of mitochondrial RPN, particularly in highly localized mitochondrial bodies such as the nebenkern4 of the grasshopper spermatid (11). It is now possible to investigate in viva the independent changes of mitochondrial and cytoplasmic pyridine nucleotide in the aerobic-anaerobic transition. In other cells, where mitochondrial localization is not sufficient for independent characterization of cytoplasmic and mitochondrial components of the fluorescence, assays of the oxidation-reduction state of the total pyridine nucleotide in individual cells in different states of metabolism and growth are possible. The combination of this differential fluorometer with the spectrophotometer described elsewhere (12, 13) for the localization of activities of respiratory and glycolytic enzymes in cells affords a new approach to the dynamic aspects of metabolic reactions.

The closure of the switch contacts and the wave form of the photocurrent and light intensity for an AC-operated light source (see below) are indicated in Fig. 1. The fluctuations of the light intensity (~100 per cent modulation) indicated on the top line cause synchronous variations which result in an asymmetrical wave form for the photocurrent, provided the fluorescent object coincides with the extremes of the excursions of the vibrating diaphragm. To measure the fluorescence intensity of the object (M) and that of a nearby “free space” (R), the switch circuit is adjusted so that it closes for a brief interval at the peaks of the photocurrent wave form (Fig. 1). The portions of the photocurrent selected by this switch are used to charge a condenser so that its potential represents the difference of the photocurrents at the two times. This potential is amplified by a “Millivac,” type 17C, and by an Esterline-Angus l-ma. recorder.

FIQ. 1. Wave forms of light intensity and photocurrent relative to the times of switch closure (alternating current operated by lamp). The vibrating diaphragm operates synchronously with the fluctuations in light intensity so that the extremes of its vibration correspond to maxima of light intensity

Fig. 2. Relative fluorescence maxima for suspensions of diploid bakers’ yeast, pentaploid yeast, and ascites tumor cells. These fluorescence emission spectra are obtained with excitation of the cell suspensions by the 366-rnr mercury line passed through the same filter used in the microfluorometer. The energy obtained through the Wratten 2A filter is analyzed by means of a grating monochromator and is plotted as a function of wave length. Significant features of the record are that no measurable energy at 366, 436, or 546 mp is received by the photocell. The cell suspensions are relatively concentrated (60 mg. per cc. for the yeast cell suspensions). The close correspondence of the amplitudes of the peaks is a consequence of adjusting the photocell dynode voltage appropriately (928).

Studies of mitochondria treated with ADP to cause the disappearance of RPN fluorescence show that a relatively small contribution of the flavoprotein of the respiratory chain remains and that flavoprotein fluorescence does not measurably change with its oxidation-reduction state. Thus, it is felt justified in these preliminary studies to attribute the major portion of the fluorescence observed to RPN. Evidence in favor of this view is indicated below, where chemical transitions affecting the oxidation-reduction state and hence the fluorescence of reduced pyridine nucleotide show that most of the fluorescence localized in the mitochondria is affected by this transition and hence is not a “fixed” background fluorescence.

Relative Intensities of Signals-A survey of various biological materials has been made to determine the relative intensities of the signals obtained and to demonstrate the feasibility of studies of their fluorescence. This study is largely incomplete, but the preliminary results summarized in Table I are rather encouraging. These fluorescence intensities range from a small value for the aerobic nebenkern of the grasshopper spermatid to a large value for the anaerobic pentaploid yeast cell. The larger currents give a signal-to-noise ratio of such magnitude that delicate indications are given, not only of the magnitude of the fluorescence, but also of changes that may occur in different metabolic states or in different parts of the cells. At higher currents, accuracies > 100 : 1 are possible. Localization of Fluorescence-The inadequate resolution of the optical microscope and the uniform distribution of the mitochondria throughout the cytoplasm of such cells as bakers’ or pentaploid yeast or ascites tumor cells offer little possibility for localizing the mitochondrial fluorescence as opposed to the cytoplasmic fluorescence.

Table I Summary of fluorescence intensities for various cell types and metabolic states

FIG. 7. Time course of the fluorescence changes of the nebenkern and of the cytoplasm in the aerobic-anaerobic transition. A, the ratio of nebenkern to cytoplasmic fluorescence, plotted as determined by records similar to those of Fig. 6. The numbers in the diagram refer to the cell studied. The abrupt upward discontinuity of the record at approximately 45 minutes occurs when anaerobiosis is expected. B, the individual measurements of the cytoplasmic (0-O) and nebenkern (0-C) fluorescence. The number of the cell used for measurement is also indicated along the scale of the abscissa (922a, b).

A fluorescence with spectral characteristics that are similar to those of reduced pyridine nucleotide of isolated mitochondria has been demonstrated to be localized in three cell configurations which cytologically show mitochondrial aggregation. The oxidation and reduction of mitochondrial pyridine nucleotide without a measurable change of cytoplasmic fluorescence suggest that compartmentalization of mitochondrial and cytoplasmic pyridine nucleotide occurs in viva, at least in the grasshopper spermatid. Studies of other material, particularly pentaploid yeast cells and ascites tumor cells, indicate that similar changes of fluorescence of the single cell are observed in the aerobic-anaerobic transition. In such cells, optical resolution does not permit localization of mitochondrial bodies. Nevertheless, the state of pyridine nucleotide in the individual cell can be investigated.

Discussion:

Radoslav S. Bozov (@Radobozov)

Your interpretations approximates wrong conclusions: 1. Oxygen is processed via mitochondrial Cu2+/3+ metalloproteins , H2O2 (King’s water) electro-negativity processing). 2. Lactate formation is an effect of cancerogenesis, a Lewis base., you lack fundamental understanding pKa issues in science and more accurately in moderns science. Such thing as protons have never been observed directly, that is a concept for explaining pH. All organic acids in bio systems are deprotonated carboxyl functional groups entering resonance state , which allows interpretation of spectra: There i s no way in real chemical science to have measured pH of a compartment and especially nano space! The physiological charge is -1: http://www.hmdb.ca/metabolites/HMDB00190
Biophysical concepts might be applied in a wrong direction! which is the case of perceiving NADH/Pyruavte/Lactate triangle , you lack conceptual frames of systems applicability in expanded biological space/energies – proteins, nucleotides, and meta states! Pyridines have nothing to do with energy states, pyridines are nitrogen capacitors , they have nothing to do with origin and implications of mutation/evolution, regulation! You lack fundamental understanding of physical implications today!
Lactate is a compensatory mechanism of the genome for copying with disregulated supply of pyruvate for synthesizing negative methyl groups, energy processed in biospaces via compression/decompressing bio systems! Remember, quantum chemistry and implications of quantum physics is not one and the same! General Relativity is applied only towards statistical cloud delocalization, that implicates induction vs deductive reasoning! Classic mechanics of optics is a neat way to do math, nothing more than that of accepting reality of none observable parameters! Lactic acid was considered an end product of metabolism and physiological fatigue for a long time! Now, we know that is not true! To contrary lactic acid is used to have healthy pluripotecy differentiation of bone marrow derived cell lines. LDH proteins demonstrate high similarity motif selection with a range of transcriprion factors via blast studies. In general DATA IS MESSED UP and likely WRONGLY INTERPRETED!

Larry H Bernstein,

I am quite sure that what I presented is the best that science has produced.  Whether there is a theoretical issue in physical interpretation is another matter.  Two key papers are by Mayekovsky and by Britton Chance.  Britton Chance only died recently at nearly 100, but he was a giant in biochemistry, and my final exam question in freshman biochemistry was – should B Chance get the Nobel Prize.  His conception was then controversial, and the ETC won out.  Nevertheless, his contributions went far beyond the explanation for the H+ transfer role in ETC.  When I was a resident in pathology, my mentor (who identified the difference between myokinase and liver AK) commented that  the only reason that Chance had not been awarded was because his work was so technologically focused. I had studied the malate dehydrogenase reaction in Nate Kaplan’s lab, and I carried out stop-flow studies of the inhibition of the mitochondrial isoenzyme by oxaloacetate.  When I went to Washington, DC at the end of the Vietnam War and the time of Watergate, I had the good fortune to be introduced to Chance in a visit to Philadelphia.  I think that I do understand acidemia, cationic and anionic balance, which is not a simple matter – after some 35 years in pathology, with a main focus on clinical pathology.   If you could step back and give a point by point elucidation of where the experimental interpretation is in error, and a point by point highlight of your explanation, it would be very helpful. I know that I am quite knowledgable about the mechanism of reactions of the pyridine nucleotide linked dehydrogenases, and the isoenzymes, and the abortive ternary complexes.  I also published in the Brit J Cancer in the 1970’s on an abnormality in the cytoplasmic MDH in fast growing murine hepatomas, and in human cancer.  I spent many months purifying the heart mitochondrial MDH to purity, and established that there was no histidine residue at the active site.

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Warburg Effect and Mitochondrial Regulation -2.1.3

Writer and Curator: Larry H Bernstein, MD, FCAP 

2.1.3 Warburg Effect and Mitochondrial Regulation

Warburg Effect and Mitochondrial Regulation- 2.1.3

Word Cloud by Daniel Menzin

2.1.3.1 Regulation of Substrate Utilization by the Mitochondrial Pyruvate Carrier

NM Vacanti, AS Divakaruni, CR Green, SJ Parker, RR Henry, TP Ciaraldi, et a..
Molec Cell 6 Nov 2014; 56(3):425–435
http://dx.doi.org/10.1016/j.molcel.2014.09.024

Highlights

  • Oxidation of fatty acids and amino acids is increased upon MPC inhibition
    •Respiration, proliferation, and biosynthesis are maintained when MPC is inhibited
    •Glutaminolytic flux supports lipogenesis in the absence of MPC
    •MPC inhibition is distinct from hypoxia or complex I inhibition

Summary

Pyruvate lies at a central biochemical node connecting carbohydrate, amino acid, and fatty acid metabolism, and the regulation of pyruvate flux into mitochondria represents a critical step in intermediary metabolism impacting numerous diseases. To characterize changes in mitochondrial substrate utilization in the context of compromised mitochondrial pyruvate transport, we applied 13C metabolic flux analysis (MFA) to cells after transcriptional or pharmacological inhibition of the mitochondrial pyruvate carrier (MPC). Despite profound suppression of both glucose and pyruvate oxidation, cell growth, oxygen consumption, and tricarboxylic acid (TCA) metabolism were surprisingly maintained. Oxidative TCA flux was achieved through enhanced reliance on glutaminolysis through malic enzyme and pyruvate dehydrogenase (PDH) as well as fatty acid and branched-chain amino acid oxidation. Thus, in contrast to inhibition of complex I or PDH, suppression of pyruvate transport induces a form of metabolic flexibility associated with the use of lipids and amino acids as catabolic and anabolic fuels.

oxidation-of-fatty-acids-and-amino-acid

oxidation-of-fatty-acids-and-amino-acids

Graphical Abstract – Oxidation of fatty acids and amino acids is increased upon MPC inhibition

Figure 2. MPC Regulates Mitochondrial Substrate Utilization (A) Citrate mass isotopomer distribution (MID) resulting from culture with [U-13C6]glucose (UGlc). (B) Percentage of 13C-labeled metabolites from UGlc. (C) Percentage of fully labeled lactate, pyruvate, and alanine from UGlc. (D) Serine MID resulting from culture with UGlc. (E) Percentage of fully labeled metabolites derived from [U-13C5]glutamine (UGln). (F) Schematic of UGln labeling of carbon atoms in TCA cycle intermediates arising via glutaminoloysis and reductive carboxylation. Mitochondrion schematic inspired by Lewis et al. (2014). (G and H) Citrate (G) and alanine (H) MIDs resulting from culture with UGln. (I) Maximal oxygen consumption rates with or without 3 mM BPTES in medium supplemented with 1 mM pyruvate. (J) Percentage of newly synthesized palmitate as determined by ISA. (K) Contribution of UGln and UGlc to lipogenic AcCoA as determined by ISA. (L) Contribution of glutamine to lipogenic AcCoA via glutaminolysis (ISA using a [3-13C] glutamine [3Gln]) and reductive carboxylation (ISA using a [5-13C]glutamine [5Gln]) under normoxia and hypoxia. (M) Citrate MID resulting from culture with 3Gln. (N) Contribution of UGln and exogenous [3-13C] pyruvate (3Pyr) to lipogenic AcCoA. 2KD+Pyr refers to Mpc2KD cells cultured with 10 mM extracellular pyruvate. Error bars represent SD (A–E, G, H, and M), SEM(I), or 95% confidence intervals(J–L, and N).*p<0.05,**p<0.01,and ***p<0.001 by ANOVA with Dunnett’s post hoc test (A–E and G–I) or * indicates significance by non-overlapping 95% confidence intervals (J–L and N).

Figure 3. Mpc Knockdown Increases Fatty Acid Oxidation. (A) Schematic of changes in flux through metabolic pathways in Mpc2KD relative to control cells. (B) Citrate MID resulting from culture with [U-13C16] palmitate conjugated to BSA (UPalm). (C) Percentage of 13C enrichment resulting from culture with UPalm. (D) ATP-linked and maximal oxygen consumption rate, with or without 20m Metomoxir, with or without 3 mM BPTES. Culture medium supplemented with 0.5 mM carnitine. Error bars represent SD (B and C) or SEM (D). *p < 0.05, **p < 0.01, and ***p < 0.001 by two-tailed, equal variance, Student’s t test(B–D), or by ANOVA with Dunnett’s post hoc test (D).

Figure 4. Metabolic Reprogramming Resulting from Pharmacological Mpc Inhibition Is Distinct from Hypoxia or Complex I Inhibition

2.1.3.2 Oxidation of Alpha-Ketoglutarate Is Required for Reductive Carboxylation in Cancer Cells with Mitochondrial Defects

AR Mullen, Z Hu, X Shi, L Jiang, …, WM Linehan, NS Chandel, RJ DeBerardinis
Cell Reports 12 Jun 2014; 7(5):1679–1690
http://dx.doi.org/10.1016/j.celrep.2014.04.037

Highlights

  • Cells with mitochondrial defects use bidirectional metabolism of the TCA cycle
    •Glutamine supplies the succinate pool through oxidative and reductive metabolism
    •Oxidative TCA cycle metabolism is required for reductive citrate formation
    •Oxidative metabolism produces reducing equivalents for reductive carboxylation

Summary

Mammalian cells generate citrate by decarboxylating pyruvate in the mitochondria to supply the tricarboxylic acid (TCA) cycle. In contrast, hypoxia and other impairments of mitochondrial function induce an alternative pathway that produces citrate by reductively carboxylating α-ketoglutarate (AKG) via NADPH-dependent isocitrate dehydrogenase (IDH). It is unknown how cells generate reducing equivalents necessary to supply reductive carboxylation in the setting of mitochondrial impairment. Here, we identified shared metabolic features in cells using reductive carboxylation. Paradoxically, reductive carboxylation was accompanied by concomitant AKG oxidation in the TCA cycle. Inhibiting AKG oxidation decreased reducing equivalent availability and suppressed reductive carboxylation. Interrupting transfer of reducing equivalents from NADH to NADPH by nicotinamide nucleotide transhydrogenase increased NADH abundance and decreased NADPH abundance while suppressing reductive carboxylation. The data demonstrate that reductive carboxylation requires bidirectional AKG metabolism along oxidative and reductive pathways, with the oxidative pathway producing reducing equivalents used to operate IDH in reverse.

Proliferating cells support their growth by converting abundant extracellular nutrients like glucose and glutamine into precursors for macromolecular biosynthesis. A continuous supply of metabolic intermediates from the tricarboxylic acid (TCA) cycle is essential for cell growth, because many of these intermediates feed biosynthetic pathways to produce lipids, proteins and nucleic acids (Deberardinis et al., 2008). This underscores the dual roles of the TCA cycle for cell growth: it generates reducing equivalents for oxidative phosphorylation by the electron transport chain (ETC), while also serving as a hub for precursor production. During rapid growth, the TCA cycle is characterized by large influxes of carbon at positions other than acetyl-CoA, enabling the cycle to remain full even as intermediates are withdrawn for biosynthesis. Cultured cancer cells usually display persistence of TCA cycle activity despite robust aerobic glycolysis, and often require mitochondrial catabolism of glutamine to the TCA cycle intermediate AKG to maintain rapid rates of proliferation (Icard et al., 2012Hiller and Metallo, 2013).

Some cancer cells contain severe, fixed defects in oxidative metabolism caused by mutations in the TCA cycle or the ETC. These include mutations in fumarate hydratase (FH) in renal cell carcinoma and components of the succinate dehydrogenase (SDH) complex in pheochromocytoma, paraganglioma, and gastrointestinal stromal tumors (Tomlinson et al., 2002Astuti et al., 2001Baysal et al., 2000Killian et al., 2013Niemann and Muller, 2000). All of these mutations alter oxidative metabolism of glutamine in the TCA cycle. Recently, analysis of cells containing mutations in FH, ETC Complexes I or III, or exposed to the ETC inhibitors metformin and rotenone or the ATP synthase inhibitor oligomycin revealed that turnover of TCA cycle intermediates was maintained in all cases (Mullen et al., 2012). However, the cycle operated in an unusual fashion characterized by conversion of glutamine-derived AKG to isocitrate through a reductive carboxylation reaction catalyzed by NADP+/NADPH-dependent isoforms of isocitrate dehydrogenase (IDH). As a result, a large fraction of the citrate pool carried five glutamine-derived carbons. Citrate could be cleaved to produce acetyl-CoA to supply fatty acid biosynthesis, and oxaloacetate (OAA) to supply pools of other TCA cycle intermediates. Thus, reductive carboxylation enables biosynthesis by enabling cells with impaired mitochondrial metabolism to maintain pools of biosynthetic precursors that would normally be supplied by oxidative metabolism. Reductive carboxylation is also induced by hypoxia and by pseudo-hypoxic states caused by mutations in the von Hippel-Lindau (VHL) tumor suppressor gene (Metallo et al., 2012Wise et al., 2011).

Interest in reductive carboxylation stems in part from the possibility that inhibiting the pathway might induce selective growth suppression in tumor cells subjected to hypoxia or containing mutations that prevent them from engaging in maximal oxidative metabolism. Hence, several recent studies have sought to understand the mechanisms by which this pathway operates. In vitro studies of IDH1 indicate that a high ratio of NADPH/NADP+ and low citrate concentration activate the reductive carboxylation reaction (Leonardi et al., 2012). This is supported by data demonstrating that reductive carboxylation in VHL-deficient renal carcinoma cells is associated with a low concentration of citrate and a reduced ratio of citrate:AKG, suggesting that mass action can be a driving force to determine IDH directionality (Gameiro et al., 2013b). Moreover, interrupting the supply of mitochondrial NADPH by silencing the nicotinamide nucleotide transhydrogenase (NNT) suppresses reductive carboxylation (Gameiro et al., 2013a). This mitochondrial transmembrane protein catalyzes the transfer of a hydride ion from NADH to NADP+ to generate NAD+ and NADPH. Together, these observations suggest that reductive carboxylation is modulated in part through the mitochondrial redox state and the balance of substrate/products.

Here we used metabolomics and stable isotope tracing to better understand overall metabolic states associated with reductive carboxylation in cells with defective mitochondrial metabolism, and to identify sources of mitochondrial reducing equivalents necessary to induce the reaction. We identified high levels of succinate in some cells using reductive carboxylation, and determined that most of this succinate was formed through persistent oxidative metabolism of AKG. Silencing this oxidative flux by depleting the mitochondrial enzyme AKG dehydrogenase substantially altered the cellular redox state and suppressed reductive carboxylation. The data demonstrate that bidirectional/branched AKG metabolism occurs during reductive carboxylation in cells with mitochondrial defects, with oxidative metabolism producing reducing equivalents to supply reductive metabolism.

Shared metabolomic features among cell lines with cytb or FH mutations

To identify conserved metabolic features associated with reductive carboxylation in cells harboring defective mitochondrial metabolism, we analyzed metabolite abundance in isogenic pairs of cell lines in which one member displayed substantial reductive carboxylation and the other did not. We used a pair of previously described cybrids derived from 143B osteosarcoma cells, in which one cell line contained wild-type mitochondrial DNA (143Bwt) and the other contained a mutation in the cytb gene (143Bcytb), severely reducing complex III function (Rana et al., 2000Weinberg et al., 2010). The 143Bwt cells primarily use oxidative metabolism to supply the citrate pool while the 143Bcytb cells use reductive carboxylation (Mullen et al., 2012). The other pair, derived from FH-deficient UOK262 renal carcinoma cells, contained either an empty vector control (UOK262EV) or a stably re-expressed wild-type FH allele (UOK262FH). Metabolites were extracted from all four cell lines and analyzed by triple-quadrupole mass spectrometry. We first performed a quantitative analysis to determine the abundance of AKG and citrate in the four cell lines. Both 143Bcytb and UOK262EV cells had less citrate, more AKG, and lower citrate:AKG ratios than their oxidative partners (Fig. S1A-C), consistent with findings from VHL-deficient renal carcinoma cells (Gameiro et al., 2013b).

Next, to identify other perturbations, we profiled the relative abundance of more than 90 metabolites from glycolysis, the pentose phosphate pathway, one-carbon/nucleotide metabolism, the TCA cycle, amino acid degradation, and other pathways (Tables S1 and S2). Each metabolite was normalized to protein content, and relative abundance was determined between cell lines from each pair. Hierarchical clustering (Fig 1A) and principal component analysis (Fig 1B) revealed far greater metabolomic similarities between the members of each pair than between the two cell lines using reductive carboxylation. Only three metabolites displayed highly significant (p<0.005) differences in abundance between the two members of both pairs, and in all three cases the direction of the difference (i.e. higher or lower) was shared in the two cell lines using reductive carboxylation. Proline, a nonessential amino acid derived from glutamine in an NADPH-dependent biosynthetic pathway, was depleted in 143Bcytb and UOK262EV cells (Fig. 1C). 2-hydroxyglutarate (2HG), the reduced form of AKG, was elevated in 143Bcytb and UOK262EV cells (Fig. 1D), and further analysis revealed that while both the L- and D-enantiomers of this metabolite were increased, L-2HG was quantitatively the predominant enantiomer (Fig. S1D). It is likely that 2HG accumulation was related to the reduced redox ratio associated with cytb and FH mutations. Although the sources of 2HG are still under investigation, promiscuous activity of the TCA cycle enzyme malate dehydrogenase produces L-2HG in an NADH-dependent manner (Rzem et al., 2007). Both enantiomers are oxidized to AKG by dehydrogenases (L-2HG dehydrogenase and D-2HG dehydrogenase). It is therefore likely that elevated 2-HG is a consequence of a reduced NAD+/NADH ratio. Consistent with this model, inborn errors of the ETC result in 2-HG accumulation (Reinecke et al., 2011). Exposure to hypoxia (<1% O2) has also been demonstrated to reduce the cellular NAD+/NADH ratio (Santidrian et al., 2013) and to favor modest 2HG accumulation in cultured cells (Wise et al., 2011), although these levels were below those noted in gliomas expressing 2HG-producing mutant alleles of isocitrate dehydrogenase-1 or -2 (Dang et al., 2009).

Figure 1 Metabolomic features of cells using reductive carboxylation

 

Finally, the TCA cycle intermediate succinate was markedly elevated in both cell lines (Fig. 1E). We tested additional factors previously reported to stimulate reductive AKG metabolism, including a genetic defect in ETC Complex I, exposure to hypoxia, and chemical inhibitors of the ETC (Mullen et al., 2012Wise et al., 2011Metallo et al., 2012). These factors had a variable effect on succinate, with impairments of Complex III or IV strongly inducing succinate accumulation, while impairments of Complex I either had little effect or suppressed succinate (Fig. 1F).

Oxidative glutamine metabolism is the primary route of succinate formation

UOK262EV cells lack FH activity and accumulate large amounts of fumarate (Frezza et al., 2011); elevated succinate was therefore not surprising in these cells, because succinate precedes fumarate by one reaction in the TCA cycle. On the other hand, TCA cycle perturbation in 143Bcytb cells results from primary ETC dysfunction, and reductive carboxylation is postulated to be a consequence of accumulated AKG (Anastasiou and Cantley, 2012Fendt et al., 2013). Accumulation of AKG is not predicted to result in elevated succinate. We previously reported that 143Bcytb cells produce succinate through simultaneous oxidative and reductive glutamine metabolism (Mullen et al., 2012). To determine the relative contributions of these two pathways, we cultured 143Bwt and 143Bcytb with [U-13C]glutamine and monitored time-dependent 13C incorporation in succinate and other TCA cycle intermediates. Oxidative metabolism of glutamine generates succinate, fumarate and malate containing four glutamine-derived 13C nuclei on the first turn of the cycle (m+4), while reductive metabolism results in the incorporation of three 13C nuclei in these intermediates (Fig. S2). As expected, oxidative glutamine metabolism was the predominant source of succinate, fumarate and malate in 143Bwt cells (Fig. 2A-C). In 143Bcytb, fumarate and malate were produced primarily through reductive metabolism (Fig. 2E-F). Conversely, succinate was formed primarily through oxidative glutamine metabolism, with a minor contribution from the reductive carboxylation pathway (Fig. 2D). Notably, this oxidatively-derived succinate was detected prior to that formed through reductive carboxylation. This indicated that 143Bcytb cells retain the ability to oxidize AKG despite the observation that most of the citrate pool bears the labeling pattern of reductive carboxylation. Together, the labeling data in 143Bcytb cells revealed bidirectional metabolism of carbon from glutamine to produce various TCA cycle intermediates.

Figure 2  Oxidative glutamine metabolism is the primary route of succinate formation in cells using reductive carboxylation to generate citrate

Pyruvate carboxylation contributes to the TCA cycle in cells using reductive carboxylation

Because of the persistence of oxidative metabolism, we determined the extent to which other routes of metabolism besides reductive carboxylation contributed to the TCA cycle. We previously reported that silencing the glutamine-catabolizing enzyme glutaminase (GLS) depletes pools of fumarate, malate and OAA, eliciting a compensatory increase in pyruvate carboxylase (PC) to supply the TCA cycle (Cheng et al., 2011). In cells with defective oxidative phophorylation, production of OAA by PC may be preferable to glutamine oxidation because it diminishes the need to recycle reduced electron carriers generated by the TCA cycle. Citrate synthase (CS) can then condense PC-derived OAA with acetyl-CoA to form citrate. To examine the contribution of PC to the TCA cycle, cells were cultured with [3,4-13C]glucose. In this labeling scheme, glucose-derived pyruvate is labeled in carbon 1 (Fig. S3). This label is retained in OAA if pyruvate is carboxylated, but removed as CO2 during conversion of pyruvate to acetyl-CoA by pyruvate dehydrogenase (PDH).

Figure 3 Pyruvate carboxylase contributes to citrate formation in cells using reductive carboxylation

Oxidative metabolism of AKG is required for reductive carboxylation

Oxidative synthesis of succinate from AKG requires two reactions: the oxidative decarboxylation of AKG to succinyl-CoA by AKG dehydrogenase, and the conversion of succinyl-CoA to succinate by succinyl-CoA synthetase. In tumors with mutations in the succinate dehydrogenase (SDH) complex, large accumulations of succinate are associated with epigenetic modifications of DNA and histones to promote malignancy (Kaelin and McKnight, 2013Killian et al., 2013). We therefore tested whether succinate accumulation per se was required to induce reductive carboxylation in 143Bcytb cells. We used RNA interference directed against the gene encoding the alpha subunit (SUCLG1) of succinyl-CoA synthetase, the last step in the pathway of oxidative succinate formation from glutamine (Fig. 4A). Silencing this enzyme greatly reduced succinate levels (Fig. 4B), but had no effect on the labeling pattern of citrate from [U-13C]glutamine (Fig. 4C). Thus, succinate accumulation is not required for reductive carboxylation.

Figure 5 AKG dehydrogenase is required for reductive carboxylation

Figure 6 AKG dehydrogenase and NNT contribute to NAD+/NADH ratio

Finally, we tested whether these enzymes also controlled the NADP+/NADPH ratio in 143Bcytb cells. Silencing either OGDH or NNT increased the NADP+/NADPH ratio (Fig. 6F,G), whereas silencing IDH2reduced it (Fig. 6H). Together, these data are consistent with a model in which persistent metabolism of AKG by AKG dehydrogenase produces NADH that supports reductive carboxylation by serving as substrate for NNT-dependent NADPH formation, and that IDH2 is a major consumer of NADPH during reductive carboxylation (Fig. 6I).

Reductive carboxylation of AKG initiates a non-conventional form of metabolism that produces TCA cycle intermediates when oxidative metabolism is impaired by mutations, drugs or hypoxia. Because NADPH-dependent isoforms of IDH are reversible, supplying supra-physiological pools of substrates on either side of the reaction drives function of the enzyme as a reductive carboxylase or an oxidative decarboxylase. Thus, in some circumstances reductive carboxylation may operate in response to a mass effect imposed by drastic changes in the abundance of AKG and isocitrate/citrate. However, reductive carboxylation cannot occur without a source of reducing equivalents to produce NADPH. The current work demonstrates that AKG dehydrogenase, an NADH-generating enzyme complex, is required to maintain a low NAD+/NADH ratio for reductive carboxylation of AKG. Thus, reductive carboxylation not only coexists with oxidative metabolism of AKG, but depends on it. Furthermore, silencing NNT, a consumer of NADH, also perturbs the redox ratio and suppresses reductive formation of citrate. These observations suggest that the segment of the oxidative TCA cycle culminating in succinate is necessary to transmit reducing equivalents to NNT for the reductive pathway (Fig 6I).

Succinate accumulation was observed in cells with cytb or FH mutations. However, this accumulation was dispensable for reductive carboxylation, because silencing SUCLG1 expression had no bearing on the pathway as long as AKG dehydrogenase was active. Furthermore, succinate accumulation was not a universal finding of cells using reductive carboxylation. Rather, high succinate levels were observed in cells with distal defects in the ETC (complex III: antimycin, cytb mutation; complex IV: hypoxia) but not defects in complex I (rotenone, metformin, NDUFA1 mutation). These differences reflect the known suppression of SDH activity when downstream components of the ETC are impaired, and the various mechanisms by which succinate may be formed through either oxidative or reductive metabolism. Succinate has long been known as an evolutionarily conserved anaerobic end product of amino acid metabolism during prolonged hypoxia, including in diving mammals (Hochachka and Storey, 1975, Hochachka et al., 1975). The terminal step in this pathway is the conversion of fumarate to succinate using the NADH-dependent “fumarate reductase” system, essentially a reversal of succinate dehydrogenase/ETC complex II (Weinberg et al., 2000, Tomitsuka et al., 2010). However, this process requires reducing equivalents to be passed from NADH to complex I, then to Coenzyme Q, and eventually to complex II to drive the reduction of fumarate to succinate. Hence, producing succinate through reductive glutamine metabolism would require functional complex I. Interestingly, the fumarate reductase system has generally been considered as a mechanism to maintain a proton gradient under conditions of defective ETC activity. Our data suggest that the system is part of a more extensive reorganization of the TCA cycle that also enables reductive citrate formation.

In summary, we demonstrated that branched AKG metabolism is required to sustain levels of reductive carboxylation observed in cells with mitochondrial defects. The organization of this branched pathway suggests that it serves as a relay system to maintain the redox requirements for reductive carboxylation, with the oxidative arm producing reducing equivalents at the level of AKG dehydrogenase and NNT linking this activity to the production of NADPH to be used in the reductive carboxylation reaction. Hence, impairment of the oxidative arm prevents maximal engagement of reductive carboxylation. As both NNT and AKG dehydrogenase are mitochondrial enzymes, the work emphasizes the flexibility of metabolic systems in the mitochondria to fulfill requirements for redox balance and precursor production even when the canonical oxidative function of the mitochondria is impaired.

2.1.3.3 Rewiring Mitochondrial Pyruvate Metabolism. Switching Off the Light in Cancer Cells

Peter W. Szlosarek, Suk Jun Lee, Patrick J. Pollard
Molec Cell 6 Nov 2014; 56(3): 343–344
http://dx.doi.org/10.1016/j.molcel.2014.10.018

Figure 1. MPC Expression and Metabolic Targeting of Mitochondrial Pyruvate High MPC expression (green) is associated with more favorable tumor prognosis, increased pyruvate oxidation, and reduced lactate and ROS, whereas low expression or mutated MPC is linked to poor tumor prognosis and increased anaplerotic generation of OAA. Dual targeting of MPC and GDH with small molecule inhibitors may ameliorate tumorigenesis in certain cancer types.

The study by Yang et al., (2014) provides evidence for the metabolic flexibility to maintain TCA cycle function. Using isotopic labeling, the authors demonstrated that inhibition of MPCs by a specific compound (UK5099) induced glutamine-dependent acetyl-CoA formation via glutamate dehydrogenase (GDH). Consequently, and in contrast to single agent treatment, simultaneous administration of MPC and GDH inhibitors drastically abrogated the growth of cancer cells (Figure 1). These studies have also enabled a fresh perspective on metabolism in the clinic and emphasized a need for high-quality translational studies to assess the role of mitochondrial pyruvate transport in vivo. Thus, integrating the biomarker of low MPC expression with dual inhibition of

MPC and GDH as a synthetic lethal strategy (Yang et al., 2014) is testable and may offer a novel therapeutic window for patients (DeBerardinis and Thompson, 2012). Indeed, combinatorial targeting of cancer metabolism may prevent early drug resistance and lead to enhanced tumor control, as shown recently for antifolate agents combined with arginine deprivation with modulation of intracellular glutamine (Szlosarek, 2014). Moreover, it will be important to assess both intertumoral and intratumoral metabolic heterogeneity going forward, as tumor cells are highly adaptable with respect to the precursors used to fuel the TCA cycle in the presence of reduced pyruvate transport. The observation by Vacanti et al. (2014) that the flux of BCAAs increased following inhibition of MPC activity may also underlie the increase in BCAAs detected in the plasma of patients several years before a clinical diagnosis of pancreatic cancer (Mayers et al., 2014). Since measuring pyruvate transport via the MPC is technically challenging, the use of 18-FDG positron emission tomography and more recently magnetic spectroscopy with hyperpolarized 13C-labeled pyruvate will need to be incorporated into these future studies (Brindle et al., 2011).

References

Bricker, D.K., Taylor, E.B., Schell, J.C., Orsak, T., Boutron, A., Chen, Y.C., Cox, J.E., Cardon, C.M., Van Vranken, J.G., Dephoure, N., et al. (2012). Science 337, 96–100.

Brindle, K.M., Bohndiek, S.E., Gallagher, F.A., and Kettunen, M.I. (2011). Magn. Reson. Med. 66, 505–519.

DeBerardinis, R.J., and Thompson, C.B. (2012). Cell 148, 1132–1144.

Herzig, S., Raemy, E., Montessuit, S., Veuthey, J.L., Zamboni, N., Westermann, B., Kunji, E.R., and Martinou, J.C. (2012). Science 337, 93–96.

Mayers, J.R., Wu, C., Clish, C.B., Kraft, P., Torrence, M.E., Fiske, B.P., Yuan, C., Bao, Y., Townsend, M.K., Tworoger, S.S., et al. (2014). Nat. Med. 20, 1193–1198.

Metallo, C.M., and Vander Heiden, M.G. (2013). Mol. Cell 49, 388–398.

Schell, J.C., Olson, K.A., Jiang, L., Hawkins, A.J., Van Vranken, J.G., et al. (2014). Mol. Cell 56, this issue, 400–413.

Szlosarek, P.W. (2014). Proc. Natl. Acad. Sci. USA 111, 14015–14016.

Vacanti, N.M., Divakaruni, A.S., Green, C.R., Parker, S.J., Henry, R.R., et al. (2014). Mol. Cell 56, this issue, 425–435.

Yang, C., Ko, B., Hensley, C.T., Jiang, L., Wasti, A.T., et al. (2014). Mol. Cell 56, this issue, 414–424.

2.1.3.4 Betaine is a positive regulator of mitochondrial respiration

Lee I
Biochem Biophys Res Commun. 2015 Jan 9; 456(2):621-5.
http://dx.doi.org:/10.1016/j.bbrc.2014.12.005

Highlights

  • Betaine enhances cytochrome c oxidase activity and mitochondrial respiration.
    • Betaine increases mitochondrial membrane potential and cellular energy levels.
    • Betaine’s anti-tumorigenic effect might be due to a reversal of the Warburg effect.

Betaine protects cells from environmental stress and serves as a methyl donor in several biochemical pathways. It reduces cardiovascular disease risk and protects liver cells from alcoholic liver damage and nonalcoholic steatohepatitis. Its pretreatment can rescue cells exposed to toxins such as rotenone, chloroform, and LiCl. Furthermore, it has been suggested that betaine can suppress cancer cell growth in vivo and in vitro. Mitochondrial electron transport chain (ETC) complexes generate the mitochondrial membrane potential, which is essential to produce cellular energy, ATP. Reduced mitochondrial respiration and energy status have been found in many human pathological conditions including aging, cancer, and neurodegenerative disease. In this study we investigated whether betaine directly targets mitochondria. We show that betaine treatment leads to an upregulation of mitochondrial respiration and cytochrome c oxidase activity in H2.35 cells, the proposed rate limiting enzyme of ETC in vivo. Following treatment, the mitochondrial membrane potential was increased and cellular energy levels were elevated. We propose that the anti-proliferative effects of betaine on cancer cells might be due to enhanced mitochondrial function contributing to a reversal of the Warburg effect.

2.1.3.5 Mitochondrial dysfunction in human non-small-cell lung cancer cells to TRAIL-induced apoptosis by reactive oxygen species and Bcl-XL/p53-mediated amplification mechanisms

Y-L Shi, S Feng, W Chen, Z-C Hua, J-J Bian and W Yin
Cell Death and Disease (2014) 5, e1579
http://dx.doi.org:/10.1038/cddis.2014.547

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a promising agent for anticancer therapy; however, non-small-cell lung carcinoma (NSCLC) cells are relatively TRAIL resistant. Identification of small molecules that can restore NSCLC susceptibility to TRAIL-induced apoptosis is meaningful. We found here that rotenone, as a mitochondrial respiration inhibitor, preferentially increased NSCLC cells sensitivity to TRAIL-mediated apoptosis at subtoxic concentrations, the mechanisms by which were accounted by the upregulation of death receptors and the downregulation of c-FLIP (cellular FLICE-like inhibitory protein). Further analysis revealed that death receptors expression by rotenone was regulated by p53, whereas c-FLIP downregulation was blocked by Bcl-XL overexpression. Rotenone triggered the mitochondria-derived reactive oxygen species (ROS) generation, which subsequently led to Bcl-XL downregulation and PUMA upregulation. As PUMA expression was regulated by p53, the PUMA, Bcl-XL and p53 in rotenone-treated cells form a positive feedback amplification loop to increase the apoptosis sensitivity. Mitochondria-derived ROS, however, promote the formation of this amplification loop. Collectively, we concluded that ROS generation, Bcl-XL and p53-mediated amplification mechanisms had an important role in the sensitization of NSCLC cells to TRAIL-mediated apoptosis by rotenone. The combined TRAIL and rotenone treatment may be appreciated as a useful approach for the therapy of NSCLC that warrants further investigation.

Abbreviations: c-FLIP, cellular FLICE-like inhibitory protein; DHE, dihydroethidium; DISC, death-inducing signaling complex; DPI, diphenylene iodonium; DR4/DR5, death receptor 4/5; EB, ethidium bromide; FADD, Fas-associated protein with death domain; MnSOD, manganese superoxide; NAC, N-acetylcysteine; NSCLC, non-small-cell lung carcinoma; PBMC, peripheral blood mononuclear cells; ROS, reactive oxygen species; TRAIL, tumor necrosis factor-related apoptosis-inducing ligand; UPR, unfolded protein response.

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) has emerged as a promising cancer therapeutic because it can selectively induce apoptosis in tumor cells in vitro, and most importantly, in vivo with little adverse effect on normal cells.1 However, a number of cancer cells are resistant to TRAIL, especially highly malignant tumors such as lung cancer.23 Lung cancer, especially the non-small-cell lung carcinoma (NSCLC) constitutes a heavy threat to human life. Presently, the morbidity and mortality of NSCLC has markedly increased in the past decade,4 which highlights the need for more effective treatment strategies.

TRAIL has been shown to interact with five receptors, including the death receptors 4 and 5 (DR4 and DR5), the decoy receptors DcR1 and DcR2, and osteoprotegerin.5 Ligation of TRAIL to DR4 or DR5 allows for the recruitment of Fas-associated protein with death domain (FADD), which leads to the formation of death-inducing signaling complex (DISC) and the subsequent activation of caspase-8/10.6 The effector caspase-3 is activated by caspase-8, which cleaves numerous regulatory and structural proteins resulting in cell apoptosis. Caspase-8 can also cleave the Bcl-2 inhibitory BH3-domain protein (Bid), which engages the intrinsic apoptotic pathway by binding to Bcl-2-associated X protein (Bax) and Bcl-2 homologous antagonist killer (BAK). The oligomerization between Bcl-2 and Bax promotes the release of cytochrome c from mitochondria to cytosol, and facilitates the formation of apoptosome and caspase-9 activation.7 Like caspase-8, caspase-9 can also activate caspase-3 and initiate cell apoptosis. Besides apoptosis-inducing molecules, several apoptosis-inhibitory proteins also exist and have function even when apoptosis program is initiated. For example, cellular FLICE-like inhibitory protein (c-FLIP) is able to suppress DISC formation and apoptosis induction by sequestering FADD.891011

Until now, the recognized causes of TRAIL resistance include differential expression of death receptors, constitutively active AKT and NF-κB,1213overexpression of c-FLIP and IAPs, mutations in Bax and BAK gene.2 Hence, resistance can be overcome by the use of sensitizing agents that modify the deregulated death receptor expression and/or apoptosis signaling pathways in cancer cells.5 Many sensitizing agents have been developed in a variety of tumor cell models.2 Although the clinical effectiveness of these agents needs further investigation, treatment of TRAIL-resistant tumor cells with sensitizing agents, especially the compounds with low molecular weight, as well as prolonged plasma half-life represents a promising trend for cancer therapy.

Mitochondria emerge as intriguing targets for cancer therapy. Metabolic changes affecting mitochondria function inside cancer cells endow these cells with distinctive properties and survival advantage worthy of drug targeting, mitochondria-targeting drugs offer substantial promise as clinical treatment with minimal side effects.141516 Rotenone is a potent inhibitor of NADH oxidoreductase in complex I, which demonstrates anti-neoplastic activity on a variety of cancer cells.1718192021 However, the neurotoxicity of rotenone limits its potential application in cancer therapy. To avoid it, rotenone was effectively used in combination with other chemotherapeutic drugs to kill cancerous cells.22

In our previous investigation, we found that rotenone was able to suppress membrane Na+,K+-ATPase activity and enhance ouabain-induced cancer cell death.23 Given these facts, we wonder whether rotenone may also be used as a sensitizing agent that can restore the susceptibility of NSCLC cells toward TRAIL-induced apoptosis, and increase the antitumor efficacy of TRAIL on NSCLC. To test this hypothesis, we initiated this study.

Rotenone sensitizes NSCLC cell lines to TRAIL-induced apoptosis

Four NSCLC cell lines including A549, H522, H157 and Calu-1 were used in this study. As shown in Figure 1a, the apoptosis induced by TRAIL alone at 50 or 100 ng/ml on A549, H522, H157 and Calu-1 cells was non-prevalent, indicating that these NSCLC cell lines are relatively TRAIL resistant. Interestingly, when these cells were treated with TRAIL combined with rotenone, significant increase in cell apoptosis was observed. To examine whether rotenone was also able to sensitize normal cells to TRAIL-mediated apoptosis, peripheral blood mononuclear cell (PBMC) isolated from human blood were used. As a result, rotenone failed to sensitize human PBMC to TRAIL-induced apoptosis, indicating that the sensitizing effect of rotenone is tumor cell specific. Of note, the apoptosis-enhancing effect of rotenone occurred independent of its cytotoxicity, because the minimal dosage required for rotenone to cause toxic effect on NSCLC cell lines was 10 μM, however, rotenone augmented TRAIL-mediated apoptosis when it was used as little as 10 nM.

Figure 1.

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To further confirm the effect of rotenone, cells were stained with Hoechst and observed under fluorescent microscope (Figure 1b). Consistently, the combined treatment of rotenone with TRAIL caused significant nuclear fragmentation in A549, H522, H157 and Calu-1 cells. Rotenone or TRAIL treatment alone, however, had no significant effect.

Caspases activation is a hallmark of cell apoptosis. In this study, the enzymatic activities of caspases including caspase-3, -8 and -9 were measured by flow cytometry by using FITC-conjugated caspases substrate (Figure 1c). As a result, rotenone used at 1 μM or TRAIL used at 100 ng/ml alone did not cause caspase-3, -8 and -9 activation. The combined treatment, however, significantly increased the enzymatic activities of them. Moreover, A549 or H522 cell apoptosis by TRAIL combined with rotenone was almost completely suppressed in the presence of z-VAD.fmk, a pan-caspase inhibitor (Figure 1d). All of these data indicate that both intrinsic and extrinsic pathways are involved in the sensitizing effect of rotenone on TRAIL-mediated apoptosis in NSCLC.

Upregulation of death receptors expression is required for rotenone-mediated sensitization to TRAIL-induced apoptosis

Sensitization to TRAIL-induced apoptosis has been explained in some studies by upregulation of death receptors,24 whereas other results show that sensitization can occur without increased TRAIL receptor expression.25 As such, we examined TRAIL receptors expression on NSCLC cells after treatment with rotenone. Rotenone increased DR4 and DR5 mRNA levels in A549 cells in a time or concentration-dependent manner (Figures 2a and b), also increased DR4 and DR5 protein expression levels (Supplementary Figure S1). Notably, rotenone failed to increase DR5 mRNA levels in H157 and Calu-1 cells (Supplementary Figure S2). To observe whether the increased DR4 and DR5 mRNA levels finally correlated with the functional molecules, we examined the surface expression levels of DR4 and DR5 by flow cytometry. The results, as shown in Figure 2c demonstrated that the cell surface expression levels of DR4 and DR5 were greatly upregulated by rotenone in either A549 cells or H522 cells.

Figure 2.

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To analyze whether the upregulation of DR4 and DR5 is a ‘side-effect’, or contrarily, necessary for rotenone-mediated sensitization to TRAIL-induced apoptosis, we blocked upregulation of the death receptors by small interfering RNAs (siRNAs) against DR4 and DR5 (Supplementary Figure S3). The results showed that blocking DR4 and DR5 expression alone significantly reduced the rate of cell apoptosis in A549 cells (Figure 2d). However, the highest inhibition of apoptosis was observed when upregulation of both receptors was blocked in parallel, thus showing an additive effect of blocking DR4 and DR5 at the same time. Similar results were also obtained in H522 cells

To analyze whether the upregulation of DR4 and DR5 is a ‘side-effect’, or contrarily, necessary for rotenone-mediated sensitization to TRAIL-induced apoptosis, we blocked upregulation of the death receptors by small interfering RNAs (siRNAs) against DR4 and DR5 (Supplementary Figure S3). The results showed that blocking DR4 and DR5 expression alone significantly reduced the rate of cell apoptosis in A549 cells (Figure 2d). However, the highest inhibition of apoptosis was observed when upregulation of both receptors was blocked in parallel, thus showing an additive effect of blocking DR4 and DR5 at the same time. Similar results were also obtained in H522 cells.

Rotenone-induced p53 activation regulates death receptors upregulation

TRAIL receptors DR4 and DR5 are regulated at multiple levels. At transcriptional level, studies suggest that several transcriptional factors including NF-κB, p53 and AP-1 are involved in DR4 or DR5 gene transcription.2 The NF-κB or AP-1 transcriptional activity was further modulated by ERK1/2, JNK and p38 MAP kinase activity. Unexpectedly, we found here that none of these MAP kinases inhibitors were able to suppress the apoptosis mediated by TRAIL plus rotenone (Figure 3a). To find out other possible mechanisms, we observed that rotenone was able to stimulate p53 phosphorylation as well as p53 protein expression in A549 and H522 cells (Figure 3b). As a p53-inducible gene, p21 mRNA expression was also upregulated by rotenone treatment in a time-dependent manner (Figure 3c). To characterize the effect of p53, A549 cells were transfected with p53 siRNA. The results, as shown in Figure 3d-1 demonstrated that rotenone-mediated surface expression levels of DR4 and DR5 in A549 cells were largely attenuated by siRNA-mediated p53 expression silencing. Control siRNA, however, failed to reveal such effect. Similar results were also obtained in H522 cells (Figure 3d-2). Silencing of p53 expression in A549 cells also partially suppressed the apoptosis induced by TRAIL plus rotenone (Figure 3e).

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Rotenone suppresses c-FLIP expression and increases the sensitivity of A549 cells to TRAIL-induced apoptosis

The c-FLIP protein has been commonly appreciated as an anti-apoptotic molecule in death receptor-mediated cell apoptosis. In this study, rotenone treatment led to dose-dependent downregulation of c-FLIP expression, including c-FLIPL and c-FLIPs in A549 cells (Figure 4a-1), H522 cells (Figure 4a-2), H441 and Calu-1 cells (Supplementary Figure S4). To test whether c-FLIP is essential for the apoptosis enhancement, A549 cells were transfected with c-FLIPL-overexpressing plasmids. As shown in Figure 4b-1, the apoptosis of A549 cells after the combined treatment was significantly reduced when c-FLIPL was overexpressed. Similar results were also obtained in H522 cells (Figure 4b-2).

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Bcl-XL is involved in the apoptosis enhancement by rotenone

Notably, c-FLIP downregulation by rotenone in NSCLC cells was irrelevant to p53 signaling (data not shown). To identify other mechanism involved, we found that anti-apoptotic molecule Bcl-XL was also found to be downregulated by rotenone in a dose-dependent manner (Figure 5a). Notably, both Bcl-XL and c-FLIPL mRNA levels remained unchanged in cells after rotenone treatment (Supplementary Figure S5). Bcl-2 is homolog to Bcl-XL. But surprisingly, Bcl-2 expression was almost undetectable in A549 cells. To examine whether Bcl-XL is involved, A549 cells were transfected with Bcl-XL-overexpressing plasmid. As compared with mock transfectant, cell apoptosis induced by TRAIL plus rotenone was markedly suppressed under the condition of Bcl-XL overexpression (Figure 5b). To characterize the mechanisms, surface expression levels of DR4 and DR5 were examined. As shown in Figure 5c, the increased surface expression of DR4 and DR5 in A549 cells, or in H522 cells were greatly reduced after Bcl-XLoverexpression (Figure 5c). In addition, Bcl-XL overexpression also significantly prevented the downregulation of c-FLIPL and c-FLIPs expression in A549 cells by rotenone treatment (Figure 5d).

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Rotenone suppresses the interaction between BCL-XL/p53 and increases PUMA transcription

Lines of evidence suggest that Bcl-XL has a strong binding affinity with p53, and can suppress p53-mediated tumor cell apoptosis.26 In this study, FLAG-tagged Bcl-XL and HA-tagged p53 were co-transfected into cells; immunoprecipitation experiment was performed by using FLAG antibody to immunoprecipitate HA-tagged p53. As a result, we found that at the same amount of p53 protein input, rotenone treatment caused a concentration-dependent suppression of the protein interaction between Bcl-XL and p53 (Figure 6a). Rotenone also significantly suppressed the interaction between endogenous Bcl-XL and p53 when polyclonal antibody against p53 was used to immunoprecipitate cellular Bcl-XL (Figure 6b). Recent study highlighted the importance of PUMA in BCL-XL/p53 interaction and cell apoptosis.27 We found here that rotenone significantly increased PUMA gene transcription (Figure 6c) and protein expression (Figure 6d) in NSCLC cells, but not in transformed 293T cell. Meanwhile, this effect was attenuated by silencing of p53 expression (Figure 6e).

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Mitochondria-derived ROS are responsible for the apoptosis-enhancing effect of rotenone

As an inhibitor of mitochondrial respiration, rotenone was found to induce reactive oxygen species (ROS) generation in a variety of transformed or non-transformed cells.2022 Consistently, by using 2′,7′-dichlorofluorescin diacetate (DCFH) for the measurement of intracellular H2O2 and dihydroethidium (DHE) for O2.−, we found that rotenone significantly triggered the .generation of H2O2(Figure 7a) and O2.− (Figure 7b) in A549 and H522 cells. To identify the origin of ROS production, we first incubated cells with diphenylene iodonium (DPI), a potent inhibitor of plasma membrane NADP/NADPH oxidase. The results showed that DPI failed to suppress rotenone-induced ROS generation (Figure 7c). Then, we generated A549 cells deficient in mitochondria DNA by culturing cells in medium supplemented with ethidium bromide (EB). These mtDNA-deficient cells were subject to rotenone treatment, and the result showed that rotenone-induced ROS production were largely attenuated in A549 ρ° cells, but not wild-type A549 cells, suggesting ROS are mainly produced from mitochondria (Figure 7d). Notably, the sensitizing effect of rotenone on TRAIL-induced apoptosis in A549 cells was largely dependent on ROS, because the antioxidant N-acetylcysteine (NAC) treatment greatly suppressed the cell apoptosis, as shown in annexin V/PI double staining experiment (Figure 7e), cell cycle analysis (Figure 7f) and caspase-3 cleavage activity assay (Figure 7g). Finally, in A549 cells stably transfected with manganese superoxide (MnSOD) and catalase, apoptosis induced by TRAIL and rotenone was partially reversed (Figure 7h). All of these data suggest that mitochondria-derived ROS, including H2O2 and O2.−, are responsible for the apoptosis-enhancing effect of rotenone.

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Rotenone promotes BCl-XL degradation and PUMA transcription in ROS-dependent manner

To understand why ROS are responsible for the apoptosis-enhancing effect of rotenone, we found that rotenone-induced suppression of BCL-XL expression can be largely reversed by NAC treatment (Figure 8a). To examine whether this effect of rotenone occurs at posttranslational level, we used cycloheximide (CHX) to halt protein synthesis, and found that the rapid degradation of Bcl-XL by rotenone was largely attenuated in A549 ρ0 cells (Figure 8b). Similarly, rotenone-induced PUMA upregulation was also significantly abrogated in A549 ρ0 cells (Figure 8c). Finally, A549 cells were inoculated into nude mice to produce xenografts tumor model. In this model, the therapeutic effect of TRAIL combined with rotenone was evaluated. Notably, in order to circumvent the potential neurotoxic adverse effect of rotenone, mice were challenged with rotenone at a low concentration of 0.5 mg/kg. The results, as shown in Figure 8d revealed that while TRAIL or rotenone alone remained unaffected on A549 tumor growth, the combined therapy significantly slowed down the tumor growth. Interestingly, the tumor-suppressive effect of TRAIL plus rotenone was significantly attenuated by NAC (P<0.01). After experiment, tumors were removed and the caspase-3 activity in tumor cells was analyzed by flow cytometry. Consistently, the caspase-3 cleavage activities were significantly activated in A549 cells from animals challenged with TRAIL plus rotenone, meanwhile, this effect was attenuated by NAC (Figure 8e). The similar effect of rotenone also occurred in NCI-H441 xenografts tumor model (Supplementary Figure S6).

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Restoration of cancer cells susceptibility to TRAIL-induced apoptosis is becoming a very useful strategy for cancer therapy. In this study, we provided evidence that rotenone increased the apoptosis sensitivity of NSCLC cells toward TRAIL by mechanisms involving ROS generation, p53 upregulation, Bcl-XL and c-FLIP downregulation, and death receptors upregulation. Among them, mitochondria-derived ROS had a predominant role. Although rotenone is toxic to neuron, increasing evidence also demonstrated that it was beneficial for improving inflammation, reducing reperfusion injury, decreasing virus infection or triggering cancer cell death. We identified here another important characteristic of rotenone as a tumor sensitizer in TRAIL-based cancer therapy, which widens the application potential of rotenone in disease therapy.

As Warburg proposed the cancer ‘respiration injury’ theory, increasing evidence suggest that cancer cells may have mitochondrial dysfunction, which causes cancer cells, compared with the normal cells, are under increased generation of ROS.33 The increased ROS in cancer cells have a variety of biological effects. We found here that rotenone preferentially increased the apoptosis sensitivity of cancer cells toward TRAIL, further confirming the concept that although tumor cells have a high level of intracellular ROS, they are more sensitive than normal cells to agents that can cause further accumulation of ROS.

Cancer cells stay in a stressful tumor microenvironment including hypoxia, low nutrient availability and immune infiltrates. These conditions, however, activate a range of stress response pathways to promote tumor survival and aggressiveness. In order to circumvent TRAIL-mediated apoptotic clearance, the expression levels of DR4 and DR5 in many types of cancer cells are nullified, but interestingly, they can be reactivated when cancer cells are challenged with small chemical molecules. Furthermore, those small molecules often take advantage of the stress signaling required for cancer cells survival to increase cancer cells sensitivity toward TRAIL. For example, the unfolded protein response (UPR) has an important role in cancer cells survival, SHetA2, as a small molecule, can induce UPR in NSCLC cell lines and augment TRAIL-induced apoptosis by upregulating DR5 expression in CHOP-dependent manner. Here, we found rotenone manipulated the oxidative stress signaling of NSCLC cells to increase their susceptibility to TRAIL. These facts suggest that cellular stress signaling not only offers opportunity for cancer cells to survive, but also renders cancer cells eligible for attack by small molecules. A possible explanation is that depending on the intensity of stress, cellular stress signaling can switch its role from prosurvival to death enhancement. As described in this study, although ROS generation in cancer cells is beneficial for survival, rotenone treatment further increased ROS production to a high level that surpasses the cell ability to eliminate them; as a result, ROS convert its role from survival to death.

2.1.3.6 PPARs and ERRs. molecular mediators of mitochondrial metabolism

Weiwei Fan, Ronald Evans
Current Opinion in Cell Biology Apr 2015; 33:49–54
http://dx.doi.org/10.1016/j.ceb.2014.11.002

Since the revitalization of ‘the Warburg effect’, there has been great interest in mitochondrial oxidative metabolism, not only from the cancer perspective but also from the general biomedical science field. As the center of oxidative metabolism, mitochondria and their metabolic activity are tightly controlled to meet cellular energy requirements under different physiological conditions. One such mechanism is through the inducible transcriptional co-regulators PGC1α and NCOR1, which respond to various internal or external stimuli to modulate mitochondrial function. However, the activity of such co-regulators depends on their interaction with transcriptional factors that directly bind to and control downstream target genes. The nuclear receptors PPARs and ERRs have been shown to be key transcriptional factors in regulating mitochondrial oxidative metabolism and executing the inducible effects of PGC1α and NCOR1. In this review, we summarize recent gain-of-function and loss-of-function studies of PPARs and ERRs in metabolic tissues and discuss their unique roles in regulating different aspects of mitochondrial oxidative metabolism.

Energy is vital to all living organisms. In humans and other mammals, the vast majority of energy is produced by oxidative metabolism in mitochondria [1]. As a cellular organelle, mitochondria are under tight control of the nucleus. Although the majority of mitochondrial proteins are encoded by nuclear DNA (nDNA) and their expression regulated by the nucleus, mitochondria retain their own genome, mitochondrial DNA (mtDNA), encoding 13 polypeptides of the electron transport chain (ETC) in mammals. However, all proteins required for mtDNA replication, transcription, and translation, as well as factors regulating such activities, are encoded by the nucleus [2].

The cellular demand for energy varies in different cells under different physiological conditions. Accordingly, the quantity and activity of mitochondria are differentially controlled by a transcriptional regulatory network in both the basal and induced states. A number of components of this network have been identified, including members of the nuclear receptor superfamily, the peroxisome proliferator-activated receptors (PPARs) and the estrogen-related receptors (ERRs) [34 and 5].

The Yin-Yang co-regulators

A well-known inducer of mitochondrial oxidative metabolism is the peroxisome proliferator-activated receptor γ coactivator 1α (PGC1α) [6], a nuclear cofactor which is abundantly expressed in high energy demand tissues such as heart, skeletal muscle, and brown adipose tissue (BAT) [7]. Induction by cold-exposure, fasting, and exercise allows PGC1α to regulate mitochondrial oxidative metabolism by activating genes involved in the tricarboxylic acid cycle (TCA cycle), beta-oxidation, oxidative phosphorylation (OXPHOS), as well as mitochondrial biogenesis [6 and 8] (Figure 1).

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Figure 1.  PPARs and ERRs are major executors of PGC1α-induced regulation of oxidative metabolism. Physiological stress such as exercise induces both the expression and activity of PGC1α, which stimulates energy production by activating downstream genes involved in fatty acid and glucose metabolism, TCA cycle, β-oxidation, OXPHOS, and mitochondrial biogenesis. The transcriptional activity of PGC1α relies on its interactions with transcriptional factors such as PPARs (for controlling fatty acid metabolism) and ERRs (for regulating mitochondrial OXPHOS).

The effect of PGC1α on mitochondrial regulation is antagonized by transcriptional corepressors such as the nuclear receptor corepressor 1 (NCOR1) [9 and 10]. In contrast to PGC1α, the expression of NCOR1 is suppressed in conditions where PGC1α is induced such as during fasting, high-fat-diet challenge, and exercise [9 and 11]. Moreover, the knockout of NCOR1 phenotypically mimics PGC1α overexpression in regulating mitochondrial oxidative metabolism [9]. Therefore, coactivators and corepressors collectively regulate mitochondrial metabolism in a Yin-Yang fashion.

However, both PGC1α and NCOR1 lack DNA binding activity and rather act via their interaction with transcription factors that direct the regulatory program. Therefore the transcriptional factors that partner with PGC1α and NCOR1 mediate the molecular signaling cascades and execute their inducible effects on mitochondrial regulation.

PPARs: master executors controlling fatty acid oxidation

Both PGC1α and NCOR1 are co-factors for the peroxisome proliferator-activated receptors (PPARα, γ, and δ) [71112 and 13]. It is now clear that all three PPARs play essential roles in lipid and fatty acid metabolism by directly binding to and modulating genes involved in fat metabolism [1314151617,18 and 19]. While PPARγ is known as a master regulator for adipocyte differentiation and does not seem to be involved with oxidative metabolism [14 and 20], both PPARα and PPARδ are essential regulators of fatty acid oxidation (FAO) [3131519 and 21] (Figure 1).

PPARα was first cloned as the molecular target of fibrates, a class of cholesterol-lowering compounds that increase hepatic FAO [22]. The importance of PPARα in regulating FAO is indicated in its expression pattern which is restricted to tissues with high capacity of FAO such as heart, liver, BAT, and oxidative muscle [23]. On the other hand, PPARδ is ubiquitously expressed with higher levels in the digestive tract, heart, and BAT [24]. In the past 15 years, extensive studies using gain-of-function and loss-of-function models have clearly demonstrated PPARα and PPARδ as the major drivers of FAO in a wide variety of tissues.

ERRS: master executors controlling mitochondrial OXPHOS

ERRs are essential regulators of mitochondrial energy metabolism [4]. ERRα is ubiquitously expressed but particularly abundant in tissues with high energy demands such as brain, heart, muscle, and BAT. ERRβ and ERRγ have similar expression patterns, both are selectively expressed in highly oxidative tissues including brain, heart, and oxidative muscle [45]. Instead of endogenous ligands, the transcriptional activity of ERRs is primarily regulated by co-factors such as PGC1α and NCOR1 [4 and 46] (Figure 1).

Of the three ERRs, ERRβ is the least studied and its role in regulating mitochondrial function is unclear [4 and 47]. In contrast, when PGC1α is induced, ERRα is the master regulator of the mitochondrial biogenic gene network. As ERRα binds to its own promoter, PGC1α can also induce an autoregulatory loop to enhance overall ERRα activity [48]. Without ERRα, the ability of PGC1α to induce the expression of mitochondrial genes is severely impaired. However, the basal-state levels of mitochondrial target genes are not affected by ERRα deletion, suggesting induced mitochondrial biogenesis is a transient process and that other transcriptional factors such as ERRγ may be important maintaining baseline mitochondrial OXPHOS [41•42 and 43]. Consistent with this idea, ERRγ (which is active even when PGC1α is not induced) shares many target genes with ERRα [49 and 50].

Conclusion and perspectives

Taken together, recent studies have clearly demonstrated the essential roles of PPARs and ERRs in regulating mitochondrial oxidative metabolism and executing the inducible effects of PGC1α (Figure 1). Both PPARα and PPARδ are key regulators for FA oxidation. While the function of PPARα seems more restricted in FA uptake, beta-oxidation, and ketogenesis, PPARδ plays a broader role in controlling oxidative metabolism and fuel preference, with its target genes involved in FA oxidation, mitochondrial OXPHOS, and glucose utilization. However, it is still not clear how much redundancy exists between PPARα and PPARδ, a question which may require the generation of a double knockout model. In addition, more effort is needed to fully understand how PPARα and PPARδ control their target genes in response to environmental changes.

Likewise, ERRα and ERRγ have been shown to be key regulators of mitochondrial OXPHOS. Knockout studies of ERRα suggest it to be the principal executor of PGC1α induced up-regulation of mitochondrial genes, though its role in exercise-dependent changes in skeletal muscle needs further investigation. Transgenic models have demonstrated ERRγ’s powerful induction of mitochondrial biogenesis and its ability to act in a PGC1α-independent manner. However, it remains to be elucidated whether ERRγ is sufficient for basal-state mitochondrial function in general, and whether ERRα can compensate for its function.

2.1.3.7 Metabolic control via the mitochondrial protein import machinery

Opalińska M, Meisinger C.
Curr Opin Cell Biol. 2015 Apr; 33:42-48
http://dx.doi.org:/10.1016/j.ceb.2014.11.001

Mitochondria have to import most of their proteins in order to fulfill a multitude of metabolic functions. Sophisticated import machineries mediate targeting and translocation of preproteins from the cytosol and subsequent sorting into their suborganellar destination. The mode of action of these machineries has been considered for long time as a static and constitutively active process. However, recent studies revealed that the mitochondrial protein import machinery is subject to intense regulatory mechanisms that include direct control of protein flux by metabolites and metabolic signaling cascades.
2.1.3.8 The Protein Import Machinery of Mitochondria—A Regulatory Hub

AB Harbauer, RP Zahedi, A Sickmann, N Pfanner, C Meisinger
Cell Metab 4 Mar 2014; 19(3):357–372

Mitochondria are essential cell. They are best known for their role as cellular powerhouses, which convert the energy derived from food into an electrochemical proton gradient across the inner membrane. The proton gradient drives the mitochondrial ATP synthase, thus providing large amounts of ATP for the cell. In addition, mitochondria fulfill central functions in the metabolism of amino acids and lipids and the biosynthesis of iron-sulfur clusters and heme. Mitochondria form a dynamic network that is continuously remodeled by fusion and fission. They are involved in the maintenance of cellular ion homeostasis, play a crucial role in apoptosis, and have been implicated in the pathogenesis of numerous diseases, in particular neurodegenerative disorders.

Mitochondria consist of two membranes, outer membrane and inner membrane, and two aqueous compartments, intermembrane space and matrix (Figure 1). Proteomic studies revealed that mitochondria contain more than 1,000 different proteins (Prokisch et al., 2004Reinders et al., 2006Pagliarini et al., 2008 and Schmidt et al., 2010). Based on the endosymbiotic origin from a prokaryotic ancestor, mitochondria contain a complete genetic system and protein synthesis apparatus in the matrix; however, only ∼1% of mitochondrial proteins are encoded by the mitochondrial genome (13 proteins in humans and 8 proteins in yeast). Nuclear genes code for ∼99% of mitochondrial proteins. The proteins are synthesized as precursors on cytosolic ribosomes and are translocated into mitochondria by a multicomponent import machinery. The protein import machinery is essential for the viability of eukaryotic cells. Numerous studies on the targeting signals and import components have been reported (reviewed in Dolezal et al., 2006,Neupert and Herrmann, 2007Endo and Yamano, 2010 and Schmidt et al., 2010), yet for many years little has been known on the regulation of the import machinery. This led to the general assumption that the protein import machinery is constitutively active and not subject to detailed regulation.

Figure 1. Protein Import Pathways of Mitochondria.  Most mitochondrial proteins are synthesized as precursors in the cytosol and are imported by the translocase of the outer mitochondrial membrane (TOM complex). (A) Presequence-carrying (cleavable) preproteins are transferred from TOM to the presequence translocase of the inner membrane (TIM23 complex), which is driven by the membrane potential (Δψ). The proteins either are inserted into the inner membrane (IM) or are translocated into the matrix with the help of the presequence translocase-associated motor (PAM). The presequences are typically cleaved off by the mitochondrial processing peptidase (MPP). (B) The noncleavable precursors of hydrophobic metabolite carriers are bound to molecular chaperones in the cytosol and transferred to the receptor Tom70. After translocation through the TOM channel, the precursors bind to small TIM chaperones in the intermembrane space and are membrane inserted by the Δψ-dependent carrier translocase of the inner membrane (TIM22 complex).
(C) Cysteine-rich proteins destined for the intermembrane space (IMS) are translocated through the TOM channel in a reduced conformation and imported by the mitochondrial IMS import and assembly (MIA) machinery. Mia40 functions as precursor receptor and oxidoreductase in the IMS, promoting the insertion of disulfide bonds into the imported proteins. The sulfhydryl oxidase Erv1 reoxidizes Mia40 for further rounds of oxidative protein import and folding. (D) The precursors of outer membrane β-barrel proteins are imported by the TOM complex and small TIM chaperones and are inserted into the outer membrane by the sorting and assembly machinery (SAM complex). (E) Outer membrane (OM) proteins with α-helical transmembrane segments are inserted into the membrane by import pathways that have only been partially characterized. Shown is an import pathway via the mitochondrial import (MIM) complex

Studies in recent years, however, indicated that different steps of mitochondrial protein import are regulated, suggesting a remarkable diversity of potential mechanisms. After an overview on the mitochondrial protein import machinery, we will discuss the regulatory processes at different stages of protein translocation into mitochondria. We propose that the mitochondrial protein import machinery plays a crucial role as regulatory hub under physiological and pathophysiological conditions. Whereas the basic mechanisms of mitochondrial protein import have been conserved from lower to higher eukaryotes (yeast to humans), regulatory processes may differ between different organisms and cell types. So far, many studies on the regulation of mitochondrial protein import have only been performed in a limited set of organisms. Here we discuss regulatory principles, yet it is important to emphasize that future studies will have to address which regulatory processes have been conserved in evolution and which processes are organism specific.

Protein Import Pathways into Mitochondria

The classical route of protein import into mitochondria is the presequence pathway (Neupert and Herrmann, 2007 and Chacinska et al., 2009). This pathway is used by more than half of all mitochondrial proteins (Vögtle et al., 2009). The proteins are synthesized as precursors with cleavable amino-terminal extensions, termed presequences. The presequences form positively charged amphipathic α helices and are recognized by receptors of the translocase of the outer mitochondrial membrane (TOM complex) (Figure 1A) (Mayer et al., 1995Brix et al., 1997van Wilpe et al., 1999Abe et al., 2000Meisinger et al., 2001 and Saitoh et al., 2007). Upon translocation through the TOM channel, the cleavable preproteins are transferred to the presequence translocase of the inner membrane (TIM23 complex). The membrane potential across the inner membrane (Δψ, negative on the matrix side) exerts an electrophoretic effect on the positively charged presequences (Martin et al., 1991). The presequence translocase-associated motor (PAM) with the ATP-dependent heat-shock protein 70 (mtHsp70) drives preprotein translocation into the matrix (Chacinska et al., 2005 and Mapa et al., 2010). Here the presequences are typically cleaved off by the mitochondrial processing peptidase (MPP). Some cleavable preproteins contain a hydrophobic segment behind the presequence, leading to arrest of translocation in the TIM23 complex and lateral release of the protein into the inner membrane (Glick et al., 1992Chacinska et al., 2005 and Meier et al., 2005). In an alternative sorting route, some cleavable preproteins destined for the inner membrane are fully or partially translocated into the matrix, followed by insertion into the inner membrane by the OXA export machinery, which has been conserved from bacteria to mitochondria (“conservative sorting”) (He and Fox, 1997Hell et al., 1998Meier et al., 2005 and Bohnert et al., 2010).  …

Regulatory Processes Acting at Cytosolic Precursors of Mitochondrial Proteins

Two properties of cytosolic precursor proteins are crucial for import into mitochondria. (1) The targeting signals of the precursors have to be accessible to organellar receptors. Modification of a targeting signal by posttranslational modification or masking of a signal by binding partners can promote or inhibit import into an organelle. (2) The protein import channels of mitochondria are so narrow that folded preproteins cannot be imported. Thus preproteins should be in a loosely folded state or have to be unfolded during the import process. Stable folding of preprotein domains in the cytosol impairs protein import.  …

Import Regulation by Binding of Metabolites or Partner Proteins to Preproteins

Binding of a metabolite to a precursor protein can represent a direct means of import regulation (Figure 2A, condition 1). A characteristic example is the import of 5-aminolevulinate synthase, a mitochondrial matrix protein that catalyzes the first step of heme biosynthesis (Hamza and Dailey, 2012). The precursor contains heme binding motifs in its amino-terminal region, including the presequence (Dailey et al., 2005). Binding of heme to the precursor inhibits its import into mitochondria, likely by impairing recognition of the precursor protein by TOM receptors (Lathrop and Timko, 1993González-Domínguez et al., 2001,Munakata et al., 2004 and Dailey et al., 2005). Thus the biosynthetic pathway is regulated by a feedback inhibition of mitochondrial import of a crucial enzyme, providing an efficient and precursor-specific means of import regulation dependent on the metabolic situation.

Figure 2. Regulation of Cytosolic Precursors of Mitochondrial Proteins

(A) The import of a subset of mitochondrial precursor proteins can be positively or negatively regulated by precursor-specific reactions in the cytosol. (1) Binding of ligands/metabolites can inhibit mitochondrial import. (2) Binding of precursors to partner proteins can stimulate or inhibit import into mitochondria. (3) Phosphorylation of precursors in the vicinity of targeting signals can modulate dual targeting to the endoplasmic reticulum (ER) and mitochondria. (4) Precursor folding can mask the targeting signal. (B) Cytosolic and mitochondrial fumarases are derived from the same presequence-carrying preprotein. The precursor is partially imported by the TOM and TIM23 complexes of the mitochondrial membranes and the presequence is removed by the mitochondrial processing peptidase (MPP). Folding of the preprotein promotes retrograde translocation of more than half of the molecules into the cytosol, whereas the other molecules are completely imported into mitochondria.

Regulation of Mitochondrial Protein Entry Gate by Cytosolic Kinases

Figure 3. Regulation of TOM Complex by Cytosolic Kinases

(A) All subunits of the translocase of the outer mitochondrial membrane (TOM complex) are phosphorylated by cytosolic kinases (phosphorylated amino acid residues are indicated by stars with P). Casein kinase 1 (CK1) stimulates the assembly of Tom22 into the TOM complex. Casein kinase 2 (CK2) stimulates the biogenesis of Tom22 as well as the mitochondrial import protein 1 (Mim1). Protein kinase A (PKA) inhibits the biogenesis of Tom22 and Tom40, and inhibits the activity of Tom70 (see B). Cyclin-dependent kinases (CDK) are possibly involved in regulation of TOM. (B) Metabolic shift-induced regulation of the receptor Tom70 by PKA. Carrier precursors bind to cytosolic chaperones (Hsp70 and/or Hsp90). Tom70 has two binding pockets, one for the precursor and one for the accompanying chaperone (shown on the left). When glucose is added to yeast cells (fermentable conditions), the levels of intracellular cAMP are increased and PKA is activated (shown on the right). PKA phosphorylates a serine of Tom70 in vicinity of the chaperone binding pocket, thus impairing chaperone binding to Tom70 and carrier import into mitochondria.

Casein Kinase 2 Stimulates TOM Biogenesis and Protein Import

Metabolic Switch from Respiratory to Fermentable Conditions Involves Protein Kinase A-Mediated Inhibition of TOM

Network of Stimulatory and Inhibitory Kinases Acts on TOM Receptors, Channel, and Assembly Factors

Protein Import Activity as Sensor of Mitochondrial Stress and Dysfunction

Figure 4. Mitochondrial Quality Control and Stress Response

(A) Import and quality control of cleavable preproteins. The TIM23 complex cooperates with several machineries: the TOM complex, a supercomplex consisting of the respiratory chain complexes III and IV, and the presequence translocase-associated motor (PAM) with the central chaperone mtHsp70. Several proteases/peptidases involved in processing, quality control, and/or degradation of imported proteins are shown, including mitochondrial processing peptidase (MPP), intermediate cleaving peptidase (XPNPEP3/Icp55), mitochondrial intermediate peptidase (MIP/Oct1), mitochondrial rhomboid protease (PARL/Pcp1), and LON/Pim1 protease. (B) The transcription factor ATFS-1 contains dual targeting information, a mitochondrial targeting signal at the amino terminus, and a nuclear localization signal (NLS). In normal cells, ATFS-1 is efficiently imported into mitochondria and degraded by the Lon protease in the matrix. When under stress conditions the protein import activity of mitochondria is reduced (due to lower Δψ, impaired mtHsp70 activity, or peptides exported by the peptide transporter HAF-1), some ATFS-1 molecules accumulate in the cytosol and can be imported into the nucleus, leading to induction of an unfolded protein response (UPRmt).

Regulation of PINK1/Parkin-Induced Mitophagy by the Activity of the Mitochondrial Protein Import Machinery

Figure 5.  Mitochondrial Dynamics and Disease

(A) In healthy cells, the kinase PINK1 is partially imported into mitochondria in a membrane potential (Δψ)-dependent manner and processed by the inner membrane rhomboid protease PARL, which cleaves within the transmembrane segment and generates a destabilizing N terminus, followed by retro-translocation of cleaved PINK1 into the cytosol and degradation by the ubiquitin-proteasome system (different views have been reported if PINK1 is first processed by MPP or not; Greene et al., 2012, Kato et al., 2013 and Yamano and Youle, 2013). Dissipation of Δψ in damaged mitochondria leads to an accumulation of unprocessed PINK1 at the TOM complex and the recruitment of the ubiquitin ligase Parkin to mitochondria. Mitofusin 2 is phosphorylated by PINK1 and likely functions as receptor for Parkin. Parkin mediates ubiquitination of mitochondrial outer membrane proteins (including mitofusins), leading to a degradation of damaged mitochondria by mitophagy. Mutations of PINK1 or Parkin have been observed in monogenic cases of Parkinson’s disease. (B) The inner membrane fusion protein OPA1/Mgm1 is present in long and short isoforms. A balanced formation of the isoforms is a prerequisite for the proper function of OPA1/Mgm1. The precursor of OPA1/Mgm1 is imported by the TOM and TIM23 complexes. A hydrophobic segment of the precursor arrests translocation in the inner membrane, and the amino-terminal targeting signal is cleaved by MPP, generating the long isoforms. In yeast mitochondria, the import motor PAM drives the Mgm1 precursor further toward the matrix such that a second hydrophobic segment is cleaved by the inner membrane rhomboid protease Pcp1, generating the short isoform (s-Mgm1). In mammals, the m-AAA protease is likely responsible for the balanced formation of long (L) and short (S) isoforms of OPA1. A further protease, OMA1, can convert long isoforms into short isoforms in particular under stress conditions, leading to an impairment of mitochondrial fusion and thus to fragmentation of mitochondria.

….

Mitochondrial research is of increasing importance for the molecular understanding of numerous diseases, in particular of neurodegenerative disorders. The well-established connection between the pathogenesis of Parkinson’s disease and mitochondrial protein import has been discussed above. Several observations point to a possible connection of mitochondrial protein import with the pathogenesis of Alzheimer’s disease, though a direct role of mitochondria has not been demonstrated so far. The amyloid-β peptide (Aβ), which is generated from the amyloid precursor protein (APP), was found to be imported into mitochondria by the TOM complex, to impair respiratory activity, and to enhance ROS generation and fragmentation of mitochondria (Hansson Petersen et al., 2008, Ittner and Götz, 2011 and Itoh et al., 2013). An accumulation of APP in the TOM and TIM23 import channels has also been reported (Devi et al., 2006). The molecular mechanisms of how mitochondrial activity and dynamics may be altered by Aβ (and possibly APP) and how mitochondrial alterations may impact on the pathogenesis of Alzheimer’s disease await further analysis.

It is tempting to speculate that regulatory changes in mitochondrial protein import may be involved in tumor development. Cancer cells can shift their metabolism from respiration toward glycolysis (Warburg effect) (Warburg, 1956, Frezza and Gottlieb, 2009, Diaz-Ruiz et al., 2011 and Nunnari and Suomalainen, 2012). A glucose-induced downregulation of import of metabolite carriers into mitochondria may represent one of the possible mechanisms during metabolic shift to glycolysis. Such a mechanism has been shown for the carrier receptor Tom70 in yeast mitochondria (Schmidt et al., 2011). A detailed analysis of regulation of mitochondrial preprotein translocases in healthy mammalian cells as well as in cancer cells will represent an important task for the future.

Conclusion

In summary, the concept of the “mitochondrial protein import machinery as regulatory hub” will promote a rapidly developing field of interdisciplinary research, ranging from studies on molecular mechanisms to the analysis of mitochondrial diseases. In addition to identifying distinct regulatory mechanisms, a major challenge will be to define the interactions between different machineries and regulatory processes, including signaling networks, preprotein translocases, bioenergetic complexes, and machineries regulating mitochondrial membrane dynamics and contact sites, in order to understand the integrative system controlling mitochondrial biogenesis and fitness.

2.1.3.9 Exosome Transfer from Stromal to Breast Cancer Cells Regulates Therapy Resistance Pathways

MC Boelens, Tony J. Wu, Barzin Y. Nabet, et al.
Cell 23 Oct 2014; 159(3): 499–513
http://www.sciencedirect.com/science/article/pii/S0092867414012392

Highlights

  • Exosome transfer from stromal to breast cancer cells instigates antiviral signaling
    • RNA in exosomes activates antiviral STAT1 pathway through RIG-I
    • STAT1 cooperates with NOTCH3 to expand therapy-resistant cells
    • Antiviral/NOTCH3 pathways predict NOTCH activity and resistance in primary tumors

Summary

Stromal communication with cancer cells can influence treatment response. We show that stromal and breast cancer (BrCa) cells utilize paracrine and juxtacrine signaling to drive chemotherapy and radiation resistance. Upon heterotypic interaction, exosomes are transferred from stromal to BrCa cells. RNA within exosomes, which are largely noncoding transcripts and transposable elements, stimulates the pattern recognition receptor RIG-I to activate STAT1-dependent antiviral signaling. In parallel, stromal cells also activate NOTCH3 on BrCa cells. The paracrine antiviral and juxtacrine NOTCH3 pathways converge as STAT1 facilitates transcriptional responses to NOTCH3 and expands therapy-resistant tumor-initiating cells. Primary human and/or mouse BrCa analysis support the role of antiviral/NOTCH3 pathways in NOTCH signaling and stroma-mediated resistance, which is abrogated by combination therapy with gamma secretase inhibitors. Thus, stromal cells orchestrate an intricate crosstalk with BrCa cells by utilizing exosomes to instigate antiviral signaling. This expands BrCa subpopulations adept at resisting therapy and reinitiating tumor growth.

stromal-communication-with-cancer-cells

stromal-communication-with-cancer-cells

Graphical Abstract

2.1.3.10 Emerging concepts in bioenergetics and cancer research

Obre E, Rossignol R
Int J Biochem Cell Biol. 2015 Feb; 59:167-81
http://dx.doi.org:/10.1016/j.biocel.2014.12.008

The field of energy metabolism dramatically progressed in the last decade, owing to a large number of cancer studies, as well as fundamental investigations on related transcriptional networks and cellular interactions with the microenvironment. The concept of metabolic flexibility was clarified in studies showing the ability of cancer cells to remodel the biochemical pathways of energy transduction and linked anabolism in response to glucose, glutamine or oxygen deprivation. A clearer understanding of the large-scale bioenergetic impact of C-MYC, MYCN, KRAS and P53 was obtained, along with its modification during the course of tumor development. The metabolic dialog between different types of cancer cells, but also with the stroma, also complexified the understanding of bioenergetics and raised the concepts of metabolic symbiosis and reverse Warburg effect. Signaling studies revealed the role of respiratory chain-derived reactive oxygen species for metabolic remodeling and metastasis development. The discovery of oxidative tumors in human and mice models related to chemoresistance also changed the prevalent view of dysfunctional mitochondria in cancer cells. Likewise, the influence of energy metabolism-derived oncometabolites emerged as a new means of tumor genetic regulation. The knowledge obtained on the multi-site regulation of energy metabolism in tumors was translated to cancer preclinical studies, supported by genetic proof of concept studies targeting LDHA, HK2, PGAM1, or ACLY. Here, we review those different facets of metabolic remodeling in cancer, from its diversity in physiology and pathology, to the search of the genetic determinants, the microenvironmental regulators and pharmacological modulators.

2.1.3.11 Protecting the mitochondrial powerhouse

M Scheibye-Knudsen, EF Fang, DL Croteau, DM Wilson III, VA Bohr
Trends in Cell Biol, Mar 2015; 25(3):158–170

Highlights

  • Mitochondrial maintenance is essential for cellular and organismal function.
    • Maintenance includes reactive oxygen species (ROS) regulation, DNA repair, fusion–fission, and mitophagy.
    • Loss of function of these pathways leads to disease.

Mitochondria are the oxygen-consuming power plants of cells. They provide a critical milieu for the synthesis of many essential molecules and allow for highly efficient energy production through oxidative phosphorylation. The use of oxygen is, however, a double-edged sword that on the one hand supplies ATP for cellular survival, and on the other leads to the formation of damaging reactive oxygen species (ROS). Different quality control pathways maintain mitochondria function including mitochondrial DNA (mtDNA) replication and repair, fusion–fission dynamics, free radical scavenging, and mitophagy. Further, failure of these pathways may lead to human disease. We review these pathways and propose a strategy towards a treatment for these often untreatable disorders.

Discussion

Radoslav Bozov –

Larry, pyruvate is a direct substrate for synthesizing pyrimidine rings, as well as C-13 NMR study proven source of methyl groups on SAM! Think about what cancer cells care for – dis-regulated growth through ‘escaped’ mutability of proteins, ‘twisting’ pathways of ordered metabolism space-time wise! mtDNA is a back up, evolutionary primitive, however, primary system for pulling strings onto cell cycle events. Oxygen (never observed single molecule) pulls up electron negative light from emerging super rich energy carbon systems. Therefore, ATP is more acting like a neutralizer – resonator of space-energy systems interoperability! You cannot look at a compartment / space independently , as dimension always add 1 towards 3+1.

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Warburg Effect Revisited – 2

Writer and Curator: Larry H. Bernstein, MD, FCAP

Finding Dysregulation in the Cancer Cell

2.1.         Warburg Effect Revisited

One of the great observations of the 20th century was the behavior of cancer cells to proliferate and rely on anaerobic glycolysis for the source of energy.  This was a restatement of the Pasteur effect, described 60 years earlier by the great French scientist in yeast experiments.  The experiments with yeast were again reperformed by Jose EDS Roselino, a Brazilian biochemist, who established an explanation for it 50 years after Warburg.  It is quite amazing the mitochondria were not yet discovered at the time that Warburg carried out the single-cell thickness measurements in his respiratory apparatus. He concluded from the observation that the cancer cells grew in a media that became acidic from producing lactic acid, that the cells were dysfunctional in the utilization of oxygen, as nonmalignant cells efficiently utilized oxygen. He also related the metabolic events to observations made by Meyerhof.  The mitochondria and the citric acid cycle at this time had not yet been discovered, and the latter was, worked out by Hans Krebs and Albert Szent-Gyorgi, both of whom worked with him on mitochondrial metabolism.  The normal cell utilizes glucose efficiently and lipids as well, generating energy through oxidative phosphorylation, with the production of ATP in a manner previously described in these posts.  Greater clarity was achieved with the discovery of Coenzyme A, and finally the electron transport chain (ETC).  This requires that the pyruvate be directed into the tricarboxylic acid cycle and to go through a series of reactions producing succinate and finally malate.

The following great achievements were made with regard to elucidating these processes:

1922 Archibald Vivian Hill United Kingdom “for his discovery relating to the production of heat in the muscle[26]
Otto Fritz Meyerhof Germany “for his discovery of the fixed relationship between the consumption of oxygen and the metabolism of lactic acid in the muscle”[26]
1931 Otto Heinrich Warburg Germany “for his discovery of the nature and mode of action of the respiratory enzyme[34]
1937 Albert Szent-Györgyi von Nagyrapolt Hungary “for his discoveries in connection with the biological combustion processes, with special reference to vitamin C and the catalysis of fumaric acid[40]
1953 Sir Hans Adolf Krebs United Kingdom “for his discovery of the citric acid cycle[53]
Fritz Albert Lipmann United States “for his discovery of co-enzyme A and its importance for intermediary metabolism”[53]
1955 Axel Hugo Theodor Theorell Sweden “for his discoveries concerning the nature and mode of action of oxidation enzymes”[55]
1978 Peter D. Mitchell United Kingdom “for his contribution to the understanding of biological energy transfer through the formulation of the chemiosmotic theory[77]
1997 Paul D. Boyer United States “for their elucidation of the enzymatic mechanism underlying the synthesis of adenosine triphosphate (ATP)”[96]
John E. Walker United Kingdom

 

 1967  Manfred Eigen   and the other half jointly to:

Ronald George Wreyford Norrish and Lord George Porter for their studies of extremely fast chemical reactions, effected by disturbing the equlibrium by means of very short pulses of energy.

1965   FRANÇOIS JACOB , ANDRÉ LWOFF And JACQUES MONOD for their discoveries concerning genetic control of enzyme and virus synthesis.

1964 KONRAD BLOCH And FEODOR LYNEN for their discoveries concerning the mechanism and regulation of the cholesterol and fatty acid metabolism.

If there is a more immediate need for energy (as in stressed muscular activity) with net oxygen insufficiency, the pyruvate is converted to lactic acid, with acidemia, and with much less ATP production, but the lactic academia and the energy deficit is subsequently compensated for.    The observation made by Jose EDS Rosalino was that yeast grown in a soil deficient in oxygen don’t put down roots.

^I. Topisirovic and N. Sonenberg

Cold Spring Harbor Symposia on Quantitative Biology, Volume LXXVI

http://dx.doi.org:/10.1101/sqb.2011.76.010785 ”A prominent feature of cancer cells is the use of aerobic glycolysis under conditions in which oxygen levels are sufficient to support energy production in the mitochondria (Jones and Thompson 2009; Cairns et al. 2010). This phenomenon, named the “Warburg effect,” after its discoverer Otto Warburg, is thought to fuel the biosynthetic requirements of the neoplastic growth (Warburg 1956; Koppenol et al. 2011) and has recently been acknowledged as one of the hallmarks of cancer (Hanahan and Weinberg 2011). mRNA translation is the most energy-demanding process in the cell (Buttgereit and Brand 1995).

Again, the use of aerobic glycolysis expression has been twisted.”

To understand my critical observation consider this: Aerobic glycolysis is the carbon flow that goes from Glucose to CO2 and water (includes Krens cycle and respiratory chain for the restoration of NAD, FAD etc.

Anerobic glyclysis is the carbon flow that goes from glucose to lactate. It uses conversion of pyruvate to lactate to regenerate NAD.

“Pasteur effect” is an expression coined by Warburg, which refers to the reduction in the carbon flow from glucose when oxygen is offered to yeasts. The major reason for that is in general terms, derived from the fact that carbon flow is regulated by several cell requirements but mainly by the ATP needs of the cell. Therefore, as ATP is generated 10 more efficiently in aerobiosis than under anaerobiosis, less carbon flow is required under aerobiosis than under anaerobiosis to maintain ATP levels. Warburg, after searching for the same regulatory mechanism in normal and cancer cells for comparison found that transformed cell continued their large flow of glucose carbons to lactate despite the presence of oxygen.

So, it is wrong to describe that aerobic glycolysis continues in the presence of oxygen. It is what it is expected to occur. The wrong thing is that anaerobic glycolysis continues under aerobiosis.
^Aurelian Udristioiu (comment)
In cells, the immediate energy sources involve glucose oxidation. In anaerobic metabolism, the donor of the phosphate group is adenosine triphosphate (ATP), and the reaction is catalyzed via the hexokinase or glucokinase: Glucose +ATP-Mg²+ = Glucose-6-phosphate (ΔGo = – 3.4 kcal/mol with hexokinase as the co-enzyme for the reaction.).

In the following step, the conversion of G-6-phosphate into F-1-6-bisphosphate is mediated by the enzyme phosphofructokinase with the co-factor ATP-Mg²+. This reaction has a large negative free energy difference and is irreversible under normal cellular conditions. In the second step of glycolysis, phosphoenolpyruvic acid in the presence of Mg²+ and K+ is transformed into pyruvic acid. In cancer cells or in the absence of oxygen, the transformation of pyruvic acid into lactic acid alters the process of glycolysis.

The energetic sum of anaerobic glycolysis is ΔGo = -34.64 kcal/mol. However a glucose molecule contains 686kcal/mol and, the energy difference (654.51 kcal) allows the potential for un-controlled reactions during carcinogenesis. The transfer of electrons from NADPH in each place of the conserved unit of energy transmits conformational exchanges in the mitochondrial ATPase. The reaction ADP³+ P²¯ + H²à ATP + H2O is reversible. The terminal oxygen from ADP binds the P2¯ by forming an intermediate pentacovalent complex, resulting in the formation of ATP and H2O. This reaction requires Mg²+ and an ATP-synthetase, which is known as the H+-ATPase or the Fo-F1-ATPase complex. Intracellular calcium induces mitochondrial swelling and aging. [12].

The known marker of monitoring of treatment in cancer diseases, lactate dehydrogenase (LDH) is an enzyme that is localized to the cytosol of human cells and catalyzes the reversible reduction of pyruvate to lactate via using hydrogenated nicotinamide deaminase (NADH) as co-enzyme.

The causes of high LDH and high Mg levels in the serum include neoplastic states that promote the high production of intracellular LDH and the increased use of Mg²+ during molecular synthesis in processes pf carcinogenesis (Pyruvate acid>> LDH/NADH >>Lactate acid + NAD), [13].

The material we shall discuss explores in more detail the dysmetabolism that occurs in cancer cells.

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?
http://pharmaceuticalintelligence.com/2014/06/21/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view-2/

Warburg Effect Revisited
http://pharmaceuticalintelligence.com/2013/11/28/warburg-effect-revisited/

AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo
http://pharmaceuticalintelligence.com/2013/03/12/ampk-is-a-negative-regulator-of-the-warburg-effect-and-suppresses-tumor-growth-in-vivo/

AKT Signaling Variable Effects
http://pharmaceuticalintelligence.com/2013/03/04/akt-signaling-variable-effects/

Otto Warburg, A Giant of Modern Cellular Biology
http://pharmaceuticalintelligence.com/2012/11/02/otto-warburg-a-giant-of-modern-cellular-biology/

The Metabolic View of Epigenetic Expression
http://pharmaceuticalintelligence.com/2015/03/28/the-metabolic-view-of-epigenetic-expression/

Metabolomics Summary and Perspective
http://pharmaceuticalintelligence.com/2014/10/16/metabolomics-summary-and-perspective/

2.1.1       Cancer Metabolism

2.1.1.1  Oncometabolites: linking altered  metabolism with cancer

Ming Yang, Tomoyoshi Soga, and Patrick J. Pollard
J Clin Invest Sep 2013; 123(9):3652–3658
http://dx.doi.org:/10.1172/JCI67228

The discovery of cancer-associated mutations in genes encoding key metabolic enzymes has provided a direct link between altered metabolism and cancer. Advances in mass spectrometry and nuclear magnetic resonance technologies have facilitated high-resolution metabolite profiling of cells and tumors and identified the accumulation of metabolites associated with specific gene defects. Here we review the potential roles of such “oncometabolites” in tumor evolution and as clinical biomarkers for the detection of cancers characterized by metabolic dysregulation.

The emerging interest in metabolites whose abnormal accumulation causes both metabolic and nonmetabolic dysregulation and potential transformation to malignancy (herein termed “oncometabolites”) has been fueled by the identification of cancerassociated mutations in genes encoding enzymes with significant roles in cellular metabolism (1–5). Loss-of-function mutations in genes encoding the Krebs cycle enzymes fumarate hydratase (FH) and succinate dehydrogenase (SDH) cause the accumulation of fumarate and succinate, respectively (6), whereas gain-offunction isocitrate dehydrogenase (IDH) mutations increase levels of D–2-hydroxyglutarate (D-2HG) (7, 8). These metabolites have been implicated in the dysregulation of cellular processes including the competitive inhibition of α-ketoglutarate–dependent (α-KG–dependent) dioxygenase enzymes (also known as 2-oxoglutarate–dependent dioxgenases) and posttranslational modification of proteins (1, 4, 9–11). To date, several lines of biochemical and genetic evidence support roles for fumarate, succinate, and D-2HG in cellular transformation and oncogenesis (3, 12).

The Journal of Clinical Investigation   http://www.jci.org   Volume 123   Number 9   September 2013

ventional gene sequencing methods may lead to false positives due to genetic polymorphism and sequencing artifacts (98). In comparison, screening for elevated 2HG levels is a sensitive and specific approach to detect IDH mutations in tumors. Whereas patient sera/plasma can be assessed in the case of AML (7, 8, 21, 99), exciting advances with proton magnetic resonance spectroscopy (MRS) have been made in the noninvasive detection of 2HG in patients with gliomas (100–103). Using MRS sequence optimization and spectral fitting techniques, Maher and colleagues examined 30 patients with glioma and showed that the detection of 2HG correlated 100% with the presence of IDH1 or IDH2 mutations (102). Andronesi et al. further demonstrated that two-dimensional correlation spectroscopy could effectively distinguish 2HG from chemically similar metabolites present in the brain (103). Negative IHC staining for SDHB correlates with the presence of SDH mutations, whether in SDHB, SDHC, or SDHD (104). This finding is most likely explained by the fact that mutations in any of the four subunits of SDH can destabilize the entire enzyme complex. PGLs/PCCs associated with an SDHA mutation show negative staining for SDHA as well as SDHB (105). Therefore, IHC staining for SDHB is a useful diagnostic tool to triage patients for genetic testing of any SDH mutation, and subsequent staining for the other subunits may further narrow the selection of genes to be tested. In contrast, detection of FH protein is often evident in HLRCC tumors due to retention of the nonfunctional mutant allele (106). However, staining of cysts and tumors for 2SC immunoreactivity reveals a striking correlation between FH inactivation and the presence of 2SC-modified protein (2SCP), which is absent in non-HLRCC tumors and normal tissue controls (106). IHC staining for 2SCP thus provides a robust diagnostic biomarker for FH deficiency (107).

Therapeutic targeting Because D-2HG is a product of neomorphic enzyme activities, curtailing the D-2HG supply by specifically inhibiting the mutant IDH enzymes provides an elegant approach to target IDH-mutant cancers. Indeed, recent reports of small-molecule inhibitors against mutant forms of IDH1 and IDH2 demonstrated the feasibility of this method. An inhibitor against IDH2 R140Q was shown to reduce both intracellular and extracellular levels of D-2HG, suppress cell growth, and increase differentiation of primary human AML cells (108). Similarly, small-molecule inhibition of IDH1 R132H suppressed colony formation and increased tumor cell differentiation in a xenograft model for IDH1 R132H glioma (58). The inhibitors exhibited a cytostatic rather than cytotoxic effect, and therefore their therapeutic efficacy over longer time periods may need further assessment (109). Letouzé et al. showed that the DNA methytransferase inhibitor decitabine could repress the migration capacities of SDHB-mutant cells (40). However, for SDH- and FH-associated cancers, a synthetic lethality approach is worth exploring because of the pleiotrophic effects associated with succinate and fumarate accumulation.

Outlook The application of next-generation sequencing technologies in the field of cancer genomics has substantially increased our understanding of cancer biology. Detection of germline and somatic mutations in specific tumor types not only expands the current repertoire of driver mutations and downstream effectors in tumorigenesis, but also sheds light on how oncometabolites may exert their oncogenic roles. For example, the identification of mutually exclusive mutations in IDH1 and TET2 in AML led to the characterization of TET2 as a major pathological target of D-2HG (34, 110). Additionally, the discovery of somatic CUL3, SIRT1, and NRF2 mutations in sporadic PRCC2 converges with FH mutation in HLRCC, in which NRF2 activation is a consequence of fumarate-mediated succination of KEAP1, indicating the functional prominence of the NRF2 pathway in PRCC2 (73). In light of this, the identification of somatic mutations in genes encoding the chromatin-modifying enzymes histone H3K36 methyltransferase (SETD2), histone H3K4 demethylase JARID1C (KDM5C), histone H3K27 demethylase UTX (KDM6A), and the SWI/SNF chromatin remodelling complex gene PBRM1 in clear cell renal cell carcinoma (111–113) highlights the importance of epigenetic modulation in human cancer and raises the potential for systematic testing in other types of tumors such as those associated with FH mutations. Technological advances such as those in gas and liquidchromatography mass spectrometry (114, 115) and nuclear magnetic resonance imaging (102) have greatly improved the ability to measure low-molecular-weight metabolites in tumor samples with high resolution (116). Combined with metabolic flux analyses employing isotope tracers and mathematical modeling, modern-era metabolomic approaches can provide direct pathophysiological insights into tumor metabolism and serve as an excellent tool for biomarker discovery. Using a data-driven approach, Jain and colleagues constructed the metabolic profiles of 60 cancer cell lines and discovered glycine consumption as a key metabolic event in rapidly proliferating cancer cells (117), thus demonstrating the power of metabolomic analyses and the relevance to future cancer research and therapeutics.

Acknowledgments The Cancer Biology and Metabolism Group is funded by Cancer Research UK and the European Research Council under the European Community’s Seventh Framework Programme (FP7/20072013)/ERC grant agreement no. 310837 to Dr. Pollard. Professor Soga receives funding from a Grant-in-Aid for scientific research on Innovative Areas, Japan (no. 22134007), and the Yamagata Prefectural Government and City of Tsuruoka.

Address correspondence to: Patrick J. Pollard, Cancer Biology and Metabolism Group, Nuffield Department of Medicine, Henry Wellcome Building for Molecular Physiology, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom. Phone: 44.0.1865287780; Fax: 44.0.1865287787; E-mail:  patrick.pollard@well.ox.ac.uk.

  1. Yang M, Soga T, Pollard PJ, Adam J. The emerging role of fumarate as an oncometabolite. Front Oncol. 2012;2:85. 2. Ward PS, Thompson CB. Metabolic reprogramming: a cancer hallmark even warburg did not anticipate. Cancer Cell. 2012;21(3):297–308. 3. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009; 324(5930):1029–1033. 4. Thompson CB. Metabolic enzymes as oncogenes or tumor suppressors. N Engl J Med. 2009; 360(8):813–815. 5. Schulze A, Harris AL. How cancer metabolism is tuned for proliferation and vulnerable to disruption. Nature. 2012;491(7424):364–373.
  1. Pollard PJ, et al. Accumulation of Krebs cycle intermediates and over-expression of HIF1alpha in tumours which result from germline FH and SDH mutations. Hum Mol Genet. 2005; 14(15):2231–2239. 7. Ward PS, et al. The common feature of leukemiaassociated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha-ketoglutarate to 2-hydroxyglutarate. Cancer Cell. 2010; 17(3):225–234.

Because D-2HG is a product of neomorphic enzyme activities, curtailing the D-2HG supply by specifically inhibiting the mutant IDH enzymes provides an elegant approach to target IDH-mutant cancers. Indeed, recent reports of small-molecule inhibitors against mutant forms of IDH1 and IDH2 demonstrated the feasibility of this method. An inhibitor against IDH2 R140Q was shown to reduce both intracellular and extracellular levels of D-2HG, suppress cell growth, and increase differentiation of primary human AML cells (108). Similarly, small-molecule inhibition of IDH1 R132H suppressed colony formation and increased tumor cell differentiation in a xenograft model for IDH1 R132H glioma (58). The inhibitors exhibited a cytostatic rather than cytotoxic effect, and therefore their therapeutic efficacy over longer time periods may need further assessment (109). Letouzé et al. showed that the DNA methytransferase inhibitor decitabine could repress the migration capacities of SDHB-mutant cells (40). However, for SDH- and FH-associated cancers, a synthetic lethality approach is worth exploring because of the pleiotrophic effects associated with succinate and fumarate accumulation.

Technological advances such as those in gas and liquid chromatography mass spectrometry (114, 115) and nuclear magnetic resonance imaging (102) have greatly improved the ability to measure low-molecular-weight metabolites in tumor samples with high resolution (116). Combined with metabolic flux analyses employing isotope tracers and mathematical modeling, modern-era metabolomic approaches can provide direct pathophysiological insights into tumor metabolism and serve as an excellent tool for biomarker discovery. Using a data-driven approach, Jain and colleagues constructed the metabolic profiles of 60 cancer cell lines and discovered glycine consumption as a key metabolic event in rapidly proliferating cancer cells (117), thus demonstrating the power of metabolomic analyses and the relevance to future cancer research and therapeutics.

Figure 1 D-2HG produced by mutant IDH1/2 affects metabolism and epigenetics by modulating activities of α-KG–dependent oxygenases. Wild-type IDH1 and IDH2 catalyze the NADP+-dependent reversible conversion of isocitrate to α-KG, whereas cancer-associated gain-of-function mutations enable mutant IDH1/2 (mIDH1/2) to catalyze the oxidation of α-KG to D-2HG, using NADPH as a cofactor. Because D-2HG is structurally similar to α-KG, its accumulation can modulate the activities of α-KG–utilizing dioxygenases. Inhibition of 5mC hydroxylase TET2 and the KDMs results in increased CpG island methylation and increased histone methylation marks, respectively, thus blocking lineage-specific cell differentiation. Inhibition of collagen prolyl and lysyl hydroxylases (C-P4Hs and PLODs, respectively) leads to impaired collagen maturation and disrupted basement membrane formation. D-2HG can also stimulate the activities of HIF PHDs, leading to enhanced HIF degradation and a diminished HIF response, which are associated with increased soft agar growth of human astrocytes and growth factor independence of leukemic cells. Together these processes exert pleiotrophic effects on cell signaling and gene expression that probably contribute to the malignancy of IDH1/2-mutant cells.
Figure 2 Candidate oncogenic mechanisms of succinate and fumarate accumulation. SDH and FH are Krebs cycle enzymes and tumor suppressors. Loss-of-function mutations in SDH and FH result in abnormal accumulation of Krebs cycle metabolites succinate (Succ) and fumarate (Fum), respectively, both of which can inhibit the activities of α-KG–dependent oxygenases. Inhibition of HIF PHDs leads to activation of HIF-mediated pseudohypoxic response, whereas inhibition of KDMs and TET family of 5mC hydroxylases causes epigenetic alterations. Fumarate is electrophilic and can also irreversibly modify cysteine residues in proteins by succination. Succination of KEAP1 in FH deficiency results in the constitutive activation of the antioxidant defense pathway mediated by NRF2, conferring a reductive milieu that promotes cell proliferation. Succination of the Krebs cycle enzyme Aco2 impairs aconitase activity in Fh1-deficient MEFs. Fumarate accumulation may also affect cytosolic pathways by inhibiting the reactions involved in the biosynthesis of arginine and purine. AcCoA, acetyl CoA; Mal, malate; OAA, oxaloacetate; Succ-CA, succinyl CoA.

2.1.1.2. Emerging concepts: linking hypoxic signaling and cancer metabolism.

Lyssiotis CA, Vander-Heiden MG, Muñoz-Pinedo C, Emerling BM.
Cell Death Dis. 2012 May 3; 3:e303
http://dx.doi.org:/10.1038/cddis.2012.41

The Joint Keystone Symposia on Cancer and Metabolism and Advances in Hypoxic Signaling: From Bench to Bedside were held in Banff, Alberta, Canada from 12 to 17 February 2012. Drs. Reuben Shaw and David Sabatini organized the Cancer and Metabolism section, and Drs. Volker Haase, Cormac Taylor, Johanna Myllyharju and Paul Schumacker organized the Advances in Hypoxic Signaling section. Accumulating data illustrate that both hypoxia and rewired metabolism influence cancer biology. Indeed, these phenomena are tightly coupled, and a joint meeting was held to foster interdisciplinary interactions and enhance our understanding of these two processes in neoplastic disease. In this report, we highlight the major themes of the conference paying particular attention to areas of intersection between hypoxia and metabolism in cancer.

One opening keynote address was delivered by Craig Thompson (Memorial Sloan-Kettering, USA), in which he provided a comprehensive perspective on the current thinking around how altered metabolism supports cancer cell growth and survival, and discussed areas likely to be important for future discovery. In particular, Thompson highlighted the essential roles of glucose and glutamine in cell growth, how glucose- and glutamine-consuming processes are rewired in cancer and how this rewiring facilitates anabolic metabolism. These topics were at the core of many of the metabolism presentations that described in detail how some metabolic alterations contribute to the properties of transformed cells.

The other keynote address was delivered by Peter Ratcliffe (University of Oxford, UK), in which he provided a historical perspective on the progress of how signaling events sense oxygen. Mammals have evolved multiple acute and long-term adaptive responses to low oxygen levels (hypoxia). This response prevents a disparity in ATP utilization and production that would otherwise result in a bioenergetic collapse when oxygen level is low. Multiple effectors have been proposed to mediate the response to hypoxia including prolyl hydroxylases, AMPK, NADPH oxidases and the mitochondrial complex III. Currently, however, the precise mechanism by which oxygen is sensed in various physiological contexts remains unknown. Indeed, this was an active point of debate, with Peter Ratcliffe favoring the prolyl hydroxylase PHD2 as the primary cellular oxygen sensor.

Anabolic glucose metabolism and the Warburg effect

Nearly a century ago, Warburg noted that cancer tissues take up glucose in excess than most normal tissues and secrete much of the carbon as lactate. Recently, headway has been made toward determining how the enhanced glucose conversion to lactate occurs and contributes to cell proliferation and survival. Heather Christofk (University of California, Los Angeles, USA) and John Cleveland (the Scripps Research Institute, USA) described a role for the lactate/pyruvate transporter MCT-1 in carbon secretion, and suggested that blocking lactate or pyruvate transport may be a strategy to target glucose metabolism in cancer cells. Kun-Liang Guan (University of California, San Diego, USA) described a novel feedback loop to control glucose metabolism in highly glycolytic cells. Specifically, he discussed how glucose-derived acetyl-CoA can be used as a substrate to modify two enzymes involved in glucose metabolism, pyruvate kinase M2 (PKM2) and phosphoenolpyruvate carboxylase (PEPCK). In both cases, acetylation leads to protein degradation and decreased glycolysis and gluconeogenesis, respectively. Data presented from Matthew Vander Heiden’s laboratory (Koch Institute/MIT, USA) illustrated that loss of pyruvate kinase activity can accelerate tumor growth, suggesting that the regulation of glycolysis may be more complex than previously appreciated. Almut Schulze (London Research Institute, UK) discussed a novel regulatory role for phosphofructokinase in controlling glucose metabolism and Jeffrey Rathmell (Duke University, USA) discussed parallels between glucose metabolism in cancer cells and lymphocytes that suggest many of these phenotypes could be a feature of rapidly dividing cells.

Glutamine addiction

Cancer cells also consume glutamine to support proliferation and survival. Alfredo Csibi (Harvard Medical School, USA) described how mTORC1 promotes glutamine utilization by indirectly regulating the activity of glutamate dehydrogenase. This work united two major themes at the meeting, mTOR signaling and glutamine metabolism, highlighting the interconnectedness of signal transduction and metabolic regulation. Richard Cerione (Cornell University, USA) described a small molecule inhibitor of glutaminase that can be used to target glutamine-addicted cancer cells. Christian Metallo (University of California, San Diego, USA), Andrew Mullen (University of Texas Southwestern Medical School, USA) and Patrick Ward (Memorial Sloan-Kettering, USA) presented data demonstrating that the carbon skeleton of glutamine can be incorporated into newly synthesized lipids. This contribution of glutamine to lipid synthesis was most pronounced in hypoxia or when the mitochondrial electron transport chain was compromised.

Signal transduction and metabolism

The protein kinases AMPK and mTOR can function as sensors of metabolic impairment, whose activation by energy stress controls multiple cellular functions. Grahame Hardie (University of Dundee, UK) and Reuben Shaw (Salk Institute, USA) highlighted novel roles for AMPK, including inhibition of viral replication, and the control of histone acetylation via phosphorylation of class IIa HDACs, respectively. Brandon Faubert (McGill University, USA) reported on an AMPK-dependent effect on glucose metabolism in unstressed cells. Brendan Manning (Harvard Medical School, USA) found that chronic activation of mTOR in the mouse liver, due to genetic ablation of this complex, promotes the development of liver cancer. Kevin Williams (University of California, Los Angeles, USA) discussed how growth signaling can control both lipid and glucose metabolism by impinging on SREBP-1, a transcription factor downstream of mTOR. AMPK-independent control of mTOR was addressed by John Blenis (Harvard Medical School, USA), who discussed the possible role of mTOR stabilizing proteins as mediators of mTOR inactivation upon energetic stress. David Sabatini (Whitehead Institute/MIT, USA) discussed several aspects of amino-acid sensing by Rag GTPases and showed that constitutive activation of the Rag GTPases leads to metabolic defects in mice.

One of the outcomes of AMPK activation and mTOR inhibition is autophagy, which can provide amino acids and fatty acids to nutrient-deprived cells. Ana Maria Cuervo (Albert Einstein College of Medicine, USA) and Eileen White (Rutgers University, USA) illuminated the role of chaperone-mediated autophagy (CMA) and macroautophagy, respectively, in tumor survival. White described a role for macroautophagy in the regulation of mitochondrial fitness, maintenance of TCA cycle and tumorigenesis induced by oncogenic Ras. Cuervo described how CMA is consistently elevated in tumor cells, and how its inactivation leads to metabolic impairment via p53-mediated downregulation of glycolytic enzymes.

Oncogene-specific changes to metabolism

Lewis Cantley (Harvard Medical School, USA) described a metabolic role for oncogenic Kras in the rewiring of glucose metabolism in pancreatic cancer. Specifically, Myc-mediated transcription (downstream of MEK-ERK signaling) both enhances glucose uptake and diverts glucose carbon into the nonoxidative pentose phosphate pathway to facilitate nucleotide biosynthesis. Alejandro Sweet-Cordero (Stanford University, USA) described how oncogenic Kras increases glycolysis and represses mitochondrial respiration (via decreased pyruvate dehydrogenase phosphatase 1 (PDP1) expression) in colon cancer. While these studies indicate that hyperstimulation of the Erk pathway suppresses PDH flux through suppression of PDP1, Joan Brugge (Harvard Medical School, USA) described studies showing that reduction of Erk signaling in normal epithelial cells also causes suppression of PDH flux, in this case through loss of repression of PDK4. The seemingly contradictory nature of these results highlighted an important theme emphasized throughout the week-long conference—that cellular context has an important role in shaping how oncogenic mutations or pathway activation rewires metabolism.

Targeting cancer metabolism

There was extensive discussion around targeting metabolism for cancer therapy. Metformin and phenformin, which act in part by mitochondrial complex I inhibition, can activate AMPK and influence cancer cell metabolism. Kevin Struhl (Harvard Medical School, USA) described how metformin can selectively target cancer stem cells, whereas Jessica Howell (Harvard Medical School, USA) described how the therapeutic activity of metformin relies on both AMPK and mTOR signaling to mediate its effect. Similarly, David Shackelford (University of California, Los Angeles, USA) demonstrated efficacy for phenformin in LKB1-deficient mouse models.

Several presentations, including those by Taru Muranen (Harvard Medical School, USA), Karen Vousden and Eyal Gottlieb (both from the Beatson Institute for Cancer Research, UK), provided insight into genetic control mechanisms that cancer cells use to promote survival under conditions of increased biosynthesis. As an example, Vousden illustrated how p53 loss can make cancer cells more dependent on exogenous serine. Several additional presentations, including those by Gottlieb, Richard Possemato (Whitehead Institute/MIT, USA), Michael Pollak (McGill University, USA) and Kevin Marks (Agios Pharmaceuticals, USA), also included data highlighting the important role of serine biosynthesis and metabolism in cancer growth. Collectively, these data highlight a metabolic addiction that may be therapeutically exploitable. Similarly, Cristina Muñoz-Pinedo (Institut d’Investigació Biomèdica, Spain) described how mimicking glucose deprivation with 2-deoxyglucose can cause programmed cell death and may be an effective cancer treatment.

Regulation of hypoxic responses

Peter Carmeliet (University of Leuven, Belgium) highlighted the mechanisms of resistance against VEGF-targeted therapies. Roland Wenger (University of Zurich, Switzerland) discussed the oxygen-responsive transcriptional networks and, in particular, the difference between the transcription factors HIF-1α and HIF-2α. Importantly, he demonstrated a rapid role for HIF-1α, and a later and more persistent response for HIF-2α. These results were central to a recurrent theme calling for the distinction of HIF-1α and HIF-2α target genes and how these responses mediate divergent hypoxic adaptations.

Advances in hypoxic signaling

Brooke Emerling (Harvard Medical School, USA) introduced CUB domain-containing protein 1 (CDCP1) and showed persuasive data on CDCP1 being a HIF-2α target gene involved in cell migration and metastasis, and suggested CDCP1 regulation as an attractive therapeutic target. Johannes Schodel (University of Oxford, UK) described an elegant HIF-ChIP-Seq methodology to define direct transcriptional targets of HIF in renal cancer.

Randall Johnson (University of Cambridge, UK) emphasized that loss of HIF-1α results in decreased lung metastasis. Lorenz Poellinger (Karolinska Institutet, Sweden) focused on how hypoxia can alter the epigenetic landscape of cells, and furthermore, how the disruption of the histone demethylase JMJD1A and/or the H3K9 methyltransferase G9a has opposing effects on tumor growth and HIF target gene expression.

Paul Schumacker (Northwestern University, USA) further emphasized the importance of mitochondrial ROS signaling under hypoxic conditions showing that ROS could be detected in the inter-membrane space of the mitochondria before activating signaling cascades in the cytosol. He also presented evidence for mitochondria as a site of oxygen sensing in diverse cell types. Similarly, Margaret Ashcroft (University College London, UK) argued for a critical role of mitochondria in hypoxic signaling. She presented on a family of mitochondrial proteins (CHCHD4) that influence hypoxic signaling and tumorigenesis and suggested that CHCHD4 is important for HIF and tumor progression.

2.1.1.3  Glutaminolysis: supplying carbon or nitrogen or both for cancer cells?

Dang CV
Cell Cycle. 2010 Oct 1; 9(19):3884-6

A cancer cell comprising largely of carbon, hydrogen, oxygen, phosphorus, nitrogen and sulfur requires not only glucose, which is avidly transported and converted to lactate by aerobic glycolysis or the Warburg effect, but also glutamine as a major substrate. Glutamine and essential amino acids, such as methionine, provide energy through the TCA cycle as well as nitrogen, sulfur and carbon skeletons for growing and proliferating cancer cells. The interplay between utilization of glutamine and glucose is likely to depend on the genetic make-up of a cancer cell. While the MYC oncogene induces both aerobic glycolysis and glutaminolysis, activated β-catenin induces glutamine synthesis in hepatocellular carcinoma. Cancer cells that have elevated glutamine synthetase can use glutamate and ammonia to synthesize glutamine and are hence not addicted to glutamine. As such, cancer cells have many degrees of freedom for re-programming cell metabolism, which with better understanding will result in novel therapeutic approaches.

Figure 1. Glutamine, glucose and glutamate are imported into the cytoplasm of a cell. Glucose is depicted to be converted primarily (large powder blue arrow) to lactate via aerobic glycolysis or the Warburg effect or channeled into the mitochondrion as pyruvate and converted to acetyl-CoA for oxidation. Glutamine is shown imported and used for different processes including glutaminolysis, which involves the conversion of glutamine to glutamate and ammonia by glutaminase (GLS). Glutamate is further oxidized via the TCA cycle to produce ATP and contribute anabolic carbon skeletons. Some cells can import glutamate and use ammonia to generate glutamine through glutamine synthetase (GLUL); glutamine could then be used for different purposes including glutathione synthesis (not shown).

The liver is organized into lobules, which have zones of cells around the perivenous region enriched with glutamine synthetase, which detoxifies ammonia by converting it to glutamine through the amination of glutamate (Fig. 1). As such, liver cancers vary in the degree of glutamine synthetase expression depending on the extent of anaplasia or de-differentiation. Highly undifferentiated liver cancers tend to be more glycolytic than those that retain some of the differentiated characteristics of liver cells. Furthermore, glutamine synthetase (considered as a direct target of activated β-catenin, which also induces ornithine aminotransferase and glutamate transporters) expression in liver cancers has been directly linked to β-catenin activation or mutations.  Hence, the work by Meng et al. illustrates, first and foremost, the metabolic heterogeneity amongst cancer cell lines, such that the ability to utilize ammonia instead of glutamine by Hep3B cells depends on the expression of glutamine synthetase. The Hep3B cells are capable of producing glutamine from glutamate and ammonia, as suggested by the observation that a glutamine-independent derivative of Hep3B has high expression of glutamine synthetase. In this regard, Hep3B could utilize glutamate directly for the production of α-ketoglutarate or to generate glutamine for protein synthesis or other metabolic processes, such as to import essential amino acids.  In contrast to Hep3B, other cell lines in the Meng et al. study were not demonstrated to be glutamine independent and thus become ammonia auxotrophs. Hence, the mode of glutamine or glucose utilization is dependent on the metabolic profile of cancer cells.
The roles of glutamine in different cancer cell lines are likely to be different depending on their genetic and epigenetic composition. In fact, well-documented isotopic labeling studies have demonstrated a role for glutamine to provide anapleurotic carbons in certain cancer and mammalian cell types. But these roles of glutaminolysis, whether providing nitrogen or anabolic carbons, should not be generalized as mutually exclusive features of all cancer cells. From these considerations, it is surmised that the expression of glutamine synthetase in different cancers will determine the extent by which these cancers are addicted to exogenous glutamine.

2.1.1.4  The Warburg effect and mitochondrial stability in cancer cells

Gogvadze V, Zhivotovsky B, Orrenius S.
Mol Aspects Med. 2010 Feb; 31(1):60-74
http://dx.doi.org:/10.1016/j.mam.2009.12.004

The last decade has witnessed a renaissance of Otto Warburg’s fundamental hypothesis, which he put forward more than 80 years ago, that mitochondrial malfunction and subsequent stimulation of cellular glucose utilization lead to the development of cancer. Since most tumor cells demonstrate a remarkable resistance to drugs that kill non-malignant cells, the question has arisen whether such resistance might be a consequence of the abnormalities in tumor mitochondria predicted by Warburg. The present review discusses potential mechanisms underlying the upregulation of glycolysis and silencing of mitochondrial activity in cancer cells, and how pharmaceutical intervention in cellular energy metabolism might make tumor cells more susceptible to anti-cancer treatment.

mitochondrial stabilization gr1

mitochondrial stabilization gr1

http://ars.els-cdn.com/content/image/1-s2.0-S0098299709000934-gr1.sml

Fig. 1. (1) Oligomerization of Bax is mediated by the truncated form of the BH3-only, pro-apoptotic protein Bid (tBid); (2) Bcl-2, Bcl-XL, Mcl-1, and Bcl-w, interact with the pro-apoptotic proteins, Bax and Bak, to prevent their oligomerization; (3) The anti-apoptotic protein Bcl-XL prevents tBid-induced closure of VDAC and apoptosis by maintaining VDAC in open configuration allowing ADT/ATP exchange and normal mitochondrial functioning; (4) MPT pore is a multimeric complex, composed of VDAC located in the OMM, ANT, an integral protein of the IMM, and a matrix protein, CyPD; (5) Interaction with VDAC allows hexokinase to use exclusively intramitochondrial ATP to phosphorylate glucose, thereby maintaining high rate of glycolysis.

mitochodrial stabilization gr2

mitochodrial stabilization gr2

http://ars.els-cdn.com/content/image/1-s2.0-S0098299709000934-gr2.sml

Fig. 2. Different sites of therapeutic intervention in cancer cell metabolism. (1) The non-metabolizable analog of glucose, 2-deoxyglucose, decreases ATP level in the cell; (2) 3-bromopyruvate suppresses the activity of hexokinase, and respiration in isolated mitochondria; (3) Phloretin a glucose transporter inhibitor, decreases ATP level in the cell and markedly enhances the anti-cancer effect of daunorubicin; (4) Dichloroacetate (DCA) shifts metabolism from glycolysistoglucoseoxidation;(5)Apoptolidin,aninhibitorofmitochondrialATPsynthase,inducescelldeathindifferentmalignantcelllineswhenapplied together with the LDH inhibitor oxamate (6).

Warburg Symposium

https://youtu.be/LpE6w6J3jU0

2.1.1.5 Oxidative phosphorylation in cancer cells

Giancarlo Solaini Gianluca SgarbiAlessandra Baracca

BB Acta – Bioenergetics 2011 Jun; 1807(6): 534–542
http://dx.doi.org/10.1016/j.bbabio.2010.09.003

Research Highlights

►Mitochondrial hallmarks of tumor cells.►Complex I of the respiratory chain is reduced in many cancer cells.►Oligomers of F1F0ATPase are reduced in cancer cells.►Mitochondrial membranes are critical to the life or death of cancer cells.

Evidence suggests that mitochondrial metabolism may play a key role in controlling cancer cells life and proliferation. Recent evidence also indicates how the altered contribution of these organelles to metabolism and the resistance of cancer mitochondria against apoptosis-associated permeabilization are closely related. The hallmarks of cancer growth, increased glycolysis and lactate production in tumours, have raised attention due to recent observations suggesting a wide spectrum of oxidative phosphorylation deficit and decreased availability of ATP associated with malignancies and tumour cell expansion. More specifically, alteration in signal transduction pathways directly affects mitochondrial proteins playing critical roles in controlling the membrane potential as UCP2 and components of both MPTP and oxphos complexes, or in controlling cells life and death as the Bcl-2 proteins family. Moreover, since mitochondrial bioenergetics and dynamics, are also involved in processes of cells life and death, proper regulation of these mitochondrial functions is crucial for tumours to grow. Therefore a better understanding of the key pathophysiological differences between mitochondria in cancer cells and in their non-cancer surrounding tissue is crucial to the finding of tools interfering with these peculiar tumour mitochondrial functions and will disclose novel approaches for the prevention and treatment of malignant diseases. Here, we review the peculiarity of tumour mitochondrial bioenergetics and the mode it is linked to the cell metabolism, providing a short overview of the evidence accumulated so far, but highlighting the more recent advances. This article is part of a Special Issue entitled: Bioenergetics of Cancer.

Mitochondria are essential organelles and key integrators of metabolism, but they also play vital roles in cell death and cell signaling pathways critically influencing cell fate decisions [1][2] and [3]. Mammalian mitochondria contain their own DNA (mtDNA), which encodes 13 polypeptides of oxidative phosphorylation complexes, 12S and 16S rRNAs, and 22 tRNAs required for mitochondrial function [4]. In order to synthesize ATP through oxidative phosphorylation (oxphos), mitochondria consume most of the cellular oxygen and produce the majority of reactive oxygen species (ROS) as by-products [5]. ROS have been implicated in the etiology of carcinogenesis via oxidative damage to cell macromolecules and through modulation of mitogenic signaling pathways [6][7] and [8]. In addition, a number of mitochondrial dysfunctions of genetic origin are implicated in a range of age-related diseases, including tumours [9]. How mitochondrial functions are associated with cancer is a crucial and complex issue in biomedicine that is still unravelled [10] and [11], but it warrants an extraordinary importance since mitochondria play a major role not only as energy suppliers and ROS “regulators”, but also because of their control on cellular life and death. This is of particular relevance since tumour cells can acquire resistance to apoptosis by a number of mechanisms, including mitochondrial dysfunction, the expression of anti-apoptotic proteins or by the down-regulation or mutation of pro-apoptotic proteins [12].

Cancer cells must adapt their metabolism to produce all molecules and energy required to promote tumor growth and to possibly modify their environment to survive. These metabolic peculiarities of cancer cells are recognized to be the outcome of mutations in oncogenes and tumor suppressor genes which regulate cellular metabolism. Mutations in genes including P53, RAS, c-MYC, phosphoinosine 3-phosphate kinase (PI3K), and mTOR can directly or through signaling pathways affect metabolic pathways in cancer cells as discussed in several recent reviews [13][14][15][16] and [17]. Cancer cells harboring the genetic mutations are also able to thrive in adverse environments such as hypoxia inducing adaptive metabolic alterations which include glycolysis up-regulation and angiogenesis factor release [18] and [19]. In response to hypoxia, hypoxia-induced factor 1 (HIF-1) [20], a transcription factor, is up-regulated, which enhances expression of glycolytic enzymes and concurrently it down regulates mitochondrial respiration through up-regulation of pyruvate dehydrogenase kinase 1 (PDK1) (see recent reviews [21] and [22]). However, several tumours have been reported to display high HIF-1 activity even in normoxic condition, now referred to as pseudohypoxia [23][24] and [25]. In addition, not only solid tumours present a changed metabolism with respect to matched normal tissues, hematological cell malignancies also are characterized by peculiar metabolisms, in which changes of mitochondrial functions are significant [26],[27] and [28], therefore indicating a pivotal role of mitochondria in tumours independently from oxygen availability.

Collectively, actual data show a great heterogeneity of metabolism changes in cancer cells, therefore comprehensive cellular and molecular basis for the association of mitochondrial bioenergetics with tumours is still undefined, despite the numerous studies carried out. This review briefly revisits the data which are accumulating to account for this association and highlights the more recent advances, particularly focusing on the metabolic and structural changes of mitochondria.

Mitochondria-related metabolic changes of cancer cells

Accumulating evidence indicate that many cancer cells have an higher glucose consumption under normoxic conditions with respect to normal differentiated cells, the so-called “aerobic glycolysis” (Warburg effect), a phenomenon that is currently exploited to detect and diagnose staging of solid and even hematological malignancies [27]. Since the initial publication by Otto Warburg over half a century ago [29], an enormous amount of studies on many different tumours have been carried out to explain the molecular basis of the Warburg effect. Although the regulatory mechanisms underlying aerobic and glycolytic pathways of energy production are complex, making the prediction of specific cellular responses rather difficult, the actual data seem to support the view that in order to favour the production of biomass, proliferating cells are commonly prone to satisfy the energy requirement utilizing substrates other than the complete oxidation of glucose (to CO2 and H2O). More precisely, only part (40 to 75%, according to [30]) of the cells need of ATP is obtained through the scarcely efficient catabolism of glucose to pyruvate/lactate in the cytoplasm and the rest of the ATP need is synthesized in the mitochondria through both the tricarboxylic acid (TCA) cycle (one ATP produced each acetyl moiety oxidized) and the associated oxidative phosphorylation that regenerates nicotinamide- and flavin-dinucleotides in their oxidized state(NAD+ and FAD). This might be due to the substrate availability as it was shown in HeLa cells, where replacing glucose with galactose/glutamine in the culture medium induced increased expression of oxphos proteins, suggesting an enhanced energy production from glutamine [31]. As a conclusion the authors proposed that energy substrate can modulate mitochondrial oxidative capacity in cancer cells. A direct evidence of this phenomenon was provided a few years later in glioblastoma cells, in which it was demonstrated that the TCA cycle flux is significantly sustained by anaplerotic alfa-ketoglutarate produced from glutamine and by acetyl moieties derived from the pyruvate dehydrogenase reaction where pyruvate may have an origin other than glucose [32]. The above changes are the result of genetic alteration and environmental conditions that induce many cancer cells to change their metabolism in order to synthesize molecules necessary to survive, grow and proliferate, including ribose and NADPH to synthesize nucleotides, and glycerol-3 phosphate to produce phospholipids. The synthesis of the latter molecules requires major amount of acetyl moieties that are derived from beta-oxidation of fatty acids and/or from cytosolic citrate (citrate lyase reaction) and/or from the pyruvate dehydrogenase reaction. Given the important requirement for NADPH in macromolecular synthesis and redox control, NADPH production in cancer cells besides being produced through the phosphate pentose shunt, may be significantly sustained by cytosolic isocitrate dehydrogenases and by the malic enzyme (see Ref. [33] for a recent review). Therefore, many cancer cells tend to have reduced oxphos in the mitochondria due to either or both reduced flux within the tricarboxylic acid cycle and/or respiration (Fig. 1). The latter being also caused by reduced oxygen availability, a typical condition of solid tumors, that will be discussed below.

Schematic illustration of mitochondrial metabolism and metabolic reprogramming in tumours gr1

Schematic illustration of mitochondrial metabolism and metabolic reprogramming in tumours gr1

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Fig. 1. Schematic illustration of mitochondrial metabolism and metabolic reprogramming in tumours. In normal cells (A), glucose is phosphorylated by HK-I, then the major part is degraded via glycolysis to pyruvate, which prevalently enters the mitochondria, it is decarboxylated and oxidized by PDH to acetyl-coenzyme A, which enters the TCA cycle where the two carbons are completely oxidized to CO2 whereas hydrogen atoms reduce NAD+ and FAD, which feed the respiratory chain (turquoise). Minor part of glycolytic G-6P is diverted to produce ribose 5-phosphate (R-5P) and NADPH, that will be used to synthesize nucleotides, whereas triose phosphates in minimal part will be used to synthesize lipids and phospholipids with the contribution of NADPH and acetyl-coenzyme A. Amino acids, including glutamine (Gln) will follow the physiological turnover of the proteins, in minimal part will be used to synthesize the nucleotides bases, and the excess after deamination will be used to produce energy. In the mitochondria inner membranes are located the respiratory chain complexes and the ATP synthase (turquoise), which phosphorylates ADP releasing ATP, that in turn is carried to the cytosol by ANT (green) in exchange for ADP. About 1–2% O2 uptaken by the mitochondria is reduced to superoxide anion radical and ROS. In cancer cells (B), where anabolism is enhanced, glucose is mostly phosphorylated by HK-II (red), which is up-regulated and has an easy access to ATP being more strictly bound to the mitochondria. Its product, G-6P, is only in part oxidized to pyruvate. This, in turn, is mostly reduced to lactate being both LDH and PDH kinase up-regulated. A significant part of G-6P is used to synthesize nucleotides that also require amino acids and glutamine. Citrate in part is diverted from the TCA cycle to the cytosol, where it is a substrate of citrate lyase, which supplies acetyl-coenzyme A for lipid and phospholipid synthesis that also requires NADPH. As indicated, ROS levels in many cancer cells increase.

Of particular relevance in the study of the metabolic changes occurring in cancer cells, is the role of hexokinase II. This enzyme is greatly up-regulated in many tumours being its gene promoter sensitive to typical tumour markers such as HIF-1 and P53 [30]. It plays a pivotal role in both the bioenergetic metabolism and the biosynthesis of required molecules for cancer cells proliferation. Hexokinase II phosphorylates glucose using ATP synthesized by the mitochondrial oxphos and it releases the product ADP in close proximity of the adenine nucleotide translocator (ANT) to favour ATP re-synthesis within the matrix (Fig. 1). Obviously, the expression level, the location, the substrate affinity, and the kinetics of the enzyme are crucial to the balancing of the glucose fate, to either allowing intermediates of the glucose oxidation pathway towards required metabolites for tumour growth or coupling cytoplasmic glycolysis with further oxidation of pyruvate through the TCA cycle, that is strictly linked to oxphos. This might be possible if the mitochondrial-bound hexokinase activity is reduced and/or if it limits ADP availability to the mitochondrial matrix, to inhibit the TCA cycle and oxphos. However, the mechanism is still elusive, although it has been shown that elevated oncogene kinase signaling favours the binding of the enzyme to the voltage-dependent anion channel (VDAC) by AKT-dependent phosphorylation [34] (Fig. 2). VDAC is a protein complex of the outer mitochondrial membrane which is in close proximity of ANT that exchanges ADP for ATP through the inner mitochondrial membrane [35]. However, the enzyme may also be detached from the mitochondrial membrane, to be redistributed to the cytosol, through the catalytic action of sirtuin-3 that deacylates cyclophilin D, a protein of the inner mitochondrial membrane required for binding hexokinase II to VDAC (Fig. 2[36]. Removing hexokinase from the mitochondrial membrane has also another important consequence in cancer cells: whatever mechanism its removal activates, apoptosis is induced [37] and [38]. These observations indicate hexokinase II as an important tool used by cancer cells to survive and proliferate under even adverse conditions, including hypoxia, but it may result an interesting target to hit in order to induce cells cytotoxicity. Indeed, a stable RNA interference of hexokinase II gene showed enhanced apoptosis indices and inhibited growth of human colon cancer cells; in accordance in vivo experiments indicated a decreased tumour growth [39].

Schematic illustration of the main mitochondrial changes frequently occurring in cancer cells gr2

Schematic illustration of the main mitochondrial changes frequently occurring in cancer cells gr2

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Fig. 2. Schematic illustration of the main mitochondrial changes frequently occurring in cancer cells. The reprogramming of mitochondrial metabolism in many cancer cells comprises reduced pyruvate oxidation by PDH followed by the TCA cycle, increased anaplerotic feeding of the same cycle, mostly from Gln, whose entry in the mitochondrial matrix is facilitated by UCP2 up-regulation. This increases also the free fatty acids uptake by mitochondria, therefore β-oxidation is pushed to produce acetyl-coenzyme A, whose oxidation contributes to ATP production. In cancer cells many signals can converge on the mitochondrion to regulate the mitochondrial membrane permeability, which may respond by elevating the MPTP (PTP) threshold, with consequent enhancement of apoptosis resistance. ROS belong to this class of molecules since it can enhance Bcl2 and may induce DNA mutations. Dotted lines indicate regulation; solid lines indicate reaction(s).

Respiratory chain complexes and ATP synthase

Beyond transcriptional control of metabolic enzyme expression by oncogenes and tumour suppressors, it is becoming evident that environmental conditions affect the mitochondrial energy metabolism, and many studies in the last decade indicate that mitochondrial dysfunction is one of the more recurrent features of cancer cells, as reported at microscopic, molecular, biochemical, and genetic level [7], [40] and [41]. Although cancer cells under several conditions, including hypoxia, oncogene activation, and mDNA mutation, may substantially differ in their ability to use oxygen, only few reports have been able to identify a strict association between metabolic changes and mitochondrial complexes composition and activity. In renal oncocytomas [42] and in lung epidermoid carcinoma [43], the NADH dehydrogenase activity and protein content of Complex I were found to be strongly depressed; subsequently, in a thyroid oncocytoma cell line [44] a similar decrease of Complex I activity was ascribed to a specific mutation in the ND1 gene of mitochondrial DNA. However, among the respiratory chain complexes, significant decrease of the only Complex I content and activity was found in K-ras transformed cells in our laboratory [45], and could not be ascribed to mtDNA mutations, but rather, based on microarray analysis of oxphos genes, we proposed that a combination of genetic (low transcription of some genes) and biochemical events (assembly factors deficiency, disorganization of structured supercomplexes, and ROS-induced structural damage) might cause the Complex I defects.

In some hereditary tumours (renal cell carcinomas) a correlation has been identified between mitochondrial dysfunctions and content of oxphos complexes [46]. For instance, the low content of ATP synthase, often observed in clear cell type renal cell carcinomas and in chromophilic tumours, seems to indicate that the mitochondria are in an inefficient structural and functional state [46]. However, it cannot be excluded that, in some cases, the structural alteration of ATP synthase may offer a functional advantage to cells exhibiting a deficient respiratory chain for instance to preserve the transmembrane electrical potential (Δψm) [47]. It is likely that low levels of ATP synthases may play a significant role in cancer cell metabolism since it has been reported that in tumours from many different tissues, carcinogenesis specifically affects the expression of F1-ATPase β subunit, suggesting alterations in the mechanisms that control mitochondrial differentiation (see for a detailed review [48]). What it seems intriguing is the overexpression of the inhibitor protein, IF1, reported in hepatocellular carcinomas [49] and [50] and in Yoshida sarcoma [51]. Normally, this protein binds to the F1 domain of the ATP synthase inhibiting its activity [52], and it is believed to limit the ATP hydrolysis occurring in the mitochondria of hypoxic cells, avoiding ATP depletion and maintaining Δψm to a level capable to avoid the induction of cell death [5]. But why is its expression in cancer cells enhanced in front of a reduced F1-ATPase β subunit?

The first possibility is that IF1 has a function similar to that in normal cells, simply avoiding excessive ATP hydrolysis therefore limiting Δψm enhancement, but in cancer cells this is unlikely due to both the reduced level of ATP synthase [46] and the high affinity of IF1 for the enzyme. A second possibility might be that cancer cells need strongly reduced oxphos to adapt their metabolism and acquire a selective growth advantage under adverse environmental conditions such as hypoxia, as it has been experimentally shown [53]. Finally, IF1 might contribute to the saving of the inner mitochondrial membrane structure since it has been reported its capability to stabilize oligomers of ATP synthase, which in turn can determine cristae shapes [54]. In this regard, recent experimental evidence has shed some light on a critical role of mitochondrial morphology in the control of important mitochondrial functions including apoptosis [55] and oxidative phosphorylation [56]. In particular, dysregulated mitochondrial fusion and fission events can now be regarded as playing a role in cancer onset and progression [57]. Accordingly, mitochondria-shaping proteins seem to be an appealing target to modulate the mitochondrial phase of apoptosis in cancer cells. In fact, several cancer tissues: breast, head-and-neck, liver, ovarian, pancreatic, prostate, renal, skin, and testis, showed a pattern suggestive of enlarged mitochondria resulting from atypical fusion [58].

Mitochondrial membrane potential in cancer cells

Critical mitochondrial functions, including ATP synthesis, ion homeostasis, metabolites transport, ROS production, and cell death are highly dependent on the electrochemical transmembrane potential, a physico-chemical parameter consisting of two components, the major of which being the transmembrane electrical potential (Δψm) (see for a recent review [59]). In normal cells, under normoxic conditions, Δψm is build up by the respiratory chain and is mainly used to drive ATP synthesis, whereas in anoxia or severe hypoxia it is generated by the hydrolytic activity of the ATP synthase complex and by the electrogenic transport of ATP in exchange for ADP from the cytosol to the matrix, operated by the adenine nucleotide translocator [17]. Dissipation of the mitochondrial membrane potential (proton leak) causes uncoupling of the respiratory chain electron transport from ADP phosphorylation by the ATP synthase complex. Proton leak functions as a regulator of mitochondrial ROS production and its modulation by uncoupling proteins may be involved in pathophysiology, including tumours. In addition, Δψm plays a role in the control of the mitochondrial permeability transition pore (MPTP), that might be critical in determining reduced sensitivity to stress stimuli that were described in neoplastic transformation [60], implying that dysregulation of pore opening might be a strategy used by tumour cells to escape death. Indeed, it has recently been reported that ERK is constitutively activated in the mitochondria of several cancer cell types, where it inhibits glycogen synthase kinase-3-dependent phosphorylation of CyP-D and renders these cells more refractory to pore opening and to the ensuing cell death [61].

It is worth mentioning a second protein of the inner mitochondrial membrane, the uncoupling protein, UCP2 (Fig. 2), which contributes to regulate Δψm. Indeed, recent observations evidenced its overexpression in various chemoresistent cancer cell lines and in primary human colon cancer. This overexpression was associated with an increased apoptotic threshold [62]. Moreover, UCP2 has been reported to be involved in metabolic reprogramming of cells, and appeared necessary for efficient oxidation of glutamine [63]. On the whole, these results led to hypothesize an important role of the uncoupling protein in the molecular mechanism at the basis of the Warburg effect, that suppose a reduced Δψm-dependent entry of pyruvate into the mitochondria accompanied by enhanced fatty acid oxidation and high oxygen consumption (see for a review [64]). However, in breast cancer Sastre-Serra et al. [65] suggested that estrogens by down-regulating UCPs, increase mitochondrial Δψm, that in turn enhances ROS production, therefore increasing tumorigenicity. While the two above points of view concur to support increased tumorigenicity, the mechanisms at the basis of the phenomenon appear on the opposite of the other. Therefore, although promising for the multiplicity of metabolic effects in which UCPs play a role (see for a recent review [66]), at present it seems that much more work is needed to clarify how UCPs are related to cancer.

A novel intriguing hypothesis has recently been put forward regarding effectors of mitochondrial function in tumours. Wegrzyn J et al. [67] demonstrated the location of the transcription factor STAT3 within the mitochondria and its capability to modulate respiration by regulating the activity of Complexes I and II, and Gough et al. [68] reported that human ras oncoproteins depend on mitochondrial STAT3 for full transforming potential, and that cancer cells expressing STAT3 have increased both Δψm and lactate dehydrogenase level, typical hallmarks of malignant transformation (Fig. 2). A similar increase of Δψm was recently demonstrated in K-ras transformed fibroblasts [45]. In this study, the increased Δψm was somehow unexpected since the cells had shown a substantial decrease of NADH-linked substrate respiration rate due to a compatible reduced Complex I activity with respect to normal fibroblasts. The authors associated the reduced activity of the enzyme to its peculiar low level in the extract of the cells that was confirmed by oxphos nuclear gene expression analysis. This significant and peculiar reduction of Complex I activity relative to other respiratory chain complexes, is recurrent in a number of cancer cells of different origin [42][44][45] and [69]. Significantly, all those studies evidenced an overproduction of ROS in cancer cells, which was consistent with the mechanisms proposed by Lenaz et al. [70] who suggested that whatever factor (i.e. genetic or environmental) initiate the pathway, if Complex I is altered, it does not associate with Complex III in supercomplexes, consequently it does not channel correctly electrons from NADH through coenzyme Q to Complex III redox centres, determining ROS overproduction. This, in turn, enhances respiratory chain complexes alteration resulting in further ROS production, thus establishing a vicious cycle of oxidative stress and energy depletion, which can contribute to further damaging cells pathways and structures with consequent tumour progression and metastasis [69].

Hypoxia and oxidative phosphorylation in cancer cells

Tumour cells experience an extensive heterogeneity of oxygen levels, from normoxia (around 2–4% oxygen tension), through hypoxia, to anoxia (< 0.1% oxygen tension). The growth of tumours beyond a critical mass > 1–2 mm3 is dependent on adequate blood supply to receive nutrients and oxygen by diffusion [88]. Cells adjacent to capillaries were found to exhibit a mean oxygen concentration of 2%, therefore, beyond this distance, hypoxia occurs: indeed, cells located at 200 μm displayed a mean oxygen concentration of 0.2%, which is a condition of severe hypoxia [89]. Oxygen shortage results in hypoxia-dependent inhibition of mitochondrial activity, mostly mediated by the hypoxia-inducible factor 1 (HIF-1)[90] and [91]. More precisely, hypoxia affects structure, dynamics, and function of the mitochondria, and in particular it has a significant inhibitory effect on the oxidative phosphorylation machinery, which is the main energy supplier of cells (see Ref. [22] for a recent review). The activation of HIF-1 occurs in the cytoplasmic region of the cell, but the contribution of mitochondria is critical being both cells oxygen sensors and suppliers of effectors of HIF-1α prolyl hydroxylase like α-ketoglutarate and probably ROS, that inhibit HIF-1α removal [92]. As reported above, mitochondria can also promote HIF-1α stabilization if the TCA flux is severely inhibited with release of intermediate molecules like succinate and fumarate into the cytosol. On the other hand, HIF-1 can modulate mitochondrial functions through different mechanisms, that besides metabolic reprogramming [7][22][93] and [94], include alteration of mitochondrial structure and dynamics[58], induction of microRNA-210 that decreases the cytochrome c oxidase (COX) activity by inhibiting the gene expression of the assembly protein COX10 [95], that also increases ROS generation. Moreover, these stress conditions could induce the anti-apoptotic protein Bcl-2, which has also been reported to regulate COX activity and mitochondrial respiration [96] conferring resistance to cells death in tumours (Fig. 2). This effect might be further enhanced upon severe hypoxia conditions, since COX is also inhibited by NO, the product of activated nitric oxide synthases [97].

The reduced respiration rate occurring in hypoxia favours the release of ROS also by Complex III, which contribute to HIF stabilization and induction of Bcl-2 [98]. In addition, hypoxia reduces oxphos by inhibiting the ATP synthase complex through its natural protein inhibitor IF1 (discussed in a previous section), which contributes to the enhancement of the “aerobic glycolysis”, all signatures of cancer transformation.

The observations reported to date indicate that cancer cells exhibit large varieties of metabolic changes which are associated with alterations in the mitochondrial structure, dynamics and function, and with tumour growth and survival. On one hand, mitochondria can regulate tumour growth through modulation of the TCA cycle and oxidative phosphorylation. The altered TCA cycle provides intermediates for both macromolecular biosynthesis and regulation of transcription factors such as HIF, and it allows cytosolic reductive power enhancement. Oxphos provides significant amounts of ATP which varies among tumour types. On the other hand, mitochondria are crucial in controlling redox homeostasis in the cell, inducing them to be either resistant or sensitive to apoptosis. All these reasons locate mitochondria at central stage to understanding the molecular basis of tumour growth and to seeking for novel therapeutical approaches.

Due to the complexity and variability of mitochondrial roles in cancer, careful evaluation of mitochondrial function in each cancer type is crucial. Deeper and more integrated knowledge of mitochondrial mechanisms and cancer-specific mitochondrial modulating means are expected for reducing tumorigenicity and/or improving anticancer drugs efficacy at the mitochondrial level. Although the great variability of biochemical changes found in tumour mitochondria, some highlighted peculiarities such as reduced TCA cycle flux, reduced oxphos rate, and reduced Complex I activity with respect to tissue specific normal counterparts are more frequent. In addition, deeper examination of supramolecular organization of the complexes in the inner mitochondrial membrane has to be considered in relation to oxphos dysfunction.

2.1.1.6  Oxidation–reduction states of NADH in vivo: From animals to clinical use

Mayevsky A, Chance B.
Mitochondrion. 2007 Sep; 7(5):330-9
http://dx.doi.org:/10.1016/j.mito.2007.05.001

Mitochondrial dysfunction is part of many pathological states in patients, such as sepsis or stroke. Presently, the monitoring of mitochondrial function in patients is extremely rare, even though NADH redox state is routinely measured in experimental animals. In this article, we describe the scientific backgrounds and practical use of mitochondrial NADH fluorescence measurement that was applied to patients in the past few years. In addition to NADH, we optically measured the microcirculatory blood flow and volume, as well as HbO(2) oxygenation, from the same tissue area. The four detected parameters provide real time data on tissue viability, which is critical for patients monitoring.

(very important article)

2.1.1.7  Mitochondria in cancer. Not just innocent bystanders

Frezza C, and Gottlieb E
Sem Cancer Biol 2009; 19: 4-11
http://dx.doi.org:/10.1016/j.semcancer.2008.11.008

The first half of the 20th century produced substantial breakthroughs in bioenergetics and mitochondria research. During that time, Otto Warburg observed abnormally high glycolysis and lactate production in oxygenated cancer cells, leading him to suggest that defects in mitochondrial functions are at the heart of malignant cell transformation. Warburg’s hypothesis profoundly influenced the present perception of cancer metabolism, positioning what is termed aerobic glycolysis in the mainstream of clinical oncology. While some of his ideas stood the test of time, they also frequently generated misconceptions regarding the biochemical mechanisms of cell transformation. This review examines experimental evidence which supports or refutes the Warburg effect and discusses the possible advantages conferred on cancer cells by ‘metabolic transformation’.

Fig.1. Mitochondria as a crossroad for catabolic and anabolic pathways in normal and cancer cells. Glucose and glutamine are important carbon sources which are metabolized in cells for the generation of energy and anabolic precursors. The pathways discussed in the text are illustrated and colour coded: red, glycolysis; white, TCA cycle; pink, non-essential amino acids synthesis; orange, pentose phosphate pathway and nucleotide synthesis; green, fatty acid and lipid synthesis; blue, pyruvate oxidation in the mitochondria; brown, glutaminolysis; black, malic enzyme reaction. Solid arrows indicate a single step reaction;dashed-dotted arrows indicate transport across membranes and dotted arrows indicate multi-step reactions. Abbreviations: HK, hexokinase; AcCoA, acetyl co-enzyme A; OAA, oxaloacetate; αKG, α-ketoglutarate.

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Fig. 2. Mitochondria as a target for multiple metabolic transformation events. Principal metabolic perturbations of cancer cells are induced by genetic reprogramming and environmental changes. The activation of Akt and MYC oncogenes and the loss of p53 tumor suppressor gene are among the most frequent events in cancer. Furthermore, all solid tumors are exposed to oxidative stress and hypoxia hence to HIF activation.These frequent changes in cancer cells trigger a dramatic metabolic shift from oxidative phosphorylation to glycolysis. In addition, direct genetic lesions of mtDNA or of nuclear encoded mitochondrial enzyme (SDH or FH) can directly abrogate oxidative phosphorylation in cancer. 3- D structures of the respiratory complexes in the scheme were retrieved from Protein DataBank (PDB:www.rcsb.org) except for complex I which was retrieved from [87]. PDB codes are as follow: SDH (II), 1 LOV; complex III (III), 1BGY; COX (IV), 1OCC; ATP synthase (V), 1QO1.

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Fig. 3. The physiological roles of SDH in the TCA cycle and the ETC and its potential roles in cancer. (A) Ribbon diagram of SDH structure (PBD code: 1LOV). The catalytic subunits: the flavoprotein (SDHA) and the iron-sulphur protein (SDHB) are depicted in red and yellow, respectively, and the membrane anchors and ubiquinone binding proteins SDHC and SDHD are depicted in cyan and green, respectively. (B) Other than being a TCA enzyme, SDH is an additional entry point to the ETC (most electrons are donated from NADH to complex I—not shown in this diagram). The electron flow in and out of complex II and III is depicted by the yellow arrows. During succinate oxidation to fumarate by SDHA, a two-electron reduction of FAD to FADH2 occurs. Electrons are transferred through their on–Sulphur centres on SDHB to ubiquinone (Q) bound to SDHC and SDHD in the inner mitochondrial membrane (IMM), reducing it to ubiquinol (QH2). Ubiquinol transfers its electrons through complex III, in a mechanism named the Q cycle, to cytochrome c (PDB: 1CXA). Electrons then flow from cytochrome c to COX where the final four-electron reduction of molecular oxygen to water occurs (not shown in this diagram). Complex III is the best characterized site of ROS production in the ETC, where a single electron reduction of oxygen to superoxide can occur (red arrow). It was proposed that obstructing electron flow within complex II might support a single electron reduction of oxygen at the FAD site (red arrow). Superoxide is dismutated to hydrogen peroxide which can then leave the mitochondria and inhibit PHD in the cytosol, leading to HIF[1] stabilization. Succinate or fumarate, which accumulate in SDH- or FH-deficient tumors, can also leave the mitochondria and inhibit PHD activity in the cytosol. The red dotted line represents the outer mitochondrial membrane (OMM).

2.1.1.8  Mitochondria in cancer cells: what is so special about them?

Gogvadze V, Orrenius S, Zhivotovsky B.
Trends Cell Biol. 2008 Apr; 18(4):165-73
http://dx.doi.org:/10.1016/j.tcb.2008.01.006

The past decade has revealed a new role for the mitochondria in cell metabolism–regulation of cell death pathways. Considering that most tumor cells are resistant to apoptosis, one might question whether such resistance is related to the particular properties of mitochondria in cancer cells that are distinct from those of mitochondria in non-malignant cells. This scenario was originally suggested by Otto Warburg, who put forward the hypothesis that a decrease in mitochondrial energy metabolism might lead to development of cancer. This review is devoted to the analysis of mitochondrial function in cancer cells, including the mechanisms underlying the upregulation of glycolysis, and how intervention with cellular bioenergetic pathways might make tumor cells more susceptible to anticancer treatment and induction of apoptosis.

Glucose utilization pathway

Glucose utilization pathway

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Figure 1. Glucose utilization pathway. When glucose enters the cell, it is phosphorylated by hexokinase to glucose-6-phosphate, which is further metabolized by glycolysis to pyruvate. Under aerobic conditions, most of the pyruvate in non-malignant cells enters the mitochondria, with only a small amount being metabolized to lactic acid. In mitochondria, pyruvate dehydrogenase (PDH) converts pyruvate into acetyl-CoA, which feeds into the Krebs cycle. Oxidation of Krebs cycle substrates by the mitochondrial respiratory chain builds up the mitochondrial membrane potential (Dc) – the driving force for ATP synthesis. By contrast, in tumor cells, the oxidative (mitochondrial) pathway of glucose utilization is suppressed, and most of the pyruvate is converted into lactate. Thus, the fate of pyruvate is determined by the relative activities of two key enzymes – lactate dehydrogenase and pyruvate dehydrogenase.

Mechanisms of mitochondrial silencing in tumors

Mechanisms of mitochondrial silencing in tumors

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Figure 2. Mechanisms of mitochondrial silencing in tumors. The activity of PDH is regulated by pyruvate dehydrogenase kinase 1 (PDK1), the enzyme that phosphorylates and inactivates pyruvate dehydrogenase. HIF-1 inactivates PDH through PDK1 induction, resulting in suppression of the Krebs cycle and mitochondrial respiration. In addition, HIF-1 stimulates expression of the lactate dehydrogenase A gene, facilitating conversion of pyruvate into lactate by lactate dehydrogenase (LDH). Mutation of p53 can suppress the mitochondrial respiratory activity through downregulation of the Synthesis of Cytochrome c Oxidase 2 (SCO2) gene, the product of which is required for the assembly of cytochrome c oxidase (COX) of the mitochondrial respiratory chain. Thus, mutation of p53 can suppress mitochondrial respiration and shift cellular energy metabolism towards glycolysis.

Production of ROS by mitochondria

In any cell, the majority of ROS are by-products of mitochondrial respiration. Approximately 2% of the molecular oxygen consumed during respiration is converted into the superoxide anion radical, the precursor of most ROS. Normally, a four-electron reduction of O2, resulting in the production of two molecules of water, is catalyzed by complex IV (COX) of the mitochondrial respiratory chain. However, the electron transport chain contains several redox centers (e.g. in complex I and III) that can leak electrons to molecular oxygen, serving as the primary source of superoxide production in most tissues. The one-electron reduction of oxygen is thermodynamically favorable for most mitochondrial oxidoreductases. Superoxide-producing sites and enzymes were recently analyzed in detail in a comprehensive review [87]. ROS, if not detoxified, oxidize cellular proteins, lipids, and nucleic acids and, by doing so, cause cell dysfunction or death. A cascade of water and lipid soluble antioxidants and antioxidant enzymes suppresses the harmful ROS activity. An imbalance that favors the production of ROS over antioxidant defenses, defined as oxidative stress, is implicated in a wide variety of pathologies, including malignant diseases. It should be mentioned that mitochondria are not only a major source of ROS but also a sensitive target for the damaging effects of oxygen radicals. ROS produced by mitochondria can oxidize proteins and induce lipid peroxidation, compromising the barrier properties of biological membranes. One of the targets of ROS is mitochondrial DNA (mtDNA), which encodes several proteins essential for the function of the mitochondrial respiratory chain and, hence, for ATP synthesis by oxidative phosphorylation. mtDNA, therefore, represents a crucial cellular target for oxidative damage, which might lead to lethal cell injury through the loss of electron transport and ATP generation. mtDNA is especially susceptible to attack by ROS, owing to its close proximity to the electron transport chain, the major locus for free-radical production, and the lack of protective histones. For example, mitochondrially generated ROS can trigger the formation of 8-hydroxydeoxyguanosine as a result of oxidative DNA damage; the level of oxidatively modified bases in mtDNA is 10- to 20-fold higher than that in nuclear DNA. Oxidative damage induced by ROS is probably a major source of mitochondrial genomic instability leading to respiratory dysfunction.

Figure 3. Stabilization of mitochondria against OMM permeabilization in tumor cells. OMM permeabilization is a key event in apoptotic cell death. (a) During apoptosis, tBid-mediated oligomerization of Bax causes OMM permeabilization and release of cytochrome c (red circles). (b) Bcl-2 protein binds Bax and prevents its oligomerization. A shift in the balance between pro- apoptotic and antiapoptotic proteins in cancer cells, in favor of the latter, reduces the availability of Bax and prevents OMM permeabilization. (c) Upregulation of hexokinase in tumors and its binding to VDAC in the OMM not only facilitates glucose phosphorylation using mitochondrially generated ATP but keeps VDAC in the open state, preventing its interaction with tBid (de).

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Figure 4. Shifting metabolism from glycolysis to glucose oxidation. Utilization of pyruvate is controlled by the relative activities of two enzymes, PDH and LDH. In cancer cells, PDH activity is suppressed by PDH kinase-mediated phosphorylation, and, therefore, instead of entering the Krebs cycle, pyruvate is converted into lactate. Several attempts have been made to redirect pyruvate towards oxidation in the mitochondria. Thus, inhibition of PDK1 by dichloroacetate might stimulate the activity of PDH and, hence, direct pyruvate to the mitochondria. A similar effect can be achieved by inhibition of LDH by oxamate. Overall, suppression of PDK1 and LDH activities will stimulate mitochondrial ATP production and might be lethal to tumor cells, even if these inhibitors are used at non-toxic doses. In addition, stimulation of mitochondrial function, for example though overexpression of mitochondrial frataxin, a protein associated with Friedreich ataxia, was shown to stimulate oxidative metabolism and inhibited growth in several cancer cell lines [86].
2.1.1.9  Glucose avidity of carcinomas

Ortega AD1, Sánchez-Aragó M, Giner-Sánchez D, Sánchez-Cenizo L, et al.
Cancer Letters 276 (2009) 125–135
http://dx.doi.org:/10.1016/j.canlet.2008.08.007

The cancer cell phenotype has been summarized in six hallmarks [D. Hanahan, R.A. Weinberg, The hallmarks of cancer, Cell 100 (1) (2000) 57-70]. Following the conceptual trait established in that review towards the comprehension of cancer, herein we summarize the basis of an underlying principle that is fulfilled by cancer cells and tumors: its avidity for glucose. Our purpose is to push forward that the metabolic reprogramming that operates in the cancer cell represents a seventh hallmark of the phenotype that offers a vast array of possibilities for the future treatment of the disease. We summarize the metabolic pathways that extract matter and energy from glucose, paying special attention to the concerted regulation of these pathways by the ATP mass-action ratio. The molecular and functional evidences that support the high glucose uptake and the “abnormal” aerobic glycolysis of the carcinomas are detailed discussing also the role that some oncogenes and tumor suppressors have in these pathways. We overview past and present evidences that sustain that mitochondria of the cancer cell are impaired, supporting the original Warburg’s formulation that ascribed the high glucose uptake of cancer cells to a defective mitochondria. A simple proteomic approach designed to assess the metabolic phenotype of cancer, i.e., its bioenergetic signature, molecularly and functionally supports Warburg’s hypothesis. Furthermore, we discuss the clinical utility that the bioenergetic signature might provide. Glycolysis is presented as the “selfish” pathway used for cellular proliferation, providing both the metabolic precursors and the energy required for biosynthetic purposes, in the context of a plethora of substrates. The glucose avidity of carcinomas is thus presented as the result of both the installment of glycolysis for cellular proliferation and of the impairment of mitochondrial activity in the cancer cell. At the end, the repression of mitochondrial activity affords the cancer cell with a cell-death resistant phenotype making them prone to malignant growth.

Fig. 1. Pathways of glucose metabolism. The model shows some of the relevant aspects of the metabolism of glucose. After entering the cell by specific transporters, glucose can be (i) catabolized by the pentose phosphate pathway (PPP) to obtain reducing power in the form of NADPH, (ii) used for the synthesis of carbohydrates or (iii) utilized by glycolysis to generate pyruvate and other metabolic intermediates that could be used in different anabolic processes (blue rectangles). In the cytoplasm, the generated pyruvate can be reduced to lactate and further exported from the cell or oxidized in the mitochondria by pyruvate dehydrogenase to generate acetyl-CoA, which is condensed with oxaloacetate in the tricarboxylic acid cycle (TCA cycle). The operation of the TCA cycle completes the oxidation of mitochondrial pyruvate. Different pathways that drain intermediates of the TCA cycle (oxaloacetate, succinyl-CoA, a-ketoglutarate and citrate) for biosynthetic purposes (blue rectangles) are represented. The transfer of electrons obtained in biological oxidations (NADH/FADH2) to molecular oxygen by respiratory complexes of the inner mitochondrial membrane (in green) is depicted by yellow lines. The utilization of the proton gradient generated by respiration for the synthesis of ATP by the H+-ATP synthase (in orange) in oxidative phosphorylation (OXPHOS) is also indicated. The incorporation of glutamine carbon skeletons into the TCA cycle is shown. The utilization of NADPH in anabolic pathways is also indicated.

Fig. 3. Fluxes of matter and energy in differentiated, proliferating and cancer cells. In differentiated cells, the flux of glycolysis is low because the requirement for precursors for anabolic purposes is low and there is a high energy yield by the oxidation of pyruvate in mitochondrial oxidative phosphorylation (OXPHOS). In this situation, mitochondrial activity produces large amounts of ROS that are normally quenched by the cellular antioxidant defense. In proliferating and cancer cells, there is a high demand of glucose to provide metabolic precursors for the biosynthesis of the macromolecules of daughter cells and because most of the energy required for anabolic purposes derives from non-efficient non-respiratory modes (glycolysis, pentose phosphate pathway) of energy generation. Limiting mitochondrial activity in these situations ensures less ROS production and their further downstream consequences. In addition, cancer cells have less overall mitochondrial complement or activity than normal cells by repressing the biogenesis of mitochondria.

Fig. 2. Genetic alterations underlying the glycolytic phenotype of cancer cells. The diagram represents the impact of gain-of-function mutations in oncogenes (ovals) and loss-of-function mutations in tumor suppressors (rectangles) in glycolysis and in the mitochondrial utilization of pyruvate in cancer cells. Hypoxia (low O2) induces the stabilization of HIF-1, which promotes transcriptional activation of the glucose transporter, glycolytic genes and PDK1. The expression of PDK1 results in the inactivation of pyruvate dehydrogenase and thus in a decreased oxidation of pyruvate in the TCA cycle concurrent to its enhanced cytoplasmic reduction to lactate by lactate dehydrogenase (LDHA). In addition, HIF1a reciprocally regulates the expression of two isoforms of the cytochrome c oxidase complex. The oncogen myc also supports an enhanced glycolytic pathway by transcriptional activation of glycolytic genes. High levels of c-myc could also promote the production of reactive oxygen species (ROS) that could damage nuclear (nDNA) and mitochondrial (mtDNA). The loss-of-function of the tumor suppressor p53 promotes an enhanced glycolytic phenotype by the repression of TIGAR expression. Likewise, loss-of-function of p53 diminished the expression of SCO2, a gene required for the appropriate assembly of cytochrome c oxidase, and thus limits the activity of mitochondria in the cancer cell.
Discussion:

Jose E S Roselino

  1. Warburg Effect revisited
    It is very interesting the series of commentaries following Warburg Effect revisited. However, it comes as no surprise that almost all of them have small or greater emphasis in the molecular biology (changes in gene expression) events of the metabolic regulation involved.
    I would like to comment on some aspects: 1- Warburg did the initial experiments following Pasteur line of reasoning that aimed at carbon flow through the cell (yeast in his case) instead of describing anything inside the cell. It is worth to recall that for the sake of his study, Pasteur considered anything inside the cell under the domain of divine forces. He, at least in defence of his work, entirely made outside the cell, considered that inside the cells was beyond human capability of understanding – He has followed vitalism as his line of reasoning in defence of his work – Interestingly, the same scientist that has ruled out spontaneous generation when Pasteurization was started. Therefore, Pasteur measured everything outside the cell (mainly sugar, ethanol – the equivalent of our lactic acid end product of anaerobic metabolism) and found that as soon as yeasts were placed in the presence of oxygen, sugar was consumed at low speed in comparison with the speed measured in anaerobiosis and ethanol was also produced at reduced speed. This is an indication of a fast biological regulatory mechanism that obviously, do not require changes in gene expression. As previously said, Warburg work translated for republishing in the Journal Biological Chemistry mentioned “grana” for mitochondria calling attention on an “inside-the-cell” component. It seems that, there is not a unique, single site of metabolism, where the Pasteur Effect – Warburg Effect seems to be elicited by the shift from anaerobiosis to aerobiosis or vice versa.
    In order to find a core for the mechanism the best approach seems to take into account one of the most important contributions of one of the greatest north-American biochemists, Briton Chance. He has made it with his polarographic method of following continuously the oxygen consumption of the cell´s mitochondria.
    Mitochondria burn organic carbon molecules under a very stringent control mechanism of oxidative-phosphorylation ATP production. Measured in the form of changes in the speed of oxygen consumption over time as Respiratory Control Ratio (RCR). When no ATP is required by the cell, oxygen consumption goes at low speed (basal or state II or IV). When ADP is offered to the mitochondria as an indication that ATP synthesis is necessary, oxygen consumption is activated in state III respiration. Low respiration means low burning activity of organic (carbon) molecules what in this case, means indirectly low glucose consumption. While high respiration is the converse – greater glucose consumption.
    Aerobic metabolism of glucose to carbonic acid and water provides a change in free energy enough for 38 molecules of ATP (the real production is +/- 32 ATP in aerobic condition) while glucose to lactic acid metabolism in anaerobiosis leads to 2 ATP production after discounting the other 2 required at initial stages of glucose metabolism.
    The low ATP yield in anaerobiosis explains the fast glucose metabolism in anaerobiosis while the control by RCR in mitochondria explains the reduction in glucose metabolism under aerobiosis as long as the ATP requirements of the cell remains the same – This is what it is assumed to happen in quiescent cells. Not necessarily in fast growing cells as cancer cells are. However, this will not be discussed here. In my first experiments in the early seventies, with M. Rouxii a dimorphic mold-yeast biological system the environmental change (aerobic – anaerobic) led to morphogenetic change presented as morphogenetic expression of the Pasteur Effect. In this case, the enzyme that replaces mitochondria in ATP production (Pyruvate Kinase) converting phosphoenolpyruvate into pyruvate together with ADP into ATP, shows changes that can be interpreted as change in gene expression together with new self-assembly of enzyme subunits. (Dimer AA – yeast in anaerobic growth or sporangiospores- converted into dimer AB in aerobic mold). In Leloir opinions at that time, PK I (AA) was only highly glycosylated, while PK II (AB) was less glycosylated without changes in gene expression.

    In case you read comments posted, you will see that the reference to aerobic glycolysis, continues to be made together with, new deranged forms of reasoning as is indicated by referring to: Mitochondrial role in ion homeostasis…
    Homeostasis is a regulation of something, ions, molecules, pH etc. that is kept outside the cell, therefore any role for mitochondria on it is only made indirectly, by its ATP production.
    However, mitochondria has a role together with other cell components in the regulation of for instance, intracellular Ca levels (Something that is not a homeostatic regulation). This is a very important point for the following reason: Homeostasis is maintained as a composite result of several differentiated cellular, tissue and organ functions. Differentiated function is something clearly missing in cancer cells. The best form to refer to the mitochondrial function regarding ions is to indicate a mitochondrial role in ion fluxes.
    In short, to indicate how an environmental event or better saying condition could favour genetic changes instead of being caused by genetic changes is to follow the same line of reasoning that is followed in understanding the role of cardioplegia. To stop heart beating is adequate for heart surgery it is also adequate for heart cells by sparing the ATP use during surgery and therefore, offering better recovery condition to the heart afterwards.
    In the case, here considered, even assuming that the genome is not made more unstable during hypoxic condition it is quite possible to understand that sharing ATP with both differentiated cell function and replication may led quality control of DNA in short supply of much needed ATP and this led to maintenance of mutations as well as less organized genome.

    • Thank you. I enjoy reading your comments. They are very instructive. I don’t really think that I comprehend the use of the term “epigenetics” and longer. In fact, it was never clear to me when I first heard it used some years ago.

      The term may have been closely wedded to the classic hypothesis of a unidirectional DNA–> RNA–> protein model that really has lost explanatory validity for the regulated cell in its environment. The chromatin has an influence, and protein-protein interactions are everywhere. As you point out, these are adjusting to a fast changing substrate milieu, and the genome is not involved. But in addition, the proteins may well have a role in suppression or activation of signaling pathways, and thereby, may well have an effect on gene expression. I don’t have any idea about how it would work, but mutations would appear to follow the metabolic condition of the cell over time. It would appear to be – genomic modification.

  2. In aerobic glucose metabolism, the oxidation of citric acid requires ADP and Mg²+, which will increase the speed of the reaction: Iso-citric acid + NADP (NAD) — isocitrate dehydrogenase (IDH) = alpha-ketoglutaric acid. In the Krebs cycle (the citric cycle), IDH1 and IDH2 are NADP+-dependent enzymes that normally catalyze the inter-conversion of D-isocitrate and alpha-ketoglutarate (α-KG). The IDH1 and IDH2 genes are mutated in > 75% of different malignant diseases. Two distinct alterations are caused by tumor-derived mutations in IDH1 or IDH2: the loss of normal catalytic activity in the production of α-ketoglutarate (α-KG) and the gain of catalytic activity to produce 2-hydroxyglutarate (2-HG), [22].
    This product is a competitive inhibitor of multiple α-KG-dependent dioxygenases, including demethylases, prolyl-4-hydroxylase and the TET enzymes family (Ten-Eleven Translocation-2), resulting in genome-wide alternations in histones and DNA methylation. [23]
    IDH1 and IDH2 mutations have been observed in myeloid malignancies, including de novo and secondary AML (15%–30%), and in pre-leukemic clone malignancies, including myelodysplastic syndrome and myeloproliferative neoplasm (85% of the chronic phase and 20% of transformed cases in acute leukemia), [24].
    Normally, cells in the body communicate via intra-cytoplasmic channels and maintain the energetic potential across cell membranes, which is 1-2.5 µmol of ATP in the form of ATP-ADP/ATP-ADP-IMP. These normal energetic values occur during normal cell division. If the intra-cellular and extra-cellular levels of Mg2+ are high, the extra-cellular charges of the cells will not be uniformly distributed.
    This change in distribution induces a high net positive charge for the cell and induces a loss of contact inhibition via the electromagnetic induction of oscillation [28, 29, 30]. Thereafter, malignant cells become invasive and metastasize.
    ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
    -22. Hartmann C, Meyer J, Balss J. Capper D, et al. Type and frequency of IDH1 and IDH2 mutations are related to astrocytic and oligodendroglial differentiation and age: a study of 1,010 diffuse gliomas. Acta Neuropathol 2009; 118: 464-474.

    23. Raymakers R.A, Langemeijer S.M., Kuiper R.P, Berends M, et al. Acquired mutations in TET2 are common in myelodysplastic syndromes. Nat. Genet 2009; 41; 838–849.

    24 Wagner K, Damm F, Gohring G., Gorlich K et al. Impact of IDH1 R132 mutations and an IDH1 single nucleotide polymorphism in cytogenetically normal acute myeloid leukemia: SNP rs11554137 is an adverse prognostic factor. J. Clin. Oncol.2010; 28: 2356–2364.
    Plant Molecular Biology 1989; 1: 271–303.

    29. Chien MM, Zahradka CE, Newel MC, Fred JW. Fas induced in B cells apoptosis require an increase in free cytosolic magnesium as in early event. J Biol Chem.1999; 274: 7059-7066.

    30. Milionis H J, Bourantas C L, Siamopoulos C K, Elisaf MS. Acid bases and electrolytes abnormalities in Acute Leukemia. Am J Hematol 1999; (62): 201-207.

    31. Thomas N Seyfried; Laura M Shelton.Cancer as a Metabolic Disease. Nutr Metab 2010; 7: 7

    – Aurelian Udristioiu, M.D,
    – Lab Director, EuSpLM,
    – City Targu Jiu, Romania
    AACC, National Academy of Biochemical Chemistry (NACB) Member, Washington D.C, USA.

 

 

 

 

 

 

 

 

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Summary, Metabolic Pathways

Author: Larry H. Bernstein, MD, FCAP 

 

This portion of a series of chapters on metabolism, proteomics and metabolomics dealt mainly with carbohydrate metabolism. Amino acids and lipids are presented more fully in the chapters that follow. There are features on the

  • functioning of enzymes and proteins,
  • on sequential changes in a chain reaction, and
  • on conformational changes that we shall also cover.

These are critical to developing a more complete understanding of life processes.

I needed to lay out the scope of metabolic reactions and pathways, and their complementary changes. These may not appear to be adaptive, if the circumstances and the duration is not clear. The metabolic pathways map in total
is in interaction with environmental conditions – light, heat, external nutrients and minerals, and toxins – all of which give direction and strength to these reactions. A developing goal is to discover how views introduced by molecular biology and genomics don’t clarify functional cellular dynamics that are not related to the classical view.  The work is vast.

Carbohydrate metabolism denotes the various biochemical processes responsible for the formation, breakdown and interconversion of carbohydrates in living organisms. The most important carbohydrate is glucose, a simple sugar (monosaccharide) that is metabolized by nearly all known organisms. Glucose and other carbohydrates are part of a wide variety of metabolic pathways across species: plants synthesize carbohydrates from carbon dioxide and water by photosynthesis storing the absorbed energy internally, often in the form of starch or lipids. Plant components are consumed by animals and fungi, and used as fuel for cellular respiration. Oxidation of one gram of carbohydrate yields approximately 4 kcal of energy and from lipids about 9 kcal. Energy obtained from metabolism (e.g. oxidation of glucose) is usually stored temporarily within cells in the form of ATP. Organisms capable of aerobic respiration metabolize glucose and oxygen to release energy with carbon dioxide and water as byproducts.

Carbohydrates are used for short-term fuel, and even though they are simpler to metabolize than fats, they don’t produce as equivalent energy yield measured by ATP.  In animals, the concentration of glucose in the blood is linked to the pancreatic endocrine hormone, insulin. . In most organisms, excess carbohydrates are regularly catabolized to form acetyl-CoA, which is a feed stock for the fatty acid synthesis pathway; fatty acids, triglycerides, and other lipids are commonly used for long-term energy storage. The hydrophobic character of lipids makes them a much more compact form of energy storage than hydrophilic carbohydrates.

Glucose is metabolized obtaining ATP and pyruvate by way of first splitting a six-carbon into two three carbon chains, which are converted to lactic acid from pyruvate in the lactic dehydrogenase reaction. The reverse conversion is by a separate unidirectional reaction back to pyruvate after moving through pyruvate dehydrogenase complex.

Pyruvate dehydrogenase complex (PDC) is a complex of three enzymes that convert pyruvate into acetyl-CoA by a process called pyruvate decarboxylation. Acetyl-CoA may then be used in the citric acid cycle to carry out cellular respiration, and this complex links the glycolysis metabolic pathway to the citric acid cycle. This multi-enzyme complex is related structurally and functionally to the oxoglutarate dehydrogenase and branched-chain oxo-acid dehydrogenase multi-enzyme complexes. In eukaryotic cells the reaction occurs inside the mitochondria, after transport of the substrate, pyruvate, from the cytosol. The transport of pyruvate into the mitochondria is via a transport protein and is active, consuming energy. On entry to the mitochondria pyruvate decarboxylation occurs, producing acetyl CoA. This irreversible reaction traps the acetyl CoA within the mitochondria. Pyruvate dehydrogenase deficiency from mutations in any of the enzymes or cofactors results in lactic acidosis.

PDH-rxns The acetyl group is transferred to coenzyme A

PDH-rxns The acetyl group is transferred to coenzyme A

http://guweb2.gonzaga.edu/faculty/cronk/biochem/images/PDH-rxns.gif

Typically, a breakdown of one molecule of glucose by aerobic respiration (i.e. involving both glycolysis and Kreb’s cycle) is about 33-35 ATP. This is categorized as:

Glycogenolysis – the breakdown of glycogen into glucose, which provides a glucose supply for glucose-dependent tissues.

Glycogenolysis in liver provides circulating glucose short term.

Glycogenolysis in muscle is obligatory for muscle contraction.

Pyruvate from glycolysis enters the Krebs cycle, also known as the citric acid cycle, in aerobic organisms.

Anaerobic breakdown by glycolysis – yielding 8-10 ATP

Aerobic respiration by Kreb’s cycle – yielding 25 ATP

The pentose phosphate pathway (shunt) converts hexoses into pentoses and regenerates NADPH. NADPH is an essential antioxidant in cells which prevents oxidative damage and acts as precursor for production of many biomolecules.

Glycogenesis – the conversion of excess glucose into glycogen as a cellular storage mechanism; achieving low osmotic pressure.

Gluconeogenesis – de novo synthesis of glucose molecules from simple organic compounds. An example in humans is the conversion of a few amino acids in cellular protein to glucose.

Metabolic use of glucose is highly important as an energy source for muscle cells and in the brain, and red blood cells.

The hormone insulin is the primary glucose regulatory signal in animals. It mainly promotes glucose uptake by the cells, and it causes the liver to store excess glucose as glycogen. Its absence

  1. turns off glucose uptake,
  2. reverses electrolyte adjustments,
  3. begins glycogen breakdown and glucose release into the circulation by some cells,
  4. begins lipid release from lipid storage cells, etc.

The level of circulatory glucose (known informally as “blood sugar”) is the most important signal to the insulin-producing cells.

  • insulin is made by beta cells in the pancreas,
  • fat is stored n adipose tissue cells, and
  • glycogen is both stored and released as needed by liver cells.
  • no glucose is released to the blood from internal glycogen stores from muscle cells.

The hormone glucagon, on the other hand, opposes that of insulin, forcing the conversion of glycogen in liver cells to glucose, and then release into the blood. Growth hormone, cortisol, and certain catecholamines (such as epinepherine) have glucoregulatory actions similar to glucagon.  These hormones are referred to as stress hormones because they are released under the influence of catabolic proinflammatory (stress) cytokines – interleukin-1 (IL1) and tumor necrosis factor α (TNFα).

Net Yield of GlycolysisThe preparatory phase consumes 2 ATP

The pay-off phase produces 4 ATP.

The gross yield of glycolysis is therefore

4 ATP – 2 ATP = 2 ATP

The pay-off phase also produces 2 molecules of NADH + H+ which can be further converted to a total of 5 molecules of ATP* by the electron transport chain (ETC) during oxidative phosphorylation.

Thus the net yield during glycolysis is 7 molecules of ATP*
This is calculated assuming one NADH molecule gives 2.5 molecules of ATP during oxidative phosphorylation.

Cellular respiration involves 3 stages for the breakdown of glucose – glycolysis, Kreb’s cycle and the electron transport system. Kreb’s cycle produces about 60-70% of ATP for release of energy in the body. It directly or indirectly connects with all the other individual pathways in the body.

The Kreb’s Cycle occurs in two stages:

  1. Conversion of Pyruvate to Acetyl CoA
  2. Acetyl CoA Enters the Kreb’s Cycle

Each pyruvate in the presence of pyruvate dehydrogenase (PDH) complex in the mitochondria gets converted to acetyl CoA which in turn enters the Kreb’s cycle. This reaction is called as oxidative  decarboxylation as the carboxyl group is removed from the pyruvate molecule in the form of CO2 thus yielding 2-carbon acetyl group which along with the coenzyme A forms acetyl CoA.

The PDH requires the sequential action of five co-factors or co-enzymes for the combined action of dehydrogenation and decarboxylation to take place. These five are TPP (thiamine phosphate), FAD (flavin adenine dinucleotide), NAD (nicotinamide adenine dinucleotide), coenzyme A (denoted as CoA-SH at times to depict role of -SH group) and lipoamide.

Acetyl CoA condenses with oxaloacetate (4C) to form a citrate (6C) by transferring its acetyl group in the presence of enzyme citrate synthase. The CoA liberated in this reaction is ready to participate in the oxidative decarboxylation of another molecule of pyruvate by PDH complex.

Isocitrate undergoes oxidative decarboxylation by the enzyme isocitrate dehydrogenase to form oxalosuccinate (intermediate- not shown) which in turn forms α-ketoglutarate (also known as oxoglutarate) which is a five carbon compound. CO2 and NADH are released in this step. α-ketoglutarate (5C) undergoes oxidative decarboxylation once again to form succinyl CoA (4C) catalysed by the enzyme α-ketoglutarate dehydrogenase complex.

Succinyl CoA is then converted to succinate by succinate thiokinase or succinyl coA synthetase in a reversible manner. This reaction involves an intermediate step in which the enzyme gets phosphorylated and then the phosphoryl group which has a high group transfer potential is transferred to GDP to form GTP.

Succinate then gets oxidised reversibly to fumarate by succinate dehydrogenase. The enzyme contains iron-sulfur clusters and covalently bound FAD which when undergoes electron exchange in the mitochondria causes the production of FADH2.

Fumarate is then by the enzyme fumarase converted to malate by hydration(addition of H2O) in a reversible manner.

Malate is then reversibly converted to oxaloacetate by malate dehydrogenase which is NAD linked and thus produces NADH.

The oxaloacetate produced is now ready to be utilized in the next cycle by the citrate synthase reaction and thus the equilibrium of the cycle shifts to the right.

The NADH formed in the cytosol can yield variable amounts of ATP depending on the shuttle system utilized to transport them into the mitochondrial matrix. This NADH, formed in the cytosol, is impermeable to the mitochondrial inner-membrane where oxidative phosphorylation takes place. Thus to carry this NADH to the mitochondrial matrix there are special shuttle systems in the body. The most active shuttle is the malate-aspartate shuttle via which 2.5 molecules of ATP are generated for 1 NADH molecule. This shuttle is mainly used by the heart, liver and kidneys. The brain and skeletal muscles use the other shuttle known as glycerol 3-phosphate shuttle which synthesizes 1.5 molecules of ATP for 1 NADH.

Glucose-6-phosphate Dehydrogenase is the committed step of the Pentose Phosphate Pathway. This enzyme is regulated by availability of the substrate NADP+. As NADPH is utilized in reductive synthetic pathways, the increasing concentration of NADP+ stimulates the Pentose Phosphate Pathway, to replenish NADPH. The importance of this pathway can easily be underestimated.  The main source for energy in respiration was considered to be tied to the high energy phosphate bond in phosphorylation and utilizes NADPH, converting it to NADP+. The pentose phosphate shunt is essential for the generation of nucleic acids, in regeneration of red cells and lens – requiring NADPH.

NAD+ serves as electron acceptor in catabolic pathways in which metabolites are oxidized. The resultant NADH is reoxidized by the respiratory chain, producing ATP.

The pyridine nucleotide transhydrogenase reaction concerns the energy-dependent reduction of TPN by DPNH. In 1959, Klingenberg and Slenczka made the important observation that incubation of isolated liver mitochondria with DPN-specific substrates or succinate in the absence of phosphate acceptor resulted in a rapid and almost complete reduction of  the intramitochondrial TPN. These and related findings led Klingenberg and co-workers (1-3) to postulate the occurrence of a ATP-controlled transhydrogenase reaction catalyzing the reduction of TPN by DPNH.  (The role of transhydrogenase in the energy-linked reduction of TPN.  Fritz Hommes, Ronald W. Estabrook, The Wenner-Gren Institute, University of Stockholm, Stockholm, Sweden. Biochemical and Biophysical Research Communications 11, (1), 2 Apr 1963, Pp 1–6. http://dx.doi.org:/10.1016/0006-291X(63)90017-2/).

Further studies observed the coupling of TPN-specific dehydrogenases with the transhydrogenase and observing the reduction of large amounts of diphosphopyridine nucleotide (DPN) in the presence of catalytic amounts of triphosphopyridine nucleotide (TPN). The studies showed the direct interaction between TPNHz and DPN, in the presence of transhydrogenase to yield products having the properties of TPN and DPNHZ. The reaction involves a transfer of electrons (or hydrogen) rather than a phosphate. (Pyridine Nucleotide Transhydrogenase  II. Direct Evidence for and Mechanism of the Transhydrogenase Reaction* by  Nathan 0. Kaplan, Sidney P. Colowick, And Elizabeth F. Neufeld. (From The Mccollum-Pratt Institute, The Johns Hopkins University, Baltimore, Maryland) J. Biol. Chem. 1952, 195:107-119.) http://www.JBC.org/Content/195/1/107.Citation
Notation: TPN, NADP; DPN, NAD+; reduced pyridine nucleotides: TPNH (NADPH2), DPNH (NADH).

Note: In this discussion there is a detailed presentation of the activity of lactic acid conversion in the mitochondria by way of PDH. In a later section there is mention of the bidirectional reaction of lactate dehydrogenase.  However, the forward reaction is dominant (pyruvate to lactate) and is described. This is not related to the kinetics of the LD reaction with respect to the defining characteristic – Km.

Biochemical Education Jan 1977; 5(1):15. Kinetics of Lactate Dehydrogenase: A Textbook Problem.
K.L. MANCHESTER. Department of Biochemistry, University of Witwatersrand, Johannesburg South Africa.

One presupposes that determined Km values are meaningful under intracellular conditions. In relation to teaching it is a simple experiment for students to determine for themselves the Km towards pyruvate of LDH in a post-mitochondrial supernatant of rat heart and thigh muscle. The difference in Km may be a factor of 3 or 4-fold.It is pertinent then to ask what is the range of suhstrate concentrations over which a difference in Km may be expected to lead to significant differences in activity and how these concentrations compare with pyruvate concentrations in the cell. The evidence of Vesell and co-workers that inhibition by pyruvate is more readily seen at low than at high enzyme concentration is important in emphasizing that under intracellular conditions enzyme concentrations may be relatively large in relation to the substrate available. This will be particularly so in relation to [NADH] which in the cytoplasm is likely to be in the ~M range.

A final point concerns the kinetic parameters for LDH quoted by Bergmeyer for lactate estimations a pH of 9 is recommended and the Km towards lactate at that pH is likely to be appreciably different from the quoted values at pH 7 — Though still at pH 9 showing a substantially lower value for lactate with the heart preparationhttp://onlinelibrary.wiley.com/doi/10.1016/0307-4412%2877%2990013-9/pdf

Several investigators have established that epidermis converts most of the glucose it uses to lactic acid even in the presence of oxygen. This is in contrast to most tissues where lactic acid production is used for energy production only when oxygen is not available. This large amount of lactic acid being continually produced within the epidermal cell must be excreted by the cell and then carried away by the blood stream to other tissues where the lactate can be utilized. The LDH reaction with pyruvate and NADH is reversible although at physiological pH the equilibrium position for the reaction lies very far to the right, i.e., in favor of lactate production. The speed of this reaction depends not only on the amount of enzyme present but also on the concentrations of the substances involved on both sides of the equation. The net direction in which the reaction will proceed depends solely on the relative concentrations of the substances on each side of the equation.
In vivo there is net conversion of pyruvate (formed from glucose) to lactate. Measurements of the speed of lactate production by sheets of epidermis floating on a medium containing glucose indicate a rate of lactate production of approximately 0.7 rn/sm/
mm/mg of fresh epidermis.Slice incubation experiments are presumably much closer to the actual in vivo conditions than
the homogenate experiments. The discrepancy between the
two indicates that in vivo conditions are far from optimal for the conversion of pyruvate to lactate. Only 1/100th of the maximal activity of the enzyme present is being achieved. The concentrations of the various substances involved are not
optimal in vivo since pyruvate and NADH concentrations are
lower than lactate and NAD concentrations and this might explain the in vivo inhibition of LDH activity. (Lactate Production And Lactate Dehydrogenase In The Human Epidermis*. KM. Halprin, A Ohkawara. J Invest Dermat 1966; 47(3): 222-6.)
http://www.nature.com/jid/journal/v47/n3/pdf/jid1966133a.pdf

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Introduction to Metabolic Pathways

Author: Larry H. Bernstein, MD, FCAP

 

Humans, mammals, plants and animals, and eukaryotes and prokaryotes all share a common denominator in their manner of existence.  It makes no difference whether they inhabit the land, or the sea, or another living host. They exist by virtue of their metabolic adaptation by way of taking in nutrients as fuel, and converting the nutrients to waste in the expenditure of carrying out the functions of motility, breakdown and utilization of fuel, and replication of their functional mass.

There are essentially two major sources of fuel, mainly, carbohydrate and fat.  A third source, amino acids which requires protein breakdown, is utilized to a limited extent as needed from conversion of gluconeogenic amino acids for entry into the carbohydrate pathway. Amino acids follow specific metabolic pathways related to protein synthesis and cell renewal tied to genomic expression.

Carbohydrates are a major fuel utilized by way of either of two pathways.  They are a source of readily available fuel that is accessible either from breakdown of disaccharides or from hepatic glycogenolysis by way of the Cori cycle.  Fat derived energy is a high energy source that is metabolized by one carbon transfers using the oxidation of fatty acids in mitochondria. In the case of fats, the advantage of high energy is conferred by chain length.

Carbohydrate metabolism has either of two routes of utilization.  This introduces an innovation by way of the mitochondrion or its equivalent, for the process of respiration, or aerobic metabolism through the tricarboxylic acid, or Krebs cycle.  In the presence of low oxygen supply, carbohydrate is metabolized anaerobically, the six carbon glucose being split into two three carbon intermediates, which are finally converted from pyruvate to lactate.  In the presence of oxygen, the lactate is channeled back into respiration, or mitochondrial oxidation, referred to as oxidative phosphorylation. The actual mechanism of this process was of considerable debate for some years until it was resolved that the mechanism involve hydrogen transfers along the “electron transport chain” on the inner membrane of the mitochondrion, and it was tied to the formation of ATP from ADP linked to the so called “active acetate” in Acetyl-Coenzyme A, discovered by Fritz Lipmann (and Nathan O. Kaplan) at Massachusetts General Hospital.  Kaplan then joined with Sidney Colowick at the McCollum Pratt Institute at Johns Hopkins, where they shared tn the seminal discovery of the “pyridine nucleotide transhydrogenases” with Elizabeth Neufeld,  who later established her reputation in the mucopolysaccharidoses (MPS) with L-iduronidase and lysosomal storage disease.

This chapter covers primarily the metabolic pathways for glucose, anaerobic and by mitochondrial oxidation, the electron transport chain, fatty acid oxidation, galactose assimilation, and the hexose monophosphate shunt, essential for the generation of NADPH. The is to be more elaboration on lipids and coverage of transcription, involving amino acids and RNA in other chapters.

The subchapters are as follows:

1.1      Carbohydrate Metabolism

1.2      Studies of Respiration Lead to Acetyl CoA

1.3      Pentose Shunt, Electron Transfer, Galactose, more Lipids in brief

1.4      The Multi-step Transfer of Phosphate Bond and Hydrogen Exchange Energy

Complex I or NADH-Q oxidoreductase

Complex I or NADH-Q oxidoreductase

Fatty acid oxidation and ETC

Fatty acid oxidation and ETC

Read Full Post »

Larry H. Bernstein, MD, FCAP, Author and Curator

Isozymes

An example of an isozyme is glucokinase, a variant of hexokinase which is not
inhibited by glucose 6-phosphate.  Its different regulatory features and lower
affinity for glucose (compared to other hexokinases), allows it to serve different
functions in cells of specific organs, such as

  • control of insulinrelease by the beta cells of the pancreas, or
  • initiation ofglycogen synthesis by liver
  • Both of these processes must only occur when glucose is abundant,or
    problems occur.

Isozymes or Isoenzymes are proteins with different structure which catalyze
the same reaction. Frequently they are oligomers made with different
polypeptide chains, so they usually differ in regulatory mechanisms and in
kinetic characteristics.

From the physiological point of view, isozymes allow the existence of similar
enzymes with different characteristics, “customized” to specific tissue
requirements or metabolic conditions.

One example of the advantages of having isoenzymes for adjusting the
metabolism to different conditions and/ or in different organs is the following:

Glucokinase and Hexokinase are typical examples of isoenzymes. In fact,
there are four Hexokinases: I, II, III and IV. Hexokinase I is present in all
mammalian tissues, and Hexokinase IV, aka Glucokinase, is found mainly
in liver, pancreas  and brain.

Both enzymes catalyze the phosphorylation of Glucose:

Glucose + ATP —–à Glucose 6 (P) + ADP

Hexokinase I has a low Km and is inhibited by glucose 6 (P).  Glucokinase
is not inhibited by Glucose 6 (P) and his Km is high. These two facts
indicate that the activity of glucokinase depends on the availability
of substrate and not on the demand of the product.

Since Glucokinase is not inhibited by glucose 6 phosphate, in
conditions of high concentrations of glucose this enzyme
continues phosphorylating glucose, which can be used for
glycogen synthesis in liver. Additionally, since Glucokinase
has a high Km, its activity does not compromise the supply
of glucose to other organs; in other words, if Glucokinase
had a low Km, and since it is not inhibited by its product, it
would continue converting glucose to glucose 6 phosphate
in the liver,  making glucose unavailable for other organs
(remember that after meals, glucose arrives first to the liver
through the portal system).

The enzyme Lactate Dehydrogenase is made of two (H-
and M-)  sub units, combined in different Permutations
and 
Combinations  depending on the tissue in which it
is present as shown in table,

Type Composition Location
LDH1 HHHH Heart and Erythrocyte
LDH2 HHHM Heart and Erythrocyte
LDH3 HHMM Brain and Kidney
LDH4 HMMM Skeletal Muscle and Liver
LDH5 MMMM Skeletal Muscle and Liver
  • While isozymes may be almost identical in function
    (defined by Michaelis constant, KM)
  • they differ in amino acidsubstitutions that change the
    electric charge of the enzyme (such as replacing
    aspartic acid with glutamic acid)
  • The sum of zwitterion charges result in identifyjng
    difference inmigratiion toward the anode by gel
    electrophoresis
    , and this forms the basis for the use
    of isozymes as molecular markers.
  • To identify isozymes, a crude protein extract is made by
    grinding animal or plant tissue with an extraction buffer,
    and the components of extract are separated according
    to their charge by gel electrophoresis.
  • They were classically purified by ion-exchange column
    chromatography after first precipitation with ammonium
    sulfate, followed by dialysis.

The cytochrome P450 isozymes play important roles in
metabolism and steroidogenesis. The multiple forms of
phosphodiesterase also play major roles in various
biological processes.

These isoforms of the enzyme are unequally distributed
in the various cells of an organism.

Further the main isoenzymes may have closely grouped
“isoforms” having unclear significance.

There are many examples of isoenzymes in cell
metabolism that distinguish cells:

  • Adenylate kinase (AL in liver, and myokinase) – that
    are distinguished by reactivity with sulfhydryl reagents
  • Pyruvate kinase
  • AMPK, and Calmodulin kinase
  • Malate, isocitrate, alcohol, and aldehyde dehydrogenase
  • Nitric oxide synthase (i, e, and n)…

References[edit]

Hunter, R. L. and C.L. Markert. (1957) Histochemical
demonstration of enzymes separated by zone electrophoresis
in starch gels. Science 125: 1294-1295

Uzunov, P. and Weiss, B.(1972) “Separation of multiple
molecular forms of cyclic adenosine 3′,5′-monophosphate
phosphodiesterase in rat cerebellum by polyacrylamide
gel electrophoresis.”  Biochim. Biophys. Acta 284:220-226.

Uzunov, P., Shein, H.M. and Weiss, B.(1974) “Multiple
forms of cyclic 3′,5′-AMP phosphodiesterase
of rat cerebrum and cloned astrocytoma and
neuroblastoma cells.” Neuropharmacology 13:377-391.

Weiss, B., Fertel, R., Figlin, R. and Uzunov, P. (1974)
“Selective alteration of the activity of the multiple forms
of adenosine 3′,5′-monophosphate phosphodiesterase
of rat cerebrum.” Mol. Pharmacol.10:615-625.

Lactate dehydrogenase

In cells, the immediate energy sources involve glucose oxidation. In anaerobic metabolism, the donor of the phosphate group is adenosine triphosphate (ATP), and the reaction is catalyzed via the hexokinase or glucokinase: Glucose +ATP-Mg²+ = Glucose-6-phosphate (ΔGo = – 3.4 kcal/mol with hexokinase as the co-enzyme for the reaction.).
In the following step, the conversion of G-6-phosphate into F-1-6-bisphosphate is mediated by the enzyme phosphofructokinase with the co-factor ATP-Mg²+. This reaction has a large negative free energy difference and is irreversible under normal cellular conditions. In the second step of glycolysis, phosphoenolpyruvic acid in the presence of Mg²+ and K+ is transformed into pyruvic acid. In cancer cells or in the absence of oxygen, the transformation of pyruvic acid into lactic acid alters the process of glycolysis.
The energetic sum of anaerobic glycolysis is ΔGo = -34.64 kcal/mol. However a glucose molecule contains 686kcal/mol and, the energy difference (654.51 kcal) allows the potential for un-controlled reactions during carcinogenesis. The transfer of electrons from NADPH in each place of the conserved unit of energy transmits conformational exchanges in the mitochondrial ATPase. The reaction ADP³+ P²¯ + H²–à ATP + H2O is reversible. The terminal oxygen from ADP binds the P2¯ by forming an intermediate pentacovalent complex, resulting in the formation of ATP and H2O. This reaction requires Mg²+ and an ATP-synthetase, which is known as the H+-ATPase or the Fo-F1-ATPase complex. Intracellular calcium induces mitochondrial swelling and aging. [12].
The known marker of monitoring of treatment in cancer diseases, lactate dehydrogenase (LDH) is an enzyme that is localized to the cytosol of human cells and catalyzes the reversible reduction of pyruvate to lactate via using hydrogenated nicotinamide deaminase (NADH) as co-enzyme.
The causes of high LDH and high Mg levels in the serum include neoplastic states that promote the high production of intracellular LDH and the increased use of Mg²+ during molecular synthesis in processes pf carcinogenesis (Pyruvate acid>> LDH/NADH >>Lactate acid + NAD), [13].
LDH is released from tissues in patients with physiological or pathological conditions and is present in the serum as a tetramer that is composed of the two monomers LDH-A and LDH-B, which can be combined into 5 isoenzymes: LDH-1 (B4), LDH-2 (B3-A1), LDH-3 (B2-A2), LDH-4 (B1-A3) and LDH-5 (A4). The LDH-A gene is located on chromosome 11, whereas the LDH-B gene is located on chromosome 12. The monomers differ based on their sensitivity to allosteric modulators. They facilitate adaptive metabolism in various tissues. The LDH-4 isoform predominates in the myocardium, is inhibited by pyruvate and is guided by the anaerobic conversion to lactate.
Total LDH, which is derived from hemolytic processes, is used as a marker for monitoring the response to chemotherapy in patients with advanced neoplasm with or without metastasis. LDH levels in patients with malignant disease are increased as the result of high levels of the isoenzyme LDH-3 in patients with hematological malignant diseases and of the high level of the isoenzymes LDH-4 and LDH-5, which are increased in patients with other malignant diseases of tissues such as the liver, muscle, lungs, and conjunctive tissues. High concentrations of serum LDH damage the cell membrane [11, 31].

Relation between LDH and Mg as Factors of Interest in the Monitoring and Prognoses of Cancer

Aurelian Udristioiu, Emergency County Hospital Targu Jiu Romania, Clinical Laboratory Medical Analyses, E-mail: aurelianu2007@yahoo.com

Lactate Dehydrogenase (LDH) is ubiquitous in animals and
man, and  it occurs in different organs of the body, each
region having a unique conformation of the subunits, but
the significance was once disputed. Perhaps the experiments
of Jakob and Monod on the lac 1 operon put to rest any
notions that isoenzymes and their conformational forms are
something of no real significance.  This concept does not
necessarily apply in all cases of isoenzyme differences, by
which I mean that there may be a difference in reactivity at
the active site.

For that matter, Jakob and Monod discovered and elucidated
allosterism.

300px-Enzyme_Model  allosterism
In biochemistryallosteric regulation is the regulation of a
protein by binding an effector molecule at a site other than
the protein’s active site.

The site the effector binds to is termed the allosteric site.
Allosteric sites allow effectors to bind to the protein, often
resulting in a conformational change. Effectors that enhance
the protein’s activity are referred to as allosteric activators,
whereas  those that decrease the protein’s activity are called
allosteric inhibitors.

Allosteric regulations are a natural example of control loops,
such as feedback from downstream products or feedforward
 from upstream substrates. Long-range allostery is especially
important in cell signaling. Allosteric regulation
is also particularly important in the cell’s ability to adjust
enzyme activity.

The term allostery comes from the Greek allos (ἄλλος), “other,”
and stereos (στερεὀς), “solid (object).” This is in reference
to the fact that the regulatory site of an allosteric protein is
physically distinct from its active site.

Jacob and Monod model of transcriptional regulation of the lac operon by lac repressor

Jacob and Monod model of  lac repressor

Most allosteric effects can be explained by the concerted
MWC model put forth by Monod, Wyman, and Changeux[2]
or by the sequential model described by Koshland, Nemethy,
and Filmer.[3] Both postulate that enzyme subunits exist in
one of two conformations, tensed (T) or relaxed (R), and
that relaxed subunits bind substrate more readily than
those in the tense state. The two models differ most in
their assumptions about subunit interaction and the pre-
existence of both states.

Allosteric_Regulation Model

Allosteric_Regulation Model

  1.  Monod, J. Wyman, J.P. Changeux. (1965). On the nature of
    allosteric transitions:A plausible model. J. Mol. Biol.;12:88-118.
  2. E. Jr Koshland, G. Némethy, D. Filmer (1966). Comparison of
    experimental binding data and theoretical models in proteins
    containing subunits. Biochemistry. Jan;5(1):365-8

The sequential model (2) of allosteric regulation holds that subunits
are not connected in such a way  that a  conformational change in
one induces a similar change in the others. Thus, all enzyme
subunits do not necessitate the  same conformation. Moreover,
the sequential model dictates that molecules of substrate
bind via an
 induced fit  protocol. In general, when a subunit
randomly collides with a molecule of substrate, the active site,
in essence, forms a  glove around its substrate.

While such an induced fit converts a subunit from the tensed
state to relaxed state, it does not propagate the conformational
change to adjacent subunits. Instead, substrate-binding at
one subunit  only slightly  alters the structure of other
subunits so that their binding sites are more receptive to
substrate.
To summarize:

  • subunits need not exist in the same conformation
  • molecules of substrate bind via induced-fit protocol
  • conformational changes are not propagated to all
    subunits

The discovery of morpheeins has revealed a previously
unforeseen mechanism to target universally essential
enzymes for species-specific drug design and discovery.
A morpheein-based inhibitor would function by  binding
to and stabilizing  the inactive morpheein form of the
enzyme, thereby shifting the equilibrium to favor that form (3).

  1. K. Jaffe, S.H. Lawrence (2008). “Expanding the
    concepts in protein structure-function relationships
    and  enzyme kinetics: Teaching using morpheeins”
    .
    Biochemistry and Molecular Biology  Education36 (4)
    : 274–283. http://dx.doi.org:/10.1002/bmb.20211.
    PMC 2575429PMID 19578473

Important related points are:

Non-regulatory allostery

A non-regulatory allosteric site refers to any non-regulatory
component of an enzyme (or any protein), that is not  itself
an amino acid. For instance, many enzymes require sodium
binding to ensure proper function. However, the sodium
does not necessarily act as a regulatory subunit; the sodium
is always present and there are no known biological processes
to add/remove sodium to regulate enzyme activity. Non-
regulatory allostery could comprise any other  ions besides
sodium (calcium, magnesium, zinc), as well as other chemicals
and possibly vitamins.

Lactate and malate dehydrogenases

LDH is a key enzyme in glycolysis. Anaerobic glycolysis is the
conversion of pyruvate into lactate acid in the absence
of oxygen. This pathway is important to glycolysis in two main
ways. The first is that

  • if pyruvate were to build up glycoysis
  • the generation of ATP would slow.

The second is anaerobic respiration

  • allows for the regeneration of NAD+ from NADH.

NAD+ is required when glyceraldehyde-3-phosphate
dehydrogenase oxidizes glyceraldehyde-3-phosphate in
glycolysis, which generates NADH. Lactate dehydrogenase
is responsible for the anaerobic conversion of NADH to
NAD+. Click here to see the residues which form
inter
actions with pyruvate in the Lactate Dehydrogenase
from Cryptosporidium  parvum (2fm3). (Wikipedia)

Glycolysis ends with the synthesis of pyruvate.  But, to be
self-functioning, it must end with lactate.  Why?  Anaerobic
means “without oxygen”.  This is tantamount to saying
“without mitochondria”.

  1. The mitochondria are especially adept at oxidizing
    NADH to NAD+. NAD+ is needed to keep the glyceraldehyde-
    3-PO4 dehydrogenase reaction functioning.
  2. If glycolysis is to continue when no oxygen is present or in
    short supply (as in a working muscle), an alternative means
    of oxidizing NADH must occur.

Pyruvate has 2 metabolic fates:

  • it can either be converted into lactate or to acetyl-CoA .
    Note that in animals and plants the electrons in  NADH
    are transferred  to pyruvate which reduces the carbonyl
    carbon in the pyruvate molecule to an alcohol. The
    reaction is catalyzed by the enzyme lactate dehydrogenase.
    Lactate (or L-lactate to be more precise)  is thus  a
    “waste product”, since it has no metabolic fate other
    than to be converted back into pyruvate in a reverse of
    the  forward reaction.
  • More importantly, the NAD+ feeds back to the glyceraldehyde-
    3-PO4 dehydrogenase reaction, which  allows glycolysis
    to continue.  Were it not for lactate formation, glycolysis
    as a self-functioning pathway could not exist.

In yeast a slightly different end of glycolysis becomes apparent.
Yeast do not synthesize lactate.  They do, however, oxidize
NADH back to NAD+ anaerobically.  How do they do this?  The
answer is they make ethanol.  In the reaction the pyruvate is
converted into acetaldehyde.  The reaction is catalyzed by a
lyase enzyme, pyruvate decarboxylase, which removes the
carboxyl group as a CO2.  Acetaldehyde is formed because
the electron pair that bonds the –COO group is not removed
by the decarboxylation.  A proton is plucked from the
environment giving the final product, acetaldehyde.
Acetaldehyde is now the substrate that will oxidize NADH to
NAD+ and in the process ethanol is formed.

There is another advantage to the pyruvate-lactate interchange.
The lactate formed by lactate  dehydrogenase  can  be
reconverted. This allows a cell to synthesize glucose from lactate.
Converting lactate to glucose is a major feature of gluconeogenesis,
an anabolic pathway that synthesizes glucose from smaller
precursors such as lactate. This is important because acetyl-CoA
cannot be converted back to pyruvate and hence cannot be a
source of carbons  for glucose biosynthesis.

ADP.  ADP is required in the 3-phosphoglycerate kinase reaction
and in the pyruvate kinase reaction.  It is formed from ATP in the
hexokinase reaction and the phosphofructokinase-I reaction.

NADH, ADP and PO4.   NADH oxidation is important in glycolysis.
NADH is converted into NAD+ in the mitochondria.  That
reaction is promoted by O2 ; NAD+ stays in the mitochondria.
Also in the mitochondria, ATP is formed by condensing ADP
with PO4.  Thus, O2 allows mitochondria to out-compete the
cytosol for ADP,  NADH and PO4, all limiting  substrates or
coenzymes.

In vertebrates, gluconeogenesis takes place mainly in the liver
and, to a lesser extent, in the cortex of kidneys. In many
animals, the process occurs during periods of fasting,
starvationlow-carbohydrate diets, or intense exercise.
The process is highly endergonic until it is coupled to the
hydrolysis of ATP or GTP, effectively making the process
exergonic. For example, the pathway leading from pyruvate
to glucose-6-phosphate requires 4 molecules of  ATP and
2 molecules of GTP to proceed spontaneously. Gluco-
neogenesis is a target of therapy for type II diabetes,
such as metformin, which inhibits glucose formation
and stimulates glucose uptake by cells.

Lactate is formed at the endstage of glycolysis with insufficient
oxygen is transported to the liver where it is converted into
pyruvate by the Cori cycle using the enzyme lactate
dehydrogenase
. In this reaction lactate loses two electrons
(becomes oxidized) and is converted to pyruvate. NAD+
gains two electrons (is reduced) and is converted to NADH.

Both lactate and NAD+ bind to the active site of the enzyme
lactate dehydrogenase and both lactate and NAD+ participate
in the catalysis reaction. In fact, catalysis could not occur
unless the coenzyme NAD+ bound to the active site.

lactat-pyr.LDH

lactat-pyr.LDH

http://academic.brooklyn.cuny.edu/biology/bio4fv/page/couple.gif

What is not shown:

  1. The liver LDH is composed of predominantly M-type subunits.
  2. The forward reaction is regulated in the H-type LDH, but not
    the M-type   enzyme by the formation of a ternary complex
    of LDH-ox. NAD-lactate
  3. The formation and breakup of the ternary complex is
    dependent on the pyruvate in the forward reaction in a
    concentration dependent manner.
  4. The M-type LDH doesn’t have this tight binding of the LDH –
    NAD+ – lactate  (see catalysis below)
  5. As lactate concentration builds in the circulation from heavy
    muscle production (M-type), or from circulatory insufficiency,
    the circulating lactic acid reaches the liver.
  6. The lactic acid is taken up by the liver, and the high
    concentration of lactic acid drives the backward reaction,
    unrestricted.

Pyruvate, the first designated substrate of the gluconeogenic
pathway, can then be used to generate glucose. Transamination
or deamination of amino acids facilitates entering of their
carbon skeleton into the cycle directly  (as pyruvate or
oxaloacetate), or indirectly via the citric acid cycle.  It is
known that odd-chain fatty acids can be  oxidized to yield
propionyl-CoA, a precursor for succinyl-CoA, which can
be converted to  pyruvate and  enter  into gluconeogenesis.

gluconeogenesis

gluconeogenesis

http://upload.wikimedia.org/wikipedia/commons/thumb/6/63/Amino_acid_catabolism.svg/300px-Amino_acid_catabolism.svg.png

Catalysis

Studies have shown that the reaction mechanism of LDH follows an ordered sequence.

mechanism of LDH reaction

mechanism of LDH reaction

In the forward reaction

  1. NADH must bind to the enzyme  Several residues are
    involved in the binding of NADH
    . Once the NADH is
    bound to the enzyme,
  2. pyruvatebinds (substrate oxamate is shown; the CH3
    group is replaced by NH2 to form oxamate). (see the
    direction of the arrow)
  3. binds to the enzyme between the nicotinamide ring
    and several LDH residues.-
  4. transfer of a hydride ion then happens quickly
  5. in either direction giving a mixture of the two ternary
    complexes,
  6. enzyme-NAD+-lactate and enzyme-NADH-pyruvate .
  7. finally L-lactate dissociates from the enzyme followed
    by NAD+[2].

What is not shown is:

  1. The dissocation of NAD+ and lactate from the H-type LDHs
    is  dependent on the pyruvate  in the forward reaction in a
    concentration dependent manner
  2. This results in inhibition of the reaction as it proceeds as
    a result of the abortive ternary complex that forms in about
    500 msec carried out in the Aminco-Morrow stop flow analyzer.
  3. The regulatory effect of the tighter binding of the LDH (H)-
    NAD+-lactate is not seen with the M-type LDH.
  4. The result of this is that the H-type LDH is regulated by the
    formation of oxidized coenzyme  bound with reduced substrate.

Genetics and Mutagenesis of Fish 1973, pp 243-276.
Developmental and Biochemical Genetics of Lactate
Dehydrogenase Isozymes in Fishes
.
G. S. WhittE. T. MillerJ. B. Shaklee
 http://link.springer.com/article/10.1007%2F978-3-642-
65700-9_23/lookinside/000.png

In the teleost there are only three of the isoenzymes.  LDH-1,
3, and 5 (H4, H2M2, M4).

 teleost

Lactic dehydrogenase isozymes in lens and cornea 
Larry BernsteinMichael KerriganHarry Maisel
Experimental Eye Research Oct 1966; 5, (4): Pp 309–314, IN23–IN28
http://dx.doi.org:/10.1016/S0014-4835(66)80041-6

Lactic dehydrogenase isozymes of bovine and rabbit lens and
cornea were analyzed by starch gel electrophoresis.
Although there was a progressive loss of enzyme activity in
the lenses of both species with increasing age, the loss of
isozymes was more clearly evident in the bovine lens. In
the adult bovine lens, 

  • lactic dehydrogenase isozyme Iwas predominant,
  • while in the adult rabbit lens, isozymes 3–5were mainly present.

The mobility of lens isozymes was identical to that of isozymes
in other tissues. Furthermore, the isozymes were not  localized
to any major specific lens crystallin.

Lactate Dehydrogenase Isozyme Patterns of Human
Platelets and Bovine Lens Fibers

Elliot S. Vesell
Science 24 Dec 1965; 150(3704): pp.1735-1737   
http://dx.doi.org:/10.1126/science

Since the platelets and lens fibers, like mature human erythrocytes,
lack a nucleus, the results strengthen the case for a

  • previously developed association between LDH-5 and the
    cell nucleus.

These three cell types are mainly anaerobic, and therefore

  • their isozyme patterns are incompatible with the theory
    that anaerobic `  tissues exhibit predominantly LDH-5
    and aerobic tissues mainly LDH-1.

Lactate dehydrogenase isozymes and their relationship
to lens cell differentiation 

James A. StewartJohn Papaconstantinou
Biochimica et Biophysica Acta (BBA) – General Subjects
26 May 1966; 121,(1): Pp 69–78
http://dx.doi.org:/10.1016/0304-4165(66)90349-7

Changes in the activity of lactate dehydrogenase (LDH) (l-lactate:
NAD+ oxidoreductase EC 1.1.1.27) isozymes are associated with
the growth and differentiation of bovine lens cells. Calf and adult
lens epithelial cells contain all 5 isozymes. The cathodal forms are
most active in the calf-epithelial cells; the anodal forms are most
active in the fiber cells
. This transition from cathodal to anodal
forms of lactate dehydrogenase in the epithelial cells is associated
with cellular aging.

During the differentiation of an epithelial cell to a fiber cell, in calf
and adult lenses there is an enhancement of 

  • the transition from cathodal forms to anodal forms. 

The regulation of lactate dehydrogenase subunit synthesis may
be associated, therefore, with

  • the replicative activity of these cells.

In cells having the greatest replicative activity (calf epithelial
cells) the cathodal isozymes are most active; in cells having a
decreased mitotic activity (adult epithelial cells) the anodal
isozymes are most active. The non-replicative

  • fiber cell of calf and adult shows a transition toward the
    anodal forms.

Although lens fiber cells have a low rate of oxidative metabolism
lactate dehydrogenase-I is the most active isozyme in these
cells. Kinetically,

  • lactate dehydrogenase-I factors other than, or in addition
    to, the regulation of carbohydrate metabolism
  • are involved in regulating the synthesis of lactate dehydrogenase subunits.

Abbreviations   LDH; lactate dehydrogenase

What is not examined to resolve the discrepancy (see the next item):

The Vessell paper was a challenge to the work in Nathan
Kaplan’s lab.  However, there is sufficient complexity revealed
in these works that there is no conceptual foundation.

  1. The analogy is to the loss of cell nuclei in crystallin lens
    fiber formation with the LDH-H type subunits (aerobic?)
  2. The findings are reproduced in several laboratories.
  3. In the lens, glucose is catabolized primarily to lactic
    acid, and is not appreciably combusted to CO2
    (J Kinoshita. Glucose metabolism of Lens)
  4. However, synthetic processes, including nuclear DNA and
    cell replication requires TPNH. This is produced by means
    of the Pentose Shunt.
  5. The most favorable conditions for the lens are achieved
    by incubating in a medium containing glucose in the
    presence of oxygen. Under these conditions of
    incubation (Kinoshita)
  • the lens remains completely transparent,
  • it maintains normal levels of high energy phosphate
    bonds and cations, and
  • it shows a high rate of arginine incorporationinto protein.

incubation in the absence of glucose, but in the presence of oxygen

  • a haze is found in the lens,
  • a drop in high energy phosphate level is observed, and
  • Changes in cation levels are apparent.
  • A 50 percent decrease in the incorporation of arginine
    into lens protein is also observed.

the most unfavorable condition for the lens is an anaerobic
incubation in a medium without glucose

Pirie2 observed that a-glycerophosphate is one of the end products
of lens metabolism. Its oxidation with DPN as the cofactor could
channel its electrons directly into the ETC to produce energy without
involving the Krebs cycle. a-Glycerophosphate is formed from intermediates of the glycolytic scheme by reduction of dihydroxy-
acetone phosphate, one of the triose phosphates produced in
glycolysis.

the dehydrogenase of the mitochondria catalyzes the transfer
of elections to form DPNH by the following reactions:

a-glycerophosphate + DPN+ ± dihydroxyacetone ……..

phosphate + DPNH.

The DPNH is channeled into the oxidative phosphorylation
mechanism to form ATP. The dihydroxyacetone phosphate
then diffuses out into the soluble cytoplasm, interacts with
the glycolytic intermediates by the reversal of the above reaction,

  • and the cyclic mechanism is begunover again.

That this type of electron transport system functions in the
lens was proposed by Pirie.
http://www.iovs.org/content/4/4/619.full.pdf

Lactate dehydrogenase activity and its isoenzymes in
concentric layers of adult bovine and calf lenses.
  
Sempol DOsinaga EZigman SKorc IKorc BSans ARadi R, et al.
Curr Eye Res. 1987 Apr;6(4):555-60.

The activity of lactate dehydrogenase (LDH) and its isoenzyme
pattern were studied in four concentric layers of adult
bovine and calf lenses. In both groups the specific activity of
the total LDH diminished progressively toward the internal
nuclear layer; the decrease was greater in the adult lenses.
The enzyme activities in the cortical layers of the calf lens
were lower than in the adult lens, but in the inner nuclear layers,
the opposite was found. All of the 5 LDH isoenzymes were found
in each layer. In both groups of animals the LDH1 isoenzyme
prevailed, followed by LDH2. No differences were found in the
percentage of each isoenzyme in the different lens layers.
The differences in the activitie(s) of LDH found may be due

  • to post-translational or post-synthetic modifications which
    may occur during the aging process.

Structural basis for altered activity of M- and H-isozyme
forms of human lactate dehydrogenase.

Read JA1, Winter VJEszes CMSessions RBBrady RL.
Author information  Proteins. 2001 May 1;43(2):175-85

Lactate dehydrogenase (LDH) interconverts pyruvate and
lactate with concomitant interconversion of NADH and NAD(+).
Although crystal structures of a variety of LDH have previously
been described, a notable absence has been any of the
three known human forms of this glycolytic enzyme. We have
now determined the crystal structures of two isoforms of
human LDH-the M form, predominantly found in muscle; and
the H form, found mainly in cardiac muscle. Both structures
have been crystallized as ternary complexes in the presence
of the NADH cofactor and oxamate, a substrate-like inhibitor.

Although each of these isoforms has different kinetic properties,
the domain structure, subunit association, and active-site regions
are indistinguishable between the two structures.

The pK(a) that governs the K(M) for pyruvate for the two isozymes
is found to differ by about 0.94 pH units, consistent with variation in
pK(a) of the active-site histidine.

The close similarity of these crystal structures suggests the distinctive
activity of these enzyme isoforms is likely to result

  • directly from variation of charged surface residues peripheral to the active site,
  • a hypothesis supported by electrostatic calculations based on each structure.

Proteins 2001;43:175-185.

Mechanistic aspects of biological redox reactions involving NADH.
Part 4. Possible mechanisms and corresponding intermediates for
the catalytic reaction in L-lactate dehydrogenase

J Molec Structure: THEOCHEM,25 Feb 1993; 279, Pp 99-125
Kathryn E. Norris, Jill E. Gready

The catalytic step in the conversion of pyruvate to L-lactate in the
enzyme L-lactate dehydrogenase involves the transfer of both a
proton and a hydride ion (A.R. Clarke, T. Atkinson and J.J. Holbrook,
TIBS, 14 (1989) 101.) However, it is not known whether the
reaction is concerted or, if a multistep process, the order in
which the transfers of the proton and the hydride ions take
place. Four possible non-concerted mechanisms can be
proposed, which differ in the order of the transfers of the
proton and hydride ion and the protonation state of the substrate
carboxylate group during the transfers. The energies and
optimized geometries of the corresponding intermediates,
protonated pyruvate, protonated pyruvic acid, deprotonated
L-lactate and deprotonated L-lactic acid, are computed using
the semiempirical AM 1 and ab initio SCF/3–21 G – methods.
These calculations are complementary to the study of
the substrates for the enzyme discussed in a previous paper
(K.E. Norris and J.E. Gready, J. Mol. Struct. (Theochem),
258 (1992) 109.) The structures and energetics of protonated
pyruvate and deprotonated L-lactate provide some
important insights into the requirements for enzymic reaction
and the characteristics of the transition state.

Pyruvate production by Enterococcus casseliflavus A-12
from gluconate in an alkaline medium

J Fermentation and Bioengineering, 1992; 73(4):287-291
H Yanase, N Mori, M Masuda, K Kita, M Shimao, N Kato

A newly isolated lactic acid bacterium, Enterococcus casseliflavus
A-12, produced pyruvic acid (16 g/l) during aerobic culture in
an alkaline medium containing sodium gluconate (50 g/l) as
the carbon source. The production was dependent on the pH
of the culture, the optimum initial pH being 10.0. With static
culture, the organism produced lactic acid (2.7 g/l) from both
gluconate and glucose. Pyruvate did not accumulate in growing
cultures on glucose, but resting cells obtained from a culture
on gluconate produced pyruvate from glucose as well as
gluconate. The enzyme profiles of the organism, which
grew on gluconate and glucose, suggested that gluconate
was metabolized via the Entner-Doudoroff and Embdem-
Meyerhof-Parnas pathways in aerobic culture, and that glucose
was oxidized mainly via the latter pathway under both aerobic
and anaerobic conditions. Gluconokinase, a key enzyme in
the aerobic metabolism of gluconate, was partially purified
from this strain and characterized.

A specific, highly active malate dehydrogenase by redesign
of a lactate dehydrogenase framework

HM WilksKW HartR FeeneyCR DunnH MuirheadWN Chiaet al.

Department of Biochemistry, University of Bristol, United Kingdom.
Science 16 Dec1988: 242(4885),  pp. 1541-1544
http://dx.doi.org:/10.1126/science.3201242

 Three variations to the structure of the nicotinamide adenine
dinucleotide (NAD)-dependent L-lactate dehydrogenase
from Bacillus stearothermophilus were made to try to
change the substrate specificity from lactate to malate:
Asp197—-Asn, Thr246—-Gly, and Gln102—-Arg).

Each modification shifts the specificity from lactate to malate, although

  • only the last (Gln102—-Arg) provides an effective and
    highly specific catalyst for the new substrate.

This synthetic enzyme has a ratio of catalytic rate (kcat) to
Michaelis constant (Km) for oxaloacetate of 4.2 x 10(6)M-1 s-1,

  • equal to that of native lactate dehydrogenase for its natural
    substrate, pyruvate, and a maximum velocity (250 s-1),
    which is double that reported for a natural malate from B.
    stearothermophilus.

Malate dehydrogenase: distribution, function and properties.

Musrati RA1, Kollárová MMernik NMikulásová D.
Author information
Gen Physiol Biophys. 1998 Sep;17; (3):193-210.

Malate dehydrogenase (MDH) (EC 1.1.1.37) catalyzes the
conversion of oxaloacetate and malate. This reaction is
important in cellular metabolism, and it is coupled with
easily detectable cofactor oxidation/reduction. It is a
rather ubiquitous enzyme, for which several isoforms
have been identified, differing in their subcellular
localization and their specificity for the cofactor NAD
or NADP. The nucleotide binding characteristics can
be altered by a single amino acid change. Multiple
amino acid sequence alignments of MDH show there is a

  • low degree of primary structural similarity, apart from
    several positions crucial for catalysis, cofactor binding
    and the subunit interface.
  • Despite the low amino acids sequence identity their
    3-dimensional structures are very similar.
  • MDH is a group of multimeric enzymes consisting of
    identical subunits usually organized as either dimer
    or tetramers with subunit molecular weights of 30-35 kDa.

Malate dehydrogenase, mitochondrial (MDH2)

UniProt Number: P40926
Alternate Names: Malate DH

Structure and Function:
Malate dehydrogenase (MDH2) is an enzyme in the citric
acid cycle that catalyzes the conversion of malate into
oxaloacetate (using NAD+) and vice versa (this is a
reversible reaction). Malate dehydrogenase is also
involved in gluconeogenesis, the synthesis of glucose
from smaller molecules.Pyruvate in the mitochondria is acted upon by pyruvate
carboxylase  to form oxaloacetate, a citric acid cycle
intermediate.In order to get the oxaloacetate out of the mitochondria,
malate dehydrogenase reduces it to malate, and it then
traverses the inner mitochondrial membrane.Once in the cytosol, the malate is oxidized back to
oxaloacetate by cytosolic malate dehydrogenase.

Finally, phosphoenol-pyruvate carboxy kinase (PEPCK)
converts oxaloacetate to phosphoenol pyruvate.

Malate Dehydrogenase (MDH)(PDB entry 2x0i) is most known
for its role in the metabolic pathway of the tricarboxylic acid cycle,
critical to cellular respiration; The enzyme has other metabolic roles in –

  •  glyoxylate bypass,
  • amino acid synthesis,
  • glucogenesis, and
  • oxidation/reduction balance .

An oxidoreductase, MDH has been extensively studied due to its
isozymes The enzyme exists in two places inside a cell:

  • the mitochondria and cytoplasm.
  • In the mitochondria, the enzyme catalyzes the reaction of
    malate to oxaloacetate;
  • in the cytoplasm, the enzyme catalyzes oxaloacetate to
    malate to allow transport.

The enzyme malate dehydrogenase is composed of either
a dimer or tetramer depending on the location of the enzyme
and the organism it is located in. During catalysis, the enzyme
subunits are

  • non-cooperative between active sites.

The mitochondrial MDH is complexly,

  • allosterically controlled by citrate, but no other known
    metabolic regulation mechanisms have been discovered.
  • the exact mechanism of regulation has yet to be discovered.

Kinetically, the pH of optimization is 7.6 for oxaloacetate
conversion and 9.6 for malate conversion. The reported
K(m) value for malate conversion is 215 uM and the V(max)
value is 87.8 uM/min.

Comment:

The mMDH and the cMDH both form ternary complex
of MDH-NAD+-OAA formed during the forward reaction,
like the LDH H-type isozyme LDH-NAD+-PYR (mot the M-type).
However, the binding of the Enz-coenzyme-substrate is not
as strong as for the H-type LDH.  .The regulatory role has
not been established.

References

  1. Minarik P, Tomaskova N, Kollarova M, Antalik M. Malate
    dehydrogenases–structure and function. Gen Physiol Biophys.
    2002 Sep;21(3):257-65. PMID:12537350
  2. Musrati RA, Kollarova M, Mernik N, Mikulasova D.
    Malate dehydrogenase: distribution, function and properties.
    Gen Physiol Biophys. 1998 Sep;17(3):193-210. PMID:9834842
  3. Boernke WE, Millard CS, Stevens PW, Kakar SN, Stevens FJ,
    Donnelly MI. Stringency of substrate specificity of
    Escherichia coli malate dehydrogenase. Arch Biochem
    Biophys. 1995 Sep 10;322(1):43-52. PMID:7574693
    doi:http://dx.doi.org/10.1006/abbi.1995.1434
  4. Goward CR, Nicholls DJ. Malate dehydrogenase: a model
    for structure, evolution, and catalysis. Protein Sci. 1994
    Oct;3(10):1883-8. PMID:7849603
    doi:http://dx.doi.org/10.1002/pro.5560031027

Kinetic determination of malate dehydrogenase isozymes.

L H Bernstein, M B Grisham

Journal of Molecular and Cellular Cardiology (Impact Factor: 5.15).
11/1978; 10(10):931-44. http://dx.doi.org/10.1016/0022-2828(78)90339-5

Source: PubMed

ABSTRACT These studies determine the levels of malate
dehydrogenase isoenzymes in cardiac muscle by a steady
state kinetic method which depends on the differential inhibition
of these isoenzyme forms by high concentrations of oxaloacetate.
This inhibition is similar to that exhibited by lactate dehydrogenase
in the presence of high concentrations of pyruvate. The results
obtained by this method are comparable in resolution to those
obtained by CM-Sephadex fractionation and by differential
centrifugation for the analyses of mitochondrial malate
dehydrogenase and cytoplasmic malate dehydrogenase in
tissues. The use of standard curves of percent inhibition of
malate dehydrogenase activity plotted against the ratio of
mitochondrial MDH activity to the total of mMDH and cMDH
activities [ malate dehydrogenase ratio] (percent m-type) is
introduced for studies of comparative mitochondrial
function in heart muscle of different species or in different
tissues of the same species.

Calmodulin and Protein Kinase C Increase Ca21-stimulated
Secretion by Modulating Membrane-attached Exocytic Machinery

YA Chen, V Duvvuri, H Schulmani, and RH Scheller
Hughes Medical Institute, Department of Molecular and Cellular
Physiology, and the iDepartment of Neurobiology, Stanford
University School of Medicine, Stanford, California 94305-5135
JBC Sep 10, 1999; 274( 37): 26469–26476

Using a reconstituted [3H]norepinephrine
release assay in permeabilized PC12 cells, we
found that essential proteins that support the triggering
stage of Ca21-stimulated exocytosis are enriched in an
EGTA extract of brain membranes. Fractionation of this
extract allowed purification of two factors that stimulate
secretion in the absence of any other cytosolic proteins.
These are calmodulin and protein kinase Ca
(PKCa). Their effects on secretion were confirmed using
commercial and recombinant proteins. Calmodulin enhances
secretion in the absence of ATP, whereas PKC
requires ATP to increase secretion, suggesting that
phosphorylation is involved in PKC- but not calmodulin
mediated stimulation. Both proteins modulate release
events that occur in the triggering stage of exocytosis.

Endothelial nitric oxide synthase (eNOS) variants in
cardiovascular disease: pharmacogenomic implications  

Indian J Med Res  May 2011;  133:  464-466

Commentary

Manjula Bhanoori

Department of Biochemistry, University College of Science,
Osmania University, Hyderabad 500 007, India

 

The maintenance of regular vascular tone substantially
depends on the bioavailability of endothelium-derived
nitric oxide (NO) synthesized by eNOS. The essential
role of NO, as the elusive endothelium-derived relaxing
factor (EDRF), was the topic of research that won the
1998 Nobel Prize in Physiology or Medicine. The eNOS
gene, as a candidate gene in the investigations on
hypertension genetics, has attracted the attention of
several researchers because of the established role
of NO in vascular homeostasis. The eNOS variants
located in the 7q35-q36 region have been investigated
for their association with CVD, particularly hypertension.
Three variants, viz., (i) G894T substitution in exon 7
resulting in a Glu to Asp substitution at codon 298 (rs1799983),
(ii) an insertion-deletion in intron 4 (4a/b) consisting of two
alleles (the a*-deletion which has four tandem 27-bp repeats
and the b*-insertion having five repeats), and (iii) a T786C
substitution in the promoter region (rs2070744), have been
extensively studied20-22. Individual SNPs often cause only
a modest change in the resulting gene expression or function.
It is, therefore, the concurrent presence of a number of SNPs
or haplotypes within a defined region of the chromosome that
determines susceptibility to disease development and progression,
particularly in case of polygenic diseases.

Shankarishan et al24 analysed for the first time the prevalence
of eNOS exon 7 Glu298Asp polymorphism in tea garden community
of North Eastern India, who are a high risk group for CVD. This study
also included indigenous Assamese population and found no
significant difference between the distribution patterns of eNOS
exon 7 Glu298Asp variants between the communities. They have
rightly mentioned that for developing public health policies and
programmes it is necessary to know the prevalence and distribution
of the candidate genes in the population, as well as trends in
different population groups. They have also observed that the
eNOS exon 7 homozygous GG wild genotype (75.8%) was
predominant in the study population followed by heterozygous
GT genotype (21.5%) and homozygous TT genotype (2.7%).
The frequency distribution of the homozygous GG, heterozygous
GT and homozygous mutant TT genotypes were comparable to
that of the north Indian and south Indian population.

Polymorphisms in the endothelial nitric oxide synthase gene have
been associated inconsistently with cardiovascular diseases.
Varying distribution of eNOS variants among ethnic groups may
explain inter-ethnic differences in nitric oxide mediated vasodilation
and response to drugs28. Different population studies showed
association of eNOS polymorphisms with variations in NO
formation and response to drugs. Cardiovascular drugs including
statins increase eNOS expression and upregulate NO formation
and this effect may be responsible for protective, pleiotropic
effects produced by statins31. With respect to hypertension,
studies have reported interactions between diuretics and
polymorphisms in eNOS gene. Particularly, the Glu298Asp
polymorphism made a statistically significant contribution to
predicting blood pressure response to diuretics.

Neuronal Nitric Oxide Synthase and Its Interaction
With Soluble Guanylate Cyclase Is a Key Factor for
the Vascular Dysfunction of Experimental Sepsis

GM. Nardi, K Scheschowitsch, D Ammar, SK de
Oliveira, TB. Arruda; J Assreuy

Vascular dysfunction plays a central role in sepsis, and it is
characterized by hypotension and hyporesponsiveness to
vasoconstrictors. Nitric oxide is regarded as a central element
of sepsis vascular dysfunction. The high amounts of nitric
oxide produced during sepsis are mainly derived from the
inducible isoform of nitric oxide synthase 2.
We have previously shown that nitric oxide synthase 2 levels
decrease in later stages of sepsis, whereas levels and activity
of soluble guanylate cyclase increase. Therefore, we studied
the putative role of other relevant nitric oxide sources, namely,

  • the neuronal (nitric oxide synthase 1) isoform, in sepsis
  • and its relationship with soluble guanylate cyclase.

We also studied the consequences of

  • nitric oxide synthase 1 blockade in the hyporesponsiveness
    to vasoconstrictors.

1) Both nitric oxide synthase 1 and soluble guanylate cyclase
are expressed in higher levels in vascular tissues during sepsis;

2) both proteins physically interact and nitric oxide synthase 1
blockade inhibits cyclic guanosine monophosphate production;

3) pharmacological blockade of nitric oxide synthase 1 using
7-nitroindazole or S-methyl-l-thiocitrulline reverts the hypo
responsiveness to phenylephrine and increases the vaso
constrictor effect of norepinephrine and phenylephrine.

Sepsis induces increased expression and physical association
of nitric oxide synthase 1/soluble guanylate cyclase and a higher
production of cyclic guanosine monophosphate that together
may help explain sepsis-induced vascular dysfunction.

In addition, selective inhibition of nitric oxide synthase 1
restores the responsiveness to vasoconstrictors.

Therefore, inhibition of nitric oxide synthase 1 (and possibly
soluble guanylate cyclase) may represent a valuable
alternative to restore the effectiveness of vasopressor
agents during late sepsis.  (Crit Care Med 2014; XX:00–00)

Nitric Oxide Synthase Inhibitors That Interact with Both Heme
Propionate and Tetrahydrobiopterin Show High Isoform Selectivity

S Kang, W Tang, H Li, G Chreifi, P Martásek, LJ. Roman,
TL. Poulos, and RB. Silverman

†Department of Chemistry, Department of Molecular Biosciences,
Chemistry of Life Processes Institute, Center for Molecular Innovation
and Drug Discovery, Northwestern University, Evanston, Illinois
‡Departments of Molecular Biology and Biochemistry, Pharmaceutical
Sciences, and Chemistry, University of California, Irvine, California,
Department of Biochemistry, University of Texas Health Science Center,
San Antonio, Texas

Overproduction of NO by nNOS is implicated in the pathogenesis of
diverse neuronal disorders. Since NO signaling is involved in
diverse physiological functions, selective inhibition of nNOS
over other isoforms is essential to minimize side effects. A series of
α-amino functionalized aminopyridine derivatives (3−8) were
designed to probe the structure−activity relationship between ligand,
heme propionate, and H4B. Compound 8R was identified as the
most potent and selective molecule of this study, exhibiting a Ki of
24 nM for nNOS, with 273-fold and 2822-fold selectivity against
iNOS and eNOS, respectively.Although crystal structures of 8R
complexed with nNOS and eNOS revealed a similar binding mode,
the selectivity stems from the distinct electrostatic environments in
two isoforms that result in much lower inhibitor binding free energy
in nNOS than in eNOS. These findings provide a basis for further
development of simple, but even more selective and potent, nNOS
inhibitors

  • Aurelian Udristioiu

    Aurelian

    Aurelian Udristioiu

    Lab Director at Emergency County Hospital Targu Jiu

    In cells, the immediate energy sources involve glucose oxidation. In anaerobic metabolism, the donor of the phosphate group is adenosine triphosphate (ATP), and the reaction is catalyzed via the hexokinase or glucokinase: Glucose +ATP-Mg²+ = Glucose-6-phosphate (ΔGo = – 3.4 kcal/mol with hexokinase as the co-enzyme for the reaction.).
    In the following step, the conversion of G-6-phosphate into F-1-6-bisphosphate is mediated by the enzyme phosphofructokinase with the co-factor ATP-Mg²+. This reaction has a large negative free energy difference and is irreversible under normal cellular conditions. In the second step of glycolysis, phosphoenolpyruvic acid in the presence of Mg²+ and K+ is transformed into pyruvic acid. In cancer cells or in the absence of oxygen, the transformation of pyruvic acid into lactic acid alters the process of glycolysis.
    The energetic sum of anaerobic glycolysis is ΔGo = -34.64 kcal/mol. However a glucose molecule contains 686kcal/mol and, the energy difference (654.51 kcal) allows the potential for un-controlled reactions during carcinogenesis. The transfer of electrons from NADPH in each place of the conserved unit of energy transmits conformational exchanges in the mitochondrial ATPase. The reaction ADP³+ P²¯ + H²–à ATP + H2O is reversible. The terminal oxygen from ADP binds the P2¯ by forming an intermediate pentacovalent complex, resulting in the formation of ATP and H2O. This reaction requires Mg²+ and an ATP-synthetase, which is known as the H+-ATPase or the Fo-F1-ATPase complex. Intracellular calcium induces mitochondrial swelling and aging. [12].
    The known marker of monitoring of treatment in cancer diseases, lactate dehydrogenase (LDH) is an enzyme that is localized to the cytosol of human cells and catalyzes the reversible reduction of pyruvate to lactate via using hydrogenated nicotinamide deaminase (NADH) as co-enzyme.
    The causes of high LDH and high Mg levels in the serum include neoplastic states that promote the high production of intracellular LDH and the increased use of Mg²+ during molecular synthesis in processes pf carcinogenesis (Pyruvate acid>> LDH/NADH >>Lactate acid + NAD), [13].
    LDH is released from tissues in patients with physiological or pathological conditions and is present in the serum as a tetramer that is composed of the two monomers LDH-A and LDH-B, which can be combined into 5 isoenzymes: LDH-1 (B4), LDH-2 (B3-A1), LDH-3 (B2-A2), LDH-4 (B1-A3) and LDH-5 (A4). The LDH-A gene is located on chromosome 11, whereas the LDH-B gene is located on chromosome 12. The monomers differ based on their sensitivity to allosteric modulators. They facilitate adaptive metabolism in various tissues. The LDH-4 isoform predominates in the myocardium, is inhibited by pyruvate and is guided by the anaerobic conversion to lactate.
    Total LDH, which is derived from hemolytic processes, is used as a marker for monitoring the response to chemotherapy in patients with advanced neoplasm with or without metastasis. LDH levels in patients with malignant disease are increased as the result of high levels of the isoenzyme LDH-3 in patients with hematological malignant diseases and of the high level of the isoenzymes LDH-4 and LDH-5, which are increased in patients with other malignant diseases of tissues such as the liver, muscle, lungs, and conjunctive tissues. High concentrations of serum LDH damage the cell membrane [11, 31].

    Relation between LDH and Mg as Factors of Interest in the Monitoring and Prognoses of Cancer

    Aurelian Udristioiu, Emergency County Hospital Targu Jiu Romania, Clinical Laboratory Medical Analyses, E-mail: aurelianu2007@yahoo.com

    Larry Bernstein likes this

  • Larry Bernstein

    Larry Bernstein

    CEO/CSO at Triplex Consulting

    The inhibition be pyruvate is related by a ternary complex formed by NAD+ formed in the catalytic forward reaction Pyruvate + NADH –> Lactate + NAD(+). The reaction can be followed in an Aminco-Morrow stop-flow analyzer and occurs in ~ 500 msec. The reaction does not occur with the muscle type LDH, and it is regulatory in function. I did not know about the role of intracellular Mg(2+) in the catalysis, as my own work was in Nate Kaplan’s lab in 1970-73.

    This difference in the behavior of the isoenzyme types was considered to be important then in elucidating functional roles, but it was challenged by Vessell earlier. The isoenzymes were first described by Clement Markert at Yale. I think, but don’t know, that the Mg++ would have a role in driving the forward reaction, but I can’t conceptualize how it might have any role in the difference between muscle and heart.

    I didn’t quite know why oncologists used it specifically. Cancer cells exhibit the reliance on the anaerobic (muscle) type enzyme, which is also typical of liver, but with respect to the adenylate kinases – the liver AK and muscle AK (myokinase) are different. That difference was discovered by Masahiro Chiga, and differences in the reaction with sulfhydryl reagents were identified by Percy Russell.

    Oddly enough, Vessell had a point. The RBC has the heart type predominance, not the M-type. He thought that it was related to the loss of nuclei from the reticulocyte. I did not buy that, and I had worked on the lens of the eye at the time.

  • Aurelian Udristioiu

    Aurelian

    Aurelian Udristioiu

    Lab Director at Emergency County Hospital Targu Jiu

    Very interesting scientific comments. Thanks. !

  • Aurelian Udristioiu

    Aurelian

    Aurelian Udristioiu

    Lab Director at Emergency County Hospital Targu Jiu

    The IDH1 and IDH2 genes are mutated in > 75% of different malignant diseases. Two distinct alterations are caused by tumor-derived mutations in IDH1 or IDH2,
    IDH1 and IDH2 mutations have been observed in myeloid malignancies, including de novo and secondary AML (15%–30%), and in pre-leukemic clone malignancies, including myelodysplastic syndrome and myeloproliferative neoplasm (85% of the chronic phase and 20% of transformed cases in acute leukemia.
    Aurelian Udristioiu, M.D
    City Targu Jiu, Romania
    AACC, NACB, Member, USA.

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Metformin, Thyroid-Pituitary Axis, Diabetes Mellitus, and Metabolism

Metformin, Thyroid-Pituitary Axis, Diabetes Mellitus, and Metabolism

Larry H, Bernstein, MD, FCAP, Author and Curator
and Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/9/27/2014/Metformin,_thyroid-pituitary_ axis,_diabetes_mellitus,_and_metabolism

The following article is a review of the central relationship between the action of
metformin as a diabetic medication and its relationship to AMPK, the important and
essential regulator of glucose and lipid metabolism under normal activity, stress, with
its effects on skeletal muscle, the liver, the action of T3 and more.

We start with a case study and a publication in the J Can Med Assoc.  Then we shall look
into key literature on these metabolic relationships.

Part I.  Metformin , Diabetes Mellitus, and Thyroid Function

Hypothyroidism, Insulin resistance and Metformin
May 30, 2012   By Janie Bowthorpe
The following was written by a UK hypothyroid patient’s mother –
Sarah Wilson.

My daughter’s epilepsy is triggered by unstable blood sugars. And since taking
Metformin to control her blood sugar, she has significantly reduced the number of
seizures. I have been doing research and read numerous academic medical journals,
which got me thinking about natural thyroid hormone and Hypothyroidism. My hunch
was that when patients develop hypothyroid symptoms, they are actually becoming
insulin resistant (IR). There are many symptoms in common between women with
polycystic ovaries and hypothyroidism–the hair loss, the weight gain, etc.
(http://insulinhub.hubpages.com/hub/PCOS-and-Hypothyroidism).

A hypothyroid person’s body behaves as if it’s going into starvation mode and so, to
preserve resources and prolong life, the metabolism changes. If hypothyroid is prolonged
or pronounced, then perhaps, chemical preservation mode becomes permanent even
with the reintroduction of thyroid hormones. To get back to normal, they need
a “jump-start” reinitiate a higher rate of metabolism. The kick start is initiated through
AMPK, which is known as the “master metabolic regulating enzyme.”
(http://en.wikipedia.org/wiki/AMP-activated protein kinase).

Guess what? This is exactly what happens to Diabetes patients when Metformin is
introduced. http://en.wikipedia.org/wiki/Metformin
Suggested articles: http://www.springerlink.com/content/r81606gl3r603167/  and
http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2265.2011.04029.x/pdf

Note the following comments/partial statements:
“Hypothyroidism is characterized by decreased insulin responsiveness”;
“the pivotal regulatory role of T3 in major metabolic pathways”.

The community knows that T3/NTH (natural thyroid hormone [Armour]) makes
hypothyroid patients feel better – but the medical establishment is averse to T3/NTH
(treating subclinical hypoT (T3/T4 euthyroid) with natural dessicated thyroid (NDT).
The medical establishment might find an alternative view about impaired metabolism
more if shown real proof that the old NDT **was/is** having the right result –i.e., the
T3 is jump-starting the metabolism by re-activating
 AMPK.

If NDT also can be used for hypothyroidism without the surmised “dangers” of NTH,
then they should consider it. [The reality in the choice is actually recombinant TH
(Synthroid)]. Metformin is cheap, stable and has very few serious side effects. I use the
car engine metaphor, and refer to glucose as our petrol, AMPK as the spark plug and
both T3 and Metformin as the ignition switches. Sometimes if you have flat batteries in
the car, it doesn’t matter how much you turn the ignition switch or pump the petrol
pedal, all it does is flatten the battery and flood the engine.

Dr. Skinner in the UK has been treating “pre-hypothyroidism” the way that some
doctors treat “pre-diabetes”. Those hypothyroid patients who get treated early
might not have had their AMPK pathways altered and the T4-T3 conversion still works.
There seems to be no reason why thyroid hormone replacement therapy shouldn’t
logically be given to ward off a greater problem down the line.

It’s my belief that there is clear and abundant academic evidence that the AMPK/
Metformin research should branch out to also look at thyroid disease.

Point – direct T3 is kicking the closed -down metabolic process back into life,
just like Metformin does for insulin resistance.
http://www.hotthyroidology.com/editorial_79.html
There is serotonin resistance! http://www.ncbi.nlm.nih.gov/pubmed/17250776

Metformin Linked to Risk of Low Levels of Thyroid Hormone

CMAJ (Canadian Medical Association Journal) 09/22/2014

Metformin, the drug commonly for treating type 2 diabetes,

  • is linked to an increased risk of low thyroid-stimulating hormone
    (TSH) levels
  • in patients with underactive thyroids (hypothyroidism),

according to a study in CMAJ (Canadian Medical Association Journal).

Metformin is used to lower blood glucose levels

  • by reducing glucose production in the liver.

previous studies have raised concerns that

  • metformin may lower thyroid-stimulating hormone levels.

Study characteristics:

  1. Retrospective  long-term
  2. 74 300 patient who received metformin and sulfonylurea
  3. 25-year study period.
  4. 5689 had treated hypothyroidism
  5. 59 937 had normal thyroid function.

Metformin and low levels of thyroid-stimulating hormone in
patients with type 2 diabetes mellitus

Jean-Pascal Fournier,  Hui Yin, Oriana Hoi Yun Yu, Laurent Azoulay  +
Centre for Clinical Epidemiology (Fournier, Yin, Yu, Azoulay), Lady Davis Institute,
Jewish General Hospital; Department of Epidemiology, Biostatistics and Occupational
Health (Fournier), McGill University; Division of Endocrinology (Yu), Jewish General
Hospital; Department of Oncology (Azoulay), McGill University, Montréal, Que., Cananda

CMAJ Sep 22, 2014,   http://dx.doi.org:/10.1503/cmaj.140688

Background:

  • metformin may lower thyroid-stimulating hormone (TSH) levels.

Objective:

  • determine whether the use of metformin monotherapy, when compared with
    sulfonylurea monotherapy,
  • is associated with an increased risk of low TSH levels(< 0.4 mIU/L)
  • in patients with type 2 diabetes mellitus.

Methods:

  • Used the Clinical Practice Research Datalink,
  • identified patients who began receiving metformin or sulfonylurea monotherapy
    between Jan. 1, 1988, and Dec. 31, 2012.
  • 2 subcohorts of patients with treated hypothyroidism or euthyroidism,

followed them until Mar. 31, 2013.

  • Used Cox proportional hazards models to evaluate the association of low TSH
    levels with metformin monotherapy, compared with sulfonylurea monotherapy,
    in each subcohort.

Results:

  • 5689 patients with treated hypothyroidism and 59 937 euthyroid patients were
    included in the subcohorts.

For patients with treated hypothyroidism:

  1. 495 events of low TSH levels were observed (incidence rate 0.1197/person-years).
  2. 322 events of low TSH levels were observed (incidence rate 0.0045/person-years)
    in the euthyroid group.
  • metformin monotherapy was associated with a 55% increased risk of low TSH
    levels 
    in patients with treated hypothyroidism (incidence rate 0.0795/person-years
    vs.0.1252/ person-years, adjusted hazard ratio [HR] 1.55, 95% confidence
    interval [CI] 1.09– 1.20), compared with sulfonylurea monotherapy,
  • the highest risk in the 90–180 days after initiation (adjusted HR 2.30, 95% CI
    1.00–5.29).
  • No association was observed in euthyroid patients (adjusted HR 0.97, 95% CI 0.69–1.36).

Interpretation: The clinical consequences of this needs further investigation.

 

Crude and adjusted hazard ratios for suppressed thyroid-stimulating hormone
levels (< 0.1 mIU/L) associated with the use metformin monotherapy, compared
with sulfonylurea monotherapy, in patients with treated hypothyroidism or
euthyroidism and type 2 diabetes
Variable No. events
suppressed
TSH levels
Person-years of
exposure
Incidence rate,
per 1000 person-years (95% CI)
Crude
HR
Adjusted HR*(95% CI)
Patients with treated hypothyroidism, = 5689
Sulfonylure,
= 762
18 503 35.8
(21.2–56.6)
1.00 1.00
(reference)
Metformin,
= 4927
130 3 633 35.8
(29.9–42.5)
1.05 0.99
(0.57–1.72)
Euthyroid patients, = 59 937
Sulfonylurea,
= 7980
12 8 576 1.4
(0.7–2.4)
1.00 1.00
(reference)
Metformin,
= 51 957
75 63 047 1.2
(0.9–1.5)
0.85 1.03
(0.52–2.03)

 

Part II. Metabolic Underpinning 
(Source: Wikipedia, AMPK and thyroid)

5′ AMP-activated protein kinase or AMPK or 5′ adenosine monophosphate-activated protein kinase
is an enzyme that plays a role in cellular energy homeostasis.
It consists of three proteins (subunits) that

  1. together make a functional enzyme, conserved from yeast to humans.
  2. It is expressed in a number of tissues, including the liver, brain, and skeletal
    muscle.
  3. The net effect of AMPK activation is stimulation of
    1. hepatic fatty acid oxidation and ketogenesis,
    2. inhibition of cholesterol synthesis,
    3. lipogenesis, and triglyceride synthesis,
    4. inhibition of adipocyte lipolysis and lipogenesis,
    5. stimulation of skeletal muscle fatty acid oxidation and muscle
      glucose uptake, and
    6. modulation of insulin secretion by pancreatic beta-cells.

The heterotrimeric protein AMPK is formed by α, β, and γ subunits. Each of these three
subunits takes on a specific role in both the stability and activity of AMPK.

  • the γ subunit includes four particular Cystathionine beta synthase (CBS) domains
    giving AMPK its ability to sensitively detect shifts in the AMP:ATP ratio.
  • The four CBS domains create two binding sites for AMP commonly referred to as
    Bateman domains. Binding of one AMP to a Bateman domain cooperatively
    increases the binding affinity of the second AMP to the other Bateman domain.
  • As AMP binds both Bateman domains the γ subunit undergoes a conformational
    change which exposes the catalytic domain found on the α subunit.
  • It is in this catalytic domain where AMPK becomes activated when
    phosphorylation takes place at threonine-172by an upstream AMPK kinase
    (AMPKK). The α, β, and γ subunits can also be found in different isoforms.

AMPK acts as a metabolic master switch regulating several intracellular systems

  1. the cellular uptake of glucose,
  2. the β-oxidation of fatty acids and
  3. the biogenesis of glucose transporter 4 (GLUT4) and
  4. mitochondria

The energy-sensing capability of AMPK can be attributed to

  • its ability to detect and react to fluctuations in the AMP:ATP ratio that take
    place during rest and exercise (muscle stimulation).

During muscle stimulation,

  • AMP increases while ATP decreases, which changes AMPK into a good substrate
    for activation.
  • AMPK activity increases while the muscle cell experiences metabolic stress
    brought about by an extreme cellular demand for ATP.
  • Upon activation, AMPK increases cellular energy levels by
    • inhibiting anabolic energy consuming pathways (fatty acid synthesis,
      protein synthesis, etc.) and
    • stimulating energy producing, catabolic pathways (fatty acid oxidation,
      glucose transport, etc.).

A recent JBC paper on mice at Johns Hopkins has shown that when the activity of brain
AMPK was pharmacologically inhibited,

  • the mice ate less and lost weight.

When AMPK activity was pharmacologically raised (AICAR see below)

  • the mice ate more and gained weight.

Research in Britain has shown that the appetite-stimulating hormone ghrelin also
affects AMPK levels.

The antidiabetic drug metformin (Glucophage) acts by stimulating AMPK, leading to

  1. reduced glucose production in the liver and
  2. reduced insulin resistance in the muscle.

(Metformin usually causes weight loss and reduced appetite, not weight gain and
increased appetite, ..opposite of expected from the Johns Hopkins mouse study results.)

Triggering the activation of AMPK can be carried out provided two conditions are met.

First, the γ subunit of AMPK

  • must undergo a conformational change so as to
  • expose the active site(Thr-172) on the α subunit.

The conformational change of the γ subunit of AMPK can be accomplished

  • under increased concentrations of AMP.

Increased concentrations of AMP will

  • give rise to the conformational change on the γ subunit of AMPK
  • as two AMP bind the two Bateman domains located on that subunit.
  • It is this conformational change brought about by increased concentrations
    of  AMP that exposes the active site (Thr-172) on the α subunit.

This critical role of AMP is further substantiated in experiments that demonstrate

  • AMPK activation via an AMP analogue 5-amino-4-imidazolecarboxamide
    ribotide (ZMP) which is derived fromthe familiar
  • 5-amino-4-imidazolecarboxamide riboside (AICAR)

AMPK is a good substrate for activation via an upstream kinase complex, AMPKK
AMPKK is a complex of three proteins,

  1. STE-related adaptor (STRAD),
  2. mouse protein 25 (MO25), and
  3. LKB1 (a serine/threonine kinase).

The second condition that must be met is

  • the phosphorylation/activation of AMPK on its activating loop at
    Thr-172of the α subunit
  • brought about by an upstream kinase (AMPKK).

The complex formed between LKB1 (STK 11), mouse protein 25 (MO25), and the
pseudokinase STE-related adaptor protein (STRAD) has been identified as

  • the major upstream kinase responsible for phosphorylation of AMPK
    on its activating loop at Thr-172

Although AMPK must be phosphorylated by the LKB1/MO25/STRAD complex,

  • it can also be regulated by allosteric modulators which
  • directly increase general AMPK activity and
  • modify AMPK to make it a better substrate for AMPKK
  • and a worse substrate for phosphatases.

It has recently been found that 3-phosphoglycerate (glycolysis intermediate)

  • acts to further pronounce AMPK activation via AMPKK

Muscle contraction is the main method carried out by the body that can provide
the conditions mentioned above needed for AMPK activation

  • As muscles contract, ATP is hydrolyzed, forming ADP.
  • ADP then helps to replenish cellular ATP by donating a phosphate group to
    another ADP,

    • forming an ATP and an AMP.
  • As more AMP is produced during muscle contraction,
    • the AMP:ATP ratio dramatically increases,
  • leading to the allosteric activation of AMPK

For over a decade it has been known that calmodulin-dependent protein kinase
kinase-beta (CaMKKbeta) can phosphorylate and thereby activate AMPK,

  • but it was not the main AMPKK in liver.

CaMKK inhibitors had no effect on 5-aminoimidazole-4-carboxamide-1-beta-4-
ribofuranoside (AICAR) phosphorylation and activation of AMPK.

  • AICAR is taken into the celland converted to ZMP,
  • an AMP analogthat has been shown to activate AMPK.

Recent LKB1 knockout studies have shown that without LKB1,

  • electrical and AICAR stimulation of muscleresults in very little
    phosphorylation of AMPK and of ACC, providing evidence that
  • LKB1-STRAD-MO25 is the major AMPKK in muscle.

Two particular adipokines, adiponectin and leptin, have even been demonstrated
to regulate AMPK. A main functions of leptin in skeletal muscle is

  • the upregulation of fatty acid oxidation.

Leptin works by way of the AMPK signaling pathway, and adiponectin also
stimulates the oxidation of fatty acids via the AMPK pathway, and

  • Adiponectin also stimulates the uptake of glucose in skeletal muscle.

An increase in enzymes which specialize in glucose uptake in cells such as GLUT4
and hexokinase II are thought to be mediated in part by AMPK when it is activated.
Increases in AMPK activity are brought about by increases in the AMP:ATP ratio
during single bouts of exercise and long-term training.

One of the key pathways in AMPK’s regulation of fatty acid oxidation is the

  • phosphorylation and inactivation of acetyl-CoA carboxylase.
  1. Acetyl-CoA carboxylase (ACC) converts acetyl-CoA (ACA) to malonyl-CoA
    (MCA), an inhibitor of carnitine palmitoyltransferase 1 (CPT-1).
  2. CPT-1 transports fatty acids into the mitochondria for oxidation.
  3. Inactivation of ACC results in increased fatty acid transport and oxidation.
  4. the AMPK induced ACC inactivation  and reduced conversion to MCA
    may occur as a result of malonyl-CoA decarboxylase (MCD)
  5. MCD as an antagonist to ACC, decarboxylatesmalonyl-CoA to acetyl-CoA
    (reversal of ACC conversion of ACA to MCA)
  6. This resultsin decreased malonyl-CoA and increased CPT-1 and fatty acid oxidation.

AMPK also plays an important role in lipid metabolism in the liver. It has long been
known that hepatic ACC has been regulated in the liver.

  1. It phosphorylates and inactivates 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR)
  2. acetyl-CoA(ACA) is converted to mevalonic acid (MVA) by ACC
    with inhibition of CPT-1
  3. HMGR converts 3-hydroxy-3-methylglutaryl-CoA, which is made from MVA
  4. which then travels down several more metabolic steps to become cholesterol.

Insulin facilitates the uptake of glucose into cells via increased expression and
translocation of glucose transporter GLUT-4. In addition, glucose is phosphorylated
by hexokinase wheni iot enters the cell. The phosphorylated form keeps glucose from
leaving the cell,

  • The decreasedthe concentration of glucose molecules creates a gradient for more
    glucose to be transported into the cell.
AMPK and thyroid hormone regulate some similar processes. Knowing these similarities,
Winder and Hardie et al. designed an experiment to see if AMPK was influenced by thyroid
hormone. They found that all of the subunits of AMPK were increased in skeletal muscle,
especially in the soleus and red quadriceps, with thyroid hormone treatment. There was
also an increase in phospho-ACC, a marker of AMPK activity.
  •  Winder WW, Hardie DG (July 1999). “AMP-activated protein kinase,
    a metabolic master switch: possible roles in type 2 diabetes”. J. Physiol. 277
    (1 Pt 1): E1–10. PMID 10409121.
  • Winder WW, Hardie DG (February 1996). “Inactivation of acetyl-CoA
    carboxylase and activation of AMP-activated protein kinase in muscle
    during exercise”. J. Physiol. 270 (2 Pt 1): E299–304. PMID 8779952.
  • Hutber CA, Hardie DG, Winder WW (February 1997). “Electrical stimulation
    inactivates muscle acetyl-CoA carboxylase and increases AMP-activated
    protein kinase”. Am. J. Physiol. 272 (2 Pt 1): E262–6. PMID 9124333
  • Durante PE, Mustard KJ, Park SH, Winder WW, Hardie DG (July 2002).
    “Effects of endurance training on activity and expression of AMP-activated
    protein kinase isoforms in rat muscles”. Am. J. Physiol. Endocrinol.
    Metab. 283 (1): E178–86. doi:10.1152/ajpendo.00404.2001. PMID 12067859
  • Corton JM, Gillespie JG, Hardie DG (April 1994). “Role of the AMP-activated
    protein kinase in the cellular stress response”. Curr. Biol. 4 (4):
    315–24. doi:10.1016/S0960-9822(00)00070-1. PMID 7922340
  • Winder WW (September 2001). “Energy-sensing and signaling by
    AMP-activated protein kinase in skeletal muscle”. J. Appl. Physiol. 91 (3):
    1017–28. PMID 11509493
  • Suter M, Riek U, Tuerk R, Schlattner U, Wallimann T, Neumann D (October
    2006). “Dissecting the role of 5′-AMP for allosteric stimulation, activation,
    and deactivation of AMP-activated protein kinase”.  J. Biol. Chem.
    281 (43): 32207–6. doi:10.1074/jbc.M606357200. PMID 16943194

 

Part III. Pituitary-thyroid axis and diabetes mellitus
The Interface Between Thyroid and Diabetes Mellitus

Leonidas H. Duntas, Jacques Orgiazzi, Georg Brabant   Clin Endocrinol. 2011;75(1):1-9.
Interaction of Metformin and Thyroid Function

Metformin acts primarily by

  • suppressing hepatic gluconeogenesis via activation of AMPK
  • It has the opposite effects on hypothalamic AMPK,
    • inhibiting activity of the enzyme.
  • the metformin effects on hypothalamic AMPK activity will
    • counteractT3 effects at the hypothalamic level.
  • AMPK therefore represents a direct target for dual regulation
    • in the hypothalamic partitioning of energy homeostasis.
  • metformin crossesthe blood–brain barrier and
    • levels in the pituitary gland are substantially increased.
  • It convincinglysuppresses TSH

A recent study recruiting 66 patients with benign thyroid nodules furthermore
demonstrated that metformin significantly decreases nodule size in patients with
insulin resistance.[76] The effect of metformin, which was produced over a
6-month treatment period, parallelled a fall in TSH concentrations and achieved a
shrinkage amounting to 30% of the initial nodule size when metformin was
administered alone and up to 55% when it was added to ongoing LT4 treatment.

These studies reveal a

  • suppressive effect of metformin on TSH secretion patterns in
    hypothyroid patients, an effect that is apparently
  • independent of T4 treatment and does not alter the TH profile.
  • A rebound of TSH secretion occurs at about 3 months following metformin
    withdrawal.

It appears that recommendations for more frequent testing, on an annual to
biannual basis, seems justified in higher risk groups like patients over 50 or 55,
particularly with suggestive symptoms, raised antibody titres or dylipidaemia.
We thus would support the suggestion of an initial TSH and TPO antibody testing
which, as discussed, will help to predict the development of hypothyroidism in
patients with diabetes.

Hypothalamic AMPK and fatty acid metabolism mediate thyroid
regulation of energy 
balance
M López,  L Varela,  MJ Vázquez,  S Rodríguez-Cuenca, CR González, …, & Vidal-Puig
Nature Medicine  29 Aug 2010; 16: 1001–1008 http://dx.doi.org:/10.1038/nm.2207

Thyroid hormones have widespread cellular effects; however it is unclear whether
their effects on the central nervous system (CNS) contribute to global energy balance.
Here we demonstrate that either

  • whole-body hyperthyroidism or central administration of triiodothyronine
    (T3) decreases

    • the activity of hypothalamic AMP-activated protein kinase (AMPK),
    • increases sympathetic nervous system (SNS) activity and
    • upregulates thermogenic markers in brown adipose tissue (BAT).

Inhibition of the lipogenic pathway in the ventromedial nucleus of the hypothalamus
(VMH) prevents CNS-mediated activation of BAT by thyroid hormone and reverses
the weight loss associated with hyperthyroidism. Similarly, inhibition of thyroid
hormone receptors in the VMH reverses the weight loss associated with hyperthyroidism.

This regulatory mechanism depends on AMPK inactivation, as genetic inhibition of this
enzyme in the VMH of euthyroid rats induces feeding-independent weight loss and
increases expression of thermogenic markers in BAT. These effects are reversed by
pharmacological blockade of the SNS. Thus, thyroid hormone–induced modulation
of AMPK activity and lipid metabolism in the hypothalamus is a major regulator of
whole-body energy homeostasis.

Metabolic Basis for Thyroid Hormone Liver Preconditioning:
Upregulation of AMP-Activated Protein Kinase Signaling
  
LA Videla,1 V Fernández, P Cornejo, and R Vargas
1Molecular and Clinical Pharmacology Program, Institute of Biomedical Sciences,
Faculty of Medicine, University of Chile, 2Faculty of Medicine, Diego Portales University,
Santiago, Chile
Academic Editors: H. M. Abu-Soud and D. Benke
The Scientific World Journal 2012; 2012, ID 475675, 10 pp
http://dx.doi.org/10.1100/2012/475675

The liver is a major organ responsible for most functions of cellular metabolism and

  • a mediator between dietary and endogenous sources of energy for extrahepatic tissues.
  • In this context, adenosine-monophosphate- (AMP-) activated protein kinase (AMPK)
    constitutes an intrahepatic energy sensor
  • regulating physiological energy dynamics by limiting anabolism and stimulating
    catabolism, thus increasing ATP availability.
  • This is achieved by mechanisms involving direct allosteric activation and
    reversible phosphorylation of AMPK, in response to signals such as

    • energy status,
    • serum insulin/glucagon ratio,
    • nutritional stresses,
    • pharmacological and natural compounds, and
    • oxidative stress status.

Reactive oxygen species (ROS) lead to cellular AMPK activation and

  • downstream signaling under several experimental conditions.

Thyroid hormone (L-3,3′,5-triiodothyronine, T3) administration, a condition
that enhances liver ROS generation,

  • triggers the redox upregulation of cytoprotective proteins
    • affording preconditioning against ischemia-reperfusion (IR) liver injury.

Data discussed in this work suggest that T3-induced liver activation of AMPK

  • may be of importance in the promotion of metabolic processes
  • favouring energy supply for the induction and operation of preconditioning
    mechanisms.

These include

  1. antioxidant,
  2. antiapoptotic, and
  3. anti-inflammatory mechanisms,
  4. repair or resynthesis of altered biomolecules,
  5. induction of the homeostatic acute-phase response, and
  6. stimulation of liver cell proliferation,

which are required to cope with the damaging processes set in by IR.

The liver functions as a mediator between dietary and endogenous sources
of energy and extrahepatic organs that continuously require energy, mainly
the brain and erythrocytes, under cycling conditions between fed and fasted states.

In the fed state, where insulin action predominates, digestion-derived glucose is
converted to pyruvate via glycolysis, which is oxidized to produce energy, whereas
fatty acid oxidation is suppressed. Excess glucose can be either stored as hepatic
glycogen or channelled into de novo lipogenesis.

In the fasted state, considerable liver fuel metabolism changes occur due to decreased
serum insulin/glucagon ratio, with higher glucose production as a consequence of
stimulated glycogenolysis and gluconeogenesis (from alanine, lactate, and glycerol).

Major enhancement in fatty acid oxidation also occurs to provide energy for liver
processes and ketogenesis to supply metabolic fuels for extrahepatic tissues. For these
reasons, the liver is considered as the metabolic processing organ of the body, and
alterations in liver functioning affect whole-body metabolism and energy homeostasis.

In this context, adenosine-monophosphate- (AMP-) activated protein kinase (AMPK)
is the downstream component of a protein kinase cascade acting as an

  • intracellular energy sensor regulating physiological energy dynamics by
  • limiting anabolic pathways, to prevent excessive adenosine triphosphate (ATP)
    utilization, and
  • by stimulating catabolic processes, to increase ATP production.

Thus, the understanding of the mechanisms by which liver AMPK coordinates hepatic
energy metabolism represents a crucial point of convergence of regulatory signals
monitoring systemic and cellular energy status

Liver AMPK: Structure and Regulation

AMPK, a serine/threonine kinase, is a heterotrimeric complex comprising

  1. a catalytic subunit α and
  2. two regulatory subunits β and γ .

The α subunit has a threonine residue (Thr172) within the activation loop of the kinase
domain, with the C-terminal region being required for association with β and γ subunits.
The β subunit associates with α and γ by means of its C-terminal region , whereas

  • the γ subunit has four cystathionine β-synthase (CBS) motifs, which
  • bind AMP or ATP in a competitive manner.

75675.fig.001 (not shown)

Figure 1: Regulation of AMP-activated protein kinase (AMPK) by
(A) direct allosteric activation and
(B) reversible phosphorylation and downstream responses maintaining
intracellular energy balance.

Regulation of liver AMPK activity involves both direct allosteric activation and
reversible phosphorylation. AMPK is allosterically activated by AMP through

  • binding to the regulatory subunit-γ, which induces a conformational change in
    the kinase domain of subunit α that protects AMPK from dephosphorylation
    of Thr172, probably by protein phosphatase-2C.

Activation of AMPK requires phosphorylation of Thr172 in its α subunit, which can be
attained by either

(i) tumor suppressor LKB1 kinase following enhancement in the AMP/ATP ratio, a
kinase that plays a crucial role in AMPK-dependent control of liver glucose and
lipid metabolism;

(ii) Ca2+-calmodulin-dependent protein kinase kinase-β (CaMKKβ) that
phosphorylates AMPK in an AMP-independent, Ca2+-dependent manner;

(iii) transforming growth-factor-β-activated kinase-1 (TAK1), an important
kinase in hepatic Toll-like receptor 4 signaling in response to lipopolysaccharide.

Among these kinases, the relevance of CaMKKβ and TAK1 in liver AMPK activation
remains to be established in metabolic stress conditions. Both allosteric and
phosphorylation mechanisms are able to elicit

  • over 1000-fold increase in AMPK activity, thus allowing
  • the liver to respond to small changes in energy status in a highly sensitive fashion.

In addition to rapid AMPK regulation through allosterism and reversible phosphorylation

  • long-term effects of AMPK activation induce changes in hepatic gene expression.

This was demonstrated for

(i) the transcription factor carbohydrate-response element-binding protein (ChREBP),

  • whose Ser568 phosphorylation by activated AMPK
  • blocks its DNA binding capacity and glucose-induced gene transcription
  • under hyperlipidemic conditions;(ii) liver sterol regulatory element-binding
    protein-1c (SREBP-1c), whose mRNA and protein expression and those of
    its target gene for fatty acid synthase (FAS)
  • are reduced by metformin-induced AMPK activation,
  • decreasing lipogenesis and increasing fatty acid oxidation due to
    malonyl-CoA depletion;

(iii) transcriptional coactivator transducer of regulated CREB activity-2 (TORC2),
a crucial component of the hepatic gluconeogenic program, was reported
to be phosphorylated by activated AMPK.

This modification leads to subsequent cytoplasmatic sequestration of TORC2 and
inhibition of gluconeogenic gene expression, a mechanism underlying

  • the plasma glucose-lowering effects of adiponectin and metformin
  • through AMPK activation by upstream LKB1.

Activation of AMPK in the liver is a key regulatory mechanism controlling glucose
and lipid metabolism,

  1. inhibiting anabolic processes, and
  2. enhancing catabolic pathways in response to different signals, including
    1. energy status,
    2. serum insulin/glucagon ratio,
    3. nutritional stresses,
    4. pharmacological and natural compounds, and
    5. oxidative stress status

Reactive Oxygen Species (ROS) and AMPK Activation

The high energy demands required to cope with all the metabolic functions
of the liver are met by

  • fatty acid oxidation under conditions of both normal blood glucose levels and
    hypoglycemia, whereas
  • glucose oxidation is favoured in hyperglycemic states, with consequent
    generation of ROS.

Under normal conditions, ROS occur at relatively low levels due to their fast processing
by antioxidant mechanisms, whereas at acute or prolonged high ROS levels, severe
oxidation of biomolecules and dysregulation of signal transduction and gene expression
is achieved, with consequent cell death through necrotic and/or apoptotic-signaling
pathways.

Thyroid Hormone (L-3,3′,5-Triiodothyronine, T3), Metabolic Regulation,
and ROS Production

T3 is important for the normal function of most mammalian tissues, with major actions
on O2 consumption and metabolic rate, thus

  • determining enhancement in fuel consumption for oxidation processes
  • and ATP repletion.

T3 acts predominantly through nuclear receptors (TR) α and β, forming

  • functional complexes with retinoic X receptor that
  • bind to thyroid hormone response elements (TRE) to activate gene expression.

T3 calorigenesis is primarily due to the

  • induction of enzymes related to mitochondrial electron transport and ATP
    synthesis, catabolism, and
  • some anabolic processes via upregulation of genomic mechanisms.

The net result of T3 action is the enhancement in the rate of O2 consumption of target
tissues such as liver, which may be effected by secondary processes induced by T3

(i) energy expenditure due to higher active cation transport,

(ii) energy loss due to futile cycles coupled to increase in catabolic and anabolic pathways, and

(iii) O2 equivalents used in hepatic ROS generation both in hepatocytes and Kupffer cells

In addition, T3-induced higher rates of mitochondrial oxidative phosphorylation are
likely to induce higher levels of ATP, which are partially balanced by intrinsic uncoupling
afforded by induction of uncoupling proteins by T3. In agreement with this view, the
cytosolic ATP/ADP ratio is decreased in hyperthyroid tissues, due to simultaneous
stimulation of ATP synthesis and consumption.

Regulation of fatty acid oxidation is mainly attained by carnitine palmitoyltransferase Iα (CPT-Iα),

  • catalyzing the transport of fatty acids from cytosol to mitochondria for β-oxidation,
    and acyl-CoA oxidase (ACO),
  • catalyzing the first rate-limiting reaction of peroxisomal β-oxidation, enzymes that are
    induced by both T3 and peroxisome proliferator-activated receptor α (PPAR-α).

Furthermore, PPAR-α-mediated upregulation of CPT-Iα mRNA is enhanced by PPAR-γ
coactivator 1α (PGC-1α), which in turn

  • augments T3 induction of CPT-Iα expression.

Interestingly, PGC-1α is induced by

  1. T3,
  2. AMPK activation, and
  3. ROS,

thus establishing potential links between

  • T3 action, ROS generation, and AMPK activation

with the onset of mitochondrial biogenesis and fatty acid β-oxidation.

Liver ROS generation leads to activation of the transcription factors

  1. nuclear factor-κB (NF-κB),
  2. activating protein 1 (AP-1), and
  3. signal transducer and activator of transcription 3 (STAT3)

at the Kupffer cell level, with upregulation of cytokine expression (TNF-α, IL-1, IL-6),
which upon interaction with specific receptors in hepatocytes trigger the expression of

  1. cytoprotective proteins (Figure 3(A)).

These responses and the promotion of hepatocyte and Kupffer-cell proliferation
represent hormetic effects reestablishing

  1. redox homeostasis,
  2. promoting cell survival, and
  3. protecting the liver against ischemia-reperfusion injury.

T3 liver preconditioning also involves the activation of the

  1. Nrf2-Keap1 defense pathway
  • upregulating antioxidant proteins,
  • phase-2 detoxifying enzymes, and
  • multidrug resistance proteins, members of the ATP binding cassette (ABC)
    superfamily of transporters (Figure 3(B))

In agreement with T3-induced liver preconditioning, T3 or L-thyroxin afford
preconditioning against IR injury in the heart, in association with

  • activation of protein kinase C and
  • attenuation of p38 and
  • c-Jun-N-terminal kinase activation ,

and in the kidney, in association with

  • heme oxygenase-1 upregulation.

475675.fig.002

http://www.hindawi.com/journals/tswj/2012/floats/475675/thumbnails/475675.fig.002_th.jpg

Figure 2: Calorigenic response of thyroid hormone (T3) and its relationship with O2
consumption, reactive oxygen species (ROS) generation, and antioxidant depletion in the liver.
Abbreviations: CYP2E1, cytochrome P450 isoform 2E1; GSH, reduced glutathione; QO2, rate
of O2 consumption; SOD, superoxide dismutase.

475675.fig.003

genomic signaling in T3 calorigenesis and ROS production 475675.fig.003

genomic signaling in T3 calorigenesis and ROS production 475675.fig.003

http://www.hindawi.com/journals/tswj/2012/floats/475675/thumbnails/475675.fig.003_th.jpg

Figure 3: Genomic signaling mechanisms in T3 calorigenesis and liver reactive oxygen
species (ROS) production leading to
(A) upregulation of cytokine expression in Kupffer cells and hepatocyte activation of genes
conferring cytoprotection,
(B) Nrf2 activation controling expression of antioxidant and detoxication proteins, and
(C) activation of the AMPK cascade regulating metabolic functions.Abbreviations: AP-1, activating protein 1; ARE, antioxidant responsive element; CaMKKβ,
Ca2+-calmodulin-dependent kinase kinase-β; CBP, CREB binding protein; CRC, chromatin
remodelling complex; EH, epoxide hydrolase; HO-1, hemoxygenase-1; GC-Ligase,
glutamate cysteine ligase; GPx, glutathione peroxidase; G-S-T, glutathione-S-transferase;
HAT, histone acetyltransferase; HMT, histone arginine methyltransferase; IL1,
interleukin 1; iNOS, inducible nitric oxide synthase; LKB1, tumor suppressor LKB1 kinase;
MnSOD, manganese superoxide dismutase; MRPs, multidrug resistance proteins; NF-κB,
nuclear factor-κB; NQO1, NADPH-quinone oxidoreductase-1; NRF-1, nuclear respiratory
factor-1; Nrf2, nuclear receptor-E2-related factor 2; PCAF, p300/CBP-associated
factor; RXR, retinoic acid receptor; PGC-1, peroxisome proliferator-activated receptor-γ
coactivator-1; QO2, rate of O2 consumption; STAT3, signal transducer and activator
of transcription 3; TAK1, transforming-growth-factor-β-activated kinase-1; TNF-α, tumor
necrosis factor-α; TR, T 3 receptor; TRAP, T3-receptor-associated protein; TRE,  T3 responsive element; UCP, uncoupling proteins; (—), reported mechanisms;
(- - - -), proposed mechanisms.

 

T3 is a key metabolic regulator coordinating short-term and long-term energy needs,
with major actions on liver metabolism. These include promotion of

(i) gluconeogenesis and hepatic glucose production, and

(ii) fatty acid oxidation coupled to enhanced adipose tissue lipolysis, with

  • higher fatty acid flux to the liver and
  • consequent ROS production (Figure 2) and
  • redox upregulation of cytoprotective proteins

affording liver preconditioning (Figure 3).

Thyroid Hormone and AMPK Activation: Skeletal Muscle and Heart

In skeletal muscle, T3 increases the levels of numerous proteins involved in

  1. glucose uptake (GLUT4),
  2. glycolysis (enolase, pyruvate kinase, triose phosphate isomerase),
  3. fatty acid oxidation (carnitine palmitoyl transferase-1, mitochondrial thioesterase I),
    and uncoupling protein-3,

effects that are achieved through enhanced transcription of TRE-containing genes

Skeletal muscle AMPK activation is characterized by

(i) being a rapid and transient response,

(ii) upstream activation by Ca2+-induced mobilization and CaMKKβ activation,

(iii) upstream upregulation of LKB1 expression, which requires association with STRAD
and MO25 for optimal phosphorylation/activation of AMPK, and

(iv) stimulation of mitochondrial fatty acid β-oxidation.

T3-induced muscle AMPK activation was found to trigger two major downstream

signaling pathways, namely,

(i) peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) mRNA
expression and phosphorylation, a transcriptional regulator for genes related to

  • mitochondrial biogenesis,
  • fatty acid oxidation, and
  • gluconeogenesis and

(ii) cyclic AMP response element binding protein (CREB) phosphorylation, which

  • in turn induces PGC-1α expression in liver tissue, thus
  • reinforcing mechanism (i).

These data indicate that AMPK phosphorylation of PGC-1α initiates many of the
important gene regulatory functions of AMPK in skeletal muscle.

In heart, hyperthyroidism increased glycolysis and sarcolemmal GLUT4 levels by the
combined effects of AMPK activation and insulin stimulation, with concomitant increase
in fatty acid oxidation proportional to enhanced cardiac mass and contractile function.

Thyroid Hormone, AMPK Activation, and Liver Preconditioning

Recent studies by our group revealed that administration of a single dose of 0.1 mg T3/kg
to rats activates liver AMPK (Figure 4; unpublished work).

  1. enhancement in phosphorylated AMPK/nonphosphorylated AMPK ratios in T3-
    treated rats over control values thatis significant in the time period of 1 to 48
    hours after hormone treatment
  2. Administration of a substantially higher dose (0.4 mg T3/kg) resulted in
    decreased liver AMPK activation at 4 h to return to control values at 6 h
    after treatment

Activation of liver AMPK by T3 may be of relevance in terms of

  • promotion of fatty acid oxidation for ATP supply,
  • supporting hepatoprotection against IR injury (Figure 3(C)).

This proposal is based on the high energy demands underlying effective liver
preconditioning for full operation of hepatic

  • antioxidant, antiapoptotic, and anti-inflammatory mechanisms,
  • oxidized biomolecules repair or resynthesis,
  • induction of the homeostatic acute-phase response, and
  • promotion of hepatocyte and Kupffer cell proliferation,

mechanisms that are needed to cope with the damaging processes set in by IR.
T3 liver preconditioning , in addition to that afforded by

  • n-3 long-chain polyunsaturated fatty acids given alone or
  • combined with T3 at lower dosages, or
  • by iron supplementation,

constitutes protective strategies against hepatic IR injury.

Studies on the molecular mechanisms underlying T3-induced liver AMPK
activation (Figure 4) are currently under assessment in our laboratory.

References

Fernández and L. A. Videla, “Kupffer cell-dependent signaling in thyroid hormone
calorigenesis: possible applications for liver preconditioning,” Current Signal
Transduction Therapy 2009; 4(2): 144–151.

Viollet, B. Guigas, J. Leclerc et al., “AMP-activated protein kinase in the regulation
of  hepatic energy metabolism: from physiology to therapeutic perspectives,” Acta
Physiologica 2009; 196(1): 81–98.

Carling, “The AMP-activated protein kinase cascade – A unifying system
for energy control,” Trends in Biochemical Sciences, 2004;. 29(1): 18–24.

E. Kemp, D. Stapleton, D. J. Campbell et al., “AMP-activated protein kinase,
super 
metabolic regulator,” Biochemical Society Transactions 2003; 31(1):
162–168
.

G. Hardie, “AMP-activated protein kinase-an energy sensor that
regulates all ;aspects of cell function,” Genes and Development,
2011; 25(18): 1895–1908.

Woods, P. C. F. Cheung, F. C. Smith et al., “Characterization of AMP-activated
protein kinase βandγ subunits Assembly of the heterotrimeric complex in vitro,”
Journal of Biological Chemistry 1996;271(17): 10282–10290.

Xiao, R. Heath, P. Saiu et al., “Structural basis for AMP binding to mammalian AMP-
activated protein kinase,” Nature 2007; 449(7161): 496–500.

more…

Impact of Metformin and compound C on NIS expression and iodine uptake in vitro and in vivo: a role for CRE in AMPK modulation of thyroid function.
Abdulrahman RM1, Boon MRSips HCGuigas BRensen PCSmit JWHovens GC.
Author information 
Thyroid. 2014 Jan;24(1):78-87.  Epub 2013 Sep 25.  PMID: 23819433
http://dx.doi.org:/10.1089/thy.2013.0041.

Although adenosine monophosphate activated protein kinase (AMPK) plays a crucial role
in energy metabolism, a direct effect of AMPK modulation on thyroid function has only
recently been reported, and much of its function in the thyroid is currently unknown.

The aim of this study was

  1. to investigate the mechanism of AMPK modulation in iodide uptake.
  2. to investigate the potential of the AMPK inhibitor compound C as an enhancer of
    iodide uptake by thyrocytes.

Metformin reduced NIS promoter activity (0.6-fold of control), whereas compound C
stimulated its activity (3.4-fold) after 4 days. This largely coincides with

  • CRE activation (0.6- and 3.0-fold).

These experiments show that AMPK exerts its effects on iodide uptake, at least partly,
through the CRE element in the NIS promoter. Furthermore, we have used AMPK-alpha1
knockout mice to determine the long-term effects of AMPK inhibition without chemical compounds.
These mice have a less active thyroid, as shown by reduced colloid volume and reduced
responsiveness to thyrotropin.

NIS expression and iodine uptake in thyrocytes

  • can be modulated by metformin and compound C.

These compounds exert their effect by

  • modulation of AMPK, which, in turn, regulates
  • the activation of the CRE element in the NIS promoter.

Overall, this suggests that AMPK modulating compounds may be useful for the
enhancement of iodide uptake by thyrocytes, which could be useful for the
treatment of thyroid cancer patients with radioactive iodine.

AMPK: Master Metabolic Regulator

© 1996–2013 themedicalbiochemistrypage.org, LLC | info
@ themedicalbiochemistrypage.org

AMPK-activating drugs metformin or phenformin might provide protection against cancer 1741-7007-11-36-5

AMPK-activating drugs metformin or phenformin might provide protection against cancer 1741-7007-11-36-5

 

AMPK and AMPK-related kinase (ARK) family 1741-7007-11-36-4

AMPK and AMPK-related kinase (ARK) family 1741-7007-11-36-4

 

central role of AMPK in the regulation of metabolism

 

 

AMP-activated protein kinase (AMPK) was first discovered as an activity that

AMPK induces a cascade of events within cells in response to the ever changing energy
charge of the cell. The role of AMPK in regulating cellular energy charge places this
enzyme at a central control point in maintaining energy homeostasis.

More recent evidence has shown that AMPK activity can also be regulated by physiological stimuli, independent of the energy charge of the cell, including hormones and nutrients.

 

Once activated, AMPK-mediated phosphorylation events

These events are rapidly initiated and are referred to as

  • short-term regulatory processes.

The activation of AMPK also exerts

  • long-term effects at the level of both gene expression and protein synthesis.

Other important activities attributable to AMPK are

  1. regulation of insulin synthesis and
  2. secretion in pancreatic islet β-cells and
  3. modulation of hypothalamic functions involved in the regulation of satiety.

How these latter two functions impact obesity and diabetes will be discussed below.

Regulation of AMPK

In the presence of AMP the activity of AMPK is increased approximately 5-fold.
However, more importantly is the role of AMP in regulating the level of phosphorylation
of AMPK. An increased AMP to ATP ratio leads to a conformational change in the γ-subunit
leading to increased phosphorylation and decreased dephosphorylation of AMPK.

The phosphorylation of AMPK results in activation by at least 100-fold. AMPK is
phosphorylated by at least three different upstream AMPK kinases (AMPKKs).
Phosphorylation of AMPK occurs in the α subunit at threonine 172 (T172) which

  • lies in the activation loop.

One kinase activator of AMPK is

  • Ca2+-calmodulin-dependent kinase kinase β (CaMKKβ)
  • which phosphorylates and activates AMPK in response to increased calcium.

The distribution of CaMKKβ expression is primarily in the brain with detectable levels
also found in the testes, thymus, and T cells. As described for the Ca2+-mediated
regulation of glycogen metabolism,

  • increased release of intracellular stores of Ca2+ create a subsequent demand for
    ATP.

Activation of AMPK in response to Ca fluxes

  • provides a mechanism for cells to anticipate the increased demand for ATP.

Evidence has also demonstrated that the serine-threonine kinase, LKB1 (also called
serine-threonine kinase 11, STK11) which is encoded by the Peutz-Jeghers syndrome
tumor suppressor gene, is required for activation of AMPK in response to stress.

The active LKB1 kinase is actually a complex of three proteins:

  1. LKB1,
  2. Ste20-related adaptor (STRAD) and
  3. mouse protein 25 (MO25).

Thus, the enzyme complex is often referred to as LKB1-STRAD-MO25. Phosphorylation
of AMPK by LKB1 also occurs on T172. Unlike the limited distribution of CaMKKβ,

  • LKB1 is widely expressed, thus making it the primary AMPK-regulating kinase.

Loss of LKB1 activity in adult mouse liver leads to

  • near complete loss of AMPK activity and
  • is associated with hyperglycemia.

The hyperglycemia is, in part, due to an increase in the transcription of gluconeogenic
genes. Of particular significance is the increased expression of

  • the peroxisome proliferator-activated receptor-γ (PPAR-γ) coactivator 1α
    (PGC-1α), which drives gluconeogenesis.
  • Reduction in PGC-1α activity results in normalized blood glucose levels in
    LKB1-deficient mice.

The third AMPK phosphorylating kinase is transforming growth factor-β-activated
kinase 1 (TAK1). However, the normal physiological conditions under which TAK1
phosphorylates AMPK are currently unclear.

The effects of AMP are two-fold:

  1. a direct allosteric activation and making AMPK a poorer substrate for
    dephosphorylation.

Because AMP affects both
the rate of AMPK phoshorylation in the positive direction and
dephosphorylation in the negative direction,

the cascade is ultrasensitive. This means that

  1. a very small rise in AMP levels can induce a dramatic increase in the activity of
    AMPK.

The activity of adenylate kinase, catalyzing the reaction shown below, ensures that

  • AMPK is highly sensitive to small changes in the intracellular [ATP]/[ADP] ratio.

2 ADP ——> ATP + AMP

Negative allosteric regulation of AMPK also occurs and this effect is exerted by
phosphocreatine. As indicated above, the β subunits of AMPK have a glycogen-binding domain, GBD. In muscle, a high glycogen content

  • represses AMPK activity and
  • this is likely the result of interaction between the GBD and glycogen,
  • the GBD of AMPK allows association of the enzyme with the regulation of glycogen metabolism
  • by placing AMPK in close proximity to one of its substrates glycogen synthase.

AMPK has also been shown to be activated by receptors that are coupled to

  • phospholipase C-β (PLC-β) and by
  • hormones secreted by adipose tissue (termed adipokines) such as leptinand adiponectin (discussed below).

Targets of AMPK

The signaling cascades initiated by the activation of AMPK exert effects on

  • glucose and lipid metabolism,
  • gene expression and
  • protein synthesis.

These effects are most important for regulating metabolic events in the liver, skeletal
muscle, heart, adipose tissue, and pancreas.

Demonstration of the central role of AMPK in the regulation of metabolism in response
to events such as nutrient- or exercise-induced stress. Several of the known physiologic
targets for AMPK are included as well as several pathways whose flux is affected by
AMPK activation. Arrows indicate positive effects of AMPK, whereas, T-lines indicate
the resultant inhibitory effects of AMPK action.

The uptake, by skeletal muscle, accounts for >70% of the glucose removal from the
serum in humans. Therefore, it should be obvious that this event is extremely important
for overall glucose homeostasis, keeping in mind, of course, that glucose uptake by
cardiac muscle and adipocytes cannot be excluded from consideration. An important fact
related to skeletal muscle glucose uptake is that this process is markedly impaired in
individuals with type 2 diabetes.

The uptake of glucose increases dramatically in response to stress (such as ischemia) and
exercise and is stimulated by insulin-induced recruitment of glucose transporters
to the plasma membrane, primarily GLUT4. Insulin-independent recruitment of glucose
transporters also occurs in skeletal muscle in response to contraction (exercise).

The activation of AMPK plays an important, albeit not an exclusive, role in the induction of
GLUT4 recruitment to the plasma membrane. The ability of AMPK to stimulate
GLUT4 translocation to the plasma membrane in skeletal muscle is by a different mechanism
than that stimulated by insulin and insulin and AMPK effects are additive.

Under ischemic/hypoxic conditions in the heart the activation of AMPK leads to the
phosphorylation and activation of the kinase activity of phosphofructokinase-2, PFK-2
(6-phosphofructo-2-kinase). The product of the action of PFK-2 (fructose-2,6-bisphosphate,
F2,6BP) is one of the most potent regulators of the rate of flux through
glycolysis and gluconeogenesis.

In liver the PKA-mediated phosphorylation of PFK-2 results in conversion of the
enzyme from a kinase that generates F2,6BP to a phosphatase that removes the
2-phosphate thus reducing the levels of the potent allosteric activator of the glycolytic
enzyme 6-phosphfructo-1-kinase, PFK-1 and the potent allosteric inhibitor
of the gluconeogenic enzyme fructose-1,6-bisphosphatase (F1,-6BPase).

It is important to note that like many enzymes, there are multiple isoforms of PFK-2
(at least 4) and neither the liver or the skeletal muscle isoforms contain the AMPK
phosphorylation sites found in the cardiac and inducible (iPFK2) isoforms of PFK-2.

Inducible PFK-2 is expressed in the monocyte/macrophage lineage in response to pro-
inflammatory stimuli. The ability to activate the kinase activity by phosphorylation of
PFK-2 in cardiac tissue and macrophages in response to ischemic conditions allows these
cells to continue to have a source of ATP via anaerobic glycolysis. This phenomenon is
recognized as the Pasteur effect: an increased rate of glycolysis in response to hypoxia.

Of pathological significance is the fact that the inducible form of PFK-2 is commonly
expressed in many tumor cells and this may allow AMPK to play an important role in
protecting tumor cells from hypoxic stress. Indeed, techniques for depleting AMPK in
tumor cells have shown that these cells become sensitized to nutritional stress upon loss
of AMPK activity.

Whereas, stress and exercise are powerful inducers of AMPK activity in skeletal muscle,
additional regulators of its activity have been identified.

Insulin-sensitizing drugs of the thiazolidinedione family (activators of PPAR-γ, see
below) as well as the hypoglycemia drug metformin exert a portion of their effects
through regulation of the activity of AMPK.

As indicated above, the activity of the AMPK activating kinase, LKB1, is critical for
regulating gluconeogenic flux and consequent glucose homeostasis. The action of
metformin in reducing blood glucose levels

  • requires the activity of LKB1 in the liver for this function.

Also, several adipokines (hormones secreted by adipocytes) either stimulate or inhibit
AMPK activation:

  1. leptin and adiponectin have been shown to stimulate AMPK activation, whereas,
  2. resistininhibits AMPK activation.

Cardiac effects exerted by activation of AMPK also include

AMPK-mediated phosphorylation of eNOS leads to increased activity and consequent
NO production and provides a link between metabolic stresses and cardiac function.

In platelets, insulin action leads to an increase in eNOS activity that is

  • due to its phosphorylation by AMPK.

Activation of NO production in platelets leads to

  • a decrease in thrombin-induced aggregation, thereby,
  • limiting the pro-coagulant effects of platelet activation.

The response of platelets to insulin function clearly indicates why disruption in insulin
action is a major contributing factor in the development of the metabolic syndrome

Activation of AMPK leads to a reduction in the level of SREBP

  • a transcription factor &regulator of the expression of numerous
    lipogenic enzymes

Another transcription factor reduced in response to AMPK activation is

  • hepatocyte nuclear factor 4α, HNF4α
    • a member of the steroid/thyroid hormone superfamily.
    • HNF4α is known to regulate the expression of several liver and
      pancreatic β-cell genes such as GLUT2, L-PK and preproinsulin.
  • Of clinical significance is that mutations in HNF4α are responsible for
    • maturity-onset diabetes of the young, MODY-1.

Recent evidence indicates that the gene for the carbohydrate-response-element-
binding protein (ChREBP) is a target for AMPK-mediated transcriptional regulation
in the liver. ChREBP is rapidly being recognized as a master regulator of lipid
metabolism in liver, in particular in response to glucose uptake.

The target of the thiazolidinedione (TZD) class of drugs used to treat type 2 diabetes is
the peroxisome proliferator-activated receptor γPPARγ which

  • itself may be a target for the action of AMPK.

The transcription co-activator, p300, is phosphorylated by AMPK

  • which inhibits interaction of p300 with not only PPARγ but also
  • the retinoic acid receptor, retinoid X receptor, and
  • thyroid hormone receptor.

PPARγ is primarily expressed in adipose tissue and thus it was difficult to reconcile how
a drug that was apparently acting only in adipose tissue could lead to improved insulin
sensitivity of other tissues. The answer to this question came when it was discovered that the TZDs stimulated the expression and release of the adipocyte hormone (adipokine),
adiponectin. Adiponectin stimulates glucose uptake and fatty acid oxidation in skeletal
muscle. In addition, adiponectin stimulates fatty acid oxidation in liver while inhibiting
expression of gluconeogenic enzymes in this tissue.

These responses to adiponectin are exerted via activation of AMPK. Another
transcription factor target of AMPK is the forkhead protein, FKHR (now referred to as
FoxO1). FoxO1 is involved in the activation of glucose-6-phosphatase expression and,
therefore, loss of FoxO1 activity in response to AMPK activation will lead to reduced
hepatic output of glucose.

This concludes a very complicated perspective that ties together the thyroid hormone
activity, the hypophysis, diabetes mellitus, and AMPK tegulation of metabolism in the
liver, skeletal muscle, adipose tissue, and heart.  I also note at this time that there
nongenetic points to be made here:

  1. The tissue specificity of isoenzymes
  2. The modulatory role of AMP:ATP ratio in phosphorylation/dephosphorylation
    effects on metabolism tied to AMPK
  3. The tie in of stress or ROS with fast reactions to protect harm to tissues
  4. The relationship of cytokine activation and release to the above metabolic events
  5. The relationship of effective and commonly used diabetes medications to AMPK
    mediated processes
  6. The preceding presentation is notable for the importance of proteomic and
    metabolomic invetigations in elucidation common chronic and nongenetic diseases

 

Read Full Post »

Extracellular evaluation of intracellular flux in yeast cells

Larry H. Bernstein, MD, FCAP, Reviewer and Curator

Leaders in Pharmaceutical Intelligence

This is the fourth article in a series on metabolomics, which is a major development in -omics, integrating transcriptomics, proteomics,  genomics, metabolic pathways analysis, metabolic and genomic regulatory control using computational mapping.  In the previous two part presentation, flux analysis was not a topic for evaluation, but here it is the major focus.  It is a study of yeast cells, and bears some relationship to the comparison of glycemia, oxidative phosphorylation, TCA cycle, and ETC in leukemia cell lines.  In the previous study – system flux was beyond the scope of analysis, and explicitly stated.  The inferences made in comparing the two lymphocytic leukemia cells was of intracellular metabolism from extracellular measurements.  The study of yeast cells is aimed at looking at cellular effluxes, which is also an important method for studying pharmacological effects and drug resistance.

Metabolomic series

1.  Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics

http://pharmaceuticalintelligence.com/2014/08/22/metabolomics-metabonomics-and-functional-nutrition-the-next-step-in-nutritional-metabolism-and-biotherapeutics/

2.  Metabolomic analysis of two leukemia cell lines. I

http://pharmaceuticalintelligence.com/2014/08/23/metabolomic-analysis-of-two-leukemia-cell-lines-_i/

3.  Metabolomic analysis of two leukemia cell lines. II.

 http://pharmaceuticalintelligence.com/2014/08/24/metabolomic-analysis-of-two-leukemia-cell-lines-ii/

4.  Extracellular evaluation of intracellular flux in yeast cells

Q1. What is efflux?

Q2. What measurements were excluded from the previous study that would not allow inference about fluxes?

Q3. Would this study bear any relationship to the Pasteur effect?

Q4 What is a genome scale network reconstruction?

Q5 What type of information is required for a network prediction model?

Q6. Is there a difference between the metabolites profiles for yeast grown under aerobic and anaerobuc conditions – under the constrainsts?

Q7.  If there is a difference in the S metabolism, would there be an effect on ATP production?

 

 

Connecting extracellular metabolomic measurements to intracellular flux
states in yeast

Monica L Mo1Bernhard Ø Palsson1 and Markus J Herrgård12*

Author Affiliations

1 Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA

2 Current address: Synthetic Genomics, Inc, 11149 N Torrey Pines Rd, La Jolla, CA 92037, USA

For all author emails, please log on.

BMC Systems Biology 2009, 3:37  doi:10.1186/1752-0509-3-37

 

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1752-0509/3/37

 

Received: 15 December 2008
Accepted: 25 March 2009
Published: 25 March 2009

© 2009 Mo et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Metabolomics has emerged as a powerful tool in the

  • quantitative identification of physiological and disease-induced biological states.

Extracellular metabolome or metabolic profiling data, in particular,

  • can provide an insightful view of intracellular physiological states in a noninvasive manner.

Results

We used an updated genome-scale

  • metabolic network model of Saccharomyces cerevisiae, iMM904, to investigate
  1. how changes in the extracellular metabolome can be used
  2. to study systemic changes in intracellular metabolic states.

The iMM904 metabolic network was reconstructed based on

  • an existing genome-scale network, iND750,
  • and includes 904 genes and 1,412 reactions.

The network model was first validated by

  • comparing 2,888 in silico single-gene deletion strain growth phenotype predictions
  • to published experimental data.

Extracellular metabolome data measured

  • of ammonium assimilation pathways 
  • in response to environmental and genetic perturbations

was then integrated with the iMM904 network

  • in the form of relative overflow secretion constraints and
  • a flux sampling approach was used to characterize candidate flux distributions allowed by these constraints.

Predicted intracellular flux changes were

  • consistent with published measurements
  • on intracellular metabolite levels and fluxes.

Patterns of predicted intracellular flux changes

  • could also be used to correctly identify the regions of
  • the metabolic network that were perturbed.

Conclusion

Our results indicate that

  • integrating quantitative extracellular metabolomic profiles
  • in a constraint-based framework
  • enables inferring changes in intracellular metabolic flux states.

Similar methods could potentially be applied

  • towards analyzing biofluid metabolome variations
  • related to human physiological and disease states.

Background

“Omics” technologies are rapidly generating high amounts of data

  • at varying levels of biological detail.

In addition, there is a rapidly growing literature and

  • accompanying databases that compile this information.

This has provided the basis for the assembly of

  • genome-scale metabolic networks for various microbial and eukaryotic organisms [111].

These network reconstructions serve

  • as manually curated knowledge bases of
  • biological information as well as
  • mathematical representations of biochemical components and
  • interactions specific to each organism.

genome-scale network reconstruction is

  • structured collection of genes, proteins, biochemical reactions, and metabolites
  • determined to exist and operate within a particular organism.

This network can be converted into a predictive model

  • that enables in silico simulations of allowable network states based on
  • governing physico-chemical and genetic constraints [12,13].

A wide range of constraint-based methods have been developed and applied

  • to analyze network metabolic capabilities under
  • different environmental and genetic conditions [13].

These methods have been extensively used to

  • study genome-scale metabolic networks and have successfully predicted, for example,
  1. optimal metabolic states,
  2. gene deletion lethality, and
  3. adaptive evolutionary endpoints [1416].

Most of these applications utilize

  • optimization-based methods such as flux balance analysis (FBA)
  • to explore the metabolic flux space.

However, the behavior of genome-scale metabolic networks can also be studied

  • using unbiased approaches such as
  • uniform random sampling of steady-state flux distributions [17].

Instead of identifying a single optimal flux distribution based on

  • a given optimization criterion (e.g. biomass production),

these methods allow statistical analysis of

  • a large range of possible alternative flux solutions determined by
  • constraints imposed on the network.

Sampling methods have been previously used to study

  1. global organization of E. coli metabolism [18] as well as
  2. to identify candidate disease states in the cardiomyocyte mitochondria [19].

Network reconstructions provide a structured framework

  • to systematically integrate and analyze disparate datasets
  • including transcriptomic, proteomic, metabolomic, and fluxomic data.

Metabolomic data is one of the more relevant data types for this type of analysis as

  1. network reconstructions define the biochemical links between metabolites, and
  2. recent advancements in analytical technologies have allowed increasingly comprehensive
  • intracellular and extracellular metabolite level measurements [20,21].

The metabolome is

  1. the set of metabolites present under a given physiological condition
  2. at a particular time and is the culminating phenotype resulting from
  • various “upstream” control mechanisms of metabolic processes.

Of particular interest to this present study are

  • the quantitative profiles of metabolites that are secreted into the extracellular environment
  • by cells under different conditions.

Recent advances in profiling the extracellular metabolome (EM) have allowed

  • obtaining insightful biological information on cellular metabolism
  • without disrupting the cell itself.

This information can be obtained through various

  • analytical detection,
  • identification, and
  • quantization techniques

for a variety of systems ranging from

  • unicellular model organisms to human biofluids [2023].

Metabolite secretion by a cell reflects its internal metabolic state, and

  • its composition varies in response to
  • genetic or experimental perturbations
  • due to changes in intracellular pathway activities
  • involved in the production and utilization of extracellular metabolites [21].

Variations in metabolic fluxes can be reflected in EM changes which can

  • provide insight into the intracellular pathway activities related to metabolite secretion.

The extracellular metabolomic approach has already shown promise

  • in a variety of applications, including
  1. capturing detailed metabolite biomarker variations related to disease and
  2. drug-induced states and
  3. characterizing gene functions in yeast [2427].

However, interpreting changes in the extracellular metabolome can be challenging

  • due to the indirect relationship between the proximal cause of the change
    (e.g. a mutation)
  • and metabolite secretion.

Since metabolic networks describe

  • mechanistic,
  • biochemical links between metabolites,

integrating such data can allow a systematic approach

  • to identifying altered pathways linked to
  • quantitative changes in secretion profiles.

Measured secretion rates of major byproduct metabolites

  • can be applied as additional exchange flux constraints
  • that define observed metabolic behavior.

For example, a recent study integrating small-scale EM data

  • with a genome-scale yeast model
  • correctly predicted oxygen consumption and ethanol production capacities
  • in mutant strains with respiratory deficiencies [28].

The respiratory deficient mutant study

  • used high accuracy measurements for a small number of
  • major byproduct secretion rates
  • together with an optimization-based method well suited for such data.

Here, we expand the application range of the model-based method used in [28]

  • to extracellular metabolome profiles,
  • which represent a temporal snapshot of the relative abundance
  • for a larger number of secreted metabolites.

Our approach is complementary to

  • statistical (i.e. “top-down”) approaches to metabolome analysis [29]
  • and can potentially be used in applications such as biofluid-based diagnostics or
  • large-scale characterization of mutants strains using metabolite profiles.

This study implements a constraint-based sampling approach on

  • an updated genome-scale network of yeast metabolism
  • to systematically determine how EM level variations

are linked to global changes in intracellular metabolic flux states.

By using a sampling-based network approach and statistical methods (Figure 1),

  • EM changes were linked to systemic intracellular flux perturbations
    in an unbiased manner
  • without relying on defining single optimal flux distributions
  • used in the previously mentioned study [28].

The inferred perturbations in intracellular reaction fluxes were further analyzed

  • using reporter metabolite and subsystem (i.e., metabolic pathway) approaches [30]
  • in order to identify dominant metabolic features that are collectively perturbed (Figure 2).

The sampling-based approach also has the additional benefit of

  • being less sensitive to inaccuracies in metabolite secretion profiles than
  • optimization-based methods and can effectively be used – in biofluid metabolome analysis.
integration of exometabolomic (EM) data

integration of exometabolomic (EM) data

Figure 1. Schematic illustrating the integration of exometabolomic (EM) data with the constraint-based framework.

(A) Cells are subjected to genetic and/or environmental perturbations to secrete metabolite patterns unique to that condition.
(B) EM is detected, identified, and quantified.
(C) EM data is integrated as required secretion flux constraints to define allowable solution space.
(D) Random sampling of solution space yields the range of feasible flux distributions for intracellular reactions.
(E) Sampled fluxes were compared to sampled fluxes of another condition to determine

  • which metabolic regions were altered between the two conditions (see Figure 2).

(F) Significantly altered metabolic regions were identified.

http://www.biomedcentral.com/content/figures/1752-0509-3-37-1.jpg

 

sampling and scoring analysis to determine intracellular flux changes

sampling and scoring analysis to determine intracellular flux changes

Figure 2. Schematic of sampling and scoring analysis to determine intracellular flux changes.

(A) Reaction fluxes are sampled for two conditions.
(B & C) Sample of flux differences is calculated by selecting random flux values from each condition

  • to obtain a distribution of flux differences for each reaction.

(D) Standardized reaction Z-scores are determined, which represent

  • how far the sampled flux differences deviates from a zero flux change.

Reaction scores can be used in

  1. visualizing perturbation subnetworks and
  2. analyzing reporter metabolites and subsystems.

http://www.biomedcentral.com/content/figures/1752-0509-3-37-2.jpg

This study was divided into two parts and describes:

(i) the reconstruction and validation of an expanded S. cerevisiae metabolic network, iMM904; and
(ii) the systematic inference of intracellular metabolic states from

  • two yeast EM data sets using a constraint-based sampling approach.

The first EM data set compares wild type yeast to the gdh1/GDH2 (glutamate dehydrogenase) strain [31],

  • which indicated good agreement between predicted metabolic changes
  • of intracellular metabolite levels and fluxes [31,32].

The second EM data set focused on secreted amino acid measurements

  • from a separate study of yeast cultured in different
    ammonium and potassium concentrations [33].

We analyzed the EM data to gain further insight into

  • perturbed ammonium assimilation processes as well as
  1. metabolic states relating potassium limitation and
  2. ammonium excess conditions to one another.

The model-based analysis of both

  • separately published extracellular metabolome datasets
  • suggests a relationship between
  1. glutamate,
  2. threonine and
  3. folate metabolism,
  • which are collectively perturbed when
    ammonium assimilation processes are broadly disrupted
  1. either by environmental (excess ammonia) or
  2. genetic (gene deletion/overexpression) perturbations.

The methods herein present an approach to

  • interpreting extracellular metabolome data and
  • associating these measured secreted metabolite variations
  • to changes in intracellular metabolic network states.

Additional file 1. iMM904 network content.

The data provided represent the content description of the iMM904 metabolic network and
detailed information on the expanded content.

Format: XLS Size: 2.7MB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional file 2. iMM904 model files.

The data provided are the model text files of the iMM904 metabolic network
that is compatible with the available COBRA Toolbox [13]. The model structure
can be loaded into Matlab using the ‘SimPhenyPlus’ format with GPR and compound information.

Format: ZIP Size: 163KB Download file

Conversion of the network to a predictive model

The network reconstruction was converted to a constraint-based model using established procedures [13].

Network reactions and metabolites were assembled into a stoichiometric matrix 

  • containing the stoichiometric coefficients of the reactions in the network.

The steady-state solution space containing possible flux distributions

  • is determined by calculating the null space of S= 0,

where is the reaction flux vector.

Minimal media conditions were set through constraints on exchange fluxes

  • corresponding to the experimental measured substrate uptake rates.

All the model-based calculations were done using the Matlab COBRA Toolbox [13]

  • utilizing the glpk or Tomlab/CPLEX (Tomopt, Inc.) optimization solvers.

Chemostat growth simulations

The iMM904 model was initially validated by

  1. simulating wild type yeast growth in aerobic and anaerobic
    carbon-limited chemostat conditions
  2. and comparing the simulation results to published experimental data

on substrate uptake and byproduct secretion in these conditions [34].

The study was performed following the approach taken to validate the iFF708 model in a previous study [35].

The predicted glucose uptake rates were determined

  1. by setting the in silico growth rate to the measured dilution rate,
    – equivalent under continuous culture growth,
  2. and minimizing the glucose uptake rate.

The accuracy of in silico predictions of

  • substrate uptake and byproduct secretion by the iMM904 model
  • was similar to the accuracy obtained using the iFF708 model
  • and results are shown in Figure S1 [see Additional file 3].

Additional file 3. Supplemental figures. 

The file provides the supplemental figures and descriptions of S1, S2, S3, and S4.

Format: PDF Size: 513KB Download file

This file can be viewed with: Adobe Acrobat Reader

Genome-scale gene deletion phenotype predictions

The iMM904 network was further validated by

  • performing genome-scale gene lethality computations
  • following established procedures to determine growth phenotypes
  1. under minimal medium conditions and
  2. compared to published data.

A modified version of the biomass function used in previous iND750 studies

  1. was set as the objective to be maximized and
  2. gene deletions were simulated by

setting the flux through the corresponding reaction(s) to zero.

The biomass function was based on the experimentally measured

  1. composition of major cellular constituents
  2. during exponential growth of yeast cells and
  3. was reformulated to include trace amounts of
  4. additional cofactors and metabolites
  5. with the assumed fractional contribution of 10-.

These additional biomass compounds were included

according to the biomass formulation used in the iLL672 study

  • to improve lethality predictions through
  • the inclusion of additional essential biomass components [3].

The model was constrained by limiting

  1. the carbon source uptake to 10 mmol/h/gDW
  2. and oxygen uptake to 2 mmol/h/gDW.

Ammonia, phosphate, and sulfate were assumed to be non-limiting.

The experimental phenotyping data was obtained

  • using strains that were auxotrophic for
  1. methionine,
  2. leucine,
  3. histidine, and
  4. uracil.

These auxotrophies were simulated

  1. by deleting the appropriate genes from the model and
  2. supplementing the in silico strain with the appropriate supplements
  3. at non-limiting, but low levels.

Furthermore, trace amounts of essential nutrients that are present

  • in the experimental minimal media formulation
  1. 4-aminobenzoate,
  2. biotin,
  3. inositol,
  4. nicotinate,
  5. panthothenate,
  6. thiamin)
  • were supplied in the simulations [3].

Three distinct methods to simulate the outcome of gene deletions were utilized:

  1. Flux-balance analysis (FBA) [36-38],
  2. Minimization of Metabolic Adjustment (MoMA) [39], and
  3. a linear version of MoMA (linearMoMA).

In the linearMoMA method, minimization of the quadratic objective function
of the original MoMA algorithm

  • was replaced by minimization of the corresponding 1-norm objective function
    (i.e. sum of the absolute values of the differences of wild type FBA solution
    and the knockout strain flux solution).

The computed results were then compared to growth phenotype data
(viable/lethal) from a previously published experimental gene deletion study [3].

The comparison between experimental and in silico deletion phenotypes involved

  • choosing a threshold for the predicted relative growth rate of
  • a deletion strain that is considered to be viable.

We used standard ROC curve analysis

  • to assess the accuracy of different prediction methods and models
  • across the full range of the viability threshold parameter,
    results shown in Figure S2 [see Additional file 3].

The ROC curve plots the true viable rate against the false viable rate

  • allowing comparison of different models and methods
  • without requiring arbitrarily choosing this parameter a priori [40].

The optimal prediction performance corresponds to

  • the point closest to the top left corner of the ROC plot
    (i.e. 100% true viable rate, 0% false viable rate).

Table 1

Table 1 Comparison of iMM904 and iLL672 gene deletion predictions and experimental data under minimal media conditions
Media Model Method True viable False viable False lethal True lethal True viable % False viable % MCC
Glucose iMM904 full FBA 647 10 32 33 95.29 23.26 0.6
iMM904 full linMOMA 644 10 35 33 94.85 23.26 0.58
iMM904 full MOMA 644 10 35 33 94.85 23.26 0.58
iMM904 red FBA 440 9 28 33 94.02 21.43 0.61
iMM904 red linMOMA 437 9 31 33 93.38 21.43 0.6
iMM904 red MOMA 437 9 31 33 93.38 21.43 0.6
iLL672 full MOMA 433 9 35 33 92.52 21.43 0.57
Galactose iMM904 full FBA 595 32 36 59 94.29 35.16 0.58
iMM904 full linMOMA 595 32 36 59 94.29 35.16 0.58
iMM904 full MOMA 595 32 36 59 94.29 35.16 0.58
iMM904 red FBA 409 12 33 56 92.53 17.65 0.67
iMM904 red linMOMA 409 12 33 56 92.53 17.65 0.67
iMM904 red MOMA 409 12 33 56 92.53 17.65 0.67
iLL672 full MOMA 411 19 31 49 92.99 27.94 0.61
Glycerol iMM904 full FBA 596 43 36 47 94.3 47.78 0.48
iMM904 full linMOMA 595 44 37 46 94.15 48.89 0.47
iMM904 full MOMA 598 44 34 46 94.62 48.89 0.48
iMM904 red FBA 410 20 34 46 92.34 30.3 0.57
iMM904 red linMOMA 409 21 35 45 92.12 31.82 0.56
iMM904 red MOMA 412 21 32 45 92.79 31.82 0.57
iLL672 full MOMA 406 20 38 46 91.44 30.3 0.55
Ethanol iMM904 full FBA 593 45 29 55 95.34 45 0.54
iMM904 full linMOMA 592 45 30 55 95.18 45 0.54
iMM904 full MOMA 592 44 30 56 95.18 44 0.55
iMM904 red FBA 408 21 27 54 93.79 28 0.64
iMM904 red linMOMA 407 21 28 54 93.56 28 0.63
iMM904 red MOMA 407 20 28 55 93.56 26.67 0.64
iLL672 full MOMA 401 13 34 62 92.18 17.33 0.68
MCC, Matthews correlation coefficient (see Methods). Note that the iLL672 predictions were obtained directly from [3] and thus the viability threshold was not optimized using the maximum MCC approach.
Mo et al. BMC Systems Biology 2009 3:37  http://dx.doi.org:/10.1186/1752-0509-3-37

 

The values reported in Table 1 correspond to selecting

  • the optimal viability threshold based on this criterion.

We summarized the overall prediction accuracy of a model and method

  • using the Matthews Correlation Coefficient (MCC) [40].

The MCC ranges from -1 (all predictions incorrect) to +1 (all predictions correct) and

  • is suitable for summarizing overall prediction performance

in our case where there are substantially more viable than lethal gene deletions.

ROC plots were produced in Matlab (Mathworks, Inc.).

 

Table 1. Comparison of iMM904 and iLL672

  • gene deletion predictions and
  • experimental data

Inferring perturbed metabolic regions based on EM profiles

The method implemented in this study is shown schematically in Figures 1 and 2

Constraining the iMM904 network 

Relative levels of quantitative EM data were incorporated into the constraint-based framework

  • as overflow secretion exchange fluxes to simulate the required low-level production of
  • experimentally observed excreted metabolites.

The primary objective of this study is to associate

  • relative metabolite levels that are generally measured for metabonomic or biofluid analyses
  • to the quantitative ranges of intracellular reaction fluxes required to produce them.

However, without detailed kinetic information or dynamic metabolite measurements available,

  • we approximated EM datasets of relative quantitative metabolite levels
  • to be proportional to the rate in which they are secreted and detected
  • (at a steady state) – into the extracellular media.

This approach is analogous to approximating uptake rates based

  • on metabolite concentrations from a previous study performing sampling analysis
  • on a cardiomyocyte mitochondrial network
  • to identify differential flux distribution ranges

for various environmental (i.e. substrate uptake) conditions [19].

The raw data was normalized by the raw maximum value of the dataset
(thus the maximum secretion flux was 1 mmol/hr/gDW) with

  • an assumed error of 10%
  • to set the lower and upper bounds and thus
  • inherently accounting for sampling calculation sensitivity.

The gdh1/GDH2 strains were flask cultured under minimal glucose media conditions; thus,

  • glucose and oxygen uptake rates were set at 15 and 2 mmol/hr/gDW, respectively,
  • for the gdh1/GDH2 strain study.

In the anaerobic case the oxygen uptake rate was set to zero, and

  • sterols and fatty acids were provided as in silico supplements as described in [35].

For the potassium limitation/ammonium toxicity study

  • the growth rate was set at 0.17 1/h, and
  • the glucose uptake rate was minimized
  • to mimic experimental chemostat cultivation conditions.

These input constraints were constant for each perturbation and comparative wild-type condition

  • such that the calculated solution spaces between the conditions
  • differed based only on variations in the output secretion constraints.

FBA optimization of EM-constrained networks

A modified FBA method with minimization of the 1-norm objective function

  • between two optimal flux distributions was used
  • to determine optimal intracellular fluxes
  • based on the EM-constrained metabolic models.

This method determines two optimal flux distributions simultaneously

  • for two differently constrained models (e.g. wild type vs. mutant) –
  • these flux distributions maximize biomass production in each case and
  • the 1-norm distance between the distributions is as small as possible
  • given the two sets of constraints.

This approach avoids problems with

  • alternative optimal solutions when comparing two FBA-computed flux distributions
  • by assuming minimal rerouting of flux distibution between a perturbed network and its reference network.

Reaction flux changes from the FBA optimization results were determined

  • by computing the relative percentage fold change for each reaction
  • between the mutant and wild-type flux distributions.

Random sampling of the steady-state solution space

We utilized artificial centering hit-and-run (ACHR) Monte Carlo sampling [19,41]

  • to uniformly sample the metabolic flux solution space
  • defined by the constraints described above.

Reactions, and their participating metabolites, found to participate in intracellular loops [42]

  • were discarded from further analysis as these reactions can have arbitrary flux values.

The following sections describe the approaches used for the analysis of the different datasets.

Sampling approach used in the gdh1/GDH2 study

Due to the overall shape of the metabolic flux solution space,

  • most of the sampled flux distributions resided close to the minimally allowed growth rate
    (i.e. biomass production) and
  • corresponded to various futile cycles that utilized substrates but
  • did not produce significant biomass.

In order to study more physiologically relevant portions of the flux space

  • we restricted the sampling to the part of the solution space
  • where the growth rate was at least 50% of the maximum growth rate
  • for the condition as determined by FBA.

This assumes that cellular growth remains an important overall objective by the yeast cells

  • even in batch cultivation conditions, but
  • that the intracellular flux distributions
  • may not correspond to maximum biomass production [43].

To test the sensitivity of the results to the minimum growth rate threshold,

  • separate Monte Carlo samples were created for each minimum threshold
  • ranging from 50% to 100% at 5% increments.

We also tested the sensitivity of the results

  • to the relative magnitude of the extracellular metabolite secretion rates
  • by performing the sampling at three different relative levels

(0 corresponding to no extracellular metabolite secretion, maximum rate of 0.5 mmol/hr/gDW,
and maximum rate of 1.0 mmol/hr/gDW).

For each minimum growth rate threshold and extracellular metabolite secretion rate,

  • the ACHR sampler was run for 5 million steps and
  • a flux distribution was stored every 5000 steps.

The sensitivity analysis results are presented in Figures S3 and S4 [see Additional File 3], and

  • the results indicate that the reaction Z-scores (see below) are not significantly affected by
  1. either the portion of the solution space sampled or
  2. the exact scaling of secretion rates.

The final overall sample used was created by combining the samples for all minimum growth rate thresholds

  • for the highest extracellular metabolite secretion rate (maximum 1 mmol/hr/gDW).

This approach allowed biasing the sampling towards

  • physiologically relevant parts of the solution space
  • without imposing the requirement of strictly maximizing a predetermined objective function.

The samples obtained with no EM data were used as control samples

  • to filter reporter metabolites/subsystems whose scores were significantly high
  • due to only random differences between sampling runs.

Sampling approach used in the potassium limitation/ammonium toxicity study

Since the experimental data used in this study was generated in chemostat conditions, and

  • previous studies have indicated that chemostat flux patterns predicted by FBA are
  • close to the experimentally measured ones [43],
  • we assumed that sampling of the optimal solution space was appropriate for this study.

In order to sample a physiologically reasonable range of flux distributions,

  • samples for four different oxygen uptake rates
    (1, 2, 3, and 4 mmol/hr/gDW with 5 million steps each)
  • were combined in the final analysis.

Standardized scoring of flux differences between perturbation and control conditions

Z-score based approach was implemented to quantify differences in flux samples between two conditions (Figure 2).
First, two flux vectors were chosen randomly,

  • one from each of the two samples to be compared and
  • the difference between the flux vectors was computed.

This approach was repeated to create a sample of 10,000 (n) flux difference vectors

  • for each pair of conditions considered (e.g. mutant or perturbed environment vs. wild type).

Based on this flux difference sample, the sample mean (μdiff,i) and standard deviation (σdiff,i)

  • between the two conditions was calculated for each reaction i. The reaction Z-score was calculated as:

 

reaction Z-score

reaction Z-score

which describes the sampled mean difference deviation

  • from a population mean change of zero (i.e. no flux difference
    between perturbation and wild type).

Note that this approach allows accounting for uncertainty in the

  • flux distributions inferred based on the extracellular metabolite secretion constraints.

This is in contrast to approaches such as FBA or MoMA that would predict

  • a single flux distribution for each condition and thus potentially
  • overestimate differences between conditions.

The reaction Z-scores can then be further used in analysis

  • to identify significantly perturbed regions of the metabolic network
  • based on reporter metabolite [44] or subsystem [30] Z-scores.

These reporter regions indicate, or “report”, dominant perturbation features

  • at the metabolite and pathway levels for a particular condition.

The reporter metabolite Z-score for any metabolite can be derived from the reaction Z-scores

  • of the reactions consuming or producing j (set of reactions denoted as Rj) as:

 

reporter z-score for any metabolite j

reporter z-score for any metabolite j

where Nis the number of reactions in Rand mmet,is calculated as

 

distributional correction for m_met,j SQRT

distributional correction for m_met,j SQRT

To account and correct for background distribution, the metabolite Z-score was normalized

  • by computing μmet,Nj and σmet,,Nj corresponding to the mean mmet and
  • its standard deviation for 1,000 randomly generated reaction sets of size Nj.

Z-scores for subsystems were calculated similarly by considering the set of reactions R

  • that belongs to each subsystem k.

Hence, positive metabolite and subsystem scores indicate a significantly perturbed metabolic region

  • relative to other regions, whereas
  • a negative score indicate regions that are not perturbed
  • more significantly than what is expected by random chance.

Perturbation subnetworks of reactions and connecting metabolites were visualized using Cytoscape [45].

Results and discussion

  1. Reconstruction and validation of iMM904 network iMM904 network content 

A previously reconstructed S. cerevisiae network, iND750,

  • was used as the basis for the construction of the expanded iMM904 network.
  • Prior to its presentation here, the
    iMM904 network content was the basis for a consensus jamboree network that was recently published
  • but has not yet been adapted for FBA calculations [46].

The majority of iND750 content was carried over and

  • further expanded on to construct iMM904, which accounts for
  1. 904 genes,
  2. 1,228 individual metabolites, and
  3. 1,412 reactions of which
  •                       395 are transport reactions.

Both the number of gene-associated reactions and the number of metabolites

  • increased in iMM904 compared with the iND750 network.

Additional genes and reactions included in the network primarily expanded the

  • lipid,
  • transport, and
  • carbohydrate subsystems.

The lipid subsystem includes

  • new genes and
  • reactions involving the degradation of sphingolipids and glycerolipids.

Sterol metabolism was also expanded to include

  • the formation and degradation of steryl esters, the
  •                      storage form of sterols.

The majority of the new transport reactions were added

  • to connect network gaps between intracellular compartments
  • to enable the completion of known physiological functions.

We also added a number of new secretion pathways

  • based on experimentally observed secreted metabolites [31].

A number of gene-protein-reaction (GPR) relationships were modified

  • to include additional gene products that are required to catalyze a reaction.

For example, the protein compounds

  • thioredoxin and
  • ferricytochrome C

were explicitly represented as compounds in iND750 reactions, but

  • the genes encoding these proteins were not associated with their corresponding GPRs.

Other examples include glycogenin and NADPH cytochrome p450 reductases (CPRs),

  1. which are required in the assembly of glycogen and
  2. to sustain catalytic activity in cytochromes p450, respectively.

These additional proteins were included in iMM904 as

  • part of protein complexes to provide a more complete
  • representation of the genes and
  • their corresponding products necessary for a catalytic activity to occur.

Major modifications to existing reactions were in cofactor biosynthesis, namely in

  • quinone,
  • beta-alanine, and
  • riboflavin biosynthetic pathways.

Reactions from previous S. cerevisiae networks associated with

  • quinone,
  • beta-alanine, and
  • riboflavin biosynthetic pathways

were essentially inferred from known reaction mechanisms based on

  • reactions in previous network reconstructions of E. coli [2,47].

These pathways were manually reviewed

  • based on current literature and subsequently replaced by
  • reactions and metabolites specific to yeast.

Additional changes in other subsystems were also made, such as

  1. changes to the compartmental location of a gene and
  2. its corresponding reaction(s),
  3. changes in reaction reversibility and cofactor specificity, and
  4. the elucidation of particular transport mechanisms.

A comprehensive listing of iMM904 network contents as well as

  • a detailed list of changes between iND750 and iMM904 is included
    [see Additional file 1].

Predicting deletion growth phenotypes

The updated genome-scale iMM904 metabolic network was validated

  • by comparing in silico single-gene deletion predictions to
  • in vivo results from a previous study used
  • to analyze another S. cerevisiae metabolic model, iLL672 [3].

This network was constructed based on the iFF708 network [22],

  • which was also the starting point for
  • reconstructing the iND750 network [2].

The experimental data used to validate the iLL672 model consisted of

3,360 single-gene knockout strain phenotypes evaluated

  • under minimal media growth conditions with
  1. glucose,
  2. galactose,
  3. glycerol, and
  4. ethanol

as sole carbon sources. Growth phenotypes for the iMM904 network were predictedusing

  1. FBA [3234],
  2. MoMA [35], and
  3. linear MoMA methods

as described in Methods and subsequently compared to the experimental data (Table 1).

Each deleted gene growth prediction comparison was classified as

  1. true lethal,
  2. true viable,
  3. false lethal, or
  4. false viable.

The growth rate threshold for considering a prediction viable was chosen

  • for each condition and method separately
  • to optimize the tradeoff between true viable and false viable predictions
    (maximum Matthews correlation coefficient, see Methods).

Since iMM904 has 212 more genes than iLL672 with experimental data, we also present results

  • for the subset of iMM904 predictions with genes included in iLL672 (reduced iMM904 set).

When the same gene sets are compared, iMM904 improves gene lethality predictions under

  • glucose,
  • galactose, and
  • glycerol conditions

over iLL672 somewhat, but is less accurate

  • at predicting growth phenotypes under the ethanol condition.

It should be noted that the iLL672 predictions were obtained directly from [3]

  • thus the growth rate threshold was not optimized similarly to iMM904 predictions.

Overall, when viability cutoff is chosen

  • as indicated above for each method separately,
  • the three prediction methods perform similarly
  1. FBA,
  2. MOMA, and
  3. linear MOMA) .

While the full gene complement in iMM904 greatly increased

  • the number of true viable predictions,
  • the full model also made significantly more false viable predictions
  • compared with reduced iMM904 and iLL672 predictions.

However, it is important to note that 143 reactions involved in dead-end biosynthetic pathways were actually

  • removed from iFF708 to build the iLL672 reconstruction [3].

These dead-ends are considered “knowledge gaps” in pathways

  • that have not been fully characterized and, as a result,
  • lead to false viable predictions when determining gene essentiality
  • if the pathway is in fact required for growth under a certain condition [2,26].

As more of these pathways are elucidated and

  • included in the model to
  • fill in existing network gaps,
  • we can expect false viable prediction rates to consequently decrease.

Thus, while a larger network has a temporarily reduced capacity to accurately predict gene deletion phenotypes,

  • it captures a more complete picture of currently known metabolic functions and
  • provides a framework for network expansion as new pathways are elucidated [48].

 

Inferring intracellular perturbation states from metabolic profiles – Aerobic and anaerobic gdh1/GDH2 mutant behavior

The gdh1/GDH2 mutant strain was previously developed [49,50]

  • to lower NADPH consumption in ammonia assimilation, which would
  • favor the NADPH-dependent fermentation of xylose.

In this strain, the NADPH-dependent glutamate dehydrogenase, Gdh1, was

  • deleted and the NADH-dependent form of the enzyme, Gdh2,
  •                     was overexpressed.

The net effect is to allow efficient assimilation of ammonia

  • into glutamate using NADH instead of NADPH as a cofactor.

While growth characteristics remained unaffected,

  • relative quantities of secreted metabolites differed between the wild-type and mutant strain
  • under aerobic and anaerobic conditions.

We analyzed EM data for the gdh1/GDH2 and wild-type strains reported

  • in [31] under aerobic and anaerobic conditions separately using
  • both FBA optimization and
  • sampling-based approaches as described in Methods.

43 measured extracellular and intracellular metabolites from the original dataset [31],

  • primarily of central carbon and amino acid metabolism,
  • were explicitly represented in the iMM904 network [see Additional file 4].

Extracellular metabolite levels were used

  • to formulate secretion constraints and
  • differential intracellular metabolites were used
  • to compare and validate the intracellular flux predictions.

Perturbed reactions from the FBA results were

  • determined by calculating relative flux changes, and
  • reaction Z-scores were calculated from the sampling analysis
  • to quantify flux changes between the mutant and wild-type strains,
  • with Z reaction > 1.96 corresponding to a two-tailed p-value < 0.05 and
  • considered to be significantly perturbed [see Additional file 4].

Additional file 4. Gdh mutant aerobic and anaerobic analysis results. 

The data provided are the full results for the exometabolomic analysis of aerobic and anerobic gdh1/GDH2 mutant.

Format: XLS Size: 669KB Download file

This file can be viewed with: Microsoft Excel Viewer

To validate the predicted results, reaction flux changes from both FBA and sampling methods were compared to differential intracellular metabolite level data measured from the same study. Intracellular metabolites involved in highly perturbed reactions (i.e. reactants and products) predicted from FBA and sampling analyses were identified and
compared to metabolites that were experimentally identified as significantly changed (< 0.05) between mutant and wild-type. Statistical measures of recall, accuracy, and
precision were calculated and represent the predictive sensitivity, exactness, and reproducibility respectively. From the sampling analysis, a considerably larger number of
significantly perturbed reactions are predicted in the anaerobic case (505 reactions, or 70.7% of active reactions) than in aerobic (394 reactions, or 49.8% of active reactions). The top percentile of FBA flux changes equivalent to the percentage of significantly perturbed sampling reactions were compared to the intracellular data. Results from both analyses are summarized in Table 2. Sampling predictions were considerably higher in recall than FBA predictions for both conditions, with respective ranges of 0.83–1
compared to 0.48–0.96. Accuracy was also higher in sampling predictions; however, precision was slightly better in the FBA predictions as expected due to the smaller
number of predicted changes. Overall, the sampling predictions of perturbed intracellular metabolites are strongly consistent with the experimental data and significantly
outperforms that of FBA optimization predictions in accurately predicting differential metabolites involved in perturbed intracellular fluxes.

Table 2. Statistical comparison of the differential intracellular metabolite data set (< 0.05) with metabolites involved in perturbed reactions predicted by FBA optimization and sampling analyses for aerobic and anaerobic gdh1/GDH2 mutant.

 

Table 2 Statistical comparison of the differential intracellular metabolite data set (p < 0.05)
with metabolites involved in perturbed reactions predicted by FBA optimization and
sampling analyses for aerobic and anaerobic gdh1/GDH2 mutant.
                           Aerobic                         Anaerobic                             Overall
FBA Sampling FBA Sampling FBA
Recall 0.48 0.83 0.96 1 0.71 0.91
Accuracy 0.55 0.62 0.64 0.64 0.6 0.63
Precision 0.78 0.69 0.64 0.63 0.68 0.66
Overall statistics indicate combined results of both conditions.
Mo et al. BMC Systems Biology 2009 3:37   http://dx.doi.org:/10.1186/1752-0509-3-37


Figure 3.
 Perturbation reaction subnetwork of gdh1/GDH2 mutant under aerobic conditions.

The network illustrates a simplified subset of highly perturbedPerturbation subnetworks can be drawn to visualize predicted significantly perturbed intracellular reactions and illustrate their connection to the observed secreted metabolites in the aerobic and anaerobic gdh1/GDH2 mutants.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under aerobic conditions.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under aerobic conditions.

Figure 3 shows an example of a simplified aerobic perturbation subnetwork consisting primarily of proximal pathways connected directly to a subset of major secreted
metabolites

  • glutamate,
  • proline,
  • D-lactate, and
  • 2-hydroxybuturate.

Figure 4 displays anaerobic reactions with Z-scores of similar magnitude to the perturbed reactions in Figure 3. The same subset of metabolites is also present in the
larger anaerobic perturbation network and indicates that the NADPH/NADH balance perturbation induced by the gdh1/GDH2 manipulation has widespread effects
beyond just altering glutamate metabolism anaerobically.

Interestingly, it is clear that the majority of the secreted metabolite pathways involve connected perturbed reactions that broadly converge on glutamate.

Note that Figures 3 and 4 only show the subnetworks that consisted of two or more connected reactions  for a number of secreted metabolites no contiguous perturbed pathway could be identified by the sampling approach. This indicates that the secreted metabolite pattern alone is not sufficient to determine which specific
production and secretion pathways are used by the cell for these metabolites.

Reactions connected to aerobically-secreted metabolites predicted from the sampling analysis of the gdh1/GDH2 mutant strain.
The major secreted metabolites

  • glutamate,
  • proline,
  • D-lactate, and
  • 2-hydroxybuturate

were also detected in the anaerobic condition. Metabolite abbreviations are found in Additional file 1.

Figure 4.

Perturbation reaction subnetwork of gdh1/GDH2 mutant under anaerobic conditions.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under anaerobic conditions