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Posts Tagged ‘AMPK’


Manipulate Signaling Pathways

Writer and Curator: Larry H Bernstein, MD, FCAP 

 

7.6  Manipulate Signaling Pathways

7.6.1 The Dynamics of Signaling as a Pharmacological Target

7.6.2 A Protein-Tagging System for Signal Amplification in Gene Expression and Fluorescence Imaging

7.6.3 IQGAPs choreograph cellular signaling from the membrane to the nucleus

7.6.4 Signaling cell death from the endoplasmic reticulum stress response

7.6.5 An Enzyme that Regulates Ether Lipid Signaling Pathways in Cancer Annotated by Multidimensional Profiling

7.6.6 Peroxisomes – A Nexus for Lipid Metabolism and Cellular Signaling

7.6.7 A nexus for cellular homeostasis- the interplay between metabolic and signal transduction pathways

7.6.8 Mechanisms-of-intercellular-signaling

7.6.9 Cathepsin B promotes colorectal tumorigenesis, cell invasion, and metastasis

 

 

7.6.1 The Dynamics of Signaling as a Pharmacological Target

Marcelo Behar, Derren Barken, Shannon L. Werner, Alexander Hoffmann
Cell  10 Oct 2013; 155(2):448–461
http://dx.doi.org/10.1016/j.cell.2013.09.018

Highlights

  • Drugs targeting signaling hubs may block specific dynamic features of the signal
  • Specific inhibition of dynamic features may introduce pathway selectivity
  • Phase space analysis reveals principles for drug targeting signaling dynamics
  • Based on these principles, NFκB dynamics can be manipulated with specificity

Summary

Highly networked signaling hubs are often associated with disease, but targeting them pharmacologically has largely been unsuccessful in the clinic because of their functional pleiotropy. Motivated by the hypothesis that a dynamic signaling code confers functional specificity, we investigated whether dynamic features may be targeted pharmacologically to achieve therapeutic specificity. With a virtual screen, we identified combinations of signaling hub topologies and dynamic signal profiles that are amenable to selective inhibition. Mathematical analysis revealed principles that may guide stimulus-specific inhibition of signaling hubs, even in the absence of detailed mathematical models. Using the NFκB signaling module as a test bed, we identified perturbations that selectively affect the response to cytokines or pathogen components. Together, our results demonstrate that the dynamics of signaling may serve as a pharmacological target, and we reveal principles that delineate the opportunities and constraints of developing stimulus-specific therapeutic agents aimed at pleiotropic signaling hubs.

http://www.cell.com/cms/attachment/2021777732/2041663648/fx1.jpg

Intracellular signals link the cell’s genome to the environment. Misregulation of such signals often cause or exacerbate disease (Lin and Karin, 2007 and Weinberg, 2007) (so-called “signaling diseases”), and their rectification has been a major focus of biomedical and pharmaceutical research (Cohen, 2002Frelin et al., 2005 and Ghoreschi et al., 2009). For the identification of therapeutic targets, the concept of discrete signaling pathways that transmit intracellular signals to connect cellular sensor/receptors with cellular core machineries has been influential. In this framework, molecular specificity of therapeutic agents correlates well with their functional or phenotypic specificity. However, in practice, clinical outcomes for many drugs with high molecular specificity has been disappointing (e.g., inhibitors of IKK, MAPK, and JNK; Berger and Iyengar, 2011DiDonato et al., 2012Röring and Brummer, 2012 and Seki et al., 2012).

Many prominent signaling mediators are functionally pleiotropic, playing roles in multiple physiological functions (Chavali et al., 2010 and Gandhi et al., 2006). Indeed, signals triggered by different stimuli often travel through shared network segments that operate as hubs before reaching the effectors of the cellular response (Bitterman and Polunovsky, 2012 and Gao and Chen, 2010). Hubs’ inherent pleiotropy means that their inhibition may have broad and likely undesired effects (Karin, 2008Berger and Iyengar, 2011,Force et al., 2007Oda and Kitano, 2006 and Zhang et al., 2008); this is a major obstacle for the efficacy of drugs targeting prominent signaling hubs such as p53, MAPK, or IKK.

Recent studies have begun to address how signaling networks generate stimulus-specific responses (Bardwell, 2006Haney et al., 2010Hao et al., 2008 and Zalatan et al., 2012). For example, the activity of some pleiotropic kinases may be steered to particular targets by scaffold proteins (Park et al., 2003,Schröfelbauer et al., 2012 and Zalatan et al., 2012). Alternatively, or in addition, some signaling hubs may rely on stimulus-specific signal dynamics to activate selective downstream branches in a stimulus-specific manner in a process known as temporal or dynamic coding or multiplexing (Behar and Hoffmann, 2010,Chalmers et al., 2007Hoffmann et al., 2002Kubota et al., 2012Marshall, 1995 and Purvis et al., 2012;Purvis and Lahav, 2013Schneider et al., 2012 and Werner et al., 2005).

Although the importance of signaling scaffolds and their pharmacological promise is widely appreciated (Klussmann et al., 2008 and Zalatan et al., 2012) and isolated studies have altered the stimulus-responsive signal dynamics (Purvis et al., 2012Park et al., 2003Sung et al., 2008 and Sung and Simon, 2004), the capacity for modulating signal dynamics for pharmacological gain has not been addressed in a systematic manner. In this work, we demonstrate by theoretical means that, when signal dynamics are targeted, pharmacological perturbations can produce stimulus-selective results. Specifically, we identify combinations of signaling hub topology and input-signal dynamics that allow for pharmacological perturbations with dynamic feature-specific or input-specific effects. Then, we investigate stimulus-specific drug targeting in the IKK-NFκB signaling hub both in silico and in vivo. Together, our work begins to define the opportunities for pharmacological targeting of signaling dynamics to achieve therapeutic specificity.

Dynamic Signaling Hubs May Be Manipulated to Mute Specific Signals

Previous work has shown how stimulus-specific signal dynamics may allow a signaling hub to selectively route effector functions to different downstream branches (Behar et al., 2007). Here, we investigated the capacity of simple perturbations to kinetic parameters (caused for example by drug treatments) to produce stimulus-specific effects. For this, we examined a simple model of an idealized signaling hub (Figure 1A), reminiscent of the NFκB p53 or of MAPK signaling modules. The hub X reacts with strong but transient activity to stimulus S1 and sustained, slowly rising activity to stimulus S2. These stimulus-specific signaling dynamics are decoded by two effector modules, regulating transcription factors TF1 and TF2. TF1, regulated by a strongly adaptive negative feedback, is sensitive only to fast-changing signals, whereas TF2, regulated by a slowly activating two-state switch, requires sustained signals for activation (Figure 1B). We found it useful to characterize the X, TF1, and TF2 responses in terms of two dynamic features, namely the maximum early amplitude (“E,” time < 15′) and the average late amplitude (“L,” 15′ < t < 6 hr). These features, calculated using a mathematical model of the network (see Experimental Procedures) show good fidelity and specificity (Komarova et al., 2005) (Figure 1C), as S1 causes strong activation of TF1 with minimal crosstalk to TF2, and vice versa for S2.

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Figure 1. Pharmacologic Perturbations with Stimulus-Specific Effects

(A) A negative-feedback module transduces input signals S1 and S2, producing outputs that are decoded by downstream effectors circuits that may distinguish between different dynamics.

(B) Unperturbed dynamics of X, TF1, and TF2 in response to S1 (red) and S2 (blue). Definition of early (E) and late (L) parts of the signal is indicated.

(C) Specificity and fidelity of E and L for TF1 and TF2, as defined in Komarova et al., 2005).

(D) Partial inhibition of X activation (A) abolishes the response to S1, but not S2, whereas a perturbation targeting the feedback regulator (FBR) suppresses the response to S2, but not S1.

(E) Perturbation phenotypes defined as difference between unperturbed and perturbed values of the indicated quantities (arbitrary scales for X, TF1, and TF2). Perturbation A inhibits E and TF1, but not TF2; perturbation FBR inhibits L and TF2, but not TF1.

(F) Virtual screening pipeline showing the experimental design and the two analysis branches for characterizing feature- and input-specific effects.

See also in Experimental Procedures and Table S1.

Seeking simple (affecting a single reaction) perturbations that selectively inhibit signaling by S1 or S2, we found that perturbation A, partially inhibiting the activation of X, was capable of suppressing hub activity in response to a range of S1 amplitudes while still allowing for activity in response to S2 (Figure 1D). Consequently, this perturbation significantly reduced TF1 activity in response to S1 but had little effect on TF2 activity elicited by S2. We also found that the most effective way to inhibit S2 signaling was by targeting the deactivation of negative feedback regulator Y (FBR). This perturbation caused almost complete abrogation of late X activity yet allows for significant levels of early activity. As a result, TF2 was nearly completely abrogated in response to S2, but stimulus S1 still produced a solid TF1 response. The early (E) and late (L) amplitudes could be used to quantify the input-signal-specific effects of these perturbations (Figure 1E).

This numerical experiment showed that it is possible to selectively suppress transient or sustained dynamic signals transduced through a common negative-feedback-containing signaling hub. Moreover, the dynamic features E and L could be independently inhibited. To study how prevalent such opportunities for selective inhibition are, we established a computational pipeline for screening reaction perturbations within multiple network topologies and in response to multiple dynamic input signals; the simulation results were analyzed to identify cases of either “input-signal-specific” inhibition or “dynamic feature-specific” inhibition (Figure 1F).

A Computational Screen to Identify Opportunities for Input-Signal-Specific Inhibition

The computational screen involved small libraries of one- and two-component regulatory modules and temporal profiles of input signals (Figure 2A), both commonly found in intracellular signaling networks. All modules (M1–M7, column on left) contained a species X that, upon stimulation by an input signal, is converted into an active form X (the output) that propagates the signal to downstream effectors. One-component modules included a reversible two-state switch (M1) and a three-state cycle with a refractory state (M2). Two-component modules contained a species Y that, upon activation via a feedback (M3 and M5) or feedforward (M4 and M6) loop, either deactivates X (M3 and M4) or inhibits (M5 and M6) its activation. We also included the afore-described topology that mimics the IκB-NFκB or the Mdm2-p53 modules (M7). Mathematical descriptions may be found in the Experimental Procedures. Although many biological signaling networks may conform to one of these simple topologies, others may be abstracted to one that recapitulates the physiologically relevant emergent properties

Figure 2. A Virtual Screen for Stimulus Specificity in Pharmacologic Perturbations

(A) Signaling modules (left) and input library (top) used in the screen. Dotted lines indicate enzymatic reactions (perturbation names indicated in letter code). Time courses of hub activity for each module/input combination for the unperturbed (black) and perturbed cases (blue indicates a decrease, red an increase in parameter value).

(B) Relative sensitivity of the stimulus response to the indicated perturbation (defined as the perturbation’s effect on the area under the curve), normalized per row.

See also Experimental ProceduresFigure S1, and Tables S2 and S3.

The library of stimuli (S1–S10; Figure 2A, top row) comprises ten input functions with different combinations of “fast” and “slow” initiation and decay phases (see Experimental Procedures). The virtual screen was performed by varying the kinetic parameter for each reaction over a range of values, thereby modeling simple perturbations of different strengths and recording the temporal profile of X abundance. To quantify stimulus-specific inhibition, we measured the area under the normalized dose-response curves (time average of X versus perturbation dose) for each module-input combination (Experimental ProceduresFigure 2B, and Figure S1 available online).

Phase Space Analysis Reveals Underlying Regulatory Principles

To understand the origin of dynamic feature-specific inhibition, we investigated the perturbation effects analytically on each module’s phase space, i.e., the space defined by X∗ and Y∗ quasi-equilibrium surfaces (Figures 4 and S4). These surfaces (“q.e. surfaces”) represent the dose response of X∗ as a function of Y∗ and a stationary input signal S (“X surface”) and the dose response of Y∗ as a function of X∗ and S (“Y surface”) (Figure 4A). The points at which the surfaces intersect correspond to the concentrations of X∗ and Y∗ in equilibrium for a given value of S. In the basal state, when S is low, the system is resting at an equilibrium point close to the origin of coordinates. When S increases, the concentrations of X∗ and Y∗ adjust until the signal settles at some stationary value (Figure 4A). Gradually, changing input signals cause the concentrations to follow trajectories close to the q.e. surfaces (quasi-equilibrium dynamics), following the line defined by the intersection of the surfaces (“q.e. line”) in the extreme of infinitely slow inputs. Fast-changing stimuli drive the system out of equilibrium, causing the trajectories to deviate markedly from the q.e. surfaces.

Two main principles emerged: (1) perturbations that primarily affect the shape of a q.e. surface tend to affect steady-state levels or responses that evolve close to quasi-equilibrium, and (2) perturbations that primarily affect the balance of timescales (X, Y activation, and S) tend to affect transient out-of-equilibrium parts of the response. These principles reflect the fact that out-of-equilibrium parts of a signal are largely insensitive to the precise shape of the underlying dose-response surfaces (they may still be bounded by them) but depend on the balance between the timescales of the biochemical processes involved. Perturbation of these balances affects how a system approaches steady state (thus affecting out-of-equilibrium and quasi-equilibrium dynamics), but not steady-state levels. To illustrate these principles, we present selected results for modules M3 and M4 and discuss additional cases in the supplement (Figure S3).

Detailed Analysis of Modules M3 and M4, Related to Figure 4

Time courses and projections of the phase space for modules M3 and M4. Color coding similar to Figure 4.

In the feedback-based modules (M3 and M5), the early peak of activity in response to rapidly changing signals is an out-of-equilibrium feature that occurs when the timescale of Y activation is significantly slower than that of X. Under these conditions, the concentration of X increases rapidly (out of equilibrium) before decaying along the X surface (in quasi-equilibrium) as more Y gets activated (Figure 4A, parameters modified to better illustrate the effects being discussed; see Table S2). For input signals that settle at some stationary level of S, Y activation eventually catches up and the concentration of X settles at the equilibrium point where the X and Y curves intersect. Gradually changing signals allow X and Yactivation to continuously adapt, and the system evolves closer to the q.e. line.

In such modules, perturbation A (X activation) changes both the shape of the q.e. surface for X and the kinetics of activation. When in the unperturbed system Y saturates, perturbation A primarily reduces Xsteady-state level (Figures 4B and 4C, left and center). When Y does not saturate in the unperturbed system, the primary effect is the reduced activation kinetics. Thus the perturbation affects the out-of-equilibrium peak (Figures 4B and 4C, center and right), with only minor reduction of steady-state levels (especially when Y’s dose response respect to X is steep). The transition from saturated to not-saturated feedback (as well as the perturbation strength) underlies the dose-dependent switch from L to E observed in the screen. In both saturated and unsaturated regimes, the shift in the shape of the surfaces does change the q.e. line and thus affects responses occurring in quasi-equilibrium. In contrast, perturbation of the feedback recovery (FBR) shifts the Y surface vertically (Figure 4D), specifically affecting the steady-state levels and late signaling; the effect on Y kinetics is limited because the reaction is relatively slow. Perturbation FBA also shifts the Y surface, but the net effect is less specific because the associated increase in the rate of Y activation tends to equalize X and Y kinetics affecting also the out-of-equilibrium peak.

In resting cells, NFκB is held inactive through its association with inhibitors IκBα, β, and ε. Upon stimulation, these proteins are phosphorylated by the kinase IKK triggering their degradation. Free nuclear NFκB activates the expression of target genes, including IκB-encoding genes, which thereby provide negative feedback (Figure 5A). The IκB-NFκB-signaling module is a complex dynamic system; however, by abstracting the control mechanism to its essentials, we show below that the above-described principles can be applied profitably.

IκB-NFκB signaling module

IκB-NFκB signaling module

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Figure 5. Modulating NFκB Signaling Dynamics

(A) The IκB-NFκB signaling module.

(B) Equilibrium dose-response relationship for NFκB versus IKK.

(C) Three IKK curves representative of three stimulation regimes; TNFc (red), TNFp (green), and LPS (blue) function as inputs into the model, which computes the corresponding NFκB activity dynamics (bottom). The quasi-equilibrium line (black) was obtained by transforming the IKK temporal profiles by the dose response in (B). Deviation from the quasi-equilibrium line for the TNF response indicates out-of-equilibrium dynamics.

(D) Coarse-grained model of the IκB-NFκB module and predicted effects of perturbations.

(E) Selected perturbations with specific effects on out-of-equilibrium (top three) or steady state (bottom two). (Left to right) Feature maps in the E-L space (E: t < 60 ′, L: 120′ < t < 300′), tangent angle at the unperturbed point (θ > 0 indicates L is more suppressed than E and vice versa), and time courses (green, TNF chronic; red, TNF pulse; blue, LPS). Only inhibitory perturbations are shown. Additional perturbations are shown in Figure S4.

See also Experimental Procedures and Table S7.

Here, we delineate the potential of achieving stimulus-specific inhibition when targeting molecular reactions within pleiotropic signaling hubs. We found that it is theoretically possible to design perturbations that (1) selectively attenuate signaling in response to one stimulus but not another, (2) selectively attenuate undesirable features of dynamic signals or enhance desirable ones, or (3) remodulate output signals to fit a dynamic profile normally associated with a different stimulus.

These opportunities—not all of them possible for every signaling module topology or biological scenario—are governed by two general principles based on timescale and dose-response relationships between upstream signal dynamics and intramodule reaction kinetics (Figure 4 and Table S4). In short, a steady-state or quasi-equilibrium part of a response may be selectively affected by perturbations that introduce changes in the relevant dose-response surfaces. Out-of-equilibrium responses that are not sensitive to the precise shape of a dose-response curve may be selectively attenuated by perturbations that modify the relative timescales. Dose responses and timescales cannot, in general, be modified independently by simple perturbations (combination treatments are required), but as we show, in some cases, one effect dominates resulting in feature or stimulus specificity.

The degree to which specific dynamic features of a signaling profile or the dynamic responses to specific stimuli can be selectively inhibited depends on how distinctly they rely on quasi-equilibrium and out-of-equilibrium control. Signals that contain both features may be partially inhibited by both types of perturbation, limiting the specific inhibition achievable by simple perturbations. In practice, this limited the degree to which NFκB signaling could be inhibited in a stimulus-specific manner (Figure 5) and the associated therapeutic dose window (Figure 6). The most selective stimulus-specific effects can be introduced when a signal is heavily dependent on a particular dynamic feature; for example, suppression of out-of-equilibrium transients will abrogate the response to stimuli that produce such transients. For a selected group of target genes, this specificity at the signal level translated directly to expression patterns (Figure 6B, middle). More generally, selective inhibition of early or late phases of a signal may allow for specific control of early and late response genes (Figure 6C), a concept that remains to be studied at genomic scales. Though the principles are general, how they apply to specific signaling pathways depends not only on the regulatory topology, but also on the dynamic regime determined by the parameters. As demonstrated with the IκB-NFκB module, analysis of a coarse-grained topology in terms of the principles may allow the prediction of perturbations with a desired specificity.

 

7.6.2 A Protein-Tagging System for Signal Amplification in Gene Expression and Fluorescence Imaging

Marvin E. Tanenbaum, Luke A. Gilbert, Lei S. Qi, Jonathan S. Weissman, Ronald D. Vale
Cell 23 Oct 2014; 159(3): 635–646
http://dx.doi.org/10.1016/j.cell.2014.09.039

Highlights

  • SunTag allows controlled protein multimerization on a protein scaffold
  • SunTag enables long-term single-molecule imaging in living cells
  • SunTag greatly improves CRISPR-based activation of gene expression

Summary

Signals in many biological processes can be amplified by recruiting multiple copies of regulatory proteins to a site of action. Harnessing this principle, we have developed a protein scaffold, a repeating peptide array termed SunTag, which can recruit multiple copies of an antibody-fusion protein. We show that the SunTag can recruit up to 24 copies of GFP, thereby enabling long-term imaging of single protein molecules in living cells. We also use the SunTag to create a potent synthetic transcription factor by recruiting multiple copies of a transcriptional activation domain to a nuclease-deficient CRISPR/Cas9 protein and demonstrate strong activation of endogenous gene expression and re-engineered cell behavior with this system. Thus, the SunTag provides a versatile platform for multimerizing proteins on a target protein scaffold and is likely to have many applications in imaging and controlling biological outputs.

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SunTag, which can recruit multiple copies of an antibody-fusion protein
Development of the SunTag, a System for Recruiting Multiple Protein Copies to a Polypeptide Scaffold Protein multimerization on a single RNA or DNA template is made possible by identifying protein domains that bind with high affinity to a relatively short nucleic acid motif. We therefore sought a protein-based system with similar properties, specifically a protein that can bind tightly to a short peptide sequence (Figures 1A and1B).Antibodies arecapable ofbindingto short,unstructured peptide sequences with high affinity and specificity, and, importantly, peptide epitopes can be designed that differ from naturally occurring sequences in the genome. Furthermore, whereas antibodies generally do not fold properly in the cytoplasm, single-chain variable fragment (scFv) antibodies, in which the epitope-binding regions of the light and heavy chains of the antibody are fused to forma single polypeptide, have been successfully expressed in soluble form in cells (Colby et al., 2004; Lecerf et al., 2001; Wo ¨rn et al., 2000).
We expressed three previously developed single-chain antibodies (Colby et al., 2004; Lecerf et al., 2001; Wo ¨rn et al., 2000) fused to EGFP in U2OS cells and coexpressed their cognate peptides (multimerized in four tandem copies) fused to the cytoplasmic side of the mitochondrial protein mitoNEET (Colca et al., 2004) (referred to here as Mito, Figure S1A). We then assayed whether the antibody-GFP fusion proteins would be recruited to the mitochondria by fluorescence microscopy, which would indicate binding between antibody and peptide (Figure 1B). Of the three antibody-peptide pairs tested, only the GCN4 antibody-peptide pair showed robust and specific binding while not disrupting normal mitochondrial morphology (Figures 1C and S1B). Thus, we focused our further efforts on the GCN4 antibody-peptide pair. The GCN4 antibody was optimized to allow intracellular expression in yeast (Wo ¨rn et al., 2000). In human cells, however, we still observed some protein aggregates of scFv-GCN4-GFP at high expression levels (Figure S2A). To improve scFv-GCN4 stability, we added a variety of N- and C-terminal fusion proteins known to enhance protein solubility and found that fusion of superfolder-GFP (sfGFP) alone
(Pe’delacq et al., 2006) or along with the small solubility tag GB1 (Gronenborn et al., 1991) to the C terminus of the GCN4 antibody almost completely eliminated protein aggregation, even at high expression levels (Figure S2A). Thus, we performed all further experiments with scFv-GCN4-sfGFP-GB1 (hereafter referred to as scFvGCN4-GFP). Very tight binding of the antibody-peptide pair in vivo is critical fortheformation ofmultimersonaproteinscaffoldbackbone.To determine the dissociation rate of the GCN4 antibody-peptide interaction, we performed fluorescence recovery after photobleaching (FRAP) experiments on scFv-GCN4-GFP bound to the mitochondrial-localized mito-mCherry-4xGCN4pep. After photobleaching, very slow GFP recovery was observed (halflife of 5–10 min [Figures 2A and 2B]), indicating that the antibody bound very tightly to the peptide. It is also important to optimize the spacing of the scFv-GCN4 binding sites within the protein scaffold so that they could be saturated by scFvGCN4 because steric hindrance of neighboring peptide binding sites is a concern. We varied the spacing between neighboring GCN4 peptides and quantified the antibody occupancy on the peptide array.

Figure 1. Identification of an Antibody-Peptide Pair that Binds Tightly In Vivo (A) Schematic of the antibody-peptide labeling strategy. (B) Schematic of the experiment described in (C) in which the mitochondrial targeting domain of mitoNEET (yellow box, mito) fused to mCherry and four tandem copies of a peptide recruits a GFP-tagged intracellular antibody to mitochondria. (C) ScFv-GCN4-GFP was coexpressed with either mito-mCherry-4xGCN4peptide (bottom) or mito-mCherry-FKBP as a control (top) in U2OS cells, and cells were imaged using spinning-disk confocal microscopy. Scale bars, 10 mm. See also Figure S1.

Figure 2. Characterization of the Off Rate and Stoichiometry of the Binding Interaction between the scFv-GCN4 Antibody and the GCN4 Peptide Array In Vivo (A) Mito-mCherry-24xGCN4pep was cotransfected with scFv-GCN4-GFP in HEK293 cells, and their colocalization on mitochondria in a single cell is shown (10 s). At 0 s, the mitochondria-localized GFP signal was photobleached in a single z plane using a 472 nm laser, and fluorescence recovery was followed by time-lapse microscopy. Scale bar, 5 mm. (B) The FRAP was quantified for 20 cells. (C–E) Indicated constructs were transfected in HEK293 cells, and images were acquired 24 hr after transfection with identical image acquisition settings. Representative images are shown in (C). Note that the GFP signal intensity in the mito-mCherry-24xGCN4pep + scFv-GCN4-GFP is highly saturated when the same scaling is used as in the other panels. Bottom row shows a zoom of a region of interest: dynamic scaling was different for the GFP and mCherry signals, so that both could be observed. Scale bars, 10 mm. (D and E) Quantifications of the GFP:mCherry fluorescence intensity ratio on mitochondria after normalization. Eachdot represents a single cell, and dashed lines indicates the average value. See also Figure S2.

Figure 3. The SunTag Allows Long-Term Single-Molecule Fluorescence Imaging in the Cytoplasm (A–H) U2OS cells were transfected with indicated SunTag24x constructs together with the scFv-GCN4-GFP-NLS and were imaged by spinning-disk confocal microscopy 24 hr after transfection. (A) A representative image of SunTag24x-CAAX-GFP is shown (left), as well as the fluorescence intensities quantification of the foci (right, blue bars). As a control, U2OS were transfected with sfGFP-CAAX and fluorescence intensities of single sfGFP-CAAX molecules were also quantified (red bars). The average fluorescence intensity of the single sfGFP-CAAX was set to 1. Dotted line marks the outline of the cell (left). Scale bar, 10 mm. (B) Cells expressing K560-SunTag24x-GFP were imaged by spinning disk confocal microscopy (image acquisition every 200 ms). Movement is revealed by a maximum intensity projection of 50 time points (left) and a kymograph (right). Scale bar, 10 mm. (C and D) Cells expressing both EB3-tdTomato and K560-SunTag24x-GFP were imaged, and moving particles were tracked manually. Red and blue tracks (bottom) indicate movement toward the cell interior and periphery, respectively (C). The duration of the movie was 20 s. Scale bar, 5 mm. Dots in (D) represent individual cells with between 5 and 20 moving particles scored per cell. The mean and SD are indicated. (E and F) Cells expressing Kif18b-SunTag24x-GFP were imaged with a 250 ms time interval. Images in (E) show a maximum intensity projection (50 time- points, left) and a kymograph (right). Speeds of moving molecules were quantified from ten different cells (F). Scale bar, 10 mm. (G and H) Cells expressing both mCherry-a-tubulin and K560rig-SunTag24x-GFP were imaged with a 600 ms time interval.The entire cell is shown in (G), whereas H shows zoomed-instills of atime series from the same cell. Open circlestrack two foci on the same microtubule,which is indicated bythe dashed line. Asterisks indicate stationary foci. Scale bars, 10 and 2 mm (G and H), respectively. See also Figure S3 and Movies S1, S2, S3, S4, S5, and S6.
The GCN4 peptide contains many hydrophobic residues (Figure 4B) and is largely unstructured in solution (Berger et al., 1999); thus, the poor expression of the peptide array could be due to its unstructured and hydrophobic nature. To test this idea, we designed several modified peptide sequence that were predicted to increase a-helical propensity and reduce hydrophobicity. One of these optimized peptides (v4, Figure 4B) was expressed moderately well as a 243 peptide array, and even higher expression was achieved with a 103 peptide array (Figure 4C). Importantly, fluorescence imaging revealed that thescFv-GCN4antibody robustlyboundto theGCN4v4peptide array in vivo and FRAP analysis suggests that the scFv-GCN4 antibody dissociates with a similar slow off rate from the GCN4
v4 peptide array as the original peptide (Figures 4D and 4E). Furthermore, K560 motility could be observed when it was tagged with the optimized v4 243 peptide array, indicating that the optimized v4 peptide array did not interfere with protein function (Movie S7). Together, these results identify a second version of the peptide array that can be used for applications requiring higher expression.
Activation of Gene Transcription Using Cas9-SunTag Because the SunTag system could be used for amplification of a fluorescence signal, we tested whether it also could be used to amplify regulatory signals involved in gene expression. Transcription of a gene is strongly enhanced by recruiting multiple copies of transcriptional activators to endogenous or artificial gene promoters (Anderson and Freytag, 1991; Chen et al., 1992; Pettersson and Schaffner, 1990). Thus, we thought that robust, artificial activation of gene transcription might also be achieved by recruiting multiple copies of a synthetic transcriptional activator to a gene using the SunTag.

Figure 4. An Optimized Peptide Array for High Expression (A) Indicated constructs were transfected in HEK293 cells and imaged 24 hr after transfection using wide-field microscopy. All images were acquired using identical acquisition parameters. Representative images are shown (left), and fluorescence intensities were quantified (n = 3) (right). (B) Sequence of the first and second generation GCN4 peptide (modified or added residues are colored blue, hydrophobic residues are red, and linker residues are yellow). (C–E) Indicated constructs were transfected in HEK293 cells and imaged 24 hr after transfection using wide-field (C) or spinning-disk confocal (D and E) microscopy. (C) Representative images are shown (left), and fluorescence intensities were quantified (n = 3) (right). (D and E) GFP signal on mitochondria was photobleached, and fluorescence recovery was determined over time. The graph (E) represents an average of six cells per condition. (E) shows an image of a representative cell before photobleaching. Scale bars in (A) and (C), 50 mm; scale bars in (D) and (E), 10 mm. Error bars in (A) and (C) represent SDs. See also Movie S7.

Figure 5. dCas9-SunTag Allows Genetic Rewiring of Cells through Activation of Endogenous Genes (A) Schematic of gene activation by dCas9-VP64 and dCas9-SunTag-VP64. dCas9 binds to a gene promoter through its sequence-specific sgRNA (red line). Direct fusion of VP64 to dCas9 (top) results in a single VP64 domain at the promoter, which poorly activates transcription of the downstream gene. In contrast, recruitment of many VP64 domains using the SunTag potently activates transcription of the gene (bottom). (B–D) K562 cells stably expressing dCas9-VP64 or dCas9-SunTag-VP64 were infected with lentiviral particles encoding indicated sgRNAs, as well as BFP and a puromycin resistance gene and selected with 0.7 mg/ml puromycin for 3 days to kill uninfected cells. (B and C) Cells were stained for CXCR4 using adirectlylabeleda-CXCR4 antibody, and fluorescence was analyzed by FACS. (D) Trans-well migration assays (see Experimental Procedures) were performed with indicated sgRNAs. Results are displayed as the fold change in directional migrating cells over control cell migration. (E) dCas9-VP64 or dCas9-SunTag-VP64 induced transcription of CDKN1B with several sgRNAs. mRNA levels were quantified by qPCR. (F) Doubling timeofcontrolcells orcells expressing indicated sgRNAs was determined (see Experimental Procedures section). Graphs in (C), (D), and (F) are averages of three independent experiments. Graph in (E) is average of two biological replicates, each with two or three technical replicates. All error bars indicate SEM. See also Figure S4

 

7.6.3 IQGAPs choreograph cellular signaling from the membrane to the nucleus

Jessica M. Smith, Andrew C. Hedman, David B. Sacks
Trends Cell Biol Mar 2015; 25(3): 171–184
http://dx.doi.org/10.1016/j.tcb.2014.12.005

Highlights

  • IQGAP proteins scaffold diverse signaling molecules.
  • IQGAPs mediate crosstalk between signaling pathways.
  • IQGAP1 regulates nuclear processes, including transcription.

Since its discovery in 1994, recognized cellular functions for the scaffold protein IQGAP1 have expanded immensely. Over 100 unique IQGAP1-interacting proteins have been identified, implicating IQGAP1 as a critical integrator of cellular signaling pathways. Initial research established functions for IQGAP1 in cell–cell adhesion, cell migration, and cell signaling. Recent studies have revealed additional IQGAP1 binding partners, expanding the biological roles of IQGAP1. These include crosstalk between signaling cascades, regulation of nuclear function, and Wnt pathway potentiation. Investigation of the IQGAP2 and IQGAP3 homologs demonstrates unique functions, some of which differ from those of IQGAP1. Summarized here are recent observations that enhance our understanding of IQGAP proteins in the integration of diverse signaling pathways.

http://ars.els-cdn.com/content/image/1-s2.0-S0962892414002153-gr2.sml

 

7.6.4 Signaling cell death from the endoplasmic reticulum stress response

Shore GC1, Papa FR, Oakes SA
Curr Opin Cell Biol. 2011 Apr; 23(2):143-9
http://dx.doi.org/10.1016%2Fj.ceb.2010.11.003

Inability to meet protein folding demands within the endoplasmic reticulum (ER) activates the unfolded protein response (UPR), a signaling pathway with both adaptive and apoptotic outputs. While some secretory cell types have a remarkable ability to increase protein folding capacity, their upper limits can be reached when pathological conditions overwhelm the fidelity and/or output of the secretory pathway.

The lumen of the ER is a unique cellular environment optimized to carry out the three primary tasks of this organelle:

  1. calcium storage and release,
  2. protein folding and secretion, and
  3. lipid biogenesis [1].

A range of cellular disturbances lead to accumulation of misfolded proteins in the ER, including

  • point mutations in secreted proteins that disrupt their proper folding,
  • sustained secretory demands on endocrine cells,
  • viral infection with ER overload of virus-encoding protein, and
  • loss of calcium homeostasis with detrimental effects on ER-resident calcium-dependent chaperones [24].

 

The tripartite UPR consists of three ER transmembrane proteins (IRE1α, PERK, ATF6) that

  • alert the cell to the presence of misfolded proteins in the ER and
  • attempt to restore homeostasis in this organelle through increasing ER biogenesis,
  1. decreasing the influx of new proteins into the ER,
  2. promoting the transport of damaged proteins from the ER to the cytosol for degradation, and
  3. upregulating protein folding chaperones [5].

The adaptive responses of the UPR can markedly expand the protein folding capacity of the cell and restore ER homeostasis [6]. However, if these adaptive outputs fail to compensate because ER stress is excessive or prolonged, the UPR induces cell death.

The cell death pathways collectively triggered by the UPR include both caspase-dependent apoptosis and caspase-independent necrosis. While many details remain unknown, we are beginning to understand how cells determine when ER stress is beyond repair and communicate this information to the cell death machinery. For the purposes of this review, we focus on the apoptotic outputs triggered by the UPR under irremediable ER stress.

Connections from the UPR to the Mitochondrial Apoptotic Pathway

Connections from the UPR to the Mitochondrial Apoptotic Pathway

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3078187/bin/nihms256554f1.jpg

Figure 1 Connections from the UPR to the Mitochondrial Apoptotic Pathway

Under excessive ER stress, the ER transmembrane sensors IRE1α and PERK send signals through the BCL-2 family of proteins to activate the mitochondrial apoptotic pathway. In response to unfolded proteins, IRE1α oligomerizes and induces endonucleolytic decay of hundreds of ER-localized mRNAs, depleting ER protein folding components and leading to worsening ER stress. Phosphorylated IRE1α also recruits TNF receptor-associated factor 2 (TRAF2) and activates apoptosis signaling kinase 1 (ASK1) and its downstream target c-Jun NH2-terminal kinase (JNK). JNK then activates pro-apoptotic BIM and inhibits anti-apoptotic BCL-2. These conditions result in dimerization of PERK and activation of its kinase domain to phosphorylate eukaryotic translation initiation factor 2α (eIF2α), which causes selective translation of activating transcription factor-4 (ATF4). ATF4 upregulates expression of the CHOP/GADD153 transcription factor, which inhibits the gene encoding anti-apoptotic BCL-2 while inducing expression of pro-apoptotic BIM. ER stress also promotes p53-dependent transcriptional upregulation of Noxa and Puma, two additional pro-apoptotic BH3-only proteins. Furthermore, high levels of UPR signaling induce initiator caspase-2 to proteolytically cleave and activate pro-apoptotic BID upstream of the mitochondrion. In addition to antagonizing pro-survival BCL-2 members, cleaved BID, BIM and PUMA activate Bax and/or Bak. Hence, in response to excessive UPR signaling, the balance of BCL-2 family proteins shifts in the direction of apoptosis and leads to the oligomerization of BAX and BAK, two multi-domain pro-apoptotic BCL-2 family proteins that then drive the permeabilization of the outer mitochondrial membrane, apoptosome formation and activation of executioner caspases such as Caspase-3. Figure adapted with permission from the Journal of Cell Science [58].

The proximal unfolded protein response sensors

UPR signaling is initiated by three ER transmembrane proteins:

  1. IRE1α,
  2. PERK, and

The most ancient ER stress sensor, IRE1α, contains

  1. an ER lumenal domain,
  2. a cytosolic kinase domain and
  3. a cytosolic RNase domain [9,10].

In the presence of unfolded proteins, IRE1α’s ER lumenal domains homo-oligomerize, leading

  • first to kinase trans-autophosphorylation and
  • subsequent RNase activation.

Dissociation of the ER chaperone BiP from IRE1α’s lumenal domain in order to engage unfolded proteins may facilitate IRE1α oligomerization [11]; alternatively, the lumenal domain may bind unfolded proteins directly [12]. PERK’s ER lumenal domain is thought to be activated similarly [13,14]. The ATF6 activation mechanism is less clear. Under ER stress, ATF6 translocates to the Golgi and is cleaved by Site-1 and Site-2 proteases to generate the ATF6(N) transcription factor [15].

All three UPR sensors have outputs that attempt to tilt protein folding demand and capacity back into homeostasis. PERK contains a cytosolic kinase that phosphorylates eukaryotic translation initiation factor 2α (eIF2α), which impedes translation initiation to reduce the protein load on the ER [16]. IRE1α splices XBP1mRNA, to produce the homeostatic transcription factor XBP1s [17,18]. Together with ATF6(N), XBP1s increases transcription of genes that augment ER size and function[19]. When eIF2α is phosphorylated, the translation of the activating transcription factor-4 (ATF4) is actively promoted and leads to the transcription of many pro-survival genes [20]. Together, these transcriptional events act as homeostatic feedback loops to reduce ER stress. If successful in reducing the amount of unfolded proteins, the UPR attenuates.

However, when these adaptive responses prove insufficient, the UPR switches into an alternate mode that promotes apoptosis. Under irremediable ER stress, PERK signaling can induce ATF-4-dependent upregulation of the CHOP/GADD153 transcription factor, which inhibits expression of the gene encoding anti-apoptotic BCL-2 while upregulating the expression of oxidase ERO1α to induce damaging ER oxidation [21,22]. Sustained IRE1α oligomerization leads to activation of apoptosis signal-regulating kinase 1 (ASK1) and its downstream target c-Jun NH2-terminal kinase (JNK) [23,24]. Phosphorylation by JNK has been reported to both activate pro-apoptotic BIM and inhibit anti-apoptotic BCL-2 (see below). Small molecule modulators of ASK1 have been shown to protect cultured cells against ER stress-induced apoptosis, emphasizing the importance of the IRE1α-ASK1-JNK output as a death signal in this pathway [25]. In response to sustained oligomerization, the IRE1α RNase also causes endonucleolytic decay of hundreds of ER-localized mRNAs [26]. By depleting ER cargo and protein folding components, IRE1α-mediated mRNA decay may worsen ER stress, and could be a key aspect of IRE1α’s pro-apoptotic program [27]. Recently, inhibitors of IRE1α’s kinase pocket have been shown to conformationally activate its adjacent RNase domain in a manner that enforces homeostatic XBP1s without causing destructive mRNA decay [27], a potentially exciting strategy for preventing ER stress-induced cell loss.

The BCL-2 family and the Mitochondrial Apoptotic Pathway

A wealth of genetic and biochemical data argues that the intrinsic (mitochondrial) apoptotic pathway is the major cell death pathway induced by the UPR, at least in most cell types. This apoptotic pathway is set in motion when several toxic proteins (e.g., cytochrome c, Smac/Diablo) are released from mitochondria into the cytosol where they lead to activation of downstream effector caspases (e.g., Caspase-3) [30]. The BCL-2 family, a large class of both pro- and anti- survival proteins, tightly regulates the intrinsic apoptotic pathway by controlling the integrity of the outer mitochondrial membrane [31]. This pathway is set in motion when cell injury leads to the transcriptional and/or post-translational activation of one or more BH3-only proteins that share sequence similarity in a short alpha helix (~9–12 a.a.) known as the Bcl-2 homology 3 (BH3) domain [32]. Once activated, BH3-only proteins lead to loss of mitochondrial integrity by disabling mitochondrial protecting proteins that drive the permeabilization of the outer mitochondrial membrane.

ER stress has been reported to activate at least four distinct BH3-only proteins (BID, BIM, NOXA, PUMA) that then signal the mitochondrial apoptotic machinery (i.e., BAX/BAK) [3335]. Each of these BH3-only proteins is activated by ER stress in a unique way. Cells individually deficient in any of these BH3-only proteins are modestly protected against ER stress-inducing agents, but not nearly as resistant as cells null for their common downstream targets BAX and BAK [36]—the essential gatekeepers to the mitochondrial apoptotic pathway. Moreover, cells genetically deficient in both Bim andPuma are more protected against ER stress-induced apoptosis than Bim or Puma single knockout cells [37].

The ER stress sensor that signals these BH3-only proteins is known in a few cases (i.e., BIM is downstream of PERK); however, we do not yet understand how the UPR communicates with most of the BH3-only proteins. Moreover, it is not known if all of the above BH3-only proteins are simultaneously set in motion by all forms of ER stress or if a subset is activated under specific pathological stimuli that injure this organelle. Understanding the molecular details of how ER damage is communicated to the mitochondrial apoptotic machinery is critical if we want to target disease specific apoptotic signals sent from the ER.

Initiator and Executor Caspases

Caspases, or cysteine-dependent aspartate-directed proteases, play essential roles in both initiating apoptotic signaling (initiator caspases- 2, 4, 8, 12) and executing the final stages of cell demise (executioner caspases- 3, 7, 9) [38]. It is not surprising that the executioner caspases (casp-3,7,9) are critical for cell death resulting from damage to this organelle. Caspase 12 was the first caspase reported to localize to the ER downstream of BAX/BAK-dependent mitochondrial permeabilization becomes activated by UPR signaling in murine cells [39],but humans fail to express a functional Caspase 12 [41. Genetic knockdown or pharmacological inhibition of caspase-2 confers resistance to ER stress-induced apoptosis [42]. How the UPR activates caspase-2 and whether other initiator caspasesare also involved remains to be determined.

Calcium and Cell Death

Although an extreme depletion of ER luminal Ca2+ concentrations is a well-documented initiator of the UPR and ER stress-induced apoptosis or necrosis, it represents a relatively non-physiological stimulus. Ca2+ signaling from the ER is likely coupled to most pathways leading to apoptosis. UPR-induced activation of ERO1-α via CHOP in macrophages results in stimulation of inositol 1,4,5-triphosphate receptor (IP3R) [43]. All three sub-groups of the Bcl-2 family at the ER regulate IP3R activity. A significant fraction of IP3R is a constituent of highly specialized tethers that physically attach ER cisternae to mitochondria (mitochondrial-associated membrane) and regulate local Ca2+ dynamics at the ER-mitochondrion interface [4546]. This results in propagation of privileged IP3R-mediated Ca2+ oscillations into mitochondria. In an extreme scenario, massive transmission of Ca2+ into mitochondria results in Ca2+ overload and cell death by caspase-dependent and –independent means [46,47]. More refined transmission regulated by the Bcl-2 axis at the ER can influence cristae junctions and the availability of cytochrome c for its release across the outer mitochondrial membrane [48]. Finally, such regulated Ca2+transmission to mitochondria is a key determinant of mitochondrial bioenergetics [49].

ER Stress-Induced Cell Loss and Disease

Mounting evidence suggests that ER stress-induced apoptosis contributes to a range of human diseases of cell loss, including diabetes, neurodegeneration, stroke, and heart disease, to name a few (reviewed in REF [50]). The cause of ER stress in these distinct diseases varies depending on the cell type affected and the intracellular and/or extracellular conditions that disrupt proteostasis. Both mutant SOD1 and mutant huntingtin proteins aggregate, exhaust proteasome activity, and result in secondary accumulations of misfolded proteins in the ER [5152].

In the case of IRE1α, it may be possible to use kinase inhibitors to activate its cytoprotective signaling and shut down its apoptotic outputs [27]. Whether similar strategies will work for PERK and/or ATF6 remains to be seen. Alternatively, blocking the specific apoptotic signals that emerge from the UPR is perhaps a more straightforward strategy to prevent ER stress-induced cell loss. To this end, small molecular inhibitors of ASK and JNK are currently being tested in a variety preclinical models of ER stress [5253,5657]. This is just the beginning, and much work needs to be done to validate the best drugs targets in the ER stress pathway.

Conclusions

The UPR is a highly complex signaling pathway activated by ER stress that sends out both adaptive and apoptotic signals. All three transmembrane ER stress sensors (IRE1α, PERK, AFT6) have outputs that initially decrease the load and increase capacity of the ER secretory pathway in an effort to restore ER homeostasis. However, under extreme ER stress, continuous engagement of IRE1α and PERK results in events that simultaneously exacerbate protein misfolding and signal death, the latter involving caspase-dependent apoptosis and caspase-independent necrosis. Advances in our molecular understanding of how these stress sensors switch from life to death signaling will hopefully lead to new strategies to prevent diseases caused by ER stress-induced cell loss.

7.6.5 An Enzyme that Regulates Ether Lipid Signaling Pathways in Cancer Annotated by Multidimensional Profiling

Chiang KP, Niessen S, Saghatelian A, Cravatt BF.
Chem Biol. 2006 Oct; 13(10):1041-50.
http://dx.doi.org/10.1016/j.chembiol.2006.08.008

Hundreds, if not thousands, of uncharacterized enzymes currently populate the human proteome. Assembly of these proteins into the metabolic and signaling pathways that govern cell physiology and pathology constitutes a grand experimental challenge. Here, we address this problem by using a multidimensional profiling strategy that combines activity-based proteomics and metabolomics. This approach determined that KIAA1363, an uncharacterized enzyme highly elevated in aggressive cancer cells, serves as a central node in an ether lipid signaling network that bridges platelet-activating factor and lysophosphatidic acid. Biochemical studies confirmed that KIAA1363 regulates this pathway by hydrolyzing the metabolic intermediate 2-acetyl monoalkylglycerol. Inactivation of KIAA1363 disrupted ether lipid metabolism in cancer cells and impaired cell migration and tumor growth in vivo. The integrated molecular profiling method described herein should facilitate the functional annotation of metabolic enzymes in any living system.

Elucidation of the metabolic and signaling networks that regulate health and disease stands as a principal goal of postgenomic research. The remarkable complexity of these molecular pathways has inspired the advancement of “systems biology” methods for their characterization [1]. Toward this end, global profiling technologies, such as DNA microarrays 2 and 3 and mass spectrometry (MS)-based proteomics 4 and 5, have succeeded in generating gene and protein signatures that depict key features of many human diseases. However, extricating from these associative relationships the roles that specific biomolecules play in cell physiology and pathology remains problematic, especially for proteins of unknown biochemical or cellular function.

The functions of certain proteins, such as adaptor or scaffolding proteins, can be gleaned from large-scale protein-interaction maps generated by technologies like yeast two-hybrid 6 and 7, protein microarrays [8], and MS analysis of immunoprecipitated protein complexes 9 and 10. In contrast, enzymes contribute to biological processes principally through catalysis. Thus, elucidation of the activities of the many thousands of enzymes encoded by eukaryotic and prokaryotic genomes requires knowledge of their endogenous substrates and products. The functional annotation of enzymes in prokaryotic systems has been facilitated by the clever analysis of gene clusters or operons 11 and 12, which correspond to sets of genes adjacently located in the genome that encode for enzymes participating in the same metabolic cascade. The assembly of eukaryotic enzymes into metabolic pathways is more problematic, however, as their corresponding genes are not, in general, physically organized into operons, but rather are scattered randomly throughout the genome.

We hypothesized that the determination of endogenous catalytic activities for uncharacterized enzymes could be accomplished directly in living systems by the integrated application of global profiling technologies that survey both the enzymatic proteome and its primary biochemical output (i.e., the metabolome). Here, we have tested this premise by utilizing multidimensional profiling to characterize an integral membrane enzyme of unknown function that is highly elevated in human cancer.

Development of a Selective Inhibitor for the Uncharacterized Enzyme KIAA1363

Previous studies using the chemical proteomic technology activity-based protein profiling (ABPP) 15, 16 and 17 have identified enzyme activity signatures that distinguish human cancer cells based on their biological properties, including tumor of origin and state of invasiveness [18]. A primary component of these signatures was the protein KIAA1363, an uncharacterized integral membrane hydrolase found to be upregulated in aggressive cancer cells from multiple tissues of origin. To investigate the role that KIAA1363 plays in cancer cell metabolism and signaling, a selective inhibitor of this enzyme was generated by competitive ABPP 20 and 21.

Previous competitive ABPP screens that target the serine hydrolase superfamily identified a set of trifluoromethyl ketone (TFMK) inhibitors that showed activity in mouse brain extracts [20]. These TFMK inhibitors showed only limited activity in living human cells (data not shown). We postulated that the activity of KIAA1363 inhibitors could be enhanced by replacing the TFMK group with a carbamate, which inactivates serine hydrolases via a covalent mechanism (Figure S1; see the Supplemental Data available with this article online). Carbamate AS115 (Figure 1A) was synthesized and tested for its effects on the invasive ovarian cancer cell line SKOV-3 by competitive ABPP (Figure 1B). AS115 was found to potently and selectively inactivate KIAA1363, displaying an IC50 value of 150 nM, while other serine hydrolase activities were not affected by this agent (IC50 values > 10 μM) (Figures 1B and 1C). AS115 also selectively inhibited KIAA1363 in other aggressive cancer cell lines that possess high levels of this enzyme, including the melanoma lines C8161 and MUM-2B (Figure S2B).

Figure 1. Characterization of AS115, a Selective Inhibitor of the Cancer-Related Enzyme KIAA1363

Profiling the Metabolic Effects of KIAA1363 Inactivation in Cancer Cells

We next compared the global metabolite profiles of SKOV-3 cells treated with AS115 to identify endogenous small molecules regulated by KIAA1363, using a recently described, untargeted liquid chromatography-mass spectrometry (LC-MS) platform for comparative metabolomics [22]. AS115 (10 μM, 4 hr) was found to cause a dramatic reduction in the levels of a specific set of lipophilic metabolites (m/z 317, 343, and 345) in SKOV-3 cells ( Figure 2A). These metabolites did not correspond to any of the typical lipid species found in cells, none of which were significantly altered by AS115 treatment ( Table S1). High-resolution MS of the m/z 317 metabolite provided a molecular formula of C19H40O3 ( Figure 2B), which suggests that this compound might represent a monoalkylglycerol ether bearing a C16:0 alkyl chain (C16:0 MAGE).  This structure assignment was corroborated by tandem MS and LC analysis, in which the endogenous m/z 317 product and synthetic C16:0 MAGE displayed equivalent fragmentation and migration patterns, respectively ( Figure S3). By extension, the m/z 343 and 345 metabolites were interpreted to represent the C18:1 and C18:0 MAGEs, respectively. A control carbamate inhibitor, URB597, which targets other hydrolytic enzymes [23], but not KIAA1363, did not affect MAGE levels in cancer cells ( Figure S4).

Pharmacological Inhibition of KIAA1363 Reduces Monoalkylglycerol Ether, MAGE, Levels in Human Cancer Cells

Pharmacological Inhibition of KIAA1363 Reduces Monoalkylglycerol Ether, MAGE, Levels in Human Cancer Cells

http://ars.els-cdn.com/content/image/1-s2.0-S1074552106003000-gr2.jpg

Figure 2. Pharmacological Inhibition of KIAA1363 Reduces Monoalkylglycerol Ether, MAGE, Levels in Human Cancer Cells

(A) Global metabolite profiling of AS115-treated SKOV-3 cells (10 μM AS115, 4 hr) with untargeted LC-MS methods [22]revealed a specific reduction in a set of structurally related metabolites with m/z values of 317, 343, and 345 (p < 0.001 for AS115- versus DMSO-treated SKOV-3 cells). Results represent the average fold change for three independent experiments. See Table S1for a more complete list of metabolite levels.

(B) High-resolution MS analysis of the sodium adduct of the purified m/z 317 metabolite provided a molecular formula of C19H40O3, which, in combination with tandem MS and LC analysis ( Figure S3), led to the determination of the structure of this small molecule as C16:0 monoalkylglycerol ether (C16:0 MAGE).

Biochemical Characterization of KIAA1363 as a 2-Acetyl MAGE Hydrolase

The correlation between KIAA1363 inactivation and reduced MAGE levels suggests that these lipids are products of a KIAA1363-catalyzed reaction. A primary route for the biosynthesis of MAGEs has been proposed to occur via the enzymatic hydrolysis of their 2-acetyl precursors 24 and 25. This 2-acetyl MAGE hydrolysis activity was first detected in cancer cell extracts over a decade ago [25], but, to date, it has eluded molecular characterization. To test whether KIAA1363 functions as a 2-acetyl MAGE hydrolase, this enzyme was transiently transfected into COS7 cells. KIAA1363-transfected cells possessed significantly higher 2-acetyl MAGE hydrolase activity compared to mock-transfected cells, and this elevated activity was blocked by treatment with AS115 (Figure 3A). In contrast, KIAA1363- and mock-transfected cells showed no differences in their respective hydrolytic activity for 2-oleoyl MAGE, monoacylglycerols, or phospholipids (e.g., platelet-activating factor [PAF], phosphatidylcholine) (Figure S5A). These data indicate that KIAA1363 selectively catalyzes the hydrolysis of 2-acetyl MAGEs to MAGEs.

KIAA1363 Regulates an Ether Lipid Signaling Network that Bridges Platelet-Activating Factor and the Lysophospholipids

Examination of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [26] suggests that the KIAA1363-MAGE pathway might serve as a unique metabolic node linking the PAF [27] and lysophospholipid [28] signaling systems in cancer cells (Figure 4A). Consistent with a direct pathway leading from MAGEs to these lysophospholipids, addition of 13C-MAGE to SKOV-3 cells resulted in the formation of 13C-labeled alkyl-LPC and alkyl-LPA (Figure 4C).
Conversely, the levels of 2-acetyl MAGE in SKOV-3 cells, as judged by metabolic labeling experiments, were significantly stabilized by treatment with AS115, which, in turn, led to an accumulation of PAF (Figure 4D).  A comparison of the metabolite profiles of SKOV-3 and OVCAR-3 cells revealed significantly higher levels of MAGE, alkyl-LPC, and alkyl-LPA in the former line (Figure 4E). These data indicate that the lysophospholipid branch of the MAGE network is elevated in aggressive cancer cells, and that this metabolic shift is regulated by KIAA1363.

Figure 4. KIAA1363 Serves as a Key Enzymatic Node in a Metabolic Network that Connects the PAF and Lysophospholipid Families of Signaling Lipids

Stable Knockdown of KIAA1363 Impairs Tumor Growth In Vivo

Figure 6. KIAA1363 Contributes to Ovarian Tumor Growth and Cancer Cell Migration

The decrease in tumorigenic potential of shKIAA1363 cells was not associated with a change in proliferation potential in vitro (Figure S8). shKIAA1363 cells were, however, impaired in their in vitro migration capacity compared to control cells (Figure 6B). Neither MAGE nor alkyl-LPC impacted cancer cell migration at concentrations up to 1 μM (Figure 6B). In contrast, alkyl-LPA (10 nM) completely rescued the reduced migratory activity of shKIAA1363 cells. Collectively, these results indicate that KIAA1363 contributes to the pathogenic properties of cancer cells in vitro and in vivo, possibly through regulating the levels of the bioactive lipid LPA.

We have determined by integrated enzyme and small-molecule profiling that KIAA1363, a protein of previously unknown function, is a 2-acetyl MAGE hydrolase that serves as a key regulator of a lipid signaling network that contributes to cancer pathogenesis. Although we cannot yet conclude which of the specific metabolites regulated by KIAA1363 supports tumor growth in vivo, the rescue of the reduced migratory phenotype of shKIAA1363 cancer cells by LPA is consistent with previous reports showing that this lipid signals through a family of G protein-coupled receptors to promote cancer cell migration and invasion 2829 and 30. LPA is also an established biomarker in ovarian cancer, and the levels of this metabolite are elevated nearly 10-fold in ascites fluid and plasma of patients with ovarian cancer [31]. Our results suggest that additional components in the KIAA1363-ether lipid network, including MAGE, alkyl LPC, and KIAA1363 itself, might also merit consideration as potential diagnostic markers for ovarian cancer. Consistent with this premise, our preliminary analyses have revealed highly elevated levels of KIAA1363 in primary human ovarian tumors compared to normal ovarian tissues (data not shown). The heightened expression of KIAA1363 in several other cancers, including breast 18 and 32, melanoma [18], and pancreatic cancer [33], indicates that alterations in the KIAA1363-ether lipid network may be a conserved feature of tumorigenesis. Considering further that reductions in KIAA1363 activity were found to impair tumor growth of both ovarian and breast cancer cells, it is possible that inhibitors of this enzyme may prove to be of value for the treatment of multiple types of cancer.

 

7.6.6 Peroxisomes – A Nexus for Lipid Metabolism and Cellular Signaling

Lodhi IJ, Semenkovich CF
Cell Metab. 2014 Mar 4; 19(3):380-92
http://dx.doi.org/10.1016%2Fj.cmet.2014.01.002

Peroxisomes are often dismissed as the cellular hoi polloi, relegated to cleaning up reactive oxygen chemical debris discarded by other organelles. However, their functions extend far beyond hydrogen peroxide metabolism. Peroxisomes are intimately associated with lipid droplets and mitochondria, and their ability to carry out fatty acid oxidation and lipid synthesis, especially the production of ether lipids, may be critical for generating cellular signals required for normal physiology. Here we review the biology of peroxisomes and their potential relevance to human disorders including cancer, obesity-related diabetes, and degenerative neurologic disease.

Peroxisomes are multifunctional organelles present in virtually all eukaryotic cells. In addition to being ubiquitous, they are also highly plastic, responding rapidly to cellular or environmental cues by modifying their size, number, morphology, and function (Schrader et al., 2013). Early ultrastructural studies of kidney and liver cells revealed cytoplasmic particles enclosed by a single membrane containing granular matrix and a crystalline core (Rhodin, 1958). These particles were linked with the term “peroxisome” by Christian de Duve, who first identified the organelle in mammalian cells when enzymes such as oxidases and catalases involved in hydrogen peroxide metabolism co-sedimented in equilibrium density gradients (De Duve and Baudhuin, 1966). Based on these studies, it was originally thought that the primary function of these organelles was the metabolism of hydrogen peroxide. Novikoff and colleagues observed a large number of peroxisomes in tissues active in lipid metabolism such as liver, brain, intestinal mucosa, and adipose tissue (Novikoff and Novikoff, 1982;Novikoff et al., 1980). Peroxisomes in different tissues vary greatly in shape and size, ranging from 0.1-0.5 μM in diameter. In adipocytes, peroxisomes tend to be small in size and localized in the vicinity of lipid droplets. Notably, a striking increase in the number of peroxisomes was observed during differentiation of adipogenic cells in culture (Novikoff and Novikoff, 1982). These findings suggest that peroxisomes may be involved in lipid metabolism.

Lazarow and de Duve hypothesized that peroxisomes in animal cells were capable of carrying out fatty acid oxidation. This was confirmed when they showed that purified rat liver peroxisomes contained fatty acid oxidation activity that was robustly increased by treatment of animals with clofibrate (Lazarow and De Duve, 1976). In a series of experiments, Hajra and colleagues discovered that peroxisomes were also capable of lipid synthesis (Hajra and Das, 1996). Over the past three decades, multiple lines of evidence have solidified the concept that peroxisomes play fundamentally important roles in lipid metabolism. In addition to removal of reactive oxygen species, metabolic functions of peroxisomes in mammalian cells include β-oxidation of very long chain fatty acids, α-oxidation of branched chain fatty acids, and synthesis of ether-linked phospholipids as well as bile acids (Figure 1). β-oxidation also occurs in mitochondria, but peroxisomal β-oxidation involves distinctive substrates and complements mitochondrial function; the processes of α-oxidation and ether lipid synthesis are unique to peroxisomes and important for metabolic homeostasis.

Structure and functions of peroxisomes

Structure and functions of peroxisomes

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951609/bin/nihms-555068-f0001.jpg

Figure 1 Structure and functions of peroxisomes

The peroxisome is a single membrane-enclosed organelle that plays an important role in metabolism. The main metabolic functions of peroxisomes in mammalian cells include β-oxidation of very long chain fatty acids, α-oxidation of branched chain fatty acids, synthesis of bile acids and ether-linked phospholipids and removal of reactive oxygen species. Peroxisomes in many, but not all, cell types contain a dense crystalline core of oxidative enzymes.

Here we highlight the established role of peroxisomes in lipid metabolism and their emerging role in cellular signaling relevant to metabolism. We describe the origin of peroxisomes and factors involved in their assembly, division, and function. We address the interaction of peroxisomes with lipid droplets and implications of this interaction for lipid metabolism. We consider fatty acid oxidation and lipid synthesis in peroxisomes and their importance in brown and white adipose tissue (sites relevant to lipid oxidation and synthesis) and disease pathogenesis.

peroxisomal biogenesis and protein import

peroxisomal biogenesis and protein import

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951609/bin/nihms-555068-f0002.jpg

Potential pathways to peroxisomal biogenesis. Peroxisomes are generated autonomously through division of pre-existing organelles (top) or through a de novo process involving budding from the ER followed by import of matrix proteins (bottom). B. Peroxisomal membrane protein import. Peroxisomal membrane proteins (PMPs) are imported post-translationally to the peroxisomal membrane. Pex19 is a soluble chaperone that binds to PMPs and transports them to the peroxisomal membrane, where it docks with a complex containing Pex16 and Pex3. Following insertion of the PMP, Pex19 is recycled back to the cytosol.

Regardless of their origin, peroxisomes require a group of proteins called peroxins for their assembly, division, and inheritance. Over 30 peroxins, encoded by Pex genes, have been identified in yeast (Dimitrov et al., 2013). At least a dozen of these proteins are conserved in mammals, where they regulate various aspects of peroxisomal biogenesis, including factors that control assembly of the peroxisomal membrane, factors that interact with peroxisomal targeting sequences allowing proteins to be shuttled to peroxisomes, and factors that act as docking receptors for peroxisomal proteins.

At least three peroxins (Pex3, Pex16 and Pex19) appear to be critical for assembly of the peroxisomal membrane and import of peroxisomal membrane proteins (PMPs) (Figure 2B). Pex19 is a soluble chaperone and import receptor for newly synthesized PMPs (Jones et al., 2004). Pex3 buds from the ER in a pre-peroxisomal vesicle and functions as a docking receptor for Pex19 (Fang et al., 2004). Pex16 acts as a docking site on the peroxisomal membrane for recruitment of Pex3 (Matsuzaki and Fujiki, 2008). Peroxisomal matrix proteins are translated on free ribosomes in the cytoplasm prior to their import. These proteins have specific peroxisomal targeting sequences (PTS) located either at the carboxyl (PTS1) or amino (PTS2) terminus (Gould et al., 1987Swinkels et al., 1991).

 

7.6.7 A nexus for cellular homeostasis- the interplay between metabolic and signal transduction pathways

Ana P Gomes, John Blenis
Current Opinion in Biotechnology Aug 2015; 34:110–117
http://dx.doi.org/10.1016/j.copbio.2014.12.007

Highlights

  • Signaling networks sense intracellular and extracellular cues to maintain homeostasis.
  • PI3K/AKT and Ras/ERK signaling induces anabolic reprogramming.
  • mTORC1 is a master node of signaling integration that promotes anabolism.
  • AMPK and SIRT1 fine tune signaling networks in response to energetic status.

In multicellular organisms, individual cells have evolved to sense external and internal cues in order to maintain cellular homeostasis and survive under different environmental conditions. Cells efficiently adjust their metabolism to reflect the abundance of nutrients, energy and growth factors. The ability to rewire cellular metabolism between anabolic and catabolic processes is crucial for cells to thrive. Thus, cells have developed, through evolution, metabolic networks that are highly plastic and tightly regulated to meet the requirements necessary to maintain cellular homeostasis. The plasticity of these cellular systems is tightly regulated by complex signaling networks that integrate the intracellular and extracellular information. The coordination of signal transduction and metabolic pathways is essential in maintaining a healthy and rapidly responsive cellular state.

AMPK and SIRT1 fine tune signaling networks in response to energetic status

AMPK and SIRT1 fine tune signaling networks in response to energetic status

 

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AMPK and SIRT1 fine tune signaling networks in response to energetic status

 

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mTORC1 is a master node of signaling integration that promotes anabolism.

 

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Fine-tuning signaling networks

 PI3K/Akt signaling-induced anabolic reprogramming

Growth factors and other ligands activate PI3K signaling upon binding and consequent activation of their cell surface receptors, such as receptor tyrosine kinases (RTKs) and G protein-coupled
receptors (GPCRs). This leads to the phosphorylation of membrane phosphatidylinositiol lipids and the recruitment and activation of several protein kinases, which perpetuate the extracellular
signals to modulate intracellular processes [3,4]. One of the most crucial signal propagators regulated by PI3K signaling is protein kinase B/Akt [3,4]. Indeed, Akt rewires metabolism in response
to environmental cues by three distinct means;
(i) by the direct phosphorylation and regulation of metabolic enzymes,
(ii) by activating/inactivating metabolism altering transcriptional factors, and
(iii) by modulating other kinases that themselves regulate metabolism [5].
Akt regulates glucose metabolism, inducing both glucose uptake and glycolytic flux by increasing the expression of the glucose transporter genes and regulating the activity of glycolytic enzymes,
respectively [6–8]. Moreover, the ability of Akt to induce glycolysis is also mediated by the regulation of Hexokinase (HK). HK performs the first step of glycolysis.

Figure 1 Anabolic rewiring induced by PI3K/Akt, Ras/ERK and mTORC1 signaling.
Extracellular signals activate two major signaling cascades controlled by the activation of PI3K and Ras. PI3K and Ras regulate Akt and ERK, which in turn induce changes in intermediate metabolism
to promote anabolic processes. In addition, they also induce the activation of  mTORC1, thus further supporting the rewiring of cellular metabolism towards anabolic processes. Through various mechanisms
Akt, ERK and mTORC1 stimulate mRNA translation, aerobic glycolysis, glutamine anaplerosis, lipid synthesis, the pentose phosphate and pyrimidine synthesis, thus producing the major components
necessary for cell growth and proliferation.

Figure 2. Regulation of intermediate metabolism by nutrient and energy sensors.
Nutrient and energy-responsive pathways fine-tune the output of signaling cascades, allowing for the correct balance between the availability of nutrients and the cellular capacity to use them effectively.
AMPK and SIRT1 respond to the energy status of the cells through sensing of AMP and NAD+ levels respectively. When energy is scarce, these sensors are activated inducing a rewiring of intermediate
metabolism to catabolic processes in order to produce energy and restore homeostasis. When nutrients (such as glucose and amino acids) and energy are available, AMPK, SIRT1, SIRT3 and SIRT6 are
repressed and mTORC1 is active, thus promoting a shift towards anabolic processes and energy production. These networks of signaling cascades, their interconnection and regulation allow the cells
to maintain energetic balance and allow for the physiological adaptation to the ever-changing environment.

 

7.6.8 Mechanisms-of-intercellular-signaling

7.6.8.1 Activation and signaling of the p38 MAP kinase pathway

Tyler Zarubin1 and Jiahuai Han
Cell Research (2005) 15, 11–18
http://dx.doi.org:/10.1038/sj.cr.7290257

The family members of the mitogen-activated protein (MAP) kinases mediate a wide variety of cellular behaviors in response to extracellular stimuli. One of the four main sub-groups, the p38 group of MAP kinases, serve as a nexus for signal transduction and play a vital role in numerous biological processes. In this review, we highlight the known characteristics and components of the p38 pathway along with the mechanism and consequences of p38 activation. We focus on the role of p38 as a signal transduction mediator and examine the evidence linking p38 to inflammation, cell cycle, cell death, development, cell differentiation, senescence and tumorigenesis in specific cell types. Upstream and downstream components of p38 are described and questions remaining to be answered are posed. Finally, we propose several directions for future research on p38.

Cellular behavior in response to extracellular stimuli is mediated through intracellular signaling pathways such as the mitogen-activated protein (MAP) kinase pathways 1. MAP kinases are members of discrete signaling cascades and serve as focal points in response to a variety of extracellular stimuli. Four distinct subgroups within the MAP kinase family have been described:

  • extracellular signal-regulated kinases (ERKs),
  • c-jun N-terminal or stress-activated protein kinases (JNK/SAPK),
  • ERK/big MAP kinase 1 (BMK1), and
  • the p38 group of protein kinases.

The focus of this review will be to highlight the characteristics of

  • the p38 kinases,
  • components of this kinase cascade,
  • activation of this pathway, and
  • the biological consequences of its activation.

p38 (p38) was first isolated as a 38-kDa protein rapidly tyrosine phosphorylated in response to LPS stimulation 23. p38 cDNA was also cloned as a molecule that binds puridinyl imidazole derivatives which are known to inhibit biosynthesis of inflammatory cytokines such as interleukin-1 (IL-1) and tumor-necrosis factor (TNF) in LPS stimulated monocytes 4. To date, four splice variants of the p38 family have been identified: p38, p38 5, p38 (ERK6, SAPK3) 67, and p38(SAPK4) 89. Of these, p38 and p38 are ubiquitously expressed while p38 and p38 are differentially expressed depending on tissue type. All p38 kinases can be categorized by a Thr-Gly-Tyr (TGY) dual phosphorylation motif 10. Sequence comparisons have revealed that each p38 isoform shares 60% identity within the p38 group but only 40–45% to the other three MAP kinase family members.

Mammalian p38s activation has been shown to occur in response to extracellular stimuli such as UV light, heat, osmotic shock, inflammatory cytokines (TNF- & IL-1), and growth factors (CSF-1) 13151617,18192021. This plethora of activators conveys the complexity of the p38 pathway and this matter is further complicated by the observation that activation of p38 is not only dependent on stimulus, but on cell type as well. For example, insulin can stimulate p38 in 3T3-L1 adipocytes 22, but downregulates p38 activity in chick forebrain neuron cells 23. The activation of p38 isoforms can be specifically controlled through different regulators and coactivated by various combinations of upstream regulators 2426.

Like all MAP kinases, p38 kinases are activated by dual kinases termed the MAP kinase kinases (MKKs). However, despite conserved dual phosphorylation sites among p38 isoforms, selective activation by distinct MKKs has been observed. There are two main MAPKKs that are known to activate p38, MKK3 and MKK6. It is proposed that upstream kinases can differentially regulate p38 isoforms as evidenced by the inability of MKK3 to effectively activate p38 while MKK6 is a potent activator despite 80% homology between these two MKKs 27. Also, it has been shown that MKK4, an upstream kinase of JNK, can aid in the activation of p38 and p38 in specific cell types 8. This data suggests then, that activation of p38 isoforms can be specifically controlled through different regulators and coactivated by various combinations of upstream regulators. Furthermore, substrate selectivity may be a reason why each MKK has a distinct function. In addition to the activation by upstream kinases, there is a MAPKK-independent mechanism of p38 MAPK activation involving TAB1 (transforming growth factor–activated protein kinase 1 (TAK1)-binding protein) 28. The activation of p38 in this pathway is achieved by the autophosphorylation of p38 after interaction with TAB1.

The activation of p38 in response to the wide range of extracellular stimuli can be seen in part by the diverse range of MKK kinases (MAP3K) that participate in p38 activation. These include TAK1 33, ASK1/MAPKKK5 34, DLK/MUK/ZPK 3536, and MEKK4 353738. Overexpression of these MAP3Ks leads to activation of both p38 and JNK pathways which is possibly one reason why these two pathways are often co-activated. Also contributing to p38 activation upstream of MAPK kinases are low molecular weight GTP-binding proteins in the Rho family such as Rac1 and Cdc42 4041. Rac1 can bind to MEKK1 or MLK1 while Cdc42 can only bind to MLK1 and both result in activation of p38 via MAP3Ks 3542.

Dephosphorylation, would seem to play a major role in the downregulation of MAP kinase activity. Many dual-specificity phosphatases have been identified that act upon various members of the MAP kinase pathway and are grouped as the MAP kinase phosphatase (MKP) family 45. Several members can efficiently dephosphorylate p38 and p38 4647; however, p38 and p38 are resistant to all known MKP family members.

The first p38 substrate identified was the MAP kinase-activated protein kinase 2 (MAPKAPK2 or MK2) 11552. This substrate, along with its closely related family member MK3 (3pk), were both shown to activate various substrates including small heat shock protein 27 (HSP27) 53, lymphocyte-specific protein 1 (LSP1) 54, cAMP response element-binding protein (CREB) 55, transcription factor ATF1 55, SRF 56, and tyrosine hydroxylase 57. p38 regulated/activated kinase (PRAK) is a p38 and/or p38activated kinase that shares 20-30% sequence identity to MK2 and is thought to regulate heat shock protein 27 (HSP27) 61. Mitogen- and stress-activated protein kinase-1 (MSK1) can be directly activated by p38 and ERK, and may mediate activation of CREB 626364.

Another group of substrates that are activated by p38 comprise transcription factors. Many transcription factors encompassing a broad range of action have been shown to be phosphorylated and subsequently activated by p38. Examples include activating transcription factor 1, 2 & 6 (ATF-1/2/6), SRF accessory protein (Sap1), CHOP (growth arrest and DNA damage inducible gene 153, or GADD153), p53, C/EBP, myocyte enhance factor 2C (MEF2C), MEF2A, MITF1, DDIT3, ELK1, NFAT, and high mobility group-box protein 1 (HBP1) 175566676869707172,73747576. An important cis-element, AP-1 appears to be influenced by p38 through several different mechanisms.  Taken together, all the data suggest that the p38 pathway has a wide variety of functions.

Abundant evidence for p38 involvement in apoptosis exists to date and is based on concomitant activation of p38 and apoptosis induced by a variety of agents such as NGF withdrawal and Fas ligation 959697. Cysteine proteases (caspases) are central to the apoptotic pathway and are expressed as inactive zymogens 98,99. Caspase inhibitors then can block p38 activation through Fas cross-linking, suggesting p38 functions downstream of caspase activation 97100. However, overexpression of dominant active MKK6b can also induce caspase activity and cell death thus implying that p38 may function both upstream and downstream of caspases in apoptosis 101102. It must be mentioned that the role of p38 in apoptosis is cell type and stimulus dependent. While p38 signaling has been shown to promote cell death in some cell lines, in different cell lines p38 has been shown to enhance survival, cell growth, and differentiation.

p38 now seems to have a role in tumorigenesis and sensescence. There have been reports that activation of MKK6 and MKK3 led to a senescent phenotype dependent upon p38 MAPK activity. Also, p38 MAPK activity was shown responsible for senescence in response to telomere shortening, H2O2 exposure, and chronic RAS oncogene signaling 117118119. A common feature of tumor cells is a loss of senescence and p38 may be linked to tumorigenesis in certain cells. It has been reported that p38 activation may be reduced in tumors and that loss of components of the p38 pathway such as MKK3 and MKK6 resulted in increased proliferation and likelihood of tumorigenic conversion regardless of the cell line or the tumor induction agent used in these studies 29.

Although all research done on the p38 pathway cannot be reviewed here, certain conclusions can still be made regarding the operation of p38 as a signal transduction mediator. The p38 family (,,,) is activated by both stress and mitogenic stimuli in a cell dependent manner and certain isoforms can either directly or indirectly target proteins to control pre/post transcription. p38 MAPKs also have the ability to activate other kinases and consequently regulate numerous cellular responses. Because p38 signaling has been implicated in cellular responses including inflammation, cell cycle, cell death, development, cell differentiation, senescence, and tumorigenesis, emphasis must be placed on p38 function with respect to specific cell types.

Regulation of the p38 pathway is not an isolated cascade and many different upstream signals can lead to p38 activation. These signals may be p38 specific (MKK3/6), general MAPKKs (MKK4), or MAPKK independent signals (TAB1). Downstream signaling pathways of p38 are quite divergent and each component may interact with other cellular components, both upstream and downstream, to coordinate cellular processes such as feedback mechanisms. Furthermore, in vivo p38 is not an isolated event and exists in the presence of other MAP kinases and a plethora of other signaling pathways. The subcellular location of p38 activation may also play a critical role determining the resulting effect and may add yet another order of complexity to the investigation of p38 function.

 

7.6.8.2 Mitogen-Activated Protein Kinase Pathways Mediated by ERK, JNK, and p38 Protein Kinases

Gary L. Johnson and Razvan Lapadat
Science 6 Dec 2002; 298: 1911-1912.

Multicellular organisms have three well-characterized subfamilies of mitogen activated protein kinases (MAPKs) that control a vast array of physiological processes. These enzymes are regulated by a characteristic phosphorelay system in which a series of three protein kinases phosphorylate and activate one another. The extracellular signal–regulated kinases (ERKs) function in the control of cell division, and inhibitors of these enzymes are being explored as anticancer agents. The c-Jun amino-terminal kinases ( JNKs) are critical regulators of transcription, and JNK inhibitors may be effective in control of rheumatoid arthritis. The p38 MAPKs are activated by inflammatory cytokines and environmental stresses.

Protein kinases are enzymes that covalently attach phosphate to the side chain of either serine, threonine, or tyrosine of specific proteins inside cells. Such phosphorylation of proteins can control their enzymatic activity, their interaction with other proteins and molecules, their location in the cell, and their propensity for degradation by proteases. Mitogen-activated protein kinases (MAPKs) compose a family of protein kinases whose function and regulation have been conserved during evolution from unicellular organisms such as brewers’ yeast to complex organisms including humans (1). MAPKs phosphorylate specific serines and threonines of target protein substrates and regulate cellular activities ranging from gene expression, mitosis, movement, metabolism, and programmed death. Because of the many important cellular functions controlled by MAPKs, they have been studied extensively to define their roles in physiology and human disease. MAPK-catalyzed phosphorylation of substrate proteins functions as a switch to turn on or off the activity of the substrate protein.

MAPKs are part of a phosphorelay system composed of three sequentially activated kinases, and, like their substrates, MAPKs are regulated by phosphorylation (Fig. 1) (2). MKK-catalyzed phosphorylation activates the MAPK and increases its activity in catalyzing the phosphorylation of its own substrates. MAPK phosphatases reverse the phosphorylation and return the MAPK to an inactive state. MKKs are highly selective in phosphorylating specific MAPKs. MAPK kinase kinases (MKKKs) are the third component of the phosphorelay system. MKKKs phosphorylate and activate specific MKKs. MKKKs have distinct motifs in their sequences that selectively confer their activation in response to different stimuli.

Fig. 1. MAPK phosphorelay systems.

The modules shown are representative of pathway connections for the respective MAPK phosphorelay systems.There are multiple component MKKKs, MKKs, and MAPKs for each system.For example, there are three Raf proteins (c-Raf1, B-Raf, A-Raf), two MKKs (MKK1 and MKK2), and two ERKs (ERK1 and ERK2) that can compose MAPK phosphorelay systems responsive to growth factors.The ERK, JNK, and p39 pathways in the STKE Connections Map demonstrate the potential complexity of these systems.

ERKs 1 and 2 are both components of a three-kinase phosphorelay module that includes the MKKK c-Raf1, B-Raf, or A-Raf, which can be activated by the proto-oncogene Ras. Mutations that convert Ras to an activated oncogene are common oncogenic mutations in many human tumors. Oncogenic Ras persistently activates the ERK1 and ERK2 pathways, which contributes to the increased proliferative rate of tumor cells. For this reason, inhibitors of the ERK pathways are entering clinical trials as potential anticancer agents.

Regulation of the JNK pathway is extremely complex and is influenced by many MKKKs. As depicted in the STKE JNK Pathway Connections Map, there are 13 MKKKs that regulate the JNKs. This diversity of MKKKs allows a wide range of stimuli to activate this MAPK pathway. JNKs are important in controlling programmed cell death or apoptosis (9). The inhibition of JNKs enhances chemotherapy-induced inhibition of tumor cell growth, suggesting that JNKs may provide a molecular target for the treatment of cancer. The pharmaceutical industry is bringing JNK inhibitors into clinical trials.

Recently, a major paradigm shift for MAPK regulation was developed for p38. The p38 enzyme is activated by the protein TAB1 (12), but TAB1 is not a MKK. Rather, TAB1 appears to be an adaptor or scaffolding protein and has no known catalytic activity. This is the first demonstration that another mechanism exists for the regulation of MAPKs in addition to the MKKK-MKKMAPK regulatory module.

The importance of MAPKs in controlling cellular responses to the environment and in regulating gene expression, cell growth, and apoptosis has made them a priority for research related to many human diseases. The ERK, JNK, and p38 pathways are all molecular targets for drug development, and inhibitors of MAPKs will undoubtedly be one of the next group of drugs developed for the treatment of human disease (13).

7.6.9 Cathepsin B promotes colorectal tumorigenesis, cell invasion, and metastasis

B Bian, S Mongrain, S Cagnol, Marie-Josée Langlois, J Boulanger, et al.
Molec Carcinogen 25 Mar 2015; 54(5). http://dx.doi.org:/10.1002/mc.22312

Cathepsin B is a cysteine proteinase that primarily functions as an endopeptidase within endolysosomal compartments in normal cells. However, during tumoral expansion, the regulation of cathepsin B can be altered at multiple levels, thereby resulting in its overexpression and export outside of the cell. This may suggest a possible role of cathepsin B in alterations leading to cancer progression. The aim of this study was to determine the contribution of intracellular and extracellular cathepsin B in growth, tumorigenesis, and invasion of colorectal cancer (CRC) cells. Results show that mRNA and activated levels of cathepsin B were both increased in human adenomas and in CRCs of all stages. Treatment of CRC cells with the highly selective and non-permeant cathepsin B inhibitor Ca074 revealed that extracellular cathepsin B actively contributed to the invasiveness of human CRC cells while not essential for their growth in soft agar. Cathepsin B silencing by RNAi in human CRC cells inhibited their growth in soft agar, as well as their invasion capacity, tumoral expansion, and metastatic spread in immunodeficient mice. Higher levels of the cell cycle inhibitor p27Kip1 were observed in cathepsin B-deficient tumors as well as an increase in cyclin B1. Finally, cathepsin B colocalized with p27Kip1 within the lysosomes and efficiently degraded the inhibitor. In conclusion, the present data demonstrate that cathepsin B is a significant factor in colorectal tumor development, invasion, and metastatic spreading and may, therefore, represent a potential pharmacological target for colorectal tumor therapy

Colorectal cancer (CRC),a major malignancy worldwide and the second leading cause of cancer death in North America, develops through multiple steps. The ability of cancers to invade and metastasize depends on the action of proteases actively taking center stage in extracellular proteolysis [2]. Of all the proteases, the cysteine protease cathepsin B is of significant importance [3]. Cathepsin B primarily functions as an endopeptidase within endolysosomal compartments in normal cells. However, during malignant transformation cathepsin B can be upregulated [3, 4]. Cathepsin B in tumors can either be secreted, bound to the cell membrane or released by shedding vesicles [4]. Expression and redistribution of active cathepsin B to the basal plasma membrane occurs in late colon adenomas [5, 6] coincident with the activation of KRAS [1]. In line with these results, Cavallo-Medved et al. [7] have demonstrated that trafficking of cathepsin B to caveolae and its secretion are regulated by active KRAS in CRC cells in culture. Accordingly, secretion of cathepsin B, increased in the extracellular environment of CRC [8, 9], is suspected to play an essential role in disrupting extracellular matrix barriers, facilitating invasion and metastasis [10-12]. These data are consistent with the link between cathepsin B protein expression in colorectal carcinomas and shortened patient survival [6].

In a recent prospective cohort study of 558 men and women with colonic tumors [13] 82% of patients had tumors that expressed cathepsin B, irrespective of stage, while the remaining 18% had tumors that did not express cathepsin B. Other studies have suggested that cathepsin B expression or activity may actually peak during early stage cancer and subsequently decline with advanced disease [14, 15]. This points to a possible role of cathepsin B in both early and late alterations leading to colonic cancer.

This study used two strategies to specifically counteract the action of cathepsin B. The first involved the use of RNA interference (RNAi) to inhibit the expression of cathepsin B protein into CRC cells while the second approach employed the highly selective cathepsin inhibitor Ca074 to block extracellular cathepsin B activity. Results suggest that extracellular cathepsin B is involved in cell invasion whereas intracellular cathepsin B controls malignant properties of CRC cells. Further, biochemical analysis suggests that intracellular cathepsin B regulates tumorigenesis by degrading the p27Kip1 cell cycle inhibitor.

mRNA and Activated Levels of Cathepsin B Are Increased in Adenomas and in Colorectal Tumors of All Stages

Cathepsin B expression was analyzed at both the mRNA and protein levels in a series of human paired specimens at various tumor stages. As shown in Figure 1A, increased transcript levels of cathepsin B were observed in colorectal tumors, regardless of tumor stage, including in adenomas. Of note, increased cathepsin B expression was more prominent in tumors exhibiting APC mutations. By contrast, there did not appear to be a significant difference relative to KRAS mutations (Figure 1B). To establish whether these increased mRNA levels could be correlated with increased cathepsin B protein levels and more importantly with increased activity, expression of the active processed forms of the protease (25 and 30 kDa) was analyzed by Western blot. Both pro-cathepsin B and active cathepsin B were also increased in colorectal tumors compared to normal tissues (Figure 1C and D). These data hence suggest that increased transcription contributes to a greater expression of active cathepsin B in CRC.

Extracellular Cathepsin B Contributes to Invasiveness of Human CRC Cells but is Dispensable for Their Growth in Soft Agar

Cathepsin B protein levels were next examined in lysates obtained from various human CRC cell lines. As shown in Figure 2A, the proactive and catalytically active processed forms of cathepsin B were detected at various levels in CRC cell lines. Selected cathepsin B presence was also confirmed in conditioned culture medium of CRC cells, again at various levels (Figure 2A, lower panel). However, while the pro-form of cathepsin B was readily observed in conditioned culture medium of all CRC cells, the catalytically-active processed forms of cathepsin B were not detected in Western blot analyses. Additionally, using a fluorescence-based enzymatic assay, no cathepsin B enzyme activity was detected in conditioned medium. Since the pro-protease form might be activated under acidic pH conditions (peri- or extracellular) and by extracellular components of the extracellular matrix, the impact of extracellular inhibition of cathepsin B activation on CRC cell invasion was verified using Biocoat Matrigel chambers. HT-29, DLD1, and SW480 CRC cell lines secreting different levels of pro-cathepsin B (Figure 2A) were tested. Experiments were performed using the highly selective and non-permeant inhibitor Ca074 to reduce extracellular cathepsin B activity. At 10 μM, Ca074 produced a >99% inhibition of recombinant cathepsin B levels while barely reducing intracellular cathepsin B, that is, 5–8%, even upon 12 h exposure to the inhibitor (data not shown). Of note, treatment with 10 μM Ca074 significantly inhibited Matrigel invasion by approximately 45–60% in HT29, DLD1, and SW480 CRC cell lines (Figure 2B). By contrast, treatment with Ca074 had no significant effect on their capacity to form colonies in soft agarose (Figure 2C).

Cathepsin B Silencing in Human CRC Cells Inhibits Tumorigenicity and Metastasis in Immunodeficient Mice

Suppression of cathepsin B expression was found to significantly attenuate the metastatic potential of CRC cells in vivo in experimental metastasis assays. Indeed, immunodeficient mice injected with control CRC cells into the tail vein showed extensive lung metastasis within 28 d, whereas cells expressing shRNA against cathepsin B exhibited reduced lung colonization (Figure 4A). Cathepsin B silencing also altered the capacity of CRC cells to form tumors in mice as assessed by subcutaneous xenograft assays. HT29 cells induced palpable tumors with a short latency period of 9 d after their injection while downregulation of cathepsin B expression in these cells severely impaired their capacity to grow as tumors (Figure 4B).

Cathepsin B Silencing in Human CRC Cells Inhibits Growth in Soft Agar and Invasion Capacity

Recombinant lentiviruses encoding anti-cathepsin B short hairpin RNA (shRNA) were developed in order to stably suppress cathepsin B expression in CRC cells. As shown in Figure 3A, intracellular cathepsin B mRNA and protein levels were decreased in HT29 and DLD1 cells in comparison to a control shRNA which had no effect. Reduction of cathepsin B expression modestly slowed the proliferation rate of HT29 and DLD1 populations in 2D cell culture (Figure 3B). Conversely, cathepsin B silencing significantly reduced the ability of HT29 and DLD1 cells to form colonies in soft agarose (Figure 3C). This indicates that intracellular cathepsin B controls anchorage-independent growth of CRC cells given the absence of Ca074 effect (Figure 2C). Moreover, cathepsin B silencing also reduced the number of invading HT29 and DLD1 cells to a similar extent as Ca074 treatment (Figure 3D vs. Figure 2B).

Cathepsin B Silencing in Human CRC Cells Inhibits Tumorigenicity and Metastasis in Immunodeficient Mice

Suppression of cathepsin B expression was found to significantly attenuate the metastatic potential of CRC cells in vivo in experimental metastasis assays. Indeed, immunodeficient mice injected with control CRC cells into the tail vein showed extensive lung metastasis within 28 d, whereas cells expressing shRNA against cathepsin B exhibited reduced lung colonization (Figure 4A). Cathepsin B silencing also altered the capacity of CRC cells to form tumors in mice as assessed by subcutaneous xenograft assays. HT29 cells induced palpable tumors with a short latency period of 9 d after their injection while downregulation of cathepsin B expression in these cells severely impaired their capacity to grow as tumors (Figure 4B).

Cathepsin B Cleaves the Cell Cycle Inhibitor p27Kip1

In order to verify whether p27Kip1 is in fact a substrate for cathepsin B, both proteins were first overexpressed in 293 T cells and cells subsequently lysed 2 d later for Western blot analysis of their respective expression. As shown in Figure 5A, forced expression of cathepsin B in 293 T cells dose-dependently reduced p27Kip1 protein levels. Next, to determine whether p27Kip1 could be degraded by cathepsin B in vitro, lysates from 293 T cells overexpressing HA-tagged p27Kip1 were incubated with purified cathepsin B and analyzed by Western blot. Figure 5B and C shows that cathepsin B degraded p27Kip1 in a time-dependent manner as visualized by the accumulation of three lower molecular mass species (26, 20, and 12 kDa) in addition to the full-length p27Kip1 protein (see arrows versus arrowhead).

Cathepsin B is capable of endopeptidase, peptidyl-dipeptidase, and carboxydipeptidase activities [18-20]. Cathepsin B also possesses a basic amino acid in the catalytic subsite in position S2 enabling the protease to preferentially split its substrates after Arg–Arg or Lys–Arg or Arg–Lys sequences. At least five of these sequences can be found within the human p27Kip1 sequence (Figure 5D). Therefore, the first amino acid of these doublets was mutated into alanine to test whether it would affect the degradation by cathepsin B. Mutation of arginine 58 (Figure 5E) and lysine 189 (Figure 5F) did not alter the cleavage profile of p27Kip1 by cathepsin B. Mutation of lysine 165 and arginine 194 also had no altering effect (not shown). On the other hand, mutation of arginine 152 into alanine markedly reduced the detection of the 20-kDa fragment (Figure 5E).

The protein stability of wild-type p27Kip1 was then compared to that of the p27Kip1 R152A/Δ189–198 mutant, which is more resistant to cathepsin B cleavage. 293T cells were transiently transfected with either wild-type p27Kip1 or p27Kip1 mutant and subsequently treated with cycloheximide to inhibit protein neosynthesis. Thereafter, cells were lysed at different time intervals in order to analyze protein expression levels of p27Kip1 forms. As shown in Figure 6A, following cycloheximide treatment, protein levels of the p27Kip1 mutant decreased much more slowly than that of wild-type protein. Specifically, 10 h after cycloheximide addition, expression of p27Kip1 protein was clearly decreased while expression of the p27Kip1 mutant remained at control (time 0) levels. Of note, forced expression of cathepsin B in 293 T cells dose-dependently reduced the wild-type form of p27Kip1 protein levels while expression of p27Kip1 R152A/Δ189–198 mutant was only very slightly affected (Figure 6B).

Colocalization of Endogenous p27Kip1 With Cathepsin B Into Lysosomes

As shown in Figure 7A, the anti-cathepsin B antibody confirmed the colocalization of cathepsin B (in green) with the lysosomal acidotropic probe LysoTracker (in red). As expected, most of p27Kip1 staining (in green) was observed in the cell nucleus (Figure 7B). However, certain areas of colocalization were observed between endogenous p27Kip1 (in green) and cathepsin B (in red) (Figure 7B, asterisks). Moreover, Western blot analyses revealed the presence of p27Kip1 protein in lysosome-enriched fractions obtained from differential centrifugation of Caco-2/15 and SW480 cell lysates (Figure 7C and D). These lysosomal fractions were enriched in lysosome-associated membrane protein 1 (LAMP1) and exhibited very low or undetectable levels of the nuclear lamin B protein.

The most extensive literature to date regarding cathepsin B highlights a key role of this protease in the invasiveness and metastasis of various carcinoma cells [3, 8, 10-12]. The present findings demonstrate that cathepsin B has not only a role in facilitating CRC invasion and metastasis, but also in mediating early premalignant processes. Results herein show that cathepsin B promotes anchorage-independent CRC cell growth, which translates in vivo to enhanced tumor growth. In addition, cathepsin B was identified as a new protease capable of proteolytic cleavage of the cell cycle inhibitor p27Kip1. This is especially relevant since the loss of p27Kip1 expression has been strongly associated with aggressive tumor behavior and poor clinical outcome in CRC [22, 23].

These data are reminiscent of the immunohistochemistry data reported by Chan et al. [13] showing that cathepsin B protein was expressed in the vast majority of colon cancers analyzed (558 tumors), which was also independent of tumor stage. The present data also revealed that increased transcription of cathepsin B was associated with the presence of mutations in APC but not in KRAS, thus emphasizing the fact that cathepsin B gene expression is already deregulated in early stages of colorectal carcinoma. Indeed, most CRCs acquire loss-of-function mutations in both copies of the APC gene, resulting in inefficient breakdown of intracellular β-catenin and enhanced nuclear signaling [27]. Given the importance of the Wnt/APC/β-catenin pathway in human tumorigenesis initiation, the present data showing an association between cathepsin B expression and APC mutations are particularly noteworthy.

 

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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

https://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

 

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AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

Reporter-Curator: Stephen J. Williams, Ph.D.

There has been a causal link between alterations in cellular metabolism and the cancer phenotype.  Reorganization of cellular metabolism, marked by a shift from oxidative phosphorylation to aerobic glycolysis for cellular energy requirements (Warburg effect), is considered a hallmark of the transformed cell.  In addition, if tumors are to survive and grow, cancer cells need to adapt to environments high in metabolic stress and to avoid programmed cell death (apoptosis). Recently, a link between cancer growth and metabolism has been supported by the discovery that the LKB1/AMPK signaling pathway as a tumor suppressor axis[1].

LKB1/AMPK/mTOR Signaling Pathway

The Liver Kinase B1 (LKB1)/AMPK  AMP-activated protein kinase/mammalian Target of Rapamycin Complex 1 (mTORC1) signaling pathway links cellular metabolism and energy status to pathways involved in cell growth, proliferation, adaption to energy stress, and autophagy.  LKB1 is a master control for 14 other kinases including AMPK, a serine-threonine kinase which senses cellular AMP/ATP ratios.  In response to cellular starvation, AMPK is allosterically activated by AMP, leading to activation of ATP-generating pathways like fatty acid oxidation and blocking anabolic pathways, like lipid and cholesterol synthesis (which consume ATP).  In addition, AMPK regulates cell growth, proliferation, and autophagy by regulating the mTOR pathway.  AMPK activates the tuberous sclerosis complex 1/2, which ultimately inhibits mTORC1 activity and inhibits protein translation.  This mTOR activity is dis-regulated in many cancers.

LKB1AMPK pathway

LKB1/AMPK in Cancer

  • Somatic mutations of the STK11 gene encoding LKB1 are detected in lung and cervical cancers
  • Therefore LKB1 may be a strong tumor suppressor
  • Pharmacologic activation of LKB1/AMPK with metformin can suppress cancer cell growth

In a recent Cell Metabolism paper[2], Brandon Faubert and colleagues describe how AMPK activity reduces aerobic glycolysis and tumor proliferation while loss of AMPK activity promotes tumor proliferation by shifting cells to aerobic glycolysis and increasing anabolic pathways in a HIF1-dependent manner.

The paper’s major findings were as follows:

  • Loss of AMPKα1 cooperates with the Myc oncogene to accelerate lymphomagenesis
  • AMPKα dysfunction enhances aerobic glycolysis (Warburg effect)
  • Inhibiting HIF-1α reverses the metabolic effects of AMPKα loss
  • HIF-1α mediates the growth advantage of tumors with reduced AMPK signaling

Summary

AMPK is a metabolic sensor that helps maintain cellular energy homeostasis. Despite evidence linking AMPK with tumor suppressor functions, the role of AMPK in tumorigenesis and tumor metabolism is unknown. Here we show that AMPK negatively regulates aerobic glycolysis (the Warburg effect) in cancer cells and suppresses tumor growth in vivo. Genetic ablation of the α1 catalytic subunit of AMPK accelerates Myc-induced lymphomagenesis. Inactivation of AMPKα in both transformed and nontransformed cells promotes a metabolic shift to aerobic glycolysis, increased allocation of glucose carbon into lipids, and biomass accumulation. These metabolic effects require normoxic stabilization of the hypoxia-inducible factor-1α (HIF-1α), as silencing HIF-1α reverses the shift to aerobic glycolysis and the biosynthetic and proliferative advantages conferred by reduced AMPKα signaling. Together our findings suggest that AMPK activity opposes tumor development and that its loss fosters tumor progression in part by regulating cellular metabolic pathways that support cell growth and proliferation.

Below is the graphical abstract of this paper.

Graphical Abstract FINAL.pptx

(Photo credit reference(2; Faubert et. al) permission from Elsevier)

However, this regulation of tumor promotion by AMPK may be more complicated and dependent on the cellular environment.

Nissam Hay from the University of Illinois College of Medicine, Chicago, Illinois, USA and his co-workers Sang-Min Jeon and Navdeep Chandel were investigating the mechanism through which LKB1/AMPK regulate the balance between cancer cell growth and apoptosis under energy stress[3]. In their system, the loss of function of either of these proteins makes cells more sensitive to apoptosis in low glucose environments, and cells deficient in either AMPK or LKB1 were shown to be resistant to oncogenic transformation.  Whereas previous studies showed (as above) AMPK opposes tumor proliferation in a HIF1-dependent manner, their results showed AMPK could promote tumor cell survival during periods of low glucose or altered redox status.

The researchers incubated LKB1-deficient cancer cells in the presence of either glucose or one of the non-metabolizable glucose analogues 2-deoxyglucose (2DG) and 5-thioglucose (5TG), and found that 2DG, but not 5TG, induced the activation of AMPK and protected the cells from apoptosis, even in cells that were deficient in LKB1.

The authors demonstrated that glucose deprivation depleted NADPH levels, increased H2O2 levels and increased cell death, and that this was accelerated in cells deficient in the enzyme glucose-6-phosphate dehydrogenase. Anti-oxidants were also found to inhibit cell death in cells deficient in either AMPK or LKB1.

Knockdown or knockout of either LKB1 or AMPK in cancer cells significantly increased levels of H2O2 but not of peroxide (O2) during glucose depletion. The glucose analogue 2DG was able to activate AMPK and maintain high levels of NADPH and low levels of H2O2 in these cells.

The nucleotide coenzyme NADPH is generated in the pentose phosphate pathway and mitochondrial metabolism, and consumed in H2O2 elimination and fatty acid synthesis. If glucose is limited mitochondrial metabolism becomes the major source of NADPH, supported by fatty acid oxidation. AMPK is known to be a regulator of fatty acid metabolism through inhibition of two acetyl-CoA carboxylases, ACC1 and ACC2.

Short interfering RNAs (siRNAs) to knock down levels of both ACC1 and ACC2 in A549 cancer cells and found that only ACC2 knockdown significantly increased peroxide accumulation and apoptosis, while over-expression of mutant ACC1 and ACC2 in LKB1-proficient cells increased H2O2 and apoptosis.

Therefore, it was concluded AMPK acts to promote early tumor growth and prevent apoptosis in conditions of energy stress through inhibiting acetyl-CoA carboxylase activity, thus maintaining NADPH levels and preventing the build-up of peroxide in glucose-deficient conditions.

This may appear to be conflicting with the previous report in this post however, it is possible that these reports reflect differences in the way cells respond to various cellular stresses, be it hypoxia, glucose deprivation, or changes in redox status.  Therefore a complex situation may arise:

  • AMPK promotes tumor progression under glucose starvation
  • AMPK can oppose tumor proliferation under a normoxic, HIF1-dependent manner
  • Could AMPK regulation be different in cancer stem cells vs. non-stem cell?

References:

1.            Green AS, Chapuis N, Lacombe C, Mayeux P, Bouscary D, Tamburini J: LKB1/AMPK/mTOR signaling pathway in hematological malignancies: from metabolism to cancer cell biology. Cell Cycle 2011, 10(13):2115-2120.

2.            Faubert B, Boily G, Izreig S, Griss T, Samborska B, Dong Z, Dupuy F, Chambers C, Fuerth BJ, Viollet B et al: AMPK is a negative regulator of the Warburg effect and suppresses tumor growth in vivo. Cell metabolism 2013, 17(1):113-124.

3.            Jeon SM, Chandel NS, Hay N: AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress. Nature 2012, 485(7400):661-665.

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