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Posts Tagged ‘Proteins and Enzymes’


Introduction to Protein Synthesis and Degradation

Curator: Larry H. Bernstein, MD, FCAP

Updated 8/31/2019

 

Introduction to Protein Synthesis and Degradation

This chapter I made to follow signaling, rather than to precede it. I had already written much of the content before reorganizing the contents. The previous chapters on carbohydrate and on lipid metabolism have already provided much material on proteins and protein function, which was persuasive of the need to introduce signaling, which entails a substantial introduction to conformational changes in proteins that direct the trafficking of metabolic pathways, but more subtly uncovers an important role for microRNAs, not divorced from transcription, but involved in a non-transcriptional role.  This is where the classic model of molecular biology lacked any integration with emerging metabolic concepts concerning regulation. Consequently, the science was bereft of understanding the ties between the multiple convergence of transcripts, the selective inhibition of transcriptions, and the relative balance of aerobic and anaerobic metabolism, the weight of the pentose phosphate shunt, and the utilization of available energy source for synthetic and catabolic adaptive responses.

The first subchapter serves to introduce the importance of transcription in translational science.  The several subtitles that follow are intended to lay out the scope of the transcriptional activity, and also to direct attention toward the huge role of proteomics in the cell construct.  As we have already seen, proteins engage with carbohydrates and with lipids in important structural and signaling processes.  They are integrasl to the composition of the cytoskeleton, and also to the extracellular matrix.  Many proteins are actually enzymes, carrying out the transformation of some substrate, a derivative of the food we ingest.  They have a catalytic site, and they function with a cofactor – either a multivalent metal or a nucleotide.

The amino acids that go into protein synthesis include “indispensable” nutrients that are not made for use, but must be derived from animal protein, although the need is partially satisfied by plant sources. The essential amino acids are classified into well established groups. There are 20 amino acids commonly found in proteins.  They are classified into the following groups based on the chemical and/or structural properties of their side chains :

  1. Aliphatic Amino Acids
  2. Cyclic Amino Acid
  3. AAs with Hydroxyl or Sulfur-containing side chains
  4. Aromatic Amino Acids
  5. Basic Amino Acids
  6. Acidic Amino Acids and their Amides

Examples include:

Alanine                  aliphatic hydrophobic neutral
Arginine                 polar hydrophilic charged (+)
Cysteine                polar hydrophobic neutral
Glutamine             polar hydrophilic neutral
Histidine                aromatic polar hydrophilic charged (+)
Lysine                   polar hydrophilic charged (+)
Methionine            hydrophobic neutral
Serine                   polar hydrophilic neutral
Tyrosine                aromatic polar hydrophobic

Transcribe and Translate a Gene

  1. For each RNA base there is a corresponding DNA base
  2. Cells use the two-step process of transcription and translation to read each gene and produce the string of amino acids that makes up a protein.
  3. mRNA is produced in the nucleus, and is transferred to the ribosome
  4. mRNA uses uracil instead of thymine
  5. the ribosome reads the RNA sequence and makes protein
  6. There is a sequence combination to fit each amino acid to a three letter RNA code
  7. The ribosome starts at AUG (start), and it reads each codon three letters at a time
  8. Stop codons are UAA, UAG and UGA

 

protein synthesis

protein synthesis

http://learn.genetics.utah.edu/content/molecules/transcribe/images/TandT.png

mcell-transcription-translation

mcell-transcription-translation

http://www.vcbio.science.ru.nl/images/cellcycle/mcell-transcription-translation_eng_zoom.gif

transcription_translation

transcription_translation

 

http://www.biologycorner.com/resources/transcription_translation.JPG

 

What about the purine inosine?

Inosine triphosphate pyrophosphatase – Pyrophosphatase that hydrolyzes the non-canonical purine nucleotides inosine triphosphate (ITP), deoxyinosine triphosphate (dITP) as well as 2′-deoxy-N-6-hydroxylaminopurine triposphate (dHAPTP) and xanthosine 5′-triphosphate (XTP) to their respective monophosphate derivatives. The enzyme does not distinguish between the deoxy- and ribose forms. Probably excludes non-canonical purines from RNA and DNA precursor pools, thus preventing their incorporation into RNA and DNA and avoiding chromosomal lesions.

Gastroenterology. 2011 Apr;140(4):1314-21.  http://dx.doi.org:/10.1053/j.gastro.2010.12.038. Epub 2011 Jan 1.

Inosine triphosphate protects against ribavirin-induced adenosine triphosphate loss by adenylosuccinate synthase function.

Hitomi Y1, Cirulli ET, Fellay J, McHutchison JG, Thompson AJ, Gumbs CE, Shianna KV, Urban TJ, Goldstein DB.

Genetic variation of inosine triphosphatase (ITPA) causing an accumulation of inosine triphosphate (ITP) has been shown to protect patients against ribavirin (RBV)-induced anemia during treatment for chronic hepatitis C infection by genome-wide association study (GWAS). However, the biologic mechanism by which this occurs is unknown.

Although ITP is not used directly by human erythrocyte ATPase, it can be used for ATP biosynthesis via ADSS in place of guanosine triphosphate (GTP). With RBV challenge, erythrocyte ATP reduction was more severe in the wild-type ITPA genotype than in the hemolysis protective ITPA genotype. This difference also remains after inhibiting adenosine uptake using nitrobenzylmercaptopurine riboside (NBMPR).

ITP confers protection against RBV-induced ATP reduction by substituting for erythrocyte GTP, which is depleted by RBV, in the biosynthesis of ATP. Because patients with excess ITP appear largely protected against anemia, these results confirm that RBV-induced anemia is due primarily to the effect of the drug on GTP and consequently ATP levels in erythrocytes.

Ther Drug Monit. 2012 Aug;34(4):477-80.  http://dx.doi.org:/10.1097/FTD.0b013e31825c2703.

Determination of inosine triphosphate pyrophosphatase phenotype in human red blood cells using HPLC.

Citterio-Quentin A1, Salvi JP, Boulieu R.

Thiopurine drugs, widely used in cancer chemotherapy, inflammatory bowel disease, and autoimmune hepatitis, are responsible for common adverse events. Only some of these may be explained by genetic polymorphism of thiopurine S-methyltransferase. Recent articles have reported that inosine triphosphate pyrophosphatase (ITPase) deficiency was associated with adverse drug reactions toward thiopurine drug therapy. Here, we report a weak anion exchange high-performance liquid chromatography method to determine ITPase activity in red blood cells and to investigate the relationship with the occurrence of adverse events during azathioprine therapy.

The chromatographic method reported allows the analysis of IMP, inosine diphosphate, and ITP in a single run in <12.5 minutes. The method was linear in the range 5-1500 μmole/L of IMP. Intraassay and interassay precisions were <5% for red blood cell lysates supplemented with 50, 500, and 1000 μmole/L IMP. Km and Vmax evaluated by Lineweaver-Burk plot were 677.4 μmole/L and 19.6 μmole·L·min, respectively. The frequency distribution of ITPase from 73 patients was investigated.

The method described is useful to determine the ITPase phenotype from patients on thiopurine therapy and to investigate the potential relation between ITPase deficiency and the occurrence of adverse events.

 

System wide analyses have underestimated protein abundances and the importance of transcription in mammals

Jingyi Jessica Li1, 2, Peter J Bickel1 and Mark D Biggin3

PeerJ 2:e270; http://dx.doi.org:/10.7717/peerj.270

Using individual measurements for 61 housekeeping proteins to rescale whole proteome data from Schwanhausser et al. (2011), we find that the median protein detected is expressed at 170,000 molecules per cell and that our corrected protein abundance estimates show a higher correlation with mRNA abundances than do the uncorrected protein data. In addition, we estimated the impact of further errors in mRNA and protein abundances using direct experimental measurements of these errors. The resulting analysis suggests that mRNA levels explain at least 56% of the differences in protein abundance for the 4,212 genes detected by Schwanhausser et al. (2011), though because one major source of error could not be estimated the true percent contribution should be higher.We also employed a second, independent strategy to determine the contribution of mRNA levels to protein expression.We show that the variance in translation rates directly measured by ribosome profiling is only 12% of that inferred by Schwanhausser et al. (2011), and that the measured and inferred translation rates correlate poorly (R2 D 0.13). Based on this, our second strategy suggests that mRNA levels explain 81% of the variance in protein levels. We also determined the percent contributions of transcription, RNA degradation, translation and protein degradation to the variance in protein abundances using both of our strategies. While the magnitudes of the two estimates vary, they both suggest that transcription plays a more important role than the earlier studies implied and translation a much smaller role. Finally, the above estimates only apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimat that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the ab-sence of mRNA, we argue that differences in translation rates can play no role in determining the expression levels for the 40% of genes that are non-expressed.

 

Related studies that reveal issues that are not part of this chapter:

  1. Ubiquitylation in relationship to tissue remodeling
  2. Post-translational modification of proteins
    1. Glycosylation
    2. Phosphorylation
    3. Methylation
    4. Nitrosylation
    5. Sulfation – sulfotransferases
      cell-matrix communication
    6. Acetylation and histone deacetylation (HDAC)
      Connecting Protein Phosphatase to 1α (PP1α)
      Acetylation complexes (such as CBP/p300 and PCAF)
      Sirtuins
      Rel/NF-kB Signal Transduction
      Homologous Recombination Pathway of Double-Strand DNA Repair
    7. Glycination
    8. cyclin dependent kinases (CDKs)
    9. lyase
    10. transferase

 

This year, the Lasker award for basic medical research went to Kazutoshi Mori (Kyoto University) and Peter Walter (University of California, San Francisco) for their “discoveries concerning the unfolded protein response (UPR) — an intracellular quality control system that

detects harmful misfolded proteins in the endoplasmic reticulum and signals the nucleus to carry out corrective measures.”

About UPR: Approximately a third of cellular proteins pass through the Endoplasmic Reticulum (ER) which performs stringent quality control of these proteins. All proteins need to assume the proper 3-dimensional shape in order to function properly in the harsh cellular environment. Related to this is the fact that cells are under constant stress and have to make rapid, real time decisions about survival or death.

A major indicator of stress is the accumulation of unfolded proteins within the Endoplasmic Reticulum (ER), which triggers a transcriptional cascade in order to increase the folding capacity of the ER. If the metabolic burden is too great and homeostasis cannot be achieved, the response shifts from

damage control to the induction of pro-apoptotic pathways that would ultimately cause cell death.

This response to unfolded proteins or the UPR is conserved among all eukaryotes, and dysfunction in this pathway underlies many human diseases, including Alzheimer’s, Parkinson’s, Diabetes and Cancer.

 

The discovery of a new class of human proteins with previously unidentified activities

In a landmark study conducted by scientists at the Scripps Research Institute, The Hong Kong University of Science and Technology, aTyr Pharma and their collaborators, a new class of human proteins has been discovered. These proteins [nearly 250], called Physiocrines belong to the aminoacyl tRNA synthetase gene family and carry out novel, diverse and distinct biological functions.

The aminoacyl tRNA synthetase gene family codes for a group of 20 ubiquitous enzymes almost all of which are part of the protein synthesis machinery. Using recombinant protein purification, deep sequencing technique, mass spectroscopy and cell based assays, the team made this discovery. The finding is significant, also because it highlights the alternate use of a gene family whose protein product normally performs catalytic activities for non-catalytic regulation of basic and complex physiological processes spanning metabolism, vascularization, stem cell biology and immunology

 

Muscle maintenance and regeneration – key player identified

Muscle tissue suffers from atrophy with age and its regenerative capacity also declines over time. Most molecules discovered thus far to boost tissue regeneration are also implicated in cancers.  During a quest to find safer alternatives that can regenerate tissue, scientists reported that the hormone Oxytocin is required for proper muscle tissue regeneration and homeostasis and that its levels decline with age.

Oxytocin could be an alternative to hormone replacement therapy as a way to combat aging and other organ related degeneration.

Oxytocin is an age-specific circulating hormone that is necessary for muscle maintenance and regeneration (June 2014)

 

Proc Natl Acad Sci U S A. 2014 Sep 30;111(39):14289-94.   http://dx.doi.org:/10.1073/pnas.1407640111. Epub 2014 Sep 15.

Role of forkhead box protein A3 in age-associated metabolic decline.

Ma X1, Xu L1, Gavrilova O2, Mueller E3.

Aging is associated with increased adiposity and diminished thermogenesis, but the critical transcription factors influencing these metabolic changes late in life are poorly understood. We recently demonstrated that the winged helix factor forkhead box protein A3 (Foxa3) regulates the expansion of visceral adipose tissue in high-fat diet regimens; however, whether Foxa3 also contributes to the increase in adiposity and the decrease in brown fat activity observed during the normal aging process is currently unknown. Here we report that during aging, levels of Foxa3 are significantly and selectively up-regulated in brown and inguinal white fat depots, and that midage Foxa3-null mice have increased white fat browning and thermogenic capacity, decreased adipose tissue expansion, improved insulin sensitivity, and increased longevity. Foxa3 gain-of-function and loss-of-function studies in inguinal adipose depots demonstrated a cell-autonomous function for Foxa3 in white fat tissue browning. Furthermore, our analysis revealed that the mechanisms of Foxa3 modulation of brown fat gene programs involve the suppression of peroxisome proliferator activated receptor γ coactivtor 1 α (PGC1α) levels through interference with cAMP responsive element binding protein 1-mediated transcriptional regulation of the PGC1α promoter.

 

Asymmetric mRNA localization contributes to fidelity and sensitivity of spatially localized systems

RJ Weatheritt, TJ Gibson & MM Babu
Nature Structural & Molecular Biology 24 Aug, 2014; 21: 833–839 http://dx.do.orgi:/10.1038/nsmb.2876

Although many proteins are localized after translation, asymmetric protein distribution is also achieved by translation after mRNA localization. Why are certain mRNA transported to a distal location and translated on-site? Here we undertake a systematic, genome-scale study of asymmetrically distributed protein and mRNA in mammalian cells. Our findings suggest that asymmetric protein distribution by mRNA localization enhances interaction fidelity and signaling sensitivity. Proteins synthesized at distal locations frequently contain intrinsically disordered segments. These regions are generally rich in assembly-promoting modules and are often regulated by post-translational modifications. Such proteins are tightly regulated but display distinct temporal dynamics upon stimulation with growth factors. Thus, proteins synthesized on-site may rapidly alter proteome composition and act as dynamically regulated scaffolds to promote the formation of reversible cellular assemblies. Our observations are consistent across multiple mammalian species, cell types and developmental stages, suggesting that localized translation is a recurring feature of cell signaling and regulation.

 

An overview of the potential advantages conferred by distal-site protein synthesis, inferred from our analysis.

 

An overview of the potential advantages conferred by distal-site protein synthesis

An overview of the potential advantages conferred by distal-site protein synthesis

 

Turquoise and red filled circle represents off-target and correct interaction partners, respectively. Wavy lines represent a disordered region within a distal site synthesis protein. Grey and red line in graphs represents profiles of t…

http://www.nature.com/nsmb/journal/v21/n9/carousel/nsmb.2876-F5.jpg

 

Tweaking transcriptional programming for high quality recombinant protein production

Since overexpression of recombinant proteins in E. coli often leads to the formation of inclusion bodies, producing properly folded, soluble proteins is undoubtedly the most important end goal in a protein expression campaign. Various approaches have been devised to bypass the insolubility issues during E. coli expression and in a recent report a group of researchers discuss reprogramming the E. coli proteostasis [protein homeostasis] network to achieve high yields of soluble, functional protein. The premise of their studies is that the basal E. coli proteostasis network is insufficient, and often unable, to fold overexpressed proteins, thus clogging the folding machinery.

By overexpressing a mutant, negative-feedback deficient heat shock transcription factor [σ32 I54N] before and during overexpression of the protein of interest, reprogramming can be achieved, resulting in high yields of soluble and functional recombinant target protein. The authors explain that this method is better than simply co-expressing/over-expressing chaperones, co-chaperones, foldases or other components of the proteostasis network because reprogramming readies the folding machinery and up regulates the essential folding components beforehand thus  maintaining system capability of the folding machinery.

The Heat-Shock Response Transcriptional Program Enables High-Yield and High-Quality Recombinant Protein Production in Escherichia coli (July 2014)

 

 Unfolded proteins collapse when exposed to heat and crowded environments

Proteins are important molecules in our body and they fulfil a broad range of functions. For instance as enzymes they help to release energy from food and as muscle proteins they assist with motion. As antibodies they are involved in immune defence and as hormone receptors in signal transduction in cells. Until only recently it was assumed that all proteins take on a clearly defined three-dimensional structure – i.e. they fold in order to be able to assume these functions. Surprisingly, it has been shown that many important proteins occur as unfolded coils. Researchers seek to establish how these disordered proteins are capable at all of assuming highly complex functions.

Ben Schuler’s research group from the Institute of Biochemistry of the University of Zurich has now established that an increase in temperature leads to folded proteins collapsing and becoming smaller. Other environmental factors can trigger the same effect.

Measurements using the “molecular ruler”

“The fact that unfolded proteins shrink at higher temperatures is an indication that cell water does indeed play an important role as to the spatial organisation eventually adopted by the molecules”, comments Schuler with regard to the impact of temperature on protein structure. For their studies the biophysicists use what is known as single-molecule spectroscopy. Small colour probes in the protein enable the observation of changes with an accuracy of more than one millionth of a millimetre. With this “molecular yardstick” it is possible to measure how molecular forces impact protein structure.

With computer simulations the researchers have mimicked the behaviour of disordered proteins.
(Courtesy of Jose EDS Roselino, PhD.

 

MLKL compromises plasma membrane integrity

Necroptosis is implicated in many diseases and understanding this process is essential in the search for new therapies. While mixed lineage kinase domain-like (MLKL) protein has been known to be a critical component of necroptosis induction, how MLKL transduces the death signal was not clear. In a recent finding, scientists demonstrated that the full four-helical bundle domain (4HBD) in the N-terminal region of MLKL is required and sufficient to induce its oligomerization and trigger cell death.

They also found a patch of positively charged amino acids on the surface of the 4HBD that bound to phosphatidylinositol phosphates (PIPs) and allowed the recruitment of MLKL to the plasma membrane that resulted in the formation of pores consisting of MLKL proteins, due to which cells absorbed excess water causing them to explode. Detailed knowledge about how MLKL proteins create pores offers possibilities for the development of new therapeutic interventions for tolerating or preventing cell death.

MLKL compromises plasma membrane integrity by binding to phosphatidylinositol phosphates (May 2014)

 

Mitochondrial and ER proteins implicated in dementia

Mitochondria and the endoplasmic reticulum (ER) form tight structural associations that facilitate a number of cellular functions. However, the molecular mechanisms of these interactions aren’t properly understood.

A group of researchers showed that the ER protein VAPB interacted with mitochondrial protein PTPIP51 to regulate ER-mitochondria associations and that TDP-43, a protein implicated in dementia, disturbs this interaction to regulate cellular Ca2+ homeostasis. These studies point to a new pathogenic mechanism for TDP-43 and may also provide a potential new target for the development of new treatments for devastating neurological conditions like dementia.

ER-mitochondria associations are regulated by the VAPB-PTPIP51 interaction and are disrupted by ALS/FTD-associated TDP-43. Nature (June 2014)

 

A novel strategy to improve membrane protein expression in Yeast

Membrane proteins play indispensable roles in the physiology of an organism. However, recombinant production of membrane proteins is one of the biggest hurdles facing protein biochemists today. A group of scientists in Belgium showed that,

by increasing the intracellular membrane production by interfering with a key enzymatic step of lipid synthesis,

enhanced expression of recombinant membrane proteins in yeast is achieved.

Specifically, they engineered the oleotrophic yeast, Yarrowia lipolytica, by

deleting the phosphatidic acid phosphatase, PAH1 gene,

which led to massive proliferation of endoplasmic reticulum (ER) membranes.

For all 8 tested representatives of different integral membrane protein families, they obtained enhanced protein accumulation.

 

An unconventional method to boost recombinant protein levels

MazF is an mRNA interferase enzyme in E.coli that functions as and degrades cellular mRNA in a targeted fashion, at the “ACA” sequence. This degradation of cellular mRNA causes a precipitous drop in cellular protein synthesis. A group of scientists at the Robert Wood Johnson Medical School in New Jersey, exploited the degeneracy of the genetic code to modify all “ACA” triplets within their gene of interest in a way that the corresponding amino acid (Threonine) remained unchanged. Consequently, induction of MazF toxin caused degradation of E.coli cellular mRNA but the recombinant gene transcription and protein synthesis continued, causing significant accumulation of high quality target protein. This expression system enables unparalleled signal to noise ratios that could dramatically simplify structural and functional studies of difficult-to-purify, biologically important proteins.

 

Tandem fusions and bacterial strain evolution for enhanced functional membrane protein production

Membrane protein production remains a significant challenge in its characterization and structure determination. Despite the fact that there are a variety of host cell types, E.coli remains the popular choice for producing recombinant membrane proteins. A group of scientists in Netherlands devised a robust strategy to increase the probability of functional membrane protein overexpression in E.coli.

By fusing Green Fluorescent Protein (GFP) and the Erythromycin Resistance protein (ErmC) to the C-terminus of a target membrane protein they wer e able to track the folding state of their target protein while using Erythromycin to select for increased expression. By increasing erythromycin concentration in the growth media and testing different membrane targets, they were able to identify four evolved E.coli strains, all of which carried a mutation in the hns gene, whose product is implicated in genome organization and transcriptional silencing. Through their experiments the group showed that partial removal of the transcriptional silencing mechanism was related to production of proteins that were essential for functional overexpression of membrane proteins.

 

The role of an anti-apoptotic factor in recombinant protein production

In a recent study, scientists at the Johns Hopkins University and Frederick National Laboratory for Cancer Research examined an alternative method of utilizing the benefits of anti-apoptotic gene expression to enhance the transient expression of biotherapeutics, specifically, through the co-transfection of Bcl-xL along with the product-coding target gene.

Chinese Hamster Ovary(CHO) cells were co-transfected with the product-coding gene and a vector containing Bcl-xL, using Polyethylenimine (PEI) reagent. They found that the cells co-transfected with Bcl-xL demonstrated reduced apoptosis, increased specific productivity, and an overall increase in product yield.

B-cell lymphoma-extra-large (Bcl-xL) is a mitochondrial transmembrane protein and a member of the Bcl-2 family of proteins which are known to act as either pro- or anti-apoptotic proteins. Bcl-xL itself acts as an anti-apoptotic molecule by preventing the release of mitochondrial contents such as cytochrome c, which would lead to caspase activation. Higher levels of Bcl-xL push a cell toward survival mode by making the membranes pores less permeable and leaky.

Introduction to Protein Synthesis and Degradation Updated 8/31/2019

N-Terminal Degradation of Proteins: The N-End Rule and N-degrons

In both prokaryotes and eukaryotes mitochondria and chloroplasts, the ribosomal synthesis of proteins is initiated with the addition of the N-formyl methionine residue.  However in eukaryotic cytosolic ribosomes, the N terminal was assumed to be devoid of the N-formyl group.  The unformylated N-terminal methionine residues of eukaryotes is then  often N-acetylated (Ac) and creates specific degradation signals, the Ac N-end rule.  These N-end rule pathways are proteolytic systems which recognize these N-degrons resulting in proteosomal degradation or autophagy.  In prokaryotes this system is stimulated by certain amino acid deficiencies and in eukaryotes is dependent on the Psh1 E3 ligase.

Two papers in the journal Science describe this N-degron in more detail.

Structured Abstract
INTRODUCTION

In both bacteria and eukaryotic mitochondria and chloroplasts, the ribosomal synthesis of proteins is initiated with the N-terminal (Nt) formyl-methionine (fMet) residue. Nt-fMet is produced pretranslationally by formyltransferases, which use 10-formyltetrahydrofolate as a cosubstrate. By contrast, proteins synthesized by cytosolic ribosomes of eukaryotes were always presumed to bear unformylated N-terminal Met (Nt-Met). The unformylated Nt-Met residue of eukaryotic proteins is often cotranslationally Nt-acetylated, a modification that creates specific degradation signals, Ac/N-degrons, which are targeted by the Ac/N-end rule pathway. The N-end rule pathways are a set of proteolytic systems whose unifying feature is their ability to recognize proteins containing N-degrons, thereby causing the degradation of these proteins by the proteasome or autophagy in eukaryotes and by the proteasome-like ClpAP protease in bacteria. The main determinant of an N‑degron is a destabilizing Nt-residue of a protein. Studies over the past three decades have shown that all 20 amino acids of the genetic code can act, in cognate sequence contexts, as destabilizing Nt‑residues. The previously known eukaryotic N-end rule pathways are the Arg/N-end rule pathway, the Ac/N-end rule pathway, and the Pro/N-end rule pathway. Regulated degradation of proteins and their natural fragments by the N-end rule pathways has been shown to mediate a broad range of biological processes.

RATIONALE

The chemical similarity of the formyl and acetyl groups and their identical locations in, respectively, Nt‑formylated and Nt-acetylated proteins led us to suggest, and later to show, that the Nt-fMet residues of nascent bacterial proteins can act as bacterial N-degrons, termed fMet/N-degrons. Here we wished to determine whether Nt-formylated proteins might also form in the cytosol of a eukaryote such as the yeast Saccharomyces cerevisiae and to determine the metabolic fates of Nt-formylated proteins if they could be produced outside mitochondria. Our approaches included molecular genetic techniques, mass spectrometric analyses of proteins’ N termini, and affinity-purified antibodies that selectively recognized Nt-formylated reporter proteins.

RESULTS

We discovered that the yeast formyltransferase Fmt1, which is imported from the cytosol into the mitochondria inner matrix, can generate Nt-formylated proteins in the cytosol, because the translocation of Fmt1 into mitochondria is not as efficacious, even under unstressful conditions, as had previously been assumed. We also found that Nt‑formylated proteins are greatly up-regulated in stationary phase or upon starvation for specific amino acids. The massive increase of Nt-formylated proteins strictly requires the Gcn2 kinase, which phosphorylates Fmt1 and mediates its retention in the cytosol. Notably, the ability of Gcn2 to retain a large fraction of Fmt1 in the cytosol of nutritionally stressed cells is confined to Fmt1, inasmuch as the Gcn2 kinase does not have such an effect, under the same conditions, on other examined nuclear DNA–encoded mitochondrial matrix proteins. The Gcn2-Fmt1 protein localization circuit is a previously unknown signal transduction pathway. A down-regulation of cytosolic Nt‑formylation was found to increase the sensitivity of cells to undernutrition stresses, to a prolonged cold stress, and to a toxic compound. We also discovered that the Nt-fMet residues of Nt‑formylated cytosolic proteins act as eukaryotic fMet/N-degrons and identified the Psh1 E3 ubiquitin ligase as the recognition component (fMet/N-recognin) of the previously unknown eukaryotic fMet/N-end rule pathway, which destroys Nt‑formylated proteins.

CONCLUSION

The Nt-formylation of proteins, a long-known pretranslational protein modification, is mediated by formyltransferases. Nt-formylation was thought to be confined to bacteria and bacteria-descended eukaryotic organelles but was found here to also occur at the start of translation by the cytosolic ribosomes of a eukaryote. The levels of Nt‑formylated eukaryotic proteins are greatly increased upon specific stresses, including undernutrition, and appear to be important for adaptation to these stresses. We also discovered that Nt-formylated cytosolic proteins are selectively destroyed by the eukaryotic fMet/N-end rule pathway, mediated by the Psh1 E3 ubiquitin ligase. This previously unknown proteolytic system is likely to be universal among eukaryotes, given strongly conserved mechanisms that mediate Nt‑formylation and degron recognition.

The eukaryotic fMet/N-end rule pathway.

(Top) Under undernutrition conditions, the Gcn2 kinase augments the cytosolic localization of the Fmt1 formyltransferase, and possibly also its enzymatic activity. Consequently, Fmt1 up-regulates the cytosolic fMet–tRNAi (initiator transfer RNA), and thereby increases the levels of cytosolic Nt-formylated proteins, which are required for the adaptation of cells to specific stressors. (Bottom) The Psh1 E3 ubiquitin ligase targets the N-terminal fMet-residues of eukaryotic cytosolic proteins, such as Cse4, Pgd1, and Rps22a, for the polyubiquitylation-mediated, proteasome-dependent degradation.

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The eukaryotic fMet/N-end rule pathway.

(Top) Under undernutrition conditions, the Gcn2 kinase augments the cytosolic localization of the Fmt1 formyltransferase, and possibly also its enzymatic activity. Consequently, Fmt1 up-regulates the cytosolic fMet–tRNAi (initiator transfer RNA), and thereby increases the levels of cytosolic Nt-formylated proteins, which are required for the adaptation of cells to specific stressors. (Bottom) The Psh1 E3 ubiquitin ligase targets the N-terminal fMet-residues of eukaryotic cytosolic proteins, such as Cse4, Pgd1, and Rps22a, for the polyubiquitylation-mediated, proteasome-dependent degradation.

 

A glycine-specific N-degron pathway mediates the quality control of protein N-myristoylation. Richard T. Timms1,2Zhiqian Zhang1,2David Y. Rhee3J. Wade Harper3Itay Koren1,2,*,Stephen J. Elledge1,2

Science  05 Jul 2019: Vol. 365, Issue 6448

The second paper describes a glycine specific N-degron pathway in humans.  Specifically the authors set up a screen to identify specific N-terminal degron motifs in the human.  Findings included an expanded repertoire for the UBR E3 ligases to include substrates with arginine and lysine following an intact initiator methionine and a glycine at the extreme N-terminus, which is a potent degron.

Glycine N-degron regulation revealed

For more than 30 years, N-terminal sequences have been known to influence protein stability, but additional features of these N-end rule, or N-degron, pathways continue to be uncovered. Timms et al. used a global protein stability (GPS) technology to take a broader look at these pathways in human cells. Unexpectedly, glycine exposed at the N terminus could act as a potent degron; proteins bearing N-terminal glycine were targeted for proteasomal degradation by two Cullin-RING E3 ubiquitin ligases through the substrate adaptors ZYG11B and ZER1. This pathway may be important, for example, to degrade proteins that fail to localize properly to cellular membranes and to destroy protein fragments generated during cell death.

Science, this issue p. eaaw4912

Structured Abstract

INTRODUCTION

The ubiquitin-proteasome system is the major route through which the cell achieves selective protein degradation. The E3 ubiquitin ligases are the major determinants of specificity in this system, which is thought to be achieved through their selective recognition of specific degron motifs in substrate proteins. However, our ability to identify these degrons and match them to their cognate E3 ligase remains a major challenge.

RATIONALE

It has long been known that the stability of proteins is influenced by their N-terminal residue, and a large body of work over the past three decades has characterized a collection of N-end rule pathways that target proteins for degradation through N-terminal degron motifs. Recently, we developed Global Protein Stability (GPS)–peptidome technology and used it to delineate a suite of degrons that lie at the extreme C terminus of proteins. We adapted this approach to examine the stability of the human N terminome, allowing us to reevaluate our understanding of N-degron pathways in an unbiased manner.

RESULTS

Stability profiling of the human N terminome identified two major findings: an expanded repertoire for UBR family E3 ligases to include substrates that begin with arginine and lysine following an intact initiator methionine and, more notably, that glycine positioned at the extreme N terminus can act as a potent degron. We established human embryonic kidney 293T reporter cell lines in which unstable peptides that bear N-terminal glycine degrons were fused to green fluorescent protein, and we performed CRISPR screens to identify the degradative machinery involved. These screens identified two Cul2 Cullin-RING E3 ligase complexes, defined by the related substrate adaptors ZYG11B and ZER1, that act redundantly to target substrates bearing N-terminal glycine degrons for proteasomal degradation. Moreover, through the saturation mutagenesis of example substrates, we defined the composition of preferred N-terminal glycine degrons specifically recognized by ZYG11B and ZER1.

We found that preferred glycine degrons are depleted from the native N termini of metazoan proteomes, suggesting that proteins have evolved to avoid degradation through this pathway, but are strongly enriched at annotated caspase cleavage sites. Stability profiling of N-terminal peptides lying downstream of all known caspase cleavages sites confirmed that Cul2ZYG11Band Cul2ZER1 could make a substantial contribution to the removal of proteolytic cleavage products during apoptosis. Last, we identified a role for ZYG11B and ZER1 in the quality control of N-myristoylated proteins. N-myristoylation is an important posttranslational modification that occurs exclusively on N-terminal glycine. By profiling the stability of the human N-terminome in the absence of the N-myristoyltransferases NMT1 and NMT2, we found that a failure to undergo N-myristoylation exposes N-terminal glycine degrons that are otherwise obscured. Thus, conditional exposure of glycine degrons to ZYG11B and ZER1 permits the selective proteasomal degradation of aberrant proteins that have escaped N-terminal myristoylation.

CONCLUSION

These data demonstrate that an additional N-degron pathway centered on N-terminal glycine regulates the stability of metazoan proteomes. Cul2ZYG11B– and Cul2ZER1-mediated protein degradation through N-terminal glycine degrons may be particularly important in the clearance of proteolytic fragments generated by caspase cleavage during apoptosis and in the quality control of protein N-myristoylation.

The glycine N-degron pathway.

Stability profiling of the human N-terminome revealed that N-terminal glycine acts as a potent degron. CRISPR screening revealed two Cul2 complexes, defined by the related substrate adaptors ZYG11B and ZER1, that recognize N-terminal glycine degrons. This pathway may be particularly important for the degradation of caspase cleavage products during apoptosis and the removal of proteins that fail to undergo N-myristoylation.

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The glycine N-degron pathway.

Stability profiling of the human N-terminome revealed that N-terminal glycine acts as a potent degron. CRISPR screening revealed two Cul2 complexes, defined by the related substrate adaptors ZYG11B and ZER1, that recognize N-terminal glycine degrons. This pathway may be particularly important for the degradation of caspase cleavage products during apoptosis and the removal of proteins that fail to undergo N-myristoylation.

 

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Extracellular evaluation of intracellular flux in yeast cells

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

Leaders in Pharmaceutical Intelligence

This is the fourth article in a series on metabolomics, which is a major development in -omics, integrating transcriptomics, proteomics,  genomics, metabolic pathways analysis, metabolic and genomic regulatory control using computational mapping.  In the previous two part presentation, flux analysis was not a topic for evaluation, but here it is the major focus.  It is a study of yeast cells, and bears some relationship to the comparison of glycemia, oxidative phosphorylation, TCA cycle, and ETC in leukemia cell lines.  In the previous study – system flux was beyond the scope of analysis, and explicitly stated.  The inferences made in comparing the two lymphocytic leukemia cells was of intracellular metabolism from extracellular measurements.  The study of yeast cells is aimed at looking at cellular effluxes, which is also an important method for studying pharmacological effects and drug resistance.

Metabolomic series

1.  Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics

https://pharmaceuticalintelligence.com/2014/08/22/metabolomics-metabonomics-and-functional-nutrition-the-next-step-in-nutritional-metabolism-and-biotherapeutics/

2.  Metabolomic analysis of two leukemia cell lines. I

https://pharmaceuticalintelligence.com/2014/08/23/metabolomic-analysis-of-two-leukemia-cell-lines-_i/

3.  Metabolomic analysis of two leukemia cell lines. II.

 https://pharmaceuticalintelligence.com/2014/08/24/metabolomic-analysis-of-two-leukemia-cell-lines-ii/

4.  Extracellular evaluation of intracellular flux in yeast cells

Q1. What is efflux?

Q2. What measurements were excluded from the previous study that would not allow inference about fluxes?

Q3. Would this study bear any relationship to the Pasteur effect?

Q4 What is a genome scale network reconstruction?

Q5 What type of information is required for a network prediction model?

Q6. Is there a difference between the metabolites profiles for yeast grown under aerobic and anaerobuc conditions – under the constrainsts?

Q7.  If there is a difference in the S metabolism, would there be an effect on ATP production?

 

 

Connecting extracellular metabolomic measurements to intracellular flux
states in yeast

Monica L Mo1Bernhard Ø Palsson1 and Markus J Herrgård12*

Author Affiliations

1 Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA

2 Current address: Synthetic Genomics, Inc, 11149 N Torrey Pines Rd, La Jolla, CA 92037, USA

For all author emails, please log on.

BMC Systems Biology 2009, 3:37  doi:10.1186/1752-0509-3-37

 

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1752-0509/3/37

 

Received: 15 December 2008
Accepted: 25 March 2009
Published: 25 March 2009

© 2009 Mo et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Metabolomics has emerged as a powerful tool in the

  • quantitative identification of physiological and disease-induced biological states.

Extracellular metabolome or metabolic profiling data, in particular,

  • can provide an insightful view of intracellular physiological states in a noninvasive manner.

Results

We used an updated genome-scale

  • metabolic network model of Saccharomyces cerevisiae, iMM904, to investigate
  1. how changes in the extracellular metabolome can be used
  2. to study systemic changes in intracellular metabolic states.

The iMM904 metabolic network was reconstructed based on

  • an existing genome-scale network, iND750,
  • and includes 904 genes and 1,412 reactions.

The network model was first validated by

  • comparing 2,888 in silico single-gene deletion strain growth phenotype predictions
  • to published experimental data.

Extracellular metabolome data measured

  • of ammonium assimilation pathways 
  • in response to environmental and genetic perturbations

was then integrated with the iMM904 network

  • in the form of relative overflow secretion constraints and
  • a flux sampling approach was used to characterize candidate flux distributions allowed by these constraints.

Predicted intracellular flux changes were

  • consistent with published measurements
  • on intracellular metabolite levels and fluxes.

Patterns of predicted intracellular flux changes

  • could also be used to correctly identify the regions of
  • the metabolic network that were perturbed.

Conclusion

Our results indicate that

  • integrating quantitative extracellular metabolomic profiles
  • in a constraint-based framework
  • enables inferring changes in intracellular metabolic flux states.

Similar methods could potentially be applied

  • towards analyzing biofluid metabolome variations
  • related to human physiological and disease states.

Background

“Omics” technologies are rapidly generating high amounts of data

  • at varying levels of biological detail.

In addition, there is a rapidly growing literature and

  • accompanying databases that compile this information.

This has provided the basis for the assembly of

  • genome-scale metabolic networks for various microbial and eukaryotic organisms [111].

These network reconstructions serve

  • as manually curated knowledge bases of
  • biological information as well as
  • mathematical representations of biochemical components and
  • interactions specific to each organism.

genome-scale network reconstruction is

  • structured collection of genes, proteins, biochemical reactions, and metabolites
  • determined to exist and operate within a particular organism.

This network can be converted into a predictive model

  • that enables in silico simulations of allowable network states based on
  • governing physico-chemical and genetic constraints [12,13].

A wide range of constraint-based methods have been developed and applied

  • to analyze network metabolic capabilities under
  • different environmental and genetic conditions [13].

These methods have been extensively used to

  • study genome-scale metabolic networks and have successfully predicted, for example,
  1. optimal metabolic states,
  2. gene deletion lethality, and
  3. adaptive evolutionary endpoints [1416].

Most of these applications utilize

  • optimization-based methods such as flux balance analysis (FBA)
  • to explore the metabolic flux space.

However, the behavior of genome-scale metabolic networks can also be studied

  • using unbiased approaches such as
  • uniform random sampling of steady-state flux distributions [17].

Instead of identifying a single optimal flux distribution based on

  • a given optimization criterion (e.g. biomass production),

these methods allow statistical analysis of

  • a large range of possible alternative flux solutions determined by
  • constraints imposed on the network.

Sampling methods have been previously used to study

  1. global organization of E. coli metabolism [18] as well as
  2. to identify candidate disease states in the cardiomyocyte mitochondria [19].

Network reconstructions provide a structured framework

  • to systematically integrate and analyze disparate datasets
  • including transcriptomic, proteomic, metabolomic, and fluxomic data.

Metabolomic data is one of the more relevant data types for this type of analysis as

  1. network reconstructions define the biochemical links between metabolites, and
  2. recent advancements in analytical technologies have allowed increasingly comprehensive
  • intracellular and extracellular metabolite level measurements [20,21].

The metabolome is

  1. the set of metabolites present under a given physiological condition
  2. at a particular time and is the culminating phenotype resulting from
  • various “upstream” control mechanisms of metabolic processes.

Of particular interest to this present study are

  • the quantitative profiles of metabolites that are secreted into the extracellular environment
  • by cells under different conditions.

Recent advances in profiling the extracellular metabolome (EM) have allowed

  • obtaining insightful biological information on cellular metabolism
  • without disrupting the cell itself.

This information can be obtained through various

  • analytical detection,
  • identification, and
  • quantization techniques

for a variety of systems ranging from

  • unicellular model organisms to human biofluids [2023].

Metabolite secretion by a cell reflects its internal metabolic state, and

  • its composition varies in response to
  • genetic or experimental perturbations
  • due to changes in intracellular pathway activities
  • involved in the production and utilization of extracellular metabolites [21].

Variations in metabolic fluxes can be reflected in EM changes which can

  • provide insight into the intracellular pathway activities related to metabolite secretion.

The extracellular metabolomic approach has already shown promise

  • in a variety of applications, including
  1. capturing detailed metabolite biomarker variations related to disease and
  2. drug-induced states and
  3. characterizing gene functions in yeast [2427].

However, interpreting changes in the extracellular metabolome can be challenging

  • due to the indirect relationship between the proximal cause of the change
    (e.g. a mutation)
  • and metabolite secretion.

Since metabolic networks describe

  • mechanistic,
  • biochemical links between metabolites,

integrating such data can allow a systematic approach

  • to identifying altered pathways linked to
  • quantitative changes in secretion profiles.

Measured secretion rates of major byproduct metabolites

  • can be applied as additional exchange flux constraints
  • that define observed metabolic behavior.

For example, a recent study integrating small-scale EM data

  • with a genome-scale yeast model
  • correctly predicted oxygen consumption and ethanol production capacities
  • in mutant strains with respiratory deficiencies [28].

The respiratory deficient mutant study

  • used high accuracy measurements for a small number of
  • major byproduct secretion rates
  • together with an optimization-based method well suited for such data.

Here, we expand the application range of the model-based method used in [28]

  • to extracellular metabolome profiles,
  • which represent a temporal snapshot of the relative abundance
  • for a larger number of secreted metabolites.

Our approach is complementary to

  • statistical (i.e. “top-down”) approaches to metabolome analysis [29]
  • and can potentially be used in applications such as biofluid-based diagnostics or
  • large-scale characterization of mutants strains using metabolite profiles.

This study implements a constraint-based sampling approach on

  • an updated genome-scale network of yeast metabolism
  • to systematically determine how EM level variations

are linked to global changes in intracellular metabolic flux states.

By using a sampling-based network approach and statistical methods (Figure 1),

  • EM changes were linked to systemic intracellular flux perturbations
    in an unbiased manner
  • without relying on defining single optimal flux distributions
  • used in the previously mentioned study [28].

The inferred perturbations in intracellular reaction fluxes were further analyzed

  • using reporter metabolite and subsystem (i.e., metabolic pathway) approaches [30]
  • in order to identify dominant metabolic features that are collectively perturbed (Figure 2).

The sampling-based approach also has the additional benefit of

  • being less sensitive to inaccuracies in metabolite secretion profiles than
  • optimization-based methods and can effectively be used – in biofluid metabolome analysis.
integration of exometabolomic (EM) data

integration of exometabolomic (EM) data

Figure 1. Schematic illustrating the integration of exometabolomic (EM) data with the constraint-based framework.

(A) Cells are subjected to genetic and/or environmental perturbations to secrete metabolite patterns unique to that condition.
(B) EM is detected, identified, and quantified.
(C) EM data is integrated as required secretion flux constraints to define allowable solution space.
(D) Random sampling of solution space yields the range of feasible flux distributions for intracellular reactions.
(E) Sampled fluxes were compared to sampled fluxes of another condition to determine

  • which metabolic regions were altered between the two conditions (see Figure 2).

(F) Significantly altered metabolic regions were identified.

http://www.biomedcentral.com/content/figures/1752-0509-3-37-1.jpg

 

sampling and scoring analysis to determine intracellular flux changes

sampling and scoring analysis to determine intracellular flux changes

Figure 2. Schematic of sampling and scoring analysis to determine intracellular flux changes.

(A) Reaction fluxes are sampled for two conditions.
(B & C) Sample of flux differences is calculated by selecting random flux values from each condition

  • to obtain a distribution of flux differences for each reaction.

(D) Standardized reaction Z-scores are determined, which represent

  • how far the sampled flux differences deviates from a zero flux change.

Reaction scores can be used in

  1. visualizing perturbation subnetworks and
  2. analyzing reporter metabolites and subsystems.

http://www.biomedcentral.com/content/figures/1752-0509-3-37-2.jpg

This study was divided into two parts and describes:

(i) the reconstruction and validation of an expanded S. cerevisiae metabolic network, iMM904; and
(ii) the systematic inference of intracellular metabolic states from

  • two yeast EM data sets using a constraint-based sampling approach.

The first EM data set compares wild type yeast to the gdh1/GDH2 (glutamate dehydrogenase) strain [31],

  • which indicated good agreement between predicted metabolic changes
  • of intracellular metabolite levels and fluxes [31,32].

The second EM data set focused on secreted amino acid measurements

  • from a separate study of yeast cultured in different
    ammonium and potassium concentrations [33].

We analyzed the EM data to gain further insight into

  • perturbed ammonium assimilation processes as well as
  1. metabolic states relating potassium limitation and
  2. ammonium excess conditions to one another.

The model-based analysis of both

  • separately published extracellular metabolome datasets
  • suggests a relationship between
  1. glutamate,
  2. threonine and
  3. folate metabolism,
  • which are collectively perturbed when
    ammonium assimilation processes are broadly disrupted
  1. either by environmental (excess ammonia) or
  2. genetic (gene deletion/overexpression) perturbations.

The methods herein present an approach to

  • interpreting extracellular metabolome data and
  • associating these measured secreted metabolite variations
  • to changes in intracellular metabolic network states.

Additional file 1. iMM904 network content.

The data provided represent the content description of the iMM904 metabolic network and
detailed information on the expanded content.

Format: XLS Size: 2.7MB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional file 2. iMM904 model files.

The data provided are the model text files of the iMM904 metabolic network
that is compatible with the available COBRA Toolbox [13]. The model structure
can be loaded into Matlab using the ‘SimPhenyPlus’ format with GPR and compound information.

Format: ZIP Size: 163KB Download file

Conversion of the network to a predictive model

The network reconstruction was converted to a constraint-based model using established procedures [13].

Network reactions and metabolites were assembled into a stoichiometric matrix 

  • containing the stoichiometric coefficients of the reactions in the network.

The steady-state solution space containing possible flux distributions

  • is determined by calculating the null space of S= 0,

where is the reaction flux vector.

Minimal media conditions were set through constraints on exchange fluxes

  • corresponding to the experimental measured substrate uptake rates.

All the model-based calculations were done using the Matlab COBRA Toolbox [13]

  • utilizing the glpk or Tomlab/CPLEX (Tomopt, Inc.) optimization solvers.

Chemostat growth simulations

The iMM904 model was initially validated by

  1. simulating wild type yeast growth in aerobic and anaerobic
    carbon-limited chemostat conditions
  2. and comparing the simulation results to published experimental data

on substrate uptake and byproduct secretion in these conditions [34].

The study was performed following the approach taken to validate the iFF708 model in a previous study [35].

The predicted glucose uptake rates were determined

  1. by setting the in silico growth rate to the measured dilution rate,
    – equivalent under continuous culture growth,
  2. and minimizing the glucose uptake rate.

The accuracy of in silico predictions of

  • substrate uptake and byproduct secretion by the iMM904 model
  • was similar to the accuracy obtained using the iFF708 model
  • and results are shown in Figure S1 [see Additional file 3].

Additional file 3. Supplemental figures. 

The file provides the supplemental figures and descriptions of S1, S2, S3, and S4.

Format: PDF Size: 513KB Download file

This file can be viewed with: Adobe Acrobat Reader

Genome-scale gene deletion phenotype predictions

The iMM904 network was further validated by

  • performing genome-scale gene lethality computations
  • following established procedures to determine growth phenotypes
  1. under minimal medium conditions and
  2. compared to published data.

A modified version of the biomass function used in previous iND750 studies

  1. was set as the objective to be maximized and
  2. gene deletions were simulated by

setting the flux through the corresponding reaction(s) to zero.

The biomass function was based on the experimentally measured

  1. composition of major cellular constituents
  2. during exponential growth of yeast cells and
  3. was reformulated to include trace amounts of
  4. additional cofactors and metabolites
  5. with the assumed fractional contribution of 10-.

These additional biomass compounds were included

according to the biomass formulation used in the iLL672 study

  • to improve lethality predictions through
  • the inclusion of additional essential biomass components [3].

The model was constrained by limiting

  1. the carbon source uptake to 10 mmol/h/gDW
  2. and oxygen uptake to 2 mmol/h/gDW.

Ammonia, phosphate, and sulfate were assumed to be non-limiting.

The experimental phenotyping data was obtained

  • using strains that were auxotrophic for
  1. methionine,
  2. leucine,
  3. histidine, and
  4. uracil.

These auxotrophies were simulated

  1. by deleting the appropriate genes from the model and
  2. supplementing the in silico strain with the appropriate supplements
  3. at non-limiting, but low levels.

Furthermore, trace amounts of essential nutrients that are present

  • in the experimental minimal media formulation
  1. 4-aminobenzoate,
  2. biotin,
  3. inositol,
  4. nicotinate,
  5. panthothenate,
  6. thiamin)
  • were supplied in the simulations [3].

Three distinct methods to simulate the outcome of gene deletions were utilized:

  1. Flux-balance analysis (FBA) [36-38],
  2. Minimization of Metabolic Adjustment (MoMA) [39], and
  3. a linear version of MoMA (linearMoMA).

In the linearMoMA method, minimization of the quadratic objective function
of the original MoMA algorithm

  • was replaced by minimization of the corresponding 1-norm objective function
    (i.e. sum of the absolute values of the differences of wild type FBA solution
    and the knockout strain flux solution).

The computed results were then compared to growth phenotype data
(viable/lethal) from a previously published experimental gene deletion study [3].

The comparison between experimental and in silico deletion phenotypes involved

  • choosing a threshold for the predicted relative growth rate of
  • a deletion strain that is considered to be viable.

We used standard ROC curve analysis

  • to assess the accuracy of different prediction methods and models
  • across the full range of the viability threshold parameter,
    results shown in Figure S2 [see Additional file 3].

The ROC curve plots the true viable rate against the false viable rate

  • allowing comparison of different models and methods
  • without requiring arbitrarily choosing this parameter a priori [40].

The optimal prediction performance corresponds to

  • the point closest to the top left corner of the ROC plot
    (i.e. 100% true viable rate, 0% false viable rate).

Table 1

Table 1 Comparison of iMM904 and iLL672 gene deletion predictions and experimental data under minimal media conditions
Media Model Method True viable False viable False lethal True lethal True viable % False viable % MCC
Glucose iMM904 full FBA 647 10 32 33 95.29 23.26 0.6
iMM904 full linMOMA 644 10 35 33 94.85 23.26 0.58
iMM904 full MOMA 644 10 35 33 94.85 23.26 0.58
iMM904 red FBA 440 9 28 33 94.02 21.43 0.61
iMM904 red linMOMA 437 9 31 33 93.38 21.43 0.6
iMM904 red MOMA 437 9 31 33 93.38 21.43 0.6
iLL672 full MOMA 433 9 35 33 92.52 21.43 0.57
Galactose iMM904 full FBA 595 32 36 59 94.29 35.16 0.58
iMM904 full linMOMA 595 32 36 59 94.29 35.16 0.58
iMM904 full MOMA 595 32 36 59 94.29 35.16 0.58
iMM904 red FBA 409 12 33 56 92.53 17.65 0.67
iMM904 red linMOMA 409 12 33 56 92.53 17.65 0.67
iMM904 red MOMA 409 12 33 56 92.53 17.65 0.67
iLL672 full MOMA 411 19 31 49 92.99 27.94 0.61
Glycerol iMM904 full FBA 596 43 36 47 94.3 47.78 0.48
iMM904 full linMOMA 595 44 37 46 94.15 48.89 0.47
iMM904 full MOMA 598 44 34 46 94.62 48.89 0.48
iMM904 red FBA 410 20 34 46 92.34 30.3 0.57
iMM904 red linMOMA 409 21 35 45 92.12 31.82 0.56
iMM904 red MOMA 412 21 32 45 92.79 31.82 0.57
iLL672 full MOMA 406 20 38 46 91.44 30.3 0.55
Ethanol iMM904 full FBA 593 45 29 55 95.34 45 0.54
iMM904 full linMOMA 592 45 30 55 95.18 45 0.54
iMM904 full MOMA 592 44 30 56 95.18 44 0.55
iMM904 red FBA 408 21 27 54 93.79 28 0.64
iMM904 red linMOMA 407 21 28 54 93.56 28 0.63
iMM904 red MOMA 407 20 28 55 93.56 26.67 0.64
iLL672 full MOMA 401 13 34 62 92.18 17.33 0.68
MCC, Matthews correlation coefficient (see Methods). Note that the iLL672 predictions were obtained directly from [3] and thus the viability threshold was not optimized using the maximum MCC approach.
Mo et al. BMC Systems Biology 2009 3:37  http://dx.doi.org:/10.1186/1752-0509-3-37

 

The values reported in Table 1 correspond to selecting

  • the optimal viability threshold based on this criterion.

We summarized the overall prediction accuracy of a model and method

  • using the Matthews Correlation Coefficient (MCC) [40].

The MCC ranges from -1 (all predictions incorrect) to +1 (all predictions correct) and

  • is suitable for summarizing overall prediction performance

in our case where there are substantially more viable than lethal gene deletions.

ROC plots were produced in Matlab (Mathworks, Inc.).

 

Table 1. Comparison of iMM904 and iLL672

  • gene deletion predictions and
  • experimental data

Inferring perturbed metabolic regions based on EM profiles

The method implemented in this study is shown schematically in Figures 1 and 2

Constraining the iMM904 network 

Relative levels of quantitative EM data were incorporated into the constraint-based framework

  • as overflow secretion exchange fluxes to simulate the required low-level production of
  • experimentally observed excreted metabolites.

The primary objective of this study is to associate

  • relative metabolite levels that are generally measured for metabonomic or biofluid analyses
  • to the quantitative ranges of intracellular reaction fluxes required to produce them.

However, without detailed kinetic information or dynamic metabolite measurements available,

  • we approximated EM datasets of relative quantitative metabolite levels
  • to be proportional to the rate in which they are secreted and detected
  • (at a steady state) – into the extracellular media.

This approach is analogous to approximating uptake rates based

  • on metabolite concentrations from a previous study performing sampling analysis
  • on a cardiomyocyte mitochondrial network
  • to identify differential flux distribution ranges

for various environmental (i.e. substrate uptake) conditions [19].

The raw data was normalized by the raw maximum value of the dataset
(thus the maximum secretion flux was 1 mmol/hr/gDW) with

  • an assumed error of 10%
  • to set the lower and upper bounds and thus
  • inherently accounting for sampling calculation sensitivity.

The gdh1/GDH2 strains were flask cultured under minimal glucose media conditions; thus,

  • glucose and oxygen uptake rates were set at 15 and 2 mmol/hr/gDW, respectively,
  • for the gdh1/GDH2 strain study.

In the anaerobic case the oxygen uptake rate was set to zero, and

  • sterols and fatty acids were provided as in silico supplements as described in [35].

For the potassium limitation/ammonium toxicity study

  • the growth rate was set at 0.17 1/h, and
  • the glucose uptake rate was minimized
  • to mimic experimental chemostat cultivation conditions.

These input constraints were constant for each perturbation and comparative wild-type condition

  • such that the calculated solution spaces between the conditions
  • differed based only on variations in the output secretion constraints.

FBA optimization of EM-constrained networks

A modified FBA method with minimization of the 1-norm objective function

  • between two optimal flux distributions was used
  • to determine optimal intracellular fluxes
  • based on the EM-constrained metabolic models.

This method determines two optimal flux distributions simultaneously

  • for two differently constrained models (e.g. wild type vs. mutant) –
  • these flux distributions maximize biomass production in each case and
  • the 1-norm distance between the distributions is as small as possible
  • given the two sets of constraints.

This approach avoids problems with

  • alternative optimal solutions when comparing two FBA-computed flux distributions
  • by assuming minimal rerouting of flux distibution between a perturbed network and its reference network.

Reaction flux changes from the FBA optimization results were determined

  • by computing the relative percentage fold change for each reaction
  • between the mutant and wild-type flux distributions.

Random sampling of the steady-state solution space

We utilized artificial centering hit-and-run (ACHR) Monte Carlo sampling [19,41]

  • to uniformly sample the metabolic flux solution space
  • defined by the constraints described above.

Reactions, and their participating metabolites, found to participate in intracellular loops [42]

  • were discarded from further analysis as these reactions can have arbitrary flux values.

The following sections describe the approaches used for the analysis of the different datasets.

Sampling approach used in the gdh1/GDH2 study

Due to the overall shape of the metabolic flux solution space,

  • most of the sampled flux distributions resided close to the minimally allowed growth rate
    (i.e. biomass production) and
  • corresponded to various futile cycles that utilized substrates but
  • did not produce significant biomass.

In order to study more physiologically relevant portions of the flux space

  • we restricted the sampling to the part of the solution space
  • where the growth rate was at least 50% of the maximum growth rate
  • for the condition as determined by FBA.

This assumes that cellular growth remains an important overall objective by the yeast cells

  • even in batch cultivation conditions, but
  • that the intracellular flux distributions
  • may not correspond to maximum biomass production [43].

To test the sensitivity of the results to the minimum growth rate threshold,

  • separate Monte Carlo samples were created for each minimum threshold
  • ranging from 50% to 100% at 5% increments.

We also tested the sensitivity of the results

  • to the relative magnitude of the extracellular metabolite secretion rates
  • by performing the sampling at three different relative levels

(0 corresponding to no extracellular metabolite secretion, maximum rate of 0.5 mmol/hr/gDW,
and maximum rate of 1.0 mmol/hr/gDW).

For each minimum growth rate threshold and extracellular metabolite secretion rate,

  • the ACHR sampler was run for 5 million steps and
  • a flux distribution was stored every 5000 steps.

The sensitivity analysis results are presented in Figures S3 and S4 [see Additional File 3], and

  • the results indicate that the reaction Z-scores (see below) are not significantly affected by
  1. either the portion of the solution space sampled or
  2. the exact scaling of secretion rates.

The final overall sample used was created by combining the samples for all minimum growth rate thresholds

  • for the highest extracellular metabolite secretion rate (maximum 1 mmol/hr/gDW).

This approach allowed biasing the sampling towards

  • physiologically relevant parts of the solution space
  • without imposing the requirement of strictly maximizing a predetermined objective function.

The samples obtained with no EM data were used as control samples

  • to filter reporter metabolites/subsystems whose scores were significantly high
  • due to only random differences between sampling runs.

Sampling approach used in the potassium limitation/ammonium toxicity study

Since the experimental data used in this study was generated in chemostat conditions, and

  • previous studies have indicated that chemostat flux patterns predicted by FBA are
  • close to the experimentally measured ones [43],
  • we assumed that sampling of the optimal solution space was appropriate for this study.

In order to sample a physiologically reasonable range of flux distributions,

  • samples for four different oxygen uptake rates
    (1, 2, 3, and 4 mmol/hr/gDW with 5 million steps each)
  • were combined in the final analysis.

Standardized scoring of flux differences between perturbation and control conditions

Z-score based approach was implemented to quantify differences in flux samples between two conditions (Figure 2).
First, two flux vectors were chosen randomly,

  • one from each of the two samples to be compared and
  • the difference between the flux vectors was computed.

This approach was repeated to create a sample of 10,000 (n) flux difference vectors

  • for each pair of conditions considered (e.g. mutant or perturbed environment vs. wild type).

Based on this flux difference sample, the sample mean (μdiff,i) and standard deviation (σdiff,i)

  • between the two conditions was calculated for each reaction i. The reaction Z-score was calculated as:

 

reaction Z-score

reaction Z-score

which describes the sampled mean difference deviation

  • from a population mean change of zero (i.e. no flux difference
    between perturbation and wild type).

Note that this approach allows accounting for uncertainty in the

  • flux distributions inferred based on the extracellular metabolite secretion constraints.

This is in contrast to approaches such as FBA or MoMA that would predict

  • a single flux distribution for each condition and thus potentially
  • overestimate differences between conditions.

The reaction Z-scores can then be further used in analysis

  • to identify significantly perturbed regions of the metabolic network
  • based on reporter metabolite [44] or subsystem [30] Z-scores.

These reporter regions indicate, or “report”, dominant perturbation features

  • at the metabolite and pathway levels for a particular condition.

The reporter metabolite Z-score for any metabolite can be derived from the reaction Z-scores

  • of the reactions consuming or producing j (set of reactions denoted as Rj) as:

 

reporter z-score for any metabolite j

reporter z-score for any metabolite j

where Nis the number of reactions in Rand mmet,is calculated as

 

distributional correction for m_met,j SQRT

distributional correction for m_met,j SQRT

To account and correct for background distribution, the metabolite Z-score was normalized

  • by computing μmet,Nj and σmet,,Nj corresponding to the mean mmet and
  • its standard deviation for 1,000 randomly generated reaction sets of size Nj.

Z-scores for subsystems were calculated similarly by considering the set of reactions R

  • that belongs to each subsystem k.

Hence, positive metabolite and subsystem scores indicate a significantly perturbed metabolic region

  • relative to other regions, whereas
  • a negative score indicate regions that are not perturbed
  • more significantly than what is expected by random chance.

Perturbation subnetworks of reactions and connecting metabolites were visualized using Cytoscape [45].

Results and discussion

  1. Reconstruction and validation of iMM904 network iMM904 network content 

A previously reconstructed S. cerevisiae network, iND750,

  • was used as the basis for the construction of the expanded iMM904 network.
  • Prior to its presentation here, the
    iMM904 network content was the basis for a consensus jamboree network that was recently published
  • but has not yet been adapted for FBA calculations [46].

The majority of iND750 content was carried over and

  • further expanded on to construct iMM904, which accounts for
  1. 904 genes,
  2. 1,228 individual metabolites, and
  3. 1,412 reactions of which
  •                       395 are transport reactions.

Both the number of gene-associated reactions and the number of metabolites

  • increased in iMM904 compared with the iND750 network.

Additional genes and reactions included in the network primarily expanded the

  • lipid,
  • transport, and
  • carbohydrate subsystems.

The lipid subsystem includes

  • new genes and
  • reactions involving the degradation of sphingolipids and glycerolipids.

Sterol metabolism was also expanded to include

  • the formation and degradation of steryl esters, the
  •                      storage form of sterols.

The majority of the new transport reactions were added

  • to connect network gaps between intracellular compartments
  • to enable the completion of known physiological functions.

We also added a number of new secretion pathways

  • based on experimentally observed secreted metabolites [31].

A number of gene-protein-reaction (GPR) relationships were modified

  • to include additional gene products that are required to catalyze a reaction.

For example, the protein compounds

  • thioredoxin and
  • ferricytochrome C

were explicitly represented as compounds in iND750 reactions, but

  • the genes encoding these proteins were not associated with their corresponding GPRs.

Other examples include glycogenin and NADPH cytochrome p450 reductases (CPRs),

  1. which are required in the assembly of glycogen and
  2. to sustain catalytic activity in cytochromes p450, respectively.

These additional proteins were included in iMM904 as

  • part of protein complexes to provide a more complete
  • representation of the genes and
  • their corresponding products necessary for a catalytic activity to occur.

Major modifications to existing reactions were in cofactor biosynthesis, namely in

  • quinone,
  • beta-alanine, and
  • riboflavin biosynthetic pathways.

Reactions from previous S. cerevisiae networks associated with

  • quinone,
  • beta-alanine, and
  • riboflavin biosynthetic pathways

were essentially inferred from known reaction mechanisms based on

  • reactions in previous network reconstructions of E. coli [2,47].

These pathways were manually reviewed

  • based on current literature and subsequently replaced by
  • reactions and metabolites specific to yeast.

Additional changes in other subsystems were also made, such as

  1. changes to the compartmental location of a gene and
  2. its corresponding reaction(s),
  3. changes in reaction reversibility and cofactor specificity, and
  4. the elucidation of particular transport mechanisms.

A comprehensive listing of iMM904 network contents as well as

  • a detailed list of changes between iND750 and iMM904 is included
    [see Additional file 1].

Predicting deletion growth phenotypes

The updated genome-scale iMM904 metabolic network was validated

  • by comparing in silico single-gene deletion predictions to
  • in vivo results from a previous study used
  • to analyze another S. cerevisiae metabolic model, iLL672 [3].

This network was constructed based on the iFF708 network [22],

  • which was also the starting point for
  • reconstructing the iND750 network [2].

The experimental data used to validate the iLL672 model consisted of

3,360 single-gene knockout strain phenotypes evaluated

  • under minimal media growth conditions with
  1. glucose,
  2. galactose,
  3. glycerol, and
  4. ethanol

as sole carbon sources. Growth phenotypes for the iMM904 network were predictedusing

  1. FBA [3234],
  2. MoMA [35], and
  3. linear MoMA methods

as described in Methods and subsequently compared to the experimental data (Table 1).

Each deleted gene growth prediction comparison was classified as

  1. true lethal,
  2. true viable,
  3. false lethal, or
  4. false viable.

The growth rate threshold for considering a prediction viable was chosen

  • for each condition and method separately
  • to optimize the tradeoff between true viable and false viable predictions
    (maximum Matthews correlation coefficient, see Methods).

Since iMM904 has 212 more genes than iLL672 with experimental data, we also present results

  • for the subset of iMM904 predictions with genes included in iLL672 (reduced iMM904 set).

When the same gene sets are compared, iMM904 improves gene lethality predictions under

  • glucose,
  • galactose, and
  • glycerol conditions

over iLL672 somewhat, but is less accurate

  • at predicting growth phenotypes under the ethanol condition.

It should be noted that the iLL672 predictions were obtained directly from [3]

  • thus the growth rate threshold was not optimized similarly to iMM904 predictions.

Overall, when viability cutoff is chosen

  • as indicated above for each method separately,
  • the three prediction methods perform similarly
  1. FBA,
  2. MOMA, and
  3. linear MOMA) .

While the full gene complement in iMM904 greatly increased

  • the number of true viable predictions,
  • the full model also made significantly more false viable predictions
  • compared with reduced iMM904 and iLL672 predictions.

However, it is important to note that 143 reactions involved in dead-end biosynthetic pathways were actually

  • removed from iFF708 to build the iLL672 reconstruction [3].

These dead-ends are considered “knowledge gaps” in pathways

  • that have not been fully characterized and, as a result,
  • lead to false viable predictions when determining gene essentiality
  • if the pathway is in fact required for growth under a certain condition [2,26].

As more of these pathways are elucidated and

  • included in the model to
  • fill in existing network gaps,
  • we can expect false viable prediction rates to consequently decrease.

Thus, while a larger network has a temporarily reduced capacity to accurately predict gene deletion phenotypes,

  • it captures a more complete picture of currently known metabolic functions and
  • provides a framework for network expansion as new pathways are elucidated [48].

 

Inferring intracellular perturbation states from metabolic profiles – Aerobic and anaerobic gdh1/GDH2 mutant behavior

The gdh1/GDH2 mutant strain was previously developed [49,50]

  • to lower NADPH consumption in ammonia assimilation, which would
  • favor the NADPH-dependent fermentation of xylose.

In this strain, the NADPH-dependent glutamate dehydrogenase, Gdh1, was

  • deleted and the NADH-dependent form of the enzyme, Gdh2,
  •                     was overexpressed.

The net effect is to allow efficient assimilation of ammonia

  • into glutamate using NADH instead of NADPH as a cofactor.

While growth characteristics remained unaffected,

  • relative quantities of secreted metabolites differed between the wild-type and mutant strain
  • under aerobic and anaerobic conditions.

We analyzed EM data for the gdh1/GDH2 and wild-type strains reported

  • in [31] under aerobic and anaerobic conditions separately using
  • both FBA optimization and
  • sampling-based approaches as described in Methods.

43 measured extracellular and intracellular metabolites from the original dataset [31],

  • primarily of central carbon and amino acid metabolism,
  • were explicitly represented in the iMM904 network [see Additional file 4].

Extracellular metabolite levels were used

  • to formulate secretion constraints and
  • differential intracellular metabolites were used
  • to compare and validate the intracellular flux predictions.

Perturbed reactions from the FBA results were

  • determined by calculating relative flux changes, and
  • reaction Z-scores were calculated from the sampling analysis
  • to quantify flux changes between the mutant and wild-type strains,
  • with Z reaction > 1.96 corresponding to a two-tailed p-value < 0.05 and
  • considered to be significantly perturbed [see Additional file 4].

Additional file 4. Gdh mutant aerobic and anaerobic analysis results. 

The data provided are the full results for the exometabolomic analysis of aerobic and anerobic gdh1/GDH2 mutant.

Format: XLS Size: 669KB Download file

This file can be viewed with: Microsoft Excel Viewer

To validate the predicted results, reaction flux changes from both FBA and sampling methods were compared to differential intracellular metabolite level data measured from the same study. Intracellular metabolites involved in highly perturbed reactions (i.e. reactants and products) predicted from FBA and sampling analyses were identified and
compared to metabolites that were experimentally identified as significantly changed (< 0.05) between mutant and wild-type. Statistical measures of recall, accuracy, and
precision were calculated and represent the predictive sensitivity, exactness, and reproducibility respectively. From the sampling analysis, a considerably larger number of
significantly perturbed reactions are predicted in the anaerobic case (505 reactions, or 70.7% of active reactions) than in aerobic (394 reactions, or 49.8% of active reactions). The top percentile of FBA flux changes equivalent to the percentage of significantly perturbed sampling reactions were compared to the intracellular data. Results from both analyses are summarized in Table 2. Sampling predictions were considerably higher in recall than FBA predictions for both conditions, with respective ranges of 0.83–1
compared to 0.48–0.96. Accuracy was also higher in sampling predictions; however, precision was slightly better in the FBA predictions as expected due to the smaller
number of predicted changes. Overall, the sampling predictions of perturbed intracellular metabolites are strongly consistent with the experimental data and significantly
outperforms that of FBA optimization predictions in accurately predicting differential metabolites involved in perturbed intracellular fluxes.

Table 2. Statistical comparison of the differential intracellular metabolite data set (< 0.05) with metabolites involved in perturbed reactions predicted by FBA optimization and sampling analyses for aerobic and anaerobic gdh1/GDH2 mutant.

 

Table 2 Statistical comparison of the differential intracellular metabolite data set (p < 0.05)
with metabolites involved in perturbed reactions predicted by FBA optimization and
sampling analyses for aerobic and anaerobic gdh1/GDH2 mutant.
                           Aerobic                         Anaerobic                             Overall
FBA Sampling FBA Sampling FBA
Recall 0.48 0.83 0.96 1 0.71 0.91
Accuracy 0.55 0.62 0.64 0.64 0.6 0.63
Precision 0.78 0.69 0.64 0.63 0.68 0.66
Overall statistics indicate combined results of both conditions.
Mo et al. BMC Systems Biology 2009 3:37   http://dx.doi.org:/10.1186/1752-0509-3-37


Figure 3.
 Perturbation reaction subnetwork of gdh1/GDH2 mutant under aerobic conditions.

The network illustrates a simplified subset of highly perturbedPerturbation subnetworks can be drawn to visualize predicted significantly perturbed intracellular reactions and illustrate their connection to the observed secreted metabolites in the aerobic and anaerobic gdh1/GDH2 mutants.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under aerobic conditions.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under aerobic conditions.

Figure 3 shows an example of a simplified aerobic perturbation subnetwork consisting primarily of proximal pathways connected directly to a subset of major secreted
metabolites

  • glutamate,
  • proline,
  • D-lactate, and
  • 2-hydroxybuturate.

Figure 4 displays anaerobic reactions with Z-scores of similar magnitude to the perturbed reactions in Figure 3. The same subset of metabolites is also present in the
larger anaerobic perturbation network and indicates that the NADPH/NADH balance perturbation induced by the gdh1/GDH2 manipulation has widespread effects
beyond just altering glutamate metabolism anaerobically.

Interestingly, it is clear that the majority of the secreted metabolite pathways involve connected perturbed reactions that broadly converge on glutamate.

Note that Figures 3 and 4 only show the subnetworks that consisted of two or more connected reactions  for a number of secreted metabolites no contiguous perturbed pathway could be identified by the sampling approach. This indicates that the secreted metabolite pattern alone is not sufficient to determine which specific
production and secretion pathways are used by the cell for these metabolites.

Reactions connected to aerobically-secreted metabolites predicted from the sampling analysis of the gdh1/GDH2 mutant strain.
The major secreted metabolites

  • glutamate,
  • proline,
  • D-lactate, and
  • 2-hydroxybuturate

were also detected in the anaerobic condition. Metabolite abbreviations are found in Additional file 1.

Figure 4.

Perturbation reaction subnetwork of gdh1/GDH2 mutant under anaerobic conditions.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under anaerobic conditions

Perturbation reaction subnetwork of gdh1.GDH2 mutant under anaerobic conditions

Subnetwork illustrates the highly perturbed anaerobic reactions of similar Z-reaction magnitude to the reactions in Figure 3.

A significantly larger number of reactions indicates mutant metabolic effects are more widespread in the anaerobic environment.
The network shows that perturbed pathways converge on glutamate, the main site in which the gdh1/GDH2 modification was introduced, which
suggests that the direct genetic perturbation effects are amplified under this environment. Metabolite abbreviations are found in Additional file 1.

To further highlight metabolic regions that have been systemically affected by the gdh1/GDH2 modification, reporter metabolite and subsystem methods [30] were used to
summarize reaction scores around specific metabolites and in specific metabolic subsystems. The top ten significant scores for metabolites/subsystems associated with more
than three reactions are summarized in Tables 3 (aerobic) and 4 (anaerobic), with Z > 1.64 corresponding to < 0.05 for a one-tailed distribution. Full data for all reactions,
reporter metabolites, and reporter subsystems is included [see Additional file 4].

Table 3. List of the top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in aerobic conditions.

Table 3
List of the top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in aerobic conditions.
Reporter metabolite Z-score No of reactions*
L-proline [c] 2.71 4
Carbon dioxide [m] 2.51 15
Proton [m] 2.19 51
Glyceraldehyde 3-phosphate [c] 1.93 7
Ubiquinone-6 [m] 1.82 5
Ubiquinol-6 [m] 1.82 5
Ribulose-5-phosphate [c] 1.8 4
Uracil [c] 1.74 4
L-homoserine [c] 1.72 4
Alpha-ketoglutarate [m] 1.71 8
Reporter subsystem Z-score No of reactions
Citric Acid Cycle 4.58 7
Pentose Phosphate Pathway 3.29 12
Glycine and Serine Metabolism 2.69 17
Alanine and Aspartate Metabolism 2.65 6
Oxidative Phosphorylation 1.79 8
Thiamine Metabolism 1.54 8
Arginine and Proline Metabolism 1.44 20
Other Amino Acid Metabolism 1.28 5
Glycolysis/Gluconeogenesis 0.58 14
Anaplerotic reactions 0.19 9
*Number of reactions categorized in a subsystem or found to be neighboring each metabolite
Mo et al. BMC Systems Biology 2009 3:37   http://dx.doi.org:/10.1186/1752-0509-3-37

Table 4. List of top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in anaerobic conditions.

 

Table 4
List of top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in anaerobic conditions.
Reporter metabolite Z-score No of reactions
Glutamate [c] 4.52 35
Aspartate [c] 3.21 11
Alpha-ketoglutarate [c] 2.66 17
Glycine [c] 2.65 7
Pyruvate [m] 2.56 7
Ribulose-5-phosphate [c] 2.43 4
Threonine [c] 2.28 6
10-formyltetrahydrofolate [c] 2.27 5
Fumarate [c] 2.27 5
L-proline [c] 2.04 4
Reporter subsystem Z-score No of reactions
Valine, Leucine, and Isoleucine Metabolism 3.97 15
Tyrosine, Tryptophan, and Phenylalanine Metabolism 3.39 23
Pentose Phosphate Pathway 3.29 11
Purine and Pyrimidine Biosynthesis 3.08 40
Arginine and Proline Metabolism 2.96 19
Threonine and Lysine Metabolism 2.74 14
NAD Biosynthesis 2.66 7
Alanine and Aspartate Metabolism 2.65 6
Histidine Metabolism 2.24 10
Cysteine Metabolism 1.85 10
Mo et al. BMC Systems Biology 2009 3:37   http://dx.doi.org:/10.1186/1752-0509-3-37
Open Data

Perturbations under aerobic conditions largely consisted of pathways involved in mediating the NADH and NADPH balance. Among the highest scoring aerobic subsystems
are TCA cycle and pentose phosphate pathway – key pathways directly involved in the generation of NADH and NADPH. Reporter metabolites involved in these
subsystems –

  • glyceraldehyde-3-phosphate,
  • ribulose-5-phosphate, and
  • alpha-ketoglutarate – were also identified.

These results are consistent with flux and enzyme activity measurements

  • of the gdh1/GDH2 strain under aerobic conditions [32],
  1. which reported significant reduction in the pentose phosphate pathway flux
  2. with concomitant changes in other central metabolic pathways.

Levels of several TCA cycle intermediates (e.g. fumarate, succinate, malate) were also elevated

  • in the gdh1/GDH2 mutant according to the differential intracellular metabolite data.

Altered energy metabolism, as indicated by

  • reporter metabolites (i.e. ubiquinone- , ubiquinol, mitochondrial proton)
  • and subsystem (oxidative phosphorylation),

is certainly feasible as NADH is a primary reducing agent for ATP production.

Pentose phosphate pathway and NAD biosynthesis also appears

  • among the most perturbed anaerobic subsystems, further suggesting
  • perturbed cofactor balance as a common, dominant effect under both conditions.

Glutamate dehydrogenase is a critical enzyme of amino acid biosynthesis as it acts as

  • the entry point for ammonium assimilation via glutamate.

Consequently, metabolic subsystems involved in amino acid biosynthesis were broadly perturbed

  • as a result of the gdh1/GDH2 modification in both aerobic and anaerobic conditions.

For example, the proline biosynthesis pathway that uses glutamate as a precursor

  • was significantly perturbed in both conditions,
  • with significantly changed intracellular and extracellular levels.

There were differences, however, in that more amino acid related subsystems were

  • significantly affected in the anaerobic case (Table 4),
  • further highlighting that altered ammonium assimilation in the mutant
  • has a more widespread effect under anaerobic conditions.

This effect is especially pronounced for

  • threonine and nucleotide metabolism,
  • which were predicted to be significantly perturbed only in anaerobic conditions.

Intracellular threonine levels were amongst the most significantly reduced

  • relative to other differential intracellular metabolites in the anaerobically grown gdh1/GDH2 strain
    (see [31] and Additional file 4), and
  • the relationship between threonine and nucleotide biosynthesis is further supported

by threonine’s recently discovered role as a key precursor in yeast nucleotide biosynthesis [51].

Other key anaerobic reporter metabolites are

  • glycine and 10-formyltetrahydrofolate,
  • both of which are involved in the cytosolic folate cycle (one-carbon metabolism).

Folate is intimately linked to biosynthetic pathways of

  • glycine (with threonine as its precursor) and purines
  • by mediating one-carbon reaction transfers necessary in their metabolism and
  • is a key cofactor in cellular growth [52].

Thus, the anaerobic perturbations identified in the analysis emphasize the close relationship

  • between threonine, folate, and nucleotide metabolic pathways as well as
  • their potential connection to perturbed ammonium assimilation processes.

Interestingly, this association has been previously demonstrated at the transcriptional level

  • as yeast ammonium assimilation (via glutamine synthesis) was found to be
  • co-regulated with genes involved in glycine, folate, and purine synthesis [53].

In summary, the overall differences in predicted gdh1/GDH2 mutant behavior

  • under aerobic and anaerobic conditions show that changes in flux states
  • directly related to modified ammonium assimilation pathway
  1. are amplified anaerobically whereas the
  2. indirect effects through NADH/NADPH balance are more significant aerobically.

Perturbed metabolic regions under aerobic conditions were predominantly

  • in central metabolic pathways involved in responding to the changed NADH/NADPH demand
  • and did not necessarily emphasize that glutamate dehydrogenase was the site of the genetic modification.

The majority of affected anaerobic pathways were involved directly

  • in modified ammonium assimilation as evidenced by

1) significantly perturbed amino acid subsystems,

2) a broad perturbation subnetwork converging on glutamate (Figure 4), and

3) glutamate as the most significant reporter metabolite (Table 4).

Potassium-limited and excess ammonium environments

A recent study reported that potassium limitation resulted in significant

  • growth retardation effect in yeast due to excess ammonium uptake
  • when ammonium was provided as the sole nitrogen source [33].

The proposed mechanism for this effect was that ammonium

  • could to be freely transported through potassium channels
  • when potassium concentrations were low in the media environment, thereby
  • resulting in excess ammonium uptake [33].

As a result, yeast incurred a significant metabolic cost

  • in assimilating ammonia to glutamate and
  • secreting significant amounts of glutamate and other amino acids
  • in potassium-limited conditions as a means to detoxify the excess ammonium.

A similar effect was observed when yeast was grown

  • with no potassium limitation,
  • but with excess ammonia in the environment.

While the observed effect of both environments (low potassium or excess ammonia) was similar,

  • quantitatively unique amino acid secretion profiles suggested that
  • internal metabolic states in these conditions are potentially different.

In order to elucidate the differences in internal metabolic states, we utilized

  • the iMM904 model and the EM profile analysis method to analyze amino acid secretion profiles
  • for a range of low potassium and high ammonia conditions reported in [33].

As before, we utilized amino acid secretion patterns as constraints to the iMM904 model,

  1. sampled the allowable solution space,
  2. computed reaction Z-scores for changes from a reference condition (normal potassium and ammonia), and
  3. finally summarized the resulting changes using reporter metabolites.

Figure 5 shows a clustering of the most significant reporter metabolites (Z ≥ 1.96 in any of the four conditions studied)

  • obtained from this analysis across the four conditions studied.

Interestingly, the potassium-limited environment perturbed only a subset of

  • the significant reporter metabolites identified in the high ammonia environments.

Both low potassium environments shared a consistent pattern of

  • highly perturbed amino acids and related precursor biosynthesis metabolites
    (e.g. pyruvate, PRPP, alpha-ketoglutarate)
  • with high ammonium environments.

The amino acid perturbation pattern (indicated by red labels in Figure 5) was present in

  • the ammonium-toxic environments, although the pattern was
  • slightly weaker for the lower ammonium concentration.

Nevertheless, the results clearly indicate that a similar

  • ammonium detoxifying mechanism that primarily perturbs pathways
  • directly related to amino acid metabolism
  • exists under both types of media conditions.

Figure 5.

Clustergram of top reporter metabolites - y in ammonium-toxic and potassium-limited conditions

Clustergram of top reporter metabolites – y in ammonium-toxic and potassium-limited conditions

Clustergram of top reporter metabolites (i.e. in yellow) in ammonium-toxic and potassium-limited conditions.

Amino acid perturbation patterns (shown in red) were shown to be consistently scored across conditions, indicating that potassium-limited environments K1 (lowest
concentration) and K2 (low concentration) elicited a similar ammonium detoxification response as ammonium-toxic environments N1 (high concentration) and N2
(highest concentration). Metabolites associated with folate metabolism (highlighted in green) are also highly perturbed in ammonium-toxic conditions. Metabolite
abbreviations are found in Additional file 1.

In addition to perturbed amino acids, a secondary effect notably appears at high ammonia levels in which metabolic regions related to folate metabolism are significantly affected. As highlighted in green in Figure 3, we predicted significantly perturbed key metabolites involved in the cytosolic folate cycle. These include tetrahydrofolate derivatives and other metabolites connected to the folate pathway, namely glycine and the methionine-derived methylation cofactors S-adenosylmethionine and S-adenosyl-homocysteine. Additionally, threonine was identified to be a key perturbed metabolite in excess ammonium conditions. These results further illustrate the close
connection between threonine biosynthesis, folate metabolism involving glycine derived from its threonine precursor, and nucleotide biosynthesis [51] that was discussed in
conjunction with the gdh1/GDH2 strain data. Taken together with the anaerobic gdh1/GDH2 data, the results consistently suggest highly perturbed threonine and folate
metabolism when amino acid-related pathways are broadly affected.

In both ammonium-toxic and potassium-limited environments, impaired cellular growth was observed, which can be attributed to high energetic costs of increased
ammonium assimilation to synthesize and excrete amino acids. However, under high ammonium environments, reporter metabolites related to threonine and folate
metabolism indicated that their perturbation, and thus purine supply, may be an additional factor in decreasing cellular viability as there is a direct relationship between
intracellular folate levels and growth rate [54]. Based on these results, we concluded that while potassiumlimited growth in yeast indeed shares physiological features with
growth in ammonium excess, its effects are not as detrimental as actual ammonium excess. The effects on proximal amino acid metabolic pathways are similar in both
environments as indicated by the secretion of the majority of amino acids. However, when our method was applied to analyze the physiological basis behind differences in
secretion profiles between low potassium and high ammonium conditions, ammonium excess was predicted to likely disrupt physiological ammonium assimilation processes,
which in turn potentially impacts folate metabolism and associated cellular growth.

Conclusion

The method presented in this study presents an approach to connecting intracellular flux states to metabolites that are excreted under various physiological conditions. We
showed that well-curated genome-scale metabolic networks can be used to integrate and analyze quantitative EM data by systematically identifying altered intracellular
pathways related to measured changes in the extracellular metabolome. We were able to identify statistically significant metabolic regions that were altered as a result of
genetic (gdh1/GD2 mutant) and environmental (excess ammonium and limited potassium) perturbations, and the predicted intracellular metabolic changes were consistent
with previously published experimental data including measurements of intracellular metabolite levels and metabolic fluxes. Our reanalysis of previously published EM data
on ammonium assimilation-related genetic and environmental perturbations also resulted in testable hypotheses about the role of threonine and folate pathways in mediating
broad responses to changes in ammonium utilization. These studies also demonstrated that the samplingbased method can be readily applied when only partial secreted
metabolite profiles (e.g. only amino acids) are available.

With the emergence of metabolite biofluid biomarkers as a diagnostic tool in human disease [55,56] and the availability of genome-scale human metabolic networks [1],
extensions of the present method would allow identifying potential pathway changes linked to these biomarkers. Employing such a method for studying yeast metabolism was possible as the metabolomic data was measured under controllable environmental conditions where the inputs and outputs of the system were defined. Measured metabolite biomarkers in a clinical setting, however, is far from a controlled environment with significant variations in genetic, nutritional, and environmental factors between different
patients. While there are certainly limitations for clinical applications, the method introduced here is a progressive step towards applying genome-scale metabolic networks
towards analyzing biofluid metabolome data as it 1) avoids the need to only study optimal metabolic states based on a predetermined objective function, 2) allows dealing with noisy experimental data through the sampling approach, and 3) enables analysis even with limited identification of metabolites in the data. The ability to establish potential
connections between extracellular markers and intracellular pathways would be valuable in delineating the genetic and environmental factors associated with a particular
disease.

Authors’ contributions

Conceived and designed the experiments: MLM MJH BOP. Performed experiments: MLM MJH. Analyzed the data: MLM MJH. Wrote the paper: MLM MJH BOP. All authors have read and approved the final manuscript.

Acknowledgements

We thank Jens Nielsen for providing the raw metabolome data for the mutant strain, and Jan Schellenberger and Ines Thiele for valuable discussions. This work was supported by NIH grant R01 GM071808. BOP serves on the scientific advisory board of Genomatica Inc.

 

References

  1. Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R, Palsson BO: Global reconstruction of the human metabolic network based on genomic and bibliomic data. 

Proc Natl Acad Sci USA 2007, 104(6):1777-1782. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Duarte NC, Herrgard MJ, Palsson B: Reconstruction and Validation of Saccharomyces cerevisiae iND750, a Fully Compartmentalized Genome-Scale Metabolic Model. 

Genome Res 2004, 14(7):1298-1309. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Kuepfer L, Sauer U, Blank LM: Metabolic functions of duplicate genes in Saccharomyces cerevisiae. 

Genome Res 2005, 15(10):1421-1430. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Nookaew I, Jewett MC, Meechai A, Thammarongtham C, Laoteng K, Cheevadhanarak S, Nielsen J, Bhumiratana S: The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism. 

BMC Syst Biol 2008, 2:71. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text

  1. Edwards JS, Palsson BO: Systems properties of the Haemophilus influenzae Rd metabolic genotype. 

J biol chem 1999, 274(25):17410-17416. PubMed Abstract | Publisher Full Text

  1. Edwards JS, Palsson BO: The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities. 

Proc Natl Acad Sci USA 2000, 97(10):5528-5533. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Thiele I, Vo TD, Price ND, Palsson B: An Expanded Metabolic Reconstruction of Helicobacter pylori (IT341 GSM/GPR): An in silico genome-scale characterization of single and double deletion mutants. 

J Bacteriol 2005, 187(16):5818-5830. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Vo TD, Greenberg HJ, Palsson BO: Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data. 

J Biol Chem 2004, 279(38):39532-39540. PubMed Abstract | Publisher Full Text

  1. Reed JL, Vo TD, Schilling CH, Palsson BO: An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). 

Genome Biology 2003, 4(9):R54.51-R54.12. BioMed Central Full Text

  1. Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, V H, Palsson BO: A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1261 ORFs and thermodynamic information. 

Molecular Systems Biology 2007, 3:121. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Suthers PF, Dasika MS, Kumar VS, Denisov G, Glass JI, Maranas CD: A genome-scale metabolic reconstruction of Mycoplasma genitalium, iPS189. 

PLos Comp Biol 2009, 5(2):e1000285. Publisher Full Text

  1. Price ND, Reed JL, Palsson BO: Genome-scale models of microbial cells: evaluating the consequences of constraints. 

Nat Rev Microbiol 2004, 2(11):886-897. PubMed Abstract | Publisher Full Text

  1. Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ: Quantitative Prediction of Cellular Metabolism with Constraint-based Models: The COBRA Toolbox. 

Nature protocols 2007, 2(3):727-738. PubMed Abstract | Publisher Full Text

  1. Reed JL, Palsson BO: Genome-Scale In Silico Models of E. coli Have Multiple Equivalent Phenotypic States: Assessment of Correlated Reaction Subsets That Comprise Network States. 

Genome Res 2004, 14(9):1797-1805. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Fong SS, Palsson BO: Metabolic gene deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. 

Nature Genetics 2004, 36(10):1056-1058. PubMed Abstract | Publisher Full Text

  1. Ibarra RU, Edwards JS, Palsson BO: Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. 

Nature 2002, 420(6912):186-189. PubMed Abstract | Publisher Full Text

  1. Schellenberger J, Palsson BØ: Use of randomized sampling for analysis of metabolic networks. 

J Biol Chem 2009, 284(9):5457-5461. PubMed Abstract | Publisher Full Text

  1. Almaas E, Kovács B, Vicsek T, Oltvai ZN, Barabási AL: Global organization of metabolic fluxes in the bacterium Escherichia coli. 

Nature 2004, 427(6977):839-843. PubMed Abstract | Publisher Full Text

  1. Thiele I, Price ND, Vo TD, Palsson BO: Candidate metabolic network states in human mitochondria: Impact of diabetes, ischemia, and diet. 

J Biol Chem 2005, 280(12):11683-11695. PubMed Abstract | Publisher Full Text

  1. Kell DB: Metabolomics and systems biology: making sense of the soup. 

Curr Opin Microbiol 2004, 7(3):296-307. PubMed Abstract | Publisher Full Text

  1. Kell DB, Brown M, Davey HM, Dunn WB, Spasic I, Oliver SG: Metabolic footprinting and systems biology: the medium is the message. 

Nat Rev Microbiol 2005, 3(7):557-565. PubMed Abstract | Publisher Full Text

  1. Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB: Metabolomics by numbers: acquiring and understanding global metabolite data. 

Trends Biotechnol 2004, 22(5):245-252. PubMed Abstract | Publisher Full Text

  1. Lenz EM, Bright J, Wilson ID, Morgan SR, Nash AF: A 1H NMR-based metabonomic study of urine and plasma samples obtained from healthy human subjects. 

J Pharm Biomed Anal 2003, 33(5):1103-1115. PubMed Abstract | Publisher Full Text

  1. Allen J, Davey HM, Broadhurst D, Heald JK, Rowland JJ, Oliver SG, Kell DB: High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. 

Nat Biotech 2003, 21(6):692-696. Publisher Full Text

  1. Nicholson JK, Connelly J, Lindon JC, Holmes E: Metabonomics: a platform for studying drug toxicity and gene function. 

Nat Rev Drug Discov 2002, 1(2):153-161. PubMed Abstract | Publisher Full Text

  1. Mortishire-Smith RJ, Skiles GL, Lawrence JW, Spence S, Nicholls AW, Johnson BA, Nicholson JK: Use of metabonomics to identify impaired fatty acid metabolism as the mechanism of a drug-induced toxicity. 

Chem Res Toxicol 2004, 17(2):165-173. PubMed Abstract | Publisher Full Text

  1. Sabatine MS, Liu E, Morrow DA, Heller E, McCarroll R, Wiegand R, Berriz GF, Roth FP, Gerszten RE: Metabolomic identification of novel biomarkers of myocardial ischemia. 

Circulation 2005, 112(25):3868-3875. PubMed Abstract | Publisher Full Text

  1. Cakir T, Efe C, Dikicioglu D, Hortaçsu AKB, Oliver SG: Flux balance analysis of a genome-scale yeast model constrained by exometabolomic data allows metabolic system identification of genetically different strains. 

Biotechnol Prog 2007, 23(2):320-326. PubMed Abstract | Publisher Full Text

  1. Bang JW, Crockford DJ, Holmes E, Pazos F, Sternberg MJ, Muggleton SH, Nicholson JK: Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods. 

J Proteome Res 2008, 7(2):497-503. PubMed Abstract | Publisher Full Text

  1. Oliveira AP, Patil KR, Nielsen J: Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks. 

BMC Syst Biol 2008, 2:17. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text

  1. Villas-Boas SG, Moxley JF, Akesson M, Stephanopoulos G, Nielsen J: High-throughput metabolic state analysis: the missing link in integrated functional genomics of yeasts. 

Biochem J 2005, 388(Pt 2):669-677. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Moreira dos Santos M, Thygesen G, Kötter P, Olsson L, Nielsen J: Aerobic physiology of redox-engineered Saccharomyces cerevisiae strains modified in the ammonium assimilation for increased NADPH availability. 

FEMS Yeast Res 2003, 4(1):59-68. PubMed Abstract | Publisher Full Text

  1. Hess DC, Lu W, Rabinowitz JD, Botstein D: Ammonium toxicity and potassium limitation in yeast. 

PLoS Biol 2006, 4(11):e351. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Nissen TL, Schulze U, Nielsen J, Villadsen J: Flux distributions in anaerobic, glucose-limited continuous cultures of Saccharomyces cerevisiae. 

Microbiology 1997, 143(Pt 1):203-218. PubMed Abstract | Publisher Full Text

  1. Famili I, Forster J, Nielsen J, Palsson BO: Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. 

Proc Natl Acad Sci USA 2003, 100(23):13134-13139. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Bonarius HPJ, Schmid G, Tramper J: Flux analysis of underdetermined metabolic networks: The quest for the missing constraints. 

Trends in Biotechnology 1997, 15(8):308-314. Publisher Full Text

  1. Edwards JS, Palsson BO: Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. 

BMC Bioinformatics 2000, 1:1. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text

  1. Varma A, Palsson BO: Metabolic Flux Balancing: Basic concepts, Scientific and Practical Use. 

Nat Biotechnol 1994, 12:994-998. Publisher Full Text

  1. Segre D, Vitkup D, Church GM: Analysis of optimality in natural and perturbed metabolic networks. 

Proc Natl Acad Sci USA 2002, 99(23):15112-15117. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H: Assessing the accuracy of prediction algorithms for classification: an overview. 

Bioinformatics 2000, 16(5):412-424. PubMed Abstract | Publisher Full Text

  1. Price ND, Schellenberger J, Palsson BO: Uniform Sampling of Steady State Flux Spaces: Means to Design Experiments and to Interpret Enzymopathies. 

Biophysical Journal 2004, 87(4):2172-2186. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Price ND, Thiele I, Palsson BO: Candidate states of Helicobacter pylori’s genome-scale metabolic network upon application of “loop law” thermodynamic constraints. 

Biophysical J 2006, 90(11):3919-3928. Publisher Full Text

  1. Schuetz R, Kuepfer L, Sauer U: Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. 

Mol Syst Biol 2007, 3:119. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Patil KR, Nielsen J: Uncovering transcriptional regulation of metabolism by using metabolic network topology. 

Proc Natl Acad Sci USA 2005, 102(8):2685-2689. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. 

Genome Res 2003, 13(11):2498-2504. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Herrgard MJ, Swainston N, Dobson P, Dunn WB, Arga KY, Arvas M, Bluthgen N, Borger S, Costenoble R, Heinemann M, et al.: A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. 

Nat Biotech 2008, 26:1155-1160. Publisher Full Text

  1. Forster J, Famili I, Fu PC, Palsson BO, Nielsen J: Genome-Scale Reconstruction of the Saccharomyces cerevisiae Metabolic Network. 

Genome Research 2003, 13(2):244-253. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Reed JL, Patel TR, Chen KH, Joyce AR, Applebee MK, Herring CD, Bui OT, Knight EM, Fong SS, Palsson BO: Systems Approach to Genome Annotation: Prediction and Validation of Metabolic Functions. 

Proc Natl Acad Sci USA 2006, 103(46):17480-17484. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Nissen TL, Kielland-Brandt MC, Nielsen J, Villadsen J: Optimization of ethanol production in Saccharomyces cerevisiae by metabolic engineering of the ammonium assimilation. 

Metab Eng 2000, 2(1):69-77. PubMed Abstract | Publisher Full Text

  1. Roca C, Nielsen J, Olsson L: Metabolic engineering of ammonium assimilation in xylose-fermenting Saccharomyces cerevisiae improves ethanol production. 

Appl Environ Microbiol 2003, 69(8):4732-4736. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Hartman JL IV: Buffering of deoxyribonucleotide pool homeostasis by threonine metabolism. 

Proc Natl Acad Sci USA 2007, 104(28):11700-11705. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

  1. Gelling CL, Piper MD, Hong SP, Kornfeld GD, Dawes IW: Identification of a novel one-carbon metabolism regulon in Saccharomyces cerevisiae. 

J Biol Chem 2004, 279(8):7072-7081. PubMed Abstract | Publisher Full Text

  1. Denis V, Daignan-Fornier B: Synthesis of glutamine, glycine and 10-formyl tetrahydrofolate is coregulated with purine biosynthesis in Saccharomyces cerevisiae. 

Mol Gen Genet 1998, 259(3):246-255. PubMed Abstract | Publisher Full Text

  1. Hjortmo S, Patring J, Andlid T: Growth rate and medium composition strongly affect folate content in Saccharomyces cerevisiae. 

Int J Food Microbiol 2008, 123(1–2):93-100. PubMed Abstract | Publisher Full Text

  1. Kussmann MRF, Affolter M: OMICS-driven biomarker discovery in nutrition and health. 

J Biotechnol 2006, 124(4):758-787. PubMed Abstract | Publisher Full Text

  1. Serkova NJ, Niemann CU: Pattern recognition and biomarker validation using quantitative 1H-NMR-based metabolomics. 

Expert Rev Mol Diagn 2006, 6(5):717-731. PubMed Abstract | Publisher Full Text

 

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Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis


Original description - :Cartoon representation...

Original description – :Cartoon representation of ubiquitin protein, highlighting the secondary structure. α-helices are coloured in blue and the β-sheet in green. The normal attachment point for a further ubiquitin molecule in polyubiquitin chain formation, lysine 48, is shown in pink. :Image was created using PyMOL (Photo credit: Wikipedia)

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis

Larry H Bernstein, MD, FACP, Curator, Reporter, AEW

The work reviewed follows a seminal contribution by two Israeli and an American molecular biologists who shared the Nobel Prize in Chemistry in 2004.

The Royal Swedish Academy of Sciences awarded the Nobel Prize in Chemistry for 2004 “for the discovery of ubiquitin-mediated protein degradation” jointly to Aaron Ciechanover Technion – Israel Institute of Technology, Haifa, Israel, Avram Hershko Technion – Israel Institute of Technology, Haifa, Israel and Irwin Rose – University of California, Irvine, USA.

Aaron Ciechanover, born 1947 (57 years) in Haifa, Israel (Israeli citizen) received a Doctor’s degree in medicine in 1975 at Hebrew University of Jerusalem, and in biology in 1982 at the Technion (Israel Institute of Technology), Haifa. He is a Distinguished Professor at the Center for Cancer and Vascular Biology, and the Rappaport Faculty of Medicine and Research Institute at the Technion, Haifa,
Israel.

Avram Hershko, born 1937 (67 years) in Karcag, Hungary (Israeli citizen) earned the Doctor’s degree in medicine in 1969 at the Hadassah and the Hebrew University Medical School, Jerusalem.  He is a Distinguished Professor at the Rappaport Family Institute for Research in Medical Sciences at the Technion (Israel Institute of Technology), Haifa, Israel.

Irwin Rose, born 1926 (78 years) in New York, USA (American citizen) achieved a Doctor’s degree in 1952 at the University of Chicago, USA. Specialist at the Department of Physiology and Biophysics, College of Medicine, University of California, Irvine, USA.

Proteins labelled for destruction
Proteins build up all living things: plants, animals and therefore us humans. In the past few decades biochemistry has come a long way towards explaining how the cell produces all its various proteins. But as to the breaking down of proteins, not so many researchers were interested. Aaron Ciechanover, Avram Hershko and Irwin Rose went against the stream and at the beginning of the 1980s discovered one of the cell’s most important cyclical processes, regulated protein degradation. For this, they are being rewarded
with the 2004 Nobel Prize in Chemistry.

The label consists of a molecule called ubiquitin. This fastens to the protein to be destroyed, accompanies it to the proteasome where it is recognised as the key in a lock, and signals that a protein is on the way for disassembly. Shortly before the protein is squeezed into the proteasome, its ubiquitin label is disconnected for re-use.

Aaron Ciechanover, Avram Hershko and Irwin Rose have brought us to realise that the cell functions as a highly-efficient checking station where proteins are built up and broken down at a furious rate. The degradation is not indiscriminate but takes place through a process that is controlled in detail so that the proteins to be broken down at any given moment are given a molecular label, a ‘kiss of death’, to be dramatic. The labelled proteins are then fed into the cells’ “waste disposers”, the so called proteasomes, where they are chopped into small pieces and destroyed.

Animation (Plug in requirement: Flash Player 6)

Thanks to the work of the three Laureates it is now possible to understand at  molecular level how the cell controls a number of central processes by breaking down certain proteins and not others. Examples of processes governed by ubiquitin-mediated protein degradation are cell division, DNA repair, quality control of newly-produced proteins, and important parts of the immune defence. When the degradation does not work correctly, we fall ill. Cervical cancer and cystic fibrosis are two examples. Knowledge of
ubiquitin-mediated protein degradation offers an opportunity to develop drugs against these diseases and others.

Aaron Ciechanover and Ronen Ben-Saadon. N-terminal ubiquitination: more protein substrates join in. TRENDS in Cell Biology 2004; 14 (3):103-106.

The ubiquitin–proteasome system (UPS) is involved in selective targeting of innumerable cellular proteins through a complex pathway that plays important roles in a broad array of processes. An important step in the proteolytic cascade is specific recognition of the substrate by one of many ubiquitin ligases, E3s, which is followed by generation of the polyubiquitin degradation signal. For most substrates, it is believed that the first ubiquitin moiety is conjugated, through its C-terminal Gly76 residue, to an 1-NH2 group of an internal Lys residue. Recent findings indicate that, for several proteins, the first ubiquitin moiety is fused linearly to the a-NH2 group of the N-terminal residue.

The ubiquitin–proteasome system (UPS). Ubiquitin is first activated to a high-energy intermediate by E1. It is then transferred to a member of the E2 family of enzymes. From E2 it can be transferred directly to the substrate (S, red) that is bound specifically to a member of the ubiquitin ligase family of proteins, E3

  • (a). This occurs when the E3 belongs to the RING finger family of ligases. In the case of a HECT-domain-containing ligase
  • (b), the activated ubiquitin is transferred first to the E3 before it is conjugated to the E3-bound substrate . Additional ubiquitin moieties are added successively to the previously conjugated moiety to generate a polyubiquitin chain.
  • The polyubiquitinated substrate binds to the 26S proteasome complex (comprising 19S and 20S sub-complexes): the substrate is degraded to short peptides, and free and reusable ubiquitin is released through the activity of de-ubiquitinating enzymes (DUBs).

Ubiquitination on an internal lysine and on the N-terminal residue of the target substrate.

  • (a) The first ubiquitin moiety is conjugated, through its C-terminal Gly76 residue, to the 1-NH2 group of an internal lysine residue of the target substrate (Kn).
  • (b) The first ubiquitin moiety is conjugated to a free a-NH2 group of the N-terminal residue, X.
  • In both cases, successive addition of activated ubiquitin moieties to internal Lys48 on the previously conjugated ubiquitin moiety leads to the synthesis of a  polyubiquitin chain that serves as the degradation signal for the 26S proteasome

 

A UPS Autophagy Review

Summary: This discussion is another in a series discussing mitochondrial metabolism, energetics and regulatory function, and dysfunction, and the process leading to apoptosis and a larger effect on disease, with a specific targeting of neurodegeneration. Why neurological and muscle damage are more sensitive than other organs is not explained easily, but recall in the article on mitochondrial oxidation-reduction reactions and repair that there are organ specific differences in the rates of organelle mutation errors and in the rates of repair. In addition, consider the effect of iron-binding in the function of the cell, and Ca2+ binding in the creation of the mechanic work or signal transmission carried out by the neuromuscular system. We target the previously mentioned role of ubiquitin-proteosome, and interaction with autophagy, mitophagy, and disease.

Keywords: autophagy, ubiquitin-proteosome, UPS, protein degradation, defective organelle removal, selective degradation, E3, neurodegenerative disease, mitochondria, mitophagy, proteolysis, ribosomes, apoptosis, Ca++, rapamycin, TORC1, atg1p kinase, ubiqitization, trafficking pathways, unfolded protein response (UPS), p52/sequestrome, IC3, nitrogen starvation, acetaldehyde dehydrogenase (Ald6p), Ut1hp, toxisomes, Pex3/14 proteins, Bax, E3 Ligase, TRAP1, TNF-a, NFkB.

Ubiquitin-Proteosome Pathway
Three recent papers, describing three apparently independent biological processes, highlight the role of the ubiquitin-proteasome system as a major, however selective, proteolytic and regulatory pathway. Using specific inhibitors to the proteasome, Rock et al. (1994) demonstrate a role for this protease in the degradation of the major bulk of cellular proteins. They also showed that antigen processing requires the ubiquitin-activating enzyme, El. This indicates that antigen processing is both ubiquitin dependent and proteasome dependent. Furthermore, inhibitors to the proteasome prevent tumor necrosis factor a (TNFa)-induced activation of mature NFKB and its entry into the nucleus. The two studies clearly demonstrate that the ubiquitin-proteasome system is involved not only in complete destruction of its protein substrates, but also in limited proteolysis and posttranslational processing in which biologically active peptides or fragments are generated. In addition, the unstable c-Jut but not the stable v-Jun, is multiubiquitinated and degraded. The escape of the oncogenic v-Jun from ubiquitin-dependent degradation suggests a novel route to malignant transformation. Presented here is a review of the components, mechanisms of action, and cellular physiology of the ubiquitin-proteasome pathway.

Experimental evidence implicates the ubiquitin system in the degradation of

  • mitotic cyclins,
  • oncoproteins,
  • the tumor suppressor protein p53,
  • several cell surface receptors,
  • transcriptional regulators, and
  • mutated and damaged proteins.

Some of the proteolytic processes occur throughout the cell cycle, whereas others are tightly programmed and occur following cell cycle-dependent posttranslational modifications of the components involved. Signaling and degradation of other proteins (cell surface receptors, for example) may occur only following structural changes or modification(s) in the target molecule that results from ligand binding. Cell cycle-and modification-dependent degradation, as well the ability of the system to destroy completely or only partially its protein substrates, reflects the complexity involved in regulated intracellular protein degradation.

Enzymes of the System
The reaction occurs in two distinct steps:

  1. signaling of the protein by covalent attachment of multiple ubiquitin molecules and
  2. degradation of the targeted protein with the release of free and reutilizable ubiquitin.

Conjugation of ubiquitin to proteins destined for degradation proceeds, in general, in a three-step mechanism.

  1. Initially, the C-terminal Gly of ubiquitin is activated by ATP to a high energy thiol ester intermediate in a reaction catalyzed by the ubiquitin-activating enzyme, El.
  2. Following activation, E2 (ubiquitin carrier protein or ubiquitin-conjugating enzyme [USC]) transfers ubiquitin from El to the substrate that is bound to a ubiquitin-protein ligase, E3.
  3. Here an isopeptide bond is formed between the activated C-terminal Gly of ubiquitin and an c-NH2 group of a Lys residue of the substrate.

As E3 enzymes specifically synthesized by processive transfer of ubiquitin moieties to Lys-48 of the previous (and already conjugated) ubiquitin molecule. In many cases, E2 transfers activated ubiquitin directly to the protein substrate. Thus, E2 enzymes also play an important role in substrate recognition, although, in most cases, this modification is of the monoubiquitin type.

The Ubiquitin-Mediated Proteolytic Pathway
(1) Activation of ubiquitin by El and E2.
(2) Binding of the protein substrate to E3.
(3) EP dependent but EM independent monoubiquitination.
(4) EP-dependent but EM independent polyubiquitination?
(5) Ed-dependent polyubiquitination.
(6) Degradation of ubiquitin-protein conjugate by the 26s protease.
(7) “Correction” function of C-terminal hydrolase(s).
(6) Release of ubiquitin from terminal proteolytic products by &terminal hydrolase(s).

It is essential for the system that ubiquitin recycles. This function is carried out by ubiquitin C-terminal hydrolases (isopeptidases). In protein degradation, hydrolase(s) is required to release ubiquitin from isopeptide linkage with Lys residues of the protein substrate at the final stage of the proteolytic process. A ubiquitin C-terminal hydrolytic activity is also required to disassemble polyubiquitin chains linked to the protein substrate, following or during the degradative process. A “proofreading” function has been proposed for hydrolases to release free protein from “incorrectly” ubiquitinated proteins. Another possibility is that ubiquitin C-terminal hydrolases are required for trimming polyubitin chains.

Hydrolases are probably required for the processing of biosynthetic precursors of ubiquitin, since most ubiquitin genes are arranged either in linear polyubiquitin arrays or are fused to ribosomal proteins. Yet another hydrolase may be required for the removal of extra amino acid residues that are encoded by certain genes at the C-termini of some polyubiquitin molecules. Ubiquitin C-terminal hydrolases may have other functions as well. High energy El-ubiquitin and E2-ubiquitin thiol esters may react with intracellular nucleophiles (such as glutathione or polyamines). Such reactions may lead to rapid depletion of free ubiquitin unless such side products are rapidly cleaved.

Recognition of Substrates
Short-lived proteins contain a region enriched with Pro, Glu, Ser, and Thr (PEST region). However, it has not been shown that this region indeed serves as a consensus proteolysis targeting signal. An interesting problem involves the evolution of the N-end rule pathway and its physiological roles. Proteins that are derived from processing of polyproteins (Sindbis virus RNA polymerase, for example) may contain destabilizing N-termini and thus are proteolyzed via the N-end rule pathway.

Using a “synthetic lethal” screen, Ota and Varshavsky attempted to isolate a mutant that requires the N-end rule pathway for viability. They characterized an extragenic suppressor of the mutation and found that it encodes a protein with a strong correlation to protein phosphotyrosine phosphatase. The target protein or the connection between dephosphorylation of phosphotyrosine and the N-end rule pathway is still obscure. In an additional study, these researchers have shown that a missense mutation in SLNI, a member of a two-component signal transduction system in yeast, is lethal in the absence, but not in the presence, of the N-end rule pathway. Further studies are required to isolate the target protein and identify the signal transduction pathway.

Two recent studies have shed light on the role of the ubiquitin system and the proteasome in the process. Michalek et al. (1993) have shown that a mutant cell that harbors a thermolabile El cannot present peptides derived from ovalbumin following inactivation of the enzyme. In contrast, presentation of a minigene-expressed antigene peptide or presentation of exogenous similar peptide was not perturbed at the nonpermissive temperature. The important conclusion of the researchers is that the processing of the protein to peptides requires the complete ubiquitin pathway. In a complementary study, Rock et al. (1994) have shown that inhibitors that block the chymotryptic activity of the proteasome also block antigen presentation, most probably by inhibiting proteolysis of the antigen (ovalbumin). Thus, it appears that processing of MHC restricted class I antigens requires both ubiquitination and subsequent degradation by the proteasome. It is likely that the proteasome catalyzes processing of these antigens as part of the 26s protease complex.
Ciechanover A. The Ubiquitin-Proteasome Proteolytic Pathway. Cell 1994; 79:13-21.
Regulation of autophagy
The protein content of the cell is determined by the balance between protein synthesis and protein degradation. At constant intracellular protein concentration, i.e. at steady state, rates of protein synthesis and degradation are equal. Although turnover of protein results in energy dissipation, regulation at the level of protein degradation effectively controls protein levels.
Intracellular proteins to be degraded in the lysosomes can get access to these organelles by the following processes:

  • macroautophagy,
  • microautophagy,
  • crinophagy and selective,
  • chaperonin mediated, direct uptake of proteins.

Overview of the involvement of signal transduction in the regulation of macroautophagic proteolysis by amino acids and cell swelling.

  1. Amino acids (AA) stimulate a protein kinase cascade via a plasma membrane receptor.
  2. Receptor activation results in activation of PtdIns 3-kinase (PI3K), possibly via a heterotrimeric Gái3 protein.
  3. followed by activation of PKC-æ, PKB/Akt, p70S6 kinase (p70S6k) and finally phosphorylation of ribosomal protein S6 (S6P).
  4. The GDP-bound form of Gái3 is required for autophagic sequestration, whereas the GTP-bound form is inhibitory.
  5. The constitutively formed phosphatidylinositol 3-phosphate (PI3P) is also required for autophagic sequestration. Therefore,

inhibition of PtdIns 3-kinase activity by

  • wortmannin (W),
  • LY294002 (LY) or
  • 3-methyladenine (3MA) prevents autophagic sequestration.

Activation of PKC-æ and PKB/Akt is mediated by the 3,4- and 3,4,5-phosphate forms of phosphatidylinositol (PI3,4P2 and PI3,4,5P3) that are produced upon activation of PtdIns 3-kinase.

As a result of this, the first step of the macroautophagic pathway is

  • inhibited by components of the cascade that are downstream of PtdIns 3-kinase.
  • inhibition of this downstream cascade by rapamycin (RAPA) accelerates autophagic sequestration.
  • cell swelling potentiates the effect of amino acids via a change in the receptor owing to membrane stretch.

Furthermore, the site of action of the different effectors of the cytoskeleton (okadaic acid, cytochalasin, nocodazole, vinblastin and colchicine) are indicated.

  • AVi,
  • initial autophagic vacuole;
  • AVd,
  • mature degradative autophagic vacuole,
  • ER, endoplasmic reticulum.

The rate of proteolysis , an important determinant of the intracellular protein content, and part of its degradation occurs in the lysosomes and is mediated by macroautophagy. In liver, macroautophagy is very active and almost completely accounts for starvation-induced proteolysis. Factors inhibiting this process include

  • amino acids,
  • cell swelling and
  • insulin.

In the mechanisms controlling macroautophagy, protein phosphorylation plays an important role.

  • Activation of a signal transduction pathway, ultimately
  • leading to phosphorylation of ribosomal protein S6,
  • accompanies Inhibition of macroautophagy.

Components of this pathway may include

  • a heterotrimeric Gi3-protein,
  • phosphatidylinositol 3-kinase and
  • p70S6 kinase.

Selectivity of Autophagy
It has been assumed for a long time that macroautophagy is a non-selective process, in which macromolecules are randomly degraded in the same ratio as they occur in the cytoplasm . However, recent observations strongly suggest that this may not always be the case, and that macroautophagy can be selective. Lysosomal protein degradation can selectively occur via ubiquitin-dependent and -independent pathways. In the perfused liver, although autophagic breakdown of protein and RNA (mainly ribosomal RNA) is sensitive to inhibition by amino acids and insulin, glucagon accelerates proteolysis but has no effect on RNA degradation.

Another example of selective autophagy is the degradation of superfluous peroxisomes in hepatocytes from clofibrate-treated rats. When hepatocytes from these rats, in which the number of peroxisomes is greatly increased, are incubated in the absence of amino acids to ensure maximal flux through the macroautophagic pathway, peroxisomes are degraded at a relative rate that exceeds that of any other component in the liver cell. The accelerated degradation of peroxisomes was sensitive to inhibition by 3-methyladenine, a specific autophagic sequestration inhibitor. Interestingly, the accelerated removal of peroxisomes was prevented by long-chain but not short-chain fatty acids. Since long-chain fatty acids are substrates for peroxisomal â-oxidation, this indicates that these organelles are removed by autophagy when they are functionally redundant.  Our hypothesis is that acylation (palmitoylation?) of a peroxisomal membrane protein protects the peroxisome against autophagic sequestration.

Under normal conditions macroautophagy may be largely unselective and serves, for example, to produce amino acids for gluconeogenesis and the synthesis of essential proteins in starvation. When cell structures are functionally redundant or when they become damaged, the autophagic system is able to recognize this and is able to degrade the structure concerned. As yet, nothing is known about the recognition signals. A possibility is that ubiquitination of membrane proteins is required to mark the structure to be degraded for autophagic sequestration.

Ubiquitin may be involved in macroautophagy
Ubiquitin not only contributes to extralysosomal proteolysis but is also involved in autophagic protein degradation. Thus, in fibroblasts ubiquitin–protein conjugates can be found in the lysosomes, as shown by immunohistochemistry and immunogold electron microscopy. Free ubiquitin can also be found inside lysosomes. Accumulations of ubiquitin–protein conjugates in filamentous, presumably lysosomal, structures are also found in a large number of neurodegenerative diseases. Mallory bodies in the liver of alcoholics also contain ubiquitin–protein conjugates.

This presence of ubiquitin–protein conjugates in filamentous inclusions in neurons and other cells can be caused by a defect in the extralysosomal ubiquitin-dependent proteolytic pathway. However, it is also possible that these filamentous inclusions represent an attempt of the cell to get rid of unwanted material (proteins, organelles) via autophagy. Direct evidence that ubiquitin may be involved in the control of macroautophagy came from experiments with CHO cells with a temperature-sensitive mutation in the ubiquitin-activating enzyme E1. Wild-type cells increased their rate of proteolysis in response to stress (amino acid depletion, increased temperature). This was prevented by the acidotropic agent ammonia or by the autophagic sequestration inhibitor 3-methyladenine, indicating that the accelerated proteolysis occurred by autophagy. In the mutant cells, there was no such increase in proteolysis in response to stress at the restrictive temperature.

Autophagy and carcinogenesis
In cancer development, cell growth is mainly induced by inhibition of protein degradation, since differences in the rate of protein synthesis between tumorigenic cells and their normal counterparts are rather small. A striking example of how reduced autophagic proteolysis can contribute to cell growth can be found in the development of liver carcinogenesis. This decrease in autophagic flux results from a decrease in the rate of autophagic sequestration and is already detectable in the early preneoplastic stage. Autophagic flux is then hardly inhibitable by amino acids nor is it inducible by catabolic stimuli
and declines in the more advanced stage of cancer development to a rate of less than 20% of that seen in normal hepatocytes. The fact that the addition of 3-methyladenine to hepatocytes from normal rats increased hepatocyte viability to the same level as observed for the tumour cells strongly suggests that the fall in autophagic proteolysis contributes to the rapid growth rate of these cells and gives them a selective advantage over the normal hepatocytes.

Underlying control mechanisms for autophagy are gradually being unravelled. It is perhaps not surprising that protein phosphorylation and signal transduction are key elements in these mechanisms. The discovery of an amino acid receptor in the plasma membrane of the hepatocyte with a signal transduction pathway coupled to it may have important repercussions, not only for the control of macroautophagy but also for the control of other pathways.

It remains to be seen whether the details of the mechanisms controlling the process in yeast are similar to those in mammalian cells. For example, it is not known whether amino acids are able to control the process as they do in mammalian cells.

Blommaart EFC, Luiken JJFP, Meijer AJ. Autophagic proteolysis: control and specificity. Histochemical Journal (1997); 29:365–385.
A Novel Type of Selective Autophagy
Eukaryotic cells use autophagy and the ubiquitin–proteasome system (UPS) as their major protein degradation pathways. Whereas the UPS is required for the rapid degradation of proteins when fast adaptation is needed, autophagy pathways selectively remove protein aggregates and damaged or excess organelles. However, little is known about the targets and mechanisms that provide specificity to this process. Here we show that mature ribosomes are rapidly degraded by autophagy upon nutrient starvation in Saccharomyces cerevisiae. Surprisingly, this degradation not only occurs by a nonselective mechanism, but also involves a novel type of selective autophagy, which we term ‘ribophagy’. A genetic screen revealed that selective degradation of ribosomes requires catalytic activity of the Ubp3p/Bre5p ubiquitin protease. Although Ubp3p and Bre5p cells strongly accumulate 60S ribosomal particles upon starvation, they are proficient in starvation sensing and in general trafficking and autophagy pathways. Moreover, ubiquitination of several ribosomal subunits and/or ribosome associated proteins was specifically enriched in Ubp3p cells, suggesting that the regulation of ribophagy by ubiquitination may be direct. Interestingly, Ubp3p cells are sensitive to rapamycin and nutrient starvation, implying that selective degradation of ribosomes is functionally important in vivo. Taken together, our results suggest a link between ubiquitination and the regulated degradation of mature ribosomes by autophagy.
Kraft C, Deplazes A, Sohrmann M,Peter M. Mature ribosomes are selectively degraded upon starvation by an autophagy pathway requiring the Ubp3p/Bre5p ubiquitin protease. Nature Cell Biology 2008; 10(5): 603-609. DOI: 10.1038/ncb1723.  www.nature.com/naturecellbiology

Mitochondrial Failure and Protein Degradation

Progressive mitochondrial failure is tightly associated with the the development of most age-related human diseases including neurodegenerative diseases, cancer, and type 2 diabetes.

This tight connection results from the double-edged sword of mitochondrial respiration, which is responsible for generating both ATP and ROS, as well as from risks that are inherent to mitochondrial biogenesis. To prevent and treat these diseases, a precise understanding of the mechanisms that maintain functional mitochondria is necessary. Mitochondrial protein quality control is one of the mechanisms that protect mitochondrial integrity, and increasing evidence implicates the cytosolic ubiquitin/proteasome system (UPS) as part of this surveillance network. In this review, we will discuss our current understanding of UPS-dependent mitochondrial protein degradation, its roles in diseases progression, and insights into future studies.

While mitochondria have their own genome, about 99% of the roughly 1000 mitochondrial proteins are encoded in the nuclear genome. Most mitochondrial proteins are therefore

  • synthesized in the cytoplasm,
  • unfolded,
  • transported across one or both mitochondrial membranes,
  • then refolded and/or assembled into complexes (Tatsuta, 2009).

Failure of this complex series of events generates unfolded or misfolded proteins within mitochondria, often disrupting critical functions.

Mitochondrial oxidative phosphorylation generates usable cellular energy in the form of ATP, but also produces reactive oxygen species (ROS) . ROS tend to react quickly, so their predominant sites of damage are mitochondrial macromolecules that are localized nearby the source of ROS production.

Exposure to oxidative stress facilitates misfolding and aggregation of these mitochondrial proteins, leading to disassembly of protein complexes and eventual loss of mitochondrial integrity.

The clearance of misfolded and aggregated proteins is constantly needed to maintain functional mitochondria.
There are several systems promoting this turnover.

  1. Mitophagy, a selective mitochondrial autophagy, mediates a bulk removal of damaged mitochondria.
  2. mitochondria intrinsically contain proteases in each of their compartments and these proteases recognize misfolded mitochondrial proteins and mediate their degradation.

Accumulating evidence shows that the ubiquitin proteasome system (UPS) plays an important role in mitochondrial protein degradation. At various cellular sites, the UPS is involved in protein degradation. With the help of ubiquitin E1–E2–E3 enzyme cascades, target proteins destined for destruction are marked by conjugation of K48-linked poly-ubiquitin chain. This poly-ubiquitinated protein is then targeted to the proteasome for degradation.

Cells treated with proteasome inhibitors exhibit elevated levels of ubiquitinated mitochondrial proteins, suggesting the potentially important roles of the proteasome on mitochondrial protein degradation. Studies have also identified mitochondrial substrates of the UPS.

  • Fzo1, an outer mitochondrial membrane (OMM) protein involved in mitochondrial fusion, is partially dependent on the proteasome for its degradation in yeast.
  • The F box protein Mdm30 mediates ubiquitination of Fzo1 by Skp1-Cullin-F-boxMdm30 ligase, which leads to proteasomal degradation.

The UPS has also been implicated in mitochondrial protein degradation in higher organisms. In mammals,

  • the OMM proteins mitofusin 1 and 2 (Mfn1/2; the mammalian orthologs of Fzo1) and Mcl1 are polyubiquitinated and degraded by the proteasome.
  • VDAC1, Tom20 and Tom70 were also suggested as targets of proteasomal degradation as they are stabilized by proteasome inhibition.
  •  inactivation of the proteasome also induces accumulation of intermembrane space (IMS) proteins and, consistent with this, the proteasome plays a role in degradation of the IMS protein, Endonuclease G.

Turnover of some inner mitochondrial membrane (IMM) proteins is also dependent upon the proteasome. Uncoupling proteins (UCPs) 2 and 3 exhibit an unusually short half-life compared with other IMM proteins, and Brand and colleagues showed that inactivation of the proteasome prevents their turnover in vivo and in a reconstituted in vitro system. Finally, mitochondrial matrix proteins can also be degraded by the proteasome.

Cdc48/p97 is involved in many cellular processes through its role in protein degradation and is targeted to different subcellular sites by adaptor proteins. For example, Cdc48/p97 is recruited to the endoplasmic reticulum with the help of two adaptor proteins, Npl4 and Ufd1. This implies the existence of specific adaptors that recruit Cdc48/p97 to mitochondria. Consistent with this notion, the authors recently identified a mitochondrial adaptor protein for Cdc48, which we named Vms1 (VCP/Cdc48-associated mitochondrial stress responsive 1). Vms1 interacts with Cdc48/p97 and Npl4, but not with Ufd1, which indicates that the Cdc48/p97–Npl4–Ufd1 complex functions in ER protein degradation while the Vms1–Cdc48/p97–Npl4 complex acts in mitochondria. In agreement with this notion, overexpression of Cdc48 or Npl4 rescues the Vms1 mutant phenotype while Ufd1 has no effect.

Normally, Vms1 is cytoplasmic. Upon mitochondrial stress, however, Vms1 recruits Cdc48 and Npl4 to mitochondria. In agreement with the role of Cdc48/p97 in OMM protein degradation, loss of the Vms1 system results in accumulation of ubiquitin-conjugated proteins in purified mitochondria as well as stabilization of Fzo1 under mitochondrial stress conditions. Accumulation of damaged and misfolded mitochondrial proteins disturbs the normal physiology of the mitochondria, leading to mitochondrial dysfunction. As expected, the Vms1 mutants progressively lose mitochondrial respiratory activity, eventually leading to cell death. The VMS1 gene is broadly conserved in eukaryotes, implying an important functional role in a wide range of organisms. The C. elegans Vms1 homolog exhibits a similar pattern of mitochondrial stress responsive translocation and is required for normal lifespan. Additionally, mammalian Vms1 also forms a stable complex with p97. Combining these observations, the authors conclude that Vms1 is a conserved component of the UPS-dependent mitochondrial protein quality control system.

The UPS regulates mitochondrial dynamics and initiation of mitophagy
The UPS regulates mitochondrial dynamics. Major proteins involved in mitochondrial fission or fusion (e.g. Mfn1/2, Drp1 and Fis1) are degraded by the UPS.  MITOL, a mitochondrial E3 ubiquitin ligase, is required for Drp1-dependent mitochondrial fission as depletion or inactivation of MITOL blocks mitochondrial fragmentation. Moreover, knockdown of USP30, an OMM-localized deubiquitinating enzyme, induces an elongated mitochondrial morphology, suggesting a defect in fission. Through this regulatory process, the UPS controls mitochondrial dynamics. Parkin, an E3 ligase involved in mitophagy, utilizes the UPS to enhance mitochondrial fission through degradation of components of the fusion machinery. By facilitating fragmentation of damaged mitochondria, which is essential for initiation of mitophagy, Parkin stimulates mitophagy. The underlying mechanisms linking the UPS to the regulation of mitochondrial dynamics remain unclear.

Accumulation of aberrant proteins and human diseases
In neurodegenerative diseases wherein aberrant pathological proteins accumulate throughout the cell, including sites in mitochondria. Amyloid precursor protein (APP), a protein associated with Alzheimer’s disease, accumulates within mitochondria and is implicated in blockade of mitochondrial protein import. A, a neurotoxic APP cleavage product, can also facilitate the formation of the mitochondrial permeability transition pore (mPTP) by binding to mPTP components VDAC1, CypD and ANT, which provokes cell death.
-Synuclein, a protein associated with the development of Parkinson’s disease, is targeted to the IMM where it binds to the mitochondrial respiratory complex I and impairs its function. -Synuclein interferes with mitochondrial dynamics as its unique interaction with the mitochondrial membrane disturbs the fusion process. Finally, in Huntington’s disease, increased association of the mutant huntingtin protein with mitochondria can impair mitochondrial trafficking. Moreover, accumulation of mutant huntingtin protein disrupts cristae structure and it facilitates mitochondrial fragmentation by activation of Drp1. These examples demonstrate the crucial importance of prompt removal of dysfunctional and/or aberrant proteins in maintaining functional mitochondria.

UPS-mediated mitochondrial protein degradation.
Misfolded and/or damaged mitochondrial proteins destined for proteasomal degradation in the cytosol are recruited to the outer mitochondrial membrane (OMM) from each mitochondrial compartment by unknown mechanisms. Upon reaching the OMM, these proteins are presented to the proteasome through a series of events. They are K48 polyubiquitinated by the cytoplasmic (e.g. Parkin) or mitochondrial ubiquitin E3 ligases. For proteasomal degradation, polyubiquitinated mitochondrial substrate proteins need to be retrotranslocated to the cytoplasm, probably, either by the proteasome per se or by the help of UPS components such as Vms1, Cdc48/p97 and Npl4. Following dislocation to the cytoplasm, these substrate proteins are degraded by the proteasome.

Treatment of diseases that arise from defects in protein quality control will depend on greater depth in our understanding of this process, which could contribute to the development of novel therapeutic approaches. For instance, both mutant SOD1, a misfolded mitochondrial protein associated with the onset of amyotrophic lateral sclerosis, and polyglutamine expanded ataxin-3, a pathogenic protein causing Machado-Joseph disease, are ubiquitinated by MITOL and then degraded by the proteasome. Facilitating the proteasomal degradation of these aberrant proteins might therefore efficiently control diseases progression and, eventually, cure the diseases. Answering these questions would partially unveil the mysterious physiology of mitochondria, which, in turn, would facilitate the development of therapeutics to prevent and cure devastating human diseases.

Heo JM, Rutter J. Ubiquitin-dependent mitochondrial protein degradation. The International Journal of Biochemistry & Cell Biology 2011; 43:1422– 1426. http://www.elsevier.com/locate/biocel
UPS Inhibitors and Apoptotic Machinery
Over the past decade, the promising results of UPSIs (UPS inhibitors) in eliciting apoptosis in various cancer cells, and the approval of the first UPSI (Bortezomib/Velcade/PS-341) for the treatment of multiple myeloma have raised interest in assessing the death program activated upon proteasomal blockage. Several reports indicate that UPSIs stimulate apoptosis in malignant cells by operating at multiple levels, possibly by inducing different types of cellular stress. Normally cellular stress signals converge on the core elements of the apoptotic machinery to trigger the cellular demise. In addition to eliciting multiple stresses, UPSIs can directly operate on the core elements of the apoptotic machinery to control their abundance. Alterations in the relative levels of anti and pro-apoptotic factors can render cancer cells more prone to die in response to other anti-cancer treatments. Aim of the present review is to discuss those core elements of the apoptotic machinery that are under the control of the UPS.

The UPS (Ubquitin-Proteasome System)
To fulfill the protein-degradation process two branches, operating at different levels, principally comprise the UPS.

  • The first branch is formed by the enzymatic activities responsible for delivering the substrate to the degradative machinery: the targeting branch.
  • The second branch is represented by the proteolytic machinery, which ultimately fragments the protein substrate into small oligopeptides.

Oligopeptides are further digested to single amino acids by cytosolic proteases.
It is important to remember that conjugation of ubiquitin to a specific protein is not sufficient to determine its degradation. In fact, mono-ubiquitination or poly-monoubiquitination and in certain cases also poly-ubiquitination of proteins are post-translational modifications related to various cellular functions including DNA repair or membrane trafficking . To deliver polypeptides for proteasomal degradation poly-ubiquitin chains of more than 4 ubiquitins must be assembled through lysine-48 linkages.

There are 3 catalytic sites for each polyubiquitin chain. These sites show specific requirements in terms of substrate specificities and catalytic activities, and they are identified as

  1. trypsin-like, which prefer to cleave after hydrophobic bonds, chymotrypsin-like, which cleave at basic residues and
  2. postglutamyl peptide hydrolase-like or
  3. caspase-like activities, which cut after acidic amino acid.

Each proteasome active site uses the side chain hydroxyl group of an NH2-terminal threonine as the catalytic nucleophile, a mechanism that distinguishes the proteasome from other cellular proteases. The presence of substrate proteolysis small size peptides ranging from 3 to 22 residues are generated. Alternative catalytic sites guarantees the efficient processing of several different substrates.

UPS Inhibitors
By UPS inhibitors (UPSI) we mean small molecules that share the ability to target and inhibit specific activities of the UPS, causing the accumulation of poly-ubiquitinated proteosomal substrates. UPSIs are heterogeneous compounds and among them bortezomib is the only one used in clinical practice.

PR-171, a modified peptide related to the natural product epoxomicin, is composed of two key elements:

  1. a peptide portion that selectively binds with high affinity in the substrate binding pocket(s) of the proteasome and
  2. an epoxyketone pharmacophore that stereospecifically interacts with the catalytic threonine residue and irreversibly inhibits enzyme activity.

In comparison to bortezomib, PR-171 exhibits equal potency, but greater selectivity, for the chymotrypsin-like activity of the proteasome. In cell culture PR-171 is more cytotoxic than bortezomib. In mice PR-171 is well tolerated and shows stronger anti-tumor activity when compared with bortezomib . Clinical studies are in progress to test the safety of PR-171 at different dose levels on some hematological cancers.

Cell Death by UPSI
In vitro experiments have unambiguously established that incubation of neoplastic cells with UPSIs including bortezomib triggers their death. Apoptosis or type I cell death relies on the timed activation of caspases, a group of cysteine proteases, which cleave selected cellular substrates after aspartic residues. Two main apoptotic pathways keep in check caspase activation.

The turnover of a large number of cellular proteins is under the control of the UPS. Thus in principle any proteosomal substrate could contribute directly or indirectly to the cell death phenotype. This is perfectly exemplified by two master regulators of cell life and death, p53 and NFkB.  UPSIs cause

  • NF-kB inhibition through reduced IkB degradation and,
  • in opposition; they promote stabilization and accumulation of p53.

c-FLIP is the most important element of the extrinsic pathway under the direct control of the UPS. Two different FLIP isoforms exist:

  1. c-FLIPL (Long) and
  2. c-FLIPS (Short).

c-FLIPL is highly homologus to caspase-8 and contains two tandem repeat Death Effector Domains (DED) and a catalytically inactive caspase-like domain. Both FLIPs can be degraded by the UPS; however they display distinct half-lives and the unique C terminus of c-FLIPS possesses a destabilizing function. The regulation of c-FLIP levels in response to UPSIs is rather controversial. Some reports indicate that UPSIs can reduce c-FLIP levels and in this manner synergize with TRAIL to promote apoptosis.

UPSIs activate multiple cellular responses and different stress signals that ultimately cause cell death. For this reason they represent broad inducers of apoptosis. In addition, since many of the available UPSIs alter the proteolytic activity of the proteasome, they represent non-specific modulators of the expression/activity of various components of the apoptotic machinery. Paradoxically they can simultaneously favor the accumulation of pro- and anti-apoptotic factors.
Brancolini C. Inhibitors of the Ubiquitin-Proteasome System and the Cell Death Machinery: How Many Pathways are Activated? Current Molecular Pharmacology, 2008; 1:24-37.

Mitochondrial Quality Control
The PINK1–Parkin pathway plays a critical role in mitochondrial quality control by selectively targeting damaged mitochondria for autophagy. The AAA-type ATPase p97 acts downstream of PINK1 and Parkin to segregate fusion-incompetent mitochondria for turnover. [Tanaka et al. (2010. J. Cell Biol. doi: 10.1083/jcb.201007013)]. p97 acts by targeting the mitochondrial fusion-promoting factor mitofusin for degradation through an endoplasmic reticulum–associated degradation (ERAD)-like mechanism.

Pallanck LJ. Culling sick mitochondria from the herd. J Cell Biol 2012;191(7):1225–1227. http://www.jcb.org/cgi/doi/10.1083/jcb.201011068

PINK1 and Parkin and Parkinson’s Disease

Studies of the familial Parkinson disease-related proteins PINK1 and Parkin have demonstrated that these factors promote the fragmentation and turnover of mitochondria following treatment of cultured cells with mitochondrial depolarizing agents. Whether PINK1 or Parkin influence mitochondrial quality control under normal physiological conditions in dopaminergic neurons, a principal cell type that degenerates in Parkinson disease, remains unclear. To address this matter, we developed a method to purify and characterize neural subtypes of interest from the adult Drosophila brain.

Using this method, we find that dopaminergic neurons from Drosophila parkin mutants accumulate enlarged, depolarized mitochondria, and that genetic perturbations that promote mitochondrial fragmentation and turnover rescue the mitochondrial depolarization and neurodegenerative phenotypes of parkin mutants. In contrast, cholinergic neurons from parkin mutants accumulate enlarged depolarized mitochondria to a lesser extent than dopaminergic neurons, suggesting that a higher rate of mitochondrial damage, or a deficiency in alternative mechanisms to repair or eliminate damaged mitochondria explains the selective vulnerability of dopaminergic neurons in Parkinson disease.

Our study validates key tenets of the model that PINK1 and Parkin promote the fragmentation and turnover of depolarized mitochondria in dopaminergic neurons. Moreover, our neural purification method provides a foundation to further explore the pathogenesis of Parkinson disease, and to address other neurobiological questions requiring the analysis of defined neural cell types.

Burmana JL, Yua S, Poole AC, Decala RB , Pallanck L. Analysis of neural subtypes reveals selective mitochondrial dysfunction in dopaminergic neurons from parkin mutants.

Autophagy in Parkinson’s Disease.
Parkinson’s disease is a common neurodegenerative disease in the elderly. To explore the specific role of autophagy and the ubiquitin-proteasome pathway in apoptosis, a specific proteasome inhibitor and macroautophagy inhibitor and stimulator were selected to investigate pheochromocytoma (PC12) cell lines transfected with human mutant (A30P) and wildtype (WT) -synuclein.

The apoptosis ratio was assessed by flow cytometry. LC3, heat shock protein 70 (hsp70) and caspase-3 expression in cell culture were determined by Western blot. The hallmarks of apoptosis and autophagy were assessed with transmission electron microscopy. Compared to the control group or the rapamycin (autophagy stimulator) group, the apoptosis ratio in A30P and WT cells was significantly higher after treatment with inhibitors of the proteasome and macroautophagy. The results of Western blots for caspase-3 expression were similar to those of flow cytometry; hsp70 protein was significantly higher in the proteasome inhibitor group than in control, but in the autophagy inhibitor and stimulator groups, hsp70 was similar to control. These findings show that inhibition of the proteasome and autophagy promotes apoptosis, and the macroautophagy stimulator rapamycin reduces the apoptosis ratio. And inhibiting or stimulating autophagy has less impact on hsp70 than the proteasome pathway.

In conclusion, either stimulation or inhibition of macroautophagy, has less impact on hsp70 than on the proteasome pathway. This study found that rapamycin decreased apoptotic cells in A30P cells independent of caspase-3 activity. Although several lines of evidence recently demonstrated crosstalk between autophagy and caspase-independent apoptosis, we could not confirm that autophagy activation protects cells from caspase-independent cell death. Undoubtedly, there are multiple connections between the apoptotic and autophagic processes.

Inhibition of autophagy may subvert the capacity of cells to remove damaged organelles or to remove misfolded proteins, which would favor apoptosis. However, proteasome inhibition activated macroautophagy and accelerated apoptosis. A likely explanation is inhibition of the proteasome favors oxidative reactions that trigger apoptosis, presumably through

1. a direct effect on mitochondria, and
2. the absence of NADPH2 and ATP

which may deinhibit the activation of caspase-2 or MOMP. Another possibility is that aggregated proteins induced by proteasome inhibition increase apoptosis.

Yang F, Yanga YP, Maoa CJ, Caoa BY, et al. Role of autophagy and proteasome degradation pathways in apoptosis of PC12 cells overexpressing human -synuclein. Neuroscience Letters 2009; 454:203–208. doi:10.1016/j.neulet.2009.03.027. http://www.elsevier.com/locate/neulet

Parkin-dependent Ubiquitination of Endogenous Bax 

Autosomal recessive loss-of-function mutations within the PARK2 gene functionally inactivate the E3 ubiquitin ligase parkin, resulting in neurodegeneration of catecholaminergic neurons and a familial form of Parkinson disease. Current evidence suggests both a mitochondrial function for parkin and a neuroprotective role, which may in fact be interrelated. The antiapoptotic effects of Parkin have been widely reported, and may involve fundamental changes in the threshold for apoptotic cytochrome c release, but the substrate(s) involved in Parkin dependent protection had not been identified. Here, we demonstrate the Parkin-dependent ubiquitination of endogenous Bax comparing primary cultured neurons from WT and Parkin KO mice and using multiple Parkin-overexpressing cell culture systems. The direct ubiquitination of purified Bax was also observed in vitro following incubation with recombinant parkin. The authors found that Parkin prevented basal and apoptotic stress induced translocation of Bax to the mitochondria. Moreover, an engineered ubiquitination-resistant form of Bax retained its apoptotic function, but Bax KO cells complemented with lysine-mutant Bax did not manifest the antiapoptotic effects of Parkin that were observed in cells expressing WT Bax. These data suggest that Bax is the primary substrate responsible for the antiapoptotic effects of Parkin, and provide mechanistic insight into at least a subset of the mitochondrial effects of Parkin.

Johnson BN, Berger AK, Cortese GP, and LaVoie MJ. The ubiquitin E3 ligase Parkin regulates the proapoptotic function of Bax. PNAS 2012, pp 6. http://www.pnas.org/cgi/doi/10.1073/pnas.1113248109
Parkin Promotes Mitochondrial Loss in Autophagy
Parkin, an E3 ubiquitin ligase implicated in Parkinson’s disease, promotes degradation of dysfunctional mitochondria by autophagy. Using proteomic and cellular approaches, we show that upon translocation to mitochondria, Parkin activates the ubiquitin–proteasome system (UPS) for widespread degradation of outer membrane proteins. This is evidenced by an increase in K48-linked polyubiquitin on mitochondria, recruitment of the 26S proteasome and rapid degradation of multiple outer membrane proteins. The degradation of proteins by the UPS occurs independently of the autophagy pathway, and inhibition of the 26S proteasome completely abrogates Parkin-mediated mitophagy in HeLa, SH-SY5Y and mouse cells. Although the mitofusins Mfn1 and Mfn2 are rapid degradation targets of Parkin, degradation of additional targets is essential for mitophagy. These results indicate that remodeling of the mitochondrial outer membrane proteome is important for mitophagy, and reveal a causal link between the UPS and autophagy, the major pathways for degradation of intracellular substrates.

Chan NC, Salazar AM, Pham AH, Sweredoski MJ, et al. Broad activation of the ubiquitin–proteasome system by Parkin is critical for mitophagy. Human Molecular Genetics 2011; 20(9): 1726–1737. doi:10.1093/hmg/ddr048.

TRAP1 and TBP7 Interaction in Refolding of Damaged Proteins
TRAP1 is a mitochondrial antiapoptotic heat shock protein. The information available on the TRAP1 pathway describes just a few well-characterized functions of this protein in mitochondria. However, our group’s use of mass spectrometry analysis identified TBP7, an AAA-ATPase of the 19S proteasomal subunit, as a putative TRAP1-interacting protein. Surprisingly, TRAP1 and TBP7 co-localize in the endoplasmic reticulum (ER), as demonstrated by biochemical and confocal/electron microscopy analyses, and directly interact, as confirmed by FRET analysis. This is the first demonstration of TRAP1 presence in this cellular compartment. TRAP1 silencing by shRNAs, in cells exposed to thapsigargin-induced ER stress, correlates with up-regulation of BiP/Grp78, thus suggesting a role of TRAP1 in the refolding of damaged proteins and in ER stress protection. Consistently, TRAP1 and/or TBP7 interference enhanced stress-induced cell death and increased intracellular protein ubiquitination. These experiments led us to hypothesize an involvement of TRAP1 in protein quality control for mistargeted/misfolded mitochondria-destined proteins, through interaction with the regulatory proteasome protein TBP7. Remarkably, the expression of specific mitochondrial proteins decreased upon TRAP1 interference as a consequence of increased ubiquitination. The proposed TRAP1 network has an impact in vivo, since it is conserved in human colorectal cancers, is controlled by ER-localized TRAP1 interacting with TBP7 and provides a novel model of ER-mitochondria crosstalk.

Amoroso MR, Matassa DS, Laudiero G, Egorova AV. TRAP1 AND THE PROTEASOME REGULATORY PARTICLE TBP7/Rpt3 INTERACT IN THE ENDOPLASMIC RETICULUM AND CONTROL CELLULAR UBIQUITINATION OF SPECIFIC MITOCHONDRIAL PROTEINS. Cell Death and Differentiation 2012; pp? DOI : 10.1038/cdd.2011.128

VMS1 and Mitochondrial Protein Degradation
We show that Ydr049 (renamed VCP/Cdc48-associated mitochondrial stress-responsive—Vms1), a member of an unstudied pan-eukaryotic protein family, translocates from the cytosol to mitochondria upon mitochondrial stress. Cells lacking Vms1 show progressive mitochondrial failure, hypersensitivity to oxidative stress, and decreased chronological life span. Both yeast and mammalian Vms1 stably interact with Cdc48/VCP/p97, a component of the ubiquitin/proteasome system with a well-defined role in endoplasmic reticulum-associated protein degradation (ERAD), wherein misfolded ER proteins are degraded in the cytosol. We show that oxidative stress triggers mitochondrial localization of Cdc48 and this is dependent on Vms1. When this system is impaired by mutation of Vms1,

  • ubiquitin-dependent mitochondrial protein degradation,
  • mitochondrial respiratory function,and
  • cell viability are compromised.

We demonstrate that Vms1 is a required component of an evolutionarily conserved system for mitochondrial protein degradation, which is
necessary to maintain

  • mitochondrial,
  • cellular, and
  • organismal viability.

Heo JM, Livnat-Levanon N, Taylor EB, Jones KT. A Stress-Responsive System
for Mitochondrial Protein Degradation. Molecular Cell 2010; 40:465–480.
DOI 10.1016/j.molcel.2010.10.021

Mitochondrial Protein Degradation
The biogenesis of mitochondria and the maintenance of mitochondrial functions depends on an autonomous proteolytic system in the organelle which is highly conserved throughout evolution. Components of this system include processing

  • peptidases and
  • ATP-dependent proteases, as well as
  • molecular chaperone proteins and
  • protein complexes with apparently regulatory functions.

While processing peptidases mediate maturation of nuclear-encoded mitochondrial preproteins, quality control within various subcompartments of mitochondria is ensured by ATP-dependent proteases which selectively remove non-assembled or misfolded polypeptides. Moreover, these proteases appear to control the activity- or steady-state levels of specific regulatory proteins and thereby ensure mitochondrial genome integrity, gene expression and protein assembly.

Kaser M and Langer T. Protein degradation in mitochondria. CELL & DEVELOPMENTAL BIOLOGY 2000; 11:181–190. doi: 10.1006/10.1006/scdb.2000.0166.

RING finger E3s

Ubiquitin-ligases or E3s are components of the ubiquitin proteasome system (UPS) that coordinate the transfer of ubiquitin to the target protein. A major class of ubiquitin-ligases consists of RING-finger domain proteins that include the substrate recognition sequences in the same polypeptide; these are known as single-subunit RING finger E3s. We are studying a particular family of RING finger E3s, named ATL, that contain a transmembrane domain and the RING-H2 finger domain; none of the member of the family contains any other previously described domain. Although the study of a few members in A. thaliana and O. sativa has been reported, the role of this family in the life cycle of a plant is still vague.

To provide tools to advance on the functional analysis of this family we have undertaken a phylogenetic analysis of ATLs in twenty-four plant genomes. ATLs were found in all the 24 plant species analyzed, in numbers ranging from 20–28 in two basal species to 162 in soybean. Analysis of ATLs arrayed in tandem indicates that sets of genes are expanding in a species-specific manner. To
get insights into the domain architecture of ATLs we generated 75 pHMM LOGOs from 1815 ATLs, and unraveled potential protein-protein interaction regions by means of yeast two-hybrid assays. Several ATLs were found to interact with DSK2a/ubiquilin through a region at the amino-terminal end, suggesting that this is a widespread interaction that may assist in the mode of action of ATLs; the region was traced to a distinct sequence LOGO. Our analysis provides significant observations on the evolution and expansion of the ATL family in addition to information on the domain structure of this class of ubiquitin-ligases that may be involved in plant adaptation to environmental stress.

Aguilar-Hernandez V, Aguilar-Henonin L, Guzman P. Diversity in the Architecture of ATLs, a Family of Plant Ubiquitin-Ligases, Leads to Recognition and Targeting of Substrates in Different Cellular Environments. PLoS ONE 2011; 6(8): e23934. doi:10.1371/journal.pone.0023934
UPS Proteolytic Function Inadequate in Proteinopathies
Proteinopathies are a family of human disease caused by toxic aggregation-prone proteins and featured by the presence of protein aggregates in the affected cells. The ubiquitin-proteasome system (UPS) and autophagy are two major intracellular protein degradation pathways. The UPS mediates the targeted degradation of most normal proteins after performing their normal functions as well as the removal of abnormal, soluble proteins. Autophagy is mainly responsible for degradation of defective organelles and the bulk degradation of cytoplasm during starvation. The collaboration between the UPS and autophagy appears to be essential to protein quality control in the cell.

UPS proteolytic function often becomes inadequate in proteinopathies which may lead to activation of autophagy, striving to remove abnormal proteins especially the aggregated forms. HADC6, p62, and FoxO3 may play an important role in mobilizing this proteolytic consortium. Benign measures to enhance proteasome function are currently lacking; however, enhancement of autophagy via pharmacological intervention and/or lifestyle change has shown great promise in alleviating bona fide proteinopathies in the cell and animal models. These pharmacological interventions are expected to be applied clinically to treat human proteinopathies in the near future.

Zheng Q, Li J, Wang X. Interplay between the ubiquitin-proteasome system and
autophagy in proteinopathies. Int J Physiol Pathophysiol Pharmacol 2009;1:127-142. http://www.ijppp.org/IJPPP904002

Ubiquitin-associated Protein-Protein Interactions

Applicability of in vitro biotinylated ubiquitin for evaluation of endogenous ubiquitin conjugation and analysis of ubiquitin-associated protein-protein interactions has been investigated. Incubation of rat brain mitochondria with biotinylated ubiquitin followed by affinity chromatography on avidin-agarose, intensive washing, tryptic digestion of proteins bound to the affinity sorbent and their mass spectrometry analysis resulted in reliable identification of 50 proteins belonging to mitochondrial and extramitochondrial compartments. Since all these proteins were bound to avidin-agarose only after preincubation of the mitochondrial fraction with biotinylated ubiquitin, they could therefore be referred to as specifically bound proteins. A search for specific
ubiquitination signature masses revealed several extramitochondrial and intramitochondrial ubiquitinated proteins representing about 20% of total number of proteins bound to avidin-agarose. The interactome analysis suggests that the identified non-ubiquitinated proteins obviously form tight complexes either with ubiquitinated proteins or with their partners and/or mitochondrial membrane components. Results of the present study demonstrate that the use of biotinylated ubiquitin may be considered as the method of choice for in vitro evaluation of endogenous ubiquitin-conjugating machinery in particular
subcellular organelles and changes in ubiquitin/organelle associated interactomes. This may be useful for evaluation of changes in interactomes induced by protein ubiquitination.

Buneeva OA, Medvedeva MV, Kopylov AT, Zgoda VG, Medvedev AE. Use of Biotinylated Ubiquitin for Analysis of Rat Brain Mitochondrial Proteome and Interactome. Int J Mol Sci 2012; 13: 11593-11609; doi:10.3390/ijms130911593
IL-6 regulation on mitochondrial remodeling/dysfunction

Muscle protein turnover regulation during cancer cachexia is being rapidly defined, and skeletal muscle mitochondria function appears coupled to processes regulating muscle wasting. Skeletal muscle oxidative capacity and the expression of proteins regulating mitochondrial biogenesis and dynamics are disrupted in severely cachectic ApcMin/+ mice. It has not been determined if these changes occur at the onset of cachexia and are necessary for the progression of muscle wasting. Exercise and anti-cytokine therapies have proven effective in preventing cachexia development in tumor bearing mice, while their effect on mitochondrial content, biogenesis and dynamics is not well understood.

The purposes of this study were to

1) determine IL-6 regulation on mitochondrial remodeling/dysfunction during the progression of cancer cachexia and
2) to determine if exercise training can attenuate mitochondrial dysfunction and the induction of proteolytic pathways during IL-6 induced cancer cachexia.

ApcMin/+ mice were examined during the progression of cachexia, after systemic interleukin (IL)-6r antibody treatment, or after IL-6 over-expression with or without exercise. Direct effects of IL-6 on mitochondrial remodeling were examined in cultured C2C12 myoblasts.

Mitochondrial content was not reduced during the initial development of cachexia, while muscle PGC-1α and fusion (Mfn1, Mfn2) protein expression was repressed.

With progressive weight loss mitochondrial content decreased, PGC-1α and fusion proteins were further suppressed, and fission protein (FIS1) was induced.

IL-6 receptor antibody administration after the onset of cachexia

  • improved mitochondrial content,
  • PGC-1α,
  • Mfn1/Mfn2 and
  • FIS1 protein expression.

IL-6 over-expression in pre-cachectic mice

  • accelerated body weight loss and muscle wasting, without reducing mitochondrial content,
  • while PGC-1α and Mfn1/Mfn2 protein expression was suppressed
  • and FIS1 protein expression induced.

Exercise normalized these IL-6 induced effects. C2C12 myotubes administered IL-6 had

  • increased FIS1 protein expression,
  • increased oxidative stress, and
  • reduced PGC-1α gene expression
  • without altered mitochondrial protein expression.

Altered expression of proteins regulating mitochondrial biogenesis and fusion are early events in the initiation of cachexia regulated by IL-6, which precede the loss of muscle mitochondrial content. Furthermore, IL-6 induced mitochondrial remodeling and proteolysis can be rescued with moderate exercise training even in the presence of high circulating IL-6 levels.

White JP, Puppa MJ, Sato S, Gao S. IL-6 regulation on skeletal muscle mitochondrial remodeling during cancer cachexia in the ApcMin/+ mouse. Skeletal Muscle 2012; 2:14-30.
http://www.skeletalmusclejournal.com/content/2/1/14

Starvation-induced Autophagy
Upon starvation cells undergo autophagy, a cellular degradation pathway important in the turnover of whole organelles and long lived proteins. Starvation-induced protein degradation has been regarded as an unspecific bulk degradation process. We studied global protein dynamics during amino acid starvation-induced autophagy by quantitative mass spectrometry and were able to record nearly 1500 protein profiles during 36 h of starvation. Cluster analysis of the recorded protein profiles revealed that cytosolic proteins were degraded rapidly, whereas proteins annotated to various complexes and organelles were degraded later at different time periods. Inhibition of protein degradation pathways identified the lysosomal/autophagosomal system as the main degradative route.

Thus, starvation induces degradation via autophagy, which appears to be selective and to degrade proteins in an ordered fashion and not completely arbitrarily as anticipated so far.

Kristensen AR, Schandorff S, Høyer-Hansen M, Nielsen MO, et al. Ordered Organelle Degradation during Starvation-induced Autophagy. Molecular & Cellular Proteomics 2008; 7:2419–2428.

Skeletal Muscle Macroautophagy
Skeletal muscles are the agent of motion and one of the most important tissues responsible for the control of metabolism. Coordinated movements are allowed by the highly organized structure of the cytosol of muscle fibers (or myofibers), the multinucleated and highly specialized cells of skeletal muscles involved in contraction. Contractile proteins are assembled into repetitive structures, the basal unit of which is the sarcomere, that are well packed into the myofiber cytosol. Myonuclei are located at the edge of the myofibers, whereas the various organelles such as mitochondria and sarcoplasmic reticulum are embedded among the myofibrils. Many different changes take place in the cytosol of myofibers during catabolic conditions:

  • proteins are mobilized
  • organelles networks are reorganized for energy needs
  • the setting of myonuclei can be modified.

Further,

  • strenuous physical activity,
  • improper dietary regimens and
  • aging

lead to mechanical and metabolic damages of myofiber organelles, especially mitochondria, and contractile proteins. During aging the protein turnover is slowed down, therefore it is easier to accumulate aggregates of dysfunctional proteins. Therefore, a highly dynamic tissue such as skeletal muscle requires a rapid and efficient system for the removal of altered organelles, the elimination of protein aggregates, and the disposal of toxic products.

The two major proteolytic systems in muscle are the ubiquitin-proteasome and the autophagy-lysosome pathways. The proteasome system requires

  • the transcription of the two ubiquitin ligases (atrogin-1 and MuRF1) and
  • the ubiquitination of the substrates.

Therefore, the ubiquitin-proteasome system can provide the rapid elimination of single proteins or small aggregates. Conversely, the autophagic system is able to degrade entire organelles and large proteins aggregates. In the autophagy-lysosome system, double-membrane vesicles named autophagosomes are able to engulf a portion of the cytosol and fuse with lysosomes, where their content is completely degraded by lytic enzymes.

The autophagy flux can be biochemicaly monitored following LC3 lipidation and p62 degradation. LC3 is the mammalian homolog of the yeast Atg8 gene, which is lipidated when recruited for the double-membrane commitment and growth. p62 (SQSTM-1) is a polyubiquitin-binding protein involved in the proteasome system and that can either reside free in the cytosol and nucleus or occur within autophagosomes and lysosomes. The GFP-LC3 transgenic mouse model allows easy detection of autophagosomes by simply monitoring the presence of bright GFP-positive puncta inside the myofibrils and beneath the plasma membrane of the myofibers, thus investigate the activation of autophagy in skeletal muscles with different contents of slow and fast-twitching myofibers and in response to stimuli such as fasting. For example, in the fast-twiching extensor digitorum longus muscle few GFP-LC3 dots were observed before starvation, while many small GFP-LC3 puncta appeared between myofibrils and in the perinuclear regions after 24 h starvation. Conversely, in the slow-twitching soleus muscle, autophagic puncta were almost absent in standard condition and scarcely induced after 24 h starvation.
Autophagy in Muscle Homeostasis
The autophagic flux was found to be increased during certain catabolic conditions, such as fasting, atrophy , and denervation , thus contributing to protein breakdown. Food deprivation is one of the strongest stimuli known to induce autophagy in muscle. Indeed skeletal muscle, after the liver, is the most responsive tissue to autophagy activation during food deprivation. Since muscles are the biggest reserve of amino acids in the body, during fasting autophagy has the vital role to maintain the amino acid pool by digesting muscular protein and organelles. In mammalian cells, mTORC1, which consists of

  • mTOR and
  • Raptor,

is the nutrient sensor that negatively regulates autophagy.

During atrophy, protein breakdown is mediated by atrogenes, which are under the forkhead box O (FoxO) transcription factors control, and activation of autophagy seems to aggravate muscle loss during atrophy. In vivo and in vitro studies demonstrated that several genes coding for components of the autophagic machinery, such as

  • LC3,
  • GABARAP,
  • Vps34,
  • Atg12 and
  • Bnip3,

are controlled by FoxO3 transcription factor. FoxO3 is able to regulate independently the ubiquitin-proteasome system and the autophagy-lysosome machinery in vivo and in vitro. Denervation is also able to induce autophagy in skeletal muscle, although at a slower rate than fasting. This effect is mediated by RUNX1, a transcription factor upregulated during autophagy; the lack of RUNX1 results in excessive autophagic flux in denervated muscle and leads to atrophy. The generation of Atg5 and Atg7 muscle-specific knockout mice have shown that with suppression of autophagy both models display muscle weakness and atrophy and a significant reduction of weight, which is correlated with the important loss of muscle tissue due to an atrophic condition. An unbalanced autophagy flux is highly detrimental for muscle, as too much induces atrophy whereas too little leads to muscle weakness and degeneration. Muscle wasting associated with autophagy inhibition becomes evident and symptomatic only after a number of altered proteins and dysfunctional organelles are accumulated, a condition that becomes evident after months or even years. On the other hand, the excessive increase of autophagy flux is able to induce a rapid loss of muscle mass (within days or weeks).
Alterations of autophagy are involved in the pathogenesis of several myopathies and dystrophies.

The maintenance of muscle homeostasis is finely regulated by the balance between catabolic and anabolic process. Macroautophagy (or autophagy) is a catabolic process that provides the degradation of protein aggregation and damaged organelles through the fusion between autophagosomes and lysosomes. Proper regulation of the autophagy flux is fundamental for the homeostasis of skeletal muscles during physiological situations and in response to stress. Defective as well as excessive autophagy is harmful for muscle health and has a pathogenic role in several forms of muscle diseases.
Grumati P, Bonaldo P. Autophagy in Skeletal Muscle Homeostasis and in Muscular Dystrophies. Cells 2012, 1, 325-345; doi:10.3390/cells1030325. ISSN 2073-4409. http://www.mdpi.com/journal/cells

Parkinson’s Disease Mutations
Mutations in parkin, a ubiquitin ligase, cause early-onset familial Parkinson’s disease (AR-JP). How Parkin suppresses Parkinsonism remains unknown. Parkin was recently shown to promote the clearance of impaired mitochondria by autophagy, termed mitophagy. Here, we show that Parkin promotes mitophagy by catalyzing mitochondrial ubiquitination, which in turn recruits ubiquitin-binding autophagic components, HDAC6 and p62, leading to mitochondrial clearance.

During the process, juxtanuclear mitochondrial aggregates resembling a protein aggregate-induced aggresome are formed. The formation of these “mito-aggresome” structures requires microtubule motor-dependent transport and is essential for efficient mitophagy. Importantly, we show that AR-JP–causing Parkin mutations are defective in supporting mitophagy due to distinct defects at

  • recognition,
  • transportation, or
  • ubiquitination of impaired mitochondria,

thereby implicating mitophagy defects in the development of Parkinsonism. Our results show that impaired mitochondria and protein aggregates are processed by common ubiquitin-selective autophagy machinery connected to the aggresomal pathway, thus identifying a mechanistic basis for the prevalence of these toxic entities in Parkinson’s disease.
Lee JY,Nagano Y, Taylor JP,Lim KL, and Yao TP. Disease-causing mutations in Parkin impair mitochondrial ubiquitination, aggregation, and HDAC6-dependent mitophagy. J Cell Biol 2010; 189(4):671-679. http://www.jcb.org/cgi/doi/10.1083/jcb.201001039

Drosophila Parkin Requires PINK1

Loss of the E3 ubiquitin ligase Parkin causes early onset Parkinson’s disease, a neurodegenerative disorder of unknown etiology. Parkin has been linked to multiple cellular processes including

  • protein degradation,
  • mitochondrial homeostasis, and
  • autophagy;

however, its precise role in pathogenesis is unclear. Recent evidence suggests that Parkin is recruited to damaged mitochondria, possibly affecting

  • mitochondrial fission and/or fusion,
  • to mediate their autophagic turnover.

The precise mechanism of recruitment and the ubiquitination target are unclear. Here we show in Drosophila cells that PINK1 is required to recruit Parkin to dysfunctional mitochondria and promote their degradation. Furthermore, PINK1 and Parkin mediate the ubiquitination of the profusion factor Mfn on the outer surface of mitochondria. Loss of Drosophila PINK1 or parkin causes an increase in Mfn abundance in vivo and concomitant elongation of mitochondria. These findings provide a molecular mechanism by which the PINK1/Parkin pathway affects mitochondrial fission/fusion as suggested by previous genetic interaction studies. We hypothesize that Mfn ubiquitination may provide a mechanism by which terminally damaged mitochondria are labeled and sequestered for degradation by autophagy.

Ziviani E, Tao RN, and Whitworth AJ. Drosophila Parkin requires PINK1 for mitochondrial translocation and ubiquitinates Mitofusin. PNAS 2010. Pp6 http://www.pnas.org/cgi/doi/10.1073/pnas.0913485107

Dynamin-related protein 1 (Drp1) in Parkinson’s
Mutations in Parkin, an E3 ubiquitin ligase that regulates protein turnover, represent one of the major causes of familial Parkinson’s disease (PD), a neurodegenerative disorder characterized by the loss of dopaminergic neurons and impaired mitochondrial functions. The underlying mechanism by which pathogenic parkin mutations induce mitochondrial abnormality is not fully understood. Here we demonstrate that Parkin interacts with and subsequently ubiquitinates dynamin-related protein 1 (Drp1), for promoting its proteasome-dependent degradation. Pathogenic mutation or knockdown of Parkin inhibits the ubiquitination and degradation of Drp1, leading to an increased level of Drp1 for mitochondrial fragmentation. These results identify Drp1 as a novel substrate of Parkin and suggest a potential mechanism linking abnormal Parkin expression to mitochondrial dysfunction in the pathogenesis of PD.

Wang H, Song P, Du L, Tian W. Parkin ubiquitinates Drp1 for proteasome-dependent degradation: implication of dysregulated mitochondrial dynamics in Parkinson’s disease.
JBC Papers in Press. Published on February 3, 2011 as Manuscript M110.144238. http://www.jbc.org/cgi/doi/10.1074/jbc.M110.144238

Pink1, Parkin, and DJ-1 Form a Complex
Mutations in the genes PTEN-induced putative kinase 1 (PINK1), PARKIN, and DJ-1 cause autosomal recessive forms of Parkinson disease (PD), and the Pink1/Parkin pathway regulates mitochondrial integrity and function. An important question is whether the proteins encoded by these genes function to regulate activities of other cellular compartments. A study in mice, reported by Xiong et al. in this issue of the JCI, demonstrates that Pink1, Parkin, and DJ-1 can form a complex in the cytoplasm, with Pink1 and DJ-1 promoting the E3 ubiquitin ligase activity of Parkin to degrade substrates via the proteasome (see the related article, doi:10.1172/ JCI37617).

This protein complex in the cytosol may or may not be related to the role of these proteins in regulating mitochondrial function or oxidative stress in vivo.
Three models for the role of the PPD complex. In this issue of the JCI, Xiong et al. report that Pink1, Parkin, and DJ-1 bind to each other and form a PPD E3 ligase complex in which Pink1 and DJ-1 modulate Parkin-dependent ubiquitination and subsequent degradation of substrates via the proteasome. Previous work suggests that the Pink1/Parkin pathway regulates mitochondrial integrity and promotes mitochondrial fission in Drosophila.

(A) Parkin and DJ-1 may be recruited to the mitochondrial outer membrane during stress and interact with Pink1. These interactions may facilitate the ligase activity of Parkin, thereby facilitating the turnover of molecules that regulate mitochondrial dynamics and mitophagy. The PPD complex may have other roles in the cytosol that result in degradative ubiquitination and/or relay information from mitochondria to other cellular compartments.
(B) Alternatively, Pink1 may be released from mitochondria after cleavage to interact with DJ-1 and Parkin in the cytosol.
A and B differ in the site of action of the PPD complex and the cleavage status of Pink1.
The complex forms on the mitochondrial outer membrane potentially containing full-length Pink1 in A, and in the cytosol with cleaved Pink1 in B.
Lack of DJ-1 function results in phenotypes that are distinct from the mitochondrial phenotypes observed in null mutants of Pink1 or Parkin in Drosophila. Thus, although the PPD complex is illustrated here as regulating mitochondrial fission, the role of DJ-1 in vivo remains to be clarified.
(C) It is also possible that the action occurs in the cytosol and is independent of the function of Pink1/Parkin in regulating mitochondrial integrity and function.

The Xiong et al. study offers an entry point for explorations of the role of Pink1, Parkin, and DJ-1 in the cytoplasm. It remains to be shown whether Parkin, in complex with Pink1 and DJ-1, carries out protein degradation in vivo.

Li H, and Guo M. Protein degradation in Parkinson disease revisited: it’s complex. commentaries. J Clin Invest.  doi:10.1172/JCI38619. http://www.jci.org

Xiong, H., et al. Parkin, PINK1, and DJ-1 form a ubiquitin E3 ligase complex promoting unfolded protein degradation. J. Clin. Invest. 2009; 119:650–660.

 Mitochondrial Ubiquitin Ligase, MITOL, protects neuronal cells

Nitric oxide (NO) is implicated in neuronal cell survival. However, excessive NO production mediates neuronal cell death, in part via mitochondrial dysfunction. Here, we report that the mitochondrial ubiquitin ligase, MITOL, protects neuronal cells from mitochondrial damage caused by accumulation of S-nitrosylated microtubule associated protein 1B-light chain 1 (LC1). S-nitrosylation of LC1 induces a conformational change that serves both to activate LC1 and to promote its ubiquination by MITOL, indicating that microtubule
stabilization by LC1 is regulated through its interaction with MITOL. Excessive NO production can inhibit MITOL, and MITOL inhibition resulted in accumulation of S-nitrosylated LC1 following stimulation of NO production by calcimycin and N-methyl-D-aspartate. LC1 accumulation under these conditions resulted in mitochondrial dysfunction and neuronal cell death. Thus, the balance between LC1 activation by S-nitrosylation and down-regulation by MITOL is critical for neuronal cell survival. Our findings may contribute significantly to an understanding of the mechanisms of neurological diseases caused by nitrosative stress-mediated mitochondrial dysfunction.

Yonashiro R, Kimijima Y, Shimura T, Kawaguchi K, et al. Mitochondrial ubiquitin ligase MITOL blocks S-nitrosylated MAP1B-light chain 1-mediated mitochondrial dysfunction and neuronal cell death. PNAS; 2012. pp 6. http://www.pnas.org/cgi/doi/10.1073/pnas.1114985109

Ubiquitin–Proteasome System in Neurodegeneration
A common histopathological hallmark of most neurodegenerative diseases is the presence of aberrant proteinaceous inclusions inside affected neurons. Because these protein aggregates are detected using antibodies against components of the ubiquitin–proteasome system (UPS), impairment of this machinery for regulated proteolysis has been suggested to be at the root of neurodegeneration. This hypothesis has been difficult to prove in vivo owing to the lack of appropriate tools. The recent report of transgenic mice with ubiquitous expression of a UPS-reporter protein should finally make it possible to test in vivo the role of the UPS in neurodegeneration.

Hernandez F, Dıaz-Hernandez M, Avila J and Lucas JJ. Testing the ubiquitin–proteasome hypothesis of neurodegeneration in vivo. TRENDS in Neurosciences 2004; 27(2): 66-68.

ALP in Parkinson’s
The ubiquitin-proteasome system (UPS) and autophagy-lysosome pathway (ALP) are the two most important mechanisms that normally repair or remove abnormal proteins. Alterations in the function of these systems to degrade misfolded and aggregated proteins are being increasingly recognized as playing a pivotal role in the pathogenesis of many neurodegenerative disorders such as Parkinson’s disease. Dysfunction of the UPS has been already strongly implicated in the pathogenesis of this disease and, more recently, growing interest has been shown in identifying the role of ALP in neurodegeneration. Mutations of a-synuclein and the increase of intracellular concentrations of non-mutant a-synuclein have been associated with Parkinson’s disease phenotype.

The demonstration that a-synuclein is degraded by both proteasome and autophagy indicates a possible linkage between the dysfunction of the UPS or ALP and the occurrence of this disorder.The fact that mutant a-synucleins inhibit ALP functioning by tightly binding to the receptor on the lysosomal membrane for autophagy pathway further supports the assumption that impairment of the ALP may be related to the development of Parkinson’s disease.

In this review, we summarize the recent findings related to this topic and discuss the unique role of the ALP in this neurogenerative disorder and the putative therapeutic potential through ALP enhancement.

Pan Y, Kondo S, Le W, Jankovic J. The role of autophagy-lysosome pathway in
neurodegeneration associated with Parkinson’s disease. Brain 2008; 131: 1969-1978. doi:10.1093/brain/awm318.

Ubiquitin-Proteasome System in Parkinson’s

There is growing evidence that dysfunction of the mitochondrial respiratory chain and failure of the cellular protein degradation machinery, specifically the ubiquitin-proteasome system, play an important role in the pathogenesis of Parkinson’s disease. We now show that the corresponding pathways of these two systems are linked at the transcriptomic level in Parkinsonian substantia nigra. We examined gene expression in medial and lateral substantia nigra (SN) as well as in frontal cortex using whole genome DNA oligonucleotide microarrays. In this study, we use a hypothesis-driven approach in analysing microarray data to describe the expression of mitochondrial and ubiquitin-proteasomal system (UPS) genes in Parkinson’s disease (PD).

Although a number of genes showed up-regulation, we found an overall decrease in expression affecting the majority of mitochondrial and UPS sequences. The down-regulated genes include genes that encode subunits of complex I and the Parkinson’s-disease-linked UCHL1. The observed changes in expression were very similar for both medial and lateral SN and also affected the PD cerebral cortex. As revealed by “gene shaving” clustering analysis, there was a very significant correlation between the transcriptomic profiles of both systems including in control brains.

Therefore, the mitochondria and the proteasome form a higher-order gene regulatory network that is severely perturbed in Parkinson’s disease. Our quantitative results also suggest that Parkinson’s disease is a disease of more than one cell class, i.e. that it goes beyond the catecholaminergic neuron and involves glia as well.

Duke DC, Moran LB, Kalaitzakis ME, Deprez M, et al. Transcriptome analysis reveals link between proteasomal and mitochondrial pathways in Parkinson’s disease. Neurogenetics 2006; 7:139-148.
Bax Degradation a Novel Mechanism for Survival in Bcl-2 overexpressed cancer cells
The authors previously reported that proteasome inhibitors were able to overcome Bcl-2-mediated protection from apoptosis, and now show that inhibition of the proteasome activity in Bcl-2-overexpressing cells accumulates the proapoptotic Bax protein to mitochondrial cytoplasm, where it interacts to Bcl-2 protein. This event was followed by release of mitochondrial cytochrome c into the cytosol and activation of caspase-mediated apoptosis. In contrast, proteasome inhibition did not induce any apparent changes in Bcl-2 protein levels. In addition, treatment with a proteasome inhibitor increased levels of ubiquitinated forms of Bax protein, without any effects on Bax mRNA expression. They also established a cell-free Bax degradation assay in which an in vitro-translated, 35S-labeled Bax protein can be degraded by a tumor cell protein extract, inhibitable by addition of a proteasome inhibitor or depletion of the proteasome or ATP. The Bax degradation activity can be reconstituted in the proteasome-depleted supernatant by addition of a purified 20S proteasome or proteasome-enriched fraction. Finally, by using tissue samples of human prostate adenocarcinoma, they demonstrated that increased levels of Bax degradation correlated well with decreased levels of Bax protein and increased Gleason scores of prostate cancer. These studies strongly suggest that ubiquitin-proteasome-mediated Bax degradation is a novel survival mechanism in human cancer cells and that selective targeting of this pathway should provide a unique approach for treatment of human cancers, especially those overexpressing Bcl-2.
In the current study, These investigators report that

  • (i) proteasome inhibition results in Bax accumulation before release of cytochrome c and induction of apoptosis, which is associated with the ability of proteasome inhibitors to overcome Bcl-2-mediated antiapoptotic function;
  • (ii) Bax is regulated by an ATP-ubiquitin-proteasome-dependent degradation pathway; and
  • (iii) decreased levels of Bax protein correlate with increased levels of Bax degradation in aggressive human prostate cancer.

Li B and Dou QP. Bax degradation by the ubiquitin-proteasome-dependent pathway: Involvement in tumor survival and progression. PNAS 2000; 97(8): 3851-3855. http://www.pnas.org

p97 and DBeQ, ATP-competitive p97 inhibitor
A major limitation to current studies on the biological functions of p97/Cdc48 is that there is no method to rapidly shut off its ATPase activity. Given the range of cellular processes in which Cdc48 participates, it is difficult to determine whether any particular phenotype observed in the existing mutants is due to a direct or indirect effect. Moreover, inhibition of p97 activity in animal cells by siRNA or expression of a dominant-negative version is challenged by its high abundance and is not suited to evaluating proximal phenotypic effects of p97 loss of function.

A specific small-molecule inhibitor of p97 would provide an important tool to investigate diverse functions of this essential ATPase associated with diverse cellular activities (AAA) ATPase and to evaluate its potential to be a therapeutic target in human disease. Cancer cells may be particularly sensitive to killing by suppression of protein degradation mechanisms, because they may exhibit a heightened dependency on these mechanisms to clear an elevated burden of quality-control substrates. For example, some cancers produce high levels of a specific protein that is a prominent quality-control substrate (e.g., Ig light chains in multiple myeloma) or produce high levels of reactive oxygen species, which can result in excessive protein damage via oxidation. Therefore, a specific p97 inhibitor would be a valuable research tool to investigate p97 function in cells.

We carried out a high-throughput screen to identify inhibitors of p97 ATPase activity. Dual-reporter cell lines that simultaneously express p97-dependent and p97-independent proteasome substrates were used to stratify inhibitors that emerged from the screen. N2,N4-dibenzylquinazoline-2,4-diamine (DBeQ) was identified as a selective,potent, reversible, and ATP-competitive p97 inhibitor.

DBeQ blocks multiple processes that have been shown by RNAi to depend on p97, including degradation of ubiquitin fusion degradation and endoplasmic reticulum-associated degradation pathway reporters, as well as autophagosome maturation. DBeQ also potently inhibits cancer cell growth and is more rapid than a proteasome inhibitor at mobilizing the executioner caspases-3 and -7.

Simultaneous inhibition of proteasome and histone deacetylase 6 (HDAC6) [which is required for autophagy results in synergistic killing of multiple myeloma cells]. Interestingly, more than one dozen human clinical trials (www.clinicaltrials.gov) combine bortezomib with the broad-spectrum HDAC inhibitor vorinostat, which is active toward HDAC6. Targeting p97
may provide an alternative route to achieving the same objective. Our results provide a rationale for targeting p97 in cancer therapy. Future work will provide molecular insight into how inhibition of p97 activity by DBeQ results in apoptosis and could strengthen the rationale for a p97-targeted cancer therapeutic.

Chou TF, Brown SJ, Minond D, Nordin BE, et al. Reversible inhibitor of p97, DBeQ, impairs both ubiquitin-dependent and autophagic protein clearance pathways. PNAS 2011; pp 6 http://www.pnas.org/cgi/doi/10.1073/pnas.1015312108

The causes of various neurodegenerative diseases, particularly sporadic cases, remain unknown, but increasing evidence suggests that these diseases may share similar molecular and cellular mechanisms of pathogenesis. One prominent feature common to most neurodegenerative diseases is the accumulation of misfolded proteins in the form of insoluble protein aggregates or inclusion bodies. Although these aggregates have different protein compositions, they all contain ubiquitin and proteasome subunits, implying a failure of the ubiquitin-proteasome system (UPS) in the removal of misfolded proteins.

A direct link between UPS dysfunction and neurodegeneration has been
provided by recent findings that genetic mutations in UPS components cause several rare, familial forms of neurodegenerative diseases. Furthermore, it is becoming increasingly clear that oxidative stress, which results from aging or exposure to environmental toxins, can directly damage UPS components, thereby contributing to the pathogenesis of sporadic forms of neurodegenerative diseases.

Aberrations in the UPS often result in defective proteasome-mediated protein degradation, leading to accumulation of toxic proteins and eventually to neuronal cell death. Interestingly, emerging evidence has begun to suggest that impairment in substrate-specific components of the UPS, such as E3 ubiquitin-protein ligases, may cause aberrant ubiquitination and neurodegeneration in a proteasome-independent manner. This provides an overview of the molecular components of the UPS and their impairment in familial and sporadic forms of neurodegenerative diseases, and summarizes present knowledge about the pathogenic mechanisms of UPS dysfunction in neurodegeneration.

Molecular mechanisms of protein ubiquitination and degradation by the UPS. Ubiquitination involves a highly specific enzyme cascade in which

  • ubiquitin (Ub) is first activated by the ubiquitinactivating enzyme (E1),
  • then transferred to an ubiquitin-conjugating enzyme (E2), and
  • finally covalently attached to the substrate by an ubiquitin-protein ligase (E3).

Ubiquitination is a reversible posttranslational modification in which the removal of Ub is mediated by a deubiquitinating enzyme (DUB).

  • Substrate proteins can be either monoubiquitinated or polyubiquitinated through successive conjugation of Ub moieties to an internal lysine residue in Ub.
  • K48-linked poly-Ub chains are recognized by the 26S proteasome, resulting in degradation of the substrate and recycling of Ub.
  • Monoubiquitination or K63-linked polyubiquitination plays a number of regulatory roles in cells that are proteasome-independent.

Parkin

Loss-of-function mutations in parkin, a 465-amino-acid RING-type E3 ligase, were first identified as the cause for autosomal recessive juvenile Parkinsonism (AR-JP) and subsequently found to account for ~50% of all recessively transmitted early-onset PD cases. Interestingly, patients with parkin mutations do not exhibit Lewy body pathology.

Possible pathogenic mechanisms by which impaired UPS components cause neurodegeneration. Genetic mutations or oxidative stress from aging and/or exposure to environmental toxins have been shown to impair the ubiquitination machinery (particularly E3 ubiquitin-protein ligases) and deubiquitinating enzymes (DUBs), resulting in abnormal ubiquitination. Depending on the type of ubiquitination affected, the impairment could cause neurodegeneration through two different mechanisms.

In the first model, aberrant K48-linked polyubiquitination resulting from impaired E3s or DUBs alters protein degradation by the proteasome, leading to accumulation of toxic proteins and subsequent neurodegeneration. The proteasomes could be directly damaged by oxidative stress or might be inhibited by protein aggregation, which exacerbates the neurotoxicity.

In the second model, aberrant monoubiquitination or K63-linked polyubiquitination resulting from impaired E3s or DUBs alters crucial non-proteasomal functions, such as gene transcription and protein trafficking, thereby causing neurodegeneration without protein aggregation.

These two models are not mutually exclusive because a single E3 or DUB enzyme, such as parkin or UCH-L1, could regulate more than one type of ubiquitination. In addition, abnormal ubiquitination and neurodegeneration could also result from mutation or oxidative stress-induced structural changes in the protein substrates that alter their recognition and degradation by the UPS.

Lian Li and Chin LS. IMPAIRMENT OF THE UBIQUITIN-PROTEASOME SYSTEM: A COMMON PATHOGENIC MECHANISM IN NEURODEGENERATIVE DISORDERS. In The Ubiquitin Proteasome System…Chapter 23. (Eds: Eds: Mario Di Napoli and Cezary Wojcik) 553-577 © 2007 Nova Science Publishers, Inc. ISBN 978-1-60021-749-4.

filedesc Schematic diagram of the ubiquitylati...

filedesc Schematic diagram of the ubiquitylation system. Created by Roger B. Dodd (Photo credit: Wikipedia)

 

Current Noteworthy Work

Nassif M and Hetz C.  Autophagy impairment: a crossroad between neurodegeneration and tauopathies.  BMC Biology 2012; 10:78. http://www.biomedcentral.com/1741-7007/10/78

Impairment of protein degradation pathways such as autophagy is emerging as a consistent and transversal pathological phenomenon in neurodegenerative diseases, including Alzheimer´s, Huntington´s, and Parkinson´s disease. Genetic inactivation of autophagy in mice has demonstrated a key role of the pathway in maintaining protein homeostasis in the brain, triggering massive neuronal loss and the accumulation of abnormal protein inclusions.  A paper in Molecular Neurodegeneration from Abeliovich´s group now suggests a role for phosphorylation of Tau and the activation of glycogen synthase kinase 3β (GSK3β) in driving neurodegeneration in autophagy-deficient neurons. We discuss the implications of this study for understanding the factors driving neurofibrillary tangle formation in Alzheimer´s disease and tauopathies.

Cajee UF, Hull R and Ntwasa M. Modification by Ubiquitin-Like Proteins: Significance in Apoptosis and Autophagy Pathways. Int. J. Mol. Sci. 2012, 13, 11804-11831; doi:10.3390/ijms130911804

Ubiquitin-like proteins (Ubls) confer diverse functions on their target proteins. The modified proteins are involved in various biological processes, including DNA replication, signal transduction, cell cycle control, embryogenesis, cytoskeletal regulation,
metabolism, stress response, homeostasis and mRNA processing. Modifiers such as SUMO, ATG12, ISG15, FAT10, URM1, and UFM have been shown to modify proteins thus conferring functions related to programmed cell death, autophagy and regulation of
the immune system. Putative modifiers such as Domain With No Name (DWNN) have been identified in recent times but not fully characterized. In this review, we focus on cellular processes involving human Ubls and their targets.

Aloy P. Shaping the future of interactome networks. (A report of the third Interactome Networks Conference, Hinxton, UK, 29 August-1 September 2007). Genome Biology 2007; 8:316 (doi:10.1186/gb-2007-8-10-316)

Complex systems are often networked, and biology is no exception. Following on from the genome sequencing projects,
experiments show that proteins in living organisms are highly connected, which helps to explain how such great complexity
can be achieved by a comparatively small set of gene products. At a recent conference on interactome networks held outside
Cambridge, UK, the most recent advances in research on cellular networks were discussed. This year’s conference focused on
identifying the strengths and weaknesses of currently resolved interaction networks and the techniques used to determine
them – reflecting the fact that the field of mapping interaction networks is maturing.

Peroutka RJ, Orcutt SJ, Strickler JE, and Butt TR. SUMO Fusion Technology for Enhanced Protein Expression and Purification in Prokaryotes and Eukaryotes. Chapter 2. in T.C. Evans, M.-Q. Xu (eds.), Heterologous Gene Expression in E. coli, Methods in Molecular Biology 705:15-29. DOI 10.1007/978-1-61737-967-3_2, © Springer Science+Business Media, LLC 2011

The preparation of sufficient amounts of high-quality protein samples is the major bottleneck for structural proteomics. The use of recombinant proteins has increased significantly during the past decades. The most commonly used host, Escherichia coli, presents many challenges including protein misfolding, protein degradation, and low solubility. A novel SUMO fusion technology appears to enhance protein expression and solubility (www.lifesensors.com). Efficient removal of the SUMO tag by SUMO protease in vitro facilitates the generation of target protein with a native N-terminus. In addition to its physiological relevance in eukaryotes, SUMO can be used as a powerful biotechnology tool for enhanced functional protein expression in prokaryotes and eukaryotes.

Juang YC, Landry MC, et al. OTUB1 Co-opts Lys48-Linked Ubiquitin Recognition to Suppress E2 Enzyme Function. Molecular Cell 2012; 45: 384–397. DOI 10.1016/j.molcel.2012.01.011

Ubiquitylation entails the concerted action of E1, E2, and E3 enzymes. We recently reported that OTUB1, a deubiquitylase, inhibits the DNA damage response independently of its isopeptidase activity. OTUB1 does so by blocking ubiquitin transfer by UBC13, the cognate E2 enzyme for RNF168. OTUB1 also inhibits E2s of the UBE2D and UBE2E families. Here we elucidate the structural mechanism by which OTUB1 binds E2s to inhibit ubiquitin transfer. OTUB1 recognizes ubiquitin-charged E2s through contacts with both donor ubiquitin and the E2 enzyme. Surprisingly, free ubiquitin associates with the canonical distal ubiquitin-binding site on OTUB1 to promote formation of the inhibited E2 complex. Lys48 of donor ubiquitin lies near the OTUB1 catalytic site and the C terminus of free ubiquitin, a configuration that mimics the products of Lys48-linked ubiquitin chain cleavage. OTUB1 therefore co-opts Lys48-linked ubiquitin chain recognition to suppress ubiquitin conjugation and the DNA damage response.

Hunter T. The Age of Crosstalk: Phosphorylation, Ubiquitination, and Beyond. Molecular Cell  2007; 28:730-738. DOI 10.1016/ j.molcel.2007.11.019.

Crosstalk between different types of posttranslational modification is an emerging theme in eukaryotic biology. Particularly prominent are the multiple connections between phosphorylation and ubiquitination, which act either positively or negatively in both directions to regulate these processes.

Tu Y, Chen C, et al. The Ubiquitin Proteasome Pathway (UPP) in the regulation of cell cycle control and DNA damage repair and its implication in tumorigenesis. Int J Clin Exp Pathol 2012;5(8):726-738. www.ijcep.com /ISSN:1936-2625/IJCEP1208018

Accumulated evidence supports that the ubiquitin proteasome pathway (UPP) plays a crucial role in protein
metabolism implicated in the regulation of many biological processes such as cell cycle control, DNA damage
response, apoptosis, and so on. Therefore, alterations for the ubiquitin proteasome signaling or functional impairments
for the ubiquitin proteasome components are involved in the etiology of many diseases, particularly in cancer
development.The authors discuss the ubiquitin proteasome pathway in the regulation of cell cycle control and DNA
damage response, the relevance for the altered regulation of these signaling pathways in tumorigenesis, and finally
assess and summarize the advancement for targeting the ubiquitin proteasome pathway in cancer therapy.

Cebollero E , Reggiori F  and Kraft C.  Ribophagy: Regulated Degradation of Protein Production Factories. Int J Cell Biol. 2012; 2012: 182834. doi:  10.1155/2012/182834 (online).

During autophagy, cytosol, protein aggregates, and organelles are sequestered into double-membrane vesicles called autophagosomes and delivered to the lysosome/vacuole for breakdown and recycling of their basic components. In all eukaryotes this pathway is important for adaptation to stress conditions such as nutrient deprivation, as well as to regulate intracellular homeostasis by adjusting organelle number and clearing damaged structures. For a long time, starvation-induced autophagy has been viewed as a nonselective transport pathway; however, recent studies have revealed that autophagy is able to selectively engulf specific structures, ranging from proteins to entire organelles. In this paper, we discuss recent findings on the mechanisms and physiological implications of two selective types of autophagy: ribophagy, the specific degradation of ribosomes, and reticulophagy, the selective elimination of portions of the ER.

Lee JH, Yu WH,…, Nixon RA.  Lysosomal Proteolysis and Autophagy Require Presenilin 1 and Are Disrupted by Alzheimer-Related PS1 Mutations. Cell 2010; 141, 1146–1158. DOI 10.1016/j.cell.2010.05.008.

Macroautophagy is a lysosomal degradative pathway essential for neuron survival. Here, we show that macroautophagy requires the Alzheimer’s disease (AD)-related protein presenilin-1 (PS1). In PS1 null blastocysts, neurons from mice hypomorphic for PS1 or
conditionally depleted of PS1, substrate proteolysis and autophagosome clearance during macroautophagy are prevented as a result of a selective impairment of autolysosome acidification and cathepsin activation. These deficits are caused by failed PS1-dependent
targeting of the v-ATPase V0a1 subunit to lysosomes. N-glycosylation of the V0a1 subunit, essential for its efficient ER-to-lysosome delivery, requires the selective binding of PS1 holoprotein to the unglycosylated subunit and the  sec61alpha/ oligosaccharyltransferase complex. PS1 mutations causing early-onset AD produce a similar lysosomal/autophagy phenotype in
fibroblasts from AD patients. PS1 is therefore essential for v-ATPase targeting to lysosomes, lysosome acidification, and proteolysis during autophagy. Defective lysosomal proteolysis represents a basis for pathogenic protein accumulations and neuronal cell death in AD and suggests previously unidentified therapeutic targets.

Pohl C and Jentsch S. Midbody ring disposal by autophagy is a post-abscission event of cytokinesis. nature cell biology 2009; 11 (1): 65-70.  DOI: 10.1038/ncb1813.

At the end of cytokinesis, the dividing cells are connected by an intercellular bridge, containing the midbody along with a single,
densely ubiquitylated, circular structure called the midbody ring (MR). Recent studies revealed that the MR serves as a target
site for membrane delivery and as a physical barrier between the prospective daughter cells. The MR materializes in telophase,
localizes to the intercellular bridge during cytokinesis, and moves asymmetrically into one cell after abscission. Daughter
cells rarely accumulate MRs of previous divisions, but how these large structures finally disappear remains unknown.
Here, we show that MRs are discarded by autophagy, which involves their sequestration into autophagosomes and delivery to
lysosomes for degradation. Notably, autophagy factors, such as the ubiquitin adaptor p62 and the ubiquitin-related protein Atg8 , associate with the MR during abscission, suggesting that autophagy is coupled to cytokinesis. Moreover, MRs accumulate in cells of patients with lysosomal storage disorders, indicating that defective MR disposal is characteristic of these diseases. Thus our findings suggest that autophagy has a broader role than previously assumed, and that cell renovation by clearing from superfluous large macromolecular assemblies, such as MRs, is an important autophagic function.

 

Hanai JI, Cao P, Tanksale P, Imamura S, et al. The muscle-specific ubiquitin ligase atrogin-1/MAFbx mediates statin-induced muscle toxicity. The Journal of Clinical Investigation  2007; 117(12):3930-3951.    http://www.jci.org

Statins inhibit HMG-CoA reductase, a key enzyme in cholesterol synthesis, and are widely used to treat hypercholesterolemia.
These drugs can lead to a number of side effects in muscle, including muscle fiber breakdown; however, the mechanisms of muscle injury by statins are poorly understood. We report that lovastatin induced the expression of atrogin-1, a key gene involved in skeletal muscle atrophy, in humans with statin myopathy, in zebrafish embryos, and in vitro in murine skeletal muscle cells. In cultured mouse myotubes, atrogin-1 induction following lovastatin treatment was accompanied by distinct morphological changes, largely absent in
atrogin-1 null cells. In zebrafish embryos, lovastatin promoted muscle fiber damage, an effect that was closely mimicked by knockdown of zebrafish HMG-CoA reductase. Moreover, atrogin-1 knockdown in zebrafish embryos prevented lovastatin-induced muscle injury. Finally, overexpression of PGC-1α, a transcriptional coactivator that induces mitochondrial biogenesis and protects against the development of muscle atrophy, dramatically prevented lovastatin-induced muscle damage and abrogated atrogin-1 induction both in fish and in cultured mouse myotubes. Collectively, our human, animal, and in vitro findings shed light on the molecular mechanism of statin-induced myopathy and suggest that atrogin-1 may be a critical mediator of the muscle
damage induced by statins.

Inami Y, Waguri S, Sakamoto A, Kouno T, et al.  Persistent activation of Nrf2 through p62 in hepatocellular carcinoma cells. J. Cell Biol. 2011; 193(2): 275–284. http://www.jcb.org/cgi/doi/10.1083/jcb.201102031

Macroautophagy (hereafter referred to as autophagy) is a cellular degradation system in which cytoplasmic components, including
organelles, are sequestered by double membrane structures called autophagosomes and the sequestered materials are
degraded by lysosomal hydrolases for supply of amino acids and for cellular homeostasis. Although autophagy has generally been considered nonselective, recent studies have shed light on another indispensable role for basal autophagy in cellular homeostasis, which is mediated by selective degradation of a specific substrate(s).  p62 is a ubiquitously expressed cellular protein that is conserved in metazoa but not in plants and fungi, and recently it has been known as one of the selective substrates for autophagy.
This protein is localized at the autophagosome formation site  and directly interacts with LC3, an autophagosome localizing protein . Subsequently, the p62 is incorporated into the autophagosome and then degraded. Therefore, impaired autophagy is accompanied by
accumulation of p62 followed by the formation of p62 and ubiquitinated protein aggregates because of the nature of both self- oligomerization and ubiquitin binding of p62.

 

Kima K, Khayrutdinov BI, Leeb CK, et al. Solution structure of the Zβ domain of human DNA-dependent activator of IFN-regulatory factors and its binding modes to B- and Z-DNAs. PNAS 2010; Early Edition ∣ pp 6. www.pnas.org/cgi/doi/10.1073/pnas.1014898107

The DNA-dependent activator of IFN-regulatory factors (DAI), also known as DLM-1/ZBP1, initiates an innate immune response by binding to foreign DNAs in the cytosol. For full activation of the immune response, three DNA binding domains at the N terminus are required: two Z-DNA binding domains (ZBDs), Zα and Zβ, and an adjacent putative B-DNA binding domain. The crystal structure of the Zβ domain of human DAI (hZβDAI) in complex with Z-DNA revealed structural features distinct from other known Z-DNA binding proteins, and it was classified as a group II ZBD. To gain structural insights into the DNA binding mechanism of hZβDAI, the solution structure of the free hZβDAI was solved, and its bindings to B- and Z-DNAs were analyzed by NMR spectroscopy. Compared to the Z-DNA–bound structure, the conformation of free hZβDAI has notable alterations in the α3 recognition helix, the “wing,” and Y145, which are critical in Z-DNA recognition. Unlike some other Zα domains, hZβDAI appears to have conformational flexibility, and structural adaptation is required for Z-DNA binding. Chemical-shift perturbation experiments revealed that hZβDAI also binds weakly to B-DNA via a different binding mode. The C-terminal domain of DAI is reported to undergo a conformational change on B-DNA binding; thus, it is possible that these changes are correlated. During the innate immune response, hZβDAI is likely to play an active role in binding to DNAs in both B and Z conformations in the recognition of foreign DNAs.

 

Epicrisis

This extensive review leaves little left unopened. We have seen the central role that the UPS system plays in normal organelle proteolysis in concert with autophagy. Impaired ubiquitination occurs from aging, and/or toxins, under oxidative stress involving E3s or DUBs.

This leads to altered gene transcripton, altered protein trafficking, and plays a role in neurodegenative disease, muscle malfunction, and cancer as well.

English: A cartoon representation of a lysine ...

English: A cartoon representation of a lysine 48-linked diubiquitin molecule. The two ubiquitin chains are shown as green cartoons with each chain labelled. The components of the linkage are indicated and shown as orange sticks. Image was created using PyMOL from PDB id 1aar. (Photo credit: Wikipedia)

Different forms of protein ubiquitylation

Different forms of protein ubiquitylation (Photo credit: Wikipedia)

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