The importance of post-translational modifications in regulating Saccharomyces cerevisiae metabolism

Metabolomic and Proteomic Profiles Reveal the Dynamics of Primary Metabolism during Seed Development of Lotus (Nelumbo nucifera)

Sacred lotus (Nelumbo nucifera) belongs to the Nelumbonaceae family. Its seeds are widely consumed in Asian countries as snacks or even medicine. Besides the market value, lotus seed also plays a crucial role in the lotus life cycle. Consequently, it is essential to gain a comprehensive understanding of the development of lotus seed. During its development, lotus seed undergoes cell division, expansion, reserve accumulation, desiccation, and maturation phases. We observed morphological and biochemical changes from 10 to 25 days after pollination (DAP) which corresponded to the reserve synthesis and accumulation phase. The volume of the seed expanded until 20 DAP with the color of the seed coat changing from yellow-green to dark green and gradually fading again. Starch and protein rapidly accumulated from 15 to 20 DAP. To further reveal metabolic adaptation, primary metabolites and proteins profiles were obtained using mass spectrometry based platforms. Metabolites and enzymes involved in sugar metabolism, glycolysis, TCA cycle and amino acid metabolism showed sequential dynamics enabling the clear separation of the different metabolic states during lotus seed development. The integration of the data revealed a highly significant metabolic switch at 15 DAP going through a transition of metabolically highly active tissue to the preparation of storage tissue. The results provide a reference data set for the evaluation of primary metabolism during lotus seed development.

Severely Impaired and Dysregulated Cytochrome P450 Expression and Activities in Hepatocellular Carcinoma: Implications for Personalized Treatment in Patients

This study aims to systematically determine the activities and expressions of cytochrome P450s (CYP) in hepatocellular carcinoma (HCC) patients to support their optimal use in personalized treatment of HCC. Activities of seven major drug-metabolizing CYP enzymes (CYP1A2, 2A6, 2C8, 2C9, 2D6, 2E1, and 3A4) were determined in tumors and pericarcinomatous tissues harvested from 26 patients with hepatitis B virus-positive HCC using probe substrates. Protein and mRNA levels of these CYPs were also measured using isotope label-free LC/MS-MS method and real-time PCR, respectively. Maximal metabolic velocity (Vmax) of CYP probe substrates was decreased by 2.5- to 30-fold in tumor microsomes, accompanied by a corresponding decrease in their protein and mRNA expression levels. However, Km values and turnover numbers of substrates in tumor microsomes were not changed. High correlations between activities and CYP protein levels were also observed, but the correlation between activities and mRNA levels was often poor. There was a major decrease in the degree of correlation in CYP expression in tumor tissues, suggesting that CYP expression levels are greatly disrupted by the tumorigenic process. Our unprecedented systemic study of the effects of HCC on CYPs demonstrated that activities of CYPs were seriously impaired and their expression patterns were severely altered by HCC. We proposed that determination of the CYP protein expression profile by LC/MS-MS in each patient is a promising approach that can be clinically used for individualized treatment of HCC.

A NAD-dependent glutamate dehydrogenase coordinates metabolism with cell division in Caulobacter crescentus

Coupling cell cycle with nutrient availability is a crucial process for all living cells. But how bacteria control cell division according to metabolic supplies remains poorly understood. Here, we describe a molecular mechanism that coordinates central metabolism with cell division in the α-proteobacterium Caulobacter crescentus. This mechanism involves the NAD-dependent glutamate dehydrogenase GdhZ and the oxidoreductase-like KidO. While enzymatically active GdhZ directly interferes with FtsZ polymerization by stimulating its GTPase activity, KidO bound to NADH destabilizes lateral interactions between FtsZ protofilaments. Both GdhZ and KidO share the same regulatory network to concomitantly stimulate the rapid disassembly of the Z-ring, necessary for the subsequent release of progeny cells. Thus, this mechanism illustrates how proteins initially dedicated to metabolism coordinate cell cycle progression with nutrient availability.

Protein kinase Ymr291w/Tda1 is essential for glucose signaling in saccharomyces cerevisiae on the level of hexokinase isoenzyme ScHxk2 phosphorylation*

The enzyme ScHxk2 of Saccharomyces cerevisiae is a dual-function hexokinase that besides its catalytic role in glycolysis is involved in the transcriptional regulation of glucose-repressible genes. Relief from glucose repression is accompanied by the phosphorylation of the nuclear fraction of ScHxk2 at serine 15 and the translocation of the phosphoenzyme into the cytosol. Different studies suggest different serine/threonine protein kinases, Ymr291w/Tda1 or Snf1, to accomplish ScHxk2-S15 phosphorylation. The current paper provides evidence that Ymr291w/Tda1 is essential for that modification, whereas protein kinases Ydr477w/Snf1, Ynl307c/Mck1, Yfr014c/Cmk1, and Ykl126w/Ypk1, which are co-purified during Ymr291w/Tda1 tandem affinity purification, as well as protein kinase PKA and PKB homolog Sch9 are dispensable. Taking into account the detection of a significantly higher amount of the Ymr291w/Tda1 protein in cells grown in low-glucose media as compared with a high-glucose environment, Ymr291w/Tda1 is likely to contribute to glucose signaling in S. cerevisiae on the level of ScHxk2-S15 phosphorylation in a situation of limited external glucose availability. The evolutionary conservation of amino acid residue serine 15 in yeast hexokinases and its phosphorylation is illustrated by the finding that YMR291W/TDA1 of S. cerevisiae and the homologous KLLA0A09713 gene of Kluyveromyces lactis allow for cross-complementation of the respective protein kinase single-gene deletion strains.

Applications of computational modeling in metabolic engineering of yeast

Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications.

Heterogeneity of glycolysis in cancers and therapeutic opportunities

Upregulated glycolysis, both in normoxic and hypoxic environments, is a nearly universal trait of cancer cells. The enormous difference in glucose metabolism offers a target for therapeutic intervention with a potentially low toxicity profile. The past decade has seen a steep rise in the development and clinical assessment of small molecules that target glycolysis. The enzymes in glycolysis have a highly heterogeneous nature that allows for the different bioenergetic, biosynthetic, and signaling demands needed for various tissue functions. In cancers, these properties enable them to respond to the variable requirements of cell survival, proliferation and adaptation to nutrient availability. Heterogeneity in glycolysis occurs through the expression of different isoforms, posttranslational modifications that affect the kinetic and regulatory properties of the enzyme. In this review, we will explore this vast heterogeneity of glycolysis and discuss how this information might be exploited to better target glucose metabolism and offer possibilities for biomarker development.

PTM-SD: a database of structurally resolved and annotated posttranslational modifications in proteins

Posttranslational modifications (PTMs) define covalent and chemical modifications of protein residues. They play important roles in modulating various biological functions. Current PTM databases contain important sequence annotations but do not provide informative 3D structural resource about these modifications. Posttranslational modification structural database (PTM-SD) provides access to structurally solved modified residues, which are experimentally annotated as PTMs. It combines different PTM information and annotation gathered from other databases, e.g. Protein DataBank for the protein structures and dbPTM and PTMCuration for fine sequence annotation. PTM-SD gives an accurate detection of PTMs in structural data. PTM-SD can be browsed by PDB id, UniProt accession number, organism and classic PTM annotation. Advanced queries can also be performed, i.e. detailed PTM annotations, amino acid type, secondary structure, SCOP class classification, PDB chain length and number of PTMs by chain. Statistics and analyses can be computed on a selected dataset of PTMs. Each PTM entry is detailed in a dedicated page with information on the protein sequence, local conformation with secondary structure and Protein Blocks. PTM-SD gives valuable information on observed PTMs in protein 3D structure, which is of great interest for studying sequence-structure- function relationships at the light of PTMs, and could provide insights for comparative modeling and PTM predictions protocols. Database URL: PTM-SD can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/PTM-SD/.

Contribution of network connectivity in determining the relationship between gene expression and metabolite concentration changes

One of the primary mechanisms through which a cell exerts control over its metabolic state is by modulating expression levels of its enzyme-coding genes. However, the changes at the level of enzyme expression allow only indirect control over metabolite levels, for two main reasons. First, at the level of individual reactions, metabolite levels are non-linearly dependent on enzyme abundances as per the reaction kinetics mechanisms. Secondly, specific metabolite pools are tightly interlinked with the rest of the metabolic network through their production and consumption reactions. While the role of reaction kinetics in metabolite concentration control is well studied at the level of individual reactions, the contribution of network connectivity has remained relatively unclear. Here we report a modeling framework that integrates both reaction kinetics and network connectivity constraints for describing the interplay between metabolite concentrations and mRNA levels. We used this framework to investigate correlations between the gene expression and the metabolite concentration changes in Saccharomyces cerevisiae during its metabolic cycle, as well as in response to three fundamentally different biological perturbations, namely gene knockout, nutrient shock and nutrient change. While the kinetic constraints applied at the level of individual reactions were found to be poor descriptors of the mRNA-metabolite relationship, their use in the context of the network enabled us to correlate changes in the expression of enzyme-coding genes to the alterations in metabolite levels. Our results highlight the key contribution of metabolic network connectivity in mediating cellular control over metabolite levels, and have implications towards bridging the gap between genotype and metabolic phenotype.

In silico analysis of protein Lys-N(𝜀)-acetylation in plants

Among post-translational modifications, there are some conceptual similarities between Lys-N(𝜀)-acetylation and Ser/Thr/Tyr O-phosphorylation. Herein we present a bioinformatics-based overview of reversible protein Lys-acetylation, including some comparisons with reversible protein phosphorylation. The study of Lys-acetylation of plant proteins has lagged behind studies of mammalian and microbial cells; 1000s of acetylation sites have been identified in mammalian proteins compared with only hundreds of sites in plant proteins. While most previous emphasis was focused on post-translational modifications of histones, more recent studies have addressed metabolic regulation. Being directly coupled with cellular CoA/acetyl-CoA and NAD/NADH, reversible Lys-N(𝜀)-acetylation has the potential to control, or contribute to control, of primary metabolism, signaling, and growth and development.

From flavors and pharmaceuticals to advanced biofuels: production of isoprenoids in Saccharomyces cerevisiae

Isoprenoids denote the largest group of chemicals in the plant kingdom and are employed for a wide range of applications in the food and pharmaceutical industry. In recent years, isoprenoids have additionally been recognized as suitable replacements for petroleum-derived fuels and could thus promote the transition towards a more sustainable society. To realize the biofuel potential of isoprenoids, a very efficient production system is required. While complex chemical structures as well as the low abundance in nature demonstrate the shortcomings of chemical synthesis and plant extraction, isoprenoids can be produced by genetically engineered microorganisms from renewable carbon sources. In this article, we summarize the development of isoprenoid applications from flavors and pharmaceuticals to advanced biofuels and review the strategies to design microbial cell factories, focusing on Saccharomyces cerevisiae for the production of these compounds. While the high complexity of biosynthetic pathways and the toxicity of certain isoprenoids still denote challenges that need to be addressed, metabolic engineering has enabled large-scale production of several terpenoids and thus, the utilization of these compounds is likely to expand in the future.

Temporal system-level organization of the switch from glycolytic to gluconeogenic operation in yeast

The diauxic shift in Saccharomyces cerevisiae is an ideal model to study how eukaryotic cells readjust their metabolism from glycolytic to gluconeogenic operation. In this work, we generated time-resolved physiological data, quantitative metabolome (69 intracellular metabolites) and proteome (72 enzymes) profiles. We found that the diauxic shift is accomplished by three key events that are temporally organized: (i) a reduction in the glycolytic flux and the production of storage compounds before glucose depletion, mediated by downregulation of phosphofructokinase and pyruvate kinase reactions; (ii) upon glucose exhaustion, the reversion of carbon flow through glycolysis and onset of the glyoxylate cycle operation triggered by an increased expression of the enzymes that catalyze the malate synthase and cytosolic citrate synthase reactions; and (iii) in the later stages of the adaptation, the shutting down of the pentose phosphate pathway with a change in NADPH regeneration. Moreover, we identified the transcription factors associated with the observed changes in protein abundances. Taken together, our results represent an important contribution toward a systems-level understanding of how this adaptation is realized.

Flux imbalance analysis and the sensitivity of cellular growth to changes in metabolite pools

Stoichiometric models of metabolism, such as flux balance analysis (FBA), are classically applied to predicting steady state rates - or fluxes - of metabolic reactions in genome-scale metabolic networks. Here we revisit the central assumption of FBA, i.e. that intracellular metabolites are at steady state, and show that deviations from flux balance (i.e. flux imbalances) are informative of some features of in vivo metabolite concentrations. Mathematically, the sensitivity of FBA to these flux imbalances is captured by a native feature of linear optimization, the dual problem, and its corresponding variables, known as shadow prices. First, using recently published data on chemostat growth of Saccharomyces cerevisae under different nutrient limitations, we show that shadow prices anticorrelate with experimentally measured degrees of growth limitation of intracellular metabolites. We next hypothesize that metabolites which are limiting for growth (and thus have very negative shadow price) cannot vary dramatically in an uncontrolled way, and must respond rapidly to perturbations. Using a collection of published datasets monitoring the time-dependent metabolomic response of Escherichia coli to carbon and nitrogen perturbations, we test this hypothesis and find that metabolites with negative shadow price indeed show lower temporal variation following a perturbation than metabolites with zero shadow price. Finally, we illustrate the broader applicability of flux imbalance analysis to other constraint-based methods. In particular, we explore the biological significance of shadow prices in a constraint-based method for integrating gene expression data with a stoichiometric model. In this case, shadow prices point to metabolites that should rise or drop in concentration in order to increase consistency between flux predictions and gene expression data. In general, these results suggest that the sensitivity of metabolic optima to violations of the steady state constraints carries biologically significant information on the processes that control intracellular metabolites in the cell.

Nucleotide degradation and ribose salvage in yeast

Nucleotide degradation is a universal metabolic capability. Here we combine metabolomics, genetics and biochemistry to characterize the yeast pathway. Nutrient starvation, via PKA, AMPK/SNF1, and TOR, triggers autophagic breakdown of ribosomes into nucleotides. A protein not previously associated with nucleotide degradation, Phm8, converts nucleotide monophosphates into nucleosides. Downstream steps, which involve the purine nucleoside phosphorylase, Pnp1, and pyrimidine nucleoside hydrolase, Urh1, funnel ribose into the nonoxidative pentose phosphate pathway. During carbon starvation, the ribose-derived carbon accumulates as sedoheptulose-7-phosphate, whose consumption by transaldolase is impaired due to depletion of transaldolase's other substrate, glyceraldehyde-3-phosphate. Oxidative stress increases glyceraldehyde-3-phosphate, resulting in rapid consumption of sedoheptulose-7-phosphate to make NADPH for antioxidant defense. Ablation of Phm8 or double deletion of Pnp1 and Urh1 prevent effective nucleotide salvage, resulting in metabolite depletion and impaired survival of starving yeast. Thus, ribose salvage provides means of surviving nutrient starvation and oxidative stress.

Regulation of yeast central metabolism by enzyme phosphorylation

As a frequent post-translational modification, protein phosphorylation regulates many cellular processes. Although several hundred phosphorylation sites have been mapped to metabolic enzymes in Saccharomyces cerevisiae, functionality was demonstrated for few of them. Here, we describe a novel approach to identify in vivo functionality of enzyme phosphorylation by combining flux analysis with proteomics and phosphoproteomics. Focusing on the network of 204 enzymes that constitute the yeast central carbon and amino-acid metabolism, we combined protein and phosphoprotein levels to identify 35 enzymes that change their degree of phosphorylation during growth under five conditions. Correlations between previously determined intracellular fluxes and phosphoprotein abundances provided first functional evidence for five novel phosphoregulated enzymes in this network, adding to nine known phosphoenzymes. For the pyruvate dehydrogenase complex E1 α subunit Pda1 and the newly identified phosphoregulated glycerol-3-phosphate dehydrogenase Gpd1 and phosphofructose-1-kinase complex β subunit Pfk2, we then validated functionality of specific phosphosites through absolute peptide quantification by targeted mass spectrometry, metabolomics and physiological flux analysis in mutants with genetically removed phosphosites. These results demonstrate the role of phosphorylation in controlling the metabolic flux realised by these three enzymes.

Linking post-translational modifications and variation of phenotypic traits

Enzymes can be post-translationally modified, leading to isoforms with different properties. The phenotypic consequences of the quantitative variability of isoforms have never been studied. We used quantitative proteomics to dissect the relationships between the abundances of the enzymes and isoforms of alcoholic fermentation, metabolic traits, and growth-related traits in Saccharomyces cerevisiae. Although the enzymatic pool allocated to the fermentation proteome was constant over the culture media and the strains considered, there was variation in abundance of individual enzymes and sometimes much more of their isoforms, which suggests the existence of selective constraints on total protein abundance and trade-offs between isoforms. Variations in abundance of some isoforms were significantly associated to metabolic traits and growth-related traits. In particular, cell size and maximum population size were highly correlated to the degree of N-terminal acetylation of the alcohol dehydrogenase. The fermentation proteome was found to be shaped by human selection, through the differential targeting of a few isoforms for each food-processing origin of strains. These results highlight the importance of post-translational modifications in the diversity of metabolic and life-history traits.