Regulation of yeast central metabolism by enzyme phosphorylation

Modular deregulation of central carbon metabolism for efficient xylose utilization in Saccharomyces cerevisiae

The tightly regulated central carbon metabolism in Saccharomyces cerevisiae, intricately linked to carbon sources utilized, poses a significant challenge to engineering efforts aimed at increasing the flux through its different pathways. Here, we present a modular deregulation strategy that enables high conversion rates of xylose through the central carbon metabolism. Specifically, employing a multifaceted approach encompassing five different engineering strategies-promoter engineering, transcription factor manipulation, biosensor construction, introduction of heterologous enzymes, and expression of mutant enzymes we engineer different modules of the central carbon metabolism at both the genetic and enzymatic levels. This leads to an enhanced conversion rate of xylose into acetyl-CoA-derived products, with 3-hydroxypropionic acid (3-HP) serving as a representative case in this study. By implementing a combination of these approaches, the developed yeast strain demonstrates a remarkable enhancement in 3-HP productivity, achieving a 4.7-fold increase when compared to our initially optimized 3-HP producing strain grown on xylose as carbon source. These results illustrate that the rational engineering of yeast central metabolism is a viable approach for boosting the metabolic flux towards acetyl-CoA-derived products on a non-glucose carbon source.

Identification and quantification of unreported sialylated N-glycan isomers with α2-3 and α2-6 linkages in the egg yolk protein phosvitin

Phosvitin (PV), a highly phosphorylated protein found in chicken egg yolk, possesses multiple bioactivities (including anti-aging and anticancer) and functional properties (including emulsifier and metal-binding capacities). The carbohydrate moiety attached to PV has been reported, but its N-glycan structure is unknown. In this study, we performed structural and quantitative analyses of N-glycans from PV using liquid chromatography-tandem mass spectrometry (MS/MS). N-glycan structures were identified using observed precursor ion m/z and MS/MS fragment ions. Each quantity was obtained relative to the total N-glycans (100%). Thirty-seven N-glycans were identified, including 22 sialylations with a negative charge (a sum of the relative quantity of each, 96.4%) comprising 13 mono- (31.6%), 7 di- (57.5%), 2 tri- (7.3%) sialylations. The sialylated N-glycan isomers with α2-3 (flexible conformation) and α2-6 (rigid conformation) linkages were distinguished using α2-3- and α2-3,6 sialidase treatments and intensity ratios of the N-acetylglucosamine and sialic acid ions (Ln/Nn) with different fragmentation stabilities. The α2-6/α2-6 (53.8%), α2-6 (31.6%), α2-3/α2-6/α2-6 (6.5%), and α2-3/α2-6 (3.7%) linkages in mono-, di, or tri-antennary structures were identified. These negatively charged structures may affect the emulsification and metal-binding capacity of PV. This is the first study to identify and quantify N-glycans in PV, including predominantly 22 sialylated N-glycan isomers with more rigid α2-6 linkages than α2-3 linkages.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Predisposing deleterious variants in the cancer-associated human kinases in the global populations

Human kinases play essential and diverse roles in the cellular activities of maintaining homeostasis and growth. Genetic mutations in the genes encoding the kinases (or phosphotransferases) have been linked with various types of cancers. In this study, we cataloged mutations in 500 kinases genes in >65,000 individuals of global populations from the Human Genetic Diversity Project (HGDP) and ExAC databases, and assessed their potentially deleterious impact by using the in silico tools SIFT, Polyphen2, and CADD. The analysis highlighted 35 deleterious non-synonymous SNVs in the ExAC and 5 SNVs in the HGDP project. Notably, a higher number of deleterious mutations was observed in the Non-Finnish Europeans (26 SNVs), followed by the Africans (14 SNVs), East Asians (13 SNVs), and South Asians (12 SNVs). The gene set enrichment analysis highlighted NTRK1 and FGFR3 being most significantly enriched among the kinases. The gene expression analysis revealed over-expression of NTRK1 in liver cancer, whereas, FGFR3 was found over-expressed in lung, breast, and liver cancers compared to their expression in the respective normal tissues. Also, 13 potential drugs were identified that target the NTRK1 protein, whereas 6 potential drugs for the FGFR3 target were identified. Taken together, the study provides a framework for exploring the predisposing germline mutations in kinases to suggest the underlying pathogenic mechanisms in cancers. The potential drugs are also suggested for personalized cancer management.

Engineering status of protein for improving microbial cell factories

With the development of metabolic engineering and synthetic biology, microbial cell factories (MCFs) have provided an efficient and sustainable method to synthesize a series of chemicals from renewable feedstocks. However, the efficiency of MCFs is usually limited by the inappropriate status of protein. Thus, engineering status of protein is essential to achieve efficient bioproduction with high titer, yield and productivity. In this review, we summarize the engineering strategies for metabolic protein status, including protein engineering for boosting microbial catalytic efficiency, protein modification for regulating microbial metabolic capacity, and protein assembly for enhancing microbial synthetic capacity. Finally, we highlight future challenges and prospects of improving microbial cell factories by engineering status of protein.

The regulatory landscape of the yeast phosphoproteome

The cellular ability to react to environmental fluctuations depends on signaling networks that are controlled by the dynamic activities of kinases and phosphatases. Here, to gain insight into these stress-responsive phosphorylation networks, we generated a quantitative mass spectrometry-based atlas of early phosphoproteomic responses in Saccharomyces cerevisiae exposed to 101 environmental and chemical perturbations. We report phosphosites on 59% of the yeast proteome, with 18% of the proteome harboring a phosphosite that is regulated within 5 min of stress exposure. We identify shared and perturbation-specific stress response programs, uncover loss of phosphorylation as an integral early event, and dissect the interconnected regulatory landscape of kinase-substrate networks, as we exemplify with target of rapamycin signaling. We further reveal functional organization principles of the stress-responsive phosphoproteome based on phosphorylation site motifs, kinase activities, subcellular localizations, shared functions and pathway intersections. This information-rich map of 25,000 regulated phosphosites advances our understanding of signaling networks.

The impact of metabolism on the adaptation of organisms to environmental change

Since Jacob and Monod's discovery of the lac operon ∼1960, the explanations offered for most metabolic adaptations have been genetic. The focus has been on the adaptive changes in gene expression that occur, which are often referred to as "metabolic reprogramming." The contributions metabolism makes to adaptation have been largely ignored. Here we point out that metabolic adaptations, including the associated changes in gene expression, are highly dependent on the metabolic state of an organism prior to the environmental change to which it is adapting, and on the plasticity of that state. In support of this hypothesis, we examine the paradigmatic example of a genetically driven adaptation, the adaptation of E. coli to growth on lactose, and the paradigmatic example of a metabolic driven adaptation, the Crabtree effect in yeast. Using a framework based on metabolic control analysis, we have reevaluated what is known about both adaptations, and conclude that knowledge of the metabolic properties of these organisms prior to environmental change is critical for understanding not only how they survive long enough to adapt, but also how the ensuing changes in gene expression occur, and their phenotypes post-adaptation. It would be useful if future explanations for metabolic adaptations acknowledged the contributions made to them by metabolism, and described the complex interplay between metabolic systems and genetic systems that make these adaptations possible.

Optogenetic decoding of Akt2-regulated metabolic signaling pathways in skeletal muscle cells using transomics analysis

Insulin regulates various cellular metabolic processes by activating specific isoforms of the Akt family of kinases. Here, we elucidated metabolic pathways that are regulated in an Akt2-dependent manner. We constructed a transomics network by quantifying phosphorylated Akt substrates, metabolites, and transcripts in C2C12 skeletal muscle cells with acute, optogenetically induced activation of Akt2. We found that Akt2-specific activation predominantly affected Akt substrate phosphorylation and metabolite regulation rather than transcript regulation. The transomics network revealed that Akt2 regulated the lower glycolysis pathway and nucleotide metabolism and cooperated with Akt2-independent signaling to promote the rate-limiting steps in these processes, such as the first step of glycolysis, glucose uptake, and the activation of the pyrimidine metabolic enzyme CAD. Together, our findings reveal the mechanism of Akt2-dependent metabolic pathway regulation, paving the way for Akt2-targeting therapeutics in diabetes and metabolic disorders.

Protocol for CAROM: A machine learning tool to predict post-translational regulation from metabolic signatures

This protocol describes CAROM, a computational tool that combines genome-scale metabolic networks (GEMs) and machine learning to identify enzyme targets of post-translational modifications (PTMs). Condition-specific enzyme and reaction properties are used to predict targets of phosphorylation and acetylation in multiple organisms. CAROM is influenced by the accuracy of GEMs and associated flux-balance analysis (FBA), which generate the inputs of the model. We demonstrate the protocol using multi-omics data from E. coli. For complete details on the use and execution of this protocol, please refer to Smith et al. (2022).

Proteomic consequences of TDA1 deficiency in Saccharomyces cerevisiae: Protein kinase Tda1 is essential for Hxk1 and Hxk2 serine 15 phosphorylation

Hexokinase 2 (Hxk2) of Saccharomyces cerevisiae is a dual function hexokinase, acting as a glycolytic enzyme and being involved in the transcriptional regulation of glucose-repressible genes. Relief from glucose repression is accompanied by phosphorylation of Hxk2 at serine 15, which has been attributed to the protein kinase Tda1. To explore the role of Tda1 beyond Hxk2 phosphorylation, the proteomic consequences of TDA1 deficiency were investigated by difference gel electrophoresis (2D-DIGE) comparing a wild type and a Δtda1 deletion mutant. To additionally address possible consequences of glucose repression/derepression, both were grown at 2% and 0.1% (w/v) glucose. A total of eight protein spots exhibiting a minimum twofold enhanced or reduced fluorescence upon TDA1 deficiency was detected and identified by mass spectrometry. Among the spot identities are-besides the expected Hxk2-two proteoforms of hexokinase 1 (Hxk1). Targeted proteomics analyses in conjunction with 2D-DIGE demonstrated that TDA1 is indispensable for Hxk2 and Hxk1 phosphorylation at serine 15. Thirty-six glucose-concentration-dependent protein spots were identified. A simple method to improve spot quantification, approximating spots as rotationally symmetric solids, is presented along with new data on the quantities of Hxk1 and Hxk2 and their serine 15 phosphorylated forms at high and low glucose growth conditions. The Δtda1 deletion mutant exhibited no altered growth under high or low glucose conditions or on alternative carbon sources. Also, invertase activity, serving as a reporter for glucose derepression, was not significantly altered. Instead, an involvement of Tda1 in oxidative stress response is suggested.

Yeast osmoregulation - glycerol still in pole position

In response to osmotic dehydration cells sense, signal, alter gene expression, and metabolically counterbalance osmotic differences. The main compatible solute/osmolyte that accumulates in yeast cells is glycerol, which is produced from the glycolytic intermediate dihydroxyacetone phosphate. This review covers recent advancements in understanding mechanisms involved in sensing, signaling, cell-cycle delays, transcriptional responses as well as post-translational modifications on key proteins in osmoregulation. The protein kinase Hog1 is a key-player in many of these events, however, there is also a growing body of evidence for important Hog1-independent mechanisms playing vital roles. Several missing links in our understanding of osmoregulation will be discussed and future avenues for research proposed. The review highlights that this rather simple experimental system—salt/sorbitol and yeast—has developed into an enormously potent model system unravelling important fundamental aspects in biology.

Proteome allocations change linearly with the specific growth rate of Saccharomyces cerevisiae under glucose limitation

Saccharomyces cerevisiae is a widely used cell factory; therefore, it is important to understand how it organizes key functional parts when cultured under different conditions. Here, we perform a multiomics analysis of S. cerevisiae by culturing the strain with a wide range of specific growth rates using glucose as the sole limiting nutrient. Under these different conditions, we measure the absolute transcriptome, the absolute proteome, the phosphoproteome, and the metabolome. Most functional protein groups show a linear dependence on the specific growth rate. Proteins engaged in translation show a perfect linear increase with the specific growth rate, while glycolysis and chaperone proteins show a linear decrease under respiratory conditions. Glycolytic enzymes and chaperones, however, show decreased phosphorylation with increasing specific growth rates; at the same time, an overall increased flux through these pathways is observed. Further analysis show that even though mRNA levels do not correlate with protein levels for all individual genes, the transcriptome level of functional groups correlates very well with its corresponding proteome. Finally, using enzyme-constrained genome-scale modeling, we find that enzyme usage plays an important role in controlling flux in amino acid biosynthesis.

A Similarity-Based Method for Predicting Enzymatic Functions in Yeast Uncovers a New AMP Hydrolase

Despite decades of research and the availability of the full genomic sequence of the baker's yeast Saccharomyces cerevisiae, still a large fraction of its genome is not functionally annotated. This hinders our ability to fully understand cellular activity and suggests that many additional processes await discovery. The recent years have shown an explosion of high-quality genomic and structural data from multiple organisms, ranging from bacteria to mammals. New computational methods now allow us to integrate these data and extract meaningful insights into the functional identity of uncharacterized proteins in yeast. Here, we created a database of sensitive sequence similarity predictions for all yeast proteins. We use this information to identify candidate enzymes for known biochemical reactions whose enzymes are unidentified, and show how this provides a powerful basis for experimental validation. Using one pathway as a test case we pair a new function for the previously uncharacterized enzyme Yhr202w, as an extra-cellular AMP hydrolase in the NAD degradation pathway. Yhr202w, which we now term Smn1 for Scavenger MonoNucleotidase 1, is a highly conserved protein that is similar to the human protein E5NT/CD73, which is associated with multiple cancers. Hence, our new methodology provides a paradigm, that can be adopted to other organisms, for uncovering new enzymatic functions of uncharacterized proteins.

Personalized phosphoproteomics identifies functional signaling

Protein phosphorylation dynamically integrates environmental and cellular information to control biological processes. Identifying functional phosphorylation amongst the thousands of phosphosites regulated by a perturbation at a global scale is a major challenge. Here we introduce 'personalized phosphoproteomics', a combination of experimental and computational analyses to link signaling with biological function by utilizing human phenotypic variance. We measure individual subject phosphoproteome responses to interventions with corresponding phenotypes measured in parallel. Applying this approach to investigate how exercise potentiates insulin signaling in human skeletal muscle, we identify both known and previously unidentified phosphosites on proteins involved in glucose metabolism. This includes a cooperative relationship between mTOR and AMPK whereby the former directly phosphorylates the latter on S377, for which we find a role in metabolic regulation. These results establish personalized phosphoproteomics as a general approach for investigating the signal transduction underlying complex biology.

Genome-scale modeling of yeast metabolism: retrospectives and perspectives

Yeasts have been widely used for production of bread, beer and wine, as well as for production of bioethanol, but they have also been designed as cell factories to produce various chemicals, advanced biofuels and recombinant proteins. To systematically understand and rationally engineer yeast metabolism, genome-scale metabolic models (GEMs) have been reconstructed for the model yeast Saccharomyces cerevisiae and nonconventional yeasts. Here, we review the historical development of yeast GEMs together with their recent applications, including metabolic flux prediction, cell factory design, culture condition optimization and multi-yeast comparative analysis. Furthermore, we present an emerging effort, namely the integration of proteome constraints into yeast GEMs, resulting in models with improved performance. At last, we discuss challenges and perspectives on the development of yeast GEMs and the integration of proteome constraints.

Metabolic signatures of regulation by phosphorylation and acetylation

Acetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets in E. coli, S. cerevisiae, and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits.

Extensive regulation of enzyme activity by phosphorylation in Escherichia coli

Protein serine/threonine/tyrosine (S/T/Y) phosphorylation is an essential and frequent post-translational modification in eukaryotes, but historically has been considered less prevalent in bacteria because fewer proteins were found to be phosphorylated and most proteins were modified to a lower degree. Recent proteomics studies greatly expanded the phosphoproteome of Escherichia coli to more than 2000 phosphorylation sites (phosphosites), yet mechanisms of action were proposed for only six phosphosites and fitness effects were described for 38 phosphosites upon perturbation. By systematically characterizing functional relevance of S/T/Y phosphorylation in E. coli metabolism, we found 44 of the 52 mutated phosphosites to be functional based on growth phenotypes and intracellular metabolome profiles. By effectively doubling the number of known functional phosphosites, we provide evidence that protein phosphorylation is a major regulation process in bacterial metabolism. Combining in vitro and in vivo experiments, we demonstrate how single phosphosites modulate enzymatic activity and regulate metabolic fluxes in glycolysis, methylglyoxal bypass, acetate metabolism and the split between pentose phosphate and Entner-Doudoroff pathways through mechanisms that include shielding the substrate binding site, limiting structural dynamics, and disrupting interactions relevant for activity in vivo.

Yeast optimizes metal utilization based on metabolic network and enzyme kinetics

Metal ions are essential to all living cells, as they can serve as cofactors of enzymes to drive catalysis of biochemical reactions. We present a constraint-based model of yeast that relates metabolism with metal ions via enzymes. The model is able to capture responses of metabolism and gene expression upon iron depletion, suggesting that yeast cells allocate iron resource in the way abiding to optimization principles. Interestingly, the model predicts up-regulation of several iron-containing enzymes that coincide with experiments, which raises the possibility that the decrease in activity due to limited iron could be compensated by elevated enzyme abundance. Moreover, the model paves the way for guiding biosynthesis of high-value compounds (e.g., p-coumaric acid) that relies on iron-containing enzymes.

Metal ions are vital to metabolism, as they can act as cofactors on enzymes and thus modulate individual enzymatic reactions. Although many enzymes have been reported to interact with metal ions, the quantitative relationships between metal ions and metabolism are lacking. Here, we reconstructed a genome-scale metabolic model of the yeast Saccharomyces cerevisiae to account for proteome constraints and enzyme cofactors such as metal ions, named CofactorYeast. The model is able to estimate abundances of metal ions binding on enzymes in cells under various conditions, which are comparable to measured metal ion contents in biomass. In addition, the model predicts distinct metabolic flux distributions in response to reduced levels of various metal ions in the medium. Specifically, the model reproduces changes upon iron deficiency in metabolic and gene expression levels, which could be interpreted by optimization principles (i.e., yeast optimizes iron utilization based on metabolic network and enzyme kinetics rather than preferentially targeting iron to specific enzymes or pathways). At last, we show the potential of using the model for understanding cell factories that harbor heterologous iron-containing enzymes to synthesize high-value compounds such as p-coumaric acid. Overall, the model demonstrates the dependence of enzymes on metal ions and links metal ions to metabolism on a genome scale.

Post-translational modifications of proteins associated with yeast peroxisome membrane: An essential mode of regulatory mechanism

Peroxisomes are single membrane‐bound organelles important for the optimum functioning of eukaryotic cells. Seminal discoveries in the field of peroxisomes are made using yeast as a model. Several proteins required for the biogenesis and function of peroxisomes are identified to date. As with proteins involved in other major cellular pathways, peroxisomal proteins are also subjected to regulatory post‐translational modifications. Identification, characterization and mapping of these modifications to specific amino acid residues on proteins are critical toward understanding their functional significance. Several studies have tried to identify post‐translational modifications of peroxisomal proteins and determine their impact on peroxisome structure and function. In this manuscript, we provide an overview of the various post‐translational modifications that govern the peroxisome dynamics in yeast.

Utilization of ethanol for itaconic acid biosynthesis by engineered Saccharomyces cerevisiae

In Saccharomyces cerevisiae, ethanol can serve as both a carbon source and NADH donor for the production of acetyl-CoA derivatives. Here we investigated the metabolic regulation of ethanol utilization for itaconic acid production by S. cerevisiae. To understand the interconnection between the TCA cycle and the glyoxylate pathway, mitochondrial membrane transporter proteins SFC1, YHM2, CTP1, DIC1, and MPC1 were knocked out and results showed that SFC1 functions as an important entrance of the glyoxylate pathway into the TCA cycle, and YHM2 is helpful to IA production but not the primary pathway for citric acid supply. To decrease the accumulation of acetic acid, the major ADP/ATP carrier of the mitochondrial inner membrane, AAC2, was upregulated and determined to accelerate ethanol utilization and itaconic acid production. RNA sequencing results showed that AAC2 overexpression enhanced IA titer by upregulating the ethanol-acetyl-CoA pathway and NADH oxidase in the mitochondrial membrane. RNA-seq analysis also suggested that aconitase ACO1 may be a rate-limiting step of IA production. However, the expression of exogenous aconitase didn't increase IA production but enhanced the rate of ethanol utilization and decreased cell growth.

Osmolyte Signatures for the Protection of Aspergillus sydowii Cells under Halophilic Conditions and Osmotic Shock

Aspergillus sydowii is a moderate halophile fungus extensively studied for its biotechnological potential and halophile responses, which has also been reported as a coral reef pathogen. In a recent publication, the transcriptomic analysis of this fungus, when growing on wheat straw, showed that genes related to cell wall modification and cation transporters were upregulated under hypersaline conditions but not under 0.5 M NaCl, the optimal salinity for growth in this strain. This led us to study osmolyte accumulation as a mechanism to withstand moderate salinity. In this work, we show that A. sydowii accumulates trehalose, arabitol, mannitol, and glycerol with different temporal dynamics, which depend on whether the fungus is exposed to hypo- or hyperosmotic stress. The transcripts coding for enzymes responsible for polyalcohol synthesis were regulated in a stress-dependent manner. Interestingly, A. sydowii contains three homologs (Hog1, Hog2 and MpkC) of the Hog1 MAPK, the master regulator of hyperosmotic stress response in S. cerevisiae and other fungi. We show a differential regulation of these MAPKs under different salinity conditions, including sustained basal Hog1/Hog2 phosphorylation levels in the absence of NaCl or in the presence of 2.0 M NaCl, in contrast to what is observed in S. cerevisiae. These findings indicate that halophilic fungi such as A. sydowii utilize different osmoadaptation mechanisms to hypersaline conditions.

The landscape of metabolic pathway dependencies in cancer cell lines

The metabolic reprogramming of cancer cells creates metabolic vulnerabilities that can be therapeutically targeted. However, our understanding of metabolic dependencies and the pathway crosstalk that creates these vulnerabilities in cancer cells remains incomplete. Here, by integrating gene expression data with genetic loss-of-function and pharmacological screening data from hundreds of cancer cell lines, we identified metabolic vulnerabilities at the level of pathways rather than individual genes. This approach revealed that metabolic pathway dependencies are highly context-specific such that cancer cells are vulnerable to inhibition of one metabolic pathway only when activity of another metabolic pathway is altered. Notably, we also found that the no single metabolic pathway was universally essential, suggesting that cancer cells are not invariably dependent on any metabolic pathway. In addition, we confirmed that cell culture medium is a major confounding factor for the analysis of metabolic pathway vulnerabilities. Nevertheless, we found robust associations between metabolic pathway activity and sensitivity to clinically approved drugs that were independent of cell culture medium. Lastly, we used parallel integration of pharmacological and genetic dependency data to confidently identify metabolic pathway vulnerabilities. Taken together, this study serves as a comprehensive characterization of the landscape of metabolic pathway vulnerabilities in cancer cell lines.

Efflux Transporters' Engineering and Their Application in Microbial Production of Heterologous Metabolites

Metabolic engineering of microbial hosts for the production of heterologous metabolites and biochemicals is an enabling technology to generate meaningful quantities of desired products that may be otherwise difficult to produce by traditional means. Heterologous metabolite production can be restricted by the accumulation of toxic products within the cell. Efflux transport proteins (transporters) provide a potential solution to facilitate the export of these products, mitigate toxic effects, and enhance production. Recent investigations using knockout lines, heterologous expression, and expression profiling of transporters have revealed candidates that can enhance the export of heterologous metabolites from microbial cell systems. Transporter engineering efforts have revealed that some exhibit flexible substrate specificity and may have broader application potentials. In this Review, the major superfamilies of efflux transporters, their mechanistic modes of action, selection of appropriate efflux transporters for desired compounds, and potential transporter engineering strategies are described for potential applications in enhancing engineered microbial metabolite production. Future studies in substrate recognition, heterologous expression, and combinatorial engineering of efflux transporters will assist efforts to enhance heterologous metabolite production in microbial hosts.

Glucose-induced internalization of the S. cerevisiae galactose permease Gal2 is dependent on phosphorylation and ubiquitination of its aminoterminal cytoplasmic tail

The hexose permease Gal2 of Saccharomyces cerevisiae is expressed only in the presence of its physiological substrate galactose. Glucose tightly represses the GAL2 gene and also induces the clearance of the transporter from the plasma membrane by ubiquitination and subsequent degradation in the vacuole. Although many factors involved in this process, especially those responsible for the upstream signaling, have been elucidated, the mechanisms by which Gal2 is specifically targeted by the ubiquitination machinery have remained elusive. Here, we show that ubiquitination occurs within the N-terminal cytoplasmic tail and that the arrestin-like proteins Bul1 and Rod1 are likely acting as adaptors for docking of the ubiquitin E3-ligase Rsp5. We further demonstrate that phosphorylation on multiple residues within the tail is indispensable for the internalization and possibly represents a primary signal that might trigger the recruitment of arrestins to the transporter. In addition to these new fundamental insights, we describe Gal2 mutants with improved stability in the presence of glucose, which should prove valuable for engineering yeast strains utilizing complex carbohydrate mixtures present in hydrolysates of lignocellulosic or pectin-rich biomass.

Mitochondrial protein phosphorylation in yeast revisited

Protein phosphorylation is one of the best-known post-translational modifications occurring in all domains of life. In eukaryotes, protein phosphorylation affects all cellular compartments including mitochondria. High-throughput techniques of mass spectrometry combined with cell fractionation and biochemical methods yielded thousands of phospho-sites on hundreds of mitochondrial proteins. We have compiled the information on mitochondrial protein kinases and phosphatases and their substrates in Saccharomyces cerevisiae and provide the current state-of-the-art overview of mitochondrial protein phosphorylation in this model eukaryote. Using several examples, we describe emerging features of the yeast mitochondrial phosphoproteome and present challenges lying ahead in this exciting field.

Pentose metabolism and conversion to biofuels and high-value chemicals in yeasts

Pentose sugars are widespread in nature and two of them, D-xylose and L-arabinose belong to the most abundant sugars being the second and third by abundance sugars in dry plant biomass (lignocellulose) and in general on planet. Therefore, it is not surprising that metabolism and bioconversion of these pentoses attract much attention. Several different pathways of D-xylose and L-arabinose catabolism in bacteria and yeasts are known. There are even more common and really ubiquitous though not so abundant pentoses, D-ribose and 2-deoxy-D-ribose, the constituents of all living cells. Thus, ribose metabolism is example of endogenous metabolism whereas metabolism of other pentoses, including xylose and L-arabinose, represents examples of the metabolism of foreign exogenous compounds which normally are not constituents of yeast cells. As a rule, pentose degradation by the wild-type strains of microorganisms does not lead to accumulation of high amounts of valuable substances; however, productive strains have been obtained by random selection and metabolic engineering. There are numerous reviews on xylose and (less) L-arabinose metabolism and conversion to high value substances; however, they mostly are devoted to bacteria or the yeast Saccharomyces cerevisiae. This review is devoted to reviewing pentose metabolism and bioconversion mostly in non-conventional yeasts, which naturally metabolize xylose. Pentose metabolism in the recombinant strains of S. cerevisiae is also considered for comparison. The available data on ribose, xylose, L-arabinose transport, metabolism, regulation of these processes, interaction with glucose catabolism and construction of the productive strains of high-value chemicals or pentose (ribose) itself are described. In addition, genome studies of the natural xylose metabolizing yeasts and available tools for their molecular research are reviewed. Metabolism of other pentoses (2-deoxyribose, D-arabinose, lyxose) is briefly reviewed.

The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.

Investigation of Proteomic and Phosphoproteomic Responses to Signaling Network Perturbations Reveals Functional Pathway Organizations in Yeast

Governance of protein phosphorylation by kinases and phosphatases constitutes an essential regulatory network in eukaryotic cells. Network dysregulation leads to severe consequences and is often a key factor in disease pathogenesis. Previous studies revealed multiple roles for protein phosphorylation and pathway structures in cellular functions from different perspectives. We seek to understand the roles of kinases and phosphatases from a protein homeostasis point of view. Using a streamlined tandem mass tag (SL-TMT) strategy, we systematically measure proteomic and phosphoproteomic responses to perturbations of phosphorylation signaling networks in yeast deletion strains. Our results emphasize the requirement for protein normalization for more complete interpretation of phosphorylation data. Functional relationships between kinases and phosphatases were characterized at both proteome and phosphoproteome levels in three ways: (1) Gene Ontology enrichment analysis, (2) Δgene-Δgene correlation networks, and (3) molecule covariance networks. This resource illuminates kinase and phosphatase functions and pathway organizations.

A biochemical network modeling of a whole-cell

All cellular processes can be ultimately understood in terms of respective fundamental biochemical interactions between molecules, which can be modeled as networks. Very often, these molecules are shared by more than one process, therefore interconnecting them. Despite this effect, cellular processes are usually described by separate networks with heterogeneous levels of detail, such as metabolic, protein-protein interaction, and transcription regulation networks. Aiming at obtaining a unified representation of cellular processes, we describe in this work an integrative framework that draws concepts from rule-based modeling. In order to probe the capabilities of the framework, we used an organism-specific database and genomic information to model the whole-cell biochemical network of the Mycoplasma genitalium organism. This modeling accounted for 15 cellular processes and resulted in a single component network, indicating that all processes are somehow interconnected. The topological analysis of the network showed structural consistency with biological networks in the literature. In order to validate the network, we estimated gene essentiality by simulating gene deletions and compared the results with experimental data available in the literature. We could classify 212 genes as essential, being 95% of them consistent with experimental results. Although we adopted a relatively simple organism as a case study, we suggest that the presented framework has the potential for paving the way to more integrated studies of whole organisms leading to a systemic analysis of cells on a broader scale. The modeling of other organisms using this framework could provide useful large-scale models for different fields of research such as bioengineering, network biology, and synthetic biology, and also provide novel tools for medical and industrial applications.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

Multiple Layers of Phospho-Regulation Coordinate Metabolism and the Cell Cycle in Budding Yeast

The coordination of metabolism and growth with cell division is crucial for proliferation. While it has long been known that cell metabolism regulates the cell division cycle, it is becoming increasingly clear that the cell division cycle also regulates metabolism. In budding yeast, we previously showed that over half of all measured metabolites change concentration through the cell cycle indicating that metabolic fluxes are extensively regulated during cell cycle progression. However, how this regulation is achieved still remains poorly understood. Since both the cell cycle and metabolism are regulated to a large extent by protein phosphorylation, we here decided to measure the phosphoproteome through the budding yeast cell cycle. Specifically, we chose a cell cycle synchronization strategy that avoids stress and nutrient-related perturbations of metabolism, and we grew the yeast on ethanol minimal medium to force cells to utilize their full biosynthetic repertoire. Using a tandem-mass-tagging approach, we found over 200 sites on metabolic enzymes and transporters to be phospho-regulated. These sites were distributed among many pathways including carbohydrate catabolism, lipid metabolism, and amino acid synthesis and therefore likely contribute to changing metabolic fluxes through the cell cycle. Among all one thousand sites whose phosphorylation increases through the cell cycle, the CDK consensus motif and an arginine-directed motif were highly enriched. This arginine-directed R-R-x-S motif is associated with protein-kinase A, which regulates metabolism and promotes growth. Finally, we also found over one thousand sites that are dephosphorylated through the G1/S transition. We speculate that the phosphatase Glc7/PP1, known to regulate both the cell cycle and carbon metabolism, may play an important role because its regulatory subunits are phospho-regulated in our data. In summary, our results identify extensive cell cycle dependent phosphorylation and dephosphorylation of metabolic enzymes and suggest multiple mechanisms through which the cell division cycle regulates metabolic signaling pathways to temporally coordinate biosynthesis with distinct phases of the cell division cycle.

The landscape of tiered regulation of breast cancer cell metabolism

Altered metabolism is a hallmark of cancer, but little is still known about its regulation. In this study, we measure transcriptomic, proteomic, phospho-proteomic and fluxomics data in a breast cancer cell-line (MCF7) across three different growth conditions. Integrating these multiomics data within a genome scale human metabolic model in combination with machine learning, we systematically chart the different layers of metabolic regulation in breast cancer cells, predicting which enzymes and pathways are regulated at which level. We distinguish between two types of reactions, directly and indirectly regulated. Directly-regulated reactions include those whose flux is regulated by transcriptomic alterations (~890) or via proteomic or phospho-proteomics alterations (~140) in the enzymes catalyzing them. We term the reactions that currently lack evidence for direct regulation as (putative) indirectly regulated (~930). Many metabolic pathways are predicted to be regulated at different levels, and those may change at different media conditions. Remarkably, we find that the flux of predicted indirectly regulated reactions is strongly coupled to the flux of the predicted directly regulated ones, uncovering a tiered hierarchical organization of breast cancer cell metabolism. Furthermore, the predicted indirectly regulated reactions are predominantly reversible. Taken together, this architecture may facilitate rapid and efficient metabolic reprogramming in response to the varying environmental conditions incurred by the tumor cells. The approach presented lays a conceptual and computational basis for mapping metabolic regulation in additional cancers.

Glycolytic flux in Saccharomyces cerevisiae is dependent on RNA polymerase III and its negative regulator Maf1

Protein biosynthesis is energetically costly, is tightly regulated and is coupled to stress conditions including glucose deprivation. RNA polymerase III (RNAP III)-driven transcription of tDNA genes for production of tRNAs is a key element in efficient protein biosynthesis. Here we present an analysis of the effects of altered RNAP III activity on the Saccharomyces cerevisiae proteome and metabolism under glucose-rich conditions. We show for the first time that RNAP III is tightly coupled to the glycolytic system at the molecular systems level. Decreased RNAP III activity or the absence of the RNAP III negative regulator, Maf1 elicit broad changes in the abundance profiles of enzymes engaged in fundamental metabolism in S. cerevisiae In a mutant compromised in RNAP III activity, there is a repartitioning towards amino acids synthesis de novo at the expense of glycolytic throughput. Conversely, cells lacking Maf1 protein have greater potential for glycolytic flux.

Glycerol as a substrate for Saccharomyces cerevisiae based bioprocesses - Knowledge gaps regarding the central carbon catabolism of this 'non-fermentable' carbon source

Glycerol is an interesting alternative carbon source in industrial bioprocesses due to its higher degree of reduction per carbon atom compared to sugars. During the last few years, significant progress has been made in improving the well-known industrial platform organism Saccharomyces cerevisiae with regard to its glycerol utilization capability, particularly in synthetic medium. This provided a basis for future metabolic engineering focusing on the production of valuable chemicals from glycerol. However, profound knowledge about the central carbon catabolism in synthetic glycerol medium is a prerequisite for such incentives. As a matter of fact, the current assumptions about the actual in vivo fluxes active on glycerol as the sole carbon source have mainly been based on omics data collected in complex media or were even deduced from studies with other non-fermentable carbon sources, such as ethanol or acetate. A number of uncertainties have been identified which particularly regard the role of the glyoxylate cycle, the subcellular localization of the respective enzymes, the contributions of mitochondrial transporters and the active anaplerotic reactions under these conditions. The review scrutinizes the current knowledge, highlights the necessity to collect novel experimental data using cells growing in synthetic glycerol medium and summarizes the current state of the art with regard to the production of valuable fermentation products from a carbon source that has been considered so far as 'non-fermentable' for the yeast S. cerevisiae.

Kinome-wide identification of phosphorylation networks in eukaryotic proteomes

Motivation

Signaling and metabolic pathways are finely regulated by a network of protein phosphorylation events. Unraveling the nature of this intricate network, composed of kinases, target proteins and their interactions, is therefore of crucial importance. Although thousands of kinase-specific phosphorylations (KsP) have been annotated in model organisms their kinase-target network is far from being complete, with less studied organisms lagging behind.

Results

In this work, we achieved an automated and accurate identification of kinase domains, inferring the residues that most likely contribute to peptide specificity. We integrated this information with the target peptides of known human KsP to predict kinase-specific interactions in other eukaryotes through a deep neural network, outperforming similar methods. We analyzed the differential conservation of kinase specificity among eukaryotes revealing the high conservation of the specificity of tyrosine kinases. With this approach we discovered 1590 novel KsP of potential clinical relevance in the human proteome.

Availability and implementation

http://akid.bio.uniroma2.it.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges

Genome-scale metabolic models (GEMs) are mathematical models that enable systematic analysis of metabolism. This modeling concept has been applied to study the metabolism of many organisms including the eukaryal model organism, the yeast Saccharomyces cerevisiae, that also serves as an important cell factory for production of fuels and chemicals. With the application of yeast GEMs, our knowledge of metabolism is increasing. Therefore, GEMs have also been used for modeling human cells to study metabolic diseases. Here we introduce the concept of GEMs and provide a protocol for reconstructing GEMs. Besides, we show the historic development of yeast GEMs and their applications. Also, we review human GEMs as well as their uses in the studies of complex diseases.

Nutrient Signaling via the TORC1-Greatwall-PP2AB55δ Pathway Is Responsible for the High Initial Rates of Alcoholic Fermentation in Sake Yeast Strains of Saccharomyces cerevisiae

Saccharomyces cerevisiae sake yeast strain Kyokai no. 7 (K7) and its relatives carry a homozygous loss-of-function mutation in the RIM15 gene, which encodes a Greatwall family protein kinase. Disruption of RIM15 in nonsake yeast strains leads to improved alcoholic fermentation, indicating that the defect in Rim15p is associated with the enhanced fermentation performance of sake yeast cells. In order to understand how Rim15p mediates fermentation control, we here focused on target-of-rapamycin protein kinase complex 1 (TORC1) and protein phosphatase 2A with the B55δ regulatory subunit (PP2A^B55δ), complexes that are known to act upstream and downstream of Rim15p, respectively. Several lines of evidence, including our previous transcriptomic analysis data, suggested enhanced TORC1 signaling in sake yeast cells during sake fermentation. Fermentation tests of the TORC1-related mutants using a laboratory strain revealed that TORC1 signaling positively regulates the initial fermentation rate in a Rim15p-dependent manner. Deletion of the CDC55 gene, encoding B55δ, abolished the high fermentation performance of Rim15p-deficient laboratory yeast and sake yeast cells, indicating that PP2A^B55δ mediates the fermentation control by TORC1 and Rim15p. The TORC1-Greatwall-PP2A^B55δ pathway similarly affected the fermentation rate in the fission yeast Schizosaccharomyces pombe, strongly suggesting that the evolutionarily conserved pathway governs alcoholic fermentation in yeasts. It is likely that elevated PP2A^B55δ activity accounts for the high fermentation performance of sake yeast cells. Heterozygous loss-of-function mutations in CDC55 found in K7-related sake strains may indicate that the Rim15p-deficient phenotypes are disadvantageous to cell survival.IMPORTANCE The biochemical processes and enzymes responsible for glycolysis and alcoholic fermentation by the yeast S. cerevisiae have long been the subject of scientific research. Nevertheless, the factors determining fermentation performance in vivo are not fully understood. As a result, the industrial breeding of yeast strains has required empirical characterization of fermentation by screening numerous mutants through laborious fermentation tests. To establish a rational and efficient breeding strategy, key regulators of alcoholic fermentation need to be identified. In the present study, we focused on how sake yeast strains of S. cerevisiae have acquired high alcoholic fermentation performance. Our findings provide a rational molecular basis to design yeast strains with optimal fermentation performance for production of alcoholic beverages and bioethanol. In addition, as the evolutionarily conserved TORC1-Greatwall-PP2A^B55δ pathway plays a major role in the glycolytic control, our work may contribute to research on carbohydrate metabolism in higher eukaryotes.

Regulation of Cellular Metabolism through Phase Separation of Enzymes

Metabolism is the sum of the life-giving chemical processes that occur within a cell. Proper regulation of these processes is essential for all organisms to thrive and prosper. When external factors are too extreme, or if internal regulation is corrupted through genetic or epigenetic changes, metabolic homeostasis is no longer achievable and diseases such as metabolic syndrome or cancer, aging, and, ultimately, death ensue. Metabolic reactions are catalyzed by proteins, and the in vitro kinetic properties of these enzymes have been studied by biochemists for many decades. These efforts led to the appreciation that enzyme activities can be acutely regulated and that this regulation is critical to metabolic homeostasis. Regulation can be mediated through allosteric interactions with metabolites themselves or via post-translational modifications triggered by intracellular signal transduction pathways. More recently, enzyme regulation has attracted the attention of cell biologists who noticed that change in growth conditions often triggers the condensation of diffusely localized enzymes into one or more discrete foci, easily visible by light microscopy. This reorganization from a soluble to a condensed state is best described as a phase separation. As summarized in this review, stimulus-induced phase separation has now been observed for dozens of enzymes suggesting that this could represent a widespread mode of activity regulation, rather than, or in addition to, a storage form of temporarily superfluous enzymes. Building on our recent structure determination of TOROIDs (TORc1 Organized in Inhibited Domain), the condensate formed by the protein kinase Target Of Rapamycin Complex 1 (TORC1), we will highlight that the molecular organization of enzyme condensates can vary dramatically and that future work aimed at the structural characterization of enzyme condensates will be critical to understand how phase separation regulates enzyme activity and consequently metabolic homeostasis. This information may ultimately facilitate the design of strategies to target the assembly or disassembly of specific enzymes condensates as a therapeutic approach to restore metabolic homeostasis in certain diseases.

The pleiotropic effects of the glutamate dehydrogenase (GDH) pathway in Saccharomyces cerevisiae

Ammonium assimilation is linked to fundamental cellular processes that include the synthesis of non-essential amino acids like glutamate and glutamine. In Saccharomyces cerevisiae glutamate can be synthesized from α-ketoglutarate and ammonium through the action of NADP-dependent glutamate dehydrogenases Gdh1 and Gdh3. Gdh1 and Gdh3 are evolutionarily adapted isoforms and cover the anabolic role of the GDH-pathway. Here, we review the role and function of the GDH pathway in glutamate metabolism and we discuss the additional contributions of the pathway in chromatin regulation, nitrogen catabolite repression, ROS-mediated apoptosis, iron deficiency and sphingolipid-dependent actin cytoskeleton modulation in S.cerevisiae. The pleiotropic effects of GDH pathway in yeast biology highlight the importance of glutamate homeostasis in vital cellular processes and reveal new features for conserved enzymes that were primarily characterized for their metabolic capacity. These newly described features constitute insights that can be utilized for challenges regarding genetic engineering of glutamate homeostasis and maintenance of redox balances, biosynthesis of important metabolites and production of organic substrates. We also conclude that the discussed  pleiotropic features intersect with basic metabolism and set a new background for further glutamate-dependent applied research of biotechnological interest.

Trans-omic Analysis Reveals Selective Responses to Induced and Basal Insulin across Signaling, Transcriptional, and Metabolic Networks

The concentrations of insulin selectively regulate multiple cellular functions. To understand how insulin concentrations are interpreted by cells, we constructed a trans-omic network of insulin action in FAO hepatoma cells using transcriptomic data, western blotting analysis of signaling proteins, and metabolomic data. By integrating sensitivity into the trans-omic network, we identified the selective trans-omic networks stimulated by high and low doses of insulin, denoted as induced and basal insulin signals, respectively. The induced insulin signal was selectively transmitted through the pathway involving Erk to an increase in the expression of immediate-early and upregulated genes, whereas the basal insulin signal was selectively transmitted through a pathway involving Akt and an increase of Foxo phosphorylation and a reduction of downregulated gene expression. We validated the selective trans-omic network in vivo by analysis of the insulin-clamped rat liver. This integrated analysis enabled molecular insight into how liver cells interpret physiological insulin signals to regulate cellular functions.

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

A challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.

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The proteome of kinase knockouts is dominated by enzyme abundance changes

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The enzyme expression profiles of kinase knockouts are non-redundant

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Metabolism is regulated by many expression changes acting in concert

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Machine learning accurately predicts the metabolome from enzyme abundance

Reprogramming Yeast Metabolism from Alcoholic Fermentation to Lipogenesis

Engineering microorganisms for production of fuels and chemicals often requires major re-programming of metabolism to ensure high flux toward the product of interest. This is challenging, as millions of years of evolution have resulted in establishment of tight regulation of metabolism for optimal growth in the organism's natural habitat. Here, we show through metabolic engineering that it is possible to alter the metabolism of Saccharomyces cerevisiae from traditional ethanol fermentation to a pure lipogenesis metabolism, resulting in high-level production of free fatty acids. Through metabolic engineering and process design, we altered subcellular metabolic trafficking, fine-tuned NADPH and ATP supply, and decreased carbon flux to biomass, enabling production of 33.4 g/L extracellular free fatty acids. We further demonstrate that lipogenesis metabolism can replace ethanol fermentation by deletion of pyruvate decarboxylase enzymes followed by adaptive laboratory evolution. Genome sequencing of evolved strains showed that pyruvate kinase mutations were essential for this phenotype.

13C-metabolic flux analysis of ethanol-assimilating Saccharomyces cerevisiae for S-adenosyl-L-methionine production

BACKGROUND

Saccharomyces cerevisiae is a host for the industrial production of S-adenosyl-L-methionine (SAM), which has been widely used in pharmaceutical and nutritional supplement industries. It has been reported that the intracellular SAM content in S. cerevisiae can be improved by the addition of ethanol during cultivation. However, the metabolic state in ethanol-assimilating S. cerevisiae remains unclear. In this study, ¹³C-metabolic flux analysis (¹³C-MFA) was conducted to investigate the metabolic regulation responsible for the high SAM production from ethanol.

RESULTS

The comparison between the metabolic flux distributions of central carbon metabolism showed that the metabolic flux levels of the tricarboxylic acid cycle and glyoxylate shunt in the ethanol culture were significantly higher than that of glucose. Estimates of the ATP balance from the ¹³C-MFA data suggested that larger amounts of excess ATP was produced from ethanol via increased oxidative phosphorylation. The finding was confirmed by the intracellular ATP level under ethanol-assimilating condition being similarly higher than glucose.

CONCLUSIONS

These results suggest that the enhanced ATP regeneration due to ethanol assimilation was critical for the high SAM accumulation.

The UBR-1 ubiquitin ligase regulates glutamate metabolism to generate coordinated motor pattern in Caenorhabditis elegans

UBR1 is an E3 ubiquitin ligase best known for its ability to target protein degradation by the N-end rule. The physiological functions of UBR family proteins, however, remain not fully understood. We found that the functional loss of C. elegans UBR-1 leads to a specific motor deficit: when adult animals generate reversal movements, A-class motor neurons exhibit synchronized activation, preventing body bending. This motor deficit is rescued by removing GOT-1, a transaminase that converts aspartate to glutamate. Both UBR-1 and GOT-1 are expressed and critically required in premotor interneurons of the reversal motor circuit to regulate the motor pattern. ubr-1 and got-1 mutants exhibit elevated and decreased glutamate level, respectively. These results raise an intriguing possibility that UBR proteins regulate glutamate metabolism, which is critical for neuronal development and signaling.

Ubiquitin-mediated protein degradation is central to diverse biological processes. The selection of substrates for degradation is carried out by the E3 ubiquitin ligases, which target specific groups of proteins for ubiquitination. The human genome encodes hundreds of E3 ligases; many exhibit sequence conservation across animal species, including one such ligase called UBR1. Patients carrying mutations in UBR1 exhibit severe systemic defects, but the biology behinds UBR1’s physiological function remains elusive. Here we found that the C. elegans UBR-1 regulates glutamate level. When UBR-1 is defective, C. elegans exhibits increased glutamate; this leads to synchronization of motor neuron activity, hence defective locomotion when animals reach adulthood. UBR1-mediated glutamate metabolism may contribute to the physiological defects of UBR1 mutations.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).