Flux control through protein phosphorylation in yeast

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.

An impact of l-histidine on the phosphorylation and stability of pyruvate kinase at low NaCl level

As one of the key rate limiting enzymes in glycolysis process, the characteristics of pyruvate kinase (PK) play an important role in regulating the muscle quality. Given the close relationship between kinase phosphorylation level and its stability, the present study investigated the impact of exogenous l-histidine (l-his) on PK phosphorylation and activity at 1% NaCl level in the early postmortem. An incubation system was also constructed and the results showed that the introduction of 0.06% l-his caused the dephosphorylation and increased the activity of PK at 1% NaCl. Compared with 1% NaCl treatment, three differential phosphorylation sites were produced when l-his was introduced. The PK secondary structure was shift from order to disorder, leading to a distinct degradation. This present study provided us with inspiration that meat quality could be improved by exogenous l-his at early postmortem under low NaCl conditions.

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 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.

Real-Time Assessment of Intracellular Metabolites in Single Cells through RNA-Based Sensors

Quantification of the concentration of particular cellular metabolites reports on the actual utilization of metabolic pathways in physiological and pathological conditions. Metabolite concentration also constitutes the readout for screening cell factories in metabolic engineering. However, there are no direct approaches that allow for real-time assessment of the levels of intracellular metabolites in single cells. In recent years, the modular architecture of natural bacterial RNA riboswitches has inspired the design of genetically encoded synthetic RNA devices that convert the intracellular concentration of a metabolite into a quantitative fluorescent signal. These so-called RNA-based sensors are composed of a metabolite-binding RNA aptamer as the sensor domain, connected through an actuator segment to a signal-generating reporter domain. However, at present, the variety of available RNA-based sensors for intracellular metabolites is still very limited. Here, we go through natural mechanisms for metabolite sensing and regulation in cells across all kingdoms, focusing on those mediated by riboswitches. We review the design principles underlying currently developed RNA-based sensors and discuss the challenges that hindered the development of novel sensors and recent strategies to address them. We finish by introducing the current and potential applicability of synthetic RNA-based sensors for intracellular metabolites.

Proteome dynamics during transition from exponential to stationary phase under aerobic and anaerobic conditions in yeast

The yeast Saccharomyces cerevisiae is a widely used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimising biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates and standardised sample preparation procedures ensured reproducibility and accuracy. Additionally, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic, standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimisation of the glycolytic pathway, resulting in the quantitative assessment of 54 proteomes. The anaerobic cultures showed remarkably less proteome-level changes compared to the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack resources to adequately adapt to starvation. This proteome dynamics study constitutes an important step towards better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engineering efforts.

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.

Understanding how yeast organizes its functional proteome is a fundamental task in systems biology. Here, the authors conduct a multiomics analysis on yeast cells cultured with different growth rates, identifying a linear dependence of the functional proteome on the growth rate.

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.

In vitro turnover numbers do not reflect in vivo activities of yeast enzymes

Turnover numbers (k_cat values) quantitatively represent the activity of enzymes, which are mostly measured in vitro. While a few studies have reported in vivo catalytic rates (k_app values) in bacteria, a large-scale estimation of k_app in eukaryotes is lacking. Here, we estimated k_app of the yeast Saccharomyces cerevisiae under diverse conditions. By comparing the maximum k_app across conditions with in vitro k_cat we found a weak correlation in log scale of R² = 0.28, which is lower than for Escherichia coli (R² = 0.62). The weak correlation is caused by the fact that many in vitro k_cat values were measured for enzymes obtained through heterologous expression. Removal of these enzymes improved the correlation to R² = 0.41 but still not as good as for E. coli, suggesting considerable deviations between in vitro and in vivo enzyme activities in yeast. By parameterizing an enzyme-constrained metabolic model with our k_app dataset we observed better performance than the default model with in vitro k_cat in predicting proteomics data, demonstrating the strength of using the dataset generated here.

Gene expression regulates metabolite homeostasis during the Crabtree effect: Implications for the adaptation and evolution of Metabolism

A key issue in both molecular and evolutionary biology has been to define the roles of genes and phenotypes in the adaptation of organisms to environmental changes. The dominant view has been that an organism's metabolic adaptations are driven by gene expression and that gene mutations, independent of the starting phenotype, are responsible for the evolution of new metabolic phenotypes. We propose an alternate hypothesis, in which the phenotype and genotype together determine metabolic adaptation both in the lifetime of the organism and in the evolutionary selection of adaptive metabolic traits. We tested this hypothesis by flux-balance and metabolic-control analysis of the relative roles of the starting phenotype and gene expression in regulating the metabolic adaptations during the Crabtree effect in yeast, when they are switched from a low- to high-glucose environment. Critical for successful short-term adaptation was the ability of the glycogen/trehalose shunt to balance the glycolytic pathway. The role of later gene expression of new isoforms of glycolytic enzymes, rather than flux control, was to provide additional homeostatic mechanisms allowing an increase in the amount and efficiency of adenosine triphosphate and product formation while maintaining glycolytic balance. We further showed that homeostatic mechanisms, by allowing increased phenotypic plasticity, could have played an important role in guiding the evolution of the Crabtree effect. Although our findings are specific to Crabtree yeast, they are likely to be broadly found because of the well-recognized similarities in glucose metabolism across kingdoms and phyla from yeast to humans.

The Post-Translational Modifications, Localization, and Mode of Attachment of Non-Covalently Bound Glucanosyltransglycosylases of Yeast Cell Wall as a Key to Understanding their Functioning

Glucan linked to proteins is a natural mega-glycoconjugate (mGC) playing the central role as a structural component of a yeast cell wall (CW). Regulation of functioning of non-covalently bound glucanosyltransglycosylases (ncGTGs) that have to remodel mGC to provide CW extension is poorly understood. We demonstrate that the main ncGTGs Bgl2 and Scw4 have phosphorylated and glutathionylated residues and are represented in CW as different pools of molecules having various firmness of attachment. Identified pools contain Bgl2 molecules with unmodified peptides, but differ from each other in the presence and combination of modified ones, as well as in the presence or absence of other CW proteins. Correlation of Bgl2 distribution among pools and its N-glycosylation was not found. Glutathione affects Bgl2 conformation, probably resulting in the mode of its attachment and enzymatic activity. Bgl2 from the pool of unmodified and monophosphorylated molecules demonstrates the ability to fibrillate after isolation from CW. Revealing of Bgl2 microcompartments and their mosaic arrangement summarized with the results obtained give the evidence that the functioning of ncGTGs in CW can be controlled by reversible post-translational modifications and facilitated due to their compact localization. The hypothetical scheme of distribution of Bgl2 inside CW is represented.

Phosphoproteomics Meets Chemical Genetics: Approaches for Global Mapping and Deciphering the Phosphoproteome

Protein kinases are important enzymes involved in the regulation of various cellular processes. To function properly, each protein kinase phosphorylates only a limited number of proteins among the thousands present in the cell. This provides a rapid and dynamic regulatory mechanism that controls biological functions of the proteins. Despite the importance of protein kinases, most of their substrates remain unknown. Recently, the advances in the fields of protein engineering, chemical genetics, and mass spectrometry have boosted studies on identification of bona fide substrates of protein kinases. Among the various methods in protein kinase specific substrate identification, genetically engineered protein kinases and quantitative phosphoproteomics have become promising tools. Herein, we review the current advances in the field of chemical genetics in analog-sensitive protein kinase mutants and highlight selected strategies for identifying protein kinase substrates and studying the dynamic nature of protein phosphorylation.

Shot-gun proteomics: why thousands of unidentified signals matter

Mass spectrometry-based proteomics has become a constitutional part of the multi-omics toolbox in yeast research, advancing fundamental knowledge of molecular processes and guiding decisions in strain and product developmental pipelines. Nevertheless, post-translational protein modifications (PTMs) continue to challenge the field of proteomics. PTMs are not directly encoded in the genome; therefore, they require a sensitive analysis of the proteome itself. In yeast, the relevance of post-translational regulators has already been established, such as for phosphorylation, which can directly affect the reaction rates of metabolic enzymes. Whereas, the selective analysis of single modifications has become a broadly employed technique, the sensitive analysis of a comprehensive set of modifications still remains a challenge. At the same time, a large number of fragmentation spectra in a typical shot-gun proteomics experiment remain unidentified. It has been estimated that a good proportion of those unidentified spectra originates from unexpected modifications or natural peptide variants. In this review, recent advancements in microbial proteomics for unrestricted protein modification discovery are reviewed, and recent research integrating this additional layer of information to elucidate protein interaction and regulation in yeast is briefly discussed.

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.

Energy metabolism controls phenotypes by protein efficiency and allocation

Cells require energy for growth and maintenance and have evolved to have multiple pathways to produce energy in response to varying conditions. A basic question in this context is how cells organize energy metabolism, which is, however, challenging to elucidate due to its complexity, i.e., the energy-producing pathways overlap with each other and even intertwine with biomass formation pathways. Here, we propose a modeling concept that decomposes energy metabolism into biomass formation and ATP-producing pathways. The latter can be further decomposed into a high-yield and a low-yield pathway. This enables independent estimation of protein efficiency for each pathway. With this concept, we modeled energy metabolism for Escherichia coli and Saccharomyces cerevisiae and found that the high-yield pathway shows lower protein efficiency than the low-yield pathway. Taken together with a fixed protein constraint, we predict overflow metabolism in E. coli and the Crabtree effect in S. cerevisiae, meaning that energy metabolism is sufficient to explain the metabolic switches. The static protein constraint is supported by the findings that protein mass of energy metabolism is conserved across conditions based on absolute proteomics data. This also suggests that enzymes may have decreased saturation or activity at low glucose uptake rates. Finally, our analyses point out three ways to improve growth, i.e., increasing protein allocation to energy metabolism, decreasing ATP demand, or increasing activity for key enzymes.

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.

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.

The Pivotal Role of Protein Phosphorylation in the Control of Yeast Central Metabolism

Protein phosphorylation is the most frequent eukaryotic post-translational modification and can act as either a molecular switch or rheostat for protein functions. The deliberate manipulation of protein phosphorylation has great potential for regulating specific protein functions with surgical precision, rather than the gross effects gained by the over/underexpression or complete deletion of a protein-encoding gene. In order to assess the impact of phosphorylation on central metabolism, and thus its potential for biotechnological and medical exploitation, a compendium of highly confident protein phosphorylation sites (p-sites) for the model organism Saccharomyces cerevisiae has been analyzed together with two more datasets from the fungal pathogen Candida albicans. Our analysis highlights the global properties of the regulation of yeast central metabolism by protein phosphorylation, where almost half of the enzymes involved are subject to this sort of post-translational modification. These phosphorylated enzymes, compared to the nonphosphorylated ones, are more abundant, regulate more reactions, have more protein–protein interactions, and a higher fraction of them are ubiquitinated. The p-sites of metabolic enzymes are also more conserved than the background p-sites, and hundreds of them have the potential for regulating metabolite production. All this integrated information has allowed us to prioritize thousands of p-sites in terms of their potential phenotypic impact. This multi-source compendium should enable the design of future high-throughput (HTP) mutation studies to identify key molecular switches/rheostats for the manipulation of not only the metabolism of yeast, but also that of many other biotechnologically and medically important fungi and eukaryotes.