Unraveling condition-dependent networks of transcription factors that control metabolic pathway activity in yeast

Non-consecutive enzyme interactions within TCA cycle supramolecular assembly regulate carbon-nitrogen metabolism

Enzymes of the central metabolism tend to assemble into transient supramolecular complexes. However, the functional significance of the interactions, particularly between enzymes catalyzing non-consecutive reactions, remains unclear. Here, by co-localizing two non-consecutive enzymes of the TCA cycle from Bacillus subtilis, malate dehydrogenase (MDH) and isocitrate dehydrogenase (ICD), in phase separated droplets we show that MDH-ICD interaction leads to enzyme agglomeration with a concomitant enhancement of ICD catalytic rate and an apparent sequestration of its reaction product, 2-oxoglutarate. Theory demonstrates that MDH-mediated clustering of ICD molecules explains the observed phenomena. In vivo analyses reveal that MDH overexpression leads to accumulation of 2-oxoglutarate and reduction of fluxes flowing through both the catabolic and anabolic branches of the carbon-nitrogen intersection occupied by 2-oxoglutarate, resulting in impeded ammonium assimilation and reduced biomass production. Our findings suggest that the MDH-ICD interaction is an important coordinator of carbon-nitrogen metabolism.

Automating the design-build-test-learn cycle towards next-generation bacterial cell factories

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.

TRIMER: Transcription Regulation Integrated with Metabolic Regulation

There has been extensive research in predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes arising from interactions among different biomolecules. For more accurate and robust prediction of target metabolic behavior under different conditions, not only metabolic reactions but also the genetic regulatory relationships involving transcription factors (TFs) affecting these metabolic reactions should be modeled. We have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with Metabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptomes to model the transcription factor regulatory network. TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors at the genome scale. We demonstrate TRIMER’s applicability to both simulated and experimental data and provide performance comparison with other existing approaches.

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TRIMER models transcription-regulated metabolism using Bayesian network modeling;

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TRIMER integrates prior knowledge (regulatory interaction) with data (expression);

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TRIMER enables metabolic behavior prediction for general knockout strategies;

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TRIMER includes a simulator as an evaluation platform for similar hybrid models;

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TRIMER reliably predicts metabolite yields for both simulated and experimental data.

The pentose phosphate pathway in industrially relevant fungi: crucial insights for bioprocessing

Abstract

The pentose phosphate pathway (PPP) is one of the most targeted pathways in metabolic engineering. This pathway is the primary source of NADPH, and it contributes in fungi to the production of many compounds of interest such as polyols, biofuels, carotenoids, or antibiotics. However, the regulatory mechanisms of the PPP are still not fully known. This review provides an insight into the current comprehension of the PPP in fungi and the limitations of this current understanding. It highlights how this knowledge contributes to targeted engineering of the PPP and thus to better performance of industrially used fungal strains.

Key points

• Type of carbon and nitrogen source as well as oxidative stress influence the PPP.

• A complex network of transcription factors regulates the PPP.

• Improved understanding of the PPP will allow to increase yields of bioprocesses.

High-throughput transcriptome sequencing and comparative analysis of Escherichia coli and Schizosaccharomyces pombe in respiratory and fermentative growth

In spite of increased complexity in eukaryotes compared to prokaryotes, several basic metabolic and regulatory processes are conserved. Here we explored analogies in the eubacteria Escherichia coli and the unicellular fission yeast Schizosaccharomyces pombe transcriptomes under two carbon sources: 2% glucose; or a mix of 2% glycerol and 0.2% sodium acetate using the same growth media and growth phase. Overall, twelve RNA-seq libraries were constructed. A total of 593 and 860 genes were detected as differentially expressed for E. coli and S. pombe, respectively, with a log2 of the Fold Change ≥ 1 and False Discovery Rate ≤ 0.05. In aerobic glycolysis, most of the expressed genes were associated with cell proliferation in both organisms, including amino acid metabolism and glycolysis. In contrast in glycerol/acetate condition, genes related to flagellar assembly and membrane proteins were differentially expressed such as the general transcription factors fliA, flhD, flhC, and flagellum assembly genes were detected in E. coli, whereas in S. pombe genes for hexose transporters, integral membrane proteins, galactose metabolism, and ncRNAs related to cellular stress were overexpressed. In general, our study shows that a conserved "foraging behavior" response is observed in these eukaryotic and eubacterial organisms in gluconeogenic carbon sources.

A proteome-integrated, carbon source dependent genetic regulatory network in Saccharomyces cerevisiae

Integrated regulatory networks can be powerful tools to examine and test properties of cellular systems, such as modelling environmental effects on the molecular bioeconomy, where protein levels are altered in response to changes in growth conditions. Although extensive regulatory pathways and protein interaction data sets exist which represent such networks, few have formally considered quantitative proteomics data to validate and extend them. We generate and consider such data here using a label-free proteomics strategy to quantify alterations in protein abundance for S. cerevisiae when grown on minimal media using glucose, galactose, maltose and trehalose as sole carbon sources. Using a high quality-controlled subset of proteins observed to be differentially abundant, we constructed a proteome-informed network, comprising 1850 transcription factor interactions and 37 chaperone interactions, which defines the major changes in the cellular proteome when growing under different carbon sources. Analysis of the differentially abundant proteins involved in the regulatory network pointed to their significant roles in specific metabolic pathways and function, including glucose homeostasis, amino acid biosynthesis, and carbohydrate metabolic process. We noted strong statistical enrichment in the differentially abundant proteome of targets of known transcription factors associated with stress responses and altered carbon metabolism. This shows how such integrated analysis can lend further experimental support to annotated regulatory interactions, since the proteomic changes capture both magnitude and direction of gene expression change at the level of the affected proteins. Overall this study highlights the power of quantitative proteomics to help define regulatory systems pertinent to environmental conditions.

Charting the Metabolic Landscape of the Facultative Methylotroph Bacillus methanolicus

Methanol is inexpensive, is easy to transport, and can be produced both from renewable and from fossil resources without mobilizing arable lands. As such, it is regarded as a potential carbon source to transition toward a greener industrial chemistry. Metabolic engineering of bacteria and yeast able to efficiently consume methanol is expected to provide cell factories that will transform methanol into higher-value chemicals in the so-called methanol economy. Toward that goal, the study of natural methylotrophs such as Bacillus methanolicus is critical to understand the origin of their efficient methylotrophy. This knowledge will then be leveraged to transform such natural strains into new cell factories or to design methylotrophic capability in other strains already used by the industry.

Bacillus methanolicus MGA3 is a thermotolerant and relatively fast-growing methylotroph able to secrete large quantities of glutamate and lysine. These natural characteristics make B. methanolicus a good candidate to become a new industrial chassis organism, especially in a methanol-based economy. Intriguingly, the only substrates known to support B. methanolicus growth as sole sources of carbon and energy are methanol, mannitol, and, to a lesser extent, glucose and arabitol. Because fluxomics provides the most direct readout of the cellular phenotype, we hypothesized that comparing methylotrophic and nonmethylotrophic metabolic states at the flux level would yield new insights into MGA3 metabolism. In this study, we designed and performed a ¹³C metabolic flux analysis (¹³C-MFA) of the facultative methylotroph B. methanolicus MGA3 growing on methanol, mannitol, and arabitol to compare the associated metabolic states. On methanol, results showed a greater flux in the ribulose monophosphate (RuMP) pathway than in the tricarboxylic acid (TCA) cycle, thus validating previous findings on the methylotrophy of B. methanolicus. New insights related to the utilization of cyclic RuMP versus linear dissimilation pathways and between the RuMP variants were generated. Importantly, we demonstrated that the linear detoxification pathways and the malic enzyme shared with the pentose phosphate pathway have an important role in cofactor regeneration. Finally, we identified, for the first time, the metabolic pathway used to assimilate arabitol. Overall, those data provide a better understanding of this strain under various environmental conditions.

IMPORTANCE Methanol is inexpensive, is easy to transport, and can be produced both from renewable and from fossil resources without mobilizing arable lands. As such, it is regarded as a potential carbon source to transition toward a greener industrial chemistry. Metabolic engineering of bacteria and yeast able to efficiently consume methanol is expected to provide cell factories that will transform methanol into higher-value chemicals in the so-called methanol economy. Toward that goal, the study of natural methylotrophs such as Bacillus methanolicus is critical to understand the origin of their efficient methylotrophy. This knowledge will then be leveraged to transform such natural strains into new cell factories or to design methylotrophic capability in other strains already used by the industry.

Adaptive Laboratory Evolution and Reverse Engineering of Single-Vitamin Prototrophies in Saccharomyces cerevisiae

Many strains of Saccharomyces cerevisiae, a popular platform organism in industrial biotechnology, carry the genetic information required for synthesis of biotin, thiamine, pyridoxine, para-aminobenzoic acid, pantothenic acid, nicotinic acid, and inositol. However, omission of these B vitamins typically leads to suboptimal growth. This study demonstrates that, for each individual B vitamin, it is possible to achieve fast vitamin-independent growth by adaptive laboratory evolution (ALE). Identification of mutations responsible for these fast-growing phenotypes by whole-genome sequencing and reverse engineering showed that, for each compound, a small number of mutations sufficed to achieve fast growth in its absence. These results form an important first step toward development of S. cerevisiae strains that exhibit fast growth on inexpensive, fully supplemented mineral media that only require complementation with a carbon source, thereby reducing costs, complexity, and contamination risks in industrial yeast fermentation processes.

Quantitative physiological studies on Saccharomyces cerevisiae commonly use synthetic media (SM) that contain a set of water-soluble growth factors that, based on their roles in human nutrition, are referred to as B vitamins. Previous work demonstrated that in S. cerevisiae CEN.PK113-7D, requirements for biotin were eliminated by laboratory evolution. In the present study, this laboratory strain was shown to exhibit suboptimal specific growth rates when either inositol, nicotinic acid, pyridoxine, pantothenic acid, para-aminobenzoic acid (pABA), or thiamine was omitted from SM. Subsequently, this strain was evolved in parallel serial-transfer experiments for fast aerobic growth on glucose in the absence of individual B vitamins. In all evolution lines, specific growth rates reached at least 90% of the growth rate observed in SM supplemented with a complete B vitamin mixture. Fast growth was already observed after a few transfers on SM without myo-inositol, nicotinic acid, or pABA. Reaching similar results in SM lacking thiamine, pyridoxine, or pantothenate required more than 300 generations of selective growth. The genomes of evolved single-colony isolates were resequenced, and for each B vitamin, a subset of non-synonymous mutations associated with fast vitamin-independent growth was selected. These mutations were introduced in a non-evolved reference strain using CRISPR/Cas9-based genome editing. For each B vitamin, the introduction of a small number of mutations sufficed to achieve a substantially increased specific growth rate in non-supplemented SM that represented at least 87% of the specific growth rate observed in fully supplemented complete SM.

IMPORTANCE Many strains of Saccharomyces cerevisiae, a popular platform organism in industrial biotechnology, carry the genetic information required for synthesis of biotin, thiamine, pyridoxine, para-aminobenzoic acid, pantothenic acid, nicotinic acid, and inositol. However, omission of these B vitamins typically leads to suboptimal growth. This study demonstrates that, for each individual B vitamin, it is possible to achieve fast vitamin-independent growth by adaptive laboratory evolution (ALE). Identification of mutations responsible for these fast-growing phenotypes by whole-genome sequencing and reverse engineering showed that, for each compound, a small number of mutations sufficed to achieve fast growth in its absence. These results form an important first step toward development of S. cerevisiae strains that exhibit fast growth on inexpensive, fully supplemented mineral media that only require complementation with a carbon source, thereby reducing costs, complexity, and contamination risks in industrial yeast fermentation processes.

Next-Generation Genome-Scale Models Incorporating Multilevel 'Omics Data: From Yeast to Human

Genome-scale modelling in eukaryotes has been pioneered by the yeast Saccharomyces cerevisiae. Early metabolic networks have been reconstructed based on genome sequence and information accumulated in the literature on biochemical reactions. Protein-protein interaction networks have been constructed based on experimental observations such as yeast-2-hybrid method. Gene regulatory networks were based on a variety of data types, including information on TF-promoter binding and gene coexpression. The aforementioned networks have been improved gradually, and methods for their integration were developed. Incorporation of omics data including genomics, metabolomics, transcriptomics, fluxome, and phosphoproteome led to next-generation genome-scale models. The methods tested on yeast have later been implemented in human, further, cellular components found to be important in yeast physiology under (ab)normal conditions, and (dis)regulation mechanisms in yeast shed light to the healthy and disease states in human. This chapter provides a historical perspective on next-generation genome-scale models incorporating multilevel 'omics data, from yeast to human.

The effect of global transcriptional regulators on the anaerobic fermentative metabolism of Escherichia coli

Global transcription factors are known to regulate the anaerobic growth of Escherichia coli on glucose. These transcription factors help the organism to sense oxygen and accordingly regulate the synthesis of mixed acid producing enzymes. Five global transcription factors, namely ArcA, Fnr, IhfA-B, Crp and Fis, are known to play an important role in the growth phenotype of the organism in the transition from anaerobic to aerobic conditions. The effect of deletion of most of these global transcription factors on the growth phenotype has not been characterized under strict anaerobic fermentation conditions. In order to enumerate the role of global transcription factors in central carbon metabolism, experiments were performed using single deletion mutants of the above mentioned global transcription regulators. The mutants demonstrated lower growth rates, ranging from 3-75% lower growth as compared to the wild-type strain along with varying glucose uptake rates. Global transcription regulators help in lowering formate and acetate synthesis, thereby effectively channeling the carbon towards redox balance (through ethanol formation) and biomass synthesis. Flux analysis of mutant strains indicated that deletion of a single transcription factor alone does not play a significant role in the normalized flux distribution of the central carbon metabolism.

Advances in analytical tools for high throughput strain engineering

The emergence of inexpensive, base-perfect genome editing is revolutionising biology. Modern industrial biotechnology exploits the advances in genome editing in combination with automation, analytics and data integration to build high-throughput automated strain engineering pipelines also known as biofoundries. Biofoundries replace the slow and inconsistent artisanal processes used to build microbial cell factories with an automated design-build-test cycle, considerably reducing the time needed to deliver commercially viable strains. Testing and hence learning remains relatively shallow, but recent advances in analytical chemistry promise to increase the depth of characterization possible. Analytics combined with models of cellular physiology in automated systems biology pipelines should enable deeper learning and hence a steeper pitch of the learning cycle. This review explores the progress, advances and remaining bottlenecks of analytical tools for high throughput strain engineering.

Genome-scale modeling of yeast: chronology, applications and critical perspectives

Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed.

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

The transcription factor bZIP14 regulates the TCA cycle in the diatom Phaeodactylum tricornutum

Diatoms are amongst the most important marine microalgae in terms of biomass, but little is known concerning the molecular mechanisms that regulate their versatile metabolism. Here, the pennate diatom Phaeodactylum tricornutum was studied at the metabolite and transcriptome level during nitrogen starvation and following imposition of three other stresses that impede growth. The coordinated upregulation of the tricarboxylic acid (TCA) cycle during the nitrogen stress response was the most striking observation. Through co-expression analysis and DNA binding assays, the transcription factor bZIP14 was identified as a regulator of the TCA cycle, also beyond the nitrogen starvation response, namely in diurnal regulation. Accordingly, metabolic and transcriptional shifts were observed upon overexpression of bZIP14 in transformed P. tricornutum cells. Our data indicate that the TCA cycle is a tightly regulated and important hub for carbon reallocation in the diatom cell during nutrient starvation and that bZIP14 is a conserved regulator of this cycle.

Systems Biology of Metabolism

Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

Targeted proteome analysis of single-gene deletion strains of Saccharomyces cerevisiae lacking enzymes in the central carbon metabolism

Central carbon metabolism is controlled by modulating the protein abundance profiles of enzymes that maintain the essential systems in living organisms. In this study, metabolic adaptation mechanisms in the model organism Saccharomyces cerevisiae were investigated by direct determination of enzyme abundance levels in 30 wild type and mutant strains. We performed a targeted proteome analysis using S. cerevisiae strains that lack genes encoding the enzymes responsible for central carbon metabolism. Our analysis revealed that at least 30% of the observed variations in enzyme abundance levels could be explained by global regulatory mechanisms. A enzyme-enzyme co-abundance analysis revealed that the abundances of enzyme proteins involved in the trehalose metabolism and glycolysis changed in a coordinated manner under the control of the transcription factors for global regulation. The remaining variations were derived from local mechanisms such as a mutant-specific increase in the abundances of remote enzymes. The proteome data also suggested that, although the functional compensation of the deficient enzyme was attained by using more resources for protein biosynthesis, available resources for the biosynthesis of the enzymes responsible for central metabolism were not abundant in S. cerevisiae cells. These results showed that global and local regulation of enzyme abundance levels shape central carbon metabolism in S. cerevisiae by using a limited resource for protein biosynthesis.

Metabolic regulation is sufficient for global and robust coordination of glucose uptake, catabolism, energy production and growth in Escherichia coli

The metabolism of microorganisms is regulated through two main mechanisms: changes of enzyme capacities as a consequence of gene expression modulation (“hierarchical control”) and changes of enzyme activities through metabolite-enzyme interactions. An increasing body of evidence indicates that hierarchical control is insufficient to explain metabolic behaviors, but the system-wide impact of metabolic regulation remains largely uncharacterized. To clarify its role, we developed and validated a detailed kinetic model of Escherichia coli central metabolism that links growth to environment. Metabolic control analyses confirm that the control is widely distributed across the network and highlight strong interconnections between all the pathways. Exploration of the model solution space reveals that several robust properties emerge from metabolic regulation, from the molecular level (e.g. homeostasis of total metabolite pool) to the overall cellular physiology (e.g. coordination of carbon uptake, catabolism, energy and redox production, and growth), while allowing a large degree of flexibility at most individual metabolic steps. These properties have important physiological implications for E. coli and significantly expand the self-regulating capacities of its metabolism.

Metabolism is a fundamental biochemical process that enables cells to operate and grow by converting nutrients into ‘building blocks’ and energy. Metabolism happens through the work of enzymes, which are encoded by genes. Thus, genes and their regulation are often thought of controlling metabolism, somewhat at the top of a hierarchical control system. However, an increasing body of evidence indicates that metabolism plays an active role in the control of its own operation via a dense network of metabolite-enzyme interactions. The system-wide role of metabolic regulation is hard to dissect and so far remains largely uncharacterized. To better understand its role, we constructed a detailed kinetic model of the carbon and energy metabolism of the bacterium Escherichia coli, a model organism in Systems and Synthetic biology. Model simulations indicate that kinetic considerations of metabolism alone can explain data from hundreds of experiments, without needing to invoke regulation of gene expression. In particular, metabolic regulation is sufficient to coordinate carbon utilization, redox and energy production, and growth, while maintaining local flexibility at individual metabolic steps. These findings indicate that the self-regulating capacities of E. coli metabolism are far more significant than previously expected, and improve our understanding on how cells work.

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com).

Recent advances in high-throughput 13C-fluxomics

The rise of high throughput (HT) strain engineering tools accompanying the area of synthetic biology is supporting the generation of a large number of microbial cell factories. A current bottleneck in process development is our limited capacity to rapidly analyze the metabolic state of the engineered strains, and in particular their intracellular fluxes. HT ¹³C-fluxomics workflows have not yet become commonplace, despite the existence of several HT tools at each of the required stages. This includes cultivation and sampling systems, analytics for isotopic analysis, and software for data processing and flux calculation. Here, we review recent advances in the field and highlight bottlenecks that must be overcome to allow the emergence of true HT ¹³C-fluxomics workflows.

Genome-Wide Mapping of Binding Sites Reveals Multiple Biological Functions of the Transcription Factor Cst6p in Saccharomyces cerevisiae

In the model eukaryote Saccharomyces cerevisiae, the transcription factor Cst6p has been reported to play important roles in several biological processes. However, the genome-wide targets of Cst6p and its physiological functions remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. Cst6p binds to the promoter regions of 59 genes with various biological functions when cells are grown on ethanol but hardly binds to the promoter at any gene when cells are grown on glucose. The retarded growth of the CST6 deletion mutant on ethanol is attributed to the markedly decreased expression of NCE103, encoding a carbonic anhydrase, which is a direct target of Cst6p. The target genes of Cst6p have a large overlap with those of stress-responsive transcription factors, such as Sko1p and Skn7p. In addition, a CST6 deletion mutant growing on ethanol shows hypersensitivity to oxidative stress and ethanol stress, assigning Cst6p as a new member of the stress-responsive transcriptional regulatory network. These results show that mapping of genome-wide binding sites can provide new insights into the function of transcription factors and highlight the highly connected and condition-dependent nature of the transcriptional regulatory network in S. cerevisiae.

Transcription factors regulate the activity of various biological processes through binding to specific DNA sequences. Therefore, the determination of binding positions is important for the understanding of the regulatory effects of transcription factors. In the model eukaryote Saccharomyces cerevisiae, the transcription factor Cst6p has been reported to regulate several biological processes, while its genome-wide targets remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. We show that the binding of Cst6p to its target promoters is condition dependent and explain the mechanism for the retarded growth of the CST6 deletion mutant on ethanol. Furthermore, we demonstrate that Cst6p is a new member of a stress-responsive transcriptional regulatory network. These results provide deeper understanding of the function of the dynamic transcriptional regulatory network in S. cerevisiae.

The transcription factor GCN4 regulates PHM8 and alters triacylglycerol metabolism in Saccharomyces cerevisiae

PHM8 is a very important enzyme in nonpolar lipid metabolism because of its role in triacylglycerol (TAG) biosynthesis under phosphate stress conditions. It is positively regulated by the PHO4 transcription factor under low phosphate conditions; however, its regulation has not been explored under normal physiological conditions. General control nonderepressible (GCN4), a basic leucine-zipper transcription factor activates the transcription of amino acids, purine biosynthesis genes and many stress response genes under various stress conditions. In this study, we demonstrate that the level of TAG is regulated by the transcription factor GCN4. GCN4 directly binds to its consensus recognition sequence (TGACTC) in the PHM8 promoter and controls its expression. The analysis of cells expressing the P _PHM8 -lacZ reporter gene showed that mutations (TGACTC-GGGCCC) in the GCN4-binding sequence caused a significant increase in β-galactosidase activity. Mutation in the GCN4 binding sequence causes an increase in PHM8 expression, lysophosphatidic acid phosphatase activity and TAG level. PHM8, in conjunction with DGA1, a mono- and diacylglycerol transferase, controls the level of TAG. These results revealed that GCN4 negatively regulates PHM8 and that deletion of GCN4 causes de-repression of PHM8, which is responsible for the increased TAG content in gcn4∆ cells.

Improving prediction fidelity of cellular metabolism with kinetic descriptions

Several modeling frameworks for describing and redirecting cellular metabolism have been developed keeping pace with the rapid development in high-throughput data generation and advances in metabolic engineering techniques. The incorporation of kinetic information within stoichiometry-only modeling techniques offers potential advantages for improved phenotype prediction and consequently more precise computational strain design. In addition to substrate-level kinetic regulatory information, the integration of a number of additional layers of regulation at the transcription, translation, and post-translation levels is sought after by many research groups. However, the practical integration of these complex biological processes into a unified framework amenable to design remains an ongoing challenge.

Applications of computational modeling in metabolic engineering of yeast

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

Developmental stage dependent metabolic regulation during meiotic differentiation in budding yeast

Background

The meiotic developmental pathway in yeast enables both differentiation of vegetative cells into haploid spores that ensure long-term survival, and recombination of the parental DNA to create genetic diversity. Despite the importance of proper metabolic regulation for the supply of building blocks and energy, little is known about the reprogramming of central metabolic pathways in meiotically differentiating cells during passage through successive developmental stages.

Results

Metabolic regulation during meiotic differentiation in budding yeast was analyzed by integrating information on genome-wide transcriptional activity, 26 enzymatic activities in the central metabolism, the dynamics of 67 metabolites, and a metabolic flux analysis at mid-stage meiosis. Analyses of mutants arresting sporulation at defined stages demonstrated that metabolic reprogramming is tightly controlled by the progression through the developmental pathway. The correlation between transcript levels and enzymatic activities in the central metabolism varies significantly in a developmental stage-dependent manner. The complete loss of phosphofructokinase activity at mid-stage meiosis enables a unique setup of the glycolytic pathway which facilitates carbon flux repartitioning into synthesis of spore wall precursors during the co-assimilation of glycogen and acetate. The need for correct homeostasis of purine nucleotides during the meiotic differentiation was demonstrated by the sporulation defect of the AMP deaminase mutant amd1, which exhibited hyper-accumulation of ATP accompanied by depletion of guanosine nucleotides.

Conclusions

Our systems-level analysis shows that reprogramming of the central metabolism during the meiotic differentiation is controlled at different hierarchical levels to meet the metabolic and energetic needs at successive developmental stages.

Electronic supplementary material

The online version of this article (doi:10.1186/s12915-014-0060-x) contains supplementary material, which is available to authorized users.

A genetic approach of wine yeast fermentation capacity in nitrogen-starvation reveals the key role of nitrogen signaling

Background

In conditions of nitrogen limitation, Saccharomyces cerevisiae strains differ in their fermentation capacities, due to differences in their nitrogen requirements. The mechanisms ensuring the maintenance of glycolytic flux in these conditions are unknown. We investigated the genetic basis of these differences, by studying quantitative trait loci (QTL) in a population of 133 individuals from the F2 segregant population generated from a cross between two strains with different nitrogen requirements for efficient fermentation.

Results

By comparing two bulks of segregants with low and high nitrogen requirements, we detected four regions making a quantitative contribution to these traits. We identified four polymorphic genes, in three of these four regions, for which involvement in the phenotype was validated by hemizygote comparison. The functions of the four validated genes, GCN1, MDS3, ARG81 and BIO3, relate to key roles in nitrogen metabolism and signaling, helping to maintain fermentation performance.

Conclusions

This study reveals that differences in nitrogen requirement between yeast strains results from a complex allelic combination. The identification of three genes involved in sensing and signaling nitrogen and specially one from the TOR pathway as affecting nitrogen requirements suggests a role for this pathway in regulating the fermentation rate in starvation through unknown mechanisms linking nitrogen signaling to glycolytic flux.

Electronic supplementary material

The online version of this article (doi: 10.1186/1471-2164-15-495) contains supplementary material, which is available to authorized users.

Metabolic constraint-based refinement of transcriptional regulatory networks

There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10⁻¹⁷²), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10⁻¹⁴) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to integrate and reconcile inconsistencies across different data-types. The algorithm and associated data are available at https://sourceforge.net/projects/gemini-data/

Cellular networks, such as metabolic and transcriptional regulatory networks (TRNs), do not operate independently but work together in unison to determine cellular phenotypes. Further, the phenotype and architecture of one network constrains the topology of other networks. Hence, it is critical to study network components and interactions in the context of the entire cell. Typically, efforts to reconstruct TRNs focus only on immediately proximal data such as gene co-expression and transcription factor (TF)-binding. Herein, we take a different strategy by linking candidate TRNs with the metabolic network to predict systems-level responses such as growth phenotypes of TF knockout strains, and compare predictions with experimental phenotype data to select amongst the candidate TRNs. Our approach goes beyond traditional data integration approaches for network inference and refinement by using a predictive network model (metabolism) to refine another network model (regulation) – thus providing an alternative avenue to this area of research. Understanding how the networks function together in a cell will pave the way for synthetic biology and has a wide-range of applications in biotechnology, drug discovery and diagnostics. Further we demonstrate how metabolic models can integrate and reconcile inconsistencies across different data-types.

Transcriptional regulation is insufficient to explain substrate-induced flux changes in Bacillus subtilis

Regulation of enzyme expression is one key mechanism by which cells control their metabolic programs. In this work, a quantitative analysis of metabolism in a model bacterium under different conditions shows that expression alone cannot explain the majority of the observed metabolic changes.

Most enzymes are indeed highly expressed in conditions where they are more active.

Quantitatively, however, the observed changes in expression between conditions do not match the changes in activity for most enzymes.

A good quantitative match is only observed for enzymes involved in the TCA cycle.

Metabolomics reveals that increased substrate availability explains only a few instances of changes in activity.

One of the key ways in which microbes are thought to regulate their metabolism is by modulating the availability of enzymes through transcriptional regulation. However, the limited success of efforts to manipulate metabolic fluxes by rewiring the transcriptional network has cast doubt on the idea that transcript abundance controls metabolic fluxes. In this study, we investigate control of metabolic flux in the model bacterium Bacillus subtilis by quantifying fluxes, transcripts, and metabolites in eight metabolic states enforced by different environmental conditions. We find that most enzymes whose flux switches between on and off states, such as those involved in substrate uptake, exhibit large corresponding transcriptional changes. However, for the majority of enzymes in central metabolism, enzyme concentrations were insufficient to explain the observed fluxes—only for a number of reactions in the tricarboxylic acid cycle were enzyme changes approximately proportional to flux changes. Surprisingly, substrate changes revealed by metabolomics were also insufficient to explain observed fluxes, leaving a large role for allosteric regulation and enzyme modification in the control of metabolic fluxes.

Food environments select microorganisms based on selfish energetic behavior

Nutrient richness, and specifically the abundance of mono- and disaccharides that characterize several food matrixes, such as milk and grape juice, has allowed the speciation of lactic acid bacteria and yeasts with a high fermentation capacity instead of energetically favorable respiratory metabolism. In these environmental contexts, rapid sugar consumption and lactic acid or ethanol production, accumulation, and tolerance, together with the ability to propagate in the absence of oxygen, are several of the "winning" traits that have apparently evolved and become specialized to perfection in these fermenting microorganisms. Here, we summarize and discuss the evolutionary context that has driven energetic metabolism in food-associated microorganisms, using the dairy species Lactococcus lactis and Streptococcus thermophilus among prokaryotes and the bakers' yeast Saccharomyces cerevisiae among eukaryotes as model organisms.

Metabolic engineering with plants for a sustainable biobased economy

Plants are bona fide sustainable organisms because they accumulate carbon and synthesize beneficial metabolites from photosynthesis. To meet the challenges to food security and health threatened by increasing population growth and depletion of nonrenewable natural resources, recent metabolic engineering efforts have shifted from single pathways to holistic approaches with multiple genes owing to integration of omics technologies. Successful engineering of plants results in the high yield of biomass components for primary food sources and biofuel feedstocks, pharmaceuticals, and platform chemicals through synthetic biology and systems biology strategies. Further discovery of undefined biosynthesis pathways in plants, integrative analysis of discrete omics data, and diversified process developments for production of platform chemicals are essential to overcome the hurdles for sustainable production of value-added biomolecules from plants.

Regulation of yeast central metabolism by enzyme phosphorylation

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

The yeast eukaryotic translation initiation factor 2B translation initiation complex interacts with the fatty acid synthesis enzyme YBR159W and endoplasmic reticulum membranes

Using affinity purifications coupled with mass spectrometry and yeast two-hybrid assays, we show the Saccharomyces cerevisiae translation initiation factor complex eukaryotic translation initiation factor 2B (eIF2B) and the very-long-chain fatty acid (VLCFA) synthesis keto-reductase enzyme YBR159W physically interact. The data show that the interaction is specifically between YBR159W and eIF2B and not between other members of the translation initiation or VLCFA pathways. A ybr159wΔ null strain has a slow-growth phenotype and a reduced translation rate but a normal GCN4 response to amino acid starvation. Although YBR159W localizes to the endoplasmic reticulum membrane, subcellular fractionation experiments show that a fraction of eIF2B cofractionates with lipid membranes in a YBR159W-independent manner. We show that a ybr159wΔ yeast strain and other strains with null mutations in the VLCFA pathway cause eIF2B to appear as numerous foci throughout the cytoplasm.

De novo sequencing, assembly and analysis of the genome of the laboratory strain Saccharomyces cerevisiae CEN.PK113-7D, a model for modern industrial biotechnology

Saccharomyces cerevisiae CEN.PK 113-7D is widely used for metabolic engineering and systems biology research in industry and academia. We sequenced, assembled, annotated and analyzed its genome. Single-nucleotide variations (SNV), insertions/deletions (indels) and differences in genome organization compared to the reference strain S. cerevisiae S288C were analyzed. In addition to a few large deletions and duplications, nearly 3000 indels were identified in the CEN.PK113-7D genome relative to S288C. These differences were overrepresented in genes whose functions are related to transcriptional regulation and chromatin remodelling. Some of these variations were caused by unstable tandem repeats, suggesting an innate evolvability of the corresponding genes. Besides a previously characterized mutation in adenylate cyclase, the CEN.PK113-7D genome sequence revealed a significant enrichment of non-synonymous mutations in genes encoding for components of the cAMP signalling pathway. Some phenotypic characteristics of the CEN.PK113-7D strains were explained by the presence of additional specific metabolic genes relative to S288C. In particular, the presence of the BIO1 and BIO6 genes correlated with a biotin prototrophy of CEN.PK113-7D. Furthermore, the copy number, chromosomal location and sequences of the MAL loci were resolved. The assembled sequence reveals that CEN.PK113-7D has a mosaic genome that combines characteristics of laboratory strains and wild-industrial strains.

Reverse engineering systems models of regulation: discovery, prediction and mechanisms

Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties.

Large-scale 13C-flux analysis reveals distinct transcriptional control of respiratory and fermentative metabolism in Escherichia coli

The authors analyze the role transcription plays in regulating bacterial metabolic flux. Of 91 transcriptional regulators studied, 2/3 affect absolute fluxes, but only a small number of regulators control the partitioning of flux between different metabolic pathways.

In contrast to the canonical respiro-fermentative glucose metabolism, fully respiratory galactose metabolism depends exclusively on the PEP-glyoxylate cycle.

Of 91 transcription factors, 2/3 affect absolute fluxes, but only one controls the distribution of fluxes on galactose and nine on glucose.

Transcriptional control of hexose flux distributions is confined to the acetyl-CoA branch point.

The PEP-glyoxylate cycle is controlled by cAMP-Crp in a hexose uptake rate-dependent manner.

Focusing on central carbon metabolism of Escherichia coli, we aim here to systematically identify transcriptional regulators that control the distribution of metabolic fluxes during aerobic growth on hexoses. To assess the condition dependence of transcriptional control of flux, we selected glucose and galactose as two substrates that are highly similar, yet lead to distinct growth rates (Soupene et al, 2003), overall metabolic rates (De Anda et al, 2006; Samir El et al, 2009) and levels of catabolite repression (Hogema et al, 1998; Bettenbrock et al, 2007).

Experimentally determined fluxes (Fischer and Sauer, 2003a) during growth on glucose and galactose reveal two distinct metabolic states. On glucose, high metabolic rates lead to high overflow metabolism and respiratory fluxes through the TCA cycle. On galactose, in contrast, metabolism was much slower without overflow metabolism and respiratory fluxes exclusively through the PEP-glyoxylate cycle (Fischer and Sauer, 2003b).

To determine which transcriptional events controlled these two distinct metabolic states, we determined intracellular fluxes in 91 transcription factor mutants. These genetic perturbations primarily affected absolute fluxes but not the distribution of fluxes. The distribution of flux between glycolysis and pentose–phosphate pathway in upper metabolism, e.g., remained constant in all mutants under all conditions. Transcriptional control of the flux distribution was exclusively seen at the acetyl-CoA branch point. On glucose, nine transcription factors controlled the distribution of fluxes at this branch point, five of which (ArcA, IHFA, IHFB, PdhR, Fur) did so presumably directly through their known targets in TCA cycle and/or respiration. Without known targets in the relevant pathways, the remaining four transcription factors (GlpR, QseB, HdfR, GlcC) may act either indirectly or directly through unknown targets. On galactose, transcriptional control focused exclusively on the PEP-glyoxylate cycle. While deletion of six transcription factors (Cra, Crp, IHFA, IHFB, Mlc, NagC) abolished or reduced the PEP-glyoxylate cycle flux, we demonstrate by substrate-limited chemostat experiments, derepression of galactose uptake and show by metabolomics that five of these transcription factors act indirectly through increased cAMP concentrations that allosterically activate Crp, the only direct transcription factors that controls the PEP-glyoxylate cycle (Nanchen et al, 2008).

Overall, our absolute flux data demonstrate that control of flux splitting during growth on hexoses was confined to the acetyl-CoA branch point in E. coli. Of the 36 transcription factors known to target genes in pathways that diverge from the acetyl-CoA branch point, only one transcription factor on galactose and five plus potentially four others on glucose showed altered flux splitting. The primary focus of steady state transcriptional control on the acetyl-CoA branch point, and thus the metabolic decision between the energetically efficient respiration and the less efficient but more rapid fermentation, was recently also demonstrated with only relative flux data for Saccharomyces cerevisiae (Fendt et al, 2010).

In contrast to glucose-grown Bacillus subtilis (Fischer et al, 2005), for batch glucose-grown E. coli, none of the investigated transcription factor mutants exhibited improved biomass productivity. However, the mutants Cra, IHF A, IHF B and NagC with increased uptake rates grew much faster at almost unaltered biomass yields. As the removal of the glucose PTS-based repression with a Crr mutant also resulted in increased galactose uptake, we provide evidence that E. coli actively represses its galactose uptake at the expense of otherwise possible rapid growth.

Despite our increasing topological knowledge on regulation networks in model bacteria, it is largely unknown which of the many co-occurring regulatory events actually control metabolic function and the distribution of intracellular fluxes. Here, we unravel condition-dependent transcriptional control of Escherichia coli metabolism by large-scale ¹³C-flux analysis in 91 transcriptional regulator mutants on glucose and galactose. In contrast to the canonical respiro-fermentative glucose metabolism, fully respiratory galactose metabolism depends exclusively on the phosphoenol-pyruvate (PEP)-glyoxylate cycle. While 2/3 of the regulators directly or indirectly affected absolute flux rates, the partitioning between different pathways remained largely stable with transcriptional control focusing primarily on the acetyl-CoA branch point. Flux distribution control was achieved by nine transcription factors on glucose, including ArcA, Fur, PdhR, IHF A and IHF B, but was exclusively mediated by the cAMP-dependent Crp regulation of the PEP-glyoxylate cycle flux on galactose. Five further transcription factors affected this flux only indirectly through cAMP and Crp by increasing the galactose uptake rate. Thus, E. coli actively limits its galactose catabolism at the expense of otherwise possible faster growth.

Regulation and control of metabolic fluxes in microbes

After about ten years of research renaissance in metabolism, the present challenge is to understand how metabolic fluxes are controlled by a complex interplay of overlapping regulatory mechanisms. Reconstruction of various regulatory network topologies is steaming, illustrating that we underestimated the broad importance of post-translational modifications such as enzyme phosphorylation or acetylation for microbial metabolism. With the growing topological knowledge, the functional relevance of these regulatory events becomes an even more pressing need. A major knowledge gap resides in the regulatory network of protein-metabolite interactions, simply because we lacked pertinent methods for systematic analyses - but a start has now been made. Perhaps most dramatic was the conceptual shift in our perception of metabolism from an engine of cellular operation to a generator of input and feedback signals for regulatory circuits that govern many important decisions on cell proliferation, differentiation, death, and naturally metabolism.