Somewhat in control--the role of transcription in regulating microbial metabolic fluxes

Enhanced flux potential analysis links changes in enzyme expression to metabolic flux

Algorithms that constrain metabolic network models with enzyme levels to predict metabolic activity assume that changes in enzyme levels are indicative of flux variations. However, metabolic flux can also be regulated by other mechanisms such as allostery and mass action. To systematically explore the relationship between fluctuations in enzyme expression and flux, we combine available yeast proteomic and fluxomic data to reveal that flux changes can be best predicted from changes in enzyme levels of pathways, rather than the whole network or only cognate reactions. We implement this principle in an 'enhanced flux potential analysis' (eFPA) algorithm that integrates enzyme expression data with metabolic network architecture to predict relative flux levels of reactions including those regulated by other mechanisms. Applied to human data, eFPA consistently predicts tissue metabolic function using either proteomic or transcriptomic data. Additionally, eFPA efficiently handles data sparsity and noisiness, generating robust flux predictions with single-cell gene expression data. Our approach outperforms alternatives by striking an optimal balance, evaluating enzyme expression at pathway level, rather than either single-reaction or whole-network levels.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

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.

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.

Intestinal epithelial cell-derived components regulate transcriptome of Lactobacillus rhamnosus GG

Introduction

Intestinal epithelial cells (IECs) provide the frontline responses to the gut microbiota for maintaining intestinal homeostasis. Our previous work revealed that IEC-derived components promote the beneficial effects of a commensal and probiotic bacterium, Lactobacillus rhamnosus GG (LGG). This study aimed to elucidate the regulatory effects of IEC-derived components on LGG at the molecular level.

Methods

Differential gene expression in LGG cultured with IEC-derived components at the timepoint between the exponential and stationary phase was studied by RNA sequencing and functional analysis.

Results

The transcriptomic profile of LGG cultured with IEC-derived components was significantly different from that of control LGG, with 231 genes were significantly upregulated and 235 genes significantly down regulated (FDR <0.05). The Clusters of Orthologous Groups (COGs) and Gene Ontology (GO) analysis demonstrated that the predominant genes enriched by IEC-derived components are involved in nutrient acquisition, including transporters for amino acids, metals, and sugars, biosynthesis of amino acids, and in the biosynthesis of cell membrane and cell wall, including biosynthesis of fatty acid and lipoteichoic acid. In addition, genes associated with cell division and translation are upregulated by IEC-derived components. The outcome of the increased transcription of these genes is supported by the result that IEC-derived components significantly promoted LGG growth. The main repressed genes are associated with the metabolism of amino acids, purines, carbohydrates, glycerophospholipid, and transcription, which may reflect regulation of metabolic mechanisms in response to the availability of nutrients in bacteria.

Discussion

These results provide mechanistic insight into the interactions between the gut microbiota and the host.

Multilayer Genetic Circuits for Dynamic Regulation of Metabolic Pathways

The dynamic regulation of metabolic pathways is based on changes in external signals and endogenous changes in gene expression levels and has extensive applications in the field of synthetic biology and metabolic engineering. However, achieving dynamic control is not trivial, and dynamic control is difficult to obtain using simple, single-level, control strategies because they are often affected by native regulatory networks. Therefore, synthetic biologists usually apply the concept of logic gates to build more complex and multilayer genetic circuits that can process various signals and direct the metabolic flux toward the synthesis of the molecules of interest. In this review, we first summarize the applications of dynamic regulatory systems and genetic circuits and then discuss how to design multilayer genetic circuits to achieve the optimal control of metabolic fluxes in living cells.

Combination of network and molecule structure accurately predicts competitive inhibitory interactions

Mining of metabolite-protein interaction networks facilitates the identification of design principles underlying the regulation of different cellular processes. However, identification and characterization of the regulatory role that metabolites play in interactions with proteins on a genome-scale level remains a pressing task. Based on availability of high-quality metabolite-protein interaction networks and genome-scale metabolic networks, here we propose a supervised machine learning approach, called CIRI that determines whether or not a metabolite is involved in a competitive inhibitory regulatory interaction with an enzyme. First, we show that CIRI outperforms the naive approach based on a structural similarity threshold for a putative competitive inhibitor and the substrates of a metabolic reaction. We also validate the performance of CIRI on several unseen data sets and databases of metabolite-protein interactions not used in the training, and demonstrate that the classifier can be effectively used to predict competitive inhibitory interactions. Finally, we show that CIRI can be employed to refine predictions about metabolite-protein interactions from a recently proposed PROMIS approach that employs metabolomics and proteomics profiles from size exclusion chromatography in E. coli to predict metabolite-protein interactions. Altogether, CIRI fills a gap in cataloguing metabolite-protein interactions and can be used in directing future machine learning efforts to categorize the regulatory type of these interactions.

Global coordination of metabolic pathways in Escherichia coli by active and passive regulation

Microorganisms adjust metabolic activity to cope with diverse environments. While many studies have provided insights into how individual pathways are regulated, the mechanisms that give rise to coordinated metabolic responses are poorly understood. Here, we identify the regulatory mechanisms that coordinate catabolism and anabolism in Escherichia coli. Integrating protein, metabolite, and flux changes in genetically implemented catabolic or anabolic limitations, we show that combined global and local mechanisms coordinate the response to metabolic limitations. To allocate proteomic resources between catabolism and anabolism, E. coli uses a simple global gene regulatory program. Surprisingly, this program is largely implemented by a single transcription factor, Crp, which directly activates the expression of catabolic enzymes and indirectly reduces the expression of anabolic enzymes by passively sequestering cellular resources needed for their synthesis. However, metabolic fluxes are not controlled by this regulatory program alone; instead, fluxes are adjusted mostly through passive changes in the local metabolite concentrations. These mechanisms constitute a simple but effective global regulatory program that coarsely partitions resources between different parts of metabolism while ensuring robust coordination of individual metabolic reactions.

A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.

iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis

Background:

Constraint-based metabolic network models have been widely used in pheno-typic prediction and metabolic engineering design. In recent years, researchers have attempted to im-prove prediction accuracy by integrating regulatory information and multiple types of “omics” data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed.

Methods and Results:

We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction bounda-ries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods.

Conclusion:

Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise pre-dictions of metabolic fluxes by using expression values normalized based on GO.

Systematic mapping of protein-metabolite interactions in central metabolism of Escherichia coli

Metabolite binding to proteins regulates nearly all cellular processes, but our knowledge of these interactions originates primarily from empirical in vitro studies. Here, we report the first systematic study of interactions between water-soluble proteins and polar metabolites in an entire biological subnetwork. To test the depth of our current knowledge, we chose to investigate the well-characterized Escherichia coli central metabolism. Using ligand-detected NMR, we assayed 29 enzymes towards binding events with 55 intracellular metabolites. Focusing on high-confidence interactions at a false-positive rate of 5%, we detected 98 interactions, among which purine nucleotides accounted for one-third, while 50% of all metabolites did not interact with any enzyme. In contrast, only five enzymes did not exhibit any metabolite binding and some interacted with up to 11 metabolites. About 40% of the interacting metabolites were predicted to be allosteric effectors based on low chemical similarity to their target's reactants. For five of the eight tested interactions, in vitro assays confirmed novel regulatory functions, including ATP and GTP inhibition of the first pentose phosphate pathway enzyme. With 76 new candidate regulatory interactions that have not been reported previously, we essentially doubled the number of known interactions, indicating that the presently available information about protein-metabolite interactions may only be the tip of the iceberg.

Conserved principles of transcriptional networks controlling metabolic flexibility in archaea

Gene regulation is intimately connected with metabolism, enabling the appropriate timing and tuning of biochemical pathways to substrate availability. In microorganisms, such as archaea and bacteria, transcription factors (TFs) often directly sense external cues such as nutrient substrates, metabolic intermediates, or redox status to regulate gene expression. Intense recent interest has characterized the functions of a large number of such regulatory TFs in archaea, which regulate a diverse array of unique archaeal metabolic capabilities. However, it remains unclear how the co-ordinated activity of the interconnected metabolic and transcription networks produces the dynamic flexibility so frequently observed in archaeal cells as they respond to energy limitation and intermittent substrate availability. In this review, we communicate the current state of the art regarding these archaeal networks and their dynamic properties. We compare the topology of these archaeal networks to those known for bacteria to highlight conserved and unique aspects. We present a new computational model for an exemplar archaeal network, aiming to lay the groundwork toward understanding general principles that unify the dynamic function of integrated metabolic-transcription networks across archaea and bacteria.

Metabolic engineering of Escherichia coli for 1,3-propanediol biosynthesis from glycerol

In this study, the engineered E. coli was constructed for efficient transformation of glycerol to 1,3-propanediol (1,3-PDO). To regenerate NADPH, the key bottleneck in 1,3-PDO production, heterologous NADP⁺-dependent glyceraldehyde-3-phosphate dehydrogenase (GAPDN, encoded by gapN) pathway was introduced, and the gapN expression level was fine-tuned with specific 5'-untranslated regions (5'-UTR) to balance the carbon flux distribution between the metabolic pathways of NADPH regeneration and 1,3-PDO biosynthesis. Additionally, glucose was added to the medium to promote glycerol utilization and cell growth. To elevate the utilization of glycerol in the presence of glucose, E. coli JA11 was constructed through destroying PEP-dependent glucose transport system while strengthening the ATP-dependent transport system. Subsequent optimization of nitrogen sources further improved 1,3-PDO production. Finally, under the optimal fermentation condition, E. coli JA11 produced 13.47 g/L 1,3-PDO, with a yield of 0.64 mol/mol, increased by 325% and 100% compared with the original engineered E. coli JA03, respectively.

Newly Identified Nucleoid-Associated-Like Protein YlxR Regulates Metabolic Gene Expression in Bacillus subtilis

Expression of genes encoding NAPs is often temporally regulated. According to results from single-cell analysis, the ylxR gene is induced by glucose and expressed in a bistable mode. These characteristics have not previously been reported for NAP gene expression. Transcriptional profiling of the ylxR disruptant revealed a change in the expression levels of approximately 400 genes, including genes for synthesis of 12 amino acids and 4 nucleotides, in addition to the SigX/M regulons. Thus, YlxR is a critical regulator of glucose response in B. subtilis.

Glucose is the most favorable carbon source for the majority of bacteria, which have several glucose-responsive gene networks. Recently, we found that in Bacillus subtilis, glucose induces expression of the extracellular sigma factor genes sigX/M. To explore the factors affecting this phenomenon, we performed a transposon mutagenesis screen for mutants with no glucose induction (GI) of sigX-lacZ and identified ylxR. YlxR is widely conserved in eubacteria. Further analysis revealed that ylxR is induced by glucose addition. In vitro DNA-binding and cytological studies suggested that YlxR is a nucleoid-associated protein (NAP) in B. subtilis. In many cases, NAPs influence transcription, recombination, and genome stability. Thus, we performed transcriptome sequencing (RNA-Seq) analysis to evaluate the impact of ylxR disruption on the transcriptome in the presence of glucose and observed that YlxR has a profound impact on metabolic gene expression in addition to that of four sigma factor genes. The wide fluctuations of gene expression may result in abolition of GI of sigX/M in the ylxR disruptant.

IMPORTANCE Expression of genes encoding NAPs is often temporally regulated. According to results from single-cell analysis, the ylxR gene is induced by glucose and expressed in a bistable mode. These characteristics have not previously been reported for NAP gene expression. Transcriptional profiling of the ylxR disruptant revealed a change in the expression levels of approximately 400 genes, including genes for synthesis of 12 amino acids and 4 nucleotides, in addition to the SigX/M regulons. Thus, YlxR is a critical regulator of glucose response in B. subtilis.

Dynamic metabolic control: towards precision engineering of metabolism

Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.

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.

Metabolic Feedback Circuits Provide Rapid Control of Metabolite Dynamics

Metabolism constitutes the basis of life, and the dynamics of metabolism dictate various cellular processes. However, exactly how metabolite dynamics are controlled remains poorly understood. By studying an engineered fatty acid-producing pathway as a model, we found that upon transcription activation a metabolic product from an unregulated pathway required seven cell cycles to reach to its steady state level, with the speed mostly limited by enzyme expression dynamics. To overcome this limit, we designed metabolic feedback circuits (MeFCs) with three different architectures, and experimentally measured and modeled their metabolite dynamics. Our engineered MeFCs could dramatically shorten the rise-time of metabolites, decreasing it by as much as 12-fold. The findings of this study provide a systematic understanding of metabolite dynamics in different architectures of MeFCs and have potentially immense applications in designing synthetic circuits to improve the productivities of engineered metabolic pathways.

Cell-to-cell variation and specialization in sugar metabolism in clonal bacterial populations

While we have good understanding of bacterial metabolism at the population level, we know little about the metabolic behavior of individual cells: do single cells in clonal populations sometimes specialize on different metabolic pathways? Such metabolic specialization could be driven by stochastic gene expression and could provide individual cells with growth benefits of specialization. We measured the degree of phenotypic specialization in two parallel metabolic pathways, the assimilation of glucose and arabinose. We grew Escherichia coli in chemostats, and used isotope-labeled sugars in combination with nanometer-scale secondary ion mass spectrometry and mathematical modeling to quantify sugar assimilation at the single-cell level. We found large variation in metabolic activities between single cells, both in absolute assimilation and in the degree to which individual cells specialize in the assimilation of different sugars. Analysis of transcriptional reporters indicated that this variation was at least partially based on cell-to-cell variation in gene expression. Metabolic differences between cells in clonal populations could potentially reduce metabolic incompatibilities between different pathways, and increase the rate at which parallel reactions can be performed.

This study addresses a fundamental question in bacterial metabolism: do all individuals in a clonal population express the same metabolic functions, or do individuals specialize on different metabolic functions and assimilate different substrates? Reports about stochastic gene expression in bacterial populations raise the possibility that transcriptional differences between individuals translate into different metabolic behaviors, but the prevalence and magnitude of such effects is currently not known. Here, we quantified the assimilation of two isotope-labeled sugars by single Escherichia coli cells using nanometer-scale secondary ion mass spectrometry, an analytical approach seldom used in systems biology. By comparing sugar assimilation and gene expression dynamics, we were able to differentiate the metabolic profiles of individual cells. We observed a previously hidden level of cell-to-cell variation in metabolism: cells differed both in the total amount of sugar they assimilated, as well as with respect to which of the two sugars they preferentially assimilated. Intriguingly, a cell’s preference in sugar assimilation was only partially based on specialization in gene expression. Taken together, this study is a step towards understanding the magnitude and the relevance of metabolic differences between genetically identical cells.

Transcriptomic buffering of cryptic genetic variation contributes to meningococcal virulence

Background

Commensal bacteria like Neisseria meningitidis sometimes cause serious disease. However, genomic comparison of hyperinvasive and apathogenic lineages did not reveal unambiguous hints towards indispensable virulence factors. Here, in a systems biological approach we compared gene expression of the invasive strain MC58 and the carriage strain α522 under different ex vivo conditions mimicking commensal and virulence compartments to assess the strain-specific impact of gene regulation on meningococcal virulence.

Results

Despite indistinguishable ex vivo phenotypes, both strains differed in the expression of over 500 genes under infection mimicking conditions. These differences comprised in particular metabolic and information processing genes as well as genes known to be involved in host-damage such as the nitrite reductase and numerous LOS biosynthesis genes. A model based analysis of the transcriptomic differences in human blood suggested ensuing metabolic flux differences in energy, glutamine and cysteine metabolic pathways along with differences in the activation of the stringent response in both strains. In support of the computational findings, experimental analyses revealed differences in cysteine and glutamine auxotrophy in both strains as well as a strain and condition dependent essentiality of the (p)ppGpp synthetase gene relA and of a short non-coding AT-rich repeat element in its promoter region.

Conclusions

Our data suggest that meningococcal virulence is linked to transcriptional buffering of cryptic genetic variation in metabolic genes including global stress responses. They further highlight the role of regulatory elements for bacterial virulence and the limitations of model strain approaches when studying such genetically diverse species as N. meningitidis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3616-7) contains supplementary material, which is available to authorized users.

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.

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).

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.

Systems-level analysis of mechanisms regulating yeast metabolic flux

Cellular metabolic fluxes are determined by enzyme activities and metabolite abundances. Biochemical approaches reveal the impact of specific substrates or regulators on enzyme kinetics but do not capture the extent to which metabolite and enzyme concentrations vary across physiological states and, therefore, how cellular reactions are regulated. We measured enzyme and metabolite concentrations and metabolic fluxes across 25 steady-state yeast cultures. We then assessed the extent to which flux can be explained by a Michaelis-Menten relationship between enzyme, substrate, product, and potential regulator concentrations. This revealed three previously unrecognized instances of cross-pathway regulation, which we biochemically verified. One of these involved inhibition of pyruvate kinase by citrate, which accumulated and thereby curtailed glycolytic outflow in nitrogen-limited yeast. Overall, substrate concentrations were the strongest driver of the net rates of cellular metabolic reactions, with metabolite concentrations collectively having more than double the physiological impact of enzymes.

Stoichiometric Representation of Gene-Protein-Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.

Functional elucidation of the non-coding RNAs of Kluyveromyces marxianus in the exponential growth phase

BACKGROUND

Non-coding RNAs (ncRNAs), which perform diverse regulatory roles, have been found in organisms from all superkingdoms of life. However, there have been limited numbers of studies on the functions of ncRNAs, especially in nonmodel organisms such as Kluyveromyces marxianus that is widely used in the field of industrial biotechnology.

RESULTS

In this study, we measured changes in transcriptome at three time points during the exponential growth phase of K. marxianus by using strand-specific RNA-seq. We found that approximately 60% of the transcriptome consists of ncRNAs transcribed from antisense and intergenic regions of the genome that were transcribed at lower levels than mRNA. In the transcriptome, a substantial number of long antisense ncRNAs (lancRNAs) are differentially expressed and enriched in carbohydrate and energy metabolism pathways. Furthermore, this enrichment is evolutionarily conserved, at least in yeast. Particularly, the mode of regulation of mRNA/lancRNA pairs is associated with mRNA transcription levels; the correlation between the pairs is positive at high mRNA transcriptional levels and negative at low levels. In addition, significant induction of mRNA and coverage of more than half of the mRNA sequence by a lancRNA strengthens the positive correlation between mRNA/lancRNA pairs.

CONCLUSIONS

Transcriptome sequencing of K. marxianus in the exponential growth phase reveals pervasive transcription of ncRNAs with evolutionarily conserved functions. Studies of the mode of regulation of mRNA/lancRNA pairs suggest that induction of lancRNA may be associated with switch-like behavior of mRNA/lancRNA pairs and efficient regulation of the carbohydrate and energy metabolism pathways in the exponential growth phase of K. marxianus being used in industrial applications.

In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

Modeling the Contribution of Allosteric Regulation for Flux Control in the Central Carbon Metabolism of E. coli

Modeling cellular metabolism is fundamental for many biotechnological applications, including drug discovery and rational cell factory design. Central carbon metabolism (CCM) is particularly important as it provides the energy and precursors for other biological processes. However, the complex regulation of CCM pathways has still not been fully unraveled and recent studies have shown that CCM is mostly regulated at post-transcriptional levels. In order to better understand the role of allosteric regulation in controlling the metabolic phenotype, we expand the reconstruction of CCM in Escherichia coli with allosteric interactions obtained from relevant databases. This model is used to integrate multi-omics datasets and analyze the coordinated changes in enzyme, metabolite, and flux levels between multiple experimental conditions. We observe cases where allosteric interactions have a major contribution to the metabolic flux changes. Inspired by these results, we develop a constraint-based method (arFBA) for simulation of metabolic flux distributions that accounts for allosteric interactions. This method can be used for systematic prediction of potential allosteric regulation under the given experimental conditions based on experimental data. We show that arFBA allows predicting coordinated flux changes that would not be predicted without considering allosteric regulation. The results reveal the importance of key regulatory metabolites, such as fructose-1,6-bisphosphate, in controlling the metabolic flux. Accounting for allosteric interactions in metabolic reconstructions reveals a hidden topology in metabolic networks, improving our understanding of cellular metabolism and fostering the development of novel simulation methods that account for this type of regulation.

Novel Functions and Regulation of Cryptic Cellobiose Operons in Escherichia coli

Presence of cellobiose as a sole carbon source induces mutations in the chb and asc operons of Escherichia coli and allows it to grow on cellobiose. We previously engineered these two operons with synthetic constitutive promoters and achieved efficient cellobiose metabolism through adaptive evolution. In this study, we characterized two mutations observed in the efficient cellobiose metabolizing strain: duplication of RBS of ascB gene, (β-glucosidase of asc operon) and nonsense mutation in yebK, (an uncharacterized transcription factor). Mutations in yebK play a dominant role by modulating the length of lag phase, relative to the growth rate of the strain when transferred from a rich medium to minimal cellobiose medium. Mutations in ascB, on the other hand, are specific for cellobiose and help in enhancing the specific growth rate. Taken together, our results show that ascB of the asc operon is controlled by an internal putative promoter in addition to the native cryptic promoter, and the transcription factor yebK helps to remodel the host physiology for cellobiose metabolism. While previous studies characterized the stress-induced mutations that allowed growth on cellobiose, here, we characterize the adaptation-induced mutations that help in enhancing cellobiose metabolic ability. This study will shed new light on the regulatory changes and factors that are needed for the functional coupling of the host physiology to the activated cryptic cellobiose metabolism.

Posttranslational regulation of microbial metabolism

Fluxes in microbial metabolism are controlled by various regulatory layers that alter abundance or activity of metabolic enzymes. Recent studies suggest a division of labor between these layers: transcriptional regulation mostly controls the allocation of protein resources, passive flux regulation by enzyme saturation and thermodynamics allows rapid responses at the expense of higher protein cost, and posttranslational regulation is utilized by cells to directly take control of metabolic decisions. We present recent advances in elucidating the role of these regulatory layers, focusing on posttranslational modifications and allosteric interactions. As the systematic mapping of posttranslational regulatory events has now become possible, the next challenge is to identify those regulatory events that are functionally relevant under a given condition.

Constructing a synthetic metabolic pathway in Escherichia coli to produce the enantiomerically pure (R, R)-2,3-butanediol

Enantiomerically pure (R, R)-2,3-butanediol has unique applications due to its special chiral group and spatial configuration. Currently, its chemical production route has many limitations. In addition, no native microorganisms can accumulate (R, R)-2,3-butanediol with an enantio-purity over 99%. Herein, we constructed a synthetic metabolic pathway for enantiomerically pure (R, R)-2,3-butanediol biosynthesis in Escherichia coli. The fermentation results suggested that introduction of the synthetic metabolic pathway redistributed the carbon fluxes to the neutral (R, R)-2,3-butanediol, and thus protected the strain against the acetic acid inhibition. Additionally, it showed that the traditionally used isopropyl beta-D-thiogalactoside (IPTG) induction displayed negative effect on (R, R)-2,3-butanediol biosynthesis in the recombinant E. coli, which was probably due to the protein burden. With no IPTG addition, the (R, R)-2,3-butanediol concentration reached 115 g/L by fed-batch culturing of the recombinant E. coli, with an enantio-purity over 99%, which is suitable for the pilot-scale production.

Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model

Computational strain-design prediction accuracy has been the focus for many recent efforts through the selective integration of kinetic information into metabolic models. In general, kinetic model prediction quality is determined by the range and scope of genetic and/or environmental perturbations used during parameterization. In this effort, we apply the k-OptForce procedure on a kinetic model of E. coli core metabolism constructed using the Ensemble Modeling (EM) method and parameterized using multiple mutant strains data under aerobic respiration with glucose as the carbon source. Minimal interventions are identified that improve succinate yield under both aerobic and anaerobic conditions to test the fidelity of model predictions under both genetic and environmental perturbations. Under aerobic condition, k-OptForce identifies interventions that match existing experimental strategies while pointing at a number of unexplored flux re-directions such as routing glyoxylate flux through the glycerate metabolism to improve succinate yield. Many of the identified interventions rely on the kinetic descriptions that would not be discoverable by a purely stoichiometric description. In contrast, under fermentative (anaerobic) condition, k-OptForce fails to identify key interventions including up-regulation of anaplerotic reactions and elimination of competitive fermentative products. This is due to the fact that the pathways activated under anaerobic condition were not properly parameterized as only aerobic flux data were used in the model construction. This study shed light on the importance of condition-specific model parameterization and provides insight on how to augment kinetic models so as to correctly respond to multiple environmental perturbations.

The return of metabolism: biochemistry and physiology of the pentose phosphate pathway

The pentose phosphate pathway (PPP) is a fundamental component of cellular metabolism. The PPP is important to maintain carbon homoeostasis, to provide precursors for nucleotide and amino acid biosynthesis, to provide reducing molecules for anabolism, and to defeat oxidative stress. The PPP shares reactions with the Entner–Doudoroff pathway and Calvin cycle and divides into an oxidative and non-oxidative branch. The oxidative branch is highly active in most eukaryotes and converts glucose 6-phosphate into carbon dioxide, ribulose 5-phosphate and NADPH. The latter function is critical to maintain redox balance under stress situations, when cells proliferate rapidly, in ageing, and for the ‘Warburg effect’ of cancer cells. The non-oxidative branch instead is virtually ubiquitous, and metabolizes the glycolytic intermediates fructose 6-phosphate and glyceraldehyde 3-phosphate as well as sedoheptulose sugars, yielding ribose 5-phosphate for the synthesis of nucleic acids and sugar phosphate precursors for the synthesis of amino acids. Whereas the oxidative PPP is considered unidirectional, the non-oxidative branch can supply glycolysis with intermediates derived from ribose 5-phosphate and vice versa, depending on the biochemical demand. These functions require dynamic regulation of the PPP pathway that is achieved through hierarchical interactions between transcriptome, proteome and metabolome. Consequently, the biochemistry and regulation of this pathway, while still unresolved in many cases, are archetypal for the dynamics of the metabolic network of the cell. In this comprehensive article we review seminal work that led to the discovery and description of the pathway that date back now for 80 years, and address recent results about genetic and metabolic mechanisms that regulate its activity. These biochemical principles are discussed in the context of PPP deficiencies causing metabolic disease and the role of this pathway in biotechnology, bacterial and parasite infections, neurons, stem cell potency and cancer metabolism.

Tuning transcription of nutrient utilization genes to catabolic rate promotes growth in a gut bacterium

Cells respond to nutrient availability by expressing nutrient catabolic genes. We report that the regulator controlling utilization of chondroitin sulphate (CS) in the mammalian gut symbiont Bacteroides thetaiotaomicron is activated by an intermediate in CS breakdown rather than CS itself. We determine that the rate-determining enzyme in CS breakdown is responsible for degrading this intermediate and establish that the levels of the enzyme increase 100-fold, whereas those of the regulator remain constant upon exposure to CS. Because enzyme and regulator compete for the intermediate, B. thetaiotaomicron tunes transcription of CS utilization genes to CS catabolic rate. This tuning results in a transient increase in CS utilization transcripts upon exposure to excess CS. Constitutive expression of the rate-determining enzyme hindered activation of CS utilization genes and growth on CS. An analogous mechanism regulates heparin utilization genes, suggesting that the identified strategy aids B. thetaiotaomicron in the competitive gut environment.

Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism

Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks. The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing. So far the predictive capability of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth maximization and parsimony criteria are as good or better than those obtained using methods that incorporate transcriptomic data. We further discuss the differences in the mathematical formulation of the methods, and their relation to the results we have obtained, as well as the connection to the underlying biological principles of metabolic regulation.

Coordination of microbial metabolism

Beyond fuelling cellular activities with building blocks and energy, metabolism also integrates environmental conditions into intracellular signals. The underlying regulatory network is complex and multifaceted: it ranges from slow interactions, such as changing gene expression, to rapid ones, such as the modulation of protein activity via post-translational modification or the allosteric binding of small molecules. In this Review, we outline the coordination of common metabolic tasks, including nutrient uptake, central metabolism, the generation of energy, the supply of amino acids and protein synthesis. Increasingly, a set of key metabolites is recognized to control individual regulatory circuits, which carry out specific functions of information input and regulatory output. Such a modular view of microbial metabolism facilitates an intuitive understanding of the molecular mechanisms that underlie cellular decision making.

Metabolic flux pattern of glucose utilization by Xanthomonas campestris pv. campestris: prevalent role of the Entner–Doudoroff pathway and minor fluxes through the pentose phosphate pathway and glycolysis

Complex metabolic flux pattern ofX. campestris.

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.

Analysis of fluorescent reporters indicates heterogeneity in glucose uptake and utilization in clonal bacterial populations

BACKGROUND

In this study, we aimed at investigating heterogeneity in the expression of metabolic genes in clonal populations of Escherichia coli growing on glucose as the sole carbon source. Different metabolic phenotypes can arise in these clonal populations through variation in the expression of glucose transporters and metabolic enzymes. First, we focused on the glucose transporters PtsG and MglBAC to analyze the diversity of glucose uptake strategies. Second, we analyzed phenotypic variation in the expression of genes involved in gluconeogenesis and acetate scavenging (as acetate is formed and excreted during bacterial growth on glucose), which can reveal, for instance, phenotypic subpopulations that cross-feed through the exchange of acetate. In these experiments, E. coli MG1655 strains containing different transcriptional GFP reporters were grown in chemostats and reporter expression was measured with flow cytometry.

RESULTS

Our results suggest heterogeneous expression of metabolic genes in bacterial clonal populations grown in glucose environments. The two glucose transport systems exhibited different level of heterogeneity. The majority of the bacterial cells expressed the reporters for both glucose transporters MglBAC and PtsG and a small fraction of cells only expressed the reporter for Mgl. At a low dilution rate, signals from transcriptional reporters for acetyl-CoA synthetase Acs and phosphoenolpyruvate carboxykinase Pck indicated that almost all cells expressed the genes that are part of acetate utilization and the gluconeogenesis pathway, respectively. Possible co-existence of two phenotypic subpopulations differing in acs expression occurred at the threshold of the switch to overflow metabolism. The overflow metabolism results in the production of acetate and has been previously reported to occur at intermediate dilution rates in chemostats with high concentration of glucose in the feed.

CONCLUSIONS

Analysis of the heterogeneous expression of reporters for genes involved in glucose and acetate metabolism raises new question whether different metabolic phenotypes are expressed in clonal populations growing in continuous cultures fed on glucose as the initially sole carbon source.

Physiological and Molecular Timing of the Glucose to Acetate Transition in Escherichia coli

The glucose-acetate transition in Escherichia coli is a classical model of metabolic adaptation. Here, we describe the dynamics of the molecular processes involved in this metabolic transition, with a particular focus on glucose exhaustion. Although changes in the metabolome were observed before glucose exhaustion, our results point to a massive reshuffling at both the transcriptome and metabolome levels in the very first min following glucose exhaustion. A new transcriptional pattern, involving a change in genome expression in one-sixth of the E. coli genome, was established within 10 min and remained stable until the acetate was completely consumed. Changes in the metabolome took longer and stabilized 40 min after glucose exhaustion. Integration of multi-omics data revealed different modifications and timescales between the transcriptome and metabolome, but both point to a rapid adaptation of less than an hour. This work provides detailed information on the order, timing and extent of the molecular and physiological events that occur during the glucose-acetate transition and that are of particular interest for the development of dynamic models of metabolism.