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1.  Bacterial adaptation through distributed sensing of metabolic fluxes 
We present a large-scale differential equation model of E. coli's central metabolism and its enzymatic, transcriptional, and posttranslational regulation. This model reproduces E. coli's known physiological behavior.We found that the interplay of known interactions in E. coli's central metabolism can indirectly recognize the presence of extracellular carbon sources through measuring intracellular metabolic flux patterns.We found that E. coli's system-level adaptations between glycolytic and gluconeogenic carbon sources are realized on the molecular level by global feedback architectures that overarch the enzymatic and transcriptional regulatory layers.We found that the capability for closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously to changing carbon sources (not requiring upstream sensing and signaling).
Adaptations to fluctuating carbon source availability are of particular importance for bacteria. To understand these adaptations, it needs to be understood how a system's behavior emerges from the interactions between the characterized molecules (Kitano, 2002b). To attain such a system understanding of bacterial metabolic adaptations to carbon source availability, the coupling between the recognition and adjustment aspects and between the enzymatic and genetic regulatory layers must be understood. For many carbon sources, neither transmembrane sensors nor regulatory proteins with sensing function have been identified. Also, it remains unclear how multiple local regulations work together to accomplish a coherent adjustment on the systems level. In this paper, we show that (1) the interplay of the known interactions in E. coli's central metabolism is capable of recognizing carbon sources indirectly, and that (2) these molecular interactions can adjust E. coli's metabolic operation between growth on glycolytic and gluconeogenic carbon sources, and that (3) this adaptation is governed by general principles.
We hypothesized that the system-level adaptations between growth on glycolytic and gluconeogenic carbon sources are accomplished by a system-wide regulation architecture that emerges when the known enzymatic and transcriptional regulations become coupled through five transcription factor (TF)–metabolite interactions. To (1) assess whether such coupled molecular interactions can indeed work together to adapt metabolic operation, and if yes, (2) to understand this system-level adaptation in molecular-level detail, we constructed a large-scale differential equation model. The model topology comprises the Embden–Meyerhoff pathway, the tricarboxylic acid (TCA) cycle, the glyoxylate (GLX) shunt, the anaplerotic reactions, the diversion of carbon flux to the GLX shunt, the uptake of glucose, the uptake and excretion of acetate, enzymatic regulation, transcriptional regulation by four TFs, and the regulation of these TFs' activities through TF–metabolite interactions. We translated the topology into differential equations by assigning the most appropriate rate law to each interaction. The kinetic model comprises 47 ordinary differential equations and 193 parameters. Parameter values were estimated through application of the ‘divide-and-conquer approach' (Kotte and Heinemann, 2009) on published experimental steady state-omics data sets.
Model simulations reproduce E. coli's known physiological behavior in an environment with fluctuating carbon source availability. But how does the in silico cell recognize acetate without a transmembrane sensor for extracellular acetate or a TF binding to intracellular acetate? Similarly, it is unclear whether the glucose sensing function of the phosphotransferase system is the exclusive mechanism to recognize glucose, or whether this sensing function is integrated into a larger sensing architecture. The model suggests that the recognition is performed indirectly through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two distinct motifs, which we termed pathway usage and flux direction, to establish defined correlations between metabolic fluxes and the levels of certain, here termed flux-signaling metabolites. The binding of these metabolites to TFs propagates the flux information to the transcriptional regulatory layer. A molecular sensor for intracellular metabolic flux is thus defined as a system of regulations and enzyme kinetics, comprising (1) either of the two motifs pathway usage or flux direction and (2) the binding of the thus established flux-signaling metabolites to TF(s).
As the in silico cell establishes and uses sensors for several intracellular metabolic fluxes, the overall sensing architecture infers the present carbon sources from a pattern of metabolic fluxes and is as such of a distributed nature. The core of this sensing architecture is formed not by transmembrane sensors but by four flux sensors, which establish flux-signaling metabolites according to the two proposed general motifs. These flux sensors use intracellular metabolic flux as a means to correlate the presence of extracellular carbon sources with the levels of intracellular metabolites. The recognition of glucose through the PTS transmembrane complex is embedded as one flux sensor in this distributed sensing architecture; the other three flux sensors function without the help of transmembrane complexes.
The in silico cell achieves the coupling between recognition and adjustment through its TFs, whose activities respond to the available carbon sources and at the same time regulate the expression of target genes. This combined recognition and adjustment, centered on the four TFs, closes four global feedback loops that overarch the metabolic and genetic layers as illustrated in Figure 6. The adaptation of the in silico cell arises from the global feedback loop-embedded, flux sensor-adjusted transcriptional regulation of the four TFs, with each TF performing one part of the overall adaptation. This adaptation incorporates both the influence of the metabolic on the genetic layer, achieved through TF–metabolite interactions, and of the genetic on the metabolic layer, achieved through the impact of adjusted enzyme levels on metabolic fluxes.
The existence of the global feedback architectures challenges the conventional view that top-level regulatory proteins recognize environmental conditions and adjust downstream metabolic operation. It suggests that the capability for closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and regulation) to changing carbon sources.
To conclude, the presented differential equation model of E. coli's central metabolism offers a consistent explanation of how a multitude of known molecular interactions fit into a coherent systems picture; the interactions work together like gear wheels that mesh with one another to adapt central metabolism between growth on the glycolytic substrate glucose and the gluconeogenic substrate acetate. The deduced general functional principles provide the missing link to understand system-level adaptations to carbon sources in molecular-level detail. The proposed principles fall under the umbrella of distributed flux sensing. The flux sensing mechanism entails the binding of TFs to flux-signaling metabolites, which are established through the motifs signaling of pathway usage and signaling of flux direction, and are embedded in global feedback loop architectures. These principles allow an autonomous adaptation of metabolic operation to growth in fluctuating environments.
The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.
PMCID: PMC2858440  PMID: 20212527
computational model; metabolism; regulation; sensing; systems biology
2.  Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions 
A human alveolar macrophage genome-scale metabolic reconstruction was reconstructed from tailoring a global human metabolic network, Recon 1, by using computational algorithms and manual curation.A genome-scale host–pathogen network of the human alveolar macrophage and Mycobacterium tuberculosis is presented. This involved integrating two genome-scale network reconstructions.The reaction activity and gene essentiality predictions of the host–pathogen model represent a more accurate depiction of infection.Integration of high-throughput data into a host-pathogen model followed by systems analysis was performed in order to elucidate major metabolic differences under different types of M. tuberculosis infection.
Mycobacterium tuberculosis (M. tb) is an insidious and highly persistent pathogen that affects one-third of the world's population (WHO, 2009). Metabolism is foundational to M. tb's infection ability and the ensuing host–pathogen interactions. In addition, M. tb has a heterogeneous clinical presentation and can infect virtually every tissue. Depending on the location of the infection, different metabolic pathways are active and inactive in both the host and pathogen cells. In this study, we sought to model the host–pathogen interactions of the human alveolar macrophage and M. tb as well as detail the metabolic differences in specific infection types using genome-scale metabolic reconstructions (Figure 4A).
Genome-scale metabolic reconstructions are knowledge bases of all known metabolic reactions of a given organism. Reconstructions have been shown to elucidate the mechanistic genotype-to-phenotype relationship through the integration of high-throughput and physiological data (Oberhardt et al, 2009). Genome-scale reconstructions are converted into mathematical models under the constraints-based reconstruction and analysis (COBRA) platform (Becker et al, 2007). COBRA models use network stoichiometry and steady-state mass balances to define a solution space of potential flux states that a network can take. Thus, the COBRA approach does not require kinetic parameters.
Recently, the global human metabolic network, Recon 1, has been reconstructed (Duarte et al, 2007). To understand the metabolic host–pathogen integrations of M. tb with its human host, we first tailored the global human metabolic network into a cell-specific metabolic reconstruction of the human alveolar macrophage. This was carried out using established computational algorithms (Becker and Palsson, 2008; Shlomi et al, 2008) and manual curation to confirm the included and excluded reactions. The human alveolar macrophage reconstruction, iAB-AMØ-1410, accounts for 1410 genes, 3012 intracellular reactions, and 2572 metabolites (Figure 4C). iAB-AMØ-1410 was able to accurately predict maximum ATP and NO production rates obtained from experimental data (Griscavage et al, 1993; Newsholme et al, 1999).
The second step to studying host–pathogen interactions was integration of the human alveolar macrophage reconstruction with an existing genome-scale metabolic model of M. tb, iNJ661 (Jamshidi and Palsson, 2007). Interfacial constraints were set to create a phagosomal environment that was hypoxic, nitrosative, rich in fatty acids, and poor in carbohydrates. From the onset, it was apparent that some oxygen (<15% of in vitro uptake) was required for proper simulations. In addition, algorithmic tailoring of the M. tb biomass objective function was performed to better represent an infectious state. The integrated host–pathogen metabolic reconstruction was dubbed iAB-AMØ-1410-Mt-661.
Analysis of the integrated host–pathogen metabolic reconstruction resulted in three main findings. First, by setting interfacial constraints and tailoring the biomass objective function, the solution space better represents an infectious state. Without adding artificial constraints to the host portion of the integrated model, the iAB-AMØ-1410 solution space is greatly reduced (Figure 4B). Macrophage glycolysis and nitric oxide production are up-regulated and macrophage ATP production, nucleotide synthesis, and amino-acid metabolism are suppressed. In addition, M. tb glycolysis is suppressed and isocitrate lyase is up-regulated for generation of acetyl-CoA. Fatty acid oxidation pathways and production of mycolic acids are increased, while production of nucleotides, peptidoglycans, and phenolic glycolipids are reduced. The modified solution space of the alveolar macrophage and M. tb better represents the infectious state.
Second, the host-pathogen model more accurately predicts M. tb gene deletion tests than the current in vitro model, iNJ661. The host-pathogen model predicted 11 essential genes and 37 unessential genes differently than iNJ661. A total of 22 of the differentially predicted genes have been experimentally characterized (Sassetti and Rubin, 2003; Sohaskey, 2008). The host-pathogen model correctly predicted 18 of the 22 genes. Thus, iAB-AMØ-1410-Mt-661 is a more accurate platform for studying infectious states of M. tb.
Finally, we sought to determine metabolic differences in both the macrophage and M. tb between three different types of infection: latent, pulmonary, and meningeal. Transcription profiling data of the macrophage for the three infections (Thuong et al, 2008) were integrated in the context of the host–pathogen network to elucidate the reaction activity of the three infections. There was wide heterogeneity in the three infection states; some of these differences are highlighted. Macrophage hyaluronan synthase and export were only active in the pulmonary infection. This is potentially interesting from a pharmaceutical viewpoint as hyaluronan has been implicated as a potential carbon source for extracellular M. tb (Hirayama et al, 2009). In addition, we detected metabolic activity differences in M. tb pathways that have been previously discussed as potential drug targets (Eoh et al, 2007; Boshoff et al, 2008). Polyprenyl metabolic reactions were only active in the latent state infection, while de novo synthesis of nicotinamide cofactors was only active in latent and meningeal M. tb infections.
Host-pathogen modeling represents a novel approach for studying metabolic interactions during infection. iAB-AMØ-1410-Mt-661 is a more accurate platform for understanding the biology and pathophysiology of M. tb infection. Most importantly, genome-scale metabolic reconstructions can act as scaffolds for integrating high-throughput data. Particularly, in this study we were able to discern reaction activity differences between different infection types.
Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host–pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host–pathogen genome-scale reconstruction, iAB-AMØ-1410-Mt-661. The integrated host–pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host–pathogen network and were able to describe three distinct pathological states. Integrated host–pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.
PMCID: PMC2990636  PMID: 20959820
computational biology; host–pathogen; Mycobacterium tuberculosis; systems biology; macrophage
3.  Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism 
A comprehensive genome-scale metabolic network of Chlamydomonas reinhardtii, including a detailed account of light-driven metabolism, is reconstructed and validated. The model provides a new resource for research of C. reinhardtii metabolism and in algal biotechnology.
The genome-scale metabolic network of Chlamydomonas reinhardtii (iRC1080) was reconstructed, accounting for >32% of the estimated metabolic genes encoded in the genome, and including extensive details of lipid metabolic pathways.This is the first metabolic network to explicitly account for stoichiometry and wavelengths of metabolic photon usage, providing a new resource for research of C. reinhardtii metabolism and developments in algal biotechnology.Metabolic functional annotation and the largest transcript verification of a metabolic network to date was performed, at least partially verifying >90% of the transcripts accounted for in iRC1080. Analysis of the network supports hypotheses concerning the evolution of latent lipid pathways in C. reinhardtii, including very long-chain polyunsaturated fatty acid and ceramide synthesis pathways.A novel approach for modeling light-driven metabolism was developed that accounts for both light source intensity and spectral quality of emitted light. The constructs resulting from this approach, termed prism reactions, were shown to significantly improve the accuracy of model predictions, and their use was demonstrated for evaluation of light source efficiency and design.
Algae have garnered significant interest in recent years, especially for their potential application in biofuel production. The hallmark, model eukaryotic microalgae Chlamydomonas reinhardtii has been widely used to study photosynthesis, cell motility and phototaxis, cell wall biogenesis, and other fundamental cellular processes (Harris, 2001). Characterizing algal metabolism is key to engineering production strains and understanding photobiological phenomena. Based on extensive literature on C. reinhardtii metabolism, its genome sequence (Merchant et al, 2007), and gene functional annotation, we have reconstructed and experimentally validated the genome-scale metabolic network for this alga, iRC1080, the first network to account for detailed photon absorption permitting growth simulations under different light sources. iRC1080 accounts for 1080 genes, associated with 2190 reactions and 1068 unique metabolites and encompasses 83 subsystems distributed across 10 cellular compartments (Figure 1A). Its >32% coverage of estimated metabolic genes is a tremendous expansion over previous algal reconstructions (Boyle and Morgan, 2009; Manichaikul et al, 2009). The lipid metabolic pathways of iRC1080 are considerably expanded relative to existing networks, and chemical properties of all metabolites in these pathways are accounted for explicitly, providing sufficient detail to completely specify all individual molecular species: backbone molecule and stereochemical numbering of acyl-chain positions; acyl-chain length; and number, position, and cis–trans stereoisomerism of carbon–carbon double bonds. Such detail in lipid metabolism will be critical for model-driven metabolic engineering efforts.
We experimentally verified transcripts accounted for in the network under permissive growth conditions, detecting >90% of tested transcript models (Figure 1B) and providing validating evidence for the contents of iRC1080. We also analyzed the extent of transcript verification by specific metabolic subsystems. Some subsystems stood out as more poorly verified, including chloroplast and mitochondrial transport systems and sphingolipid metabolism, all of which exhibited <80% of transcripts detected, reflecting incomplete characterization of compartmental transporters and supporting a hypothesis of latent pathway evolution for ceramide synthesis in C. reinhardtii. Additional lines of evidence from the reconstruction effort similarly support this hypothesis including lack of ceramide synthetase and other annotation gaps downstream in sphingolipid metabolism. A similar hypothesis of latent pathway evolution was established for very long-chain fatty acids (VLCFAs) and their polyunsaturated analogs (VLCPUFAs) (Figure 1C), owing to the absence of this class of lipids in previous experimental measurements, lack of a candidate VLCFA elongase in the functional annotation, and additional downstream annotation gaps in arachidonic acid metabolism.
The network provides a detailed account of metabolic photon absorption by light-driven reactions, including photosystems I and II, light-dependent protochlorophyllide oxidoreductase, provitamin D3 photoconversion to vitamin D3, and rhodopsin photoisomerase; this network accounting permits the precise modeling of light-dependent metabolism. iRC1080 accounts for effective light spectral ranges through analysis of biochemical activity spectra (Figure 3A), either reaction activity or absorbance at varying light wavelengths. Defining effective spectral ranges associated with each photon-utilizing reaction enabled our network to model growth under different light sources via stoichiometric representation of the spectral composition of emitted light, termed prism reactions. Coefficients for different photon wavelengths in a prism reaction correspond to the ratios of photon flux in the defined effective spectral ranges to the total emitted photon flux from a given light source (Figure 3B). This approach distinguishes the amount of emitted photons that drive different metabolic reactions. We created prism reactions for most light sources that have been used in published studies for algal and plant growth including solar light, various light bulbs, and LEDs. We also included regulatory effects, resulting from lighting conditions insofar as published studies enabled. Light and dark conditions have been shown to affect metabolic enzyme activity in C. reinhardtii on multiple levels: transcriptional regulation, chloroplast RNA degradation, translational regulation, and thioredoxin-mediated enzyme regulation. Through application of our light model and prism reactions, we were able to closely recapitulate experimental growth measurements under solar, incandescent, and red LED lights. Through unbiased sampling, we were able to establish the tremendous statistical significance of the accuracy of growth predictions achievable through implementation of prism reactions. Finally, application of the photosynthetic model was demonstrated prospectively to evaluate light utilization efficiency under different light sources. The results suggest that, of the existing light sources, red LEDs provide the greatest efficiency, about three times as efficient as sunlight. Extending this analysis, the model was applied to design a maximally efficient LED spectrum for algal growth. The result was a 677-nm peak LED spectrum with a total incident photon flux of 360 μE/m2/s, suggesting that for the simple objective of maximizing growth efficiency, LED technology has already reached an effective theoretical optimum.
In summary, the C. reinhardtii metabolic network iRC1080 that we have reconstructed offers insight into the basic biology of this species and may be employed prospectively for genetic engineering design and light source design relevant to algal biotechnology. iRC1080 was used to analyze lipid metabolism and generate novel hypotheses about the evolution of latent pathways. The predictive capacity of metabolic models developed from iRC1080 was demonstrated in simulating mutant phenotypes and in evaluation of light source efficiency. Our network provides a broad knowledgebase of the biochemistry and genomics underlying global metabolism of a photoautotroph, and our modeling approach for light-driven metabolism exemplifies how integration of largely unvisited data types, such as physicochemical environmental parameters, can expand the diversity of applications of metabolic networks.
Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology.
PMCID: PMC3202792  PMID: 21811229
Chlamydomonas reinhardtii; lipid metabolism; metabolic engineering; photobioreactor
4.  Tradeoff between enzyme and metabolite efficiency maintains metabolic homeostasis upon perturbations in enzyme capacity 
Substrate metabolite concentrations are inversely related to the in vivo capacity of their converting enzymes.Local metabolite responses represent a passive mechanism to achieve metabolic homeostasis upon perturbations in enzyme capacity.Enzyme capacity and metabolite concentration control the metabolic reaction rate.
Physiological behavior emerges from complex dynamic interactions between transcripts, enzymes, and metabolites, the constituents of metabolism, and its regulatory network (Sauer, 2006). Although large data sets can be generated on all these variables, data integration, in particular across different omics levels, is becoming the key challenge (Stitt and Fernie, 2003; Sauer et al, 2007). In this study, we identify a general relationship between substrates of an enzymatic reaction and enzymatic capacity in central carbon metabolism that allows the prediction of changes in metabolite concentration based on changes in enzyme capacity and vise versa. To elucidate whether general relationships exist between metabolite concentrations and enzyme capacities (i.e. the outcome of enzyme abundance combined with activity), we propose three hypothetical and alternative governing principles. The first hypothesis postulates a minimization of metabolite concentration at a given flux. In this case, no correlation between alterations in metabolite concentrations and enzyme capacities is expected. The second hypothesis postulates a tradeoff between metabolite concentration and enzyme capacity. In this case, a negative correlation between differences in concentrations of substrate metabolites and differences in enzyme capacity is expected. The third hypothesis postulates a minimization of enzyme capacity at a given flux. In this case, we expect a positive correlation between differences in concentrations of product metabolites and differences in enzyme capacity. As hypotheses I–III imply different relationships between enzyme capacities and metabolite concentrations, identification of the prevailing situation in microbial metabolism requires quantitative in vivo metabolite concentration and enzyme capacity data upon moderate changes in enzyme capacity. As a first test, we chose wild type Saccharomyces cerevisiae and an otherwise isogenic mutant with a complete deletion of the transcription factor Gcr2p, an activator of glycolysis (Chambers et al, 1995). This mutant exhibits altered transcript abundances, enzyme activities, and metabolite concentrations within closely connected reactions in glycolysis and in the tricarboxylic acid cycle (Uemura and Fraenkel, 1990, 1999; Sasaki and Uemura, 2005). To quantify the relationship between metabolite concentrations and enzyme capacities, we determined transcript, enzyme, and metabolite abundances in wild type and GCR2 mutant in batch culture on glucose minimal medium. Transcript and enzyme abundances are used as surrogates for enzyme capacities. The most significant correlation was observed for fold-changes in substrate metabolite concentrations with fold-changes in enzyme abundance. Not unexpectedly, enzyme abundances were a significantly better approximation for enzyme capacities than transcript abundances. A further improved correlation was achieved by considering all diverging enzymes that react upon a given substrate metabolite simultaneously rather than considering them as a separate reaction (Figure 4). The high correlation between substrate metabolite and enzyme fold-changes suggests a tradeoff between enzyme capacity and metabolite concentrations in central metabolism. To test the general validity for central carbon metabolism of the above-identified tradeoff between reaction substrate metabolite concentrations and enzyme abundances, we performed four independent validations: a statistical, a literature based, and two experimental ones. Statistically, we verified that the correlation between substrate metabolites and enzymes could not have been found by chance. On the basis of the literature data, we performed the above correlation analysis with literature data. All available data followed the proposed correlation, thus providing further evidence for the general validity of this relationship. As a more serious challenge of the identified correlation, we designed an experiment where the absolute flux alterations are large and additionally the flux directions are altered. We expected the substrate metabolites to occur at higher concentrations in the mutant than in the wild type. This expectation was fulfilled by the experimental data in all cases, thereby further corroborating the negative correlation between enzyme capacity and metabolite concentrations. So far, our experimental evidence was based on perturbing multiple enzyme abundances through a transcription factor mutant. To ensure that our findings are also valid for single-reaction perturbations, we modulated individual abundances of the four glycolytic enzymes Pgi1p, Tpi1p, Eno2p, and Cdc19p using strains whose endogenous genomic promotor was replaced by a Tet-controlled promotor (Mnaimneh et al, 2004) (Figure 7). Thus, we determined intracellular metabolites concentrations during exponential growth in the strains with modulated enzyme abundance. Our above-identified correlation predicts metabolite concentrations to increase only for the substrate of the such perturbed reaction and all other metabolite concentrations to remain constant. This prediction was verified. We demonstrate here that global or local alterations in enzyme abundance correlate negatively with enzyme reaction substrate concentration at least in central carbon metabolism. This implies a tradeoff between enzyme and metabolite efficiency in metabolic networks. These findings can be interpreted as a passive network mechanism to maintain close-to-wild-type homeostasis of central carbon metabolism upon perturbations that alter the enzyme capacity. The alterations are compensated by converse changes in reaction substrate concentrations, thereby minimizing the difference in metabolic flux that is caused by the alteration.
What is the relationship between enzymes and metabolites, the two major constituents of metabolic networks? We propose three alternative relationships between enzyme capacity and metabolite concentration alterations based on a Michaelis–Menten kinetic; that is enzyme capacities, metabolite concentrations, or both could limit the metabolic reaction rates. These relationships imply different correlations between changes in enzyme capacity and metabolite concentration, which we tested by quantifying metabolite, transcript, and enzyme abundances upon local (single-enzyme modulation) and global (GCR2 transcription factor mutant) perturbations in Saccharomyces cerevisiae. Our results reveal an inverse relationship between fold-changes in substrate metabolites and their catalyzing enzymes. These data provide evidence for the hypothesis that reaction rates are jointly limited by enzyme capacity and metabolite concentration. Hence, alteration in one network constituent can be efficiently buffered by converse alterations in the other constituent, implying a passive mechanism to maintain metabolic homeostasis upon perturbations in enzyme capacity.
PMCID: PMC2872607  PMID: 20393576
design principle; metabolic network; metabolomics; proteomics; transcriptome
5.  Three serendipitous pathways in E. coli can bypass a block in pyridoxal-5′-phosphate synthesis 
Overexpression of seven different genes restores growth of a ΔpdxB strain of E. coli, which cannot make pyridoxal phosphate (PLP), on M9/glucose.None of the enzymes encoded by these genes has a promiscuous 4-phosphoerythronate dehydrogenase activity that can replace the activity of PdxB.Overexpression of these genes restores PLP synthesis by three different serendipitous pathways that feed into the normal PLP synthesis pathway downstream of the blocked step.Reactions in one of these pathways are catalyzed by low-level activities of enzymes of unknown function and a promiscuous activity of an enzyme that normally has a role in another pathway; one reaction appears to be non-enzymatic.
Most metabolic enzymes are prodigious catalysts that have evolved to accelerate chemical reactions with high efficiency and specificity. However, many enzymes have inefficient promiscuous activities, as well, as a result of the assemblage of highly reactive catalytic residues and cofactors in active sites. Although promiscuous activities are generally orders of magnitude less efficient than well-evolved activities (O'Brien and Herschlag, 1998, 2001; Wang et al, 2003; Taylor Ringia et al, 2004), they often enhance reaction rates by orders of magnitude relative to those of uncatalyzed reactions (O'Brien and Herschlag, 1998, 2001). Thus, promiscuous activities provide a reservoir of novel catalytic activities that can be recruited to serve new functions.
The evolutionary potential of promiscuous enzymes extends beyond the recruitment of single enzymes to serve new functions. Microbes contain hundreds of enzymes—E. coli contains about 1700 (Freilich et al, 2005)—raising the possibility that promiscuous enzymes can be patched together to generate ‘serendipitous' pathways that are not part of normal metabolism. We distinguish serendipitous pathways from latent or cryptic pathways, which are bona fide pathways involving dedicated enzymes that are produced only under particular environmental circumstances. In contrast, serendipitous pathways are patched together from enzymes that normally serve other functions and are not regulated in a coordinated manner in response to the need to synthesize or degrade a metabolite.
In this study, we describe the discovery of three serendipitous pathways that allow synthesis of pyridoxal phosphate (PLP) in a strain of E. coli that lacks 4-phosphoerythronate dehydrogenase (PdxB) when one of the seven different genes is overexpressed. These genes were identified in a multicopy suppression experiment in which a library of E. coli genes (from the ASKA collection) was introduced into a ΔpdxB strain of E. coli that is unable to synthesize PLP. Surprisingly, none of the enzymes encoded by these genes has a promiscuous 4-phosphoerythronate (4PE) dehydrogenase activity that can substitute for the missing PdxB. Rather, overproduction of these enzymes appears to facilitate at least three serendipitous pathways that draw material from other metabolic pathways and feed into the normal PLP synthesis pathway downstream of the blocked step (Figure 1).
We have characterized one of these pathways in detail (Figure 3). The first step, dephosphorylation of 3-phosphohydroxypyruvate, is catalyzed by YeaB, a predicted NUDIX hydrolase of unknown function. Although catalysis is inefficient (kcat=5.7×10−5 s−1 and kcat/KM>0.028 M−1 s−1), the enzymatic rate is 4×107-fold faster than the rate of the uncatalyzed reaction, and is sufficient to support PLP synthesis when YeaB is overproduced. The second step in the pathway is decarboxylation of 3-hydroxypyruvate (3HP). Although we found two enzymes (1-deoxyxylulose-5-phosphate synthase and the catalytic domain of α-ketoglutarate dehydrogenase) that catalyze this reaction with low but respectable activity in vitro, their involvement in pathway 1 was ruled out by genetic methods. Surprisingly, the non-enzymatic rate of decarboxylation of 3HP appears to be sufficient to support PLP synthesis. The third step in the pathway, condensation of glycolaldehyde and glycine to form 4-hydroxy-L-threonine, is catalyzed by LtaE, a low-specificity threonine aldolase whose physiological role is not known. The final step, phosphorylation of 4-hydroxy-L-threonine, is catalyzed by homoserine kinase (ThrB), which is required for synthesis of threonine. The promiscuous phosphorylation of 4-hydroxy-L-threonine is 80-fold slower than the physiological phosphorylation of homoserine. The involvement of LtaE and ThrB in pathway 1 was confirmed by genetic experiments showing that overexpression of yeaB no longer restores growth of ΔpdxB strains lacking either ltaE or thrB.
Although pathway 1 is inefficient, it provides the ΔpdxB strain with the ability to grow under conditions in which survival is otherwise impossible. In general, serendipitous assembly of an inefficient pathway from promiscuous activities of available enzymes will be important whenever the pathway provides increased fitness. This might occur when a critical metabolite is no longer available from the environment, and survival depends on assembly of a new biosynthetic pathway. A second circumstance in which an inefficient serendipitous pathway might improve fitness is the appearance of a novel compound in the environment that can be exploited as a source of carbon, nitrogen or phosphorous. Finally, chemotherapeutic agents that block metabolic pathways in bacteria or cancer cells could provide selective pressure for assembly of serendipitous pathways that allow synthesis of the end product of the blocked pathway and thus a previously unappreciated source of drug resistance. In all of these cases, even an inefficient pathway can provide a selective advantage over other cells in a particular environmental niche, allowing survival and subsequent mutations that elevate the efficiency of the pathway.
Our work is consistent with the hypothesis that the recognized metabolic network of E. coli is underlain by a denser network of reactions due to promiscuous enzymes that use and generate recognized metabolites, but also unusual metabolites that normally have no physiological role. The findings reported here highlight the abundance of cryptic capabilities in the E. coli proteome that can be drawn on to generate novel pathways. Such pathways could provide a starting place for assembly of more efficient pathways, both in nature and in the hands of metabolic engineers.
Bacterial genomes encode hundreds to thousands of enzymes, most of which are specialized for particular functions. However, most enzymes have inefficient promiscuous activities, as well, that generally serve no purpose. Promiscuous reactions can be patched together to form multistep metabolic pathways. Mutations that increase expression or activity of enzymes in such serendipitous pathways can elevate flux through the pathway to a physiologically significant level. In this study, we describe the discovery of three serendipitous pathways that allow synthesis of pyridoxal-5′-phosphate (PLP) in a strain of E. coli that lacks 4-phosphoerythronate (4PE) dehydrogenase (PdxB) when one of seven different genes is overexpressed. We have characterized one of these pathways in detail. This pathway diverts material from serine biosynthesis and generates an intermediate in the normal PLP synthesis pathway downstream of the block caused by lack of PdxB. Steps in the pathway are catalyzed by a protein of unknown function, a broad-specificity enzyme whose physiological role is unknown, and a promiscuous activity of an enzyme that normally serves another function. One step in the pathway may be non-enzymatic.
PMCID: PMC3010111  PMID: 21119630
metabolic bypass; multicopy suppression; promiscuity; pyridoxal-5′-phosphate; serendipitous pathway
6.  Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network 
In the paper we present a metabolic reconstruction and flux-balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network of the parasite accounts for 1001 reactions and 616 metabolites. Enzyme–gene associations were established for 366 genes and 75% of all enzymatic reactions.The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. The model also can be used to efficiently integrate mRNA-expression data to improve the accuracy of metabolic predictions.Using FBA of the reconstructed metabolic network, we identified 40 enzymatic drug targets (i.e. in silico essential genes) with no or very low sequence identity to human proteins.We experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor.
Malaria remains one of the most severe public health challenges worldwide (WHO, 2008). Although several available drugs have been successful in controlling malaria in the past, most of them are rapidly losing efficacy due to acquired drug resistance in the most lethal causative agent, Plasmodium falciparum (Mackinnon and Marsh, 2010). This creates an urgent need for new drugs and treatments, as well as better understanding of the parasite physiology. With this in mind, we built a genome-scale flux-balance model of the P. falciparum metabolism. Given the complex life cycle of Plasmodium, the flux-balanced model is of direct relevance to the ongoing search to identify new therapeutic drug targets. The model can be used to explore diverse metabolic states of the parasite and identify essential metabolic genes in the context of known alternative pathways (Oberhardt et al, 2009).
The reconstructed model, which is based on Plasmodium-specific databases, genomic annotations, and literature reports, includes 366 genes, 1001 reactions, 616 metabolic species, and 4 cellular compartments. We applied flux-balance analysis (FBA) (Orth et al, 2010) to identify the genes and reactions that are required to produce a set of necessary biomass components. Interestingly, compared with the yeast metabolic network (Duarte et al, 2004), a model eukaryote with a similar genome size, the Plasmodium network has a significantly higher proportion of essential genes; we confirmed this result using a comparative analysis of known gene knockouts in the two microbes. This low level of genetic robustness, which is likely due to the parasitic lifestyle, suggests that many metabolic genes of the parasite can be used as effective drug targets. Indeed, based on the in silico analysis we identified 40 essential P. falciparum genes with no or very little sequence identity to their human homologs.
We used a recently described small-molecule inhibitor (compound 1_03; Sorci et al, 2009) to experimentally verify one of the enzymes identified as essential: nicotinate mononucleotide adenylyltransferase (NMNAT; Figure 2A). This enzyme, and the corresponding NAD synthesis and recycling pathway, have been recently used for anti-microbial development (Magni et al, 2009). However, to the best of our knowledge, they have not been used against P. falciparum. The compound 1_03 was able to completely block host cell escape and reinvasion by arresting parasites in the trophozoite growth stage (Figure 2B). These results demonstrate that the inhibitory compound may be a good starting lead for new anti-malarials.
Importantly, the metabolic model of the parasite can be also used to integrate various genomic data, such as gene expression (Oberhardt et al, 2009). To illustrate these possibilities, we applied gene-expression data as constraints for the flux-balance model (Colijn et al, 2009) in order to predict changes in metabolic exchange fluxes. We found that the model was able to correctly predict the changes in external metabolite concentrations (Olszewski et al, 2009) with about 70% accuracy (Figure 3). The availability of a human metabolic network reconstruction (Duarte et al, 2007) would allow, in the future, to analyze the combined parasite–host network, which would deepen understanding of the P. falciparum metabolic vulnerabilities.
Future improvements of the presented P. falciparum metabolic model, for example incorporation of missing activities and yet undiscovered pathways, will lead to a better understanding of parasite physiology. Ultimately, the improved understanding should significantly accelerate the identification and development of desperately needed new drugs against this devastating disease.
Genome-scale metabolic reconstructions can serve as important tools for hypothesis generation and high-throughput data integration. Here, we present a metabolic network reconstruction and flux-balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network accounts for 1001 reactions and 616 metabolites. Enzyme–gene associations were established for 366 genes and 75% of all enzymatic reactions. Compared with other microbes, the P. falciparum metabolic network contains a relatively high number of essential genes, suggesting little redundancy of the parasite metabolism. The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. Moreover, using constraints based on gene-expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy. Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins. To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor.
PMCID: PMC2964117  PMID: 20823846
flux-balance analysis; Plasmodium falciparum metabolism; systems biology
7.  Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery 
Chromosome 1 of Vibrio vulnificus tends to contain larger portion of essential or housekeeping genes on the basis of the genomic analysis and gene knockout experiments performed in this study, while its chromosome 2 seems to have originated and evolved from a plasmid.The genome-scale metabolic network model of V. vulnificus was reconstructed based on databases and literature, and was used to identify 193 essential metabolites.Five essential metabolites finally selected after the filtering process are 2-amino-4-hydroxy-6-hydroxymethyl-7,8-dihydropteridine (AHHMP), D-glutamate (DGLU), 2,3-dihydrodipicolinate (DHDP), 1-deoxy-D-xylulose 5-phosphate (DX5P), and 4-aminobenzoate (PABA), which were predicted to be essential in V. vulnificus, absent in human, and are consumed by multiple reactions.Chemical analogs of the five essential metabolites were screened and a hit compound showing the minimal inhibitory concentration (MIC) of 2 μg/ml and the minimal bactericidal concentration (MBC) of 4 μg/ml against V. vulnificus was identified.
Discovering new antimicrobial targets and consequently new antimicrobials is important as drug resistance of pathogenic microorganisms is becoming an increasingly serious problem in human healthcare management (Fischbach and Walsh, 2009). There clearly exists a gap between genomic studies and drug discovery as the accumulation of knowledge on pathogens at genome level has not successfully transformed into the development of effective drugs (Mills, 2006; Payne et al, 2007). In this study, we dissected the genome of a microbial pathogen in detail, and subsequently developed a systems biological strategy of employing genome-scale metabolic modeling and simulation together with metabolite essentiality analysis for effective drug targeting and discovery. This strategy was used for identifying new drug targets in an opportunistic pathogen Vibrio vulnificus CMCP6 as a model.
V. vulnificus is a Gram-negative halophilic bacterium that is found in estuarine waters, brackish ponds, or coastal areas, and its Biotype 1 is an opportunistic human pathogen that can attack immune-compromised patients, and causes primary septicemia, necrotized wound infections, and gastroenteritis. We previously found that many metabolic genes were specifically induced in vivo, suggesting that specific metabolic pathways are essential for in vivo survival and virulence of this pathogen (Kim et al, 2003; Lee et al, 2007). These results motivated us to carry out systems biological analysis of the genome and the metabolic network for new drug target discovery.
V. vulnificus CMCP6 has two chromosomes. We first re-sequenced genomic regions assembled in low quality and low depth, and subsequently re-annotated the whole genome of V. vulnificus. Horizontal gene transfer was suspected to be responsible for the diversification of each chromosome of V. vulnificus, and the presence of metabolic genes was more biased to chromosome 1 than chromosome 2. Further studies on V. vulnificus genome revealed that chromosome 2 is more prone to diversification for better adaptation to the environment than its chromosome 1, while chromosome 1 tends to expand their genetic repertoire while maintaining the core genes at a constant level.
Next, a genome-scale metabolic network VvuMBEL943 was reconstructed based on literature, databases and experiments for systematic studies on the metabolism of this pathogen and prediction of drug targets. The VvuMBEL943 model is composed of 943 reactions and 765 metabolites, and covers 673 genes. The model was validated by comparing its simulated cell growth phenotype obtained by constraints-based flux analysis with the V. vulnificus-specific experimental data previously reported in the literature. In this study, constraints-based flux analysis is an optimization-based simulation method that calculates intracellular fluxes under the specific genetic and environmental condition (Kim et al, 2008). As a result, 17 growth phenotypes were correctly predicted out of 18 cases, which demonstrate the validity of VvuMBEL943.
The main objective of constructing VvuMBEL943 in this study is to predict potential drug targets by system-wide analysis of the metabolic network for the effective treatment of V. vulnificus. To achieve this goal, a set of drug target candidates was predicted by taking a metabolite-centric approach. Metabolite essentiality analysis is a concept recently introduced for the study of cellular robustness to complement conventional reaction or gene-centric approach (Kim et al, 2007b). Metabolite essentiality analysis observes changes in flux distribution by removing each metabolite from the in silico metabolic network. Hence, metabolite essentiality predicts essential metabolites whose absence causes cell death. By selecting essential metabolites, it is possible to directly screen only their structural analogs, which substantially reduces the number of chemical compounds to screen from the chemical compound library. As a result of implementing this approach, 193 metabolites were initially identified to be essential to the cell. These essential metabolites were then further filtered based on the predetermined criteria, mainly organism specificity and multiple connectivity associated with each metabolite, in order to reduce the number of initial target candidates towards identifying the most effective ones.
Five essential metabolites finally selected are 2-amino-4-hydroxy-6-hydroxymethyl-7,8-dihydropteridine (AHHMP), D-glutamate (DGLU), 2,3-dihydrodipicolinate (DHDP), 1-deoxy-D-xylulose 5-phosphate (DX5P), and 4-aminobenzoate (PABA). Enzymes that consume these essential metabolites were experimentally verified to be essential, which indeed demonstrates the essentiality of these five metabolites. On the basis of the structural information of these five essential metabolites, whole-cell screening assay was performed using their analogs for possible antibacterial discovery. We screened 352 chemical analogs of the essential metabolites selected from the chemical compound library, and found a hit compound 24837, which shows the minimal inhibitory concentration (MIC) of 2 μg/ml and minimal bactericidal concentration (MBC) of 4 μg/ml, showing good antibacterial activity without further structural modification. Although this study demonstrates a proof-of-concept, the approaches and their rationale taken here should serve as a general strategy for discovering novel antibiotics and drugs based on systems-level analysis of metabolic networks.
Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re-sequenced and fully re-annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome-scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost-effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole-cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.
PMCID: PMC3049409  PMID: 21245845
drug discovery; drug targeting; genome analysis; metabolic network; Vibrio vulnificus
8.  Chemical combinations elucidate pathway interactions and regulation relevant to Hepatitis C replication 
SREBP-2, oxidosqualene cyclase (OSC) or lanosterol demethylase were identified as novel sterol pathway-associated targets that, when probed with chemical agents, can inhibit hepatitis C virus (HCV) replication.Using a combination chemical genetics approach, combinations of chemicals targeting sterol pathway enzymes downstream of and including OSC or protein geranylgeranyl transferase I (PGGT) produce robust and selective synergistic inhibition of HCV replication. Inhibition of enzymes upstream of OSC elicit proviral responses that are dominant to the effects of inhibiting all downstream targets.Inhibition of the sterol pathway without inhibition of regulatory feedback mechanisms ultimately results in an increase in HCV replication because of a compensatory upregulation of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) expression. Increases in HMGCR expression without inhibition of HMGCR enzymatic activity ultimately stimulate HCV replication through increasing the cellular pool of geranylgeranyl pyrophosphate (GGPP).Chemical inhibitors that ultimately prevent SREBP-2 activation, inhibit PGGT or encourage the production of polar sterols have great potential as HCV therapeutics if associated toxicities can be reduced.
Chemical inhibition of enzymes in either the cholesterol or the fatty acid biosynthetic pathways has been shown to impact viral replication, both positively and negatively (Su et al, 2002; Ye et al, 2003; Kapadia and Chisari, 2005; Sagan et al, 2006; Amemiya et al, 2008). FBL2 has been identified as a 50 kDa geranylgeranylated host protein that is necessary for localization of the hepatitis C virus (HCV) replication complex to the membranous web through its close association with the HCV protein NS5A and is critical for HCV replication (Wang et al, 2005). Inhibition of the protein geranylgeranyl transferase I (PGGT), an enzyme that transfers geranylgeranyl pyrophosphate (GGPP) to cellular proteins such as FBL2 for the purpose of membrane anchoring, negatively impacts HCV replication (Ye et al, 2003). Conversely, chemical agents that increase intracellular GGPP concentrations promote viral replication (Kapadia and Chisari, 2005). Statin compounds that inhibit 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), the rate-limiting enzyme in the sterol pathway (Goldstein and Brown, 1990), have been suggested to inhibit HCV replication through ultimately reducing the cellular pool of GGPP (Ye et al, 2003; Kapadia and Chisari, 2005; Ikeda et al, 2006). However, inhibition of the sterol pathway with statin drugs has not yielded consistent results in patients. The use of statins for the treatment of HCV is likely to be complicated by the reported compensatory increase in HMGCR expression in vitro and in vivo (Stone et al, 1989; Cohen et al, 1993) in response to treatment. Enzymes in the sterol pathway are regulated on a transcriptional level by sterol regulatory element-binding proteins (SREBPs), specifically SREBP-2 (Hua et al, 1993; Brown and Goldstein, 1997). When cholesterol stores in cells are depleted, SREBP-2 activates transcription of genes in the sterol pathway such as HMGCR, HMG-CoA synthase, farnesyl pyrophosphate (FPP) synthase, squalene synthase (SQLS) and the LDL receptor (Smith et al, 1988, 1990; Sakai et al, 1996; Brown and Goldstein, 1999; Horton et al, 2002). The requirement of additional downstream sterol pathway metabolites for HCV replication has not been completely elucidated.
To further understand the impact of the sterol pathway and its regulation on HCV replication, we conducted a high-throughput combination chemical genetic screen using 16 chemical probes that are known to modulate the activity of target enzymes relating to the sterol biosynthesis pathway (Figure 1). Using this approach, we identified several novel antiviral targets including SREBP-2 as well as targets downstream of HMGCR in the sterol pathway such as oxidosqualene cyclase (OSC) and lanosterol demethylase. Many of our chemical probes, specifically SR-12813, farnesol and squalestatin, strongly promoted replicon replication. The actions of both farnesol and squalestatin ultimately result in an increase in the cellular pool of GGPP, which is known to increase HCV replication (Ye et al, 2003; Kapadia and Chisari, 2005; Wang et al, 2005).
Chemical combinations targeting enzymes upstream of squalene epoxidase (SQLE) at the top of the sterol pathway (Figure 4A) elicited Bateson-type epistatic responses (Boone et al, 2007), where the upstream agent's response predominates over the effects of inhibiting all downstream targets. This was especially notable for combinations including simvastatin and either U18666A or squalestatin, and for squalestatin in combination with Ro48-8071. Treatment with squalestatin prevents the SQLS substrate, farnesyl pyrophosphate (FPP) from being further metabolized by the sterol pathway. As FPP concentrations increase, the metabolite can be shunted away from the sterol pathway toward farnesylation and GGPP synthetic pathways, resulting in an increase in host protein geranylgeranylation, including FBL2, and consequently replicon replication. This increase in replicon replication explains the source of the observed epistasis over Ro48-8071 treatment.
Combinations between probes targeting enzymes downstream of and including OSC produced robust synergies with each other or with a PGGT inhibitor. Figure 4B highlights examples of antiviral synergy resulting from treatment of cells with an OSC inhibitor in combination with an inhibitor of either an enzyme upstream or downstream of OSC. A combination of terconazole and U18666A is synergistic without similar combination effects in the host proliferation screen. Likewise, clomiphene was also synergistic when added to replicon cells in combination with U18666A. One of the greatest synergies observed downstream in the sterol pathway is a combination of amorolfine and AY 9944, suggesting that there is value in developing combinations of drugs that target enzymes in the sterol pathway, which are downstream of HMGCR.
Interactions with the protein prenylation pathway also showed strong mechanistic patterns (Figure 4C). GGTI-286 is a peptidomimetic compound resembling the CAAX domain of a protein to be geranylgeranylated and is a competitive inhibitor of protein geranylgeranylation. Simvastatin impedes the antiviral effect of GGTI-286 at low concentrations but that antagonism is balanced by comparable synergy at higher concentrations. At the low simvastatin concentrations, a compensatory increase in HMGCR expression leads to increased cellular levels of GGPP, which are likely to result in an increase in PGGT enzymatic turnover and decreased GGTI-286 efficacy. The antiviral synergy observed at the higher inhibitor concentrations is likely nonspecific as synergy was also observed in a host viability assay. Further downstream, however, a competitive interaction was observed between GGTI-286 and squalestatin, where the opposing effect of one compound obscures the other compound's effect. This competitive relationship between GGTI and SQLE explains the epistatic response observed between those two agents. For inhibitors of targets downstream of OSC, such as amorolfine, there are strong antiviral synergies with GGTI-286. Notably, combinations with OSC inhibitors and GGTI-286 were selective, in that comparable synergy was not found in a host viability assay. This selectivity suggests that jointly targeting OSC and PGGT is a promising avenue for future HCV therapy development.
This study provides a comprehensive and unique perspective into the impact of sterol pathway regulation on HCV replication and provides compelling insight into the use of chemical combinations to maximize antiviral effects while minimizing proviral consequences. Our results suggest that HCV therapeutics developed against sterol pathway targets must consider the impact on underlying sterol pathway regulation. We found combinations of inhibitors of the lower part of the sterol pathway that are effective and synergistic with each other when tested in combination. Furthermore, the combination effects observed with simvastatin suggest that, though statins inhibit HMGCR activity, the resulting regulatory consequences of such inhibition ultimately lead to undesirable epistatic effects. Inhibitors that prevent SREBP-2 activation, inhibit PGGT or encourage the production of polar sterols have great potential as HCV therapeutics if associated toxicities can be reduced.
The search for effective Hepatitis C antiviral therapies has recently focused on host sterol metabolism and protein prenylation pathways that indirectly affect viral replication. However, inhibition of the sterol pathway with statin drugs has not yielded consistent results in patients. Here, we present a combination chemical genetic study to explore how the sterol and protein prenylation pathways work together to affect hepatitis C viral replication in a replicon assay. In addition to finding novel targets affecting viral replication, our data suggest that the viral replication is strongly affected by sterol pathway regulation. There is a marked transition from antagonistic to synergistic antiviral effects as the combination targets shift downstream along the sterol pathway. We also show how pathway regulation frustrates potential hepatitis C therapies based on the sterol pathway, and reveal novel synergies that selectively inhibit hepatitis C replication over host toxicity. In particular, combinations targeting the downstream sterol pathway enzymes produced robust and selective synergistic inhibition of hepatitis C replication. Our findings show how combination chemical genetics can reveal critical pathway connections relevant to viral replication, and can identify potential treatments with an increased therapeutic window.
PMCID: PMC2913396  PMID: 20531405
chemical genetics; combinations and synergy; hepatitis C; replicon; sterol biosynthesis
9.  Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation 
BMC Systems Biology  2011;5:94.
Higher plants and algae are able to fix atmospheric carbon dioxide through photosynthesis and store this fixed carbon in large quantities as starch, which can be hydrolyzed into sugars serving as feedstock for fermentation to biofuels and precursors. Rational engineering of carbon flow in plant cells requires a greater understanding of how starch breakdown fluxes respond to variations in enzyme concentrations, kinetic parameters, and metabolite concentrations. We have therefore developed and simulated a detailed kinetic ordinary differential equation model of the degradation pathways for starch synthesized in plants and green algae, which to our knowledge is the most complete such model reported to date.
Simulation with 9 internal metabolites and 8 external metabolites, the concentrations of the latter fixed at reasonable biochemical values, leads to a single reference solution showing β-amylase activity to be the rate-limiting step in carbon flow from starch degradation. Additionally, the response coefficients for stromal glucose to the glucose transporter kcat and KM are substantial, whereas those for cytosolic glucose are not, consistent with a kinetic bottleneck due to transport. Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and β-amylase kcat and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole. The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways. Exploration of the steady-state solution space with respect to concentrations of 6 external metabolites and 8 dynamic metabolite concentrations show that stromal metabolism is strongly coupled to starch levels, and that transport between compartments serves to lower coupling between metabolic subsystems in different compartments.
We find that in the reference steady state, starch cleavage is the most significant determinant of carbon flux, with turnover of oligosaccharides playing a secondary role. Independence of stationary point with respect to initial dynamic variable values confirms a unique stationary point in the phase space of dynamically varying concentrations of the model network. Stromal maltooligosaccharide metabolism was highly coupled to the available starch concentration. From the most highly converged trajectories, distances between unique fixed points of phase spaces show that cytosolic maltose levels depend on the total concentrations of arabinogalactan and glucose present in the cytosol. In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions.
PMCID: PMC3148208  PMID: 21682905
10.  Oncogenic K-Ras decouples glucose and glutamine metabolism to support cancer cell growth 
A systems approach using 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD), and transcriptional profiling was applied to investigate the role of oncogenic K-Ras in metabolic transformation.K-Ras transformed cells exhibit an increased glycolytic rate and lower flux through the oxidative tricarboxylic acid (TCA) cycle.K-Ras transformed cells show a relative increase in glutamine anaplerosis and reductive TCA metabolism.Transcriptional changes driven by oncogenic K-Ras suggest control nodes associated with the metabolic reprogramming of cancer cells.
The ras and myc oncogenes drive pleiotropic changes in cell signaling, nutrient uptake, and intracellular metabolism (Chiaradonna et al, 2006b; Yuneva et al, 2007; Wise et al, 2008; Vander Heiden et al, 2009). Mutated ras proteins, identified in 25% of human cancers (Bos, 1989; Downward, 2003), correlate with an increased rate of glucose consumption, lactate accumulation, altered expression of mitochondrial genes, increased ROS production, and reduced mitochondrial activity (Bos, 1989; Downward, 2003; Vizan et al, 2005; Chiaradonna et al, 2006a; Yun et al, 2009; Baracca et al, 2010; Weinberg et al, 2010). Furthermore, K-Ras transformed cancer cells are dependent upon glucose and glutamine availability, since their withdrawal induces apoptosis and cell-cycle arrest, respectively (Ramanathan et al, 2005; Telang et al, 2006; Yun et al, 2009). However, the precise metabolic effects downstream of oncogenic Ras signaling as well as the mechanisms by which intracellular glucose and glutamine metabolism change have not been completely elucidated.
In this report, we have investigated the reprogramming of central carbon metabolism in cancer cells and its regulation by the K-ras oncogene, applying a systems level approach using 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD), and transcriptional profiling. These data reveal a coordinated decoupling of glycolysis and the tricarboxylic acid (TCA) cycle. K-Ras transformed mouse and human cells exhibited a high glucose to lactate flux and relatively lower oxidative metabolism of pyruvate. Such changes were supported by increased expression of glycolytic genes as well as several pyruvate dehydrogenase kinases. In contrast to glucose, the contribution of glutamine carbon to TCA cycle intermediates through both oxidative and reductive metabolism was significantly increased upon K-Ras transformation. Despite this increase in glutamine anaplerosis, oxidative TCA flux was significantly decreased. Additionally, we observed elevated levels of glutamine-derived nitrogen in various biosynthetic metabolites in transformed cells, including amino acids, 5-oxoproline, and the nucleobase adenine. Consistent with these changes, we detected increased transcription of genes associated with glutamine metabolism and nucleotide biosynthesis in cells expressing oncogenic K-Ras.
Taken together, these findings indicate an important role of oncogenic K-Ras in cancer cell metabolism. The observed decoupling of glucose and glutamine metabolism enables the efficient utilization of both carbon and nitrogen from glutamine for biosynthetic processes. In accord with these alterations, oncogenic K-Ras induces gene expression changes that may drive this metabolic reprogramming. Finally, these results may enable the identification of metabolic and transcriptional targets throughout the network and allow more effective cancer therapies.
Oncogenes such as K-ras mediate cellular and metabolic transformation during tumorigenesis. To analyze K-Ras-dependent metabolic alterations, we employed 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD) of 15N-labeled glutamine, and transcriptomic profiling in mouse fibroblast and human carcinoma cell lines. Stable isotope-labeled glucose and glutamine tracers and computational determination of intracellular fluxes indicated that cells expressing oncogenic K-Ras exhibited enhanced glycolytic activity, decreased oxidative flux through the tricarboxylic acid (TCA) cycle, and increased utilization of glutamine for anabolic synthesis. Surprisingly, a non-canonical labeling of TCA cycle-associated metabolites was detected in both transformed cell lines. Transcriptional profiling detected elevated expression of several genes associated with glycolysis, glutamine metabolism, and nucleotide biosynthesis upon transformation with oncogenic K-Ras. Chemical perturbation of enzymes along these pathways further supports the decoupling of glycolysis and TCA metabolism, with glutamine supplying increased carbon to drive the TCA cycle. These results provide evidence for a role of oncogenic K-Ras in the metabolic reprogramming of cancer cells.
PMCID: PMC3202795  PMID: 21847114
cancer; metabolic flux analysis; metabolism; Ras; transcriptional analysis
11.  Chemical combination effects predict connectivity in biological systems 
Chemical synergies can be novel probes of biological systems.Simulated response shapes depend on target connectivity in a pathway.Experiments with yeast and cancer cells confirm simulated effects.Profiles across many combinations yield target location information.
Living organisms are built of interacting components, whose function and dysfunction can be described through dynamic network models (Davidson et al, 2002). Systems Biology involves the iterative construction of such models (Ideker et al, 2001), and may eventually improve the understanding of diseases using in silico simulations. Such simulations may eventually permit drugs to be prioritized for clinical trials, reducing potential risks and increasing the likelihood of successful outcomes. Given the complexity of biological systems, constructing realistic models will require large and diverse sets of connectivity data.
Chemical combinations provide a new window into biological connectivity. Information gleaned from targeted combinations, such as paired mutations (Tong et al, 2004), has proven to be especially useful for revealing functional interactions between components. We have been screening chemical combinations for therapeutic synergies (Borisy et al, 2003; Zimmermann et al, 2007), collecting full-dose matrices where combinations are tested in all possible pairings of serially diluted single agent doses (Figure 1). Such screens yield a variety of response surfaces with distinct shapes for combinations that work through different known mechanisms, suggesting that combination effects may contain information on the nature of functional connections between drug targets.
Simulations of biological pathways predict synergistic responses to inhibitors that depend on target connectivity. We explored theoretical predictions by simulating a metabolic pathway with pairs of inhibitors aimed at different targets with varying doses. We found that the shape of each combination response depended on how the inhibitor pair's targets were connected in the pathway (Figure 2). The predicted response shapes were robust to plausible variations in the simulated pathway that did not affect the network topology (e.g., kinetic assumptions, parameter values, and nonlinear response functions), but were very sensitive to topological alterations in the modelled network (e.g., feedback regulation or changing the type of junction at a branch point). These findings suggest that connectivity of the inhibitor targets has a major influence on combination response morphology.
The predicted shapes were experimentally confirmed in yeast combination experiments. The proliferation experiment used drugs focused on the sterol biosynthesis pathway, which is mostly linear between the targets covered in this study, and is known to be regulated by negative feedback (Gardner et al, 2001). The combinations between sterol inhibitors confirmed expectations from our simulations, showing dose-additive responses for pairs targeting the same enzyme and strong synergies across enzymes of the shape predicted in our simulations for linear pathways under negative feedback. Combinations across pathways showed much more variable responses with a trend towards less synergy on average.
Further experimental support was obtained from human cells. A combination screen of 90 annotated drugs in a human tumour cell line (HCT116) proliferation assay produced strong synergies for combinations within pathways and more variable effects between targeted functions. Synergy profiles (sets of all synergy scores involving each drug) also showed a greater degree of similarity for pairs of drugs with related targets. Finally, the most extreme outliers were dominated by inhibitors of kinases that are especially critical for HCT116 proliferation (Awwad et al, 2003), with effects that are consistent across mechanistic replicates, showing that chemical combinations can highlight biologically relevant cellular processes.
This study demonstrates the potential of chemical combinations for exploring functional connectivity in biological systems. This information complements genetic studies by providing more details through variable dosing, by directly targeting single domains of multi-domain proteins, and by probing cell types that are not amenable to mutagenesis. Responses from large chemical combination screens can be used to identify molecular targets through chemical–genetic profiling (Macdonald et al, 2006), or to directly constrain network models by means of a prediction-validation procedure (Ideker et al, 2001). This initial exploration can be extended to cover a wider range of response shapes and network topologies, as well as to combinations of three or more chemical agents. Moreover, this approach may even be applicable to non-biological systems where responses to targeted perturbations can be measured.
Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.
PMCID: PMC1828746  PMID: 17332758
chemical genetics; combinations and synergy; metabolic and regulatory networks; simulation and data analysis
12.  Dynamic interaction networks in a hierarchically organized tissue 
We have integrated gene expression profiling with database and literature mining, mechanistic modeling, and cell culture experiments to identify intercellular and intracellular networks regulating blood stem cell self-renewal.Blood stem cell fate in vitro is regulated non-autonomously by a coupled positive–negative intercellular feedback circuit, composed of megakaryocyte-derived stimulatory growth factors (VEGF, PDGF, EGF, and serotonin) versus monocyte-derived inhibitory factors (CCL3, CCL4, CXCL10, TGFB2, and TNFSF9).The antagonistic signals converge in a core intracellular network focused around PI3K, Raf, PLC, and Akt.Model simulations enable functional classification of the novel endogenous ligands and signaling molecules.
Intercellular (between cell) communication networks are required to maintain homeostasis and coordinate regenerative and developmental cues in multicellular organisms. Despite the recognized importance of intercellular networks in regulating adult stem and progenitor cell fate, the specific cell populations involved, and the underlying molecular mechanisms are largely undefined. Although a limited number of studies have applied novel bioinformatic approaches to unravel intercellular signaling in other cell systems (Frankenstein et al, 2006), a comprehensive analysis of intercellular communication in a stem cell-derived, hierarchical tissue network has yet to be reported.
As a model system to explore intercellular communication networks in a hierarchically organized tissue, we cultured human umbilical cord blood (UCB)-derived stem and progenitor cells in defined, minimal cytokine-supplemented liquid culture (Madlambayan et al, 2006). To systematically explore the molecular and cellular dynamics underlying primitive progenitor growth and differentiation, gene expression profiles of primitive (lineage negative; Lin−) and mature (lineage positive; Lin+) populations were generated during phases of stem cell expansion versus depletion. Parallel phenotypic and subproteomic experiments validated that mRNA expression correlated with complex measures of proteome activity (protein secretion and cell surface expression). Using a curated list of secreted ligand–receptor interactions and published expression profiles of purified mature blood populations, we implemented a novel algorithm to reconstruct the intercellular signaling networks established between stem cells and multi-lineage progeny in vitro. By correlating differential expression patterns with stem cell growth, we predict cell populations, pathways, and secreted ligands associated with stem cell self-renewal and differentiation (Figure 3A).
We then tested the correlative predictions in a series of cell culture experiments. UCB progenitor cell cultures were supplemented with saturating amounts of 18 putative regulatory ligands, or cocultured with purified mature blood lineages (megakaryocytes, monocytes, and erythrocytes), and analyzed for effects on total cell, progenitor, and primitive progenitor growth. At the primitive progenitor level, 3/5 novel predicted stimulatory ligands (EGF, PDGFB, and VEGF) displayed significant positive effects, 5/7 predicted inhibitory factors (CCL3, CCL4, CXCL10, TNFSF9, and TGFB2) displayed negative effects, whereas only 1/5 non-correlated ligand (CXCL7) displayed an effect. Also consistent with predictions from gene expression data, megakaryocytes and monocytes were found to stimulate and inhibit primitive progenitor growth, respectively, and these effects were attributable to differential secretome profiles of stimulatory versus inhibitory ligands.
Cellular responses to external stimuli, particularly in heterogeneous and dynamic cell populations, represent complex functions of multiple cell fate decisions acting both directly and indirectly on the target (stem cell) populations. Experimentally distinguishing the mode of action of cytokines is thus a difficult task. To address this we used our previously published interactive model of hematopoiesis (Kirouac et al, 2009) to classify experimentally identified regulatory ligands into one of four distinct functional categories based on their differential effects on cell population growth. TGFB2 was classified as a proliferation inhibitor, CCL4, CXCL10, SPARC, and TNFSF9 as self-renewal inhibitors, CCL3 a proliferation stimulator, and EGF, VEGF, and PDGFB as self-renewal stimulators.
Stem and progenitor cells exposed to combinatorial extracellular signals must propagate this information through intracellular molecular networks, and respond appropriately by modifying cell fate decisions. To explore how our experimentally identified positive and negative regulatory signals are integrated at the intracellular level, we constructed a blood stem cell self-renewal signaling network through extensive literature curation and protein–protein interaction (PPI) network mapping. We find that signal transduction pathways activated by the various stimulatory and inhibitory ligands converge on a limited set of molecular control nodes, forming a core subnetwork enriched for known regulators of self-renewal (Figure 6A). To experimentally test the intracellular signaling molecules computationally predicted as regulators of stem cell self-renewal, we obtained five small molecule antagonists against the kinases Phosphatidylinositol 3-kinase (PI3K), Raf, Akt, Phospholipase C (PLC), and MEK1. Liquid cultures were supplemented with the five molecules individually, and resultant cell population outputs compared against model simulations to deconvolute the functional effects on proliferation (and survival) versus self-renewal. This analysis classifies inhibition of PI3K and Raf activity as selectively targeting self-renewal, PLC as selectively targeting survival, and Akt as selectively targeting proliferation; MEK inhibition appears non-specific for these processes.
This represents the first systematic characterization of how cell fate decisions are regulated non-autonomously through lineage-specific interactions with differentiated progeny. The complex intercellular communication networks can be approximated as an antagonistic positive–negative feedback circuit, wherein progenitor expansion is modulated by a balance of megakaryocyte-derived stimulatory factors (EGF, PDGF, VEGF, and possibly serotonin) versus monocyte-derived inhibitory factors (CCL3, CCL4, CXCL10, TGFB2, and TNFSF9). This complex milieu of endogenous regulatory signals is integrated and processed within a core intracellular signaling network, resulting in modulation of cell-level kinetic parameters (proliferation, survival, and self-renewal). We reconstruct a stem cell associated intracellular network, and identify PI3K, Raf, Akt, and PLC as functionally distinct signal integration nodes, linking extracellular and intracellular signaling. These findings lay the groundwork for novel strategies to control blood stem cell self-renewal in vitro and in vivo.
Intercellular (between cell) communication networks maintain homeostasis and coordinate regenerative and developmental cues in multicellular organisms. Despite the importance of intercellular networks in stem cell biology, their rules, structure and molecular components are poorly understood. Herein, we describe the structure and dynamics of intercellular and intracellular networks in a stem cell derived, hierarchically organized tissue using experimental and theoretical analyses of cultured human umbilical cord blood progenitors. By integrating high-throughput molecular profiling, database and literature mining, mechanistic modeling, and cell culture experiments, we show that secreted factor-mediated intercellular communication networks regulate blood stem cell fate decisions. In particular, self-renewal is modulated by a coupled positive–negative intercellular feedback circuit composed of megakaryocyte-derived stimulatory growth factors (VEGF, PDGF, EGF, and serotonin) versus monocyte-derived inhibitory factors (CCL3, CCL4, CXCL10, TGFB2, and TNFSF9). We reconstruct a stem cell intracellular network, and identify PI3K, Raf, Akt, and PLC as functionally distinct signal integration nodes, linking extracellular, and intracellular signaling. This represents the first systematic characterization of how stem cell fate decisions are regulated non-autonomously through lineage-specific interactions with differentiated progeny.
PMCID: PMC2990637  PMID: 20924352
cellular networks; hematopoiesis; intercellular signaling; self-renewal; stem cells
13.  k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design 
PLoS Computational Biology  2014;10(2):e1003487.
Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.
Author Summary
Computational strain design procedures aim at assisting metabolic engineering efforts by identifying metabolic interventions leading to the targeted overproduction of a desired chemical using network models of cellular metabolism. The effect of metabolite concentrations and substrate-level enzyme regulation cannot be captured with stoichiometry-only metabolic models and analysis methods. Here, we introduce k-OptForce, an optimization-based strain design framework incorporating the mechanistic details afforded by kinetic models, whenever available, into a genome-scale stoichiometric-based modeling formalism. The resulting optimization problems pose significant computational challenges due to the bilevel nature of the formulation and the nonconvex terms in the constraints. A tractable reformulation and solution procedure is introduced for solving the optimization problems. k-OptForce uses kinetic information to (re)apportion reaction fluxes in the network by identifying interventions comprised of both direct enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Our results show that the introduction of kinetic expressions can significantly alter the identified interventions compared to those identified with stoichiometry-alone analysis. In particular, additional modifications are required in some cases to avoid the violation of metabolite concentration bounds, while in other cases, the kinetic constraints yield metabolic flux distributions that favor the overproduction of the desired product thereby requiring fewer direct interventions.
PMCID: PMC3930495  PMID: 24586136
14.  Optimal regulatory strategies for metabolic pathways in Escherichia coli depending on protein costs 
Pathways in Escherichia coli show large differences in the extent to which enzymes from the same pathway are expressed in a coordinated manner.Using dynamic optimization, we show that regulation of the initial and terminal reactions of a pathway is the minimum requirement for a precise control of flux.We find that in E. coli a regulation of initial and terminal reactions is predominantly used to control pathways with low costs of enzymes while a regulation of all enzymes occurs if protein costs are high.A trade-off between minimization of protein investment and minimization of response time can explain the preference for transcriptional regulation at key positions (leading to high protein costs, but low response time) or coordinated transcriptional regulation of all enzymes (leading to low protein costs, but high response time).
The increasing availability and decreasing prices of experimental techniques have led to an explosion in the number of available experimental data sets (Ishii et al, 2007; Lu et al, 2007; Bennett et al, 2009; Lewis et al, 2010). However, approaches to integrate these diverse data sets into a coherent model of cellular mechanisms have lagged behind (Palsson and Zengler, 2010). In this study, we want to contribute to this effort through the analysis of a large number of data sets in order to identify global principles in the regulation of metabolism in Escherichia coli. While previous studies have shed light onto the link between the transcriptional regulation of metabolism and its structure (Ihmels et al, 2004; Reed and Palsson, 2004; Schwartz et al, 2007; Seshasayee et al, 2009), the extent to which transcriptional regulation controls metabolism has remained elusive.
To address this problem, we investigated the coexpression of enzymes within the same pathway in all biochemically annotated subsystems of E. coli metabolism. As a reference for metabolic pathways, we used elementary flux patterns, a recently introduced concept for pathway analysis in genome-scale metabolic networks (Kaleta et al, 2009). Through this analysis, we found that while pathways in many subsystems of metabolism show a high degree of coexpression, pathways in the subsystems cofactor and prosthetic group biosynthesis, glycerophospholipid metabolism, murein recycling, nucleotide salvage pathway and pentose phosphate pathway show only weak coexpression. We refer to these subsystems with a low coordination of transcriptional regulation as transcriptionally sparsely regulated subsystems.
In order to understand these different patterns of regulation, we constructed a simplified model of a linear metabolic pathway that converts a substrate s via four intermediates into a product p. We then used dynamic optimization to identify a regulatory program (i.e. a time course for the enzyme concentrations), which allows the cell to maintain the concentration of the product p in a changing environment while obeying a set of physiological constraints. As an objective function we used the minimization of the level of transcriptional regulation, specified through absolute deviations of enzyme concentrations from their initial values, and the minimization of protein costs. Protein costs are measured as the sum of the initial enzyme concentrations.
The optimization results revealed that for a full control of the flux through a pathway, transcriptional regulation of initial and terminal reactions of the pathway is sufficient (sparse transcriptional regulation). Regulation of the first reaction is required to control the flux into the pathway, and hence, the intermediate concentrations. In contrast, regulation at the terminal position is required to tightly control the rate of synthesis of the product. By performing the same optimization for randomly chosen kinetic parameters, we found that this pattern is also optimal in most cases with differences in the catalytic properties of enzymes. Moreover, we found that with increasing enzyme costs (i.e. increasing enzyme concentrations), there is a shift from sparse transcriptional regulation to coordinated transcriptional regulation of all enzymes within a pathway (pervasive transcriptional regulation).
To verify these predictions, we analyzed the position-specific frequency of regulatory events in the pathways of the transcriptionally sparsely regulated subsystems. We could confirm that there is a significant increase in the frequency of transcriptional regulation at the end and a less pronounced increase at the beginning of pathways. Performing the same analysis for post-translational regulation, we found that there is a statistically significant increase at the beginning of pathways. Thus, the control at the beginning of pathways is achieved through a combination of transcriptional and post-translational regulation. In other subsystems that were not identified as transcriptionally sparsely regulated, we did not find this pattern of transcriptional regulation while the same pattern of post-translational regulation could be observed. By analyzing protein abundance data, we confirmed that particularly pathways within subsystems, for which enzyme costs are low, are transcriptionally sparsely regulated.
Having confirmed the predictions made by the optimization, we found that there appears to be a mechanism favoring sparse transcriptional regulation in pathways with low-cost enzymes. We suggest an evolutionary trade-off between the cellular objectives of protein cost minimization and response time minimization as a cause of this mechanism. The optimal strategy to reduce average protein costs is to transcriptionally control enzymes within a pathway. However, responses on a transcriptional level are usually very slow. In contrast, short response times can be achieved through a constitutive expression of enzymes with a focused regulation of key steps within a pathway. The interplay between the two cellular objectives leads to the observation that particularly pathways with highly abundant and thus costly enzymes are transcriptionally pervasively regulated (Figure 7A). In contrast, pathways with low abundance enzymes are transcriptionally sparsely regulated (Figure 7B). In agreement with these results, we found that pathways such as the pentose phosphate pathway, for which rapid response times are required, are sparsely regulated even if they contain costly enzymes (Figure 7C). Finally, if the fitness advantage achieved through following either of the cellular objectives is low, sparse transcriptional regulation is the minimum requirement to control flux through a pathway (Figure 7D).
In summary, our results demonstrate that, in contrast to the classical picture, regulation of key positions of metabolic pathways is sufficient for full control of flux and is implemented in vivo. This pattern of sparse regulation is particularly useful if a higher fitness advantage can be achieved through rapid response times compared to the fitness advantage achieved through the reduced protein cost of pervasive transcriptional regulation.
Analysis of optimal strategies for the control of metabolic pathways in Escherichia coli reveals that the extent of transcriptional regulation reflects an evolutionary trade-off between the minimization of response time and protein costs.
While previous studies have shed light on the link between the structure of metabolism and its transcriptional regulation, the extent to which transcriptional regulation controls metabolism has not yet been fully explored. In this work, we address this problem by integrating a large number of experimental data sets with a model of the metabolism of Escherichia coli. Using a combination of computational tools including the concept of elementary flux patterns, methods from network inference and dynamic optimization, we find that transcriptional regulation of pathways reflects the protein investment into these pathways. While pathways that are associated to a high protein cost are controlled by fine-tuned transcriptional programs, pathways that only require a small protein cost are transcriptionally controlled in a few key reactions. As a reason for the occurrence of these different regulatory strategies, we identify an evolutionary trade-off between the conflicting requirements to reduce protein investment and the requirement to be able to respond rapidly to changes in environmental conditions.
PMCID: PMC3159982  PMID: 21772263
cost-optimal regulatory strategies; evolutionary optimization; genome-scale metabolic networks; proteomics; transcriptomics
15.  Host Defense against Viral Infection Involves Interferon Mediated Down-Regulation of Sterol Biosynthesis 
PLoS Biology  2011;9(3):e1000598.
Upon infection, our immune cells produce a small protein called interferon, which in turn signals a protective response through a series of biochemical reactions that involves lowering the cells' ability to make cholesterol by targeting a gene essential for controlling the pathway for cholesterol metabolism.
Little is known about the protective role of inflammatory processes in modulating lipid metabolism in infection. Here we report an intimate link between the innate immune response to infection and regulation of the sterol metabolic network characterized by down-regulation of sterol biosynthesis by an interferon regulatory loop mechanism. In time-series experiments profiling genome-wide lipid-associated gene expression of macrophages, we show a selective and coordinated negative regulation of the complete sterol pathway upon viral infection or cytokine treatment with IFNγ or β but not TNF, IL1β, or IL6. Quantitative analysis at the protein level of selected sterol metabolic enzymes upon infection shows a similar level of suppression. Experimental testing of sterol metabolite levels using lipidomic-based measurements shows a reduction in metabolic output. On the basis of pharmacologic and RNAi inhibition of the sterol pathway we show augmented protection against viral infection, and in combination with metabolite rescue experiments, we identify the requirement of the mevalonate-isoprenoid branch of the sterol metabolic network in the protective response upon statin or IFNβ treatment. Conditioned media experiments from infected cells support an involvement of secreted type 1 interferon(s) to be sufficient for reducing the sterol pathway upon infection. Moreover, we show that infection of primary macrophages containing a genetic knockout of the major type I interferon, IFNβ, leads to only a partial suppression of the sterol pathway, while genetic knockout of the receptor for all type I interferon family members, ifnar1, or associated signaling component, tyk2, completely abolishes the reduction of the sterol biosynthetic activity upon infection. Levels of the proteolytically cleaved nuclear forms of SREBP2, a key transcriptional regulator of sterol biosynthesis, are reduced upon infection and IFNβ treatment at both the protein and de novo transcription level. The reduction in srebf2 gene transcription upon infection and IFN treatment is also found to be strictly dependent on ifnar1. Altogether these results show that type 1 IFN signaling is both necessary and sufficient for reducing the sterol metabolic network activity upon infection, thereby linking the regulation of the sterol pathway with interferon anti-viral defense responses. These findings bring a new link between sterol metabolism and interferon antiviral response and support the idea of using host metabolic modifiers of innate immunity as a potential antiviral strategy.
Author Summary
Currently, little is known about the crosstalk between the body's immune and metabolic systems that occurs after viral infection. This work uncovers a previously unappreciated physiological role for the cholesterol-metabolic pathway in protecting against infection that involves a molecular link with the protein interferon, which is made by immune cells and known to “interfere” with viral replication. We used a clinically relevant model based on mouse cytomegalovirus (CMV) infection of bone-marrow-derived cells. Upon infection these cells produce high levels of interferon as part of the innate-immune response, which we show in turn signals through the interferon receptor resulting in lowering enzyme levels on the cholesterol pathway. We observed this effect with a range of other viruses, and in each case it leads to a notable drop in the metabolites involved in the cholesterol pathway. We found that the control mechanism involves regulation by interferon of an essential transcription factor, named SREBP-2, which coordinates the gene activity of the cholesterol pathway. This mechanism may explain clinical observations of reduced cholesterol levels in patients receiving interferon treatment. Our initial investigation into how lowered cholesterol might protect against viral infection reveals that the protection is not due to a requirement of the virus for cholesterol itself but instead involves a particular side-branch of the pathway that chemically links lipids to proteins. Drugs such as statins and small interfering RNAs that block this part of the pathway are also shown to protect against CMV infection of cells in culture and in mice. This provides the first example of targeting a host metabolic pathway in order to protect against an acute infection.
PMCID: PMC3050939  PMID: 21408089
16.  HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology 
We present HepatoNet1, a manually curated large-scale metabolic network of the human hepatocyte that encompasses >2500 reactions in six intracellular and two extracellular compartments.Using constraint-based modeling techniques, the network has been validated to replicate numerous metabolic functions of hepatocytes corresponding to a reference set of diverse physiological liver functions.Taking the detoxification of ammonia and the formation of bile acids as examples, we show how these liver-specific metabolic objectives can be achieved by the variable interplay of various metabolic pathways under varying conditions of nutrients and oxygen availability.
The liver has a pivotal function in metabolic homeostasis of the human body. Hepatocytes are the principal site of the metabolic conversions that underlie diverse physiological functions of the liver. These functions include provision and homeostasis of carbohydrates, amino acids, lipids and lipoproteins in the systemic blood circulation, biotransformation, plasma protein synthesis and bile formation, to name a few. Accordingly, hepatocyte metabolism integrates a vast array of differentially regulated biochemical activities and is highly responsive to environmental perturbations such as changes in portal blood composition (Dardevet et al, 2006). The complexity of this metabolic network and the numerous physiological functions to be achieved within a highly variable physiological environment necessitate an integrated approach with the aim of understanding liver metabolism at a systems level. To this end, we present HepatoNet1, a stoichiometric network of human hepatocyte metabolism characterized by (i) comprehensive coverage of known biochemical activities of hepatocytes and (ii) due representation of the biochemical and physiological functions of hepatocytes as functional network states. The network comprises 777 metabolites in six intracellular (cytosol, endoplasmic reticulum and Golgi apparatus, lysosome, mitochondria, nucleus, and peroxisome) and two extracellular compartments (bile canaliculus and sinusoidal space) and 2539 reactions, including 1466 transport reactions. It is based on the manual evaluation of >1500 original scientific research publications to warrant a high-quality evidence-based model. The final network is the result of an iterative process of data compilation and rigorous computational testing of network functionality by means of constraint-based modeling techniques. We performed flux-balance analyses to validate whether for >300 different metabolic objectives a non-zero stationary flux distribution could be established in the network. Figure 1 shows one such functional flux mode associated with the synthesis of the bile acid glycochenodeoxycholate, one important hepatocyte-specific physiological liver function. Besides those pathways directly linked to the synthesis of the bile acid, the mevalonate pathway and the de novo synthesis of cholesterol, the flux mode comprises additional pathways such as gluconeogenesis, the pentose phosphate pathway or the ornithine cycle because the calculations were routinely performed on a minimal set of exchangeable metabolites, that is all reactants were forced to be balanced and all exportable intermediates had to be catabolized into non-degradable end products. This example shows how HepatoNet1 under the challenges of limited exchange across the network boundary can reveal numerous cross-links between metabolic pathways traditionally perceived as separate entities. For example, alanine is used as gluconeogenic substrate to form glucose-6-phosphate, which is used in the pentose phosphate pathway to generate NADPH. The glycine moiety for bile acid conjugation is derived from serine. Conversion of ammonia into non-toxic nitrogen compounds is one central homeostatic function of hepatocytes. Using the HepatoNet1 model, we investigated, as another example of a complex metabolic objective dependent on systemic physiological parameters, how the consumption of oxygen, glucose and palmitate is affected when an external nitrogen load is converted in varying proportions to the non-toxic nitrogen compounds: urea, glutamine and alanine. The results reveal strong dependencies between the available level of oxygen and the substrate demand of hepatocytes required for effective ammonia detoxification by the liver.
Oxygen demand is highest if nitrogen is exclusively transformed into urea. At lower fluxes into urea, an intriguing pattern for oxygen demand is predicted: oxygen demand attains a minimum if the nitrogen load is directed to urea, glutamine and alanine with relative fluxes of 0.17, 0.43 and 0.40, respectively (Figure 2A). Oxygen demand in this flux distribution is four times lower than for the maximum (100% urea) and still 77 and 33% lower than using alanine and glutamine as exclusive nitrogen compounds, respectively. This computationally predicted tendency is consistent with the notion that the zonation of ammonia detoxification, that is the preferential conversion of ammonia to urea in periportal hepatocytes and to glutamine in perivenous hepatocytes, is dictated by the availability of oxygen (Gebhardt, 1992; Jungermann and Kietzmann, 2000). The decreased oxygen demand in flux distributions using higher proportions of glutamine or alanine is accompanied by increased uptake of the substrates glucose and palmitate (Figure 2B). This is due to an increased demand of energy and carbon for the amidation and transamination of glutamate and pyruvate to discharge nitrogen in the form of glutamine and alanine, respectively. In terms of both scope and specificity, our model bridges the scale between models constructed specifically to examine distinct metabolic processes of the liver and modeling based on a global representation of human metabolism. The former include models for the interdependence of gluconeogenesis and fatty-acid catabolism (Chalhoub et al, 2007), impairment of glucose production in von Gierke's and Hers' diseases (Beard and Qian, 2005) and other processes (Calik and Akbay, 2000; Stucki and Urbanczik, 2005; Ohno et al, 2008). The hallmark of these models is that each of them focuses on a small number of reactions pertinent to the metabolic function of interest embedded in a customized representation of the principal pathways of central metabolism. HepatoNet1, currently, outperforms liver-specific models computationally predicted (Shlomi et al, 2008) on the basis of global reconstructions of human metabolism (Duarte et al, 2007; Ma and Goryanin, 2008). In contrast to either of the aforementioned modeling scales, HepatoNet1 provides the combination of a system-scale representation of metabolic activities and representation of the cell type-specific physical boundaries and their specific transport capacities. This allows for a highly versatile use of the model for the analysis of various liver-specific physiological functions. Conceptually, from a biological system perspective, this type of model offers a large degree of comprehensiveness, whereas retaining tissue specificity, a fundamental design principle of mammalian metabolism. HepatoNet1 is expected to provide a structural platform for computational studies on liver function. The results presented herein highlight how internal fluxes of hepatocyte metabolism and the interplay with systemic physiological parameters can be analyzed with constraint-based modeling techniques. At the same time, the framework may serve as a scaffold for complementation of kinetic and regulatory properties of enzymes and transporters for analysis of sub-networks with topological or kinetic modeling methods.
We present HepatoNet1, the first reconstruction of a comprehensive metabolic network of the human hepatocyte that is shown to accomplish a large canon of known metabolic liver functions. The network comprises 777 metabolites in six intracellular and two extracellular compartments and 2539 reactions, including 1466 transport reactions. It is based on the manual evaluation of >1500 original scientific research publications to warrant a high-quality evidence-based model. The final network is the result of an iterative process of data compilation and rigorous computational testing of network functionality by means of constraint-based modeling techniques. Taking the hepatic detoxification of ammonia as an example, we show how the availability of nutrients and oxygen may modulate the interplay of various metabolic pathways to allow an efficient response of the liver to perturbations of the homeostasis of blood compounds.
PMCID: PMC2964118  PMID: 20823849
computational biology; flux balance; liver; minimal flux
17.  Contribution of Network Connectivity in Determining the Relationship between Gene Expression and Metabolite Concentration Changes 
PLoS Computational Biology  2014;10(4):e1003572.
One of the primary mechanisms through which a cell exerts control over its metabolic state is by modulating expression levels of its enzyme-coding genes. However, the changes at the level of enzyme expression allow only indirect control over metabolite levels, for two main reasons. First, at the level of individual reactions, metabolite levels are non-linearly dependent on enzyme abundances as per the reaction kinetics mechanisms. Secondly, specific metabolite pools are tightly interlinked with the rest of the metabolic network through their production and consumption reactions. While the role of reaction kinetics in metabolite concentration control is well studied at the level of individual reactions, the contribution of network connectivity has remained relatively unclear. Here we report a modeling framework that integrates both reaction kinetics and network connectivity constraints for describing the interplay between metabolite concentrations and mRNA levels. We used this framework to investigate correlations between the gene expression and the metabolite concentration changes in Saccharomyces cerevisiae during its metabolic cycle, as well as in response to three fundamentally different biological perturbations, namely gene knockout, nutrient shock and nutrient change. While the kinetic constraints applied at the level of individual reactions were found to be poor descriptors of the mRNA-metabolite relationship, their use in the context of the network enabled us to correlate changes in the expression of enzyme-coding genes to the alterations in metabolite levels. Our results highlight the key contribution of metabolic network connectivity in mediating cellular control over metabolite levels, and have implications towards bridging the gap between genotype and metabolic phenotype.
Author Summary
Regulation of metabolic activity in response to environmental and genetic perturbations is fundamental to the growth and maintenance of all cells. A primary regulatory process used by cells to control the activity of their metabolic network is the alteration in the expression of enzyme-coding genes. How these alterations regulate metabolite concentrations is an important question in the quest towards unraveling the genotype-phenotype relationship. The link between the expression levels of enzymes and metabolite concentrations is governed by the kinetics of individual reactions, which in turn are interlinked with each other due to the complex connectivity structure of metabolic networks. Although the enzyme-metabolite relationship is relatively well studied at the level of individual reactions, our understanding of the regulation of metabolite levels in complex networks has remained incomplete. In this study, we show that the constraints imposed by the network connectivity are key determinants of the relationship between gene expression and metabolite concentration changes. Our results provide mechanistic insight into the function of complex metabolic networks and have implications for health and biotechnological applications.
PMCID: PMC3998873  PMID: 24762675
18.  Chemical Basis of Metabolic Network Organization 
PLoS Computational Biology  2011;7(10):e1002214.
Although the metabolic networks of the three domains of life consist of different constituents and metabolic pathways, they exhibit the same scale-free organization. This phenomenon has been hypothetically explained by preferential attachment principle that the new-recruited metabolites attach preferentially to those that are already well connected. However, since metabolites are usually small molecules and metabolic processes are basically chemical reactions, we speculate that the metabolic network organization may have a chemical basis. In this paper, chemoinformatic analyses on metabolic networks of Kyoto Encyclopedia of Genes and Genomes (KEGG), Escherichia coli and Saccharomyces cerevisiae were performed. It was found that there exist qualitative and quantitative correlations between network topology and chemical properties of metabolites. The metabolites with larger degrees of connectivity (hubs) are of relatively stronger polarity. This suggests that metabolic networks are chemically organized to a certain extent, which was further elucidated in terms of high concentrations required by metabolic hubs to drive a variety of reactions. This finding not only provides a chemical explanation to the preferential attachment principle for metabolic network expansion, but also has important implications for metabolic network design and metabolite concentration prediction.
Author Summary
The metabolic networks of the three domains of life exhibit the same scale-free organization, which has been hypothetically explained in terms of preferential attachment principle. Here we reveal that the scale-free organization of metabolic networks may have a chemical basis. Through a chemoinformatic analysis on metabolic networks of Kyoto Encyclopedia of Genes and Genomes (KEGG), Escherichia coli and Saccharomyces cerevisiae, it was found that the metabolites with higher degrees of connectivity (hubs) are of relatively stronger polarity. The reason underlying this phenomenon is that to drive a variety of reactions, metabolic hubs have to be highly concentrated. Since the intracellular environments are hydrophilic, metabolic hubs have to be strong-polar to reach high concentrations. This finding has direct implications for metabolic network design and provides a chemical explanation to the preferential attachment principle, which has been validated by numerical simulations of metabolic network expansion. In addition, the correlations between metabolite concentration, metabolic network topology and metabolite chemical properties also suggest that we can use chemical and topological properties of metabolites to predict their intracellular concentrations. A support vector regression model has been successfully established to predict the metabolite concentrations for Escherichia coli.
PMCID: PMC3192814  PMID: 22022254
19.  A synthetic library of RNA control modules for predictable tuning of gene expression in yeast 
The authors describe a library of synthetic RNA control elements that provide programmable post-transcriptional regulation of gene expression in yeast. This toolkit is then used to study endogenous regulation of the ergosterol biosynthetic pathway.
Rnt1p hairpins can act as effective posttranscriptional gene regulatory elements in the yeast Saccharomyces cerevisiae.Modification of the cleavage efficiency box (CEB) region of an Rnt1p hairpin can modulate Rnt1p cleavage rates, and thus the resulting gene regulatory activities of the hairpin control elements.A library of Rnt1p hairpins can act as a set of synthetic control modules that provide predictable tuning of gene expression over a wide range of expression levels.The Rnt1p-based control elements can be combined with any promoter to support titration of regulatory strategies encoded in transcriptional regulators, including feedback control around endogenous proteins.
The design of complex biological systems encoding desired functions require the development of genetic tools for the precise control of protein levels in cells (Elowitz and Leibler, 2000; Gardner et al, 2000; Basu et al, 2004). For example, in the design of engineered metabolic networks, the tuning of enzyme levels is often critical for overcoming metabolic burden (Jones et al, 2000; Jin et al, 2003), the accumulation of toxic intermediates (Zhu et al, 2001; Pfleger et al, 2006) and detrimental consequences associated with the redirection of cellular resources from native pathways (Alper et al, 2005b; Paradise et al, 2008). Various examples of libraries of genetic control modules have been described that have been generated through the randomization of well-characterized gene expression control elements (Basu et al, 2004; Pfleger et al, 2006; Anderson et al, 2007). However, most of these studies have been conducted in Escherichia coli such that there is a lack of similar tools for other cellular chassis.
The budding yeast, Saccharomyces cerevisiae, is a relevant organism in industrial processes, including biosynthesis and biomanufacturing strategies (Ostergaard et al, 2000; Szczebara et al, 2003; Nguyen et al, 2004; Veen and Lang, 2004; Ro et al, 2006; Hawkins and Smolke, 2008). The majority of existing methods for tuning gene expression in yeast are through transcriptional control mechanisms in the form of inducible and constitutive promoter systems (Hawkins and Smolke, 2006; Nevoigt et al, 2006; Nevoigt et al, 2007). RNA-based control modules based on posttranscriptional mechanisms may offer an advantage in that they can be coupled to any promoter of choice, providing for enhanced control strategies and finer resolution tuning of protein expression levels. Although posttranscriptional control elements, such as internal ribosome entry sites and AU-rich elements, have been applied to regulate heterologous gene expression in yeast (Vasudevan and Peltz, 2001; Zhou et al, 2001; Lautz et al, 2010), these control elements have exhibited substantial variability in activity and have not been engineered as synthetic libraries exhibiting a wide range of predictable gene regulatory activities.
RNase III enzymes are a class of enzymes that cleave double-stranded RNA. The S. cerevisiae RNase III enzyme, Rnt1p, exhibits a number of unique features that allow it to recognize very specific RNA hairpin substrates that harbor a consensus AGNN tetraloop sequence. Despite extensive characterization of this enzyme and its demonstrated role in processing non-coding RNA and mRNA, neither natural nor synthetic Rnt1p substrates have been used to control gene expression levels in yeast. Therefore, we developed a genetic control system based on directed Rnt1p processing of a target transcript. Specifically, Rnt1p hairpins were immediately flanked by a clamp sequence (that insulates the hairpin structure from surrounding sequences) and placed downstream of a gene of interest, where they direct cleavage and thus inactivate the transcript, resulting in rapid transcript degradation. We validated this Rnt1p-based control system with two Rnt1p hairpins based on previous in vitro studies and demonstrated that Rnt1p hairpins can act as gene control modules in yeast.
Previous in vitro studies had identified three key regions in Rnt1p hairpins: the cleavage efficiency box (CEB), the binding stability box and the initial binding and positioning box (Lamontagne et al, 2003). The CEB region affects the processing of the hairpin stem by Rnt1p, such that nucleotide (nt) modifications in this region are expected to specifically modulate the cleavage rate. We created an Rnt1p hairpin library by randomizing the CEB region (12 nt). This library was placed downstream of a fluorescent reporter protein and a cell-based screening assay was used to identify functional members of the library that resulted in lowered fluorescence levels. The functional Rnt1p hairpin library comprises 16 unique sequences that span a large gene regulatory range—from 8 to 85% (Figure 3A)—and are fairly evenly distributed across this range. The negative controls for each sequence (constructed by mutating the required consensus tetraloop sequence) demonstrated that the majority of gene knockdown observed from each hairpin is due to Rnt1p processing (Figure 3B). A correlation analysis on the transcript and protein levels for each library hairpin construct indicated a strong positive correlation and a strong preservation of rank order between the two in vivo regulatory measurements (Figure 3C). Characterization of the hairpin library in a different genetic context supported the broader utility of these control modules for providing predictable gene control.
We applied the Rnt1p control modules to titrating a key enzyme component of the endogenous ergosterol biosynthesis network—the ERG9 genetic target. Squalene synthase, encoded by the ERG9 gene, is responsible for catalyzing the conversion of two molecules of farnesyl pyrophosphate to squalene, the first precursor in the ergosterol biosynthetic pathway in S. cerevisiae (Poulter and Rilling, 1981; Figure 6A). We integrated several members of the Rnt1p hairpin library downstream of the native ERG9 gene to cover the regulatory range of the library (Figure 6B). A strong positive correlation and preservation of rank order was observed between the ERG9 transcript levels and their yEGFP3 counterparts (Figure 6C). However, ERG9 expression levels did not fall below ∼40%, regardless of the Rnt1p hairpin strength, indicating that a previously identified endogenous feedback mechanism associated with the native ERG9 promoter acts to maintain ERG9 expression levels at that threshold value. In addition, most strains exhibited high relative ergosterol levels and growth rates, except for two strains harboring synthetic Rnt1p hairpins that resulted in the lowest expression levels, which exhibited a significant reduction in the amount of ergosterol produced and growth rate (Figure 6D and E). Our studies indicate that the endogenous feedback mechanism can be acting to increase ERG9 expression levels to the desired set point in the slow-growing strains, but the perturbations introduced in these strains may result in other impacts on the pathway that inhibit the endogenous control systems from restoring cellular growth to wild-type rates. These studies support the unique ability of the synthetic Rnt1p hairpin library to systematically titrate pathway enzyme levels by introducing precise perturbations around major control points while maintaining native cellular control strategies acting through transcriptional mechanisms.
Advances in synthetic biology have resulted in the development of genetic tools that support the design of complex biological systems encoding desired functions. The majority of efforts have focused on the development of regulatory tools in bacteria, whereas fewer tools exist for the tuning of expression levels in eukaryotic organisms. Here, we describe a novel class of RNA-based control modules that provide predictable tuning of expression levels in the yeast Saccharomyces cerevisiae. A library of synthetic control modules that act through posttranscriptional RNase cleavage mechanisms was generated through an in vivo screen, in which structural engineering methods were applied to enhance the insulation and modularity of the resulting components. This new class of control elements can be combined with any promoter to support titration of regulatory strategies encoded in transcriptional regulators and thus more sophisticated control schemes. We applied these synthetic controllers to the systematic titration of flux through the ergosterol biosynthesis pathway, providing insight into endogenous control strategies and highlighting the utility of this control module library for manipulating and probing biological systems.
PMCID: PMC3094065  PMID: 21364573
gene expression control; metabolic flux control; RNA controller; Rnt1p hairpin; synthetic biology
20.  Dynamics and Design Principles of a Basic Regulatory Architecture Controlling Metabolic Pathways 
PLoS Biology  2008;6(6):e146.
The dynamic features of a genetic network's response to environmental fluctuations represent essential functional specifications and thus may constrain the possible choices of network architecture and kinetic parameters. To explore the connection between dynamics and network design, we have analyzed a general regulatory architecture that is commonly found in many metabolic pathways. Such architecture is characterized by a dual control mechanism, with end product feedback inhibition and transcriptional regulation mediated by an intermediate metabolite. As a case study, we measured with high temporal resolution the induction profiles of the enzymes in the leucine biosynthetic pathway in response to leucine depletion, using an automated system for monitoring protein expression levels in single cells. All the genes in the pathway are known to be coregulated by the same transcription factors, but we observed drastically different dynamic responses for enzymes upstream and immediately downstream of the key control point—the intermediate metabolite α-isopropylmalate (αIPM), which couples metabolic activity to transcriptional regulation. Analysis based on genetic perturbations suggests that the observed dynamics are due to differential regulation by the leucine branch-specific transcription factor Leu3, and that the downstream enzymes are strictly controlled and highly expressed only when αIPM is available. These observations allow us to build a simplified mathematical model that accounts for the observed dynamics and can correctly predict the pathway's response to new perturbations. Our model also suggests that transient dynamics and steady state can be separately tuned and that the high induction levels of the downstream enzymes are necessary for fast leucine recovery. It is likely that principles emerging from this work can reveal how gene regulation has evolved to optimize performance in other metabolic pathways with similar architecture.
Author Summary
Single-cell organisms must constantly adjust their gene expression programs to survive in a changing environment. Interactions between different molecules form a regulatory network to mediate these changes. While the network connections are often known, figuring out how the network responds dynamically by looking at a static picture of its structure presents a significant challenge. Measuring the response at a finer time scales could reveal the link between the network's function and its structure. The architecture of the system we studied in this work—the leucine biosynthesis pathway in yeast—is shared by other metabolic pathways: a metabolic intermediate binds to a transcription factor to activate the pathway genes, creating an intricate feedback structure that links metabolism with gene expression. We measured protein abundance at high temporal resolution for genes in this pathway in response to leucine depletion and studied the effects of various genetic perturbations on gene expression dynamics. Our measurements and theoretical modeling show that only the genes immediately downstream from the intermediate are highly regulated by the metabolite, a feature that is essential to fast recovery from leucine depletion. Since the architecture we studied is common, we believe that our work may lead to general principles governing the dynamics of gene expression in other metabolic pathways.
A quantitative, high-temporal resolution study of gene induction in a metabolic pathway reveals an intricate connection between the regulatory architecture and the dynamic response of the system, pointing to possible principles underlying the design of these pathways.
PMCID: PMC2429954  PMID: 18563967
21.  Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli 
We have designed an experimental/computational framework for studying complex phenotypes in bacteria.Our framework relies on whole-genome fitness profiling coupled with a module-level analysis to discover pathways that directly affect fitness.As a proof-of-principle, we studied ethanol tolerance in Escherichia coli and we identified key pathways that contribute to this phenotype.We then validated our findings through genetic manipulations, gene-expression profiling, metabolite-level measurements, and stable-isotope labeling.
Elucidating the genetic basis of complex phenotypes remains a fundamental challenge in biology. We have developed a systematic framework for comprehensive genetic analysis of microbial phenotypes. Our approach combines the power of fitness profiling (Girgis et al, 2007; Amini et al, 2009) with the sensitivity of module-level analysis (Goodarzi et al, 2009a) to identify key genetic modules that directly affect a phenotype under study. We applied our technology to ethanol tolerance, a complex phenotype with broad industrial relevance. Ethanol affects a variety of cellular components and pathways, including but not limited to membrane integrity (Dombek and Ingram, 1984), enzyme activities (Millar et al, 1982), and proton flux (D'Amore et al, 1990). Given the diversity of targets, the emergence of ethanol tolerance requires modifications to multiple pathway (D'Amore and Stewart, 1987).
To reveal the genetic basis of ethanol tolerance in Escherichia coli, we used two high-coverage mutant libraries (a transposon library and an overexpression library) to assess the fitness consequences of single-locus perturbations. Each cell in our transposon library contains a random transposon insertion in its genome (Girgis et al, 2007); whereas the cells in the overexpression library carry 1–3 kb genomic fragments cloned into a cloning vector (Amini et al, 2009). We grew these libraries under mild (4% v/v) and harsh (5.5% v/v) ethanol concentrations. On growth, the abundance of each transposon insertion or overexpression mutant changes as a function of its fitness, a process that can be monitored through parallel genetic footprinting and microarray hybridization (Figure 1A). This results in a global fitness profile, where the contribution of each genetic locus to ethanol tolerance can be quantified in parallel. However, in the context of ethanol tolerance and other complex phenotypes, single-locus perturbations typically result in modest changes in fitness. Although these small differences can be amplified through multiple rounds of selection, the number of generations is limited as spontaneous beneficial mutations emerge in the population and cause strong biases in the resulting fitness profiles. To boost our analytical power without introducing these biases in the data, we used a module-level computational method to discover the pathways and components that are strongly associated with the data as opposed to focusing on the genes individually (Goodarzi et al, 2009a). Genes function in the context of pathways and modules and module-level analyses increase statistical power through combining information from multiple genes functioning as part of a given pathway (Subramanian et al, 2005).
The module-level analysis of the fitness scores from both libraries revealed a diverse set of pathways that have a direct function in ethanol tolerance. Some of these pathways, including heat-shock stress response and osmoregulation, are known modifiers of ethanol tolerance; whereas others such as acid-stress response and fimbrial structures are novel pathways. Among our findings was the important function of three regulatory proteins: FNR, ArcA, and CafA. Knocking out FNR/ArcA that upregulates aerobic respiration proteins and TCA cycle components results in a marked increase in ethanol tolerance. Similarly, knocking out CafA, a post-transcriptional regulator of alcohol dehydrogenase, is beneficial for tolerance. Given these observations, we hypothesized that selection for ethanol tolerance can result in higher ethanol degradation.
As a large fraction of discovered pathways belonged to central metabolism, we used metabolomics to evaluate our findings. To directly assess the metabolic consequences of adaptation to ethanol, we evolved ethanol-tolerant strains in minimal media plus glucose for ∼30 and 160 generations. We then compared the steady-state level of metabolites in these strains to that of the wild type (Figure 1B). In agreement with our fitness profiling results, we observed a significant increase in TCA cycle metabolites in one of our ethanol-tolerant strains. Higher concentrations of TCA cycle components along with a high free coenzyme A (CoA) to acetyl-coenzyme A (acetyl-CoA) ratio hinted at the capacity of this strain to metabolize ethanol. To test this hypothesis, we performed stable-isotope labeling on our ethanol-tolerant strain versus wild type. After growth on labeled ethanol, we measured the fraction of metabolites that were labeled at each timepoint (Figure 1B). Our results confirmed that the ethanol-tolerant strain has the capacity to consume ethanol through its conversion into acetyl-CoA and further assimilation in the TCA cycle.
By using a variety of systems-level approaches, we have been able to genetically dissect ethanol tolerance in E. coli. We have shown that fitness profiling, in combination with module-level analysis tools, can serve as a powerful approach for revealing the genetic basis of complex phenotypes. The fact that laboratory evolution ended up using the very modules that we discovered, highlights the biological and adaptive relevance of the proposed framework.
Understanding the genetic basis of adaptation is a central problem in biology. However, revealing the underlying molecular mechanisms has been challenging as changes in fitness may result from perturbations to many pathways, any of which may contribute relatively little. We have developed a combined experimental/computational framework to address this problem and used it to understand the genetic basis of ethanol tolerance in Escherichia coli. We used fitness profiling to measure the consequences of single-locus perturbations in the context of ethanol exposure. A module-level computational analysis was then used to reveal the organization of the contributing loci into cellular processes and regulatory pathways (e.g. osmoregulation and cell-wall biogenesis) whose modifications significantly affect ethanol tolerance. Strikingly, we discovered that a dominant component of adaptation involves metabolic rewiring that boosts intracellular ethanol degradation and assimilation. Through phenotypic and metabolomic analysis of laboratory-evolved ethanol-tolerant strains, we investigated naturally accessible pathways of ethanol tolerance. Remarkably, these laboratory-evolved strains, by and large, follow the same adaptive paths as inferred from our coarse-grained search of the fitness landscape.
PMCID: PMC2913397  PMID: 20531407
adaptation; ethanol tolerance; evolution; fitness profiling
22.  Metabolomic and transcriptomic stress response of Escherichia coli 
GC-MS-based analysis of the metabolic response of Escherichia coli exposed to four different stress conditions reveals reduction of energy expensive pathways.Time-resolved response of E. coli to changing environmental conditions is more specific on the metabolite as compared with the transcript level.Cease of growth during stress response as compared with stationary phase response invokes similar transcript but dissimilar metabolite responses.Condition-dependent associations between metabolites and transcripts are revealed applying co-clustering and canonical correlation analysis.
The response of biological systems to environmental perturbations is characterized by a fast and appropriate adjusting of physiology on every level of the cellular and molecular network.
Stress response is usually represented by a combination of both specific responses, aimed at minimizing deleterious effects or repairing damage (e.g. protein chaperones under temperature stress) and general responses which, in part, comprise the downregulation of genes related to translation and ribosome biogenesis. This in turn is reflected by growth cessation or reduction observed under essentially all stress conditions and is an important strategy to adjust cellular physiology to the new condition.
E. coli has been intensively investigated in relation to stress responses. Thus far, however, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. To better understand system response to perturbation, we designed a time-resolved experiment to compare and integrate metabolic and transcript changes of E. coli using four stress conditions including non-lethal temperature shifts, oxidative stress, and carbon starvation relative to cultures grown under optimal conditions covering both states before and directly after stress application, resumption of growth after stress-induced lag phase, and finally the stationary phase.
Metabolic changes occurring after stress application were characterized by a reduction in metabolites of central metabolism (TCA cycle and glycolysis) as well as an increase in free amino acids. Whereas the latter is probably due to protein degradation and stalling of translation, the former supports and extends conclusions based on transcriptome data demonstrating a major decrease in energy-consuming processes as a general stress response. Further comparative analysis of the response on the metabolome and transcriptome, however, revealed in addition to these similarities major differences. Thus, the response on the metabolome displayed a significantly higher specificity towards the specific stress as compared with the transcriptome. Further, when comparing the metabolome of cells ceasing growth due to stress application with cells ceasing growth due to reaching stationary phase the metabolome response differed to a significant extent between both growth arrest phases, whereas the transcriptome response showed significant overlap again, suggesting that the response of E. coli on the metabolome level displays a higher level of significance as compared with the transcriptome level.
Subsequently, both data sets were jointly analyzed using co-clustering and canonical correlation approaches to identify coordinated changes on the transcriptome and the metabolite level indicative metabolite–transcript associations. A first outcome of this study was that no association was preserved during all conditions analyzed but rather condition-specific associations were observed. One set of associations found was between metabolites from the oxidative pentose phosphate pathway such as glc-6-P, 6-P-gluconic acid, ribose-5-P, and E-4-P and metabolites from the glycolytic pathway (3PGA and PEP in addition to glc-6-P with two genes encoding pathway enzymes, that is rpe encoding ribulose phosphate 3-epimerase and pps encoding PEP synthase.
A second example comprises metabolites of the TCA cycle such as pyruvic acid, 2-ketoglutaric acid, fumaric acid, malic acid, and succinic acid and the mqo gene encoding malate-quinone oxidoreductase (MQO). MQO catalyses the irreversible oxidation of malate to oxaloacetate that in turn regulates the activity of citrate synthase, which is a major rate determining enzyme of the TCA cycle. The strong association between mqo gene expression and multiple members of the TCA cycle as well as pyruvate suggest mqo expression to have a major function for the regulation of the TCA cycle, which need to be experimentally validated.
Multiple further associations identified show on the one hand the power of integrative systems oriented approaches for developing new hypothesis, on the other hand their condition-dependent behavior shows the extreme flexibility of the biological systems studied thus requesting a much more intense effort toward parallel analysis of biological systems under several environmental conditions.
Environmental fluctuations lead to a rapid adjustment of the physiology of Escherichia coli, necessitating changes on every level of the underlying cellular and molecular network. Thus far, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. Here, we incorporate the metabolite composition together with gene expression data to provide a more comprehensive insight on system level stress adjustments by describing detailed time-resolved E. coli response to five different perturbations (cold, heat, oxidative stress, lactose diauxie, and stationary phase). The metabolite response is more specific as compared with the general response observed on the transcript level and is reflected by much higher specificity during the early stress adaptation phase and when comparing the stationary phase response to other perturbations. Despite these differences, the response on both levels still follows the same dynamics and general strategy of energy conservation as reflected by rapid decrease of central carbon metabolism intermediates coinciding with downregulation of genes related to cell growth. Application of co-clustering and canonical correlation analysis on combined metabolite and transcript data identified a number of significant condition-dependent associations between metabolites and transcripts. The results confirm and extend existing models about co-regulation between gene expression and metabolites demonstrating the power of integrated systems oriented analysis.
PMCID: PMC2890322  PMID: 20461071
Escherichia coli; metabolomic; response to stress; time course; transcriptomic
23.  An in silico platform for the design of heterologous pathways in nonnative metabolite production 
BMC Bioinformatics  2012;13:93.
Microorganisms are used as cell factories to produce valuable compounds in pharmaceuticals, biofuels, and other industrial processes. Incorporating heterologous metabolic pathways into well-characterized hosts is a major strategy for obtaining these target metabolites and improving productivity. However, selecting appropriate heterologous metabolic pathways for a host microorganism remains difficult owing to the complexity of metabolic networks. Hence, metabolic network design could benefit greatly from the availability of an in silico platform for heterologous pathway searching.
We developed an algorithm for finding feasible heterologous pathways by which nonnative target metabolites are produced by host microorganisms, using Escherichia coli, Corynebacterium glutamicum, and Saccharomyces cerevisiae as templates. Using this algorithm, we screened heterologous pathways for the production of all possible nonnative target metabolites contained within databases. We then assessed the feasibility of the target productions using flux balance analysis, by which we could identify target metabolites associated with maximum cellular growth rate.
This in silico platform, designed for targeted searching of heterologous metabolic reactions, provides essential information for cell factory improvement.
PMCID: PMC3506926  PMID: 22578364
24.  Interplay between Siderophores and Colibactin Genotoxin Biosynthetic Pathways in Escherichia coli 
PLoS Pathogens  2013;9(7):e1003437.
In Escherichia coli, the biosynthetic pathways of several small iron-scavenging molecules known as siderophores (enterobactin, salmochelins and yersiniabactin) and of a genotoxin (colibactin) are known to require a 4′-phosphopantetheinyl transferase (PPTase). Only two PPTases have been clearly identified: EntD and ClbA. The gene coding for EntD is part of the core genome of E. coli, whereas ClbA is encoded on the pks pathogenicity island which codes for colibactin. Interestingly, the pks island is physically associated with the high pathogenicity island (HPI) in a subset of highly virulent E. coli strains. The HPI carries the gene cluster required for yersiniabactin synthesis except for a gene coding its cognate PPTase. Here we investigated a potential interplay between the synthesis pathways leading to the production of siderophores and colibactin, through a functional interchangeability between EntD and ClbA. We demonstrated that ClbA could contribute to siderophores synthesis. Inactivation of both entD and clbA abolished the virulence of extra-intestinal pathogenic E. coli (ExPEC) in a mouse sepsis model, and the presence of either functional EntD or ClbA was required for the survival of ExPEC in vivo. This is the first report demonstrating a connection between multiple phosphopantetheinyl-requiring pathways leading to the biosynthesis of functionally distinct secondary metabolites in a given microorganism. Therefore, we hypothesize that the strict association of the pks island with HPI has been selected in highly virulent E. coli because ClbA is a promiscuous PPTase that can contribute to the synthesis of both the genotoxin and siderophores. The data highlight the complex regulatory interaction of various virulence features with different functions. The identification of key points of these networks is not only essential to the understanding of ExPEC virulence but also an attractive and promising target for the development of anti-virulence therapy strategies.
Author Summary
The synthesis of numerous molecules involved in the virulence potential and fitness of pathogenic bacteria requires a particular enzyme family, i.e. phosphopantetheinyl transferases (PPTases). To date, the synthesis of a given bioactive metabolite was thought to require a specific PPTase. As PPTases are being investigated as promising targets for antibacterial development, we addressed the question of a possible functional interchangeability between PPTases in Escherichia coli. PPTases are known to be involved in the synthesis of low-molecular weight iron chelators (siderophores), and of a genotoxin named colibactin. Here we demonstrated interplay between the synthesis pathways leading to the production of siderophores and of colibactin. We showed that inactivation of both PPTases abolished the virulence of extra-intestinal pathogenic E. coli (ExPEC) in a mouse sepsis model. To our knowledge, this is the first demonstration of interplay between multiple PPTases-requiring pathways leading to the biosynthesis of functionally distinctive virulence factors, in a given microorganism. The extensive substrate specificity of PPTase ClbA could account for the co-selection and co-evolution of genomic islands encoding colibactin and yersiniabactin siderophore.
PMCID: PMC3708854  PMID: 23853582
25.  KiMoSys: a web-based repository of experimental data for KInetic MOdels of biological SYStems 
BMC Systems Biology  2014;8:85.
The kinetic modeling of biological systems is mainly composed of three steps that proceed iteratively: model building, simulation and analysis. In the first step, it is usually required to set initial metabolite concentrations, and to assign kinetic rate laws, along with estimating parameter values using kinetic data through optimization when these are not known. Although the rapid development of high-throughput methods has generated much omics data, experimentalists present only a summary of obtained results for publication, the experimental data files are not usually submitted to any public repository, or simply not available at all. In order to automatize as much as possible the steps of building kinetic models, there is a growing requirement in the systems biology community for easily exchanging data in combination with models, which represents the main motivation of KiMoSys development.
KiMoSys is a user-friendly platform that includes a public data repository of published experimental data, containing concentration data of metabolites and enzymes and flux data. It was designed to ensure data management, storage and sharing for a wider systems biology community. This community repository offers a web-based interface and upload facility to turn available data into publicly accessible, centralized and structured-format data files. Moreover, it compiles and integrates available kinetic models associated with the data.
KiMoSys also integrates some tools to facilitate the kinetic model construction process of large-scale metabolic networks, especially when the systems biologists perform computational research.
KiMoSys is a web-based system that integrates a public data and associated model(s) repository with computational tools, providing the systems biology community with a novel application facilitating data storage and sharing, thus supporting construction of ODE-based kinetic models and collaborative research projects.
The web application implemented using Ruby on Rails framework is freely available for web access at, along with its full documentation.
PMCID: PMC4236735  PMID: 25115331
Dynamic modeling; Repository; Data sharing; Accessible dynamic data; Associated kinetic models

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