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Evidence suggests that novel enzyme functions evolved from low‐level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems‐level adaptations are not well characterized. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set, or underground metabolism, can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non‐native substrates in Escherichia coli K‐12 MG1655. After as few as approximately 20 generations, evolved populations repeatedly acquired the capacity to grow on five predicted non‐native substrates—D‐lyxose, D‐2‐deoxyribose, D‐arabinose, m‐tartrate, and monomethyl succinate. Altered promiscuous activities were shown to be directly involved in establishing high‐efficiency pathways. Structural mutations shifted enzyme substrate turnover rates toward the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome‐scale model simulations of metabolism with enzyme promiscuity. Synopsis Computational modeling of underground metabolism, laboratory evolution and omics analyses reveal that enzyme promiscuity can play a major role during adaptation to new growth environments and indicate that the genes underlying the phenotypic innovations can be predicted. Enzyme promiscuity can confer a fitness benefit in novel growth environments and open routes for achieving innovative growth states. Mutation events which enable growth on non‐native carbon sources can be structural or regulatory in nature and single mutation events related to a promiscuous activity can be sufficient to support growth while some cases require multiple mutations. Metabolic network analysis and constraint‐based modeling can predict adaptation to non‐native carbon sources through promiscuous enzyme activities. Laboratory evolution can be used to select for enzymes with structural mutations enabling an improved substrate affinity for a non‐native carbon source. Graphical Abstract Computational modeling of underground metabolism, laboratory evolution and omics analyses reveal that enzyme promiscuity can play a major role during adaptation to new growth environments and indicate that the genes underlying the phenotypic innovations can be predicted.

Photoacclimation consists of short- and long-term strategies used by photosynthetic organisms to adapt to dynamic light environments. Observable photophysiology changes resulting from these strategies have been used in coarse-grained models to predict light-dependent growth and photosynthetic rates. However, the contribution of the broader metabolic network, relevant to species-specific strategies and fitness, is not accounted for in these simple models. We incorporated photophysiology experimental data with genome-scale modeling to characterize organism-level, light-dependent metabolic changes in the model diatom Phaeodactylum tricornutum. Oxygen evolution and photon absorption rates were combined with condition-specific biomass compositions to predict metabolic pathway usage for cells acclimated to four different light intensities. Photorespiration, an ornithine-glutamine shunt, and branched-chain amino acid metabolism were hypothesized as the primary intercompartment reductant shuttles for mediating excess light energy dissipation. Additionally, simulations suggested that carbon shunted through photorespiration is recycled back to the chloroplast as pyruvate, a mechanism distinct from known strategies in photosynthetic organisms. Our results suggest a flexible metabolic network in P. tricornutum that tunes intercompartment metabolism to optimize energy transport between the organelles, consuming excess energy as needed. Characterization of these intercompartment reductant shuttles broadens our understanding of energy partitioning strategies in this clade of ecologically important primary producers.

To elucidate the molecular mechanisms underlying non‐alcoholic fatty liver disease (NAFLD), we recruited 86 subjects with varying degrees of hepatic steatosis (HS). We obtained experimental data on lipoprotein fluxes and used these individual measurements as personalized constraints of a hepatocyte genome‐scale metabolic model to investigate metabolic differences in liver, taking into account its interactions with other tissues. Our systems level analysis predicted an altered demand for NAD + and glutathione (GSH) in subjects with high HS. Our analysis and metabolomic measurements showed that plasma levels of glycine, serine, and associated metabolites are negatively correlated with HS, suggesting that these GSH metabolism precursors might be limiting. Quantification of the hepatic expression levels of the associated enzymes further pointed to altered de novo GSH synthesis. To assess the effect of GSH and NAD + repletion on the development of NAFLD, we added precursors for GSH and NAD + biosynthesis to the Western diet and demonstrated that supplementation prevents HS in mice. In a proof‐of‐concept human study, we found improved liver function and decreased HS after supplementation with serine (a precursor to glycine) and hereby propose a strategy for NAFLD treatment. Synopsis Personalized modeling and metabolic measurements identified altered GSH and NAD + metabolism as a prevailing feature in NAFLD. These findings suggested a potential treatment strategy for NAFLD patients based on increased oxidation of fat and increased synthesis of GSH. We developed personalized genome‐scale metabolic models for NAFLD patients. We found that altered GSH and NAD + metabolism is a prevailing feature in NAFLD. Plasma and liver levels of glycine and serine were lower in NAFLD patients. Supplementation of precursors for glutathione and NAD + decreased HS in mice. Serine supplementation decreased liver fat and improved markers of liver function in humans. Graphical Abstract Personalized modeling and metabolic measurements identified altered GSH and NAD + metabolism as a prevailing feature in NAFLD. These findings suggested a potential treatment strategy for NAFLD patients based on increased oxidation of fat and increased synthesis of GSH.

Vibrio natriegens is a Gram‐negative bacterium with an exceptional growth rate that has the potential to become a standard biotechnological host for laboratory and industrial bioproduction. Despite this burgeoning interest, the current lack of organism‐specific qualitative and quantitative computational tools has hampered the community's ability to rationally engineer this bacterium. In this study, we present the first genome‐scale metabolic model (GSMM) of V. natriegens . The GSMM (iLC858) was developed using an automated draft assembly and extensive manual curation and was validated by comparing predicted yields, central metabolic fluxes, viable carbon substrates, and essential genes with empirical data. Mass spectrometry‐based proteomics data confirmed the translation of at least 76% of the enzyme‐encoding genes predicted to be expressed by the model during aerobic growth in a minimal medium. iLC858 was subsequently used to carry out a metabolic comparison between the model organism Escherichia coli and V. natriegens , leading to an analysis of the model architecture of V. natriegens ' respiratory and ATP‐generating system and the discovery of a role for a sodium‐dependent oxaloacetate decarboxylase pump. The proteomics data were further used to investigate additional halophilic adaptations of V. natriegens . Finally, iLC858 was utilized to create a Resource Balance Analysis model to study the allocation of carbon resources. Taken together, the models presented provide useful computational tools to guide metabolic engineering efforts in V. natriegens . Synopsis iLC858 is the first constructed and validated genome‐scale metabolic model for the fast‐growing bacterium Vibrio natriegens. This new model enabled the study of V. natriegens metabolism, halophilic adaptions, and resource analysis. Construction and curation of a draft genome‐scale metabolic model reconstruction iLC858 for V. natriegens were performed. Validation of iLC858 showed a good correlation between model predictions and experimental data. A comparison to the metabolism of model organism E. coli led to the discovery of a new role for a sodium‐dependent oxaloacetate decarboxylase in V. natriegens growth under aerobic conditions. Proteomics data provided novel insights into halophilic adaptations and resource allocation of V. natriegens. Graphical Abstract iLC858 is the first constructed and validated genome‐scale metabolic model for the fast‐growing bacterium Vibrio natriegens . This new model enabled the study of V. natriegens metabolism, halophilic adaptions, and resource analysis.

Tremendous progress has been made to control the COVID‐19 pandemic caused by the SARS‐CoV‐2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS‐CoV‐2 infection using genome‐scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS‐CoV‐2 infection. We next applied the GEM‐based metabolic transformation algorithm to predict anti‐SARS‐CoV‐2 targets that counteract the virus‐induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco‐2 cells. Further generating and analyzing RNA‐sequencing data of remdesivir‐treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti‐SARS‐CoV‐2 drug. Our study provides clinical data‐supported candidate anti‐SARS‐CoV‐2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy. SYNOPSIS Metabolic modeling of 12 SARS‐CoV‐2 datasets identifies novel single or combinatory antiviral targets by reverting the virus‐induced host metabolic reprogramming. Meta‐analysis of SARS‐CoV‐2‐induced expression changes reveals extensive host metabolic alterations. rMTA algorithm predicted 81 single metabolic targets and 87 targets for combination with remdesivir for anti‐SARS‐CoV‐2. Selected candidate single targets were successfully validated with an immunofluorescence‐based siRNA assay in Caco‐2 cells. Graphical Abstract Metabolic modeling of 12 SARS‐CoV‐2 datasets identifies novel single or combinatory antiviral targets by reverting the virus‐induced host metabolic reprogramming.

Over the last decade, the field of cancer metabolism has mainly focused on studying the role of tumorigenic metabolic rewiring in supporting cancer proliferation. Here, we perform the first genome‐scale computational study of the metabolic underpinnings of cancer migration. We build genome‐scale metabolic models of the NCI‐60 cell lines that capture the Warburg effect (aerobic glycolysis) typically occurring in cancer cells. The extent of the Warburg effect in each of these cell line models is quantified by the ratio of glycolytic to oxidative ATP flux (AFR), which is found to be highly positively associated with cancer cell migration. We hence predicted that targeting genes that mitigate the Warburg effect by reducing the AFR may specifically inhibit cancer migration. By testing the anti‐migratory effects of silencing such 17 top predicted genes in four breast and lung cancer cell lines, we find that up to 13 of these novel predictions significantly attenuate cell migration either in all or one cell line only, while having almost no effect on cell proliferation. Furthermore, in accordance with the predictions, a significant reduction is observed in the ratio between experimentally measured ECAR and OCR levels following these perturbations. Inhibiting anti‐migratory targets is a promising future avenue in treating cancer since it may decrease cytotoxic‐related side effects that plague current anti‐proliferative treatments. Furthermore, it may reduce cytotoxic‐related clonal selection of more aggressive cancer cells and the likelihood of emerging resistance. Synopsis A computational analysis based on genome‐scale metabolic models shows that the extent of the Warburg effect is highly associated with cancer cell migration across different cell lines and identifies anti‐migratory targets. Genome‐scale metabolic models of each the NCI‐60 cell lines correctly capture the Warburg effect. The extent of the Warburg effect, as quantified by the ratio between glycolytic and oxidative ATP flux rate (AFR), positively associates with cancer cell migration across the different cell lines. siRNA knockdown of 13 genes predicted to reduce the AFR attenuates cell migration while having almost no effect on cell proliferation. In agreement with the predictions, a significant reduction in the ratio of glycolytic/oxidative capacity is observed following these gene perturbations. Graphical Abstract A computational analysis based on genome‐scale metabolic models shows that the extent of the Warburg effect is highly associated with cancer cell migration across different cell lines and identifies anti‐migratory targets.

Metabolic alterations play an important role in cancer and yet, few metabolic cancer driver genes are known. Here we perform a combined genomic and metabolic modeling analysis searching for metabolic drivers of colorectal cancer. Our analysis predicts FUT9, which catalyzes the biosynthesis of Ley glycolipids, as a driver of advanced‐stage colon cancer. Experimental testing reveals FUT9's complex dual role; while its knockdown enhances proliferation and migration in monolayers, it suppresses colon cancer cells expansion in tumorspheres and inhibits tumor development in a mouse xenograft models. These results suggest that FUT9's inhibition may attenuate tumor‐initiating cells (TICs) that are known to dominate tumorspheres and early tumor growth, but promote bulk tumor cells. In agreement, we find that FUT9 silencing decreases the expression of the colorectal cancer TIC marker CD44 and the level of the OCT4 transcription factor, which is known to support cancer stemness. Beyond its current application, this work presents a novel genomic and metabolic modeling computational approach that can facilitate the systematic discovery of metabolic driver genes in other types of cancer. Synopsis A combined computational and experimental analysis reveals FUT9 as a new, context‐dependent driver of colon cancer. FUT9 expression is required in tumor initiating cells while its loss favors bulk tumor growth and supports tumor aggressiveness. A combined genomic and metabolic modeling analysis is performed to identify metabolic drivers of colorectal cancer. FUT9, which catalyzes the biosynthesis of Ley glycolipids in the Golgi compartment, emerges as a driver of advanced stage colon cancer. FUT9 activity has different effects on tumor initiating cells vs. bulk tumor cells, supporting the former but attenuating the latter. The presented combined computational and experimental approach can be applied for the systematic discovery of metabolic driver genes in other cancer types. Graphical Abstract A combined computational and experimental analysis reveals FUT9 as a new, context‐dependent driver of colon cancer. FUT9 expression is required in tumor initiating cells while its loss favors bulk tumor growth and supports tumor aggressiveness.

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks.

Salt tolerant organisms are increasingly being used for the industrial production of high‐value biomolecules due to their better adaptability compared to mesophiles. Chromohalobacter canadensis is one of the early halophiles to show promising biotechnology potential, which has not been explored to date. Advanced high throughput technologies such as whole‐genome sequencing allow in‐depth insight into the potential of organisms while at the frontiers of systems biology. At the same time, genome‐scale metabolic models (GEMs) enable phenotype predictions through a mechanistic representation of metabolism. Here, we sequence and analyze the genome of C. canadensis 85B, and we use it to reconstruct a GEM. We then analyze the GEM using flux balance analysis and validate it against literature data on C. canadensis. We show that C. canadensis 85B is a metabolically versatile organism with many features for stress and osmotic adaptation. Pathways to produce ectoine and polyhydroxybutyrates were also predicted. The GEM reveals the ability to grow on several carbon sources in a minimal medium and reproduce osmoadaptation phenotypes. Overall, this study reveals insights from the genome of C. canadensis 85B, providing genomic data and a draft GEM that will serve as the first steps towards a better understanding of its metabolism, for novel applications in industrial biotechnology. The whole genome of Chromohalobacter canadensis 85B was sequenced and its genome‐scale metabolic model was reconstructed and validated, enabling a better understanding of its metabolism. We show that C. canadensis is a metabolically versatile organism with many features for stress and osmotic adaptation, including pathways for compatible solute and polyhydroxybutyrate (PHB) synthesis. The model reveals the ability to grow on several carbon sources in minimal medium, and produce PHBs and compatible solutes such as ectoine, 5‐hydroxyectoine, and betaine.

Heme is an oxygen carrier and a cofactor of both industrial enzymes and food additives. The intracellular level of free heme is low, which limits the synthesis of heme proteins. Therefore, increasing heme synthesis allows an increased production of heme proteins. Using the genome-scale metabolic model (GEM) Yeast8 for the yeast Saccharomyces cerevisiae, we identified fluxes potentially important to heme synthesis. With this model, in silico simulations highlighted 84 gene targets for balancing biomass and increasing heme production. Of those identified, 76 genes were individually deleted or overexpressed in experiments. Empirically, 40 genes individually increased heme production (up to threefold). Heme was increased by modifying target genes, which not only included the genes involved in heme biosynthesis, but also those involved in glycolysis, pyruvate, Fe-S clusters, glycine, and succinyl-coenzyme A (CoA) metabolism. Next, we developed an algorithmic method for predicting an optimal combination of these genes by using the enzyme-constrained extension of the Yeast8 model, ecYeast8. The computationally identified combination for enhanced heme production was evaluated using the heme ligand-binding biosensor (Heme-LBB). The positive targets were combined using CRISPR-Cas9 in the yeast strain (IMX581-HEM15-HEM14-HEM3-Δshm1-HEM2-Δhmx1-FET4-Δgcv2-HEM1-Δgcv1-HEM13), which produces 70-fold-higher levels of intracellular heme.