Novel Bacteroides Vulgatus strain protects against gluten-induced break of human celiac gut epithelial homeostasis: a pre-clinical proof-of-concept study.

BACKGROUND AND AIMS: We have identified a decreased abundance of microbial species known to have a potential anti-inflammatory, protective effect in subjects that developed Celiac Disease (CeD) compared to those who did not. We aim to confirm the potential protective role of one of these species, namely Bacteroides vulgatus, and to mechanistically establish the effect of bacterial bioproducts on gluten-dependent changes on human gut epithelial functions. METHODS: We identified, isolated, cultivated, and sequenced a unique novel strain (20220303-A2) of B. vulgatus found only in control subjects. Using a human gut organoid system developed from pre-celiac patients, we monitored epithelial phenotype and innate immune cytokines at baseline, after exposure to gliadin, or gliadin plus B. vulgatus cell free supernatant (CFS). RESULTS: Following gliadin exposure, we observed increases in epithelial cell death, epithelial monolayer permeability, and secretion of pro-inflammatory cytokines. These effects were mitigated upon exposure to B. vulgatus 20220303-A2 CFS, which had matched phenotype gene product mutations. These protective effects were mediated by epigenetic reprogramming of the organoids treated with B. vulgatus CFS. CONCLUSIONS: We identified a unique strain of B. vulgatus that may exert a beneficial role by protecting CeD epithelium against a gluten-induced break of epithelial tolerance through miRNA reprogramming. IMPACT: Gut dysbiosis precedes the onset of celiac disease in genetically at-risk infants. This dysbiosis is characterized by the loss of protective bacterial strains in those children who will go on to develop celiac disease. The paper reports the mechanism by which one of these protective strains, B. vulgatus, ameliorates the gluten-induced break of gut epithelial homeostasis by epigenetically re-programming the target intestinal epithelium involving pathways controlling permeability, immune response, and cell turnover.

Microbiome signatures of progression toward celiac disease onset in at-risk children in a longitudinal prospective cohort study.

Other than exposure to gluten and genetic compatibility, the gut microbiome has been suggested to be involved in celiac disease (CD) pathogenesis by mediating interactions between gluten/environmental factors and the host immune system. However, to establish disease progression markers, it is essential to assess alterations in the gut microbiota before disease onset. Here, a prospective metagenomic analysis of the gut microbiota of infants at risk of CD was done to track shifts in the microbiota before CD development. We performed cross-sectional and longitudinal analyses of gut microbiota, functional pathways, and metabolites, starting from 18 mo before CD onset, in 10 infants who developed CD and 10 matched nonaffected infants. Cross-sectional analysis at CD onset identified altered abundance of six microbial strains and several metabolites between cases and controls but no change in microbial species or pathway abundance. Conversely, results of longitudinal analysis revealed several microbial species/strains/pathways/metabolites occurring in increased abundance and detected before CD onset. These had previously been linked to autoimmune and inflammatory conditions (e.g., Dialister invisus, Parabacteroides sp., Lachnospiraceae, tryptophan metabolism, and metabolites serine and threonine). Others occurred in decreased abundance before CD onset and are known to have anti-inflammatory effects (e.g., Streptococcus thermophilus, Faecalibacterium prausnitzii, and Clostridium clostridioforme). Additionally, we uncovered previously unreported microbes/pathways/metabolites (e.g., Porphyromonas sp., high mannose-type N-glycan biosynthesis, and serine) that point to CD-specific biomarkers. Our study establishes a road map for prospective longitudinal study designs to better understand the role of gut microbiota in disease pathogenesis and therapeutic targets to reestablish tolerance and/or prevent autoimmunity.

k-OptForce: integrating kinetics with flux balance analysis for strain design.

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.

A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data.

In contrast to stoichiometric-based models, the development of large-scale kinetic models of metabolism has been hindered by the challenge of identifying kinetic parameter values and kinetic rate laws applicable to a wide range of environmental and/or genetic perturbations. The recently introduced ensemble modeling (EM) procedure provides a promising remedy to address these challenges by decomposing metabolic reactions into elementary reaction steps and incorporating all phenotypic observations, upon perturbation, in its model parameterization scheme. Here, we present a kinetic model of Escherichia coli core metabolism that satisfies the fluxomic data for wild-type and seven mutant strains by making use of the EM concepts. This model encompasses 138 reactions, 93 metabolites and 60 substrate-level regulatory interactions accounting for glycolysis/gluconeogenesis, pentose phosphate pathway, TCA cycle, major pyruvate metabolism, anaplerotic reactions and a number of reactions in other parts of the metabolism. Parameterization is performed using a formal optimization approach that minimizes the discrepancies between model predictions and flux measurements. The predicted fluxes by the model are within the uncertainty range of experimental flux data for 78% of the reactions (with measured fluxes) for both the wild-type and seven mutant strains. The remaining flux predictions are mostly within three standard deviations of reported ranges. Converting the EM-based parameters into a Michaelis-Menten equivalent formalism revealed that 35% of Km and 77% of kcat parameters are within uncertainty range of the literature-reported values. The predicted metabolite concentrations by the model are also within uncertainty ranges of metabolomic data for 68% of the metabolites. A leave-one-out cross-validation test to evaluate the flux prediction performance of the model showed that metabolic fluxes for the mutants located in the proximity of mutations used for training the model can be predicted more accurately. The constructed model and the parameterization procedure presented in this study pave the way for the construction of larger-scale kinetic models with more narrowly distributed parameter values as new metabolomic/fluxomic data sets are becoming available for E. coli and other organisms.

OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities.

Microorganisms rarely live isolated in their natural environments but rather function in consolidated and socializing communities. Despite the growing availability of high-throughput sequencing and metagenomic data, we still know very little about the metabolic contributions of individual microbial players within an ecological niche and the extent and directionality of interactions among them. This calls for development of efficient modeling frameworks to shed light on less understood aspects of metabolism in microbial communities. Here, we introduce OptCom, a comprehensive flux balance analysis framework for microbial communities, which relies on a multi-level and multi-objective optimization formulation to properly describe trade-offs between individual vs. community level fitness criteria. In contrast to earlier approaches that rely on a single objective function, here, we consider species-level fitness criteria for the inner problems while relying on community-level objective maximization for the outer problem. OptCom is general enough to capture any type of interactions (positive, negative or combinations thereof) and is capable of accommodating any number of microbial species (or guilds) involved. We applied OptCom to quantify the syntrophic association in a well-characterized two-species microbial system, assess the level of sub-optimal growth in phototrophic microbial mats, and elucidate the extent and direction of inter-species metabolite and electron transfer in a model microbial community. We also used OptCom to examine addition of a new member to an existing community. Our study demonstrates the importance of trade-offs between species- and community-level fitness driving forces and lays the foundation for metabolic-driven analysis of various types of interactions in multi-species microbial systems using genome-scale metabolic models.

Mathematical optimization applications in metabolic networks.

Genome-scale metabolic models are increasingly becoming available for a variety of microorganisms. This has spurred the development of a wide array of computational tools, and in particular, mathematical optimization approaches, to assist in fundamental metabolic network analyses and redesign efforts. This review highlights a number of optimization-based frameworks developed towards addressing challenges in the analysis and engineering of metabolic networks. In particular, three major types of studies are covered here including exploring model predictions, correction and improvement of models of metabolism, and redesign of metabolic networks for the targeted overproduction of a desired compound. Overall, the methods reviewed in this paper highlight the diversity of queries, breadth of questions and complexity of redesigns that are amenable to mathematical optimization strategies.

An integrated computational and experimental study for overproducing fatty acids in Escherichia coli.

Increasing demands for petroleum have stimulated sustainable ways to produce chemicals and biofuels. Specifically, fatty acids of varying chain lengths (C6-C16) naturally synthesized in many organisms are promising starting points for the catalytic production of industrial chemicals and diesel-like biofuels. However, bio-production of fatty acids from plants and other microbial production hosts relies heavily on manipulating tightly regulated fatty acid biosynthetic pathways. In addition, precursors for fatty acids are used along other central metabolic pathways for the production of amino acids and biomass, which further complicates the engineering of microbial hosts for higher yields. Here, we demonstrate an iterative metabolic engineering effort that integrates computationally driven predictions and metabolic flux analysis techniques to meet this challenge. The OptForce procedure was used for suggesting and prioritizing genetic manipulations that overproduce fatty acids of different chain lengths from C6 to C16 starting with wild-type E. coli. We identified some common but mostly chain-specific genetic interventions alluding to the possibility of fine-tuning overproduction for specific fatty acid chain lengths. In accordance with the OptForce prioritization of interventions, fabZ and acyl-ACP thioesterase were upregulated and fadD was deleted to arrive at a strain that produces 1.70 g/L and 0.14 g fatty acid/g glucose (∼39% maximum theoretical yield) of C14₋16 fatty acids in minimal M9 medium. These results highlight the benefit of using computational strain design and flux analysis tools in the design of recombinant strains of E. coli to produce free fatty acids.

Computational design and engineering of an Escherichia coli strain producing the nonstandard amino acid para-aminophenylalanine.

Introducing heterologous pathways into host cells constitutes a promising strategy for synthesizing nonstandard amino acids (nsAAs) to enable the production of proteins with expanded chemistries. However, this strategy has proven challenging, as the expression of heterologous pathways can disrupt cellular homeostasis of the host cell. Here, we sought to optimize the heterologous production of the nsAA para-aminophenylalanine (pAF) in Escherichia coli. First, we incorporated a heterologous pAF biosynthesis pathway into a genome-scale model of E. coli metabolism and computationally identified metabolic interventions in the host's native metabolism to improve pAF production. Next, we explored different approaches of imposing these flux interventions experimentally and found that the upregulation of flux in the chorismate biosynthesis pathway through the elimination of feedback inhibition mechanisms could significantly raise pAF titers (∼20-fold) while maintaining a reasonable pAF production-growth rate trade-off. Overall, this study provides a promising strategy for the biosynthesis of nsAAs in engineered cells.

Microbiota and Metabolomic Patterns in the Breast Milk of Subjects with Celiac Disease on a Gluten-Free Diet.

The intestinal microbiome may trigger celiac disease (CD) in individuals with a genetic disposition when exposed to dietary gluten. Research demonstrates that nutrition during infancy is crucial to the intestinal microbiome engraftment. Very few studies to date have focused on the breast milk composition of subjects with a history of CD on a gluten-free diet. Here, we utilize a multi-omics approach with shotgun metagenomics to analyze the breast milk microbiome integrated with metabolome profiling of 36 subjects, 20 with CD on a gluten-free diet and 16 healthy controls. These analyses identified significant differences in bacterial and viral species/strains and functional pathways but no difference in metabolite abundance. Specifically, three bacterial strains with increased abundance were identified in subjects with CD on a gluten-free diet of which one (Rothia mucilaginosa) has been previously linked to autoimmune conditions. We also identified five pathways with increased abundance in subjects with CD on a gluten-free diet. We additionally found four bacterial and two viral species/strains with increased abundance in healthy controls. Overall, the differences observed in bacterial and viral species/strains and in functional pathways observed in our analysis may influence microbiome engraftment in neonates, which may impact their future clinical outcomes.

Characterization of the blood microbiota in children with Celiac disease.

Celiac Disease (CD) is an autoimmune disorder triggered by gluten ingestion that can develop in genetically predisposed individuals. Alterations in the gut microbiota have been suggested to contribute to development of autoimmune conditions including CD. Recent work suggests the existence of a blood microbiota. Evidence that alterations in the blood microbiota potentially influence the development of chronic immune based diseases is increasing. However, there is no published literature regarding the blood microbiota in children, including those with CD. This study aimed to characterize the diversity and taxonomic composition of the blood microbiota of children with CD compared to controls. Whole blood samples were collected from children with active CD, CD in remission, and control subjects and 16S rRNA sequencing was utilized to analyze the blood microbiota. We found 16s rRNA present throughout all pediatric blood samples, providing evidence for the presence of a pediatric blood microbiota. We found significant differences in beta diversity and in abundance of certain taxa (Campylobacterales order, Odoribacteraceae and Helicobacteraceae families, Odoribacter genus and species, and Bacteroides acidifaciens species) between subjects with active CD and controls. These taxa have been previously reported to be associated with immune response and gut-inflammatory diseases. We did not find significant differences between subjects with active and remission CD or between remission CD and controls. Conclusions: We provide evidence for a pediatric blood microbiota and identified higher beta diversity and alterations in the composition of blood microbiota in subjects with active CD compared to controls.

Gut microbiota in Celiac Disease: microbes, metabolites, pathways and therapeutics.

INTRODUCTION: Current evidence supports a vital role of the microbiota on health outcomes, with alterations in an otherwise healthy balance linked to chronic medical conditions like celiac disease (CD). Recent advances in microbiome analysis allow for unparalleled profiling of the microbes and metabolites. With the growing volume of data available, trends are emerging that support a role for the gut microbiota in CD pathogenesis. AREAS COVERED: In this article, the authors review the relationship between factors such as genes and antibiotic exposure on CD onset and the intestinal microbiota. The authors also review other microbiota within the human body (like the oropharynx) that may play a role in CD pathogenesis. Finally, the authors discuss implications for disease modification and the ultimate goal of prevention. The authors reviewed literature from PubMed, EMBASE, and Web of Science. EXPERT OPINION: CD serves as a unique opportunity to explore the role of the intestinal microbiota on the development of chronic autoimmune disease. While research to date provides a solid foundation, most studies have been case-control and thus do not have capacity to explore the mechanistic role of the microbiota in CD onset. Further longitudinal studies and integrated multi-omics are necessary for investigating CD pathogenesis.

Multi-omics analysis reveals the influence of genetic and environmental risk factors on developing gut microbiota in infants at risk of celiac disease.

BACKGROUND: Celiac disease (CD) is an autoimmune digestive disorder that occurs in genetically susceptible individuals in response to ingesting gluten, a protein found in wheat, rye, and barley. Research shows that genetic predisposition and exposure to gluten are necessary but not sufficient to trigger the development of CD. This suggests that exposure to other environmental stimuli early in life, e.g., cesarean section delivery and exposure to antibiotics or formula feeding, may also play a key role in CD pathogenesis through yet unknown mechanisms. Here, we use multi-omics analysis to investigate how genetic and early environmental risk factors alter the development of the gut microbiota in infants at risk of CD. RESULTS: Toward this end, we selected 31 infants from a large-scale prospective birth cohort study of infants with a first-degree relative with CD. We then performed rigorous multivariate association, cross-sectional, and longitudinal analyses using metagenomic and metabolomic data collected at birth, 3 months and 6 months of age to explore the impact of genetic predisposition and environmental risk factors on the gut microbiota composition, function, and metabolome prior to the introduction of trigger (gluten). These analyses revealed several microbial species, functional pathways, and metabolites that are associated with each genetic and environmental risk factor or that are differentially abundant between environmentally exposed and non-exposed infants or between time points. Among our significant findings, we found that cesarean section delivery is associated with a decreased abundance of Bacteroides vulgatus and Bacteroides dorei and of folate biosynthesis pathway and with an increased abundance of hydroxyphenylacetic acid, alterations that are implicated in immune system dysfunction and inflammatory conditions. Additionally, longitudinal analysis revealed that, in infants not exposed to any environmental risk factor, the abundances of Bacteroides uniformis and of metabolite 3-3-hydroxyphenylproprionic acid increase over time, while those for lipoic acid and methane metabolism pathways decrease, patterns that are linked to beneficial immunomodulatory and anti-inflammatory effects. CONCLUSIONS: Overall, our study provides unprecedented insights into major taxonomic and functional shifts in the developing gut microbiota of infants at risk of CD linking genetic and environmental risk factors to detrimental immunomodulatory and inflammatory effects. Video Abstract.

Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities.

Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial "games". We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.

Synthetic Ecology of Microbes: Mathematical Models and Applications.

As the indispensable role of natural microbial communities in many aspects of life on Earth is uncovered, the bottom-up engineering of synthetic microbial consortia with novel functions is becoming an attractive alternative to engineering single-species systems. Here, we summarize recent work on synthetic microbial communities with a particular emphasis on open challenges and opportunities in environmental sustainability and human health. We next provide a critical overview of mathematical approaches, ranging from phenomenological to mechanistic, to decipher the principles that govern the function, dynamics and evolution of microbial ecosystems. Finally, we present our outlook on key aspects of microbial ecosystems and synthetic ecology that require further developments, including the need for more efficient computational algorithms, a better integration of empirical methods and model-driven analysis, the importance of improving gene function annotation, and the value of a standardized library of well-characterized organisms to be used as building blocks of synthetic communities.

d-OptCom: Dynamic multi-level and multi-objective metabolic modeling of microbial communities.

Most microbial communities change with time in response to changes and/or perturbations in environmental conditions. Temporal variations in interspecies metabolic interactions within these communities can significantly affect their structure and function. Here, we introduce d-OptCom, an extension of the OptCom procedure, for the dynamic metabolic modeling of microbial communities. It enables capturing the temporal dynamics of biomass concentration of the community members and extracellular concentration of the shared metabolites, while integrating species- and community-level fitness functions. The applicability of d-OptCom was demonstrated by modeling the dynamic co-growth of auxotrophic mutant pairs of E. coli and by computationally assessing the dynamics and composition of a uranium-reducing community comprised of Geobacter sulfurreducens, Rhodoferax ferrireducens, and Shewanella oneidensis. d-OptCom was also employed to examine the impact of lactate vs acetate addition on the relative abundance of uranium-reducing species. These studies highlight the importance of simultaneously accounting for both species- and community-level fitness functions when modeling microbial communities, and demonstrate that the incorporation of uptake kinetic information can substantially improve the prediction of interspecies flux trafficking. Overall, this study paves the way for the dynamic multi-level and multi-objective analysis of microbial ecosystems.

Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks.

The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.

Down-regulation of HtrA1 activates the epithelial-mesenchymal transition and ATM DNA damage response pathways.

Expression of the serine protease HtrA1 is decreased or abrogated in a variety of human primary cancers, and higher levels of HtrA1 expression are directly related to better response to chemotherapeutics. However, the precise mechanisms leading to HtrA1 down regulation during malignant transformation are unclear. To investigate HtrA1 gene regulation in breast cancer, we characterized expression in primary breast tissues and seven human breast epithelial cell lines, including two non-tumorigenic cell lines. In human breast tissues, HtrA1 expression was prominent in normal ductal glands. In DCIS and in invasive cancers, HtrA1 expression was greatly reduced or lost entirely. HtrA1 staining was also reduced in all of the human breast cancer cell lines, compared with the normal tissue and non-tumorigenic cell line controls. Loss of HtrA1 gene expression was attributable primarily to epigenetic silencing mechanisms, with different mechanisms operative in the various cell lines. To mechanistically examine the functional consequences of HtrA1 loss, we stably reduced and/or overexpressed HtrA1 in the non-tumorigenic MCF10A cell line. Reduction of HtrA1 levels resulted in the epithelial-to-mesenchymal transition with acquisition of mesenchymal phenotypic characteristics, including increased growth rate, migration, and invasion, as well as expression of mesenchymal biomarkers. A concomitant decrease in expression of epithelial biomarkers and all microRNA 200 family members was also observed. Moreover, reduction of HtrA1 expression resulted in activation of the ATM and DNA damage response, whereas overexpression of HtrA1 prevented this activation. Collectively, these results suggest that HtrA1 may function as a tumor suppressor by controlling the epithelial-to-mesenchymal transition, and may function in chemotherapeutic responsiveness by mediating DNA damage response pathways.

Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data.

BACKGROUND: Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., iMM904) remains significantly less predictive than the latest E. coli model (i.e., iAF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model. RESULTS: In this paper we make use of the automated GrowMatch procedure for restoring consistency with single gene deletion experiments in yeast and extend the procedure to make use of synthetic lethality data using the genome-scale model iMM904 as a basis. We identified and vetted using literature sources 120 distinct model modifications including various regulatory constraints for minimal and YP media. The incorporation of the suggested modifications led to a substantial increase in the fraction of correctly predicted lethal knockouts (i.e., specificity) from 38.84% (87 out of 224) to 53.57% (120 out of 224) for the minimal medium and from 24.73% (45 out of 182) to 40.11% (73 out of 182) for the YP medium. Synthetic lethality predictions improved from 12.03% (16 out of 133) to 23.31% (31 out of 133) for the minimal medium and from 6.96% (8 out of 115) to 13.04% (15 out of 115) for the YP medium. CONCLUSIONS: Overall, this study provides a roadmap for the computationally driven correction of multi-compartment genome-scale metabolic models and demonstrates the value of synthetic lethals as curation agents.