Integrating highly quantitative proteomics and genome-scale metabolic modeling to study pH adaptation in the human pathogen Enterococcus faecalis

Proteome profiling of Campylobacter jejuni 81-176 at 37 °C and 42 °C by label-free mass spectrometry

BACKGROUND

The main natural reservoir for Campylobacter jejuni is the avian intestinal tract. There, C. jejuni multiplies optimally at 42 °C - the avian body temperature. After infecting humans through oral intake, the bacterium encounters the lower temperature of 37 °C in the human intestinal tract. Proteome profiling by label-free mass spectrometry (DIA-MS) was performed to examine the processes which enable C. jejuni 81-176 to thrive at 37 °C in comparison to 42 °C. In total, four states were compared with each other: incubation for 12 h at 37 °C, for 24 h at 37 °C, for 12 h at 42 °C and 24 h at 42 °C.

RESULTS

It was shown that the proteomic changes not only according to the different incubation temperature but also to the length of the incubation period were evident when comparing 37 °C and 42 °C as well as 12 h and 24 h of incubation. Altogether, the expression of 957 proteins was quantifiable. 37.1 - 47.3% of the proteins analyzed showed significant differential regulation, with at least a 1.5-fold change in either direction (i.e. log2 FC ≥ 0.585 or log2 FC ≤ -0.585) and an FDR-adjusted p-value of less than 0.05. The significantly differentially expressed proteins could be arranged in 4 different clusters and 16 functional categories.

CONCLUSIONS

The C. jejuni proteome at 42 °C is better adapted to high replication rates than that at 37 °C, which was in particular indicated by the up-regulation of proteins belonging to the functional categories "replication" (e.g. Obg, ParABS, and NapL), "DNA synthesis and repair factors" (e.g. DNA-polymerase III, DnaB, and DnaE), "lipid and carbohydrate biosynthesis" (e.g. capsular biosynthesis sugar kinase, PrsA, AccA, and AccP) and "vitamin synthesis, metabolism, cofactor biosynthesis" (e.g. MobB, BioA, and ThiE). The relative up-regulation of proteins with chaperone function (GroL, DnaK, ClpB, HslU, GroS, DnaJ, DnaJ-1, and NapD) at 37 °C in comparison to 42 °C after 12 h incubation indicates a temporary lower-temperature proteomic response. Additionally the up-regulation of factors for DNA uptake (ComEA and RecA) at 37 °C compared to 42 °C indicate a higher competence for the acquisition of extraneous DNA at human body temperature.

Differences of protein expression in enterococcus faecalis biofilm during resistance to environmental pressures

BACKGROUND

Enterococcus faecalis biofilm was frequently found on the failed treated root canal wall, which survived by resisting disinfectant during endodontic treatment.Many researches have been conducted to explore the mechanisms of persistence of this pathogen in unfavorable conditions. However, no comprehensive proteomics studies have been conducted to investigate stress response in Enterococcus faecalis caused by alkali and NaOCl.

OBJECTIVE

Enterococcus faecalis (E.f) has been recognized as a main pathogen of refractory apical periodontitis, its ability to withstand environmental pressure is the key to grow in the environment of high alkaline and anti-bacterial drug that causes chronic infection in the root canal. This study aims to focus on the protein expression patterns of E.f biofilm under extreme pressure environment".

METHODS

Enterococcus faecalis biofilm model was established in vitro. Liquid Chromatograph-Mass Spectrometer (LC-MS/MS)-based label free quantitative proteomics approach was applied to compare differential protein expression under different environmental pressures (pH 10 and 5% sodium hypochlorite (NaOCl)). And then qPCR and Parallel Reaction Monitoring Verification (PRM) were utilized to verify the consequence of proteomics.

RESULTS

The number of taxa in this study was higher than those in previous studies, demonstrating the presence of a remarkable number of proteins in the groups of high alkaline and NaOCl. Proteins involved in ATP-binding cassette (ABC) transporter were significantly enriched in experimental samples. We identified a total of 15 highly expressed ABC transporters in the high alkaline environment pressure group, with 7 proteins greater than 1.5 times.

CONCLUSIONS

This study revealed considerable changes in expression of proteins in E.f biofilm during resistance to environmental pressures. The findings enriched our understanding of association between the differential expression proteins and environmental pressures.

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.

Parameter inference for enzyme and temperature constrained genome-scale models

The metabolism of all living organisms is dependent on temperature, and therefore, having a good method to predict temperature effects at a system level is of importance. A recently developed Bayesian computational framework for enzyme and temperature constrained genome-scale models (etcGEM) predicts the temperature dependence of an organism's metabolic network from thermodynamic properties of the metabolic enzymes, markedly expanding the scope and applicability of constraint-based metabolic modelling. Here, we show that the Bayesian calculation method for inferring parameters for an etcGEM is unstable and unable to estimate the posterior distribution. The Bayesian calculation method assumes that the posterior distribution is unimodal, and thus fails due to the multimodality of the problem. To remedy this problem, we developed an evolutionary algorithm which is able to obtain a diversity of solutions in this multimodal parameter space. We quantified the phenotypic consequences on six metabolic network signature reactions of the different parameter solutions resulting from use of the evolutionary algorithm. While two of these reactions showed little phenotypic variation between the solutions, the remainder displayed huge variation in flux-carrying capacity. This result indicates that the model is under-determined given current experimental data and that more data is required to narrow down the model predictions. Finally, we made improvements to the software to reduce the running time of the parameter set evaluations by a factor of 8.5, allowing for obtaining results faster and with less computational resources.

Variations in protein expression associated with oral cancer

BACKGROUND

Differential protein expression of the oral microbiome is related to human diseases, including cancer.

OBJECTIVE

In order to reveal the potential relationship between oral bacterial protein expression in oral squamous cell carcinoma (OSCC), we designed this study.

METHODS

We obtained samples of the same patient from cancer lesion and anatomically matched normal site. Then, we used the label free quantitative technique based on liquid chromatography tandem mass spectrometry (LC-MS/MS) to analyze the bacteria in the samples of oral squamous cell carcinoma at the protein level, so as to detect the functional proteins.

RESULTS

Protein diversity in the cancer samples was significantly greater than in the normal samples. We identified a substantially higher number of the taxa than those detected in previous studies, demonstrating the presence of a remarkable number of proteins in the groups. In particular, proteins involved in energy production and conversion, proton transport, hydrogen transport and hydrogen ion transmembrane transport, ATP-binding cassette (ABC) transporter, PTS system, and L-serine dehydratase were enriched significantly in the experimental group. Moreover, some proteins associated with Actinomyces and Fusobacterium were highly associated with OSCC and provided a good diagnostic outcome.

CONCLUSION

The present study revealed considerable changes in the expression of bacterial proteins in OSCC and enrich our understanding in this point.

Antibiotic Resistance Diagnosis in ESKAPE Pathogens-A Review on Proteomic Perspective

Antibiotic resistance has emerged as an imminent pandemic. Rapid diagnostic assays distinguish bacterial infections from other diseases and aid antimicrobial stewardship, therapy optimization, and epidemiological surveillance. Traditional methods typically have longer turn-around times for definitive results. On the other hand, proteomic studies have progressed constantly and improved both in qualitative and quantitative analysis. With a wide range of data sets made available in the public domain, the ability to interpret the data has considerably reduced the error rates. This review gives an insight on state-of-the-art proteomic techniques in diagnosing antibiotic resistance in ESKAPE pathogens with a future outlook for evading the "imminent pandemic".

Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.

Expanding the search for small-molecule antibacterials by multidimensional profiling

New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.

All Driven by Energy Demand? Integrative Comparison of Metabolism of Enterococcus faecalis Wildtype and a Glutamine Synthase Mutant

Lactic acid bacteria (LAB) play a significant role in biotechnology, e.g., food industry and also in human health. Many LAB genera have developed a multidrug resistance in the past few years, causing a serious problem in controlling hospital germs worldwide. Enterococcus faecalis accounts for a large part of the human infections caused by LABs. Therefore, studying its adaptive metabolism under various environmental conditions is particularly important to promote the development of new therapeutic approaches. In this study, we investigated the effect of glutamine auxotrophy (ΔglnA mutant) on metabolic and proteomic adaptations of E. faecalis in response to a changing pH in its environment. Changing pH values are part of the organism's natural environment in the human body and play a role in the food industry. We compared the results with those of the wildtype. Using a genome-scale metabolic model constrained by metabolic and proteomic data, our integrative method allows us to understand the bigger picture of the adaptation strategies of this bacterium. The study showed that energy demand is the decisive factor in adapting to a new environmental pH. The energy demand of the mutant was higher at all conditions. It has been reported that ΔglnA mutants of bacteria are energetically less effective. With the aid of our data and model we are able to explain this phenomenon as a consequence of a failure to regulate glutamine uptake and the costs for the import of glutamine and the export of ammonium. Methodologically, it became apparent that taking into account the nonspecificity of amino acid transporters is important for reproducing metabolic changes with genome-scale models because it affects energy balance. IMPORTANCE The integration of new pH-dependent experimental data on metabolic uptake and release fluxes, as well as of proteome data with a genome-scale computational model of a glutamine synthetase mutant of E. faecalis is used and compared with those of the wildtype to understand why glutamine auxotrophy results in a less efficient metabolism and how-in comparison with the wildtype-the glutamine synthetase knockout impacts metabolic adjustments during acidification or simply exposure to lower pH. We show that forced glutamine auxotrophy causes more energy demand and that this is likely due to a disregulated glutamine uptake. Proteome changes during acidification observed for the mutant resemble those of the wildtype with the exception of glycolysis-related genes, as the mutant is already energetically stressed at a higher pH and the respective proteome changes were in effect.

Inspecting the Solution Space of Genome-Scale Metabolic Models

Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results.

Downregulation of beclin 1 restores arsenite-induced impaired autophagic flux by improving the lysosomal function in the brain

Arsenite is a toxic metalloid that causes various adverse effects in the brain. However, the underlying mechanisms of arsenite-induced neurotoxicity remain poorly understood. In this study, both adult beclin 1^+/+ and beclin 1^+/- mice were employed to establish a model of chronic arsenite exposure by treating with arsenite via drinking water for 6 months. The results clearly demonstrated that exposure to arsenite profoundly caused damage to the cerebral cortex, induced autophagy and impaired autophagic flux in the cerebral cortex. Heterozygous disruption of beclin 1 in animals remarkably alleviated the neurotoxic effects of arsenite. To verify the results obtained in the animals, a permanent U251 cell line was used. After treating of cells with arsenite, similar phenomenon was also observed, showing the significant elevation in the expression levels of autophagy-related genes. Importantly, lysosomal dysfunction caused by arsenite was observed in vitro and in vivo. Either knockdown of beclin 1 in cells or heterozygous disruption of beclin 1 in animals remarkably alleviated the lysosomal dysfunction induced by arsenite. These findings indicate that downregulation of beclin 1 could restore arsenite-induced impaired autophagic flux possibly through improving lysosomal function, and correct that regulation of autophagy via beclin 1 would be an alternative approach for the treatment of arsenite neurotoxicity.

Toward modeling metabolic state from single-cell transcriptomics

Background

Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge.

Scope of Review

We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals.

Major Conclusions

Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

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Single-cell RNA sequencing and prior metabolic knowledge enable metabolic predictions.

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When compared to bulk, single-cell modeling is linked to unique insights and challenges.

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Computational modelling approaches differ in applicability and newly provided insights.

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The use of prior metabolic knowledge paves the way for mechanistic machine-learning.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Understanding the host-microbe interactions using metabolic modeling

The human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using in silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce to experimental biologists the possible applications of flux balance analysis in the host-microbiota interaction field and discusses its potential use to improve human health. Video abstract.

Spirulina-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis

This study used an in silico metabolic engineering strategy for modifying the metabolic capabilities of Spirulina under specific conditions as an approach to modifying culture conditions in order to generate the intended outputs. In metabolic models, the basic metabolic fluxes in steady-state metabolic networks have generally been controlled by stoichiometric reactions; however, this approach does not consider the regulatory mechanism of the proteins responsible for the metabolic reactions. The protein regulatory network plays a critical role in the response to stresses, including environmental stress, encountered by an organism. Thus, the integration of the response mechanism of Spirulina to growth temperature stresses was investigated via simulation of a proteome-based GSMM, in which the boundaries were established by using protein expression levels obtained from quantitative proteomic analysis. The proteome-based flux balance analysis (FBA) under an optimal growth temperature (35 °C), a low growth temperature (22 °C) and a high growth temperature (40 °C) showed biomass yields that closely fit the experimental data obtained in previous research. Moreover, the response mechanism was analyzed by the integration of the proteome and protein-protein interaction (PPI) network, and those data were used to support in silico knockout/overexpression of selected proteins involved in the PPI network. The Spirulina, wild-type, proteome fluxes under different growth temperatures and those of mutants were compared, and the proteins/enzymes catalyzing the different flux levels were mapped onto their designated pathways for biological interpretation.

Genome scale metabolic models and analysis for evaluating probiotic potentials

Probiotics are live beneficial microorganisms that can be consumed in the form of dairy and food products as well as dietary supplements to promote a healthy balance of gut bacteria in humans. Practically, the main challenge is to identify and select promising strains and formulate multi-strain probiotic blends with consistent efficacy which is highly dependent on individual dietary regimes, gut environments, and health conditions. Limitations of current in vivo and in vitro methods for testing probiotic strains can be overcome by in silico model guided systems biology approaches where genome scale metabolic models (GEMs) can be used to describe their cellular behaviors and metabolic states of probiotic strains under various gut environments. Here, we summarize currently available GEMs of microbial strains with probiotic potentials and propose a knowledge-based framework to evaluate metabolic capabilities on the basis of six probiotic criteria. They include metabolic characteristics, stability, safety, colonization, postbiotics, and interaction with the gut microbiome which can be assessed by in silico approaches. As such, the most suitable strains can be identified to design personalized multi-strain probiotics in the future.

Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines.

Tn-Core: A Toolbox for Integrating Tn-seq Gene Essentiality Data and Constraint-Based Metabolic Modeling

The design of synthetic cells requires a detailed understanding of the relevance of genes and gene networks underlying complex cellular phenotypes. Transposon-sequencing (Tn-seq) and constraint-based metabolic modeling can be used to probe the core genetic and metabolic networks underlying a biological process. Integrating these highly complementary experimental and in silico approaches has the potential to yield a highly comprehensive understanding of the core networks of a cell. Specifically, it can facilitate the interpretation of Tn-seq data sets and identify gaps in the data that could hinder the engineering of the cellular system, while also providing refined models for the accurate predictions of cellular metabolism. Here, we present Tn-Core, the first easy-to-use computational pipeline specifically designed for integrating Tn-seq data with metabolic modeling, prepared for use by both experimental and computational biologists. Tn-Core is a MATLAB toolbox that contains several custom functions, and it is built upon existing functions within the COBRA Toolbox and the TIGER Toolbox. Tn-Core takes as input a genome-scale metabolic model, Tn-seq data, and optionally RNA-seq data, and returns: (i) a context-specific core metabolic model; (ii) an evaluation of redundancies within core metabolic pathways, and optionally (iii) a refined genome-scale metabolic model. A simple, user-friendly workflow, requiring limited knowledge of metabolic modeling, is provided that allows users to run the analyses and export the data as easy-to-explore files of value to both experimental and computational biologists. We demonstrate the utility of Tn-Core using Sinorhizobium meliloti, Pseudomonas aeruginosa, and Rhodobacter sphaeroides genome-scale metabolic reconstructions as case studies.

Biomedical applications of genome-scale metabolic network reconstructions of human pathogens

The growing global threat of antibiotic resistant human pathogens has coincided with improved methods for developing and using genome-scale metabolic network reconstructions. Consequently, there has been an increase in the number of high-quality reconstructions of relevant human and zoonotic pathogens. Novel biomedical applications of pathogen reconstructions focus on three key aspects of pathogen behavior: the evolution of antibiotic resistance, virulence factor production, and host-pathogen interactions. New methods using these reconstructions aim to improve understanding of microbe pathogenicity and guide the development of new therapeutic strategies. This review summarizes the latest ways that genome-scale metabolic network reconstructions have been used to study human pathogens and suggests future applications with the potential to mitigate infectious disease.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.

Systems biology of lactic acid bacteria: For food and thought

Lactic acid bacteria (LAB) ferment plants, fish, meats and milk and turn them into tasty food products with increased shelf life; other LAB help digesting food and create a healthy environment in the intestine. The economic and societal importance of these relatively simple and small bacteria is immense. In this review we hope to show that their adaptations to nutrient-rich environments provides fascinating and often puzzling behaviours that give rise to many fundamental evolutionary biological questions in need of a systems biology approach. We will provide examples of such questions, compare the (metabolic) behaviour of LAB to that of other model organisms, and provide the latest insights, if available.

More than just a gut feeling: constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes

The human gut is colonized with a myriad of microbes, with substantial interpersonal variation. This complex ecosystem is an integral part of the gastrointestinal tract and plays a major role in the maintenance of homeostasis. Its dysfunction has been correlated to a wide array of diseases, but the understanding of causal mechanisms is hampered by the limited amount of cultured microbes, poor understanding of phenotypes, and the limited knowledge about interspecies interactions. Genome-scale metabolic models (GEMs) have been used in many different fields, ranging from metabolic engineering to the prediction of interspecies interactions. We provide showcase examples for the application of GEMs for gut microbes and focus on (i) the prediction of minimal, synthetic, or defined media; (ii) the prediction of possible functions and phenotypes; and (iii) the prediction of interspecies interactions. All three applications are key in understanding the role of individual species in the gut ecosystem as well as the role of the microbiota as a whole. Using GEMs in the described fashions has led to designs of minimal growth media, an increased understanding of microbial phenotypes and their influence on the host immune system, and dietary interventions to improve human health. Ultimately, an increased understanding of the gut ecosystem will enable targeted interventions in gut microbial composition to restore homeostasis and appropriate host-microbe crosstalk.