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.

[1]  Christopher A. Voigt,et al.  Discovery of Reactive Microbiota-Derived Metabolites that Inhibit Host Proteases , 2017, Cell.

[2]  F. Montecucco,et al.  Evidence for the Gut Microbiota Short-Chain Fatty Acids as Key Pathophysiological Molecules Improving Diabetes , 2014, Mediators of inflammation.

[3]  P. Blainey The future is now: single-cell genomics of bacteria and archaea. , 2013, FEMS microbiology reviews.

[4]  Edward J. O'Brien,et al.  Using Genome-scale Models to Predict Biological Capabilities , 2015, Cell.

[5]  R. Lasken Genomic sequencing of uncultured microorganisms from single cells , 2012, Nature Reviews Microbiology.

[6]  Ronan M. T. Fleming,et al.  A community-driven global reconstruction of human metabolism , 2013, Nature Biotechnology.

[7]  C. Robert,et al.  Culture of previously uncultured members of the human gut microbiota by culturomics , 2016, Nature Microbiology.

[8]  Fangfang Xia,et al.  Genome-scale bacterial transcriptional regulatory networks: reconstruction and integrated analysis with metabolic models , 2014, Briefings Bioinform..

[9]  E. Borenstein,et al.  Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules , 2013, Proceedings of the National Academy of Sciences.

[10]  Nathan D. Price,et al.  Metabolic Constraint-Based Refinement of Transcriptional Regulatory Networks , 2013, PLoS Comput. Biol..

[11]  Carlos González-Alcón,et al.  Modeling of leishmaniasis infection dynamics: novel application to the design of effective therapies , 2012, BMC Systems Biology.

[12]  Bernhard O. Palsson,et al.  Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways , 2015, PLoS Comput. Biol..

[13]  Jennifer L Reed,et al.  Refining metabolic models and accounting for regulatory effects. , 2014, Current opinion in biotechnology.

[14]  M. Kleerebezem,et al.  Complete genome sequence of Lactobacillus plantarum WCFS1 , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Jeffrey D. Orth,et al.  Systematizing the generation of missing metabolic knowledge , 2010, Biotechnology and bioengineering.

[16]  W. D. de Vos,et al.  Production of butyrate from lysine and the Amadori product fructoselysine by a human gut commensal , 2015, Nature Communications.

[17]  Bas Teusink,et al.  In Silico Reconstruction of the Metabolic Pathways of Lactobacillus plantarum: Comparing Predictions of Nutrient Requirements with Those from Growth Experiments , 2005, Applied and Environmental Microbiology.

[18]  M. Espey,et al.  Role of oxygen gradients in shaping redox relationships between the human intestine and its microbiota. , 2013, Free radical biology & medicine.

[19]  W. D. de Vos,et al.  Development of a minimal growth medium for Lactobacillus plantarum , 2010, Letters in applied microbiology.

[20]  Daniel Segrè,et al.  Environments that Induce Synthetic Microbial Ecosystems , 2010, PLoS Comput. Biol..

[21]  A. Hoppe What mRNA Abundances Can Tell us about Metabolism , 2012, Metabolites.

[22]  R. Vilu,et al.  Nutritional requirements and media development for Lactococcus lactis IL1403 , 2014, Applied Microbiology and Biotechnology.

[23]  Min Kyung Kim,et al.  Methods for integration of transcriptomic data in genome-scale metabolic models , 2014, Computational and structural biotechnology journal.

[24]  Adam M. Feist,et al.  The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli , 2008, Nature Biotechnology.

[25]  Johannes Zimmermann,et al.  BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities , 2017, PLoS Comput. Biol..

[26]  M. Pop,et al.  Metagenomic Analysis of the Human Distal Gut Microbiome , 2006, Science.

[27]  Bernhard O. Palsson,et al.  A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1 , 2010, BMC Systems Biology.

[28]  Uri Gophna,et al.  Oscillospira: a Central, Enigmatic Component of the Human Gut Microbiota. , 2016, Trends in microbiology.

[29]  Peter D. Karp,et al.  A genome-scale metabolic flux model of Escherichia coli K–12 derived from the EcoCyc database , 2014, BMC Systems Biology.

[30]  H. Harmsen,et al.  Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe , 2014, Journal of bacteriology.

[31]  W. D. de Vos,et al.  Towards metagenome-scale models for industrial applications--the case of Lactic Acid Bacteria. , 2013, Current opinion in biotechnology.

[32]  Marc-Thorsten Hütt,et al.  Uncoupling of mucosal gene regulation, mRNA splicing and adherent microbiota signatures in inflammatory bowel disease , 2016, Gut.

[33]  M. Wésolowski-Louvel,et al.  Soil eukaryotic functional diversity, a metatranscriptomic approach , 2007, The ISME Journal.

[34]  Daniel Machado,et al.  Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism , 2014, PLoS Comput. Biol..

[35]  D. Stahl,et al.  Metabolic modeling of a mutualistic microbial community , 2007, Molecular systems biology.

[36]  W. D. de Vos,et al.  Improved taxonomic assignment of human intestinal 16S rRNA sequences by a dedicated reference database , 2015, BMC Genomics.

[37]  Rick L. Stevens,et al.  High-throughput generation, optimization and analysis of genome-scale metabolic models , 2010, Nature Biotechnology.

[38]  Roded Sharan,et al.  Competitive and cooperative metabolic interactions in bacterial communities. , 2011, Nature communications.

[39]  D. Askew,et al.  Doxycycline-regulated gene expression in the opportunistic fungal pathogen Aspergillus fumigatus , 2005, BMC Microbiology.

[40]  D. Schüler,et al.  Single-cell genomics of uncultivated deep-branching magnetotactic bacteria reveals a conserved set of magnetosome genes. , 2016, Environmental microbiology.

[41]  Hesso Farhan,et al.  Parallel Exploitation of Diverse Host Nutrients Enhances Salmonella Virulence , 2013, PLoS pathogens.

[42]  Radhakrishnan Mahadevan,et al.  Genome-scale metabolic modeling of a clostridial co-culture for consolidated bioprocessing. , 2010, Biotechnology journal.

[43]  H. Flint,et al.  The impact of nutrition on intestinal bacterial communities. , 2017, Current opinion in microbiology.

[44]  Daniel Segrè,et al.  Species interactions differ in their genetic robustness , 2015, Front. Microbiol..

[45]  Ioannis G. Tollis,et al.  A computational exploration of bacterial metabolic diversity identifying metabolic interactions and growth-efficient strain communities , 2011, BMC Systems Biology.

[46]  H. Sokol,et al.  Identification of an anti-inflammatory protein from Faecalibacterium prausnitzii, a commensal bacterium deficient in Crohn’s disease , 2015, Gut.

[47]  Adam M. Feist,et al.  Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli , 2013, Molecular systems biology.

[48]  E. Zoetendal,et al.  Effect of diet on the intestinal microbiota and its activity , 2014, Current opinion in gastroenterology.

[49]  Eugen Bauer,et al.  Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires , 2015, Microbiome.

[50]  B. Birren,et al.  The “Most Wanted” Taxa from the Human Microbiome for Whole Genome Sequencing , 2012, PloS one.

[51]  Partho Sen,et al.  Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome. , 2015, Cell metabolism.

[52]  Marcus J. Claesson,et al.  Genome-scale analyses of health-promoting bacteria: probiogenomics , 2009, Nature Reviews Microbiology.

[53]  P. Bork,et al.  A human gut microbial gene catalogue established by metagenomic sequencing , 2010, Nature.

[54]  W. D. de Vos,et al.  The first 1000 cultured species of the human gastrointestinal microbiota , 2014, FEMS microbiology reviews.

[55]  P. Turnbaugh,et al.  Xenobiotics Shape the Physiology and Gene Expression of the Active Human Gut Microbiome , 2013, Cell.

[56]  Jens Nielsen,et al.  From next-generation sequencing to systematic modeling of the gut microbiome , 2015, Front. Genet..

[57]  J. Lennon,et al.  Relationships between protein-encoding gene abundance and corresponding process are commonly assumed yet rarely observed , 2014, The ISME Journal.

[58]  Vinay Satish Kumar,et al.  GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions , 2009, PLoS Comput. Biol..

[59]  F. Doyle,et al.  Dynamic flux balance analysis of diauxic growth in Escherichia coli. , 2002, Biophysical journal.

[60]  P. Baldrian,et al.  Microbial genomics, transcriptomics and proteomics: new discoveries in decomposition research using complementary methods , 2014, Applied Microbiology and Biotechnology.

[61]  Bas Teusink,et al.  Exploring Metabolic Pathway Reconstruction and Genome-Wide Expression Profiling in Lactobacillus reuteri to Define Functional Probiotic Features , 2011, PloS one.

[62]  D. Raoult,et al.  The impact of culturomics on taxonomy in clinical microbiology , 2017, Antonie van Leeuwenhoek.

[63]  B. Palsson,et al.  Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation , 2005, BMC Microbiology.

[64]  Jonathan R. Karr,et al.  A Whole-Cell Computational Model Predicts Phenotype from Genotype , 2012, Cell.

[65]  H. Flint,et al.  Role of the gut microbiota in nutrition and health , 2018, British Medical Journal.

[66]  B. Palsson,et al.  A protocol for generating a high-quality genome-scale metabolic reconstruction , 2010 .

[67]  Frank T. Bergmann,et al.  Integrating highly quantitative proteomics and genome-scale metabolic modeling to study pH adaptation in the human pathogen Enterococcus faecalis , 2016, npj Systems Biology and Applications.

[68]  P. Hylemon,et al.  Bile acids and the gut microbiome , 2014, Current opinion in gastroenterology.

[69]  Lu Wang,et al.  The NIH Human Microbiome Project. , 2009, Genome research.

[70]  Jason A. Papin,et al.  Comparative Metabolic Systems Analysis of Pathogenic Burkholderia , 2013, Journal of bacteriology.

[71]  N. Price,et al.  Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis , 2010, Proceedings of the National Academy of Sciences.

[72]  M. Fischbach,et al.  Molecular analysis of model gut microbiotas by imaging mass spectrometry and nanodesorption electrospray ionization reveals dietary metabolite transformations. , 2012, Analytical chemistry.

[73]  Alex H. Lang,et al.  Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. , 2014, Cell reports.

[74]  R. Mackie,et al.  Ecology of Uncultivated Oscillospira Species in the Rumen of Cattle, Sheep, and Reindeer as Assessed by Microscopy and Molecular Approaches , 2003, Applied and Environmental Microbiology.

[75]  M. Blaut,et al.  Human intestinal microbiota: Characterization of a simplified and stable gnotobiotic rat model , 2011, Gut microbes.

[76]  J. Foster,et al.  Psychobiotics and the gut–brain axis: in the pursuit of happiness , 2015, Neuropsychiatric disease and treatment.

[77]  W. Garrett,et al.  The Microbial Metabolites, Short-Chain Fatty Acids, Regulate Colonic Treg Cell Homeostasis , 2013, Science.

[78]  Sharon I. Greenblum,et al.  Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease , 2011, Proceedings of the National Academy of Sciences.

[79]  Yufang Jin,et al.  A conceptual cellular interaction model of left ventricular remodelling post-MI: dynamic network with exit-entry competition strategy , 2010, BMC Systems Biology.

[80]  Yixin Chen,et al.  MicrobesFlux: a web platform for drafting metabolic models from the KEGG database , 2012, BMC Systems Biology.

[81]  M. Mellow,et al.  Fecal Microbiota Transplant for Treatment of Clostridium difficile Infection in Immunocompromised Patients , 2014, The American Journal of Gastroenterology.

[82]  William R. Harcombe,et al.  NOVEL COOPERATION EXPERIMENTALLY EVOLVED BETWEEN SPECIES , 2010, Evolution; international journal of organic evolution.

[83]  Jun Sun,et al.  Constraint-based modeling analysis of the metabolism of two Pelobacter species , 2010, BMC Systems Biology.

[84]  Xiao Han,et al.  A computational procedure for identifying master regulator candidates: a case study on diabetes progression in Goto-Kakizaki rats , 2012, BMC Systems Biology.

[85]  Elhanan Borenstein,et al.  Computational systems biology and in silico modeling of the human microbiome , 2012, Briefings Bioinform..

[86]  Ilias Tagkopoulos,et al.  An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli , 2014, Molecular systems biology.

[87]  Susumu Goto,et al.  Data, information, knowledge and principle: back to metabolism in KEGG , 2013, Nucleic Acids Res..

[88]  Intawat Nookaew,et al.  The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum , 2013, PLoS Comput. Biol..

[89]  Jens Roat Kultima,et al.  Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes , 2014, Nature Biotechnology.

[90]  Bernhard O. Palsson,et al.  Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions , 2012, BMC Systems Biology.

[91]  Nitin Kumar,et al.  Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation , 2016, Nature.

[92]  Ronan M. T. Fleming,et al.  Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota , 2016, Nature Biotechnology.

[93]  N. Ottman Host immunostimulation and substrate utilization of the gut symbiont Akkermansia muciniphila , 2015 .

[94]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[95]  Isabel Rocha,et al.  Integration of Biomass Formulations of Genome-Scale Metabolic Models with Experimental Data Reveals Universally Essential Cofactors in Prokaryotes , 2015, Metabolic engineering.

[96]  Eytan Ruppin,et al.  A Novel Nutritional Predictor Links Microbial Fastidiousness with Lowered Ubiquity, Growth Rate, and Cooperativeness , 2014, PLoS Comput. Biol..

[97]  W. D. de Vos,et al.  A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice , 2016, Nature Medicine.

[98]  Bas Teusink,et al.  Modelling strategies for the industrial exploitation of lactic acid bacteria , 2006, Nature Reviews Microbiology.

[99]  William J. Riehl,et al.  RegPrecise 3.0 – A resource for genome-scale exploration of transcriptional regulation in bacteria , 2013, BMC Genomics.

[100]  D. Wozniak,et al.  Understanding the control of Pseudomonas aeruginosa alginate synthesis and the prospects for management of chronic infections in cystic fibrosis , 2005, Molecular microbiology.

[101]  Shaowu Zhang,et al.  Prediction of metabolic fluxes from gene expression data with Huber penalty convex optimization function. , 2017, Molecular bioSystems.

[102]  Jochen Förster,et al.  Modeling Lactococcus lactis using a genome-scale flux model , 2005, BMC Microbiology.

[103]  E. Zoetendal,et al.  Duodenal infusion of donor feces for recurrent Clostridium difficile. , 2013, The New England journal of medicine.

[104]  Bas Teusink,et al.  Analysis of Growth of Lactobacillus plantarum WCFS1 on a Complex Medium Using a Genome-scale Metabolic Model* , 2006, Journal of Biological Chemistry.

[105]  Jason A. Papin,et al.  Applications of genome-scale metabolic reconstructions , 2009, Molecular systems biology.

[106]  B. Palsson,et al.  Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods , 2012, Nature Reviews Microbiology.

[107]  E. Shanahan,et al.  Isolation of Genetically Tractable Most-Wanted Bacteria by Metaparental Mating , 2015, Scientific Reports.

[108]  Tom M. Conrad,et al.  Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models , 2010, Molecular systems biology.

[109]  D. Jonkers,et al.  Review article: the role of butyrate on colonic function , 2007, Alimentary pharmacology & therapeutics.

[110]  Intawat Nookaew,et al.  Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction , 2014, BMC Systems Biology.

[111]  Peer Bork,et al.  Metabolic dependencies drive species co-occurrence in diverse microbial communities , 2015, Proceedings of the National Academy of Sciences.

[112]  Jason A. Papin,et al.  * Corresponding authors , 2006 .

[113]  Anne Kümmel,et al.  In silico genome-scale reconstruction and validation of the Staphylococcus aureus metabolic network. , 2005, Biotechnology and bioengineering.