Modeling approaches for probing cross-feeding interactions in the human gut microbiome

Pedro Saa1,2, Arles Urrutia1, Claudia Silva-Andrade3, Alberto J. Martín3, Daniel Garrido1
1Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, CHILE
2Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna, 4860 Santiago, Chile
3Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile

Tài liệu tham khảo

Nayfach, 2019, New insights from uncultivated genomes of the global human gut microbiome, Nature, 568, 505, 10.1038/s41586-019-1058-x Gasaly, 2021, Impact of bacterial metabolites on gut barrier function and host immunity: A focus on bacterial metabolism and its relevance for intestinal inflammation, Front Immunol, 12, 1807, 10.3389/fimmu.2021.658354 Cronin, 2021, Dietary fibre modulates the gut microbiota, Nutrients, 13, 1655, 10.3390/nu13051655 Coyte, 2015, The ecology of the microbiome: Networks, competition, and stability, Science, 350, 663, 10.1126/science.aad2602 Tsukuda, 2021, Key bacterial taxa and metabolic pathways affecting gut short-chain fatty acid profiles in early life, ISME J, 15, 2574, 10.1038/s41396-021-00937-7 Aires, 2021, First 1000 days of life: Consequences of antibiotics on gut microbiota, Front Microbiol, 12, 681427, 10.3389/fmicb.2021.681427 Sommer, 2017, The resilience of the intestinal microbiota influences health and disease, Nat Rev Microbiol, 15, 630, 10.1038/nrmicro.2017.58 Goyal, 2021, Ecology-guided prediction of cross-feeding interactions in the human gut microbiome, Nat Commun, 12, 1335, 10.1038/s41467-021-21586-6 Clark, 2021, Design of synthetic human gut microbiome assembly and butyrate production, Nat Commun, 12, 10.1038/s41467-021-22938-y Gutiérrez, 2019, Species deletions from microbiome consortia reveal key metabolic interactions between gut microbes, MSystems, 4, 10.1128/mSystems.00185-19 Pacheco, 2019, A multidimensional perspective on microbial interactions, FEMS Microbiol Lett, 366, 10.1093/femsle/fnz125 Coquant, 2020, Impact of N-Acyl-homoserine lactones, quorum sensing molecules, on gut immunity, Front Immunol, 11, 1827, 10.3389/fimmu.2020.01827 Ding, 2021, Crosstalk between sIgA-coated bacteria in infant gut and early-life health, Trends Microbiol, 29, 725, 10.1016/j.tim.2021.01.012 García-Bayona, 2021, Mobile Type VI secretion system loci of the gut Bacteroidales display extensive intra-ecosystem transfer, multi-species spread and geographical clustering, PLOS Genet, 17, e1009541, 10.1371/journal.pgen.1009541 Gong, 2021, Antimicrobial peptides in gut health: A review, Front Nutr, 8, 711, 10.3389/fnut.2021.751010 Huus, 2021, Diversity and dynamism of IgA−microbiota interactions, Nat Rev Immunol, 21, 514, 10.1038/s41577-021-00506-1 Donaldson, 2016, Gut biogeography of the bacterial microbiota, Nat Rev Microbiol, 14, 20, 10.1038/nrmicro3552 Klymiuk, 2021, Characterization of the luminal and mucosa-associated microbiome along the gastrointestinal tract: results from surgically treated preterm infants and a murine model, Nutr, 13, 1030 Kettle, 2015, Modelling the emergent dynamics and major metabolites of the human colonic microbiota, Environ Microbiol, 17, 1615, 10.1111/1462-2920.12599 Louis, 2017, Formation of propionate and butyrate by the human colonic microbiota, Environ Microbiol, 19, 29, 10.1111/1462-2920.13589 D'Souza, 2018, Ecology and evolution of metabolic cross-feeding interactions in bacteria, Nat Prod Rep, 35, 455, 10.1039/C8NP00009C Belzer, 2017, Microbial metabolic networks at the mucus layer lead to diet-independent butyrate and vitamin B 12 production by intestinal symbionts, mBio, 8, 10.1128/mBio.00770-17 Sung, 2017, Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis, Nat Commun, 8, 10.1038/ncomms15393 Saa, 2017, Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks, Biotechnol Adv, 35, 981, 10.1016/j.biotechadv.2017.09.005 Altamirano, 2020, Inferring composition and function of the human gut microbiome in time and space: A review of genome-scale metabolic modelling tools, Comput Struct Biotechnol J, 18, 3897, 10.1016/j.csbj.2020.11.035 Lewis, 2012, Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods, Nat Rev Microbiol, 10, 291, 10.1038/nrmicro2737 Zomorrodi, 2012, A multi-level optimization framework for the metabolic modeling and analysis of microbial communities, PLoS Comput Biol, 8, e1002363, 10.1371/journal.pcbi.1002363 Heirendt, 2019, Creation and analysis of biochemical constraint-based models using the COBRA Toolbox vol 3.0, Nat Protoc, 14, 639, 10.1038/s41596-018-0098-2 Zomorrodi, 2014, D-OptCom: dynamic multi-level and multi-objective metabolic modeling of microbial communities, ACS Synth Biol, 3, 247, 10.1021/sb4001307 Heinken, 2021, Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis, Npj Syst Biol Appl, 7, 19, 10.1038/s41540-021-00178-6 Baldini, 2020, Parkinson’s disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions, BMC Biol, 18, 10.1186/s12915-020-00775-7 Hertel, 2019, Integrated analyses of microbiome and longitudinal metabolome data reveal microbial-host interactions on sulfur metabolism in Parkinson’s Disease, Cell Rep, 29, 1767, 10.1016/j.celrep.2019.10.035 Renwick, 2021, Culturing human gut microbiomes in the laboratory, Annu Rev Microbiol, 75, 49, 10.1146/annurev-micro-031021-084116 Strandwitz, 2019, GABA-modulating bacteria of the human gut microbiota, Nat Microbiol, 4, 396, 10.1038/s41564-018-0307-3 Bengtsson-Palme, 2020, Microbial model communities: To understand complexity, harness the power of simplicity, Comput Struct Biotechnol J, 18, 3987, 10.1016/j.csbj.2020.11.043 Van den Abbeele, 2013, Butyrate-producing Clostridium cluster XIVa species specifically colonize mucins in an in vitro gut model, ISME J, 7, 949, 10.1038/ismej.2012.158 Medina, 2018, Simulation and modeling of dietary changes in the infant gut microbiome, FEMS Microbiol Ecol, 94 Kim, 2012, Human gut-on-a-chip inhabited by microbial flora that experiences intestinal peristalsis-like motions and flow, Lab Chip, 12, 2165, 10.1039/c2lc40074j Molly, 1993, Development of a 5-step multi-chamber reactor as a simulation of the human intestinal microbial ecosystem, Appl Microbiol Biotechnol, 39, 254, 10.1007/BF00228615 Marzorati, 2014, The HMITM module: a new tool to study the Host-Microbiota Interaction in the human gastrointestinal tract in vitro, BMC Microbiol, 14, 133, 10.1186/1471-2180-14-133 Shah, 2016, A microfluidics-based in vitro model of the gastrointestinal human–microbe interface, Nat Commun, 7, 10.1038/ncomms11535 Shoaie, 2015, Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome, Cell Metab, 22, 320, 10.1016/j.cmet.2015.07.001 Shoaie, 2013, Understanding the interactions between bacteria in the human gut through metabolic modeling, Sci Rep, 3, 10.1038/srep02532 Everard, 2013, Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity, Proc Natl Acad Sci USA, 110, 9066, 10.1073/pnas.1219451110 Egan, 2014, Cross-feeding by Bifidobacterium breve UCC2003 during co-cultivation with Bifidobacterium bifidum PRL2010 in a mucin-based medium, BMC Microbiol, 14, 10.1186/s12866-014-0282-7 Bunesova, 2018, Mucin cross-feeding of infant Bifidobacteria and Eubacterium hallii, Microb Ecol, 75, 228, 10.1007/s00248-017-1037-4 van Best, 2020, Bile acids drive the newborn’s gut microbiota maturation, Nat Commun, 11, 10.1038/s41467-020-17183-8 Holert, 2014, Evidence of distinct pathways for bacterial degradation of the steroid compound cholate suggests the potential for metabolic interactions by interspecies cross-feeding, Environ Microbiol, 16, 1424, 10.1111/1462-2920.12407 Shetty, 2017, Intestinal microbiome landscaping: Insight in community assemblage and implications for microbial modulation strategies, FEMS Microbiol Rev, 41, 182, 10.1093/femsre/fuw045 Holscher, 2017, Dietary fiber and prebiotics and the gastrointestinal microbiota, Gut Microbes, 8, 172, 10.1080/19490976.2017.1290756 Rivière, 2016, Bifidobacteria and butyrate-producing colon bacteria: Importance and strategies for their stimulation in the human gut, Front Microbiol, 7, 10.3389/fmicb.2016.00979 Laverde Gomez, 2019, Formate cross-feeding and cooperative metabolic interactions revealed by transcriptomics in co-cultures of acetogenic and amylolytic human colonic bacteria, Environ Microbiol, 21, 259, 10.1111/1462-2920.14454 Smith, 2021, Examination of hydrogen cross-feeders using a colonic microbiota model, BMC Bioinf, 22, 3, 10.1186/s12859-020-03923-6 Ravcheev, 2014, Systematic genomic analysis reveals the complementary aerobic and anaerobic respiration capacities of the human gut microbiota, Front Microbiol, 5, 674, 10.3389/fmicb.2014.00674 Cavaliere, 2017, Cooperation in microbial communities and their biotechnological applications, Environ Microbiol, 19, 2949, 10.1111/1462-2920.13767 Rakoff-Nahoum, 2016, The evolution of cooperation within the gut microbiota, Nature, 533, 255, 10.1038/nature17626 Rogowski, 2015, Glycan complexity dictates microbial resource allocation in the large intestine, Nat Commun, 6, 10.1038/ncomms8481 Cuskin, 2015, Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism, Nature, 517, 165, 10.1038/nature13995 Thomson, 2018, Human milk oligosaccharides and infant gut bifidobacteria: Molecular strategies for their utilization, Food Microbiol, 75, 37, 10.1016/j.fm.2017.09.001 Cremer, 2019, Cooperation in microbial populations: theory and experimental model systems, J Mol Biol, 431, 4599, 10.1016/j.jmb.2019.09.023 Damore, 2012, Understanding microbial cooperation, J Theor Biol, 299, 31, 10.1016/j.jtbi.2011.03.008 Magnúsdóttir, 2017, Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota, Nat Biotechnol, 35, 81, 10.1038/nbt.3703 Ghoul, 2016, The ecology and evolution of microbial competition, Trends Microbiol, 24, 833, 10.1016/j.tim.2016.06.011 Duncan, 2004, Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product, Appl Environ Microbiol, 70, 5810, 10.1128/AEM.70.10.5810-5817.2004 Dal Co, 2020, Short-range interactions govern the dynamics and functions of microbial communities, Nat Ecol Evol, 4, 366, 10.1038/s41559-019-1080-2 van Tatenhove-Pel, 2021, Microbial competition reduces metabolic interaction distances to the low µm-range, ISME J, 15, 688, 10.1038/s41396-020-00806-9 Cremer, 2016, Effect of flow and peristaltic mixing on bacterial growth in a gut-like channel, Proc Natl Acad Sci USA, 113, 11414, 10.1073/pnas.1601306113 Kaczmarek, 2017, Complex interactions of circadian rhythms, eating behaviors, and the gastrointestinal microbiota and their potential impact on health, Nutr Rev, 75, 673, 10.1093/nutrit/nux036 Matenchuk, 2020, Sleep, circadian rhythm, and gut microbiota, Sleep Med Rev, 53, 10.1016/j.smrv.2020.101340 Zarrinpar, 2014, Diet and feeding pattern affect the diurnal dynamics of the gut microbiome, Cell Metab, 20, 1006, 10.1016/j.cmet.2014.11.008 Thaiss, 2014, Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis, Cell, 159, 514, 10.1016/j.cell.2014.09.048 Leone, 2015, Effects of diurnal variation of gut microbes and high-fat feeding on host circadian clock function and metabolism, Cell Host Microbe, 17, 681, 10.1016/j.chom.2015.03.006 Geng, 2021, CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention, Proc Natl Acad Sci, 118, 10.1073/pnas.2019336118 Silverman, 2018, Dynamic linear models guide design and analysis of microbiota studies within artificial human guts, Microbiome, 6, 202, 10.1186/s40168-018-0584-3 McNally, 2018, Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss, BMC Syst Biol, 12, 69, 10.1186/s12918-018-0588-4 Zelezniak, 2015, Metabolic dependencies drive species co-occurrence in diverse microbial communities, Proc Natl Acad Sci USA, 112, 6449, 10.1073/pnas.1421834112 Tian, 2020, Deciphering functional redundancy in the human microbiome, Nat Commun, 11, 10.1038/s41467-020-19940-1 Paine, 1969, A note on trophic complexity and community stability, Am Nat, 103, 91, 10.1086/282586 Wang, 2019, Evidence for a multi-level trophic organization of the human gut microbiome, PLOS Comput Biol, 15, e1007524, 10.1371/journal.pcbi.1007524 Antonella, 2021, A diverse community to study communities: integration of experiments and mathematical models to study microbial consortia, J Bacteriol, 199, e00865 Bosi, 2017, Perspectives and challenges in microbial communities metabolic modeling, Front Genet, 8, 88, 10.3389/fgene.2017.00088 Perez-Garcia, 2016, Metabolic network modeling of microbial interactions in natural and engineered environmental systems, Front Microbiol, 7, 673, 10.3389/fmicb.2016.00673 Santibáñez, 2019, A tool for statistical and multi-objective calibration of Rule-based models, Sci Rep, 9, 10.1038/s41598-019-51546-6 Qian, 2021, Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models, Curr Opin Microbiol, 62, 84, 10.1016/j.mib.2021.05.003 Venturelli, 2018, Deciphering microbial interactions in synthetic human gut microbiome communities, Mol Syst Biol, 14, 10.15252/msb.20178157 Friedman, 2017, Community structure follows simple assembly rules in microbial microcosms, Nat Ecol Evol, 1, 109, 10.1038/s41559-017-0109 Pinto, 2017, Modeling metabolic interactions in a consortium of the infant gut microbiome, Front Microbiol, 8, 1, 10.3389/fmicb.2017.02507 Sacher, 2011, Improved calibration of a solid substrate fermentation model, Electron J Biotechnol, 14, 7 Julien-Laferrière, 2016, A combinatorial algorithm for microbial consortia synthetic design, Sci Rep, 6, 10.1038/srep29182 Thiele, 2010, A protocol for generating a high-quality genome-scale metabolic reconstruction, Nat Protoc, 5, 93, 10.1038/nprot.2009.203 Henry, 2010, High-throughput generation, optimization and analysis of genome-scale metabolic models, Nat Biotechnol, 28, 977, 10.1038/nbt.1672 Wang, 2018, RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor, PLOS Comput Biol, 14, e1006541, 10.1371/journal.pcbi.1006541 Dias, 2015, Reconstructing genome-scale metabolic models with merlin, Nucleic Acids Res, 43, 3899, 10.1093/nar/gkv294 Kanehisa, 2016, KEGG as a reference resource for gene and protein annotation, Nucleic Acids Res, 44, D457, 10.1093/nar/gkv1070 Caspi, 2016, The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases, Nucleic Acids Res, 44, D471, 10.1093/nar/gkv1164 Thiele, 2014, fastGapFill: efficient gap filling in metabolic networks, Bioinformatics, 30, 2529, 10.1093/bioinformatics/btu321 Zimmermann, 2021, gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models, Genome Biol, 22, 81, 10.1186/s13059-021-02295-1 Machado, 2018, Fast automated reconstruction of genome-scale metabolic models for microbial species and communities, Nucleic Acids Res, 46, 7542, 10.1093/nar/gky537 Aite, 2018, Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models, PLOS Comput Biol, 14, e1006146, 10.1371/journal.pcbi.1006146 Belcour, 2020, Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species, Elife, 9, 10.7554/eLife.61968 Heinken, 2020, AGORA2: Large scale reconstruction of the microbiome highlights wide-spread drug-metabolising capacities, BioRxiv Heinken, 2021, DEMETER: efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations, Bioinformatics, 37, 3974, 10.1093/bioinformatics/btab622 Heinken, 2019, Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease, Microbiome, 7, 75, 10.1186/s40168-019-0689-3 Heinken, 2013, Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut, Gut Microbes, 4, 28, 10.4161/gmic.22370 Heinken, 2015, Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework, Gut Microbes, 6, 120, 10.1080/19490976.2015.1023494 Lim, 2020, Large-scale metabolic interaction network of the mouse and human gut microbiota, Sci Data, 7 Sen, 2019, Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview, Metabolites, 9, 22, 10.3390/metabo9020022 Orth, 2010, What is flux balance analysis?, Nat Biotechnol, 28, 245, 10.1038/nbt.1614 Price, 2004, Genome-scale models of microbial cells: evaluating the consequences of constraints, Nat Rev Microbiol, 2, 886, 10.1038/nrmicro1023 Gu, 2019, Current status and applications of genome-scale metabolic models, Genome Biol, 20, 121, 10.1186/s13059-019-1730-3 Thiele, 2020, Personalized whole-body models integrate metabolism, physiology, and the gut microbiome, Mol Syst Biol, 16, 10.15252/msb.20198982 Baloni, 2020, Metabolic network analysis reveals altered bile acid synthesis and metabolism in Alzheimeŕs Disease, Cell Reports Med, 1, 100138, 10.1016/j.xcrm.2020.100138 Pacheco, 2019, Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems, Nat Commun, 10, 103, 10.1038/s41467-018-07946-9 Mahadevan, 2003, The effects of alternate optimal solutions in constraint-based genome-scale metabolic models, Metab Eng, 5, 264, 10.1016/j.ymben.2003.09.002 Mahadevan, 2002, Dynamic flux balance analysis of diauxic growth in Escherichia coli, Biophys J, 83, 1331, 10.1016/S0006-3495(02)73903-9 Abdel-Haleem, 2020, Integrated Metabolic Modeling, Culturing, and Transcriptomics Explain Enhanced Virulence of Vibrio cholerae during Coinfection with Enterotoxigenic Escherichia coli, MSystems, 5, 10.1128/mSystems.00491-20 Khandelwal, 2013, Community Flux Balance Analysis for Microbial Consortia at Balanced Growth, PLoS ONE, 8, e64567, 10.1371/journal.pone.0064567 Chan, 2017, Predicting microbial abundances while ensuring community stability, PLoS Comput Biol, 13, e1005539, 10.1371/journal.pcbi.1005539 Chan, 2019, Predicting the Longitudinally and Radially Varying Gut Microbiota Composition using Multi-Scale Microbial Metabolic Modeling, Processes, 7, 10.3390/pr7070394 Harcombe, 2014, Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics, Cell Rep, 7, 1104, 10.1016/j.celrep.2014.03.070 Bauer, 2017, BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities, PLOS Comput Biol, 13, e1005544, 10.1371/journal.pcbi.1005544 Rinninella, 2019, What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases, Microorganisms, 7, 14, 10.3390/microorganisms7010014 Kunasegaran, 2021, The Modulation of Gut Microbiota Composition in the Pathophysiology of Gestational Diabetes Mellitus: A Systematic Review, Biol, 10, 1027, 10.3390/biology10101027 Sultan, 2021, Metabolic Influences of Gut Microbiota Dysbiosis on Inflammatory Bowel Disease, Front Physiol, 12, 1489, 10.3389/fphys.2021.715506 Clapp, 2017, Gut microbiota’s effect on mental health: The gut-brain axis, Clin Pract, 7, 131, 10.4081/cp.2017.987 Nishida, 2018, Gut microbiota in the pathogenesis of inflammatory bowel disease, Clin J Gastroenterol, 11, 1, 10.1007/s12328-017-0813-5 Diener, 2020, MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota, MSystems, 5, 10.1128/mSystems.00606-19 Hu, 2002, Dietary pattern analysis: a new direction in nutritional epidemiology, Curr Opin Lipidol, 13, 3, 10.1097/00041433-200202000-00002 Foster, 2012, Competition, not cooperation, dominates interactions among culturable microbial species, Curr Biol, 22, 1845, 10.1016/j.cub.2012.08.005 Descheemaeker, 2020, Stochastic logistic models reproduce experimental time series of microbial communities, Elife, 9, 10.7554/eLife.55650 Vega, 2017, Stochastic assembly produces heterogeneous communities in the Caenorhabditis elegans intestine, PLOS Biol, 15, e2000633, 10.1371/journal.pbio.2000633 Shashkova, 2016, Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations, PLoS ONE, 11, e0148386, 10.1371/journal.pone.0148386 Lin, 2018, GutLogo: Agent-based modeling framework to investigate spatial and temporal dynamics in the gut microbiome, PLoS ONE, 13, e0207072, 10.1371/journal.pone.0207072 Ibrahim, 2021, Modelling microbial communities: Harnessing consortia for biotechnological applications, Comput Struct Biotechnol J, 19, 3892, 10.1016/j.csbj.2021.06.048 Buetti-Dinh, 2020, Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations, BMC Bioinf, 21, 10.1186/s12859-019-3337-9 Pfeiffer, 1999, METATOOL: For studying metabolic networks, Bioinformatics, 15, 251, 10.1093/bioinformatics/15.3.251 Arabameri, 2020, Detection of colorectal carcinoma based on microbiota analysis using generalized regression neural networks and nonlinear feature selection, IEEE/ACM Trans Comput Biol Bioinforma, 17, 547 Fukui, 2020, Usefulness of machine learning-based gut microbiome analysis for identifying patients with irritable bowels syndrome, J Clin Med, 9, 2403, 10.3390/jcm9082403 Flynn, 2018, Spatial variation of the native colon microbiota in healthy adults, Cancer Prev Res, 11, 393, 10.1158/1940-6207.CAPR-17-0370 Zampieri, 2019, Machine and deep learning meet genome-scale metabolic modeling, PLOS Comput Biol, 15, e1007084, 10.1371/journal.pcbi.1007084 Walsh, 2021, DOME: recommendations for supervised machine learning validation in biology, Nat Methods, 18, 1122, 10.1038/s41592-021-01205-4 Faust, 2012, Microbial Co-occurrence Relationships in the Human Microbiome, PLOS Comput Biol, 8, e1002606, 10.1371/journal.pcbi.1002606 Chen, 2021, The IMG/M data management and analysis system vol 6.0: new tools and advanced capabilities, Nucleic Acids Res, 49, D751, 10.1093/nar/gkaa939 Methé, 2012, A framework for human microbiome research, Nature, 486, 215, 10.1038/nature11209 Vanderhaeghen, 2015, Methanogen communities in stools of humans of different age and health status and co-occurrence with bacteria, FEMS Microbiol Lett, 362, fnv092, 10.1093/femsle/fnv092 Warren, 2013, Co-occurrence of anaerobic bacteria in colorectal carcinomas, Microbiome, 1, 10.1186/2049-2618-1-16 Thingholm, 2019, Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition, Cell Host Microbe, 26, 252, 10.1016/j.chom.2019.07.004 Baldassano, 2016, Topological distortion and reorganized modular structure of gut microbial co-occurrence networks in inflammatory bowel disease, Sci Rep, 6, 26087, 10.1038/srep26087 Kuenzig, 2018, Co-occurrence of asthma and the inflammatory bowel diseases: A systematic review and meta-analysis, Clin Transl Gastroenterol, 9 Maini Rekdal, 2019, Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism, Science, 364, 10.1126/science.aau6323 Ghannam, 2021, Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring, Comput Struct Biotechnol J, 19, 1092, 10.1016/j.csbj.2021.01.028 Garza, 2018, Towards predicting the environmental metabolome from metagenomics with a mechanistic model, Nat Microbiol, 3, 456, 10.1038/s41564-018-0124-8 Lugli, 2019, Unveiling genomic diversity among members of the species bifidobacterium pseudolongum, a widely distributed gut commensal of the animal kingdom, Appl Environ Microbiol, 85, 10.1128/AEM.03065-18 Lieven, 2020, MEMOTE for standardized genome-scale metabolic model testing, Nat Biotechnol, 38, 272, 10.1038/s41587-020-0446-y Honda, 2016, The microbiota in adaptive immune homeostasis and disease, Nature, 535, 75, 10.1038/nature18848 Gardner, 2019, Multiscale Multiobjective Systems Analysis (MiMoSA): an advanced metabolic modeling framework for complex systems, Sci Rep, 9, 10.1038/s41598-019-53188-0 Borer, 2019, Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH), PLOS Comput Biol, 15, e1007127, 10.1371/journal.pcbi.1007127 Dukovski, 2021, A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS), Nat Protoc, 16, 5030, 10.1038/s41596-021-00593-3