Metabolic dependencies drive species co-occurrence in diverse microbial communities
Open accessAbstract
Significance: Although metabolic interactions have long been implicated in the assembly of microbial communities, their general prevalence has remained largely unknown. In this study, we systematically survey, by using a metabolic modeling approach, the extent of resource competition and metabolic cross-feeding in over 800 microbial communities from diverse habitats. We show that interspecies metabolic exchanges are widespread in natural communities, and that such exchanges can provide group advantage under nutrient-poor conditions. Our results highlight metabolic dependencies as a major driver of species co-occurrence. The presented methodology and mechanistic insights have broad implications for understanding compositional variation in natural communities as well as for facilitating the design of synthetic microbial communities.
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Flux Balance Analysis models have been used to predict and understand the behavior of microbes given their environment.
“For instance, Flux Balance Analysis (FBA) models have been used to predict and understand the behavior of microbes given their environment (Orth et al., 2010;Thiele et al., 2013;Zelezniak et al., 2015).”
From The Microbiota-Gut-Brain Axis (2019) · cites this paper as Zelezniak et al., 2015
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Guindon, S., Dufayard, J.-F., Lefort, V., Anisimova, M., Hordijk, W., & Gascuel, O. (2010) · Systematic Biology · doi:10.1093/sysbio/syq010 · Cited by 18,376 · see citing works
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624 · see citing works
Self-citationWhat is flux balance analysis?
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903 · see citing works
Microbial co-occurrence relationships in the human microbiome
Faust, K., Sathirapongsasuti, J. F., Izard, J., Segata, N., Gevers, D., Raes, J., & Huttenhower, C. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002606 · Cited by 1,527 · see citing works
High-throughput generation, optimization and analysis of genome-scale metabolic models
Henry, C. S., DeJongh, M., Best, A. A., Frybarger, P. M., Linsay, B., & Stevens, R. L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1672 · Cited by 1,090 · see citing works
Flagged first, then by citations
Research works printed but not linked: 25
- Baker BJ, Banfield JF (2003) Microbial communities in acid mine drainage. FEMS Mi- crobiol Ecol 44(2):139-152.
- Andersen R, Chapman SJ, Artz RRE (2013) Microbial communities in natural and dis- turbed peatlands: A review. Soil Biol Biochem 57:979-994.
- Fuhrman JA (2009) Microbial community structure and its functional implications. Nature 459(7244):193-199.
- Flint HJ, Scott KP, Louis P, Duncan SH (2012) The role of the gut microbiota in nu- trition and health. Nat Rev Gastroenterol Hepatol 9(10):577-589.
- Tremaroli V, Bäckhed F (2012) Functional interactions between the gut microbiota and host metabolism. Nature 489(7415):242-249.
- Levy R, Borenstein E (2013) Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc Natl Acad Sci USA 110(31):12804-12809.
- Foster KR, Bell T (2012) Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol 22(19):1845-1850.
- Hibbing ME, Fuqua C, Parsek MR, Peterson SB (2010) Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol 8(1):15-25.
- HilleRisLambers J, Adler PB, Harpole WS, Levine JM, Mayfield MM (2012) Rethinking community assembly through the lens of coexistence theory. Annu Rev Ecol Evol Syst 43(1):227-248.
- Morris BE, Henneberger R, Huber H, Moissl-Eichinger C (2013) Microbial syntrophy: Interaction for the common good. FEMS Microbiol Rev 37(3):384-406.
- Kuramitsu HK, He X, Lux R, Anderson MH, Shi W (2007) Interspecies interactions within oral microbial communities. Microbiol Mol Biol Rev 71(4):653-670.
- Kato S, Haruta S, Cui ZJ, Ishii M, Igarashi Y (2008) Network relationships of bacteria in a stable mixed culture. Microb Ecol 56(3):403-411.
- Barve A, Wagner A (2013) A latent capacity for evolutionary innovation through exaptation in metabolic systems. Nature 500(7461):203-206.
- Klitgord N, Segrè D (2010) Environments that induce synthetic microbial ecosystems. PLOS Comput Biol 6(11):e1001002.
- Zomorrodi AR, Maranas CD (2012) OptCom: A multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLOS Comput Biol 8(2):e1002363.
- Zhuang K, et al. (2011) Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J 5(2):305-316.
- Chaffron S, Rehrauer H, Pernthaler J, von Mering C (2010) A global network of co- existing microbes from environmental and whole-genome sequence data. Genome Res 20(7):947-959.
- Barberán A, Bates ST, Casamayor EO, Fierer N (2012) Using network analysis to ex- plore co-occurrence patterns in soil microbial communities. ISME J 6(2):343-351.
- Hom EF, Murray AW (2014) Plant-fungal ecology. Niche engineering demonstrates a latent capacity for fungal-algal mutualism. Science 345(6192):94-98.
- Stadie J, Gulitz A, Ehrmann MA, Vogel RF (2013) Metabolic activity and symbiotic interactions of lactic acid bacteria and yeasts isolated from water kefir. Food Micro- biol 35(2):92-98.
- Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27-30.
- Krzywinski M, et al. (2009) Circos: An information aesthetic for comparative geno- mics. Genome Res 19(9):1639-1645.
- Ciccarelli FD, et al. (2006) Toward automatic reconstruction of a highly resolved tree of life. Science 311(5765):1283-1287.
- Mende DR, Sunagawa S, Zeller G, Bork P (2013) Accurate and universal delineation of prokaryotic species. Nat Methods 10(9):881-884.
- Muller J, Creevey CJ, Thompson JD, Arendt D, Bork P (2010) AQUA: Automated quality improvement for multiple sequence alignments. Bioinformatics 26(2):263-265.
Cited by
Sorted by citations
Biofilms: an emergent form of bacterial life
Flemming, H.-C., Wingender, J., Szewzyk, U., Steinberg, P., Rice, S. A., & Kjelleberg, S. (2016) · Nature Reviews Microbiology · doi:10.1038/nrmicro.2016.94 · Cited by 5,979 · see citing works
Cryan, J. F., O'Riordan, K. J., Cowan, C. S. M., Sandhu, K. V., Bastiaanssen, T. F. S., Boehme, M., Codagnone, M. G., Cussotto, S., Fulling, C., Golubeva, A. V., et al. (2019) · Physiological Reviews · doi:10.1152/physrev.00018.2018 · Cited by 5,286 · see citing works
Microbial imbalance in inflammatory bowel disease patients at different taxonomic levels
Alam, M. T., Amos, G. C. A., Murphy, A. R. J., Murch, S., Wellington, E. M. H., & Arasaradnam, R. P. (2020) · Gut Pathogens · doi:10.1186/s13099-019-0341-6 · Cited by 380 · see citing works
Reconstructing organisms in silico: genome-scale models and their emerging applications
Fang, X., Lloyd, C. J., & Palsson, B. O. (2020) · Nature Reviews Microbiology · doi:10.1038/s41579-020-00440-4 · Cited by 292 · see citing works
Nielsen, J. (2017) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061516-044757 · Cited by 252 · see citing works
Aite, M., Chevallier, M., Frioux, C., Trottier, C., Got, J., Cortés, M. P., Mendoza, S. N., Carrier, G., Dameron, O., Guillaudeux, N., et al. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006146 · Cited by 137 · see citing works
“Prediction of phenotypes has allowed GSMs to be applied for several purposes such as, guiding metabolic engineering efforts to achieve an increased production of target metabolites [3], identification of drug targets [4] and more recently, prediction of interactions in microbial communities [5].”
Modeling metabolism of the human gut microbiome
Magnúsdóttir, S., & Thiele, I. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.12.005 · Cited by 127 · see citing works
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Giri, S., Waschina, S., Kaleta, C., & Kost, C. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.06.023 · Cited by 89 · see citing works
Targeting the human gut microbiome with small-molecule inhibitors
Woo, A. Y. M., Aguilar Ramos, M. A., Narayan, R., Richards-Corke, K. C., Wang, M. L., Sandoval-Espinola, W. J., & Balskus, E. P. (2023) · Nature Reviews Chemistry · doi:10.1038/s41570-023-00471-4 · Cited by 42 · see citing works
- Authors
- Zelezniak, A., Andrejev, S., Ponomarova, O., Mende, D. R., Bork, P., & Patil, K. R.
- Affiliations
- European Molecular Biology Laboratory (DE)
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2015-05-04
- Volume
- 112
- Issue
- 20
- Pages
- 6449-6454
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4443341
- OpenAlex
- W2102681753
- Cited by
- 930
- References
- 6
- Citations by year
- 2026: 116 · 2025: 117 · 2024: 109 · 2023: 102 · 2022: 105 · 2021: 81 · 2020: 74 · 2019: 52 · 2018: 62 · 2017: 48 · 2016: 50 · 2015: 13
- FWCI
- 25.05
- Type
- journal article
- Language
- en
- Update notices
- correction (2015-12-14)
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Keywords
- microbial community assembly, species co-occurrence, metabolic dependencies, resource competition, metabolite exchange, genome-scale metabolic modeling, flux balance analysis, collaborative interaction, amino acid substitutions, nutrient limitation
- MeSH terms
- Models, Biological, Phylogeny, Species Specificity, Symbiosis, Statistics, Nonparametric, Metabolic Networks and Pathways, Microbial Interactions, Microbial Consortia
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