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Polarization of microbial communities between competitive and cooperative metabolism

Nature Ecology & Evolution volume 5, pages 195–203 (2021)Cite this article

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Matters Arising to this article was published on 05 December 2022

Abstract

Resource competition and metabolic cross-feeding are among the main drivers of microbial community assembly. Yet the degree to which these two conflicting forces are reflected in the composition of natural communities has not been systematically investigated. Here, we use genome-scale metabolic modelling to assess the potential for resource competition and metabolic cooperation in large co-occurring groups (up to 40 members) across thousands of habitats. Our analysis reveals two distinct community types, which are clustered at opposite ends of a spectrum in a trade-off between competition and cooperation. At one end are highly cooperative communities, characterized by smaller genomes and multiple auxotrophies. At the other end are highly competitive communities, which feature larger genomes and overlapping nutritional requirements, and harbour more genes related to antimicrobial activity. The latter are mainly present in soils, whereas the former are found in both free-living and host-associated habitats. Community-scale flux simulations show that, whereas competitive communities can better resist species invasion but not nutrient shift, cooperative communities are susceptible to species invasion but resilient to nutrient change. We also show, by analysing an additional data set, that colonization by probiotic species is positively associated with the presence of cooperative species in the recipient microbiome. Together, our results highlight the bifurcation between competitive and cooperative metabolism in the assembly of natural communities and its implications for community modulation.

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Fig. 1: Co-occurring microbial communities are polarized in the competition–cooperation landscape.
Fig. 2: Members of the cooperative and competitive communities show distinct genomic and phylogenetic characteristics, and have different abundance profiles.
Fig. 3: Cooperative communities occupy more diverse habitats.
Fig. 4: Members of both competitive and cooperative communities are observed across the four main phyla.
Fig. 5: Cooperative and competitive communities show contrasting sensitivity to abiotic and biotic perturbations.

Data availability

All of the data required to generate the results and figures presented in this article are publicly available in the following repository: https://github.com/cdanielmachado/cooccurrence.

Code availability

All of the code required to generate the results and figures presented in this article are publicly available in the following repository: https://github.com/cdanielmachado/cooccurrence.

References

  1. Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).

    Article  CAS  PubMed  Google Scholar 

  2. Raaijmakers, J. M. & Mazzola, M. Soil immune responses. Science 352, 1392–1393 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).

    Article  PubMed  Google Scholar 

  4. Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lawson, C. E. et al. Common principles and best practices for engineering microbiomes. Nat. Rev. Microbiol. 17, 725–741 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Armstrong, R. A. & McGehee, R. Competitive exclusion. Am. Nat. 115, 151–170 (1980).

    Article  Google Scholar 

  11. Tan, J., Zuniga, C. & Zengler, K. Unraveling interactions in microbial communities - from co-cultures to microbiomes. J. Microbiol. 53, 295–305 (2015).

    Article  PubMed  Google Scholar 

  12. Zengler, K. & Zaramela, L. S. The social network of microorganisms—how auxotrophies shape complex communities. Nat. Rev. Microbiol. 16, 383–390 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2, 589 (2011).

    Article  PubMed  Google Scholar 

  14. Levy, R. & Borenstein, E. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc. Natl Acad. Sci. USA 110, 12804–12809 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Billick, I. & Case, T. J. Higher order interactions in ecological communities: what are they and how can they be detected? Ecology 75, 1529–1543 (1994).

    Article  Google Scholar 

  17. Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 0109 (2017).

    Article  Google Scholar 

  18. Morin, M., Pierce, E. C. & Dutton, R. J. Changes in the genetic requirements for microbial interactions with increasing community complexity. eLife 7, e37072 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Freilich, S. et al. The large-scale organization of the bacterial network of ecological co-occurrence interactions. Nucleic Acids Res. 38, 3857–3868 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6, 343–351 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Faust, K. et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol. 8, e1002606 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zomorrodi, A. R. & Maranas, C. D. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput. Biol. 8, e1002363 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Khandelwal, R. A., Olivier, B. G., Röling, W. F. M., Teusink, B. & Bruggeman, F. J. Community flux balance analysis for microbial consortia at balanced growth. PLoS ONE 8, e64567 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Chan, S. H. J., Simons, M. N. & Maranas, C. D. SteadyCom: predicting microbial abundances while ensuring community stability. PLoS Comput. Biol. 13, e1005539 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Mee, M. T., Collins, J. J., Church, G. M. & Wang, H. H. Syntrophic exchange in synthetic microbial communities. Proc. Natl Acad. Sci. USA 111, E2149–E2156 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ponomarova, O. et al. Yeast creates a niche for symbiotic lactic acid bacteria through nitrogen overflow. Cell Syst. 5, 345–357 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chaffron, S., Rehrauer, H., Pernthaler, J. & Mering, C. V. A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res. 20, 947–959 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Amarasekare, P. Interference competition and species coexistence. Proc. R. Soc. Lond. B 269, 2541–2550 (2002).

    Article  Google Scholar 

  32. Blin, K. et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 47, W81–W87 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Crump, B. C., Amaral-Zettler, L. A. & Kling, G. W. Microbial diversity in arctic freshwaters is structured by inoculation of microbes from soils. ISME J. 6, 1629–1639 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. O’Brien, S. L. et al. Spatial scale drives patterns in soil bacterial diversity. Environ. Microbiol. 18, 2039–2051 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Zarraonaindia, I. et al. The soil microbiome influences grapevine-associated microbiota. mBio 6, e02527-14 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lax, S. et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345, 1048–1052 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Nolan, M. J. et al. Molecular-based investigation of Cryptosporidium and Giardia from animals in water catchments in southeastern Australia. Water Res. 47, 1726–1740 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Haig, S.-J., Quince, C., Davies, R. L., Dorea, C. C. & Collins, G. Replicating the microbial community and water quality performance of full-scale slow sand filters in laboratory-scale filters. Water Res. 61, 141–151 (2014).

    Article  CAS  PubMed  Google Scholar 

  39. Koehler, A. V., Haydon, S. R., Jex, A. R. & Gasser, R. B. Cryptosporidium and Giardia taxa in faecal samples from animals in catchments supplying the city of Melbourne with drinking water (2011 to 2015). Parasit. Vectors 9, 315 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).

    Article  CAS  PubMed  Google Scholar 

  41. Rivière, A., Gagnon, M., Weckx, S., Roy, D. & Vuyst, L. D. Mutual cross-feeding interactions between Bifidobacterium longum subsp. longum NCC2705 and Eubacterium rectale ATCC 33656 explain the bifidogenic and butyrogenic effects of arabinoxylan oligosaccharides. Appl. Environ. Microbiol. 81, 7767–7781 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  42. D’Souza, G. et al. Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat. Prod. Rep. 35, 455–488 (2018).

    Article  PubMed  Google Scholar 

  43. Pacheco, A. R. & Segrè, D. A multidimensional perspective on microbial interactions. FEMS Microbiol. Lett. 366, fnz125 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Pacheco, A. R., Moel, M. & Segrè, D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun. 10, 103 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Barton, M. D., Delneri, D., Oliver, S. G., Rattray, M. & Bergman, C. M. Evolutionary systems biology of amino acid biosynthetic cost in yeast. PLoS ONE 5, e11935 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Campbell, K. et al. Self-establishing communities enable cooperative metabolite exchange in a eukaryote. eLife 4, e09943 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  47. D’Souza, G. & Kost, C. Experimental evolution of metabolic dependency in bacteria. PLoS Genet. 12, e1006364 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Harcombe, W. Novel cooperation experimentally evolved between species. Evolution 64, 2166–2172 (2010).

    PubMed  Google Scholar 

  49. Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zmora, N. et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell 174, 1388–1405.e21 (2018).

    Article  CAS  PubMed  Google Scholar 

  51. Valen, L. V. A new evolutionary law. Evol. Theory 1, 1–30 (1973).

    Google Scholar 

  52. Morris, J. J., Lenski, R. E. & Zinser, E. R. The black queen hypothesis: evolution of dependencies through adaptive gene loss. mBio 3, e00036-12 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649–662 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Bosch, A. A. T. M., Biesbroek, G., Trzcinski, K., Sanders, E. A. M. & Bogaert, D. Viral and bacterial interactions in the upper respiratory tract. PLoS Pathog. 9, e1003057 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Peleg, A. Y., Hogan, D. A. & Mylonakis, E. Medically important bacterial–fungal interactions. Nat. Rev. Microbiol. 8, 340–349 (2010).

    Article  CAS  PubMed  Google Scholar 

  56. Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: reconstruction analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Jones, D. T., Taylor, W. R. & Thornton, J. M. The rapid generation of mutation data matrices from protein sequences. Bioinformatics 8, 275–282 (1992).

    Article  CAS  Google Scholar 

  58. Ciccarelli, F. D. Toward automatic reconstruction of a highly resolved tree of life. Science 311, 1283–1287 (2006).

    Article  CAS  PubMed  Google Scholar 

  59. Mende, D. R., Sunagawa, S., Zeller, G. & Bork, P. Accurate and universal delineation of prokaryotic species. Nat. Methods 10, 881–884 (2013).

    Article  CAS  PubMed  Google Scholar 

  60. Sievers, F. et al. Fast scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539–539 (2014).

    Article  Google Scholar 

  61. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2018).

    Article  Google Scholar 

  63. Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Article  Google Scholar 

  65. Bollback, J. P. SIMMAP: stochastic character mapping of discrete traits on phylogenies. BMC Bioinformatics 7, 88 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge N. Zmora and E. Elinav for providing data and feedback on the probiotics study, K. Blin for assistance with the antiSMASH database, and S. Schmidt for fruitful discussions. This project has received funding from the Horizon 2020 research and innovation programme of the European Union under grant agreement no. 686070. This work was supported by the UK Medical Research Council (project no. MC_UU_00025/11).

Author information

Authors and Affiliations

  1. European Molecular Biology Laboratory (EMBL), Heidelberg, Germany

    Daniel Machado, Oleksandr M. Maistrenko, Sergej Andrejev, Yongkyu Kim, Peer Bork & Kiran R. Patil

  2. Department of Biotechnology and Food Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Daniel Machado

  3. Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany

    Kaustubh R. Patil

  4. Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Kaustubh R. Patil

  5. Medical Research Council (MRC) Toxicology Unit, University of Cambridge, Cambridge, UK

    Kiran R. Patil

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Contributions

D.M. developed the co-occurrence computation method and performed the simulations and data analysis. D.M. and S.A. implemented the software for metabolic modelling. O.M.M. performed the phylogenetic analysis. Y.K. mapped the OTUs to reference genomes. P.B. supervised the phylogenetic analysis. Kaustubh R. Patil and Kiran R. Patil conceived the study. D.M. and Kiran R. Patil wrote the manuscript. All authors read and revised the final manuscript.

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Correspondence to Kiran R. Patil.

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The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Joshua Goldford and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Mapping of OTUs to reference genomes.

a, comparison of sample diversity in terms of OTUs (blue) and genomes (orange); b, comparison of species prevalence in terms of OTUs and genomes across samples; c, species abundance distribution; d, total abundance of each sample that is captured by the mapped genomes in comparison to the ratio of genomes to OTUs.

Extended Data Fig. 2 Summary of higher-order co-occurrence analysis.

a, Average number of samples where all species in a co-occurring community can be found together as a function of community size (computed for the threshold values used in this work); b, Principal component analysis of co-occurring communities computed using different abundance thresholds. Marker size indicates co-occurring community size (up to 30 species) and marker shapes indicates independent runs of the algorithm (3 runs for each threshold). Numbers on x and y axis indicate percentage of explained variance for the respective principle component.

Extended Data Fig. 3 Simulation results using an independent data set.

Simulation results for competition (MRO score) and cooperation potential (MIP score) for microbial communities obtained from Chaffron et al.1. Blue dots represent co-occurring communities of different sizes (up to 1000 per size) and grey dots represent randomly-assembled communities of similar size (1000 communities per size).

Extended Data Fig. 4 Summary of metabolite requirements for growth and cross-fed metabolites.

a, compounds competed for in cooperative communities; b, compounds competed for in competitive communities; c, cross-fed compounds in cooperative communities; d, cross-fed compounds in competitive communities. Compound classification according to the Human Metabolome Database (HMDB). Only the ten most frequent compound classes are colored and labeled.

Extended Data Fig. 5 Abundance of co-occurring species as a function of the total number of co-occurring partners present in a sample.

The colored line denotes the average abundance for each type of community, the shadowed area indicates standard deviation, and the dashed grey line indicates the average species abundance across all species and samples (in all cases the average is calculated as the mean value in log-space, that is representing the geometric mean of the relative abundance values).

Extended Data Fig. 6 Abundance stability in co-occurring communities.

Community stability measured in terms of: a, individual stability (lower coefficient of variation per species indicates higher stability); b, group stability (lower cosine distance indicates higher covariation of species abundance within each community).

Extended Data Fig. 7 Auxotrophy frequency is associated with amino acid production cost.

Spearman correlation between amino acid production costs and their auxotrophy frequency across species participating in cooperative communities. The data on amino acid production costs was obtained from Barton et al (ref. 45).

Extended Data Fig. 8 Acquisition of amino acid auxotrophies.

Analysis of evidence for recent acquisition of amino acid auxotrophies using two complementary approaches: taxonomy based (T), measuring the fraction of auxotrophic species at genus level; phylogeny based (P), estimating the probability of the auxotrophy existing in the most recent ancestor of the species. Green color in both columns is indicative of a consensus.

Extended Data Fig. 9 Sensitivity of co-occurring communities to different types of perturbations.

This is an extended version of Fig. 5 where the simulations are performed in aerobic and anaerobic environments separately. For abiotic perturbations we further test the effect of removal of compounds from the growth medium.

Extended Data Fig. 10 Association between the presence cooperative species and colonization of the human gut microbiome.

Presence of cooperative species in the lower gastrointestinal (LGI) tract of patients permissive to probiotic colonization (P), patients resistant to colonization (R) and control patients (C). Asterisks indicate significance of Wilcoxon signed-rank test (p < 0.05).

Supplementary information

Supplementary Information

Supplementary Figs. 1–3.

Reporting Summary

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Supplementary Table 1

Table with all the models used in this work, including the following information: assembly accession code, strain ID, species, genus, family, order, class, phylum, genome length, number of open reading frames, number of annotated metabolic genes, number of enzymatic reactions, number of transport reactions, number of gap-filling reactions, number of internal metabolites, number of external metabolites, and frequency in cooperative and competitive communities.

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Machado, D., Maistrenko, O.M., Andrejev, S. et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat Ecol Evol 5, 195–203 (2021). https://doi.org/10.1038/s41559-020-01353-4

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