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Environmental stimuli drive a transition from cooperation to competition in synthetic phototrophic communities

An Author Correction to this article was published on 05 November 2019

This article has been updated

Abstract

Phototrophic communities of photosynthetic algae or cyanobacteria and heterotrophic bacteria or fungi are pervasive throughout the environment1. How interactions between members contribute to the resilience and affect the fitness of phototrophic communities is not fully understood2,3. Here, we integrated metatranscriptomics, metabolomics and phenotyping with computational modelling to reveal condition-dependent secretion and cross-feeding of metabolites in a synthetic community. We discovered that interactions between members are highly dynamic and are driven by the availability of organic and inorganic nutrients. Environmental factors, such as ammonia concentration, influenced community stability by shifting members from collaborating to competing. Furthermore, overall fitness was dependent on genotype and streamlined genomes improved growth of the entire community. Our mechanistic framework provides insights into the physiology and metabolic response to environmental and genetic perturbation of these ubiquitous microbial associations.

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Fig. 1: Interwoven metabolic interactions in the phototrophic community.
Fig. 2: Community interactions depend on culture conditions.
Fig. 3: Nitrate availability and genetic drift stimulate community cooperation.
Fig. 4: Member-specific genomic capabilities help to overcome lethal genetic gaps in the community.

Data availability

The phototrophic community model, as well as individual models are available in Supplementary Dataset 1 and are described in Supplementary Table 1. Models constrained with different biomass compositions are also provided in Supplementary Dataset 1 and described in Supplementary Table 4. Supplementary Information is also available at https://github.com/cristalzucsd/PhototrophicCommunities. All sequencing reads were deposited in the Sequence Read Archive under BioProject PRJNA496045, with specific numbers listed in Supplementary Table 12.

Code availability

Computer code will be provided upon request from the corresponding author.

Change history

  • 05 November 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

We acknowledge P. Stirling at the T. Fox Laboratory and P. Hieter and M. Smith Laboratories at the University of British Columbia, Vancouver, Canada for kindly providing several fungal strains. We also acknowledge D. Zielinski at University of California, San Diego for providing input at all stages of this work. This material is based on work supported by the National Science Foundation under Awards 1332344 and CBET-1804187 and the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research under Awards DE-SC0012658 and DE-SC0019388. C.Z. was in part supported by Mexican National Research Council, CONACYT, fellowship No. 237897.

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Contributions

C.Z., M.J.B. and K.Z. conceived the study. C.Z. developed computational methods, performed simulations and designed experiments. C.-T.L. and G.Y. performed physiological experiments regarding community stability. M.G. generated metabolomics data. M.M.A.-B. generated RNA-sequencing data. T.L., L.J. and L.Z. performed additional experiments and computational analyses. C.Z. compiled and analysed modelling and experimental outcomes. C.Z. and K.Z. wrote the manuscript with input from all co-authors.

Corresponding author

Correspondence to Karsten Zengler.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–11, Supplementary Results, Supplementary Table legends, Supplementary Dataset legend and Supplementary References.

Reporting Summary

Supplementary Tables 1–12

Supplementary Table 1, model characteristics; Supplementary Table 2, metabolites in the SMP; Supplementary Table 3, applied constraints single and community models; Supplementary Table 4, predicted growth rates at different biomass composition of the phototrophic community; Supplementary Table 5, predicted metabolites improving the community growth rate at different biomass compositions; Supplementary Table 6, predicted essential genes at different biomass compositions for C. vulgaris (alga) in monoculture (189 genes); Supplementary Table 7, predicted essential genes at different biomass compositions for C. vulgaris (alga) in co-culture (115 genes); Supplementary Table 8, essential genes for the phototrophic community; Supplementary Table 9, experimentally tested KOs and predicted metabolic exchange; Supplementary Table 10, reactions associated to genes in the alga C. vulgaris, that after being knocked out increase the growth rate; Supplementary Table 11, reactions associated to genes in S. cerevisiae that after being knocked out increase the growth rate; Supplementary Table 12, properties of expression analysis samples and expression results.

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Zuñiga, C., Li, CT., Yu, G. et al. Environmental stimuli drive a transition from cooperation to competition in synthetic phototrophic communities. Nat Microbiol 4, 2184–2191 (2019). https://doi.org/10.1038/s41564-019-0567-6

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