Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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


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.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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 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.


  1. Flemming, H.-C. & Wuertz, S. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17, 247–260 (2019).

    CAS  PubMed  Google Scholar 

  2. Zuñiga, C., Zaramela, L. & Zengler, K. Elucidation of complexity and prediction of interactions in microbial communities. Microb. Biotechnol. 10, 1500–1522 (2017).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  4. de Vera, J.-P. et al. Survival potential and photosynthetic activity of lichens under Mars-like conditions: a laboratory study. Astrobiology 10, 215–227 (2010).

    CAS  PubMed  Google Scholar 

  5. Prieto-Barajas, C. M., Valencia-Cantero, E. & Santoyo, G. Microbial mat ecosystems: structure types, functional diversity, and biotechnological application. Electron. J. Biotechnol. 31, 48–56 (2018).

    Google Scholar 

  6. Amin, S. A., Parker, M. S. & Armbrust, E. V. Interactions between diatoms and bacteria. Microbiol. Mol. Biol. Rev. 76, 667–684 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Insarova, I. D. & Blagoveshchenskaya, E. Y. Lichen symbiosis: Search and recognition of partners. Biol. Bull. 43, 408–418 (2016).

    Google Scholar 

  8. Hill, D. J. The growth of lichens with special reference to the modelling of circular thalli. Lichenologist 13, 265–287 (1981).

    Google Scholar 

  9. Grube, M., Cardinale, M., de Castro, J. V., Müller, H. & Berg, G. Species-specific structural and functional diversity of bacterial communities in lichen symbioses. ISME J. 3, 1105–1115 (2009).

    PubMed  Google Scholar 

  10. Bolhuis, H., Cretoiu, M. S. & Stal, L. J. Molecular ecology of microbial mats. FEMS Microbiol. Ecol. 90, 335–350 (2014).

    CAS  PubMed  Google Scholar 

  11. Zhalnina, K., Zengler, K., Newman, D. & Northen, T. R. Need for laboratory ecosystems to unravel the structures and functions of soil microbial communities mediated by chemistry. mBio 9, e01175-18 (2018).

    PubMed  PubMed Central  Google Scholar 

  12. Zengler, K. et al. EcoFABs: advancing microbiome science through standardized fabricated ecosystems. Nat. Methods 16, 567–571 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Hom, E. F. Y. & Murray, A. W. Niche engineering demonstrates a latent capacity for fungal–algal mutualism. Science 345, 94–98 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Li, T. et al. Mimicking lichens: incorporation of yeast strains together with sucrose-secreting cyanobacteria improves survival, growth, ROS removal, and lipid production in a stable mutualistic co-culture production platform. Biotechnol. Biofuels 10, 55 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Zengler, K. & Palsson, B. O. A road map for the development of community systems (CoSy) biology. Nat. Rev. Microbiol. 10, 366–372 (2012).

    CAS  PubMed  Google Scholar 

  16. Zuñiga, C. et al. Genome-scale metabolic model for the green alga Chlorella vulgaris UTEX 395 accurately predicts phenotypes under autotrophic, heterotrophic, and mixotrophic growth conditions. Plant Physiol. 172, 589–602 (2016).

    PubMed  PubMed Central  Google Scholar 

  17. Mo, M. L., Palsson, B. Ø. & Herrgard, M. J. Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst. Biol. 3, 37 (2009).

    PubMed  PubMed Central  Google Scholar 

  18. Oliveira, N. M., Niehus, R. & Foster, K. R. Evolutionary limits to cooperation in microbial communities. Proc. Natl Acad. Sci. USA 111, 17941–17946 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Guimarães, P. R., Pires, M. M., Jordano, P., Bascompte, J. & Thompson, J. N. Indirect effects drive coevolution in mutualistic networks. Nature 550, 511–514 (2017).

    PubMed  Google Scholar 

  20. Du, B., Zielinski, D. C., Monk, J. M. & Palsson, B. O. Thermodynamic favorability and pathway yield as evolutionary tradeoffs in biosynthetic pathway choice. Proc. Natl Acad. Sci. USA 115, 11339–11344 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Stegman, M. R., Cottrell, M. T. & Kirchman, D. L. Leucine incorporation by aerobic anoxygenic phototrophic bacteria in the Delaware estuary. ISME J. 8, 2339–2348 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Dahlman, L., Persson, J., Näsholm, T. & Palmqvist, K. Carbon and nitrogen distribution in the green algal lichens Hypogymnia physodes and Platismatia glauca in relation to nutrient supply. Planta 217, 41–48 (2003).

    CAS  PubMed  Google Scholar 

  23. Palmqvist, K., Franklin, O. & Näsholm, T. Symbiosis constraints: Strong mycobiont control limits nutrient response in lichens. Ecol. Evol. 7, 7420–7433 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. Goff, L. J. (ed.). Algal symbiosis: a continuum of interaction strategies (Cambridge Univ. Press, 2011).

  25. Jovan, S., Riddell, J., Padgett, P. E. & Nash, T. H. Eutrophic lichens respond to multiple forms of N: implications for critical levels and critical loads research. Ecol. Appl. 22, 1910–1922 (2012).

    PubMed  Google Scholar 

  26. Navarrete, A. et al. Physiological status and community composition of microbial mats of the Ebro Delta, Spain, by signature lipid biomarkers. Microb. Ecol. 39, 92–99 (2000).

    CAS  PubMed  Google Scholar 

  27. Zuñiga, C. et al. Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris. Plant Physiol. 176, 450–462 (2018).

    PubMed  Google Scholar 

  28. Basan, M. et al. Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528, 99–104 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Liu, J. K. et al. Predicting proteome allocation, overflow metabolism, and metal requirements in a model acetogen. PLOS Comput. Biol. 15, e1006848 (2019).

    PubMed  PubMed Central  Google Scholar 

  30. Klitgord, N. & Segrè, D. Environments that induce synthetic microbial ecosystems. PLoS Comput. Biol. 6, e1001002 (2010).

    PubMed  PubMed Central  Google Scholar 

  31. Wink, M. Evolution of secondary metabolites from an ecological and molecular phylogenetic perspective. Phytochemistry 64, 3–19 (2003).

    CAS  PubMed  Google Scholar 

  32. Reznik, E., Mehta, P. & Segrè, D. Flux imbalance analysis and the sensitivity of cellular growth to changes in metabolite pools. PLoS Comput. Biol. 9, e1003195 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Rakoff-Nahoum, S., Foster, K. R. & Comstock, L. E. The evolution of cooperation within the gut microbiota. Nature 533, 255–259 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 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).

    PubMed  PubMed Central  Google Scholar 

  35. Ackermann, M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol. 13, 497–508 (2015).

    CAS  PubMed  Google Scholar 

  36. Good, B. H., McDonald, M. J., Barrick, J. E., Lenski, R. E. & Desai, M. M. The dynamics of molecular evolution over 60,000 generations. Nature 551, 45–50 (2017).

    PubMed  PubMed Central  Google Scholar 

  37. Breslow, D. K. et al. A comprehensive strategy enabling high-resolution functional analysis of the yeast genome. Nat. Methods 5, 711–718 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Zengler, K. et al. Cultivating the uncultured. Proc. Natl Acad. Sci. USA 99, 15681–15686 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Kim, Y.-M. et al. Diel metabolomics analysis of a hot spring chlorophototrophic microbial mat leads to new hypotheses of community member metabolisms. Front. Microbiol. 6, 209 (2015).

    PubMed  PubMed Central  Google Scholar 

  40. Lynch, M. Streamlining and simplification of microbial genome architecture. Annu. Rev. Microbiol. 60, 327–349 (2006).

    CAS  PubMed  Google Scholar 

  41. Schellenberger, J. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc. 6, 1290–1307 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Nagarajan, H. et al. Characterization and modelling of interspecies electron transfer mechanisms and microbial community dynamics of a syntrophic association. Nat. Commun. 4, 2809 (2013).

    PubMed  Google Scholar 

  43. Matthews, B. W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 405, 442–451 (1975).

    CAS  PubMed  Google Scholar 

  44. Henard, C. A., Guarnieri, M. T. & Knoshaug, E. P. The Chlorella vulgaris S-nitrosoproteome under nitrogen-replete and -deplete conditions. Front. Bioeng. Biotechnol. 4, 100 (2017).

    PubMed  PubMed Central  Google Scholar 

  45. Krueger, F. Trim Galore!: A Wrapper Tool Around Cutadapt and FastQC to Consistently Apply Quality and Adapter Trimming to FastQ Files (2015).

  46. Agarwala, R. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 46, D8–D13 (2018).

    Google Scholar 

  47. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  PubMed  Google Scholar 

  48. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

Download references


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.

Author information

Authors and Affiliations



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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing