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Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere

Abstract

Multicellular organisms, including plants, are colonized by microorganisms, some of which are beneficial to growth and health. The assembly rules for establishing plant microbiota are not well understood, and neither is the extent to which their members interact. We conducted drop-out and late introduction experiments by inoculating Arabidopsis thaliana with synthetic communities from a resource of 62 native bacterial strains to test how arrival order shapes community structure. As a read-out we tracked the relative abundance of all strains in the phyllosphere of individual plants. Our results showed that community assembly is historically contingent and subject to priority effects. Missing strains could, to various degrees, invade an already established microbiota, which was itself resistant and remained largely unaffected by latecomers. Additionally, our results indicate that individual strains of Proteobacteria (Sphingomonas, Rhizobium) and Actinobacteria (Microbacterium, Rhodococcus) have the greatest potential to affect community structure as keystone species.

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Fig. 1: Concept of drop-out and late introduction experiments using synthetic microbiota.
Fig. 2: Relative abundance in control community.
Fig. 3: Proteobacteria class drop-out and late introduction experiment.
Fig. 4: Single-strain drop-outs.
Fig. 5: Causal network (P ≤ 0.01) based on single-strain drop-outs.
Fig. 6: Correlation between node out degree and effect size following node removal.

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Data availability

Raw data can be found in the European Nucleotide Archive under accession number PRJEB32997.

Code availability

The code used to analyse all data and generate figures can be found at https://github.com/cmfield/carlstrom2019. No unpublished algorithms or methods were used.

References

  1. Fischbach, M. A. Microbiome: focus on causation and mechanism. Cell 174, 785–790 (2018).

    Article  CAS  Google Scholar 

  2. Vorholt, J. A., Vogel, C., Carlstrom, C. I. & Müller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2017).

    Article  CAS  Google Scholar 

  3. Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157 (2018).

    Article  Google Scholar 

  4. 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 

  5. Müller, D. B., Schubert, O. T., Röst, H., Aebersold, R. & Vorholt, J. A. Systems-level proteomics of two ubiquitous leaf commensals reveals complementary adaptive traits for phyllosphere colonization. Mol. Cell Proteom. 15, 3256–3269 (2016).

    Article  Google Scholar 

  6. Gourion, B., Rossignol, M. & Vorholt, J. A. A proteomic study of Methylobacterium extorquens reveals a response regulator essential for epiphytic growth. Proc. Natl Acad. Sci. USA 103, 13186–13191 (2006).

    Article  CAS  Google Scholar 

  7. Abreu, C., Ortiz Lopez, A. & Gore, J. Pairing off: a bottom-up approach to the human gut microbiome. Mol. Syst. Biol. 14, e8425 (2018).

    Article  Google Scholar 

  8. Brugiroux, S. et al. Genome-guided design of a defined mouse microbiota that confers colonization resistance against Salmonella enterica serovar Typhimurium. Nat. Microbiol. 2, 16215 (2016).

    Article  CAS  Google Scholar 

  9. Goodman, A. L. et al. Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc. Natl Acad. Sci. USA 108, 6252–6257 (2011).

    Article  CAS  Google Scholar 

  10. Bonilla-Rosso, G. & Engel, P. Functional roles and metabolic niches in the honey bee gut microbiota. Curr. Opin. Microbiol. 43, 69–76 (2018).

    Article  CAS  Google Scholar 

  11. Rawls, J. F., Samuel, B. S. & Gordon, J. I. Gnotobiotic zebrafish reveal evolutionarily conserved responses to the gut microbiota. Proc. Natl Acad. Sci. USA 101, 4596–4601 (2004).

    Article  CAS  Google Scholar 

  12. Bodenhausen, N., Bortfeld-Miller, M., Ackermann, M. & Vorholt, J. A. A synthetic community approach reveals plant genotypes affecting the phyllosphere microbiota. PLoS Genet. 10, e1004283 (2014).

    Article  Google Scholar 

  13. Lebeis, S. et al. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 349, 860–864 (2015).

    Article  CAS  Google Scholar 

  14. Niu, B., Paulson, J. N., Zheng, X. & Kolter, R. Simplified and representative bacterial community of maize roots. Proc. Natl Acad. Sci. USA 114, E2450–E2459 (2017).

    Article  CAS  Google Scholar 

  15. Herrera Paredes, S. et al. Design of synthetic bacterial communities for predictable plant phenotypes. PLoS Biol. 16, e2003962 (2018).

    Article  Google Scholar 

  16. Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528, 364–369 (2015).

    Article  CAS  Google Scholar 

  17. Müller, D. B., Vogel, C., Bai, Y. & Vorholt, J. A. The plant microbiota: systems-level insights and perspectives. Annu. Rev. Genet. 50, 211–234 (2016).

    Article  Google Scholar 

  18. Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10, 828–840 (2012).

    Article  CAS  Google Scholar 

  19. Meyer, K. M. & Leveau, J. H. Microbiology of the phyllosphere: a playground for testing ecological concepts. Oecologia 168, 621–629 (2012).

    Article  Google Scholar 

  20. Woodward, F. I. & Lomas, M. R. Vegetation dynamics—simulating responses to climatic change. Biol. Rev. 79, 643–670 (2004).

    Article  CAS  Google Scholar 

  21. Innerebner, G., Knief, C. & Vorholt, J. A. Protection of Arabidopsis thaliana against leaf-pathogenic Pseudomonas syringae by Sphingomonas strains in a controlled model system. Appl. Environ. Microbiol. 77, 3202–3210 (2011).

    Article  CAS  Google Scholar 

  22. Ritpitakphong, U. et al. The microbiome of the leaf surface of Arabidopsis protects against a fungal pathogen. New Phytol. 210, 1033–1043 (2016).

    Article  CAS  Google Scholar 

  23. Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, e2001793 (2017).

    Article  Google Scholar 

  24. Knief, C., Ramette, A., Frances, L., Alonso-Blanco, C. & Vorholt, J. A. Site and plant species are important determinants of the Methylobacterium community composition in the plant phyllosphere. ISME J. 4, 719–728 (2010).

    Article  CAS  Google Scholar 

  25. Laforest-Lapointe, I. & Whitaker, B. K. Decrypting the phyllosphere microbiota: progress and challenges. Am. J. Bot. 106, 171–173 (2019).

    PubMed  Google Scholar 

  26. Copeland, J. K., Yuan, L., Layeghifard, M., Wang, P. W. & Guttman, D. S. Seasonal community succession of the phyllosphere microbiome. Mol. Plant Microbe Interact. 28, 274–285 (2015).

    Article  CAS  Google Scholar 

  27. Laforest-Lapointe, I., Messier, C. & Kembel, S. W. Host species identity, site and time drive temperate tree phyllosphere bacterial community structure. Microbiome 4, 27 (2016).

    Article  Google Scholar 

  28. Kembel, S. W. et al. Relationships between phyllosphere bacterial communities and plant functional traits in a neotropical forest. Proc. Natl Acad. Sci. USA 111, 13715–13720 (2014).

    Article  CAS  Google Scholar 

  29. Horton, M. W. et al. Genome-wide association study of Arabidopsis thaliana leaf microbial community. Nat. Commun. 5, 5320 (2014).

    Article  Google Scholar 

  30. Redford, A. J., Bowers, R. M., Knight, R., Linhart, Y. & Fierer, N. The ecology of the phyllosphere: geographic and phylogenetic variability in the distribution of bacteria on tree leaves. Environ. Microbiol. 12, 2885–2893 (2010).

    Article  Google Scholar 

  31. Finkel, O. M., Burch, A. Y., Lindow, S. E., Post, A. F. & Belkin, S. Geographical location determines the population structure in phyllosphere microbial communities of a salt-excreting desert tree. Appl. Environ. Microbiol. 77, 7647–7655 (2011).

    Article  CAS  Google Scholar 

  32. Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).

    Article  Google Scholar 

  33. Chase, J. M. Community assembly: when should history matter? Oecologia 136, 489–498 (2003).

    Article  Google Scholar 

  34. Peay, K. G., Belisle, M. & Fukami, T. Phylogenetic relatedness predicts priority effects in nectar yeast communities. Proc. Biol. Sci. 279, 749–758 (2012).

    Article  Google Scholar 

  35. Werner, G. D. & Kiers, E. T. Order of arrival structures arbuscular mycorrhizal colonization of plants. New Phytol. 205, 1515–1524 (2015).

    Article  CAS  Google Scholar 

  36. Fukami, T. et al. Assembly history dictates ecosystem functioning: evidence from wood decomposer communities. Ecol. Lett. 13, 675–684 (2010).

    Article  Google Scholar 

  37. Hiscox, J. et al. Priority effects during fungal community establishment in beech wood. ISME J. 9, 2246–2260 (2015).

    Article  Google Scholar 

  38. van Gremberghe, I. et al. Priority effects in experimental populations of the cyanobacterium Microcystis. Environ. Microbiol. 11, 2564–2573 (2009).

    Article  Google Scholar 

  39. Adame-Alvarez, R. M., Mendiola-Soto, J. & Heil, M. Order of arrival shifts endophyte-pathogen interactions in bean from resistance induction to disease facilitation. FEMS Microbiol. Lett. 355, 100–107 (2014).

    Article  CAS  Google Scholar 

  40. Braun-Kiewnick, A., Jacobsen, B. & Sands, D. Biological control of Pseudomonas syringae pv. syringae, the causal agent of basal kernel blight of barley, by antagonistic Pantoea agglomerans. Phytopathology 90, 368–375 (2000).

    Article  CAS  Google Scholar 

  41. Wilson, M. & Lindow, S. E. Interactions between the biological control agent Pseudomonas fluorescens A506 and Erwinia amylovora in pear blossoms. Phytopathology 83, 117–123 (1992).

    Article  Google Scholar 

  42. Maignien, L., DeForce, E. A., Chafee, M. E., Eren, A. M. & Simmons, S. L. Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities. mBio 5, e00682–00613 (2014).

    Article  Google Scholar 

  43. Delmotte, N. et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc. Natl Acad. Sci. USA 106, 16428–16433 (2009).

    Article  CAS  Google Scholar 

  44. Bodenhausen, N., Horton, M. W. & Bergelson, J. Bacterial communities associated with the leaves and the roots of Arabidopsis thaliana. PLoS ONE 8, e56329 (2013).

    Article  CAS  Google Scholar 

  45. Rottjers, L. & Faust, K. From hairballs to hypotheses—biological insights from microbial networks. FEMS Microbiol. Rev. 42, 761–780 (2018).

    Article  CAS  Google Scholar 

  46. Duran, P. et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell 175, 973–983 (2018).

    Article  CAS  Google Scholar 

  47. Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  49. Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).

    Article  CAS  Google Scholar 

  50. Rottjers, L. & Faust, K. Can we predict keystones? Nat. Rev. Microbiol. 17, 193 (2019).

    Article  CAS  Google Scholar 

  51. Huse, S. M., Ye, Y., Zhou, Y. & Fodor, A. A. A core human microbiome as viewed through 16S rRNA sequence clusters. PLoS ONE 7, e34242 (2012).

    Article  CAS  Google Scholar 

  52. Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

    Article  CAS  Google Scholar 

  53. Hall, A. B., Tolonen, A. C. & Xavier, R. J. Human genetic variation and the gut microbiome in disease. Nat. Rev. Genet. 18, 690–699 (2017).

    Article  CAS  Google Scholar 

  54. Martinez, I. et al. Experimental evaluation of the importance of colonization history in early-life gut microbiota assembly. eLife 7, e36521 (2018).

    Article  Google Scholar 

  55. Kinkel, L. L. & Lindow, S. E. Invasion and exclusion among coexisting Pseudomonas syringae strains on leaves. Appl. Environ. Microbiol. 59, 3447–3454 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Lindow, S. E., Arny, D. C. & Upper, C. D. Biological control of frost injury: an isolate of Erwinia herbicola antagonistic to ice nucleation active bacteria. Phytopathology 73, 1097–1102 (1983).

    Article  Google Scholar 

  57. Jousset, A. et al. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J. 11, 853–862 (2017).

    Article  Google Scholar 

  58. Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc. Natl Acad. Sci. USA 103, 12115–12120 (2006).

    Article  CAS  Google Scholar 

  59. Lynch, M. D. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229 (2015).

    Article  CAS  Google Scholar 

  60. Shade, A. et al. Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. mBio 5, e01371–01314 (2014).

    Article  Google Scholar 

  61. Chelius, M. K. & Triplett, E. W. The diversity of Archaea and bacteria in association with the roots of Zea mays L. Microb. Ecol. 41, 252–263 (2001).

    Article  CAS  Google Scholar 

  62. Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95 (2012).

    Article  CAS  Google Scholar 

  63. Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).

    Article  CAS  Google Scholar 

  64. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    Article  CAS  Google Scholar 

  65. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).

    Article  CAS  Google Scholar 

  66. Pruesse, E., Peplies, J. & Glockner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).

    Article  CAS  Google Scholar 

  67. Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).

    Article  CAS  Google Scholar 

  68. 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).

    Article  Google Scholar 

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Acknowledgements

DNA sequencing was performed at the Functional Genomics Centre Zurich. We thank D. Müller and C. Vogel for helpful discussions and support with initial strain selection. This work was funded through a European Research Council Advanced Grant (PhyMo; grant number 668991) to J.A.V. and by ETH Zurich. S.S. is grateful for financial support by the Helmut Horten Foundation.

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Authors and Affiliations

Authors

Contributions

C.I.C. and J.A.V. conceived the project. C.I.C., M.B.-M. and B.M. carried out the plant experiments. C.I.C. and M.B.-M. extracted DNA from samples. C.I.C. prepared DNA libraries for sequencing. C.M.F. wrote the code for data analysis and visualization. S.S. guided data analysis. C.I.C., C.M.F. and J.A.V. wrote the manuscript with input from S.S. All authors read the manuscript and approved it.

Corresponding author

Correspondence to Julia A. Vorholt.

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The authors declare no competing interests.

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Supplementary Information

Supplementary Information

Supplementary Figs. 1–12 and Tables 1 and 2.

Reporting Summary

Supplementary Dataset 1

Relative abundance (median and interquartile range) of all strains in the control community (1 independent replicate, n = 48).

Supplementary Dataset 2

Relative abundance of all strains in all samples of the control community (1 independent replicate, n = 48).

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Carlström, C.I., Field, C.M., Bortfeld-Miller, M. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat Ecol Evol 3, 1445–1454 (2019). https://doi.org/10.1038/s41559-019-0994-z

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