Abstract
Plants grow within a complex web of species that interact with each other and with the plant1–10. These interactions are governed by a wide repertoire of chemical signals, and the resulting chemical landscape of the rhizosphere can strongly affect root health and development7–9,11–18. Here, to understand how interactions between microorganisms influence root growth in Arabidopsis, we established a model system for interactions between plants, microorganisms and the environment. We inoculated seedlings with a 185-member bacterial synthetic community, manipulated the abiotic environment and measured bacterial colonization of the plant. This enabled us to classify the synthetic community into four modules of co-occurring strains. We deconstructed the synthetic community on the basis of these modules, and identified interactions between microorganisms that determine root phenotype. These interactions primarily involve a single bacterial genus (Variovorax), which completely reverses the severe inhibition of root growth that is induced by a wide diversity of bacterial strains as well as by the entire 185-member community. We demonstrate that Variovorax manipulates plant hormone levels to balance the effects of our ecologically realistic synthetic root community on root growth. We identify an auxin-degradation operon that is conserved in all available genomes of Variovorax and is necessary and sufficient for the reversion of root growth inhibition. Therefore, metabolic signal interference shapes bacteria–plant communication networks and is essential for maintaining the stereotypic developmental programme of the root. Optimizing the feedbacks that shape chemical interaction networks in the rhizosphere provides a promising ecological strategy for developing more resilient and productive crops.
Original language | American English |
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Pages (from-to) | 103-108 |
Number of pages | 6 |
Journal | Nature |
Volume | 587 |
Issue number | 7832 |
DOIs | |
State | Published - 5 Nov 2020 |
Bibliographical note
Funding Information:Acknowledgements We thank S. Barth, J. Shen, M. Priegel, D. Chudasma, D. Panda, I. Castillo, N. Del Risco, C. Lindberg and R. Pérez-Torres for technical assistance; D. Pelletier and P. Schulze-Lefert for strains; the Dangl laboratory microbiome group, A. Stepanova, J. Alonso, J. Brumos, J. Kieber, J. Reed and I. Greenhut for useful discussions; and D. Lundberg and A. Bishopp for critical comments on the manuscript. This work was supported by NSF grant IOS-1917270 and by Office of Science (BER), US Department of Energy, Grant DE-SC0014395 to J.L.D. J.L.D. is an Investigator of the Howard Hughes Medical Institute, supported by the HHMI. O.M.F. was supported by NIH NRSA Fellowship F32-GM117758. C.R.F. was supported by an NSERC postdoctoral fellowship (532852-2019).
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© 2020, The Author(s), under exclusive licence to Springer Nature Limited.