Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly

  • Nature Microbiologyvolume 3pages470480 (2018)
  • doi:10.1038/s41564-018-0129-3
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Like all higher organisms, plants have evolved in the context of a microbial world, shaping both their evolution and their contemporary ecology. Interactions between plant roots and soil microorganisms are critical for plant fitness in natural environments. Given this co-evolution and the pivotal importance of plant–microbial interactions, it has been hypothesized, and a growing body of literature suggests, that plants may regulate the composition of their rhizosphere to promote the growth of microorganisms that improve plant fitness in a given ecosystem. Here, using a combination of comparative genomics and exometabolomics, we show that pre-programmed developmental processes in plants (Avenabarbata) result in consistent patterns in the chemical composition of root exudates. This chemical succession in the rhizosphere interacts with microbial metabolite substrate preferences that are predictable from genome sequences. Specifically, we observed a preference by rhizosphere bacteria for consumption of aromatic organic acids exuded by plants (nicotinic, shikimic, salicylic, cinnamic and indole-3-acetic). The combination of these plant exudation traits and microbial substrate uptake traits interact to yield the patterns of microbial community assembly observed in the rhizosphere of an annual grass. This discovery provides a mechanistic underpinning for the process of rhizosphere microbial community assembly and provides an attractive direction for the manipulation of the rhizosphere microbiome for beneficial outcomes.

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This study was supported by the DOE, Office of Science, Office of Biological Environmental Research, including a Genomic Sciences programme award no. DE-SC0010570, DE-SC0016247 and DE-SC0014079 to M.K.F. Work performed at the Lawrence Berkeley National Laboratory including DOE Early Career Awards to D.L. and T.R.N., and work performed at the DOE JGI ( and at the DOE Joint BioEnergy Institute ( are supported by the DOE, Office of Science, Office of Biological and Environmental Research through Contract No. DE-AC02-05CH11231. D.L. was also supported in part by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 659910. Isolate genome sequencing was conducted by the DOE JGI, a DOE Office of Science User Facility, under a Community Science Program award to E.L.B., supported by the Office of Science of the DOE under Contract no. DE-AC02-05CH11231. We thank C. Castanha for background information on soil temperature and are very grateful to our talented former undergraduate students B. Jargalsaikhan, R. Hossainkhil, D. Ly, S. Ouedraogo and Y. Nguyen for their assistance with maintenance of the bacterial isolate collection.

Author information


  1. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    • Kateryna Zhalnina
    • , Katherine B. Louie
    • , Nasim Mansoori
    • , Dominique Loqué
    • , Benjamin P. Bowen
    •  & Trent R. Northen
  2. Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    • Kateryna Zhalnina
    • , Zhao Hao
    • , Ulisses Nunes da Rocha
    • , Heejung Cho
    • , Ulas Karaoz
    • , Mary K. Firestone
    •  & Eoin L. Brodie
  3. Joint BioEnergy Institute, Biosystems Engineering Division, Lawrence Berkeley National Laboratory, Emeryville, CA, USA

    • Nasim Mansoori
    •  & Dominique Loqué
  4. Department of Environmental Microbiology, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany

    • Ulisses Nunes da Rocha
  5. Lincoln Science Centre, AgResearch Ltd, Christchurch, New Zealand

    • Shengjing Shi
  6. Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA

    • Heejung Cho
    •  & Dominique Loqué
  7. INSA de Lyon, CNRS, UMR5240, Microbiologie, Adaptation et Pathogénie, Université Claude Bernard Lyon 1, Villeurbanne, France

    • Dominique Loqué
  8. Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA

    • Mary K. Firestone
    •  & Eoin L. Brodie


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K.Z., T.R.N., M.K.F. and E.L.B. developed the hypotheses. K.Z., K.B.L., N.M., U.N.d.R., S.S. and D.L. performed the experimental analyses. K.Z., H.C., U.K., Z.H., U.N.d.R. and B.P.B. analysed the data. K.Z., T.R.N., M.K.F. and E.L.B. wrote the paper. All authors provided comments and edits on the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Trent R. Northen or Eoin L. Brodie.

Supplementary information

  1. Supplementary Information

    Supplementary Methods, Supplementary Tables 1 & 2 and Supplementary Figures 1–14.

  2. Life Sciences Reporting Summary

  3. Supplementary Data Set 1

    Additional details on bacterial isolates described in the text, their genome identifiers and their observed response to root growth.

  4. Supplementary Data Set 2

    Additional details on transporters and CAZy enzymes identified in the genomes of isolates described in the text, their gene identifiers, classification and differences in their distributions between rhizosphere positive and negative responders.

  5. Supplementary Data Set 3

    Additional details on identification of metabolites in Avena barbata exudation described in the text, their theoretical and measured m/z, retention times and relative abundances at different time points of plant growth.

  6. Supplementary Data Set 4

    Additional details on identification of metabolites consumed by isolates from Avena barbata exudates described in the text, their theoretical and measured m/z, retention times and differences in uptake between rhizosphere positive and negative responders.

  7. Supplementary Data Set 5

    Additional details on prediction of isolate response to root growth based on exometabolite profiles that were used to build a principal component regression (PCR) model.