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Heritable microbiome variation is correlated with source environment in locally adapted maize varieties

Nature Plants volume 10, pages 598–617 (2024)Cite this article

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Abstract

Beneficial interactions with microorganisms are pivotal for crop performance and resilience. However, it remains unclear how heritable the microbiome is with respect to the host plant genotype and to what extent host genetic mechanisms can modulate plant–microbiota interactions in the face of environmental stresses. Here we surveyed 3,168 root and rhizosphere microbiome samples from 129 accessions of locally adapted Zea, sourced from diverse habitats and grown under control and different stress conditions. We quantified stress treatment and host genotype effects on the microbiome. Plant genotype and source environment were predictive of microbiome abundance. Genome-wide association analysis identified host genetic variants linked to both rhizosphere microbiome abundance and source environment. We identified transposon insertions in a candidate gene linked to both the abundance of a keystone bacterium Massilia in our controlled experiments and total soil nitrogen in the source environment. Isolation and controlled inoculation of Massilia alone can contribute to root development, whole-plant biomass production and adaptation to low nitrogen availability. We conclude that locally adapted maize varieties exert patterns of genetic control on their root and rhizosphere microbiomes that follow variation in their home environments, consistent with a role in tolerance to prevailing stress.

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Fig. 1: Overall diversity and heritability of microbiome among abiotic stresses.
Fig. 2: Genomic, environmental and microbial prediction of host–microbe interactions and plant traits.
Fig. 3: Dominant and heritable bacterial families of maize root and rhizosphere microbiome under abiotic stresses.
Fig. 4: Source habitats facilitate microbiome-driven root phenotypic association with nitrogen availability.
Fig. 5: Massilia alone can modulate lateral root development and growth performance under nitrogen-poor soil.
Fig. 6: Schematic illustration of the role of host plant genetic variation and gene regulation in bacterial microbiota-mediated lateral root development in maize.

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Data availability

All raw maize genotyping data, bacterial 16S and fungal ITS data in this paper were deposited in the Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) under the BioProject ID PRJNA889703 and PRJNA1015142. The SSUrRNA database from SILVA database (release 138, 2020, https://www.arb-silva.de/) and UNITE database (v8.3, 2021, https://unite.ut.ee/) were used for analysing the bacterial 16S and fungal ITS sequences, respectively. Geographical coordinates and elevation information of the collection sites for maize landraces were retrieved from the public database of the US National Plant Germplasm System (https://www.grin-global.org/). Climatic and soil descriptor data were collected from WorldClim (https://www.worldclim.org), the NCEP/NCAR reanalysis project (https://psl.noaa.gov/data/reanalysis/reanalysis.shtml), NASA SRB (https://asdc.larc.nasa.gov/project/SRB), Climate Research Unit (https://www.uea.ac.uk), SoilGrids (https://www.isric.org/explore/soilgrids) and the Global Soil Dataset (http://globalchange.bnu.edu.cn). Root tissue gene expression data were extracted from qTeller (https://qteller.maizegdb.org/) and annotated from UniProt database (https://www.uniprot.org/). Maize mutants were retrieved from BonnMu resources (https://www.inres.uni-bonn.de/cfg/en/c-f-g/research/bonnmu). All statistical data are provided with this paper. Source data are provided with this paper.

Code availability

We deposited customized scripts in the following GitHub repository: https://github.com/Danning16/MaizeMicrobiome2022.

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Acknowledgements

We thank C. Gardner (United States Department of Agriculture, Ames, USA) and the International Maize and Wheat Improvement Center (CIMMYT) for germplasm contribution. We thank A. Glogau for soil and plant nutrient determination and S. Siemens and A. Brox for soil and root DNA extractions (University of Bonn, Bonn, Germany). We thank Y. Wang and H. Liu (State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, China) for providing us the SNP matrix data in foxtail millet. We thank D. Ning and J. Zhou (University of Oklahoma, Norman, USA) for their suggestions on microbiome data analysis. This work is supported by Deutsche Forschungsgemeinschaft (DFG) grants HO2249/9-3, HO2249/12-1 to F.H. and YU272/4-1 and Emmy Noether Programme 444755415 to P.Y., the German Excellence Strategy – EXC 2070 – grant 390732324 to P.Y. and G.S., and DFG Priority Program (SPP2089) ‘Rhizosphere Spatiotemporal Organisation - A Key to Rhizosphere Functions’ grant 403671039 to F.H. and P.Y. Germplasm propagation is funded by the TRA Sustainable Futures (University of Bonn) as part of the Excellence Strategy of the federal and state governments. X.C.’s research is supported by The Changjiang Scholarship, Ministry of Education, China, State Cultivation Base of Eco-agriculture for Southwest Mountainous Land (Southwest University, Chongqing, China), and the National Maize Production System in China (grant no. CARS-02-15). R.J.H.S. is funded by USDA Hatch Appropriations under project number PEN04734 and accession number 1021929.

Author information

Author notes

  1. These authors contributed equally: Xiaoming He, Danning Wang, Yong Jiang, Meng Li, Manuel Delgado-Baquerizo.

Authors and Affiliations

  1. College of Resources and Environment, and Academy of Agricultural Sciences, Southwest University (SWU), Chongqing, People’s Republic of China

    Xiaoming He & Xinping Chen

  2. Emmy Noether Group Root Functional Biology, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany

    Xiaoming He, Danning Wang & Peng Yu

  3. Crop Functional Genomics, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany

    Xiaoming He, Danning Wang, Caroline Marcon, Li Guo, Marcel Baer, Frank Hochholdinger & Peng Yu

  4. Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany

    Yong Jiang & Jochen C. Reif

  5. Department of Plant Science, Pennsylvania State University, State College, PA, USA

    Meng Li, Chloee McLaughlin & Ruairidh J. H. Sawers

  6. Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Sevilla, Spain

    Manuel Delgado-Baquerizo

  7. Department of Physiology and Cell Biology, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany

    Yudelsy A. T. Moya & Nicolaus von Wirén

  8. Plant Nutrition, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany

    Marion Deichmann & Gabriel Schaaf

  9. Biostatistics Unit, University of Hohenheim, Stuttgart, Germany

    Hans-Peter Piepho

  10. Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA

    Zhikai Yang & Jinliang Yang

  11. Institute for Epidemiology and Pathogen Diagnostics, Julius Kühn-Institut – Federal Research Centre for Cultivated Plants (JKI), Braunschweig, Germany

    Bunlong Yim & Kornelia Smalla

  12. Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium

    Sofie Goormachtig

  13. Center for Plant Systems Biology, VIB, Ghent, Belgium

    Sofie Goormachtig

  14. Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Netherlands

    Franciska T. de Vries

  15. Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany

    Hubert Hüging

  16. Institute of Nutrition and Food Sciences, Department of Food Microbiology and Hygiene, University of Bonn, Bonn, Germany

    Mareike Baer

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  1. Xiaoming He
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Contributions

P.Y., X.C. and F.H. designed the study. P.Y. coordinated and managed the whole project. X.H. performed the culture and harvest of the phytochamber experiments. D.W. analysed the microbiome data and performed all statistical analyses. Y.J. and J.C.R. performed genetic analyses. C. McLaughlin and R.J.H.S. performed machine learning and environmental genome-wide association analyses. M.D.-B. performed ecological analyses. B.Y. and K.S. contributed bacterial strains from maize. X.H., Marcel Baer and Mareike Baer performed bacterial inoculation experiments. X.H. and L.G. extracted all DNA samples. M.L., Z.Y. and J.Y. performed genomic prediction analyses. P.Y. and H.-P.P. discussed and designed the large pot experiment. C. Marcon and F.H. contributed the Mu-transposon induced lines. M.D., G.S., Y.A.T.M. and N.v.W. conducted soil and plant nutrient analyses. H.H. performed the preparation of soil from Dikopshof long-term experimental station. X.H., D.W., Y.J., M.L., M.D.-B., R.J.H.S., J.C.R., X.C., F.H. and P.Y. wrote the paper. All authors read and approved the final version of the paper.

Corresponding authors

Correspondence to Ruairidh J. H. Sawers, Jochen C. Reif, Frank Hochholdinger, Xinping Chen or Peng Yu.

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Nature Plants thanks Davide Bulgarelli, Devin Coleman-Derr and Ertao Wang for their contribution to the peer review of this work.

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Supplementary information

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Supplementary Figs. 1–35 and Table 1.

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Supplementary Datasets 1–26 (description is shown individually in actual excel sheet itself).

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Source Data Fig. 4

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Source Data Fig. 5

Statistical source data.

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He, X., Wang, D., Jiang, Y. et al. Heritable microbiome variation is correlated with source environment in locally adapted maize varieties. Nat. Plants 10, 598–617 (2024). https://doi.org/10.1038/s41477-024-01654-7

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