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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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.
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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.
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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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DOI: https://doi.org/10.1038/s41477-024-01654-7
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