Association analyses of host genetics, root-colonizing microbes, and plant phenotypes under different nitrogen conditions in maize

Abstract

The root-associated microbiome (rhizobiome) affects plant health, stress tolerance, and nutrient use efficiency. However, it remains unclear to what extent the composition of the rhizobiome is governed by intraspecific variation in host plant genetics in the field and the degree to which host plant selection can reshape the composition of the rhizobiome. Here we quantify the rhizosphere microbial communities associated with a replicated diversity panel of 230 maize (Zea mays L.) genotypes grown in agronomically relevant conditions under high N (+N) and low N (-N) treatments. We analyze the maize rhizobiome in terms of 150 abundant and consistently reproducible microbial groups and we show that the abundance of many root-associated microbes is explainable by natural genetic variation in the host plant, with a greater proportion of microbial variance attributable to plant genetic variation in -N conditions. Population genetic approaches identify signatures of purifying selection in the maize genome associated with the abundance of several groups of microbes in the maize rhizobiome. Genome-wide association study was conducted using the abundance of microbial groups as rhizobiome traits, and identified n = 622 plant loci that are linked to the abundance of n = 104 microbial groups in the maize rhizosphere. In 62/104 cases, which is more than expected by chance, the abundance of these same microbial groups was correlated with variation in plant vigor indicators derived from high throughput phenotyping of the same field experiment. We provide comprehensive datasets about the three-way interaction of host genetics, microbe abundance, and plant performance under two N treatments to facilitate targeted experiments towards harnessing the full potential of root-associated microbial symbionts in maize production.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting file.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Michael A Meier

    Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Gen Xu

    Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Martha G Lopez-Guerrero

    Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Guangyong Li

    Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Christine Smith

    Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Brandi Sigmon

    Department of Plant Pathology, University of Nebraska-Lincoln, Lincoln, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Joshua R Herr

    Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3425-292X
  8. James R Alfano

    Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Yufeng Ge

    Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. James C Schnable

    Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, United States
    For correspondence
    schnable@unl.edu
    Competing interests
    The authors declare that no competing interests exist.
  11. Jinliang Yang

    Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, United States
    For correspondence
    jinliang.yang@unl.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0999-3518

Funding

National Science Foundation (Cooperative Agreement OIA-1557417)

  • Jinliang Yang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Rebecca Bart, The Donald Danforth Plant Science Center, United States

Version history

  1. Preprint posted: November 2, 2021 (view preprint)
  2. Received: November 23, 2021
  3. Accepted: July 25, 2022
  4. Accepted Manuscript published: July 27, 2022 (version 1)
  5. Version of Record published: September 13, 2022 (version 2)

Copyright

© 2022, Meier et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,452
    Page views
  • 694
    Downloads
  • 5
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Michael A Meier
  2. Gen Xu
  3. Martha G Lopez-Guerrero
  4. Guangyong Li
  5. Christine Smith
  6. Brandi Sigmon
  7. Joshua R Herr
  8. James R Alfano
  9. Yufeng Ge
  10. James C Schnable
  11. Jinliang Yang
(2022)
Association analyses of host genetics, root-colonizing microbes, and plant phenotypes under different nitrogen conditions in maize
eLife 11:e75790.
https://doi.org/10.7554/eLife.75790

Further reading

    1. Genetics and Genomics
    Emily I Green, Etienne Jaouen ... Eric Marois
    Research Article

    Lipophorin is an essential, highly expressed lipid transport protein that is secreted and circulates in insect hemolymph. We hijacked the Anopheles coluzzii Lipophorin gene to make it co-express a single-chain version of antibody 2A10, which binds sporozoites of the malaria parasite Plasmodium falciparum. The resulting transgenic mosquitoes show a markedly decreased ability to transmit Plasmodium berghei expressing the P. falciparum circumsporozoite protein to mice. To force the spread of this anti-malarial transgene in a mosquito population, we designed and tested several CRISPR/Cas9-based gene drives. One of these is installed in, and disrupts, the pro-parasitic gene Saglin and also cleaves wild-type Lipophorin, causing the anti-malarial modified Lipophorin version to replace the wild type and hitch-hike together with the Saglin drive. Although generating drive-resistant alleles and showing instability in its gRNA-encoding multiplex array, the Saglin-based gene drive reached high levels in caged mosquito populations and efficiently promoted the simultaneous spread of the antimalarial Lipophorin::Sc2A10 allele. This combination is expected to decrease parasite transmission via two different mechanisms. This work contributes to the design of novel strategies to spread antimalarial transgenes in mosquitoes, and illustrates some expected and unexpected outcomes encountered when establishing a population modification gene drive.

    1. Evolutionary Biology
    2. Genetics and Genomics
    Alex Mas Sandoval, Sara Mathieson, Matteo Fumagalli
    Research Article

    Cultural and socioeconomic differences stratify human societies and shape their genetic structure beyond the sole effect of geography. Despite mating being limited by sociocultural stratification, most demographic models in population genetics often assume random mating. Taking advantage of the correlation between sociocultural stratification and the proportion of genetic ancestry in admixed populations, we sought to infer the former process in the Americas. To this aim, we define a mating model where the individual proportions of the genome inherited from Native American, European and sub-Saharan African ancestral populations constrain the mating probabilities through ancestry-related assortative mating and sex bias parameters. We simulate a wide range of admixture scenarios under this model. Then, we train a deep neural network and retrieve good performance in predicting mating parameters from genomic data. Our results show how population stratification shaped by socially constructed racial and gender hierarchies have constrained the admixture processes in the Americas since the European colonisation and the subsequent Atlantic slave trade.