Cross-species predictive modeling reveals conserved drought responses between maize and sorghum

Jeremy Pardo, Ching Man Wai, Maxwell Harman, Annie Nguyen, Karl A. Kremling, Maria Cinta Romay, Nicholas Lepak, Taryn L. Bauerle, Edward S. Buckler, Addie M. Thompson, Robert VanBuren*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Drought tolerance is a highly complex trait controlled by numerous interconnected pathways with substantial variation within and across plant species. This complexity makes it difficult to distill individual genetic loci underlying tolerance, and to identify core or conserved drought-responsive pathways. Here, we collected drought physiology and gene expression datasets across diverse genotypes of the C4 cereals sorghum and maize and searched for signatures defining water-deficit responses. Differential gene expression identified few overlapping drought-associated genes across sorghum genotypes, but using a predictive modeling approach, we found a shared core drought response across development, genotype, and stress severity. Our model had similar robustness when applied to datasets in maize, reflecting a conserved drought response between sorghum and maize. The top predictors are enriched in functions associated with various abiotic stress-responsive pathways as well as core cellular functions. These conserved drought response genes were less likely to contain deleterious mutations than other gene sets, suggesting that core drought-responsive genes are under evolutionary and functional constraints. Our findings support a broad evolutionary conservation of drought responses in C4 grasses regardless of innate stress tolerance, which could have important implications for developing climate resilient cereals.

Original languageEnglish (US)
Article numbere2216894120
JournalProceedings of the National Academy of Sciences of the United States of America
Volume120
Issue number10
DOIs
StatePublished - Mar 7 2023

Funding

ACKNOWLEDGMENTS. This work is supported by NSF MCB‐1817347 and the United States Department of Agriculture National Institute of Food and Agriculture (USDA-NIFA 2022-67013-36118) to R.V. M.H. was a participant in the Plant Genomics Research Experience for Undergraduates Program funded by NSF Division of Biological Infrastructure (NSF-DBI 1358474). J.P. was supported by predoctoral training award T32-GM110523 from the National Institute of General Medical Sciences of the NIH.

Keywords

  • C4 grasses
  • drought
  • maize
  • predictive modeling
  • transfer learning

ASJC Scopus subject areas

  • General

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