Bioinformatics and meta-analysis of expression data to investigate transcriptomic response of wheat root to abiotic stresses

Biosystems. 2024 Mar:237:105165. doi: 10.1016/j.biosystems.2024.105165. Epub 2024 Feb 29.


Abiotic stresses are predominant and main causes of the losses in the crop yield. A complexity of systems biology and involvement of numerous genes in the response to abiotic factors have challenged efforts to create tolerant cultivars with sustainable production. The root is the main organ of the plant and determines a plant tolerance under stressful conditions. In this study, we carried out a meta-analysis of expression datasets from wheat root to identify differentially expressed genes, followed by the weighted gene co-expression network analysis (WGCNA) to construct the weighted gene co-expression network. The aim was to identify consensus differentially expressed genes with regulatory functions, gene networks, and biological pathways involved in response of wheat root to a set of abiotic stresses. The meta-analysis using Fisher method (FDR<0.05) identified consensus 526 DEGs from 55,367 probe sets. Although the annotated expression data are limited for wheat, the functional analysis based on the data from model plants could identify the up-regulated seven regulatory genes involved in chromosome organization and response to oxygen-containing compounds. WGCNA identified four gene modules that were mostly associated with the ribosome biogenesis and polypeptide synthesis. This study's findings enhance our understanding of key players and gene networks related to wheat root response to multiple abiotic stresses.

Keywords: Abiotic stress; Hub genes; Ribosomal proteins; Weighted gene co-expression network; Wheat root response.

Publication types

  • Meta-Analysis

MeSH terms

  • Computational Biology
  • Gene Expression Profiling
  • Gene Expression Regulation, Plant* / genetics
  • Stress, Physiological / genetics
  • Triticum* / genetics