Integrative analysis of co-expression networks and codon usage bias in maize under biotic stress

TL;DR

This study explores gene networks and codon usage in maize under biotic stress to identify potential targets for improving stress tolerance.

Contribution

The study integrates co-expression networks and codon usage bias to reveal novel insights into maize's biotic stress response.

Findings

Four modules showed strong positive correlations with biotic stress traits, while one showed a negative correlation.

Brown and grey modules were enriched in defense-related pathways like diterpene phytoalexin and lipid oxidation.

Codon usage analysis revealed adaptive mechanisms for efficient translation under stress.

Abstract

Understanding the complex networks underlying the biotic stress response in maize is crucial for developing effective approaches to improve tolerance. We identified 1449 differentially expressed genes (DEGs) by meta-analysis of the public microarray gene expression profile. Weighted Gene Co-expression Network Analysis on the DEGs resulted in positive module-trait correlation (0.71, 0.69, 0.58, and 0.46) in the brown, grey, blue, and green modules, respectively, and negative correlation in the turquoise module. The module membership (MM) and gene significance (GS) were strongly correlated (0.65 and 0.6) in the brown and grey modules, respectively. The enrichment in diterpene phytoalexin and diterpenoid biosynthetic process suggests the involvement of the brown module in the synthesis of compounds necessary for the defense against pathogens. For the grey module, the significant GO terms were related to lipid oxidation, oxylipin, and fatty acid biosynthetic process. Identification of DEGs encoding transcription factors revealed that the MYB, NAC, WRKY, and C2C2 families had the highest membership, each with six members. Noteworthy genes identified include zealexin A1 synthase, CPP synthase, linoleate 9S-lipoxygenase3 (lox3), linoleate 9S-lipoxygenase1 (lox1), and MYB8, were among the top 5% genes with the highest GS and MM values in the brown and grey modules. Codon usage analysis revealed specific preferences under biotic stress, characterized by high Codon Adaptation Index (CAI) and Relative Synonymous Codon Usage (RSCU) values, suggesting an adaptive mechanism for efficient translation and gene regulation during stress. This comprehensive study identified potential targets for genetic engineering aimed at optimizing gene expression for improved stress tolerance.

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