Genome-wide identification and characterization of m6A regulatory genes in Soybean: Insights into evolution, miRNA interactions, and stress responses

Abstract

N6-methyladenosine (m6A) is one of the most prevalent mRNA modifications in eukaryotes, playing a crucial role in plant development and stress responses. The m6A modification is regulated by three key components: writers (which add methyl groups), erasers (which remove them), and readers (which interpret the modification). Despite its significance, the role of m6A regulatory genes in plants, particularly in soybean, remains largely unexplored. This study identified 42 m6A regulatory genes in soybean through a comprehensive genome-wide analysis. Structural analysis revealed diverse gene architectures and functional variations across different subgroups. A total of 18 gene duplication events were identified, predominantly evolving under purifying selection. Network analysis and hub gene identification suggested weaker interactions among eraser proteins. Moreover, interaction analysis between miRNAs and soybean m6A regulatory genes indicated a stronger miRNA-mediated regulation of writer components compared to erasers and readers. Expression profiling under various stress conditions highlighted distinct regulatory patterns, where GmECT9, GmECT13, and GmECT17 were highly expressed in roots and nodules, while GmMTB2 and GmALKBH9B2 exhibited maximum upregulation and downregulation, respectively, under combined water deficit and heat stress. Additionally, in the case of mosaic virus infection, GmALKBH9B4 showed significant downregulation, whereas GmMTB1, GmMTB2, and GmALKBH9B1 were upregulated. This study provides a foundational framework for understanding m6A regulatory genes in soybean. The genome-wide insights presented here contribute to a deeper understanding of the molecular mechanisms governing m6A regulation and its potential implications for stress responses and plant development.

Joy Prokash Debnath

Graduate Research Assistant

My research interests include computational biology and machine learning.