Comparative transcriptome analysis reveals key pathways and regulatory networks in early resistance of Glycine max to soybean mosaic virus

• Published in: Frontiers in microbiology Vol. 14; p. 1241076
• Main Authors: Li, Han, Liu, Jinyang, Yuan, Xingxing, Chen, Xin, Cui, Xiaoyan
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 19.10.2023
• Subjects: DEGs; Microbiology; PPI network; RNA sequencing; soybean; soybean mosaic virus
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2023.1241076

As a high-value oilseed crop, soybean [ Glycine max (L.) Merr.] is limited by various biotic stresses during its growth and development. Soybean mosaic virus (SMV) is a devastating viral infection of soybean that primarily affects young leaves and causes significant production and economic losses; however, the synergistic molecular mechanisms underlying the soybean response to SMV are largely unknown. Therefore, we performed RNA sequencing on SMV-infected resistant and susceptible soybean lines to determine the molecular mechanism of resistance to SMV. When the clean reads were aligned to the G. max reference genome, a total of 36,260 genes were identified as expressed genes and used for further research. Most of the differentially expressed genes (DEGs) associated with resistance were found to be enriched in plant hormone signal transduction and circadian rhythm according to Kyoto Encyclopedia of Genes and Genomes analysis. In addition to salicylic acid and jasmonic acid, which are well known in plant disease resistance, abscisic acid, indole-3-acetic acid, and cytokinin are also involved in the immune response to SMV in soybean. Most of the Ca 2+ signaling related DEGs enriched in plant-pathogen interaction negatively influence SMV resistance. Furthermore, the MAPK cascade was involved in either resistant or susceptible responses to SMV, depending on different downstream proteins. The phytochrome interacting factor-cryptochrome-R protein module and the MEKK3/MKK9/MPK7-WRKY33-CML/CDPK module were found to play essential roles in soybean response to SMV based on protein-protein interaction prediction. Our findings provide general insights into the molecular regulatory networks associated with soybean response to SMV and have the potential to improve legume resistance to viral infection.