Integrative System Biology Analysis of Transcriptomic Responses to Drought Stress in Soybean (Glycine max L.)

Abstract

Drought is a major abiotic stressor that causes yield losses and limits the growing area for most crops. Soybeans are an important legume crop that is sensitive to water-deficit conditions and suffers heavy yield losses from drought stress. To improve drought-tolerant soybean cultivars through breeding, it is necessary to understand the mechanisms of drought tolerance in soybeans. In this study, we applied several transcriptome datasets obtained from soybean plants under drought stress in comparison to those grown under normal conditions to identify novel drought-responsive genes and their underlying molecular mechanisms. We found 2168 significant up/downregulated differentially expressed genes (DEGs) and 8 core modules using gene co-expression analysis to predict their biological roles in drought tolerance. Gene Ontology and KEGG analyses revealed key biological processes and metabolic pathways involved in drought tolerance, such as photosynthesis, glyceraldehyde-3-phosphate dehydrogenase and cytokinin dehydrogenase activity, and regulation of systemic acquired resistance. Genome-wide analysis of plants’ cis-acting regulatory elements (CREs) and transcription factors (TFs) was performed for all of the identified DEG promoters in soybeans. Furthermore, the PPI network analysis revealed significant hub genes and the main transcription factors regulating the expression of drought-responsive genes in each module. Among the four modules associated with responses to drought stress, the results indicated that GLYMA_04G209700, GLYMA_02G204700, GLYMA_06G030500, GLYMA_01G215400, and GLYMA_09G225400 have high degrees of interconnection and, thus, could be considered as potential candidates for improving drought tolerance in soybeans. Taken together, these findings could lead to a better understanding of the mechanisms underlying drought responses in soybeans, which may useful for engineering drought tolerance in plants.

Overexpression of soybean GmNAC19 and GmGRAB1 enhances root growth and water-deficit stress tolerance in soybean

“…Yet, functional characterization of relevant genes is often missing in gene discovery, which is absolutely required for crop improvement. Many systematic studies have identified a number of candidate genes for growth and water stress response, providing useful genetic resources for functional analyses and future development of improved soybean ( Le et al., 2011 ; Le et al., 2012 ; Neves-Borges et al., 2012 ; Chai et al., 2015 ; Ha et al., 2015 ; Song et al., 2016 ; Hussain et al., 2017 ; Melo et al., 2018 ; Zhou et al., 2020 ; Shahriari et al., 2022 ; Xuan et al., 2022 ). However, the functionality of majority of these candidate genes is yet to be elucidated.…”

“…Several studies have used bioinformatic tools such as differential gene expression analysis and homologous sequence similarity to the genes with known function in rice and Arabidopsis to identify candidate genes for root growth and water-deficit tolerance in soybean. These systematic-based analyses detected transcriptional factors of the MYB, bHLH, AP2-EREBP, NAC, WRKY, bZIP, and C2H2-zinc finger gene families as prime factors ( Le et al., 2011 ; Le et al., 2012 ; Neves-Borges et al., 2012 ; Chai et al., 2015 ; Ha et al., 2015 ; Song et al., 2016 ; Hussain et al., 2017 ; Melo et al., 2018 ; Zhou et al., 2020 ; Shahriari et al., 2022 ; Xuan et al., 2022 ). However, the majority of these studies lack the functional characterization of these candidate genes.…”

Integrative systems biology analysis of barley transcriptome ─ hormonal signaling against biotic stress

“…To find the upstream flanking region of DEGs (1000 bp) [ 74 ], a BLAST against the Ensemble plant database was performed [ 75 ]. Then, the MEME [ 76 ] database was used to identify the conserved cis-acting elements.…”

Abiotic Stress in Crop Production

“…Meta-analyses are efficient approaches to the identification of important genes in abiotic stress responses. Recently, these bioinformatic analyses uncovered important abiotic stress targets in soya, wheat, and rice [ 200 , 201 , 202 ]. The results of the transcriptomic studies, which are available in the Expression Atlas database, provide a rich resource that allows analyses and comparisons of the effects of different stressors [ 203 ].…”