Identification and Comparative Analysis of Differential Gene Expression in Soybean Leaf Tissue under Drought and Flooding Stress Revealed by RNA-Seq

Abstract

Drought and flooding are two major causes of severe yield loss in soybean worldwide. A lack of knowledge of the molecular mechanisms involved in drought and flood stress has been a limiting factor for the effective management of soybeans; therefore, it is imperative to assess the expression of genes involved in response to flood and drought stress. In this study, differentially expressed genes (DEGs) under drought and flooding conditions were investigated using Illumina RNA-Seq transcriptome profiling. A total of 2724 and 3498 DEGs were identified under drought and flooding treatments, respectively. These genes comprise 289 Transcription Factors (TFs) representing Basic Helix-loop Helix (bHLH), Ethylene Response Factors (ERFs), myeloblastosis (MYB), No apical meristem (NAC), and WRKY amino acid motif (WRKY) type major families known to be involved in the mechanism of stress tolerance. The expression of photosynthesis and chlorophyll synthesis related genes were significantly reduced under both types of stresses, which limit the metabolic processes and thus help prolong survival under extreme conditions. However, cell wall synthesis related genes were up-regulated under drought stress and down-regulated under flooding stress. Transcript profiles involved in the starch and sugar metabolism pathways were also affected under both stress conditions. The changes in expression of genes involved in regulating the flux of cell wall precursors and starch/sugar content can serve as an adaptive mechanism for soybean survival under stress conditions. This study has revealed the involvement of TFs, transporters, and photosynthetic genes, and has also given a glimpse of hormonal cross talk under the extreme water regimes, which will aid as an important resource for soybean crop improvement.

Metagenome-Wide Association Study and Machine Learning Prediction of Bulk Soil Microbiome and Crop Productivity

“…On the other hand, biotic features such as pathogen, pest (Hartman et al, 2015), and beneficial symbiosis such as root nodulation (Tkacz and Poole, 2015) are also involved in yield performance. Moreover, an important biotic feature is the genetics of the crop variety (the genotype), which includes the genetics of the photosynthesis and productivity performance (Dhanapal et al, 2016; Li et al, 2016), the genetics of water and nitrogen utilizing efficiency (Dhanapal et al, 2015; Chen et al, 2016), the genetics of disease and pest resistance (Chang et al, 2016; Revelle, 2016), and the genetic influence on structuring the rhizosphere microbiome (Jin et al, 2009; Babujia et al, 2016; de Almeida Lopes et al, 2016). In our study, we discovered additional factors underlying crop productivity when the above factors were identical or similar.…”

Identification and Analysis of NaHCO3 Stress Responsive Genes in Wild Soybean (Glycine soja) Roots by RNA-seq

“…Therefore, there might be some overlapped responsive genes between alkaline and salt stress, as well as other stresses. We compared the expression patterns of the alkalinity-responsive genes encoding NF-YA transcription factors and transporters in this study (Supplementary Table S8) with the published RNA-seq data in soybean under salt (Belamkar et al, 2014), drought (Belamkar et al, 2014; Chen et al, 2016), flooding (Chen et al, 2016), potassium deficiency (Wang et al, 2012), and shade stresses (Gong et al, 2014). Among these 39 alkalinity-responsive genes, there are 20, 16, 8, 9, and 8 genes were also detected as DEGs in soybean response to salt, drought, flooding, potassium deficiency, and shade, respectively.…”

Analysis of EF-Hand Proteins in Soybean Genome Suggests Their Potential Roles in Environmental and Nutritional Stress Signaling

“…Microarray or high-throughput sequencing datasets for soybean in responses to cold, drought, phosphorus deficiency, and symbiotic bacteria, were also retrieved from previously published data (Libault et al, 2010a; Le et al, 2012; Maruyama et al, 2012; Chen et al, 2016; Zeng et al, 2016b). …”