Background: Cold is one of the main abiotic stresses that severely affect plant growth and development, and crop productivity as well. Transcriptional changes during cold stress have already been intensively studied in various plant species. However, the gene networks involved in the regulation of differential cold tolerance between tobacco varieties with contrasting cold resistance are quite limited.
Results: Here, we conducted multiple time-point transcriptomic analyses using Tai tobacco (TT, cold susceptibility) and Yan tobacco (YT, cold resistance) with contrasting cold responses. We identified similar DEGs in both cultivars after comparing with the corresponding control (without cold treatment), which were mainly involved in response to abiotic stimuli, metabolic processes, kinase activities. Through comparison of the two cultivars at each time point, in contrast to TT, YT had higher expression levels of the genes responsible for environmental stresses. By applying Weighted Gene Co-Expression Network Analysis (WGCNA), we identified two main modules: the pink module was similar while the brown module was distinct between the two cultivars. Moreover, we obtained 100 hub genes, including 11 important transcription factors (TFs) potentially involved in cold stress, 3 key TFs in the brown module and 8 key TFs in the pink module. More importantly, according to the genetic regulatory networks (GRNs) between TFs and other genes or TFs by using GENIE3, we identified 3 TFs (ABI3/VP1, ARR-B and WRKY) mainly functioning in differential cold responses between two cultivars, and 3 key TFs (GRAS, AP2-EREBP and C2H2) primarily involved in cold responses.
Conclusion: Collectively, our study provides valuable resources for transcriptome- based gene network studies of cold responses in tobacco. It helps to reveal how key cold responsive TFs or other genes are regulated through network. It also helps to identify the potential key cold responsive genes for the genetic manipulation of tobacco cultivars with enhanced cold tolerance in the future.
Keywords: Co-expression network; Cold treatment; Differential gene expression; Tobacco; Transcriptome.
© 2022. The Author(s).