WGCNA Analysis Identifies the Hub Genes Related to Heat Stress in Seedling of Rice (Oryza sativa L.)

Abstract

Frequent high temperature weather affects the growth and development of rice, resulting in the decline of seed–setting rate, deterioration of rice quality and reduction of yield. Although some high temperature tolerance genes have been cloned, there is still little success in solving the effects of high temperature stress in rice (Oryza sativa L.). Based on the transcriptional data of seven time points, the weighted correlation network analysis (WGCNA) method was used to construct a co–expression network of differentially expressed genes (DEGs) between the rice genotypes IR64 (tolerant to heat stress) and Koshihikari (susceptible to heat stress). There were four modules in both genotypes that were highly correlated with the time points after heat stress in the seedling. We further identified candidate hub genes through clustering and analysis of protein interaction network with known–core genes. The results showed that the ribosome and protein processing in the endoplasmic reticulum were the common pathways in response to heat stress between the two genotypes. The changes of starch and sucrose metabolism and the biosynthesis of secondary metabolites pathways are possible reasons for the sensitivity to heat stress for Koshihikari. Our findings provide an important reference for the understanding of high temperature response mechanisms and the cultivation of high temperature resistant materials.

Transcriptome analysis and mining of genes related to shade tolerance in foxtail millet ( Setaria italica (L.) P. Beauv.)

“…To understand the gene expression network during STR of foxtail millet, the expression values of expressed genes (FPKM > 1) identified in this study were used to construct co-expression networks. An appropriate soft thresholding value can effectively reduce the correlation noise, making the network conform to scale-free network attributes [27]. In the current study, optimal soft thresholding value of β was 28 with R 2 as 0.85 (electronic supplementary material, figure S1).…”

“…An appropriate soft thresholding value can effectively reduce the correlation noise, making the network conform to scale-free network attributes [27]. In the current study, optimal soft thresholding value of β was 28 with R 2 as 0.85 (electronic supplementary material, figure S1).…”

Transcriptomic and metabolomic changes in postharvest sugarbeet roots reveal widespread metabolic changes in storage and identify genes potentially responsible for respiratory sucrose loss

“…Network analysis of protein-protein interactions identified a pyruvate kinase gene as the central hub for the nearly 2000 genes that correlated to root respiration rate in WGCNA. Hubs are genes that are highly interconnected to other genes within co-expression modules and often function as regulators or biomarkers for module-correlated phenotypic traits ( Chen et al., 2017 ; Wang et al., 2022 ). Pyruvate kinase catalyzes the conversion of phospho enol pyruvate to pyruvate in the final enzymatic reaction in the glycolytic pathway.…”

Integrated multi‐omic data and analyses reveal the response pathways of green onion (Allium fistulosum L.) to high‐temperature stress

“…Despite decreased photosynthetic activity during high temperatures, rice plants still accumulate a large amount of sugar in their leaves (Jagadish et al 2015). Alterations in starch and sucrose metabolism and modifications in the biosynthetic pathways of secondary metabolites could potentially contribute to the heat stress sensitivity observed in Oryza sativa L. (Wang et al 2022). Glycolytic processes are abundant in both early and long-term thermal responses in rice (Jung et al 2012).…”