Hub Gene Mining and Co-Expression Network Construction of Low-Temperature Response in Maize of Seedling by WGCNA

Abstract

Weighted gene co-expression network analysis (WGCNA) is a research method in systematic biology. It is widely used to identify gene modules related to target traits in multi-sample transcriptome data. In order to further explore the molecular mechanism of maize response to low-temperature stress at the seedling stage, B144 (cold stress tolerant) and Q319 (cold stress sensitive) provided by the Maize Research Institute of Heilongjiang Academy of Agricultural Sciences were used as experimental materials, and both inbred lines were treated with 5 °C for 0 h, 12 h, and 24 h, with the untreated material as a control. Eighteen leaf samples were used for transcriptome sequencing, with three biological replicates. Based on the above transcriptome data, co-expression networks of weighted genes associated with low-temperature-tolerance traits were constructed by WGCNA. Twelve gene modules significantly related to low-temperature tolerance at the seedling stage were obtained, and a number of hub genes involved in low-temperature stress regulation pathways were discovered from the four modules with the highest correlation with target traits. These results provide clues for further study on the molecular genetic mechanisms of low-temperature tolerance in maize at the seedling stage.

DIA-Based Quantitative Proteomics in the Flower Buds of Two Malus sieversii (Ledeb.) M. Roem Subtypes at Different Overwintering Stages

“…To further understand the DEPs in the flower buds of GL and HC at the three overwintering stages, and to determine the key proteins that affected the cold tolerance of the flower buds, we analyzed the DEPs using weighted gene co-expression network analysis (WGCNA) [ 22 , 23 ]. In this study, all the DEPs based on expression patterns were divided into different expression modules as shown in Figure 5 a; 10 modules (represented in 10 colors) were identified (detailed information is provided in Table S7 including all the comparison groups), and the turquoise module contained the largest number of proteins, with 1620 proteins.…”

Analysis of huanglongbing-associated RNA-seq data reveals disturbances in biological processes within Citrus spp. triggered by Candidatus Liberibacter asiaticus infection

“…In the realm of plant science, WGCNA has proven to be a powerful tool for exploring complex genetic traits and plant responses to various environmental factors ( Mutinda et al., 2023 ). Unveiling these hub genes can lead us to the genetic controls underlying critical physiological traits, stress responses, and adaptation strategies in plants ( Yu et al., 2023 ). Utilizing 289 transcriptome samples of Dendrobium catenatum , key genes including DcCHIL , DcFLS , DcDFR , and DcWRKY3/4 were determined to react to methyl Jasmonate treatment.…”

“…Weighted gene co-expression network analysis (WGCNA) offers a robust computational approach to decipher complex biological systems at the genetic level ( Langfelder and Horvath, 2008 ). The performance of WGCNA in identifying co-expression modules has become a critical tool in plant transcriptional regulation ( Lu et al., 2019 ; Zhu et al., 2019 ; Yu et al., 2023 ). WGCNA is helpful to identify hub genes that control plant growth, development, and responses to environmental stimuli ( Yao et al., 2023 ; Yu et al., 2023 ).…”

“…The performance of WGCNA in identifying co-expression modules has become a critical tool in plant transcriptional regulation ( Lu et al., 2019 ; Zhu et al., 2019 ; Yu et al., 2023 ). WGCNA is helpful to identify hub genes that control plant growth, development, and responses to environmental stimuli ( Yao et al., 2023 ; Yu et al., 2023 ). For instance, multiple transcription factors associated with regulation of salt response in rice were identified by WGCNA ( Zhu et al., 2019 ).…”

Mining Heat-Resistant Key Genes of Peony Based on Weighted Gene Co-Expression Network Analysis

“…WGCNA is a system biology method used to describe the gene association patterns between different samples [ 7 ]. It can be used to identify highly co−variant gene sets (module), and the genes within the modules are highly positively correlated.…”