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[…] n Col vs. WT, but not Xbp1 vs. WT or CX vs. WT, and the DEGs in Col vs. WT and CX vs. WT, but not Xbp1 vs. WT, in the subsequent analysis., The database for annotation, visualization and integrated discovery (DAVID) online software can provide a comprehensive set of functional annotation tools (). In order to analyze the identified DEGs on the functional level, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed for all DEGs using DAVID software (). Subsequently, p-values were calculated using the hypergeometric test (). Gene count ≥2 and p-values <0.05 were set as the cut-off criterion for pathway enrichment analysis., iRegulon (), available as a Cytoscape () plugin, implements a genome-wide ranking-and-recovery approach to detect enriched TF motifs and their optimal sets of direct target genes (). Besides, iRegulon allows the integration of predicted regulatory binding sites directly into a biological network (). In the original study by Cameron et al (), DEGs in Col vs. WT and CX vs. WT and Xbp1 vs. WT, as well as the DEGs between in Col vs. WT and Xbp1 vs. WT, but not CX vs. WT, were analyzed. In this study, we screened out the overlapping DEGs with consistent expression changes (both upregulated or both downregulated) in the 3 groups (Xbp1 vs. WT, Col vs. WT and CX vs. WT). The lists of overlapping DEGs with consistent expression change were subjected to iRegulon () and used to predict their transcriptional regulators using the following parameters: minimum identity between orthologous genes, 0.05; and maximum false discovery rate on motif similarity, 0.001. The predicted TF-DEG pairs with normalized enrichment scores (NES) () >4.5 were selected for further analysis and the regulatory networks were constructed., To further analyze the more potential genes associated with SMCD, we intended to perform gene co-expression analysis of DEGs. For reducing the deviation, we used all the DEGs as the scope of gene co-expression analysis., Weighted correlation network analysis (WGCNA) can be used for finding modules (clusters) of genes with high correlations, or relating modules to external sample traits (). A robust correlation coefficient emphasizes high correlations of genes and results in a weighted network (). In this study, WGCNA R software package (), which can cluster the most highly co-expressed genes in defined modules, was applied to detect modules of co-expressed genes of all the DEGs identified in Xbp1 vs. WT, Col vs. WT, and CX vs. WT. If the absolute value of the correlation coefficient was high, the co-expressed genes clustered in modules would have high gene co-expression trend consistency and the modules would be significantly related to external sample traits., In order to analyze gene co-expression modules on the functional level, Gene Ontology (GO) enrichment analysis was carried out using DAVID online tool () to obtain the enriched biological process (BP) terms. A hypergeometric test () was applied to examine the significance of this enrichment analysis. The count number ≥2 and p-value <0.05 were used as the cut-off criterion., The Search Tool for the Retrieval of Interacting Genes (STRING) database can be used as it provides easy access to known and predicted protein interactions (). The interaction probabilities of proteins in STRING are provided with a confidence score (). A protein with a confidence score >0.4 is deemed to have medium confidence of interaction with other proteins (). In the present study, the STRING database was used to select the PPIs among the DEG […]

Pipeline specifications

Software tools Cytoscape, iRegulon, WGCNA, DAVID