Computational protocol: Transcriptome Network Analysis Reveals Aging-Related Mitochondrial and Proteasomal Dysfunction and Immune Activation in Human Thyroid

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Protocol publication

[…] WGCNA was used to identify associations of gene expression changes in thyroid gland tissue with aging or autoimmunity (). In this analysis, pairwise correlations between the expression values of each gene were used to construct modules which denote the coexpression network. It is established by an undirected connection between each gene with significant relationships. The module eigengene is characterized as the first principal component of each module. It reflects the most illustrative gene expression of each sample in a module. This WGCNA approach has many advantages when compared with traditional differential expression analysis, including fine focus on coexpression gene patterns which allows precise identification of biologically meaningful modules containing related genes.To identify the relationship between the modules and the clinical characteristics, the correlations between module eigengene and clinical traits, such as age, were determined. Moreover, WGCNA also computes correlation between gene and clinical traits. Aforementioned correlation tests were performed by determining Pearson correlation coefficients. The variance stabilizing transformed (VST) expression values from DESeq2 (v1.14.1) were used for the analysis according to the developer's instructions (,). DESeq2 is a type of R package for differentially expressed gene analysis using RNA-seq data. This method can be utilized not only for differentially expressed gene analysis, but also transforms raw expression level to log2 scale normalized values according to library size for subsequent analysis. Here, the VST values are used to eliminate the dependence of the variance on the mean, especially the high variance of the raw count data when the mean is low.When we constructed the coexpression gene networks, according to the scale-free topology criterion, the optimal power was selected. The scale-free networks display highly heterogeneous features and their topologies are determined by a few greatly linked nodes (hubs) which connect the rest of the less linked nodes to the system (). [...] The Cancer Genome Atlas (TCGA) is a collaboration between the National Cancer Institute and the National Human Genome Research Institute to conduct comprehensive genomic research on 33 types of cancer. As described in TCGA study, we calculated the thyroid differentiation score (TDS) using 16 genes that have functions in thyroid metabolism: DIO1, DIO2, DUOX1, DUOX2, FOXE1, GLIS3, NKX2-1, PAX8, SLC26A4, SLC5A5, SLC5A8, TG, THRA, THRB, TSHR, and TPO (). The VST expression values from DESeq2 were first centered at the median across all samples (,). Then, the TDS was determined as the average of the 16 genes in each sample:TDS = Mean of median VST expression across 16 genes.The association between aging and TDS were measured by one-way ANOVA and test for linearity using IBM SPSS Statistics Version 23.0. […]

Pipeline specifications

Software tools WGCNA, DESeq2, SPSS
Applications Miscellaneous, RNA-seq analysis