Reads were first aligned to the human reference genome (hg19) and current gene definitions using TopHat2 aligner (58). Reads aligning to each known transcript were counted and the follow-up analyses performed using the Bioconductor package for next-generation sequencing data analysis (59). The quality of raw, aligned, and counts data was assessed using fastQC (60) and RNA-SeQC (61) tools, and by examining correlation patterns of normalized data between different samples. Two samples that showed poor correlation with the rest of the samples and unusually high levels of read duplication were excluded from the analysis. The differential gene expression analysis was based on the negative-binomial statistical model of read counts as implemented in the edgeR Bioconductor package (62) for each separate comparison. Differential expressions with FDR-adjusted P values (63) less than 0.1 were considered to be statistically significant. For cluster analysis, gene expression levels were normalized by calculating log2 reads per kilobase million (RPKM) levels (64). Expression profiles of differentially expressed genes in the heatmap were clustered using the Bayesian infinite mixture model (65), and the samples were clustered using average linkage hierarchical clustering based on pairwise Pearson’s correlations as the measure of similarity. The enrichment analysis of up- and downregulated genes in gene sets defined by MSigDB (66) and Gene Ontologies (67) was performed using logistic regression–based LRPath methodology (68) as implemented in the CLEAN package (69). The enrichment analysis of transcription factor targets was performed by submitting lists of differentially expressed genes to Enrichr (70). The genes associated with Cu were identified by searching GeneCards (71). The statistical significance of enrichment of genes related to copper was assessed using Fisher’s exact test (72). Enrichr has been used to perform enrichment analysis using ChEA and ENCODE libraries of curated sets of transcription factor targets identified by ChIP-Seq. All raw RNA-Seq and demographic data from patients who granted informed consent to share such data are made available at the Database of Genotypes and Phenotypes (dbGaP) under accession number phs2083.v1.

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