An end to end workflow for differential gene expression using Affymetrix microarrays

In this article, we walk through an end-to-end Affymetrix microarray differential expression workflow using Bioconductor packages. This workflow is directly applicable to current "Gene" type arrays, e.g. the HuGene or MoGene arrays but can easily adapted to similar platforms. The data re-analyzed is a typical clinical microarray data set that compares inflammed and non-inflammed colon tissue in two disease subtypes. We will start from the raw data CEL files, show how to import them into a Bioconductor ExpressionSet, perform quality control and normalization and finally differential gene expression (DE) analysis, followed by some enrichment analysis. As experimental designs can be complex, a self contained introduction to linear models is also part of the workflow.

CHST3 and CHST13 polymorphisms as predictors of bosentan-induced liver toxicity in Japanese patients with pulmonary arterial hypertension

Bosentan, an endothelin receptor antagonist, has been widely used as a first-line drug for the treatment of pulmonary arterial hypertension (PAH). In addition, bosentan is approved for patients with digital ulcers related to systemic sclerosis. Liver dysfunction is a major adverse effect of bosentan and may lead to discontinuation of therapy. The purpose of this study was to identify genomic biomarkers to predict bosentan-induced liver injury. A total of 69 PAH patients were recruited into the study. An exploratory analysis of 1936 single-nucleotide polymorphisms (SNPs) in 231 genes involved in absorption, distribution, metabolism, and elimination of multiple medications using Affimetrix DMET™ (Drug Metabolism Enzymes and Transporters) chips was performed. We extracted 16 SNPs (P <  0.05) using the Jonckheere-Terpstra trend test and multiplex logistic analysis; we identified two SNPs in two genes, CHST3 and CHST13, which are responsible for proteoglycan sulfation and were significantly associated with bosentan-induced liver injury. We constructed a predictive model for bosentan-induced liver injury (area under the curve [AUC]: 0.89, sensitivity: 82.61%, specificity: 86.05%) via receiver operating curve (ROC) analysis using 2 SNPs and 2 non-genetic factors. Two SNPs were identified as potential predictive markers for bosentan-induced liver injury in Japanese patients with pulmonary arterial hypertension. This is the first pharmacogenomics study linking proteoglycan sulfating genes to drug-induced liver dysfunction, a frequently observed clinical adverse effect of bosentan therapy. These results may provide a way to personalize PAH medicine as well as provide novel mechanistic insights to drug-induced liver dysfunction.