ABSTRACT: Background: Patients suffering from primary sclerosing cholangitis (PSC) often also have ulcerative colitis (UC). Cross-disease genetic and microbiome studies across PSC und UC patients indicated that UC in PSC is a separate disease entity from primary UC, but expression studies for PSC are lacking. In this study, we performed a molecular comparison of whole blood expression levels in PSC only, PSC patients with additional UC diagnosis (PSC/UC), and UC, using a large collection of whole blood transcriptome data. Methods: We conducted whole blood RNA-Seq experiments for 495 UC patients, 220 PSC patients (of whom 177 have also a UC diagnosis), and 320 healthy controls from Germany and Norway. Differential expression analyses, gene ontology and coexpression analyses and random forest machine learning were performed to identify genes, ontologies and transcriptional features that discriminate diagnoses. Results: The blood transcriptome in UC and PSC is dominated by neutrophil activation genes. In UC, but not in PSC (neither PSC alone nor PSC/UC), there is upregulation of genes for ribosomes, mitochondria and energy metabolism genes in conjunction with antibody transcript expression. In PSC, there is an increase in modules related to apoptosis and expression of genes of interferon-I-related ontologies. Random forest analysis could poorly discriminate PSC alone from PSC/UC (AUROC 0.56), but could discriminate PSC, UC, and controls with high accuracy (AUROC UC vs controls 0.95, PSC vs controls 0.88, UC vs PSC 0.986). The main coexpression modules are enriched in neutrophil degranulation and antibody production genes relevant for distinguishing PSC, UC, and controls. Conclusions: PSC and UC share neutrophil-related transcriptional upregulation in whole blood (e.g. S100A12). UC is characterised by upregulation of modules involved in antibody production (MZB1, IGJ) and increased metabolic activity (PDK4, ribosomal genes), while PSC differs from UC by interferon (IFIT1) and apoptosis upregulation (G0S2). Supported by machine learning results, PSC and UC are interpreted as molecularly separate entities, while PSC/UC and PSC are indistinguishable.
