Discovery of new candidate genes for rheumatoid arthritis through integration of genetic association data with expression pathway analysis.
Klementy Shchetynsky, Lina-Marcella Diaz-Gallo, Lasse Folkersen, Aase Haj Hensvold, Anca Irinel Catrina, Louise Berg, Lars Klareskog, Leonid Padyukov
Author Information
Klementy Shchetynsky: Rheumatology Unit, Department of Medicine Centre of Molecular Medicine, CMM:L8:04, Karolinska Institutet/Karolinska University Hospital Solna, 171 61, Stockholm, Sweden. klementy.shchetynsky@ki.se. ORCID
Lina-Marcella Diaz-Gallo: Rheumatology Unit, Department of Medicine Centre of Molecular Medicine, CMM:L8:04, Karolinska Institutet/Karolinska University Hospital Solna, 171 61, Stockholm, Sweden.
Lasse Folkersen: Rheumatology Unit, Department of Medicine Centre of Molecular Medicine, CMM:L8:04, Karolinska Institutet/Karolinska University Hospital Solna, 171 61, Stockholm, Sweden.
Aase Haj Hensvold: Rheumatology Unit, Department of Medicine Centre of Molecular Medicine, CMM:L8:04, Karolinska Institutet/Karolinska University Hospital Solna, 171 61, Stockholm, Sweden.
Anca Irinel Catrina: Rheumatology Unit, Department of Medicine Centre of Molecular Medicine, CMM:L8:04, Karolinska Institutet/Karolinska University Hospital Solna, 171 61, Stockholm, Sweden.
Louise Berg: Rheumatology Unit, Department of Medicine Centre of Molecular Medicine, CMM:L8:04, Karolinska Institutet/Karolinska University Hospital Solna, 171 61, Stockholm, Sweden.
Lars Klareskog: Rheumatology Unit, Department of Medicine Centre of Molecular Medicine, CMM:L8:04, Karolinska Institutet/Karolinska University Hospital Solna, 171 61, Stockholm, Sweden.
Leonid Padyukov: Rheumatology Unit, Department of Medicine Centre of Molecular Medicine, CMM:L8:04, Karolinska Institutet/Karolinska University Hospital Solna, 171 61, Stockholm, Sweden.
BACKGROUND: Here we integrate verified signals from previous genetic association studies with gene expression and pathway analysis for discovery of new candidate genes and signaling networks, relevant for rheumatoid arthritis (RA). METHOD: RNA-sequencing-(RNA-seq)-based expression analysis of 377 genes from previously verified RA-associated loci was performed in blood cells from 5 newly diagnosed, non-treated patients with RA, 7 patients with treated RA and 12 healthy controls. Differentially expressed genes sharing a similar expression pattern in treated and untreated RA sub-groups were selected for pathway analysis. A set of "connector" genes derived from pathway analysis was tested for differential expression in the initial discovery cohort and validated in blood cells from 73 patients with RA and in 35 healthy controls. RESULTS: There were 11 qualifying genes selected for pathway analysis and these were grouped into two evidence-based functional networks, containing 29 and 27 additional connector molecules. The expression of genes, corresponding to connector molecules was then tested in the initial RNA-seq data. Differences in the expression of ERBB2, TP53 and THOP1 were similar in both treated and non-treated patients with RA and an additional nine genes were differentially expressed in at least one group of patients compared to healthy controls. The ERBB2, TP53. THOP1 expression profile was successfully replicated in RNA-seq data from peripheral blood mononuclear cells from healthy controls and non-treated patients with RA, in an independent collection of samples. CONCLUSION: Integration of RNA-seq data with findings from association studies, and consequent pathway analysis implicate new candidate genes, ERBB2, TP53 and THOP1 in the pathogenesis of RA.
