Protein and DNA methylation-based scores as surrogate markers for interferon system activation in patients with primary Sjögren’s syndrome

Björk A, Richardsdotter Andersson E, Imgenberg-Kreuz J, Thorlacius GE, Mofors J, Syvänen AC, Kvarnström M, Nordmark G, Wahren-Herlenius M

RMD Open 6 (1) e000995 [2020-01-00; online 2020-01-07]

Standard assessment of interferon (IFN) system activity in systemic rheumatic diseases depends on the availability of RNA samples. In this study, we describe and evaluate alternative methods using plasma, serum and DNA samples, exemplified in the IFN-driven disease primary Sjögren's syndrome (pSS). Patients with pSS seropositive or negative for anti-SSA/SSB and controls were included. Protein-based IFN (pIFN) scores were calculated from levels of PD-1, CXCL9 and CXCL10. DNA methylation-based (DNAm) IFN scores were calculated from DNAm levels at RSAD2, IFIT1 and IFI44L. Scores were compared with mRNA-based IFN scores measured by quantitative PCR (qPCR), Nanostring or RNA sequencing (RNAseq). mRNA-based IFN scores displayed strong correlations between B cells and monocytes (r=0.93 and 0.95, p<0.0001) and between qPCR and Nanostring measurements (r=0.92 and 0.92, p<0.0001). The pIFN score in plasma and serum was higher in patients compared with controls (p<0.0001) and correlated well with mRNA-based IFN scores (r=0.62-0.79, p<0.0001), as well as with each other (r=0.94, p<0.0001). Concordance of classification as 'high' or 'low' IFN signature between the pIFN score and mRNA-based IFN scores ranged from 79.5% to 88.6%, and the pIFN score was effective at classifying patients and controls (area under the curve, AUC=0.89-0.93, p<0.0001). The DNAm IFN score showed strong correlation to the RNAseq IFN score (r=0.84, p<0.0001) and performed well in classifying patients and controls (AUC=0.96, p<0.0001). We describe novel methods of assessing IFN system activity in plasma, serum or DNA samples, which may prove particularly valuable in studies where RNA samples are not available.

Clinical Biomarkers [Service]

NGI Uppsala (SNP&SEQ Technology Platform) [Service]

National Genomics Infrastructure [Service]

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PubMed 31958277

DOI 10.1136/rmdopen-2019-000995

Crossref 10.1136/rmdopen-2019-000995