Katherine James, Simon J Cockell, Colin S Gillespie, Anil Wipat, Jennifer Hallinan, Benedikt M Kessler, Roman Fischer, Simon J Bowman, Bridget Griffiths & The UKPSSR Study Group
Objectives: Primary Sjögren's Syndrome (pSS) is a chronic autoimmune disease characterised by a range of symptoms including dryness, fatigue, depression, anxiety, pain and an increased risk of lymphoma. Patient populations are heterogeneous in their symptoms, making the accurate identification of pSS biomarkers non-trivial. This study aims to identify symptom-specific changes in protein abundance using serum samples from the UK Primary Sjögren's Syndrome Registry (UKPSSR), a cohort of clinically well-characterised pSS patients and healthy controls.
Methods: Patients were chosen from the UKPSSR database and grouped based on their levels of dryness, fatigue, depression, anxiety, and pain. Serum samples from each subject group were pooled and analysed by LC-MS/MS analysis using a Thermo LTQ Orbitrap Velos. Bioinformatic analyses and statistical modelling were then used to characterise the proteins and identify relationships between protein abundance and symptom levels.
Results: A total of 107 proteins were found to have significant changes in abundance between patient groups (ANOVA p>0.05, based on analytical duplicates). The majority of these proteins were immunoglobulins and components of the complement and coagulation cascades. Statistical modelling indicated that several of these abundance changes correlated with symptom levels. In particular, decreases in immunoglobulin chain regions are associated with the high fatigue and high pain groups, while slight decreases in complement components C1R and C4A were associated with dryness.
Conclusions: Differences in blood serum abundance of several proteins can be detected between groups of pSS patients with heterogeneous symptom profiles. These observations suggest that specific proteins may be biomarkers for the individual symptoms of pSS. Future analysis on a patient by patient basis could potentially reveal symptom-specific bio-fingerprints for individual symptom profiles.
