Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus

Kevin Y C Su; John A Reynolds; Rachel Reed; Rachael Da Silva; Janet Kelsall; Ivona Baricevic-Jones; David Lee; Anthony D Whetton; Nophar Geifman; Neil McHugh; Ian N Bruce; the MASTERPLANS and BILAG-BR consortia

doi:10.1186/s12014-023-09420-1

Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus

Clin Proteomics. 2023 Jul 29;20(1):29. doi: 10.1186/s12014-023-09420-1.

Authors

Kevin Y C Su^#^{1

2}, John A Reynolds^#^{3

4}, Rachel Reed⁵, Rachael Da Silva⁵, Janet Kelsall⁵, Ivona Baricevic-Jones⁵, David Lee⁵, Anthony D Whetton^{5

6}, Nophar Geifman⁶, Neil McHugh⁷, Ian N Bruce^{8

9}; the MASTERPLANS and BILAG-BR consortia

Affiliations

¹ Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
² Rheumatology Department, Sandwell and West Birmingham NHS Trust, Birmingham, UK.
³ Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK. j.a.reynolds.1@bham.ac.uk.
⁴ Rheumatology Department, Sandwell and West Birmingham NHS Trust, Birmingham, UK. j.a.reynolds.1@bham.ac.uk.
⁵ Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
⁶ Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
⁷ Department of Pharmacy and Pharmacology, University of Bath, Bath, UK.
⁸ Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK.
⁹ NIHR Manchester Biomedical Research Centre, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.

^# Contributed equally.

Abstract

Objective: Systemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients.

Method: Plasma was collected from patients with active SLE who were enrolled in the British Isles Lupus Assessment Group Biologics Registry (BILAG-BR). The plasma proteome was analysed using a data-independent acquisition method, Sequential Window Acquisition of All theoretical mass spectra mass spectrometry (SWATH-MS). Unsupervised, data-driven clustering algorithms were used to delineate groups of patients with a shared proteomic profile.

Results: In 223 patients, six clusters were identified based on quantification of 581 proteins. Between the clusters, there were significant differences in age (p = 0.012) and ethnicity (p = 0.003). There was increased musculoskeletal disease activity in cluster 1 (C1), 19/27 (70.4%) (p = 0.002) and renal activity in cluster 6 (C6) 15/24 (62.5%) (p = 0.051). Anti-SSa/Ro was the only autoantibody that significantly differed between clusters (p = 0.017). C1 was associated with p21-activated kinases (PAK) and Phospholipase C (PLC) signalling. Within C1 there were two sub-clusters (C1A and C1B) defined by 49 proteins related to cytoskeletal protein binding. C2 and C6 demonstrated opposite Rho family GTPase and Rho GDI signalling. Three proteins (MZB1, SND1 and AGL) identified in C6 increased the classification of active renal disease although this did not reach statistical significance (p = 0.0617).

Conclusions: Unsupervised proteomic analysis identifies clusters of patients with active SLE, that are associated with clinical and serological features, which may facilitate biomarker discovery. The observed proteomic heterogeneity further supports the need for a personalised approach to treatment in SLE.

Keywords: Arthritis; Cluster; Nephritis; Proteomics; SLE (systemic lupus erythematosus); SWATH.