Dermatomyositis (DM) is a multisystemic immune mediated disease presenting with heterogeneous clinical features. Disease activity relies on biomarkers such as creatine kinase (CK) which can be unreliable, notably in cases with extra-muscular manifestations. Novel proteomic platforms have the potential to identify inflammatory biomarkers for personalized monitoring. The objective of this study was to identify proteins associated with disease activity in DM by using the Olink Target 96 Inflammation panel.
Six DM patients from a multicenter inflammatory myopathy registry with available longitudinal biobanked sera were identified. All patients met the 2017 EULAR/ACR criteria for adult idiopathic inflammatory myopathy. Disease activity was categorized based on the International Myositis Assessment and Clinical Study (IMACS) physician global assessment (PhGA) as low (PhGA 0-3) or moderate/high (PhGA 4-10). The IMACS core set measures used for the 2016 ACR/EULAR Total Improvement Score were extracted. Serum samples were analyzed using proximity extension technology (Olink Proteomics Inc., Watertown, MA), simultaneously targeting 92 proteins involved in inflammatory processes. Results were reported as normalized protein expression (NPX) values (log2 scale) with higher NPX values representing higher protein concentrations. Mean NPX difference (ΔNPX) for each protein comparing moderate/high and low disease activity were calculated using paired t-tests with Benjamini-Hochberg correction for multiple testing.
Six female DM patients with ages ranging from 34 to 53 years were included (Table 1). Clinical features at timepoint 1 included rash (n=6), muscle weakness (n=5), interstitial lung disease (n=5), Raynaud’s (n=3), arthritis (n=1) and dysphagia (n=1). CK values ranged from 37-6039 U/L. Autoantibodies included anti-MDA5, -TIF1y, -Mi2, -Ro52, and -Ku. At timepoint 1, 5 patients had moderate/high disease activity and at the timepoint 2, 5 patients had low disease activity. Eleven proteins were significantly upregulated when comparing moderate/high vs low disease activity. The strongest ΔNPX was observed for monocyte chemoattractant proteins, MCP-2 (3.0), MCP-1 (2.4), MCP-4 (2.2) (all adj. p=0.02) and C-X-C motif chemokine 11 (CXCL11, 3.0, adj. p=0.05). Other upregulated proteins included CX3CL1 (1.57), CCL11 (1.42), PD-L1 (1.27), IL-4 (1.1), CD40 (0.92), IL-15RA (0.9) and CSF-1 (0.61) (all adj. p=0.05).
Table 1.
Demographic and clinical characteristics
Using serum inflammatory profiles, we identified proteins upregulated in DM patients with moderate/high disease activity compared to low disease activity in a small exploratory cohort. Those included chemokines involved in monocyte and T-cell recruitment that could represent potential biomarkers for disease activity monitoring. Further studies in larger DM cohorts are warranted to evaluate the role of longitudinal proteomic profiling in personalized disease activity monitoring.
Natasha Le Blanc, V. Leclair, Marie Hudson et al.· Journal of Rheumatology· 0 citations
Systemic autoimmune rheumatic diseases (SARDs) are a heterogeneous group of autoimmune conditions characterized by immune system dysregulation leading to chronic inflammation and tissue damage. The overlapping clinical manifestations make differential diagnosis challenging, highlighting the need for novel biomarkers to facilitate early diagnosis, stratification, and personalized treatment. Five SARDs including idiopathic inflammatory myopathies (n = 210), rheumatoid arthritis (n = 84), systemic sclerosis (n = 100), Sjögren disease (n = 99), and systemic lupus erythematosus (n = 99), as well as healthy controls (n = 400) and controls with acute infectious diseases (n = 218) were selected for plasma protein profiling using Olink Explore 1536. Differential abundance analysis and machine learning were used to identify proteins with both known and novel association to SARDs. The five SARDs share hundreds of proteins with consistently altered abundance compared to both healthy and infectious controls, reflecting common underlying molecular dysregulation. Despite the overlap, we identify multiple proteins with higher abundance specific to individual SARDs. Machine learning further enables accurate classification of the five SARDs, identifying a panel of 48 proteins with high discriminatory performance, several of which are also supported by differential abundance analysis. Altogether, this explorative cross-sectional study demonstrates the importance of a pan-disease approach, including also infectious and healthy controls, to identify robust and disease-informative protein panels for improved classification of SARDs. Protein levels from this study are available open access through the Human Protein Atlas, facilitating further plasma proteome research on autoimmune disease. Systemic autoimmune rheumatic diseases (SARDs) are a group of diseases caused by a malfunctioning immune system that attacks and damages the body´s own tissues and organs. They can be difficult to distinguish and diagnose correctly because they share similar symptoms; therefore, additional molecular indicators could be helpful to aid in diagnosis. In this study, blood samples were collected from patients representing five SARDs. These samples were measured using a technology named Olink Explore, which measures around 1500 proteins in the blood at the same time. Using protein measurements from 592 patients, as well as from control groups, we identified proteins that may help distinguish the five diseases from one another. The proteins, if confirmed in future studies, could help clinicians in the diagnosis of these diseases with higher precision. Kenrick et al. investigate levels of more than 1500 proteins in blood across five systemic autoimmune rheumatic diseases using proximity extension assay. They demonstrate the importance of a pan-disease approach and identify disease-specific proteins that differentiate systemic autoimmune rheumatic diseases.
J. Kenrick, C. Preger, M. B. Álvez et al.· Communications Medicine· 0 citations
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