POS0488 IDENTIFICATION OF NOVEL DISEASE BIOMARKERS IN SYSTEMIC SCLEROSIS THROUGH HIGH-THROUGHPUT PROTEOMICS

Autor: R. Ortega Castro, F. U. Pilar, M. Martinez-Monllor, L. Muñoz-Barrera, I. Sanchez-Pareja, M. C. Ábalos-Aguilera, N. Barbarroja Puerto, E. Collantes Estevez, M. Á. Aguirre-Zamorano, C. Perez-Sanchez, C. Lopez-Pedrera
Rok vydání: 2022
Předmět:
Zdroj: Annals of the Rheumatic Diseases. 81:498.3-499
ISSN: 1468-2060
0003-4967
Popis: BackgroundSystemic sclerosis (SSc) is an autoimmune disease characterized by vasculopathy, tissue fibrosis and activation of the innate and adaptive immune system. Clinical features of the disease consist of skin thickening and internal organ involvement. Due to the heterogeneous nature of the disease, there is an unmet need of biomarkers for diagnosis, disease progression and response to treatment.ObjectivesThe aim of this study was to explore new serum proteomic fingerprints of clinically defined forms of SSc.MethodsHighly specific detection of nighty two proteins from a panel related to organ damage was performed, by using the breakthrough technology proximity extension immunoassay (PEA, Olink), in the serum of 72 patients with SSC and 18 age-matched healthy donors (HD). Main disease complications in the SSC cohort, including lung fibrosis, skin fibrosis, renal, vascular, and esophageal involvement were assessed, and prevalence of circulating autoantibodies was tested, along with standard demographic and inflammatory parameters. Unsupervised hierarchical clustering methodologies were applied to identify subgroups of patients based on their proteomic profiles. Gene ontology enrichment was used to interrogate the biological meaning of the distinctive molecular signatures identified.ResultsSixteen circulating proteins related to organ damage were coordinately altered in the serum of SSc patients in relation to HD. Unsupervised clustering analyses differentiated 3 patients clusters presenting different proteomic profiles. Clinically, patients belonging to cluster 1 were characterized by a significant prevalence of multiple organ involvement (84%) in relation to clusters 2 (52%) and 3 (43%), mostly encompassing lung and skin fibrosis and esophageal dysmotility. Immunologically, cluster 1 further displayed the highest percentage of positivity for anti-scl70 antibodies.Nineteen serum proteins, not previously reported in the serum of SSC patients (BANK1, BID, CALR, ERBB2IP, FGR, FOSB, FOXO1, INPPL1, MAEA, MAGED1, MAP4K5, NBN, NCF2, PRKAB1, RASSF2, RCOR1, SMAD1, STXBP3, VASH1) were found deregulated between clusters, with a significant increase in the levels of all of them in cluster 1 compared with clusters 2 and 3. These deregulated proteins were mostly involved in biological processes such as cell proliferation, apoptosis, cell adhesion, migration, and immune response. Among them, two were functionally linked with cutaneous diseases [Calreticulin (CALR) and B-cell scaffold protein with ankryn repeats (BANK1)], two with digestive disorders (Tyrosine-protein kinase Fgr (FGR) and syntaxin-binding protein 3 (STXBP3)] and three with lung disfunction [protein FosB (FOSB), mothers against decapentaplegic homolog 1 (SMAD1) and forkhead box protein O1 (FOXO1)]. Interestingly, levels of some overexpressed proteins in C1 [BH3-interacting domain death agonist (BID), phosphatidylinositol 3,4,5-triphosphate 5-phosphatase 2 (INPPL1), Erbin (ERBB2IP), BANK1 and FOSB] were further related to the positivity for anti-scl70, the specific SSC-autoantibody known to be mostly associated to a bad prognosis and multiple organ involvement in SSC patients.Conclusion:1) Stratification based on serum proteomic profile could be of use for a better clinical classification of SSc patients, adding new insights to the underlying pathophysiological mechanisms.2) Combination of disease classifying autoantibodies with principal pathophysiological processes and serum proteomic profiles can help to elucidate and strengthen the diagnosis as well as the prognosis in SSC.AcknowledgementsSupported by ISCIII (RICOR-RD21/0002/0033) co-financed with FEDER.Disclosure of InterestsNone declared.
Databáze: OpenAIRE
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