Stratification According to Autoantibody Status in Systemic Sclerosis Reveals Distinct Molecular Signatures

Bénédicte ROUVIERE¹, Christelle Le dantec², Eléonore Bettacchioli³, Lorenzo Beretta⁴, Céline Cao², Christophe Jamin², Jacques-Olivier Pers⁵, Nathan Foulquier², Martin Kerick⁶, Javier Martin⁶, Marta Alarcon-Riquelme⁷, Claire de Moreuil⁸, Divi Cornec³ and Sophie Hillion², and PRECISESADS Clinical Consortium, PRECISESADS Metabolomic Study Group, ¹CHRU de Brest, LBAI. UMR 1227, University of Brest, Brest, France, ²LBAI. UMR 1227, University of Brest, Brest, France, ³Service de Rhumatologie, CHU de Brest, Brest, France, ⁴Scleroderma Unit, Fondazione IRCCS Ca' Granda, University of Milan, Milan, Italy, ⁵University of Brest, Brest, France, ⁶Instituto de Parasitología y Biomedicina 'López-Neyra', CSIC, PTS Granada, Spain, Granada, Spain, ⁷Fundación Progreso y Salud, Andalusian Government, Granada, Spain, ⁸Service de médecine interne, CHU de Brest, Brest, France

Meeting: ACR Convergence 2024

Keywords: autoantigens, Systemic sclerosis

Session Information

Date: Sunday, November 17, 2024

Title: Systemic Sclerosis & Related Disorders – Basic Science Poster I

Session Type: Poster Session B

Session Time: 10:30AM-12:30PM

Background/Purpose: Systemic sclerosis is a heterogeneous disease, complicating its management. Its complexity and the insufficiency of clinical manifestations alone to delineate homogeneous patient groups further challenge this task. However, autoantibodies could serve as relevant markers for the pathophysiological mechanisms driving the disease. Identifying specific immunological mechanisms based on patients’ serological statuses might facilitate a deeper understanding of the diversity of the disease.

Methods: A cohort of 206 systemic sclerosis patients enrolled in the PRECISESADS cross-sectional study was examined. Patients were stratified based on their anti-centromere (ACA) and anti-SCL70 (SCL70) antibody statuses. Comprehensive omics analyses, including transcriptomic, flow cytometric, cytokine, and metabolomic data, were analyzed to characterize the differences between these patient groups.

Results: Patients with SCL70 antibodies showed severe clinical features such as diffuse cutaneous sclerosis and pulmonary fibrosis, and were biologically distinguished by unique transcriptomic profiles. They exhibit a pro-inflammatory and fibrotic biologic signature associated with impaired tissue remodeling and increased carnitine metabolism. Conversely, ACA-positive patients exhibited an immunomodulation and tissue homeostasis signature and increased phospholipid metabolism.

Conclusion: Systemic sclerosis patients display varying biological profiles based on their serological status. The findings highlight the potential utility of serological status as a discriminating factor in disease severity and suggest its relevance in tailoring treatment strategies and future research directions.

A) Expression levels of each DEGs in every patient are visualized in a heatmap for each Ab group after centering and scaling the data. Each column corresponds to a patient, and each row represents a DEGs. The annotation at the top of the heatmap distinguishes the groups: orange for anti-SLC70-positive patients, blue for anti-ACA-positive patients, red for treated patients and green for untreated. (B) Venn diagram analysis of the different comparisons of patients group (SCL, ACA, Untreated SCL, Untreated ACA) with controls.

The BloodGen3Module R package, developed by Rinchai et al., facilitates group comparison analysis at a gene module-level, defining 382 modules covering 14,168 transcripts based on co-clustering observations across 16 different states, including autoimmune and infectious diseases, primary immune deficiencies, cancer, and pregnancy, representing 985 unique transcriptome profiles. Within these 382 modules (gene sets), a reduced set of 38 aggregate modules (A1 to A38) was created, consisting of module sets and functionally annotated pathways, ontologies, and literature term enrichments (figure on the right). The annotated fingerprint grid plots show the percentage of genes in each module with increased expression (red spot), decreased expression (blue spot), or no change (white spot), applying thresholds of an absolute fold change cutoff of >1.5 and a false discovery rate-adjusted p-value of <0.05. When no consensus annotation is attributed, the position on the annotation grid is colored in white, and the grey corresponds to a position with no module attributed. The figure on the left shows the comparaison between SCL70 patients and ACA patients

Disclosures: B. ROUVIERE: None; C. Le dantec: None; E. Bettacchioli: None; L. Beretta: None; C. Cao: None; C. Jamin: None; J. Pers: None; N. Foulquier: None; M. Kerick: None; J. Martin: None; M. Alarcon-Riquelme: None; C. de Moreuil: GlaxoSmithKlein(GSK), 6; D. Cornec: None; S. Hillion: None.

To cite this abstract in AMA style:

ROUVIERE B, Le dantec C, Bettacchioli E, Beretta L, Cao C, Jamin C, Pers J, Foulquier N, Kerick M, Martin J, Alarcon-Riquelme M, de Moreuil C, Cornec D, Hillion S. Stratification According to Autoantibody Status in Systemic Sclerosis Reveals Distinct Molecular Signatures [abstract]. Arthritis Rheumatol. 2024; 76 (suppl 9). https://acrabstracts.org/abstract/stratification-according-to-autoantibody-status-in-systemic-sclerosis-reveals-distinct-molecular-signatures/. Accessed .

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