Multimodal Interrogation of Autoimmune Disease Patients Reveals Highly Diverse Biomarker Signatures 2259778

Pan-disease blood protein profiles of rheumatic autoimmune diseases

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.

Phenotypic and Metabolic Signatures in Chronic Autoimmune Inflammation 2308477

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by systemic chronic inflammation that can lead to heterogeneous clinical manifestations, from mild cutaneous disease to multi-organ damage and failure. Understanding how immune cell composition, their activation status, and metabolic changes contribute to disease is important to identify new biomarkers and therapeutic targets. In this study, we combined spectral flow cytometry and CITE-Seq to profile immune cell subsets and metabolic changes at the single-cell level. Peripheral white blood cells from SLE patients, psoriatic arthritis (PsA) patients, and healthy donors were analyzed using a 50-marker panel that evaluated immune subsets, activation and exhaustion markers, including metabolic dependencies by using SCENITH. The same antibody panel was used in CITE-Seq to validate flow cytometric findings and explore additional metabolic and inflammation pathways. High-dimensional computational analysis was used to identify immune subpopulations with specific metabolic signatures that distinguished SLE from PsA and healthy individuals. Across 44 immune cell populations, we observed significant immunophenotypic and immunometabolic changes in both diseases. B cell analysis revealed altered frequencies, different translational activity, and metabolic dependence in early developmental and antigen-experienced cell subsets. SCENITH profiling showed distinct metabolic signatures in CD4+ and CD8+ T cells, with more pronounced differences in naive and memory subsets. In fact, in SLE, T cells exhibited strong dependence on OXPHOS and limited sensitivity to glycolytic inhibition. Finally, PsA patients presented increased NKT-cell frequencies, indicating an involvement of lipid antigens. Overall, these findings describe several immune and metabolic dysregulations in SLE and PsA patients, which could be used as potential biomarkers for disease progression and therapeutic intervention. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project 321115009 (GRK 2336/3) Basic Autoimmunity (BA)

Distinct Multiomic Signatures of Environmental Exposure Drive Immune Dysregulation and Disease Activity in Early Multiple Sclerosis

Background and Objectives Environmental factors are pivotal in determining multiple sclerosis (MS) susceptibility, yet their specific influence on the immune system at the time of diagnosis remains incompletely understood. The aim of this study was to map the systemic effects of modifiable risk factors, specifically smoking, body mass index (BMI), serum vitamin D, UV exposure, and latent viral immunity, on immune function in patients with treatment-naive MS. Methods A multiomics analysis was performed on a monocentric cohort of 96 treatment-naive patients. The study used a combination of bulk transcriptomics across 6 immune cell lineages, serum proteomics, cell-cell communication modeling, and high-dimensional flow cytometry to identify unique immunologic patterns linking environmental exposures to disease pathology. Results Smoking was found to induce an upregulation of the tissue-homing receptor GPR15 across lineages, particularly in NK cells, which correlated with higher disability levels. Smoking also amplified a proinflammatory milieu (increased CXCL17 and LAMP3). Higher potential UV exposure seemed to counteract these effects by lowering IFI30 and LYZ expression; notably, elevated IFI30 correlated with the presence of gadolinium-enhancing lesions. Regarding metabolic factors, high BMI exerted a linear effect on monocyte metabolism, driving cholesterol biosynthesis (MVD and SQLE) and increasing proinflammatory markers (FABP4 and LEP) associated with blood-brain barrier compromise. Discussion These findings elucidate the molecular mechanisms by which modifiable risk factors contribute to MS pathology. The data imply distinct clinical approaches for risk management: while smoking cessation must likely be absolute to reverse pathogenic trafficking signals (such as GPR15 upregulation), the linear effect of BMI suggests that incremental weight loss confers proportional immunologic benefits. These insights strongly support the integration of lifestyle interventions as a core component of early MS management.

Autoimmune disease patients with comorbid cancer exhibit prominent immunometabolic crosstalk.

Role of IFIT1 and IFIT3 in systemic lupus erythematosus: modeling a diagnosis and exploring immune regulation

IFIT1 and IFIT3 represent promising biomarkers for diagnosing SLE and appear to mediate key immune and metabolic disturbances and serve as an accurate and reliable instrument for early diagnosis and personalized therapy.

Complement Pathway Heterogeneity in Rheumatoid Arthritis Uncovered Through Longitudinal Serum Proteomics

Rheumatoid arthritis (RA) is a heterogeneous autoimmune disease. Despite established serological classification and advances in therapeutic strategies, around 40% of patients exhibit inadequate responses to first-line DMARDs. Current clinical tools remain limited in their ability to predict treatment response and risk of joint erosion, posing a significant challenge to precision medicine. There is a critical need for novel biomarkers that can reliably forecast disease outcomes and guide therapeutic decisions. Integrating protein signatures with clinical features may enhance risk stratification in rheumatic diseases.[1] In this study, we employed data-independent acquisition mass spectrometry (DIA-MS) to perform a comprehensive longitudinal serum proteomic analysis in RA patients, aiming to identify biomarkers predictive of disease activity and progression. This study was conducted on serum samples collected from 107 DMARD- and steroid-free RA patients (48 seronegative, 59 seropositive) enrolled in the EUPA cohort ( NCT00512239 ), between 2005 and 2019, at the CIUSSS de l’Estrie-CHUS, Québec, Canada.[2] Serial sera collected at baseline and at the 12-month follow-up visit were analyzed by DIA-MS. MS data were analyzed using DIA-NN software for peptides/proteins identification and quantification. Statistical analyses were performed using R: Differentially abundant proteins (DAPs) across clusters were identified by Mann-Whitney U test with Bonferroni correction. KEGG enrichment analyses were conducted using the PathfindR R package, with BH correction. Generalized estimating equations (GEE) models were adjusted for age, sex, serology, and symptom duration, with FDR correction using Storey’s q-value. Proteomic profiling quantified 869 serum proteins, of which 368 passed quality control and filtering for downstream statistical analyses. 1-Principal component analysis (PCA) followed by hierarchical clustering of baseline (Figure 1A), and 12-month proteomic data suggested patients could be grouped into 2 clusters. 2-KEGG pathway analysis of DAPs between patient clusters showed significant enrichment of complement and coagulation cascades pathway. 3-These clusters exhibited distinct serum levels of complement-related proteins (Figure 1B). 4-C68omparison of patient trajectories between baseline and 12-month serum-based clusters (Figure 1C), revealed that transitions between clusters were accompanied by significant modulation of complement-related protein levels (Figure 1D). 5-GEE models identified 29 proteins (q-value ≤0.05) associated with binomial 12-month disease activity, defined as DAS28-CRP ≤2.6 or ≥3.2, which were enriched for KEGG complement and coagulation cascades pathway. Figure 1. A) Hierarchical Clustering on PCA was performed on RA patients’ baseline serum proteome. B) Baseline serum complement pathway (KEGG hsa04610) protein levels were normalized by z-scores. Average z-scores per patient are shown as a color gradient over PCA plot. C) Sankey plot of patient trajectories between baseline and 12-month proteome-based clusters. D) Average serum complement proteins z-scores of patients grouped by cluster trajectories BH-adjusted p ≤ 0.05 = * and ≤ 0.001 = ***. This study highlights the heterogeneity of circulating complement components in early RA patients, suggesting that complement protein profiles may reflect underlying disease mechanisms beyond general inflammation and could inform future biomarker-driven approaches to RA management. [1.] Carrasco-Zanini J. Nat Med 2024;30:2489-98. [2.] Carrier N. J Rheumatol 2024;52:119-27.