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Record W4411425692 · doi:10.1016/j.ard.2025.06.778

POS1431 CIRCULATING CD14+ MONOCYTES DISPLAY A PRO-MIGRATORY AND PRO-INFLAMMATORY PHENOTYPE IN INTERSTITIAL LUNG DISEASE ASSOCIATED WITH RHEUMATOID ARTHRITIS

2025· article· en· W4411425692 on OpenAlexaboutno aff
Lygia Stewart, Kieran Woolcock, Mario Ferraioli, B. Crestani, Philippe Dieudé, E. Ebstein, Madeleine Jaillet, Pierre‐Antoine Juge, Victoria Keillor, Alice Y. Tong, Daniel J. Anderson, Aurélie Najm

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatoid arthritisInterstitial lung diseaseCD14PhenotypeImmunologyLungArthritisInflammationPathologyInternal medicineImmune systemGene

Abstract

fetched live from OpenAlex

Background: Interstitial lung diseases (ILD) represent a group of heterogeneous pulmonary fibrotic diseases often associated with rheumatoid arthritis (RA). The lack of reliable diagnostic or prognostic biomarkers, standardised treatments, and poor prognosis, as well as disease heterogeneity (usual interstitial pneumonia (UIP) and non-specific interstitial pneumonitis (NSIP)) represent a substantial clinical challenge, which can have a major impact on patient wellbeing. This is further confounded by the current knowledge gap in disease pathogenesis. Current evidence suggests an important role of the myeloid compartment both in RA and ILD, particularly in idiopathic pulmonary fibrosis (IPF). Both circulating monocytes and tissue resident macrophages have been shown to contribute to disease pathogenesis by driving and maintaining inflammation in synovial tissue in RA as well as in both chronic (IPF) and acute (COVID-19) lung disease [1, 2, 3]. Objectives: The aim of our study was to analyse the transcriptional profiles of circulating monocytes in people living with RA-ILD (both UIP and NSIP) to better understand their role in disease pathogenesis compared to healthy controls and people living with RA without ILD and ILD without RA (both UIP and NSIP). Methods: We included 39 patients in the analysis (13 RA, 10 RA ILD UIP, 8 RA ILD NSIP and 8 age and sex matched HC). CD14+ Monocytes were isolated from peripheral blood mononuclear cells using a magnetic negative selection kit with CD16 depletion. We then performed RNA sequencing on the samples. Results: There were no significant differences between groups in terms of disease duration, disease activity, conventional synthetic and biologic DMARDs and glucocorticoid dose. Circulating monocytes in RA-ILD UIP display a different transcriptomic profile compared to HC (Figure 1) and RA-ILD NSIP. Interestingly, expression of several chemokines facilitating tissue migration including CCL2 (Figure 2) and CCL7 were increased in RA-ILD UIP monocytes; compared to age and sex matched HC, RA, and RA-ILD NSIP. In addition, monocytes displayed increased capability to recruit other immune cells such as neutrophils and T cells as evidenced by the increased expression of CXCL16, CXCL1, CXCL2 , and CXCL3 . Finally, monocytes in RA-ILD displayed a pro inflammatory phenotype, as demonstrated by the increased expression of IL6, LIF, HIF1A, S100A10, S100A11, ADAMTS1 . Figure 1Principal component analysis reveals global differences in transcriptomic profiles between HC and RA-ILD UIP patients. Figure 2Circulating monocytes in RA-ILD UIP display increased CCL2 expression priming them to be trafficked to tissues, compared to RA and HC. Conclusion: Circulating monocytes in RA-ILD, particularly UIP, display differential transcriptomic profiles linked to key trafficking and inflammatory genes. This suggests that these cells are primed to migrate to tissues such as joints and the lungs. Utilising this specific phenotypic state as a disease stratifying biomarker would provide therapeutic avenues to explore and improve our understanding of the pathogenesis of both RA and ILD. REFERENCES: [1] Joy GM, Arbiv OA, Wong CK, Lok SD, Adderley NA, Dobosz KM, Johannson KA, Ryerson CJ. Prevalence, imaging patterns and risk factors of interstitial lung disease in connective tissue disease: a systematic review and meta-analysis. Eur Respir Rev. 2023 Mar 8;32(167):220210. doi: 10.1183/16000617.0210-2022. PMID: 36889782; PMCID: PMC10032591. [2] Poole, Jill A. et al. "Expansion of distinct peripheral blood myeloid cell subpopulations in patients with rheumatoid arthritis-associated interstitial lung disease." International immunopharmacology 127 (2023): 111330. [3] Sullivan, D.I., Ascherman, D.P. Rheumatoid Arthritis-Associated Interstitial Lung Disease (RA-ILD): Update on Prevalence, Risk Factors, Pathogenesis, and Therapy. Curr Rheumatol Rep 26, 431–449 (2024). https://doi.org/10.1007/s11926-024-01155-8 Acknowledgements: NIL . Disclosure of Interests: Lynn Stewart: None declared, Kieran Woolcock: None declared, Mario Ferraioli: None declared, Bruno Crestani BMS, GSK, SANOFI, Menarini, Boehringer Ingelheim, Abbvie, Astra Zeneca, Chiesi, GSK, Boehringer Ingelheim, Boehringer Ingelheim, Roche, Sanofi, Philippe Dieudé: None declared, Marie-Pierre Debray: None declared, Esther Ebstein: None declared, Madeleine Jaillet: None declared, Pierre-Antoine Juge: None declared, Victoria Keillor: None declared, Andrew Tong: None declared, Emily Smith: None declared, David Anderson: None declared, Aurelie Najm: None declared. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.260
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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