The Transcriptomic Profile of Monocytes from Patients With Sjögren’s Syndrome Is Associated With Inflammatory Parameters and Is Mimicked by Circulating Mediators
Bibliographic record
Abstract
monocytes from patients with pSS, non-Sjögren's sicca (nSS), and healthy controls (HC). We demonstrated that the transcriptomic profile of pSS patients is enriched in intermediate and non-classical monocyte profiles, and confirmed the increased frequency of non-classical monocytes in pSS patients by flow-cytometry analysis. Weighted gene co-expression network analysis identified four molecular signatures in monocytes from pSS patients, functionally annotated for processes related with translation, IFN-signaling, and toll-like receptor signaling. Systemic and local inflammatory features significantly correlated with the expression of these signatures. Furthermore, genes highly associated with clinical features in pSS were identified as hub-genes for each signature. Unsupervised hierarchical cluster analysis of the hub-genes identified four clusters of nSS and pSS patients, each with distinct inflammatory and transcriptomic profiles. One cluster showed a significantly higher percentage of pSS patients with higher prevalence of anti-SSA autoantibodies, interferon-score, and erythrocyte sedimentation rate compared to the other clusters. Finally, we showed that the identified transcriptomic differences in pSS monocytes were induced in monocytes of healthy controls by exposure to serum of pSS patients. Representative hub-genes of all four signatures were partially inhibited by interferon-α/β receptor blockade, indicating that the circulating inflammatory mediators, including type I interferons have a significant contribution to the altered transcriptional profile of pSS-monocytes. Our study suggests that targeting key circulating inflammatory mediators, such as type I interferons, could offer new insights into the important pathways and mechanisms driving pSS, and holds promise for halting immunopathology in Sjögren's Syndrome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".