MicroRNA-130a Contributes to Type-2 Classical DC-activation in Sjögren's Syndrome by Targeting Mitogen- and Stress-Activated Protein Kinase-1
Bibliographic record
Abstract
Objectives. Considering the critical role of microRNAs (miRNAs) in regulation of cell activation, we investigated their role in circulating type-2 conventional dendritic cells (cDC2s) of patients with primary Sjögren’s syndrome (pSS) compared to healthy controls (HC). Methods. CD1c-expressing cDC2s were isolated from peripheral blood. A discovery cohort (15 pSS, 6 HC) was used to screen the expression of 758 miRNAs and a replication cohort (15 pSS, 11 HC) was used to confirm differential expression of 18 identified targets. Novel targets for two replicated miRNAs were identified by SILAC in HEK-293T cells and validated in primary cDC2s. Differences in cytokine production between pSS and HC cDC2s were evaluated by intracellular flow-cytometry. cDC2s were cultured in the presence of MSK1-inhibitors to investigate their effect on cytokine production. Results. Expression of miR-130a and miR-708 was significantly decreased in cDC2s from pSS patients compared to HC in both cohorts, and both miRNAs were downregulated upon stimulation via endosomal TLRs. Upstream mediator of cytokine production MSK1 was identified as a novel target of miR-130a and overexpression of miR-130a reduced MSK1 expression in cDC2s. pSS cDC2s showed higher MSK1 expression and an increased fraction of IL-12 and TNF-α-producing cells. MSK1-inhibition reduced cDC2 activation and production of IL-12, TNF-α and IL-6. Conclusions. The decreased expression of miR-130a and miR-708 in pSS cDC2s seems to reflect cell activation. miR-130a targets MSK1, which regulates pro-inflammatory cytokine production, and we provide proof-of-concept for MSK1-inhibition as a therapeutic avenue to impede cDC2 activity in pSS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".