Senolytic treatment to rescue hallmarks of senescence in lymph node fibroblasts from patients with rheumatoid arthritis: Implications for premature aging and potential therapeutic intervention in early rheumatoid arthritis
Bibliographic record
Abstract
Cellular senescence, a state of proliferation arrest, is implicated in the pathogenesis of age-related diseases such as rheumatoid arthritis (RA). The pathogenesis of RA, characterized by immune dysregulation and systemic autoimmunity preceding clinical onset of disease, may involve early accumulation of senescent lymph node (LN) fibroblasts driving immune tolerance breakdown. This study aims to explore the hallmarks of senescence in LN fibroblasts during the earliest phases of RA and evaluate the effects of dasatinib. Human LN fibroblasts were isolated from inguinal LN needle biopsies from autoantibody-positive individuals at risk of developing RA (RA-risk individuals), RA patients, and seronegative healthy volunteers. Senescence hallmarks and the effects of dasatinib treatment were assessed using quantitative gene expression analysis, flow cytometry, microscopy, and live-cell imaging. Cell size, granularity, and autofluorescence were significantly greater in RA LN fibroblasts compared with controls. Altered gene expression of senescence-associated genes was observed in RA LN fibroblasts. Elevated senescence-associated β-galactosidase activity, more lipofuscin-positive granules, and DNA damage were observed in RA-risk and RA LN fibroblasts. Notably, RA(-risk) LN fibroblasts presented impaired DNA damage repair capacity. Dasatinib treatment significantly improved the size and ability of the LN fibroblast pool to repair DNA damage. We observed multiple senescence hallmarks in RA LN fibroblasts and, to a lesser extent, in RA-risk LN fibroblasts, which could be partially restored by senescent cell removal via dasatinib treatment. These findings suggest a role for senescent LN fibroblasts in RA pathogenesis and highlight the potential of dasatinib as a potential therapeutic intervention to mitigate senescence-associated defects in RA.
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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) | 0.001 | 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 | 0.001 |
| 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".