Oxidative stress-induced senescence mediates inflammatory and fibrotic phenotypes in fibroblasts from systemic sclerosis patients
Bibliographic record
Abstract
OBJECTIVE: SSc is an autoimmune connective tissue disorder characterized by inflammation and fibrosis. Although constitutive activation of fibroblasts is proposed to be responsible for the fibrotic and inflammatory features of the disease, the underlying mechanism remains elusive, and effective therapeutic targets are still lacking. The aim of this study was to evaluate the role of oxidative stress-induced senescence and its contribution to the pro-fibrotic and pro-inflammatory phenotypes of fibroblasts from SSc patients. METHODS: Dermal fibroblasts were isolated from SSc (n = 13) and healthy (n = 10) donors. Fibroblasts' intracellular and mitochondrial reactive oxygen species (ROS) were determined by flow cytometry. Mitochondrial function was measured by Seahorse XF24 analyser. Fibrotic and inflammatory gene expressions were assessed by qPCR and key pro-inflammatory components of the fibroblasts' secretome (IL-6 and IL-8) were quantified by ELISA. RESULTS: Compared with healthy fibroblasts, SSc fibroblasts displayed higher levels of both intracellular and mitochondrial ROS. Oxidative stress in SSc fibroblasts induced the expression of fibrotic genes and activated the TGF-β-activated kinase 1 (TAK1)-IκB kinase β (IKKβ)-IFN regulatory factor 5 (IRF5) inflammatory signalling cascade. These cellular responses paralleled the presence of a DNA damage response, a senescence-associated secretory phenotype and a fibrotic response. Treatment of SSc fibroblasts with ROS scavengers reduced their pro-inflammatory secretome production and fibrotic gene expression. CONCLUSIONS: Oxidative stress-induced cellular senescence in SSc fibroblasts underlies their pro-inflammatory and pro-fibrotic phenotypes. Targeting redox imbalance of SSc fibroblasts enhances their in vitro functions and could be of relevance for SSc therapy.
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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 | 0.001 | 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) | 0.002 | 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".