The profibrotic and senescence phenotype of old lung fibroblasts is reversed or ameliorated by genetic and pharmacological manipulation of Slc7a11 expression
Bibliographic record
Abstract
Increased senescence and expression of profibrotic genes in old lung fibroblasts contribute to disrepair responses. We reported that primary lung fibroblasts from old mice have lower expression and activity of the cystine transporter Slc7a11/xCT than cells from young mice, resulting in changes in both the intracellular and extracellular redox environments. This study examines the hypothesis that low Slc7a11 expression in old lung fibroblasts promotes senescence and profibrotic gene expression. The levels of mRNA and protein of Slc7a11, senescence markers, and profibrotic genes were measured in primary fibroblasts from the lungs of old (24 mo) and young (3 mo) mice. In addition, the effects of genetic and pharmacological manipulation of Slc7a11 were investigated. We found that decreased expression of Slc7a11 in old cells was associated with elevated markers of senescence (p21, p16, p53, and β-galactosidase) and increased expression of profibrotic genes (Tgfb1, Smad3, Acta2, Fn1, Col1a1, and Col5a1). Silencing of Slc7a11 in young cells replicated the aging phenotype, whereas overexpression of Slc7a11 in old cells decreased expression of senescence and profibrotic genes. Young cells were induced to express the senescence and profibrotic phenotype by sulfasalazine, a Slc7a11 inhibitor, whereas treatment of old cells with sulforaphane, a Slc7a11 inducer, decreased senescence without affecting profibrotic genes. Like aging cells, idiopathic pulmonary fibrosis fibroblasts show decreased Slc7a11 expression and increased profibrotic markers. In short, old lung fibroblasts manifest a profibrotic and senescence phenotype that is modulated by genetic or pharmacological manipulation of Slc7a11.
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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".