A Senescence Bystander Effect in Human Lung Fibroblasts
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a chronic disease characterised by a dense fibrosing of the lung parenchyma. An association between IPF and cellular senescence is well established and several studies now describe a higher abundance of senescent fibroblasts and epithelial cells in the lungs of IPF patients compared with age-matched controls. The cause of this abnormal accumulation of senescent cells is unknown but evidence suggests that, once established, senescence can be transferred from senescent to non-senescent cells. In this study, we investigated whether senescent human lung fibroblasts (LFs) and alveolar epithelial cells (AECs) could induce a senescent-like phenotype in “naïve” non-senescent LFs in vitro. Primary cultures of LFs from adult control donors (Ctrl-LFs) with a low baseline of senescence were exposed to conditioned medium (CM) from: (i) Ctrl-LFs induced to become senescent using H2O2 or etoposide; (ii) LFs derived from IPF patients (IPF-LFs) with a high baseline of senescence; or (iii) senescence-induced A549 cells, an AEC line. Additionally, ratios of non-senescent Ctrl-LFs and senescence-induced Ctrl-LFs (100:0, 0:100, 50:50, 90:10, 99:1) were co-cultured and their effect on induction of senescence measured. We demonstrated that exposure of naïve non-senescent Ctrl-LFs to CM from senescence-induced Ctrl-LFs and AECs and IPF-LFs increased the markers of senescence including nuclear localisation of phosphorylated-H2A histone family member X (H2AXγ) and expression of p21, IL-6 and IL-8 in Ctrl-LFs. Additionally, co-cultures of non-senescent and senescence-induced Ctrl-LFs induced a senescent-like phenotype in the non-senescent cells. These data suggest that the phenomenon of “senescence-induced senescence” can occur in vitro in primary cultures of human LFs, and provides a possible explanation for the abnormal abundance of senescent cells in the lungs of IPF patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
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".