Cadmium exposure following early-life respiratory syncytial virus infection promotes lung fibrosis through autophagy inhibition
Bibliographic record
Abstract
Early-life respiratory syncytial virus (RSV) infection (eRSV) is a major cause of severe respiratory illness in children and increases the risk of asthma and lung dysfunction later in life. Cadmium (Cd), a toxic environmental metal, exacerbates these risks when combined with eRSV. Our previous research demonstrated that eRSV reprograms lung metabolism and amplifies Cd toxicity, driving inflammation, and metabolic disruption through protein palmitoylation. Recent studies showed that inhibiting mTORC1 with rapamycin (Rapa) mitigates Cd-induced metabolic disruption and profibrotic signaling in lung fibroblasts. In this study, we employed a mouse model to investigate the role of mTORC1 in mediating the effects of chronic low-dose Cd exposure (3.3 mg CdCl2/L in drinking water for 16 wk) following eRSV (eRSV+Cd). The impact of mTORC1 inhibition was assessed using Rapa (14 ppm), with downstream autophagy markers analyzed as indicators of mTORC1 activity. Mice in the eRSV+Cd group showed significantly elevated levels of cytokines, chemokines, inflammatory cells, and collagen deposition, indicating stimulation of inflammation and fibrosis. Rapa treatment markedly reduced these pathological markers. Metabolomic profiling and single-cell RNA sequencing revealed disruptions in autophagy-associated metabolites and genes in the eRSV+Cd group, which were reversed by Rapa. Taken together, this study highlights the critical role of the mTORC1 activation-autophagy inhibition pathway in mediating the exacerbated inflammatory response and lung fibrosis induced by Cd exposure following eRSV. These results underscore the potential of targeting the mTORC1-autophagy pathway with Rapa as a therapeutic strategy to mitigate lung damage in individuals affected by these environmental and infectious insults.
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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".