TGF-β regulates m 6 A RNA methylation after PM 2.5 exposure
Bibliographic record
Abstract
Abstract Atmospheric particulate matter exposure has adverse effects on human health, but its molecular mechanism is complex and need to be explored in depth over time. m6A RNA methylation is an important epigenetic modification that regulates gene expression at the post-transcriptional level. Our previous animal exposure studies found that PM2.5 exposure up-regulated m6A RNA methylation in lung, but the regulatory pathway is currently unclear. PM2.5 can activate transforming growth factor-β (TGF-β), and Smad2/3, a downstream factor of TGF-β, can affect m6A RNA methylation by binding to the RNA methyltransferase complex. Based on the above evidences, the current study aimed to investigate the role of TGF-β signal pathway in PM2.5-induced m6A RNA methylation through animal and A549 cell exposure model. Our results showed that PM2.5 could induce upregulation of m6A RNA methylation, accompanied by increased expression of TGF-β, Smad3, methyltransferase-like 3 (METTL3), methyltransferase-like 14 (METTL14) in both lungs of mice and A549 cell line. Furthermore, the TGF-β inhibition cellular experiments determined that PM2.5 exposure altered the level of m6A RNA methylation, expression of TGF-β, Smad3. Accordingly, it is clear that TGF-β plays an indispensable role in m6A RNA methylation after PM2.5 exposure. Our study demonstrates that PM2.5 exposure influence RNA m6A methylation through the TGF-β signal pathway.
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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".