The Flip Side of the Coin: METTL3 Serves as a Novel Cellular Senescence Accelerator via Negative Regulation of ITGA9
Bibliographic record
Abstract
N6-Methyladenosine (m6A), a prevalent and dynamically regulated chemical modification, has recently emerged as a crucial post-transcriptional regulator of gene expression, and affected diverse eukaryotic biological processes. However, the role of m6A modification in aging research was still rarely reported. Herein, we uncovered that both the m6A modification level and the expression level of the methyltransferase METTL3 were significantly elevated during the aging process, as observed in the physiological aging mouse model in vivo, and the cellular senescence model in vitro. Furthermore, the silencing of METTL3 staved off the senescent phenotype of MEF cells, as evidenced by the downregulation of p16, decreased β-galactosidase activity and enhanced cell proliferative capacity, while METTL3 overexpression accelerated cellular senescence. Subsequently, a METTL3 transgenic mouse was generated, which exhibited a more pronounced senescence phenotype and a shortened lifespan. To deepen into the understanding of the molecular mechanisms of m6A and METTL3 in the aging process, high-throughput MeRIP sequencing was performed on young and senescent MEFs, and identified ITGA9 as a critical downstream m6A target, which might be negatively regulated by m6A modification or METTL3 through translation inhibition. And loss- or gain-of-function experiments unveiled that ITGA9 remarkably delayed the senescence of MEF cells. Additionally, the inhibition of ITGA9 reversed the impact of METTL3 silencing on delaying senescence, while ITGA9 overexpression counteracted the effect of ectopic expression of METTL3 on advancing cellular senescence. In aggregate, our data suggested that METTL3 promoted cellular senescence by m6A-dependent translational suppression of ITGA9, which was of great significance to alleviate the organismal aging process and age-related diseases.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".