Deletion of METTL14, a key methylation regulator, attenuates vascular ageing
Bibliographic record
Abstract
BACKGROUND AND AIMS: Vascular ageing often accompanies inflammation, contributing to the onset of local or systemic vascular diseases. Nevertheless, limited research focuses on pivotal factors triggering chronic vascular inflammation and associated pathological changes. This study aimed to investigate the role of methyltransferase-like protein 14 (METTL14) in inflammation in the pathogenesis of vascular ageing. METHODS: The natural ageing mouse model, D-galactose induced ageing mouse model, and endothelial cell-specific METTL14 knockout mice were generated. The roles of METTL14 in vascular ageing were investigated in human, mice, and various endothelial cells. RESULTS: Up-regulation of METTL14 was observed in the aortic endothelial cells of aged mice, aged humans, and senescent human umbilical vein endothelial cells, human aortic endothelial cells, and mice aortic endothelial cells. Endothelium-specific knockdown or knockout of METTL14 notably inhibited arterial stiffness, arterial remodelling, and endothelial senescence, whereas endothelium-specific overexpression of METTL14 yielded opposing effects. At the cellular level, METTL14 knockdown ameliorated cellular senescence, inflammatory responses, and oxidative stress in senescent endothelial cells. Mechanistically, METTL14 facilitated m6A modification of Toll-like receptor 4 (TLR4) mRNA, thereby enhancing its stability. Knockdown of TLR4 reversed the detrimental effects of METTL14 on vascular ageing. Importantly, vascular ageing, along with related atherosclerosis and arteriosclerosis, positively correlated with blood METTL14 and TLR4 elevations in humans. CONCLUSIONS: This study hints at the role of METTL14/TLR4 signalling in the pathogenesis of vascular ageing, and METTL14 knockdown emerges as a potential therapeutic strategy for mitigating vascular ageing and associated vascular 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".