Glucose Is Involved in the Dynamic Regulation of m6A in Patients With Type 2 Diabetes
Bibliographic record
Abstract
Context: N6-methyladenosine (m6A) in mRNA is the most abundant and reversible modification. However, the mechanism behind the decrease in m6A in patients with type 2 diabetes (T2D) has not yet been thoroughly investigated. Objective: To clarify whether glucose is involved in the dynamic regulation of m6A in T2D and to identify a possible underlying mechanism. Methods: Liquid chromatography/electrospray ionization/tandem mass spectrometry and quantitative PCR were performed to determine the m6A content and the mRNA expression of target genes in 102 patients with T2D and 107 controls. An additional 12 patients with normal fasting blood glucose, emergency hyperglycemia, or emergency hypoglycemia, as well as HepG2 cells with high-glucose treatment and FTO knockout or overexpression were used to confirm the initial observations in patients. Results: In patients with T2D, the m6A content was decreased, and mRNA expression levels of FTO, METTL3, METTL14, and WTAP were increased. Interestingly, the m6A content was negatively associated with mRNA expression levels of METTL3, METTL14, and FTO. Moreover, FTO was positively correlated with serum glucose. In HepG2 cells, high glucose upregulated FTO protein, whereas it had no significant effect on METTL3 or METTL14. Additionally, mRNA expression levels of FOXO1, G6PC, and DGAT2 were significantly increased and positively correlated with FTO and serum glucose in patients. Conclusions: Our data revealed that in patients with T2D, high-glucose-enhanced FTO mRNA expression resulted in a decrease in m6A. The lower m6A content might be responsible for the upregulation of methyltransferases. Additionally, FTO induced mRNA expression of FOXO1, G6PC, and DGAT2 and was closely associated with glucose metabolism.
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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 | 0.001 | 0.001 |
| 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".