AICAR enhances insulin signaling via downregulation of miR-29
Bibliographic record
Abstract
As an activator of AMPK, the effect of AICAR on insulin signaling has not been investigated extensively. PI3K-Akt is a critical node involved in the insulin signaling pathway. We observed that concomitant with the activation of AMPK by AICAR, the protein level of PI3K p85α and the insulin-induced phosphorylation of Akt were enhanced in mouse primary hepatocytes. Previously, we identified a group of AMPK-regulated miRNAs in hepatocytes. It is not clear whether miRNAs are related to the regulation of insulin signaling by AMPK. Here, we confirmed the negative regulation of miR-29 family members by AICAR treatment in mouse primary hepatocytes. Our results indicated that p85α is a direct target of miR-29 and is negatively regulated by miR-29b in hepatocytes. In agreement with the findings in vitro, we found that the expression of miR-29 and the protein levels of p85α were inversely correlated in the liver of fasted mice. Overexpression of miR-29b reduced the insulin-induced phosphorylation of Akt in hepatocytes, suggesting that miR-29 could serve as a negative regulator of insulin signaling. Furthermore, we found that overexpression of miR-29 could attenuate the effect of AICAR on p85α expression. Taken together, our results indicated that activation of AMPK may enhance insulin signaling via downregulation of miR-29.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".