Metformin induces the AP-1 transcription factor network in normal dermal fibroblasts
Bibliographic record
Abstract
Metformin is a widely-used treatment for type 2 diabetes and is reported to extend health and lifespan as a caloric restriction (CR) mimetic. Although the benefits of metformin are well documented, the impact of this compound on the function and organization of the genome in normal tissues is unclear. To explore this impact, primary human fibroblasts were treated in culture with metformin resulting in a significant decrease in cell proliferation without evidence of cell death. Furthermore, metformin induced repositioning of chromosomes 10 and 18 within the nuclear volume indicating altered genome organization. Transcriptome analyses from RNA sequencing datasets revealed that alteration in growth profiles and chromosome positioning occurred concomitantly with changes in gene expression profiles. We further identified that different concentrations of metformin induced different transcript profiles; however, significant enrichment in the activator protein 1 (AP-1) transcription factor network was common between the different treatments. Comparative analyses revealed that metformin induced divergent changes in the transcriptome than that of rapamycin, another proposed mimetic of CR. Promoter analysis and chromatin immunoprecipitation assays of genes that changed expression in response to metformin revealed enrichment of the transcriptional regulator forkhead box O3a (FOXO3a) in normal human fibroblasts, but not of the predicted serum response factor (SRF). Therefore, we have demonstrated that metformin has significant impacts on genome organization and function in normal human fibroblasts, different from those of rapamycin, with FOXO3a likely playing a role in this response.
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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".