Adipogenic differentiation of human mesenchymal stromal cells is down‐regulated by microRNA‐369‐5p and up‐regulated by microRNA‐371
Bibliographic record
Abstract
Long-term culture of human mesenchymal stromal cells (MSC) has implications on their proliferation and differentiation potential and we have demonstrated that this is associated with up-regulation of the five microRNAs miR-29c, miR-369-5p, miR-371, miR-499, and let-7f. In this study, we examined the role of these senescence-associated microRNAs for cellular aging and differentiation of MSC. Proliferation was reduced upon transfection with miR-369-5p, miR-371, and miR-499. Adipogenic differentiation was impaired by miR-369-5p whereas it was highly increased by miR-371. This was accompanied by respective gene expression changes of some adipogenic key molecules (adiponectin and fatty acid-binding protein 4 [FABP4]). Furthermore luciferase reporter assay indicated that FABP4 is a direct target of miR-369-5p. Microarray analysis upon adipogenic or osteogenic differentiation revealed down-regulation of several microRNAs albeit miR-369-5p and miR-371 were not affected. Expression of the de novo DNA methyltransferases DNMT3A and DNMT3B was up-regulated by transfection of miR-371 whereas expression of DNMT3A was down-regulated by miR-369-5p. In summary, we identified miR-369-5p and miR-371 as antagonistic up-stream regulators of adipogenic differentiation and this might be indirectly mediated by epigenetic modifications.
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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 | 0.001 |
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".