Faculty Opinions recommendation of miR-145 and miR-143 regulate smooth muscle cell fate and plasticity.
Bibliographic record
Abstract
MicroRNAs (miRNAs) are regulators of myriad cellular events, but evidence for a single miRNA that can efficiently differentiate multipotent stem cells into a specific lineage or regulate direct reprogramming of cells into an alternative cell fate has been elusive. Here we show that miR-145 and miR-143 are co-transcribed in multipotent murine cardiac progenitors before becoming localized to smooth muscle cells, including neural crest stem-cell-derived vascular smooth muscle cells. miR-145 and miR-143 were direct transcriptional targets of serum response factor, myocardin and Nkx2-5 (NK2 transcription factor related, locus 5) and were downregulated in injured or atherosclerotic vessels containing proliferating, less differentiated smooth muscle cells. miR-145 was necessary for myocardin-induced reprogramming of adult fibroblasts into smooth muscle cells and sufficient to induce differentiation of multipotent neural crest stem cells into vascular smooth muscle. Furthermore, miR-145 and miR-143 cooperatively targeted a network of transcription factors, including Klf4 (Kruppel-like factor 4), myocardin and Elk-1 (ELK1, member of ETS oncogene family), to promote differentiation and repress proliferation of smooth muscle cells. These findings demonstrate that miR-145 can direct the smooth muscle fate and that miR-145 and miR-143 function to regulate the quiescent versus proliferative phenotype of smooth muscle cells. PMID: 19578358 Funding information This work was supported by: NHLBI NIH HHS, United States Grant ID: R01 HL062572 NHLBI NIH HHS, United States Grant ID: R01 HL091168 NHLBI NIH HHS, United States Grant ID: R01 HL091168-01A1 NCRR NIH HHS, United States Grant ID: C06 RR018928 NHLBI NIH HHS, United States Grant ID: HL091168 NHLBI NIH HHS, United States Grant ID: R01 HL132574 NHLBI NIH HHS, United States Grant ID: R01 HL062572-12 NHLBI NIH HHS, United States Grant ID: HL62572 More Less keyboard_arrow_down
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.456 | 0.287 |
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".