MicroRNA miR-23a cluster promotes osteocyte differentiation by regulating TGF-β signalling in osteoblasts
Bibliographic record
Abstract
Osteocytes are the terminally differentiated cell type of the osteoblastic lineage and have important functions in skeletal homeostasis. Although the transcriptional regulation of osteoblast differentiation has been well characterized, the factors that regulate differentiation of osteocytes from mature osteoblasts are poorly understood. Here we show that miR-23a∼27a∼24-2 (miR-23a cluster) promotes osteocyte differentiation. Osteoblast-specific miR-23a cluster gain-of-function mice have low bone mass associated with decreased osteoblast but increased osteocyte numbers. By contrast, loss-of-function transgenic mice overexpressing microRNA decoys for either miR-23a or miR-27a, but not miR24-2, show decreased osteocyte numbers. Moreover, RNA-sequencing analysis shows altered transforming growth factor-β (TGF-β) signalling. Prdm16, a negative regulator of the TGF-β pathway, is directly repressed by miR-27a with concomitant alteration of sclerostin expression, and pharmacological inhibition of TGF-β rescues the phenotypes observed in the gain-of-function transgenic mice. Taken together, the miR-23a cluster regulates osteocyte differentiation by modulating the TGF-β signalling pathway through targeting of Prdm16.
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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".