MicroRNA miR-378 Regulates Nephronectin Expression Modulating Osteoblast Differentiation by Targeting GalNT-7
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small fragments of single-stranded RNA containing 18-24 nucleotides, and are generated from endogenous transcripts. MicroRNAs function in post-transcriptional gene silencing by targeting the 3'-untranslated region (UTR) of mRNAs, resulting in translational repression. We have developed a system to study the role of miRNAs in cell differentiation. We have found that one of the miRNAs tested in our system (miR-378, also called miR-378*) plays a role in modulating nephronectin-mediated differentiation in the osteoblastic cell line, MC3T3-E1. Nephronectin is an extracellular matrix protein, and we have demonstrated that its over-expression enhanced osteoblast differentiation and bone nodule formation. Furthermore, we found that the nephronectin 3'-untranslated region (3'UTR) contains a binding site for miR-378. Stable transfection of MC3T3-E1 cells with miR-378 inhibited cell differentiation. MC3T3-E1 cells stably transfected with nephronectin exhibited higher rates of differentiation and nodule formation as compared with cells transfected with nephronectin containing the 3'UTR in the early stages of development, suggesting that endogenous miR-378 is present and active. However, in the later stages of MC3T3-E1 development, the differentiation rates were opposite, with higher rates of differentiation and nodule formation in the cells over-expressing the 3'UTR of nephronectin. This appeared to be the consequence of competition between nephronectin and UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 7 (GalNAc-T7 or GalNT7) for miR-378 binding, resulting in increased GalNT7 activity, which in turn lead to increased nephronectin glycosylation and product secretion, thereby resulting in a higher rate of osteoblast differentiation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".