MiR-106a-5p modulates apoptosis and metabonomics changes by TGF-β/Smad signaling pathway in cleft palate
Bibliographic record
Abstract
BACKGROUND: The molecular mechanisms of abnormal palatogenesis were investigated in this study. A key regulator, miR-106a-5p, and its target pathway were analyzed. OBJECTIVES: This research is trying to clarify the underlying mechanism of the modulation of miRNA transcription during the formation of cleft palate by 7T and 9.4T NMR metabolomic platforms. METHOD: Differentially expressed miRNAs and mRNAs were analyzed by microarray analysis and verified by qRT-PCR. The protein expression in TGFβ signaling pathways were analyzed by Western Blotting. The relationship between miR-106a-5p and TGFβ were analyzed by luciferase reporter assay. Cell apoptosis were analyzed by flow cytometer. And finally, the metabonomics were analyzed by NMR and multivariate data analysis models (MVDA). RESULTS: The expression of miR-106a-5p increased in cleft palatal tissue and negatively correlated with the protein level of Tgfbr2. The luciferase assay further proved that the tgfbr2 was a direct target of miR-106a-5p. In another aspect, miR-106a-5p increased apoptosis level in palatal mesenchymal cells, possibly because its inhibition of TGFβ signaling pathway. Moreover, low cholesterol and choline levels with high citric acid and lipid levels were observed by 7T and 9.4T NMR metabonomic analysis, which inferred the disorder of cell membrane synthesis in cleft palate formation. Furthermore, transformation from choline to phosphatidylcholine regulated by miR-106a-5p was also disrupted, resulting in phosphatidic choline synthesis disorder and reduced cell membrane synthesis. CONCLUSIONS: The regulatory mechanism of cleft palate was studied at transcriptional and metabolomics levels, which may provide important information in understanding the primary cause of this abnormality.
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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".