Global miRNA expression and correlation with mRNA levels in primary human bone cells
Bibliographic record
Abstract
MicroRNAs (miRNAs) are important post-transcriptional regulators that have recently introduced an additional level of intricacy to our understanding of gene regulation. The aim of this study was to investigate miRNA-mRNA interactions that may be relevant for bone metabolism by assessing correlations and interindividual variability in miRNA levels as well as global correlations between miRNA and mRNA levels in a large cohort of primary human osteoblasts (HOBs) obtained during orthopedic surgery in otherwise healthy individuals. We identified differential expression (DE) of 24 miRNAs, and found 9 miRNAs exhibiting DE between males and females. We identified hsa-miR-29b, hsa-miR-30c2, and hsa-miR-125b and their target genes as important modulators of bone metabolism. Further, we used an integrated analysis of global miRNA-mRNA correlations, mRNA-expression profiling, DE, bioinformatics analysis, and functional studies to identify novel target genes for miRNAs with the potential to regulate osteoblast differentiation and extracellular matrix production. Functional studies by overexpression and knockdown of miRNAs showed that, the differentially expressed miRNAs hsa-miR-29b, hsa-miR-30c2, and hsa-miR-125b target genes highly relevant to bone metabolism, e.g., collagen, type I, α1 (COL1A1), osteonectin (SPARC), Runt-related transcription factor 2 (RUNX2), osteocalcin (BGLAP), and frizzled-related protein (FRZB). These miRNAs orchestrate the activities of key regulators of osteoblast differentiation and extracellular matrix proteins by their convergent action on target genes and pathways to control the skeletal gene expression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".