The let-7f-5p–Nme4 pathway mediates tumor necrosis factor α-induced impairment in osteogenesis of bone marrow-derived mesenchymal stem cells
Bibliographic record
Abstract
Although tumor necrosis factor α (TNF-α)-mediated inflammation significantly impacts osteoporosis, the mechanisms underlying the osteogenic differentiation defects of bone marrow-derived mesenchymal stem cells (BM-MSCs) caused by TNF-α remain poorly understood. We found that TNF-α stimulation of murine BM-MSCs significantly upregulated the expression levels of several microRNAs (miRNAs), including let-7f-5p, but this increase was significantly reversed by treatment with the kinase inhibitor BAY 11-7082. To study gain- or loss of function, we transfected cells with an miRNA inhibitor or miRNA mimic. We then demonstrated that let-7f-5p impaired osteogenic differentiation of BM-MSCs in the absence and presence of TNF-α, as evidenced by alkaline phosphatase and alizarin red staining as well as quantitative assays of the mRNA levels of bone formation marker genes in differentiated BM-MSCs. Moreover, let-7f-5p targets the 3' untranslated region of Nucleoside diphosphate kinase 4 (Nme4) mRNA and negatively regulates Nme4 expression in mouse BM-MSCs. Ectopic expression of Nme4 completely reversed the inhibitory effects of the let-7f-5p mimic on osteogenic differentiation of mouse BM-MSCs. Furthermore, inhibition of let-7f-5p or overexpression of Nme4 in BM-MSCs restored in-vivo bone formation in an ovariectomized animal model. Collectively, our work indicates that let-7f-5p is involved in TNF-α-mediated reduction of BM-MSC osteogenesis via targeting Nme4.
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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) | 0.001 | 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 | 0.001 |
| 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".