Lipid nanoparticle-mediated silencing of osteogenic suppressor GNAS leads to osteogenic differentiation of mesenchymal stem cells in vivo
Bibliographic record
Abstract
Approved drugs for the treatment of osteoporosis can prevent further bone loss but do not stimulate bone formation. Approaches that improve bone density in metabolic diseases are needed. Therapies that take advantage of the ability of mesenchymal stem cells (MSCs) to differentiate into various osteogenic lineages to treat bone disorders are of particular interest. Here we examine the ability of small interfering RNA (siRNA) to enhance osteoblast differentiation and bone formation by silencing the negative suppressor gene GNAS in bone MSCs. Using clinically validated lipid nanoparticle (LNP) siRNA delivery systems, we show that silencing the suppressor gene GNAS in vitro in MSCs leads to molecular and phenotypic changes similar to those seen in osteoblasts. Further, we demonstrate that these LNP-siRNAs can transfect a large proportion of mice MSCs in the compact bone following intravenous injection. Transfection of MSCs in various animal models led to silencing of GNAS and enhanced differentiation of MSCs into osteoblasts. These data demonstrate the potential for LNP delivery of siRNA to enhance the differentiation of MSCs into osteoblasts, and suggests that they are a promising approach for the treatment of osteoporosis and other bone diseases.
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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".