Organotins Are Potent Activators of PPARγ and Adipocyte Differentiation in Bone Marrow Multipotent Mesenchymal Stromal Cells
Bibliographic record
Abstract
Adipocyte differentiation in bone marrow is potentially deleterious to both bone integrity and lymphopoiesis. Here, we examine the hypothesis that organotins, common environmental contaminants that are dual ligands for peroxisome proliferator-activated receptor (PPAR) γ and its heterodimerization partner retinoid X receptor (RXR), are potent activators of bone marrow adipogenesis. A C57Bl/6-derived bone marrow multipotent mesenchymal stromal cell (MSC) line, BMS2, was treated with rosiglitazone, a PPARγ agonist, bexarotene, an RXR agonist, or a series of organotins. Rosiglitazone and bexarotene potently activated adipocyte differentiation; however, bexarotene had a maximal efficacy of only 20% of that induced by rosiglitazone. Organotins (tributyltin [TBT], triphenyltin, and dibutyltin) also stimulated adipocyte differentiation (EC₅₀ of 10-20 nM) but with submaximal, structure-dependent efficacy. In coexposures, both bexarotene and TBT enhanced rosiglitazone-induced adipogenesis. To investigate the contribution of PPARγ to TBT-induced adipogenesis, we examined expression of PPARγ2, as well as its transcriptional target FABP4. TBT-induced PPARγ2 and FABP4 protein expression with an efficacy intermediate between rosiglitazone and bexarotene, similar to lipid accumulation. A PPARγ antagonist and PPARγ-specific small hairpin RNA suppressed TBT-induced differentiation, although to a lesser extent than rosiglitazone-induced differentiation, suggesting that TBT may engage alternate pathways. TBT and bexarotene, but not rosiglitazone, also induced the expression of TGM2 (an RXR target) and ABCA1 (a liver X receptor target). The results show that an environmental contaminant, acting with the same potency as a therapeutic drug, induces PPARγ-dependent adipocyte differentiation in bone marrow MSCs. Activation of multiple nuclear receptor pathways by organotins may have significant implications for bone physiology.
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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 | 0.000 |
| 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".