Dexamethasone induced miR-155 up-regulation in differentiating 3T3-L1 preadipocytes does not affect adipogenesis
Bibliographic record
Abstract
Dexamethasone is a synthetic glucocorticoid that is widely used as an adipogenic inducer in both murine and human in vitro models. Glucocorticoids have been shown to regulate early transcriptional events in adipogenesis. MicroRNAs (miRNAs) have been also implicated in the regulation of preadipocyte differentiation; however, the effects of glucocorticoids on miRNA expression levels during this process have not been studied. In this study we investigated the effects of glucocorticoids on the expression levels of miR-155 in differentiating 3T3-L1 preadipocytes. We found that miR-155 levels were up-regulated (2.4-fold) by glucocorticoids in differentiating 3T3-L1 preadipocytes, and this enhancement was abolished in the presence of RU486, a glucocorticoid receptor antagonist. In contrast, treatment with rosiglitazone, another adipogenic inducer decreased the expression levels of miR-155 in these cells. Further, our data show that endogenous miR-155 is unlikely to be involved in adipogenesis as we show that both dexamethasone and rosiglitazone induced adipogenesis to similar levels. Furthermore, using miR-155 inhibitor, we showed that the dexamethasone mediated miR-155 enhancement did not alter adipogenesis. Our data show that dexamethasone but not rosiglitazone increases miR-155 expression and that the increased expression of miR-155 is not involved in the dexamethasone-mediated adipogenesis in the 3T3-L1 model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".