The <i>DLK</i> gene is a transcriptional target of PPARγ
Bibliographic record
Abstract
DLK (dual leucine zipper-bearing kinase) is a key regulator of development, cell differentiation and apoptosis. Interestingly, recent studies have shown that DLK expression is up-regulated in 3T3-L1 cells induced to differentiate into adipocytes and that DLK knockdown impairs the expression of PPARγ (peroxisome-proliferator-activated receptor γ), a master regulator of adipogenesis. Because the PPARγ agonist rosiglitazone was found to increase DLK expression in 3T3-L1 cells, we hypothesized that PPARγ is required for the transcriptional activation of the DLK gene. To test this hypothesis, we first examined the effects of pharmacological inhibition or shRNA (small-hairpin RNA)-mediated depletion of PPARγ on DLK accumulation in 3T3-L1 cells undergoing differentiation. In addition to blocking adipocyte conversion of 3T3-L1 cells, inhibition of PPARγ suppressed DLK expression at both the mRNA and protein levels. Moreover, supporting a role for PPARγ in DLK regulation, two potential PPARγ-binding sites identified by bioinformatic tools at positions -611 and -767 upstream of the DLK gene transcriptional start site were shown by electrophoretic mobility-shift assay and chromatin immunoprecipitation to bind PPARγ and its essential heterodimer partner retinoid X receptor as differentiation proceeds. Collectively, these results show that DLK is a novel transcriptional target of PPARγ with functional PPARγ-binding sites in its promoter.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".