Transglutaminase 2—a novel inhibitor of adipogenesis
Bibliographic record
Abstract
Differentiation of preadipocytes to lipid storing adipocytes involves extracellular signaling pathways, matrix remodeling and cytoskeletal changes. A number of factors have been implicated in maintaining the preadipocyte state and preventing their differentiation to adipocytes. We have previously reported that a multifunctional and protein crosslinking enzyme, transglutaminase 2 (TG2) is present in white adipose tissue. In this study, we have investigated TG2 function during adipocyte differentiation. We show that TG2 deficient mouse embryonic fibroblasts (Tgm2-/- MEFs) display increased and accelerated lipid accumulation due to increased expression of major adipogenic transcription factors, PPARγ and C/EBPα. Examination of Pref-1/Dlk1, an early negative regulator of adipogenesis, showed that the Pref-1/Dlk1 protein was completely absent in Tgm2-/- MEFs during early differentiation. Similarly, Tgm2-/- MEFs displayed defective canonical Wnt/β-catenin signaling with reduced β-catenin nuclear translocation. TG2 deficiency also resulted in reduced ROCK kinase activity, actin stress fiber formation and increased Akt phosphorylation in MEFs, but did not alter fibronectin matrix levels or solubility. TG2 protein levels were unaltered during adipogenic differentiation, and was found predominantly in the extracellular compartment of MEFs and mouse WAT. Addition of exogenous TG2 to Tgm2+/+ and Tgm2-/- MEFs significantly inhibited lipid accumulation, reduced expression of PPARγ and C/EBPα, promoted the nuclear accumulation of β-catenin, and recovered Pref-1/Dlk1 protein levels. Our study identifies TG2 as a novel negative regulator of adipogenesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".