Posttranslational regulation of thioredoxin-interacting protein
Bibliographic record
Abstract
Thioredoxin-interacting protein (Txnip) is a metabolic regulator, which modulates insulin sensitivity and likely plays a role in type 2 diabetes. We studied the regulation of Txnip in 3T3-L1 adipocytes. Cells were incubated under different conditions and Txnip was measured by immunoblotting. We confirmed that high glucose markedly increases Txnip expression by promoting transcription. Insulin decreases Txnip protein levels. Rapamycin under most conditions decreased Txnip, suggesting that mTOR complex-1 is involved. The acute effects of insulin are mainly posttranscriptional; insulin (100 nM) accelerates Txnip degradation more than tenfold. This effect is cell type specific. It works in adipocytes, preadipocytes and in L6 myotubes but not in HepG2 or in HEK 293 cells or in a pancreatic β-cell line. The ubiquitin/proteasome pathway is involved. Degradation of Txnip occurred within 15 min in the presence of 3 nM insulin and overnight with 0.6 nM insulin. Proteasomal Txnip degradation is not mediated by a cysteine protease or an anti-calpain enzyme. Okadaic acid (OKA), an inhibitor of phosphoprotein phosphatases (pp), markedly reduced Txnip protein and stimulated its further decrease by insulin. The latter occurred after incubation with 1 or 1000 nM OKA, suggesting that insulin enhances the phosphorylation of a pp2A substrate. Incubation with 0.1 μM Wortmannin, a PI3 kinase inhibitor, increased Txnip protein twofold and significantly inhibited its insulin-induced decrease. Thus, while OKA mimics the effect of insulin, Wortmannin opposes it. In summary, insulin stimulates Txnip degradation by a PI3 kinase-dependent mechanism, which activates the ubiquitin/proteasome pathway and likely serves to mitigate insulin resistance.
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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".