Regulation of TRIB1 abundance in hepatoma models
Bibliographic record
Abstract
Abstract Tribbles related homolog 1 (TRIB1) contributes to lipid and glucose homeostasis by facilitating the degradation of cognate cargos by the proteasome. We previously reported that TRIB1 was unstable in non-hepatic cellular models. Moreover, inclusion of proteasome inhibitors failed to prevent TRIB1 loss, consistent with the involvement of proteasome independent degradative processes. In view of the key role of TRIB1 in liver function, we continue our exploration of TRIB1 regulation pathways in two commonly used human hepatocyte models, HuH-7 and HepG2 cells. Proteasome inhibitors potently upregulated both endogenous and recombinant TRIB1 mRNA and protein levels. Increased transcript abundance was independent of MAPK activation while ER stress was a relatively mild inducer. Despite increasing TRIB1 protein abundance and stabilizing bulk ubiquitination, proteasome inhibition failed to stabilize TRIB1, pointing to the predominance of proteasome independent protein degradation processes controlling TRIB1 protein abundance in hepatomas. Proteasome inhibition via downregulation of its PSMB3 regulatory subunit, in contrast to its chemical inhibition, had minimal impact on TRIB1 levels. Moreover, immunoprecipitation experiments showed no evidence of TRIB1 ubiquitination. Cytoplasmic retained TRIB1 was unstable, indicating that TRIB1 lability is regulated prior to its nuclear import. Substitution of the TRIB1 PEST-like region with a GST helical region or N-terminal deletions failed to fully stabilize TRIB1. Finally, inclusion of protease or autophagy inhibitors in vivo did not rescue TRIB1 stability. This work excludes proteasome-mediated degradation as a significant contributor to TRIB1 instability and identifies transcriptional regulation as a prominent mechanism regulating TRIB1 abundance in liver models in response to proteasome inhibition.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".