TRIB1 Is Regulated Post-Transcriptionally by Proteasomal and Non-Proteasomal Pathways
Bibliographic record
Abstract
The TRIB1 gene has been associated with multiple malignancies, plasma triglycerides and coronary artery disease (CAD). Despite the clinical significance of this pseudo-kinase, there is little information on the regulation of TRIB1. Previous studies reported TRIB1 mRNA to be unstable, hinting that TRIB1 might be subject to post-transcriptional regulation. This work explores TRIB1 regulation, focusing on its post-transcriptional aspects. In 3 distinct model systems (HEK293T, HeLa and arterial smooth muscle cells) TRIB1 was undetectable as assessed by western blot. Using recombinant TRIB1 as a proxy, we demonstrate TRIB1 to be highly unstable at the protein and RNA levels. By contrast, recombinant TRIB1 was stable in cellular extracts. Blocking proteasome function led to increased protein steady state levels but failed to rescue protein instability, demonstrating that the 2 processes are uncoupled. Unlike as shown for TRIB2, CUL1 and TRCPβ did not play a role in mediating TRIB1 instability although TRCPβ suppression increased TRIB1 expression. Lastly, we demonstrate that protein instability is independent of TRIB1 subcellular localization. Following the identification of TRIB1 nuclear localization signal, a cytosolic form was engineered. Despite being confined to the cytosol, TRIB1 remained unstable, suggesting that instability occurs at a stage that precedes its nuclear translocation and downstream nuclear function. These results uncover possible avenues of intervention to regulate TRIB1 function by identifying two distinct regulatory axes that control TRIB1 at the post-transcriptional level.
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 | 0.000 | 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 | 0.001 |
| 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".