Deciphering the role of the lncRNA TRIBAL in hepatocyte models
Bibliographic record
Abstract
We recently reported that the long non-coding RNA TRIBAL/TRIB1AL was required to sustain key hepatocyte functions. Here, we identify HepaRG cells as a model for studying TRIBAL and provide additional validation and functional insights. In contrast to HepG2 and HuH-7 cells, differentiated HepaRG cells showed similarities to primary hepatocytes in response to TRIBAL suppression. TRIBAL suppression was associated with reduced HNF4A and MLXIPL abundance in hepatocytes and HepaRG cells. TRIBAL targeting using a panel of cognate antisense oligonucleotides confirmed specificity. A comparison of TRIBAL-suppressed hepatocyte and HepaRG transcriptomics identified extensive functional overlap. Biological ontologies associated with key hepatic metabolic functions were predicted to be inhibited in both models. Comparative analyses with TRIB1-suppressed HepaRG cells, a central metabolic regulator vicinal to TRIBAL, also revealed extensive functional congruence with TRIBAL. Interestingly, TRIBAL transduction failed to restore function in TRIBAL-suppressed cells, which may be linked to structural differences, as supported by contrasting RNAse R sensitivities between the endogenous and transduced forms. In summary, these findings support the use of HepaRG cells as an experimental model to study TRIBAL and underscore its importance in regulating key hepatocyte genes essential for metabolic function.
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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) | 0.001 | 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".