Regulation of gene expression by the immunoproteasome (P5049)
Bibliographic record
Abstract
Abstract Background: Proteasomes play crucial roles in regulating fundamental cellular processes in eukaryotic cells. Catalytic subunits β1, β2 and β5 of the proteasome are replaced by LMP2, MECL-1 and LMP7 to form the immunoproteasome (IPr). We have shown that the IPr modulates mRNA levels of several clusters of genes in dendritic cells, but the precise mechanism by which it regulates gene expression is unknown. Results: By comparing transcriptome of Lmp7+/+/Mecl1+/+ (WT) and Lmp7-/-/Mecl1-/- (dKO) thymocytes by microarray, we found that the IPr affects the expression of 50 transcripts. The genes source of these transcripts are clustered in the genome, and are enriched in cell cycle-associated functions. Moreover, an overlap of 2% is seen by comparing with transcripts modulated by the IPr in dendritic cells. Chymotrypsin-like activity is higher in dKO than in WT cells, due to β5 overexpression. Furthermore, ubiquitinated proteins levels are similar between WT and dKO cells whereas free ubiquitin is lower in dKO cells. We next investigated levels of ubiquitin on histones, since it represent an important reservoir of ubiquitin and it can regulate transcriptional activity. Our results show that H2B, but not H2A, ubiquitination is increased in dKO cells. Conclusion: The effect of IPr on transcription is tissue-specific and could be due to increased ubiquitinated H2B levels. While proteasomes clearly regulate transcription, the impact of IPr on transcription has never been investigated.
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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) | 0.003 | 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".