Deletion of Immunoproteasome Subunits Imprints on the Transcriptome and Has a Broad Impact on Peptides Presented by Major Histocompatibility Complex I molecules
Bibliographic record
Abstract
Proteasome-mediated proteolysis plays a crucial role in many basic cellular processes. In addition to constitutive proteasomes (CPs), which are found in all eukaryotes, jawed vertebrates also express immunoproteasomes (IPs). Evidence suggests that the key role of IPs may hinge on their impact on the repertoire of peptides associated to major histocompatibility complex (MHC) I molecules. Using a label-free quantitative proteomics approach, we identified 417 peptides presented by MHC I molecules on primary mouse dendritic cells (DCs). By comparing MHC I-associated peptides (MIPs) eluted from primary DCs and thymocytes, we found that the MIP repertoire concealed a cell type-specific signature correlating with cell function. Notably, mass spectrometry analyses of DCs expressing or not IP subunits MECL1 and LMP7 showed that IPs substantially increase the abundance and diversity of MIPs. Bioinformatic analyses provided evidence that proteasomes harboring LMP7 and MECL1 have specific cleavage preferences and recognize unstructured protein regions. Moreover, while differences in MIP repertoire cannot be attributed to potential effects of IPs on gene transcription, IP subunits deficiency altered mRNA levels of a set of genes controlling DC function. Regulated genes segregated in clusters that were enriched in chromosomes 4 and 8. Our peptidomic studies performed on untransfected primary cells provide a detailed account of the MHC I-associated immune self. This work uncovers the dramatic impact of IP subunits MECL1 and LMP7 on the MIP repertoire and their non-redundant influence on expression of immune-related genes.
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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) | 0.002 | 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".