Global Analysis of the Mammalian MHC class I Immunopeptidome at the Organism-Wide Scale
Bibliographic record
Abstract
ABSTRACT Understanding the molecular principles that govern the composition of the mammalian MHC-I immunopeptidome (MHC-Ii) across different primary tissues is fundamentally important to predict how T cell respond in different contexts in vivo . Here, we performed a global analysis of the mammalian MHC-Ii from 29 and 19 primary human and mouse tissues, respectively. First, we observed that different HLA-A, -B and -C allotypes do not contribute evenly to the global composition of the MHC-Ii across multiple human tissues. Second, we found that peptides that are presented in a tissue-dependent and -independent manner share very distinct properties. Third, we discovered that proteins that are evolutionarily hyperconserved represent the primary source of the MHC-Ii at the organism-wide scale. Finally, we uncovered new components of the antigen processing and presentation network that may drive the high level of heterogeneity of the MHC-Ii across different tissues in mammals. This study opens up new avenues toward a system-wide understanding of antigen presentation in vivo and may serve as ground work to understand tissue-dependent T cell responses in autoimmunity, infectious diseases and cancer.
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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) | 0.001 | 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".