Impact of genomic polymorphisms on the repertoire of human MHC class I-associated peptides
Bibliographic record
Abstract
For decades, the global impact of genomic polymorphisms on the repertoire of peptides presented by major histocompatibility complex (MHC) has remained a matter of speculation. Here we present a novel approach that enables high-throughput discovery of polymorphic MHC class I-associated peptides (MIPs), which play a major role in allorecognition. On the basis of comprehensive analyses of the genomic landscape of MIPs eluted from B lymphoblasts of two MHC-identical siblings, we show that 0.5% of non-synonymous single nucleotide variations are represented in the MIP repertoire. The 34 polymorphic MIPs found in our subjects are encoded by bi-allelic loci with dominant and recessive alleles. Our analyses show that, at the population level, 12% of the MIP-coding exome is polymorphic. Our method provides fundamental insights into the relationship between the genomic self and the immune self and accelerates the discovery of polymorphic MIPs (also known as minor histocompatibility antigens). Mass spectrometry (MS) has furthered our understanding of MHC class I-associated peptides (MIPs), but the technique is inadequate for studying MIP-associated polymorphisms. Here, the authors combine high-throughput MS with exome and transcriptome sequencing to identify polymorphic MIPs from two female siblings.
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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 | 0.001 |
| 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".