High-throughput characterization of MHC I-associated peptides presented by B lymphoblastoid cells (100.11)
Bibliographic record
Abstract
Abstract Sensing of the MHC I-peptide (MHC I-p) repertoire allows elimination of infected or transformed cells. High-throughput strategies used to study MHC I-p include i) the use of secreted forms of MHC I transfected into model cell lines, or ii) chemical or metabolic labeling of MHC I, which is limited to certain MHCs and to cell culture model systems. Here, we used a quantitative proteomics approach to characterize and compare for the first time the MHC I-p repertoire associated to unlabeled HLA-A,B molecules of untransfected B lymphoblastoid cells (B-LCLs). Peptides were obtained from B-LCLs from 3 subjects by mild acid elution, fractionated by liquid chromatography and analyzed by nanoLC-MS/MS on a LTQ-Orbitrap mass spectrometer. We have identified more than 1000 unique 8-11mers associated to 10 HLA-A,B alleles: HLA-A*0101,0201,0205,0301,2902; B*0801,1501,4403,5001,5701. We found MHC-dependent differences in the predicted binding affinity of identified peptides. We used bioinformatic tools to identify the genes source of peptides and to characterize and compare the proteins encoding the MHC I-p repertoire. Peptides were encoded by genes located in all chromosomes and the mitochondria, and were significantly enriched for genes located in chromosomes 17 and 6. Our analysis revealed an enrichment of genes implicated in cell cycle, transcription and protein synthesis, as well as in immune and viral infection-related pathways, reflecting a B cell-specific signature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".