The Human Immune System Recognizes Neopeptides Derived from Mitochondrial DNA Deletions
Bibliographic record
Abstract
Mutations in mitochondrial (mt) DNA accumulate with age and can result in the generation of neopeptides. Immune surveillance of such neopeptides may allow suboptimal mitochondria to be eliminated, thereby avoiding mt-related diseases, but may also contribute to autoimmunity in susceptible individuals. To date, the direct recognition of neo-mtpeptides by the adaptive immune system has not been demonstrated. In this study we used bioinformatics approaches to predict MHC binding of neopeptides identified from known deletions in mtDNA. Six such peptides were confirmed experimentally to bind to HLA-A*02. Pre-existing human CD4(+) and CD8(+) T cells from healthy donors were shown to recognize and respond to these neopeptides. One remarkably promiscuous immunodominant peptide (P9) could be presented by diverse MHC molecules to CD4(+) and/or CD8(+) T cells from 75% of the healthy donors tested. The common soil microbe, Bacillus pumilus, encodes a 9-mer that differs by one amino acid from P9. Similarly, the ATP synthase F0 subunit 6 from normal human mitochondria encodes a 9-mer with a single amino acid difference from P9 with 89% homology to P9. T cells expanded from human PBMCs using the B. pumilus or self-mt peptide bound to P9/HLA-A2 tetramers, arguing for cross-reactivity between T cells with specificity for self and foreign homologs of the altered mt peptide. These findings provide proof of principal that the immune system can recognize peptides arising from spontaneous somatic mutations and that such responses might be primed by foreign peptides and/or be cross-reactive with self.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".