Viral adaptation to immune selection pressure by HLA class I–restricted CTL responses targeting epitopes in HIV frameshift sequences
Bibliographic record
Abstract
CD8+ cytotoxic T lymphocyte (CTL)-mediated immune responses to HIV contribute to viral control in vivo. Epitopes encoded by alternative reading frame (ARF) peptides may be targeted by CTLs as well, but their frequency and in vivo relevance are unknown. Using host genetic (human leukocyte antigen [HLA]) and plasma viral sequence information from 765 HIV-infected subjects, we identified 64 statistically significant (q<0.2) associations between specific HLA alleles and sequence polymorphisms in alternate reading frames of gag, pol, and nef that did not affect the regular frame protein sequence. Peptides spanning the top 20 HLA-associated imprints were used to test for ex vivo immune responses in 85 HIV-infected subjects and showed responses to 10 of these ARF peptides. The most frequent response recognized an HLA-A*03-restricted +2 frame-encoded epitope containing a unique A*03-associated polymorphism at position 6. Epitope-specific CTLs efficiently inhibited viral replication in vitro when viruses containing the wild-type sequence but not the observed polymorphism were tested. Mutating alternative internal start codons abrogated the CTL-mediated inhibition of viral replication. These data indicate that responses to ARF-encoded HIV epitopes are induced during natural infection, can contribute to viral control in vivo, and drive viral evolution on a population level.
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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".