Genotypic and Mechanistic Characterization of Subtype-Specific HIV Adaptation to Host Cellular Immunity
Bibliographic record
Abstract
The identification of HIV polymorphisms reproducibly selected under pressure by specific HLA alleles and the elucidation of their impact on viral function can help identify immunogenic viral regions where immune escape incurs a fitness cost. However, our knowledge of HLA-driven escape pathways and their functional costs is largely limited to HIV subtype B and, to a lesser extent, subtype C. Our study represents the first characterization of HLA-driven adaptation pathways in HIV subtypes A1 and D, which dominate in East Africa, and the first statistically rigorous characterization of differential HLA-driven escape across viral subtypes. The results support a considerable impact of viral genetic context on HIV adaptation to host HLA, where HIV subtype-specific sequence variation influences both epitope-HLA binding and the fitness costs of escape. Integrated bioinformatic and mechanistic characterization of these and other instances of differential escape could aid rational cytotoxic T-lymphocyte-based vaccine immunogen selection for both subtype-specific and universal HIV vaccines.
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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".