For Protection from HIV-1 Infection, More Might Not Be Better: a Systematic Analysis of HIV Gag Epitopes of Two Alleles Associated with Different Outcomes of HIV-1 Infection
Bibliographic record
Abstract
A subset of women in the Pumwani Sex Worker Cohort, established in 1985 in Nairobi, Kenya, remains uninfected despite repeated high-risk exposure (HIV-exposed, seronegative [HESN]) through active sex work. This HESN phenotype is associated with several alleles of human leukocyte antigens (HLAs) and specific CD8(+) and CD4(+) T cell responses to HIV-1. The associations of HLA alleles with differential HIV-1 infection are most likely due to their different abilities to present antigen and the different immune responses they induce. The characteristics of epitopes of HLA alleles associated with different outcomes of HIV-1 infection might therefore point to a vital clue for developing an effective vaccine. In this study, we systematically analyzed HIV-1 clade A and D Gag CD8(+) T cell epitopes of two HLA class I alleles associated with different outcomes of HIV-1 infection. Binding affinity and off-rates of the identified epitopes were determined. Gamma interferon (IFN-γ) enzyme-linked immunospot (ELISpot) assays with patient peripheral blood mononuclear cells (PBMCs) validated the epitopes. Epitope-specific CD8(+) T cells were further phenotyped for memory markers with tetramer staining. Our study showed that the protective allele A*01:01 recognizes only three Gag epitopes. By contrast, B*07:02, the allele associated with susceptibility, binds 30 epitope variants. These two alleles differ most importantly in the spectrum of Gag epitopes they can present and not in affinity, off-rates, the location of the epitopes, or epitope-specific Tem/Tcm frequencies. The binding of more epitopes and strong IFN-gamma ELISpot responses are associated with susceptibility to HIV-1 infection, while more focused antigen recognition of multiple subtypes is protective. Rational vaccine design should take these observations into account.
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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 | 0.001 | 0.001 |
| 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 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".