Epitope Mapping of HIV-Specific CD8 <sup>+</sup> T Cell Responses by Multiple Immunological Readouts Reveals Distinct Specificities Defined by Function
Bibliographic record
Abstract
The limited success of HIV vaccine candidates to date highlights our need to better characterize protective cell-mediated immunity (CMI). While HIV-specific CD8(+) T cell responses have been defined largely by measuring gamma interferon (IFN-γ), these responses are not always protective, and it is unclear whether the same epitopes would predominate if other functional parameters were examined. Here, we assessed the epitope specificity of HIV-specific CD8(+) T cell responses by multiparametric flow cytometry, measuring five CD8(+) T cell functions (IFN-γ, macrophage inflammatory protein 1β [MIP-1β], tumor necrosis factor alpha [TNF-α], interleukin-2 [IL-2], and proliferative capacity) in 24 chronically HIV-infected individuals. Sixty-nine epitope-specific responses to 50 epitopes within p24 were measured. Surprisingly, most epitope-specific responses were IFN-γ negative (50/69 responses). Many responses had polyfunctional (33%) and proliferative (19%) components. An inverse association between IL-2 and proliferation responses was also observed, contrary to what was described previously. We confirm that long-term nonprogressors (LTNP) have more polyfunctional responses and also have higher-magnitude and broader p24-specific proliferation and higher levels of IL-2 and TNF-α production than do progressing controls. Together, these data suggest that the specificity of CD8(+) T cell responses differs depending on the immunological readout, with a 3.5-fold increase in breadth detected by including multiple parameters. Furthermore, the identification of epitopes that elicit polyfunctional responses reinforces the need for the comprehensive evaluation of HIV vaccine candidates, and these epitopes may represent novel targets for CMI-based 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".