Comparison of the depth of vaccine-elicited HIV-1 Env epitope-specific CD8+ T lymphocyte responses
Bibliographic record
Abstract
One of the major challenges in the development of an effective HIV-1 vaccine is the extraordinary genetic diversity of the virus. Immunizations of nonhuman primates using consensus and mosaic immunogens have been shown to elicit cross-reactive CD8+T lymphocyte responses that increase the depth of epitope recognition. However, one of the limitations of vaccine-induced epitope-specific CD8+T lymphocytes includes lack of protection against diverse strains and emergent forms of HIV-1 due to altered T cell receptor (TCR) affinity for variant peptide:MHC class I complexes. In this study, we immunized a cohort of fifteen Mamu-A*01+ rhesus monkeys with either a 3-valent mosaic Env, group M consensus Env, or single clade B Env vaccine and compared the ability of the CD8+T lymphocyte populations elicited by each immunogen to recognize variants of an HIV-1 envelope epitope sequence p41A (YI9). We identified vaccine-induced CD8+T lymphocytes populations using tetramers constructed with 9 variants of p41A epitope. We assessed the ability of those variant peptides to activate CTL by measuring cytokine production and CD107a expression. We evaluated proliferation using carboxyfluorescein succinimidyl ester. Finally, we investigated the functional avidity of these CD8+T lymphocytes for the variant peptide:Mamu-A*01 complexes using surface plasmon resonance technology. Our data show that Env immunizations can generate cross-reactive CD8+T lymphocytes that recognize 2 of 9 (22%) of the variants of p41A epitope, with higher responses induced by the consensus and the 3-valent mosaic immunogens (variant from clade C and variant from clade A/E) compared to the single clade Env immunogen. Tetramer-binding data also show that CD8+ T lymphocytes from monkeys immunized with mosaic immunogen have a trend of higher binding to majority of the variant peptides tested. This underscores the potential of mosaic immunogens for generating cellular immune responses with greater depth.
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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".