Creating HIV-1 reverse transcriptase cytotoxic T lymphocyte target structures by HLA-A2 heavy chain modifications
Bibliographic record
Abstract
Vigorous HIV-1-specific CD8(+) cytotoxic T lymphocyte (CTL) responses play an important role in the control of HIV-1 replication and the induction of a strong, broadly cross-reactive CTL response remains an important goal of HIV vaccine development. It is known that the display of high levels of class I MHC-viral peptide complexes at the cell surface of target cells is necessary to elicit a strong CTL response. We now report two strategies to enhance the presentation of defined HIV-1 epitope-specific CTL target structures, by incorporating subdominant HIV-1 reverse transcriptase (RT) CTL epitope sequences into the human class I MHC molecule HLA-A2. We show that either incorporation of HIV-1 CTL epitopes into the signal sequence of HLA or tethering of epitopes to the HLA-A2 heavy chain provide simple ways to create effective CTL target structures that can be recognized and lysed by human HLA-A2-restricted RT-specific CD8(+) CTL. Moreover, cells expressing these epitope-containing HLA-A2 constructs stimulated the generation of primary epitope-specific CTL in vitro. These strategies offer new options in the design of plasmid DNA-based vaccines or immunotherapeutics for the induction of CTL responses against subdominant HIV-1 epitopes.
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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 | 0.001 |
| 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".