Altering Effects of Antigenic Variations in HIV-1 on Antiviral Effectiveness of HIV-Specific CTLs
Bibliographic record
Abstract
The mutational escape of HIV-1 from established CTL responses is becoming evident. However, it is not yet clear whether antigenic variations of HIV-1 may have an additional effect on the differential antiviral effectiveness of HIV-specific CTLs. Herein, we characterized HIV-specific CTL responses toward Pol, Env, and Nef optimal epitopes presented by HLA-B*35 during a chronic phase of HIV-1 infection. We found CTL escape variants within Pol and Nef epitopes that affected recognition by TCRs, although there was no mutation within the Env epitope. An analysis of peptide-HLA tetrameric complexes revealed that CD8 T cells exclusively specific for the Nef variant were generated following domination by the variant viruses. The variant-specific cells were capable of killing target cells and producing antiviral cytokines but showed impaired Ag-specific proliferation ex vivo, whereas wild-type specific cells had potent activities. Moreover, clonotypic CD8 T cells specific for the Pol variant showed diminished proliferation, whereas Env-specific ones had no functional heterogeneity. Taken together, our data indicate that antigenic variations that abolished TCR recognition not only resulted in escape from established CTL responses but also eventually generated another subset of variant-specific CTLs having decreased antiviral activity, causing an additional negative effect on antiviral immune responses during a chronic HIV infection.
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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".