CTL Clonotypes with Higher TCR Affinity Have Better Ability to Reduce the HIV Latent Reservoir
Bibliographic record
Abstract
Abstract The success of the shock and kill strategy for the HIV cure depends both on the reactivation of the latent reservoir and on the ability of the immune system to eliminate infected cells. As latency reversal alone has not shown any impact in the size of the latent reservoir, ensuring that effector CTLs are able to recognize and kill HIV-infected cells could contribute to reservoir reduction. In this study, we investigated which functional aspects of human CTLs are associated with a better capacity to kill HIV-infected CD4+ T cells. We isolated Gag- and Nef-specific CTL clones with different TCR sequences from the PBMC of donors in acute and chronic infection. High-affinity clonotypes that showed IFN-γ production preserved even when the CD8 coreceptor was blocked, and clones with high Ag sensitivity exhibited higher efficiency at reducing the latent reservoir. Although intrinsic cytotoxic capacity did not differ according to TCR affinity, clonotypes with high TCR affinity showed a better ability to kill HIV-infected CD4+ T cells obtained from in vivo–infected PBMC and subjected to viral reactivation. Strategies aiming to specifically boost and maintain long-living memory CTLs with high TCR affinity in vivo prior to latency-reversing treatment might improve the efficacy of the shock and kill approach to reduce the latent reservoir.
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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) | 0.003 | 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".