Latent HIV reservoirs exhibit inherent resistance to elimination by CD8+ T cells
Bibliographic record
Abstract
The presence of persistent, latent HIV reservoirs in CD4+ T cells obstructs current efforts to cure infection. The so-called kick-and-kill paradigm proposes to purge these reservoirs by combining latency-reversing agents with immune effectors such as cytotoxic T lymphocytes. Support for this approach is largely based on success in latency models, which do not fully reflect the makeup of latent reservoirs in individuals on long-term antiretroviral therapy (ART). Recent studies have shown that CD8+ T cells have the potential to recognize defective proviruses, which comprise the vast majority of all infected cells, and that the proviral landscape can be shaped over time due to in vivo clonal expansion of infected CD4+ T cells. Here, we have shown that treating CD4+ T cells from ART-treated individuals with combinations of potent latency-reversing agents and autologous CD8+ T cells consistently reduced cell-associated HIV DNA, but failed to deplete replication-competent virus. These CD8+ T cells recognized and potently eliminated CD4+ T cells that were newly infected with autologous reservoir virus, ruling out a role for both immune escape and CD8+ T cell dysfunction. Thus, our results suggest that cells harboring replication-competent HIV possess an inherent resistance to CD8+ T cells that may need to be addressed to cure 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".