The HIV-1 proviral landscape reveals that Nef contributes to HIV-1 persistence in effector memory CD4+ T cells
Bibliographic record
Abstract
Despite long-term antiretroviral therapy (ART), HIV-1 persists within a reservoir of CD4+ T cells that contribute to viral rebound if treatment is interrupted. Identifying the cellular populations that contribute to the HIV-1 reservoir and understanding the mechanisms of viral persistence are necessary to achieve an effective cure. In this regard, through Full-Length Individual Proviral Sequencing, we observed that the HIV-1 proviral landscape was different and changed with time on ART across naive and memory CD4+ T cell subsets isolated from 24 participants. We found that the proportion of genetically intact HIV-1 proviruses was higher and persisted over time in effector memory CD4+ T cells when compared with naive, central, and transitional memory CD4+ T cells. Interestingly, we found that escape mutations remained stable over time within effector memory T cells during therapy. Finally, we provided evidence that Nef plays a role in the persistence of genetically intact HIV-1. These findings posit effector memory T cells as a key component of the HIV-1 reservoir and suggest Nef as an attractive therapeutic target.
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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".