Short Communication: HIV Type 1 Accumulates in Influenza-Specific T Cells in Subjects Receiving Seasonal Vaccination in the Context of Effective Antiretroviral Therapy
Bibliographic record
Abstract
Whether or not HIV-1 continues to infect cells in individuals treated with effective antiretroviral therapy (ART) remains controversial. Here, we determined whether the redistribution of the HIV-1 proviral burden with respect to antigen specificity of CD4(+) cells would provide evidence for ongoing infection cycles in vivo. HIV-1 preferentially infects antigen-stimulated CD4(+) T cells. In the setting of prolonged effective ART, we postulated that if infection cycles were occurring, influenza-specific CD4(+) T cells, activated by influenza vaccination, would preferentially accumulate proviral burden. Peripheral blood mononuclear cells (PBMCs) were collected from HIV-1-infected subjects who had been treated with effective ART for >5 years, before and after influenza vaccination. CD4(+) T cells were sorted by antigen specificity and HIV-1 proviral burdens were determined. Levels of HIV-1 production upon in vitro antigenic stimulation were also measured. At baseline, influenza-specific CD4(+) T cells carried higher HIV-1 proviral loads than HIV-1-p55-specific CD4(+) T cells. Upon influenza vaccination we observed trends toward elevated levels of HIV-1 proviral DNA in influenza and HIV-1-p55-specific, but not tetanus toxoid or cytomegalovirus (CMV)-specific CD4(+) T cells. Higher levels of HIV-1 virions were produced upon influenza stimulation in postvaccination as compared to baseline samples. While the trends toward increased proviral burdens in influenza-specific cells failed to reach statistical significance, our observation of disproportionately high levels of provirus in influenza-specific cells at baseline indicates that this may represent a real increase that is cumulative over multiple annual vaccinations. This has implications for the eradication of HIV-1 by adding to the evidence that the resting CD4(+) T cell viral reservoir is continually replenished in ART-treated subjects.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".