High levels of genetically intact HIV in HLA-DR+ memory T cells indicates their value for reservoir studies
Bibliographic record
Abstract
OBJECTIVE: The contribution of HLA-DR+ memory CD4 T cells to the HIV reservoir during prolonged antiretroviral therapy is unclear as these cells are commonly excluded when assessing for replication-competent HIV. To address this issue, we examined the distribution of genetically intact HIV DNA within HLA-DR- and HLA-DR+ memory CD4 T cells and the RNA transcriptional profile of these cells during antiretroviral therapy. DESIGN/METHODS: Full-length DNA sequencing was used to examine the HIV DNA landscape within HLA-DR+ and HLA-DR- memory CD4 T cells. RNA quantification and sequencing was used to interrogate the relationship between HLA-DR status and HIV RNA transcription. RESULTS: HLA-DR+ CD4 T cells contained a high frequency of genetically intact HIV genomes, contributing over half of the genetically intact viral sequences to the reservoir. Expansions of genetically identical sequences were identified in all T-cell subsets, indicating that cellular proliferation maintains genetically intact and defective viral DNA during therapy. Intracellular HIV RNA levels in HLA-DR+ and HLA-DR- T cells were not statistically different by either long terminal repeat quantitative PCR quantification or single-genome RNA sequencing of the p6-RT region. CONCLUSION: The high proportion of intact viral DNA sequences in the proliferative HLA-DR+ subset suggests they are critical in maintaining HIV infection during effective therapy. As such, these cells should be included in any immune intervention targeting HIV during effective therapy.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".