HIVepsilon-seq—scalable characterization of intact persistent proviral HIV reservoirs in women
Bibliographic record
Abstract
ABSTRACT The persistence of replication-competent HIV-1 in people living with HIV (PLWH) is a barrier to a cure for HIV. Early-phase studies of clinical interventions to deplete the intact persistent HIV-1 reservoir are ongoing. However, the ability to distinguish intact proviruses is limited by sequence variation and the predominance of defective proviruses. In this study, we developed HIVepsilon-seq (HIVε-seq), a novel assay to analyze 33 samples consisting of unfractionated peripheral blood mononuclear cells and/or resting CD4 + T cells from antiretroviral therapy (ART)-treated durably suppressed PLWH, including samples from 17 female participants. HIVε-seq combines simplified target enrichment and long-read sequencing methodology with advanced bioinformatic analysis to increase the depth, characterization, and detection of intact persistent HIV-1 provirus. HIVε-seq detected persistent sequence-intact HIV-1 proviruses in samples from both male and female participants and represents a robust, scalable assay to detect intact proviral sequences that could be applied to studies of HIV cure interventions. IMPORTANCE The lack of a reliable method to accurately detect when replication-competent HIV has been cleared is a major challenge in developing a cure. This study introduces a new approach called the HIVepsilon-seq (HIVε-seq) assay, which uses long-read sequencing technology and bioinformatics to scrutinize the HIV genome at the nucleotide level, distinguishing between defective and intact HIV. This study included 30 participants on antiretroviral therapy, including 17 women, and was able to discriminate between defective and genetically intact viruses at the single DNA strand level. The HIVε-seq assay is an improvement over previous methods, as it requires minimal sample, less specialized lab equipment, and offers a shorter turnaround time. The HIVε-seq assay offers a promising new tool for researchers to measure the intact HIV reservoir, advancing efforts towards finding a cure for this devastating disease.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".