Adaptation of Droplet Digital PCR-Based HIV Transcription Profiling to Digital PCR and Association of HIV Transcription and Total or Intact HIV DNA
Bibliographic record
Abstract
In most people living with HIV (PLWH) on effective antiretroviral therapy (ART), cell-associated viral transcripts are readily detectable in CD4+ T cells despite the absence of viremia. Quantification of HIV RNA species provides insights into the transcriptional activity of proviruses that persist in cells and tissues throughout the body during ART ('HIV reservoir'). One such technique for HIV RNA quantitation, 'HIV transcription profiling', developed in the Yukl laboratory, measures a series of HIV RNA species using droplet digital PCR. To take advantage of advances in digital (d)PCR, we adapted the 'HIV transcription profiling' technique to Qiagen's dPCR platform (QIAcuity) and compared its performance to droplet digital (dd)PCR (Bio-Rad QX200 system). Using RNA standards, the two technologies were tested in parallel and assessed for multiple parameters including sensitivity, specificity, linearity, and intra- and inter-assay variability. The newly validated dPCR assays were then applied to samples from PLWH to determine HIV transcriptional activity relative to HIV reservoir size. We report that HIV transcriptional profiling was readily adapted to dPCR and assays performed similarly to ddPCR, with no differences in assay characteristics. We applied these assays in a cohort of 23 PLWH and found that HIV reservoir size, based on genetically intact proviral DNA, does not predict HIV transcriptional activity. In contrast, levels of total DNA correlated with levels of most HIV transcripts (initiated, proximally and distally elongated, unspliced, and completed, but not multiply spliced), suggesting that a considerable proportion of HIV transcripts likely originate from defective proviruses. These findings may have implications for measuring and assessing curative strategies and clinical trial outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".