Improving HIV Outgrowth by Optimizing Cell-Culture Conditions and Supplementing With all-trans Retinoic Acid
Bibliographic record
Abstract
The persistence of replication-competent HIV reservoirs in people living with HIV (PLWH) receiving antiretroviral therapy (ART) is a barrier to cure. Therefore, their accurate quantification is essential for evaluating the efficacy of new therapeutic interventions and orienting the decision to interrupt ART. Quantitative viral outgrowth assays (QVOAs) represent the “gold standard” for measuring the size of replication-competent HIV reservoirs. However, they require large numbers of cells and are technically challenging. This justifies the need for the development of novel simplified methods adapted for small biological samples. Herein, we sought to simplify the viral outgrowth procedure (VOP) by i) using memory CD4+ T-cells, documented to be enriched in HIV reservoirs ii) optimizing cell-culture conditions, and iii) supplementing with all-trans retinoic acid (ATRA), a positive regulator of HIV replication. Memory CD4+ T-cells were sorted from the peripheral blood of ART-treated (HIV+ART; n=14) and untreated (HIV+; n=5) PLWH. The VOP was first performed with one original replicate of 1x106 cells/well in 48-well plates. Cells were stimulated via CD3/CD28 for 3 days, washed to remove residual CD3/CD28 Abs, split every 3 days for optimal cell density, and cultured in the presence or the absence of ATRA for 12 days. Soluble and intracellular HIV-p24 levels were quantified by ELISA and flow cytometry, respectively. Optimal cell-culture density achieved by splitting improved HIV outgrowth detection. ATRA promoted superior/accelerated detection of replication-competent HIV in all HIV+ART individuals tested, including those with low/undetectable viral outgrowth in the absence of ATRA. Finally, this VOP was used to design a simplified ATRA-based QVOA by including 4 and 6 original replicates of 1x106 cells/well in 48-well plates and 2x105 cells/well in 96-well plates, respectively. Consistently, the number of infectious units per million cells (IUPM) was significantly increased in the presence of ATRA. In conclusion, we demonstrate that memory CD4+ T-cell splitting for optimal density in culture and ATRA supplementation significantly improved the efficacy of HIV outgrowth in a simplified ATRA-based QVOA performed in the absence of feeder/target cells or indicator cell lines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".