Characterizing HIV-1 reservoir dynamics in subtypes B and C to advance treatment and cure strategies
Bibliographic record
Abstract
Human immunodeficiency virus type 1 (HIV-1) is the cause of acquired immunodeficiency syndrome (AIDS), a disease that has claimed over 40 million lives. The development of antiretroviral therapy (ART) transformed HIV-1 infection from a fatal disease into a chronic condition. However, challenges remain: ART is inaccessible to many globally, and those with access may experience HIV-related health issues. Although ART prevents HIV-1 replication, the viral DNA integrated into human DNA remains hidden in immune cells in a dormant state, forming what is known as the viral reservoir. ART cannot eliminate this viral DNA, allowing the virus to reactivate if ART is stopped, thus necessitating lifelong treatment. Proviral DNA can be either intact, meaning it can infect new cells, or defective, due to mutations or missing DNA fragments, which render it unable to replicate. Accurate quantification of both forms is essential for assessing treatment effectiveness and potential cure strategies. The Intact Proviral DNA Assay (IPDA) is a PCR test developed to quantify both forms. Initially optimized for HIV-1 subtype B, which is common in high-income countries, this assay was not applicable to subtype C, which accounts for approximately 47% of global infections, primarily in Sub-Saharan Africa. To address this gap, we developed an optimized version of the IPDA capable of quantifying both subtypes B and C. Using this assay in a study of 82 people living with HIV subtype C, we found that prior to treatment, the amount of defective HIV-1 DNA was closely linked to viral load, viral activity, and immune response. Notably, after 96 weeks of ART, the intact viral reservoir declined more than the defective DNA. These findings suggest that defective HIV-1 DNA is not merely passive but may be associated with an increased risk of HIV-related conditions, underscoring the importance of eliminating this DNA to halt disease progression. Additionally, in a study involving 30 individuals with low-level viremia, about half suppressed their viremia after switching to the high genetic barrier drug Darunavir, while the other half did not respond. In the non-responding group, we observed correlations between the defective viral DNA and viral activity. This indicates that defective proviruses may contribute to ongoing viral production, potentially leading to adverse health effects due to an increased immune response. One of the strategies to eliminate viral reservoirs is the "induce and reduce" approach, which involves two steps: first, "inducing" the virus out of hiding using latency reversal agents (LRAs), and second, allowing the immune system to recognize and eliminate the now-active infected cells. We investigated whether combining different classes of LRAs could enhance the reactivation of latent HIV-1. Our in vitro and ex vivo results indicated that combinations involving panobinostat were most effective at reactivating dormant HIV-1 without significantly harming healthy cells. Future research should focus more on finding LRA combinations that effectively reverse latency while employing strategies that can eliminate the induced reservoir. Moreover, we emphasize the importance of including anatomical reservoirs, especially the central nervous system, in cure strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".