ArticlePLoS pathogens2013
An in-depth comparison of latent HIV-1 reactivation in multiple cell model systems and resting CD4+ T cells from aviremic patients.
Article in PLoS pathogens, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 312 papers.
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Who cites it
312 citing papers in PubMed, 418 citations in OpenAlex.
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- World free HIV: the novel therapeutic approaches for eliminating latent HIV infection.Virology journal · 2026Review
- The effect of combining HIV latency reversal with inhibition of phosphoinositide-3 kinases or B-cell lymphoma-2 on the HIV reservoir.PLoS pathogens · 2026Article
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- HIV proviral transcription and infectivity are enhanced by neddylation.Journal of virology · 2025Article
- Retinol Binding Protein 4 reactivates latent HIV-1 by triggering canonical NF-κB, JAK/STAT5 and JNK signalling.Signal transduction and targeted therapy · 2025Article
- Using single cell technologies to understand HIV latency models.Current opinion in HIV and AIDS · 2025Review
- Fast pH-Driven Solubilization Method of Realgar (AsAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Profiling HIV1-host protein-protein interaction networks in patient-derived exosome proteins: impact on pathophysiology and innate immune pathways.Virology journal · 2025Article
- Modeling simian immunodeficiency virus (SIV) latency in primary rhesus macaque CD4bioRxiv : the preprint server for biology · 2025Article
- MrHAMER2: high-accuracy long-read RNA sequencing to decode isoform-specific variation in viral transcripts during latency.bioRxiv : the preprint server for biology · 2025Article
- BET degraders reveal BRD4 disruption of 7SK and P-TEFb is critical for effective reactivation of latent HIV in CD4+ T-cells.Journal of virology · 2025Article
252 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
23 authors at 9 institutions in 2 countries.
Funding
Abstract
The possibility of HIV-1 eradication has been limited by the existence of latently infected cellular reservoirs. Studies to examine control of HIV latency and potential reactivation have been hindered by the small numbers of latently infected cells found in vivo. Major conceptual leaps have been facilitated by the use of latently infected T cell lines and primary cells. However, notable differences exist among cell model systems. Furthermore, screening efforts in specific cell models have identified drug candidates for "anti-latency" therapy, which often fail to reactivate HIV uniformly across different models. Therefore, the activity of a given drug candidate, demonstrated in a particular cellular model, cannot reliably predict its activity in other cell model systems or in infected patient cells, tested ex vivo. This situation represents a critical knowledge gap that adversely affects our ability to identify promising treatment compounds and hinders the advancement of drug testing into relevant animal models and clinical trials. To begin to understand the biological characteristics that are inherent to each HIV-1 latency model, we compared the response properties of five primary T cell models, four J-Lat cell models and those obtained with a viral outgrowth assay using patient-derived infected cells. A panel of thirteen stimuli that are known to reactivate HIV by defined mechanisms of action was selected and tested in parallel in all models. Our results indicate that no single in vitro cell model alone is able to capture accurately the ex vivo response characteristics of latently infected T cells from patients. Most cell models demonstrated that sensitivity to HIV reactivation was skewed toward or against specific drug classes. Protein kinase C agonists and PHA reactivated latent HIV uniformly across models, although drugs in most other classes did not.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.