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Small-molecule screening using a human primary cell model of HIV latency identifies compounds that reverse latency without cellular activation.

ABSTRACT: The development of highly active antiretroviral therapy (HAART) to treat individuals infected with HIV-1 has dramatically improved patient outcomes, but HAART still fails to cure the infection. The latent viral reservoir in resting CD4+ T cells is a major barrier to virus eradication. Elimination of this reservoir requires reactivation of the latent virus. However, strategies for reactivating HIV-1 through nonspecific T cell activation have clinically unacceptable toxicities. We describe here the development of what we believe to be a novel in vitro model of HIV-1 latency that we used to search for compounds that can reverse latency. Human primary CD4+ T cells were transduced with the prosurvival molecule Bcl-2, and the resulting cells were shown to recapitulate the quiescent state of resting CD4+ T cells in vivo. Using this model system, we screened small-molecule libraries and identified a compound that reactivated latent HIV-1 without inducing global T cell activation, 5-hydroxynaphthalene-1,4-dione (5HN). Unlike previously described latency-reversing agents, 5HN activated latent HIV-1 through ROS and NF-kappaB without affecting nuclear factor of activated T cells (NFAT) and PKC, demonstrating that TCR pathways can be dissected and utilized to purge latent virus. Our study expands the number of classes of latency-reversing therapeutics and demonstrates the utility of this in vitro model for finding strategies to eradicate HIV-1 infection.

SUBMITTER: Yang HC

PROVIDER: S-EPMC2769176 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Small-molecule screening using a human primary cell model of HIV latency identifies compounds that reverse latency without cellular activation.

Yang Hung-Chih HC Xing Sifei S Shan Liang L O'Connell Karen K Dinoso Jason J Shen Anding A Zhou Yan Y Shrum Cynthia K CK Han Yefei Y Liu Jun O JO Zhang Hao H Margolick Joseph B JB Siliciano Robert F RF

The Journal of clinical investigation 20091001 11

The development of highly active antiretroviral therapy (HAART) to treat individuals infected with HIV-1 has dramatically improved patient outcomes, but HAART still fails to cure the infection. The latent viral reservoir in resting CD4+ T cells is a major barrier to virus eradication. Elimination of this reservoir requires reactivation of the latent virus. However, strategies for reactivating HIV-1 through nonspecific T cell activation have clinically unacceptable toxicities. We describe here th ...[more]

PMID: 19805909

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Project description:Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

Dataset Information

Small-molecule screening using a human primary cell model of HIV latency identifies compounds that reverse latency without cellular activation.

Publications

Small-molecule screening using a human primary cell model of HIV latency identifies compounds that reverse latency without cellular activation.

Similar Datasets

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