Publication: Treatment with integrase inhibitor suggests a new interpretation of HIV RNA decay curves that reveals a subset of cells with slow integration
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Date
2017
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Public Library of Science
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Cardozo, E. Fabian, Adriana Andrade, John W. Mellors, Daniel R. Kuritzkes, Alan S. Perelson, and Ruy M. Ribeiro. 2017. “Treatment with integrase inhibitor suggests a new interpretation of HIV RNA decay curves that reveals a subset of cells with slow integration.” PLoS Pathogens 13 (7): e1006478. doi:10.1371/journal.ppat.1006478. http://dx.doi.org/10.1371/journal.ppat.1006478.
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Abstract
The kinetics of HIV-1 decay under treatment depends on the class of antiretrovirals used. Mathematical models are useful to interpret the different profiles, providing quantitative information about the kinetics of virus replication and the cell populations contributing to viral decay. We modeled proviral integration in short- and long-lived infected cells to compare viral kinetics under treatment with and without the integrase inhibitor raltegravir (RAL). We fitted the model to data obtained from participants treated with RAL-containing regimes or with a four-drug regimen of protease and reverse transcriptase inhibitors. Our model explains the existence and quantifies the three phases of HIV-1 RNA decay in RAL-based regimens vs. the two phases observed in therapies without RAL. Our findings indicate that HIV-1 infection is mostly sustained by short-lived infected cells with fast integration and a short viral production period, and by long-lived infected cells with slow integration but an equally short viral production period. We propose that these cells represent activated and resting infected CD4+ T-cells, respectively, and estimate that infection of resting cells represent ~4% of productive reverse transcription events in chronic infection. RAL reveals the kinetics of proviral integration, showing that in short-lived cells the pre-integration population has a half-life of ~7 hours, whereas in long-lived cells this half-life is ~6 weeks. We also show that the efficacy of RAL can be estimated by the difference in viral load at the start of the second phase in protocols with and without RAL. Overall, we provide a mechanistic model of viral infection that parsimoniously explains the kinetics of viral load decline under multiple classes of antiretrovirals.
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Keywords
Biology and Life Sciences, Microbiology, Virology, Viral Transmission and Infection, Viral Load, Medical Microbiology, Microbial Pathogens, Viral Pathogens, Immunodeficiency Viruses, HIV, HIV-1, Medicine and Health Sciences, Pathology and Laboratory Medicine, Pathogens, Organisms, Viruses, Biology and life sciences, RNA viruses, Retroviruses, Lentivirus, Cell Biology, Cellular Types, Animal Cells, Blood Cells, White Blood Cells, T Cells, Immune Cells, Immunology, Physical Sciences, Physics, Nuclear Physics, Nuclear Decay, Chemistry, Radiochemistry, Mathematical and Statistical Techniques, Mathematical Models, Biochemistry, Enzymology, Enzyme Inhibitors, Protease Inhibitors, Macrophages
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