Modeling of the HIV-1 Life Cycle in Productively Infected Cells to Predict Novel Therapeutic Targets

Abstract

There are many studies that model the within-host population dynamics of Human Immunodeficiency Virus Type 1 (HIV-1) infection. However, the within-infected-cell replication of HIV-1 remains to be not comprehensively addressed. There exist rather few quantitative models describing the regulation of the HIV-1 life cycle at the intracellular level. In treatment of HIV-1 infection, there remain issues related to side-effects and drug-resistance that require further search “...for new and better drugs, ideally targeting multiple independent steps in the HIV-1 replication cycle” (as highlighted recently by Tedbury & Freed, The Future of HIV-1 Therapeutics, 2015). High-resolution mathematical models of HIV-1 growth in infected cells provide an additional analytical tool in identifying novel drug targets. We formulate a high-dimensional model describing the biochemical reactions underlying the replication of HIV-1 in target cells. The model considers a nonlinear regulation of the transcription of HIV-1 mediated by Tat and the Rev-dependent transport of fully spliced and singly spliced transcripts from the nucleus to the cytoplasm. The model is calibrated using available information on the kinetics of various stages of HIV-1 replication. The sensitivity analysis of the model is performed to rank the biochemical processes of HIV-1 replication with respect to their impact on the net production of virions by one actively infected cell. The ranking of the sensitivity factors provides a quantitative basis for identifying novel targets for antiviral therapy. Our analysis suggests that HIV-1 assembly depending on Gag and Tat-Rev regulation of transcription and mRNA distribution present two most critical stages in HIV-1 replication that can be targeted to effectively control virus production. These processes are not covered by current antiretroviral treatments.

Cooperative nature of viral replication

“…Previous work has modeled thoroughly the infection cycle of certain viruses ( 24 , 25 ). To test for cooperativity in a more realistic scenario, we used two previous deterministic ODE models describing the entire infection cycles of HIV-1 ( 26 ) and hepatitis C virus (HCV) ( 27 ), as well as a stochastic model for influenza virus A (IVA) ( 23 ). We examined the dynamics of the number of progeny infectious particles ( V t ) as a function of the number of founder particles per cell ( V 0 ) using the parameter values reported in the original publications.…”

“…We tested for cooperativity in a previously published quantitative deterministic ODE model describing the entire cellular infection cycle of HIV-1, from virion-receptor binding to viral progeny maturation ( 26 ). A comprehensive description of the model equations and parameters is available from the original publication.…”

A New Approach to Developing Long-Acting Injectable Formulations of Anti-HIV Drugs: Poly(Ethylene Phosphoric Acid) Block Copolymers Increase the Efficiency of Tenofovir against HIV-1 in MT-4 Cells

“…Initiation of HIV reverse transcription (defined as reaching 1% of its total progression) occurs as early as 3 h after the virus-to-cell-membrane fusion [ 49 ] that is preceded by HIV attachment to its cellular receptors; fusion events are detected after a time lag of 10–15 min following virus and cell co-culture [ 50 ], reaching maximum with a t 1/2 of 19 min [ 51 ] (see also [ 52 ]). We therefore examined transformations of TDF and 1P4-TFD by determining their metabolites in cell extracts following 4 h of incubation with MT-4 cells.…”

Viral Infection Dynamics Model Based on a Markov Process with Time Delay between Cell Infection and Progeny Production

“…In subsequent work, the full and the hybrid scheme has to be generalized by accounting for a greater number of interacting components (like in the model described in [39]), for a distributed delay, and for more than one delay. This will enable to develop more realistic virus infection models that shall help to better understand regulation and sensitivity of the underlying biological processes.…”