Characterizing Transcriptional Dynamics of HIV-1 in T-cells and Macrophages Using a Three-State LTR Model

Tin Phan
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA

Catherine DeMarino
Laboratory of Molecular Virology, School of Systems Biology, George Mason University, Manassas, VA, USA

Fatah Kashanchi
Laboratory of Molecular Virology, School of Systems Biology, George Mason University, Manassas, VA, USA

Yang Kuang
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA

Daniel M. Anderson
Department of Mathematical Sciences, George Mason University, Fairfax, VA, USA

Maria Emelianenko
Department of Mathematical Sciences, George Mason University, Fairfax, VA, USA

Abstract

HIV-1 affects tens of millions of people worldwide. In this work, we extend a novel three-state model of HIV-1 transcription to study the differences in the transcription process of HIV-1 in T-cells and macrophages. In particular, we find that the activation of the HIV-1 promoter in macrophages appears to take place rapidly as the Tat protein approaches a critical threshold. In contrast, the same process occurs smoother in T-cells. By examining the self-feedback loop of Tat, we observe distinct characteristic differences of the transcriptional feedback loop between macrophages and T-cells. A systematic analysis shows the stability of the positive steady state in limiting cases, with the global stability in the general case remaining an open question. Moreover, our numerical simulations and analysis demonstrate that the transcription-inhibitor's effect can be enhanced by synchronizing with standard treatments, such as combination antiretroviral therapy, to reduce the total dosages and toxicity.

Keywords: HIV-1 transcription ,F07#13 ,Transcriptional inhibitor ,Treatment combination ,Transcriptional feedback loop ,Mathematical modeling

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