Differences in Transcriptional Dynamics Between T-cells and Macrophages as Determined by a Three-State Mathematical Model

Sci Rep. 2020 Feb 10;10(1):2227. doi: 10.1038/s41598-020-59008-0.

Abstract

HIV-1 viral transcription persists in patients despite antiretroviral treatment, potentially due to intermittent HIV-1 LTR activation. While several mathematical models have been explored in the context of LTR-protein interactions, in this work for the first time HIV-1 LTR model featuring repressed, intermediate, and activated LTR states is integrated with generation of long (env) and short (TAR) RNAs and proteins (Tat, Pr55, and p24) in T-cells and macrophages using both cell lines and infected primary cells. This type of extended modeling framework allows us to compare and contrast behavior of these two cell types. We demonstrate that they exhibit unique LTR dynamics, which ultimately results in differences in the magnitude of viral products generated. One of the distinctive features of this work is that it relies on experimental data in reaction rate computations. Two RNA transcription rates from the activated promoter states are fit by comparison of experimental data to model predictions. Fitting to the data also provides estimates for the degradation/exit rates for long and short viral RNA. Our experimentally generated data is in reasonable agreement for the T-cell as well macrophage population and gives strong evidence in support of using the proposed integrated modeling paradigm. Sensitivity analysis performed using Latin hypercube sampling method confirms robustness of the model with respect to small parameter perturbations. Finally, incorporation of a transcription inhibitor (F07#13) into the governing equations demonstrates how the model can be used to assess drug efficacy. Collectively, our model indicates transcriptional differences between latently HIV-1 infected T-cells and macrophages and provides a novel platform to study various transcriptional dynamics leading to latency or activation in numerous cell types and physiological conditions.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anti-HIV Agents / pharmacology*
  • Anti-HIV Agents / therapeutic use
  • Cell Line
  • Drug Resistance, Viral / drug effects
  • Drug Resistance, Viral / genetics
  • Drug Resistance, Viral / immunology
  • Gene Expression Regulation, Viral / immunology*
  • HIV Infections / blood
  • HIV Infections / drug therapy*
  • HIV Infections / immunology
  • HIV Long Terminal Repeat / genetics
  • HIV-1 / drug effects
  • HIV-1 / genetics*
  • HIV-1 / immunology
  • Humans
  • Macrophages / immunology*
  • Macrophages / virology
  • Models, Genetic
  • Models, Immunological
  • Primary Cell Culture
  • RNA, Viral / genetics
  • RNA, Viral / metabolism
  • T-Lymphocytes / immunology*
  • T-Lymphocytes / virology
  • Transcription, Genetic / drug effects
  • Transcription, Genetic / immunology
  • Virus Replication / drug effects
  • Virus Replication / genetics
  • Virus Replication / immunology

Substances

  • Anti-HIV Agents
  • RNA, Viral