Literature DB >> 25016044

Stochastic population switch may explain the latent reservoir stability and intermittent viral blips in HIV patients on suppressive therapy.

Sunpeng Wang1, Libin Rong2.   

Abstract

Highly active antiretroviral therapy can suppress plasma viral loads of HIV-1 infected individuals to below the detection limit of standard clinical assays. However, low-level viremia still persists. Many patients also have transient viral load measurements above the detection limit (the so-called "viral blips"). The latent reservoir consisting of latently infected CD4+ T cells represents a major obstacle to HIV-1 eradication. These cells can be activated to produce virions but the size of the latent reservoir is relatively stable. The mechanisms underlying low viral load persistence, emergence of intermittent viral blips and stability of the latent reservoir are not well understood. Cellular and viral transcription factors play an important role in the establishment and maintenance of HIV-1 latency. Infected cells with intermediate transcriptional activities may either revert to a latent state or become highly activated and produce virions due to intracellular perturbations. Here we develop a mathematical model that includes such stochastic population switch. We demonstrate that the model can generate a stable latent reservoir, intermittent viral blips, as well as low-level viremia persistence. Latently infected cells with intermediate transcription activities may maintain their size through a high level of homeostatic proliferation, while cells with low transcriptional activities are likely to be maintained through the reversion from cells with intermediate transcription activities. Simulations also suggest that treatment intensification or activation therapy may not help to eradicate the latent reservoir. Blocking the proliferation of latently infected cells might be a good strategy. These results provide more insights into the long-term dynamics of virus and latently infected cells in HIV patients on suppressive therapy and may help to develop novel treatment strategies.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Activation therapy; HIV latency; Mathematical model; Stochastic simulation; Viral persistence

Mesh:

Year:  2014        PMID: 25016044     DOI: 10.1016/j.jtbi.2014.06.042

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  10 in total

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3.  Increased inflammation in sanctuary sites may explain viral blips in HIV infection.

Authors:  E Fabian Cardozo; Michael J Piovoso; Ryan Zurakowski
Journal:  IET Syst Biol       Date:  2016-08       Impact factor: 1.615

4.  Dynamics of a new HIV model with the activation status of infected cells.

Authors:  Ting Guo; Zhipeng Qiu; Mingwang Shen; Libin Rong
Journal:  J Math Biol       Date:  2021-04-15       Impact factor: 2.164

Review 5.  Dynamic models of viral replication and latency.

Authors:  Pejman Mohammadi; Angela Ciuffi; Niko Beerenwinkel
Journal:  Curr Opin HIV AIDS       Date:  2015-03       Impact factor: 4.283

6.  Comparison of the Aptima HIV-1 Quant Dx assay with the COBAS AmpliPrep/COBAS TaqMan HIV-1 v2.0 Test for HIV-1 viral load quantification in plasma samples from HIV-1-infected patients.

Authors:  Serena Longo; Isabella Bon; Giuseppina Musumeci; Alessia Bertoldi; Vanessa D'Urbano; Leonardo Calza; Maria Carla Re
Journal:  Health Sci Rep       Date:  2018-03-13

7.  Viral Blips After Treatment Initiation During Acute Human Immunodeficiency Virus Infection.

Authors:  Trevor A Crowell; Suteeraporn Pinyakorn; Carlo Sacdalan; Eugène Kroon; Donn J Colby; Suwanna Puttamaswin; Sasiwimol Ubolyam; Rapee Trichavaroj; Oratai Butterworth; Ellen Turk; Corinne Mccullough; Nicolas Chomont; Mark de Souza; Merlin L Robb; Nittaya Phanuphak; Jintanat Ananworanich
Journal:  Clin Infect Dis       Date:  2020-06-10       Impact factor: 9.079

8.  Modeling the Slow CD4+ T Cell Decline in HIV-Infected Individuals.

Authors:  Sunpeng Wang; Patricia Hottz; Mauro Schechter; Libin Rong
Journal:  PLoS Comput Biol       Date:  2015-12-28       Impact factor: 4.475

9.  Immunological biomarkers predict HIV-1 viral rebound after treatment interruption.

Authors:  Jacob Hurst; Matthias Hoffmann; Matthew Pace; James P Williams; John Thornhill; Elizabeth Hamlyn; Jodi Meyerowitz; Chris Willberg; Kersten K Koelsch; Nicola Robinson; Helen Brown; Martin Fisher; Sabine Kinloch; David A Cooper; Mauro Schechter; Giuseppe Tambussi; Sarah Fidler; Abdel Babiker; Jonathan Weber; Anthony D Kelleher; Rodney E Phillips; John Frater
Journal:  Nat Commun       Date:  2015-10-09       Impact factor: 14.919

Review 10.  Persistent HIV-1 replication during antiretroviral therapy.

Authors:  Javier Martinez-Picado; Steven G Deeks
Journal:  Curr Opin HIV AIDS       Date:  2016-07       Impact factor: 4.283

  10 in total

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