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Simulating within host human immunodeficiency virus 1 genome evolution in the persistent reservoir.

Bradley R Jones1, Jeffrey B Joy1

  • 1BC Centre for Excellence in HIV/AIDS, 608-1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada.

Virus Evolution
|May 27, 2021
PubMed
Summary

New software models human immunodeficiency virus 1 (HIV-1) evolution, including its persistent reservoir. The Least Squares method accurately estimates viral integration dates, aiding cure research.

Keywords:
fitnesshuman immunodeficiency virus 1simulationviral latency

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Area of Science:

  • Computational Biology
  • Virology
  • Evolutionary Biology

Background:

  • Viral evolution, particularly Human Immunodeficiency Virus 1 (HIV-1), is complex and challenging to study.
  • HIV-1's ability to integrate into host genomes creates a persistent reservoir, hindering durable cures.
  • Understanding the dynamics of this reservoir, including integration frequency and longevity, is crucial for developing effective treatments.

Purpose of the Study:

  • To develop and utilize novel simulation software for modeling HIV-1 evolution within a host.
  • To incorporate a multi-compartment model representing both active and latent HIV-1 viral populations.
  • To evaluate and compare different date estimation methods for determining HIV-1 integration dates within the persistent reservoir.

Main Methods:

  • Extended SANTA-SIM software to include multiple viral population compartments.
  • Developed a two-compartment model (active and latent) for HIV-1 evolution.
  • Assessed five date estimation methods: Closest Sequence, Clade, Linear Regression, Least Squares, and Maximum Likelihood.

Main Results:

  • The Least Squares method demonstrated superior performance in estimating integration dates.
  • Achieved high concordance (0.80) between real and estimated integration dates.
  • Exhibited the lowest absolute error among the tested methods, with statistically significant results (P < 0.01).

Conclusions:

  • The developed software is a valuable tool for validating bioinformatics software.
  • The findings enhance understanding of the temporal dynamics within the persistent HIV-1 reservoir.
  • Accurate date estimation is critical for characterizing HIV-1 persistence and informing cure strategies.