Estimating the Timing of Early Simian-Human Immunodeficiency Virus Infections: a Comparison between Poisson Fitter and BEAST
Abstract
Many HIV prevention strategies are currently under consideration where it is highly informative to know the study participants’ times of infection. These can be estimated using viral sequence data sampled early in infection. However, there are several scenarios that, if not addressed, can skew timing estimates. These include multiple transmitted/founder (TF) viruses, APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like)-mediated mutational enrichment, and recombination. Here, we suggest a pipeline to identify these problems and resolve the biases that they introduce. We then compare two modeling strategies to obtain timing estimates from sequence data. The first, Poisson Fitter (PF), is based on a Poisson model of random accumulation of mutations relative to the TF virus (or viruses) that established the infection. The second uses a coalescence-based phylogenetic strategy as implemented in BEAST. The comparison is based on timing predictions using plasma viral RNA (cDNA) sequence data from 28 simian-human immunodeficiency virus (SHIV)-infected animals for which the exact day of infection is known. In this particular setting, based on nucleotide sequences from samples obtained in early infection, the Poisson method yielded more accurate, more precise, and unbiased estimates for the time of infection than did the explored implementations of BEAST. The inferencemore »
- Authors:
-
[1];
[3];
[1];
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States); New Mexico Consortium, Los Angeles, NM (United States)
- Univ. of Pennsylvania, Philadelphia, PA (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Johns Hopkins Univ., Baltimore, MD (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE; Bill and Melinda Gates Foundation; National Institutes of Health (NIH)
- OSTI Identifier:
- 1614836
- Report Number(s):
- LA-UR-19-26789
Journal ID: ISSN 2150-7511
- Grant/Contract Number:
- 89233218CNA000001; P01AI131251; 1146996; 1145056; 1206647
- Resource Type:
- Accepted Manuscript
- Journal Name:
- mBio (Online)
- Additional Journal Information:
- Journal Name: mBio (Online); Journal Volume: 11; Journal Issue: 2; Journal ID: ISSN 2150-7511
- Publisher:
- American Society for Microbiology
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; biological science; evolution; HIV; SHIV; transmission
Citation Formats
Giorgi, Elena Edi, Li, Hui, Bhattacharya, Tanmoy, Shaw, George M., Korber, Bette Tina Marie, and Griffin, Diane E. Estimating the Timing of Early Simian-Human Immunodeficiency Virus Infections: a Comparison between Poisson Fitter and BEAST. United States: N. p., 2020.
Web. doi:10.1128/mBio.00324-20.
Giorgi, Elena Edi, Li, Hui, Bhattacharya, Tanmoy, Shaw, George M., Korber, Bette Tina Marie, & Griffin, Diane E. Estimating the Timing of Early Simian-Human Immunodeficiency Virus Infections: a Comparison between Poisson Fitter and BEAST. United States. https://doi.org/10.1128/mBio.00324-20
Giorgi, Elena Edi, Li, Hui, Bhattacharya, Tanmoy, Shaw, George M., Korber, Bette Tina Marie, and Griffin, Diane E. Tue .
"Estimating the Timing of Early Simian-Human Immunodeficiency Virus Infections: a Comparison between Poisson Fitter and BEAST". United States. https://doi.org/10.1128/mBio.00324-20. https://www.osti.gov/servlets/purl/1614836.
@article{osti_1614836,
title = {Estimating the Timing of Early Simian-Human Immunodeficiency Virus Infections: a Comparison between Poisson Fitter and BEAST},
author = {Giorgi, Elena Edi and Li, Hui and Bhattacharya, Tanmoy and Shaw, George M. and Korber, Bette Tina Marie and Griffin, Diane E.},
abstractNote = {Many HIV prevention strategies are currently under consideration where it is highly informative to know the study participants’ times of infection. These can be estimated using viral sequence data sampled early in infection. However, there are several scenarios that, if not addressed, can skew timing estimates. These include multiple transmitted/founder (TF) viruses, APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like)-mediated mutational enrichment, and recombination. Here, we suggest a pipeline to identify these problems and resolve the biases that they introduce. We then compare two modeling strategies to obtain timing estimates from sequence data. The first, Poisson Fitter (PF), is based on a Poisson model of random accumulation of mutations relative to the TF virus (or viruses) that established the infection. The second uses a coalescence-based phylogenetic strategy as implemented in BEAST. The comparison is based on timing predictions using plasma viral RNA (cDNA) sequence data from 28 simian-human immunodeficiency virus (SHIV)-infected animals for which the exact day of infection is known. In this particular setting, based on nucleotide sequences from samples obtained in early infection, the Poisson method yielded more accurate, more precise, and unbiased estimates for the time of infection than did the explored implementations of BEAST. The inference of the time of infection is a critical parameter in testing the efficacy of clinical interventions in protecting against HIV-1 infection. For example, in clinical trials evaluating the efficacy of passively delivered antibodies (Abs) for preventing infections, accurate time of infection data are essential for discerning levels of the Abs required to confer protection, given the natural Ab decay rate in the human body. In such trials, genetic sequences from early in the infection are regularly sampled from study participants, generally prior to immune selection, when the viral population is still expanding and genetic diversity is low. In this particular setting of early viral growth, the Poisson method is superior to the alternative approach based on coalescent methods. This approach can also be applied in human vaccine trials, where accurate estimates of infection times help ascertain if vaccine-elicited immune protection wanes over time.},
doi = {10.1128/mBio.00324-20},
journal = {mBio (Online)},
number = 2,
volume = 11,
place = {United States},
year = {Tue Mar 24 00:00:00 EDT 2020},
month = {Tue Mar 24 00:00:00 EDT 2020}
}
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