Title: Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification

Abstract

To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics canmore » reach 90% accuracy under idealized situations. Under realistic within-host HIV-1 evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5–50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling and that adding biomarkers improves reconstructions of real HIV-1 transmission histories. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.« less

Authors:

Lundgren, Erik John  ^[1];

• Search DOE PAGES for author "Lundgren, Erik John"
• Search DOE PAGES for ORCID "0000-0001-8840-8067"

Romero-Severson, Ethan  ^[1];

• Search DOE PAGES for author "Romero-Severson, Ethan"
• Search DOE PAGES for ORCID "0000-0002-8082-1225"

Albert, Jan  ^[2];

• Search DOE PAGES for author "Albert, Jan"
• Search DOE PAGES for ORCID "0000-0001-9020-0521"

Leitner, Thomas Kenneth  ^[1]

• Search DOE PAGES for author "Leitner, Thomas Kenneth"
• Search DOE PAGES for ORCID "0000-0001-8160-2588"

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+ Show Author Affiliations

1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2. Karolinska Institute, Stockholm (Sweden); Karolinska University Hospital, Stockholm (Sweden)

Publication Date:

Fri Aug 26 00:00:00 EDT 2022

Research Org.:

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE National Nuclear Security Administration (NNSA); National Institutes of Health (NIH); National Institute of Allergy and Infectious Diseases (NIAID)

OSTI Identifier:

1897429

Report Number(s):

LA-UR-21-31408
Journal ID: ISSN 1553-7358

Grant/Contract Number:  

89233218CNA000001; R01AI087520; R01AI152897

Resource Type:

Accepted Manuscript

Journal Name:

PLoS Computational Biology (Online)

Additional Journal Information:

Journal Name: PLoS Computational Biology (Online); Journal Volume: 18; Journal Issue: 8; Journal ID: ISSN 1553-7358

Publisher:

Public Library of Science

Country of Publication:

United States

Language:

English

Subject:

60 APPLIED LIFE SCIENCES; biomarkers; phylogenetics; HIV epidemiology; HIV-1; HIV; biomarker epidemiology; virus testing; public and occupational health