Letter | Published:

Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants

Nature Microbiologyvolume 3pages983988 (2018) | Download Citation

Abstract

The growth of human immunodeficiency virus (HIV) sequence databases resulting from drug resistance testing has motivated efforts using phylogenetic methods to assess how HIV spreads1,2,3,4. Such inference is potentially both powerful and useful for tracking the epidemiology of HIV and the allocation of resources to prevention campaigns. We recently used simulation and a small number of illustrative cases to show that certain phylogenetic patterns are associated with different types of epidemiological linkage5. Our original approach was later generalized for large next-generation sequencing datasets and implemented as a free computational pipeline6. Previous work has claimed that direction and directness of transmission could not be established from phylogeny because one could not be sure that there were no intervening or missing links involved7,8,9. Here, we address this issue by investigating phylogenetic patterns from 272 previously identified HIV transmission chains with 955 transmission pairs representing diverse geography, risk groups, subtypes, and genomic regions. These HIV transmissions had known linkage based on epidemiological information such as partner studies, mother-to-child transmission, pairs identified by contact tracing, and criminal cases. We show that the resulting phylogeny inferred from real HIV genetic sequences indeed reveals distinct patterns associated with direct transmission contra transmissions from a common source. Thus, our results establish how to interpret phylogenetic trees based on HIV sequences when tracking who-infected-whom, when and how genetic information can be used for improved tracking of HIV spread. We also investigate limitations that stem from limited sampling and genetic time-trends in the donor and recipient HIV populations.

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Acknowledgements

We thank N. Hengartner for advice on statistical analyses, C. Fraser for suggesting the use of Bayes’ rule to illustrate the broader inference problem, and J. Macke and W. Abfalterer for help with database annotation and searches. This study was supported by a NIH/NIAID grant (R01AI087520) and a NIH–DOE interagency agreement (AI2013183).

Author information

Affiliations

  1. Theoretical Biology and Biophysics Group, MS K710, Los Alamos National Laboratory, Los Alamos, NM, USA

    • Thomas Leitner
    •  & Ethan Romero-Severson

Authors

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Contributions

T.L. designed the study and compiled the data, T.L. and E.R.-S. analysed the data and wrote the paper.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Thomas Leitner.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–3 and Supplementary Table 1 footnote.

  2. Reporting Summary

  3. Supplementary Table 1

    Excel file summarizing data features per epidemiological pair.

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https://doi.org/10.1038/s41564-018-0204-9

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