A transmission-virulence evolutionary trade-off explains attenuation of HIV-1 in Uganda
Abstract
Evolutionary theory hypothesizes that intermediate virulence maximizes pathogen fitness as a result of a trade-off between virulence and transmission, but empirical evidence remains scarce. We bridge this gap using data from a large and long-standing HIV-1 prospective cohort, in Uganda. We use an epidemiological-evolutionary model parameterised with this data to derive evolutionary predictions based on analysis and detailed individual-based simulations. We robustly predict stabilising selection towards a low level of virulence, and rapid attenuation of the virus. Accordingly, set-point viral load, the most common measure of virulence, has declined in the last 20 years. Our model also predicts that subtype A is slowly outcompeting subtype D, with both subtypes becoming less virulent, as observed in the data. Reduction of set-point viral loads should have resulted in a 20% reduction in incidence, and a three years extension of untreated asymptomatic infection, increasing opportunities for timely treatment of infected individuals.
Data availability
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Data from: A transmission-virulence evolutionary trade-off explains attenuation of HIV-1 in UgandaAvailable at Dryad Digital Repository under a CC0 Public Domain Dedication.
Article and author information
Author details
Funding
European Commission (Intra European Fellowship 657768)
- François Blanquart
World Bank Group
- Mary Kate Grabowski
- Fred Nalugoda
- David Serwadda
- Michael A Eller
- Merlin L Robb
- Ronald Gray
- Godfrey Kigozi
- Oliver Laeyendecker
- Gertrude Nakigozi
- Thomas C Quinn
- Steven J Reynolds
- Maria J Wawer
Henry M. Jackson Foundation (W81XWH-07-2-0067)
- Mary Kate Grabowski
- Fred Nalugoda
- David Serwadda
- Michael A Eller
- Merlin L Robb
- Ronald Gray
- Godfrey Kigozi
- Oliver Laeyendecker
- Gertrude Nakigozi
- Thomas C Quinn
- Steven J Reynolds
- Maria J Wawer
U.S. Department of Defense (W81XWH-07-2-0067)
- Mary Kate Grabowski
- Fred Nalugoda
- David Serwadda
- Michael A Eller
- Merlin L Robb
- Ronald Gray
- Godfrey Kigozi
- Oliver Laeyendecker
- Gertrude Nakigozi
- Thomas C Quinn
- Steven J Reynolds
- Maria J Wawer
National Institutes of Health (R01AI108490; P30AI027757)
- Joshua Herbeck
European Research Council (PBDR-339251)
- Christophe Fraser
National Institute of Allergy and Infectious Diseases (R01 Al 29314; R01 AI34826; UO1 AI11171-01-02)
- Mary Kate Grabowski
- Fred Nalugoda
- David Serwadda
- Michael A Eller
- Merlin L Robb
- Ronald Gray
- Godfrey Kigozi
- Oliver Laeyendecker
- Gertrude Nakigozi
- Thomas C Quinn
- Steven J Reynolds
- Maria J Wawer
National Institute of Child Health and Human Development (5P30 HD 06268)
- Mary Kate Grabowski
- Fred Nalugoda
- David Serwadda
- Michael A Eller
- Merlin L Robb
- Ronald Gray
- Godfrey Kigozi
- Oliver Laeyendecker
- Gertrude Nakigozi
- Thomas C Quinn
- Steven J Reynolds
- Maria J Wawer
John E. Fogarty Foundation for Persons with Intellectual and Developmental Disabilities (5D43TW00010)
- Mary Kate Grabowski
- Fred Nalugoda
- David Serwadda
- Michael A Eller
- Merlin L Robb
- Ronald Gray
- Godfrey Kigozi
- Oliver Laeyendecker
- Gertrude Nakigozi
- Thomas C Quinn
- Steven J Reynolds
- Maria J Wawer
John Snow Inc. (5024-30)
- Mary Kate Grabowski
- Fred Nalugoda
- David Serwadda
- Michael A Eller
- Merlin L Robb
- Ronald Gray
- Godfrey Kigozi
- Oliver Laeyendecker
- Gertrude Nakigozi
- Thomas C Quinn
- Steven J Reynolds
- Maria J Wawer
Pfizer (5024-30)
- Mary Kate Grabowski
- Fred Nalugoda
- David Serwadda
- Michael A Eller
- Merlin L Robb
- Ronald Gray
- Godfrey Kigozi
- Oliver Laeyendecker
- Gertrude Nakigozi
- Thomas C Quinn
- Steven J Reynolds
- Maria J Wawer
Rockefeller Foundation
- Mary Kate Grabowski
- Fred Nalugoda
- David Serwadda
- Michael A Eller
- Merlin L Robb
- Ronald Gray
- Godfrey Kigozi
- Oliver Laeyendecker
- Gertrude Nakigozi
- Thomas C Quinn
- Steven J Reynolds
- Maria J Wawer
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Informed consent was obtained from all the participants in the Rakai Community Cohort Study.The Scientific and Ethics Committee of the Uganda Virus Research Institute (UVRI) of the Ministry of Health provides the Institutional Review Board approval and monitoring of all Rakai research.
Reviewing Editor
- Richard A Neher, Max Planck Institute for Developmental Biology, Germany
Publication history
- Received: August 10, 2016
- Accepted: November 1, 2016
- Accepted Manuscript published: November 5, 2016 (version 1)
- Accepted Manuscript updated: November 8, 2016 (version 2)
- Version of Record published: November 18, 2016 (version 3)
Copyright
© 2016, Blanquart et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Epidemiology and Global Health
- Evolutionary Biology
Detecting factors associated with transmission is important to understand disease epidemics, and to design effective public health measures. Clustering and terminal branch lengths (TBL) analyses are commonly applied to genomic data sets of Mycobacterium tuberculosis (MTB) to identify sub-populations with increased transmission. Here, I used a simulation-based approach to investigate what epidemiological processes influence the results of clustering and TBL analyses, and whether differences in transmission can be detected with these methods. I simulated MTB epidemics with different dynamics (latency, infectious period, transmission rate, basic reproductive number R0, sampling proportion, sampling period, and molecular clock), and found that all considered factors, except for the length of the infectious period, affect the results of clustering and TBL distributions. I show that standard interpretations of this type of analyses ignore two main caveats: (1) clustering results and TBL depend on many factors that have nothing to do with transmission, (2) clustering results and TBL do not tell anything about whether the epidemic is stable, growing, or shrinking, unless all the additional parameters that influence these metrics are known, or assumed identical between sub-populations. An important consequence is that the optimal SNP threshold for clustering depends on the epidemiological conditions, and that sub-populations with different epidemiological characteristics should not be analyzed with the same threshold. Finally, these results suggest that different clustering rates and TBL distributions, that are found consistently between different MTB lineages, are probably due to intrinsic bacterial factors, and do not indicate necessarily differences in transmission or evolutionary success.
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- Epidemiology and Global Health
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