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
Version 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
Background:
Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD).
Methods:
We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40–80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004–2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC).
Results:
ML model Elastic Net (EN) had the best AUC (95% CI) of 0.851 (0.847–0.856), which was 7.0% relatively higher than by LR 0.795 (0.790–0.801). Sensitivity and specificity of EN were 88.2 and 65.9% vs. 73.0 and 72.8% by LR. The top 15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR, and metabolites related to lipids, lipoproteins, fatty acids, and ketone bodies.
Conclusions:
Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites.
Funding:
This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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- Epidemiology and Global Health
The NIH-funded RECOVER study is collecting clinical data on patients who experience a SARS-CoV-2 infection. As patient representatives of the RECOVER Initiative’s Mechanistic Pathways task force, we offer our perspectives on patient motivations for partnering with researchers to obtain results from mechanistic studies. We emphasize the challenges of balancing urgency with scientific rigor. We recognize the importance of such partnerships in addressing post-acute sequelae of SARS-CoV-2 infection (PASC), which includes ‘long COVID,’ through contrasting objective and subjective narratives. Long COVID’s prevalence served as a call to action for patients like us to become actively involved in efforts to understand our condition. Patient-centered and patient-partnered research informs the balance between urgency and robust mechanistic research. Results from collaborating on protocol design, diverse patient inclusion, and awareness of community concerns establish a new precedent in biomedical research study design. With a public health matter as pressing as the long-term complications that can emerge after SARS-CoV-2 infection, considerate and equitable stakeholder involvement is essential to guiding seminal research. Discussions in the RECOVER Mechanistic Pathways task force gave rise to this commentary as well as other review articles on the current scientific understanding of PASC mechanisms.