Evidence map›Paper›PMID 24699231›Full record

ArticlePLoS computational biology2014

The genealogical population dynamics of HIV-1 in a large transmission chain: bridging within and among host evolutionary rates.

Bram Vrancken, Andrew Rambaut, Marc A Suchard, Alexei Drummond, Guy Baele, Inge Derdelinckx, Eric Van Wijngaerden, Anne-Mieke Vandamme, Kristel Van Laethem, Philippe Lemey

Abstract read
In one paragraph

Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

68 citing papers in PubMed, 1 synthesis or guideline pooled it.

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8 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Bram VranckenDepartment of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
Andrew RambautInstitute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.
Marc A SuchardDepartment of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, California, United States of America; Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, California, United States of America; Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles Los Angeles, California, United States of America.
Alexei DrummondAllan Wilson Centre for Molecular Ecology and Evolution, University of Auckland, Auckland, New Zealand.
Guy BaeleDepartment of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
Inge DerdelinckxUniversity Hospitals Leuven, Leuven, Belgium.
Eric Van WijngaerdenUniversity Hospitals Leuven, Leuven, Belgium.
Anne-Mieke VandammeDepartment of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium; Centro de Malária e Outras Doenças Tropicais Instituto de Higiene e Medicina Tropical and Unidade de Microbiologia, Universidade Nova de Lisboa, Lisboa, Portugal.
Kristel Van LaethemDepartment of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
Philippe LemeyDepartment of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.

Funding

Wellcome Trust 095831
6 · The paper itself

Abstract

Transmission lies at the interface of human immunodeficiency virus type 1 (HIV-1) evolution within and among hosts and separates distinct selective pressures that impose differences in both the mode of diversification and the tempo of evolution. In the absence of comprehensive direct comparative analyses of the evolutionary processes at different biological scales, our understanding of how fast within-host HIV-1 evolutionary rates translate to lower rates at the between host level remains incomplete. Here, we address this by analyzing pol and env data from a large HIV-1 subtype C transmission chain for which both the timing and the direction is known for most transmission events. To this purpose, we develop a new transmission model in a Bayesian genealogical inference framework and demonstrate how to constrain the viral evolutionary history to be compatible with the transmission history while simultaneously inferring the within-host evolutionary and population dynamics. We show that accommodating a transmission bottleneck affords the best fit our data, but the sparse within-host HIV-1 sampling prevents accurate quantification of the concomitant loss in genetic diversity. We draw inference under the transmission model to estimate HIV-1 evolutionary rates among epidemiologically-related patients and demonstrate that they lie in between fast intra-host rates and lower rates among epidemiologically unrelated individuals infected with HIV subtype C. Using a new molecular clock approach, we quantify and find support for a lower evolutionary rate along branches that accommodate a transmission event or branches that represent the entire backbone of transmitted lineages in our transmission history. Finally, we recover the rate differences at the different biological scales for both synonymous and non-synonymous substitution rates, which is only compatible with the 'store and retrieve' hypothesis positing that viruses stored early in latently infected cells preferentially transmit or establish new infections upon reactivation.

Indexed as

Evolution, MolecularHost-Pathogen InteractionsHIV-1HIV InfectionsHumans

Identifiers

PMID24699231
PMCPMC3974631

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.