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Modeling HIV-1 Drug Resistance as Episodic Directional Selection

Overview of attention for article published in PLoS Computational Biology, May 2012
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Title
Modeling HIV-1 Drug Resistance as Episodic Directional Selection
Published in
PLoS Computational Biology, May 2012
DOI 10.1371/journal.pcbi.1002507
Pubmed ID
Authors

Ben Murrell, Tulio de Oliveira, Chris Seebregts, Sergei L. Kosakovsky Pond, Konrad Scheffler

Abstract

The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 2%
Netherlands 1 2%
France 1 2%
United Kingdom 1 2%
Canada 1 2%
Taiwan 1 2%
United States 1 2%
Unknown 50 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 19%
Researcher 11 19%
Student > Bachelor 8 14%
Student > Master 6 11%
Professor 5 9%
Other 10 18%
Unknown 6 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 42%
Computer Science 6 11%
Biochemistry, Genetics and Molecular Biology 5 9%
Medicine and Dentistry 4 7%
Immunology and Microbiology 4 7%
Other 8 14%
Unknown 6 11%