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Standing Genetic Variation and the Evolution of Drug Resistance in HIV

Overview of attention for article published in PLoS Computational Biology, June 2012
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Title
Standing Genetic Variation and the Evolution of Drug Resistance in HIV
Published in
PLoS Computational Biology, June 2012
DOI 10.1371/journal.pcbi.1002527
Pubmed ID
Authors

Pleuni Simone Pennings

Abstract

Drug resistance remains a major problem for the treatment of HIV. Resistance can occur due to mutations that were present before treatment starts or due to mutations that occur during treatment. The relative importance of these two sources is unknown. Resistance can also be transmitted between patients, but this process is not considered in the current study. We study three different situations in which HIV drug resistance may evolve: starting triple-drug therapy, treatment with a single dose of nevirapine and interruption of treatment. For each of these three cases good data are available from literature, which allows us to estimate the probability that resistance evolves from standing genetic variation. Depending on the treatment we find probabilities of the evolution of drug resistance due to standing genetic variation between 0 and 39%. For patients who start triple-drug combination therapy, we find that drug resistance evolves from standing genetic variation in approximately 6% of the patients. We use a population-dynamic and population-genetic model to understand the observations and to estimate important evolutionary parameters under the assumption that treatment failure is caused by the fixation of a single drug resistance mutation. We find that both the effective population size of the virus before treatment, and the fitness of the resistant mutant during treatment, are key-arameters which determine the probability that resistance evolves from standing genetic variation. Importantly, clinical data indicate that both of these parameters can be manipulated by the kind of treatment that is used.

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Geographical breakdown

Country Count As %
India 1 <1%
United States 1 <1%
Switzerland 1 <1%
Canada 1 <1%
Unknown 133 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 22%
Student > Master 23 17%
Researcher 19 14%
Student > Bachelor 15 11%
Professor > Associate Professor 9 7%
Other 23 17%
Unknown 18 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 36%
Biochemistry, Genetics and Molecular Biology 19 14%
Medicine and Dentistry 16 12%
Physics and Astronomy 4 3%
Nursing and Health Professions 3 2%
Other 24 18%
Unknown 21 15%