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Standing genetic variation and the evolution of drug resistance in HIV.

Pennings PS - PLoS Comput. Biol. (2012)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America. pennings@fas.harvard.edu

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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Possible combinations of population size and fitness and the effect of population sizes on the probability that DRMs are present before treatment.Figure 2a: Continuous line: combinations of population size before treatment () and fitness of mutant virus during therapy () that lead to the observed probability that resistance mutations from standing genetic variation become established (). Dashed line: combinations of population size during treatment () and fitness of mutant virus during therapy () that lead to the observed probability that resistance mutations from standing genetic variation become established (). Open dot:  and , closed dot: , . Figure 2b: Probability that a patient has any pre-existing DRMs before the start of therapy for different population sizes, and . Open dot: .
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pcbi-1002527-g002: Possible combinations of population size and fitness and the effect of population sizes on the probability that DRMs are present before treatment.Figure 2a: Continuous line: combinations of population size before treatment () and fitness of mutant virus during therapy () that lead to the observed probability that resistance mutations from standing genetic variation become established (). Dashed line: combinations of population size during treatment () and fitness of mutant virus during therapy () that lead to the observed probability that resistance mutations from standing genetic variation become established (). Open dot: and , closed dot: , . Figure 2b: Probability that a patient has any pre-existing DRMs before the start of therapy for different population sizes, and . Open dot: .

Mentions: The data and the results from simulations and predictions (using equations 2 and 4) are shown in figure 2. The percentage of patients with resistance after one year is lower in the simulations than in the analytical predictions, because in the simulations, it takes time for a mutation to increase in frequency and be detected. We assume that it is detected as soon as it is more frequent than the wildtype, the result is that in the simulations (and probably in reality) is lower in the first year than in the other two years. It is unclear how large this effect is in reality, but it means that the 6% we find is a conservative estimate of the role of standing genetic variation. If it would take 3 months for a mutation to increase in frequency and become detected, then in year 1 would be 75% of its value in the later years, and would be approximately 7% in stead of 6%.


Standing genetic variation and the evolution of drug resistance in HIV.

Pennings PS - PLoS Comput. Biol. (2012)

Possible combinations of population size and fitness and the effect of population sizes on the probability that DRMs are present before treatment.Figure 2a: Continuous line: combinations of population size before treatment () and fitness of mutant virus during therapy () that lead to the observed probability that resistance mutations from standing genetic variation become established (). Dashed line: combinations of population size during treatment () and fitness of mutant virus during therapy () that lead to the observed probability that resistance mutations from standing genetic variation become established (). Open dot:  and , closed dot: , . Figure 2b: Probability that a patient has any pre-existing DRMs before the start of therapy for different population sizes, and . Open dot: .
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3369920&req=5

pcbi-1002527-g002: Possible combinations of population size and fitness and the effect of population sizes on the probability that DRMs are present before treatment.Figure 2a: Continuous line: combinations of population size before treatment () and fitness of mutant virus during therapy () that lead to the observed probability that resistance mutations from standing genetic variation become established (). Dashed line: combinations of population size during treatment () and fitness of mutant virus during therapy () that lead to the observed probability that resistance mutations from standing genetic variation become established (). Open dot: and , closed dot: , . Figure 2b: Probability that a patient has any pre-existing DRMs before the start of therapy for different population sizes, and . Open dot: .
Mentions: The data and the results from simulations and predictions (using equations 2 and 4) are shown in figure 2. The percentage of patients with resistance after one year is lower in the simulations than in the analytical predictions, because in the simulations, it takes time for a mutation to increase in frequency and be detected. We assume that it is detected as soon as it is more frequent than the wildtype, the result is that in the simulations (and probably in reality) is lower in the first year than in the other two years. It is unclear how large this effect is in reality, but it means that the 6% we find is a conservative estimate of the role of standing genetic variation. If it would take 3 months for a mutation to increase in frequency and become detected, then in year 1 would be 75% of its value in the later years, and would be approximately 7% in stead of 6%.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America. pennings@fas.harvard.edu

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.

Show MeSH
Related in: MedlinePlus