Limits...
Recombination rate and selection strength in HIV intra-patient evolution.

Neher RA, Leitner T - PLoS Comput. Biol. (2010)

Bottom Line: By examining temporal changes in the genetic composition of the population, we estimate the effective recombination to be rho = 1.4+/-0.6 x 10(-5) recombinations per site and generation.These results provide a basis for a more detailed understanding of the evolution of HIV.With the methods developed here, more precise and more detailed studies will be possible as soon as data with higher time resolution and greater sample sizes are available.

View Article: PubMed Central - PubMed

Affiliation: Kavli Institute for Theoretical Physics, University of California, Santa Barbara, California, United States of America. neher@kitp.ucsb.edu

ABSTRACT
The evolutionary dynamics of HIV during the chronic phase of infection is driven by the host immune response and by selective pressures exerted through drug treatment. To understand and model the evolution of HIV quantitatively, the parameters governing genetic diversification and the strength of selection need to be known. While mutation rates can be measured in single replication cycles, the relevant effective recombination rate depends on the probability of coinfection of a cell with more than one virus and can only be inferred from population data. However, most population genetic estimators for recombination rates assume absence of selection and are hence of limited applicability to HIV, since positive and purifying selection are important in HIV evolution. Yet, little is known about the distribution of selection differentials between individual viruses and the impact of single polymorphisms on viral fitness. Here, we estimate the rate of recombination and the distribution of selection coefficients from time series sequence data tracking the evolution of HIV within single patients. By examining temporal changes in the genetic composition of the population, we estimate the effective recombination to be rho = 1.4+/-0.6 x 10(-5) recombinations per site and generation. Furthermore, we provide evidence that the selection coefficients of at least 15% of the observed non-synonymous polymorphisms exceed 0.8% per generation. These results provide a basis for a more detailed understanding of the evolution of HIV. A particularly interesting case is evolution in response to drug treatment, where recombination can facilitate the rapid acquisition of multiple resistance mutations. With the methods developed here, more precise and more detailed studies will be possible as soon as data with higher time resolution and greater sample sizes are available.

Show MeSH

Related in: MedlinePlus

Estimating recombination rates from time resolved data.Panel A shows the probability of finding a haplotype that is not detected at time  in the sample at  as a function of the separation  of the sites. The data labeled ‘recombinant haplotypes’ refers to those combinations, that can be generated by recombination from the alleles detected at time  and displays a pronounced distance dependence. The data labeled ‘other haplotypes’ refers to pairs containing at least one allele not detected at time , implying an additional mutation or undersampling. The data is averaged over all time points, all patients, and those pairs of polymorphic sites, where both alleles at both sites are seen at least 3 times. Panel B shows the probability of finding the missing haplotype as a function of the product of distance  and time interval . The fit to the data is shown in black with fit parameters indicated in the legend.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2813257&req=5

pcbi-1000660-g002: Estimating recombination rates from time resolved data.Panel A shows the probability of finding a haplotype that is not detected at time in the sample at as a function of the separation of the sites. The data labeled ‘recombinant haplotypes’ refers to those combinations, that can be generated by recombination from the alleles detected at time and displays a pronounced distance dependence. The data labeled ‘other haplotypes’ refers to pairs containing at least one allele not detected at time , implying an additional mutation or undersampling. The data is averaged over all time points, all patients, and those pairs of polymorphic sites, where both alleles at both sites are seen at least 3 times. Panel B shows the probability of finding the missing haplotype as a function of the product of distance and time interval . The fit to the data is shown in black with fit parameters indicated in the legend.

Mentions: For each pair of biallic sites at time that was found in three of the four possible haplotypes, we asked whether the missing haplotype is observed the at time (comp. Figure 1) and calculated the frequency of this event as a function of the separation of the two sites, as shown in Figure 2A. This frequency increases with the separation of the two sites from about 0.1 to about 0.35 at 500 bp separation, in line with the expectation that recombination is more rapid between distant sites. To corroborate that this distance dependence is indeed due to recombination, we performed the following similar analysis: The curve labelled “other haplotypes” in Figure 2A shows the frequency of observing a haplotype at time , which contains alleles not observed at time , again averaged over all available data. Any such haplotype could have arisen by mutation in the time interval between and , or could have been present at time but not sampled. It cannot, however, be assembled by recombination from the alleles found at time . The important observation is that the frequency of observing such a haplotype does not increase with distance. This is consistent with our expectation that an additional mutation or undersampling should be independent of an additional polymorphism nearby. The clear separation between the two classes of haplotypes suggests that the contribution from homoplasy and sampling can be accounted for by a distance independent constant.


Recombination rate and selection strength in HIV intra-patient evolution.

Neher RA, Leitner T - PLoS Comput. Biol. (2010)

Estimating recombination rates from time resolved data.Panel A shows the probability of finding a haplotype that is not detected at time  in the sample at  as a function of the separation  of the sites. The data labeled ‘recombinant haplotypes’ refers to those combinations, that can be generated by recombination from the alleles detected at time  and displays a pronounced distance dependence. The data labeled ‘other haplotypes’ refers to pairs containing at least one allele not detected at time , implying an additional mutation or undersampling. The data is averaged over all time points, all patients, and those pairs of polymorphic sites, where both alleles at both sites are seen at least 3 times. Panel B shows the probability of finding the missing haplotype as a function of the product of distance  and time interval . The fit to the data is shown in black with fit parameters indicated in the legend.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2813257&req=5

pcbi-1000660-g002: Estimating recombination rates from time resolved data.Panel A shows the probability of finding a haplotype that is not detected at time in the sample at as a function of the separation of the sites. The data labeled ‘recombinant haplotypes’ refers to those combinations, that can be generated by recombination from the alleles detected at time and displays a pronounced distance dependence. The data labeled ‘other haplotypes’ refers to pairs containing at least one allele not detected at time , implying an additional mutation or undersampling. The data is averaged over all time points, all patients, and those pairs of polymorphic sites, where both alleles at both sites are seen at least 3 times. Panel B shows the probability of finding the missing haplotype as a function of the product of distance and time interval . The fit to the data is shown in black with fit parameters indicated in the legend.
Mentions: For each pair of biallic sites at time that was found in three of the four possible haplotypes, we asked whether the missing haplotype is observed the at time (comp. Figure 1) and calculated the frequency of this event as a function of the separation of the two sites, as shown in Figure 2A. This frequency increases with the separation of the two sites from about 0.1 to about 0.35 at 500 bp separation, in line with the expectation that recombination is more rapid between distant sites. To corroborate that this distance dependence is indeed due to recombination, we performed the following similar analysis: The curve labelled “other haplotypes” in Figure 2A shows the frequency of observing a haplotype at time , which contains alleles not observed at time , again averaged over all available data. Any such haplotype could have arisen by mutation in the time interval between and , or could have been present at time but not sampled. It cannot, however, be assembled by recombination from the alleles found at time . The important observation is that the frequency of observing such a haplotype does not increase with distance. This is consistent with our expectation that an additional mutation or undersampling should be independent of an additional polymorphism nearby. The clear separation between the two classes of haplotypes suggests that the contribution from homoplasy and sampling can be accounted for by a distance independent constant.

Bottom Line: By examining temporal changes in the genetic composition of the population, we estimate the effective recombination to be rho = 1.4+/-0.6 x 10(-5) recombinations per site and generation.These results provide a basis for a more detailed understanding of the evolution of HIV.With the methods developed here, more precise and more detailed studies will be possible as soon as data with higher time resolution and greater sample sizes are available.

View Article: PubMed Central - PubMed

Affiliation: Kavli Institute for Theoretical Physics, University of California, Santa Barbara, California, United States of America. neher@kitp.ucsb.edu

ABSTRACT
The evolutionary dynamics of HIV during the chronic phase of infection is driven by the host immune response and by selective pressures exerted through drug treatment. To understand and model the evolution of HIV quantitatively, the parameters governing genetic diversification and the strength of selection need to be known. While mutation rates can be measured in single replication cycles, the relevant effective recombination rate depends on the probability of coinfection of a cell with more than one virus and can only be inferred from population data. However, most population genetic estimators for recombination rates assume absence of selection and are hence of limited applicability to HIV, since positive and purifying selection are important in HIV evolution. Yet, little is known about the distribution of selection differentials between individual viruses and the impact of single polymorphisms on viral fitness. Here, we estimate the rate of recombination and the distribution of selection coefficients from time series sequence data tracking the evolution of HIV within single patients. By examining temporal changes in the genetic composition of the population, we estimate the effective recombination to be rho = 1.4+/-0.6 x 10(-5) recombinations per site and generation. Furthermore, we provide evidence that the selection coefficients of at least 15% of the observed non-synonymous polymorphisms exceed 0.8% per generation. These results provide a basis for a more detailed understanding of the evolution of HIV. A particularly interesting case is evolution in response to drug treatment, where recombination can facilitate the rapid acquisition of multiple resistance mutations. With the methods developed here, more precise and more detailed studies will be possible as soon as data with higher time resolution and greater sample sizes are available.

Show MeSH
Related in: MedlinePlus