title: Stochastic processes strongly influence HIV-1 evolution during suboptimal protease-inhibitor therapy.
abstract: It has long been assumed that HIV-1 evolution is best described by deterministic evolutionary models because of the large population size. Recently, however, it was suggested that the effective population size (Ne) may be rather small, thereby allowing chance to influence evolution, a situation best described by a stochastic evolutionary model. To gain experimental evidence supporting one of the evolutionary models, we investigated whether the development of resistance to the protease inhibitor ritonavir affected the evolution of the env gene. Sequential serum samples from five patients treated with ritonavir were used for analysis of the protease gene and the V3 domain of the env gene. Multiple reverse transcription-PCR products were cloned, sequenced, and used to construct phylogenetic trees and to calculate the genetic variation and Ne. Genotypic resistance to ritonavir developed in all five patients, but each patient displayed a unique combination of mutations, indicating a stochastic element in the development of ritonavir resistance. Furthermore, development of resistance induced clear bottleneck effects in the env gene. The mean intrasample genetic variation, which ranged from 1.2% to 5.7% before treatment, decreased significantly (P < 0.025) during treatment. In agreement with these findings, Ne was estimated to be very small (500-15,000) compared with the total HIV-1 RNA copy number. This study combines three independent observations, strong population bottlenecking, small Ne, and selection of different combinations of protease-resistance mutations, all of which indicate that HIV-1 evolution is best described by a stochastic evolutionary model.
authors.affiliation: Eijkman-Winkler Institute, Department of Virology, University Hospital Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
authors.forename: M
authors.initials: M
authors.lastname: Nijhuis
authors.forename: C A
authors.initials: CA
authors.lastname: Boucher
authors.forename: P
authors.initials: P
authors.lastname: Schipper
authors.forename: T
authors.initials: T
authors.lastname: Leitner
authors.forename: R
authors.initials: R
authors.lastname: Schuurman
authors.forename: J
authors.initials: J
authors.lastname: Albert
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy* / Q000821:virology*; D000595:Amino Acid Sequence; D001483:Base Sequence; D017931:DNA Primers; D004352:Drug Resistance, Microbial; D019143:Evolution, Molecular; D015686:Gene Products, env / Q000737:chemistry* / Q000235:genetics; D015752:Genes, env; D014644:Genetic Variation; D005838:Genotype; D017320:HIV Protease Inhibitors / Q000627:therapeutic use*; D015497:HIV-1 / Q000187:drug effects / Q000235:genetics* / Q000502:physiology; D006801:Humans; D008969:Molecular Sequence Data; D010802:Phylogeny; D020133:Reverse Transcriptase Polymerase Chain Reaction; D019438:Ritonavir / Q000627:therapeutic use*; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D013269:Stochastic Processes
chemical_list: D017931:DNA Primers; D015686:Gene Products, env; D017320:HIV Protease Inhibitors; D019438:Ritonavir