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.

Figure 1

Serum HIV-1 RNA levels (solid lines) and CD4 cell counts (dotted lines) from start of ritonavir therapy in patients 125(A), 127(B), 129(C), 134(D), and 224(E). The times for detection of protease amino acid changes associated with selection during ritonavir treatment are indicated.

Figure 2

Amino acid sequence of part of the HIV-1 envelope protein in patients 125, 127, 129, 134, and 224 from start of ritonavir therapy as compared with the sequence of HIV-1 consensus B. Pretherapy sequences are listed for the antiviral-therapy naive patients 125 and 127. Uppercase letters refer to an amino acid change that dominated the population, whereas heterogeneous amino acid positions are indicated with lowercase letters. Particular positions known to be involved in glycosylation (g), antigenicity (a), and phenotype (p) are indicated in gray.

Figure 3

Phylogenetic trees constructed by using the neighbor-joining method and V3 nucleotide sequences from patients 125, 127, 129, 134, and 224. □, sequences obtained from samples drawn 100–200 days before start of ritonavir treatment; ○, sequences obtained at start ■, sequences obtained early after start; and •, sequences obtained later after start. “NSI” and “SI” denote sequences from patient 129 with nonsyncytium-inducing and syncytium-inducing phenotypes, respectively. The trees were rooted against the database sequence JRCSF, and the length of branch leading to the root has been shortened by a factor of 2.