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. 2005 Feb;79(3):1666-77.
doi: 10.1128/JVI.79.3.1666-1677.2005.

Genetic recombination of human immunodeficiency virus type 1 in one round of viral replication: effects of genetic distance, target cells, accessory genes, and lack of high negative interference in crossover events

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Genetic recombination of human immunodeficiency virus type 1 in one round of viral replication: effects of genetic distance, target cells, accessory genes, and lack of high negative interference in crossover events

Terence D Rhodes et al. J Virol. 2005 Feb.

Abstract

Recombination is a major mechanism that generates variation in populations of human immunodeficiency virus type 1 (HIV-1). Mutations that confer replication advantages, such as drug resistance, often cluster within regions of the HIV-1 genome. To explore how efficiently HIV-1 can assort markers separated by short distances, we developed a flow cytometry-based system to study recombination. Two HIV-1-based vectors were generated, one encoding the mouse heat-stable antigen gene and green fluorescent protein gene (GFP), and the other encoding the mouse Thy-1 gene and GFP. We generated derivatives of both vectors that contained nonfunctional GFP inactivated by different mutations. Recombination in the region between the two inactivating mutations during reverse transcription could yield a functional GFP. With this system, we determined that the recombination rates of markers separated by 588, 300, 288, and 103 bp in one round of viral replication are 56, 38, 31, and 12%, respectively, of the theoretical maximum measurable recombination rate. Statistical analyses revealed that at these intervals, recombination rates and marker distances have a near-linear relationship that is part of an overall quadratic fit. Additionally, we examined the segregation of three markers within 600 bp and concluded that HIV-1 crossover events do not exhibit high negative interference. We also examined the effects of target cells and viral accessory proteins on recombination rate. Similar recombination rates were observed when human primary CD4(+) T cells and a human T-cell line were used as target cells. We also found equivalent recombination rates in the presence and absence of accessory genes vif, vpr, vpu, and nef. These results illustrate the power of recombination in generating viral population variation and predict the rapid assortment of mutations in the HIV-1 genome in infected individuals.

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Figures

FIG. 1.
FIG. 1.
Viral vectors and protocol used to measure HIV-1 recombination rates. (A) General structures of the vectors. All listed vectors have similar structures but differ in the encoded marker genes. Asterisk, inactivating mutation in GFP. (B) Protocol used to measure the recombination rates of HIV-1.
FIG. 2.
FIG. 2.
Representative flow cytometry analyses of mock-infected cells, producer cells, and cells infected with control plasmids. (A and B) Analyses of mock-infected 293T cells stained with anti-HSA and anti-Thy-1 antibodies. (C and D) Analyses of a producer cell line used for virus production. This cell line was sequentially infected with ON-H0 and ON-T3 at low MOIs; HSA+ and Thy-1+ cells were enriched by sorting, stained with antibodies, and analyzed. (E and F) Analyses of uninfected Hut/CCR5 target cells stained with anti-HSA and anti-Thy-1 antibodies. (G) Analysis of Hut/CCR5 target cells infected with ON-fHIG virus and stained with anti-HSA antibody. (H) Analysis of Hut/CCR5 cells infected with ON-fTIG virus and stained with anti-Thy-1 antibody. In all panels, the x and y axes denote the expression of a particular marker as indicated.
FIG. 3.
FIG. 3.
Representative flow cytometry analyses of mock-infected and infected target cells. (A and B) Analyses of mock-infected Hut/CCR5 target cells stained with anti-HSA and anti-Thy-1 antibodies. (C and D) Analyses of Hut/CCR5 target cells infected with virus harvested from a producer cell line harboring both ON-H0 and ON-T6.
FIG. 4.
FIG. 4.
Theoretical distribution of GFP genotypes and phenotypes in the progeny generated from doubly infected cells after one round of viral replication. Vectors ON-H0 and ON-T6 are used as examples (shown as H0 and T6, respectively). Several assumptions were made in the calculated theoretical frequency. First, the 25%:50%:25% distribution of the virion content was based on the assumptions that H0 and T6 were expressed at similar levels in the producer cells and virion RNAs were packaged randomly. Second, the 12.5% distribution of each genotype was based on the assumption that the H0 and T6 markers segregated as unlinked markers. GFP, cells that did not express functional GFP; GFP+, cells expressing functional GFP.
FIG. 5.
FIG. 5.
Effects of accessory proteins and target cells on HIV-1 recombination. The y axis represents the percentage of the theoretical maximum measurable recombination rate (TMMRR); the x axis represents distances between markers. White and gray bars represent the average recombination rates measured in the presence or absence, respectively, of accessory genes vif, vpr, vpu, and nef in the experimental system with Hut/CCR5 cells as target cells. Black bars represent the recombination rate in the presence of accessory genes with activated primary T cells as the target cells. All of the histograms show the average of three independent experiments; standard deviations are shown as error bars.
FIG. 6.
FIG. 6.
Distribution of the GFP+ phenotype in HSA+ and Thy-1+ cells. The y axis denotes the percentage of GFP+ cells; the x axis denotes the vector pairs used. White bars represent GFP+ cells within the HSA+ populations; black bars represent GFP+ cells within the Thy-1+ populations.
FIG. 7.
FIG. 7.
Relationship between HIV-1 recombination rate and marker distance. (A) Near-linear relationship between recombination rate and marker distance in the 0.1 to 0.6 kb range. (B) The relationship between HIV-1 recombination rate and marker distances has a quadratic fit. All data points are shown as triangles; the quadratic fit is shown as a black line, and the 95% confidence limits are shown as dotted lines. (C) Simulation of observed recombination rate. This simulation is based on the assumptions that the frequency of crossover event is proportional to the marker distance, crossover occurs randomly throughout the genome, and crossovers are independent events. The x axis represent marker distance; the y axis represent the percentage of the theoretical maximum measurable recombination rate (TMMRR).

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