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. 2013 Apr;189(1):53-7.
doi: 10.1016/j.jviromet.2013.01.004. Epub 2013 Jan 21.

Quantitation of HIV DNA Integration: Effects of Differential Integration Site Distributions on Alu-PCR Assays

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Free PMC article

Quantitation of HIV DNA Integration: Effects of Differential Integration Site Distributions on Alu-PCR Assays

Troy Brady et al. J Virol Methods. .
Free PMC article

Abstract

In many studies of HIV replication, it is useful to quantify the number of HIV proviruses in cells against a background of unintegrated forms of the HIV DNA. A popular method for doing so involves quantitative PCR using one primer complementary to the HIV long terminal repeat (LTR), and a second primer complementary to a cellular Alu repeat, so that PCR product only forms from templates where a provirus is integrated in the human genome near an Alu repeat. However, several recent studies have identified conditions that alter distributions of HIV integration sites relative to genes. Because Alu repeats are enriched in gene rich regions, this raises the question of whether altered integration site distributions might confound provirus abundance measurements using the Alu-PCR method. Here modified versions of the HIV tethering protein LEDGF/p75 were used to retarget HIV integration outside of transcription units, and show that this has a negligible effect on Alu-PCR quantitation of proviral abundance. Thus altered integration targeting, at least to the degree achieved here, is not a major concern when using the Alu-PCR assay.

Figures

Figure 1
Figure 1
Methods for quantifying proviral abundance using the Alu-PCR assay. Arrows indicate the positions of PCR primers, the line with two balls indicates the Taqman probe.
Figure 2
Figure 2
Retargeting HIV DNA integration in cells using fusions of LEDGF/p75 to CBX. (A) Integration frequency in genes. The proportion of integration sites falling within a RefSeq gene is shown with the frequency of a random distribution represented by the horizontal line at one. (B) Genomic heatmap of integration site associations with genomic features. Each column represents a dataset, each row a genomic feature. Some genomic features were tested at several interval sizes surrounding the integration site. Increasing shades of red show favored association with a feature, increasing shades of blue show disfavored association compared with a random distribution. For a description of the ROC area method used to construct the heat map see (Berry et al., 2006). (C) Epigenetic heatmap of integration site associations with epigenetic marks. Heatmap structure is similar to the Genomic heatmap but with epigenetic marks indicated in the rows and shades of blue showing favoring and yellow showing disfavoring. Chip-Seq data on epigenetic marks in HeLa cells used to generate the heatmap was taken from (Meylan et al., 2011). (D) Average number of Alu repeats in a specified window surrounding integration sites. The sizes of the genomic intervals surrounding each integration site (“Window”) is shown at the right.
Figure 3
Figure 3
Effects of different integration site distributions on detection by the Alu-PCR assay. The graph compares the relative provirus copy number determined using the one-step late RT assay (blue) versus the Alu-PCR assay (red). Engineered HeLa cells were infected with an HIV-based vector and DNA harvested 14 days after infection. The long time was used to allow unintegrated DNA to be diluted out by growth of cells. The x-axis shows the cell type studied, the y-axis shows relative provirus copies/genome normalized to wild-type, with standard error. Data represent four technical replicates. The sample with the largest difference between assays (22%) was LEDGF BC. Statistical tests comparing all Alu-PCR versus all Late-RT samples (Kruskal-Wallis) showed no significant differences. Follow up tests of individual pairs of samples (Wilcoxon rank-sum test) also showed no significant differences.

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