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Weak palindromic consensus sequences are a common feature found at the integration target sites of many retroviruses.

ABSTRACT: Integration into the host genome is one of the hallmarks of the retroviral life cycle and is catalyzed by virus-encoded integrases. While integrase has strict sequence requirements for the viral DNA ends, target site sequences have been shown to be very diverse. We carefully examined a large number of integration target site sequences from several retroviruses, including human immunodeficiency virus type 1, simian immunodeficiency virus, murine leukemia virus, and avian sarcoma-leukosis virus, and found that a statistical palindromic consensus, centered on the virus-specific duplicated target site sequence, was a common feature at integration target sites for these retroviruses.

SUBMITTER: Wu X

PROVIDER: S-EPMC1069554 | biostudies-literature | 2005 Apr

REPOSITORIES: biostudies-literature

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Weak palindromic consensus sequences are a common feature found at the integration target sites of many retroviruses.

Wu Xiaolin X Li Yuan Y Crise Bruce B Burgess Shawn M SM Munroe David J DJ

Journal of virology 20050401 8

Integration into the host genome is one of the hallmarks of the retroviral life cycle and is catalyzed by virus-encoded integrases. While integrase has strict sequence requirements for the viral DNA ends, target site sequences have been shown to be very diverse. We carefully examined a large number of integration target site sequences from several retroviruses, including human immunodeficiency virus type 1, simian immunodeficiency virus, murine leukemia virus, and avian sarcoma-leukosis virus, a ...[more]

PMID: 15795304

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Project description:BackgroundAll retroviruses, including human immunodeficiency virus (HIV), must integrate a DNA copy of their genomes into the genome of the infected host cell to replicate. Although integrated retroviral DNA, known as a provirus, can be found at many sites in the host genome, integration is not random. The adaption of linker-mediated PCR (LM-PCR) protocols for high-throughput integration site mapping, using randomly-sheared genomic DNA and Illumina paired-end sequencing, has dramatically increased the number of mapped integration sites. Analysis of samples from human donors has shown that there is clonal expansion of HIV infected cells and that clonal expansion makes an important contribution to HIV persistence. However, analysis of HIV integration sites in samples taken from patients requires extensive PCR amplification and high-throughput sequencing, which makes the methodology prone to certain specific artifacts.ResultsTo address the problems with artifacts, we use a comprehensive approach involving experimental procedures linked to a bioinformatics analysis pipeline. Using this combined approach, we are able to reduce the number of PCR/sequencing artifacts that arise and identify the ones that remain. Our streamlined workflow combines random cleavage of the DNA in the samples, end repair, and linker ligation in a single step. We provide guidance on primer and linker design that reduces some of the common artifacts. We also discuss how to identify and remove some of the common artifacts, including the products of PCR mispriming and PCR recombination, that have appeared in some published studies. Our improved bioinformatics pipeline rapidly parses the sequencing data and identifies bona fide integration sites in clonally expanded cells, producing an Excel-formatted report that can be used for additional data processing.ConclusionsWe provide a detailed protocol that reduces the prevalence of artifacts that arise in the analysis of retroviral integration site data generated from in vivo samples and a bioinformatics pipeline that is able to remove the artifacts that remain.

Dataset Information

Weak palindromic consensus sequences are a common feature found at the integration target sites of many retroviruses.

Publications

Weak palindromic consensus sequences are a common feature found at the integration target sites of many retroviruses.

Similar Datasets

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