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PLoS Genet. 2014 Apr 10;10(4):e1004250. doi: 10.1371/journal.pgen.1004250. eCollection 2014 Apr.

Chromatin landscapes of retroviral and transposon integration profiles.

Author information

1
Computational Cancer Biology Group, Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands; Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands.
2
Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands; Division of Molecular Genetics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
3
Division of Molecular Genetics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
4
Wellcome Trust Sanger Institute, Genome Campus, Hinxton-Cambridge, United Kingdom.
5
Department of Medicine II; Klinikum Rechts der Isar; Technische Universität München, German Cancer Research Center (DKFZ), Heidelberg, & German Cancer Consortium (DKTK), Heidelberg, Germany.
6
Division of Molecular Genetics, The Netherlands Cancer Institute, Amsterdam, The Netherlands; Skoltech Center for Stem Cell Research, Skolkovo Institute for Science and Technology, Skolkovo, Odintsovsky, Moscow, Russia.
7
Computational Cancer Biology Group, Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands; Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands; Delft Bioinformatics Lab, Faculty of EEMCS, TU Delft, Delft, The Netherlands.
8
Delft Bioinformatics Lab, Faculty of EEMCS, TU Delft, Delft, The Netherlands.

Abstract

The ability of retroviruses and transposons to insert their genetic material into host DNA makes them widely used tools in molecular biology, cancer research and gene therapy. However, these systems have biases that may strongly affect research outcomes. To address this issue, we generated very large datasets consisting of ~ 120,000 to ~ 180,000 unselected integrations in the mouse genome for the Sleeping Beauty (SB) and piggyBac (PB) transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed ~ 80 (epi)genomic features to generate bias maps at both local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome. More distinct preferences were observed for the two transposons, with PB showing remarkable resemblance to bias profiles of the Murine Leukemia Virus. Furthermore, we present a model where target site selection is directed at multiple scales. At a large scale, target site selection is similar across systems, and defined by domain-oriented features, namely expression of proximal genes, proximity to CpG islands and to genic features, chromatin compaction and replication timing. Notable differences between the systems are mainly observed at smaller scales, and are directed by a diverse range of features. To study the effect of these biases on integration sites occupied under selective pressure, we turned to insertional mutagenesis (IM) screens. In IM screens, putative cancer genes are identified by finding frequently targeted genomic regions, or Common Integration Sites (CISs). Within three recently completed IM screens, we identified 7%-33% putative false positive CISs, which are likely not the result of the oncogenic selection process. Moreover, results indicate that PB, compared to SB, is more suited to tag oncogenes.

PMID:
24721906
PMCID:
PMC3983033
DOI:
10.1371/journal.pgen.1004250
[Indexed for MEDLINE]
Free PMC Article

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