Deciphering the code for retroviral integration target site selection.

Upon cell invasion, retroviruses generate a DNA copy of their RNA genome and integrate retroviral cDNA within host chromosomal DNA. Integration occurs throughout the host cell genome, but target site selection is not random. Each subgroup of retrovirus is distinguished from the others by attraction...

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Main Authors: Federico Andrea Santoni, Oliver Hartley, Jeremy Luban
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2010-11-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC2991247?pdf=render
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spelling doaj-caf5b523d99a416ebaac78ab1ebe05522020-11-25T01:13:12ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582010-11-01611e100100810.1371/journal.pcbi.1001008Deciphering the code for retroviral integration target site selection.Federico Andrea SantoniOliver HartleyJeremy LubanUpon cell invasion, retroviruses generate a DNA copy of their RNA genome and integrate retroviral cDNA within host chromosomal DNA. Integration occurs throughout the host cell genome, but target site selection is not random. Each subgroup of retrovirus is distinguished from the others by attraction to particular features on chromosomes. Despite extensive efforts to identify host factors that interact with retrovirion components or chromosome features predictive of integration, little is known about how integration sites are selected. We attempted to identify markers predictive of retroviral integration by exploiting Precision-Recall methods for extracting information from highly skewed datasets to derive robust and discriminating measures of association. ChIPSeq datasets for more than 60 factors were compared with 14 retroviral integration datasets. When compared with MLV, PERV or XMRV integration sites, strong association was observed with STAT1, acetylation of H3 and H4 at several positions, and methylation of H2AZ, H3K4, and K9. By combining peaks from ChIPSeq datasets, a supermarker was identified that localized within 2 kB of 75% of MLV proviruses and detected differences in integration preferences among different cell types. The supermarker predicted the likelihood of integration within specific chromosomal regions in a cell-type specific manner, yielding probabilities for integration into proto-oncogene LMO2 identical to experimentally determined values. The supermarker thus identifies chromosomal features highly favored for retroviral integration, provides clues to the mechanism by which retrovirus integration sites are selected, and offers a tool for predicting cell-type specific proto-oncogene activation by retroviruses.http://europepmc.org/articles/PMC2991247?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Federico Andrea Santoni
Oliver Hartley
Jeremy Luban
spellingShingle Federico Andrea Santoni
Oliver Hartley
Jeremy Luban
Deciphering the code for retroviral integration target site selection.
PLoS Computational Biology
author_facet Federico Andrea Santoni
Oliver Hartley
Jeremy Luban
author_sort Federico Andrea Santoni
title Deciphering the code for retroviral integration target site selection.
title_short Deciphering the code for retroviral integration target site selection.
title_full Deciphering the code for retroviral integration target site selection.
title_fullStr Deciphering the code for retroviral integration target site selection.
title_full_unstemmed Deciphering the code for retroviral integration target site selection.
title_sort deciphering the code for retroviral integration target site selection.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2010-11-01
description Upon cell invasion, retroviruses generate a DNA copy of their RNA genome and integrate retroviral cDNA within host chromosomal DNA. Integration occurs throughout the host cell genome, but target site selection is not random. Each subgroup of retrovirus is distinguished from the others by attraction to particular features on chromosomes. Despite extensive efforts to identify host factors that interact with retrovirion components or chromosome features predictive of integration, little is known about how integration sites are selected. We attempted to identify markers predictive of retroviral integration by exploiting Precision-Recall methods for extracting information from highly skewed datasets to derive robust and discriminating measures of association. ChIPSeq datasets for more than 60 factors were compared with 14 retroviral integration datasets. When compared with MLV, PERV or XMRV integration sites, strong association was observed with STAT1, acetylation of H3 and H4 at several positions, and methylation of H2AZ, H3K4, and K9. By combining peaks from ChIPSeq datasets, a supermarker was identified that localized within 2 kB of 75% of MLV proviruses and detected differences in integration preferences among different cell types. The supermarker predicted the likelihood of integration within specific chromosomal regions in a cell-type specific manner, yielding probabilities for integration into proto-oncogene LMO2 identical to experimentally determined values. The supermarker thus identifies chromosomal features highly favored for retroviral integration, provides clues to the mechanism by which retrovirus integration sites are selected, and offers a tool for predicting cell-type specific proto-oncogene activation by retroviruses.
url http://europepmc.org/articles/PMC2991247?pdf=render
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