HIV integration site selection: Analysis by massively parallel pyrosequencing reveals association with epigenetic modifications

Abstract

Integration of retroviral DNA into host cell DNA is a defining feature of retroviral replication. HIV integration is known to be favored in active transcription units, which promotes efficient transcription of the viral genes, but the molecular mechanisms responsible for targeting are not fully clarified. Here we used pyrosequencing to map 40,569 unique sites of HIV integration. Computational prediction of nucleosome positions in target DNA indicated that integration sites are periodically distributed on the nucleosome surface, consistent with favored integration into outward-facing DNA major grooves in chromatin. Analysis of integration site positions in the densely annotated ENCODE regions revealed a wealth of new associations between integration frequency and genomic features. Integration was particularly favored near transcription-associated histone modifications, including H3 acetylation, H4 acetylation, and H3 K4 methylation, but was disfavored in regions rich in transcription-inhibiting modifications, which include H3 K27 trimethylation and DNA CpG methylation. Statistical modeling indicated that effects of histone modification on HIV integration were partially independent of other genomic features influencing integration. The pyrosequencing and bioinformatic methods described here should be useful for investigating many aspects of retroviral DNA integration.

Integrase, LEDGF/p75 and HIV replication

“…Although harder to address experimentally, it has not been ruled out that the ensemble of host cell enzymes that carries out gap repair is not properly entrained in the absence of p75 and the semi-joined provirus is "repaired" by its removal; however, the increases in 2-LTR circles that have been seen in p75-deficient cells weigh against this. Note also that p75 knockout did not affect the wellestablished tendency of retroviral integrations to occur at weakly conserved yet virus and/or genus-specific DNA sequences with palindrome-approximating symmetry [48,61,116,136,162,163], indicating cofactor-independent, presumably IN-autonomous selectivity that depends on the local DNA sequence irrespective of larger scale genomic features or the G/C content of the DNA [101].…”

“…Of equivalent interest is deciphering the roles cellular proteins are likely to play in genome-wide patterns of retroviral integration [34][35][36][37][38][39][40][41][42][43][44][45][46]. Experimental capabilities in this area changed with the availability of the human genome sequence, commensurately advancing bioinformatics, and more recently, a second wave of high-throughput sequencing methodologies [47,48]). Before such analyses, most evidence had suggested that retroviral integration was largely random except for highly local biases related to distortion of the double helix as it accommodates to nucleosome structure [49][50][51][52][53][54].…”

“…HIV integration is also strongly disfavored in centromeric heterochromatin [34,61], which is transcriptionally repressed and a poor substrate for viral transcription [62,63]. Analyses that exploit deeper genome annotation have potential to reveal further mechanistic possibilities [48].…”

Lens epithelium-derived growth factor fusion proteins redirect HIV-1 DNA integration

“…chromatin | histone marks | lentiviral vectors R etroviruses can integrate their DNAs at many sites in host DNA (1), but different retroviruses have different integration site preferences (2)(3)(4)(5)(6)(7)(8)(9). HIV-1 and simian immunodeficiency virus DNAs preferentially integrate into expressed genes, murine leukemia virus (MLV) DNA preferentially integrates near transcriptional start sites (TSSs), and avian sarcoma leukosis virus (ASLV) and human T cell leukemia virus (HTLV) DNAs integrate nearly randomly, showing a slight preference for genes.…”

Web-Based Analysis of (Epi-) Genome Data Using EpiGRAPH and Galaxy

“…genomic regions at which viruses such as HIV are incorporated into the host DNA), based on sequenced integration sites (Wang, et al, 2007). We will have to make sure that the control set does not contain more repetitive regions than the set of integration sites, because the latter dataset is artificially biased against repetitive regions, and this should be reflected in the control set.…”