Motivation

Gene therapy with retroviral vectors can induce adverse effects when those vectors integrate in sensitive genomic regions. Retroviral vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of integration sites and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of false discovery rates providing statistical methods for comparing retroviral vectors.

Results

A scan statistic for comparing two vectors using multiple window widths is proposed with software to detect clustering differentials and compute false discovery rates. Application to several sets of experimentally determined HIV integration sites demonstrates the software. Simulated datasets of various sizes and signal strengths are used to determine the power to discover clusters and evaluate a convenient lower bound. This provides a toolkit for planning evaluations of new gene therapy vectors.

Availability and implementation

The geneRxCluster R package containing a simple tutorial and usage hints is available from http://www.bioconductor.org.

The completion of the human genome sequence has made possible genome-wide studies of retroviral DNA integration. Here we report an analysis of 3,127 integration site sequences from human cells. We compared retroviral vectors derived from human immunodeficiency virus (HIV), avian sarcoma-leukosis virus (ASLV), and murine leukemia virus (MLV). Effects of gene activity on integration targeting were assessed by transcriptional profiling of infected cells. Integration by HIV vectors, analyzed in two primary cell types and several cell lines, strongly favored active genes. An analysis of the effects of tissue-specific transcription showed that it resulted in tissue-specific integration targeting by HIV, though the effect was quantitatively modest. Chromosomal regions rich in expressed genes were favored for HIV integration, but these regions were found to be interleaved with unfavorable regions at CpG islands. MLV vectors showed a strong bias in favor of integration near transcription start sites, as reported previously. ASLV vectors showed only a weak preference for active genes and no preference for transcription start regions. Thus, each of the three retroviruses studied showed unique integration site preferences, suggesting that virus-specific binding of integration complexes to chromatin features likely guides site selection.

The integration of the HIV-1 genome into the host genome is an essential step in the life cycle of the virus and it plays a critical role in the expression, long-term persistence, and reactivation of HIV expression. To better understand the local genomic environment surrounding HIV-1 proviruses, we assessed the influence of non-canonical B-form DNA (non-B DNA) on the HIV-1 integration site selection. We showed that productively and latently infected cells exhibit different integration site biases towards non-B DNA motifs. We identified a correlation between the integration sites of the latent proviruses and non-B DNA features known to potently influence gene expression (e.g., cruciform, guanine-quadruplex (G4), triplex, and Z-DNA). The reactivation potential of latent proviruses with latency reversal agents also correlated with their proximity to specific non-B DNA motifs. The perturbation of G4 structures in vitro using G4 structure-destabilizing or -stabilizing ligands resulted in a significant reduction in integration within 100 base pairs of G4 motifs. The stabilization of G4 structures increased the integration within 300-500 base pairs from G4 motifs, increased integration near transcription start sites, and increased the proportion of latently infected cells. Moreover, we showed that host lens epithelium-derived growth factor (LEDGF)/p75 and cleavage and polyadenylation specificity factor 6 (CPSF6) influenced the distribution of integration sites near several non-B DNA motifs, especially G4 DNA. Our findings identify non-B DNA motifs as important factors that influence productive and latent HIV-1 integration and the reactivation potential of latent proviruses.

Abstract Background HIV infection can be treated effectively with antiretroviral agents, but the persistence of a latent reservoir of integrated proviruses prevents eradication of HIV from infected individuals. The chromosomal environment of integrated proviruses has been proposed to influence HIV latency, but the determinants of transcriptional repression have not been fully clarified, and it is unclear whether the same molecular mechanisms drive latency in different cell culture models. Results Here we compare data from five different in vitro models of latency based on primary human T cells or a T cell line. Cells were infected in vitro and separated into fractions containing proviruses that were either expressed or silent/inducible, and integration site populations sequenced from each. We compared the locations of 6,252 expressed proviruses to those of 6,184 silent/inducible proviruses with respect to 140 forms of genomic annotation, many analyzed over chromosomal intervals of multiple lengths. A regularized logistic regression model linking proviral expression status to genomic features revealed no predictors of latency that performed better than chance, though several genomic features were significantly associated with proviral expression in individual models. Proviruses in the same chromosomal region did tend to share the same expressed or silent/inducible status if they were from the same cell culture model, but not if they were from different models. Conclusions The silent/inducible phenotype appears to be associated with chromosomal position, but the molecular basis is not fully clarified and may differ among in vitro models of latency.

Analysis of sites of newly integrated DNA in cellular genomes is important to several fields, but methods for analyzing and visualizing these datasets are still under development. Here, we describe tools for data analysis and visualization that take as input integration site data from our INSPIIRED pipeline. Paired-end sequencing allows inference of the numbers of transduced cells as well as the distributions of integration sites in target genomes. We present interactive heatmaps that allow comparison of distributions of integration sites to genomic features and that support numerous user-defined statistical tests. To summarize integration site data from human gene therapy samples, we developed a reproducible report format that catalogs sample population structure, longitudinal dynamics, and integration frequency near cancer-associated genes. We also introduce a novel summary statistic, the UC50 (unique cell progenitors contributing the most expanded 50% of progeny cell clones), which provides a single number summarizing possible clonal expansion. Using these tools, we characterize ongoing longitudinal characterization of a patient from the first trial to treat severe combined immunodeficiency-X1 (SCID-X1), showing successful reconstitution for 15 years accompanied by persistence of a cell clone with an integration site near the cancer-associated gene CCND2. Software is available at https://github.com/BushmanLab/INSPIIRED.

Genome-wide siRNA screens have identified host cell factors important for efficient HIV infection, among which are nuclear pore proteins such as RanBP2/Nup358 and the karyopherin Transportin-3/TNPO3. Analysis of the roles of these proteins in the HIV replication cycle suggested that correct trafficking through the pore may facilitate the subsequent integration step. Here we present data for coupling between these steps by demonstrating that depletion of Transportin-3 or RanBP2 altered the terminal step in early HIV replication, the selection of chromosomal sites for integration. We found that depletion of Transportin-3 and RanBP2 altered integration targeting for HIV. These knockdowns reduced HIV integration frequency in gene-dense regions and near gene-associated features, a pattern that differed from that reported for depletion of the HIV integrase binding cofactor Psip1/Ledgf/p75. MLV integration was not affected by the Transportin-3 knockdown. Using siRNA knockdowns and integration targeting analysis, we also implicated several additional nuclear proteins in proper target site selection. To map viral determinants of integration targeting, we analyzed a chimeric HIV derivative containing MLV gag, and found that the gag replacement phenocopied the Transportin-3 and RanBP2 knockdowns. Thus, our data support a model in which Gag-dependent engagement of the proper transport and nuclear pore machinery mediate trafficking of HIV complexes to sites of integration.

Integration of new DNA into cellular genomes mediates replication of retroviruses and transposons; integration reactions have also been adapted for use in human gene therapy. Tracking the distributions of integration sites is important to characterize populations of transduced cells and to monitor potential outgrow of pathogenic cell clones. Here, we describe a pipeline for quantitative analysis of integration site distributions named INSPIIRED (integration site pipeline for paired-end reads). We describe optimized biochemical steps for site isolation using Illumina paired-end sequencing, including new technology for suppressing recovery of unwanted contaminants, then software for alignment, quality control, and management of integration site sequences. During library preparation, DNAs are broken by sonication, so that after ligation-mediated PCR the number of ligation junction sites can be used to infer abundance of gene-modified cells. We generated integration sites of known positions in silico, and we describe optimization of sample processing parameters refined by comparison to truth. We also present a novel graph-theory-based method for quantifying integration sites in repeated sequences, and we characterize the consequences using synthetic and experimental data. In an accompanying paper, we describe an additional set of statistical tools for data analysis and visualization. Software is available at https://github.com/BushmanLab/INSPIIRED.