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 to unselected integrations in the mouse genome for the Sleeping Beauty (SB) and piggyBac (PB) transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed (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.
DNA integrating elements, such as transposons and retroviruses, are an important tool in many areas of molecular biology, e. g. gene therapy [1], [2], oncogene discovery [3], [4], gene regulation [5], [6], and functional genetics [7], [8]. A current limitation to the use of retroviruses and transposons is that, even without selective pressure, integration loci are not uniformly distributed across the genome. There are significant biases, the molecular determinants of which are still largely unknown. Such biases can pose problems, for example in the discovery of novel cancer genes by insertional mutagenesis (IM), because it can be difficult to distinguish clusters of integrations arising purely through integration bias from those giving a selective growth advantage to the cell. More insight into target site selection would also benefit gene therapy, where adverse integrational activation of oncogenes resulting from treatment with retroviral vectors has been observed [9]. Three of the main integrating elements currently used in the fields mentioned above are the Sleeping Beauty transposon (SB), the piggyBac transposon (PB), and the mouse mammary tumor virus (MMTV). During the last decade, some studies have reported on integration biases in the mouse genome for one or more of these systems. SB does not integrate randomly on a micro-scale, since it is dependent on local DNA deformability, and the presence of a TA dinucleotide at the site of integration [10], [11]. At larger scales integration target site selection was found to be relatively random [12], [13], although (sometimes conflicting) associations have been observed for CpG islands, gene density, and actively transcribed loci [14], [15]. PB integration is TTAA-specific, although slight variations on this target sequence have been observed [16]. PB was found to be biased towards transcriptional units, CpG islands and transcription start sites (TSSs) and actively transcribed loci, and in general marks of open chromatin [14], [17]–[19]. MMTV is the least well-characterized of the three. In mouse and human cell lines, no bias was detected with respect to genes, TSSs and CpG islands, and MMTV was suggested to be the retrovirus least biased in its target site selection [20]. While the studies mentioned above have provided valuable insights into retroviral and transposon target site selection for SB, PB and MMTV in the mouse genome, they do have some limitations with respect to gaining insight into de novo integration target site selection. These limitations can be subdivided into three categories. First, there are limitations regarding the individual integration datasets. For example, some datasets were generated using cells that were enriched with a selectable marker, e. g. [15], [18], [19]. Also, considering only the datasets that were not under selective pressure, the sample sizes were fairly small compared to current standards, mostly in the range of several tens to several hundreds of integrations, e. g. [14]–[16]. Note that having large numbers of integrations is important for gaining sufficient statistical power to detect even relatively weak biases. Second, some limitations complicate the comparison between integration datasets. For example, integration datasets have been compared that differed substantially in the cell lines used, as well as the degree of selection imposed on those cell lines, e. g. [15]. Third, other limitations concern the features used to analyze the integration datasets. For example, integration datasets have been compared to features in non-matching cell types [15], while for example Murine Leukemia Virus target site selection within the human genome has been suggested [21] and shown [22] to have a cell type dependent component. Interesting to note here is that for a resurrected human endogenous retrovirus, no cell type specific integration into the human genome could be detected [23]. Also, many studies focused only on a limited number of features, e. g. genomic features [17], [20], or genomic features and DNase I hypersensitivity [15]. Moreover, the features, such as ChIP-seq profiles, were not necessarily preprocessed in similar ways, for example in terms of sequence alignment, e. g. [18]. This complicates the comparison of features across different systems. To address these questions, we generated large datasets of SB and PB integrations in mouse embryonic stem cells (mESC). In order to directly compare the two transposons, they were mobilized from the same construct containing inverted repeats (IRs) for both PB and SB. This eliminates any other possible cause than the IR (or the transposon-specific transposase) for the observed differences between the two systems. In addition, we generated a large dataset of MMTV integrations in normal murine mammary gland epithelial (NMuMG) cells. All three datasets were generated under minimal selective pressure, and are henceforward referred to as unselected integration profiles. They are considerably larger than previously published datasets of unselected integrations, , , and integrations respectively. We associated the three integration profiles with a large number of genomic and epigenomic features. In particular, for SB and PB, the recent explosive growth in publicly available ChIP-seq datasets [24] enabled us to analyze a large number of epigenomic features (), all in mESCs. To allow for a better comparison between these datasets, they were preprocessed from the raw sequence reads in exactly the same way. Additionally, the impact of selective pressure on an unselected integration profile, which is important in using IM for cancer gene discovery, has never been addressed extensively. Previous work can be classified as either knowledge-based or data-driven. The knowledge-based approaches use modeling of previously described integration biases to avoid CIS calls that can be explained by these biases, such as SB TA sequence specificity [25], or -retroviral TSS specificity and lentiviral gene specificity [26]. Alternatively, by assuming that a genic region harboring a true positive CIS should contain significantly more integrations than its flanking genes, three out of nine CISs from gene therapeutic clinical trials were labeled false positive [27]. These knowledge-based approaches are necessarily limited in their modeling of integration bias, for example in the number of features that are considered. Conversely, data-driven approaches treat integration bias as a black box, and compare integration datasets that were under substantial selective pressure to integration datasets that lacked this pressure. Using this approach, one study suggested a 47% false positive rate for a MuLV tumor screen [28], and another observed 6 control CISs from SB integrations present in mouse tail DNA, where 79 CISs could be found in the corresponding SB tumor screen [29]. To analyze the impact of selective pressure on integration bias profiles, we take the data-driven approach and compare our three unselected datasets with CIS integration profiles from three previously published tumor screens [3], [30]–[32]. Taken together, this allows us to present the most extensive analysis of SB, PB and MMTV target site selection to date, the results of which include previously undetected biases and differences between selected and unselected integration profiles. Another focal point of the analysis is the influence of scale. By analyzing differences between small-scale (within from integrations) and large-scale (or further from integrations) associations of genomic and epigenomic features with the proximity of integrations, we reveal a hierarchical organization in integration bias. On a global scale, target sites of different systems are selected in similar ways, whereas differences mainly exist in fine-tuning on a local scale.
We started our analysis by studying the sequence specificity at integration sites of the three systems. As expected, PB and SB mostly, but not exclusively, integrate at TTAA and TA sites respectively, accounting for 93% (PB) and 94% (SB) of the integrations (Figure S1). The remaining integrations show sequences that are relatively similar to these motifs and often differ by only one nucleotide (Figure S1). MMTV has little integration site sequence specificity. Focusing instead on integration-flanking sequences (50 bp on either side), de novo motif discovery using the HOMER software [34] revealed no enrichment of non-trivial motifs (i. e. not TTAA for PB, and not TA for SB), except for two motifs in the case of MMTV (binding TFAP2A and Tcfap2e respectively; Figure S2). The bias of an integrating element with respect to genes is of particular interest in IM and gene therapy. In IM screens, more integrations near genes are desired whereas in gene therapy integrations proximal to genes pose a potential threat to the patient, since such integrations may give rise to cancer. We, therefore, compared the integration density of the three systems in and around genes, by aligning all genes (Figure 1A). PB shows a strong bias for TSSs. The PB bias profile with respect to genes is remarkably similar to that of MuLV, see e. g. [35], [36], as well as Figure S3 which shows singleton MuLV profiles, based on a previously published tumor screen [3], [37]. While PB has a strong bias for TSSs, SB is enriched uniformly along the body of genes. MMTV shows the weakest bias, although it does slightly prefer TSSs, and has a mild bias against gene bodies. The significance of the observations made above was assessed using the binomial test, and visualized at a 5% FDR threshold in Figures 1B and 1C, for different gene and transcript related regions. Refer to Table S2 for the raw p-values associated with these statistical tests. It confirms the strong bias of PB for TSSs, and indicates that PB prefers integrating with its transcription unit oriented towards the TSS. When landing within genes, MMTV prefers a sense orientation relative to the host gene, and SB and PB an antisense orientation. In general, MMTV shows the least biased profile, with weak but significant biases for TSSs, and against genic regions. PB integrations are mainly enriched in the 5′UTR, weakly enriched in exons and the 3′UTR, and biased against introns. This pattern is highly similar to that observed for singleton MuLV integrations (Figure S4). Regarding orientation biases within transcripts, MMTV shows sense orientation biases for exons and introns, whereas the two transposons show only antisense orientation biases, SB in introns, and PB in 3′UTRs and exons. Next, we analyzed the influence of expression status of endogenous genes on integration bias. This revealed interesting differences between the unselected integration profiles of the three systems (Figure 2), significance of which was assessed by the Cochran-Armitage trend test, unless mentioned otherwise. Across genes and TSSs, PB is strongly influenced by the a priori gene expression levels (for genic, TSS_upstream and TSS_downstream). For SB this same positive trend is only apparent for intragenic integrations (), whereas around TSSs, the numbers of integrations decrease with increasing gene expression (and for TSS_upstream and TSS_downstream respectively). Within weakly expressed genes, there is a depletion of PB integrations (binomial test;). MMTV target site selection is largely independent from the expression levels of endogenous genes (, and for genic, TSS_upstream and TSS_upstream respectively). Although it is evident that there are more MMTV integrations in TSS regions than within genes (Figures 1 and 2), this preference is clearly independent from gene expression. In addition to gene structure, the integration site selection of an integrating element can also be influenced by other features such as organization of the genome, state of chromatin compaction and transcription factor binding events, as well as by epigenetic modifications. Considering that an important barrier for integration of viral or transposon DNA into host DNA can be how tightly the DNA is packed in chromatin, we looked at the influence of the a priori chromatin organization on the unselected integration profiles. Hi-C [38] is a technique for studying chromatin compaction and organization by determining interaction frequencies between different genomic loci on a genome-wide scale. Analysis of Hi-C data has suggested that the genome is organized into chromatin modules, called topologically associated domains (TADs), which are stable across different cell types [39]. TADs are separated by less organized (showing fewer 3D interactions) regions called TAD boundaries. It is conceivable that chromatin is relatively less compact in and near TAD boundaries as compared to TADs. We asked if this 3D organization of the genome has any influence on the integration bias of systems. In general, we found that more integrations are close to the interface between TADs and their boundaries (Figure 3), i. e. all systems have a preference for inserting at the border of TADs, which are tightly organized, but not necessarily in the less organized chromatin of boundary regions. It is interesting to note that for MMTV, which is generally the least biased system, the bias for the TAD - TAD boundary interface is stronger than that of SB (Cochran-Mantel-Haenszel test in a window of 10 kb on either side of the interface;). In the previous two sections, we demonstrated that there are strong biases of the unselected integration profiles with respect to genes, transcripts, gene expression, and genome organization. However, these features themselves have strong spatial ties with other features, such as histone marks and transcription factor binding. Therefore, we asked the following two questions. First, how do these features associate with integration proximity? Second, do they provide extra information with respect to integration bias, in addition to what gene proximity and gene expression provide? The features we analyzed are listed in Table 2. To maximize comparability between the features, the ChIP-seq datasets were preprocessed from the raw sequencing reads in exactly the same way. Since these features are not available in NMuMG cells, which were used for generating the MMTV integrations, we restricted all analyses based on these data to SB and PB. First, we analyzed the orientation biases with respect to these features (Figures S5 and S6). This showed that the two transposons preferably integrate with the transcription cassette cloned in them oriented towards regions of high feature signal. Although for individual marks this bias is not very substantial, it is highly consistent across different marks, especially for SB. It is important to note that an orientation bias of these systems relative to genes cannot explain this bias for SB, and only partly for PB (Figures S5 and S6). Using a limited number of mostly genomic features, it has been observed before that associations of integration occurrence with these features depend on the scale chosen for the analysis [15]. Therefore, we analyzed our genomic and epigenomic features across different scales, by comparing feature scores at the site of integration with feature scores at increasing distances (scales) from the integration site. Features were then clustered based on their association profiles across scales (Figure 4A). Resulting associations can be positive, i. e. higher feature scores at integration sites compared to their neighborhood, or negative, i. e. lower feature scores at integration sites compared to their neighborhood. A clustering of the association profiles results in four groups of features generally associated with activation (Clusters 1,2, 3 and 5), and three groups associated with repression (Clusters 6,7 and 8). The remaining Cluster 5 is more mixed (Figure 4C). Another characterization of the resulting clusters is into groups of features for which the behavior is either fairly similar (Clusters 2,3, 6 and 8), or groups for which SB and PB behave very differently (Clusters 1,4, 5, and 7). Especially striking when observing the differences is that PB is positively associated (Clusters 1,4 and 5) with far more features than SB (Cluster 7). Since for both PB and SB, association with the many gene-related features in Cluster 3 is mostly positive, this indicates that SB does prefer gene-rich regions and active genes over heterochromatin, but in these regions, compared to PB, generally avoids regulatory units such as histone modifications and transcription factor bound regions. Interestingly, the single cluster positively associating with SB but negatively with PB (Cluster 7) contains mostly repressive features. Combined, these observations suggest that PB is much more biased to active chromatin than SB, whereas SB is also partly biased towards more repressed chromatin. The scale-based approach reveals that the sign of association can change across different scales. For example, SB shows negative association with some of the features in Cluster 3 on a small scale, but a positive association on larger scales. This implies that SB has a bias for larger scale domains containing these features such as Ctr9, H3K79me2 and 5hmC, but within these domains integration sites will generally avoid overlap with these marks. Conversely, association changing from positive to negative for increasing scales is seen for example for PB and Stat3 in Cluster 5. This indicates that PB prefers domains relatively devoid of these features. However, given a PB integration in such a domain, it will be mildly biased towards Stat3. The above observations suggest a hierarchy in target site selection, which is further illustrated by the fact that some features, for both SB and PB, are consistently non-significant at smaller scales (Figures 4A and 4B). For example, at smaller distances from integrations, associations with features such as gene proximity and expression, CpG island proximity, replication timing and H3K9me2 are not significant for both SB and PB. They are however consistently significant on larger scales. On the other hand, most transcription factors and other histone marks show strong associations already at small scales. Henceforward, features that are significant only at larger scales will be referred to as ‘macrofeatures’, as opposed to ‘microfeatures’, which are significant already at smaller scales (refer to Table S3 for a list of macrofeatures and microfeatures). For a selected set of macrofeatures that were available for NMuMG cells, we performed a similar analysis showing that these features also behave as macrofeatures in MMTV, an unrelated system (Figure S7). Biases of unselected integration profiles with respect to the macrofeatures are similar across the systems and scales. This suggests that on a large scale, integration bias is regulated in similar ways for both systems, and that this large scale bias is mainly determined by the macrofeatures. However, within a distance of, macrofeatures provide no information with regard to integration locus, contrary to the microfeatures. This indicates that microfeatures may in fact be determinants of integration bias at a higher resolution, which prompted us to ask the following question: Are macrofeatures needed at all to explain integration proximity, or are microfeatures sufficient for this purpose? To address this question, we needed to take into account that the features in Figure 4 show a high degree of multicollinearity. Multicollinearity implies that a strong association between a certain feature and integration proximity may potentially be explained by the association of with another feature that also strongly associates with integration proximity, i. e. integration proximity may be conditionally independent from, given. Then, rephrasing the question above, for each system we wanted to identify a set of features such that integration proximity is conditionally independent from all other features, given this set of features. BANJO [40] is designed to identify such conditional independencies in the form of Bayesian networks [41], and thus allowed us to determine for each feature its importance for integration proximity. For this, we used two measures derived from the Bayesian networks. The first measure (‘log10% bootstraps’; see Material and Methods) represents the confidence that a feature is truly relevant for integration proximity. The second measure (‘log10 mean CMI’, or conditional mutual information; see Material and Methods) represents the strength of association between integration proximity and a feature. Interestingly, the results show that seven macrofeatures are consistently of great importance, i. e. of high-confidence and strongly associating, in both systems (Figure 5). These features are gene/TSS/TTS proximity, TSS expression (the expression of the gene with the nearest TSS), replication timing, CpG island proximity, and Hi-C alpha (a measure of chromatin compaction; see Material and Methods section). This shows that in addition to microfeatures for explaining local differences between systems (Figure 4), macrofeatures are needed to explain integration bias in each of these systems on large scales (Figure 5). Furthermore, it indicates that on a large scale, biases of the two systems are similar, and that differences between the systems are mainly found in the microfeatures. Insertional mutagenesis (IM) using retroviruses and transposons is an important tool in the discovery of new putative cancer genes. These elements mutate the genome by inserting into the host DNA. Mutations providing cells with a proliferative and/or survival advantage can cause tumors. Because the integration loci can be retrieved using sequencing, these mutations can act as cancer gene tags, allowing discovery of novel cancer genes, e. g. [3], [4], [29], [37], [42]–[44]. However, integration biases can pose problems because they can be difficult to distinguish from the accumulation of integrations in cells that are under selective pressure to retain these integrations. Therefore, we compared the unselected integration profiles with CIS integration profiles [3], [30]–[32]. Generally, the orientation biases of CIS integrations for genes and transcripts are much stronger than those of the unselected integrations (Figure S4). This indicates that in tumors, the orientation bias is mainly the result of selective pressure. For all systems and especially for PB and SB, there are significantly more unselected integrations than CIS integrations in regions other than genes and TSSs (Figure 6A; visualized at a 5% FDR threshold based on the binomial test. Refer to Table S2 for the raw p-values). Additionally, biases of unselected integrations for intergenic CIS regions (100 kb from genes) are relatively strong, compared to the biases for genic CIS regions (+/−100 kb) (Figure 6C; visualized at a 5% FDR threshold based on the binomial test. Refer to Table S2 for the raw p-values). Combined, these observations show that in regions far from genes, unselected integration profiles correlate relatively strongly with CIS profiles, compared to regions close to genes. Therefore, to avoid calling spurious CISs in IM screens, higher statistical stringency should be required for CISs found far from genes. Combined, these observations suggest that CISs found far away from genes are more likely to be spurious, i. e. arise from the integration bias of the system. Conversely, the observations suggest that true CISs, i. e. CISs arising from selective pressure, are more often found in the vicinity of genes than would be expected based on an unselected integration profile. To identify potentially spurious CISs, we tested for all CISs if the corresponding CIS regions contained significantly more CIS integrations than unselected integrations. This revealed a number of potentially spurious CISs, 13%, 33% and 7. 4% of all CISs for SB, PB and MMTV respectively (Figure 6D, Tables S4, S5 and S6). For MMTV, it could be confirmed that potentially spurious CISs tended to be relatively far away from genes (One-sided Mann-Whitney U test;). For SB and PB, when ranking CISs according to increasing p-value, the potentially spurious CISs consisted mainly of lower ranking CISs (Mann-Whitney U test; and for SB and PB respectively). Next, we asked whether integration bias has an influence on the types of CISs that are found in screens. For this purpose, we separated CISs into activating and repressing CISs, based on orientation homogeneity and occurrence within or outside genes. We observed more activating CISs for PB than for SB (Figure 6B). Considering that the constructs used for the SB and PB tumor screens are similar [32], [45], this indicates that for use in IM screens, PB is more efficient at finding oncogenes, whereas SB would find more tumor suppressor genes.
In this study, we have analyzed the integration biases of unselected integrations of a retrovirus and two transposons. For generating these sets of integrations, cells were grown in culture for three to four weeks. This implies that a few of our integration loci could potentially have been selected for. However, non-acute retroviruses, such as MMTV, induce tumors only very slowly (months to years) due to the absence of oncogenes in their genome [46]. Similarly, our transposon constructs can be described as non-acute in the sense that they do not carry oncogenes. Moreover, they do not contain any gene-trap or enhancer-trap elements, limiting the potential of disrupting endogenous gene expression. Hence, three or four weeks of cell culturing is a very short time frame compared to the latency to integration-induced tumor formation, and the influence of selected integration loci will be minimal at best. This is also supported by the observations that 1) we find a large number of unique integration sites, whereas in the case of substantial selection a relatively small number of (selected) integrations would be expected, and 2) while both PB and SB were mobilized from the same construct, we do obtain completely different insertion profiles for each transposon. The main differences between the three systems regarding integration bias are summarized in Table 3. Generally, MMTV was observed to be the system least biased in its integration profile. Although many associations were found to be significant, they were generally not very substantial. In this context, it is surprising that only a small set of oncogenes has been tagged with this virus in IM screens [31], [47]. This could be due to activation of a limited set of promoters by the MMTV enhancer. Alternatively, certain unknown aspects of murine mammary tissue biology might allow only a limited number of tumorigenic mechanisms. In any case, our data rules out integration bias as a reason for the limited potential of IM by MMTV. Recent availability of data describing the three-dimensional architecture of the genome [39] has allowed us to identify TAD - TAD boundary interfaces as hotspots of integration. Of the three systems, PB is most strongly affected, from a strong enrichment at TAD interfaces to a strong depletion towards the inner regions of TADs. While MMTV is largely indifferent regarding these inner TAD regions, its bias for TAD - TAD boundary interfaces is relatively strong. Although the TADs were defined in a different cell type (Tables 1 and 2), they have been shown to be stable across different cell types [39]. Altogether, our data show that integration target site selection is strongly associated with the topological organization of the genome. The two transposons were found to have very different integration profiles. Generally, it is unknown to what extent certain sequences cloned into an IM construct affect the integration bias of that construct, which complicates the interpretation of differences observed between systems. However, the construct that was used in this study contained both the SB and PB IRs. Therefore, any difference between the two profiles can only be explained by the IR or the transposon-specific transposase, indicating that the IR and transposase are major defining elements of integration bias. Although the SB and PB profiles are very different, they were shown to share a bias for activating macrofeatures and a bias against repressive macrofeatures. Analysis of a subset of macrofeatures for MMTV suggested that these may also operate as macrofeatures in a wider range of systems. Differences between SB and PB were mostly seen for the microfeatures. Together, these observations support a model where integration sites are selected at two levels (Figure 7). On larger scales, both systems target the same type of domains, determined by the macrofeatures. In particular, gene/TSS/TTS proximity, TSS expression, replication timing, CpG island proximity, and Hi-C alpha were found consistently indispensable for integration site selection in both systems (Figure 5). Once these domains have been selected, fine tuning of integration site selection is dependent on different microfeatures for each system. These microfeatures appear to be indispensable for integration site selection as well (Figure 4). For the current study, the mESC model system was selected because it is the most thoroughly studied model system, with the broadest availability of (epi) genomic datasets. While limited cell type specificities have been demonstrated for retroviral integration profiles [22], earlier studies have used epigenomic features in non-matching cell types to analyze retroviral integration profiles [15], [23], noting that differences due to experimental error are generally greater than differences due to cell type [23]. We do not expect our transposon integration profiles to be highly cell type specific. This is supported by a supplementary analysis comparing the SB and PB integration profiles to a selected set of epigenomic features available for both mESC and mouse embryonic fibroblasts (mEF) [48]–[52], which shows that the mEF associations are highly similar to the mESC associations (Figure S8). This strong similarity suggests that cell type specificities are relatively weak. Nevertheless, it is interesting to note that the mEF associations are consistently slightly weaker than the mESC associations, indicating that cell type specificities, while weak, do exist. In large scale IM screens, identification of overwhelming numbers of CISs is a serious impediment in distinguishing true CISs from spurious ones. In such screens, a true CIS arises through tumorigenic selection, whereas a spurious CIS is defined by the a priori integration bias of the IM system that was used. Our large datasets of unselected integrations can be used as a valuable resource for prioritizing candidate cancer genes emerging from IM screens. As a proof of principle, we showed that a substantial number of CIS regions in three recent IM screens do not contain more integrations than would be expected based on the unselected integration profiles. Although the cell types between PB and SB tumor screens and their corresponding unselected profiles are different, this nevertheless indicates that these CISs can potentially be explained by an a priori integration bias, and therefore likely represent passenger mutations. In conclusion, the large numbers of integrations for three of the main systems used in IM, unselected integrations from cell lines and selected integrations from tumor screens, as well as the wide range of publicly available datasets, has enabled us to assess integration bias at unprecedented resolution, and assess its relation to CIS designation.
All integration data generated in this study are made available on http: //mutapedia. nki. nl.
Retroviruses and transposons are widely used in cancer research and gene therapy. However, these systems show integration biases that may strongly affect results. To address this issue, we generated very large datasets consisting of to unselected integrations for the Sleeping Beauty and piggyBac transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed (epi) genomic features to generate bias maps at local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome, and a striking similarity was observed between piggyBac and the Murine Leukemia Virus. Moreover, we find that target site selection is directed at multiple scales. At larger scales, it is similar across systems, and directed by a set of domain-oriented features, including chromatin compaction, replication timing, and CpG islands. Notable differences between systems are defined at smaller scales by a diverse range of epigenetic features. As a practical application of our findings, we determined that three recent insertional mutagenesis screens - commonly used for cancer gene discovery - contained 7%–33% putative false positive integration hotspots.
viral classification
microbiology
retroviruses
molecular cell biology
genetic elements
epigenomics
chromatin
genomics
chromosome biology
medical microbiology
microbial pathogens
systems biology
cell biology
virology
viral pathogens
genetics
transposable elements
biology and life sciences
dna transposons
computational biology
mobile genetic elements
Chromatin Landscapes of Retroviral and Transposon Integration Profiles