Viruses are small infectious particles that can only reproduce with the help of a host. Once they are inside their victim, they hijack the cells’ genetic material and reprogram it to become a virus factory that produces more virus particles. Parvoviruses, for example, are among the simplest of viruses and need all resources a cell has to offer to successfully replicate.

This process often takes place at so-called replication centers that contain these necessary factors. It was previously thought that parvoviruses set up such centers randomly, and gather the required molecules such as proteins to these sites. However, it was not well understood how they do this.

Now, Majumder et al. have developed a new method that enabled them to study in detail how parvoviruses gain access to the resources of the cell they need to initiate and amplify replication. The results show that parvoviruses set up their replication centers at sites on the host DNA that are already rich in proteins needed to repair and then replicate damaged DNA. Some of these sites already exist in the cell’s genetic material as a consequence of naturally occurring processes, but others are created during infection by the virus. These findings may have important implications for how other viruses may establish their replication.

Viruses, including parvoviruses, are important pathogens. Like many microbes, viruses can be beneficial for our health and environment. Others, however, can be harmful. A clearer understanding of how viruses establish and amplify an infection may provide new treatment opportunities.

DNA viruses that replicate in the nucleus depend on host cellular functions for transcriptional and replication machinery to express and amplify their genomes. Accessing these functions is critical to productive infection, yet successful establishment of replication must also overcome cellular antiviral activity, which for larger DNA viruses includes innate immune responses, epigenetic silencing, the cellular DNA-damage response (DDR), and antiviral activity found associated with PML bodies (Weitzman et al., 2010). Replication of many DNA viruses takes place in distinct micro-nuclear compartments termed replication centers that are rich in factors viruses must interact with - either positively or negatively - to productively replicate (Schmid et al., 2014). It is not fully clear, however, how nuclear-replicating viruses initiate replication centers in order to optimize access to factors and functions they need to either utilize or inactivate.

Parvoviruses are small non-enveloped icosahedral viruses that are important pathogens in many animal species including humans. Minute Virus of Mice (MVM) is an autonomously replicating parvovirus that is lytic in murine cells and transformed human cells (Cotmore and Tattersall, 2014). The viral genome is approximately 5 kb and possesses inverted terminal repeats at each end that serve as origins of replication (Cotmore and Tattersall, 2014). MVM encodes two non-structural proteins: the larger non-structural phosphoprotein NS1 performs a number of functions required for viral replication, while NS2 plays important, currently undefined, roles during infection of the normal murine host (Cotmore and Tattersall, 2014).

Parvoviruses are the only known viruses of vertebrates that contain single-stranded linear DNA genomes, and thus, they present novel replicative DNA structures to cells during infection. They depend heavily on cellular functions for replication, and unlike the DNA tumor viruses, do not drive quiescent cells into S-phase. However, following S-phase entry, cellular DNA polymerase δ converts the single stranded viral DNA genome into a double stranded molecule that serves as a template for transcription of the viral genes (Cotmore and Tattersall, 2013). As MVM infection progresses through S-phase, it induces substantial cellular DNA damage and evokes a robust, ATM-dependent DNA damage response (DDR, [Adeyemi et al., 2010]). Infection is characterized by a pre-mitotic cell cycle arrest that is both p21 and CHK1 independent (Adeyemi and Pintel, 2012; 2014). During this block virus replication proceeds for many hours, and ATM inhibitors reduce ongoing viral replication (Adeyemi et al., 2010).

Parvoviruses establish replication factories in the nucleus (termed Autonomous Parvovirus-Associated Replication, or APAR, bodies) where active transcription of viral genes and viral replication takes place (Bashir et al., 2001). Low resolution confocal microscopy originally showed that DDR sensor and response proteins, cell cycle regulators, DNA polymerases and RNA polymerase II accumulate in MVM APAR bodies where they co-localize with replicating viral DNA and NS1 (Adeyemi et al., 2010; Bashir et al., 2001; Ruiz et al., 2011). More recently, higher magnification microscopic studies revealed that phosphorylated histone H2A variant γ-H2AX, as well as other DDR proteins ([Ruiz et al., 2011] see Figure 1, below), seemed to reside adjacent to, rather than within, early APAR bodies. This gave rise to the suggestion that these DDR factors may reside on cellular DNA in the vicinity of viral replication centers (Ruiz et al., 2011). The induction of cellular DNA damage leads to almost instantaneous recruitment of DDR factors to the break site which coordinate complex signaling cascades that recruit DNA damage sensors, repair mediators and effectors (Hashiguchi et al., 2007; Polo and Jackson, 2011). Endogenous sites of damage are also often associated with interference between replication and transcription polymerases (Durkin and Glover, 2007). DNA breaks, therefore, serve as cellular depots of DDR proteins, and factors involved in DNA replication and expression (Hashiguchi et al., 2007; Polo and Jackson, 2011). While it is plausible that during infection damaged cellular DNA is relocated to sites of MVM replication, it seemed possible, alternatively, that the virus initially established its replication centers at damaged cellular sites where these DDR, replication, and expression factors were already present.

Figure 1

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It has been suggested that a number of viruses, including hepatitis B virus (HBV) and human papillomavirus (HPV), associate with sites of DNA damage, including early-replicating or common, fragile sites (ERFs, and CFSs, respectively) at early times during their infections (Durkin and Glover, 2007; Jang et al., 2014; Tubbs and Nussenzweig, 2017). The HPV genome was shown to be tethered to CFSs by the chromatin modifier BRD4, which facilitates subsequent integration into the host genome utilizing the cellular DDR machinery (Feitelson and Lee, 2007; Jang et al., 2014). These studies utilized chromatin immunoprecipitation (ChIP) assays of the HPV E2 protein to identify sites of HPV localization to cellular CFSs, which were then validated by 3D-FISH assays of the viral and cellular DNA (Jang et al., 2014). Consistent with these findings, crosslinked-ChIP assays have recently demonstrated that FANCD2, required for maintaining fragile site stability and coordinating their replication, also associates with HPV genomes at replication centers (Madireddy et al., 2016; Spriggs and Laimins, 2017). However, an unbiased way to map the interaction between the viral and cellular genomes directly has been largely unavailable until recently.

The development of Chromosome Conformation Capture (3C) technologies has enabled detailed analysis of cis-interactions between separated regions of the genome, as well as aspects of chromatin packaging (Dixon et al., 2016). When combined with high-throughput sequencing, these techniques (termed 4C, 5C, and Hi-C assays) have become valuable tools for studying the details of nuclear configuration (Denker and de Laat, 2016). Specifically, 4C and Hi-C assays provide genome-wide interaction data through unbiased deep sequencing. Hi-C assays are designed to provide information on all nuclear interactions whereas 4C assays enable higher resolving power and a deeper interrogation of genomic associations by utilizing inverse PCRs to amplify only the ‘bait’ (Lajoie et al., 2015). In this study, we report the novel adaptation of high-throughput circular chromosome conformation capture assay (4C) for use in trans which we term V3C (Viral Chromosome Conformation Capture). This assay, which should be generally adaptable, has allowed us to characterize, on a genome-wide scale, the direct association of the linear MVM genome with discreet regions of the cellular genome. These sites, termed Virus Association Domains (VADs), correlated initially with sites of cellular DNA damage that in mock-infected cells also exhibited damage as cells progressed through S-phase. As infection progressed these sites expanded, and additional sites of DNA damage were induced. MVM subsequently associated with the newly induced sites as infection amplified. Sites of association identified biochemically were confirmed microscopically, and in addition, MVM could be targeted specifically to sites of DNA damage artificially engineered into the cellular chromosome. Development of the V3C assay has allowed us to suggest the following model. Soon after nuclear entry MVM homes first to sites of pre-existing endogenous DNA damage to initiate infection at sites that provide cellular factors necessary for its replication. Subsequently, as cellular DNA damage accrues, virus spreads additionally to these sites of damage to amplify infection. The V3C-seq assay should be useful for characterizing the interaction of many DNA viruses that associate with the cellular genome, and provide a useful tool to characterize the molecular events leading to the initiation of infection.

When DNA viruses enter the nucleus they must locate to sites suitable to sustain replication. Small DNA viruses, such as parvoviruses, require multiple cellular factors for the expression and replication of their genomes. It may be that DNA viruses set up replication centers essentially randomly, and factors necessary for replication are recruited to these sites. An alternative model, suggested by the present work, is that incoming DNA viruses can initially locate to cellular sites that maintain factors necessary for virus replication. Sites of cellular DNA damage present such an opportunity (Hashiguchi et al., 2007; Polo and Jackson, 2011). Using a high-throughput conformational capture assay developed here for use in trans, we show that at early times post infection MVM interacted directly with sites of cellular DNA damage that in mock infected cells also exhibited DNA damage upon entry into S-phase. As infection progressed, interaction of the MVM genome, as well as the presence of the viral replication protein NS1, increased at these sites, suggesting they were sites of ongoing viral replication. ChIP-seq analysis for γ-H2AX and BRCA1 demonstrated that DNA damage increased during infection, and MVM subsequently associated also with newly induced sites. This model is supported by our findings that MVM NS1 and the MVM genome could be re-localized to artificially induced sites of cellular DNA damage. These observations support a model consistent with the notion that MVM initially establishes replication at cellular DNA damage sites that provide replication and expression machinery, as well as other DDR factors, and as infection progresses induces additional sites of cellular DNA damage, using these to amplify infection. It should be noted, however, that even at the earliest time points examined, multiple virus genomes have accumulated at sites of replication. Thus, specific localization of input genomes remains circumstantial. Consistent with our overall model, we have previously demonstrated that an ATM inhibitor applied during infection specifically reduced virus replication (Adeyemi et al., 2010). Also, as might be expected, we find that HU pretreatment of permissive rat F111 cells, which have lower levels of endogenous DNA damage as indicated by lower levels of γ-H2AX, resulted in increased MVM replication by 20 hpi (Figure 3—figure supplement 2B).

Because VADs correlated strongly with γ-H2AX ChIP-seq sites identified on mock-infected cells as they progressed through S-phase, and on non-infected, HU-treated A9 cells, the predominantly identified γ-H2AX signal associated with MVM during infection very likely resides on cellular DNA. However it remains possible that γ-H2AX and/or other DDR signaling proteins are directly associated with the viral genome during infection. In this regard, while there is a clear potential role in MVM replication for DNA polymerase-δ and gene expression factors potentially present at DNA damage sites, the possible roles of other DDR proteins in parvovirus replication warrants additional study.

Recently Shah and O’Shea have elegantly demonstrated a bipartite cellular DDR to adenovirus (Ad) infection that initially targets the virus while sparing the cell. The Mre11-Rad50-Nbs1 (MRN) complex first inhibits adenovirus replication without inducing a global response that could interfere with cellular proliferation or viability. As Ad overcomes this block, utilizing the Ad E4Orf6/E1B 55 kDa complex, and replicates to high levels, a global DDR is subsequently induced, and cellular DNA breaks were found to sequester DDR proteins from adenovirus preventing its replication (Shah and O'Shea, 2015). While MVM localizes to sites of cellular DNA damage as replication ensues, the model that we propose for parvovirus infection is different. It is consistent with the parvovirus life cycle, which depends upon the induction of a cell cycle arrest, is susceptible to ATM inhibitors, and which continually induces cellular DNA damage during its infection. Additionally, in contrast to adenovirus, MVM is much more dependent on host cell factors for its replication and expression (Cotmore and Tattersall, 2014), and it does not have an extensive genetic capacity to encode functions that inactivate the cellular DDR as does adenovirus (Ou et al., 2012; Querido et al., 2001; Stracker et al., 2002).

Chromosome conformation assays have been traditionally used to analyze the 3D folding principles of the cellular genome, including the regulation of promoter-enhancer loops and structural folding loops (Dixon et al., 2016). However, these assays can also serve as valuable tools to study the interaction between the host genome and invading virus. In the context of virus-infected cells, Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET) assays have enabled the comprehensive mapping of EBV interaction with the cellular genome (Jiang et al., 2017). These studies, which combine ChIP-seq with chromosome conformation capture techniques, utilize the proximal interaction of distally located DNA regions bound by shared protein elements. They have demonstrated that Epstein Barr Virus enhancers can regulate the expression of the cellular Myc oncogene in lymphoblastoid cells via long-range promoter-enhancer looping, thereby contributing the EBV-mediated cellular transformation (Jiang et al., 2017). However, mapping of the virus-host interactome by ChIA-PET experiments can be limited by the necessity of having a priori knowledge of the proteins mediating this interaction. The EBV interactome has also been characterized using in-situ Hi-C assays, which generate chromosome conformation maps that provide a snapshot of how every restriction enzyme site associates with every other site throughout the genome. These studies showed that the latent EBV episome associates with gene poor regions, but relocalizes to gene-rich regions of the genome upon reactivation (Moquin et al., 2017). While highly informative, these studies did not have the resolving power of the assays described here for MVM.

For MVM, a single inverse-PCR viewpoint was sufficient to map its interactome. Moreover, using inverse PCR using primers complementary to the viral genome to generate the sequencing library ensured that the V3C-seq assay detects only MVM-host hybrid junctions. This enabled higher resolving power and a deeper interrogation of genomic associations, allowing the detection of both frequent and infrequent MVM interaction sites. The drawback of V3C-seq, however, is that it does not assay changes in nuclear architecture in response to viral infection, which invariably is altered during parvovirus replication, particularly in late stages.

Looping interactions have previously been observed at distinct loci in genomic regions proximal to some integrated viral genomes. For example, the integrated Murine Leukemia Virus (MuLV) genome has beRen, BRen, Ben shown to influence the folding properties of its proximally located Myc promoter, which in turn contributes to cellular oncogenesis (Zhang et al., 2012). In other studies, it has been shown in cell line models that the integrated HIV genome associates distally with an uncharacterized chromosomal region to promote reactivation of latent HIV (Dieudonné et al., 2009). Chromosome conformation capture analyses focused on elucidating the topological conformation of the viral genome have also revealed the conformational structure adopted by the gammaherpesvirus Kaposi’s Sarcoma Associated Herpesvirus (KSHV), which forms distinct promoter-enhancer loops mediated by the cellular architectural proteins CTCF and Cohesin (Kang et al., 2011). In this study we have utilized chromosome conformation capture technology for the first time to map the trans interaction of a lytic virus with the cellular genome in a non-biased way. However, in addition to assaying the inter-chromosomal interaction cellular sites, long-range interactions of integrated viruses such as MuLV and HIV with distal elements, or TADs, may also be assayed using V3C-seq.

Inspection of VADs show that regions of MVM interaction vary greatly in size. The size of the peaks correlates with the number of interactions, and so indicate that some regions are more densely populated by virus than others. As can be seen in the whole-genome analysis, there is quite a variation in VADs across different chromosomes (Figure 2—figure supplement 2). While most chromosomes have multiple VADS, most have only a few larger VADs of 5–15 Mb. Whether the size of the VADs correlates to the success of infection at that site is not known. There are a number of chromosomes [chromosomes 1, 13, 14, 18, X and Y (Figure 2—figure supplement 2 and Figure 3—figure supplement 1)] that have few sites of damage, as assessed by γ-H2AX ChIP-seq, as well as few VADs, reinforcing their correlation.

VADs identified by V3C-seq showed striking overlap with sites of damage in infected cells, at sites of damage incurred in uninfected cells as they progressed into S-phase, but surprisingly, also at sites in uninfected cells treated with HU. This overlap suggested that there may be a predilection in the cellular chromosome for sites sensitive to the induction of damage for which MVM has an affinity. In this vein, we find that most VADs (and by implication sites of endogenous and infection-induced damage and damage induced by HU) also overlap strikingly with Topologically Associated Domains (TADs), determined for murine CH12 B-cells, by Hi-C assays (Figure 4—figure supplement 2A–D, [Rao et al., 2014]). Such studies, in combination with high-resolution imaging, have shown that the mammalian genome is folded into megabase-sized cis-interacting regions (TADs). TADs have been divided into two main compartments: A (primarily euchromatin-like), and B (primarily heterochromatin-like) (Rao et al., 2014). The A compartment is characterized by being gene dense, containing highly expressed genes, and active chromatin marks. The A1 subcompartment was seen to finish replicating at the beginning of S-phase, while the A2 subcompartment was described as continuing to replicate into the middle of S-phase. Overlaying published chromatin marks from murine 3T3 cells induced with the DNA damaging agent aphidicolin (Kraushaar et al., 2013) suggests that MVM associations may be with the A compartment (Figure 4—figure supplement 2E). This compartment of cellular DNA would provide the necessary machinery for the expression and replication of MVM within S-phase, and would be consistent with MVM initially associating with sites that emerge as ERFs during cellular replication, which would be predicted to replicate in compartment A.

The VADs identified in our study occupy multiple adjacent TADs. This suggests that several adjacent TADs may contain the necessary environment to support MVM expression and replication. The mechanisms that maintain TAD borders are elusive, but structural proteins such as CTCF and cohesin have been implicated in these processes (Dixon et al., 2016; Rao et al., 2014, 2017). Indeed, Canela et al., have shown that CTCF and RAD21 form the anchors to cis-interacting cellular loops that are susceptible to topoisomerase TOP2B mediated DNA breaks (Canela et al., 2017). The MVM NS1 protein, which also interacts at TADs, is a known DNA binding protein with double-stranded nickase activity; however, the mechanisms by which the viral genome associates with the cellular sites remains to be determined.

Our results suggest that host chromatin states may play a significant role in permissiveness to DNA damage, and thereby influence MVM localization for replication. Consistent with this model, the HPV E2 protein has been found associated with actively transcribing genes and active chromatin, presumably to facilitate expression of HPV through hijacking the host transcriptional machinery at these sites (Jang et al., 2009). Indeed, profiling of sites on the cellular genomic that undergo replication stress have shown that these regions are encased in a protective chromatin environment to facilitate efficient repair (Kim et al., 2018). Such regions, either pre-existing prior to infection, or induced by virus, may provide the supportive environment necessary for successful infection. The generally adaptable, high-throughput V3C-seq assay allows genome-wide identification of the direct associations of viral genomes with distinct regions of the cellular chromosome. It should be useful for characterizing the interaction of many DNA viruses that associate with the cellular genome, and thus provide a useful tool to characterize the molecular events leading to the initiation of their infections.

Sequencing data have been deposited in GEO under accession code GSE112957.

1. 1. Kinjal Majumder
   2. Juexin Wang
   3. Maria Boftsi
   4. Matthew S Fuller
   5. Jordan E Rede
   6. Trupti Joshi
   7. David J Pintel

   (2018) Parvovirus Minute Virus of Mice Localizes to Sites of Cellular DNA Damage to Establish and Amplify its Lytic Infection

   Publicly available at the NCBI Gene Expression Omnibus (accession no: GSE112957).

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112957

1. 1. Rao SS
   2. Huntley MH
   3. Durand NC
   4. Stamenova EK

   (2014) A three-dimensional map of the human genome at kilobase resolution reveals prinicples of chromatin looping

   Publicly available at the NCBI Gene Expression Omnibus (accession no: GSE63525).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63525
2. 1. Kraushaar DC
   2. Jin W
   3. Maunakea A
   4. Abraham B

   (2013) Genome-wide incorporation dynamics reveal distinct categories of turnover for the histone variant H3.3

   Publicly available at the NCBI Gene Expression Omnibus (accession no: GSE51505).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51505
3. 1. Barlow JH
   2. Faryabi RB
   3. Callén E
   4. Wong N

   (2013) Genome-wide mapping of early replication fragile sites (ERFs)

   Publicly available at the NCBI Gene Expression Omnibus (accession no: GSE43504).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43504

1. 1. Adeyemi RO
   2. Landry S
   3. Davis ME
   4. Weitzman MD
   5. Pintel DJ

   (2010) Parvovirus minute virus of mice induces a DNA damage response that facilitates viral replication

   PLoS Pathogens 6:e1001141.

   https://doi.org/10.1371/journal.ppat.1001141

   • PubMed
   • Google Scholar
2. 1. Adeyemi RO
   2. Pintel DJ

   (2012) Replication of minute virus of mice in murine cells is facilitated by virally induced depletion of p21

   Journal of Virology 86:8328–8332.

   https://doi.org/10.1128/JVI.00820-12

   • PubMed
   • Google Scholar
3. 1. Adeyemi RO
   2. Pintel DJ

   (2014) The ATR signaling pathway is disabled during infection with the parvovirus minute virus of mice

   Journal of Virology 88:10189–10199.

   https://doi.org/10.1128/JVI.01412-14

   • PubMed
   • Google Scholar
4. 1. Afgan E
   2. Baker D
   3. van den Beek M
   4. Blankenberg D
   5. Bouvier D
   6. Čech M
   7. Chilton J
   8. Clements D
   9. Coraor N
   10. Eberhard C
   11. Grüning B
   12. Guerler A
   13. Hillman-Jackson J
   14. Von Kuster G
   15. Rasche E
   16. Soranzo N
   17. Turaga N
   18. Taylor J
   19. Nekrutenko A
   20. Goecks J

   (2016) The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update

   Nucleic Acids Research 44:W3–W10.

   https://doi.org/10.1093/nar/gkw343

   • PubMed
   • Google Scholar
5. 1. Barlow JH
   2. Faryabi RB
   3. Callén E
   4. Wong N
   5. Malhowski A
   6. Chen HT
   7. Gutierrez-Cruz G
   8. Sun HW
   9. McKinnon P
   10. Wright G
   11. Casellas R
   12. Robbiani DF
   13. Staudt L
   14. Fernandez-Capetillo O
   15. Nussenzweig A

   (2013) Identification of early replicating fragile sites that contribute to genome instability

   Cell 152:620–632.

   https://doi.org/10.1016/j.cell.2013.01.006

   • PubMed
   • Google Scholar
6. 1. Bashir T
   2. Horlein R
   3. Rommelaere J
   4. Willwand K

   (2000) Cyclin A activates the DNA polymerase Delta -dependent elongation machinery in vitro: a Parvovirus DNA replication model

   PNAS 97:5522–5527.

   https://doi.org/10.1073/pnas.090485297

   • PubMed
   • Google Scholar
7. 1. Bashir T
   2. Rommelaere J
   3. Cziepluch C

   (2001) In vivo accumulation of cyclin A and cellular replication factors in autonomous Parvovirus minute virus of mice-associated replication bodies

   Journal of Virology 75:4394–4398.

   https://doi.org/10.1128/JVI.75.9.4394-4398.2001

   • PubMed
   • Google Scholar
8. Software

   1. Bolstad BM

   (2013)

   preprocessCore: A Collection of Pre-Processing Functions, version R package version, 1(0)

   preprocessCore: A Collection of Pre-Processing Functions.
9. 1. Canela A
   2. Maman Y
   3. Jung S
   4. Wong N
   5. Callen E
   6. Day A
   7. Kieffer-Kwon KR
   8. Pekowska A
   9. Zhang H
   10. Rao SSP
   11. Huang SC
   12. Mckinnon PJ
   13. Aplan PD
   14. Pommier Y
   15. Aiden EL
   16. Casellas R
   17. Nussenzweig A

   (2017) Genome organization drives chromosome fragility

   Cell 170:507–521.

   https://doi.org/10.1016/j.cell.2017.06.034

   • PubMed
   • Google Scholar
10. 1. Christensen J
    2. Cotmore SF
    3. Tattersall P

    (1995)

    Minute virus of mice transcriptional activator protein NS1 binds directly to the transactivation region of the viral P38 promoter in a strictly ATP-dependent manner

    Journal of virology 69:5422–5430.

    • PubMed
    • Google Scholar
11. 1. Cotmore SF
    2. Tattersall P

    (2013) Parvovirus diversity and DNA damage responses

    Cold Spring Harbor Perspectives in Biology 5:a012989.

    https://doi.org/10.1101/cshperspect.a012989

    • PubMed
    • Google Scholar
12. 1. Cotmore SF
    2. Tattersall P

    (2014) Parvoviruses: small does not mean simple

    Annual Review of Virology 1:517–537.

    https://doi.org/10.1146/annurev-virology-031413-085444

    • PubMed
    • Google Scholar
13. 1. Dekker J
    2. Marti-Renom MA
    3. Mirny LA

    (2013) Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data

    Nature Reviews Genetics 14:390–403.

    https://doi.org/10.1038/nrg3454

    • PubMed
    • Google Scholar
14. 1. Denker A
    2. de Laat W

    (2016) The second decade of 3C technologies: detailed insights into nuclear organization

    Genes & Development 30:1357–1382.

    https://doi.org/10.1101/gad.281964.116

    • PubMed
    • Google Scholar
15. 1. Dieudonné M
    2. Maiuri P
    3. Biancotto C
    4. Knezevich A
    5. Kula A
    6. Lusic M
    7. Marcello A

    (2009) Transcriptional competence of the integrated HIV-1 provirus at the nuclear periphery

    The EMBO Journal 28:2231–2243.

    https://doi.org/10.1038/emboj.2009.141

    • PubMed
    • Google Scholar
16. 1. Dixon JR
    2. Gorkin DU
    3. Ren B

    (2016) Chromatin domains: the unit of chromosome organization

    Molecular Cell 62:668–680.

    https://doi.org/10.1016/j.molcel.2016.05.018

    • PubMed
    • Google Scholar
17. 1. Duggavathi R
    2. Volle DH
    3. Mataki C
    4. Antal MC
    5. Messaddeq N
    6. Auwerx J
    7. Murphy BD
    8. Schoonjans K

    (2008) Liver receptor homolog 1 is essential for ovulation

    Genes & Development 22:1871–1876.

    https://doi.org/10.1101/gad.472008

    • PubMed
    • Google Scholar
18. 1. Durand NC
    2. Robinson JT
    3. Shamim MS
    4. Machol I
    5. Mesirov JP
    6. Lander ES
    7. Aiden EL

    (2016) Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom

    Cell Systems 3:99–101.

    https://doi.org/10.1016/j.cels.2015.07.012

    • PubMed
    • Google Scholar
19. 1. Durkin SG
    2. Glover TW

    (2007) Chromosome fragile sites

    Annual Review of Genetics 41:169–192.

    https://doi.org/10.1146/annurev.genet.41.042007.165900

    • PubMed
    • Google Scholar
20. 1. Feitelson MA
    2. Lee J

    (2007) Hepatitis B virus integration, fragile sites, and hepatocarcinogenesis

    Cancer Letters 252:157–170.

    https://doi.org/10.1016/j.canlet.2006.11.010

    • PubMed
    • Google Scholar
21. 1. Freeman AE
    2. Igel HJ
    3. Price PJ

    (1975) Carcinogenesis in vitro

    In Vitro 11:107–116.

    https://doi.org/10.1007/BF02624083

    • Google Scholar
22. 1. Fuller MS
    2. Majumder K
    3. Pintel DJ

    (2017) Minute virus of mice inhibits transcription of the cyclin B1 gene during infection

    Journal of Virology 91:e00428–17.

    https://doi.org/10.1128/JVI.00428-17

    • PubMed
    • Google Scholar
23. 1. Hagège H
    2. Klous P
    3. Braem C
    4. Splinter E
    5. Dekker J
    6. Cathala G
    7. de Laat W
    8. Forné T

    (2007) Quantitative analysis of chromosome conformation capture assays (3C-qPCR)

    Nature Protocols 2:1722–1733.

    https://doi.org/10.1038/nprot.2007.243

    • PubMed
    • Google Scholar
24. 1. Hashiguchi K
    2. Matsumoto Y
    3. Yasui A

    (2007) Recruitment of DNA repair synthesis machinery to sites of DNA damage/repair in living human cells

    Nucleic Acids Research 35:2913–2923.

    https://doi.org/10.1093/nar/gkm115

    • PubMed
    • Google Scholar
25. 1. Jang MK
    2. Kwon D
    3. McBride AA

    (2009) Papillomavirus E2 proteins and the host BRD4 protein associate with transcriptionally active cellular chromatin

    Journal of Virology 83:2592–2600.

    https://doi.org/10.1128/JVI.02275-08

    • PubMed
    • Google Scholar
26. 1. Jang MK
    2. Shen K
    3. McBride AA

    (2014) Papillomavirus genomes associate with BRD4 to replicate at fragile sites in the host genome

    PLoS Pathogens 10:e1004117.

    https://doi.org/10.1371/journal.ppat.1004117

    • PubMed
    • Google Scholar
27. 1. Jiang S
    2. Zhou H
    3. Liang J
    4. Gerdt C
    5. Wang C
    6. Ke L
    7. Schmidt SCS
    8. Narita Y
    9. Ma Y
    10. Wang S
    11. Colson T
    12. Gewurz B
    13. Li G
    14. Kieff E
    15. Zhao B

    (2017) The Epstein-Barr Virus Regulome in Lymphoblastoid Cells

    Cell Host & Microbe 22:561–573.

    https://doi.org/10.1016/j.chom.2017.09.001

    • PubMed
    • Google Scholar
28. 1. Kang H
    2. Wiedmer A
    3. Yuan Y
    4. Robertson E
    5. Lieberman PM

    (2011) Coordination of KSHV latent and lytic gene control by CTCF-cohesin mediated chromosome conformation

    PLoS Pathogens 7:e1002140.

    https://doi.org/10.1371/journal.ppat.1002140

    • PubMed
    • Google Scholar
29. 1. Kent WJ
    2. Sugnet CW
    3. Furey TS
    4. Roskin KM
    5. Pringle TH
    6. Zahler AM
    7. Haussler D

    (2002) The human genome browser at UCSC

    Genome Research 12:996–1006.

    https://doi.org/10.1101/gr.229102

    • PubMed
    • Google Scholar
30. 1. Kim J
    2. Sturgill D
    3. Sebastian R
    4. Khurana S
    5. Tran AD
    6. Edwards GB
    7. Kruswick A
    8. Burkett S
    9. Hosogane EK
    10. Hannon WW
    11. Weyemi U
    12. Bonner WM
    13. Luger K
    14. Oberdoerffer P

    (2018) Replication Stress Shapes a Protective Chromatin Environment across Fragile Genomic Regions

    Molecular Cell 69:36–47.

    https://doi.org/10.1016/j.molcel.2017.11.021

    • PubMed
    • Google Scholar
31. 1. Kollek R
    2. Tseng BY
    3. Goulian M

    (1982)

    DNA polymerase requirements for Parvovirus H-1 DNA replication in vitro

    Journal of Virology 41:982–989.

    • PubMed
    • Google Scholar
32. 1. Kraushaar DC
    2. Jin W
    3. Maunakea A
    4. Abraham B
    5. Ha M
    6. Zhao K

    (2013) Genome-wide incorporation dynamics reveal distinct categories of turnover for the histone variant H3.3

    Genome Biology 14:R121.

    https://doi.org/10.1186/gb-2013-14-10-r121

    • PubMed
    • Google Scholar
33. 1. Lajoie BR
    2. Dekker J
    3. Kaplan N

    (2015) The Hitchhiker's guide to Hi-C analysis: practical guidelines

    Methods 72:65–75.

    https://doi.org/10.1016/j.ymeth.2014.10.031

    • PubMed
    • Google Scholar
34. 1. Landt SG
    2. Marinov GK
    3. Kundaje A
    4. Kheradpour P
    5. Pauli F
    6. Batzoglou S
    7. Bernstein BE
    8. Bickel P
    9. Brown JB
    10. Cayting P
    11. Chen Y
    12. DeSalvo G
    13. Epstein C
    14. Fisher-Aylor KI
    15. Euskirchen G
    16. Gerstein M
    17. Gertz J
    18. Hartemink AJ
    19. Hoffman MM
    20. Iyer VR
    21. Jung YL
    22. Karmakar S
    23. Kellis M
    24. Kharchenko PV
    25. Li Q
    26. Liu T
    27. Liu XS
    28. Ma L
    29. Milosavljevic A
    30. Myers RM
    31. Park PJ
    32. Pazin MJ
    33. Perry MD
    34. Raha D
    35. Reddy TE
    36. Rozowsky J
    37. Shoresh N
    38. Sidow A
    39. Slattery M
    40. Stamatoyannopoulos JA
    41. Tolstorukov MY
    42. White KP
    43. Xi S
    44. Farnham PJ
    45. Lieb JD
    46. Wold BJ
    47. Snyder M

    (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia

    Genome Research 22:1813–1831.

    https://doi.org/10.1101/gr.136184.111

    • PubMed
    • Google Scholar
35. 1. Langmead B
    2. Salzberg SL

    (2012) Fast gapped-read alignment with Bowtie 2

    Nature Methods 9:357.

    https://doi.org/10.1038/nmeth.1923

    • PubMed
    • Google Scholar
36. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The Sequence Alignment/Map format and SAMtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
37. 1. Lossaint G
    2. Larroque M
    3. Ribeyre C
    4. Bec N
    5. Larroque C
    6. Décaillet C
    7. Gari K
    8. Constantinou A

    (2013) FANCD2 binds MCM proteins and controls replisome function upon activation of s phase checkpoint signaling

    Molecular Cell 51:678–690.

    https://doi.org/10.1016/j.molcel.2013.07.023

    • PubMed
    • Google Scholar
38. 1. Madireddy A
    2. Kosiyatrakul ST
    3. Boisvert RA
    4. Herrera-Moyano E
    5. García-Rubio ML
    6. Gerhardt J
    7. Vuono EA
    8. Owen N
    9. Yan Z
    10. Olson S
    11. Aguilera A
    12. Howlett NG
    13. Schildkraut CL

    (2016) FANCD2 facilitates replication through common fragile sites

    Molecular Cell 64:388–404.

    https://doi.org/10.1016/j.molcel.2016.09.017

    • PubMed
    • Google Scholar
39. 1. Medvedovic J
    2. Ebert A
    3. Tagoh H
    4. Tamir IM
    5. Schwickert TA
    6. Novatchkova M
    7. Sun Q
    8. Huis In 't Veld PJ
    9. Guo C
    10. Yoon HS
    11. Denizot Y
    12. Holwerda SJ
    13. de Laat W
    14. Cogné M
    15. Shi Y
    16. Alt FW
    17. Busslinger M

    (2013) Flexible long-range loops in the VH gene region of the Igh locus facilitate the generation of a diverse antibody repertoire

    Immunity 39:229–244.

    https://doi.org/10.1016/j.immuni.2013.08.011

    • PubMed
    • Google Scholar
40. 1. Moquin SA
    2. Thomas S
    3. Whalen S
    4. Warburton A
    5. Fernandez SG
    6. McBride AA
    7. Pollard KS
    8. Miranda JJL

    (2017) The Epstein-Barr virus episome maneuvers between nuclear chromatin compartments during reactivation

    Journal of Virology 92:e01413–01417.

    https://doi.org/10.1128/JVI.01413-17

    • Google Scholar
41. 1. Ou HD
    2. Kwiatkowski W
    3. Deerinck TJ
    4. Noske A
    5. Blain KY
    6. Land HS
    7. Soria C
    8. Powers CJ
    9. May AP
    10. Shu X
    11. Tsien RY
    12. Fitzpatrick JA
    13. Long JA
    14. Ellisman MH
    15. Choe S
    16. O'Shea CC

    (2012) A structural basis for the assembly and functions of a viral polymer that inactivates multiple tumor suppressors

    Cell 151:304–319.

    https://doi.org/10.1016/j.cell.2012.08.035

    • PubMed
    • Google Scholar
42. Software

    1. Pagès H
    2. Aboyoun P
    3. Gentleman R
    4. DebRoy S

    (2017)

    Biostrings: Efficient manipulation of biological strings, version R Package Version, 2(0)

    Biostrings: Efficient manipulation of biological strings.
43. 1. Polo SE
    2. Jackson SP

    (2011) Dynamics of DNA damage response proteins at DNA breaks: a focus on protein modifications

    Genes & Development 25:409–433.

    https://doi.org/10.1101/gad.2021311

    • PubMed
    • Google Scholar
44. 1. Qi LS
    2. Larson MH
    3. Gilbert LA
    4. Doudna JA
    5. Weissman JS
    6. Arkin AP
    7. Lim WA

    (2013) Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression

    Cell 152:1173–1183.

    https://doi.org/10.1016/j.cell.2013.02.022

    • PubMed
    • Google Scholar
45. 1. Querido E
    2. Blanchette P
    3. Yan Q
    4. Kamura T
    5. Morrison M
    6. Boivin D
    7. Kaelin WG
    8. Conaway RC
    9. Conaway JW
    10. Branton PE

    (2001) Degradation of p53 by adenovirus E4orf6 and E1B55K proteins occurs via a novel mechanism involving a Cullin-containing complex

    Genes & Development 15:3104–3117.

    https://doi.org/10.1101/gad.926401

    • PubMed
    • Google Scholar
46. 1. Quinlan AR
    2. Hall IM

    (2010) BEDTools: a flexible suite of utilities for comparing genomic features

    Bioinformatics 26:841–842.

    https://doi.org/10.1093/bioinformatics/btq033

    • PubMed
    • Google Scholar
47. 1. Ramírez F
    2. Ryan DP
    3. Grüning B
    4. Bhardwaj V
    5. Kilpert F
    6. Richter AS
    7. Heyne S
    8. Dündar F
    9. Manke T

    (2016) deepTools2: a next generation web server for deep-sequencing data analysis

    Nucleic Acids Research 44:W160–W165.

    https://doi.org/10.1093/nar/gkw257

    • PubMed
    • Google Scholar
48. 1. Rao SS
    2. Huntley MH
    3. Durand NC
    4. Stamenova EK
    5. Bochkov ID
    6. Robinson JT
    7. Sanborn AL
    8. Machol I
    9. Omer AD
    10. Lander ES
    11. Aiden EL

    (2014) A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping

    Cell 159:1665–1680.

    https://doi.org/10.1016/j.cell.2014.11.021

    • PubMed
    • Google Scholar
49. 1. Rao SSP
    2. Huang SC
    3. Glenn St Hilaire B
    4. Engreitz JM
    5. Perez EM
    6. Kieffer-Kwon KR
    7. Sanborn AL
    8. Johnstone SE
    9. Bascom GD
    10. Bochkov ID
    11. Huang X
    12. Shamim MS
    13. Shin J
    14. Turner D
    15. Ye Z
    16. Omer AD
    17. Robinson JT
    18. Schlick T
    19. Bernstein BE
    20. Casellas R
    21. Lander ES
    22. Aiden EL

    (2017) Cohesin loss eliminates all loop domains

    Cell 171:305–320.

    https://doi.org/10.1016/j.cell.2017.09.026

    • PubMed
    • Google Scholar
50. 1. Rogakou EP
    2. Boon C
    3. Redon C
    4. Bonner WM

    (1999) Megabase chromatin domains involved in DNA double-strand breaks in vivo

    The Journal of Cell Biology 146:905–916.

    https://doi.org/10.1083/jcb.146.5.905

    • PubMed
    • Google Scholar
51. 1. Ruiz Z
    2. Mihaylov IS
    3. Cotmore SF
    4. Tattersall P

    (2011) Recruitment of DNA replication and damage response proteins to viral replication centers during infection with NS2 mutants of Minute Virus of Mice (MVM)

    Virology 410:375–384.

    https://doi.org/10.1016/j.virol.2010.12.009

    • PubMed
    • Google Scholar
52. 1. Sanjana NE
    2. Shalem O
    3. Zhang F

    (2014) Improved vectors and genome-wide libraries for CRISPR screening

    Nature Methods 11:783–784.

    https://doi.org/10.1038/nmeth.3047

    • PubMed
    • Google Scholar
53. 1. Schmid M
    2. Speiseder T
    3. Dobner T
    4. Gonzalez RA

    (2014) DNA virus replication compartments

    Journal of Virology 88:1404–1420.

    https://doi.org/10.1128/JVI.02046-13

    • PubMed
    • Google Scholar
54. 1. Schneider CA
    2. Rasband WS
    3. Eliceiri KW

    (2012) NIH image to ImageJ: 25 years of image analysis

    Nature Methods 9:671–675.

    https://doi.org/10.1038/nmeth.2089

    • PubMed
    • Google Scholar
55. 1. Shah GA
    2. O'Shea CC

    (2015) Viral and cellular genomes activate distinct DNA damage responses

    Cell 162:987–1002.

    https://doi.org/10.1016/j.cell.2015.07.058

    • PubMed
    • Google Scholar
56. 1. Shih HY
    2. Krangel MS

    (2010) Distinct contracted conformations of the Tcra/Tcrd locus during Tcra and Tcrd recombination

    The Journal of Experimental Medicine 207:1835–1841.

    https://doi.org/10.1084/jem.20100772

    • PubMed
    • Google Scholar
57. 1. Spriggs CC
    2. Laimins LA

    (2017) FANCD2 binds human papillomavirus genomes and associates with a distinct set of DNA repair proteins to regulate viral replication

    mBio 8:e02340-16.

    https://doi.org/10.1128/mBio.02340-16

    • PubMed
    • Google Scholar
58. 1. Stracker TH
    2. Carson CT
    3. Weitzman MD

    (2002) Adenovirus oncoproteins inactivate the Mre11-Rad50-NBS1 DNA repair complex

    Nature 418:348–352.

    https://doi.org/10.1038/nature00863

    • PubMed
    • Google Scholar
59. 1. Tattersall P
    2. Bratton J

    (1983)

    Reciprocal productive and restrictive virus-cell interactions of immunosuppressive and prototype strains of minute virus of mice

    Journal of Virology 46:944–955.

    • PubMed
    • Google Scholar
60. 1. Tubbs A
    2. Nussenzweig A

    (2017) Endogenous DNA damage as a source of genomic instability in cancer

    Cell 168:644–656.

    https://doi.org/10.1016/j.cell.2017.01.002

    • PubMed
    • Google Scholar
61. 1. Weitzman MD
    2. Lilley CE
    3. Chaurushiya MS

    (2010) Genomes in conflict: maintaining genome integrity during virus infection

    Annual Review of Microbiology 64:61–81.

    https://doi.org/10.1146/annurev.micro.112408.134016

    • PubMed
    • Google Scholar
62. 1. Xu S
    2. Grullon S
    3. Ge K
    4. Peng W

    (2014)

    In Stem Cell Transcriptional Networks

    97–111, Spatial clustering for identification of ChIP-enriched regions (SICER) to map regions of histone methylation patterns in embryonic stem cells, In Stem Cell Transcriptional Networks, New York, Humana Press, 10.1007/978-1-4939-0512-6_5.

    • Google Scholar
63. 1. Zang C
    2. Schones DE
    3. Zeng C
    4. Cui K
    5. Zhao K
    6. Peng W

    (2009) A clustering approach for identification of enriched domains from histone modification ChIP-Seq data

    Bioinformatics 25:1952–1958.

    https://doi.org/10.1093/bioinformatics/btp340

    • PubMed
    • Google Scholar
64. 1. Zhang J
    2. Markus J
    3. Bies J
    4. Paul T
    5. Wolff L

    (2012) Three murine leukemia virus integration regions within 100 kilobases upstream of c-myb are proximal to the 5' regulatory region of the gene through DNA looping

    Journal of Virology 86:10524–10532.

    https://doi.org/10.1128/JVI.01077-12

    • PubMed
    • Google Scholar

Author details

1. Kinjal Majumder

   Department of Molecular Microbiology and Immunology, Christopher S. Bond Life Sciences Center, Columbia, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   km3k5@health.missouri.edu

   Competing interests

   No competing interests declared

2. Juexin Wang

   1. Department of Electrical Engineering and Computer Science, Christopher S. Bond Life Sciences Center, Columbia, United States
   2. Christopher S. Bond Life Sciences Center, Columbia, United States

   Contribution

   Data curation, Software, Formal analysis

   Competing interests

   No competing interests declared

3. Maria Boftsi

   Pathobiology Area Graduate Program, Christopher S. Bond Life Sciences Center, Columbia, United States

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

4. Matthew S Fuller

   Ultragenyx Pharmaceutical, Christopher S. Bond Life Sciences Center, Columbia, United States

   Contribution

   Conceptualization, Investigation, Methodology

   Competing interests

   is employed by Ultragenyx Pharmaceutical. There are no other competing interests to declare.

5. Jordan E Rede

   Department of Molecular Microbiology and Immunology, Christopher S. Bond Life Sciences Center, Columbia, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Trupti Joshi

   1. Department of Electrical Engineering and Computer Science, Christopher S. Bond Life Sciences Center, Columbia, United States
   2. Christopher S. Bond Life Sciences Center, Columbia, United States
   3. Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, Columbia, United States
   4. MU Informatics Institute, University of Missouri-Columbia, Columbia, United States

   Contribution

   Software, Formal analysis

   Competing interests

   No competing interests declared

7. David J Pintel

   Department of Molecular Microbiology and Immunology, Christopher S. Bond Life Sciences Center, Columbia, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   pinteld@missouri.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9959-2848

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