The human leukemia virus HTLV-1 alters the structure and transcription of host chromatin in cis

The dynamics and higher-order folding of chromatin play a critical role in gene regulation. Higher-order chromatin structure is determined by several factors, among which the best characterized is CCCTC-binding factor (CTCF) (Ong and Corces, 2014). CTCF binds a non-palindromic 20-nucleotide DNA motif at ~50,000 sites in the human genome, and acts chiefly (Phillips and Corces, 2009) by regulating the formation of chromatin loops of ~100 kb to ~2 Mb, which control the contacts made between promoters and enhancers and so regulate gene expression (Bartman et al., 2016; Gibcus and Dekker, 2013; Schwarzer and Spitz, 2014). Aberrant higher-order chromatin organization can result in abnormal patterns of transcription, and mutations in CTCF are linked to human disease (Corces and Corces, 2016; Flavahan et al., 2016; Hnisz et al., 2016).

Recently we found (Satou et al., 2016) that CTCF binds to a nucleotide motif in the Human T lymphotropic virus type 1 (HTLV-1; also known as the human T-cell leukemia virus), when HTLV-1 is integrated - as the provirus - into the host cell genome. HTLV-1 is a primate retrovirus that infects ~10 million people in the tropics and subtropics (Bangham, 2018). The infection is asymptomatic in ~90% of human hosts; the remaining 10% of HTLV-1-infected hosts develop either a chronic inflammatory disease, most commonly involving the central nervous system, or an aggressive malignancy of CD4⁺ T cells known as adult T-cell leukemia/lymphoma (ATL). Unlike HIV-1 infection, there remains no satisfactory treatment for either the inflammatory or malignant diseases caused by HTLV-1.

A typical host of HTLV-1 carries between 10⁴ and 10⁵ clones of infected T cells, each clone carrying a single copy of the provirus in a unique genomic location (Laydon et al., 2014). The large number of HTLV-1-infected clones appears to be established early in infection, after which persistent clonal proliferation maintains a stable hierarchy of HTLV-1-infected clones for the remainder of the host’s life. The viral regulatory proteins, Tax and HBZ (HTLV-1 bZIP), which are encoded respectively by the sense and antisense strands of the provirus, play indispensable roles in pathogenesis (Matsuoka and Jeang, 2011a). Tax is a transcriptional transactivator which dysregulates many host genes, while HBZ acts as a negative regulator of Tax-mediated host gene transcription and viral expression. Both Tax and HBZ contribute to persistent proliferation of the infected T cell in vivo, and it is now thought that the consequent accumulation of replicative mutations is a key driver in HTLV-1 oncogenesis. However, until recently (Cook et al., 2014; Kataoka et al., 2015; Rosewick et al., 2017) insertional mutagenesis has not been considered important in causing ATL.

The observation that CTCF binds to the HTLV-1 provirus (Satou et al., 2016) raised the hypothesis that CTCF bound to the provirus can form abnormal chromatin loops by dimerizing with CTCF in the flanking host genome. Using chromosome conformation capture (3C), we previously demonstrated the presence of a single CTCF-dependent chromatin loop in a long-term in vitro T cell line (Satou et al., 2016). In the present study we extended this analysis, using circular chromosome conformation capture (4C) and RNA-seq, to examine systematically the impact of the HTLV-1 provirus on the structure and expression of the host genome in T cell clones isolated from HTLV-1-infected subjects. We show that the HTLV-1 provirus forms reproducible abnormal chromatin contacts with sites in the host genome in cis as far as 1.4 Mb from the provirus. Some of these abnormal chromatin contacts depend on CTCF binding to the provirus. Further, we demonstrate clone-specific deregulation of host transcription in cis both immediately flanking the integrated provirus (up to 50 kb upstream and downstream) and at distant sites as far as 300 kb from the provirus. Since HTLV-1 is integrated in >10⁴ different genetic locations in a typical host, and there are tens of thousands of CTCF-binding sites (CTCF-BS) in the human genome (Krijger and de Laat, 2016), these results imply that HTLV-1 has the potential to cause deregulation of host transcription at a very large number of loci in each infected host.

In this paper, we refer to loci in the host genome relative to the orientation of the HTLV-1 provirus. Thus, a locus ‘downstream’ of the provirus is located 3′ to the 3′ LTR, whether the provirus is integrated in the positive or negative sense in the host genome.

HTLV-1 forms chromatin loops with the flanking host genome

To test the hypothesis that HTLV-1 integration alters the host chromatin structure, we performed a genome-wide search for chromosomal positions that contact the HTLV-1 provirus, using a modified version of circular chromosome conformation capture (4C). 4C is a powerful tool to study the 3D chromatin looping between a specified genomic region (the 'viewpoint') with respect to the rest of the genome. The standard 4C protocol has limitations in that it is only semiquantitative (Denker and de Laat, 2016). To improve the quantification, we adapted our protocol for quantification of HTLV-1 integration sites (Gillet et al., 2011; Gillet et al., 2017). We followed the 4C protocol described by van de Werken et al. (van de Werken et al., 2012), but instead of using a second restriction enzyme, we sonicated the library, added adapters and performed ligation-mediated PCR (see Materials and methods). This modification confers two advantages. First, it precludes the bias towards detection of chromatin contacts that lie near a given restriction site. Second, the amplicon length serves as a unique molecular identifier, enabling relative quantification (Gillet et al., 2017) of the chromatin contacts. We refer to this modified method as quantitative 4C (q4C); a similar approach (UMI-4C) has been described by others (Schwartzman et al., 2016).

We applied q4C to test the hypothesis that the HTLV-1 provirus forms chromatin contacts with the host genome in a series of T cell clones isolated by limiting dilution from circulating CD4⁺ T lymphocytes of HTLV-1-infected individuals (Cook et al., 2012) (Table 1; Supplementary file 1). Each clone has a single copy of the provirus in a unique integration site. As the viewpoint in q4C, we used the 679 bp NlaIII fragment containing the proviral CTCF-BS (Figure 1A). Chromatin contacts were identified using a protocol based on a hidden Markov model (Materials and methods).

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Table 1

Subject	Clone(s)
TBJ	3.60, 3.83
TCX	8.13, 8.8
TCT	10.1
TBW	11.50, 11.63, 11.65, 13.50(U)
TBX	TBX4B
HAY	6.25, 6.30(U)

We detected reproducible q4C peaks (long-range chromatin contacts between the provirus and the host genome) in 9 of the 10 infected T-cell clones examined (Figure 1B,C; Figure 1—figure supplement 1–10). The number of peaks per clone varied between 0 and 15, with a median of 3 peaks per clone (Figure 1C); There were significantly more peaks downstream of the integration site than upstream (p=0.03, Chi-squared goodness-of-fit test; Figure 1—figure supplement 11A). The distance between identified peaks and the provirus of the respective clone varied between 12.9 kb and 1.4 Mb, with a median of 85 kb (Figure 1D). The distance between each peak and the integration site did not significantly differ between upstream and downstream peaks (p=0.13, Wilcoxon test; Figure 1—figure supplement 11B).

We wished to identify whether the HTLV-1 provirus makes preferential contacts with the host genome in cis. The provirus is present in a single copy per cell (Figure 2A) (Cook et al., 2012). First, to determine whether the q4C reads were derived from a single chromosome (i.e. were monoallelic) or from both homologous chromosomes (biallelic), we identified single-nucleotide polymorphisms (SNPs) present in the respective donor subject by whole-genome sequencing (see Materials and methods). The alleles identified at heterozygous SNPs in q4C reads demonstrated that the observed ligation events were confined to a single strand (i.e. were monoallelic), with a range of at least 4 Mb from the provirus (Figure 2B).

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Next, we used the heterozygous SNPs to distinguish the chromosome carrying the provirus from its homologous chromosome, using computationally-determined phased haplotypes (see Materials and methods). In this way, the infected chromosome could be distinguished from the uninfected chromosome for at least 100 kb either side of the integration site in 8 of the 10 clones (Figure 2C; Figure 2—figure supplement 1–5). To validate the haplotype-calling, we identified heterozygous SNPs in long-range PCR products, amplified either between the provirus and the host genome (infected haplotype) or across the proviral integration site (uninfected haplotype) (Figure 2C; Figure 2—figure supplement 1–5). The results (Figure 2C) showed that the reproducible contacts between the host genome and the HTLV-1 provirus were exclusively made in cis, that is, on the infected chromosome.

Certain long-range chromatin contacts are CTCF-dependent

We wished to test the hypothesis that the observed abnormal long-range chromatin contacts are associated with the presence of CTCF-BS identified in the T cell clones by ChIP-seq. The results showed that that CTCF-BS were enriched at the chromatin contacts in the host genome: ~50% of q4C peaks overlapped with at least one CTCF-BS (Figure 3A); in 10% of peaks there were two CTCF-BS. The frequency of CTCF-BS overlapping the observed 4C peaks was significantly higher than random expectation (p=4.73 * 10⁻⁴, Fisher’s exact test; Figure 3—figure supplement 1). Consistent with recent findings by others (de Wit et al., 2015; Sanborn et al., 2015), where a peak overlapped a CTCF-BS the viral and host CTCF binding motifs were present in convergent or tandem orientation in 80% of cases (Figure 3B). However, not all nearby tandem or convergent host CTCF-BS formed detectable contacts with the provirus: see, for example, clones 8.13 and TBX4B (Figure 1—figure supplement 5 and 8, respectively). The presence of a CTCF-BS was associated with a significantly greater observed q4C peak height (p=0.025, Wilcoxon test), and there was a significant positive trend between the q4C peak height and the number of CTCF sites within the peak (Figure 3C; p=0.016, Spearman’s test). Finally, there was a significant positive correlation between the number of observed chromatin contacts in a clone and the number of CTCF-BS within 0.5 Mb of the integration site (Figure 3D; Pearson’s correlation test); this correlation remained significant up to 1.16 Mb from the provirus.

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We then tested whether CTCF binding to the provirus is required for formation of provirus-host contacts. We used a CRISPR-modified cell line (ED) (Satou et al., 2016), derived from an individual with the HTLV-1-associated malignancy adult T-cell leukemia (ATL) (Figure 3E). We also applied CRISPR-Cas9 ribonucleoprotein transfection (Schumann et al., 2015) to knock out the CTCF-BS in a non-malignant T-cell clone (TBX4B; Figure 3F). We carried out q4C analysis on cells from the wild-type (WT) clone, and the mutant (Mut) clone containing a mutated CTCF-BS. The results show that the loss of CTCF binding was associated with a loss of 5 of the eight observed contacts; three of the five lost contacts overlapped a CTCF-BS in the host.

HTLV-1 alters contiguous host transcription

The results obtained by q4C demonstrated that HTLV-1 integration can alter host chromatin looping. We wished to investigate the impact of the HTLV-1 provirus on transcription in the host genome both immediately flanking the provirus and at distant loci. We carried out strand-specific mRNA-sequencing (RNA-seq) on the HTLV-1-infected T cell clones, and quantified the density of reads mapping to discrete 1 kb windows up to 2 Mb from the respective proviral integration site. The results showed upregulated transcription in the host genome immediately flanking the HTLV-1 provirus, in all clones examined (Figure 4A,B; Figure 1—figure supplement 1–10, Figure 4—figure supplement 1). This upregulated transcription was observed either upstream or downstream of the provirus, or both, with a predominant increase downstream in the same sense as the HTLV-1 plus-strand and upstream in the opposite sense (Figure 4C). We then performed this analysis separately to compare the clones with high HTLV-1 plus-strand expression and those with low plus-strand expression (defined respectively as those clones with a tax read intensity in the RNA-seq above or below the median intensity of all clones). The results showed that, whereas abnormal upstream antisense expression was present in most clones, an increase in same-sense transcription was specific to those clones with high plus-strand expression (Figure 4D).

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The observed upregulation of transcription was frequently intergenic, but we also observed instances of clone-specific gene expression. For example, in clone TBX4B, HTLV-1 is inserted between exons of the gene PNPLA (Figure 3F). This gene was not expressed in the other T cell clones, but was highly expressed in TBX4B both downstream and upstream of the integration site, in the same sense as the proviral plus-strand. The presence of abnormal same-sense host transcription upstream of the 5′ LTR suggests that the transcription was driven by the proviral enhancer.

HTLV-1 alters host transcription in cis

To test the hypothesis that the observed abnormal transcription of the host genome was confined to the chromosome carrying the provirus, we quantified the allelic imbalance in the SNPs identified in RNA-seq reads up to 2 Mb from the integration sites. The results showed that in heterozygous SNPs identified in the clone-specific transcripts, transcription was predominantly homozygous, that is, monoallelic (Figure 5A).

Figure 5

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To test whether this monoallelic transcription was derived from the infected chromosome or its homologous chromosome, we used the haplotype-calling approach described above to assign heterozygous SNPs present in the RNA-seq reads within 100 kb of the proviral integration site to either the infected or the uninfected chromosome. The results (Figure 5B,C,D) showed that, where the transcripts could be assigned to one chromosome, the observed clone-specific transcripts were overwhelmingly derived from the infected chromosome, whereas transcripts that were not specifically upregulated were expressed from either chromosome at a similar frequency (Figure 5B, left side of left panel; Figure 5C,D).

The mammalian genome is not randomly arranged in the nucleus, but is folded in a highly ordered manner at a series of successively larger spatial scales (Bickmore, 2013). At the smallest scale, chromatin is organized in a series of reproducible loops that are formed by bringing together specific genomic loci which are separated on the linear genome by up to ~2 Mb (Dekker and Heard, 2015; Dixon et al., 2012; Nora et al., 2012). The zinc finger protein CTCF plays a central part in establishing and maintaining these chromatin loops: the non-palindromic CTCF-BS is found at the borders of ~80% of loops. Certain other chromatin-associated proteins, such as PRC1 (Schoenfelder et al., 2015), and transcription factors including AP-1 (Phanstiel et al., 2017) and YY1 (Beagan et al., 2017), can also cause looping of chromatin. The resulting chromatin loops in turn play a critical part in controlling gene expression, by regulating the contacts made between enhancers and promoters (Bartman et al., 2016; Gibcus and Dekker, 2013; Schwarzer and Spitz, 2014). Disruption of chromatin loops can deregulate gene expression and cause developmental abnormalities (Lupiáñez et al., 2015) or diseases such as cancer (Corces and Corces, 2016; Flavahan et al., 2016; Hnisz et al., 2016).

The discovery that the HTLV-1 provirus binds CTCF (Satou et al., 2016) therefore raised the hypothesis that the provirus forms chromatin loops with the neighbouring host genome and thereby deregulates host transcription. To test this hypothesis, we used a panel of non-malignant CD4⁺ T cell clones naturally infected with HTLV-1. Each clone carries a single copy HTLV-1 provirus in a known genomic location (Cook et al., 2012); all 10 clones studied were competent to express the plus-strand proviral genes except clones 6.25 and 8.13.

The results of chromosome conformation analysis (q4C) presented here reveal reproducible contacts between the HTLV-1 provirus and the flanking host genome at least 1.4 Mb from the integration site. Each clone has a unique pattern of chromatin contacts, depending on the site of integration of the provirus in the host genome. The allelic bias observed in the q4C data, with the preferential detection of SNPs on the chromosome carrying the provirus, indicates that the provirus makes preferential contacts in cis with host chromatin at distances at least 4 Mb from the provirus. Preferential contacts in cis may extend beyond this distance, but detection is likely to be limited by the sensitivity of the q4C technique.

Four observations indicate that certain chromatin contacts made between the provirus and the host genome depend on the presence of CTCF. First, in each of two clones (ED and TBX4B; Figure 3E,F), knocking out the CTCF BS in the provirus without altering the coding sequence of the tax gene abrogated a major contact with a site in the host genome, respectively ~50 and 17 kb downstream, where CTCF was shown to bind. Second, of the 44 host loci identified by the peak-calling algorithm as making contact with the provirus, 22 contained one or more CTCF-BS (p=4.73 * 10⁻⁴, Fisher’s exact test). Third, consistent with recent observations made by others (de Wit et al., 2015; Sanborn et al., 2015), 12 of the 13 (92.3%) CTCF-BS in the host contact sites whose orientation could be ascertained were oriented towards the CTCF-BS in HTLV-1. Fourth, both the number and height of q4C peaks were correlated with the number of CTCF-BS in the host genome (Figure 4C,D). However, not all host chromatin contact sites contain CTCF-BS, and certain peaks remained detectable by q4C after knock-out of the proviral CTCF-BS. These observations are consistent with the findings that certain chromatin-associated proteins other than CTCF can also give rise to chromatin looping (Schoenfelder et al., 2015; Phanstiel et al., 2017; Beagan et al., 2017).

The formation of chromatin loops between the HTLV-1 provirus and the host genome in turn raised the possibility that host transcription is deregulated. At least two mechanisms of such deregulation can be suggested. First, the abnormal chromatin loops might alter the normal contacts made between enhancers and promoters in the host genome, either creating new contacts or abrogating pre-existing contacts. Second, the new chromatin loop might bring the proviral LTR near a host promoter and cause abnormal transcription from the promoter.

We show here that HTLV-1 can deregulate host transcription both at sites contiguous with the provirus and at non-contiguous, distant sites. Again, each clone has a unique pattern of transcription. In the host genome immediately flanking the provirus, we detected frequent abnormal (clone-specific) transcription, predominantly in the opposite transcriptional sense to the provirus upstream of the 5′ LTR and, to a lesser extent, in the same transcriptional sense downstream of the 3′ LTR. The observed abnormal transcription involved both intergenic regions and identified host genes (Figure 4B, Figure 1—figure supplement 1–10, Figure 4—figure supplement 2); in addition, we observed examples of abnormal transcription and abnormal splicing of host genes (Figure 4—figure supplement 2).

Kataoka et al. (Kataoka et al., 2015) and Rosewick et al. (Rosewick et al., 2017) have reported evidence of transcription initiated on the minus strand of the provirus and continuing upstream into the host genome in transformed lymphocyte clones carrying HTLV-1 or the related bovine leukemia virus (BLV). The present results are consistent with these recent reports, demonstrating that abnormal host transcription flanking the provirus in cis is a general feature of HTLV-1 infection. Our results further suggest that an important mechanism of the observed abnormal transcription is chromatin looping between the provirus and the host genome. The abnormal transcription is bidirectional: both downstream of the provirus in the same sense as the proviral plus-strand, and upstream in the opposite sense. The observation of clone-specific transcription upstream of the 5′ LTR in the plus-strand sense (Figure 4A), especially in high tax-expressing cells (Figure 4D), suggest that the HTLV-1 promoter/enhancer can drive abnormal expression from nearby transcription start sites.

In addition to the abnormal transcription frequently detected in regions of the host genome contiguous with the provirus, each clone also had a unique pattern of abnormal transcription at non-contiguous, distant sites. The great majority of these clone-specific transcripts came from the chromosome carrying the provirus, not its homologous ‘uninfected’ chromosome, as shown by the allelic bias observed in the sequence reads (Figure 5D). Similarly, the observed allelic bias in the q4C reads extended to at least 1 Mb upstream and downstream of the provirus (Figure 2B). These results are consistent with the notion that the abnormal transcription observed at sites distant from the provirus results from contacts made between the provirus and the host genome.

While certain CTCF-dependent chromatin loops mediate enhancer-promoter interactions, there is growing evidence that the main function of CTCF-dependent loops is structural and is largely shared between cell types, whereas loops formed by other chromatin-binding proteins such as YY1 (Weintraub et al., 2017) or chromatin-modifying proteins such as PRC1 (Eagen et al., 2017) mediate dynamic enhancer-promoter interactions and thus play a central part in determining cell-type-specific transcription. It remains to be tested whether each respective instance of HTLV-1-associated abnormal host transcription observed here depends on chromatin looping induced by CTCF or another chromatin-binding protein.

Retroviruses have long been known to disrupt host gene expression by insertional mutagenesis, either by integration of the provirus within a gene or by activating expression of a flanking host gene (Uren et al., 2005; Coffin et al., 1997; Nusse and Varmus, 1982); indeed, the first report of insertional oncogenesis was activation of c-myc by avian leukosis virus (Hayward et al., 1981). Upstream host oncogenes have been shown to be activated by integrated retroviruses, for example mouse mammary tumour virus (Nusse and Varmus, 1982) and murine leukemia virus (MLV); activation may occur either by an effect of the proviral enhancer or by readthrough transcription from the LTR into the host genome (Rasmussen et al., 2010). Integration downstream of a host oncogene conferred a strong selective advantage on certain clones transduced with a gene therapy vector derived from MLV, resulting in a number of cases of leukemia (Hacein-Bey-Abina et al., 2008; 2003). In mice, it has also been shown that MLV can cause abnormal host transcription by activating a distant host gene (Lazo et al., 1990; Sokol et al., 2014; Babaei et al., 2015). In a study of MLV-induced leukemia, Sokol et al. (Sokol et al., 2014) suggested that the MLV proviral enhancer was brought near the oncogene by chromatin looping; if so, it is likely that chance integration of MLV at this particular locus enabled the virus to exploit a normal chromatin loop normally present in the mouse genome. In contrast to this adventitious effect of MLV, our results show that the HTLV-1 provirus itself can cause both abnormal chromatin contacts in cis with distant host loci and abnormal host transcription at distant loci. These findings demonstrate that HTLV-1 has a range of insertional mutagenesis that can extend to the megabase scale.

The altered chromosomal looping and consequent effects on host genome transcription depend on the proviral integration site and the presence of nearby CTCF-BS, and so differ in each infected T-cell clone. The changes in chromatin structure caused by HTLV-1 might not only create novel loops but also destroy pre-existing CTCF-dependent loops that are present in uninfected cells. Further work is needed to investigate the impact of altered looping and transcription on the persistence, infectivity and oncogenic potential of the infected T-cell clones.

We postulate that CTCF confers a survival or replication advantage to HTLV-1, because of the high degree of sequence conservation of the proviral CTCF binding site and the coincidence of the binding site with a border of epigenetic modifications associated with transcriptional activity (Satou et al., 2016). However, the nature and mechanism of this putative advantage are not yet known.

Schmidt et al (Schmidt et al., 2012) showed that retrotransposons propagated CTCF-BS throughout the genome of several mammalian lineages during evolution; however, present-day exogenous retroviruses have not previously been shown to alter the higher-order structure of host chromatin, to our knowledge. It was recently reported (Goodman et al., 2018) that the spumaretrovirus foamy virus encodes a CTCF-BS in its long terminal repeats. The impact of these binding sites on host chromatin structure and transcription have not been investigated; Goodman et al (Goodman et al., 2018) suggested that the presence of CTCF bound to the LTRs might block enhancer effects of the foamy virus LTR, and so might account for the low observed degree of genotoxicity of the foamy virus.

The HTLV-1 transcriptional transactivator protein Tax drives expression not only of the proviral plus stand but also of several host genes, notably CD25 (IL2RA), IFNG, IL6, IL15, GM-CSF, TNFB, and CCL22 (Matsuoka and Jeang, 2011b). While these effects may contribute to the persistence and replication of the virus, and to the risk of the inflammatory and malignant diseases associated with HTLV-1, the effects are not clone-specific. The present results show that, in addition to these generic effects, HTLV-1 has the potential to disrupt host gene expression in cis at both contiguous and non-contiguous sites. Since the virus infects between 10⁴ and 10⁵ different clones of T cells in a typical host, each carrying the provirus at a different genomic location, we conclude that the virus deregulates tens of thousands of host genes in a typical infected host.

Sequence data have been deposited at the European Genome-phenome Archive (EGA, http://www.ebi.ac.uk/ega/), hosted by the European Bioinformatics Institute (accession number EGAS00001002259). Custom scripts used to analyse this data are available at https://github.com/ImperialCollegeLondon/q4C-2018.

1. 1. Melamed A
   2. Yaguchi H
   3. Miura M
   4. Witover A
   5. Fitzgerald TW
   6. Bimey E
   7. Bangham CRM

   (2018) The human leukemia virus HTLV-1 alters the structure and transcription of host chromatin in cis

   Publicly available at the European Genome-phenome Archive (accession no: EGAS00001002259).

   https://www.ebi.ac.uk/ega/studies/EGAS00001002259

1. 1. Babaei S
   2. Akhtar W
   3. de Jong J
   4. Reinders M
   5. de Ridder J

   (2015) 3D hotspots of recurrent retroviral insertions reveal long-range interactions with cancer genes

   Nature Communications 6:6381.

   https://doi.org/10.1038/ncomms7381

   • PubMed
   • Google Scholar
2. 1. Bangham CRM

   (2018) Human T cell leukemia virus type 1: persistence and pathogenesis

   Annual Review of Immunology 36:43–71.

   https://doi.org/10.1146/annurev-immunol-042617-053222

   • PubMed
   • Google Scholar
3. 1. Bartman CR
   2. Hsu SC
   3. Hsiung CC
   4. Raj A
   5. Blobel GA

   (2016) Enhancer regulation of transcriptional bursting parameters revealed by forced chromatin looping

   Molecular Cell 62:237–247.

   https://doi.org/10.1016/j.molcel.2016.03.007

   • PubMed
   • Google Scholar
4. 1. Beagan JA
   2. Duong MT
   3. Titus KR
   4. Zhou L
   5. Cao Z
   6. Ma J
   7. Lachanski CV
   8. Gillis DR
   9. Phillips-Cremins JE

   (2017) YY1 and CTCF orchestrate a 3D chromatin looping switch during early neural lineage commitment

   Genome Research 27:1139–1152.

   https://doi.org/10.1101/gr.215160.116

   • PubMed
   • Google Scholar
5. 1. Bickmore WA

   (2013) The spatial organization of the human genome

   Annual Review of Genomics and Human Genetics 14:67–84.

   https://doi.org/10.1146/annurev-genom-091212-153515

   • PubMed
   • Google Scholar
6. 1. Coffin JM
   2. Hughes SH
   3. Varmus HE

   (1997)

   Retroviruses

   The Interactions of Retroviruses and their Hosts, Retroviruses, NY, Cold Spring Harbor.

   • Google Scholar
7. 1. Cook LB
   2. Melamed A
   3. Niederer H
   4. Valganon M
   5. Laydon D
   6. Foroni L
   7. Taylor GP
   8. Matsuoka M
   9. Bangham CR

   (2014) The role of HTLV-1 clonality, proviral structure, and genomic integration site in adult T-cell leukemia/lymphoma

   Blood 123:3925–3931.

   https://doi.org/10.1182/blood-2014-02-553602

   • PubMed
   • Google Scholar
8. 1. Cook LB
   2. Rowan AG
   3. Melamed A
   4. Taylor GP
   5. Bangham CR

   (2012) HTLV-1-infected T cells contain a single integrated provirus in natural infection

   Blood 120:3488–3490.

   https://doi.org/10.1182/blood-2012-07-445593

   • PubMed
   • Google Scholar
9. 1. Corces MR
   2. Corces VG

   (2016) The three-dimensional cancer genome

   Current Opinion in Genetics & Development 36:1–7.

   https://doi.org/10.1016/j.gde.2016.01.002

   • PubMed
   • Google Scholar
10. 1. de Wit E
    2. Vos ES
    3. Holwerda SJ
    4. Valdes-Quezada C
    5. Verstegen MJ
    6. Teunissen H
    7. Splinter E
    8. Wijchers PJ
    9. Krijger PH
    10. de Laat W

    (2015) CTCF binding polarity determines chromatin looping

    Molecular Cell 60:676–684.

    https://doi.org/10.1016/j.molcel.2015.09.023

    • PubMed
    • Google Scholar
11. 1. Dekker J
    2. Heard E

    (2015) Structural and functional diversity of topologically associating domains

    FEBS Letters 589:2877–2884.

    https://doi.org/10.1016/j.febslet.2015.08.044

    • PubMed
    • Google Scholar
12. 1. Delaneau O
    2. Howie B
    3. Cox AJ
    4. Zagury JF
    5. Marchini J

    (2013) Haplotype estimation using sequencing reads

    The American Journal of Human Genetics 93:687–696.

    https://doi.org/10.1016/j.ajhg.2013.09.002

    • PubMed
    • Google Scholar
13. 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
14. 1. DePristo MA
    2. Banks E
    3. Poplin R
    4. Garimella KV
    5. Maguire JR
    6. Hartl C
    7. Philippakis AA
    8. del Angel G
    9. Rivas MA
    10. Hanna M
    11. McKenna A
    12. Fennell TJ
    13. Kernytsky AM
    14. Sivachenko AY
    15. Cibulskis K
    16. Gabriel SB
    17. Altshuler D
    18. Daly MJ

    (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data

    Nature Genetics 43:491–498.

    https://doi.org/10.1038/ng.806

    • PubMed
    • Google Scholar
15. 1. Dixon JR
    2. Selvaraj S
    3. Yue F
    4. Kim A
    5. Li Y
    6. Shen Y
    7. Hu M
    8. Liu JS
    9. Ren B

    (2012) Topological domains in mammalian genomes identified by analysis of chromatin interactions

    Nature 485:376–380.

    https://doi.org/10.1038/nature11082

    • PubMed
    • Google Scholar
16. 1. Eagen KP
    2. Aiden EL
    3. Kornberg RD

    (2017) Polycomb-mediated chromatin loops revealed by a subkilobase-resolution chromatin interaction map

    PNAS 114:8764–8769.

    https://doi.org/10.1073/pnas.1701291114

    • PubMed
    • Google Scholar
17. 1. Flavahan WA
    2. Drier Y
    3. Liau BB
    4. Gillespie SM
    5. Venteicher AS
    6. Stemmer-Rachamimov AO
    7. Suvà ML
    8. Bernstein BE

    (2016) Insulator dysfunction and oncogene activation in IDH mutant gliomas

    Nature 529:110–114.

    https://doi.org/10.1038/nature16490

    • PubMed
    • Google Scholar
18. 1. Gibcus JH
    2. Dekker J

    (2013) The hierarchy of the 3D genome

    Molecular Cell 49:773–782.

    https://doi.org/10.1016/j.molcel.2013.02.011

    • PubMed
    • Google Scholar
19. 1. Gillet NA
    2. Malani N
    3. Melamed A
    4. Gormley N
    5. Carter R
    6. Bentley D
    7. Berry C
    8. Bushman FD
    9. Taylor GP
    10. Bangham CR

    (2011) The host genomic environment of the provirus determines the abundance of HTLV-1-infected T-cell clones

    Blood 117:3113–3122.

    https://doi.org/10.1182/blood-2010-10-312926

    • PubMed
    • Google Scholar
20. 1. Gillet NA
    2. Melamed A
    3. Bangham CR

    (2017) High-Throughput mapping and clonal quantification of retroviral integration sites

    Methods in Molecular Biology 1582:127–141.

    https://doi.org/10.1007/978-1-4939-6872-5_10

    • PubMed
    • Google Scholar
21. 1. Goodman MA
    2. Arumugam P
    3. Pillis DM
    4. Loberg A
    5. Nasimuzzaman M
    6. Lynn D
    7. van der Loo JCM
    8. Dexheimer PJ
    9. Keddache M
    10. Bauer TR
    11. Hickstein DD
    12. Russell DW
    13. Malik P

    (2018) Foamy virus vector carries a strong insulator in its long terminal repeat which reduces its genotoxic potential

    Journal of Virology 92:e01639-17.

    https://doi.org/10.1128/JVI.01639-17

    • PubMed
    • Google Scholar
22. 1. Hacein-Bey-Abina S
    2. Garrigue A
    3. Wang GP
    4. Soulier J
    5. Lim A
    6. Morillon E
    7. Clappier E
    8. Caccavelli L
    9. Delabesse E
    10. Beldjord K
    11. Asnafi V
    12. MacIntyre E
    13. Dal Cortivo L
    14. Radford I
    15. Brousse N
    16. Sigaux F
    17. Moshous D
    18. Hauer J
    19. Borkhardt A
    20. Belohradsky BH
    21. Wintergerst U
    22. Velez MC
    23. Leiva L
    24. Sorensen R
    25. Wulffraat N
    26. Blanche S
    27. Bushman FD
    28. Fischer A
    29. Cavazzana-Calvo M

    (2008) Insertional oncogenesis in 4 patients after retrovirus-mediated gene therapy of SCID-X1

    Journal of Clinical Investigation 118:3132–3142.

    https://doi.org/10.1172/JCI35700

    • PubMed
    • Google Scholar
23. 1. Hacein-Bey-Abina S
    2. Von Kalle C
    3. Schmidt M
    4. McCormack MP
    5. Wulffraat N
    6. Leboulch P
    7. Lim A
    8. Osborne CS
    9. Pawliuk R
    10. Morillon E
    11. Sorensen R
    12. Forster A
    13. Fraser P
    14. Cohen JI
    15. de Saint Basile G
    16. Alexander I
    17. Wintergerst U
    18. Frebourg T
    19. Aurias A
    20. Stoppa-Lyonnet D
    21. Romana S
    22. Radford-Weiss I
    23. Gross F
    24. Valensi F
    25. Delabesse E
    26. Macintyre E
    27. Sigaux F
    28. Soulier J
    29. Leiva LE
    30. Wissler M
    31. Prinz C
    32. Rabbitts TH
    33. Le Deist F
    34. Fischer A
    35. Cavazzana-Calvo M

    (2003) LMO2-associated clonal T cell proliferation in two patients after gene therapy for SCID-X1

    Science 302:415–419.

    https://doi.org/10.1126/science.1088547

    • PubMed
    • Google Scholar
24. 1. Hayward WS
    2. Neel BG
    3. Astrin SM

    (1981) Activation of a cellular onc gene by promoter insertion in ALV-induced lymphoid leukosis

    Nature 290:475–480.

    https://doi.org/10.1038/290475a0

    • PubMed
    • Google Scholar
25. 1. Hnisz D
    2. Weintraub AS
    3. Day DS
    4. Valton AL
    5. Bak RO
    6. Li CH
    7. Goldmann J
    8. Lajoie BR
    9. Fan ZP
    10. Sigova AA
    11. Reddy J
    12. Borges-Rivera D
    13. Lee TI
    14. Jaenisch R
    15. Porteus MH
    16. Dekker J
    17. Young RA
    18. Ch L

    (2016) Activation of proto-oncogenes by disruption of chromosome neighborhoods

    Science 351:1454–1458.

    https://doi.org/10.1126/science.aad9024

    • PubMed
    • Google Scholar
26. 1. Kataoka K
    2. Nagata Y
    3. Kitanaka A
    4. Shiraishi Y
    5. Shimamura T
    6. Yasunaga J
    7. Totoki Y
    8. Chiba K
    9. Sato-Otsubo A
    10. Nagae G
    11. Ishii R
    12. Muto S
    13. Kotani S
    14. Watatani Y
    15. Takeda J
    16. Sanada M
    17. Tanaka H
    18. Suzuki H
    19. Sato Y
    20. Shiozawa Y
    21. Yoshizato T
    22. Yoshida K
    23. Makishima H
    24. Iwanaga M
    25. Ma G
    26. Nosaka K
    27. Hishizawa M
    28. Itonaga H
    29. Imaizumi Y
    30. Munakata W
    31. Ogasawara H
    32. Sato T
    33. Sasai K
    34. Muramoto K
    35. Penova M
    36. Kawaguchi T
    37. Nakamura H
    38. Hama N
    39. Shide K
    40. Kubuki Y
    41. Hidaka T
    42. Kameda T
    43. Nakamaki T
    44. Ishiyama K
    45. Miyawaki S
    46. Yoon SS
    47. Tobinai K
    48. Miyazaki Y
    49. Takaori-Kondo A
    50. Matsuda F
    51. Takeuchi K
    52. Nureki O
    53. Aburatani H
    54. Watanabe T
    55. Shibata T
    56. Matsuoka M
    57. Miyano S
    58. Shimoda K
    59. Ogawa S

    (2015) Integrated molecular analysis of adult T cell leukemia/lymphoma

    Nature Genetics 47:1304–1315.

    https://doi.org/10.1038/ng.3415

    • PubMed
    • Google Scholar
27. 1. Krijger PH
    2. de Laat W

    (2016) Regulation of disease-associated gene expression in the 3D genome

    Nature Reviews Molecular Cell Biology 17:771–782.

    https://doi.org/10.1038/nrm.2016.138

    • PubMed
    • Google Scholar
28. 1. Lawrence M
    2. Huber W
    3. Pagès H
    4. Aboyoun P
    5. Carlson M
    6. Gentleman R
    7. Morgan MT
    8. Carey VJ

    (2013) Software for computing and annotating genomic ranges

    PLoS Computational Biology 9:e1003118.

    https://doi.org/10.1371/journal.pcbi.1003118

    • PubMed
    • Google Scholar
29. 1. Laydon DJ
    2. Melamed A
    3. Sim A
    4. Gillet NA
    5. Sim K
    6. Darko S
    7. Kroll JS
    8. Douek DC
    9. Price DA
    10. Bangham CR
    11. Asquith B

    (2014) Quantification of HTLV-1 clonality and TCR diversity

    PLoS Computational Biology 10:e1003646.

    https://doi.org/10.1371/journal.pcbi.1003646

    • PubMed
    • Google Scholar
30. 1. Lazo PA
    2. Lee JS
    3. Tsichlis PN

    (1990) Long-distance activation of the Myc protooncogene by provirus insertion in Mlvi-1 or Mlvi-4 in rat T-cell lymphomas

    PNAS 87:170–173.

    https://doi.org/10.1073/pnas.87.1.170

    • PubMed
    • Google Scholar
31. 1. Li H
    2. Durbin R

    (2009) Fast and accurate short read alignment with Burrows-Wheeler transform

    Bioinformatics 25:1754–1760.

    https://doi.org/10.1093/bioinformatics/btp324

    • PubMed
    • Google Scholar
32. 1. Lupiáñez DG
    2. Kraft K
    3. Heinrich V
    4. Krawitz P
    5. Brancati F
    6. Klopocki E
    7. Horn D
    8. Kayserili H
    9. Opitz JM
    10. Laxova R
    11. Santos-Simarro F
    12. Gilbert-Dussardier B
    13. Wittler L
    14. Borschiwer M
    15. Haas SA
    16. Osterwalder M
    17. Franke M
    18. Timmermann B
    19. Hecht J
    20. Spielmann M
    21. Visel A
    22. Mundlos S

    (2015) Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions

    Cell 161:1012–1025.

    https://doi.org/10.1016/j.cell.2015.04.004

    • PubMed
    • Google Scholar
33. 1. Martin M

    (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads

    EMBnet.journal 17:10–12.

    https://doi.org/10.14806/ej.17.1.200

    • Google Scholar
34. 1. Matsuoka M
    2. Jeang KT

    (2011a) Human T-cell leukemia virus type 1 (HTLV-1) and leukemic transformation: viral infectivity, tax, HBZ and therapy

    Oncogene 30:1379–1389.

    https://doi.org/10.1038/onc.2010.537

    • PubMed
    • Google Scholar
35. 1. Matsuoka M
    2. Jeang KT

    (2011b) Human T-cell leukemia virus type 1 (HTLV-1) and leukemic transformation: viral infectivity, tax, HBZ and therapy

    Oncogene 30:1379–1389.

    https://doi.org/10.1038/onc.2010.537

    • PubMed
    • Google Scholar
36. Software

    1. Melamed A

    (2018) q4C-2018, version 4753d13

    GitHub.

    https://github.com/ImperialCollegeLondon/q4C-2018
37. 1. Nora EP
    2. Lajoie BR
    3. Schulz EG
    4. Giorgetti L
    5. Okamoto I
    6. Servant N
    7. Piolot T
    8. van Berkum NL
    9. Meisig J
    10. Sedat J
    11. Gribnau J
    12. Barillot E
    13. Blüthgen N
    14. Dekker J
    15. Heard E

    (2012) Spatial partitioning of the regulatory landscape of the X-inactivation centre

    Nature 485:381–385.

    https://doi.org/10.1038/nature11049

    • PubMed
    • Google Scholar
38. 1. Nusse R
    2. Varmus HE

    (1982) Many tumors induced by the mouse mammary tumor virus contain a provirus integrated in the same region of the host genome

    Cell 31:99–109.

    https://doi.org/10.1016/0092-8674(82)90409-3

    • PubMed
    • Google Scholar
39. 1. Ong CT
    2. Corces VG

    (2014) CTCF: an architectural protein bridging genome topology and function

    Nature Reviews Genetics 15:234–246.

    https://doi.org/10.1038/nrg3663

    • PubMed
    • Google Scholar
40. 1. Phanstiel DH
    2. Van Bortle K
    3. Spacek D
    4. Hess GT
    5. Shamim MS
    6. Machol I
    7. Love MI
    8. Aiden EL
    9. Bassik MC
    10. Snyder MP

    (2017) Static and dynamic DNA loops form AP-1-Bound activation hubs during macrophage development

    Molecular Cell 67:1037–1048.

    https://doi.org/10.1016/j.molcel.2017.08.006

    • PubMed
    • Google Scholar
41. 1. Phillips JE
    2. Corces VG

    (2009) CTCF: master weaver of the genome

    Cell 137:1194–1211.

    https://doi.org/10.1016/j.cell.2009.06.001

    • PubMed
    • Google Scholar
42. 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
43. 1. Rasmussen MH
    2. Ballarín-González B
    3. Liu J
    4. Lassen LB
    5. Füchtbauer A
    6. Füchtbauer EM
    7. Nielsen AL
    8. Pedersen FS

    (2010) Antisense transcription in gammaretroviruses as a mechanism of insertional activation of host genes

    Journal of Virology 84:3780–3788.

    https://doi.org/10.1128/JVI.02088-09

    • PubMed
    • Google Scholar
44. 1. Rosewick N
    2. Durkin K
    3. Artesi M
    4. Marçais A
    5. Hahaut V
    6. Griebel P
    7. Arsic N
    8. Avettand-Fenoel V
    9. Burny A
    10. Charlier C
    11. Hermine O
    12. Georges M
    13. Van den Broeke A

    (2017) Cis-perturbation of cancer drivers by the HTLV-1/BLV proviruses is an early determinant of leukemogenesis

    Nature Communications 8:15264.

    https://doi.org/10.1038/ncomms15264

    • PubMed
    • Google Scholar
45. 1. Sanborn AL
    2. Rao SS
    3. Huang SC
    4. Durand NC
    5. Huntley MH
    6. Jewett AI
    7. Bochkov ID
    8. Chinnappan D
    9. Cutkosky A
    10. Li J
    11. Geeting KP
    12. Gnirke A
    13. Melnikov A
    14. McKenna D
    15. Stamenova EK
    16. Lander ES
    17. Aiden EL

    (2015) Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes

    PNAS 112:E6456–E6465.

    https://doi.org/10.1073/pnas.1518552112

    • PubMed
    • Google Scholar
46. 1. Satou Y
    2. Miyazato P
    3. Ishihara K
    4. Yaguchi H
    5. Melamed A
    6. Miura M
    7. Fukuda A
    8. Nosaka K
    9. Watanabe T
    10. Rowan AG
    11. Nakao M
    12. Bangham CR

    (2016) The retrovirus HTLV-1 inserts an ectopic CTCF-binding site into the human genome

    PNAS 113:3054–3059.

    https://doi.org/10.1073/pnas.1423199113

    • PubMed
    • Google Scholar
47. 1. Schmidt D
    2. Schwalie PC
    3. Wilson MD
    4. Ballester B
    5. Gonçalves A
    6. Kutter C
    7. Brown GD
    8. Marshall A
    9. Flicek P
    10. Odom DT

    (2012) Waves of retrotransposon expansion remodel genome organization and CTCF binding in multiple mammalian lineages

    Cell 148:335–348.

    https://doi.org/10.1016/j.cell.2011.11.058

    • PubMed
    • Google Scholar
48. 1. Schoenfelder S
    2. Sugar R
    3. Dimond A
    4. Javierre BM
    5. Armstrong H
    6. Mifsud B
    7. Dimitrova E
    8. Matheson L
    9. Tavares-Cadete F
    10. Furlan-Magaril M
    11. Segonds-Pichon A
    12. Jurkowski W
    13. Wingett SW
    14. Tabbada K
    15. Andrews S
    16. Herman B
    17. LeProust E
    18. Osborne CS
    19. Koseki H
    20. Fraser P
    21. Luscombe NM
    22. Elderkin S

    (2015) Polycomb repressive complex PRC1 spatially constrains the mouse embryonic stem cell genome

    Nature Genetics 47:1179–1186.

    https://doi.org/10.1038/ng.3393

    • PubMed
    • Google Scholar
49. 1. Schumann K
    2. Lin S
    3. Boyer E
    4. Simeonov DR
    5. Subramaniam M
    6. Gate RE
    7. Haliburton GE
    8. Ye CJ
    9. Bluestone JA
    10. Doudna JA
    11. Marson A

    (2015) Generation of knock-in primary human T cells using Cas9 ribonucleoproteins

    PNAS 112:10437–10442.

    https://doi.org/10.1073/pnas.1512503112

    • PubMed
    • Google Scholar
50. 1. Schwartzman O
    2. Mukamel Z
    3. Oded-Elkayam N
    4. Olivares-Chauvet P
    5. Lubling Y
    6. Landan G
    7. Izraeli S
    8. Tanay A

    (2016) UMI-4C for quantitative and targeted chromosomal contact profiling

    Nature Methods 13:685–691.

    https://doi.org/10.1038/nmeth.3922

    • PubMed
    • Google Scholar
51. 1. Schwarzer W
    2. Spitz F

    (2014) The architecture of gene expression: integrating dispersed cis-regulatory modules into coherent regulatory domains

    Current Opinion in Genetics & Development 27:74–82.

    https://doi.org/10.1016/j.gde.2014.03.014

    • PubMed
    • Google Scholar
52. 1. Sokol M
    2. Wabl M
    3. Ruiz IR
    4. Pedersen FS

    (2014) Novel principles of gamma-retroviral insertional transcription activation in murine leukemia virus-induced end-stage tumors

    Retrovirology 11:36.

    https://doi.org/10.1186/1742-4690-11-36

    • PubMed
    • Google Scholar
53. 1. Uren AG
    2. Kool J
    3. Berns A
    4. van Lohuizen M

    (2005) Retroviral insertional mutagenesis: past, present and future

    Oncogene 24:7656–7672.

    https://doi.org/10.1038/sj.onc.1209043

    • PubMed
    • Google Scholar
54. 1. van de Werken HJ
    2. de Vree PJ
    3. Splinter E
    4. Holwerda SJ
    5. Klous P
    6. de Wit E
    7. de Laat W

    (2012) 4c technology: protocols and data analysis

    Methods in Enzymology 513:89–112.

    https://doi.org/10.1016/B978-0-12-391938-0.00004-5

    • PubMed
    • Google Scholar
55. 1. Visser I
    2. Speekenbrink M

    (2010) depmixS4 : an R package for hidden markov models

    Journal of Statistical Software 36:1–21.

    https://doi.org/10.18637/jss.v036.i07

    • Google Scholar
56. 1. Weintraub AS
    2. Li CH
    3. Zamudio AV
    4. Sigova AA
    5. Hannett NM
    6. Day DS
    7. Abraham BJ
    8. Cohen MA
    9. Nabet B
    10. Buckley DL
    11. Guo YE
    12. Hnisz D
    13. Jaenisch R
    14. Bradner JE
    15. Gray NS
    16. Young RA

    (2017) YY1 Is a Structural Regulator of Enhancer-Promoter Loops

    Cell 171:1573–1588.

    https://doi.org/10.1016/j.cell.2017.11.008

    • PubMed
    • Google Scholar
57. 1. Wu TD
    2. Nacu S
    3. Td W
    4. Fast NS

    (2010) Fast and SNP-tolerant detection of complex variants and splicing in short reads

    Bioinformatics 26:873–881.

    https://doi.org/10.1093/bioinformatics/btq057

    • PubMed
    • Google Scholar
58. 1. Zhang Y
    2. Liu T
    3. Meyer CA
    4. Eeckhoute J
    5. Johnson DS
    6. Bernstein BE
    7. Nusbaum C
    8. Myers RM
    9. Brown M
    10. Li W
    11. Liu XS

    (2008) Model-based analysis of ChIP-Seq (MACS)

    Genome Biology 9:R137.

    https://doi.org/10.1186/gb-2008-9-9-r137

    • PubMed
    • Google Scholar

Author details

1. Anat Melamed

   Division of Infectious Diseases, Imperial College London, London, United Kingdom

   Contribution

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

   Contributed equally with

   Hiroko Yaguchi

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7312-3138

2. Hiroko Yaguchi

   Division of Infectious Diseases, Imperial College London, London, United Kingdom

   Contribution

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

   Contributed equally with

   Anat Melamed

   For correspondence

   h.yaguchi@imperial.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6184-0033

3. Michi Miura

   Division of Infectious Diseases, Imperial College London, London, United Kingdom

   Contribution

   Resources, Formal analysis, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6943-3782

4. Aviva Witkover

   Division of Infectious Diseases, Imperial College London, London, United Kingdom

   Contribution

   Resources, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

5. Tomas W Fitzgerald

   The European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom

   Contribution

   Software, Formal analysis, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2370-8496

6. Ewan Birney

   The European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom

   Contribution

   Conceptualization, Resources, Supervision, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8314-8497

7. Charles RM Bangham

   Division of Infectious Diseases, Imperial College London, London, United Kingdom

   Contribution

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

   For correspondence

   c.bangham@imperial.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2624-3599

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