Introduction

Pluripotent embryonic stem (ES) cells can self-renew indefinitely and generate all cell lineages in an organism, including the germ line1,2,3. Induced pluripotent stem (iPS) cells are genetically reprogrammed to an ES cell-like state by over-expression of genes that maintain ES cell properties4. Both types of cells are valuable tools in disease modeling, drug development and, particularly, cell replacement therapy. Understanding molecular mechanisms underlying pluripotency is a pressing task if we are to take full advantage of these cell types.

Recent studies have provided insight into chromatin folding patterns in 3D space as a mechanism governing stem cell pluripotency5. In fact, in addition to the linear arrangement of information encoded on DNA fibers, the 3-D architecture of the genome is increasingly thought to be a defining factor in gene regulation6. Both critical stem cell activities, self-renewal and pluripotency, require a distinct chromosome organization: one “stable” enough to maintain cellular memory but “plastic” enough to assume a germ-layer specific conformation7.

A determinant of genome architecture is long-range chromatin-chromatin interactions occurring genome-wide. Since 2002, when chromosome conformation capture (3C) was first used to investigate interactions between two genomic segments8, a repertoire of high-throughput assays (4C9, 4C-seq10,11, 5C12, Hi-C13 and ChIA-PET14) has been developed to create chromatin interaction maps. These methods have greatly advanced our understanding of genome organization. Among them, circularized chromosome conformation capture coupled with next-generation sequencing (4C-seq) is an unbiased “one-versus-all” approach used to detect all genomic regions interacting with a region of interest11,15. Previously, we applied enzyme-based 4C-seq to capture genome-wide regions targeting the Oct4 locus, a region regulating ES cell pluripotency5,16. Furthermore, we have applied sonication-based 4C-seq to identify regions strongly interacting with a specified “bait”16,17. This modified approach was found as effective as enzyme-based methods but minimized bias due to enzyme digestion17.

Oct4 gene expression is governed by upstream distal and proximal enhancers18. The former is responsible for Oct4 expression in ES cells, while the latter controls expression in epiblasts18. Oct4 regulation by the distal enhancer is achieved by spatial proximity via looping7,19. Furthermore, there is evidence that enhancer elements can regulate genes hundreds to thousands of base pairs away. Therefore, we reasoned that Oct4 distal enhancer could regulate both nearby and distant genes on the same or even different chromosomes and that these interactions could be effectively detected by 4C-seq technology using the Oct4 distal enhancer as “bait”.

Here, we report that the Oct4 distal enhancer interacts with genomic loci that exhibit open chromosome features and contain active histone marks. Genes residing at these loci were expressed at levels higher than genes in other regions. We also demonstrate that long-range chromosomal interaction correlates with Oct4 gene transcription and show that somatic cells reprogrammed to iPS cells establish long-range chromosome interactions at the Oct4 locus before activating Oct4 transcription. When we compared the interactome of a transgenic Oct4 distal enhancer with its endogenous counterpart in iPS cells using sonication-based 4C-seq, we observed similar interacting loci. Overall, this analysis yields insight into high-fidelity interacting regions likely critical for gene expression in mouse pluripotent stem cells.

Results

Identification of endogenous and transgenic Oct4 enhancer interactomes in mouse ES and iPS cells

We applied a sonication-based 4C-seq technique to identify interacting partners of an Oct4 distal enhancer “bait” in three pluripotent lines, including mouse ES cells, mouse transgenic ES cells and mouse transgenic iPS cells (Fig. 1). The transgenic ES cell line contained both endogenous and transgenic Oct4 distal enhancers18, enabling us to obtain two sets of 4C interactomes in that line. Thus, we obtained five sets of 4C interactomes: one for mouse ES cells, two for mouse transgenic ES cells and two for mouse transgenic iPS cells. Notations used to identify Oct4 enhancer mouse lines were: MES, endogenous enhancer in wild-type ES cells; MES-E, endogenous enhancer in transgenic ES cells; MES-G, transgenic enhancer in transgenic ES cells; MIPS-E, endogenous enhancer in transgenic iPS cells; and MIPS-G, transgenic enhancer in transgenic mouse iPS cells. We obtained two biological replicates per experiment.

Figure 1
figure 1

Study design.

Left boxes indicate cell lines used; right boxes indicate 4C-seq datasets obtained from indicated mouse ES or iPS cells. Notations are defined as 4C-seq targeting: MES, endogenous Oct4 distal enhancer in wild-type ES cells; MES-E, endogenous enhancer in transgenic ES cells; MES-G, transgenic enhancer in transgenic ES cells; MIPS-E, endogenous enhancer in transgenic iPS cells; and MIPS-G, transgenic enhancer in transgenic mouse iPS cells. Datasets targeting transgenic Oct4 distal enhancer are designated “G” for GFP, while “E” indicates endogenous enhancer in a transgenic line. The transgenic Oct4 enhancers are shown as red dots while endogenous enhancers are shown as blue dots in left boxes.

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Details relevant to sonication-based 4C are found in Gao et al.17. Briefly, chromosome crosslinking via fixation captured spatial proximities within the nucleus and a circular structure was formed by sonication followed by ligation. We then employed “bait”-specific primers to capture genomic regions interacting with an Oct4 distal enhancer “bait” by inverse nested PCR. Two sets of primers were designed to target endogenous or exogenous enhancers (see Methods). 4C libraries were then constructed and subjected to next-generation sequencing.

Based on our previously established protocol, we employed a paired-end tag (PET) mapping strategy15,17 in which short paired tags are extracted from DNA fragment ends. In our hands, this is an optimal approach to identify bait-interacting regions by spotting reads that are mosaics of the bait and interacting regions17. Here, we define the “bait” as a ~0.6 kb region including a 300-bp extension from locations of the second set of PCR primers (Fig. 2). Overall, we identified thousands of distal sites in 10 datasets (see Table 1). Here, we focused primarily on inter-chromosomal interactions as they account for most of the interaction pool.

Table 1 Summary of metrics in 4C-seq analysis.
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Figure 2
figure 2

Nested PCR primers targeting the Oct4 distal enhancer.

Oct4-specific primers (red arrowheads for the 1st set, green arrowheads for the 2nd set) target the endogenous enhancer in mouse ES, iPS, transgenic ES and transgenic iPS cells. GFP-specific primers (red arrowheads for the 1st set, green and cyan arrowheads for the 2nd set.) target the transgenic enhancer in both transgenic cell types. The only difference in primers targeting endogenous and transgenic enhancers is the forward primer of 2nd set, shown as cyan arrowhead for transgenic experiment while green arrowhead for endogenous experiment. Blue boxes represent conserved regions CR3 (right) and CR4 (left) found in the distal enhancer in human, bovine, rat and mouse Oct4 orthologs. TSS, transcription start site.

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Reproducibility of inter-chromosomal interactions

We initially determined reproducibility of inter-chromosomal interactions between biological replicates by counting interactions in every 2Mb genomic bin and correlating them between biological replicates. For inter-chromosomal interactions generated in replicates, Pearson’s correlation coefficient was >0.4 in all five 4C experiment groups (Fig. 3). This finding suggests that the chromosome conformation capture strategy captures only a subset of diverse interactions occurring within the nucleus.

Figure 3
figure 3

Scatter plot of density of inter-chromosomal interactions.

For each of five experiment contexts, data points are generally scattered along a 45° diagonal. Color scale indicates data point density. The Pearson correlation coefficient is shown in each panel.

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To evaluate consistency between biological replicates in a different manner, we employed a strategy devised by Favorov et al.20, namely, to measure relative distance between two sets of genomic intervals. When we plotted distribution of relative distance between 4C sites in two biological replicates, we observed a significant enrichment in frequency around the relative distance 0 (Fig. 4), indicating consistency between replicates.

Figure 4
figure 4

Distribution of relative distances between 4C sites in two biological replicates.

observed: distribution of relative distance between 4C sites in two biological replicates of 4C samples; random: distribution of relative distance between two set of sites with no spatial correlation.

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Identification of significantly enriched interacting regions

Application of our custom computational pipeline identified thousands of sites interacting with the bait region (Table 1). To minimize noise due to random collisions within the nucleus and identify only biologically relevant sites, we applied a statistical model with a permutation-based false discovery rate (FDR) procedure. A z-score was assigned to each site based on the number of interacting sites within a 2Mb window around that site. FDR was calculated by random permutation of the data 100 times and the 0.05 threshold was chosen to select positive sites. Positive sites and nearby interacting sites within +/−1 Mb (Supplementary Table 1) were grouped as enriched interacting regions and overlapping regions were further merged (Table 1).

Comparison of enriched interacting regions identified from 4C-seq data

Statistical analysis identified dozens of enriched interacting regions in each of the five experimental contexts (Fig. 5, Supplementary Table 2), with numerous regions shared between two replicates. Such regions represent high-confidence interactions of potential biological significance. Of note, we identified some regions that interact with both the endogenous and transgenic Oct4 distal enhancer (13 for MES and 24 for MIPS, Supplementary Table 2), suggesting that both bind a similar set of genomic loci and that these regions could be relevant to stem cell fate. Of note, 9 regions (Supplementary Table 2) are consistently identified in MES and MIPS. In addition, we concluded that information in individual datasets could be masked if we focused only on high-fidelity regions. Thus, in the following association analysis, we compared interacting sites in individual datasets.

Figure 5
figure 5

Venn diagram of interacting regions between biological replicates and between endogenous and exogenous interactomes.

The number of regions overlapping between biological replicates in all five experiments (A–E) is shown. 13 high-fidelity regions are identified between MES-E and MES-G and 24 between MIPS-E and MIPS-G (F,G).

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Transgenic and endogenous Oct4 enhancer interactomes are similarly enriched with active nuclear compartments

Hi-C studies suggest a “two-compartment” model of nuclear chromatin, in which “open” compartments are enriched with active histone features, while “closed” compartments lack such marks13,21. To examine association of our 4C interactomes of the Oct4 enhancer with histone marks, we examined enrichment of those marks around 4C sites. For all 10 datasets, histone marks related to gene activation, including H3K4me1, H3K27ac, H3K4me3 and H3K9ac, were enriched around identified 4C sites (+/− 0.5 Mb) compared with randomly shuffled genomic sites. By contrast, enrichment for the repressive mark H3K27me3 and the heterochromatin mark H3K9me3 was not significant (Fig. 6A for mouse ES cells; Fig. 6B for mouse iPS cells). This result supports the idea that the Oct4 enhancer physically interacts with active genomic regions and reveals a consistent pattern of histone marks in endogenous and transgenic enhancer interactomes.

Figure 6
figure 6

Enrichment analysis of histone marks around 4C sites in mouse ES (A) and iPS (B) cells.

4C sites are enriched with enhancer (H3K4me1 and H3K27ac) and promoter (H3K4me3 and H3K9ac) marks in both endogenous and transgenic enhancer interactomes relative to random sites. In contrast, no obvious enrichment of repressive marks (H3K9me3 and H3K27me3) is observed in sites interacting with the endogenous or transgenic enhancer.

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Chromatin compartments are also defined by DNA replication domains22 and some studies show that DNA replication timing shapes the genomic landscape in specific cell types23,24,25. These findings suggest that early and late DNA replication might occur in different chromatin compartments. Using DNA replication timing data of three ES cell lines and three iPS cell lines (see Methods), we established that Oct4 is located at an early DNA replication domain (log2 transformed early/late replication timing ratio: mean = 1.334, standard deviation = 0.382, p-value = 0.00018). Thus, we asked whether the interactomes identified were similarly correlated with replication timing. We found that Oct4 enhancer interacting sites overlapped primarily with early DNA replication domains in mouse ES and iPS cells (p < 2.2e-16, Wilcoxon rank-sum test, Fig. 7). Early DNA replication domains are reportedly correlated with active gene transcription22, suggesting that contact of the Oct4 enhancer with distant interacting regions has functional significance.

Figure 7
figure 7

Enrichment analysis of early DNA replication timing domains around 4C sites.

Distribution of log2 transformed early/late replication timing ratio of regions +/− 50 kb from interacting sites indicates a shift toward positive values relative to genome-wide counterparts. DNA replication timing data for ES cells (ESC_46C, ESC_D3 and ESC_TT2) were used for analysis of MES, MES-E and MES-G, while data for iPS cells (iPSC, iPSC_1D4 and iPSC_2D4) were used for analysis of MIPS-E and MIPS-G.

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DNase I hypersensitivity (DHS) sites are universal features of active cis-regulatory sequences26. We counted DHS sites around +/− 0.5 Mb of 4C sites and at random sites in 10 pluripotent datasets (Fig. 8) and observed significant enrichment around 4C compared with random sites (p < 2.2e-16, Wilcoxon rank-sum test). The enrichment pattern of transgenic enhancer datasets was similar to that of endogenous enhancer datasets, further supporting the idea that the Oct4 distal enhancer resides in accessible chromatin and likely contains elements regulating stem cell activity.

Figure 8
figure 8

DNA hypersensitivity site counts in the proximity of 4C sites.

Boxplot shows the distribution of counts of DHSs around +/− 0.5 Mb of 4C sites. Biological replicates of comparable experiment contexts are labeled with the same color. The red line shows median level of enrichment in random sample.

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DNA methylation marked by hydroxymethylcytosine (5-hmC) also regulates pluripotent cell activity27. Thus, we counted 5-hmC peaks28 genome-wide within +/− 0.5 Mb of 4C sites (Fig. 9). 5-hmC sites were relatively more enriched in the proximity of Oct4 enhancer interactomes than were randomly selected sites (p < 2.2e-16, Wilcoxon rank-sum test) and that pattern was comparable between endogenous and transgenic enhancer interactomes.

Figure 9
figure 9

5-hmC site enrichment in the proximity 4C sites.

Boxplot shows the distribution of counts of 5-hmC sites around +/− 0.5 Mb of 4C sites. Biological replicates of comparable experiment contexts are labeled with the same color. The red line shows median level of enrichment in random sample.

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Oct4 enhancer interactomes are adjacent to transcription start sites and CpG sites

To correlate Oct4 enhancer interactomes with annotated gene locations, we examined distribution of genomic distances of 4C sites to nearby genomic elements, including to transcription start sites (TSSs) and CpG sites, which regulate gene expression and govern chromatin organization29,30. Kernel density was plotted to show the distribution of distance from our 4C sites to TSSs (Fig. 10A) and to CpG sites (Fig. 10B). As shown, the plots show sharp peaks around the zero relative position. Compared to the randomly simulated sites in the whole genome, we observed that peaks of enhancer interactomes were steeper in a statistically significant manner (p < 2.2e-16), suggesting that endogenous and transgenic Oct4 enhancers preferentially interact with gene-rich regions. Proximity of interactomes to defined TSSs supports the concept of “transcription factories”, in which multiple active gene loci are co-regulated31,32. Proximity of interactomes to CpG sites also supports the two-compartment model of interphase chromosome organization.

Figure 10
figure 10

Distribution of distances to TSSs (A)and CpG sites (B).

In both kernel density plots, the peak of random sites is significantly less steep around zero location than that of 4C sites in all 10 datasets (p < 2.2e-16).

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Expression of Oct4 enhancer interacting genes is higher than random genes

Since Oct4 enhancer interactomes are closely associated with genes, we identified all genes overlapping with or closest to 4C sites. Gene counts from 10 datasets are summarized in Table 2. We also collected RNA-seq data to examine expression levels of those genes. All interactomes identified in pluripotent cells were associated with genes expressed at a higher level than genes near randomly selected sites (Fig. 11). Genes consistently identified between biological replicates are listed in Supplementary Table 3. The overlapping genes found in endogenous and transgenic enhancer interactomes in MES and MIPS are listed in Supplementary Table 4.

Table 2 Counts of genes identified around 4C sites.
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Figure 11
figure 11

RNA-seq association study of targeted gene expression levels.

The average FPKM (fragments per kilobase of exon per million fragments mapped) value was calculated for each of 10 datasets and compared with that of random set of genes. Gene expression levels are elevated in 4C regions relative to randomly selected regions in indicated cells. (p < 0.05 except for MES-E-2; Welch’s two sample t-test).

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The Oct4 enhancer interactome is enriched in transcription factor binding sites

Transcription factors can mediate chromatin-chromatin interactions by binding to two DNA segments33. We thus asked whether the Oct4 enhancer interactome is enriched with transcription factor binding sites. To determine this, we analyzed ChIP-seq profiles of 13 sequence-specific transcription factors (Nanog, Oct4, STAT3, Smad1, Sox2, Zfx, c-Myc, n-Myc, Klf4, Esrrb, Tcfcp2l1, E2f1 and CTCF) and 2 transcription regulators (p300 and Suz12) reported previously34. We obtained the binding sites for the 15 TFs (Methods) and compared normalized and background-subtracted ChIP-seq tag counts around 4C and random sites using the Wilcoxon rank-sum test. Binding sites for Oct4, Tcfcp2l1, Klf4 and Esrrb were enriched in a statistically significant manner around +/− 1 kb of 4C sites in all 10 datasets (p < 1e-04), while Zfx was significantly enriched in 9 datasets (p < 1e-04) (Fig. 12, Table 3). These transcription factors may mediate chromatin interactions governing stem cell fate.

Table 3 Transcription factor enrichment around 4C sites identified in 10 datasets.
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Figure 12
figure 12

Boxplots of five transcription factors (Oct4, Klf4, Tcfcp2I1, Esrrb, Zfx) enrichment around the enhancer interacting sites (red) and random sites (blue) in 10 datasets.

ChIP-seq tags within +/− 1 kb of an interacting site were counted and normalized to 10 million total tags.

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Discussion

To study the role of the Oct4 gene locus in establishing and maintaining pluripotency, previous studies have mapped its interaction partners in mouse5 and human16 ES cells. To our knowledge, ours is the first interactome map established for a transgenic enhancer, allowing comparison with endogenous enhancer interactomes. Our work shows that even if the Oct4 distal enhancer resides at a different locus, its interaction partners are comparable to those of the endogenous enhancer.

Here, we applied sonication-based 4C-seq to identify loci interacting with the Oct4 distal enhancer bait in transgenic mouse ES cells. We found that interacting partners tend to occupy active genomic regions labeled by active histone modifications and other epigenetic marks. This observation suggests that physical contact between the Oct4 enhancer and other loci may be crucial for activity of transcription of factors regulating pluripotency. Particularly, the Oct4 enhancer are closer to other enhancer regions in 3D space, thus may interact and synergize with enhancers of other genes functioning in pluripotent stem cells. In fact, synergy among enhancers has been previously shown in analysis of the mouse immunoglobulin kappa (Igκ) gene35,36,37,38. Besides, the fact that Oct4 enhancer interacts with a set of actively transcribed genes in pluripotent cells supports the association of genomic architecture with gene expression, as reported in a previous study39. Genes residing in the 4C regions identified were actively expressed, revealing how far-reaching chromosome-chromosome interactions can have functional consequences in cells.

We observed high reproducibility in our study, both between biological replicates and between endogenous and transgenic enhancer interactomes. However, we saw some variation in outcomes potentially due to the methodologies employed. The Pearson’s correlation coefficient of >0.4 is large enough in evaluating reproducibility between replicates and is comparable to similar studies of this type. Chromatin-chromatin interactions are highly dynamic and transient40 and 4C-seq takes a snapshot of chromatin interaction patterns across millions of cells. By comparison, a previous study of the CTCF-mediated chromatin interactome in pluripotent cells using ChIA-PET reported that overlap between two biological replicates was 38%41.

4C-seq provides genome-wide candidate of regions and genes that interact with Oct4 enhancer. However, validation of the function of individual locus and gene is still required for unveiling the specific function. DNA fluorescence in situ hybridization (FISH) is a powerful approach to reveal nuclear positioning of a pair of loci42,43,44 and thus could be applied to validate specific interacting candidates suggested by 4C-seq. In addition, chromosome conformation capture (3C)8 could be used to quantify contact frequencies between selected genomic sites in a more targeted manner.

In summary, we report consistent interactome profiles for both the endogenous and transgenic Oct enhancer and conclude that interactions we observed are likely relevant to gene expression and pluripotency. It is important to keep in mind that we are relying on millions of cells and that our observations reflect behavior of cell populations. Such profiles should be interpreted with caution when predicting the function of a single cell.

Methods

Cell culture

Mouse E14 ES cells were grown in culture dishes coated with 0.1% gelatin in Glasgow Minimum Essential Medium (GMEM) supplemented with 15% fetal bovine serum (FBS), 100 nM nonessential amino acids, 1% sodium pyruvate, 200 mM glutamate, 1% penicillin streptomycin, 50 uM β-mercaptoethanol and 10 ng/mL LIF. Medium was replaced every 24 hours.

Generation of sonication-based 4C libraries

Immediately before library preparation, 10 million cells were cross-linked in culture dishes with freshly prepared 1% formaldehyde. To extract chromatin, cells were detached and treated with Triton X100 buffer (0.25% Triton X100, 10 mM EDTA, 10 mM Tris-HCl, pH8.0, 100 mM NaCl, 1 × protease inhibitor cocktail). Isolated chromatin pellets were resuspended in SDS lysis buffer (1% SDS, 5 mM EDTA, 50 mM Tris-HCl, pH8.0, 1 × protease inhibitor cocktail) and sonicated to an average size of 500-bp. Fragments were diluted, blunt-end repaired and ligated with T4 ligase for 24 hours at 4. Reverse crosslinking was carried out at 65 °C for 20 hours with proteinase K. Protein-free DNA was purified as template for nested PCR using two sets of primers. Primers targeting endogenous Oct4 enhancer are as follows. 1st set, Forward 1: ACAGGCACTCTGAGGGCTAT, Reverse 1: TCGTTCAGAGCATGGTGTAGG; 2nd set, Forward 2: GTAATGGGATCCTCAGACTGGG, Reverse 2: AGGCTGTGTGATTCACCCTG. Primers targeting transgenic Oct4 enhancer are the same as those targeting endogenous enhancer, except for the Forward 2: GTAATGGGATCGTGACCCAAGG (Fig. 2). Purified PCR products were further sonicated to an average size of 200-bp, which were sequenced using an Illumina HiiSeq2000 Sequencer.

Computational analysis of sonication-based 4C libraries

4C libraries were sequenced using 90-bp paired-end reads. Using this method, we extracted 20-bp end tags from forward and reverse reads and aligned them to the mouse reference genome assembly (mm10) separately using Burrows-Wheeler Aligner (BWA45). We retained only uniquely mapped reads, with both paired end reads of mapping quality MAPQ >20. Junction reads were identified as one end-tag mapping uniquely to the “bait” region and the other to genomic locations >300-bp away on the same chromosome or different chromosome. We paid particular attention to distal junction reads in which two tags were either 1) on the same chromosome separated by >10 kb, or 2) on different chromosomes. Distal junction reads were further processed to identify distal chromatin interactions. Tags within a 100-bp window were interpreted as PCR products from a single ligation event and merged as one unique distal interacting site. Unique distal sites with only one read were excluded as background noise.

Significantly enriched interacting domain calling

We applied a statistical model to identify contact regions with significantly higher interaction frequency than that expected from background10. Every interacting site i on chromosome W with length Lw was examined within window w with length lw. The number of interacting sites was defined as Ci,w and a z score was assigned to the window based on the following calculation:

in which μw is the expected number of interacting sites in window w on chromosome W.

We then applied an FDR-based approach to determine statistical significance of each interacting site. We randomly permutated z-score data obtained using the above calculations 100 times for every chromosome and selected sites with FDR ≤ 0.05 as significant interacting sites. An FDR for each site was calculated by counting randomly permutated z-scores that were larger than the experimentally determined z-score. All interacting sites within +/− 1 Mb range of significant interacting sites were merged into an enriched domain. Overlapping domains, if any, were further merged into a final candidate region.

Integrative association study

We retrieved ChIP-seq data for histone modifications, DHS data and RNA-seq data from ENCODE Project Portal. RNA-seq raw data were processed using Tophat and Cufflinks. ChIP-seq peak files of 15 DNA-binding proteins were retrieved from the GEO database (GSE11431). A file containing genome-wide 5-hmc peaks in mouse ES cells was provided by Dr. Hao Wu28. TTSs and CpG sites were retrieved from UCSC Genome Bioinformatics site. DNA replication timing data for the mouse genome in 3 ES cell lines and 3 iPS cell lines were downloaded from the GEO database. The cell lines and GEO accessions are as follows: 46C (GSM450272), D3 (GSM450273), TT2 (GSM450274), iPSC (GSM450275), iPSC 1D4 (GSM450276), iPSC 2D4 (GSM450277). Segments with mean replication timing ratio above one (log2 transformed early/late replication timing ratio above zero) were defined as early replication regions.

Other methods

Statistical analysis was carried out using R (http://www.r-project.org/). Conversion of genomic coordinates between genome assemblies was executed using the liftOver tool from the UCSC Genome Bioinformatics Site.

Additional Information

How to cite this article: Cai, M. et al. Analysis of a transgenic Oct4 enhancer reveals high fidelity long-range chromosomal interactions. Sci. Rep. 5, 14558; doi: 10.1038/srep14558 (2015).