- We also noticed that although a fraction of these clusters of enhancers do overlap with super-enhancers, the majority of the enhancer clusters are different from the known super-enhancers.. - Early experimental studies identify enhancers by“enhancer trap”, which has established our rudimentary understand- ing of enhancers in spite of its low-throughput and time- consuming nature [10, 11]. - A large number of enhancers have been discovered so far. - This set of enhancers is arguably the largest set of mammalian enhancers with supporting experimental evidence [28].. - This set of enhancers represents the most comprehensive set of computationally predicted human enhancers currently available although they are much less reliable. - In addition to individual enhancers, super-enhancers are identified, each of which is a group of enhancers in a genomic region that col- lectively control the expression of genes involved in cell- identities [29, 30].. - In order to study IEPs, we calculated the bipartite clus- tering coefficient (BCC) of enhancers in each cell line or cell type, with two sets of enhancers and five sets of experimentally supported IEPs (Methods, Fig. - With the goal to investi- gate how different enhancers share their target genes, the BCC is a perfect measurement, which can measure the percentage of target genes pairs of enhancers may share in a given set of IEPs (Fig. - We observed that the BCC of enhancers was usually larger than 0.90. - This indicates that when any pair of enhancers interact with one com- mon target gene, both enhancers are likely to interact with all target genes of these two enhancers.. - The average BCC of the enhancers in this example is . - noticed that the BCC of enhancers was no smaller than 0.97 in all cell lines with enough IEPs (Table 1 and Supplementary Table S1). - We further calculated the aver- age BCC of the enhancers interacting with more than one gene. - In other words, the target genes of any pair of enhancers usually are either the same or completely different.. - To assess the statistical significance of the above obser- vation, we studied the BCC of enhancers in randomly generated IEPs (Supplementary Table S1). - These random lEPs were constructed using the same set of enhancers and promoters but randomized interactions, where we ran- domly chose promoters to interact with an enhancer so that the same enhancer had the same number of inter- actions as it had originally. - With these random IEPs in the eight cell lines, we barely had a handful of enhancers sharing pro- moters with other enhancers in any cell line, suggesting that it is not by chance that multiple enhancers inter- act with a common set of target genes in the Rao et al.’s looplists (Supplementary Tables S1). - For all four cell lines we could calculate the BCC, the BCC of enhancers was and 0, respectively, which was much smaller than the BCC of enhancers in the above sets of real IEPs (p-value = 0, Supplementary Table S1). - When we considered the BCC of enhancers interacting with mul- tiple genes, the BCC values were no larger than 0.34 for random IEPs, while it was no smaller than 0.96 for the real IEPs, also suggesting that the observation that the. - BCC of enhancers being close to 1 was not by chance (Supplementary Table S1).. - Under the cutoffs 30, 50 and 100, the BCC of enhancers in all seven cell lines except GM12878 was no smaller than and 0.92, respec- tively (Supplementary Table S1). - We noticed that the BCC of enhancers was 0.97 in GM12878 with the cut- off 400. - Note that in HMEC, HUVEC, KBM7 and NHEK, the BCC of enhancers was no smaller than 0.92 even under the cutoff 100. - Moreover, the BCC of enhancers was increasing with more stringently defined IEPs, suggesting that the BCC of enhancers is close to 1 if it is not 1 (Supplementary Table S1).. - In order to assess the statistical significance of the observed BCC of enhancers in IEPs from different cutoffs, similarly, we compared the above BCC of enhancers with that from randomly generated IEPs (Supplementary Table S1). - Again, for every cutoff in every cell line, the BCC of enhancers for random IEPs was much smaller than the BCC of enhancers for real IEPs (p-value = 0). - For instance, under the cutoff 50, the BCC of enhancers was no larger. - If we considered only enhancers interacting with multiple target genes, the BCC of enhancers for random IEPs was about two times smaller than that for real IEPs. - When we calculated the BCC of enhancers using the IEPs defined by Jin et al.. - Although the IEPs were from different labs and from different experimental pro- cedures, in all cases, the BCC of enhancers was larger than 0.80 and the majority of enhancers interacting with multiple promoters had their individual BCCs larger than 0.90, suggesting that the BCC of enhancers is likely to be 1 in these samples. - Finally, we repeated the above analyses with the ChromHMM enhancers instead of the FANTOM enhancers, because the number of the FANTOM enhancers was relatively small compared with the esti- mated number of enhancers and there were much more ChromHMM enhancers than FANTOM enhancers [24].. - That is, the BCC of enhancers for IEPs in a cell line was close to 1. - In almost all cases, the majority of enhancers with multiple promoters had their individual BCCs larger than 0.90.. - In summary, the BCC of enhancers was likely to be close to 1 for different sets of IEPs, data from different labs, different experimental protocols, different cell lines. - The analy- ses based on IEPs from different cutoffs suggest that the BCC of enhancers is quite robust, although it is smaller when more loosely defined IEPs are used. - These analyses suggest that what we observed may be an intrinsic prop- erty of enhancers. - Two target genes tend to interact with exactly the same set or two completely different sets of enhancers. - We studied the BCC of promoters in each set of the afore- mentioned IEPs to see whether the similar hypothesis was true for the BCC of promoters. - Our data showed that the BCC of promoters was likely to be 1 as well, although this was not so evident as the BCC of enhancers in certain cases.. - First, we studied the BCC of promoters with IEPs based on the looplists [9]. - We also calculated the BCC of pro- moters in randomly simulated IEP datasets, where we kept the same sets of enhancers and promoters but ran- domly selected enhancers to interact with promoters so that every promoter had the same number of interacting enhancers as it had in the original set of IEPs. - The BCC of promoters was 0.52 at best in any cell line in these random datasets, suggesting that it was not by chance that the BCC of promoters was close to 1 in all cell lines (Supplementary Table S2).. - Second, we studied the BCC of promoters based on lEPs defined with different cutoffs [9] (Supplementary Table S2). - When we used the FANTOM enhancers, the BCC of promoters was often close to 1. - For instance, with the cutoff 400 for GM12878 and the cutoff 100 for other cell lines, the BCC of promoters was no smaller than 0.91 in all the cell lines . - For different cutoffs, it was usually no smaller than the BCC of enhancers, which was close to 1 in most cases. - For instance, with the cutoff 400 for GM12878 and the cutoff 100 for other cell lines, the BCC of pro- moters varied from 0.64 to 0.91 in different cell lines. - Although the BCC of promoters was not as large as the BCC of enhancers when the ChromHMM enhancers were used, the actual BCC of promoters could also be close to 1. - interactions and thus a low BCC of promoters. - Moreover, the BCC of promoters was always increasing with more and more stringently defined IEPs. - Although we did not observe that the BCC of promoters was close to 1 at the cutoff 100 we tried, it was indeed close to 1 when the looplists defined by Rao et al. - Third, we studied the BCC of promoters based on lEPs from other studies (Fig. - The low BCC of promoters for the original IEPs may be partially due to the promot- ers Jin et al. - In terms of the ChIA-PET data [6], when we used the FANTOM enhancers, the BCC of pro- moters was 0.86 in K562 and 0.86 in MCF7. - For the SPRITE data on the GM12878 cell line [42], the BCC values of promoters were no smaller than 0.89 and 0.71 in the IEPs defined with the FANTOM and ChromHMM enhancers, respectively. - In other words, a gene usually interacts with all enhancers of another gene or interacts with a completely different set of enhancers from this second gene.. - Enhancers form clusters that have special characteristics Since the BCC of enhancers is close to 1, we can orga- nize enhancers into clusters, where every enhancer in the same cluster is likely to interact wtih the same set of tar- get genes. - The average number of enhancers in a cluster varied from 2 to 5 in different cell lines. - However, there was a small frac- tion of enhancers in a cluster that were more than 50 kbps away from each other. - Since enhancers in a cluster were consecutive in the genome and the majority of enhancers in a cluster were close to each other, they seemed like the super-enhancers.. - 2 Clusters of enhancers with Hi-C reads. - For example, the yellow cluster of enhancers interact with NIT1 and PFDN2 gene promoters with 687 Hi-C reads. - The enhancers in a cluster were usually within the same TAD, with no smaller than 98.08% of enhancers in a cluster within a TAD in every cell line, independent of IEPs and enhancers used. - Moreover, the BCC of enhancers became closer and closer to 1 when the criteria to define IEPs became more and more stringent. - These analyses suggested that the BCC of enhancers in a cell line or a cell type was likely to be close to 1 if it is not 1.. - Similarly, we observed that two promoters were likely to interact with either the same set of enhancers or two disjoint sets of enhancers. - 4 The overlap of the enhancer clusters with the super-enhancers. - a The percentage of the enhancer clusters overlapping with the super-enhancers. - when the ChromHMM enhancers and the sets of IEPs that were defined with loose criteria were used, this might be due to the imperfectness of enhancers and IEPs we had.. - More importantly, the fact that the BCC of enhancers was close to 1 implied that the BCC of the promoters should be close to 1 as well based on the definition of the BCC.. - The BCC of enhancers being close to 1 suggested that enhancers form clusters to interact with the target genes. - The BCC of enhancers was not 1 sometimes, which implied that when a group of enhancers interacts with a set of target genes, the majority of target genes interact with each enhancer in this group while the rest interact with only a subset of enhancers in this group. - The percentage of the par- tially shared target genes by a group of enhancers varied from 0% to 6.57%. - In practice, several aspects may prevent the BCC of enhancers and the BCC of promoters from being 1.. - We also studied the functional similarities between the targets of enhancers in the same clusters. - We measured the sequence similiarity of enhancers within clusters in a cell line as well (Methods).. - We found that the pairs of enhancers in the same clusters. - There is no doubt that a proportion of enhancers only interacting with individual target genes.. - To study IEPs, we considered two sets of enhancers (Fig. - did not explicitly spec- ify the enhancers and promoters, we overlapped these links with the two sets of enhancers and the GEN- CODE promoters to define two sets of IEPs. - The super-enhancers in a cell line were com- pared with the clusters of enhancers that interact with the same set of target genes in the same cell line identified in this study.. - For a pair of enhancers (or a pair of genes), say u and v, their BCC is defined as,. - Similarly, if u and v are a pair of genes, BCC ( u, v ) measures the percentage of enhancers both u and v interact with among all the enhancers they interact with. - Correspondingly, the BCC of an individual enhancer (or gene), say u, is defined as,. - is the set of enhancers (or genes) that share at least one target gene (or enhancer) with u. - Clusters of enhancers that interact with the common set of genes. - We built an enhancer graph, with nodes representing enhancers and edges representing pairs of enhancers interacting with at least one common target gene. - Enhancers in a clique formed a cluster of enhancers that interact with the same set of genes (Fig. - To assess the statistical significance of the observed BCC values in a given set of IEPs, we compared the observed BCC of enhancers (promoters) in this original set of IEPs with that in random IEPs, respectively. - To assess whether enhancers within a cluster have more sequence similarity to each other, we aligned every pair of enhancers within a cluster for every cluster in a cell line.. - We then measured the similarity of a pair of enhancers as the percentage of identities in the corresponding align- ment [54]. - In this way, we obtained the similarity scores of pairs of enhancers with clusters in a cell line. - Similarly, we obtained the similarity scores of pairs of enhancers that are randomly selected in the same cell line. - To assess whether enhancers in a cluster tend to be close to each other in a cell line, We also compared the relative distance of pairs of enhancers within clusters in a cell line with pairs of enhancers ran- domly chosen in the same cell line by the Mann-Whitney test. - In addition, we measured the function similarity of enhancers in clusters by the GREAT tool [46], with the. - input of the targets genes of enhancers in clusters in a cell line. - 1e-05) associated with the target genes of the enhancer clusters.. - Clusters of enhancers. - Average percentage of enhancers in the same clusters mapped in a common TAD or TAD gap. - Enhancer networks revealed by correlated DNAse hypersensitivity states of enhancers
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