Nucleosome organization in the vicinity of transcription factor binding sites in the human genome
© Nie et al.; licensee BioMed Central Ltd. 2014
Received: 24 December 2013
Accepted: 10 June 2014
Published: 19 June 2014
The binding of transcription factors (TFs) to specific DNA sequences is an initial and crucial step of transcription. In eukaryotes, this process is highly dependent on the local chromatin state, which can be modified by recruiting chromatin remodelers. However, previous studies have focused mainly on nucleosome occupancy around the TF binding sites (TFBSs) of a few specific TFs. Here, we investigated the nucleosome occupancy profiles around computationally inferred binding sites, based on 519 TF binding motifs, in human GM12878 and K562 cells.
Although high nucleosome occupancy is intrinsically encoded at TFBSs in vitro, nucleosomes are generally depleted at TFBSs in vivo, and approximately a quarter of TFBSs showed well-positioned in vivo nucleosomes on both sides. RNA polymerase near the transcription start site (TSS) has a large effect on the nucleosome occupancy distribution around the binding sites located within one kilobase to the nearest TSS; fuzzier nucleosome positioning was thus observed around these sites. In addition, in contrast to yeast, repressors, rather than activators, were more likely to bind to nucleosomal DNA in the human cells, and nucleosomes around repressor sites were better positioned in vivo. Genes with repressor sites exhibiting well-positioned nucleosomes on both sides, and genes with activator sites occupied by nucleosomes had significantly lower expression, suggesting that actions of activators and repressors are associated with the nucleosome occupancy around their binding sites. It was also interesting to note that most of the binding sites, which were not in the DNase I-hypersensitive regions, were cell-type specific, and higher in vivo nucleosome occupancy were observed at these binding sites.
This study demonstrated that RNA polymerase and the functions of bound TFs affected the local nucleosome occupancy around TFBSs, and nucleosome occupancy patterns around TFBSs were associated with the expression levels of target genes.
KeywordsNucleosome occupancy Transcription factor binding site Clustering
Transcription factors (TFs) bind to specific DNA sequences and interact with components of the RNA polymerase complex, or with other complexes, to regulate transcription in a cell type-specific manner, and this process is highly dependent on the chromatin structure in eukaryotes [1–3]. The basic unit of chromatin structure is the nucleosome, consisting of histone octamers wrapped in 147 base pairs (bps) of DNA [4, 5]. Eukaryotic genomic DNA is assembled into nucleosomes and is further packaged into chromatin to achieve high compaction. Nucleosomes can directly regulate the accessibility of TFs and transcriptional machinery to the DNA sequences . Sequences in nucleosome-depleted regions are easier to access, while the accessibility of DNA within nucleosomes depends on nucleosome dynamics [7, 8]. Although histone-DNA complexes are very stable, histones are constantly evicted and reassembled onto DNA templates in a locus-specific manner. Previous studies have suggested that promoters and other regulatory sequences are typically nucleosome-depleted, whereas transcribed regions tend to be occupied by well-positioned nucleosomes, which are maintained by nucleosome-remodeling activities [9, 10]. The occupancy patterns and dynamic positioning of nucleosomes thus play crucial roles in regulating eukaryotic transcription.
Nucleosomes influence the accessibility of TFs to DNA. TFs can, in turn, directly or indirectly recruit remodeling complexes, or other coregulators, to modify the local chromatin state. The binding of several TFs, such as the insulator binding protein CTCF [11, 12], the RE1-silencing transcription factor (REST/NRSF)  and the multifunctional TF YY1 , has been suggested to initiate nucleosome depletion at TF binding sites (TFBSs) and the phased nucleosome arrays in the flanking regions in human cells. Nearly 3,000 TFs have been predicted computationally in the DNA-binding domain (DBD) database , and detailed manual curation has confirmed at least 1,400 TFs in the human genome . However, chromatin immunoprecipitation followed by sequencing (ChIP-seq), a technique for measuring genome-wide TF binding profiles, is only applied to one TF in a single experiment, making it difficult to identify binding locations for large numbers of factors in the specific cell type. Previous studies have, therefore, focused mainly on nucleosome occupancy around binding sites of a few specific TFs [11–13, 17]. Computational methods have the advantage of being able to determine the accurate profiles for many factors in a specific sample [18, 19]. Like many computational methods, CENTIPEDE , based on a hierarchical Bayesian mixture model, requires position weight matrices of known TF binding motifs; therefore, its ability is dependent on the availability of TF binding motifs. However, CENTIPEDE incorporates cell-specific experimental data to infer binding sites in a particular cell type, making it more accurate for predicting TFBSs.
To better understand the relationship between nucleosome positioning and TF binding, we focused on the CENTIPEDE-inferred binding sites for 519 TF binding motifs, representing up to a third of the human TF repertoire, and examined the nucleosome occupancy around these binding sites in human GM12878 and K562 cells. We further classified the binding sites by the distances of sites relative to the nearest gene and the functions of the bound TFs, to test whether the nucleosome occupancy exhibited distinct patterns. We finally clustered the nucleosome occupancy profiles around TFBSs and investigated their relevance to gene expression.
Nucleosome occupancy around TF binding sites
Low nucleosome signals in vivo are necessary for most TFBSs, as TFs may compete with nucleosomes to slide or evict them for access to the specific DNA . It is generally believed that nucleosomes are depleted before TFs bind to their sites. However, a recent study argues that nucleosome eviction occurs after TF binding and, in fact, requires TF binding, suggesting that nucleosome loss may not be a prerequisite for TF binding . The barrier model could explain the well-positioned nucleosomes around TFBSs. Binding of TFs can form barriers and other nucleosomes are stacked against them to generate the phased nucleosome arrays by ATP-dependent chromatin remodelers [4, 10]. Despite DNA sequences encoding nucleosome occupancy at certain regions, TF binding can drive nucleosomes to occupy intrinsically unfavorable DNA sequences or evict nucleosomes from intrinsically favorable sites.
Nucleosome occupancy around proximal and distal binding sites
Nucleosome occupancy around activator and repressor binding sites
Nucleosome occupancy around DNase I-hypersensitive and -resistant sites
Clustering nucleosome occupancy around TF binding sites
Nucleosome occupancy patterns correlate with gene expression
Transcription is regulated by the dynamic binding of TFs to the underlying DNA sequences in a cell type-specific manner . Most eukaryotic genomic DNA is packaged into nucleosomes, and TF binding is thus strongly associated with the local chromatin structure surrounding TFBSs [4, 5]. Chromatin can affect the recognition and binding of TFs; TFs can, in turn, direct the chromatin remodeling complexes to their target regions . We examined the nucleosome occupancy profiles around TFBSs to better understand the intricate relationships between TF binding and chromatin structure, and we also investigated the correlations between binding sites with different patterns of nucleosome occupancy and gene expression.
Although previous studies have demonstrated the nucleosome occupancy profiles around the binding sites for several specific TFs, our findings expand the current knowledge of nucleosome occupancy at TFBSs, based on the greater number of TFs. First, TF binding regions are generally nucleosome-depleted as a result of TF and nucleosome interactions. TFs can directly compete with nucleosomes and evict them from the DNA, while some TFs are aided by pioneer factors to bind to DNA [6–8]. Pioneer factors, such as FoxA, GATA, and PU.1, can bind to nucleosomal DNA and displace nucleosomes to help other TFs access their sites . Although nucleosome-depleted regions are necessary for most TFBSs, some TFs may bind to nucleosomal DNA without nucleosome reorganization. Previous studies have suggested that TF NF-κB p50 can bind to nucleosomal DNA without perturbing the overall structure of the nucleosome . We found that many TFBSs were located within DNase-I resistant regions, and these DNase-I resistant sites were cell-type specific. The cluster analysis using the CAGT software also indicated that a small proportion of TFBSs were indeed occupied by nucleosomes. It should be also noted that TFs might directly bind to sites in the nucleosome-depleted regions, especially in the proximal promoter near TSSs. Second, better-positioned nucleosomes were observed around the repressor sites compared with those around the activator sites. Repressors were more likely to bind to nucleosomal DNA, which might require catalyzed remodeling, in the human genome. The higher dependence on chromatin remodeling complexes might contribute to the stronger nucleosome positioning around the repressor sites. Besides, repressors are more associated with closed chromatin compared with activators. The highly positioned nucleosomes might result from the recruitment of different chromatin remodelers. Third, although a quarter of TFBSs showed arrays of well-positioned nucleosomes on both sides, the majority of TFBSs exhibited one or more well-positioned nucleosomes on one side, and a small proportion of TFBSs were occupied by nucleosomes in vivo. Correlating these different patterns of nucleosome occupancy with the expression levels of target genes indicated that genes with TFBSs exhibiting well-positioned nucleosomes on both sides or occupied by nucleosomes, had significantly lower expression levels. The analysis of gene expression for proximal activator and repressor binding sites further indicated that genes with repressor sites exhibiting well-positioned nucleosomes on both sides, and genes with activator sites occupied by nucleosomes had significantly lower expression, suggesting that actions of activators and repressors are associated with the nucleosome occupancy around their binding sites.
The DNA sequence, TF binding and chromatin remodeling events are important determinants of in vivo nucleosome organization in human cells. In this study, we systematically investigated the nucleosome occupancy profiles around TFBSs and their relevance to gene expression in human GM12878 and K562 cells. The nucleosomes were generally depleted at TFBSs in vivo, and asymmetric patterns of nucleosome occupancy were more pervasive around TFBSs. However, approximately a quarter of TFBSs showed well-positioned nucleosomes on both sides, and a small proportion of TFBSs were occupied by nucleosomes. Compared with the distal sites, proximal sites showed fuzzier nucleosome positioning. These proximal sites were located within 1 kb of TSSs, and RNA polymerase complexes near the TSSs had a large effect on the nucleosome occupancy distributions around these sites. Compared with activator sites, nucleosomes around repressor sites were better positioned. In addition, nucleosome occupancy patterns around TFBSs were correlated with the expression levels of target genes. Genes with repressor sites exhibiting well-positioned nucleosomes on both sides, and genes with activator sites occupied by nucleosomes had significantly lower expression.
The in vitro nucleosomes were assembled through combining the human genomic DNA with recombinantly derived histone octamers , and the raw sequenced reads for in vitro nucleosomes were obtained from the NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) using the accession number GSE25133. The sequenced reads were first mapped to the hg19 human genome using the Bowtie aligner , allowing a maximum of two mismatches. Then duplicate mapped reads were removed and the rest of reads were shifted by 73 bp in the 5′ to 3′ direction. Reads within a 60-bp window were finally counted and normalized to construct the in vitro nucleosome occupancy profile along the human genome, as in the processing of in vivo nucleosome reads.
The peaks of enriched signals in DNase I hypersensitivity experiments , generated by the Crawford lab, were downloaded from the UCSC FTP server. Crawford’s group mapped DNase-seq reads to the hg19 human genome using the BWA aligner , calculated the signal enrichment at each genomic coordinate using the F-Seq software , and identified peaks from the F-Seq density signals. These DNase I-hypersensitive regions reflected the openness of the chromatin and the accessibility of the genome in GM12878 and K562 cells.
TF binding sites for 519 binding motifs estimated with CENTIPEDE  were downloaded from http://centipede.uchicago.edu/SimpleMulti/. The total numbers of binding sites in the GM12878 and K562 cells were 368,127 and 340,094, respectively. The initial downloaded data were mapped to the hg18 human reference genome, and the binding locations were therefore converted from hg18 to hg19 using liftOver, provided by the UCSC Genome Browser. CENTIPEDE scanned the human genome with a TF binding motif to obtain candidate binding sites and computed a posterior probability for each candidate site to identify the real binding sites. It should be noted that although CENTIPEDE predicted binding sites using 519 TF binding motifs, only 220 and 260 binding motifs (Additional file 10) were included in the GM12878 and K562 cells, respectively, to ensure that each of the binding sites in downloaded files had a posterior probability greater than 0.999.
The aligned RNA-seq reads in GM12878 and K562 cells , were generated by the Caltech and downloaded from the UCSC FTP server. These paired-end reads were aligned to the hg19 genome and stored in the BAM format. The hg19 RefSeq gene annotation data  were also obtained from the UCSC FTP server. In order to determine the TSS position and the expression level of each of the genes, we first removed non-protein-coding transcripts from the hg19 RefSeq file. Then, we used the transcript assembly and quantification software Cufflinks  with default settings to calculate the expression value of each RefSeq transcript, which was quantified in fragments per kilobase of exon per million mapped fragments . For alternatively spliced transcripts encoding the same protein, only the transcript with the highest expression value was used. A total of 19,019 TSSs of RefSeq genes in both GM12878 and K562 cells were obtained to investigate the nucleosome occupancy around TSSs and define the distance between a binding site and the nearest TSS. We further classified genes into four categories on the basis of their expression levels. Genes with expression levels less than the first quartile, between the first and second quartiles, between the second and third quartiles, and greater than the third quartile were considered as very lowly, lowly, medium and highly expressed genes, respectively.
Nucleosome occupancy around TF binding sites
For a group of CENTIPEDE sites, we extracted the nucleosome signal in a ±2-kb window around each binding site and averaged nucleosome signals over all sites to represent the nucleosome occupancy around the binding sites. In addition, considering the confounding factors of nearby TSSs, we assigned each of the binding sites to the nearest gene based on its distance to the TSSs and reversed the shape profile of binding sites on the negative strand before averaging, to avoid a misleading aggregation.
Identification of activators and repressors
All activators and repressors were first retrieved from the UniProt database , a comprehensive resource for protein sequence and annotation data, to determine whether a TF was an activator or repressor. The search terms for activators and repressors were “activator AND organism:human AND reviewed:yes” and “repressor AND organism:human AND reviewed:yes”, respectively. Some multifunctional TFs, such as YY1 and CTCF, were annotated as both activators and repressors and were further removed in the analysis. We finally identified 20 activators and four repressors in the GM12878 cell line, and 25 activators and six repressors in the K562 cell line (Additional file 2).
Clustering nucleosome occupancy around TF binding sites
We extracted the nucleosome signals in a ±500-bp window around each binding site, and clustered these nucleosome signals using the CAGT software . CAGT uses the k-medians algorithm to obtain a relatively large number of compact clusters, and then redundant clusters are merged using the hierarchical agglomerative clustering. The number of clusters was set to 40 and a correlation-based distance function was used in the k-medians clustering in our analyses. Hierarchical agglomerative clustering iteratively merged the two most similar clusters and mirror clusters were also merged. If the number of clusters was set to 1, a distance threshold was set to 0.4 and two closest clusters with a distance below the threshold would be merged in the hierarchical agglomerative clustering. In addition, binding sites, whose nucleosome signal profiles had variance below a threshold of 0.1, were removed prior to the k-medians clustering.
Transcription factor binding site
Transcription start site
Clustered AGgregation tool.
We thank Hongde Liu for discussions on the statistical analysis. We thank the two anonymous reviewers for their valuable suggestions. This work was supported by the National Basic Research Program of China (2012CB316501) and the National Natural Science Foundation of China (61073141).
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