TFregulomeR reveals transcription factors' context-specific features and functions

Abstract

Transcription factors (TFs) are sequence-specific DNA binding proteins, fine-tuning spatiotemporal gene expression. Since genomic occupancy of a TF is highly dynamic, it is crucial to study TF binding sites (TFBSs) in a cell-specific context. To date, thousands of ChIP-seq datasets have portrayed the genomic binding landscapes of numerous TFs in different cell types. Although these datasets can be browsed via several platforms, tools that can operate on that data flow are still lacking. Here, we introduce TFregulomeR (https://github.com/benoukraflab/TFregulomeR), an R-library linked to an up-to-date compendium of cistrome and methylome datasets, implemented with functionalities that facilitate integrative analyses. In particular, TFregulomeR enables the characterization of TF binding partners and cell-specific TFBSs, along with the study of TF's functions in the context of different partnerships and DNA methylation levels. We demonstrated that TFs' target gene ontologies can differ notably depending on their partners and, by re-analyzing well characterized TFs, we brought to light that numerous leucine zipper TFBSs derived from ChIP-seq experiments documented in current databases were inadequately characterized, due to the fact that their position weight matrices were assembled using a mixture of homodimer and heterodimer binding sites. Altogether, analyses of context-specific transcription regulation with TFregulomeR foster our understanding of regulatory network-dependent TF functions.

Figure 1.

TFregulomeR key functionalities. (A) TFregulomeR consists of a toolbox linked to a large timely updated compendium of motif PWMs along with DNA methylation derived from MethMotif and GTRD. Users are also allowed to include their own genomic regions (e.g. ChIP-seq peaks) in TFregulomeR for peak meta-analysis. The PWM annotations recorded in TFregulomeR data compendium have been manually curated regarding species, organ, sample type, cell or tissue origin, disease state and data source. (B) TFregulomeR supports query of context in/dependent cistrome, TF interactome as well as cis-regulatory module (CRM). (C) Its functionalities allow (i) the study of TF co-factors along with DNA methylation and read enrichments, (ii) the characterization of context-specific binding sites and (iii) context-specific genomic and functional annotations. (D) Furthermore, TFregulomeR enables a direct conversion of newly generated PWM models to objects compatible with TFBSTools. These PWM models can also be exported to MEME and TRANSFAC formatted files for further downstream analyses using third-party resources such as RSAT.

Figure 2.

Analysis of binding partners and motifs in K562 context-specific CEBPB peaks. (A) Among 7914 CEBPB peaks in K562, 9.11% of peaks were unique to K562 while 1.14% of peaks were shared across all cell types. MethMotif logos display the motif enrichments along with DNA methylation states in K562 shared and exclusive CEBPB peaks. The blue, orange and green bars stacked above motif logo denote the numbers of CpGs homogenously unmethylated, homogenously methylated and heterogeneously methylated, respectively. (B) The heatmap shows the cofactor binding profiles in 16 sub-ensembles of K562 CEBPB peaks segregated according to their number of cell types where they are enriched. Each row represents a TF, each column denotes K562 CEBPB sub-ensemble peaks, and color intensity denotes a specified TF co-binding percentage within a given sub-ensemble of peaks. Here, the TF with co-binding percentages <5% in all 16 sub-ensembles were excluded, and the heatmap underwent row-wise hierarchical clustering based on Euclidean distance. (C) These three plots show, from the top to the bottom, the co-binding percentages, methylated CG percentages within the co-binding peaks, as well as ChIP-seq read enrichment scores in the co-binding peaks for CEBPB-CEBPD (blue) and CEBPB-ATF4 combinations (red) across 16 K562 CEBPB sub-ensembles. In the middle plot, the overall methylated CG percentages across 16 K562 CEBPB sub-ensembles are further reported in black. (D) The MethMotif logos display sequence preferences together with DNA methylation states enriched in the K562 shared CEBPB peaks with/without CEBPD co-binding loci, and in the K562 exclusive CEBPB peaks with/without ATF4 co-binding loci. (E) CEBPB motif logos extracted from different TF-binding profile databases.

Figure 3.

Analysis of MAFF binding partners and motifs in cell-specific peak regions. (A) MAFF motif logos from different TF-binding profile databases. (B) The MethMotif logos display the MAFF motif enrichments along with the DNA methylation states in cell-specific peak regions of three cell types. The blue, orange and green bars stacked above motif logo denote the numbers of CGs homogenously unmethylated, homogenously methylated and heterogeneously methylated respectively. (C) MAFF co-binding factors in the cell-specific regions are reported in a heatmap for each cell type (shades of red). DNA methylation states for the co-factors with more than 10% co-binding percentage are also portrayed in the regions (±100 bp around peak summits) co-bound by MAFF with those factors as the heatmaps (shades of blue). In this DNA methylation heatmap, each row represents a co-factor, while each column shows a methylation score interval. The color intensity implies the percentage of CGs with methylation scores in the given interval. (D) The MethMotif logo on the left displays sequence and DNA methylation preferences in the K562 MAFF cell-specific binding regions without the co-occurrences of NFE2 and NFE2L2. The right sides of this panel show the co-factor binding profiles in the regions, together with the DNA methylation for each co-factor with more than 10% co-binding percentage.

Figure 4.

Analysis of ATF3 binding partners and functions. (A) Five MethMotif logos display the ATF3 sequence and DNA methylation preferences in five different cell types. The blue, orange and green bars stacked above motif logo denote the numbers of CGs homogenously unmethylated, homogenously methylated and heterogeneously methylated respectively. (B) The heatmaps represent the cofactor profiles around all ATF3 binding sites across five cell types. Furthermore, for each cell type, a pie chart illustrates the genomic locations of ATF3 binding loci, while the density plot profiles the distribution of distances between ATF3 peak summits and the nearest gene promoters. (C) The Venn diagrams denote the overlaps of ATF3 binding sites across different cell types. (D) The bubble plots show the enriched ontologies of targeted genes by different ATF3 binding subsets. The bubble size is proportional to the adjusted p-value, while the color intensity represents the number of targeted genes. Due to the long lists of gene ontology results from ATF3 binding loci in HCT116 and K562 cells, some informative terms were selected.