Abstract
BackgroundOur appreciation of the critical role of the genome’s 3D organization in gene regulation is steadily increasing. Recent 3C-based deep sequencing techniques elucidated a hierarchy of structures that underlie the spatial organization of the genome in the nucleus. At the top of this hierarchical organization are chromosomal territories and the megabase-scale A/B compartments that correlate with transcriptional activity within cells. Below them are the relatively cell-type-invariant topologically associated domains (TADs), characterized by high frequency of physical contacts between loci within the same TAD, and are assumed to function as regulatory units. Within TADs, chromatin loops bring enhancers and target promoters to close spatial proximity. Yet, we still have only rudimentary understanding how differences in chromatin organization between different cell types affect cell-type-specific gene expression programs that are executed under basal and challenged conditions.ResultsHere, we carried out a large-scale meta-analysis that integrated Hi–C data from thirteen different cell lines and dozens of ChIP-seq and RNA-seq datasets measured on these cells, either under basal conditions or after treatment. Pairwise comparisons between cell lines demonstrate a strong association between modulation of A/B compartmentalization, differential gene expression and transcription factor (TF) binding events. Furthermore, integrating the analysis of transcriptomes of different cell lines in response to various challenges, we show that A/B compartmentalization of cells under basal conditions significantly correlates not only with gene expression programs and TF binding profiles that are active under the basal condition but also with those induced in response to treatment. Yet, in pairwise comparisons between different cell lines, we find that a large portion of differential TF binding and gene induction events occur in genomic loci assigned to A compartment in both cell types, underscoring the role of additional critical factors in determining cell-type-specific transcriptional programs.ConclusionsOur results further indicate the role of dynamic genome organization in regulation of differential gene expression between different cell types and the impact of intra-TAD enhancer–promoter interactions that are established under basal conditions on both the basal and treatment-induced gene expression programs.
Highlights
Our appreciation of the critical role of the genome’s 3D organization in gene regulation is steadily increasing
We carried out a large-scale meta-analysis, integrating Hi–C data from 13 different cell lines and dozens of ChIP-seq and RNA-seq datasets recorded in the same cellular systems at basal conditions and in response to various treatments, to further elucidate the intricate interplay between the hierarchical 3D organization of the genome and gene regulation
For each chromosome separately, we used gene density to determine whether positive or negative values of the PC that represents the A/B compartmentalization corresponds to A compartment. (Centromeric regions were not included in the A/B partitions since no chromatin interactions are identified by Hi–C in these regions.) Table 1 summarizes the total genomic size and number of genes assigned to the A and B compartments in each cell line
Summary
Our appreciation of the critical role of the genome’s 3D organization in gene regulation is steadily increasing. The open (A-type) compartments cluster together in the nuclear interior, whereas the closed (B-type) compartments tend to cluster near the nuclear periphery [4] These chromosomal compartments contain ~ 100 kb–1 Mb scale subunits called topologically associating domains (TADs). At the bottom of the hierarchy are ~ 10 Kb–1 Mb chromatin-looping interactions, bringing enhancers (E) and promoters (P) that are located at high distance along the linear DNA sequence to close spatial proximity. Such E–P loops, a portion of which is cell type specific, mostly occur within TADs and unfrequently cross over TAD boundaries [4, 10]. The 3D organization of the genome has a pronounced cell-tocell stochastic variability, and the snapshots obtained by 3C-based analyses are typically the result of averaging over a large ensemble of cells
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