Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages

Abstract

We have analyzed publicly available K562 Hi-C data, which enable genome-wide unbiased capturing of chromatin interactions, using a Mixture Poisson Regression Model and a power-law decay background to define a highly specific set of interacting genomic regions. We integrated multiple ENCODE Consortium resources with the Hi-C data, using DNase-seq data and ChIP-seq data for 45 transcription factors and 9 histone modifications. We classified 12 different sets (clusters) of interacting loci that can be distinguished by their chromatin modifications and which can be categorized into two types of chromatin linkages. The different clusters of loci display very different relationships with transcription factor-binding sites. As expected, many of the transcription factors show binding patterns specific to clusters composed of interacting loci that encompass promoters or enhancers. However, cluster 9, which is distinguished by marks of open chromatin but not by active enhancer or promoter marks, was not bound by most transcription factors but was highly enriched for three transcription factors (GATA1, GATA2 and c-Jun) and three chromatin modifiers (BRG1, INI1 and SIRT6). To investigate the impact of chromatin organization on gene regulation, we performed ribonucleicacid-seq analyses before and after knockdown of GATA1 or GATA2. We found that knockdown of the GATA factors not only alters the expression of genes having a nearby bound GATA but also affects expression of genes in interacting loci. Our work, in combination with previous studies linking regulation by GATA factors with c-Jun and BRG1, provides genome-wide evidence that Hi-C data identify sets of biologically relevant interacting loci.