Differences in nanoscale organization of DNase I hypersensitive and insensitive chromatin in single human cells

Methodological advances in conformation capture techniques have fundamentally changed our understanding of chromatin architecture. However, the nanoscale organization of chromatin and its cell-to-cell variance are less studied. By using a combination of high throughput super-resolution microscopy and coarse-grained modelling we investigated properties of active and inactive chromatin in interphase nuclei. Using DNase I hypersensitivity as a criterion, we have selected prototypic active and inactive regions from ENCODE data that are representative for K-562 and more than 150 other cell types. By using oligoFISH and automated STED microscopy we systematically measured physical distances of the endpoints of 5kb DNA segments in these regions. These measurements result in high-resolution distance distributions which are right-tailed and range from very compact to almost elongated configurations of more than 200 nm length for both the active and inactive regions. Coarse-grained modeling of the respective DNA segments suggests that in regions with high DNase I hypersensitivity cell-to-cell differences in nucleosome occupancy determine the histogram shape. Simulations of the inactive region cannot sufficiently describe the compaction measured by microscopy, although internucleosomal interactions were elevated and the linker histone H1 was included in the model. These findings hint at further organizational mechanisms while the microscopy-based distance distribution indicates high cell-to-cell differences also in inactive chromatin regions. The analysis of the distance distributions suggests that direct enhancer-promoter contacts, which most models of enhancer action assume, happen for proximal regulatory elements in a probabilistic manner due to chromatin flexibility.

Trans-Ancestral Fine-Mapping and Epigenetic Annotation as Tools to Delineate Functionally Relevant Risk Alleles at IKZF1 and IKZF3 in Systemic Lupus Erythematosus

Background: Prioritizing tag-SNPs carried on extended risk haplotypes at susceptibility loci for common disease is a challenge. Methods: We utilized trans-ancestral exclusion mapping to reduce risk haplotypes at IKZF1 and IKZF3 identified in multiple ancestries from SLE GWAS and ImmunoChip datasets. We characterized functional annotation data across each risk haplotype from publicly available datasets including ENCODE, RoadMap Consortium, PC Hi-C data from 3D genome browser, NESDR NTR conditional eQTL database, GeneCards Genehancers and TF (transcription factor) binding sites from Haploregv4. Results: We refined the 60 kb associated haplotype upstream of IKZF1 to just 12 tag-SNPs tagging a 47.7 kb core risk haplotype. There was preferential enrichment of DNAse I hypersensitivity and H3K27ac modification across the 3′ end of the risk haplotype, with four tag-SNPs sharing allele-specific TF binding sites with promoter variants, which are eQTLs for IKZF1 in whole blood. At IKZF3, we refined a core risk haplotype of 101 kb (27 tag-SNPs) from an initial extended haplotype of 194 kb (282 tag-SNPs), which had widespread DNAse I hypersensitivity, H3K27ac modification and multiple allele-specific TF binding sites. Dimerization of Fox family TFs bound at the 3′ and promoter of IKZF3 may stabilize chromatin looping across the locus. Conclusions: We combined trans-ancestral exclusion mapping and epigenetic annotation to identify variants at both IKZF1 and IKZF3 with the highest likelihood of biological relevance. The approach will be of strong interest to other complex trait geneticists seeking to attribute biological relevance to risk alleles on extended risk haplotypes in their disease of interest.

Genome-wide Prediction of DNase I Hypersensitivity Using Gene Expression

We evaluate the feasibility of using a biological sample’s transcriptome to predict its genome-wide regulatory element activities measured by DNase I hypersensitivity (DH). We develop BIRD, Big Data Regression for predicting DH, to handle this high-dimensional problem. Applying BIRD to the Encyclopedia of DNA Element (ENCODE) data, we found that gene expression to a large extent predicts DH, and information useful for prediction is contained in the whole transcriptome rather than limited to a regulatory element’s neighboring genes. We show that the predicted DH predicts transcription factor binding sites (TFBSs), prediction models trained using ENCODE data can be applied to gene expression samples in Gene Expression Omnibus (GEO) to predict regulome, and one can use predictions as pseudo-replicates to improve the analysis of high-throughput regulome profiling data. Besides improving our understanding of the regulome-transcriptome relationship, this study suggests that transcriptome-based prediction can provide a useful new approach for regulome mapping.