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Douglas Higgs and colleagues report a high-throughput approach, called Capture-C, to analyze interactions between cis regulatory elements. Using Capture-C, the authors interrogated hundreds of specific interactions at high resolution in a single experiment. Gene expression during development and differentiation is regulated in a cell- and stage-specific manner by complex networks of intergenic and intragenic cis -regulatory elements whose numbers and representation in the genome far exceed those of structural genes. Using chromosome conformation capture, it is now possible to analyze in detail the interaction between enhancers, silencers, boundary elements and promoters at individual loci, but these techniques are not readily scalable. Here we present a high-throughput approach (Capture-C) to analyze cis interactions, interrogating hundreds of specific interactions at high resolution in a single experiment. We show how this approach will facilitate detailed, genome-wide analysis to elucidate the general principles by which cis -acting sequences control gene expression. In addition, we show how Capture-C will expedite identification of the target genes and functional effects of SNPs that are associated with complex diseases, which most frequently lie in intergenic cis -acting regulatory elements.

The severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) disease (COVID-19) pandemic has caused millions of deaths worldwide. Genome-wide association studies identified the 3p21.31 region as conferring a twofold increased risk of respiratory failure. Here, using a combined multiomics and machine learning approach, we identify the gain-of-function risk A allele of an SNP, rs17713054G>A, as a probable causative variant. We show with chromosome conformation capture and gene-expression analysis that the rs17713054-affected enhancer upregulates the interacting gene, leucine zipper transcription factor like 1 ( LZTFL1 ). Selective spatial transcriptomic analysis of lung biopsies from patients with COVID-19 shows the presence of signals associated with epithelial–mesenchymal transition (EMT), a viral response pathway that is regulated by LZTFL1 . We conclude that pulmonary epithelial cells undergoing EMT, rather than immune cells, are likely responsible for the 3p21.31-associated risk. Since the 3p21.31 effect is conferred by a gain-of-function, LZTFL1 may represent a therapeutic target. SNP rs17713054 in the 3p21.31 COVID-19 risk locus is identified as a probable causative variant for disease association. Chromatin conformation and gene expression data indicate that LZTFL1 is impacted by rs17713054 in pulmonary epithelial cells.

Bulk ATAC-seq assays have been used to map and profile the chromatin accessibility of regulatory elements such as enhancers, promoters, and insulators. This has provided great insight into the regulation of gene expression in many cell types in a variety of organisms. To date, ATAC-seq has most often been used to provide an average evaluation of chromatin accessibility in populations of cells. The development of a single cell approach (scATAC-seq) assay enables researchers to evaluate chromatin accessibility in individual cells and identify sub-groups in mixed populations of cells. To investigate the full potential of single-cell epigenomic data, we have comprehensively compared the information derived from bulk ATAC-seq and scATAC-seq in populations of cells. We found that the chromatin architecture signal is the same using bulk ATAC-seq and scATAC-seq to analyse aliquots of the same cell population. However, scATAC-seq provides substantially higher data quality compared to bulk ATAC-seq improving the sensitivity to detect relatively weak, but functionally important ATAC-seq signals. Furthermore, we found that scATAC-seq identified differences in what was previously assumed to be a homogenous population of cells. Finally, we determined the number of cells required to generate aggregated open chromatin profiles from single cells and to identify biologically meaningful clusters after pseudo-bulking of data. This study illustrates the added value of using scATAC-seq rather than bulk ATAC-seq in evaluating both homogeneous and heterogeneous populations of cells. This paper provides a comprehensive guide on the benefits of using scATAC-seq data to study gene regulation.

Pooling barcoded 3C libraries and simultaneously capturing interactions at many loci of interest generates reproducible cis - and trans -interaction maps at high resolution from low amounts of input material. This allows for the comparison of interactions in different cell types using common software designed for differential analysis of sequence count data, rather than requiring software specifically designed for 3C experiments. Methods for analyzing chromosome conformation in mammalian cells are either low resolution or low throughput and are technically challenging. In next-generation (NG) Capture-C, we have redesigned the Capture-C method to achieve unprecedented levels of sensitivity and reproducibility. NG Capture-C can be used to analyze many genetic loci and samples simultaneously. High-resolution data can be produced with as few as 100,000 cells, and single-nucleotide polymorphisms can be used to generate allele-specific tracks. The method is straightforward to perform and should greatly facilitate the investigation of many questions related to gene regulation as well as the functional dissection of traits examined in genome-wide association studies.

Central nervous system-expressed long non-coding RNAs (lncRNAs) are often located in the genome close to protein coding genes involved in transcriptional control. Such lncRNA-protein coding gene pairs are frequently temporally and spatially co-expressed in the nervous system and are predicted to act together to regulate neuronal development and function. Although some of these lncRNAs also bind and modulate the activity of the encoded transcription factors, the regulatory mechanisms controlling co-expression of neighbouring lncRNA-protein coding genes remain unclear. Here, we used high resolution NG Capture-C to map the cis -regulatory interaction landscape of the key neuro-developmental Paupar-Pax6 lncRNA-mRNA locus. The results define chromatin architecture changes associated with high Paupar - Pax6 expression in neurons and identify both promoter selective as well as shared cis -regulatory-promoter interactions involved in regulating Paupar - Pax6 co-expression. We discovered that the TCF7L2 transcription factor, a regulator of chromatin architecture and major effector of the Wnt signalling pathway, binds to a subset of these candidate cis -regulatory elements to coordinate Paupar and Pax6 co-expression. We describe distinct roles for Paupar in Pax6 expression control and show that the Paupar DNA locus contains a TCF7L2 bound transcriptional silencer whilst the Paupar transcript can act as an activator of Pax6 . Our work provides important insights into the chromatin interactions, signalling pathways and transcription factors controlling co-expression of adjacent lncRNAs and protein coding genes in the brain.

The genome is organized via CTCF–cohesin-binding sites, which partition chromosomes into 1–5 megabase (Mb) topologically associated domains (TADs), and further into smaller sub-domains (sub-TADs). Here we examined in vivo an ∼80 kb sub-TAD, containing the mouse α-globin gene cluster, lying within a ∼1 Mb TAD. We find that the sub-TAD is flanked by predominantly convergent CTCF–cohesin sites that are ubiquitously bound by CTCF but only interact during erythropoiesis, defining a self-interacting erythroid compartment. Whereas the α-globin regulatory elements normally act solely on promoters downstream of the enhancers, removal of a conserved upstream CTCF–cohesin boundary extends the sub-TAD to adjacent upstream CTCF–cohesin-binding sites. The α-globin enhancers now interact with the flanking chromatin, upregulating expression of genes within this extended sub-TAD. Rather than acting solely as a barrier to chromatin modification, CTCF–cohesin boundaries in this sub-TAD delimit the region of chromatin to which enhancers have access and within which they interact with receptive promoters. Hanssen et al.  show that CTCF–cohesin binding sites at the α-globin gene cluster function as boundaries to restrict the interaction of enhancers with the flanking chromatin, thus preventing abnormal gene expression.

Mammalian gene expression patterns are controlled by regulatory elements, which interact within topologically associating domains (TADs). The relationship between activation of regulatory elements, formation of structural chromatin interactions and gene expression during development is unclear. Here, we present Tiled-C, a low-input chromosome conformation capture (3C) technique. We use this approach to study chromatin architecture at high spatial and temporal resolution through in vivo mouse erythroid differentiation. Integrated analysis of chromatin accessibility and single-cell expression data shows that regulatory elements gradually become accessible within pre-existing TADs during early differentiation. This is followed by structural re-organization within the TAD and formation of specific contacts between enhancers and promoters. Our high-resolution data show that these enhancer-promoter interactions are not established prior to gene expression, but formed gradually during differentiation, concomitant with progressive upregulation of gene activity. Together, these results provide new insight into the close, interdependent relationship between chromatin architecture and gene regulation during development. The relationship between regulatory elements, chromatin interactions and gene expression during development remains poorly understood. Here the authors present Tiled-C, a low-input 3C approach to study genome architecture at high resolution, and apply it to mouse erythroid differentiation in vivo, finding that enhancer-promoter interactions are formed gradually during differentiation, concomitant with progressive upregulation of gene activity.

Enhancer elements are a key regulatory feature of many important genes. Several general features including the presence of specific histone modifications are used to demarcate potentially active enhancers. Here we reveal that putative enhancers marked with H3 lysine 79 (H3K79) di or trimethylation (me2/3) (which we name H3K79me2/3 enhancer elements or KEEs) can be found in multiple cell types. Mixed lineage leukemia gene ( MLL ) rearrangements (MLL-r) such as MLL-AF4 are a major cause of incurable acute lymphoblastic leukemias (ALL). Using the DOT1L inhibitor EPZ-5676 in MLL-AF4 leukemia cells, we show that H3K79me2/3 is required for maintaining chromatin accessibility, histone acetylation and transcription factor binding specifically at KEEs but not non-KEE enhancers. We go on to show that H3K79me2/3 is essential for maintaining enhancer-promoter interactions at a subset of KEEs. Together, these data implicate H3K79me2/3 as having a functional role at a subset of active enhancers in MLL-AF4 leukemia cells. Histone 3 lysine 79 is mono (me1), di (me2), or tri (me3) methylated by the methyltransferase DOT1L. Here the authors reveal a group of enhancers defined by H3K79me2/3 which regulates enhancer-promoter interactions and other key enhancer features in MLL-AF4 leukemia cells.

Specific communication between gene promoters and enhancers is critical for accurate regulation of gene expression. However, it remains unclear how specific interactions between multiple regulatory elements contained within a single chromatin domain are coordinated. Recent technological advances which can detect multi-way chromatin interactions at single alleles can provide insights into how multiple regulatory elements cooperate or compete for transcriptional activation. Here, we use such an approach to investigate how interactions of the α-globin enhancers are distributed between multiple promoters in a mouse model in which the α-globin domain is extended to include several additional genes. Our data show that gene promoters do not form mutually exclusive interactions with enhancers, but all interact simultaneously in a single complex. These findings suggest that promoters do not structurally compete for interactions with enhancers, but form a regulatory hub structure, which is consistent with recent models of transcriptional activation occurring in non-membrane bound nuclear compartments. The coordination of interactions between multiple regulatory elements and genes within a chromatin domain remains poorly understood. Here, the authors use a method to detect multi-way chromatin interactions in a mouse model in which the α-globin domain is extended to include several additional genes, finding that the promoters do not form mutually exclusive interactions with the enhancers, but all interact simultaneously in a single complex.

The mouse X-inactivation center ( Xic ) locus represents a powerful model for understanding the links between genome architecture and gene regulation, with the non-coding genes Xist and Tsix showing opposite developmental expression patterns while being organized as an overlapping sense/antisense unit. The Xic is organized into two topologically associating domains (TADs) but the role of this architecture in orchestrating cis -regulatory information remains elusive. To explore this, we generated genomic inversions that swap the Xist/Tsix transcriptional unit and place their promoters in each other’s TAD. We found that this led to a switch in their expression dynamics: Xist became precociously and ectopically upregulated, both in male and female pluripotent cells, while Tsix expression aberrantly persisted during differentiation. The topological partitioning of the Xic is thus critical to ensure proper developmental timing of X inactivation. Our study illustrates how the genomic architecture of cis -regulatory landscapes can affect the regulation of mammalian developmental processes. Swapping the Xist/Tsix transcriptional units and placing their promoters in each other’s topologically associating domain shows that the topological partitioning of the X-inactivation center is critical to ensure proper X inactivation during development.