Enhancers predominantly regulate gene expression during differentiation via transcription initiation.

Gene transcription occurs via a cycle of linked events, including initiation, promoter-proximal pausing, and elongation of RNA polymerase II (Pol II). A key question is how transcriptional enhancers influence these events to control gene expression. Here, we present an approach that evaluates the level and change in promoter-proximal transcription (initiation and pausing) in the context of differential gene expression, genome-wide. This combinatorial approach shows that in primary cells, control of gene expression during differentiation is achieved predominantly via changes in transcription initiation rather than via release of Pol II pausing. Using genetically engineered mouse models, deleted for functionally validated enhancers of the α- and ß-globin loci, we confirm that these elements regulate Pol II recruitment and/or initiation to modulate gene expression. Together, our data show that gene expression during differentiation is regulated predominantly at the level of initiation and that enhancers are key effectors of this process.

Defining genome architecture at base-pair resolution.

In higher eukaryotes, many genes are regulated by enhancers that are 104-106 base pairs (bp) away from the promoter. Enhancers contain transcription-factor-binding sites (which are typically around 7-22 bp), and physical contact between the promoters and enhancers is thought to be required to modulate gene expression. Although chromatin architecture has been mapped extensively at resolutions of 1 kilobase and above; it has not been possible to define physical contacts at the scale of the proteins that determine gene expression. Here we define these interactions in detail using a chromosome conformation capture method (Micro-Capture-C) that enables the physical contacts between different classes of regulatory elements to be determined at base-pair resolution. We find that highly punctate contacts occur between enhancers, promoters and CCCTC-binding factor (CTCF) sites and we show that transcription factors have an important role in the maintenance of the contacts between enhancers and promoters. Our data show that interactions between CTCF sites are increased when active promoters and enhancers are located within the intervening chromatin. This supports a model in which chromatin loop extrusion1 is dependent on cohesin loading at active promoters and enhancers, which explains the formation of tissue-specific chromatin domains without changes in CTCF binding.

DeepC: predicting 3D genome folding using megabase-scale transfer learning.

Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from megabase-scale DNA sequence. DeepC predicts domain boundaries at high resolution, learns the sequence determinants of genome folding and predicts the impact of both large-scale structural and single base-pair variations.

Recapitulation of erythropoiesis in congenital dyserythropoietic anaemia type I (CDA-I) identifies defects in differentiation and nucleolar abnormalities.

The investigation of inherited disorders of erythropoiesis has elucidated many of the principles underlying the production of normal red blood cells and how this is perturbed in human disease. Congenital Dyserythropoietic Anaemia type 1 (CDA-I) is a rare form of anaemia caused by mutations in two genes of unknown function: CDAN1 and CDIN1 (previously called C15orf41), whilst in some cases, the underlying genetic abnormality is completely unknown. Consequently, the pathways affected in CDA-I remain to be discovered. To enable detailed analysis of this rare disorder we have validated a culture system which recapitulates all of the cardinal haematological features of CDA-I, including the formation of the pathognomonic 'spongy' heterochromatin seen by electron microscopy. Using a variety of cell and molecular biological approaches we discovered that erythroid cells in this condition show a delay during terminal erythroid differentiation, associated with increased proliferation and widespread changes in chromatin accessibility. We also show that the proteins encoded by CDAN1 and CDIN1 are enriched in nucleoli which are structurally and functionally abnormal in CDA-I. Together these findings provide important pointers to the pathways affected in CDA-I which for the first time can now be pursued in the tractable culture system utilised here.

Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints.

In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specific DNA binding motifs. Here, we present Sasquatch, a new computational approach that uses DNase footprint data to estimate and visualize the effects of noncoding variants on transcription factor binding. Sasquatch performs a comprehensive k-mer-based analysis of DNase footprints to determine any k-mer's potential for protein binding in a specific cell type and how this may be changed by sequence variants. Therefore, Sasquatch uses an unbiased approach, independent of known transcription factor binding sites and motifs. Sasquatch only requires a single DNase-seq data set per cell type, from any genotype, and produces consistent predictions from data generated by different experimental procedures and at different sequence depths. Here we demonstrate the effectiveness of Sasquatch using previously validated functional SNPs and benchmark its performance against existing approaches. Sasquatch is available as a versatile webtool incorporating publicly available data, including the human ENCODE collection. Thus, Sasquatch provides a powerful tool and repository for prioritizing likely regulatory SNPs in the noncoding genome.

Iron overload induces dysplastic erythropoiesis and features of myelodysplasia in Nrf2-deficient mice.

Iron overload (IOL) is hypothesized to contribute to dysplastic erythropoiesis. Several conditions, including myelodysplastic syndrome, thalassemia and sickle cell anemia, are characterized by ineffective erythropoiesis and IOL. Iron is pro-oxidant and may participate in the pathophysiology of these conditions by increasing genomic instability and altering the microenvironment. There is, however, lack of in vivo evidence demonstrating a role of IOL and oxidative damage in dysplastic erythropoiesis. NRF2 transcription factor is the master regulator of antioxidant defenses, playing a crucial role in the cellular response to IOL in the liver. Here, we crossed Nrf2-/- with hemochromatosis (Hfe-/-) or hepcidin-null (Hamp1-/-) mice. Double-knockout mice developed features of ineffective erythropoiesis and myelodysplasia including macrocytic anemia, splenomegaly, and accumulation of immature dysplastic bone marrow (BM) cells. BM cells from Nrf2/Hamp1-/- mice showed increased in vitro clonogenic potential and, upon serial transplantation, recipients disclosed cytopenias, despite normal engraftment, suggesting defective differentiation. Unstimulated karyotype analysis showed increased chromosome instability and aneuploidy in Nrf2/Hamp1-/- BM cells. In HFE-related hemochromatosis patients, NRF2 promoter SNP rs35652124 genotype TT (predicted to decrease NRF2 expression) associated with increased MCV, consistent with erythroid dysplasia. Our results suggest that IOL induces ineffective erythropoiesis and dysplastic hematologic features through oxidative damage in Nrf2-deficient cells.

Predicting Chromatin Interactions from DNA Sequence Using DeepC.

The genome 3D structure is central to understanding how disease-associated genetic variants in the noncoding genome regulate their target genes. Genome architecture spans large-scale structures determined by fine-grained regulatory elements, making it challenging to predict the effects of sequence and structural variants. Experimental approaches for chromatin interaction mapping remain costly and time-consuming, limiting their use for interrogating changes of chromatin architecture associated with genomic variation at scale. Computational models to predict chromatin interactions have either interpreted chromatin at coarse resolution or failed to capture the long-range dependencies of larger sequence contexts. To bridge this gap, we previously developed deepC, a deep neural network approach to predict chromatin interactions from DNA sequence at megabase scale. deepC employs dilated convolutional layers to achieve simultaneously a large sequence context while interpreting the DNA sequence at single base pair resolution. Using transfer learning of convolutional weights trained to predict a compendium of chromatin features across cell types allows deepC to predict cell type-specific chromatin interactions from DNA sequence alone. Here, we present a detailed workflow to predict chromatin interactions with deepC. We detail the necessary data pre-processing steps, guide through deepC model training, and demonstrate how to employ trained models to predict chromatin interactions and the effect of sequence variations on genome architecture.

Direct correction of haemoglobin E ß-thalassaemia using base editors.

Haemoglobin E (HbE) ß-thalassaemia causes approximately 50% of all severe thalassaemia worldwide; equating to around 30,000 births per year. HbE ß-thalassaemia is due to a point mutation in codon 26 of the human HBB gene on one allele (GAG; glutamatic acid → AAG; lysine, E26K), and any mutation causing severe ß-thalassaemia on the other. When inherited together in compound heterozygosity these mutations can cause a severe thalassaemic phenotype. However, if only one allele is mutated individuals are carriers for the respective mutation and have an asymptomatic phenotype (ß-thalassaemia trait). Here we describe a base editing strategy which corrects the HbE mutation either to wildtype (WT) or a normal variant haemoglobin (E26G) known as Hb Aubenas and thereby recreates the asymptomatic trait phenotype. We have achieved editing efficiencies in excess of 90% in primary human CD34 + cells. We demonstrate editing of long-term repopulating haematopoietic stem cells (LT-HSCs) using serial xenotransplantation in NSG mice. We have profiled the off-target effects using a combination of circularization for in vitro reporting of cleavage effects by sequencing (CIRCLE-seq) and deep targeted capture and have developed machine-learning based methods to predict functional effects of candidate off-target mutations.

Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases.

BACKGROUND: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome. METHODS: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants. RESULTS: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving. CONCLUSIONS: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing.

The chromatin remodeller ATRX facilitates diverse nuclear processes, in a stochastic manner, in both heterochromatin and euchromatin.

The chromatin remodeller ATRX interacts with the histone chaperone DAXX to deposit the histone variant H3.3 at sites of nucleosome turnover. ATRX is known to bind repetitive, heterochromatic regions of the genome including telomeres, ribosomal DNA and pericentric repeats, many of which are putative G-quadruplex forming sequences (PQS). At these sites ATRX plays an ancillary role in a wide range of nuclear processes facilitating replication, chromatin modification and transcription. Here, using an improved protocol for chromatin immunoprecipitation, we show that ATRX also binds active regulatory elements in euchromatin. Mutations in ATRX lead to perturbation of gene expression associated with a reduction in chromatin accessibility, histone modification, transcription factor binding and deposition of H3.3 at the sequences to which it normally binds. In erythroid cells where downregulation of α-globin expression is a hallmark of ATR-X syndrome, perturbation of chromatin accessibility and gene expression occurs in only a subset of cells. The stochastic nature of this process suggests that ATRX acts as a general facilitator of cell specific transcriptional and epigenetic programmes, both in heterochromatin and euchromatin.

Dynamic Runx1 chromatin boundaries affect gene expression in hematopoietic development.

The transcription factor RUNX1 is a critical regulator of developmental hematopoiesis and is frequently disrupted in leukemia. Runx1 is a large, complex gene that is expressed from two alternative promoters under the spatiotemporal control of multiple hematopoietic enhancers. To dissect the dynamic regulation of Runx1 in hematopoietic development, we analyzed its three-dimensional chromatin conformation in mouse embryonic stem cell (ESC) differentiation cultures. Runx1 resides in a 1.1 Mb topologically associating domain (TAD) demarcated by convergent CTCF motifs. As ESCs differentiate to mesoderm, chromatin accessibility, Runx1 enhancer-promoter (E-P) interactions, and CTCF-CTCF interactions increase in the TAD, along with initiation of Runx1 expression from the P2 promoter. Differentiation to hematopoietic progenitor cells is associated with the formation of tissue-specific sub-TADs over Runx1, a shift in E-P interactions, P1 promoter demethylation, and robust expression from both Runx1 promoters. Deletion of promoter-proximal CTCF sites at the sub-TAD boundaries has no obvious effects on E-P interactions but leads to partial loss of domain structure, mildly affects gene expression, and delays hematopoietic development. Together, our analysis of gene regulation at a large multi-promoter developmental gene reveals that dynamic sub-TAD chromatin boundaries play a role in establishing TAD structure and coordinated gene expression.

Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features.

The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom's 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia.

High-resolution targeted 3C interrogation of cis-regulatory element organization at genome-wide scale.

Chromosome conformation capture (3C) provides an adaptable tool for studying diverse biological questions. Current 3C methods generally provide either low-resolution interaction profiles across the entire genome, or high-resolution interaction profiles at limited numbers of loci. Due to technical limitations, generation of reproducible high-resolution interaction profiles has not been achieved at genome-wide scale. Here, to overcome this barrier, we systematically test each step of 3C and report two improvements over current methods. We show that up to 30% of reporter events generated using the popular in situ 3C method arise from ligations between two individual nuclei, but this noise can be almost entirely eliminated by isolating intact nuclei after ligation. Using Nuclear-Titrated Capture-C, we generate reproducible high-resolution genome-wide 3C interaction profiles by targeting 8055 gene promoters in erythroid cells. By pairing high-resolution 3C interaction calls with nascent gene expression we interrogate the role of promoter hubs and super-enhancers in gene regulation.

Identification of LZTFL1 as a candidate effector gene at a COVID-19 risk locus.

The severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) 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.

Single-allele chromatin interactions identify regulatory hubs in dynamic compartmentalized domains.

The promoters of mammalian genes are commonly regulated by multiple distal enhancers, which physically interact within discrete chromatin domains. How such domains form and how the regulatory elements within them interact in single cells is not understood. To address this we developed Tri-C, a new chromosome conformation capture (3C) approach, to characterize concurrent chromatin interactions at individual alleles. Analysis by Tri-C identifies heterogeneous patterns of single-allele interactions between CTCF boundary elements, indicating that the formation of chromatin domains likely results from a dynamic process. Within these domains, we observe specific higher-order structures that involve simultaneous interactions between multiple enhancers and promoters. Such regulatory hubs provide a structural basis for understanding how multiple cis-regulatory elements act together to establish robust regulation of gene expression.

Targeting Fibroblast Growth Factor Receptor 1 for Treatment of Soft-Tissue Sarcoma.

Purpose: Altered FGFR1 signaling has emerged as a therapeutic target in epithelial malignancies. In contrast, the role of FGFR1 in soft-tissue sarcoma (STS) has not been established. Prompted by the detection and subsequent therapeutic inhibition of amplified FGFR1 in a patient with metastatic leiomyosarcoma, we investigated the oncogenic properties of FGFR1 and its potential as a drug target in patients with STS.Experimental Design: The frequency of FGFR1 amplification and overexpression, as assessed by FISH, microarray-based comparative genomic hybridization and mRNA expression profiling, SNP array profiling, and RNA sequencing, was determined in three patient cohorts. The sensitivity of STS cell lines with or without FGFR1 alterations to genetic and pharmacologic FGFR1 inhibition and the signaling pathways engaged by FGFR1 were investigated using viability assays, colony formation assays, and biochemical analysis.Results: Increased FGFR1 copy number was detected in 74 of 190 (38.9%; cohort 1), 13 of 79 (16.5%; cohort 2), and 80 of 254 (31.5%; cohort 3) patients. FGFR1 overexpression occurred in 16 of 79 (20.2%, cohort 2) and 39 of 254 (15.4%; cohort 3) patients. Targeting of FGFR1 by RNA interference and small-molecule inhibitors (PD173074, AZD4547, BGJ398) revealed that the requirement for FGFR1 signaling in STS cells is dictated by FGFR1 expression levels, and identified the MAPK-ERK1/2 axis as critical FGFR1 effector pathway.Conclusions: These data identify FGFR1 as a driver gene in multiple STS subtypes and support FGFR1 inhibition, guided by patient selection according to the FGFR1 expression and monitoring of MAPK-ERK1/2 signaling, as a therapeutic option in this challenging group of diseases. Clin Cancer Res; 23(4); 962-73. ©2016 AACR.