Interpreting 4C-Seq data: how far can we go?

Identification and Copy Number Variant Analysis of Enhancer Regions of Genes Causing Spinocerebellar Ataxia

Currently, routine diagnostics for spinocerebellar ataxia (SCA) look for polyQ repeat expansions and conventional variations affecting the proteins encoded by known SCA genes. However, ~40% of the patients still remain without a genetic diagnosis after routine tests. Increasing evidence suggests that variations in the enhancer regions of genes involved in neurodegenerative disorders can also cause disease. Since the enhancers of SCA genes are not yet known, it remains to be determined whether variations in these regions are a cause of SCA. In this pilot project, we aimed to identify the enhancers of the SCA genes ATXN1, ATXN3, TBP and ITPR1 in the human cerebellum using 4C-seq, publicly available datasets, reciprocal 4C-seq, and luciferase assays. We then screened these enhancers for copy number variants (CNVs) in a cohort of genetically undiagnosed SCA patients. We identified two active enhancers for each of the four SCA genes. CNV analysis did not reveal any CNVs in the enhancers of the four SCA genes in the genetically undiagnosed SCA patients. However, in one patient, we noted a CNV deletion with an unknown clinical significance near one of the ITPR1 enhancers. These results not only reveal elements involved in SCA gene regulation but can also lead to the discovery of novel SCA-causing genetic variants. As enhancer variations are being increasingly recognized as a cause of brain disorders, screening the enhancers of ATXN1, ATXN3, TBP and ITPR1 for variations other than CNVs and identifying and screening enhancers of other SCA genes might elucidate the genetic cause in undiagnosed patients.

Circular Chromosome Conformation Capture (4C-Seq) Analysis of HBV-Host Chromosome DNA Interactome

Hepatitis B virus (HBV) infects hepatocytes that are in the G0/G1 phase with intact nuclear membrane and organized chromosome architecture. In the nucleus of the infected cells, HBV covalently closed circular (ccc) DNA, an episomal minichromosome, serves as the template for all viral transcripts and the reservoir of persistent infection. Nuclear positioning of cccDNA can be assessed by the spatial distance between viral DNA and host chromosomal DNA through Circular Chromosome Conformation Capture (4C) combined with high-throughput sequencing (4C-seq). The 4C-seq analysis relies on proximity ligation and is commonly used for mapping genomic DNA regions that communicate within a host chromosome. The method has been tailored for studying nuclear localization of HBV episomal cccDNA in relation to the host chromosomes. In this study, we present a step-by-step protocol for 4C-seq analysis of HBV infection, including sample collection and fixation, 4C DNA library preparation, sequence library preparation, and data analysis. Although limited by proximity ligation of DNA fragments, 4C-seq analysis provides useful information of HBV localization in 3D genome, and aids the understanding of viral transcription in light of host chromatin conformation.

Core regulatory circuitries in defining cancer cell identity across the malignant spectrum

Gene expression programmes driving cell identity are established by tightly regulated transcription factors that auto- and cross-regulate in a feed-forward manner, forming core regulatory circuitries (CRCs). CRC transcription factors create and engage super-enhancers by recruiting acetylation writers depositing permissive H3K27ac chromatin marks. These super-enhancers are largely associated with BET proteins, including BRD4, that influence higher-order chromatin structure. The orchestration of these events triggers accessibility of RNA polymerase machinery and the imposition of lineage-specific gene expression. In cancers, CRCs drive cell identity by superimposing developmental programmes on a background of genetic alterations. Further, the establishment and maintenance of oncogenic states are reliant on CRCs that drive factors involved in tumour development. Hence, the molecular dissection of CRC components driving cell identity and cancer state can contribute to elucidating mechanisms of diversion from pre-determined developmental programmes and highlight cancer dependencies. These insights can provide valuable opportunities for identifying and re-purposing drug targets. In this article, we review the current understanding of CRCs across solid and liquid malignancies and avenues of investigation for drug development efforts. We also review techniques used to understand CRCs and elaborate the indication of discussed CRC transcription factors in the wider context of cancer CRC models.

Analysis of 4C-seq data: A comparison of methods

The circular chromosome conformation capture technique followed by sequencing (4C-seq) has been used in a number of studies to investigate chromosomal interactions between DNA fragments. Computational pipelines have been developed and published that offer various possibilities of 4C-seq data processing and statistical analysis. Here, we present an overview of four of such pipelines (fourSig, FourCSeq, 4C-ker and w4Cseq) taking into account the most important stages of computations. We provide comparisons of the methods and discuss their advantages and possible weaknesses. We illustrate the results with the use of data obtained for two different species, in a study devoted to vernalization control in Arabidopsis thaliana by the FLOWERING LOCUS C (FLC) gene and to long-range chromatin interactions in mouse embryonic stem cells.

Functional genomic approaches to elucidate the role of enhancers during development

Successful development depends on the precise tissue-specific regulation of genes by enhancers, genetic elements that act as switches to control when and where genes are expressed. Because enhancers are critical for development, and the majority of disease-associated mutations reside within enhancers, it is essential to understand which sequences within enhancers are important for function. Advances in sequencing technology have enabled the rapid generation of genomic data that predict putative active enhancers, but functionally validating these sequences at scale remains a fundamental challenge. Herein, we discuss the power of genome-wide strategies used to identify candidate enhancers, and also highlight limitations and misconceptions that have arisen from these data. We discuss the use of massively parallel reporter assays to test enhancers for function at scale. We also review recent advances in our ability to study gene regulation during development, including CRISPR-based tools to manipulate genomes and single-cell transcriptomics to finely map gene expression. Finally, we look ahead to a synthesis of complementary genomic approaches that will advance our understanding of enhancer function during development. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Developmental Biology > Developmental Processes in Health and Disease Laboratory Methods and Technologies > Genetic/Genomic Methods.

Computational Methods for Assessing Chromatin Hierarchy

The hierarchical organization of chromatin is known to associate with diverse cellular functions; however, the precise mechanisms and the 3D structure remain to be determined. With recent advances in high-throughput next generation sequencing (NGS) techniques, genome-wide profiling of chromatin structures is made possible. Here, we provide a comprehensive overview of NGS-based methods for profiling "higher-order" and "primary-order" chromatin structures from both experimental and computational aspects. Experimental requirements and considerations specific for each method were highlighted. For computational analysis, we summarized a common analysis strategy for both levels of chromatin assessment, focusing on the characteristic computing steps and the tools. The recently developed single-cell level techniques based on Hi-C and ATAC-seq present great potential to reveal cell-to-cell variability in chromosome architecture. A brief discussion on these methods in terms of experimental and data analysis features is included. We also touch upon the biological relevance of chromatin organization and how the combination with other techniques uncovers the underlying mechanisms. We conclude with a summary and our prospects on necessary improvements of currently available methods in order to advance understanding of chromatin hierarchy. Our review brings together the analyses of both higher- and primary-order chromatin structures, and serves as a roadmap when choosing appropriate experimental and computational methods for assessing chromatin hierarchy.

Identification of multi-loci hubs from 4C-seq demonstrates the functional importance of simultaneous interactions

Use of low resolution single cell DNA FISH and population based high resolution chromosome conformation capture techniques have highlighted the importance of pairwise chromatin interactions in gene regulation. However, it is unlikely that associations involving regulatory elements act in isolation of other interacting partners that also influence their impact. Indeed, the influence of multi-loci interactions remains something of an enigma as beyond low-resolution DNA FISH we do not have the appropriate tools to analyze these. Here we present a method that uses standard 4C-seq data to identify multi-loci interactions from the same cell. We demonstrate the feasibility of our method using 4C-seq data sets that identify known pairwise and novel tri-loci interactions involving the Tcrb and Igk antigen receptor enhancers. We further show that the three Igk enhancers, MiEκ, 3′Eκ and Edκ, interact simultaneously in this super-enhancer cluster, which add to our previous findings showing that loss of one element decreases interactions between all three elements as well as reducing their transcriptional output. These findings underscore the functional importance of simultaneous interactions and provide new insight into the relationship between enhancer elements. Our method opens the door for studying multi-loci interactions and their impact on gene regulation in other biological settings.

4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments

4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.

Inferring regulatory element landscapes and transcription factor networks from cancer methylomes

Recent studies indicate that DNA methylation can be used to identify transcriptional enhancers, but no systematic approach has been developed for genome-wide identification and analysis of enhancers based on DNA methylation. We describe ELMER (Enhancer Linking by Methylation/Expression Relationships), an R-based tool that uses DNA methylation to identify enhancers and correlates enhancer state with expression of nearby genes to identify transcriptional targets. Transcription factor motif analysis of enhancers is coupled with expression analysis of transcription factors to infer upstream regulators. Using ELMER, we investigated more than 2,000 tumor samples from The Cancer Genome Atlas. We identified networks regulated by known cancer drivers such as GATA3 and FOXA1 (breast cancer), SOX17 and FOXA2 (endometrial cancer), and NFE2L2, SOX2, and TP63 (squamous cell lung cancer). We also identified novel networks with prognostic associations, including RUNX1 in kidney cancer. We propose ELMER as a powerful new paradigm for understanding the cis-regulatory interface between cancer-associated transcription factors and their functional target genes.