Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq

Fang, Rongxin; Yu, Miao; Li, Guoqiang; Chee, Sora; Liu, Tristin; Schmitt, Anthony; Ren, Bing

doi:10.1038/cr.2016.137

Cited by 291 publications

(247 citation statements)

References 15 publications

(22 reference statements)

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“…Very recently, PLAC-seq and HiChIP were introduced as another many-to-all approach that enables identification of long-range interactions associated with specific proteins of interest (Fang et al 2016, Mumbach et al 2016). In these methods, first DNA fragmentation and in situ proximity ligation are performed in the cross-linked cells just as in in situ Hi-C, and then chromatin immunoprecipitation using specific antibodies is carried out, followed by enrichment of biotinylated ligation junctions.…”

Section: Tools To Explore 3d Genome Organizationmentioning

confidence: 99%

“…Contacts within the same compartment are enriched, whereas contacts between different compartments are depleted. The Hi-C data are from mouse embryonic stem cells (Fang et al 2016) and are visualized with Juicebox (Durand et al 2016). ( b ) Principal-component analysis (PCA) on the observed/expected matrix partitions each chromosome into two compartments, A and B, based on the first principal component (PC1).…”

Section: Figurementioning

confidence: 99%

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The Three-Dimensional Organization of Mammalian Genomes

Ren

2017

Annu. Rev. Cell Dev. Biol.

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331

284

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Animal development depends on not only the linear genome sequence that embeds millions of cis-regulatory elements, but also the three-dimensional (3D) chromatin architecture that orchestrates the interplay between cis-regulatory elements and their target genes. Compared to our knowledge of the cis-regulatory sequences, the understanding of the 3D genome organization in human and other eukaryotes is still limited. Recent advances in technologies to map the 3D genome architecture have greatly accelerated the pace of discovery. Here, we review emerging concepts of chromatin organization in mammalian cells, discuss the dynamics of chromatin conformation during development, and highlight important roles for chromatin organization in cancer and other human diseases.

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Section: Tools To Explore 3d Genome Organizationmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

The Three-Dimensional Organization of Mammalian Genomes

Ren

2017

Annu. Rev. Cell Dev. Biol.

Self Cite

331

284

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“…Recently, to improve the resolution and sensitivity of Hi-C assays, in situ protocols have been developed. In addition, for focussed interaction mapping oligonucleotide capture technology has been used to enrich for regions of interest (such as promoters) in the Hi-C library prior to high-throughput sequencing [119][120][121] or chromatin immunoprecipitation combined with Hi-C to interrogate individual protein-centric conformation maps 122 The use of oligo-capture technology significantly improves sensitivity and resolution without the associated increase in sequencing costs required for high-resolution analysis of traditional Hi-C libraries. 113 Importantly, Chromosome conformation capture-based assays have been used to successfully identify targets of disease-associated variation in other cell types 111,112,120,123 including human CD4 and CD8 populations, 124 and data from a genome-wide H3K27ac Hi CHIP (acetylated histone CHIP and conformation capture) performed in na€ ıve T cells, Th17 and Treg, have annotated accessible chromatin interacting with regulatory elements, 125 shedding new light on the lineage-specific interactome in these cells.…”

Section: Mapping the 3d Genomementioning

confidence: 99%

Unravelling the molecular basis for regulatory T‐cell plasticity and loss of function in disease

et al. 2018

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Regulatory T cells (Treg) are critical for preventing autoimmunity and curtailing responses of conventional effector T cells (Tconv). The reprogramming of T‐cell fate and function to generate Treg requires switching on and off of key gene regulatory networks, which may be initiated by a subtle shift in expression levels of specific genes. This can be achieved by intermediary regulatory processes that include microRNA and long noncoding RNA‐based regulation of gene expression. There are well‐documented microRNA profiles in Treg and Tconv, and these can operate to either reinforce or reduce expression of a specific set of target genes, including FOXP3 itself. This type of feedforward/feedback regulatory loop is normally stable in the steady state, but can alter in response to local cues or genetic risk. This may go some way to explaining T‐cell plasticity. In addition, in chronic inflammation or autoimmunity, altered Treg/Tconv function may be influenced by changes in enhancer–promoter interactions, which are highly cell type‐specific. These interactions are impacted by genetic risk based on genome‐wide association studies and may cause subtle alterations to the gene regulatory networks controlled by or controlling FOXP3 and its target genes. Recent insights into the 3D organisation of chromatin and the mapping of noncoding regulatory regions to the genes they control are shedding new light on the direct impact of genetic risk on T‐cell function and susceptibility to inflammatory and autoimmune conditions.

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“…While the repeated independent findings of association of PCSK2 variations with parameters involved in diabetes risk in a variety of different populations clearly implies functionality, whether these represent direct or indirect effects is not yet clear. Further work mapping possible chromatin interactions of intronic variants [32] represents an interesting avenue for further research which could lead to possible indirect gene targets. In this work we have attempted to approach this problem by examining coding variants for this enzyme, one of which, although rare in the general population, was found to be enriched in the Old Order Amish, suggesting a founder effect.…”

Section: Discussionmentioning

confidence: 99%

Functional analysis of PCSK2 coding variants: A founder effect in the Old Order Amish population

Winters

Ramos‐Molina

Jarvela

et al. 2017

Diabetes Research and Clinical Practice

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Aims In humans, noncoding variants of PCSK2, the gene encoding prohormone convertase 2 (PC2), have been previously associated with risk for and age of onset of type 2 diabetes (T2D). The aims of this study were to identify coding variants in PCSK2; to determine their possible association with glucose handling; and to determine functional outcomes for coding variants in biochemical studies. Methods Exome-wide genotyping was performed on 1725 Old Order Amish (OOA) subjects. PCSK2 coding variants were tested for association with diabetes-related phenotypes. In vitro analyses using transfected human PC2-encoding constructs were performed to determine the impact of each mutation on PC2 activity. Results We identified 10 rare missense coding variants in PCSK2 in various genomic databases. R430W (rs200711626) is greatly enriched in the OOA population (MAF 4.3%). This variant is almost twice as common (MAF 7.4%) in OOA individuals with T2D as in OOA individuals with normal or with normal/impaired glucose tolerance (MAF 3.9% and 2.9%, respectively; p= 0.25 and p=0.10). In vitro experiments revealed a broadening of the pH optimum for the R430W variant, which may result in increased activity against PCSK2 substrates. Conclusions Although the association of the R430W variation with T2D in the OOA population did not reach significance, based upon the broadened pH profile of R430W PC2, we speculate that the presence of this substitution may result in altered processing of PCSK2 substrates, ultimately leading to increased conversion to diabetes.

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Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq

Cited by 291 publications

References 15 publications

The Three-Dimensional Organization of Mammalian Genomes

The Three-Dimensional Organization of Mammalian Genomes

Unravelling the molecular basis for regulatory T‐cell plasticity and loss of function in disease

Functional analysis of PCSK2 coding variants: A founder effect in the Old Order Amish population

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