Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture–on-chip (4C)

Simonis, Marieke; Klous, Petra; Splinter, Erik; Moshkin, Yuri M.; Willemsen, Rob; Wit, Elzo de; Steensel, Bas van; Laat, Wouter de

doi:10.1038/ng1896

Cited by 1,241 publications

(1,151 citation statements)

References 21 publications

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“…In Hi-C experiment, formaldehyde is used to cross-link cells, resulting in covalent links between spatially adjacent chromatin segments [10]. This procedure is just the same as 3C[1] and 4C[18], which are used to support the transcription factory mechanism[3], and implies the dynamic instinct of gene localization. So our result based on Hi-C could be good evidence to illustrate that transcription factories are genome-wide common in human nucleus.…”

Section: Resultsmentioning

confidence: 99%

Human transcriptional interactome of chromatin contribute to gene co-expression

Dong

Chen

et al. 2010

BMC Genomics

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BackgroundTranscriptional interactome of chromatin is one of the important mechanisms in gene transcription regulation. By chromatin conformation capture and 3D FISH experiments, several chromatin interactions cases among sequence-distant genes or even inter-chromatin genes were reported. However, on genomics level, there is still little evidence to support these mechanisms. Recently based on Hi-C experiment, a genome-wide picture of chromatin interactions in human cells was presented. It provides a useful material for analysing whether the mechanism of transcriptional interactome is common.ResultsThe main work here is to demonstrate whether the effects of transcriptional interactome on gene co-expression exist on genomic level. While controlling the effects of transcription factors control similarities (TCS), we tested the correlation between Hi-C interaction and the mutual ranks of gene co-expression rates (provided by COXPRESdb) of intra-chromatin gene pairs. We used 6,084 genes with both TF annotation and co-expression information, and matched them into 273,458 pairs with similar Hi-C interaction ranks in different cell types. The results illustrate that co-expression is strongly associated with chromatin interaction. Further analysis using GO annotation reveals potential correlation between gene function similarity, Hi-C interaction and their co-expression.ConclusionsAccording to the results in this research, the intra-chromatin interactome may have relation to gene function and associate with co-expression. This study provides evidence for illustrating the effect of transcriptional interactome on transcription regulation.

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Section: Resultsmentioning

confidence: 99%

Human transcriptional interactome of chromatin contribute to gene co-expression

Dong

Chen

et al. 2010

BMC Genomics

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“…For each overlapping interaction, the anchor region that does not overlap with the bait is extracted and – along with the data associated with that interaction – used to construct a object. This process yields data for intervals on the linear genome, which is similar to the output of 4C experiments 7 that measure the intensity of interactions between the bait and all other regions. The “linearized” format may be preferable when a specific region can be defined as the bait, as intervals on the linear genome are easier to interpret than interactions in two-dimensional space.…”

Section: Overview Of Available Methodsmentioning

confidence: 99%

Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments

2016

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The study of genomic interactions has been greatly facilitated by techniques such as chromatin conformation capture with high-throughput sequencing (Hi-C). These genome-wide experiments generate large amounts of data that require careful analysis to obtain useful biological conclusions. However, development of the appropriate software tools is hindered by the lack of basic infrastructure to represent and manipulate genomic interaction data. Here, we present the InteractionSet package that provides classes to represent genomic interactions and store their associated experimental data, along with the methods required for low-level manipulation and processing of those classes. The InteractionSet package exploits existing infrastructure in the open-source Bioconductor project, while in turn being used by Bioconductor packages designed for higher-level analyses. For new packages, use of the functionality in InteractionSet will simplify development, allow access to more features and improve interoperability between packages.

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“…For instance, the duplicated 1.4 Mb DNA segment of chromosome 17p12 (c17p12) may have disrupted the normal genome structure. Intergene or interchromosomal interaction has been demonstrated to regulate genomic structure and expression of genes 26, 27. Normal trans interactions between two alleles of c17p12 (interchromatid) are likely altered by introducing an additional copy of c17p12 in CMT1A.…”

Section: Highly Variable Levels Of Pmp22mentioning

confidence: 99%

“…The chromosomal structure derived from the trans/intrachromosomal interactions has been achieved using C3 technique 28. More advanced versions of the technique (C4 or C5) are now also available 26, 27. To determine if the dynamic changes of the duplicated c17p12 structure are pathogenic, single cell clones (such as fibroblasts from human skin biopsy or immature Schwann cells by iPSC technology) may be isolated from humans with CMT1A.…”

Section: Highly Variable Levels Of Pmp22mentioning

confidence: 99%

Caveats in the Established Understanding of CMT1A

2017

Ann Clin Transl Neurol

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Charcot‐Marie‐Tooth disease type‐1A (CMT1A) is one of the most common types of inherited peripheral nerve diseases. It is caused by the trisomy of chromosome 17p12 (c17p12), a large DNA segment of 1.4 Mb containing PMP22 plus eight other genes. The size of c17p12 is formidable for any cloning technique to manipulate, and thus precludes production of models in vitro and in vivo that can precisely recapitulate the genetic alterations in humans with CMT1A. This limitation and other factors have led to several assumptions, which have yet been carefully scrutinized, serving as key principles in our understanding of the disease. For instance, one extra copy of c17p12 in patients with CMT1A results in a higher gene dosage of PMP22, thereby expected to produce a higher level of PMP22 mRNA/proteins that cause the disease. However, there has been increasing evidence that PMP22 levels are highly variable among patients with CMT1A and may fall into the normal range at a given time point. This raises an alternative mechanism causing the disease by dysregulation of PMP22 expression or excessive fluctuation of PMP22 levels, not the absolute increase of PMP22. This has become a pressing issue since recent clinical trials using ascorbic acid failed to alter the clinical outcome of CMT1A patients, leaving no effective therapy for the disease. In this article, we will discuss how this fundamental issue might be investigated. In addition, several other key issues in CMT1A will be discussed, including potential mechanisms responsible for the uniform slowing of conduction velocities. A clear understanding of these issues could radically change how therapies should be developed against CMT1A.

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Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture–on-chip (4C)

Cited by 1,241 publications

References 21 publications

Human transcriptional interactome of chromatin contribute to gene co-expression

Human transcriptional interactome of chromatin contribute to gene co-expression

Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments

Caveats in the Established Understanding of CMT1A

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