Global Genome Conformational Programming during Neuronal Development Is Associated with CTCF and Nuclear FGFR1—The Genome Archipelago Model

During the development of mouse embryonic stem cells (ESC) to neuronal committed cells (NCC), coordinated changes in the expression of 2851 genes take place, mediated by the nuclear form of FGFR1. In this paper, widespread differences are demonstrated in the ESC and NCC inter- and intra-chromosomal interactions, chromatin looping, the formation of CTCF- and nFGFR1-linked Topologically Associating Domains (TADs) on a genome-wide scale and in exemplary HoxA-D loci. The analysis centered on HoxA cluster shows that blocking FGFR1 disrupts the loop formation. FGFR1 binding and genome locales are predictive of the genome interactions; likewise, chromatin interactions along with nFGFR1 binding are predictive of the genome function and correlate with genome regulatory attributes and gene expression. This study advances a topologically integrated genome archipelago model that undergoes structural transformations through the formation of nFGFR1-associated TADs. The makeover of the TAD islands serves to recruit distinct ontogenic programs during the development of the ESC to NCC.

Download Full-text

In Situ Hi-C Library Preparation for Plants to Study Their Three-Dimensional Chromatin Interactions on a Genome-Wide Scale

Methods in Molecular Biology - Plant Gene Regulatory Networks ◽

10.1007/978-1-4939-7125-1_11 ◽

2017 ◽

pp. 155-166 ◽

Cited By ~ 3

Author(s):

Chang Liu

Keyword(s):

Three Dimensional ◽

Library Preparation ◽

Chromatin Interactions ◽

Genome Wide ◽

A Genome ◽

Wide Scale

Download Full-text

Genome-Wide Technologies to Study RNA–Chromatin Interactions

Non-Coding RNA ◽

10.3390/ncrna6020020 ◽

2020 ◽

Vol 6 (2) ◽

pp. 20 ◽

Cited By ~ 1

Author(s):

Masaki Kato ◽

Piero Carninci

Keyword(s):

Nuclear Organization ◽

Oligonucleotide Probes ◽

Hybridization Capture ◽

Proximity Ligation ◽

Advantages And Disadvantages ◽

Chromatin Interactions ◽

Genome Wide ◽

A Genome ◽

Wide Scale ◽

Non Coding Rnas

An increasing number of studies have revealed that long non-coding RNAs (lncRNAs) play important roles in gene regulation and nuclear organization. Although the mechanisms are still largely unknown, many lncRNAs have been shown to interact with chromatin. Thus, one approach to understanding the function of these lncRNAs is to identify their sites of genomic interaction. Hybridization capture methods using oligonucleotide probes have been used for years to study chromatin-associated RNA. Recently, several groups have developed novel methods based on proximity ligation to investigate RNA–chromatin interactions at a genome-wide scale. This review discusses these technologies and highlights their advantages and disadvantages for the consideration of potential users.

Download Full-text

Hi-C analyses with GENOVA: a case study with cohesin variants

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab040 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Robin H van der Weide ◽

Teun van den Brand ◽

Judith H I Haarhuis ◽

Hans Teunissen ◽

Benjamin D Rowland ◽

...

Keyword(s):

R Package ◽

Loop Formation ◽

Chromosome Conformation ◽

Genome Wide ◽

Score Analysis ◽

A Genome ◽

Contact Frequency ◽

Wide Scale ◽

Genomic Regions ◽

User Friendly

Abstract Conformation capture-approaches like Hi-C can elucidate chromosome structure at a genome-wide scale. Hi-C datasets are large and require specialised software. Here, we present GENOVA: a user-friendly software package to analyse and visualise chromosome conformation capture (3C) data. GENOVA is an R-package that includes the most common Hi-C analyses, such as compartment and insulation score analysis. It can create annotated heatmaps to visualise the contact frequency at a specific locus and aggregate Hi-C signal over user-specified genomic regions such as ChIP-seq data. Finally, our package supports output from the major mapping-pipelines. We demonstrate the capabilities of GENOVA by analysing Hi-C data from HAP1 cell lines in which the cohesin-subunits SA1 and SA2 were knocked out. We find that ΔSA1 cells gain intra-TAD interactions and increase compartmentalisation. ΔSA2 cells have longer loops and a less compartmentalised genome. These results suggest that cohesinSA1 forms longer loops, while cohesinSA2 plays a role in forming and maintaining intra-TAD interactions. Our data supports the model that the genome is provided structure in 3D by the counter-balancing of loop formation on one hand, and compartmentalization on the other hand. By differentially controlling loops, cohesinSA1 and cohesinSA2 therefore also affect nuclear compartmentalization. We show that GENOVA is an easy to use R-package, that allows researchers to explore Hi-C data in great detail.

Download Full-text

Hi-C Analyses with GENOVA: a case study with cohesin variants

10.1101/2021.01.22.427620 ◽

2021 ◽

Author(s):

Robin H. van der Weide ◽

Teun van den Brand ◽

Judith H.I. Haarhuis ◽

Hans Teunissen ◽

Benjamin D. Rowland ◽

...

Keyword(s):

R Package ◽

Loop Formation ◽

Genome Wide ◽

Score Analysis ◽

A Genome ◽

Contact Frequency ◽

Wide Scale ◽

Specific Locus ◽

Genomic Regions ◽

User Friendly

AbstractConformation capture-approaches like Hi-C can elucidate chromosome structure at a genome-wide scale. Hi-C datasets are large and require specialised software. Here, we present GENOVA: a user-friendly software package to analyse and visualise conformation capture data. GENOVA is an R-package that includes the most common Hi-C analyses, such as compartment and insulation score analysis. It can create annotated heatmaps to visualise the contact frequency at a specific locus and aggregate Hi-C signal over user-specified genomic regions such as ChIP-seq data. Finally, our package supports output from the major mapping-pipelines. We demonstrate the capabilities of GENOVA by analysing Hi-C data from HAP1 cell lines in which the cohesin-subunits SA1 and SA2 were knocked out. We find that ΔSA1 cells gain intra-TAD interactions and increase compartmentalisation. ΔSA2 cells have longer loops and a less compartmentalised genome. These results suggest that cohesinSA1 forms longer loops, while cohesinSA2 plays a role in forming and maintaining intra-TAD interactions. Our data supports the model that the genome is provided structure in 3D by the counter-balancing of loop formation on one hand, and compartmentalization on the other hand. By differentially controlling loops, cohesinSA1 and cohesinSA2 therefore also affect nuclear compartmentalization. We show that GENOVA is an easy to use R-package, that allows researchers to explore Hi-C data in great detail.

Download Full-text

Genome-wide Kinase-Chromatin Interactions Reveal the Regulatory Network of ERK Signaling in Human Embryonic Stem Cells

Molecular Cell ◽

10.1016/j.molcel.2013.04.030 ◽

2013 ◽

Vol 50 (6) ◽

pp. 844-855 ◽

Cited By ~ 61

Author(s):

Jonathan Göke ◽

Yun-Shen Chan ◽

Junli Yan ◽

Martin Vingron ◽

Huck-Hui Ng

Keyword(s):

Stem Cells ◽

Embryonic Stem Cells ◽

Human Embryonic Stem Cells ◽

Regulatory Network ◽

Embryonic Stem ◽

Erk Signaling ◽

Chromatin Interactions ◽

Genome Wide

Download Full-text

F-Box Genes in the Wheat Genome and Expression Profiling in Wheat at Different Developmental Stages

Genes ◽

10.3390/genes11101154 ◽

2020 ◽

Vol 11 (10) ◽

pp. 1154

Author(s):

Min Jeong Hong ◽

Jin-Baek Kim ◽

Yong Weon Seo ◽

Dae Yeon Kim

Keyword(s):

Developmental Stages ◽

Brachypodium Distachyon ◽

Triticum Aestivum L ◽

Wheat Genome ◽

Post Translational Modification ◽

Genome Wide ◽

A Genome ◽

High Sequence Homology ◽

Wide Scale

Genes of the F-box family play specific roles in protein degradation by post-translational modification in several biological processes, including flowering, the regulation of circadian rhythms, photomorphogenesis, seed development, leaf senescence, and hormone signaling. F-box genes have not been previously investigated on a genome-wide scale; however, the establishment of the wheat (Triticum aestivum L.) reference genome sequence enabled a genome-based examination of the F-box genes to be conducted in the present study. In total, 1796 F-box genes were detected in the wheat genome and classified into various subgroups based on their functional C-terminal domain. The F-box genes were distributed among 21 chromosomes and most showed high sequence homology with F-box genes located on the homoeologous chromosomes because of allohexaploidy in the wheat genome. Additionally, a synteny analysis of wheat F-box genes was conducted in rice and Brachypodium distachyon. Transcriptome analysis during various wheat developmental stages and expression analysis by quantitative real-time PCR revealed that some F-box genes were specifically expressed in the vegetative and/or seed developmental stages. A genome-based examination and classification of F-box genes provide an opportunity to elucidate the biological functions of F-box genes in wheat.

Download Full-text

Genome-wide mapping of unexplored essential regions in the Saccharomyces cerevisiae genome: evidence for hidden synthetic lethal combinations in a genetic interaction network

Nucleic Acids Research ◽

10.1093/nar/gku576 ◽

2014 ◽

Vol 42 (15) ◽

pp. 9838-9853 ◽

Cited By ~ 6

Author(s):

Saeed Kaboli ◽

Takuya Yamakawa ◽

Keisuke Sunada ◽

Tao Takagaki ◽

Yu Sasano ◽

...

Keyword(s):

Saccharomyces Cerevisiae ◽

Genetic Interaction ◽

Interaction Network ◽

Essential Genes ◽

Genetic Interactions ◽

Lethal Gene ◽

Synthetic Lethal ◽

Genome Wide ◽

A Genome ◽

Wide Scale

Abstract Despite systematic approaches to mapping networks of genetic interactions in Saccharomyces cerevisiae, exploration of genetic interactions on a genome-wide scale has been limited. The S. cerevisiae haploid genome has 110 regions that are longer than 10 kb but harbor only non-essential genes. Here, we attempted to delete these regions by PCR-mediated chromosomal deletion technology (PCD), which enables chromosomal segments to be deleted by a one-step transformation. Thirty-three of the 110 regions could be deleted, but the remaining 77 regions could not. To determine whether the 77 undeletable regions are essential, we successfully converted 67 of them to mini-chromosomes marked with URA3 using PCR-mediated chromosome splitting technology and conducted a mitotic loss assay of the mini-chromosomes. Fifty-six of the 67 regions were found to be essential for cell growth, and 49 of these carried co-lethal gene pair(s) that were not previously been detected by synthetic genetic array analysis. This result implies that regions harboring only non-essential genes contain unidentified synthetic lethal combinations at an unexpectedly high frequency, revealing a novel landscape of genetic interactions in the S. cerevisiae genome. Furthermore, this study indicates that segmental deletion might be exploited for not only revealing genome function but also breeding stress-tolerant strains.

Download Full-text

Machine-learning annotation of human splicing branchpoints

10.1101/094003 ◽

2016 ◽

Cited By ~ 3

Author(s):

Bethany Signal ◽

Brian S Gloss ◽

Marcel E Dinger ◽

Timothy R Mercer

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Gene Splicing ◽

Genetic Encoding ◽

Genome Wide ◽

Common Genetic Variants ◽

A Genome ◽

Wide Scale ◽

The Impact ◽

Splicing Patterns

ABSTRACTBackgroundThe branchpoint element is required for the first lariat-forming reaction in splicing. However due to difficulty in experimentally mapping at a genome-wide scale, current catalogues are incomplete.ResultsWe have developed a machine-learning algorithm trained with empirical human branchpoint annotations to identify branchpoint elements from primary genome sequence alone. Using this approach, we can accurately locate branchpoints elements in 85% of introns in current gene annotations. Consistent with branchpoints as basal genetic elements, we find our annotation is unbiased towards gene type and expression levels. A major fraction of introns was found to encode multiple branchpoints raising the prospect that mutational redundancy is encoded in key genes. We also confirmed all deleterious branchpoint mutations annotated in clinical variant databases, and further identified thousands of clinical and common genetic variants with similar predicted effects.ConclusionsWe propose the broad annotation of branchpoints constitutes a valuable resource for further investigations into the genetic encoding of splicing patterns, and interpreting the impact of common- and disease-causing human genetic variation on gene splicing.

Download Full-text

Emerging Technologies for Genome-Wide Profiling of DNA Breakage

Frontiers in Genetics ◽

10.3389/fgene.2020.610386 ◽

2021 ◽

Vol 11 ◽

Author(s):

Matthew J. Rybin ◽

Melina Ramic ◽

Natalie R. Ricciardi ◽

Philipp Kapranov ◽

Claes Wahlestedt ◽

...

Keyword(s):

Genome Instability ◽

Dna Double Strand Breaks ◽

Single Nucleotide ◽

Strand Breaks ◽

Single Strand Breaks ◽

Genome Wide ◽

A Genome ◽

Wide Scale ◽

Nucleotide Resolution ◽

Genomic Regions

Genome instability is associated with myriad human diseases and is a well-known feature of both cancer and neurodegenerative disease. Until recently, the ability to assess DNA damage—the principal driver of genome instability—was limited to relatively imprecise methods or restricted to studying predefined genomic regions. Recently, new techniques for detecting DNA double strand breaks (DSBs) and single strand breaks (SSBs) with next-generation sequencing on a genome-wide scale with single nucleotide resolution have emerged. With these new tools, efforts are underway to define the “breakome” in normal aging and disease. Here, we compare the relative strengths and weaknesses of these technologies and their potential application to studying neurodegenerative diseases.

Download Full-text