Genome-wide and sister chromatid-resolved profiling of protein occupancy in replicated chromatin with ChOR-seq and SCAR-seq

2021 ◽  
Author(s):  
Nataliya Petryk ◽  
Nazaret Reverón-Gómez ◽  
Cristina González-Aguilera ◽  
Maria Dalby ◽  
Robin Andersson ◽  
...  
Keyword(s):  
Sister Chromatid ◽  
Genome Wide ◽  
Protein Occupancy

scholarly journals Predicting context specific enhancer-promoter interactions from ChIP-Seq time course data

2017 ◽  
Author(s):  
Tomasz Dzida ◽  
Mudassar Iqbal ◽  
Iryna Charapitsa ◽  
George Reid ◽  
Henk Stunnenberg ◽  
...  
Keyword(s):  
Estrogen Receptor Alpha ◽  
Time Course ◽  
Target Genes ◽  
Mcf7 Cells ◽  
Genome Wide ◽  
A Genome ◽  
Machine Learning Approach ◽  
Protein Occupancy ◽  
Context Specific ◽  
Over Time

We have developed a machine learning approach to predict context specific enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

scholarly journals Condensin facilitates sister chromatid separation in actively transcribed DNA regions by relieving the obstructive effect of transcription

2019 ◽  
Author(s):  
Norihiko Nakazawa ◽  
Orie Arakawa ◽  
Mitsuhiro Yanagida
Keyword(s):  
Sister Chromatid ◽  
Gene Locus ◽  
Time Lapse ◽  
Chromosomal Dna ◽  
Ssdna Binding ◽  
Genome Wide ◽  
Sister Chromatid Separation ◽  
Mitotic Chromosome Condensation ◽  
Ssdna Binding Proteins

AbstractThe evolutionarily conserved protein complex, condensin, is central to chromosome dynamics, including mitotic chromosome condensation and segregation. Genome-wide localization of condensin is correlated with transcriptional activity; however, the significance of condensin accumulation in transcribed regions remains unclear. Here, we demonstrate that condensin relieves the obstructive effect of mitotic transcription on sister chromatid separation in fission yeast, Schizosaccharomyces pombe. Time-lapse visualization of sister chromatid DNA separation revealed that mutant condensin causes delayed segregation specifically at mitotically transcribed, condensin-bound gene locus, ecm33+. Contrarily, the delay was abolished by transcriptional shut-off of the actively transcribed gene. We also showed that delayed separation at a heat shock-inducible gene locus, ssa1+, in condensin mutants was significantly alleviated by deletion of the gene. Since condensin has ability to remove ssDNA-binding proteins and RNA from unwound ssDNAs or DNA-RNA hybrids in vitro, we propose a model that condensin-mediated removal of mitotic transcripts from chromosomal DNA is the primary mechanism of sister chromatid separation.

scholarly journals Cohesin recruits the Esco1 acetyltransferase genome wide to repress transcription and promote cohesion in somatic cells

2015 ◽  
Vol 112 (36) ◽  
pp. 11270-11275 ◽  
Author(s):  
Sadia Rahman ◽  
Mathew J. K. Jones ◽  
Prasad V. Jallepalli
Keyword(s):  
Cell Cycle ◽  
Gene Silencing ◽  
Sister Chromatid ◽  
Target Genes ◽  
Somatic Cells ◽  
S Phase ◽  
Cohesin Complex ◽  
Genome Wide ◽  
Chromatid Cohesion ◽  
Eukaryotic Genomes

The cohesin complex links DNA molecules and plays key roles in the organization, expression, repair, and segregation of eukaryotic genomes. In vertebrates the Esco1 and Esco2 acetyltransferases both modify cohesin’s Smc3 subunit to establish sister chromatid cohesion during S phase, but differ in their N-terminal domains and expression during development and across the cell cycle. Here we show that Esco1 and Esco2 also differ dramatically in their interaction with chromatin, as Esco1 is recruited by cohesin to over 11,000 sites, whereas Esco2 is infrequently enriched at REST/NRSF target genes. Esco1’s colocalization with cohesin occurs throughout the cell cycle and depends on two short motifs (the A-box and B-box) present in and unique to all Esco1 orthologs. Deleting either motif led to the derepression of Esco1-proximal genes and functional uncoupling of cohesion from Smc3 acetylation. In contrast, other mutations that preserved Esco1’s recruitment separated its roles in cohesion establishment and gene silencing. We conclude that Esco1 uses cohesin as both a substrate and a scaffold for coordinating multiple chromatin-based transactions in somatic cells.

scholarly journals Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3742 ◽  
Author(s):  
Tomasz Dzida ◽  
Mudassar Iqbal ◽  
Iryna Charapitsa ◽  
George Reid ◽  
Henk Stunnenberg ◽  
...  
Keyword(s):  
Estrogen Receptor Alpha ◽  
Time Course ◽  
Target Genes ◽  
Mcf7 Cells ◽  
Genome Wide ◽  
A Genome ◽  
Machine Learning Approach ◽  
Protein Occupancy ◽  
Time Course Data ◽  
Over Time

We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

scholarly journals Predicting context specific enhancer-promoter interactions from ChIP-Seq time course data

2017 ◽  
Author(s):  
Tomasz Dzida ◽  
Mudassar Iqbal ◽  
Iryna Charapitsa ◽  
George Reid ◽  
Henk Stunnenberg ◽  
...  
Keyword(s):  
Estrogen Receptor Alpha ◽  
Time Course ◽  
Target Genes ◽  
Mcf7 Cells ◽  
Genome Wide ◽  
A Genome ◽  
Machine Learning Approach ◽  
Protein Occupancy ◽  
Context Specific ◽  
Over Time

We have developed a machine learning approach to predict context specific enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

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