The replication initiation determinant protein (RepID) modulates replication by recruiting CUL4 to chromatin

Jang, Sang‐Min; Ya, Zhang; Utani, Koichi; Fu, Haiqing; Redon, Christophe E.; Marks, Anna B.; Smith, Owen K.; Redmond, Catherine J.; Baris, Adrian; Tulchinsky, Danielle A.; Aladjem, Mirit I.

doi:10.1038/s41467-018-05177-6

Cited by 45 publications

(65 citation statements)

References 68 publications

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“…To that end, we have explored several avenues for triggering re-replication. Consistent with our previous observations, partial genome re-replication was triggered by inhibiting SKP2, a key component of CRL1, in a RepID-deficient cell background 26 ( Supplementary Fig. 1a top).…”

Section: Introductionsupporting

confidence: 92%

Dynamics of replication origin over-activation

Redon

Utani

et al. 2020

Preprint

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We determined replication patterns in cancer cells in which the controls that normally prevent excess replication were disrupted ("re-replicating cells"). Single-fiber analyses suggested that replication origins were activated at a higher frequency in re-replicating cells. However, nascent strand sequencing demonstrated that re-replicating cells utilized the same pool of potential replication origins as normally replicating cells. Surprisingly, rereplicating cells exhibited a skewed initiation frequency correlating with replication timing.These patterns differed from the replication profiles observed in non-re-replicating cells exposed to replication stress, which activated a novel group of dormant origins not typically activated during normal mitotic growth. Hence, disruption of the molecular interactions that regulates origin initiation can activate two distinct pools of potential replication origins: rereplicating cells over-activate flexible origins while replication stress in normal mitotic growth activates dormant origins.Our previous studies have shown that RepID, a replicator-specific binding protein essential for site-specific initiation of DNA replication in mammalian cells 25 , recruits CRL4 to chromatin in a PCNA-independent manner prior to the onset of S phase and promotes CDT1 degradation.RepID deficiency is associated with delayed CDT1 degradation, resulting in limited genome rereplication 26 . Partial genome re-replication can also be caused by inhibition of DOT1L, a methyltransferase which catalyzes the demethylation of histone H3 on lysine 79 (H3K79Me2), thereby removing a histone modification typically associated with a group of replication origins 27 . Because massive re-replication can drive cell death specifically in checkpointcompromised cancer cells, both CDT1 stabilization by inactivation of ubiquitin-mediated degradation and inhibition of DOT1L are currently being explored as novel anti-cancer therapeutic strategies [28][29][30][31][32][33] .Given that genome re-replication is a common avenue to genomic instability and considering its potential as a strategy for chemotherapy, it is important to understand in detail its mechanics and downstream consequences. Here, we report that re-replication exhibits aberrant replication fork dynamics and occurs more slowly than the routine genome duplication that takes place during normal growth. The consequences of the persistent presence of pre-replication complexes on chromatin include massive DNA damage and the induction of senescence. Unlike other instances of replication origin over-activation, such as when replication slows down as a result of nucleotide depletion or in cells exposed to DNA damaging conditions, the re-replication process preferentially utilizes a subset of the replication origin pool typically used during normal growth. Results:

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Section: Introductionsupporting

confidence: 92%

Dynamics of replication origin over-activation

Redon

Utani

et al. 2020

Preprint

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“…Improved technologies and decreasing sequencing costs enable in-depth analysis of chromatin and gene expression changes for genome-wide comparisons. These integrative multi-omics studies help to elucidate the functionalities of coding and non-coding parts of the genome, their influence on development of complex disease such as cancers [1][2][3][4], and their translational implications [5][6][7]. Currently many studies focus on identifying protein-DNA interactions through sequencing (ChIP-seq) [8,9].…”

Section: Introductionmentioning

confidence: 99%

BAMscale: quantification of next-generation sequencing peaks and generation of scaled coverage tracks

Gross¹,

Alvarez

Murai³

et al. 2020

Preprint

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Background: Next-generation sequencing allows genome-wide analysis of changes in chromatin states and gene expression. Data analysis of these increasingly used methods either requires multiple analysis steps, or extensive computational time. We sought to develop a tool for rapid quantification of sequencing peaks, and an efficient method to produce coverage tracks for accurate visualization that can be intuitively interpreted by experimentalists with minimal bioinformatics background. We demonstrate its strength by integrating data from several types of sequencing approaches. Results: We have developed BAMscale, a one-step tool that processes a wide set of sequencing datasets. To demonstrate the usefulness of BAMscale, we analyzed multiple sequencing datasets from chromatin immunoprecipitation sequencing (ChIP-seq) data, chromatin state change data (Assay for Transposase-Accessible Chromatin using sequencing: ATAC-seq, DNA double strand break mapping sequencing: END-seq), DNA replication data (Okazaki fragments sequencing: OK-seq, Nascent-strand sequencing: NS-seq, single-cell replication timing sequencing: scRepli-seq) and RNA-seq data. The outputs consist of raw and normalized peak scores (multiple normalizations) in text format and scaled bigWig coverage tracks that are directly accessible to data visualization programs. Our tool can effectively analyze large sequencing datasets (~100Gb size) in minutes, outperforming currently available tools.Conclusions: BAMscale is a tool that can be used to accurately quantify and normalize identified peaks directly from BAM files, as well as create coverage tracks for visualization in genome browsers. BAMScale can be implemented for a wide set of methods for calculating coverage tracks such as ChIP-seq / ATAC-seq, as well as splice aware RNA-seq, END-seq and OK-seq for which no dedicated software is available. BAMscale is freely available on github (https://github.com/ncbi/BAMscale).

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“…Improved technologies and decreasing sequencing costs enable in-depth analyses of chromatin and gene expression changes for genome-wide comparisons. These integrative multi-omics studies elucidate the functionalities of coding and non-coding parts of the genome, their influence on development of complex disease such as cancers [1][2][3][4] and their translational implications [5][6][7]. Currently many studies focus on identifying protein-DNA interactions through sequencing (ChIP-seq) [8,9].…”

Section: Introductionmentioning

confidence: 99%

BAMscale: quantification of next-generation sequencing peaks and generation of scaled coverage tracks

Gross¹,

Alvarez

Murai³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background Next-generation sequencing allows genome-wide analysis of changes in chromatin states and gene expression. Data analysis of these increasingly used methods either requires multiple analysis steps, or extensive computational time. We sought to develop a tool for rapid quantification of sequencing peaks from diverse experimental sources and an efficient method to produce coverage tracks for accurate visualization that can be intuitively displayed and interpreted by experimentalists with minimal bioinformatics background. We demonstrate its strength and usability by integrating data from several types of sequencing approaches. ResultsWe have developed BAMscale , a one-step tool that processes a wide set of sequencing datasets. To demonstrate the usefulness of BAMscale , we analyzed multiple sequencing datasets from chromatin immunoprecipitation sequencing data (ChIP-seq), chromatin state change data (Assay for Transposase-Accessible Chromatin using sequencing: ATAC-seq, DNA double-strand break mapping sequencing: END-seq), DNA replication data (Okazaki fragments sequencing: OK-seq, Nascent-strand sequencing: NS-seq, single-cell replication timing sequencing: scRepli-seq) and RNAseq data. The outputs consist of raw and normalized peak scores (multiple normalizations) in text format and scaled bigWig coverage tracks that are directly accessible to data visualization programs.BAMScale also includes a visualization module facilitating direct, on-demand quantitative peak comparisons that can be used by experimentalists. Our tool can effectively analyze large sequencing datasets (~100Gb size) in minutes, outperforming currently available tools.Conclusions BAMscale accurately quantifies and normalizes identified peaks directly from BAM files, and creates coverage tracks for visualization in genome browsers. BAMScale can be implemented for a wide set of methods for calculating coverage tracks, including ChIP-seq and ATAC-seq, as well as methods that currently require specialized, separate tools for analyses, such as splice aware RNA-seq, END-seq and OK-seq for which no dedicated software is available. BAMscale is freely available on github ( https://github.com/ncbi/BAMscale ).

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The replication initiation determinant protein (RepID) modulates replication by recruiting CUL4 to chromatin

Cited by 45 publications

References 68 publications

Dynamics of replication origin over-activation

Dynamics of replication origin over-activation

BAMscale: quantification of next-generation sequencing peaks and generation of scaled coverage tracks

BAMscale: quantification of next-generation sequencing peaks and generation of scaled coverage tracks

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