Cohesin-dependent chromosome loop extrusion is limited by transcription and stalled replication forks

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Abstract

Genome function depends on regulated chromosome folding, and loop extrusion by the protein complex cohesin is essential for this multilayered organization. The chromosomal positioning of cohesin is controlled by transcription, and the complex also localizes to stalled replication forks. However, the role of transcription and replication in chromosome looping remains unclear. Here, we show that reduction of chromosome-bound RNA polymerase weakens normal cohesin loop extrusion boundaries, allowing cohesin to form new long-range chromosome cis interactions. Stress response genes induced by transcription inhibition are also shown to act as new loop extrusion boundaries. Furthermore, cohesin loop extrusion during early S phase is jointly controlled by transcription and replication units. Together, the results reveal that replication and transcription machineries are chromosome-folding regulators that block the progression of loop-extruding cohesin, opening for new perspectives on cohesin’s roles in genome function and stability.

Abstract

Spatial genome organization is controlled by transcription and stalled replication machineries that limit cohesin loop extrusion.

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Most cited references 49

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Fast gapped-read alignment with Bowtie 2.

Ben Langmead, Steven L Salzberg (2013)

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Bo Li, Colin Dewey (2011)

Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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Author and article information

Contributors

Kristian Jeppsson:

ORCID: https://orcid.org/0000-0002-4085-9771

Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Toyonori Sakata: Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: ResourcesRole: ValidationRole: Visualization

Ryuichiro Nakato:

ORCID: https://orcid.org/0000-0003-3019-5817

Role: Data curationRole: Formal analysisRole: InvestigationRole: SoftwareRole: Visualization

Stefina Milanova:

ORCID: https://orcid.org/0000-0001-9865-857X

Role: Formal analysisRole: InvestigationRole: ResourcesRole: ValidationRole: VisualizationRole: Writing - review & editing

Katsuhiko Shirahige:

ORCID: https://orcid.org/0000-0002-7862-1144

Role: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: ValidationRole: Visualization

Camilla Björkegren:

ORCID: https://orcid.org/0000-0001-7354-9270

Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Journal

Journal ID (nlm-ta): Sci Adv

Journal ID (iso-abbrev): Sci Adv

Journal ID (publisher-id): sciadv

Journal ID (hwp): advances

Title: Science Advances

Publisher: American Association for the Advancement of Science

ISSN (Electronic): 2375-2548

Publication date Collection: June 2022

Publication date (Electronic, pub): 10 June 2022

Volume: 8

Issue: 23

Electronic Location Identifier: eabn7063

Affiliations

[1 ]Karolinska Institutet, Department of Biosciences and Nutrition, Neo, Hälsovägen 7c, 141 83 Huddinge, Sweden.

[2 ]Karolinska Institutet, Department of Cell and Molecular Biology, Biomedicum, Tomtebodavägen 16, 171 77 Stockholm, Sweden.

[3 ]Institute for Quantitative Bioscience, Tokyo University, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan.

Author notes

[* ]Corresponding author. Email: kristian.jeppsson@ 123456ki.se (K.J.); camilla.bjorkegren@ 123456ki.se (C.B.)

Author information

Kristian Jeppsson https://orcid.org/0000-0002-4085-9771

Ryuichiro Nakato https://orcid.org/0000-0003-3019-5817

Stefina Milanova https://orcid.org/0000-0001-9865-857X

Katsuhiko Shirahige https://orcid.org/0000-0002-7862-1144

Camilla Björkegren https://orcid.org/0000-0001-7354-9270

Article

Publisher ID: abn7063

DOI: 10.1126/sciadv.abn7063

PMC ID: 9187231

PubMed ID: 35687682

SO-VID: 3d24427e-29c2-4264-8036-b7b6fed983df

Copyright © Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 28 January 2021

Date revision received : 15 December 2021

Date accepted : 27 April 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100012538, Swedish Cancer Foundation;

Funded by: FundRef http://dx.doi.org/10.13039/501100003788, Helge Ax:son Johnsons Stiftelse;

Funded by: FundRef http://dx.doi.org/10.13039/501100006636, Swedish Research Council for Health, Working Life and Welfare;

Funded by: JSPS Postdoctoral Fellowship for Overseas Researchers;

Funded by: JST CREST;

Award ID: JPMJCR18S5

Funded by: the Scandinavia-Japan Sasakawa Foundation;

Funded by: Centre for Innovative Medicine (CIMED);

Funded by: KI-UTokyo collaboration;

Funded by: Grant-in-Aid for Scientific Research(S) from JSPS;

Award ID: 20H05686

Funded by: Grant-in-Aid for Transformative Research Areas from MEXT;

Award ID: 20H05940

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Copyeditor Vivian Hernandez

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Cohesin-dependent chromosome loop extrusion is limited by transcription and stalled replication forks

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Most cited references 49

STAR: ultrafast universal RNA-seq aligner.

Fast gapped-read alignment with Bowtie 2.

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

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Cited by 22

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