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      Model-based analysis of chromatin interactions from dCas9-Based CAPTURE-3C-seq

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          Abstract

          Deciphering long-range chromatin interactions is critical for understanding temporal and tissue-specific gene expression regulated by cis- and trans-acting factors. By combining the chromosome conformation capture (3C) and biotinylated dCas9 system, we previously established a method CAPTURE-3C-seq to unbiasedly identify high-resolution and locus-specific long-range DNA interactions. Here we present the statistical model and a flexible pipeline, C3S, for analysing CAPTURE-3C-seq or similar experimental data from raw sequencing reads to significantly interacting chromatin loci. C3S provides all steps for data processing, quality control and result illustration. It can automatically define the bin size based on the binding peak of the dCas9-targeted regions. Furthermore, it supports the analysis of intra- and inter-chromosomal interactions for different mammalian cell types. We successfully applied C3S across multiple datasets in human K562 cells and mouse embryonic stem cells (mESC) for detecting known and new chromatin interactions at multiple scales. Integrative and topological analysis of the interacted loci at the human β-globin gene cluster provides new insights into mechanisms in developmental gene regulation and network structure in local chromosomal architecture. Furthermore, computational results in mESCs reveal a role for chromatin interacting loops between enhancers and promoters in regulating alternative transcripts of the pluripotency gene OCT4.

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          Most cited references22

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          Chromosome Conformation Capture Carbon Copy (5C): a massively parallel solution for mapping interactions between genomic elements.

          Physical interactions between genetic elements located throughout the genome play important roles in gene regulation and can be identified with the Chromosome Conformation Capture (3C) methodology. 3C converts physical chromatin interactions into specific ligation products, which are quantified individually by PCR. Here we present a high-throughput 3C approach, 3C-Carbon Copy (5C), that employs microarrays or quantitative DNA sequencing using 454-technology as detection methods. We applied 5C to analyze a 400-kb region containing the human beta-globin locus and a 100-kb conserved gene desert region. We validated 5C by detection of several previously identified looping interactions in the beta-globin locus. We also identified a new looping interaction in K562 cells between the beta-globin Locus Control Region and the gamma-beta-globin intergenic region. Interestingly, this region has been implicated in the control of developmental globin gene switching. 5C should be widely applicable for large-scale mapping of cis- and trans- interaction networks of genomic elements and for the study of higher-order chromosome structure.
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            Robust 4C-seq data analysis to screen for regulatory DNA interactions.

            Regulatory DNA elements can control the expression of distant genes via physical interactions. Here we present a cost-effective methodology and computational analysis pipeline for robust characterization of the physical organization around selected promoters and other functional elements using chromosome conformation capture combined with high-throughput sequencing (4C-seq). Our approach can be multiplexed and routinely integrated with other functional genomics assays to facilitate physical characterization of gene regulation.
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              Interchromosomal associations between alternatively expressed loci.

              The T-helper-cell 1 and 2 (T(H)1 and T(H)2) pathways, defined by cytokines interferon-gamma (IFN-gamma) and interleukin-4 (IL-4), respectively, comprise two alternative CD4+ T-cell fates, with functional consequences for the host immune system. These cytokine genes are encoded on different chromosomes. The recently described T(H)2 locus control region (LCR) coordinately regulates the T(H)2 cytokine genes by participating in a complex between the LCR and promoters of the cytokine genes Il4, Il5 and Il13. Although they are spread over 120 kilobases, these elements are closely juxtaposed in the nucleus in a poised chromatin conformation. In addition to these intrachromosomal interactions, we now describe interchromosomal interactions between the promoter region of the IFN-gamma gene on chromosome 10 and the regulatory regions of the T(H)2 cytokine locus on chromosome 11. DNase I hypersensitive sites that comprise the T(H)2 LCR developmentally regulate these interchromosomal interactions. Furthermore, there seems to be a cell-type-specific dynamic interaction between interacting chromatin partners whereby interchromosomal interactions are apparently lost in favour of intrachromosomal ones upon gene activation. Thus, we provide an example of eukaryotic genes located on separate chromosomes associating physically in the nucleus via interactions that may have a function in coordinating gene expression.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: Writing – original draftRole: Writing – review & editing
                Role: SoftwareRole: Validation
                Role: Data curationRole: MethodologyRole: Resources
                Role: Funding acquisitionRole: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: Project administrationRole: Supervision
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                31 July 2020
                2020
                : 15
                : 7
                : e0236666
                Affiliations
                [1 ] Department of Molecular and Cellular Biosciences, Rowan University, Glassboro, New Jersey, United States of America
                [2 ] Department of Biological Sciences, Center for Systems Biology, University of Texas, Dallas, Richardson, Texas, United States of America
                [3 ] Department of Melanoma Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
                [4 ] Children’s Medical Center Research Institute, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
                [5 ] MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
                [6 ] Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Tsinghua University, Beijing, China
                [7 ] Department of Automation, Tsinghua University, Beijing, China
                Università degli Studi di Milano, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-6827-4321
                Article
                PONE-D-20-08710
                10.1371/journal.pone.0236666
                7394367
                32735574
                6bf62ba1-af09-48ea-b0f7-84b0ea1c5db1
                © 2020 Chen et al

                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 author and source are credited.

                History
                : 26 March 2020
                : 10 July 2020
                Page count
                Figures: 6, Tables: 0, Pages: 16
                Funding
                Funded by: Camden Health Research Initiative
                Award ID: Startup fund
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01MH109616
                Award Recipient : Michael Zhang
                Funded by: funder-id http://dx.doi.org/10.13039/100000968, American Heart Association;
                Award ID: 18POST34060219
                Award Recipient :
                MQZ was supported by the NIH grant R01MH109616, the Cecil H. and Ida Green Endowment, and the SKR&DPC grant (2017YFA0505503). XL was supported by the American Heart Association postdoctoral fellowship (18POST34060219).
                Categories
                Research Article
                Biology and Life Sciences
                Cell Biology
                Chromosome Biology
                Chromatin
                Biology and Life Sciences
                Genetics
                Epigenetics
                Chromatin
                Biology and Life Sciences
                Genetics
                Gene Expression
                Chromatin
                Biology and Life Sciences
                Genetics
                Genomics
                Animal Genomics
                Mammalian Genomics
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Biology and Life Sciences
                Cell Biology
                Chromosome Biology
                Chromosomes
                Autosomes
                Chromosome Pairs
                Biology and Life Sciences
                Cell Biology
                Chromosome Biology
                Chromosomes
                Chromosome Pairs
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Biology and life sciences
                Computational biology
                Genome analysis
                Chromatin immunoprecipitation
                ChIA PET
                Biology and life sciences
                Genetics
                Genomics
                Genome analysis
                Chromatin immunoprecipitation
                ChIA PET
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Physical Sciences
                Materials Science
                Materials
                Insulators
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                All relevant data are within the paper and its Supporting Information files.

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