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      Engineering CpG island DNA methylation in pluripotent cells through synthetic CpG-free ssDNA insertion

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          Summary

          Cellular differentiation requires global changes to DNA methylation (DNAme), where it functions to regulate transcription factor, chromatin remodeling activity, and genome interpretation. Here, we describe a simple DNAme engineering approach in pluripotent stem cells (PSCs) that stably extends DNAme across target CpG islands (CGIs). Integration of synthetic CpG-free single-stranded DNA (ssDNA) induces a target CpG island methylation response (CIMR) in multiple PSC lines, Nt2d1 embryonal carcinoma cells, and mouse PSCs but not in highly methylated CpG island hypermethylator phenotype (CIMP)+ cancer lines. MLH1 CIMR DNAme spanned the CGI, was precisely maintained through cellular differentiation, suppressed MLH1 expression, and sensitized derived cardiomyocytes and thymic epithelial cells to cisplatin. Guidelines for CIMR editing are provided, and initial CIMR DNAme is characterized at TP53 and ONECUT1 CGIs. Collectively, this resource facilitates CpG island DNAme engineering in pluripotency and the genesis of novel epigenetic models of development and disease.

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          Highlights

          • Integration of CpG-free ssDNA at CGIs induces de novo DNAme in PSCs

          • CpG island methylation response (CIMR) DNA is functional through differentiation

          • CIMR guidelines facilitate genome-wide DNAme editing in pluripotency

          Motivation

          Pluripotent DNAme edits may be carried through differentiation, alter cell fate, and provide epigenetic models of development and disease, which may be used in future therapeutic DNAMe reversion strategies. To overcome barriers for DNAme editing across entire CpG islands, we investigated whether CpG island methylation responses occur in response to synthetic CpG-free ssDNA integration. We subsequently aimed to optimize CIMR DNAMe editing in pluripotency.

          Abstract

          Integration of a designer CG-free synthetic ssDNA can stimulate de novo DNAMe at target CGIs. Based on this observation, Tompkins et al. describe a genome-wide DNA methylation editing technique that spreads DNAme across target CpG islands (CGIs), is globally specific, and is functionally maintained through directed differentiation.

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          Most cited references60

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Contributors
                Journal
                Cell Rep Methods
                Cell Rep Methods
                Cell Reports Methods
                Elsevier
                2667-2375
                04 May 2023
                22 May 2023
                04 May 2023
                : 3
                : 5
                : 100465
                Affiliations
                [1 ]Department of Diabetes Complications and Metabolism, Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA 91010, USA
                [2 ]Integrative Genomics Core, City of Hope, Duarte, CA 91010, USA
                Author notes
                []Corresponding author jtompkins@ 123456coh.org
                [3]

                These authors contributed equally

                [4]

                These authors contributed equally

                [5]

                Lead contact

                Article
                S2667-2375(23)00084-X 100465
                10.1016/j.crmeth.2023.100465
                10261899
                37323577
                0335e331-a607-49bb-8007-86b11a1a1796
                © 2023 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 16 August 2022
                : 14 February 2023
                : 12 April 2023
                Categories
                Report

                epigenetic editing,pluripotent stem cells,esc,dna methylation,cg island,cas9,mlh1,cardiomyocyte,thymic epithelial cell,p53

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