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      Multi-omics analyses demonstrate a critical role for EHMT1 methyltransferase in transcriptional repression during oogenesis

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          Abstract

          EHMT1 (also known as GLP) is a multifunctional protein, best known for its role as an H3K9me1 and H3K9me2 methyltransferase through its reportedly obligatory dimerization with EHMT2 (also known as G9A). Here, we investigated the role of EHMT1 in the oocyte in comparison to EHMT2 using oocyte-specific conditional knockout mouse models ( Ehmt2 cKO, Ehmt1 cKO, Ehmt1/2 cDKO), with ablation from the early phase of oocyte growth. Loss of EHMT1 in Ehmt1 cKO and Ehmt1/2 cDKO oocytes recapitulated meiotic defects observed in the Ehmt2 cKO; however, there was a significant impairment in oocyte maturation and developmental competence in Ehmt1 cKO and Ehmt1/2 cDKO oocytes beyond that observed in the Ehmt2 cKO. Consequently, loss of EHMT1 in oogenesis results, upon fertilization, in mid-gestation embryonic lethality. To identify H3K9 methylation and other meaningful biological changes in each mutant to explore the molecular functions of EHMT1 and EHMT2, we performed immunofluorescence imaging, multi-omics sequencing, and mass spectrometry (MS)–based proteome analyses in cKO oocytes. Although H3K9me1 was depleted only upon loss of EHMT1, H3K9me2 was decreased, and H3K9me2-enriched domains were eliminated equally upon loss of EHMT1 or EHMT2. Furthermore, there were more significant changes in the transcriptome, DNA methylome, and proteome in Ehmt1/2 cDKO than Ehmt2 cKO oocytes, with transcriptional derepression leading to increased protein abundance and local changes in genic DNA methylation in Ehmt1/2 cDKO oocytes. Together, our findings suggest that EHMT1 contributes to local transcriptional repression in the oocyte, partially independent of EHMT2, and is critical for oogenesis and oocyte developmental competence.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Fast gapped-read alignment with Bowtie 2.

            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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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                January 2023
                January 2023
                : 33
                : 1
                : 18-31
                Affiliations
                [1 ]Epigenetics Programme, Babraham Institute, Cambridge CB22 3AT, United Kingdom;
                [2 ]Millennium Institute on Immunology and Immunotherapy, Laboratory of Integrative Biology (LIBi), Centro de Excelencia en Medicina Traslacional (CEMT), Scientific and Technological Bioresource Nucleus (BIOREN), Universidad de La Frontera, 4810296, Temuco, Chile;
                [3 ]Centre for Trophoblast Research, University of Cambridge, Cambridge CB2 3EG, United Kingdom;
                [4 ]Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridge CB2 3EG, United Kingdom;
                [5 ]Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 0RE, United Kingdom;
                [6 ]Bioinformatics Group, Babraham Institute, Cambridge CB22 3AT, United Kingdom;
                [7 ]Wellcome-MRC Institute of Metabolic Science–Metabolic Research Laboratories, Cambridge CB2 0QQ, United Kingdom
                Author notes
                Author information
                http://orcid.org/0000-0003-4706-4792
                http://orcid.org/0000-0002-4063-5575
                http://orcid.org/0000-0002-0034-8029
                http://orcid.org/0000-0002-3854-4084
                http://orcid.org/0000-0002-4650-8745
                http://orcid.org/0000-0002-9762-5634
                Article
                9509184
                10.1101/gr.277046.122
                9977154
                36690445
                dd3242b3-3cce-4b25-8d95-707676b9bb89
                © 2023 Demond et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 June 2022
                : 22 December 2022
                Page count
                Pages: 14
                Funding
                Funded by: Babraham Institute , doi 10.13039/100012067;
                Funded by: UK Biotechnology and Biological Sciences Research Council
                Award ID: BBS/E/B/000C0423
                Funded by: Medical Research Council Canada , doi 10.13039/501100007155;
                Award ID: MR/K011332/1
                Award ID: MR/S000437/1
                Funded by: Fondecyt
                Award ID: 3200676
                Funded by: Centre for Trophoblast Research
                Funded by: Cancer Research UK , doi 10.13039/501100000289;
                Funded by: CRUK , doi 10.13039/501100000289;
                Award ID: A29580
                Funded by: Cambridge Institute
                Categories
                Research

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