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      CHD1 controls H3.3 incorporation in adult brain chromatin to maintain metabolic homeostasis and normal lifespan

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          SUMMARY

          The ATP-dependent chromatin remodeling factor CHD1 is essential for the assembly of variant histone H3.3 into paternal chromatin during sperm chromatin remodeling in fertilized eggs. It remains unclear, however, if CHD1 has a similar role in normal diploid cells. Using a specifically tailored quantitative mass spectrometry approach, we show that Chd1 disruption results in reduced H3.3 levels in heads of Chd1 mutant flies. Chd1 deletion perturbs brain chromatin structure in a similar way as H3.3 deletion and leads to global de-repression of transcription. The physiological consequences are reduced food intake, metabolic alterations, and shortened lifespan. Notably, brain-specific CHD1 expression rescues these phenotypes. We further demonstrate a strong genetic interaction between Chd1 and H3.3 chaperone Hira. Thus, our findings establish CHD1 as a factor required for the assembly of H3.3-containing chromatin in adult cells and suggest a crucial role for CHD1 in the brain as a regulator of organismal health and longevity.

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          In brief

          In postmitotic brain cells, histones lost during transcription must be replenished by replication-independent mechanisms. Schoberleitner et al. show that the chromatin remodeling factor CHD1 is involved in this process. CHD1 loss results in reduced histone H3.3 levels, global chromatin perturbation, transcriptional dysregulation, and defects in feeding behavior, metabolism, and lifespan.

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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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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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              Fast and accurate short read alignment with Burrows–Wheeler transform

              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                22 October 2021
                05 October 2021
                22 November 2021
                : 37
                : 1
                : 109769
                Affiliations
                [1 ]Institute of Molecular Biology, Biocenter, Medical University of Innsbruck, Innsbruck 6020, Austria
                [2 ]Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
                [3 ]Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck 6020, Austria
                [4 ]Institute of Human Genetics, Medical University of Innsbruck, Innsbruck 6020, Austria
                [5 ]Department of Biotechnology and Biochemistry, Cinvestav Unidad Irapuato, Irapuato 36824, Mexico
                [6 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                I.S., J.S., and A.L. conceived the project and designed the experiments. I.S., A.H., J.S., K.P., and V.P. performed phenotype characterization experiments. I.B., D.R., and L.R. analyzed NGS data. G.O., D.C.G., M.A.K., and R.W. performed metabolite mass spec experiments and data analysis. M.B. isolated histones. E.N.A. and D.V.F. developed H3.3 quantification by MRM-HR. A.L. performed ATAC-seq experiments. A.L., D.V.F., and I.S. analyzed the data and wrote the manuscript with help from I.B., M.A.K., R.K., and E.N.A.

                Article
                NIHMS1746301
                10.1016/j.celrep.2021.109769
                8607513
                34610319
                16119ce3-5672-4c77-8400-9098f93b847e

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

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                Cell biology
                Cell biology

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