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      An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease

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          Abstract

          Protein aggregation is the hallmark of neurodegeneration but the molecular mechanisms underlying late-onset Alzheimer’s disease (AD) remain unclear. Here we integrated transcriptomic, proteomic and epigenomic analyses of post-mortem human brains to identify molecular pathways involved in AD. RNA-seq analysis revealed upregulation of transcription- and chromatin-related genes, including the histone acetyltransferases for H3K27ac and H3K9ac. An unbiased proteomic screening singled out H3K27ac and H3K9ac as main enrichments specific to AD. In turn, epigenomic profiling revealed gains of H3K27ac and H3K9ac linked to transcription, chromatin, and disease pathways in AD. Increasing genome-wide H3K27ac and H3K9ac in a fly model of AD exacerbated amyloid-β42-driven neurodegeneration. Together, these findings suggest that AD involves a reconfiguration of the epigenome, where H3K27ac and H3K9ac impact disease pathways by dysregulating transcription- and chromatin-gene feedback loops. The identification of this process highlights potential epigenetic strategies for early-stage disease treatment.

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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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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              Is Open Access

              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat Genet
                Nature genetics
                1061-4036
                1546-1718
                25 April 2021
                28 September 2020
                October 2020
                05 May 2021
                : 52
                : 10
                : 1024-1035
                Affiliations
                [1 ]Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
                [2 ]Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
                [3 ]Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
                [4 ]Current address: Department of Biochemistry, Albert Einstein College of Medicine, The Bronx, NY 10462, USA
                [5 ]Department of Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA
                [6 ]Department of Pathology & Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
                [7 ]Department of Chemistry, Department of Biochemistry and Molecular Biology, and Institute for Biophysical Dynamics, Howard Hughes Medical Institute, University of Chicago, Chicago, IL 60637, USA
                [8 ]Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
                Author notes

                Author contributions

                R.N., N.M.B. and S.L.B. conceived the project. R.N. performed ChIP-seq, RNA-seq, mass spectrometry experiments and supervised most of the analyses. Y.L. performed ChIP-seq and RNA-seq analyses. G.D. performed comparisons with published RNA-seq data and AD SNP enrichment analysis. S.D. performed mass spectrometry and STRING analysis. A.B., A.R.S. and O.S. performed fly experiments. R.N. extracted genomic DNA and J.N. performed 5hmC-Seal. X.C. processed 5hmC data. A.A.W. performed AD eQTL enrichment analysis. C.H., L.W., B.A.G., J.Q.T., N.M.B. and S.L.B. contributed to methodology and resources. R.N., N.M.B. and S.L.B. wrote the manuscript. All authors reviewed the manuscript and discussed the work.

                [* ] Corresponding author: bergers@ 123456pennmedicine.upenn.edu (S.L.B); nbonini@ 123456sas.upenn.edu (N.M.B)
                Article
                NIHMS1622396
                10.1038/s41588-020-0696-0
                8098004
                32989324
                73bba08d-c3b2-42b9-9fae-9c602f807359

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                Genetics
                Genetics

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