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      Novel Alzheimer risk genes determine the microglia response to amyloid‐β but not to TAU pathology

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          Abstract

          Polygenic risk scores have identified that genetic variants without genome‐wide significance still add to the genetic risk of developing Alzheimer's disease ( AD). Whether and how subthreshold risk loci translate into relevant disease pathways is unknown. We investigate here the involvement of AD risk variants in the transcriptional responses of two mouse models: APPswe/ PS1 L166P and Thy‐ TAU22. A unique gene expression module, highly enriched for AD risk genes, is specifically responsive to Aβ but not TAU pathology. We identify in this module 7 established AD risk genes ( APOE , CLU , INPP5D , CD33, PLCG2 , SPI1, and FCER1G ) and 11 AD GWAS genes below the genome‐wide significance threshold ( GPC2, TREML2, SYK, GRN, SLC2A5, SAMSN1, PYDC1, HEXB, RRBP1, LYN, and BLNK), that become significantly upregulated when exposed to Aβ. Single microglia sequencing confirms that Aβ, not TAU, pathology induces marked transcriptional changes in microglia, including increased proportions of activated microglia. We conclude that genetic risk of AD functionally translates into different microglia pathway responses to Aβ pathology, placing AD genetic risk downstream of the amyloid pathway but upstream of TAU pathology.

          Abstract

          It is unknown how genetic risk of Alzheimer's disease (AD) manifests itself at the molecular and the cellular level in the brain. Analysis of a TAUtg and an APPtg mouse models show that genetic risk of AD is mainly reflected in the transcriptional responses of microglia to amyloid‐β pathology.

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          Most cited references71

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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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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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                b.strooper@ukdri.ucl.ac.uk
                mark.fiers@kuleuven.vib.be
                Journal
                EMBO Mol Med
                EMBO Mol Med
                10.1002/(ISSN)1757-4684
                EMMM
                embomm
                EMBO Molecular Medicine
                John Wiley and Sons Inc. (Hoboken )
                1757-4676
                1757-4684
                17 January 2020
                06 March 2020
                : 12
                : 3 ( doiID: 10.1002/emmm.v12.3 )
                : e10606
                Affiliations
                [ 1 ] VIB Center for Brain & Disease Research Leuven Belgium
                [ 2 ] Laboratory for the Research of Neurodegenerative Diseases Department of Neurosciences Leuven Brain Institute (LBI) KU Leuven (University of Leuven) Leuven Belgium
                [ 3 ] INSERM, CHU Lille, LabEx DISTALZ, UMR‐S 1172, Alzheimer & Tauopathies Université Lille Lille France
                [ 4 ] UK Dementia Research Institute University College London London UK
                Author notes
                [*] [* ] Corresponding author. Tel: +32 4957 71044; E‐mail: b.strooper@ 123456ukdri.ucl.ac.uk

                Corresponding author. Tel: +32 4944 95150; E‐mail: mark.fiers@ 123456kuleuven.vib.be

                [†]

                These authors contributed equally to this work as first authors

                Author information
                https://orcid.org/0000-0001-9233-972X
                https://orcid.org/0000-0002-7046-3348
                https://orcid.org/0000-0001-5564-8179
                https://orcid.org/0000-0003-1164-4306
                https://orcid.org/0000-0001-5691-431X
                https://orcid.org/0000-0001-5455-5819
                https://orcid.org/0000-0001-5694-2409
                Article
                EMMM201910606
                10.15252/emmm.201910606
                7059012
                31951107
                9319996f-757a-458d-a369-d788762059fa
                © 2020 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 March 2019
                : 20 December 2019
                : 20 December 2019
                Page count
                Figures: 9, Tables: 0, Pages: 18, Words: 14740
                Funding
                Funded by: Leuven Universitair Fonds (Opening the Future Campaign)
                Funded by: Alzheimer Research Foundation
                Award ID: P16017
                Funded by: Fonds voor Wetenschappelijk Onderzoek (FWO) , open-funder-registry 10.13039/501100003130;
                Award ID: 1139520N
                Award ID: 12A5316N
                Award ID: G0C9219N
                Funded by: Alzheimer's Association (AA) , open-funder-registry 10.13039/100000957;
                Award ID: AARF‐16‐442853
                Funded by: KU Leuven (Katholieke Universiteit Leuven) , open-funder-registry 10.13039/501100004040;
                Funded by: Vlaams Instituut voor Biotechnologie (VIB) , open-funder-registry 10.13039/501100004727;
                Funded by: Vlaamse Overheid (Government of Flanders) , open-funder-registry 10.13039/501100002913;
                Funded by: Vlaams Initiatief voor Netwerken voor Dementie Onderzoek (VIND)
                Funded by: Geneeskundige Stichting Koningin Elisabeth
                Funded by: Bax‐Vanluffelen Chair for Alzheimer's Disease
                Funded by: Agence National Recherche
                Award ID: ANR‐16‐COEN‐0007
                Funded by: UK Research and Innovation|MRC|Medical Research Foundation , open-funder-registry 10.13039/100014013;
                Funded by: Alzheimer Society , open-funder-registry 10.13039/501100000143;
                Funded by: Alzheimer Research UK
                Categories
                Article
                Articles
                Custom metadata
                2.0
                06 March 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.7 mode:remove_FC converted:06.03.2020

                Molecular medicine
                alzheimer's disease,genetic risk,microglia,rna‐seq,single cell,chromatin, epigenetics, genomics & functional genomics,neuroscience

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