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      A Microglial Signature Directing Human Aging and Neurodegeneration-Related Gene Networks

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          Abstract

          Aging is regarded as a major risk factor for neurodegenerative diseases. Thus, a better understanding of the similarities between the aging process and neurodegenerative diseases at the cellular and molecular level may reveal better understanding of this detrimental relationship. In the present study, we mined publicly available gene expression datasets from healthy individuals and patients affected by neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease) across a broad age spectrum and compared those with mouse aging and mouse cell-type specific gene expression profiles. We performed weighted gene co-expression network analysis (WGCNA) and found a gene network strongly related with both aging and neurodegenerative diseases. This network was significantly enriched with a microglial signature as imputed from cell type-specific sequencing data. Since mouse models are extensively used for the study of human diseases, we further compared these human gene regulatory networks with age-specific mouse brain transcriptomes. We discovered significantly preserved networks with both human aging and human disease and identified 17 shared genes in the top-ranked immune/microglia module, among which we found five human hub genes TYROBP, FCER1G, ITGB2, MYO1F, PTPRC, and two mouse hub genes Trem2 and C1qa. Taken together, these results support the hypothesis that microglia are key players involved in human aging and neurodegenerative diseases, and suggest that mouse models should be appropriate for studying these microglial changes in human.

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          Most cited references23

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          FQC Dashboard: integrates FastQC results into a web-based, interactive, and extensible FASTQ quality control tool

          Abstract Summary FQC is software that facilitates quality control of FASTQ files by carrying out a QC protocol using FastQC, parsing results, and aggregating quality metrics into an interactive dashboard designed to richly summarize individual sequencing runs. The dashboard groups samples in dropdowns for navigation among the data sets, utilizes human-readable configuration files to manipulate the pages and tabs, and is extensible with CSV data. Availability and implementation FQC is implemented in Python 3 and Javascript, and is maintained under an MIT license. Documentation and source code is available at: https://github.com/pnnl/fqc. Contact joseph.brown@pnnl.gov
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            Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways.

            Because mouse models play a crucial role in biomedical research related to the human nervous system, understanding the similarities and differences between mouse and human brain is of fundamental importance. Studies comparing transcription in human and mouse have come to varied conclusions, in part because of their relatively small sample sizes or underpowered methodologies. To better characterize gene expression differences between mouse and human, we took a systems-biology approach by using weighted gene coexpression network analysis on more than 1,000 microarrays from brain. We find that global network properties of the brain transcriptome are highly preserved between species. Furthermore, all modules of highly coexpressed genes identified in mouse were identified in human, with those related to conserved cellular functions showing the strongest between-species preservation. Modules corresponding to glial and neuronal cells were sufficiently preserved between mouse and human to permit identification of cross species cell-class marker genes. We also identify several robust human-specific modules, including one strongly correlated with measures of Alzheimer disease progression across multiple data sets, whose hubs are poorly-characterized genes likely involved in Alzheimer disease. We present multiple lines of evidence suggesting links between neurodegenerative disease and glial cell types in human, including human-specific correlation of presenilin-1 with oligodendrocyte markers, and significant enrichment for known neurodegenerative disease genes in microglial modules. Together, this work identifies convergent and divergent pathways in mouse and human, and provides a systematic framework that will be useful for understanding the applicability of mouse models for human brain disorders.
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              The iPlant Collaborative: Cyberinfrastructure for Plant Biology

              The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                24 January 2019
                2019
                : 13
                : 2
                Affiliations
                [1] 1Health Informatics Advanced Science Masters Program, Arizona State University , Tempe, AZ, United States
                [2] 2Department of Neurology, University of California, Los Angeles , Los Angeles, CA, United States
                [3] 3Department of Bioinformatics, University of California, Los Angeles , Los Angeles, CA, United States
                [4] 4Neural Regeneration Group, Institute of Reconstructive Neurobiology, University of Bonn , Bonn, Germany
                [5] 5Institute for Medical Informatics and Biometry, Faculty of Medicine “Carl Gustav Carus”, TU Dresden , Dresden, Germany
                [6] 6Allen Institute for Brain Science , Seattle, WA, United States
                [7] 7Department of Translational Brain Research, German Center for Neurodegenerative Diseases , Munich, Germany
                [8] 8Center for Neuropathology and Prion Research, Ludwig Maximilian University of Munich , Munich, Germany
                [9] 9Department of Ophthalmology, Emory University , Atlanta, GA, United States
                Author notes

                Edited by: Rupert W. Overall, German Center for Neurodegenerative Diseases (DZNE), Germany

                Reviewed by: Björn Spittau, Universitätsmedizin Rostock, Germany; Khyobeni Mozhui, University of Tennessee Health Science Center (UTHSC), United States; Michael Oldham, University of California, San Francisco, United States

                *Correspondence: Shradha Mukherjee, smukher2@ 123456gmail.com Felix L. Struebing, felix.struebing@ 123456med.uni-muenchen.de

                These authors have contributed equally to this work

                This article was submitted to Neurogenomics, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2019.00002
                6353788
                30733664
                33abab53-7700-4d2d-aee8-372fb20ca86e
                Copyright © 2019 Mukherjee, Klaus, Pricop-Jeckstadt, Miller and Struebing.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 August 2018
                : 03 January 2019
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 59, Pages: 12, Words: 0
                Funding
                Funded by: Horizon 2020 Framework Programme 10.13039/100010661
                Categories
                Neuroscience
                Original Research

                Neurosciences
                aging,neurodegeneration,microglia,alzheimer,parkinson,bioinformatics,gene networks,wgcna
                Neurosciences
                aging, neurodegeneration, microglia, alzheimer, parkinson, bioinformatics, gene networks, wgcna

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