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      Analysis of transcription factor- and ncRNA-mediated potential pathogenic gene modules in Alzheimer’s disease

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          Abstract

          Alzheimer’s disease (AD) is a progressive neurodegenerative disease that ranks as the fourth most common cause of death in developed countries. In our study, genes differentially expressed between AD and healthy individuals were identified and used to construct protein-protein interaction (PPI) networks. The AD-related PPI network was used to identify functional modules, and enrichment analysis showed that they were significantly involved in “Alzheimer’s disease”, “apoptosis”, and related pathways. We predicted non-coding RNAs and transcription factors that may regulate the functional modules. The expression of hub genes and transcription factors was validated in an independent data set. The results in this study provide several candidates for further research on mechanisms of AD pathogenesis.

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          Detecting overlapping protein complexes in protein-protein interaction networks.

          Tamás Nepusz, Haiyuan Yu, Alberto Paccanaro (2012)
          We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.
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            Genetic Evidence Implicates the Immune System and Cholesterol Metabolism in the Aetiology of Alzheimer's Disease

            Lesley Jones, Peter A. Holmans, Marian Hamshere … (2010)
            Background Late Onset Alzheimer's disease (LOAD) is the leading cause of dementia. Recent large genome-wide association studies (GWAS) identified the first strongly supported LOAD susceptibility genes since the discovery of the involvement of APOE in the early 1990s. We have now exploited these GWAS datasets to uncover key LOAD pathophysiological processes. Methodology We applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS dataset. The principal findings were then tested in an independent GWAS dataset. Principal Findings We found a significant overrepresentation of association signals in pathways related to cholesterol metabolism and the immune response in both of the two largest genome-wide association studies for LOAD. Significance Processes related to cholesterol metabolism and the innate immune response have previously been implicated by pathological and epidemiological studies of Alzheimer's disease, but it has been unclear whether those findings reflected primary aetiological events or consequences of the disease process. Our independent evidence from two large studies now demonstrates that these processes are aetiologically relevant, and suggests that they may be suitable targets for novel and existing therapeutic approaches.
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              RAID v2.0: an updated resource of RNA-associated interactions across organisms

              Qi-ying Yi, Yue Zhao, Chunhua Li … (2016)
              With the development of biotechnologies and computational prediction algorithms, the number of experimental and computational prediction RNA-associated interactions has grown rapidly in recent years. However, diverse RNA-associated interactions are scattered over a wide variety of resources and organisms, whereas a fully comprehensive view of diverse RNA-associated interactions is still not available for any species. Hence, we have updated the RAID database to version 2.0 (RAID v2.0, www.rna-society.org/raid/) by integrating experimental and computational prediction interactions from manually reading literature and other database resources under one common framework. The new developments in RAID v2.0 include (i) over 850-fold RNA-associated interactions, an enhancement compared to the previous version; (ii) numerous resources integrated with experimental or computational prediction evidence for each RNA-associated interaction; (iii) a reliability assessment for each RNA-associated interaction based on an integrative confidence score; and (iv) an increase of species coverage to 60. Consequently, RAID v2.0 recruits more than 5.27 million RNA-associated interactions, including more than 4 million RNA–RNA interactions and more than 1.2 million RNA–protein interactions, referring to nearly 130 000 RNA/protein symbols across 60 species.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Aging (Albany NY)
                Journal ID (iso-abbrev): Aging (Albany NY)
                Journal ID (publisher-id): Aging
                Title: Aging (Albany NY)
                Publisher: Impact Journals
                ISSN (Electronic): 1945-4589
                Publication date Collection: 31 August 2019
                Publication date (Electronic): 16 August 2019
                Volume: 11
                Issue: 16
                Pages: 6109-6119
                Affiliations
                [1 ]Department of Neurology, Youjiang Medical University for Nationalities, Baise, Guangxi 533000, People’s Republic of China
                [2 ]Department of Nephrology, Youjiang Medical University for Nationalities, Baise, Guangxi 533000, People’s Republic of China
                [3 ]Department of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 533022, People’s Republic of China
                Author notes
                [*]

                Equal contribution

                Correspondence to: Donghua Zou; email: danvor0922@hotmail.com
                Correspondence to: Xuebin Li; email: yyfylxb@163.com
                Article
                Publisher ID: 102169 Publisher ID: 102169
                DOI: 10.18632/aging.102169
                PMC ID: 6738443
                PubMed ID: 31422384
                SO-VID: 814ebf9a-b28c-42ed-aa7a-4a99811a781c
                Copyright © Copyright © 2019 Zou et al.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 3.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 18 May 2019
                Date accepted : 05 August 2019
                Categories
                Subject: Research Paper

                ScienceOpen disciplines: Cell biology
                Keywords: alzheimer’s disease,differential expression analysis,modularization,protein-protein interaction network
                Data availability:
                ScienceOpen disciplines: Cell biology
                Keywords: alzheimer’s disease, differential expression analysis, modularization, protein-protein interaction network

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