An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases
Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strat...
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description | Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration. |
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Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0143045</identifier><identifier>PMID: 26575483</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adherens Junctions - metabolism ; Alzheimer Disease - genetics ; Alzheimer Disease - metabolism ; Alzheimer's disease ; Alzheimers disease ; Apoptosis ; Blood-brain barrier ; Cell interactions ; Chronic illnesses ; Cluster Analysis ; Clustering ; Cognitive ability ; Disorders ; Gene expression ; Genes ; Genetic Predisposition to Disease ; Humans ; Huntington Disease - genetics ; Huntington Disease - metabolism ; Huntington's disease ; Huntingtons disease ; Infectious diseases ; Laboratories ; Medical treatment ; Metabolic Networks and Pathways - genetics ; Modules ; Movement disorders ; Neurodegeneration ; Neurodegenerative diseases ; Neurological diseases ; Neurosciences ; Parkinson Disease - genetics ; Parkinson Disease - metabolism ; Parkinson's disease ; Parkinsons disease ; Pathways ; Probability ; Process engineering ; Protein interaction ; Protein Interaction Maps ; Proteins ; Tight junctions ; Tight Junctions - metabolism ; Transcriptome</subject><ispartof>PloS one, 2015-11, Vol.10 (11), p.e0143045-e0143045</ispartof><rights>2015 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Li et al 2015 Li et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-d90bba232fcb01e3e84541060efb0d3d3c6d753125ff4dc945f41e8e9daa4bc33</citedby><cites>FETCH-LOGICAL-c526t-d90bba232fcb01e3e84541060efb0d3d3c6d753125ff4dc945f41e8e9daa4bc33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4648499/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4648499/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26575483$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Cookson, Mark R.</contributor><creatorcontrib>Li, Ping</creatorcontrib><creatorcontrib>Nie, Yaling</creatorcontrib><creatorcontrib>Yu, Jingkai</creatorcontrib><title>An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration.</description><subject>Adherens Junctions - metabolism</subject><subject>Alzheimer Disease - genetics</subject><subject>Alzheimer Disease - metabolism</subject><subject>Alzheimer's disease</subject><subject>Alzheimers disease</subject><subject>Apoptosis</subject><subject>Blood-brain barrier</subject><subject>Cell interactions</subject><subject>Chronic illnesses</subject><subject>Cluster Analysis</subject><subject>Clustering</subject><subject>Cognitive ability</subject><subject>Disorders</subject><subject>Gene expression</subject><subject>Genes</subject><subject>Genetic Predisposition to Disease</subject><subject>Humans</subject><subject>Huntington Disease - genetics</subject><subject>Huntington Disease - metabolism</subject><subject>Huntington's disease</subject><subject>Huntingtons disease</subject><subject>Infectious diseases</subject><subject>Laboratories</subject><subject>Medical treatment</subject><subject>Metabolic Networks and Pathways - genetics</subject><subject>Modules</subject><subject>Movement disorders</subject><subject>Neurodegeneration</subject><subject>Neurodegenerative diseases</subject><subject>Neurological diseases</subject><subject>Neurosciences</subject><subject>Parkinson Disease - genetics</subject><subject>Parkinson Disease - metabolism</subject><subject>Parkinson's disease</subject><subject>Parkinsons disease</subject><subject>Pathways</subject><subject>Probability</subject><subject>Process engineering</subject><subject>Protein interaction</subject><subject>Protein Interaction Maps</subject><subject>Proteins</subject><subject>Tight junctions</subject><subject>Tight Junctions - metabolism</subject><subject>Transcriptome</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNptkk1v1DAQhiMEoqXlHyCwxIXLLv5OckGqlhZWKrRS4WxN7PFuVkm8tZOi_feku2nVIk62Z955PDN6s-wdo3MmcvZ5E4bYQTPfhg7nlElBpXqRHbNS8JnmVLx8cj_K3qS0oVSJQuvX2RHXKleyEMeZPevIufdo-_oOyQ_s18GRPpClw66v_Y7crCGiI9fQr__ALhHoHFmEtg0duQDbhziGxseK_MQhBocr7DDCnva1TggJ02n2ykOT8O10nmS_L85_Lb7PLq--LRdnlzOruO5nrqRVBVxwbyvKUGAhlWRUU_QVdcIJq12uBOPKe-lsKZWXDAssHYCsrBAn2YcDd9uEZKb9JMNyIUqueMFHxfKgcAE2ZhvrFuLOBKjNPhDiykDsa9ugAUl9brWSkjspKgmQi9IprXBM-EqNrC_Tb0PVorPjviI0z6DPM129NqtwZ6SWhSzLEfBpAsRwO2DqTVsni00DHYZh37cqqWSMjtKP_0j_P508qGwMKUX0j80wau4981Bl7j1jJs-MZe-fDvJY9GAS8RebWsC-</recordid><startdate>20151117</startdate><enddate>20151117</enddate><creator>Li, Ping</creator><creator>Nie, Yaling</creator><creator>Yu, Jingkai</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20151117</creationdate><title>An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases</title><author>Li, Ping ; Nie, Yaling ; Yu, Jingkai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c526t-d90bba232fcb01e3e84541060efb0d3d3c6d753125ff4dc945f41e8e9daa4bc33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Adherens Junctions - metabolism</topic><topic>Alzheimer Disease - genetics</topic><topic>Alzheimer Disease - metabolism</topic><topic>Alzheimer's disease</topic><topic>Alzheimers disease</topic><topic>Apoptosis</topic><topic>Blood-brain barrier</topic><topic>Cell interactions</topic><topic>Chronic illnesses</topic><topic>Cluster Analysis</topic><topic>Clustering</topic><topic>Cognitive ability</topic><topic>Disorders</topic><topic>Gene expression</topic><topic>Genes</topic><topic>Genetic Predisposition to Disease</topic><topic>Humans</topic><topic>Huntington Disease - genetics</topic><topic>Huntington Disease - metabolism</topic><topic>Huntington's disease</topic><topic>Huntingtons disease</topic><topic>Infectious diseases</topic><topic>Laboratories</topic><topic>Medical treatment</topic><topic>Metabolic Networks and Pathways - genetics</topic><topic>Modules</topic><topic>Movement disorders</topic><topic>Neurodegeneration</topic><topic>Neurodegenerative diseases</topic><topic>Neurological diseases</topic><topic>Neurosciences</topic><topic>Parkinson Disease - genetics</topic><topic>Parkinson Disease - metabolism</topic><topic>Parkinson's disease</topic><topic>Parkinsons disease</topic><topic>Pathways</topic><topic>Probability</topic><topic>Process engineering</topic><topic>Protein interaction</topic><topic>Protein Interaction Maps</topic><topic>Proteins</topic><topic>Tight junctions</topic><topic>Tight Junctions - metabolism</topic><topic>Transcriptome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Ping</creatorcontrib><creatorcontrib>Nie, Yaling</creatorcontrib><creatorcontrib>Yu, Jingkai</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Ping</au><au>Nie, Yaling</au><au>Yu, Jingkai</au><au>Cookson, Mark R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2015-11-17</date><risdate>2015</risdate><volume>10</volume><issue>11</issue><spage>e0143045</spage><epage>e0143045</epage><pages>e0143045-e0143045</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26575483</pmid><doi>10.1371/journal.pone.0143045</doi><oa>free_for_read</oa></addata></record> |
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subjects | Adherens Junctions - metabolism Alzheimer Disease - genetics Alzheimer Disease - metabolism Alzheimer's disease Alzheimers disease Apoptosis Blood-brain barrier Cell interactions Chronic illnesses Cluster Analysis Clustering Cognitive ability Disorders Gene expression Genes Genetic Predisposition to Disease Humans Huntington Disease - genetics Huntington Disease - metabolism Huntington's disease Huntingtons disease Infectious diseases Laboratories Medical treatment Metabolic Networks and Pathways - genetics Modules Movement disorders Neurodegeneration Neurodegenerative diseases Neurological diseases Neurosciences Parkinson Disease - genetics Parkinson Disease - metabolism Parkinson's disease Parkinsons disease Pathways Probability Process engineering Protein interaction Protein Interaction Maps Proteins Tight junctions Tight Junctions - metabolism Transcriptome |
title | An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases |
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