Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection
Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one d...
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| description | Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/. |
| doi_str_mv | 10.1371/journal.pone.0041202 |
| format | Article |
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With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0041202</identifier><identifier>PMID: 22848443</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acids ; Alginic acid ; Artificial intelligence ; Bacterial Proteins - metabolism ; Biochemistry ; Bioinformatics ; Biology ; Case studies ; Chronic obstructive lung disease ; Chronic obstructive pulmonary disease ; Computer Science ; Cystic fibrosis ; Drug Delivery Systems ; Drug Discovery - methods ; Drug resistance ; Drug therapy ; Drugs ; E coli ; Escherichia coli ; Experiments ; Genomes ; Genomics ; Health aspects ; Humans ; Infections ; Informatics ; Internet ; Learning algorithms ; Lung diseases ; Lungs ; Machine learning ; Medicine ; Models, Biological ; Multidrug resistance ; Obstructive lung disease ; Opportunist infection ; Organisms ; Pathogens ; Pharmacology ; Predictions ; Prokaryotes ; Protein interaction ; Protein-protein interactions ; Proteins ; Proteome - metabolism ; Pseudomonas aeruginosa ; Pseudomonas aeruginosa - metabolism ; Pseudomonas Infections - drug therapy ; Pseudomonas Infections - metabolism ; RhlR protein ; Saccharomyces cerevisiae ; Servers ; Sigma factor ; Studies ; Target recognition ; Transcription</subject><ispartof>PloS one, 2012-07, Vol.7 (7), p.e41202</ispartof><rights>COPYRIGHT 2012 Public Library of Science</rights><rights>2012 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://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>Zhang et al. 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-616e8b714e5dead75bf601f359d0e77ad48635a368dfce4d37c4c9e2507d3da73</citedby><cites>FETCH-LOGICAL-c692t-616e8b714e5dead75bf601f359d0e77ad48635a368dfce4d37c4c9e2507d3da73</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/PMC3404098/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3404098/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53770,53772,79347,79348</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22848443$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Kaufmann, Gunnar F.</contributor><creatorcontrib>Zhang, Minlu</creatorcontrib><creatorcontrib>Su, Shengchang</creatorcontrib><creatorcontrib>Bhatnagar, Raj K</creatorcontrib><creatorcontrib>Hassett, Daniel J</creatorcontrib><creatorcontrib>Lu, Long J</creatorcontrib><title>Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/.</description><subject>Acids</subject><subject>Alginic acid</subject><subject>Artificial intelligence</subject><subject>Bacterial Proteins - metabolism</subject><subject>Biochemistry</subject><subject>Bioinformatics</subject><subject>Biology</subject><subject>Case studies</subject><subject>Chronic obstructive lung disease</subject><subject>Chronic obstructive pulmonary disease</subject><subject>Computer Science</subject><subject>Cystic fibrosis</subject><subject>Drug Delivery Systems</subject><subject>Drug Discovery - methods</subject><subject>Drug resistance</subject><subject>Drug therapy</subject><subject>Drugs</subject><subject>E coli</subject><subject>Escherichia coli</subject><subject>Experiments</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Infections</subject><subject>Informatics</subject><subject>Internet</subject><subject>Learning algorithms</subject><subject>Lung diseases</subject><subject>Lungs</subject><subject>Machine learning</subject><subject>Medicine</subject><subject>Models, Biological</subject><subject>Multidrug resistance</subject><subject>Obstructive lung disease</subject><subject>Opportunist infection</subject><subject>Organisms</subject><subject>Pathogens</subject><subject>Pharmacology</subject><subject>Predictions</subject><subject>Prokaryotes</subject><subject>Protein interaction</subject><subject>Protein-protein interactions</subject><subject>Proteins</subject><subject>Proteome - metabolism</subject><subject>Pseudomonas aeruginosa</subject><subject>Pseudomonas aeruginosa - metabolism</subject><subject>Pseudomonas Infections - drug therapy</subject><subject>Pseudomonas Infections - metabolism</subject><subject>RhlR protein</subject><subject>Saccharomyces cerevisiae</subject><subject>Servers</subject><subject>Sigma factor</subject><subject>Studies</subject><subject>Target recognition</subject><subject>Transcription</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</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>eNqNkm2L1DAQx4so3nn6DUQDguCLXdMmbdo3wnH4sHBwh09vwzSZdnN2kzVJ1fPTm73tHVtQkBAyTH7zn2H4Z9nTnC5zJvLXV270Fobl1llcUsrzghb3suO8YcWiKii7fxAfZY9CuKK0ZHVVPcyOiqLmNefsOPt96VEbFY2zBKxOF4brYAJxHYlrJFvvIhpLjI3oQUW3wRSTy4CjdhtnIRBAP_bGugAkOoIW2gGJxfjT-W-LFgJqohNBIvgeIwk44E2_x9mDDoaAT6b3JPvy7u3nsw-L84v3q7PT84WqmiIuqrzCuhU5x1IjaFG2XUXzjpWNpigEaF5XrARW1bpTyDUTiqsGi5IKzTQIdpI93-tuBxfktLYgc1aInDJRF4lY7Qnt4EpuvdmAv5YOjLxJON9L8NGoAaUuGt6yquNV2XLdIjRa1JjGqxVlXVslrTdTt7HdoFZoo4dhJjr_sWYte_dDMk45beok8GIS8O77iCH-Y-SJ6iFNZWznkpjamKDkKReC8rKhNFHLv1DpaNwYlYzTmZSfFbyaFSQm4q_YwxiCXH36-P_sxdc5-_KAXSMMcR3cMO58EOYg34PKuxA8dneby6nc-f52G3Lnezn5PpU9O9z6XdGt0dkfBDYAVg</recordid><startdate>20120724</startdate><enddate>20120724</enddate><creator>Zhang, Minlu</creator><creator>Su, Shengchang</creator><creator>Bhatnagar, Raj K</creator><creator>Hassett, Daniel J</creator><creator>Lu, Long J</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>IOV</scope><scope>ISR</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>AEUYN</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>5PM</scope><scope>DOA</scope></search><sort><creationdate>20120724</creationdate><title>Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection</title><author>Zhang, Minlu ; Su, Shengchang ; Bhatnagar, Raj K ; Hassett, Daniel J ; Lu, Long J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-616e8b714e5dead75bf601f359d0e77ad48635a368dfce4d37c4c9e2507d3da73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Acids</topic><topic>Alginic acid</topic><topic>Artificial intelligence</topic><topic>Bacterial Proteins - metabolism</topic><topic>Biochemistry</topic><topic>Bioinformatics</topic><topic>Biology</topic><topic>Case studies</topic><topic>Chronic obstructive lung disease</topic><topic>Chronic obstructive pulmonary disease</topic><topic>Computer Science</topic><topic>Cystic fibrosis</topic><topic>Drug Delivery Systems</topic><topic>Drug Discovery - methods</topic><topic>Drug resistance</topic><topic>Drug therapy</topic><topic>Drugs</topic><topic>E coli</topic><topic>Escherichia coli</topic><topic>Experiments</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Health aspects</topic><topic>Humans</topic><topic>Infections</topic><topic>Informatics</topic><topic>Internet</topic><topic>Learning algorithms</topic><topic>Lung diseases</topic><topic>Lungs</topic><topic>Machine learning</topic><topic>Medicine</topic><topic>Models, Biological</topic><topic>Multidrug resistance</topic><topic>Obstructive lung disease</topic><topic>Opportunist infection</topic><topic>Organisms</topic><topic>Pathogens</topic><topic>Pharmacology</topic><topic>Predictions</topic><topic>Prokaryotes</topic><topic>Protein interaction</topic><topic>Protein-protein interactions</topic><topic>Proteins</topic><topic>Proteome - metabolism</topic><topic>Pseudomonas aeruginosa</topic><topic>Pseudomonas aeruginosa - metabolism</topic><topic>Pseudomonas Infections - drug therapy</topic><topic>Pseudomonas Infections - metabolism</topic><topic>RhlR protein</topic><topic>Saccharomyces cerevisiae</topic><topic>Servers</topic><topic>Sigma factor</topic><topic>Studies</topic><topic>Target recognition</topic><topic>Transcription</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Minlu</creatorcontrib><creatorcontrib>Su, Shengchang</creatorcontrib><creatorcontrib>Bhatnagar, Raj K</creatorcontrib><creatorcontrib>Hassett, Daniel J</creatorcontrib><creatorcontrib>Lu, Long J</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</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 One Sustainability</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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With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>22848443</pmid><doi>10.1371/journal.pone.0041202</doi><tpages>e41202</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Acids Alginic acid Artificial intelligence Bacterial Proteins - metabolism Biochemistry Bioinformatics Biology Case studies Chronic obstructive lung disease Chronic obstructive pulmonary disease Computer Science Cystic fibrosis Drug Delivery Systems Drug Discovery - methods Drug resistance Drug therapy Drugs E coli Escherichia coli Experiments Genomes Genomics Health aspects Humans Infections Informatics Internet Learning algorithms Lung diseases Lungs Machine learning Medicine Models, Biological Multidrug resistance Obstructive lung disease Opportunist infection Organisms Pathogens Pharmacology Predictions Prokaryotes Protein interaction Protein-protein interactions Proteins Proteome - metabolism Pseudomonas aeruginosa Pseudomonas aeruginosa - metabolism Pseudomonas Infections - drug therapy Pseudomonas Infections - metabolism RhlR protein Saccharomyces cerevisiae Servers Sigma factor Studies Target recognition Transcription |
| title | Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T09%3A00%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Prediction%20and%20analysis%20of%20the%20protein%20interactome%20in%20Pseudomonas%20aeruginosa%20to%20enable%20network-based%20drug%20target%20selection&rft.jtitle=PloS%20one&rft.au=Zhang,%20Minlu&rft.date=2012-07-24&rft.volume=7&rft.issue=7&rft.spage=e41202&rft.pages=e41202-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0041202&rft_dat=%3Cgale_plos_%3EA477045900%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1327103782&rft_id=info:pmid/22848443&rft_galeid=A477045900&rft_doaj_id=oai_doaj_org_article_d294b36f465b4dbea9d78eb718c03fb6&rfr_iscdi=true |