Nietert, Manuel Manfred

[["dc.bibliographiccitation.firstpage","1072"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Journal of Personalized Medicine"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Vinhoven, Liza"],["dc.contributor.author","Voskamp, Malte"],["dc.contributor.author","Nietert, Manuel Manfred"],["dc.date.accessioned","2022-01-11T14:07:52Z"],["dc.date.available","2022-01-11T14:07:52Z"],["dc.date.issued","2021"],["dc.description.abstract","The MINERVA platform is currently the most widely used platform for visualizing and providing access to disease maps. Disease maps are systems biological maps of molecular interactions relevant in a certain disease context, where they can be used to support drug discovery. For this purpose, we extended MINERVA’s own drug and chemical search using the MINERVA plugin starter kit. We developed a plugin to provide a linkage between disease maps in MINERVA and application-specific databases of candidate therapeutics. The plugin has three main functionalities; one shows all the targets of all the compounds in the database, the second is a compound-based search to highlight targets of specific compounds, and the third can be used to find compounds that affect a certain target. As a use case, we applied the plugin to link a disease map and compound database we previously established in the context of cystic fibrosis and, herein, point out possible issues and difficulties. The plugin is publicly available on GitLab; the use-case application to cystic fibrosis, connecting disease maps and the compound database CandActCFTR, is available online."],["dc.description.abstract","The MINERVA platform is currently the most widely used platform for visualizing and providing access to disease maps. Disease maps are systems biological maps of molecular interactions relevant in a certain disease context, where they can be used to support drug discovery. For this purpose, we extended MINERVA’s own drug and chemical search using the MINERVA plugin starter kit. We developed a plugin to provide a linkage between disease maps in MINERVA and application-specific databases of candidate therapeutics. The plugin has three main functionalities; one shows all the targets of all the compounds in the database, the second is a compound-based search to highlight targets of specific compounds, and the third can be used to find compounds that affect a certain target. As a use case, we applied the plugin to link a disease map and compound database we previously established in the context of cystic fibrosis and, herein, point out possible issues and difficulties. The plugin is publicly available on GitLab; the use-case application to cystic fibrosis, connecting disease maps and the compound database CandActCFTR, is available online."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/jpm11111072"],["dc.identifier.pii","jpm11111072"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/97883"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-507"],["dc.relation.eissn","2075-4426"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Mapping Compound Databases to Disease Maps—A MINERVA Plugin for CandActBase"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1278"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","Biomolecules"],["dc.bibliographiccitation.volume","12"],["dc.contributor.affiliation","Voskamp, Malte; 1Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany"],["dc.contributor.affiliation","Vinhoven, Liza; 1Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany"],["dc.contributor.affiliation","Stanke, Frauke; 2Clinic for Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany"],["dc.contributor.affiliation","Hafkemeyer, Sylvia; 4Mukoviszidose Institut gGmbH, In den Dauen 6, 53117 Bonn, Germany"],["dc.contributor.affiliation","Nietert, Manuel Manfred; 1Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany"],["dc.contributor.author","Voskamp, Malte"],["dc.contributor.author","Vinhoven, Liza"],["dc.contributor.author","Stanke, Frauke"],["dc.contributor.author","Hafkemeyer, Sylvia"],["dc.contributor.author","Nietert, Manuel Manfred"],["dc.contributor.editor","Song, Jiangning"],["dc.date.accessioned","2022-10-04T10:21:49Z"],["dc.date.available","2022-10-04T10:21:49Z"],["dc.date.issued","2022"],["dc.date.updated","2022-11-11T13:13:13Z"],["dc.description.abstract","An adequate visualization form is required to gain an overview and ultimately understand the complex and diverse biological mechanisms of diseases. Recently, disease maps have been introduced for this purpose. A disease map is defined as a systems biological map or model that combines metabolic, signaling, and physiological pathways to create a comprehensive overview of known disease mechanisms. With the increase in publications describing biological interactions, efforts in creating and curating comprehensive disease maps is growing accordingly. Therefore, new computational approaches are needed to reduce the time that manual curation takes. Test mining algorithms can be used to analyse the natural language of scientific publications. These types of algorithms can take humanly readable text passages and convert them into a more ordered, machine-usable data structure. To support the creation of disease maps by text mining, we developed an interactive, user-friendly disease map viewer. The disease map viewer displays text mining results in a systems biology map, where the user can review them and either validate or reject identified interactions. Ultimately, the viewer brings together the time-saving advantages of text mining with the accuracy of manual data curation."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.doi","10.3390/biom12091278"],["dc.identifier.pii","biom12091278"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/114511"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-600"],["dc.publisher","MDPI"],["dc.relation.eissn","2218-273X"],["dc.rights","CC BY-SA 4.0"],["dc.title","Integrating Text Mining into the Curation of Disease Maps"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","12351"],["dc.bibliographiccitation.firstpage","12351"],["dc.bibliographiccitation.issue","20"],["dc.bibliographiccitation.journal","International Journal of Molecular Sciences"],["dc.bibliographiccitation.volume","23"],["dc.contributor.affiliation","Vinhoven, Liza; 1Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany"],["dc.contributor.affiliation","Stanke, Frauke; 2Clinic for Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany"],["dc.contributor.affiliation","Hafkemeyer, Sylvia; 4Mukoviszidose Institut, In den Dauen 6, 53117 Bonn, Germany"],["dc.contributor.affiliation","Nietert, Manuel Manfred; 1Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany"],["dc.contributor.author","Vinhoven, Liza"],["dc.contributor.author","Stanke, Frauke"],["dc.contributor.author","Hafkemeyer, Sylvia"],["dc.contributor.author","Nietert, Manuel Manfred"],["dc.contributor.editor","Guerrini, Gabriella"],["dc.contributor.editor","Giovannoni, Maria P."],["dc.date.accessioned","2022-12-01T08:31:41Z"],["dc.date.available","2022-12-01T08:31:41Z"],["dc.date.issued","2022"],["dc.date.updated","2022-11-11T13:12:17Z"],["dc.description.abstract","Cystic fibrosis is a genetic disease caused by mutation of the CFTR gene, which encodes a chloride and bicarbonate transporter in epithelial cells. Due to the vast range of geno- and phenotypes, it is difficult to find causative treatments; however, small-molecule therapeutics have been clinically approved in the last decade. Still, the search for novel therapeutics is ongoing, and thousands of compounds are being tested in different assays, often leaving their mechanism of action unknown. Here, we bring together a CFTR-specific compound database (CandActCFTR) and systems biology model (CFTR Lifecycle Map) to identify the targets of the most promising compounds. We use a dual inverse screening approach, where we employ target- and ligand-based methods to suggest targets of 309 active compounds in the database amongst 90 protein targets from the systems biology model. Overall, we identified 1038 potential target–compound pairings and were able to suggest targets for all 309 active compounds in the database."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft DFG"],["dc.identifier.doi","10.3390/ijms232012351"],["dc.identifier.pii","ijms232012351"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118235"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-621"],["dc.publisher","MDPI"],["dc.relation.eissn","1422-0067"],["dc.relation.isreplacedby","hdl:2/118235"],["dc.rights","Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)."],["dc.title","Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Pharmacology"],["dc.bibliographiccitation.volume","12"],["dc.contributor.affiliation","Nietert, Manuel Manfred; \r\n\r\n1\r\nDepartment of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Vinhoven, Liza; \r\n\r\n1\r\nDepartment of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Auer, Florian; \r\n\r\n3\r\nInstitute for Informatics, University of Augsburg, Augsburg, Germany"],["dc.contributor.affiliation","Hafkemeyer, Sylvia; \r\n\r\n4\r\nMukoviszidose Institut gGmbH, Bonn, Germany"],["dc.contributor.affiliation","Stanke, Frauke; \r\n\r\n5\r\nGerman Center for Lung Research (DZL), Partner Site BREATH, Hannover, Germany"],["dc.contributor.author","Nietert, Manuel Manfred"],["dc.contributor.author","Vinhoven, Liza"],["dc.contributor.author","Auer, Florian"],["dc.contributor.author","Hafkemeyer, Sylvia"],["dc.contributor.author","Stanke, Frauke"],["dc.date.accessioned","2022-01-11T14:06:16Z"],["dc.date.available","2022-01-11T14:06:16Z"],["dc.date.issued","2021"],["dc.date.updated","2022-02-09T13:20:22Z"],["dc.description.abstract","Background: Cystic fibrosis (CF) is a genetic disease caused by mutations in CFTR , which encodes a chloride and bicarbonate transporter expressed in exocrine epithelia throughout the body. Recently, some therapeutics became available that directly target dysfunctional CFTR, yet research for more effective substances is ongoing. The database CandActCFTR aims to provide detailed and comprehensive information on candidate therapeutics for the activation of CFTR-mediated ion conductance aiding systems-biology approaches to identify substances that will synergistically activate CFTR-mediated ion conductance based on published data. Results: Until 10/2020, we derived data from 108 publications on 3,109 CFTR-relevant substances via the literature database PubMed and further 666 substances via ChEMBL; only 19 substances were shared between these sources. One hundred and forty-five molecules do not have a corresponding entry in PubChem or ChemSpider, which indicates that there currently is no single comprehensive database on chemical substances in the public domain. Apart from basic data on all compounds, we have visualized the chemical space derived from their chemical descriptors via a principal component analysis annotated for CFTR-relevant biological categories. Our online query tools enable the search for most similar compounds and provide the relevant annotations in a structured way. The integration of the KNIME software environment in the back-end facilitates a fast and user-friendly maintenance of the provided data sets and a quick extension with new functionalities, e.g., new analysis routines. CandActBase automatically integrates information from other online sources, such as synonyms from PubChem and provides links to other resources like ChEMBL or the source publications. Conclusion: CandActCFTR aims to establish a database model of candidate cystic fibrosis therapeutics for the activation of CFTR-mediated ion conductance to merge data from publicly available sources. Using CandActBase, our strategy to represent data from several internet resources in a merged and organized form can also be applied to other use cases. For substances tested as CFTR activating compounds, the search function allows users to check if a specific compound or a closely related substance was already tested in the CF field. The acquired information on tested substances will assist in the identification of the most promising candidates for future therapeutics."],["dc.description.abstract","Background: Cystic fibrosis (CF) is a genetic disease caused by mutations in CFTR , which encodes a chloride and bicarbonate transporter expressed in exocrine epithelia throughout the body. Recently, some therapeutics became available that directly target dysfunctional CFTR, yet research for more effective substances is ongoing. The database CandActCFTR aims to provide detailed and comprehensive information on candidate therapeutics for the activation of CFTR-mediated ion conductance aiding systems-biology approaches to identify substances that will synergistically activate CFTR-mediated ion conductance based on published data. Results: Until 10/2020, we derived data from 108 publications on 3,109 CFTR-relevant substances via the literature database PubMed and further 666 substances via ChEMBL; only 19 substances were shared between these sources. One hundred and forty-five molecules do not have a corresponding entry in PubChem or ChemSpider, which indicates that there currently is no single comprehensive database on chemical substances in the public domain. Apart from basic data on all compounds, we have visualized the chemical space derived from their chemical descriptors via a principal component analysis annotated for CFTR-relevant biological categories. Our online query tools enable the search for most similar compounds and provide the relevant annotations in a structured way. The integration of the KNIME software environment in the back-end facilitates a fast and user-friendly maintenance of the provided data sets and a quick extension with new functionalities, e.g., new analysis routines. CandActBase automatically integrates information from other online sources, such as synonyms from PubChem and provides links to other resources like ChEMBL or the source publications. Conclusion: CandActCFTR aims to establish a database model of candidate cystic fibrosis therapeutics for the activation of CFTR-mediated ion conductance to merge data from publicly available sources. Using CandActBase, our strategy to represent data from several internet resources in a merged and organized form can also be applied to other use cases. For substances tested as CFTR activating compounds, the search function allows users to check if a specific compound or a closely related substance was already tested in the CF field. The acquired information on tested substances will assist in the identification of the most promising candidates for future therapeutics."],["dc.identifier.doi","10.3389/fphar.2021.689205"],["dc.identifier.eissn","1663-9812"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/97866"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-507"],["dc.relation.eissn","1663-9812"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Comprehensive Analysis of Chemical Structures That Have Been Tested as CFTR Activating Substances in a Publicly Available Database CandActCFTR"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","7590"],["dc.bibliographiccitation.issue","14"],["dc.bibliographiccitation.journal","International Journal of Molecular Sciences"],["dc.bibliographiccitation.volume","22"],["dc.contributor.affiliation","Vinhoven, Liza; \t\t \r\n\t\t Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany, liza.vinhoven@med.uni-goettingen.de"],["dc.contributor.affiliation","Stanke, Frauke; \t\t \r\n\t\t Clinic for Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany, mekus.frauke@mh-hannover.de\t\t \r\n\t\t Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), The German Center for Lung Research, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany, mekus.frauke@mh-hannover.de"],["dc.contributor.affiliation","Hafkemeyer, Sylvia; \t\t \r\n\t\t Mukoviszidose Institut gGmbH, In den Dauen 6, 53117 Bonn, Germany, shafkemeyer@muko.info"],["dc.contributor.affiliation","Nietert, Manuel Manfred; \t\t \r\n\t\t Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany, manuel.nietert@med.uni-goettingen.de\t\t \r\n\t\t CIDAS Campus Institute Data Science, Goldschmidtstraße 1, 37077 Göttingen, Germany, manuel.nietert@med.uni-goettingen.de"],["dc.contributor.author","Vinhoven, Liza"],["dc.contributor.author","Stanke, Frauke"],["dc.contributor.author","Hafkemeyer, Sylvia"],["dc.contributor.author","Nietert, Manuel Manfred"],["dc.date.accessioned","2021-08-12T07:45:58Z"],["dc.date.available","2021-08-12T07:45:58Z"],["dc.date.issued","2021"],["dc.date.updated","2022-09-06T07:25:31Z"],["dc.description.abstract","Different causative therapeutics for CF patients have been developed. There are still no mutation-specific therapeutics for some patients, especially those with rare CFTR mutations. For this purpose, high-throughput screens have been performed which result in various candidate compounds, with mostly unclear modes of action. In order to elucidate the mechanism of action for promising candidate substances and to be able to predict possible synergistic effects of substance combinations, we used a systems biology approach to create a model of the CFTR maturation pathway in cells in a standardized, human- and machine-readable format. It is composed of a core map, manually curated from small-scale experiments in human cells, and a coarse map including interactors identified in large-scale efforts. The manually curated core map includes 170 different molecular entities and 156 reactions from 221 publications. The coarse map encompasses 1384 unique proteins from four publications. The overlap between the two data sources amounts to 46 proteins. The CFTR Lifecycle Map can be used to support the identification of potential targets inside the cell and elucidate the mode of action for candidate substances. It thereby provides a backbone to structure available data as well as a tool to develop hypotheses regarding novel therapeutics."],["dc.description.abstract","Different causative therapeutics for CF patients have been developed. There are still no mutation-specific therapeutics for some patients, especially those with rare CFTR mutations. For this purpose, high-throughput screens have been performed which result in various candidate compounds, with mostly unclear modes of action. In order to elucidate the mechanism of action for promising candidate substances and to be able to predict possible synergistic effects of substance combinations, we used a systems biology approach to create a model of the CFTR maturation pathway in cells in a standardized, human- and machine-readable format. It is composed of a core map, manually curated from small-scale experiments in human cells, and a coarse map including interactors identified in large-scale efforts. The manually curated core map includes 170 different molecular entities and 156 reactions from 221 publications. The coarse map encompasses 1384 unique proteins from four publications. The overlap between the two data sources amounts to 46 proteins. The CFTR Lifecycle Map can be used to support the identification of potential targets inside the cell and elucidate the mode of action for candidate substances. It thereby provides a backbone to structure available data as well as a tool to develop hypotheses regarding novel therapeutics."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/ijms22147590"],["dc.identifier.pii","ijms22147590"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/88588"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-448"],["dc.relation.eissn","1422-0067"],["dc.relation.orgunit","Institut für Medizinische Bioinformatik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","CFTR Lifecycle Map—A Systems Medicine Model of CFTR Maturation to Predict Possible Active Compound Combinations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]