1
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Rohith AN, Karki R, Veith TL, Preisendanz HE, Duncan JM, Kleinman PJA, Cibin R. Prioritizing conservation practice locations for effective water quality improvement using the Agricultural Conservation Planning Framework (ACPF) and the Soil and Water Assessment Tool (SWAT). J Environ Manage 2024; 349:119514. [PMID: 37976641 DOI: 10.1016/j.jenvman.2023.119514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/08/2023] [Accepted: 10/31/2023] [Indexed: 11/19/2023]
Abstract
Adopting the right agricultural conservation practices (CPs) at the right place is critical to maximizing water quality benefits. The Agricultural Conservation Planning Framework (ACPF) tool identifies all potential CPs and their locations within a target watershed based on the landscape characteristics. The ACPF tool suggests hundreds of CP locations in a watershed, making it challenging to prioritize the CP implementation. We develop and demonstrate an efficient approach using a multicriteria ranking technique for prioritizing the CPs suggested by ACPF, considering pollution hotspots and CP cost and effectiveness to support decision-makers. The pollution hotspots are estimated with simulations from an ecohydrological model, the Soil and Water Assessment Tool (SWAT). The CP cost and effectiveness were estimated from CP installation costs and pollutant reduction efficiencies from the literature. The methodology was demonstrated in the Conewago and Mahantango watersheds in Pennsylvania, US, for grassed waterways (GWWs) and water and sediment control basins (WASCOBs) for sediment load reduction. Multiple CP prioritization scenarios were evaluated with individual and combined criteria for reductions in total sediment load, yield (loading per area), and cost. In single criterion-based prioritization, the cost-based and load-based prioritization indicated cost efficiency and fewer CPs, respectively. The yield-based approach correctly prioritized CPs in sediment loss hotspots in the case study watersheds; however, it needed more CPs to meet the target reductions. The multicriteria approach efficiently prioritized CPs in sediment hotspots to meet target reductions. Although this approach was demonstrated in two case study watersheds and for sediment loss reduction, it is applicable for any location or pollutant for which similar input variables can be provided, thereby providing a means for prioritizing the results of ACPF for implementation in the Mid-Atlantic region of the US.
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Affiliation(s)
- A N Rohith
- The Pennsylvania State University, Department of Agricultural and Biological Engineering, University Park, PA, USA
| | - R Karki
- The Pennsylvania State University, Department of Agricultural and Biological Engineering, University Park, PA, USA; University of Maryland, College of Agriculture and Natural Resources, College Park, MD, USA
| | - T L Veith
- United States Department of Agriculture-Agricultural Research Service, Pasture Systems & Watershed Management Research Unit, University Park, PA, USA
| | - H E Preisendanz
- The Pennsylvania State University, Department of Agricultural and Biological Engineering, University Park, PA, USA
| | - J M Duncan
- The Pennsylvania State University, Department of Ecosystem Science and Management, University Park, PA, USA
| | - P J A Kleinman
- United States Department of Agriculture-Agricultural Research Service, Soil Management and Sugarbeet Research Unit, Fort Collins, CO, USA
| | - R Cibin
- The Pennsylvania State University, Department of Agricultural and Biological Engineering, University Park, PA, USA; The Pennsylvania State University, Department of Civil and Environmental Engineering, University Park, PA, USA.
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2
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Karki R, Gadiya Y, Gribbon P, Zaliani A. Pharmacophore-Based Machine Learning Model To Predict Ligand Selectivity for E3 Ligase Binders. ACS Omega 2023; 8:30177-30185. [PMID: 37636935 PMCID: PMC10448689 DOI: 10.1021/acsomega.3c02803] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/06/2023] [Indexed: 08/29/2023]
Abstract
E3 ligases are enzymes that play a critical role in ubiquitin-mediated protein degradation and are involved in various cellular processes. Pharmacophore analysis is a useful approach for predicting E3 ligase binding selectivity, which involves identifying key chemical features necessary for a ligand to interact with a specific protein target cavity. While pharmacophore analysis is not always sufficient to accurately predict ligand binding affinity, it can be a valuable tool for filtering and/or designing focused libraries for screening campaigns. In this study, we present a fast and an inexpensive approach using a pharmacophore fingerprinting scheme known as ErG, which is used in a multi-class machine learning classification model. This model can assign the correct E3 ligase binder to its known E3 ligase and predict the probability of each molecule to bind to different E3 ligases. Practical applications of this approach are demonstrated on commercial libraries such as Asinex for the rational design of E3 ligase binders. The scripts and data associated with this study can be found on GitHub at https://github.com/Fraunhofer-ITMP/E3_binder_Model.
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Affiliation(s)
- Reagon Karki
- Fraunhofer
Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, Germany
- Fraunhofer
Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
| | - Yojana Gadiya
- Fraunhofer
Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, Germany
- Fraunhofer
Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
- Bonn-Aachen
International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany
| | - Philip Gribbon
- Fraunhofer
Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, Germany
- Fraunhofer
Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
| | - Andrea Zaliani
- Fraunhofer
Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, 22525 Hamburg, Germany
- Fraunhofer
Cluster of Excellence for Immune-Mediated Diseases (CIMD), Theodor Stern Kai 7, 60590 Frankfurt, Germany
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3
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Karki R, Gadiya Y, Zaliani A, Gribbon P. Mpox Knowledge Graph: A comprehensive representation embedding chemical entities and associated biology of Mpox. Bioinformatics Advances 2023; 3:vbad045. [PMID: 37187795 PMCID: PMC10181838 DOI: 10.1093/bioadv/vbad045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/20/2023] [Accepted: 03/31/2023] [Indexed: 04/05/2023]
Abstract
Abstract
The outbreak of Mpox virus (MPXV) infection in May 2022 is declared a global health emergency by WHO. A total of 84330 cases have been confirmed as of 5th January, 2023 and the numbers are on the rise. The MPXV pathophysiology and its underlying mechanisms are unfortunately not yet understood. Likewise, the knowledge of biochemicals and drugs used against MPXV and their downstream effects is sparse. In this work, using Knowledge Graph (KG) representations we have depicted chemical and biological aspects of MPXV. To achieve this, we have collected and rationally assembled several biological study results, assays, drug candidates, and pre-clinical evidence to form a dynamic and comprehensive network. The KG is compliant with FAIR annotations allowing seamless transformation and integration to/with other formats and infrastructures.
Availability and implementation
The programmatic scripts for Mpox KG are publicly available at https://github.com/Fraunhofer-ITMP/mpox-kg. It is hosted publicly at https://doi.org/10.18119/N9SG7D.
Supplementary information
Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Reagon Karki
- Discovery Research ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) , Schnackenburgallee 114, 22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD) , Theodor Stern Kai 7, Frankfurt, 60590, Germany
| | - Yojana Gadiya
- Discovery Research ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) , Schnackenburgallee 114, 22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD) , Theodor Stern Kai 7, Frankfurt, 60590, Germany
| | - Andrea Zaliani
- Discovery Research ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) , Schnackenburgallee 114, 22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD) , Theodor Stern Kai 7, Frankfurt, 60590, Germany
| | - Philip Gribbon
- Discovery Research ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP) , Schnackenburgallee 114, 22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD) , Theodor Stern Kai 7, Frankfurt, 60590, Germany
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4
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Berg H, Wirtz Martin MA, Altincekic N, Alshamleh I, Kaur Bains J, Blechar J, Ceylan B, de Jesus V, Dhamotharan K, Fuks C, Gande SL, Hargittay B, Hohmann KF, Hutchinson MT, Korn SM, Krishnathas R, Kutz F, Linhard V, Matzel T, Meiser N, Niesteruk A, Pyper DJ, Schulte L, Trucks S, Azzaoui K, Blommers MJJ, Gadiya Y, Karki R, Zaliani A, Gribbon P, Almeida MDS, Anobom CD, Bula AL, Buetikofer M, Caruso ÍP, Felli IC, Da Poian AT, de Amorim GC, Fourkiotis NK, Gallo A, Ghosh D, Gomes-Neto F, Gorbatyuk O, Hao B, Kurauskas V, Lecoq L, Li Y, Mebus-Antunes NC, Mompean M, Neves-Martins TC, Ninot-Pedrosa M, Pinheiro AS, Pontoriero L, Pustovalova Y, Riek R, Robertson A, Abi Saad MJ, Treviño MA, Tsika AC, Almeida FC, Bax A, Henzler-Wildman K, Hoch JC, Jaudzems K, Laurents DV, Orts J, Pieratelli R, Spyroulias GA, Duchardt-Ferner E, Ferner J, Fuertig B, Hengesbach M, Löhr F, Qureshi N, Richter C, Saxena K, Schlundt A, Sreeramulu S, Wacker A, Weigand JE, Wirmer-Bartoschek J, Woehnert J, Schwalbe H. Comprehensive Fragment Screening of the SARS‐CoV‐2 Proteome Explores Novel Chemical Space for Drug Development. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202205858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Hannes Berg
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | | | - Nadide Altincekic
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Islam Alshamleh
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Jasleen Kaur Bains
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Julius Blechar
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Betül Ceylan
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Vanessa de Jesus
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | | | - Christin Fuks
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Santosh L. Gande
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Bruno Hargittay
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | | | - Marie T. Hutchinson
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | | | - Robin Krishnathas
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Felicitas Kutz
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Verena Linhard
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Tobias Matzel
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Nathalie Meiser
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Anna Niesteruk
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Dennis J. Pyper
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Linda Schulte
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Sven Trucks
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Kamal Azzaoui
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Marcel J J Blommers
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Yojana Gadiya
- Fraunhofer Institute for Molecular Biology and Applied Ecology ScreeningPort: Fraunhofer-Institut fur Translationale Medizin und Pharmakologie ITMP Drug Discovery Research ScreeningPort Screening Unit GERMANY
| | - Reagon Karki
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP: Fraunhofer-Institut fur Translationale Medizin und Pharmakologie ITMP Screening Unit GERMANY
| | - Andrea Zaliani
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP: Fraunhofer-Institut fur Translationale Medizin und Pharmakologie ITMP Screening Unit GERMANY
| | - Philip Gribbon
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP: Fraunhofer-Institut fur Translationale Medizin und Pharmakologie ITMP Screening Unit GERMANY
| | - Marcius da Silva Almeida
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Institue for Medical Biochemistry BRAZIL
| | - Cristiane Dinis Anobom
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Department of Biochemistry BRAZIL
| | - Anna Lina Bula
- Latvian Institute of Organic Synthesis: Latvijas Organiskas sintezes instituts Institute of Organic Synthesis LATVIA
| | - Matthias Buetikofer
- ETH Zurich: Eidgenossische Technische Hochschule Zurich Institute für Physikalische Chemie GERMANY
| | - Ícaro Putinhon Caruso
- Sao Paulo State University Julio de Mesquita Filho: Universidade Estadual Paulista Julio de Mesquita Filho Department of Physics BRAZIL
| | - Isabella Caterina Felli
- University of Florence: Universita degli Studi di Firenze Magnetic Resonance Center (CERM) ITALY
| | - Andrea T Da Poian
- Sao Paulo State University Julio de Mesquita Filho: Universidade Estadual Paulista Julio de Mesquita Filho Department of Physics GERMANY
| | - Gisele Cardoso de Amorim
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Multidisciplinary Center for Research in Biology BRAZIL
| | - Nikolaos K Fourkiotis
- University of Patras - Patras Campus: Panepistemio Patron Department of Pharmacy GREECE
| | - Angelo Gallo
- University of Patras - Patras Campus: Panepistemio Patron Department of Pharmacy GREECE
| | - Dhiman Ghosh
- ETH Zurich: Eidgenossische Technische Hochschule Zurich Institute for Physical Chemistry SWITZERLAND
| | | | - Oksana Gorbatyuk
- UConn Health Department of Molecular Biology and Biophysics UNITED STATES
| | - Bing Hao
- UConn Health Department of Molecular Biology and Biopyhsics UNITED STATES
| | - Vilius Kurauskas
- UW Madison: University of Wisconsin Madison Department of Biochemistry UNITED STATES
| | - Lauriane Lecoq
- Universite de Lyon Molecular Microbiology and Structural Biochemistry FRANCE
| | - Yunfeng Li
- UConn Health Department of Molecular Biology and Biophysics UNITED STATES
| | - Nathane Cunha Mebus-Antunes
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Institute of Medical Biochemistry BRAZIL
| | - Miguel Mompean
- Estacion Biologica de Donana CSIC "Rocasolano" Institute for Physical Chemistry SPAIN
| | - Thais Cristtina Neves-Martins
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Institute of Medical Biochemistry BRAZIL
| | - Marti Ninot-Pedrosa
- Universite Lyon 1 IUT Lyon 1 Molecular Microbiology and Structural Biochemistry FRANCE
| | - Anderson S Pinheiro
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Department of Biochemistry BRAZIL
| | - Letizia Pontoriero
- University of Florence: Universita degli Studi di Firenze Center for Magnetic Resonance ITALY
| | - Yulia Pustovalova
- UConn Health Department of Molecular Biology and Biophysics UNITED STATES
| | - Roland Riek
- ETH Zürich: Eidgenossische Technische Hochschule Zurich Institute for Physical Chemistry SWITZERLAND
| | - Angus Robertson
- NIAMDD: National Institute of Diabetes and Digestive and Kidney Diseases Laboratory of Chemical Physics UNITED STATES
| | - Marie Jose Abi Saad
- University of Vienna: Universitat Wien Department of Pharmaceutical Sciences AUSTRIA
| | - Miguel A Treviño
- CSIC: Consejo Superior de Investigaciones Cientificas "Rocasolano" Institute for Physical Chemistry SPAIN
| | - Aikaterini C Tsika
- University of Patras - Patras Campus: Panepistemio Patron Department of Pharmacy GREECE
| | - Fabio C.L. Almeida
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Institute of Medical Biochemistry BRAZIL
| | - Ad Bax
- National Institute of Diabetes and Digestive and Kidney Diseases Laboratory of Chemical Physics UNITED STATES
| | | | - Jeffrey C Hoch
- UConn Health Department of Molecular Biology and Biophysics UNITED STATES
| | - Kristaps Jaudzems
- Institute of Organic Synthesis of the Latvian Academy of Sciences: Latvijas Organiskas sintezes instituts Institute for Organic Chemistry LATVIA
| | - Douglas V Laurents
- Estacion Biologica de Donana CSIC "Rocasolano" Institute for Physical Chemistry SPAIN
| | - Julien Orts
- University of Vienna: Universitat Wien Department of Pharmaceutical Sciences AUSTRIA
| | - Roberta Pieratelli
- University of Florence: Universita degli Studi di Firenze Center for Magnetic Resonance ITALY
| | - Georgios A Spyroulias
- University of Patras - Patras Campus: Panepistemio Patron Department of Pharmacy GREECE
| | | | - Jan Ferner
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Boris Fuertig
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Martin Hengesbach
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Frank Löhr
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Nusrat Qureshi
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Christian Richter
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Krishna Saxena
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Andreas Schlundt
- Goethe-Universitat Frankfurt am Main Department for Biosciences GERMANY
| | - Sridhar Sreeramulu
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Anna Wacker
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Julia E Weigand
- TU Darmstadt: Technische Universitat Darmstadt Department of Biology GERMANY
| | | | - Jens Woehnert
- Goethe-Universitat Frankfurt am Main Department of Biological Sciences GERMANY
| | - Harald Schwalbe
- Goethe-Universitat Frankfurt am Main Institut für Organische Chemie und Chemische Biologie Max-von-Laue-Str. 7 60438 Frankfurt GERMANY
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5
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Berg H, Wirtz Martin MA, Altincekic N, Alshamleh I, Kaur Bains J, Blechar J, Ceylan B, de Jesus V, Dhamotharan K, Fuks C, Gande SL, Hargittay B, Hohmann KF, Hutchinson MT, Korn SM, Krishnathas R, Kutz F, Linhard V, Matzel T, Meiser N, Niesteruk A, Pyper DJ, Schulte L, Trucks S, Azzaoui K, Blommers MJJ, Gadiya Y, Karki R, Zaliani A, Gribbon P, Almeida MDS, Anobom CD, Bula AL, Buetikofer M, Caruso ÍP, Felli IC, Da Poian AT, de Amorim GC, Fourkiotis NK, Gallo A, Ghosh D, Gomes-Neto F, Gorbatyuk O, Hao B, Kurauskas V, Lecoq L, Li Y, Mebus-Antunes NC, Mompean M, Neves-Martins TC, Ninot-Pedrosa M, Pinheiro AS, Pontoriero L, Pustovalova Y, Riek R, Robertson A, Abi Saad MJ, Treviño MA, Tsika AC, Almeida FC, Bax A, Henzler-Wildman K, Hoch JC, Jaudzems K, Laurents DV, Orts J, Pieratelli R, Spyroulias GA, Duchardt-Ferner E, Ferner J, Fuertig B, Hengesbach M, Löhr F, Qureshi N, Richter C, Saxena K, Schlundt A, Sreeramulu S, Wacker A, Weigand JE, Wirmer-Bartoschek J, Woehnert J, Schwalbe H. Comprehensive Fragment Screening of the SARS‐CoV‐2 Proteome Explores Novel Chemical Space for Drug Development. Angew Chem Int Ed Engl 2022; 61:e202205858. [PMID: 36115062 PMCID: PMC9539013 DOI: 10.1002/anie.202205858] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Indexed: 11/17/2022]
Abstract
SARS‐CoV‐2 (SCoV2) and its variants of concern pose serious challenges to the public health. The variants increased challenges to vaccines, thus necessitating for development of new intervention strategies including anti‐virals. Within the international Covid19‐NMR consortium, we have identified binders targeting the RNA genome of SCoV2. We established protocols for the production and NMR characterization of more than 80% of all SCoV2 proteins. Here, we performed an NMR screening using a fragment library for binding to 25 SCoV2 proteins and identified hits also against previously unexplored SCoV2 proteins. Computational mapping was used to predict binding sites and identify functional moieties (chemotypes) of the ligands occupying these pockets. Striking consensus was observed between NMR‐detected binding sites of the main protease and the computational procedure. Our investigation provides novel structural and chemical space for structure‐based drug design against the SCoV2 proteome.
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Affiliation(s)
- Hannes Berg
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | | | - Nadide Altincekic
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Islam Alshamleh
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Jasleen Kaur Bains
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Julius Blechar
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Betül Ceylan
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Vanessa de Jesus
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | | | - Christin Fuks
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Santosh L. Gande
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Bruno Hargittay
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | | | - Marie T. Hutchinson
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | | | - Robin Krishnathas
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Felicitas Kutz
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Verena Linhard
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Tobias Matzel
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Nathalie Meiser
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Anna Niesteruk
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Dennis J. Pyper
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Linda Schulte
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Sven Trucks
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Kamal Azzaoui
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Marcel J J Blommers
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Yojana Gadiya
- Fraunhofer Institute for Molecular Biology and Applied Ecology ScreeningPort: Fraunhofer-Institut fur Translationale Medizin und Pharmakologie ITMP Drug Discovery Research ScreeningPort Screening Unit GERMANY
| | - Reagon Karki
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP: Fraunhofer-Institut fur Translationale Medizin und Pharmakologie ITMP Screening Unit GERMANY
| | - Andrea Zaliani
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP: Fraunhofer-Institut fur Translationale Medizin und Pharmakologie ITMP Screening Unit GERMANY
| | - Philip Gribbon
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP: Fraunhofer-Institut fur Translationale Medizin und Pharmakologie ITMP Screening Unit GERMANY
| | - Marcius da Silva Almeida
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Institue for Medical Biochemistry BRAZIL
| | - Cristiane Dinis Anobom
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Department of Biochemistry BRAZIL
| | - Anna Lina Bula
- Latvian Institute of Organic Synthesis: Latvijas Organiskas sintezes instituts Institute of Organic Synthesis LATVIA
| | - Matthias Buetikofer
- ETH Zurich: Eidgenossische Technische Hochschule Zurich Institute für Physikalische Chemie GERMANY
| | - Ícaro Putinhon Caruso
- Sao Paulo State University Julio de Mesquita Filho: Universidade Estadual Paulista Julio de Mesquita Filho Department of Physics BRAZIL
| | - Isabella Caterina Felli
- University of Florence: Universita degli Studi di Firenze Magnetic Resonance Center (CERM) ITALY
| | - Andrea T Da Poian
- Sao Paulo State University Julio de Mesquita Filho: Universidade Estadual Paulista Julio de Mesquita Filho Department of Physics GERMANY
| | - Gisele Cardoso de Amorim
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Multidisciplinary Center for Research in Biology BRAZIL
| | - Nikolaos K Fourkiotis
- University of Patras - Patras Campus: Panepistemio Patron Department of Pharmacy GREECE
| | - Angelo Gallo
- University of Patras - Patras Campus: Panepistemio Patron Department of Pharmacy GREECE
| | - Dhiman Ghosh
- ETH Zurich: Eidgenossische Technische Hochschule Zurich Institute for Physical Chemistry SWITZERLAND
| | | | - Oksana Gorbatyuk
- UConn Health Department of Molecular Biology and Biophysics UNITED STATES
| | - Bing Hao
- UConn Health Department of Molecular Biology and Biopyhsics UNITED STATES
| | - Vilius Kurauskas
- UW Madison: University of Wisconsin Madison Department of Biochemistry UNITED STATES
| | - Lauriane Lecoq
- Universite de Lyon Molecular Microbiology and Structural Biochemistry FRANCE
| | - Yunfeng Li
- UConn Health Department of Molecular Biology and Biophysics UNITED STATES
| | - Nathane Cunha Mebus-Antunes
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Institute of Medical Biochemistry BRAZIL
| | - Miguel Mompean
- Estacion Biologica de Donana CSIC "Rocasolano" Institute for Physical Chemistry SPAIN
| | - Thais Cristtina Neves-Martins
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Institute of Medical Biochemistry BRAZIL
| | - Marti Ninot-Pedrosa
- Universite Lyon 1 IUT Lyon 1 Molecular Microbiology and Structural Biochemistry FRANCE
| | - Anderson S Pinheiro
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Department of Biochemistry BRAZIL
| | - Letizia Pontoriero
- University of Florence: Universita degli Studi di Firenze Center for Magnetic Resonance ITALY
| | - Yulia Pustovalova
- UConn Health Department of Molecular Biology and Biophysics UNITED STATES
| | - Roland Riek
- ETH Zürich: Eidgenossische Technische Hochschule Zurich Institute for Physical Chemistry SWITZERLAND
| | - Angus Robertson
- NIAMDD: National Institute of Diabetes and Digestive and Kidney Diseases Laboratory of Chemical Physics UNITED STATES
| | - Marie Jose Abi Saad
- University of Vienna: Universitat Wien Department of Pharmaceutical Sciences AUSTRIA
| | - Miguel A Treviño
- CSIC: Consejo Superior de Investigaciones Cientificas "Rocasolano" Institute for Physical Chemistry SPAIN
| | - Aikaterini C Tsika
- University of Patras - Patras Campus: Panepistemio Patron Department of Pharmacy GREECE
| | - Fabio C.L. Almeida
- Federal University of Rio de Janeiro: Universidade Federal do Rio de Janeiro Institute of Medical Biochemistry BRAZIL
| | - Ad Bax
- National Institute of Diabetes and Digestive and Kidney Diseases Laboratory of Chemical Physics UNITED STATES
| | | | - Jeffrey C Hoch
- UConn Health Department of Molecular Biology and Biophysics UNITED STATES
| | - Kristaps Jaudzems
- Institute of Organic Synthesis of the Latvian Academy of Sciences: Latvijas Organiskas sintezes instituts Institute for Organic Chemistry LATVIA
| | - Douglas V Laurents
- Estacion Biologica de Donana CSIC "Rocasolano" Institute for Physical Chemistry SPAIN
| | - Julien Orts
- University of Vienna: Universitat Wien Department of Pharmaceutical Sciences AUSTRIA
| | - Roberta Pieratelli
- University of Florence: Universita degli Studi di Firenze Center for Magnetic Resonance ITALY
| | - Georgios A Spyroulias
- University of Patras - Patras Campus: Panepistemio Patron Department of Pharmacy GREECE
| | | | - Jan Ferner
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Boris Fuertig
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Martin Hengesbach
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Frank Löhr
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Nusrat Qureshi
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Christian Richter
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Krishna Saxena
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Andreas Schlundt
- Goethe-Universitat Frankfurt am Main Department for Biosciences GERMANY
| | - Sridhar Sreeramulu
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Anna Wacker
- Goethe-Universitat Frankfurt am Main Biochemistry, Chemistry, Pharmacy GERMANY
| | - Julia E Weigand
- TU Darmstadt: Technische Universitat Darmstadt Department of Biology GERMANY
| | | | - Jens Woehnert
- Goethe-Universitat Frankfurt am Main Department of Biological Sciences GERMANY
| | - Harald Schwalbe
- Goethe-Universitat Frankfurt am Main Institut für Organische Chemie und Chemische Biologie Max-von-Laue-Str. 7 60438 Frankfurt GERMANY
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6
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Domingo-Fernández D, Baksi S, Schultz B, Gadiya Y, Karki R, Raschka T, Ebeling C, Hofmann-Apitius M, Kodamullil AT. COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology. Bioinformatics 2021; 37:1332-1334. [PMID: 32976572 PMCID: PMC7558629 DOI: 10.1093/bioinformatics/btaa834] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/26/2020] [Accepted: 09/11/2020] [Indexed: 02/07/2023] Open
Abstract
Summary The COVID-19 crisis has elicited a global response by the scientific community that has led to a burst of publications on the pathophysiology of the virus. However, without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats. Availability and implementation The COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754 Sankt Augustin, Germany.,Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany
| | - Shounak Baksi
- Causality Biomodels, KINFRA Hi-Tech Park, Cochin, Kerala 683503, India
| | - Bruce Schultz
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754 Sankt Augustin, Germany
| | - Yojana Gadiya
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754 Sankt Augustin, Germany.,Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany
| | - Reagon Karki
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754 Sankt Augustin, Germany.,Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany
| | - Tamara Raschka
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754 Sankt Augustin, Germany.,Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany
| | - Christian Ebeling
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754 Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754 Sankt Augustin, Germany.,Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754 Sankt Augustin, Germany.,Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany
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7
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Schultz B, Zaliani A, Ebeling C, Reinshagen J, Bojkova D, Lage-Rupprecht V, Karki R, Lukassen S, Gadiya Y, Ravindra NG, Das S, Baksi S, Domingo-Fernández D, Lentzen M, Strivens M, Raschka T, Cinatl J, DeLong LN, Gribbon P, Geisslinger G, Ciesek S, van Dijk D, Gardner S, Kodamullil AT, Fröhlich H, Peitsch M, Jacobs M, Hoeng J, Eils R, Claussen C, Hofmann-Apitius M. A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization. Sci Rep 2021; 11:11049. [PMID: 34040048 PMCID: PMC8155020 DOI: 10.1038/s41598-021-90296-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 05/04/2021] [Indexed: 02/08/2023] Open
Abstract
The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.
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Affiliation(s)
- Bruce Schultz
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Andrea Zaliani
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Christian Ebeling
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Jeanette Reinshagen
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Denisa Bojkova
- Institute for Medical Virology, University Hospital Frankfurt, 60590, Frankfurt am Main, Germany
| | - Vanessa Lage-Rupprecht
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Reagon Karki
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Sören Lukassen
- Center for Digital Health, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Yojana Gadiya
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Neal G Ravindra
- Center for Biomedical Data Science, Yale School of Medicine, Yale University, 333 Cedar Street, New Haven, CT, 06510, USA
| | - Sayoni Das
- Unit 8B Bankside, PrecisionLife Ltd., Hanborough Business Park, Long Hanborough, Oxfordshire, OX29 8LJ, UK
| | - Shounak Baksi
- Causality BioModels Pvt Ltd., Kinfra Hi-Tech Park, Kerala Technology Innovation Zone- KTIZ, Kalamassery, Cochin, 683503, India
| | - Daniel Domingo-Fernández
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Manuel Lentzen
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Mark Strivens
- Unit 8B Bankside, PrecisionLife Ltd., Hanborough Business Park, Long Hanborough, Oxfordshire, OX29 8LJ, UK
| | - Tamara Raschka
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Jindrich Cinatl
- Institute for Medical Virology, University Hospital Frankfurt, 60590, Frankfurt am Main, Germany
| | - Lauren Nicole DeLong
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Phil Gribbon
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Gerd Geisslinger
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
- Pharmazentrum Frankfurt/ZAFES, Institut Für Klinische Pharmakologie, Klinikum Der Goethe-Universität Frankfurt, 60590, Frankfurt am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 60596, Frankfurt am Main, Germany
| | - Sandra Ciesek
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 60596, Frankfurt am Main, Germany
- Institute for Medical Virology, University Hospital Frankfurt, 60590, Frankfurt am Main, Germany
- DZIF, German Centre for Infection Research, External Partner Site, 60596, Frankfurt am Main, Germany
| | - David van Dijk
- Center for Biomedical Data Science, Yale School of Medicine, Yale University, 333 Cedar Street, New Haven, CT, 06510, USA
| | - Steve Gardner
- Unit 8B Bankside, PrecisionLife Ltd., Hanborough Business Park, Long Hanborough, Oxfordshire, OX29 8LJ, UK
| | - Alpha Tom Kodamullil
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Holger Fröhlich
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Manuel Peitsch
- Philipp Morris International R&D, Biological Systems Research, R&D Innovation Cube T1517.07, Quai Jeanrenaud 5, 2000, Neuchâte, Switzerland
| | - Marc Jacobs
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany
| | - Julia Hoeng
- Philipp Morris International R&D, Biological Systems Research, R&D Innovation Cube T1517.07, Quai Jeanrenaud 5, 2000, Neuchâte, Switzerland
| | - Roland Eils
- Center for Digital Health, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Carsten Claussen
- ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525, Hamburg, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Institutszentrum Birlinghoven, 53754, Sankt Augustin, Germany.
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8
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Golriz Khatami S, Domingo-Fernández D, Mubeen S, Hoyt CT, Robinson C, Karki R, Iyappan A, Kodamullil AT, Hofmann-Apitius M. A Systems Biology Approach for Hypothesizing the Effect of Genetic Variants on Neuroimaging Features in Alzheimer's Disease. J Alzheimers Dis 2021; 80:831-840. [PMID: 33554913 PMCID: PMC8075382 DOI: 10.3233/jad-201397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/07/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Neuroimaging markers provide quantitative insight into brain structure and function in neurodegenerative diseases, such as Alzheimer's disease, where we lack mechanistic insights to explain pathophysiology. These mechanisms are often mediated by genes and genetic variations and are often studied through the lens of genome-wide association studies. Linking these two disparate layers (i.e., imaging and genetic variation) through causal relationships between biological entities involved in the disease's etiology would pave the way to large-scale mechanistic reasoning and interpretation. OBJECTIVE We explore how genetic variants may lead to functional alterations of intermediate molecular traits, which can further impact neuroimaging hallmarks over a series of biological processes across multiple scales. METHODS We present an approach in which knowledge pertaining to single nucleotide polymorphisms and imaging readouts is extracted from the literature, encoded in Biological Expression Language, and used in a novel workflow to assist in the functional interpretation of SNPs in a clinical context. RESULTS We demonstrate our approach in a case scenario which proposes KANSL1 as a candidate gene that accounts for the clinically reported correlation between the incidence of the genetic variants and hippocampal atrophy. We find that the workflow prioritizes multiple mechanisms reported in the literature through which KANSL1 may have an impact on hippocampal atrophy such as through the dysregulation of cell proliferation, synaptic plasticity, and metabolic processes. CONCLUSION We have presented an approach that enables pinpointing relevant genetic variants as well as investigating their functional role in biological processes spanning across several, diverse biological scales.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
| | - Christine Robinson
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Reagon Karki
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Anandhi Iyappan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (Fraunhofer SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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9
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Karki R, Madan S, Gadiya Y, Domingo-Fernández D, Kodamullil AT, Hofmann-Apitius M. Data-Driven Modeling of Knowledge Assemblies in Understanding Comorbidity Between Type 2 Diabetes Mellitus and Alzheimer's Disease. J Alzheimers Dis 2020; 78:87-95. [PMID: 32925069 PMCID: PMC7683056 DOI: 10.3233/jad-200752] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background: Recent studies have suggested comorbid association between Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) through identification of shared molecular mechanisms. However, the inference is pre-dominantly literature-based and lacks interpretation of pre-disposed genomic variants and transcriptomic measurables. Objective: In this study, we aim to identify shared genetic variants and dysregulated genes in AD and T2DM and explore their functional roles in the comorbidity between the diseases. Methods: The genetic variants for AD and T2DM were retrieved from GWAS catalog, GWAS central, dbSNP, and DisGeNet and subjected to linkage disequilibrium analysis. Next, shared variants were prioritized using RegulomeDB and Polyphen-2. Afterwards, a knowledge assembly embedding prioritized variants and their corresponding genes was created by mining relevant literature using Biological Expression Language. Finally, coherently perturbed genes from gene expression meta-analysis were mapped to the knowledge assembly to pinpoint biological entities and processes and depict a mechanistic link between AD and T2DM. Results: Our analysis identified four genes (i.e., ABCG1, COMT, MMP9, and SOD2) that could have dual roles in both AD and T2DM. Using cartoon representation, we have illustrated a set of causal events surrounding these genes which are associated to biological processes such as oxidative stress, insulin resistance, apoptosis and cognition. Conclusion: Our approach of using data as the driving force for unraveling disease etiologies eliminates literature bias and enables identification of novel entities that serve as the bridge between comorbid conditions.
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Affiliation(s)
- Reagon Karki
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
| | - Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Yojana Gadiya
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
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10
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de Jong J, Emon MA, Wu P, Karki R, Sood M, Godard P, Ahmad A, Vrooman H, Hofmann-Apitius M, Fröhlich H. Deep learning for clustering of multivariate clinical patient trajectories with missing values. Gigascience 2019; 8:giz134. [PMID: 31730697 PMCID: PMC6857688 DOI: 10.1093/gigascience/giz134] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 09/23/2019] [Accepted: 10/19/2019] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratification problem translates into a complex problem of clustering multivariate and relatively short time series because (i) these diseases are multifactorial and not well described by single clinical outcome variables and (ii) disease progression needs to be monitored over time. Additionally, clinical data often additionally are hindered by the presence of many missing values, further complicating any clustering attempts. FINDINGS The problem of clustering multivariate short time series with many missing values is generally not well addressed in the literature. In this work, we propose a deep learning-based method to address this issue, variational deep embedding with recurrence (VaDER). VaDER relies on a Gaussian mixture variational autoencoder framework, which is further extended to (i) model multivariate time series and (ii) directly deal with missing values. We validated VaDER by accurately recovering clusters from simulated and benchmark data with known ground truth clustering, while varying the degree of missingness. We then used VaDER to successfully stratify patients with Alzheimer disease and patients with Parkinson disease into subgroups characterized by clinically divergent disease progression profiles. Additional analyses demonstrated that these clinical differences reflected known underlying aspects of Alzheimer disease and Parkinson disease. CONCLUSIONS We believe our results show that VaDER can be of great value for future efforts in patient stratification, and multivariate time-series clustering in general.
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Affiliation(s)
- Johann de Jong
- UCB Biosciences GmbH, Alfred-Nobel-Strasse 10, 40789 Monheim, Germany
| | - Mohammad Asif Emon
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Konrad-Adenauer-Strasse, 53754 Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, University of Bonn, Konrad-Adenauer-Strasse, 53115 Bonn, Germany
| | - Ping Wu
- UCB Pharma, Bath Road 216, Slough SL1 3WE, UK
| | - Reagon Karki
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Konrad-Adenauer-Strasse, 53754 Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, University of Bonn, Konrad-Adenauer-Strasse, 53115 Bonn, Germany
| | - Meemansa Sood
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Konrad-Adenauer-Strasse, 53754 Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, University of Bonn, Konrad-Adenauer-Strasse, 53115 Bonn, Germany
| | - Patrice Godard
- UCB Pharma, Chemin du Foriest 1, 1420 Braine-l’Alleud, Belgium
| | - Ashar Ahmad
- Bonn-Aachen International Center for IT, University of Bonn, Konrad-Adenauer-Strasse, 53115 Bonn, Germany
| | - Henri Vrooman
- Erasmus MC, University Medical Center Rotterdam, Department of Radiology, Doctor Molewaterplein 40, PO Box 2040, 3000 CA Rotterdam, Netherlands
- Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Department of Medical Informatics, PO Box 2040, 3000 CA Rotterdam, Netherlands
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Konrad-Adenauer-Strasse, 53754 Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, University of Bonn, Konrad-Adenauer-Strasse, 53115 Bonn, Germany
| | - Holger Fröhlich
- UCB Biosciences GmbH, Alfred-Nobel-Strasse 10, 40789 Monheim, Germany
- Bonn-Aachen International Center for IT, University of Bonn, Konrad-Adenauer-Strasse, 53115 Bonn, Germany
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11
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Karki R, Kodamullil AT, Hoyt CT, Hofmann-Apitius M. Quantifying mechanisms in neurodegenerative diseases (NDDs) using candidate mechanism perturbation amplitude (CMPA) algorithm. BMC Bioinformatics 2019; 20:494. [PMID: 31604427 PMCID: PMC6788110 DOI: 10.1186/s12859-019-3101-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 09/16/2019] [Indexed: 12/21/2022] Open
Abstract
Background Literature derived knowledge assemblies have been used as an effective way of representing biological phenomenon and understanding disease etiology in systems biology. These include canonical pathway databases such as KEGG, Reactome and WikiPathways and disease specific network inventories such as causal biological networks database, PD map and NeuroMMSig. The represented knowledge in these resources delineates qualitative information focusing mainly on the causal relationships between biological entities. Genes, the major constituents of knowledge representations, tend to express differentially in different conditions such as cell types, brain regions and disease stages. A classical approach of interpreting a knowledge assembly is to explore gene expression patterns of the individual genes. However, an approach that enables quantification of the overall impact of differentially expressed genes in the corresponding network is still lacking. Results Using the concept of heat diffusion, we have devised an algorithm that is able to calculate the magnitude of regulation of a biological network using expression datasets. We have demonstrated that molecular mechanisms specific to Alzheimer (AD) and Parkinson Disease (PD) regulate with different intensities across spatial and temporal resolutions. Our approach depicts that the mitochondrial dysfunction in PD is severe in cortex and advanced stages of PD patients. Similarly, we have shown that the intensity of aggregation of neurofibrillary tangles (NFTs) in AD increases as the disease progresses. This finding is in concordance with previous studies that explain the burden of NFTs in stages of AD. Conclusions This study is one of the first attempts that enable quantification of mechanisms represented as biological networks. We have been able to quantify the magnitude of regulation of a biological network and illustrate that the magnitudes are different across spatial and temporal resolution.
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Affiliation(s)
- Reagon Karki
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Endenicher Allee 19a, 53115, Bonn, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Endenicher Allee 19a, 53115, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Endenicher Allee 19a, 53115, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754, Sankt Augustin, Germany. .,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Endenicher Allee 19a, 53115, Bonn, Germany.
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12
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Domingo-Fernández D, Kodamullil AT, Iyappan A, Naz M, Emon MA, Raschka T, Karki R, Springstubbe S, Ebeling C, Hofmann-Apitius M. Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): a web server for mechanism enrichment. Bioinformatics 2018. [PMID: 28651363 PMCID: PMC5870765 DOI: 10.1093/bioinformatics/btx399] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Motivation The concept of a 'mechanism-based taxonomy of human disease' is currently replacing the outdated paradigm of diseases classified by clinical appearance. We have tackled the paradigm of mechanism-based patient subgroup identification in the challenging area of research on neurodegenerative diseases. Results We have developed a knowledge base representing essential pathophysiology mechanisms of neurodegenerative diseases. Together with dedicated algorithms, this knowledge base forms the basis for a 'mechanism-enrichment server' that supports the mechanistic interpretation of multiscale, multimodal clinical data. Availability and implementation NeuroMMSig is available at http://neurommsig.scai.fraunhofer.de/. Contact martin.hofmann-apitius@scai.fraunhofer.de. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn 53113, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn 53113, Germany
| | - Anandhi Iyappan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn 53113, Germany
| | - Mufassra Naz
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn 53113, Germany
| | - Mohammad Asif Emon
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn 53113, Germany
| | - Tamara Raschka
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn 53113, Germany
| | - Reagon Karki
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn 53113, Germany
| | - Stephan Springstubbe
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany
| | - Christian Ebeling
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53754, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn 53113, Germany
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13
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Kodamullil AT, Iyappan A, Karki R, Madan S, Younesi E, Hofmann-Apitius M. Of Mice and Men: Comparative Analysis of Neuro-Inflammatory Mechanisms in Human and Mouse Using Cause-and-Effect Models. J Alzheimers Dis 2018; 59:1045-1055. [PMID: 28731442 PMCID: PMC5545904 DOI: 10.3233/jad-170255] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Perturbance in inflammatory pathways have been identified as one of the major factors which leads to neurodegenerative diseases (NDD). Owing to the limited access of human brain tissues and the immense complexity of the brain, animal models, specifically mouse models, play a key role in advancing the NDD field. However, many of these mouse models fail to reproduce the clinical manifestations and end points of the disease. NDD drugs, which passed the efficacy test in mice, were repeatedly not successful in clinical trials. There are numerous studies which are supporting and opposing the applicability of mouse models in neuroinflammation and NDD. In this paper, we assessed to what extend a mouse can mimic the cellular and molecular interactions in humans at a mechanism level. Based on our mechanistic modeling approach, we investigate the failure of a neuroinflammation targeted drug in the late phases of clinical trials based on the comparative analyses between the two species.
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Affiliation(s)
- Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
| | - Anandhi Iyappan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
| | - Reagon Karki
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
| | - Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
| | - Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
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Karki R, Kodamullil AT, Hofmann-Apitius M. Comorbidity Analysis between Alzheimer's Disease and Type 2 Diabetes Mellitus (T2DM) Based on Shared Pathways and the Role of T2DM Drugs. J Alzheimers Dis 2018; 60:721-731. [PMID: 28922161 PMCID: PMC5611890 DOI: 10.3233/jad-170440] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background: Various studies suggest a comorbid association between Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) indicating that there could be shared underlying pathophysiological mechanisms. Objective: This study aims to systematically model relevant knowledge at the molecular level to find a mechanistic rationale explaining the existing comorbid association between AD and T2DM. Method: We have used a knowledge-based modeling approach to build two network models for AD and T2DM using Biological Expression Language (BEL), which is capable of capturing and representing causal and correlative relationships at both molecular and clinical levels from various knowledge resources. Results: Using comparative analysis, we have identified several putative “shared pathways”. We demonstrate, at a mechanistic level, how the insulin signaling pathway is related to other significant AD pathways such as the neurotrophin signaling pathway, PI3K/AKT signaling, MTOR signaling, and MAPK signaling and how these pathways do cross-talk with each other both in AD and T2DM. In addition, we present a mechanistic hypothesis that explains both favorable and adverse effects of the anti-diabetic drug metformin in AD. Conclusion: The two computable models introduced here provide a powerful framework to identify plausible mechanistic links shared between AD and T2DM and thereby identify targeted pathways for new therapeutics. Our approach can also be used to provide mechanistic answers to the question of why some T2DM treatments seem to increase the risk of AD.
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Affiliation(s)
- Reagon Karki
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
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15
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Emon MAEK, Kodamullil AT, Karki R, Younesi E, Hofmann-Apitius M. Using Drugs as Molecular Probes: A Computational Chemical Biology Approach in Neurodegenerative Diseases. J Alzheimers Dis 2018; 56:677-686. [PMID: 28035920 PMCID: PMC5271458 DOI: 10.3233/jad-160222] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Neurodegenerative diseases including Alzheimer’s disease are complex to tackle because of the complexity of the brain, both in structure and function. Such complexity is reflected by the involvement of various brain regions and multiple pathways in the etiology of neurodegenerative diseases that render single drug target approaches ineffective. Particularly in the area of neurodegeneration, attention has been drawn to repurposing existing drugs with proven efficacy and safety profiles. However, there is a lack of systematic analysis of the brain chemical space to predict the feasibility of repurposing strategies. Using a mechanism-based, drug-target interaction modeling approach, we have identified promising drug candidates for repositioning. Mechanistic cause-and-effect models consolidate relevant prior knowledge on drugs, targets, and pathways from the scientific literature and integrate insights derived from experimental data. We demonstrate the power of this approach by predicting two repositioning candidates for Alzheimer’s disease and one for amyotrophic lateral sclerosis.
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Affiliation(s)
- Mohammad Asif Emran Khan Emon
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
| | - Reagon Karki
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
| | - Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany
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16
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Fluck J, Madan S, Ansari S, Kodamullil AT, Karki R, Rastegar-Mojarad M, Catlett NL, Hayes W, Szostak J, Hoeng J, Peitsch M. Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL). Database (Oxford) 2016; 2016:baw113. [PMID: 27554092 PMCID: PMC4995071 DOI: 10.1093/database/baw113] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 07/07/2016] [Indexed: 01/21/2023]
Abstract
Success in extracting biological relationships is mainly dependent on the complexity of the task as well as the availability of high-quality training data. Here, we describe the new corpora in the systems biology modeling language BEL for training and testing biological relationship extraction systems that we prepared for the BioCreative V BEL track. BEL was designed to capture relationships not only between proteins or chemicals, but also complex events such as biological processes or disease states. A BEL nanopub is the smallest unit of information and represents a biological relationship with its provenance. In BEL relationships (called BEL statements), the entities are normalized to defined namespaces mainly derived from public repositories, such as sequence databases, MeSH or publicly available ontologies. In the BEL nanopubs, the BEL statements are associated with citation information and supportive evidence such as a text excerpt. To enable the training of extraction tools, we prepared BEL resources and made them available to the community. We selected a subset of these resources focusing on a reduced set of namespaces, namely, human and mouse genes, ChEBI chemicals, MeSH diseases and GO biological processes, as well as relationship types ‘increases’ and ‘decreases’. The published training corpus contains 11 000 BEL statements from over 6000 supportive text excerpts. For method evaluation, we selected and re-annotated two smaller subcorpora containing 100 text excerpts. For this re-annotation, the inter-annotator agreement was measured by the BEL track evaluation environment and resulted in a maximal F-score of 91.18% for full statement agreement. In addition, for a set of 100 BEL statements, we do not only provide the gold standard expert annotations, but also text excerpts pre-selected by two automated systems. Those text excerpts were evaluated and manually annotated as true or false supportive in the course of the BioCreative V BEL track task. Database URL:http://wiki.openbel.org/display/BIOC/Datasets
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Affiliation(s)
- Juliane Fluck
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Sumit Madan
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Sam Ansari
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
| | - Alpha T Kodamullil
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Reagon Karki
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | | | | | - William Hayes
- Selventa, One Alewife Center, Cambridge, MA 02140, USA
| | - Justyna Szostak
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
| | - Julia Hoeng
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
| | - Manuel Peitsch
- Philip Morris International R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, Neuchâtel, 2000, Switzerland
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Rudroff T, Kindred J, Koo P, Karki R, Hebert J. Asymmetric glucose uptake in leg muscles of patients with Multiple Sclerosis during walking detected by [18F]-FDG PET/CT. NeuroRehabilitation 2014; 35:813-23. [DOI: 10.3233/nre-141179] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- T. Rudroff
- Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, USA
| | - J.H. Kindred
- Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, USA
| | - P.J. Koo
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, USA
| | - R. Karki
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, USA
| | - J.R. Hebert
- Department of Physical Medicine & Rehabilitation – Physical Therapy Program, University of Colorado School of Medicine, Aurora, CO, USA
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Klingensmith WC, Koo PJ, Summerlin A, Fehrenbach BW, Karki R, Shulman BC, Raeburn CD, McIntyre RC. Parathyroid Imaging: The Importance of Pinhole Collimation with Both Single- and Dual-Tracer Acquisition. J Nucl Med Technol 2013; 41:99-104. [DOI: 10.2967/jnmt.112.118208] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Abstract
BACKGROUND Determining the sex of deceased is easy when a complete skeleton is available for examination. On the whole, the bones are heavier, larger and markings of muscular attachments are more pronounced in the male than in the female. OBJECTIVE The purpose of this study was attempted to evolve an easily applied formula to enable the assessment of sex in an unknown clavicles and to know about comparative differences between the right and left clavicles, from certain metrical parameters. METHODS The study was an observational, cross-sectional and descriptive in nature. The present study was conducted on 257 adult clavicles out of which, 135 were of the right side and 122 of left side. The maximum length of the clavicle (in mm) was taken. RESULTS The length of the right clavicles varies from 108 mm to 178 mm with an average of 143.21 mm ± 11.13 mm S.D. The length of the left clavicles varies from 111 mm to 181 mm with an average 145.53 mm ± 11.04 mm S.D. It has been observed that the left clavicle was longer than the right clavicle by 2.32 mm. There was no such single character which can determine the sex of all clavicles. Depending on the length alone, the sex can be decided in 13.33% male and 4.44% female right clavicles and 16.39% male and 9.83% female bones if the left clavicle is considered. CONCLUSION The left clavicle was longer than the right clavicle. The determination of sex from the clavicle has a great medico legal significance to the toxicologists. It also helps the anthropologists in their study of evolution of mankind and migration of races.
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Affiliation(s)
- M K Haque
- Department of Anatomy, Kathmandu University School Of Medical Sciences, Dhulikhel, Nepal
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Karki R, Bhatta DR, Malla S, Dumre SP, Upadhyay BP, Dahal S, Acharya D. Resistotypes of Vibrio cholerae 01 Ogawa Biotype El Tor in Kathmandu, Nepal. Nepal Med Coll J 2011; 13:84-87. [PMID: 22364087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Cholera continued to be a major diarrheal illness in Nepal and antibiotic resistance has appeared as a serious problem in cholera management. The study aimed at analyzing the distribution pattern of the resistotypes (R-types) of Vibrio cholerae in the Kathmandu valley, Nepal. During June 2008 to January 2009, 210 diarrheal specimens received at National Public Health Laboratory from suspected cholera patients were subjected to standard bacteriological investigation including biotyping and serotyping. Antimicrobial susceptibility pattern of V. cholerae isolates was determined by Kirby Bauer disc diffusion method following CLSI guidelines. A total of 57 (27%) V. cholerae isolated were recovered, all of which belonged to 01 Ogawa Biotype EL Tor. Based on antibiogram, V. cholerae isolates in our study revealed three distinct R-types: R-type I, R-type II and R-type III. All three R types showed resistance to furazolidone, nalidixic acid and cotrimoxazole while sensitive to ciprofloxacin and tetracycline. Additional resistance to ampicillin and erythromycin was observed respectively in R-type II and III. Different R-types showed unique month wise variations (P < 0.05). Differentiation of V. cholerae strains into R-types is an important tool. In addition to direct patient management, it may have implication in identifying the source and spread of infection, and understanding the distribution pattern in a particular geographical region.
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Affiliation(s)
- R Karki
- Central Department of Microbiology, Tribhuvan University, Kathmandu, Nepal
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22
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Kamath MP, Shenoy BD, Tiwari SB, Karki R, Udupa N, Kotian M. Prolonged release biodegradable vesicular carriers for rifampicin--formulation and kinetics of release. Indian J Exp Biol 2000; 38:113-8. [PMID: 11218826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
An attempt has been made to design suitable liposome and niosome-encapsulated drug delivery system for rifampicin and evaluated the same in vitro and in vivo. A modified lipid layer hydration method was employed to prepare these vesicular carriers. The formulated systems were characterized in vitro for size distribution analysis, drug entrapment, drug release profiles and vesicular stability at different conditions of storage. In vivo drug kinetics was evaluated in normal, healthy albino rats for niosomal formulation upon subcutaneous injection and various pharmacokinetic parameters were determined. Niosomes and liposomes exhibited mean diameter of 9.73 and 11.87 microns with entrapment efficiencies of 30.5 and 34.2% respectively. Both the products exhibited sustained release characteristics in vitro with zero order drug release kinetics up to initial 10 hr. Stability evaluation indicated that both formulations were not significantly leaky over a period of one month. Niosomal formulation elevated plasma elimination half life and decreased elimination rate constants for rifampicin in vivo suggested that encapsulation retarded the removal of the drug from circulation compared to free drug due to slow drug release into systemic circulation. A five-fold increase in the area under plasma rifampicin concentration-time curve for niosomal rifampicin as compared to free drug indicated better bioavailability of encapsulated drug. It is evident from this study that niosomes and liposomes could be promising delivery systems for rifampicin with prolonged drug release profiles and reasonably good stability characteristics.
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Affiliation(s)
- M P Kamath
- Department of Pharmaceutics, College of Pharmaceutical Sciences, Manipal 576 119, India
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Rai SKC, Thapa HB, Sharma MK, Dhakhwa K, Karki R. The distribution of refractive errors among children attending Lumbini Eye Institute, Nepal. Nepal J Ophthalmol 1970; 4:90-5. [DOI: 10.3126/nepjoph.v4i1.5858] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Introduction: Uncorrected refractive error is an important cause of childhood blindness and visual impairment. Objective: To describe the patterns of refractive errors among children attending the outpatient clinic at the Department of Pediatric Ophthalmology, Lumbini Eye Institute, Bhairahawa, Nepal. Subjects and methods: Records of 133 children with refractive errors aged 5 - 15 years from both the urban and rural areas of Nepal and the adjacent territory of India attending the hospital between September and November 2010 were examined for patterns of refractive errors. The SPSS statistical software was used to perform data analysis. Results: The commonest type of refractive error among the children was astigmatism (47 %) followed by myopia (34 %) and hyperopia (15 %). The refractive error was more prevalent among children of both the genders of age group 11-15 years as compared to their younger counterparts (RR = 1.22, 95 % CI = 0.66 – 2.25). The refractive error was more common (70 %) in the rural than the urban children (26 %). The rural females had a higher (38 %) prevalence of myopia than urban females (18 %). Among the children with refractive errors, only 57 % were using spectacles at the initial presentation. Conclusions: Astigmatism is the commonest type of refractive error among the children of age 5 - 15 years followed by hypermetropia and myopia. Refractive error remains uncorrected in a significant number of children. DOI: http://dx.doi.org/10.3126/nepjoph.v4i1.5858 NEPJOPH 2012; 4(1): 90-95
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