disease maps; network analysis; protein interactions; systems biology; Proteins; Software; Systems Biology; Proteins/genetics; Biochemistry; Molecular Biology
Abstract :
[en] Protein function is often interpreted using molecular interaction diagrams, encoding roles a given protein plays in various molecular mechanisms. Information about disease-related mechanisms can be inferred from disease maps, knowledge repositories containing manually constructed systems biology diagrams. Disease maps hosted on the Molecular Interaction Network VisuAlization (MINERVA) Platform are individually accessible through a REST API interface of each instance, making it challenging to systematically explore their contents. To address this challenge, we introduce the MINERVA Net web service, a repository of open-access disease maps allowing users to publicly share minimal information about their maps. The MINERVA Net repository provides REST API endpoints of particular disease maps, which then can be individually queried for content. In this article, we describe the concept of MINERVA Net and illustrate its use by comparing proteins and their interactions in three different disease maps.
Disciplines :
Biotechnology
Author, co-author :
Gawron, Piotr ; LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
Smula, Ewa ; LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
SCHNEIDER, Reinhard ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
OSTASZEWSKI, Marek ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
External co-authors :
no
Language :
English
Title :
Exploration and comparison of molecular mechanisms across diseases using MINERVA Net.
Publication date :
February 2023
Journal title :
Protein Science: A Publication of the Protein Society
Fujita KA, Ostaszewski M, Matsuoka Y, Ghosh S, Glaab E, Trefois C, et al. Integrating pathways of Parkinson's disease in a molecular interaction map. Mol Neurobiol. 2014;49(1):88–102.
Gawron P, Ostaszewski M, Satagopam V, Gebel S, Mazein A, Kuzma M, et al. MINERVA-a platform for visualization and curation of molecular interaction networks. NPJ Syst Biol Appl. 2016;2:16020.
Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A, et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 2022;50(D1):D687–92.
Glaab E, Schneider R. Comparative pathway and network analysis of brain transcriptome changes during adult aging and in Parkinson's disease. Neurobiol Dis. 2015;74:1–13.
Green DR, Galluzzi L, Kroemer G. Mitochondria and the autophagy–inflammation–cell death axis in organismal aging. Science. 2011;333(6046):1109–12.
Hampel H, Hardy J, Blennow K, Chen C, Perry G, Kim SH, et al. The amyloid-β pathway in Alzheimer's disease. Mol Psychiatry. 2021;26(10):5481–503.
Hanspers K, Kutmon M, Coort SL, Digles D, Dupuis LJ, Ehrhart F, et al. Ten simple rules for creating reusable pathway models for computational analysis and visualization. PLoS Comput Biol. 2021;17(8):e1009226.
Hoksza D, Gawron P, Ostaszewski M, Hasenauer J, Schneider R. Closing the gap between formats for storing layout information in systems biology. Brief Bioinform. 2021;22(1):608.
Hoksza D, Gawron P, Ostaszewski M, Schneider R. MolArt: a molecular structure annotation and visualization tool. Bioinformatics. 2018;34(23):4127–8.
Hoksza D, Gawron P, Ostaszewski M, Smula E, Schneider R. MINERVA API and plugins: opening molecular network analysis and visualization to the community. Bioinformatics. 2019;35(21):4496–8.
Kitano H, Funahashi A, Matsuoka Y, Oda K. Using process diagrams for the graphical representation of biological networks. Nat Biotechnol. 2005;23(8):961–6.
Lo Surdo P, Calderone A, Iannuccelli M, Licata L, Peluso D, Castagnoli L, et al. DISNOR: a disease network open resource. Nucleic Acids Res. 2018;46(D1):D527–34.
Mazein A, Ostaszewski M, Kuperstein I, Watterson S, Le Novère N, Lefaudeux D, et al. Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms. NPJ Syst Biol Appl. 2018;4:21.
Ogishima S, Mizuno S, Kikuchi M, Miyashita A, Kuwano R, Tanaka H, et al. AlzPathway, an updated map of curated signaling pathways: towards deciphering Alzheimer's disease pathogenesis. Methods Mol Biol. 2016;1303:423–32.
Ostaszewski, M. (2022) Clusters of interactions common between the Parkinson's disease map and the ageing map. File deposited on Zenodo https://doi.org/10.5281/zenodo.7448588.
Ostaszewski M, Kieffer E, Danoy G, Schneider R, Bouvry P. Clustering approaches for visual knowledge exploration in molecular interaction networks. BMC Bioinformatics. 2018;19(1):308.
Paolicelli RC, Sierra A, Stevens B, Tremblay M-E, Aguzzi A, Ajami B, et al. Microglia states and nomenclature: a field at its crossroads. Neuron. 2022;110(21):3458–83.
Perfetto L, Pastrello C, Del-Toro N, Duesbury M, Iannuccelli M, Kotlyar M, et al. The IMEx coronavirus interactome: an evolving map of Coronaviridae-host molecular interactions. Database. 2020;2020:baaa096.
UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49(D1):D480–9.
Wyss-Coray T. Ageing, neurodegeneration and brain rejuvenation. Nature. 2016;539(7628):180–6.
