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The global integrative network: integration of signaling and metabolic pathways
The crosstalk between signaling and metabolic pathways has been known to play key roles in human diseases and plant biological processes. The integration of signaling and metabolic pathways can provide an essential reference framework for crosstalk analysis. However, current databases use distinct s...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755797/ https://www.ncbi.nlm.nih.gov/pubmed/36533264 http://dx.doi.org/10.1007/s42994-022-00078-1 |
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author | Lin, Yuying Yan, Shen Chang, Xiao Qi, Xiaoquan Chi, Xu |
author_facet | Lin, Yuying Yan, Shen Chang, Xiao Qi, Xiaoquan Chi, Xu |
author_sort | Lin, Yuying |
collection | PubMed |
description | The crosstalk between signaling and metabolic pathways has been known to play key roles in human diseases and plant biological processes. The integration of signaling and metabolic pathways can provide an essential reference framework for crosstalk analysis. However, current databases use distinct structures to present signaling and metabolic pathways, which leads to the chaos in the integrated networks. Moreover, for the metabolic pathways, the metabolic enzymes and the reactions are disconnected by the current widely accepted layout of edges and nodes, which hinders the topological analysis of the integrated networks. Here, we propose a novel “meta-pathway” structure, which uses the uniformed structure to display the signaling and metabolic pathways, and resolves the difficulty in linking the metabolic enzymes to the reactions topologically. We compiled a comprehensive collection of global integrative networks (GINs) by merging the meta-pathways of 7077 species. We demonstrated the assembly of the signaling and metabolic pathways using the GINs of four species—human, mouse, Arabidopsis, and rice. Almost all of the nodes were assembled into one major network for each of the four species, which provided opportunities for robust crosstalk and topological analysis, and knowledge graph construction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42994-022-00078-1. |
format | Online Article Text |
id | pubmed-9755797 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-97557972022-12-16 The global integrative network: integration of signaling and metabolic pathways Lin, Yuying Yan, Shen Chang, Xiao Qi, Xiaoquan Chi, Xu aBIOTECH Research Article The crosstalk between signaling and metabolic pathways has been known to play key roles in human diseases and plant biological processes. The integration of signaling and metabolic pathways can provide an essential reference framework for crosstalk analysis. However, current databases use distinct structures to present signaling and metabolic pathways, which leads to the chaos in the integrated networks. Moreover, for the metabolic pathways, the metabolic enzymes and the reactions are disconnected by the current widely accepted layout of edges and nodes, which hinders the topological analysis of the integrated networks. Here, we propose a novel “meta-pathway” structure, which uses the uniformed structure to display the signaling and metabolic pathways, and resolves the difficulty in linking the metabolic enzymes to the reactions topologically. We compiled a comprehensive collection of global integrative networks (GINs) by merging the meta-pathways of 7077 species. We demonstrated the assembly of the signaling and metabolic pathways using the GINs of four species—human, mouse, Arabidopsis, and rice. Almost all of the nodes were assembled into one major network for each of the four species, which provided opportunities for robust crosstalk and topological analysis, and knowledge graph construction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42994-022-00078-1. Springer Nature Singapore 2022-09-21 /pmc/articles/PMC9755797/ /pubmed/36533264 http://dx.doi.org/10.1007/s42994-022-00078-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Lin, Yuying Yan, Shen Chang, Xiao Qi, Xiaoquan Chi, Xu The global integrative network: integration of signaling and metabolic pathways |
title | The global integrative network: integration of signaling and metabolic pathways |
title_full | The global integrative network: integration of signaling and metabolic pathways |
title_fullStr | The global integrative network: integration of signaling and metabolic pathways |
title_full_unstemmed | The global integrative network: integration of signaling and metabolic pathways |
title_short | The global integrative network: integration of signaling and metabolic pathways |
title_sort | global integrative network: integration of signaling and metabolic pathways |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755797/ https://www.ncbi.nlm.nih.gov/pubmed/36533264 http://dx.doi.org/10.1007/s42994-022-00078-1 |
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