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Construction of disease-specific cytokine profiles by associating disease genes with immune responses
The pathogenesis of many inflammatory diseases is a coordinated process involving metabolic dysfunctions and immune response—usually modulated by the production of cytokines and associated inflammatory molecules. In this work, we seek to understand how genes involved in pathogenesis which are often...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022887/ https://www.ncbi.nlm.nih.gov/pubmed/35404985 http://dx.doi.org/10.1371/journal.pcbi.1009497 |
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author | Liu, Tianyun Wang, Shiyin Wornow, Michael Altman, Russ B. |
author_facet | Liu, Tianyun Wang, Shiyin Wornow, Michael Altman, Russ B. |
author_sort | Liu, Tianyun |
collection | PubMed |
description | The pathogenesis of many inflammatory diseases is a coordinated process involving metabolic dysfunctions and immune response—usually modulated by the production of cytokines and associated inflammatory molecules. In this work, we seek to understand how genes involved in pathogenesis which are often not associated with the immune system in an obvious way communicate with the immune system. We have embedded a network of human protein-protein interactions (PPI) from the STRING database with 14,707 human genes using feature learning that captures high confidence edges. We have found that our predicted Association Scores derived from the features extracted from STRING’s high confidence edges are useful for predicting novel connections between genes, thus enabling the construction of a full map of predicted associations for all possible pairs between 14,707 human genes. In particular, we analyzed the pattern of associations for 126 cytokines and found that the six patterns of cytokine interaction with human genes are consistent with their functional classifications. To define the disease-specific roles of cytokines we have collected gene sets for 11,944 diseases from DisGeNET. We used these gene sets to predict disease-specific gene associations with cytokines by calculating the normalized average Association Scores between disease-associated gene sets and the 126 cytokines; this creates a unique profile of inflammatory genes (both known and predicted) for each disease. We validated our predicted cytokine associations by comparing them to known associations for 171 diseases. The predicted cytokine profiles correlate (p-value<0.0003) with the known ones in 95 diseases. We further characterized the profiles of each disease by calculating an “Inflammation Score” that summarizes different modes of immune responses. Finally, by analyzing subnetworks formed between disease-specific pathogenesis genes, hormones, receptors, and cytokines, we identified the key genes responsible for interactions between pathogenesis and inflammatory responses. These genes and the corresponding cytokines used by different immune disorders suggest unique targets for drug discovery. |
format | Online Article Text |
id | pubmed-9022887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-90228872022-04-22 Construction of disease-specific cytokine profiles by associating disease genes with immune responses Liu, Tianyun Wang, Shiyin Wornow, Michael Altman, Russ B. PLoS Comput Biol Research Article The pathogenesis of many inflammatory diseases is a coordinated process involving metabolic dysfunctions and immune response—usually modulated by the production of cytokines and associated inflammatory molecules. In this work, we seek to understand how genes involved in pathogenesis which are often not associated with the immune system in an obvious way communicate with the immune system. We have embedded a network of human protein-protein interactions (PPI) from the STRING database with 14,707 human genes using feature learning that captures high confidence edges. We have found that our predicted Association Scores derived from the features extracted from STRING’s high confidence edges are useful for predicting novel connections between genes, thus enabling the construction of a full map of predicted associations for all possible pairs between 14,707 human genes. In particular, we analyzed the pattern of associations for 126 cytokines and found that the six patterns of cytokine interaction with human genes are consistent with their functional classifications. To define the disease-specific roles of cytokines we have collected gene sets for 11,944 diseases from DisGeNET. We used these gene sets to predict disease-specific gene associations with cytokines by calculating the normalized average Association Scores between disease-associated gene sets and the 126 cytokines; this creates a unique profile of inflammatory genes (both known and predicted) for each disease. We validated our predicted cytokine associations by comparing them to known associations for 171 diseases. The predicted cytokine profiles correlate (p-value<0.0003) with the known ones in 95 diseases. We further characterized the profiles of each disease by calculating an “Inflammation Score” that summarizes different modes of immune responses. Finally, by analyzing subnetworks formed between disease-specific pathogenesis genes, hormones, receptors, and cytokines, we identified the key genes responsible for interactions between pathogenesis and inflammatory responses. These genes and the corresponding cytokines used by different immune disorders suggest unique targets for drug discovery. Public Library of Science 2022-04-11 /pmc/articles/PMC9022887/ /pubmed/35404985 http://dx.doi.org/10.1371/journal.pcbi.1009497 Text en © 2022 Liu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Liu, Tianyun Wang, Shiyin Wornow, Michael Altman, Russ B. Construction of disease-specific cytokine profiles by associating disease genes with immune responses |
title | Construction of disease-specific cytokine profiles by associating disease genes with immune responses |
title_full | Construction of disease-specific cytokine profiles by associating disease genes with immune responses |
title_fullStr | Construction of disease-specific cytokine profiles by associating disease genes with immune responses |
title_full_unstemmed | Construction of disease-specific cytokine profiles by associating disease genes with immune responses |
title_short | Construction of disease-specific cytokine profiles by associating disease genes with immune responses |
title_sort | construction of disease-specific cytokine profiles by associating disease genes with immune responses |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022887/ https://www.ncbi.nlm.nih.gov/pubmed/35404985 http://dx.doi.org/10.1371/journal.pcbi.1009497 |
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