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A Network Approach to Predict Pathogenic Genes for Fusarium graminearum
Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interacti...
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949387/ https://www.ncbi.nlm.nih.gov/pubmed/20957229 http://dx.doi.org/10.1371/journal.pone.0013021 |
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author | Liu, Xiaoping Tang, Wei-Hua Zhao, Xing-Ming Chen, Luonan |
author_facet | Liu, Xiaoping Tang, Wei-Hua Zhao, Xing-Ming Chen, Luonan |
author_sort | Liu, Xiaoping |
collection | PubMed |
description | Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which demonstrate the effectiveness of the proposed method. The results presented in this paper not only can provide guidelines for future experimental verification, but also shed light on the pathogenesis of the destructive fungus F. graminearum. |
format | Text |
id | pubmed-2949387 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-29493872010-10-18 A Network Approach to Predict Pathogenic Genes for Fusarium graminearum Liu, Xiaoping Tang, Wei-Hua Zhao, Xing-Ming Chen, Luonan PLoS One Research Article Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which demonstrate the effectiveness of the proposed method. The results presented in this paper not only can provide guidelines for future experimental verification, but also shed light on the pathogenesis of the destructive fungus F. graminearum. Public Library of Science 2010-10-04 /pmc/articles/PMC2949387/ /pubmed/20957229 http://dx.doi.org/10.1371/journal.pone.0013021 Text en Liu et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Liu, Xiaoping Tang, Wei-Hua Zhao, Xing-Ming Chen, Luonan A Network Approach to Predict Pathogenic Genes for Fusarium graminearum |
title | A Network Approach to Predict Pathogenic Genes for Fusarium graminearum
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title_full | A Network Approach to Predict Pathogenic Genes for Fusarium graminearum
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title_fullStr | A Network Approach to Predict Pathogenic Genes for Fusarium graminearum
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title_full_unstemmed | A Network Approach to Predict Pathogenic Genes for Fusarium graminearum
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title_short | A Network Approach to Predict Pathogenic Genes for Fusarium graminearum
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title_sort | network approach to predict pathogenic genes for fusarium graminearum |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949387/ https://www.ncbi.nlm.nih.gov/pubmed/20957229 http://dx.doi.org/10.1371/journal.pone.0013021 |
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