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A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder

Magnaporthe oryzae Oryzae (MoO) pathotype is a devastating fungal pathogen of rice; however, its pathogenic mechanism remains poorly understood. The current research is primarily focused on single-omics data, which is insufficient to capture the complex cross-kingdom regulatory interactions between...

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Autores principales: Zhao, Enshuang, Dong, Liyan, Zhao, Hengyi, Zhang, Hao, Zhang, Tianyue, Yuan, Shuai, Jiao, Jiao, Chen, Kang, Sheng, Jianhua, Yang, Hongbo, Wang, Pengyu, Li, Guihua, Qin, Qingming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10607591/
https://www.ncbi.nlm.nih.gov/pubmed/37888263
http://dx.doi.org/10.3390/jof9101007
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author Zhao, Enshuang
Dong, Liyan
Zhao, Hengyi
Zhang, Hao
Zhang, Tianyue
Yuan, Shuai
Jiao, Jiao
Chen, Kang
Sheng, Jianhua
Yang, Hongbo
Wang, Pengyu
Li, Guihua
Qin, Qingming
author_facet Zhao, Enshuang
Dong, Liyan
Zhao, Hengyi
Zhang, Hao
Zhang, Tianyue
Yuan, Shuai
Jiao, Jiao
Chen, Kang
Sheng, Jianhua
Yang, Hongbo
Wang, Pengyu
Li, Guihua
Qin, Qingming
author_sort Zhao, Enshuang
collection PubMed
description Magnaporthe oryzae Oryzae (MoO) pathotype is a devastating fungal pathogen of rice; however, its pathogenic mechanism remains poorly understood. The current research is primarily focused on single-omics data, which is insufficient to capture the complex cross-kingdom regulatory interactions between MoO and rice. To address this limitation, we proposed a novel method called Weighted Gene Autoencoder Multi-Omics Relationship Prediction (WGAEMRP), which combines weighted gene co-expression network analysis (WGCNA) and graph autoencoder to predict the relationship between MoO–rice multi-omics data. We applied WGAEMRP to construct a MoO–rice multi-omics heterogeneous interaction network, which identified 18 MoO small RNAs (sRNAs), 17 rice genes, 26 rice mRNAs, and 28 rice proteins among the key biomolecules. Most of the mined functional modules and enriched pathways were related to gene expression, protein composition, transportation, and metabolic processes, reflecting the infection mechanism of MoO. Compared to previous studies, WGAEMRP significantly improves the efficiency and accuracy of multi-omics data integration and analysis. This approach lays out a solid data foundation for studying the biological process of MoO infecting rice, refining the regulatory network of pathogenic markers, and providing new insights for developing disease-resistant rice varieties.
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spelling pubmed-106075912023-10-28 A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder Zhao, Enshuang Dong, Liyan Zhao, Hengyi Zhang, Hao Zhang, Tianyue Yuan, Shuai Jiao, Jiao Chen, Kang Sheng, Jianhua Yang, Hongbo Wang, Pengyu Li, Guihua Qin, Qingming J Fungi (Basel) Article Magnaporthe oryzae Oryzae (MoO) pathotype is a devastating fungal pathogen of rice; however, its pathogenic mechanism remains poorly understood. The current research is primarily focused on single-omics data, which is insufficient to capture the complex cross-kingdom regulatory interactions between MoO and rice. To address this limitation, we proposed a novel method called Weighted Gene Autoencoder Multi-Omics Relationship Prediction (WGAEMRP), which combines weighted gene co-expression network analysis (WGCNA) and graph autoencoder to predict the relationship between MoO–rice multi-omics data. We applied WGAEMRP to construct a MoO–rice multi-omics heterogeneous interaction network, which identified 18 MoO small RNAs (sRNAs), 17 rice genes, 26 rice mRNAs, and 28 rice proteins among the key biomolecules. Most of the mined functional modules and enriched pathways were related to gene expression, protein composition, transportation, and metabolic processes, reflecting the infection mechanism of MoO. Compared to previous studies, WGAEMRP significantly improves the efficiency and accuracy of multi-omics data integration and analysis. This approach lays out a solid data foundation for studying the biological process of MoO infecting rice, refining the regulatory network of pathogenic markers, and providing new insights for developing disease-resistant rice varieties. MDPI 2023-10-12 /pmc/articles/PMC10607591/ /pubmed/37888263 http://dx.doi.org/10.3390/jof9101007 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhao, Enshuang
Dong, Liyan
Zhao, Hengyi
Zhang, Hao
Zhang, Tianyue
Yuan, Shuai
Jiao, Jiao
Chen, Kang
Sheng, Jianhua
Yang, Hongbo
Wang, Pengyu
Li, Guihua
Qin, Qingming
A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder
title A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder
title_full A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder
title_fullStr A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder
title_full_unstemmed A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder
title_short A Relationship Prediction Method for Magnaporthe oryzae–Rice Multi-Omics Data Based on WGCNA and Graph Autoencoder
title_sort relationship prediction method for magnaporthe oryzae–rice multi-omics data based on wgcna and graph autoencoder
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10607591/
https://www.ncbi.nlm.nih.gov/pubmed/37888263
http://dx.doi.org/10.3390/jof9101007
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