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

Author:

Zhao Enshuang1,Dong Liyan12,Zhao Hengyi1,Zhang Hao13ORCID,Zhang Tianyue1,Yuan Shuai3,Jiao Jiao1,Chen Kang1,Sheng Jianhua1,Yang Hongbo3,Wang Pengyu3,Li Guihua4ORCID,Qin Qingming5ORCID

Affiliation:

1. College of Computer Science and Technology, Jilin University, Changchun 130012, China

2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China

3. College of Software, Jilin University, Changchun 130012, China

4. College of Plant Science, Key Laboratory of Zoonosis Research, Ministry of Education, Jilin University, Changchun 130012, China

5. Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, MI 65211-7310, USA

Abstract

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.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Plant Science,Ecology, Evolution, Behavior and Systematics,Microbiology (medical)

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