Identification of Drought Stress-Responsive Genes in Rice by Random Walk with Multi-Restart Probability on MultiPlex Biological Networks

Exploring drought stress-responsive genes in rice is essential for breeding drought-resistant varieties. Rice drought resistance is controlled by multiple genes, and mining drought stress-responsive genes solely based on single omics data lacks stability and accuracy. Multi-omics correlation analysi...

Full description

Saved in:
Bibliographic Details
Published in:International journal of molecular sciences Vol. 25; no. 17; p. 9216
Main Authors: Liu, Jiacheng, Zhu, Liu, Cao, Dan, Zhu, Xinghui, Zhang, Hongyan, Zhang, Yinqiong, Liu, Jing
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.09.2024
Subjects:
Algorithms
Computer centers
Drought
Droughts
Eigenvectors
Gene Expression Profiling - methods
Gene Expression Regulation, Plant
Gene Regulatory Networks
Genes
Genes, Plant
Genomes
Kinases
Oryza - genetics
Plant Proteins - genetics
Plant Proteins - metabolism
Probability
Protein Interaction Maps - genetics
Proteins
Seeds
Stress, Physiological - genetics
China
rice
MultiPlex biological networks
drought stress-responsive genes
random walk with multi-restart probability
eigenvector centrality
ISSN:1422-0067, 1661-6596, 1422-0067
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Exploring drought stress-responsive genes in rice is essential for breeding drought-resistant varieties. Rice drought resistance is controlled by multiple genes, and mining drought stress-responsive genes solely based on single omics data lacks stability and accuracy. Multi-omics correlation analysis and biological molecular network analysis provide robust solutions. This study proposed a random walk with a multi-restart probability (RWMRP) algorithm, based on the Restarted Random Walk (RWR) algorithm, to operate on rice MultiPlex biological networks. It explores the interactions between biological molecules across various levels and ranks potential genes. RWMRP uses eigenvector centrality to evaluate node importance in the network and adjusts the restart probabilities accordingly, diverging from the uniform restart probability employed in RWR. In the random walk process, it can be better to consider the global relationships in the network. Firstly, we constructed a MultiPlex biological network by integrating the rice protein–protein interaction, gene pathway, and gene co-expression network. Then, we employed RWMRP to predict the potential genes associated with rice tolerance to drought stress. Enrichment and correlation analyses resulted in the identification of 12 drought-related genes. We further conducted quantitative real-time polymerase chain reaction (qRT-PCR) analysis on these 12 genes, ultimately identifying 10 genes responsive to drought stress.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms25179216