Identification of Key Gene Network Modules and Hub Genes Associated with Wheat Response to Biotic Stress Using Combined Microarray Meta-analysis and WGCN Analysis

Wheat ( Triticum aestivum ) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising...

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Published in	Molecular biotechnology Vol. 65; no. 3; pp. 453 - 465
Main Authors	Nemati, Mahdi, Zare, Nasser, Hedayat-Evrigh, Nemat, Asghari, Rasool
Format	Journal Article
Language	English
Published	New York Springer US 01.03.2023 Springer Nature B.V
Subjects	Algorithms Biochemistry Biological Techniques Biotechnology biotic stress Calories Cell Biology Chemistry Chemistry and Materials Science Crop production data collection DNA microarrays foods Gene Expression Profiling gene expression regulation Gene Regulatory Networks Genes Genetic engineering Human Genetics Humans Meta-analysis microarray technology Modules Molecular modelling Original Paper Plant Breeding Protein Science Statistical methods stress tolerance Stress, Physiological - genetics Triticum - genetics Triticum aestivum Wheat Hub genes Microarray Biological process KEGG pathway Co-expression network
Online Access	Get full text
ISSN	1073-6085 1559-0305 1559-0305
DOI	10.1007/s12033-022-00541-w

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Abstract	Wheat ( Triticum aestivum ) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising solutions to handle losses caused by biotic stress factors, a comprehensive understanding of molecular mechanisms and identifying key genes is a critical step to obtaining success. Here, a network-based meta-analysis approach based on two main statistical methods was used to identify key genes and molecular mechanisms of the wheat response to biotic stress. A total of 163 samples (21,792 genes) from 10 datasets were analyzed. Fisher Z test based on the p -value and REM method based on effect size resulted in 533 differentially expressed genes ( p < 0.001 and FDR < 0.001). WGCNA analysis using a dynamic tree-cutting algorithm was used to construct a co-expression network and three significant modules were detected. The modules were significantly enriched by 16 BP terms and 4 KEGG pathways (Benjamini–Hochberg FDR < 0.001). A total of nine hub genes (a top 1.5% of genes with the highest degree) were identified from the constructed network. The identification of DE genes, gene–gene co-expressing network, and hub genes may contribute to uncovering the molecular mechanisms of the wheat response to biotic stress.
AbstractList	Wheat (Triticum aestivum) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising solutions to handle losses caused by biotic stress factors, a comprehensive understanding of molecular mechanisms and identifying key genes is a critical step to obtaining success. Here, a network-based meta-analysis approach based on two main statistical methods was used to identify key genes and molecular mechanisms of the wheat response to biotic stress. A total of 163 samples (21,792 genes) from 10 datasets were analyzed. Fisher Z test based on the p-value and REM method based on effect size resulted in 533 differentially expressed genes (p < 0.001 and FDR < 0.001). WGCNA analysis using a dynamic tree-cutting algorithm was used to construct a co-expression network and three significant modules were detected. The modules were significantly enriched by 16 BP terms and 4 KEGG pathways (Benjamini-Hochberg FDR < 0.001). A total of nine hub genes (a top 1.5% of genes with the highest degree) were identified from the constructed network. The identification of DE genes, gene-gene co-expressing network, and hub genes may contribute to uncovering the molecular mechanisms of the wheat response to biotic stress.Wheat (Triticum aestivum) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising solutions to handle losses caused by biotic stress factors, a comprehensive understanding of molecular mechanisms and identifying key genes is a critical step to obtaining success. Here, a network-based meta-analysis approach based on two main statistical methods was used to identify key genes and molecular mechanisms of the wheat response to biotic stress. A total of 163 samples (21,792 genes) from 10 datasets were analyzed. Fisher Z test based on the p-value and REM method based on effect size resulted in 533 differentially expressed genes (p < 0.001 and FDR < 0.001). WGCNA analysis using a dynamic tree-cutting algorithm was used to construct a co-expression network and three significant modules were detected. The modules were significantly enriched by 16 BP terms and 4 KEGG pathways (Benjamini-Hochberg FDR < 0.001). A total of nine hub genes (a top 1.5% of genes with the highest degree) were identified from the constructed network. The identification of DE genes, gene-gene co-expressing network, and hub genes may contribute to uncovering the molecular mechanisms of the wheat response to biotic stress. Wheat (Triticum aestivum) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising solutions to handle losses caused by biotic stress factors, a comprehensive understanding of molecular mechanisms and identifying key genes is a critical step to obtaining success. Here, a network-based meta-analysis approach based on two main statistical methods was used to identify key genes and molecular mechanisms of the wheat response to biotic stress. A total of 163 samples (21,792 genes) from 10 datasets were analyzed. Fisher Z test based on the p-value and REM method based on effect size resulted in 533 differentially expressed genes (p < 0.001 and FDR < 0.001). WGCNA analysis using a dynamic tree-cutting algorithm was used to construct a co-expression network and three significant modules were detected. The modules were significantly enriched by 16 BP terms and 4 KEGG pathways (Benjamini-Hochberg FDR < 0.001). A total of nine hub genes (a top 1.5% of genes with the highest degree) were identified from the constructed network. The identification of DE genes, gene-gene co-expressing network, and hub genes may contribute to uncovering the molecular mechanisms of the wheat response to biotic stress. Wheat ( Triticum aestivum ) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising solutions to handle losses caused by biotic stress factors, a comprehensive understanding of molecular mechanisms and identifying key genes is a critical step to obtaining success. Here, a network-based meta-analysis approach based on two main statistical methods was used to identify key genes and molecular mechanisms of the wheat response to biotic stress. A total of 163 samples (21,792 genes) from 10 datasets were analyzed. Fisher Z test based on the p -value and REM method based on effect size resulted in 533 differentially expressed genes ( p < 0.001 and FDR < 0.001). WGCNA analysis using a dynamic tree-cutting algorithm was used to construct a co-expression network and three significant modules were detected. The modules were significantly enriched by 16 BP terms and 4 KEGG pathways (Benjamini–Hochberg FDR < 0.001). A total of nine hub genes (a top 1.5% of genes with the highest degree) were identified from the constructed network. The identification of DE genes, gene–gene co-expressing network, and hub genes may contribute to uncovering the molecular mechanisms of the wheat response to biotic stress. Wheat (Triticum aestivum) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising solutions to handle losses caused by biotic stress factors, a comprehensive understanding of molecular mechanisms and identifying key genes is a critical step to obtaining success. Here, a network-based meta-analysis approach based on two main statistical methods was used to identify key genes and molecular mechanisms of the wheat response to biotic stress. A total of 163 samples (21,792 genes) from 10 datasets were analyzed. Fisher Z test based on the p-value and REM method based on effect size resulted in 533 differentially expressed genes (p < 0.001 and FDR < 0.001). WGCNA analysis using a dynamic tree-cutting algorithm was used to construct a co-expression network and three significant modules were detected. The modules were significantly enriched by 16 BP terms and 4 KEGG pathways (Benjamini–Hochberg FDR < 0.001). A total of nine hub genes (a top 1.5% of genes with the highest degree) were identified from the constructed network. The identification of DE genes, gene–gene co-expressing network, and hub genes may contribute to uncovering the molecular mechanisms of the wheat response to biotic stress.
Author	Zare, Nasser Asghari, Rasool Nemati, Mahdi Hedayat-Evrigh, Nemat
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Snippet	Wheat ( Triticum aestivum ) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and... Wheat (Triticum aestivum) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and...
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SubjectTerms	Algorithms Biochemistry Biological Techniques Biotechnology biotic stress Calories Cell Biology Chemistry Chemistry and Materials Science Crop production data collection DNA microarrays foods Gene Expression Profiling gene expression regulation Gene Regulatory Networks Genes Genetic engineering Human Genetics Humans Meta-analysis microarray technology Modules Molecular modelling Original Paper Plant Breeding Protein Science Statistical methods stress tolerance Stress, Physiological - genetics Triticum - genetics Triticum aestivum Wheat
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Title	Identification of Key Gene Network Modules and Hub Genes Associated with Wheat Response to Biotic Stress Using Combined Microarray Meta-analysis and WGCN Analysis
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