- Open Access
Computational analysis of microarray data of Arabidopsis thaliana challenged with Alternaria brassicicola for identification of key genes in Brassica
Journal of Genetic Engineering and Biotechnology volume 18, Article number: 17 (2020)
Alternaria blight, a recalcitrant disease caused by Alternaria brassicae and Alternaria brassicicola, has been recognized for significant losses of oilseed crops especially rapeseed-mustard throughout the world. Till date, no resistance source is available against the disease; hence, plant breeding methods cannot be used to develop disease-resistant varieties. Therefore, in the present study, efforts have been made to identify resistance and defense-related genes as well as key components of JA-SA-ET-mediated pathway involved in resistance against Alternaria brasscicola through computational analysis of microarray data and network biology approach. Microarray profiling data from wild type and mutant Arabidopsis plants challenged with Alternaria brassicicola along with control plant were obtained from the Gene Expression Omnibus (GEO) database. The data analysis, including DEGs extraction, functional enrichment, annotation, and network analysis, was used to identify genes associated with disease resistance and defense response.
A total of 2854 genes were differentially expressed in WT9C9; among them, 1327 genes were upregulated and 1527 genes were downregulated. A total of 1159 genes were differentially expressed in JAM9C9; among them, 809 were upregulated and 350 were downregulated. A total of 2516 genes were differentially expressed in SAM9C9; among them, 1355 were upregulated and 1161 were downregulated. A total of 1567 genes were differentially expressed in ETM9C9; among them, 917 were upregulated and 650 were downregulated. Besides, a total of 2965 genes were differentially expressed in contrast WT24C24; among them, 1510 genes were upregulated and 1455 genes were downregulated. A total of 4598 genes were differentially expressed in JAM24C24; among them, 2201 were upregulated and 2397 were downregulated. A total of 3803 genes were differentially expressed in SAM24C24; among them, 1819 were upregulated and 1984 were downregulated. A total of 4164 genes were differentially expressed in ETM24C24; among them, 1895 were upregulated and 2269 were downregulated. The upregulated genes of Arabidopsis thaliana were mapped and annotated with CDS sequences of Brassica rapa obtained from PlantGDB database. Additionally, PPI network of these genes were constructed to investigate the key components of hormone-mediated pathway involved in resistance during pathogenesis.
The obtained information from present study can be used to engineer resistance to Alternaria blight caused by Alternaria brasscicola through molecular breeding or genetic manipulation-based approaches for improving Brassica oilseed productivity.
Alternaria blight, a recalcitrant disease caused by Alternaria brassicae and Alternaria brassicicola, has been recognized for significant losses of oilseed crops especially rapeseed-mustard throughout the world. Till date, no resistance source is available against the disease; hence, plant breeding methods cannot be used to develop disease-resistant varieties. Oilseed crops especially Brassica (rapeseed-mustard) play a critical role in the Indian agricultural economy, next to food grains, in terms of area, production, and value. It is grown in 53 countries across the six continents, with India being the world’s second largest grower after China [1, 2]. Despite that, India has to import large amount of edible oils from other countries to meet its domestic demands . In future, the demand for oilseed production is expected to extensively increase due to increase in population and income. The only way to increase oilseed productivity is to protect mustard crops from the attack of various biotic and abiotic stresses .
Fungi and oomycete are the main threats causing major losses in oilseed crops; more than thirty diseases are incurred in mustard crops in India [5, 6]. Alternaria blight, caused by Alternaria brassicae and Alternaria brassicicola, holds major importance based on the economic yield losses in Brassica crops [6, 7]. The yield losses due to Alternaria blight disease have been estimated to range from 35 to 46% in India and up to 70% in the world with no demonstrated source of transferable resistance in any of the hosts [8, 9]. Disease management strategies employing fungicidal chemicals are not only environmentally hazardous but also inadequate to control the disease caused by Alternaria brasscicola. Quick evolution through genetic variations of new pathogenic strains has further been problematical for breeders to develop resistance in crop plants. Alternaria is a necrotrophic fungal pathogen which produces lesions on leaves, siliquae, and stems influencing quantity as well as quality of seed by diminishing oil content, size, and color [10, 11].
Phytohormones affect several aspects of growth and differentiation in crop plants and are involved in both abiotic and biotic stress responses in plants. Among the plant hormones, jasmonic acid (JA), salicylic acid (SA), and ethylene (ET), which are known for differentially controlling defense responses against biotrophic and necrotrophic pathogens, are recognized as the immunity hormones [12, 13]. The accumulation of these hormones triggers the activation of a cascade of defense-signaling pathways. However, the final outcome of the defense response is greatly influenced by the production, timing, and composition of the hormonal blend produced [14,15,16,17,18]. Although there are exceptions, in general, it can be stated that SA-dependent defenses and JA/ET-dependent defenses participate in defense against biotrophic and necrotrophic pathogens and against insect herbivores respectively [7, 19,20,21]. Jasmonic acid (JA)-dependent defense signaling pathway has been reported to restrict the growth of necrotrophic fungal pathogens [2, 22, 23]. The expression of some MAP kinases has been associated with increase in JA level in plants and JA-dependent genes. For example, expression of MAPK4 is linked with induction of JA-dependent genes/proteins, and MAPK6 which triggers the basal defense is also activated by JA [7, 24]. The downregulation of MAPK4 as observed during pathogenesis of Alternaria blight is an indication of decrease in JA-dependent defense against the pathogen. Since the pathogen is a hemibiotrophic which uses both biotrophic and necrotrophic mode of infection, hence, it was thought that downregulation of JA-mediated defense could facilitate necrotrophic colonization of pathogen on host. However, no information is available about intricacy of such signaling cascades involved in the pathogenesis, though some evidences of antagonism of Alternaria toxin and zeatin are reported in this system . Plant breeders are unable to develop resistance against Alternaria blight due to lack of knowledge of resistant genes linked with defense responses. Although some progress has been made in recent years to understand the molecular basis of pathogenesis of Alternaria blight, the target molecules affected by disease is not identified .
In the view of the above facts, there is a need of genomics- and bioinformatics-based approaches to decipher the complexity of signaling cascades through analysis of available microarray data of host-pathogen interaction for identification of defense-related gene(s) involved in hormone-mediated resistance which can be utilized for the development of disease-resistant Brassica crops through genetic manipulation of key candidate gene(s) or by utilizing molecular breeding approaches for sustainable agriculture.
Source of DNA microarray data
The microarray datasets, GSE50526 with GPL198 [ATH1-121501] Affymetrix Arabidopsis ATH1 Genome array platform, were obtained from the Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information . The data samples were obtained from the Arabidopsis leaves which were challenged with the Alternaria brassicicola infection at 9 and 24 h. The GSE50526 dataset contains 29 leaf samples of wild type and JA-SA-ET mutant plant that is also challenged with the infection of Alternaria brassicicola along with the control.
Pre-processing of raw data
All 29 sample files (.CEL files) were subjected to the R software library (version 3.4.0) (https://www.r-project.org/). The Affy library of Bioconductor was used to read CEL files. Subsequently, simpleaffy library was used to check the quality of raw data (https://www.bioconductor.org/). GCRMA algorithm was applied for normalization and summarization of the probes . The obtained normalized expression values were utilized for further analysis.
Screening and annotation of differentially expressed genes
The linear modeling approach was employed for screening of differentially expressed genes (DEGs). The limma library in R/Bioconductor was used to build the linear models and contrasts of interest . To obtain DEGs, moderated t statistic has been applied. The multiplicity of testing was done using the Benjamini and Hochberg (BH) correction adjusted for false discovery rate (FDR). The threshold adjusted p value was set as < 0.05, and fold-change threshold was set to > 1.5. The decideTests function was implemented to fetch out up- and downregulated probes present in each contrasts. The library org.At.tair.db, ath1121501.db, and annotate was used to get Gene Symbol, EntrezID, and TAIR accession number of up- and downregulated probes [29, 30].
Enrichment analysis of the DEGs
The gene ontology (GO) enrichment analysis, i.e., biological process, molecular function, and cellular component of up- and downregulated genes were performed by GeneCodis (http://genecodis.cnb.csic.es/). Besides, pathway analysis was also done using Kyoto Encyclopedia of Genes and Genome (KEGG) by the same tool [31,32,33]. The threshold value was set at p < 0.05.
Mapping of identified upregulated gene(s) sequences in Arabidopsis thaliana on Brassica rapa
All annotated upregulated gene sequences involved in defense response to fungi of each contrasts at 9 and 24 h have been taken, merged to prepare a single text file for every contrasts, i.e., WTC, JAMC, SAMC, and ETMC. Many genes were found to be upregulated in both conditions, i.e., 9 and 24 h in each contrast during analysis; therefore, duplicate sequences were removed, and the rest are considered for analysis in such condition. The complete CDS protein and nucleotide sequences of Arabidopsis thaliana each gene were downloaded from TAIR (https://www.arabidopsis.org/) database through batch download using accession numbers, whereas available CDS sequences of Brassica from Brassica rapa genome (n = 41019; 14.52 MB) were downloaded from BrGDB, part of the PlantGDB database (www.plantgdb.org) (accessed on 21 July, 2017). These sequences were used to construct a local database of B. rapa CDS sequence. Further, the retrieved sequences of upregulated genes of Arabidopsis thaliana form TAIR were taken as a query to perform local BLAST search against constructed local database of the B. rapa sequences to determine the closeness among them . The top BLAST hits of B. rapa sequences that pose higher identity and lower e-value with A. thaliana sequences were taken for further investigations.
Characterization and comparative analysis of identified up-regulated gene(s) through molecular phylogeny and domain prediction
A single text file holding A. thaliana and its corresponding B. rapa sequences was created for each contrast taken in the study. Multiple sequence alignment was performed using CLUSTALX . The molecular phylogeny was done by using aligned files to build a phylogenetic tree using NJ methods to visualize the relatedness between sequences using TreeView and iTOL [36, 37]. The presence of conserved domains in each sequence of every contrast was also determined via Conserved Domain Database (CDD) at the National Center for Biotechnology Information for characterization of gene(s) involved in disease resistance and defense responses against Alternaria blight in Brassica spp .
Protein-protein interactions (PPIs) network construction and analysis of upregulated DEGs
The protein sequences of upregulated genes retrieved from TAIR were used to obtain PPIs network for JAMC, SAMC, and ETMC contrast from STRING (Search Tool for Retrieval of Interacting Genes/Protein) database . STRING holds information about the experimental and predicted PPI obtained from scientific literature, which are based on their co-expression, neighborhood, co-occurrence, and gene fusion experimentation. The extended network for selected contrast was constructed based on high confidence score, which is considered as valid link. The obtained networks were visualized and analyzed topologically by Cytoscape 3.4.0 (http://www.cytoscape.org/) using Network Analyzer 3.3.1 to identify key components involved in resistance during pathogenesis of Alternaria blight with respect to JA-, SA-, and ET-mediated signaling pathway [40, 41]. A brief workflow is provided in Fig. 1 on the data and methods used in this analysis.
Identification of upregulated and downregulated DEGs in wild and mutant plants of A. thaliana challenged with Alternaria brassicicola
In the biological systems, downregulation is the mechanism by which a cell, in response to an external stimulus, decreases the amount of a cellular component, such as RNA or protein. Besides, the complementary mechanism involving increase in these components is called upregulation which plays tremendous role during plant-pathogen interactions. After pre-processing of data, 22,810 probes were obtained on the basis of the cutoff criteria. A total of 1327 upregulated and 1527 downregulated probes were identified in wild-type pathogen-treated plant compared with the control at 9 h (WT9C9) whereas 1510 upregulated and 1455 downregulated probes were identified at 24 h (WT24C24); 809 upregulated and 350 downregulated at 9 h (JAM9C9) whereas 2201 upregulated and 2397 downregulated probes at 24 h (JAM24C24) were identified in jasmonic acid mutant plant challenged with pathogen compared with control plant; 1355 upregulated and 1161 downregulated at 9 h (SAM9C9) whereas 1819 upregulated and 1984 downregulated probes at 24 h (SAM24C24) were identified in salicylic acid mutant plant challenged with pathogen compared with control. Besides, 917 upregulated and 650 downregulated probes at 9 h (ETM9C9) as well as 1895 upregulated and 2269 downregulated probes at 24 h (ETM24C24) were also identified in ethylene mutant plant challenged with pathogen compared with the control plant (Table 1). The list of top-ten up- and downregulated genes in each contrasts are listed in Supplementary Table 1-16.
Genes that respond to the conditions have been identified by comparing their expression levels in treatment and control samples. Out of total DEGs, only annotated probes having unique accession number were used for construction of Venn diagram because many probes code the same genes. In the present study, 1312 up and 1506 downregulated probes were annotated in WT9C9 whereas 1497 up and 1436 downregulated probes were annotated in WT24C24; 801 up and 342 downregulated probes were annotated in JAM9C9 whereas 2179 up and 2367 downregulated probes were annotated in JAM24C24; 1336 up and 1143 downregulated probes were annotated in SAM9C9 whereas 1805 up and 1954 downregulated probes were annotated in SAM24C24; 905 up and 642 downregulated probes were annotated in ETM9C9 whereas 1875 up and 2235 downregulated probes were annotated in ETM24C24. During analysis, NHL10 and HCHIB were identified as important genes which are involved in defense responses during pathogenesis of Alternaria blight in Arabidopsis thaliana.
Venn diagrams can be used for several purposes, such as comparing different lists of genes or proteins to define and represent similarity and differences in two dimensions. During Venn diagram construction and analysis, it was found that 152, 42, 220, and 40 genes are unique in WT9C9, JAM9C9, SAM9C9, ETM9C9, respectively. Besides, 602 are found common in WT9C9, JAM9C9, SAM9C9, and ETM9C9; 58 genes are found common in WT9C9, JAM9C9 and SAM9C9; 40 are common in WT9C9, JAM9C9, and ETM9C9; 151 are common in WT9C9, SAM9C9, and ETM9C9; 7 are common in JAM9C9, SAM9C9, and ETM9C9; 15 are common inWT9C9 and JAM9C9; 256 are common between WT9C9 and SAM9C9; 29 are common between WT9C9 and ETM9C9; 17 are common between JAM9C9 and SAM9C9; 13 are common in JAM9C9 and ETM9C9; and 15 genes are common between SAM9C9 and ETM9C9 at 9 h during upregulation. Upon analysis of downregulation of genes at 9 h, it was found that 624, 53, 425, and 67 genes are unique in WT9C9, JAM9C9, SAM9C9, and ETM9C9, respectively. Besides, 186 genes are common among WT9C9, JAM9C9, SAM9C9, and ETM9C9; 37 are common among WT9C9, JAM9C9, and SAM9C9; 24 are common among WT9C9, JAM9C9, and ETM9C9; 185 are common among WT9C9, SAM9C9, and ETM9C9; 6 are common in JAM9C9, SAM9C9, and ETM9C9; 21 are common among WT9C9 and JAM9C9; 273 are common among WT9C9 and SAM9C9; 152 are common among WT9C9 and ETM9C9; 12 are common among JAM9C9 and SAM9C9; 3 are common among JAM9C9 and ETM9C9; and 18 genes are common among SAM9C9 and ETM9C9.
Among the upregulated genes at 24 h of treatment, it was found that 70, 685, and 178 genes are unique in WT24C24, JAM24C24, SAM24C24, and ETM24C24 respectively. Besides, 929 genes are common among WT24C24, JAM24C24, SAM24C24, and ETM24C24; 63 are common among WT24C24, JAM24C24, and SAM24C24; 50 are common among WT24C24, JAM24C24, and ETM24C24; 226 are common among WT24C24, SAM24C24, and ETM24C24; 139 are common among JAM24C24, SAM24C24, and ETM24C24; 39 are common among WT24C24 and JAM24C24; 95 are common among WT24C24 and SAM24C24; 13 are common among WT24C24 and ETM24C24; 47 are common among JAM24C24 and SAM24C24; 213 are common among JAM24C24 and ETM24C24; and 116 genes are found common among SAM24C24 and ETM24C24. In case of downregulation, 18 genes are found unique in WT24C24, 580 are unique in JAM24C24, 246 are unique in SAM24C24, and 237 are unique in ETM24C24. Besides, 1062 genes are common among WT24C24, JAM24C24, SAM24C24, and ETM24C24; 33 genes are common among WT24C24, JAM24C24, and SAM24C24; 63 are common among WT24C24, JAM24C24, and ETM24C24; 174 are common among WT24C24, SAM24C24, and ETM24C24; 194 are common among JAM24C24, SAM24C24, and ETM24C24; 14 are common among WT24C24 and JAM24C24; 55 are common among WT24C24 and SAM24C24; 13 genes are common among WT24C24 and ETM24C24; 56 are common among JAM24C24 and SAM24C24; 358 are common among JAM24C24 and ETM24C24; and 130 are found common among SAM24C24 and ETM24C24. All the unique and common DEGs of each set are shown in Fig. 2.
Enrichment analysis of up- and downregulated DEGs
Further biological knowledge were fetched from the list of DEGs that are known to perform biological process or involved in different key regulatory networks with respect to plant-pathogen interactions. The enrichment analysis of each contrast were done in terms of gene ontology (GO) analysis, i.e., biological process, molecular function, and cellular components as well as KEGG analysis for decoding the role of DEGs in plant systems during pathogenesis of Alternaria brassicicola.
In contrast WT9C9 during upregulation, the significant GO term for biological function was protein phosphorylation (GO 0006468) whereas 38 genes were detected which are involved in the defense response to fungus (GO 0050832), for molecular function was protein binding (GO 0005515), and for cellular component was plasma membrane (GO 0005886). In downregulation condition of WT9C9, the significant GO term for biological process was metabolic process (GO 0008152), for molecular function was ATP binding (GO 0005524), and for cellular component was chloroplast (GO 0009507). In upregulation condition of JAM9C9, the significant GO term for biological process was protein phosphorylation (GO 0006468), whereas 35 genes were involved in defense response to fungus (GO 0050832), for molecular function was kinase activity (GO 0016301), for cellular component was plasma membrane (GO 0005886). In case of downregulation of JAM9C9, the significant GO term for biological process was metabolic process (GO 0008152), for molecular function was DNA binding (GO 0003677), for cellular component was chloroplast (GO 0009507). In upregulation condition of SAM9C9, the significant GO term for biological process was regulation of transcription, DNA dependent (GO:0006355), whereas 34 genes were involved in defense response to fungus, for molecular function was sequence-specific DNA binding transcription factor activity (GO 0003700), for cellular component was plasma membrane (GO 0005886). Besides, in downregulation condition of SAM9C9, the significant GO term for biological process was protein phosphorylation (GO 0006468), for molecular function was sequence-specific DNA binding transcription factor activity (GO 0003700), and for cellular component was chloroplast (GO 0009507). In upregulation condition of ETM9C9, the significant GO term for biological process was response to chitin (GO 0010200), whereas 37 genes were involved in defense response to fungus (GO 0050832), for molecular function was kinase activity (GO 0016301), for cellular component was plasma membrane (GO 0005886). Besides, in downregulation of ETM9C9, the significant GO term for biological process was proteolysis (GO 0006508), for molecular function was protein binding (GO 0005515), and cellular component was chloroplast (GO 0009507). In contrast WT24C24 during upregulation, the significant GO term for biological function was metabolic process (GO 0008152) whereas 34 genes were detected which are involved in the defense response to fungus (GO 0050832), for molecular function was protein binding (GO 0005515), for cellular component was cytosol (GO 0005829). In downregulation condition of WT24C24, the significant GO term for biological process was metabolic process (GO 0008152), for molecular function was catalytic activity (GO 0003824), and for cellular component was chloroplast (GO 0009507). In upregulation condition of JAM24C24, the significant GO term for biological process was metabolic process (GO 0008152), whereas 48 genes were involved in defense response to fungus (GO 0050832), for molecular function was protein binding (GO 0005515), for cellular component was cytosol (GO 0005829). In case of downregulation of JAM24C24, the significant GO terms for biological process, molecular function, and cellular components were translation (GO 0006412), structural constituent of ribosome (GO 0003735), and chloroplast (GO 0009507), respectively. In up-regulation condition of SAM24C24, the significant GO terms for biological process, molecular function, and cellular component were metabolic process (GO 0008152), whereas 33 genes were involved in defense response to fungus, protein binding (GO 0005515), and cytosol (GO 0005829), respectively. Besides, in downregulation condition of SAM24C24, the significant GO terms for biological process, molecular function, and cellular components were metabolic process (GO 0008152), catalytic activity (GO 0003824), and chloroplast (GO:0009507), respectively. In upregulation condition of ETM24C24, the significant GO terms for biological process, molecular function, and cellular component were metabolic process (GO 0008152), whereas 39 genes were involved in defense response to fungus (GO 0050832), protein binding (GO 0005515), and cytosol (GO 0005829), respectively. Besides, in downregulation of ETM24C24, the significant GO terms for biological process, molecular function, and cellular component were translation (GO 0006412), catalytic activity (GO 0003824), and chloroplast (GO 0009507), respectively.
Pathways analysis is a useful tool for understanding the interrelationship between different biological components to recognize key pathway. The KEGG pathway enrichment analysis was done to further evaluate up- and downregulated genes involved in different biological function. The significant pathway term was sorted based on P value. Our analysis revealed that amino sugar and nucleotide sugar metabolism (KEGG 00520) was the most significant pathway of upregulated condition in WT9C9. While, in downregulated condition of WT9C9, starch and sucrose metabolism (KEGG 00500) was the most significant pathway; biosynthesis of secondary metabolites (KEGG 01110) was the significant pathway in JAM9C9 upregulated, whereas plant hormone signal transduction (KEGG 04075) was in downregulated condition of JAM9C9. Amino sugar and nucleotide sugar metabolism (KEGG 00520) was the significant pathway in SAM9C9 upregulated, whereas glycosphingolipid biosynthesis—globo series (KEGG 00603) was in downregulated condition of SAM9C9; glutathione metabolism (KEGG 00480) was the significant pathway in ETM9C9 upregulated, whereas peroxisome (KEGG 04146) was in downregulated condition of ETM9C9. Oxidative phosphorylation (KEGG 00190) was the most significant pathway of upregulated condition in WT24C24. While in downregulated condition of WT24C24, starch and sucrose metabolism (KEGG 00500) was the most significant pathway; amino sugar and nucleotide sugar metabolism (KEGG 00520) was the significant pathway in JAM24C24 upregulated, whereas starch and sucrose metabolism (KEGG 00500) was in down-regulated condition of JAM24C24; starch and sucrose metabolism (KEGG 00500) was the significant pathway of upregulated and downregulated condition in SAM24C24 and ETM24C24. Furthermore, plant-hormone signal transduction (KEGG 04075) and plant-pathogen interaction (KEGG 04626) were revealed to be highly enriched in upregulated conditions. Therefore, the plant hormone-based signaling network plays significant role during pathogenesis and triggering defense to plant systems towards pest and pathogens.
Identification and characterization of genes in Brassica based on upregulated DEGs triggered during resistance against A. brassicicola in A. thaliana
Based on the gene ontology analysis, the genes triggered in Arabidopsis thaliana during resistance to fungal pathogen (upregulated) have been taken for further analysis. A total of 47, 52, 45, and 49 unique genes were chosen from WTC, JAMC, SAMC, and ETMC respectively at 9 and 24 h. Out of these, 41, 42, 40, and 42 genes were annotated in WTC, JAMC, SAMC, and ETMC, respectively through BLAST analysis against constructed local database of Brassica rapa based on bit score, identity, and e-value (Supplementary table 17-20).
The identified sequences of Brassica rapa were further subjected to domain prediction for functional characterization and molecular phylogeny analysis with Arabidopsis for their relatedness prediction among them. The number of predicted domain and their positions along with short names for WTC, JAMC, SAMC, and ETMC are given in Tables 2, 3, 4, and 5 respectively. Based on obtained results, it can be interpreted that they might be involved in disease resistance and defense responses during pathogenesis. To examine the evolutionary relationship among identified Arabidopsis sequences with respect to similar Brassica rapa sequences obtained through BLAST analysis, phylogenetic tree for WTC, JAMC, SAMC, and ETMC were constructed to determine the relationship among them (Figs. 3, 4, 5, 6).
Topological analysis and visualization of PPI network for identification of key components involved through JA-SA-ET-mediated resistance
After analysis, the identified upregulated genes of Arabidopsis thaliana involved in defense response to fungal pathogen at 9 and 24 h for the contrast JAMC, SAMC, and ETMC were chosen to build extended PPI network based on confidence score > 0.7 as cutoff. The constructed networks were visualized and analyzed by Cytoscape 3.4.0 and Network Analyzer 3.3.1. Network analysis revealed that the JAMC network has 34 nodes, 68 edges, 3 connected components, 0 isolated node, 4.0 average number of neighbors, 600 shortest paths, 2.807 characteristic path lengths, 7 network diameters, and 1 network radius; SAMC network has 33 nodes, 56 edges, 5 connected components, 0 isolated node, 3.394 average number of neighbors, 380 shortest paths, 2.053 characteristic path lengths, 4 network diameters, and 1 network radius; ETMC network has 36 nodes, 57 edges, 6 connected components, 0 isolated node, 3.167 average number of neighbors, 420 shortest paths, 2.167 characteristic path lengths, 5 network diameters, and 1 network radius (Table 6).
The visual parameter of NetworkAnalyzer was used to map hub nodes in the networks using the visual style to map node size “Degree” and node color “BetweenessCentrality” to investigate the key components of JA-SA-ET-mediated pathway triggered during resistance. The nodes MP, IAA19, AXR3, IAA1, ARF6, and XLG2 were found as significant components and XLG2, WRKY33, and CZF1 are found as hub nodes under contrast JAMC, SAMC, and ETMC, which play tremendous role during plant-pathogen interaction (Figs. 7, 8, 9).
In the present study, efforts have been made to identify and characterize resistant and defense-related genes triggered during resistance towards Alternaria blight, a recalcitrant disease caused by Alternaria brassicicola and Alternaria brassicae in Arabidopsis and Brassica. Besides, the key components of JA-SA-ET involved in defense response were also investigated. The identified top ten resistant and defense-related genes are listed in Tables S2-S17. These genes could be utilized for development of molecular markers linked with disease resistance which can further be utilized in molecular breeding program. Moreover, the results can also be utilized for transgenesis, directed mutagenesis, cisgenesis, and gene editing for development of resistant Brassica plants against Alternaria blight.
The identification of resistance (R) and defense-related genes unlocked interesting possibilities for prevention and management of diseases caused by several pathogens . However, such genes are available in limited numbers which can be deployed in plants to engineer defense against limited number of pathogens. On the other hand, efficient application of microarray technology and functional genomics tools allow us to discover important candidate genes through stimulating better understanding of disease resistance and plant defense signaling. It could disclose novel insights on the interactions among signaling pathways and other processes of plant systems involved in plant-pathogen interactions [43, 44]. Various studies as conducted in recent past on the signaling machinery towards necrotrophic fungal pathogens have helped to dissect various components.
The knowledge on molecular mechanism of host pathogen interaction is considered to be prerequisite for engineering disease-resistant varieties of Brassica against Alternaria blight disease. Throughout plant-pathogen interactions, our knowledge of responses has taken a big leap forward. Nonetheless, over the course of this decade, we still have several aspects and challenges to address different questions associated to these interactions . It is believed that the huge data on expression of resistance and defense related genes with respect to plant-pathogen interaction can be analyzed to identify key candidate gene(s) which can be modified by genetic engineering or molecular breeding approaches to engineer disease resistance in Brassica.
Recently efforts have been made in dissecting the different components of defense signal transduction pathways activated towards different pathogens. Jasmonic acid/ethylene and salicylic acid-mediated signaling pathways are activated against necrotrophic and biotrophic fungal pathogens [7, 46]. Arabidopsis thaliana has already been demonstrated as host for Alternaria blight disease of Brassica . Therefore, it is being felt that Alternaria brassicicola-Arabidopsis thaliana could be used as one of the excellent model system for deciphering the intricacy of Alternaria blight in Brassica . Pre-processing of microarray data is the phenomenon of extracting and transforming the intensities of raw fluorescence into a signal normalized for biological variations and experimental errors . Here, GCRMA (Guanine Cytosine Robust Multi-Array Analysis) method was used for background correction of downloaded microarray data from NCBI GEO . It converts background adjusted probe intensities into expression measures as same has been used by RMA (Robust Multi-array Average) for normalization and summarization of data . It performs much better than the other commonly used methods for normalization  to identify upregulated and downregulated DEGs. The present study has demonstrated different sets of differentially expressed JA, SA, and ET responsive genes (DEGs) at 9 and 24 h after infection of Alternaria brasscicola. All the upregulated and downregulated DEGs given in parenthesis for each contrast, i.e., wild-type pathogen-treated plant with control plant (WT9C9: 1327up, 1527down; WT24C24: 1510up, 1455down); jasmonic acid mutant pathogen-treated plant with control (JAM9C9: 809up, 350down; JAM24C24: 2201up, 2397down); salicylic acid mutant pathogen-treated plant with control (SAM9C9: 1355up, 1161down; SAM24C24: 1819up, 1984down); ethylene mutant-treated plant with control (ETM9C9: 917up, 650down; ETM24C24: 1895up, 2269down) at 9 h and 24 h after Alternaria brassicicola infection. During data analysis and annotation, among many defense related genes, NHL10 and HCHIB were recognized as resistance genes. The NHL10 is non-race-specific disease resistance gene (NDR1) , and HCHIB is involved in ethylene/jasmonate-mediated systemic acquired resistance during pathogenesis. They are involved in defense responses during pathogenesis of Alternaria blight in Arabidopsis thaliana [51, 52]. Besides, enrichment analyses of the DEGs led to formation of gene ontology to define the significant genes involved in biological processes, molecular function, cellular components, and pathways. The genes involved in defense response towards fungi in Arabidopsis thaliana were mapped on Brassica rapa genome sequences to identify and characterize the similar Brassica sequences. Characterization and comparative analysis of identified genes were carried through molecular phylogeny analysis and domain prediction to identify resistance and defense-related genes in Brassica. Infection of Alternaria brasscicola led to upregulation of various genes such as WRKY, peroxidase, p450 oxidases, and chitinase which mediate defense response in Arabidopsis and Brassica upon infection with pathogen [53,54,55,56]. It has been observed that the expression of these genes increase more in the presence of JA and SA than the wild-type plants at 24 h post infection. This indicates that expression of defense-related genes increase post infection of Alternaria brasscicola to combat pathogen’s spread and this effect is enhanced by JA and SA which are well-known defense inducers. However, there were observed few genes which were downregulated in the presence of JA and SA. These genes appear to be the ones which are involved in pathogenesis process and that they are downregulated by JA or SA to trigger defense response against the pathogen. Protein-protein interaction networks of genes involved in defense response towards fungi were constructed from contrast JAMC, SAMC, and ETMC to determine the key components of JA-SA-ET-mediated pathway involved in disease resistance through network analysis. The genes, CZF1; WRKY; Movement Protein (MP); INDOLE-3-ACETIC ACID INDUCIBLE 19 (IAA19); Auxin-responsive gene (AXR3); INDOLE-3-ACETIC ACID INDUCIBLE 1 (IAA1); auxin response factor 6 (ARF6); and extra large G-protein 2 (XLG2) involved in DNA-binding transcription factor activity, GTP binding, GTPase activity, and protein binding as well as defense response towards fungi were investigated . However, XLG2 which is found in all contrast in the category of hubs is a well-characterized gene playing significant role in disease resistance .
In the present computational study, among many defense-related genes, NHL10 and HCHIB were identified as major genes which are involved in defense responses during pathogenesis of Alternaria blight in Arabidopsis thaliana. Besides, the key components of the three main signaling pathway, viz., jasmonic acid, salicylic acid, and ethylene-mediated pathway triggered during resistance were also identified. The genes, viz., CZF1, WRKY, MP, IAA19, AXR3, IAA1, ARF6, and XLG2 were found as potential candidate genes of these signaling pathways. Additionally, XLG2 was found to be one of the most promising key genes involved in defense response against Alternaria brasscicola fungal pathogen. Furthermore, the genes involved in defense response to Alternaria brasscicola were also identified and characterized in Brassica rapa by taking Arabidopsis as a model system. The finding from the present study may provide a way to understand the intricate molecular mechanism of Brassica-Alternaria pathosystem. This may further be used for devising strategies based on molecular breeding or genetic engineering approaches to develop designer resistant Brassica crops for robust oilseed productivity and sustainability, and securing food and nutritional security of rapidly growing world population.
Availability of data and materials
National Center for Biotechnology Information
Gene Expression Omnibus
Basic Local Alignment Search Tool
Guanine Cytosine Robust Multi-Array Analysis
Wild-type pathogen-treated plant vs control plant
Jasmonic acid mutant with pathogen-treated plant vs control plant
Salicylic acid mutant with pathogen-treated plant vs control plant
Ethylene mutant with pathogen-treated plant vs control plant
Differentially expressed genes
The Arabidopsis Information Resource
Average number of neighbors
Characteristics path length
Multi-edge node pair
Shekhawat, K., Rathore, S.S., Premi, O.P., Kandpal, B.K. and Chauhan, J.S. (2012) Advances in agronomic management of Indian mustard (Brassica juncea (L.) Czernj. Cosson): an overview. International journal of Agronomy, 2012.
Pathak RK, Baunthiyal M, Shukla R, Pandey D, Taj G, Kumar A (2017) In silico identification of mimicking molecules as defense inducers triggering jasmonic acid mediated immunity against alternaria blight disease in brassica species. Front Plant Sci 8:609
ISOB (2017) Souvenir, International Seminar on Oilseed Brassica, Jaipur, India.
Pathak RK, Baunthiyal M, Pandey N, Pandey D, Kumar A (2017) Modeling of the jasmonate signaling pathway in Arabidopsis thaliana with respect to pathophysiology of Alternaria blight in Brassica. Sci Rep 7(1):16790
Saharan, G. S. (1992) Disease resistance, In Breeding Oilseed Brassicas, eds K. S. Labana, S. S. Banga, and S. K. Banga (Daryaganj: Narosa Publishing House), 181–205.
Meena PD, Awasthi RP, Chattopadhyay C, Kolte SJ, Kumar A (2016) Alternaria blight: a chronic disease in rapeseed-mustard. Journal of Oilseed Brassica 1(1):1–11
Pandey D, Rajendran SRCK, Gaur M, Sajeesh PK, Kumar A (2016) Plant defense signaling and responses against necrotrophic fungal pathogens. J Plant Growth Regul 35(4):1159–1174
Mishra A, Pandey D, Goel A, Kumar A (2010) Molecular cloning and in silico analysis of functional homologues of hypersensitive response gene(s) induced during pathogenesis of Alternaria blight in two genotypes of Brassica. J Proteomics Bioinform 3(8):244–248
Chandrashekar N, Ali S, Rawat S, Grover A (2015) Gene expression profiling of Arabidopsis thaliana chitinase genes in response to Alternaria brassicae challenge. Indian Phytopathology 68(1):106–111
Mathpal P, Punetha H, Tewari AK, Agrawal S (2016) Biochemical defense mechanism in rapeseed-mustard genotypes against Alternaria blight disease. Journal of Oilseed Brassica 1(2):87–94
Pathak RK, Taj G, Pandey D, Kasana VK, Baunthiyal M, Kumar A (2016) Molecular modeling and docking studies of phytoalexin(s) with pathogenic protein(s) as molecular targets for designing the derivatives with anti-fungal action on Alternaria spp. of Brassica. Plant Omics 9(3):172
Bari R, Jones JD (2009) Role of plant hormones in plant defence responses. Plant Mol Biol 69(4):473–488
Tsuda K, Katagiri F (2010) Comparing signaling mechanisms engaged in pattern-triggered and effector-triggered immunity. Curr Opin Plant Biol 13(4):459–465
De Vos M, Van Oosten VR, Van Poecke RM, Van Pelt JA, Pozo MJ, Mueller MJ, Buchala AJ, Métraux JP, Van Loon LC, Dicke M, Pieterse CM (2005) Signal signature and transcriptome changes of Arabidopsis during pathogen and insect attack. Mol Plant-Microbe Interact 18(9):923–937
Mur LA, Carver TL, Prats E (2005) NO way to live; the various roles of nitric oxide in plant–pathogen interactions. J Exp Bot 57(3):489–505
Koornneef A, Pieterse CM (2008) Cross talk in defense signaling. Plant Physiol 146(3):839–844
Leon-Reyes A, Spoel SH, De Lange ES, Abe H, Kobayashi M, Tsuda S, Millenaar FF, Welschen RA, Ritsema T, Pieterse CM (2009) Ethylene modulates the role of NONEXPRESSOR OF PATHOGENESIS-RELATED GENES1 in cross talk between salicylate and jasmonate signaling. Plant Physiol 149(4):1797–1809
Pieterse CM, Leon-Reyes A, Van der Ent S, Van Wees SC (2009) Networking by small-molecule hormones in plant immunity. Nat Chem Biol 5(5):308
Kessler A, Baldwin IT (2002) Plant responses to insect herbivory: the emerging molecular analysis. Annu Rev Plant Biol 53(1):299–328
Glazebrook J (2005) Contrasting mechanisms of defense against biotrophic and necrotrophic pathogens. Annu Rev Phytopathol 43:205–227
Howe GA, Jander G (2008) Plant immunity to insect herbivores. Annu Rev Plant Biol 59:41–66
Pathak RK, Taj G, Pandey D, Arora S, Kumar A (2013) Modeling of the MAPK machinery activation in response to various abiotic and biotic stresses in plants by a system biology approach. Bioinformation 9(9):443
Yang YX, J Ahammed G, Wu C, Fan SY, Zhou YH (2015) Crosstalk among jasmonate, salicylate and ethylene signaling pathways in plant disease and immune responses. Curr Protein Pept Sci 16(5):450–461
Petersen M, Brodersen P, Naested H, Andreasson E, Lindhart U, Johansen B, Nielsen HB, Lacy M, Austin MJ, Parker JE, Sharma SB (2000) Arabidopsis MAP kinase 4 negatively regulates systemic acquired resistance. Cell 103(7):1111–1120
Pandey D, Kumar A, Garg GK (2001) Antagonism between Alternaria brassicae toxin and zeatin in cell culture of Brassica juncea (L.) Czern and Coss cv. Divya. Physiol Mol Biol Plants 7:181–184
Edgar R, Barrett T (2006) NCBI GEO standards and services for microarray data. Nat Biotechnol 24(12):1471
Harr B, Schlötterer C (2006) Comparison of algorithms for the analysis of Affymetrix microarray data as evaluated by co-expression of genes in known operons. Nucleic Acids Res 34(2):e8–e8
Smyth, G.K., Ritchie, M., Thorne, N., Wettenhall, J., Shi, W. and Hu, Y (2002) limma: linear models for microarray and RNA-Seq data user’s guide.
Maglott D, Ostell J, Pruitt KD, Tatusova T (2010) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res 39(suppl_1):D52–D57
Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, Sasidharan R, Muller R, Dreher K, Alexander DL, Garcia-Hernandez M, Karthikeyan AS (2011) The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res 40(D1):D1202–D1210
Carmona-Saez, P., Chagoyen, M., Tirado, F., Carazo, J.M. and Pascual-Montano, A., 2007. GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists. Genome biology, 8(1), p.R3.
Nogales-Cadenas R, Carmona-Saez P, Vazquez M, Vicente C, Yang X, Tirado F, Carazo JM, Pascual-Montano A (2009) GeneCodis: interpreting gene lists through enrichment analysis and integration of diverse biological information. Nucleic Acids Res 37(suppl_2):W317–W322
Tabas-Madrid D, Nogales-Cadenas R, Pascual-Montano A (2012) GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Res 40(W1):W478–W483
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410
Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD (2007) Clustal W and Clustal X version 2.0. bioinformatics 23(21):2947–2948
Page RD (2003) Visualizing phylogenetic trees using TreeView. Curr Protoc Bioinformatics 1:6–2
Letunic I, Bork P (2016) Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res 44(W1):W242–W245
Marchler-Bauer A, Derbyshire MK, Gonzales NR, Lu S, Chitsaz F, Geer LY, Geer RC, He J, Gwadz M, Hurwitz DI, Lanczycki CJ (2014) CDD: NCBI’s conserved domain database. Nucleic Acids Res 43(D1):D222–D226
Szklarczyk, D., Morris, J.H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N.T., Roth, A., Bork, P. and Jensen, L. J (2016) The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic acids research, p.gkw937.
Assenov Y, Ramírez F, Schelhorn SE, Lengauer T, Albrecht M (2007) Computing topological parameters of biological networks. Bioinformatics 24(2):282–284
Kohl, M., Wiese, S. and Warscheid, B., 2011. Cytoscape: software for visualization and analysis of biological networks. In Data mining in proteomics (pp. 291-303). Humana Press.
Pandolfi V, Ribamar Costa Ferreira Neto J, Daniel da Silva M, Lindinalva Barbosa Amorim L, Carolina Wanderley-Nogueira A, Lane de Oliveira Silva R, Akio Kido E, Crovella S, Maria Benko Iseppon A (2017) Resistance (R) genes: applications and prospects for plant biotechnology and breeding. Curr Protein Pept Sci 18(4):323–334
Gururani, M.A., Venkatesh, J., Upadhyaya, C.P., Nookaraju, A., Pandey, S.K. and Park, S.W (2012) Plant disease resistance genes: current status and future directions. Physiol Mol Plant Pathol, 78, pp.51-65.
Kumar A, Pathak RK, Gupta SM, Gaur VS, Pandey D (2015) Systems biology for smart crops and agricultural innovation: filling the gaps between genotype and phenotype for complex traits linked with robust agricultural productivity and sustainability. Omics: a journal of integrative biology 19(10):581–601
Imam J, Singh PK, Shukla P (2016) Plant microbe interactions in post genomic era: perspectives and applications. Front Microbiol 7:1488
Mondal R, Pandey D, Taj G, Kumar A (2016) Reciprocal relationship between expression of MAP kinase 4 and MAP kinase 6 during pathogenesis of Alternaria blight in Arabidopsis thaliana. Biotech Today: An International Journal of Biological Sciences 6(1):60–64
Kannan P, Pandey D, Gupta AK, Punetha H, Taj G, Kumar A (2012) Expression analysis of MAP2K9 and MAPK6 during pathogenesis of Alternaria blight in Arabidopsis thaliana ecotype Columbia. Mol Biol Rep 39(4):4439–4444
Mohapatra SK and Krishnan, A (2011) Microarray data analysis. In Plant reverse genetics (pp. 27-43). Humana Press, Totowa. NJ.
Wu, Z.J. and Irizarry, R (2010) Description of gcrma package (https://www.bioconductor.org/packages/3.7/bioc/vignettes/gcrma/inst/doc/gcrma2.0.pdf).
Zheng MS, Takahashi H, Miyazaki A, Hamamoto H, Shah J, Yamaguchi I, Kusano T (2004) Up-regulation of Arabidopsis thaliana NHL10 in the hypersensitive response to cucumber mosaic virus infection and in senescing leaves is controlled by signalling pathways that differ in salicylate involvement. Planta 218(5):740–750
Pré M, Atallah M, Champion A, De Vos M, Pieterse CM, Memelink J (2008) The AP2/ERF domain transcription factor ORA59 integrates jasmonic acid and ethylene signals in plant defense. Plant Physiol 147(3):1347–1357
Bernoux M, Timmers T, Jauneau A, Briere C, de Wit PJ, Marco Y, Deslandes L (2008) RD19, an Arabidopsis cysteine protease required for RRS1-R–mediated resistance, is relocalized to the nucleus by the Ralstonia solanacearum PopP2 effector. Plant Cell 20(8):2252–2264
Shinde BA, Dholakia BB, Hussain K, Aharoni A, Giri AP, Kamble AC (2018) WRKY1 acts as a key component improving resistance against Alternaria solani in wild tomato, Solanum arcanum Peralta. Plant Biotechnol J 16(8):1502–1513
Kámán-Tóth E, Dankó T, Gullner G, Bozsó Z, Palkovics L, Pogány M (2019) Contribution of cell wall peroxidase-and NADPH oxidase-derived reactive oxygen species to Alternaria brassicicola-induced oxidative burst in Arabidopsis. Mol Plant Pathol 20(4):485–499
Berardini, T.Z., Reiser, L., Li, D., Mezheritsky, Y., Muller, R., Strait, E. and Huala, E., 2015. The Arabidopsis information resource: making and mining the “gold standard” annotated reference plant genome. genesis 53.
Rawat S, Ali S, Mittra B, Grover A (2017) Expression analysis of chitinase upon challenge inoculation to Alternaria wounding and defense inducers in Brassica juncea. Biotechnology reports 13:72–79
Zhu H, Li GJ, Ding L, Cui X, Berg H, Assmann SM, Xia Y (2009) Arabidopsis extra large G-protein 2 (XLG2) interacts with the Gβ subunit of heterotrimeric G protein and functions in disease resistance. Mol Plant 2(3):513–525
Authors are grateful to the Department of Biotechnology, G. B. Pant Institute of Engineering & Technology, Pauri Garhwal, Uttarakhand for providing all the necessary facilities.
R. K. Pathak would like to thank Council of Scientific and Industrial Research (CSIR), Govt. of India for providing him a Senior Research Fellowship for pursuing Ph.D.
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Pathak, R.K., Baunthiyal, M., Pandey, D. et al. Computational analysis of microarray data of Arabidopsis thaliana challenged with Alternaria brassicicola for identification of key genes in Brassica. J Genet Eng Biotechnol 18, 17 (2020). https://doi.org/10.1186/s43141-020-00032-y
- Microarray data analysis
- GO analysis
- Network analysis