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Article

Identifying Critical Regulators in the Viral Stress Response of Wheat (Triticum aestivum L.) Using Large-Scale Transcriptomics Data

by
Amir Ghaffar Shahriari
1,*,
Imre Majláth
2,*,
Massume Aliakbari
3,
Mohamad Hamed Ghodoum Parizipour
4,
Aminallah Tahmasebi
5,
Fatemeh Nami
6,
Ahmad Tahmasebi
6 and
Mohsen Taherishirazi
6
1
Department of Agriculture and Natural Resources, Higher Education Center of Eghlid, Eghlid 7381943885, Iran
2
Centre for Agricultural Research, Agricultural Institute, Brunszvik u. 2., H-2462 Martonvásár, Hungary
3
Department of Crop and Plant Breeding, Shiraz University, Shiraz 7144113131, Iran
4
Department of Plant Protection, Faculty of Agriculture, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani 6341773637, Iran
5
Department of Agriculture, Minab Higher Education Center, University of Hormozgan, Bandar Abbas 7916193145, Iran
6
Institute of Biotechnology, School of Agriculture, Shiraz University, Shiraz 7144113131, Iran
*
Authors to whom correspondence should be addressed.
Agronomy 2023, 13(10), 2610; https://doi.org/10.3390/agronomy13102610
Submission received: 31 August 2023 / Revised: 6 October 2023 / Accepted: 9 October 2023 / Published: 13 October 2023
(This article belongs to the Section Crop Breeding and Genetics)
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Abstract

:
Wheat (Triticum aestivum L.) cultivation has been globally restricted by many plant viruses such as the Wheat streak mosaic virus (WSMV), Barley stripe mosaic virus (BSMV), and Brome mosaic virus (BMV). Herein, the transcriptome of wheat was in silico analyzed under mono- (WSMV, BSMV, or BMV), bi- (BMV&BSMV, BMV&WSMV, and BSMV&WSMV), and tripartite (WSMV, BSMV, and BMV) infections using the RNA-seq technique. Total numbers of 1616/270, 5243/690 and 5589/2183 differentially expressed genes (DEGs) were up/down-regulated during the bipartite infection of BMV&BSMV, BMV&WSMV and BSMV&WSMV, respectively, while the tripartite infection resulted in the up/down-regulation of 6110/2424 DEGs. The NAC and bHLH were the most commonly presented transcription factor (TF) families in WSMV, BMV, and BSMV infection, while C2H2, bHLH, and NAC were the TF families involved in BMV&WSMV, BMV&BSMV, and BSMV&WSMV infections, respectively. The RLK-Pelle_DLSV was the most commonly expressed protein kinase (PK) family in all infection patterns. Promoter analysis showed that the motifs involved in gene expression, CUL4 RING ubiquitin ligase complex, stress response, brassinosteroid response, and energy-related pathways were significantly induced in wheat plants under bipartite infections. The gene expression network analysis showed that a defense-related gene, i.e., allene oxide synthase (AOS) gene, serves as a crucial hub in tripartite infections.

1. Introduction

Plant viruses are a considerable category of biotrophs causing adverse implications for plant-based industries globally [1]. These agents can infect innumerable plant species which they replicate and, simultaneously, incite various microscopic and macroscopic disorders [2]. Due to the social, political, and economic importance of wheat (Triticum aestivum L.) in human life, a large number of virological studies have been conducted on the plant species and various wheat-infecting viruses have been consequently characterized. Wheat streak mosaic virus (WSMV), Barley stripe mosaic virus (BSMV), and Brome mosaic virus (BMV) are three wheat-infecting viruses that are responsible for the significant loss associated with wheat cultivations all over the world [3].
WSMV is a mite-transmitted virus from the genus Tritimovirus and the family Potyviridae with a positive-sense single-stranded (+ ss) RNA genome and filamentous particle that infects gramineous plants [4]. WSMV-infected plants typically exhibit decreased growth, streak patterns of leaf yellowing, and irregular discoloration [5,6]. WSMV infection can reduce the wheat yield by up to 100% in some locations [7]. Moreover, WSMV together with other viruses such as Triticum mosaic virus and High Plains virus exhibit synergistic interactions, increasing the crop yield in cultivated wheat [7].
BSMV has been taxonomically placed in the genus Hordevirus and the family Virgaviridae [8]. It is a rod-shaped cereal-infecting (+) ss-RNA virus that can be transmitted by mechanical inoculation or infected seeds [9]. BSMV causes yellow streaks or spots, mosaics, and dwarfism in the infected hosts [8,10]. It has been reported that BSMV frequently infects plants, reducing the yields by up to 25% [11,12].
BMV is a small icosahedral (+) ss-RNA plant virus belonging to the genus Bromovirus, family Bromoviridae, in the Alphavirus-like superfamily [13,14]. Although it commonly infects grasses, BMV can be found in dicotyledonous plants, such as soybean [15]. The symptoms of BMV infection include stunted growth, leaf lesions, leaf mosaic, and, rarely, overall death [14]. BMV can cause a significant reduction in the yield as a 61% yield loss has been reported in soft red winter wheat [16].
Due to the overlapping host range of plant viruses, it is likely that more than one virus simultaneously infects a single plant species. The mixed infection of a plant by some viruses may lead to at least four different interactions including (1) neutralism, (2) synergism, (3) antagonism, and (4) synergism/antagonism [17]. To date, the accumulation of genomic DNA/RNA, symptom severity, and transmission mode have been considered the main factors affecting the way by which plant viruses interact with each other within mixed infections [18,19,20,21,22,23]. However, a plant virus successfully infects its host when it can counteract host defense mechanisms and other defense-related agents [24]. Thus, a plant virus utilizes a variety of host-originated resources to facilitate their infection [25]. It has been generally accepted that alternation of gene expression is a key factor in virus-infected plants [26,27,28,29,30,31,32,33,34]. Therefore, transcript analysis has been extensively carried out to study plant–virus interactions [35,36,37]. Transcription factors (TFs) are important players in biotic stresses including viral infections. They are DNA-binding proteins that regulate gene expression by annealing to certain sequences of DNA in the promoter, up-/down-regulating the gene expression [38,39,40]. The type of plant–virus interaction highly depends on different factors such as the virus, host, and vector type, and multiple viral infections, therefore, affecting the interaction [17,41]. Particularly, an antagonistic interaction has been shown during the co-infection of wheat by BMV and BSMV, while the co-infection of BMV/BSMV with WSMV/Triticum mosaic virus (TriMV) exhibits a synergistic interaction within the host plant [42]. Moreover, multiple infections caused by WSMV, TriMV, and BMV/BSMV have led to a synergistic interaction. Similarly, the quadripartite interactions in mixed infections of wheat by WSMV, TriMV, BMV, and BSMV have been found to be a synergistic type, although BSMV accumulation has been drastically reduced [42]. Therefore, it is highly important to investigate the transcriptome responses during the various types of single and mixed interactions (synergism and antagonism) between the three main wheat-infecting viruses (WSMV, BSMV and BMV). Additionally, the application of genomic approaches would give a better picture of plant–virus interaction [24,43]. There is a lack of knowledge in the literature about the virus-responsive genes of WSMV, BSMV, and BMV-infected wheat. This study was performed to reveal the transcriptome response of T. aestivum plants to single and mixed infections by WSMV, BSMV, and BMV. Novel approaches, including gene ontology (GO) and biological pathway (Kyoto Encyclopedia of genes and genomes, KEGG) analyses and gene network analysis, have been used to determine the plant response to viral infections. The action of TFs and protein kinases (PKs) was also demonstrated during the infection.

2. Materials and Methods

To evaluate the variation in the gene expression patterns of wheat plants in the response to different infection patterns including monopartite infection (single infections of WSMV, BSMV, or BMV), bipartite infection (double infections of BMV&BSMV, BMV&WSMV, or BSMV&WSMV), and tripartite infection (mixed infection of WSMV, BSMV, and BMV), the RNA-seq data were downloaded from three independent studies deposited in the Sequence Read Archive (SRA) repository of NCBI. The data were cleaned using the Galaxy webserver (Europe core) and file quality was evaluated by FASTQ. The Trimmomatic tool was used in case of data trimming. Data normalization was performed by R software (ver. 4.3.2). DEGs were identified by statistical analysis of the data using the package edgeR incorporated in R software (ver. 4.3.2). The details of the data are presented in Table 1. The Longhorn genotype of T. aestivum seedlings (14-day-old) was mechanically inoculated with an American isolate of WSMV.
The inoculated seedlings were incubated in a growth chamber under the conditions of 18 °C day/15 °C night and 12 h light/12 h dark photoperiods. Control plants were inoculated using the inoculation buffer (0.01 M of KH2PO4 (pH 7.4)) [44]. In the case of BMV and BSMV, both sides of one-week-old seedlings were rub inoculated and the inoculated plants were kept in a growth chamber under the conditions of 20 °C temperature and 16D/8L photoperiod (Ding et al., unpublished data).

2.1. The RNA-Seq

The RNA-seq analysis was performed using the Galaxy platform (European server). The DEGs of each study were obtained separately by the edgeR package in the R software (ver. 3.14.0) with a cutoff value of 5%. Then, downstream analyses were performed on the filtered DEGs during all infection patterns and the Venn diagrams were drawn and common DEGs were identified and analyzed.

2.2. Functional Enrichment Analysis of the DEGs

The AgriGO online tool [45] was used to reveal the gene ontology (GO) of differentially genes expressed under different wheat–virus interactions. Additionally, gene regulation was elucidated in multiple infections of wheat plants by WSMV, BMV and BSMV, applying GO and Singular Enrichment Analysis (SEA). The GO enrichment analysis of DEGs in the virus-infected plants was performed according to Du et al. (2010) [46].
The functional or pathway mapping of the DEGs was applied to determine the biological meaning of the KEGG pathway by the KOBAS ver. 3.0 database [47]. The functional annotation was determined using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) [48]. The false discovery rate (FDR) < 0.05 was determined accordingly.

2.3. The Analysis of Transcription Factors, Protein Kinases, and Promoters

For the analysis of TF and PK, the corresponding sequences were first obtained from the Ensembl genome browser (https://plants.ensembl.org/index.html (accessed on 25 July 2023)) and then analyzed using the iTAK database [49]. For promoter analysis, the sequences were extracted from the Ensembl genome browser and analyzed by the MEME database (https://meme-suite.org/meme/tools/meme (accessed on 12 September 2023)). Their TFs were identified by the TOMTOM database (https://meme-suite.org/meme/tools/tomtom (accessed on 20 September 2023)) and their ontology was analyzed by the GOMO database (https://meme-suite.org/meme/tools/gomo (accessed on 18 August 2023)) [50]. The KEGG pathway was drawn using R software (ver. 4.1.2).

2.4. Validation Analysis

The hub genes were drawn by Cytoscape and their common features were identified by a Venn diagram (https://bioinformatics.psb.ugent.be/webtools/Venn/ (accessed on 20 September 2023)).

2.5. Validation Analysis of Shared DEGs

To validate the expression analysis of the identified 34 common genes among the three studies, we utilized a 10-fold cross-validation approach to assess the efficacy of these genes in distinguishing between the control and stressed samples. This validation process divided the initial expression dataset into two sets: a training set and a test set [51].

2.6. Homology Analysis of Overlapping DEGs

Nucleotide sequences of all 34 shared genes were retrieved from Ensembl Plants [52]. These sequences were subjected to homology analysis using BlastX search (E-value ≤ 0.00001) in CLC Genomics Workbench 10 to find their homologous in rice and Arabidopsis.

2.7. Gene Network Analysis

Shared genes that had a significant hit among Arabidopsis proteins were subjected to enrichment analysis using the Pathway Studio software (ver. 7.1) [53] with a cut-off p-value < 0.05. Significant pathways were networked using the same software with a hierarchical layout. All nodes and edges of the predicted gene network were transferred into Cytoscape 3.10.0 software [54,55]. Gene network topology analysis was performed using the same software. Closeness centrality and betweenness centrality [56,57] parameters were used to assign more important (hub) gene(s).

3. Results

3.1. DEG Analysis

To reveal the mechanism of gene expression in wheat plants infected by WSMV, BSMV, and BMV, the DEGs were assessed in mono-, bi-, and tri-partite infections. The three viral infections in wheat plants triggered a significant gene change in the global expression patterns. Bi-partite infection of the host plant by BMV&BSMV, BMV&WSMV and BSMV&WSMV led to altered regulation of 3600, 5897, and 7713 genes, respectively (Figure 1a–c). Moreover, tri-partite infections of WSMV, BSMV and BMV resulted in the changed expression of 8368 genes (Figure 1d). Mono-partite infection of the wheat plants by WSMV, BSMV, and BMV resulted in the changed expression of 5011, 2816, and 1049 genes, respectively. Moreover, the number of up-/down-regulated genes in response to mono-partite infections was determined. The data showed that 1616/270, 5243/690 and 5589/2183 genes were up/down-regulated during bi-partite infection of BMV&BSMV, BMV&WSMV, BSMV&WSMV, respectively (Figure 2 and Figure 3). Additionally, tri-partite infections of WSMV, BSMV, and BMV resulted in the up/down-regulation of 6110/2424 genes (Figure 2 and Figure 3). The information on the most up-and down-regulated genes is presented in Tables S1 and S2, respectively. The GO analysis of general gene expression and DEGs regulated under different wheat–virus interactions exhibited the biological events, cell composition, and functional groups of biomolecules (Figure 4). The genes involved in defense responses, protein functions, and translation events were significantly stimulated in wheat plants co-infected by BMV&BSMV and BSMV&WSMV. These genes included the defense response (139), carbohydrate metabolic process (69), and polysaccharide binding (37) in BMV and BSMV infection. In BSMV&WSMV infection, genes including defense response to bacterium (40), protein transport (96), and translation (180) were significantly triggered in wheat plants (Figure 4). The genes including ATP binding (200), carbohydrate binding (180), NAD binding (131), polysaccharide binding (185), kinase activity (129) and defense response to a bacterium (142) and oomycetes (129) were stimulated in BSMV&WSMV infection. Tri-partite infection of wheat plants by WSMV, BSMV, and BMV resulted in significant regulation of defense-related and molecular binding genes (Figure 5). In the infection pattern, WSMV could stimulate the genes involved in the defense response to bacterium (124), defense response to oomycetes (114), ATP binding (200), carbohydrate binding (206), and NAD binding (110).
When wheat plants were co-infected by BMV&BSMV, 15 pathways were significantly enriched with altered DEGs in the KEGG assay, which include thiamine metabolism, starch and sucrose metabolism, plant–pathogen interaction, monoterpenoid biosynthesis, metabolic pathways, MAPK signaling pathway, glutathione metabolism, fatty acid elongation, diterpenoid biosynthesis, cysteine, and methionine metabolism, cyanoamino acid metabolism, biosynthesis of various plant secondary metabolites, biosynthesis of secondary metabolites, benzoxazinoid biosynthesis and alanine, aspartate and glutamate metabolism (Figure 6).
Moreover, co-infection of wheat plants by BMV&WSMV resulted in the significant enrichment of 17 pathways including tyrosine metabolism, ribosome, protein processing in endoplasmic reticulum, proteasome, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, glycolysis/gluconeogenesis, glutathione metabolism, fatty acid degradation, citrate cycle (TCA cycle), carbon metabolism, biosynthesis of amino acids, arginine biosynthesis, aminoacyl–tRNA biosynthesis, alanine, aspartate and glutamate metabolism, ABC transporters and 2–oxocarboxylic acid metabolism (Figure 6). No KEGG pathway of DEGs was found during bi-partite infection of BSMV&WSMV.

3.2. TF and PK Assays

The TF activity in wheat plants under mono- and bi-partite infections was evaluated. The results showed that specific wheat-originated TFs, including AP2/ERF-ERF, B3, bHLH, C3H, C2C2-Dof, GRAS, WRKY, MYB, HB-other, NAC, C2H2, and MADS-MIKC, were detected in all wheat–virus interactions (Table 2). The specific C2C2-CO-like TFs were activated in wheat plants infected by BMV, and co-infected by BMV&WSMV and BMV&BSMV. Except for mono-partite infection by BMV and bi-partite infection by BSMV&WSMV, other infection patterns led to activated HSF TFs in wheat plants (Table 2). Moreover, mono-partite infection by BSMV and co-infection by BMV&BSMV resulted in the activation of the specific GeBP TFs in the plants. The highest numbers of genes activated during WSMV, BMV, and BSMV infection belonged to NAC (38), NAC (13), and bHLH (30) TF families, respectively. Furthermore, during bi-partite infections of BMV&WSMV, BMV&BSMV, and BSMV&WSMV, C2H2 (36), bHLH (34) and NAC (57) TF families had the highest numbers of activated genes (Table 2).
Table 3 represents the PK families activated in wheat plants under mono- and bi-partite infections by BMV, BSMV, and WSMV. The highest numbers of genes activated during WSMV, BMV, and BSMV belonged to the RLK-Pelle_DLSV PK family with 47, 24, and 66 numbers, respectively. Similarly, during bi-partite infections of the BMV&WSMV, BMV&BSMV, and BSMV&WSMV, RLK-Pelle_DLSV PK family had the highest numbers of 76, 78, and 55 activated genes, respectively (Table 3).

3.3. Motif Analysis

In total, 11 motifs were induced in wheat plants under bi-partite infections by BMV&BSMV (Table 4). Two energy-related motifs with ATP binding functions (# 8, 9) were induced in the virus-infected plants. Four motifs (# 1–3, 10) were found to be functional in the transcription process. Moreover, three motifs (# 3, 9, 11) were identified that are involved in the CUL4 RING ubiquitin ligase complex. Motifs 1 and 10 were also induced in the virus-infected plants, which function as transcription regulators. In the case of wheat plants co-infected by BMV&WSMV, a total number of 11 motifs were found to be induced (Table 5). Motifs 8, 9, 10, and 11 are involved in transcription activity. The CUL4 RING ubiquitin ligase complex was found to be associated with three motifs (# 1, 3, 5) induced in the virus-infected plants. One motif (Motif 9) was identified that functions in response to brassinosteroid stimulus. Also, a stress-related motif (# 10) and an energy-related motif (# 4) were induced in the virus-infected plants.

3.4. Validation Analysis of Shared DEGs

The 10-fold cross-validation approach validated 34 shared DEGs’ efficiency in distinguishing stress and control conditions. The result indicated that the control and stress samples were accurately classified (Table S3). Our findings demonstrated that the expression levels of these genes enabled accurate classification of the majority of samples with a classification accuracy of 86.11%. Therefore, these genes were used in this study.

3.5. Homology and Gene Network Analysis

BlastX results showed that of 34 identified shared genes, 25 genes had a significant hit among Arabidopsis proteins. In addition, all common genes had a homologous gene among rice proteins (Table S3). Pathway Studio software (ver. 7.1) highlighted genes with functional information (Figure 7a). Moreover, the predicted network was expanded by Pathway Studio software through the addition of several components identified as those related to these networked genes, including small molecules (JA, salicylate, ABA, nitric oxide (NO), ROS and so on), functional classes (lip oxidase, allene-oxide cyclase, PLDalpha, and abscisic-aldehyde: oxygen oxidoreductase) and cell processes (systemic acquired resistance, apoptosis, oxidative stress, cell death and so on) (Figure 7a), which represented the main pathways of wheat responses to virus infections.
The derived gene network encompassed 30 nodes and 87 edges. Network topological analysis of the predicted gene network using Cytoscape 3.4 software (ver. 3.4.0) evealed that based on the closeness centrality and betweenness centrality parameters, the gene encoding allene oxide synthase (AOS) significantly influences the predicted network (Figure 7b); therefore, it was considered an important (hub) gene.

4. Discussion

It has been shown that mixed infection by plant viruses from different genera results in synergistic or antagonistic interactions within the plant hosts [17,42,58]. The genomic approaches are capable of predicting a variety of plant–virus interactions [17,41,59]. One of these approaches could be the extraction of information obtained from infection-responsive genes in virus-challenged plants. In this study, the expression profile of common genes was assessed during viral infection within wheat plants. DEG analysis showed that the expression of several response-related genes has been altered in wheat plants infected by WSMV, BSMV, and BMV. Due to the reliance of plant viruses on their host cell machinery, their infection might alter the gene expression profile of host cells [60]. It has been demonstrated that viruses can interfere with biological events such as metabolic pathways [61]. In the present study, GO analysis showed the presence of energy-related genes in wheat–virus interactions that have been similarly identified in other studies [41,59,62,63]. This might play a key role in wheat’s response to WSMV, BSMV, and/or BMV. The DEGs of glutathione metabolism and autophagy pathways were significantly induced in wheat plants co-infected by BMV&BSMV, BMV&WSMV, and BSMV&WSMV. It has been shown that glutathione has antioxidant activity and is considered an important regulator of redox signaling involved in plant defense against viruses [64]. It has been considered that autophagy is activated by different stresses such as viral infections [65,66]. Moreover, autophagy plays a role in defense hormone signaling and cell death [67,68]. Nuclear import of specific proteins might be vital in reprograming cellular processes defending against stress [69], which might reflect the protein export activity during viral infections. The DEGs of the endoplasmic reticulum (ER) were altered in wheat plants infected by WSMV, BSMV, and/or BMV, which is active in several cellular processes such as calcium homeostasis, protein, and membrane lipid synthesis [70,71]. It has been shown that the majority of viruses hijack ER resident chaperones to act in their replication and translocation [71]. Furthermore, proteasome-related DEGs were identified in wheat plants co-infected by BMV&WSMV. Ubiquitin-26S proteasome system (UPS) has been considered a key mechanism for protein degradation [72], plant defense [73,74,75], and viral infection [73,76,77,78]. The highest number of genes activated during all infection patterns belonged to the RLK-Pelle_DLSV PK family, which has been shown to play a role in stress signaling [79,80,81]. Three motifs were identified to be induced in wheat plants under bi-partite infections by BMV&BSMV, which are involved in CUL4 RING ubiquitin ligase. This complex plays a role in chromatin regulation and is generally hijacked by viruses [82]. Moreover, at least six motifs were induced in the virus-infected plants that are involved in transcription regulation. This reinforced the idea that the virus infection alters the gene expression of the host plant [83,84,85,86]. In wheat plants co-infected by BMV&WSMV, a motif was identified to be involved in brassinosteroid response. This phytohormone has been considered to play a role in virus defense pathways [34,87,88,89].
The allene oxide synthase (AOS) gene was identified as a central hub shared in tri-partite infections of WSMV, BSMV, and BMV. The AOS gene is an essential component of the jasmonic acid (JA) biosynthesis pathway, which is a key regulator of plant defense against pathogens [90]. During viral infections, the AOS gene is upregulated, leading to the synthesis of oxylipin signaling molecules, including jasmonates (JA and its derivatives) [90,91]. The JA pathway plays a pivotal role in the activation of defense responses against viral pathogens, including WSMV, BSMV, and BMV [92].
At the molecular level, the activation of the AOS gene leads to the production of JA, which subsequently regulates the expression of downstream target genes involved in defense against viral pathogens. The AOS enzyme catalyzes the conversion of fatty acid hydroperoxides to allene oxides, which are then converted to JA through subsequent enzymatic reactions. The synthesis of JA involves the action of several enzymes, including lipoxygenases (LOXs), allene oxide synthase (AOS), allene oxide cyclase (AOC), and 12-oxophytodienoic acid reductase (OPR) [90,93,94]. These enzymes, in coordination with AOS, ensure the efficient production of JA for defense signaling.
Upon synthesis, JA acts as a signaling molecule, initiating a cascade of defense responses in the infected plant. JA perception and signal transduction involve the JA receptor complex composed of COI1 (CORONATINE INSENSITIVE 1) and JAZ proteins (JASMONATE ZIM DOMAIN). In the absence of JA, JAZ proteins repress the activity of transcription factors, preventing the expression of defense-related genes. However, in the presence of JA, COI1 interacts with JAZ proteins, leading to their degradation and subsequent activation of transcription factors, such as MYC2 [95,96,97]. MYC2 then binds to the promoter regions of defense-related genes, initiating their upregulation [98,99].
The activation of defense-related genes mediated by the AOS gene and JA pathway leads to the synthesis of various defense compounds and the induction of defense mechanisms. These include the production of antimicrobial peptides, such as thionins and defensins, which possess direct antiviral activity. Furthermore, the AOS gene and JA pathway regulate the synthesis of secondary metabolites involved in defense against viral pathogens. For instance, JA signaling stimulates the production of phytoalexins, which are antimicrobial compounds that inhibit the replication and spread of viruses [90,100,101].
Specifically, JA has been found to play a role in the regulation of protease inhibitors, which is one of the defense responses against potyviral infection [102]. The involvement of defense signaling pathways mediated by JA, salicylic acid (SA) and ethylene (ET) would shape the overall defense strategy against viral infections. The SA pathway is associated with defense against biotrophic pathogens, whereas the ET pathway is involved in both biotrophic and necrotrophic pathogen defense [103,104,105]. The integration and balance of these signaling pathways contribute to an effective defense response against viral infections.
Moreover, the AOS gene engages in protein–protein interactions and regulatory mechanisms that further modulate its activity and function during viral infections. The AOS protein interacts with other enzymes involved in JA biosynthesis, such as lipoxygenases (LOXs) and allene oxide cyclase (AOC), forming protein complexes that facilitate the efficient conversion of substrates and the production of JA [91,106,107]. These interactions ensure the proper functioning of the JA pathway in defense signaling.
Additionally, post-translational modifications, such as phosphorylation and acetylation, can regulate the activity and stability of the AOS protein, impacting its function in the defense response. Phosphorylation of AOS by protein kinases and acetylation by acetyltransferases can modulate its enzymatic activity and protein–protein interactions, providing an additional layer of regulation [90,94,108].
Numerous studies have identified genes associated with defense responses that are upregulated upon AOS activation during viral infections. These target genes include pathogenesis-related (PR) genes, encoding proteins such as PR1, PR2 (β-1,3-glucanases), and PR5 (thaumatin-like proteins), which are involved in antiviral defense and systemic acquired resistance (SAR) [104,105,109,110]. Other target genes may encode enzymes involved in secondary metabolite synthesis, such as terpenoids and phenylpropanoids, which contribute to defense against viral pathogens [101,111,112].
Nevertheless, it has been demonstrated that AOS plays a key element in the oxylipin pathway that mediates vector attraction in viral diseases [102]. Since vector feeding could affect the gene expression of host plant [113,114], it is highly recommended to determine the alternation of host gene expression in response to vector-transmitted viruses.
In summary, our findings revealed that the AOS gene serves as a crucial hub in the plant–virus interaction, orchestrating defense responses against WSMV, BSMV, and BMV infections. Its activation leads to the synthesis of JA and jasmonates, which regulate the expression of defense-related genes involved in antimicrobial compound synthesis, signaling, and systemic defense. The interplay with other defense signaling pathways, including SA and ET, and the modulation of AOS activity through protein–protein interactions and post-translational modifications contribute to the complexity of the defense response. Our findings about the AOS gene can be the starting point of studies on the response of crop plants, especially wheat, to combined viral contamination. Therefore, it is necessary to conduct additional studies. Further research is highly recommended to validate the role of AOS using experimental methods such as quantitative PCR. Understanding the intricate molecular details of the AOS gene provides valuable insights into the regulatory networks and potential targets for developing strategies to improve crop protection against devastating viral diseases. These findings revealed the pathways and genes that are frequently activated during plant–virus interactions, pointing to potential new antiviral targets for enhancing plant resistance to mixed viral infections through genetic engineering approaches.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13102610/s1, Table S1: The details of the most up-regulated genes of wheat plants in response to different viruses; Table S2: The details of the most down-regulated genes of wheat plants in response to different viruses; Table S3: Cross-validation results of identified shared DEGs.

Author Contributions

A.G.S., A.T. (Aminallah Tahmasebi), M.H.G.P. and F.N. designed the project, collected and analyzed the data, and wrote the manuscript. M.A. and A.T. (Ahmad Tahmasebi) wrote and reviewed the manuscript. I.M. and M.T. authored and reviewed the manuscript before approving the final draft. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the project No. TKP2021-NKTA-06 funded by the Ministry of Innovation and Technology from the Hungarian National Research Development and Innovation Fund, in the frame of the Thematic Excellence Program 2021. We also acknowledge support from the Higher Education Center of Eghlid.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Global transcription of differentially expressed genes in Triticum aestivum under different viral infections. Venn diagram of genes whose expression was altered in response to bi-partite infections of BMV&BSMV (a), BMV&WSMV (b), BSMV&WSMV (c), and tri-partite infections of WSMV&BSMV&BMV (d).
Figure 1. Global transcription of differentially expressed genes in Triticum aestivum under different viral infections. Venn diagram of genes whose expression was altered in response to bi-partite infections of BMV&BSMV (a), BMV&WSMV (b), BSMV&WSMV (c), and tri-partite infections of WSMV&BSMV&BMV (d).
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Figure 2. The up-regulation of DEGs in Triticum aestivum under bipartite infections of BMV&BSMV (a), BMV&WSMV (b), BSMV&WSMV (c), and tri-partite infection of WSMV, BSMV and BMV (d).
Figure 2. The up-regulation of DEGs in Triticum aestivum under bipartite infections of BMV&BSMV (a), BMV&WSMV (b), BSMV&WSMV (c), and tri-partite infection of WSMV, BSMV and BMV (d).
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Figure 3. The DEGs of Triticum aestivum down-regulated in response to bipartite infections of BMV&BSMV (a), BMV&WSMV (b), BSMV&WSMV (c), and tripartite infection of WSMV, BSMV, and BMV (d).
Figure 3. The DEGs of Triticum aestivum down-regulated in response to bipartite infections of BMV&BSMV (a), BMV&WSMV (b), BSMV&WSMV (c), and tripartite infection of WSMV, BSMV, and BMV (d).
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Figure 4. The counts of DEGs of Triticum aestivum samples co-infected by BMV&BSMV, BMV&WSMV and BSMV&WSMV. The horizontal axis shows GO categories according to three main classes (biological process, molecular function, and cellular component). The vertical axis shows the number of corresponding genes.
Figure 4. The counts of DEGs of Triticum aestivum samples co-infected by BMV&BSMV, BMV&WSMV and BSMV&WSMV. The horizontal axis shows GO categories according to three main classes (biological process, molecular function, and cellular component). The vertical axis shows the number of corresponding genes.
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Figure 5. The results of gene ontology (GO) analysis of general gene expression in Triticum aestivum co-infected by BMV, BSMV, and WSMV. The horizontal axis shows the overrepresented GO categories in the three main GO classes and the vertical axis shows the number of corresponding genes.
Figure 5. The results of gene ontology (GO) analysis of general gene expression in Triticum aestivum co-infected by BMV, BSMV, and WSMV. The horizontal axis shows the overrepresented GO categories in the three main GO classes and the vertical axis shows the number of corresponding genes.
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Figure 6. The pathway mapping of the DEGs from the KEGG analysis in Triticum aestivum co-infected by BMV&BSMV and BMV&WSMV (FDR < 0.05). The vertical axis shows the name of the enriched KEGG pathway. The color of the pathways is based on the FDR, respectively.
Figure 6. The pathway mapping of the DEGs from the KEGG analysis in Triticum aestivum co-infected by BMV&BSMV and BMV&WSMV (FDR < 0.05). The vertical axis shows the name of the enriched KEGG pathway. The color of the pathways is based on the FDR, respectively.
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Figure 7. Gene network of up-regulated TFs in response to tri-partite infections of WSMV, BSMV, and BMV in Triticum aestivum. (a) Network topological analysis of the predicted gene network. (b) The lines represent the interaction between differentially expressed genes.
Figure 7. Gene network of up-regulated TFs in response to tri-partite infections of WSMV, BSMV, and BMV in Triticum aestivum. (a) Network topological analysis of the predicted gene network. (b) The lines represent the interaction between differentially expressed genes.
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Table 1. The details of the sample data retrieved from SRA and NCBI.
Table 1. The details of the sample data retrieved from SRA and NCBI.
Accession NumberTypePlatformSample NumberTissueReleased
ControlTreatmentTotal
SRP349660WSMV infectionIllumina HiSeq 20008816Leaf30 April 2022
ERP128185BSMV infectionIllumina NovaSeq 60006612Leaf & root6 June 2022
ERP128185BMV infectionIllumina NovaSeq 6000448Leaf & root6 June 2022
Table 2. The transcription factor (TF) families altered in Triticum aestivum under mono- and bi-partite infections by BMV, BSMV and WSMV.
Table 2. The transcription factor (TF) families altered in Triticum aestivum under mono- and bi-partite infections by BMV, BSMV and WSMV.
TF FamiliesGenes Number
WSMVBMVBSMVBMV&WSMVBMV&BSMVBSMV&WSMV
AP2/ERF-AP2ND2ND22ND
AP2/ERF-ERF23310301227
AP2/ERF-RAV1NDND2ND2
B321143152
bHLH673013347
bZIP6ND5959
C2C2-CO-likeND2ND22ND
C2C2-Dof451964
C2C2-GATA1NDND1ND1
C2C2-LSD1NDND1ND1
C2C2-YABBYNDND3ND3ND
C2H22868361430
C3H514948
CPP51ND615
CSD3NDND3ND3
E2F-DP2NDND2ND2
EILNDND1ND1ND
GARP-G2-likeND22422
GeBPNDND1ND1ND
GRAS8321259
GRFNDND3ND3ND
HB-BELL1NDND2ND2
HB-HD-ZIP5ND7575
HB-other111221
HB-PHDNDND1ND1ND
HB-WOXNDND1ND1ND
HSF5ND161ND
LIM1NDND1ND1
LOB3ND1313
MADS-MIKC218392
MADS-M-typeNDND1ND1ND
MYB5361188
MYB-related4377104
NAC381310612257
NF-X11NDND3ND3
NF-YAND3235ND
NF-YBNDND1ND1ND
OFPND3ND33ND
RWP-RKNDND2ND2ND
Tify4NDND4ND4
Trihelix53ND735
WRKY14126241716
ND: not detected.
Table 3. The protein kinase (PK) families altered in Triticum aestivum under mono- and bi-partite infections by BMV, BSMV and WSMV.
Table 3. The protein kinase (PK) families altered in Triticum aestivum under mono- and bi-partite infections by BMV, BSMV and WSMV.
PK FamiliesGenes Number
WSMVBMVBSMVBMV&WSMVBMV&BSMVBSMV&WSMV
AGC-Pl NDND2ND2ND
CAMK_AMPK 1NDND1ND1
CAMK_CAMKL-CHK1412534
CAMK_CDPK 9ND313313
CK1_CK1 NDND2ND2ND
CMGC_CDK-CRK7-CDK9 33ND633
CMGC_CDKL-Os 112231
CMGC_GSK 2NDND2ND2
CMGC_MAPK ND1212ND
Group-Pl-4 2NDND2ND2
IRE1 1NDND1ND1
NAK 3NDND3ND3
RLK-Pelle_CR4L 131441
RLK-Pelle_CrRLK1L-1 114141
RLK-Pelle_DLSV472466767855
RLK-Pelle_ExtensinND1111ND
RLK-Pelle_L-LEC291016412232
RLK-Pelle_LRK10L-2 8ND10121012
RLK-Pelle_LRR-I-1 124361
RLK-Pelle_LRR-II 2NDND4ND4
RLK-Pelle_LRR-III ND1112ND
RLK-Pelle_LRR-VI-1NDND1ND1ND
RLK-Pelle_LRR-VI-2 6NDND6ND6
RLK-Pelle_LRR-VII-2NDNDND1ND15
RLK-Pelle_LRR-VIII-11ND2121
RLK-Pelle_LRR-Xb-151ND6126
RLK-Pelle_LRR-Xb-2 3NDND3ND3
RLK-Pelle_LRR-XI-1 9312131510
RLK-Pelle_LRR-XII-120716331926
RLK-Pelle_LRR-XIIIaNDND1ND1ND
RLK-Pelle_LRR-XIV 1NDND1ND1
RLK-Pelle_LysM 5NDND5ND5
RLK-Pelle_PERK-1 2NDND5ND5
RLK-Pelle_RLCK-IV ND2123ND
RLK-Pelle_RLCK-Ixb262882
RLK-Pelle_RLCK-V NDND1ND1ND
RLK-Pelle_RLCK-VIIa-2125320815
RLK-Pelle_RLCK-VIII 6ND1616
RLK_Pelle RLCK-XINDNDND11ND
RLK-Pelle_SD-2b13813211818
RLK_Pelle URK-3NDNDND11ND
RLK-Pelle_WAK 17719232419
RLK-Pelle_WAK_LRK10L-15145391925
STE_STE11 4ND1717
STE_STE7NDNDND211
TKL_GdtNDNDNDND1ND
TKL-Pl-4NDNDND1ND1
TKL-Pl-6 3NDND13ND13
ND: not detected.
Table 4. The motif details in Triticum aestivum under bi-partite infections by BMV&BSMV.
Table 4. The motif details in Triticum aestivum under bi-partite infections by BMV&BSMV.
MotifMotif LogoE-ValueWidthBest Match in JASPARSignificant GO Terms Identified by GOMO
Motif 1Agronomy 13 02610 i0016.90 × 10−4229MA1723.1 (PRDM9)MF: transcription factor activity
MA1833.1 (Zm00001d049364)CC: plasma membrane
MA2022.1 (LOB)BP: regulation of transcription
Motif 2Agronomy 13 02610 i0025.70 × 10−3229MA1267.1 (DOF5.8)MF: transcription factor activity
MA1268.1 (CDF5)
MA1274.1 (DOF3.6)
Motif 3Agronomy 13 02610 i0035.20 × 10−1829MA1890.1 (Klf15)MF: translation initiation factor activity
MA1833.1 (Zm00001d049364)CC: CUL4 RING ubiquitin ligase complex
MA1821.1 (Zm00001d020595)BP: translation
Motif 4Agronomy 13 02610 i0041.60 × 10−1127MA0752.1 (ZNF410)MF: structural constituent of ribosome
MA0410.1 (UGA3)CC: chloroplast thylakoid membrane
MA1642.1 (NEUROG2)BP: trichome branching
Motif 5Agronomy 13 02610 i0054.00 × 10−1841MA1819.1 (Zm00001d005892)MF: structural constituent of ribosome
MA1817.1 (Zm00001d020267)CC: chloroplast stroma
MA2022.1 (LOB)BP: DNA replication initiation
Motif 6Agronomy 13 02610 i0063.30 × 10−914MA1125.1 (ZNF384)
MA0277.1 (AZF1)CC: nucleus
MA0548.2 (AGL15)
Motif 7Agronomy 13 02610 i0076.50 × 10−921MA1257.1 (ERF9)MF: nucleotide binding
MA1246.1 (LEP)CC: chloroplast stroma
MA1239.1 (ERF104)BP: translation
Motif 8Agronomy 13 02610 i0082.20 × 10−541MA1833.1 (Zm00001d049364)MF: ATP binding
MA1246.1 (LEP)CC: nucleus
MA1257.1 (ERF9)BP: potassium ion transport
Motif 9Agronomy 13 02610 i0094.20 × 10−941MA1267.1 (DOF5.8)MF: ATP binding
MA1871.1 (FoxM)CC: CUL4 RING ubiquitin ligase complex
MA1866.1 (FoxI-a)BP: DNA replication initiation
Motif 10Agronomy 13 02610 i0101.20 × 10−341MA0762.1 (ETV2)MF: transcription factor activity
MA1367.1 (AT1G76870)CC: plasma membrane
MA0679.2 (ONECUT1)BP: regulation of transcription
Motif 11Agronomy 13 02610 i0113.80 × 10−329MA1630.2 (ZNF281)MF: DNA-directed DNA polymerase activity
MA1713.1 (ZNF610)CC: CUL4 RING ubiquitin ligase complex
MA0528.2 (ZNF263)BP: translation
Table 5. The motif details in Triticum aestivum under bi-partite infections by BMV&WSMV.
Table 5. The motif details in Triticum aestivum under bi-partite infections by BMV&WSMV.
MotifMotif LogoE-ValueWidthBest Match in JASPARSignificant GO Terms Identified by GOMO
Motif 1Agronomy 13 02610 i0124.90 × 10−8829MA1833.1 (Zm00001d049364)MF: translation initiation factor activity
MA1817.1 (Zm00001d020267)BP: DNA replication initiation
MA1820.1 (Zm00001d024324)CC: CUL4 RING ubiquitin ligase complex
Motif 2Agronomy 13 02610 i0132.90 × 10−5741MA1890.1 (Klf15)ND
MA1892.1 (Klf5-like)
MA1653.1 (ZNF148)
Motif 3Agronomy 13 02610 i0143.20 × 10−3515MA1274.1 (DOF3.6)MF: structural constituent of ribosome
MA1268.1 (CDF5)BP: megagametogenesis
MA1267.1 (DOF5.8)CC: CUL4 RING ubiquitin ligase complex
Motif 4Agronomy 13 02610 i0154.80 × 10−2541MA1818.1 (Zm00001d052229)MF: ATP binding
MA1833.1 (Zm00001d049364)BP: xanthophyll biosynthetic process
MA1817.1 (Zm00001d020267)CC: chloroplast stroma
Motif 5Agronomy 13 02610 i0166.90 × 10−1929MA1267.1 (DOF5.8)MF: structural constituent of ribosome
MA1274.1 (DOF3.6)BP: translation
MA1268.1 (CDF5)CC: CUL4 RING ubiquitin ligase complex
Motif 6Agronomy 13 02610 i0175.60 × 10−1541MA1833.1 (Zm00001d049364)MF: structural constituent of ribosome
MA1819.1 (Zm00001d005892)BP: translation
MA1832.1 (Zm00001d002364)CC: respiratory chain complex I
Motif 7Agronomy 13 02610 i0181.30 × 10−1521MA0528.2 (ZNF263)ND
MA1226.1 (DREB2G)
MA1890.1 (Klf15)
Motif 8Agronomy 13 02610 i0192.00 × 10−841MA1354.1 (AT4G12670)MF: transcription factor activity
MA1210.2 (HAT22)BP: regulation of transcription
MA0389.1 (SRD1)CC: nucleus
Motif 9Agronomy 13 02610 i0202.90 × 10−821MA1267.1 (DOF5.8)MF: transcription factor activity
MA1281.1 (DOF5.1)BP: response to brassinosteroid stimulus
MA1277.1 (DOF1.7)CC: nucleus
Motif 10Agronomy 13 02610 i0213.00 × 10−650MA1007.1 (PHYPADRAFT_173530)MF: transcription factor activity
MA1023.1 (PHYPADRAFT_28324)BP: response to water deprivation
MA0986.1 (DREB2C)CC: nucleus
Motif 11Agronomy 13 02610 i0222.10 × 10−521MA0687.1 (SPIC)MF: transcription factor activity
MA0752.1 (ZNF410)BP: regulation of transcription
MA0410.1 (UGA3)CC: nucleus
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Shahriari, A.G.; Majláth, I.; Aliakbari, M.; Ghodoum Parizipour, M.H.; Tahmasebi, A.; Nami, F.; Tahmasebi, A.; Taherishirazi, M. Identifying Critical Regulators in the Viral Stress Response of Wheat (Triticum aestivum L.) Using Large-Scale Transcriptomics Data. Agronomy 2023, 13, 2610. https://doi.org/10.3390/agronomy13102610

AMA Style

Shahriari AG, Majláth I, Aliakbari M, Ghodoum Parizipour MH, Tahmasebi A, Nami F, Tahmasebi A, Taherishirazi M. Identifying Critical Regulators in the Viral Stress Response of Wheat (Triticum aestivum L.) Using Large-Scale Transcriptomics Data. Agronomy. 2023; 13(10):2610. https://doi.org/10.3390/agronomy13102610

Chicago/Turabian Style

Shahriari, Amir Ghaffar, Imre Majláth, Massume Aliakbari, Mohamad Hamed Ghodoum Parizipour, Aminallah Tahmasebi, Fatemeh Nami, Ahmad Tahmasebi, and Mohsen Taherishirazi. 2023. "Identifying Critical Regulators in the Viral Stress Response of Wheat (Triticum aestivum L.) Using Large-Scale Transcriptomics Data" Agronomy 13, no. 10: 2610. https://doi.org/10.3390/agronomy13102610

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