Gene network modeling and pathway analysis of maize transcriptomes in response to Maize Iranian mosaic virus

doi:10.21203/rs.3.rs-2249512/v1

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Gene network modeling and pathway analysis of maize transcriptomes in response to Maize Iranian mosaic virus

https://doi.org/10.21203/rs.3.rs-2249512/v1

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Maize Iranian mosaic virus (MIMV, family Rhabdoviridae) is one of the factors limiting cereal production in Iran. MIMV can affect maize transcriptome, alternative splicing, circular RNAs and miRNA profiles during infection and active biological pathways in the maize cells. In the present study, we sought to find critical genes and key pathways involved in MIMV infection and analyzed gene networks, pathways and promoters using transcriptome data. We determined the hub genes involved in pathways related to the proteasome and ubiquitin. These hub genes played a key role in the response to MIMV infection. Gene ontology (GO) and pathway analysis showed that metabolic processes are most affected in MIMV infection. In addition, the results showed the important role of the cellular endoplasmic reticulum in MIMV infection. Network cluster analysis confirmed the result of GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Potential miRNAs were identified as target hub genes using the psRNATarget server. The discovered miRNAs belonged to miR166, miR167, miR169, miR395, miR399, miR408 and miR482 families, which are involved in various pathogenicity or resistance processes against MIMV or other viruses. The results of this study provide a list of hub genes, important pathways and new insights for future development of virus-resistant transgenic crops and clarify the basic mechanism of plant response. They may also be a source of information on the role of key genes and the pathway controlling the responses.

Biological networks

Gene ontology

Kyoto Encyclopedia of Genes and Genomes

Maize Iranian mosaic virus

miRNAs

Promoter analysis

Protein-protein interactions

Maize Iranian mosaic virus (MIMV) infects cereal crops and some gramineous weeds (Massah et al. 2008), which belongs to the genus Nucleorhabdovirus of the family Rhabdoviridae, and is transmitted by the small brown planthopper (Laodelphax striatellus) (Izadpanah and Parvin 1979). The comparison of MIMV with related viruses and the serological, biological, morphological, and molecular characteristics of this virus has made it a unique virus in the genus Nucleorhabdovirus (Ammar et al. 2005, Massah et al. 2008). Transcriptome investigation of maize plants during MIMV infection has shown that pathways of immune receptor signaling, metabolic pathways, RNA silencing, hormone-mediated protein degradation, protein kinase, ATP-binding activity, and fatty acid metabolism are activated (Ghorbani, Izadpanah and Dietzgen 2018a).

Genetic analysis has confirmed that viral infection of the plants is related to viral factors, host factosr and their interaction (Garcia-Ruiz 2019). Recent advances in understanding the molecular mechanisms involved in plant-pathogen interactions and advances in biotechnology have created a suitable base for generating resistant plants. For example, microRNAs play an important role in resistance regulation. MicroRNA168 is an important component of the RNA silencing complex that negatively regulates plant growth and immunity. Suppression of miR168 in rice improves grain yield and enhances immunity to Magnaporthe oryzae (Zhao et al. 2022). mRNA and MicroRNA contents of the maize plant can be affected by MIMV, and replication of the virus in the infected tissue of the plant causes significant metabolic changes (Ghorbani et al. 2018a).

The interaction of genes and proteins in the infected plant determines the function of the cell against the virus (Zhao et al. 2016). To be more precise, it is the group of genes that are involved in these types of interactions (Zhang and Horvath 2005). Identifying these gene groups by reconstructing pathways and networks from transcriptome, proteome, and metabolome data can be a powerful tool for modeling plant responses to various stressors and provide a better understanding of how plants respond (Pazhamala et al. 2021). Analysis of the regulatory network of miRNA-mRNA interactions during soybean mosaic virus infection revealed virus-host interactions and identified key genes that can regulate microRNAs (Chen et al. 2016).

Our previous studies identified genes, miRNAs, alternative splicing and circular RNAs with different expression and pathways involved in virus infection of maize and alterations in miRNA profile. The RNA-Seq technique has been successfully used to study the transcriptional and miRNA profiles of maize. To better understand the plant response to virus infection, we analyzed the profile of mRNAs and miRNAs in infected and uninfected plants and then focused on transcripts related to the plant-host interaction pathway (Ghorbani et al. 2018a, Ghorbani et al. 2022), (Ghorbani et al. 2019, Ghorbani et al. 2018c). In this study, we used bioinformatic analysis to examine genes involved in MIMV infection presented in previous studies. We investigated key Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction networks (PPI), specifically focusing on potential gene hubs that may play a role in maize infection with MIMV.

Data collection of the genes involved in MIMV infection

In our previous study (Ghorbani et al. 2018a), genes overexpressed in MIMV infection were identified by analysis of the transcriptome profile of maize. The genes presented in this study were selected with fold change > 2 and <-2 using genes identified for network analysis (Supplementary Table 1).

Ppi Networks And Hub Analysis

To assess the interactions between the listed genes (Supplementary Table 1), analyses were made in the web-based application of STRING ver.10 (http://string-db.org) with a minimum required interaction score of 0.150 (low confidence), and the PPI list was created. The PPI list was then imported into Cytoscape (version 3.9.1). CytoHubba software (version 0.1) was used to plot the PPI network and identify hub proteins between all nodes. Four computational algorithms from Cyto-Hubba, including MCC, Degree, MNCC, and MNC, were used to detect and rank the hub genes, and four proteins for each algorithm were chosen as hub proteins. The hub genes and their interactions in a subnetworks were shown.

Gene Ontology And Pathway Enrichment Analysis Of Differentially Expressed Genes

The KEGG was used for enrichment analysis of hub subnetwork genes, and the web-based application of STRING was used for GO, including molecular function (MF), cellular components (CC), and biological process (BP).

Cluster Analysis Of The Network (Dup: Abstract ?)

CytoCluster (version 2.1.0) was used to cluster network nodes. The protein complex identification algorithm (IPCA) (Li et al. 2017) was used for cluster analysis of the subnetwork. The threshold was set to 10, and the genes of each cluster were further analyzed in STRING ver.10 to find the KEGG pathways involving these genes.

Promoter motif analysis of hub genes

The 1 kbp upstream flanking regions (UFRs) of hub genes were extracted from Ensembl Plants Web Services (http://plants.ensembl.org). Conserved motifs in the sequences were identified using MEME Suite (version 5.4.1) (meme.nbcr.net/meme/intro.html) (Bailey et al. 2009) with their default parameters, except for thresholds for P and E values of < 0.01, respectively. The Tomtom tool (version 5.4.1) (http://meme-suite.org/tools/tomtom) was used to remove redundant motifs and identify known CRE based on JASPAR CORE 2022 database with thresholds for P and E values of < 0.01 and < 0.1, respectively. (Gupta et al. 2007) The GoMo tool (http://meme-suite.org/tools/gomo) was also used to identify potential roles for motifs (Buske et al. 2010).

Identification Of Potential Mirnas Of Hub Genes

To identify potential miRNAs that are related to the hub genes, their sequences were used for the psRNATarget server (http://plantgrn.noble.org/psRNATarget/) with default parameters except for the maximum expectation that was set to 4 and compared with the 80 published miRNAs of Zea mays.

Virus infection of maize plants

Maize plants showed typical MIMV symptoms, including yellow striping, chlorosis, mosaic, and stunting after 15 days of infection under greenhouse conditions. RT-PCR confirmed MIMV in infected plants. Healthy and infected plants were sequenced as whole transcriptome sequencing with three biological repetitions; more details were described in our previous study (Ghorbani et al. 2018a).

Ppi Networks And The Hub Analysis

In our current study, 1900 genes were subjected to bioinformatic analysis of DEGs derived from our previous study (Ghorbani et al. 2018a). All up and down-regulated genes in maize plants infected with MIMV were used. The PPI networks were drawn using STRING and Cytoscape (Fig. 1). STRING (http://string-db.org) is a database of predicted and known protein interactions (Szklarczyk et al. 2015) and Cytoscape is an open-source platform for molecular interaction network visualization and data integration (Smoot et al. 2011).

Figure 1 shows the PPI network of genes that were up and down-regulated during MIMV infection. This figure shows that when maize was infected with the virus a network of host proteins was active in maize. Also, MIMV proteins make a network that is necessary for infection (Ghorbani, Izadpanah and Dietzgen 2018b). In other words, it confirms the new concepts of ‘network for network’ theory in virus-host interaction (Eskandarzade et al. 2022).

Using the four algorithms mentioned in Material and Methods, 12 hub proteins with the most interactions and significant roles in the network among all protein interactions were identified (Fig. 2). The description of hub proteins s is shown in Table 1. The subnetwork formed by these identified hub genes is shown in Fig. 2.

Table 1

Ranking of hub genes identified in maize infected with MIMV by using CytoHubba.
Rank	Gene ID	Ranking Method	Fold change	Gene description
1, 4	GRMZM2G073571	MCC, Degree	2.307072516	Putative CRAL/TRIO domain containing, Sec14p-like phosphatidylinositol transfer family protein (774 aa)
1	GRMZM2G368908	MCC	2.542857143	26S proteasome non-ATPase regulatory subunit 7 homolog A (314 aa)
1	GRMZM2G061745	MCC	2.090334807	26S proteasome non-ATPase regulatory subunit 7 homolog A (310 aa)
1	GRMZM2G029583	MCC	2.355453351	26S proteasome non-ATPase regulatory subunit 6; Uncharacterized protein (389 aa)
1	GRMZM2G463267	DMNC	2.505050505	26S proteasome non-ATPase regulatory subunit 13 homolog A (386 aa)
1	GRMZM5G868757	DMNC	2.186418109	Uncharacterized protein loc100283395 precursor; 26S proteasome non-ATPase regulatory subunit 13; Uncharacterized protein (441 aa)
3	GRMZM2G072682	DMNC	4.200514139	Chaperone protein which promotes assembly of the 20S proteasome as part of a heterodimer with PSMG1 (281 aa)
4	AC149828.2_FG002	DMNC	2.784810127	Proteasome subunit alpha type (214 aa)
1	GRMZM5G877815	MNC, Degree	28.33333333	Ubiquitin-small subunit ribosomal protein s27ae; 40S ribosomal protein S27a (156 aa)
2	GRMZM2G419891	MNC, Degree	4.054784421	Ubiquitin2; Putative ubiquitin family protein; Ubiquitin2 (535 aa)
3	GRMZM2G387076	MNC, Degree	2.618453865	VAMP-like protein (359 aa)
4	GRMZM2G035341	MNC	2.375323555	RING-box protein 1a; Putative RING zinc finger domain superfamily protein; Uncharacterized protein (123 aa)

All hub genes are upregulated during infection and most of them have fold change close to 2 (Table 1). Some genes may not have the highest level of expression despite their important role in MIMV-maize interaction. PPI network analysis can discover key genes in the biological process.

Two identified genes, i.e. GRMZM5G877815 and GRMZM2G419891 (Table 1), encode ubiquitin-small subunit ribosomal protein s27ae, 40S ribosomal protein S27a (156 aa) and putative ubiquitin family protein, Ubiquitin2 (535 aa). Ubiquitin, a 76 amino acid protein, is a highly conserved protein in eukaryotes. Ubiquitination is one of the post-translational modifications during which ubiquitin units are attached to the target protein. This enzymatic process can connect chains of several ubiquitin units to the target protein. The result of the ubiquitination process includes proteasomal degradation, activation, or determination of the protein's cellular location (Alcaide-Loridan and Jupin 2012, Verchot 2016). Ubiquitin binding can act as a molecular switch between different functions or affect the ability of viral proteins to interact with host-specific factors (Ikeda and Dikic 2008).

Different stages of the ubiquitination process are carried out by three enzymes: the ubiquitin-activating enzyme (E1), the ubiquitin-conjugating enzyme (E2), and the ubiquitin ligase (E3) (Scheffner, Nuber and Huibregtse 1995). In Arabidopsis thaliana, of the of the genes encode 6% proteins of the ubiquitin-proteasome system, including two E1, 37 E2 and 1400 E3 proteins. The E3-Ub ligases consist of four subfamilies, including HECT (homologous to the carboxyl terminus of E6- AP), RING (really interesting new gene), U-box, and CRL (cullin- RING ligase). CRL consists of several subunits, whereas the other subfamilies are single polypeptides. RING-box protein 1 is one of the proteins that complete the CRL complex by joining with its carboxy-terminal (C) region. CRLs are involved in plant growth and development, auxin and JA signaling, and regulation of antiviral responses (Lobaina 2022). GRMZM2G035341 is one of the genes ID, which encodes RING -box protein 1a as a hub gene. It also encodes the putative RING zinc finger domain superfamily protein (Table 1). RING zinc finger proteins function as E3 ubiquitin ligases that have a conserved RING domain. This protein has a main role in plant growth, development, and stress responses. By binding to specific gene sequences in plants, they interact with different proteins and play a role in signal transduction and gene expression. They cause growth and adaptation to the environment (Han et al. 2022).

GRMZM2G368908, GRMZM2G061745, GRMZM2G029583, GRMZM2G463267, and GRMZM5G868757 are gene IDs that were identified as 26S proteasome non-ATPase regulating different subunits and AC149828.2_FG002 as proteasome subunit alpha. Moreover, GRMZM2G072682 was identified as a Chaperone protein which promotes the assembly of the 20S proteasome as part of a heterodimer with PSMG1 (Table 1). Chaperones are proteins whose function is to promote the folding of proteins and peptides synthesized in the cell (Zhao, Raines and Huang 2020). They also enhance the accumulation of viral RNA in the ER (Verchot 2016). Ubiquitin-mediated proteasomal degradation is an important method of controlling the amount of protein in the cell, which is carried out by the proteasome 26s system. The proteasome degradation system 26s consists of the regulatory part 19s and the protease 20s. Abnormal proteins are targeted for degradation by the proteasome ubiquitin system (Sharma et al. 2016). Infection of maize with Maize chlorotic dwarf virus, Maize fine streak virus, and MIMV caused up-regulation of transcript expression for proteins in ubiquitin-proteasome pathways such as 20S proteasome and 26S proteasome (Ghorbani et al. 2018a, Cassone et al. 2014). The activity of the 26S proteasome, together with the cellular and viral proteins associated with the ER, is thought to be critical for the regulation of viral infection in plants. For instance, the 26S proteasome subunit RPN9 contributes to the systemic spread of tobacco mosaic virus and turnip mosaic virus in plants (Verchot 2016). Thus, as hubs in the network, these genes can play an important role in the propagation and spread of the virus in maize.

Among the identified genes in this study (Table 1), GRMZM2G073571 encodes putative CRAL/TRIO domain containing, Sec14p-like phosphatidylinositol transfer family protein (774 aa). The CRAL_TRIO protein domain binds to the small lipophilic ligands. Its importance lies in the regulation of signaling in the cell, which is related to the small GTPases (Gupta et al. 2012). Lipid signaling pathways determine the central approach of cellular regulation (Bankaitis, Mousley and Schaaf 2010).

Finally, VAMP-like protein (359 aa) (Table 1), were also identified in the present study. Histone 3 was up-regulated in Nicotiana benthamiana during Chinese wheat mosaic virus (CWMV) infection (Yuan et al. 2021) and colocalized with nuclear shuttle protein and movement protein during infection with Bean dwarf mosaic virus (BDMV) (Zhou et al. 2011).

Gene Ontology And Pathway Enrichment Analysis Of Subnetwork Genes In Maize Infected With Mimv

Subnetwork genes are genes that interact with hub genes so that they can clarify key pathways and important processes in maize cells in response to MIMV. In this study, we sought to determine these biological pathways and processes using GO and KEGG pathways analysis. GO is a model for describing the gene products of organisms. In GO analysis, BP, MF and CC of gene products are described (Yon Rhee et al. 2008).

Analysis of the ontology of subnetwork genes using a web-based STRING and KEGG tool revealed that a series of BP, including response to stimuli, regulation of biological processes, metabolic processes, and placement under these conditions, are enriched. The predominant (≥ 50% observed genes) GO terms found for MF are significantly enriched in various binding activities, ribonucleotide binding, nucleotide binding, purine ribonucleotide binding, purine ribonucleoside triphosphate binding, anion binding, ion binding, heterocyclic compound binding, organic cyclic compound binding, hydrolase activity, and catalytic activity (Fig. 3). During viral infection, the viral protein interacts with the 20S proteasome components and modulates their catalytic activity. This property of the 20S proteasome increases the accumulation of Papaya ringspot virus in its natural host, Papaya. This is critical for the virus, and the protease and RNAase activity of the proteasome causes this modulation (Sahana et al. 2012, Ballut et al. 2005). Nucleotide-binding, ion binding, hydrolase activity, and heterocyclic compound binding are functions related to the plant defense response (Dixon et al. 2002, Carmo et al. 2017).

In this study, the biological categories of cellular and metabolic processes have the most up-regulated transcripts. Also, BP are significantly enriched in the organic substance metabolic process, cellular metabolic process, primary metabolic process, nitrogen compound metabolic process, organonitrogen compound metabolic process, macromolecule metabolic process, protein metabolic process, cellular macromolecule metabolic process, cellular protein metabolic process, localization, and establishment of localization (≥ 50% observed genes) (Fig. 4). GO Analysis of the transcriptome in Triticum aestivumi infected with wheat dwarf virus has shown that metabolic processes such as DNA metabolic process is related to response to biological stimuli and defense response (Liu et al. 2020). Initial infection of potatoes with Potato virus Y increased metabolites in infected leaves, as demonstrated by gene expression analysis (Kogovšek et al. 2016).

The predominant (≥ 50% observed genes) GO terms found for CC are an anatomical entity, protein-containing complex, intracellular, cytoplasm, organelle, intracellular organelle, membrane-bounded organelle, membrane, and intracellular membrane-bounded. This result showed subnetwork genes are related to the whole parts of the cell. Previous research has confirmed that plant viruses use CC to perform proviral functions (Hyodo and Okuno 2020). Our data show up-regulation of hub subnetwork gene transcripts related to a defense response and plant-pathogen interaction in maize plants infected with MIMV.

To obtain information on the important biological functions and pathways in virus infection we used KEGG pathways enrichment of subnetwork genes. KEGG is a database for assigning specific pathways to groups of DEGs and linking omics data to higher-level functional data (Kanehisa and Goto 2000). The major pathways involved in MIMV infection are proteasome, endocytosis, ubiquitin-mediated proteolysis, protein processing in the ER, SNARE interactions in vesicular transport, autophagy-other, phagosome, and protein export (≥ 40% of observed genes), based on KEGG enrichment analysis (Fig. 6).

Most of the hub genes listed in Table 1 are involved in pathways related to the proteasome and ubiquitin. The main pathways identified by KEGG confirm the importance of these genes (Fig. 6). Another pathway involved in virus infection is endocytosis. Endocytosis is an important function in the regulation of plasma membrane proteins and many cellular processes during a pathogen attack. Host factors of potyviruses, dynamin-like proteins, cause infection in plants using the endocytosis process (Wu et al. 2018). DRP1 and AP2β, two proviral host factors, cause turnip mosaic virus infection through the process of endocytosis and endosomal trafficking (Wu et al. 2020).

In this study, protein processing in the ER is one of the main pathways during infection. The ER is a functionally complex organelle that integrates stress responses to biotic and abiotic stress in plants. ER Stress and unfolded protein response signaling is induced by the overexpression of viral proteins and changes in the lipid membrane (Park and Park 2019). In addition, the ER releases vesicles associated with RNA viral factories (Laliberté 2014), and the superfamily of SNARE proteins (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) supports vesicle trafficking and fusion in plants. Syp71, a member of the SNAREs, is the essential factor for TuMV infection by mediating the fusion of virus-induced vesicles with chloroplasts (Wei et al. 2013).

One of the complex interactions between viral proteins and host factors is the autophagy process, which has two distinct viral and antiviral functions. In this study, autophagy is also mentioned as one of the determining pathways in infection. Some viruses alter the autophagy mechanism in plants for their replication in plant cells. In contrast, autophagy is involved in plant defense against viral infections and plays a role, for example, in defense against TMV, Cotton leaf curl Multan virus (CLCuMV), and other geminiviruses. Programmed cell death (PCD) and reactive oxygen species are regulated by autophagy during viral infection (Huang et al. 2020).

Protein export, listed in Fig. 6, is also involved in the viral infection cycle. Viruses encode proteins that find their way for entering and exiting the nucleus. These proteins use the nuclear import and export pathways of the host (Krichevsky et al. 2006). The ORF3 protein of the groundnut rosette virus is exported from the nucleus and makes cytoplasmic viral ribonucleoprotein particles that are transported with phloem (Kim et al. 2007).

Cluster Analysis Of The Network

The results of cluster analysis of biological networks, one of the most important strategies for determining functional modules, and predicting protein complexes and network biomarkers, reveal the structure of biological networks. The use of CytoCluster's algorithms depends on user requirements. In this study, six clustering algorithms were used. The IPCA algorithm, a density-based clustering algorithm, illustrates dense subgraphs in protein interaction networks. The weight of each edge is calculated by counting the common neighbors of the two nodes connected to it. The sum of the weights of the incident edges gives the weight of each node. The seed is determined by the weight of this node. First, a seed is regarded as a cluster, then a cluster is formed by IPCA with the recursive addition of nodes from its neighbors according to the priority of the nodes. The interaction probability of a node and the shortest path between nodes and the nodes in the cluster are two conditions for adding a node to a cluster (Li et al. 2017).

In this study, cluster analysis of the subnetwork yielded ninety clusters, of which we selected clusters ranking 1 through 5 (Table 2). Pathways common to all five clusters include proteasome, ubiquitin-mediated proteolysis, and protein processing in the ER. However, the pathway of endocytosis is represented only in cluster rank 1. The overlapping between genes counted in the KEGG pathways and the result of Cluster analysis confirmed that five pathways are the main pathway in viral infection. The pathways identified in this study using cluster analysis are the major pathways in our previous study (Ghorbani et al. 2018a). Network analysis using RNA-Seq data and clustering has helped us to illustrate the most important ones in response to MIMV infection.

Table 2

Summary of clusters (rank 1 to 5) resulting from cluster analysis of the subnetwork of expressed hub genes in MIMV-infected maize using the CytoCluster app.
Cluster	Rank	Nodes	Edges	Functions
1	1	90	2676	Proteasome Ubiquitin mediated proteolysis Protein processing in the endoplasmic reticulum Endocytosis
2	2	85	2596	Proteasome Ubiquitin mediated proteolysis Protein processing in the endoplasmic reticulum
3	3	85	2541	Proteasome Ubiquitin mediated proteolysis Protein processing in the endoplasmic reticulum
4	4	84	2581	Proteasome Ubiquitin mediated proteolysis Protein processing in the endoplasmic reticulum
5	5	82	2572	Proteasome Ubiquitin mediated proteolysis Protein processing in the endoplasmic reticulum

Promoter Motif Analysis Of Hub Genes

The UFRs (1 kbp) of hub genes were analyzed to discover the conserved motifs and consensus cis-regulatory elements (CREs). The UFRs were retrieved from Ensemble Plants. Using MEME, six significant motifs with lengths ranging from 11 to 29 bp were discovered in the promoters of the genes. Several biological functions were identified by GOMo analysis for the transcription factor motifs (TFs) including BP, MF, and CC. GO revealed that these CREs were involved in lipid transport, regulation of transcription, DNA-dependent RNA polymerase, translation, and auxin-mediated signaling pathways. These CREs were involved in MF such as transcription factor activity, structural constituent of ribosome, and pseudouridine synthase activity. In addition, the predominant GO terms found for CC were significantly enriched in the chloroplast, anchored to the membrane, nucleus, plasma membrane, endomembrane system, and cytosolic small ribosomal subunit.

In our study, auxin-mediated signaling pathways and lipid transport have been specified as the main processes in which TFs are involved during infection. Plant-virus interactions cause changes in the plant transcriptome and morphogenesis and affect plant hormone homeostasis and signaling. In addition, plant organogenesis, development, and growth are controlled by auxin, the signaling of which changes the various mRNA and protein levels of its components. Different families of plant viruses have expanded several tactics to disrupt auxin signaling. Impaired auxin signaling by viruses is one of the factors important for viral transmission, replication, movement, and symptom formation (Müllender et al. 2021). Moreover, lipid flow in plants is manipulated by viruses for their replication as they use lipid metabolism, targeting, and transport to form new membranes (de Castro, Tenorio and Risco 2016). Therefore, this CREs have an important role in viral infections and it can be noted in MIMV-maize interaction.

Two functions of plant molecules that the virus uses for pathogenicity are related to the ribosome and pseudouridine, which is identified in this study as MF of TFs. Ribosomes and their proteins involved in the cellular process of protein biosynthesis are used by viruses for survival and replication, interacting with viral mRNA and proteins. Besides, ribosomal proteins have been presented in some research as a component of host cell defense signals against the virus. The discovery of their role in viral infection may open new therapeutic opportunities (Li 2019). Little is known about the mechanisms and functions of RNA pseudouridylation in plants. Some pseudouridine synthases encoded in the Arabidopsis genome have been annotated as chloroplast enzymes (Manavski et al. 2021). Functional characterization of pseudouridine synthase 4 during brome mosaic virus (BMV) infection in Nicotiana benthamiana involves binding to BMV-positive strand RNA and disruption of encapsidation, leading to a reduction in viral RNA accumulation and systemic movement of BMV (Garcia-Ruiz 2019).

Finally, prompters’ analysis showed that CREs that were determined for hub genes have an important role in viral infection. All functional predictions of these CREs can be related to MIMV infection, which was described in this study and our previous studies (Ghorbani et al. 2018a, Ghorbani et al. 2018b, Ghorbani et al. 2018c).

Identification Of Mirnas That Target Hub Genes

MicroRNAs (miRNAs), a class of endogenous and small non-coding RNAs, regulate gene expression in most eukaryotes (Wang, Mei and Ren 2019). In maize, miRNAs are involved in the development of leaf, shoot, and root as well as cellular processes such as signal transduction, stress response, sucrose and cellulose synthesis, and the ubiquitin protein degradation pathway (Zhang, Pan and Anderson 2006). miRNAs potentially affect metabolism and development in response to MIMV infection by binding CircRNAs in maize (Samarfard et al. 2022).

In our previous study, miRNA profiles of infected maize were compared with those of uninfected maize and their role during MIMV infection was predicted (Ghorbani et al. 2022). One of the objectives of the current study was to identify miRNAs that can target the hub genes that we introduced. Hence, the Web-based psRNATarget program was used to predict potential miRNAs that targeted the hub genes. A total of 18 miRNAs were found, all of which belonged to 7 conserved families (Fig. 7).

Among the hub genes, GRMZM2G029583 and GRMZM2G073571 are targeted by more miRNAs. The discovered miRNAs belong to miR166, miR167, miR169, miR395, miR399, miR408, and miR482 families, and are involved in various BPs. MiR166 plays a role in maize leaf development and formation (Nogueira et al. 2007). zma-miR167 and zma-miR482 are involved in plant growth (Liu et al. 2019) and zma-miR167 targets auxin response factor (ARF) genes (Khraiwesh, Zhu and Zhu 2012). In addition, some miRNAs are related to plant infection response. The miR398, miR160, miR482 and miR2118 are involved in maize resistance to viral and fungal infections (Shivaprasad et al. 2012, Li et al. 2014). miR166, miR396, and miR399 are miRNA families involved in maize stress response (Zhou et al. 2016). Overall, a comparison of this result with our previous study about miRNA profile in response to MIMV showed all miRNA that target hub genes were up or down regulated in response to MIMV. Mir169 was up-regulated in response to MIMV which target GRMZM2G073571 and GRMZM2G419891 (Ghorbani et al. 2022). These results confirm the key role of hub genes in response to MIMV in maize.

The current study identified twelve hub genes in maize during MIMV infection. Most of them are involved in signaling pathways related to the proteasome and ubiquitin. We have shown association of functional GO with KEGG pathways and found that many of the altered biological functions are controlled by metabolic processes. These analyzes also confirmed the potential importance of cellular ER in MIMV pathogenesis, which is involved in disease signaling during infection. The results of promoter analysis indicated CREs that control hub genes' expression related to metabolism or functions related to pathogenesis or resistance. In addition, important miRNAs of hub genes were discovered which play a role in disease response. The results of this study may provide new insights into the development of virus-resistant transgenic crops in the future, and the results of plant-virus interaction dynamics could also be used to effectively control viral infections. In addition, the genes identified in this study could be promising diagnostic biomarkers for MIMV infections.

Ethical Approval

The research reported here did not involve experimentation with human participants or animals.

Competing interests

The authors declare that they have no conflict of interest.

Authors' contributions

AG provided and analyzed data, AG and MR wrote the main manuscript text and KI reviewed the manuscript

Funding

Not applicable

Availability of data and materials

All data in this manuscript was mentioned in the text and more details can be released as requested.

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Table 3 is available in the Supplementary Files section

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Gene network modeling and pathway analysis of maize transcriptomes in response to Maize Iranian mosaic virus

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Abstract

Figures

Introduction

Methods

Data collection of the genes involved in MIMV infection

Ppi Networks And Hub Analysis

Gene Ontology And Pathway Enrichment Analysis Of Differentially Expressed Genes

Cluster Analysis Of The Network (Dup: Abstract ?)

Promoter motif analysis of hub genes

Identification Of Potential Mirnas Of Hub Genes

Results And Discussion

Virus infection of maize plants

Ppi Networks And The Hub Analysis

Gene Ontology And Pathway Enrichment Analysis Of Subnetwork Genes In Maize Infected With Mimv

Cluster Analysis Of The Network

Promoter Motif Analysis Of Hub Genes

Identification Of Mirnas That Target Hub Genes

Conclusion

Declarations

References

Table

Additional Declarations

Supplementary Files

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