- Original article
- Open Access
RNA-Seq analysis of gene expression changes triggered by Xanthomonas oryzae pv. oryzae in a susceptible rice genotype
- Rezwan Tariq†1,
- Zhiyuan Ji†1, 2,
- Chunlian Wang†1,
- Yongchao Tang1,
- Lifang Zou2,
- Hongda Sun1,
- Gongyou Chen2Email author and
- Kaijun Zhao1Email authorView ORCID ID profile
© The Author(s). 2019
- Received: 14 March 2019
- Accepted: 24 May 2019
- Published: 24 June 2019
Xanthomonas oryzae pv. oryzae (Xoo) is a destructive disease in most of the rice growing regions worldwide. Xoo injects the transcriptional activator-like (TAL) effector protein into the host cell to induce the susceptibility (S) gene(s) for spreading the disease. In the current study, a susceptible rice genotype, JG30, was inoculated with wild Xoo strain PXO99A and its mutant PH without any TAL effector, to retrieve the differentially expressed genes (DEGs) having a role in susceptibility.
RNA-Seq data analysis showed that 1143 genes were significantly differentially expressed (p-value ≤0.05) at 12, 24, 36 and 48 h post inoculation (hpi). Expression patterns, evaluated by quantitative real-time PCR (qRT-PCR), of randomly selected eight genes were similar to the RNA-Seq data. KEGG pathway classified the DEGs into photosynthesis and biosynthesis of phenylpropanoid pathway. Gene ontology (GO) analysis categorized the DEGs into the biological pathway, cellular component, and molecular function. We identified 43 differentially expressed transcription factors (TFs) belonging to different families. Also, clusters of the DEGs representing kinase and peroxidase responsive genes were retrieved. MapMan pathway analysis representing the expression pattern of genes expressed highly in biotic stress and metabolic pathways after PXO99A infection relative to PH.
DEGs were identified in susceptible rice genotype inoculated with PXO99A relative to mutant strain PH. The identified 1143 DEGs were predicted to be included in the different biological processes, signaling mechanism and metabolic pathways. The Jasmonic acid (JA) responsive genes were identified to be downregulated in PXO99A infected leaves. This study would be useful for the researchers to reveal the potential functions of genes involved in the rice susceptibility to PXO99A infection.
- Xanthomonas oryzae
- Differentially expressed genes
Rice is a widely consumed crop, directly feeding more people than any other crop. It is the staple food in Asia, Africa and Latin America. Although rice in its various forms, has been under intense cultivation for more than 6000 years, it represents a laboratory for the investigation of bacterial diseases (Khush 1997). Currently, bacterial blight is a devastating disease in most of the rice growing regions, caused by the gram-negative bacterial pathogen, Xanthomonas oryzae pv. oryzae (Xoo) (Gnanamanickam et al. 1999). Naturally, Xoo enters inside the rice leaf through hydathodes of the leaf margin and multiplies into the intercellular spaces of epithelial tissues, then moves to the xylem vessel for systemic infection (Noda and Kaku 1999).
During infection, pathogenic Xoo injects transcriptional activator-like (TAL) effector protein via type III secretion system into host plant cell to promote the disease (Boch et al. 2009). The TALE may activate the susceptible (S) gene contributing to the disease progression or trigger the resistance (R) gene resulting to activate the host defense mechanism against pathogen. Interestingly, natural TAL effector proteins have conserved repeat region of 34 amino acids repeat, an N terminus region for type III secretion system, C-terminus containing acidic transcription activation domain and nuclear localization signal (Boch et al. 2009). Central conserved repeat region of TAL effectors determines the targeted genes in the host plant cell. The nucleotide specificity between TAL effector and host DNA sequence is determined by the number and order of 34 amino acids repeats (Moscou and Bogdanove 2009). Crystal structure elaborated that twelfth amino acid stabilizes the loop and thirteen amino acid residue interacts with the targeted nucleotide, projecting into the major groove (Boch and Bonas 2010; Deng et al. 2012).
Single immune receptor activates the different genes in host plant cell that need to be modulated. It was found that transcriptional reprogramming is a major feature of plant immunity and is governed by TFs and co-regulatory proteins associated within discrete transcriptional complexes (Moore et al. 2011). Upon immune receptor activation, selected TFs integrate with different signaling pathways in various ways. Among all TFs, OsWRKY exhibit significant role against various diseases from seedling to adult stage of the plant through induction of the salicylic acid (SA), jasmonic acid (JA), abscisic acid and gibberellic acid pathways (Jimmy and Babu 2015). For example, OsWRKY45–2 exhibited Xoo resistance resulted in increased accumulation of JA (Tao et al. 2009); whereas, MYB TFs have promising importance in biotic resistance. Several MYB TFs have been reported with function in plant immunity, e.g., AtMYB30, AtMYB44, AtMYB108 in Arabidopsis, and HvMYB6 in barley (Ambawat et al. 2013; Chang et al. 2013). Moreover, boosting the innate immune response, plants produce different reactive oxygen species (ROS) to restrict the pathogen entry into the plant cell through the strengthening of the cell wall and cellulose deposition (Torres et al. 2006). Peroxisomes are the major sites for the accumulation of H2O2, as a result of the oxidative metabolism. For instance, NADPH oxidase and peroxidase class III are key proteins in ROS generation during the “oxidative burst” initiated in early defense reactions (Tripathy and Oelmüller 2012).
It is quite interesting to explore the genetic bases of the susceptibility in rice in response to the wild and mutant strains of Xoo at different time points. In the recent years, the rapid development in omics technology including transcriptomics has emerged a tremendous understanding of global gene networks to different diseases. RNA-Seq is considered unbiased technology, used to detect the DEGs with a broader dynamic range of expression level (Wang et al. 2009). The objective of the present study was to elucidate the DEGs at different time points in a susceptible rice genotype, JG30, after inoculation of the wild and mutant strains of Xoo, PXO99A and PH strains, respectively. PXO99A was isolated in the Philippines, and it is virulent toward a large number of rice varieties representing the diverse genetic resources of resistance. Unlike PXO99A, PH is a TALE free mutant of PXO99A (Ji et al. 2016). Comprehensive analysis of RNA-Seq data identified several DEGs; moreover, there were different clusters of the DEGs, i.e., TFs, peroxidase responsive genes etc., were retrieved, involved in different biological pathways.
Transcriptome profiling of JG30 genotype for DEGs
To determine the expression pattern of the JG30 genotype after PXO99A and PH inoculations, a pairwise comparison between PXO99A vs PH strains was made at a specific time point. A threshold level of p-value was set to 0.05 and revealed 1143 significant DEGs between PXO99A vs PH at different time points (Additional file 1: Table S2). An overview of the comparative analysis between PXO99A vs PH revealed that there were more up-regulated genes than downregulated genes (Fig. 1c, d). Briefly, there were 263 (192 upregulated and 71 downregulated), 541 (335 upregulated and 206 downregulated), 265 (97 upregulated and 168 downregulated), and 174 (89 upregulated and 85 downregulated) DEGs were identified at 12, 24, 36 and 48 hpi in PXO99A vs PH.
Identification of the differentially expressed TFs
Identification of the kinase and peroxidase responsive genes in RNA-Seq data
The peroxidase responsive genes are important in plants controlling different processes such as development, growth, response to biotic and abiotic stress, and programmed cell death (Bailey-Serres and Mittler 2006). In our RNA-Seq experiment, 50 significant differentially expressed peroxidase responsive genes were identified at different time points (Fig. 3b and Additional file 1: Table S5). Among the 50 genes, 29 genes were upregulated and 21 genes were downregulated. At 12 hpi, two DEGs (LOC_Os07g02440.1 and LOC_Os09g35940.1) were observed to be upregulated, and five DEGs (LOC_Os01g73200.1, LOC_Os02g41954.1, LOC_Os04g49210.1, LOC_Os04g55740.1, and LOC_Os05g38420.1) were downregulated in PXO99A inoculated JG30 leaves relative to PH. At 24 hpi, 19 DEGs were upregulated and 14 DEGs were downregulated in PXO99A vs PH. At 36 hpi, nine DEGs were upregulated and only two genes (LOC_Os06g15990.1 and LOC_Os08g36860.1) were downregulated. Nevertheless, at 48 hpi, six genes (LOC_Os05g40384.1, LOC_Os06g37224.1, LOC_Os07g01410.1, LOC_Os08g39660.1, LOC_Os09g10340.1, and LOC_Os10g36848.1) were upregulated and three genes (LOC_Os01g62490.1, LOC_Os03g16610.1, and LOC_Os08g39840.1) were downregulated in PXO99A inoculated leaves than that of PH. It is depicted that downregulated peroxidase responsive genes might have role in the resistance against PXO99A in rice.
Pathway enrichment analysis
The two DEGs encoding auxins and nine ethylene-related genes were upregulated; on the contrary, five DEGs of ethylene, three JA and one brassinosteroids responsive genes were downregulated after PXO99A infection. Afterward, 18 DEGs related to the cell wall were identified; among these 18 DEGs, 11 genes were upregulated and seven were downregulated in PXO99A infected leaves as compared to the PH. Likewise, among the 31 proteolysis DEGs, 22 were upregulated and 9 nine genes were downregulated.
Gene ontology enrichment analysis
The GO analysis functionally characterizes the DEGs into three different categories, i.e., biological process, cellular component, and molecular function. The GO analysis was done using the AgriGO online tool. The GO analysis of all the DEGs is shown in the Additional file 1: Table S9. The significant GO terms after infection of PXO99A and PH at each time point were retrieved using the false discovery rate (FDR) ≤ 0.05 (Additional file 1: Table S10). In PXO99A vs PH at 12 hpi, the GO terms were significantly enriched in biological process (10), cellular component (6) and molecular function (7). At 24 hpi, 56 significant enriched GO were identified, including 28 biological processes, five cellular function, and 23 molecular function related terms. In PXO99A vs PH at 36 hpi, the significant GO terms were classified into the biological process (5), cellular component (1) and molecular function (13). Unlike 36 hpi, only five enriched GO terms were identified, including biological process (4) and molecular function (1).
RNA-Seq data validation
Plants are attacked by different pathogens that seriously damage their productivity. Plants response to these stresses by adopting different physiological, cellular and molecular mechanisms. A crucial step in plant defense against a pathogon is to timely counter the stress in a rapid and an efficient manner. In our RNA-Seq experiment, the JG30 leaves exhibited two distinct morphological symptoms after PXO99A and PH infection. PXO99A infection overcomes the PAMP-triggered immunity (PTI) owing to the fact that the secreted TAL effectors successfully interfere with or inhibit the defense response. However, the PH strain, without the TAL effector proteins, was countered by plant PTI and halted the infection. In detail, protein kinases are involved in mediating the different signaling process in the plant-pathogen interaction. The serine/threonine responsive genes (LOC_Os05g46760.1 and LOC_Os02g02120.1) were observed to be upregulated at 24 hpi in PXO99A vs PH. The previous studies elaborated that serine/threonine kinase responsive genes are important in resistance against Pseudomonas syringae in tomato (Lin and Martin 2007). The most of the kinase responsive DEGs were upregulated in our experiment in susceptible leaves of JG30 after PXO99A infection comparative to PH strain; on the contrary, kinase and signaling responsive genes are playing role in PTI as pattern recognition receptors (PPRs) and activate the plant defense system in a short time interval (<10 min) (Abramovitch et al. 2006; Pfeilmeier 2017); moreover, PRRs activation triggers the signaling responsive genes which further upregulate the expression of > 300 plant genes (Cheng et al. 2017; Thilmony et al. 2006). According to the literature, to counter the PTI, pathogens deliver the effector protein into the plant cell to suppress the basal defense. In our study, PXO99A injected the TAL effector proteins through type III secretion system into the plant cell to suppress the PTI and induce ETS. In the absence of ETI, JG30 leaves showed susceptibility symptoms. Unlike PXO99A inoculated samples, PH infected leaves exhibited resistance symptoms owing to the absence of effector protein in the PH strain. In a different transcriptome study, a rice genotype with Xa23 gene, exhibited the resistance against PXO99A in form of hypersensitivity cell death reaction (Tariq et al. 2018).
Likewise, plant immune receptors activate several genes to tackle the biotic stress and coordinate their stress response with growth to maximize their fitness. TFs interact with the cis- regulatory elements in the promoter regions of stress-related genes and up-regulate the expression of many genes to activate the biotic stress tolerance (Agarwal et al. 2010). In our experiment, OsWRKY14 (LOC_Os01g53040.1), OsWRKY17 (LOC_Os03g21710.1), and OsWRKY21 (LOC_Os01g60640.1) were upregulated in PXO99A infected leaves than that of PH. WRKY17 was exhibited to be a negative regulator of basal resistance to Pseudomonas syringae pv. tomato in Arabidopsis thaliana (Journot-Catalino et al. 2006). Whereas, overexpression of OsWRKY21 was found to be involved in the expression of calcium and calcium/calmodulin genes, important in plants as a messenger in modulating diverse physiological processes for stress adaptation (Park et al. 2005). In wheat, WRKY14 was reported to be upregulated in resistance cultivar against yellow dwarf virus infection (Wang et al. 2013).
AP2-ERF TFs are unique to plants, playing a vital role throughout the plant life cycle, e.g., leaf epidermal cell identity and response to various biotic and environmental stresses (Riechmann and Meyerowitz 1998). AP2-ERF TFs activate the defense-related genes, i.e., PR, osmotin, beta-1,3-glucanase and chitinase responsive genes, in Arabidopsis thaliana under different biotic stress conditions (Moffat et al. 2012; Zarei et al. 2011); similarly, ERF proteins regulate the ethylene biosynthesis pathway in Arabidopsis thaliana against Botrytis cinerea (Zhao et al. 2012). Moreover, OsERF922 was reported to negatively regulate the resistance to Magnoparthe oryzae in rice (Liu et al. 2012). AP2-ERF TFs might have regulated the PR response genes in our experiment.
The different MYB responsive TFs, i.e., MYB51 (LOC_Os08g33150.1) and R2R3-MYB (LOC_Os07g37210.1) were observed to be upregulated in the PXO99A infected leaves of JG30. It was found that AtMYB51 is associated with wound response against insect herbivores (Johnson and Dowd 2004); The BOTRYTIS SUSCEPTIBLE1 gene encodes the MYB51, interacting with the signaling pathway in wound response, which ultimately triggers the peroxidase responsive genes (Mengiste et al. 2003). In another study, AtMYB51 was observed to regulate the indolic glucosinolate biosynthesis in leaves after pathogen attack (Gigolashvili et al. 2007). R2R3-MYB TFs control the wide variety of processes, including phenylpropanoid mechanism and secondary cell wall formation (Soler et al. 2015); in our study, R2R3-MYB TF (LOC_Os07g37210.1) may be negatively regulating the resistance mechanism against PXO99A infection.
The positive upregulations of the two different NAC TFs, i.e., OsNAC95 (LOC_Os06g51070.1) and OsNAC22 (LOC_Os03g04070.1) were observed in PXO99A infected leaves. In previous studies, OsNAC22 was observed to be overexpressed in rice infected with rice dwarf virus and rice black-streaked dwarf virus (Nuruzzaman et al. 2015). We predict that OsNAC22 might be related to the response induced by PXO99A.
Likewise, the cluster of DEGs enriched in KEGG pathway related to the photosynthesis exhibited to be downregulated after PXO99A infection than that of PH. Besides the KEGG pathway, the DEGs involved in the light reaction were enriched in MapMan metabolic pathway. Previous studies indicate that downregulation of the photosynthesis responsive genes reflects the usage of energy and resources to defend the invading pathogens (Jain et al. 2017; Yu et al. 2014). Pathogen infection restricts the photosynthesis activity and availability of nutrient sources for the pathogen (Berger et al. 2007); in Arabidopsis, the photosynthesis was decreased after P. syringae infection (Bonfig et al. 2006). Moreover, the genes involved in the photosynthesis reaction were repressed by Xoo in rice (Narsai et al. 2013). It was found in another study that Rhizoctonia solani infection leads to repressing the photosynthesis, increases the secondary metabolism, ROS accumulation and cell death in rice (Helliwell et al. 2013).
Plant hormones, JA and ET, play a diverse role in resistance against pathogens to a remarkable spectrum. In our experiment, the DEGs (LOC_Os12g37260.1, LOC_Os08g39840.1, and LOC_Os06g11290.1) representing the JA hormones were observed to be downregulated in PXO99A inoculated leaves. It was observed that JA regulates the expression of PR genes in rice exhibiting the role in resistance to M. oryzae (Agrawal et al. 2000); furthermore, an increased accumulation of JA in rice was observed, causing resistance to Xoo (Tao et al. 2009). The upregulation of the ET responsive gene, OsACS2, encoding the allene oxide synthase illustrated the broad spectrum resistance to M. oryzae and R. solani (Helliwell et al. 2013).
The plant cell wall is the physical barrier to restrict the entry of pathogen, acting as a passive defense barrier. In our experiment, the cell wall-related genes were differentially expressed at different time points in PXO99A vs PH. Downregulation and upregulation of the cell wall-related genes depicted to have an impact on abiotic and biotic stresses (Bacete et al. 2018). The cell wall component, lignin, was induced by different plant hormones that regulate the plant defense. In Arabidopsis, infection of P. syringae and X.compestris resulted in overexpression of lignin biosynthesis genes (Mohr and Cahill 2007). Similarly, hemicellulose is cell wall polysaccharides, such as xylans and xyloglucans that exhibited the resistance to Plectosphaerella cucumerina in Arabidopsis (Delgado-Cerezo et al. 2012).
Overall, the RNA-Seq data of different time points identified the DEGs. This study highlighted the genes differentially expressed only in susceptibility condition in rice after PXO99A infection. The expression patterns of the genes expressed in susceptibility provide new information for researchers to explore the susceptibility mechanism conferred by PXO99A.
Plant materials and growth conditions
The seeds of a rice genotype, JG30, were surface sterilized in 70% ethanol for 5 min and washed with sterilized water. Afterward, water soaked the sterilized rice seeds for overnight. After pre-germination, rice seeds were sown in pots and kept them in the green house of Chinese Academy of Agricultural Sciences, Beijing, and P.R. China. The condition of the green house in which rice seeds were grown was: 25/30 °C under a 14 h light /10 h dark cycle with 80% RH.
Inoculation of different Xoo strains
Two different Xoo strains, PXO99A and mutant of PXO99A (PH), were used for inoculation. Particularly, PH strain is without the TAL effector gene, responsible for pathogenicity symptoms in the host plant. Initially, PXO99A and PH were subcultured on TSA plate (tryptophan, 10 g/L; sucrose, 10 g/L; glutamic acid, 1 g/L and agar, 5 g/200 ml) for 48 h. The inoculum was prepared by suspending the bacterial strains in sterilized water and concentration was measured by determining the OD600 (Optical density at 600 nm) between 0.9 and 1.0. The leaves of JG30 genotype were infected by using scissors dipped in bacterial suspensions to clip leaves 1-2 cm down from the tip of the leaf blade. After 2 weeks of post inoculation, lesions were observed from the cut surface to the distal-most position of the leaf blade exhibiting water-soaked lesions. For RNA-Seq, both strains, PXO99A and PH, were inoculated into 50 days old leaves of JG30 genotype by needless syringe. Inoculated leaves with three biological replicates were harvested at 12 hpi, 24 hpi, 36 hpi, and 48 hpi, respectively. The harvested leaves were immediately frozen in liquid nitrogen and stored at − 80 °C until RNA extraction.
RNA extraction and Illumina sequencing
Total RNA of inoculated (12 hpi, 24 hpi, 36 hpi, and 48 hpi) and mock (J0) leaf samples were extracted through TRIZOL® reagent (TIANGEN, Beijing, China) according to the manufacturer’s protocol. Extracted RNA from different samples was purified by using RNase-free DNase I (TaKaRa, Kyoto, Japan) to remove the genomic DNA contamination. Total RNA concentration in different samples was calculated using NanoDrop microvolume spectrophotometer (Thermo Scientific NanoDrop Products, Waltham, MA, USA). Thereafter, the Illumina HiSeq2500 platform was used for RNA-Seq. cDNA library construction and sequencing were done by Novogene Bioinformatics Technology Co., Ltd., Beijing, China.
Analysis of the RNA-Seq data
Quality reads of the raw RNA-Seq data were processed by the fastQC application v0.11.2 (Anders and Huber 2010); each paired-end data had the insert size 200–300 bp. The low quality reads and reads containing adapters were removed by the Trimmomatic (0.36.5) tool to get the clean reads data (Bolger et al. 2014). Afterward, paired-end clean reads were aligned to the available reference genome of rice (http://rice.plantbiology.msu.edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/pseudomolecules/) using HISAT2 (2.1.0) (Kim et al. 2015). StringTie (1.3.4) was employed to count the number of reads mapped to each gene and quantification of the gene expression level in FPKM (number of fragments per kilobase of the transcript sequence per million base pairs sequenced) (Pertea et al. 2015). The differential expression analysis between PXO99A and PH inoculated samples was performed using the DESeq2 R package (2.11.38). Genes with Log2 fold change (Log2FC) ≥1 (up-regulated) or Log2FC ≤1 (down-regulated) were considered as DEGs in comparative analysis.
Functional classification and pathway enrichment analysis
The functional enrichment analysis including GO analysis was performed to identify which DEGs were significantly involved in each GO term. GO enrichment analysis was performed by AgriGO software (Du et al. 2010); GO term with FDR ≤ 0.05 was considered significantly enriched by DEGs. The KEGG pathway analysis was executed to retrieve the enriched pathways with p-value ≤0.05. Additionally, MapMan package was employed to get the graphical representation of the DEGs playing role in biotic stress response and metabolic pathways (Thimm et al. 2004).
Validation of RNA-Seq data
The expression pattern of the differentially expressed genes was done by qRT-PCR to validate the RNA-Seq data. The transcript sequences of the eight nominated genes were retrieved from Phytozome v12.1. The primers of the nominated genes were designed using AmplifX 1.5.4 software, and the primers used in the qRT-PCR were given in Additional file 1: Table S11. Ubiquitin was used as an internal control in qRT-PCR; the reaction was performed in a 96-wells plate on an ABI prism 7500 Real-Time PCR system (Applied Biosystem, Foster City, CA, USA) using SYBR Green Master ROX (TaKaRa). The relative expression level of the selected DEGs was calculated with the 2-ΔΔCT method (Livak and Schmittgen 2001). The reaction was carried out using three biological replicates with three technical replicates.
In this study, two different strains of Xoo, PXO99A and PH, were inoculated into the JG30 genotype leaves to uncover the differentially expressed genes at different time points. A total of 1143 genes were differentially expressed in JG30 genotype. GO and pathway analysis revealed that DEGs were involved in biological regulation, response to biotic stimulus, response to oxidative stress, lipid transport, photosynthesis, and light reaction. The antenna responsive genes having role in the photosynthesis were downregulated in PXO99A infected leaf samples. In plant-pathogen interaction pathway, JA, brassinosteroids and ethylene responsive genes were downregulated in PXO99A infected leaf samples than that of the PH. Moreover, most of the genes representing the cell wall and secondary metabolites were downregulated in susceptibility condition. We also identified the genes of different TF families, kinases and peroxidase responsive genes that were differentially expressed between PXO99A and PH inoculated leaf samples. This study highlighted the possible candidate genes that may play role in susceptibility in rice after Xoo infection.
We thank Zheng Wei for technical assistance in inoculating the rice plants.
This work was supported by grants from the National Priority Program-Breeding New Rice Varieties for Southern China Area (2017YFD0100100), the Innovation Program of Chinese Academy of Agricultural Sciences to Kaijun Zhao and Chunlian Wang. Rezwan Tariq received support from the China Scholarship Council (CSC) for Scholarship grant.
KZ and GC conceived and designed the research; RT, CW and LZ designed some of the experiments; RT, CW, ZJ, YT and HS performed experiments; KZ, RT and ZJ wrote the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Consent for publication
The manuscript has been approved by all authors.
The authors declare that they have no competing interests.
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