ABASTRCAT
Tomato Fusarium wilt caused by Fusarium oxysporum f. sp. lycopersici (FOL) is a destructive disease of tomato worldwide which causes severe yield loss of the crops. As exploring gene expression and function approaches constitute an initial point for investigating pathogen-host interaction, we performed a transcriptional analysis to unravel regulated genes in tomato infected by FOL. Differentially expressed genes (DEG) upon inoculation with FOL were presented at twenty-four hours post-inoculation including four treatments: Moneymaker_H2O, Moneymaker_FOL, Motelle_H2O and Motelle_FOL. A total of more than 182.6 million high quality clean reads from the four libraries were obtained. A large overlap was found in DEGs between susceptible tomato cultivar Moneymaker and resistant tomato cultivar Motelle. All Gene Ontology terms were mainly classified into catalytic activity, metabolic process and binding. However, Gene Ontology enrichment analysis evidenced specific categories in infected Motelle. Statistics of pathway enrichment of DEGs resulted that the taurine and hypotaurine metabolism, the stibenoid, diarylheptanoid and gingerol biosynthesis, the starch and sucrose metabolism were the top three pathway affected in both groups. Interestingly, plant-pathogen pathway was greatly regulated in Motelle treated with FOL. Combining with qRT-PCR facilitated the identification of regulated pathogenicity associated genes upon infected resistant or susceptible tomato. Our data showed that a coordinated machinery played a critical role in prompting the response, which could help in generating models of mediated resistance responses with assessment of genomic gene expression patterns.
INTRODUCTION
Fusarium oxysporum f. sp. lycopersici (hereafter referred to as FOL) is a biotrophic pathogen which is the causal agent of tomato wilt. Accumulating data indicate that F. oxysporum is a large species complex, with more than 150 host-specific forms causing disease in vegetables, fruit trees, wheat, corn, cotton and ornamental crops (Di Pietro et al. 2003; Leslie and Summerell 2006). F. oxysporum infects vascular bundles in the plant host, leading to clogged vessels, yellowing of leaves, wilting and finally death of the whole plant. According to their specific pathogenicity to tomato cultivars, three physiological races (Di Pietro et al. 2003; Leslie and Summerell 2006; Takken and Rep 2010) of F. oxysporum are distinguished (Kawabe et al. 2005).
Tomato (Solanum lycopersicum) is a worldwide economic crop, and also has been studied as a crucial model plant for studying the genetics and molecular basis of resistance mechanisms. Four plant resistance (R) genes have been discovered in cultivated tomato from wild tomato species including the I and I-2 genes from S. pimpinellifolium, and the I-3 and I-7 gene from S. pennellii. Among these four R genes, so far, I-2, I-3 and I-7 have been cloned, encode an NB-LRR protein like most known R genes (Ori et al. 1997; Simons et al. 1998; Kawabe et al. 2005; Catanzariti et al. 2015; Gonzalez-Cendales et al. 2016). Previous works have demonstrated that the I-2 and I-3 gene confers resistance to race 2 and race 3 strains of FOL, respectively (Simons et al. 1998; Catanzariti et al. 2015). The I-2 locus encodes an R protein that recognizes the avr2 gene product from F. oxysporum (race 2) (Houterman et al. 2009). The I-3 encodes an S-receptor-like kinase (SRLK) genes that confers Avr3-dependent resistance to FOL (race 3) (Catanzariti et al. 2015). Previously, two near-isogenic tomato cultivars susceptible Moneymaker (i-2/i-2) and resistant Motelle (I-2/I-2) were recruited to study the interaction between tomato and FOL (Ouyang et al. 2014). The genotypes of these two tomato cultivars are for I-2 and respond to FOL infection (Di Pietro and Roncero 1998; De Ilarduya et al. 2001; Yu and Zou 2008). We unrevealed the microRNA diversifications responding to FOL infection in tomato by high-throughput RNA sequencing (RNA-seq) approach (Ouyang et al. 2014). Basically, transcriptome analysis is a very important tool to discover the molecular basis of plant-pathogen interaction globally, allowing dissection of the pattern of pathogen activities and molecular repertoires available for defense responses in host plant. By taking advantage of RNA-seq technology, a few of transcriptome profiling studies of plants following inoculation with Fusarium fungus have been reported, including studies in banana (Guo et al. 2014), cabbage (Xing et al. 2016), watermelon (Liu et al. 2015), mango (Liu et al. 2016), and Arabidopsis (Chen et al. 2014; Gupta et al. 2014). Upon to pathogens infection, plants activate a few of defense responses to resistant diseases caused by according pathogens. Resistance response may associated with hypersensitive reaction (HR), structural alterations, reactive oxygen species (ROS) accumulation, synthesis of secondary metabolites and defense molecules (Park et al. 2003; Shah 2003; Ros et al. 2004).
The objects of this study were to determine the transcript profile between susceptible Moneymaker and resistant Motelle tomato plants in response to FOL infection and to reveal genes underlying the innate immune response against the fungal pathogen. To achieve these goals, we performed transcriptome analysis using RNA-seq approach. In addition to genes known to response to pathogen infection, our results also uncovered a bunch of novel fungal pathogen-responsive genes for further functional characterization, and provided a broader view of the dynamics of tomato defense transcriptome triggered by FOL infection.
MATERIALS and METHODS
Tomato materials and fungal culture
Two tomato near-isogenic cultivars (cv.) Motelle (I-2/I-2) and Moneymaker (i-2/i-2) that exhibit different susceptibilities to the root pathogen FOL were used for plant infection and libraries construction. Profiling experiments were performed on two-week-old tomato seedlings grown at 25°C with a 16/8-h light/dark cycle. The wild-type Fusarium oxysporum f. sp lycopersici strain used for all experiments is FGSC 9935 (also referred to as FOL 4287 or NRRL 34936). Two-week-old tomato seedlings were removed from soil and roots incubated in a solution of FOL conidia at a concentration of 1x108/ml for 30 min. Control tomato plants were treated with water. Plants were then replanted in soil and maintained in a growth chamber at 25°C for 24 h with constant light. Plants were removed from soil, and roots were rinsed and excised, then immediately frozen in liquid nitrogen and stored at −80°C.
RNA extraction, library preparation, and sequencing
Total RNA was isolated from roots using TRIzol® Reagent (#15596026, Life Technologies, CA, USA) according to the manufacturer’s recommendations. After the total RNA extraction and DNase I treatment, magnetic beads with Oligo (dT) were used to isolate mRNA. Mixed with the fragmentation buffer, the mRNA was sheared into short fragments. Then cDNA was synthesized using the mRNA fragments as templates. cDNAs were purified and resolved with EB buffer for end reparation and single nucleotide A (adenine) addition followed by adding adapters to cDNAs. After agarose gel electrophoresis, the suitable cDNAs were selected for the PCR amplification as templates. During the quality control (QC) steps, Agilent 2100 Bioanaylzer and ABI StepOnePlus Real-Time PCR System were used in quantification and qualification of the sample library. The libraries were sequenced using Illumina HiSeqTM 2000.
RNA-seq analysis, normalization of sequence reads and identification of differentially expressed genes (DEGs)
Primary sequencing data that produced by Illumina HiSeqTM 2000, called as raw reads, were subjected to QC. After QC, raw reads were filtered into clean reads which were aligned to the reference sequences as described by previous report. (Trapnell et al. 2012). All sequence reads were trimmed to remove the low-quality sequences. The trimmed reads were then aligned to the tomato reference genome downloaded from the Sol Genomics Network using Bowtie v0.12.5 (Langmead et al. 2009) and TopHat v2.0.0 (Trapnell et al. 2009; Trapnell et al. 2012) with default settings. Cufflinksv0.9.3 (Trapnell et al. 2010) was used to calculate transcript abundance based on fragments per kilo base of transcript permillion fragments mapped (FPKM) using all parameters on default settings. The transcript was considered as expressed when the FPKM value was greater than 0.1 and the lower boundary for FPKM value was greater than zero at 95% confidence interval. Once the transcript abundance was calculated for individual sample files using Cufflinks, the output files were further merged pairwise for each comparison (in vitro comparison between two populations, in planta comparison between two populations and in planta versus in vitro for each population) using Cufflinks utility program-Cuffmerge (Trapnell et al. 2012). The pairwise comparisons of gene expression profiles between the two populations were done using the Cuffdiff program of the Cufflinks version 1.3.0 (Trapnell et al. 2010). The genes were considered significantly differentially expressed if Log2 FPKM (fold change) was ≥1.0 and false discovery rate (FDR, the adjusted P value) was <0.01. The q-value which was a positive FDR analogue of the p-value was set to <0.01 (Storey and Tibshirani 2003).
Functional categorization of DEGs
DEGs were functionally categorized online for all pairwise comparisons according to the Munich Information Center for Protein Sequences (MIPS) functional catalogue (Ruepp et al. 2004). The functional categories and subcategories were regarded as enriched in the genome if an enrichment P- and FDR-value was below <0.05. The Kyoto Encyclopediaof Genes and Genomes (KEGG) pathway analyses were performed using interface on blast2GO (Blast2GO v2.6.0, http://www.blast2go.com/b2ghome) for all DEGs to identify gene enrichment on a specific pathway.
Gene Ontology (GO) and pathway enrichment analysis
Gene Ontology (GO) and pathway enrichment were performed using DAVID software (Smyth 2005). Graphs of the top 20 enriched GO terms for each library were generated using the Cytoscape Enrichment Map plugin (Smoot et al. 2011; Merico et al. 2010).
Quantitative real time-PCR (qRT-PCR) analysis
qRT-PCR analysis was performed according to our previous protocol (Ouyang et al. 2014). The reverse transcription reaction was done on 1 μg of total RNA using the SMART MMLV Reverse Transcriptase (Takara, Mountain View, CA). cDNA was diluted two times and used as template for quantitative RT-PCR, which was performed with the CFX96 real-time PCR system (Bio-Rad, Hercules, California, USA). Primers used for qRT-PCR were designed from 3-UTR for individual gene. For each cDNA sample, three replications were performed. Each reaction mixture (20 μL) contained 1 μL of cDNA template, 10 μL of SYBR1 Green PCR Master Mix (Applied Biosystems, Foster, CA) and 1 μL of each primer (10 μM). Relative expression levels of genes were normalized using the 18S rRNA as internal control, and were calculated as the fold change by comparison between in water treated and in FOL treated samples.
Statistical analyses
All data in this study were subjected to ANOVA analysis or Student’s t-test analysis using SPSS 11.5 (SPSS Company, Chicago, IL).
RESULTS
General features of Moneymaker and Motelle transcriptomes
We investigated transcriptomes in roots of tomato during infection with the tomato wilt disease fungus FOL through construction of transcript libraries and RNA-seq. By taking advantage of two near-isogenic cultivars that show differential interaction with FOL - Moneymaker (susceptible) and Motelle (resistant), we generated four libraries including: Moneymaker treated with water (MM_H2O), Moneymaker treated with FOL (MM_Foxy), Motelle treated with water (Mot_H2O) and Motelle treated with FOL (Mot_Foxy). Using Illumina sequencing, we obtained a total of more than 182.6 million high quality clean reads from the four libraries. Of these, 45,616,330 from MM_H2O, 45,635,428 from MM_Foxy, 45,680,034 from Mot_H2O, and 45,661,734 from Mot_Foxy. The number of expressed transcripts were 22,796 and 22,639 from MM_H2O and MM_Foxy library respectively, and 22,825 and 22,725 from Mot _H2O and Mot _Foxy library respectively (Table 1). Sequence reads presented reasonable correlation between related two populations (t >0.95, p = 0.29) (Figure 1). A number of 21,808 and 21,753 genes were co-expressed between MM_H2O and MM_Foxy library and Mot _H2O and Mot _Foxy library, respectively. The co-expressed genes increased slightly to 21,887 between MM_Foxy and Mot _Foxy library (Figure 2). The scatter of all expressed genes of each pair were presented in figure 3.
Of the sequence reads from MM_H2O and MM_Foxy library, 75.49% and 67.89% were mapped to the reference genome of tomato, respectively. For Mot _H2O and Mot _Foxy library, 75.87% and 70.46% were aligned to the reference genome of tomato, respectively. Among the reads mapped to the tomato genome, perfect match reads were 63.00% and 55.52% for MM_H2O and MM_Foxy library respectively, and 62.61% and 57.41% for Mot _H2O and Mot _Foxy library respectively (Table 2).
Analysis of differentially expressed genes (DEGs) and functional classification of DEGs by gene ontology (GO) enrichment analysis
After expression levels Fragments Per Kilobase of exon model per Million mapped reads (FPKM) for each gene were calculated. Differentially expressed genes (DEGs) were defined as genes with fold-change > 2 fold and Padjust value < 0.05. A total number of 3,942 and 4,168 genes showed significantly differential expression in MM_H2O vs. MM_Foxy library and Mot _H2O vs. Mot _Foxy library, respectively.
Among these DEGs, 221/219 genes were down-regulated, and 261/415 genes were up-regulated (MM_H2O vs. MM_Foxy/ Mot _H2O vs. Mot _Foxy) (Figure 4). A majority of these DEGs were overlapped in both water and FOL treated two tomato cultivars.
To explore the distribution of DEGs, gene ontology (GO) enrichment analyses were conducted based on these DEGs. A total of 530 and 769 GO terms were discovered in MM_H2O vs. MM_Foxy and Mot _H2O vs. Mot _Foxy library, respectively. For both libraries, all GO terms were assigned to three groups including the biological process, the cellular component and the molecular. All GO terms were mainly classified into catalytic activity (104 out of 530 in MM_H2O vs. MM_Foxy library, and 141 out of 769 in Mot _H2O vs. Mot _Foxy library, (the same define in the following text), metabolic process (81 out of 530, and 118 out of 769), and binding (72 out of 530, and 104 out of 769). For the class of response to stimulus, however, no significant change was presented between these two libraries (31 out of 530, and 36 out of 769) (Figure 5).
To further understand the biological functions, top 20 statistics of pathway enrichment of DEGs were performed to discover the affection of FOL to host plant. Total of 356 and 469 DEGs from MM_H2O vs. MM_Foxy library and Mot _H2O vs. Mot _Foxy library respective were annotated for pathway enrichment. The taurine and hypotaurine metabolism, the stibenoid, diarylheptanoid and gingerol biosynthesis, the starch and sucrose metabolism were the top three pathway affected in both groups, but in different ranking. The metabolic pathway was the most abundant DEGs in both groups with 122 out of 356 and 148 out of 469 DEGs in MM_H2O vs. MM_Foxy library and Mot _H2O vs. Mot _Foxy library, respectively (Figure 6). Be worth mentioning, plant-pathogen pathway was ranked in the 24th (24 out of 356 DEGs) in MM_H2O vs. MM_Foxy library, however, it was presented in the 8th (40 out of 469 DEGs) in Mot _H2O vs. Mot _Foxy library (Figure 6). When compared with Mot _H2O vs. Mot_Foxy library, 19 DEGs were presented in MM_H2O vs. MM_Foxy library.
Expression profiles of DEGs selected in plant-pathogen interaction by qRT-PCR
To verify the DEGs in plant-pathogen interaction pathway, ten disease related DEGs were selected to characterize the gene expression profiles between water and FOL treated Moneymaker and Motelle by qRT-PCR using primers listed in table 3. These DEGs were Solyc00g174330 (Pathogenesis related protein PR-1), Solyc09g007010 (Pathogenesis related protein PR-1), Solyc02g084890 (Cc-nbs-lrr, resistance protein), Solyc07g054120 (LRR receptor-like serine/threonine-protein kinase, RLP), Solyc10g011910 (WRKY transcription factor 23), Solyc03g124110 (Pathogenesis-related transcriptional factor and ERF, DNA-binding), Solyc03g026280 (Pathogenesis-related transcriptional factor and ERF, DNA-binding), Solyc12g009240 (Pathogenesis-related transcriptional factor and ERF, DNA-binding) and Solyc02g080070 (RLK, Receptor like protein, putative resistance protein with an antifungal domain).
The results of qRT-PCR showed the similar pattern with sequencing results with minute difference. Among these DEGs, Solyc00g174330, Solyc10g011910, Solyc03g124110, Solyc02g084890 and Solyc12g009240 were induced greatly in Motelle affected by FOL, however, no significant changes were present in Moneymaker between water and FOL treatment (Figure 7).
DISCUSSION
In this study, we explored the availability of near-isogenic susceptible and resistant cultivars of tomato infected by FOL to uncover a global transcriptomic profile of tomato-FOL interaction using Illumina sequencing. The components of plant responding to pathogen challenging may lead to understand the underlying defense mechanisms. Plants have evolved a complicate defense system against pathogens including cascade signaling activation, the regulation of gene expression, synthesis of defensive metabolites as well as hormone balancing (Mukhtar et al. 2011; Andolfo et al. 2014). So far, by taking advantage of high-throughput RNAsequencing (RNA-seq) approach, a few of transcriptome studies discovering the F. oxysporum-host interaction have been reported in plants such as banana, watermelon, mango and Arabidopsis (Chen et al. 2014; Guo et al. 2014; Gupta et al. 2014; Liu et al. 2015; Liu et al. 2016; Xing et al. 2016), shedding light on the crosstalking among different signaling pathways involving in plant-pathogen interaction.
When plant is attacked by pathogen, the host reprograms metabolism balance between development and the resources to support defense to pathogen, involving biological process, cellular components and molecular functions (Mithöfer and Boland 2012). Upon to our results, the tomato-FOL interaction basically followed the typical reaction of biotrophic phase pathogens infection. Gene Ontology analysis of DEGs between two tomato cultivars revealed specific enriched categories in both interactions. In resistant tomato cultivar Motelle, cellular component organization or biogenesis, signaling, molecular transducer activity, and signal transducer activity were evidenced when compared to susceptive tomato cultivar Moneymaker. Among them, cellular component organization or biogenesis was a critical metabolic activities required by plants to survive under fungus-inflicted stresses (Paul et al. 2011). Generally, the genes involved in GO analysis presented in Motelle more than in Moneymaker upon FOL infection which was due to different resistant cultivar.
Two main mechanisms, pathogen-associated molecular patterns (PAMPs) or microbe-associated molecular patterns (MAMPs) (Boller and Felix 2009; Cui et al. 2014; Yang and Huang 2014) and the adaptive immune system composed of resistance (R) genes (Dangl and Jones 2001; Van Ooijen et al. 2007; Marone et al. 2013), are involved in plant responses to pathogenic microorganisms in plant. At least five different classes of R genes have been classified based on functional domain (Van Ooijen et al. 2007). Among these classes, a nucleotide-binding site (NBS) and leucine-reach repeats (LRRs) (NBS-LRR) is known as the most numerous R-gene class (Dangl and Jones 2001). Previously, we reported that tomato endogenic microRNA slmiR482f and slmiR5300 conferred to tomato wilt disease resistance. Two predicted mRNA targets each of slmiR482f and slmiR5300, encoded protein with full or partial NBS domains respectively, confirmed to exhibit function of resistance to FOL (Ouyang et al. 2014). A few of investments have been demonstrated that NB-LRR proteins are required for the recognition of a specific Avr and disease resistance in several plant species, including rice, N. benthamiana, Arabidopsis and wheat (Sinapidou et al. 2004; Peart et al. 2005; Lee et al. 2009; Loutre et al. 2009; Narusaka et al. 2009; Okuyama et al. 2011; Ouyang et al. 2014). The corresponding R genes were located tightly in physical linkage. However, in spite this physical linkage, not all these R gene pairs were homologous (Sinapidou et al. 2004; Lee et al. 2009). We found that genes related to plant-pathogen interaction were activated in resistant cultivar Moltelle once treated with FOL. Our qRT-PCR results demonstrated that some of these genes were up-regulated specifically in Motelle but not in Moneymaker. In particular, most of these genes were NBS-LRR or like genes which may imply that NBS-LRR genes played a critical role in resistance to FOL in tomato. Investigation of differentially regulated pathogen-induced NBS-LRR genes could lead to uncover the specific modulation patterns upon FOL infection in tomato.
To conclude, our abroad genome transcriptome RNA-seq data provided a comprehensive overview of the gene expression profiles between two different tomato cultivars Moneymaker and Motelle treated with FOL. Our results will facilitate further analysis of putative molecular mechanism of resistance in tomato upon to FOL, which eventually lead to improvement of Fusarium wilt disease resistance in tomato. It remains to be determined whether or how these candidate pathogen-related genes confirmed by qRT-PCR are overexpressed/knockouted in Moneymaker/Motelle plant to reveal the Fusarium wilt disease resistance. In this scenario, we would expect that overexpressing of these candidate pathogen-related genes will enhance resistance to F. oxysporum and would therefore develop a useful molecular tool to uncover functional roles for the increasing number of discovered genes in tomato.
ACKNOWLEDGMENTS
We gratefully acknowledge support from JSSF: BK20161330, Jiangsu Province, China.