Microscopic analysis of C. fulvum invasion in the two tomato lines
Light microscopy was used to observe the infection process between C. fulvum and Ontario7816 or Moneymaker leaves (Fig. 1). As shown in Fig. 1a, no difference between Ontario7816 and Moneymaker was observed at 0 dpi, hyphae grew into the stomata in both Ontario7816 and Moneymaker at 4 dpi (Fig. 1e). In Ontario7816 leaves, a small number of HR areas appeared at 8 dpi (Fig. 1b), and the necrosis area gradually grew at 10 dpi (Fig. 1c) until more necrosis lesions appeared in both mesophyll cells and leaf veins between 12 and 21 dpi (Fig. 1d). Hyphae emerged through the stomata in Moneymaker at 8 dpi (Fig. 1f), and the emerged hyphae continued to increase and grow at 10 dpi, with the last few infected cells starting to undergo necrosis at 10-21 dpi (Fig. 1 g-h). Based on this observation, we collected samples from each treatment at 4 and 8 dpi for RNA-seq and qRT-PCR verification.
Analysis of SA and JA responses to C. fulvum infection
To explore the SA and JA responses to C. fulvum infection, HPLC-MS/MS was used to measure their contents. As shown in Fig. 2, compared with the SA content of CK-Cf16, the SA content of Cf16 increased rapidly to a peak at 8 dpi and was far higher than the SA content of MM between 4 and 12 dpi. Moreover, the SA content of MM was generally lower than the SA content of CK-MM. The JA content of Cf16 had the greatest value at 4 dpi, followed by a rapid reduction between 12 and 21 dpi but had a higher value than the JA content of the other samples at 4-16 dpi. These results suggested that SA and JA played important roles in regulating the plant response and enhancing plant defense in tomato plants infected with C. fulvum and had a quick response at the early stages of infection.
Summary of RNA-seq data
To determine the transcriptome profiles of Ontario7816 and Moneymaker following C. fulvum infection, we performed RNA-seq analysis on these two lines at 4 and 8 dpi. Three biological replicates were made at each time point for each treatment. In this study, an average of ~6.87 Gb was generated from each sample using the BGISEQ-500 platform (Fig. S1, Table S1). The raw data were deposited in the NCBI Sequence Read Archive under the accession number GSE133678. As shown in Table S1, more than 98% of reads were ≥ 20%, and more than 91% of clean reads had a quality score of ≥ 30%. After the reads were filtered, 64.15-72.64 million clean reads were generated, and at least 93.29% of these reads were mapped to the tomato reference genome, among which more than 78.26% were aligned to unique locations. Ultimately, 18,514 novel transcripts were generated with 12,790 unknown splicing events for known genes, 2,047 novel coding transcripts without any known features, and 3,677 transcripts for long noncoding RNA.
DEGs in response to C. fulvum
A gene was defined as significantly differentially expressed when an adjusted P-value ≤ 0.001 and log2fold-change ≥ 2. The two standards were used to identify DEGs in the resistant and susceptible lines in response to C. fulvum at 4 and 8 dpi. All FPKM values for every gene and the fold-changes and adjusted P-values for DEGs are shown in Tables S2 and S3, respectively. As shown in Table 1, the number of DEGs was highest in CK_Cf_4dpi-vs-Cf_4dpi, indicating that Ontario7816 carries more defense-response genes than Moneymaker, and the number of DEGs at 8 dpi was markedly less than the number of DEGs at 4 dpi between Ontario7816 and Moneymaker. However, the number of upregulated genes was higher than the number of downregulated genes in the two tomato lines at 4 dpi. Overall, 8,526 and 6,938 genes were differentially expressed in the resistant and susceptible lines at 4 dpi, respectively, of which 3,382 and 2,203 genes were up- and downregulated, respectively. In addition, among three comparisons CK_Cf_4dpi-vs-Cf_4dpi, CK_Cf_8dpi-vs-Cf_8dpi and Cf_4dpi-vs-Cf_8dpi, 1,350 DEGs were shared, which further suggests that a common group of genes was activated or deactivated upon C. fulvum infection (Fig. 3).
GO and KEGG enrichment analyses of DEGs
To determine the functions of DEGs involved in the response to C. fulvum, we performed GO classification and KEGG functional enrichment analyses with the Phyper function of R software. GO is divided into three major functional categories: biological process, cellular component and molecular function. For the DEGs in Ontario7816, the significant GO terms were mostly enriched in biological regulation, cellular process, metabolic process and response to stimulus in the biological process category, and these terms were related to disease resistance. In the cellular component category, most of the DEGs were assigned to cell, membrane, membrane part and organelle, which were found to be specific to the resistant line. The significantly enriched terms in the molecular function category were binding, catalytic activity, transcription regulator activity and transporter activity, and among them, binding and catalytic activity terms are known to play an important role in plant hormone signal transduction (Fig. 4). Therefore, the corresponding genes of these terms might play critical roles in response to C. fulvum infection.
KEGG pathway enrichment analysis was used to investigate the biological pathways associated with DEGs. As shown in Fig. 5a, the pathways “Plant hormone signal transduction” and “Plant-pathogen interaction” were significantly enriched, and the number of genes and the rich ratios of these two pathways were significantly higher than those of other pathways. Other disease-resistance pathways were also enriched, such as “MAPK signaling pathway-plant”, “Benzoxazinoid biosynthesis” and “Phosphatidylinositol signaling system”. Overall, “Plant hormone signal transduction” and “Plant-pathogen interaction” may be pivotal pathways in regulating the resistance response to C. fulvum infection in tomato. In the KEGG pathway analysis based on upregulated DEGs among CK_Cf_4dpi-vs-Cf_4dpi, CK_Cf_8dpi-vs-Cf_8dpi and Cf_4dpi-vs-Cf_8dpi and shown in Fig. 5b, the pathways “Plant-pathogen interaction” and “Plant hormone signal transduction” were also significantly enriched, further confirming the existence of a common core of DEGs in response to C. fulvum infection. Meanwhile, we performed KEGG pathway analysis based on upregulated unique DEGs in CK_Cf_4dpi-vs-Cf_4dpi (Fig. 5c) and found that the “Plant-pathogen interaction” pathway was also significantly enriched, and 10 DEGs in this pathway were identified (Table 4).
Table 2 shows that common DEGs related to disease-resistance pathways were significantly upregulated in Ontario7816 and Moneymaker at 4 dpi with C. fulvum. Among the DEGs of the “Plant-pathogen interaction” pathway, 26 genes were significantly differentially expressed between CK_Cf_4dpi-vs-Cf_4dpi and CK_MM_4dpi-vs-MM_4dpi, while 25 DEGs in the significantly enriched pathway “Plant hormone signal transduction” were identified (Table 3). Overall, plant hormones may play a key role in the Cf-16 tomato response to C. fulvum infection.
Table 1, Table 2, Table 3 and Table 4 were shown at the end of this document.
Gene coexpression network analysis
Weighted gene coexpression network analysis is a common algorithm for constructing gene coexpression networks . A total of 13 different modules were obtained using a gene dendrogram colored according to the correlations between gene expression levels (Fig. 6a). Among them, genes in MEred and MEgreenyellow were highly expressed in Cf_4dpi, and genes in MEpurple had a relatively high expression in Cf_4dpi and MM_4dpi (Fig. 6b). We performed KEGG analysis for the three modules. For the MEred module, pathways related to “Plant-pathogen interaction”, “Oxidative phosphorylation” and “Phenylalanine, tyrosine and tryptophan biosynthesis” were enriched, whereas for MEgreenyellow, pathways related to “Pentose phosphate pathway”, “Flavonoid biosynthesis”, “Phenylpropanoid biosynthesis” and “Plant hormone signal transduction” were enriched (Fig. S2).
Validation of RNA-seq data by qRT-PCR
To verify the RNA-seq data, 16 DEGs were chosen for qRT-PCR using three biological replicates. These 16 genes were selected from significantly enriched KEGG pathways (such as “Plant hormone signal transduction”, “Plant-pathogen interaction” and “Metabolic pathways”). The expression data of qRT-PCR were consistent with the RNA-seq results, indicating a similar trend between the transcriptome analysis and qRT-PCR data (Fig. 7).