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Article

Transcriptome-Based Analysis of Tomato Genotypes Resistant to Bacterial Spot (Xanthomonas perforans) Race T4

by
Rui Shi
1,2 and
Dilip R. Panthee
1,*
1
Department of Horticultural Science, Mountain Horticultural Crops Research & Extension Center, North Carolina State University, Mills River, NC 28759, USA
2
Department of Crop and Soil Science, North Carolina State University, Raleigh, NC 27695-7620, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(11), 4070; https://doi.org/10.3390/ijms21114070
Submission received: 9 May 2020 / Revised: 27 May 2020 / Accepted: 1 June 2020 / Published: 6 June 2020
(This article belongs to the Section Molecular Plant Sciences)
Browse Figures
Versions Notes

Abstract

:
Bacterial spot (BS) is one of the most devastating foliar bacterial diseases of tomato and is caused by multiple species of Xanthomonas. We performed the RNA sequencing (RNA-Seq) analysis of three tomato lines with different levels of resistance to Xanthomonas perforans race T4 to study the differentially expressed genes (DEGs) and transcript-based sequence variations. Analysis between inoculated and control samples revealed that resistant genotype Solanum pimpinellifolium accession PI 270443 had more DEGs (834), followed by susceptible genotype tomato (S. lycopersicum L) breeding line NC 714 (373), and intermediate genotype tomato breeding line NC 1CELBR (154). Gene ontology (GO) terms revealed that more GO terms (51) were enriched for upregulated DEGs in the resistant genotype PI 270443, and more downregulated DEGs (67) were enriched in the susceptible genotype NC 714. DEGs in the biotic stress pathway showed more upregulated biotic stress pathway DEGs (67) for PI 270443 compared to more downregulated DEGs (125) for the susceptible NC 714 genotype. Resistant genotype PI 270443 has three upregulated DEGs for pathogenesis-related (PR) proteins, and susceptible genotype NC 714 has one downregulated R gene. Sequence variations called from RNA-Seq reads against the reference genome of susceptible Heinz 1706 showed that chr11, which has multiple reported resistance quantitative trait loci (QTLs) to BS race T4, is identical between two resistant lines, PI 270443 and NC 1CELBR, suggesting that these two lines share the same resistance QTLs on this chromosome. Several loci for PR resistance proteins with sequence variation between the resistant and susceptible tomato lines were near the known Rx4 resistance gene on chr11, and additional biotic stress associated DEGs near to the known Rx4 resistance gene were also identified from the susceptible NC 714 line.

1. Introduction

Bacterial spot (BS) is a foliar disease of tomato caused by multiple species of Xanthomonas. There are four physiological races of Xanthomonas, including races T1 to T4, which are distributed throughout the world, particularly in warm and humid regions, and cause a significant yield loss every year. Association between races and species is classified as race T1 (X. euvesicatoria, Xe), race T2 (X. vesicatoria, Xv), race T3 and T4 (X. perforens, Xp), and no race designations (X. gardneri, Xg) [1,2].
Resistance to BS is both a monogenic and polygenic trait [2]. Multiple resistance genes and loci for BS have been identified in tomato and have been summarized by Pei et al. [3]. Hawaii 7998 is a differential genotype for identifying race T1, which includes Xanthomonas spp.—carrying the avrRxv gene [4]. Hawaii 7998 remains the most reliable source of resistance to race T1 [3,5], which is conferred by three independent loci (rx1 and rx2 on opposite arms of chromosome 1 and rx3 on chromosome 5) and may be modified by three susceptible loci on chromosomes 3, 9, and 11 [6]. The dominant allele Rx3 confers resistance in the field, explaining 41% of the phenotypic variation [7] and is considered to be the most effective locus for T1 resistance. It is not clear yet whether rx3 and Rx3 are alleles of the same gene or closely associated genes on the same chromosome [3].
Resistance to race T2 has been documented in Hawaii 7983, which expressed partial resistance over multiple seasons [8]. As Xp contains the major races (T3 and T4) that cause many annual epidemics in tomato growing regions [2,9], much work has been done to identify and breed resistance against this group of the Xanthomonas.
Race T3 resistance has been identified in several lines, including Hawaii 7981, and Solanum pimpinellifolium accessions PI 126932 and PI 128216, each conferring a hypersensitive response (HR) in the presence of the pathogenic expression of the avrXv3 gene, and partial resistance in field assessments [10]. Race T4 resistance has been identified in LA 716 (S. pennellii), being conferred by Xv4. The previously mentioned line PI 114490 also showed strong resistance to race T4.
Mapping and characterization of loci help to develop resistant breeding lines. The quantitative trait loci (QTLs) associated with resistance to BS race T4 were identified in the populations derived from genotype Hawaii 7998 and PI 114490 and mapped to chromosome 11 and chromosome 3, respectively [11].
Resistance to BS is very complex, and fulfilling the introgression of resistance into the desirable genetic background is very challenging. Although the QTLs with the reasonably high level of R2 value (29.4% and 4.8%, respectively, from chromosomes 11 and 3) to BS race T4 had been identified using QTL mapping method [11], its introgression into the breeding lines to achieve the desired level of BS resistance has not been obtained. In this context, it is reasonable to investigate the gene regulation network in more detail so that the resistance mechanism can be understood better. Differential gene expression analysis by conducting RNA sequencing (RNA-Seq) experiments has been used to understand such mechanisms in different species [12]. Recently, Du et al. [13] investigated the expression profiles of genes in response to BS race T3 infection and found that 78 genes were upregulated in PI 114490 (resistant parent), and 15 genes were upregulated in OH 88,119 (susceptible parent) six days after inoculation (dai). With information on gene expression in response to race T3 available, it is logical to investigate the genes specially expressed on exposure to BS race T4 in tomato, which has not been reported to date. With the availability of detailed annotation for most of the genes in the tomato genome, functional classification of the genes and pathway analysis is now much more convenient. A gene expression analysis approach using RNA-Seq analysis technology has been applied to unravel the gene function and defense mechanism in tomato, soybean, and several other plant species [13,14]. This approach is useful to identify the gene(s) associated with complex traits by comparing the detailed network of gene regulation between host and pathogens and eventually determining the phenotypic trait [12,15]. This approach is suitable to identify the gene network involved in conferring resistance to BS.
The available tomato genome sequence [16] also presents an opportunity to identify sequence polymorphism in the form of single nucleotide polymorphisms (SNPs), insert and deletion (INDELs) genome-wide, which can be developed into molecular markers for breeding program [17,18,19]. RNA-Seq data is a valuable resource for the detection of SNP/INDELs from gene transcripts, a subset of the whole genome sequence, but can be more relevant to functional analysis. For instance, to detect nonsynonymous SNP/INDELs, which lead to gene function alternation due to amino acid sequence change [18].
In North Carolina, X. perforans race T4 is the dominant race [20]. There are limited QTL analysis information and genetic dissection information available as of now. In this situation, we evaluated a few tomato breeding lines for BS resistance. Based on this screening, we selected three lines with a relatively good level of resistance and susceptibility to BS. These lines were used for transcriptome-based analysis in this study.

2. Results

2.1. Tomato Lines with Different Level of Resistance to BS

The resistance level of 30 tomato breeding lines was evaluated by inoculation with BS (X. perforans) race T4 in the greenhouse at Mountain Horticultural Crops Research and Extension Center, Mills River, NC, USA. Leaf samples of these lines were collected and frozen in liquid nitrogen 48 h after BS inoculation and then stored at −80 °C before RNA extraction and RNA-Seq analysis.
The resistance level of these 30 tomato lines, as shown by the area under disease progress curve (AUDPC) based on the data from greenhouse experiments, was used as a single indicator to select the genotype for disease resistance. We started scoring the plants 21 days after planting (dap) and continued until 35 dap. Among these tomato genotypes, accession PI 270443 was the most resistant line, followed by PI 114490, CLN-2413A, LA 2093, LA4277, and Fla. 7060_Xv4 with an AUDPC value of less than 280 (Table 1). Average disease incidence took place about 5.5 days after inoculation (dai) in LA2093, whereas it was 6 dai in PI 114490, LA4277, and Fla.7060_Xv4. In the case of PI 270443 and CLN2413A, the average disease incidence was 7 dai. Among the tomato lines, Heinz 1706 was the most susceptible line, followed by NC 714, NC 6 Grape, NC EBR7, and Hawaii 7981 with an AUDPC value of more than 608 (Table 1). The time for the first disease symptom appearance was not much different in the group of susceptible lines, which ranged from 5.5 to 6 days. Even in the wide array of lines, NC 1CELBR was intermediate in its response to the BS along with other lines, including NC 25P, NCEBR8, CLN-2418A, NC 161L, NC EBR6, Money Maker, LA2653, and NC123S. The level of disease development reported in terms of AUDPC in these lines ranged from 420 to 540 (Table 1). The disease developed very quickly in the susceptible lines, whereas the rate of disease development was very slow in the resistant lines. Hawaii 7998, which is widely used as a resistant line to BS, was very close to NC 1CELBR in AUDPC value, whereas NC 714 was the most susceptible line.
Based on the results presented in Table 1, three tomato lines were selected to represent a different level of resistance to BS for RNA extraction and subsequent RNA-Seq analysis. These three lines were PI 270443, the most resistant tomato line, NC 714, one of the most susceptible tomato breeding lines, and NC 1CELBR, a medium resistance line (Table 1, Figure 1). Although Heinz 1706 is the most susceptible line in our list, it is a processing tomato, but our ultimate interest is to develop genomic resources using large-fruit fresh-market tomato breeding lines. Therefore, we selected NC 714 as the most susceptible lines instead of NC 5Grape, a small fruit tomato and Heinz 1706, a processing variety. NC 1CELBR is also a large-fruited fresh-market breeding line (Table 1, Figure 1).

2.2. RNA-Seq Library Information

To investigate the differences in transcriptome associated with the inoculation of BS race T4, 12 RNA-Seq libraries were constructed and sequenced with two biological replicates for three tomato lines (lines) inoculated with BS (IN) or without BS inoculation (CK). A total of 267 million reads were generated for these RNA-Seq libraries. These reads were processed to remove low-quality reads and then mapped to the Heinz 1706 genome assembly (SL3.0). The mapped RNA-Seq reads were quality filtered and resulted in a total of 234 million mapped reads (at least 6 million per library) for subsequent bioinformatics analysis (Table 2). The data of these RNA-Seq libraries have been deposited in NCBI’s Gene Expression Omnibus [21] and are accessible through GEO Series accession number GSE135232.
The first step of our analysis was to check the relationship of all RNA-Seq libraries based on variation distance calculated from RNA-Seq read counts for annotated genes using the plotMDS function of the Edge R package [22]. Ideally, replicated samples from the same group should cluster together in the plot, while samples from different sample groups form separate clusters [22]. In this plot, we found that samples of each tomato lines are grouped. However, RNA-Seq libraries for uninoculated (CK) and inoculated (IN) samples separated slightly from each other for the same tomato line (Figure 2). This plot pattern also indicated that gene expression variation between lines was more significant than BS inoculation treatment. Based on such expression profiles, we adopted the strategy of analyzing genes by comparing expression changes between treatment (control and inoculated) samples within the same genotype first, and then compare inoculation-induced DEGs between different tomato lines.

2.3. DEGs Induced by BS Inoculation

Differentially expressed genes (DEGs) were screened between control and inoculated tomatoes for each breeding lines based on a 2-fold change of transcript abundance and FDR <0.05. A total of 1161 differentially expressed genes without overlap were identified from this study. Among them, PI 270443 had 834 (346 upregulated, and 488 downregulated), NC 1CELBR had 154 (71 upregulated, 83 downregulated), and NC 714 had 373 (93 upregulated, 277 downregulated) DEGs between control and inoculated samples, respectively (Figure 3A, Table S1).
Overall, there were 445 upregulated genes and 677 downregulated genes without overlap in the three lines. Among them, only 35 genes were common (seven upregulated and 28 downregulated) among all three tomato lines, and these genes represent a common reaction to BS inoculation (Figure 3B). More combinations, as shown in Figure 3B, also indicated that PI 270443 has 289 up and 362 downregulated DEGs specific to PI 270443 over two other lines, and these genes might be associated with strong resistance reaction to pathogen attacks. In comparison, NC 714 had 63 and 146 up and downregulated DEGs specific to NC 714 over two other lines, and these DEGs might be associated with several susceptible reactions to pathogen attack. DEGs specific to NC 1CELBR might be more complicated to interpret, but its common DEGs with PI 270443 might be more helpful for resistance, and DEGs shared with NC 714 may be associated with susceptibility.

2.4. Gene Ontology Term Enrichment from BS Inoculation-Induced DEGs

Functional analysis of DEGs for different tomato lines was conducted based on gene ontology (GO) enrichment analysis using AgriGO based on tomato ITAG3.2 version annotation and background. We expected that some GO terms were enriched (over-represented) after BS inoculation, and provide clues to understand the reaction of BS inoculation for different tomato lines. Based on this analysis, we found more GO terms enriched for upregulated DEGs in resistant genotype PI 270443, and more GO terms were enriched for downregulated DEGs in susceptible genotype NC 714 (Figure 4, Table S2).
In detail, there were 51 GO terms enriched among the 346 upregulated DEGs from PI 270443. Among the enriched GO terms, the important GO terms included protein binding, cell parts, cell, intracellular, intracellular part, intracellular organelle, organelle, nucleic acid binding, DNA binding, and protein complex (Table S2). Additional important GO enriched terms were intracellular non-membrane-bounded organelle, non-membrane-bounded organelle, and macromolecular complex, among others.
For 488 downregulated DEGs from PI 270443, there were 11 GO enriched terms with a relatively low level of enrichment, and all these ontologies are assigned as molecular functions. The important GO terms were catalytic activity, nucleic acid binding transcription factor activity, transcription factor activity, sequence-specific DNA binding, hydrolase activity, acting on glycosyl bonds, hydrolase activity, hydrolyzing O-glycosyl compounds, serine hydrolase activity, and serine-type peptidase activity, among others (Figure 4, Table S2).
There were only two GO terms enriched for NC 1CELBR among its 71 upregulated DEGs, which are transferase activity for hexosyl groups and glycosyl groups. Nine GO terms were enriched for 81 downregulated DEGs from NC 1CELBR, including membrane part, hydrolase activity, hydrolyzing O-glycosyl compounds, hydrolase activity (acting on glycosyl bonds, an integral component of the membrane), cellular catabolic process, and defense response (Figure 4, Table S2).
For the 96 upregulated DEGs from NC 714, there were 21 GO enriched terms. The significantly enriched terms were associated with cellular components such as DNA and chromosome (Table S2). For the 277 downregulated DEGs from NC 714, there were 67 GO terms enriched, including response to stress (GO:0006950), and biotic stimulus (GO:0009607), protein kinase activity, peroxidase activity, antioxidant activity, response to biotic stimulus, cell recognition, chitin-binding, protein phosphorylation, regulation of the cellular process, cellular metabolic process, and response to oxidative stress, among others (Figure 4, Table S2).
Overall, there were several enriched GO terms for upregulated DEGs from PI 270443, which might be associated with the resistance of this genotype. Contrary to this, more enriched GO terms for the downregulated DEGs of NC 714 might be associated with the susceptibility of NC 714. It should be noted that there was no overlap of GO terms for all upregulated DEGs from these three lines, but all 21 GO terms enriched in the upregulated DEGs of NC 714 overlapped with the GO enrichments from PI 270443. The lack of GO overlap between NC 1CELBR upregulated DEGs and the other two lines might be associated with few DEGs identified in this genotype. There were four GO terms enriched for downregulated DEGs for all these three lines, which might refer to a common reaction to BS. These GO terms include nucleic acid binding transcription factor activity, transcription factor activity, sequence-specific DNA binding hydrolase activity, and acting on glycosyl bonds hydrolase activity, hydrolyzing O-glycosyl compounds.

2.5. BS Inoculation Induced DEGs in the Biotic Stress Pathway

To understand the responses of these tomato lines to BS infection, MapMan was used to classify the DEGs of these tomato lines based on the more comprehensive annotation of tomato genes as described in the methods section. This analysis was conducted to decipher the involvement of DEGs in various cellular processes using the MapMan software. In Figure 5, we present the distribution of DEGs in the biotic stress pathway, which is associated with disease resistance and development.
Overall, highly resistant genotype PI 270443 had more upregulated DEGs (67) in the biotic stress pathway (Figure 5A), followed by 34 upregulated DEG in NC 1CELBR (Figure 5C), and 21 upregulated DEGs in NC714 (Figure 5E). On the other hand, upregulated DEGs in PI 270443 had more bins (catalogs defined for genes relating to functional processes in MapMan) and more DEGs in each bin as well. For instance, PI 270443 had three upregulated PR-protein genes in the core of biotic stress pathway (dark area in Figure 5A) and had DEGs associated with ABA and peroxidase, which were not present in other two lines. There were also more DEGs associated with the cell wall, proteolysis, signaling, Erythroblast transformation specific domain-containing transcription factor (ERF), and secondary metabolites for PI 270443 than in the other two lines. Although the overall DEGs and enriched GO terms for NC 1CELBR seemed underestimated in our analysis, this medium resistant tomato line still had more upregulated DEGs in biotic stress pathway than that of NC 714. The DEGs were associated with auxins, ethylene, redox-associated genes, glutathione-S-transferase, heat shock proteins, and secondary metabolites.
Regarding downregulated DEGs in this biotic stress pathway, PI 270443 had 154 DEGs (Figure 5B), followed by 126 in NC 714 (Figure 5F), and 33 in NC 1CELBR (Figure 5D). Regarding each catalog, the pattern of PI 270443 and NC 714 was similar in number, but NC 1CELBR had few DEGs in each bin (Figure 5D). One noticeable pattern was that one DEG (solyc12g097000) defined as R gene, which is essential for disease resistance, was downregulated in NC 714 (Figure 5F), and no such DEG was found in the biotic stress pathway for the other two lines (Figure 5B,D).
Table 3 presents the overlapping DEGs in the biotic stress pathway for these tomato lines. It shows that among 67 DEGs upregulated in PI 270443, only two were commonly upregulated DEGs in all three lines, which might be associated with a common reaction to the inoculation of BS race T4. There were 16 DEGs upregulated in both PI 270443 and NC 1CELBR, which are resistant to BS. One noticeable feature was that half of them are involved in the secondary metabolism of flavonoids. Furthermore, the CCoAOMT gene, which was reported to be disease-resistance-associated in maize [24,25,26,27,28,29,30], was found as the common upregulated DEG in these two resistant tomato lines. Regarding upregulated DEGs specific to PI 270443, some groups of genes were overrepresented, including those associated with cell wall cellulose synthesis, APETALA2/ethylene-responsive element binding protein family, protein degradation related, and stress-related genes. On the other hand, among the 125 downregulated DEGs in NC 714, which might be associated with susceptibility to BS race T4, there were 15 commonly downregulated genes in all three lines, including two secondary metabolisms, lignin genes C3H and F5H, and three genes encoding β-1,3 glucan endo-1,3-β-glucosidase. These DEGs, we believe, are common reactions to the inoculation of BS. Downregulated DEGs specific to NC 714 included three CCoAOMT genes, five WRKY domain transcription factor genes, and five genes related to protein degradation/ubiquitin in the list of the common downregulated gene for PI 270443 and NC 714 (Table 3).

2.6. BS Inoculation-Induced DEGs in Other Immunity Levels.

We attempted to evaluate the possible involvement of genes in the level of plant immunity based on DEGs associated with effector-triggered immunity (ETI) and pattern-triggered immunity (PTI) identified by Pombo et al. [31]. They reported Epk1 as a novel protein kinase for the induction of host response for the recognition of three beneficial bacterial effectors. We found a pattern of ETI and PTI associated with the level of resistance in three lines. For instance, there were more ETI-specific genes found in upregulated DEGs of resistance genotype. PI 270443 had 30 upregulated DEGs specific to ETI, whereas NC 1CELBR and NC 714 had 12 and 11 genes, respectively. Regarding PTI-specific genes, only one (Solyc07g008440) gene was identified among upregulated DEGs from PI 270443, but no PTI-specific gene was found in NC 1CELBR and NC 714. Genes commonly associated with ETI and PTI (ETI-PTI) which might be involved in a common resistance pathway to pathogens can be found in both PI 270443 (7) and NC 1CELBR (6), the two lines with strong and medium resistance to BS, but there were no genes in NC 714, which is the most susceptible to BS (Table 4).
There were fewer ETI/PTI-associated DEGs induced by BS inoculation in our study compared to Pombo et al. [31]. For instance, Pombo et al. [31] found 2805/4274 DEGs from 6 h after inoculation (hai) using the vacuum-infiltration method on leaf tissue. In contrast, we found 57/139, 18/38, and 26/105 DEGs in PI270443, NC 1CEBEL, and NC714 induced by inoculation using the spray method, respectively. These numbers are comparable reported by Du et al. [13] from their RNA-Seq analysis based on 6 hai spray-inoculated samples.

2.7. Transcriptome-Based Sequence Variation in Three Selected Tomato Lines

The SNP molecular marker analysis was conducted based on a sequence of RNA-Seq reads mapped to reference genome sequence of tomato Heinz 1706 (SL3.0), which is the most susceptible to BS race T4. Overall, a total of 23,253 SNP/INDELs sites were called in PI 270443, NC 1CELBR, and NC 714 against Heinz 1706. Among these SNP/INDELs, 18,238 could be located within the sequence of 6676 gene models (annotation version ITAG3.2). Distribution of these SNP/INDELs and associated genes on the individual chromosome are listed in Table 5. As SNP/INDELs or related genes in individual tomato lines, these were defined as a homo form of alternative sequence to Heinz 1706, i.e., GT:1/1 in the genotype section of the VAR file. More detailed SNP/INDELs raw information can also be found in Table S3.
These results showed that in NC 714, the most susceptible tomato breeding line had more SNP/INDELs against the reference genome of similar susceptible Heinz 1706. In contrast, the most resistant PI 270443, which is expected to be different from Heinz 1706 (susceptible to BS), had fewer SNP/INDELs. The situation of the individual chromosome was different. For instance, chr11 and chr12 of NC 714 had relatively fewer SNP/INDELs and associated genes than Heinz 1706, but PI 270443 and NC 1CELBR had more SNP/INDELs and associated genes, respectively.
On further evaluation of the distribution of sequence variation, as shown in the locations of genes with SNP/INDELs on each chromosome (Figure 6A), we noticed that the patterns of genes with SNP/INDELs on chr11 SNP/INDELs from PI 270443 and NC 1CELBR were almost the same, which can be further confirmed by comparison of SNP/INDELs between these two lines, resulting in over 95% identity on chr11 (Figure 6D). One critical insight of this pattern was that chr11 has resistance QTLs, as identified in the previous study, and these QTLs contribute 25% resistance to race T4 [11]. This pattern suggested that QTLs on chr11 might contribute to resistance of PI 270443 and NC 1CELBR.
In addition to a similar sequence variation pattern on chr11, which has resistance QTL as reported before [11], these two lines also share some common sequence variation on chr12 (Figure 6D), which has QTLs related to susceptibility to race T4 of BS [11]. Therefore, such common sequence variation in PI 270443 and NC 1CELBR against the reference genome of susceptible Heinz 1706 might be associated with reduced susceptible QTLs to the BS. SNP/INDELs on other chromosomes in these three lines were more diverse, such as chr3 and chr4, as shown in Figure 6B,C, even though it was reported that chr3 has resistance QTL to BS [11].

2.8. Analysis Combined with Transcriptome-Based Sequence Variation and Biotic Stress-Associated DEGs in Three Selected Tomato Lines

To further explore the application of RNA-Seq analysis in gene response to BS resistance, we checked all DEGs or sequence variation of genes with biotic stress pathway or putative involvement in disease resistance from chr11 (Table 6). Based on the table, it seems that most of chr11 (from 5 Mb to end) of PI 470443 and NC 1CELBR are identical, but different from NC 714 in terms of SNP/INDELs distribution. However, there was no apparent association between SNP/INDELs with upregulated DEGs with biotic stress pathways in this region. For instance, many genes had identical SNP/INDELs to PI 470443 and NC 1CELBR, but were different from NC 714. However, none of the upregulated DEGs in both PI 470443 and NC 1CELBR had SNP/INDELs, except one upregulated DEGs associated with disease resistance (solyc11g066580, secondary metabolism, flavonoids, Dihydroflavonols, flavonoid 3’’-monooxygenase in biotic pathway associated). In a reverse direction, we found one downregulated DEG (solyc11g020230, signaling. receptor kinases.crinkly like) and two upregulated DEGs (solyc11g066060, stress.abiotic.heat; and solyc11g069960, signaling. receptor kinases.leucine rich repeat III.) in NC 714 induced by BS. These genes are not DEGs in the other two lines with resistance to BS. All these genes contain identical SNPs in PI 470443 and NC 1CELBR, but not in NC 714. Therefore, these genes may be associated with BS resistance.

3. Discussion

The primary objective of this study was to determine and identify the unique DEGs in response to the BS race T4 so that we can identify the specific genes associated with BS resistance in tomato. It has been a challenge to determine the resistance genes, and hence to introgress the resistance into the breeding lines with desirable fruit quality in tomato despite many efforts towards the identification of QTL associated with BS resistance. In this study, the response of tomato lines to BS race T4 was as expected. For example, PI 114490, which is resistant to all races of Xanthomonas, also had less disease severity.
On the other hand, NC 714, which does not have any disease resistance to any race of Xanthomonas, was found to develop severe disease. An exciting aspect of this study was that PI 270443 had less disease than PI 114490. In several papers, PI 114490 has been reported to have resistance to all available races [10,11,32,33,34,35]. In our study, however, a genotype more resistant than PI 114490 is available, and the genes identified from the genotype may provide more robust information towards the identification of resistance genes. It should be noted that the race of BS reported from NC is race T4 [20]. This indicated that the source of resistance might be associated with a single race. This will still be a beneficial material to address this critical issue. Race T4 is widely distributed in FL and NC tomato-growing regions [20].
In exploring RNA-Seq library relationships by using plotting analysis, we found that the expression variation between different tomato lines was much bigger than inoculation treatment within the same tomato genotype based on calculated variation distance. Such a pattern may be associated with the genomic background of tomato lines. For instance, PI270443 (S. pimpinellifolium) is a wild species of tomato, and the tomato breeding line NC 1CELBR contains the genome segment of S. pimpinellifolium. Therefore, heterogeneous expression profile patterns between these tomato lines are expected. Genes in tomato lines with the S. pimpinellifolium genome are more diverse in sequence than tomato breeding lines. Such sequence diversity can lead to the underestimation of expression of wild tomato genes based on RNA-Seq read alignment of wild tomato genes to the Heinz 1706 tomato reference genome assembly. To overcome these effects in gene expression analysis, we adopted the strategy of comparing gene expression within the same line for screening BS inoculation-induced DEGs and then analyzing the resultant genes between different lines.
RNA-Seq analysis by this approach successfully identified DEGs associated with critical biological and cellular processes. While some DEGs are unique to the Xanthomonas race T4, others are already reported either with a plant defense system or with stress tolerance. In most of the gene expression or RNA-Seq studies, many pathways are involved, although biotic stress pathways were primarily focused on specific disease resistance-associated genes.
As expected, there was a significant difference between inoculated and control breeding lines for DEGs when evaluated based on RNA-Seq analysis. Du et al. [13] performed gene expression analysis using race T3 of X. perforans in tomato and identified more DEGs in resistant line PI 114490 than in susceptible line OH88119, and identified different sets of genes associated with cellular and molecular processes. Our analysis revealed that resistant accession PI 270443 had more DEGs compared to susceptible line NC 714, but NC 1CELBR, with a medium level of resistance, had fewer DEGs induced by inoculation of BS race T4. However, when we closely evaluated the upregulated DEGs in the biotic stress pathway, we identified more biotic stress-associated DEGs in NC 1CELBR than in NC 714, even though the total number of upregulated DEGs or enriched GO terms in NC 1CELBR were much less than that in NC 714.
All identified DEGs are valuable resources for the evaluation of plant reaction to BS infection, and GO term enrichment is the most adopted strategy to show the overall response based on over-representation. However, in this study, we also adopted MapMan-based analysis to identify genes associated with disease resistance. Our results showed a noticeable feature of a higher number of upregulated genes induced by the inoculation of BS in the resistant tomato line and a more significant number of downregulated genes found in the susceptible line after X. perforans race T4 inoculation. We tend to think that such patterns would provide an excellent opportunity to find resistance-related genes in resistant genotypes and genes associated with disease development in susceptible genotypes. For instance, in the core genes of the biotic stress pathway, we found that only resistant accession PI 270443 had three upregulated PR genes (loci solyc09g092300, solyc08g081790, and solyc09g092310) associated with inoculation of BS race T4. On the other hand, only the susceptible NC 714 line had a downregulated R gene (locus solyc12g097000) among the three lines. These genes were not reported in the study conducted by Du et al. [13]. A possible reason for this is that we analyzed samples collected two days after inoculation (48 hai), which is different from the condition of samples collected by Du et al. [13] after 6 hai and 6 dai. Additionally, the genetic background of their susceptible variety is substantially different from the tomato lines we analyzed.
Regarding the other bins of putative genes involved in the biotic stress pathway, we identified genes for glycosyl hydrolases (GH). The GHs comprise a large assembly of enzymes that hydrolyze the glycosidic bond between carbohydrates or between carbohydrates and noncarbohydrate moieties. GHs are grouped into various families based on amino acid sequence similarities. These proteins perform diverse functions in both plants and microbes. Many pathogenesis-related (PR) proteins belong to the GH group. GH family 17 and families 18 and 19, which contain β-1,3 glucanases and chitinases, respectively, form an essential part of the defense arsenal of plants against fungal pathogens [36,37,38].
We also found many genes associated with secondary metabolites involved in the reaction to inoculation of BS. For instance, in addition to many upregulated genes for secondary metabolites of flavonoids, we identified that CCoAMOT (solyc09g082660) was upregulated in PI 270443 after inoculation, and several CCoAMOTs (solyc02g093230, solyc02g093250, and solyc02g093270) were downregulated in NC 714 only. It should be mentioned that CCoAMOT has been reported as a gene involved in disease resistance [38,39,40]. On the other hand, C3H (solyc10g078220) and F5H (solyc12g042480) for lignin biosynthesis were downregulated in all tomato lines, which might be a common reaction to the inoculation of BS.
Genes with regulation functions involve more complex interactions. They need more power to analyze, but we still found that after inoculation of Xanthomonas race T4, there were more ethylene-responsive transcript factor genes upregulated in resistant PI 270443, and many WRKYs, on the other hand, were downregulated in susceptible genotype NC 714. Such reaction seems to be different from that reported by Du et al. [13], since WRKYs, including Solyc03g116890, Solyc04g051690, Solyc06g066370, and Solyc08g082110, were upregulated in both resistance line PI 114490 and susceptible line OH 88,119 after 6 hai, but downregulated 6 dai in these two lines in the BS race T3 inoculation experiment. This is possibly due to the different inoculation time of tomato samples with BS.
The BS inoculation-induced DEGs can also be interpreted via two interlinked layers of immunity to pathogen assault in the plant. One is pattern-triggered immunity (PTI) by detecting features of pathogen-associated molecular patterns (PAMPs). Then, induced expression of mitogen-activated protein kinase (MAPK) leads to further reaction to the pathogen. Another layer relies on the detection of the effector associated with the pathogen, and is therefore termed as effector-triggered immunity (ETI), usually associated with localized programmed cell death (PCD) as the hypersensitive response (HR) to pathogen invasion [41,42].
The identified ETI/PTI-associated DEGs induced by BS inoculation in our study are less represented in the ETI/PTI gene list than found by Pombo et al. [31]. We believe that this phenomenon is associated with the pathogen inoculation method. For instance, vacuum infiltration is more efficient in forcing pathogen into the host tissue and inducing significant reactions. While in spray inoculation, fewer host cells of the leaves are infected due to the physical and chemical barriers on the leaf surface to block the pathogen invasion [13]. In addition, we suspected that vacuum-filtration-induced reaction to pathogen invasion is more likely a localized defense response, and spray inoculation might involve a nonlocalized system acquired resistance (SAR) reaction in cells near infected cells, and therefore, the overall transcriptome-based reaction is less representative in vacuum filtrated samples.
Although the number of identified ETI/PTI DEGs are limited in this study, this analysis still provides clues towards the overall immunity pattern to BS. Induction of ETI-specific DEGs was identified in all tested tomato lines, while PTI-specific DEGs were only found in PI 470443, the genotype with strong resistance to BS, and common ETI/PTI DEGs were be identified in the PI 270443 and NC 1CELBR genotypes with resistance to BS, but not in the susceptible NC 714 genotype.
DEG analysis can reveal overall functions specific to the biotic stress pathway for genes associated with disease resistance and susceptibility processes. Sequence variation within expressed genes can also contribute to disease resistance even if they are not DEGs. Sequence variation in coding regions can alter the function of genes, and thus may affect disease resistance. For instance, the sequence variation in Rx4 was found to be associated with resistance to race T3 BS in tomato [3]. Therefore, we have taken advantage of transcriptome information in this study to call SNP/INDELs and hope to find genes with sequence variation between BS-resistant and susceptible tomato lines.
Our results showed more SNP/INDELs in NC 714 against Heinz 1706, suggesting a more diverse genetic background between these two BS-susceptible lines. However, such sequence variation on chr11 was small, which suggested that both lines might lack resistance genes to BS from wild tomato at this chromosome. The chr11 of PI 270443 and NC 1CELBR were identical based on SNP/INDELs distribution, but different from that of NC 714 and Heinz 1706. This suggested that chr11 contributes to the resistance of tomato lines to BS. For instance, chr11 was reported to have QTLs contributing ~(R2 = 29.4%) resistance to BS [11]. Chromosome 3 ~(R2 = 4.3%), which has been reported to contain BS resistance elements, did not show such a pattern in the two resistant tomato lines, and might explain the different resistance levels in PI 270443 and NC 1CEBLR to BS infection. To focus more on BS race T4 resistance genes, we feel that it would be relevant to specifically concentrate efforts on chr11.
Line PI 128216, derived from S. pimpinellifolium, has been reported to carry the gene Rx4 located on chromosome 11, which confers hypersensitivity and field resistance to race T3. An NBS-LRR class of resistance genes was fine-mapped near a 45.1 kb region between the pcc17 and pcc14 molecular markers. Six SNPs and one INDEL were also found in this region, which was found to be useful for the marker-assisted selection (MAS) of this gene [3].
The recessive genes, rx1 and rx2, conferring resistance to race T1, were derived from Hawaii 7998 and mapped onto chr1, whereas Rx3 was mapped onto chr3 [4]. Modifying susceptible alleles have been reported from chr3, 5, 9, and 11. The genes avrXv3, Xv3/avrXv3, and Xv3/Rx4 derived from Hawaii 7981 and PI 126932, respectively, were located on chr11, conferring resistance to race T3 [43,44].
By blasting the published cloned Rx4 candidate transcript sequence for S. lycopersicum lines Hawaii 7981, OH88119, and S. pimpinellifolium PI 128216PI 128216 (JF743044.1, JF743043.1, and JF743045.1, respectively), we found that the candidate Rx4 gene is a homolog of solyc11g069020, for which we identified no SNP/INDELs in this study (Table 6), possibly due to their low expression levels in the tissues tested. On the other hand, we found multiple PR genes around this Rx4 candidate gene (<2 Mb, Table 6), and these PR genes have SNP/INDELs specific to the resistant tomato line PI 270443 and medium resistant line NC 1CELBR compared to susceptible lines NC 714 and Heinz 1706 (Table 5). Although these PR genes are not identified as BS-induced DEGs in these two lines, they might still function diversely from their homologs in susceptible tomato plants, and might function as multiple loci for resistance to BS race T4.
RNA-Seq-based analysis usually focuses on gene expression profile, such as DEG identification. Here, we tried to combine it with transcriptome-based sequence variation to explore the possible reason for different reactions of tested tomato lines to BS inoculation. We illustrated a simple case by focusing on chr11. This chromosome seems highly similar in the two resistant tomato lines. Therefore, any genes with sequence variation and DEGs between the two resistant lines and the susceptible line are of great interest for future analysis. Using this comparison, we found some DEGs of interest. For instance, we identified some DEGs that are putatively involved in biotic stress pathways from chr11 in susceptible tomato line NC 714, while these genes were not differentially expressed in resistant lines PI 27043 and NC 1CELBR. On the other hand, these genes contain SNPs in resistant lines PI 270443 and NC 1CELBR, but no sequence variation was detected in NC714 (Table 6) or Heinz 1706. Based on this data, we suggest that these genes might be responsible for the sensitive response of NC 714. Two of these DEGs are located at 50.2 Mb and 54.9 Mb of chr11, respectively, near the location of the Rx4 homolog (53.9 Mb).

4. Materials and Methods

4.1. Plant Materials

Thirty tomato lines grown in a greenhouse were evaluated for their resistance to bacterial spot (X. perforans). These tomato lines were sown in 4P soil mixture (Fafard®, Agawam, MA, USA) in 24-cell trays in the greenhouse at the Mountain Horticultural Crops Research & Extension Center, Mills River, NC, USA. Six plants per genotype were planted in duplicate in a completely randomized design. Plants were fertilized using a mixture of fertilizer containing a ratio of 20:20:20 of nitrogen, phosphorus, and potassium, respectively. Standard greenhouse treatments for insects and fungal disease management were used, but copper was not applied to control bacterial diseases.
Based on the disease score, three tomato lines (S. pimpinellifolium L. accession PI 270443, and S. lycopersicum L. breeding lines NC 1CELBR, and NC 714) were selected as tested lines to be used for the RNA-Seq analysis. PI 270443, a small-fruited tomato, was found to have the least level of BS and was used as the resistant genotype in this study. NC 714 is a large-fruited tomato breeding line with excellent horticultural traits developed in a NC State breeding program [45]. It developed the most BS disease. NC 1CELBR is also a large-fruited, late blight-resistant tomato breeding line developed from a NC State University tomato breeding program [46]. Late blight resistance comes from one of the S. pimpinellifolium (LA3707) lines. It had a medium level of BS disease resistance and was used as a tomato line in this study.

4.2. Bacterial Spot Inoculation and Disease Evaluation

Plants were artificially inoculated with Isolate 9 of X. perforans, which was found to be extremely virulent to many tomato cultivars. This is a field isolate collected from infected tissue of a tomato plant in western NC and characterized as X. perforans race T4 using differential tomato lines [5,47,48,49] by Dr. Jefferey B. Jones’ lab, University of Florida, Gainesville, Florida. The strain was maintained in pure culture and stored at −80 °C. The isolate was grown on yeast dextrose chalk (YDC) agar medium [50] at 28 °C for 24–48 h and was then overlaid with sterile distilled water. The bacteria were dislodged from the plates, and the resulting bacterial suspensions were pooled in a sterile glass container. The suspension was standardized by determining its optical density at 600 nm using an LKB Biochrom Ultrospec II Spectrophotometer (American Laboratory Training, East lyme, CT, USA) and diluted as needed to obtain an OD600 of 0.3 (approximately 2–5 × 108 CFU/mL). Diluted cells were immediately used for inoculations.
For greenhouse inoculations, humidity around the plants was maintained using V5100NS humidifiers (Vicks Ultrasonic Humidifiers, Hudson, NY, USA) from 24 h before inoculation to 48 h after inoculation and by covering the seedlings with clear plastic. Four to six weeks after transplanting, the seedlings were sprayed with the bacterial suspension until foliar runoff using a hand sprayer around sunset. Sterile water was used for mock inoculation. Leaf tissue samples after inoculation were collected in liquid nitrogen and stored at −80 °C until further processed.
Greenhouse plants were scored for foliar symptoms on the most severely infected leaves using the Horsfall–Barratt scale, where 0% = 1, 1–3% = 2, 3–6% = 3, 6–12% = 4, 12–25% = 5, 25–50% = 6, 50–75% = 7, 75–87% = 8, 87–94% = 9, 94–97% = 10, 97–100% = 11 and 100% dead tissue = 12 [51]. However, the data is reported in percentage.
Area under the disease progress curve was calculated based on weekly disease severity assessments. AUDPC is a quantitative summary of disease severity over time and compares average disease severity between pairs of adjacent time points [52]. The AUDPC is calculated as follows:
A U D P C = ∑ i = 1 n − 1 y i + y i + 1 2 × ( t i + 1 − t i )
where yi is the assessment of the disease at the ith observation, ti is the time at the ith observation, and n is the total number of observations.

4.3. RNA Extraction and RNA-Seq Library Construction

Leaves from three tomato genotypes were collected in liquid nitrogen as three replicates at 48 h after inoculation (hai). Frozen samples were stored in a −80 °C freezer. Before RNA extraction, frozen leaves were ground into fine powder by using pestle and mortar in liquid nitrogen. About 100 mg ground tissue sample was transferred into a 1.5 mL tube for the extraction of total RNA using the Qiagen Plant RNeasy mini kit (Qiagen, Hilden, Germany). The RNA quality and quantity were evaluated by using Nanodrop (Fisher Scientific, Waltham, MA, USA) and MOPs gel electrophoresis [53]. Total RNA samples were used for RNA-Seq library construction using NEBNext® UltraTM Directional RNA Library Prep Kit for Illumina (New England BioLabs, Ipswich, MA, USA). We followed the protocol for the normal insertion size.

4.4. RNA-Seq Deep Sequencing, Data Processing, Mapping, and Differential Gene Expression Analysis

RNA-Seq data was generated on Illumina HiSeq2500 instruments in 150 bp read mode at Genomic Sciences Laboratory, North Carolina State University, Raleigh, NC, USA and reads were provided in FASTq format. All reads were quality checked using FastQC [54], and trimmed to eliminate poor quality bases (Q30) using fastq-mcf function.
Reads were mapped against tomato Heinz 1706 genome assembly SL3.0 using Hisat2 version 2.1.0 [55]. Reads mapped to gene assembly were manipulated using Samtools [56] for sorting/indexing, and the raw count of reads mapped to the annotated gene model (ITAG3.2 version) was extracted using Bedtools version 2.25.0 [57]. Information on raw counts of mapped RNA-Seq reads to annotated gene models were analyzed using R package EdgeR [22], including exploration of RNA-Seq libraries relationship using the plotMDS function, gene expression normalization followed TMM algorithm [58], and differentially expressed gene (DEG) identification followed classical approach. Criteria of DEG is gene expression fold change >2.0 times between the compared group of samples, and statistics level in the form of false discovery rate (FDR) <0.05.

4.5. Gene Functional and Pathway Analysis

Biological function evaluation for DEGs was conducted using the online GO analysis toolkit AgriGO2.0 [59] following an option of using ITAG3.2 version transcript ID and suggested ITAG3.2 background.
MapMan software (version 3.6.0RC1) was used to evaluate the DEGs’ function in the different pathways [60] using a list of DEGs with fold change in log2 between control and inoculated samples for each tomato line as input data. Gene annotation in the pathway analysis was prepared via Mercator online software within the PlabiPD website (http://www.plabipd.de) based on the ITAG3.2 protein sequence and followed the default annotation parameter.

4.6. SNP/INDEL Identification

Sequence variations were extracted using the mpileup function of Samtools package [56] from the mapping result file in the format as BAM. For even comparison, these BAM files were the result of mapping the same amount of clean RNA-Seq reads (20 million) from two control RNA-Seq libraries for each tomato line. To get high-quality SNP/INDELs, data in raw variant calling format (VCF, version 4.0) files were filtered for minimum depth (DP) 10 and SNP/INDELs quality (Q) over 30 [18]. To screen SNP/INDELs specific to individual tomato lines, loci with homozygous SNP/INDEL against Heinz 1706, i.e., in the form of 1/1 in genotype (GT) section of VCF output file), were selected.
Genes with SNP/INDELs were screened using vlookup function in Excel environment, and location information of genes with SNP/INDELs on the chromosome was converted into CSV format and used as input for R QTL package [61] to generate a genetic map for visualization. Online software Venny2.1 online [62] and BioVenn [63] were used to generate Venn diagrams or extract overlapping information for DEGs, SNP/INDELs.

5. Conclusions

We selected three distinct tomato lines based on their different response to BS. We performed RNA-Seq analysis for these lines to investigate DEGs induced two days after inoculation with BS race T4. Comparing functional involvement in various processes and past studies, we identified unique differentially expressed genes in resistance accession PI 270443, such as upregulated PR-protein genes (solyc09g092300, solyc08g081790, and solyc09g092310) specific to this study. On the other hand, a disease-associated R gene (solyc12g097000) was found downregulated in susceptible line NC 714. In addition to these differentially expressed genes, we used transcriptome-based RNA-Seq analysis to call SNP/INDELs from expressed genes between the three different tomato lines and found that most of the molecular markers from resistant tomato lines PI 270443 and NC 1CELBR were on chr11. Several biotic stress-associated genes, including PR-protein genes, were identified with sequence variation in addition to known Rx4 resistance genes. All these findings are a valuable resource for tomato breeding aiming to develop plants resistant to BS race T4.

Supplementary Materials

Supplementary materials can be found at https://www.mdpi.com/1422-0067/21/11/4070/s1. Table S1. DEGs identified between inoculated and mock sample for each tomato lines. Table S2. Enriched GO terms. Table S3. SNP/INDELs across all chromosomes.

Author Contributions

Conceptualization, D.R.P.; Formal analysis, R.S.; Funding acquisition, D.R.P.; Investigation, R.S.; Methodology, R.S. and D.R.P.; Project administration, D.R.P.; Software, R.S.; Supervision, D.R.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was partially supported by the College of Agriculture and Life Science (CALS) Agriculture Service of North Carolina State University, and National Science Foundation (Grant# IOS-1546625) to Dilip R. Panthee.

Acknowledgments

Help provided by Ann Piotrowski, Ragy Ibrahem, Ibrahim AlBallat, Mario De Jesus Velasco Alvarado, and Pragya Adhikari is highly appreciated.

Conflicts of Interest

The authors declare no conflict of interest.

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Ijms 21 04070 g001 550
Figure 1. The resistance level of three selected tomato lines for BS.
Figure 1. The resistance level of three selected tomato lines for BS.
Ijms 21 04070 g001
Ijms 21 04070 g002 550
Figure 2. Multidimensional scaling plot for the relationship of each RNA-Seq library. Distance between libraries was calculated as the leading log fold change for biological variation between libraries [23]. The label of “IN” represents BS-inoculated samples, and “CK” represents a control sample without inoculation of BS.
Figure 2. Multidimensional scaling plot for the relationship of each RNA-Seq library. Distance between libraries was calculated as the leading log fold change for biological variation between libraries [23]. The label of “IN” represents BS-inoculated samples, and “CK” represents a control sample without inoculation of BS.
Ijms 21 04070 g002
Ijms 21 04070 g003 550
Figure 3. DEGs in three tomato lines induced by BS inoculation. (A) The number of upregulated (blue bar) and downregulated DEGs (red bar, with the label of “-”) in tomato lines with respect to the inoculation of X. perforans race T4. (B,C) Venn diagram showing the upregulated (blue outlined) and downregulated (red outlined) differentially expressed genes (DEGs) in three tomato lines PI 270443, NC 1CELBR, and NC 714. An entire list of differentially expressed genes (DEGs) in all three tomato lines is provided as Supplementary Material (Table S1).
Figure 3. DEGs in three tomato lines induced by BS inoculation. (A) The number of upregulated (blue bar) and downregulated DEGs (red bar, with the label of “-”) in tomato lines with respect to the inoculation of X. perforans race T4. (B,C) Venn diagram showing the upregulated (blue outlined) and downregulated (red outlined) differentially expressed genes (DEGs) in three tomato lines PI 270443, NC 1CELBR, and NC 714. An entire list of differentially expressed genes (DEGs) in all three tomato lines is provided as Supplementary Material (Table S1).
Ijms 21 04070 g003
Ijms 21 04070 g004 550
Figure 4. GO terms enriched for DEGs in different tomato lines after BS inoculation. Labels in green and red represent upregulated and downregulated DEGs, respectively. Bars colored in blue, green, and orange represent a biological process, cellular component, and molecular function GO category, respectively. Scale is log10 (1/p value).
Figure 4. GO terms enriched for DEGs in different tomato lines after BS inoculation. Labels in green and red represent upregulated and downregulated DEGs, respectively. Bars colored in blue, green, and orange represent a biological process, cellular component, and molecular function GO category, respectively. Scale is log10 (1/p value).
Ijms 21 04070 g004
Ijms 21 04070 g005 550
Figure 5. BS inoculation-induced DEGs in the biotic pathway for different tomato lines. Illustration of (A) PI 270443 upregulated DEGs (blue color) (B) PI 270443 downregulated DEGs (red color). (C) NC 1CELBR upregulated DEGs (blue color) (D) NC 1CELBR downregulated DEGs (red color). (E) NC 714 upregulated DEGs (blue color) (F) NC 714 downregulated DEGs (red color) in the biotic stress pathway via MapMan.
Figure 5. BS inoculation-induced DEGs in the biotic pathway for different tomato lines. Illustration of (A) PI 270443 upregulated DEGs (blue color) (B) PI 270443 downregulated DEGs (red color). (C) NC 1CELBR upregulated DEGs (blue color) (D) NC 1CELBR downregulated DEGs (red color). (E) NC 714 upregulated DEGs (blue color) (F) NC 714 downregulated DEGs (red color) in the biotic stress pathway via MapMan.
Ijms 21 04070 g005
Ijms 21 04070 g006 550
Figure 6. Analysis of sequence variation identified from transcriptomes of three tomato lines. (A) Distribution of genes with SNP/INDELs on different chromosomes. (B–E) are Venn diagrams for overlaying SNP/INDELs identified from three tomato lines for chromosome 3, 4, 11, and 12 (labeled as chr03, chr04, chr11, and chr12).
Figure 6. Analysis of sequence variation identified from transcriptomes of three tomato lines. (A) Distribution of genes with SNP/INDELs on different chromosomes. (B–E) are Venn diagrams for overlaying SNP/INDELs identified from three tomato lines for chromosome 3, 4, 11, and 12 (labeled as chr03, chr04, chr11, and chr12).
Ijms 21 04070 g006
Table
Table 1. Bacterial spot disease development in the tomato breeding lines. Tomato plants were inoculated 15 days after planting (dap). While the first symptom was observed on different days in different lines, we started scoring the lines for bacterial spot (BS) six days after inoculation (dai) of the plants and scored in every two days interval. Data presented in percentage. Last column has the area under disease progress curve (AUDPC) data, which can be used as a single indicator in disease screening.
Table 1. Bacterial spot disease development in the tomato breeding lines. Tomato plants were inoculated 15 days after planting (dap). While the first symptom was observed on different days in different lines, we started scoring the lines for bacterial spot (BS) six days after inoculation (dai) of the plants and scored in every two days interval. Data presented in percentage. Last column has the area under disease progress curve (AUDPC) data, which can be used as a single indicator in disease screening.
Genotype First Symptom dai6 dai
(21 dap)		9 dai
(24 dap)		11 dai
(26 dap)		13 dai
(28 dap)		15 dai (30 dap)17 dai (32 dap)19 dai (34 dap)AUDPC
PI 2704437.01.01.04.510.022.520.017.5135.5
PI 1144906.01.01.012.520.022.535.037.5221.5
CLN-2413A7.01.03.08.022.527.532.532.5222.5
LA20935.51.01.04.520.032.535.035.0223.0
LA42776.01.01.510.017.542.542.537.5267.8
Fla 7060_Xv46.01.03.010.015.035.050.050.0279.0
NC 25P6.01.03.017.532.537.545.047.5321.5
NCEBR86.51.03.017.525.045.050.042.5326.5
CLN-2418A6.01.010.520.535.040.045.042.5351.3
NC161L6.52.08.025.530.040.047.547.5356.5
NCEBR67.01.03.012.542.545.050.050.0359.0
Money maker6.01.09.527.535.045.052.552.5397.8
LA26535.51.510.017.550.045.050.047.5399.8
NC123S6.51.015.522.532.552.547.550.0400.3
VF366.510.05.525.535.047.552.552.5402.3
Pto-S5.51.06.017.540.060.057.555.0421.5
Hawaii 79986.01.515.530.545.047.550.035.0422.0
CLN-1466EA6.01.020.532.042.542.547.545.0426.8
NC 1CELBR6.01.516.032.547.537.550.055.0432.3
NC 30P7.01.010.520.540.057.557.555.0433.8
NC 1CS6.51.010.521.042.555.057.555.0434.8
NC 22L-1(2008)6.55.010.525.052.557.560.055.0478.8
HI79976.01.011.022.550.060.060.067.5481.5
Pto-R6.08.020.041.047.557.560.065.0539.0
Hawaii 79816.05.517.050.060.070.065.067.5608.3
NCEBR76.01.09.525.050.085.090.095.0620.3
NC 6Grape6.015.020.540.065.077.577.575.0668.8
NC 714 5.56.015.040.065.085.087.585.0686.5
NC 5Grape5.58.025.055.067.572.580.080.0704.5
Heinz 17066.03.525.055.067.587.592.590.0762.8
Table
Table 2. Summary of RNA-Seq library information.
Table 2. Summary of RNA-Seq library information.
Sample IDNo. of Raw ReadsNo. of Clean ReadsNo. of Reads Mapped to the GenomeNo. of Reads Mapped to Genome after Filtering
PI270443—CK117,401,32017,366,77615,679,41014,493,893
PI270443—CK216,652,43316,627,27714,877,66014,493,893
NCCELBR1—CK122,569,40822,539,26120,742,99720,134,965
NCCELBR1—CK215,898,55415,872,14713,799,11713,418,896
NC 714—CK116,716,90916,672,95315,014,16014,652,561
NC 714—CK28,045,4708,028,4886,783,4926,615,839
PI270443—IN115,187,27815,150,12812,830,08212,458,547
PI270443—IN210,417,14410,390,4788,475,4068,204,475
NCCELBR1—IN126,100,65926,015,05324,082,17623,340,952
NCCELBR1—IN237,792,19437,721,27535,177,77934,073,612
NC 714—IN157,979,11857,873,64654,422,29852,813,125
NC 714—IN222,812,96622,762,93019,933,27519,364,162
Table
Table 3. DEGs in the biotic stress pathway associated with resistance and susceptibility. The upper part of the table lists all upregulated biotic stress pathway DEGs in three lines with expression change (log2) between inoculated and control samples, and the lower part of the table list all downregulated biotic stress pathway DEGs in the susceptible line NC 714. Expression change in the blue and red background is for upregulated and downregulated DEGs, respectively. The intensity of the two colors indicates the level of expression of genes changes in both directions.
Table 3. DEGs in the biotic stress pathway associated with resistance and susceptibility. The upper part of the table lists all upregulated biotic stress pathway DEGs in three lines with expression change (log2) between inoculated and control samples, and the lower part of the table list all downregulated biotic stress pathway DEGs in the susceptible line NC 714. Expression change in the blue and red background is for upregulated and downregulated DEGs, respectively. The intensity of the two colors indicates the level of expression of genes changes in both directions.
Upregulated
Gene Loci		PI
270443		NC 1CELBRNC 714Gene Annotation Bin Defined in MapMan Analysis
solyc10g0858701.11.62.3secondary metabolism.flavonoids.flavonols
solyc05g0081201.41.71.7stress.abiotic.heat
solyc12g0063801.31.4 hormone metabolism.ethylene.synthesis-degradation
solyc04g0781402.91.5 redox.ascorbate and glutathione
solyc02g0813402.51.1 misc.glutathione S transferases
solyc07g0564401.01.2 misc.glutathione S transferases
solyc01g0587201.91.5 signalling.calcium
solyc09g0826603.61.6 secondary metabolism.phenylpropanoids.lignin biosynthesis.CCoAOMT
solyc09g0591701.11.2 secondary metabolism.flavonoids.anthocyanins
solyc08g0800402.81.7 secondary metabolism.flavonoids.anthocyanins.leucocyanidin dioxygenase
solyc05g0535502.11.3 secondary metabolism.flavonoids.chalcones.naringenin-chalcone synthase
solyc09g0915101.91.0 secondary metabolism.flavonoids.chalcones.naringenin-chalcone synthase
solyc02g0850204.11.4 secondary metabolism.flavonoids.dihydroflavonols.dihydroflavonol 4-reductase
solyc02g0838601.21.0 secondary metabolism.flavonoids.dihydroflavonols.flavanone 3-hydroxylase
solyc11g0665803.01.7 secondary metabolism.flavonoids.dihydroflavonols.flavonoid 3-monooxygenase
solyc10g0834401.31.0 secondary metabolism.flavonoids.flavonols.flavonol-3-O-rhamnosyltransferase
solyc12g0985904.01.5 hormone metabolism.auxin.synthesis-degradation
solyc08g0623403.42.1 stress.abiotic.heat
solyc08g0749901.3 1.9cell wall.pectin*esterases.acetyl esterase
solyc09g0089901.4 cell wall.cellulose synthesis
solyc09g0090101.2 cell wall.cellulose synthesis
solyc10g0836701.1 cell wall.cellulose synthesis
solyc02g0801601.1 cell wall.modification
solyc11g0659701.3 secondary metabolism.simple phenols
solyc12g0890501.7 secondary metabolism.wax
solyc06g0824201.7 misc.glutathione S transferases
solyc02g0708701.5 protein.targeting.secretory pathway.unspecified
solyc09g0106201.7 protein.postranslational modification
solyc05g0515802.0 signalling.light
solyc03g1159302.2 signalling.calcium
solyc06g0844501.3 signalling.G-proteins
solyc11g0137401.2 signalling.G-proteins
solyc09g0074201.6 cell wall.hemicellulose synthesis.glucuronoxylan
solyc06g0752201.1 cell wall.cell wall proteins.AGPs.AGP
solyc04g0806203.1 cell wall.degradation.mannan-xylose-arabinose-fucose
solyc11g0666701.2 secondary metabolism.phenylpropanoids.lignin biosynthesis
solyc06g0834452.0 misc.O-methyl transferases
solyc06g0834501.7 misc.O-methyl transferases
solyc10g0862701.5 secondary metabolism.flavonoids.anthocyanins
solyc05g0103201.2 secondary metabolism.flavonoids.chalcones
solyc02g0908901.1 hormone metabolism.abscisic acid.synthesis-degradation.synthesis.zeaxanthin epoxidase
solyc08g0791501.4 hormone metabolism.auxin.induced-regulated-responsive-activated
solyc01g1105701.3 hormone metabolism.auxin.induced-regulated-responsive-activated
solyc11g0063001.3 hormone metabolism.brassinosteroid.synthesis-degradation.BRs.DET2
solyc01g1047401.5 hormone metabolism.ethylene.induced-regulated-responsive-activated
solyc09g0923001.6 stress.biotic
solyc09g0923101.4 stress.biotic
solyc08g0817901.6 stress.biotic.PR-proteins
solyc06g0723301.7 stress.abiotic.heat
solyc03g1235401.6 stress.abiotic.heat
solyc08g0786951.5 stress.abiotic.heat
solyc11g0661001.2 stress.abiotic.heat
solyc05g0058651.3 stress.abiotic.unspecified
solyc06g0097101.5 RNA.regulation of transcription.MYB domain transcription factor family
solyc10g0763702.4 RNA.regulation of transcription.AP2/EREBP, APETALA2/Ethylene-responsive element binding protein family
solyc03g1208401.5 RNA.regulation of transcription.AP2/EREBP, APETALA2/Ethylene-responsive element binding protein family
solyc05g0524101.3 RNA.regulation of transcription.AP2/EREBP, APETALA2/Ethylene-responsive element binding protein family
solyc08g0822101.1 RNA.regulation of transcription.AP2/EREBP, APETALA2/Ethylene-responsive element binding protein family
solyc09g0596301.2 protein.degradation.ubiquitin
solyc12g0105001.8 protein.degradation.ubiquitin.E3.RING
solyc04g0075001.4 protein.degradation.ubiquitin.E3.RING
solyc10g0856601.1 protein.degradation.ubiquitin.E3.SCF.FBOX
solyc01g1042301.0 protein.degradation.ubiquitin.E3.SCF.FBOX
solyc06g0718301.4 protein.degradation.ubiquitin.E3.BTB/POZ Cullin3.BTB/POZ
solyc06g0720161.3 protein.degradation.AAA type
solyc02g0799904.1 signalling.receptor kinases.DUF 26
solyc11g0120201.0 signalling.calcium
Downregulated Gene lociPI
270443		NC 1CELBRNC 714Gene annotation (Bin defined in MapMan)
solyc01g105070−2.6−2.0−3.1misc.peroxidases
solyc10g078220−4.1−2.5−3.4secondary metabolism.phenylpropanoids.lignin biosynthesis.C3H
solyc12g042480−1.5−2.0−1.6secondary metabolism.phenylpropanoids.lignin biosynthesis.F5H
solyc05g052040−2.4−1.6−2.2hormone metabolism.ethylene.signal transduction
solyc01g106620−3.7−2.3−2.5stress.biotic
solyc04g054690−2.5−-2.0−2.7redox.ascorbate and glutathione.ascorbate
solyc01g059965−4.4−2.3−3.2misc.beta 1,3 glucan hydrolases.glucan endo-1,3-beta-glucosidase
solyc01g059980−4.4−2.3−3.2misc.beta 1,3 glucan hydrolases.glucan endo-1,3-beta-glucosidase
solyc01g060020−4.4−2.3−3.2misc.beta 1,3 glucan hydrolases.glucan endo-1,3-beta-glucosidase
solyc02g036370−1.3−1.5−2.8RNA.regulation of transcription.MYB-related transcription factor family
solyc05g009790−1.6−1.7−2.0RNA.regulation of transcription.AP2/EREBP, APETALA2/Ethylene-responsive element binding protein family
solyc03g095770−2.8−1.1−1.9RNA.regulation of transcription.WRKY domain transcription factor family
solyc09g014990−1.8−2.7−4.2RNA.regulation of transcription.WRKY domain transcription factor family
solyc03g083470−1.7−2.2−2.3signalling.receptor kinases.wheat LRK10 like
solyc05g009010−1.1−1.1−1.6signalling.receptor kinases.wheat LRK10 like
solyc01g006300−1.0−1.5misc.peroxidases
solyc03g113950−1.3−1.2signalling.calcium
solyc05g007710−2.0−3.4RNA.regulation of transcription.MYB domain transcription factor family
solyc01g095630−1.1−1.4RNA.regulation of transcription.WRKY domain transcription factor family
solyc08g008280−1.3−1.6RNA.regulation of transcription.WRKY domain transcription factor family
solyc12g055710−3.3−4.0protein.degradation.ubiquitin.E3.RING
solyc02g076980−2.5−2.5protein.degradation.cysteine protease
solyc02g080040−2.5−1.7signalling.receptor kinases.DUF 26
solyc08g080670−2.5 −2.5stress.abiotic
solyc11g018775−2.5 −3.2misc.glutathione S transferases
solyc11g018777−2.3 −3.2misc.glutathione S transferases
solyc11g018800−2.9 −2.7misc.glutathione S transferases
solyc11g018805−2.3 −3.2misc.glutathione S transferases
solyc04g048900−1.7 −1.6signalling.calcium
solyc10g006700−1.2 −1.2signalling.calcium
solyc01g095580−1.0 −1.1hormone metabolism.auxin.induced-regulated-responsive-activated
solyc08g008087−1.4 −1.4hormone metabolism.ethylene.synthesis-degradation.1-aminocyclopropane-1-carboxylate synthase
solyc03g093560−1.7 −1.9hormone metabolism.ethylene.signal transduction
solyc05g051200−2.9 −1.9hormone metabolism.ethylene.signal transduction
solyc05g052050−1.5 −1.4hormone metabolism.ethylene.signal transduction
solyc08g078190−1.0 −2.1hormone metabolism.ethylene.signal transduction
solyc07g008590−1.3 −1.4stress.biotic.PR-proteins
solyc07g008620−2.5 −2.3stress.biotic.PR-proteins
solyc03g098730−.11 −2.9stress.biotic.PR-proteins.proteinase inhibitors.trypsin inhibitor
solyc07g055710−1.8 −2.7stress.abiotic.heat
solyc08g023660−1.3 −1.2stress.biotic
solyc09g090130−1.7 −1.6RNA.regulation of transcription.MYB domain transcription factor family
solyc06g068460−2.1 −1.4RNA.regulation of transcription.WRKY domain transcription factor family
solyc09g015770−2.2 −1.8RNA.regulation of transcription.WRKY domain transcription factor family
solyc08g079860−1.0 −3.1protein.degradation.subtilases
solyc08g079870−2.3 −3.3protein.degradation.subtilases
solyc01g066430−1.1 −1.8protein.degradation.ubiquitin.E3.RING
solyc11g068710−1.2 −1.7protein.degradation.ubiquitin.E3.SCF.FBOX
solyc08g068860−1.7 −1.2RNA.regulation of transcription.unclassified
solyc03g033790−3.0 −2.6protein.degradation.AAA type
solyc04g074000−1.9 −2.6signalling.receptor kinases.leucine rich repeat XII
solyc04g074050−1.2 −1.9signalling.receptor kinases.leucine rich repeat XII
solyc02g014030−2.7 −2.0signalling.receptor kinases.Catharanthus roseus-like RLK1
solyc09g011330−1.3 −1.4misc.myrosinases-lectin-jacalin
solyc01g067020−1.9 −2.0signalling.receptor kinases.leucine rich repeat III
solyc11g066270 −1.3cell wall.modification
solyc08g080640 -3.2stress.abiotic
solyc08g080650 −1.6stress.abiotic
solyc12g019740 −1.0redox.thioredoxin
solyc04g009860 −1.4hormone metabolism.ethylene.synthesis-degradation
solyc07g008240 −1.1redox.heme
solyc07g053550 −1.6redox.glutaredoxins
solyc04g071890 −1.2misc.peroxidases
solyc09g011560 −1.9misc.glutathione S transferases
solyc09g011590 −1.5misc.glutathione S transferases
solyc09g011630 −1.3misc.glutathione S transferases
solyc00g187050 −2.5protein.degradation
solyc03g025670 −2.0signalling.in sugar and nutrient physiology
solyc03g118810 −1.1signalling.calcium
solyc03g119250 −1.7signalling.calcium
solyc10g006660 −1.6signalling.calcium
solyc10g079420 −1.1signalling.calcium
solyc06g005170 −1.2signalling.MAP kinases
solyc12g017240 −1.2cell wall.degradation.mannan-xylose-arabinose-fucose
solyc04g014400 −1.4cell wall.degradation.pectate lyases and polygalacturonases
solyc07g052230 −1.0cell wall.pectin*esterases.PME
solyc02g093230 −1.4secondary metabolism.phenylpropanoids.lignin biosynthesis.CCoAOMT
solyc02g093250 −2.4secondary metabolism.phenylpropanoids.lignin biosynthesis.CCoAOMT
solyc02g093270 −1.1secondary metabolism.phenylpropanoids.lignin biosynthesis.CCoAOMT
solyc04g078290 −1.4secondary metabolism.sulfur-containing.glucosinolates.synthesis.indole.cytochrome P450 monooxygenase
solyc03g080190 −1.7secondary metabolism.flavonoids.dihydroflavonols
solyc07g049530 −2.5hormone metabolism.ethylene.synthesis-degradation.1-aminocyclopropane-1-carboxylate oxidase
solyc03g093540 −1.3hormone metabolism.ethylene.signal transduction
solyc03g093550 −1.2hormone metabolism.ethylene.signal transduction
solyc04g014530 −1.3hormone metabolism.ethylene.signal transduction
solyc07g053740 −1.0hormone metabolism.ethylene.signal transduction
solyc10g009110 −1.4hormone metabolism.ethylene.signal transduction
solyc01g006540 −1.3hormone metabolism.jasmonate.synthesis-degradation.lipoxygenase
solyc12g097000 −1.7stress.biotic
solyc10g076500 −1.7stress.biotic
solyc02g090380 −-1.1stress.biotic
solyc04g007320 −1.5stress.biotic.PR-proteins
solyc06g068500 −1.2stress.abiotic.heat
solyc08g008370 −1.1stress.abiotic.touch/wounding
solyc01g008620 −3.4misc.beta 1,3 glucan hydrolases.glucan endo-1,3-beta-glucosidase
solyc09g008250 −1.7RNA.regulation of transcription.MYB domain transcription factor family
solyc03g116890 −3.1RNA.regulation of transcription.WRKY domain transcription factor family
solyc04g051690 −3.6RNA.regulation of transcription.WRKY domain transcription factor family
solyc06g066370 −1.5RNA.regulation of transcription.WRKY domain transcription factor family
solyc08g067340 −3.9RNA.regulation of transcription.WRKY domain transcription factor family
solyc08g082110 −2.7RNA.regulation of transcription.WRKY domain transcription factor family
solyc01g087810 −1.2protein.degradation.subtilases
solyc08g079900 −4.2protein.degradation.subtilases
solyc06g074770 −1.4protein.degradation.ubiquitin
solyc11g005640 −1.3protein.aa activation
solyc01g079530 −1.2protein.degradation.ubiquitin.E3.RING
solyc03g034020 −1.0protein.degradation.ubiquitin.E3.RING
solyc11g010330 −1.4protein.degradation.ubiquitin.E3.RING
solyc05g005150 −2.1protein.degradation.ubiquitin.E3.SCF.FBOX
solyc03g111710 −1.1protein.degradation.ubiquitin.E3.BTB/POZ Cullin3.BTB/POZ
solyc02g077040 −2.9protein.degradation.cysteine protease
solyc11g066250 −1.1protein.degradation.serine protease
solyc02g087540 −2.8protein.degradation.AAA type
solyc10g007280 −2.3protein.degradation.AAA type
solyc04g076990 −1.1signalling.receptor kinases.leucine rich repeat XI
solyc04g074020 −2.1signalling.receptor kinases.leucine rich repeat XII
solyc04g074030 −2.7signalling.receptor kinases.leucine rich repeat XII
solyc09g072810 −1.4signalling.receptor kinases.leucine rich repeat XII
solyc02g080010 −1.9signalling.receptor kinases.DUF 26
solyc05g009000 −1.5signalling.receptor kinases.wheat LRK10 like
solyc03g078360 −2.1protein.postranslational modification
solyc11g005630 −1.7misc.myrosinases-lectin-jacalin
solyc10g076550 −1.5signalling.receptor kinases.wall associated kinase
solyc11g020230 −1.2signalling.receptor kinases.crinkly like
solyc08g016210 −1.9stress.biotic
Table
Table 4. Effector-triggered immunity (ETI) and pattern-triggered immunity (PTI)-associated genes among BS-induced upregulated DEGs. DEG expression change listed as (log2) between inoculated and control samples underlines PI270443, NC 1CELBR, and NC714. The intensity of the blue background represents the degree of upregulation.
Table 4. Effector-triggered immunity (ETI) and pattern-triggered immunity (PTI)-associated genes among BS-induced upregulated DEGs. DEG expression change listed as (log2) between inoculated and control samples underlines PI270443, NC 1CELBR, and NC714. The intensity of the blue background represents the degree of upregulation.
Loci IDLayerPI 270443NC 1CELBRNC 714Function Annotation (ITAG3.2)
Solyc02g079990ETI4.09 Cysteine-rich recETIor-kinase-like protein
Solyc03g115930ETI2.17 Calcium-binding EF-hand family protein
Solyc07g063170ETI2.01 Sodium/calcium exchanger family protein
Solyc01g107400ETI1.98 IAA-amido synthetase
Solyc01g109120ETI1.90 Transducin/WD40 repeat-like superfamily protein
Solyc11g069700ETI1.831.41 Elongation factor 1-alpha
Solyc10g080370ETI1.60 LOW QUALITY:Transmembrane protein, putative
Solyc01g104740ETI1.521.63 Multiprotein-bridging factor, putative
Solyc09g082710ETI1.52 Histone H2A
Solyc04g082380ETI1.28 BnaA03g07530D protein
Solyc12g006380ETI1.271.43 2-oxoglutarate-dependent dioxygenase
Solyc02g092110ETI1.25 Phytosulfokines 3 family protein
Solyc02g083860ETI1.221.02 flavanone 3-dioxygenase
Solyc06g075800ETI1.17 1.03Histone H2B
Solyc07g065410ETI1.15 LOW QUALITY:Melanin-concentrating hormone recETIor 1
Solyc08g082210ETI1.14 AP2/EREBP transcription factor
Solyc03g116170ETI1.14 Nucleosome assembly protein family
Solyc03g096670ETI1.14 Protein phosphatase 2C
Solyc02g080150ETI1.14 CAI-1 autoinducer sensor kinase/phosphatase cqsS isoform 1
Solyc06g072430ETI1.111.22 BAG family molecular chaperone regulator 5
Solyc10g007010ETI1.11 Cytochrome c oxidase copper chaperone, putative
Solyc11g066840ETI1.11 Histone deacetylase-like protein-like
Solyc03g120390ETI1.10 Auxin responsive protein IPR003311 AUX_IAA protein
Solyc10g085870ETI1.091.562.30Glycosyltransferase
Solyc12g042650ETI1.08 40S ribosomal protein S12
Solyc10g006560ETI1.06 Histone H2A
Solyc10g018810ETI1.05 60S ribosomal protein L7A-like protein
Solyc01g091840ETI1.05 UDP-galactose transporter
Solyc12g005270ETI1.02 Histone H2A
Solyc03g007770ETI1.01 S-type anion channel
Solyc10g085880ETI 2.73 Glycosyltransferase
Solyc12g062520ETI 2.64 AP-2 complex subunit mu
Solyc07g056430ETI 1.15 Glutathione S-transferase-like protein
Solyc09g092500ETI 1.141.09Glycosyltransferase
Solyc07g056480ETI 1.03 glutathione S-transferase/peroxidase
Solyc09g092490ETI 1.03 Glycosyltransferase
Solyc11g069960ETI 1.82RLK-1
Solyc10g086410ETI 1.42LEHSC270 hsc-2heat shock protein cognate 70
Solyc06g076020ETI 1.41heat shock protein 70 kD
Solyc09g092520ETI 1.39xyloglucan endotransglycosylase
Solyc11g066060ETI 1.39heat shock protein 70
Solyc09g005120ETI 1.21DnaJ domain-containing protein
Solyc05g007150ETI 1.06UDP-galactose transporter, putative
Solyc08g065850ETI 1.00Arabinogalactan pETIide 14
Solyc08g062340ETI-PTI3.402.12 Heat-shock protein, putative
Solyc01g058720ETI-PTI1.931.50 Calcium-binding EF-hand
Solyc06g072330ETI-PTI1.70 DNAJ protein, putative, expressed
Solyc01g086670ETI-PTI1.57 LOW QUALITY:Expressed protein-RZ53
Solyc02g089660ETI-PTI1.27 Titin
Solyc11g008530ETI-PTI1.11 Dicer-like 2d
Solyc08g075540ETI-PTI1.08 alternative oxidase 1au
Solyc01g107780ETI-PTI 3.08 Glycosyltransferase
Solyc06g008620ETI-PTI 1.74 LOW QUALITY:tolB protein-like protein
Solyc01g109090ETI-PTI 1.38 LOW QUALITY:mRNA, clone: RTFL01-34-C05
Solyc12g042600ETI-PTI 1.33 Glycosyltransferase
Solyc07g008440PTI1.03 Purine permease-like protein
Table
Table 5. Distribution of single nucleotide polymorphisms (SNP)/insertion–deletions (INDELs) on different chromosomes of tomato.
Table 5. Distribution of single nucleotide polymorphisms (SNP)/insertion–deletions (INDELs) on different chromosomes of tomato.
SNP/INDELs Loci (Gene with SNP/INDELs in Parentheses)
ChrTotalPI 270443NC 1CELBRNC 714
11850 (728)781 (274)734 (248)1219 (481)
21285 (531)437 (167)563 (191)875 (397)
32162 (720)595 (230)739 (247)1575 (584)
43306 (825)1143 (298)2061 (488)1826 (556)
51266 (455)633 (168)544 (161)958 (376)
61149 (432414 (125)378 (110)949 (379)
71242 (366)447 (146)398 (143)937(309)
81359 (466)517 (139)559 (152)1036 (400)
92210 (535)675 (200)1522 (345)1264 (397)
101869 (473)825 (242)1244 (289)904 (273)
113835 (716)2893 (544)2894 (545)1699 (341)
121223 (377)944 (311)938 (301)488 (152)
Total23,253 (6676)10,666 (2865)12,942 (3239)14,063 (4667)
Table
Table 6. Biotic stress pathway DEGs or genes with SNP/INDELs on chr11 in position (POS) listed as Million base (Mb). In the section of SNP/INDELs, “S”, “I”, and “D” represent sequence variation in the homo form of SNP, INSERT, and DELETION alternative form against the reference genome of Heinz 1706. The DEG fold changes are log2 of gene expression change induced by BS inoculation (IN/CK). Gene functions annotation bin were annotated by MapMan package in this study. DEG fold change background color intensity corresponds to the level of change.
Table 6. Biotic stress pathway DEGs or genes with SNP/INDELs on chr11 in position (POS) listed as Million base (Mb). In the section of SNP/INDELs, “S”, “I”, and “D” represent sequence variation in the homo form of SNP, INSERT, and DELETION alternative form against the reference genome of Heinz 1706. The DEG fold changes are log2 of gene expression change induced by BS inoculation (IN/CK). Gene functions annotation bin were annotated by MapMan package in this study. DEG fold change background color intensity corresponds to the level of change.
Loci_ID POS
Mb		SNP/INDELDEG Fold ChangeFunction Annotation Bin in MapMan for Biotic Stress Pathway Genes
PI 270443NC 1CELBRNC 714PI 270443NC 1CELBRNC 714
solyc11g0050100.0 S signalling.G-proteins
solyc11g0050600.1 S hormone metabolism.auxin.induced-regulated-responsive-activated
solyc11g0051300.1 S stress.abiotic.touch/wounding
solyc11g0051500.3 I cell wall.cell wall proteins.LRR
solyc11g0056300.5 S signalling.receptor kinases.S-locus glycoprotein like
solyc11g0056400.5 −1.3protein.degradation.ubiquitin
solyc11g0059100.7 S signalling.phosphinositides.phosphatidylinositol 4-kinase
solyc11g0061800.9 S hormone metabolism.ethylene.signal transduction
solyc11g0063001.0 1.3 hormone metabolism.brassinosteroid.synthesis-degradation.BRs.DET2
solyc11g0065901.2 S secondary metabolism.sulfur-containing.glucosinolates.synthesis.shared. Phenylacetaldoxime monooxygenase
solyc11g0082502.5 S protein.degradation
solyc11g0082602.5 S protein.degradation.cysteine protease
solyc11g0082802.6 S protein.degradation
solyc11g0084502.7 S redox.thioredoxin
solyc11g0088503.0 S protein.degradation.serine protease
solyc11g0089603.1 S signalling.receptor kinases.leucine rich repeat II
solyc11g0103103.3 S protein.degradation.ubiquitin.E3.RING
solyc11g0103303.4III −1.4protein.degradation.ubiquitin.E3.RING
solyc11g0104703.5 S signalling.14-3-3 proteins
solyc11g0104803.5 SI protein.degradation
solyc11g0106003.7 S hormone metabolism.auxin.induced-regulated-responsive-activated
solyc11g0106503.7DSDSDS secondary metabolism.phenylpropanoids
solyc11g0107403.8 SDA secondary metabolism.flavonoids.anthocyanins
solyc11g0107603.8 SA secondary metabolism.flavonoids.anthocyanins
solyc11g0107903.8S S secondary metabolism.flavonoids.anthocyanins
solyc11g0108103.8 S secondary metabolism.flavonoids.anthocyanins
solyc11g0108503.9 S secondary metabolism.isoprenoids.non-mevalonate pathway.DXS
solyc11g0109404.0 S RNA.regulation of transcription.C2C2(Zn) DOF zinc finger family
solyc11g0109604.0 S secondary metabolism.phenylpropanoids.lignin biosynthesis.CAD
solyc11g0110004.0 S signalling.receptor kinases.misc
solyc11g0110204.0 signalling.receptor kinases.leucine rich repeat III
solyc11g0110504.1 A RNA.regulation of transcription.MYB domain transcription factor family
solyc11g0110604.2 SD stress.biotic.PR-proteins
solyc11g0110804.2 S stress.biotic.PR-proteins
solyc11g0111204.2 SI signalling.calcium
solyc11g0112004.3 SI stress.abiotic
solyc11g0112504.3 S redox.ascorbate and glutathione.ascorbate
solyc11g0112604.3 S signalling.in sugar and nutrient physiology
solyc11g0113404.4 S secondary metabolism.phenylpropanoids.lignin biosynthesis.CAD
solyc11g0114404.5 S−1.2 protein.degradation.aspartate protease
solyc11g0115084.6 S protein.degradation.ubiquitin.E3.SCF.FBOX
solyc11g0115404.6 S protein.degradation.ubiquitin.E3.SCF.FBOX
solyc11g0115464.6 S protein.degradation.ubiquitin.E3.SCF.FBOX
solyc11g0115484.6 S protein.degradation.ubiquitin.E3.SCF.FBOX
solyc11g0116304.7 S hormone metabolism.auxin.induced-regulated-responsive-activated
solyc11g0116404.7 SS hormone metabolism.auxin.induced-regulated-responsive-activated
solyc11g0116704.7 S hormone metabolism.auxin.induced-regulated-responsive-activated
solyc11g0117004.7SSS hormone metabolism.auxin.induced-regulated-responsive-activated
solyc11g0118504.8SS? cell wall.hemicellulose synthesis.glucuronoxylan
solyc11g0118804.8SSS signalling.receptor kinases.DUF 26
solyc11g0120205.0SS 1.0 signalling.calcium
solyc11g0120405.0SS protein.degradation.ubiquitin
solyc11g0124105.2SDSD redox.ascorbate and glutathione.ascorbate.L-galactose-1-phosphate phosphatase
solyc11g0124605.3ISIS signalling.G-proteins
solyc11g0125105.3SS signalling.light
solyc11g0125505.4SS protein.degradation.ubiquitin.E3.SCF.FBOX
solyc11g0125805.4SS misc.beta 1,3 glucan hydrolases
solyc11g0127105.5SS signalling.in sugar and nutrient physiology
solyc11g0130105.9SS signalling.phosphinositides.phosphatidylinositol-4-phosphate 5-kinase
solyc11g0131706.0SS secondary metabolism.phenylpropanoids
solyc11g0137407.1SSS1.2 signalling.G-proteins
solyc11g0138307.3SS signalling. phosphinositides.phosphatidylinositol-4-phosphate 5-kinase
solyc11g0138807.3SS signalling.receptor kinases.S-locus glycoprotein like
solyc11g0169307.6SISI signalling.receptor kinases.leucine rich repeat X
solyc11g0170407.8SS protein.degradation.cysteine protease
solyc11g0170707.9SS hormone metabolism.brassinosteroid.signal transduction.other
solyc11g0172708.1SDSD signalling.receptor kinases.leucine rich repeat X
solyc11g0173008.2SS signalling.light.COP9 signalosome
solyc11g0173358.2SDSD protein.degradation.ubiquitin.E3.SCF.FBOX
solyc11g0185508.7SS redox.ascorbate and glutathione.ascorbate
solyc11g0186708.9SS stress.abiotic.heat
solyc11g0187759.5 −2.5 −3.2misc.glutathione S transferases
solyc11g0187779.5 −2.3 −3.2misc.glutathione S transferases
solyc11g0188009.6SS −2.9 −2.7misc.glutathione S transferases
solyc11g0188059.6 −2.3 −3.2misc.glutathione S transferases
solyc11g0199209.8SS redox.thioredoxin.PDIL
solyc11g02004010.0II stress.abiotic.heat
solyc11g02023010.6SS −1.2signalling.receptor kinases.crinkly like
solyc11g02028010.7SS signalling.receptor kinases.leucine rich repeat XI
solyc11g02238013.7SSS signalling.phosphinositides
solyc11g02781019.1SS protein.degradation.ubiquitin.E3.RING
solyc11g03073023.2SS secondary metabolism.flavonoids.dihydroflavonols.flavonoid 3’’-monooxygenase
solyc11g03222026.1SISISI−3.7 hormone metabolism.jasmonate.synthesis-degradation.12-Oxo-PDA-reductase
solyc11g03222526.1S? −4.7 hormone metabolism.jasmonate.synthesis-degradation.12-Oxo-PDA-reductase
solyc11g03327026.5SS signalling.MAP kinases
solyc11g04004040.3SS signalling.light
solyc11g04005040.2SS signalling.G-proteins
solyc11g04034038.1SS cell wall.degradation.cellulases and beta -1,4-glucanases
solyc11g04293034.2SS protein.degradation.ubiquitin.E3.SCF.SKP
solyc11g04313033.8SS signalling.phosphinositides
solyc11g04431033.3SSS protein.degradation
solyc11g04445033.0 −1.2 stress.abiotic.heat
solyc11g04456032.7SS hormone metabolism.abscisic acid.signal transduction
solyc11g04491032.3SDSD −2.0 cell wall.degradation.mannan-xylose-arabinose-fucose
solyc11g04494032.2SS signalling.receptor kinases.crinkly like
solyc11g04524031.6SS protein.degradation
solyc11g04552030.6SS −1.2 hormone metabolism.ethylene.synthesis-degradation
solyc11g05668047.7SS signalling.receptor kinases.leucine rich repeat XI
solyc11g06226049.4SS protein.degradation.ubiquitin.E3.RING
solyc11g06243049.8SS signalling.light.COP9 signalosome
solyc11g06244049.8SS redox.ascorbate and glutathione.ascorbate
solyc11g06479050.2SS protein.degradation
solyc11g06483050.3SDSDD protein.degradation.ubiquitin.E3.RING
solyc11g06483550.4II hormone metabolism.ethylene.signal transduction
solyc11g06488050.4SS signalling.G-proteins
solyc11g06495050.6SS RNA.regulation of transcription.bZIP transcription factor family
solyc11g06495350.6SS RNA.regulation of transcription.bZIP transcription factor family
solyc11g06500050.6SDSD protein.degradation.ubiquitin.E3.SCF.FBOX
solyc11g06519050.8SS protein.degradation.ubiquitin.E2
solyc11g06521050.9SS protein.degradation.ubiquitin.ubiquitin protease
solyc11g06560051.4SS cell wall.modification
solyc11g06566051.5SS signalling.calcium
solyc11g06597051.9 1.3 secondary metabolism.simple phenols
solyc11g06604052.0III protein.degradation.ubiquitin.E3.RING
solyc11g06605052.0SS RNA.regulation of transcription.C2C2(Zn) DOF zinc finger family
solyc11g06606052.0SS 1.4stress.abiotic.heat
solyc11g06609052.1SS stress.abiotic
solyc11g06610052.1 1.2 stress.abiotic.heat
solyc11g06615052.1SSS cell wall.precursor synthesis.UXS
solyc11g06625052.3SS −1.1protein.degradation
solyc11g06627052.3SISISD −1.3cell wall.modification
solyc11g06632052.3SS cell wall.hemicellulose synthesis.glucuronoxylan
solyc11g06651052.6SS protein.degradation.ubiquitin.E3.RING
solyc11g06658052.6 3.01.7 secondary metabolism.flavonoids.dihydroflavonols.flavonoid 3″-monooxygenase
solyc11g06659052.7SS protein.degradation
solyc11g06667052.7SSS1.2 secondary metabolism.phenylpropanoids.lignin biosynthesis
solyc11g06672052.8SS cell wall.precursor synthesis.AXS
solyc11g06673052.8SDSD signalling.light
solyc11g06678052.9SSS protein.degradation
solyc11g06682052.9SISII cell wall.cellulose synthesis
solyc11g06844053.5DIDII misc.beta 1,3 glucan hydrolases.glucan endo-1,3-beta-glucosidase
solyc11g06866053.6SS protein.degradation.cysteine protease
solyc11g06871053.6III−1.2 −1.7protein.degradation.ubiquitin.E3.SCF.FBOX
solyc11g06901053.9SS cell wall.degradation.mannan-xylose-arabinose-fucose
solyc11g06902053.9 −2.0 stress.biotic.PR-proteins (Rx4)
solyc11g06905054.0SS secondary metabolism.phenylpropanoids.lignin biosynthesis.4CL
solyc11g06940054.3SS redox.thioredoxin.PDIL
solyc11g06960054.5SS protein.degradation.ubiquitin.E3.RING
solyc11g06962054.6SS stress.biotic.PR-proteins
solyc11g06966054.5SS stress.biotic.PR-proteins
solyc11g06980054.7SS hormone metabolism.jasmonate.synthesis-degradation.allene oxidase synthase
solyc11g06981054.7SS protein.degradation.cysteine protease
solyc11g06992554.8SISII stress.biotic.PR-proteins
solyc11g06995054.9SS protein.degradation.metalloprotease
solyc11g06996054.9SS 1.8signalling.receptor kinases.leucine rich repeat III
solyc11g06999055.0SS stress.biotic.PR-proteins
solyc11g07134055.1SS cell wall.cell wall proteins.HRGP
solyc11g07142355.2SS stress.biotic.PR-proteins
solyc11g07150055.3SS RNA.regulation of transcription.MYB-related transcription factor
solyc11g07152055.3SS misc.beta 1,3 glucan hydrolases.glucan endo-1,3-beta-glucosidase
solyc11g07160055.3SS −1.3 hormone metabolism.abscisic acid.synthesis-degradation
solyc11g07161055.3SSS hormone metabolism.abscisic acid.synthesis-degradation
solyc11g07162055.3SS hormone metabolism.abscisic acid.synthesis-degradation
solyc11g07164055.4SSD cell wall.degradation.cellulases and beta -1,4-glucanases
solyc11g07170055.4SDSD protein.degradation.ubiquitin
solyc11g07184055.5SSS signalling.calcium
solyc11g07187055.5SSS protein.degradation.ubiquitin.E2
solyc11g07191055.5DD signalling.G-proteins
solyc11g07192055.5SSS protein.degradation.ubiquitin.E3.SCF.FBOX
solyc11g07193055.5SS stress.abiotic.heat
solyc11g07205055.6SS protein.degradation.ubiquitin.E3.SCF.FBOX
solyc11g07207055.6SS protein.degradation.ubiquitin.E3.BTB/POZ Cullin3.BTB/POZ
solyc11g07229055.8SSS stress.biotic.signalling
solyc11g07254056.0SS protein.degradation.ubiquitin.E3.unspecified
solyc11g07259056.1SS protein.degradation.ubiquitin.E3.RING
solyc11g07312056.5III RNA.regulation of transcription.MYB domain transcription factor

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Shi, R.; Panthee, D.R. Transcriptome-Based Analysis of Tomato Genotypes Resistant to Bacterial Spot (Xanthomonas perforans) Race T4. Int. J. Mol. Sci. 2020, 21, 4070. https://doi.org/10.3390/ijms21114070

AMA Style

Shi R, Panthee DR. Transcriptome-Based Analysis of Tomato Genotypes Resistant to Bacterial Spot (Xanthomonas perforans) Race T4. International Journal of Molecular Sciences. 2020; 21(11):4070. https://doi.org/10.3390/ijms21114070

Chicago/Turabian Style

Shi, Rui, and Dilip R. Panthee. 2020. "Transcriptome-Based Analysis of Tomato Genotypes Resistant to Bacterial Spot (Xanthomonas perforans) Race T4" International Journal of Molecular Sciences 21, no. 11: 4070. https://doi.org/10.3390/ijms21114070

APA Style

Shi, R., & Panthee, D. R. (2020). Transcriptome-Based Analysis of Tomato Genotypes Resistant to Bacterial Spot (Xanthomonas perforans) Race T4. International Journal of Molecular Sciences, 21(11), 4070. https://doi.org/10.3390/ijms21114070

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