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Rapid identification of candidate genes for resistance to tomato late blight disease using next-generation sequencing technologies

  • Ramadan A. Arafa,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Resources, Visualization, Writing – original draft

    Current address: Department of Frontier Science, Kazusa DNA Research Institute, Chiba, Japan

    Affiliation Plant Pathology Research Institute, Agricultural Research Center, Giza, Egypt

  • Mohamed T. Rakha,

    Roles Conceptualization, Project administration, Resources, Supervision, Writing – review & editing

    Current address: World Vegetable Center, Shanhua, Tainan, Taiwan

    Affiliation Department of Horticulture, Faculty of Agriculture, University of Kafrelsheikh, Kafr El-Sheikh, Egypt

  • Nour Elden K. Soliman ,

    Contributed equally to this work with: Nour Elden K. Soliman, Olfat M. Moussa

    Roles Supervision, Validation

    Affiliation Department of Plant Pathology, Faculty of Agriculture, Cairo University, Giza, Egypt

  • Olfat M. Moussa ,

    Contributed equally to this work with: Nour Elden K. Soliman, Olfat M. Moussa

    Roles Supervision, Validation

    Affiliation Department of Plant Pathology, Faculty of Agriculture, Cairo University, Giza, Egypt

  • Said M. Kamel,

    Roles Supervision, Validation

    Affiliation Plant Pathology Research Institute, Agricultural Research Center, Giza, Egypt

  • Kenta Shirasawa

    Roles Data curation, Formal analysis, Investigation, Supervision, Validation, Writing – original draft

    shirasaw@kazusa.or.jp

    Affiliation Department of Frontier Science, Kazusa DNA Research Institute, Chiba, Japan

Rapid identification of candidate genes for resistance to tomato late blight disease using next-generation sequencing technologies

  • Ramadan A. Arafa, 
  • Mohamed T. Rakha, 
  • Nour Elden K. Soliman, 
  • Olfat M. Moussa, 
  • Said M. Kamel, 
  • Kenta Shirasawa
PLOS
x

Abstract

Tomato late blight caused by Phytophthora infestans (Mont.) de Bary, also known as the Irish famine pathogen, is one of the most destructive plant diseases. Wild relatives of tomato possess useful resistance genes against this disease, and could therefore be used in breeding to improve cultivated varieties. In the genome of a wild relative of tomato, Solanum habrochaites accession LA1777, we identified a new quantitative trait locus for resistance against blight caused by an aggressive Egyptian isolate of P. infestans. Using double-digest restriction site–associated DNA sequencing (ddRAD-Seq) technology, we determined 6,514 genome-wide SNP genotypes of an F2 population derived from an interspecific cross. Subsequent association analysis of genotypes and phenotypes of the mapping population revealed that a 6.8 Mb genome region on chromosome 6 was a candidate locus for disease resistance. Whole-genome resequencing analysis revealed that 298 genes in this region potentially had functional differences between the parental lines. Among of them, two genes with missense mutations, Solyc06g071810.1 and Solyc06g083640.3, were considered to be potential candidates for disease resistance. SNP and SSR markers linking to this region can be used in marker-assisted selection in future breeding programs for late blight disease, including introgression of new genetic loci from wild species. In addition, the approach developed in this study provides a model for identification of other genes for attractive agronomical traits.

Introduction

Plants suffer from many biotic and abiotic stresses [1], which reduce quantity and quality of crop production worldwide. Late blight disease is caused by the hemibiotrophic oomycete Phytophthora infestans (Mont.) de Bary, one of the most destructive plant pathogens. Phytophthora infestans is well known as the causative agent of the Great Famine in Ireland between 1845 and 1852, which devastated potato production (Solanum tuberosum) [2]. After potato, tomato (S. lycopersicum L.) is the second most agriculturally important crop in the Solanaceae family. The annual global productivity of tomato has increased dramatically, to 170 million tons in 2014 [3]. However, tomato can also be damaged by the late blight disease, particularly in cool temperatures, high relative humidity (RH), and rainy or foggy conditions [4], resulting in 100% economic losses in open fields and greenhouses.

Tomato has been used in molecular genetic and genomic studies as a model for fruiting plants [5] because of its compact genome (~950 Mb) and the simple diploid genome composition of family Solanaceae. The genome sequence of tomato [6] has enabled discovery of genome-wide single-nucleotide polymorphisms (SNPs) and development of advanced molecular markers [710]. Although the genetic diversity of the cultivated tomato is limited [11], its wild relatives S. pennellii, S. habrochaites, S. peruvianum, and S. pimpinellifolium have many useful traits potentially applicable to improvement of the agricultural varieties. Therefore, introduction of wild tomato species into tomato breeding programs could facilitate development of new tomato lines [1215]. Indeed, five race-specific resistance (R) genes that confer various levels of resistances against P. infestans isolates Ph-1, Ph-2, Ph-3, Ph-4, and Ph-5 have been identified [1622] and applied to molecular breeding by marker-assisted selection (MAS) [20]. However, a serious problem in breeding by interspecific crossing is linkage drag, in which undesirable traits linked to target traits in the wild relatives are introgressed in elite cultivars [23, 24].

In the genomics era, advanced molecular markers and genotyping technologies have helped to solve this problem [25, 26]. Simple sequence repeat (SSR) markers are useful for genomics and breeding in tomato [2729]; however, analysis of large numbers of genome-wide SSR markers across multiple samples, such as breeding materials, is time-consuming and laborious. However, next-generation sequencing (NGS) technologies, including high-throughput sequencing and sophisticated bioinformatics techniques, can overcome these limitations. Restriction site–associated DNA sequencing (RAD-Seq) [3032] and an alternative technique, double-digest RAD-Seq (ddRAD-Seq) [33], can skim through the genome with low cost and high throughput. These methods can be successfully implemented in gene mapping, including quantitative trait locus (QTL) analysis and genome-wide association studies (GWAS), of a vast array of crops [32, 3438]. On the other hand, whole-genome resequencing (WGRS) enables prediction of the effects of sequence variants on gene function throughout the genome [3943]. Therefore, a combination of RAD-Seq and WGRS analysis represents a powerful strategy for rapidly identifying candidate genes responsible for traits of interests.

Development of new tomato lines with resistance to late blight disease would be a straightforward, effective, and environmentally safe approach to managing late blight disease. Therefore, in this study, we aimed to identify map positions of genetic loci derived from a wild tomato relative, S. habrochaites that control resistance to late blight disease caused by P. infestans. We applied a ddRAD-Seq pipeline that we developed in a previous study [33] to genetic mapping of the resistance loci, and then we used a WGRS strategy to predict candidate genes for late blight disease resistance.

Materials and methods

Plant materials

A cultivated tomato (S. lycopersicum), Castlerock, and its wild relative, S. habrochaites (LA1777), were used in this study. Castlerock was chosen because it is susceptible to late blight disease, and LA1777 was selected because it is resistant to the Egyptian P. infestans population, as shown in a previous study by our group [15]. Seeds of Castlerock and LA1777 were provided by the Horticulture Research Institute, Agricultural Research Center (ARC), Egypt, and the Tomato Genetic Research Center (TGRC), Davis, CA, USA, respectively. An F2 population (n = 344) was generated from an interspecific cross between Castlerock and LA1777.

Isolation and purification of P. infestans isolate

Isolation of the P. infestans population was conducted by placing host infected tissues under organic potato slices in converted Petri dishes containing water agar and incubating at 18°C for 7–10 days. Sporangia were picked from the abundant sporulation on the top of the slices and transferred directly onto the recommended media. Rye sucrose agar (RSA) medium [44] (60 g of rye grains, 20 g of sucrose, and 20 g of agar per liter) was used for isolation, growth, and maintenance of P. infestans isolates. Pure culture of P. infestans was conducted on rye slants at 18°C, and the cultures were preserved as a stock for further studies. P. infestans isolate EG_12 was selected from the stock of the Plant Pathology Research Institute, ARC, which was overcome five tomato genotypes containing R genes (Ph-1, Ph-2, and Ph-3) as well as Super Strain B, a susceptible tomato cultivar control based on virulence test [15].

Inoculum preparation and late blight assessment

Seeds of F2 progeny and the parental lines Castlerock and LA1777, as well as the susceptible control (cv. Castlerock), were sown in 209 cell seedling trays with peat moss–vermiculite mixture (1:1 volume) in a greenhouse (25 ± 2°C, 16/8 h day/night). Plants were watered and fertilized regularly with N:P:K 19:19:19, and all traditional agricultural transactions were applied to maintain the plants under appropriate and healthy conditions. Eight weeks after sowing, all trays were moved from the greenhouse to growth room at the Plant Pathology Research Institute, ARC, for artificial inoculation with P. infestans EG_12 and late blight assessment.

Inoculum preparation of isolate EG_12 was performed as described [15]. Prior to artificial inoculation, the suspension was chilled at 4°C for 2–4 h [45] to allow cleavage of sporangia and release of zoospores.

After inoculum preparation, the conditions in the growth room were adjusted to 20±2°C and 100% RH for 48 h in darkness, followed by 20°C, up to 90% RH [46], and 10/14 h day/night for 10 days. All tested plants were hand-sprayed with an atomizer to cover all parts of the foliage and kept in a growth room under the conditions described above. The plants were wrapped with a plastic sheet to keep RH above 90%. F2 plants were evaluated individually for late blight disease at 10 days post inoculation (dpi) by visually scoring disease severity according to a numerical rating (0–6) as described [47] with some modifications: 0, immune; 1, highly resistant; 2, resistant; 3, moderately resistant; 4, moderately susceptible; 5, susceptible; 6, highly (91–100%) susceptible. All inoculated plants were scored when the susceptible control exhibited 100% disease severity (complete death).

DNA extraction and sequencing analysis

Total genomic DNA was extracted from young leaves of the two parents and the F2 progeny using the DNeasy Plant Mini Kit (Qiagen Inc., Hilden, Germany). Genotypes were analyzed using ddRAD-Seq technology with the restriction enzymes PstI and MspI (S1 Table). The ddRAD-Seq libraries were constructed and sequenced on a HiSeq 2000 platform (Illumina, San Diego, CA, USA) in paired-end 93 bp mode as described [33].

The two parents were further subjected to WGRS. Paired-end sequencing libraries with an insert size of 600 bp were prepared as described [48]. The nucleotide sequences were determined using massively parallel sequencing by synthesis on an Illumina HiSeq2000 (Illumina) in paired-end 93 bp mode.

Computational data processing and association analysis

Primary data processing of ddRAD-Seq and WGS sequence reads was performed as described in our previous studies [33, 49] with some modifications. Low-quality sequences were removed and adapters were trimmed using PRINSEQ (version 0.20.4) [50] and fastx_clipper in the FASTX-Toolkit (version 0.0.13) (http://hannonlab.cshl.edu/fastx_toolkit). The filtered reads were mapped onto tomato genome SL3.0 [6], used as a reference sequence, with Bowtie 2 (version 2.1.0; parameters:—minins 100—no-mixed) [51]. The resultant sequence alignment/map format (SAM) files were converted to binary sequence alignment/map format (BAM) files and subjected to SNP calling using the mpileup option of SAMtools (version 0.1.19; parameters: default) [52] to yield a variant call format (VCF) file including SNP information. Moreover, to obtain high-confidence SNP markers, VCF files were filtered with VCFtools (version 0.1.14) [53]. The parameters for VCFtools were as follows:—maf 0.05—max-alleles 2—min-alleles 2—minDP 10—minQ 10—non-ref-ac 2—max-non-ref-ac 2—max-missing 0.75 for WGRS data; and—remove-indels—minDP 5—minQ 20—max-missing 1—min-alleles 2—max-alleles 2 for ddRAD-Seq data. Annotations of SNP effects on gene functions were predicted using SnpEff (version 4.2) [54]. The association analysis between phenotype and genotype data was performed using the generalized linear model (GLM) of trait analysis by association, evolution, and linkage (TASSEL) version 5.2.33 [55].

SSR marker analysis

A total of 13 expressed sequence tag (EST)-derived SSR markers (TES markers) and ten genome-derived SSR markers (TGS markers) (S2 Table) were selected from the candidate genome regions on chromosome 6 for late blight resistance, as described in the Kazusa Marker Database (http://marker.kazusa.or.jp) [29]. These markers were used for polymorphic analysis.

Data availability

Nucleotide sequence data for the ddRAD-Seq and WGRS analyses are available in the DDBJ Sequence Read Archive under accession numbers DRA005972 and DRA005973.

Results

Phenotypic assessment of disease response for the F2 population

To identify QTLs associated with late blight resistance, an F2 mapping population of 383 plants, as well as the susceptible and resistant parents, were infected with an Egyptian isolate of P. infestans EG_12, and disease severity was evaluated on a numerical scale (0–6). All tested materials were individually scored 10 days after artificial inoculation, when the susceptible control plants reached the highest score of disease severity. The evaluated population was divided into seven categories based on the scale. The F2 population exhibited broad variations in reaction to the pathogen, ranging from complete resistant (0) to highly susceptible (6). In addition, varying degrees of disease severity were detected in all tested plants. Among the F2 population, a disease severity score of 4 was most prevalent (79 plants, 22.97%), followed by score of 6 (76 plants, 22.09%). On the other hand, a score of 1 (highly resistant) was least prevalent (26 plants, 7.56%) (Fig 1). Also, the whole-plant assay under environmentally controlled conditions confirmed that the parent S. habrochaites accession LA1777 was resistant, whereas the cultivated tomato cv. Castlerock was highly susceptible, with severe late blight symptoms (completely blighted, 100%) (Fig 2). Therefore, the tomato wild accession LA1777 should be considered a genetic resource for identification of QTLs associated with late blight resistance.

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Fig 1. Disease severity rating 0–6 of F2 mapping population (n = 344) of the cross cv. Castlerock (S. lycopersicum) x S. habrochaites accession LA1777 to aggressive Egyptian isolate of P. infestans.

https://doi.org/10.1371/journal.pone.0189951.g001

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Fig 2. Screening the parental lines for resistance to P. infestans isolate EG_12 using whole-plant assay under controlled conditions.

(A) Highly susceptible parent cv. Castlerock, (B) highly resistant parent S. habrochaites accession LA1777.

https://doi.org/10.1371/journal.pone.0189951.g002

Association analysis with SNPs based on ddRAD-Seq

In the ddRAD-Seq analysis of the parental lines and a subset of the F2 population (n = 150), a mean of 616,763 reads was obtained for each sample. The total numbers of high-quality paired reads of the parental lines, cv. Castlerock and S. habrochaites accession LA1777, were 1,010,157 and 367,193, respectively (S3 Table). The read numbers obtained in this study is enough for the following linkage analysis [33]. The alignment rate to the reference tomato genome build SL3.0 was approximately 90.0% in the F2 population, whereas those of the two parents were 93.4% (Castlerock) and 88.99% (LA1777). From the alignment data, 11,348 SNP candidates were obtained, of which 6,514 were selected as a high-quality data set (S4 Table) based on criteria described in Materials and Methods. The mean number of SNPs per chromosome (excluding 17 SNPs on sequences unassigned to the tomato chromosomes) was 543, with a variant rate of one SNP every 123,921 bases, ranging from 354 SNPs on chromosome 9 (1 SNP/205,950 bp) to 780 on chromosome 2 (1 SNP/71,766 bp). The SNPs comprised 3,406 downstream gene variants following 3,374 intron variants and 2,371 upstream gene variants. The physical positions of the 6,514 SNPs were distributed over all 12 chromosomes (Fig 3 and S1 Fig), but the distribution patterns were highly biased: most of the SNPs were located at both ends of each chromosome, which are gene-rich euchromatic regions; an exception to this pattern is chromosome 2, which has repetitive rDNA sequences at the top of the chromosome.

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Fig 3. Representation of high-confidence single nucleotide polymorphism (SNP) markers along chromosome 6 of tomato mapped on SL3.0 version of the tomato reference genome.

Candidate genomic region tightly related to plant disease resistance was predicted on ch06 based on SnpEff annotation, (A) the double-digest restriction site–associated DNA sequencing (ddRAD-Seq), and (B) the whole-genome shotgun resequencing (WGRS) technologies. The remaining chromosomes ch00 –ch12 are shown in S1 Fig.

https://doi.org/10.1371/journal.pone.0189951.g003

To detect genetic loci for resistance to P. infestans isolate EG_12, GWAS were performed with 6,514 high-confidence SNPs from the ddRAD-Seq and phenotypic data. Based on GLM with false discovery rate (FDR) of 0.1 [56], 124 SNPs on a 6.8 Mb region of chromosome 6 (42,859,404 bp to 49,665,578 bp), including 665 predicted genes, were significantly associated with phenotypic variation. Among those, the SNP at 48,363,490 bp on chromosome 6 exhibited the highest association with late blight disease resistance (Fig 4).

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Fig 4. Manhattan plots for genome-wide association studies of generalized linear model (GLM) analysis of late blight disease resistance using TASSEL software.

The SNP markers were generated using NGS technology, double-digest restriction site–associated DNA sequencing (ddRAD-Seq).

https://doi.org/10.1371/journal.pone.0189951.g004

Validation of the associated loci by SSR marker analysis

To validate the results of the association studies, we subjected the remaining F2 lines (n = 194) not analyzed with the ddRAD-Seq to genotyping analysis with 23 SSR markers that were physically and genetically close to the candidate region (S2 Table). Out of the 23 SSRs, 5 markers (TES0422, TES0014, TES1344, TES0945, and TES0213) exhibited polymorphism between the parental lines, Castlerock and LA1777 (Table 1). Therefore, we analyzed the genotypes of the additional 194 lines, as well as the 150 lines used for ddRAD-Seq, using the five selected SSR markers. As expected, the phenotypes of F2 lines with homozygous alleles from LA1777 or Castlerock differed significantly (resistant in the case of LA1777 alleles, and susceptible in the case of Castlerock alleles), even though severe segregation distortion that is often reported in intercrossing populations [29] and references therein was observed in this locus. This additional SSR analysis confirmed the results of the GWAS using ddRAD-Seq technology.

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Table 1. Genotyping of F2 mapping population with five EST-SSR markers.

https://doi.org/10.1371/journal.pone.0189951.t001

Whole-genome shotgun resequencing

To identify sequence variations in the candidate genetic locus, we performed WGRS analysis on the parents. Totals of 174.9 and 189.9 million high-quality reads (17-18x genome coverage) for Castlerock and LA1777, respectively, were obtained and mapped onto the reference genome sequence, with alignment rates of 96.9% for Castlerock and 70.7% for LA1777 (S5 Table).

Across the genome including “chromosome 0”, genome sequences not assigned to any chromosomes, we identified a total of 4,180,666 high-quality sequence variations (one sequence variation every 198 bp), including 4,022,951 SNPs and 157,715 indels. The ratio of transitions/transversions (Ts/Tv) was calculated to be 1.08. The SNPs were positioned on all tomato chromosomes without large gaps (Fig 3 and S1 Fig), as observed for the genome positions of SNPs detected by ddRAD-Seq. Among the 4,180,666 sites, 14,755 (0.27%) sequence variations in 2,557 genes were predicted by the SnpEff software to possess high-impact (e.g., nonsense or frame-shift mutations) on gene functions, whereas 57,390 (1.038%) polymorphisms in 15,934 genes were predicted to have moderate impacts (e.g., missense mutations) (S6 Table).

On the other hand, in the 6.8 Mb candidate locus on chromosome 6, we identified 8,367 polymorphic sites (7,684 SNPs and 683 indels) at 1 variation/814 bases with a Ts/Tv ratio of 1.36. Among the 8,367 sites, 168 (0.87%) sequence variations in 24 genes were predicted to have high impacts, and 516 (2.67%) polymorphisms in 274 genes were predicted to have moderate impacts. In the candidate regions, the ratio of high-impact variations versus moderate-impact variations was 3-fold higher than in the genome overall, whereas variation density was lower. Among them, two genes located in the interval between the significant SNPs were considered as potential candidates for blight disease resistance genes. One was Solyc06g071810.1 encoding the leucine-rich repeat (LRR) receptor–like serine/threonine-protein kinase FEI 1 having a missense mutation at the 39th position (Asp in Castlerock, Glu in LA1777), while the other was Solyc06g083640.3 for a LRR family protein with a missense mutation at the 111th position (Gln in Castlerock and Lys in LA1777).

Discussion

In this study, we identified a resistance locus for late blight disease on chromosome 6 of tomato. This locus is at a different genome position than previously reported resistance loci [22, 57, 58], and should therefore be considered novel. The result of GWAS was validated by the SSR analysis of the additional F2 lines (Table 1). In general, to confirm the accuracy of the genetic analysis of GWAS and QTL analysis, the results are validated by genotyping with DNA markers in the candidate regions. Three types of plant materials are potentially used for the validation: 1) an additional biparental population derived from the same crossing in the genetic analysis (as in this study); 2) near-isogenic lines (NILs) having target loci of the donor (e.g., a wild relative) with genetic background of the recurrent line (e.g., a cultivated line); and 3) a group of genetically divergent lines like natural populations or core collections maintaining genetic diversity of genetic pools. Among them, NILs would be the most useful materials to investigate the effects of the candidate locus on the phenotypes, and to identify the genes controlling the phenotypes by a map-based cloning strategy. However, it would take a long time and labors to develop NILs because of recurrent backcrossings with marker-assisted selection. In Tomato Genetic Resource Center, University of California, Davis, series of NILs covering the entire genome of LA1777 in the background of S. lycopersicum E6203 have been registered [59]; however, NILs for chromosome 6 is not available at the time of writing unfortunately. On the other hand, although a group of genetically divergent lines could be useful for the validation, no resistance lines against P. infestans EG_12 have identified except for S. habrochaites LA1777 [15]. This meant that this approach might be not suitable for the case of this study.

It should be possible to breed new varieties with high disease resistance by combining the new locus with previously reported genes [19, 20]. Such a ‘gene pyramid’ strategy resulting in durable resistance could contribute to successful management of new populations of P. infestans, which are resistant not only to well-known R genes, but also to certified fungicides, e.g., metalaxyl [60, 61]. Because we have characterized many P. infestans isolates [15, 62], as well as tomato wild relatives highly resistant to these isolates [15], further novel resistance loci could be identified from these materials using an approach similar to the one employed in this study.

The genotyping analysis was completed in a short time by taking advantage of two NGS technologies, ddRAD-Seq and WGRS. In the former type of analysis, the number of detectable SNPs depends on genetic diversity (i.e., the so-called genetic distance) of the materials [32, 63, 64]. In this study, because the parental lines were genetically divergent, the number of obtained SNPs was 6,514. This result is consistent with a previous report in which 8,784 SNPs were obtained from an interspecific cross between different species [65]. In intercrossing, or crossing between closely related species, even though the number of SNPs obtained by ddRAD-Seq might be small [66], WGRS has the potential to overcome this issue [43]. Therefore, lab work is no longer a limiting factor in the discovery of new genetic loci.

ddRAD-Seq analysis and WGRS are powerful tools for gene mapping. Previously, it was common to employ SSR and SNP markers for such analysis [28, 29, 67]. However, because these methods are time-consuming and laborious, it used to be difficult to analyze multiple populations at once. Furthermore, even if genetic loci could be narrowed down to small genomic regions, subsequent sequencing of the target regions was necessary for identification of candidate genes of interest. By contrast, ddRAD-Seq analysis can be performed in parallel across multiple mapping populations. In addition, WGRS is the most effective and easiest method for identifying sequence variations in candidate regions. In this study, the alignment rate of the sequence reads to reference sequence was lower in LA1777 than in Castlerock, likely because LA1777 is a wild species belonging to the Eriopersicon subsection, which is distantly associated with cultivated lines such as Castlerock and Heinz 1706 [10].

The distribution patterns of SNPs over the genome was highly biased, with higher density at the distal ends of chromosomes and lower density in pericentromeric regions. This observation was consistent with some previous studies [6, 29, 66] but discordant with another [10]. On the other hand, the density of SNPs identified by the WGRS in this study (512.8 SNPs per 100 kb) was higher than that in a previous study using only cultivated lines (11.9–98.9 SNPs per 100 kb) [33], confirming that wild tomato relatives are genetically distant from cultivated tomato. Thus, it is possible for the WGRS technology to dissect target quantitative traits at nucleotide scale.

Furthermore, WGRS also makes it possible to predict the effects of sequence variations on gene function, which facilitates the identification of candidate genes. In this study, we identified three candidate R genes encoding a nucleotide-binding site leucine-rich repeat (NBS-LRR) protein, a mitogen-activated protein kinase kinase kinase (MAPKKK), and a receptor-like protein kinase (RLK); these gene families are involved in disease resistance and signaling pathways linked to plant innate immunity not only in tomato, but also in other plant species [6871]. Indeed, outside the candidate region, we identified moderate-impact SNPs in genes encoding serine/threonine-protein kinases. These genes play important roles in disease resistance and biological defense systems, inducing reactive oxygen species (ROS) bursts and stimulating MAP kinases, as demonstrated in Arabidopsis [72]. Thus, these genes might confer high disease resistance on LA1777. Furthermore, LA1777 possesses other R genes to many types of biotic stresses [73, 74], because it has not undergone the domestication process, which decreases the level of resistances [75]. The combination of ddRAD-Seq and WGRS could facilitate identification of genes of interest in LA1777. In addition to the genotyping methods, comparative genomics and transcriptomics in tomato and its relatives are useful methods in the post–genome sequencing era [6, 39, 41, 42].

The resolution of genetic mapping depends on the frequency of chromosome recombination in the population, which unfortunately remains uncontrollable. Therefore, even though ddRAD-Seq and WGRS are available, identification of target genes requires fine-mapping. Accordingly, we performed additional DNA marker analysis with SSRs and/or SNPs in the target regions. In the future, due to decreasing sequencing costs for NGS analysis, it will become feasible to perform WGRS across entire mapping populations, not only the parental lines, potentially making fine-mapping with SSR markers and SNPs unnecessary. Disruption of gene functions using genome-editing technologies is also an effective approach for elucidating the functions of genes responsible for target traits.

In conclusion, using the ddRAD-Seq and WGRS NGS technologies, we identified a new resistance locus for late blight disease caused by P. infestans. DNA markers linked to the locus could be used in MAS in future breeding programs aimed at increasing resistance to this disease. In addition, this approach provides a model for identifying not only additional R genes from tomato relatives and P. infestans isolates, which our group identified in a previous study [15, 62], but also other genes responsible for desirable agronomical traits. Furthermore, our results confirmed that, as previously reported [15, 58], S. habrochaites accession LA1777 represents a useful genetic resource for smart tomato breeding programs, genetics, and genomics studies.

Supporting information

S1 Fig. Physical positions of SNP markers across the tomato chromosomes (Chr00 –Chr12) except ch06 using R package.

The SNP markers were generated using the next-generation sequencing technologies and mapped on the reference genome of tomato SL3.0 version, (A) the double-digest restriction site–associated DNA sequencing (ddRAD-Seq), (B) the whole-genome shotgun resequencing (WGRS) approaches.

https://doi.org/10.1371/journal.pone.0189951.s001

(PDF)

S1 Table. Sequences of oligonucleotides used in ddRAD-Seq.

https://doi.org/10.1371/journal.pone.0189951.s002

(XLSX)

S2 Table. Information of TES and TGS-SSR markers used in the current study.

https://doi.org/10.1371/journal.pone.0189951.s003

(XLSX)

S3 Table. Number of paired-reads and alignment rate of ddRAD-Seq data for F2 population mapped onto the tomato reference genome SL3.0.

https://doi.org/10.1371/journal.pone.0189951.s004

(XLSX)

S4 Table. Distribution of the SNP markers on the 12 tomato chromosomes from WGRS and ddRAD-Seq analysis.

https://doi.org/10.1371/journal.pone.0189951.s005

(XLSX)

S5 Table. Number of paired-reads and alignment rate of cv. Castlerock and LA1777 generated from WGRS analysis mapped onto the tomato reference genome SL3.0 version.

https://doi.org/10.1371/journal.pone.0189951.s006

(XLSX)

S6 Table. Putative impact of SNPs on gene functions in the tomato genome of WGRS and candidate regions data.

https://doi.org/10.1371/journal.pone.0189951.s007

(XLSX)

Acknowledgments

We are grateful to S. Sasamoto, C. Mimani, and H. Tsuruoka at the Kazusa DNA Research Institute for their technical assistance. The authors would also like to thank the Tomato Genetic Resources Center, USA, for providing us with the tomato wild accession used in this study. Also, we want to thank prof. Elmahdy Metwally for his help for crossing and develop the F2 seeds.

References

  1. 1. Lukyanenko AN. Disease resistance in tomato. In: Kalloo G, editor. Genetic Improvement of Tomato. Berlin Heidelberg: Springer-Verlag; 1991. pp. 99–119.
  2. 2. Fry W. Phytophthora infestans: the plant (and R gene) destroyer. Mol Plant Pathol. 2008;9(3):385–402. pmid:18705878
  3. 3. FAO Statistical Databases [Internet]. FAOSTAT: Food and agriculture organization of the United Nations, Statistics Division—[cited 2017]. Available from: http://www.fao.org/faostat/en/#data/QC
  4. 4. Govers F. Late blight: the perspective from the pathogen. In: Haverkort AJ, Struik PC, editors. Potato in progress: Science meets practice. Wageningen Netherland: Academic Publishers; 2005. pp. 245–54.
  5. 5. Bernatzky R, Tanksley SD. Toward a saturated linkage map in tomato based on isozymes and random cDNA sequences. Genetics. 1986 Apr 1;112(4):887–98. pmid:17246322
  6. 6. The Tomato Genome Consortium. The tomato genome sequence provides insights into fleshy fruit evolution, Nature. 2012;485, 635–41. pmid:22660326
  7. 7. Shirasawa K, Fukuoka H, Matsunaga H, Kobayashi Y, Kobayashi I, Hirakawa H, et al. Genome-wide association studies using single nucleotide polymorphism markers developed by re-sequencing of the genomes of cultivated tomato. DNA Res. 2013a;20(6):593–603. pmid:23903436
  8. 8. Kobayashi M, Nagasaki H, Garcia V, Just D, Bres C, Mauxion JP, et al. Genome-wide analysis of intraspecific DNA polymorphism in ‘Micro-Tom’, a model cultivar of tomato (Solanum lycopersicum). Plant Cell Physiol. 2014;55(2):445–54. pmid:24319074
  9. 9. Celik I, Gurbuz N, Uncu AT, Frary A, Doganlar S. Genome-wide SNP discovery and QTL mapping for fruit quality traits in inbred backcross lines (IBLs) of solanum pimpinellifolium using genotyping by sequencing. BMC Genomics. 2017;18(1):1. pmid:28049423
  10. 10. Sahu KK, Chattopadhyay D. Genome-wide sequence variations between wild and cultivated tomato species revisited by whole genome sequence mapping. BMC Genomics. 2017;18(1):430. pmid:28576139
  11. 11. Miller JC, Tanksley SD. RFLP analysis of phylogenetic relationships and genetic variation in the genus Lycopersicon. TAG Theor Appl Genet. 1990;80(4):437–48. pmid:24221000
  12. 12. Firdaus S, van Heusden AW, Hidayati N, Supena ED, Visser RG, Vosman B. Resistance to Bemisia tabaci in tomato wild relatives. Euphytica. 2012;187(1):31–45.
  13. 13. Haggard JE, Johnson EB, Clair DA. Linkage relationships among multiple QTL for horticultural traits and late blight (P. infestans) resistance on chromosome 5 introgressed from wild tomato Solanum habrochaites. G3: Genes, Genomes, Genetics. 2013;3(12):2131–46.
  14. 14. Haggard JE, Johnson EB, Clair DA. Multiple QTL for horticultural traits and quantitative resistance to Phytophthora infestans linked on Solanum habrochaites chromosome 11. G3: Genes, Genomes, Genetics. 2015;5(2):219–33.
  15. 15. Arafa RA, Moussa OM, Soliman NE, Shirasawa K, Kamel SM, Rakha MT. Resistance to Phytophthora infestans in tomato wild relatives. Afr. J. Agric. Res. 2017;12(26):2188–96.
  16. 16. Gallegly ME, Marvel ME. Inheritance of resistance to tomato race 0 of Phytophthora infestans. Phytopathology. 1955;45:103–9.
  17. 17. Peirce LC. Linkage tests with Ph conditioning resistance to race 0, Phytophthora infestans. Rep. Tomato Genet. Coop. 1971;21:30.
  18. 18. Eshed Y, Zamir D. An introgression line population of Lycopersicon pennellii in the cultivated tomato enables the identification and fine mapping of yield-associated QTL. Genetics. 1995;141(3):1147–62. pmid:8582620
  19. 19. Kole C, Ashrafi H, Lin G, Foolad M. Identification and molecular mapping of a new R gene, Ph-4, conferring resistance to late blight in tomato. Proceedings of Solanaceae Conference; 2006; University of Wisconsin, Madison, Abstract 449.
  20. 20. Foolad MR, Merk HL, Ashrafi H. Genetics, genomics and breeding of late blight and early blight resistance in tomato. Crit Rev Plant Sci. 2008;27(2):75–107.
  21. 21. Robert VJ, West MA, Inai S, Caines A, Arntzen L, Smith JK, et al. Marker-assisted introgression of blackmold resistance QTL alleles from wild Lycopersicon cheesmanii to cultivated tomato (L. esculentum) and evaluation of QTL phenotypic effects. Mol Breed. 2001;8(3):217–33.
  22. 22. Brouwer DJ, Jones ES, Clair DA. QTL analysis of quantitative resistance to Phytophthora infestans (late blight) in tomato and comparisons with potato. Genome. 2004;47(3):475–92. pmid:15190365
  23. 23. Brouwer DJ, Clair DS. Fine mapping of three quantitative trait loci for late blight resistance in tomato using near isogenic lines (NILs) and sub-NILs. Theor Appl Genet. 2004;108(4):628–38. pmid:14586504
  24. 24. Ashrafi H, Kinkade M, Foolad MR. A new genetic linkage map of tomato based on a Solanum lycopersicum× S. pimpinellifolium RIL population displaying locations of candidate pathogen response genes. Genome. 2009;52(11):935–56. pmid:19935918
  25. 25. Shirasawa K, Hirakawa H. DNA marker applications to molecular genetics and genomics in tomato. Breed Sci. 2013b;63(1):21–30. pmid:23641178
  26. 26. Víquez-Zamora M, Vosman B, van de Geest H, Bovy A, Visser RG, Finkers R, et al. Tomato breeding in the genomics era: insights from a SNP array. BMC Genomics. 2013;14(1):354.
  27. 27. Jimenez-Gomez JM, Alonso-Blanco C, Borja A, Anastasio G, Angosto T, Lozano R, et al. Quantitative genetic analysis of flowering time in tomato. Genome. 2007;50(3):303–15. pmid:17502904
  28. 28. Ohyama A, Asamizu E, Negoro S, Miyatake K, Yamaguchi H, Tabata S, et al. Characterization of tomato SSR markers developed using BAC-end and cDNA sequences from genome databases. Mol Breed. 2009;23(4):685–91.
  29. 29. Shirasawa K, Asamizu E, Fukuoka H, Ohyama A, Sato S, Nakamura Y, et al. An interspecific linkage map of SSR and intronic polymorphism markers in tomato. Theor Appl Genet. 2010;121(4):731–9. pmid:20431859
  30. 30. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PloS One. 2008;3(10):e3376. pmid:18852878
  31. 31. Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH. Stacks: building and genotyping loci de novo from short-read sequences. G3: Genes, genomes, genetics. 2011;1(3):171–82.
  32. 32. Davey JW, Cezard T, Fuentes‐Utrilla P, Eland C, Gharbi K, Blaxter ML. Special features of RAD sequencing data: implications for genotyping. Mol Ecol. 2013;22(11):3151–64. pmid:23110438
  33. 33. Shirasawa K, Hirakawa H, Isobe S. Analytical workflow of double-digest restriction site-associated DNA sequencing based on empirical and in silico optimization in tomato. DNA Res. 2016a;23(2):145–53. pmid:26932983
  34. 34. Chutimanitsakun Y, Nipper RW, Cuesta-Marcos A, Cistué L, Corey A, Filichkina T, et al. Construction and application for QTL analysis of a restriction site associated DNA (RAD) linkage map in barley. BMC genomics. 2011;12(1):4.
  35. 35. Pfender WF, Saha MC, Johnson EA, Slabaugh MB. Mapping with RAD (restriction-site associated DNA) markers to rapidly identify QTL for stem rust resistance in Lolium perenne. Theor Appl Genet. 2011;122(8):1467–80. pmid:21344184
  36. 36. Truong HT, Ramos AM, Yalcin F, de Ruiter M, van der Poel HJ, Huvenaars KH, et al. Sequence-based genotyping for marker discovery and co-dominant scoring in germplasm and populations. PLoS One. 2012;7(5):e37565. pmid:22662172
  37. 37. Wang N, Fang L, Xin H, Wang L, Li S. Construction of a high-density genetic map for grape using next generation restriction-site associated DNA sequencing. BMC Plant Biol. 2012;12(1):148.
  38. 38. Etter PD, Bassham S, Hohenlohe PA, Johnson EA, Cresko WA. SNP discovery and genotyping for evolutionary genetics using RAD sequencing. In: Orgogozo V, Rockman MV, editors. Molecular Methods for Evolutionary Genetics, Methods in Molecular Biology. The Netherlands: Springer; 2011. pp. 157–78.
  39. 39. Bolger A, Scossa F, Bolger ME, Lanz C, Maumus F, Tohge T, et al. The genome of the stress-tolerant wild tomato species Solanum pennellii. Nature Genet. 2014;46(9):1034–8. pmid:25064008
  40. 40. Aflitos S, Schijlen E, Jong H, Ridder D, Smit S, Finkers R, et al. Exploring genetic variation in the tomato (Solanum section Lycopersicon) clade by whole‐genome sequencing. Plant J. 2014;80(1):136–48. pmid:25039268
  41. 41. Causse M, Desplat N, Pascual L, Le Paslier MC, Sauvage C, Bauchet G, et al. Whole genome resequencing in tomato reveals variation associated with introgression and breeding events. BMC Genomics. 2013;14(1):791.
  42. 42. Lin T, Zhu G, Zhang J, Xu X, Yu Q, Zheng Z, et al. Genomic analyses provide insights into the history of tomato breeding. Nature Genet. 2014;46(11):1220–6. pmid:25305757
  43. 43. Shirasawa K, Kuwata C, Watanabe M, Fukami M, Hirakawa H, Isobe S. Target amplicon sequencing for enotyping genome-wide single nucleotide polymorphisms identified by whole-genome resequencing in Peanut. Plant Genome. 2016b;9(3).
  44. 44. Caten CE, Jinks JL. Spontaneous variability of single isolates of Phytophthora infestans. I. Cultural variation. Can J Bot. 1968;46(4):329–48.
  45. 45. Ivanović M, Mijatović M, Zečević B, Niepold F. Occurrence of New Populations and Mating Types of Phytophthora infestans (Mont) de Bary in Serbia. Acta Hortic. 2004; 729: 499–502.
  46. 46. Dorrance AE, Inglis DA. Assessment of greenhouse and laboratory screening methods for evaluating potato foliage for resistance to late blight. Plant Dis. 1997;81(10):1206–13.
  47. 47. Chunwongse J, Chunwongse C, Black L, Hanson P. Molecular mapping of the Ph-3 gene for late blight resistance in tomato. J Hortic Sci Biotechnol. 2002;77(3):281–6.
  48. 48. Shirasawa K, Hirakawa H, Nunome T, Tabata S, Isobe S. Genome‐wide survey of artificial mutations induced by ethyl methanesulfonate and gamma rays in tomato. Plant Biotechnol J. 2016c;14(1):51–60. pmid:25689669
  49. 49. Shirasawa K, Tanaka M, Takahata Y, Ma D, Cao Q, Liu Q, et al. A high-density SNP genetic map consisting of a complete set of homologous groups in autohexaploid sweetpotato (Ipomoea batatas). Sci Rep. 2017;7.
  50. 50. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27(6):863–4. pmid:21278185
  51. 51. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9. pmid:22388286
  52. 52. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. pmid:19505943
  53. 53. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27(15):2156–8. pmid:21653522
  54. 54. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012;6(2):80–92. pmid:22728672
  55. 55. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23(19):2633–5. pmid:17586829
  56. 56. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995:289–300.
  57. 57. Smart CD, Tanksley SD, Mayton H, Fry WE. Resistance to Phytophthora infestans in Lycopersicon pennellii. Plant Dis. 2007;91(8):1045–9.
  58. 58. Li J, Liu L, Bai Y, Finkers R, Wang F, Du Y, et al. Identification and mapping of quantitative resistance to late blight (Phytophthora infestans) in Solanum habrochaites LA1777. Euphytica. 2011;179(3):427–38.
  59. 59. Monforte AJ, Tanksley SD. Development of a set of near isogenic and backcross recombinant inbred lines containing most of the Lycopersicon hirsutum genome in a L. esculentum genetic background: a tool for gene mapping and gene discovery. Genome 2000;43(5):803–813. pmid:11081970
  60. 60. Saville A, Graham K, Grünwald NJ, Myers K, Fry WE, Ristaino JB. Fungicide sensitivity of US genotypes of Phytophthora infestans to six oomycete-targeted compounds. Plant Dis. 2015;99(5):659–66.
  61. 61. Montes MS, Nielsen BJ, Schmidt SG, Bødker L, Kjøller R, Rosendahl S. Population genetics of Phytophthora infestans in Denmark reveals dominantly clonal populations and specific alleles linked to metalaxyl‐M resistance. Plant Pathol. 2016;65(5):744–53.
  62. 62. Arafa RA, Soliman NEK, Moussa OM, Kamel SM, Shirasawa K. Characterization of Egyptian Phytophthora infestans population using simple sequence repeat markers. J. Gen. Plant Pathol. Forthcoming.
  63. 63. Zhao Y, Gowda M, Liu W, Würschum T, Maurer HP, Longin FH, et al. Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet. 2012;124(4):769–76. pmid:22075809
  64. 64. Yamamoto E, Matsunaga H, Onogi A, Kajiya-Kanegae H, Minamikawa M, Suzuki A, et al. A simulation-based breeding design that uses whole-genome prediction in tomato. Sci. Rep. 2016;6:19454. pmid:26787426
  65. 65. Sim SC, Durstewitz G, Plieske J, Wieseke R, Ganal MW, Van Deynze A, et al. Development of a large SNP genotyping array and generation of high-density genetic maps in tomato. PLoS One. 2012;7(7):e40563. pmid:22802968
  66. 66. Chen AL, Liu CY, Chen CH, Wang JF, Liao YC, Chang CH, et al. Reassessment of QTLs for late blight resistance in the tomato accession L3708 using a restriction site associated DNA (RAD) linkage map and highly aggressive isolates of Phytophthora infestans. PloS One. 2014;9(5):e96417. pmid:24788810
  67. 67. Sim SC, Van Deynze A, Stoffel K, Douches DS, Zarka D, Ganal MW, et al. High-density SNP genotyping of tomato (Solanum lycopersicum L.) reveals patterns of genetic variation due to breeding. PloS One. 2012;7(9):e45520. pmid:23029069
  68. 68. Melech‐Bonfil S, Sessa G. Tomato MAPKKKε is a positive regulator of cell‐death signaling networks associated with plant immunity. Plant J. 2010;64(3):379–91. pmid:21049563
  69. 69. Mace E, Tai S, Innes D, Godwin I, Hu W, Campbell B, et al. The plasticity of NBS resistance genes in sorghum is driven by multiple evolutionary processes. BMC Plant Biol. 2014;14(1):253.
  70. 70. Devran Z, Kahveci E, Özkaynak E, Studholme DJ, Tör M. Development of molecular markers tightly linked to Pvr4 gene in pepper using next-generation sequencing. Mol Breed. 2015;35(4):101. pmid:25798050
  71. 71. Li Y, Ruperao P, Batley J, Edwards D, Davidson J, Hobson K, et al. Genome analysis identified novel candidate genes for ascochyta blight resistance in chickpea using whole genome re-sequencing data. Front Plant Sci. 2017;8.
  72. 72. Lin ZJ, Liebrand TW, Yadeta KA, Coaker GL. PBL13 is a serine/threonine protein kinase that negatively regulates Arabidopsis immune responses. Plant Physiol. 2015: 2950–62. pmid:26432875
  73. 73. Al Abdallat AM, Al Debei HS, Asmar H, Misbeh S, Quraan A, Kvarnheden A. An efficient in vitro-inoculation method for Tomato yellow leaf curl virus. Virol. J. 2010;7(1):84.
  74. 74. Momotaz A, Scott JW, Schuster DJ. Identification of quantitative trait loci conferring resistance to Bemisia tabaci in an F2 population of Solanum lycopersicum× Solanum habrochaites accession LA1777. J Am Soc Hortic Sci. 2010;135(2):134–42.
  75. 75. Bergougnoux V. The history of tomato: from domestication to biopharming. Biotechnol Adv. 2014;32(1):170–89. pmid:24211472