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Article

Genomic Analysis Highlights Putative Defective Susceptibility Genes in Tomato Germplasm

by
Ruiling Li
1,†,
Alex Maioli
1,†,
Sergio Lanteri
1,
Andrea Moglia
1,
Yuling Bai
2 and
Alberto Acquadro
1,*
1
Plant Genetics and Breeding, Department of Agricultural, Forest and Food Science (DISAFA), University of Torino, 10095 Grugliasco, Italy
2
Plant Breeding, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Plants 2023, 12(12), 2289; https://doi.org/10.3390/plants12122289
Submission received: 31 March 2023 / Revised: 16 May 2023 / Accepted: 8 June 2023 / Published: 12 June 2023
(This article belongs to the Special Issue Trends and Prospects of Genetic and Molecular Research in Plant)
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Abstract

:
Tomato (Solanum lycopersicum L.) is one of the most widely grown vegetables in the world and is impacted by many diseases which cause yield reduction or even crop failure. Breeding for disease resistance is thus a key objective in tomato improvement. Since disease arises from a compatible interaction between a plant and a pathogen, a mutation which alters a plant susceptibility (S) gene facilitating compatibility may induce broad-spectrum and durable plant resistance. Here, we report on a genome-wide analysis of a set of 360 tomato genotypes, with the goal of identifying defective S-gene alleles as a potential source for the breeding of resistance. A set of 125 gene homologs of 10 S-genes (PMR 4, PMR5, PMR6, MLO, BIK1, DMR1, DMR6, DND1, CPR5, and SR1) were analyzed. Their genomic sequences were examined and SNPs/indels were annotated using the SNPeff pipeline. A total of 54,000 SNPs/indels were identified, among which 1300 were estimated to have a moderate impact (non-synonymous variants), while 120 were estimated to have a high impact (e.g., missense/nonsense/frameshift variants). The latter were then analyzed for their effect on gene functionality. A total of 103 genotypes showed one high-impact mutation in at least one of the scouted genes, while in 10 genotypes, more than 4 high-impact mutations in as many genes were detected. A set of 10 SNPs were validated through Sanger sequencing. Three genotypes carrying high-impact homozygous SNPs in S-genes were infected with Oidium neolycopersici, and two highlighted a significantly reduced susceptibility to the fungus. The existing mutations fall within the scope of a history of safe use and can be useful to guide risk assessment in evaluating the effect of new genomic techniques.

1. Introduction

Tomato (Solanum lycopersicum L.) is one of the most widely grown vegetables in the world. The species is subjected to many diseases, which cause substantial economic losses. Breeding for disease resistance is thus a key objective in tomato improvement. Breeders’ efforts have been mainly focused on the introgression of resistance genes (R-genes) in elite genotypes, a strategy which is time consuming and often not durable [1], however, as most resistance genes confer race-specific resistance and are frequently overcome by a pathogen’s new virulent race.
Most pathogens require the cooperation of the host to establish a compatible interaction, which is mediated by host susceptibility (S) genes [2,3,4]. The identification of S-genes’ spontaneous mutants represents an emerging breeding strategy for durable and broad-spectrum resistance [5]. A mutated S-gene named Mildew Locus O (MLO) was found to induce resistance to Blumeria graminis f. sp. hordei in barley and has been used for over 70 years in breeding programs [6]. Many MLO orthologs have been identified in several monocots and eudicots species [7], including tomato, and it was found that mutations in MLO confer resistance to powdery mildew as demonstrated with various mutagenesis approaches including chemical mutagenesis, RNAi, and CRISPR-Cas9 [8,9,10,11,12]. Homoeoalleles in hexaploid bread wheat have also been modified using transcription activator-like effector nuclease (TALEN) and CRISPR-Cas9 to confer heritable resistance to powdery mildew [13]. Mutations in the PMR4 (powdery-mildew-resistant) gene have also been found to induce not only resistance to powdery mildew but also late blight [14]. Its CRISPR/Cas9-based disabling reduced susceptibility to both pathogens in tomato [15,16] and in potato to late blight as well as several diseases [17]. PMR5, a pectin acetyltransferase, and PMR6, a pectate-lyase-like gene, increased powdery mildew resistance in their Arabidopsis mutants, despite the mutants showing higher susceptibility to multiple strains of Botrytis cinerea [18,19,20]. In Arabidopsis, mutation of the BIK1 (Botrytis-induced kinase1) gene, which belongs to the family of RLCKs, has been found to play a role in defense against pathogens and insects acting specifically or redundantly in immune signaling [21], and it induced strong resistance to Plasmodiophora brassicae [22], although it also increased the susceptibility to green peach aphids [23].
The DND1 (Defense No Death 1) gene is a cyclic nucleotide-gated ion channel protein, and Arabidopsis mutants showed broad-spectrum resistance against several fungal, bacterial, and viral pathogens due to disturbance of the Ca2+/calmodulin-dependent signaling pathway [24]. In potato and tomato, RNAi silencing of DND1 orthologs led to resistance to late blight and to two powdery mildew species (Oidium neolycopersici and Golovinomyces orontii) [17,25].
Other genes have been observed to play important roles in regulating plant defense mechanisms against different pathogens in plants (DMR1, DMR6, CPR1-1, CPR5-2, and SR1). In Arabidopsis, the DMR1 gene (coding homoserine kinase) mediates susceptibility mechanisms occurring in both vegetative and reproductive plant tissues during infection by both obligate biotrophic oomycete and hemi-biotrophic fungal pathogens. Its mutation conferred enhanced resistance to Fusarium graminearum and F. culmorum, which cause ear blight disease in cereals [26]. Recently, the ortholog of AtDMR1 was efficiently disabled through the CRISPR/Cas9-mediated editing system in Ocimum basilicum, but no results on its effect on pathogen resistance were reported by Navet and Tian (2020) [27]. In Arabidopsis, the resistance to Cauliflower mosaic virus (CaMV) is regulated by the salicylic acid (SA) and jasmonic acid/ethylene (JA/ET) signaling pathways. Mutations in the constitutive expressor of PR genes (CPR1-1 and CPR5-2), involved in their biosynthetic pathway, resulted in constitutive activation of SA-dependent defense signaling and increased resistance to systemic infection of CaMV [28]. Furthermore, the cpr mutants, including cpr5, exhibited both EDS-1-dependent and -independent components of plant disease resistance [29]. The SR1 (signal responsive1) gene is a calmodulin-binding transcription factor, modulating plant defense. A gain-of-function mutation in SR1 by gene editing in Arabidopsis enhanced disease resistance to powdery mildew and regulated ET-induced senescence by directly regulating non-race-specific disease resistance1 (NDR1) and ethylene insensitive3 (EIN3) genes [30]. Similarly, in tobacco, sr1 mutants (ater1 to ater7) generated via T-DNA activation tagging were less susceptible to tobacco mosaic virus due to reduced microtubule dynamics [31]. One of the most intriguing S-genes is DOWNY MILDEW RESISTANCE 6 (DMR6), encoding for a 2-oxoglutarate (2OG)- and Fe(II)-dependent oxygenase, which has salicylic acid (SA) 5-hydroxylase activity and thus reduces the active SA pool [32]. Inactivation of DMR6 results in increased SA acid levels [33,34]. Tomato sldmr6-1 mutants, characterized by high accumulation of SA, showed enhanced resistance against evolutionarily distinct classes of pathogens: bacteria, oomycetes, and fungi [34].
The EFSA (European Food Safety Authority) has recently released scientific opinions on plants obtained through new genomic techniques, i.e., targeted mutagenesis based on gene editing, cisgenesis, and intragenesis, and elaborated criteria for the risk assessment of plants obtained through new genomic techniques [35]. EFSA proposed six main criteria to assess risk assessment [36] among which was the history of safe use. If familiarity and/or history of safe use can be demonstrated as a result of traditional usage and/or widespread cultivation, the donor plant and/or gene/allele and the associated trait can be subjected to a reduced risk assessment [37]. In other words, the risk assessment will consider both the probability for such an allele to be obtained by conventional breeding or to be already in place in the breeders’ gene pool. A genomic survey on the genetic diversity already present in the germplasm of a species can assist this step. The knowledge of existing defective alleles in the germplasm of a species can assist this risk assessment step and provide a resource for tomato genomic-assisted breeding programs as well as tailored gene editing approaches for resistance to biotic stresses.
Here, we analyzed in a set of 360 resequenced tomato genotypes, a set of 125 genes belonging to ten S-gene families (PMR4, PMR5, PMR6, MLO, BIK1, DMR1, DMR6, DND1, CPR5, and SR1), with the goal of evaluating the frequency of high-impact mutations and highlight potential sources of broad-spectrum and recessively inherited resistance. The identified mutations were screened to assess their likely impact on protein functionality. Genotypes carrying high-impact homozygous SNPs in S-genes were assayed for resistance to O. neolycopersici, of which two highlighted reduced susceptibility. Moreover, sgRNA sequences were designed for eight S-genes, and they were made available for the creation of optimal gene editing constructs.

2. Results and Discussion

In order to identify natural mutant alleles of tomato S-genes, we analyzed the genomic diversity of the cultivated tomato germplasm consisting of a set of 360 genotypes (Table S1). The data were divided into different datasets: (1) a collection of 168 big-fruited S. lycopersicum accessions (fruit weight = 111.33 ± 68.19) and 17 modern commercial hybrids (F1), altogether called BIG); (2) a collection of 53 S. pimpinellifolium accessions (fruit weight = 2.04 ± 0.85 g, called PIM); (3) a collection of 112 S. lycopersicum var. cerasiforme accessions (fruit weight = 13.29 ± 9.54, called CER). The whole collection of 360 genotypes was referred to as ALL. We selected 10 S-genes (Table S2), of which some are known to reduce susceptibility to pathogens when knocked out or knocked down [2]. The selected S-genes including PMR4, PMR5, PMR6, MLO, BIK1, DMR1, DMR6, DND1, CPR5, and SR1, which facilitate host compatibility by being involved in host recognition and penetration, negative regulation of host immunity, or pathogen proliferation. This work represents the first examination at a genomic level of S-genes and existing putative defective alleles in the Solanaceae family.
Initially, a blastP analysis was performed (Figure 1) to identify homologs from the 10 chosen genes. A total of 125 S-gene homologs were obtained and used for further analyses (Table 1). The genome sequences of 360 accessions [38] were analyzed (Table S1, genotypes) for SNP mining, and 11,620,517 SNPs/indels were detected across 34,725 tomato gene locations. The number of SNPs over 185 accessions (BIG) was 7,744,233 (67%). In the 125 gene member subset (Table 1), 54,000 SNPs/indels were observed using the SNPeff pipeline. Among these, 51,000 had no effect on protein function, with them being synonymous with SNPs or located in intergenic regions. A total of 1500 SNPs had a low impact, and 1300 had a moderate impact. A total of 119 high-impact SNPs were observed. The distribution of these SNPs was studied among the 10 S-genes (Figure 2).
Despite differences in the number and type of genes considered, recent analyses on the nucleotide diversity of S-genes in other species such as apple [39,40] and grape [41] have been conducted. The number and density of SNPs observed in grape (V. vinifera) was ~15 SNPs per Kb (1SNP every 66 bp), while in both wild species and hybrid/wild Vitis species, it was 18 SNPs per Kb (1 SNP every 55 bp) [41]; in apple (M. domestica), in Mlo-like genes, values of ~41 SNPs per Kb and 1SNP every 24 bp were observed [39]. These values were higher than the ones we obtained, i.e., 1 SNP every 1031 bp in the whole dataset and 1 SNP every 472 bp in tomato (BIG), reflecting the different genetic structures of the species, the homozygosity level, and their domestication history.
Our analyses (Table 1) showed that when both wild and cultivated tomato genotypes were considered, the number of SNPs and their density were higher (119 SNPs with a density of 1 SNP per gene). However, when only “big tomato” genotypes were considered, the number of SNPs and their density was halved (58 SNPs with a density of 0.5 SNPs per gene); this suggests that there is a specific reservoir of S-gene alleles in the wild tomato germplasm that can be used for breeding. Haplotype analysis of the 119 SNPs was conducted, revealing the presence of specific conserved haplotypes (Figure S1) that were clearly distinguishable from other haplotypes, providing useful information for breeders.
We analyzed the potential impact of 119 highly detrimental mutations, including frameshift-inducing mutations that result in major damage such as knock-out mutations. However, there are also many moderate-impact mutations (1326) that may lead to changes in protein conformation and function. Although we did not delve into these effects in detail, they are worth monitoring in order to gain a deeper understanding of altered S-genes. Among the 119 SNPs, 10 were validated in 10 genotypes readily available within the research group facilities (http://eurisco.ecpgr.org, accessed on 1 April 2022) through Sanger sequencing with a 90% validation rate (Table S3); indeed, some non-validated SNPs were mutations detected in a heterozygous condition or possessed the same allelic profile as the reference; the emergence of such heterozygous/reference-like SNPs during the validation step can be explained by the high genetic diversity existing within the analyzed germplasm set (Figure 3), as observed by Li et al. [15].
The number of SNPs in each family was related to their length, but the SNP density appeared higher in certain genes (Table 1, PMR 4, PMR5, PMR6, MLO, BIK1, and CPR5) and lower in others (DMR1, DND1, SR1, and DMR6). This difference might be due to the fact that some genes are single-copy genes or present in a nodal position (hub) within the cell regulation network, hardly supporting deleterious SNPs [42]. On the contrary, the presence of multiple genes in a gene family may mitigate the impact of deleterious mutations [43]. In specific cases, such as DMR1, a single-copy tomato gene exhibited a deleterious mutation (a gained stop codon) in homozygosity, but its potential impact on protein functionality was likely reduced, as the causative SNP was located in the last six codons of the gene (1129/1134) (Table S4). In some others (e.g., BIK1-like genes), many occurrences were observed since all the 51 serine-threonine kinases, belonging to the RLCK (clade VII) repertoire, were analyzed.
Based on the nucleotide diversity (Pi) analysis, we observed that bottleneck events appear to be present in some S-genes (Figure S2, BIK-Solyc01g028830 and Solyc05g007050; DMR1-Solyc04g008760; DND-Solyc02g088560; MLO-Solyc06g082820; PMR4-Solyc01g006350; and PMR6-Solyc06g071020) considering BIG varieties in relation to the other two groups (PIM and CER). In general, we found that the PIM group showed the highest diversity in S alleles, while the CER group exhibited a moderate level of diversity, and the BIG group showed the least diversity in accordance with the known tomato history of domestication. Indeed, in few cases the genetic variation is reduced in BIG and CER in a similar way while not in PIM (e.g., MLO-Solyc02g077570 and PMR4-Solyc01g006350); in others (e.g., BIK-Solyc06g005500; BIK-Solyc06g083500; and PMR6-Solyc06g071020), the genetic variation is reduced in all the three groups.

2.1. Homozygous SNPs/Indels

The number of genotypes with two SNPs was 174 (whole dataset) and 36 (BIG tomatoes), while those with three or more SNPs were 114 and 14 (Table 2, Figure 4), respectively. This high representation can be explained by the presence of multigene families such as BIK1-like which might present some degree of redundancy. While examining those high-impact mutations, the results revealed that certain mutations appeared frequently in the cultivated germplasm and were preserved across various genotypes, as displayed in Table S5. One example is BIK1 (Solyc05g024290, SNP in chr5:31013858), which could be maintained under selective pressure in clustering genotypes within the germplasm materials (Figure 3, e.g., Rowpac, M-82, Santa Chiara, Hunt101, Puno I, and E-6203). The genotypes carrying a high number of SNPs (three or more) were approximately a dozen (e.g., Panama, N 739, Rowpac, Micro-Tom, Guayaquil, Droplet, M-82, Hawaii 7998, and KR2), and information about these SNPs is provided in Table S4. Certain mutations, such as BIK1-like/Solyc01g008860 and DMR1-like/Solyc04g008760 in specific genotypes (e.g., N-739/TS-074), appeared to be of lower relevance as they were present in the final percentile of the sequence length (Table S4).

2.2. Heterozygous SNPs/Indels

The incidence of deleterious SNPs in S-genes in a heterozygous condition was comparatively lesser than that of homozygous ones, as observed in both the complete germplasm collection (ALL) and the BIG tomato varieties (Table 2, Figure 4). This frequency may be due to the genetic structure of tomato as an inbred species, which tends to have a low number of heterozygous mutations [15]. However, the number appears relatively high because such mutations, although harmful, can be maintained in the genome if the normal allelic copy continues to function. This high frequency is particularly noticeable in the case of multiple member S-genes (e.g., BIK1-like genes) which may exhibit some redundancy and have no effects, or due to the position of the SNP within the gene (e.g., DMR1/Solyc04g008760 in TS-113 and BIK1-like/Solyc01g008860 in Chiclayo, Table S5). If two SNPs are considered, the number of genotypes was 89 (ALL) and 10 (BIG), while if three SNPs are considered, the number of genotypes decreases to 54 (ALL) and 3 (BIG). Some heterozygous mutants for S-genes were also identified, which have a 50% chance of acquiring resistance through natural mutagenic effects.

2.3. sgRNA Design

Introgression of S-genes’ alleles through breeding into elite varieties is possible, but it is a long and labor-intensive process and has limitations due to linkage drag. To address this issue, in analogy with the work from Prajapati and Nain [44], sgRNA sequences were designed for eight of the proposed S-genes (Table S6) and made available to a wider audience through the creation of optimal gene editing constructs. In total, 113 sgRNAs were designed, considering only the highly specific categories (A0, B0, A0.1, and B0.1) for CRISPR-Cas9-mediated genome editing to minimize off-target events. Specifically, 39 A0, 20 A0.1, 48 B0, and 6 B0.1 sgRNAs were designed. Each gene was equipped with at least one useful sgRNA, with PMR4, PMR5, PMR6, MLO1, and BIK1 having the most sgRNAs at 13, 15, 20, 8, and 50, respectively.

2.4. Disease Assay

As a preliminary assay, five genotypes, readily available within the research group facilities (http://eurisco.ecpgr.org, accessed on 1 April 2022), were selected for a disease assay to assess their resistance to O. neolycopersici (On). They included three varieties (PunoI/TS-108, Droplet/TS-296, and M82/TS-003) with deleterious SNPs and two varieties with no deleterious SNPs in the S-genes (VF-36/TS-01 and Moneymaker/TS-02). M-82 carried three mutated genes (BIK1-like Solyc05g024290 and Solyc04g050970, and PMR4-like/Solyc01g073750), which introduced a stop codon and produced truncated proteins. Puno-I carried two mutated genes (BIK1/Solyc05g024290 and PMR4/Solyc01g073750) in the middle of the gene, resulting in truncated proteins. Droplet had four high-impact mutations, including one in the BIK1-like gene (Solyc04g050970), two in the Mlo1-like gene (Solyc02g077570), and one in the PMR4-like gene (Solyc01g073750). These varieties showed sequences that predicted the presence of truncated susceptibility proteins in a homozygous state. To assess whether these selected varieties with deleterious SNPs in S-genes had higher resistance to powdery mildew, we inoculated all of them with O. neolycopersici and evaluated the disease index (Table 3, Table S7 and Figure S3). Two of them (Puno1 and M-82) showed reduced susceptibility to O. neolycopersici based on visual scoring of disease symptoms, while no significant differences in the disease index were observed in the others. The reason for this incomplete resistance may lie in the genes under consideration (BIK1-like: Solyc05g024290 and Solyc04g050970). The RLCK family encodes for a series (~50) of serine/threonine protein kinases with a role in post-translational regulation through, in the case of BIK-1, the phosphorylation of FLS2 and BAK1 [45,46]. The latter gene is involved in pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) signaling, including calcium signaling and defense responses downstream of FLS2. With the RLCK subfamily VII being a large clade (46 members in Arabidopsis; 51 in the present work), whose members play a role both specifically or redundantly in immune signaling, some BIK1-like genes could have a vicarious role in case of the emergence of mutant forms (e.g., Solyc04g050970 (49.186.199 bp, chromosome 4) in M82 and Solyc05g024290 (31.013.858 bp, chromosome 5) in the PunoI and M82 genotypes. Moreover, the Mlo1-like (Solyc02g077570) and PMR4-like (Solyc01g073750) genes were found to differ from the SlMlo1 and PMR4 genes (Table 1), which were previously known to provide complete resistance in the presence of a loss-of-function allele. Our research was an extensive genomic study incorporating a small pilot study on the impact of mutations on pathogenesis. We carried out pathogenesis assays using plant material readily available at our academic institutions. However, restrictions imposed by the recent Nagoya protocol on plant material transfer and difficulties in obtaining material for phytosanitary reasons limited our scope. We propose further research on accessions such as Panama, N739, and Rowpac (which have 6, 5, and 5 homozygous deleterious SNPs respectively)—a poorly characterized plant material that deserves further investigation. These materials should also be analyzed using different fungal pathogens (Phytophthora infestans, Botrytis, etc.) or bacteria (Pseudomonas syringeae).

3. Materials and Methods

3.1. Data Mining on S-Genes

A preliminary blastP (https://ftp.ncbi.nlm.nih.gov/blast, accessed on 1 March 2022) analysis allowed us to identify the possible orthologs for susceptibility genes, using information from different plant species (from Schie and Takken [2]; Table S1) and by considering as a preferential choice criterion the e-value (range 0–1 × 10−10) and the percentage of similarity and the query coverage. Since many genes were present in multigene families, the filtering criteria varied, and previous functional annotations were used to filter out non appropriate candidates.

3.2. SNP/Indel Data

The genotypic data discussed in Lin et al.’s study [38] were retrieved from SGN (ftp://ftp.solgenomics.net/genomes/tomato_360, accessed on 1 April 2022) as raw vcf files. The data derived from 360 genotypes (Table S1) were divided into different datasets: (1) a collection of 168 big-fruited S. lycopersicum accessions and 17 modern commercial hybrids (F1), altogether called BIG); (2) a collection of 53 S. pimpinellifolium accessions (called PIM); (3) a collection of 112 S. lycopersicum var. cerasiforme accessions (called CER). the whole collection of 360 genotypes was referred to as ALL. A principal component analysis (PCA) was conducted using the R-based ClustVis suite (https://biit.cs.ut.ee/clustvis, accessed on 1 June 2022). The dataset used for PCA was the whole dataset pruned and filtered using vcftools (https://vcftools.github.io, accessed on 1 June 2022), using the option --max-missing = 0.2, for filtering loci. The genetic variation of the S alleles was analyzed using the nucleotide diversity (Pi) index implemented in vcftools (https://vcftools.github.io, accessed on 1 June 2022) among the different groups (PIM, CER, and BIG). We focused on a 100 kb region, centered around each deleterious SNP with a 5 k window.

3.3. SNP Annotation

The SNP data were newly annotated using the v2.5 assembly with ITAG2.4 information. The SnpEff v5.0 program was adopted to infer functional annotation of any SNPs/indels and any potential deleterious effect on protein structure [47]. The effect of each SNP/indel was classified into four of classes of effects: (1) high effect, as variants changing the frameshift, thereby introducing/eliminating stop codons or modifying splice sites; (2) moderate effect, as variants altering the aminoacidic sequence; (3) low effect, as synonymous variants in coding regions; and (4) modifier effect, as variants located outside the coding sequence (non-transcribed regions or introns). Annotated vcf files from each individual were merged into a single file to integrate the entirety of the information. Bedtools intersect (https://github.com/arq5x/bedtools2, accessed on 1 June 2022) was used to screen for overlaps between the genomic features related to the S-genes (in gff format), and the SNP positions emerged from the SnpEff analysis; genomic coordinates were lifted over from SL2.50 to SL5.0 [48]. Functionally annotated SNPs from both the BIG and ALL dataset were inspected for different categories (high, moderate, and low impact) and were considered and counted for each accession, through custom bash scripts. Conserved deleterious SNPs were utilized as informative markers for generating haplotypes of SNPs, and the resulting haplotype information was analyzed around the S-genes using the software tool Tassel. All the categories were decomposed into homozygous and heterozygous SNPs/indels. A subset of SNPs was validated through Sanger sequencing (BMR Genomics Service, Padova, Italy) of the PCR-amplified gene fragments using the primers listed in Table S3.

3.4. Single Guide RNA (sgRNA) Design on Target Genes

The CRISPR-PLANT v2 platform (http://omap.org/crispr2/CRISPRsearch.html, accessed on 1 July 2022) was used to design sgRNAs in S-genes using the gene code as a query for the scan of the SL2.5 genome. We selected sgRNAs only present in exons, discarding the ones with a high possibility to give off-targets. Then, the rest of the sgRNAs were selected using their quality, based on the mismatch score in their seed sequence. The sgRNAs were divided by the CRISPR-PLANT software into different quality classes (A0, B0, A0.1, B0.1, A1, B1, A2, and B2), with A0 being the most specific and B2 being the least specific. The sgRNA sequence of each selected S-gene and the relative quality is reported in Table S4; only the A0, A0.1, B0, and B0.1 classes were reported in the output as highly specific sgRNAs for CRISPR-Cas9 mediated genome editing.

3.5. Disease Assay

Thirty seeds of selected accession, three with mutations (M-82, Puno-I, and Droplet) and two controls (VF-36 and MoneyMaker) were sowed and then inoculated with the Wageningen University isolate of O. neolycopersici (On) by spraying 4-week-old plants with a suspension of conidiospores obtained from the leaves of infected tomato Moneymaker plants and adjusted to a concentration of 3.5 × 104 spores per ml. The Moneymaker variety was used as the susceptible control. The inoculated plants were grown at 20  ±  2  °C with 70  ±  15% relative humidity and a day length of 16 h in a greenhouse of Unifarm of Wageningen University & Research, the Netherlands, and placed randomly within the greenhouse. Disease index scoring was carried out 10 and 12 days after inoculation. Symptoms were scored visually using a scale from 0 to 3 as described by Huibers et al. [14]. Statistical differences between each variety and the control were analyzed using a two-tailed t-test (* p < 0.05).

4. Conclusions

In this study, we conducted a comprehensive genomic survey of various tomato genotypes to identify putative defective alleles of susceptibility genes. Our analysis revealed the presence of natural homozygous/heterozygous deleterious alleles, which we further validated through Sanger sequencing. Interestingly, we observed that certain genotypes carrying high-impact homozygous SNPs in S-genes exhibited significantly reduced susceptibility to O. neolycopersici. These findings offer valuable insights for plant genetics and have the potential to enhance genomic-assisted breeding programs focused on developing resistance to biotic stresses. Nonetheless, it is important to acknowledge that incorporating desirable alleles into elite genotypes can be a time-consuming process with challenges such as linkage drag. To address this, we have also explored the application of a gene editing approach using single guide RNA (sgRNA) design. This alternative method shows promise in disabling targeted genes, presenting a powerful means to obtain elite tomato genotypes resistant to biotic stresses. Furthermore, our genomic survey can contribute to the evaluation and risk assessment of new genomic techniques by tracking existing alleles in the context of their “History of Safe Use” [36].
This study underscores the significance of publicly available data in enabling further analyses and, more importantly, highlights the wealth of potentially beneficial alleles already present in the existing tomato breeding pool. If proven to reduce disease susceptibility, these genes could serve as long-lasting sources of tolerance against various pathogens.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants12122289/s1. Table S1: List of accessions; Table S2: List of S-genes; Table S3: List of primers and SNP validation; Table S4: SNP raw data (vcf); Table S5: Numbers of SNPs in S-genes; Table S6: List of designed sgRNAs; Table S7: Disease index data; Figure S1: S-gene haplotypes; Figure S2: Nucleotide diversity analysis (Pi) of S-genes around deleterious SNPs in the PIM, CER, and BIG groups; Figure S3: Pathogen assay performed with Oidium neolycopersici.

Author Contributions

Conceptualization, S.L., Y.B. and A.A.; methodology, A.A. and A.M. (Alex Maioli); formal analysis, R.L. and A.M. (Alex Maioli); investigation, R.L., A.M. (Alex Maioli), A.M. (Andrea Moglia) and A.A.; resources, A.A. and Y.B.; data curation, A.A., A.M. (Andrea Moglia) and A.M. (Alex Maioli); writing—original draft preparation, R.L., A.M. (Alex Maioli) and A.A.; writing—review and editing, S.L., Y.B. and A.M. (Andrea Moglia); visualization, A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the ‘Cassa di Risparmio di Cuneo’ (CRC) Foundation under the research project Pathogen Resistance introduction in commercially important hOrticultural Species in PiEdmonT (PROSPEcT, www.crispr-plants.unito.it, accessed on 16 May 2023). This research was supported by the China Scholarship Council (CSC, NO. 201909370091).

Data Availability Statement

The sequencing data used in this study are openly available in the NCBI database (SRA/SRP045767).

Acknowledgments

We thank the G2PSOL project (www.g2p-sol.eu, accessed on 16 May 2023) and Paola Ferrante (ENEA) for providing the tomato DNA for SNP validation.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Plants 12 02289 g001 550
Figure 1. Flowchart of the high-impact SNP mining process within the available sequenced tomato germplasm (the data were originally retrieved from Lin et al. 2014 [38]).
Figure 1. Flowchart of the high-impact SNP mining process within the available sequenced tomato germplasm (the data were originally retrieved from Lin et al. 2014 [38]).
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Plants 12 02289 g002 550
Figure 2. (a) Distribution of high-impact SNPs in the S-gene families (N° SNPs); (b) relative SNP density (N° SNPs/gene).
Figure 2. (a) Distribution of high-impact SNPs in the S-gene families (N° SNPs); (b) relative SNP density (N° SNPs/gene).
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Plants 12 02289 g003 550
Figure 3. Genotypes accumulating multiple mutations in S-genes. In light blue are the reported genotypes with five or more SNPs, in green are the genotypes with four SNPs, in gray are the genotypes with three SNPs, in red are the genotypes with two SNPs, and in black are the rest of the genotypes (0–1 SNPs).
Figure 3. Genotypes accumulating multiple mutations in S-genes. In light blue are the reported genotypes with five or more SNPs, in green are the genotypes with four SNPs, in gray are the genotypes with three SNPs, in red are the genotypes with two SNPs, and in black are the rest of the genotypes (0–1 SNPs).
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Figure 4. Genotypes accumulating mutations in S-genes in (a) homozygous and (b) heterozygous states.
Figure 4. Genotypes accumulating mutations in S-genes in (a) homozygous and (b) heterozygous states.
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Table
Table 1. Statistics on SNPs/indels within S-genes related to the 360 panel. The numbers are always formed by two values X/Y, where X is the number of SNPs observed in the 360 panel and Y is the number of SNPs observed in the tomato panel. BIG = 168 S. lycopersicum + 17 F1 hybrid genotypes; ALL = 168 S. lycopersicum + 17 F1 hybrid genotypes + 53 S. pi + 112 S. cerasiforme + 10 wild tomatoes.
Table 1. Statistics on SNPs/indels within S-genes related to the 360 panel. The numbers are always formed by two values X/Y, where X is the number of SNPs observed in the 360 panel and Y is the number of SNPs observed in the tomato panel. BIG = 168 S. lycopersicum + 17 F1 hybrid genotypes; ALL = 168 S. lycopersicum + 17 F1 hybrid genotypes + 53 S. pi + 112 S. cerasiforme + 10 wild tomatoes.
S-Gene FamilyTomato OrthologGenesHigh
Impact		High Impact (SNP/Gene)Moderate ImpactLow ImpactN° Variants
(Total)		Total SNP/Gene
BIGALLBIGALLBIGALLBIGALLBIGALLBIGALL
PMR4Solyc07g05398098120.91.39519916628824734033274.8448.1
PMR5Solyc06g082070225190.20.917227415125733415267151.9239.4
PMR6Solyc11g0081402217230.81.0104188120187806512,989366.6590.4
DMR1Solyc04g0087601111.01.066612147215147.0215.0
DMR6Solyc03g0801902130.51.5719719434775217.0387.5
DND1Solyc02g0885603220.70.716381846410806136.7268.7
MLO1Solyc04g049090136160.51.2671206012153097787408.4599.0
CPR5Solyc04g0541701000.00.02669653873653.0873.0
BIK1Solyc10g0847705118410.40.823745227250012,78921,376250.8419.1
SR1Solyc01g1052301020.02.09244158925789.0257.0
Total-12558119--7151326810145433,71054,378--
Average-136120.51.0721338114533715438269.5429.7
Table
Table 2. Detailed statistics on the allelic richness of the tomato genotypes (BIG, from Lin et al. 2014 [38]) considering the high-impact SNPs in the whole gene dataset and in the selected S-genes.
Table 2. Detailed statistics on the allelic richness of the tomato genotypes (BIG, from Lin et al. 2014 [38]) considering the high-impact SNPs in the whole gene dataset and in the selected S-genes.
High-Impact SNPsHigh-Impact SNPs in S-Genes
GenotypeNameTGRC/PI-CGN/EACategoriesTotalHom.Heteroz.TotalHom.Heteroz.
TS-214Panama-/-/-Landrace62056951761
TS-074N 739-/-/-Fresh market64758760550
TS-186RowpacLA3214/-/-Modern processing44542322550
TS-007Micro-TomLA3911/-/-Modern fresh market901724177440
TS-224GuayaquilLA0410/PI 258474/-Landrace/Latin American cultivar77976712440
TS-296Droplet-/-/--71966851440
TS-409--/PI124161/-Landrace15261263263440
TS-003M-82LA3475/-/-Modern processing51542491330
TS-004Hawaii 7998LA3856/-/-Inbreed line69260686330
TS-011KR2-/-/-Modern fresh market565392173532
TS-135Hacienda RosarioLA0466/PI 258469/-Landrace/Latin American cultivar33430133330
TS-150TarapotoLA2285/-/-Landrace/Latin American cultivar35232626330
TS-190Santa Chiara-/-/-Cultivar43736671330
TS-277Hunt100LA3144/-/-Modern processing26623630330
TS-005EdkawiLA2711/-/-Vintage fresh market19111675321
TS-012yoku improvement-/-/-Modern fresh market505400105422
TS-078--/-/EA02895Processing tomato30027327220
TS-089--/-/EA01185Processing tomato45737186321
TS-090--/-/EA02753Cocktail tomato36828682220
TS-108Puno I-/-/EA01989Processing tomato33431222220
TS-121NC EBR-6LA3846/-/-Modern fresh market26722542220
TS-122RutgersLA1090/-/-Vintage fresh market705812220
TS-127Hacienda CaleraLA0113/-/-Landrace/Latin American cultivar1589886703321
TS-143Florida 7547LA4025/-/-Modern fresh market18216319220
TS-147--/-/--48240478220
TS-171UC-82LA1706/-/-Modern processing33430529321
TS-204Florida 7060LA3840/-/-Modern fresh market24720245220
TS-220Barnaulski Konservnyi-/-/-Cultivar53545580220
TS-225--/PI330336/EA05747Processing tomato17210864321
TS-226Microtom-/-/-Cultivar43640036321
TS-228M-82-/-/-Cultivar39836929220
TS-234--/-/EA01371Processing tomato23421915220
TS-237PlatenseLA3243/-/-Vintage fresh market19014545220
TS-245--/-/EA03126Processing tomato31424866422
TS-276--/-/EA03650Cocktail/processing tomato16012436321
TS-292--/-/EA06902Processing tomato29827820220
TS-002MoneymakerLA2706/-/-Vintage fresh market20715156211
TS-008E-6203LA4024/-/-Modern processing38030278413
TS-009Ailsa CraigLA2838A/-/-Vintage fresh market18212854211
TS-041--/-/EA02435Cocktail tomato26221844110
TS-043Moneymaker-/-/EA00840Fresh market16613036110
TS-045--/PI303718/EA05578Processing tomato19817622110
TS-047--/-/EA01960Processing tomato14412519110
TS-049EarlianaLA3238/-/-Vintage processing14913910110
TS-051--/-/--12710027110
TS-05205-4126 (97-49-2)-/-/-Cultivar32828147211
TS-055--/-/EA00448-17611759110
TS-058--/-/EA03577Processing tomato13111912110
TS-059--/-/EA02898Processing tomato690516174110
TS-068ChiclayoLA0395/-/-Latin American cultivar16401851455918
TS-069HuachinangoLA1459/-/-Latin American cultivar24723116110
TS-073Quarantino-/-/--12610521110
TS-076--/-/EA01230Processing tomato15612927110
TS-081--/-/EA02761Processing tomato18215527110
TS-085--/-/--474237237312
TS-086--/-/EA01684-13911821110
TS-095Moneymaker-/-/-Fresh market17614729211
TS-100--/-/EA03456Processing13411717110
TS-112--/-/EA03083Processing tomato17514827110
TS-115--/-/EA03426Processing tomato24322221110
TS-117Scatolone di bolsena-/-/-Landrace214104110110
TS-125--/-/EA00422Processing tomato241137104211
TS-128PearsonLA0012/-/-Vintage processing24521431110
TS-132PrimabelLA3903/-/-Vintage fresh market13611620110
TS-133Peto95-43LA3528/-/-Modern processing30726443110
TS-137Spagnoletta-/-/-Landrace305136169110
TS-142Roma-/-/-Vintage cultivar13612214211
TS-151T-5LA2399/-/-Modern fresh market62552996211
TS-152Santa Cruz BLA1021/-/-Landrace/Latin American cultivar17716017110
TS-155Condine RedLA0533/-/-Vintage fresh market, monogenic13011911110
TS-157--/-/EA03648Processing tomato12110417110
TS-160--/-/EA03533Processing tomato22118536110
TS-163MarmandeLA1504/-/-Vintage fresh market12911415110
TS-166PiuraLA0404/-/-Landrace/Latin American cultivar17816315211
TS-167TegucigalpaLA0147/-/-Landrace/Latin American cultivar15813523110
TS-168--/-/-Landrace33725681110
TS-174--/-/EA00304Processing tomato21219121110
TS-176--/-/EA02669Processing tomato1971907110
TS-177--/-/EA01155Processing tomato12710819110
TS-180--/-/EA02728Processing tomato1168234110
TS-183--/-/EA02764Processing tomato15413321110
TS-184TarapotoLA2283/-/--338225113211
TS-193Pantano dArdea-/-/-Landrace17012149110
TS-194--/-/--16714324110
TS-197Libanese-/-/-Landrace16512243110
TS-198--/-/EA00512-15312924110
TS-200Hot setLA3320/-/-Cultivar18713552110
TS-203Bell pepper-like-/-/-Landrace17711067110
TS-206Prince BorgheseLA0089/-/-Vintage fresh market26224110
TS-211NC 84173LA4354/-/-Modern fresh market42536659110
TS-215Vrbikanske Nizke-/-/-Cultivar18312657211
TS-235--/-/EA00892Processing tomato46442110
TS-239NC EBR-5LA3845/-/-Modern fresh market12610917211
TS-242AyacuchoLA0134C/-/-Latin American cultivar530332198110
TS-251--/PI647249/EA04001-15012822110
TS-256-LA2260/0/EA00744Latin American cultivar47741562110
TS-261-LA1511/-/EA01444Wild species246145101211
TS-263Rio GrandeLA3343/-/-Processing tomato21318330110
TS-264King Humbert #1LA0025/-/-Vintage fresh market13411915110
TS-268--/-/EA01915Cultivar14713017110
TS-274--/-/EA03613Cocktail/processing tomato26624125110
TS-278Early Santa ClaraLA0517/-/-Vintage processing20718720110
TS-400--/-/-Inbred line45339855110
Table
Table 3. Disease assay with O. neolycopersici performed on four varieties and a control variety (Moneymaker). The disease score (DS) values reported here were compared with the ones derived from the controls. Statistical differences among the varieties/control were analyzed with a two-tailed t test (p < 0.05).
Table 3. Disease assay with O. neolycopersici performed on four varieties and a control variety (Moneymaker). The disease score (DS) values reported here were compared with the ones derived from the controls. Statistical differences among the varieties/control were analyzed with a two-tailed t test (p < 0.05).
VarietyCodeTypeDS (0–3)Std. Errornp-ValueReduction (%)Class
VF-36TS-1control3.00020--a
Money MakerTS-2assayed2.960.03280.3261891.2%a
DropletTS-296assayed2.870.09150.1643184.4%a
M-82TS-003assayed2.420.14330.00036719.2%b
Puno-ITS-108assayed2.670.11210.00490011.1%b
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Li, R.; Maioli, A.; Lanteri, S.; Moglia, A.; Bai, Y.; Acquadro, A. Genomic Analysis Highlights Putative Defective Susceptibility Genes in Tomato Germplasm. Plants 2023, 12, 2289. https://doi.org/10.3390/plants12122289

AMA Style

Li R, Maioli A, Lanteri S, Moglia A, Bai Y, Acquadro A. Genomic Analysis Highlights Putative Defective Susceptibility Genes in Tomato Germplasm. Plants. 2023; 12(12):2289. https://doi.org/10.3390/plants12122289

Chicago/Turabian Style

Li, Ruiling, Alex Maioli, Sergio Lanteri, Andrea Moglia, Yuling Bai, and Alberto Acquadro. 2023. "Genomic Analysis Highlights Putative Defective Susceptibility Genes in Tomato Germplasm" Plants 12, no. 12: 2289. https://doi.org/10.3390/plants12122289

APA Style

Li, R., Maioli, A., Lanteri, S., Moglia, A., Bai, Y., & Acquadro, A. (2023). Genomic Analysis Highlights Putative Defective Susceptibility Genes in Tomato Germplasm. Plants, 12(12), 2289. https://doi.org/10.3390/plants12122289

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