Abstract
Zebra-stem of tomato is a disorder characterized by leaf necrosis, wilting, and a stripped pattern on stems of mature plants. Wilting, necrosis, and death of seedlings are also observed. The physiological and genetic causes of zebra-stem are poorly characterized. Anecdotal evidence has suggested pedigrees with S. pimpinellifolium and bacterial speck resistance in the genetic background are often prone to this disorder. We demonstrate a genetic cause using composite interval mapping and association analysis approaches to define quantitative trait loci (QTLs) that contribute to the disorder. A biparental population of F4 partially inbred families was developed for the initial analysis, and four subsequent backcross or F2 populations were used for subsequent validation. Significant QTLs on chromosomes 5 and 10 were identified, explaining ∼60% and 40% of the variation, respectively. Polymorphisms in the Pto locus are strongly associated with the QTL on chromosome 5. The two loci were derived from different parentage, and a significant interaction effect was demonstrated, resulting in the characteristic zebra-stem symptoms when combined.
Physiological or abiotic disorders of plants refer to symptoms caused by nonliving causal factors. Such disorders may be difficult to distinguish from diseases caused by biological agents such as pathogens, parasites, or insects. The definition and categorization of physiological disorders can be ambiguous in literature descriptions, although disorders are generally thought of as distinct from disease. In a comprehensive review article, the causes of abiotic disorders have been described as genetic predisposition, environmental stresses, nutritional deficiency or excess, and damage caused by chemicals, cultural practices, or animals (Schutzki and Cregg 2007). An industry field guide of tomato diseases treated insect damage and pesticide injury as subsets of abiotic disorders (De Ruiter and Seminis 2017). In contrast, other reviews of tomato physiological disorders excluded physical and chemical damage (Masarirambi et al. 2009; Peet 2009; Savvas et al. 2008).
Genetic, environmental, and nutritional stresses have been described as separate categories of physiological disorders in tomato, although the environmental and genetic components are often confounded (De Ruiter and Seminis 2017; Peet 2009). For example, blossom-end rot (BER) has been commonly considered a nutritional disorder associated with calcium deficiency with stress-inducing reactive oxygen species leading to damage (Adams and Ho 1993; Hagassou et al. 2019). However, there is also a genetic component for BER with QTLs affecting severity identified on chromosomes 3, 4, and 11 (Topcu et al. 2021). Fruit pox and gold fleck on tomato fruits have been classified as genetic disorders, and symptoms vary by cultivar (De Ruiter and Seminis 2017). A recessive gene ( fp) controls the presence of fruit pox, and a dominant gene (Gdf) contributes to gold fleck (Crill et al. 1973). At the same time, high temperature and rapid growth contribute to more severe symptoms (Masarirambi et al. 2009). Differences in the definition and categorization of physiological disorders may be attributable to the contribution of both genetic and environmental causes leading to a quantitatively variable response.
Zebra-stem is a less common physiological disorder of tomato. There has been no clear relationship between the occurrence of zebra-stem and specific biotic or abiotic stress, although an association between lines and accessions that contain the Pto and Fen genes has been noted (De Ruiter and Seminis 2017; Ricker 2002). There is no documented evidence of environmental factors that affect symptom severity. Zebra-stem is considered a genetic disorder characterized by foliar necrosis and a distinct striped pattern on the stem (Ricker 2002). Nonpathogenic necrosis caused by genetic predisposition has multiple descriptions in the literature. Hybrid necrosis is a broad term that describes tissue necrosis, wilting, yellowing, and even lethality of F1 progeny formed within and between species. Hybrid necrosis has been postulated to affect gene flow by creating genetic barriers among populations and species (Bomblies and Weigel 2007). Details and examples of hybrid necrosis have been reviewed, and cases have been attributed to divergent plant immune responses (Bomblies and Weigel 2007; Calvo-Baltanás et al. 2021; Li and Weigel 2021). Autogenous necrosis, also called autonecrosis, describes a spontaneous immune response in plants that is similar to a hypertensive reaction but is activated without the presence of pathogens (Bomblies and Weigel 2007). Conflicts between R genes, whether nucleotide-binding site leucine-rich repeat (NLR) genes or non-NLR immune receptors associated with disease resistance, have been implicated in a negative epistatic interaction that induces autonecrosis (Bomblies et al. 2007; Calvo-Baltanás et al. 2021; Chae et al. 2016). A specific example of R-gene mediated autonecrosis has been described based on the interaction of the Cf-2 resistance gene, which mediates resistance to C. fulvum, and the unlinked S. lycopersicum allele Rcr3esc (ne) (Krüger et al. 2002).
This study was conducted to define the genetic basis of zebra-stem. The disorder was measured based on the incidence and severity of symptoms. A partially inbred F4 population was evaluated, and subsequent BC and F2 populations were developed and evaluated for validation. Single-nucleotide polymorphism (SNP) genotyping and quantitative trait locus (QTL) mapping using interval mapping and a subsequent single-marker trait analysis were used to define regions of the genome, gene action, and interactions of loci that contribute to zebra-stem.
Materials and Methods
Plant materials.
Plant populations and evaluations are presented graphically in Fig. 1. We developed a biparental population using ‘Ohio 2K9-5533-1’ as the stigma/ovule parent and ‘Ohio FG02-188’ as the pollen parent. The inbred line 2K9-5533-1 carries resistance genes for tomato spotted wilt virus (Sw5), Phytophthora infestans (Ph3), and Pseudomonas syringae pv tomato (Pto) with Sw5 and Ph3 in the coupling phase (Robbins et al. 2010). FG02-188 has quantitative resistance to bacterial canker, Clavibacter michiganensis subsp. michiganensis (Cmm), which is believed to be derived from Bulgaria 12. FG02-188 is a processing tomato line, whereas 2K9-5533-1 retains 50% fresh-market Roma genetic background. In Summer 2019, 129 F4 families were planted in the field as plots and evaluated for zebra-stem. The partially inbred population was derived from 159 individual F2 plants from the field in Summer 2018. Seed were extracted from single F2 plants, and F3 plants were advanced in the greenhouse using single seed descent in Fall 2018. The entire F4 population was advanced to F5 using single seed descent, and selections with zebra-stem symptoms (2K19-8012, 2K19-8027-3, 2K19-8104, 2K19-8115, 2K19-8189, and 2K19-8209) were advanced and used for further experiments.
Flowchart showing plant material, experimental methods, and analyses used in this study.
Citation: HortScience 59, 7; 10.21273/HORTSCI17766-24
Two selections based on zebra-stem symptoms, 2K19-8027 and 2K19-8115, were used as pollen parents to cross with three different inbred lines FG02-188, 2K17-2130, and UGP02 as the ovule parents. Hybrids (F1) were self-pollinated to generate four F2 populations for validation of QTLs: 2K20-8501 derived from FG02-188 × 2K19-8027; 2K20-8502 derived from 2K17-2130 × 2K19-8027; 2K20-8503 derived from UGP02 × 2K19-8115; and 2K20-8504 derived from 2K17-2130 × 2K19-8115. FG02-188 was the pollen parent of 2K19-8027; therefore, 2K20-8501 was a first-generation backcross. 2K17-2130 was an elite processing tomato line adapted to humid environments with bacterial spot resistance. The ovule parent UGP02 was an elite processing tomato line adapted to arid environments. Approximately 100 F2 individuals from each of these four populations were seeded into 288 cell flats and transplanted to the field for evaluation in Summer 2020.
Experimental design.
The partially inbred F4 population was evaluated in the field during Summer 2019, in both Fremont and Wooster, OH, USA. The subsequent F2 populations were evaluated in Summer 2020 in Fremont, OH, USA. The F4 population was arranged as an augmented design with one of each of the 159 lines represented once in the field at each location. Commercial cultivars UG16112 (United Genetics Seeds, Co., Hollister, CA, USA), H3402 (Heinz Seed, Stockton, CA, USA) and PS696 (synonym Perfectpeel; Seminis, St. Louis, MO, USA) were included as over-replicated zebra-stem-resistant checks in row, column, and quadrant blocks to account for variations within the field. Plots consisted of 20 plants for each F4 family or check cultivar. Individual plots were planted 1.5 m apart; individual plants within a plot were planted 28 cm apart. The F2 populations were planted with 56 cm spacing between plants using 100 plants for each population. Rows were spaced 1.5 m apart.
Zebra-stem rating and analysis.
The F4 population was rated for the presence or absence of necrosis in seedlings while in greenhouses and of zebra-stem symptoms in mature plants in the field in Wooster and Fremont, OH, USA. Some of the lines within the F4 families were still segregating. A rating of “0” represented a plot of 20 plants with no zebra-stem. A rating of “1” indicated a plot segregating for zebra-stem. A rating of “2” indicated uniform zebra-stem symptoms. The field was evaluated twice, first in early September ∼80 d post-transplanting, when plants had green fruit, and again in late September, when fruits were mature and the phenotype became easier to observe. The BC and F2 populations were rated as individual plants using ratings of “0” and “2”. Following the rating period, leaf tissue and seed were collected from 20 to 24 individuals in each BC or F2 population representing the extremes with and without clear zebra-stem symptoms. Tissue was used for DNA extraction and genotyping.
Single plant selections from the F4, 2K19-8012, 2K19-8027-3, 2K19-8104, 2K19-8189, and 2K19-8209 were evaluated under excessive and regular irrigation treatments as F5 families in the greenhouse during Spring 2022, in Wooster, OH, USA. The five F5 families were planted separately in 10-cm pots with ∼25 to 30 seedlings in each pot. Two pots were planted for each family, one for excessive and one for regular irrigation treatments. Each family served as a replication. Irrigation treatments were applied at 10:00 AM and 2:00 PM, with one with excessive irrigation (300 mL) and the other with regular irrigation (100 mL) as a control. The water content of the potting media was measured with a Decagon ProCheck hygrometer (METER Group, Pullman, WA, USA), and the pot weight was recorded before and after irrigation. Zebra-stem was rated based on the presence or absence of symptoms, and biomass was based on an average of the individual seedling weight at the end of the experiment. Treatment effects were evaluated based on the linear model as follows: phenotype = treatment + replicate + error. All statistical analyses were performed using the R core package (version 3.6.3) (R Core Team 2020).
Genotyping.
The F4, BC, and F2 populations were genotyped with 384 SNP markers. The SNPs were derived from 7720 polymorphisms described previously (Sim et al. 2012a) and optimized as a community resource for genotyping. Optimization was designed to maximize genome distribution and polymorphic information content in cultivated tomato (Gill et al. 2019). The markers covered the entire genome based on the genetic position estimated from a high-density genetic map with markers added to gaps according to the physical position from the reference genome SL2.40 release (Sim et al. 2012b). The PlexSeq (AgriPlex Genomics, Cleveland, OH, USA) amplicon-based sequencing platform was used for SNP genotyping. Raw genotyping data were returned as SNP calls. The SNP calls were phased to the “A” and “B” format, with those homozygous for the FG02-188 allele designated as “A”, and those homozygous for the Ohio 2K9-5533-1 allele designated as “B”. Monomorphic markers and those with more than 20% missing values were removed from the analysis. Missing values were scored as “NA.” Marker segregation ratios of 1:1 (A:B) were examined using the “geno.table” function of the “R/qtl” package (version 1.48-1) (Broman et al. 2003) in R (version 3.6.3) (R Core Team 2020), and markers with skewed segregation were removed when the χ2 probabilities were less than 10−4. Following these quality control and filtering steps, 135 markers, 48 markers, 104 markers, 66 markers, and 100 markers were retained for the F4 population, BC family 2K20-8501, F2 2K20-8502, F2 2K20-8503, and F2 2K20-8504, respectively.
Composite interval mapping.
We used composite interval mapping to identify QTLs for zebra-stem in the F4 population. First, a linkage map based on 135 polymorphic SNP markers was constructed using the “R/qtl” package (version 1.48-1) (Broman et al. 2003). The genetic map was constructed with the “formLinkageGroups” function in “R/qtl” (version 1.48-1) (Broman et al. 2003). The minimum logarithm of the odds (LOD) scores (“min.lod”) were set at 2, and the maximum recombination frequencies (“max.rf”) were set at 0.25 to form single linkage groups for each chromosome separately. Genetic positions as defined by “R/qtl” were also compared with the SNP physical positions derived from tomato reference genome sequence SL4.0 (SGN 2020). Based on physical positions, marker orders were adjusted using “switch.order” at each linkage group. Then, the genetic positions were recalculated using the “summaryMap” function in “R/qtl”, and the map was visualized using “plotMap.” To examine map quality, we tested the correlation and collinearity between the genetic and physical positions of markers on each chromosome using regression approaches.
Composite interval mapping was conducted using the “R/qtl” package (version 1.48-1) (Broman et al. 2003). We first set the mapping interval to 1 cM using “steps”; then, we used the “cim” function with 1000 permutations to determine the significant LOD cutoffs at 5% and 10%. Haley-Knott regression was used as the estimation method (Haley and Knott 1992). The window for a single covariate marker was set to 20 cM. Results were graphed using the base R “plot” function (version 3.6.3) (R Core Team 2020).
QTL validation within the BC and F2 populations.
We also conducted a marker–trait association analysis of the BC and F2 populations to confirm the QTLs identified in the F4 population (Edwards et al. 1987). Because zebra-stem was rated and scored categorically in the BC and F2 populations, the Kruskal-Wallis test by ranks was used as a nonparametric alternative for association analyses (Kruskal and Wallis 1952). The significance cutoffs for the Kruskal-Wallis analysis of variance (ANOVA) were set at P = 0.001. The analysis was conducted with the R core package (version 3.6.3) (R Core Team 2020). The effects of allele substitution and phenotypic variation explained by significant markers were also estimated (Edwards et al. 1987). In addition, using the fixed effect linear model (Y = M1i:M2i + E), we examined marker interactions between QTLs on different chromosomes, where Y was the phenotypic score for each individual i, M1 and M2 were the alleles of unlinked markers, and E was the error. Boxplots of zebra-stem scores by marker genotypes were plotted using “geom_boxplot” from “ggplot2” (version 3.3.3) (Wickham, 2016).
Molecular marker development.
Molecular markers based on insertion/deletions (InDel) were also developed for regions flanking SNP markers that showed significant associations with QTLs. The DNA sequence for the parent FG02-188 was obtained through a collaborative Tomato Pan Genome Consortium with NR Gene (Ness-Ziona, Israel). We extracted 1 to 3 Mb of the region flanking significant QTLs from the tomato reference genome sequences (H1706) using the Jbrowser tools from the Sol Genetic Network (SGN 2020). Then, we compared H1706 and FG02-188 sequences using the Basic Local Alignment Search Technique function for nucleotide sequences (BLASTN) (Altschul et al. 1990) using an overlapping interval approach with windows of 10 to 20 kbp. The BLAST analysis was performed using resources available on the Ohio Super Computer (OnDemand version 2.0.13). Primers that amplify InDel markers were developed using Primer3 with default settings (Untergasser et al. 2012). Markers identified as polymorphic between FG02-188 and 2K9-5533-1 included ZS50895-7 (F:AAGCGGCTTGAATTATTTGTTC; R:CGAGTGAAAATGAATAATCAAAAA) and ZS8835-4 (F:CCAAAAAGTCAATTTAGTCAATCAAA; R:CCCCAAATTGGCCATATAAA), which were used to genotype the F4 population and BC and F2 validation populations.
Results and Discussion
Zebra-stem symptoms.
Necrotic symptoms were observed in seedlings and mature plants of the F4 progeny and, subsequently, in the BC and F2 populations developed for validation (Fig. 1). Similar symptoms have been described previously as zebra-stem (De Ruiter and Seminis 2017; Ricker 2002). The seedling symptoms presented as necrosis and wilt on stems and young leaves, whereas the mature plants showed a distinct striped pattern on stems and necrosis on leaves (Fig. 2). Plants with severe symptoms lost foliage early; therefore, the fruits of the affected plants were subject to sunburn.
Tomato seedlings in the greenhouse and mature tomato plants in the field showing zebra-stem phenotypes: necrosis of seedlings (left) and distinct striped pattern on the stem and necrosis of leaves (middle and right).
Citation: HortScience 59, 7; 10.21273/HORTSCI17766-24
Symptoms of transplants were more pronounced in the Fremont greenhouses, and we hypothesized that the severity was caused by overwatering seedlings. We tested this hypothesis using F5 families and controlled irrigation treatments. The two irrigation treatments had differences in pot water retention as measured by weight (P = 0.0027) and hygrometer readings (P = 6.51E-6). The control treatment had an average media water capacity of 82.1% relative to the excessive irrigation treatment, with hygrometer readings of 0.39 g/m3 and 0.53 g/m3, respectively. The excessively irrigated seedlings had visible necrosis (Fig. 2) and produced less plant biomass than the controls that were not watered as often (Fig. 3), and the effect of the irrigation treatment on seedling biomass was significant (P = 0.041).
Boxplot showing the seedling biomass of the control and excessive irrigation treatment groups used to evaluate the effect on zebra-stem symptom severity at seedling development.
Citation: HortScience 59, 7; 10.21273/HORTSCI17766-24
Genetic analysis.
Of the 159 F4 families, 50 had zebra-stem and 109 were free from any zebra-stem symptoms. The different incidence of zebra-stem in progeny suggested segregation. We tested the fit of one-gene (1:1) and two-gene (1:3) models for Mendelian inheritance in the partially inbred population. The χ2 test for a single-gene model was 21.65 (P < 0.001), and that for a two-gene model was 3.34 (P = 0.676), with 1 degree of freedom. Therefore, we failed to reject the two-gene model. As a result, the frequency of zebra-stem symptoms in the RIL population was consistent with that of a two-gene model for Mendelian inheritance.
A genetic map was first constructed for SNPs segregating in the F4 population as a prerequisite for the QTL analysis. The map consisted of 12 linkage groups with one group for each chromosome (Table 1). The map covered 690.4 cM with 135 SNP markers (Table 1). The genetic positions of the SNP markers were significantly correlated with their physical positions in the reference genome (Table 1). The limited number of markers on chromosomes 11 and 12 affected the significance of linear correlations on those two chromosomes, although the marker order was identical based on physical and genetic map rank-order comparisons. Details of the genetic map and estimates of coverage and quality are listed in Table 1.
Genetic map (FG02-188 × 2K9-5533-1) coverage and collinearity with the physical map.
Significant QTLs for zebra-stem were identified on chromosomes 5 and 10 with composite interval mapping. Based on 1000 permutations, the thresholds of significance at 5% were LOD = 2.64 for the mature plant phenotype and LOD = 2.65 for the seedling phenotype. Highly significant QTLs on chromosome 5 (LOD = 12.85) and 10 (LOD = 8.23) were identified based on field ratings of mature plants (Fig. 4). A significant QTL on chromosome 5 (LOD = 2.75) and a marginally nonsignificant QTL on chromosome 10 (LOD = 2.62) were identified based on the ratings of seedlings (Fig. 4). Because the seedling symptoms were more affected by the irrigation regime, the variation associated with the ratings of seedlings may have been greater than that of the ratings of mature plants, resulting in higher error in QTL signal detection.
Composite interval mapping of zebra-stem quantitative trait loci (QTLs) with the F4 population. The orange lines were QTL signals for zebra-stem calculated based on the adult plant phenotype, and the green lines were based on the seedling phenotype. The x-axis represents the 12 chromosomes of tomato in cM. The y-axis represents significance as the logarithm of the odds (LOD) score. The dashed grey line indicates the 5% threshold with N = 1000 permutations using the Haley-Knott (HK) regression method. The LOD thresholds were 2.64 for adult phenotypes and 2.65 for seedling phenotypes.
Citation: HortScience 59, 7; 10.21273/HORTSCI17766-24
The SNP markers (solcap_snp_sl_5050, solcap_snp_sl_50895, and solcap_snp_sl_50902) significantly associated with zebra-stem on chromosome 5 were located between 6045160 and 6466400 bp on the SL4.0 physical map. This is the same location for the resistance gene Pto, which originated from the wild species S. pimpinellifolium (Riely and Martin 2001). The correlation of zebra-stem with homozygous resistance to bacterial speck race 0 (Pseudomonas syringae pv. tomato) has been noted previously (De Ruiter and Seminis 2017; Mazo-Molina et al. 2020; Ricker 2002). The SNP marker solcap_snp_sl_50895 lies within Solyc05g013280 (Prf), a member of a leucine-rich repeat in the middle of the Pto gene cluster, and marker solcap_snp_sl_50902 lies within Solyc05g013290 (Fen), a Pto-like serine/threonine kinase that confers sensitivity to Fenthion (SGN 2020). Both SNPs are within the Pto locus, confirmed the presence of this resistance locus in 2K9-5533-1, demonstrated the segregation of the locus in the F4 population, and showed a strong association between the Pto locus and zebra-stem. The significant SNP marker (solcap_snp_sl_8835) at the QTL on chromosome 10 was located at physical position 63495697 bp based on the physical map SL4.0 and was inherited from FG02-188 (Table 2).
Chromosome and physical positions of markers showing significant associations with zebra-stem symptoms, including the effects of allele substitution, R2, P values, and parental alleles contributing to the disorder.
The effects of parental alleles and allele substitutions were also evaluated (Table 2). For mapping purposes, FG02-188 alleles were coded as A and the alternative parent 2K9-5533-1 was coded as B. The 2K9-5533-1 allele B on chromosome 5 is associated with larger effects for zebra-stem, whereas the FG02-188 allele A on chromosome 10 was associated with the disorder (Table 2).
QTL validation in BC and F2 populations.
QTLs were validated in BC and F2 progenies derived from crossing specific F5 lines selected from the F4 population to FG02-188, 2K17-2130, and UGP02 lines (Fig. 1). The QTLs at the same or nearby physical locations were confirmed on chromosomes 5 and 10 (Table 2). Significant SNP markers associated with zebra-stem on chromosome 5 included solcap_snp_sl_18272 and solcap_snp_sl_13481 at 3802285 to 3947971 bp, and solcap_snp_sl_5050, solcap_snp_sl_50895, and solcap_snp_sl_50902 at 6045160 to 6466400 bp. A significant SNP marker at the QTL on chromosome 10 (solcap_snp_sl_15094) was located at physical position 63756617 bp (Table 2). The chromosome 10 marker solcap_snp_sl_15094 was not included in the mapping analysis of the F4 population because of filtering for missing data; conversely, solcap_snp_sl_8835 was not included in the analysis of the F2 progeny because of monomorphism. However, these markers are located within the same 300-kbp region (63495697 and 63756617) and mark the same QTL (Table 2).
As part of the validation, we developed primers flanking InDel variation as molecular markers. The marker ZS50895-7 was located on chromosome 5 at 6027852 to 6027992 bp, whereas the marker ZS8835-4 was located on chromosome 10 at 63499484 to 63499721 bp based on the SL4.0 physical map. ZS50895-7 detected a 6-bp insertion with a band of 147 bp associated with a lack of zebra-stem symptoms and a band of 141 bp associated with zebra-stem. ZS50895-7 explained 54% of the phenotypic variation in the F4 population and 41% of the variation in the F2 (Table 2). The marker ZS8835-4 amplified a 238-bp fragment in FG02-188 and a 215-bp fragment in 2K9-5533-1, with the larger fragment associated with zebra-stem in the F4 population, explaining 43% of the variation. ZS8835-4 was monomorphic in the F2 populations (Table 2).
Genetic interactions.
QTL interactions between chromosomes 5 and 10 were significant in the F4 population as well as in the BC and F2 progeny (Table 3). The interaction of alleles from different parents, allele B on chromosome 5, and allele A on chromosome 10 were associated with a higher incidence of zebra-stem than that of the other allele combinations (Fig. 5C).
Means of zebra-stem symptom severity for allele combinations showing an interaction between chromosomes 5 and 10.
Boxplots showing the allele effects of individual quantitative trait loci (QTLs) and the QTL interactions between chromosomes 5 and 10 in the F4 population based on genotyping (Agriplex Genomics, Cleveland, OH, USA) and the phenotyping results in the field.
Citation: HortScience 59, 7; 10.21273/HORTSCI17766-24
Therefore, zebra-stem results from the interaction of alleles from different parents and leads to necrosis symptoms in progeny. The phenomenon of hybrid necrosis in plants has been observed in several species, including Arabidopsis thaliana, and studied (Barragan et al. 2021; Bomblies et al. 2007). Hybrid necrosis is described as an autoimmune response caused by the epistatic interaction of different alleles involved in immunity contributed by divergent parents (Bomblies et al. 2007). Tomato Cf-2-mediated resistance to C. fulvum requires the presence of a specific allele at the unlinked Rcr3 gene (Dixon et al. 2000). Tomato plants displayed a normal resistance phenotype when the S. pimpinellifolium-derived Cf-2 resistance gene on chromosome 6 was present with the S. pimpinellifolium Rcr3pim (Ne) allele on chromosome 2. However, when the Cf-2 allele from S. pimpinellifolium is paired with the S. lcyopersicum allele Rcr3esc (ne), the interaction results in autonecrosis (Krüger et al. 2002). We speculated that a similar autonecrosis mechanism may cause zebra-stem. The association with the Pto locus and the interaction of loci on chromosomes 5 and 10 leading to zebra-stem are consistent with allele-dependent necrosis.
In conclusion, through a multigeneration analysis, we have demonstrated that the interaction of alleles from different parents contributes to zebra-stem. We also provided tools in the form of SNP and InDel markers for selection in the context of breeding programs. The parent material and progeny selections described may provide germplasms for further investigations of the mechanism of necrosis.
References Cited
Adams P, Ho LC. 1993. Effects of environment on the uptake and distribution of calcium in tomato and on the incidence of blossom-end rot. Plant Soil. 154(1):127–132. https://doi.org/10.1007/BF00011081.
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. 1990. Basic local alignment search tool. J Mol Biol. 215:403–410. https://doi.org/10.1016/S0022-2836(05)80360-2.
Barragan AC, Collenberg M, Wang J, Lee RQ, Cher WY, Rabanal FA, Ashkenazy H, Weigel D, Chae E. 2021. A truncated singleton NLR causes hybrid necrosis in Arabidopsis thaliana. Mol Biol Evol. 38(2):557–574. https://doi.org/10.1093/molbev/msaa245.
Bomblies K, Lempe J, Epple P, Warthmann N, Lanz C, Dangl JL, Weigel D. 2007. Autoimmune response as a mechanism for a Dobzhansky-Muller-type incompatibility syndrome in plants. PLoS Biol. 5(9):e236. https://doi.org/10.1371/journal.pbio.0050236.
Bomblies K, Weigel D. 2007. Hybrid necrosis: Autoimmunity as a potential gene-flow barrier in plant species. Nat Rev Genet. 8:382–393. https://doi.org/10.1038/nrg2082.
Broman KW, Wu H, Sen S, Churchill GA. 2003. R/qtl: QTL mapping in experimental crosses. Bioinformatics. 19(7):889–890. https://doi.org/10.1093/bioinformatics/btg112.
Calvo-Baltanás V, Wang J, Chae E. 2021. Hybrid incompatibility of the plant immune system: An opposite force to heterosis equilibrating hybrid performances. Front Plant Sci. 11:576796. https://doi.org/10.3389/fpls.2020.576796.
Chae E, Tran DT, Weigel D. 2016. Cooperation and conflict in the plant immune system. PLoS Pathogens. 12(3):e1005452. https://doi.org/10.1371/journal.ppat.1005452.
Crill P, Burgis DS, Jones JP, Strobel JW. 1973. The fruit pox and gold fleck syndrome of tomato. Phytopathology. 63:1285–1287. https://doi.org/10.1094/Phyto-63-1285.
De Ruiter and Seminis. 2017. Abiotic disorders: Zebra stem. Tomato disease field guide. https://www.norseco.com/pdf/Tomato_Disease.pdf. [accessed 8 Apr 2022].
Dixon MS, Catherine G, Thomas CM, van der Biezen EA, Jones JD. 2000. Genetic complexity of pathogen perception by plants: The example of Rcr3, a tomato gene required specifically by Cf-2. Proc Natl Acad Sci USA. 97(16):8807–8814. https://doi.org/10.1073/pnas.97.16.8807.
Edwards MD, Stuber CW, Wendel JF. 1987. Molecular-marker-facilitated investigations of quantitative-trait loci in maize. I. Numbers, genomic distribution and types of gene action. Genetics. 116(1):113–125. https://doi.org/10.1093/genetics/116.1.113.
Gill U, Scott JW, Shekasteband R, Ogundiwin E, Schuit C, Francis DM, Sim SC, Smith H, Hutton SF. 2019. Ty-6, a major begomovirus resistance gene on chromosome 10, is effective against Tomato yellow leaf curl virus and Tomato mottle virus. Theor Appl Genet. 132(5):1543–1554. https://doi.org/10.1007/s00122-019-03298-0.
Hagassou D, Francia E, Ronga D, Buti M. 2019. Blossom end-rot in tomato (Solanum lycopersicum L.): A multi-disciplinary overview of inducing factors and control strategies. Scientia Hortic. 249:49–58. https://doi.org/10.1016/j.scienta.2019.01.042.
Haley C, Knott S. 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity. 69:315–324. https://doi.org/10.1038/hdy.1992.131.
Krüger J, Thomas CM, Golstein C, Dixon MS, Smoker M, Tang S, Mulder L, Jones JD. 2002. A tomato cysteine protease required for Cf-2-dependent disease resistance and suppression of autonecrosis. Science. 296(5568):744–747. https://doi.org/10.1126/science.1069288.
Kruskal WH, Wallis WA. 1952. Use of ranks in one-criterion variance analysis. J Am Stat Assoc. 47(260):583–621. https://doi.org/10.1080/01621459.1952.10483441.
Li L, Weigel D. 2021. One hundred years of hybrid necrosis: Hybrid autoimmunity as a window into the mechanisms and evolution of plant-pathogen interactions. Annu Rev Phytopathol. 59:213–237. https://doi.org/10.1146/annurev-phyto-020620-114826.
Masarirambi MT, Mhazo N, Oseni TO, Shongwe VD. 2009. Common physiological disorders of tomato (Lycopersicon esculentum) fruit found in Swaziland. Journal of Agriculture and Social Science. 5:123–127. https://www.fspublishers.org/published_papers/3682_.pdf.
Mazo-Molina C, Mainiero S, Haefner BJ, Bednarek R, Zhang J, Feder A, Shi K, Strickler SR, Martin GB. 2020. Ptr1 evolved convergently with RPS2 and Mr5 to mediate recognition of AvrRpt2 in diverse solanaceous species. Plant J. 103:1433–1445. https://doi.org/10.1111/tpj.14810.
Peet MM. 2009. Physiological disorders in tomato fruit development. Acta Hortic. (821):151–159. https://doi.org/10.17660/ActaHortic.2009.821.16.
R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. [accessed 26 Nov 2020].
Riely BK, Martin GB. 2001. Ancient origin of pathogen recognition specificity conferred by the tomato disease resistance gene Pto. Proc Natl Acad Sci USA. 98(4):2059–2064. https://doi.org/10.1073/pnas.98.4.2059.
Ricker MD. 2002. Association of zebra-stem symptoms on processing tomato with the Pto gene. Phytopathology. 92(6 Suppl):S69. https://eurekamag.com/research/034/452/034452847.php.
Robbins MD, Masud MAT, Panthee DR, Gardner RG, Francis DM, Stevens MR. 2010. Marker-assisted selection for coupling phase resistance to Tomato spotted wilt virus and Phytophthora infestans (late blight) in tomato. HortScience. 45(10):1424–1428. https://doi.org/10.21273/HORTSCI.45.10.1424.
Savvas D, Ntatsi G, Passam HC. 2008. Plant nutrition and physiological disorders in greenhouse grown tomato, pepper and eggplant. Eur J Plant Sci Biotechnol. 2(1):45–61. http://www.globalsciencebooks.info/Online/GSBOnline/images/0812/EJPSB_2(SI1)/EJPSB_2(SI1)45-61o.pdf.
Schutzki RE, Cregg B. 2007. Abiotic plant disorders: Symptoms, signs and solutions. A diagnostic guide to problem solving. Michigan State University. https://www.canr.msu.edu/resources/abiotic_plant_disorders_symptoms_signs_and_solutions_e2996. [accessed 8 Apr 2022].
Sim SC, Van Deynze A, Stoffel K, Douches DS, Zarka D, Ganal MW, Chetelat RT, Hutton SF, Scott JW, Gardner RG, Panthee DR, Mutschler M, Myers JR, Francis DM. 2012a. High-density SNP genotyping of tomato (Solanum lycopersicum L.) reveals patterns of genetic variation due to breeding. PLoS One. 7(9):e45520. https://doi.org/10.1371/journal.pone.0045520.
Sim SC, Durstewitz G, Plieske J, Wieseke R, Ganal MW, Van Deynze A, Hamilton JP, Buell CR, Causse M, Wijeratne S, Francis DM. 2012b. Development of a large SNP genotyping array and generation of high-density genetic maps in tomato. PLoS One. 7(7):e40563. https://doi.org/10.1371/journal.pone.0040563.
Sol Genomics Network (SGN). 2020. International tomato genome sequencing project. https://solgenomics.net. [accessed 26 Nov 2020].
Topcu Y, Sapkota M, Illa-Berenguer E, Nambeesan SU, van der Knaap E. 2021. Identification of blossom-end rot loci using joint QTL-seq and linkage-based QTL mapping in tomato. Theor Appl Genet. 134(9):2931–2945. https://doi.org/10.1007/s00122-021-03869-0.
Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG. 2012. Primer3–new capabilities and interfaces. Nucleic Acids Res. 40(15):e115. https://doi.org/10.1093/nar/gks596.
Wickham H. 2016. ggplot2: Elegant graphics for data analysis. https://ggplot2.tidyverse.org. [accessed 26 Nov 2020].