Development of Clustered Resistance Gene Analogs-Based Markers of Resistance to<i> Phytophthora capsici</i> in Chili Pepper

Kim, Nayoung; Kang, Won-Hee; Lee, Jundae; Yeom, Seon-In

doi:https://doi.org/10.1155/2019/1093186

BioMed Research International

On this page

Abstract Introduction Materials and Methods Results and Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments Supplementary Materials References Copyright Related Articles

Special Issue

Plant Genetics and Gene Study

View this Special Issue

Research Article | Open Access

Volume 2019 | Article ID 1093186 | https://doi.org/10.1155/2019/1093186

Development of Clustered Resistance Gene Analogs-Based Markers of Resistance to Phytophthora capsici in Chili Pepper

Nayoung Kim,¹Won-Hee Kang,²Jundae Lee,³and Seon-In Yeom^1,2

Guest Editor: Yuri Shavrukov

Received12 Sept 2018

Revised24 Nov 2018

Accepted04 Dec 2018

Published03 Jan 2019

Abstract

The soil-borne pathogen Phytophthora capsici causes severe destruction of Capsicum spp. Resistance in Capsicum against P. capsici is controlled by numerous minor quantitative trait loci (QTLs) and a consistent major QTL on chromosome 5. Molecular markers on Capsicum chromosome 5 have been developed to identify the predominant genetic contributor to resistance but have achieved little success. In this study, previously reported molecular markers were used to reanalyze the major QTL region on chromosome 5 (6.2 Mbp to 139.2 Mbp). Candidate resistance gene analogs (RGAs) were identified in the extended major QTL region including 14 nucleotide binding site leucine-rich repeats, 3 receptor-like kinases, and 1 receptor-like protein. Sequence comparison of the candidate RGAs was performed between two Capsicum germplasms that are resistant and susceptible, respectively, to P. capsici. 11 novel RGA-based markers were developed through high-resolution melting analysis which were closely linked to the major QTL for P. capsici resistance. Among the markers, CaNB-5480 showed the highest cosegregation rate at 86.9% and can be applied to genotyping of the germplasms that were not amenable by previous markers. With combination of three markers such as CaNB-5480, CaRP-5130 and CaNB-5330 increased genotyping accuracy for 61 Capsicum accessions. These could be useful to facilitate high-throughput germplasm screening and further characterize resistance genes against P. capsici in pepper.

1. Introduction

Hot pepper (Capsicum spp.) is an economically important crop that belongs to the Solanaceae family along with tobacco, potato, and tomato. Hot pepper provides many essential vitamins, and capsaicin is used as a major spicy flavoring in most global cuisines [1]. In 2016, the main pepper-producing countries grew 38 million tons on about 3.7 Mha [2]. The world production and trade value of hot pepper consistently increased during the last decade.

Pathogenic fungi, oomycetes, bacteria, and viruses cause economic damage and crop loss. Pepper production in negatively impacted by approximately 87 different pathogens and diseases [3]. Among them, Phytophthora capsici is a highly destructive, broad-host-range oomycetes that was first described in pepper in New Mexico in 1922 [4]. The host range of P. capsici includes Solanaceae, Cucurbitaceae, lima beans, and other plants. P. capsici is found in North America, South America, Asia, Africa, and Europe. P. capsici causes severe disease symptoms such as foliar blight, stem blight, and root, stem, fruit, and foliar rot. The economic impact of P. capsici on worldwide vegetable production has been valued at over one billion dollars per year [5].

P. capsici forms oospores that survive for years in the soil and can migrate through water and wind during warm (25–28°C) and wet conditions such as those during rainy seasons [6]. After P. capsici becomes established at a location, it can be very difficult to control. There is no effective chemical and agricultural strategy. Resistance against P. capsici has been reported in cultivated peppers including cultivars ‘AC2258,’ ‘PI201232,’ ‘PI201234,’ and ‘CM334’ [7, 8]. Among the resistant cultivars, Capsicum annuum ‘CM334’ shows a very high degree of resistance to multiple races of P. capsici and reported resistance to root rot, crown rot, fruit rot, and foliar blight [9–11].

Previous studies indicated that resistance of pepper to P. capsici is under polygenic control governed by complex quantitative trait loci (QTLs) [12–15]. Identification and QTL mapping of genes conferring partial resistance against P. capsici in pepper is essential for breeding cultivars with P. capsici resistance. The majority of QTLs for P. capsici resistance are conserved on Capsicum chromosome 5. Numerous molecular markers have been developed within the major QTL region, but little progress has been made in developing functional gene-based markers of P. capsici resistance, and no P. capsici-resistance gene (R gene) has yet been characterized. Recently, high-quality de novo sequenced Capsicum genomes, including the ‘CM334’ genome, were reported [16, 17]. This genomic information from the hot pepper could be applied to plant breeding and the selection of crops with specific traits such as disease resistance or large fruit size [16, 18, 19]. The development of functional gene-based markers and the further characterization of genes that confer resistance against P. capsici is key factor for efforts to breed disease-resistant pepper cultivars.

Plants have multiple layers of defense against pathogen attacks, including preformed barriers for continuous defense and programmed immune responses based on pathogen recognition [20–22]. Resistance gene analogs (RGAs) are proteins involved in plant immune responses. RGAs have highly conserved structures, which include nucleotide binding site leucine-rich repeat (NBS-LRR) proteins, receptor-like proteins (RLPs), and receptor-like kinases (RLKs) [21, 23, 24]. RGAs have been cloned as resistance genes in various plants such as wheat (Lr10 to pathogen), barley (Mla6), rice (Xa1), maize (Rp1-D), Arabidopsis (RPM1), lettuce (Rgc2), tobacco (N), and tomato (Prf) [25–32]. RGAs can be used to identify and characterize genes.

In this study, the sequences of previously developed markers were screened to extend the major QTL region on Capsicum chromosome 5 and the RGAs were reanalyzed within that region to identify candidate resistance genes. Marker development on candidate genes was performed by comparing single nucleotide polymorphisms (SNPs) between the P. capsici-resistant cultivar C. annuum ‘CM334’ and the P. capsici-susceptible cultivar C. annuum ‘Daepoongcho’. A total of 61 Capsicum accessions were used to validate the newly developed RGA-based markers through high-resolution melting (HRM) analysis. These markers could be useful to validate the genotyping of germplasms and to further characterize resistance genes against P. capsici.

2. Materials and Methods

2.1. Plant Materials

61 Capsicum accessions (44 resistant, 3 moderately resistant, and 14 susceptible cultivars; Table S1) [33–38] were used to validate cosegregation of molecular markers. All plants were grown in 32-cell trays filled with soil. The plants were kept in a growth chamber at 25°C with a 16 h/8 h (light/darkness) photoperiod.

2.2. Pathogen Preparation and Plant Inoculation with P. capsici

The preparation of the P. capsici inoculum was described in previous study [11]. P. capsici was grown for 7 days in potato dextrose agar medium at 27°C and mycelial plugs (6 mm in diameter) were transferred on V8 agar medium for zoospore production. After 5 days, the mycelia grown on V8 agar medium were damaged using spreader and incubated under fluorescent lights for 2 days. The zoosporangia were shocked by incubating at 4°C with sterile water for 1 h 30 min to initiate release zoospores, followed by 30 min at 28°C for equilibration. The zoospores were collected and the concentration was adjusted to 1 × 10⁵ zoospores · mL⁻¹ counted by hemocytometer, and 2 mL of suspension was drenched at the root of each six-true-leaf stage of pepper plant. The inoculated pepper plants were kept at 25°C with a 16-h light photoperiod condition. Evaluation of disease symptoms was assessed using disease index of 0 to 3 scale (0 = no symptom; 1 = leaf wilting but no necrosis or less than 30% of the leaf wilted; 2 = leaf wilting and slightly necrosis stem or less than 60% of the leaf wilted; 3 = plant dead) [37]. The classification of the pepper lines, as resistant, moderately resistant or susceptible, was made based on the average disease index of each line, where if the scale was < 1, the pepper line was considered as resistant (R), 1 ≤ disease index < 2 confer as moderately resistant (MR), and if the scale was < 3 as susceptible (S). The results of the phenotype correspond to those as shown in Kim et al., 2017 [38].

2.3. Genomic DNA Extraction

Genomic DNA was extracted from the young leaves of plant samples using a slightly modified cetyltrimethylammonium bromide (CTAB) method [39]. First, the leaves were grinded using a pestle and mixed them with CTAB buffer, polyvinylpyrrolidone, and β-mercaptoethanol. The samples were incubated at 65°C for 1 h, added chloroform with isoamyl alcohol (24:1), and centrifuged them at 4°C 15,814 g for 15 min. Supernatant was transferred to a new 1.5 mL tube and incubated it for 30 min at −20°C with isopropyl alcohol. The precipitated genomic DNA was washed using 70% ethanol and centrifuged it at 4°C 15,814g for 10 min. DNA pellet was dissolved in deionized water and treated it with ribonuclease A. Concentration of genomic DNA was measured by a NanoDrop™ Spectrophotometer (ND-2000, ThermoFisher Scientific, Waltham, MA, USA) and then diluted the sample to a final DNA concentration of 20 ng·μL⁻¹.

2.4. Major QTL-Related Marker Analysis

The DNA sequences of 5 simple sequence repeat (SSR), 8 cleaved amplified polymorphic sequence (CAPS), and 18 SNP markers from the published maps of pepper chromosome 5 were used and compared with the C. annuum ‘CM334’ genome version 1.55 (http://genome.pepper.snu.ac.kr/) using BLASTn. The major QTL region on chromosome 5 was extended including all of the previously identified molecular markers and then candidate resistance genes from the extended major QTL region were selected.

2.5. Candidate Gene Selection and High-Resolution Melting Analysis

RGAs were identified according to the domain structure of the genes in the extended major QTL region. Domain structure analysis was conducted using SMART (http://smart.embl-heidelberg.de/) and Pfam (http://pfam.xfam.org/). The RGA sequences were expanded including an additional 1 kb from both ends of the sequences using an in-house pipeline. A multiple sequence alignment tool MUSCLE (http://www.ebi.ac.uk/) was used to identify SNPs between C. annuum ‘CM334’ and C. annuum ‘Daepoongcho’ for developing RGA-based markers. The candidate genes were selected based on the locations of the SNPs within the expanded QTL region.

Peptide and DNA sequences were downloaded from the Pepper Genome Platform (http://genome.pepper.snu.ac.kr/) and designed primers using primer3plus (http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi; Table 1) for HRM analysis of molecular markers. HRM analysis was performed to validate the cosegregation of the newly developed RGA-based markers among 61 Capsicum accessions using LightCycler® Real-Time PCR (Roche, Basel, Switzerland). The reaction solution had a total volume of 20 μL and contained 40 ng genomic DNA, 1 μL each of two primers at 10 pmol·μL⁻¹, 0.1 μL EasyTaq® DNA polymerase (Transgene Biotech, Beijing, China), 2 μL 10× EasyTaq® buffer (Transgene Biotech, Beijing, China), 1 μL 2.5 mM dNTPs (Transgene Biotech, Beijing, China), 1 μL SYTO®9 green fluorescent nucleic acid stain (Life Technologies, Carlsbad, CA, USA), and sterilized water. The PCR conditions were 5 min denaturation at 95°C followed by 10 s at 95°C and annealing with extension 20 s at 60°C for 40 cycles. The melting curve stage was a progression of 95°C for 1 min, 40°C for 1 min, and 65°C for 1 s with fluorescence estimated at 0.2°C intervals. Genotyping of the molecular markers was analyzed through High-Resolution Melt software version 1.1 (Roche, Basal, Switzerland).

3. Results and Discussion

3.1. Candidate RGA Markers with Integration of Genetic and Genomic Data on Pepper Chromosome 5

Genetic and genomic data on pepper chromosome 5 were integrated to determine the extended region of a major resistance QTL. The following DNA sequences and QTL information were used in this study: CAPS markers mapped to the Pc.5.2, Pc.5.3 region of ‘H3’ × ‘Vania’ (HV), and ‘Perenial’ × ‘Yolo Wonder’ (PY) [40]; SNP markers within the Pc5.1 region of ‘Early Jalapeno’ × ‘CM334’ (EC), the Phyto5 region of ‘YCM334’ × ‘Tean,’ and the Pc5.2 region of ‘CM334’ × ‘Chilsungcho’ (CC) and ‘NB1’ × ‘Bhut Jolokia’ [41–44]; and SSR markers mapped to the Pc5.1 region of ‘Manganji’ × ‘CM334’ [45]. The genomic locations of the previously developed markers were identified using BLASTn with the C. annuum ‘CM334’ genome. The primer sequences and the genomic location of several representative markers are shown in Table 1.

A physical map of chromosome 5 was drawn including the locations of the markers and obtained the extended major QTL region for the selection of candidate resistance genes. The extended QTL region spanned from 6.2 Mbp (U196349) to 139.2 Mbp (P5-SNAP-CM) and contained 10 QTLs and 845 genes from 56 scaffolds (Table 1 and Figure 1). The genes included NB-ARC domain containing protein, GRAS family transcription factor, cytochrome P450, and proteins of unknown function.

Figure 1

Integration of genetic and genomic data on chromosome 5. The genetic map of chromosome 5 is on the left, and the physical map is on the right. Quantitative trait loci (QTL) of the previously developed markers are shown on left side of the genetic map. The physical map is drawn to show the actual chromosome size, which is 233.3 Mbp in ‘CM334.’ The extended QTL region spans from 6.2 Mbp to 139.2 Mbp and in shown in yellow color. The other colors indicate information about the RGA-based markers within the QTL region, which are shown on the right side of the physical map. Previously developed markers are integrated with the genetic map and physical map with their actual locations on chromosome 5. Orange: NBS-LRR-based marker, green: RLK-based marker, and blue: RLP-based marker.

Using the domain analysis tool, 18 RGAs in the extended QTL region including 14 NBS-LRR proteins, 3 RLKs, and 1 RLP were identified (Table S2). Previously, Rehrig et al., 2014, reported several RGAs nearby QTL peak on chromosome 5 using ‘CM334’ genome (ver.1.5), but they did not developed RGA-based markers to evaluate P. capsici-resistance resources. In this study, additional five RGAs were identified from the updated ‘CM334’ genome (ver. 1.55) as shown in Table S2. The 14 NBS-LRR proteins consisted of seven partial type NBS-LRRs and seven full type NBS-LRRs. All three RLKs had a kinase-TM-kinase domain. The RLP had a signal peptide-LRR domain. The majority of candidate genes that have been cloned as genes in plant species are RGAs. To date, more than 314 functional genes have been identified in plants [46]. Among them, 80% encode NBS-LRR proteins (191/314) or RLPs/RLKs (60/314). In Solanaceae, all of the cloned genes against Phytophthora infestans were identified RGAs such as Rpi-blb1, Rpi-blb2, R2, R3a, and ELR [47–51]. Therefore, the identification of RGAs and development of RGA-based markers could be useful in the further characterization of P. capsici-resistance genes.

3.2. Development of SNP Markers

Eighteen RGAs were reanalyzed to identify SNPs between resistant and susceptible P. capsici germplasms by multiple sequence alignment. Among the 18 RGAs, 11 had SNPs and were selected as candidate genes for the development of molecular markers (Table 2 and Table S2). The RGA-based markers had four SNP types (A/C, A/G, T/C, and T/G) and had from one to three SNPs between the resistant and susceptible germplasms (Table 2). Using HRM analysis, nine NBS-LRR-based markers (CaNB-5390, CaNB-5410, CaNB-5440, CaNB-5480, CaNB-5550, CaNB-5720, CaNB-5330, CaNB-5530, and CaNB-5170), one RLK-based marker (CaRK-5470), and 1 RLP-based marker (CaRP-5130) were developed. The HRM curves clearly distinguished among three genotypes for each of the markers: resistant homozygous, resistant heterozygous, and susceptible homozygous (Figure 2).

The RGA-based SNP markers were applied to genotyping of 61 Capsicum accessions with previously developed markers such as 142964 [42] and Phyto5SAR [43]. In a previous report, the region spanning from 20.2 Mbp to 29.29 Mbp on chromosome 5 was reported as the major core QTL region [43]. Phyto5SAR was closely linked to the major core QTL, and was used to assess the genotyping accuracy of the new developed markers. The genotyping results of the newly developed markers are shown on Table 3 and Table S3, which were divided into four groups as genotyping accuracy on cultivars. The phenotypes in cultivars belong to Group 1 and Group 2 were matched with genotyping results by six markers (Table 3). The cosegregation rates varied from 37.7% to 86.9% (Figure 3(a)). Several markers adjacent to Phyto5SAR had a tendency to show a high cosegregation rate, which were CaRP-5130 (82%), CaNB-5330 (83.6%), CaRK-5470 (72.1%), and CaNB-5530 (82%). Although far from Phyto5SAR on the physical map, CaNB-5480 showed the highest cosegregation rate (86.9%) except for eight cultivars (13.1%). CaNB-5480 could be applicable to genotyping of germplasms that are not amenable to genotyping (R31-33; R35-37) using Phyto5SAR. In a previous study, the combination of molecular markers increased genotyping accuracy of Cf-9 locus in tomato cultivars [52]. In our study, by combining two or three markers, the genotyping accuracy was increased compared to that in single RGA maker (Figure 3). Combination of three markers such as CaNB-5480, CaRP-5130, and CaNB-5330 cosegregated with the resistance and susceptible phenotypes in pepper accessions used in this study (Figure 3(b)). Genotyping results of CaNB-5480 and Phyto5SAR showed 98.4% genotyping accuracy except for one cultivar (R43) and CaNB-5130 can apply to the genotyping of the R43 cultivar. The genotyping accuracy of combination of two or three markers ranged from 78.6% to 100% (Figure 3(b)). Taken together, combination with highly linked markers could be used efficiently for genotyping of pepper varieties for further pepper breeding.

(a)

(b)

Figure 3

Cosegregation rate obtained by genotyping RGA-based markers in 61 cultivars. (a) The cosegregation rate of the each marker. The x-axis shows the names of the molecular markers. The y-axis shows the cosegregation rate. Newly developed markers are indicated by grey bars. White bars show previously developed markers 142964 and Phyto5SAR. (b) The genotyping accuracy when applied with combination of RGA markers at 61 cultivars. The x-axis shows combination of the markers and y-axis shows genotyping accuracy. Grey bars show genotyping accuracy of resistant and moderately resistant cultivars. Genotyping accuracy of susceptible cultivars is indicated by white bars.

The variation of the cosegregation rate (37.7% to 86.9%) suggested the conversion of the major QTL region between the genetic and physical maps. Kim and associates [17] constructed pseudomolecules using a high-density map with 6,281 markers derived from C. annuum ‘Perennial’ and C. annuum ‘Dempsey’. Scaffold anchoring was conducted with genetic maps of a cross between C. annuum ‘NuMexRNAKY’ and C. frutescens acc. 2814-6 [53]. Neither previous study used ‘CM334’-related populations. Genomic variation between ‘CM334’ and other pepper germplasms could represent differences in cosegregation [54]. Further reanalyses of the pepper genomic structure could reveal reanchoring or rearrangement within or among chromosomes.

To date, several markers linked to the major resistance QTL in pepper have been developed including restriction fragment length polymorphisms (RFLPs), randomly amplified polymorphic DNA (RAPD), amplified fragment length polymorphisms (AFLPs), and SNPs, which could lead to marker-assisted selection for breeding P. capsici-resistant lines [55, 56]. Wang and associates [57] also developed SSR markers that tightly linked to the resistance gene in pepper line ‘PI201234’. However, the previously developed markers from the different genetic maps could be insufficient to determine whether the QTL region includes a QTL that is conserved among different progenies. P. capsici isolates with different virulence factors and/or inoculation concentrations also have variable disease phenotypes, which cause variation in cosegregation rates of molecular markers [43, 57]. Rehrig and associates [44] reported cosegregated CaDMR1 with QTL Pc5.1 as a candidate for resistance to P. capsici in pepper, but its function was not determined yet. Here, newly closely linked RGA-based markers of P. capsici resistance were developed, which can be used to genotype breeding sources and to further characterize genes. Our markers may be used in combination with other markers such as CaNB-5480, CaRP-5130, and CaNB-5330 to efficiently determine the phenotypes of pepper germplasms. Such data would also be sufficient to determine the resistance gene spectrum in the QTL region on chromosome 5.

4. Conclusion

In this study, 11 novel RGA-based markers were developed that are linked to major QTL for P. capsici resistance. Among the markers, CaNB-5480 showed the most closely linked marker to major QTL. With combination of CaNB-5480, CaRP-5130 and CaNB-5330 provide the most accurate assessment of genotyping among 61 Capsicum accessions. Together, as combination with other markers, it could be more efficiently phenotyping of pepper germplasms and to further characterize resistance genes against P. capsici.

Data Availability

The data used to support the findings of this study are included within the article and supplementary materials.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

This work was supported by grants from the National Research Foundation of Korea (NRF) from the Korea Government (Ministry of Science and ICT) (NRF-2017R1E1A1A01072843) and the Cooperative Research Program (Project no. PJ010939062018) for Agriculture Science & Technology Development from the Rural Development Administration of the Korean Government. Nayoung Kim was supported by a scholarship from the BK21 Plus Program from the Ministry of Education.

Supplementary Materials

Supp. Table S1: plant materials used to validate the molecular markers in the study. Supp. Table S2: the information of the reanalyzed RGAs in this study. Supp. Table S3: sequence variation (SNPs) between resistant and susceptible plants in pepper varieties. (Supplementary Materials)

References

L. R. Howard and R. E. Wildman, “Antioxidant Vitamin and Phytochemical Content of Fresh and Processed Pepper Fruit (Capsicum annuum),” in Handbook of Nutraceuticals and Functional Foods, pp. 173–199, CRC Press, 2016.
View at: Publisher Site | Google Scholar
FAOSTAT, http://www.fao.org/faostat/en/#data/QC.
Common Names of Plant Diseases, http://www.apsnet.org/publications/commonnames/Pages/Pepper.aspx.
L. H. Leonian, “Stem and fruit blight of peppers caused by Phytophthora capsici sp. nov,” Phytopathology, vol. 12, no. 9, pp. 401–408, 1922.
View at: Google Scholar
M. K. Hausbeck and K. H. Lamour, “Phytophthora capsici on vegetable crops: Research progress and management challenges,” Plant Disease, vol. 88, no. 12, pp. 1292–1303, 2004.
View at: Publisher Site | Google Scholar
K. H. Lamour, R. Stam, J. Jupe, and E. Huitema, “The oomycete broad-host-range pathogen Phytophthora capsici,” Molecular Plant Pathology, vol. 13, no. 4, pp. 329–337, 2012.
View at: Publisher Site | Google Scholar
R. Bartual, E. A. Carbonell, J. I. Marsal, J. C. Tello, and T. Campos, “Gene action in the resistance of peppers (Capsicum annuum) to Phytophthora stem blight (Phytophthora capsici L.),” Euphytica, vol. 54, no. 2, pp. 195–200, 1991.
View at: Google Scholar
L. M. Oelke, P. W. Bosland, and R. Steiner, “Differentiation of race specific resistance to phytophthora root rot and foliar blight in Capsicum annuum,” Journal of the American Society for Horticultural Science, vol. 128, no. 2, pp. 213–218, 2003.
View at: Google Scholar
M. K. Hausbeck and J. M. Foster, “Resistance of pepper to phytophthora crown, root, and fruit rot is affected by isolate virulence,” Plant Disease, vol. 94, no. 1, pp. 24–30, 2010.
View at: Publisher Site | Google Scholar
S. J. Walker and P. W. Bosland, “Inheritance of phytophthora root rot and foliar blight resistance in pepper,” Journal of the American Society for Horticultural Science, vol. 124, no. 1, pp. 14–18, 1999.
View at: Google Scholar
S.-I. Yeom, H.-K. Baek, S.-K. Oh et al., “Use of a secretion trap screen in pepper following Phytophthora capsici infection reveals novel functions of secreted plant proteins in modulating cell death,” Molecular Plant-Microbe Interactions, vol. 24, no. 6, pp. 671–684, 2011.
View at: Publisher Site | Google Scholar
V. Lefebvre and A. Palloix, “Both epistatic and additive effects of QTLs are involved in polygenic induced resistance to disease: a case study , the interaction pepper - Phytophthora capsici Leonian,” Theoretical and Applied Genetics, vol. 93, no. 4, pp. 503–511, 1996.
View at: Publisher Site | Google Scholar
E. A. Ogundiwin, T. F. Berke, M. Massoudi et al., “Construction of 2 intraspecific linkage maps and identification of resistance QTLs for Phytophthora capsici root-rot and foliar-blight diseases of pepper (Capsicum annuum L.),” Genome, vol. 48, no. 4, pp. 698–711, 2005.
View at: Publisher Site | Google Scholar
O. Sy, P. W. Bosland, and R. Steiner, “Inheritance of phytophthora stem blight resistance as compared to phytophthora root rot and phytophthora foliar blight resistance in Capsicum annuum L,” Journal of the American Society for Horticultural Science, vol. 130, no. 1, pp. 75–78, 2005.
View at: Google Scholar
A. Thabuis, A. Palloix, S. Pflieger, A.-M. Daubèze, C. Caranta, and V. Lefebvre, “Comparative mapping of Phytophthora resistance loci in pepper germplasm: evidence for conserved resistance loci across Solanaceae and for a large genetic diversity,” Theoretical and Applied Genetics, vol. 106, no. 8, pp. 1473–1485, 2003.
View at: Publisher Site | Google Scholar
S. Kim, J. Park, S. Yeom et al., “New reference genome sequences of hot pepper reveal the massive evolution of plant disease-resistance genes by retroduplication,” Genome Biology, vol. 18, no. 1, Article ID 210, 2017.
View at: Publisher Site | Google Scholar
S. Kim, M. Park, and S.-I. Yeom, “Genome sequence of the hot pepper provides insights into the evolution of pungency in Capsicum species,” Nature Genetics, vol. 46, no. 3, pp. 270–278, 2014.
View at: Publisher Site | Google Scholar
J. Chunthawodtiporn, T. Hill, K. Stoffel, and A. van Deynze, “Quantitative trait loci controlling fruit size and other horticultural traits in bell pepper (Capsicum annuum),” The Plant Genome, vol. 11, no. 1, Article ID 160125, 2018.
View at: Google Scholar
S.-B. Kim, W.-H. Kang, H. N. Huy et al., “Divergent evolution of multiple virus-resistance genes from a progenitor in Capsicum spp,” New Phytologist, vol. 213, no. 2, pp. 886–899, 2017.
View at: Publisher Site | Google Scholar
H.-A. Lee and S.-I. Yeom, “Plant NB-LRR proteins: tightly regulated sensors in a complex manner,” Briefings in Functional Genomics, vol. 14, no. 4, pp. 233–242, 2015.
View at: Publisher Site | Google Scholar
M. K. Sekhwal, P. Li, I. Lam, X. Wang, S. Cloutier, and F. M. You, “Disease resistance gene analogs (RGAs) in plants,” International Journal of Molecular Sciences, vol. 16, no. 8, pp. 19248–19290, 2015.
View at: Publisher Site | Google Scholar
S.-I. Yeom, E. Seo, S.-K. Oh, K. W. Kim, and D. Choi, “A common plant cell-wall protein HyPRP1 has dual roles as a positive regulator of cell death and a negative regulator of basal defense against pathogens,” The Plant Journal, vol. 69, no. 5, pp. 755–768, 2012.
View at: Publisher Site | Google Scholar
E. Seo, S. Kim, S.-I. Yeom, and D. Choi, “Genome-wide comparative analyses reveal the dynamic evolution of nucleotide-binding leucine-rich repeat gene family among solanaceae plants,” Frontiers in Plant Science, vol. 7, Article ID 1205, 2016.
View at: Publisher Site | Google Scholar
W.-H. Kang and S.-I. Yeom, “Genome-wide Identification, Classification, and Expression Analysis of the Receptor-Like Protein Family in Tomato,” The Plant Pathology Journal, vol. 34, no. 5, pp. 435–444, 2018.
View at: Google Scholar
“Center SGE: Resistance Gene N: Similarity to Toll and the Interleukin-1 Receptor,” Cell, vol. 78, no. 102, pp. 1101–1115, 1994.
View at: Google Scholar
C. Feuillet, S. Travella, N. Stein, L. Albar, A. Nublat, and B. Keller, “Map-based isolation of the leaf rust disease resistance gene Lr10 from the hexaploid wheat (Triticum aestivum L.) genome,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 100, no. 25, pp. 15253–15258, 2003.
View at: Publisher Site | Google Scholar
M. R. Grant, L. Godiard, E. Straube et al., “Structure of the Arabidopsis RPM1 gene enabling dual specificity disease resistance,” Science, vol. 269, no. 5225, pp. 843–846, 1995.
View at: Publisher Site | Google Scholar
D. Halterman, F. Zhou, F. Wei, R. P. Wise, and P. Schulze-Lefert, “The MLA6 coiled-coil, NBS-LRR protein confers AvrMla6-dependent resistance specificity to Blumeria graminis f. sp. hordei in barley and wheat,” The Plant Journal, vol. 25, no. 3, pp. 335–348, 2001.
View at: Publisher Site | Google Scholar
B. C. Meyers, K. A. Shen, P. Rohani, B. S. Gaut, and R. W. Michelmore, “Receptor-like genes in the major resistance locus of lettuce are subject to divergent selection,” The Plant Cell, vol. 10, no. 11, pp. 1833–1846, 1998.
View at: Publisher Site | Google Scholar
J. M. Salmeron, G. E. D. Oldroyd, C. M. T. Rommens et al., “Tomato Prf is a member of the leucine-rich repeat class of plant disease resistance genes and lies embedded within the Pto kinase gene cluster,” Cell, vol. 86, no. 1, pp. 123–133, 1996.
View at: Publisher Site | Google Scholar
S. Yoshimura, U. Yamanouchi, Y. Katayose et al., “Expression of Xa1, a bacterial blight-resistance gene in rice, is induced by bacterial inpculation,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 95, no. 4, pp. 1663–1668, 1998.
View at: Publisher Site | Google Scholar
N. Collins, J. Drake, M. Ayliffe et al., “Molecular characterization of the maize Rp1-D rust resistance haplotype and its mutants,” The Plant Cell, vol. 11, no. 7, pp. 1365–1376, 1999.
View at: Publisher Site | Google Scholar
J. Hu, Z. Pang, Y. Bi et al., “Genetically diverse long-lived clonal lineages of Phytophthora capsici from pepper in Gansu, China,” Journal of Phytopathology, vol. 103, no. 9, pp. 920–926, 2013.
View at: Publisher Site | Google Scholar
W. Lee, J. Lee, J. Han, B. Kang, and J. Yoon, “Validity Test for Molecular Markers Associated with Resistance to Phytophthora Root Rot in Chili Pepper (Capsicum annuum L.),” Korean Journal of Horticultural Science & Technology, vol. 30, no. 1, pp. 64–72, 2012.
View at: Publisher Site | Google Scholar
B. L. Candole, P. J. Conner, and P. Ji, “Evaluation of phytophthora root rot-resistant Capsicum annuum accessions for resistance to phytophthora foliar blight and phytophthora stem blight,” Agricultural Sciences, vol. 3, no. 5, pp. 732–737, 2012.
View at: Publisher Site | Google Scholar
P. W. Bosland, “A seedling screen for Phytophthora root rot of pepper, Capsicum annuum,” Plant Disease, vol. 75, no. 10, pp. 1048–1050, 1991.
View at: Publisher Site | Google Scholar
S.-J. Jo, S.-A. Shim, K. S. Jang, Y. H. Choi, J.-C. Kim, and G. J. Choi, “Resistance of chili pepper cultivars to isolates of Phytophthora capsici,” Korean Journal of Horticultural Science & Technology, vol. 32, no. 1, pp. 66–76, 2014.
View at: Publisher Site | Google Scholar
H. Kim, J. B. Yoon, and J. Lee, “Development of Fluidigm SNP Type Genotyping Assays for Marker-assisted Breeding of Chili Pepper (Capsicum annuum L.),” Korean Journal of Horticultural Science & Technology, vol. 35, no. 4, pp. 465–479, 2017.
View at: Publisher Site | Google Scholar
M. G. Murray and W. F. Thompson, “Rapid isolation of high molecular weight plant DNA,” Nucleic Acids Research, vol. 8, no. 19, pp. 4321–4326, 1980.
View at: Publisher Site | Google Scholar
S. Mallard, M. Cantet, A. Massire, A. Bachellez, S. Ewert, and V. Lefebvre, “A key QTL cluster is conserved among accessions and exhibits broad-spectrum resistance to Phytophthora capsici: a valuable locus for pepper breeding,” Molecular Breeding, vol. 32, no. 2, pp. 349–364, 2013.
View at: Publisher Site | Google Scholar
H.-J. Kim, S.-H. Nahm, H.-R. Lee et al., “BAC-derived markers converted from RFLP linked to Phytophthora capsici resistance in pepper (Capsicum annuum L.),” Theoretical and Applied Genetics, vol. 118, no. 1, pp. 15–27, 2008.
View at: Publisher Site | Google Scholar
J. Lee, S. J. Park, S. C. Hong, J.-H. Han, D. Choi, and J. B. Yoon, “QTL mapping for capsaicin and dihydrocapsaicin content in a population of Capsicum annuum 'NB1' × Capsicum chinense 'Bhut Jolokia',” Plant Breeding, vol. 135, no. 3, pp. 376–383, 2016.
View at: Publisher Site | Google Scholar
W.-Y. Liu, J.-H. Kang, H.-S. Jeong et al., “Combined use of bulked segregant analysis and microarrays reveals SNP markers pinpointing a major QTL for resistance to Phytophthora capsici in pepper,” Theoretical and Applied Genetics, vol. 127, no. 11, pp. 2503–2513, 2014.
View at: Publisher Site | Google Scholar
W. Z. Rehrig, H. Ashrafi, T. Hill, J. Prince, and A. Van Deynze, “CaDMR1 cosegregates with QTL Pc5.1 for resistance to phytophthora capsici in pepper (Capsicum annuum),” The Plant Genome, vol. 7, no. 2, 2014.
View at: Google Scholar
Y. Minamiyama, M. Tsuro, T. Kubo, and M. Hirai, “QTL analysis for resistance to Phytophthora capsici in pepper using a high density SSR-based map,” Breeding Science, vol. 57, no. 2, pp. 129–134, 2007.
View at: Publisher Site | Google Scholar
J. Kourelis and R. A. L. Van Der Hoorn, “Defended to the nines: 25 years of resistance gene cloning identifies nine mechanisms for R protein function,” The Plant Cell, vol. 30, no. 2, pp. 285–299, 2018.
View at: Publisher Site | Google Scholar
J. Du, E. Verzaux, A. Chaparro-Garcia et al., “Elicitin recognition confers enhanced resistance to Phytophthora infestans in potato,” Nature Plants, vol. 1, Article ID 15034, 2015.
View at: Publisher Site | Google Scholar
S. Huang, E. A. G. Van Der Vossen, H. Kuang et al., “Comparative genomics enabled the isolation of the R3a late blight resistance gene in potato,” The Plant Journal, vol. 42, no. 2, pp. 251–261, 2005.
View at: Publisher Site | Google Scholar
A. A. Lokossou, T.-H. Park, G. van Arkel et al., “Exploiting knowledge of R/Avr genes to rapidly clone a new LZ-NBS-LRR family of late blight resistance genes from potato linkage group IV,” Molecular plant-microbe interactions, vol. 22, no. 6, pp. 630–641, 2009.
View at: Publisher Site | Google Scholar
J. Song, J. M. Bradeen, S. K. Naess et al., “Gene RB cloned from Solanum bulbocastanum confers broad spectrum resistance to potato late blight,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 100, no. 16, pp. 9128–9133, 2003.
View at: Publisher Site | Google Scholar
E. A. G. Van Der Vossen, J. Gros, A. Sikkema et al., “The Rpi-blb2 gene from Solanum bulbocastanum is an Mi-1 gene homolog conferring broad-spectrum late blight resistance in potato,” The Plant Journal, vol. 44, no. 2, pp. 208–222, 2005.
View at: Publisher Site | Google Scholar
B. Kim, I. S. Hwang, H.-J. Lee, and C.-S. Oh, “Combination of newly developed SNP and InDel markers for genotyping the Cf-9 locus conferring disease resistance to leaf mold disease in the tomato,” Molecular Breeding, vol. 37, no. 5, Article ID 59, 2017.
View at: Google Scholar
S. C. Yarnes, H. Ashrafi, S. Reyes-Chin-Wo, T. A. Hill, K. M. Stoffel, and A. Van Deynze, “Identification of QTLs for capsaicinoids, fruit quality, and plant architecture-related traits in an interspecific Capsicum RIL population,” Genome, vol. 56, no. 1, pp. 61–74, 2013.
View at: Publisher Site | Google Scholar
B. R. Glosier, E. A. Ogundiwin, G. S. Sidhu, D. R. Sischo, and J. P. Prince, “A differential series of pepper (Capsicum annuum) lines delineates fourteen physiological races of Phytophthora capsici: Physiological races of P. capsici in pepper,” Euphytica, vol. 162, no. 1, pp. 23–30, 2008.
View at: Publisher Site | Google Scholar
A. Thabuis, V. Lefebvre, G. Bernard et al., “Phenotypic and molecular evaluation of a recurrent selection program for a polygenic resistance to Phytophthora capsici in pepper,” Theoretical and Applied Genetics, vol. 109, no. 2, pp. 342–351, 2004.
View at: Publisher Site | Google Scholar
A. Thabuis, A. Palloix, B. Servin et al., “Marker-assisted introgression of 4 Phytophthora capsici resistance QTL alleles into a bell pepper line: validation of additive and epistatic effects,” Molecular Breeding, vol. 14, no. 1, pp. 9–20, 2004.
View at: Publisher Site | Google Scholar
P. Wang, L. Wang, J. Guo, W. Yang, and H. Shen, “Molecular mapping of a gene conferring resistance to Phytophthora capsici Leonian race 2 in pepper line PI201234 (Capsicum annuum L.),” Molecular Breeding, vol. 36, no. 6, Article ID 66, 2016.
View at: Google Scholar

Copyright

Copyright © 2019 Nayoung Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

2502

Downloads

1850

Citations