Abstract
Black rot, a disease of significance affecting vegetable Brassica crops, is primarily caused by the bacterium Xanthomonas campestris pv. campestris (Xcc). When the disease spreads extensively in the field, it can lead to substantial yield losses, particularly under favorable environmental conditions. Controlling the spread of this disease is challenging, and the primary approach involves utilizing resistant cultivars or disease-free seeds. Among the various methods available for identifying different Xcc races, Polymerase Chain Reaction (PCR)-based molecular markers have proven to be highly reliable. To date, the PCR method has successfully identified Xcc races 1 to 7. In this study, molecular markers were developed for races 4 and 9 through the sequencing and alignment of the whole genome sequences of Xcc races, closely related Xanthomonas campestris (Xc) pathovars, and two Xanthomonas species. These designed markers were subsequently validated by PCR with bacterial genomic DNA samples from Xcc races and 7 other bacteria. The results indicated successful amplification only for race 4 and race 9, yielding amplicon sizes of 1080 bp and 830 bp, respectively, while the other strains failed to amplify. Furthermore, the amplicons from races 4 and 9 were cloned and sequenced, confirming that both races exhibited matching sequences after alignment. Consequently, the molecular marker method offers a rapid and efficient means of differentiating between Xcc races 4 and 9 within a few hours, presenting itself as a viable alternative to conventional methods that rely on the use of differential cultivars of Brassicaceae for identifying Xcc races.
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Key words: Black rot, Cabbage, Molecular marker, Xanthomonas campestris pv. campestris, PCR
Introduction
Xanthomonas campestris pathovar
campestris (
Xcc) is a small, aerobic, rod-shaped, and Gram-negative bacterium, which causes the black rot disease in cabbage and other
Brassica vegetables. It is considered the most serious disease that infects almost all the important cruciferous crops such as cabbage, cauliflower, broccoli, brussels sprouts, kohlrabi, and kale as well as the model plant
Arabidopsis (
An ShiQi et al. 2011;
Soengas et al. 2007;
Vicente et al. 2001).
Xcc enters the plants through the hydathodes and wounded leaves and develops V-shaped yellow lesions with necrotic and blackening veins at the leaf margins (
Cook et al. 1952;
Williams 1981). In Korea,
Xcc is one of the most damaging diseases in cabbage-growing fields (
Kim 1986).
Conventional approaches for identifying the causal agent of plant diseases have traditionally involved the utilization of visual symptom assessment, microscopy techniques, and culturing methods (
Ward et al. 2004). Nevertheless, reliance on these straightforward, cost-effective, and time-consuming methods can sometimes lead to erroneous conclusions, particularly when there are striking similarities between disease symptoms and the morphological characteristics of pathogens. This inherent limitation highlights the heavy dependence of these methods on prior knowledge and experimental expertise (
Nezhad 2014). Up to now,
Xcc races have been classified into eleven races based on race determination using the differential cultivars method (
Cruz et al. 2017;
Fargier et al. 2007;
Kamoun et al. 1992;
Vicente et al. 2001). Among eleven races, races 1 and 4 are predominant in
Brassica oleracea whereas race 6 is predominant in
Brassica rapa (
Lema et al. 2012;
Vicente et al. 2013). Although race 9 of
Xanthomonas is not predominant in every part of the world, the recent development in the seed trade between the countries poses a potential threat of spreading the race to other countries. Therefore, it is important to detect the presence of these races. However, the identification of
Xcc races based on differential cultivars is time-consuming and labor-intensive. Furthermore, the process of culturing pathogens for identification purposes is time-consuming and, in some cases, not feasible due to the inability to culture certain pathogens (
Cardenas et al. 2008;
Rinke et al. 2013).
The rapid advancements in molecular biotechnology have carried out the way for the application of rapid and dependable detection methods, including PCR (
Ward et al. 2004). The development of race-specific molecular markers with PCR-based techniques is very effective, quick, and simple. Molecular identification of pathogens through PCR saves time (within a few hours) and is less laborious for detecting plant pathogenic bacterial races (
Song et al. 2014). The emergence of Next-Generation Sequencing (NGS), has introduced a pioneering approach to diagnostics, and methods for detecting and identifying phytopathogens (
Chalupowicz et al. 2019). These advancements have paved the way for DNA-based NGS, which encompasses many steps including variant/mutation annotation and interpretation (
Qin 2019). Various variations, including single nucleotide polymorphisms (SNPs), insertions and deletions (INDELs), and structural variations can be discovered using the population genomics datasets based on NGS (
Potgieter et al. 2020). These variations can be exploited to design molecular markers and can be used for plant pathogen diagnosis (
Afrin et al. 2020;
Rubel et al. 2019a). Many researchers have used PCR-based molecular markers to identify bacterial and fungal pathogens. Two (Sequence Characterized Amplified Region) SCAR markers were developed to detect the fungus
Fusarium oxysporum f. sp.
melonis race 2, caused vascular wilt of cucurbits (
Luongo et al. 2012). The new
Xanthomonas oryzae pv.
oryzae K3a in rice plants was identified using a race-specific marker-based PCR technique (
Song et al. 2014). Race 3 of
Pseudomonas syringae pv.
phaseolicola in halo blight of common bean was identified by PCR-based molecular markers (
Schaad et al. 1995). This study aimed to develop a specific molecular marker for the identification of
Xcc races 4 and 9.
Materials and Methods
Bacterial strains and culture conditions
The 16 bacterial strains were used in this study, including nine
Xcc races (races 1-9); three
Xc pathovars:
X. campestris pv.
incanae (
Xci),
X. campestris pv.
raphani (
Xcr), and
X. campestris pv.
zinniae (
Xcz); two
Xanthomonas species:
X. axonopodis pv.
dieffenbachiae (
Xad), and
X. campestris pv.
vesicatoria (
Xcv) and two other plant pathogenic bacteria:
Pseudomonas syringae pv.
maculicola (
Psm), and
Erwinia carotovora subsp.
carotovora (
Ecc) (
Table 1). All the bacterial isolates were grown on King's B medium (KB) for 48 hours at 30℃ (
King et al. 1954).
The bacterial DNA was extracted from 16 bacterial strains using the QIAamp DNA Mini Kit (Qiagen, Valencia, CA), according to the manufacturer's instructions. The concentration and quality of all the DNA after extraction were measured using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, USA). Then, the DNA was stored at -20℃ for further experiments.
Whole genome re-sequencing and alignment of Xcc races
The whole genome sequences of
Xcc races: race 1 (B100 and CFBP1869), race 3 (ATCC33913), race 4 (CFBP5817), race 9 (str.8004), and
Xci (CFBP1606R),
Xcr (str.756C) and
Xcv (str.85-10) were taken from the National Center for Biotechnology Information (NCBI) database (
www.ncbi.nlm.nih.gov). The genomes of races 2, 5, 6, 7, and 8 were sequenced and aligned with the available genome sequences mentioned above using the Integrative Genomics Viewer (IGV) (
https://software.broadinstitute.org/software/igv/) to identify the variant regions.
The flanking DNA sequences near the variant regions were used to design primers using Primer3 (
https://primer 3.ut.ee/), thereafter twenty primer pairs were designed (Supplementary Table S1). PCR was performed with a 10 μL reaction mixture containing 1 μL (30 ng μL
-1) of DNA, 0.5 μL of each 10 pmol forward and reverse primers, 5 μL of 2X Prime Taq Premix (GenetBio, Daejeon, Korea) and 3 μL of sterile distilled water. The PCR conditions for race 4 and race 9-specific markers were adjusted with denaturation at 94℃ for 2 minutes followed by 30 cycles (94℃ for 20 seconds, 70℃ for 30 seconds, and 72℃ for 20 seconds) and terminated by a final elongation at 72℃ for 2 minutes, 25℃ overnight (Supplementary Table S1). The PCR products were analyzed with gel electrophoresis using 1.5% agarose at 100 V for 30 minutes and visualized with a gel documentation system under UV light (320 nm). The size of PCR products was determined using HiQ 100 bp DNA ladder. Additionally, reported primers for specific amplification of
Xcc races 1-7 (Supplementary Table S1) were used for the validation using
Xcc race 9 DNA.
To evaluate the sensitivity and specificity of 'XccR9-2F2-2R1' primer, 30 ng μL-1 DNA of races 4 and 9 was taken and serially diluted up to 10-4 dilution (30 ng μL-1, 3 ng μL-1, 0.3 ng μL-1, 0.03 ng μL-1, 0.003 ng μL-1). Thereafter, 1 μL of DNA from each dilution was used for PCR amplification using the PCR conditions described above.
Cloning and sequencing of races 4 and 9
The PCR amplicons of
Xcc races 4 and 9 using the 'XccR9-2F2-2R1' primer were further cloned and sequenced. The visible bands were excised from the agarose gels (0.8%) and the amplicons were purified in mini-columns using the Wizard SV gel and PCR cleanup system (Promega, Madison, WI, USA). The amplicons were cloned into
E. coli (DH5α) using the TOPcloner blunt kit (Enzynomics, Daejeon, Korea) following the manufacturer's instructions. Thereafter, the single colonies grown overnight on solid Luria Bertani (LB) media containing ampicillin (1 mg mL
-1) were picked. Subsequently, these selected colonies were cultured in liquid LB media to isolate plasmid DNA. The plasmid DNA isolations were purified with QIAprep Spin Miniprep Kit (50). The alignment of cloned sequences was performed with Multiple Sequence Alignment by ClustalW online (
https://www.genome.jp/tools-bin/clustalw). Additionally, the cloned and sequenced amplicons were subjected to ORF prediction using NCBI tool ORF Finder (https://
www.ncbi.nlm.nih.gov/orffinder/). The predicted ORF sequences were searched for protein homology in the NCBI database using the Blastp tool (
https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins).
The susceptible cabbage lines were artificially inoculated with strains of
Xcc races 1 to 9. Three plants for each race were inoculated at 35 days after sowing, and the three youngest leaves were inoculated using the clipping method followed by dipping into a bacterial suspension (10
8-10
9 CFU/mL) of
Xcc races (
Vicente et al. 2001). After 2 weeks, when the symptoms appeared on the leaves, three infected leaves were collected for bio-PCR assay. Afterward, the leaves with black rot symptoms were cut about 1 cm, sliced into small pieces, and soaked in 2 mL of sterile water for 1 hour at room temperature. Following this, 1 mL of bacterial ooze was transferred into a 1.5 mL microcentrifuge tube and incubated at 65℃ for 10 min. Eventually, 5 μL of the exudates were used for the bio-PCR assessment with the PCR condition stated above.
Results
Identification of variant regions and primer designing
The comparison of whole genome sequences, including
Xcc races (1-9), two
Xc pathovars (
Xci and
Xcr), and one other
Xanthomonas species (
Xcv), and the alignment of these available complete genome sequences allowed us to identify the variant regions (
Fig. 1). Variant regions specific to
Xcc race 4 and race 9 were identified using the Integrative Genomics Viewer (IGV) tool. Flanking genomic sequences of these variants were then utilized to initially design twenty pairs of primers to detect the
Xcc races 4 and 9 (Supplementary Table S1). Out of these 20 pairs of primers, only one primer pair namely 'XccR9-2F2-2R1' (forward primer sequence- CGAACAGCAAAGGCAGATACAG; reverse primer sequence - CATGCGCATAGCGGCCCGCCTTG) able to differentiate
Xcc races 4 and 9 from
Xcc races 1-8,
Xc pathovars,
Xanthomonas species and other plant pathogenic bacteria. None of the markers developed for
Xcc race 1-7 gave a specific reported amplicon with
Xcc race 9 DNA (
Figs. 2b-
2h).
In this study, we developed a marker for early and quick detection of
Xcc race 4 and race 9. The designed primers were validated with bacterial DNA of
Xcc races,
Xc pathovars,
Xanthomonas species, and other plant pathogenic bacteria by PCR amplifications (
Table 1). Among twenty designed primers, only one primer pair 'XccR9-2F2-2R1' was able to differentiate the
Xcc race 9 with an amplicon size of 830 bp (
Fig. 2i and Supplementary Fig. S1). Interestingly this primer gave an amplicon size of 1080 bp with
Xcc race 4 DNA whereas all the other
Xcc races (races 1, 2, 3, 5, 6, 7, and 8),
Xc pathovars (
Xci,
Xcr, and
Xcz),
Xanthomonas species (
Xad and
Xcv), and other plant pathogenic bacteria (
Psm and
Ecc) used in this study were unamplified (
Fig. 2i). Despite this, the primer pair 'XccR9-2F2-2R1' amplified an amplicon in
Xcc race 4 and race 9, but it was able to differentiate between both races (
Fig. 2i). Therefore, the primer pair 'XccR9-2F2-2R1' can be used as a diagnostic molecular marker for detecting both races 4 and 9.
We further conducted tests to evaluate the efficiency and sensitivity of the newly developed marker 'XccR9-2F2-2R1'. Remarkably, this marker successfully detected DNA from
Xcc race 4 and race 9 even at a very low concentration of 0.003 ng
µL
-1(
Fig. 3).
Cloning and sequencing of the PCR amplicons produced by the 'XccR9-2F2-2R1' primer in races 4 and 9 DNA revealed a 190 bp deletion in race 9 when compared to race 4. Therefore, this marker can be used proficiently to differentiate race 4 and race 9 with amplicon sizes of 830 bp and 1080 bp, respectively (Supplementary Figs. S2a, S2b).
The cloned and sequenced amplicons were subjected to Open Reading Frame (ORF) prediction using NCBI tool ORF Finder. The number of ORFs found in race 9 and race 4 cloned fragments was six and nine, respectively. The largest ORF found was 843 nucleotides and 630 nucleotides in length and encoded for 280 amino acids and 206 amino acids in race 4 and race 9, respectively (Supplementary Figs. S2c, S2d). This protein has significant homology with the protein-encoding IS5 transposase family, but the protein sequence of the race 9 amplicon has a 71 bp deletion when compared to the protein sequence of the race 4 amplicon (Fig. S2d).
Bio-PCR assessment
A bio-PCR assay was conducted to validate the competency of the developed markers in detecting the presence of
Xcc races 4 and 9 from infected leaves. Cabbage leaves were artificially infected with various strains of
Xcc races (1-9) strain. None of the samples infected with
Xcc races 1, 2, 3, 5, 6, 7, and 8 showed amplification, whereas races 4 and 9 yielded amplicon sizes of 1080 bp and 830 bp, respectively. Therefore, the results demonstrated amplification with a base-pare size equivalent to positive control DNA (gDNA of
Xcc race 4 and race 9) (
Fig. 4). This finding affirmed the potential of the novel developed marker that can directly detect the bacterial ooze from soaking leaves of cabbage without the genomic DNA extraction of bacteria within a few hours with an accurate and reliable identification.
Discussion
The establishment of races and differentiation of plant pathogens is associated with the presence of
Avr genes in pathogens and its interaction with the corresponding R gene in the plant system, which became the basis of race differentiation using the differential cultivars. However, the system of race differentiation using differential cultivars requires rigorous efforts in growing plants, microbial culturing, optimization of culturing conditions, and skilled personnel. On the other hand, the use of molecular diagnostics of plant pathogens is less tedious. Identifying the race of a plant pathogen is an important criterion for the management of the disease. There have been various reports on the molecular diagnosis of plant-pathogenic bacteria and fungi (
Hariharan et al. 2021;
Tewari et al. 2019). The utilization of race-specific molecular markers in this study facilitated rapid, reliable, and precise identification of
Xcc race 4 and race 9. The availability of genomic sequences for
Xcc races (races 1- 9) and other
Xc pathovars (
Xci,
Xcr, and
Xcv) simplified the identification of highly variable genomic regions and the development of specific markers for race 4 and race 9 (
Fig. 1). Alignment of genome sequences provides useful data about the variants. However, these variants need to be analyzed and interpreted with the help of certain tools. IGV is one of the tools for the visualization and interpretation of variants (
Robinson et al. 2017). This study also used the IGV software for variant calling to design the specific primers. The previously developed marker by
Rubel et al. (2017) specifically detected
Xcc race 4 (
Fig. 2e, Supplementary Table S1), whereas our marker provides the advantage of simultaneous detection of
Xcc races 4 and 9. The race-specific markers developed in this study provided rapid and effective tools, requiring minimal labor and offering high reliability, for specifically detecting and differentiating races 4 and 9 from other
Xcc races. In contrast, the cultivar-based race determination method requires extensive fieldwork and additional labor. Our 'XccR9-2F2-2R1' primer displayed the ability to identify
Xcc races 4 and 9 through PCR amplification even at very low concentrations of genomic DNA of races 4 and 9, highlighting the high efficiency and sensitivity of the marker quality (
Fig. 3).
Moreover, PCR-based molecular marker techniques have been successfully employed for the detection of bacterial and fungal pathogens.
Xanthomonas pathovars were detected in
Brassica seeds and plants, and
X. campestris was specifically distinguished from other
Xanthomonas using PCR (
Berg et al. 2005). In Korea,
Rubel et al. (2017) developed specific markers using SCAR for the detection of
Xcc races 1 and 4.
Wang et al. (2010), in China, reported highly reliable PCR-based SCAR markers for detecting wheat stripe rust races CYR32 and CYR33 caused by
Puccinia striiformis f. sp.
tritici. Pasquali et al. (2007), successfully employed an inter-retrotransposon sequence-characterized amplified region (IR-SCAR) marker to identify race 1 of
F. oxysporum f. sp.
lactucae in lettuce.
PCR-based methods have also proven effective in identifying other pathogens, including race 3 of
Pseudomonas syringae pv.
phaseolicola in halo blight of common bean (
Schaad et al. 1995),
Xcc in black rot disease (
Singh et al. 2014),
Xoo in rice bacterial blight (
Song et al. 2014), and
Colletotrichum spp. causing anthracnose disease in sweet persimmon (
Iee et al. 2002). Therefore, this PCR-based molecular marker holds promise for the rapid identification of
Xcc race 4 and race 9 in cabbage fields affected by black rot disease caused by
Xcc worldwide.
Insertion sequences (IS) and transposons play a significant role in bacterial pathogenicity and evolution, including
Xanthomonas, by facilitating genome rearrangement (
Ferreira et al. 2015). In the case of
Xanthomonas, it has been reported that IS elements might potentially introduce inversions and rearrangements, thereby contributing to race diversity (
Ochiai et al. 2005). In this study, we conducted sequence analysis of the amplicons from race 4 and race 9 using the 'XccR9-2F2-2R1' marker, which indicated the presence of transposases belonging to the IS5 family. However, the presence of a complete gene in race 4 likely aided its adaptation through genome rearrangement, and further studies are required to confirm the role of this gene.
In conclusion, this study developed a molecular marker-based PCR amplification for the specific identification of races 4 and 9 through the realignment of whole genome sequences of Xcc races (1-9), two Xanthomonas species (Xci and Xcr), and one other Xc pathovar (Xcv). The newly developed marker (XccR9-2F2-2R1) illustrated a quick and reliable detection of race 4 and race 9 among various bacterial strains such as Xcc races, Xanthomonas species, and other plant pathogenic bacteria. Therefore, to our knowledge, this novel marker proved as a useful and rapid diagnostic tool for accurate and reliable detection of Xcc races 4 and 9. Additionally, this marker can be used as an alternative method compared to a traditional one using differential cultivars of Brassicas for race identification, which is time-consuming and labor-intensive.
Acknowledgments
This study was supported by grants from Ministry of Agriculture, Food and Rural Affairs (MAFRA) (322059- 03-2-HD050) and Regional Innovation Strategy (RIS) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021RIS-002). We would thank Dr. Pilar Soengas, Department of Plant Genetics, Spain for providing Xcc race 8. We also thank Dr. Joana G. Vicente, University of Warwick, UK for providing Xcc races (races 1-7) and Xc pathovars. We thank the Korean Agriculture Culture Collection (KACC), Korea for providing Xanthomonas species, and ICMP collection, New Zealand for providing isolates of plant pathogenic bacteria.
Fig. 1. a) Alignment of the whole genome sequences of Xcc races (1-9), two other Xc. pathovars (incanae and raphani), and another Xanthomonas species (X. campestris pv. vesicatoria); b) Line diagram representation of PCR amplification pattern with XccR9-2F2-2R1 primer; Dashed lines represent deletion region; Solid black bar represents similar genomic regions; Green arrow and blue arrows represent forward and reverse primers, respectively.
Fig. 2Agarose gel electrophoresis of PCR products of Xcc race 4 and race 9 DNA using race a 9-specific primer, and Xcc race specific primer and specific primer sets of Xcc races 1-7 reported by previous studies. a) Xcc specific (Xcc-53, 930-bp); b) Xcc race 1-specific (Xcc-47R1, 1089-bp); c) Xcc race 2-specific (Xcc-R2-89-2, 929-bp); d) Xcc race 3-specific (XccR3-49, 867-bp); e) Xcc race 4-specific (Xcc2-46R4, 578-bp); f) Xcc race 5-specific (XccR5-89.2, 1515-bp); g) Xcc race 6-specific (XccR6-60, 693-bp); h) Xcc race 7-specific (Race 7-1F-1R, 600-bp); i) Xcc race 9-specific (XccR9-2F2-2R1, 830-bp) amplified from genomic DNA of Xcc races, Xc. pathovars and other plant pathogenic bacteria. Lane M: DNA ladder-100 bp; Lanes 1-9: Xcc races (1-9); Lane 10: X. campestris pv. incanae (WHRI-6377); Lane 11: X. campestris pv. raphani (WHRI-8305); Lane 12: X. campestris pv. zinniae (KACC17126); Lane 13: X. axonopodis pv. dieffenbachiae (KACC17821); Lane 14: X. campestris pv. vesicatoria (KACC11153); Lane 15: Pseudomonas syringae pv. maculicola (ICMP13051); Lane 16: Erwinia carotovora subsp. carotovora (ICMP12464).
Fig. 3Efficient detection of different rates of DNA concentrations by PCR amplification using XccR9-2F2-2R1 primer. a) Detection of genomic DNA of race 4; b) Detection of genomic DNA of race 9. Lane M: DNA ladder (100-bp); Lane 1: 30 ng μL-1; Lane 2: 3 ng μL-1; Lane 3: 0.3 ng μL-1; Lane 4: 0.03 ng μL-1; Lane 5: 0.003 ng μL-1.
Fig. 4Bio-PCR assessment using race 9-specific primer (XccR9-2F2-2R1) to detect 27 samples from infected cabbage leaves after inoculation with strains of Xcc races 1-9. Lane M: DNA ladder (100-bp); Lane 1-3: race 1; Lane 4-6: race 2; Lane 7-9: race 3; Lane 10-12: race 4; Lane 13-15: race 5; Lane 16-18: race 6; Lane 19-21: race 7; Lane 22-24: race 8; Lane 25-27: race 9; +ve1: positive control of gDNA of race 4, +ve2: positive control of gDNA of race 9; -ve: negative control of distilled water.
Table 1List of bacterial strains used in this study.
Table 1
|
SL. |
Bacterial Strains |
Races |
Host |
Country |
Collection Year |
References |
|
1 |
X. campestris pv. campestris (HRIW-3811) |
1 |
B. oleracea
|
US |
2017 |
, Vicente et al. (2001)
|
|
|
2 |
X. campestris pv. campestris (HRIW-3849A) |
2 |
B. oleracea var. botrytis
|
US |
2017 |
|
|
3 |
X. campestris pv. campestris (HRIW-5212) |
3 |
B. oleracea var. gemmifera
|
UK |
2017 |
|
|
4 |
X. campestris pv. campestris (HRIW-1279A) |
4 |
B. oleracea var. capitata
|
UK |
2017 |
|
|
5 |
X. campestris pv. campestris (HRIW-3880) |
5 |
B. oleracea var. capitata
|
Australia |
2017 |
|
|
6 |
X. campestris pv. campestris (HRIW-6181) |
6 |
B. rapa
|
Portugal |
2017 |
|
|
7 |
X. campestris pv. campestris (HRIW-8450A) |
7 |
B. oleracea var. capitata
|
UK |
2017 |
|
|
8 |
X. campestris pv. campestris (MBG-145.3) |
8 |
B. rapa
|
Spain |
2017 |
, Lema et al. (2012)
|
|
|
9 |
X. campestris pv. campestris (NCPPB-1145) |
9 |
- |
UK |
2022 |
NCPPB |
|
|
10 |
X. campestris pv. incanae
(WHRI-6377) |
- |
Matthiola incana
|
UK |
2017 |
, Vicente et al. (2001)
|
|
|
11 |
X. campestris pv. raphani
(WHRI-8305) |
2 |
B. rapa var. perviridis
|
UK |
2017 |
|
|
12 |
X. campestris pv. zinniae (KACC17126) |
- |
Zinnia elegans
|
South Korea (Suwon) |
2017 |
KACC |
|
|
13 |
X. axonopodis pv. dieffenbachiae (KACC17821) |
- |
Anthurium andreanum
|
South Korea (Yongin) |
2017 |
|
|
14 |
X. campestris pv. vesicatoria (KACC11153) |
- |
- |
South Korea |
2017 |
|
|
15 |
Pseudomonas syringae pv. maculicola (ICMP13051) |
- |
B. oleracea var. capitata
|
New Zealand |
2016 |
ICMP |
|
|
16 |
Erwinia carotovora subsp. carotovora (ICMP12464) |
- |
B. oleracea var. capitata
|
New Zealand |
2016 |
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