characterization and variability of soft rot-causing bacteria in ... - SIPaV

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Journal of Plant Pathology (2011), 93 (1), 173-181

Edizioni ETS Pisa, 2011

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CHARACTERIZATION AND VARIABILITY OF SOFT ROT-CAUSING BACTERIA IN CHINESE CABBAGE IN NORTH EASTERN BRAZIL I.C.M. Alvarado1, S.J. Michereff1, R.L.R. Mariano1, E.B. Souza1, A.M. Quezado-Duval2, L.V. Resende1, E. Cardoso1 and E.S.G. Mizubuti3 1 Universidade

Federal Rural de Pernambuco, Departamento de Agronomia, 52171-900 Recife, Pernambuco, Brazil 2 Embrapa Hortaliças, 70359-970 Brasília, Distrito Federal, Brazil 3 Universidade Federal de Viçosa, Departamento de Fitopatologia, 36571-000 Viçosa, Minas Gerais, Brazil

SUMMARY

Yield of Chinese cabbage (Brassica pekinensis) may be limited by the occurrence of the soft rot caused by pectinolytic bacteria. Thirty-nine bacterial isolates associated with soft rot in Chinese cabbage, obtained from northeastern Brazil, were identified as Pectobacterium carotovorum subsp. carotovorum (Pcc) based on biochemical tests and URP-PCR. The variability of all isolates was assessed with reference to disease components, i.e. incubation period (IP), initial severity (ISEV), final severity (FSEV) and area under the disease progress curve (AUDPC), sensitivity to 12 antibiotics and the banding pattern of REP, ERIC and BOX in Rep-PCR. Based on IP, ISEV, FSEV and AUDPC, the isolates were distributed in six similarity groups after cluster analysis. There was significant correlation (P≤0.05) between IP and ISEV. Based on the sensitivity to antibiotics, Pcc isolates were distributed in 14 groups. Significant correlations between sensitivity to gentamicin and IP (r = -0.41), as well as between sensitivity to clindamycin and FSEV (r = -0.45) were detected. There was high genetic variability among the 39 isolates based on the molecular markers. A total of 32 similarity groups were formed. No significant correlations were found between the linkage distances of molecular markers and either the disease components or antibiotic sensitivity. Overall, there was high variability in populations of Pcc affecting Chinese cabbage in north-eastern Brazil. Key words: Pectobacterium carotovorum subsp. carotovorum, Brassica pekinensis, disease components, resistance to antibiotics, molecular markers.

INTRODUCTION

Pernambuco state (north eastern Brazil), is one of the main producers of Chinese cabbage (Brassica pekinensis L.), the average yield between 2001 and 2005 being estimated to be 75.6 t/year. Camocim de São Félix is the Corresponding author: S.J. Michereff Fax: +55.81.33206205 E-mail: [email protected]

most important producing county, and accounts for 45% of the total crop (Ceasa-PE, 2007). Yield of Chinese cabbage can be limited by diseases among which soft rots, caused by pectinolytic bacteria, are the most destructive worldwide (Mew et al., 1976; Kikumoto, 1980; Ren et al., 2001). Soft rots are frequent in Pernambuco state. For instance, in a survey conducted in 2004 the disease was recorded in all fields of Chinese cabbage sampled in the Agreste and in the Zona da Mata regions. Disease prevalence was 100%, and an incidence of up to 67% was recorded in a sampled field (Silva et al., 2007). The initial symptom of soft rot in Chinese cabbage is the maceration of leaf base tissues that are in contact with soil. The disease can progress quickly and symptoms can be observed in the main stem. The whole plant can collapse after a few days (Kikumoto, 1980). The main bacterial species associated with soft rots in Chinese cabbage is Pectobacterium carotovorum (Jones) Hauben et al., predominantly P. carotovorum subsp. carotovorum (Jones) Hauben et al. (Mew et al., 1976; Ren et al., 2001). However, P. carotovorum subsp. odoriferum (Gallois et al.) Hauben et al. (Seo et al., 2004) has also been reported to cause soft rot in this crop. Classification of the Pectobacterium spp. is mainly based on biochemical and phenotypic characteristics (Seo et al., 2004). One important characteristic is the capacity to grow and form a depression in crystal-violet pectate medium (CVP) (Hyman et al., 2002). According to De Boer and Kelman (2001), P. carotovorum can be differentiated from other species of the genus based on the ability to grow at 37ºC, production of acid from αmethyl glucoside, indol production, phosphatase activity, and sucrose reduction. Nevertheless, phenotypic identification of the bacteria is time-consuming and reliability is questionable (Toth et al., 2001). The high genetic and phenotypic diversity of Pectobacterium spp. makes identification a challenging issue (Seo et al., 2000, 2002). Molecular methods are rapid, sensitive, cost effective, and widely used in diagnostic bacteriology (Janse, 2005). They also can quantify pathogen variability or polymorphism (Hu et al., 2007). The genetic variability of P. carotovorum subsp. caro-


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tovorum (Pcc) has been investigated by restriction fragment length polymorphism (RFLP), rep-PCR (Seo et al., 2000) using BOX, ERIC, and REP primers that correspond to the conserved repetitive elements of the bacterial genome (Louws et al., 1999). Pcc isolates have also been studied using DNA sequences of the 16S and 23S of rDNA (Fessehaie et al., 2002) and the intergenic region 16S-23S (ITS) (Toth et al., 2001). However, for practical purposes (see below), the association of genetic variability assessed with molecular markers must be complemented with the analysis of variation regarding variables directly related to disease development and management strategies, such as disease components and sensitivity to chemical compounds. Bacterial isolates can differ in severity of the disease they cause and sensitivity to antibiotics. Quantification of disease components related to the infection cycle is important to infer for determining pathogenic variations. Adoption of this approach makes it possible to assess differences among isolates regarding the speed of host tissue colonization, thus the possible severity of disease in the field (Costa et al., 2001; Silveira et al., 2003). Sensitivity of bacterial isolates to antibiotics can also vary. The effectiveness of these antibiotics can be affected by differential responses among isolates. Knowledge about the diversity of soft rot-causing bacteria in Chinese cabbage can potentially contribute to design more effective disease management strategies (Seo et al., 2000). Despite the importance of both crop and disease in the Agreste region (Pernambuco state), no thorough investigation of the population of these plant pathogenic bacteria has been conducted. The objective of this study was to characterize soft rot-causing bacteria that affect Chinese cabbage in the main producing areas of Pernambuco state and to quantify the amount of variability among isolates of Pcc based on disease components, sensitivity to antibiotics and molecular makers.

MATERIALS AND METHODS

Sampling and characterization of bacterial isolates. Chinese cabbages with typical soft rot symptoms collected from different producing areas in Camocim de São Félix during 2004 and 2005, were taken to the laboratory and selective isolation of P. carotovorum was carried out in sweet pepper as described (Takatsu et al., 1981). Preliminary identification of the isolates was done using Casamino acid-peptone-glucose culture medium (CPG). Young Pectobacterium colonies (36-48 h) observed under a stereoscope with oblique illumination, had a “broken glass” appearance (Kelman and Dickey, 1995). Thirty nine isolates with these characteristics were subjected to the following tests: Gram coloration, oxidation/fermentation, oxidase, catalase, soft rot in potato tuber


(Hyman et al., 2002) and pathogenicity in detached leaves of Chinese cabbage (cv. Komachi). Isolate identification at the subspecies level was done based on the following tests: growth at 37°C; reducing substances from sucrose; utilization of α-methyl glucoside; acid production from sorbitol; melibiose and lactose; sensitivity to erythromycin; growth in 5% NaCl, and production of phosphatase and lecithinase (Hyman et al., 2002). Additional tests such as hydrolysis of casein (Dye, 1969) and colony color on nutrient agar-glycerol-MnCl2 medium (NGM) were done to differentiate Pcc from Dickeya chrysanthemi (Burkholder et al.) Samson et al. (Lee and Yu, 2005). Pure cultures were preserved in sterilized distilled water (SDW) and stored in the culture collection of the Laboratório de Fitobacteriologia of the Universidade Federal Rural de Pernambuco. DNA extraction. Total genomic DNA of each isolate was extracted from 36 to 48 h old cultures grown in 5 ml of nutrient broth and incubated at 25±4°C. The concentration of the bacterial suspension was adjusted to A600 = 0.3 using a photocolorimeter. DNA was extracted using the CTAB method (Wilson, 1999). An aliquot of 1.5 ml of bacterial suspension was transferred to microtubes, centrifuged twice at 14,000 g for 2 min, and the supernatant was discarded. The pellet was resuspended in SDW and centrifuged at 14,000 g for 3 min. The pellet was resuspended with 567 µl of TE buffer, pH 8. A volume of 30 µl of 10% SDS and 3 µl proteinase K (20 mg ml-1) were added to the tubes and vortexed. Tubes were kept at a 37°C for 1.5 h. After incubation, 100 µl of 5 M NaCl were added to each tube and tubes were shaken. CTAB/NaCl (4.1 g of NaCl and 10 g of CTAB in 100 ml of water) was added and tubes were kept in a water bath at 65°C for 10 min. After incubation, 780 µl of chlorophorm-isoamilic alcohol (24:1) were pipetted, tubes were manually agitated for 10 min, and centrifuged at 14,000 g for 5 min. Supernatant was transferred to a new tube, phenolchlorophorm-isoamilic alcohol (25:24:1) was added, tubes were manually agitated for 10 min, and centrifuged at 14,000 g for 5 min. Supernatant was transferred to new tubes and 360 µl of isopropanol were added to precipitate DNA by gently agitation. After precipitation, tubes were kept at -70oC for 10 min and centrifuged at 14.000 g for 20 min. Supernatant was discarded and DNA was washed with 70% ethanol followed by a centrifugation at 14.000 g for 10 min. Ethanol was discarded and tubes were placed in SpeedVac for 5 min to dry. DNA was resuspended with 50 µl TE buffer pH 8 with RNase (10 µg ml-1). The amount of DNA was estimated by electrophoresis in agarose gel (0.9%) by adding 2 µl of Sybr Gold (Invitrogen, USA) to each sample which were visualized in an image analysis system (Vilber Lourmat, France). DNA samples were stored at 4ºC.


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Primers EXPCCR (5’-GCCGTAATTGCCTACCTGCTTAAG-3’) and EXPCCF (5’-GAACTTCGCACCGCCGACCTTCTA-3’) were used in the URP (universal rice primer)-PCR protocol (Kang et al., 2003). Nested-PCR was carried out with the URP-PCR product using primers INPCCR (5’-GGCCAAGCAGTGCCTGTATATCC-3’) and INPCCF (5’-TTCGATCACGCAACCTGCATTACT-3’) (Kang et al., 2003). In both PCR protocols gelatin and KCl were not used since gelatin improves reaction efficacy which was not necessary and KCl was already in the Taq polymerase buffer (Invitrogen, Brazil). PCR products were separated in 2% agarose gel in TBE 0.5% buffer at 100 V for 1 h and documentation was secured in the Vilber Lourmat image system. P. carotovorum subsp. carotovorum Pcc 867 was used as positive control and P. betavasculorum Pb 787 as negative control (IBSBF Phytobacteria Culture Collection, Laboratório de Bacteriologia Vegetal, Instituto Biológico - CEIB, P.O. Box 70, Campinas, SP, Brazil). Variability of isolates. Variability of the 39 isolates of Pcc obtained from Camocim de São Félix was assessed based on pathogenic (disease components), phenotypic (sensitivity to antibiotics), and molecular markers (REPPCR). Disease components. Isolates were grown in nutrient agar-yeast extract-dextrose medium (NYDA) for 36-48 h and kept at 28±2°C. A cell suspension was obtained by flooding Petri plates with SDW. Concentration of each isolate suspension was adjusted with a photocolorimeter (M3, Metronic) at A570 = 0.36, which corresponds to approximately 109 CFU ml-1. Tests were conducted in 43-day-old Chinese cabbage cv. Komachi plants grown in a greenhouse (25-30°C and RH = 71.7±18.2%). The second fully developed leaf of each plant was inoculated in the basal portion of the petiole with the deposition of a 5 µl droplet of bacterial suspension after wounding with a sterilized toothpick. After inoculation plants were kept in a moist chamber (plastic bags with the inner part moistened with distilled water) at 25-30ºC for 6 h. Disease assessments started 1 h after inoculation and were carried out at hourly intervals up to 6 h after inoculation. Afterwards, disease assessments were conducted at 6 h intervals up to 48 h after inoculation. The following variables were recorded: (i) incubation period (IP), defined as the number of hours between inoculation and symptom appearance; (ii) initial soft rot severity (ISEV) value, 6 h after inoculation, rated with an arbitrary scale ranging from 1 to 9 (Ren et al., 2001), where: 1 = no lesion at the inoculation site; 2 = lesions smaller than 5 mm; 3 = lesions between 5 and 10 mm; 4 = lesions larger than 10 mm, but not reaching the leaf blade; 5 = lesions in the leaf blade and in the main stem;

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6 = stem infected, but inoculated leaf is not affected; 7 = stem and uninoculated leaves are infected; 8 = whole plant almost dead; 9 = dead plant; (iii) final soft rot severity (FSEV), 48 h after inoculation, rated with the above scale; (iv) area under the disease progress curve (AUDPC), which was calculated as [Σ (yi + yi+1)/2. dti]/n, where yi and yi+1 are severity values recorded in two consecutive time intervals, dti is the time interval between assessments, and n is the duration of the assessment period (Fry, 1978). The experiment was set in a completely randomized design with 5 replicates. Each pot with a plant was considered an experimental unit. Sensitivity to antibiotics. Sensitivity of isolates to 12 antibiotics was determined by antibiogram analysis based on agar diffusion. Cell suspensions in SDW of each isolate were prepared from 36 to 48 h-old cultures developed in test tubes containing NYDA. Aliquots of 3 ml of the cell suspension were transferred to Erlenmeyers containing 100 ml of molten semi-solid NYDA medium, homogenized by vigorous shaking and the mixture was poured into Petri plates. After solidification of the culture medium, paper discs containing amoxicillin (AMO) 10 µg, cefoxitin (CFO) 30 µg, clindamycin (CLI) 2 µg, erythromycin (ERI) 15 µg, gentamicin (GEN) 10 µg, nalidixic acid (NAL) 30 µg, oxacillin (OXA) 1 µg, rifampicin (RIF) 5 µg, trimetoprim (TRI) 5 µg, teicoplanin (TEC) 30 µg, tetracycline (TET) 30 µg, and vancomycin (VAN) 30 µg were placed in four equidistant points in a Petri plate. Plates were kept in an incubator at 28±2ºC for 24 h. After incubation, the diameter of the inhibition halo was measured in two perpendicular directions. The average value of the two measurements was subjected to statistical analysis. The experiment was set in a completely randomized design with 5 replicates. Each plate containing one disc of an antibiotic was considered an experimental unit. Rep-PCR analysis. DNA of each isolate was extracted as described above. The amount of DNA was quantified by electrophoresis in a 0.8% agarose gel, stained with ethydium bromide and visualized with the Eagle Eye II documentation system (Stratagene, USA). RepPCR analysis was carried out with the ERIC, BOX, and REP primers (Louws et al., 1994). PCR products were resolved by electrophoresis in a 1.5% agarose gel running in TBE 0.5% buffer at 80V for 2 h and visualized with the Eagle Eye II system. Data analysis. Disease components and sensitivity to antibiotics data were subjected to multivariate analysis of variance (MANOVA) to test variation among isolates. One MANOVA analysis was conducted for the disease components data set and a separate analysis was con-


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ducted for the sensitivity to antibiotics data set. After testing for variation among isolates, cluster analysis was carried out aimed at finding groups of isolates with similar properties. Isolates were grouped based on the Euclidean distance and the unweighted pair-group method with arithmetic average (UPGMA). Additionally, correlation (Pearson) analyses were conducted among disease components (IP, ISEV, FSEV, and AUDPC) and between these variables and sensitivity to antibiotics and the linkage distance calculated for the cluster analysis with the molecular marker. All statistical analyses were conducted with the STATISTICA for Windows program (StatSoft Inc., Tulsa, USA, 2000). For the molecular marker data a haplotype of each isolate was constructed based on the banding pattern for each primer. The presence or absence of a band in a locus was assumed to represent allelic state 1 or 0, respectively. The simple match coefficient was used to estimate similarity (Kosman and Leonard, 2005). Finally, the correlation between pathogenic and molecular variation was assessed by analyzing the two distance matrices (Euclidean distance based on disease components and the genetic distance for the REP-PCR) with the Mantel test (Mantel, 1967). The null hypothesis of no correlation (Ho: ρ=0) was tested after constructing a simulated data set with 1000 permutations.

RESULTS AND DISCUSSION

Characterization of isolates. All bacterial colonies growing on CPG medium had the typical “broken glass” aspect (Kelman and Dickey, 1995). Isolates were Gram-negative, oxidase negative, catalase positive, had fermentative-oxidative metabolism, caused soft rot in potato tubers, and were pathogenic to detached leaves of Chinese cabbage. These isolates were classified as Pectobacterium. Identification at the species and subspecies level resulted in the classification of all isolates as Pcc. Isolates grew at 37°C, did not utilize α-methyl glucoside, did not produce acid from sorbitol, produced acid from lactose, were resistant to erythromycin, grew in 5% NaCl, and did not produce phosphatase and lecithinase. Sucrose reduction was observed for 10.2% of the isolates and 12.8% did not produce acid from melibiose. The results regarding sucrose and melibiose metabolism are not uncommon, possibly due to higher genotypic and phenotypic diversity of Pcc in relation to other subspecies of the P. carotovorum complex (Avrova et al., 2002). Growth capacity at 36°C and sucrose reduction are not suitable to differentiate P. carotovorum from P. atrosepticum (Seo et al., 2002). Thus, other methods should be employed to identify the subspecies (Yap et al., 2004). The casein hydrolysis test (Dye, 1969) and the colony color on NGM medium (cream color) confirmed the subspecies as Pcc (Lee and Yu, 2005).


Furthermore, identity of isolates was confirmed by typical banding pattern with PCR tests, i.e. one 555 bp band from URP-PCR and a 380 bp band from nested-PCR that are characteristic of Pcc (Kang et al., 2003). Disease components. Based on several tests (Wilks’ Lambda, Pillai’s Trace, Hotelling-Lawley’s Trace and Roy’s Greatest Root), there was a highly significant effect of isolates (P