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Research article

J. Microbiol. Biotechnol. 2018; 28(2): 227-235

Published online February 28, 2018 https://doi.org/10.4014/jmb.1710.10021

Copyright © The Korean Society for Microbiology and Biotechnology.

Metagenomic Approach to Identifying Foodborne Pathogens on Chinese Cabbage

Daeho Kim 1, Sanghyun Hong 2, You-Tae Kim 3, Sangryeol Ryu 1, Hyeun Bum Kim 2 and Ju-Hoon Lee 3*

1Department of Food and Animal Biotechnology, Department of Agricultural Biotechnology, Research Institute of Agricultureand Life Sciences, and Center for Food and Bioconvergence, Seoul National University, Seoul 08826, Republic of Korea, 2Department of Animal Resources Science, Dankook University, Cheonan 31116, Republic of Korea, 3Department of Food Science and Biotechnology, Institute of Life Science and Resources, Kyung Hee University, Youngin 17104, Republic of Korea

Correspondence to:Ju-Hoon  Lee
juhlee@khu.ac.kr

Received: October 17, 2017; Accepted: November 15, 2017

Abstract

Foodborne illness represents a major threat to public health and is frequently attributed to pathogenic microorganisms on fresh produce. Recurrent outbreaks often come from vegetables that are grown close to or within the ground. Therefore, the first step to understanding the public health risk of microorganisms on fresh vegetables is to identify and describe microbial communities. We investigated the phyllospheres on Chinese cabbage (Brassica rapa subsp. pekinensis, N = 54). 16S rRNA gene amplicon sequencing targeting the V5- V6 region of 16S rRNA genes was conducted by employing the Illumina MiSeq system. Sequence quality was assessed, and phylogenetic assessments were performed using the RDP classifier implemented in QIIME with a bootstrap cutoff of 80%. Principal coordinate analysis was performed using a weighted Fast UniFrac matrix. The average number of sequence reads generated per sample was 34,584. At the phylum level, bacterial communities were composed primarily of Proteobacteria and Bacteroidetes. The most abundant genera on Chinese cabbages were Chryseobacterium, Aurantimonadaceae_g, Sphingomonas, and Pseudomonas. Diverse potential pathogens, such as Pantoea, Erwinia, Klebsiella, Yersinia, Bacillus, Staphylococcus, Salmonella, and Clostridium were also detected from the samples. Although further epidemiological studies will be required to determine whether the detected potential pathogens are associated with foodborne illness, our results imply that a metagenomic approach can be used to detect pathogenic bacteria on fresh vegetables.

Keywords: Phyllosphere, Chinese cabbage, foodborne illness, 16S rRNA gene, bacterial diversity

Introduction

Chinese cabbage (Brassica rapa subsp. pekinensis) is one of the most popular vegetables in Northeast Asia, and its consumption in Europe and North America has also been increasing [1]. It is an annual, cool-season cruciferous vegetable that has compactly arranged light green leaves with white veins [2]. Brassica species have beneficial components, such as dietary fiber, vitamin C, and anticancer compounds [3]. Consequently, the consumption of fresh or minimally processed vegetables, including Chinese cabbage, has recently risen. This is due to their high nutritional value and beneficial effects on human health [4]. Recurrent foodborne outbreaks often come from vegetables that are grown close to or within the ground. As such, more foodborne outbreaks have been associated with fresh produce, including Chinese cabbage [5, 6]. In 2006, the Centers for Disease Control and Prevention reported that the widespread outbreaks of foodborne disease were caused by Escherichia coli O157:H7 in fresh spinach in the USA [7]. Romaine lettuce contaminated with Shiga toxin-producing Escherichia coli (STEC) O157:H7 also led to massive foodborne outbreaks in the USA in 2011 [8]. In Japan, pickled napa cabbage was contaminated with STEC O157 in 2012, resulting in widespread foodborne outbreaks [9].

A conventional culture method has traditionally been used to identify pathogens in samples. However, this method is not only laborious and time-consuming, but also unable to detect a variety of bacteria [10, 11]. To overcome these limitations, a non-culture method, metagenome sequencing [12], has been widely used to explore microbial diversity using the advent of next-generation sequencing (NGS) [13-15]. Sequence-driven targeted metagenomics is based on PCR amplicon sequencing, targeting 16S ribosomal RNA (16S rRNA) genes as taxonomic markers [12]. Because 16S rRNA genes consist of conserved and variable regions, they can be used to identify the operational taxonomic unit (OTU) information of a certain sample [12, 16]. For instance, the microbiota derived from the surface or within plant tissues (the phyllosphere) was extensively studied by analyzing 16S rRNA genes [17, 18]. The identification of bacterial communities associated with fresh produce, such as sprouts, fruits, and vegetables, was also performed by analyzing 16S rRNA genes [19, 20]. Although there are an increasing number of studies related to human pathogens within certain plants [21-23], information about potential pathogens in Chinese cabbage is still limited.

The first step to understanding the public health risk of microorganisms on fresh vegetables is to identify and describe microbial communities. Therefore, we investigated the phyllospheres on Chinese cabbage (B. rapa subsp. pekinensis) from South Korean farms. Bacterial communities from each Chinese cabbage sample were analyzed using NGS and bioinformatic tools. In addition, geographic variation in the microbiota present on the Chinese cabbages was investigated in association with their potential risk with regard to foodborne pathogens.

Materials and Methods

Preparation of Samples

A total of 54 Chinese cabbage samples were collected from 18 locations (3 samples per location) in South Korea (Table S1). The edible portion of the Chinese cabbage was harvested and stored in a sterilized plastic bag. Samples were transported to the laboratory within 5 h in a cooler containing ice. Extraction of DNA from all samples was performed on the same day.

Twenty-five grams of each sample was placed in a Stomacher bag (Labplas, Canada) with 225 ml of buffered peptone water (Oxoid, UK) and homogenized for 2 min using a BagMixer 400 P (Interscience, France). The residual particulates, such as plant leaves, were filtered using a strainer with a pore size of 1.0 mm. The filtered samples were transferred to clean conical tubes. Then, the samples were centrifuged at 10,000 ×g for 10 min at 4°C, and the supernatants were discarded. Each pellet was resuspended with 5 ml of TES buffer (0.1 M NaCl (Sigma, Germany), 1 mM ethylenediaminetetraacetic acid (EDTA) (Amresco, USA), and 10 mM Tris-HCl, pH 8.0 (GeneDEPOT, USA)) to wash the pellet [24]. Samples were then centrifuged at 10,000 ×g for 10 min at 4°C. The washing step was repeated three times, and the final pellet was stored at -80°C before extraction of DNA from the total microbial community.

Extraction of Bacterial DNA

The pellet was resuspended with 400 µl of Tris-EDTA (TE) buffer (pH 8) (GeneDEPOT) and 50 µl of lysozyme solution (100 mg/ml)(Sigma), and then incubated at 37°C for 1 h. After incubation, the mixture was chilled in a deep freezer at -80°C for 10 min, and incubated again at 37°C for 10 min. It was then treated with 200 µl of proteinase K solution (2 mg/ml proteinase K (Sigma), 2%sodium dodecyl sulfate (Sigma), and 0.35 M EDTA, pH 8.0)) and incubated at 56°C for 1 h. The tube was centrifuged at 21,130 ×g for 1 min at room temperature (RT), and the supernatant was transferred to a clean microcentrifuge tube. The mixture was treated with an equal volume of phenol-chloroform-isoamyl alcohol (v/v) (25:24:1) (Sigma), inverted several times, and centrifuged at 21,130 ×g for 5 min at RT [25]. The supernatant was collected and treated with an equal volume of chloroform (Sigma) to remove residual phenol and then centrifuged again, and the aqueous phase was transferred to a new tube. Samples were then incubated with 3 µl of RNase A (100 mg/ml) (Sigma) at 37°C for 1 h to remove the RNA. For the elimination of RNase A, phenol-chloroform extraction was repeatedly performed in the same procedure [25]. After transferring the aqueous phase to a microcentrifuge tube, a 10%volume of 3 M sodium acetate (pH 6.2) (Sigma) and two volumes of ice-cold absolute ethanol (Merck KGaA, Germany) were added to the solution [26]. The mixture was inverted several times and centrifuged at 21,130 ×g for 20 min at 4°C. The supernatant was discarded, and the remaining pellet was washed with 1 ml of ice-cold 70% ethanol. After centrifugation at 21,130 ×g for 20 min at 4°C, the supernatant was discarded and the pellet was air-dried at room temperature. The isolated DNA was resuspended in 50 µl of TE buffer and incubated at 55°C for 1 h to completely dissolve the DNA. The DNA obtained was quantified using a Colibri Microvolume Spectrometer (Titertek-Berthold, Germany) and stored at -20°C for further analysis [24].

16S rRNA Gene Primers for Illumina MiSeq Sequencing

To amplify the V5-V6 region [27] of bacterial 16S rRNA genes and minimize the amplification of 16s rRNA fragments derived from chloroplast and plant mitochondria, the 799F-mod3 (5’-CMGGATTAGATACCCKGGT-3’) and 1114R (5’-GGGTTGCGC TCGTTGC -3’) primer set was used [28].

Illumina MiSeq Library Construction and Sequencing

Each sample was prepared for sequencing the V5 and V6 regions of the 16S rRNA gene using the 16S rRNA-specific primers with attached overhang adapters according to the protocol of Illumina MiSeq technology. The amplification mix contained 5× PrimeSTAR Buffer (Mg2+) (Takara Bio, Inc., Japan), 2.5 mM concentrations of each of deoxynucleotide triphosphates (dNTPs), 2.5 U/µl of PrimeSTAR HS DNA Polymerase, 10 pmol of each primer, and 40 ng of DNA in a reaction volume of 50 µl. The thermal cycling parameters were as follows: initial denaturation at 98°C for 3 min, followed by 35 cycles of 98°C for 10 sec, 55°C for 15 sec, and 72°C for 30 sec, and a final 3-min extension at 72oC. The PCR products were purified using Wizard SV Gel and the PCR Clean-Up System (Promega Corp., USA). Dual indices and Illumina sequencing adapters were tagged by Index PCR using the Nextera XT Index Kit, and the final library was purified using AMPure XP beads (Beckman Coulter, USA). Library quantification was performed using qPCR (KAPA library quantification kits for Illumina sequencing platforms) following the manufacturer’s instructions. Samples were qualified using the LabChip GX HT DNA High Sensitivity Kit (PerkinElmer, USA). All samples were pooled using an equal amount of DNA before sequencing. Paired-end (2 × 300 bp) sequencing was performed using the MiSeq platform (Illumina, USA).

Bioinformatic Analysis

Raw paired-end reads were merged using the mothur pipeline alignment method [29]. Barcode and primer sequences were trimmed off from the sequences, and those with the maximum number of eight homopolymers were discarded. Chimeras were removed from the sequences using UCHIME [30]. OTU picking, taxonomic assignment, diversity analysis, and visualization were performed using the Quantitative Insights into Microbial Ecology (QIIME) pipeline [31]. OTUs were defined and clustered using UCLUST at 3% divergence (97% similarity) [32]. Phylogenetic assignment and classification were performed using the RDP classifier implemented in CHIME [33]. During classification, when sequences could not be assigned into a sublevel, the initial letter of each unknown taxon level was written at the end of the name (i.e., Aurantimonadaceae_g; genus = g, family = f). Shannon indices, Simpson indices, and Chao1 values were calculated to check the diversity of the microbiota obtained from the samples. The PCoA plots were used to visualize the similarities or dissimilarities of microbiota distances among the sample groups.

Statistical Analysis

The one-way ANOVA test was used to determine the statistically significant differences in taxonomic profiles among the samples. The statistical analysis was carried out using SAS 9.3 (SAS Institute Inc., USA). Duncan’s multiple-range test was used to assess the significant differences in the relative abundance of bacterial taxonomic profiles between groups, and p-values <0.05 were considered significant.

Results

Bacterial Community Composition of Chinese Cabbage Harvested from Different Regions in South Korea

Illumina sequencing data for Chinese cabbage. To examine the composition of bacterial communities, 54 metagenomes originating from Chinese cabbages harvested in Gangwon-do, Chungcheong-do, and Jeolla-do were subjected to 16S rRNA Illumina sequencing analysis. A total of 373,055, 860,030 and 662,968 DNA sequence reads were generated for the Chinese cabbage samples from Gangwon-do, Chungcheong-do, and Jeolla-do, respectively (Table S2). The average number of sequence reads generated per Chinese cabbage was 31,087.92 for Gangwon-do, 35,834.58 for Chungcheong-do, and 36,831.56 for Jeolla-do. The total number of OTUs at a 97%identity level was 10,060, 16,523, and 21,100 for Gangwon-do, Chungcheong-do, and Jeolla-do, respectively.

iversity comparison among samples. Bacterial diversity and species richness were analyzed using an OTU definition with a similarity cutoff of 97%. Sequence reads from the plant chloroplasts (Streptophyta) and mitochondria (Raphanus) were removed from the data before analyzing the microbial diversity. Farms FD, FK, and FR showed the higher diversity and richness values for bacterial communities among farms in Gangwon-do, Chuncheong-do, and Jeolla-do, respectively (Table S3). Furthermore, the bacterial diversity and richness in Chinese cabbage samples from each location were significantly different (p < 0.05) (Fig. 1, Table 1). The mean value of Shannon indices in samples from Jeolla-do was 5.59, which was higher than that of the other two locations (Table 1). The average values of Chao 1 from Gangwon-do, Chuncheong-do, and Jeolla-do were 1,967.5, 1,521.1, and 2,757.0, respectively. Moreover, the values of the Simpson index were 0.90, 0.89, and 0.94, respectively (Table 1).

Table 1 . Average values of diversity indices for Chinese cabbages by location..

LocationDNA sequence readsOTUsShannonSimpsonChao1
Gangwon-do31,087838.334.630.901,967.5
Chungcheong-do35,834688.464.340.891,521.1
Jeolla-do36,8311,172.225.590.942,757.0


Figure 1. The number of operational taxonomic unit (OTUs), Shannon indices, and Chao1 indices for Chinese cabbage microbiota. The x-axis indicates different locations (Gangwon-do, Chungcheong-do, and Jeolla-do). The y-axis corresponds to (A) the number of OTUs, (B) bacterial diversity (Shannon values), and (C) Chao1 values from individual Chinese cabbage samples (N = 54). The midline (black bar) delineates the median at each location, and the asterisk represents significant differences among locations (p < 0.05).

Regional Variation of Bacterial Community in Chinese Cabbage

Phylogenetic assessments of sequences at the phylum level are shown in Fig. 2A. We detected 29 phyla, 169 orders, 322 families, and 767 genera from all the samples used in the study. At most farms, Proteobacteria (69.19 ± 8.08%) and Bacteroidetes (27.09 ± 7.59%) were dominant phyla in the Chinese cabbages, accounting for more than 90% of the total sequences. Other phyla, including the Actinobacteria and Firmicutes, comprised less than 5% of the total community on average.

Figure 2. Taxonomic assignments of sequences at the phylum (A) and genus (B) levels for Chinese cabbage. Phylogenetic assignments of sequences were conducted using the RDP classifier implemented in the software package QIIME. The “other” category contains taxonomic groups accounting for less than 0.5% and 1.7% of all sequences at the phylum and genus levels, respectively. The initial letter of each unknown taxon level is written at the end of the name (genus = g). X-axis: the first letter denotes “Farm” and the second letter after F means the Farm ID as denoted by uppercase letter of the alphabet, A,B,C etc.

The microbiota of Chinese cabbages at the genus level are presented in Fig. 2B. The four dominant genera (more than 5% abundance of the total sequences) were Chryseobacterium (12.21 ± 7.91%), Aurantimonadaceae_g (10.12 ± 7.04%), Sphingomonas (9.95 ± 3.70%), and Pseudomonas (9.17 ± 6.50%). Among them, Chryseobacterium is a member of the phylum Bacteroidetes (Class Flavobacteriia), whereas the other three genera (Aurantimonadaceae_g, Sphingomonas, and Pseudomonas) are members of the phylum Proteobacteria. The genus Pantoea was predominant in farms FI, FM, and FN with relative abundances of 14.76%, 5.11%, and 9.82%, respectively. Regional comparisons of the Illumina sequencing data revealed that the relative abundances of Pseudomonas, Flavobacterium, Pantoea, Enterobacteriaceae_g, and Oxalobacteraceae_g were significantly higher in samples from Jeolla-do than other provinces, whereas the relative abundances of Aurantimonadaceae_g and Sphingobacterium were highest in Gangwon-do (p < 0.05) (Fig. 3). Furthermore, other potential pathogenic bacteria were detected from numerous samples aside from Pseudomonas and Pantoea (Table S4).

Figure 3. Bacterial composition at the genus level for Chinese cabbage, grouped by location. Phylogenetic assignments of sequences were conducted using the RDP classifier implemented in the software package QIIME. The “other” category contains taxonomic groups accounting for less than 1.7% of all sequences. Mean values (± SEM) are plotted. Error bars denote standard error, and the asterisk represents significant differences among locations (p < 0.05).

To visualize the relative distances between the bacterial communities in each sample, a three-dimensional principal coordinate analysis (PCoA) plot was constructed (Fig. 4). The plot was generated by analyzing the metagenome sequencing data of Chinese cabbages (N = 54) harvested in three different regions: Gangwon-do, Chungcheong-do, and Jeolla-do. Our results showed that the microbiota of the Chinese cabbages harvested in Gangwon-do and Chungcheong-do were similar, whereas the samples from Jeolla-do showed distinct microbial compositions compared with the other provinces.

Figure 4. Bacterial communities associated with different locations visualized by PCoA. A three-dimensional PCoA plot of the Illumina sequencing data from Chinese cabbages (N = 54) was generated using the software package QIIME. Samples associated with Gangwon-do (clustered by the red ellipse), Chungcheong-do (clustered by the green ellipse), and Jeollado (clustered by the blue ellipse) are shown as single points.

Relative Abundance Comparison between Pseudomonas and Sphingomonas spp. in Chinese Cabbages

To investigate a possible correlation between Pseudomonas and Sphingomonas spp., the relative abundance of each in Chinese cabbages was compared (Fig. 5). In general, Pseudomonas spp. were more prevalent in samples with a low proportion of Sphingomonas spp.

Figure 5. A comparison of the relative abundance of Pseudomonas and Sphingomonas spp. Phylogenetic assignments of sequences were conducted using the RDP classifier implemented in the software package QIIME. Samples were aligned in ascending order of relative abundance for Sphingomonas spp.

Discussion

Vegetables, including Chinese cabbage, are often consumed raw or minimally processed, such as those added in salads, and they are considered to be part of a healthy diet as they contain high nutritional value and provide beneficial effects on human health [4]. As foodborne outbreaks related to consumption of fresh produce have increased [5, 6], it is important to prevent contamination of agricultural products by pathogens.

Microbial safety of fresh vegetables may be dependent on various environmental conditions, such as temperature, relative humidity, and seasonal changes, since bacterial communities are dynamically affected by various environmental conditions [17, 18, 34]. In this study, the microbiota of 54 Chinese cabbages harvested from 18 South Korean farms in autumn were analyzed using Illumina MiSeq technology. We found that the microbiota of the Chinese cabbages varied depending on the region from where they were harvested (Gangwon-do, Chungcheong-do, or Jeolla-do).

Our results showed that the bacterial diversity and richness in Chinese cabbage samples from different regions were significantly different (p <0.05). It is known that the phyllosphere is significantly affected by changes in habitat, temperature, UV radiation, relative humidity, leaf wetness, and pH [17, 34-36]. Among these and other factors, Finkel et al. [35] suggested that geographic distance is the most important factor influencing bacterial community composition. In addition, various environmental conditions might explain the similarities between the microbiota of the samples from different regions. The relative humidity and average temperature of Jeolla-do are higher than those of the other locations. It has been shown that microbial richness seems to increase in warm and humid climates [37]. However, further investigation is needed to explain the differences in the microbiota from the different regions, since factors related to bacterial richness and diversity are complex.

When analyzing the bacterial community structure of Chinese cabbage, Proteobacteria and Bacteroidetes were found to be dominant in most regions (Fig. 2A). The existence of these phyla has consistently been reported for other vegetables, such as spinach and lettuce [20, 38-40]. At the class level, a regional difference on the microbiota was also observed. The relative abundance of three classes within the Proteobacteria, namely, Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria, varied significantly according to the region where the Chinese cabbage was harvested. Similarly, Sphingobacteriia (a class within the Bacteroidetes) was significantly abundant in Gangwon-do. The community composition of plant microbiota, particularly regarding Betaproteobacteria, also differed significantly among samples from different locations [35]. Genera such as Pseudomonas, Pantoea, Herbaspirillum, and Enterobacteriaceae_g, which are commonly detected in vegetables, were also identified in Chinese cabbages [20, 37, 38]. In this study, sequences that mapped to genera Chryseobacterium, Aurantimonadaceae_g, Sphingomonas, and Pseudomonas were enriched in Chinese cabbages. Among these, Chryseobacterium is a member of the phylum Bacteroidetes (class Flavobacteriia). The other three genera Aurantimonadaceae_g, Sphingomonas, and Pseudomonas are members of the phylum Proteobacteria.

Chryseobacterium is a genus within the family Flavo-bacteriaceae, as proposed by Vandamme et al. [41]. It is widely distributed in the natural environment, including in water, chilled fish, shellfish, and cauliflower [42, 43]. It can serve as a plant pathogen or symbiont [44], and it has caused human disease outbreaks via contaminated water [42, 45]. The family Aurantimonadaceae is within the class Alphaproteobacteria, and is associated with the order Rhizobiales. It has been isolated from various sources such as plant tissues, water, soil, and air [46]. However, outbreaks related to foodborne infection with these bacteria have rarely been reported.

The genus Sphingomonas has been detected in plants and soil [47, 48]. It can act as a plant-protective genus by suppressing disease symptoms and decreasing pathogen growth [47]. Plants produce photoassimilates like carbon, nitrogen, and energy for bacteria, and as such, plants serve as nutrient reservoirs [49]. From this perspective, some strains of Sphingomonas competitively consume the nutrients that leak from plant leaves, suppressing the growth of other bacteria like Pseudomonas spp. [47]. Interestingly, a low abundance of Pseudomonas spp. was observed in samples with a high proportion of Sphingomonas spp. Further experiments will be required to explain this observation because many factors affect the phyllosphere (e.g., competition for space for colonization and production of antimicrobial compounds by the plant) [47, 50].

The genus Pseudomonas is a potential human pathogen that is dominant in spinach, lettuce, and red cabbage [51]. Geographically, Chinese cabbages that were collected from Jeolla-do had a significantly higher relative abundance of these bacteria than cabbages from the other regions. This observation may be related to the high humidity in Jeolla-do, as some Pseudomonas spp. are resistant to environmental stress under humid conditions [52, 53].

The genus Pantoea is generally found in vegetables such as spinach and lettuce [20, 44], and it can serve as a human pathogen causing bacteremia [54]. According to one report, Pantoea agglomerans was seemingly aided by the presence of Pseudomonas savastanoi [55].

A variety of potential pathogens such as Pantoea, Erwinia, Klebsiella, Yersinia, Bacillus, Staphylococcus, Salmonella, and Clostridium were detected from various samples, regardless of location. Therefore, more cautious management is needed to prevent foodborne outbreaks from Chinese cabbage.

This study has provided the first description of bacterial communities residing in Chinese cabbages. The structure of the microbiota and variations by region were also discussed. It has been suggested that various environmental factors, especially geographical factors, influence the microbiota of Chinese cabbages. Moreover, bacteria-bacteria or plant-bacteria interactions represent crucial relationships affecting the microbiota of Chinese cabbages. Further investigation to better understand the formation of different microbial communities is needed.

Supplemental Materials

Acknowledgments

The present study was supported by a research fund (14162MFDS972) from the Ministry of Food and Drug Safety, Republic of Korea.

Conflict of Interest


The authors have no financial conflicts of interest to declare.

Fig 1.

Figure 1.The number of operational taxonomic unit (OTUs), Shannon indices, and Chao1 indices for Chinese cabbage microbiota. The x-axis indicates different locations (Gangwon-do, Chungcheong-do, and Jeolla-do). The y-axis corresponds to (A) the number of OTUs, (B) bacterial diversity (Shannon values), and (C) Chao1 values from individual Chinese cabbage samples (N = 54). The midline (black bar) delineates the median at each location, and the asterisk represents significant differences among locations (p < 0.05).
Journal of Microbiology and Biotechnology 2018; 28: 227-235https://doi.org/10.4014/jmb.1710.10021

Fig 2.

Figure 2.Taxonomic assignments of sequences at the phylum (A) and genus (B) levels for Chinese cabbage. Phylogenetic assignments of sequences were conducted using the RDP classifier implemented in the software package QIIME. The “other” category contains taxonomic groups accounting for less than 0.5% and 1.7% of all sequences at the phylum and genus levels, respectively. The initial letter of each unknown taxon level is written at the end of the name (genus = g). X-axis: the first letter denotes “Farm” and the second letter after F means the Farm ID as denoted by uppercase letter of the alphabet, A,B,C etc.
Journal of Microbiology and Biotechnology 2018; 28: 227-235https://doi.org/10.4014/jmb.1710.10021

Fig 3.

Figure 3.Bacterial composition at the genus level for Chinese cabbage, grouped by location. Phylogenetic assignments of sequences were conducted using the RDP classifier implemented in the software package QIIME. The “other” category contains taxonomic groups accounting for less than 1.7% of all sequences. Mean values (± SEM) are plotted. Error bars denote standard error, and the asterisk represents significant differences among locations (p < 0.05).
Journal of Microbiology and Biotechnology 2018; 28: 227-235https://doi.org/10.4014/jmb.1710.10021

Fig 4.

Figure 4.Bacterial communities associated with different locations visualized by PCoA. A three-dimensional PCoA plot of the Illumina sequencing data from Chinese cabbages (N = 54) was generated using the software package QIIME. Samples associated with Gangwon-do (clustered by the red ellipse), Chungcheong-do (clustered by the green ellipse), and Jeollado (clustered by the blue ellipse) are shown as single points.
Journal of Microbiology and Biotechnology 2018; 28: 227-235https://doi.org/10.4014/jmb.1710.10021

Fig 5.

Figure 5.A comparison of the relative abundance of Pseudomonas and Sphingomonas spp. Phylogenetic assignments of sequences were conducted using the RDP classifier implemented in the software package QIIME. Samples were aligned in ascending order of relative abundance for Sphingomonas spp.
Journal of Microbiology and Biotechnology 2018; 28: 227-235https://doi.org/10.4014/jmb.1710.10021

Table 1 . Average values of diversity indices for Chinese cabbages by location..

LocationDNA sequence readsOTUsShannonSimpsonChao1
Gangwon-do31,087838.334.630.901,967.5
Chungcheong-do35,834688.464.340.891,521.1
Jeolla-do36,8311,172.225.590.942,757.0

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