metagenomic approach to identifying foodborne pathogens on

February 2018⎪Vol. 28⎪No. 2

J. Microbiol. Biotechnol. (2018), 28(2), 227–235https://doi.org/10.4014/jmb.1710.10021 Research Article jmbReview

Metagenomic Approach to Identifying Foodborne Pathogens onChinese CabbageDaeho Kim1†, Sanghyun Hong2†, You-Tae Kim3, Sangryeol Ryu1, Hyeun Bum Kim2*, and Ju-Hoon Lee3*

1Department of Food and Animal Biotechnology, Department of Agricultural Biotechnology, Research Institute of Agriculture and 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

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].

Received: October 17, 2017

Revised: November 15, 2017

Accepted: November 15, 2017

First published online

November 25, 2017

*Corresponding authors

H.B.K.

Phone: +82-41-550-3653;

Fax: +82-41-559-7881;

E-mail: [email protected]

J.-H.L.

Phone: +82-31-201-2618;

Fax: +82-31-204-8116;

E-mail: [email protected]

†These authors contributed

equally to this work.

upplementary data for this

paper are available on-line only at

http://jmb.or.kr.

pISSN 1017-7825, eISSN 1738-8872

Copyright© 2018 by

The Korean Society for Microbiology

and Biotechnology

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

S

S

228 Kim et al.

J. Microbiol. Biotechnol.

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

Phyllospheres on Chinese Cabbage 229


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 98oC for 3 min, followed by

35 cycles of 98oC for 10 sec, 55oC for 15 sec, and 72oC 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

Fig. 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).

230 Kim et al.


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.

Diversity 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).

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.

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

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

Location DNA sequence reads OTUs Shannon Simpson Chao1

Gangwon-do 31,087 838.33 4.63 0.90 1,967.5

Chungcheong-do 35,834 688.46 4.34 0.89 1,521.1

Jeolla-do 36,831 1,172.22 5.59 0.94 2,757.0

Fig. 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.



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).

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.

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.

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

Fig. 3. Bacterial composition at the genus level for Chinese

cabbage, grouped by location.


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).

Fig. 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 Jeolla-

do (clustered by the blue ellipse) are shown as single points.

232 Kim et al.


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

Fig. 5. A comparison of the relative abundance of Pseudomonas

and Sphingomonas spp.


RDP classifier implemented in the software package QIIME. Samples

were aligned in ascending order of relative abundance for Sphingomonas

spp.



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.

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.

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