metagenomic analysis of kimchi, the korean traditional ... · 2 25 26 kimchi, the korean...

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Metagenomic Analysis of Kimchi, the Korean Traditional 1

Fermented Food 2

3

Ji Young Jung1, Se Hee Lee

1, Jeong Myeong Kim

1, Moon Su Park

1, Jin-Woo Bae

2, Yoonsoo 4

Hahn1, Eugene L. Madsen

3 and Che Ok Jeon

1,* 5

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1Schools of Biological Sciences, Research Center for Biomolecules and Biosystems, Chung-7

Ang University, Seoul, 156-756, Republic of Korea 8

2Department of Biology, Kyung Hee University, Seoul, 130-701, Republic of Korea 9

3Department of Microbiology, Cornell University, Ithaca, NY, 14853-8101, U.S.A. 10

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Running title: Microbiome of kimchi 13

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*Corresponding author: Che Ok Jeon. 15

Mailing address: Department of Life Science, Chung-Ang University, 221, HeukSeok-Dong, 16

Seoul, 156-756, Republic of Korea. 17

Tel: +82-2-820-5864. 18

Fax: +82-2-821-8132. 19

E-mail: [email protected] 20

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Subject Category: microbial population and community ecology 22

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Key words: kimchi, metagenomics, lactic acid bacteria, pyrosequencing, fermented food 24

Copyright © 2011, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.Appl. Environ. Microbiol. doi:10.1128/AEM.02157-10 AEM Accepts, published online ahead of print on 11 February 2011

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Kimchi, the Korean culture's traditional food, is made from vegetables by fermentation. 26

In this study, metagenomic approaches were used to monitor changes in bacterial 27

populations, metabolic potential, and overall genetic features of the microbial 28

community during the 29-day fermentation. Metagenomic DNA was extracted from 29

kimchi samples obtained periodically and followed by sequencing using a 454 GS-FLX 30

Titanium system, which yielded a total of 701,556 reads, with an average read length of 31

438 bp. Phylogenetic analysis based on 16S rRNA genes from the metagenome indicated 32

that the kimchi microbiome was dominated by members of three genera; Leuconostoc, 33

Lactobacillus, and Weissella. Assignment of metagenomic sequences to SEED categories 34

of MG-RAST revealed a genetic profile characteristic of heterotrophic lactic acid 35

fermentation of carbohydrates, which was supported by the detection of mannitol, 36

lactate, acetate, and ethanol as fermentation products. When the metagenomic reads 37

were mapped onto the database of completed genomes, the Leuconostoc mesenteroides s38

ubsp. mesenteroides ATCC 8293 and Lactobacillus sakei subsp. sakei 23K genomes 39

were highly represented. These same two genera were confirmed to be important in 40

kimchi fermentation when the majority of kimchi metagenomic sequences showed very 41

high identity to Leuconostoc mesenteroides and Lactobacillus genes. Besides microbial 42

genome sequences, a surprisingly large number of phage DNA sequences were identified 43

from the cellular fractions, possibly indicating that a high proportion of cells were 44

infected by bacteriophage during fermentation. Overall, these results provide insights 45

into the kimchi microbial community and also shed light on fermentation processes 46

carried out broadly by complex microbial communities. 47

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Kimchi is a traditional food, emblematic of Korean culture, that is fermented from vegetables 50

such as Chinese cabbage and radish. In recent years, kimchi's health-promoting characteristics 51

have been recognized (40, 54). Kimchi is usually processed with various seasoning 52

ingredients such as red pepper powder, garlic, ginger, green onion, fermented seafood 53

(jeotgal), and salts at low temperatures to ensure proper ripening and preservation. 54

Spontaneous fermentation without the use of starter cultures or sterilization leads to the 55

growth of various microorganisms during kimchi preparation. Moreover, the influence of 56

ingredients and fermentation conditions on the microbial community are unexplored, and a 57

rational approach to control of the microbial community for improvement of kimchi taste 58

(flavor) is currently almost impossible. 59

Microbes involved in kimchi fermentation have been studied previously (26, 27). 60

Taxonomic studies of bacteria isolated and purified from kimchi fermenting cultures have 61

shown that lactic acid bacteria (LAB) including Leuconostoc (Le.) mesenteroides, Le. kimchii, 62

Le. citreum, Le. gasicomitatum, Le. gelidum, Lactobacillus (Lb.) brevis, Lb. curvatus, Lb. 63

plantarum, Lb. sakei, Lactococcus lactis, Pediococcus pentosaceus, Weissella (W) confusa, W. 64

kimchii, and W. koreensis are likely to be key players responsible for kimchi fermentation (2, 65

22, 24, 28, 42, 51). However, culture-based approaches have known limitations in terms of 66

reproducibility and challenges in the culturability of some bacteria. Therefore, the set of 67

cultured isolates may not reflect the true microbial composition of fermented kimchi. 68

The use of 16S rRNA genes as a phylogenetic marker that does not require isolation and 69

cultivation of microorganisms has resulted in a tremendous amount of information about 70

naturally occurring microbial communities in various habitats (9, 48). When applied to kimchi, 71

the construction of 16S rRNA clone libraries has extended information about kimchi 72

microbiology (42). Denaturing gradient gel electrophoresis and amplified ribosomal DNA 73

restriction analysis, followed by sequencing of 16S rRNA genes, were additional techniques 74


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used to elucidate microbial community involved in kimchi fermentation (24, 28). Although 75

these culture-independent approaches can estimate species richness within a kimchi microbial 76

community, they are unable to resolve the true genotypic diversity because diversity of 16S 77

rRNA gene sequences may not correlate well with genotypic and phenotypic diversity (60). 78

Thus, although progress using 16S RNA gene-analysis and culture-based methods has been 79

significant, current understanding of kimchi microbiology is piecemeal and features 80

significant limitations regarding community dynamics and function during fermentation (12, 81

29, 38). 82

Metagenomic approaches based on the random sequencing of environmental DNA can 83

provide a wealth of information (free of PCR bias) about gene content, metabolic potential, 84

and the function of microbial communities (10). Recently, the development of next-generation 85

DNA sequencing technologies (50), such as 454-pyrosequencing has led large-scale 86

metagenomic sequencing of environmental habitats such as activated sludge (16, 49), coastal 87

ocean (36), glacier ice (52), and the human distal gut (45). 88

It is well known that kimchi fermentation is generally characterized by the production of 89

metabolites such as organic acids (lactic and acetic acids) and other flavoring compounds, 90

which are important components in defining kimchi quality (20). The presence of various 91

metabolites can reflect the presence of related genes in the microbial population, and give a 92

more direct collective phenotypic view of the kimchi microbiome as a consequence of the end 93

products of gene expression. Fermentation rate is also very important to kimchi taste and 94

flavor development during fermentation (25). 1H nuclear magnetic resonance (NMR) is one of 95

the most adequate, easy, and nondestructive multinuclear techniques to simultaneously 96

monitor several substances present in one sample (15, 30). 97

Here, we applied metagenomic approaches using pyrosequencing technology developed by 98

454 Life Sciences to kimchi. In addition, we conducted a metabolite analysis of fermenting 99


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kimchi using 1H NMR to collectively understand the overall features of the kimchi 100

microbiome and to investigate relationships between metabolite production, metabolic 101

potential, and composition of the kimchi microbial community during fermentation. 102

103

MATERIALS AND METHODS 104

Preparation of kimchi and sampling. An industrial scale batch of kimchi was prepared by 105

Daesang F&F Corporation (http://www.chonggafood.com) using their production line. Briefly, 106

Chinese cabbage (Brassica rapa subsp. pekinensis) were soaked in a solution of 15% (w/v) 107

solar salt for 5 to 6 h. The soaked Chinese cabbages were washed with tap water 3 times and 108

drained excessive water. The salted Chinese cabbages were mixed with various seasoning 109

ingredient mixtures including red pepper powder, radish, garlic, ginger, green onion, sugar, 110

and fermented seafood (jeotgal) and were packaged into airtight plastic bags with 1-kg 111

portions. Thirty 1 kg kimchi bags were stored at the standard fermentation temperature (4°C). 112

Periodically over the 29-day fermentation period, 3 bags were sacrificed and subjected to 3 113

layers of coarse gauze (Daehan, Korea) to remove only large kimchi particles and the filtrates 114

were pooled. The pH of each filtrate was measured immediately and the filtrates were 115

centrifuged (8,000 rpm for 20 min at 4°C) to harvest microorganisms. The pellets and 116

supernatants were stored at -80°C for extraction of bulk genomic DNA and metabolite 117

analysis, respectively. One ml of each sample was centrifuged separately for the measurement 118

of 16S rRNA gene copies using quantitative real time-PCR (qRT-PCR). The kimchi samples 119

were coded as J"x", where "x" designates the sample day during fermentation. 120

121

Quantitative real time-PCR (qRT-PCR) to determine 16S rRNA gene copy number. To 122

estimate the total number of bacteria during the kimchi fermentation, total genomic DNA 123

from pellets derived from 1.0 ml of kimchi filtrate by centrifugation was extracted using a 124


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FastDNA SPIN Kit (MPbio, Solon, OH, USA) according to manufacturer's instructions. 125

Genomic DNA concentrations were measured using an ELISA reader equipped with Take3TM

126

Multi-Volume Plate (SynergyMx, BioTek, CA, USA). The genomic DNA was serially diluted 127

ranging from 50 µg/ml ~ 200 µg/ml and was used as template to amplify 16S rRNA genes 128

from each group of bacteria using primers, 340F: 5'-CCTACGGGAGGCAGCAG-3' and 129

758R: 5'-CTACCAGGGTATCTAATCC-3' (21). The PCR mixture consisted of 2 µl of 130

genomic DNA, 0.3 µM total of the primer set (340F, 758R), 10 µl of 2X iQTM

SYBR®

Green 131

Supermix (Bio-Rad, Hercules, CA, USA), and water to a total of 20 µl. The qRT-PCR 132

amplifications were conducted using the CFX96TM

Real-Time PCR system (Bio-Rad, 133

Hercules, CA, USA) with the following qRT-PCR conditions: initial denaturation at 95°C for 134

3 min; followed by 40 cycles of 95°C for 45 sec, 59°C for 45 sec, and 72°C for 50 sec. 135

Bacterial standard curves were generated on the basis of the concentration of a pCR2.1 vector 136

(Invitrogen, CA, USA) carrying the 16S rRNA gene of Leuconostoc mesenteroides subsp. 137

mesenteroides ATCC 8293T. The 16S rRNA gene copy number of each sample was calculated 138

as described previously (41, 46). 139

140

1H-NMR spectroscopic analysis of kimchi metabolites. The analysis of metabolites, 141

including organic acids and monosaccharides in kimchi supernatants during fermentation, was 142

performed using 1H-NMR spectroscopy according to the modification of the methods 143

described previously (30, 53). Briefly, 15 ml of kimchi supernatant without cells was adjusted 144

to pH 6.0 and then lyophilized. The freeze-dried powders were dissolved in 600 µl of D2O 145

(10%, w/v) with 0.5 mM sodium 2,2-dimethyl-2-silapentane-5-sulfonate (DSS) and 146

centrifuged at 13,000 rpm for 5 min. The resulting supernatants were transferred into 5 mm 147

NMR tubes and 1H-NMR spectra were acquired using a Varian Inova-600 MHz NMR 148

spectrometer (Varian Inc., Palo Alto, CA). Identification and quantification of individual 149


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metabolites was performed using the Profiler module of the Chenomx NMR Suite v. 6.1 150

(Chenomx. Inc., Edmonton, Canada). 151

152

Genomic DNA extraction and pyrosequencing. Total genomic DNA from pellets was 153

extracted using a FastDNA SPIN Kit (MPbio) according to manufacturer's instructions. The 154

extracted crude DNA was loaded on 1% (w/v) low-melting agarose (Promega, WI, USA) and 155

electrophoresed at 100 V for 1 hr to remove impurities. A strip from each side of the gel was 156

removed and stained to localize the genomic DNA. DNA-containing regions from the rest of 157

the unstained gel were removed, digested with β-agarase I, and DNA recovery was conducted 158

as described by the manufacturer (NEB, MA, USA). The concentration of purified genomic 159

DNA was measured with an ELISA reader equipped with Take3TM

Multi-Volume Plate 160

(SynergyMx, BioTek, CA, USA) and the DNA samples were concentrated to a minimum of 161

100 ng/µl using a speed vacuum centrifuge (EYELA CVE-2000, Japan). The total genomic 162

DNA was nebulized to obtain a mean fragment size of ~680 bp, confirmed by electrophoresis, 163

for pyrosequencing. The fragmented genomic DNA was individually tagged using multiplex 164

identifier (MID) adaptors according to the manufacturer’s instructions (Roche, Mannheim, 165

Germany), which allowed sorting of the pyrosequencing-derived sequencing reads based on 166

MID adaptors automatically after pyrosequencing. Pyrosequencing of the 10 tagged genomic 167

DNA samples was performed using the 454 GS-FLX Titanium system (Roche, Mannheim, 168

Germany) by Macrogen (Korea). 169

Sequencing data sets were subjected to systematic checks to remove replicates, duplicates, 170

and low quality reads. Briefly, replicate and duplicate sequences, which are known artifacts of 171

the pyrosequencing approach, were first checked through a web based program 172

(http://microbiomes.msu.edu/replicates/) (18) using the default settings (sequence identity 173

cutoff: 0.9; length identity requirement: 0; number of beginning base pairs to check: 3) of the 174


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open-source program Cd-hit (31). Replicates and duplicates were removed, and the remaining 175

sequence data were then trimmed using the LUCY program (7) with a stringency similar to 176

Phred scores of 20 or higher (-error 0.01), and long reads over 100 bp. Only high quality 177

sequence data obtained after LUCY trimming were used for further subtraction with SeqClean 178

(http://compbio.dfci.harvard.edu/tgi/software/). Resulting clean sequence data sets were then 179

used for further analysis. 180

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Comparative analysis of metagenomic data. Unassembled clean reads were annotated using 182

the MG-RAST server, an open-access metagenome curation and analysis platform 183

(http://metagenomics.nmpdr.org/) (35). To analyze bacterial community changes during 184

kimchi fermentation, 16S rRNA gene sequences from all sequences were identified by 185

comparing the read data set against the RDP II database (8), using recommended parameters 186

(minimum alignment length ≥ 50 bp, E-value = 0.01 cutoffs). 16S rRNA gene phylotypes 187

were classified at the genus, family, order, class, and phylum-level based on the taxonomic 188

categorizations of the MG-RAST server, respectively. To identify putative protein-encoding 189

sequences, the sequences were also analyzed using the SEED platform 190

(http://www.theseed.org/), which comprises all available genomic data from genome 191

sequencing centers (1). Kimchi microbial community metabolic potential was determined by 192

assigning functional annotation to metagenomic sequences with subsequent sequence 193

assignment to SEED subsystems using the recommended parameters (minimum alignment 194

length ≥50 bp, E-value = 0.01 cutoffs). Statistical analyses were conducted on the number of 195

sequencing reads within each subsystem rather than on the number of total metagenomic 196

reads. Therefore, abundance of metabolic genes in the MG-RAST server was calculated based 197

on the relative proportion of sequencing reads within each subsystem. Metabolic potential of 198

the kimchi microbiome was compared with other metagenomic data sets of the MG-RAST 199


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server. 200

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Individual genome recruitment plots. The degree to which completed, whole-genome 202

sequences were represented in metagenomic reads was assessed using the "recruitment plot" 203

procedure of Ghai et al., (17). Sequence fragments from unassembled clean reads derived 204

from the 454 GS-FLX Titanium system were compared to all complete and draft microbial 205

genomes using the BLASTN algorithm. Whole clean metagenomic reads were matched to 206

multiple reference genomes and were matched multiple times to the same genome (17). The 207

criteria for counting a given sequence were a minimum percent identity of 95% and a 208

minimum alignment of 90 bp. Whole clean reads were multiply-matched to the four highest 209

scoring genomes: Leuconostoc mesenteroides subsp. mesenteroides ATCC 8293T 210

(NC_008531), Lactobacillus sakei 23K sakei (NC_007576), the partially assembled genome 211

of Weissella paramesenteroides ATCC 33313, and Leuconostoc citreum KM20 (NC_010471). 212

The data were plotted using the plotting program gnuplot (http://www.gnuplot.info/). 213

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Assembly of pyrosequencing-derived reads and analysis of assembled contigs. 215

Pyrosequencing-derived sequencing reads were assembled using the 454 Newbler Assembler 216

(ver. 2.0.01.14, Roche) by Macrogen (Korea). Low complexity sequence regions (simple 217

sequence repeats) were identified and excluded from consideration during initial pair-wise 218

comparison, but were included during final alignment and consensus building. Assembled 219

contigs longer than 10 kb were assigned to their taxonomic affiliations by BLASTN 220

comparisons to the GenBank nonredundant nucleotide (nr/nt) database and selections of the 221

top BLASTN hits for phylogenetic affiliation as described previously (43, 44). Clustered 222

regularly interspaced short palindromic repeats (CRISPR) elements were searched from 223

assembled contigs longer than 10 kb using a web-based CRISPRFinder (19). 224


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Phylogenetic analysis of phage-related contigs and analysis of their abundance. Four 226

contigs (contig01645, contig27827, contig02778, and contig03545), assigned as putative 227

bacteriophage genome fragments among the assembled contigs, were analyzed in detail. 228

Molecular phylogenetic analysis of the putative phage contigs was performed using 229

bacteriophage major capsid proteins (MCPs). Putative MCP sequences were aligned with 230

homologous MCP sequences and retrieved from the NCBI database using the MUSCLE 231

program (13). Phylogenetic trees were constructed using MEGA (ver. 4.0) based on deduced 232

amino acid sequences (55). The abundance of putative phage reads in the cellular fractions of 233

kimchi supernatants during fermentation was estimated by multiple matching of the 234

metagenomic data sets against the four putative phage contigs, with the criteria of a minimum 235

percent identity of 95% and a minimum overlap of 90 bp as individual genome recruitment 236

plots were performed above. 237

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Nucleotide accession numbers. The kimchi metagenomic data sets are publicly available in 239

the MG-RAST system under the following project identifiers (IDs 4450209.3 ~ 4450218.3) 240

and the NCBI Short Read Achieve (Accession # :SRA023444). Assembled contig sequences 241

with more than 10 kb have been deposited in GenBank (AEKF01000001 ~ AEKF01000127). 242

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RESULTS AND DISCUSSION 244

Metagenomics makes it possible to study microbial communities by deciphering genetic 245

information from DNA extracted directly from naturally occurring habitats. Various 246

metagenomic approaches have been applied to environmental samples to characterize the 247

microbial and viral communities present in an ecosystem, thereby elucidating metabolic 248

capabilities and native taxa (11, 47, 56, 57, 59). Advantages of metagenomic studies include 249


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avoiding the need to culture individual microorganisms, elimination of cloning and PCR 250

biases, and reduced cost per sequencing read. At present, the 454 GS-FLX Titanium platform 251

generates relatively long read lengths (400~500 bp), which makes it possible to reliably 252

assemble metagenomic reads. Here, we applied pyrosequencing technology to analyze the 253

microbiome of kimchi, the most popular fermented food in Korea. In addition, we analyzed 254

the metabolites of fermenting kimchi using 1H NMR to investigate the link between kimchi 255

microbial community metagenome and phenotype. 256

257

General features of kimchi fermentation. Kimchi fermentation is an anaerobic process, and 258

sampling kimchi supernatants from one large fermentation batch can cause oxygen infiltration, 259

disturbing the normal fermentation process and thereby influencing the microbial community 260

and metabolite production. Therefore, thirty sealed bags, each containing 1 kg of kimchi that 261

was manufactured by a one-batch process (incubated at the standard 4°C temperature), were 262

prepared for metagenome and metabolite analysis. The pH profile of kimchi supernatants over 263

the 30 day-fermentation process (Fig. 1) was similar to those of typical kimchi fermentation 264

(41). In this study, the pH was about 5.5 at the beginning and decreased sharply after 16 days. 265

After 27 days of fermentation, the pH reached below 4.5 and became stable. 266

A qRT-PCR approach was used to enumerate the total number of bacteria present during 267

the kimchi fermentation process. The exact determination of bacterial cell number by qRT-268

PCR in a microbial community is impossible because the copy number of chromosomal 16S 269

rRNA gene operons varies with species type (14); however, the data does allow for an 270

estimation of cell number. In our study, the total number of bacteria in kimchi samples was 271

estimated using a standard curve (coefficients (R2) value= 0.999) prepared from the 16S 272

rRNA gene of Leuconostoc mesenteroides subsp. mesenteroides ATCC 8293. The 16S rRNA 273

gene copy numbers of kimchi samples increased from an initial value (sample J1, day 1) of 274


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2.6 × 108

copies/ml to the highest value (sample J25, day 25) of 3.0 × 1010

copies/ml during 275

the kimchi fermentation. After the day 25 sample, the bacterial population decreased steadily. 276

The increase of bacterial abundance correlated well with a decrease in pH value (Fig. 1). 277

278

Identification and quantification of metabolites during the kimchi fermentation. It is 279

known that kimchi flavor is principally dependent on free sugars, amino acids, and organic 280

acids, production of which are surely influenced by the microbial community during 281

fermentation (20). Therefore, we analyzed the change of metabolites and microbial 282

communities during kimchi fermentation using 1H-NMR spectroscopy to investigate their 283

relationships. Identification and quantification of metabolites using 1H-NMR peaks were 284

performed based on comparison with chemical shifts of standard compounds using Chenomx 285

NMR suite software. 1H-NMR spectra of the kimchi supernatants were associated with the 286

presence of various metabolites, including carbohydrates, amino acids, and organic acids as 287

expected. In particular, intense parts of the 1H-NMR spectra were in the 0.5-2.0 ppm and 3.0-288

5.0 ppm ranges, corresponding to organic acids and carbohydrates, respectively (data not 289

shown). 290

In this study, fructose and glucose were detected as major free sugars (Fig. 2). Free sugars 291

play important roles in the taste of kimchi because free sugars are not only sweeteners but 292

also serve as carbon sources for microorganisms, including LAB, to produce various products. 293

The level of free sugars decreased slowly in the early phases; between days 16 and 23, their 294

concentrations decreased relatively quickly. After day 23, carbohydrate levels were relatively 295

constant (Fig. 2). With the decrease in free sugars, lactate, acetate, and ethanol were detected 296

as fermentation products, indicating that heterotrophic lactic acid fermentation occurred 297

during fermentation. It was also found that a considerable amount of mannitol accumulated 298

during the fermentation (Fig. 2), which is a naturally occurring six-carbon polyol produced 299


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from the reduction of fructose by LAB in vegetable fermentations (5, 61). The presence of 300

mannitol in foods results in a refreshing taste with tooth-friendly properties and is a good 301

replacement for sugars in diabetic foods (61). In general, the profile of fermentation products 302

inversely correlated with the level of free sugars. However, the level of fermentation products, 303

especially mannitol, increased continually at the early and late phases, and free sugars were 304

relatively constant, which might be caused by the continuous liberation of free sugar from 305

cabbage. The change in free sugars and fermentation products were closely correlated with 306

pH change and growth of the bacterial population (Fig. 1). However, ethanol levels were very 307

low, which might be explained by the loss of ethanol during sample preparation (freeze 308

drying) for NMR analysis. 309

310

Statistics of sequences generated by 454-pyrosequencing. Pyrosequencing of ten kimchi 311

samples during fermentation yielded a total of 701,566 reads by single run of 10 tagged 312

samples (Table 1). However, there was considerable variation in the number of sequences 313

obtained from each 454 sequencing sample, which could be explained by a difference in the 314

amount of genomic DNA from each sample, even though the total amount of DNA was 315

adjusted before pooling for sequencing. It is well known that the 454 sequencers produce 316

artificial replicated and duplicated reads, which might produce misleading conclusions (18). 317

Therefore, replicates, duplicates, low quality and short read length sequences (<100 bp) were 318

removed from the raw data set through web-based approaches. Here, these culled sequences 319

constituted 18.9~21.3% depending on the kimchi sample, which was a typical output for the 320

Roche GS-FLX system (39). Finally, 560,907 clean sequencing reads (79.95% of total reads) 321

with an average sequence length of 438 bp were obtained. This allowed clear assembly and 322

annotation analysis. The G+C% content of the data sets ranged from 30~40%, which reflects 323

the predominance of low G+C Gram-positive LAB. Table 1 summarizes the statistics for 324


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pyrosequencing-derived data sets from the kimchi samples. 325

326

Phylogenetic compositions of the bacterial communities. Among the pyrosequencing-327

derived data sets, 16S rRNA genes accounted for 0.17~0.67% with an average of 0.54% 328

(Table 1). The number of 16S rRNA genes increased as the fermentation progressed and the 329

16S rRNA gene frequencies were slightly higher than in cultured genome prokaryotic 330

genomes (36); perhaps reflecting kimchi’s enrichment in LAB, which contain several copies 331

of 16S rRNA genes within relatively small genomes (~2 Mb). Sequences were classified at 332

each level using the MG-RAST server RDP (16S rRNA gene) database (E-value = 0.01 using 333

a minimum alignment length ≥ 50 bp). Phylogenetic classifications of the 16S rRNA gene 334

sequences are shown in Fig. 3. 335

Genomic DNA from kimchi substrates such as Chinese cabbage, red pepper, garlic, and 336

fermented seafood could be a reason for the relatively low frequency of 16S rRNA genes in 337

the early fermentation phases (J1 = 0.17%, J7 = 0.19%). In fact, in early fermentation the 338

proportion of unclassified phylotypic groups including unclassified Bacteria and unclassified 339

Deferribacterales were highest (J1 = ~97%), and they might have originated from Chinese 340

cabbage and kimchi additives. However, the proportion of unclassified phylotypic groups 341

decreased gradually as the fermentation progressed due to an increase in bacterial abundance. 342

Over the entire kimchi fermentation, somewhat greater 20% of the 16S rRNA gene sequences 343

for all samples fell into unclassified phylotypic groups. Archaeal 16S rRNA gene sequences 344

were not detected. 345

Figure 3 shows that the kimchi fermentation was governed by the distinct population 346

dynamics of three genera, Leuconostoc, Lactobacillus and Weissella. Among them, the genus 347

Leuconostoc was most abundant over the kimchi fermentation, followed by Lactobacillus and 348

Weissella. Leuconostoc dominated at the beginning stages of kimchi fermentation, but as the 349


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fermentation progressed the abundance of Lactobacillus and Weissella increased. After the 350

middle stages of fermentation, Leuconostoc, Lactobacillus and Weissella became the 351

predominant bacterial groups in the microbial community and their cumulative abundance 352

reached to approximately 80% after day 23 (sample J23), which comprised more than 98% of 353

all bacterial groups, except for unclassified phylotypic groups. However, after day 23, the 354

abundance of Leuconostoc increased again, and Lactobacillus and Weissella gradually 355

decreased. 356

Previous studies reported that in kimchi fermentation, Leuconostoc species typically 357

dominate during the beginning of fermentation with low acidity at high temperature, whereas 358

Weissella and Lactobacillus species have the capacity to grow under high acidity and low 359

temperature, indicating that fermentation temperature and acidity are important determinants 360

of the microbial population (6, 28, 29). However, our results showed that Leuconostoc species 361

dominated, even at low temperature (4°C) and high acidity. Although Leuconostoc species 362

were more dominant than Weissella and Lactobacillus species at the beginning of 363

fermentation, Leuconostoc species increased again and Lactobacillus and Weissella decreased 364

at the end of the fermentation. These results suggest that there could be another important 365

determinant of microbial communities in addition to temperature and acidity. Data in Figs. 2 366

and 3 strongly indicate that the metabolism of free sugars and the production of lactate, 367

acetate, and mannitol closely correlated with the growth of Leuconostoc, Lactobacillus and 368

Weissella. 369

370

Comparative functional analysis of the kimchi microbiome. To explore the metabolic 371

potential of the kimchi microbiome during fermentation, all sequences were analyzed using 372

the SEED subsystem publicly available on the MG-RAST server. Assignment of orthologous 373

sequences in the metagenome by comparison with both protein and nucleotide databases were 374


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categorized (28 metabolic categories, 519 subsystems). An average of 45.09% of total 375

sequences were matched to SEED functional categories of organisms present in the MG-376

RAST database at an E-value cutoff of 0.01 (Table 1). The level of reads categorized by 377

SEED were very low for the early fermentation, as only 3.33% and 7.45% of reads from J1 378

and J7 samples were categorized into the SEED functional categories, respectively. This 379

might be explained by a contamination of genomic DNA by cabbage and other kimchi 380

ingredients. As the kimchi fermentation progressed, matching levels of the metagenomic 381

sequence reads to SEED functional categories increased sharply due to the increase in 382

bacterial abundance (Fig. 1). 383

Results of assigning these metagenomic sequences to functional categories are shown in 384

Figs. 4 and 5. As expected, patterns in the data confirmed that kimchi fermentation is 385

accomplished predominantly by heterofermentative LAB, therefore carbohydrate categories 386

including metabolism of mono-, di-, and oligosaccharides and fermentation were key 387

categories for kimchi fermentation. Thus, among the many genes in the kimchi metagenome, 388

our analyses focused on carbohydrate categories. The comparative representation with several 389

other environmental metagenomic data sets showed that genes involved in the fermentation 390

process were enriched (Table 2). Carbohydrate metabolism yielded an average of 14.49% of 391

all matches (Fig. 4). The fraction of the kimchi microbiome reads in the carbohydrate 392

category was higher than those of microbiomes derived from the termite gut (12.89%), acid 393

mine drainage (AMD) biofilm (12.17%), enhanced biological phosphorus removal (EBPR) 394

sludge (10.72%), farm soil (11.25%) and Sargasso Sea (12.23%). Analysis of metabolic 395

potential within the carbohydrate category indicated that the kimchi microbiome was enriched 396

for genes involved and monosaccharide, di- and oligosaccharide fermentation. The 397

metagenomic read fractions of monosaccharide (28.70%), di- and oligosaccharide (28.38%), 398

and fermentation (14.38%) within the carbohydrate category in the kimchi microbiome were 399


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also higher than in the microbiomes derived from the termite gut (24.35%, 21.64%, 5.24%), 400

AMD biofilm (7.12%, 24.1%, 10.51%), EBPR sludge (11.55%, 14.32%, 6.77%), farm soil 401

(18.41%, 15.3%, 7.52%) and Sargasso Sea (18.04%, 13.9%, 9.18%). The fermentation 402

metabolism subcategory was mostly associated with various lactate fermentations (65.93%) 403

and acetoin and butanediol metabolism (28.10%). In particular, lactate fermentation genes in 404

the kimchi microbiome were predominantly enriched compared to the termite gut (33.33%), 405

AMD biofilm (29.29%), EBPR sludge (27.19%), farm soil (25.66%), and Sargasso Sea 406

(16.07%). Moreover, metabolic genes involved in carbohydrate metabolism and fermentation 407

generally increased as the kimchi fermentation progressed (Figs. 4 and 5). In summary, the 408

above-mentioned results showed that the kimchi microbiome had high metabolic versatility 409

with respect to lactic acid fermentations, which were also supported by the profiles of free 410

sugars and fermentation products (Fig. 2). 411

412

Individual genome recruitment. Genome recruitment analysis is an insightful way to 413

examine the degree to which microorganisms in a given habitat are represented by completed 414

genomic sequences (17). Therefore, the metagenomic sequence reads were compared to all 415

complete and draft microbial genomes available from the NCBI 416

(http://www.ncbi.nlm.nih.gov/sites/genome) using the criterion of 95% nucleotide identity. 417

The two most highly recruited genomes were Leuconostoc mesenteroides and Lactobacillus 418

sakei, respectively (Fig. 6A). Prior reports have indicated that Le. mesenteroides is one of the 419

prominent species in kimchi fermentation (24, 34). However L. sakei has been better known 420

as a psychrotrophic LAB associated with fresh meat and fish (4). The reads that aligned with 421

the Le. mesenteroides subsp. mesenteroides ATCC 8293 and L. sakei subsp. sakei 23K 422

genomes often featured extremely high sequence identities (Figs. 6B and 6C), suggesting that 423

LAB closely related to these two reference strains were abundant in the fermented kimchi 424


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samples. It seems clear that close relatives of these specific strains contribute to a large 425

fraction of the heterotrophic lactic acid fermentation in kimchi. Nevertheless, there were some 426

significant differences between the genomes of the two reference strains and the LAB 427

genomes of the kimchi samples, since certain reference sequences were not represented within 428

the metagenome sequence, implying that the kimchi habitat may feature specific selective 429

pressures and support diverse populations not yet isolated or sequenced. 430

Although Leuconostoc citreum KM20 was originally isolated from Chinese cabbage 431

kimchi (23), its degree of genomic match to the metagenome was relatively low (Fig. 6D). 432

Phylogenetic analysis based on 16S rRNA gene sequences showed that the genus Weissella 433

was one of the predominant bacterial groups during kimchi fermentation (Fig. 3). However, 434

the draft genome of W. paramesenteroides ATCC 33313 matched poorly to the metagenome 435

sequence (Fig. 6E). This was surprising and suggests that the metagenome sequences from 436

Weissella might be derived from other Weissella species such as W. koreensis S-5623T, 437

isolated from Chinese cabbage kimchi (27). 438

439

Assembly of pyrosequencing-derived sequencing reads and assignment of assembled 440

contigs. A very important aspect of metagenomic sequencing is the possibility of assembling 441

contigs derived from a single microorganism. Therefore, sequencing reads from the kimchi 442

metagenome, which comprised a total of 560,907 clean reads, were assembled resulting in a 443

total of 27,835 contigs. However, many sequences on the contigs were found to be split due to 444

frame shifts mostly caused by homopolymeric sequences, which are difficult to resolve by 445

454-pyrosequencing technology. The longest contig was 270,682 bps in size. One hundred 446

twenty seven contigs longer than 10 kb in size were classified according to their taxonomic 447

affiliations by BLASTN comparisons to the GenBank nonredundant nucleotide (nr/nt) 448

database and selection of the top BLASTN hits (see Table S1 in the supplemental material). 449


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Most contigs were clearly derived from the predominant LAB, Leuconostoc mesenteroides 450

subsp. mesenteroides ATCC8293 and Lactobacillus sakei subsp. sakei 23K at the species level 451

(>97% sequence identity). Surprisingly, four contigs, 02778, 27827, 01645, and 03545 of 452

139,667 bp, 137,689 bp, 79,287 bp, and 10,233 bp, respectively, were classified as putative 453

bacteriophage fragments. Three of these contigs, 02778, 27827, and 01645 contained genes 454

for a putative bacteriophage major capsid protein (MCP), which is used frequently for phage 455

phylogenic analysis (3). Phylogenetic analysis based on the MCP sequences showed that 456

contigs, 02778, 27827, and 01645 were related to LAB phages (data not shown). Although 457

contig 03545 did not contain a gene for the putative bacteriophage MCP, most of genes in the 458

contig sequence were matched with the Leuconostoc phage 1-A4 complete genome (33) with 459

high identity (92%), indicating that contig 03545 was clearly able to be assigned as a 460

Leuconostoc phage (see Table S1 in the supplemental material). CRISPR elements are sites 461

containing multiple short palindromic repeats (23-47bp) that flank short “spacers” composed 462

of viral DNA (58); their presence in a bacterial genome represents a key evidence of 463

bacteriophage attack (58). Four CRISPR elements were detected from 127 contigs with longer 464

than 10 kb, suggesting that bacterial cells might be suffered from bacteriophage infection. 465

466

Relative abundance of the phage-related contigs. Bacteriophages are ubiquitous and 467

abundant components of microbial communities, and have significant influence upon bacterial 468

diversity, horizontal gene transfer, and genetic diversity in environmental habitats (43). By 469

counting the number of reads mapped to each phage-related contig and normalizing by the 470

total read count, it was possible to estimate the relative abundance of phages. Figure 7 shows 471

the frequency at which reads were observed for each phage-related contig in the kimchi 472

microbial samples. Putative phage sequences were detected early in the J13 sample (day 13), 473

and the numbers subsequently increased steadily throughout the fermentation. However, 474


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putative phage sequences increased suddenly in the J25 sample, and their abundance 475

amounted to approximately 7% of the total reads. This large number of putative phage 476

sequences in the metagenomic data sets was unexpected because it was thought that most 477

phage (if present) escaped from the cell fraction obtained by centrifugation. Possible 478

explanations for phage detection include: the phages were replicating within the kimchi 479

microbiome and free phage particles adhered to either particulate matter or bacterial cells at 480

the time of harvesting (10, 17). 481

In any event, the high abundance of phage sequences in the cellular fraction from Kimchi 482

indicate that a remarkably high proportion of bacterial cells were suffering from infection and 483

perhaps from lytic phage attack. Although the frequency of putative phage genes was high at 484

the end of the fermentation, such levels are consistent with previously reported ranges of 485

phage-infected bacterial cells (10). It was inferred that the decrease in bacterial population 486

after day 25 (Fig. 1) and the major changes of microbial community between days 23 and 25 487

(Fig. 2) were affected by phage-host dynamics that occur during kimchi curing. With the 488

decrease of bacterial abundance after day 25, the putative phage sequences also slowly 489

decreased. Previous studies have reported that fermentation temperature and acidity are 490

important determinants of the microbial community composition in kimchi fermentation (6, 491

28). However, our results showed that in addition to temperature and acidity, a new influence, 492

bacteriophage was another clearly important determinant of microbial community dynamics 493

like sauerkraut fermentation (32, 33, 37), especially at the end stages of kimchi fermentation. 494

495

Conclusions. Changes in the overall features and metabolic potential of the kimchi-496

fermenting microbial community were investigated by examining a large pyrosequencing-497

derived data set, in conjunction with metabolite analysis. The metagenome and metabolites 498

revealed that the kimchi microbiome had high metabolic potential with respect to 499


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heterotrophic lactic-acid fermentations. Based on the abundances of 16S rRNA genes and 500

representation of whole genomic sequences in the metagenome, we conclude that active 501

microbial populations in kimchi fermentation were dominated by members of three genera, 502

Leuconostoc, Lactobacillus, and Weissella, which are consistent with many previous results (6, 503

24, 28). Besides microbial genome sequences, a surprisingly high abundance of phage DNA 504

sequences were identified, indicating that bacteriophage influence the kimchi microbial 505

community and may be a key determinant of kimchi microbial community dynamics. 506

Metagenomic analysis of the kimchi samples over time made it possible to monitor not only 507

microbial community composition but also the overall features and metabolic potential during 508

fermentation. Future analysis of microbial abundances and interactions will likely provide 509

insights into how the kimchi microbial community influences metabolite production and, 510

ultimately, kimchi flavor. 511

512

ACKNOWLEDGMENTS 513

We thank professors J.S. Chun and C.J. Cha, and Drs. W.S. Park, B.H. Ryu, and Y.S. Ko for 514

their valuable help and collaboration during this study. This work was supported by the 515

Technology Development Program for Agriculture and Forestry (TDPAF) of the Ministry for 516

Agriculture, Forestry and Fisheries and the 21C Frontier Microbial Genomics and Application 517

Center Program (grant #: MG05-0104-4-0) of the Ministry of Education, Science and 518

Technology, Republic of Korea. 519

520

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J. Mahaffy, A. B. Martin-Cuadrado, A. Mira, J. Nulton, L. Pašic, S. Rayhawk, J. 721

Rodriguez-Mueller, F. Rodriguez-Valera, P. Salamon, S. Srinagesh, T. F. Thingstad, T. 722

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community dynamics in four aquatic environments. ISME J. 4:739-751. 724

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Investigation of archaeal and bacterial diversity in fermented seafood using barcoded 727

pyrosequencing. ISME J. 3:745-751. 728


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49. Sanapareddy, N., T. J. Hamp, L. C. Gonzalez, H. A. Hilger, A. A. Fodor, and S. M. 730

Clinton. 2009. Molecular diversity of a North Carolina wastewater treatment plant as 731

revealed by pyrosequencing. Appl. Environ. Microbiol. 75:1688-1696. 732

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characterization of a bacteriocin produced by Pediococcus pentosaceus K23-2 isolated from 738

kimchi. J. Appl. Microbiol. 105:331-339. 739

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52. Simon, C., A. Wiezer, A. W. Strittmatter, and R. Daniel. 2009. Phylogenetic diversity 741

and metabolic potential revealed in a glacier ice metagenome. Appl. Environ. Microbiol. 742

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53. Son, H. S., K. M. Kim, F. van den Berg, G. S. Hwang, W. M. Park, C. H. Lee, and Y. 745

S. Hong. 2008. 1H nuclear magnetic resonance-based metabolomic characterization of wines 746

by grape varieties and production areas. J. Agric. Food Chem. 56:8007-8016. 747

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54. Song, Y. O. 2004. The functional properties of kimchi for the health benefits. J. Food Sci. 749

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genetics analysis (MEGA) software version 4.0. Mol. Biol. Evol. 24:1596-1599. 753


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56. Tringe, S. G., C. von Mering, A. Kobayashi, A. A. Salamov, K. Chen, H. W. Chang, M. 755

Podar, J. M. Short, E. J. Mathur, J. C. Detter, P. Bork, P. Hugenholtz, and E. M. Rubin. 756

2005. Comparative metagenomics of microbial communities. Science 308:554-557. 757

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57. Tyson, G. W., J. Chapman, P. Hugenholtz, E. E. Allen, R. J. Ram, P. M. Richardson, 759

V. V. Solovyev, E. M. Rubin, D. S. Rokhsar, and J. F. Banfield. 2004. Community 760

structure and metabolism through reconstruction of microbial genomes from the environment. 761

Nature 428:37-43. 762

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58. Tyson, G. W., and J. F. Banfield. 2008. Rapidly evolving CRISPRs implicated in 764

acquired resistance of microorganisms to viruses. Environ. Microbiol. 10:200-207. 765

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59. Venter, J. C., K. Remington, J. F. Heidelberg, A. L. Halpern, D. Rusch, J. A. Eisen, D. 767

Wu, I. Paulsen, K. E. Nelson, W. Nelson, D. E. Fouts, S. Levy, A. H. Knap, M. W. Lomas, 768

K. Nealson, O. White, J. Peterson, J. Hoffman, R. Parsons, H. Baden-Tillson, C. 769

Pfannkoch, Y.-H. Rogers, and H. O. Smith. 2004. Environmental genome shotgun 770

sequencing of the Sargasso Sea. Science 304:66-74. 771

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60. Wilmes, P., S. L. Simmons, V. J. Denef, and J. F. Banfield. 2009. The dynamic genetic 773

repertoire of microbial communities. FEMS Microbiol. Rev. 33:109-132. 774

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61. Wisselink, H. W., R. A. Weusthuis, G. Eggink, J. Hugenholtz, and G. J. Grobben. 776

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778

FIGURE LEGENDS 779

FIG. 1. Change in pH and 16S rRNA gene copy number for total bacterial numbers during 780

kimchi fermentation. Measurements of 16S rRNA gene copy numbers were performed 781

independently in triplicate. Error bars represent the standard deviation. 782

783

FIG. 2. Quantification of identified key metabolites in kimchi supernatants during 784

fermentation by 1H-NMR. Data are given as means ± standard error, calculated from three 785

replicates. Quantification was determined using the Chenomx NMR Suite v. 6.1 with 2,2-786

dimethyl-2-silapentane-5-sulfonate (DSS) as the internal standard. 787

788

FIG. 3. Phylogenetic taxonomic composition of the kimchi microbiome during fermentation. 789

16S rRNA gene sequences were classified to the genus level using the MG-RAST sever based 790

on the RDP (16S rRNA gene) database (E-value, 0.01; minimum alignment length, ≥50 bp). 791

792

FIG. 4. Predicted metabolic profiles of kimchi metagenomic data sets obtained from SEED 793

subsystems. The MG-RAST server based on SEED subsystems provided categories for the 794

metagenomic data sets. The MG-RAST recommended parameters (E-value, 0.01; minimum 795

sequence length, ≥50 bp) were used. 796

797

FIG. 5. Predicted metabolic profiles of kimchi metagenomic data sets related to carbohydrate 798

metabolism and fermentation. Categories for the metagenomic data sets were provided by the 799

MG-RAST server based on SEED subsystems. The MG-RAST recommended parameters (E-800

value, 0.01; minimum sequence length, ≥50 bp) were used. 801

802


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FIG. 6. Recruitment of whole clean reads against complete or draft genomes (A). Insets 803

are the recruitment plots for the four top highest recruiting genomes; Leuconostoc 804

mesenteroides subsp. mesenteroides ATCC 8293, (B); Lactobacillus sakei subsp. sakei 23805

K, (C); Leuconostoc citreum KM20, (D); and Weissella paramesenteroides ATCC 33313, 806

(E). Lines within the recruitment plots indicate the level of 95% nucleotide identity. 807

808

FIG. 7. Relative abundance of putative phage contigs in whole unassembled clean reads 809

during kimchi fermentation. Kimchi metagenomic data sets were matched against four 810

putative phage contigs using the BLASTN algorithm with the criteria of a minimum percent 811

identity of 95% and a minimum overlap of 90 bp. 812


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813

TABLE 1. Summary of the pyrosequencing-derived data sets, and statistical analysis of, 814

kimchi fermentation samples. 815

Kimchi samples J1 J7 J13 J16 J18 J21 J23 J25 J27 J29 Total

Total size of sequences (bp) 11417220 15705531 9806465 13411871 8009837 27622094 28190450 104312483 49419903 25183023 281661657

Number of reads 27337 38388 23287 31647 18846 66096 67771 249849 118313 60022 701556

Size of clean sequences (bp) 9646351 13090858 8301534 11390298 6765854 23238193 23565573 86509803 41595359 21345546 245449369

Number of clean reads 22006 30202 18885 25737 15208 53106 53919 197709 95322 48813 560907

Average length of clean reads (bp) 438 433 439 442 444 437 437 437 436 437 438

Average GC (%) of clean reads§ 37.53 37.25 37.54 38.62 38.03 37.38 37.49 38.13 37.81 37.26 37.70

Number (%†) of

predicted 16S rRNA genes*

38

(0.17)

57

(0.19)

90

(0.48)

199

(0.77)

101

(0.66)

300

(0.56)

334

(0.62)

1316

(0.67)

592

(0.62)

252

(0.52)

3279

(0.59)

Number (%†) of reads categorized

by SEED subsystem*

732

(3.33)

2249

(7.45)

8059

(42.67)

12419

(48.25)

8865

(58.29)

23849

(44.91)

24486

(46.11)

102770

(51.98)

43808

(45.96)

23406

(47.95)

252936

(45.09)

§Fractional GC contents of DNA sequence was calculated by GEECEE program (http://emboss.bioinformatics.nl/cgi-816

bin/emboss/geecee). 817

†The numbers in parentheses indicate the percentages of respective read numbers for clean read numbers. 818

*An E-value of less than 0.01 and a minimum alignment length (≥ 50 bp) was used to predict 16S rRNA and functional genes 819

using the MG-RAST server. 820 on May 16, 2019 by guest

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821

TABLE 2. Comparative representation of the major metabolic groups of the kimchi 822

microbiome versus several other environmental metagenomic data sets publicly available in 823

MG-RAST 824

Relative abundance (%)*

SEED class SEED category Kimchi

Termite gut

(4442701.3)§

AMD biofilm

(4441137.3)§

Farm soil

(4441091.3)§

EBPR sludge

(4441092.3)§

Sargasso Sea

(4441571.3)§

Subsystem

hierarchy 1 Carbohydrate 14.49 12.89 12.17 11.25 10.72 12.23

Uptake system 0.07 0.64 0.11 1.80 0.51 0.51

Di-and oligosaccharides 28.38 21.64 24.10 15.30 14.33 13.90

Central carbohydrate metabolism 18.05 26.16 39.86 26.67 30.47 28.63

Amino sugars 1.66 7.90 1.88 4.71 2.65 2.47

One-carbon metabolism 0.69 2.60 3.28 8.15 11.09 8.76

Organic acids 2.82 3.22 6.12 5.34 8.33 8.10

Quorum sensing and biofilm formation 0.35 0.55 0.16 0.40 0.61 0.54

Fermentation 14.38 5.24 10.51 7.52 6.77 9.18

CO2 fixation 0.74 2.17 3.60 4.46 7.78 2.33

Sugar alcohols 4.17 4.98 3.24 6.96 5.68 7.35

Methanogenesis - 0.43 0.02 0.22 0.22 0.10

Monosaccharides 28.70 24.35 7.12 18.41 11.55 18.04

Subsystem

hierarchy 2

Carbohydrate - 0.13 - 0.07 0.01 0.09

Butanol biosynthesis 2.60 10.16 3.57 20.21 21.20 21.27

Fermentation (mixed acid) 2.72 8.54 5.79 3.54 7.92 3.90

Fermentation (lactate) 65.93 33.33 29.29 25.66 27.19 16.08

Acetyl-CoA fermentation to butylate 0.65 18.29 43.71 30.97 32.33 42.23

Subsystem

Acetoin, butanediol metabolism 28.10 29.67 17.64 19.62 11.35 16.54

* Relative abundance means the relative proportion of sequencing reads performing specific metabolisms within each subsystem. 825 § Project identifying numbers in the MG-RAST system. 826


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827

Figure 1 828

829

830

831

Days

0 5 10 15 20 25 30

16

S r

RN

A g

ene

cop

ies

/ml

108

109

1010

1011

pH

4.2

4.6

5.0

5.4

5.816S rRNA gene copies

pH

832


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Figure 2 834

835

836

837

838

Days

0 5 10 15 20 25 30

Co

nce

ntr

ati

on

(m

M)

0

20

40

60

80Glucose

Fructose

Sucrose

Mannitol

Lactate

Acetate

Ethanol

839


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840

Figure 3 841

842

843

844

845

846


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847

Figure 4 848

849

850

851

852

853


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854

Figure 5 855

856

857

858

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Figure 6 861

862

863

864

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Figure 7 867

868

869

870

Kimchi samples

J1 J7 J13 J16 J18 J21 J23 J25 J27 J29

Rel

ati

ve

ab

un

dan

ce (

%)

0

1

2

3

4

5

6

7

8

contig 03545

contig 01645

contig 27827

contig 02778

871

872

873


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