Food Science and Technology Research
Online ISSN : 1881-3984
Print ISSN : 1344-6606
ISSN-L : 1344-6606
Technical paper
Microbial Community Characteristics in Industrial Matured Chinese paocai, a Fermented Vegetable Food, from Different Factories
Huipeng LiangAn ZhangZhengyun WuShupin ChengWenping YuWenxue Zhang
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2016 Volume 22 Issue 5 Pages 595-604

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Abstract

Paocai was a traditional fermented food in China and produced by spontaneous fermentation of various vegetables. The microbial community occurring in industrial matured Chinese paocai (IMCP) from different factories was investigated by PCR-denaturing gradient gel electrophoresis (DGGE) and quantitative PCR (qPCR). The dominant bacteria were different in IMCP from different factories. Three genera including Debaryomyces, Pichia and Candida were identified and Debaryomyces was mutual and dominant yeast in all IMCP. Fifteen bands from DGGE profiles of lactic acid bacteria (LAB) were identified and four genera were detected. Alkalibacterium and Lactobacillus were dominant in all IMCP. In conclusion, the IMCP from different factories possessed different dominant bacteria. The community of LAB in IMCP from different factories was different, and Lactobacillus and Alkalibacterium were main LABs in all IMCP. The quantity of LAB was quantitated and shown to exist at 107 – 109 copies in the IMCP from different factories.

Introduction

Fermented vegetables are usually produced by spontaneous fermentation of the raw materials with various naturally-occurring bacteria in brine water (Tanganurat et al., 2009). Paocai is one of the typical representatives of traditional fermented vegetable foods in China, especially in Sichuan province. It was recorded that Chinese made paocai as early as more than 3000 years ago in Zhou Dynasty (Xiong et al., 2012). Paocai, commonly served as side dish or used as an appetizer, is a popular food items prepared as a result of spontaneous fermentation and in recent years it has been recognized as a healthy functional food because of its healthcare function such as antibiosis, anticancer, anti-senescence, anti-obesity and so on (Li et al., 2005; Yan et al., 2008). Nowadays there is an increasing consumer demand for various paocai on account of its convenience, tasty, nutrition and health. To improve the yield and quality of paocai, it is necessary to understand fully the microbial community in various paocai. As far as we know, the conventional culture method is often time-consuming and laboursome. In recent years, plenty of powerful molecular ecological methods, such as PCR-DGGE, qPCR, Amplified ribosomal DNA restriction analysis (ARDRA), fluorescent in situ hybridization (FISH), etc., have been widely used to analyze the microbial community in the field of food (Yeun et al., 2014; Iwobi et al., 2015; Luo et al., 2014; Ding et al., 2014), which could overcome the shortcomings of culture approaches and detect the uncultured microbes as well. PCR-DGGE, a classical molecular ecological technic, has been widely applied to directly reveal and rapidly monitor the microbial community in the process of fermentation (Nie et al., 2015, 2013). Yeun et al. (2014) identified the most prevalent LAB in salted Chinese cabbage samples by using PCR-DGGE and SDS-PAGE. Yeun et al. (2013) analyzed the changes in the bacterial microflora of two commercial kimchi, salted cabbage, and ingredient mix samples during 30 days of fermentation at 4°C and 10°C by using PCR-DGGE. And for all we know, LABs are important in many food fermentations because they contribute to sensory characteristics and preservative effects (Holzapfel, 1995). During the fermentation, LAB utilise carbohydrate substrates available in the fermentation system and produce organic acids, especially lactic acid, which not only play an important role in the taste and aroma of the product but also lower the product's pH to ensure quality and safety (Zhou et al., 2014). As a complementary method, qPCR technology which has been used to quantitatively determine microbe in various environments (Mokhtari et al., 2013; Kao et al., 2007) was used to quantitate the quantity of LAB in this study.

The major objective of this study, therefore, was to investigate the characteristic and the differences of the microbial community structures in IMCP from different factories. For the best of our knowledge, this is the first report to reveal the diversity and differences of microbial community in traditional IMCP by using the combined PCR-DGGE and qPCR methods.

Materials and Methods

(1) Sampling and chemical analysis    Samples of IMCP were collected from three paocai Condiment Company limited (C1, C2 and C3), located in Meishan, Sichuan province of China, in March 2014. The varieties of paocai samples included Qingcai (P1, Brassica chinensis var chinensis) and Zhacai (P2, Brassica juncea var.tumida). The simples were stored at −20°C for further analysis. The pH value of the samples from different factories was measured with a pH meter (PHS-3C, China). Total titratable acidity (TTA) was titrated using 0.1 N NaOH to final pH 8.2 with phenolphthalein as the indicator and expressed in lactic acid. Salinity was measured by a digital salt meter (ATAGO, Japan). All analyses were conducted in duplicate and the average values are presented.

(2) DNA extraction and PCR amplification    The total DNA was extracted using the Ezup Column Genomic DNA Purification Kit (Sangon Biotech, China) and stored at −20°C until used.

The PCR was carried out in a MyCycler™ Thermal (Bio Rad, USA). The primers of WBAC1 (5′-GTC GTC AGC TCG TGT CGT GAG A-3′) and WBAC2-GC (5′-CGC CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GGG GCC CGG GAA CGT ATT CAC CGC G-3′) were used to amplify the bacterial 16S rRNA gene (Lopez et al., 2003). The GC clamp sequence attached to primer was underlined. For analysis of fungal diversity, PCR amplification of the 18S rRNA gene was performed using primers NS3-GC (5′-CGC CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GGG GGC AAG TCT GGT GCC AGC AGC C-3′) and YM951r (5′-TTG GCA AAT GCT TTC GC-3′) (Haruta et al. 2006). LAB group-specific PCR was performed using the primers of LacF-GC (5′-CGC CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GGG GAG CAG TAG GGA ATC TTC CA-3′) and LacR (5′-ATT YCA CCG CTA CAC ATG-3′) targeting the region of 16S rRNA gene (Walter et al., 2001). All the primers were synthesized by the Sangon Biotech Company (Shanghai, China). The PCR reaction contained 50 µL of 2×PCR Mix (TIANGEN Biotech, Beijing, China), 20 pmol primers, template DNA and distilled water. The PCR program for bacteria was 95°C for 5 min, followed by 30 cycles of 95°C for 60 s, annealing at 67°C for 30 s and elongation at 72°C for 60 s, and finally a 5 min elongation step at 72°C. The PCR of 18S rRNA gene was initiated at 94°C for 5 min, followed by 30 cycles of denaturation at 94°C for 60 s, annealing at 53°C for 60 s and extension at 72°C for 60 s, and a final extension at 72°C for 5 min. PCR amplification of LAB-group was performed as follows: 94°C for 2min, followed by 35 cycles of 94°C for 30 s, annealing at 61°C for 60 s and elongation at 68°C for 60 s, and finally a 7 min elongation step at 68°C. The PCR products were checked by electrophoresis on a 2% agarose gel.

(3) PCR-DGGE analysis of microbial communities    The PCR products were analyzed by DGGE using the D-Code™ Universal Mutation Detection System (Bio-Rad, Hercules, CA, USA). DGGE was performed with 80% polyacrylamide gel (Acrylamide/Bisacrylamide, 37.5:1) with a linear gradient of 30 – 55% denaturant for the bacterial community, 20 – 40% for the fungal community and 25 – 45 for LAB-group (a 100% denaturant corresponds to 7 M urea and 40% [v/v] formamide). Electrophoresis was run for 4 h at 200 V for bacteria, 5h at 160 V for fungi and 4.5 h at 130 V for the LAB at 60°C in 1×TAE buffer respectively. After electrophoresis, the gel was stained with SYBR Green I and visualized using a Gel Doc™ XR (Bio-Rad, USA). Bands patterns of the DGGE profiles were analyzed by Quantity One software (Bio-Rad, USA). The band Richness (S), Shannon (H) and Pielou (E) index were determined based on the number and relative quantity of the DGGE bands (Shannon, 1949; Pielou, 1966). The S showed that the number of bands in DGGE profiles. The H gives the proportional abundance of species and reacts sensitively to rare species and when all species are represented by the same number of individuals, the H reaches its maximum value. The E was presented to describe the uniformity of the distribution of the individuals on the number of bands. Principal component analysis (PCA) was performed using Canoco for Windows v4.5 software (Wageningen UR, Netherlands).

(4) Excision of representative DGGE bands and sequencing    Bands of interest from DGGE profiles were excised. The DNA eluted from excised bands was amplified using the primers mentioned above with no GC-clamp and the amplified products were sent to company for cloning and sequencing (Sangon, Shanghai, China). The sequence information was acquired by aligning the results with the sequences in Gen Bank using the BLAST search program at the National Center for Biotechnology Information (NCBI) (i) and the Classifier search program at the Ribosomal Database Project (RDP) (ii).

(5) qPCR analysis of LAB in IMCP samples    The total quantity of LAB was quantitated by employing the qPCR with SYBR Green I. The primers of LacF and LacR mentioned above with no GC-clamp were used to perform the qPCR in a Light cycler Nano System (Rocha, Switzerland) (Walter et al., 2001). The amplification reactions were carried out in a total volume of 20 µL containing SYBR® Green PCR Real time PCR Master mix (Toyobo, Japan), 2 pmol each primer, DNA template and distilled water. According to the protocol described by Walter, a modified form of amplification program was as follows: 50°C for 2 min, initial denaturation at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 s, annealing at 60°C for 30 s, and extension at 72°C for 15 s, with collection of fluorescence signal at the end of each cycle.

The melting curve analysis was carried out after amplification by heating gradually from 60°C to 95°C at 0.1°C/s with continuous fluorescence monitoring. The standard curve, evaluated by correlation coefficient (R2), was constructed using plasmids, which were prepared from the 16S rRNA gene library using Mini Plasmid Kit (Tiangen, Beijing, China). All samples were performed in triplicate.

Results and Discussion

In China, paocai is a traditional fermented food which drives by spontaneous fermentation microorganism. Although many studies have revealed that a variety of microbial species contribute to the production of kimchi products (Yeun et al., 2013; Jeong et al., 2013; Park et al., 2012), little is known about the microbial communities in IMCP samples made in southwest China.

(1) Physico-chemical analyses of IMCP samples    The pH, which is considered as fundamental variables determining the success of vegetable fermentation, is an important indicator for determining the maturity of paocai (Penas et al., 2010). The pH, TTA and salinity of IMCP brine samples from different factories were shown in Table 1. The pH values of IMCP in our investigation were in a common range and the pH profile was similar with those researches on fermented vegetables (Yu et al., 2012; Liu et al., 2015). The pH values of P1 were higher than that of P2. The pH values were 3.67 – 4.23 for all IMCP samples. The sample C2 of P1 and P2 had the lowest pH (4.04 and 3.67) and the highest TTA (0.34 and 0.67) and salinity (8.00 and 8.80) (Table 1). The pH values of IMCP samples from different factories were different and this was potentially connected with their differences in microbial community.

Table 1. The diversity indices of microbial community in IMCP samples from different factories.
Samples* pH TTA Salinity (NaCl%) Bacteria Fungi LAB-group
Richness (S) Shannon (H) Pielou (E) Richness (S) Shannon (H) Pielou (E) Richness (S) Shannon (H) Pielou (E)
P1 C1 4.14 0.20 4.60 14 2.17 0.82 6 1.20 0.67 12 1.77 0.77
C2 4.04 0.34 8.00 16 1.96 0.71 5 0.69 0.43 9 1.51 0.69
C3 4.23 0.30 6.70 22 2.32 0.75 6 1.38 0.77 10 1.83 0.79
P2 C1 4.01 0.17 3.60 19 2.41 0.82 6 1.67 0.93 10 1.63 0.71
C2 3.67 0.67 8.80 15 1.78 0.66 7 1.53 0.79 10 1.99 0.86
C3 4.00 0.40 6.50 16 2.16 0.78 7 1.33 0.68 11 2.01 0.84
*  P1 and P2 denoted two kinds of vegetables samples. C1, C2 and C3 represented the samples collected from three different factories respectively.

(2) Diversity of microbial community in IMCP samples    The microbial community DGGE profile of IMCP from different factories was shown in Fig. 1. A total of 27 bands were observed within all the samples from bacterial DGGE profile and the S of each lane were shown in Table 1. The S of C3 and C1 were the highest and lowest in P1 samples. While the S of C1 and C2 was the highest and lowest in P2 samples. Based on DGGE profile of bacterial community, the ranges of H in P1 and P2 samples were 1.96 – 2.32 and 1.78 – 2.41 respectively (Table 1). The H of C2 was the lowest in P1 and P2 samples. This may be due to their lowest pH and highest salinity. In fungal DGGE profile, the S in P1 and P2 samples are comparable and the ranges of H were 0.69–1.38 and 1.33 – 1.67 respectively (Table 1). The microbial groups shifted sequentially from less acid and salt tolerant to more acid and salt tolerant groups adapted to the acidic and salt environmental condition (Fierer and Jackson, 2006; Plengvidhya et al., 2007). Therefore, the relatively low pH of the samples may predict a low microbial diversity. And the bacterial and fungal H of C3 (2.32 and 1.38) and C1 (2.41 and 1.67) were the highest in P1 and P2 samples respectively. The E of P1 samples showed no significant differences with those of P2 samples (Table 1).

Fig. 1.

The PCR-DGGE profile of 16S rRNA gene extracted from bacterial community in IMCP samples from different factories. P1 and P2 denoted two kinds of vegetables samples. C1, C2 and C3 represented the samples collected from three different factories respectively. The bands indicated by the arrows and numbers were excised and sequenced, and the alignment results were listed in Table 2.

Table 2. The identities of 16S and 18S rRNA gene sequences of bands excised from DGGE gel by using the BLAST and RDP search tools.
Banda Closest sequence/microorganismb Phylogenetic affiliation Identity (%) Alignment length (bp) Accession No.
Bacteria
1 Clostridium butyricum strain CB8 Clostridium 99 327 KJ558433.1
2 Lactobacillus zymae strain KCC-14 Lactobacillus 99 328 KC625331.1
3 Uncultured Lactobacillaceae bacterium clone PC-B50 Lactobacillus 99 328 JQ809294.1
4 Uncultured Pediococcus sp. clone PC-B14 Pediococcus 99 328 JQ809283.1
5 Alkalibacterium kapii strain MGR70 Alkalibacterium 97 327 KF151857.1
6 Alkalibacterium gilvum strain 5AE-1 Alkalibacterium 100 328 AB690572.1
7 Pediococcus ethanolidurans strain lao3-6-1 Pediococcus 100 243 KJ690914.1
8 Lactobacillus hammesii strain Kw2S11L1 Lactobacillus 99 329 JF427720.1
9 Uncultured bacterium clone Ll142-2L4 Acinetobacter 100 330 FJ671990.1
10,16 Uncultured bacterium clone Woods-Hole_a1567 Rhodobacteraceae 99 307, 302 KF798445.1
11 Tetragenococcus halophilus strain JCM 20256 Tetragenococcus 99 329 AB911552.1
12 Uncultured bacterium clone TV_B8 Brevundimonas 100 289 JX575614.1
13 Clostridium sp. S11-3-10 Clostridium 99 329 AB838978.1
14 Lactobacillus sp. NBRC 101665 Lactobacillus 99 328 AB681518.1
15 Tetragenococcus halophilus strain 7–8 Tetragenococcus 98 329 HQ384302.1
17 Lactobacillus sp. B4(2014) Lactobacillus 100 328 KM259929.1
18 Uncultured bacterium isolate DGGE gel band A28 Sphingomonas 100 328 KC736689.1
19 Idiomarina loihiensis strain DY032-2 Idiomarina 98 330 KJ466005.1
20,21 Lactobacillus farciminis strain Y124D Lactobacillus 98 328, 329 AB889729.1
22,26 Uncultured bacterium clone C184 Alcanivorax 99 330 KC523578.1
23 Pseudomonas halophila isolate P-halo Halovibrio 98 329 HG964480.1
24 Brevundimonas sp. BBDP1026 Brevundimonas 98 306 EF471236.1
25 Uncultured bacterium DGGE gel band L18 Aminobacterium 99 331 AB856317.1
27 Uncultured Chloroflexi bacterium clone SL73 Sphaerobacter 98 316 JX240517.1
Fungi
1,4 Debaryomyces hansenii isolate LJ-2-Y-1 Saccharomycetaceae 100 402 KJ781291.1
2 Pichia triangularis strain BC 211 Saccharomycetaceae 100 400 AY227018.1
3 Pichia farinosa strain CO-2 Saccharomycetaceae 99 402 EU106163.1
5 Uncultured Debaryomyces clone: ZQC98 Saccharomycetaceae 100 401 AB986207.1
6,7,8,9 Uncultured Candida clone: ZLB6 Saccharomycetaceae 99 389–390 AB986197.1
LAB
1,13 Lactobacillus ginsenosidimutans strain EMML 3041 Lactobacillus 99,100 347 HQ389549.1
2,4,5,7, 11,12 Uncultured Lactobacillaceae bacterium clone PC-B50 Lactobacillus 99,100 347 JQ809294.1
3 Uncultured Lactobacillus sp. clone: 2X7 Lactobacillus 99 347 LC002932.1
6 Pediococcus ethanolidurans strain MF14 Pediococcus 100 347 KJ994507.1
8 Leuconostoc sp. P Leuconostoc 99 347 DQ061074.1
9 Lactobacillus alimentarius gene strain: JCM 1095 Lactobacillus 99 347 LC055606.1
10 Alkalibacterium gilvum gene strain: 5AE-1 Alkalibacterium 99 347 AB690572.1
14,15 Lactobacillus sp. NBRC 107199 Lactobacillus 96 347 AB682498.1

Only highest homology matches are presented.

a  the number of bands was in accordance with that in Fig. 1 and Fig. 3.

b  sequences were compared with known sequences in NCBI database.

(3) Microbial community analysis of IMCP samples    In order to understand the dominant microbe in IMCP samples from different factories, a total of 27 representative bands of bacterial PCR-DGGE indicated in Fig. 1 were sequenced and the results were shown in Table 2. All the bands were identified and fell into four phyla including Firmicutes (15 bands), Proteobacteria (10 bands), Synergistetes (1 band) and Chloroflexi (1 band). The similarity of all band sequences was nearly ≥ 99% comparing with those available in the GenBank database. After the fermentation, IMCP samples of different kinds or from different factories possessed the different dominated bacteria. In samples P1, the dominant bacteria of C1 sample were Brevundimonas (bands 12), Clostridium (band 13) and Sphingomonas (band 18). While in C2 sample Alkalibacterium (bands 5 and 6), Brevundimonas and Tetragenococcus (band 15) were prevalent. Uncultured bacterium (band 9, 10 and 16) and Tetragenococcus (bands 11) were dominant in C3 sample. In samples P2, Uncultured bacterium (band 9, 12, 16 and 18), Clostridium and Lactobacillus (band 14) were dominant bacteria in C1 sample. In C2 sample of P2, Pediococcus (band 7) and uncultured bacterium (band 12) were preponderant. While in C3 sample, uncultured bacterium (band 12) and Clostridium were prevalent. Among these genera Lactobacillus is regarded as one of main bacteria that produce plenty of lactic acid and one of dominant LAB in vegetable fermentations (Xiong et al., 2013). Some researchers have demonstrated that Lactobacillus is contributed to the depletion of nitrite accumulated during fermentation in paocai (Yan et al., 2008; Rai et al., 2010). Then Lactobacillus is not only beneficial to the taste and aroma but also to ensure the safety of the IMCP. Brevundimonas detected in all samples was found in various environments but its functions on the fermentation need further researches (Ryu et al., 2007). Clostridium which could metabolize organic substances to organic acids such as caproic acid, butylic acid, etc. (Zhang et al., 2005), was possible involved in the flavor components in paocai. Sphingomonas dominated in C1 of P1 and P2 sample especially have been reported to produce antimicrobial compounds and exhibited antimicrobial activity against several pathogens (Romanenko et al., 2008). The genus Alkalibacterium dominated in C2 of P1 samples especially comprises marine LAB and they produce lactic acid as the main product of glucose fermentation (Ishikawa et al., 2011, 2013). Tetragenococcus halophilus dominated in C2 of P1 samples is a moderately halophilic Gram- positive LAB and has been widely found in salted products (Fukuda et al., 2002; Taira et al. 2007). Pediococcus dominated in C2 of P2 samples were LAB found on plants and in many fermented foods (Dobson et al., 2002; Simpson et al., 2002). The existence of Alkalibacterium, Tetragenococcus and Pediococcus maybe lead to a relative low pH in C2 of P1 and P2 samples (Table 1).

For fungal PCR-DGGE (Fig. 2), a total of 9 bands were excised and sequenced. The results from the sequencing of the highlighted bands are shown in Table 2. All sequenced bands were affiliated with three genera, including Debaryomyces (bands 1, 4 and 5), Pichia (bands 2 and 3) and Candida (bands 6, 7, 8 and 9). Obviously, PCR-DGGE profile analyses showed that Debaryomyces predominated in all IMCP samples. Debaryomyces hansenii reportedly produces vitamins, amino acid and so on and contributes to the development of flavor in cheese and fermented sausages (Breuer et al. 2006; Cano-García et al. 2014). Some strains, pertaining to the species D. hansenii, have the ability to produce aroma compounds, such as aldehydes, ketones, alcohols, esters and sulphur compounds (Cano-García et al. 2014). Inferentially, D. hansenii is also involved in the development of flavor in IMCP.

Fig. 2.

The PCR-DGGE profile of 18S rRNA gene extracted from fungal community in IMCP samples from different factories. P1 and P2 denoted two kinds of vegetables samples. C1, C2 and C3 represented the samples collected from three different factories respectively. The bands indicated by the arrows and numbers were excised and sequenced, and the alignment results were listed in Table 2.

(4) Multivariate analysis of DGGE profiles    PCA was performed based on the relative quantity of microbial DGGE band profile to analyze the correlation between microorganisms and the IMCP samples from different factories. The PCA plots were shown in Fig. 3. The principal components PC1 and PC2 of IMCP samples accounted for 50.30% and 24.90% of the variance. The PCA ordination of the genera and sample variables demonstrated that Brevundimonas, Alcanivorax and Pediococcus were correlated with C1 of P1, C2 and C3 of P2 samples (Fig. 3). Alkalibacterium and Tetragenococcus were correlated with C2 of P1 samples while Pediococcus was correlated with C2 of P2 samples. This may be due to the higher salinity of C2 samples. Halovibrio, unclassified-Rhodobacteraceae, Acinetobacter and Candida were correlated with C3 of P1 samples, and Lactobacillus and Alkalibacterium were correlated with C3 of P2 samples (Fig. 3).

Fig. 3.

Principal component analysis (PCA) of microbial composition in IMCP samples from different factories. P1 and P2 denoted two kinds of vegetables samples. C1, C2 and C3 represented the samples collected from three different factories respectively. The directions of the arrows indicate the relative loading on the first and second principal components.

Plants provide a nutrient-rich niche for the growth of microorganisms, particularly bacteria (Pereira et al., 2012). The bacteria which attached to the raw vegetables present significant differences associated with regions, seasons, vegetable varieties as well as cultivation patterns, resulting in the differences of microflora in different fermented vegetable products (Xiong et al., 2012; Islam et al., 2010; Zhang et al., 2013; Haque et al., 2015). Potentially, this is the main reason for the difference of bacteria in different varieties IMCP samples from different factories. In addition, another reason for the difference of microflora appeared among various fermented vegetable products were probably related to chemical and physical factors including substrates, NaCl concentration and fermentation temperature (Xiong et al., 2012; Cho et al., 2006).

(5) PCR-DGGE analysis of LAB in IMCP samples    The DGGE pattern of LAB was showed in Fig. 4, and a total of 15 bands were observed. The S of C1 and C2 in P1 samples was the highest and lowest, while the S in P2 samples was comparative (Table 1). The H of C3 (1.83 and 2.01) was the highest in P1 and P2 samples respectively. In addition, the E range of LAB in IMCP samples community was from 0.69 to 0.86.

Fig. 4.

The PCR-DGGE profile of LAB-group community in IMCP samples from different factories. P1 and P2 denoted two kinds of vegetables samples. C1, C2 and C3 represented the samples collected from three different factories respectively. The bands indicated by the arrows and numbers were excised and sequenced, and the alignment results were listed in Table 2.

In Fig. 4, fifteen bands were sequenced and four genera including Alkalibacterium, Lactobacillus, Pediococcus, Leuconostoc were identified (≥ 96%). Lactobacillus (band 7) and Alkalibacterium (band 10) were mutual and preponderant in all IMCP samples. Many previous studies have shown that LAB including Leuconostoc, Lactobacillus, Weissella, Lactococcus, and Pediococcus, are key players responsible for kimchi fermentation (Chang and Chang, 2010; Lee and Lee, 2010; Lee et al., 2010). Alkalibacterium, Lactobacillus and Pediococcus were already detected in the bacterial DGGE profiles. But the genus Alkalibacterium was found for the first time to be a dominated LAB in IMCP. And Lactobacillus alimentarius was detected more abundant in P1 samples than that in P2 samples. Pediococcus sp. was found to be dominant LAB in mukeunji aged for at least 2 years (Hong et al., 2015). In addition, Leuconostoc was detected while it was not in bacterial DGGE profiles, and it was more abundant in P2 samples than that in P1 samples. Some species affiliated to Leuconostoc was detected in many fermented vegetables such as Chinese sauerkraut, leek and kimchi (Xiong et al., 2012; Hong et al., 2015; Wouters et al., 2013). Marshall (1987) has pointed out that Leuconostoc mesenteroides ssp. cremoris and Leuconostoc lactis are two important organisms for aroma production. Then Leuconostoc was possibly contributed to the aroma of IMCP.

(6) Quantitative of LAB in IMCP samples from different factories    LABs play an important role in IMCP fermentation from the results of DGGE analysis. The quantity of LAB was monitored using qPCR. Compared with the conventional quantitative method, the qPCR approach can quickly quantitate both cultivable and non-cultivable microorganisms in samples. The amplification curves were of sigmoidal form. Melting curves, as an additional quality control step for the analysis of qPCR data to verify the specificity of amplified products, can distinguish false positive signals due to non-specific amplification or primer-dimers (Soares et al., 2013). The melting curves of LAB in IMCP samples only had one peak, indicating that the specificity of primers which were used was good. No primer dimers or non-specific amplification products were visible for all samples. The standard curve of LAB for P1 and P2 samples was as Eq. 1 and Eq. 2.

  
  

The R2 (0.99 and 0.99) suggested a good correlation between Cq and the logarithm of template concentrations. The efficiency of amplification calculated according to the slope of the standard curve was 99.30% and 96.60%, which indicated an optimum PCR efficiency. The quantity of LAB in IMCP samples from different factories was shown in Table 3. The quantities of LAB were shown to exist at 107 – 109 copies in the IMCP samples from different factories. The results obtained by qPCR showed that the quantity of LAB in C1 and C3 was the highest in P1 and P2 samples respectively (Table 3). The quantity of LAB in IMCP of different kinds or from different factories was different. The difference of the quantity of LAB in P1 samples was more obvious than that in P2 samples (Table 3). The factors that affected the quantity of LAB in IMCP included the vegetable varieties, naturally occurring microbial populations in the raw materials as well as the environmental conditions such as pH, temperature and salt concentration.

Table 3. The quantity of LAB in IMCP samples from different factories by qPCR.
Samples* Concentration of LAB (copies/mL)
P1 C1 (1.20 ± 0.02) × 109
C2 (2.73 ± 0.13) × 108
C3 (8.30 ± 0.39) × 107
P2 C1 (1.78 ± 0.01) × 108
C2 (1.63 ± 0.00) × 108
C3 (1.10 ± 0.01) × 109
*  P1 and P2 denoted two kinds of vegetables samples. C1, C2 and C3 represented the samples collected from three different factories respectively.

Conclusion

Combined PCR-DGGE and qPCR analysis may be a valuable and mutual complementary way to elucidate the microbial community in IMCP samples and this is the first report about the microbial community structure and diversity in IMCP from different factories by combined PCR-DGGE and qPCR analyses. The microbial diversity was different in IMCP of different kinds or from different factories. The results of bacterial DGGE profiles showed that a total of 27 bands were sequenced and four phyla, including Firmicutes, Proteobacteria, Synergistetes and Chloroflexi, were identified. Fourteen genera including Lactobacillus, Alkalibacterium, Pediococcus, Clostridium, Tetragenococcus, Acinetobacter, Idiomarina, Alcanivorax, Halovibrio, Brevundimonas, Sphingomonas, Aminobacterium, Sphaerobacter and unclassified_Rhodobacteraceae were detected. The IMCP samples of different kinds or from different factories possessed different dominant bacteria. For fungal PCR-DGGE (Fig. 2), a total of 9 bands were sequenced and affiliated with three genera, including Debaryomyces, Pichia and Candida. Obviously, Debaryomyces was mutual and dominant yeast in all IMCP samples. Fifteen bands from LAB DGGE profiles were identified and four genera including Alkalibacterium, Lactobacillus, Pediococcus, Leuconostoc were detected. Lactobacillus and Alkalibacterium were mutual and preponderant in all IMCP samples. And the genus Alkalibacterium was found for the first time to be a dominated LAB in IMCP. The quantity of LAB was monitored by qPCR and shown to exist at 107 – 109 copies in the IMCP samples from different factories The quantity of LAB in IMCP of different kinds or from different factories was different.

Acknowledgment    The research was financially supported by grants from the National Science and Technology Project (NO. 2012BAD31B04) of the Ministry of Science and Technology of the People's Republic of China, and the Key Science and Technology Project (NO. 2013NZ0055) of the Sichuan Science and Technology Department.

Supplementary Fig. 1.

The standard curve for LAB community in IMCP samples from different factories (C1, C2 and C3). P1 and P2 denoted two kinds of vegetables samples.

Supplementary Fig. 2.

The melting curves for LAB community in IMCP samples from different factories (C1, C2 and C3). P1 and P2 denoted two kinds of vegetables samples.

References
 
© 2016 by Japanese Society for Food Science and Technology
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