Food Science and Technology Research
Online ISSN : 1881-3984
Print ISSN : 1344-6606
Original papers
The Effects of Rebaudioside A on Microbial Diversity in Mouse Intestine
Shengjie LiTingtao ChenSuqin DongYonghua XiongHua WeiFeng Xu
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2014 Volume 20 Issue 2 Pages 459-467

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Abstract

Rebaudioside A (RA), a natural high potency sweetener, is isolated from the leaves of the Stevia rebaudiana plant, and has potential for wide use in our diet today. To investigate the effect of RA on the changes of microbial diversity in animal intestinal tract, the viable cell count and denaturing gradient gel electrophoresis (DGGE) method were used to monitor the microbial number and species in vivo, and before that, we also evaluated its infuence on the growth of some selected bacteria in vitro. Our results indicated that the RA had little or no effect on the growth of E. coli O157 H7, S. typhimurium, L. monocytogenes and B. longum, while could promote the growth of L. plantarum and inhibit that of S. aureus in vitro; moreover, the viable cell count and DGGE results confrmed that the RA posed little pressure on the composition of total bacteria, enterobacteria and lactobacilli in vivo. In conclusion, RA posed little pressure on the growth and the composition of microbes, suggesting it is safe for gut microbes.

Introduction

Rebaudioside A (RA) is a kind of steviol glycoside obtained from Stevia rebaudiana Bertoni and has been applied widely to food and beverage industries in Japan, Korea and South America, and acted as dietary supplements in the United States (JECFA, 2005). RA is a natural sweetener, whose sweetness is 250 – 450 times than sucrose, and is safe for diabetes (type II), phenylketonuria, and Candida patients (Geuns, 2010). Structurally, one molecular RA is composed by one main steviol and four glucose, and metabolism studies showed that it is metabolized to steviol which is the safety evaluation form of RA for the risk assessment (Carakostas et al., 2008). Previous studies also indicated that RA shares similar pharmacokinetic characters in mammals, such as pigs, rats and humans (Wheeler et al., 2008).

RA has been a focus for decades, its structure (Steinmetz Lin, 2009; Upreti et al., 2012), pharmacokinetic (Wheeler et al., 2008), stability properties (PRAKASH et al., 2012; WÖlwer-Rieck et al., 2010), clinical safety (Hsieh et al., 2003), and toxicologies, including chronic toxicity (Curry and Roberts, 2008; Nikiforov and Eapen, 2008), genotoxicity (Williams and Burdock, 2009), carcinogenicity (Toyoda et al., 1997; Xili et al., 1992), reproductive and developmental toxicity (Curry et al., 2008) had been fully studied. In addition, some beneficial functions of RA, including nontoxicological effects, antioxidant, antihypertensive, anti-Inflammatory and immunomodulatory activities, and anti-hyperglycemic activities had also been proved (Boonkaewwan et al., 2006; Chan et al., 2000; Gregersen et al., 2004; Jeppesen et al., 2002; Lee et al., 2001; Tadhani et al., 2007). However, few studies were carried out to study the effects of RA on the microbial diversity in the gastrointestinal tract.

In this study, the culture-dependent and -independent methods were applied to study the infuence of RA on the intestinal major bacteria in vivo, and its effects on some bacteria were also evaluated. As a powerful tool, denaturing gradient gel electrophoresis (DGGE) is based on the direct analysis of DNA extracted from the microbial environment, allowing us to recover the full diversity of the enriching bacteria and to characterize the predominant bacteria in the culture at the species level (Chen et al., 2012), and had been widely applied to monitor the microbial diversity in soil, polluted water, fermented food, wines and animal intestinal tracts. And the combination of viable cell count and DGGE will better evaluate the effect of RA on the intestinal microbiota.

Materials and Methods

Bacteria strains and RA preparation    Six common bacteria, namely Escherichia coli O157:H7, Salmonella typhimurium ATCC 13311, Staphylococcus aureus CGMCC 26001, Listeria monocytogenes CMCC 54007, Lactobacillus plantarum ACCC 11095, and Bifdobacterium longum ATCC 15707, were used as indicator strains to evaluate the effects of RA on their growth in vitro. Before the experiment, all the microorganisms were activated two times using LB (Lysogeny Broth, for culturing E. coli, S. typhimurium, S. aureus and L. monocytogenes) and MRS (Man-Rogosa-Sharpe, for culturing L. plantarum and B. longum, Qingdao Hope Bio-technology Co. Ltd, Qingdao, China) mediums.

The RA (Ganzhou Julong High-tech Industrial Co., Ltd, Jiangxi, China.) was diluted in distilled water and reached the final concentration of 10% (w/v), and sterile fltered using the 0.22 µm Millipore filter (0.22 µm, Millipore Express PES Membrane, Carrigtwohill, Co. cork, Ireland.), and then stored at −20°C for further use.

Effect of RA on the growth of different bacteria in vitro    The RA was added into LB and MRS broths to reach the final concentration of 0.01, 0.10, 0.50, and 1.00% (w/v), and the sterile saline buffer was used as the control group. The growth testing was performed in 96 well microtiter plates every 1 h by adding 200 µL cultures, and optical density at the absorbance of 600 nm was recorded using the enzyme-labeling instrument (PERLONG, Beijing, China). The experiment was performed in triplicates for each sample, and each value was described using Mean according to the method of Tidona et al.'s (Tidona et al., 2011).

The Animal design    The SPF BALB/c mice (6 – 8 week-old, 18.0 ± 2.0 g) were purchased from the Hebei Municipal Center for Disease Control and Prevention, Wuhan, China. Animals were kept at 24°C and 12/12 h light-dark cycle, and housed individually. After one-week-balance in Laboratory Animal Center of Nanchang University, ffteen mice were randomly divided into three groups (5 mice per group), with the orally administrations of 0 mg/mL (Control), 0.5 mg/mL (LD) and 5.0 mg/mL (HD) RA (JECFA 2005) 200 µL each mice daily until experiment finished. Mice faeces were collected at 0 (frst day of intervention), 7, 14, 21, and 28 days, and were used for cell count. Faecal samples after 28 days were also used for DNA extraction.

Cell viable count    Fresh faecal samples were treated within 2 hours. The samples were serially diluted 10-fold with saline water, and 300 µL drops of each dilution were separately plated on BHI (brain-heart infusion, Beijing Land Bridge Technology Co. Ltd, Beijing, China) agar supplemented with 10% sterile skimmed milk for total anaerobic bacteria (incubated anaerobically at 37°C for 36 h), MRS agar for lactobacilli (anaerobically, 37°C, 24h), SBM (Slanetz-Bartley medium, OXOID Ltd, Basingstoke, Hampshire, England) agar for enterococci (aerobically, 37°C, 24 h), and MAC (MacConkey, OXOID Ltd, Basingstoke, Hampshire, England) agar for enterobacteria (aerobically, 37°C, 24 h), respectively (Chen et al., 2011b).

DNA extraction and PCR amplification    Total genomic DNA from the Day 28 faecal samples (fve samples for each group) was extracted according to the bead-beating method (Donskey et al., 2003). After extraction, the total DNA was precipitated with 80% ethanol and suspended in 50 µL TE buffer [Tris-HCl buffer, pH 8.0, containing 1.0 mmol/L ethylenediaminetetraacetic acid (EDTA)] and stored at −20°C for further analysis. Then PCR amplification was performed using the 2 × PCR Taq MasterMix (CWBIO, Beijing, China) in a Biosci PCR system. For the PCR amplification of microbial population, the primer pairs used in this study were listed in Table 1. Based on the manufacturer's instruction, the PCR reaction (25 µL volumes) used 12.5 µL 2 × PCR Taq MasterMix, 0.5 µL of each primer, and 0.5 µL 10-fold diluted DNA template and lastly 11.0 µL RNase-Free Water. The samples were amplifed in a Biosci PCR system using the same procedure, with 30 cycles of 94°C for 30 s, 56°C for 30 s, and 72°C for 30 s. Aliquots of 5 µL were analyzed by electrophoresis on anagarose gel (1.0%) to check the size and amounts of amplicons.

Table 1. The PCR primers used in this study
Target organism Primer Sequence(----) Reference
Total bacteria -ATTACCGCGGCTGCTGG-
518 (R) + -GC clamp-TACGGGAGGCAGCAG-
Entrobacteriaceae Ec1055 (F) -ATGGCTGTCGTCAGCT- (Chen et al., 2011a; Chen et al., 2011b; Jeyaram et al., 2008; Wang and Lee, 1997)
Ec1392 (R) + -GC clamp-ACGGGCGGTGTGTAC-
Lactobacilli Lac (F) -AGCAGTAGGGAATCTTCCA-
Lac (R) + -GC clamp-ATTYCACCGCTACACATG-

F, forward primer; R, reverse primer; +, GC clamp (CGCCCGGGGCGCGCCCCGGGCGGGGCGGGGGCACGGGGG)

Denaturing Gradient Gel Electrophoresis (DGGE) analyze    Amplicons described above were used for sequence separation by DGGE. DGGE was performed on 8% (w/v) polyacrylamide gels in running buffer containing 1 × TAE [20 mmol L−1 Tris, 10 mmol L−1 acetate, 0.5 mmol L−1 EDTA (pH 8.0)] and a denaturing gradient of 35 – 65% urea (Sigma) and formamide (Sigma) for bacteria, enterobacteria and lactobacilli (where 100% is defined as 7 mol/L urea and 40% (v/v) formamide). Gels were run on a Dcode™ System (Bio-Rad, Hercules, CA, USA). The electrophoresis was initiated by pre-running for 5 min at a voltage of 220 V, and subsequently running at a fxed voltage of 85 V for 16 h at 60°C. The gel was stained with AgNO3 and developed after completion of electrophoresis. The obtained DGGE patterns were subsequently normalized and analyzed with Bio-Rad Quantity One 4.4.0 software. During processing, the background was subtracted, differences in the intensity of the lanes were compensated during normalization and the correlation matrix was calculated. Clustering was done with Pearson correlation and the un-weighted pair group method with arithmetic mean (UPGMA) method (Chen et al., 2011a). When analyzing genetic diversity, Richness (S), Shannon-Wiener indices (H') and Evenness (E) were used according to the following equations (Luo et al., 2004):

  
  

Where pi is the ratio between the ith band intensity and the total intensity of all bands, S is the total number of bands in each sample.

Sequencing of DGGE bands    The bands of interest DNA were excised from gels using a sterile blade, poured into 20 µL distilled deionized water, and was kept overnight at 4°C to allow DNA diffusion out of the polyacrylamide matrix. These DNA samples were used to perform secondary PCR amplifications using the homologous primers but without a GC clamp under the conditions indicated above. The PCR products were purified using the QIA quick PCR purification kit, sub-cloned with the pMD19-T vector system I (Takara Biotechnology) according to the manufacturer's instructions, transformed into the competent cell of Escherichia coli DH5α, and sequenced using in Sangon Biotech Co. Ltd (Shanghai, China). These results were blasted in NCBI website.

Statistical analysis    All of the data are reported as mean ± SD or mean, with n indicating the number of experiments. Results were analyzed using the SPSS 13.0 (SPSS Inc., Chicago, USA) software by means of independent one-way ANOVA tests in each sampling point. The differences among the three mice groups were assessed by means of the LSD (Fisher's Least significant difference) multiple comparison test (p < 0.05).

Results and Discussions

The effects of the RA on the growth of selected strains in vitro    In the present study, two Gram-negative pathogens (E. coli O157:H7 and S. typhimurium ATCC 13311), two Gram-positive pathogens (S. aureus CGMCC 26001 and L. monocytogenes CMCC 54007) and two probiotics (B. longum ATCC 15707 and L. plantarum ACCC 11095) were selected to test the effects of RA on their growth.

The growth curve results indicated that RA posed little effects on the growth of E. coli O157:H7, S. typhimurium ATCC 13311, L. monocytogenes CMCC 54007 and L. plantarum ACCC 11095, compared to the control group (Fig. 1-A, -B, -C, -F); but it seemed that RA could decrease the number of S. aureus in vitro and showed an concentration-dependent manner, which means that 0.5% RA can significantly inhibit the growth of S.aureus (p < 0.05), while 1.0% RA is extremely significant (p < 0.01) from the middle lag to steady phage (Fig. 1-D). These results were also observed by Ghosh et al. and Jayaraman et al. who found that various solvent extracts of Stevia rebaudiana leaves could inhibit some bacteria and fungal, but no explanation was provided (Ghosh et al., 2008; Jayaraman et al., 2008). In addition, some researchers mentioned that the secondary metabolite “Stevioside” of RA may be responsible for the antimicrobial activity (Debnath, 2008). No differences were observed on the growth of B. longum as well (Fig. 1-E), indicating that B. longum appeared to be resistant or unaffected by RA. Interestingly, our results indicated that the RA could stimulate the growth of L. plantarum ACCC 11095 obviously at 0.50% and 1.00% concentrations (p < 0.05). The results indicated that the RA might inhibit the growth of pathogens and promote the growth of probiotics, which was good news for the application of RA as a food additive.

Fig. 1.

Growth curves (optical density, OD = 600 nm) of selected bacteria on different concentrations (have been shown in the pictures) of RA. A (E.coli O157:H7), B (S. typhimurium ATCC 13311), C (L. monocytogenes CMCC 54007), D (S. aureus CGMCC 26001), E (B. longum ATCC 15707), F (L. plantarum ACCC 11095). Each value was expressed using Mean of triplicates for each testing and significant difference was described in the results part.

The effects of RA on the microbial number in vivo    Trillions of microbes inhabit the human intestine, forming a complex ecological community that infuence normal physiology and play an important role in maintaining the humans health, including protecting against entero-pathogens (Candela et al., 2008), extracting nutrients and energy (Eckburg et al., 2005), and contributing to normal immune function (Olszak et al., 2012). Some diseases, such as obesity, malnutrition, infammatory bowel, neurological disorders and cancer would occur if the microbial balance was disrupted. As a food additive, RA should not disrupt the normal bacterial balance, or should not promote the growth of pathogens.

To evaluate the effects of RA on the viable number of total anaerobic bacteria, enterococci, enterobacteria, and lactobacilli, some selected media were used in this study. The viable count results indicated that no obvious changes were observed in groups of total anaerobic bacteria, enterococci, enterobacteria, and lactobacilli both in the LD RA and HD RA groups (Fig. 2), indicating that RA did not significantly influence the intestinal microbiota composition after administration and these results was in accordance with the previous studies in vitro (Gardana et al., 2003).

Fig. 2.

Effect of different concentrations of RA on numbers of the intestinal bacteria. A, enterobacteria; B, enterococci; C, lactobacilli; D, total bacteria. White bar, the control group; ray bar, 0.5 mg/mL RA group; black bar, 5.0 mg/mL RA group. No significant difference was observed in each group.

DGGE analysis of total bacteria    During the administration, band 1 (Uncultured Bacteroidales bacterium), 2 (Uncultured bacterium), 4 (Uncultured Bacteroides sp.), 6 (Uncultured bacterium), 7 (Uncultured bacterium), 8 (Uncultured bacterium) and 9 (Escherichia coli) occupied the dominant positions in all the stages, indicating their tolerance to the different concentrations of RA (Table 2 and Fig. 3-A). The analysis of un-weighted pair-group method with arithmetic means (UPGMA) showed that all the mice in the control group clustered into one cluster, and possessed the minimum similarity of 76%, which guaranteed the stability of the control group; moreover, the minimum similarity of LD RA group (82%) and HD RA group (79%), as well as the minimum similarity among control group, LD RA group and HD RA group (69%) had ensured the slight influence of RA on the total bacteria in mice intestine (Fig. 3-B). In addition, the genetic diversity indices showed that no significant discrepancy was observed in species Richness (S), Shannon-Wiener index (H' A) and the maximum Shannon-Wiener index (H'max B) indices (Table 3)

Table 2. Identification of bacteria by sequencing of the DGGE bands amplifed by PCR from DNA of fecal samples using different primers.
Primers Strain No. Closest relatives GeneBank No. Similarity
V3 universal primers 1 Uncultured Bacteroidales bacterium AB702754.1 99%
2 Uncultured bacterium JQ695608.1 100%
3 Uncultured bacterium JQ894202.1 100%
4 Uncultured Bacteroides sp. JF710634.1 100%
5 Bacteroides chinchillae AB547637.1 100%
6 Uncultured bacterium JQ894297.1 100%
7 Uncultured bacterium JX013201.1 100%
8 Uncultured bacterium JF259521.1 100%
9 Escherichia coli JX290090.1 100%
10 ND
Enterobacteriaceae primers
1 Shigella fexneri JX307691.1 99%
2 Stenotrophomonas maltophilia JX293297.1 100%
3 Pseudomonas geniculata AB734811.1 98%
4 Mucispirillum schaedleri NR_042896.1 98%
5 Xanthomonas retrofexus JQ890537.1 100%
6 Uncultured bacterium HM124111.1 100%
7 Unidentifed bacterium AY345532.1 97%
8 Akkermansia muciniphila CP001071.1 99%
9 Escherichia coli JQ863234.1 100%
10 ND
Lactobacilli primers
1 Lactobacillus johnsonii JQ989153.1 99%
2 Lactobacillus taiwanensis HE573918.1 99%
3 Lactobacillus crispatus AY335500.1 99%
4 Lactobacillus intestinalis FR683097.1 99%
5 Lactobacillus animalis JN713320.1 99%
6 Lactobacillus agilis JQ837458.1 99%
7 Lactobacillus reuteri EF439674.1 99%
8 Lactobacillus taiwanensis HE573918.1 99%
9 Uncultured bacterium clone JQ085223.1 99%

ND, not detectable; Sequences were compared with those held in the GenBank database using the local BLAST program

Fig. 3.

DGGE profle (A) and UPGMA analysis (B) of the fecal microbiota using universal primers. C1–C5 refers to the fve fecal samples from control group, L1–L5 from LD RA group and H1–H5 from HD RA group after intervention 28 days. The corresponding strains were seen in Table 2; and the diversity index were shown in Table 3.

Table 3. Diversity indices calculated from the DGGE using V3 universal primers
Group Richness (S) H' A H'max B Enveness (E)
Control LD RA HD RA 32.4 ± 0.55 3.46 ± 0.14
3.48 ± 0.02 0.994 ± 0.001 31.0 ± 0.71 3.41 ± 0.20 3.43 ± 0.02
0.993 ± 0.004 30.4 ± 0.89 3.40 ± 0.03 3.42 ± 0.03 0.995 ± 0.001

H'A, Shannon-Wiener index; H'max B, the maximum Shannon-Wiener index. Results are shown as mean ± SD.

DGGE analysis of enterobacteria    Enterobacteria are a large gram-negative bacilli group that are often sojourned in the human and animal intestinal. Enterobacteria belong to the common microbiota in the intestinal tract, and would become the pathogens and caused disease accompanied with the changes of inner environments (Paterson, 2006). In our study, the DGGE results of enterobacteria showed that the band 1 (Shigella flexneri) and 6 (Uncultured bacterium) had occupied the dominant positions and received little change (Fig. 4-A and Table 2) among the three groups; however, the band 2 (Stenotrophomonas maltophilia), 3 (Pseudomonas geniculata), and 4 (Mucispirillum schaedleri) possessed the dominant position in HD RA group which indicated that the RA had increased their amount in mice intestine tract. The result of UPGMA showed that the similarity among these groups was beyond 61.0% (Fig. 4-B) and the diversity indices showed no significant changes were observed among the S, H’ A and H'max B index (Table 4).

Fig. 4.

DGGE profle (A) and UPGMA analysis (B) of the fecal microbiota using enterobacteriaceae primers. C1–C5 refers to the fve fecal samples from control group, L1–L5 from LD RA group and H1–H5 from HD RA group after intervention 28 days. The corresponding strains were seen in Table 2; and the diversity index were shown in Table 4.

Table 4. Diversity indices calculated from the DGGE using enterobacteriaceae primers.
Group Richness (S) H' A H'max B Enveness (E)
Control 21.2 ± 2.17 3.01 ± 0.11 3.05 ± 0.11 0.988 ± 0.002
LD RA 21.4 ± 0.55 3.02 ± 0.03 3.06 ± 0.03 0.986 ± 0.004
HD RA 20.8 ± 1.79 3.00 ± 0.08 3.08 ± 0.09 0.989 ± 0.001

H'A, Shannon-Wiener index; H'max B, the maximum Shannon-Wiener index. Results are shown as mean ± SD.

DGGE analysis of lactobacilli    For human, the lactobacilli play an important role in gastrointestinal tract, which maintain the intestinal health and regulation the immunity of the host (Gorbach, 1990). In this study, our DGGE results indicated that band 1 (Lactobacillus johnsonii), 2 (Lactobacillus taiwanensis), 3 (Lactobacillus crispatus), 5 (Lactobacillus animalis) and 7 (Lactobacillus reuteri) didn't change, but 6 (Lactobacillus agilis) and 8 (Lactobacillus taiwanensis) occupied the dominant positions in mice intestinal tract and received little change when administrated different concentration of RA (Table 2 and Fig. 5-A). However, the results of diversity indices in HD RA group suggested that the species richness was significant higher than that of the control group when administrated RA (p < 0.05), and the maximum Shannon-Wiener index (H'max B) also received a significant increase (p < 0.05) (Table 5). It seemed that the RA can increase the diversity of probiotics, which will beneft the health of the host. Accordingly, the minimum similarity of HD RA group and control group, and LD RA group is only 38% (Fig. 5-B), while the minimum similarity of control group and LD RA group is as high as 60%. This suggested that the low dose of RA slightly changed the lactobacilli diversity, while the high dose of RA will significantly increased the lactobacilli species, which may pose a positive effect on host health.

Fig. 5.

DGGE profle (A) and UPGMA analysis (B) of the fecal microbiota using lactobacilli primers. C1–C5 refers to the fve fecal samples from control group, L1–L5 from LD RA group and H1–H5 from HD RA group after intervention 28 days. The corresponding strains were seen in Table 2; and the diversity index were shown in Table 5.

Table 5. Diversity indices calculated from the DGGE using lactobacilli primers
Group Richness (S) H' A H'max B Enveness (E)
Control 8.9 ± 0.84 2.20 ± 0.09 2.28 ± 0.08 0.96 ± 0.01
LD RA 10.8 ± 1.10 2.27 ± 0.11 2.38 ± 0.11 0.96 ± 0.14
HD RA 11.2 ± 0.84* 2.26 ± 0.07 2.41 ± 0.08* 0.94 ± 0.04

H'A, Shannon-Wiener index; H'max B, the maximum Shannon-Wiener index. Results are shown as mean ± SD, *p < 0.05.

Conclusion

In the present study, the combination of viable cell count and DGGE was firstly used to monitor the influence of RA on gut microbiota, and our results indicated that RA cloud not alter the diversity of gut microbiota efficiently but maybe affect the number of some bacterial genus in vivo. These results may be due to the degradation of RA into steviol and glucose by gut microbes in the gastrointestinal tract. After degradation, steviol was absorbed and transformed into steviol glucuronide, which has beneficial detoxification effects in the liver (Carakostas et al., 2008). Meanwhile, glucose was mainly absorbed by the enterocyte, and had limit effects on blood pressure and glucose homeostasis in human body had been observed (Dyrskog et al., 2005). Residual glucose remained in the gut was not able to cause the energy competition among gut microbes and significantly induce the changes of gut microbiota diversity (Gardana et al., 2003); however, minor changes maybe occurred in the numbers of specific genus. In conclusion, Rebaudiosid A posed little pressure on the growth and the composition of gut microbes, indicating its safety for the gut microbiota in this study.

Acknowledgments    This project was sponsored by the National Natural Science Foundation of China (NSF31170091, 31360377, 31260363), the Ganpo Talent Engineering 555 Project, the Academic and Technical Leaders Training Program for Major Subjects of Jiangxi Province (2009) and the Research Program of the State Key Laboratory of Food Science and Technology of Nanchang University (SKLF-TS-200916, SKLF-ZZA-201302).

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© 2014 by Japanese Society for Food Science and Technology
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