Accepted Manuscript Metabolite profiling of fermented ginseng extracts by GC-MS Seong-Eun Park, Seung-Ho Seo, Kyoung In Lee, Chang-Su Na, Hong-Seok Son PII:
S1226-8453(16)30172-5
DOI:
10.1016/j.jgr.2016.12.010
Reference:
JGR 234
To appear in:
Journal of Ginseng Research
Received Date: 7 September 2016 Revised Date:
7 November 2016
Accepted Date: 20 December 2016
Please cite this article as: Park S-E, Seo S-H, Lee KI, Na C-S, Son H-S, Metabolite profiling of fermented ginseng extracts by GC-MS, Journal of Ginseng Research (2017), doi: 10.1016/ j.jgr.2016.12.010. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Metabolite profiling of fermented ginseng extracts by GC-MS
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Seong-Eun Parka, Seung-Ho Seoa, Kyoung In Leeb, Chang-Su Naa, Hong-Seok Sona*
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Republic of Korea
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School of Korean Medicine, Dongshin University, Naju, Jeonnam 58245, Republic of Korea
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Biotechnology Industrialization Center, Dongshin University, Naju, Jeonnam 58410,
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Running title: Metabolite profiling of fermented ginseng
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* Corresponding author
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Tel.: +82 61 330 3513
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Fax: +82 61 330 3519
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E-mail address:
[email protected] (H.-S. Son).
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Abstract
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Background Ginseng contains many small metabolites such as amino acids, fatty acids, and carbohydrates
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as well as ginsenosides. However, little is known about the relationships between
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microorganisms and metabolites during the entire ginseng fermentation process. We
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investigate metabolic changes during ginseng fermentation according to the inoculation of
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food-compatible microorganisms.
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Methods
GC-MS datasets, coupled with the multivariate statistical method for the purpose of latent-
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information extraction and sample classification, were used for the evaluation of ginseng
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fermentation. Four different starter cultures (Saccharomyces bayanus, Bacillus subtilis,
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Lactobacillus plantarum, and Leuconostoc mesenteroide) were used for the ginseng extract
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fermentation.
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Results
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The PCA score plot and heatmap showed a clear separation between ginseng extracts
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fermented with S. bayanus and other strains. The highest levels of fructose, maltose, and
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galactose in the ginseng extracts were found in ginseng extracts fermented with B. subtilis.
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The levels of succinic acid, and malic acid in the ginseng extract fermented with S. bayanus,
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as well as the levels of lactic acid, malonic acid, and hydroxypruvic acid in the ginseng
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extract fermented with lactic acid bacteria (L. plantarum, and L. mesenteroide) were the
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highest. In the results of taste features analysis using E-tongue, the fermented ginseng
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extracts with lactic acid bacteria were significantly distinguished from other groups by a high
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index of sour taste probably due to the high lactic acid contents.
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Conclusion 2
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These results suggest that a metabolomics approach based on GC-MS can be useful tool to
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understand ginseng fermentation and evaluate the fermentative characteristics of starter
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cultures.
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Keywords : GC-MS, metabolomics, ginseng, fermentation, E-tongue
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1. Introduction Ginseng (Panax ginseng Meyer), a traditional medicinal herb in Asia, has been used for a
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long time due to its various medicinal functions [1], such as anti-cancer [2], anti-
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inflammatory [3], and anti-depressant effect [4]. Many studies have focused on the
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conversion of ginsenosides, which are 30-carbon glycosides derived from the triterpenoid
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dammarane, into more active deglycosylated forms using microbial or enzymatic methods to
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change the biological activity. The conversion of ginsenosides into smaller deglycosylated
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forms changes the biological activities such as the anti-allergic [5] and anti-diabetic effects
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[6].
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As ginsenosides have been highly characterized for their major active components [7],
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most reports only deal with some ginsenosides, ignoring the full action of metabolites.
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However, ginseng also contains many small metabolites such as amino acids, fatty acids, and
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carbohydrates [8,9]. The investigation of metabolic changes in ginseng during fermentation is
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important to evaluate the fermentation characteristics and assess the fermentative behaviors
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of microorganisms. As compounds in ginseng are not always easily detected by classical
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analysis, many metabolites should be analyzed, and powerful analytical methods for the
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determination of the metabolites are needed.
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Metabolomics can directly delineate the physiological and biochemical status by its
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“metabolome profile” as a whole, and therefore, can provide systems information that differs
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from other traditional approaches [10]. There are a few existing reports regarding the
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fingerprinting or metabolic profiling of ginseng using analytical methods such as 1H NMR
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[9,11], HPLC [12,13], and FT-IR [14]. However, little is known about the relationships
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between microorganisms and metabolites during the entire ginseng fermentation process.
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Thus, this study was performed to investigate metabolic changes during ginseng fermentation
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according to the inoculation of food-compatible microorganisms. GC-MS datasets, coupled
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with multivariate statistical methods for the purpose of latent-information extraction and
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sample classification, were used to be understand ginseng fermentation.
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2. Materials and methods 2.1. Strain cultures
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Lactobacillus plantarum KCCM 11322 was purchased from the Korean Culture Center of
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Microorganisms (KCCM, Seoul, Korea). Leuconostoc mesenteroides subsp. mesenteroides
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KCTC 3718 and Bacillus subtilis KCTC 2023 were purchased from the Korean Collection
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for Type Cultures (KCTC, Daejeon, Korea). Saccharomyces bayanus EC-1118 (Lalvin,
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Montreal, Canada) was also used for comparison.
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Before inoculation with ginseng extracts, strains were pre-cultured at 37°C for 48 h in de
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Man Rogosa Sharpe broth (Difco, Sparks, MD, USA) for L. plantarum and L. mesenteroides,
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Nutrient broth (Difco, Sparks, MD, USA) for B. subtilis, and Yeast Malt broth (Difco, Sparks,
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MD, USA) for S. bayanus to obtain a final cell count of above 107 CFU/mL.
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2.2. Fermentation conditions using ginseng
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Six-year-old ginseng roots with a disheveled-hair shape cultivated at Geumsan (Korea)
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were used in this study. Ginseng was hot-air-dried for 2 days and then was prepared into a
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powder form using a blender. 15 g of ginseng powder was dissolved with 300 mL of distilled
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water and extracted at 121°C for 15 min by autoclaving. The mixture was then air-cooled to
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room temperature for inoculation. The ginseng extracts were inoculated with 1% (v/v) of
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each starter culture and fermentation was performed at 30°C for 4 days. Samples were taken
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on the 1st, 2nd, and 4th days of fermentation for analyses. Three replicate measurements were carried out for each starter culture.
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2.3. Viable cell counts
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One milliliter of ginseng was aseptically transferred into a conical tube prior to the
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preparation of 1/10 serial dilutions for microbiological analysis. L. plantarum and L.
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mesenteroides counts were determined after growing the bacteria in Man Rogosa Sharpe agar
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and incubating at 37°C for 48 h. The counts of S. bayanus and B. subtilis were determined in
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Yeast Malt agar and Potato Dextrose agar, by incubation at 30°C for 48 h. Tests were carried
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out in triplicate and the results were expressed as log CFU/mL.
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2.4. pH and titratable acidity
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After centrifuging for 5 min at 10,000 rpm, the supernatant liquid was used in all of the test
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systems. pH was determined using a pH meter (pH-250L, ISTEK, Seoul, Korea) and the
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means of three measurements were recorded. Titratable acidity as lactic acid was determined
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by titrating to pH 8.3 with 0.1 N NaOH.
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2.5. Ginsenosides analysis
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Eight ginsenoside peaks were detected using LC (Agilent 1200 Series) coupled with 6410A
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triple quadrupole mass spectroscopy (Agilent, USA). Samples were ionized and detected by
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ESI/MS with the selected ion monitoring (SIM) mode for negative ions. The following ions
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were extracted for the quantitative analysis of ginsenosides: m/z 784.5 (ginsenoside Rg2), m/z
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799.5 (ginsenoside Rf and Rg1), m/z 945.6 (ginsenoside Re and Rd), m/z 1077.6 (ginsenoside
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Rb2 and Rc), and m/z 1107.6 (ginsenoside Rb1). The quantity of the ginsenosides was
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purchased from Sigma-Aldrich (St Louis, MO, USA). The nebulizer gas was set to 10 L/min
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at a temperature of 320 °C and the capillary voltages were set to 4 kV. Separation was
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achieved using a XDB-C18 column (50 mm × 4.6 mm i.d., 1.8 µm, Agilent, USA), with a
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column oven temperature of 35°C. The mobile phase was composed of (A) 5 mM ammonium
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acetate-formic acid (0.1%, v/v) and (B) methanol. B was kept at 50% for 2 min and then
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gradually increased to 90% for 25 min. The flow rate was kept at 0.35 mL/min, and 5 µL of
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the sample solution was injected in each run.
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2.6. Electronic tongue
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The α-ASTREE II Electronic Tongue System (Alpha M.O.S. Toulouse, France) was used to
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determine the taste profiles of the fermented ginseng samples. The e-tongue is composed of a
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48 position autosampler, an array of chemical sensors with cross-selectivity and a
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chemometrics software package. The sensor set consisted of seven working electrodes (SRS,
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GPS, STS, UMS, SPS, SWS, and BRS sensors) and a reference electrode (Ag/AgCl
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electrode). The integral signal during the measurement of each sample, which comprises a
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vector with seven individual sensor measurements, was transformed into intensity values
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representing the five basic tastes as follows: SRS (sourness, astringency, and bitterness), GPS
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(sourness, saltiness, and metallic), STS (saltiness, spiciness, and metallic), UMS (umami,
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saltiness, and astringency), SPS (metallic, spiciness, and umami), SWS (sweetness and
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sourness), and BRS (bitterness and astringency). Test conditions were as follows: sample
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volume, 20 mL; analysis time, 3 min; acquisition time, 2 min; sample temperature, room
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temperature; and sensor cleaning solution, 5% ethanol. After sensory measurement for each
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sample solution, a wash cycle was performed to ensure that there was no sample carryover to
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the next analysis and to ensure good reproducibility. Three replicate measurements were
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carried out for each sample.
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2.7. GC-MS analysis
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The samples were derivatized using methoxyamine hydrochloride (MEOX) in pyridine and
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N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) containing trimethychlorosilane
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(1%, v/v). Samples were analyzed with a 6890N gas chromatography (Agilent, Santa Clara,
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USA) equipped with a 5973N mass selective detector. Separation was achieved using a DB-
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5MS capillary column (30 m × 0.25 mm i.d., 0.25 µm film thickness). The GC-MS was
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operated as previously described (Park et al, 2016). Mass spectra (m/z scanning range of 50–
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550) were recorded at 2 scans/s with electron impact ionization at 70 eV. Ribitol served as an
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internal standard to monitor batch reproducibility and to correct for minor variations that
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occurred during sample preparation and analysis. MSD ChemStation software (Agilent, USA)
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was used to acquire mass spectrometric data. The mass spectra of all detected compounds
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were compared with spectra in the NIST and Wiley library for identification. Metabolites
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were identified only when the quality value of the mass spectra compared to the spectra
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library was 90 or higher. All metabolite identifications were manually validated to reduce
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deconvolution errors during automated data-processing and to eliminate false identifications.
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For quantitation methods, the most specific fragment ion in the spectra of each identified
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metabolite was determined to the quantification ion, and their summed abundance was
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integrated; fragment ions due to trimethylsilylation (i.e. m/z 73.1 and 147.1) were excluded
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from the determination of metabolite abundance.
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2.8. Data processing and statistical analysis 8
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[15]. Metabolite content was calculated according to the peak area of the metabolites and the
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peak area of the internal standard ribitol on the same chromatograph. The generated
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normalized metabolite contents (variables) were imported into SIMCA-P version 14.0
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(Umetrics, Umea, Sweden) for multivariate statistical analysis. The quality of the models was
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described by R2 and Q2 values. R2 represents the variation explained by the model, whereas
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Q2 indicates how well the model predicts new data [16]. The heat map was generated using
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the R statistical computing environment (http://www.r-project.org/). The heat map color
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drawn by R with ggplot2 represents the z-score transformed raw data for fermented ginseng
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metabolites.
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Statistical analyses were performed using the SPSS version 21 statistical package for
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Windows (SPSS Inc., Chicago, IL, USA). ANOVA and Duncan’s multiple range tests were
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applied to the data to determine significant differences, and a value of p
