World J Microbiol Biotechnol (2014) 30:887–892 DOI 10.1007/s11274-013-1498-7

ORIGINAL PAPER

Comparative analysis of flavonoids and polar metabolites from hairy roots of Scutellaria baicalensis and Scutellaria lateriflora Jae Kwang Kim • Young Seon Kim • YeJi Kim • Md. Romij Uddin • Yeon Bok Kim • Haeng Hoon Kim • Soo Yun Park • Mi Young Lee • Sun Ok Chung • Sang Un Park

Received: 4 January 2013 / Accepted: 16 September 2013 / Published online: 27 October 2013 Ó Springer Science+Business Media Dordrecht 2013

Abstract Baicalin, baicalein, and wogonin were accumulated in hairy roots derived from Scutellaria lateriflora and Scutellaria baicalensis. The levels of baicalein and baicalin were 6.8 and 5.0 times higher, respectively, in S. baicalensis than in S. lateriflora. A total of 47 metabolites were detected and identified in Scutellaria species by GCTOF MS. The metabolites from the two species were subjected to principal component analysis (PCA) to evaluate differences. PCA fully distinguished between the two species. The results showed that individual phenolic acids and phenylalanine, precursors for the phenylpropanoid biosynthetic pathway, were higher in S. baicalensis than in S. lateriflora. This GC-TOF MS-based metabolic profiling approach was a viable alternative method to differentiate metabolic profiles between species. Keywords Hairy root culture  Metabolomics  Principal component analysis  Scutellaria baicalensis  Scutellaria lateriflora Jae Kwang Kim and Young Seon Kim have contributed equally to this work.

Introduction The genus Scutellaria in the family Lamiaceae has over 350 species, many of which are medicinally active. Scutellaria baicalensis and Scutellaria lateriflora are the most widely studied medicinal plants (Cole et al. 2008; Boyle et al. 2011). In western medicine, S. baicalensis Georgi, Baikal skullcap has been used to treat inflammation, respiratory tract infections, diarrhea, dysentery, jaundice/liver disorders, hypertension, hemorrhaging, and insomnia (Gasiorowski et al. 2011; Wang et al. 2012). S. lateriflora L., American skullcap, has been traditionally used by Native Americans and Europeans as a sedative and to treat various nervous disorders such as anxiety (Awad et al. 2003; Zhang et al. 2009). Extracts of S. lateriflora have been shown to exhibit anxiolytic properties (Awad et al. 2003). Hairy root cultures have been widely proven to be an efficient alternative system for the production of secondary metabolites in many plant species because of their genetic and biochemical stability, rapid growth rate, and ability to synthesize natural compounds at levels comparable to

J. K. Kim Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Korea

H. H. Kim Department of Well-Being Resources, Sunchon National University, 413 Jungangno, Suncheon, Jeollanam-do 540-742, Korea

Y. S. Kim  M. Y. Lee KM-Based Herbal Drug Research Group Researcher, Korea Institute of Oriental Medicine, 1672 Yuseongdae-ro, Yuseong-gu, Daejeon 305-811, Korea

S. Y. Park National Academy of Agricultural Science, Rural Development Administration, Suwon 441-707, Korea

YeJ. Kim  Md. R. Uddin  Y. B. Kim  S. U. Park (&) Department of Crop Science, College of Agriculture and Life Sciences, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 305-764, Korea e-mail: [email protected]

S. O. Chung (&) Department of Biosystems Machinery Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 305-764, Korea e-mail: [email protected]

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plants grown in vivo (Giri and Narasu 2000; Guillon et al. 2006). However, comparative analysis of the bioactive phytochemical content in hairy roots of S. lateriflora and S. baicalensis with respect to the levels of baicalin, baicalein, and wogonin has not been reported. Secondary metabolites are derived from central or primary metabolic processes in plants. The primary metabolite profile is closely related to the organism’s phenotype and includes important nutritional characteristics (Kok et al. 2008). In addition, metabolite profiling combined with chemometrics has assisted functional genomics research (Kim et al. 2013). Metabolomics allows for classification of samples with diverse biological status, origin, or quality using chemometric techniques, such as principal components analysis (PCA) and partial least square-discriminate analysis. This study aimed to evaluate the flavonoid biomarker content of hairy roots derived from S. lateriflora and S. baicalensis. In addition, hydrophilic metabolic profiling (including phenolics) in S. lateriflora and S. baicalensis roots using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) coupled with chemometrics was applied to determine the phenotypic variation and relationships between their contents.

Materials and methods Establishment of hairy roots from S. baicalensis and S. lateriflora Hairy root cultures of S. baicalensis and S. lateriflora were established and maintained as described previously (Kim et al. 2012). In brief, hairy roots from S. baicalensis and S. lateriflora were subcultured on fresh agar-solidified Murashige and Skoog medium (MS medium) (1962) and then transferred to MS liquid culture medium for experiments. The pH of the medium was adjusted with NaOH to 5.8, and the medium was then autoclaved for 20 min. Hairy root cultures were maintained at 25 °C on a rotary shaker (100 rpm) in a growth chamber with a 16-h photoperiod and cool white fluorescent lights (flux rate of 35 mol s-1 m-2). Hairy root cultures were maintained in MS liquid medium and subcultured every 15 days. Three flasks were used for each culture, and experiments were performed in duplicate. Extraction and analysis of flavones from hairy roots of S. baicalensis and S. lateriflora Hairy roots of S. baicalensis and S. lateriflora (0.05 g) were frozen in liquid nitrogen, ground to a fine powder using a mortar and pestle, and extracted with 10 mL of 70 % ethanol for 1 h at 60 °C. After centrifugation, the supernatant was filtered through a 0.45-lm poly filter and analyzed by HPLC.

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The analysis was monitored at 275 nm and performed using a C18 column (250 mm 9 4.6 mm, 5 lm; RStech, Daejon, Korea). The mobile phase was a gradient prepared from mixtures of acetonitrile, methanol, and 0.2 % acetic acid; the column was maintained at 30 °C. The flow rate was set at 1.0 mL min-1, and the injection volume was 20 lL. Results were calculated using a standard curve.

GC-TOF MS analysis of polar metabolites Extraction of polar metabolites was performed in accordance with previously described procedures (Kim et al. 2013). Samples were freeze-dried and disrupted in liquid nitrogen. Ground samples (20 mg) were extracted with 1 mL of a mixed solvent of methanol/water/chloroform (2.5:1:1 by volume). Sixty microliters of ribitol solution (0.2 mg mL-1) was added as an internal standard (IS). Extraction was performed at 37 °C with a mixing frequency of 1,200 rpm using a thermomixer compact (Eppendorf AG, Germany). The solutions were then centrifuged at 16,0009g for 3 min. The polar phase (0.8 mL) was transferred into a new tube, and 0.4 mL water was added. The well-mixed content of the tube was centrifuged at 16,0009g for 3 min. The methanol/water phase was then dried in a centrifugal concentrator (CVE-2000, Eyela, Japan) for 2 h, followed by a drying process in a freeze dryer for 16 h. Methoxime-derivatization was carried out by adding 160 lL of methoxyamine hydrochloride (20 mg mL-1) in pyridine and shaking at 30 °C for 90 min. Trimethylsilyl (TMS) etherification was carried out by adding 160 lL of Nmethy-N-(trimethylsilyl) trifluoroacetamide (MSTFA) at 37 °C for 30 min. GC-TOF MS was performed using an Agilent 7890A gas chromatograph (Agilent, Atlanta, GA, USA) which was coupled to a Pegasus HT TOF mass spectrometer (LECO, St. Joseph, MI). Derivatized sample (1 lL) was separated on a 30-m 9 0.25-mm I.D. fusedsilica capillary column coated with 0.25-lm CP-SIL 8 CB low bleed (Varian Inc., Palo Alto, CA, USA). The split ratio was set at 1:25. The injector temperature was 230 °C. The helium gas flow rate through the column was 1.0 mL min-1. The temperature program was as follows: starting temperature of 80 °C, maintained for 2 min, followed by an increase to 320 °C at 15 °C min-1, and a 10-min hold at 320 °C. The transfer line and ion-source temperatures were 250 and 200 °C, respectively. The scanned mass range was 85–600 m/z, and the detector voltage was set at 1,700 V. Quantification was performed using selected ions. ChromaTOF software was used to assist with peak location. Peak identification was performed by comparison with reference compounds and the use of an in-house library. In addition, identification of several metabolites was performed using direct comparison of the sample mass chromatogram with

World J Microbiol Biotechnol (2014) 30:887–892

Fig. 1 Hairy roots of S. baicalensis cultured on solid medium (a) and in liquid medium (b). Hairy roots of S. lateriflora cultured on solid medium (c) and in liquid medium (d)

those of commercially available standard compounds obtained by a similar MO/TMS derivatization and GC-TOF MS analysis. The quantitative calculations of all analytes were based on the peak area ratios relative to that of the IS. Statistical analysis Quantification data acquired from GC-TOF MS was subjected to PCA (SIMCA-P version 12.0; Umetrics, Umea˚, Sweden) to evaluate the relationships in terms of similarities or dissimilarities among groups of multivariate data. The PCA output consisted of score plots to visualize the contrast between different samples and loading plots to explain the cluster separation. The data file was scaled with unit variance scaling before all variables were subjected to PCA.

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containing 200 mg L-1 timentin every 2 weeks. Hairy root clones of S. baicalensis (Fig. 1b) and S. lateriflora (Fig. 1d) were transfered to MS liquid culture medium for 3 months. Our results revealed that S. baicalensis accumulated baicalin and baicalein flavones at much higher levels than S. lateriflora (Fig. 2). The levels of baicalein and baicalin were 6.8 and 5.0 times higher, respectively, in S. baicalensis than in S. lateriflora. The accumulation of wogonin was similar between S. baicalensis and S. lateriflora. We conducted comprehensive metabolic phenotyping of primary metabolites in S. lateriflora and S. baicalensis, and low-molecular-weight molecules from hairy roots were identified by GC-TOF MS. In total, 47 metabolites, including 19 amino acids, 16 organic acids, 8 sugars, 3 sugar alcohols, and 1 amine, were detected and identified in Scutellaria species (Fig. 3). The corresponding retention times and their fragment patterns were consistent with our previous data (Kim et al. 2013). Of the identified metabolites, 4 phenolics (ferulic, salicylic, sinapinic, and vanillic acid) were found in the samples. In our study, the data obtained for the 47 detected metabolites were subjected to PCA to evaluate differences in the metabolite profiles of S. lateriflora and S. baicalensis (Fig. 4). The PCA results, which allow identification of compounds exhibiting the greatest variance within a population and determination of closely related compounds (Kim et al. 2013) were determined by plotting the principal component scores. The 2 highest ranking principal components accounted for 87.9 % of the total variance within the data set. The first principal component, accounting for 74.8 % of the total variance, resolved the metabolite profile of S. baicalensis from that of S. lateriflora. To further investigate the contributors to the principal components, the metabolic loadings in principal components were compared. In principal component 1, the corresponding loadings were positive for all phenolic acids, suggesting that phenolic acids were present in higher amounts in S. baicalensis than in S. lateriflora. Furthermore, in principal component 1, the loadings were also positive for phenylalanine, the major amino acid donor for the synthesis of phenolic acids. These results provided correlations between metabolites that participate in closely related pathways and demonstrated the robustness of the present experimental system.

Discussion Result We previously established hairy root cultures of S. baicalensis for the production of flavones (Kim et al. 2012). The induced hairy roots of S. baicalensis (Fig. 1a) and S. lateriflora (Fig. 1c) transferred on agar-solidified MS medium

The advantages of GC-TOF MS analysis are the relatively high reproducibility and possible adaption for highthroughput analysis due to its rapid spectra accumulation times. Furthermore, the high mass spectra similarities among peaks make the use of mathematical algorithms for deconvolution of closely overlapping peaks possible.

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Fig. 2 The production of flavones (baicalin, baicalein, and wogonin) from hairy root cultures of S. baicalensis and S. lateriflora grown for 15 days in media

Sb(GR), Baicalin, 23.304

Sl(A.GR), Baicalin, 4.713

Sb(GR), Baicalein , 20.533

Sl(A.GR), Baicalein , 3.038

Sl(A.GR)

Sb(GR)

Sl(A.GR), Sb(GR), Wogonin , Wogonin 3.418 , 3.665

Fig. 3 Selected ion chromatograms of metabolites extracted from hairy roots of S. baicalensis as MO/TMS derivatives separated on a 30 m 9 0.25-mm I.D. fused silica capillary column coated with 0.25-lm CP-SIL 8 CB low bleed. Peak identification: 1, pyruvic acid [retention time (RT): 4.56 min, quantification ion (QI): 174]; 2, lactic acid (RT: 4.65 min, QI: 147); 50 , valine (RT: 5.05 min, QI: 146); 3, alanine (RT: 5.15 min, QI: 116); 4, glycolic acid (RT: 6.25 min, QI: 147); 5, valine (RT: 6.37 min, QI: 144); 6, serine (RT: 6.81 min, QI: 116); 7, ethanolamine (RT: 6.89 min, QI: 174); 8, glycerol (RT: 6.91 min, QI: 147); 9, leucine (RT: 6.94 min, QI: 158); 10, isoleucine (RT: 7.156 min, QI: 158); 11, proline (RT: 7.23 min, QI: 142); 12, nicotinic acid (RT: 7.26 min, QI: 180); 13, glycine (RT: 7.29 min, QI: 174); 14, succinic acid (RT: 7.36 min, QI: 147); 15, glyceric acid (RT: 7.46 min, QI: 147); 16, fumaric acid (RT: 7.70 min, QI: 245); 60 , serine (RT: 7.75 min, QI: 204); 17, threonine (RT: 7.98 min, QI: 219); 18, b-alanine (RT: 8.40 min, QI: 174); 19, malic acid (RT: 8.88 min, QI: 147); 20, salicylic acid (RT: 9.15 min, QI: 267); 21, aspartic acid (RT: 9.17 min, QI: 100); 22, methionine (RT: 9.21 min,

QI: 176); 23, pyroglutamic acid (RT: 9.28 min, QI: 156); 24, 4-aminobutyric acid (RT: 9.29 min, QI: 174); 25, threonic acid (RT: 9.44 min, QI: 147); 26, arginine (RT: 9.93 min, QI: 142); 27, glutamic acid (RT: 9.96 min, QI: 246); 28, phenylalanine (RT: 10.09 min, QI: 218); 29, xylose (RT: 10.18 min, QI: 103); 30, asparagine (RT: 10.36 min, QI: 116); 31, vanillic acid (RT: 11.12 min, QI: 297); 32, glutamine (RT: 11.13 min, QI: 156); 33, shikimic acid (RT: 11.29 min, QI: 204); 34, citric acid (RT: 11.41 min, QI: 273); 35, quinic acid (RT: 11.66 min, QI: 345); 36, fructose (RT: 11.76 min, QI: 103); 360 , fructose (RT: 11.82 min, QI: 103); 37, galactose (RT: 11.89 min, QI: 147); 38, glucose (RT: 11.94 min, QI: 147); 39, mannose (RT: 12.07 min, QI: 147); 40, mannitol (RT: 12.14 min, QI: 319); 41, inositol (RT: 13.21 min, QI: 305); 42, ferulic acid (RT: 13.33 min, QI: 338); 43, tryptophan (RT: 14.06 min, QI: 202); 45, sucrose (RT: 16.16 min, QI: 217); 46, trehalose (RT: 16.69 min, QI: 191); 47, raffinose (RT: 19.74 min, QI: 217); IS, IS (ribitol)

As a clustering technique, PCA is most commonly used to identify how one sample is different from another, which variables contribute the most to the observed differences, and whether those variables are correlated or uncorrelated (Kim et al. 2010). The primary application of

metabolomics in plants includes screening mutant collections (Messerli et al. 2007), quality control and quality assessment of food and crop products (Jumtee et al. 2009), and the development of traditional medicine (Tarachiwin et al. 2008).

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World J Microbiol Biotechnol (2014) 30:887–892 Fig. 4 Scores (a) and loading (b) plots of principal components 1 and 2 of the PCA results obtained from metabolite data of S. baicalensis and S. lateriflora

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Component 2 (13.1% of total variance)

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S. baicalensis S.S.lateriflora lateriflora

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Component 1 (74.8% of total variance) SIMCA-P+ 12 - 2012-10-05 08:52:42 (UTC+9)

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Pyroglutamic acid

Glutamic acid

B

Methionine

0.20 Succinic acid

Phenylalanine

Component 2

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Arginine

Ferulic acid Aspartic acid Salicylic acid Lactic acid Citric acid Glycerol Xylose Mannitol Quinic acid Asparagine

Fructose Serine Glycolic acid Galactose Glutamine Mannose Tryptophan Malic acid Glucose Trehalose Glyceric acid Sucrose Inositol beta-Alanine Fumaric acid Alanine Nicotinic acid Pyruvic acid Ethanolamine Proline

Vanillic acid

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Valine Leucine

Threonic acid

-0.20 Threonine

Isoleucine

Glycine

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Raffinose Sinapinic acid

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Component 1 SIMCA-P+ 12 - 2012-10-09 04:54:48 (UTC+9)

Secondary metabolite differences may be due to differences in growth conditions of samples. Standardization of growth conditions could be achieved through the use of in vitro culture of Scutellaria species where growth conditions can be tightly regulated. Thus, in this study, we compared the main flavonoid contents between S. baicalensis and S. lateriflora hairy-root cultures. Roots of S. baicalensis contained high levels of baicalein and baicalin

relative to those of S. lateriflora. In addition, we identified 47 polar metabolites in Scutellaria and applied PCA to the metabolite profiles obtained for S. baicalensis and S. lateriflora. PCA allowed the two species to be fully distinguished, suggesting that reasonable score ranges of the components could be used for sample selection according to the correlation between variables and these two components. Metabolomics can assist in dissecting the

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mechanisms that regulate the conversion of primary metabolites into secondary metabolites in plants. Correlations between the concentrations of various metabolites can be examined to gain information regarding metabolic associations. Using GC-TOF MS, we were able to evaluate differences in the metabolite profiles of S. lateriflora and S. baicalensis. Metabolic loading in component 1 suggested that S. baicalensis had relatively high phenolic acid and phenylalanine levels, indicating that metabolite profiling combined with chemometrics can be used as a powerful tool for assessing food quality and tracking metabolic pathways. Acknowledgments This work (K13101) was supported by the Korea Institute of Oriental Medicine (KIOM) grant funded by the Korea government and in part this study was supported by the National Academy of Agricultural Science (Code PJ0068342012), Rural Development Administration, Republic of Korea.

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