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ORIGINAL ARTICLE
Metabolite fingerprinting, pathway analyses, and bioactivitycorrelations for plant species belonging to the Cornaceae,Fabaceae, and Rosaceae families
Su Young Son1 • Na Kyung Kim1• Sunmin Lee1 • Digar Singh1 • Ga Ryun Kim2
•
Jong Seok Lee2 • Hee-sun Yang2 • Joohong Yeo2 • Sarah Lee2 • Choong Hwan Lee1
Received: 1 April 2016 / Accepted: 26 May 2016
� Springer-Verlag Berlin Heidelberg 2016
Abstract
Key message A multi-parallel approach gauging the
mass spectrometry-based metabolite fingerprinting
coupled with bioactivity and pathway evaluations could
serve as an efficacious tool for inferring plant taxo-
nomic orders.
Abstract Thirty-four species from three plant families,
namely Cornaceae (7), Fabaceae (9), and Rosaceae (18)
were subjected to metabolite profiling using gas chro-
matography–time-of-flight-mass spectrometry (GC–TOF-
MS) and ultrahigh performance liquid chromatography–
linear trap quadrupole-ion trap-mass spectrometry
(UHPLC–LTQ-IT-MS/MS), followed by multivariate
analyses to determine the metabolites characteristic of
these families. The partial least squares discriminant
analysis (PLS-DA) revealed the distinct clustering pattern
of metabolites for each family. The pathway analysis fur-
ther highlighted the relatively higher proportions of flavo-
nols and ellagitannins in the Cornaceae family than in the
other two families. Higher levels of phenolic acids and
flavan-3-ols were observed among species from the Rosa-
ceae family, while amino acids, flavones, and isoflavones
were more abundant among the Fabaceae family members.
The antioxidant activities of plant extracts were measured
using ABTS, DPPH, and FRAP assays, and indicated that
extracts from the Rosaceae family had the highest activity,
followed by those from Cornaceae and Fabaceae. The
correlation map analysis positively links the proportional
concentration of metabolites with their relative antioxidant
activities, particularly in Cornaceae and Rosaceae. This
work highlights the pre-eminence of the multi-parallel
approach involving metabolite profiling and bioactivity
evaluations coupled with metabolic pathways as an effi-
cient methodology for the evaluation of plant phylogenies.
Keywords Metabolite fingerprinting � Pathways �Bioactivity � Plant families � Mass spectrometry
Introduction
The diversity of plant species is governed by their geo-
graphical distribution and climatic conditions (Qian 2002).
The peninsular biome of Korea witnesses four distinct
seasons in a year (Lee et al. 2002) and thus, hosts a rich
ecological biodiversity harboring some of the exclusive
plant species (Kim 2006). These numerous plant species
are characterized by their prolific spectrum of natural
products and metabolic plasticity in response to environ-
mental parameters such as temperature, climate, and rain-
fall (Rim et al. 2000; Pandey and Rizvi 2009; Sytar et al.
2015). Plants carry a huge assortment of metabolites with
overwhelming applications in the vital areas of human
welfare including health, pharmaceuticals, nutrition, and
agriculture. Conspicuously, Cornus species (Family:
Communicated by Y.-Il Park.
Electronic supplementary material The online version of thisarticle (doi:10.1007/s00299-016-2006-y) contains supplementarymaterial, which is available to authorized users.
& Sarah Lee
& Choong Hwan Lee
1 Department of Bioscience and Biotechnology, Konkuk
University, Seoul 05029, Korea
2 National Institute of Biological Resources, Environmental
Research Complex, Inchon 22689, Korea
123
Plant Cell Rep
DOI 10.1007/s00299-016-2006-y
Cornaceae) are widely used in ornamental gardening due to
their autumn coloring, berry fruits, and timber suitable for
the manufacture of musical instruments (Seeram et al.
2002; Lee et al. 2014; Forman et al. 2015a). Besides these,
Cornaceae plant extracts have traditionally been used by
Asian populations for the treatment of various ailments,
including colds, flu, and diarrhea, owing to their recently
reported antioxidant, antimicrobial, anti-malarial, anti-di-
abetic, and anti-proliferative effects (Forman et al. 2015b).
The Fabaceae family, with over 18,000 known species
having uncustomary floral structures and abundant fruits, is
often harvested for grains, pasture, and agro-forestry use,
and they are an excellent source of health proteins, dietary
fibers, and various phytochemicals (Messina 1999; Graham
and Vance 2003). Moreover, metabolites from Fabaceae
plants reportedly have potential health benefits, including
anticancer, antimicrobial, anti-obesity, and anti-diabetic
effects (Gepts et al. 2005). The rose family (Rosaceae),
with over 100 genera and 3000 known species, further
represents a preeminent group among the flowering plants
including many ornamental and fleshy fruit-bearing spe-
cies. Rosaceae plant extracts have widely been used as
therapeutic agents in Asia (Ju et al. 2009), owing to their
spectrum of bioactive metabolites with antioxidant (Mon-
tazeri et al. 2011), estrogenic, and anti-proliferative activ-
ities (Kang et al. 2006).
Metabolomics, an interdisciplinary ‘‘omics’’, has been
increasingly used for global, unbiased, and both qualitative
and quantitative evaluations of different metabolomes
(Bundy et al. 2009). The untargeted or global metabo-
lomics aim for comprehensive evaluation of the widest
possible metabolic coverage of an organism without any
bias. Gas chromatography–mass spectrometry (GC–MS)
and Liquid chromatography–mass spectrometry (LC–MS)
are a few of the holistic methods for the arduous exami-
nation of different classes of plant metabolites (Arbona
et al. 2009; Wahyuni et al. 2013). These techniques have
recently been applied to evaluate untargeted metabolic
profiles from various plant species; however, these data are
rarely coupled with corresponding bioactivity assays and
associated metabolic pathways. Previous studies in our
laboratory have reported the chemotaxonomic characteri-
zation for six different plant families (Aceraceae, Betu-
laceae, Fagaceae, Rosaceae, Asteraceae, and Fabaceae)
from the Korean temperate climate, which signifies the
ecological relevance of metabolomics in plant phylogenies
(Lee et al. 2015).
In the present work, we describe the metabolite char-
acterization coupled with bioactivity assays and associated
pathways for 34 species of plants from three different
families. The families Rosaceae and Fabaceae were
included in the study based on the initial partial least
squares discriminant analyses (PLS-DA), and their relative
importance both in Korea and worldwide. The family
Cornaceae was also included in the investigation because
of its assortment of bioactive metabolites reported in our
previous works. In this study, we propose an integrated
high-throughput comprehensive methodology twining the
comparative metabolite fingerprinting with quantitative
estimations for bioactivity phenotypes and associated
metabolic pathways to correlate the plant taxon.
Materials and methods
Chemicals and reagents
High-performance liquid chromatography (HPLC)-grade
methanol, acetonitrile, water, and hexane were purchased
from Fisher Scientific (Pittsburgh, PA, USA). Gallic acid,
naringin, 6-hydroxy-2,5,7,8-tetramethylchroman-2-car-
boxylic acid (Trolox), methoxyamine hydrochloride, pyr-
idine, N-methyl-N-(trimethylsilyl) trifluoroacetamide
(MSTFA), 2,20-azino-bis(3-ethylbenzothiazoline-6-sul-fonic acid) diammonium salt (ABTS), 1,1-diphenyl-2-
picrylhydrazyl (DPPH), hydrogen chloride (HCl), 2,4,6-
Tris(2-pyridyl)-s-triazine (TPTZ), ferric chloride (FeCl3),
Folin and Ciocalteu’s phenol reagent, diethylene glycol,
formic acid, myricetin (purity, C96 %), epicatechin (pu-
rity, C98 %), quinic acid (analytical grade), 4-O-caf-
feoylquinic acid (purity, C98 %), rutin (purity, C94 %),
ellagic acid (purity, C95 %), quercitrin (analytical grade),
myricetin (purity, C96 %), genistein (purity, C98 %),
daidzein (purity, C98 %), formononetin (purity, C99 %),
luteolin (purity, C98 %), diosmetin (analytical grade),
acacetin (purity, C97 %), and the standard compounds
were purchased from Sigma-Aldrich (St. Louis, MO,
USA). All chemicals and solvents were of analytical grade.
Plant materials
Three plant families, Cornaceae, Fabaceae, and Rosaceae,
were investigated in this study. Detailed information about
the corresponding plant species with their zones and dates
of collections is listed in Table 1. The six samples of
Cornaceae, excluding Cornus macrophylla, were procured
from the Korea Plant Extract Bank (KPEB,
Chengcheongbuk-do, Korea) and the remainder of the 28
plant species (1 Cornaceae, 9 Fabaceae, and 18 Rosaceae)
were provided by the National Institute of Biological
Resources (NIBR, Incheon, Korea). All the plant samples
were indigenously collected from two megalopolises,
seven provinces, and one special self-governing province
of the Republic of Korea. The samples were extracted
immediately and stored under deep-freeze conditions
(-80 �C) until procurement. Plant samples at the KPEB
Plant Cell Rep
123
and NIBR were extracted following the procedure descri-
bed below. The procured plant samples included the extract
from aerial above-ground parts.
Sample preparation
The plant samples from KPEB were subjected to ultrasonic
(SDN-900H, SD Ultrasonic Cleaner, Seoul, Republic of
Korea) extraction with 99.9 % methyl alcohol for 3 days.
The extraction procedure was performed at the KPEB
facilities using standard operating protocols (15 min
ultrasonic pulse followed by 2 h stand by, at the rate of ten
times per day). Samples from NIBR were first dried under
shade and each sample (100 mg) was extracted thrice with
70 % ethanol (1 L). The plant extracts were then concen-
trated using a rotary evaporator (N-1000SWD, Eyela,
Tokyo, Japan) at 45 �C for 24 h, and filtered.
The extracted samples were derivatized for gas chro-
matography–time-of-flight-mass spectrometry (GC–TOF-
MS) analyses through oximation and silylation steps. First,
the concentrated extracts were oximated with 50 lL of
methoxyamine hydrochloride (20 mg/mL) in pyridine at
30 �C for 90 min. Subsequently, the samples were silylated
with 50 lL of MSTFA at 37 �C for 30 min. For ultrahigh
Table 1 Samples used in this study
No. Family Genus Species Collection areas in Korea Collection date
1 Cornaceae Aucuba japonica Naesujeon, Ulleung-eup, Ulleung-gun, Gyeongsangbuk-do 2000-10-10
2 Cornus alba Oksan-ri, Baegam-myeon, Cheoin-gu, Yongin-si, Gyeonggi-do 2001-04-19
3 Cornus controversa Oksan-ri, Baegam-myeon, Cheoin-gu, Yongin-si, Gyeonggi-do 2001-03-11
4 Cornus kousa Oksan-ri, Baegam-myeon, Cheoin-gu, Yongin-si, Gyeonggi-do 2001-06-08
5 Cornus macrophylla Jeodong-ri, Ulleung-eup, Ulleung-gun, Gyeongsangbuk-do 2014-07-16
6 Cornus officinalis Gisan-ri, Gwangtan-myeon, Paju-si, Gyeonggi-do 2007-07-20
7 Cornus walteri Pyeonghwal-ri, Samsan-myeon, Haenam-gun, Jeollanam-do 2001-05-17
8 Rosaceae Chaenomeles sinensis Ojeong-dong, Daedeok-gu, Daejeon 2014-08-10
9 Crataegus pinnatifida Gurae-ri, Sangdong-eup, Yeongwol-gun, Gangwon-do 2014-08-30
10 Eriobotrya japonica Jiro-ri, Byeongyeong-myeon, Gangjin-gun, Jeollanam-do 2014-08-13
11 Pourthiaea villosa Seonheul-ri, Jocheon-eup, Jeju-si, Jeju special self-governing province 2014-08-24
12 Prunus armeniaca Ojeong-dong, Daedeok-gu, Daejeon 2014-07-20
13 Prunus yedoensis Janghyeon-ri, Cheongna-myeon, Boryeong-si, Chungcheongnam-do 2014-08-07
14 Prunus maackii Gurae-ri, Sangdong-eup, Yeongwol-gun, Gangwon-do 2014-08-30
15 Prunus padus Gohan-ri, Gohan-eup, Jeongseon-gun, Gangwon-do 2014-05-22
16 Prunus sp. Gomo-ri, Soheul-eup, Pocheon-si, Gyeonggi-do 2014-08-08
17 Pyrus ussuriensis Icheon-ri, Sangbuk-myeon, Ulju-gun, Ulsan 2014-08-01
18 Rosa multiflora Nadae-ri, Yaro-myeon, Hapcheon-gun, Gyeongsangnam-do 2014-08-21
19 Rubus coreanus Sogye-ri, Hwanggan-myeon, Yeongdong-gun, Chungcheongbuk-do 2014-08-14
20 Rubus crataegifolius Nadae-ri, Yaro-myeon, Hapcheon-gun, Gyeongsangnam-do 2014-08-21
21 Rubus phoenicolasius Nadae-ri, Yaro-myeon, Hapcheon-gun, Gyeongsangnam-do 2014-08-21
22 Sanguisorba officinalis Nadae-ri, Yaro-myeon, Hapcheon-gun, Gyeongsangnam-do 2014-08-21
23 Sorbus commixta Jeodong-ri, Ulleung-eup, Ulleung-gun, Gyeongsangbuk-do 2014-07-16
24 Spiraea prunifolia Ungyo-ri, Bangnim-myeon, Pyeongchang-gun, Gangwon-do 2014-08-08
25 Spiraea salicifolia Ungyo-ri, Bangnim-myeon, Pyeongchang-gun, Gangwon-do 2014-08-08
26 Fabaceae Albizia julibrissin Daechi-ri, Daechi-myeon, Cheongyang-gun, Chungcheongnam-do 2014-08-06
27 Desmodium caudatum Seonheul-ri, Jocheon-eup, Jeju-si, Jeju special self-governing province 2014-08-24
28 Lespedeza bicolor Sin-ri, Goryeong-eup, Goryeong-gun, Gyeongsangbuk-do 2014-07-23
29 Lespedeza cuneata Geogi-ri, Jusang-myeon, Geochang-gun, Gyeongsangnam-do 2014-08-22
30 Lespedeza maximowiczii Gohan-ri, Gohan-eup, Jeongseon-gun, Gangwon-do 2014-08-30
31 Pueraria lobata Mamyeong-ri, Naechon-myeon, Pocheon-si, Gyeonggi-do 2014-08-04
32 Robinia pseudoacacia Sin-ri, Goryeong-eup, Goryeong-gun, Gyeongsangbuk-do 2014-10-23
33 Sophora flavescens Hanggok-ri, Gunbuk-myeon, Okcheon-gun, Chungcheongbuk-do 2014-08-18
34 Sophora japonica Geogi-ri, Jusang-myeon, Geochang-gun, Gyeongsangnam-do 2014-08-22
Plant Cell Rep
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performance liquid chromatography–LTQ XL-linear-ion
trap-mass spectrometry/mass spectrometry (UHPLC–LTQ
XL-IT-MS/MS) and ultra-performance liquid chromatog-
raphy–quadrupole-time-of-flight-mass spectrometry
(UPLC–Q-TOF-MS) analyses, 20 mg/mL (w/v) ethanol
(70 %) suspensions of the samples were filtered using a
0.2 lm PTFE membrane.
Primary metabolite analysis using GC–TOF-MS
The primary metabolites were analyzed on a GC–TOF-MS
system using an Agilent 7890A GC system (Palo Alto, CA)
equipped with an Agilent 7693 auto-sampler and TOF
Pegasus III mass spectrometer (Leco, St. Joseph, MI,
USA). An Rtx-5MS column (30 m length 9 0.25 mm
i.d. 9 0.25 m film thickness, J & W Scientific, Folsom,
CA, USA) was employed with helium as the carrier gas at a
constant flow rate of 1.5 mL/min. Injector and transfer line
temperatures were set at 250 and 240 �C, respectively. TheGC oven temperature was programmed at 75 �C for 2 min
with a 3-min hold time and the ramping of 15 �C/min to
300 �C as the final temperature. The mass acquisition rate
was set at 10 scans/s for a scan range of 45–1000 m/z with
70 eV of ionization energy in EI mode, and 1 lL of the
derivatized sample was placed in an auto-sampler and
injected with a split ratio of 20:1.
Secondary metabolite analysis using UHPLC–LTQ
XL-MS/MS and UPLC–Q-TOF-MS
The secondary metabolite analysis was performed on
UHPLC–LTQXL-MS/MS using the LTQXL-ion trap-mass
spectrometer equipped with an electrospray interface
(Thermo Fisher Scientific, San Jose, CA) coupled with
DIONEX UltiMate 3000 RS Pump, RS Autosampler, RS
Column Compartment, and RS Diode Array Detector (Dio-
nex Corporation, Sunnyvale, USA). The samples were sep-
arated on a Thermo Scientific Syncronis C18 UHPLC
column with a 1.7 lm particle size. The mobile phase con-
sisted of 0.1 % formic acid in water (solvent A) and 0.1 %
formic acid in acetonitrile (solvent B), with the gradient flow
program as follows: the initial solvent conditionwas 10 %of
solvent B; the gradient was then gradually increased from
10 % solvent B to 100 % solvent B over 18 min. Following
this, solvent B was decreased to 10 % and maintained for
22 min. The flow rate was maintained at 0.3 mL/min with
10 lL of the injection volume. The photodiode array
detector was set at a wavelength range of 200–600 nm and
managed by 3D field. Mass spectra were obtained by elec-
trospray ionization in negative ion mode within a mass range
of 150–1000 m/z. The operating parameters used were as
follows: source voltage, ±5 kV; capillary voltage, 39 V;
capillary temperature, 275 �C. Tandem MS analysis was
performed by scan-type turbo data-dependent scanning
under the conditions used for negative-mode MS scanning.
UPLC–Q-TOF-MS analysis was performed on a Waters
Micromass QTOF Premier using a UPLC ACQUITY sys-
tem (Waters, Milford, MA) equipped with a binary solvent
delivery apparatus, an auto-sampler, and an ultraviolet
(UV) detector. The column selected was an ACQUITY
UPLCBEH C18 column (100 mm 9 2.1 mm 9 1.7 lmparticle size, Waters Corp.). The operation parameters were
set as follows: injection volume, 5 lL; flow rate, 0.3 mL/
min; column temperature, 37 �C. The mobile phase con-
sisted of 0.1 % formic acid in water (A) and 0.1 % formic
acid in acetonitrile (B). The gradient program was set as
follows: 5 % solvent B was maintained initially for 1 min
followed by a gradual increase to 100 % over 9 min, and
then maintained at 100 % B for 1 min, with a subsequent
decrease to 5 % over 3 min. The total runtime was 13 min.
The MS data were collected in the range of 100–1000 m/z
using Waters Q-TOF Premier system (Micromass MS
Technologies, Manchester, UK) under negative- and posi-
tive-ion modes. The capillary voltage and cone voltage
were set at 2.5 kV and 50 V, respectively. The source
temperature was set at 100 �C, with the desolvation gas
(nitrogen) and cone gas (nitrogen) flow rates tuned to 600
and 50 L/h, respectively.
Data processing and statistical analysis
The GC–TOF-MS data acquisition files were converted
into netCDF (*.cdf) format using ChromaTOF software
(LECO Corporation). The parameters were adjusted as
follows: the baseline offset was tuned below 0.5, while the
average data points were set to auto with an average peak
width of 2. The UHPLC–LTQ-IT-MS/MS data were
acquired with Xcalibur software (version 2.00, Thermo
Fisher Scientific), and raw data were subsequently con-
verted to netCDF (*.cdf) format using Xcalibur software.
The MS data files were then processed using MetAlign
software (RIKILT-Institute of Food Safety, Wageningen,
The Netherlands) to evaluate the retention times, normal-
ized peak intensities, and accurate masses. The resulting
data were exported to Excel files (Microsoft, Redmond,
WA, USA), and multivariate statistical analyses were
performed using SIMCA-P? software (version 12.0,
Umetrics, Umea, Sweden). Principal component analysis
(PCA) and the loading plots were employed to compare
different metabolites among the three plant families (Cor-
naceae, Fabaceae, and Rosaceae). The variable importance
in the projection (VIP) value and analysis of variance
(ANOVA) methods was used for the tentative evaluation of
significantly different metabolites identified from both
GC–TOF-MS (at VIP[1.0, p\ 0.05) and UHPLC–LTQ-
IT-MS/MS (at VIP[0.7, p\ 0.05) analyses. PASW
Plant Cell Rep
123
Statistics (version 18.0, SPSS, Inc., Chicago, IL) was used
to test differences in antioxidant activities, total phenolic
content (TPC), and total flavonoid content (TFC) by
Duncan’s multiple-range test, and to calculate Pearson’s
correlation coefficient between metabolites and the corre-
sponding antioxidant activities. The correlation between
metabolites and antioxidant activities was visualized with
correlation maps using MEV software version 4.8.
Determination of antioxidant activities by ABTS,
DPPH, and FRAP assays
The method described by Re et al. (1999) was used with
some modifications to perform ABTS assay. Briefly, 7 mM
ABTS was dissolved in 2.45 mM potassium persulfate
solution, and the mixture was stored for 12 h in the dark at
room temperature. The solution was diluted with deionized
water until the absorbance reached 0.7 ± 0.02 at 750 nm
using a microplate reader (Spectronic Genesys 6, Thermo
Electron, Madison, WI, USA). Each of the three plant
family sample extracts (10 lL) was mixed with 190 lL of
diluted ABTS solution in 96-well plate, and was incubated
at 37 �C in the dark for 6 min. Following the incubation,
the absorbance was recorded for the reacted samples at
750 nm using a microplate reader.
The DPPH assay was performed using the methods
described by Dietz et al. (2005) with few modifications.
The plant extracts (20 lL) from each of the species from
the three families were added to 0.2 mM DPPH ethanol
solution (180 lL) in a 96-well plate, and incubated for
20 min in the dark at room temperature. The absorbance
was recorded at 515 nm using a microplate reader.
The FRAP assay was performed using the modified
procedure formerly described by Prieto et al. (1999). The
FRAP reagent was mixed with 300 mM sodium acetate
buffer (pH 3.6), 10 mM TPTZ, and 20 mM ferric chloride
at a ratio of 10:1:1. Every plant sample (10 lL) was mixed
with 300 lL of FRAP reagent in a 96-well plate and
incubated in the dark at 37 �C for 6 min. The absorbance at
593 nm was then measured.
All the activity assays were conducted in triplicates and
the results are presented as the Trolox equivalent antioxi-
dant capacity, with a concentration range of 0.0156–1 mM.
The activity assays were performed for all of the plant
samples earlier subjected to mass spectrometry analysis.
Determination of total phenolic and flavonoid
contents
TPCs were determined using the method described by
Singleton et al. (1999) with some modifications. The
sample extracts (10 lL) from each of the three plant
families were mixed with 100 lL 0.2 N Folin and Cio-
calteu’s phenol reagent in 96-well plates. After 6 min of
reaction in the dark, 80 lL of 7.5 % Na2CO3 solution (in
distilled water) was added to the mixture, which was then
incubated for 60 min at room temperature. The absorbance
was measured at 750 nm using a microplate reader. The
results are presented as the gallic acid equivalent concen-
tration, with the concentration range of 31.25–500 ppm.
TFCs were determined following the method described
by Singleton et al. (1999) with some modifications. The
plant sample extracts (20 lL) were mixed with 20 lL of
NaOH, and 180 lL of 90 % diethylene glycol (in distilled
water) in a 96-well plate, and incubated for 60 min at room
temperature, followed by absorbance measurement at
405 nm using a microplate reader. The results are expres-
sed as the naringin equivalent concentration, with the
concentration range varying from 15.625 to 200 ppm. All
the assays were carried out in triplicates.
Quantification of selected nine metabolites
from Cornaceae, Fabaceae, and Rosaceae
The standard compounds were obtained for specifically
identified compounds, namely epicatechin (28), quercitrin
(41), luteolin (48), luteolin 4-methyl ether (49), apigenin
4-methyl ether (50), daidzein (51), genistein (52), caf-
feoylquinic acid (55), and ellagic acid (58) from three plant
families (Cornaceae, Fabaceae, and Rosaceae). The com-
pound standards were diluted as follows: 0.004 mg/mL for
genistein (52); 0.05 mg/mL for daidzein (51) and luteolin
(48); 0.15 mg/mL for epicatechin (28), luteolin 4-methyl
ether (49), apigenin 4-methyl ether (50), and 4-O-caf-
foylquinic acid (55); 0.2 mg/mL for quercitrin (41) and
ellagic acid (58) in microcentrifuge tubes (2 mL). The
compound stocks were serially diluted to the required
concentrations. The series concentrations for the standard
compounds were prepared to formulate the standard curve
and the corresponding regression equation in UHPLC–
LTQ-IT-MS/MS operations.
Results and discussion
Metabolite fingerprinting has emerged as an efficacious
discipline in modern metabolomics that enables researchers
to gain a comprehensive insight into the complex metabolic
relatedness and distinction among the different plant hier-
archies. In general, metabolic profiling could be extended
and interpreted to complement traditional methods of plant
phylogenies.
Plant Cell Rep
123
Metabolite fingerprinting for plant species
from three different families (Cornaceae, Fabaceae,
and Rosaceae)
Thirty-four native Korean plant species were selected for
metabolite analyses using GC–TOF-MS and UHPLC–
LTQ-IT-MS/MS to discriminate different metabolites
among the three Families (Cornaceae, Fabaceae, and
Rosaceae). The species-specific metabolites were evaluated
using multivariate analyses including the supervised partial
least squares discrimination analysis (PLS-DA) as well as
the unsupervised principle component analysis (PCA). The
PLS-DA score plot for GC–TOF-MS data showed the
distinguished primary metabolite patterns among each of
the plant families by PLS1 (12.9 %) and PLS2 (7.0 %),
respectively (Fig. 1a). The quality parameters for PLS-DA
were signified using R2X, R2Y, and Q2. The fractions of the
sum of squares towards the selected components were
represented by R2X and R2Y values of 0.301 and 0.988,
respectively. The Q2 value of 0.868 further signifies the
fraction of the total variation for the X and Y components.
The PCA score plot also resulted in a pattern similar to the
PLS-DA (Fig. S1). The discriminated metabolites among
Cornaceae, Fabaceae, and Rosaceae were selected with
variable importance in the projection value (VIP[1.0) and
p value (p\ 0.05). The selected metabolites were subse-
quently identified using the standard compounds followed
by the comparison of resulting mass fragmentation patterns
with the NIST library. A total of 34 metabolites were
characterized and divided into broad sub-groups such as
organic acids (3), amino acids (11), sugars and sugar
alcohols (13), others (epicatechin and myricetin), and non-
identified (5) compounds (Table S1).
The PLS-DA score plot for secondary metabolites ana-
lyzed through UHPLC–LTQ-IT-MS/MS in negative ion
mode exhibits a similar pattern of distinct metabolic enti-
ties (Fig. 1b). PLS-DA analysis showed 13.2 % of total
variability. Cornaceae, Rosaceae, and Fabaceae were
clearly separated by PLS1 (6.8 %) and PLS2 (6.4 %), with
R2X (0.195), R2Y (0.997), and Q2 (0.822). A total of 46
different secondary metabolites were recognized as being
considerably different among the three plant families
depending upon the variable importance in the projection
(VIP[0.7) and p value (p\ 0.05). These metabolites were
tentatively identified based on various parameters, viz.,
retention time, mass spectra, MSn fragment, kmax, ele-
mental compositions, DmDa, and i-Fit data derived from
the UHPLC–LTQ-IT-MS/MS and UPLC–Q-TOF-MS
spectra for standard compounds and through the published
references (Table S2). A total of 46 different metabolites
were identified including flavonols (11), flavones (5), iso-
flavones (3), phenolic acids (3), ellagitannins (2), flavan-3-
ols (1), mannitol, di-O-galloyl-glucose, lespecyrtin D1,
hydroxyl-octadecatrienoic acids, and 17 non-identified
Fig. 1 Partial least-square discriminant analysis (PLS-DA) score plot
results derived from a GC–TOF-MS and b UHPLC–LTQ-IT-MS/MS
data of the three plant families. Each of the plant families are
symbolized as follows: red triangle Cornaceae, blue triangle
Rosaceae, and green triangle Fabaceae (color figure online)
Plant Cell Rep
123
compounds. Intriguingly, the multivariate analyses results
exhibited the clear clustering among the different plant
family members irrespective of their varying periods of
storage (Fig. 1, Fig. 1S). Hence, it can further be inferred
that plant extract when maintain under deep-freeze condi-
tions (-80 �C) can be utilized for metabolomics analysis
even after extended periods of storage.
Discriminative metabolites from the three plant families
were indicated using the loading plots derived from PLS-
DA datasets (Fig. 2a) coupled with the class representation
for primary chemical structures (Fig. 2b). The identified
secondary metabolites were sorted into six classes of
phenolic compounds that were further categorized for
Cornaceae, Fabaceae, and Rosaceae plant families. The
loading plot indicated that flavonols (kaempferol deriva-
tives, quercetin derivatives, myricetin, and myricetin
derivatives) and ellagitannins (tellimagrandin II and ellagic
acid) were specifically related to the Cornaceae family. In
agreement with our results, similar classes of metabolites
have been reported in the leaves and fruits of Cornus mas
and the sarcocarp of C. officinalis (Cao et al. 2011;
Badalica-Petrescu et al. 2014). Moreover, the ellagic and
iridoid compounds have been detected in the roots of C.
capitata (Tanaka et al. 2001). Our study further correlates
phenolic acids (quinic acid derivatives) and flavan-3-ol
(epicatechin and cinchonain I) specifically to the Rosaceae,
confirming earlier reports where quinic acids derivatives
were identified in the fruits of Prunus domestica and in the
leaves of Crataegus species (C. laevigata, C. monogyna, C.
nigra, and C. pentagyna) (Cadiz-Gurrea et al. 2014;
Kuczkowiak et al. 2014). The present investigation indi-
cated the presence of epicatechin and cinchonain I in the
leaves of Eriobotrya japonica, as reported previously by
Qa’dan et al. (2009). The flavones (apigenin derivatives,
luteolin, and luteolin derivatives) and isoflavones (daid-
zein, genistein, and formononetin) were detected in
Fig. 2 A Loading plots (PLS-DA) for Cornaceae, Fabaceae, and
Rosaceae samples analyzed using UHPLC–LTQ-IT-MS/MS. Each of
the three plant families are symbolized as follows: red triangle
Cornaceae, blue triangle Rosaceae, and green triangle Fabaceae.
B The classes of compounds: a flavonols, b ellagitannins, c phenolic
acids, d flavan-3-ols, e flavones, and f isoflavones detected in the three
plant families (color figure online)
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relatively higher concentrations in Fabaceae than in the
other two families. Flavones and isoflavones were also
reported from Bauhinia L. species, Genista tinctoria and
Lespedeza maximowiczii (also included in present study),
thus validating our results (Tuczkiewica et al. 2004; Veitch
2013; Farag et al. 2015; Kim et al. 2015). In general, the
present work indicates that Cornaceae, Fabaceae, and
Rosaceae show clear metabolite groupings as indicated
through the multivariate statistical analyses. Furthermore,
the identified metabolites from each of the families could
be distinguished into different classes of phenolic deriva-
tives. The classified metabolites, such as ellagitannins,
flavonols, phenolic acids flavan-3-ols, flavones, and iso-
flavones were correlated specifically to their corresponding
families.
Metabolic pathway correlations and the relative
contents of the discriminant metabolites in the three
plant families
The final selected primary and secondary metabolites were
linked to the corresponding metabolic pathways to evaluate
their relative contents among the three plant families
(Fig. 3). In the case of the primary metabolites, shikimic
acid (2), gallic acid (3), arabinose (16), fructose (21),
glucose (22), and myo-inositol (25) were relatively higher
in Cornaceae than in Fabaceae and Rosaceae. The fruits of
C. mas have been reported as containing significant pro-
portions of organic acids (4.6–7.4 %) and gallic acid was
identified (Deng et al. 2013). Gallic acid is a known pre-
cursor of hydrolysable tannin, which is synthesized from an
intermediate precursor named dehydroshikimic acid (Os-
sipov et al. 2003). Shikimic acid found in C. stolonifera
Michx is an essential intermediate compound for the
biosynthesis of aromatic amino acids, viz., phenylalanine,
tyrosine, and tryptophan, and has pharmaceutical signifi-
cance (Li et al. 1966; Jung et al. 2011). The highest con-
centrations of glucose and fructose derived from the stems
of C. sericea L. were detected in highest concentration
during fall after cold acclimation (Ashworth et al. 1993).
The family of specific amino acids (valine, isoleucine,
glycine, lysine, proline, serine, threonine, and phenylala-
nine) and organic acids (lactic acid, acetic acid, malic acid,
succinic acid, and citric acid) were earlier reported in
Cornus species (C. officinalis, and C. caprae hircus) (Xu
et al. 2009; Jung et al. 2011). The relative proportions of
Fig. 3 Schematic diagram of the primary and secondary metabolic
pathways and relative contents of metabolites in Cornaceae,
Fabaceae, and Rosaceae. COR Cornaceae, ROS Rosaceae, FAB
Fabaceae. The pathway was modified from the KEGG database
(http://www.genome.jp/kegg/). The Y-axis of the graphs represents
peak areas in log scale. Data are the three plant families’ mean value,
with the error bars representing deviation values (COR, n = 7; ROS,
n = 18; FAB, n = 9). The relatively higher contents of the metabo-
lites are represented by red boxes (Cornaceae), blue boxes (Rosa-
ceae), and green boxes (Fabaceae) (color figure online)
Plant Cell Rep
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gluconic acid (24), allose (26), and melibiose (27) were
observed to be comparatively higher in Rosaceae than in
Cornaceae and Fabaceae. From the perspective of bioac-
tivity phenotypes, gluconic acid was reported in Potentilla
anserina (Rosaceae), which catalyzes the periplasmic
oxidation of glucose (Rashid et al. 2007; Werra et al.
2009). Similarly, allose is related with antibacterial flavo-
noids glycosides to Prunus armeniaca (Rashid et al. 2007),
whereas melibiose in P. dulcis was related to heat and
drought stress (Dey 1979; Rabert et al. 2015). Here, we
report that malonic acid (1), D-ribonic acid (15), xylitol
(17), fucose (18), tagatofuranose (19), pinitol (20), dulcitol
(23), and eleven amino acids (4–14) were observed in
relatively higher proportions in Fabaceae than in Cor-
naceae and Rosaceae. Previously, the prominent amino
acids, viz., alanine, valine, isoleucine, proline, serine,
threonine, aspartic acid, GABA, glutamic acid, phenylala-
nine, and tryptophan, have been identified in L. maxi-
mowiczii, Sophora alopecuroides L. and several other
species (Lotus corniculatus and Medicago 9 varia) of the
Fabaceae family (Scherling et al. 2010; Wang et al. 2013;
Kim et al. 2015). Functionally, amino acids such as proline
and glutamic acid are directly or indirectly related to the
regulation of plant responses to diverse environmental
stimuli, including light and mineral availability as well as
biotic and abiotic stresses. Threonine and tryptophan con-
tribute to the nutritional quality of plant-based foods (Galili
and Hofgen 2002). Phenylalanine is an important
metabolite in plant metabolism and is crucial for the syn-
thesis of flavonoids (Burbulis and Winkel-Shirley 1999;
Vogt 2010). Although the functions of GABA is not well
known in plants, it reportedly accumulates in response to
hypoxia, cold, heat shock, drought, or mechanical stresses,
and often participates in signal transduction (Facchini et al.
2000).
The present findings suggest that organic acids, sugars,
and sugar alcohols were not found to exhibit any specific
distribution pattern among the three plant families. In
contrast, the proportions of amino acids were particularly
high in Fabaceae, followed by Cornaceae and Rosaceae.
We can further presume that secondary metabolites flowed
from the shikimate pathway via the phenylpropanoid
pathway and finally to the flavonoid pathway. Elaborating
further our conjectures, we found that ellagitannins and
flavonols such as myricetin, myricetin derivatives, quer-
cetin derivatives, kaempferol derivatives, ellagic acid, and
tellimagrandin II metabolism were relatively higher in
Cornaceae than in Fabaceae and Rosaceae (Fig. 3). The
fruits of C. mas L. have earlier been studied for the qual-
itative–quantitative evaluation of flavonols, such as quer-
cetin derivatives and kaempferol derivatives (Pawlowska
et al. 2010). Additionally, hydrolyzable tannins have been
isolated from the fruits and seeds of C. officinalis (Hatano
et al. 1989; Lee et al. 2011). Moreover, flavonols such as
myricetin and kaempferol derivatives with antioxidant
activities were reported from C. kousa fruits (Vareed et al.
2007). Our pathway analysis suggests that ellagitannins
were synthesized from shikimic acid via gallic acid through
the shikimate pathway (Fig. 3). Presumably, the ellagi-
tannins are formed from galloyl glucose by the coupling
reaction in dicotyledons predominantly found among
Hamamelidae, Dilleniidae, and Rosidae (Bate-Smith et al.
1975). Flavonoids such as flavonols, flavones, isoflavones,
and flavan-3-ols were identified in Cornaceae, Fabaceae,
and Rosaceae. Flavonoids, the phenolic compound of
plants, have been studied for their chemical diversity and
associated roles including bioactivities, insect resistance,
and defense metabolites (Pandey et al. 2014). Flavonoid
metabolism leading to the biosynthesis of lignins and
sinapate esters consists of numerous intermediates includ-
ing isoflavones, flavones, flavonols, flavandiols, chalcones,
and anthocyanins (Winkel-Shirley 2001). The insight
gained through understanding the structures of complex
synthetic enzymes enabled researchers to understand the
various aspects of flavonoid biosynthesis related to its
enzymology and subcellular organization (Winkel-Shirley
2001). The occurrence of quercetin and kaempferol (fla-
vonol O-glycosides compounds) distinguishes Cornaceae
genera, viz., Cornus, Aucuba, and Mastixia, from the fla-
vone O-glycosides containing Helwingiaceae (genus Hel-
wingia) (Iwashina 2000). Interestingly, the proportions and
metabolism of isoflavones and flavones, viz., apigenin
derivatives, luteolin, luteolin derivatives, genistein, daid-
zein, and formononetin, were relatively higher in Fabaceae
than in Cornaceae and Rosaceae. The isoflavones (daid-
zein, daidzein derivatives, genistein, and genistein deriva-
tives) and flavones (apigenin, apigenin derivative, luteolin,
and luteolin derivatives) were previously identified from
the leaf and stem samples of L. maximowiczii and G.
tinctoria L. Moreover, genistein and daidzein are pre-
dominantly synthesized in soybean and other Fabaceae
genera (Jung et al. 2000; Tuczkiewica et al. 2004; Kim
et al. 2015). These metabolites have crucial roles, partic-
ularly in plant defense from pest invasion and the onset of
root nodulation through the establishment of symbiotic
relationship with nitrogen-fixing rhizobial bacteria (Jung
et al. 2000; Galili and Hofgen 2002). Nitrogen fixation in
plants occurs in close conjunction with amino acid meta-
bolism and protein synthesis (Fowden 1967). Isoflavone-
mediated root nodulation and nitrogen fixation is often
thought to be associated with higher rates of amino acid
metabolism in Fabaceae plants. Phenolic acids and flavan-
3-ols such as quinic acid derivatives, epicatechin, and
cinchonain I were found in relatively higher proportions in
Rosaceae followed by those in Cornaceae and Fabaceae,
respectively. The fruits of Rosaceae family such as
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123
strawberry, blackberry, plum, cherry, and Sorbus domes-
tica are reportedly rich in various phenolic acids like caf-
feic acid, syringic acid, quinic acid derivatives, and vanillic
acid (Termentzi et al. 2008; Vasco et al. 2009). Quinic acid
derivatives mediate the transformation of phenylalanine to
a variety of secondary metabolites such as lignins, stilbe-
nes, and flavonoids through the phenyl propanoid pathway,
which is specific to plants (Winkel-Shirley 1999). These
metabolites perform several crucial functions in plants
including growth, defense, UV protection, and reproduc-
tion (Winkel-Shirley 1999). Shikimate and quinate esters
serve as the specific substrates for the enzyme Cyp98A3,
which catalyze the hydroxylation of the meta-form of the
hydrocinnamoyl shikimate and quinate esters (Vogt 2010).
Functionally, some hydroxycinnamoyl quinates such as
caffeoyl quinate, primarily function as plant defense
compounds or antioxidant molecules, and thus synthesized
in elevated proportions among Rosaceae (Winkel-Shirley
1999; Vogt 2010). Hence, our study formulates a correla-
tion between the complex metabolite profiles from
different plant families (Cornaceae, Fabaceae and Rosa-
ceae), which further envisages the levels of family-specific
molecules in corresponding metabolic pathways.
Correlations between metabolites and antioxidant
activities among three plant families
Antioxidant activities (ABTS, DPPH, and FRAP), TPC,
and TFC were measured to compare the 34 different spe-
cies of Cornaceae, Fabaceae, and Rosaceae. The average
values for the antioxidant activities, TPC, and TFC towards
the metabolites extracted from these 34 different species
were grouped among Cornaceae, Fabaceae, and Rosaceae
(Fig. 4). The average antioxidant activities and TPC were
found to be the highest among the Rosaceae followed by
Cornaceae and Fabaceae, respectively. Although the levels
of antioxidant activities and TPC were comparable among
Cornaceae and Rosaceae; however, they were significantly
lower in Fabaceae. In contrast, the TFC levels were highest
among Fabaceae followed by Rosaceae and Cornaceae,
Fig. 4 Antioxidant activity assays: a ABTS, b DPPH, c FRAP,
d total phenolic content, and e total flavonoid content for the three
plant families average value. The same letter indicates values that are
not significantly different by Duncan’s multiple range tests at 5 %
significance level. COR Cornaceae, ROS Rosaceae, FAB Fabaceae
(color figure online)
Plant Cell Rep
123
respectively. However, the variations for flavonoid levels
were statistically insignificant among all three plant fami-
lies at p\ 0.05.
To better represent the correlation of metabolites with
their antioxidant activities, the correlation map was visual-
ized (Fig. 5). The Pearson’s correlation coefficients among
the relative contents of 27 selected metabolites and their
antioxidant activities (ABTS, DPPH, and FRAP) were cal-
culated. According to the correlation assay, 14 metabolites
showed positive correlation while the remaining 13 showed
negative correlation. Specifically, the five metabolites
including epicatechin (28), quinic acid (54), 4-O-caf-
foylquinic acid (55), coumaroyl-caffeoylquinic acid, and
cinchonain I (59) were found in relatively higher proportions
in Rosaceae than in the other two families. Further, the nine
metabolites including quercetin galloylglucoside (35),
myricetin-O-arabinopyranoside (36), quercetin-O-xyloside
(40), quercitrin (41), kaempferol-O-galactopyranoside (42),
galloylmyricitrin (43), galloylquercitrin (44), tellimagrandin
II (57), and ellagic acid (58) were detected in relatively high
concentrations among the Cornaceae plants. On the other
hand, the remaining 13 metabolites were observed to be in
relatively higher proportions among Cornaceae and Rosa-
ceae, exhibiting positive correlation coefficients
(0.6[ r[ 0)with antioxidant activities. In congruencewith
our results, certain metabolites such as flavonols (myricetin,
quercetin, and kaempferol) fromC. kousa and phenolic acids
(quinic acid and quinic acid derivatives) from blackberry,
chokeberry, strawberry, and cherry have been shown to
exhibit antioxidant activities (Lidija et al. 2007; Vareed et al.
2007; Podio et al. 2015). The relative concentrations of the
eight metabolites, viz., apigenin-C-hexoside (46), apigenin-
methyl ether-rutinoside (47), luteolin (48), luteolin 4-methyl
ether (49), apigenin 4-methyl ether (50), daidzein (51),
genistein (52), and formononetin (53), were proportionally
higher in Fabaceae. However, these metabolites showed a
Fig. 5 Correlation patterns between the secondary metabolite levels
and antioxidant activity assays (ABTS, DPPH, and FRAP). Groups of
secondary metabolites such as flavan-3-ols, phenolic acids, ellagitan-
nins, flavonols, isoflavones, and flavones were identified as signifi-
cantly different metabolites through PLS-DA. Each square indicates
Pearson’s correlation coefficient of a pair of metabolites and
antioxidant activities. Red and blue colors represent positive
(0\ r\ 0.6) and negative (-0.6\ r\ 0) correlations, respectively
(color figure online)
Plant Cell Rep
123
negative correlation coefficients (0[ r[-0.6) with
respect to their antioxidant activities. Although, the iso-
flavones (genistein, daidzein, and formononetin) and fla-
vones (apigenin, luteolin, and their derivatives) from Salvia
officinalis, Olea europaea L. leaves, and Rooibos tea were
reported to exhibit significant antioxidant activities (Ruiz-
Larrea et al. 1997; von Gadow et al. 1997; Garcia et al. 2000;
Lu and Foo 2001).
The antioxidant activities (using ABTS method) and
average metabolite concentrations were evaluated to for-
mulate their correlations among each of the plant families
(Table 2). The nine selected metabolites were selected for
their previously reported antioxidant activities (Record
et al. 1995; Cao et al. 1997; Pekkarinen et al. 1999). The
relative EC50b value for each metabolite was derived from
the ABTS graph against the concentration range for stan-
dard compounds. The relative EC50b values for the
metabolites were observed in decreasing order as follows:
epicatechin (28)[ genistein (52)[ luteolin (48)[ ellagic
acid (58)[ daidzein (51)[ quercitrin (41)[ luteolin
4-methyl ether (49)[ apigenin 4-methyl ether
(50)[ caffeoylquinic acid (55). The highest antioxidant
activity (EC50b = 1.00) recorded was approximately
fourfold higher than its least observed value
(EC50b = 4.34). Previously, the antioxidant activities of
compounds were attributed to the number of (C2–C3)
double bonds and keto-groups in their C-ring, which was
the reason given for the decreasing order of activities of
quercetin followed by rutin and (?)-catechin, respectively
(Pekkarinen et al. 1999). Additionally, the antioxidant
activities for flavonols including myricetin, quercetin, and
kaempferol were reported as being directly proportional to
the number of phenolic hydroxyl groups in their B-ring. In
the case of the isoflavones (genistein and daidzein), higher
antioxidant activities were observed in the presence of a
free 40-hydroxy group in their structures (Ruiz-Larrea et al.
1997). In general, phenolic hydroxyl groups are assumed to
enhance the antioxidant activities of phenolic acids (4-O-
caffeoylquinic acid), while methoxylation of the hydroxyl
groups causes an activity reduction (Lu and Foo 2001). The
cumulative concentrations for the nine selected metabolites
among the three plant families were detected highest in
Rosaceae (110.76 mg/L), followed by Cornaceae
(99.45 mg/L), and Fabaceae (27.85 mg/L), respectively.
These results further support the findings that antioxidant
activities of the extracts from Fabaceae were relatively
lower than Cornaceae and Rosaceae families. Moreover,
this explains the negative correlation between the
metabolite concentrations in Fabaceae and their antioxidant
activities.
Table 2 Assessment of
antioxidant activity and
quantitative estimations of the
selected nine metabolites of
Cornaceae, Fabaceae, and
Rosaceae (color figure online)
MetaboliteABTS Concentration (mg/L)
c
EC50
a(mg/L) Relative EC
50
bCornaceae Rosaceae Fabaceae
Epicatechin (28) 36.01 ± 1.15 1.00 0 20.11 0 Genistein (52) 36.70 ± 0.48 1.02 0 0.11 0.61Luteolin (48) 49.48 ± 0.42 1.37 0 0.09 0.14Ellagic acid (58) 50.70 ± 0.68 1.41 78.76 47.22 11.94Daidzein (51) 63.34 ± 1.82 1.76 0 0 0.04Quercitrin (41) 68.07 ± 0.19 1.89 20.69 2.30 5.00Luteolin 4-methyl ether (49) 116.76 ± 1.65 3.24 0 0 0.87Apigenin 4-methyl ether (50) 128.20 ± 0.55 3.56 0 0 9.254-O-Caffeoylquinic acid (55) 156.21 ± 1.36 4.34 0 40.93 0
Total concentration 99.45 110.76 27.85
The color scheme is as follows: lower limit value, 0 (white); upper limit, 78 (red)a The effective concentration of antioxidant compounds necessary to decrease the radical concentration by
50 %; data are expressed as mean ± standard deviation (Cornaceae, n = 7; Rosaceae, n = 18; Fabaceae,
n = 9)b Relative to epicatechinc Nine metabolites were measured via quantitative estimation and represented using a heat map with
relative content indicated by heat scale
Plant Cell Rep
123
Conclusions
In the present work, we propose a high-throughput unbi-
ased strategy for metabolite fingerprinting with pathway
and bioactivity correlative methods towards the charac-
terization of economically important plant families (Cor-
naceae, Fabaceae, and Rosaceae). An estimation of the
family-specific library metabolites using GC–TOF-MS and
UHPLC–LTQ-IT-MS/MS with multivariate analyses and
their relative proportions in respective pathways further
predicts the idea of conserved phytochemicals across the
plant hierarchies. As evident from the correlation map
analysis, it is highly likely that the proportional contents of
specific classes of metabolites influence their antioxidant
activities in the sample extracts from each family. The
present approach has the potential to complement the
ongoing global efforts to elucidate the complex
metabolotypes for biological species subjected to varying
natural or experimental conditions.
Author contribution statement CHL and SL designed
this research. SYS performed the experiments and data
analysis. SYS, NKK, SL, DS, GRK, and JSL conducted the
data interpretation. The six samples of Cornaceae,
excluding Cornus macrophylla, were procured from the
Korea Plant Extract Bank and the remainder of the 28 plant
species were provided by the National Institute of Bio-
logical Resources. GRK and JSL participated in sample
preparation. HSY and JY revised the paper. SYS wrote the
paper. All authors approved the final manuscript.
Acknowledgments This work was supported by a Grant from the
National Institute of Biological Resources (NIBR), funded by the
Ministry of Environment (MOE) of the Republic of Korea
(NIBR201628102).
Compliance with ethical standards
Conflict of interest The authors declare that they have no competing
interests.
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