Arabian Journal of Chemistry (2023) 16, 104911King Saud University
Arabian Journal of Chemistry
www.ksu.edu.sa
www.sciencedirect.comORIGINAL ARTICLEVasodilatory constituents of essential oil from
Nardostachys jatamansi DC.: Virtual screening,
experimental validation and the potential molecular
mechanisms* Corresponding authors at: Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu
West Area, Tuanbo New Town, Jinghai District, Tianjin, 301617, China.
E-mail addresses: zhanglihua200061@163.com (L.-H. Zhang), wangqilong_00@tjutcm.edu.cn (Q. Wang), wuhonghua2011@tjutcm
(H.-H. Wu).
1 Co-first authors: B.-X. Xue and S.-X. Liu contributed equally to this work and should be considered co-first authors.
Peer review under responsibility of King Saud University.
Production and hosting by Elsevier
https://doi.org/10.1016/j.arabjc.2023.104911
1878-5352 
 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Bian-Xia Xue a,1, Si-Xia Liu a,1, Patrick Kwabena Oduro a,
Nana Ama Mireku-Gyimah b, Li-Hua Zhang a,*, Qilong Wang a,*, Hong-Hua Wu a,*aState Key Laboratory of Component-based Chinese Medicine, Institute of Traditional Chinese Medicine, Tianjin University
of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin 301617,
People’s Republic of China
bDepartment of Pharmacognosy and Herbal Medicine, School of Pharmacy, College of Health Sciences, University of Ghana,
Legon-Accra, GhanaReceived 18 December 2022; accepted 9 April 2023
Available online 17 April 2023KEYWORDS
Nardostachys jatamansi DC;
Essential oil;
GC/MS;
Vasodilatation;
Spectrum-effect relationship;
Network pharmacologyAbstract Recent studies have demonstrated that the essential oil of Nardostachys jatamansi
(EONJ) has potential health benefits, such as reducing blood pressure, protecting the heart, and
relaxing blood vessels. However, the active constituents and the underlying molecular mechanisms
are still unclear. In this study, we utilized GC–MS to identify the chemical constituents of EONJ
and tested its vasodilatory effects on constricted isolated thoracic aorta rings from mice. We then
combined network pharmacology with spectrum-effect relationship approaches to select potential
marker ingredients and uncover the possible vascular protective mechanism of EONJ. Among
the four main chemicals screened in this work, b-maaliene and patchouli alcohol exhibited varying
levels of vasodilatory effects on isolated, constricted thoracic aortic rings. Bioinformatics and net-
work pharmacology analyses revealed that EONJ’s primary biological regulation targets were
NOS3 and PTGS2. Moreover, EONJ may exert its vasodilatory effects by modulating lipid andRoad,
.edu.cn
2 B.-X. Xue et al.atherosclerosis and the PI3K-AKT signaling pathways. This study provides the first report on the
vasodilatory effects and possible molecular mechanisms of EONJ. The findings may serve as a ref-
erence for quality evaluation, identification of bioactive markers, and future applications of EONJ
in the prevention and treatment of vascular and cardiac diseases.

 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open
access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).1. Introduction
Nardostachys jatamansi DC., belonging to the Caprifoliaceae family of
perennial herbs, is an endangered medicinal and aromatic plant dis-
tributed predominantly at the high elevation of 3300–5000 m in China,
Nepal, India, Bhutan, and other countries (Chauhan et al., 2011;
Rehman and Ahmad, 2019). N. jatamansi has been used as a medicine
for a long time, and it is often given to people with diseases of the ner-
vous, digestive, cardiovascular systems, and skin. Notably, N. jata-
mansi has great therapeutic potential in the treatment of
cardiovascular diseases, especially hypertension (Bhat and Malik,
2020; Lyle et al., 2009). Recently, a clinical trial was conducted to show
that N. jatamansi can significantly decrease systolic and diastolic blood
pressure in patients with essential hypertension (Bhat and Malik,
2020). The aqueous extract of N. chinensis Batalin exhibited hypoten-
sive and protective effects in hypertensive rats with two-kidney one-
clip (2K1C)-induced myocardial hypertrophy (Aisa et al., 2017). The
methanol extract of N. jatamansi can inhibit angiotensin-converting
enzymes leading to a probable decrease in blood pressure (Bose
et al., 2019). More importantly, our previous study demonstrated a sig-
nificant vasodilatory effect of the total methanolic extract and the ethyl
acetate fraction of N. jatamansi (Fang et al. (2022). The two low polar
constituents, (-)-aristolone and kanshone H, are the major compounds
with significant vasodilation properties. Owing to this, we are curious
to know whether the essential oil of N. jatamansi (EONJ) has similar
vasodilatory activity.
To the best of our knowledge, the EONJ is one of the most impor-
tant components of N. jatamansi, with various industrial applications
such as shampoo, soap, bath foam, perfume, incense in religious cere-
monies, and so on. It is also noted as one of the key quality control
markers of N. jatamansi in the Chinese Pharmacopoeia, which stipu-
lates that the EONJ content should not be less than2.0% (mL/g)
(China Pharmacopoeia Committee, 2020b). To date, gas
chromatography-mass spectrometry (GC–MS) has been frequently
employed for the qualitative and semi-quantitative profiling of EONJ.
It has been investigated in depth by a considerable number of scholars
(Chauhan et al., 2017; Gen et al., 2011; Han et al., 2017; Jin et al.,
2018a; Jin et al., 2018b; Maiwulanjiang et al., 2015; Takemoto et al.,
2008; Tanaka and Komatsu, 2008; Wang et al., 2015; Wu et al.,
2015). The major constituents of EONJ vary depending on species
and origins. They might also change along with the drying degree as
well as the processing temperature of the herb (Chauhan et al., 2017;
Wang et al., 2021). Moreover, it has been extensively investigated for
various pharmacological activities. It is notable for its promising effi-
cacy in terms of cardiovascular disease, such as the treatment of car-
diomyocyte death, salt-sensitive hypertension, arrhythmias, and
myocardial ischemia–reperfusion injury (Cao et al., 2010; Jiang
et al., 2017; Maiwulanjiang et al., 2014; Yang et al., 2010; Yang
et al., 2018). Maiwulanjiang et al. have discovered that the EONJ is
the main ingredient that could trigger vasodilation. However, the
specific active constituents remain unknown (Maiwulanjiang et al.,
2015).
Recently, screening bioactive candidates from herbal medicines has
become a serious challenge owing to their intrinsic properties of diverse
components, multi-targets, integrative regulation, and complex mech-
anisms (Zhang et al., 2018). Traditionally, the screening of bioactive
ingredients in herbal medicines has been carried out by conventionalmethods, viz. extraction, purification, structural identification of phy-
toconstituents and finally bioassays, which are time-consuming and
inefficient. Nowadays, numerous researchers have exploited
fingerprint-activity relationship models to uncover promising bioactive
constituents, including hierarchical cluster analysis (HCA) (Liu et al.,
2021), principal component analysis (PCA) (Cai et al., 2019; Liu
et al., 2021), gray correlation analysis (GCA) (Chen et al., 2019;
Xiao et al., 2018; Yuan et al., 2022; Zhang et al., 2019a), partial least
squares regression analysis (PLSR) (Guo et al., 2020; Shawky et al.,
2018; Zhang et al., 2019b), bivariate correlation analysis (BCA)
(Yuan et al., 2022; Pang et al., 2022), multiple linear regression analysis
(MLRA) (Cai et al., 2019; Kong et al., 2017; Zhang et al., 2018), and
back propagation-artificial neural network modeling (BP-ANN) (Guo
et al., 2020; Wang et al., 2017). Furthermore, the network pharmaco-
logical approach has often been integrated into the systematic virtual
screening of the core targets and the primary bioactive ingredients
from herbal medicines (Cheng et al., 2021; He et al., 2019; Jia et al.,
2021; Zeng et al., 2022).
Hence, the GC–MS technique was applied to analyze the chemical
compositions of four EONJs, whose significant vasodilatory effects
were reported herein. Furthermore, a combination of the spectrum-
effect relationship and network pharmacology was integrated for min-
ing the vasodilatory constituents of EONJ and their potential targets,
as well as the possible molecular mechanisms.
2. Materials and methods
2.1. Plant material and reagents
The roots and rhizomes of Nardostachys jatamansi DC. were
purchased from Beijing Tong Ren Tang Tianjin Nankai Phar-
macy Co. Ltd. (batch number: 20211220; origin: Sichuan pro-
vince, China) and were authenticated by Prof. Tianxiang Li of
Tianjin University of Traditional Chinese Medicine, China.
Voucher specimens were deposited in the State Key Labora-
tory of Component Chinese Medicines, Institute of Chinese
Medicine, Tianjin University of Traditional Chinese Medicine.
NJ-B, the essential oil of sample B of N. jatamansi, sourced
from Sichuan, was kindly supplied and produced by one of
our partners in a Wenxin Keli-related project, Shandong
BUCHANG Pharmaceutical Co. Ltd. NJ-C, the essential oil
of sample C of N. jatamansi (batch number: 8208), namely
’Spikenard C’, was originated in Nepal and made in the UK
by Shirley Price Aromatherapy Ltd., 8 Hawley Road, Hinckley
Leicestershire UK LE10 OPR. NJ-D, the essential oil of sam-
ple D of N. jatamansi (batch number: 210259A), namely
’Spikenard D’, was purchased from do
TERRA Intl, LLC,
389 South 1300 West Pleasant Grove, UTAH 84062, U.S.A.,
and the place of origin was unknown. C8–C40 alkanes calibra-
tion standard (SIGMA-ALDRICH, 40147-U, USA) was
employed as the retention index standard in this work, consist-
ing of a mixture of aliphatic hydrocarbons dissolved in n-
hexane. Sodium chloride (NaCl), sodium bicarbonate
(NaHCO3), and D-glucose were purchased from Damao
Vasodilatory ingredients of essential oil 3Chemical Reagent Factory (Tianjin, China). Magnesium sul-
fate heptahydrate (MgSO4
7H2O) was obtained from Tianjin
Guangfu Technology Development Co. (Tianjin, China).
Potassium chloride (KCl) was available from Boote (Tianjin)
Chemical Trading Co. (Tianjin, China). Potassium dihydrogen
phosphate anhydrous (KH2PO4), Crystalline calcium chloride
(CaCl2
2H2O) were acquired from Tianjin Bailens Biotechnol-
ogy Co. (Tianjin, China). Dimethyl sulfoxide (DMSO),
U46619(9,11-methanoepoxy PGH2), acetylcholine (Ach), and
sodium nitroprusside (SNP) used in the experiment were pur-
chased from Sigma-Aldrich (St. Louis, MO). Isoflurane was
sourced from Rayward Life Technology Co., Ltd (Shenzhen,
China). Patchouli alcohol was purchased from Sichuan Vic-
tory Biological Technology Co., Ltd (China) with a purity
determined by GC analysis as above 98%.
2.2. Extraction and sample preparation
The essential oil was extracted from 30 g powder of dried N.
jatamansi (sieved through 50 mesh) by hydro-distillation for
4 h in 600 mL of distilled water, using Clevenger-type appara-
tus and following the standard procedure described in the Chi-
nese Pharmacopoeia (2020 Edition, Part IV) (China
Pharmacopoeia Committee, 2020a), until there was no signifi-
cant increase of the volume of oil collected. The volume of
volatile oil was measured and recorded by the graduated tube
and the yield was calculated. The yields were expressed as the
volume of oil/weight of plant material (% v/w). The essential
oil collected was dried over anhydrous sodium sulfate, dis-
solved in ethyl acetate (1:500, v/v), filtered through a 0.22 lm
microporous membrane, and stored in an amber-colored
sealed vial at a 4 C in a refrigerator for further analysis. Thus,
the essential oil sample A of N. jatamansi (NJ-A) was
obtained. Similarly, NJ-B, NJ-C and NJ-D were dissolved in
ethyl acetate (1:500, v/v), filtered through a 0.22 lm microp-
orous membrane, and kept in an amber-colored sealed vial
at 4 C for the subsequent experiments.
2.3. Isolation and identification of the main constituents
NJ-B (2 mL, 1.83 g) was dissolved in methanol-
tetrahydrofuran (1:1) and filtered by 0.45 lm microporous fil-
ter before isolation of the main constituents by preparative
HPLC. NJ-B was separated on a Gilson GX-281
preparative-HPLC system equipped with a Luna C18(2) col-
umn (250  21.2 mm, 5 lm) at room temperature. The mobile
phase was composed of H2O (A) and acetonitrile (B) and
implemented in the gradient elution as follows: 80  100%
A for 12 min; 100% A was kept for 15 min. The flow rate
was set as 15 mL/min, and the detection wavelength was
selected at 220 and 254 nm. The purity of the isolated phyto-
chemicals was determined by GC–MS and LC-MS. Their
structures were identified by analysis of the GC–MS, 1H,
and 13C NMR data, as well as the comparison of their
NMR data with those reported. Three main constituents were
then afforded and identified as valerena-4,7(11)-diene (3.28%,
60.0 mg, tR = 2.25 min, purity of 99.14%) (Paul et al., 2001),
calarene (18.03%, 330 mg, tR = 2.57 min, purity of 99.21%)
(Abraham et al., 1992; Nagashima et al., 1997;Furusawa
et al., 2006) and b-maaliene (3.39%, 62.0 mg, tR = 2.89 min,
purity of 96.00%) (Abraham et al., 1992).Valerena-4,7(11)-diene: Colorless oil; [a]20 D 0.32 (c
0.125, ACN); UV (ACN) kmax 214 nm; CD (c 1.00, ACN) k
(De) 204 (-0.43) nm, 228 (1.02) nm; 1H NMR (400 MHz,
CDCl3) dH (mult, J in Hz): 0.76 (3H, d, 7.2), 1.28–1.36 (2H,
m), 1.49–1.57 (2H, m), 1.64 (3H, s), 1.66 (3H, s), 1.69 (3H,
s), 1.79–1.85 (2H, m), 1.95 (1H, m), 2.16–2.21 (2H, m), 2.93
(1H, m), 3.40 (1H, dd, 5.2, 9.2), 5.47 (1H, br d, 9.2); 13C
NMR (101 MHz, CDCl3) dC: 136.2, 129.9, 128.5, 126.3,
47.5, 37.7, 33.8, 33.7, 28.8, 26.7, 26.3, 24.7, 17.9, 13.5, 12.2;
GC-EIMS m/z 204 (100%), 189 (60%), 161 (57%), 147
(52%), 133 (45%), 119 (51%), 109 (56%), 107 (51%), 105
(69%), 91 (40%), 79 (22%), 67 (21%), 55 (18%), 41 (29%).
Calarene: Colorless oil; [a]20 D + 0.80 (c 0.125, ACN); UV
(ACN) kmax 207 nm; CD (c 1.00, ACN) k(De) 216 (-1.39) nm;
1H NMR (400 MHz, CDCl3) dH (mult, J in Hz): 0.56 (1H, d,
9.2), 0.74 (1H, dt, 3.6, 9.6), 0.97 (3H, d, 6.8), 0.98 (3H, s), 1.01
(3H, s), 1.07 (3H, s), 1.34–1.45 (3H, m), 1.71–1.76 (2H, m),
1.92–2.01 (3H, m), 2.22 (1H, m), 5.24 (1H, br t, 2.4); 13C
NMR (101 MHz, CDCl3) dC: 144.3, 120.5, 36.9, 36.8, 33.6,
30.1, 30.0, 27.4, 25.9, 23.1, 21.0, 19.7, 18.7, 16.7, 16.2; GC-
EIMS m/z 204 (12%), 189 (18%), 161 (100%), 147 (13%),
133 (19%), 119 (28%), 105 (34%), 91 (19%), 79 (11%), 69
(11%), 53 (8%), 41 (16%).
b-Maaliene: Colorless oil; [a]20 D 0.48 (c 0.125, ACN);
UV (ACN) kmax 214 nm; CD (c 1.00, ACN) k(De) 205 (-
0.20) nm, 233 (+2.34) nm; 1H NMR (400 MHz, CDCl3) dH
(mult, J in Hz): 0.73 (1H, t, 8.0), 0.84 (3H, s), 0.88–0.98 (3H,
m), 0.99 (3H, s), 1.09 (3H, s), 1.16–1.22 (2H, m), 1.40 (1H,
m), 1.56 (1H, m), 1.59 (3H, s), 1.61–1.66 (2H, m), 1.86–2.04
(2H, m); 13C NMR (101 MHz, CDCl3) dC: 131.8, 129.8,
38.7, 36.9, 32.5, 32.4, 29.3, 24.5, 24.3, 20.7, 19.9, 19.5, 18.1,
16.5, 15.6; GC-EIMS m/z 204 (85%), 189 (100%), 161
(100%), 147 (22%), 133 (56%), 119 (45%), 107 (30%), 105
(62%), 91 (40%), 79 (15%), 67 (15%), 55 (16%), 41 (28%).
2.4. GC–MS analysis
The GC–MS profiling was performed with an Agilent 7890B
gas chromatography system equipped with an Agilent 7000D
quadrupole mass spectrometry detector (Agilent Technologies,
Santa Clara, CA, USA). Chromatographic separation was
conducted on an HP-5MS quartz capillary column
(30 m  0.25 mm, 0.25 lm film thickness, 19091S-433, J&W
Scientific, Folsom, CA, USA). Helium (99.999% purity) was
used as the carrier gas at a 1.0 mL/min flow rate. The initial
column temperature was 50C, with heating to 150C at
15C /min, where it was kept for 3 min, followed by a rise at
a rate of 2C/min to 162C, finally increasing at 20C/ min
to 250C. The total running time was 20.667 min. The inlet
temperature was held at 250C with an injection volume of
1 lL sample solution and a split ratio of 1:10. The ion source
(EI) and quadruple temperature were set at 230C and 150C,
respectively, with a mass scanning range of 40–650 amu and an
ionization voltage of 70 eV. The solvent delay time was estab-
lished at 3.6 min to avoid the appearance of solvent peaks. Rel-
ative retention indices (RRI) against retention time were
calculated using the same column with C8–C40 as the reference
standard and under the same operating conditions. Chemical
constituents in volatile oil were characterized by matching
the mass spectra to the spectra of reference compounds stored
in the NIST 17.0 library, comparing their Kovats retention
4 B.-X. Xue et al.indices against C8–C40 n-alkanes, and comparing them with
those reported in the literature. The relative contents of indi-
vidual components were calculated in terms of GC peak area
normalization without correction factors. Each essential oil
determination was carried out in triplicate.
2.5. Animals
SPF grade C57BL/6 mice, male, 8 weeks, weight 18–22 g, were
purchased from SPF (Beijing) biotechnology co., Ltd, housed
in a barrier environment at the Animal Center of Tianjin
University of Traditional Chinese Medicine, at 25C and
50% humidity. The ventilation system was good, and the
12 h light and dark cycles were strictly maintained with food
and water ad libitum. All experimental animal procedures were
per the Regulations of the People’s Republic of China on the
Administration of Laboratory Animals. Prior to the start of
the experiment, the mice were acclimatized for 7 days.
2.6. Evaluation of the vasodilatory activity
C57BL/6 mice (male, 8–10 weeks) were euthanized by isoflu-
rane through inhalation. The thoracic aorta was removed
and placed in Krebs-Henseleit (K-H) solution, including
(mM): NaCl (118), NaHCO3 (24.0), KCl (4.7), MgSO4
7H2O
(1.2), KH2PO4 (1.8), CaCl2
2H2O (11.1), D-glucose (11). After
carefully separating the excess tissue and fat around the tho-
racic aorta, the aorta was cut into 4 mm-length vascular rings.
The aortic rings were placed in a bath at 37C, 5 mL K-H solu-
tion, and continuously injected with a mixture of 95%
O2 + 5%CO2 in a multi-channel ex vivo vascular tonometry
system 630AM (Danish Myo Technology A/S, Denmark).
Aortic rings were precontracted with 10-8 mol/L U46619. After
the aortic rings reached their maximum contraction value, Ach
（10-9–10-5 mol/L）was added sequentially at 2 min intervals.
Recording the diastolic curve, the endothelium of the aortic
rings was intact if the maximum diastolic rate reached over
80%. The intact endothelium aorta was used to measure the
vasorelaxation activity of the sample. After precontraction of
the aortic rings with U46619, different concentrations of sam-
ples were added cumulatively at 5 min intervals. The relaxation
rate was calculated using the following formula (Eq. (1)).
¼ GU46619  GXX%
GU46619   100% ð1ÞG0
GU46619: maximum contractility due to U46619; GX: con-
tractility after 5 min of drug action; G0: Resting contractility.
2.7. Modeling of the GC spectrum-vasodilatory activity
relationship
GCA and PLSR were employed in the spectrum-effect rela-
tionship modeling between the relative contents (percentage,
%) of forty-eight constituents in EONJ and the vasodilatory
activity data of each EONJ utilizing Excel 2016 for Windows
10 and RStudio software, respectively. The average diastolic
rate of each EONJ at the concentration of 300 lg/mL was
taken into account due to considerable variability under this
concentration, which is favorable for the discovery of the
bioactive constituents. The combination of GCA and PLSRmethods enabled a mutual verification of the spectrum-effect
relationship analysis between pharmacodynamic indices and
chromatographic peaks, enhancing the credibility of these
efficacy-associated compounds selected out of the constituents
in the volatile oils of N. jatamansi.
2.7.1. Gray correlation analysis (GCA)
GCA was conducted for the modeling of the spectrum-effect
relationship utilizing Excel 2016. The data on the vasodilatory
activities of the four EONJs served as reference sequences (par-
ent sequences), and the data on the relative contents of forty-
eight components obtained by GC–MS were treated as com-
parison sequences (sub-sequences) accordingly. Correlation
coefficients (ni) of the reference and comparison sequences
derived from the GCA, representing the proximity of the asso-
ciated factor sequence to the behavioral feature, were calcu-
lated using the mean value method. The higher the value ni,
the closer the sequence of correlation factors to the behavioral
characteristics. The correlation degree (r) was obtained by cal-
culating the mean value of the correlation coefficient between
each indicator and the corresponding value of the reference
sequence, which was designed to reflect the correlation
between each evaluation object and the parent sequence
(Chen et al., 2019; Huang et al., 2020; Xiao et al., 2018;
Yuan et al., 2022; Zhang et al., 2019a). The contribution mag-
nitude of each component to potency was assessed according
to the ranking of the correlation degree, where r higher than
0.6 represented the existence of correlation while r greater than
0.8 reflected the high correlation (Chen et al., 2020; Yuan
et al., 2022). The concrete calculation steps have been reported
in detail in a published paper (Xiao et al., 2018).
Standardized processing of data (Eq. (2)):
Xi
XiðkÞ ¼ P ði ¼ 0; 1; 2; 
 
 
 :; n; k ¼ 1; 2; 
 
 
 ; mÞ ð2Þ1
mX
X0 (k) denotes the reference sequence.
Calculation of correlation coefficients (n) (Eq. (3)):
ð Þ ¼ miniminkjX0ðkÞ  XiðkÞj þ qmaximaxkjX0ðkÞ  Xn iðkÞji k jX0ðkÞ  XiðkÞj þ qmaximaxkjX0ðkÞ  XiðkÞj
ð3Þ
q represents the resolution coefficient, the smaller the q, the
greater the resolution. q value range from 0 to 1, and it is taken
as 0.5 in this work.
Calculation of correlation degree (r) (Eq. (4)):
ð Þ ¼ 1
Xm
r0i k niðkÞ ð4Þ
m
k¼12.7.2. Partial least squares regression analysis (PLSR)
Partial least squares regression model between the vasodilatory
activity and GC–MS spectrums was established to explore
potential bioactive constituents utilizing RStudio. For this
method, the X-matrix consisted of the relative content data
of the individual constituents of the GC–MS spectrum. At
the same time, the Y-matrix was composed of the vasorelaxant
activity data of the four EONJs. It was followed by a regres-
sion procedure. More specifically, the two sets of data were
standardized, and the regression was analyzed subsequently
to obtain the regression coefficient (r0) for the X variable rela-
Vasodilatory ingredients of essential oil 5tive to the Y variable. In the algorithm, a decomposition of
the X variable predicts the Y variable. In contrast, the algo-
rithm enables the discovery of several latent variables which
can maximally explain the X variable and be correlated with
the Y variable.
2.8. Network pharmacological workflow
2.8.1. Prediction of the drug-like properties and toxicological
parameters
In this study, the chemical structures of the forty-eight phyto-
chemicals identified from the four EONJs were retrieved or
verified via several online databases such as PubChem
(https://pubchem.ncbi.nlm.nih.gov), ChemSpider (https://
www.chemspider.com) and Chemical Book (https://
www.chemicalbook.com/ProductIndex.aspx), as well as the
reported literature data on N. jatamansi. Their 2D structure
drawing was accomplished by ChemDraw 19.0 software.
The SwissADME web tool (https://www.swissadme.ch) was
employed to obtain the corresponding canonical SMILES cod-
ing of the compounds as well as the drug-like properties com-
prising molecular weight (MW), number of hydrogen bond
donors (Hdon), number of hydrogen bond acceptors (Hacc),
lipid-water partition coefficient (LogP) and number of rotat-
able bonds (Rbon). Lipinski rule, viz. Rule of Five (RO5,
MW  500, Hdon  5, Hacc  10, LogP  5 and
Rbon  10) (Yang et al. 2020; Zeng et al. 2022), was applied
for the bioavailability evaluation based on the 2D structures
of these compounds. Synthetic accessibility score (SAscore)
was collected using the ADMETlab 2.0 database (https://ad-
metmesh.scbdd.com), with SAscore greater than 6 indicating
difficult synthesis and SAscore < 6 representing easy synthesis
(Zeng et al. 2022).
In addition, drug toxicology plays a critical role in pre-
clinical investigations. Therefore, the toxicological parameters
of the forty-eight compounds were calculated by the Protox II
webserver (https://tox-new.charite.de/protox_II/) (for predict-
ing the toxicity of chemicals) and the toxicological endpoints
(hepatotoxicity, carcinogenicity, immunotoxicity, mutagenic-
ity, cytotoxicity, and acute oral toxicity (LD50, mg/kg) were
in binary format reported as active or inactive. Finally, a gra-
phic visualization was done utilizing the online bioinformatics
website (https://www.bioinformatics.com.cn/).
2.8.2. Collection of predictive targets and CVD-associated
targets
The PharmMapper (https://www.lilab-ecust.cn/pharmmapper/
), SwissTarget Prediction (https://www.swisstargetprediction.
ch/), TCMID (https://www.megabionet.org/tcmid/), TCMSP
(https://tcmsp-e.com/) and SEA (https://sea.bkslab.org/) data-
bases were chosen to predict the potential targets of the forty-
eight constituents of EONJ. The OMIM (https://www.omim.
org/), TTD (https://db.idrblab.net/ttd/), GeneCards (https://
www.genecards.org/), DisGenet (https://www.disgenet.org/)
and Drugbank (https://go.drugbank.com/) databases were
retrieved for the possible targets concerning CVD by using
the keyword ’cardiovascular disease’. Furthermore, the Uni-
prot database (https://www.uniprot.org/) was employed to
translate the target names into the corresponding gene name.
The collection and organization of the target information were
completed by Microsoft Excel software (version 2016). Finally,the potential targets of the forty-eight phytochemicals were
intersected with those related to CVD utilizing the Venn dia-
gram to obtain the target proteins of EONJ-CVD.
2.8.3. Protein-Protein interaction (PPI) network
The PPI network of the compound targets for EONJ-CVD
and EONJ was constructed by STRING database (https://
string.embl.de/), with ’Organism’ set to ’Homo sapiens’, ’Min-
imum required interaction score’ selected as ’medium confi-
dence (0.400)’, and the remaining parameters were left
unchanged. Next, the interaction information was downloaded
to be imported into Cytoscape software (v 3.7.1) for better
optimization and visualization of the PPI network. Network
Analyzer (a Cytoscape plugin) is applied to calculate the
degree values and ranking. The color and size of the node cor-
relate with its degree value. The larger the node’s degree and
the darker the color are, the higher the node’s importance in
the PPI network. Accordingly, the top core targets in the
PPI network could be screened out.
2.8.4. Construction of ’EONJ-ingredients-target-CVD’ network
Based on the protein interaction target data of EONJ and
CVD, the active components of EONJ that could act on
CVD were introduced, and ’EONJ-ingredients-common
target-CVD’ network diagrams were established by Cytoscape
3.7.1 software for visualizing. The network structure was ana-
lyzed by Network Analyzer with the node sorting and ranking
based on degree value, and the potential active makers of
EONJ against CVD were filtered. The Vertical Drop Line dia-
gram, displaying the numbers of CVD-related targets targeted
by the EONJ phytochemicals, was prepared using Origin 2021
software.
2.8.5. Pathway enrichment analysis
After the collection of the common targets of EONJ-CVD,
Disease Ontology (DO) and Kyoto Encyclopedia of Genes
and Genomes (KEGG) pathway enrichment analyses were
performed with the DOSE, Bioconductor, and ClusterProfiler
R packages. The top 20 DO, and KEGG terms sorted by gene
count enriched were visualized by R 4.1.0 software. Cytoscape
3.7.1 was again deployed to construct ’target-pathway’ net-
works. To further reveal the relationship among the biological
functions, signal pathways involved in common targets, and
their potential scientific connotations in biological networks,
Gene Ontology (GO) functional enrichment, and KEGG path-
way enrichment analyses were implemented using ClueGO
plugin of Cytoscape widely utilized. Supplementarily, DO
annotate genes based on human disease. GO analysis focuses
on the biological process (BP) of the target. In contrast,
KEGG pathway enrichment analysis was performed to probe
the potential biological pathways related to the target.
2.9. Statistic analysis
The experimental results are presented by Mean ± SEM; Data
comparisons between the two groups were independent of each
other Sample T-test, multi-group comparison using one-way
ANOVA. Values of p < 0.05 were considered statistically sig-
nificant. Statistical analyses were performed using GraphPad
Prism 8 and SPSS 21.0 Software.
6 B.-X. Xue et al.3. Results
3.1. GC–MS profiling of the four EONJs
NJ-A, with an aromatic odor, was obtained as a pale yellow-
green oily liquid (0.8 mL) from the dried rhizomes of N. jata-
mansi by hydro-distillation with a yield of 2.67% (v/w) above
the limit requirement (1.8%) in the Chinese Pharmacopoeia
(China Pharmacopoeia Committee, 2020b). The GC–MS total
ion flow chromatograms (TIC) of the four EONJs are shown
in Fig. 1. The corresponding identification results, including
the retention time (RT), relative retention index (RRI), and
the relative content of each constituent, are listed in Table 1.
A total of forty-eight compounds were identified, as shown
in Fig. 1. The main components were sesquiterpenes, account-
ing for 77.08% of the numbers of the total components. At the
same time, monoterpenes were also present in EONJ, repre-
senting about 22.92%. Among them, thirty-seven chemical
constituents from NJ-A, forty chemical constituents from
NJ-B, thirty-three chemical constituents from NJ-C, and
thirty-eight chemical constituents from NJ-D were character-
ized, contributing to 91.89%, 92.78%, 95.23%, and 83.32%
of the total essential oil, respectively. The main constituents
in each essential oil of N. jatamansi were: NJ-A, calarene
(19.47%), ledene oxide-(II) (12.13%), patchouli alcohol
(11.36%), and maaliol (6.74%); NJ-B, calarene (35.15%), b-
maaliene (9.54%), valerena-4,7(11)-diene (6.31%); NJ-C, b-
maaliene (32.23%), calarene (25.74%), patchouli alcohol
(6.23%), (-)-aristolene (6.13%); and NJ-D, valencene
(12.51%), calarene (10.1%), guaia-6,9-diene (8.22%). Accord-
ingly, calarene is one of the most predominant constituents of
NJ-A, NJ-B, NJ-C, and NJ-D. This result is generally consis-Fig. 1 Total ion chromatograms of EONJs and the chemicaltent with those reported (Liu and Liu, 2014; Maiwulanjiang
et al., 2015; Wang et al., 2010).
The dendrogram depicted in Fig. 2 displayed the results of
agglomerative hierarchical clustering analyses on the forty-
eight compounds identified in the four EONJs. NJ-A and
NJ-B fall into one cluster because of their high similarity in
chemical composition and the relative content of each com-
mon constituent. NJ-C seems more alike to NJ-A and NJ-B
compared to NJ-D, differing marginally in the contents of
the common constituents. Among these EONJs, NJ-C con-
tains the minimum number of constituents, while NJ-D pos-
sesses the largest number of constituents. NJ-D is quite
different from other EONJs in chemical composition and the
relative content of each common constituent. So, it could be
speculated that NJ-D may originate from a different variety
of N. jatamansi against those of other EONJs.
Overall, seventeen constituents, including calarene, b-
maaliene, valerena-4,7(11)-diene, (-)-aristolene, seychellene,
valencene, patchouli alcohol, maaliol, a-maaliene, (-)- globu-
lol, (E)-b-caryophyllene, ledene oxide-(II), a-bulnesene, c-
himachalane, a-patchoulene, b-elemene, and b-patchoulene
were found to be the major common constituents in EONJs,
accounting for 69.53%, 79.31%, 89.27% and 47.28% of the
whole chemical constituents of NJ-A, NJ-B, NJ-C, and NJ-
D, respectively. Notably, bicyclosesquiphellandrene (4.39%),
a-cadinol (3.92%), jatamansone (3.78%), and spirojatamol
(2.74%) were prevalent ingredients of NJ-D, in which these
constituents as aristolone, spathulenol, trans-b-ionone, and
aromadendrene epoxide were absent instead. Guaia-6,9-diene
could be detected at 0.88%, 0.22%, and 8.22% in NJ-A, NJ-
B, and NJ-D, respectively, whereas it has not been detected
in NJ-C. Further, there were great differences in the relative
contents of those common constituents, including b-structures of forty-eight ingredients identified by GC–MS.
Vasodilatory ingredients of essential oil 7
Table 1 Phytochemical identification of four EONJs based on GC–MS analysis.
No. RΤ (min) Compound RRIa RRIL Formula CAS No. Percent (% peak area)
NJ-A NJ-B NJ-C NJ-D
1 4.033 b-Pinene / 979 C10H16 127–91-3 0.01 0.16 0.03 1.7
2 4.500 p-Cymene 1038 1023 C10H14 99–87-6 / 0.01 / 0.01
3 4.514 D-Limonene 1039 1029 C10H16 5989–27-5 / 0.02 0.01 0.09
4 4.554 1,8-Cineol 1043 1031 C10H18O 470–82-6 0.03 0.32 0.02 0.24
5 6.005 Terpinen-4-ol 1191 1177 C10H18O 562–74-3 0.01 0.03 / 0.06
6 6.506 Methyl thymyl ether 1245 1237 C11H16O 1017–56-8 0.04 0.09 0.01 0.1
7 6.602 Isothymol methyl ether 1255 1245 C11H16O 31574–44-4 0.11 0.26 0.03 0.02
8 6.930 1,4-Dimethyltetralin 1288 1283 C12H16 4175–54-6 0.11 0.05 0.02 0.03
9 7.150 Methyl myrtenate 1310 1301 C11H16O2 30649–97-9 / / / 0.46
10 7.436 Myrtenyl acetate 1336 1326 C12H18O2 1079–01-2 0.09 0.1 0.02 0.13
11 7.588 d-EIemene 1349 1338 C15H24 20307–84-0 0.02 0.04 0.24 /
12 8.217 b-Patchoulene 1402 1380 C15H24 514–51-2 0.15 0.56 0.71 2.27
13 8.271 b-Elemene 1406 1389 C15H24 515–13-9 0.11 0.24 0.73 0.3
14 8.616 b-Maaliene 1429 1415 C15H24 489–29-2 4.76 9.54 32.23 1.68
15 8.743 (-)-Aristolene 1438 1429 C15H24 87–44-5 2.56 4.63 6.13 0.85
16 8.886 (E)-b-Caryophyllene 1447 1440 C15H24 154098–14-3 1.06 1.83 0.85 0.86
17 8.954 Calarene 1451 1430 C15H24 17334–55-3 19.47 35.15 25.74 10.1
18 9.045 Guaia-6,9-diene 1457 1451 C15H24 36577–33-0 0.88 0.22 / 8.22
19 9.16 Seychellene 1464 1446 C15H24 20085–93-2 1.78 3.24 2.11 3.6
20 9.257 Valerena-4,7(11)-diene 1470 1456 C15H24 351222–66-7 3.16 6.31 2.75 4.26
21 9.366 a-Patchoulene 1477 1467 C15H24 560–32-7 0.76 1.49 3 2.89
22 9.43 allo-Aromadendrene 1481 1465 C15H24 125135–08-2 / / / 1.65
23 9.489 c-Himachalane 1484 1480 C15H28 53111–25-4 0.34 0.49 0.32 0.92
24 9.568 c-Gurjunene 1489 1477 C15H24 22567–17-5 / 0.34 1.91 1.03
25 9.735 trans-b-Ionone 1499 1488 C13H20O 79–77-6 3.51 1.65 0.55 /
26 9.784 Bicyclosesquiphellandrene 1502 1495 C15H24 54324–03-7 / / / 4.39
27 9.963 Valencene 1511 1496 C15H24 4630–07-3 1.51 1.82 1.71 12.51
28 10.167 d-Guaiene 1521 1510 C15H24 3691–11-0 0.26 0.48 2.65 0.95
29 10.368 Nootkatene 1531 1511 C15H22 5090–61-9 / / / 0.65
30 10.466 a-Maaliene 1536 1522 C15H24 489–28-1 0.94 1.46 0.88 3.03
31 10.585 b-Vatirenene 1541 1540 C15H22 27840–40-0 0.05 0.02 / 0.95
32 11.537 Maaliol 1584 1574 C15H26O 527–90-2 6.74 2.45 0.81 0.46
33 11.777 Spathulenol 1595 1577 C15H24O 6750–60-3 4.79 1.78 0.76 /
34 11.786 Spirojatamol 1595 1585 C15H26O 128487–46-7 / / / 2.74
35 11.929 (-)-Globulol 1601 1590 C15H26O 489–41-8 2.44 0.91 0.62 0.36
36 12.411 Isoaromadendrene epoxide 1620 1610 C15H24O / 2.1 0.44 0.12 /
37 12.915 Selin-6-en-4a-ol 1639 1636 C15H26O 118173–08-3 0.5 0.2 0.11 /
38 13.064 a-Cadinol 1645 1653 C15H26O 481–34-5 / / / 3.92
39 13.105 Isospathulenol 1646 1638 C15H24O 88395–46-4 1.25 0.24 / /
40 13.707 Aromadendrene epoxide 1668 1672 C15H24O 85710–39-0 2.13 0.78 0.62 /
41 13.909 Patchouli alcohol 1675 1663 C15H26O 5986–55-0 11.36 4.17 6.23 1.28
42 14.070 Ledene oxide-(II) 1680 1631 C15H24O / 12.13 4.54 1.8 0.96
43 14.238 Jatamansone 1686 1675 C15H26O 1803–39-0 / / / 3.78
44 14.915 Valerenol 1712 1736 C15H24O 101628–22-2 0.66 0.77 0.14 /
45 15.553 Valerenal 1738 1720 C15H22O 4176–16-3 1.11 0.71 / 0.17
46 16.629 Aristolone 1781 1763 C15H22O 6831–17-0 4.87 3.49 1.37 /
47 16.875 Nootkatone 1790 1806 C15H22O 4674–50-4 / / / 5.7
48 17.245 (Z)-Isovalencenal 1800 1812 C15H22O 137695–20-6 0.09 1.75 / /
Total (%) 91.89 92.78 95.23 83.32
Monoterpene hydrocarbons 0.12 0.24 0.06 1.83
Oxygenated monoterpenes 3.79 2.45 0.63 1.01
Total monoterpenoids 3.91 2.69 0.69 2.84
Sesquiterpene hydrocarbons 37.81 67.86 81.96 59.30
Oxygenated sesquiterpenes 50.17 22.23 12.58 21.18
Total sesquiterpenoids 87.98 90.09 94.54 80.48
RRIa, Relative retention index calculated against homologous series of alkanes (C8–C40) on HB-5MS capillary column.
RRIL, Relative retention index reported in the literature about GC–MS analysis.by HB-5MS capillary column.
8 B.-X. Xue et al.maaliene, valencene, patchouli alcohol, ledene oxide-(II), (-)-
aristolene, and maaliol among the four EONJs. This phe-
nomenon illustrated that the composition of EONJ varies
greatly in type and content depending on the origin. The chem-
ical profile of EONJ is susceptible to those changes in growing,
collecting, storage, and processing conditions, prompting us to
conduct an urgent future study on the global quality control of
EONJs and their processed products.
3.2. Vasodilatory activity induced in mice aorta rings by four
EONJs
Aortic rings were contracted with U46619 (10-8 mol/L), and
vasodilator responses were measured with four EONJs added
at different final concentrations (50, 150, 300, 500, and
750 lg/mL). As shown in Table 2 and Fig. 3, the vasoconstric-
tion caused by U46619 could be significantly relaxed after the
treatment of four EONJs compared to the Veh group. NJ-A,
NJ-B, NJ-C, and NJ-D exhibited similar significant vasodila-
tory activity with EC50 values of 350.1 lg/mL, 251.3 lg/mL,
210.4 lg/mL, and 292.2 lg/mL, respectively. The mean relax-
ation rates of the four EONJs at 300 lg/mL were used as the
representative vasodilatory data for further study of the chro-
matographic spectrum-vasodilatory activity relationship, aim-
ing to facilitate the preliminary discovery of vasodilatory
contribution ingredients.
3.3. Analysis of the spectrum-effect relationship by GCA and
PLSR models
In this experiment, spectrum-effect relationship models were
established between the GC–MS spectrum (viz. TIC chromato-
graphic relative contents of the forty-eight constituents) and
the vasorelaxant activities of 300 lg/mL EONJs by two means
of chemometrics (GCA and PLSR).
The GCA result was illustrated in Table S1 and Fig. 4A.
Correlation degrees (r) of the identified peaks to the vasodila-
tory efficacy were between 0.5371 and 0.8571. Specifically,
there were thirty-five components, each with a correlation
degree above 0.6, indicating that most of the components of
EONJ possess high relevance to the whole vasorelaxant effi-Fig. 2 Agglomerative hierarchical clustering analysis of tcacy of EONJ, and the vasodilatory effect of EONJ may be
the comprehensive activity of those ingredients with high rele-
vance. Herein, a correlation degree of 0.65 was taken as the
cut-off. Twenty-eight peaks with high contribution were
accordingly screened out, including P2, P5, P6, P7, P8, P10,
P12, P13, P14, P15, P16, P17, P19, P20, P21, P23, P24, P25,
P28, P30, P32, P33, P35, P37, P40, P41, P42, and P45.
Further, the PLSR result was shown in Table S2 and
Fig. 4B. Correlation coefficient (r0) of the forty-eight peaks
to the vasodilatory efficacy was in a range of -0.0680 and
0.0674 (Fig. 4B). Among them, seventeen peaks (P2, P4, P7,
P11, P12, P13, P14, P15, P16, P17, P19, P20, P21, P24, P28,
P41, and P48) showed positive correlations with the vasodila-
tory activity. In contrast, the remaining peaks exhibited nega-
tive ones. It can be speculated that these seventeen components
may exert beneficial perturbation for the vasodilatory activity
of EONJ. Among them, ten peaks (P11, P13, P14, P15, P17,
P21, P24, P28, P41, and P48) were selected due to their signif-
icant contributions in the correlation coefficients ranked by
magnitude.
Based on the above-mentioned GCA and PLSR analytical
results, eight relevant phytoconstituents, including b-elemene
(P13), b-maaliene (P14), (-)-aristolene (P15), calarene (P17),
a-patchoulene (P21), c-gurjunene (P24), d-guaiene (P28) and
patchouli alcohol (P41), were finally selected out as the key
facilitators of vasodilatory activity. Interestingly, b-maaliene
(P14), (-)-aristolene (P15), calarene (P17), and patchouli alco-
hol (P41) have been previously reported as the major active
constituents with high relative contents in EONJs (Bose
et al., 2019; Geng et al., 2011; Tanaka and Komatsu, 2008;
Wang et al., 2010), which meet, to some extent, the principle
of consistency of content-potency and are expected to be
selected as the biomarkers for quality control of EONJs.
3.4. Network pharmacology
3.4.1. Drug-like properties and toxicological parameters of the
48 EONJ ingredients
The 2D structures of the forty-eight phytochemical con-
stituents were presented in Fig. 1. The SMILES for their struc-
tures were specifically collated (Table S3) and used forhe ingredients of four EONJs based on GC–MS data.
Vasodilatory ingredients of essential oil 9
Table 2 Vasodilatory activities of the four EONJs.
Essential oil Relaxation (%) EC50 (lg/mL)
50 lg/mL 150 lg/mL 300 lg/mL 500 lg/mL 750 lg/mL
NJ-A -0.88 ± 3.14 7.36 ± 5.59 35.53 ± 2.29** 73.76 ± 3.39*** 92.32 ± 3.11*** 350.1
NJ-B 4.71 ± 1.24 23.71 ± 4.35** 52.25 ± 5.47*** 77.88 ± 4.74*** 90.11 ± 3.01*** 251.3
NJ-C 2.67 ± 2.78 22.12 ± 2.09** 61.16 ± 3.97*** 90.55 ± 5.69*** 92.82 ± 4.54*** 210.4
NJ-D 2.22 ± 0.26 19.33 ± 1.95** 46.80 ± 3.19*** 74.00 ± 3.60*** 88.05 ± 2.57*** 292.2
n = 3,**p < 0.01 vs. Veh., ***p < 0.001 vs. Veh., The vehicle group (Veh) was treated with an equal volume of DMSO.
Fig. 3 Vasodilatory activity of the four EONJs (A) The representative tracing of aortic ring relaxation for four EONJs; (B) The line
graph of vasodilatory activity on four EONJs (n = 3. ***p < 0.001 vs. Veh.).simulating their drug-like properties (RO5 and SAscore), as
listed in Table S4. The results indicated that all these forty-
eight compounds conformed to the RO5 principle. Their sim-
ulated SAscore values were all less than 6 as they may be rel-
atively easier to synthesize. Hence, these forty-eight
phytochemicals of EONJs were all selected for the subsequent
investigations.
Firstly, in view of the vital role of potential toxicological
properties or possible side effects of the major phytocon-
stituents in the field of preclinical development for herbal
medicines, toxicological parameters of the above forty-eight
constituents were calculated, as shown in Table S5. As shown
in Fig. S10, the result revealed that none of the EONJ phyto-
chemicals turned out to be hepatotoxic or cytotoxic. However,
(-)-aristolene, (E)-b-caryophyllene, calarene, bicyclos-
esquiphellandrene, spirojatamol, selin-6-en-4a-ol, a-cadinol,
and isospathulenol were figured out to be immunotoxic, 1,4-
dimethyltetralin tended to be mutagenic. At the same time,
p-cymene, methyl thymyl ether, and isothymol methyl ether
seemed to be carcinogenic.
3.4.2. Prediction and collection of the potential targets
As illustrated in Fig. 5, a total of 319 potential targets of
EONJ constituents were finally collated by integrating the
130, 77, 89, 113, and 76 predictive targets from PharmMapper,
SwissTarget Prediction, TCMID, TCMSP, and SEA data-
bases, respectively. And 1461 CVD-associated targets were
afforded by combining the 680, 65, 510, 358, and 95 targets
obtained from OMIM, TTD, GeneCards, DisGenet, andDrugbank databases, respectively. Finally, 63 common targets
were screened out through the intersection of the EONJ con-
stituents’ predictive targets and the CVD-associated targets.
3.4.3. DO enrichment analysis of the 319 predictive targets
DO functional enrichment was further conducted to evaluate
the relevance of the 319 constituents’ predictive targets to
human diseases, resulting in the enrichment of a total of 405
DO terms. As depicted in Fig. 6, the most prominent DO terms
were obesity (DOID: 9970), overnutrition (DOID: 654), nutri-
tion disease (DOID: 374), tauopathy (DOID: 680), Alzhei-
mer’s disease (DOID: 10652), coronary artery disease
(DOID: 3393), brain disease (DOID: 936), myocardial infarc-
tion (DOID: 5844), a developmental disorder of mental health
(DOID: 0060037), autism spectrum disorder (DOID:
0060041), autistic disorder (DOID:12849), arteriosclerotic car-
diovascular disease (DOID: 2348), atherosclerosis
(DOID:1936), etc. The detailed information of the top 20
DO terms enriched significantly is listed in Table S6. The
top-enriched DO terms were mainly brain diseases, cardiovas-
cular diseases, and obesity. Specifically, there were 46 targets
for coronary artery disease, 40 for myocardial infarction, 39
for arteriosclerotic cardiovascular disease, and 39 for arte-
riosclerosis. As is well known, obesity and overnutrition are
the major risk factors for various metabolic disorders and car-
diovascular diseases (Lovren et al., 2015). This, to some extent,
was consistent with those reported pharmacological activities
of N. jatamansi (Bhat and Malik, 2020; Saroya and Singh,
2018), as summarized in our previously published review
10 B.-X. Xue et al.
Fig. 4 The GCA and PLSR plots of the relationship between the GC–MS spectrum and the vasodilatory activities of EONJs (A) Grey
correlation degree plot of each constituent to the vasodilatory efficacy (GCA); (B) Regression coefficient plot of each constituent to the
vasodilatory efficacy (PLSR).(Wang et al., 2021) that N. jatamansi has been prescribed in
ethnic medical systems of different countries mainly for neuro-
logical, digestive, cardiovascular, and epidermal disorders.
3.4.4. Screening of the key targets
As shown in Fig. 7A–B, the PPI network was constructed for
the 319 predictive targets of EONJ, among which the top 15
core targets were ALB, MAPK3, IL1B, SRC, EGFR, ESR1,
PPARG, CASP3, HIF1A, PTGS2, MAPK1, MAPK14,
PPARA, NOS3, and SLC6A4. Similarly, the PPI network of
the 63 common targets between EONJ and CVD was estab-
lished with 63 nodes and 361 edges, as presented in Fig. 7C–
D, while the top 10 core targets were ALB, IL1B, PPARG,
MAPK3, NOS3, CASP3, EGFR, PPARA, PTGS2, and
HIF1A. It is worth mentioning that the numbers in red, shown
in the bar chart of Fig. 7D, represented the degree ranking of
the core targets in the PPI network of the 319 EONJ’s predic-
tive targets. Combining the analysis results of the two PPI net-
works mentioned above, ten key targets were screened out,
including ALB, IL1B, PPARG, MAPK3, CASP3, EGFR,
PTGS2, NOS3, PPARA, and HIF1A.
Some identified cardiovascular diseases, such as hyperlipi-
demia, hypertension, and diabetes, could increase the produc-
tion of ROS and decrease the generation of endothelial NO.
Under physiological conditions, NOS3, one of the endothelial
nitric oxide synthases (eNOS), can synthesize NO, an essentialvasoprotective factor of the endothelium. NOS3 plays a vital
role in the treatment of CVD (Forstermann et al., 2017).
PTGS2, as a cyclooxygenase, was proven to be a core gene
in human coronary atherosclerosis. This may be attributed
to the fact that cyclooxygenase affects the production of pros-
taglandins in response to increased oxidative stress and thereby
affects vascular tone (Zhao et al., 2021; Zhou et al., 2021).
Thus, NOS3 and PTGS2 were selected as the crucial targets
because of their high relevance to hypertension.
By the way, as shown in Fig. 7E, the major constituents of
EONJ, including aristolone (19 targets), b-patchoulene (18 tar-
gets), isospathulenol (15 targets), valencene (15 targets), calar-
ene (15 targets), b-maaliene (15 targets), nootkatone (14
targets), patchouli alcohol (13 targets), a-maaliene (13 targets),
(-)-aristolene (13 targets) and p-cymene (13 targets), occupy the
largest number of targets. Considering the high relative con-
tents, the satisfactory performance in the abovementioned
spectrum-effect relationship analyses, and the attractive
drug-like properties, the four major ingredients (b-maaliene,
(-)-aristolene, calarene, and patchouli alcohol) were considered
as the candidate active ingredients.
3.4.5. The ’EONJ-ingredients-targets-CVD’ network
Based on the above-analyzed data, an ’EONJ-ingredients-tar
gets-CVD’ network concerning 48 ingredients and their rela-
tive targets was constructed (Fig. 8), resulting in 113 nodes
Vasodilatory ingredients of essential oil 11
Fig. 5 Number of EONJ’s predictive targets and CVD-associated targets.
Fig. 6 The bubble diagram and bar chart on the top 20 significantly enriched Disease Ontology (DO) terms (A) Bubble diagram; (B) Bar
chart (DO terms with corrected p-value < 0.05 were considered significantly enriched; GeneRatio of the x-axis represents the ratio of
targets in the background terms; Bubble size represents the number of genes enriched; Bubble color represents p-value).and 539 edges. Potential active ingredients and targets of
EONJ are shown in different shapes and colors. Blue circles
represent phytochemical ingredients, and green diamonds rep-
resent common targets between EONJ and CVD. And theedge represents the correlation among EONJ, active ingredi-
ents, targets, and CVD. Some of the target nodes, including
ALB, CYP19A1, AR, PPARA, ESR1, ITGAL, MAPK1,
and ESR2, appeared larger due to their larger degree values,
12 B.-X. Xue et al.
Fig. 7 The screening of core targets and bioactive phytochemicals. (A) The PPI network of 319 targets associated with 48
phytochemicals of EONJ; (B) The top 20 targets ranked by degree value were identified as the core targets related to EONJ; (C) The PPI
network of 63 common targets between EONJ and CVD; (D) The top 25 targets ranked by degree value were classified as the core targets
related to EONJ and CVD; (E) The vertical drop line diagram on 48 ingredients of EONJ and the numbers of their targets associated with
CVD.
Vasodilatory ingredients of essential oil 13
Fig. 8 ’EONJ-ingredients-targets-CVD’ network.suggesting that these targets are more likely to be targeted by
most of the EONJ ingredients.
3.4.6. GO enrichment and KEGG pathway analysis of the 63
common targets
GO enrichment and KEGG pathway analysis were further
conducted for the 63 common targets. GO enrichment revealed
the existence of 92 GO terms related to biological processes
(BP) with 136 GO terms connections (Fig. 9), demonstrating
that the main biological functions enriched include the positive
regulation of vasoconstriction, nuclear receptor activity, regu-
lation of cardiac muscle tissue growth, regulation of blood ves-
sel endothelial cell migration, positive regulation of lipid
biosynthetic process, positive regulation of the fatty acid meta-
bolic process, positive regulation of prostaglandin biosynthetic
process, response to lipopolysaccharide, regulation of MAP
kinase activity, regulation of protein secretion, regulation of
biomineral tissue development and the diterpenoid metabolic
process, as well as some other biological processes.
As displayed in Fig. 10A and Table S7, the KEGG pathway
analysis revealed that the main pathways involved were chem-
ical carcinogenesis-receptor activation, serotonergic synapse,
lipid and atherosclerosis, arachidonic acid metabolism, toxo-
plasmosis, etc. On the other hand, the 63 common candidate
targets were further imported into the Clue GO plugin to carry
out KEGG pathway analysis. As shown in Fig. 10B, the main
pathways involved could be screened out, including chemical
carcinogenesis, lipid and atherosclerosis, serotonergic synapse,
PPAR signaling pathway, regulation of lipolysis in adipocytes,
fat digestion and absorption, and ovarian steroidogenesis. The
results showed us that the chemical carcinogenesis, lipid and
atherosclerosis, and serotonergic synapse pathways might play
vital roles in the treatment of EONJ against CVD. EONJ may
exert its effects through multiple signaling pathways. The net-
work diagram of co-targets involved in the top 20 KEGG
pathways was presented in Fig. 11.3.5. Vasodilatory activities of four available major EONJ
constituents
Four available major EONJ constituents, including valerena-
4,7(11)-diene, calarene, b-maaliene, and patchouli alcohol,
were furtherly subjected to the abovementioned vascular tone
test to evaluate their vasodilatory activities. As shown in
Fig. 12 and Table 3, the result turned out that patchouli alco-
hol exhibited excellent vasodilatory activity with an EC50 value
of 51.75 lg/mL. In contrast, b-maaliene possessed weak
vasodilatory activity at 180 lg/mL and 380 lg/mL of high con-
centration. And other two constituents (calarene and valerena-
4,7(11)-diene) could not significantly relax the vasoconstriction
caused by U46619 compared to the Veh group.
4. Discussion
This work employed two spectrum-effect relationship models
and a network pharmacological approach to identify the effec-
tive constituents in EONJ for combating CVD. The ultimate
aim was to determine both the multi-target therapeutic poten-
tial and the molecular mechanisms involved. The screening
process led to the identification of two significant bioactive
compounds, patchouli alcohol, and b-maaliene, as well as
two crucial targets, NOS3 and PTGS2, that make EONJ a
potential treatment for CVD.
According to previous studies, patchouli alcohol, a calcium
antagonist, has been shown to have significant vasodilatory
effects via an endothelial non-dependent pathway (Hu et al.,
2018), which corroborates the findings of this study. However,
there is no evidence to suggest that b-maaliene, (-)-aristolene,
or calarene have therapeutic benefits against hypertension.
Instead, these compounds have significant sedative effects
(Takemoto et al., 2008; Takemoto et al., 2009), which are com-
monly prescribed for prehypertension, stage I hypertension,
and stage II hypertension (Parr et al., 2012). Thus, it is possible
14 B.-X. Xue et al.
Fig. 9 The network plot shows biological processes of GO enrichment analysis (Each node indicates the enriched GO function term, the
more significant the p-value is, the larger the node is; different colors represent different functional groups, the bold font is treated as the
functional representation, and the line between nodes means the correlation among functions, and the diamond arrow refers to the
regulatory effect, the triangular arrow shows the positive regulation).that b-maaliene, (-)-aristolene, calarene, and valerena-4,7(11)-
diene could aid in the prevention and treatment of hyperten-
sion. Interestingly, b-maaliene, (-)-aristolene, and calarene
share an aristolane-like sesquiterpenoid scaffold, suggesting
that they may be biosynthetic precursors or natural substrates
before natural oxidation. This is important because (-)-
aristolone and kanshone H, which have been found to have
significant vasodilatory effects in alleviating hypertension,
are derived from such oxidation (Fang et al., 2022). Therefore,
it is worth exploring whether exposing EONJ to air for a cer-
tain period of time would lower blood pressure or enhance its
vasodilatory effects. Further research is needed to determine
whether the major ingredients in EONJ have a synergistic
effect in relaxing blood vessels.
To further analyze the relationship between the relative
contents of normalized common peak areas and the corre-
sponding vasodilatory activity data, Pearson correlation coef-
ficients were obtained using Origin 2021 software. As shown in
Figure S11, red points indicate a positive correlation, while
blue points indicate a negative correlation. P41 represents
patchouli alcohol, and RR represents vasodilation rate. The
result of the Pearson correlation analysis was consistent with
the findings of the partial least squares regression (Fig. 4B).
Several peaks (P11, P13, P14, P15, P17, P21, P24, P28, P41)were found to have significant positive correlations with
EONJ’s vasodilatory activity, suggesting that these con-
stituents may have a beneficial impact on vasodilation. Fur-
thermore, the Pearson correlation analysis revealed
correlations among the 48 ingredients, indicating that there
might be a synergistic effect among the volatile ingredients in
EONJ.
Our investigation into the potential benefits of N. jatamansi
led to the discovery that the herb’s compounds may have the
ability to regulate the activities of two enzymes: endothelial
nitric oxide synthase (NOS3 or eNOS) and cyclooxygenase 2
(PTSG2 or COX-2), as revealed by our network pharmacology
analysis. NOS3 produces nitric oxide, which aids in the relax-
ation of vascular smooth muscles and improves blood flow
through a cGMP-mediated signal transduction pathway
(Forstermann et al., 2017). PTGS2 is an inducible enzyme that
produces prostanoids, enhancing vasodilation and preventing
blood clots (Zhao et al., 2021; Zhou et al., 2021). Therefore,
we propose that the herb’s compounds could promote or
induce the activity of these two enzymes, leading to enhanced
vasodilation.
The KEGG analysis conducted in our study indicated sig-
nificant enrichment of the lipid and atherosclerosis signaling
pathways (Fig. S12). The PI3K and AKT proteins are essential
Vasodilatory ingredients of essential oil 15
Fig. 10 KEGG pathway enrichment analysis (A) The bar chart on the top 20 significantly enriched KEGG pathway terms by RStudio;
(B) The network diagram of KEGG pathway enrichment analysis by clueGO. (Each node indicates the enriched KEGG pathway term, the
more significant the p-value is, the larger the node is; different colors represent different pathway groups, the bold font is treated as the
pathway representation, and the line between nodes means the correlation among pathways).
16 B.-X. Xue et al.
Fig. 11 The network diagram of ’Target-Pathway’.
Fig. 12 Dose-vasorelaxant activity graph of b-maaliene, calar-
ene, valerena-4,7(11)-diene and patchouli alcohol (n = 3.
*p < 0.05, ***p < 0.001 vs. Veh.).regulators in these pathways and have been identified as
upstream regulators. Further, the PPI analysis revealed that
all ten selected key targets interact with PI3K and AKT
(Fig. S13), indicating that EONJ could potentially treat
CVD by modulating the PI3K-AKT signaling pathway.
AKT, also known as PKB, plays a crucial role in improving
contractile function in the failing heart, and its downregulation
is associated with cardiomyocyte damage and apoptosis (Fujio
et al., 2000; Shiojima et al., 2012; Wu et al., 2000). Severalstudies have highlighted the significance of the PI3K-AKT sig-
naling pathway in the treatment of CVD (Fujio et al., 2000; Li
et al., 2020; Maiwulanjiang et al., 2014; Shiojima et al., 2012;
Wu et al., 2000). EONJ has been shown to prevent oxidative
stress-induced cell death by decreasing the production of intra-
cellular ROS in cardiac myocytes, inducing antioxidant
enzymes, and activating the phosphorylation of AKT
(Maiwulanjiang et al., 2014). Additionally, high doses of
EONJ could reduce myocardial infarction after ischemia–
reperfusion and lower the levels of CK-MB and cTnT. This
study’s differential expression of the AKT pathway and phos-
phorylated proteins suggested that EONJ could protect the
heart and reduce myocardial injury by regulating the PI3K-
AKT pathway (Yang et al., 2018). Endothelium-derived nitric
oxide (NO) is a key vasodilator, while Ang II is one of the
endogenous vasoconstrictors. An increase in NO and a
decrease in Ang II can induce a vasodilation (Zhang et al.,
2019a). The effect of EONJ on vasodilation may also be medi-
ated by the phosphorylation of endothelial nitric oxide syn-
thase (eNOS) via AKT phosphorylation (Maiwulanjiang
et al., 2015).
5. Conclusion
This study provides a comprehensive analysis of the vasodilatory activ-
ity of the essential oil of N. jatamansi, its marker constituents, and the
underlying molecular mechanisms. By integrating GC–MS spectrum-
Vasodilatory ingredients of essential oil 17
Table 3 Vasodilatory activities of b-maaliene, calarene, valerena-4,7(11)-diene and patchouli alcohol.
Compound Relaxation (%) EC50 (lg/mL)
5 lg/mL 30 lg/mL 80 lg/mL 180 lg/mL 380 lg/mL
Patchouli alcohol 1.79 ± 1.37 22.91 ± 3.86** 71.71 ± 5.45*** 98.20 ± 0.40*** – 51.75
Valerena-4,7(11)-diene 0.89 ± 1.51 2.34 ± 2.58 3.99 ± 2.73 4.01 ± 5.63 6.11 ± 4.06 –
Calarene 0.99 ± 0.74 4.91 ± 0.86 7.61 ± 0.71 6.94 ± 2.31 9.77 ± 1.61 –
b-Maaliene 0.19 ± 1.64 3.32 ± 3.16 10.24 ± 2.01 16.52 ± 3.78* 29.08 ± 5.58* –
n = 3, *p < 0.05 vs. Veh, **p < 0.01 vs. Veh, ***p < 0.001 vs. Veh.vasodilatory activity relationship, network pharmacology, and vascu-
lar tone tests, we identified two bioactive compounds (patchouli alco-
hol and b-maaliene) and two critical biological targets (NOS3 and
PTGS2) for N. jatamansi in the treatment of cardiovascular diseases.
Our findings suggest that N. jatamansi exerts its vasodilatory effect
mainly through lipid and atherosclerosis signaling as well as the
PI3K-AKT pathway. This work provides valuable information for
the quality evaluation, identification of bioactive markers, and poten-
tial industrial applications of N. jatamansi for cardiovascular and
related disorders.
Declaration of Competing Interest
The authors declare that they have no known competing
financial interests or personal relationships that could have
appeared to influence the work reported in this paper.
Acknowledgments
The authors would like to express their gratitude to the Tianjin
Committee of Science and Technology of China (No.
21ZYJDJC00080 and 22ZYJDSS00040), the National Key
Research and Development Program of China
(2019YFC1708803), the Innovation Team and Talents Culti-
vation Program of National Administration of Traditional
Chinese Medicine (No: ZYYCXTD-D-202002, ZYYCXTD-
C-202203), and the National Key Research and Development
Project of China (No. 2018YFC1707905 and
2018YFC1707403) for supporting this research.
Ethical approval
No studies involving human participants were conducted by
any of the authors of this article.
Author contributions
The conception and design of the research were performed by
H.-H. Wu and Q.-L. Wang. B.-X. Xue and S.-X. Liu con-
ducted the experiments and data analysis collaboratively. B.-
X. Xue was responsible for drafting the manuscript with criti-
cal input from H.-H. Wu and Q.-L. Wang. P.-K. Oduro con-
tributed to the analysis and visualization of the network
pharmacology data. N.-A. M.-Gyimah and L.-H. Zhang
assisted with the manuscript revision.Appendix A. Supplementary material
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.arabjc.2023.104911.
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