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BIOMOLECULAR AND HEALTH SCIENCE JOURNAL 2019 OCTOBER, VOL 02 (02)
ORIGINAL ARTICLE
68
Anti-eczema Mechanism of Action of Nigella sativa
for Atopic Dermatitis: Computer-aided Prediction
and Pathway Analysis Based on Protein-chemical
Interaction Networks
Meidyta Sinantryana Widyaswari1, Iis Noventi2, Herdiantri Sufriyana3,4*
1Department of of Dermatology and Venereology, College of Medicine, University of Nahdlatul Ulama Surabaya, Indonesia
2Department of Medical-Surgical Nursing, College of Nursing and Midwifery, University of Nahdlatul Ulama Surabaya,
Indonesia
3Department of Medical Physiology, College of Medicine, University of Nahdlatul Ulama Surabaya, Surabaya, Indonesia
4Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University,
Taipei, Taiwan
A R T I C L E I N F O A B S T R A C T
Article history:
Received 26 August 2019
Received in revised form 14
October 2019
Accepted 21 October 2019
Keywords:
Nigella sativa,
atopic dermatitis,
protein-chemical interaction
networks,
PASS.
Introduction: Black cumin (Nigella sativa) is widely used to treat various diseases. It is also
believed to relief skin conditions accompanied by itching symptom, such as atopic dermatitis
(AD) or eczema. However, the anti-eczema mechanism of action is still unclear. The aims of this
syudy was to identify anti-eczema mechanism of action of N. sativa for AD using computer aided
prediction and pathway analysis based on protein-chemical networks.
Methods: We utilized dataset consisting chemical compounds of N. sativa from KNApSAcK. It
is a comprehensive species-metabolite relationship database. Using canonical SMILES strings that
encode molecular structures of each compound, we predicted the probabilities of activity (Pa) for
anti-eczema eect based on PASS algorithms. The compounds with Pa >0.7 were included for
pathway analysis based on protein-chemical interaction networks in STITCH database. We selected
interactomes built by experimental data, gene co-expression, closest gene position, fusion, co-
occurence, computational prediction, and other secondary data.
Results: Thirty-ve active compounds of N. sativa have been utilized and 19 of them have potential
anti-eczema eects. Oleic acid and lauric acid were predicted with Pa-value of 0.947 and 0.920
for anti-eczema eect, respectively. However, only lauric acid was conrmed having a plausible
mechanism of action via LY96-TLR4- PIK3R1 pathway for lipopolysaccharide receptor activity
(false discovery rate [FDR] = 0.0243) and low-density lipoprotein particle receptor binding (FDR
= 0.0118).
Conclusion: Lauric acid in N. sativa has potential antieczema eect to prevent relaps in AD patients
by controlling opportunistic bacterial infection that aggravates itching symptom in this condition.
© 2019 Biomolecular and Health Science Journal. All rights reserved
Introduction
Atopic dermatitis (AD), or eczema, is a chronic,
recurrent skin inammation accompanied by severe
itching and dry skin on certain area of skin predilection.
It commonly occurs at early childhood and relapse
at adulthood. Ten percent of the cases may last up to
adolescence or adulthood. Prevalence of AD varied among
countries, which have increased worldwide, especially in
industrialized countries. The prevalence was 10-20% in
children, while it was 1-3% in adults. This multifactorial
disease has unclear etiopathogenesis, but it generally
involves complex interactions of skin barrier function,
immune system disorders, genetics, and other factors,
including environment, diet, infection, and stress.1
Mismanagement of AD often occurs because of its
similarity to other chronic skin diseases. This disease
commonly occurs in primary care.2 As a chronic skin
inammatory disease, a primary choice of long-term
treatment is topical drugs, particularly those with a steroid
compound. Its use may not be acceptable for the patients
who believe this as a chemical substance which is harmful
for long-term use. In several cultures, particularly in
Indonesia, it is widely accepted that medicinal plants are
*Correspondence: herdiantrisufriyana@unusa.ac.id
© 2019 Biomolecular and Health Science Journal. All rights reserved
Available at https://e-journal.unair.ac.id/BHSJ
BIOMOLECULAR AND HEALTH SCIENCE JOURNAL 2019 OCTOBER, VOL 02 (02) 69
safe for long-term use. Black cumin is one of the medicinal
plants. It may improve quality of life of the patients and
reduce severity of the disease.3
Black cumin (Nigella sativa) is a plant that grows up
to 20-90 cm in height, nely-separated leaves, and owers
with multiple colors. These are white, yellow, pink, pale
blue and pale purple. Each ower consists 5-10 crowns.
The large fruits contain several seeds. This plant is one of
the most common medicinal plants that were investigated.
It is because of its wide spectrum of applications in
traditional medicine from many countries worldwide. The
seeds are commonly used to treat various diseases. Most
of the therapeutic eects are contributed by thymoquinone
which is the main active ingredient in the seed’s oil.
Several studies have demonstrated that this plant could be
used as an antidiabetic, anticancer, immunomodulator, pain
reliever, antimicrobial, antioxidant, hepatoprotector, and
others.4 However, an active substance of N. sativa, which
has antieczema eect, is still unclear.
To perform a preclinical research eciently, we need to
conduct preliminary study identifying the active substance
and its mechanism action. We utilized dataset of active
compounds from a database of plants. A classication
algorithm can be used to predict the antieczema eect
based on a structure-activity relationship models derive
from other chemical substances. The mechanism of action
can be predicted by protein-chemical interactomes using
over representation analysis. This study aims to identify
active compounds of N. sativa, which have antieczema
eect, and its mechanism of action for AD using computer-
aided prediction and pathway analysis based on protein-
chemical interaction networks.
Methods
Study design and data source
We applied a descriptive exploratory study design. This
study utilized dataset from KNApSAcK database (http://
kanaya.naist.jp/KNApSAcK/) at April 13th, 2018. Active
compounds’ data of N. sativa were retrieved without
considering which parts of plant had been taken to identify
these compounds. We only selected those which have
available data in protein-chemical interaction database. In
addition, these compounds should have canonical structure
information as inferred from simplied molecular-input
line-entry system (SMILES) in PubChem database (https://
pubchem.ncbi.nlm.nih.gov/).
Prediction of antieczema eect
Selected compounds were predicted for their eects
by quantitative structure-activity relationship (QSAR)
modeling. We used prediction of activity spectra for
substances (PASS) web application (http://pharmaexpert.
ru/PASSonline) to apply its classication algorithm based
on the QSAR model. This web application classied the
selected compounds into several classication systems.
We only looked for those that were classied by its
pharmacological eect, particularly with probability of
activity (Pa) >0.7 for antieczema.
Pathway analysis to identify antieczema mechanism of
action
To identify mechanisms of action for these compounds,
we used search tool for interacting chemicals (STITCH)
platform (http://stitch.embl.de). By over representation
analysis (ORA), this platform annotated the compounds
with existing protein-chemical interactomes in STITCH
database. We congured ORA to analyze the compounds
based on experimental data, gene co-expression, closest
gene position, gene fusion, co-occurence, computational
prediction, and other secondary data. We congured
interaction condence to be 0.400 for the minimum value.
Other parameter conguration was maximum 10 interactors
for each layer up to the second interaction layer.
The platform also retrieved information gene ontology
(GO) database for functional enrichment analysis. The
information consisted of biological process, molecular
function, and cellular component, which are involved in
the interactomes. Individual GO information was used to
identify which one was related to antieczema based on
previous studies.
We also determined a gene set consisted of genes which
involved in the antieczema interactomes. Pathway analysis
was conducted by including this gene set with that related
to AD based on previous studies. Eventually, this analysis
identied antieczema mechanism of action in silico.
Results
Retrieved active compounds of Nigella sativa
Active compounds N. sativa were retrieved from
KNApSAcK database (Table 1). Thirty-ve active
compounds were obtained. Although this database did not
provide which parts of the plant that the compounds were
identied, previous studies showed that thymoquinone was
mostly identied in seed’s oil.4, 5
The active compounds with antieczema eect
The active compounds have been selected based on the
criteria (Table 2). Nineteen of 35 active compounds have
predicted antieczema eect with Pa-value of >0.7 and
available in protein-chemical interactomes of STITCH
database. Six compounds on STITCH database were
identical with those in PubChem database. Canonical
SMILES codes of these compounds from PubChem were
used as features to predict the antieczema eect. Oleic acid
was a compound of N. sativa that had highest biological
activity is (Pa-value=0.947). However, pathway analysis
showed that lauric acid had relevant antieczema eect with
Pa-value which was closest to that of oleic acid (Pa=0.920).
Predicted antieczema mechanism of action
Pathway analysis was conducted to identify antieczema
mechanism of action (Figure 1). Several interactomes
over-represented oleic acid and lauric acid. The
interactomes included genes/transcripts/proteins coded by
the gene name. These were FAAH, PMP2, RBP1, APOE,
APOB, LDLR, PCSK9, GLTP, ALB, UBC, MTRNR2L2,
LTB4R2, GPR68, LY96, TLR4 and FCGRT. In addition,
the interactomes also included other chemical substances.
Therefore, two of active compounds contained by N.
sativa were active substances that had been known having
interactions with several proteins
To conrm which interactomes were related to the
anti-eczema mechanism of action, functional enrichment
analysis was carried out (Table 3). Majority of the GO
information were related to cholesterol metabolism and
interactions with lipopolysaccharides (LPS). Interaction
BIOMOLECULAR AND HEALTH SCIENCE JOURNAL 2019 OCTOBER, VOL 02 (02) 70
with LPS was considered having a role in pathophysiology
of AD because of LY96 and TLR4. Increasing transepidermal
water loss, allergic sensitization, and thickness of epidermis
were demonstrated on AD lesion of a mice model with TLR4
deciency that had been exposed to allergen derived from
Aspergillus fumigatus for 3 weeks.7 Oleic acid may also play a
role in determining occurrence of AD via cholesterol metabolic
pathway. Hyperlipidemia was found as comorbidity (10.35%)
in 30,354 patients with AD compared to those without AD.8
A gene set related to AD were determined based on a
previous study.9 This consisted of LOR, KRT17, SPRR2D,
SPRR1A, SPRR1B, IVL, CCR7, CCL19, PIK3R1, and
STAT1. These gene names along with LY96, TLR4, and lauric
acid were analyzed by STITCH platform (Figure 2). Lauric
acid, LY96, and TLR4 were over-represented by interactomes
that were annotated as LPS detection for the biological
process, LPS receptor activity for the molecular function, and
LPS receptor complex for the cellular components. This was
considered having a greater role in antieczema mechanism of
action compared to other GO information related to cholesterol
metabolism. The LY96 and TLR4 had interactions with
MYD88 that connected them to AD via PIK3R1. The score
was 0.576 for interaction of MYD88 and the gene set.
Table 1. Active compounds of Nigella sativa retrieved from KNApSAcK database.
Active compounds Molecular formula Molecular weight (kD)
Thymol C10H14O 150.1044651
Carvacrol C10H14O 150.1044651
alpha-Thujene C10H16 136.1252005
alpha-Pinene C10H16 136.1252005
beta-Pinene C10H16 136.1252005
Myrcene C10H16 136.1252005
Lauric acid C12H24O2 200.17763
Oleic acid C18H34O2 282.2558803
Anisaldehyde C8H8O2 136.0524295
Apiol C12H14O4 222.0892089
Estragol C10H12O 148.088815
Myristicin C11H12O3 192.0786443
(+)-R-Citronellol C10H20O 156.1514153
p-Cymene C10H14 134.1095505
(+)-Fenchone C10H16O 152.1201151
alpha-Phellandrene C10H16 136.1252005
gamma-Terpinene C10H16 136.1252005
Longifolene C15H24 204.1878008
(Z,Z,Z)-Octadeca-9,12,15-trienoic acid C18H30O2 278.2245802
Thymoquinone C10H12O2 164.0837296
Kaempferol 3-glucosyl-(1->2)-galactosyl-(1->2)-
glucoside C33H40O21 772.2062083
Quercetin 3-glucosyl-(1->2)-galactosyl-(1->2)-glucoside C33H40O22 788.201123
Quercetin 3-(6''''-feruloylglucosyl)-(1->2)-
galactosyl-(1->2)-glucoside C43H48O25 964.2484671
Fuzitine C20H24NO4 342.1705333
Nigellicine C13H14N2O3 246.1004423
Nigellidine C18H18N2O2 294.1368278
Nigellimine C12H13NO2 203.0946287
4-Terpineol C10H18O 154.1357652
4(10)-Thujene C10H16 136.1252005
Nigeglanine C12H14N2O 202.1106131
Nonane C9H20 128.1565006
Carvone C10H14O2 166.0993797
alpha-Longipinene C15H24 204.1878008
Dihydrocarvone C10H16O 152.1201151
Nigellidine 4-O-sulte C18H18N2O5S 374.0936424
BIOMOLECULAR AND HEALTH SCIENCE JOURNAL 2019 OCTOBER, VOL 02 (02) 71
Table 2. Active compounds of Nigella sativa with antieczema aect.*
Active compounds Pa-value**
Oleic acid 0.947
Lauric acid 0.920
beta-Pinene (pinene***) 0.902
Nonane 0.895
Dihydrocarvone (trans-dihydrocarvone***) 0.889
p-Cymene 0.884
Estragol (estragole***) 0.881
Longifolene 0.878
alpha-Thujene (thujene***) 0.866
gamma-Terpinene 0.854
4-Terpineol (terpinen-4-ol atau 4-carvomenthenol***) 0.838
Myrcene 0.836
Carvacrol 0.811
Myristicin 0.795
Apiol (apiole***) 0.794
Thymol 0.788
alpha-Phellandrene 0.772
Thymoquinone 0.762
Carvone 0.737
* Available in STITCH database, predicted as antieczema, and Pa-value >0.7
** Probability of activity
*** Synonyms in STITCH and PubChem
Figure 1. Interactomes of protein and chemical substances including active compounds of Nigella sativa. Color of lines
denoted specic types of action, which were activation (green), binding (blue), phenotype (cyan), reaction (black), in-
hibition (red), catalysis (purple), posttranslational modication (pink), and transcriptional regulation (yellow). End
shape of arrow denoted action eects, which were positive (triangle), negative (vertical line), and unspecied (round).
BIOMOLECULAR AND HEALTH SCIENCE JOURNAL 2019 OCTOBER, VOL 02 (02) 72
Table 3. Gene ontology (GO) from interactomes involving oleic acid and lauric acid based on functional enrichment analysis.
Gene ontology Pathway identier Pathway description Count in gene set False discovery
rate
Biological
process
GO:0042159 Lipoprotein catabolic process 3 0.00491
GO:0032805
Positive regulation of low density
lipoprotein particle receptor cata-
bolic process
20.0176
GO:0042632 Cholesterol homeostasis 4 0.0176
GO:0001523 Retinoid metabolic process 4 0.0217
GO:0034381 Plasma lipoprotein particle clearance 3 0.0217
GO:0032497 Detection of lipopolysaccharide 2 0.0295
GO:0007603 Phototransduction, visible light 4 0.0321
GO:0008203 Cholesterol metabolic process 4 0.041
GO:0006869 Lipid transport 50.0462
GO:0008202 Steroid metabolic process 50.0462
Molecular
function
GO:0050750 Low-density lipoprotein particle re-
ceptor binding 3 0.0118
GO:0071813 Lipoprotein particle binding 3 0.013
GO:0070326 Very-low-density lipoprotein particle
receptor binding 20.0174
GO:0001875 Lipopolysaccharide receptor activity 2 0.242
Cellular
component
GO:0034362 Low-density lipoprotein particle 3 0.0029
GO:1990666 PCSKg-LDLR complex 2 0.0029
GO:0046696 Lipopolysaccharide receptor complex 2 0.00579
GO:0034363 Intermediate-density lipoprotein par-
ticle 2 0.013
Figure 2 Interactomes of protein and chemical substances including ten gene names involved in AD along with LY96, TLR4
and lauric acid. Color of lines denoted specic types of action, which were activation (green), binding (blue), phenotype
(cyan), reaction (black), inhibition (red), catalysis (purple), posttranslational modication (pink), transcriptional regulation
(yellow), and unspecied (grey). End shape of arrow denoted action eects, which were positive (triangle), negative (vertical
line), and unspecied (round).
BIOMOLECULAR AND HEALTH SCIENCE JOURNAL 2019 OCTOBER, VOL 02 (02) 73
Discussion
Lauric acid was predicted having antieczema eect
for AD with a plausible mechanism of action. It was
indicated by interaction of lauric acid with LY96 and
TLR4. Concurrently, both gene names were connected
to PIK3R1 which was a part of interactomes involving
proteins related to AD. The GO information of these
interactomes indicated that LPS was involved in
antieczema mechanism of action for lauric acid as an
active compound of N. sativa.
Topical administration of N. sativa seed’s oil showed
antieczema eect in patients who applied this oil twice
a day for 28 days. The nineteen patients demonstrated
lower hand eczema severity index (HECSI) compared to
those eighteen patients given eucerin as negative controls
(P=0.003). Likewise, the dermatology life quality index
(DLQI) was also lower (P<0.0001). There was no
dierence with patients given betamethasone as positive
controls for HECSI (P=0.99) or DLQI (P=0.38).4
Conversely, there was no dierence in ecacy for
twenty patients who applied the formulation of N. sativa
oil on one hand and placebo on the other hand. The
ecacy was no dierence in terms of severity, pruritis,
transepidermal water loss, and skin hydration.6 However,
this might occur due to dierence in composition of active
compounds. It might also occur because of dierent
composition of inactive compounds in the formulation.
The LY96 protein that binds to TLR4 as its receptor
was connected to interactomes related to AD via MYD88.
The interaction score of MYD88 with the interactomes
was moderately signicance. Clinical manifestations
of patients with MYD88 gene deciency indicated
inammation of the skin. The MYD88 protein is an
adapter protein receiving signals from extracellular to
intracellular proteins, particularly by binding CBLB in
this study, a negative regulator of T-cell activation.
In turn, the CBLB protein was indicated by the
pathway as a catalyzer for PIK3R1 as the downstream
protein related to AD, which encodes a regulatory subunit
of phosphoinositide 3-kinase.12 The role of PIK3R1 in AD
were investigated by few studies,13, 14 indicating a potential
novel target for AD treatment. This protein extensively
interacted with other proteins, such as STAT1 that acts
as a transcription factor involved in chemokine signaling
pathway, including that in peripheral mononuclear cells of
AD patients.15
Another protein, which is KRT17, was also connected
with the extensive interactions involving PIK3R1. The
gene of KRT17 was upregulated along with IVL in canine
AD.16 The IVL expression demonstrated reduction of
genes expression for proteins that maintain epidermal
barrier, which were EGFR, e-cadherin, occludin,
envoplakin, and periplakin.17, 18
The inammation of skin involving MYD88 was also
due to infection by two opportunistic bacteria which were
Staphilococcus aureus and Pseudomonas aeruginosa.10
Atopic dermatitis is a common skin disease involving
microbial infection. This was shown by dominance of S.
aureus, including those that were resistant to methicillin,
like many Streptococcus sp. and P. aeruginosa.11 The
antieczema eect may occur by increasing activity
of these proteins to control opportunistic bacterial
population by detection of LPS and activation of innate
immunity. Inammation allegedly persists for controlling
opportunistic bacteria; thus, the severity of AD is reduced.
Conversely, if opportunistic bacteria are out of control,
more severe inammatory response may occur. Eventually,
this may prevent relapse occurrence in AD patients.
Our study presents prediction of pharmacological
eect and pathway analysis that showed lauric acid
contains in N. sativa has a potential role as antieczema
to relieve inammation in AD. Computer-aided prediction
and the pathway analysis may save time for ligand-based
drug discovery. This in silico analysis can be a starting
point to conduct in vitro and in vivo testing.
However, computational prediction may possess a
risk of bias. This depends on available datasets that were
utilized to train the prediction model. Features of the
chemical structure in active compounds of N. sativa may
be unobserved in the datasets. This may increase false
discovery rate of the prediction model.
Conclusion
Lauric acid, via LY96-TLR4-PIK3R1 pathway, was found
to be potential target to prevent relapse in AD patients.
Topical administration of N. sativa seed’s oil may be more
acceptable in several cultures in Indonesia. However,
prevention of relapse still requires further investigation
to prove the ecacy to prevent AD relapse as well as
toxicity testing for chronic administration.
Further computational investigation will be an analysis of
potential binding site for lauric acid to the corresponding
proteins. Anity of the active compounds will determine
its feasibility for wet laboratory testing. This study may
help to estimate proper dosage for preclinical study.
Conict of Interest
The author stated there is no conict of interest
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