Comprehensive Network Pharmacological Analysis and In Vitro Verification Reveal the Potential Active Ingredients and Potential Mechanisms of Frankincense and Myrrh in Knee Osteoarthritis

Abstract

Background: Frankincense and myrrh (FM) are often used together to treat knee osteoarthritis (KOA). However, the underlying mechanism of its treatment of KOA remains unclear. Objective: To analyze the active components of FM through network pharmacology and in vitro experiments, and to explore its potential therapeutic mechanism in the treatment of KOA. Materials and methods: The protein mapping relationship between potential drug targets and disease targets was screened and constructed through the database. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using R software. Discovery Studio software was used to perform molecular docking. The active components of FM were identified using liquid chromatography–mass spectrometry (LC-MS). In addition, experimental verification was carried out by Cell Counting Kit-8 detection, Western blot, and immunofluorescence analysis. Results: Combining the results of network pharmacology and LC-MS, 31 active compounds and 94 target genes of FM were identified. The common genes of FM and KOA suggest that FM exerts anti-KOA effect by regulating genes such as Transcription factor Jun (JUN), Interleukin-6 (IL-6), Interleukin-1 beta (IL-1β), C-X-C motif chemokine ligand 8 (CXCL8), Transcription factor p65 (RELA), and Mitogen-activated protein kinase 1 (MAPK1). GO enrichment analysis showed that FM exerted therapeutic effects on KOA by regulating biological processes such as cell proliferation, cell migration, and apoptosis. In addition, KEGG enrichment analysis involved signaling pathways such as fluid shear stress, the TNF, PI3K-Akt, NF-κB, and MAPK. Consistently, in vivo experiments showed that FM inhibited IL-1β-induced MAPK activation and attenuated inflammation in mouse chondrocytes. Furthermore, FM inhibited IL-1β-induced phosphorylation of p65 and the process of p65 translocation from the cytoplasm into the nucleus. Conclusions: Our results provide conclusive evidence and deepen the current understanding of FM in the treatment of KOA and further support its clinical application.

Keywords

Network pharmacology inflammation MAPK NF-κB osteoarthritis LC-MS

Introduction

Knee osteoarthritis (KOA) is a universal problem worldwide and a common disease encountered in primary care settings. A survey showed that more than 200 million people worldwide suffer from osteoarthritis (OA), and the knee joint is one of the most frequent sites.¹ According to statistics, 6.5 of every 1000 people suffer from KOA.² In addition to pain, KOA also causes joint stiffness, restricted daily activities, joint swelling, joint instability, and lower limb muscle weakness, thereby reducing the patient's quality of life and causing serious social and economic burdens.³ With the extension of the life cycle, the severity of the disease and the symptoms worsen.³ The aging of the global population is becoming increasingly prominent and plans to maintain and promote the management of chronic diseases are becoming increasingly important. In the treatment of KOA, drugs are mainly related to the relief of pain symptoms, which is a main appeal for many patients with KOA.

Medicinal plants have been used in traditional herbal medicine for thousands of years as a promising alternative treatment, and the incidence of adverse events is usually equal to that of traditional medicines.³ In Ayurvedic medical systems, natural medicines have been used to treat chronic diseases for many years. In traditional Chinese medicine, Frankincense and myrrh (FM) has been used in combination in the same prescription for thousands of years, and their combination exerts a better therapeutic effect on diseases.⁴ FM generally has a stronger synergistic effect than a single drug.⁵ Deng et al summarized the frequency of use of herbal medicines for the treatment of KOA, and the results showed that the frequency of use of FM was 43.2% and 40.6%.⁶ The association rule analysis conducted by Gao et al showed that Boswellia myrrh is the most commonly used group of drugs to treat KOA.⁷

Frankincense is a resin exuded from the bark of Boswellia carterii Birdw. It is mainly produced in North Ethiopia, Somalia, and India.⁸ Frankincense contains various chemical components, mainly triterpenes, diterpenes, monoterpenes, and carbohydrates, and possesses a variety of pharmacological activities.⁹ Modern pharmacological studies have shown that frankincense extract and its monomeric compounds exert good anti-inflammatory, antitumor, and antiulcer effects and regulate glucose and lipid metabolism disorders. According to a clinical study, acetyl-11-keto-β-boswellic acid and β-boswellic acid improve the knee joint space of patients with OA, relieve knee pain, and stiffness, and reduce the serum C-reactive protein level.¹⁰ Myrrh is the dry resin of Burseraceae Commiphora myrrha Engl.⁵ The name myrrh appears in the Bible, and it was originally used in religious ceremonies and as a spice.¹¹ Over time, it has been gradually used as a disease treatment. Modern pharmacological studies have shown that myrrh possesses various pharmacological activities, such as anti-inflammatory, analgesic, and antimicrobial activities. The main chemical components include monoterpenes, sesquiterpenes, triterpenes, steroids, and lignans.¹² The analgesic effects of FM may be mediated by the regulation of TRPV1, thereby reducing the mechanical allodynia and thermal hypersensitivity caused by chronic contractile injury.¹³ Another study showed that myrrh reduces the expression of proinflammatory mediators and improves endogenous antioxidant activity, thereby improving inflammatory bowel disease in rats.¹⁴ In addition, myrrh is used as an anticancer drug in the treatment of various cancers, including glioblastoma, breast cancer, and prostate cancer.^15,16

The mechanism of action of FM in the treatment of KOA has been partially explored, but the complexity of the herbal medicine and the diversity of its targets require further elucidation of the mechanism underlying its therapeutic effect on KOA. Natural medicine treats diseases with multiple targets and multiple channels. Current basic research still focuses on the treatment of a disease with a single monomer or a natural drug compound. Thus, exploration of how complex pharmaceutical ingredients act in the complex human environment is difficult. In recent years, with the rise of network science, such as systems biology and network pharmacology, combination drugs have become a hot topic in modern drug research, and new trends in the development and use of modern drugs have been reported.¹⁷ Based on this information, this article adopts the network pharmacology research method to reveal the mysteries underlying the synergistic effects of FM on KOA and provide further evidence for the use of FM as a treatment for diseases. By exploring the molecular mechanisms and targets of FM in the treatment of KOA, new ideas are expected to guide drug research and clinical applications.

Methods

Network Pharmacology Analysis

Compound Composition, Collection, and Screening

TCMSP¹⁸ (https://tcmspw.com/tcmsp.php) and BATMAN-TCM¹⁹ (http://bionet.ncpsb.org.cn/batman-tcm/) were used to collect herbal and drug target components. We searched the chemical composition information of FM in TCMSP using the following screening conditions to obtain active drug ingredients: oral bioavailability (OB) ≥30% and drug likelihood (DL) ≥0.18. According to the BATMAN-TCM operation steps, the score cutoff of “drug-target” was set to ≥20, with an adjusted P value < 0.05.

FM and KOA-Related Target Acquisition

In our previous study, we searched and screened genes related to KOA.²⁰ Using the UniProt database (https://www.uniprot.org/UniProt/), we converted the information and names of the KOA-related targets. Venn diagrams were constructed to show the logical connections between drugs and disease targets. Mapping was carried out between the target of the active ingredient of the drug and the target of KOA, and the overlapping targets were selected as the common targets of FM for KOA.

Construction of the Protein Interaction Network and Screening of Core Genes

We imported the obtained common targets into the STRING database, analyzed the protein interaction network (PPI), and drew the PPI network diagram. The following conditions were used: multiple proteins, Homo sapiens, and a confidence level of 0.7. The TSV file was imported into Cytoscape software, and the “CytoNCA” App was used to analyze the topological properties and obtain the degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), network centrality (NC), and local average connectivity (LAC)-based method. The core target was selected based on the values of DC, BC, CC, EC, NC, and LAC. First, the median of the six indicators was calculated, and the median of the obtained results was calculated to obtain the core target. We independently screened pivotal genes using the CytoHubba App to verify the reliability of the results . CytoHubba generates the attribute ranking of nodes in the network. This research used the maximal clique centrality (MCC) topology analysis algorithm.

Construction of the Network of Active Drug Ingredients and Disease Targets

The active ingredients of herbs and the screening results of KOA targets were imported into Cytoscape software to construct a network of active ingredients of FM and KOA targets. A visual analysis was performed on the built network. In this network, each node represents a target or active ingredient, and each edge represents the relationship between biomolecules.

GO and KEGG Enrichment Analyses

A relevant analysis was carried out using R software to further explore the biological processes (BPs) of FM in the treatment of KOA and its regulatory signaling pathways. The bioconductor collection toolkit in R software was used to perform the GO functional enrichment analysis and KEGG pathway enrichment analysis of common genes with P value cutoff <0.05 and q value cutoff <0.05 as the limiting conditions.

Molecular Docking

The active ingredients of the drug were used as ligands, and the target protein was used as the receptor. DS software was used for molecular docking analysis. Ligand information was obtained from the Chemicalbook database (https://www.chemicalbook.com/) and TCMSP. The crystal structure of the receptor was downloaded from the RCSB PDB database. First, the receptor and ligand were preprocessed using DS software. Then, the binding site was defined and edited, and the Dock Ligands (LibDock) module was run. The following settings were used: high quality, BEST conformation method, parallel processing, and the default values for the other parameters. The docking results are displayed as LibDock scores. The higher the score, the more likely the active ingredient of the drug will bind to the target.

Chemicals and Reagents

Ru Xiang (Frankincense, Boswellia carterii Birdw, 210701) and Mo Yao (Myrrh, Commiphora myrrha [Nees] Engl, 210701) were purchased from the Affiliated Hospital of Nanjing University of Chinese Medicine (Nanjing, China). The voucher specimens (nos. NJUTCM090210 and NJUTCM090606) were deposited at the Herbarium of the Nanjing University of Chinese Medicine, China. Dulbecco's modified Eagle's medium (DMEM) and fetal bovine serum (FBS) were purchased from Invitrogen Gibco Inc. (Carlsbad, CA, USA). Recombinant mouse IL-1 beta (IL-1β) was obtained from R&D (R&D Systems, Minneapolis, MN, USA). Cell Counting Kit-8 was purchased from Beyotime Biotech (Beyotime Biotech, Shanghai, China). Collagen II, NF-kB p65, p-NF-kB p65 (Ser536), IKB alpha, ERK, p-ERK (Thr202/Tyr204), p38 MAPK, p-p38 MAPK, JNK1/2/3, and p-JNK1/2/3 (Thr183 + Tyr185) were obtained from Affinity Biosciences LD (Affinity Biosciences, Cincinnati, OH, USA).

Drug Preparation

According to the experimental design, 6 g of frankincense (Ru Xiang) and 6 g of myrrh (Mo Yao) were obtained from the Affiliated Hospital of Nanjing University of Chinese Medicine. These drugs were identified by Prof. Wang Peimin. The herbs were mixed, and the mixture was boiled in distilled water and filtered. The filtrate was evaporated with a rotary evaporator and then dried to a constant weight in a vacuum drying oven for further in vitro experiments. FM was dissolved in phosphate buffered saline (PBS) to a stock concentration of 40 mg/mL and stored at −80 °C. The working concentration of FM was prepared by diluting the stock solution in PBS, filtering through a 0.22-µm filter, and storing at 4 °C.

LC–MS for Component Identification

Ten milligrams of the lyophilized drug was weighed and dissolved in 0.5 ml of ultrapure water. Then, 80 µl of the solution was pipetted, mixed with 20 µl of pure acetonitrile, and placed at −20 °C overnight. After centrifugation (14 000 rpm*10 min), the supernatant was used for detection.

The detection system was ultrafast liquid chromatography (UFLC, Shimadzu, Kyoto, Japan) with Triple-ToFTM 5600 (Q-TOF, AB Sciex, Framingham, MA, USA). The ion source was electron spray ionization (ESI), and the control system was Analyst TF 1.6 software (AB Sciex, Singapore). The chromatographic column was a Waters Symmetry ShieldTM RP18 C18 (250 mm × 4.6 mm, 5 μm, Waters, USA), and the column temperature was 30 °C. The mobile phase was 0.2% formic acid water (A) and acetonitrile (B), with a flow rate of 1 ml/min. Elution gradient: 0-2 min, 2% B; 2-18 min, 30% B; 18-30 min, 60% B; 30-38 min, 100% B; 38-40 min, 100% B; 40-44 min, 2% B; 44-48 min, 2% B. The samples were analyzed in positive and negative ion modes, and the injection volume was 10 µL. Mass spectrometry parameters: data-dependent acquisition (IDA) in dynamic background subtraction mode (DBS), acquisition range of m/z 50-1500 Da, and an error of 50 mDa. The corresponding top 10 substances were fragmented, and MS2 information was collected. Ion source temperature: ± 550 °C; CE: ± 40 V; DP: ± 100 V; CUR: 40 V; GS1, GS2: 40 V; ISVF: 5500 V (pos)/4500 V (neg).

Peak picking and alignment were performed by MS-DIAL (ver. 4.60). The pubchem database was used to search the MS/MS spectra, remove the recognition results of |ppm|>20 and invalid comparison, and reconfirm the compounds with SN > 3 and a total score of 80 points or more.

Obtaining Primary Chondrocytes

This study was approved by the Animal Ethics Committee of Nanjing University of Chinese Medicine (202104A017) and complied with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Twenty C57BL/6 male mice (9 weeks) were obtained from the Experimental Animal Center of Nanjing University of Chinese Medicine (Nanjing, China) with license number SCXK (ZHE)-2019-0001. The C57BL/6 male mice were sacrificed with an overdose of sodium pentobarbital. The articular cartilage of the femoral condyle and tibial plateau was extracted under aseptic conditions. The tissue was cut into pieces (1 mm³) and digested with 0.2% collagen II solution for 4 h. Chondrocytes were seeded in a 10 cm² Petri dish at a density of 2 × 10⁵/mL and placed in an environment at 37 °C and 5% CO₂. The cells used in the subsequent experiments were in the second generation and entered the logarithmic growth phase.

Chondrocyte Identification

Chondrocytes (second generation) were seeded into 6-well plates at 2 × 10⁴ cells/well. When the cells were more than 50% confluent, they were rinsed twice with PBS and toluidine stain solution and dye were added for 5 min at room temperature. The same amount of distilled water was added and mixed for 15 min. Finally, the cells were washed twice with distilled water and observed under a microscope (DMi1, Leica, Germany).

CCK-8 Assay

The cells were seeded in a 96-well plate at a density of 6000 cells/well. Then, different concentrations (0, 25, 50, 100, 200 and 400 μg/mL) of FM were used for 24/48 h of treatment. Then, 10 μL of CCK-8 solution was added to each well and incubated at 37 °C for 2 h. The absorbance was detected at 450 nm with multimode plate reader (EnSpire, PerkinElmer, USA).

Experimental Design

Except for the blank group, all other groups were treated with 10 ng/mL IL-1β. After that, FM (100, 200, 400 μg/mL) was added to the drug groups and incubated at 37 °C in a humidified atmosphere containing 5% CO² for 24 h. The protein was collected from the supernatant and stored at −80 °C.

Immunofluorescence

Chondrocytes were treated in 6-well plates (2 × 10⁴), fixed with 4% (v/v) paraformaldehyde, permeabilized using 0.5% Triton X-100, blocked with 3% goat serum, incubated overnight at 4 °C with antibodies against p65 and collagen II, and incubated with anti-mouse or anti-rabbit Alexa Fluor 594 sary antibodies (Abcam, USA) for 30 min. The cells were counterstained with DAPI for 5 min and visualized using a fluorescence microscope (DMi8, Leica, Germany).

Western Blotting

Western blotting was used to detect the protein levels of p38, p-p38, JNK, p-JNK, ERK1/2, p-ERK1/2, P65, p-P65, and IκB. RIPA Lysis Buffer (RIPA) and Phenylmethylsulfonyl fluoride (PMSF) were used to extract the total protein from chondrocytes. After brief sonication, the cell lysates were centrifuged at 12 000 rpm at 4 °C for 20 min. The BCA Protein Assay Kit (Beyotime) was used to determine the protein concentration. Forty micrograms of protein was loaded on SDS-PAGE gels and transferred to polyvinylidene fluoride(PVDF) membranes (Bio-Rad, USA). After blocking with 5% skimmed milk powder for 2 h at room temperature, the membranes were washed with tris-buffered saline Tween-20 (TBST) 3 times for 10 min each. Then, the membranes were incubated with the targeted primary antibody overnight at 4 °C. After washing, the blots were incubated with horseradish peroxidase-conjugated goat antirabbit or anti-mouse IgG secondary antibodies for 2 h at room temperature. ImageQuant LAS 4000 mini (LAS 4000, Cytiva, USA) was used for signal detection. Image J software was used to analyze the gray value of each band.

Statistical Analysis

Data analysis was conducted using SPSS version 22.0 software (SPSS) and GraphPad Prism 8.0. The results are expressed as the mean ± standard deviation (SD). Statistical comparisons were performed using Student's t test, one-way analysis of variance, or two-way analysis of variance. A statistically significant difference was defined as P < 0.05.

Results

Constructing a “Drug Component-Disease Target” Network

According to the screening conditions and after deleting duplicate data, 31 active ingredients were finally included, as shown in Table 1. The drug targets were mapped with the disease targets to obtain 94 common target genes, as shown in Figure 1A and Table 2.

Figure 1.

Drug-disease target network construction and core target screening. (A) Common target acquisition. (B) The blue quadrilateral represents the common gene, and the green inverted triangle represents the active ingredient of frankincense and myrrh. (C) Core gene screening in the PPI network.

Table 1.

Active Ingredients Parameters of Frankincense and Myrrh.

MOL ID	Name	OB (%)	DL
MOL001001	Quercetin-3-O-β-D-glucuronide	30.66	0.74
MOL001002	Ellagic acid	43.06	0.43
MOL001004	Pelargonidin	37.99	0.21
MOL001006	Poriferasta-7,22E-dien-3beta-ol	42.98	0.76
MOL001013	Mansumbinoic acid	48.1	0.32
MOL001026	Myrrhanol C	39.96	0.58
MOL001028	(8R)-3-oxo-8-hydroxy-polypoda -13E,17E,21-triene	44.83	0.59
MOL001029	Myrrhanones B	34.39	0.67
MOL001031	Epimansumbinol	61.81	0.4
MOL001033	Diayangambin	63.84	0.81
MOL001040	(2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one	42.36	0.21
MOL001045	(13E,17E,21E)-8-hydroxypolypodo-13,17,21-trien-3-one	44.34	0.58
MOL001049	16-hydroperoxymansumbin-13(17)-en-3β-ol	41.05	0.49
MOL001061	(16S, 20R)-dihydroxydammar-24-en-3-one	37.34	0.78
MOL001062	15α-hydroxymansumbinone	37.51	0.44
MOL001063	28-acetoxy-15α-hydroxymansumbinone	41.85	0.67
MOL001095	Isofouquierone	40.95	0.78
MOL001126	[(5aS,8aR,9R)-8-oxo-9-(34,5-trimethoxyphenyl)-5,5a,6,9-tetrahydroisobenzofurano[6,5-f][1,3]benzodioxol-8a-yl] acetate	44.08	0.9
MOL001131	Phellamurin_qt	56.6	0.39
MOL001138	(3R,20S)-3,20-dihydroxydammar- 24-ene	37.49	0.75
MOL001156	3-methoxyfuranoguaia-9- en-8-one	35.15	0.18
MOL001175	Guggulsterone	42.45	0.44
MOL000358	Beta-sitosterol	36.91	0.75
MOL000449	Stigmasterol	43.83	0.76
MOL000490	Petunidin	30.05	0.31
MOL000098	Quercetin	46.43	0.28
MOL000988	4,17(20)-(cis)-pregnadiene-3,16-dione	51.42	0.48
MOL000996	Guggulsterol IV	33.59	0.74
MOL001243	3alpha-Hydroxy-olean-12-en-24-oic-acid	39.32	0.75
MOL001255	Boswellic acid	39.55	0.75
MOL001295	Phyllocladene	33.4	0.27
MOL001001	Quercetin-3-O-β-D-glucuronide	30.66	0.74
MOL001002	Ellagic acid	43.06	0.43
MOL001004	Pelargonidin	37.99	0.21
MOL001006	Poriferasta-7,22E-dien-3beta-ol	42.98	0.76
MOL001013	Mansumbinoic acid	48.1	0.32
MOL001026	Myrrhanol C	39.96	0.58
MOL001028	(8R)-3-oxo-8-hydroxy-polypoda -13E,17E,21-triene	44.83	0.59
MOL001029	Myrrhanones B	34.39	0.67
MOL001031	Epimansumbinol	61.81	0.4
MOL001033	Diayangambin	63.84	0.81
MOL001040	(2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one	42.36	0.21
MOL001045	(13E,17E,21E)-8-hydroxypolypodo-13,17,21-trien-3-one	44.34	0.58
MOL001049	16-hydroperoxymansumbin-13(17)-en-3β-ol	41.05	0.49
MOL001061	(16S, 20R)-dihydroxydammar-24-en-3-one	37.34	0.78
MOL001062	15α-hydroxymansumbinone	37.51	0.44
MOL001063	28-acetoxy-15α-hydroxymansumbinone	41.85	0.67
MOL001095	Isofouquierone	40.95	0.78
MOL001126	[(5aS,8aR,9R)-8-oxo-9-(34,5-trimethoxyphenyl)-5,5a,6,9-tetrahydroisobenzofurano[6,5-f][1,3]benzodioxol-8a-yl] acetate	44.08	0.9
MOL001131	Phellamurin_qt	56.6	0.39
MOL001138	(3R,20S)-3,20-dihydroxydammar- 24-ene	37.49	0.75
MOL001156	3-methoxyfuranoguaia-9- en-8-one	35.15	0.18
MOL001175	Guggulsterone	42.45	0.44

Table 2.

The Common Gene of Frankincense and Myrrh in the Treatment of KOA.

Gene Symbol	OMIM ID	Description
PPARA	170998	Peroxisome Proliferator Activated Receptor Alpha
IL6	147620	Interleukin 6
PTGS2	600262	Prostaglandin-Endoperoxide Synthase 2
PON1	168820	Paraoxonase 1
MMP2	120360	Matrix Metallopeptidase 2
MMP1	120353	Matrix Metallopeptidase 1
IL1A	147760	Interleukin 1 Alpha
IL1B	147720	Interleukin 1 Beta
PLAU	191840	Plasminogen Activator, Urokinase
MMP3	185250	Matrix Metallopeptidase 3
MMP9	120361	Matrix Metallopeptidase 9
AR	313700	Androgen Receptor
ESR1	133430	Estrogen Receptor 1
SERPINE1	173360	Serpin Family E Member 1
PTGS1	176805	Prostaglandin-Endoperoxide Synthase 1
BCL2	151430	BCL2 Apoptosis Regulator
THBD	188040	Thrombomodulin
PPARG	601487	Peroxisome Proliferator Activated Receptor Gamma
ALOX5	152390	Arachidonate 5-Lipoxygenase
AKR1B1	103880	Aldo-keto reductase family 1 member B1
PPARD	600409	Peroxisome proliferator-activated receptor delta
IL10	124092	Interleukin 10
CRP	123260	C-Reactive Protein
CXCL8	146930	C-X-C Motif Chemokine Ligand 8
COL1A1	120150	Collagen Type I Alpha 1 Chain
RUNX2	600211	RUNX Family Transcription Factor 2
COL3A1	120180	Collagen Type III Alpha 1 Chain
CCL2	158105	C-C Motif Chemokine Ligand 2
VEGFA	192240	Vascular Endothelial Growth Factor A
SPP1	166490	Secreted Phosphoprotein 1
IFNG	147570	Interferon Gamma
ICAM1	147840	Intercellular Adhesion Molecule 1
NOS2	163730	Nitric Oxide Synthase 2
VCAM1	192225	Vascular Cell Adhesion Molecule 1
IL2	147680	Interleukin 2
CCND1	168461	Cyclin D1
STAT1	600555	Signal Transducer And Activator Of Transcription 1
JUN	165160	Jun Proto-Oncogene, AP-1 Transcription Factor Subunit
IGFBP3	146732	Insulin Like Growth Factor Binding Protein 3
MAPK14	600289	Mitogen-Activated Protein Kinase 14
FOS	164810	Fos Proto-Oncogene, AP-1 Transcription Factor Subunit
CASP3	600636	Caspase 3
CD40LG	300386	CD40 Ligand
AKT1	164730	AKT Serine/Threonine Kinase 1
MAPK1	176948	Mitogen-Activated Protein Kinase 1
EGF	131530	Epidermal Growth Factor
CXCL10	147310	C-X-C Motif Chemokine Ligand 10
SELE	131210	Selectin E
IGF2	147470	Insulin Like Growth Factor 2
OPRM1	600018	Opioid Receptor Mu 1
F3	134390	Coagulation Factor III, Tissue Factor
MPO	606989	Myeloperoxidase
NFKBIA	164008	NFKB Inhibitor Alpha
MYC	190080	MYC Proto-Oncogene, BHLH Transcription Factor
CDKN1A	116899	Cyclin Dependent Kinase Inhibitor 1A
CXCL2	139110	C-X-C Motif Chemokine Ligand 2
SOD1	147450	Superoxide Dismutase 1
HIF1A	603348	Hypoxia Inducible Factor 1 Subunit Alpha
F7	613878	Coagulation Factor VII
RELA	164014	RELA Proto-Oncogene, NF-KB Subunit
ESR2	601663	Estrogen Receptor 2
BAX	600040	BCL2 Associated X, Apoptosis Regulator
NR3C1	138040	Nuclear Receptor Subfamily 3 Group C Member 1
GJA1	121014	Gap Junction Protein Alpha 1
PLAT	173370	Plasminogen Activator, Tissue Type
CASP8	601763	Caspase 8
GSTM1	138350	Glutathione S-Transferase Mu 1
EGFR	131550	Epidermal Growth Factor Receptor
CTSD	116840	Cathepsin D
CXCL11	604852	C-X-C Motif Chemokine Ligand 11
NR1I3	603881	Nuclear Receptor Subfamily 1 Group I Member 3
PRKCA	176960	Protein Kinase C Alpha
HMOX1	141250	Heme Oxygenase 1
EIF6	602912	Eukaryotic Translation Initiation Factor 6
NOS3	163729	Nitric Oxide Synthase 3
HAS2	601636	Hyaluronan Synthase 2
NFE2L2	600492	Nuclear Factor, Erythroid 2 Like 2
PARP1	173870	Poly(ADP-Ribose) Polymerase 1
ERBB2	164870	Erb-B2 Receptor Tyrosine Kinase 2
E2F1	189971	E2F Transcription Factor 1
CASP9	602234	Caspase 9
CAV1	601047	Caveolin 1
BIRC5	603352	Baculoviral IAP Repeat Containing 5
CYP3A4	124010	Cytochrome P450 Family 3 Subfamily A Member 4
GSK3B	605004	Glycogen Synthase Kinase 3 Beta
CHUK	600664	Component Of Inhibitor Of Nuclear Factor Kappa B Kinase Complex
AHR	600253	Aryl Hydrocarbon Receptor
IRF1	147575	Interferon Regulatory Factor 1
PGR	607311	Progesterone Receptor
HSPB1	602195	Heat Shock Protein Family B (Small) Member 1
GSTP1	134660	Glutathione S-Transferase Pi 1
TP63	603273	Tumor Protein P63
ADRB2	109690	Adrenoceptor Beta 2
CCNB1	123836	Cyclin B1

Cytoscape software was used to construct a network of active ingredients and common gene relationships. After importing the data, a network of 125 points and 174 connections was obtained. The blue nodes represent a common gene, and the green nodes represent the active ingredient, as shown in Figure 1B. Taking the degree as the main reference basis for the topological analysis, the top 5 active ingredients were quercetin, beta-sitosterol, ellagic acid, pelargonidin, and petunidin, and the parameters used for the topological analysis of the remaining active ingredients are shown in Supplementary Table 1. The topological analysis results are shown in Table 3. These compounds with higher degrees may be the key compounds in FM that treat KOA.

Table 3.

Topology Parameters in the PPI Network.

NO.	Name	Degree	Eigenvector	LAC	Betweenness	Closeness	Network
1	JUN	28	0.33206794	7	752.75305	0.2636656	18.799557
2	RELA	24	0.28291473	6.25	505.28482	0.2570533	14.89462
3	AKT1	24	0.21761675	4.1666665	850.9638	0.25465837	12.070793
4	IL6	22	0.23858611	5.818182	696.6581	0.2554517	12.763512
5	MAPK1	22	0.24269721	4.909091	891.2355	0.2570533	9.894697
6	ESR1	17	0.19492176	5.0588236	293.48517	0.24698795	9.188174
7	MAPK14	17	0.21506457	4.8235292	274.0686	0.24550898	8.510977
8	CXCL8	16	0.16960101	5.375	388.4959	0.24624625	10.353896
9	FOS	16	0.21152838	5.375	198.27281	0.25	9.124525
10	IL1B	15	0.18253209	5.866667	229.11449	0.24624625	8.550253
11	VEGFA	15	0.14451039	3.7333333	527.11615	0.25308642	6.418759
12	EGFR	15	0.14243847	3.3333333	430.85114	0.24924012	5.9298534
13	STAT1	14	0.15536147	3.4285715	301.45212	0.24698795	5.019411
14	MYC	14	0.19494553	6.285714	89.83442	0.24260356	8.794597
15	CCND1	13	0.15430571	4	390.67526	0.24332345	6.214286
16	CCL2	12	0.15506938	5.3333335	122.32302	0.23768117	6.7186146
17	NR3C1	12	0.16521293	4.6666665	63.920788	0.23699422	6.228896
18	IFNG	10	0.1322631	4.2	68.33403	0.23229462	5.0674605
19	NFKBIA	10	0.13417685	4.6	30.62471	0.23295455	6.4952383
20	IL10	10	0.111237235	5	17.926323	0.2204301	5.9900794
21	CASP8	10	0.08135375	1.2	379.72717	0.23361823	3.1
22	IL2	10	0.16686663	5.2	58.53426	0.24260356	5.7777777
23	EGF	10	0.07499734	2.6	184.0309	0.23631124	4.642857
24	CXCL2	9	0.10110616	4.888889	50.72975	0.22465754	6.589286
25	GSK3B	9	0.1074815	2.6666667	187.33163	0.23428571	3.25
26	MMP9	8	0.0651293	2.25	144.6444	0.22465754	2.9714286
27	PRKCA	8	0.081657164	2	101.56526	0.23229462	2.4571428
28	CXCL10	8	0.07062195	3.5	29.247889	0.21578947	5
29	ICAM1	8	0.0788866	3.25	121.53465	0.21750663	4.9166665
30	HIF1A	8	0.11251304	3.5	35.947105	0.2404692	4.071429
31	CDKN1A	8	0.088875234	3.25	98.30535	0.22969188	4.7238097
32	IRF1	7	0.09373853	2.5714285	44.68776	0.22714682	3.1666667
33	ERBB2	7	0.08003712	3.142857	35.80046	0.23163842	3.75
34	MMP2	7	0.025702778	2.2857144	83.16423	0.2152231	3.2333333
35	IL1A	7	0.113102086	3.4285715	18.169638	0.22841226	4.0333333
36	IGF2	7	0.03662141	2	123.20987	0.22222222	2.8333333
37	AR	7	0.079273835	2	47.229637	0.22651933	2.4166667
38	NOS2	7	0.10527593	3.4285715	47.22882	0.22777778	4.133333
39	NOS3	7	0.08787569	2.857143	32.049404	0.23229462	3.5833333
40	PPARA	6	0.102211796	4.6666665	1.8434523	0.22589532	5.6
41	MMP3	6	0.02804547	1.3333334	59.36763	0.21465969	1.7
42	PTGS2	6	0.0668497	1.6666666	198.35912	0.22404371	2
43	BCL2	6	0.07427376	2.3333333	162.76048	0.22714682	2.8
44	CHUK	6	0.07204759	1.6666666	27.392275	0.22841226	2
45	CASP3	6	0.05044793	1.6666666	130.43419	0.2204301	3.2
46	IGFBP3	5	0.026908575	1.6	36.42498	0.21578947	2.0833333
47	MMP1	5	0.014441599	2.4	5.433112	0.19951338	3
48	SERPINE1	5	0.021458331	1.2	172.4718	0.2113402	1.5
49	CXCL11	5	0.039746996	2.8	2.047619	0.2029703	3.75
50	VCAM1	5	0.03890193	1.6	54.456722	0.21025641	2.75
51	PGR	5	0.06034375	2	9.869783	0.21808511	2.5
52	CAV1	5	0.04994457	1.6	15.910841	0.22102426	2
53	SPP1	4	0.024882099	0.5	27.50098	0.21025641	0.6666667
54	NFE2L2	4	0.05455349	1.5	5.257434	0.21750663	2
55	HMOX1	4	0.06432898	2.5	2.283041	0.21866667	3.3333333
56	ESR2	4	0.058345698	2	3.0217032	0.21808511	2.6666667
57	OPRM1	4	0.030065533	3	0	0.2014742	4
58	CCNB1	4	0.022436112	1.5	158	0.200489	2
59	CD40LG	4	0.06722683	2	1.8000222	0.22343324	2.6666667
60	E2F1	4	0.038275998	2.5	14.647294	0.21243523	3.3333333
61	CASP9	4	0.028159562	1.5	54.31594	0.2118863	2.6666667
62	PPARG	3	0.065363765	2	0	0.21808511	3
63	COL1A1	3	0.009036742	0.6666667	32.38636	0.2014742	1.5
64	HSPB1	3	0.049688112	2	0	0.21808511	3
65	RUNX2	2	0.021176852	0	12.446522	0.20971867	0
66	MPO	2	0.019364938	0	121.91468	0.20812182	0
67	SELE	2	0.00929444	1	0	0.18222222	2
68	PLAU	2	0.012987975	0	4.4128428	0.20347394	0
69	CTSD	2	0.020585315	0	6.800861	0.2118863	0
70	CRP	2	0.032242887	1	0	0.20971867	2
71	COL3A1	2	0.00714939	1	0	0.19477434	2
72	PARP1	2	0.00622625	1	0	0.18222222	2
73	TP63	2	0.009036179	0	10.761274	0.19248827	0
74	BAX	2	0.00660034	0	7.5380955	0.18764302	0
75	PTGS1	2	0.001962429	0	7.7635965	0.17864923	0
76	ALOX5	2	0.005435109	0	39.84892	0.18594104	0
77	ADRB2	2	0.017232398	1	0	0.20654912	2
78	PLAT	1	0.001703567	0	0	0.17558886	0
79	GJA1	1	0.019209607	0	0	0.20603015	0
80	F7	1	0	0	0	0.012195122	0
81	F3	1	0	0	0	0.012195122	0
82	BIRC5	1	0.001778939	0	0	0.16803278	0
83	SOD1	1	0.017236838	0	0	0.20448878	0

Analysis of the Target Protein Interaction Network

The STRING database platform was used to predict protein interactions and draw protein relationship network diagrams. Ninety-four common genes were imported into the STRING database to obtain FM PPI networks acting on KOA, with a total of 83 nodes and 313 interactive connections. Cytoscape software was used for visualization. The topological parameters of the network nodes were calculated using the CytoNCA App, and the DC, CC, BC, EC, NC and LAC values were obtained. The first screening parameter settings were DC≥6.000, EC≥0.065, LAC≥2.250, BC≥39.849, CC≥0.220, NC≥3.000, a total of 23 nodes, and 101 interaction connections. The second screening parameter settings were DC≥14.000, EC≥0.165, LAC≥4.824, BC≥229.115, CC≥0.246, NC≥6.589, a total of 8 nodes, and 20 interaction connections. In addition, we searched for the core target again using the CytoHubba App to predict the most relevant core target. First, the target network node values (node scores) were calculated, the top 8 CytoHubba nodes were selected according to the MCC algorithm, the first-stage nodes were assessed, and the shortest path and the expanded subnetwork were displayed. The screening process is shown in Figure 1C.

GO Enrichment Analysis

According to the results of the GO enrichment analysis, 2213 BPs were enriched, and were involved in the negative regulation of apoptosis signaling pathways, regulation of reactive oxygen species metabolism, response to tumor necrosis factor, mechanical stimulation, cellular responses to biological stimulation, and oxidative stress. One hundred thirty-five items were enriched in the molecular function (MF) category, among which DNA binding transcription activation activity, cytokine receptor binding, cytokine, receptor ligand, nuclear receptor activity, and RNA polymerase II transcription factor binding were the top enriched terms. Based on the results of the cell fraction (CC) enrichment analysis, 54 items were enriched, which were mainly related to cell membrane parts such as membrane rafts, membrane microdomains, membrane regions, and caveolae. The 20 most notable items are presented in a bubble chart and a GO enrichment network diagram in Figure 2A.

Figure 2.

Go and KEGG enrichment analyses. (A) GO biological function enrichment analysis. The size of the P value is represented by the color; redder colors indicate smaller P values and closer relationships, and bluer colors indicate larger P values and more distant relationships. The size of the origin indicates the number of related genes; the larger the size, the more related the genes are, and a smaller size indicates the opposite result. BPs: biological process; CCs: cellular component; MFs: molecular function. (B) KEGG pathway enrichment analysis. The smaller the P value is, the more significant the enrichment result. (C) TNF signaling pathway.

Analysis of Enriched KEGG Pathways

The KEGG pathway enrichment analysis results reveal 143 enriched pathways. Among the top 20 signaling pathways, fluid shear stress and atherosclerosis and the TNF, IL-17, HIF-1, and toll-like receptor signaling pathways are closely related to the mechanism of FM treatment in KOA. In addition, apoptosis, the NF-κB signaling pathway, the PI3K-Akt signaling pathway, cellular senescence, the MAPK signaling pathway, the p53 signaling pathway, the mTOR signaling pathway, arachidonic acid metabolism, and the Hippo signaling pathway were included in the enrichment results, as shown in Figure 2B. The TNF signaling pathway is shown in Figure 2C.

Molecular Docking Analysis

By screening the core targets, 6 important targets were obtained, including JUN, IL6, IL1B, CXCL8, RELA, and MAPK1. Using Discovery Studio software, these six intersecting targets and the active ingredients obtained from the screen were subjected to molecular docking analysis. The software evaluates the binding ability of the ligand and the receptor through the binding between the targets and generates a LibDock score after calculation and fitting. The larger the value, the higher the binding activity, and the easier it is for the drug to bind to the receptor. Topological analysis showed that quercetin ranked first in the prediction. We show the docking process of quercetin and 6 core targets in Figure 3A and B. Other docking results are shown in Supplementary Table 2.

Figure 3.

Molecular docking of compounds with core targets. (A) a1 and a2: Docking process of quercetin with MAPK1; b1 and b2: Docking process of quercetin with CXCL8; c1 and c2: Docking process of quercetin with JUN; d1 and d2: Docking process of quercetin with RELA; e1 and e2: Docking process of quercetin with IL6; f1 and f2: Docking process of quercetin with IL1B. (B) Heatmap of the LibDock Score for molecular docking. The redder the color, the higher the LibDock Score, and the greener the color, the lower the LibDock Score.

FM Component Separation and Identification

Through component annotation of the data, a total of 72 possible compounds were identified, of which 28 compounds were only recognized in the positive ion mode, 36 metabolites were only recognized in the negative ion mode, and were 8 common components, as shown in Figure 4A to C. Supplementary Table 3 provides details and some representative MS/MS spectra. The 72 kinds of ingredients included 13 kinds of flavonoids, 11 kinds of amino acids, 9 kinds of terpenes, 8 kinds of phenylpropanoids, 4 kinds of phenols, 4 kinds of fatty acids, and 11 kinds of other ingredients, as shown in Figure 4D. The positive and negative ion identification results are shown in Figure 4E and F.

Figure 4.

FM ingredients identified by LC–MS. (A) Twenty-eight ingredients were identified in positive ion mode. (B) Thirty-six ingredients were identified in negative ion mode. (C) Shared ingredients of the positive and negative modes. (D) Major categories of identified ingredients. (E) Confirmation of the mass spectrogram obtained in positive ion mode. (F) Confirmation of mass spectrograms obtained in negative ion mode.

Toluidine Blue Staining to Identify Chondrocytes

Second-generation chondrocytes with good growth were selected. Adherent blue chondrocytes were scattered and distributed like paving stones. After staining, the cell cytoplasm was light blue, and the nucleus was dark blue and spherical, as shown in Figure 5A and B. Since collagen II is mainly produced by chondrocytes, collagen II immunofluorescence was used to identify the chondrocytes. Under the inverted phase contrast microscope, the cytoplasm of chondrocytes was blue (Figure 5C and D).

Figure 5.

Identification of mouse articular chondrocytes and cytotoxicity assay of FM on chondrocytes. (A) Toluidine blue staining of chondrocytes (magnification, up: × 100) (magnification, down: × 200). (B) Identification of chondrocytes by collagen II immunofluorescence (magnification: × 100). (C-D) 24/48 h detection of chondrocyte viability by CCK-8 analysis.

Cytotoxicity Assay of FM Using Chondrocytes

The CCK-8 method was used to detect the cytotoxicity of FM to chondrocytes. The results show (Figure 5E and F) that FM did not inhibit the viability of chondrocytes after treatment at 25, 50, 100, 200, and 400 μg/mL for 24/48 h, which indicates that FM has no cytotoxicity to chondrocytes at the selected dose and exposure time.

FM Inhibits IL-1β-Induced Chondrocyte Damage in Mice by Inhibiting MAPK Signaling Pathways

Combined with the pathway map and core target prediction, we chose the NF-κB and MAPK signaling pathways for verification. The NF-κB and MAPK pathways are involved in the expression of inflammatory mediators induced by IL-1β. First, we investigated whether the inhibitory effect of FM on its expression is mediated by the MAPK signaling cascade. As shown in Figure 6A to D, compared with the untreated group, p38, JNK, and ERK were significantly phosphorylated under IL-1β stimulation. FM inhibited the activation of p38, JNK, and ERK. Inhibition of the activation of the MAPK pathway may be one of the mechanisms by which FM protects mouse chondrocytes.

Figure 6.

Effect of FM on IL-1β-induced MAPK activation in chondrocytes. Chondrocytes were pretreated with FM at different concentrations for 4 h and then incubated with or without IL-1β (10 ng/mL) for 24 h. Western blot analysis (A) and relevant quantitative analysis (B-D) of p38, p-p38, Jnk, p-Jnk, Erk, and p-Erk. *P < 0.05 versus the control group. #P < 0.05 versus the model group.

Effects of FM on IL-1β-Induced NF-κB Signal Activation in Chondrocytes

Since p65 is an indicator of NF-κB activation after IL-1β stimulation, the protein level of p65 was measured. As shown in Figure 7A to C, we found that p-p65 increased after IL-1β stimulation, while FM inhibited its phosphorylation. In addition, we found that IL-1β stimulated the degradation of IkBα in mouse chondrocytes. The immunofluorescence results show that p65 protein was mainly located in the cytoplasm of mouse chondrocytes not treated with IL-1β; after IL-1β treatment, p65 protein translocated into the nucleus (Figure 7D). FM treatment reversed IL-1β-induced p65 expression and prevented the translocation of p65 from the cytoplasm to the nucleus, as shown in Figure 7D and E. These results indicate that FM has a protective effect on mouse OA chondrocytes by inhibiting the activation of the NF-κB pathway.

Figure 7.

Effect of FM on IL-1β-induced NF-κB signal activation in chondrocytes. Chondrocytes were pretreated with FM at different concentrations for 4 h and then incubated with or without IL-1β (10 ng/mL) for 24 h. Western blot analysis (A) and relevant quantitative analysis (B & C) of p65, p-p65, and IκB in chondrocytes. *P < 0.05 versus the control group. #P < 0.05 versus the model group. (D-E) Immunofluorescence analysis of p65 nuclear translocation. Blue, DAPI; Red, p65.

Discussion

According to some previous studies, Boswellia myrrh inhibits inflammation. FM has been used for centuries. The resin extract of Boswellia serrata used in Ayurvedic medicine prevents the destruction of cartilage by MMP-3 and reduces the synthesis or activation of MMP-9, MMP-13, and nitric oxide, thereby inhibiting the dissolution of collagen and cartilage and effectively treating inflammatory diseases, especially arthritis.²¹ Clinical studies performing a 3-way comparison of Aflapin^® and 5-Loxin^® with placebo in 60 patients within 90 days reported a significantly greater improvement in the pain and function of the two treatment groups than in the placebo group.²² Frankincense extract prevents collagen degradation and inhibits the production of proinflammatory mediators and MMPs.²³ In addition, frankincense extract inhibits IL-β-induced chondrocyte apoptosis.²³ Although the mechanism of FM in the treatment of KOA has been explored, our understanding of it is still insufficient.

Quercetin is a plant-derived polyphenol with a wide range of biological effects and anti-inflammatory, antiviral and anticancer activities.²⁴ In particular, quercetin exerts a beneficial effect on inflammation and immune function.^25,26 Studies have shown that quercetin inhibits the inflammation and apoptosis of chondrocytes, regulates the polarization of synovial macrophages to M2 macrophages, weakens the oxidative stress response, inhibits the degradation of chondrocyte extracellular matrix, and protects articular cartilage.²⁷ β-Sitosterol is a phytosterol and tetracyclic triterpenoid.²⁸ β-Sitosterol shows good pharmacological antioxidant, anti-inflammatory, immunoregulatory, antitumor, and other effects. According to basic research, β-sitosterol treatment reduces LPS-induced increases in the levels of inflammatory mediators, including IL-6, iNOS, TNF-α, and COX-2.²⁹ Stigmasterol is also a phytosterol with various pharmacological activities, such as antitumor, blood cholesterol-lowering, and anti-OA properties. Stigmasterol reduces the degradation of cartilage caused by OA by inhibiting the degradation of inflammatory mediators and the matrix.^30,31 Ellagic acid is a natural antioxidant that inhibits the IL-1β-induced expression of iNOS, COX-2, NO, TNF-α, PGE2, and IL-6 and downregulates the expression of MMP-13 and ADAMTS-5.^29,32 Pelargonidin has antioxidant, anti-inflammatory, and antidiabetic activities.^33,34 In addition, PEL has the potential to improve Alzheimer's disease and atherosclerosis.^35,36 Mansumbinoic acid is an extract of myrrh with strong anti-inflammatory activity. Studies have shown that the molar titer of mansumbinoic acid is on the same order of magnitude as those of indomethacin and prednisolone.³⁷

According to the analysis of the PPI network topology, 6 core targets were identified: JUN, IL-6, IL-1β, CXCL8, RELA, and MAPK1. These may be potential targets on which FM acts to treat OA. According to the enrichment analysis of the KEGG pathway and the results of previous studies, among the selected pathways for FM treatment of KOA, the NF-κB and MAPK signaling pathways have a strong correlation.

MAPK is an activated serine/threonine protein kinase that is mainly involved in the regulation of cell growth, differentiation, apoptosis, and the inflammatory response.³⁸ Jun is a member of the mitogen-activated protein kinase family that plays a role in cell proliferation, apoptosis, survival, stress, and apoptosis by inducing transcription or the activation of transcription factors for downstream target genes.³⁹ The downregulation of Jun reduces the expression of inflammatory factors and inhibits endothelial cell apoptosis.⁴⁰ In OA, IL-6 released from joint tissues binds to IL-6R, which activates the immune system, and monocytes are recruited to the inflamed joint area.⁴¹ IL-1β participates in a series of cellular processes, such as cell proliferation, differentiation, and apoptosis.³⁸ IL-1β plays an important role in the pathogenesis of OA and is currently used to induce OA models in vitro. NF-κB, which is triggered by proinflammatory cytokines and ECM degradation products, plays an important role in the pathogenesis of OA.^42,43 Activated NF-κB is an important part of OA events because NF-κB regulates the expression of a variety of cytokines, chemokines, and matrix-degrading enzymes.⁴⁴ Under physiological conditions, p65 exists in the cytoplasm. When stimulated by IL-1β, p65 is released, phosphorylation increases, and it rapidly transfers to the nucleus and promotes the expression of a large number of inflammatory factors. Our results show that FM inhibits p65 phosphorylation and nuclear translocation, indicating that the NF-κB pathway has an inhibitory effect in chondrocytes. The MAPK pathway mainly includes ERK1/2, Jnk, and p38, which can be activated by phosphorylation of upstream signals. According to the western blotting results, FM can reduce the phosphorylation of Erk, p38, and JNK induced by IL-1β, thereby inhibiting MAPK signaling. Therefore, the potential mechanism by which FM prevents cartilage degeneration is related to the inhibition of the NF-κB and MAPK pathways.

Conclusions

In summary, this study preliminarily explored the potential mechanism of FM as a treatment for KOA through the combined actions of multiple targets and pathways. Our results show that FM can inhibit chondrocyte inflammatory factors in vitro by inhibiting the NF-κB and MAPK signaling pathways. Furthermore, this study provides a theoretical basis and research data for the clinical application of FM in the treatment of KOA and reveals the material basis and potential mechanism underlying the efficacy of FM in the KOA treatment.

Supplemental Material

sj-xls-1-npx-10.1177_1934578X221116984 - Supplemental material for Comprehensive Network Pharmacological Analysis and In Vitro Verification Reveal the Potential Active Ingredients and Potential Mechanisms of Frankincense and Myrrh in Knee Osteoarthritis

Supplemental material, sj-xls-1-npx-10.1177_1934578X221116984 for Comprehensive Network Pharmacological Analysis and In Vitro Verification Reveal the Potential Active Ingredients and Potential Mechanisms of Frankincense and Myrrh in Knee Osteoarthritis by Xiaoqing Shi, Lishi Jie, Peng Wu, Jun Mao, Peimin Wang, Zixiu Liu and Songjiang Yin in Natural Product Communications

Supplemental Material

sj-xls-2-npx-10.1177_1934578X221116984 - Supplemental material for Comprehensive Network Pharmacological Analysis and In Vitro Verification Reveal the Potential Active Ingredients and Potential Mechanisms of Frankincense and Myrrh in Knee Osteoarthritis

Supplemental material, sj-xls-2-npx-10.1177_1934578X221116984 for Comprehensive Network Pharmacological Analysis and In Vitro Verification Reveal the Potential Active Ingredients and Potential Mechanisms of Frankincense and Myrrh in Knee Osteoarthritis by Xiaoqing Shi, Lishi Jie, Peng Wu, Jun Mao, Peimin Wang, Zixiu Liu and Songjiang Yin in Natural Product Communications

Supplemental Material

sj-docx-3-npx-10.1177_1934578X221116984 - Supplemental material for Comprehensive Network Pharmacological Analysis and In Vitro Verification Reveal the Potential Active Ingredients and Potential Mechanisms of Frankincense and Myrrh in Knee Osteoarthritis

Supplemental material, sj-docx-3-npx-10.1177_1934578X221116984 for Comprehensive Network Pharmacological Analysis and In Vitro Verification Reveal the Potential Active Ingredients and Potential Mechanisms of Frankincense and Myrrh in Knee Osteoarthritis by Xiaoqing Shi, Lishi Jie, Peng Wu, Jun Mao, Peimin Wang, Zixiu Liu and Songjiang Yin in Natural Product Communications

Footnotes

Acknowledgements

We gratefully acknowledge the contributions of Juan Zhao (Nanjing University of Chinese Medicine) to the data analysis.

Author Contributions

SXQ and JLS contributed equally to this paper and designed the study. Experimental work and data collection were conducted by SXQ and LZX. SXQ and JLS analyzed and interpreted the data. SXQ and WP drafted the manuscript. WPM and YSJ provided critical comments and revised the manuscript. LZX, YSJ and ZNS helped perform the analysis and with constructive discussions. All authors read and approved the final version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The present study was supported by the Natural Science Foundation of Jiangsu Province (SBK2020041404), the National Natural Science Foundation of China (82074460), the Universities Natural Science Research Project of Jiangsu Province (20KJB360003), and the Research and Practice Innovation Program for Postgraduates in Jiangsu Province (KYCX21_1684). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability Statement

The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding authors.

ORCID iD

Xiaoqing Shi

Supplemental Material

Supplemental material for this article is available online.

References

Martel-Pelletier

Barr

Cicuttini

, et al. Osteoarthritis. Nat Rev Dis Primers. 2016;2:16072.

Prieto-Alhambra

Judge

Javaid

, et al. Incidence and risk factors for clinically diagnosed knee, hip and hand osteoarthritis: influences of age, gender and osteoarthritis affecting other joints. Ann Rheum Dis. 2014;73(9):1659‐1664.

Palazzo

Nguyen

Lefevre-Colau

, et al. Risk factors and burden of osteoarthritis. Ann Phys Rehabil Med. 2016;59(3):134‐138.

Lee

Kim

. Commiphora extract mixture ameliorates monosodium iodoacetate-induced osteoarthritis. Nutrients. 2020;12(5):1477.

Cao

Wei

, et al. Seeing the unseen of the combination of two natural resins, frankincense and myrrh: changes in chemical constituents and pharmacological activities. Molecules. 2019;24(17):3076.

Deng

Liu

Lou

, et al. Analysis of the rule of prescription for external use of knee osteoarthritis based on data mining. J Hunan Univ Chin Med. 2020;40(09):1136‐1141.

Gao

Xue

, et al. Analysis of formulation regularity of Chinese patent medicine for knee osteoarthritis. China Pharmacy. 2019;30(15):2096‐2100.

Roy

Parama

Banik

, et al. An update on pharmacological potential of boswellic acids against chronic diseases. Int J Mol Sci. 2019;20(17):4101.

Ning

Wang

Liu

, et al. Integrating strategies of herbal metabolomics, network pharmacology, and experiment validation to investigate frankincense processing effects. Front Pharmacol. 2018;9:1482.

10.

Majeed

Narayanan

, et al. A pilot, randomized, double-blind, placebo-controlled trial to assess the safety and efficacy of a novel Boswellia serrata extract in the management of osteoarthritis of the knee. Phytother Res. 2019;33(5):1457‐1468.

11.

Dafni

Böck

. Medicinal plants of the Bible-revisited. J Ethnobiol Ethnomed. 2019;15(1):57.

12.

Sun

Hua

Liu

, et al. Myrrh induces the apoptosis and inhibits the proliferation and migration of gastric cancer cells through down-regulating cyclooxygenase-2 expression. Biosci Rep. 2020;40(5):BSR20192372.

13.

Wang

, et al. A combined water extract of frankincense and myrrh alleviates neuropathic pain in mice via modulation of TRPV1. Neural Plast. 2017;2017:3710821.

14.

Fatani

Alrojayee

Parmar

, et al. Myrrh attenuates oxidative and inflammatory processes in acetic acid-induced ulcerative colitis. Exp Ther Med. 2016;12(2):730‐738.

15.

Yao

Ding

Jia

, et al. Anti-tumor effect of beta-elemene in glioblastoma cells depends on p38 MAPK activation. Cancer Lett. 2008;264(1):127‐134.

16.

Yeo

Ali

Hayward

, et al. β-Bisabolene, a sesquiterpene from the essential oil extract of Opoponax (Commiphora guidottii), exhibits cytotoxicity in breast cancer cell lines. Phytother Res. 2016;30(3):418‐425.

17.

Hopkins

. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11):682‐690.

18.

Wang

, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13.

19.

Liu

Guo

Wang

, et al. BATMAN-TCM: a bioinformatics analysis tool for molecular mechANism of traditional Chinese medicine. Sci Rep. 2016;6:21146.

20.

Huang

Shi

, et al. Network pharmacology approach to uncover the mechanism governing the effect of simiao powder on knee osteoarthritis. Biomed Res Int. 2020;2020:6971503.

21.

Ammon

. Boswellic acids (components of frankincense) as the active principle in treatment of chronic inflammatory diseases. Wien Med Wochenschr. 2002;152(15–16):373‐378.

22.

Sengupta

Krishnaraju

Vishal

, et al. Comparative efficacy and tolerability of 5-loxin and Aflapin against osteoarthritis of the knee: a double blind, randomized, placebo controlled clinical study. Int J Med Sci. 2010;7(6):366‐377.

23.

Blain

Ali

Duance

. Boswellia frereana (frankincense) suppresses cytokine-induced matrix metalloproteinase expression and production of pro-inflammatory molecules in articular cartilage. Phytother Res. 2010;24(6):905‐912.

24.

Yao

Han

, et al. Quercetin, inflammation and immunity. Nutrients. 2016;8(3):167.

25.

Chirumbolo

. The role of quercetin, flavonols and flavones in modulating inflammatory cell function. Inflamm Allergy Drug Targets. 2010;9(4):263‐285.

26.

Agrawal

Blunden

. Quercetin: antiviral significance and possible COVID-19 integrative considerations. Nat Prod Commun. 2020;15(12):1‐10.

27.

Gui

Zhou

, et al. Quercetin alleviates rat osteoarthritis by inhibiting inflammation and apoptosis of chondrocytes, modulating synovial macrophages polarization to M2 macrophages. Free Radic Biol Med. 2019;145:146‐160.

28.

Klingberg

Andersson

Mulligan

, et al. Food sources of plant sterols in the EPIC Norfolk population. Eur J Clin Nutr. 2008;62(6):695‐703.

29.

Sun

Gao

Hou

, et al. β-Sitosterol alleviates inflammatory response via inhibiting the activation of ERK/p38 and NF-κB pathways in LPS-exposed BV2 cells. Biomed Res Int. 2020;2020:7532306.

30.

Chen

, et al. Stigmasterol blocks cartilage degradation in rabbit model of osteoarthritis. Acta Biochim Pol. 2012;59(4):537‐541.

31.

Gabay

Sanchez

Salvat

, et al. Stigmasterol: a phytosterol with potential anti-osteoarthritic properties. Osteoarthr Cartil. 2010;18(1):106‐116.

32.

Galano

Francisco Marquez

Pérez-González

. Ellagic acid: an unusually versatile protector against oxidative stress. Chem Res Toxicol. 2014;27(5):904‐918.

33.

Wang

Sun

Cao

, et al. Antioxidant and pro-oxidant properties of acylated pelargonidin derivatives extracted from red radish (Raphanus sativus var. Niger, Brassicaceae). Food Chem Toxicol. 2010;48(10):2712‐2718.

34.

Min

Park

, et al. Anti-septic effects of pelargonidin on HMGB1-induced responses in vitro and in vivo. Arch Pharm Res. 2016;39(12):1726‐1738.

35.

Sohanaki

Baluchnejadmojarad

Nikbakht

, et al. Pelargonidin improves memory deficit in amyloid β25-35 rat model of Alzheimer's disease by inhibition of glial activation, cholinesterase, and oxidative stress. Biomed Pharmacother. 2016;83:85‐91.

36.

Son

Jeong

Kim

, et al. Pelargonidin attenuates PDGF-BB-induced aortic smooth muscle cell proliferation and migration by direct inhibition of focal adhesion kinase. Biochem Pharmacol. 2014;89(2):236‐245.

37.

Duwiejua

Zeitlin

Waterman

, et al. Anti-inflammatory activity of resins from some species of the plant family Burseraceae. Planta Med. 1993;59(1):12‐16.

38.

Chow

Chin

. The role of inflammation in the pathogenesis of osteoarthritis. Mediators Inflamm. 2020;2020:8293921.

39.

Meng

Xia

. c-Jun, at the crossroad of the signaling network. Protein Cell. 2011;2(11):889‐898.

40.

Liu

Wang

, et al. Exendin-4 protects HUVECs from tunicamycin-induced apoptosis via inhibiting the IRE1a/JNK/caspase-3 pathway. Endocrine. 2017;55(3):764‐772.

41.

Scheller

Chalaris

Schmidt-Arras

, et al. The pro- and anti-inflammatory properties of the cytokine interleukin-6. Biochim Biophys Acta. 2011;1813(5):878‐888.

42.

Gui

Gan

, et al. Enhanced SOCS3 in osteoarthiritis may limit both proliferation and inflammation. Biotech Histochem. 2017;92(2):107‐114.

43.

Marcu

Otero

Olivotto

, et al. NF-kappaB signaling: multiple angles to target OA. Curr Drug Targets. 2010;11(5):599‐613.

44.

Sun

Zhang

, et al. Sam68 promotes NF-κB activation and apoptosis signaling in articular chondrocytes during osteoarthritis. Inflamm Res. 2015;64(11):895‐902.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.53 MB

0.02 MB