puc/PMC10019655.txt

==== Front
PLoS One
PLoS One
plos
PLOS ONE
1932-6203
Public Library of Science San Francisco, CA USA

10.1371/journal.pone.0282875
PONE-D-22-34202
Research Article
Biology and Life Sciences
Organisms
Eukaryota
Plants
Herbs
Biology and Life Sciences
Anatomy
Biological Tissue
Connective Tissue
Adipose Tissue
Adipocytes
Medicine and Health Sciences
Anatomy
Biological Tissue
Connective Tissue
Adipose Tissue
Adipocytes
Biology and Life Sciences
Cell Biology
Cellular Types
Animal Cells
Connective Tissue Cells
Adipocytes
Biology and Life Sciences
Anatomy
Biological Tissue
Connective Tissue
Connective Tissue Cells
Adipocytes
Medicine and Health Sciences
Anatomy
Biological Tissue
Connective Tissue
Connective Tissue Cells
Adipocytes
Biology and Life Sciences
Physiology
Physiological Parameters
Body Weight
Obesity
Biology and Life Sciences
Genetics
Gene Expression
Computer and Information Sciences
Network Analysis
Biology and Life Sciences
Developmental Biology
Cell Differentiation
Adipocyte Differentiation
Medicine and Health Sciences
Pharmacology
Biology and Life Sciences
Anatomy
Body Fluids
Semen
Medicine and Health Sciences
Anatomy
Body Fluids
Semen
Biology and Life Sciences
Physiology
Body Fluids
Semen
Network pharmacology predicts combinational effect of novel herbal pair consist of Ephedrae herba and Coicis semen on adipogenesis in 3T3-L1 cells
Network pharmacology predict combinational effect of Ephedrae herba and Coicis semen
https://orcid.org/0000-0002-3179-9439
Lim Dong-Woo Conceptualization Methodology Writing – original draft 1 2
https://orcid.org/0000-0003-1559-4326
Yu Ga-Ram Methodology Writing – review & editing 1
https://orcid.org/0000-0002-1368-6684
Kim Jai-Eun Validation Writing – review & editing 3 *
Park Won-Hwan Funding acquisition Supervision 1 *
1 Department of Diagnostic, College of Korean Medicine, Dongguk University, Goyang, Republic of Korea
2 Institute of Korean Medicine, Dongguk University, Goyang, Republic of Korea
3 Department of Pathology, College of Korean Medicine, Dongguk University, Goyang, Republic of Korea
Wang Chun-Hua Editor
Foshan University, CHINA
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: diapwh@dongguk.ac.kr (W-HP); herbqueen@dongguk.ac.kr (J-EK)
16 3 2023
2023
18 3 e028287519 12 2022
24 2 2023
© 2023 Lim et al
2023
Lim et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Background

Herbal combinations are regarded as basic strategy in oriental medicine with various purposes. Ephedrae herba (EH) and Coicis semen (CS) are two herbal medicines used to treat obesity in many herbal prescriptions, yet the effect and significance of this herbal pair have not been evaluated.

Purpose

This study is to elucidate the effect of a novel herbal pair, EH-CS, on obesity and identify the key synergistic mechanism underlying it.

Methods

We investigated the network of herbs comprising the anti-obesity herbal prescriptions. Using the tools of network pharmacology, we investigated the compound-target interactions of EH and CS in combination to predict their effects in combination. Five EH-CS samples with different EH to CS ratios were prepared to investigate their efficacies in adipocytes.

Results

1-mode network analysis of herbs in prescriptions based on literature review revealed the importance of EH-CS in anti-obesity prescriptions. The herbal combination comprised of equivalent weights (1:1) of EH and CS most potently reduced mature adipocyte adiposity, although several markers of adipogenesis and lipid synthesis were more suppressed by pure EH. PTGS2 (COX-2 gene) expression, a common target of EH and CS as deduced by compound-target network analysis, was affected by EH-CS extract treatments. However, EH at high concentration (25 μg/ml) notably increased PTGS2 expression without adversely affecting cell viability. However, EH-CS combination of the same concentration markedly decreased PTGS2 gene expression.

Conclusion

These results show that the compounds in CS and EH act in concert to enhance the pharmacological effect of EH, but control unexpected effects of EH treatment.

korean national research foundation 2021R1A6A3A01086718 https://orcid.org/0000-0002-3179-9439
Lim Dong-Woo LDW. (only author that was awarded fund) This work was supported by the Basic Science Research Program of the Korean National Research Foundation funded by the Ministry of Education (Grant no. 2021R1A6A3A01086718). https://www.nrf.re.kr/index The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data AvailabilityAll relevant data are within the paper and its Supporting Information files.
Data Availability

All relevant data are within the paper and its Supporting Information files.
==== Body
pmc1. Introduction

Drug combinations are often proposed as novel prospective treatments and pose considerable challenges to drug developers [1]. However, in oriental medicine, the concept of multicomponent prescriptions is universally accepted, and such prescriptions are used as primary treatments [2]. There is a perception of herbal pairs used for certain effects of formulae in oriental medicine, like modulating efficacy, toxicity and bioavailability (or absorption) [3]. Recent progress in analysis of herbal medicine on structural similarities at the systems level have elucidated certain pairs of natural compounds work in combination [4], and many authors have reported on the combinatorial effects of various herbal pairs, such as Radix Sophorae Flavescentis- Fructus Cnidii [5], Chuanxiong Rhizoma-Cyperi Rhizoma [6], and Danshen–Honghua (Salviae miltiorrhizae radix-Flos Carthami) [7]. Such studies are on-going, and researchers continue to identify novel herbal combinations that, for example, improve drug efficacies or reduce side effects.

Ephedrae herba (EH) is the rhizome of the perennial plant, Ephedra sinica, which has long been used to treat the common cold, arthralgia, and asthma [8], and is now prescribed by physicians to induce weight loss [9,10]. The pharmaceutical potential of EH on metabolic diseases or syndromes is supported by scientific evidence of increased thermogenesis achieved by sympathetic neuron control [11] and the browning effect on white adipose tissue [12]. Furthermore, EH is frequently used with other herbs [13] to obtain various effects and ameliorate EH treatment-associated unsolicited symptoms [14].

On the other hand, Coicis semen (CS, the seed of Coix lacryma-jobi) is also used to treat obesity and diabetes in traditional medicine [15]. CS has been reported to have anti-diabetic [16], anti-oxidative [17], and anti-tumor properties [18], which have been attributed to its phytochemical constituents, such as polyphenols, flavonoids, lignans, and phytosterols [15]. Several anti-obesity herbal prescriptions containing CS continue to be used because of its safety [19] and efficacy [20] profiles.

A few researchers have raised questions about possible interactions between the two herbs (EH-CS) for its co-occurrence in many prescriptions. An analysis of precedent research on clinical herbal prescriptions by Song et al. revealed that EH and CS are the herbs most commonly used to treat obesity [21]. We also tentatively suggested the possibility of unidentified synergic effects of these two herbs based on a review of common herbs used in anti-obesity prescriptions in previous in vivo study [22]. However, these studies only suggested the possibility and did not provide supporting evidence of their effects. Despite the possibility of pharmacological benefits that the empirical use of the combination may have, there has been no intensive research on it. Network pharmacology, an integrative tool for analyzing pharmacological mechanism of herbal medicine [23], can be used to decipher complex interactions between numerous targets and compounds derived from EH and CS.

Prostaglandin-endoperoxide synthase2 (PTGS2) is inducible enzyme expressed in inflamed conditions leading to biosynthesis of prostaglandins [24]. It has been noted that inhibition of COX activity affects adipocyte differentiation via decreasing inflammatory cytokines [25]. A member of the nuclear hormone receptor coactivator family, nuclear receptor coactivator 2 (NCOA2) controls adipogenesis, lipid metabolism, and fat absorption to maintain metabolic balance [26]. Adrenoreceptor beta 2 (ADRB2) is connected to elevated noradrenaline release brought on by exposure to cold, which activates lipolysis and thermogenesis [27]. Interleukin-6 (IL6) is pro-inflammatory mediator which is suggested as cause of systemic low grade inflammation in obesity [28]. By offering a variety of strategies in various metabolic pathways, those genes can be used as targets for the treatment of obesity.

In this study, network pharmacology analysis was used to predict key targets and decipher the combinational effects of EH-CS herbal pair with their constituents. We evaluated the anti-adipogenic effects of EH-CS herbal combinations in a mature 3T3-L1 adipocyte model. To confirm the synergistic effect of EH-CS combinations, we tested the effects of five EH-CS samples prepared using different EH to CS ratios (EH-CS; 0–100, 25–75, 50–50, 75–25, 100–0, percent w/w). Mechanisms probably responsible for the effects of EH-CS as predicted by network pharmacologic analysis were verified in a palmitate-induced inflammatory preadipocyte model treated with separated concentrations of EH-CS sample. Finally, we tried to find the reasonable explanation to describe the different effects caused by EH-CS combinational samples with the clinical implication.

2. Materials and methods

2.1 HPLC-DAD-UV analysis

Chromatographic analyses of EH-CS and standard ingredients were performed using a HPLC system (Agilent 1260 infinity HPLC, Agilent, CA, USA) equipped with a binary pump, UV-Diode array detector, degasser, and auto-sampler. Standard compounds for EH-CS were obtained from Sigma (Sigma Aldrich, St. Louis, Missouri, USA). Standard compounds and EH-CS extracts were diluted with pure ethanol to 5 mg/mL and filtered. The components of EH-CS were analyzed using an Agilent Eclipse XDB-C18 chromatographic column (150×4.6 mm, 5 μm pore size) at a flow rate of 2 mL/min, a column temperature of 25˚C, and a detection wavelength of 192 nm. Two mobile phase conditions were used; 1) isocratic (A–water with 0.05% formic acid and B–acetonitrile) of A:B = 50:50 consistently for 20 min; or 2) isocratic (A–water with 0.05% formic acid and B–acetonitrile) of A:B = 95:5 consistently for 20 min.

2.2 1-Mode network analysis of herbs in prescriptions

We built a 1-mode network of herbs used in anti-obesity prescriptions. To collect information on herbal constituents in obesity prescriptions published online, we searched original research articles on the anti-obesity effects of herbal prescriptions. Nine herbal prescriptions were selected based on preference of clinical usage. To examine combinations of herbs used in anti-obesity prescriptions, herbal compositions of prescriptions (contains at least two herbs) were arranged in a binary matrix (2-mode network) with herbs in rows and prescriptions in columns, in which ’0’ indicated absence and ’1’ indicated presence [29]. These 2-mode networks were transformed into 1-mode herb x herb network using the methods suggested by Breiger [30]. Similarity between herbs was assessed by analyzing Jaccard coefficients with values ranging from 0 to 1 using XLSTAT Excel add-in software [31]. An herb network was built using herbs as nodes and undirected edges as relationships in Cytoscape version 3.8.2. Node sizes represented degrees and edge width in the herb network represents Jaccard coefficients between nodes, and node colors represented the frequency of herb appearances. A heatmap of unweighted Pearson’s correlations was created using XLSTAT.

2.3. Acquisition of potential active ingredients and targets of EH-CS combinations using web databases

The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP, https://old.tcmsp-e.com/tcmsp.php (accessed on 29 June 2021)) was used as a repository to collect data about the ingredients and targets of EH-CS. Two ADME properties of drug likeness (DL) (≥0.18) and oral bioavailability (OB) (≥30%) were used to identify potential bioactive ingredients in each herb. Pubchem_CID was used to identify ingredients. Proteins targeted by each ingredient acquired from the TCMSP were validated using the Uniprot database (https://www.uniprot.org/ (accessed on 29 June 2021)) [32]. All target proteins were validated and converted into official gene names in Homo sapiens using the Genecards database ((https://www.genecards.org/) (accessed on 29 June 2021)) [33]. Lists of ingredients and targets were sorted and uploaded as groups for each herb to the bioinformatics and evolutionary genomics website (http://bioinformatics.psb.ugent.be/webtools/Venn/ (accessed on 20 July 2021)) to obtain Venn diagrams.

2.4. Key EH-CS target screening for obesity using the STRING database and Cytoscape

Targets related to obesity were obtained by selecting overlapping targets identified using the Genecards database (accessed on 10 July 2021) and the Disgenet web database (https://www.disgenet.org/ (accessed on 10 July 2021)) [34]. All targets of EH-CS ingredients were arranged as lists and uploaded to the STRING database (https://string-db.org/ (accessed on 13 July 2021)) [35] to construct a protein-protein interaction (PPI) network. The minimum required interaction score was set at 0.4, and isolated target nodes without known interactions were discarded. PPI network interactions were exported to Cytoscape [36], and the network file was imported into Cytoscape version 3.8.2; topological analysis of networks was performed using a built-in network analyzer. Two topological parameters, "degree" and "betweenness centrality" were adopted as criteria for selecting key targets of EH-CS combinations. Targets above average for degree and betweenness centrality were regarded as key targets.

2.5. Construction of a compound-target network

A network of herbal compound-targets was constructed and visualized in Cytoscape and processed in PowerPoint software. Nodes represent herb ingredients, and targets, and edges represent interactions between nodes. Node colors of compounds represent source herbs (CS-blue, EH-red, EH-CS-purple). Node colors of targets represent separate three clusters. The clusters of key targets were created using on-board function in STRING database according to the kmeans algorithm.

2.6. The KEGG pathway and gene ontology enrichment analyses in the R package

The Kyoto Encyclopedia of Genes and Genomes (KEGG) [37] pathway and gene ontology (GO) enrichment analyses [38] were performed by uploading key target genes to the DAVID database platform (https://david.ncifcrf.gov/ (accessed on 13 July 2021)) [39]. A list of all key genes was uploaded, and the identifier was set as "official gene symbol". Annotations of key genes were identified in the KEGG pathway and three GO terms, that is, BP (biological process), CC (cellular component), and MF (molecular function). The top 20 results with the lowest P-values in each category were visualized as a bubble chart containing P-values, gene counts, and gene ratios. Bubble plots were created using R Studio and the ’ggplot2’ package with public R script, which was modified for the present study [40].

2.7. Herbal extraction preparation

Five Ephedrae herba and Coicis semen (EH-CS) combinations were used in this study (0:100, 25:70, 50:50, 75:25, and 100:0 (w/w)). Herbs were obtained from Humanherb (Gyeongsangbukdo, South Korea). Different ratios of EH to CS were extracted in hot water for 1 h at 95˚C. Crude extracts were filtered, condensed using a rotary evaporator (Buchi, Switzerland) at 95˚C, and freeze-dried to obtain respective powders, which were eluted with Dulbecco’s Phosphate Buffered Saline (DPBS) and filtered through a 0.22 μm syringe filter before use.

2.8. Cell culture and cell differentiation

3T3-L1 preadipocytes (ATCC CL-173) were grown in Dulbecco’s Modified Eagle Medium (DMEM, Gibco, Carlsbad, CA, USA), supplemented with 10% fetal bovine serum (FBS) and 100 U/mL penicillin and streptomycin (Gibco, USA). Cells were incubated at 37˚C in a humidified 5% CO2 atmosphere and maintained at ~70% confluence before being used in experiments.

3T3-L1 preadipocytes were seeded on 12-well plates at 2 x 105 cells per well in DMEM supplemented with 10% FBS and incubated to full confluence (100%) and then for a further 2 days. Differentiation was initiated by exchanging the medium with differentiation medium (DMEM supplemented with 10% FBS, 1 μM dexamethasone, 0.5 mM 3-isobutyl-1-methylxanthine, 10 μg/ml insulin) for 72 h, and cells were further incubated in maturation medium (DMEM supplemented with 10% FBS containing 10 μg/ml insulin) for 1 day (for real-time PCR) or 8 days (for ORO staining and Western blotting).

2.9 Cell viability assessment

For viability determinations we used previous protocol with slight modification [41]. Cells were seeded in 96-well plates in FBS-free DMEM at 2 x 103 cells per well and then incubated with various concentrations of EH-CS combinations for 24 h. Viabilities were measured using an Ez-Cytox kit (Daeil Lab., Seoul, South Korea) according to the manufacturer’s instructions. Optical densities (ODs) were then measured at 450 nm using a microplate spectrophotometer (VersaMax, Molecular Devices, CA, USA).

2.10 Oil red O staining

For ORO staining, we used modified protocols from previous study [42]. Mature 3T3-L1 cells (adipocytes) were washed with DPBS, fixed with 5% formalin for 1 h at room temperature, washed once with 60% isopropanol, and dried. A stock solution of ORO was prepared by filtering a solution of 0.175 g of ORO powder in 50 ml of isopropanol and diluting the filtrate with distilled water at a ratio of 3: 2. Cells were stained with ORO solution for 15 min, washed with distilled water, air-dried, and examined under an inverted microscope system equipped with a camera (DMI 6000, Leica, Wetzlar, Germany). For quantitative analysis, stains were re-dissolved in isopropanol, and absorbances were measured at 520 nm using a spectrophotometer (VersaMax).

2.11 The palmitate-induced preadipocyte inflammatory model

3T3-L1 cells (preadipocytes) were seeded on 12-well plates without differentiation factors in culture medium and incubated for 24 h in PA containing medium for 24 h [43]. PA medium was prepared by conjugating palmitate (0.5 mM) in 1% bovine serum albumin (BSA)-containing DMEM for 1 h at 55˚C, as described by Kim et al. [44] with slight modification.

2.12 Real-time quantitative PCR

Total RNA was isolated from preadipocytes or adipocytes using Trizol reagent (Thermo Fisher Scientific, USA). Reverse transcription was performed using an AccuPower RT PreMix (Bioneer, Daejeon, South Korea) and oligo (dt) 18 primers (Invitrogen, Carlsbad, CA, USA). cDNA amplification was performed using a LightCycler 480 PCR system (Roche, Basel, Switzerland). PCR reaction mixes contained 10 μl of 2x SYBR Green Master Mix (Roche, Switzerland), 8 μl of ultrapure water, 10 pmol/μl of primers, and 1 μl of template cDNA. PCR was performed using an initial denaturation step (95°C for 10 min), 45 amplification cycles (denaturation at 95°C for 10 s, annealing at 56~62°C for 20 s, and extension at 72°C for 20 s). Melting curve analysis was performed at 95°C for 5 min for quality check. Threshold cycle (Ct) value was calculated to quantify PCR results. Relative expression levels were calculated by dividing gene Ct values by that of β-actin. All data were acquired using a LightCycler 480 instrument and software. Primers sequences are presented in Table 1.

10.1371/journal.pone.0282875.t001 Table 1 Primer sequence used in this study.

Genes	Sequence (5’-3’)	Tm (˚C)	
PPARγ	Forward	agtgacttggctatatttatagctgtcatt	65.3	
Reverse	tgtcttggatgtcctcgatgg	61.3	
FABP4	Forward	cagaagtgggatggaaagtcg	61.3	
Reverse	cgactgactattgtagtgtttga	59.3	
CEBPA	Forward	gcgcaagagccgagataaag	60.5	
Reverse	cacggctcagctgttcca	58.4	
SCD1	Forward	atatcctggtttccctgggt	58.4	
Reverse	cagcggtactcactggc	57.2	
FASN	Forward	cctccaagactgactcgg	58.4	
Reverse	cagtgtgctcaggttcagtt	58.4	
ACC1	Forward	tggcgtccgctctgtgata	59.5	
Reverse	catggcgacttctgggttg	59.5	
SREBF1	Forward	ggaacagacactggccga	58.4	
Reverse	aagtcactgtcttggttgttgat	59.3	
PTGS2	Forward	gcgacatactcaagcaggagca	64	
Reverse	agtggtaaccgctcaggtgttg	64	
IL6	Forward	ccacttcacaagtcggaggctta	64.7	
Reverse	gcaagtgcatcatcgttgttcatac	64.2	
TNF	Forward	aagcctgtagcccacgtcgta	63.3	
Reverse	ggcaccactagttggttgtctttg	65.3	
β-Actin	Forward	gacggccaggtcatcactattg	64	
Reverse	ccacaggattccatacccaaga	62.1	

2.13 Western blot

Preadipocyte protein levels were determined by Western blot. Briefly, cells were washed and lysed with radioimmunoprecipitation assay (RIPA) buffer (Thermo Fisher Scientific, Rockford, IL, USA) containing an enzyme inhibitor cocktail (Gendepot, Barker, TX, USA). Protein concentrations were estimated using the BCA kit (Thermo Fisher Scientific). Same amounts of protein lysates were loaded into 10% SDS-PAGE gels, electrophoresed, and transferred to PVDF membranes using an electrophoretic transfer cell (Bio-rad, Hercules, CA, USA). Membranes were then blocked with 5% BSA in TBS-T (TBS containing 0.1% Tween 20) for 2 h at room temperature, incubated with primary antibodies (1:1000 dilution in TBS-T) overnight with gentle agitation, rinsed with TBS-T, and incubated with secondary antibodies (1:3000 dilution in TBS-T) at room temperature for 2 h. Chemiluminescent blots were developed using ECL buffer (Super Signal West Pico, Thermo Fisher Scientific), and images were captured using the Fusion Solo imaging system (Vilber Lourmat, France).

2. 14 Elisa

Concentrations of secretory inflammatory cytokines in cell culture supernatants were measured using Quantikine mouse ELISA kits (R&D Systems, Inc. Minneapolis, MN, USA). Briefly, conditioned media of palmitate-induced preadipocytes (Section 4.10) and IL-6 concentrations were determined according to the manufacturer’s instructions. Optical densities were measured at 450 nm using a microplate spectrophotometer (VersaMax).

2.15 Statistical analysis

The significances of differences between non-treated 3T3-L1 cells and differentiated cells and between sample-treated differentiated cells and differentiated cells were determined by one-way ANOVA in Graphpad Prism 5.0 (Graphpad Software, USA). Results are presented as the means ± SDs of at least three independent experiments, and statistical significance was accepted for P-values < 0.05. Figures and tables were created using Graphpad Prism 5.0.

3. Results

3.1 Profiling and identification of major compounds in EH-CS by HPLC

All EH-CS samples were analyzed by HPLC fingerprinting using standard compounds. The peaks of HPLC chromatogram identified as a retention time of 1.070 min and 17.373 min which corresponded to ephedrine (Fig 1A) and stigmasterol (Fig 1B). Ephedrine was detected only in EH-containing samples (s2, s3, s4, s5). Meanwhile, Stigmasterol content was detected in s3, s4, s5.

10.1371/journal.pone.0282875.g001 Fig 1 (a) HPLC analysis results for EH-CS samples and standard (Ephedrine) in isocratic condition (A–water with 0.05% formic acid and B–acetonitrile) of A:B = 50:50. Ephedrine was identified at a retention time of 1.070 min. (b) HPLC analysis results for EH-CS samples and standard (Stigmasterol) in isocratic condition (A–water with 0.05% formic acid and B–acetonitrile) of A:B = 95:5. Stigmasterol was identified at a retention time of 1.737 min.

3.2 Analysis of the Herb-Herb 1 mode network

The 1-mode Herb network revealed a higher frequency of Ephedrae herba (EH), Coicis semen (CS), and Glycyrrhizae Radix (GR) use in anti-obesity prescriptions (Fig 2A). High degree and betweenness centrality figures of EH and CS in herb network demonstrates their importance in these prescriptions. Unexpectedly, Jaccard similarity coefficiency results between herbs based on their frequencies showed a higher value for minor herbs due to their consistent absence in most prescriptions. Pearson’s correlation matrix showed hierarchical distance between two clusters (CS and EH cluster), which is relatively far apart (Fig 2B). We speculate that this result was caused by reference article sampling method only limited to anti-obesity herbal prescriptions, which has low coexistence in between EH and CS.

10.1371/journal.pone.0282875.g002 Fig 2 (a) 1-mode network of herbs in herbal prescriptions (b) Heatmap of Pearson’s correlation matrix of herbs in anti-obesity herbal prescriptions. AS, Armeniacae Semen; EH, Ephedrae Herba; ZR, Zingiberis Rhizoma; GF, Gypsum Fibrosum; MH, Menthae Herba; CR, Cnidii Rhizoma; AnGR, Angelicae Gigantis Radix; PR, Paeoniae Radix; AR, Atractylodis Rhizoma; GR, Glycyrrhizae Radix; SF, Schisandrae Fructus; CaS, Castaneae Semen; LT, Liriope Tuber; AcGR, Acori Graminei Rhizoma; PR, Platycodonis Radix; RS, Raphani Semen; CS, Coicis Semen.

3.3 Active compound screening and key targets of EH-CS in combination

TCMSP database showed that 15 and 7 ingredients in EH and CS, respectively, met OB and DL criteria (Table 2). Mandenol and stigmasterol were common constituents of EH and CS. (Fig 3A, Table 2).

10.1371/journal.pone.0282875.g003 Fig 3 (a) Venn diagram showing the distribution of potent active compounds in EH-CS combinations (b) Venn diagram showing all the targets of EH and CS related to obesity. Data were obtained from TCMSP.

10.1371/journal.pone.0282875.t002 Table 2 List of potential active compounds in Ephedrae herba and Coicis semenSource.

	Molecular name	Pubchem CID	MW	OB	DL	
CS/EH
Compound
(2)	Mandenol	5282184	308.56	42.0	0.19	
Stigmasterol	5280794	412.77	43.83	0.76	
EH compound
(15)	Eriodictyol	440735	288.27	71.79	0.24	
Truflex OBP	66540	334.5	43.74	0.24	
Genkwanin	5281617	284.28	37.13	0.24	
Naringenin	932	272.27	59.29	0.21	
Beta-sitosterol	222284	414.79	36.91	0.75	
Quercetin	5280343	302.25	46.43	0.28	
Herbacetin	5280544	302.25	36.07	0.27	
Clionasterol (gamma-sitosterol)	457801	414.79	36.91	0.75	
Campesterol	173183	400.76	37.58	0.71	
Kaempferol	5280863	286.25	41.88	0.24	
24-Ethylcholest-4-en-3-one	15596633	412.77	36.08	0.76	
Pectolinarigenin	5320438	314.31	41.17	0.3	
Supraene	638072	410.8	33.55	0.42	
Luteolin	5280445	286.25	36.16	0.25	
Diosmetin	5281612	300.28	31.14	0.27	
CS compound
(7)	Coixenolide	46173943	591.08	32.4	0.43	
Hydrosqualene	11975273	410.8	33.55	0.42	
2-Monoolein	5319879	356.61	34.23	0.29	
Sitosterol alpha1	9548595	426.8	43.28	0.78	
CLR (Cholesterol)	5997	386.73	37.87	0.68	
Monooleoylglycerol	11451146	356.61	34.13	0.3	
Sitosterol (3-epi-beta-sitosterol)	12303645	414.79	36.91	0.75	

Lists of potential targets of active ingredients from EH and CS were obtained and compared with lists of obesity-related targets obtained from web databases (Fig 3B). As a result, 2199 targets were identified from disease databases, as compared with 223 EH targets and 31 CS targets. Interestingly, the 31 CS targets were all included among EH targets. Finally, total 152 genes targeted by EH and CS were found to be related to obesity.

3.4 Construction of a PPI (protein-protein interaction) network and screening of key targets

The 152 obesity-related target genes were uploaded into the STRING database to obtain a PPI network, which was then analyzed in Cytoscape. Network parameters of betweenness centrality and degree were evaluated. Seventeen solitary proteins without a known interaction were removed. Finally, 31 target proteins were found to be highly interconnected and thus were selected as key EH-CS targets. A list of key targets arranged by degree and betweenness centrality is provided in Table 3. Fig 4 shows the PPI network consisted of 31 key targets as nodes and 308 interactions between targets as edges. IL6 was found to be a core target gene of the PPI network with the highest degree of 28 (Table 3).

10.1371/journal.pone.0282875.g004 Fig 4 PPI network of the 31 key targets of EH-CS.

Thirty-one nodes and 308 interactions between nodes are represented. Red nodes represent genes with dominant BP terms (positive regulation of transcription from RNA), blue nodes represent genes with dominant CC terms (nucleoplasm), and green nodes represent genes with dominant MF terms (enzyme binding).

10.1371/journal.pone.0282875.t003 Table 3 Degrees and betweenness centralities of the 31 key target genes targeted by EH-CS as predicted by the PPI network structure.

Gene name	Target name	Degree	Betweenness
Centrality	
IL6	Interleukin 6	28	0.034933	
TNF	Tumor Necrosis Factor	27	0.029773	
AKT1	AKT Serine/Threonine Kinase 1	26	0.02412	
ESR1	Estrogen Receptor 1	26	0.044226	
TP53	Tumor Protein P53	26	0.022326	
VEGFA	Vascular Endothelial Growth Factor A	26	0.017908	
EGFR	Epidermal Growth Factor Receptor	25	0.015777	
MAPK3	Mitogen-Activated Protein Kinase 3	25	0.014045	
PTGS2	Prostaglandin-Endoperoxide Synthase 2	25	0.03103	
EGF	Epidermal Growth Factor	24	0.006448	
HSP90AA1	Heat Shock Protein 90 Alpha Family Class A Member 1	24	0.011566	
PPARA	Peroxisome Proliferator Activated Receptor Alpha	24	0.02819	
CASP3	Caspase 3	23	0.00302	
FOS	Fos Proto-Oncogene, AP-1 Transcription Factor Subunit	23	0.010306	
MYC	MYC Proto-Oncogene, BHLH Transcription Factor	23	0.00302	
CCND1	Cyclin D1	22	0.009491	
MAPK1	Mitogen-Activated Protein Kinase 1	22	0.004374	
MAPK8	Mitogen-Activated Protein Kinase 8	22	0.0021	
STAT1	Signal Transducer And Activator Of Transcription 1	21	0.001201	
APP	Amyloid Beta Precursor Protein	20	0.007925	
CASP8	Caspase 8	20	6.54E-04	
IL4	Interleukin 4	19	0.00576	
PRKCA	Protein Kinase C Alpha	17	1.04E-04	
IGFBP3	Insulin Like Growth Factor Binding Protein 3	16	0.002014	
CYP3A4	Cytochrome P450 Family 3 Subfamily A Member 4	13	0.019348	
F2	Coagulation Factor II, Thrombin	13	0.007026	
CYP1A1	Cytochrome P450 Family 1 Subfamily A Member 1	10	0.006373	
APOB	Apolipoprotein B	8	5.92E-04	
ADRB2	Adrenoceptor Beta 2	7	4.47E-04	
NCOA1	Nuclear Receptor Coactivator 1	7	0.00119	
AKR1C3	Aldo-Keto Reductase Family 1 Member C3	4	2.30E-04	

3.5. KEGG pathway and GO enrichment analysis of the key target genes

We uploaded 31 key genes into the DAVID database and obtained 20 results for the KEGG pathway and GO enrichment analysis using smallest P-values (Fig 5). Of the 20 pathways, 11 were human disease pathways and 9 signaling pathways. Of the 9 signaling pathways, 5 pathways were involved in signal transduction, 2 in the immune system, and 2 in the endocrine system. Among the signaling pathways, TNF and thyroid hormone signaling pathways were most significantly enriched.

10.1371/journal.pone.0282875.g005 Fig 5 (a) Gene enrichment analysis of the KEGG pathway Gene ontology (GO) enrichment analysis of (b) biological processes, (c) cellular components, and (d) molecular function of key targets of EH-CS.

For BP terms, drug response and positive regulation of transcription from RNA polymerase II promoter were the most prominent results. For CC terms, nucleoplasm was the most significant term, followed by protein-containing complex, and for MF terms, enzyme binding and protein-containing complex binding were the most significant. Target genes related to the most prominent BP, CC, and MF terms were annotated and colored red, blue, and green, respectively, in the PPI network (Fig 4).

3.6 Compound-target network and target clustering of EH-CS

Using the key targets of EH-CS elicited by PPI network analysis, we constructed 2-dimensional compound-target network (Fig 6). The complete compound-target network consisted of 45 nodes and 81 edges. EH and CS contained 13 and 3 active compounds, respectively, and 2 compounds were shared (stigmasterol and mandenol). Thirty-one targets were linked with EH compounds, 3 of which were also linked with CS compounds. However, no key target was solely linked with CS. The three common key targets shared by the two herbs were Prostaglandin Endoperoxide Synthase 2 (PTGS2, also called COX2), Nuclear Receptor Coactivator 1 (NCOA1), and Adrenoreceptor Beta 2 (ADRB2).

10.1371/journal.pone.0282875.g006 Fig 6 Visualization of the compound-target network of EH-CS.

The upper layer represents herbal compounds from herbs and the lower layer represents networks of three modules (clusters) of 31 potential targets. Target clusters were created using kmeans clustering tools in STRING database.

Key targets were clustered into 3 groups (cluster 1–3), and their significant BP terms are presented (Table 4). In cluster 1, cellular response to reactive oxygen species was most prominent BP term. In cluster 2, positive regulation of apoptotic process was second most significant BP term following the response to glucocorticoid. In cluster 3, positive regulation of protein kinase B signaling, positive regulation of phosphorylation, and positive regulation of MAP kinase activity were the three most important BP terms.

10.1371/journal.pone.0282875.t004 Table 4 Description of Top 3 biological processes of target clusters within 31 key targets of EH-CS compounds for obesity as determined using STRING database.

Cluster	GO identifier	BP Term	Gene count	P-value	
Cluster 1	GO:0034614	cellular response to reactive oxygen species	6	1.60E-11	
GO:0071276	cellular response to cadmium ion	6	2.70E-11	
GO:0006974	cellular response to DNA damage stimulus	6	4.30E-07	
Cluster 2	GO:0051384	response to glucocorticoid	4	3.50E-06	
GO:0043065	positive regulation of apoptotic process	5	9.70E-06	
GO:0032355	response to estradiol	4	1.60E-05	
Cluster 3	GO:0051897	positive regulation of protein kinase B signaling	5	6.40E-08	
GO:0042327	positive regulation of phosphorylation	4	1.30E-07	
GO:0043406	positive regulation of MAP kinase activity	4	2.40E-06	

3.7 Cytotoxicity of EH-CS samples on preadipocytes

3T3-L1 cells were treated with EH-CS of five different EH to CS ratios (s1-s5) at concentrations from 0 to 50 μg/ml (Fig 7A). Sample s1 (100% CS extract) at 50 μg/ml did not have any significant cytotoxic effect on preadipocytes. However, as the EH ratio increased, significant cytotoxicity was observed; for example, s5 (100% EH) reduced cell viability to ~90% at 30 μg/ml. Thus, we decided to use a maximum concentration of 25 μg/ml in subsequent studies.

10.1371/journal.pone.0282875.g007 Fig 7 Effect of EH-CS combinations on 3T3-L1 preadipocytes and lipid contents of mature adipocytes.

(a) Cell viabilities after treatment with EH-CS combinations (b) Oil Red O (ORO) staining results. Results are presented as the means ± SD of five different experiments. ###P < 0.001 versus non-differentiated 3T3-L1 preadipocytes, and *P < 0.05 versus differentiated adipocytes.

3.8 Inhibitory effects of EH-CS on adipocyte differentiation

EH-CS treatments of preadipocytes at 25 μg/ml for 8 days significantly inhibited differentiation, as demonstrated by loss of lipid accumulation in Oil Red O (ORO) staining (Fig 7B). In particular, s3 (EH:CS = 1:1 (w/w)) reduced lipid accumulation most. Pravastatin (50 μg/ml) was used as a positive control.

3.9 Regulatory effects of EH-CS on adipocyte differentiation and lipogenesis markers

Treatment-induced changes in phosphorylated AMPK levels (an important modulator of differentiation and energy expenditure) in adipocytes were investigated by western blot. As illustrated in Fig 8A and 8B, AMPK phosphorylation/AMPK was significantly increased by s3 and s5 as compared with that of differentiated adipocytes.

10.1371/journal.pone.0282875.g008 Fig 8 Relative protein and gene expression levels of adipocytes differentiation markers as determined by western blot and real-time quantitative PCR in adipocytes.

(a) Representative blot of p-AMPK and AMPK. (b) Band intensities were measured densitometrically and divided by that of non-phosphorylated AMPK. Real-time quantitative PCR results for (c) PPAR gamma, (d) FABP4, and (e) C/EBPa. Relative gene expressions were calculated by dividing each Ct value by that of β-actin. Results are presented as the means ± SDs of three different experiments. ###P < 0.001 versus preadipocytes, and *P < 0.05, **P < 0.01, and ***P < 0.001 versus differentiated adipocytes.

Real-time PCR was conducted to confirm the inhibitory properties and investigate the mechanisms responsible for the adipogenic and lipogenic effects of EH-CS in preadipocytes (Figs 8 and 9). At the early stage of differentiation (24 h), adipocytes showed significant increases in the expressions of the PPAR, FABP4, and CEBP genes, which ranged from 10 to 700-fold. However, treatment with EH-CS samples markedly reduced these increases. In particular, sample s3 potently inhibited the expressions of these adipogenic genes.

10.1371/journal.pone.0282875.g009 Fig 9 Relative gene expression levels of lipid synthesis markers were analyzed by real-time quantitative PCR in adipocytes.

Real-time quantitative PCR results of (a) SCD1, (b) FASN, (c) ACC1, and (d) SREBF1. Relative gene expressions were calculated by dividing Ct values by that of β-actin. Results are presented as the means ± SD of three different experiments. ##P < 0.01 and ###P < 0.001 versus 3T3-L1 preadipocytes, **P < 0.01, and ***P < 0.001 versus differentiated adipocytes.

The lipogenic genes investigated were SCD1, FASN, ACC1, and SREBF1 (Fig 9). The expressions of these four genes were significantly increased during differentiation, but these increases were suppressed by EH-CS treatment, but not in a consistent manner. SCD1 and FASN (markers of lipogenesis) were significantly down-regulated by all five samples. However, the gene expressions of ACC1 and SREBF responded to EH-CS treatments inconsistently (Fig 9C and 9D), and s2 and s3 caused no significant change in therapeutic expressions.

3.10 Relative protein levels and gene expression levels of core targets of EH-CS combinations

The synergistic effects of EH-CS combinations on obesity were not fully explained by the expressional changes of lipogenic genes. As we predicted it as key modulator in network pharmacology analysis, we examined IL6, TNF, and PTGS2 (COX-2) gene changes in palmitate-stimulated (0.5 mM) preadipocytes incubated with the five EH-CS samples at 10 or 25 μg/ml (Fig 10). The expressions of these key markers were significantly upregulated by 5-, 3, and 7-fold, respectively by palmitate. Palmitate-induced increases in IL6 and PTGS2 gene expressions were significantly inhibited by most samples, except IL6 by s4 and PTGS2 by s5 at 25 μg/ml (Fig 10A and 10D), while s3 at 25 μg/ml had the strongest inhibitory effect. Interestingly, in contrast to other samples (s1-s3), EH enriched samples (s4 and s5) at 25 μg/ml increased pro-inflammatory mediators within the cell viability range.

10.1371/journal.pone.0282875.g010 Fig 10 Relative protein levels and gene expression levels of inflammatory markers, predicted by network pharmacology analysis, were subjected to western blot and real-time quantitative PCR in palmitate-treated (0.5 mM) preadipocytes.

Real-time quantitative PCR results for (a) PTGS2, (d) IL6, and (f) TNF. (b) Representative blots of PTGS2 and β-actin. (c) Band intensities were measured densitometrically and divided by that of β-actin. Relative gene expressions were calculated by dividing Ct values by that of β-actin. Results are presented as the means ± SDs of three different experiments. ##P < 0.01, and ###P < 0.001 versus non-treated 3T3-L1 preadipocytes, and *P < 0.05, **P < 0.01, and ***P < 0.001 versus non-treated adipocytes.

4. Discussion

Synergy is a commonly used concept in herbal medicine which can have advantages over single compound-based treatments [45]. By definition, synergy is said to exist when the combined effect of constituents is greater than the effect expected by summing their individual effects [46]. The rationale of combination therapy is that a drug combination has a greater effect than single drug components by targeting multiple nodes in pathological pathways to overcome disease [47].

As herbal extracts are complex mixtures of numerous compounds, it is extremely difficult to predict their pharmaceutical potentials [48]. Furthermore, the synergistic and antagonistic effects of herbal extracts are even more difficult to study because these effects involve considerations of interactions between numerous constituents [46]. Network pharmacology based on omics tools and web pharmaceutical databases provides a novel means of systematically analyzing complex interactions between compounds and their biological functions. For instance, Zhang et al. predicted synergistic effects between ingredients of a herbal combination in a TCM formula for rheumatoid arthritis using a self-developed network pharmacology platform [49], and subsequently suggested that drug synergism might be the result of modulation of a feedback loop in the network [50].

Another group screened for effective combinations of herbal medicines and scrutinized their modes of action for the treatment of endometriosis using network pharmacology and data mining approaches [51]. This technique involves combining numerous databases and computational tools and provides a feasible means of understanding the effects of herbal combinations [52], empowering molecular fundamental for modernization of herbal prescription.

In the current study, the anti-obesity effects and mechanisms of different EH-CS samples on adipocytes were scrutinized. The study shows that: 1) EH extract has a greater impact on adipocyte viabilites thus limiting maximal concentration in vitro, 2) CS extract alone is not effective at reducing lipid accumulation in adipocytes in the long-term but does not reduce cell viability, and 3) EH-CS samples, especially s3 (EH-CS = 50:50), appear to have advantages over samples with higher EH ratios (s4, s5) in terms of efficacy and cell stability.

At a concentration of 25 μg/ml EH-CS combinations had variable effects on the expressions adipogenic genes, such as PPAR gamma, FABP4, and CEBPα, that were similar to or greater than those of EH (s5) (Fig 8). Their effects on phosphorylated AMPK levels suggested that s3 and s5 had similar inhibitory impacts on adipocyte differentiation. However, neural lipid contents in mature adipocytes were only significantly reduced by s3 (Fig 7B). There was a study that reports insulin-mimetic effects of CS, therefore, the adiposity of mature 3T3-L1 was increased by CS treatment on the contrary [53]. Regarding the EH, there was no reliable study that reports the efficacy of sole EH extract on 3T3-L1 preadipocytes differentiation or other similar in vitro systems. Additional experiments were conducted to explain the discrepancy between the inhibitory effects of EH-CS on adipogenesis gene expression and lipid accumulation.

The inconsistency in inhibition of COX-2 protein by different EH-CS ratios in high dose is critical point and implicating a lot in this study. The most prominent target predicted by compound-target network analysis was PTGS2 (COX-2), which was expected to be modulated by EH and CS (Fig 6). We verified the effects of EH-CS on PTGS2, IL-6, and TNF-a using a well-established PA-induced preadipocyte inflammatory model [54]. Furthermore, the gene expressions of IL-6 and COX-2 were significantly more increased by s4 and s5 at 25 μg/ml than at 10 μg/ml even the concentration was within the limit of cell viability (90% cell viability) (Fig 10A and 10D). In other samples, however, the pro-inflammatory property was not observed. Rather, they showed better anti-inflammatory activity in higher dose. This indicates detrimental effects of EH is successfully controlled by CS while preserving the anti-adipogenic efficacy, allowing us to use the combination safely in higher dose. This phenomenon is not common in other herbal combinations.

As was described, COX-2 mRNA and protein expressions were inhibited by EH-CS combinations, and s3 had the greatest effect (Fig 10A and 10B). It has been reported that modulation of inflammatory status in adipose tissue microenvironment is critical for adipogenesis, thus it is considered a therapeutic target [55]. Interestingly, CS extract (s1) tended to be less effective than EH extract (s5) at modulating the expressions of adipogenic genes or lipid synthesis markers in mature adipocytes (Figs 8 and 9). This suggests compounds in CS act in concert with those in EH to augment the pharmacological effect of EH but suppress EH-induced inflammation. Thus, our results suggest that EH and CS combinations can be used to reduce the amount of EH administered while preserving treatment effectiveness and improving safety.

It has been reported that the extracted amount of ephedrine (a major biologically active constituent of Ephedrae herba) from hot water extract of Ephedrae herba can be reduced by 57–83% when it was combined with other herbs [14]. If we review several obesity-related studies on herbal prescriptions containing the EH-CS combination, the weight ratios of EH to CS used were 40:60 [22], 30:70 [56], or 25:75 [57] or contained an even higher percentage of CS [58], which we ascribe to empirical learning of traditional medicine. Therefore, it appears additional study is needed to fully optimize the EH to CS ratio as per their clinical demands.

As a phytosterol found in many soybeans, stigmasterol has been extensively reviewed for its various benefits on health, including its outstanding anti-oxidant and anti-diabetic activities [59]. It has been reported to attenuate insulin resistance and hyperlipidemia in vivo, which are significant clinical features of obesity [60]. Linolenic acid, an essential fatty acid and also a polyunsaturated fatty acid with significant effects on obesity, is the source of the ethyl ester mandenol. Alpha linolenic acid has been reported to improve cholesterol homeostasis in HFD-fed mice model [61], and obesity-associated non-alcoholic liver disease [62]. As we illustrated in network pharmacologic analysis (Fig 6), these compounds might work in combination with other active compounds to attenuate obesity via modulating major targets including PTGS2, ADRB2, and NCOA2.

The present study has several limitations that warrant consideration. First, it is possible that other targets were not included in the network pharmacologic analysis due to their molecular features since we adopted cutoff in OB and DL. Many natural ingredients are known to be metabolized into smaller, biologically active, absorbable molecules by gut microbiota [63] and digestive enzymes [64,65]. Therefore, we suggest that detailed consideration of herbal constituents is required prior to in silico analysis to improve the accuracy and reliability of the data obtained.

Second, we performed in silico analysis combined with in vitro study, but no in vivo study. Thus our results do not take into account several factors related to drug efficacy and side effects associated with digestion, appetite (food intake), the nervous system (especially the potential sympathomimetic effect of EH and its alkaloid ephedrine [66]), and hormonal changes. We carefully estimate that the inhibitory efficacy of the sample on lipid accumulation which is seemingly not outstanding is due to the limitations of the in vitro system, where various factors are restricted.

Third, experiments performed at the molecular level were less than comprehensive. Based on compound-target network analysis, common targets of EH and CS, such as PTGS2, NCOA1, and ADRB2, and their associated molecules were investigated (Fig 6). In the future, we intend to investigate key compounds pair from two herbs. Notably, synergism between caffeine and ephedrine in the context of metabolism is well recognized [67]. However, little information is available on compounds that antagonize or modulate the side effects of ephedrine.

The present study proposes presence of unique combinational effect of two herbs used in treating obesity. Nonetheless, additional in vivo and clinical studies are required to confirm that the effects observed in adipocyte model manifest as anti-obesity effects in vivo.

Supporting information

S1 Graphical abstract (PNG)

Click here for additional data file.

S1 Raw images (PDF)

Click here for additional data file.

Abbreviations

BP biological process

BSA bovine serum albumin

CC cellular component

CS Coicis semen

DL drug likeness

DMEM Dulbecco’s Modified Eagle Medium

DPBS Dulbecco’s Phosphate Buffered saline

EH Ephedrae herba

FBS fetal bovine serum

GO gene ontology

KEGG Kyoto Encyclopedia of Genes and Genomes

MF molecular function

OB oral bioavailability

ORO oil red o

PA palmitic acid

PPI protein-protein interaction

TCMSP The traditional Chinese medicine systems pharmacology database and analysis platform

10.1371/journal.pone.0282875.r001
Decision Letter 0
Wang Chun-Hua Academic Editor
© 2023 Chun-Hua Wang
2023
Chun-Hua Wang
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
30 Jan 2023

PONE-D-22-34202Network pharmacology predicts combinational effect of novel herbal pair consist of Ephedrae herba and Coicis semen on adipogenesis in 3T3-L1 cellsPLOS ONE

Dear Dr. Kim,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Mar 16 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Chun-Hua Wang

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf.

2. Please ensure all the website links in the manuscript works. For instance, we are unable to access https://old.tcmsp-e.com/tcmsp.php.

3. Thank you for stating the following financial disclosure: 

 "LDW. (only author that was awarded fund)

This work was supported by the Basic Science Research Program of the Korean National Research Foundation funded by the Ministry of Education (Grant no. 2021R1A6A3A01086718 ).

https://www.nrf.re.kr/index

NO."

  

Please state what role the funders took in the study.  If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." 

If this statement is not correct you must amend it as needed. 

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

4. PLOS ONE now requires that authors provide the original uncropped and unadjusted images underlying all blot or gel results reported in a submission’s figures or Supporting Information files. This policy and the journal’s other requirements for blot/gel reporting and figure preparation are described in detail at https://journals.plos.org/plosone/s/figures#loc-blot-and-gel-reporting-requirements and https://journals.plos.org/plosone/s/figures#loc-preparing-figures-from-image-files. When you submit your revised manuscript, please ensure that your figures adhere fully to these guidelines and provide the original underlying images for all blot or gel data reported in your submission. See the following link for instructions on providing the original image data: https://journals.plos.org/plosone/s/figures#loc-original-images-for-blots-and-gels. 

  

In your cover letter, please note whether your blot/gel image data are in Supporting Information or posted at a public data repository, provide the repository URL if relevant, and provide specific details as to which raw blot/gel images, if any, are not available. Email us at plosone@plos.org if you have any questions.

5. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Partly

Reviewer #3: Partly

Reviewer #4: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: No

Reviewer #4: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The present manuscript from Lim and colleagues, entitled “Network pharmacology predicts combinational effect of novel herbal pair consist of Ephedrae herba and Coicis semen on adipogenesis in 3T3-L1 cells” applied network pharmacology analysis to propose a combinational effect of EH-CS herbal pair in treating obesity, and they further evaluated this effect in vitro with a 3T3-L1 adipocyte model.

1. Overall, this study is too simple. The anti-obesity effect of EH-CS combination remains on the surface, some description in the results is inconsistent with the actual figure, and discussion regards distinct effects caused by different doses in the same combination is lacking.

2. The manuscript needs to be polished the English grammar or spelling by a native speaker.

Obvious mistakes are everywhere:

1) Article (the/a) usage problems throughout the manuscript.

2) Abstract: line 27-“ we investigated the compound-target interactions of EH and CS in combination to predict their effects in combination” repeated in combination, the second one is excess.

3) Introduction: the last line is missing punctuation (full stop).

3. As a general rule for writing, titles/labels/legends for tables are always placed at the top of the table, while all the table captions were placed below the tables in this manuscript.

4. The picture does not convey the meaning.

1) In figure 3b, the descriptions don’t correspond to the actual Venn diagram. There is an overlap between CS target and the Obesity target, while the description and the number in the diagram indicate 0.

2) There is no symbol to indicate a significant difference in figure 7a .

3) Figure 7a, 30 μg/ml seems to increase the cell viability instead of decrease it. And I think you mean it reduces the cell viability to 90% instead of reducing by 90%.

5. The quality of the Immunoblot images is poor in Figure 8 and 10, especially the gray analysis results of Western blot are inconsistent with the actual images in figure 10. The original image of the entire PVDF membrane needs to be provided. The internal reference antibody is needed in Western blot in figure 8.

6. Regarding IL6, TNF, and PTGS2 (COX-2) gene changes in palmitate-stimulated (0.5 mM) preadipocytes incubated with the five EH-CS samples, 10 and 25 μg/ml seem to have the opposite effect. But the authors haven’t discussed this inconsistency at all.

Reviewer #2: As the author mentioned in the discussion, CS increased the adiposity of matured 3T3L1; however, lipid contents in the 3T3L1 cells were reduced by CS-EH combined extracts (Fig. 7b).

However, the expression of differentiation-related genes or the degree of phosphorylation of AMPK was not different from that of the EH-only treatment group, suggesting that the mixed treatment of CS and EH would have a substantial lipid reduction effect through the activity of pathways other than the differentiation-related genes. The author then suggested the inhibition of COX-2 as a significant pathway. However, the secretion of inflammatory cytokines such as IL-6 and TNF was not excellent in inhibition compared to the low-concentration EH-only treatment group.

I'd like to hear the author's opinion on this comment.

Reviewer #3: The authors aimed to investigate the combinational effect of novel herbal pair consist of Ephedrae herba and Coicis semen on adipogenesis in 3T3-L1 cells by the Network pharmacology. Although interesting, authors should add and clarify the following requirements:

1. Study title is “Network pharmacology predicts combinational effect of novel herbal pair consist of Ephedrae herba and Coicis semen on adipogenesis in 3T3-L1 cells”. The relationship between the study subjects should be elaborated in detail in the Introduction section.

2. Through network pharmacology analysis, the authors screened three common targets (PTGS2, NCOA1, and ADRB2), two common compounds (stigmasterol and mandenol), and one core target (IL-6) in EH-CS drug pairs. The authors should discuss the effects of these targets and compounds in obesity, separately.

3. In the introduction, the authors describe the beneficial effects of Ephedrae herba and Coicis semen on ameliorating obesity, but do not address why obesity was chosen as a disease to be explored in this study.

4. The authors screened the key targets of EH-CS from the PPI network. However, the authors should describe in more detail how to build the PPI network from the STRING database. Which specific parameters were set and why the parameter 0.4 was chosen?

5. In section 3.4, “The 152 obesity-related target genes were uploaded into the STRING database to obtain a PPI network, which was then analyzed in Cytoscape”. The figure of Cytoscape should be supplemented.

6. Kindly provide the citation details for the methods adopted in Cell Viability Assessment, Oil Red O Staining, and The Palmitate-induced Preadipocyte Inflammatory Model.

7. Result: the figure between 3.7 Cytotoxicity of EH-CS Samples on Preadipocytes and 3.8 Inhibitory Effects of EH-CS on Adipocyte Differentiation should be kept separate for data processing.

Some minor issues:

1. In line 327, is it "Fig.4" or "Fig.5"?

2. The title of the table should be located at the top of the table.

3 It is recommended that authors standardize the expression of numbers, when to express them in English (such as three) and when to express them in Arabic numerals (such as 152)?

4. The full names of TCMSP, OB, and DL in lines 126-127 should be supplemented, while the full names in lines 283-285 should be deleted.

5. Line 376: AMPK phosphorylation ratio should be corrected to AMPK phosphorylation / AMPK?

6. Line 208-209: the revised content should be deleted.

7. This manuscript needs to be checked for spelling and grammar errors

Reviewer #4: Please, check manuscript about spelling check. For example, in line 179, 'CO2' be better to be changed 'CO2(the underscript style of 2)'.

In the addition,

In line 199, '3: 2.' be checked.

In line 246, 'means±SDs' be better to be changed to 'mean ± SDs' (taking a blank).

In line 252, 'com-pounds' be better to be changed to 'compounds'.

In line 247 and 319, 'p-values' be better to be changed to 'P-values (an italic style of 'P').

In line 397, 403, 422, 423, 'P<' or 'P <' needs to be unified into one style.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Yuanyuan Deng

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

10.1371/journal.pone.0282875.r002
Author response to Decision Letter 0
Submission Version1
20 Feb 2023

Notifications to reviewers

- Annotated Line number in this “Response to reviewers” file indicates Line number in “Revised manuscript with track changes” file.

- Please also read “Response to reviewers.docx” file for revision figures used in this rebuttal document. Thank you very much.

Review Comments to the Author

Reviewer #1: The present manuscript from Lim and colleagues, entitled “Network pharmacology predicts combinational effect of novel herbal pair consist of Ephedrae herba and Coicis semen on adipogenesis in 3T3-L1 cells” applied network pharmacology analysis to propose a combinational effect of EH-CS herbal pair in treating obesity, and they further evaluated this effect in vitro with a 3T3-L1 adipocyte model.

� We really appreciate for your sincere comments on the manuscript. Reading your critical opinion greatly helped us to reconsider on publishing our manuscript with the presented data. We feel grateful that we still have a chance to explain the major points of the manuscript to reviewers.

� Many anti-obesity drugs were efficacious in reducing body weight for a short period, but the drug’s effectiveness was not sustained in long-term observation or serious side effects appeared.

� Ephedrae herba (EH) has long been used to treat obesity, however, the side effects of EH has been a significant issue by medical practitioners. EH was used in combination with other herbs to treat obesity; many of the cases were CS. We described it in Line 79.

� First, we tried to find reasonable evidences for existence of the herbal combination (EH-CS). After reviewing numerous Korean articles on various herbal prescriptions used on obesity, we have concluded that the combination is found frequently and is a significant pair in many prescriptions when compared with other herbal combinations.

� Therefore, we analyzed the interactions between herbs consisting anti-obesity prescription and presented it as 1-mode herb network (Figure 1).

� Then we used network pharmacological approaches (NP) to assume the potential mechanisms of the herbal combination. Prior to investigation, principal investigator of this study had the impression of definite roles of EH and CS in prescriptions, each was explained as Monarch (jun, 君) and Minister (chen, 臣) of herbal formula theory in Oriental medicine.

� To our surprise, the idea seems to be sound as demonstrated by NP results. All biological targets of CS were all included in EH targets which can be tentatively interpreted as below;

- The spectrum of whole pharmacological activities of EH is broader than CS.

- CS is supporting or modulating the partial function of EH by targeting the same proteins.

� EH and CS have several targets in common (20 targets) against obesity. These co-regulated targets might explain the decreased toxicity of EH and increased efficacy of EH-CS combination in clinic.

� Cell based study showed combinational effect of the EH-CS. However, the therapeutic efficacy of EH-CS combinations (s1-s5) on lipid accumulation was not corresponding to the anti-obesity mechanism (lipogenesis or adipose differentiation). But we found significant benefit of using EH-CS combination in suppressing deleterious effects induced by EH by inhibiting inflammatory markers.

� We have addressed all issues commented by reviewers. We really hope reviewers are satisfied with our revised manuscript.

1. Overall, this study is too simple. The anti-obesity effect of EH-CS combination remains on the surface, some description in the results is inconsistent with the actual figure, and discussion regards distinct effects caused by different doses in the same combination is lacking.

� We feel sorry if you felt the data of our manuscript is too simple.

� However, although it may seem simple, this paper has done three dimensions of analysis; Literature review, network pharmacologic analysis, and in vitro analysis. Also these data are supported by HPLC analysis.

� We admit that in vitro experiment data is not rigorous; however, we insist that the in vitro data is not the only part in this paper.

� We tried to improve the quality of this manuscript by responding all of comments by reviewers.

� The inconsistency of efficacy on inhibiting pro-inflammatory markers by s4 and s5 between two doses (10 and 25 μg/ml) implicates a lot in this study. The issue is addressed in reviewer comments 6.

(6. Regarding IL6, TNF, and PTGS2 (COX-2) gene changes in palmitate-stimulated (0.5 mM) preadipocytes incubated with the five EH-CS samples, 10 and 25 μg/ml seem to have the opposite effect. But the authors haven’t discussed this inconsistency at all.)

� We reinforced the discussion part to explain the meaning of inconsistency. .

� In discussion part Line 492, we added

- “The inconsistency in inhibition of COX-2 protein by different EH-CS ratios in high dose is critical point and implicating a lot in this study.”

� In discussion part Line 497, we added

- “even the concentration is within the limit of cell viability (90% cell viability)”

� In discussion part Line 498, we added

- “In other samples, however, the pro-inflammatory property was not observed. Rather, they showed better anti-inflammatory activity in higher dose. This indicates detrimental effects of EH was successfully controlled by CS while preserving the anti-adipogenic efficacy, allowing us to use the combination safely in higher dose. This phenomenon is not common in other herbal combinations.”

2. The manuscript needs to be polished the English grammar or spelling by a native speaker.

Obvious mistakes are everywhere:

1) Article (the/a) usage problems throughout the manuscript.

� We are terribly sorry for the grammatical errors. Despite we had thorough English proofreading by native speaker, some mistakes are still remaining after minor manuscript revision.

2) Abstract: line 27-“ we investigated the compound-target interactions of EH and CS in combination to predict their effects in combination” repeated in combination, the second one is excess.

� We thank you for your comment. We removed the excessive ‘in combination’ from the manuscript.

3) Introduction: the last line is missing punctuation (full stop).

� We corrected the sentence by adding punctuation.

3. As a general rule for writing, titles/labels/legends for tables are always placed at the top of the table, while all the table captions were placed below the tables in this manuscript.

� We appreciate for your valuable comment on placement of captions.

� We admit that it needs to be placed correctly. So we corrected it immediately.

4. The picture does not convey the meaning.

1) In figure 3b, the descriptions don’t correspond to the actual Venn diagram. There is an overlap between CS target and the Obesity target, while the description and the number in the diagram indicate 0.

� We are grateful for giving us a chance to explain.

� As we described in our manuscript, all CS targets (31 targets) were included in EH targets (described in result chapter 3.3 Active compound screening and key targets of EH-CS in combination; ‘interestingly, the 31 CS targets were included among EH targets.’). So, there is not a single target that only belongs to CS.

� That explains the ‘0’ in the venn diagram. We hope you satisfied with our explanation.

2) There is no symbol to indicate a significant difference in figure 7a .

� We appreciate for your comment on the significance test result of Figure 7a.

� We performed the statistical analysis with cell viability data (One way anova with Tukey post-hoc test, Revision Figure 1). However, there was no significant reduction in cell viability as per the statistical analysis.

� However, as we considered 90 percent of cell viability as good condition, we used 25 μg/ml as maximal concentrations of in vitro study.

� In some cases, viability (mean value as percentage) is heavily reduced while the statistical significance is not met (p>0.05). And opposite case is also possible. In our case, we think that the mean value of cell viability should come first than the statistical significance, since we are intended to find safe dose for further in vitro study.

� We hope you are satisfied with our explanation.

Revision Figure 1 Statistical analysis result of cell viability.

3) Figure 7a, 30 μg/ml seems to increase the cell viability instead of decrease it. And I think you mean it reduces the cell viability to 90% instead of reducing by 90%.

� We appreciate for your valuable comments.

� We confirmed the raw data of cell viability. No error was found in the raw data, but unfortunately, SD value was relatively large in the concentration (30 μg/ml).

� Despite we cannot rule out the possibility that it was due to experimental or measurement error, we consider 30 μg/ml of EH seems to be safe for cell condition.

� We needed to choose maximal concentration considering stability of all cells treated with five samples. In case of the s5 sample, the concentration value for cell viability seems to lay around 28.6 μg/ml, which was estimated by linear regression estimation. We described the deduction process in below (Revision Figure 2).

� Therefore, we acknowledged that the concentration of 25 μg/ml was acceptable and continued in vitro study.

� To avoid possible misunderstanding, we corrected the word (by 90%) as you recommended (to 90%).

Revision Figure 2 Estimation of limit concentration of s5 for 90% cell viability

5. The quality of the Immunoblot images is poor in Figure 8 and 10, especially the gray analysis results of Western blot are inconsistent with the actual images in figure 10. The original image of the entire PVDF membrane needs to be provided. The internal reference antibody is needed in Western blot in figure 8.

� Thank you for giving us sincere comments on western blot images.

� We want to be clear that the results analysis and blot images are accurate. We feel sorry that the standard deviations (SDs) of blot intensity are relatively large in some conditions. We did three times of western blot (the COX-2 protein). We obtained immunoblot images from three independent experiments on different days (We assume that it’s the reason for large SDs).

� We already submitted all our western blot images to journal. We also attached the images in here for reviewer to confirm (Review Figure 3). Please check it.

� We are not sure what exactly ‘the reference antibody’ means. In case you mean housekeeping gene like beta-actin as ‘the reference antibody’, here we presented with beta-actin images for the phosphorylated-AMPK/AMPK signals (Review Figure 4).

� We also revised figure 8 by adding beta-actin images. We really hope you are satisfied with our explanation.

Revision Figure 3 Three immunoblot images of COX-2 obtained in different days (for Figure 10b).

Revision Figure 4 Immunoblot images of beta-actin obtained in different days (for Figure 8a)

6. Regarding IL6, TNF, and PTGS2 (COX-2) gene changes in palmitate-stimulated (0.5 mM) preadipocytes incubated with the five EH-CS samples, 10 and 25 μg/ml seem to have the opposite effect. But the authors haven’t discussed this inconsistency at all.

� We would like to thank you for bringing up critical points on discussion part.

� As you pointed out, the inconsistency of COX-2 protein inhibition by different EH-CS ratios is critical point and implicating a lot in this study.

� As we presented, the cell viability was notably affected by EH treatment. Moreover, the inflammation was induced at 25 μg/ml of s4 and s5 recognized by increased COX-2 protein and IL6 mRNA levels.

� The point is that, when cells were treated with EH-enriched samples (s4, s5) at 25 μg/ml, the samples have detrimental effects even though the concentration is within the limit concentration (90% cell viability). This phenomenon is not usual with other herbal samples.

� However, in other samples, the inflammation-inducing property was not observed at high concentration. Rather, they showed better activity (inflammatory inhibition) in higher concentration. This might be due to the pharmacological activities of CS and is what we wanted to emphasize in our study.

� Our team is already conducting next study using this EH-CS combination to investigate the synergistic efficacy using combination of single compound (from CS) and herb (EH).

� We added some points in discussion part to emphasize the significance of our findings.

- In Line 500, “This indicates detrimental effects of EH is successfully controlled by CS while preserving the anti-adipogenic efficacy, allowing us to use the combination safely in higher dose. This phenomenon is not common in other herbal combinations.”

Reviewer #2: As the author mentioned in the discussion, CS increased the adiposity of matured 3T3L1; however, lipid contents in the 3T3L1 cells were reduced by CS-EH combined extracts (Fig. 7b).

However, the expression of differentiation-related genes or the degree of phosphorylation of AMPK was not different from that of the EH-only treatment group, suggesting that the mixed treatment of CS and EH would have a substantial lipid reduction effect through the activity of pathways other than the differentiation-related genes.

The author then suggested the inhibition of COX-2 as a significant pathway. However, the secretion of inflammatory cytokines such as IL-6 and TNF was not excellent in inhibition compared to the low-concentration EH-only treatment group.

I'd like to hear the author's opinion on this comment.

� We appreciate for your sincere comments on our manuscript. We are grateful that we had another chance to explain about it.

� We humbly suggest you to focus on the other area than the AMPK and its downstream pathways.

� In fact, this study was intended to find the reason why combination is necessarily used rather than the EH or CS alone. As you can see, the EH-CS was not showing outstanding efficacy in reducing lipid accumulation in matured adipocytes, as compared to other prominent materials used in other studies by peer researchers.

� In our opinion, the reason is the efficacy of CS in controlling side effects caused by EH. Therefore, we predicted the common targets to be the mechanisms to control side effects. We further investigated the network pharmacology-deduced common targets of CS and EH.

� As you can see in figure 8 and 9, EH alone exerted strongest inhibiting activity in adipogenesis along with enhanced activity of AMPK. However, ironically, the inhibition of lipid accumulation in s5 was poor. This needed to be explained.

� We investigated the expression of COX-2, which is a remarkable inflammatory marker and common target of both EH and CS. It showed significant increase in high dose (25 μg/ml) of EH-enriched samples (s4 and s5) as compared to low dose (10 μg/ml), even the concentration is within the limit concentration of cell viability. This is not usual phenomenon in other drugs. We suspect this phenomenon was induced due to detrimental effects of EH on cell physiology with unidentified mechanism. It is well known that the pro-inflammatory condition can significantly stimulate adipogenesis in preadipocytes.

� However, the s3 or other CS-enriched samples (s1, s2) showed better performances in reducing inflammatory markers expression in higher dose (except few cases). This means that, EH can be used safer and better when combined with CS.

� In summary, EH has strong efficacy in inhibiting adipogenesis while it also has inflammatory/toxicity issue. But the use of EH in combination with CS greatly resolves the issue while it preserving the efficacy.

� This type of combinational effect has not been reported very much. This mutual pharmacological activity of two herbs is somewhat similar to what they described in herbal formula theory in Oriental medicine.

� The usage of two herbs in combination has been in empirical field used by practitioners. We think now is the right time to report the herbal combination into scientific field.

Reviewer #3: The authors aimed to investigate the combinational effect of novel herbal pair consist of Ephedrae herba and Coicis semen on adipogenesis in 3T3-L1 cells by the Network pharmacology. Although interesting, authors should add and clarify the following requirements:

1. Study title is “Network pharmacology predicts combinational effect of novel herbal pair consist of Ephedrae herba and Coicis semen on adipogenesis in 3T3-L1 cells”. The relationship between the study subjects should be elaborated in detail in the Introduction section.

� We are grateful for your sincere comments on introduction part. As you pointed out, the rationale for using network pharmacologic approach needs to be described in introduction.

� Therefore, in Line 86-88, we wrote as “Network pharmacology, an integrative tool for analyzing pharmacological mechanism of herbal medicine [23], can be used to decipher complex interactions between numerous targets and compounds derived from EH and CS.”

2. Through network pharmacology analysis, the authors screened three common targets (PTGS2, NCOA1, and ADRB2), two common compounds (stigmasterol and mandenol), and one core target (IL-6) in EH-CS drug pairs. The authors should discuss the effects of these targets and compounds in obesity, separately.

� We appreciate your sincere advice to write more about important targets and compounds discovered through network pharmacology.

� We wrote more about key targets in line 89-98 with new references ([24-28]).

� “Prostaglandin-endoperoxide synthase2 (PTGS2) is inducible enzyme expressed in inflamed conditions leading to biosynthesis of prostaglandins [24]. It has been noted that inhibition of COX activity affects adipocyte differentiation via decreasing inflammatory cytokines [25]. A member of the nuclear hormone receptor coactivator family, nuclear receptor coactivator 2 (NCOA2) controls adipogenesis, lipid metabolism, and fat absorption to maintain metabolic balance [26]. Adrenoreceptor beta 2 (ADRB2) is connected to elevated noradrenaline release brought on by exposure to cold, which activates lipolysis and thermogenesis [27]. Interleukin-6 (IL6) is pro-inflammatory mediator which is suggested as cause of systemic low grade inflammation in obesity [28]. By offering a variety of strategies in various metabolic pathways, those genes can be used as targets for the treatment of obesity.”

� We also wrote more about key compounds in line 519-527 with new references ([59-62])

� “As a phytosterol found in many soybeans, stigmasterol has been extensively reviewed for its various benefits on health, including its outstanding anti-oxidant and anti-diabetic activities [59]. It has been reported to attenuate insulin resistance and hyperlipidemia in vivo, which are significant clinical features of obesity [60]. Linolenic acid, an essential fatty acid and also a polyunsaturated fatty acid with significant effects on obesity, is the source of the ethyl ester mandenol. Alpha linolenic acid has been reported to improve cholesterol homeostasis in HFD-fed mice model [61], and obesity-associated non-alcoholic liver disease [62]. As we illustrated in network pharmacologic analysis (Fig 6), these compounds might work in combination with other active compounds to attenuate obesity via modulating major targets including PTGS2, ADRB2, and NCOA2.”

3. In the introduction, the authors describe the beneficial effects of Ephedrae herba and Coicis semen on ameliorating obesity, but do not address why obesity was chosen as a disease to be explored in this study.

� Thank you for giving us kind comments on introduction part.

� As you indicated, EH-CS combination has been frequently used in treating obesity by clinical practitioners. This was clearly described in introduction part (Line 79-82).

� We think it is a basic strategy to investigate efficacy of herbs (or natural products) as per their traditional medicinal usages.

4. The authors screened the key targets of EH-CS from the PPI network. However, the authors should describe in more detail how to build the PPI network from the STRING database. Which specific parameters were set and why the parameter 0.4 was chosen?

� Thank you for raising an important point on construction of PPI network.

� The parameter of 0.4 was chosen as it is a figure of medium confidence which is most frequently used. If we raise the criteria, the interactions between target proteins will be loosening as a result.

� There was no other modification in setting up PPI network. In fact, there aren’t many modifiable settings in STRING, except some options related to visualization (like font, presentation of protein name, disable bubble design etc.). These options does not affect the structure of network .

5. In section 3.4, “The 152 obesity-related target genes were uploaded into the STRING database to obtain a PPI network, which was then analyzed in Cytoscape”. The figure of Cytoscape should be supplemented.

� We welcome your valuable suggestion.

� We think that the total PPI network consists of 152 obesity-related target is not much informative for readers. Including it in the manuscript would consume too much space, as the figure does not provide any crucial information. The total network was only used for extracting core network in this study.

� Furthermore, the core network consists of 31 target genes from total network is already attached as figure 4.

� Therefore, we decided not to insert the image in main manuscript. If you insist, however, we will change it.

� Here we attached the original image of 152 obesity-related PPI network for your understanding (Revision figure 5). We are confident that reviewer will agree with our opinion.

Revision Figure 5 Total PPI network of EH-CS consists of 152 target genes.

6. Kindly provide the citation details for the methods adopted in Cell Viability Assessment, Oil Red O Staining, and The Palmitate-induced Preadipocyte Inflammatory Model.

� We feel gratitude for suggesting us good points on references for specific experimental methods.

� We added two references ([41] for cell viability assessment, [42] for oil red o staining) as per your suggestion.

� Reference [43] is already cited in the main manuscript for palmitate-induced inflammatory model for preadipocytes.

7. Result: the figure between 3.7 Cytotoxicity of EH-CS Samples on Preadipocytes and 3.8 Inhibitory Effects of EH-CS on Adipocyte Differentiation should be kept separate for data processing.

� We appreciate your valuable comments on figure 7.

� We understand that you asked the two figures (Fig. 7A and Fig. 7B) should be separated into different figures (Fig. 7, Fig. 8).

� However, we assume this rearrangement of figure will deteriorate overall quality of the manuscript and give bad impression since the total number of figures is going to be large (total 11 figures).

� We gently ask you to withdraw the opinion. However, if you insist, then we will separate the figure as you requested.

Some minor issues:

1. In line 327, is it "Fig.4" or "Fig.5"?

� We confirmed it. The Fig. 4 is correct. The figure 4 is annotated in both section 3.4 and 3.5.

2. The title of the table should be located at the top of the table.

� We checked and corrected it immediately.

3 It is recommended that authors standardize the expression of numbers, when to express them in English (such as three) and when to express them in Arabic numerals (such as 152)?

� We regarded it as reasonable to express numbers in English when it modifies a significant numbers of sample, trials, core targets less than 10. On the contrary, huge numbers with less significance were expressed in Arabic numerals as it doesn’t need to be expressed in English.

� If you still think it needs to be changed, we will gladly change it.

4. The full names of TCMSP, OB, and DL in lines 126-127 should be supplemented, while the full names in lines 283-285 should be deleted.

� Sorry for the mistake. We corrected it.

5. Line 376: AMPK phosphorylation ratio should be corrected to AMPK phosphorylation / AMPK?

� We agree with your suggestion. We corrected it.

6. Line 208-209: the revised content should be deleted.

� We corrected it by using track change function.

7. This manuscript needs to be checked for spelling and grammar errors

� We fully admit that our manuscript needs to be checked for grammatical errors.

� Other reviewers also pointed out the same issue. We read the manuscript carefully and revised it.

� We appreciate again for your warm and kind comments.

Reviewer #4: Please, check manuscript about spelling check. For example, in line 179, 'CO2' be better to be changed 'CO2(the underscript style of 2)'.

� We appreciate for your kind comments on our manuscript. We carefully read and improved the quality of our manuscript.

� We replaced it with underscript character (CO2).

In the addition,

In line 199, '3: 2.' be checked.

� We checked it. We changed it to '3 : 2’ (spacing after 3).

In line 246, 'means±SDs' be better to be changed to 'mean ± SDs' (taking a blank).

� We corrected it as per your suggestion.

In line 252, 'com-pounds' be better to be changed to 'compounds'.

� Sorry for the mistake. It must be derived from the copy-pasting process from the last version of manuscript. We corrected it immediately.

In line 247 and 319, 'p-values' be better to be changed to 'P-values (an italic style of 'P').

� We agree with your suggestion on Italicizing ‘P’ used in ‘P-value’. We corrected it in line 247 and 319. (Line 268 and 343 in revised manuscript)

� In addition, we Italicized P in Line 179, 180 (Line in revised manuscript). Also, we changed P to Italics in Table 4.

In line 397, 403, 422, 423, ‘P<’ or ‘P <’ needs to be unified into one style.

� We decided to unify all space rule after ‘less than sign’ (<).

� Therefore, we corrected with it throughout the manuscript to have space before ‘<’ sign.

� We want to express our deepest thank for your kind comments.

Attachment Submitted filename: Response to reviewers.docx

Click here for additional data file.

10.1371/journal.pone.0282875.r003
Decision Letter 1
Wang Chun-Hua Academic Editor
© 2023 Chun-Hua Wang
2023
Chun-Hua Wang
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
27 Feb 2023

Network pharmacology predicts combinational effect of novel herbal pair consist of Ephedrae herba and Coicis semen on adipogenesis in 3T3-L1 cells

PONE-D-22-34202R1

Dear Dr. Kim,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Chun-Hua Wang

Academic Editor

PLOS ONE

Additional Editor Comments:

The authors have addressed the issues raised by the reviewers, so, it can be accepted in this version.

10.1371/journal.pone.0282875.r004
Acceptance letter
Wang Chun-Hua Academic Editor
© 2023 Chun-Hua Wang
2023
Chun-Hua Wang
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
2 Mar 2023

PONE-D-22-34202R1

Network pharmacology predicts combinational effect of novel herbal pair consist of Ephedrae herba and Coicis semen on adipogenesis in 3T3-L1 cells

Dear Dr. Kim:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Chun-Hua Wang

Academic Editor

PLOS ONE
==== Refs
References

1 Reddy AS , Zhang S . Polypharmacology: drug discovery for the future. Expert Rev Clin Pharmacol. 2013;6 (1 ):41–7. Epub 2013/01/01. doi: 10.1586/ecp.12.74 ; PubMed Central PMCID: PMC3809828.23272792
2 Wang Y , Yang H , Chen L , Jafari M , Tang J . Network-based modeling of herb combinations in traditional Chinese medicine. Brief Bioinform. 2021;22 (5 ). Epub 2021/04/10. doi: 10.1093/bib/bbab106 ; PubMed Central PMCID: PMC8425426.33834186
3 Chen L , Hu C , Hood M , Kan J , Gan X , Zhang X , et al . An Integrated Approach Exploring the Synergistic Mechanism of Herbal Pairs in a Botanical Dietary Supplement: A Case Study of a Liver Protection Health Food. Int J Genomics. 2020;2020 :9054192. Epub 2020/05/01. doi: 10.1155/2020/9054192 ; PubMed Central PMCID: PMC7171619.32351982
4 Kim HU , Ryu JY , Lee JO , Lee SY . A systems approach to traditional oriental medicine. Nat Biotechnol. 2015;33 (3 ):264–8. Epub 2015/03/10. doi: 10.1038/nbt.3167 .25748918
5 Zhong J , Liu Z , Zhou X , Xu J . Synergic Anti-Pruritus Mechanisms of Action for the Radix Sophorae Flavescentis and Fructus Cnidii Herbal Pair. Molecules. 2017;22 (9 ). Epub 2017/09/05. doi: 10.3390/molecules22091465 ; PubMed Central PMCID: PMC6151778.28869563
6 Guo L , Gong M , Wu S , Qiu F , Ma L . Identification and quantification of the quality markers and anti-migraine active components in Chuanxiong Rhizoma and Cyperi Rhizoma herbal pair based on chemometric analysis between chemical constituents and pharmacological effects. J Ethnopharmacol. 2020;246 :112228. Epub 2019/09/13. doi: 10.1016/j.jep.2019.112228 .31513838
7 Wang YL , Zhang Q , Yin SJ , Cai L , Yang YX , Liu WJ , et al . Screening of blood-activating active components from Danshen-Honghua herbal pair by spectrum-effect relationship analysis. Phytomedicine. 2019;54 :149–58. Epub 2019/01/23. doi: 10.1016/j.phymed.2018.09.176 .30668364
8 Ge H , Wang YF , Xu J , Gu Q , Liu HB , Xiao PG , et al . Anti-influenza agents from Traditional Chinese Medicine. Nat Prod Rep. 2010;27 (12 ):1758–80. Epub 2010/10/14. doi: 10.1039/c0np00005a .20941447
9 Kim B-S , Song M-y , Kim H . The anti-obesity effect of Ephedra sinica through modulation of gut microbiota in obese Korean women. Journal of ethnopharmacology. 2014;152 (3 ):532–9. doi: 10.1016/j.jep.2014.01.038 24556223
10 Boozer CN , Daly PA , Homel P , Solomon JL , Blanchard D , Nasser JA , et al . Herbal ephedra/caffeine for weight loss: a 6-month randomized safety and efficacy trial. Int J Obes Relat Metab Disord. 2002;26 (5 ):593–604. Epub 2002/05/29. doi: 10.1038/sj.ijo.0802023 .12032741
11 Diepvens K , Westerterp KR , Westerterp-Plantenga MS . Obesity and thermogenesis related to the consumption of caffeine, ephedrine, capsaicin, and green tea. Am J Physiol Regul Integr Comp Physiol. 2007;292 (1 ):R77–85. Epub 2006/07/15. doi: 10.1152/ajpregu.00832.2005 .16840650
12 Park SJ , Shon DH , Ryu YH , Ko Y . Extract of Ephedra sinica Stapf Induces Browning of Mouse and Human White Adipocytes. Foods. 2022;11 (7 ). Epub 2022/04/13. doi: 10.3390/foods11071028 ; PubMed Central PMCID: PMC8998140.35407115
13 Kim H-J , Han C-H , Lee E-J , Song Y-K , Shin B-C , Kim Y-K . A clinical practice guideline for Ma-huang (Ephedra sinica) prescription in obesity. Journal of Korean Medicine for Obesity Research. 2007;7 (2 ):27–37.
14 Hwang M-J , Shin H-D , Song M-Y . Literature review of herbal medicines on treatment of obesity since 2000; mainly about ephedra herba. Journal of Korean Medicine for Obesity Research. 2007;7 (1 ):39–54.
15 Devaraj RD , Jeepipalli SP , Xu B . Phytochemistry and health promoting effects of Job’s tears (Coix lacryma-jobi)-A critical review. Food Bioscience. 2020;34 :100537.
16 Watanabe M , Kato M , Ayugase J . Anti-diabetic effects of adlay protein in type 2 diabetic db/db mice. Food Science and Technology Research. 2012;18 (3 ):383–90.
17 Manosroi A , Sainakham M , Chankhampan C , Abe M , Manosroi W , Manosroi J . Potent in vitro anti-proliferative, apoptotic and anti-oxidative activities of semi-purified Job’s tears (Coix lachryma-jobi Linn.) extracts from different preparation methods on 5 human cancer cell lines. Journal of ethnopharmacology. 2016;187 :281–92. doi: 10.1016/j.jep.2016.04.037 27125591
18 Lu X , Liu W , Wu J , Li M , Wang J , Wu J , et al . A polysaccharide fraction of adlay seed (Coix lachryma-jobi L.) induces apoptosis in human non-small cell lung cancer A549 cells. Biochemical and Biophysical Research Communications. 2013;430 (2 ):846–51. doi: 10.1016/j.bbrc.2012.11.058 23200838
19 STRONG JM . 28-day Repeated Dose Oral Toxicity Test of Coix lacryma-jobi L. var. ma-yuen Stapf in Rats. 日本補完代替医療学会誌. 2009;6 (3 ):131–5.
20 Yuan H , Ma Q , Cui H , Liu G , Zhao X , Li W , et al . How can synergism of traditional medicines benefit from network pharmacology? Molecules. 2017;22 (7 ):1135. doi: 10.3390/molecules22071135 28686181
21 Song Y-K , Cha Y-Y , Ko S-G . Analysis of the obesity-related research for each constituent herb of Euiiin-tang. Journal of Korean Medicine for Obesity Research. 2014;14 (2 ):72–9.
22 Jang J-W , Lim D-W , Chang J-U , Kim J-E . The Combination of Ephedrae herba and Coicis semen in Gambihwan Attenuates Obesity and Metabolic Syndrome in High-Fat Diet–Induced Obese Mice. Evidence-Based Complementary and Alternative Medicine. 2018;2018 . doi: 10.1155/2018/5614091 30210573
23 Zhang G-B , Li Q-Y , Chen Q-L , Su S-B . Network pharmacology: a new approach for Chinese herbal medicine research. Evidence-based complementary and alternative medicine. 2013;2013 . doi: 10.1155/2013/621423 23762149
24 Chan P-C , Liao M-T , Hsieh P-S . The dualistic effect of COX-2-mediated signaling in obesity and insulin resistance. International Journal of Molecular Sciences. 2019;20 (13 ):3115. doi: 10.3390/ijms20133115 31247902
25 Banhos Danneskiold-Samsøe N , Sonne SB , Larsen JM , Hansen AN , Fjære E , Isidor MS , et al . Overexpression of cyclooxygenase-2 in adipocytes reduces fat accumulation in inguinal white adipose tissue and hepatic steatosis in high-fat fed mice. Scientific reports. 2019;9 (1 ):1–13.30626917
26 Lu Y , Habtetsion TG , Li Y , Zhang H , Qiao Y , Yu M , et al . Association of NCOA2 gene polymorphisms with obesity and dyslipidemia in the Chinese Han population. International Journal of Clinical and Experimental Pathology. 2015;8 (6 ):7341. 26261634
27 Kurylowicz A , Jonas M , Lisik W , Jonas M , Wicik ZA , Wierzbicki Z , et al . Obesity is associated with a decrease in expression but not with the hypermethylation of thermogenesis-related genes in adipose tissues. Journal of translational medicine. 2015;13 :1–10.25591711
28 Eder K , Baffy N , Falus A , Fulop AK . The major inflammatory mediator interleukin-6 and obesity. Inflammation Research. 2009;58 :727–36. doi: 10.1007/s00011-009-0060-4 19543691
29 Chu H-M , Kim C-H , Moon Y-J , Sung K-K , Lee S-K . A Comparative Study on the Herb Network of Prescriptions in the Dongui-Bogam Wind Chapter. The Journal of Internal Korean Medicine. 2017;38 (6 ):1007–20.
30 Breiger RL . The duality of persons and groups. Social forces. 1974;53 (2 ):181–90.
31 Barboza K , Beretta V , Kozub PC , Salinas C , Morgenfeld MM , Galmarini CR , et al . Microsatellite analysis and marker development in garlic: distribution in EST sequence, genetic diversity analysis, and marker transferability across Alliaceae. Molecular genetics and genomics. 2018;293 (5 ):1091–106. doi: 10.1007/s00438-018-1442-5 29705936
32 UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research. 2021;49 (D1 ):D480–D9. doi: 10.1093/nar/gkaa1100 33237286
33 Stelzer G , Rosen N , Plaschkes I , Zimmerman S , Twik M , Fishilevich S , et al . The GeneCards suite: from gene data mining to disease genome sequence analyses. Current protocols in bioinformatics. 2016;54 (1 ):1.30. 1–1. 3. doi: 10.1002/cpbi.5 27322403
34 Piñero J , Bravo À , Queralt-Rosinach N , Gutiérrez-Sacristán A , Deu-Pons J , Centeno E , et al . DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic acids research. 2016:gkw943. doi: 10.1093/nar/gkw943 27924018
35 Szklarczyk D , Gable AL , Nastou KC , Lyon D , Kirsch R , Pyysalo S , et al . The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic acids research. 2021;49 (D1 ):D605–D12. doi: 10.1093/nar/gkaa1074 33237311
36 Shannon P , Markiel A , Ozier O , Baliga NS , Wang JT , Ramage D , et al . Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research. 2003;13 (11 ):2498–504. doi: 10.1101/gr.1239303 14597658
37 Kanehisa M , Furumichi M , Sato Y , Ishiguro-Watanabe M , Tanabe M . KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 2021;49 (D1 ):D545–D51. Epub 2020/10/31. doi: 10.1093/nar/gkaa970 ; PubMed Central PMCID: PMC7779016.33125081
38 Gene Ontology C. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 2021;49 (D1 ):D325–D34. Epub 2020/12/09. doi: 10.1093/nar/gkaa1113 ; PubMed Central PMCID: PMC7779012.33290552
39 Jiao X , Sherman BT , Huang da W , Stephens R , Baseler MW , Lane HC , et al . DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics. 2012;28 (13 ):1805–6. Epub 2012/05/01. doi: 10.1093/bioinformatics/bts251 ; PubMed Central PMCID: PMC3381967.22543366
40 Bonnot T , Gillard MB , Nagel DH . A simple protocol for informative visualization of enriched gene ontology terms. Bio-protocol. 2019:e3429–e.
41 Lim D-W , Bose S , Wang J-H , Choi HS , Kim Y-M , Chin Y-W , et al . Modified SJH alleviates FFAs-induced hepatic steatosis through leptin signaling pathways. Scientific Reports. 2017;7 (1 ):45425. doi: 10.1038/srep45425 28358008
42 Lim D-W , Kim H , Kim Y-M , Chin Y-W , Park W-H , Kim J-E . Drug repurposing in alternative medicine: herbal digestive Sochehwan exerts multifaceted effects against metabolic syndrome. Scientific Reports. 2019;9 (1 ):9055. doi: 10.1038/s41598-019-45099-x 31227732
43 Bradley RL , Fisher FF , Maratos-Flier E . Dietary fatty acids differentially regulate production of TNF-alpha and IL-10 by murine 3T3-L1 adipocytes. Obesity (Silver Spring). 2008;16 (5 ):938–44. Epub 2008/03/22. doi: 10.1038/oby.2008.39 ; PubMed Central PMCID: PMC4862864.18356844
44 Kim JY , Lee HJ , Lee SJ , Jung YH , Yoo DY , Hwang IK , et al . Palmitic Acid-BSA enhances Amyloid-beta production through GPR40-mediated dual pathways in neuronal cells: Involvement of the Akt/mTOR/HIF-1alpha and Akt/NF-kappaB pathways. Sci Rep. 2017;7 (1 ):4335. Epub 2017/07/01. doi: 10.1038/s41598-017-04175-w ; PubMed Central PMCID: PMC5489526.28659580
45 Williamson EM . Synergy and other interactions in phytomedicines. Phytomedicine. 2001;8 (5 ):401–9. Epub 2001/11/07. doi: 10.1078/0944-7113-00060 .11695885
46 Caesar LK , Cech NB . Synergy and antagonism in natural product extracts: when 1+ 1 does not equal 2. Natural product reports. 2019;36 (6 ):869–88. doi: 10.1039/c9np00011a 31187844
47 Araujo RP , Doran C , Liotta LA , Petricoin EF . Network-targeted combination therapy: a new concept in cancer treatment. Drug Discovery Today: Therapeutic Strategies. 2004;1 (4 ):425–33.
48 Pelkonen O , Xu Q , Fan T-P . Why is research on herbal medicinal products important and how can we improve its quality? Journal of Traditional and Complementary Medicine. 2014;4 (1 ):1–7. doi: 10.4103/2225-4110.124323 24872927
49 Zhang B , Wang X , Li S . An integrative platform of TCM network pharmacology and its application on a herbal formula, Qing-Luo-Yin. Evidence-based complementary and alternative medicine. 2013;2013 . doi: 10.1155/2013/456747 23653662
50 Li S , Zhang B , Zhang N . Network target for screening synergistic drug combinations with application to traditional Chinese medicine. BMC Syst Biol. 2011;5 Suppl 1 :S10. Epub 2011/06/28. doi: 10.1186/1752-0509-5-S1-S10 ; PubMed Central PMCID: PMC3121110.21689469
51 Zheng W , Wu J , Gu J , Weng H , Wang J , Wang T , et al . Modular characteristics and mechanism of action of herbs for endometriosis treatment in Chinese medicine: a data mining and network pharmacology–based identification. Frontiers in pharmacology. 2020;11 :147. doi: 10.3389/fphar.2020.00147 32210799
52 Yang M , Chen J-L , Xu L-W , Ji G . Navigating traditional Chinese medicine network pharmacology and computational tools. Evidence-based complementary and alternative medicine. 2013;2013 . doi: 10.1155/2013/731969 23983798
53 Kim J , Choi Y , Ju Y , Park S , Lee M , Kim H , et al . Effect of insulin-like action and insulin sensitizing on 3T3-L1 adipocytes from coicis semen. J Korean Orient Med. 2002;23 (1 ):83–91.
54 Wang M , Chen Y , Xiong Z , Yu S , Zhou B , Ling Y , et al . Ginsenoside Rb1 inhibits free fatty acidsinduced oxidative stress and inflammation in 3T3L1 adipocytes. Mol Med Rep. 2017;16 (6 ):9165–72. Epub 2017/10/11. doi: 10.3892/mmr.2017.7710 .28990058
55 Fuggetta MP , Zonfrillo M , Villiva C , Bonmassar E , Ravagnan G . Inflammatory Microenvironment and Adipogenic Differentiation in Obesity: The Inhibitory Effect of Theobromine in a Model of Human Obesity In Vitro. Mediators Inflamm. 2019;2019 :1515621. Epub 2019/02/26. doi: 10.1155/2019/1515621 ; PubMed Central PMCID: PMC6360562.30804705
56 Park K , Ho LH , KYUNG SY . Anti-inflammation and anti-obesity effects of Euiiin-tang granules on high fat diet-induced obese C57BL/6J mice. Journal of Korean Medicine Rehabilitation. 2012;22 (2 ):47–66.
57 Han J , Shin Y , Oh J , Keum D . Anorexigenic effect of Taeyeumjowuitang (Taiyintiaowei-tang) in obese Zucker rat. J Orient Rehab Med. 2005;15 :131–45.
58 Kim K-S , Song J-C . Effects of Chegameyiin-tang extract on the change of the weight, tissue in epididymal fat, blood, leptin and uncoupled protein in visceral fat of obesity rats induced by high fat diet. Journal of Korean Medicine for Obesity Research. 2001;1 (1 ):85–100.
59 Bakrim S , Benkhaira N , Bourais I , Benali T , Lee LH , El Omari N , et al . Health Benefits and Pharmacological Properties of Stigmasterol. Antioxidants (Basel). 2022;11 (10 ). Epub 2022/10/28. doi: 10.3390/antiox11101912 ; PubMed Central PMCID: PMC9598710.36290632
60 Wang J , Huang M , Yang J , Ma X , Zheng S , Deng S , et al . Anti-diabetic activity of stigmasterol from soybean oil by targeting the GLUT4 glucose transporter. Food & nutrition research. 2017;61 (1 ):1364117. doi: 10.1080/16546628.2017.1364117 28970778
61 O’Reilly ME , Lenighan YM , Dillon E , Kajani S , Curley S , Bruen R , et al . Conjugated linoleic acid and alpha linolenic acid improve cholesterol homeostasis in obesity by modulating distinct hepatic protein pathways. Molecular nutrition & food research. 2020;64 (7 ):1900599. doi: 10.1002/mnfr.201900599 31917888
62 Takic M , Pokimica B , Petrovic-Oggiano G , Popovic T . Effects of Dietary α-Linolenic Acid Treatment and the Efficiency of Its Conversion to Eicosapentaenoic and Docosahexaenoic Acids in Obesity and Related Diseases. Molecules. 2022;27 (14 ):4471.35889342
63 Wang HY , Qi LW , Wang CZ , Li P . Bioactivity enhancement of herbal supplements by intestinal microbiota focusing on ginsenosides. Am J Chin Med. 2011;39 (6 ):1103–15. Epub 2011/11/16. doi: 10.1142/S0192415X11009433 ; PubMed Central PMCID: PMC3349338.22083984
64 Ng ZX , See AN . Effect of in vitro digestion on the total polyphenol and flavonoid, antioxidant activity and carbohydrate hydrolyzing enzymes inhibitory potential of selected functional plant‐based foods. Journal of Food Processing and Preservation. 2019;43 (4 ):e13903.
65 Qin W , Ketnawa S , Ogawa Y . Effect of digestive enzymes and pH on variation of bioavailability of green tea during simulated in vitro gastrointestinal digestion. Food Science and Human Wellness. 2022;11 (3 ):669–75.
66 Brown CE , Trauth SE , Grippo RS , Gurley BJ , Grippo AA . Combined effects of ephedrine-containing dietary supplements, caffeine, and nicotine on morphology and ultrastructure of rat hearts. Journal of Caffeine Research. 2012;2 (3 ):123–32. doi: 10.1089/jcr.2012.0021 24761270
67 Dulloo A , Seydoux J , Girardier L . Peripheral mechanisms of thermogenesis induced by ephedrine and caffeine in brown adipose tissue. International journal of obesity. 1991;15 (5 ):317–26. 1885257