Abstract
Natural bioactive compounds and plant constituents are considered to
have a positive anti-inflammatory effect. This study aimed to establish
a screening technique for anti-inflammatory function in foods based on
label-free Raman imaging. A visible anti-inflammatory analysis method
based on coherent anti-Stokes Raman scattering (CARS) was established
with an LPS-induced RAW264.7 cell model. Dynamic changes in proteins
and lipids were determined at laser pump light wavelengths of
2956 cm^−1 and 2856 cm^−1, respectively. The method was applied to a
plant-based formula (JC) with anti-inflammatory activity. Q-TOF-MS and
HPLC analyses revealed the main active constituents of JC as quercetin,
kaempferol, l-glutamine, and sodium copper chlorophyllin. In in vitro
and in vivo verification experiments, JC showed significant
anti-inflammatory activity by regulating the TLR4/NF-κB pathway. In
conclusion, this study successfully established a label-free and
visible method for screening anti-inflammatory constituents in
plant-based food products, which will facilitate the evaluation of
functional foods.
Keywords: Anti-inflammatory, Plant-based ingredient, Raman imaging
technique, Coherent anti-stokes Raman scattering
Highlights
* •
An effective label-free evaluation method for functional food were
established.
* •
Coherent anti-Stokes Raman scattering (CARS) imaging was used.
* •
A plant-based formula with anti-inflammatory activity was
evaluated.
* •
The active components were quercetin, l-glutamine and sodium copper
chlorophyllin.
* •
Anti-inflammatory effect was associated with TLR4 and NF-kappa B
pathways.
1. Introduction
In modern society, involving stress, exposure to environmental toxins,
and consumption of highly processed foods, there is a growing awareness
of the benefits of a diet rich in plant-based foods for the prevention
and, indeed, treatment of chronic diseases. Accumulating evidence
supports the role of plant constituents in promoting physiological
functions beyond basic nutrition ([41]Buathong & Duangsrisai, 2023).
The traditional therapeutic application of herbs and extracts has
globally laid a foundation centuries ago, and the current market for
plant-based functional foods, food supplements, and drugs has shown an
explosive growth. Modern pharmacology provides convincing evidence to
support such products, and the antioxidant and anti-inflammatory
properties of plant constituents are a primary focus of study.
Inflammation is a natural immune response to stress, injury, infection,
or toxic stimuli, and chronic inflammatory diseases such as metabolic
syndrome, dementia, and cancer account for more than half of the total
deaths worldwide. The pathogenesis of inflammation is complex, but
production of nitric oxide (NO) and release of cytokines are
accompanying factors widely used for the preliminary evaluation of
agents with anti-inflammatory activity ([42]Hou, Chen, Yang, & Ji,
2020). Pro-inflammatory cytokines produced by immune cells are commonly
regarded as the main target for evaluation ([43]Khan et al., 2014), and
anti-inflammatory activities on macrophage cell lines are a primary
mode of assessment. Measuring NO production, mRNA expression,
expression of inflammatory modulators (interleukin-1β/2/5/6/8/10, tumor
necrosis factor α [TNF-α], prostaglandin E2-PGE2, and others), and key
proteins in macrophage cells RAW264.7, or other cell types, can provide
evidence for anti-inflammatory activity in vitro ([44]Gresa-Arribas et
al., 2012). For in vivo studies, mouse, rat, and zebrafish are
generally selected as animal models ([45]Brugman, 2016; [46]Inada,
Hirota, & Shingu, 2015). Lipopolysaccharide (LPS), xylene, arachidonic
acid, croton oil, dextran sulfate sodium (DSS) and
2,4,6-trinitrobenzene sulfonic acid are frequently employed to induce
an inflammatory response in these models ([47]Antoniou et al., 2016;
[48]Perse & Cerar, 2012). Although these methods of inflammation
evaluation are used in standard practice, their complexity and errors
arising from the use of a single activity test can have serious
drawbacks (D. [49]Zhang, Wang, Slipchenko, & Cheng, 2014).
The ability to obtain spatiotemporal function information from
biological systems in a label-free manner can solve many problems
currently emerging in activity screening applications ([50]Camp &
Cicerone, 2015). A real-time, visible monitoring technique that
provides a comprehensive evaluation index of both pharmacodynamic and
mechanism information would be highly desirable for development
([51]Tan et al., 2022; D. [52]Zhang et al., 2014). Coherent Raman
scattering (CRS) microscopy provides feasibility for noninvasive
label-free visualization of endogenous biomolecules in biological
samples, by detecting vibrations of chemical bonds within molecules
([53]Shi et al., 2023). Unlike the spontaneous Raman scattering, CRS
utilizes a nonlinear process to enhance the Raman signal for fast
imaging. As a CRS technology, coherent anti-Stokes Raman scattering
(CARS) microscopy offers high spatial resolution and sufficient
chemical contrast without single photon fluorescence; thus, it has been
successfully applied in the field of life sciences ([54]Steuwe et al.,
2014). Many of the applications of CARS microscopy focus on imaging
lipids or lipid droplets (LDs) to study their metabolism under certain
conditions([55]Borek-Dorosz et al., 2022; C. [56]Zhang & Boppart,
2020). LDs play a crucial role in lipid metabolism and cell signaling,
and the dysregulation of lipid metabolism can lead to many diseases. By
acquiring CARS images at different wavenumbers, more detailed
components such as LDs, proteins, and DNA can be visualized inside
cells or tissues([57]Fung & Shi, 2020).
Based on the label-free CARS imaging technique, this study established
a method for screening compounds with anti-inflammatory activity; And a
plant-based formula (JC) with the potential anti-inflammatory activity
were evaluate. In the context of a network pharmacological analysis, in
vitro and in vivo tests were undertaken on JC to verify its
anti-inflammatory activity and to explore the mechanisms involved.
Thus, this study established an effective label-free Raman imaging
technique for screening anti-inflammatory function in foods.
2. Materials and methods
2.1. Materials
Dulbecco's modified Eagle medium (DMEM) and fetal bovine serum (FBS)
were purchased from HyClone (Logan, UT, USA). 100× Penicillin,
streptomycin (P/S), and NO product kit were purchased from Sangon
Biotech (Shanghai, China). Trypsin (0.25%) and Nuclear and Cytoplasmic
Protein Extraction Kit were obtained from Beyotime (Shanghai, China).
Dimethyl sulfoxide and MTT were purchased from Sigma–Aldrich (St.
Louis, MO, USA). Enzyme-linked immunosorbent assay (ELISA) kits for
mouse interleukin (IL)-1β and IL-10 were purchased from NeoBioscience
(Shenzhen, China). LPS was sourced from Macklin (Shanghai, China). RNA
extraction kit was purchased from Yuduo (Shanghai, China). SYBR Green
polymerase chain reaction (PCR) kit and RevertAid First Strand cDNA
Synthesis Kit were purchased from Thermo (Waltham, MA, USA).
Bicinchoninic acid (BCA) Protein Assay Kit was obtained from Biosharp
(Anhui, China). Polyvinylidene difluoride (PVDF) membranes were
purchased from Millipore (Darmstadt, Germany). Phosphate-buffered
saline (PBS) was sourced from Puhe (Wuxi, China). The primary
antibodies (NF-Κb p65, TBP) used for western blot analyses were
purchased from Abcam (Cambridge, London, UK); PKC-γ and GAPDH were
sourced from HuaBio (Hangzhou, China) and Proteintech (Chicago, IL,
USA), respectively. The JC was obtained from Nature's Sunshine Products
Inc. (Shanghai, China).
2.2. LPS-induced cell model and CARS imaging
RAW264.7 cell line was purchased from Chinese Tissue Culture
Collections (CTCC) and cultured in DMEM (supplemented with 10% FBS and
1% P/S) in a humidified incubator at 5% CO[2] and 37 °C.
Four groups were established for the CARS-based anti-inflammatory
assay: (1) Control group (untreated cell lines); (2) LPS group (LPS),
wherein the final concentration of LPS was 1 μg/mL; (3) LPS + quercetin
group (LQ), wherein the final concentrations of LPS and quercetin were
1 μg/mL and 2 μg/mL, respectively; (4) LPS + JC group (LJC), wherein
the final concentration of LPS and JC were 1 μg/mL and 16 μg/mL,
respectively. Stock solutions of LPS, quercetin, and JC were prepared
to 1.0, 1.0, and 32.2 mg/mL, respectively, in PBS solution, and samples
were diluted to the appropriate concentration for each experiment.
The wave number range for CARS was deduced as follows. Based on a
network pharmacology calculation and a literature review, the
interacting proteins and signaling pathways of the main constituents
could be summarized by AKT-related pathways. The amino acid sequences
of each protein based on their quantity from highest to lowest were
sorted, and the amino acids in the top two ranks were selected. The
side chain group structures of these amino acids were used as the
criteria for defining the CARS wavenumber range.
The CARS sample preparation process was as follows. First, a
double-sided tape was applied onto a glass slide, and a sample space
(approx.10 mm) was hollowed out at the center of the tape.
Subsequently, a small amount of culture medium containing cells
(1–2 μL) was added into the sample space. A piece of cover glass was
overlaid to ensure that the droplets did not touch the double-sided
tape. Finally, the upper and lower sides of the sample were adhered to
the water mirror and the oil mirror, respectively, to collect the CARS
image. Cells were evaluated after 24 and 48 h. A supercontinuum fiber
laser, serving as the pump beam for CARS signal excitation, provided a
pulse train with a repetition rate of 40 MHz and an adjustable output
between 400 and 2400 nm. The total power and pulse duration were 4 W
and 58 ps, respectively. Another pulse train was employed as the Stokes
beam, with the excitation source being centered at 1064 nm, a pulse
duration of 186 fs, and a pulse power of 1.1 W. The optical system
structure and parameters were designed according to a published study
([58]Zhang et al., 2022). Data processing was performed with ImageJ
(version v1.52i). The intensity ratio of bright and dark areas,
assessed by manual selection, was used as an evaluation index, and the
calculation methods were in accordance with the built-in analysis tool.
The signal-to-noise ratio (SNR) was also employed as an evaluation
index.
2.3. Network pharmacology analysis
2.3.1. Predicting potential targets of JC
Targets of JC were searched from TCMSP database (screened based on
OB ≥ 30%, DL ≥ 0.18, [59]https://old.tcmsp-e.com/tcmsp.php) and Swiss
Target Prediction System ([60]http://www.swisstargetprediction.ch/,
accessed on August 12, 2022). Duplicates were removed, and information
from the UniProt database ([61]https://www.uniprot.org/, accessed on
June 30, 2022) was used to standardize the target names into gene
names.
2.3.2. Inflammation-associated target genes
The keyword “resist inflammation” was inputted into the GeneCards
database ([62]https://www.genecards.org/, accessed on August 8, 2022)
to search for target genes. Plant formula and inflammation-associated
targets genes were imported into the Venny 2.1 online mapping tool
platform ([63]https://bioinfogp.cnb.csic.es/tools/venny/index.html) to
obtain intersecting genes.
2.3.3. Drug–target–disease network construction
Cytoscape 3.9.1 software was applied for visual analyses of the
drug–target–disease network using the Network Analyzer plug-in for
network topology analysis to obtain degree values and betweenness
centrality.
2.3.4. Protein–protein interaction network construction
Protein–protein interaction (PPI) network data were obtained from the
STRING online database ([64]http://cn.string-db.org, accessed on August
12, 2022) by inputting the aforementioned intersecting target genes.
The parameter “Organisms” was limited to “Homo sapiens,” and the medium
confidence score was set at >0.900, with the disconnected nodes having
removed. The PPI network was visualized using Cytoscape.
2.3.5. Enrichment of gene ontology and Kyoto encyclopedia of genes and
genomes pathways
The Metascape database ([65]http://metascape.org, accessed on August
13, 2022) was utilized to analyze the Gene Ontology (GO) and Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathway enrichments. The top
20 data in each enrichment analysis were further visualized with an
online platform for data analysis and visualization
([66]https://www.bioinformatics.com.cn, last accessed on August 13,
2022).
2.4. Quadrupole–time of flight–mass spectrometry analysis
An Agilent 1290 Infinity UHPLC system was used for this analysis
(Agilent 6545 Quadrupole–Time of Flight–Mass Spectrometer with a dual
jet stream electrospray ion source; Agilent, USA). An Endeavorsil
1.8 μm C18 column (50 × 2.1 mm) was employed for detecting both
positive and negative modes. The binary gradient elution system
consisted of (A) 0.1% (v/v) formic acid dissolved in acetonitrile and
(B) 0.1% (v/v) formic acid dissolved in water. Separation was achieved
using the following gradient: 0 min, 5% A; 0.5 min, 5% A; 5 min, 30% A;
9.5 min, 90% A; 9.75 min, 5% A; and 12 min, 5% A. The flow rate was set
at 0.3 mL/min at a column temperature of 40 °C, and all sample
injection volumes were 5 μL. The mass range was from m/z 100 to 1200.
The mass spectrometer operated was as follows: spray voltage, 4000 V in
positive mode and 3500 V in negative mode; gas temperature, 350 °C;
drying gas flow rate, 8 L/min; nebulizer pressure, 35 psig; and
fragmentor, 175 V (115 V for Glucoraphanin). Data were collected via
Agilent MassHunter workstation software and evaluated by MassHunter
qualitative and quantitative analysis software.
2.5. HPLC analysis
A high-performance liquid chromatography (HPLC) system was used
(SPD-2 A UV detector, CTO-10AS column oven, LC-2AT pump; Shimadzu,
Japan). An Inertsil ODS-SP C18 (4.6 × 250 nm, 5 μm) column was
employed. The binary gradient elution system consisted of (A)
chromatography grade methanol and (B) 0.05 mol/L potassium dihydrogen
phosphate solution. Separation of the components was achieved by using
the following gradient: 0 min, 100% B; 10 min, 95% B; 25 min, 80% B;
30 min, 100% B; flow rate of 1 mL/min and a column temperature of
30 °C. Separation was carried out using the following gradient. All
samples were injected at a volume of 20 μL (per injection) during the
assay, and detection was performed at a wavelength of 235 nm.
2.6. In vitro experiments
2.6.1. Cell viability assay
RAW264.7 cells in the logarithmic growth phase were uniformly seeded
into 96-well plates at a density of 1 × 10^5 cells/well. After 24-h
incubation, according to the double-dilution method, the control group
and JC groups (0.5, 1.01, 2.01, 4.02, 8.05, 16.1, and 32.2 μg/mL) were
set up with three replicate wells. The MTT method was used to measure
the optical density (OD) values in different wells using a microplate
analyzer at 490 nm. The survival rate of RAW264.7 cells was calculated
according to the following formula:
[MATH: survival rate=ODdrug−ODblankODcontrol−ODblank×100% :MATH]
2.6.2. Nitric oxide assay
RAW264.7 cells (2 × 10^5 cells/well) were seeded in 12-well plates and
incubated for 24 h. Cells were treated with different concentrations
(0, 8, 16, 32, and 64 μg/mL) for JC and 1 μg/mL LPS for 24, 48, and
72 h. The medium was collected at each time point to determine NO
production.
2.6.3. Reverse transcription–polymerase chain reaction assay
The groups were set as control group, LPS group (1 μg/mL LPS), and JC
group (16 μg/mL W and 1 μg/mL LPS). The preparation of the cells and
tissues was carried out according to the kit instructions. mRNA was
extracted from each group using the RNAsimple total RNA Kit. cDNA was
synthesized by reverse transcription using the PrimeScript™ 1st Strand
cDNA Synthesis Kit. A 20-μL Real-time PCR system was established, and
cDNA was reverse transcribed by real-time fluorescent quantitative PCR
assay. The mRNA expression profiles were normalized to that of GAPDH.
The primers used for this assay were as follows: GAPDH forward
5′-GGTGAAGGTCGGTGTGAACG-3′ and reverse 5′-CTCGCTCCTGGAAGATGGTG-3′; TLR2
forward 5′-TGGTTCCCTGCTCGTTCT-3′ and reverse 5′-CTCAAATGATTCTGGCTC-3′;
TLR4 forward 5′-GAGCCGTTGGTGTATCTT-3′ and reverse
5′’-GTTGCCGTTTCTTGTTCT-3′; IL-6 forward 5′-ATGATGGATGCTACCAAACT-3′ and
reverse 5′-TATCTCTCTGAAGGACTCTG-3′; IL-1β forward
5′-TGTGTAATGAAAGACGG-3′ and reverse 5′-TGTGAGGTGCTGATGTA-3′; TNF-α
forward 5′-CGTCGTAGCAAACCACC3′ and reverse 5′-CCCTTGAAGAGAACCTG-3′.
2.6.4. Enzyme-linked immunosorbent assay (ELISA)
RAW264.7 cells growing on 12-well plates were divided into three
groups. In addition to the control group, RAW264.7 cells were treated
with LPS (1 μg/mL) in the presence or absence of JC (16 μg/mL) for 24,
48, and 72 h. ELISA kits were used to assess the secretion of IL-1β and
IL-10 from the supernatants of cell cultures using the ELISA Kit.
2.6.5. Western blot analysis
The cells (5 × 10^5 cells/well) were cultured in 60-mm culture dishes,
and the treatment method was the same as that of ELISA. After
incubating the cells for 72 h, the cellular nuclear and cytoplasmic
proteins were extracted using the Nuclear and Cytoplasmic Protein
Extraction Kit. Total protein concentration was detected according to
the BCA method, and the standard curve was constructed according to the
following formula:
[MATH: A=0.5644Cmg/ml+0.0239 :MATH]
The sodium dodecyl sulfate–polyacrylamide gel electrophoresis method
was used to visualize the adhesive proteins (concentrated gel: 80 V,
separator gel: 120 V). The gel was transferred onto a PVDF membrane and
was blocked with confining liquid for 1 h (5% skimmed milk). NF-κB p65
(1:2000), PKC-γ (1:1000), TBP (1:1000), and GAPDH (1:1000) antibodies
were added to the membranes and incubated overnight at 4 °C. The
membranes were washed five times (10 min each time) with Tris-buffered
saline with Tween 20 and incubated with the corresponding
peroxidase-conjugated secondary antibody (1:5000 dilution) for 1 h at
room temperature. The PVDF membranes were covered using an
electrochemiluminescence kit and were photographed using a ChemiScope
5300 Pro instrument.
2.7. In vivo experiments
Thirty-six BALB/c female 4-week-old mice with bodyweight of
20.0 ± 1.0 g were purchased from Xi'an Ensiweier Biotechnology (license
number: SCXK (Xiang) 2019–0004). The mice were housed in a clean,
specific pathogen-free grade environment at constant temperature and
humidity. All experiments were conducted under internationally accepted
guidelines for laboratory animal use and care, approved by the
Committee for Animal Use and Care in the Laboratory (Laboratory Animal
Ethics No. CDDEACL2022–37).
The mice were divided into three groups, as shown in [67]Fig. 1: (1)
control group (n = 3), wherein saline was administered intragastrically
(ig); (2) DSS group (n = 15), wherein 600 μL of 2.5% DSS was
administered three times a day from day 7 to day 14 (ig); (3) JC group
(n = 18), wherein 650 mg/d of JC was administered from day 1 to day 44
(ig) and 2.5% DSS was added the same way as that in the DSS group from
day 7 to day 14. The mental state, feeding, and daily activities of the
mice were observed every day. Disease activity indices (DAIs) were
recorded on days 5, 11, 23, 30, and 44. The scoring criteria for the
DAI were as follows: 0 points for no weight loss, normal stool
consistency, and no blood in the stool; 1 point for weight loss of
1%–5%; 2 points for weight loss of 5%–10%, semi-liquid stool, and
positive occult blood in the stool; 3 points for weight loss of
10%–15%; and 4 points for weight loss >15%, loose stool, and visible
blood in the stool. In the JC and DSS groups, three mice from each
group were killed by cervical dislocation on days 15, 23, 30, and 44.
Blood and intestinal tissue samples were collected.
Fig. 1.
[68]Fig. 1
[69]Open in a new tab
Design of in vivo experiment.
Hematoxylin-eosin (HE) staining was performed to visualize the tissue
structure. Colon and duodenal tissues were fixed with 4%
paraformaldehyde for HE staining. The remaining colon tissues were
stored in liquid nitrogen for western blot analysis. Intestinal tissues
were embedded in paraffin and then sliced into 5-mm-thick sections.
2.8. Statistical analysis
Experimental results were presented as mean ± standard deviation (SD)
through triplicate experiments. Statistical analysis was performed
using GraphPad Prism software (GraphPad Software Inc., Avenida, CA,
USA). The p values were calculated using the t-test. p < 0.05 was
considered as statistically significant, and n.s. indicated not
statistically significant. Details of each statistical analysis are
provided in the figure captions.
3. Results and discussion
3.1. Construction of the CARS imaging method for anti-inflammatory assay
Fluorescent labels have been used widely for real-time imaging of
biomolecule dynamics. However, because of their molecular size,
fluorescent protein labels might destroy or significantly perturb the
biological activities of small biomolecules ([70]Georgakoudi & Quinn,
2012). As a chemically selective, highly sensitive, and high-speed
imaging technique with submicron resolution, CARS imaging can be used
to perform quantitative assessment of the metabolic activities of
biomolecules (e.g., lipids, proteins, and nucleic acids) in single live
cells with a label-free protocol ([71]Yue & Cheng, 2016). Thus, this
study constructed a validation method for anti-inflammatory drugs based
on CARS imaging.
A primary analysis of the mechanism revealed that LPS-induced
inflammation can alter protein and lipid contents in cells. In the
carbon‑hydrogen (C-H) region, CH[3] asymmetric stretch (2956 cm^−1)
([72]Corasi Ortiz, Xie, Ribbe, & Ben-Amotz, 2006), CH[3] stretching
vibrations (2947 cm^−1) ([73]Caspers et al., 2001), CH stretch of
lipids and proteins (2931 cm^−1), and CH[2] symmetric stretch
(2856 cm^−1) ([74]Koljenović, Schut, Vincent, Kros, & Puppels, 2005)
were selected by controlling the wavelength of the pump light. As shown
in [75]Fig. 2A, LPS-induced RAW264.7 cells were imaged with CARS at
different wavenumbers. After 48 h since LPS-induced inflammation,
RAW264.7 cells exhibited changes in the amount of intracellular
substances at different wavenumber. To acquire the signal changes in a
better manner, two data analysis modes were established. The first mode
focused on the intensity ratio between bright and dark areas in cells,
which served as a comparison between active and inactive physiological
regions of the cells. The intensity ratio was calculated by using the
average of three cells in the same wavenumber CARS images at different
time points in each group. The second mode, based on the SNR to show
changes in cellular physiological activity compared with that in the
external environment, was calculated from the ratio of the mean value
of the bright region to the background standard deviation. The
intensity ratio and SNR are shown in Table S1. From 2956 cm^−1 to
2856 cm^−1, the intensity ratio presented a downward trend and the SNR
remained relatively stable with no significant trend. Thus, 2956 cm^−1
and 2856 cm^−1 were chosen as the imaging parameters for evaluating
anti-inflammatory activity.
Fig. 2.
[76]Fig. 2
[77]Open in a new tab
Analysis of CARS imaging results. (A) CARS imaging of LPS-induced
inflammation in four wavenumber ranges; (B) Distinction of RAW264.7
cells CARS imaging after different drug treatments at 24 h and 48 h and
(C) Gray statistical map of intensity ratio and SNR.
The CARS imaging results are shown in [78]Fig. 2B and C. Generally, the
2959 cm^−1 spectral range was identified with protein imaging, while
2856 cm^−1 was associated with lipid imaging. The intensity ratio of
the LPS group in CARS imaging at 2959 cm^−1 was higher than that of the
LQ group, with the ratio for LJC being the lowest, and the data showed
minimal changes between 24 h and 48 h time points. These results
suggested that both quercetin and JC could inhibit the inflammatory
effects of LPS. As for the intensity ratio at 2856 cm^−1, the results
showed an increasing trend over time for all groups, except JC. The SNR
results indicated that the CARS signal intensity showed only little
variation relative to noise with the exception of that for the LPS
group after 24 h. A semi-quantitative analysis of intensity ratio could
better intuitively reflect the anti-inflammatory activity. The
inconsistent changes between the two vibrational bands (as shown in
[79]Fig. 2C) might be attributed to two reasons: (1) the main
functional groups contributing to the signals are different; and (2)
the final concentration of the anti-inflammatory drugs is lower,
leading to lower activity.
In conclusion, the CARS imaging results provided a compelling
indication that JC might be a potential anti-inflammatory plant-based
formula. The following tests were performed to confirm the
anti-inflammatory activity of JC, both in vitro and in vivo.
3.2. Network pharmacology analysis
Network pharmacology analysis was carried out initially, before
accurate verification experiments. After removing duplicates and
standardizing target names, a total of 210 target constituents of JC
were filtered from TCMSP and Swiss Target Prediction databases. As
shown in [80]Fig. 3A, the size of the blue rhombuses and polygons,
corresponding to target genes and JC constituents, respectively,
represents the degree value. Setting “resist inflammation” as the
keyword, 1249 of 9117 inflammation-associated targets were screened
from the GeneCards database, with a relevance score of >5. The 210
targets of JC were mapped with the 1249 anti-inflammation targets on a
Venn diagram ([81]Fig. 3B), identifying 126 targets with potential
therapeutic effects for inflammatory conditions. A PPI network exported
from the STRING database was optimized using Cytoscape to gain a better
visualization and analyze the interaction of the targets ([82]Fig. 3C)
through a topology analysis. Subsequently, the node degree and
betweenness centrality above the average value were used to select
significant targets, including AKT1, TNF, IL-10, and MAPK1 (Table S2).
Similarly, topological analysis of the drug–target–disease network
identified quercetin, kaempferol, and arachidonic acid as significantly
active compounds ([83]Fig. 3D; Table S3).
Fig. 3.
[84]Fig. 3
[85]Open in a new tab
Network pharmacology analysis of JC for anti-inflammatory effects. (A)
Plant formula-target network; (B) Venn diagram showing the intersection
of drug targets and anti-inflammatory targets; (C) PPI network of
common target protein; (D) Drug−target−disease network. The blue
rhombus nodes represent common targets, and the polygon node represents
represent ingredients of the plant-based formula. (E) KEGG pathway
analysis; (F) Molecular function analysis; (G) Biological process
analysis; (H) Cellular component analysis. (For interpretation of the
references to colour in this figure legend, the reader is referred to