Abstract
Dendrobium mixture (DM) is a patented Chinese herbal medicine indicated
which has anti-inflammatory and improved glycolipid metabolism.
However, its active ingredients, targets of action, and potential
mechanisms are still uncertain. Here, we investigate the role of DM as
a prospective modulator of protection against non-alcoholic fatty liver
disease (NAFLD) induced by type 2 diabetes mellitus (T2DM) and
illustrate the molecular mechanisms potentially involved. The network
pharmacology and TMT-based quantitative protomics analysis were
conducted to identify potential gene targets of the active ingredients
in DM against NAFLD and T2DM. DM was administered to the mice of DM
group for 4 weeks, and db/m mice (control group) and db/db mice (model
group) were gavaged by normal saline. DM was also given to
Sprague-Dawley (SD) rats, and the serum was subjected to the palmitic
acid-induced HepG2 cells with abnormal lipid metabolism. The mechanism
of DM protection against T2DM-NAFLD is to improve liver function and
pathological morphology by promoting peroxisome proliferator-activated
receptor γ (PPARγ) activation, lowering blood glucose, improving
insulin resistance (IR), and reducing inflammatory factors. In db/db
mice, DM reduced RBG, body weight, and serum lipids levels, and
significantly alleviated histological damage of liver steatosis and
inflammation. It upregulated the PPARγ corresponding to the prediction
from the bioinformatics analysis. DM significantly reduced inflammation
by activating PPARγ in both db/db mice and palmitic acid-induced HepG2
cells.
Keywords: dendrobium mixture, non-alcoholic fatty liver disease, type 2
diabetes mellitus, network pharmacology, TMT-based quantitative
protomics, inflammation, PPAR gamma
1 Introduction
NAFLD produces two key components of the metabolic syndrome (MetS),
glucose and triglycerides ([36]Ferguson and Finck, 2021; [37]Stefan and
Cusi, 2022). The association between T2DM and NAFLD is complex and
bidirectional, and the two often co-occur in the context of the
metabolic syndrome. Consequently, the liver is a key determinant of
abnormal glucolipid metabolism. The “twice hit” theory states that
NAFLD can further deteriorate and develop into non-alcoholic
steatohepatitis (NASH), which mainly manifests as a fatty buildup and
inflammation in the liver. Studies have demonstrated that T2DM
predisposes to the development of NAFLD through IR and hyperglycemia,
and its more severe form, NASH, may contribute to the risk of cirrhosis
and malignant tumors of the liver ([38]Jarvis et al., 2020; [39]Targher
et al., 2021). The alarming thing is that there are still unapproved
drug therapies for the disease. It is particularly crucial to explore
effective treatment methods and explain the molecular mechanism
underlying T2DM with NAFLD.
Dendrobium mixture (DM) (patent. no. ZL201110408411.0) consisted of
Huang Qi [HQ, Astragalus membranaceus (Fisch.) Bge.], Shi Hu (SH,
Dendrobium nobile Lindl.), Dan Shen (DS, Salvia miltiorrhiza Bge.), Niu
Xi (NX, Achyranthes bidentata Bl.), Wu Wei Zi [WWZ, Schisandra
chinensis (Turcz.) Baill.], and Zhi Mu (ZM, Anemarrhena asphodeloides
Bge.), etc. ([40]Chen et al., 2021; [41]Wang et al., 2022; [42]Zheng et
al., 2022). Our works here already showed that DM can be advantageous
in reducing IR, improving disorders of glucolipid metabolism,
anti-oxidation damage, reducing inflammation, and gradually improving
diabetic liver and kidney function ([43]Lin et al., 2018; [44]Chen et
al., 2021). Therefore, we hope that this study will allow us to explore
the targets of action of the active ingredients of dm, as well as to
explain some of the mechanisms underlying the treatment of T2DM-NAFLD.
We also expect to provide fundamental evidence for future drug
research.
Network pharmacology identifies key gene and protein networks
associated with target diseases through database information collection
and software analysis. Network pharmacology has also gained popularity
in studying complex herbal combinations where multiple bioactive
components act and interact with multiple genes and protein targets
([45]Hopkins, 2008). Tandem mass tagging (TMTs) is a frequently used
technique for differential proteomics and has been widely used for
screening disease markers, drug action targeting, animal and plant
therapeutic mechanism, animal and plant development and differentiation
mechanism, and other fields. Due to their high sensitivity, wide range,
high throughput, and high efficiency, TMTs are the application of the
most preferred proteomics technology in recent years ([46]Moulder et
al., 2018).
The protein changes and potential mechanisms of NAFLD and T2DM after DM
intervention will be analyzed by us in this work by TMTs, and then the
main active ingredients of DM and their predicted targets of action in
NAFLD and T2DM will then be analyzed by network pharmacology theory. We
investigated the pharmacological effect of the active ingredients of DM
in the therapy of T2DM combined with NAFLD mice based on multiple
analyses of bioinformatics.
2 Materials and methods
2.1 DM preparation
The composition and preparation of DM decoction followed our previous
study ([47]Chen et al., 2021; [48]Zheng et al., 2022). All herbs (108 g
of the raw materials per day) were first steeped in 800 mL of water and
brought to a boil over high heat, then simmered on low heat for 15 min.
Filter the liquid and repeat the process with 800 mL of water for the
remaining herbs. Finally, the herbal solution from the two decoctions
is concentrated in 1.62 g/mL of ingredients in the formula. The main
active ingredients in DM included catalpol, harpaside, puerarin,
timosaponin BII, astragaloside IV, tanshinone IIA and schisandrin
([49]Zheng et al., 2022).
2.2 Animal model and experimental intervention
Ten male db/m mice (8 weeks old, 20 ± 2 g), and forty male db/db mice
(8 weeks old, 36 ± 2 g) from Nanjing Junke Bio-Technology Co., Ltd. The
db/db mice are Leptin receptor gene deficient mice which have been
widely used as a model of diabetes and non-alcoholic fatty liver
disease and usually db/m mice were used as the control group for the
experiments ([50]Guilbaud et al., 2019; [51]Suriano et al., 2021). The
animals were kept in the Animal Experiment Center of Fujian University
of Traditional Chinese Medicine. Gavage dosages in DM were determined
by consideration of the equivalent conversion of human dose for a mouse
model and several studies, including an animal model ([52]Nair and
Jacob, 2016). The dosage of Chinese medicine conforms to the standards
stipulated in the current Chinese Pharmacopoeia ([53]Cai et al., 2019).
The Control group used 10 db/m mice, and 20 db/db mice, randomly
grouped according to blood glucose and body weight, with 10 mice in
each group, were used as the model group and the DM group,
respectively. All groups were gavaged with normal saline except for the
DM group, and the dose of DM group was converted according to the adult
clinical equivalent dose, and the DM concentration was 16.2 g/kg/d. The
gavage volume of all mice was 0.1 mL/10 g. The gavage was performed at
9:00 a.m. every day, and the body weight was monitored weekly.
After the end of gavage, the blood was taken, fasted without water for
8 h before taking, and 5% uratan was used for intraperitoneal
injection; after the mice were anesthetized, the eyes were fully
exposed and blood was collected by removing the eyeballs, and blood was
gathered in 1.5 mL centrifuge tubes, and place the centrifuge in a
low-temperature centrifuge (4°C, 8,000 g, 15 min). Liver tissues were
separated and stored partially in 4% paraformaldehyde and partially at
−80°C after aspiration of excess liquid. This study obtained approval
from the Experimental Animal Ethics Committee of Fujian University of
Traditional Chinese Medicine (approval number: FJTCM IACUC2021079).
2.3 TMT-based quantitative protomics analysis
2.3.1 Sample preparation with TMT reagents
Liver tissue was lysed in SDT buffer, UA buffer was repeatedly
ultrafiltered, and iodoacetamide was added for incubation. Finally, the
proteins were digested. A C18 column (Sigma, United States) was used to
desalinate the peptides. After protein quantification, sample labeling
was performed. Reagents were used iTRAQ reagent (Applied Biosystems)
and TMT reagent (Thermo Scientific, United States).
2.3.2 LC-MS/MS analysis
Peptides were coupled to a C18 reversed-phase analytical column (Thermo
Scientific Easy Column) for linear gradient separation in buffer and
LC-MS/MS analysis using a Q Exactive mass spectrometer (Thermo
Scientific) and Easy nLC (Thermo Fisher Scientific).
2.3.3 Bioinformatic analysis
Differential proteins were mapped and annotated by gene ontology (GO)
terminology (Blast2GO). The signaling pathways involved in the
differential proteins were determined by the Kyoto Encyclopedia of
Genes and Genomes (KEGG) database. Functional categories and pathways
were considered significant when the p < 0.05.
2.4 Network pharmacology analysis
2.4.1 Collecting targets for DM for T2DM and NAFLD
The bioactive compounds of the herbal components of DM were analyzed by
searching the TCM Systematic Pharmacology Database and Analysis
Platform (TCMSP) and the Encyclopedia of Chinese Medicines (ETCM)
([54]Ru et al., 2014; [55]Xu et al., 2019). The two-dimensional
structure maps of the selected compounds were obtained from the PubChem
database after the gene targets (probability >0.9) of the selected
compounds were predicted by the Swiss Target Prediction Database
prediction ([56]Daina et al., 2019). Disease genes associated with
T2DM-NAFLD were searched using five databases, including the GeneCards
database, the Online Mendelian Inheritance in Mandatabase, the
therapeutic target database, the DrugBank database, and the DisGeNet
database. The search terms were specified as “type 2 diabetes” and
“non-alcoholic fatty liver”, and the species was limited to “Homo
sapiens”. The targets of DM predicted by the databases in T2DM and
NAFLD were summarized for the next analysis.
2.4.2 Construction of core subnetwork
Venny2.1 ([57]https://bioinfogp.cnb.csic.es/tools/venny/) was used to
visualize overlapping genes of DM therapeutic targets with disease
targets of T2DM and NAFLD. Predicted targets for DM treatment of
T2DM-NAFLD were uploaded into the STRING database resulting in
protein-protein interaction (PPI) network. To analyze its results even
further, the data source of PPI network will be imported into Cytoscape
3.9.1, and the top five molecular targets were screened by CytoHubba
plug-in functions to construct the “disease-target-drug” network
([58]Figure 1B).
FIGURE 1.
[59]FIGURE 1
[60]Open in a new tab
(A) Volcano plot showing significantly up- (red) or downregulated
(bule) proteins between the DM group and the model group. The gray
points are proteins whose expression levels did not change
significantly. (B) GO terms of the DSEPs in the DM treatment group
compared with the model group.
2.4.3 GO and KEGG pathway enrichment analyses
Therapeutic targets for each bioactive compound in DM were uploaded
using the Metascape database, and the results of GO functional
enrichment and KEGG pathway enrichment analysis were downloaded for
further screening ([61]Zhou et al., 2019).
2.5 Glucose and lipid metabolism analysis
Random blood glucose (RBG) levels in the blood samples retrieved from
the tail vein of each mouse were measured weekly using the glucose
meter (Roche, United States). At the end of the experiment, the serum
levels of triglycerides (TG) (Solarbio, China, BC0625), total
cholesterol (TC) (Solarbio, China, BC 1985), low-density lipoprotein
cholesterol (LDL-C) (Solarbio, China, BC5335), high-density lipoprotein
cholesterol (HDL-C) (Solarbio, China, BC5325), aspartate
aminotransferase (AST) (Sigma-Aldrich, United States, MAK055) and
alanine aminotransferase (ALT) (Sigma-Aldrich, United States,
MAK052-1 KT) were measured using commercial kits based on the protocols
from the manufacturers.
2.6 Liver index
After the mice were sacrificed, the intact liver was taken out and
photographed. The liver index was calculated by the following equation:
[MATH: Liver index=liver weight/body weight×100% :MATH]
2.7 Hematoxylin and eosin (HE) staining
After blood was obtained from the mice, the intact livers were
photographed and weighed. Liver tissue was placed in a solution of 4%
paraformaldehyde and it was kept at −80°C. The liver was sectioned and
the thickness was limited to 5 μm. Xylene and gradient alcohol was used
for dewaxing, and hematoxylin and eosin were used for staining and
sealed with neutral resin. Neutral resin was used to glue coverslips,
which were observed and photographed using a Nikon 55i microscope
(Nikon, Japan). The Non-Alcoholic Fatty Liver Activity Score (NAS) was
applied in this drug treatment trial ([62]Kleiner et al., 2005). In the
NAS scoring system, hepatocellular steatosis was scored 0–3,
hepatocellular ballooning was scored 0–2, and inflammation within the
liver lobules was scored 0–3.
2.8 Cell culture and induction of palmitic acid
HepG2 cells were acquired from the department of biochemistry, Fujian
University of Traditional Chinese Medicine. HepG2 cells were cultured
in DMEM high sugar medium (Gibco, United States, 11995065) consisting
of 10% fetal bovine serum (FBS; Gibco, United States, 10099141) and 1%
penicillin-streptomycin (Gibco, United States, 15140163). High glucose
medium combined with 250 μM palmitic acid (PA, Solarbio, China, H8780)
intervened in the cells for 24 h to induce cellular steatosis.
2.9 Preparation and screening of serum containing DM
After 40 male Sprague-Dawley (SD) rats were adaptively fed for 1 week,
15 mice were then randomly selected as the control group and given
normal saline, while the rest of the mice were fed DM (10.8 g/kg/d once
a day). All the mice were fed for 7 days. On the last day (fasting
without water for 12 h), 20% urethane was administered for anesthesia.
After blood was drawn by abdominal arter, it was centrifuged at 8,000 g
for 15 min. The serum was collected from each mouse and combined within
the same group. The serum sample was sterilized in a 56°C water bath,
filtered by 0.22-micron filter, and stored at −80°C until analysis. The
HepG2 cells were treated with DMEM media only or DMEM media with PA at
250 μM for 24 h. The PA-induced cells were then intervened with diluted
serum from normal saline-treated SD rats or DM-treated SD rats,
respectively. The cell or cell supernatant was subjected to the
following bioassays.
2.10 Oil red O staining
Frozen sections of liver tissue from each group and crawling slices of
HepG2 cells treated with different serum drugs were fractionated with
60% isopropanol for 10 min; oil red O (Sigma-Aldrich, United States,
0625) was stained for 10 min, washed with PBS, fractionated with 60%
isopropanol for 15 s, stained with hematoxylin (Solarbio, China,
H8070), blow-dried crawling slices, and glycerol gelatin The films were
sealed and photographed for storage. The films were mounted with
glycerol gelatin (Beyotime, China, C0187). The films were observed and
photographed under the light microscope (Nikon 55i microscope, Japan).
2.11 Western blot analysis
RIPA lysis (Beyotime, China, P0013B) buffer containing protease
inhibitors was used to lyse liver tissue and HepG2 cells by ultrasonic
lysis at low temperature and centrifugation. Protein content
determination according to BCA kit instructions (PC0020, Solarbio,
China). Proteins were separated by electrophoresis, transferred to PVDF
membranes (IPFL00010, Millipore, United States), and blocked by
incubation with blot blocking buffer for 10 min ([63]P30500, NCM
Biotec, China) at room temperature. The membrane is then placed into a
primary antibody box containing anti-PPARγ (Cell Signaling Technology,
United States, #2435), anti-PGC-1α (Cell Signaling Technology, United
States, #2178), and anti-β-actin (Proteintech, United States, 2D4H5)
for binding at 4°C overnight. All primary antibodies were diluted at
1:1000. The membranes were then eluted with TBST and incubated with the
secondary antibodies including HRP-conjugated Goat anti-rabbit
(Proteintech, America, 10366-1-AP) and HRP-conjugated Goat anti-mouse
(Proteintech, America, 3H9D1) for 1 h. The grayscale values of the
protein bands were analyzed using Image Lab software, and the ratio of
the target protein to the reference protein was calculated and then
statistically analyzed.
2.12 Measurements of cytokine
Concentrations of serum and HepG2 cells treated with serum drugs of
IL1β, IL6, and TNFα were measured by ELISA kit (Abcam, United States,
Mouse IL-6 ELISA Kit, ab222503), (Abcam, United States, Human IL-6
ELISA Kit, ab178013), (Abcam, United States, Mouse IL-1β ELISA Kit,
ab197742), (Abcam, United States, Human IL-1β ELISA Kit, ab214025),
(Abcam, United States, Human TNFα ELISA Kit, ab181421), (Abcam, United
States, Mouse TNFα ELISA Kit, ab208348). The serum and cell samples
were subjected to the 96-well plates and coupled with antibody
cocktails after various treatments and co-incubated for 2 h. After
another wash, the TMB Development Solution was incubated for 10 minis.
The stop solution was added and the amount was measured by their
corresponding calibration curves.
2.13 Statistical analysis
Data are shown as mean ± standard deviation. Data conforming to a
normal distribution were compared between multiple groups by one-way
ANOVA analysis, and the Tukey test was performed between groups when
the variances were equal; data not conforming to a normal distribution
were compared between groups by a non-parametric test. p < 0.05 was
considered a statistically significant difference.
3 Results
3.1 Identification of differentially significant expressed proteins in the
liver after dm treatment
In significantly different protein screens, expression fold changes
(FC) were compared, and when FC was greater than 1.
[MATH: 2˙
:MATH]
-fold or less than 0.8
[MATH: 3˙
:MATH]
-fold and p < 0.05 (t-test) was the criterion for significant up- and
downregulation of protein quantity between groups. As shown in the
volcano plot, blue dots indicate proteins downregulated in the DM group
compared to the model group, red represents upregulated proteins, and
proteins with no difference are in gray ([64]Figure 1A). The top 15
differentially significant expressed proteins (DSEPs) (up and
downregulated) are listed in [65]Tables 1, [66]2. Dimethylaniline
monooxygenase [N-oxide-forming] 3 (Fmo3), Farnesyl pyrophosphate
synthase (Fdps), Cytohesin-3 (Cyth3), Kynurenine--oxoglutarate
transaminase 3 (Kyat3), Isopentenyl-diphosphate Delta-isomerase 1
(Idi1), Sulfotransferase 1E1 (Sult1e1), etc. showed notable
upregulation. Downregulated proteins included vacuolar protein
sorting-associated protein 8 homolog (Vps8), dual specificity protein
phosphatase 12 (Dusp12), fascin (Fscn1), putative bifunctional
UDP-N-acetylglucosamine transferase and deubiquitinase ALG13(Alg13),
p53 and DNA damage-regulated protein 1(Pdrg1), etc.
TABLE 1.
Selected significantly upregulated proteins in the Dendrobium mixture
(DM) group compared with the model (MOD) group.
Gene name Protein name Fold change (DM/MOD) p-Value
Fmo3 Dimethylaniline monooxygenase [N-oxide-forming] 3 2.15177974
0.03486014
Fdps Farnesyl pyrophosphate synthase 1.81523714 0.03248932
Cyth3 Cytohesin-3 1.79518214 0.04138613
Kyat3 Kynurenine--oxoglutarate transaminase 3 1.58382788 0.01615924
Idi1 Isopentenyl-diphosphate Delta-isomerase 1 1.57733989 0.0196337
Sult1e1 Sulfotransferase 1E1 1.5722069 0.03604566
Got1 Aspartate aminotransferase, cytoplasmic 1.4780465 0.0007515
Mgst3 Microsomal glutathione S-transferase 3 1.46398517 0.02826162
Cth Cystathionine gamma-lyase 1.45567752 0.00466174
Ass1 Argininosuccinate synthase 1.44866073 0.00327519
Agxt Serine--pyruvate aminotransferase, mitochondrial 1.44621756
0.00657333
Aldh1l1 Cytosolic 10-formyltetrahydrofolate dehydrogenase 1.44055477
0.02204724
Acly ATP-citrate synthase 1.40603532 0.03317841
Sult1a1 Sulfotransferase 1A1 1.39086313 0.01578916
Hmgn2 Non-histone chromosomal protein HMG-17 1.38870532 0.03481204
[67]Open in a new tab
TABLE 2.
Selected significantly downregulated proteins in the Dendrobium mixture
(DM) group compared with the model (MOD) group.
Gene name Protein name Fold change (DM/MOD) p-Value
Vps8 Vacuolar protein sorting-associated protein 8 homolog 0.83332451
0.02896317
Dusp12 Dual specificity protein phosphatase 12 0.83275542 0.03357374
Fscn1 Fascin 0.83055614 0.00436564
Alg13 Putative bifunctional UDP-N-acetylglucosamine transferase and
deubiquitinase ALG13 0.83048436 0.01074288
Tmem205 Transmembrane protein 205 0.82983434 0.00795468
Acp2 Lysosomal acid phosphatase 0.8296204 0.01669829
Pdrg1 p53 and DNA damage-regulated protein 1 0.82799098 0.00195912
Prkci Protein kinase C iota type 0.82685673 0.02009944
Celf2 CUGBP Elav-like family member 2 0.82614131 0.04124738
Acot1 Acyl-coenzyme A thioesterase 1 0.82596656 0.01460239
Arap1 Arf-GAP with Rho-GAP domain, ANK repeat and PH domain-containing
protein 1 0.82455461 0.02439473
Pros1 Vitamin K-dependent protein S 0.82229932 0.02561268
Tex2 Testis-expressed protein 2 0.82113709 0.01997845
Ces1f Carboxylesterase 1F 0.82083517 0.01359083
Gpat4 Glycerol-3-phosphate acyltransferase 4 0.81824183 0.00331068
[68]Open in a new tab
FC (Fold Change) refers to the multiple of the difference in the
expression of the same protein between two samples; FC > 1.
[MATH: 2˙
:MATH]
indicates upregulated proteins, and FC < 0.8
[MATH: 3˙
:MATH]
indicates downregulated proteins.
3.2 GO functional enrichment analyses of TMTs
Here, we show the top 20 results of GO analysis ([69]Figure 1B). Lipid
metabolism and biosynthetic processes, steroid metabolism and
biosynthetic processes, alcohol metabolism processes, and sterol
metabolism processes were significantly enriched in BP terms after DM
intervention. In the MF group, oxidoreductase activity, catalytic
activity, and steroid dehydrogenase activity were significantly
enriched. In the CC term, we found that the endoplasmic reticulum was
remarkably enriched. These results suggest that DM administration
resulted in a greater enrichment of lipid metabolic processes and
oxidative stress in the endoplasmic reticulum.
3.3 KEGG pathway enrichment analysis of TMTs
DSEPs after DM intervention in db/db mice were uploaded to KEGG pathway
enrichment analysis, thus exploring potential pathways for DM
intervention. Here, we show the 20 pathways with the highest enrichment
([70]Figure 2A). We performed a preliminary classification of these
results, in which five pathways related to amino acid metabolism,
including valine, leucine and isoleucine degradation, tryptophan
metabolism, lysine degradation, cysteine and methionine metabolism,
glycine and serine and threonine metabolism. The pathway related to
lipid metabolism consists of steroid hormone biosynthesis, biosynthesis
of unsaturated fatty acids, glycerolipid metabolism, fatty acid
degradation, and primary bile acid biosynthesis. The pathway related to
transport and catabolism consists of peroxisome, endocytosis, and
lysosome. The related pathway of Xenobiotics biodegradation and
metabolism include cytochrome P450 and Drug metabolism - other enzymes.
The beta-Alanine metabolism belongs to Metabolism. The cholesterol
metabolism belongs to the digestive system. A PPAR signaling pathway is
part of the endocrine system, which involves the most abundant protein
changes. According to our results, after DM intervention, a total of 11
genes on the PPAR pathway were significantly altered, including lipid
biosynthesis (Scd1), lipid degradation (Adipoq), lipid storage (Plin2),
lipid transport (Scp2), lipid synthesis (Fads2), β-oxidation (Acsl1,
Acsl4, Acox2, Ehhadh), liposynthesisand cholesterol metabolism (Hmgcs1)
([71]Figure 2B). In the PPAR signaling pathway, the Hmgcs1 and Fads2
genes were upregulated, and the Scp2, Ehhadh, Acsl1, Acaa1b, Acox2,
Plin2, Scd1, Acsl4, and Adipoq genes were downregulated, all of which
were closely related to lipid metabolism. Furthermore, STRING analysis
was used to visualize the interaction network of significantly altered
genes ([72]Figure 2C).
FIGURE 2.
[73]FIGURE 2
[74]Open in a new tab
(A) The top 20 KEGG pathways enriched in DSEPs. (B) Heat map of
differentially expressed genes in peroxisome proliferators-activated
receptor (PPAR) signaling pathway. (C) Search tool for recurring
instances of neighboring genes (STRING) network visualization of the
genes in differentially expressed genes in the PPAR signaling pathway.
Edges represent protein-protein associations.
3.4 Therapeutic target genes of DM in T2DM and NAFLD
We acquired 1,543 disease genes for T2DM and 2,263 for NAFLD. The
intersection of the target genes in T2DM and NAFLD was then mapped. The
Venn diagram revealed 92 overlapping therapeutic targets of T2DM and
NAFLD ([75]Figure 3A). Through the TCMSP, ETCM, and literature
databases, 115 main active compounds were identified as key drug-like
components from six ingredients of DM (Shi Hu: n = 9, Wu Wei Zi: n = 8,
Huang Qi: n = 16, Dan Shen, n = 56, Niu Xi: n = 15, Zhi Mu: n = 11).
Eventually, there were 271 total gene targets associated with the main
active compounds ([76]Supplementary Material S1). The protein-protein
interaction network drawn from the STRING database indicated that the
proteins encoded by these target genes have complicated associations.
FIGURE 3.
[77]FIGURE 3
[78]Open in a new tab
(A) Venn of the gene targets of DM, T2DM and NAFLD; (B) Network of
“drug-gene-disease”. The Orange hexagon represents DM, and the green
circles represent components of the DM. The blue square highlighted the
gene targets with high degrees. The yellow triangle represents two
diseases of NAFLD and T2DM;(C) Top 20 GO terms relevant to the action
of DM against NAFLD and T2DM. (D) Top 20 KEGG relevant to the action of
DM against NAFLD and T2DM. The count of each KEGG pathway is indicated
by the circle size, and the -log10 p-value was indicated by the color.
3.5 PPI network and core subnetwork
The PPI network was imported into Cytoscape for further analysis. The
final five key genes were screened after two filtrations using
CytoHubba to construct the interactive network of “drug-gene
target-disease” ([79]Figure 3B).
3.6 GO functional enrichment and KEGG pathway enrichment analysis of
therapeutic target genes
The 92 overlapping genes were mapped for enrichment analysis of the GO
terms and KEGG pathways. The results of GO enrichment analysis of DM
including molecular function, cellular components, and biological
processes are shown in [80]Figure 3C. The top 20 KEGG pathways that may
play the most significant role in the mechanisms action of DM are shown
in [81]Figure 3D. Majority of these pathways were associated with
inflammation and metabolism.
3.7 DM regulated glycolipid metabolism in T2DM-NAFLD mice
The random blood glucose (RBG) in db/db mice gradually increased to
>25 mmol/L with age ([82]Figure 4A). It is to be noted that due to the
limitation of the glucose meter, when the blood glucose of the mice
exceeded the maximum value of the glucose meter (33.3 mmol/L), it was
recorded as 33.3 mmol/L. Compared with the model group, RBG showed a
decreasing trend after DM administration for 2 weeks, a significant
decrease after DM administration for 3 weeks, and similar results in
the fourth week. In [83]Figure 4B, the body weight in the model group
(db/db mice, 39.9 ± 1.20 g) before treatment was greatly (p < 0.001)
higher than the control group (db/m mice, 27.30 ± 1.64 g). The body
weight in the DM treatment group (40.3 ± 1.26 g) was comparable to the
model group before treatment. However, at week 4, the body weight in
the DM group reduced (45.5 ± 3.0 g) lower than the model group (53.40 ±
3.17 g) (p < 0.001). The serum levels of TC (5.41 ± 0.67 mmol/L vs.
2.36 ± 0.35 mmol/L), TG (2.70 ± 0.15 mmol/L vs. 0.83 ± 0.13 mmol/L),
HDL (11.34 ± 1.45 mmol/L vs. 7.52 ± 0.88 mmol/L) and LDL (5.37 ±
0.81 mmol/L vs. 1.70 ± 0.49 mmol/L), AST (83.70 ± 12.41 U/L vs. 24.56 ±
3.92 U/L), ALT (166.40 ± 37.79 U/L vs. 15.09 ± 3.56 mmol/L) in the
model group were all obviously higher than the control group (p <
0.001) ([84]Figure 4C). After DM treatment, the serum levels of TC
(3.88 ± 0.49 mmol/L), TG (1.44 ± 0.29 mmol/L), HDL (8.67 ± 0.88 mmol/L)
and LDL (3.87 ± 0.55 mmol/L), AST (54.93 ± 8.46 U/L), ALT (55.93 ±
10.70 U/L) were reduced through comparing to the model group (p <
0.001).
FIGURE 4.
[85]FIGURE 4
[86]Open in a new tab
(A) DM reduced the elevated RBG level (mmol/L) in db/db mice (n = 10
per group); (B) DM reduced the body weight (g) in db/db mice (n = 10
per group). ***p < 0.001 vs. Mod as assessed by one-way ANOVA with
Tukey’s multiple comparison test;(C) The serum levels of TC (mmol/L),
TG (mmol/L), HDL-C (mmol/L), LDL-C (mmol/L), AST (U/L) and ALT (U/L) I
n the db/db mice (n = 10 mice per group). ***p < 0.001 vs. Mod group as
assessed by one-way ANOVA with Tukey’s multiple comparison test; Con:
control group; Mod: model group; DM: DM treatment group.
3.8 DM attenuated liver damage in T2DM-NAFLD mice
Macroscopical differences were observed in the liver between the
control and model group, with the liver in the model group manifested
as hepatomegaly ([87]Figure 5A). Hematoxylin staining of the livers of
control mice showed that the liver tissue was structurally intact, with
neatly arranged hepatocytes of uniform size and no obviously fatty
degeneration. In contrast, db/db mice clearly showed significant
lipoatrophy. The liver index showed that the liver in the model group
was more higher than that in the control group (p < 0.001). NAS
analysis also showed that the model group was significantly higher than
that in the control group and was attenuated by DM (p < 0.05). In
addition, the percentage of hepatocyte lipid accumulation was
significantly higher in db/db mice (51.13% ± 2.35%, p < 0.001) and
significantly attenuated by DM (24.8% ± 2.84%, p < 0.001).
FIGURE 5.
[88]FIGURE 5
[89]Open in a new tab
(A) Effect of DM on morphology and histopathologic changes in the
liver. Representative sections of the liver specimen, H&E staining
(scale bars = 100 μm); (B) DM reduced the liver weight/BW, NAS, and
lipid droplets percentage (%) in the liver. *p < 0.05 and***p < 0.001
vs. Mod group as assessed by one-way ANOVA with Tukey’s multiple
comparison test. (C) The effect of DM on serum TNF-α, IL-1β, and IL-6
(pg/mL), ***p < 0.001 vs. Mod group as assessed by one-way ANOVA with
Tukey’s multiple comparison test. (D) Western blot images (n = 3
individual experiments); (E) their statistical analysis of protein
expressions of PPARγand PGC-1α in liver tissue. *p < 0.05 and**p < 0.01
vs. Mod group as assessed by one-way ANOVA with Tukey’s multiple
comparison test.
3.9 DM upregulated PPARγ, reduced inflammation in T2DM-NAFLD mice
As shown in [90]Figure 5C, the inflammatory cytokines IL-1β, TNF-α, and
IL-6 were all increased greatly in model group (p < 0.001), whereas DM
significantly inhibited their productions (p < 0.001). These results
noticed that the effect of DM was associated with an inhibited
susceptibility to inflammations. As suggested by the network
pharmacology and TMT-based quantitative protomics analysis that
intracellular signaling pathways related to PPAR were involved in the
pharmacological actions of DM, experimental verification was conducted
on the diabetic mice. In the diabetic group, we observed significantly
reduced PPARγ (p < 0.01) and induced PGC-1α (p<0.01) ([91]Figure 5D).
However, the intervention of DM significantly increased PPARγ
expressions (p < 0.05) and reduced significantly higher levels of
PGC-1α (p<0.01), which corresponded to the predicted action of DM in
the network pharmacology and TMT-based quantitative protomics analysis.
3.10 Serum containing DM regulated the steatosis in PA-induced HepG2 cells
Oil red o staining was used to evaluate the degree of lipid
accumulation in Hepatocyte with or without DM treatment. As shown in
[92]Figure 6A, the co-incubation of PA and SC groups for 24 h induced
an obvious intracellular lipid accumulation as evidenced by the strong
red staining. The statistical analysis in [93]Figure 6A demonstrated
that the lipid droplet percentage was approximately 3 times that of the
control group (p < 0.001). However, the lipid droplets in the DM group
were significantly lower than that in the model group (p < 0.001)
([94]Figure 6B).
FIGURE 6.
[95]FIGURE 6
[96]Open in a new tab
(A) Oil Red O staining for HepG2 cells; (B) quantitative analysis of
lipid. DM reduced the lipid accumulation in HepG2 cells induced by the
24 h coincubation of PA 250 μM. Visual observation of lipid content was
captured by microscope (×200): the control cells treated with only
media (Con), cells treated with PA (250 μM) for 24 h (Mod), cells
pretreated with PA 250 μM for 24 h and then cultured with serum from
mice administered with saline only (diluted to 2.5%) for 24 h (Serum
Control, SC), and cells pretreated with PA 250 μM for 24 h and then
cultured with serum DM for 24 h (DM). The quantitative analysis of
cellular steatosis was measured through deposited Oil Red O in the
cells. Statistical significance was determined by one-way ANOVA and the
values are mean ± STD. ***p < 0. 001 vs. Mod group. (C) The effect of
cells TNF-α, IL-1β and IL-6 (pg/mL), **p < 0. 01 and ***p < 0.001 vs.
Mod group as assessed by one-way ANOVA with Tukey’s multiple comparison
test. (D) Western blot images (n = 3 individual experiments); (E) their
statistical analysis of protein expressions of PPARγand PGC-1α in HepG2
cells. **p < 0.01 vs. Mod group as assessed by one-way ANOVA with
Tukey’s multiple comparison test.
3.11 Mechanisms of action of DM by integrating bioinformatics analysis and
experimental validation in HepG2 cells
To test the expression of inflammation, cytokine analysis was performed
using Elisa kit measured HepG2 cells ([97]Figure 6C). After PA induced,
the levels of IL-1, TNF, and IL-6 were greatly higher in the model and
serum control groups (p < 0.001), showing that PA promoted the
secretion of cellular inflammatory factors and that saline was not
significantly effective. In contrast, inflammation levels in the DM
group were significantly lower than those in the mod group (p < 0.001or
p < 0.01). The mechanisms action of DM was investigated based on the
bioinformatic analysis that the action of DM was relevant to the PPAR
pathway. To further investigate the mechanisms of action of serum DM,
Western blot analysis was performed to examine the level of PPARγ as
guided by the bioinformatic analysis. In [98]Figure 6D, PA increased
the level of PGC-1α (p<0.01). With the effect of saline being
insignificant, the serum containing DM significantly induced
significantly higher levels of PPARγ (p < 0.01), and reduced the
expression of PGC-1α (p<0.01). These results suggest that the key
factors for the therapeutic effect of serum are the incoming component
of DM rather than the serum itself, and the possible involvement of
PPARγ in the treatment of DM in serum, which is consistent with
bioinformatics analysis and animal experimental investigations.
4 Discussion
The liver takes up a part in many physiological processes by regulating
glucolipid metabolism throughout the body. In the context of MetS, IR
and relative insulin deficiency result in increased lipolysis of
adipose tissue, which increases fatty acid uptake by the liver, and
hyperinsulinemia and IR will further increase lipid accumulation in the
liver and increase the hepatic load. Thus, liver function is a key
determinant of metabolic abnormalities. Due to the complex relationship
between T2DM and NAFLD, the most current clinical therapies are not
fully adequate for the treatment of complex diseases. Therefore, we are
looking for a promising drug as a therapeutic approach. Chinese
medicine has multi-target and multi-level characteristics, which
provide unique superiority in the prophylaxis and therapy of complex
diseases. Nevertheless, at the same time, due to the multiple and
complex ingredients of DM, identifying them and describing their
respective mechanisms in T2DM-NAFLD is a huge and complex project. In
this research, the combination of network pharmacology and TMTs was
used for predicting the active ingredients, therapeutic targets and
important pathways in DM.
Identification of bioactive compounds is critical in studying
mechanisms of action of complex herbal formulations. The network
pharmacology analysis conducted in the present study identified varied
bioactive that contribute mostly to the actions of DM including
kaempferol, luteolin, baicalein, diosgenin, wogonin, quercetin, etc.
Each of the bioactive compounds has been independently demonstrated to
be effective against NAFLD based on cellular or animal studies, either
by preventing the deterioration from simple steatosis to NASH or
protecting against the “two hits” in NAFLD ([99]Li et al., 2013;
[100]Chen et al., 2017; [101]Zhu et al., 2018; [102]Yang et al., 2019;
[103]Sun et al., 2020; [104]Liu et al., 2021; [105]Ochiai et al., 2021;
[106]Tie et al., 2021; [107]Li et al., 2022; [108]Zhou et al., 2022).
Related studies of these active ingredients indicate that DM is
indispensable in the treatment of T2DM-NAFLD. Therefore, our results
first demonstrated the capability of DM in attenuating hepatic
pathological changes in db/db mice as evidenced by reduced RBG, body
weight, blood lipids, ransaminase, liver weight, lipid droplets, and
lipid accumulation.
In this study, we network pharmacology and TMTs results for comparative
analysis to further identify key genetic targets in the pathogenesis of
NAFLD and T2DM. The results of network pharmacology pointed that AKT1,
TNF, IL-6, PPARG and IL-1 β are the most critical core targets of DM
for T2DM-NAFLD. The effect of DM on reducing inflammatory factors in
diabetic rats and regulating Akt1 in diabetic mice has been confirmed
in previous studies. In this study, In this study, protein changes in
db/db mice after DM treatment as determined by TMT-based proteomics
methods were focused. At the same time, go analysis of proteomics once
again verified that DM was closely related to lipid metabolism,
inflammation, and cytokine receptor binding, etc.
The leading KEGG pathway that linked these two diseases was the PPAR
signaling pathway in diabetic complications by two bioinformatics.
Analysis based on TMT shows that DM has significantly downregulated
nine genes and upregulated two genes in the PPAR pathway. The
downregulation of plin2, scp2, and scd1 has demonstrated their positive
involvement in improving lipid metabolism and reducing inflammation
([109]Libby et al., 2018; [110]Tao et al., 2020; [111]Xu et al., 2022).
In the past two decades, PPARγ has attracted much attention as a
transcription factor regulating glucose and lipid homeostasis
([112]Wang et al., 2020). Among the PPAR subtypes, PPARγ is mainly
distributed in adipose tissue and can regulate the expression of target
genes after being recognized, combined, and activated by endogenous and
exogenous ligands. These target genes involved many biological effects
such as cell energy metabolism, material metabolis, and cell
proliferation. PPARγ agonists increased the sensitivity of peripheral
tissues to insulin, including increased muscle uptake and utilization
of glucose, inhibition of liver gluconeogenesis, inhibition of fatty
acid decomposition and synthesis in adipose tissue, and promotion of
adipocyte remodeling ([113]Yessoufou and Wahli, 2010; [114]Zhong et
al., 2018; [115]2017). Therefore, PPARγ agonist as an insulin
sensitizer is effective in the treatment of diabetes. Actually, whether
some PPARγ agonists produce adverse reactions such as lipogenesis and
edema depends on the intensity of PPARγ target gene regulation. Some
agonists stimulate PPARγ activity, which can regulate the transcription
of target genes related to therapeutic effects, but not the
transcription of target genes related to adverse reactions. This is the
fundamental reason why some agonists can retain therapeutic effects and
avoid adverse reactions, its essence is the “selective” result of PPARγ
activity regulation. PPARγ cofactors include coinhibitors and
coactivators, the coactivators such as PPARγ coactivator one α(PGC-1α)
and mediator complex subunit 1 (MED1), etc. PGC-1α is a key gene
involved in regulating gluconeogenesis which plays an anti-diabetes
role by inhibiting gluconeogenesis after being selectively inhibited
([116]Kolli et al., 2014). The selective recruitment of cofactors
determines the selectivity of PPARγ regulatory target genes. For
instance, INT131 acts as an agonist of PPARγ but does not recruit MED1,
which is a critical factor in regulating fat production. Therefore,
INT131 can selectively reduce blood glucose without an obvious adverse
reaction to fat production ([117]Higgins and Mantzoros, 2008). The
effect of DM was verified on this pathway. Our results suggested that
DM effectively regulated the activated PPAR pathway, through the
actions of upregulating the expressions of PPARγ, downregulating the
expressions of PGC-1α, and reducing the inflammatory actions. A similar
observation was obtained from our in vitro investigation. The serum
containing DM obtained from SD rats was tested on the PA-induced HepG2
cells. Serum-containing DM reduced the lipids accumulation as shown by
the oil red o staining. It also showed the regulatory effects on PPARγ
and PGC-1α protein expressions. Taken together, our data suggested DM
as a prospective therapy agent in treating NAFLD in T2DM, through
selectively activating PPARγ without obvious adverse reaction.
5 Conclusion
Although the abilities of DM to orchestrate hepatic physiological
processes have been discerned by modulating PPARγ and PGC1α, their
specific contributions to T2DM with NAFLD remain unclear. In addition,
we do not know if these bioactive compounds would be present in the
serum and/or target organs at the contractions that could elicit
meaningful pharmacological effects. Future studies are needed to solve
the puzzles to advance our understanding the of functions of DM in the
complex landscape of the human liver disease.
Funding Statement
This work was supported by the National Natural Science Foundation of
China Projects (No. 81973827).
Data availability statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found below: ProteomeXchange Consortium
([118]http://proteomecentral.proteomexchange.org) via the iProX partner
repository with the dataset identifier PXD039727.
Ethics statement
The animal study was reviewed and approved by the Ethics Committee) of
Fujian University of Traditional Chinese Medicine (approval number:
FJTCM IACUC2021079) (approval number: FJTCM IACUC2021027). Written
informed consent was obtained from the owners for the participation of
their animals in this study.
Author contributions
Conceptualization, SZ and HS; methodology, SZ and JZ; software, SZ;
validation, SZ and XL; data curation, XW and WY; writing—original draft
preparation, SZ; writing—review and editing, HS; supervision, HS;
project administration, HS. All authors have read and agreed to the
published version of the manuscript.
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary material
The Supplementary Material for this article can be found online at:
[119]https://www.frontiersin.org/articles/10.3389/fphar.2023.1112554/fu
ll#supplementary-material
[120]Click here for additional data file.^ (686.9KB, pdf)
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