Based on Network Pharmacology and Molecular Docking Technology

to Explore the Mechanism of Coptis in the Treatment of Diabetic

Nephropathy

Yuexing Ma

1,2,3,+,*

, Zixuan Luo

2,3,+

, Simin Liu

2,3

, Haoyi Zheng

2,3

, Rongbin Pan

4,*

, Zhixin Zhu

2,3,*

Zirong Peng

2,3,*

, Mengyu Hou

, Xuening Huang

and Xin Qiao

Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, 330004, China

Science and Technology College, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, 330004, China

Nanchang Medical College, Nanchang, Jiangxi, 330004, China

Jiangzhong Cancer Research Center, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, 330004, China

Rongbin Pan’s e-mail: PRB2019@jxutcm.edu.cn; Yuexing Ma ’s e-mail: ma-yuexing@qq.com;

+The same contribution to research

Keywords:

Huanglian, Diabetic Nephropathy, Network Pharmacology, Molecular Docking.

Abstract:

Objective Based on network pharmacology and molecular docking to explore the mechanism of Coptis in the

treatment of diabetic nephropathy. Methods Search and screen the main active ingredients in Coptis chinensis

through the TCM System Pharmacology Database and Platform (TCMSP) and obtain the corresponding

targets. Through the four databases of GeneCards, OMIM, PharmGkb, and TTD, the genes related to diabetic

nephropathy are searched and merged, and the target genes of the effective component target and the disease-

related gene intersection are obtained through the R language. Cytoscape 3.8.0 software was used to construct

a drug component-target-disease regulation network, and a protein interaction network was constructed

through the STRING online website. Using R language, GO enrichment analysis and KEGG enrichment

analysis were performed on the potential targets of Huanglian in the treatment of diabetic nephropathy. In

AutoDockTools-1.5.6, the molecular docking of key target proteins and main active ingredients is realized.

As a result, 10 active ingredients of Coptidis for treating diabetic nephropathy were obtained, including:

berberine, quercetin, etc.; corresponding to 104 target genes, including: PTSG2, CCL2, MAPK1, etc. Among

them, PTSG2 is the core of the PPI network Protein; KEGG pathway enriched to obtain 166 pathways,

including: IL-17 signaling pathway, TNF signaling pathway, NF-kappa B signaling pathway, VEGF signaling

pathway, etc. The results of molecular docking showed that berberine (berberine) has binding properties to

PTSG2. Conclusion Through network pharmacology, the target and mechanism of Coptidis in the treatment

of diabetic nephropathy are predicted.1 Introduction.

1 INTRODUCTION

1.1 Diabetes Pathogenesis and

Treatment Research

Diabetes (diabetes mellitus, DM) is a metabolic

disease caused by insufficient insulin secretion or the

inability of insulin to act. Continued maintenance of

high blood sugar and long-term metabolic disorders

may cause damage to the whole body tissues and

organs, especially the kidneys, and their dysfunction

and failure.

Diabetic kidney disease (DKD) is the most

common microvascular complication of diabetes. It is

a kidney disease caused by DM. (Neal, 2017); (Xing,

2021) It has become the leading cause of end-stage

kidney disease globally (Perkovic, 2019); (Verma,

2018); (Xing, 2021), and its prevalence is increasing

year by year (Liu, 2013). Clinical manifestations are

generally proteinuria, hypertension, edema, etc. (Ritz,

2010); (Xing, 2021), and in severe cases, it can even

cause renal failure and life-threatening. The

pathogenesis of DKD is complicated. Modern

research believes that the occurrence of DKD may be

related to oxidative stress, inflammation, metabolic

status, activation of NF-κB, and activation of the

renin-angiotensin-aldosterone system. (Haraguchi,

2020); (Xing, 2021) Among them, the inflammatory

638

Ma, Y., Luo, Z., Liu, S., Zheng, H., Pan, R., Zhu, Z., Peng, Z., Hou, M., Huang, X. and Qiao, X.

Based on Network Pharmacology and Molecular Docking Technology to Explore the Mechanism of Coptis in the Treatment of Diabetic Nephropathy.

DOI: 10.5220/0011250500003443

In Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics (ICBEB 2022), pages 638-645

ISBN: 978-989-758-595-1

response plays an important role in the pathogenesis

of DKD. (Friedman, 2004); (Li, 2008) Inflammatory

factors include IL, NF-κB, TNF-α, TGF-β1, etc. (Lu,

2012); inflammatory factors not only cause kidney

Damage can also activate some signal channels to

aggravate the inflammatory response (Lu, 2010),

which further stimulates the development of DKD. At

present, Western medicine lacks effective treatment

for DKD, and studies have shown that Chinese and

Western medicine treatment will be the development

trend of the treatment of DKD.

1.2 Research Purpose and Introduction

From ancient times to the present, there have been

many discussions on Xiaoke in the literature and

classics, and the earliest relevant discussion appears

in the "Huangdi Neijing" (Wang, 2011); (Zhang,

2017). Diabetic nephropathy belongs to the category

of "diabetes" or "nephropathy" in traditional Chinese

medicine, and Huanglian is used to treat diabetic a

lot." Huanglian has a bitter taste and a cold nature. It

has the effects of clearing heat and dampness, purging

fire and detoxification. It is used for damp heat,

fullness of damp heat, vomiting and sourness, red

eyes, diminishing thirst, etc. Materia Medica Justice"

says: "Coptis rhizome has great bitterness and severe

cold, bitterness and dampness, cold overcomes heat,

and can vent all excess damp and fire, and the residual

heat of heart, spleen, liver, and kidney; the fire of

gallbladder, stomach, large intestine, and small

intestine, nothing will be incurable." (Peng, 2018)

Modern pharmacological studies (Fu, 2021);

(WANG, 2019) have shown that Coptidis has anti-

inflammatory, hypoglycemic, antitoxin, and anti-

tumor effects. Berberine, the main component of

Coptis, can effectively treat diabetic nephropathy.

(Li, 2016); (Yang, 2019)

Therefore, this article intends to use network

pharmacology methods to collect and analyze

relevant data from major databases. Through methods

such as drawing, tabulation, screening, and docking,

we will explore and verify the target of Coptidis on

DKD at the molecular level, and predict its

mechanism of action.

2 MATERIAL AND METHODS

2.1 Database and Software Preparation

Commonly used software: PERL (strawberry-perl-

5.32.1.1-64bit), R language (R-4.0.4-win), Cytoscape

(Cytoscape_v3.8.0), ChemOffice (Chem3D.exe),

PyMOL, AutoDockTools-1.5.6, vina and other

Databases: TCMSP, GeneCard, OMIM, PharmGKB,

TTD

2.2 Acquirement of the Effective

Components of Coptis and Its

Target

The TCSMP database was used to search for Coptidis

Rhizoma Coptidis, and the effective components of

Coptidis Rhizoma Coptidis and its corresponding

target data were screened based on the criteria of oral

bioavailability (OB) ≥ 30% and druglikeness (DL) ≥

0.18 in the TCMSP database. Obtain the target name

of the corresponding person in the UniProt database.

2.3 Acquisition and Screening of

Diabetic Nephropathy Related

Genes

Search with "Diabetic Kidney Disease" and "diabetic

nephropathy" in GeneCards, OMIM, TTD, and

PharmGKB databases to obtain DKD-related gene

sets and make their intersection Venn diagrams.

2.4 Obtain the Intersection of the

Target of the Effective Component

of Coptis and DKD-Related Genes

Use R language and corresponding R language scripts

to perform online analysis on the target of Coptidis

active ingredient and DKD disease-related genes,

draw the Venn diagram of the intersection of drug

ingredient targets and disease-related genes, and get

the intersection genes. This intersection gene is the

target of Coptidis for DKD.

2.5 Constructing the Mechanism

Network of Coptis Chinensis in

Regulating DKD

Upload the relevant files to the graphical display and

analysis software Cytoscape_v3.8.0, use Cytoscape

to construct a drug-component-target network

relationship diagram, and analyze the corresponding

relationship between the effective components and

Based on Network Pharmacology and Molecular Docking Technology to Explore the Mechanism of Coptis in the Treatment of Diabetic

Nephropathy

639

the target, and the results are usually displayed by

Degree. The higher the degree value of the target

gene, the greater the number of connected nodes,

which means that this node is more important in the

network relationship graph.

2.6

Construct (PPI) Protein Interaction

Network and Screen Core Genes

Upload the intersection gene file of the effective

component target of Coptis chinensis and DKD

disease-related genes to the STRING online website,

select the species as "Human", enter the website to

select all genes, set the minimum required interaction

score to lowest confidence (0.4000), and then

analyze, Get the PPI protein interaction network and

related documents. Finally, the file was imported into

Cytoscape to obtain the final core gene through three

screenings.

2.7

Enrichment Analysis of GO and

KEGG Pathways

Using R and its scripts, set qvalueFilter=0.05 (P

value≤0.05) and working directory of related files.

Then perform GO (gene ontology) enrichment

analysis and KEGG (Kyoto encyclopedia of genes

and genomes) pathway enrichment analysis in R-

4.0.4-win respectively, Obtain available enrichment

bubble chart and pathway enrichment histogram.

2.8

Molecular Docking Verifies the

Binding Relationship Between the

Active Ingredient and the Target

Download the 2D structure of the small molecule

ligand from the PubChem database

(http://pubchem.ncbi.nlm.nih.gov/), import it into

ChemOffice to convert the 2D structure of the small

molecule ligand into a 3D design, and optimize it to

the minor free energy structure, Get the 3D structure

of small molecule ligand. Download the protein

receptor structure of the selected active ingredient

from the PDB database (http://www.rcsb.org), import

the PyMOL software to remove water molecules and

small molecule ligands to obtain the protein receptor

file. Use AutoDockTools-1.5.6 software to

hydrogenate the protein receptor obtained in the

previous step and then convert the small molecule

ligand and protein receptor file format to determine

the functional pockets on the protein receptor

roughly. Use vina to perform molecular docking

between the target and the target protein to obtain the

score of the molecular docking result. The lower the

free energy, the better the binding.

3 RESULTS

3.1

Screening and Intersection of

Active Ingredient Targets and

Disease-Related Genes

Through the TCM System Pharmacology Database

and Platform (TCMSP), we searched and screened 14

main active ingredients in Coptis and 148

corresponding targets. In the GeneCards, OMIM,

TTD, and PharmGKB databases, 3506 DKD (DN)

disease-related genes (after deduplication) were

explored. The Venn diagram is drawn by the

intersection of the active ingredient targets and

disease-related genes (see Figure 1), and ten active

ingredients of Coptidis for treating diabetic

nephropathy include: berberine, etc., and 104

corresponding target genes include: PTSG2, MMP3,

MAPK1, etc

Figure 1: Venn diagram of the intersection of the

corresponding target of the practical components of

Coptidis and DKD-related genes.

3.2

The Mechanism Network of

Coptidis Regulating DKD

The analysis software Cytoscape_v3.8.0 was used to

construct and visualize the drug component-target

network of Coptidis regulated DKD (see Figures 2).

There are 277 nodes, including ten active ingredients,

104 target genes, and 163 edges. Among them,

PTGS2, ESR1, AR, and PTGS1 have slightly more

connections, and the Degree value is higher, which

may play an essential role in the treatment.

ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics

640

Figure 2: a: Rhizoma Coptidis regulates the DKD network, in which genes PTGS1, PTGS2, ESR1, AR, etc. are more

connected b: Coptis Rhizoma controls the DKD network, in which the active ingredients MOL000098, MOL000758,

MOL001454, etc. are more connected.

3.3 PPI Protein Interaction Network

The lowest confidence (0.4000) was screened through

the STRING online website to construct and visualize

the protein interaction network of the target genes for

the treatment of diabetic nephropathy (DKD) in

Coptis Chinensis (see Figure 3 a). There are 103

nodes and 1466 edges in the PPI network. The result

of importing the network into Cytoscape is visualized

as network 1. Select DC (Betweenness), CC

(Closeness), DC (Degree), EC (Eigenvector), and

LAC which are all greater than the median value.

Thirty-eight nodes generate network 2, and 17 nodes

generate network three by repeating the screening

criteria (see Figure 3 bcd). Network 3 is the core

network gene. Among them, the target genes may be

related to the treatment of diabetic nephropathy

(DKD) by Huanglian, from which genes can be

selected for molecular docking.

Figure 3: a: The PPI network of Coptis treatment of DKD

targets, where each sphere represents a protein or gene, and

the number of lines with different colors represents the

relationship b: the PPI network of a picture is imported into

the visualized network diagram of Cytoscape c: the gene

network is shown in b Diagram of the first-level core

network screened by specific criteria d: Diagram b:

Diagram of the core gene network filtered by specific

criteria

Based on Network Pharmacology and Molecular Docking Technology to Explore the Mechanism of Coptis in the Treatment of Diabetic

Nephropathy

641

3.4

GO and KEGG Enrichment

Analysis

Figure 4 ： a: GO enrichment analysis bubble chart, the

larger the value of generation, the more significant the

enrichment, the redder the bubble color, the more relevant

b: the histogram of KEGG pathway enrichment analysis,

the longer the column indicates that the gene is in this

pathway, The more significant the enrichment, the redder

the color suggests, the more relevant

The results of GO enrichment analysis (see Figure 4a)

show that BP mainly includes cellular Response to

chemical stress, Response to oxidative stress,

Response to nutrient levels, Response to the drug, etc.

KEGG pathway enrichment analysis has 165

pathways, and the top 30 pathways are plotted as a

histogram (see Figure4b). The results showed that

Lipids and atherosclerosis, AGE-RAGE signaling

pathway in diabetic complications, Prostate cancer,

IL-17 signaling pathway, TNF signaling pathway,

NF-kappa B signaling pathway, and other metabolic

pathways have significant gene enrichment. Screen

the IL-17 signaling pathway, TNF signaling pathway,

NF-kappa B signaling pathway, and VEGF signaling

based on the selected PTGS2 and refer to relevant

literature VEGF signaling pathway and draw a table

(see Table 1)

Table 1：According to the specific data of the four signal

pathways determined by PTGS2, including name, number

of genes, specific gene names, etc.

Serial

numbe

Path

way

Number

of genes

Gene

hsa046

IL-

signa

ling

path

way

PTGS2/HSP90AA1/MMP3/

RELA/FOS/

MMP9/MAPK1/JUN/CASP

3/NFKBIA/

CASP8/MMP1/CCL2/CXCL

8/IFNG/

CXCL10

hsa046

TNF

signa

ling

path

way

PTGS2/MMP3/RELA/FOS/

MMP9/MAPK1/

JUN/CASP3/NFKBIA/CAS

P8/ICAM1/CCL2/

SELE/VCAM1/CXCL10

hsa040

NF-

kapp

a B

signa

ling

path

way

PTGS2/RELA/BCL2/BCL2

L1/PLAU/NFKBIA

/ICAM1/VCAM1/CXCL8/P

RKCB/PARP1/

CD40LG

hsa043

VEG

signa

ling

path

way

PTGS2/CASP9/MAPK1/RA

F1/PRKCA/PRKCB

/HSPB1

3.5

Molecular Docking Results

Table 2: PTGS2 molecular docking result scoring table

(select the first five docking positions with the lowest free

energy)

mode affinity(kcal/mol) dist from

best

mode

(rmsd

l.b.

)

dist from

best

mode

(rmsd

u.b.

)

1 -9.2 0.000 0.000

2 -9.0 37.418 38.338

3 -8.9 47.593 48.959

4 -8.8 2.188 3.850

5 -8.7 1.948 7.680

The molecular docking technology was used to verify

the binding degree of the docking between the

corresponding receptor protein corresponding to the

receptor protein berberine and the related small-

molecule ligand of PTGS2, and the minimum free

energy was selected and visualized with PyMOL (see

Figure 5). The docking results show that the free

ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics

642

energy of the receptor protein and the corresponding

small molecule ligand is low, indicating that the target

protein has a better binding ability than the small

molecule (see Table 2)

Figure 5: Select the minimum free energy docking form,

and use PyMOL to visualize the panorama and details.

3.6

PTSG2, CCL2 Gene Mutation

Analysis

It is predicted that the genes related to DKD may be

PTGS2 and CCL2. Studies have shown that increased

expression of renal tubules MCP-1/CCL2 will

promote kidney damage, and the level of urine MCP-

1/CCL2 will gradually increase with the progress of

DKD, and the severity of it will deepen. (Morii,

2003); (Zhu, 2013). It shows that CCL2 and its

pathway are closely related to DKD. They are

analyzed through the online website

(http://www.cbioportal.org/). The analysis results are

shown in Figure 6: a: PTSG2 has Missense Mutation,

Truncating Mutation unknown gene mutation, and

CCLI2 has a strange gene mutation in Missense

Mutation. b: PTGS2 may have genetic mutations in

Kidney Renal Clear Cell Carcinoma, Renal Clear Cell

Carcinoma, Kidney Renal Papillary Cell Carcinoma,

and Kidney Renal Papillary Cell Carcinoma. Among

them, Kidney Renal Clear Cell Carcinoma and Renal

Clear Cell Carcinoma are more likely to occur. Nearly

40% of the genetic mutations in Renal Clear Cell

Carcinoma will be amplified. CCL2 is less likely to

be mutated in Kidney Renal Clear Cell Carcinoma,

but nearly 50% of the possible genetic mutations will

be strengthened after the genetic mutation.

Figure 6: A: Comparative analysis of gene mutations of

PTGS2 and CCL2 in kidney-related cancers. B: Analysis of

PTGS2 mutations [1:Structural variant data 2:Mutation

data 3:CAN data a:Kidney Renal Clear Cell Carcinoma

(TCGA, Firehose Legacy) b:Renal Clear Cell Carcinama

(UTOkyo, Nat Genet 2013) c:Kidney Renal Papillary Cell

Carcinama (TCGA, Firehose Legacy) d:Clear Cell Renal

Cell Caricinoma (DFCI, Science 2019) e:Kidney

Chromophobe(TCGA, Cancer Cell 2014) f:Kidney Renal

Clear Cell Carcinoma(BGI, Nat Genet 2012) g: Kidney

Renal Clear Cell Carcinoma (IRC, Nat Genet 2014) h:Renal

Non-Clear Cell Carcinoma (Genentech, Nat Genet 2014)

i:Unclassificied Renal Cell Cacinoma (MSK, Nature 2016)

C: Analysis of CCL2 mutations. [1:Structural variant data

2:Mutation data 3:CAN data j: Kidney Renal Clear Cell

Carcinoma(TCGA, Firehose Legacy) k: Clear Cell Renal

Cell Caricinoma(DFCI, Science 2019) l: Kidney

Chromophobe(TCGA, Cancer Cell 2014) m: Kidney

Renal Clear Cell Carcinoma(BGI, Nat Genet 2012) n:

Kidney Renal Clear Cell Carcinoma(IRC, Nat Genet 2014)

o: Kidney Renal Papillary Cell Carcinama(TCGA, Firehose

Legacy) p: Renal Clear Cell Carcinama(UTOkyo, Nat Genet

2013) q: Renal Non-Clear Cell Carcinoma(Genentech,

Nat Genet 2014) r: Unclassificied Renal Cell

Cacinoma(MSK, Nature 2016)] (In B and C, the green part

represents Mutation, and the red part represents

Amplification)

Figure 7: Comparison of the expression map of the two

genes in normal kidney tissue and tumor tissue. Image is

taken from Human Protein Atlas

(http://www.proteinatlas.org) online database

Based on Network Pharmacology and Molecular Docking Technology to Explore the Mechanism of Coptis in the Treatment of Diabetic

Nephropathy

643

4 DISCUSSIONS

Through network pharmacology data collection,

screening, and analysis, some key genes (Figure 3d)

and signal pathways (Figure 4b) were obtained. In the

process of searching the literature, we learned that

berberine can treat DKD renal insufficiency (Niksic

L, 2005) and protect the kidneys (Lan, 2010). After

screening the core gene and the corresponding target

of berberine to take the intersection, it was finally

determined to select the only intersection gene

PTGS2. Four related signal pathways were chosen

from the first 30 pathways with significant KEGG

enrichment (Table 1). Finally, PTGS2 was

molecularly docked (Figure 5). The results showed

that the lowest score was -9.2 (kcal/mol) (Table 2),

indicating that PTGS2 and its related pathways may

be essential genes and pathways regulating berberine

treatment of DKD.

Studies have shown that the occurrence of DKD

may be related to inflammation, metabolic status,

activation of NF-κB, etc. Inflammation plays a vital

role in the pathogenesis of diabetic nephropathy,

causing kidney damage and activating some signal

channels to exacerbate inflammation reactions.

Inflammatory factors include interleukin (IL), nuclear

factor-κB (NF-κB), tumor necrosis factor-α (TNF-α),

transforming growth factor-β1 (TGF-β1), etc. Among

them, IL may be related to IL- 17 signaling pathway,

TNF-α is related to TNF signaling pathway, and NF-

κB is associated with NF -kappa B signaling pathway.

Berberine is likely to regulate these inflammatory

factors to regulate the DKD metabolic pathway to

protect the kidney and treat DKD.

Studies have shown that berberine has a relatively

apparent anti-inflammatory effect, mainly by

inhibiting the production and activity of

inflammatory factors. It can reduce the activity of

neutrophil phospholipase A2 and reduce the

production of prostaglandin E2 in inflammatory

tissues (Hu, 2014). Prostaglandin-endoperoxide

synthase (PTGS), also known as cyclooxygenase, is a

key enzyme in prostaglandin biosynthesis, closely

related to the synthesis of prostaglandin E2, and

PTGS2 is one of the two types of PTGS Inducible

may be involved in the synthesis of prostaglandin E2.

Based on this speculation, berberine may regulate

PTGS2 to regulate related metabolic pathways to

achieve anti-inflammatory effects and reduce kidney

damage.

In addition, the gene mutation analysis of PTGS2

and CCL2 in section 2.6 (Figure 6) also shows that

PTGS2 is more likely and more prone to gene

mutations in related kidney diseases than CCL2,

which also illustrate the relationship of PTGS2 and

pathogenesis of DKD is closer.

5 CONCLUSIONS

In summary, based on network pharmacology,

predictive analysis verified that PTGS2 and its related

pathways may be important genes and pathways

regulating berberine treatment of DKD. Berberine is

likely to further inhibit the synthesis and release of

some inflammatory factors (such as IL, NF-κB, TNF-

α, TGF-β1, etc.) by inhibiting PTGS2, thereby

achieving regulation of DKD metabolic pathways

(TNF signaling pathway, IL-17 signaling pathway,

NF-kappa B signaling pathway, VEGF signaling

pathway) to achieve anti-inflammatory effects, and

ultimately reduce kidney damage, protect the

kidneys, and achieve the effect of treating DKD.

In this study, a series of methods were used to find

the relationship between berberine and the DKD

disease gene PTGS2, the effective component of

Coptidis Rhizome, and to verify the feasibility of

Coptis Rhizoma (berberine) for regulating PTGS2 in

the treatment of DKD. It provides a new idea for the

research on the target and mechanism of Coptidis in

the treatment of diabetic nephropathy in the future.

It is hoped that this study can provide reference

for other researchers who are starting to develop the

mechanism of action of Coptis in the treatment of

DKD in the future.

REFERENCES

Friedman AN, Hunsicker LG, Selhub J, et al.Clinical and

nuitritional correlates of C-reactive protein in

type2diabetic nephropathy. Atherosclero-sls, 2004,

172:121-125.

Fu L; Fu Q; Li J; Tong X. Research progress on the

chemical constituents and pharmacological effects of

Rhizoma Coptidis[J]. Journal of Traditional Chinese

Medicine,2021,49(02):87-92.

Haraguchi R, Kohara Y, Matsubayashi K, et al. New

Insights into the Pathogenesis of Diabetic Nephropathy:

Proximal Renal Tubules Are Primary Target of

Oxidative Stress in Diabetic Kidney[J]. Acta

histochemica et cytochemica official journal of the

Japan Society of Histochemistry and Cytochemistry,

2020, 46(12):1723-1731.

Hu SP, Wang Y, Yu P-Y, et al. In vitro anti-inflammatory

effects of the main components of Huanglian Jiedu

Decoction[J]. China Modern Applied Pharmacy, 2014,

31, (10): 1171-1174

Lan T, Shen XY, Liu PQ, et al.Berberine ameliorates renal

ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics

644

injury in di-abetic C57BL/6 mice:Involvement of

suppression of SphK–S1P sig-naling pathway[J].Arch

Biochem Biophys, 2010, 502 (2) :112-120.

Li JP, Wu CZ, Yue W, et al. Research progress of new

clinical uses and new dosage forms of berberine[J].

China Pharmacy, 2016, 27(22):3154-3158.

Liu ZH. Nephrology in China[J]. Nature Reviews

Nephrology, 2013, 9 (9) :523-528

Li XS. Inflammation-related factors and diabetic

nephropathy[J]. Medicine, 2008, (02):214-216.

Lu F, Tang LQ. The role of inflammatory factors in diabetic

nephropathy-related signal pathways[J]. China

Pharmacy, 2010, 21 (18) :1706-1710.

Lu J-J; Liu S. Research progress on the effects of berberine

on diabetes and its complications[J]. Anhui Medicine,

2012, (11): 1566-1569.

Morii T, Fujita H, Narita T, et al.Association of monocyte

chemoattractant protein-1 with renal tubular damage in

diabetic nephropathy[J].J Diabetes Complications,

2003, 17 (1) :11-15.

Neal B, Perkovic V, Mahaffey KW, et al. CANVAS

Program Collaborative Group. Canagliflozin and

Cardiovascular and Renal Events in Type 2 Diabetes. N

Engl J Med, 2017, 377(7):644-657.

Niksic L, Martin PY.BMP-7 (Bone morphogenetic protein-

7): afuture for chronic renal failure? [J]. Rev Med

Suisse, 2005, 1 (8) :568-573.

Perkovic V, Jardine MJ,Neal B,et al. CREDENCE Trial

Investigators. Canagliflozin and Renal Outcomes in

Type 2 Diabetes and Nephropathy. N Engl J Med, 2019,

380(24):2295-2306.

Peng C. Pharmacology of Traditional Chinese Medicine-

(New Century Fourth Edition) -Thirteenth Five-Year

Plan for Higher Education in Chinese Medicine

Industry [M]. China Press of Traditional Chinese

Medicine, 2018.

Ritz E, Wolf G. Pathogenesis, Clinical Manifestations, and

Natural History of Diabetic Nephropathy[J].

Comprehensive Clinical Nephrology (Fourth Edition),

2010, 42(7):359-376.

Verma S, Mazer CD, Fitchett D, et al. Empagliflozin

reduces cardiovascular events, mortality and renal

events in participants with type 2 diabetes after

coronary artery bypass graft surgery: subanalysis of the

EMPA-REG OUTCOME randomised trial.

Diabetologia, 2018, 61(8):1712-1723.

WANG J, WANG L, LOU G H,et al Coptidis Rhizoma:a

comprehensive review of its traditional uses, botany,

phytochemistry ,pharmacology and toxicology [J].

Pharm Biol,2019,57(1):193.

Wang HT. Neijing [M]. Beijing: People's Health Publishing

Madness, 2011; 203.

Xing T-T. The protective effect of crocetin on kidney

damage in diabetic rats by inhibiting oxidative stress

and inflammation[C]. Jinzhou Medical University,

2021.

Yang NY; Zhang QC; Zhu HX; Fu TM; Li B; Duan JA;

Tang ZS. Research progress and utilization strategy of

resource chemical components of Coptis alkaloids[J].

Chinese Traditional and Herbal Medicine, 2019,

50(20):5080-5087.

Zhang Y.A study of ancient and modern medicine thinking

in diabetic nephropathy[C]. Beijing University of

Chinese Medicine, 2017.

Zhu BY; Huang LJ; Yu J-Y. The research progress of

chemokines and their receptors and diabetic

nephropathy[J]. Jiangsu Medicine, 2013, (02):210-213.

Based on Network Pharmacology and Molecular Docking Technology to Explore the Mechanism of Coptis in the Treatment of Diabetic

Nephropathy

645