Compound XiongShao Capsule (CXSC), a traditional herb mixture, has shown significant clinical efficacy against diabetic peripheral neuropathy (DPN). However, its multicomponent and multitarget features cause difficulty in deciphering its molecular mechanisms. Our study aimed to identify the key active ingredients and potential pharmacological mechanisms of CXSC in treating DPN by network pharmacology and provide scientific evidence of its clinical efficacy. CXSC active ingredients were identified from both the Traditional Chinese Medicine Systems Pharmacology database, with parameters of oral bioavailability ≥ 30% and drug-likeness ≥ 0.18, and the Herbal Ingredients’ Targets (HIT) database. The targets of those active ingredients were identified using ChemMapper based on 3D-structure similarity and using HIT database. DPN-related genes were acquired from microarray dataset GSE95849 and five widely used databases (TTD, Drugbank, KEGG, DisGeNET, and OMIM). Next, we obtained candidate targets with therapeutic effects against DPN by mapping active ingredient targets and DPN-related genes and identifying the proteins interacting with those candidate targets using STITCH 5.0. We constructed an “active ingredients-candidate targets-proteins” network using Cytoscape 3.61 and identified key active ingredients and key targets in the network. We identified 172 active ingredients in CXSC, 898 targets of the active ingredients, 110 DPN-related genes, and 38 candidate targets with therapeutic effects against DPN. Three key active ingredients, namely, quercetin, kaempferol, and baicalein, and 25 key targets were identified. Next, we input all key targets into ClueGO plugin for KEGG enrichment and molecular function analyses. The AGE-RAGE signaling pathway in diabetic complications and MAP kinase activity were determined as the main KEGG pathway and molecular function involved, respectively. We determined quercetin, kaempferol, and baicalein as the key active ingredients of CXSC and the AGE-RAGE signaling pathway and MAP kinase activity as the main pharmacological mechanisms of CXSC against DPN, proving the clinical efficacy of CXSC against DPN.
Diabetic peripheral neuropathy (DPN) is one of the most common complications of diabetes, affecting approximately 50% of people with diabetes [
The pathogenesis of DPN is complex and multifactorial [
Despite advancements in the understanding of DPN pathogenesis, prevention and therapy of DPN mainly focus on glucose control and lifestyle modification [
Recently, Traditional Chinese Medicine (TCM) has shown efficacy in treating DPN [
However, the active ingredients of CXSC and their potential pharmacological mechanisms have not been fully studied. In a previous study, network pharmacology was used to discover the active ingredients and elucidate the mechanisms of herbal formulae [
Flowcharts of the network pharmacology analysis. Left: summary of the identification of representative ingredients of CXSC and targets with therapeutic effects against DPN. Right: summary of the determination of the pharmacological mechanisms of CXSC.
Active ingredients of CXSC were collected both from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database [
The TCMSP database is a unique TCM platform for identifying the relationships between drugs, targets, and diseases [
The validated targets of the active ingredients of CXSC were collected from the HIT database [
Known genes of DPN were identified from five currently available databases using “diabetic peripheral neuropathy” as the keyword. In addition, the main differentially expressed genes (DEGs) between DPN patients and diabetic patients were extracted from microarray data GSE95849 [
The five databases were: the Therapeutic Target Database [
Candidate targets with therapeutic effects against DPN were identified by mapping the targets of the active ingredients of CXSC and the genes related to DPN [
A network of “active ingredients-candidate targets-proteins” was constructed using the Cytoscape 3.61 software [
A total of 172 active ingredients (Supplementary Table
The number of overlapped active ingredients between herbs.
Active ingredients | Total | Herbs |
---|---|---|
beta-sitosterol | 7 | |
Sitosterol | 6 | |
quercetin | 5 | |
Stigmasterol | 3 | |
acetic acid | 3 | |
kaempferol | 3 | |
(-)-taxifolin | 2 | |
CLR | 2 | |
hederagenin | 2 | |
FA | 2 | |
(+)-catechin | 2 | |
baicalein | 2 | |
ecdysterone | 2 | |
Mandanol | 2 | |
A total of 898 targets of the active ingredients of CXSC (Supplementary Table
The targets that overlapped between at least 7 herbs. The triangle nodes represent the herbs, whereas the rectangle nodes represent the targets. The targets distributed in a circle are targeted by the same number of herbs. The number of herbs and number of targets are expressed as “n” and “T,” respectively.
A total of 51 DEGs were extracted from microarray data GSE95849, including 23 upregulated genes and 28 downregulated genes, as shown in Figure
Volcano map of 51 DEGs. Red nodes represent upregulated genes and green nodes represent downregulated genes.
A total of 38 candidate targets were shown to have potential pharmacological effects against DPN, including 11 predicted targets (INS, MAPK14, MMP2, NOS3, SCN9A, SLC6A4, SLC6A3, OPRM1, TUBB1, ABCC8, and KCNJ11) and 27 validated targets (AKT1, BAX, BCL2, CASP3, JUN, MAPK1, MAPK8, TNF, VEGFA, IL1B, IL6, PRKCA, PRKCB, RELA, SELE, STAT1, PLAU, CCND1, COL1A1, CXCL8, EDN1, F3, ICAM1, ACE, SLC6A2, and CYP1A2). Specifically, TNF, IL1B, IL6, and CXCL8 were involved in inflammatory reactions; AKT1, BAX, BCL2, CASP3, and MAPK8 were related to apoptosis; and MAPK1, MAPK8, and MAPK14 were associated with MAPK activity. In addition, there were 86 proteins that interacted with those candidate targets.
An “active ingredients-candidate targets-proteins” network of 38 candidate targets and their interacting proteins was constructed using the Cytoscape 3.61 software (Figure
The information about the 25 key targets.
Gene | UniProt | Description | Degree | Source |
---|---|---|---|---|
AKT1 | P31749 | RAC-alpha serine/threonine-protein kinase | 83 | validated |
OPRM1 | P35372 | Mu-type opioid receptor | 76 | predicted |
MAPK8 | P45983 | Mitogen-activated protein kinase 8 | 72 | validated |
SLC6A4 | P31645 | Sodium-dependent serotonin transporter | 67 | predicted |
SLC6A2 | P23975 | Sodium-dependent noradrenaline transporter | 60 | validated |
SLC6A3 | Q01959 | Sodium-dependent dopamine transporter | 56 | predicted |
JUN | P05412 | Transcription factor AP-1 | 53 | validated |
MAPK14 | Q16539 | Mitogen-activated protein kinase 14 | 51 | predicted |
NOS3 | P29474 | Nitric oxide synthase | 50 | predicted |
TNF | P01375 | Tumor necrosis factor | 40 | validated |
ABCC8 | Q09428 | ATP-binding cassette sub-family C member 8 | 37 | proteins |
FOS | P01100 | Proto-oncogene c-Fos | 35 | validated |
TUBB1 | Q9H4B7 | Tubulin beta-1 chain | 34 | predicted |
MAPK1 | P28482 | Mitogen-activated protein kinase 1 | 34 | validated |
NFKB1 | P19838 | Nuclear factor NF-kappa-B p105 subunit | 31 | proteins |
BCL2 | P10415 | Apoptosis regulator Bcl-2 | 30 | validated |
KCNJ11 | Q14654 | ATP-sensitive inward rectifier potassium channel 11 | 29 | predicted |
PLAU | P00749 | Urokinase-type plasminogen activator | 28 | validated |
RELA | Q04206 | Transcription factor p65 | 23 | validated |
TP53 | P04637 | Cellular tumor antigen p53 | 23 | proteins |
BAX | Q07812 | Apoptosis regulator BAX | 21 | validated |
VEGFA | P15692 | Vascular endothelial growth factor A | 21 | validated |
STAT1 | P42224 | Signal transducer and activator of transcription 1-alpha/beta | 19 | validated |
FOXO1 | Q12778 | Forkhead box protein O1 | 19 | proteins |
UBC | P0CG48 | Polyubiquitin-C | 18 | proteins |
“Active ingredients-candidate targets-proteins” network. The triangle nodes represent active ingredients, the rectangle nodes represent validated targets, the diamond nodes represent predicted targets, and the circular nodes represent interacting proteins. The color of the nodes is shown in a gradient from purple to transparent according to descending order of the degree value. The three key active ingredients and 25 key targets are listed in the center circle.
The chemical structure of the 3 key active ingredients.
The enrichment analysis results showed enrichment in the KEGG signaling pathways and molecular functions. As shown in Figure
KEGG signaling pathways.
Molecular functions analysis.
TCM pharmacological mechanisms have always been associated with multiple components and multiple targets that are difficult to explain. However, the holistic ideas of TCM have been revealed by emerging network pharmacology approaches studying the relationships between drugs, targets, and diseases [
The “active ingredients-targets-proteins” network showed quercetin, kaempferol, and baicalein as the key active ingredients of CXSC that exhibit therapeutic effects against DPN. Moreover, these key ingredients targeted 308 (34.3%), 296 (33.0%), and 244 (27.2%) targets, respectively. Furthermore, these three key ingredients targeted 22 of the 38 candidate targets with therapeutic effects against DPN, as well as 21 of the 25 key targets of CXSC, as shown in Figure
Data of the three key active ingredients (quercetin, kaempferol, and baicalein). Pink rectangles represent 38 candidate targets with therapeutic effects against DPN, whereas green rectangles represent the 25 key targets. Two-colored rectangles are targets that overlapped between the two categories. Targets in the purple box are targets of quercetin, kaempferol, and baicalein.
Kaempferol and baicalein, the key active ingredients of CXSC, were previously confirmed to reduce the formation of AGEs, thereby reducing inflammatory responses in diabetic rat nerves [
In addition, AGEs can activate p38 MAPK, leading to neuron apoptosis [
In conclusion, the multicomponent and multitarget features of the therapeutic effects of CXSC against DPN were effectively elucidated through network pharmacology approach. Quercetin, kaempferol, and baicalein were determined as the key active ingredients of CXSC. In addition, the AGE-RAGE signaling pathway and regulation of MAPK activity were shown as the main pharmacological mechanisms of the therapeutic effects of CXSC against DPN, thereby providing scientific evidence of the clinical efficacy of CXSC against DPN.
The data of our research can be acquired from the Supplementary Materials uploaded with this article.
The authors declare no conflicts of interest.
Yu Meixiang and Yang Wanhua designed, wrote, and revised the manuscript. Yu Meixiang and Song Xin performed the experiments; Li Ziwei, Ma Xiaoqin, and Hao Chenxia revised the manuscript and provided technical or material support.
This project was supported by the key scientific research projects of the Science and Technology Commission of Shanghai (no.17401901100).
Supplementary Table 1. 172 active ingredients in Compound XiongShao Capsule. 172 active ingredients in Compound XiongShao Capsule were downloaded from both the Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database (