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BY 4.0 license Open Access Published by De Gruyter Open Access November 28, 2022

Mechanism study of Cordyceps sinensis alleviates renal ischemia–reperfusion injury

  • Yan Liang , Di Zhang , Jianguang Gong , Wenfang He , Juan Jin EMAIL logo and Qiang He EMAIL logo
From the journal Open Chemistry

Abstract

Cordyceps sinensis (C. sinensis) is a kind of traditional Chinese medicine commonly used to protect renal function and relieve kidney injury. This study aimed to reveal the renal protective mechanism of C. sinensis in renal ischemia–reperfusion injury (RIRI). First, we obtained 8 active components and 99 common targets of C. sinensis against RIRI from public databases. Second, we have retrieved 38 core targets through STRING database analysis. Third, Gene Ontology analysis of 38 core targets is indicated that C. sinensis treatment RIRI may related hormone regulation, oxidative stress, cell proliferation, and immune regulation. Kyoto Encyclopedia of Genes and Genomes enrichment analysis of 38 core targets is indicated that C. sinensis treatment RIRI may involve in PI3K–Akt, HIF-1, and MAPK signaling pathways, as well as advanced glycation end product (AGE)–receptor for AGE (RAGE) signaling pathway in diabetic complications. Lastly, molecular docking was used to detect the binding activity and properties of active components and core target using molecular docking. And the results showed that eight active components of C. sinensis had low affinity with core targets. In conclusion, C. sinensis may improve RIRI by regulating oxidative stress and immunity through PI3K–Akt, HIF-1, and MAPK pathways.

1 Introduction

Acute kidney injury (AKI) is a group of extensive clinical syndromes characterized by sudden decline in renal function, affecting 7–18% of hospitalized patients and 30–70% of critically ill patients [1]. AKI is closely related to the progression of chronic kidney disease (CKD) and end-stage renal disease (ESRD) [2,3], and the 1-year mortality associated with cancer and cardiovascular disease significantly increased after AKI survivors were discharged from the hospital [4]. The etiological spectrum of AKI is very wide, including infection, nephrotoxic drugs, trauma surgery, and hypovolemic shock, among which ischemia–reperfusion injury (IRI) is one of the most common causes [5]. The general principles of management of AKI include determination of volume status, fluid resuscitation with isotonic crystalloids, treatment of volume overload with diuretics, discontinuation of nephrotoxic drugs, and adjustment of prescribed medication according to renal function [6]. Hemodynamic management is crucial for patients with RIRI.

Traditional Chinese medicines develop significant parts in alleviating disease progression and protecting renal function due to their unique pharmacological effects, which contain their complex active components, and acting on multiple targets, like Cordyceps sinensis (C. sinensis), a sort of fungus that grows inside larva. Studies have shown that C. sinensis has antioxidation [7], immune regulation [8], metabolic regulation [9], anti-tumor [10,11,12], anti-apoptosis [10], anti-inflammatory, anti-fibrosis [13,14], and other biological activities. Furthermore, C. sinensis is commonly used to delay the progression of CKD and ESRD [15,16,17] and is also significant in the treatment of AKI [18]. C. sinensis can reduce the serum creatinine (SCr) level and increase the levels of endogenous creatinine clearance (Ccr), serum albumin, and hemoglobin. Simultaneously, it can improve lipid metabolism disorders [16]. Importantly, previous study found C. sinensis against renal ischemia–reperfusion injury (RIRI) in rats [19]. However, a comprehensive, extensive, and systematic research of the molecular mechanism of C. sinensis in the treatment of RIRI is very necessary.

In this study, we systematically analyzed the active components, targets, and the interaction network of C. sinensis and RIRI from two aspects: network pharmacology and molecular docking (Figure 1). Briefly, this study provides comprehensive insight into the molecular mechanism of action of C. sinensis in the treatment of RIRI.

Figure 1 
               The workflow of this study.
Figure 1

The workflow of this study.

2 Materials and methods

2.1 The active components of C. sinensis

The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) database (https://old.tcmsp-e.com/tcmsp.php; https://old.tcmsp-e.com/tcmsp.php) is an authoritative systematic pharmacology database containing a variety of Chinese herbal medicine components, target proteins, and related diseases. Each Chinese herbal medicine was provided its pharmacokinetic information, namely absorption, distribution, metabolism, and excretion, and including oral bioavailability (OB), drug-likeness (DL), intestinal epithelial permeability (Caco-2), blood–brain barrier, aqueous solubility (AlogP), and H-bond donor/receptor (Hdon/Hacc) [20]. OB and DL are crucial to the efficacy of drug components. OB represents the degree and speed of the compound bioavailability, and DL is on behalf of the compounds as a possible drug. In this research, the related components of C. sinensis were obtained using “Cordyceps sinensis” as a keyword and filtered by “OB ≥ 30%” and “DL ≥ 0.18”.

2.2 Potential targets of the active components

The active components of C. sinensis mainly exerted their therapeutic effect through the related targets. In addition to acquiring the targets of the active components directly from the TCMSP and Encyclopedia of Traditional Chinese Medicine (http://www.nrc.ac.cn:9090/ETCM/; http://www.nrc.ac.cn:9090/ETCM/) [21] database, we also used SuperPred database (http://prediction.charite.de; http://prediction.charite.de) [22] to predict possible targets of active components through their Canonical SMILES structure and selected the targets of “possibility ≥60%.” We applied the Uniprot database (https://www.uniprot.org; https://www.uniprot.org) to obtain the corresponding gene symbols and Uniprot IDs of the target proteins.

2.3 Targets collection of RIRI

The target genes associated with disease were obtained from GeneCards (https://www.genecards.org; https://www.genecards.org) [23], Online Mendelian Inheritance in Man (OMIM) (https://www.omim.org; https://www.omim.org) [24], and DisGeNET (https://www.disgenet.org/; https://www.disgenet.org/) [25] databases. In GeneCards, we set “relevance score ≥10.00” as the filter condition. In OMIM Database, “Gene Map” in “Advanced Search” was performed for the search of disease keywords. Targets with “pLI ≥ 0.05” were selected from the results obtained with DisGeNET. The final disease-related targets were collected by removing duplicate values in the three databases.

2.4 Screening the core targets of C. sinensis for RIRI treatment

We use Venny 2.1 online tool (https://bioinfogp.cnb.csic.es/tools/venny/; https://bioinfogp.cnb.csic.es/tools/venny/) to get the intersection targets of active components and RIRI. The intersection targets obtained above were used to make a protein–protein interaction (PPI) network from STRING database (https: //cn.string-db.org) [26] and exported it to the “TSV” format. Then, we use “combined score ≥0.7” as the screening condition and analyzed its topology properties (degree) in Cytoscape 3.6.0 software [27]. The targets of “degree ≥ median” are the core targets of C. sinensis in the treatment of RIRI.

2.5 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of core targets

To explore the molecular mechanism of C. sinensis treating RIRI at the three levels of molecular function (MF), cellular component (CC), and biological process (BP), we used the ggplot2 package and clusterProfiler package [28] of R 3.6.3 software to perform the GO and KEGG [29] pathway enrichment analyses and select “p adjust <0.05” as the screening threshold. Therefore, GO terms display the top 10 and KEGG pathways display the top 20.

2.6 Construction of the active component–target–pathway network

For further exploring the interaction relationships among the active components, core targets, and pathways, we used Cytoscape 3.6.0 to construct the active component–target–pathway network: 8 active components, 38 core targets, and top 20 KEGG pathways.

2.7 Molecular docking

To understand the binding activity between the active components of C. sinensis and core targets, we performed molecular docking. The three-dimensional (3D) structure of the active component linoleyl acetate was obtained from ChemSpider (www.chemspider.com), while the composition of other active components structure was obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov; https://pubchem.ncbi.nlm.nih.gov). Subsequently, the 3D crystal structure of the receptor protein was obtained from Protein Data Bank (PDB) (https://www.pdbus.org; https://www.pdbus.org) database. The screening conditions for the crystal structure of the receptor protein were as follows [30]: (1) the source is Homo sapiens; (2) the resolution of the crystal is less than 3 Å; (3) the experimental method of the crystal is X-ray diffraction; (4) more complete protein structure; and (5) the target protein has more than a small-molecule co-crystallized structure. Autodock Vina [31] was then performed to predict the binding mode and free energy score of the active component and target protein. The lower the affinity, the better the spatial and energy matching. In parallel, PyMol was used to visualize the spatial binding conformation of the active component and the target protein.

3 Results

3.1 Active components and targets of C. sinensis

A total of 38 active components of C. sinensis were searched from the TCMSP database, and there were seven compounds that met the screening criteria “OB ≥ 30%” and “DL ≥ 0.18,” namely linoleyl acetate, arachidonic acid, CLR, beta-sitosterol, peroxyergosterol, cerevisterol, and cholesteryl palmitate, and cordycepin is a key compound with important functions found in the literature (Table 1). The target numbers of the 8 active components were 154, 60, 151, 138, 82, 113, 102, and 79. After removing duplicates, there were 372 targets of C. sinensis.

Table 1

The important information about active components of C. sinensis

Mol ID Molecule name OB (%) DL
MOL001439 Arachidonic acid 45.57 0.2
MOL008998 Cerevisterol 39.52 0.77
MOL000358 Beta-sitosterol 36.91 0.75
MOL008999 Cholesteryl palmitate 31.05 0.45
MOL000953 Cholesterol 37.87 0.68
MOL011169 Peroxyergosterol 44.39 0.82
MOL001645 Linoleyl acetate 42.1 0.2
Cordycepin

3.2 The targets prediction of RIRI

The search keywords were “acute ischemia–reperfusion kidney injury” and “renal ischemia reperfusion injury,” 1,688 targets were collected in GeneCards and 256 targets were searched in OMIM. 146 targets were obtained by entering “reperfusion injury” into the DisGeNET database. Finally, a total of 825 targets were collected after intersection of the three databases. By Venn diagram, 99 intersection targets of C. sinensis and RIRI were obtained (Figure 2).

Figure 2 
                  Intersection targets between C. sinensis and RIRI. The blue circle represents the targets of active components of C. sinensis; the yellow circle means the disease-related targets; and the middle region represents the intersection targets.
Figure 2

Intersection targets between C. sinensis and RIRI. The blue circle represents the targets of active components of C. sinensis; the yellow circle means the disease-related targets; and the middle region represents the intersection targets.

3.3 Analysis of active components–targets–disease network

Ninety-nine interaction targets were analyzed by Cytoscape to construct an active component–target–disease network, with 108 nodes and 311 edges (Figure 3). The degrees of 8 active components, arachidonic acid, linoleyl acetate, beta-sitosterol, cholesterol, peroxyergosterol, cerevisterol, cholesteryl palmitate, and cordycepin, were 37, 12, 35, 24, 23, 37, 29, and 15, respectively (Table 2), which were important elements in the network.

Figure 3 
                  Active components–intersection targets–disease network of C. sinensis against RIRI. The 99 green ellipses in the middle represent intersection targets. The surrounding triangle nodes mean the active components. The bottom diamond represents RIRI. The interactions between the components, disease, and their targets were connected by edges.
Figure 3

Active components–intersection targets–disease network of C. sinensis against RIRI. The 99 green ellipses in the middle represent intersection targets. The surrounding triangle nodes mean the active components. The bottom diamond represents RIRI. The interactions between the components, disease, and their targets were connected by edges.

Table 2

Degree value of 8 main active components of C. sinensis

Mol ID Molecule name Degree
MOL001439 Arachidonic acid 37
MOL008998 Cerevisterol 37
MOL000358 Beta-sitosterol 35
MOL008999 Cholesteryl palmitate 29
MOL000953 Cholesterol 24
MOL011169 Peroxyergosterol 23
Cordycepin 15
MOL001645 Linoleyl acetate 12

3.4 PPI network analysis

PPI networks of 99 targets were prepared by using STRING database. Then, the network was imported into Cytoscape software, where the node colors and sizes indicate the degree's size, and using the centiscape2.2 plugin to screen out the 38 core targets of degree ≥ median (9.82) (Figure 4a). The bar graphic showed the top 20 related targets (Figure 4b), including signal transducer and activator of transcription 3 (STAT3), JUN, epidermal growth factor receptor (EGFR), heat shock protein (HSP)90AA1, RELA, Caspase-3 (CASP3), hypoxia-inducible factor (HIF) 1A, mitogen-activated protein kinase 1 (MAPK1), protein kinase C alpha (PRKCA), and EGF (Table 3), which may play important function in the process of C. sinensis treatment of RIRI.

Figure 4 
                  Thirty-eight core targets of C. sinensis for RIRI. (a) Thirty-eight core targets between C. sinensis resist RIRI with degree ≥ median, and the larger and the closer to the blue nodes represent the larger degree value. (b) Top 20 protein targets sort by degree.
Figure 4

Thirty-eight core targets of C. sinensis for RIRI. (a) Thirty-eight core targets between C. sinensis resist RIRI with degree ≥ median, and the larger and the closer to the blue nodes represent the larger degree value. (b) Top 20 protein targets sort by degree.

Table 3

Information on 38 core targets

No. Uniprot ID Gene symbol Protein name Degree
1 P40763 STAT3 Signal transducer and activator of transcription 3 35
2 P05412 JUN Transcription factor Jun 35
3 P00533 EGFR Epidermal growth factor receptor 30
4 P07900 HSP90AA1 Heat shock protein HSP 90-alpha 30
5 Q04206 RELA Transcription factor p65 26
6 P42574 CASP3 Caspase-3 25
7 Q16665 HIF1A Hypoxia-inducible factor 1-alpha 23
8 P28482 MAPK1 Mitogen-activated protein kinase 1 23
9 P17252 PRKCA Protein kinase C alpha type 22
10 P01133 EGF Pro-epidermal growth factor 21
11 P42345 MTOR Serine/threonine-protein kinase mTOR 21
12 P27986 PIK3R1 Phosphatidylinositol 3-kinase regulatory subunit alpha 20
13 P01308 INS Insulin 20
14 P03372 ESR1 Estrogen receptor 19
15 P42224 STAT1 Signal transducer and activator of transcription 1-alpha/beta 19
16 P37231 PPARG Peroxisome proliferator-activated receptor gamma 18
17 P24385 CCND1 G1/S-specific cyclin-D1 18
18 Q13547 HDAC1 Histone deacetylase 1 18
19 P14780 MMP9 Matrix metalloproteinase-9 18
20 P60484 PTEN Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN 17
21 P61073 CXCR4 C-X-C chemokine receptor type 4 16
22 P19838 NFKB1 Nuclear factor NF-kappa-B p105 subunit 16
23 O00206 TLR4 Toll-like receptor 4 15
24 P05771 PRKCB Protein kinase C beta type 15
25 P04150 NR3C1 Glucocorticoid receptor 15
26 P35354 PTGS2 Prostaglandin G/H synthase 2 13
27 P05556 ITGB1 Integrin beta-1 13
28 Q05655 PRKCD Protein kinase C delta type 13
29 P35968 KDR Vascular endothelial growth factor receptor 2 13
30 P01137 TGFB1 Transforming growth factor beta-1 proprotein 12
31 Q07869 PPARA Peroxisome proliferator-activated receptor alpha 11
32 Q14790 CASP8 Caspase-8 11
33 O15111 CHUK Inhibitor of nuclear factor kappa-B kinase subunit alpha 11
34 P29474 NOS3 Nitric oxide synthase 11
35 Q00535 CDK5 Cyclin-dependent-like kinase 5 10
36 P19438 TNFRSF1A Tumor necrosis factor receptor superfamily member 1A 10
37 Q14289 PTK2B Protein-tyrosine kinase 2-beta 10
38 P35228 NOS2 Nitric oxide synthase 10

3.5 GO function and KEGG enrichment analyses

To systematically understand the molecular mechanism of C. sinensis treating RIRI, GO and KEGG enrichment analyses of 38 core targets were conducted. In GO analysis, 93 terms related to MF were enriched, mainly including repressing transcription factor binding, ubiquitin-like protein ligase binding, ubiquitin protein ligase binding, phosphatase binding, DNA-binding transcription activator activity, and RNA polymerase II specific (p adjust <0.05). There were 52 terms related to CC, membrane raft, membrane microdomain, and membrane region were the main enriched regions (p adjust <0.05). There were 1860 BP related terms, mainly including response to peptide hormone, oxidative stress, epithelial cell proliferation, reactive oxygen species metabolic process, lipopolysaccharide, and molecule of bacterial origin (p adjust <0.05). The top 10 items of the three parts selected according to the gene ratio are shown in Figure 5. A total of 168 pathways were obtained through KEGG pathway enrichment analysis, and the top 20 were displayed after screening with gene ratio (p adjust <0.05, Figure 6). In these pathways, PI3K–Akt signaling pathway, HIF-1 signaling pathway, advanced glycation end product (AGE)–receptor for AGE (RAGE) signaling pathway in diabetic complications, and MAPK signaling pathway maybe the most significant pathways. The gene symbols of pathways and BP closely related to the disease are shown in Table 4. Through Cytoscape software to construct the interaction network of active components, core targets, and pathways, we can further understand the molecular mechanism of the treatment of diseases by the active components of C. sinensis at the systemic level (Figure 7).

Figure 5 
                  GO functional analysis. GO terms and −log10 (p adjust) are represented by the x-axis and y-axis, respectively.
Figure 5

GO functional analysis. GO terms and −log10 (p adjust) are represented by the x-axis and y-axis, respectively.

Figure 6 
                  KEGG enrichment results. Gene ratio and pathways are represented by the x-axis and y-axis, respectively. The size and color of the dots mean the gene counts and the value of p adjust, respectively.
Figure 6

KEGG enrichment results. Gene ratio and pathways are represented by the x-axis and y-axis, respectively. The size and color of the dots mean the gene counts and the value of p adjust, respectively.

Table 4

The pathways and BPs related to the therapeutic effect of C. sinensis

ID Pathway or biology process Related gene symbol
hsa04151 PI3K–Akt signaling pathway CCND1 CHUK EGF EGFR MTOR HSP90AA1 INS ITGB1 KDR NFKB1 NOS3 PIK3R1 PRKCA MAPK1 PTEN RELA TLR4
hsa04066 HIF-1 signaling pathway EGF EGFR MTOR HIF1A INS NFKB1 NOS2 NOS3 PIK3R1 PRKCA PRKCB MAPK1 RELA STAT3 TLR4
hsa04933 AGE–RAGE signaling pathway in diabetic complications CCND1 CASP3 JUN NFKB1 NOS3 PIK3R1 PRKCA PRKCB PRKCD MAPK1 RELA STAT1 STAT3 TGFB1
hsa04010 MAPK signaling pathway CASP3 CHUK EGF EGFR INS JUN KDR NFKB1 PRKCA PRKCB MAPK1 RELA TGFB1 TNFRSF1A
hsa04931 Insulin resistance MTOR INS NFKB1 NOS3 PIK3R1 PPARA PRKCB PRKCD PTEN RELA STAT3 TNFRSF1A
GO:0043434 Response to peptide hormone CHUK MTOR INS NFKB1 PIK3R1 PPARA PPARG PRKCB PRKCD PTEN PTGS2 RELA STAT1 STAT3 TGFB1
GO:0006979 Response to oxidative stress CASP3 CHUK EGFR PTK2B HIF1A INS JUN MMP9 NOS3 PRKCD MAPK1 PTGS2 RELA STAT1 TLR4
GO:0050673 Epithelial cell proliferation CCND1 EGFR ESR1 MTOR HIF1A JUN KDR PPARG PRKCA MAPK1 PTEN STAT1 STAT3 TGFB1
GO:0072593 Reactive oxygen species metabolic process EGFR PTK2B MTOR HIF1A HSP90AA1 INS NOS2 NOS3 PRKCD PTGS2 STAT3 TGFB1 TLR4
GO:0032496 Response to lipopolysaccharide CASP3 CASP8 CHUK JUN NFKB1 NOS2 NOS3 PRKCA MAPK1 PTGS2 RELA TGFB1 TLR4
GO:0002237 Response to molecule of bacterial origin CASP3 CASP8 CHUK JUN NFKB1 NOS2 NOS3 PRKCA MAPK1 PTGS2 RELA TGFB1 TLR4
Figure 7 
                  Active component–target–pathway network. The green ellipse, rose red hexagons, and green triangles represent the active components, core targets, and pathways, respectively. The red ellipses mean C. sinensis and RIRI. The interactions were connected by edges.
Figure 7

Active component–target–pathway network. The green ellipse, rose red hexagons, and green triangles represent the active components, core targets, and pathways, respectively. The red ellipses mean C. sinensis and RIRI. The interactions were connected by edges.

3.6 Molecular docking

Autodock Vina was used to perform blind docking and calculated the binding activity between the small-ligand molecule and the receptor protein through the algorithm. In general, the first mode is the best, since it has the lowest affinity (kcal/mol). The energy matching results of the eight active components of C. sinensis and three corresponding core targets with higher degrees are shown in Table 5. The affinity of arachidonic acid-CASP3 was −7 kcal/mol, and there were hydrogen bond interaction with TYR195 on chain A and hydrophobic interaction with TYR195 on chain C (Figure 8a). The binding energy of the best mode Cerevisterol-EGFR was −9.8 kcal/mol, and it had hydrogen bond interaction with the two amino acid residues GLN-976 and LEU-979, and the small molecule was in the hydrophobic cavity formed by LEU-747, PHE-723, LYS-860, LEU-858, ILE-759, and ALA-755 (Figure 8b). The affinity energy of the best mode BETA-sitosterol-CASP3 was −10.5 kcal/mol, surrounded by hydrophobic cavity formed by PHE-266, Pro-201, TYR-197, TYR-195, LEU-136, Pro-201, and TYR-197 (Figure 8c). The affinity of the best mode cholesteryl palmitate-PRKCA was −7.3 kcal/mol and formed hydrogen bonds with an arginine residue (ARG-608) and hydrophobic interactions with TYR-512, PHE-498, PHE-547, MET-551, and ILE-667 (Figure 8d). The affinity between cholesterol-PRKCA was −8.9 kcal/mol, and it formed hydrogen bonds with GLU-474 and ARG-608 and was in the hydrophobic cavity formed by ILE-667, MET-551, TYR-512, ILE-510, PHE-498, and PHE-547 (Figure 8e). The affinity between peroxyergosterol-HSP90AA1 was −9.7 kcal/mol, and it had hydrogen bond interactions with LYS-84 and LYS-153 and formed hydrophobic interactions with ILE-203, ASN-155, ARG-201, and PRO-179 (Figure 8f). The affinity of the best mode Linoleyl acetate-NFKB1 was −5.0 kcal/mol, and there was a hydrogen bond interaction with the amino acid residue HIS-64 (Figure 8g). The affinity between cordycepin-HIF1A was −7.2 kcal/mol, and it formed hydrogen bond interactions with ASP-250, ARG-370, TYR-380, GLY-349, and VAL-311 and formed hydrophobic interaction with PRO-373 (Figure 8h). Of course, these are only the best possible binding modes predicted by the algorithm and the binding modes between specific components and targets need to be further verified.

Table 5

Molecular docking results of the active components and key targets

No. Mol ID Molecule name Gene symbol PDB ID Affinity (kcal/mol)
1 MOL001439 Arachidonic acid CASP3 5IAE −7
RELA 3QXY −6.9
MAPK1 2Y9Q −6.2
2 MOL008998 Cerevisterol EGFR 6V6O −9.8
HSP90AA1 3K99 −8.9
STAT3 6NJS −7.4
3 MOL000358 Beta-sitosterol CASP3 5IAE −10.5
STAT3 6NJS −6.6
JUN 5FV8 −6.4
4 MOL008999 Cholesteryl palmitate PRKCA 3IW4 −7.3
STAT3 6NJS −6.8
PIK3R1 2IUI −6.3
5 MOL000953 Cholesterol PRKCA 3IW4 −8.9
MTOR 4DRJ −7.4
STAT3 6NJS −7
6 MOL011169 Peroxyergosterol HSP90AA1 3K99 −9.7
STAT3 6NJS −8.6
HIF1A 5LAS −7.5
7 MOL001645 Linoleyl acetate NFKB1 2O61 −5
PIK3R1 2IUI −4.5
STAT1 1BF5 −3.8
8 Cordycepin HIF1A 5LAS −7.2
CXCR4 3ODU −6.9
MAPK1 2Y9Q −6.6
Figure 8 
                  The protein–ligand of the docking stimulation. (a–h) Docking between eight active components and their possible optimal targets. The yellow dotted line represents hydrogen bond; the number represents hydrogen bond distance; the gray stick structure represents the active small molecules, and the yellow stick structure represents amino acid residues.
Figure 8

The protein–ligand of the docking stimulation. (a–h) Docking between eight active components and their possible optimal targets. The yellow dotted line represents hydrogen bond; the number represents hydrogen bond distance; the gray stick structure represents the active small molecules, and the yellow stick structure represents amino acid residues.

4 Discussion

The course of AKI develops rapidly, and hospitalized patients often die from the clinical consequences of severe renal impairment. IRI is one of the leading causes of AKI in countries around the world, which is mainly induced by common clinical factors, such as shock, low cardiac output, organ transplantation, thromboembolism, and cardiac bypass surgery [32,33], and effective therapies are still lacking. Identifying the etiology, ensuring volume supply, dialysis, and applying drugs to protect renal function are the main treatments for RIRI. C. sinensis is an entomogenous fungus that is often used as a nutritional supplement to nourish the body. Some studies have shown that C. sinensis can reduce RIRI in rats; this is partly due to the retarding of cell senescence after renal reperfusion by targeting stromal cell-derived factor-1α (SDF-1α)/chemokine (C-X-C motif) receptor 4 (CXCR4) expression [19]. Therefore, network pharmacology and molecular docking were applied to analyze the active components, action targets, and biological pathways of C. sinensis in treating RIRI, eventually to discover the pharmacological mechanism of C. sinensis.

We have collected 8 active components and 99 common targets of C. sinensis treating RIRI, which fully reflected the characteristics of C. sinensis through multi-component and multi-target exerting the synergistic therapy. The eight components mainly include arachidonic acid, linoleyl acetate, beta-sitosterol, cholesterol, peroxyergosterol, cerevisterol, cholesteryl palmitate, and cordycepin. Studies have shown that the metabolites of arachidonic acid, epoxyeicosatrienoic acids, have anti-inflammatory [34,35], regulation of glucose and lipid metabolism [36], cardiovascular protection and inhibition of apoptosis [37], anti-fibrosis [38,39], and other pharmacological effects. Two other metabolites of arachidonic acid, 12-hydroperoxyeicosatetraenoic acid and 12-hydroxy-eicosatetraenoic acid, can inhibit renin activity [40]. While beta-sitosterol is a common sterol component in plants and has extensive biological activities, such as antioxidant and anti-apoptosis [41], regulation of intestinal flora [42], anti-inflammatory [43,44,45], anti-diabetes and obesity [46], and tumor inhibition [47]. Recent studies have shown that beta-sitosterol attenuates RIRI through anti-oxidative stress and inhibition of inflammatory responses [48]. Peroxyergosterol is a kind of C28-sterol, and studies have shown that it has the functions of inhibiting tumor cell proliferation [49], anti-inflammatory [50], antibacterial [51], anti-trypanosomiasis [52], and immune response regulation [53]. Cerevisterol is a compound identified from endophytic fungi, with anti-inflammatory [54], ameliorating hypoxia [7], and other biological activities. Cholesteryl palmitate is a kind of lipid with anti-inflammatory and antibacterial properties [55,56]. Cordycepin, also known as 30-deoxyadenosine, is a kind of natural nucleoside analogues of C. sinensis, which has a variety of pharmacological activities. For example, it can reduce inflammation and lipid accumulation [57,58], regulate intestinal flora and reduce obesity [59], inhibit cell senescence [60] and oxidative stress [61,62], and have anti-cancer activity [63,64]. Animal experiments have shown that cordycepin can protect RIRI by inhibiting inflammation, reducing apoptosis, and oxidative stress levels [65]. Cordycepin also relieves hyperuricemia by modulating the expression of uric acid transporter 1 (URAT1) in kidney [66]. Therefore, the research on the drug components and molecular mechanism of C. sinensis is of great significance to the treatment of kidney diseases and medical care.

GO and KEGG enrichment analysis results also showed that the roles of these 38 core targets were closely related to insulin resistance, response to peptide hormone, response to oxidative stress, epithelial cell proliferation, reactive oxygen species metabolic process, response to lipopolysaccharide, and response to molecule of bacterial origin are closely related (Table 4). Among them, PI3K–Akt signaling pathway, HIF-1 signaling pathway, AGE–RAGE signaling pathway in diabetic complications, and MAPK signaling pathway were involved in the pathological process of RIRI. The PI3K–Akt pathway plays important effects in regulating cell mitosis, apoptosis, and cell proliferation [67]. Upregulation of PI3K/Akt phosphorylation levels can improve RIRI and reduce kidney damage [68,69]. AGEs accumulated and bound to their receptor RAGE can stimulate oxidative stress and inflammatory response in the body, accelerate kidney damage and DKD progression [70], and greatly increase the incidence of RIRI in patients. Macrophages are activated by cytokines, such as IL-6, IL-1β, and TNF-a; they will activate the downstream MAPK (JNK/P38/ERK)-NF-κB signaling pathway, so that NF-κB is transferred to the nucleus and participates in the inflammatory process of RIRI [71]. HIF-1 is a heterodimer composed of oxygen-sensitive ɑ subunit and constitutively expressed β subunit, which is the main regulator of hypoxia. Under normal oxygen conditions, HIF-1 is usually unstable and degraded by ubiquitination proteasome [72]. In a porcine partial nephrectomy-induced RIRI model, HIF-1ɑ expression was reduced in the clamped kidney and significantly increased after 60 min of hypoxia and during reperfusion recovery [73]. Prolyl hydroxylase domain (PHD) is one of the key regulatory proteins of HIF, and the use of PHD inhibitors can significantly improve renal function in RIRI rats by stabilizing HIF [74].

Topological analysis showed that the active components of C. sinensis may mainly bind to 38 core targets, such as STAT3, JUN, EGFR, HSP90AA1, RELA, CASP3, HIF1A, MAPK1, PRKCA, and EGF, and participate in related pathways or BPs to exert pharmacology effect. The active component–target–pathway network indicated that C. sinensis plays an indispensable role in the treatment of renal injury through its pharmacological activity of multi-component, multi-target, and multi-pathway.

5 Conclusions

There are 8 active components and 38 potential targets of C. sinensis in RIRI treatment. CASP3, EGFR, PRKCA, HSP90AA1, nuclear factor of kappa light polypeptide gene enhancer in B-cell 1 (NFKB1), and HIF1A may be the effective targets of C. sinensis protecting against RIRI. GO and KEGG enrichment analyses indicate that C. sinensis exerted its therapeutic effect mainly by participating in the biological functions, such as hormone regulation, oxidative stress, cell proliferation, and immune regulation. However, the deficiency of this study is that it has not been experimentally verified, such as lacking in vivo experimental data support. In a word, this study provides a new theoretical and molecular basis for the treatment of RIRI with C. sinensis and maybe amplify the application and research of C. sinensis.


# These authors contributed equally to this research.


Acknowledgments

Not applicable.

  1. Funding information: This research was supported by the Zhejiang Province Chinese Medicine Modernization Program [grant number 2020ZX001] and the “Pioneer” and “Leading Goose” R&D Program of Zhejiang [2022C03118].

  2. Author contributions: Y.L. – writing – original draft, formal analysis; D.Z. – investigation; J.G. – data curation; W.H. – data curation; J.J. – conceptualization, funding acquisition; Q.H. – funding acquisition, writing-review and editing.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Ethic approval: The conducted research is not related to either human or animal use.

  5. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-09-21
Revised: 2022-10-18
Accepted: 2022-10-26
Published Online: 2022-11-28

© 2022 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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