A network pharmacology approach to explore the mechanisms of action of Epimrdii Herba-Coicis Semen for treatment of Alzheimer's disease

doi:10.21203/rs.3.rs-20651/v1

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A network pharmacology approach to explore the mechanisms of action of Epimrdii Herba-Coicis Semen for treatment of Alzheimer's disease

https://doi.org/10.21203/rs.3.rs-20651/v1

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Background: Traditional Chinese medicine (TCM) can treat diseases through its “multi-component, multi-target, multi-pathway” mechanisms. Especially have advantages in the treatment of diseases with complicated pathogenesis, such as Alzheimer’s disease (AD). Tonifying the kidney and strengthening the spleen is one of the common methods of Chinese Medicine to treat AD. The TCM combination of Epimrdii Herba and Coicis Semen can be used as the main drugs of a prescription for tonifying the kidney and strengthening the spleen. However, the mechanisms for Epimrdii Herba-Coicis Semen (EH-CS) to treat AD is vague. The purpose of this study was to explore the mechanisms of EH-CS on AD using a network pharmacological method.

Methods: We retrieved the chemical compounds and targets of Epimrdii Herba-Coicis Semen from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). We screened the active ingredients based on the pharmacokinetic parameters (ADME). The Human Gene Database (GeenCards) was used to obtain disease targets of Alzheimer’s disease. Then we drew a venn diagram to obtain common targets of Chinese medicine and disease. Based on the topological properties, we screened the key targets. The protein-protein interaction (PPI) network was constructed using the STRING database, and the "Traditional Chinese Medicine-active ingredient-target" network was constructed using Cytoscape software. The key targets were respectively uploaded to the Metascape and DAVID database for GO and KEGG pathway analysis.

Results: We obtained 31 active compounds for EH-CS. Flavonoids play important roles in the treatment of AD. A total of 29 key targets, including AKT1, MAPK1, and TP53, etc. The biological processes involve response to lipopolysaccharide, neuron death, neuroinflammatory response, etc. The main pathways include TNF signaling pathways, MAPK signaling pathways, PI3K-Akt signaling pathways and other signaling pathways.

Conclusion: The network pharmacology method is an effective tool for exploring the mechanisms of TCM. Based on network pharmacology, this study systematically explained the potential mechanisms of EH-CS on AD. It provides a valuable reference for the development of AD drugs.

Pharmacodynamics

Pharmacokinetics

Network pharmacology

Traditional Chinese medicine

Alzheimer's disease

Epimrdii Herba

Coicis Semen

Alzheimer’s disease (AD) is a neurodegenerative disease, which is the most common type of dementia. Patients often experience symptoms of cognitive impairment and memory loss. [1] With the aging of the population, the incidence of AD is on the rise which will bring the burden on society and families. [2] AD has complicated pathogenic mechanisms. At present, chemical drugs are approved for clinical applications of treating AD, such as taclaline, donepezil, memantine, etc. do not alter the course of disease and combined with adverse reactions. [3]Therefore, it is an urgent need to find drugs that can slow or prevent the development of AD with fewer side effects.

In recent years, traditional Chinese medicine（TCM）has received widespread attention because it has many formulas and each herb contains a variety of active ingredients. It has shown large advantages in acting on complex diseases. Due to the complex and diverse chemical composition, it is difficult to study the underlying mechanisms of TCM in the treatment of diseases, which limit the promotion of TCM in clinical applications. The proposed of using network pharmacology to explore the mechanisms of the drug was reported by Hopkin in 2008.4]Network pharmacology, as a comprehensive method based on network target analysis, can describe the complex interactions between biological systems, drugs, and diseases. It emphasizes the current model of drug development from “single drug, single target” transition to “network targets, multi-components”.5] It conforms to the holistic concept of Chinese medicine and provides feasible strategies for the successful development of modern medicines. It also provides a new perspective for the research of TCM and its formulas through the analysis of the underlying mechanisms of TCM by multiple components, multiple targets and multiple pathways.

In the theory of TCM, AD and other forms of dementia is recognized that closely correlated to “deficiency and aging of Five Organs”. [1] Among the Five Organs, the kidney is the innate foundation, and the spleen is the acquired foundation. The kidney produces the marrow, and the brain is the sea of the marrow, the kidney essence is sufficient, the brain will also be sufficient; The spleen is the source of qi and blood biochemistry, which is the basis of brain nutrients. Deficiency of the kidney and spleen is one of the physiological characteristics of the elderly and it will cause the brain is deficient, which easily leads to dementia and other symptoms. Therefore, kidney deficiency [6] and spleen deficiency [7] are the pathological basis of AD. Tonifying the kidney and strengthening the spleen is often used as one of the basic principle of TCM to treat dementia such as AD. Epimrdii Herba (Epimedium) [8] is a TCM originating from Epimedium Brevicornu Maxim., Epimedium sagittatum Maxim., Epimedium pubescens Maxim., or Epimedium koreanum Nakai. It has been reported to treat diseases [9] such as cancer, osteoporosis, cardiovascular disease and brain disease, it also improves sexual function. Coicis Semen is a mature dry kernel of the gramineous plant Coix lacryma-jobi L. var. mayuen (Roman.) Stapf. Its efficacy includes anti-inflammatory, anti-cancer, anti-hyperlipidemia, and inhibition of diabetic nephropathy. [10] According to the theory of Chinese medicine, Epimrdii Herba can be attributed to the kidney meridian, and Coicis Semen can be attributed to the spleen meridian. Therefore, they are used in combination to nourish the kidney and strengthen the spleen, improving AD.

It has been reported that Epimrdii Herba can improve the function of the nervous system in AD treatment. [11]However, few studies have been done on Epimrdii Herba-Coicis Semen (EH-CS) in the treatment of AD and people lack a comprehensive understanding of its mechanism of action. In this study, we used a network pharmacological method to predict and analyze the EH-CS. It is hoped that this research will contribute to a deeper understanding of the mechanism of this TCM combination treat AD and provide a theoretical basis for further research and clinical application. It can also provide a new direction for the development of AD drugs. The flowchart of the procedures of our study is shown in Fig. 1.

Screening of active ingredients and their targets

The traditional Chinese medicine system pharmacology database and analysis platform (TCMSP) [12, 13] is a unique platform that can capture the relationship between drugs, targets and diseases, and provide a chemical screening process based on pharmacokinetics parameters (ADME) features. TCMSP was applied to search for relevant compounds of Epimrdii Herba and Coicis Semen, and screened active ingredients based on ADME: Oral bioavailability (OB) represents the pharmacological percentage of oral drugs entering the systemic circulation; Drug-likeness (DL) refers to the similarity of a compound with a known drug. We selected compounds with OB≥30% and DL≥0.18 [14]as active ingredients.

We searched for the relevant target of the active ingredient from TCMSP and deleted the duplicate target to keep the unique item. Next, these target proteins were entered the Uniprot database [15] to standardize naming. Species selection as “Homo Sapiens”. Deletion of genetic information that cannot be found, and complementing common subtypes for targets that unspecified subtypes.

Acquisition of disease targets

GeneCards [16] is a database that integrates gene-centric data from more than 100 web sources, providing a comprehensive and user friendly information. [17] To obtain the relevant targets of Alzheimer’s disease, we used “Alzheimer’s disease” as the keyword to search in GeneCards.

Prediction of potential targets

The targets of active ingredients and disease were uploaded to the Draw Venn Diagram website [18] to draw a Venn diagram online and get the intersection of the two parts, that is, the common targets of TCM and disease.

Construction of “Traditional Chinese Medicine-active ingredient-target” Network

For the purpose of attaining a systematic understanding of the complex relationships among TCM, compounds, and targets, “TCM-active ingredient” and “active ingredient-target” interaction network was constructed in Cytoscape 3.7.2 software [19], and the “NetworkAnalyzer” tool in software was used to analyze active ingredient-target network to predict the key active ingredients. Then we used “merge” tool in software to merge the two networks to get a “TCM-active ingredient-target” network.

PPI network construction and selection of key target

We uploaded the common target of drugs and disease to the STRING database [20, 21] and selected the species as “Homo Sapiens”. We set the minimum required interaction score is 0.900 to construct a PPI (protein-protein interaction) network with high confidence and hide targets without interaction. Then “tsv” format data file for the result of PPI was exported from STRING. We imported this file into Cytoscape 3.7.2 software and used the “NetworkAnalyzer” tool to perform a topology analysis on the PPI network. Two topological properties, “degree” and “betweenness centrality”, were used as criteria to measure the topological importance of targets. The larger the protein’s degree and betweenness centrality, the more crucial that protein in this network.

Gene enrichment analysis

In order to GO (Gene Ontology) enrichment analysis, we uploaded key targets to Metascape [22, 23] platform, which provides comprehensive gene list annotation and analysis resources. During enrichment analysis, it can absorb overlapping or related redundant parts into representative clusters, showing the most important results. We set the species as “H. sapiens”. Then used Custom Analysis and check only “GO Molecular Functions”, “GO Biological Processes” and “GO Cellular Components”. The other settings are unchanged (P-value <0.01). Similarly, key targets were uploaded to the DAVID database [24, 25] and selected identifier as “OFFICIAL GENE SYMBOL”. Then species were set as “Homo Sapiens” to perform KEGG （Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. We selected the pathway of false rate discovery (FDR) < 0.05.

Screening of active ingredients

A total of 130 compounds of Epimrdii Herba and 38 compounds of Coicis Semen were retrieved by searching TCMSP, and 31 compounds were screened as active ingredients with OB≥ 30% and DL ≥ 0.18. Including 23 in Epimrdii Herba,9 in Coicis Semen. There is 1 compound common to both. It is shown in Table 1.

Table 1 List of active compounds of Epimrdii Herba and Coicis Semen

Compound	Degree	OB	DL	source
quercetin	125	46.43	0.28	Epimrdii Herba
kaempferol	53	41.88	0.24	Epimrdii Herba
luteolin	52	36.16	0.25	Epimrdii Herba
Anhydroicaritin	33	45.41	0.44	Epimrdii Herba
C-Homoerythrinan, 1,6-didehydro-3,15,16-trimethoxy-, (3.beta.)-	30	39.14	0.49	Epimrdii Herba
8-(3-methylbut-2-enyl)-2-phenyl-chromone	25	48.54	0.25	Epimrdii Herba
8-Isopentenyl-kaempferol	24	38.04	0.39	Epimrdii Herba
Stigmasterol	22	43.83	0.76	Coicis Semen
Chryseriol	18	35.85	0.27	Epimrdii Herba
Yinyanghuo C	12	45.67	0.5	Epimrdii Herba
1,2-bis(4-hydroxy-3-methoxyphenyl)propan-1,3-diol	11	52.31	0.22	Epimrdii Herba
DFV	10	32.76	0.18	Epimrdii Herba
Yinyanghuo E	10	51.63	0.55	Epimrdii Herba
Yinyanghuo A	9	56.96	0.77	Epimrdii Herba
6-hydroxy-11,12-dimethoxy-2,2-dimethyl-1,8-dioxo-2,3,4,8-tetrahydro-1H-isochromeno[3,4-h]isoquinolin-2-ium	6	60.64	0.66	Epimrdii Herba
Sitosterol alpha1	4	43.28	0.78	Coicis Semen
Linoleyl acetate	3	42.1	0.2	Epimrdii Herba
olivil	3	62.23	0.41	Epimrdii Herba
Magnograndiolide	2	63.71	0.19	Epimrdii Herba
sitosterol	2	36.91	0.75	Epimrdii Herba/Coicis Semen
Mandenol	2	42	0.19	Coicis Semen
CLR	2	37.87	0.68	Coicis Semen
24-epicampesterol	1	37.58	0.71	Epimrdii Herba
poriferast-5-en-3beta-ol	1	36.91	0.75	Epimrdii Herba
Icariside A7	1	31.91	0.86	Epimrdii Herba
Anhydroicaritin-3-O-alpha-L-rhamnoside	0	41.58	0.61	Epimrdii Herba
Icariin	0	41.58	0.61	Epimrdii Herba
2-Monoolein	0	34.23	0.29	Coicis Semen
[(2R)-2,3-dihydroxypropyl] (Z)-octadec-9-enoate	0	34.13	0.3	Coicis Semen
(6Z,10E,14E,18E)-2,6,10,15,19,23-hexamethyltetracosa-2,6,10,14,18,22-hexaene	0	33.55	0.42	Coicis Semen
Coixenolide	0	32.4	0.43	Coicis Semen

Acquisition of potential targets

We obtained 225 targets related to active ingredients from TCMSP and 8606 targets related to AD from the GeneCards database. 186 targets of EH-CS for treating AD were obtained by drawing the venn diagram. (Fig. 2)

Construction of network

According to the above results, the network of Chinese medicine-active ingredients and the network of active ingredients-targets was constructed and combined using Cytoscape 3.7.2 to obtain a Traditional Chinese Medicine-active ingredients-target network, as shown in Fig. 3. Network includes 31 active ingredient nodes, 186 target nodes and 2 TCM nodes, a total of 219 nodes.492 edges represent interrelationship between Chinese medicine, compounds, and targets. (6 compounds are not linked to the target). We analyzed the network of the active ingredient-target, obtained the degree of each active compound. (Table 2) The greater a compound’s degree, the more likely it is a key compound. The results, as shown in Table2, indicate that the top 8 compounds with the degree are quercetin (degree = 125), kaempferol (degree = 53), luteolin (degree = 52), Anhydroicaritin (degree = 33), C-Homoerythrinan,1,6-didehydro–3,15,16-trimethoxy-, (3. beta.)- (degree = 30), 8- (3-methylbut–2-enyl)–2-phenyl-chromone (degree = 25), 8-Isopentenyl-kaempferol (degree = 24), and Stigmasterol (degree = 22). They may be the key compounds for EH-CS to treat AD. In addition, Icariin is a major active compound of Epimrdii Herba, which has a neuroprotective effect in the treatment of AD. [11, 26] It is also one of the important compounds.

PPI network and screening of key target

We used the STRING database to construct the PPI network (Fig. 4). Then analyzed it using Cytoscape software. A total of 171 nodes can interact in this network, and there are 813 edges representing interaction in nodes. According to the topological attributes of the network, the average degree of the interaction network is 9.51, and the average betweenness centrality is 1.52 × 10^–2. The distribution of degree and betweenness centrality are shown in Fig. 5. A total of 29 nodes were screened out for their degree and betweenness centrality were greater than the average (Table 2), which might play an important role in EH-CS to treat AD. Among them, the degrees of AKT1, MAPK1, TP53, JUN, HSP90AA1, TNF, and APP are all greater than 30.

Table 2 Key targets and their degree values and betweenness centrality

Target name	Gene	Degree	Betweenness Centrality
RAC-alpha serine/threonine-protein kinase	AKT1	40	0.09603348
Mitogen-activated protein kinase 1	MAPK1	39	0.12393318
Cellular tumor antigen p53	TP53	38	0.07668474
Transcription factor AP-1	JUN	33	0.06972905
Heat Shock Protein 90kDa Alpha A1	HSP90AA1	33	0.08025483
Tumor necrosis factor	TNF	32	0.05990612
Amyloid beta A4 protein	APP	32	0.12457522
Transcription factor p65	RELA	29	0.03612238
Interleukin-8	CXCL8	26	0.05097551
Interleukin-6	IL6	24	0.02872362
Mitogen-activated protein kinase 8	MAPK8	23	0.03557485
Retinoblastoma-associated protein	RB1	23	0.02902652
Mitogen-activated protein kinase 14	MAPK14	23	0.03032019
Vascular endothelial growth factor A	VEGFA	22	0.04147238
Epidermal growth factor receptor	EGFR	22	0.05380639
Proto-oncogene c-Fos	FOS	21	0.01879884
Myc proto-oncogene protein	MYC	21	0.0251559
Estrogen receptor	ESR1	21	0.02754115
Protein kinase C alpha type	PRKCA	21	0.05494384
G1/S-specific cyclin-D1	CCND1	20	0.01847442
Protein kinase C beta type	PRKCB	18	0.01757137
Pro-epidermal growth factor	EGF	17	0.03109745
Retinoic acid receptor RXR-alpha	RXRA	17	0.07908122
Caspase-8	CASP8	16	0.03068141
5-hydroxytryptamine 1A receptor	HTR1A	15	0.02085024
Caspase-3	CASP3	14	0.0237043
Nitric oxide synthase, inducible	NOS2	12	0.02045239
Peroxisome proliferator-activated receptor alpha	PPARA	12	0.03588719
Matrix metalloproteinase-9	MMP9	11	0.01970359

GO enrichment analysis

We are input 29 key targets to perform GO enrichment analysis (p <0.01), and the result showed 20 main clusters (Fig. 6), containing 1 molecular function (MF) is the transcription factor binding. The others are biological process (BP), such as response to lipopolysaccharide, cytokine-mediated signaling pathway, neuron death, neuroinflammatory response, and so on.

KEGG pathway enrichment analysis

We mapped the 29 key targets onto the 97 KEGG pathways (FDR<0.05). The top 25 pathways were selected and sorted by FDR, containing 17 disease pathways and 8 signaling pathways: MAPK signaling pathways, TNF signaling pathways, Toll-like receptor signaling pathways, NOD-like receptor signaling pathways, HIF–1 signaling pathways, ErbB signaling pathways, PI3K-Akt signaling pathways, and Prolactin signaling pathways. (Table 3)

Table 3 Pathways of the top 25 FDR

Pathway ID	Pathway name	Count	FDR
hsa05200	Pathways in cancer	23	1.10×10^-20
hsa05161	Hepatitis B	18	1.70×10^-20
hsa05219	Bladder cancer	10	1.20×10^-12
hsa04010	MAPK signaling pathway	15	3.30×10^-12
hsa05205	Proteoglycans in cancer	14	3.60×10^-12
hsa05142	Chagas disease (American trypanosomiasis)	12	3.70×10^-12
hsa05133	Pertussis	11	3.70×10^-12
hsa04668	TNF signaling pathway	12	4.10×10^-12
hsa05223	Non-small cell lung cancer	10	8.50×10^-12
hsa05160	Hepatitis C	12	2.30×10^-11
hsa05212	Pancreatic cancer	10	2.90×10^-11
hsa04620	Toll-like receptor signaling pathway	11	6.60×10^-11
hsa04621	NOD-like receptor signaling pathway	9	3.80×10^-10
hsa04066	HIF-1 signaling pathway	10	7.90×10^-10
hsa05210	Colorectal cancer	9	8.20×10^-10
hsa05214	Glioma	9	1.10×10^-09
hsa04932	Non-alcoholic fatty liver disease (NAFLD)	11	1.60×10^-09
hsa05164	Influenza A	11	6.30×10^-09
hsa05132	Salmonella infection	9	6.60×10^-09
hsa04012	ErbB signaling pathway	9	9.30×10^-09
hsa05215	Prostate cancer	9	9.70×10^-09
hsa04151	PI3K-Akt signaling pathway	13	2.20×10^-08
hsa05231	Choline metabolism in cancer	9	2.70×10^-08
hsa05145	Toxoplasmosis	9	5.10×10^-08
hsa04917	Prolactin signaling pathway	8	5.70×10^-08

Count means the number of gene related to the pathways. FDR show as “Benjamini”

in DAVID database.

At present, the main etiology of Alzheimer’s disease is still not being completely cleared, but many theories about its pathogenesis have been put forward. It has been reported that AD is characterized by the amyloid β (Aβ) plaques and the neurofibrillary tangles (NFTs) caused by hyperphosphorylated tau proteins. Additionally, the mechanisms of AD are also involved in neuroinflammation, oxidative stress, and loss of cholinergic neurons. [27]In our study, we conjectured the active compounds, targets and pathways which related to AD. They have been discussed in the existing literature.

Active ingredients

Some active ingredients which act on more disease targets may be the key compounds for EH-CS to treat AD. Quercetin has the neuroprotection effect has been extensively studied. It has been shown to the mechanisms of treating AD include inhibition of Aβ aggregation, Tau protein phosphorylation and activity of acetylcholinesterase, as well as the reduction of oxidative stress and neuroinflammation. [27] kaempferol was demonstrated can attenuate glutamate-induced oxidative stress in hippocampal neuronal cells (HT22) and reverse memory impairment in mouse models of AD. [28, 29] Luteolin can inhibit degeneration of cholinergic neurons and oxidative injuries to against Aβ-induced cognitive impairment in AD rats.[30]A study has shown that Stigmasterol can be a neuroprotective agent to reduce the toxicity of Aβ25–35 by regulating oxidative stress, apoptotic responses, and pro-inflammatory mediators.[31] Icariin is the main active ingredient of Epimedium. Although it is connected with no targets in our work, it has been extensively reported in the treatment of AD. Icariin has anti-inflammatory, antioxidant, anti-apoptosis and other pharmacological activities. It can prevent and/or treat AD by reducing the level of Aβ, and inhibiting the hyperphosphorylation of tau and other mechanisms. [11]To sum up, one phenomenon that emerge from these findings is except for stigmasterol is derived from Coicis Semen, the rest of active compounds of the top 8 are all from Epimedium. Therefore, we conjecture that Epimedium plays a major role in the treatment of AD, and Coicis Semen plays the role of auxiliary efficiency. In particular, quercetin, kaempferol, luteolin, Icariin, and Anhydroicaritin are all flavonoids. Flavonoids have anti-inflammatory and antioxidant activities, which are potential drugs to prevent neurodegenerative diseases. [32]About Anhydroicaritin, C-Homoerythrinan,1,6-didehydro–3,15,16-trimethoxy-,(3.beta.)-, 8-(3-methylbut–2-enyl)–2-phenyl-chromone and 8-Isopentenyl-kaempferol has few related research reports in AD, future studies on these compounds are therefore recommended.

Targets

Our study suggests that there are 29 targets may be the key targets for EH-CS treatment of AD, and AKT1, MAPK1, TP53, JUN, HSP90AA1, TNF, and APP have the greater degree value. AKT is an essential protein in PI3K-Akt signaling pathway. AKT down-regulation may be related to the pathogenesis in AD brain, and its activation may play a therapeutic role in neurodegenerative diseases. [33] In our results, proteins belonging to MAPK family includes MAPK1,MAPK8, and MAPK14. The MAPK family involved in hyperphosphorylation of tau protein.[34]MAPK14 (p38-α) is a subtype of p38 MAPK, inhibition of p38 MAPK may be an effective strategy for treating AD. [35]The induction of TP53(p53) in neurons can cause neuronal damage, it is related to AD. [36] HSP90AA1 is a subtype of heat shock protein 90 (Hsp90). Hsp90 is one of chaperone proteins and Hsp90 inhibitors can decrease tau level and reduce Aβ-induced toxicity effectively. [37]TNF-α is a proinflammatory cytokine. The overexpression of TNF-α is related to Aβ aggregation and hyperphosphorylation of tau protein. It has been shown that TNF-α reduction is able to reduce Aβ plaque and hyperphosphorylation of tau protein. [38]APP is a complete transmembrane protein, which produces Aβ40 and Aβ42 after being abnormally cleaved by β -secretase and γ-secretase. Aβ oligomers will aggregate to form senile plaques and produce neurotoxicity, which is closely related to the pathogenesis of AD. [39]In addition to the above targets, MMP9(degree = 11) is a major participant in neurodegeneration and neuroinflammation, which can activate markers of inflammation and regulate the permeability of the blood-brain barrier in AD. [40] CASP8(degree = 16) and CASP3(degree = 14) belong to the Caspase family, which can cleave APP. Caspases can increase Aβ levels during apoptosis, and interrupt microglia activation to produce neuroinflammation. They play deleterious roles in AD, so caspase inhibition could be a mechanism for treating AD.[41]

Biological processes

The GO enrichment analysis showed that the response to lipopolysaccharide had the lowest P-value. Lipopolysaccharide (LPS) [42] can interact with toll-like receptor 4(TLR4) on the surface of microglia, activate TLR4 and its downstream signaling pathways, affect NF-κB and other cytokines, and cause the release of proinflammatory cytokines, thereby inducing the occurrence of neuroinflammation. Therefore, regulation of LPS interaction with TLR4 may be a potential therapeutic strategy for neuroinflammatory-related diseases. In addition, the neuron death and neuroinflammatory response in the enrichment results are also related to the onset of AD.

Pathways

KEGG pathway enrichment analysis shows the main pathways where the drug act on: TNF signaling pathway has been reported that is key inflammatory pathway activated in AD. [43]MAPK signaling pathways may regulate neuronal apoptosis, β-and γ-secretase activity, and phosphorylation of APP and tau in the pathogenesis of AD. [44] Dysfunction of PI3K-Akt signaling will increase activity of GSK–3β, leading to hyperphosphorylation of tau. [33] In the brains of AD patients, PI3K-Akt signaling is debilitated, [45] so activation of the PI3K-Akt signaling pathway may helpful for treating AD. It has been reported that Toll-like receptors (TLRs) play key roles in the clearance of Aβ by microglia cell, and drugs that target Toll-like receptor signaling pathways are expected to be used to treat neurodegenerative diseases. [46] NLRP1, NLRP3, and NLRP10 in the NLRs (nod like receptors) family have been shown to be related to AD, [47] and HIF–1 has neuroprotective effects when neurons are damaged. [48] Regarding ErbB signaling pathway and Prolactin signaling pathway in AD, there are few reports on them at present.

Neuroinflammation in AD

In our study, we can find that the mechanism of treating AD by EH-CS involves many possible pathogenic factors, contains the regulation of Aβ deposition, tau protein phosphorylation, neuroinflammation, oxidative stress, and cholinergic neurons. According to reports, Epimrdii Herba and Coicis Semen both have anti-inflammatory effects. Therefore, an implication of this is the possibility that inhibiting neuroinflammation is an important mechanism of EH-CS to treat AD. In Alzheimer’s disease, Aβ plaques and neurofibrillary tangles (NFTs) are both considered causes and consequences of neuroinflammation. [49]The activation of microglia is the first sign of neuroinflammation. It will lead to the production of pro-inflammatory factors, trigger neuroinflammation, and eventually cause neuronal damage. [50] The main pathways related to neuroinflammation in EH-CS include MAPK signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, and Toll-like receptor signaling pathway. TLR4 signaling pathway in the Toll-like receptor signaling pathway plays an important role in AD. MAPK signaling pathway includes p38MAPK pathway, JNK pathway, and ERK pathway, they are also often associated with oxidative stress. Cells damaged by oxidative stress also produce inflammatory mediators. [43, 50, 51]Quercetin and other flavonoids can play neuroprotective roles through these pathways.[52, 53]

Blood - brain barrier (BBB)

Drugs acting on the central nervous system need to pass through the BBB. We need to further explore the ability of each active ingredient to pass through the BBB. Active ingredients with high penetrability are more conducive to the treatment of AD, and active ingredients with low penetrability can be used as prodrugs of anti-AD drugs.

Despite the above interpretations are warranted, we admit that still needs further experimental testing to support them. Some active ingredients and action pathways have few reports of relating to AD, and they may not play a major role in the prevention and treatment of AD, but they are not excluded as potential research objects, which provides a new clue for subsequent research. Despite these promising results, questions remain. We can summarize the limitations of this study: First, this study only analyzes the mechanism of action of drugs, not the dosage of drugs. However, in the actual application of TCM, the dosage often affects the effect. This is an important issue for future research. Second, because the research results are constantly updated, the information in the database may be incomplete. For example, the lack of information about icariin targets in database. Therefore, we need to be combined with more review of literature to enrich our results.

In this work, we have constructed a model of systematic pharmacology. It was the first to reveal the possible mechanism of EH-CS in AD using network pharmacology methods. We obtained 31 active compounds,186 potential targets (including 29 key targets), 20 important biological processes and molecular function, and 25 crucial pathways. The flavonoids in Epimedium and Stigmasterol in Coicis Semen as the main active components for EH-CS, by acting on AKT1, MAPK1, TP53 and other key targets to regulate important signaling pathways such as TNF signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway etc.

Because of the complex pathogenic mechanisms of AD, the previous development of drugs was mostly based on a single target may be one reason for the failure. Therefore, TCM with less toxic and side effects and multi-component, multi-target, and multi-path effects have more advantages. Most of the predicted active ingredients, potential targets, biological functions, and pathways in this study are consistent with literature reports, indicating that the study has reliability and can provide a reliable basis for tapping the potential value of TCM.

TCM: Traditional Chinese medicine; AD: Alzheimer’s disease; EH-CS: Epimrdii Herba-Coicis Semen; TCMSP: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform; ADME: pharmacokinetics parameters; OB: Oral bioavailability; DL: Drug-likeness; PPI: protein-protein interaction; FDR: false rate discovery; Aβ: amyloid β; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; LPS: Lipopolysaccharide; NLRs: nod like receptors; TRL4: toll-like receptor 4; BBB: blood - brain barrier;

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

Not applicable.

Authors' contributions

YXZ designed and conducted this study, acquired and analyzed the data. All authors read and approval of the final

manuscript.

Acknowledgements

Not applicable.

Authors' information

¹School of Pharmaceutical Sciences, Fujian University of Traditional Chinese Medicine, Fujian 350122, China.

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A network pharmacology approach to explore the mechanisms of action of Epimrdii Herba-Coicis Semen for treatment of Alzheimer's disease

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Version 1

Abstract

Figures

Background

Methods

Screening of active ingredients and their targets

Acquisition of disease targets

Prediction of potential targets

Construction of “Traditional Chinese Medicine-active ingredient-target” Network

PPI network construction and selection of key target

Gene enrichment analysis

Results

Screening of active ingredients

Acquisition of potential targets

Construction of network

PPI network and screening of key target

GO enrichment analysis

KEGG pathway enrichment analysis

Discussion

Active ingredients

Targets

Biological processes

Pathways

Neuroinflammation in AD

Blood - brain barrier (BBB)

Conclusion

Abbreviations

Declarations

References

Status:

Version 1