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Identifying the Mechanisms and Molecular Targets of Yizhiqingxin Formula on Alzheimer’s Disease: Coupling Network Pharmacology with GEO Database

Authors Zhang T , Pan L, Cao Y, Liu N, Wei W, Li H 

Received 28 June 2020

Accepted for publication 9 September 2020

Published 15 October 2020 Volume 2020:13 Pages 487—502

DOI https://doi.org/10.2147/PGPM.S269726

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Martin H Bluth



Tingting Zhang,1,2,* Linlin Pan,3,* Yu Cao,4 Nanyang Liu,2 Wei Wei,1,2 Hao Li2

1College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, ShanDong Province, People’s Republic of China; 2Department of Geratology, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, People’s Republic of China; 3Department of Chinese Medicine Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, People’s Republic of China; 4Geriatric Laboratory, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Hao Li
Department of Geratology, Xiyuan Hospital, China Academy of Chinese Medical Science, Haidian District, Beijing, People’s Republic of China
Tel +86 10 6283 5631
Email [email protected]

Background: Yizhiqingxin formula (YZQX) is a promising formula for the treatment of Alzheimer’s disease (AD) with significant clinical effects. Here, we coupled a network pharmacology approach with the Gene Expression Omnibus (GEO) database to illustrate comprehensive mechanisms and screen for molecular targets of YZQX for AD treatment.
Methods: First, active ingredients of YZQX were screened for the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database with the absorption, distribution, metabolism, and excretion (ADME) parameters. Subsequently, putative targets of active ingredients were predicted using the DrugBank database. AD-related targets were retrieved by analyzing published microarray data (accession number GSE5281). Protein–protein interaction (PPI) networks of YZQX putative targets and AD-related targets were constructed visually and merged to identify candidate targets for YZQX against AD using Cytoscape 3.7.2 software. We performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to further clarify the biological functions of the candidate targets. The gene-pathway network was established to filter for key target genes.
Results: Forty-three active ingredients were identified, and 193 putative target genes were predicted. Seven hundred and ten targets related to AD were screened with |log2 FC| > 1 and P < 0.05. Based on the PPI network, 110 target genes of YZQX against AD were identified. Moreover, 32 related pathways including the PI3K-Akt signaling pathway, MAPK signaling pathway, ubiquitin-mediated proteolysis, apoptosis and the NF-kappa B signaling pathway were significantly enriched. In the gene-pathway network, MAPK1, AKT1, TP53, MDM2, EGFR, RELA, SRC, GRB2, CUL1, and MYC targets are putative core genes for YZQX in AD treatment.
Conclusion: YZQX against AD may exert its neuroprotective effect via the PI3K-Akt signaling pathway, MAPK signaling pathway, and ubiquitin-mediated proteolysis. YZQX may be a promising drug that can be used in the treatment of AD.

Keywords: Yizhiqingxin formula, Alzheimer’s disease, network pharmacology, mechanism, molecular target

Introduction

Alzheimer’s disease (AD) is the major cause of dementia globally, affecting 60–80% of patients,1 which is considered an enormous public health hazard by the World Health Organization.2 As a slowly progressive neurodegenerative disorder, the clinical characteristic symptoms of AD include memory deficits, cognitive dysfunction, and inability to perform normal daily living activities in the latter stages. This seems to be mostly associated with extracellular senile plaques (SPs) and intracellular neurofibrillary tangles (NFTs).3 The pathophysiology of AD is driven by the deposition of different types of amyloid-beta peptide (Aβ) and hyperphosphorylation of the au protein.4,5 The Aβ deposition in the brain originates not only from the Aβ component in the brain but also from the periphery.6 Of note, previous studies have revealed that mutations in presenilin (PSEN) suppressed the activity of γ-secretase and Aβ generation, thereby triggering AD.7 Moreover, the interactions of Aβ and tau with cytoplasmic and organelle proteins also play a pivotal role in the pathogenesis of AD.8 Although great progress has been made regarding our understanding of AD pathogenesis and the course of the disease since the first case was reported by Alois Alzheimer in 1907,9 there are still no pharmacotherapies available to cure or reverse disease progression. Currently, four drugs for the pharmacologic therapy of AD have been approved by the US Food and Drug Administration (FDA): donepezil, rivastigmine, galantamine, and memantine. However, these treatments are often accompanied by side effects and a heavy financial burden.10

Recently, the drive for new therapeutic strategies has focused on traditional Chinese medicine (TCM), which is a unique therapeutic modality, and has been practiced clinically by Chinese for thousands of years due to its better clinical efficacy, fewer side effects, and lower resistance. Importantly, TCM has been an effective treatment of neurological diseases and verified in vitro and in vivo.11 Yizhiqingxin formula (YZQX) is composed of three Chinese medicines, including radix of Panax ginseng (Chinese name: Renshen), rhizome of Coptis chinensis (Chinese name: Huanglian), and rhizome of Conioselinum anthriscoides (Chinese name: Chuanxiong). Data from our previous study suggested that YZQX promoted autophagy by inhibiting the mTOR signaling pathway, thereby improving brain function and decreasing Aβ accumulation in APP/PS1 mice.12 Moreover, complex diseases and syndromes treated with TCM are controlled via a multi-ingredient, multi-target, and multi-pathway method.13 Thus, the pharmacological mechanisms and molecular targets of YZQX remain to be adequately studied using innovative approaches.

Network pharmacology has emerged as a powerful and promising tool, which plays a pivotal role in screening the active substances of TCM, revealing potential targets, and elucidating specific mechanisms.14 Moreover, the network pharmacology of TCM focuses on a holistic and systematic understanding of a complex network of interrelationships among components, targets, and diseases.15,16 In particular, the application of network pharmacology in TCM provides researchers a novel opportunity to acquire systematic insights into TCM, which may pave the way to a new direction for the investigation of underlying pharmacological mechanisms and safety assessment of TCM. In addition, the transcription profile characteristics might unprecedentedly change along with the innovations in microarray technologies and public microarray data repository establishment.17,18

Hence, in the present study, we coupled a network pharmacology approach with the Gene Expression Omnibus database (GEO) to further illustrate comprehensive mechanisms, explore underlying pathways, and screen for molecular targets of YZQX for the treatment of AD. First, we screened for active ingredients of YZQX and predicted their putative targets through the search of related databases. Differentially expressed genes (DEGs) between AD and healthy individuals were identified by analyzing microarray data from the GEO database. We identified core networks and targets through the protein–protein interaction (PPI) network method. Moreover, by gene ontology (GO) and pathway analysis, the molecular mechanisms of action of YZQX were clarified. The study flowchart is presented in Figure 1.

Figure 1 Workflow for Yizhiqingxin formula treatment of Alzheimer’s disease.

Methods

Screening of Active Ingredients in YZQX

All chemical ingredients in YZQX were manually acquired from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database (http://tcmspw.com/tcmsp.php),19 which serves as a unique systematic pharmacology platform to study TCM. The absorption, distribution, metabolism, and excretion (ADME) model20 was used to predict the pharmacokinetic properties of chemical ingredients. In this process, we employed two vital parameters among all ADME-related properties, including oral bioavailability (OB) and drug-likeness (DL), to identify bioactive ingredients of YZQX. OB represents the efficiency of bioactive ingredients reaching the systemic circulation.21 DL is a qualitative indicator applied in drug design to estimate the resemblance between an ingredient and a certified drug structure.22 In our study, our threshold criteria of OB and DL were greater than 30% and 0.18, respectively.

Identification of Potential Targets

Identification of putative targets of YZQX chemical compounds was performed with DrugBank (https://www.drugbank.ca/),23 which is a web platform that combines detailed medicine data with abundant drug target information. First, we input all active ingredients into DrugBank to acquire all targets for each ingredient. Then, with species limited to “Homo sapiens”, the UniProt database (https://www.uniprot.org/) was used to convert proteins into genes. Eventually, all putative targets of YZQX were retrieved after removing duplicated targets. In addition, we used Cytoscape 3.7.2 software to establish and visualize the compound-target network of YZQX based on the obtained results.

Differentially Expressed Gene Search, Identification, and Analysis

Expression profiling data from GSE5281 were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/) based on the microarray platform GPL570 (Affymetrix Human Gene Expression Array), which contained 74 samples from healthy individuals and 87 AD samples. Based on the annotation information in the platform, probe IDs were used to identify the corresponding genes. DEGs between patients with AD and healthy individuals were screened using the package limma of R software according to P < 0.05, and |log2 fold change (FC)| > 1 and were visualized using a volcano plot.

Protein–Protein Interaction Network Construction

The PPI networks of YZQX putative targets and AD-related DEGs were established and visualized using the BisoGenet24 plug-in of Cytoscape 3.7.2. In this process, two PPI networks were built according to the available PPI databases from the Biomolecular Interaction Network Database (BIND), Biological General Repository for Interaction Datasets (BioGRID), Database of Interacting Proteins (DIP), Human Protein Reference Database (HPRD), IntAct Molecular Interaction Database (IntAct), and Molecular INTeraction Database (MINT).

Network Merge and Analysis

A merged network was thereafter constructed according to the overlapping data from the two PPI networks built earlier. The network topological features of nodes in the merged interaction network were calculated and analyzed using Cytoscape 3.7.2 software plug-in CytoNCA25 using the following six crucial topological parameters: betweenness centrality (BC), closeness centrality (CC), degree centrality (DC), eigenvector centrality (EC), local average connectivity-based method (LAC), and network centrality (NC). BC is defined as the total number of shortest paths through a node. If the number of shortest paths passing through a node is larger, then intermediary centrality is higher.26 CC is a measure of the mean distance from a node to other nodes, reflecting the degree of closeness of one node to other nodes.25 DC refers to the number of links to one node, which reflects the interaction frequency of one node with adjacent nodes.27 EC calculates the centrality for a node relative to the centrality of its neighbors, which is proportional to the sum of the centrality scores of neighboring nodes.28 LAC represents the mean local connectivity of its neighbors, which could be used to determine a protein’s significance.29 NC measures a node’s significance according to the number of edges it connects and the clustering coefficients of the edges.30

First, the degree of centrality was calculated. Notably, if the degree of centrality of a node was more than twice the median degree of centrality of all nodes in a network, the gene that corresponds to that node served as “a big hub” in the network.31 According to this topological indicator, the network was further extracted for the ensuing analysis. Subsequently, to maximize the screening of key genes in the network, we adopted the corresponding median values of other indicators as the threshold values of the hub nodes in the network analysis. Eventually, a core sub-network was created based on the above indicators, where these hub genes were considered to have more nodes to transmit information and higher node information transmission efficiency.

GO and KEGG Pathway Analysis of the Core Network

We employed the GO and KEGG pathway analysis to further clarify the biological interpretations of hub genes in the core network. For gene classification and enrichment analyses, clusterProfiler,32 a new ontology-based package of R version 3.6.0 software, was applied to improve understanding of higher-order functions of the biological system. GO consists of three parts: biological process (BP), molecular function (MF), and cellular component (CC). Of note, in both the GO or KEGG functional categories, false discovery rate (FDR) <0.05 was considered significant.

The top 20 terms of GO analysis were selected and further presented visually using the package GOplot in R version 3.6.0 software. In addition, a bubble plot was used to present KEGG enrichment analysis with color-coding: the smaller the P-value is in red, and the larger the P-value is in blue. The sizes of the dots represent the gene ratio. In addition, we constructed a gene-KEGG pathway network using Cytoscape version 3.7.2 software.

Results

Screening of Bioactive Ingredients and Putative Targets from YZQX

After applying the criteria of OB ≥ 30% and DL ≥ 0.18, all bioactive ingredients of Chinese herbs in YZQX were identified in the TCMSP database. There were 43 bioactive ingredients from filtered YZQX, including 7 in Chuanxiong, 14 in Huanglian, and 22 in Renshen. The chemical ingredients of these Chinese herbs did not overlap with each other. Eventually, all 43 candidate ingredients were chosen for further investigation. The drug names, molecular names, and ADME-related parameters of these compounds are listed in Table 1. The top five ingredients of OB were Corchoroside A_qt (OB = 104.95%), Celabenzine (OB = 101.88%), Moupinamide (OB = 86.71%), FA (OB = 68.96%), and Aposiopolamine (OB = 66.65%). The top five DL components included worenine (DL = 0.87), coptisine (DL = 0.86), fumarine (DL = 0.83), gomisin B (DL = 0.83), and berlambine (DL = 0.82).

Table 1 The Final Selected Ingredients in YZQX for Analysis

According to the target screening of the bioactive ingredients in the DrugBank database, a total of 505 target genes in 3 Chinese herbs in YZQX were found, of which, there were 39 targets in Chuanxiong, 251 targets in Huanglian, and 214 targets in Renshen. After removing duplicate targets, 193 potential target genes were selected for the 43 ingredients of YZQX. Moreover, the UniProt database was used to translate the official names of potential targets so that they could be used in network construction for further biological characterization. Detailed information is presented in Table S1.

Identification of AD-Related DEGs

Differential genetic analysis between AD and healthy individuals was performed with |log2 FC| > 1 and P < 0.05. Ultimately, 710 DEGs were identified. A volcano plot of the distribution of DEGs is shown in Figure 2; among them, 415 up-regulated genes are represented by red dots, and 295 down-regulated genes are represented by green dots.

Figure 2 Volcano plot of differentially expressed genes. The red dots represent significantly up-regulated genes, the green dots represent significantly down-regulated genes.

Construction of a Compound-Putative Target Network of YZQX

Chinese herbal compounds can interfere with diseases by regulating a network through binding multiple targets. Therefore, a network, compound-target, was established to predict these targets through the acquisition of detailed information on the bioactive ingredients of YZQX. This network consisted of 230 nodes and 538 edges (Figure 3), indicating the interactions of chemical compounds and putative targets.

Figure 3 Compound- target network of YZQX. Blue Diamonds represent targets contained in YZQX, yellow squares represent Chinese Herbs, purple vs represent ingredients of Chuanxiong, light red vs represent ingredients of Huanglian, and red vs represent ingredients of Renshen.

PPI Network Construction, Merging, and Analysis

PPI network analysis contributes to the in-depth understanding of the molecular mechanism of diseases from a systematic perspective and quantifies the function of specific proteins.33 Hence, we visually constructed PPI networks of YZQX putative targets (Figure 4A), which contained 6322 nodes and 154 133 edges. The PPI network constructed for AD-related targets specifically consisted of 8052 nodes and 187 535 edges (Figure 4B). In the PPI network, nodes and edges represent interacting proteins and interactions, respectively.

Figure 4 Identification of core targets of YZQX against AD. (A) YZQX putative targets PPI network. (B) AD-related targets PPI network. (C) The interactive PPI network of YZQX putative targets and AD-related targets. (D) PPI network of significant proteins extracted from C. (E) PPI network of candidate YZQX targets for AD treatment extracted from D.

Ultimately, these two PPI networks were merged to identify the candidate targets for YZQX against AD, which helped to clarify the underlying mechanism of action of YZQX in AD. The results demonstrated that the YZQX-interacting PPI network comprised 4601 nodes and 131,267 edges in total (Figure 4C). Subsequently, the topological properties of the aforementioned merged PPI network were analyzed according to six key parameters: BC, CC, DC, EC, LAC, and NC, screened targets above two-fold median values of DC as well as more than median values of BC, CC, EC, LAC, and NC as hub genes, thereby establishing the core network of the AD-treated effect of YZQX. Since the median degree of all nodes was 36, the cutoff value of the first screening was DC >72, and the results were cast on 1044 nodes and 47,693 edges (Figure 4D). Subsequently, these 1044 vital targets were screened. The second cutoff values were BC > 433.632, CC > 0.512, DC > 232.000, EC > 0.019, LAC > 18.436, and NC > 20.060. As a result, the second extracted network consisted of 110 nodes and 2269 edges (Figure 4E), which was a core network for YZQX against AD. When the 110 nodes were sorted in descending order presented in Table 2, NTRK1 (degree = 1289), CUL3 (degree = 826), APP (degree = 806), HSP90AA1 (degree = 767), EGFR (degree = 744), TP53 (degree = 705), ESR1 (degree = 688), XPO1 (degree = 687), MCM2 (degree = 651), and HSP90AB1 (degree = 640) were the major hub nodes in the core network.

Table 2 The Key Parameter Values of 110 Core Targets

Enrichment Analysis of the Core Network

To further evaluate the 110 candidate targets, enrichment analysis was performed using the package clusterProfiler in R. The results of GO enrichment analysis demonstrated that 110 genes of the core network were significantly enriched in 1640 GO terms (FDR < 0.05), including 1383 in BP, 121 in CC, and 136 in MF. Detailed information on GO analysis is presented in Table S2. Moreover, the top 20 terms are presented in Figure 5. The results indicated that the most representative GO terms included the regulation of DNA-binding transcription factor activity, regulation of cell cycle phase transition, negative regulation of cell cycle process, positive regulation of cell cycle, regulation of apoptotic signaling pathway, nuclear chromatin, transcription factor complex, protein-DNA complex, ubiquitin ligase complex, ubiquitin-protein ligase binding, ubiquitin-like protein ligase binding, cell adhesion molecule binding, DNA-binding transcription activator activity, RNA polymerase II-specific, ubiquitin-like protein transferase activity, and activating transcription factor binding, which suggested the well-documented biological effects on cell proliferation, ubiquitin-proteasome system, and apoptosis.

Figure 5 Go analysis of core targets. (A) Biological process; (B) Cellular component; (C) Molecular function.

In addition, a total of 32 related pathways according to the KEGG analysis were identified (FDR < 0.05) (Figure 6), mainly including the PI3K-Akt signaling pathway, Cell cycle, Cellular senescence, MAPK signaling pathway, ubiquitin-mediated proteolysis, apoptosis and NF-kappa B signaling pathway, and p53 signaling pathway.

Figure 6 KEGG pathway enrichment of core targets of YZQX against AD. Pathways that had significant changes of p.adjust <0.05 were identified. The dot size represents number of genes and color represents p.adjust value.

Gene-Pathway Network Analysis

Based on the analysis of KEGG by clusterProfiler of R, a gene-pathway network was established with the aforementioned signal pathways and the corresponding target genes, which are displayed in Figure 7. This gene-pathway network showed interactions in multiple pathways involving cross-talk of the transitive relationship between the pathway terms and genes. A total of 102 nodes and 247 edges were found in the gene-pathway network. The topological analysis of 32 pathways and 70 genes was calculated with a certain degree. According to Figure 7, it was preliminarily speculated that the above ingredients of YZQX could be used for the treatment of AD via the PI3K-Akt signaling pathway, cell cycle, MAPK signaling pathway, ubiquitin-mediated proteolysis, and cellular senescence due to the high representation of MAPK1, AKT1, TP53, MDM2, EGFR, RELA, SRC, GRB2, CUL1, and MYC targets.

Figure 7 Gene-Pathway Network. The topological analysis of 32 pathways and 70 genes was calculated with the degree. The yellow circles represent target genes and the red vs represent pathways. Big size represents the larger degree.

Discussion

AD is an age-related heterogeneous disease, while effective treatments remain scarce. YZQX is a promising formula for the treatment of AD in TCM clinical practice with significant clinical effects, which has been demonstrated in previous studies.12,33 Hence, this study performed a comprehensive analysis of network pharmacology coupled with gene expression profiling to further identify the underlying mechanisms and therapeutic targets of YZQX in AD. The findings identified 110 key target genes, 33 related signal pathways, and 43 chemical compounds for YZQX in the treatment of patients with AD. By constructing the gene-KEGG network, 10 common genes including MAPK1, AKT1, TP53, MDM2, RELA, EGFR, SRC, MYC, GRB2, and CUL1, were considered as key target genes of YZQX treating AD.

A compound-target network of YZQX was generated in the present study, which demonstrated that the majority of compounds affected multiple targets; for example, quercetin, kaempferol, beta-sitosterol, stigmasterol, fumarine, (R)-canadine, and myricanone acted on 141, 56, 28, 27, 27, 26, and 23 targets, respectively. Moreover, the majority of YZQX compounds may have overlapping targets, which provided a synergistic effect, suggesting that YZQX acts in a multi-component and multi-target way. Quercetin is a natural flavonoid often found in fruits and vegetables and has anti-inflammatory, antioxidant, and neuroprotective effects.34,35 The long-term preventive administration of quercetin led to a meaningful improvement in the development of histopathological features and cognitive dysfunction in triple transgenic mouse models of AD.36 A growing body of evidence demonstrates that quercetin may contribute to neuroprotective actions against AD mainly through inhibiting the aggregation of Aβ, the formation of NFTs, β-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1), acetylcholinesterase (AChE), and others.37 Importantly, the neuroprotective effects of quercetin are primarily associated with MAPK signaling cascades and PI3K/Akt pathways.37 Kaempferol is also a flavonoid, which is abundant in multiple types of foods and beverages, such as tea, broccoli, apples, strawberries, and beans,38 with antioxidant, anti-inflammatory, and neuroprotective properties.39,40 The neuroprotective effects of kaempferol were mediated via regulating the protein expression levels of Bcl-2, apoptosis-inducing factor (AIF), and mitogen-activated protein kinase (MAPK).40 Beta-Sitosterol is one of the most extensively distributed plant sterols, with a structure similar to cholesterol.41 Studies performed on dietary plant sterols suggested that it could accumulate in the brain through the blood-brain barrier, thereby potentially affecting brain function.42 Moreover, beta-Sitosterol can change the shear mode of amyloid precursor protein (APP),43 as well as prevent the deposition of Aβ and enhance the improvement of cognitive dysfunction in APP/PS1 mice.44 The pathogenesis of AD is complicated, and it is widely accepted that neurodegeneration can be triggered by a series of interactions including inflammation, oxidative stress, and apoptotic cell death.4547 In the present study, due to their antioxidant, anti-inflammatory, and neuroprotective properties, quercetin, kaempferol, and beta-sitosterol may be key compounds for YZQX.

In addition, a PPI network of YZQX against AD was screened with 110 nodes and 2269 edges, thus highlighting a potential role in AD. YZQX probably exerts therapeutic effects on AD by regulating these particular core targets. Furthermore, we performed functional enrichment analysis of these core protein targets and found that the mechanisms of YZQX against AD were closely related to the following pathways: (1) PI3K-Akt signaling pathway, (2) MAPK signaling pathway, (3) ubiquitin-mediated proteolysis, (4) cell cycle, cellular senescence, apoptosis, (5) Wnt signaling pathway, (6) ErbB signaling pathway, and (7) NF-κB signaling pathway. Many signaling pathways have been associated with AD. The PI3K-Akt signaling pathway participates in various cell functions such as autophagy, cell survival, protein synthesis, and glycolysis. Furthermore, Akt is also a key survival-promoting factor that inhibits apoptotic signaling. The PI3K/Akt/mTOR signaling pathway modulates autophagy and clears protein aggregates during neurodegeneration.48 When it was over-activated, the level of neuronal autophagy was inhibited and clearance of intracellular Aβ and tau was delayed, which also aggravated the production of amyloid plaques and NFTs of the AD brain to a certain extent.49 The MAPK signaling pathway is one of the classic inflammation pathways, composed of JNK, ERK, and p38. Studies have suggested that the activated MAPK pathway may be involved in the pathogenesis of AD via the following mechanisms: induction of neuronal apoptosis5053 as well as transcription and enzymatic activation of β- and γ-secretases.54,55 Moreover, Schnöder et al found that in an AD mouse model, inhibiting neuronal p38-MAPK enhanced autophagy and promoted BACE1 degradation, thereby reducing Aβ generation in neurons and Aβ load in the brain.56 Moreover, as a eukaryotic cell intracellular major protein degradation system, mounting evidence has implicated ubiquitin-mediated proteolysis in the pathogenesis of AD.57,58 Ubiquitin can bind to proteins and label them for degradation; for example, it can bind to APP and γ-secretase activated protein, which are associated with the etiology of AD.59,60 Accordingly, in principle, some of the symptoms of AD were ameliorated by modulating the function of the ubiquitin-proteasome pathway components.61 Consequently, YZQX may be neuroprotective through related signaling pathways in the process of AD treatment.

To reveal key targets of YZQX against AD in the related pathways, we also constructed a gene-pathway network. The results demonstrated that MAPK1 showed the maximum degree and therefore, it may be considered as the core target gene. In addition to MAPK1, other core target genes including AKT1, TP53, MDM2, RELA, EGFR, and MYC obtained from this network, elicit a very potent vital effect on the process of YZQX against AD. As a natural negative regulatory factor of MAPKs, MAPK1 plays a significant role in the dephosphorylation of MAPKs.62 Evidence provided by Meng et al revealed that MDM2 is a vital information transmitter that activates AKT and suppresses p53-induced cell apoptosis.63

In summary, we adopted a network pharmacology approach to elucidate the underlying molecular mechanisms and target genes of YZQX against AD in the present study. Quercetin, kaempferol, and beta-Sitosterol, which regulate most of the targets, may be considered as key compounds of YZQX. Furthermore, YZQX may exert its regulatory function via the following pathways: PI3K-Akt signaling pathway, MAPK signaling pathway, and ubiquitin-mediated proteolysis. MAPK1, AKT1, TP53, MDM2, RELA, EGFR, and MYC were the core targets in the gene-pathway network of YZQX against AD. YZQX and its components may be promising drugs that can be used to treat AD.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research was supported by the National Science and Technology Major Project for “Essential new drug research and development” (NO.2019ZX09301114), the National Natural Science Foundation of China (NO. 81873350), and received funding from the Beijing Natural Science Foundation (NO. 7202174).

Disclosure

The authors report no conflicts of interest in this work.

References

1. Mebane-Sims I. 2020 Alzheimer’s disease facts and figures. Alzheimers Dement. 2020.

2. Lane CA, Hardy J, Schott JM. Alzheimer’s disease. Eur J Neurol. 2018;25(1):59–70. doi:10.1111/ene.13439

3. Solanki I, Parihar P, Parihar MS. Neurodegenerative diseases: from available treatments to prospective herbal therapy. Neurochem Int. 2016;95:100–108. doi:10.1016/j.neuint.2015.11.001

4. Majd S, Power JH, Grantham HJ. Neuronal response in Alzheimer’s and Parkinson’s disease: the effect of toxic proteins on intracellular pathways. BMC Neurosci. 2015;16(69).

5. Morris M, Maeda S, Vossel K, Mucke L. The many faces of tau. Neuron. 2011;70(3):410–426. doi:10.1016/j.neuron.2011.04.009

6. Uddin MS, Kabir MT, Tewari D, et al. Revisiting the role of brain and peripheral Aβ in the pathogenesis of Alzheimer’s disease. J Neurol Sci. 2020;416:116974. doi:10.1016/j.jns.2020.116974

7. Kabir MT, Uddin MS, Setu JR, et al. Exploring the role of PSEN mutations in the pathogenesis of Alzheimer’s disease. Neurotox Res. 2020.

8. Uddin MS, Al Mamun A, Rahman MA, et al. Emerging proof of protein misfolding and interaction in multifactorial Alzheimer’s disease. Curr Top Med Chem. 2020;20. doi:10.2174/1568026620666200601161703

9. Alzheimer A. Uber eine eigenartige Erkrankung der Hirnride. Centralblatt Nervenheilkunde Psychiatr. 1907;30:177–179.

10. Patterson C. World Alzheimer report 2018. The state of the art of dementia research: new frontiers. An analysis of prevalence, incidence, cost and trends. Alzheimers Dis Int. 2018.

11. Ong WY, Farooqui T, Koh HL, Farooqui AA, Ling EA. Protective effects of ginseng on neurological disorders. Front Aging Neurosci. 2015;7:129.

12. Yang Y, Wang Z, Cao Y, et al. Yizhiqingxin formula alleviates cognitive deficits and enhances autophagy via mTOR signaling pathway modulation in early onset alzheimer’s disease mice. Front Pharmacol. 2019;10:1041. doi:10.3389/fphar.2019.01041

13. Liu X, Wu J, Zhang D, Wang K, Duan X, Zhang X. A network pharmacology approach to uncover the multiple mechanisms of hedyotis diffusa willd. on colorectal cancer. Evid Based Complement Alternat Med. 2018;2018:6517034. doi:10.1155/2018/7802639

14. Hopkins AL. Network pharmacology. Nat Biotechnol. 2007;25(10):1110–1111. doi:10.1038/nbt1007-1110

15. Cao H, Li S, Xie R. Exploring the mechanism of dangguiliuhuang decoction against hepatic fibrosis by network pharmacology and experimental validation. Front Pharmacol. 2018;9:187. doi:10.3389/fphar.2018.00187

16. Huang T, Ning Z, Hu D. Uncovering the mechanisms of chinese herbal medicine (mazirenwan) for functional constipation by focused network pharmacology approach. Front Pharmacol. 2018;9:270. doi:10.3389/fphar.2018.00270

17. Butte A. The use and analysis of microarray data. Nat Rev Drug Discov. 2002;1(12):951–960.

18. Lu Y, Huggins P, Bar-Joseph Z. Cross species analysis of microarray expression data. Bioinformatics. 2009;25(12):1476–1483. doi:10.1093/bioinformatics/btp247

19. Ru J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6(1):13. doi:10.1186/1758-2946-6-13

20. Barton HA, Pastoor TP, Baetcke K, et al. The acquisition and application of absorption, distribution, metabolism, and excretion (ADME) data in agricultural chemical safety assessments. Crit Rev Toxicol. 2006;36(1):9–35. doi:10.1080/10408440500534362

21. Xu X, Zhang W, Huang C, et al. A novel chemometric method for the prediction of human oral bioavailability. Int J Mol Sci. 2012;13(6):6964–6982. doi:10.3390/ijms13066964

22. Tao W, Xu X, Wang X, et al. Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. J Ethnopharmacol. 2013;145(1):1–10. doi:10.1016/j.jep.2012.09.051

23. Law V, Knox C, Djoumbou Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014;42(Databaseissue):D1091–1097. doi:10.1093/nar/gkt1068

24. Martin A, Ochagavia ME, Rabasa LC, Miranda J, Fernandez-de-Cossio J, Bringas R. BisoGenet: a new tool for gene network building, visualization and analysis. BMC Bioinform. 2010;11(1):91. doi:10.1186/1471-2105-11-91

25. Tang Y, Li M, Wang J, Pan Y, Wu FX. CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. BioSystems. 2015;127:67–72. doi:10.1016/j.biosystems.2014.11.005

26. Newman MEJ. A measure of betweenness centrality based on random walks. Soc Networks. 2005;27(1):39–54. doi:10.1016/j.socnet.2004.11.009

27. Jeong H, Mason SP, Barabási AL, Oltvai ZN. Lethality and centrality in protein networks. Nature. 2001;411(6833):41–42. doi:10.1038/35075138

28. Bonacich P. Power and centrality: a family of measures. Am J Sociol. 1987;92(5):1170–1182. doi:10.1086/228631

29. Li M, Wang J, Chen X, Wang H, Pan Y. A local average connectivity-based method for identifying essential proteins from the network level. Comput Biol Chem. 2011;35(3):143–150. doi:10.1016/j.compbiolchem.2011.04.002

30. Wang J, Li M, Wang H, Pan Y. Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Trans Comput Biol Bioinform. 2012;9(4):1070–1080. doi:10.1109/TCBB.2011.147

31. Li S, Zhang ZQ, Wu LJ, Zhang XG, Li YD, Wang YY. Understanding ZHENG in traditional Chinese medicine in the context of neuro-endocrine-immune network. IET Syst Biol. 2007;1(1):51–60. doi:10.1049/iet-syb:20060032

32. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–287. doi:10.1089/omi.2011.0118

33. Wang F, Feng J, Yang Y, et al. The Chinese herbal formula fuzheng quxie decoction attenuates cognitive impairment and protects cerebrovascular function in SAMP8 mice. Neuropsychiatr Dis Treat. 2018;14:3037–3051. doi:10.2147/NDT.S175484

34. Moradi SZ, Momtaz S, Bayrami Z, Farzaei MH, Abdollahi M. Nanoformulations of herbal extracts in treatment of neurodegenerative disorders. Front Bioeng Biotechnol. 2020;8:238.

35. Suganthy N, Devi KP, Nabavi SF, Braidy N, Nabavi SM. Bioactive effects of quercetin in the central nervous system: focusing on the mechanisms of actions. Biomed Pharmacother. 2016;84:892–908. doi:10.1016/j.biopha.2016.10.011

36. Paula PC, Angelica Maria SG, Luis CH, Gloria Patricia CG. Preventive effect of quercetin in a triple transgenic alzheimer’s disease mice model. Molecules. 2019;24(12):2287. doi:10.3390/molecules24122287

37. Zaplatic E, Bule M, Shah SZA, Uddin MS, Niaz K. Molecular mechanisms underlying protective role of quercetin in attenuating Alzheimer’s disease. Life Sci. 2019;224:109–119. doi:10.1016/j.lfs.2019.03.055

38. Chen AY, Chen YC. A review of the dietary flavonoid, kaempferol on human health and cancer chemoprevention. Food Chem. 2013;138(4):2099–2107. doi:10.1016/j.foodchem.2012.11.139

39. Tsai MS, Wang YH, Lai YY, et al. Kaempferol protects against propacetamol-induced acute liver injury through CYP2E1 inactivation, UGT1A1 activation, and attenuation of oxidative stress, inflammation and apoptosis in mice. Toxicol Lett. 2018;290:97–109. doi:10.1016/j.toxlet.2018.03.024

40. Yang EJ, Kim GS, Jun M, Song KS. Kaempferol attenuates the glutamate-induced oxidative stress in mouse-derived hippocampal neuronal HT22 cells. Food Funct. 2014;5(7):1395–1402. doi:10.1039/c4fo00068d

41. Benesch MG, McElhaney RN. A comparative calorimetric study of the effects of cholesterol and the plant sterols campesterol and brassicasterol on the thermotropic phase behavior of dipalmitoylphosphatidylcholine bilayer membranes. Biochim Biophys Acta. 2014;1838(7):1941–1949. doi:10.1016/j.bbamem.2014.03.019

42. Burg VK, Grimm HS, Rothhaar TL, et al. Plant sterols the better cholesterol in Alzheimer’s disease? A mechanistical study. J Neurosci. 2013;33(41):16072–16087. doi:10.1523/JNEUROSCI.1506-13.2013

43. Wang J, Wu F, Shi C. Substitution of membrane cholesterol with β-sitosterol promotes nonamyloidogenic cleavage of endogenous amyloid precursor protein. Neuroscience. 2013;247:227–233. doi:10.1016/j.neuroscience.2013.05.022

44. Ye JY, Li L, Hao QM, Qin Y, Ma CS. β-Sitosterol treatment attenuates cognitive deficits and prevents amyloid plaque deposition in amyloid protein precursor/presenilin 1 mice. Korean J Physiol Pharmacol. 2020;24(1):39–46. doi:10.4196/kjpp.2020.24.1.39

45. Guo LL, Guan ZZ, Huang Y, Wang YL, Shi JS. The neurotoxicity of β-amyloid peptide toward rat brain is associated with enhanced oxidative stress, inflammation and apoptosis, all of which can be attenuated by scutellarin. Exp Toxicol Pathol. 2013;65(5):579–584. doi:10.1016/j.etp.2012.05.003

46. Uddin MS, Kabir MT, Mamun AA, et al. Pharmacological approaches to mitigate neuroinflammation in Alzheimer’s disease. Int Immunopharmacol. 2020;84:106479.

47. Persson T, Popescu BO, Cedazo-Minguez A. Oxidative stress in Alzheimer’s disease: why did antioxidant therapy fail? Oxid Med Cell Longev. 2014;2014:427318. doi:10.1155/2014/427318

48. Heras-Sandoval D, Pérez-Rojas JM, Hernández-Damián J, Pedraza-Chaverri J. The role of PI3K/AKT/mTOR pathway in the modulation of autophagy and the clearance of protein aggregates in neurodegeneration. Cell Signal. 2014;26(12):2694–2701. doi:10.1016/j.cellsig.2014.08.019

49. Li Q, Liu Y, Sun M. Autophagy and Alzheimer’s disease. Cell Mol Neurobiol. 2017;37(3):377–388. doi:10.1007/s10571-016-0386-8

50. Chiarini A, Dal Pra I, Marconi M, Chakravarthy B, Whitfield JF, Armato U. Calcium-sensing receptor (CaSR) in human brain’s pathophysiology: roles in late-onset Alzheimer’s disease (LOAD). Curr Pharm Biotechnol. 2009;10(3):317–326. doi:10.2174/138920109787847501

51. Puig B, Gómez-Isla T, Ribé E, et al. Expression of stress-activated kinases c-Jun N-terminal kinase (SAPK/JNK-P) and p38 kinase (p38-P), and tau hyperphosphorylation in neurites surrounding betaA plaques in APP Tg2576 mice. Neuropathol Appl Neurobiol. 2004;30(5):491–502. doi:10.1111/j.1365-2990.2004.00569.x

52. Marques CA, Keil U, Bonert A, et al. Neurotoxic mechanisms caused by the Alzheimer’s disease-linked Swedish amyloid precursor protein mutation: oxidative stress, caspases, and the JNK pathway. J Biol Chem. 2003;278(30):28294–28302. doi:10.1074/jbc.M212265200

53. Hashimoto Y, Tsuji O, Niikura T, et al. Involvement of c-Jun N-terminal kinase in amyloid precursor protein-mediated neuronal cell death. J Neurochem. 2003;84(4):864–877. doi:10.1046/j.1471-4159.2003.01585.x

54. Tamagno E, Parola M, Bardini P, et al. Beta-site APP cleaving enzyme up-regulation induced by 4-hydroxynonenal is mediated by stress-activated protein kinases pathways. J Neurochem. 2005;92(3):628–636. doi:10.1111/j.1471-4159.2004.02895.x

55. Shen C, Chen Y, Liu H, et al. Hydrogen peroxide promotes Abeta production through JNK-dependent activation of gamma-secretase. J Biol Chem. 2008;283(25):17721–17730. doi:10.1074/jbc.M800013200

56. Schnöder L, Hao W, Qin Y, et al. Deficiency of neuronal p38α MAPK attenuates amyloid pathology in Alzheimer disease mouse and cell models through facilitating lysosomal degradation of BACE1. J Biol Chem. 2016;291(5):2067–2079. doi:10.1074/jbc.M115.695916

57. Layfield R, Cavey JR, Lowe J. Role of ubiquitin-mediated proteolysis in the pathogenesis of neurodegenerative disorders. Ageing Res Rev. 2003;2(4):343–356. doi:10.1016/S1568-1637(03)00025-4

58. Hegde AN, Smith SG, Duke LM, Pourquoi A, Vaz S. Perturbations of ubiquitin-proteasome-mediated proteolysis in aging and Alzheimer’s disease. Front Aging Neurosci. 2019;11:324. doi:10.3389/fnagi.2019.00324

59. Chu J, Li JG, Hoffman NE, Madesh M, Praticò D. Degradation of gamma secretase activating protein by the ubiquitin-proteasome pathway. J Neurochem. 2015;133(3):432–439. doi:10.1111/jnc.13011

60. Hong L, Huang HC, Jiang ZF. Relationship between amyloid-beta and the ubiquitin-proteasome system in Alzheimer’s disease. Neurol Res. 2014;36(3):276–282. doi:10.1179/1743132813Y.0000000288

61. Gong B, Radulovic M, Figueiredo-Pereira ME, Cardozo C. The ubiquitin-proteasome system: potential therapeutic targets for Alzheimer’s disease and spinal cord injury. Front Mol Neurosci. 2016;9:4. doi:10.3389/fnmol.2016.00004

62. Thiel G, Rössler OG. Resveratrol stimulates AP-1-regulated gene transcription. Mol Nutr Food Res. 2014;58(7):1402–1413. doi:10.1002/mnfr.201300913

63. Meng J, Li Y, Zhang M, et al. A combination of curcumin, vorinostat and silibinin reverses Aβ-induced nerve cell toxicity via activation of AKT-MDM2-p53 pathway. PeerJ. 2019;7:e6716. doi:10.7717/peerj.6716

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