Abstract

Background

   Shanzhuyu (the dried mature sarcocarp of Cornus officinalis Sieb. et
   Zucc., DMSCO) is a Chinese herb that can be used for the treatment of
   Alzheimer’s disease (AD), but its mechanism remains unknown. The
   present study aimed to investigate the active ingredients and effective
   mechanisms of DMSCO for the treatment of AD based on a network
   pharmacology approach.

Methods

   The active components of DMSCO were collected from the TCMSP and ETCM
   databases and the target proteins of these compounds were predicted
   using TCMSP, SwissTargetPrediction and the STITCH database. The
   AD-related target proteins were identified from the OMIM, DisGeNet, GEO
   and GeneCards databases. The network interaction model of the
   compound-target-disease was established and was used to obtain the key
   targets of DMSCO on AD through network topology analysis. Subsequently,
   gene enrichment in Gene Ontology (GO) and KEGG pathways were conducted
   using the David 6.8 online tool.

Results

   A total of 30 DMSCO effective compounds and 209 effective drug targets
   were obtained. A total of 172 AD-related genes and 37 shared targets of
   DMSCO and AD were identified. A total of 43 key targets for the
   treatment of AD were obtained from the topological analysis of the
   DMSCO-AD target network. These key targets were involved in a variety
   of biological processes, including amyloid deposition, apoptosis,
   autophagy, inflammatory response and oxidative stress and pathways,
   such as the PI3K-AKT, MAPK and TNF pathways. Three key compounds,
   namely ursolic acid, anethole and β-sitosterol were obtained from the
   analysis of the key targets.

Conclusions

   Ursolic acid, anethole and β-sitosterol may be the main active
   components of DMSCO in the treatment of AD. DMSCO can treat AD by
   regulating amyloid deposition, apoptosis, autophagy, inflammatory
   response and oxidative stress via the PI3K-AKT, MAPK and other
   signaling pathways.

   Keywords: Alzheimer’s disease, Chinese herb, Cornus officinalis Sieb.
   et Zucc., Network pharmacology

Background

   Alzheimer’s disease (AD) is a neurodegenerative disease with clinical
   manifestations of progressive memory loss, cognitive impairment and
   personality changes. Currently, approximately 50 million people
   worldwide have dementia and more than 35% of the population over the
   age of 80 has this disease [[39]1]. Approximately 5 million new cases
   of dementia are reported each year and the population is expected to
   grow to 118 million by 2050 [[40]2]. The pathological feature of AD is
   loss of neurons, formation of senile plaques and neurofibrillary
   tangles involving the amyloid β (Aβ) and the tau protein, as well as
   oxidative stress and inflammation [[41]3]. Studies that target Aβ and
   the tau protein are under investigation. However, they have not yielded
   satisfactory results [[42]4, [43]5]. Current drugs, such as Donepezil
   and Memantine, are not effective to meet clinical needs. Therefore, it
   is necessary to study the treatment of AD from the perspective of
   multi-target therapy.

   Traditional Chinese medicine (TCM) has been used to treat AD related
   diseases for thousands of years and significant clinical evidences have
   been accumulated. Shanzhuyu (the dried mature sarcocarp of Cornus
   officinalis Sieb. et Zucc., DMSCO) is a commonly used Chinese herb for
   the treatment of AD, which has the ability to tonify the liver and
   kidney. DMSCO is an important component of the Chinese herbal formula
   for AD-related disease treatment, such as Di-Huang-Yin-Zi,
   Liu-Wei-Di-Huang pills [[44]6, [45]7]. DMSCO can inhibit
   Aβ[1–42]-induced apoptosis and inflammation, tau hyperphosphorylation
   and aggregation [[46]8–[47]10]. It can also inhibit cholinesterase and
   beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) [[48]11].
   The active ingredients of DMSCO and their associated effective
   mechanism with regard to the treatment of AD require further
   investigation.

   Network pharmacology is a new subject that has emerged recently.
   Network pharmacology can aid the exploration of the direct targets of
   the active ingredients of the Chinese herbs, define their functions in
   the context of molecular network [[49]12]. In the present study, the
   active ingredients of DMSCO and its associated effective mechanism in
   the treatment of AD were systematically analyzed by establishing a
   “compound-target-pathway” network. The results indicated that DMSCO
   contained multiple active ingredients that could treat AD and that its
   mechanism was associated with the regulation of amyloid deposition,
   apoptosis, autophagy, inflammatory response and oxidative stress via
   the PI3K-AKT, MAPK and other signaling pathways.

Methods

Identification and screening of chemical ingredients of DMSCO

   At present, several databases are available with regard to TCM
   ingredients. Traditional Chinese Medicine System Pharmacology (TCMSP,
   [50]http://lsp.nwu.edu.cn/browse.php) is a comprehensive TCM platform
   containing 499 herbs and more than 2.9 × 10^4 chemical components,
   providing comprehensive information on Chinese herbal ingredients,
   including chemical structure, oral bioavailability (OB), intestinal
   epithelial permeability, half-life, drug similarity and drug targets
   [[51]13]. The Encyclopedia of Traditional Chinese Medicine (ETCM,
   [52]http://www.nrc.ac.cn:9090/ETCM/) is also a commonly used TCM
   database, containing comprehensive information on Chinese herbs, TCM
   formulations and their ingredients [[53]14]. TCMSP uses authoritative
   algorithms to predict the pharmacokinetic properties of compounds, such
   as absorption, distribution, metabolism and drug excretion (ADME) in
   order to provide comprehensive scores. In the present study, the
   chemical components of DMSCO were collected through literature research
   and via the TCMSP and ETCM databases. The ADME parameters OB ≥ 30% and
   Drug-likeness (DL) ≥ 0.18 were used to screen the potential active
   ingredients from the TCMSP database [[54]15]. In addition, a
   Drug-likeness Weight ≥ 0.49 was used to retrieve the active ingredients
   of DMSCO from the ETCM database [[55]14].

Investigation and prediction of compound-related targets

   Based on the chemical similarity and pharmacophore model, the present
   study used TCMSP, STITCH ([56]http://stitch.embl.de/) and
   SwissTargetPrediction ([57]http://www.swisstargetprediction.ch/) to
   retrieve and predict the related targets of compounds in DMSCO. STITCH
   is a database containing various structural and predictive interactions
   of compounds that support target prediction based on structural
   similarity [[58]16]. In the present study, a confidence score ≥ 0.7 was
   used as the screening criterion. SwissTargetPrediction is a database
   used for predicting compound targets based on 2D and 3D structures of
   known compounds [[59]17]. The probability value ≥ 0.5 served as the
   target screening standard in the present study.

Identification of AD-related targets

   AD-related genes were screened using online mendelian inheritance in
   man (OMIM, [60]https://omim.org/) [[61]18], DisGeNET
   ([62]http://www.disgenet.org/) [[63]19], GeneCards
   ([64]https://www.genecards.org/) [[65]20], and Gene Expression Omnibus
   (GEO) databases ([66]http://www.ncbi.nlm.nih.gov/geo). DisGeNET is a
   comprehensive platform developed to solve problems regarding the
   genetic basis of human disease. The platform was searched using the
   keyword “Alzheimer’s Disease” and disease related genes were identified
   based on a score ≥ 0.4. AD-related genes with GeneCards Inferred
   Functionality Score (GIFtS) ≥ 52 were selected from the GeneCards
   database. We further used the GEO2R online tool
   ([67]http://www.ncbi.nlm.nih.gov/geo/geo2r/) to select AD-related genes
   from the [68]GSE36980 dataset (15 AD patients and 33 healthy subjects).
   The criteria for screening differentially expressed genes were
   P ≤ 0.05, fold change (FC) ≥ 1.5. The target ID was converted to the
   gene symbol by retrieving either the UniProtKB
   ([69]https://www.uniprot.org/) or the STRING
   ([70]https://string-db.org/) databases.

Network construction and topological analysis

   PPI (protein-protein network) was constructed via the STRING database
   and the targets with a confidence score ≥ 0.7 were selected. The
   following network and topology analyses were performed using the
   Cytoscape 3.6.0 software: 1. The compound-target network of DMSCO; 2.
   The AD-related target network; 3. The DMSCO potential target-AD target
   interaction network; 4. The networks of shared targets between DMSCO
   and AD targets. Degree centrality (DC), betweenness centrality (BC) and
   closeness centrality (CC) are the most common topology parameters used
   to evaluate the central properties of nodes in a network. In the DMSCO
   potential target-AD target interaction network, the parameter settings
   of DC ≥ 3 × median DC, BC ≥ median BC and CC ≥ median CC were used to
   screen the key targets of DMSCO.

GO and KEGG pathway enrichment analysis

   DAVID 6. 8 ([71]https://david.ncifcrf.gov/) is an online biological
   information repository and analysis tool for extracting biological
   information regarding gene functional annotation and pathways
   enrichment [[72]21]. Drug targets and key targets of DMSCO acting on AD
   were imported into the DAVID 6.8 database and the species were defined
   as “Homo sapiens”, whereas the target genes were identified as
   “official gene symbol”. Gene Ontology (GO) and KEGG pathway analysis
   were performed.

Results

DMSCO compound-target network

   According to the search results of TCMSP, DMSCO exhibited a total of
   226 chemical components, including mainly iridoids, pentacyclic
   triterpenoid acids and their corresponding esters, polysaccharides and
   tannins. A total of 20 compounds were screened by OB ≥ 30% and
   DL ≥ 0.18. A total of 55 types of compounds were retrieved from ETCM
   and 20 compounds were filtered by Drug-likeness Weight ≥ 0.49. A
   thorough literature search using Chinese and international references