Next Article in Journal
The Relative Validity of the Menzies Remote Short-Item Dietary Assessment Tool (MRSDAT) in Aboriginal Australian Children Aged 6–36 Months
Next Article in Special Issue
Leucine Supplementation Does Not Attenuate Skeletal Muscle Loss during Leg Immobilization in Healthy, Young Men
Previous Article in Journal
Intestinal Absorption and Antioxidant Activity of Grape Pomace Polyphenols
Previous Article in Special Issue
Chemopreventive Activities of Sulforaphane and Its Metabolites in Human Hepatoma HepG2 Cells
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 10
Issue 5
10.3390/nu10050589
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

In Silico Investigation of the Pharmacological Mechanisms of Beneficial Effects of Ginkgo biloba L. on Alzheimer’s Disease

by
Hongxiang Li
1,2,
Xiaoyuan Sun
1,2,
Fan Yu
1,2,
Lijia Xu
1,2,*,
Jianhua Miu
3 and
Peigen Xiao
1,2
1
Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, 151 Malianwa North Road, Beijing 100193, China
2
Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Ministry of Education, 151 Malianwa North Road, Beijing 100193, China
3
Guangxi Institute of Medicinal Plant Development, Nanning, 189 Changgang Road, Nanning 520023, China
*
Author to whom correspondence should be addressed.
Nutrients 2018, 10(5), 589; https://doi.org/10.3390/nu10050589
Submission received: 1 April 2018 / Revised: 1 May 2018 / Accepted: 8 May 2018 / Published: 10 May 2018
(This article belongs to the Special Issue Advances in Dietary Supplements)
Browse Figures
Versions Notes

Abstract

:
Based on compelling experimental and clinical evidence, Ginkgo biloba L. exerts a beneficial effect in ameliorating mild to moderate dementia in patients with Alzheimer’s disease (AD) and other neurological disorders, although the pharmacological mechanisms remain unknown. In the present study, compounds, their putative target proteins identified using an inverse docking approach, and clinically tested AD-related target proteins were systematically integrated together with applicable bioinformatics methods in silico. The results suggested that the beneficial effects of G. biloba on AD may be contributed by the regulation of hormone sensitivity, improvements in endocrine homeostasis, maintenance of endothelial microvascular integrity, and proteolysis of tau proteins, particularly prior to amyloid β-protein (Aβ) plaque formation. Moreover, we identified six putative protein targets that are significantly related to AD, but have not been researched or have had only preliminary studies conducted on the anti-AD effects of G. biloba. These mechanisms and protein targets are very significant for future scientific research. In addition, the existing mechanisms were also verified, such as the reduction of oxidative stress, anti-apoptotic effects, and protective effects against amyloidogenesis and Aβ aggregation. The discoveries summarized here may provide a macroscopic perspective that will improve our understanding of the molecular mechanism of medicinal plants or dietary supplements, as well as new clues for the future development of therapeutic strategies for AD.

Graphical Abstract

1. Introduction

The number of patients with Alzheimer’s disease (AD) is predicted to increase exponentially during the next few decades [1]. The current therapies for AD are based on five main strategies [2]: cholinergic treatment, antiglutamatergic treatment, vitamins and antioxidants, nonsteroidal anti-inflammatory drugs (NSAIDs), and pharmacological management of neuropsychiatric symptoms. However, single targeted therapies has often been unsuccessful, due to fact that the pathogenesis and etiology of AD have not yet been completely elucidated [3]. In contrast, Ginkgo biloba L. has long been thought to be “multivalent” [4] and it has a definite positive effect in ameliorating mild to moderate dementia in patients with AD and other neurological disorders associated with old age [5].
G. biloba-related entries are all included in the Dietary Supplements chapter of the United States Pharmacopoeia National Formulary (USP41-NF36), including Ginkgo, Powdered Ginkgo Extract, Ginkgo Capsules, and Ginkgo Tablets. The leaves and seeds of G. biloba have been used medicinally in China for hundreds of years [5], and were approved by China’s Ministry of Health as “medicine food homology” (MFH) materials in 2002. EGb761, a standardized and well-defined product extract of Ginkgo biloba leaves, is currently one of the most popular herbal drugs. Thus, there is sufficient evidence of the efficacy and safety of G. biloba.
However, G. biloba contains a variety of compounds with potential synergistic effects that make it difficult to clarify its pharmacological mechanism. With the development of structural biology and computational chemistry, the exploration of medicinal plants and natural sources has moved into a new epoch. The inverse docking approach has been developed and used to facilitate the discovery of new drugs [6]. PharmMapper is a web server used to identify potential drug targets through inverse docking by matching the query compound to an in-house pharmacophore model database, with 23,236 proteins covering 16,159 druggable pharmacophore models and 51,431 ligandable pharmacophore models, as of January 2018 [7].
In this paper, we extensively employ currently available public databases and integrated bioinformatics methodologies and the inverse docking approach to describe a novel paradigm for constructing easily interpretable networks. This might enable us to reveal the pharmacological mechanism of G. biloba in its beneficial effects on AD.

2. Materials and Methods

2.1. Data Collection

● Candidate Compounds of G. biloba
In the present study, the compounds were collected from The Traditional Chinese Medicine System Pharmacology Database and Analysis Platform [8] (TCMSP, http://ibts.hkbu.edu.hk/LSP/tcmsp.php). The TCMSP provides absorptions, distribution, metabolism, and excretion (ADME)-related pharmacokinetic properties, including bioavailability (OB), drug-likeness (DL), blood–brain barrier (BBB), etc. Values OB ≥ 30% and DL ≥ 0.18 were affirmed as ADME screening criteria for candidate compounds. Some studies confirmed that under pathological conditions such as AD, EGb761 is able to cross the BBB [4], but EGb761 has a limited ability to cross the BBB under normal physiological conditions. Thus, BBB penetration may be an important factor that alters the effects of EGb761 or G. biloba in vivo. Therefore, we removed the BBB cutoff value of ≥−0.3 from the ADME screening criteria.
● Alzheimer’s disease associated protein targets
Information on AD-associated protein targets was identified from GeneCards [9] (http://www.genecards.org/) and the Comparative Toxicogenomics Database [10] (CTD, http://ctdbase.org/), which is a robust, publicly available database that provides comprehensive, user-friendly information on chemical-gene/protein interactions, and chemical-disease and gene-disease relationships. We also referred to the corresponding target protein’s unique UniProtKB ID in the UniProt database (http://www.uniprot.org/), composing an AD-associated target protein database. It is noteworthy that these two databases provide an expert review ranking of protein targets based on scientific research and literature, labeled by Relevance score (GeneCard) and Inference Score (CTD). A higher score indicates a higher correlation with AD.

2.2. Inverse Docking Analysis

The 2-dimensional (2D) and 3D structures of candidate compounds were drawn using ChemBioOffice 2012 (PerkinElmer Inc., Cambridge, MA, USA). The mol2 files (mol2) of the 3D molecular structures of 25 candidate compounds were uploaded to the PharmMapper [7] (http://lilab.ecust.edu.cn/pharmmapper/) and the Human Protein Targets Only database was selected for target prediction. Results include Protein Data Bank (PDB) database codes (PDBIDs), UniProtKB ID, target names, FitScores, and z’score. FitScores was adopted as the principal scoring to rank the proteins; among them, those with FitScores ≥ 4.5 were selected as the putative target proteins. The putative target proteins and AD-related target proteins were validated one by one, according to their unique UniProtKB ID, producing conditionally filtered results of AD-associated target proteins of candidate compounds.

2.3. Gene Ontology and KEGG Pathway Enrichment

The Database for Annotation, Visualization, and Integrated Discovery [11] (DAVID, v6.8, https://david.ncifcrf.gov/) was employed to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The p-value was used to examine the significance of the GO/pathway term enrichment with a modified Fisher’s exact test. The Benjamini value was used to globally correct the enrichment p-values of individual term members [11]. Those GO/pathway terms with a p-value of ≤0.05 and Benjamini value of ≤0.5 were regarded as significant and interesting.

2.4. Composite Network Integration

On the strength of above-mentioned target identification results, combined with Protein–Protein Interaction (PPI) data from STRING (https://string-db.org/) and pathway enrichment data from DAVID, Cytoscape 3.6.0 (Institute for Systems Biology, Seattle, WA, USA) [12] (http://www.cytoscape.org/) software was employed to construct Compound-Target (CT), Compound-Target-Disease and Compound-Group-Target-Pathway (CGTP) network models. Subsequently, engaging the NetworkAnalyzer plugin in Cytoscape, the parameters of the network topology were analyzed to get the Average Shortest Path Length (ASPL) and the Betweenness Centrality (BC), etc., and the more substantially contributing nodes were obtained. Besides this, R 3.4.3 software (R Core Team, Auckland, Tamaki-Makau-Rau, New Zealand) (https://www.r-project.org/) was employed to visualize the quantitative information.

3. Results

3.1. The Candidate Compounds and Putative Target Proteins

Figure 1 shows a schematic of the methodology and a summary of the results from each step. From the 307 native G. biloba compounds collected from the TCMSP database, 25 compounds were screened by ADME and prepared for further study as the candidate compounds as shown in Figure 2 and Supplementary Materials Table S1. The 25 compounds were divided into 6 categories: 12 flavonoids (quercetin, catechin, genkwanin, etc.), 5 terpene lactones (bilobalide, ginkgolide B, etc.), 3 sterols (beta-sitosterol, stigmasterol, and campest-5-en-3beta-ol), 3 fatty acid esters (mandenol, ethyl oleate, and linolenic acid ethyl ester), 1 polyprenol (flavoxanthin), and 1 lignan (sesamin). These compounds are the main components or active functional ingredients of G. biloba [4,13,14].
A total of 2500 target proteins were docked with the 25 candidate compounds. Among these target proteins, 97 were screened by ADME and named the putative target proteins. All results from the inverse docking calculation are presented in Supplementary Materials Table S2.
We compared the 97 putative target proteins for commonality and properties, and the results are shown in Figure 3. Panel (a) more intuitively shows that compounds from different categories mapped to different target proteins. Moreover, panel (b) depicts a Venn diagram that clearly shows the terpene lactone group associated with 26 exclusive protein targets—approximately one-third of the protein targets. Thus, we speculated that this finding may be consistent with the observation that terpene lactones are the predominant and unique primary bioactive substances in G. biloba. In other words, we inferred that the specificity of the inverse docking calculation was distinct.
The gene entries related to AD were collected from the CTD and GeneCards databases. As a result, 21,249 and 7262 gene entries were collected from each database, respectively. The prioritized Inference Scores for the corresponding annotations are listed in descending order in Supplementary Materials Tables S3-1 and S4-1. The corresponding gene entries were converted into 108,145 and 58,432 UniProtKB IDs, respectively. The results are listed in Supplementary Materials Tables S3-2 and S4-2. This procedure was initiated by matching the unique UniProtKB IDs to determine the magnitude of the correlation between the putative target proteins and AD. We adopted the arithmetic average of the two scores from the CTD and GeneCards databases as integration scores, screening the top third of the putative target proteins for MOA (Molecular Mechanisms of Action) analysis. The one-third ratio was determined after several preliminary experiments. The results are listed in Supplementary Materials Table S5.

3.2. Exploration of the Molecular Mechanisms of Action

The top 30 putative target proteins were selected based on their integration score, and GO and KEGG pathway enrichment analyses were initiated. After filtering by a parameter p-value cutoff of ≤0.05, 84 GO terms and 30 KEGG pathway terms were returned, as shown in Figure 4 and Figure 5 and Supplementary Materials Tables S6 and S7. A total of 84 GO terms are included: 51 for Biological Processes, 19 for Molecular Function, and 14 for Cellular Component. According to physiological function, these biological processes can be divided into 6 modules, as shown in Table 1.
In order to show the result of the KEGG pathway enrichment in an intuitive and explicit way, a bubble diagram was employed. As shown in Figure 5, p-values are given the highest priority, in ascending order. Dual specificity mitogen-activated protein kinase kinase 1 (MAP2K1), GTPase HRas (HRAS), mitogen-activated protein kinase 14 (MAPK14), mitogen-activated protein kinase 10 (MAPK10), and proto-oncogene tyrosine-protein kinase src (SRC) were the most frequently occurring protein targets. According to the pathogenesis of AD, these KEGG pathway terms can be divided into 5 modules, as shown in Table 2.

3.3. An Integrated Network Model Analysis

The Compound-Target-Disease network model contained 123 nodes and 369 edges, as shown in Figure 6. The top 10 putative target proteins identified based on integration scores are highlighted, and the other putative target proteins are in gray, as shown in panel (b) of Figure 6. We identified 7 flavonoids, 3 terpene lactones, 3 sterols, 3 fatty acid esters, 1 polyprenol, and 1 lignan associated with these proteins. The results suggest that flavonoids and terpene lactones may primarily contribute to the anti-AD effects of G. biloba. The top 10 putative target proteins associated with AD, in turn, were nitric oxide synthase (NOS3), neprilysin (NEP), beta-secretase 1 (BACE1), estrogen receptor (ESR1), amine oxidase B (MAOB), prothrombin (F2), serum albumin (ALB), transthyretin (TTR), matrix metalloproteinase-3 (MMP3), and interleukin-2 (IL2).
As shown in Figure 7, three subnetworks were integrated into the Compound-Group-Target-Pathway (CGTP) network, including the Protein-Protein Interaction (PPI) network, Compound-Group-Target (CGT) network, and Target-Pathway (TP) network. The PPI network is the premise and basis to obtain nodes with more substantial contributions. Nodes with a shorter ASPL and higher BC were considered as vital ones. In the PPI network, serum albumin (ALB), estrogen receptor (ESR1), and a proto-oncogene tyrosine-protein kinase (SRC) were the top three, consistent with their molecular functions of transport, connection, and signal communication. Clustering and topology approaches were utilized to identify individual variations and similarities among various protein targets. Of the top 30 putative protein targets, 3 well-organized clusters with 30 KEGG pathway terms were identified. The protein targets in cluster A were associated with 26 KEGG pathway terms, and targets in cluster C were associated with 4 KEGG pathway terms.

4. Discussion and Conclusions

Currently, an increasing number of people are consuming dietary supplements for health. Due to the amazing vitality induced by G. biloba, it has achieved therapeutic applications as a dietary supplement. Despite the fact that it has a definite positive effect in ameliorating mild to moderate dementia in patients with AD, its mechanism remains elusive. This prompted us to determine the pharmacological mechanism of G. biloba by inverse docking and system pharmacological approaches.
We collected 307 native G. biloba compounds from TCMSP, and 25 compounds were screened by ADME. Further calculations using PharmMapper identified 2500 target proteins, 97 of which were obtained by screening criteria. From the CTD and GeneCards databases, we collected 21,249 and 7262 AD-associated gene entries, respectively. The subsequent step was performed using UniProt and resulted in 108,145 and 58,432 UniprotKB IDs, respectively. Based on the integration score, the top 30 putative target proteins were selected to further explore the MOA. The functions of these putative target proteins include the following: antioxidant activity [15], protective effects on the mitochondria [16], anti-apoptotic [17], anti-inflammatory [18], protective effects on amyloidogenesis and amyloid β-protein (Aβ) aggregation [19,20], ion homeostasis [21], modulation of the phosphorylation of the tau protein [15], and induction of hormone synthesis [15]. These findings are consistent with existing experimental evidence [13,14,22]. Subsequently, 84 GO terms and 30 KEGG pathway terms were returned and classified. Finally, 3 networks were constructed and integrated.
In order to better understand the relationship between enriched KEGG pathways and putative target proteins, manual annotation was conducted based on KEGG pathway maps, as shown in Figure 8. The left panel shows the potential MOAs that are directly related to Aβ synthesis, transport, degradation, and clearance. The right panel shows MOAs that are indirectly linked or irrelevant to Aβ. Interestingly, the putative target proteins in the left panel are rarely enriched in the KEGG pathway terms, but the putative target proteins in the right panel are enriched in many signaling pathways. It seems that the putative target proteins in the left panel are more like “lone rangers”, while the putative target proteins in the right panel play a physiological role by interacting with other proteins; this might also be related to most of them being protein kinases. The results in Figure 7 also confirm this viewpoint, and the putative target proteins in cluster B are mostly distributed in the left panel of Figure 8. This clustering may be a possible explanation for the findings from the DAVID enrichment algorithm, but it does not hinder the process of discovering their unique roles in AD pathology. These proteins, such as Endothelial Nitric Oxide Synthase (NOS3) [23,24,25,26,27], neprilysin (NEP) [28,29], Beta-secretase (BACE) [30,31,32,33,34], Monoamine oxidases (MAOs) [35,36,37], Prothrombin (F2) [38], Serum albumin (ALB) [39], Thyroid hormone (TTR) [32], and Matrix metalloproteinase 3 (MMP3) [40], are directly or indirectly involved in Aβ synthesis, processing, aggregation, degradation and transport; the formation of neurofibrillary tangles (NFTs); and tau proteolysis. In the right panel, the hormone-related signaling pathways were on top of the KEGG pathway enrichment results. Therefore, we hypothesize that hormone-related signaling pathways may play an important role in the anti-AD effects of G. biloba. These signaling pathways include the (hsa04917) prolactin signaling pathway, (hsa04915) estrogen signaling pathway, (hsa04912) GnRH signaling pathway, (hsa04910) insulin signaling pathway, (hsa04921) oxytocin signaling pathway, (hsa04919) thyroid hormone signaling pathway, (hsa04722) neurotrophin signaling pathway, and (hsa04071) sphingolipid signaling pathway. Based on accumulating evidence [41,42], non-Aβ-related pathways are also an important factor in AD etiology, particularly prior to Aβ plaque formation. Prolactin [43], estrogen [44,45], oxytocin [46,47], thyroid hormone [48], and insulin [49] potentially play substantial roles in non-Aβ-related mechanisms. As for the studies on G. biloba related to the above-mentioned hormones, estrogen- and insulin-related studies have been extensive, but there has been less research on prolactin, oxytocin, and thyroid hormone. Based on the results of present study, it is worth further research.
As illustrated in Table 3, we identified 6 putative protein targets that were significantly related to AD, but have not been researched or have had only preliminary studies conducted on the anti-AD effects of G. biloba. Neprilysin (NEP), estrogen receptor (ESR), Prothrombin (F2), Serum albumin (ALB), Thyroid hormone (TTR), and Matrix metalloproteinase 3 (MMP3) are significantly associated with the development of AD. These above-mentioned protein targets are highly matched with several representative compounds in G. biloba, but the specific actions and properties must be further verified and probed in future experiments.
In conclusion, the establishment of networks between AD-related protein targets and compounds in G. biloba may have important implications for elucidating the mechanisms underlying the beneficial effects of G. biloba on AD. Conceivably, a novel therapeutic strategy for AD may be developed from the protein targets and pathways identified in the present study. Hopefully, a novel paradigm presented in this study would help facilitate natural medicine development and the construction of a herbal compound library.

Supplementary Materials

The following are available online at https://www.mdpi.com/2072-6643/10/5/589/s1. Table S1: Result of 25 candidate compounds of Ginkgo biloba. Table S2: Inverse Docking Results of the 25 candidate compounds of Ginkgo biloba L. Table S3-1: AD-associated gene information collected from Comparative Toxicogenomics Database (CTD). Table S3-2: AD-associated target proteins information collected from Comparative Toxicogenomics Database (CTD). Table S4-1: AD-associated gene information collected from GeneCards. Table S4-2: AD-associated target proteins information collected from GeneCards. Table S5: Top 30 putative target proteins according to Integrated Score. Table S6: Results of Gene Ontology enrichment for top 30 putative target proteins. Table S7: Results of pathway enrichment for top 30 putative target proteins.

Author Contributions

J.M., L.X. and P.X. conceived and designed the experiments; H.L. performed the experiments and wrote the paper; X.S. and F.Y. analyzed the data.

Funding

This work was supported by the CAMS Innovation Fund for Medical Sciences (CIFMS 2016-I2M-1-012) and Guangxi Science and Technology Foundation and Talents Project (Gui Ke AD17129044).

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviation

ADAlzheimer’s Disease
2D2-dimensional
ADMEabsorptions, distribution, metabolism, and excretion
ALBserum albumin
ARAndrogen receptor
ASPLAverage Shortest Path Length
Aβamyloid β-protein
BACE1beta-secretase 1
BBBblood brain barrier
BCBetweenness Centrality
CCNA2Cyclin-A2
CDK2Cyclin-dependent kinase 2
CGTPCompound-Group-Target-Pathway
CTCompound-Target
CTDComparative Toxicogenomics Database
DAVIDThe Database for Annotation, Visualization, and Integrated Discovery
DLdrug-likeness
ELANENeutrophil elastase
ERBB2Receptor tyrosine-protein kinase erbB-2
ESR1Estrogen receptor 1
F2prothrombin
FDPSFarnesyl pyrophosphate synthase
FDRfalse discovery rate
FGFR1Fibroblast growth factor receptor 1
FOXOforkhead box O
GOGene Ontology
GSTA1Glutathione S-transferase A1
GSTM2Glutathione S-transferase Mu 2
GSTP1Glutathione S-transferase P
HRASGTPase HRas
HSP90AA1Heat shock protein HSP 90-alpha
IL2interleukin-2
KEGGKyoto Encyclopedia of Genes and Genomes
MAOBamine oxidase B
MAP2K1Dual specificity mitogen-activated protein kinase kinase 1
MAPKmitogen-activated protein kinase
MAPK10mitogen-activated protein kinase 10
MAPK14mitogen-activated protein kinase 14
MFHmedicine food homology
MMP3matrix metalloproteinase-3
MOAMolecular Mechanisms of Action
NEPneprilysin
NODnucleotide bindingoligomerization domain
NOS3synthase
NR3C2Mineralocorticoid receptor
NSAIDsnonsteroidal anti-inflammatory drugs
OBbioavailability
PI3KPhosphoinositide 3-kinase
PPIProtein–Protein Interaction
PPIAPeptidyl-prolyl cis-trans isomerase A
PTPN1Tyrosine-protein phosphatase non-receptor type 1
RBP4Retinol-binding protein 4
RNAribonucleic acid
RnRHGonadotropin-releasing hormone
SRCproto-oncogene tyrosine-protein kinase src
TCMSPThe Traditional Chinese Medicine System Pharmacology Database and Analysis Platform
TNFTumor Necrosis Factor
TRPTransient receptor potential
TTRtransthyretin
USP41-NF36United States Pharmacopoeia National Formulary
VDRVitamin D3 receptor
VEGFvascular endothelial growth factor

References

  1. Selkoe, D.J. Preventing Alzheimer’s disease. Science 2012, 337, 1488–1492. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Lleó, A.; Greenberg, S.M.; Growdon, J.H. Current Pharmacotherapy for Alzheimer’s Disease. Ann. Rev. Med. 2006, 57, 513–533. [Google Scholar] [CrossRef] [PubMed]
  3. Selkoe, D.J. Resolving controversies on the path to Alzheimer’s therapeutics. Nat. Med. 2011, 17, 1060. [Google Scholar] [CrossRef] [PubMed]
  4. Wolffram, S.; Ader, P.; Rimbach, G.; Packer, L.; Maguire, J.J.; Schultz, P.G.; Gohil, K. The in vivo Neuromodulatory Effects of the Herbal Medicine Ginkgo Biloba. Proc. Natl. Acad. Sci. USA 2001, 98, 6577. [Google Scholar]
  5. Howes, M.J.; Houghton, P.J. Ethnobotanical treatment strategies against Alzheimer’s disease. Curr. Alzheimer Res. 2012, 9, 67–85. [Google Scholar] [CrossRef] [PubMed]
  6. Chen, Y.Z.; Zhi, D.G. Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins Struct. Funct. Bioinform. 2001, 43, 217–226. [Google Scholar] [CrossRef]
  7. Wang, X.; Shen, Y.; Wang, S.; Li, S.; Zhang, W.; Liu, X.; Lai, L.; Pei, J.; Li, H. PharmMapper 2017 update: A web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res. 2017, 45, W356–W360. [Google Scholar] [CrossRef] [PubMed]
  8. Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform. 2014, 6, 13. [Google Scholar] [CrossRef] [PubMed]
  9. Safran, M.; Dalah, I.; Alexander, J.; Rosen, N.; Stein, T.I.; Shmoish, M.; Nativ, N.; Bahir, I.; Doniger, T.; Krug, H. GeneCards Version 3: The human gene integrator. Database 2010, 2010. [Google Scholar] [CrossRef] [PubMed]
  10. Davis, A.P.; Grondin, C.J.; Lennon-Hopkins, K.; Saraceni-Richards, C.; Sciaky, D.; King, B.L.; Wiegers, T.C.; Mattingly, C.J. The Comparative Toxicogenomics Database’s 10th year anniversary: Update 2015. Nucleic Acids Res. 2015, 43, D914–D920. [Google Scholar] [CrossRef] [PubMed]
  11. Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009, 4, 44. [Google Scholar] [CrossRef] [PubMed]
  12. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef] [PubMed]
  13. Shi, C.; Liu, J.; Wu, F.; Yew, D.T. Ginkgo biloba Extract in Alzheimer’s Disease: From Action Mechanisms to Medical Practice. Int. J. Mol. Sci. 2010, 11, 107–123. [Google Scholar] [CrossRef] [PubMed]
  14. Tapan Kumar, M.; Yasinalli, T.; Zubaidha, P.K. Phytochemical and medicinal importance of Ginkgo biloba L. Nat. Prod. Res. 2014, 28, 746–752. [Google Scholar]
  15. Ahlemeyer, B.; Krieglstein, J. Neuroprotective effects of Ginkgo biloba extract. Cell. Mol. Life Sci. 2003, 60, 1779–1792. [Google Scholar] [CrossRef] [PubMed]
  16. Shi, C. Protective effects of Ginkgo biloba extract (EGb761) and its constituents quercetin and ginkgolide B against β-amyloid peptide-induced toxicity in SH-SY5Y cells. Chem. Biol. Interact. 2009, 181, 115–123. [Google Scholar] [CrossRef] [PubMed]
  17. Smith, J.V.; Luo, Y. Studies on molecular mechanisms of Ginkgo biloba extract. Appl. Microbiol. Biotechnol. 2004, 64, 465–472. [Google Scholar] [PubMed]
  18. Maclennan, K.M.; Darlington, C.L.; Smith, P.F. The CNS effects of Ginkgo biloba extracts and ginkgolide B. Prog. Neurobiol. 2002, 67, 235–257. [Google Scholar] [CrossRef]
  19. Smith, J.V.; Luo, Y. Elevation of oxidative free radicals in Alzheimer’s disease models can be attenuated by Ginkgo biloba extract EGb 761. J. Alzheimers Dis. 2003, 5, 287–300. [Google Scholar] [CrossRef] [PubMed]
  20. Bastianetto, S.; Ramassamy, C.; Doré, S.; Christen, Y.; Poirier, J.; Quirion, R. The Ginkgo biloba extract (EGb 761) protects hippocampal neurons against cell death induced by beta-amyloid. Eur. J. Neurosci. 2000, 12, 1882–1890. [Google Scholar] [CrossRef] [PubMed]
  21. Berrocal, M.; Marcos, D.; Sepúlveda, M.R.; Pérez, M.; Avila, J.; Mata, A.M. Altered Ca2+ dependence of synaptosomal plasma membrane Ca2+-ATPase in human brain affected by Alzheimer’s disease. FASEB J. 2009, 23, 1826–1834. [Google Scholar] [CrossRef] [PubMed]
  22. Yin, Y.; Ren, Y.; Wu, W.; Wang, Y.; Cao, M.; Zhu, Z.; Wang, M.; Li, W. Protective effects of bilobalide on Aβ(25-35) induced learning and memory impairments in male rats. Pharmacol. Biochem. Behav. 2013, 106, 77–84. [Google Scholar] [CrossRef] [PubMed]
  23. Doreulee, N.; Sergeeva, O.A.; Yanovsky, Y.; Chepkova, A.N.; Selbach, O.; Gödecke, A.; Schrader, J.; Haas, H.L. Cortico-striatal synaptic plasticity in endothelial nitric oxide synthase deficient mice. Brain Res. 2003, 964, 159–163. [Google Scholar] [CrossRef]
  24. Austin, S.A.; Santhanam, A.V.; Hinton, D.J.; Choi, D.S.; Katusic, Z.S. Endothelial nitric oxide deficiency promotes Alzheimer’s disease pathology. J. Neurochem. 2013, 127, 691–700. [Google Scholar] [CrossRef] [PubMed]
  25. Austin, S.A.; Santhanam, A.V.; Katusic, Z.S. Endothelial Nitric Oxide Modulates Expression and Processing of Amyloid Precursor ProteinNovelty and Significance. Circ. Res. 2010, 107, 1498–1502. [Google Scholar] [CrossRef] [PubMed]
  26. Haul, S.; Gödecke, A.; Schrader, J.; Haas, H.L.; Luhmann, H.J. Impairment of neocortical long-term potentiation in mice deficient of endothelial nitric oxide synthase. J. Neurophysiol. 1999, 81, 494–497. [Google Scholar] [CrossRef] [PubMed]
  27. Austin, S.A.; D’Uscio, L.V.; Katusic, Z.S. Supplementation of nitric oxide attenuates AβPP and BACE1 protein in cerebral microcirculation of eNOS-deficient mice. J. Alzheimers Dis. 2013, 33, 29–33. [Google Scholar] [PubMed]
  28. Howell, S.; Nalbantoglu, J.; Crine, P. Neutral endopeptidase can hydrolyze β-amyloid(1–40) but shows no effect on β-amyloid precursor protein metabolism. Peptides 1995, 16, 647–652. [Google Scholar] [CrossRef]
  29. Takaki, Y.; Iwata, N.; Tsubuki, S.; Taniguchi, S.; Toyoshima, S.; Lu, B.; Gerard, N.P.; Gerard, C.; Lee, H.J.; Shirotani, K. Biochemical identification of the neutral endopeptidase family member responsible for the catabolism of amyloid beta peptide in the brain. J. Biochem. 2000, 128, 897–902. [Google Scholar] [CrossRef] [PubMed]
  30. Colciaghi, F.; Borroni, B.; Zimmermann, M.; Bellone, C.; Longhi, A.; Padovani, A.; Cattabeni, F.; Christen, Y.; Di, L.M. Amyloid precursor protein metabolism is regulated toward alpha-secretase pathway by Ginkgo biloba extracts. Neurobiol. Dis. 2004, 16, 454–460. [Google Scholar] [CrossRef] [PubMed]
  31. Baker, L.D.; Cross, D.J.; Minoshima, S.; Belongia, D.; Watson, G.S.; Craft, S. Insulin resistance and Alzheimer-like reductions in regional cerebral glucose metabolism for cognitively normal adults with prediabetes or early type 2 diabetes. Arch. Neurol. 2011, 68, 51–57. [Google Scholar] [CrossRef] [PubMed]
  32. Subhadra, B.; Schaller, K.; Seeds, N.W. Neuroserpin up-regulation in the Alzheimer’s disease brain is associated with elevated thyroid hormone receptor-β1 and HuD expression. Neurochem. Int. 2013, 63, 476–481. [Google Scholar] [CrossRef] [PubMed]
  33. Yan, R.; Vassar, R. Targeting the β secretase BACE1 for Alzheimer’s disease therapy. Lancet Neurol. 2014, 13, 319–329. [Google Scholar] [CrossRef]
  34. Vassar, R.; Kovacs, D.M.; Yan, R.; Wong, P.C. The β-Secretase Enzyme BACE in Health and Alzheimer’s Disease: Regulation, Cell Biology, Function, and Therapeutic Potential. J. Neurosci. 2009, 29, 12787–12794. [Google Scholar] [CrossRef] [PubMed]
  35. White, H.L.; Scates, P.W.; Cooper, B.R. Extracts of Ginkgo biloba leaves inhibit monoamine oxidase. Life Sci. 1996, 58, 1315–1321. [Google Scholar] [CrossRef]
  36. Pardon, M.C.; Joubert, C.; Perez-Diaz, F.; Christen, Y.; Launay, J.M.; Cohen-Salmon, C. In vivo regulation of cerebral monoamine oxidase activity in senescent controls and chronically stressed mice by long-term treatment with Ginkgo biloba extract (EGb 761). Mech. Aging Dev. 2000, 113, 157–168. [Google Scholar] [CrossRef]
  37. Chaurasiya, N.D.; Ganesan, S.; Nanayakkara, N.P.; Dias, L.R.; Walker, L.A.; Tekwani, B.L. Inhibition of human monoamine oxidase A and B by 5-phenoxy 8-aminoquinoline analogs. Bioorg. Med. Chem. Lett. 2012, 22, 1701–1704. [Google Scholar] [CrossRef] [PubMed]
  38. Aliev, G.; Seyidova, D.; Lamb, B.T.; Obrenovich, M.E.; Siedlak, S.L.; Vinters, H.V.; Friedland, R.P.; Lamanna, J.C.; Smith, M.A.; Perry, G. Mitochondria and vascular lesions as a central target for the development of Alzheimer’s disease and Alzheimer disease-like pathology in transgenic mice. Neurol. Res. 2003, 25, 665–674. [Google Scholar] [CrossRef] [PubMed]
  39. Biere, A.L.; Ostaszewski, B.; Stimson, E.R.; Hyman, B.T.; Maggio, J.E.; Selkoe, D.J. Amyloid beta-peptide is transported on lipoproteins and albumin in human plasma. J. Biol. Chem. 1996, 271, 32916–32922. [Google Scholar] [CrossRef] [PubMed]
  40. Li, G.H.; Arora, P.D.; Chen, Y.; Mcculloch, C.A.; Liu, P. Multifunctional roles of gelsolin in health and diseases. Med. Res. Rev. 2012, 32, 999–1025. [Google Scholar] [CrossRef] [PubMed]
  41. Atwood, C.S.; Meethal, S.V.; Liu, T.; Wilson, A.C.; Gallego, M.; Smith, M.A.; Bowen, R.L. Dysregulation of the hypothalamic-pituitary-gonadal axis with menopause and andropause promotes neurodegenerative senescence. J. Neuropathol. Exp. Neurol. 2005, 64, 93–103. [Google Scholar] [CrossRef] [PubMed]
  42. Meethal, S.V.; Smith, M.A.; Bowen, R.L.; Atwood, C.S. The gonadotropin connection in Alzheimer’s disease. Endocrine 2005, 26, 317–325. [Google Scholar] [CrossRef]
  43. Pedrós, I.; Petrov, D.; Artiach, G.; Abad, S.; Ramon-Duaso, C.; Sureda, F.; Pallàs, M.; Beas-Zarate, C.; Folch, J.; Camins, A. Adipokine pathways are altered in hippocampus of an experimental mouse model of Alzheimer’s disease. J. Nutr. Health Aging 2015, 19, 403–412. [Google Scholar] [CrossRef] [PubMed]
  44. Dye, R.V.; Miller, K.J.; Singer, E.J.; Levine, A.J. Hormone Replacement Therapy and Risk for Neurodegenerative Diseases. Int. J. Alzheimers Dis. 2012, 2012, 258454. [Google Scholar] [CrossRef] [PubMed]
  45. Long, J.; He, P.; Shen, Y.; Li, R. New evidence of mitochondria dysfunction in the female Alzheimer’s brain: Deficiency of estrogen receptor-β. J. Alzheimers Dis. 2012, 30, 545–558. [Google Scholar] [PubMed]
  46. Jackson, H.M.; Soto, I.; Graham, L.C.; Carter, G.W.; Howell, G.R. Clustering of transcriptional profiles identifies changes to insulin signaling as an early event in a mouse model of Alzheimer’s disease. BMC Genom. 2013, 14, 831. [Google Scholar] [CrossRef] [PubMed]
  47. Burbach, J.P.; Voorhuis, T.A.; van Tol, H.H.; Ivell, R. In situ hybridization of oxytocin messenger RNA: Macroscopic distribution and quantitation in rat hypothalamic cell groups. Biochem. Biophys. Res. Commun. 1987, 145, 10–14. [Google Scholar] [CrossRef]
  48. Hollowell, J.G.; Staehling, N.W.; Flanders, W.D.; Hannon, W.H.; Gunter, E.W.; Spencer, C.A.; Braverman, L.E. Serum TSH, T(4), and thyroid antibodies in the United States population (1988 to 1994): National Health and Nutrition Examination Survey (NHANES III). J. Clin. Endocr. Metab. 2002, 87, 489–499. [Google Scholar] [CrossRef] [PubMed]
  49. Riederer, P.; Bartl, J.; Laux, G.; Grünblatt, E. Diabetes type II: A risk factor for depression-Parkinson-Alzheimer? Neurotox. Res. 2011, 19, 253–265. [Google Scholar] [CrossRef] [PubMed]
  50. Huang, S.M.; Mouri, A.; Kokubo, H.; Nakajima, R.; Suemoto, T.; Higuchi, M.; Staufenbiel, M.; Noda, Y.; Yamaguchi, H.; Nabeshima, T. Neprilysin-sensitive synapse-associated amyloid-beta peptide oligomers impair neuronal plasticity and cognitive function. J. Biol. Chem. 2006, 281, 17941–17951. [Google Scholar] [CrossRef] [PubMed]
  51. Zhao, L.; Yao, J.; Mao, Z.; Chen, S.; Wang, Y.; Brinton, R.D. 17β-Estradiol regulates insulin-degrading enzyme expression via an ERβ/PI3-K pathway in hippocampus: Relevance to Alzheimer’s prevention. Neurobiol. Aging 2011, 32, 1949–1963. [Google Scholar] [CrossRef] [PubMed]
  52. Wang, L.; Andersson, S.; Warner, M.; Gustafsson, J.A. Morphological abnormalities in the brains of estrogen receptor beta knockout mice. Proc. Natl. Acad. Sci. USA 2001, 98, 2792–2796. [Google Scholar] [CrossRef] [PubMed]
  53. Waters, E.M.; Yildirim, M.; Janssen, W.G.M.; Lou, W.Y.W.; Mcewen, B.S.; Morrison, J.H.; Milner, T.A. Estrogen and aging affect the synaptic distribution of estrogen receptor α-immunoreactivity in the CA1 region of female rat hippocampus. Brain Res. 2011, 1379, 86–97. [Google Scholar] [CrossRef] [PubMed]
  54. Zhao, L.; Mao, Z.; Schneider, L.S.; Brinton, R.D. Estrogen receptor β-selective phytoestrogenic formulation prevents physical and neurological changes in a preclinical model of human menopause. Menopause 2011, 18, 1131–1142. [Google Scholar] [CrossRef] [PubMed]
  55. Kalgutkar, A.S.; Dalvie, D.K., Jr.; Castagnoli, N.; Taylor, T.J. Interactions of nitrogen-containing xenobiotics with monoamine oxidase (MAO) isozymes A and B: SAR studies on MAO substrates and inhibitors. Chem. Res. Toxicol. 2001, 14, 1139–1162. [Google Scholar] [CrossRef] [PubMed]
  56. Arai, T.; Miklossy, J.; Klegeris, A.; Guo, J.P.; Mcgeer, P.L. Thrombin and prothrombin are expressed by neurons and glial cells and accumulate in neurofibrillary tangles in Alzheimer disease brain. J. Neuropathol. Exp. Neurol. 2006, 65, 19–25. [Google Scholar] [CrossRef] [PubMed]
  57. Era, S.; Kuwata, K.; Imai, H.; Nakamura, K.; Hayashi, T.; Sogami, M. Age-related change in redox state of human serum albumin. Biochim. Biophys. Acta 1995, 1247, 12–16. [Google Scholar] [CrossRef]
  58. Guerin-Dubourg, A.; Catan, A.; Bourdon, E.; Rondeau, P. Structural modifications of human albumin in diabetes. Diabetes Metab. 2012, 38, 171–178. [Google Scholar] [CrossRef] [PubMed]
  59. Johansson, P.; Almqvist, E.G.; Johansson, J.O.; Mattsson, N.; Hansson, O.; Wallin, A.; Blennow, K.; Zetterberg, H.; Svensson, J. Reduced cerebrospinal fluid level of thyroxine in patients with Alzheimer’s disease. Psychoneuroendocrinology 2013, 38, 1058–1066. [Google Scholar] [CrossRef] [PubMed]
  60. Ray, I.; Chauhan, A.; Wegiel, J.; Chauhan, V. Gelsolin inhibits the fibrillization of amyloid beta-protein, and also defibrillizes its preformed fibrils. Brain Res. 2000, 853, 344–351. [Google Scholar] [CrossRef]
  61. Hirko, A.C.; Meyer, E.M.; King, M.A.; Hughes, J.A. Peripheral Transgene Expression of Plasma Gelsolin Reduces Amyloid in Transgenic Mouse Models of Alzheimer’s Disease. Mol. Ther. J. Am. Soc. Gene Ther. 2007, 15, 1623–1629. [Google Scholar] [CrossRef] [PubMed]
Nutrients 10 00589 g001 550
Figure 1. Methodology roadmap and summarized results for study.
Figure 1. Methodology roadmap and summarized results for study.
Nutrients 10 00589 g001
Nutrients 10 00589 g002 550
Figure 2. The 2D molecular structures, names, and codes of the 25 candidate compounds.
Figure 2. The 2D molecular structures, names, and codes of the 25 candidate compounds.
Nutrients 10 00589 g002
Nutrients 10 00589 g003 550
Figure 3. Illustration of the Compound–Target (CT) network. (a) Orange circles represent candidate compounds that are grouped together by structural category. The blue square represents the putative target proteins that were directly associated with candidate compounds. (b) The distribution of 97 putative target proteins among different compound categories, as shown in the Venn diagram.
Figure 3. Illustration of the Compound–Target (CT) network. (a) Orange circles represent candidate compounds that are grouped together by structural category. The blue square represents the putative target proteins that were directly associated with candidate compounds. (b) The distribution of 97 putative target proteins among different compound categories, as shown in the Venn diagram.
Nutrients 10 00589 g003
Nutrients 10 00589 g004 550
Figure 4. GO enrichment scatter plot for the top 30 putative target proteins. Only GO terms with a p-value ≤ 0.05 are shown in panel (a), and terms with a p-value ≤ 0.001 are shown in panel (b).
Figure 4. GO enrichment scatter plot for the top 30 putative target proteins. Only GO terms with a p-value ≤ 0.05 are shown in panel (a), and terms with a p-value ≤ 0.001 are shown in panel (b).
Nutrients 10 00589 g004
Nutrients 10 00589 g005 550
Figure 5. KEGG pathway enrichment bubble diagram for the top 30 putative target proteins (p-value ≤ 0.05).
Figure 5. KEGG pathway enrichment bubble diagram for the top 30 putative target proteins (p-value ≤ 0.05).
Nutrients 10 00589 g005
Nutrients 10 00589 g006 550
Figure 6. Illustration of the Compound-Target-Disease network. Rounded squares in different colors represent different groups of compounds; the code is shown on each rounded square. Red triangles represent putative target proteins, and triangles shown in darker colors and larger sizes represent greater associations with AD. All candidate compounds and putative target proteins are shown in panel (a). The top 10 putative target proteins and the candidate compounds directly related to them are highlighted based on the relevance score; other target proteins are grayed out in panel (b).
Figure 6. Illustration of the Compound-Target-Disease network. Rounded squares in different colors represent different groups of compounds; the code is shown on each rounded square. Red triangles represent putative target proteins, and triangles shown in darker colors and larger sizes represent greater associations with AD. All candidate compounds and putative target proteins are shown in panel (a). The top 10 putative target proteins and the candidate compounds directly related to them are highlighted based on the relevance score; other target proteins are grayed out in panel (b).
Nutrients 10 00589 g006
Nutrients 10 00589 g007 550
Figure 7. Illustration of the Compound-Group-Target-Pathway (CGTP) network. Diamonds in different colors represent different compound groups. Gray circles represent enriched KEGG pathway terms, and the size of each circle represents the number of genes that have been enriched. White rounded rectangles represent the top 30 putative target proteins. The wide pink bands represent the protein-protein interactions.
Figure 7. Illustration of the Compound-Group-Target-Pathway (CGTP) network. Diamonds in different colors represent different compound groups. Gray circles represent enriched KEGG pathway terms, and the size of each circle represents the number of genes that have been enriched. White rounded rectangles represent the top 30 putative target proteins. The wide pink bands represent the protein-protein interactions.
Nutrients 10 00589 g007
Nutrients 10 00589 g008 550
Figure 8. Schematic representation of the interaction effects of enriched KEGG pathway terms and putative target proteins identified in the present study. Green rectangles represent putative target proteins identified in this study. Carmine characters represent enriched KEGG pathway terms. The left panel shows potential MOAs that are directly related to Aβ synthesis, transport, degradation, and clearance. The right panel shows MOAs that are indirectly linked or irrelevant to Aβ.
Figure 8. Schematic representation of the interaction effects of enriched KEGG pathway terms and putative target proteins identified in the present study. Green rectangles represent putative target proteins identified in this study. Carmine characters represent enriched KEGG pathway terms. The left panel shows potential MOAs that are directly related to Aβ synthesis, transport, degradation, and clearance. The right panel shows MOAs that are indirectly linked or irrelevant to Aβ.
Nutrients 10 00589 g008
Table
Table 1. The classification results of Biological Process terms.
Table 1. The classification results of Biological Process terms.
CategoryBiological Process Termsp-ValueBonferroniBenjaminiFDR
signal transductionGO:0007165~signal transduction0.00010.06110.06110.1472
GO:0043406~positive regulation of MAP kinase activity0.00010.08600.04390.2099
GO:0007265~Ras protein signal transduction0.00020.13880.04860.3487
GO:0010628~positive regulation of gene expression0.00100.47530.07741.4961
GO:0045944~positive regulation of transcription from RNA polymerase II promoter0.02481.00000.384531.2264
GO:0006367~transcription initiation from RNA polymerase II promoter0.00220.75970.10393.2779
GO:0018105~peptidyl-serine phosphorylation0.01961.00000.342825.4995
GO:0018108~peptidyl-tyrosine phosphorylation0.00230.76600.09863.3386
synthesis and metabolismGO:0043651~linoleic acid metabolic process0.00040.21770.04790.5724
GO:1901687~glutathione derivative biosynthetic process0.00070.33960.06680.9652
GO:0006749~glutathione metabolic process0.00420.93090.16326.0551
hormone relatedGO:0043401~steroid hormone mediated signaling pathway0.00430.93710.15886.2622
GO:0030520~intracellular estrogen receptor signaling pathway0.03571.00000.464941.7726
protein metabolicGO:0006508~proteolysis0.01020.99860.257314.1863
GO:0050435~beta-amyloid metabolic process0.02221.00000.369928.4455
GO:0044267~cellular protein metabolic process0.01761.00000.341723.1933
inflammatory cascade reactionGO:0050900~leukocyte migration0.00120.53190.07311.7588
GO:0048010~vascular endothelial growth factor receptor signaling pathway0.00680.98730.20539.7017
GO:0010544~negative regulation of platelet activation0.01370.99990.318218.6122
GO:0071222~cellular response to lipopolysaccharide0.01621.00000.329621.5741
GO:0051024~positive regulation of immunoglobulin secretion0.01881.00000.341524.6631
cell apoptosisGO:0098869~cellular oxidant detoxification0.00020.13880.04860.3487
GO:0071732~cellular response to nitric oxide0.00030.15200.04040.3845
GO:0038128~ERBB2 signaling pathway0.00190.71120.09832.8617
GO:2001237~negative regulation of extrinsic apoptotic signaling pathway0.00190.71120.09832.8617
GO:0050999~regulation of nitric-oxide synthase activity0.04401.00000.478548.8128
GO:0043410~positive regulation of MAPK cascade0.00860.99590.239912.0357
Table
Table 2. The classification results of KEGG pathway enrichment analysis.
Table 2. The classification results of KEGG pathway enrichment analysis.
CategoryKEGG Pathway Termsp-ValueBonferroniBenjaminiFDR
inflammation-generating processhsa04370: VEGF signaling pathway0.00010.00830.00170.0712
hsa04664: Fc epsilon RI signaling pathway0.00180.21960.02052.0929
hsa04660: T cell receptor signaling pathway0.00580.55370.05246.6494
hsa04668: TNF signaling pathway0.00630.58270.05327.1833
hsa04015: Rap1 signaling pathway0.00630.58480.05047.2230
hsa04621: NOD-like receptor signaling pathway0.01660.90070.085017.8802
hsa04750: Inflammatory mediator regulation of TRP channels0.04830.99890.157144.1896
apoptosishsa04151: PI3K-Akt signaling pathway0.00120.14920.01601.3682
hsa04068: FoxO signaling pathway0.00120.15810.01551.4573
hsa04010: MAPK signaling pathway0.01240.82210.075513.6915
hsa00480: Glutathione metabolism0.01440.86450.076815.6705
hsa05205: Proteoglycans in cancer0.00060.08460.01100.7506
hsa05200: Pathways in cancer0.00230.27100.02402.6595
hsa04014: Ras signaling pathway0.04680.99870.155943.0804
hsa04012: ErbB signaling pathway0.00360.39450.03524.1891
hsa05204: Chemical carcinogenesis0.03340.99080.125532.9823
hormone synthesis and transporthsa04917: Prolactin signaling pathway0.00000.00060.00060.0053
hsa04915: Estrogen signaling pathway0.00000.00320.00080.0272
hsa04912: GnRH signaling pathway0.00030.03900.00570.3386
hsa04910: Insulin signaling pathway0.01300.83560.075514.2708
hsa04921: Oxytocin signaling pathway0.01870.92560.091819.8791
hsa04919: Thyroid hormone signaling pathway0.00770.65690.05478.7191
hsa04722: Neurotrophin signaling pathway0.00890.70840.05989.9765
hsa04071: Sphingolipid signaling pathway0.00080.10770.01260.9668
hsa04921: Oxytocin signaling pathway0.01870.92560.091819.8791
drug metabolismhsa00982: Drug metabolism-cytochrome P4500.00180.21960.02052.0929
hsa00980: Metabolism of xenobiotics by cytochrome P4500.02900.98270.115629.2357
ontogeny processhsa04540: Gap junction0.03980.99630.137137.9955
hsa04810: Regulation of actin cytoskeleton0.00650.59100.04847.3404
hsa04520: Adherens junction0.02680.97650.110627.3786
Table
Table 3. The mechanisms of 6 putative protein targets related to AD and the current status of research on G. biloba.
Table 3. The mechanisms of 6 putative protein targets related to AD and the current status of research on G. biloba.
Protein TargetsAD-Related Mechanisms/EtiologyDocking CompoundsResearch Related to G. biloba for Anti-AD
Neprilysin (NEP)(1) Aβ degradation enzymes [28,29](+)-Catechin
Diosmetin
Genkwanin		No research
(2) maintain blood–brain barrier (BBB) integrity
(3) participate in neuroinflammation [50]
Estrogen receptor (ESR)(1) upregulated insulin-degrading enzyme (IDE) [51]GenkwaninNo research
(2) maintaining steroid homeostasis [52]
(3) altering synaptic plasticity [53,54]
(4) participate in neurons oxidative stress-mediated injury [55]
Prothrombin (F2)(1) coagulation cascade and endothelial cell integrity [38]Ginkgolide JNo research
(2) ideal molecular-biological indicator for AD [56]
(3) proteolyzes the microtubule-associated protein tau [56]
(4) inhibits phosphorylation of tau [56]
Serum albumin (ALB)(1) bounded and transported Aβ, maintaining a constant concentration level in the brain [39]Ethyl oleate
Flavoxanthin		No research
(2) Aβ excretion from the brain to the blood [57,58]
Thyroid hormone (TTR)(1) up-regulation of expression of neuroserpin in neurons [32]Beta-sitosterol
Stigmasterol
Mandenol
Ethyl oleate
Flavoxanthin		No research
(2) hyperthyroidism increases the risk of AD [59]
Matrix metalloproteinase 3 (MMP3)(1) main Plasma gelsolin (GSN)-degrading enzyme [40]Ginkgolide B
Ginkgolide J		No research
(2) inhibits the fibrillation of the Aβ [60]
(3) a diagnostic biomarker for AD [61]

Share and Cite

MDPI and ACS Style

Li, H.; Sun, X.; Yu, F.; Xu, L.; Miu, J.; Xiao, P. In Silico Investigation of the Pharmacological Mechanisms of Beneficial Effects of Ginkgo biloba L. on Alzheimer’s Disease. Nutrients 2018, 10, 589. https://doi.org/10.3390/nu10050589

AMA Style

Li H, Sun X, Yu F, Xu L, Miu J, Xiao P. In Silico Investigation of the Pharmacological Mechanisms of Beneficial Effects of Ginkgo biloba L. on Alzheimer’s Disease. Nutrients. 2018; 10(5):589. https://doi.org/10.3390/nu10050589

Chicago/Turabian Style

Li, Hongxiang, Xiaoyuan Sun, Fan Yu, Lijia Xu, Jianhua Miu, and Peigen Xiao. 2018. "In Silico Investigation of the Pharmacological Mechanisms of Beneficial Effects of Ginkgo biloba L. on Alzheimer’s Disease" Nutrients 10, no. 5: 589. https://doi.org/10.3390/nu10050589

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop