Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
PreprintCited by 11Computational Drug Discovery MethodsFungal Biology and ApplicationsPhytochemistry and Biological Activities

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Effect of Poria cocos Terpenes: Verifying Modes of Action using Molecular Docking, Drug-Induced Transcriptomes, and Diffusion Network

Open access
Park, Musun, Lee, Seo-Young, Lee, Haeseung, Yi, Jin-Mu·2023·International Journal of Molecular Sciences
DOI 10.22541/au.169649175.59025957/v1PMC PMC11083729

Preprint — published as 10.3390/ijms25094636 · Paper page

Open this paper in CoChat — free accountOpen PDF

Abstract

Background and Purpose: The presence of unique terpenes in Poria cocos (PC) enables it to treat various diseases. However, the modes of action (MOA) of PC have not been investigated. Hence, the present study was conducted to address this research gap via molecular docking and drug-induced transcriptome analyses. Experimental Approach: For molecular docking analysis, data on PC terpenes and druggable proteomes were collected from public databases. Transcriptome data generated using intestinal and brain cell lines were also collected. We compared the results of docking analysis and transcriptome analysis and constructed a diffusion network (DN) to bridge the gap between the results of the two analyses. The DN analysis was also proposed to confirm its MOA against Alzheimer’s disease (AD). Key Results: The docking and transcriptome analysis of PC allowed for the prediction of its existing clinical efficacy. Differences between the docking and the transcriptome analysis were also confirmed; however, the results of both analyses were complementary. Finally, the DN was constructed for AD, and the submodules of AD with which terpenes interacted were identified. The AD DN analysis confirmed the detailed pharmacological mechanism of PC. Conclusion and Implications: This study identified the MOA of PC using biological data and in silico methods. The method proposed herein can help identify the pharmacological MOA of complex natural products.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.22541/au.169649175.59025957/v1

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 ORA results using lanostane terpenes and seco-lanostane terpenes. ORA was performed on the EnrichR platform using proteins that specifically bind to lanostane and seco-lanostane terpenes, which are unique terpenes of PC. The lightest color has a higher combined score, and as the color becomes darker, the combined score decreases. Gray are terms that did not pass the significance test. (A) ORA results using the KEGG gene set. (B) ORA results using the GO biological process gene set. (C) ORA results using the OMIM disease gene set. ORA, Over-representation analysis. PC, Poria cocos F.A. Wolf; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; OMIM, Online Mendelian Inheritance in Man.
Figure 1 ORA results using lanostane terpenes and seco-lanostane terpenes. ORA was performed on the EnrichR platform using proteins that specifically bind to lanostane and seco-lanostane terpenes, which are unique terpenes of PC. The lightest color has a higher combined score, and as the color becomes darker, the combined score decreases. Gray are terms that did not pass the significance test. (A) ORA results using the KEGG gene set. (B) ORA results using the GO biological process gene set. (C) ORA results using the OMIM disease gene set. ORA, Over-representation analysis. PC, Poria cocos F.A. Wolf; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; OMIM, Online Mendelian Inheritance in Man.CC BY · PubMed Central
Figure 2 GSEA results using the SW1783 cell line–based PC-induced transcriptomes. GSEA was performed using the KEGG pathway gene set. Poria Cocos_High, Poria Cocos_Middle, and Poria Cocos_Low are the results of analyzing the gene expression values obtained by treating the PC water extract with high (500 µg/mL), medium (100 µg/mL), and low (20 µg/mL) concentrations, respectively. Wortmannin, LY294002, and thioridazine are positive controls. Wortmannin and LY294002 are known to inhibit inflammation by acting on the cell cycle, and thioridazine is a known dopaminergic receptor antagonist. GSEA, gene set enrichment analysis. PC, Poria cocos F.A. Wolf; KEGG, Kyoto Encyclopedia of Genes and Genomes. * p < 0.05, ** p < 0.01.
Figure 2 GSEA results using the SW1783 cell line–based PC-induced transcriptomes. GSEA was performed using the KEGG pathway gene set. Poria Cocos_High, Poria Cocos_Middle, and Poria Cocos_Low are the results of analyzing the gene expression values obtained by treating the PC water extract with high (500 µg/mL), medium (100 µg/mL), and low (20 µg/mL) concentrations, respectively. Wortmannin, LY294002, and thioridazine are positive controls. Wortmannin and LY294002 are known to inhibit inflammation by acting on the cell cycle, and thioridazine is a known dopaminergic receptor antagonist. GSEA, gene set enrichment analysis. PC, Poria cocos F.A. Wolf; KEGG, Kyoto Encyclopedia of Genes and Genomes. * p < 0.05, ** p < 0.01.CC BY · PubMed Central
Figure 3 MOA analysis of PC using AD–related DN. (A) DN associated with AD. In layer 1, terms related to AD were selected from the results of MDA, and in layer 5, DEGs were selected and a DN was built using them. Orange circles indicate proteins included in the KEGG AD gene set. (B) MOA analysis result of PC using AD–related DN. Each layer was rebuilt using only the orange circles in the network above. Layers 2 and 3 were grouped so that it was possible to identify which protein signal transduction was initiated in layer 1. The AD submodule that each group specifically acts on was confirmed using KEGG Mapper. MOA, mode of action; PC, Poria cocos F.A. Wolf; AD, Alzheimer’s disease; DN, diffusion network; MDA, molecular docking analysis, DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Figure 3 MOA analysis of PC using AD–related DN. (A) DN associated with AD. In layer 1, terms related to AD were selected from the results of MDA, and in layer 5, DEGs were selected and a DN was built using them. Orange circles indicate proteins included in the KEGG AD gene set. (B) MOA analysis result of PC using AD–related DN. Each layer was rebuilt using only the orange circles in the network above. Layers 2 and 3 were grouped so that it was possible to identify which protein signal transduction was initiated in layer 1. The AD submodule that each group specifically acts on was confirmed using KEGG Mapper. MOA, mode of action; PC, Poria cocos F.A. Wolf; AD, Alzheimer’s disease; DN, diffusion network; MDA, molecular docking analysis, DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.CC BY · PubMed Central
Figure 4 Antioxidant activity of PC. (A) Pretreatment of water (WEPC) and 70% ethanol (EEPC) extracts of PC inhibits LPS–mediated ROS production in the BV2 microglial cells. Intracellular ROS was monitored by DCF–DA staining (left, scale bar 400 μm) and their fluorescence intensities were quantified (right). Black bars, without LPS treatment; gray bars, with LPS treatment. (B) Changes in Nos2 (left) and Hmox1 (right) mRNA levels in BV2 microglial cells by PC in the absence (black bars) and the presence (gray bars) of LPS stimulation were determined by qPCR. (C) Activation of Nrf2/Hmox1 antioxidant signaling pathway by PC in the absence and the presence of LPS stimulation was observed by Western blotting (top). Expression of Nrf2 (left) and Hmox1 (right) proteins were quantified by image analysis. NAC was used as a positive control with antioxidant activity. β–actin was included as a loading control. Data are presented as means ± SD (n = 3 except for figure (A), n = 6). # p < 0.05, ## p < 0.01, ### p < 0.001 vs. vehicle-treated control without LPS treatment. * p < 0.05, ** p < 0.01, *** p < 0.001 vs. vehicle-treated control with LPS treatment.
Figure 4 Antioxidant activity of PC. (A) Pretreatment of water (WEPC) and 70% ethanol (EEPC) extracts of PC inhibits LPS–mediated ROS production in the BV2 microglial cells. Intracellular ROS was monitored by DCF–DA staining (left, scale bar 400 μm) and their fluorescence intensities were quantified (right). Black bars, without LPS treatment; gray bars, with LPS treatment. (B) Changes in Nos2 (left) and Hmox1 (right) mRNA levels in BV2 microglial cells by PC in the absence (black bars) and the presence (gray bars) of LPS stimulation were determined by qPCR. (C) Activation of Nrf2/Hmox1 antioxidant signaling pathway by PC in the absence and the presence of LPS stimulation was observed by Western blotting (top). Expression of Nrf2 (left) and Hmox1 (right) proteins were quantified by image analysis. NAC was used as a positive control with antioxidant activity. β–actin was included as a loading control. Data are presented as means ± SD (n = 3 except for figure (A), n = 6). # p < 0.05, ## p < 0.01, ### p < 0.001 vs. vehicle-treated control without LPS treatment. * p < 0.05, ** p < 0.01, *** p < 0.001 vs. vehicle-treated control with LPS treatment.CC BY · PubMed Central
Figure 5 Method for constructing the DN. The DN was constructed to integrate results from MDA and DTA. (A) DN construction method. A new layer was assigned to the protein derived from the MDA results and the interacting proteins, and the process of connecting the previous layer and the new layer was repeated three times to add a total of three layers. In the fourth layer, the network was completed by adding connections that interact with genes derived from DTA. Numbers (1) through (4) indicate the order in which DNs are constructed. (B) DN modification method. In the fourth layer, nodes that did not have a connection with the last layer were deleted. In this manner, the network was completed by sequentially deleting nodes using the backpropagation method up to the second layer. Numbers (1) through (4) indicate the order in which DNs are constructed. Nodes marked in yellow indicate nodes that will be removed in the next sequence. DN, diffusion network; MDA, molecular docking analysis; DTA, drug-induced transcriptome analysis.
Figure 5 Method for constructing the DN. The DN was constructed to integrate results from MDA and DTA. (A) DN construction method. A new layer was assigned to the protein derived from the MDA results and the interacting proteins, and the process of connecting the previous layer and the new layer was repeated three times to add a total of three layers. In the fourth layer, the network was completed by adding connections that interact with genes derived from DTA. Numbers (1) through (4) indicate the order in which DNs are constructed. (B) DN modification method. In the fourth layer, nodes that did not have a connection with the last layer were deleted. In this manner, the network was completed by sequentially deleting nodes using the backpropagation method up to the second layer. Numbers (1) through (4) indicate the order in which DNs are constructed. Nodes marked in yellow indicate nodes that will be removed in the next sequence. DN, diffusion network; MDA, molecular docking analysis; DTA, drug-induced transcriptome analysis.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Metadata

Authors
Park, M., Lee, S.-Y., Lee, H., & Yi, J.-M.
Affiliations
  • Korea Institute of Oriental Medicine (KR)
  • Korea Institute of Oriental Medicine
  • Pusan National University (KR)
Corresponding author
Park, Musun
ORCID
Venue
International Journal of Molecular Sciences
Publisher
Wiley
Published
2023-10-05
Volume
25
Issue
9
Pages
4636-4636
ISSN
1422-0067 (ISSN-L)
DOI
10.22541/au.169649175.59025957/v1
PMC
PMC11083729
OpenAlex
W4395082416
Cited by
11
References
1
Citations by year
2026: 4 · 2025: 5 · 2024: 2
FWCI
2.33
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • International Journal of Molecular Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Research Foundation
  • Korea Institute of Oriental Medicine (KSN1722122, KSN1731122, NSN2312040)
  • National Research Foundation of Korea (NSN2311040, NSN2312040)
  • Ministry of Science and ICT, South Korea
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Health Sciences › Medicine › Pharmacology (medical) › Fungal Biology and Applications
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Phytochemistry and Biological Activities
Keywords
Poria cocos, terpenes, Alzheimer's disease, microglial cells, antioxidant activity, molecular docking
MeSH terms
Wolfiporia, Alzheimer Disease, Animals, Antioxidants, Humans, Terpenes, Microglia, Computational Biology, Gene Expression Profiling, Transcriptome, Molecular Docking Simulation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.