Lim, Kweon, Kim, Lee, Leem, Kim, Kang, and Bae: Exploration of the Potential and Mechanisms of Diabetic Cognitive Disorder Modulation by Daehwangmokdanpi-tang through a Network Pharmacological Approach
Original Article
The Journal of Korean Medicine 2024; 45(2): 23-40.
Exploration of the Potential and Mechanisms of Diabetic Cognitive Disorder Modulation by Daehwangmokdanpi-tang through a Network Pharmacological Approach
1Department of Pharmacology, College of Korean Medicine, Wonkwang University
2Hanbang Cardio-Renal Syndrome Research Center, Wonkwang University
3Department of Korean Neuropsychiatry Medicine, College of Korean Medicine, Wonkwang University
4Korean Medicine-Cognitive Disorder Research Center, Wonkwang University
5Research center of Traditional Korean medicine, Wonkwang University
6Department of Diagnostics, College of Korean Medicine, Wonkwang University
7Department of Herbology, College of Korean Medicine, Dong-Eui University
Correspondence to: Dong-Gu Kim, Department of herbology, College of Korean Medicine, Dong-Eui University, 52-57, Yangjeong-ro, Busanjin-gu, Busan, 47227, South Korea, Tel: +82-51-890-3371, E-mail: kdg2409@deu.ac.kr
Correspondence to: Hyung Won Kang, Department of Korean Neuropsychiatry Medicine, College of Korean Medicine, Wonkwang University, 460 Iksandae-ro, Iksan, 54538 Jeonbuk, South Korea, Tel: +82-63-850-6831, E-mail: dskhw@wku.ac.kr
Correspondence to: Gi-Sang Bae, Department of Pharmacology, College of Korean Medicine, Wonkwang University, 460 Iksandae-ro, Iksan, 54538 Jeonbuk, South Korea, Tel: +82-63-850-6842, E-mail: baegs888@wku.ac.kr
§ These authors contributed equally to this work.
Received April 15, 2024 Revised May 7, 2024 Accepted May 7, 2024
Abstract
Objectives
This study utilized a network pharmacology approach to investigate the potential therapeutic effects and underlying mechanisms of Daehwangmokdanpi-tang (DHMDPT) in diabetic cognitive disorder (DCD).
Methods
The compounds of DHMDPT and their target genes were obtained from the OASIS and PubChem databases. These putative target genes were compared with known targets of DCD to identify potential correlations. Using Cytoscape 3.10.2, a network was constructed to highlight key target genes. To further elucidate the underlying mechanisms, functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Finally, CB-DOCK was used to assess binding affinities and confirm the interactions.
Results
The results showed that a total of 27 compounds and 439 related genes were identified from DHMDPT. Among these, 373 genes interacted with the DCD gene set, indicating a close relationship between the effects of DHMDPT and DCD. Through GO enrichment analysis and KEGG pathways, ‘Regulation of Apoptotic Process’, ‘Cytokine-Mediated signaling pathway’, and ‘AGE-RAGE signaling pathway in diabetic complications’ were identified as the functional pathways of the 18 key target genes of DHMDPT on DCD. Additionally, molecular docking was performed to assess the binding affinities of the six most highly associated key target genes of DCD with active compounds.
Conclusions
Using a network pharmacology approach, which included molecular docking, DHMDPT was found to be highly relevant to DCD. This study could serve as a foundation for further research on the cognitive enhancement effects of DHMDPT in DCD.
Venn diagram illustrating the intersection targets between the target genes of Daehwangmokdanpi-tang (DHMDPT) and the gene sets associated with diabetic cognitive disorder.
Fig. 2
Process of topological screening: (A) Network showing the intersection targets between the target genes of DHMDPT and the gene sets related to diabetic cognitive disorder. (B) and (C) represent the networks of the initial and final screenings of key targets, respectively.
Fig. 3
Biological processes, cellular components, and molecular functions related to the targets of DHMDPT were identified using the GO enrichment analysis database. GO terms were listed in order of significance based on their p-values.
Fig. 4
Biological processes related to the targets of DHMDPT were identified using the KEGG pathways database. (A) Pathways were listed in order of significance based on their p-values. (B) SRPLOT was used to illustrate the associations between target genes and KEGG pathways.
Fig. 5
The network illustrating the relationship between the tang-herb-compound-target-pathway of DHMDPT in the treatment of diabetic cognitive disorder. (Abbreviations: PS - Persicae Semen; MRC - Mountan Radicis Cortex; TS - Trichosanthis Semen; RR - Rhei Radix.)
Fig. 6
Results from molecular docking studies between the proteins encoded by the six key target genes and six selected compounds. These compounds were chosen based on their descending degree within the tang-herb -compound-target-pathway network of DHMDPT for the treatment of diabetic cognitive disorder.
Table 1
Physicochemical properties of the compounds in DHMDPT optimized for enhanced oral bioavailability
Positive Regulation Of Cellular Biosynthetic Process
GO:0031328
1.13392 * 10−12
HSP90AA1;IL6;IL1B;AKT1;CTNNB1;TNF;T LR4;INS
Positive Regulation Of Intracellular Signal Transduction
GO:1902533
5.1694 * 10−12
HSP90AA1;CD4;IL6;SRC;IL1B;TNF;TLR4;T P53;EGFR;INS
Positive Regulation Of Macromolecule Metabolic Process
GO:0010604
8.35353 * 10−12
IL6;MYC;IL1B;STAT3;AKT1;TNF;TLR4;TP 53;INS
Positive Regulation Of Nucleic Acid-Templated Transcription
GO:1903508
9.27673 * 10−12
CD4;IL6;MYC;IL1B;STAT3;AKT1;CTNNB1 ;TNF;TP53;EGFR
Positive Regulation Of Cellular Process
GO:0048522
1.74957 * 10−11
IL6;MYC;IL1B;BCL2;AKT1;CTNNB1;TNF; TLR4;EGFR;INS
Positive Regulation Of DNA-binding Transcription Factor Activity
GO:0051091
1.83931 * 10−11
IL6;IL1B;STAT3;AKT1;CTNNB1;TNF;TLR4 ;INS
Positive Regulation Of Nitric Oxide Biosynthetic Process
GO:0045429
2.56366 * 10−11
HSP90AA1;IL1B;AKT1;TNF;TLR4
Cytokine-Mediated Signaling Pathway
GO:0019221
2.61009 * 10−11
CD4;IL6;SRC;IL1B;STAT3;AKT1;TNF;TP53
Regulation Of Nitric-Oxide Synthase Activity
GO:0050999
3.1193 * 10−11
IL1B;AKT1;TNF;EGFR;INS
Positive Regulation Of Nitric Oxide Metabolic Process
GO:1904407
3.1193 * 10−11
HSP90AA1;IL1B;AKT1;TNF;TLR4
Transmembrane Receptor Protein Tyrosine Kinase Signaling Pathway
GO:0007169
5.79508 * 10−11
CD4;SRC;CASP3;STAT3;AKT1;MMP9;EGF R;INS
sTable 3
Analysis of KEGG pathways for the 18 key targets.
Pathway
p-value
Genes
AGE-RAGE signaling pathway in diabetic complications
1.92 * 10−12
IL6;CASP3;IL1B;STAT3;BCL2;AKT1;TNF
HIF-1 signaling pathway
3.56 * 10−12
IL6;STAT3;BCL2;AKT1;TLR4;EGFR;INS
PI3K-Akt signaling pathway
6.51 * 10−12
HSP90AA1;IL6;MYC;BCL2;AKT1;TLR4;TP53;EGFR;INS
MAPK signaling pathway
7.64 * 10−11
MYC;CASP3;IL1B;AKT1;TNF;TP53;EGFR;INS
IL-17 signaling pathway
1.63 * 10−10
HSP90AA1;IL6;CASP3;IL1B;TNF;MMP9
TNF signaling pathway
4.73 * 10−10
IL6;CASP3;IL1B;AKT1;TNF;MMP9
Estrogen signaling pathway
1.61 * 10−9
HSP90AA1;SRC;BCL2;AKT1;MMP9;EGFR
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