A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease
Abstract
:1. Introduction
2. Results
2.1. The In-House Library of Uncaria Alkaloids and Drug-Likeness Screening
2.2. Putative Target Prediction for the Candidate Targets of Uncaria Alkaloids
2.3. Identification of Approved Druggable Targets in AD and Hypertension
2.4. Pathways in Compound-Target Network
2.5. Network Construction, Topological Analysis, and GO/KEGG Function and Pathway Analysis
2.6. Pathways in Target-Disease Network
2.7. Pathways in Target-Pathway Network
2.8. Integrative Network Analysis and Target Selection
2.9. Molecular Docking and Biological Validation
3. Discussion
4. Materials and Methods
4.1. Compound Library Construction and Drug-Likeness Screening
4.2. Prediction of Uncaria Alkaloid Targets and Their Overall Biological Functions
4.3. Identification of Druggable Alzheimer’s Disease (AD) and Hypertension (HTN) Associated Disease Targets
4.4. Network Construction, Topological Analysis and GO/KEGG Function Analysis
4.5. Molecular Docking
4.6. In Vitro Butyrylcholinesterase (BChE) Enzyme Activity Assays
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AD | Alzheimer’s disease |
BChE | butyrylcholinesterase |
HTN | hypertension |
PPI | protein–protein interaction |
TCM | Traditional Chinese Medicine |
References
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# | Gene Symbol | Degree | Subgraph | Eigenvector | Information | LAC | Betweenness | Closeness | Network |
---|---|---|---|---|---|---|---|---|---|
1 | MAOA | 16 | 1850.135 | 0.356 | 5.360 | 6.375 | 81.651 | 0.759 | 13.078 |
2 | ACHE | 14 | 1413.283 | 0.311 | 5.162 | 5.143 | 63.508 | 0.710 | 9.577 |
3 | BCHE | 13 | 1311.436 | 0.300 | 5.048 | 4.923 | 38.836 | 0.688 | 8.028 |
4 | DRD2 | 12 | 1230.716 | 0.290 | 4.924 | 6.333 | 35.935 | 0.667 | 9.991 |
5 | HTR1A | 11 | 1166.267 | 0.282 | 4.786 | 6.545 | 20.340 | 0.629 | 9.071 |
6 | HTR2A | 10 | 623.717 | 0.203 | 4.632 | 4.800 | 24.033 | 0.595 | 7.526 |
7 | ADRA2B | 9 | 868.194 | 0.242 | 4.461 | 6.444 | 7.798 | 0.595 | 8.000 |
8 | ADRA2C | 9 | 868.194 | 0.242 | 4.461 | 6.444 | 7.798 | 0.595 | 8.000 |
9 | ADRA2A | 9 | 868.194 | 0.242 | 4.461 | 6.444 | 7.798 | 0.595 | 8.000 |
10 | CYP3A4 | 9 | 688.133 | 0.216 | 4.461 | 4.889 | 10.826 | 0.629 | 6.405 |
11 | ALB | 8 | 406.076 | 0.165 | 4.268 | 2.750 | 47.899 | 0.611 | 3.429 |
12 | CYP2D6 | 8 | 606.406 | 0.203 | 4.268 | 4.500 | 9.607 | 0.611 | 5.238 |
13 | ADRA1D | 7 | 428.160 | 0.169 | 4.049 | 4.857 | 1.567 | 0.537 | 6.000 |
14 | ADRA1B | 7 | 428.160 | 0.169 | 4.049 | 4.857 | 1.567 | 0.537 | 6.000 |
15 | ADRA1A | 7 | 428.160 | 0.169 | 4.049 | 4.857 | 1.567 | 0.537 | 6.000 |
16 | CYP2C19 | 7 | 271.666 | 0.133 | 4.049 | 3.429 | 39.552 | 0.564 | 4.333 |
17 | CHRM2 | 6 | 348.284 | 0.151 | 3.798 | 5.000 | 0.000 | 0.458 | 6.000 |
18 | CHRM4 | 6 | 348.284 | 0.151 | 3.798 | 5.000 | 0.000 | 0.458 | 6.000 |
19 | CHRM5 | 5 | 178.487 | 0.107 | 3.508 | 4.000 | 0.000 | 0.512 | 5.000 |
20 | CYP1A2 | 5 | 193.747 | 0.112 | 3.508 | 3.200 | 1.269 | 0.537 | 4.000 |
21 | DRD1 | 4 | 252.070 | 0.131 | 3.170 | 2.500 | 0.167 | 0.524 | 3.333 |
22 | PPARG | 2 | 7.872 | 0.019 | 2.287 | 0.000 | 2.700 | 0.400 | 0.000 |
23 | PTGS1 | 2 | 6.536 | 0.016 | 2.287 | 0.000 | 1.583 | 0.379 | 0.000 |
Ligand | Total Scores |
---|---|
3F9_A | 5.524 |
3F9_B | 4.6765 |
Corynoxine B | 5.7701 |
Corynoxeine (C) | 5.4715 |
Corynoxine | 5.58 |
3-dihydrocadambine (DHC) | 5.0741 |
Geissoschiziner (GSM) | 5.886 |
Hirsuteine (HTE) | 6.7146 |
Hirsutine (HTI) | 6.4769 |
Isocorynoxeine (IC) | 6.196 |
Isorhynchophylline (IR) | 6.1038 |
isomitraphylline | 4.3782 |
Rhynchophylline (R) | 4.8015 |
Uncarine B | 4.8631 |
Vincosamide (VCA) | 5.9499 |
Galantamine hydrobromide | 4.8609 |
Drofenine hydrochloride | 9.7639 |
Conc. (μM) | Inhibition Rate (%) | ||||
---|---|---|---|---|---|
Hirsutine (HTI) | Hirsuteine (HTE) | Geissoschiziner (GSM) | Corynoxeine | Isocorynoxeine | |
50 | 50.71 ± 1.51 | 21.23 ± 0.74 | 0.07 ± 0.99 | - | - |
100 | 65.83 ± 2.49 | 36.03 ± 0.67 | 7.34 ± 0.66 | - | - |
200 | 71.69 ± 0.18 | 54.44 ± 0.92 | 22.24 ± 1.32 | (−1.86) ± 1.65 | (−1.86) ± 0.55 |
300 | 73.28 ± 0.67 | 60.51 ± 0.74 | n.a. | 1.76 ± 0.98 | 3.09 ± 1.58 |
400 | 73.60 ± 0.67 | 64.13 ± 0.18 | n.a. | 16.82 ± 3.39 | 15.17 ± 3.50 |
500 | 73.17 ± 1.15 | 65.19 ± 0.96 | 39.83 ± 1.52 | 19.43 ± 3.21 | 21.77 ± 0.90 |
IC50 | 67.97 | 202.3 | n.a. | n.a. | n.a. |
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Wu, W.; Zhang, Z.; Li, F.; Deng, Y.; Lei, M.; Long, H.; Hou, J.; Wu, W. A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease. Int. J. Mol. Sci. 2020, 21, 1766. https://doi.org/10.3390/ijms21051766
Wu W, Zhang Z, Li F, Deng Y, Lei M, Long H, Hou J, Wu W. A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease. International Journal of Molecular Sciences. 2020; 21(5):1766. https://doi.org/10.3390/ijms21051766
Chicago/Turabian StyleWu, Wenyong, Zijia Zhang, Feifei Li, Yanping Deng, Min Lei, Huali Long, Jinjun Hou, and Wanying Wu. 2020. "A Network-Based Approach to Explore the Mechanisms of Uncaria Alkaloids in Treating Hypertension and Alleviating Alzheimer’s Disease" International Journal of Molecular Sciences 21, no. 5: 1766. https://doi.org/10.3390/ijms21051766