Medicinal value of asiaticoside for Alzheimer’s disease as assessed using single-molecule-detection fluorescence correlation spectroscopy, laser-scanning microscopy, transmission electron microscopy, and in silico docking
Open accessAbstract
BACKGROUND: Identifying agents that inhibit amyloid beta peptide (Aβ) aggregation is the ultimate goal for slowing Alzheimer's disease (AD) progression. This study investigated whether the glycoside asiaticoside inhibits Aβ1-42 fibrillation in vitro. METHODS: Fluorescence correlation spectroscopy (FCS), evaluating the Brownian diffusion times of moving particles in a small confocal volume at the single-molecule level, was used. If asiaticoside inhibits early Aβ1-42 fibrillation steps, more Aβs would remain free and rapidly diffuse in the confocal volume. In contrast, "weaker or no inhibition" permits a greater number of Aβs to polymerize into oligomers, leading to fibers and gives rise to slow diffusion times in the solution. Trace amounts of 5-carboxytetramethylrhodamine (TAMRA)-labeled Aβ1-42 in the presence of excess unlabeled Aβ1-42 (10 μM) was used as a fluorescent probe. Steady-state and kinetic-Thioflavin T (ThT) fluorospectroscopy, laser-scanning fluorescence microscopy (LSM), and transmission electron microscopy (TEM) were also used to monitor fibrillation. Binding of asiaticoside with Aβ1-42 at the atomic level was computationally examined using the Molegro Virtual Docker and PatchDock. RESULTS: With 1 h of incubation time for aggregation, FCS data analysis revealed that the diffusion time of TAMRA-Aβ1-42 was 208 ± 4 μs, which decreased to 164 ± 8.0 μs in the presence of asiaticoside, clearly indicating that asiaticoside inhibited the early stages Aβ1-42 of fibrillation, leaving more free Aβs in the solution and permitting their rapid diffusion in the confocal volume. The inhibitory effects were also evidenced by reduced fiber formation as assessed by steady-state and kinetic ThT fluorospectroscopy, LSM, and TEM. Asiaticoside elongated the lag phase of Aβ1-42 fibrillation, indicating the formation of smaller amyloid species were impaired in the presence of asiaticoside. Molecular docking revealed that asiaticoside binds with amyloid intra- and inter-molecular amino acid residues, which are responsible for β-sheet formation and longitudinal extension of fibrils. CONCLUSION: Finally, asiaticoside prevents amyloidogenesis that precedes neurodegeneration in patients with Alzheimer's disease.
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.
Integrity signals
Registry
Cites
Statistics
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Madecassoside and asiaticoside are incorporated into the treatment of various neurodegenerative disorders like Alzheimer's and Parkinson's diseases.
“and are incorporated into the treatment of various neurodegenerative disorders like Alzheimer's and Parkinson's diseases. 33 , 34”
From Therapeutic Properties and Pharmacological Activities of Asiaticoside and Madecassoside: A Review (2023) · cites this paper as [33]
The Discussion (page unavailable) links both compounds to Alzheimer’s-related research: it reports improved memory in AD-model rats given madecassoside and inhibition of Aβ fibrillation by asiaticoside. The Conclusions (page unavailable) recommend further development of asiaticoside for clinical use, not that either compound is already incorporated into treatment. The passages do not address Parkinson’s disease. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMadecassoside inhibits Aβ1-42 fibrillation and improves memory loss in AD model rats.
“In addition, a previous study by the same team has also accounted for the role of madecassoside in inhibiting Aβ1–42 fibrillation as well as improving memory loss of AD model rats. 33”
From Therapeutic Properties and Pharmacological Activities of Asiaticoside and Madecassoside: A Review (2023) · cites this paper as [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Iwatsubo, T., Odaka, A., Suzuki, N., Mizusawa, H., Nukina, N., & Ihara, Y. (1994) · Neuron · doi:10.1016/0896-6273(94)90458-8 · Cited by 1,774 · see citing works
Isolation and quantification of soluble Alzheimer's β-peptide from biological fluids
Seubert, P., Vigo-Pelfrey, C., Esch, F., Lee, M., Dovey, H., Davis, D., Sinha, S., Schiossmacher, M., Whaley, J., Swindlehurst, C., et al. (1992) · Nature · doi:10.1038/359325a0 · Cited by 1,731 · see citing works
Dhanasekaran, M., Holcomb, L. A., Hitt, A. R., Tharakan, B., Porter, J. W., Young, K. A., & Manyam, B. V. (2009) · Phytotherapy Research · doi:10.1002/ptr.2405 · Cited by 175 · see citing works
Flagged first, then by citations
Research works printed but not linked: 33
- [1]Selkoe DJ The molecular pathology of Alzheimer’s disease Neuron 1991 6 4 487 98 10.1016/0896-6273(91)90052-2 1673054 · DOI · Paper page
- [7]Hashimoto M Hossain S Shimada T Sugioka K Yamasaki H Fujii Y Docosahexaenoic acid provides protection from impairment of learning ability in Alzheimer’s disease model rats J Neurochem 2002 81 1084 91 10.1046/j.1471-4159.2002.00905.x 12065621 · DOI
- [8]Hashimoto M Shahdat HM Yamashita S Katakura M Tanabe Y Fujiwara H Docosahexaenoic acid disrupts in vitro amyloid beta(1–40) fibrillation and concomitantly inhibits amyloid levels in cerebral cortex of Alzheimer’s disease model rats J Neurochem 2008 107 1634 46 10.1111/j.1471-4159.2008.05731.x 19014387 · DOI
- [9]Kapoor L Handbook of Ayurvedic medicinal plants 1990 Boca Raton, Fla, USA CRC Press
- [10]Mook-Jung I Shin JE Yun SH Huh K Koh JY Park HK Protective effects of asiaticoside derivatives against beta-amyloid neurotoxicity J Neurosci Res 1999 58 417 25 10.1002/(SICI)1097-4547(19991101)58:3<417::AID-JNR7>3.0.CO;2-G 10518115 · DOI
- [12]Edelstein SJ Schaad O Changeux JP Single binding versus single channel recordings: a new approach to study ionotropic receptors Biochemistry 1997 36 13755 60 10.1021/bi9718301 9374851 · DOI
- [13]Haupts U Maiti S Schwille P Webb WW Dynamics of fluorescence fluctuations in green fluorescent protein observed by fluorescence correlation spectroscopy Proc Natl Acad Sci U S A 1998 95 13573 8 10.1073/pnas.95.23.13573 9811841 PMC24860 · DOI
- [14]Piehler J New methodologies for measuring protein interaction in vivo and in vitro Curr Opin Struct Biol 2005 15 4 14 10.1016/j.sbi.2005.01.008 15718127 · DOI
- [15]Hossain S Grande M Ahmadkhanov G Pramanik A Binding of the Alzheimer amyloid beta-peptide to neuronal cell membranes by fluorescence correlation spectroscopy Exp Mol Pathol 2007 82 169 74 10.1016/j.yexmp.2007.01.008 17398282 · DOI
- [16]Tjernberg L Pramanik A Björling S Thyberg P Thyberg J Nordsted C Amyloid β-peptide polymerization studied by fluorescence correlation spectroscopy Chem Biol 1999 6 53 62 10.1016/S1074-5521(99)80020-9 9889152 · DOI
- [17]Cavasotto CN A bagyan RA Protein flexibility in ligand docking and virtual screening to protein kinases J Mol Biol 2004 12 209 25 10.1016/j.jmb.2004.01.003 15001363 · DOI
- [18]Schoichet BK Virtual screening of chemical libraries Nature 2004 43 862 5 10.1038/nature03197 PMC1360234 15602552 · DOI · Paper page
- [19]Koppen H Virtual screening – what does it us? Curr Opin Drug Disc Dev 2009 12 397 407 19396741
- [20]Miwa K Hashimoto M Hossain S Katakura M Shido O Evaluation of the inhibitory effect of docosahexaenoic acid and arachidonic acid on the initial stage of amyloid β1-42 polymerization by fluorescence correlation spectroscopy Adv Alzheimers Dis 2013 2 66 72 10.4236/aad.2013.22009 · DOI
- [21]Hossain S Hashimoto M Katakura M Miwa K Shimada T Shido O Mechanism of docosahexaenoic acid-induced inhibition of in vitro Abeta1-42 fibrillation and Abeta1-42-induced toxicity in SH-S5Y5 cells J Neurochem 2009 111 2 568 79 10.1111/j.1471-4159.2009.06336.x 19686246 · DOI
- [23]Thomsen R Christensen MH MolDock: a new technique for high-accuracy molecular docking J Med Chem 2006 49 11 3315 21 10.1021/jm051197e 16722650 · DOI
- [24]Lührs T Ritter C Adrian M Riek-Loher D Bohrmann B Döbeli H 3D structure of Alzheimer’s amyloid-beta (1–42) fibrils Proc Natl Acad Sci U S A 2005 102 48 17342 7 10.1073/pnas.0506723102 16293696 PMC1297669 · DOI
- [25]Lyskov S Gray JJ The RosettaDock server for local protein-protein docking Nucleic Acids Res 2008 36 Web Server Issue W233 8 10.1093/nar/gkn216 18442991 PMC2447798 · DOI
- [26]Dosztányi Z Mészáros B Simon I ANCHOR: web server for predicting protein binding regions in disordered proteins Bioinformatics 2009 25 20 2745 6 10.1093/bioinformatics/btp518 19717576 PMC2759549 · DOI
- [27]Garbuzynskiy SO Lobanov MY Galzitskaya OV Fold Amyloid: a method of prediction of amyloidogenic regions from protein sequence Bioinformatics 2010 26 Oxford, England 326 32 10.1093/bioinformatics/btp691 20019059 · DOI
- [28]Conchillo-Sole O de Groot NS Aviles FX Vendrell J Daura X Ventura S AGGRESCAN: a server for the prediction and evaluation of “hot spots” of aggregation in polypeptides BMC Bioinformatics 2007 8 65 1 17 17324296 PMC1828741
- [29]Fang Y Gao S Ta D Middaugh CR Fang J Identification of properties important to protein aggregation using feature selection BMC Bioinformatics 2013 14 314 10.1186/1471-2105-14-314 24165390 PMC3819749 · DOI
- [30]Kawabata T Detection of multi-scale pockets on protein surfaces using mathematical morphology Proteins 2010 78 5 1195 221 10.1002/prot.22639 19938154 · DOI
- [31]Laurie ATR Jackson RM Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites Bioinformatics 2005 21 9 1908 16 10.1093/bioinformatics/bti315 15701681 · DOI
- [32]Chenm H-L Zhou H-X Prediction of interface residues in protein-protein complexes by a consensus neural network method: test against NMR data Proteins 2005 61 1 21 35 10.1002/prot.20514 16080151 · DOI
- [33]Darnell SJ Page D Mitchell JC Automated decision-tree approach to predicting protein protein interaction hot spots Proteins 2007 68 4 813 23 10.1002/prot.21474 17554779 · DOI
- [34]Zhu X Mitchell JC KFC2: a knowledge-based hot spot prediction method based on interface solvation, atomic density and plasticity features Proteins 2011 79 9 2671 83 10.1002/prot.23094 21735484 · DOI
- [35]Duhovny D Nussinov R Wolfson HJ Guigó R Gusfield D Efficient unbound docking of rigid molecules Proceedings of the 2′nd Workshop on Algorithms in Bioinformatics(WABI) Rome, Italy, Lecture Notes in Computer Science (LNCS) 2452 2002 Berlin Heidelberg Springer Verlag 185 200
- [37]Greenwald J Riek R Biology of Amyloid: structure, function, and regulation Structure (Review) 2010 8 10 1244 60 10.1016/j.str.2010.08.009 20947013 · DOI
- [38]Ehrnhoefer D Bieschke J Boeddrich A Herbst M Masino L Lurz R EGCG redirects amyloidogenic polypeptides into unstructured off-pathway oligomers Nat Struct Mol Biol 2008 15 558 66 10.1038/nsmb.1437 18511942 · DOI
- [39]Soumyanath A Zhong YP Gold SA Yu X Koop DR Bourdette D Centella asiatica accelerates nerve regeneration upon oral administration and contains multiple active fractions increasing neurite elongation in-vitro J Pharm Pharmacol 2005 57 3 1221 9 10.1211/jpp.57.9.0018 16105244 · DOI · Paper page
- [40]Wanakhachornkrai O Pongrakhananon V Chunhacha P Wanasuntronwong A Vattanajun A Tantisira B Neuritogenic effect of standardized extract of Centella asiatica ECa233 on human neuroblastoma cells BMC Complement Altern Med 2013 13 204 10.1186/1472-6882-13-204 23915016 PMC3750251 · DOI
- [42]Kirschner DA Abraham C Selkoe DJ X-ray diffraction from intraneuronal paired helical filaments and extraneuronal amyloid fibers in Alzheimer disease indicates cross-beta conformation Proc Natl Acad Sci U S A 1986 83 2 503 7 10.1073/pnas.83.2.503 3455785 PMC322888 · DOI
Cited by
Sorted by citations
Therapeutic Properties and Pharmacological Activities of Asiaticoside and Madecassoside: A Review
Bandopadhyay, S., Mandal, S., Ghorai, M., Jha, N. K., Kumar, M., Radha, Ghosh, A., Proćków, J., Pérez de la Lastra, J. M., & Dey, A. (2023) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.17635 · Cited by 220 · see citing works
He, Z., Hu, Y., Niu, Z., Zhong, K., Liu, T., Yang, M., Ji, L., & Hu, W. (2023) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2022.115865 · Cited by 93 · see citing works
Tan, S. C., Bhattamisra, S. K., Chellappan, D. K., & Candasamy, M. (2021) · Applied Sciences · doi:10.3390/app11188475 · Cited by 72 · see citing works
Biosynthesis and bioactivities of triterpenoids from Centella asiatica: Challenges and opportunities
Zeng, W., Li, H., Liu, S., Luo, Z., Chen, J., & Zhou, J. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108541 · Cited by 27 · see citing works
Peptidomimetics That Inhibit and Partially Reverse the Aggregation of Aβ<sub>1–42</sub>
Stark, T., Lieblein, T., Pohland, M., Kalden, E., Freund, P., Zangl, R., Grewal, R., Heilemann, M., Eckert, G. P., Morgner, N., et al. (2017) · Biochemistry · doi:10.1021/acs.biochem.7b00223 · Cited by 17 · see citing works
- Authors
- Hossain, S., Hashimoto, M., Katakura, M., Mamun, A. A., & Shido, O.
- Affiliations
- Shimane University (JP)
- Corresponding author
- Michio Hashimoto
- Venue
- BMC Complementary and Alternative Medicine
- Published
- 2015-04-14
- Volume
- 15
- Issue
- 1
- Pages
- 118-118
- ISSN
- 1472-6882 (ISSN-L)
- PMC
- PMC4422550
- OpenAlex
- W2018644003
- Cited by
- 38
- References
- 3
- Citations by year
- 2025: 5 · 2024: 2 · 2023: 6 · 2022: 5 · 2021: 2 · 2020: 3 · 2019: 4 · 2018: 2 · 2017: 4 · 2016: 2 · 2015: 3
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Complementary and Alternative Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Shimane University
- Japan Society for the Promotion of Science (26350120)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Complementary and alternative medicine › Medicinal Plants and Neuroprotection
- Health Sciences › Medicine › Pharmacology › Cholinesterase and Neurodegenerative Diseases
- Keywords
- asiaticoside, Alzheimer's disease, amyloid-beta 42, amyloid fibrillation inhibition, single-molecule diffusion, thioflavin T, molecular docking, ligand binding, glycosides, lag phase, β-sheet formation, anti-amyloid agents, natural products, medicinal plants, Withania somnifera
- MeSH terms
- Alzheimer Disease, Amyloid, Fluorescence, Humans, Phytotherapy, Microscopy, Peptide Fragments, Plant Extracts, Rhodamines, Spectrum Analysis, Triterpenes, Amyloid beta-Peptides, Centella, 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