Hostname: page-component-76fb5796d-45l2p Total loading time: 0 Render date: 2024-04-29T22:24:38.916Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation and validation of synergistic effects of amyloid-beta inhibitor–gold nanoparticles complex on Alzheimer’s disease using deep neural network approach

Published online by Cambridge University Press:  21 January 2019

Aman Chandra Kaushik ,
Ajay Kumar ,
Zhennan Peng ,
Abbas Khan ,
Muhammad Junaid ,
Arif Ali ,
Shiv Bharadwaj  and
Dong-Qing Wei Open the ORCID record for Dong-Qing Wei [Opens in a new window]
Aman Chandra Kaushik
Affiliation:
State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
Ajay Kumar
Affiliation:
Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung City 804, Taiwan
Zhennan Peng
Affiliation:
College of Life Sciences, Lanzhou University, Lanzhou 730000, China
Abbas Khan
Affiliation:
State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
Muhammad Junaid
Affiliation:
State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
Arif Ali
Affiliation:
State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
Shiv Bharadwaj
Affiliation:
Nanotechnology Research and Application Center, Sabanci University, 34956 Istanbul, Turkey
Dong-Qing Wei*
Affiliation:
State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
*
a)Address all correspondence to this author. e-mail: dqwei@weislab.com
Get access

Abstract

Numerous studies have reported that amyloid-beta 42 (Aβ-42) protein is a high-profile risk factor associated with the onset and progression of Alzheimer’s disease (AD). Accumulation of extracellular senile plaques, synaptic degeneration, and intracellular neurofibrillary tangles were recorded as essential features that facilitate the onset of Aβ-42, resulting in AD. Hence, we attempted a new screening technique to discover potential inhibitors against Aβ-42 using an in silico deep neural network approach. We screened PubChem compounds library and found wgx-50 as a potential inhibitor of Aβ-42. Also, synergistic effects of wgx-50–gold nanoparticles (AuNPs) complex induced significant inhibition of Aβ-42, compared with those of wgx-50 alone. Further, molecular docking analysis, systems biology approach, and time course simulation confirmed that synergistic effects of wgx-50–AuNPs complex have potential application in the treatment for AD. Additionally, we proposed the biological circuit for AD induced by Aβ-42 that can be used to monitor the effect of drugs on AD.

Keywords

Alzheimer’s diseaseamyloid-beta 42AuNPbiochemical pathway
Type
Article
Information
Journal of Materials Research , Volume 34 , Issue 11: Focus Issue: (Nano)materials for Biomedical Applications , 14 June 2019 , pp. 1845 - 1853
Copyright
Copyright © Materials Research Society 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Kaushik, A.C., Kumar, A., Dwivedi, V.D., Bharadwaj, S., Kumar, S., Bharti, K., Kumar, P., Chaudhary, R.K., Mishra, S.K.: Deciphering the biochemical pathway and pharmacokinetic study of amyloid β-42 with superparamagnetic iron oxide nanoparticles (SPIONs) using systems biology approach. Mol. Neurobiol. 55, 3224–3236 (2018).CrossRefGoogle ScholarPubMed
Austen, B.M., Frears, E.R., and Davies, H.: The use of Seldi ProteinChip™ arrays to monitor production of Alzheimer’s β-amyloid in transfected cells. J. Pept. Sci. 6, 459–469 (2000).3.0.CO;2-B>CrossRefGoogle Scholar
Beher, D., Wrigley, J.D., Owens, A.P., and Shearman, M.S.: Generation of C-terminally truncated amyloid-β peptides is dependent on γ-secretase activity. J. Neurochem. 82, 563–575 (2002).CrossRefGoogle ScholarPubMed
Marksteiner, J., Hinterhuber, H., and Humpel, C.: Cerebrospinal fluid biomarkers for diagnosis of Alzheimer’s disease: Beta-amyloid (1-42), tau, phospho-tau-181 and total protein. Drugs Today 43, 423 (2007).CrossRefGoogle Scholar
R. Brookmeyer, E. Johnson, K. Ziegler-Graham, H.M. Arrighi: Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement. 3, 186–191 (2007).CrossRefGoogle Scholar
Goedert, M. and Spillantini, M.G.: A century of Alzheimer’s disease. Science 314, 777–781 (2006).CrossRefGoogle ScholarPubMed
Saido, T.C., Iwatsubo, T., Mann, D.M., Shimada, H., Ihara, Y., Kawashima, S.: Dominant and differential deposition of distinct β-amyloid peptide species, AβN3(pE), in senile plaques. Neuron 14, 457–466 (1995).CrossRefGoogle Scholar
Hardy, J.A. and Higgins, G.A.: Alzheimer’s disease: The amyloid cascade hypothesis. Science 256, 184–185 (1992).CrossRefGoogle ScholarPubMed
Kayed, R., Head, E., Thompson, J.L., McIntire, T.M., Milton, S.C., Cotman, C.W., Glabe, C.G.: Common structure of soluble amyloid oligomers implies common mechanism of pathogenesis. Science 300, 486–489 (2003).CrossRefGoogle ScholarPubMed
Kawahara, M. and Kuroda, Y.: Molecular mechanism of neurodegeneration induced by Alzheimer’s beta-amyloid protein: Channel formation and disruption of calcium homeostasis. Brain Res. Bull. 53, 389–397 (2000).CrossRefGoogle ScholarPubMed
Takashima, A., Noguchi, K., Sato, K., Hoshino, T., Imahori, K.: Tau protein kinase I is essential for amyloid beta-protein-induced neurotoxicity. Proc. Natl. Acad. Sci. U. S. A. 90, 7789–7793 (1993).CrossRefGoogle ScholarPubMed
Yatin, S.M., Aksenova, M., Aksenov, M., Markesbery, W.R., Aulick, T., and Butterfield, D.A.: Temporal relations among amyloid beta-peptide-induced free-radical oxidative stress, neuronal toxicity, and neuronal defensive responses. J. Mol. Neurosci. 11, 183–197 (1998).CrossRefGoogle ScholarPubMed
Querfurth, H.W., Jiang, J., Geiger, J.D., Selkoe, D.J.: Caffeine stimulates amyloid beta-peptide release from beta-amyloid precursor protein-transfected HEK293 cells. J. Neurochem. 69, 1580–1591 (1997).CrossRefGoogle ScholarPubMed
Capone, R., Jang, H., Kotler, S.A., Connelly, L., Teran Arce, F., Ramachandran, S., Kagan, B.L., Nussinov, R., Lal, R.: All-D-enantiomer of beta-amyloid peptide forms ion channels in lipid bilayers. J. Chem. Theory Comput. 8, 1143–1152 (2012).CrossRefGoogle ScholarPubMed
Quist, A., Doudevski, I., Lin, H., Azimova, R., Ng, D., Frangione, B., Kagan, B., Ghiso, J., Lal, R.: Amyloid ion channels: A common structural link for protein-misfolding disease. Proc. Natl. Acad. Sci. U. S. A. 102, 10427–10432 (2005).CrossRefGoogle ScholarPubMed
Bhatia, R., Lin, H., and Lal, R.: Fresh and globular amyloid β protein (1-42) induces rapid cellular degeneration: Evidence for AβP channel-mediated cellular toxicity. FASEB J. 14, 1233–1243 (2000).CrossRefGoogle ScholarPubMed
Lu, M., Jolly, M.K., and Ben-Jacob, E.: Toward decoding the principles of cancer metastasis circuits. Cancer Res. 74, 4574–4587 (2014).CrossRefGoogle ScholarPubMed
Jolly, M.K., Huang, B., Lu, M., Mani, S.A., Levine, H., and Ben-Jacob, E.: Towards elucidating the connection between epithelial–mesenchymal transitions and stemness. J. R. Soc. Interface 11, 20140962 (2014).CrossRefGoogle ScholarPubMed
Kiel, C., Yus, E., and Serrano, L.: Engineering signal transduction pathways. Cell 140, 33–47 (2010).CrossRefGoogle ScholarPubMed
McMillen, D., Kopell, N., Hasty, J., and Collins, J.: Synchronizing genetic relaxation oscillators by intercell signaling. Proc. Natl. Acad. Sci. U.S.A. 99, 679–684 (2002).CrossRefGoogle ScholarPubMed
Densmore, D. and Hassoun, S.: Design automation for synthetic biological systems. IEEE Des. Test Comput. 29, 7–20 (2012).CrossRefGoogle Scholar
Khalil, A.S. and Collins, J.J.: Synthetic biology: Applications come of age. Nat. Rev. Genet. 11, 367–379 (2010).CrossRefGoogle ScholarPubMed
Ozbudak, E.M., Thattai, M., Kurtser, I., Grossman, A.D., and Van Oudenaarden, A.: Regulation of noise in the expression of a single gene. Nat. Genet. 31, 69–73 (2002).CrossRefGoogle ScholarPubMed
Tang, M., Wang, Z., Zhou, Y., Xu, W., Li, S., Wang, L., Wei, D., Qiao, Z.: A novel drug candidate for Alzheimer’s disease treatment: wgx-50 derived from zanthoxylum bungeanum. J. Alzheim. Dis. 34, 203–213 (2013).CrossRefGoogle ScholarPubMed
Hou, S., Gu, R.X., and Wei, D.Q.: Inhibition of beta-amyloid channels with a drug candidate wgx-50 revealed by molecular dynamics simulations. J. Chem. Inf. Model. 57, 2811–2821 (2017).CrossRefGoogle ScholarPubMed
Fan, H.M., Gu, R.X., Wang, Y.J, Pi, Y.L., Zhang, Y.H., Xu, Q., Wei, D.Q.: Destabilization of Alzheimer’s Aβ42 protofibrils with a novel drug candidate wgx-50 by molecular dynamics simulations. J. Phys. Chem. B. 119, 11196–11202 (2015).CrossRefGoogle ScholarPubMed
Kaushik, A.C., Sahi, S.: Boolean network model for GPR142 against Type 2 diabetes and relative dynamic change ratio analysis using systems and biological circuits approach. Systems and synthetic biology 1;9(1-2):45–54 (2015).Google Scholar
Adleman, L.M.: Molecular computation of solutions to combinatorial problems. Nature 369, 40 (1994).Google Scholar
Boneh, D., Dunworth, C., Lipton, R.J., and Sgall, J.: On the computational power of DNA. Discrete Appl. Math. 71, 79–94 (1996).CrossRefGoogle Scholar
Kari, L., Gloor, G., and Yu, S.: Using DNA to solve the bounded post correspondence problem. Theor. Comput. Sci. 231, 193–203 (2000).CrossRefGoogle Scholar
Ogihara, M. and Ray, A.: Simulating Boolean circuits on a DNA computer. Algorithmica 25, 239–250 (1999).CrossRefGoogle Scholar
Benenson, Y., Gil, B., Ben-Dor, U., Adar, R., and Shapiro, E.: An autonomous molecular computer for logical control of gene expression. Nature 429, 423–429 (2004).CrossRefGoogle ScholarPubMed
PubChem Home Page: Available at: http://pubchem.ncbi.nlm.nih.gov/ (accessed November 22, 2017).Google Scholar
Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., and Olson, A.J.: AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791 (2009).CrossRefGoogle ScholarPubMed
Kaushik, A.C., Bharadwaj, S., Kumar, S., Wei, D.Q.: Nano-particle mediated inhibition of Parkinson’s disease using computational biology approach. Sci. Rep. 8, 9169 (2018).CrossRefGoogle ScholarPubMed
Burch, C.: Logisim a graphical system for logic circuit design and simulation. J. Educ. Resour. Comput. 2, 5–16 (2002).CrossRefGoogle Scholar
Supplementary material: File

Kaushik et al. supplementary material

Kaushik et al. supplementary material 1

Download Kaushik et al. supplementary material(File)
File 494.6 KB