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G-protein-coupled receptors function as logic gates for nanoparticle binding using systems and synthetic biology approach

Published online by Cambridge University Press:  20 February 2019

Aman Chandra Kaushik
Affiliation:
State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
Xueying Mao
Affiliation:
Qianweichang College, Shanghai University, Shanghai 200444, China
Cheng-Dong Li
Affiliation:
State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
Yan Li
Affiliation:
College of Computer Science and Information Technology, Henan Normal University, Xixiang 453007, China
Dong-Qing Wei*
Affiliation:
State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
Shakti Sahi*
Affiliation:
School of Biotechnology, Gautam Buddha University, Greater Noida 201312, India
*
a)Address all correspondence to these authors. e-mail: dqwei@sjtu.edu.cn
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Abstract

G-protein-coupled receptor 142 (GPR142) belongs to rhodopsin family. GPR142 and GPR119, both Gq-coupled receptors, are expressed in pancreatic β cells of pancreas; their activation eventually leads to triggering of insulin secretion. In this paper, through a systems and synthetic biology approach, the effect of a common hit compound has been investigated in GPR142 and GPR119 pathways. This hit that has the potential to be developed as a lead for nanodrug was obtained through high-throughput virtual screening. The hit compound was further docked with nanoparticles (GOLD, SPION, and CeO2). The probable effect of this potential hit on insulin secretion in type 2 diabetes and its dynamic behavior was explored. Kinetic simulation was performed for cross-validation of its role in both the pathways. This study opens up a probable avenue in therapy of type 2 diabetes through regulation of GPR142 and GPR119 receptors. The biological circuit constructed may further have an application as a modulator to control the up- and downregulation of the biochemical pathway and can be implemented as sensors or nanochips for therapy.

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Copyright © Materials Research Society 2019 

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References

Zhu, X., Huang, W., and Qian, H.: GPR119 agonists: a novel strategy for type 2 diabetes treatment. In: Oluwafemi O. Oguntibeju, editor. Diabetes mellitus–insights and perspectives. Rijeka: InTech; 2013. doi: 10.5772/48444. Available from: https://www.intechopen.com/books/diabetes-mellitus-insights-and-perspectives/gpr119-agonists-a-novel-strategy-for-type-2-diabetes-treatment.Google Scholar
Overton, H., Fyfe, M., and Reynet, C.: GPR119, a novel G protein-coupled receptor target for the treatment of type 2 diabetes and obesity. Br. J. Pharmacol. 153, S76 (2008).CrossRefGoogle Scholar
Kahn, S.E., Hull, R.L., and Utzschneider, K.M.: Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444, 840 (2006).CrossRefGoogle ScholarPubMed
Kaushik, A.C., Kumar, S., Wei, D.Q., and Sahi, S.: Structure based virtual screening studies to identify novel potential compounds for GPR142 and their relative dynamic analysis for study of type 2 diabetes. Front. Chem. 6, 23 (2018).CrossRefGoogle ScholarPubMed
Fredriksson, R., Höglund, P.J., Gloriam, D.E., Lagerström, M.C., and Schiöth, H.B.: Seven evolutionarily conserved human rhodopsin G protein-coupled receptors lacking close relatives. FEBS Lett. 554, 381 (2003).CrossRefGoogle ScholarPubMed
Shah, U. and Kowalski, T.J.: GPR119 Agonists for the Potential Treatment of Type 2 Diabetes and Related Metabolic Disorders. Vitamins & Hormones, 84, 415448, Academic Press, Cambridge, Massachusetts, 2010.Google Scholar
Lu, M., Jolly, M.K., and Ben-Jacob, E.: Toward decoding the principles of cancer metastasis circuits. Cancer Res. 74, 4574 (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 (2010).CrossRefGoogle ScholarPubMed
Bhalla, U.S. and Iyengar, R.: Emergent properties of networks of biological signaling pathways. Science 283, 381 (1999).CrossRefGoogle ScholarPubMed
McMillen, D., Kopell, N., Hasty, J., and Collins, J.: Synchronizing genetic relaxation oscillators by inter cell signaling. Proc. Natl. Acad. Sci. U. S. A 99, 679 (2002).CrossRefGoogle Scholar
Densmore, D. and Hassoun, S.: Design automation for synthetic biological systems. IEEE Des. Test Comput. Mag. 29, 7 (2012).CrossRefGoogle Scholar
Hasty, J., Isaacs, F., Dolnik, M., McMillen, D., and Collins, J.J.: Designer gene networks: Toward fundamental cellular control. Chaos 11, 207 (2001).CrossRefGoogle Scholar
Davidson, E.H., Rast, J.P., Oliveri, P., Ransick, A., Calestani, C., Yuh, C.H., Minokawa, T., Amore, G., Hinman, V., Arenas-Mena, C., Otim, O.: A genomic regulatory network for development. Science 295, 1669 (2002).CrossRefGoogle Scholar
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 (2002).CrossRefGoogle ScholarPubMed
Kitano, H.: Computational systems biology. Nature 420, 206 (2002).CrossRefGoogle ScholarPubMed
Khalil, A.S. and Collins, J.J.: Synthetic biology: Applications come of age. Nat. Rev. Genet. 11, 367 (2010).CrossRefGoogle ScholarPubMed
Friesner, R.A., Murphy, R.B., Repasky, M.P., Frye, L.L., Greenwood, J.R., Halgren, T.A., Sanschagrin, P.C., and Mainz, D.T.: Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J. Med. Chem. 49, 61776196 (2006).CrossRefGoogle ScholarPubMed
Halgren, T.A., Murphy, R.B., Friesner, R.A., Beard, H.S., Frye, L.L., Pollard, W.T., and Banks, J.L.: Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 47, 17501759 (2004).CrossRefGoogle ScholarPubMed
Friesner, R.A., Banks, J.L., Murphy, R.B., Halgren, T.A., Klicic, J.J., Mainz, D.T., Repasky, M.P., Knoll, E.H., Shelley, M., and Perry, J.K.: Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 17391749 (2004).CrossRefGoogle ScholarPubMed
Farid, R., Day, T., Friesner, R.A., and Pearlstein, R.A.: New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies. Bioorg. Med. Chem. 14, 31603173 (2006).CrossRefGoogle ScholarPubMed
Sherman, W., Day, T., Jacobson, M.P., Friesner, R.A., and Farid, R.: Novel procedure for modeling ligand/receptor induced fit effects. J. Med. Chem. 49, 534553 (2006).CrossRefGoogle ScholarPubMed
Sherman, W., Beard, H.S., and Farid, R.: Use of an induced fit receptor structure in virtual screening. Chem. Biol. Drug Des. 67, 8384 (2006).CrossRefGoogle ScholarPubMed
Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., and Kummer, U.: COPASI—A complex pathway simulator. Bioinformatics 22, 30673074 (2006).CrossRefGoogle ScholarPubMed
Inubushi, T., Kamemura, N., Oda, M., Sakurai, J., Nakaya, Y., Harada, N., Suenaga, M., Matsunaga, Y., Ishidoh, K., and Katunuma, N.: L-tryptophan suppresses rise in blood glucose and preserves insulin secretion in type-2 diabetes mellitus rats. J. Nutr. Sci. Vitaminol. 58, 415422 (2012).CrossRefGoogle ScholarPubMed
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, 32243236 (2018).CrossRefGoogle ScholarPubMed
Shaikh, T., Pandey, A., Talpur, F.N., Kaushik, A., Niazi, J.H.: Gold nanoparticles based sensor for in vitro analysis of drug-drug interactions using imipramine and isoniazid drugs: A proof of concept approach. Sens. Actuators, B 252, 10551062 (2017).CrossRefGoogle Scholar
Kaushik, A.C., Bharadwaj, S., Kumar, S., and Wei, D.Q.: Nano-particle mediated inhibition of Parkinson’s disease using computational biology approach. Sci. Rep. 8, 9169 (2018).CrossRefGoogle ScholarPubMed
Chen, C., Huang, H., and Wu, C.H.: Protein Bioinformatics Databases and Resources (Protein Bioinformatics, Humana Press, New York, NY, 2017).CrossRefGoogle ScholarPubMed
Pundir, S., Martin, M.J., and O’Donovan, C.: UniProt Protein Knowledgebase (Protein Bioinformatics, Humana Press, New York, NY, 2017).CrossRefGoogle ScholarPubMed
Kaushik, A.C. and Sahi, S.: Boolean network model for GPR142 against type 2 diabetes and relative dynamic change ratio analysis using systems and biological circuits approach. Synth. Syst. Biol. 9, 45 (2015).CrossRefGoogle ScholarPubMed
Kaushik, A.C. and Sahi, S.: Deciphering evolutionarily conserved orphan G-protein-coupled receptors from homolog cluster. Int. J. Bioinf. Res. Appl. 13, 264 (2017).CrossRefGoogle Scholar
Kaushik, A.C. and Sahi, S.: Insights into unbound–bound states of GPR142 receptor in a membrane-aqueous system using molecular dynamics simulations. J. Biomol. Struct. Dyn. 36, 1788 (2018).CrossRefGoogle Scholar
Harmar, A.J., Hills, R.A., Rosser, E.M., Jones, M., Buneman, O.P., Dunbar, D.R., Greenhill, S.D., Hale, V.A., Sharman, J.L., Bonner, T.I., Catterall, W.A.: IUPHAR-DB: The IUPHAR database of G protein-coupled receptors and ion channels. Nucleic Acids Res. 37, D680 (2008).CrossRefGoogle ScholarPubMed
Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M.: Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res. 42, D199 (2013).CrossRefGoogle ScholarPubMed
Muto, A., Kotera, M., Tokimatsu, T., Nakagawa, Z., Goto, S., and Kanehisa, M.: Modular architecture of metabolic pathways revealed by conserved sequences of reactions. J. Chem. Inf. Model. 53, 613 (2013).CrossRefGoogle ScholarPubMed
Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A.C., and Kanehisa, M.: KAAS: An automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W182 (2007).CrossRefGoogle ScholarPubMed
Moriya, Y., Shigemizu, D., Hattori, M., Tokimatsu, T., Kotera, M., Goto, S., Kanehisa, M.: PathPred: An enzyme-catalyzed metabolic pathway prediction server. Nucleic Acids Res. 38, W138W143 (2010).CrossRefGoogle ScholarPubMed
Yamanishi, Y., Hattori, M., Kotera, M., Goto, S., and Kanehisa, M.: Enzyme predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs. Bioinformatics 25, i179 (2009).CrossRefGoogle ScholarPubMed
Kaushik, A.C., Bharadwaj, S., Kumar, S., and Wei, D.Q.: Nano-particle mediated inhibition of Parkinson's disease using computational biology approach. Sci. Rep. 8, 9169 (2018).Google Scholar
Kanehisa, M.: Toward pathway engineering: A new database of genetic and molecular pathways. Sci. Technol. Jpn. 59, 34 (1996).Google Scholar
Kanehisa, M.: A database for post-genome analysis. Trends Genet. 13, 375376 (1997).CrossRefGoogle ScholarPubMed
Kanehisa, M.: The KEGG Database, ‘In Silico’ Simulation of Biological Processes: Novartis Foundation Symposium 247 (John Wiley & Sons, Ltd, Chichester, UK, 2002); pp. 91103.CrossRefGoogle Scholar
Kanehisa, M. and Goto, S.: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 2730 (2000).CrossRefGoogle ScholarPubMed
Kanehisa, M., Goto, S., Kawashima, S., and Nakaya, A.: The KEGG databases at GenomeNet. Nucleic Acids Res. 30, 4246 (2002).CrossRefGoogle ScholarPubMed
Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y., and Hattori, M.: The KEGG resource for deciphering the genome. Nucleic Acids Res. 32(Suppl. 1), D277D280 (2004).CrossRefGoogle ScholarPubMed
Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K.F., Itoh, M., Kawashima, S., Katayama, T., Araki, M., and Hirakawa, M.: From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Res. 34(Suppl. 1), D354D357 (2006).CrossRefGoogle ScholarPubMed
Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., and Tokimatsu, T.: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36(Suppl. 1), D480D484 (2007).CrossRefGoogle ScholarPubMed
Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., and Hirakawa, M.: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38(Suppl. 1), D355D360 (2009).CrossRefGoogle ScholarPubMed
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., and Tanabe, M.: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109D114 (2011).CrossRefGoogle ScholarPubMed
Kanehisa, M.: Organizing and Computing Metabolic Pathway Data in Terms of Binary Relations (Pacific Symposium on Biocomputing, Citeseer, 1997).Google Scholar
Okuda, S., Yamada, T., Hamajima, M., Itoh, M., Katayama, T., Bork, P., Goto, S., and Kanehisa, M.: KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res. 36(Suppl. 2), W423W426 (2008).CrossRefGoogle ScholarPubMed
Kotera, M., Yamanishi, Y., Moriya, Y., Kanehisa, M., and Goto, S.: GENIES: Gene network inference engine based on supervised analysis. Nucleic Acids Res. 40, W162W167 (2012).CrossRefGoogle ScholarPubMed
Nakaya, A., Katayama, T., Itoh, M., Hiranuka, K., Kawashima, S., Moriya, Y., Okuda, S., Tanaka, M., Tokimatsu, T., Yamanishi, Y., and KEGG, O.C.: A large-scale automatic construction of taxonomy-based ortholog clusters. Nucleic Acids Res. 41, D353D357 (2012).CrossRefGoogle ScholarPubMed
Hattori, M., Okuno, Y., Goto, S., and Kanehisa, M.: Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J. Am. Chem. Soc. 125, 1185311865 (2003).CrossRefGoogle ScholarPubMed
Hattori, M., Tanaka, N., Kanehisa, M., and Goto, S.: SIMCOMP/SUBCOMP: Chemical structure search servers for network analyses. Nucleic Acids Res. 38(Suppl. 2), W652W656 (2010).CrossRefGoogle ScholarPubMed
Oh, M., Yamada, T., Hattori, M., Goto, S., and Kanehisa, M.: Systematic analysis of enzyme-catalyzed reaction patterns and prediction of microbial biodegradation pathways. J. Chem. Inf. Model. 47, 17021712 (2007).CrossRefGoogle ScholarPubMed
Funahashi, A., Morohashi, M., Kitano, H., and Tanimura, N.: CellDesigner: A process diagram editor for gene-regulatory and biochemical networks. Biosilico 1, 159162 (2003).CrossRefGoogle Scholar
Funahashi, A., Jouraku, A., Matsuoka, Y., and Kitano, H.: Integration of CellDesigner and SABIO-RK. Silico Biol. 7(Suppl. 2), 8190 (2007).Google ScholarPubMed
Funahashi, A., Matsuoka, Y., Jouraku, A., Morohashi, M., Kikuchi, N., and Kitano, H.: CellDesigner 3.5: A versatile modeling tool for biochemical networks. Proc. IEEE 96, 12541265 (2008).CrossRefGoogle Scholar
Arkin, A., Ross, J., and McAdams, H.H.: Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-infected Escherichia coli cells. Genetics 149, 16331648 (1998).Google ScholarPubMed
Mendes, P.: GEPASI: A software package for modelling the dynamics, steady states and control of biochemical and other systems. Bioinformatics 9, 563571 (1993).CrossRefGoogle ScholarPubMed
Burch, C.: Logisim: A graphical system for logic circuit design and simulation. J. Educ. Resour. Comput. 2, 516 (2002).CrossRefGoogle Scholar
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