Molecular Neurobiology

, Volume 55, Issue 4, pp 3224–3236 | Cite as

Deciphering the Biochemical Pathway and Pharmacokinetic Study of Amyloid βeta-42 with Superparamagnetic Iron Oxide Nanoparticles (SPIONs) Using Systems Biology Approach

  • Aman Chandra Kaushik
  • Ajay Kumar
  • Vivek Dhar Dwivedi
  • Shiv Bharadwaj
  • Sanjay Kumar
  • Kritika Bharti
  • Pavan Kumar
  • Ravi Kumar ChaudharyEmail author
  • Sarad Kumar MishraEmail author


Alzheimer’s disease (AD) pathogenesis leads to the appearance of senile plaques due to the production and deposition of the β-amyloid peptide (Aβ). Superparamagnetic iron oxide nanoparticles (SPIONs) have potential role in the detection and imaging of Aβ plaques in AD. SPIONs have shown appropriate potential in the diagnosis and treatment of AD. In the present study, the pharmacokinetics of SPIONs and its effect in the biochemical pathway of AD were analyzed using collected information. During analysis, the interaction of SPIONs with amyloid beta-42 (Aβ42), a biomarker for AD progression, has been shown. Nodes represent the entities and edges represent the relation (interactions) of one node to another node. Aβ42 and their interaction with other entities making up biochemical network are involved in AD mechanism in presence of SPION. The kinetic simulation was done to investigate pharmacokinetics of SPIONs for AD, where concentration was assigned of nanoparticles and other entities were applied as a kinetic irreversible simple Michaelis–Menten or mass action kinetics. Simulation was done in presence and absence of SPIONs to investigate pharmacokinetic effect in AD and explore the mechanism of Aβ42 in presence of SPIONs. This study may lead to better understanding, which is required to target the metabolism of Aß42 peptide, a pivotal player in this pathology.


Alzheimer’s disease Pharmacokinetics SPIONs Amyloid beta-42 Biochemical network 


  1. 1.
    Kumar A, Narayanan K, Chaudhary RK, Mishra S, Kumar S, Vinoth KJ, Padmanabhan P, Gulyás B (2016) Current perspective of stem cell therapy in neurodegenerative and metabolic diseases. Mol Neurobiol:1–21Google Scholar
  2. 2.
    Kumar DKV, Moir RD (2017) The emerging role of innate immunity in Alzheimer’s disease. Neuropsychopharmacology 42(1):362–363. doi: 10.1038/npp.2016.226 CrossRefGoogle Scholar
  3. 3.
    Nabers A, Ollesch J, Schartner J, Kötting C, Genius J, Hafermann H, Klafki H, Gerwert K et al (2016) Amyloid-β-secondary structure distribution in cerebrospinal fluid and blood measured by an immuno-infrared-sensor: A biomarker candidate for Alzheimer’s disease. Anal Chem 88(5):2755–2762CrossRefPubMedGoogle Scholar
  4. 4.
    Olsson B, Lautner R, Andreasson U, Ohrfelt A, Portelius E, Bjerke M, Holtta M, Rosen C et al (2016) CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: A systematic review and meta-analysis. Lancet Neurol 15(7):673–684. doi: 10.1016/51474-4422(16)00070-3 CrossRefPubMedGoogle Scholar
  5. 5.
    Walsh DM, Selkoe DJ (2004) Oligomers on the brain: The emerging role of soluble protein aggregates in neurodegeneration. Protein Peptide Lett 11(3):213–228CrossRefGoogle Scholar
  6. 6.
    Carrette O, Demalte I, Scherl A, Yalkinoglu O, Corthals G, Burkhard P, Hochstrasser DF, Sanchez JC (2003) A panel of cerebrospinal fluid potential biomarkers for the diagnosis of Alzheimer’s disease. Proteomics 3(8):1486–1494CrossRefPubMedGoogle Scholar
  7. 7.
    Davies H, Lomas L, Austen B (1999) Profiling of amyloid beta peptide variants using SELDI protein Chip arrays. BioTechniques 27(6):1258–1261PubMedGoogle Scholar
  8. 8.
    Tesco G, Koh YH, Kang EL, Cameron AN, Das S, Sena-Esteves M, Hiltunen M, Yang SH et al (2007) Depletion of GGA3 stabilizes BACE and enhances beta-secretase activity. Neuron 54(5):721–737. doi: 10.1016/j.neuron.2007.05.012 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Burguillos MA, Deierborg T, Kavanagh E, Persson A, Hajji N, Garcia-Quintanilla A, Cano J, Brundin P et al (2011) Caspase signalling controls microglia activation and neurotoxicity. Nature 472(7343):319–U214. doi: 10.1038/nature09788 CrossRefPubMedGoogle Scholar
  10. 10.
    Ho L, Guo Y, Spielman L, Petrescu O, Haroutunian V, Purohit D, Czernik A, Yemul S et al (2001) Altered expression of a-type but not b-type synapsin isoform in the brain of patients at high risk for Alzheimer’s disease assessed by DNA microarray technique. Neurosci Lett 298(3):191–194CrossRefPubMedGoogle Scholar
  11. 11.
    Ryan NS, Nicholas JM, Weston PSJ, Liang YY, Lashley T, Guerreiro R, Adamson G, Kenny J et al (2016) Clinical phenotype and genetic associations in autosomal dominant familial Alzheimer’s disease: A case series. Lancet Neurol 15(13):1326–1335. doi: 10.1016/S1474-4422(16)30193-4 CrossRefPubMedGoogle Scholar
  12. 12.
    Urfer-Buchwalder A, Urfer R (2017) Identification of a nuclear respiratory factor 1 recognition motif in the apolipoprotein E variant APOE4 linked to Alzheimer’s disease. Sci Rep-Uk 7:40668. doi: 10.1038/Srep40668 CrossRefGoogle Scholar
  13. 13.
    Nilsberth C, Westlind-Danielsson A, Eckman CB, Condron MM, Axelman K, Forsell C, Stenh C, Luthman J et al (2001) The‘Arctic’APP mutation (E693G) causes Alzheimer’s disease by enhanced Aβ protofibril formation. Nat Neurosci 4(9):887–893CrossRefPubMedGoogle Scholar
  14. 14.
    Veugelen S, Saito T, Saido TC, Chávez-Gutiérrez L, De Strooper B (2016) Familial Alzheimer’s disease mutations in Presenilin generate Amyloidogenic Aβ peptide seeds. Neuron 90(2):410–416CrossRefPubMedGoogle Scholar
  15. 15.
    Ho L, Gineste C, Pompl P, Dang A, Schall M, Pasinetti G (2002) Expression of Psoriasin and Xystain C in the CSF of Early Alzheimer’s Disease. In: Originally presented in abstract form at the 2nd Annual Meeting of the Society of Neuroscience, Orlando, FLGoogle Scholar
  16. 16.
    Loring J, Wen X, Lee J, Seilhamer J, Somogyi R (2001) A gene expression profile of Alzheimer’s disease. DNA Cell Biol 20(11):683–695CrossRefPubMedGoogle Scholar
  17. 17.
    Shoji M (2002) Cerebrospinal fluid Abeta40 and Abeta42: Natural course and clinical usefulness. Front Biosci 7:d997–1006PubMedGoogle Scholar
  18. 18.
    Steed MM, Tyagi SC (2011) Mechanisms of cardiovascular remodeling in hyperhomocysteinemia. Antioxid Redox Signal 15(7):1927–1943CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Wang J, Ho L, Zhao Z, Seror I, Humala N, Dickstein DL, Thiyagarajan M, Percival SS et al (2006) Moderate consumption of cabernet sauvignon attenuates Aβ neuropathology in a mouse model of Alzheimer’s disease. FASEB J 20(13):2313–2320CrossRefPubMedGoogle Scholar
  20. 20.
    Busquets MA, Sabate R, Estelrich J (2014) Potential applications of magnetic particles to detect and treat Alzheimer’s disease. Nanoscale Res Lett 9:538. doi: 10.1186/1556-276x-9-538 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Chatterjee K, Sarkar S, Rao KJ, Paria S (2014) Core/shell nanoparticles in biomedical applications. Adv Colloid Interfac 209:8–39. doi: 10.1016/j.cis.2013.12.008 CrossRefGoogle Scholar
  22. 22.
    Gendelman HE, Anantharam V, Bronich T, Ghaisas S, Jin H, Kanthasamy AG, Liu X, McMillan J et al (2015) Nanoneuromedicines for degenerative, inflammatory, and infectious nervous system diseases. Nanomed Nanotechnol Biol Med 11(3):751–767CrossRefGoogle Scholar
  23. 23.
    Kabanov AV, Gendelman H (2007) Nanomedicine in the diagnosis and therapy of neurodegenerative disorders. Prog Polym Sci 32(8):1054–1082CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Posadas I, Monteagudo S, Ceña V (2016) Nanoparticles for brain-specific drug and genetic material delivery, imaging and diagnosis. Nanomedicine 11(7):833–849CrossRefPubMedGoogle Scholar
  25. 25.
    Mahmoudi M, Akhavan O, Ghavami M, Rezaee F, Ghiasi SMA (2012) Graphene oxide strongly inhibits amyloid beta fibrillation. Nano 4(23):7322–7325Google Scholar
  26. 26.
    Padmanabhan P, Kumar A, Kumar S, Chaudhary RK, Gulyás B (2016) Nanoparticles in practice for molecular-imaging applications: An overview. Acta Biomater 41:1–16CrossRefPubMedGoogle Scholar
  27. 27.
    Mirsadeghi S, Dinarvand R, Ghahremani MH, Hormozi-Nezhad MR, Mahmoudi Z, Hajipour MJ, Atyabi F, Ghavami M et al (2015) Protein corona composition of gold nanoparticles/nanorods affects amyloid beta fibrillation process. Nano 7(11):5004–5013Google Scholar
  28. 28.
    Hellstrand E, Boland B, Walsh DM, Linse S (2009) Amyloid β-protein aggregation produces highly reproducible kinetic data and occurs by a two-phase process. ACS Chem Neurosci 1(1):13–18CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Mahmoudi M, Hosseinkhani H, Hosseinkhani M, Boutry S, Simchi A, Journeay WS, Subramani K, Laurent S (2010) Magnetic resonance imaging tracking of stem cells in vivo using iron oxide nanoparticles as a tool for the advancement of clinical regenerative medicine. Chem Rev 111(2):253–280CrossRefPubMedGoogle Scholar
  30. 30.
    Mahmoudi M, Shokrgozar MA (2012) Multifunctional stable fluorescent magnetic nanoparticles. Chem Commun 48(33):3957–3959CrossRefGoogle Scholar
  31. 31.
    Mahmoudi M, Simchi A, Imani M (2010) Recent advances in surface engineering of superparamagnetic iron oxide nanoparticles for biomedical applications. J Iran Chem Soc 7(2):S1–S27CrossRefGoogle Scholar
  32. 32.
    Nighoghossian N, Wiart M, Cakmak S, Berthezène Y, Derex L, Cho T-H, Nemoz C, Chapuis F et al (2007) Inflammatory response after ischemic stroke. Stroke 38(2):303–307CrossRefPubMedGoogle Scholar
  33. 33.
    Saleh A, Schroeter M, Ringelstein A, Hartung H-P, Siebler M, Mödder U, Jander S (2007) Iron oxide particle-enhanced MRI suggests variability of brain inflammation at early stages after ischemic stroke. Stroke 38(10):2733–2737CrossRefPubMedGoogle Scholar
  34. 34.
    Amiri H, Bustamante R, Millán A, Silva NJ, Pinol R, Gabilondo L, Palacio F, Arosio P et al (2011) Magnetic and relaxation properties of multifunctional polymer-based nanostructured bioferrofluids as MRI contrast agents. Magn Reson Med 66(6):1715–1721CrossRefPubMedGoogle Scholar
  35. 35.
    Amiri H, Mahmoudi M, Lascialfari A (2011) Superparamagnetic colloidal nanocrystal clusters coated with polyethylene glycol fumarate: A possible novel theranostic agent. Nano 3(3):1022–1030Google Scholar
  36. 36.
    Hosseini F, Panahifar A, Adeli M, Amiri H, Lascialfari A, Orsini F, Doschak MR et al (2013) Synthesis of pseudopolyrotaxanes-coated superparamagnetic iron oxide nanoparticles as new MRI contrast agent. Colloids Surf B: Biointerfaces 103:652–657CrossRefPubMedGoogle Scholar
  37. 37.
    Wang Y-XJ (2011) Superparamagnetic iron oxide based MRI contrast agents: Current status of clinical application. Quant Imaging Med Surg 1(1):35–40PubMedPubMedCentralGoogle Scholar
  38. 38.
    Cai W, Chen X (2007) Nanoplatforms for targeted molecular imaging in living subjects. Small 3(11):1840–1854CrossRefPubMedGoogle Scholar
  39. 39.
    Krol S, Macrez R, Docagne F, Defer G, Laurent S, Rahman M, Hajipour MJ, Kehoe PG et al (2012) Therapeutic benefits from nanoparticles: The potential significance of nanoscience in diseases with compromise to the blood brain barrier. Chem Rev 113(3):1877–1903CrossRefPubMedGoogle Scholar
  40. 40.
    Zhou J, Fa H, Yin W, Zhang J, Hou C, Huo D, Zhang D, Zhang H (2014) Synthesis of superparamagnetic iron oxide nanoparticles coated with a DDNP-carboxyl derivative for in vitro magnetic resonance imaging of Alzheimer’s disease. Mater Sci Eng C 37:348–355CrossRefGoogle Scholar
  41. 41.
    Cao C-Y, Shen Y-Y, Wang J-D, Li L, Liang G-L (2013) Controlled intracellular self-assembly of gadolinium nanoparticles as smart molecular MR contrast agents. Sci Rep 3:1024CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Kanal E (2012) Gadolinium-based magnetic resonance contrast agents for neuroradiology: An overview. Magn Reson Imaging Clin N Am 20(4):625–631CrossRefPubMedGoogle Scholar
  43. 43.
    Othman M, Desmaële D, Couvreur P, Vander Elst L, Laurent S, Muller RN, Bourgaux C, Morvan E et al (2011) Synthesis and physicochemical characterization of new squalenoyl amphiphilic gadolinium complexes as nanoparticle contrast agents. Org Biomol Chem 9(11):4367–4386CrossRefPubMedGoogle Scholar
  44. 44.
    Laurent S, Vander Elst L, Fu Y, Muller RN (2004) Synthesis and physicochemical characterization of Gd− DTPA− B (sLex) a, a new MRI contrast agent targeted to inflammation. Bioconjug Chem 15(1):99–103CrossRefPubMedGoogle Scholar
  45. 45.
    Sillerud LO, Solberg NO, Chamberlain R, Orlando RA, Heidrich JE, Brown DC, Brady CI, Vander Jagt TA et al (2013) SPION-enhanced magnetic resonance imaging of Alzheimer’s disease plaques in AβPP/PS-1 transgenic mouse brain. J Alzheimers Dis 34(2):349–365PubMedPubMedCentralGoogle Scholar
  46. 46.
    Tafoya MA, Madi S, Sillerud LO (2016) Superparamagnetic nanoparticle-enhanced MRI of Alzheimer’s disease plaques and activated microglia in 3X transgenic mouse brains: contrast optimization. J Magn Reson Imaging. doi: 10.1002/jmri.25563
  47. 47.
    Yang J, Wadghiri YZ, Hoang DM, Tsui W, Sun Y, Chung E, Li Y, Wang A et al (2011) Detection of amyloid plaques targeted by USPIO-Aβ1–42 in Alzheimer’s disease transgenic mice using magnetic resonance microimaging. NeuroImage 55(4):1600–1609CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Wong A, Ye M, Levy A, Rothstein J, Bergles D, Searson PC (2013) The blood-brain barrier: An engineering perspective. Front Neuroeng 6:7CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Upadhyay RK (2014) Drug delivery systems, CNS protection, and the blood brain barrier. Biomed Res Int. doi: 10.1155/2014/869269
  50. 50.
    Busquets MA, Sabaté R, Estelrich J (2014) Potential applications of magnetic particles to detect and treat Alzheimer’s disease. Nanoscale Res Lett 9(1):538CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Wu W, He Q, Jiang C (2008) Magnetic iron oxide nanoparticles: Synthesis and surface functionalization strategies. Nanoscale Res Lett 3(11):397CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Kim C-B, Lim E-G, Shin SW, Krause HJ, Hong H (2016) Magnetic immunoassay platform based on the planar frequency mixing magnetic technique. Biosens Bioelectron 83:293–299CrossRefPubMedGoogle Scholar
  53. 53.
    Sang Z, Pan W, Wang K, Ma Q, Yu L, Yang Y, Bai P, Leng C et al (2017) Design, synthesis and evaluation of novel ferulic acid-O-alkylamine derivatives as potential multifunctional agents for the treatment of Alzheimer’s disease. Eur J Med Chem 130:379–392CrossRefPubMedGoogle Scholar
  54. 54.
    Shidore M, Machhi J, Shingala K, Murumkar P, Sharma MK, Agrawal N, Tripathi A, Parikh Z et al (2016) Benzylpiperidine-linked Diarylthiazoles as potential anti-Alzheimer’s agents: Synthesis and biological evaluation. J Med Chem 59(12):5823–5846CrossRefPubMedGoogle Scholar
  55. 55.
    Dias KST, de Paula CT, dos Santos T, Souza IN, Boni MS, Guimarães MJ, da Silva FM, Castro NG et al (2017) Design, synthesis and evaluation of novel feruloyl-donepezil hybrids as potential multitarget drugs for the treatment of Alzheimer’s disease. Eur J Med Chem 130:440–457CrossRefPubMedGoogle Scholar
  56. 56.
    Keri RS, Quintanova C, Marques SM, Esteves AR, Cardoso SM, Santos MA (2013) Design, synthesis and neuroprotective evaluation of novel tacrine–benzothiazole hybrids as multi-targeted compounds against Alzheimer’s disease. Bioorg Med Chem 21(15):4559–4569CrossRefPubMedGoogle Scholar
  57. 57.
    Shaik JB, Palaka BK, Penumala M, Eadlapalli S, Darla Mark M, Ampasala DR, Vadde R, Amooru Gangaiah D (2016) Synthesis, biological evaluation, and molecular docking of 8-imino-2-oxo-2H, 8H–pyrano [2, 3-f] chromene analogs: new dual AChE inhibitors as potential drugs for the treatment of Alzheimer’s disease. Chem Biol Drug Des 88:43–53. doi: 10.1111/cbdd.12732
  58. 58.
    da Silva Gonçalves A, França TCC, Vital de Oliveira O (2016) Computational studies of acetylcholinesterase complexed with fullerene derivatives: A new insight for Alzheimer disease treatment. J Biomol Struct Dyn 34(6):1307–1316CrossRefPubMedGoogle Scholar
  59. 59.
    Brambilla D, Verpillot R, Le Droumaguet B, Nicolas J, Taverna M, Kóňa J, Lettiero B, Hashemi SH et al (2012) PEGylated nanoparticles bind to and alter amyloid-beta peptide conformation: Toward engineering of functional nanomedicines for Alzheimer’s disease. ACS Nano 6(7):5897–5908CrossRefPubMedGoogle Scholar
  60. 60.
    Li H, Luo Y, Derreumaux P, Wei G (2011) Carbon nanotube inhibits the formation of β-sheet-rich oligomers of the Alzheimer’s amyloid-β (16-22) peptide. Biophys J 101(9):2267–2276CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M (2014) Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res 42(D1):D199–D205CrossRefPubMedGoogle Scholar
  62. 62.
    Muto A, Kotera M, Tokimatsu T, Nakagawa Z, Goto S, Kanehisa M (2013) Modular architecture of metabolic pathways revealed by conserved sequences of reactions. J Chem Inf Model 53(3):613–622CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M (2007) KAAS: An automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 35(suppl 2):W182–W185CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Kanehisa M, Goto S, Kawashima S, Nakaya A (2002) The KEGG databases at GenomeNet. Nucleic Acids Res 30(1):42–46CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Okuda S, Yamada T, Hamajima M, Itoh M, Katayama T, Bork P, Goto S, Kanehisa M (2008) KEGG atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res 36(suppl 2):W423–W426CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Kotera M, Yamanishi Y, Moriya Y, Kanehisa M, Goto S (2012) GENIES: gene network inference engine based on supervised analysis. Nucleic Acids Res 40(W1):W162–W167. doi: 10.1093/nar/gks459
  68. 68.
    Funahashi A, Morohashi M, Kitano H, Tanimura N (2003) CellDesigner: A process diagram editor for gene-regulatory and biochemical networks. Biosilico 1(5):159–162CrossRefGoogle Scholar
  69. 69.
    Funahashi A, Jouraku A, Matsuoka Y, Kitano H (2007) Integration of CellDesigner and SABIO-RK. In Silico Biol 7(2 Supplement):81–90Google Scholar
  70. 70.
    Funahashi A, Matsuoka Y, Jouraku A, Morohashi M, Kikuchi N, Kitano H (2008) CellDesigner 3.5: A versatile modeling tool for biochemical networks. Proc IEEE 96(8):1254–1265CrossRefGoogle Scholar
  71. 71.
    Mendes P (1993) GEPASI: A software package for modelling the dynamics, steady states and control of biochemical and other systems. Comput Appl Biosci CABIOS 9(5):563–571PubMedGoogle Scholar
  72. 72.
    Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L et al (2006) COPASI—A complex pathway simulator. Bioinformatics 22(24):3067–3074CrossRefPubMedGoogle Scholar
  73. 73.
    Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH (2009) PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 37(2):W623–633Google Scholar
  74. 74.
    Colletier J-P, Laganowsky A, Landau M, Zhao M, Soriaga AB, Goldschmidt L, Flot D, Cascio D et al (2011) Molecular basis for amyloid-β polymorphism. Proc Natl Acad Sci 108(41):16938–16943CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Guo Z, Mohanty U, Noehre J, Sawyer TK, Sherman W, Krilov G (2010) Probing the α-helical structural stability of stapled p53 peptides: Molecular dynamics simulations and analysis. Chem Biol Drug Des 75(4):348–359CrossRefPubMedGoogle Scholar
  78. 78.
    Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA (2006) Sacerdoti FD Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the 2006 ACM/IEEE conference on Supercomputing, ACM, p 84Google Scholar
  79. 79.
    Berendsen HJ, Postma JV, van Gunsteren WF, DiNola A, Haak J (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81(8):3684–3690CrossRefGoogle Scholar
  80. 80.
    Shivakumar D, Williams J, Wu Y, Damm W, Shelley J, Sherman W (2010) Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J Chem Theory Comput 6(5):1509–1519CrossRefPubMedGoogle Scholar
  81. 81.
    Kaushik AC, Sahi S (2015) Boolean network model for GPR142 against type 2 diabetes and relative dynamic change ratio analysis using systems and biological circuits approach. Syst Synth Biol 9(1–2):45–54CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Zhang B, Tian Y, Zhang Z (2014) Network biology in medicine and beyond. Circ Cardiovasc Genet 7(4):536–547CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Wu X, Al Hasan M, Chen JY (2014) Pathway and network analysis in proteomics. J Theor Biol 362:44–52CrossRefPubMedGoogle Scholar
  84. 84.
    Jiang D, Rauda I, Han S, Chen S, Zhou F (2012) Aggregation pathways of the amyloid β (1–42) peptide depend on its colloidal stability and ordered β-sheet stacking. Langmuir 28(35):12711–12721CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Murphy MP, LeVine H III (2010) Alzheimer’s disease and the amyloid-β peptide. J Alzheimers Dis 19(1):311–323CrossRefPubMedPubMedCentralGoogle Scholar
  86. 86.
    O’Brien RJ, Wong PC (2011) Amyloid precursor protein processing and Alzheimer’s disease. Annu Rev Neurosci 34:185–204CrossRefPubMedPubMedCentralGoogle Scholar
  87. 87.
    Hashimoto M, Takenouchi T, Mallory M, Masliah E, Takeda A, Culvenor JG, McLean CA, Campbell BC et al (2000) The role of NAC in amyloidogenesis in Alzheimer’s disease. Am J Pathol 156(2):734–735CrossRefPubMedPubMedCentralGoogle Scholar
  88. 88.
    Fernndez-Novoa L, Cacabelos RN (1996) Blood levels of histamine, IL-1, and TNF-c in patients with mild to moderate Alzheimer disease. Mol Chem Neuropathol 29:237. doi: 10.1007/BF02815005
  89. 89.
    Alvarez XA, Franco A, Fernández-Novoa L, Cacabelos R (1996) Blood levels of histamine, IL-1β, and TNF-α in patients with mild to moderate alzheimer disease. Mol Chem Neuropathol 29(2):237–252CrossRefPubMedGoogle Scholar
  90. 90.
    Jarrett JT, Lansbury PT (1993) Seeding “one-dimensional crystallization” of amyloid: A pathogenic mechanism in Alzheimer’s disease and scrapie? Cell 73(6):1055–1058CrossRefPubMedGoogle Scholar
  91. 91.
    Marr RA, Hafez DM (2014) Amyloid-beta and Alzheimer’s disease: the role of neprilysin-2 in amyloid-beta clearance. Front Aging Neurosci 6:187. doi: 10.3389/fnagi.2014.00187
  92. 92.
    Nishitsuji K, Hosono T, Uchimura K, Michikawa M (2011) Lipoprotein lipase is a novel amyloid β (Aβ)-binding protein that promotes glycosaminoglycan-dependent cellular uptake of Aβ in astrocytes. J Biol Chem 286(8):6393–6401CrossRefPubMedGoogle Scholar
  93. 93.
    Danysz W, Parsons CG (2012) Alzheimer’s disease, β-amyloid, glutamate, NMDA receptors and memantine–searching for the connections. Br J Pharmacol 167(2):324–352CrossRefPubMedPubMedCentralGoogle Scholar
  94. 94.
    Shirwany NA, Payette D, Xie J, Guo Q (2007) The amyloid beta ion channel hypothesis of Alzheimer’s disease. Neuropsychiatr Dis Treat 3(5):597PubMedPubMedCentralGoogle Scholar
  95. 95.
    Green KN, Demuro A, Akbari Y, Hitt BD, Smith IF, Parker I, LaFerla FM (2008) SERCA pump activity is physiologically regulated by presenilin and regulates amyloid β production. J Cell Biol 181(7):1107–1116CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Martinez JA, Zhang Z, Svetlov SI, Hayes RL, Wang KK, Larner SF (2010) Calpain and caspase processing of caspase-12 contribute to the ER stress-induced cell death pathway in differentiated PC12 cells. Apoptosis 15(12):1480–1493CrossRefPubMedGoogle Scholar
  97. 97.
    Pritchard SM, Dolan PJ, Vitkus A, Johnson GV (2011) The toxicity of tau in Alzheimer disease: Turnover, targets and potential therapeutics. J Cell Mol Med 15(8):1621–1635CrossRefPubMedPubMedCentralGoogle Scholar
  98. 98.
    Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT (2011) Neuropathological alterations in Alzheimer disease. Cold Spring Harbor Perspect Med 1(1):a006189CrossRefGoogle Scholar
  99. 99.
    Paulson JB, Ramsden M, Forster C, Sherman MA, McGowan E, Ashe KH (2008) Amyloid plaque and neurofibrillary tangle pathology in a regulatable mouse model of Alzheimer’s disease. Am J Pathol 173(3):762–772CrossRefPubMedPubMedCentralGoogle Scholar
  100. 100.
    Mahmoudi M, Quinlan-Pluck F, Monopoli MP, Sheibani S, Vali H, Dawson KA, Lynch I (2013) Influence of the physiochemical properties of superparamagnetic iron oxide nanoparticles on amyloid β protein fibrillation in solution. ACS Chem Neurosci 4(3):475–485CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Aman Chandra Kaushik
    • 1
    • 2
  • Ajay Kumar
    • 3
  • Vivek Dhar Dwivedi
    • 4
  • Shiv Bharadwaj
    • 5
  • Sanjay Kumar
    • 6
  • Kritika Bharti
    • 7
  • Pavan Kumar
    • 1
  • Ravi Kumar Chaudhary
    • 1
    Email author
  • Sarad Kumar Mishra
    • 8
    Email author
  1. 1.School of BiotechnologyGautam Buddha UniversityGreater NoidaIndia
  2. 2.The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health SciencesBen-Gurion University of the NegevBeer-ShevaIsrael
  3. 3.School of EngineeringGautam Buddha UniversityGreater NoidaIndia
  4. 4.Faculty of Science and EnvironmentMahatma Gandhi Chitrakoot Rural UniversityChitrakootIndia
  5. 5.Nanotechnology Research and Application CenterSabanci UniversityIstanbulTurkey
  6. 6.Molecular Structural Biology DivisionCSIR-Central Drug Research InstituteLucknowIndia
  7. 7.Department of BiotechnologyJaypee Institute of Information TechnologyNoidaIndia
  8. 8.Department of BiotechnologyDeen Dayal Upadhyay Gorakhpur UniversityGorakhpurIndia

Personalised recommendations