ACS Publications. Most Trusted. Most Cited. Most Read
Finke–Watzky Two-Step Nucleation–Autocatalysis Model of S100A9 Amyloid Formation: Protein Misfolding as “Nucleation” Event
My Activity

Figure 1Loading Img
    Letter

    Finke–Watzky Two-Step Nucleation–Autocatalysis Model of S100A9 Amyloid Formation: Protein Misfolding as “Nucleation” Event
    Click to copy article linkArticle link copied!

    View Author Information
    Department of Medical Biochemistry and Biophysics, Umeå University, SE-901 87 Umeå, Sweden
    Department of General Chemistry, Sumy State University, 40007 Sumy, Ukraine
    § Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Vilnius University, LT-10257 Vilnius, Lithuania
    Other Access OptionsSupporting Information (1)

    ACS Chemical Neuroscience

    Cite this: ACS Chem. Neurosci. 2017, 8, 10, 2152–2158
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acschemneuro.7b00251
    Published July 31, 2017
    Copyright © 2017 American Chemical Society

    Abstract

    Click to copy section linkSection link copied!
    Abstract Image

    Quantitative kinetic analysis is critical for understanding amyloid mechanisms. Here we demonstrate the application of generic Finke–Watzky (F-W) two-step nucleation–autocatalytic growth model to the concentration-dependent amyloid kinetics of proinflammatory α-helical S100A9 protein at pH 7.4 and at 37 and 42 °C. The model is based on two pseudoelementary reaction steps applied without further analytical constraints, and its treatment of S100A9 amyloid self-assembly demonstrates that initial misfolding and β-sheet formation, defined as “nucleation” step, spontaneously takes place within individual S100A9 molecules at higher rate than the subsequent fibrillar growth. The latter, described as an autocatalytic process, will proceed if misfolded amyloid-prone S100A9 is populated on a macroscopic time scale. Short lengths of S100A9 fibrils are consistent with the F-W model. The analysis of fibrillar length distribution by the Beker–Döring model demonstrates independently that such distribution is solely determined by slow fibril growth and there is no fragmentation or secondary pathways decreasing fibrillar length.

    Copyright © 2017 American Chemical Society

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. Add or change your institution or let them know you’d like them to include access.

    Supporting Information

    Click to copy section linkSection link copied!

    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acschemneuro.7b00251.

    • Method and figure on the thermal unfolding of S100A9 in the presence of various concentrations of CaCl2 (0–30 mM) and under the range of pH from 4 to 10 (PDF)

    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    Click to copy section linkSection link copied!
    Citation Statements
    Explore this article's citation statements on scite.ai

    This article is cited by 46 publications.

    1. Ieva Baronaitė, Darius Šulskis, Aurimas Kopu̅stas, Marijonas Tutkus, Vytautas Smirnovas. Formation of Calprotectin Inhibits Amyloid Aggregation of S100A8 and S100A9 Proteins. ACS Chemical Neuroscience 2024, 15 (9) , 1915-1925. https://doi.org/10.1021/acschemneuro.4c00093
    2. Efrat Naaman, Amanda Qarawani, Rony Ben-Zvi Elimelech, Michal Harel, Shahaf Sigal-Dror, Shadi Safuri, Vytautas Smirnovas, Ieva Baronaite, Nina V. Romanova, Ludmilla A. Morozova-Roche, Shiri Zayit-Soudry. The Surprising Nonlinear Effects of S100A9 Proteins in the Retina. ACS Chemical Neuroscience 2024, 15 (4) , 735-744. https://doi.org/10.1021/acschemneuro.3c00650
    3. Robab Jahangir, Iqra Munir, Gurkan Yesiloz. One-Step Synthesis of Ultrasmall Nanoparticles in Glycerol as a Promising Green Solvent at Room Temperature Using Omega-Shaped Microfluidic Micromixers. Analytical Chemistry 2023, 95 (47) , 17177-17186. https://doi.org/10.1021/acs.analchem.3c01697
    4. Goro Nishide, Keesiang Lim, Maiki Tamura, Akiko Kobayashi, Qingci Zhao, Masaharu Hazawa, Toshio Ando, Noritaka Nishida, Richard W. Wong. Nanoscopic Elucidation of Spontaneous Self-Assembly of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Open Reading Frame 6 (ORF6) Protein. The Journal of Physical Chemistry Letters 2023, 14 (38) , 8385-8396. https://doi.org/10.1021/acs.jpclett.3c01440
    5. Si Sun, Hong-Wen Liang, Hao Wang, Quanming Zou. Light-Triggered Self-Assembly of Peptide Nanoparticles into Nanofibers in Living Cells through Molecular Conformation Changes and H-Bond Interactions. ACS Nano 2022, 16 (11) , 18978-18989. https://doi.org/10.1021/acsnano.2c07895
    6. Rebeka Szabó, Gábor Lente. A Comparison of the Stochastic and Deterministic Approaches in a Nucleation–Growth Type Model of Nanoparticle Formation. Chemistry of Materials 2021, 33 (13) , 5430-5436. https://doi.org/10.1021/acs.chemmater.0c04688
    7. Himanshu Chaudhary, Igor A. Iashchishyn, Nina V. Romanova, Mark A. Rambaran, Greta Musteikyte, Vytautas Smirnovas, Michael Holmboe, C. André Ohlin, Željko M. Svedružić, Ludmilla A. Morozova-Roche. Polyoxometalates as Effective Nano-inhibitors of Amyloid Aggregation of Pro-inflammatory S100A9 Protein Involved in Neurodegenerative Diseases. ACS Applied Materials & Interfaces 2021, 13 (23) , 26721-26734. https://doi.org/10.1021/acsami.1c04163
    8. Manuela Leri, Himanshu Chaudhary, Igor A. Iashchishyn, Jonathan Pansieri, Željko M. Svedružić, Silvia Gómez Alcalde, Greta Musteikyte, Vytautas Smirnovas, Massimo Stefani, Monica Bucciantini, Ludmilla A. Morozova-Roche. Natural Compound from Olive Oil Inhibits S100A9 Amyloid Formation and Cytotoxicity: Implications for Preventing Alzheimer’s Disease. ACS Chemical Neuroscience 2021, 12 (11) , 1905-1918. https://doi.org/10.1021/acschemneuro.0c00828
    9. Jonathan Pansieri, Lucija Ostojić, Igor A. Iashchishyn, Mazin Magzoub, Cecilia Wallin, Sebastian K.T.S. Wärmländer, Astrid Gräslund, Mai Nguyen Ngoc, Vytautas Smirnovas, Željko Svedružić, Ludmilla A. Morozova-Roche. Pro-Inflammatory S100A9 Protein Aggregation Promoted by NCAM1 Peptide Constructs. ACS Chemical Biology 2019, 14 (7) , 1410-1417. https://doi.org/10.1021/acschembio.9b00394
    10. Igor A. Iashchishyn, Marina A. Gruden, Roman A. Moskalenko, Tatiana V. Davydova, Chao Wang, Robert D. E. Sewell, Ludmilla A. Morozova-Roche. Intranasally Administered S100A9 Amyloids Induced Cellular Stress, Amyloid Seeding, and Behavioral Impairment in Aged Mice. ACS Chemical Neuroscience 2018, 9 (6) , 1338-1348. https://doi.org/10.1021/acschemneuro.7b00512
    11. Andrey V Kuznetsov. Effect of diffusivity of amyloid beta monomers on the formation of senile plaques. Mathematical Medicine and Biology: A Journal of the IMA 2024, 41 (4) , 346-362. https://doi.org/10.1093/imammb/dqae019
    12. Andrey V. Kuznetsov. Simulating the Growth of TATA-Box Binding Protein-Associated Factor 15 Inclusions in Neuron Soma. Journal of Biomechanical Engineering 2024, 146 (12) https://doi.org/10.1115/1.4066386
    13. Viktorija Karalkevičiūtė, Ieva Baronaitė, Aistė Peštenytė, Dominykas Veiveris, Gediminas Usevičius, Mantas Šimėnas, Mantas Žiaunys, Vytautas Smirnovas, Darius Šulskis. Calcium-mediated amyloid co-aggregation of S100A1 and S100A8 proteins. 2024https://doi.org/10.1101/2024.11.26.625466
    14. Qi Tian, Zhijie Li, Ziang Yan, Shengming Jiang, Xincan Zhao, Lei Wang, Mingchang Li. Inflammatory role of S100A8/A9 in the central nervous system non-neoplastic diseases. Brain Research Bulletin 2024, 218 , 111100. https://doi.org/10.1016/j.brainresbull.2024.111100
    15. Andrey V. Kuznetsov. The growth rate of senile plaques is determined by the competition between the rate of deposition of free Aβ aggregates into plaques and the autocatalytic production of free Aβ aggregates. Journal of Theoretical Biology 2024, 593 , 111900. https://doi.org/10.1016/j.jtbi.2024.111900
    16. Andrey V. Kuznetsov. Criterion for assessing accumulated neurotoxicity of alpha-synuclein oligomers in Parkinson’s disease. 2024https://doi.org/10.1101/2024.09.23.614584
    17. Ana P. Carapeto, Carlos Marcuello, Patrícia F. N. Faísca, Mário S. Rodrigues. Morphological and Biophysical Study of S100A9 Protein Fibrils by Atomic Force Microscopy Imaging and Nanomechanical Analysis. Biomolecules 2024, 14 (9) , 1091. https://doi.org/10.3390/biom14091091
    18. Ana O. Tiroli‐Cepeda, Leonardo A. Linhares, Annelize Z. B. Aragão, Jemmyson R. de Jesus, Ana P. Wasilewska‐Sampaio, Fernanda G. De Felice, Sérgio T. Ferreira, Júlio C. Borges, Douglas M. Cyr, Carlos H. I. Ramos. Type I Hsp40s/ DnaJs aggregates exhibit features reminiscent of amyloidogenic structures. The FEBS Journal 2024, 291 (17) , 3904-3923. https://doi.org/10.1111/febs.17215
    19. Manuela Leri, Dan Sun, Željko M. Svedružic, Darius Šulskis, Vytautas Smirnovas, Massimo Stefani, Ludmilla Morozova-Roche, Monica Bucciantini. Pro-inflammatory protein S100A9 targeted by a natural molecule to prevent neurodegeneration onset. International Journal of Biological Macromolecules 2024, 276 , 133838. https://doi.org/10.1016/j.ijbiomac.2024.133838
    20. Bo Chen, Bin Di. Endogenous Ligands of TLR4 in Microglia: Potential Targets for Related Neurological Diseases. Current Drug Targets 2024, 25 (14) , 953-970. https://doi.org/10.2174/0113894501316051240821060249
    21. Andrey V. Kuznetsov. A criterion characterizing accumulated neurotoxicity of Aβ oligomers in Alzheimer’s disease. 2024https://doi.org/10.1101/2024.08.19.608707
    22. Mark Cornell Manning, Ryan E. Holcomb, Robert W. Payne, Joshua M. Stillahn, Brian D. Connolly, Derrick S. Katayama, Hongcheng Liu, James E. Matsuura, Brian M. Murphy, Charles S. Henry, Daan J. A. Crommelin. Stability of Protein Pharmaceuticals: Recent Advances. Pharmaceutical Research 2024, 41 (7) , 1301-1367. https://doi.org/10.1007/s11095-024-03726-x
    23. Mantas Ziaunys, Darius Sulskis, Kamile Mikalauskaite, Andrius Sakalauskas, Ruta Snieckute, Vytautas Smirnovas. S100A9 Inhibits and Redirects Prion Protein 89-230 Fragment Amyloid Aggregation. Archives of Biochemistry and Biophysics 2024, 86 , 110087. https://doi.org/10.1016/j.abb.2024.110087
    24. Andrey V. Kuznetsov. Numerical and Analytical Simulation of the Growth of Amyloid-β Plaques. Journal of Biomechanical Engineering 2024, 146 (6) https://doi.org/10.1115/1.4064969
    25. Andrey V. Kuznetsov. Numerical modeling of senile plaque development under conditions of limited diffusivity of amyloid-β monomers. Journal of Theoretical Biology 2024, 587 , 111823. https://doi.org/10.1016/j.jtbi.2024.111823
    26. Andrey V. Kuznetsov. Lewy body radius growth: The hypothesis of the cube root of time dependency. Journal of Theoretical Biology 2024, 581 , 111734. https://doi.org/10.1016/j.jtbi.2024.111734
    27. Jiannan Wang, Lijun Dai, Sichun Chen, Zhaohui Zhang, Xin Fang, Zhentao Zhang. Protein–protein interactions regulating α-synuclein pathology. Trends in Neurosciences 2024, 47 (3) , 209-226. https://doi.org/10.1016/j.tins.2024.01.002
    28. Mantas Ziaunys, Darius Sulskis, Kamile Mikalauskaite, Andrius Sakalauskas, Ruta Snieckute, Vytautas Smirnovas. S100A9 Inhibits and Redirects Prion Protein 89-230 Fragment Amyloid Aggregation. 2024https://doi.org/10.1101/2024.02.06.579161
    29. Yogesh Prabhu, Abhilasha Jain, R. Lakshmi Narayan, Priyanka Saini, S. Vincent, W.H. Ryu, E.S. Park, Jatin Bhatt. Crystallization kinetics and nanoindentation studies of Cu46Zr40Ti8.5Al5.5 glassy alloy. Journal of Non-Crystalline Solids 2024, 625 , 122753. https://doi.org/10.1016/j.jnoncrysol.2023.122753
    30. Shamasree Ghosh, Shanmugam Tamilselvi, Chloe Williams, Sanduni W. Jayaweera, Igor A. Iashchishyn, Darius Šulskis, Jonathan D. Gilthorpe, Anders Olofsson, Vytautas Smirnovas, Željko M. Svedružić, Ludmilla A. Morozova-Roche. ApoE Isoforms Inhibit Amyloid Aggregation of Proinflammatory Protein S100A9. International Journal of Molecular Sciences 2024, 25 (4) , 2114. https://doi.org/10.3390/ijms25042114
    31. Darius Šulskis, Mantas Žiaunys, Andrius Sakalauskas, Rūta Sniečkutė, Vytautas Smirnovas. Formation of amyloid fibrils by the regulatory 14-3-3 ζ protein. Open Biology 2024, 14 (1) https://doi.org/10.1098/rsob.230285
    32. Xingmei Qi, Yu Wang, Hairui Yu, Ruifang Liu, Axel Leppert, Zihan Zheng, Xueying Zhong, Zhen Jin, Han Wang, Xiaoli Li, Xiuzhe Wang, Michael Landreh, Ludmilla A. Morozova‐Roche, Jan Johansson, Sidong Xiong, Igor Iashchishyn, Gefei Chen. Spider Silk Protein Forms Amyloid‐Like Nanofibrils through a Non‐Nucleation‐Dependent Polymerization Mechanism. Small 2023, 19 (46) https://doi.org/10.1002/smll.202304031
    33. Konstantia Nathanael, Sibo Cheng, Nina M. Kovalchuk, Rossella Arcucci, Mark J.H. Simmons. Optimization of microfluidic synthesis of silver nanoparticles: A generic approach using machine learning. Chemical Engineering Research and Design 2023, 193 , 65-74. https://doi.org/10.1016/j.cherd.2023.03.007
    34. Rimgailė Tamulytė, Evelina Jankaitytė, Zigmantas Toleikis, Vytautas Smirnovas, Marija Jankunec. Pro-inflammatory protein S100A9 alters membrane organization by dispersing ordered domains. Biochimica et Biophysica Acta (BBA) - Biomembranes 2023, 1865 (3) , 184113. https://doi.org/10.1016/j.bbamem.2022.184113
    35. Rebeka Szabó, Gábor Lente. Deterministic approximation for the nucleation-growth type model of nanoparticle formation: A validation against stochastic simulations. Chemical Engineering Journal 2022, 446 , 137377. https://doi.org/10.1016/j.cej.2022.137377
    36. Jennifer Mills, Norman Wagner. Rheokinetic modeling of N-A-S–H gel formation related to alkali-activated aluminosilicate materials. Rheologica Acta 2022, 61 (8-9) , 601-612. https://doi.org/10.1007/s00397-022-01351-2
    37. Zigmantas Toleikis, Raitis Bobrovs, Agne Janoniene, Alons Lends, Mantas Ziaunys, Ieva Baronaite, Vytautas Petrauskas, Kristine Kitoka, Vytautas Smirnovas, Kristaps Jaudzems. Interactions between S100A9 and Alpha-Synuclein: Insight from NMR Spectroscopy. International Journal of Molecular Sciences 2022, 23 (12) , 6781. https://doi.org/10.3390/ijms23126781
    38. Rebeka Szabó, Gábor Lente. General nucleation-growth type kinetic models of nanoparticle formation: possibilities of finding analytical solutions. Journal of Mathematical Chemistry 2021, 59 (7) , 1808-1821. https://doi.org/10.1007/s10910-021-01265-z
    39. Zigmantas Toleikis, Mantas Ziaunys, Lina Baranauskiene, Vytautas Petrauskas, Kristaps Jaudzems, Vytautas Smirnovas. S100A9 Alters the Pathway of Alpha-Synuclein Amyloid Aggregation. International Journal of Molecular Sciences 2021, 22 (15) , 7972. https://doi.org/10.3390/ijms22157972
    40. Lili Arabuli, Igor A. Iashchishyn, Nina V. Romanova, Greta Musteikyte, Vytautas Smirnovas, Himanshu Chaudhary, Željko M. Svedružić, Ludmilla A. Morozova-Roche. Co-Aggregation of S100A9 with DOPA and Cyclen-Based Compounds Manifested in Amyloid Fibril Thickening without Altering Rates of Self-Assembly. International Journal of Molecular Sciences 2021, 22 (16) , 8556. https://doi.org/10.3390/ijms22168556
    41. Mehdi Eshraghi, Aida Adlimoghaddam, Amir Mahmoodzadeh, Farzaneh Sharifzad, Hamed Yasavoli-Sharahi, Shahrokh Lorzadeh, Benedict C. Albensi, Saeid Ghavami. Alzheimer’s Disease Pathogenesis: Role of Autophagy and Mitophagy Focusing in Microglia. International Journal of Molecular Sciences 2021, 22 (7) , 3330. https://doi.org/10.3390/ijms22073330
    42. N.N. Yusof, S.K. Ghoshal, S.A. Jupri. Luminescence of Neodymium Ion-Activated Magnesium Zinc Sulfophosphate Glass: Role of Titanium Nanoparticles Sensitization. Optical Materials 2020, 109 , 110390. https://doi.org/10.1016/j.optmat.2020.110390
    43. Jonathan Pansieri, Igor A. Iashchishyn, Hussein Fakhouri, Lucija Ostojić, Mantas Malisauskas, Greta Musteikyte, Vytautas Smirnovas, Matthias M. Schneider, Tom Scheidt, Catherine K. Xu, Georg Meisl, Tuomas P. J. Knowles, Ehud Gazit, Rodolphe Antoine, Ludmilla A. Morozova-Roche. Templating S100A9 amyloids on Aβ fibrillar surfaces revealed by charge detection mass spectrometry, microscopy, kinetic and microfluidic analyses. Chemical Science 2020, 11 (27) , 7031-7039. https://doi.org/10.1039/C9SC05905A
    44. Chiharu Mizuguchi, Miho Nakagawa, Norihiro Namba, Misae Sakai, Naoko Kurimitsu, Ayane Suzuki, Kaho Fujita, Sayaka Horiuchi, Teruhiko Baba, Takashi Ohgita, Kazuchika Nishitsuji, Hiroyuki Saito. Mechanisms of aggregation and fibril formation of the amyloidogenic N-terminal fragment of apolipoprotein A-I. Journal of Biological Chemistry 2019, 294 (36) , 13515-13524. https://doi.org/10.1074/jbc.RA119.008000
    45. Joana S. Cristóvão, Cláudio M. Gomes. S100 Proteins in Alzheimer’s Disease. Frontiers in Neuroscience 2019, 13 https://doi.org/10.3389/fnins.2019.00463
    46. Amirmostafa Amirjani, Davoud Fatmehsari Haghshenas. Modified Finke–Watzky mechanisms for the two-step nucleation and growth of silver nanoparticles. Nanotechnology 2018, 29 (50) , 505602. https://doi.org/10.1088/1361-6528/aae3dd
    47. Chao Wang, Igor A. Iashchishyn, Jonathan Pansieri, Sofie Nyström, Oxana Klementieva, John Kara, Istvan Horvath, Roman Moskalenko, Reza Rofougaran, Gunnar Gouras, Gabor G. Kovacs, S. K. Shankar, Ludmilla A. Morozova-Roche. S100A9-Driven Amyloid-Neuroinflammatory Cascade in Traumatic Brain Injury as a Precursor State for Alzheimer’s Disease. Scientific Reports 2018, 8 (1) https://doi.org/10.1038/s41598-018-31141-x
    48. I. A. Kuznetsov, A. V. Kuznetsov. Simulating the effect of formation of amyloid plaques on aggregation of tau protein. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 2018, 474 (2220) , 20180511. https://doi.org/10.1098/rspa.2018.0511
    49. Istvan Horvath, Igor A. Iashchishyn, Roman A. Moskalenko, Chao Wang, Sebastian K. T. S. Wärmländer, Cecilia Wallin, Astrid Gräslund, Gabor G. Kovacs, Ludmilla A. Morozova-Roche. Co-aggregation of pro-inflammatory S100A9 with α-synuclein in Parkinson’s disease: ex vivo and in vitro studies. Journal of Neuroinflammation 2018, 15 (1) https://doi.org/10.1186/s12974-018-1210-9
    50. I. A. Kuznetsov, A. V. Kuznetsov. How the formation of amyloid plaques and neurofibrillary tangles may be related: a mathematical modelling study. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 2018, 474 (2210) , 20170777. https://doi.org/10.1098/rspa.2017.0777

    ACS Chemical Neuroscience

    Cite this: ACS Chem. Neurosci. 2017, 8, 10, 2152–2158
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acschemneuro.7b00251
    Published July 31, 2017
    Copyright © 2017 American Chemical Society

    Article Views

    2938

    Altmetric

    -

    Citations

    Learn about these metrics

    Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

    Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

    The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.