[{"has_accepted_license":"1","publication":"PLOS Computational Biology","article_number":"e1010586","month":"10","project":[{"_id":"eba2549b-77a9-11ec-83b8-a81e493eae4e","call_identifier":"H2020","name":"Non-Equilibrium Protein Assembly: from Building Blocks to Biological Machines","grant_number":"802960"},{"_id":"eba0f67c-77a9-11ec-83b8-cc8501b3e222","grant_number":"96752","name":"The evolution of trafficking: from archaea to eukaryotes"}],"oa_version":"Published Version","keyword":["Computational Theory and Mathematics","Cellular and Molecular Neuroscience","Genetics","Molecular Biology","Ecology","Modeling and Simulation","Ecology","Evolution","Behavior and Systematics"],"language":[{"iso":"eng"}],"type":"journal_article","date_published":"2022-10-17T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"publication_identifier":{"issn":["1553-7358"]},"status":"public","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","related_material":{"link":[{"relation":"software","url":"https://github.com/sharonJXY/3-filament-model"}]},"file":[{"success":1,"relation":"main_file","access_level":"open_access","creator":"dernst","file_id":"12359","checksum":"bada6a7865e470cf42bbdfa67dd471d2","file_size":2641067,"date_created":"2023-01-24T10:45:01Z","file_name":"2022_PLoSCompBio_Jiang.pdf","content_type":"application/pdf","date_updated":"2023-01-24T10:45:01Z"}],"issue":"10","author":[{"last_name":"Jiang","first_name":"Xiuyun","full_name":"Jiang, Xiuyun"},{"full_name":"Harker-Kirschneck, Lena","first_name":"Lena","last_name":"Harker-Kirschneck"},{"id":"3adeca52-9313-11ed-b1ac-c170b2505714","first_name":"Christian Eduardo","last_name":"Vanhille-Campos","full_name":"Vanhille-Campos, Christian Eduardo"},{"full_name":"Pfitzner, Anna-Katharina","last_name":"Pfitzner","first_name":"Anna-Katharina"},{"full_name":"Lominadze, Elene","last_name":"Lominadze","first_name":"Elene"},{"full_name":"Roux, Aurélien","last_name":"Roux","first_name":"Aurélien"},{"full_name":"Baum, Buzz","last_name":"Baum","first_name":"Buzz"},{"id":"bf63d406-f056-11eb-b41d-f263a6566d8b","orcid":"0000-0002-7854-2139","full_name":"Šarić, Anđela","first_name":"Anđela","last_name":"Šarić"}],"scopus_import":"1","_id":"12152","intvolume":"        18","title":"Modelling membrane reshaping by staged polymerization of ESCRT-III filaments","date_created":"2023-01-12T12:08:10Z","article_processing_charge":"No","department":[{"_id":"AnSa"}],"publication_status":"published","file_date_updated":"2023-01-24T10:45:01Z","ec_funded":1,"quality_controlled":"1","article_type":"original","publisher":"Public Library of Science","external_id":{"isi":["000924885500005"]},"isi":1,"citation":{"chicago":"Jiang, Xiuyun, Lena Harker-Kirschneck, Christian Eduardo Vanhille-Campos, Anna-Katharina Pfitzner, Elene Lominadze, Aurélien Roux, Buzz Baum, and Anđela Šarić. “Modelling Membrane Reshaping by Staged Polymerization of ESCRT-III Filaments.” <i>PLOS Computational Biology</i>. Public Library of Science, 2022. <a href=\"https://doi.org/10.1371/journal.pcbi.1010586\">https://doi.org/10.1371/journal.pcbi.1010586</a>.","ieee":"X. Jiang <i>et al.</i>, “Modelling membrane reshaping by staged polymerization of ESCRT-III filaments,” <i>PLOS Computational Biology</i>, vol. 18, no. 10. Public Library of Science, 2022.","ama":"Jiang X, Harker-Kirschneck L, Vanhille-Campos CE, et al. Modelling membrane reshaping by staged polymerization of ESCRT-III filaments. <i>PLOS Computational Biology</i>. 2022;18(10). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1010586\">10.1371/journal.pcbi.1010586</a>","apa":"Jiang, X., Harker-Kirschneck, L., Vanhille-Campos, C. E., Pfitzner, A.-K., Lominadze, E., Roux, A., … Šarić, A. (2022). Modelling membrane reshaping by staged polymerization of ESCRT-III filaments. <i>PLOS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1010586\">https://doi.org/10.1371/journal.pcbi.1010586</a>","ista":"Jiang X, Harker-Kirschneck L, Vanhille-Campos CE, Pfitzner A-K, Lominadze E, Roux A, Baum B, Šarić A. 2022. Modelling membrane reshaping by staged polymerization of ESCRT-III filaments. PLOS Computational Biology. 18(10), e1010586.","mla":"Jiang, Xiuyun, et al. “Modelling Membrane Reshaping by Staged Polymerization of ESCRT-III Filaments.” <i>PLOS Computational Biology</i>, vol. 18, no. 10, e1010586, Public Library of Science, 2022, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1010586\">10.1371/journal.pcbi.1010586</a>.","short":"X. Jiang, L. Harker-Kirschneck, C.E. Vanhille-Campos, A.-K. Pfitzner, E. Lominadze, A. Roux, B. Baum, A. Šarić, PLOS Computational Biology 18 (2022)."},"year":"2022","date_updated":"2023-08-04T09:03:21Z","abstract":[{"text":"ESCRT-III filaments are composite cytoskeletal polymers that can constrict and cut cell membranes from the inside of the membrane neck. Membrane-bound ESCRT-III filaments undergo a series of dramatic composition and geometry changes in the presence of an ATP-consuming Vps4 enzyme, which causes stepwise changes in the membrane morphology. We set out to understand the physical mechanisms involved in translating the changes in ESCRT-III polymer composition into membrane deformation. We have built a coarse-grained model in which ESCRT-III polymers of different geometries and mechanical properties are allowed to copolymerise and bind to a deformable membrane. By modelling ATP-driven stepwise depolymerisation of specific polymers, we identify mechanical regimes in which changes in filament composition trigger the associated membrane transition from a flat to a buckled state, and then to a tubule state that eventually undergoes scission to release a small cargo-loaded vesicle. We then characterise how the location and kinetics of polymer loss affects the extent of membrane deformation and the efficiency of membrane neck scission. Our results identify the near-minimal mechanical conditions for the operation of shape-shifting composite polymers that sever membrane necks.","lang":"eng"}],"day":"17","doi":"10.1371/journal.pcbi.1010586","ddc":["570"],"volume":18,"acknowledgement":"A.S . received an award from European Research Council (https://erc.europa.eu, “NEPA\"\r\n802960), and an award from the Royal Society (https://royalsociety.org, UF160266). L. H.-K.\r\nreceived an award from the Biotechnology and Biological Sciences Research Council (https://\r\nwww.ukri.org/councils/bbsrc/). E. L. received an award from the University College London (https://www.ucl.ac.uk/biophysics/news/2022/feb/applications-biop-brian-duff-and-ipls-summerundergraduate-studentships-now-open, Brian Duff Undergraduate Summer Research Studentship). B.B. and A.S. received an award from Volkswagen Foundation https://www.volkswagenstiftung.de/en/foundation, Az 96727), and an award from Medical Research Council (https://www.ukri.org/councils/mrc, MC_CF1226). A. R. received an\r\naward from the Swiss National Fund for Research (https://www.snf.ch/en, 31003A_130520,\r\n31003A_149975, and 31003A_173087) and an award from the European Research Council\r\nConsolidator (https://erc.europa.eu, 311536). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."},{"file":[{"creator":"dernst","file_id":"12442","access_level":"open_access","relation":"main_file","success":1,"file_name":"2022_FrontiersNeuroscience_Weiffert2.pdf","content_type":"application/pdf","date_updated":"2023-01-30T09:15:13Z","checksum":"e67d16113ffb4fb4fa38a183d169f210","file_size":19798610,"date_created":"2023-01-30T09:15:13Z"}],"status":"public","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publication_identifier":{"issn":["1662-453X"]},"oa":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"type":"journal_article","date_published":"2022-09-20T00:00:00Z","keyword":["General Neuroscience"],"language":[{"iso":"eng"}],"oa_version":"Published Version","article_number":"943355","month":"09","has_accepted_license":"1","publication":"Frontiers in Neuroscience","acknowledgement":"This work was supported by grants from the Swedish Research Council (grant no. 2015-00143) and the European Research Council (grant no. 340890).","volume":16,"ddc":["570"],"day":"20","doi":"10.3389/fnins.2022.943355","abstract":[{"text":"Amyloid formation is linked to devastating neurodegenerative diseases, motivating detailed studies of the mechanisms of amyloid formation. For Aβ, the peptide associated with Alzheimer’s disease, the mechanism and rate of aggregation have been established for a range of variants and conditions <jats:italic>in vitro</jats:italic> and in bodily fluids. A key outstanding question is how the relative stabilities of monomers, fibrils and intermediates affect each step in the fibril formation process. By monitoring the kinetics of aggregation of Aβ42, in the presence of urea or guanidinium hydrochloride (GuHCl), we here determine the rates of the underlying microscopic steps and establish the importance of changes in relative stability induced by the presence of denaturant for each individual step. Denaturants shift the equilibrium towards the unfolded state of each species. We find that a non-ionic denaturant, urea, reduces the overall aggregation rate, and that the effect on nucleation is stronger than the effect on elongation. Urea reduces the rate of secondary nucleation by decreasing the coverage of fibril surfaces and the rate of nucleus formation. It also reduces the rate of primary nucleation, increasing its reaction order. The ionic denaturant, GuHCl, accelerates the aggregation at low denaturant concentrations and decelerates the aggregation at high denaturant concentrations. Below approximately 0.25 M GuHCl, the screening of repulsive electrostatic interactions between peptides by the charged denaturant dominates, leading to an increased aggregation rate. At higher GuHCl concentrations, the electrostatic repulsion is completely screened, and the denaturing effect dominates. The results illustrate how the differential effects of denaturants on stability of monomer, oligomer and fibril translate to differential effects on microscopic steps, with the rate of nucleation being most strongly reduced.","lang":"eng"}],"citation":{"ieee":"T. Weiffert <i>et al.</i>, “Influence of denaturants on amyloid β42 aggregation kinetics,” <i>Frontiers in Neuroscience</i>, vol. 16. Frontiers Media, 2022.","chicago":"Weiffert, Tanja, Georg Meisl, Samo Curk, Risto Cukalevski, Anđela Šarić, Tuomas P. J. Knowles, and Sara Linse. “Influence of Denaturants on Amyloid Β42 Aggregation Kinetics.” <i>Frontiers in Neuroscience</i>. Frontiers Media, 2022. <a href=\"https://doi.org/10.3389/fnins.2022.943355\">https://doi.org/10.3389/fnins.2022.943355</a>.","ama":"Weiffert T, Meisl G, Curk S, et al. Influence of denaturants on amyloid β42 aggregation kinetics. <i>Frontiers in Neuroscience</i>. 2022;16. doi:<a href=\"https://doi.org/10.3389/fnins.2022.943355\">10.3389/fnins.2022.943355</a>","apa":"Weiffert, T., Meisl, G., Curk, S., Cukalevski, R., Šarić, A., Knowles, T. P. J., &#38; Linse, S. (2022). Influence of denaturants on amyloid β42 aggregation kinetics. <i>Frontiers in Neuroscience</i>. Frontiers Media. <a href=\"https://doi.org/10.3389/fnins.2022.943355\">https://doi.org/10.3389/fnins.2022.943355</a>","ista":"Weiffert T, Meisl G, Curk S, Cukalevski R, Šarić A, Knowles TPJ, Linse S. 2022. Influence of denaturants on amyloid β42 aggregation kinetics. Frontiers in Neuroscience. 16, 943355.","short":"T. Weiffert, G. Meisl, S. Curk, R. Cukalevski, A. Šarić, T.P.J. Knowles, S. Linse, Frontiers in Neuroscience 16 (2022).","mla":"Weiffert, Tanja, et al. “Influence of Denaturants on Amyloid Β42 Aggregation Kinetics.” <i>Frontiers in Neuroscience</i>, vol. 16, 943355, Frontiers Media, 2022, doi:<a href=\"https://doi.org/10.3389/fnins.2022.943355\">10.3389/fnins.2022.943355</a>."},"year":"2022","date_updated":"2023-08-04T09:48:56Z","external_id":{"isi":["000866287100001"]},"isi":1,"publisher":"Frontiers Media","article_type":"original","quality_controlled":"1","file_date_updated":"2023-01-30T09:15:13Z","article_processing_charge":"No","date_created":"2023-01-16T09:56:43Z","department":[{"_id":"AnSa"}],"publication_status":"published","intvolume":"        16","title":"Influence of denaturants on amyloid β42 aggregation kinetics","scopus_import":"1","_id":"12251","author":[{"first_name":"Tanja","last_name":"Weiffert","full_name":"Weiffert, Tanja"},{"first_name":"Georg","last_name":"Meisl","full_name":"Meisl, Georg"},{"full_name":"Curk, Samo","first_name":"Samo","last_name":"Curk"},{"full_name":"Cukalevski, Risto","first_name":"Risto","last_name":"Cukalevski"},{"id":"bf63d406-f056-11eb-b41d-f263a6566d8b","last_name":"Šarić","first_name":"Anđela","full_name":"Šarić, Anđela","orcid":"0000-0002-7854-2139"},{"full_name":"Knowles, Tuomas P. J.","first_name":"Tuomas P. J.","last_name":"Knowles"},{"last_name":"Linse","first_name":"Sara","full_name":"Linse, Sara"}]}]