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Understanding cytoskeletal avalanches using mechanical stability analysis
Eukaryotic cells are mechanically supported by a polymer network called the cytoskeleton, which consumes chemical energy to dynamically remodel its structure. Recent experiments in vivo have revealed that this remodeling occasionally happens through anomalously large displacements, reminiscent of ea...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521716/ https://www.ncbi.nlm.nih.gov/pubmed/34611021 http://dx.doi.org/10.1073/pnas.2110239118 |
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author | Floyd, Carlos Levine, Herbert Jarzynski, Christopher Papoian, Garegin A. |
author_facet | Floyd, Carlos Levine, Herbert Jarzynski, Christopher Papoian, Garegin A. |
author_sort | Floyd, Carlos |
collection | PubMed |
description | Eukaryotic cells are mechanically supported by a polymer network called the cytoskeleton, which consumes chemical energy to dynamically remodel its structure. Recent experiments in vivo have revealed that this remodeling occasionally happens through anomalously large displacements, reminiscent of earthquakes or avalanches. These cytoskeletal avalanches might indicate that the cytoskeleton’s structural response to a changing cellular environment is highly sensitive, and they are therefore of significant biological interest. However, the physics underlying “cytoquakes” is poorly understood. Here, we use agent-based simulations of cytoskeletal self-organization to study fluctuations in the network’s mechanical energy. We robustly observe non-Gaussian statistics and asymmetrically large rates of energy release compared to accumulation in a minimal cytoskeletal model. The large events of energy release are found to correlate with large, collective displacements of the cytoskeletal filaments. We also find that the changes in the localization of tension and the projections of the network motion onto the vibrational normal modes are asymmetrically distributed for energy release and accumulation. These results imply an avalanche-like process of slow energy storage punctuated by fast, large events of energy release involving a collective network rearrangement. We further show that mechanical instability precedes cytoquake occurrence through a machine-learning model that dynamically forecasts cytoquakes using the vibrational spectrum as input. Our results provide a connection between the cytoquake phenomenon and the network’s mechanical energy and can help guide future investigations of the cytoskeleton’s structural susceptibility. |
format | Online Article Text |
id | pubmed-8521716 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-85217162021-10-27 Understanding cytoskeletal avalanches using mechanical stability analysis Floyd, Carlos Levine, Herbert Jarzynski, Christopher Papoian, Garegin A. Proc Natl Acad Sci U S A Biological Sciences Eukaryotic cells are mechanically supported by a polymer network called the cytoskeleton, which consumes chemical energy to dynamically remodel its structure. Recent experiments in vivo have revealed that this remodeling occasionally happens through anomalously large displacements, reminiscent of earthquakes or avalanches. These cytoskeletal avalanches might indicate that the cytoskeleton’s structural response to a changing cellular environment is highly sensitive, and they are therefore of significant biological interest. However, the physics underlying “cytoquakes” is poorly understood. Here, we use agent-based simulations of cytoskeletal self-organization to study fluctuations in the network’s mechanical energy. We robustly observe non-Gaussian statistics and asymmetrically large rates of energy release compared to accumulation in a minimal cytoskeletal model. The large events of energy release are found to correlate with large, collective displacements of the cytoskeletal filaments. We also find that the changes in the localization of tension and the projections of the network motion onto the vibrational normal modes are asymmetrically distributed for energy release and accumulation. These results imply an avalanche-like process of slow energy storage punctuated by fast, large events of energy release involving a collective network rearrangement. We further show that mechanical instability precedes cytoquake occurrence through a machine-learning model that dynamically forecasts cytoquakes using the vibrational spectrum as input. Our results provide a connection between the cytoquake phenomenon and the network’s mechanical energy and can help guide future investigations of the cytoskeleton’s structural susceptibility. National Academy of Sciences 2021-10-12 2021-10-05 /pmc/articles/PMC8521716/ /pubmed/34611021 http://dx.doi.org/10.1073/pnas.2110239118 Text en Copyright © 2021 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences Floyd, Carlos Levine, Herbert Jarzynski, Christopher Papoian, Garegin A. Understanding cytoskeletal avalanches using mechanical stability analysis |
title | Understanding cytoskeletal avalanches using mechanical stability analysis |
title_full | Understanding cytoskeletal avalanches using mechanical stability analysis |
title_fullStr | Understanding cytoskeletal avalanches using mechanical stability analysis |
title_full_unstemmed | Understanding cytoskeletal avalanches using mechanical stability analysis |
title_short | Understanding cytoskeletal avalanches using mechanical stability analysis |
title_sort | understanding cytoskeletal avalanches using mechanical stability analysis |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521716/ https://www.ncbi.nlm.nih.gov/pubmed/34611021 http://dx.doi.org/10.1073/pnas.2110239118 |
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