Identification of electroporation sites in the complex lipid organization of the plasma membrane
- University of Lubljana, Slovenia
- KTH Royal Institute of Technology, Sweden
Abstract
The plasma membrane of a biological cell is a complex assembly of lipids and membrane proteins, which tightly regulate transmembrane transport. When a cell is exposed to strong electric field, the membrane integrity becomes transiently disrupted by formation of transmembrane pores. This phenomenon termed electroporation is already utilized in many rapidly developing applications in medicine including gene therapy, cancer treatment, and treatment of cardiac arrythmias. However, the molecular mechanisms of electroporation are not yet sufficiently well understood; in particular, it is unclear where exactly pores form in the complex organization of the plasma membrane. In this study we combine coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis to identify how formation of pores depends on the local lipid organization. We show that pores do not form homogeneously across the membrane, but colocalize with domains that have specific features, the most important being high density of polyunsaturated lipids. We further show that knowing the lipid organization is sufficient to reliably predict poration sites with machine learning. Additionally, by analysing poration kinetics with Bayesian survival analysis we show that poration does not depend solely on local lipid arrangement, but also on membrane mechanical properties and the polarity of the electric field. Finally, we discuss how the combination of atomistic and coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis can guide the design of future experiments and help us to develop an accurate description of plasma membrane electroporation on the whole-cell level. Achieving this will allow us to shift the optimization of electroporation applications from blind trial-and-error approaches to mechanistic-driven design.
Data availability
The current manuscript is a computational study, so no data have been generated for this manuscript. Analysis codes are available at https://github.com/learems/Electroporation-CGmem-MemSurfer, https://github.com/learems/Electroporation-CGmem-MachineLearning, https://docs.pymc.io/en/v3/pymc-examples/examples/survival_analysis/survival_analysis.html and https://github.com/sperezconesa/electroporation_modeling
Article and author information
Author details
Funding
Science for Life Laboratory
- Ilaria Testa
- Lucie Delemotte
Vetenskapsrådet (2018-04905)
- Lucie Delemotte
Gustafssons Stiftelse
- Lucie Delemotte
Horizon 2020 Marie Skłodowska-Curie (893077)
- Lea Rems
Slovenian Research Agency (J2-2503)
- Lea Rems
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Qiang Cui, Boston University, United States
Version history
- Preprint posted: October 16, 2021 (view preprint)
- Received: October 16, 2021
- Accepted: February 22, 2022
- Accepted Manuscript published: February 23, 2022 (version 1)
- Version of Record published: March 10, 2022 (version 2)
Copyright
© 2022, Rems et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Identification of electroporation sites in the complex lipid organization of the plasma membrane
eLife 11:e74773.
https://doi.org/10.7554/eLife.74773
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