Identification of electroporation sites in the complex lipid organization of the plasma membrane

Rems, Lea (author), Tang, Xinru (author), Zhao, Fangwei (author), Pérez-Conesa, Sergio (author), Testa, Ilaria (author), Delemotte, Lucie (author)

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 trans-membrane pores. This phenomenon termed electroporation is already utilized in many rapidly developing applications in medicine including gene therapy, cancer treatment, and treatment of cardiac arrhythmias. 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 blindtrial- and- error approaches to mechanistic-driven design.

• Language: English
• Keywords: plasma membrane, electric field, pores, molecular dynamics simulation, machine learning, Bayesian survival analysis
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2022
• Number of pages: Str. 1-26
• Numbering: Vol. 11, e74773
• PID: 20.500.12556/RUL-152746
• UDC: 577
• ISSN on article: 2050-084X
• DOI: 10.7554/eLife.74773
• COBISS.SI-ID: 100814851
• Publication date in RUL: 05.12.2023

Record is a part of a journal

• Title: eLife
• Shortened title: eLife
• Publisher: eLife Sciences Publications
• ISSN: 2050-084X
• COBISS.SI-ID: 523069721

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: celična membrana, električno polje, pore, simulacije molekularne dinamike, strojno učenje, Bayesova analiza preživetja

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: J2-2503
• Name: Vpliv visokonapetostnih električnih pulzov na membranske proteine pri elektroporaciji

• Funder: EC - European Commission
• Funding programme: H2020
• Project number: 893077
• Name: Controlling the susceptibility of biological cells to pulsed electric field treatment by using ion channel modulators
• Acronym: EPmIC