Identification of electroporation sites in the complex lipid organization of the plasma membrane
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Evaluation Summary:
Using coarse-grained simulations, machine learning analysis and Bayesian inference modeling, the authors explored features that dictate the location and kinetics of electroporation in complex lipid membranes. The resulting understanding and modeling will lead to an effective multi-scale approach for predicting the kinetics of electroporation and guiding the design of experimental protocols for inducing electroporation in broad applications such as tumor treatment, gene therapy and vaccination against cancer.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)
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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 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 blind trial-and-error approaches to mechanistic-driven design.
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Evaluation Summary:
Using coarse-grained simulations, machine learning analysis and Bayesian inference modeling, the authors explored features that dictate the location and kinetics of electroporation in complex lipid membranes. The resulting understanding and modeling will lead to an effective multi-scale approach for predicting the kinetics of electroporation and guiding the design of experimental protocols for inducing electroporation in broad applications such as tumor treatment, gene therapy and vaccination against cancer.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)
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Reviewer #1 (Public Review):
In this computational study, the authors conducted extensive coarse-grained molecular dynamics simulations to study the location and kinetics of electroporation of complex lipid membranes. It was evident that electroporation does not occur uniformly in the membrane, but preferentially at certain locations. Analysis of the results using several machine learning models found that the local lipid composition is essential to the poration probability. While this observation is somewhat expected, the machine learning approach leads to a quantitative and predictive model, which is valuable. On the other hand, poration kinetics depend on additional lipid properties, such as the local area compressibility modulus. Again, this is somewhat expected qualitatively, but the Bayesian inference approach leads to a …
Reviewer #1 (Public Review):
In this computational study, the authors conducted extensive coarse-grained molecular dynamics simulations to study the location and kinetics of electroporation of complex lipid membranes. It was evident that electroporation does not occur uniformly in the membrane, but preferentially at certain locations. Analysis of the results using several machine learning models found that the local lipid composition is essential to the poration probability. While this observation is somewhat expected, the machine learning approach leads to a quantitative and predictive model, which is valuable. On the other hand, poration kinetics depend on additional lipid properties, such as the local area compressibility modulus. Again, this is somewhat expected qualitatively, but the Bayesian inference approach leads to a quantitative model that contains physically meaningful parameters (Eq. 3). Therefore, as the authors pointed out, by combining extensive coarse-grained simulations and these analyses, one is able to establish quantitative models for the poration kinetics at the whole-cell level, which are valuable for guiding the design/optimization of experimental protocols for electroporation in different applications.
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Reviewer #2 (Public Review):
In this work the authors illustrate some of the molecular mechanisms of membrane electroporation, using coarse-grained molecular dynamics simulations of complex, ~60 lipid type, membrane mimics of both a brain plasma membrane (BPM) and an average mammalian plasma membrane (APM). They repeatedly simulated electroporation with both hyperpolarized and depolarized applied electric fields and carefully analyzed the resulting simulations: mapping the time and location of pore formation and the local lipid composition and membrane properties. They successfully described the pore kinetics using Bayesian survival analysis, characterized the local bilayer environment at preferred pore formation sites, and trained machine learning models to accurately predict pore forming sites from the local bilayer environment. …
Reviewer #2 (Public Review):
In this work the authors illustrate some of the molecular mechanisms of membrane electroporation, using coarse-grained molecular dynamics simulations of complex, ~60 lipid type, membrane mimics of both a brain plasma membrane (BPM) and an average mammalian plasma membrane (APM). They repeatedly simulated electroporation with both hyperpolarized and depolarized applied electric fields and carefully analyzed the resulting simulations: mapping the time and location of pore formation and the local lipid composition and membrane properties. They successfully described the pore kinetics using Bayesian survival analysis, characterized the local bilayer environment at preferred pore formation sites, and trained machine learning models to accurately predict pore forming sites from the local bilayer environment. Together that illustrates the importance of both global and local bilayer properties and the role of different lipid types such as polyunsaturated lipids in electroporation.
Strengths of this work include the appropriate use of coarse-grained simulations to access larger length and time scales as well as complex plasma membrane mimics. Combined with clever and thorough analysis, that allowed them to show the strong spatial dependence of poration sites on local lipid composition. The use of coarse-grained simulations is also the main weakness of this work because, as pointed out by the authors, they have significant limitations. Having further reduced degrees of freedom compared to all-atom simulations, coarse-grained force fields average over or implicitly include shorter and faster motion(s). The Martini force field used here has successfully captured a wide range of membrane dynamics and functions, including several demonstrations of electroporation in simple systems, but its lack of directional hydrogen bonding and the mapping of four water molecules to a single coarse-grained bead limits its ability to capture the finer details of pore formation.
These predictions are still of significant interest and hopefully will motivate further work, such as: 1) validation - using finer resolution modeling as well as experiments, and 2) extensions - such as those proposed in the manuscript, as well as combining local lipid neighborhood analysis with pore initiation rate analysis which would allow for finer decomposition of relative importance of different local vs global properties.
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Reviewer #3 (Public Review):
The open questions targeted by this study refer to the proneness of membranes to electroporation as a function of membrane composition. The authors use two previously setup coarse grained membrane systems with a complex composition mimicking average mammal and brain plasma membranes, and expose them to nanosecond electroporating pulses. Pore formation and kinetics is studied using coarse grained molecular dynamics simulations, machine learning and Bayesian survival analysis. The authors demonstrate that this combination of approaches is a powerful tool to investigate the individual role of a variety of components of such complex heterogeneous systems in the electroporation phenomena.
A very valuable outcome of this study is the finding that pores colocalize with specific compositional regions in the …
Reviewer #3 (Public Review):
The open questions targeted by this study refer to the proneness of membranes to electroporation as a function of membrane composition. The authors use two previously setup coarse grained membrane systems with a complex composition mimicking average mammal and brain plasma membranes, and expose them to nanosecond electroporating pulses. Pore formation and kinetics is studied using coarse grained molecular dynamics simulations, machine learning and Bayesian survival analysis. The authors demonstrate that this combination of approaches is a powerful tool to investigate the individual role of a variety of components of such complex heterogeneous systems in the electroporation phenomena.
A very valuable outcome of this study is the finding that pores colocalize with specific compositional regions in the membrane. From molecular dynamics studies, the authors find that, among other factors, in regions of higher local concentration of polyunsaturated lipids the membrane is easier to porate. Gangliosides appear to also substantially affect poration. Furthermore, the work shows dependence of the poration kinetics on the stretching elasticity of the membrane. It also puts forward the potential of the combination of the employed methods to help better understand and infer the kinetics of pore formation under short (sub)nanosecond electric pulses from numerical analysis.
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