[Paper Review] Cracking donuts and sorting lipids: geometry controls archaeal membrane stability and lipid organization
The paper uses coarse-grained MD simulations of toroidal vesicles to show how archaeal membrane structure (bilayer vs bolalipid monolayer) and curvature jointly influence shape stability and lipid sorting, with mean curvature driving u-shaped bolalipid enrichment and membrane remodeling.
Cells are defined by lipid membranes that differ in their structure across the tree of life. While the membranes of most bacteria and eukaryotes consist of single-headed bilayer lipids, the membranes of archaea are composed of mixtures of single-headed bilayer lipids and double-headed bolalipids. Archaeal bolalipids can adopt straight or u-shaped conformations, enabling them - together with bilayer lipids - to control whether membranes form bilayer or monolayer structures. Yet, the physical principles governing archaeal membranes remain largely unexplored, especially how membrane structure couples to externally imposed curvature during membrane remodeling. Here, we perform coarse-grained molecular dynamics simulations of toroidal vesicles to systematically probe the effects of all relevant combinations of mean and Gaussian curvatures on shape stability and lipid organization. We find that soft bilayer membranes can sustain all curvatures induced, whereas rigid bolalipid monolayer membranes either transition to different vesicle shapes or rupture. Bilayer-mimicking u-shaped bolalipids and bilayer lipids are spatially accumulated in regions of high mean membrane curvature independent of Gaussian curvature. Our work identifies curvature-composition coupling as a physical signature of archaeal membrane remodeling.
Motivation & Objective
- Investigate how archaeal membrane structure (bilayer vs bolalipid monolayer) affects remodeling under curvature.
- Determine how external curvature influences shape stability and topological transitions.
- Quantify curvature-induced lipid sorting between bolalipids and bilayer lipids in mixed membranes.
Proposed method
- Extend Cooke and Deserno bilayer model to include bolalipids with adjustable rigidity k_bola.
- Represent lipids as bead-spring chains with harmonic angle potentials controlling stiffness.
- Simulate toroidal vesicles spanning combinations of mean and Gaussian curvatures to map stability.
- Compute reduced volume nu to quantify shape and use Euler characteristic to count pores/handles.
- Relate u-shaped bolalipid fraction to curvature via linearized free-energy expansion.
- Analyze spatial lipid distributions on torus and fit curvature–composition relations.

Experimental results
Research questions
- RQ1How does archaeal membrane composition (bilayer lipid content and bolalipid rigidity) determine toroidal vesicle stability under curvature?
- RQ2How do mean and Gaussian curvatures influence lipid sorting between bolalipids and bilayer lipids?
- RQ3Can curvature-composition coupling predict topological remodeling pathways such as fission or pore formation?
- RQ4What are the conditions under which toroidal vesicles transition to spherical or planar morphologies?
- RQ5Do straight vs u-shaped bolalipids preferentially populate regions of high curvature, and is this determined by curvature?
Key findings
- Soft bilayer membranes remain stable across curvature variations, while rigid bolalipid monolayers rupture or undergo pore formation.
- In mixed membranes, u-shaped bolalipids and bilayer lipids accumulate in regions of high mean curvature independent of Gaussian curvature.
- Pores form heterogeneously on inner/outer torus surfaces where mean curvature is maximal, suggesting curvature-stress relief mechanisms.
- Torodal vesicles with higher bolalipid rigidity show transitions to spherical shapes or rupture, increasing fission energy with k_bola.
- In mixtures with high bilayer fraction, toroidal shapes persist, and lipid sorting reflects curvature-driven organization.
- Curvature sorting of u-shaped bolalipids scales linearly with squared mean curvature, consistent with a theoretical continuum model.

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This review was created by AI and reviewed by human editors.