1 Effects of Shape on Interaction Dynamics of Tetrahedral Nanoplastics and the Cell Membrane

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Effects of Shape on Interaction Dynamics of Tetrahedral Nanoplastics and the Cell

Membrane

Xin Yong1*, Ke Du2

1 Department of Mechanical Engineering, Binghamton University, Binghamton, NY 13902,

USA.

2 Department of Chemical and Environmental Engineering, University of California,

Riverside, CA 92521, USA.

Abstract

Cellular uptake of nanoplastics is instrumental in their environmental accumulation and

transfers to humans through the food chain. Despite extensive studies using spherical plastic

nanoparticles, the influence of the morphological characteristics of environmentally released

nanoplastics is understudied. Using dissipative particle dynamics (DPD) simulations, we

modeled the interactions between hydrophobic nanotetrahedra and a cell membrane, featuring

high shape anisotropy and large surface curvature seen for environmental nanoplastics. We

observe robust uptake of nanotetrahedra with sharp vertices and edges by the lipid membrane.

Two local energy minimum configurations of nanotetrahedra embedded in the membrane

bilayer were identified for particles of large sizes. Further analysis of particle dynamics within

the membrane shows that the two interaction states exhibit distinct translational and rotational

dynamics in the directions normal and parallel to the plane of the membrane. The membrane

confinement significantly arrests the out-of-plane motion, resulting in caged translation and

subdiffusive rotation. While the in-plane diffusion remains Brownian, we find that the

translational and rotational modes decouple from each other as nanotetrahedra size increases.

The rotational diffusion decreases by a greater extent compared to the translational diffusion,

deviating from the continuum theory predictions. These results provide fundamental insight

into the shape effect on the nanoparticle dynamics in crowded lipid membranes.

*Email: xyong@binghamton.edu

Introduction

Synthetic polymers are ubiquitous in our society and daily life1 and their immense utility

simultaneously leads to a tremendous increase in plastic waste in landfills and the

environment.2–5 Nanoplastics can be defined as plastic fragments with sizes ranging from 1 nm

– 1 µm and can exhibit colloidal behavior.6,7 The majority of nanoplastics occurring in the

environment are formed by fragmentation and degradation of microscopic and macroscopic

plastic objects,8–11 while a small fraction is directly released from industrial products and

applications.12,13 Much of the recent research regarding plastic pollution has been dedicated to

microplastics. However, nanoplastics can behave very differently from their micron-sized

counterparts. Small dimensions, large surface areas, and specific colloidal properties could

facilitate the penetration of nanoplastics across the blood-brain and gut-blood barriers.14 which

presents an emerging threat to ecosystems and human health.15,16

The cell membrane is an important biological barrier for the uptake of nanoplastics. It is

known that nanoparticles utilize two major pathways for transporting across the lipid

membrane: direct penetration by passive diffusion and energy-consuming active translocation

termed as endocytosis.17–20 Both uptake pathways are governed by complex interactions of

nanoparticles with the lipid bilayer, and the internalization process sensitively depends on

particle size, shape, elasticity, chemical functionality, and surface charge.21–24 Previous studies

on nanoplastics predominantly used commercially available polystyrene latex nanospheres.16

Such engineered models mimic nanoplastics sampled in the environment for size but do not

capture highly irregular surface morphology resulting from natural weathering,9,25,26 which can

lead to biased results.27,28 The lack of a comprehensive understanding of how shape anisotropy

and large surface curvature influence cellular uptake of nanoplastics hampers our ability to

assess the impact to the environment and humans accurately.

Experimentally, probing the transmembrane transport of nanoparticles challenges

currently used sampling and detection techniques. Although microfluidic single-cell

analysis29,30 has provided much insight, the spatiotemporal resolutions of the reported

experimental setups are still limited in in situ characterization of anisotropic nanoplastics

interacting with the cell membrane in complex biological matrices. This leaves clear

opportunities for computational simulations. For nanoparticles of sizes comparable to or

smaller than the membranes, the finite thickness and lipid structure of the membrane become

important in the interaction.31 Particle-based simulation techniques have been widely used to

model the penetration, accumulation, and spontaneous translocation of small nanoparticles in

lipid membranes.32–35 It has been shown that passive diffusion can result in the trapping of

nanoplastics in the hydrophobic core of the bilayer.36–38 A very recent coarse-grained molecular

dynamics study using the MARTINI force field comprehensively investigated the

biomembrane interaction of nanoplastics of five representative polymer types and three aging

properties. The simulations revealed that interfacial processes, including nanoplastic

translocation, shape transformation, and membrane perturbation, are governed by the

competition of polymer–polymer and polymer–lipid interactions.39 Despite extensive research

on the interactions between anisotropic nanoparticles and cell membranes,40–46 only a handful

of previous studies explore the dynamics of colloid-membrane interaction in the context of

environmentally relevant nanoplastics, possessing unique characteristics of irregular shape and

specific surface chemistry. Moreover, most studies have focused on membrane insertion or

translocation of a single particle or membrane-mediated assembly of multiple particles. To the

extent of our knowledge, the detailed dynamics of anisotropic particles trapped within the

membrane have not been explored on the quantitative level.

In this work, we explore the dynamics of tetrahedral nanoplastics interacting with a model

plasma membrane using dissipative particle dynamics (DPD) simulations. We develop a new

coarse-grained model of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) bilayers (Fig. 1a)

to accurately reproduce physical bilayer properties, which approximate those of eukaryotic

plasma membranes.31 The simulations elucidate the detailed dynamics of hydrophobic

nanotetrahedra of varying sizes (Fig. 1b) interacting with the model membrane, focusing on

the quantitative characterization of long-time dynamics. We find different interaction states of

anisotropic nanotetrahedra in the membrane when the particle sizes become comparable to or

larger than the membrane thickness. More interestingly, these states feature distinct

translational and rotational dynamics of nanoparticles. Our results not only contribute to a

comprehensive understanding of the shape effect in nanoplastic-membrane interaction but also

provide insights into colloidal dynamics in crowded environments. Although this work focuses

on nanotetrahedra with a general hydrophobic chemistry, which represent only a group of

polymers within diverse nanoplastics detected in the environment,47,48 the establishment of the

new membrane model and dynamics analysis will serve as the starting point for studying

irregular nanoplastics.

Methods

A. Dissipative particle dynamics

DPD is a mesoscale method that has been widely used in simulating multiphase,

multicomponent systems because of its computational efficiency and ease of implementation.

The DPD model describes units of larger molecules or a group of smaller molecules as a single

bead.49,50 In contrast to Brownian dynamics, DPD includes explicit solvents and therefore is

capable of capturing hydrodynamic interactions51–53 important for membrane simulations. The

DPD beads interact with each other through a pairwise conservative force supplemented by

pairwise friction and random forces. The conservative force is a soft-core repulsion given

by

   

, where  is the maximum repulsion between beads  and ;  

 

 is the inter-bead distance; and    

 

 represents the force direction.



 is the cutoff radius of the interaction and is typically the same for all beads. The dissipative

force 

 is proportional to the relative velocity of two beads    , given by 



  , where  is a friction coefficient related to fluid viscosity. The random

force is a Gaussian white noise 

 , where  is the noise amplitude and 

is a random variable satisfying    and     

.  and  are arbitrary weight functions dependent on the interparticle distance. They

typically take the form of      

 together with  

( is the Boltzmann constant) to satisfy the fluctuation-dissipation theorem for a system in

equilibrium at temperature . The bead motion is governed by the Newton’s second

law  with the total force 

 

 



 . The sum is carried out

for all beads  within the cutoff radius 

 from bead

. As per convention of DPD simulations,

the beads have the same mass  . The results are presented in reduced units using 

 as

the characteristic length scale and the energy of thermal fluctuation  at room temperature

as the characteristic energy. The characteristic time scale is denoted as . For simplicity,

,,

, and  are all set to one. The equation of motion is integrated using the velocity-Verlet

algorithm with a time step   . The value of  is set at 4.5 to ensure numerical stability

of the integrator and rapid equilibration of the system temperature.

B. Model plasma membrane and tetrahedral nanoparticles

Inspired by the MARTINI model,54 a DOPC molecule is composed of 14 beads with a

H2E2(C5)2 architecture as shown in Fig. 1a. The zwitterionic phosphorylcholine group is

represented by two hydrophilic beads (H). Two beads of intermediate hydrophilicity model the

glycerol linking unit (E), with one of them connected to the head beads in a linear sequence.

The oleoyl tails are composed of five hydrophobic beads (C) and attached to the two E beads,

respectively. The central triplet (H-E-E) takes an off-linear configuration, which results in an

h-shape lipid model. The lipid membrane is surrounded by an aqueous solvent composed of

individual DPD beads. Each solvent (W) bead represents several water molecules. In this model,

one interaction site corresponds to, on average, four heavy atoms.54–56 This coarse-graining

mapping not only preserves the asymmetric structure of phospholipids but also recognizes the

chemical specificity of the glycerol ester moiety.

The bonded interactions are introduced between chemically connected sites, including

bond and angle interactions. All adjacent lipid beads are attached together using harmonic

springs with the potential   

   with a bond strength   







and an equilibrium bond length  

. The bond strength is similar to the one used in the

MARTINI model and the value equilibrium bond length is taken from previous DPD

models.57,58 The DPD interactions are excluded between bonded beads. Hydrocarbon chain

stiffness is imposed by harmonic angle potentials applied on three adjacent C beads, given by

  

  , where  is the bending constant and  is the equilibrium angle.59

The DPD interactions between second nearest neighbors are not excluded. For single bonds,

the angle potential parameters are  and . The angles involving the cis

double bond are assigned with higher bending stiffness  with an equilibrium

angle of 120°.54 The triplets H-E-C and E-C-C use the same angle potentials as for the lipid

tails, while the H-E-E triplet experiences a special angle potential for cis-bond bonds to

maintain the h-shape topology.

Each plastic nanoparticle is modeled as a cluster of hydrophobic DPD beads (denoted

by P), which are constrained to move as a rigid body. The constituent beads are assembled in

a face-centered-cubic (fcc) structure. The corresponding number density of beads in a particle

is set to 



 to prevent lipid and solvent beads from unphysically penetrating the particle.58,60

The resulting lattice constant is 

. Regular tetrahedrons of different sizes are carved from

the fcc assembly with four faces being the , , , and  planes (see Fig.

1b). The edge lengths, , of nanotetrahedra simulated in this work are 3.1, 5.6, 8.0 and 10.5



. The total numbers of beads in each particle are 56, 220, 560, and 1140, respectively.

The dynamics of this system, including solvated lipid bilayers and nanoparticles, are

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摘要：

1EffectsofShapeonInteractionDynamicsofTetrahedralNanoplasticsandtheCellMembraneXinYong1*,KeDu21DepartmentofMechanicalEngineering,BinghamtonUniversity,Binghamton,NY13902,USA.2DepartmentofChemicalandEnvironmentalEngineering,UniversityofCalifornia,Riverside,CA92521,USA.AbstractCellularuptakeofnanoplast...

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