Negative lipid membranes enhance the adsorption of TAT-decorated elastin-like polypeptide micelles.

A cell-penetrating peptide (CPP) is a short amino-acid sequence capable of efficiently translocating across the cellular membrane of mammalian cells. However, the potential of CPPs as a delivery vector is hampered by the strong reduction of its translocation efficiency when it bears an attached molecular cargo. To overcome this problem, we used previously developed diblock copolymers of elastin-like polypeptides (ELPBCs), which we end functionalized with TAT (transactivator of transcription), an archetypal CPP built from a positively charged amino acid sequence of the HIV-1 virus. These ELPBCs self-assemble into micelles at a specific temperature and present the TAT peptide on their corona. These micelles can recover the lost membrane affinity of TAT and can trigger interactions with the membrane despite the presence of a molecular cargo. Herein, we study the influence of membrane surface charge on the adsorption of TAT-functionalized ELP micelles onto giant unilamellar vesicles (GUVs). We show that the TAT-ELPBC micelles show an increased binding constant toward negatively charged membranes compared to neutral membranes, but no translocation is observed. The affinity of the TAT-ELPBC micelles for the GUVs displays a stepwise dependence on the lipid charge of the GUV, which, to our knowledge, has not been reported previously for interactions between peptides and lipid membranes. By unveiling the key steps controlling the interaction of an archetypal CPP with lipid membranes, through regulation of the charge of the lipid bilayer, our results pave the way for a better design of delivery vectors based on CPPs.

Real-time signal processing via chemical reactions for a microfluidic molecular communication system.

Signal processing over the molecular domain is critical for analysing, modifying, and synthesising chemical signals in molecular communication systems. However, the lack of chemical signal processing blocks and the wide use of electronic devices to process electrical signals in existing molecular communication platforms can hardly meet the biocompatible, non-invasive, and size-miniaturised requirements of applications in various fields, e.g., medicine, biology, and environment sciences. To tackle this, here we design and construct a liquid-based microfluidic molecular communication platform for performing chemical concentration signal processing and digital signal transmission over distances. By specifically designing chemical reactions and microfluidic geometry, the transmitter of our platform is capable of shaping the emitted signals, and the receiver is able to threshold, amplify, and detect the chemical signals after propagation. By encoding bit information into the concentration of sodium hydroxide, we demonstrate that our platform can achieve molecular signal modulation and demodulation functionalities, and reliably transmit text messages over long distances. This platform is further optimised to maximise data rate while minimising communication error. The presented methodology for real-time chemical signal processing can enable the implementation of signal processing units in biological settings and then unleash its potential for interdisciplinary applications.

Real-Time Monitoring and Control of Nanoparticle Formation.

Methods capable of controlling synthesis at the level of an individual nanoparticle are a key step toward improved reproducibility and scalability in engineering complex nanomaterials. To address this, we combine the spatially patterned activation of the photoreductant sodium pyruvate with interferometric scattering microscopy to achieve fast, label-free monitoring and control of hundreds of gold nanoparticles in real time. Individual particle growth kinetics are well-described by a two-step nucleation-autocatalysis model but with a distribution of individual rate constants that change with reaction conditions.

Ripple-like instability in the simulated gel phase of finite size phosphocholine bilayers.

Atomistic molecular dynamics simulations have reached a degree of maturity that makes it possible to investigate the lipid polymorphism of model bilayers over a wide range of temperatures. However if both the fluid Lα and tilted gel [Formula: see text] states are routinely obtained, the [Formula: see text] ripple phase of phosphatidylcholine lipid bilayers is still unsatifactorily described. Performing simulations of lipid bilayers made of different numbers of DPPC (1,2-dipalmitoylphosphatidylcholine) molecules ranging from 32 to 512, we demonstrate that the tilted gel phase [Formula: see text] expected below the pretransition cannot be obtained for large systems (equal or larger than 94 DPPC molecules) through common simulations settings or temperature treatments. Large systems are instead found in a disordered gel phase which display configurations, topography and energies reminiscent from the ripple phase [Formula: see text] observed between the pretransition and the main melting transition. We show how the state of the bilayers below the melting transition can be controlled and depends on thermal history and conditions of preparations. A mechanism for the observed topographic instability is suggested.

MLLPA: A Machine Learning-assisted Python module to study phase-specific events in lipid membranes.

Machine Learning-assisted Lipid Phase Analysis (MLLPA) is a new Python 3 module developed to analyze phase domains in a lipid membrane based on lipid molecular states. Reading standard simulation coordinate and trajectory files, the software first analyze the phase composition of the lipid membrane by using machine learning tools to label each individual molecules with respect to their state, and then decompose the simulation box using Voronoi tessellations to analyze the local environment of all the molecules of interest. MLLPA is versatile as it can read from multiple format (e.g., GROMACS, LAMMPS) and from either all-atom (e.g., CHARMM36) or coarse-grain models (e.g., Martini). It can also analyze multiple geometries of membranes (e.g., bilayers, vesicles). Finally, the software allows for training with more than two phases, allowing for multiple phase coexistence analysis.

A machine learning study of the two states model for lipid bilayer phase transitions.

We have adapted a set of classification algorithms, also known as machine learning, to the identification of fluid and gel domains close to the main transition of dipalmitoyl-phosphatidylcholine (DPPC) bilayers. Using atomistic molecular dynamics conformations in the low and high temperature phases as learning sets, the algorithm was trained to categorise individual lipid configurations as fluid or gel, in relation with the usual two-states phenomenological description of the lipid melting transition. We demonstrate that our machine can learn and sort lipids according to their most likely state without prior assumption regarding the nature of the order parameter of the transition. Results from our machine learning study provide strong support in favour of a two-states model approach of membrane fluidity.

Interaction of Giant Unilamellar Vesicles with the Surface Nanostructures on Dragonfly Wings.

The waxy epicuticle of dragonfly wings contains a unique nanostructured pattern that exhibits bactericidal properties. In light of emerging concerns of antibiotic resistance, these mechano-bactericidal surfaces represent a particularly novel solution by which bacterial colonization and the formation of biofilms on biomedical devices can be prevented. Pathogenic bacterial biofilms on medical implant surfaces cause a significant number of human deaths every year. The proposed mechanism of bactericidal activity is through mechanical cell rupture; however, this is not yet well understood and has not been well characterized. In this study, we used giant unilamellar vesicles (GUVs) as a simplified cell membrane model to investigate the nature of their interaction with the surface of the wings of two dragonfly species, Austrothemis nigrescens and Trithemis annulata, sourced from Victoria, Australia, and the Baix Ebre and Terra Alta regions of Catalonia, Spain. Confocal laser scanning microscopy and cryo-scanning electron microscopy techniques were used to visualize the interactions between the GUVs and the wing surfaces. When exposed to both natural and gold-coated wing surfaces, the GUVs were adsorbed on the surface, exhibiting significant deformation, in the process of membrane rupture. Differences between the tensile rupture limit of GUVs composed of 1,2-dioleoyl- sn-glycero-3-phosphocholine and the isotropic tension generated from the internal osmotic pressure were used to indirectly determine the membrane tensions, generated by the nanostructures present on the wing surfaces. These were estimated as being in excess of 6.8 mN m-1, the first experimental estimate of such mechano-bactericidal surfaces. This simple model provides a convenient bottom-up approach toward understanding and characterizing the bactericidal properties of nanostructured surfaces.

Cargo self-assembly rescues affinity of cell-penetrating peptides to lipid membranes.

Although cationic cell-penetrating peptides (CPPs) are able to bind to cell membranes, thus promoting cell internalization by active pathways, attachment of cargo molecules to CPPs invariably reduces their cellular uptake. We show here that CPP binding to lipid bilayers, a simple model of the cell membrane, can be recovered by designing cargo molecules that self-assemble into spherical micelles and increase the local interfacial density of CPP on the surface of the cargo. Experiments performed on model giant unilamellar vesicles under a confocal laser scanning microscope show that a family of thermally responsive elastin-like polypeptides that exhibit temperature-triggered micellization can promote temperature triggered attachment of the micelles to membranes, thus rescuing by self-assembly the cargo-induced loss of the CPP affinity to bio-membranes.