Direct Observation of Sophorolipid Micelle Docking in Model Membranes and Cells by Single Particle Studies Reveals Optimal Fusion Conditions

Defect-Engineered Metal-Organic Frameworks as Nanocarriers for Pharmacotherapy: Insights into Intracellular Dynamics at The Single Particle Level

Nanoscale Metal-Organic Frameworks (nanoMOFs) are widely implemented in a host of assays involving drug delivery, biosensing catalysis, and bioimaging. However, the cell pathways and cell fate remain poorly understood. Here, a new fluorescent nanoMOF integrating ATTO 655 into surface defects of colloidal UiO-66 is synthesized, allowing to track the spatiotemporal localization of Single nanoMOF in live cells. Density functional theory reveals the stronger binding of ATTO 655 to the Zr6 cluster nodes compared with phosphate and Alendronate Sodium. Parallelized tracking of the spatiotemporal localization of thousands of nanoMOFs and analysis using machine learning platforms reveals whether nanoMOFs remain outside as well as their cellular internalization pathways. To quantitatively assess their colocalization with endo/lysosomal compartments, a colocalization proxy approach relying on the nanoMOF detection of particles in one channel to the signal in the corresponding endo/lysosomal compartments channel, considering signal versus local background intensity ratio and signal-to-noise ratio is developed. This strategy mitigates colocalization value inflation from high or low signal expression in endo/lysosomal compartments. The results accurately measure the nanoMOFs' colocalization from early to late endosomes and lysosomes and emphasize the importance of understanding their intracellular dynamics based on single-particle tracking for optimal and safe drug delivery.

Review on sophorolipids – a promising microbial bio-surfactant

Surfactants are amphiphilic molecules used primarily for cleaning. Petroleum-based surfactants have a high production rate, but are non-biodegradable and destructive to the environment. Environmentally friendly biosurfactants are therefore becoming increasingly important. In addition to not being toxic; they are environmentally safe and mild to the skin. Depending on their structure, there are different types of biosurfactants. One of the types are the glycolipids, they are low molecular weight biosurfactants, and consist of sophorolipids. Sophorolipids are getting more attention as alternative to petroleum-based surfactants due to excellent stability at various pH levels, temperatures, and salinities. In addition to being anti-microbial, they have excellent wetting and foaming abilities and act as emulsifiers. There are numerous applications of sophorolipids in food, agriculture, biomedicine, cosmetics and personal care.

Enhanced hexamerization of insulin via assembly pathway rerouting revealed by single particle studies

Insulin formulations with diverse oligomerization states are the hallmark of interventions for the treatment of diabetes. Here using single-molecule recordings we firstly reveal that insulin oligomerization can operate via monomeric additions and secondly quantify the existence, abundance and kinetic characterization of diverse insulin assembly and disassembly pathways involving addition of monomeric, dimeric or tetrameric insulin species. We propose and experimentally validate a model where the insulin self-assembly pathway is rerouted, favoring monomeric or oligomeric assembly, by solution concentration, additives and formulations. Combining our practically complete kinetic characterization with rate simulations, we calculate the abundance of each oligomeric species from nM to mM offering mechanistic insights and the relative abundance of all oligomeric forms at concentrations relevant both for secreted and administrated insulin. These reveal a high abundance of all oligomers and a significant fraction of hexamer resulting in practically halved bioavailable monomer concentration. In addition to providing fundamental new insights, the results and toolbox presented here can be universally applied, contributing to the development of optimal insulin formulations and the deciphering of oligomerization mechanisms for additional proteins.

The dopamine transporter antiports potassium to increase the uptake of dopamine

The dopamine transporter facilitates dopamine reuptake from the extracellular space to terminate neurotransmission. The transporter belongs to the neurotransmitter:sodium symporter family, which includes transporters for serotonin, norepinephrine, and GABA that utilize the Na⁺ gradient to drive the uptake of substrate. Decades ago, it was shown that the serotonin transporter also antiports K⁺, but investigations of K⁺-coupled transport in other neurotransmitter:sodium symporters have been inconclusive. Here, we show that ligand binding to the Drosophila- and human dopamine transporters are inhibited by K⁺, and the conformational dynamics of the Drosophila dopamine transporter in K⁺ are divergent from the apo- and Na⁺-states. Furthermore, we find that K⁺ increases dopamine uptake by the Drosophila dopamine transporter in liposomes, and visualize Na⁺ and K⁺ fluxes in single proteoliposomes using fluorescent ion indicators. Our results expand on the fundamentals of dopamine transport and prompt a reevaluation of the impact of K⁺ on other transporters in this pharmacologically important family.

The dopamine transporter, DAT, controls dopamine signaling by facilitating its reuptake using the Na⁺ gradient as driving force. Here, the authors uncover that an antiport of K⁺ ions also contributes to setting the rate of DAT-mediated dopamine clearance.

Interaction of an acidic sophorolipid biosurfactant with phosphatidylcholine model membranes

Sophorolipids (SLs) constitute a group of unique biosurfactants (BS) in the light of their outstanding properties, among which their antimicrobial activities stand out. SLs can exist mainly in an acidic and a lactonic form, both of which display inhibitory activity. Given the amphipathic nature of SLs it is feasible that these antimicrobial actions are the result of the perturbation of the physicochemical properties of targeted membranes. Thus, in this work we have carried out a biophysical study to unveil the molecular details of the interaction of an acidic SL with a model phospholipid membrane made of 1,2-dipalmitoy-sn-glycero-3-phosphocholine (DPPC). Using differential scanning calorimetry it was found that SL altered the phase behaviour of DPPC at low molar fractions, producing fluid phase immiscibility with the result of formation of biosurfactant-enriched domains within the phospholipid bilayer. Fourier-transform infrared spectroscopy showed that SL interacted with DPPC increasing ordering of the phospholipid acyl chain palisade and hydration of the lipid/water interface. Small angle X-ray scattering showed that SL did not modify bilayer thickness in the biologically relevant L_α fluid phase. SL was found to induce contents leakage in 1-palmitoy-2-oleoy-sn-glycero-3-phosphocholine (POPC) unilamellar liposomes, at sublytic concentrations below the cmc. This SL-induced membrane permeabilization at concentrations below the onset for membrane solubilization can be the result of the formation of laterally segregated domains, which might contribute to provide a molecular basis for the reported antimicrobial actions of SLs.

Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion

Single-particle tracking (SPT) analysis of individual biomolecules is an indispensable tool for extracting quantitative information from dynamic biological processes, but often requires some a priori knowledge of the system. Here we present “single-particle diffusional fingerprinting,” a more general approach for extraction of diffusional patterns in SPT independently of the biological system. This method extracts a set of descriptive features for each SPT trajectory, which are ranked upon classification to yield mechanistic insights for the species under comparison. We demonstrate its capacity to yield a dictionary of diffusional traits across multiple systems (e.g., lipases hydrolyzing fat, transcription factors diffusing in cells, and nanoparticles in mucus), supporting its use on multiple biological phenomena (e.g., drug delivery, receptor dynamics, and virology).

Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug nanocarrier delivery. The inherently complex diffusion in such biological systems can vary drastically both in time and across systems, consequently imposing considerable analytical challenges, and currently requires an a priori knowledge of the system. Here we introduce a method for SPT data analysis, processing, and classification, which we term “diffusional fingerprinting.” This method allows for dissecting the features that underlie diffusional behavior and establishing molecular identity, regardless of the underlying diffusion type. The method operates by isolating 17 descriptive features for each observed motion trajectory and generating a diffusional map of all features for each type of particle. Precise classification of the diffusing particle identity is then obtained by training a simple logistic regression model. A linear discriminant analysis generates a feature ranking that outputs the main differences among diffusional features, providing key mechanistic insights. Fingerprinting operates by both training on and predicting experimental data, without the need for pretraining on simulated data. We found this approach to work across a wide range of simulated and experimentally diverse systems, such as tracked lipases on fat substrates, transcription factors diffusing in cells, and nanoparticles diffusing in mucus. This flexibility ultimately supports diffusional fingerprinting’s utility as a universal paradigm for SPT diffusional analysis and prediction.

Single-Molecule Study of Thermomyces lanuginosus Lipase in a Detergency Application System Reveals Diffusion Pattern Remodeling by Surfactants and Calcium

Lipases comprise one of the major enzyme classes in biotechnology with applications within, e.g., baking, brewing, biocatalysis, and the detergent industry. Understanding the mechanisms of lipase function and regulation is therefore important to facilitate the optimization of their function by protein engineering. Advances in single-molecule studies in model systems have provided deep mechanistic insights on lipase function, such as the existence of functional states, their dependence on regulatory cues, and their correlation to activity. However, it is unclear how these observations translate to enzyme behavior in applied settings. Here, single-molecule tracking of individual Thermomyces lanuginosus lipase (TLL) enzymes in a detergency application system allowed real-time direct observation of spatiotemporal localization, and thus diffusional behavior, of TLL enzymes on a lard substrate. Parallelized imaging of thousands of individual enzymes allowed us to observe directly the existence and quantify the abundance and interconversion kinetics between three diffusional states that we recently provided evidence to correlate with function. We observe redistribution of the enzyme's diffusional pattern at the lipid-water interface as well as variations in binding efficiency in response to surfactants and calcium, demonstrating that detergency effectors can drive the sampling of lipase functional states. Our single-molecule results combined with ensemble activity assays and enzyme surface binding efficiency readouts allowed us to deconvolute how application conditions can significantly alter protein functional dynamics and/or surface binding, both of which underpin enzyme performance. We anticipate that our results will inspire further efforts to decipher and integrate the dynamic nature of lipases, and other enzymes, in the design of new biotechnological solutions.

DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning

Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring ~1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET's capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.