Kinetic Monte Carlo modeling of multivalent binding of CTB proteins with GM1 receptors

Nanocube-Based Fluidic Glycan Array

The nature of cell membrane fluidity permits glycans, which are attached to membrane proteins and lipids, to freely diffuse on cell surfaces. Through such two-dimensional motion, some weakly binding glycans can participate in lectin binding processes, eventually changing lectin binding behaviors. This chapter discusses a plasmonic nanocube sensor that allows users to detect lectin binding kinetics in a cell membrane mimicking environment. This assay only requires standard laboratory spectrometers, including microplate readers. We describe the basics of the technology in detail, including sensor fabrication, sensor calibration, data processing, a general protocol for detecting lectin-glycan interactions, and a troubleshooting guide.

A slip-spring framework to study relaxation dynamics of entangled wormlike micelles with kinetic Monte Carlo algorithm

HYPOTHESIS

Wormlike micelles (WLMs) formed due to the self-assembly of amphiphiles in aqueous solution have similar viscoelastic properties as polymers. Owing to this similarity, in this work, it is postulated that kinetic Monte Carlo (kMC) sampling of slip-springs dynamics, which is able to model the rheology of polymers, can also be extended to capture the relaxation dynamics of WLMs.

THEORY

The proposed modeling framework considers the following relaxation mechanisms: reptation, union-scission, and constraint release. Specifically, each of these relaxation mechanisms is simulated as separate kMC events that capture the relaxation dynamics while considering the living nature of WLMs within the slip-spring framework. As a case study, the model is implemented to a system of sodium oleate and sodium chloride to predict the linear rheology and the characteristic relaxation times associated with the individual relaxation mechanisms at different pH and salt concentrations.

FINDINGS

Linear rheology predictions were found to be in good agreement with experimental data. Furthermore, the calculated relaxation times highlighted that reptation contributed to a continuous increase in viscosity while union-scission contributed to the decrease in viscosity of WLM solutions at a higher salinity and pH. This manifests the proposed model's capability to provide insights into the key processes governing WLM's rheology.

Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling

Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose dynamics should be corrected by the ANN by examining the correlation between each state and outputs through relative order. Second, an L2-regularized least-squares problem is solved to infer values of the correction terms that are necessary to minimize the discrepancy between the model predictions and available measurements. Third, an ANN is developed to generalize relationships between the values of the correction terms and the system dynamics. Lastly, the original first-principle model is coupled with the developed ANN to finalize the hybrid model development so that the model will possess generalized prediction capabilities while retaining the model interpretability. We have successfully validated the proposed methodology with two case studies, simplified apoptosis and lipopolysaccharide-induced NFκB signaling pathways, to develop hybrid models with in silico and in vitro measurements, respectively.

An intracellular signaling pathway is often represented by a set of nonlinear ordinary differential equations, which translate our current knowledge about the signaling pathway into a testable mathematical model. However, predictions from such models are often subject to high uncertainty since many signaling pathways are only partially known beforehand. In this study, we propose a systematic approach to develop a hybrid model to improve model accuracy by combining machine learning and the first-principle modeling. Specifically, model correction terms are learned from discrepancy between model predictions and measurements, and these terms are added to the first-principle model to enhance the prediction accuracy. Once these correction terms are learned from the data, an artificial neural network (ANN) model is developed to find an empirical relation between the model and the correction terms so that the developed ANN can be used to posses improved predictive capabilities even in new operating conditions (i.e., generalizability). The final hybrid model is then constructed by coupling the first-principle model with the developed ANN.

Structured clustering of the glycosphingolipid GM1 is required for membrane curvature induced by cholera toxin

AB₅ bacterial toxins and polyomaviruses induce membrane curvature as a mechanism to facilitate their entry into host cells. How membrane bending is accomplished is not yet fully understood but has been linked to the simultaneous binding of the pentameric B subunit to multiple copies of glycosphingolipid receptors. Here, we probe the toxin membrane binding and internalization mechanisms by using a combination of superresolution and polarized localization microscopy. We show that cholera toxin subunit B (CTxB) can induce membrane curvature only when bound to multiple copies of its glycosphingolipid receptor, GM1, and the ceramide structure of GM1 is likely not a determinant of this activity as assessed in model membranes. A mutant CTxB capable of binding only a single GM1 fails to generate curvature either in model membranes or in cells, and clustering the mutant CTxB-single-GM1 complexes by antibody cross-linking does not rescue the membrane curvature phenotype. We conclude that both the multiplicity and specific geometry of GM1 binding sites are necessary for the induction of membrane curvature. We expect this to be a general rule of membrane behavior for all AB₅ toxins and polyomaviruses that bind glycosphingolipids to invade host cells.

Red fluorescent AuNDs with conjugation of cholera toxin subunit B (CTB) for extended-distance retro-nerve transporting and long-time neural tracing

A retrograde transportation nerve probe, Au nanodots-cholera toxin B subunit (AuNDs-CTB), are prepared and fully characterized, which emit bright red fluorescence and show high quantum yield (7.2%) and good stability. The fluorescence emitted by the AuNDs is constant across a wide pH range (4-10) and after prolonged UV irradiation (>4 h). Previously, CTB has shown targeting characteristic for nerve cells with high sensitivity and effectiveness. After linking CTB to AuNDs through amidation reactions, AuNDs-CTB are obtained with excellent fluorescence property, nerve target characteristic, and, particularly, neural retrograde transportation feature. The red emission of the AuNDs-CTB is well distinguished from the blue autofluorescence of normal tissues, which provides potential for detection by naked eyes. Further, the fluorescence emission intensity maintains for 10 days in vivo, suggesting great utility for long-time monitoring and sensing of the nerve tissue. Furthermore, the AuNDs-CTB with bright red fluorescence can travel through the peripheral nerve to the spinal cord rapidly by retrograde transportation. The transportation occurs for a long distance (>5 cm) within only 2 days after injection of the AuNDs-CTB into the sciatic nerve. The present study exhibits a novel method for nerve visualization and drug delivery. STATEMENT OF SIGNIFICANCE: Au nanodots (AuNDs) conjugated with cholera toxin subunit B (CTB) have been developed for nerve labeling and neural retro-transporting. The red fluorescence from AuNDs-CTB is stable in vitro (pH 4-10 and 4 h UV irradiation) and in vivo (for a long time, more than 10 days). When injecting AuNDs-CTB into the sciatic nerve located at the midpiece of the thigh, the targeted nerve emits bright red fluorescence under UV light. Furthermore, the nerve can retrograde transport the AuNDs-CTB to the spinal cord for a distance of more than 5 cm just in 2 days. This work exhibits a novel method for nerve visualization by naked eyes and demonstrates the potential for intraoperative navigation.

The influence of heteromultivalency on lectin-glycan binding behavior

We recently discovered that the nature of lectin multivalency and glycolipid diffusion on cell membranes could lead to the heteromultivalent binding (i.e., a single lectin simultaneously binding to different types of glycolipid ligands). This heteromultivalent binding may even govern the lectin-glycan recognition process. To investigate this, we developed a kinetic Monte Carlo simulation, which only considers the fundamental physics/chemistry principles, to model the process of lectin binding to glycans on cell surfaces. We found that the high-affinity glycan ligands could facilitate lectin binding to other low-affinity glycan ligands, even though these low-affinity ligands are barely detectable in microarrays with immobilized glycan ligands. Such heteromultivalent binding processes significantly change lectin binding behaviors. We hypothesize that living organisms probably utilize this mechanism to regulate the downstream lectin functions. Our finding not only offers a mechanism to describe the concept that lectins are pattern recognition molecules, but also suggests that the two overlooked parameters, surface diffusion of glycan ligand and lectin binding kinetics, can play important roles in glycobiology processes. In this paper, we identified the critical parameters that influence the heteromultivalent binding process. We also discussed how our discovery can impact the current lectin-glycan analysis.

Evaluation of hetero-multivalent lectin binding using a turbidity-based emulsion agglutination assay

Lectin hetero-multivalency, binding to two or more different types of ligands, has been demonstrated to play a role in case of both LecA (a Pseudomonas aeruginosa adhesin) and Cholera Toxin subunit B (a Vibrio cholerae toxin). In order to screen the ligand candidates that involve in hetero-multivalent binding from large molecular libraries, we present a turbidity-based emulsion agglutination (TEA) assay that can be conducted in a high-throughput format using the standard laboratory instruments and reagents. The benefit of this assay is that it relies on the use of emulsions that can be formed using ultrasonication, minimizing the bottleneck of substrate surface functionalization. By measuring the change in turbidity, we could quantify the lectin-induced aggregation rate of oil droplets to determine the relative binding strength between different ligand combinations. The TEA results are consistent with our prior binding results using a nanocube sensor. The developed TEA assay can serve as a high-throughput and customizable tool to screen the potential ligands involved in hetero-multivalent binding.