Biophysical reports最新文献

Liposomes are used as model membranes in many scientific fields. Various methods exist to prepare liposomes, but common procedures include thin-film hydration followed by extrusion, freeze-thaw, and/or sonication. These procedures can produce liposomes at specific concentrations and lipid compositions, and researchers often assume that the concentration and composition of their liposomes are similar or identical to what would be expected if no lipid loss occurred. However, lipid loss and concomitant biasing of lipid composition can in principle occur at any preparation step due to nonideal mixing, lipid-surface interactions, etc. Here, we report a straightforward HPLC-ELSD method to quantify the lipid concentration and composition of liposomes and apply that method to study the preparation of simple cholesterol/POPC liposomes. We examine common liposome preparation steps, including vortexing during resuspension, lipid film hydration, extrusion, freeze-thaw, and sonication. We found that the resuspension step can play an outsized role in determining the lipid loss (up to ∼50% under seemingly rigorous procedures). The extrusion step yielded smaller lipid losses (∼10-20%). Freeze-thaw and sonication could both be employed to improve lipid yields. Hydration times up to 60 min and increasing cholesterol concentrations up to 50 mol % had little influence on lipid recovery. Fortunately, even conditions with large lipid loss did not substantially influence the target membrane composition, as long as the lipid mixture was below the cholesterol solubility limit. From our results, we identify best practices for producing maximum levels of lipid recovery and minimal changes to lipid composition during liposome preparation for cholesterol/POPC liposomes.

In vitro assays of ion transport are an essential tool for understanding molecular mechanisms associated with ATP-dependent pumps. Because ion transport is generally electrogenic, principles of electrophysiology are applicable, but conventional tools like patch-clamp are ineffective due to relatively low turnover rates of the pumps. Instead, assays have been developed to measure either voltage or current generated by transport activity of a population of molecules either in cell-derived membrane fragments or after reconstituting purified protein into proteoliposomes. In order to understand the nuances of these assays and to characterize effects of various operational parameters, we have developed a numerical model to simulate data produced by two relevant assays: fluorescence from voltage-sensitive dyes and current recorded by capacitive coupling on solid supported membranes. Parameters of the model, which has been implemented in Python, are described along with underlying principles of the computational algorithm. Experimental data from KdpFABC, a K⁺ pump associated with P-type ATPases, are presented, and model parameters have been adjusted to mimic these data. In addition, effects of key parameters such as nonselective leak conductance and turnover rate are demonstrated. Finally, simulated data are used to illustrate the effects of capacitive coupling on measured current and to compare alternative methods for quantification of raw data.

Electron paramagnetic resonance (EPR) is a powerful tool for elucidating both static and dynamic conformational alterations in macromolecules. However, to effectively utilize EPR for such investigations, the presence of paramagnetic centers, known as spin labels, is required. The process of spin labeling, particularly for nucleotides, typically demands intricate organic synthesis techniques. In this study, we introduce a unique addition-elimination reaction method with a simple spin-labeling process, facilitating the monitoring of structural changes within nucleotide sequences. Our investigation focuses on three distinct labeling positions with a DNA sequence, allowing the measurement of distance between two spin labels. The experimental mean distances obtained agreed with the calculated distances, underscoring the efficacy of this straightforward spin-labeling approach in studying complex biological processes such as transcription mechanism using EPR measurements.

A common type of cytoskeletal morphology involves multiple microtubules converging with their minus ends at the microtubule organizing center (MTOC). The cargo-motor complex will experience ballistic transport when bound to microtubules or diffusive transport when unbound. This machinery allows for sequestering and subsequent dispersal of dynein-transported cargo. The general principles governing dynamics, efficiency, and tunability of such transport in the MTOC vicinity are not fully understood. To address this, we develop a one-dimensional model that includes advective transport toward an attractor (such as the MTOC) and diffusive transport that allows particles to reach absorbing boundaries (such as cellular membranes). We calculated the mean first passage time (MFPT) for cargo to reach the boundaries as a measure of the effectiveness of sequestering (large MFPT) and diffusive dispersal (low MFPT). We show that the MFPT experiences a dramatic growth, transitioning from a low to high MFPT regime (dispersal to sequestering) over a window of cargo on-/off-rates that is close to in vivo values. Furthermore, increasing either the on-rate (attachment) or off-rate (detachment) can result in optimal dispersal when the attractor is placed asymmetrically. Finally, we also describe a regime of rare events where the MFPT scales exponentially with motor velocity and the escape location becomes exponentially sensitive to the attractor positioning. Our results suggest that structures such as the MTOC allow for the sensitive control of the spatial and temporal features of transport and corresponding function under physiological conditions.

The effectiveness of antitumor chimeric antigen receptor (CAR) therapy mainly dealt with an elevated sensitivity of CAR cells to target cells. However, CAR therapies are associated with nonspecific side effects: on-target off-tumor toxicity. Sensitivity and specificity of CAR cells are the most important properties of the recognition process of target cells among other cells. Current developments are mainly concentrated on exploring molecular biology methods for designing CAR cells with the highest sensitivity, while the problem of the CAR cell specificity is rarely considered. For the assessment of CAR cell specificity, we suggest that, in addition to an elevated level of CAR-antigen affinity, the ability of CARs for clustering should be taken into account. We assume that the CAR cell cytotoxicity is determined by CAR clustering. The latter is treated within the framework of nucleation theory. The master equation for the probability of CAR cell cytotoxicity is derived. The size of a critical CAR cluster is found to be one of two most essential parameters. The conditions for necessary sensitivity and sufficient specificity are explored. Relevant parametric diagrams are derived. Possible applications of the method for assessing the specificity of developing CAR therapies are discussed.

The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an artificial neural network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where the TF concentration relative to half-maximal RNA concentration and gene product copy number influences transcription and translation via weighted multiplication before undergoing a nonlinear activation function. This process yields protein concentration as the output, effectively turning the entire GRN into a gene regulatory neural network (GRNN). In this paper, we establish nonlinear classifiers for molecular machine learning using the inherent sigmoidal nonlinear behavior of gene expression. The eigenvalue-based stability analysis, tailored to system parameters, confirms maximum-stable concentration levels, minimizing concentration fluctuations and computational errors. Given the significance of the stabilization phase in GRNN computing and the dynamic nature of the GRN, alongside potential changes in system parameters, we utilize the Lyapunov stability theorem for temporal stability analysis. Based on this GRN-to-GRNN mapping and stability analysis, three classifiers are developed utilizing two generic multilayer sub-GRNNs and a sub-GRNN extracted from the Escherichia coli GRN. Our findings also reveal the adaptability of different sub-GRNNs to suit different application requirements.

Phage display and mirror-image phage display are commonly used techniques for the identification of binders that are specific to predefined targets. Recent studies demonstrated the effectiveness of next-generation sequencing (NGS) by increasing the amount of information extracted from selections. This allows for a better analysis and increases the possibility to select effective binders. A potential downside to NGS analysis of phage display selections is the increased workload that is needed to analyze the obtained information. Here, we report on the development of TSAT (target-specific analysis tool), software for user-friendly and efficient analysis of peptide sequence data from NGS of phage display selections.

Self-organizing spiral waves of excitation occur in many complex excitable systems. In the heart, for example, they are associated with the occurrence of fatal cardiac arrhythmias such as tachycardia and fibrillation, which can lead to sudden cardiac death. The control of these waves is therefore necessary for the treatment of the disease. In this letter, I present an innovative approach to control cardiac arrhythmias using low (nonfreezing) temperatures. This approach differs from all previous established techniques in that it involves no drugs, no genetic modification, no injection of foreign bodies, no application of voltage shocks (high or low, single or pulsed), and no curative damage to the heart. It relies on regional cooling of cardiac tissue to create a transient inhomogeneity in the electrophysiological properties. This inhomogeneity can then be manipulated to control the dynamics of the reentrant waves. This approach is, to my knowledge, the most sustainable theoretical proposal for the treatment of cardiac arrhythmias in the clinic.

Significant efforts have been made to characterize the biophysical properties of proteins. Small proteins have received less attention because their annotation has historically been less reliable. However, recent improvements in sequencing, proteomics, and bioinformatics techniques have led to the high-confidence annotation of small open reading frames (smORFs) that encode for functional proteins, producing smORF-encoded proteins (SEPs). SEPs have been found to perform critical functions in several species, including humans. While significant efforts have been made to annotate SEPs, less attention has been given to the biophysical properties of these proteins. We characterized the distributions of predicted and curated biophysical properties, including sequence composition, structure, localization, function, and disease association of a conservative list of previously identified human SEPs. We found significant differences between SEPs and both larger proteins and control sets. In addition, we provide an example of how our characterization of biophysical properties can contribute to distinguishing protein-coding smORFs from noncoding ones in otherwise ambiguous cases.

The quantification of physical properties of biological matter gives rise to novel ways of understanding functional mechanisms. One of the basic biophysical properties is the mass density (MD). It affects the dynamics in sub-cellular compartments and plays a major role in defining the opto-acoustical properties of cells and tissues. As such, the MD can be connected to the refractive index (RI) via the well known Lorentz-Lorenz relation, which takes into account the polarizability of matter. However, computing the MD based on RI measurements poses a challenge, as it requires detailed knowledge of the biochemical composition of the sample. Here we propose a methodology on how to account for assumptions about the biochemical composition of the sample and respective RI measurements. To this aim, we employ the Biot mixing rule of RIs alongside the assumption of volume additivity to find an approximate relation of MD and RI. We use Monte-Carlo simulations and Gaussian propagation of uncertainty to obtain approximate analytical solutions for the respective uncertainties of MD and RI. We validate this approach by applying it to a set of well-characterized complex mixtures given by bovine milk and intralipid emulsion and employ it to estimate the MD of living zebrafish (Danio rerio) larvae trunk tissue. Our results illustrate the importance of implementing this methodology not only for MD estimations but for many other related biophysical problems, such as mechanical measurements using Brillouin microscopy and transient optical coherence elastography.