Biophysical reports最新文献

We investigate to what extent yeast complementation assays, which in principle can provide large amounts of training data for machine-learning models, yield quantitative correlations between growth rescue and single-channel recordings. If this were the case, yeast complementation results could be used as surrogate data for machine-learning-based channel design. Therefore, we mutated position L94 at the cavity entry of the model K⁺ channel Kcv_PBCV1 to all proteinogenic amino acids. The function of the wild-type channel and its mutants was investigated by reconstituting them in planar lipid bilayers and by their ability to rescue the growth of a yeast strain deficient in K⁺ uptake. The single-channel data show a distinct effect of mutations in this critical position on unitary conductance and open probability, with no apparent causal relationship between the two functional parameters. We also found that even conservative amino acid replacements can alter the unitary conductance and/or open probability and that most functional changes show no systematic relationship with the physicochemical nature of the amino acids. This emphasizes that the functional influence of an amino acid on channel function cannot be reduced to a single chemical property. Mutual comparison of single-channel data and yeast complementation results exhibit only a partial correlation between their electrical parameters and their potency of rescuing growth. Hence, complementation data alone are not sufficient for enabling functional channel design; they need to be complemented by additional parameters such as the number of channels in the plasma membrane.

This study investigates the use of spectral phasor analysis, hyperspectral imaging, and 6-acetyl-2-dimethylaminonaphthalene (ACDAN) fluorescence to explore key protein transitions: unfolding, amyloid aggregation, and liquid-liquid phase separation. We show that ACDAN fluorescence can sensitively detect subtle conformational changes before the complete protein unfolds, revealing early microenvironmental shifts. During amyloid formation, ACDAN identifies solvent dipolar relaxation events undetectable by conventional thioflavin T, providing critical insight into early aggregation events. Additionally, we map the physicochemical properties of protein biocondensates and highlight distinct microenvironments within these condensates, emphasizing the significance of dipolar relaxation in phase-separated systems. The approach provides a flexible and user-friendly toolkit for studying protein transitions, which can be easily implemented in commercial spectrofluorometers and microscopes.

In this paper, the mechanical properties of collagen fibrils in the cortical bone and cortical-trabecular bone interface of the human mandible and maxilla have been investigated. Force-indentation curves on wet collagen fibrils are taken by applying the atomic force microscopy nanoindentation technique, and the elastic modulus is measured. The distribution of stress and strain is determined by considering an elastic medium when it is deformed by a rigid cone. Afterward, by applying the nonlocal elasticity theory and the indentation parameters, the nonlocal parameter of the collagen fibrils is calculated at the nanoscale. Finally, the elastic modulus and nonlocal modulus of the collagen fibrils are compared. According to the results, the highest and lowest values of the elastic modulus of the collagen fibrils are determined in the maxillary cortical-trabecular bone interface (4.16 ± 0.18 MPa) and mandibular cortical bone (2.03 ± 0.14 MPa), respectively. In general, in collagen fibrils, this parameter is higher in the maxillary bone than in the mandibular one. In the upper and lower jaws, the elastic modulus of collagen fibrils in the cortical-trabecular bone interface is higher than that of the cortical bone. In mandibular and maxillary bone collagen fibrils, the range of nonlocal parameter and scaling parameter e₀ are computed as (0.430 ± 0.013-0.483 ± 0.011 nm) and (0.269 ± 0.006-0.302 ± 0.006), respectively. Also, the highest value of this parameter is recorded in the maxillary cortical-trabecular bone interface. The difference between the nanoscale modulus of collagen fibrils and the elastic modulus at large length scales is significant.

Super-resolution microscopy offers the ability to visualize molecular structures in biological samples with unprecedented detail. However, the full potential of these techniques is often hindered by a lack of automated, user-independent workflows. Here, we present an open-source toolkit that automates dSTORM super-resolution microscopy using deep learning for segmentation and object detection. This standalone program enables reliable segmentation of diverse biomedical images, even in low-contrast samples, surpassing existing solutions. Integrated into the imaging pipeline, it rapidly processes high-content data in minutes, reducing manual labor. Demonstrated by biological examples, such as microtubules in cell culture and the βII-spectrin in nerve fibers, our approach makes super-resolution imaging faster, more robust, and easy to use, even by nonexperts. This broadens its potential applications in biomedicine, including high-throughput experimentation.

All living systems display remarkable spatial and temporal precision, despite operating in intrinsically fluctuating environments. It is even more surprising given that biological phenomena are regulated by multiple chemical reactions that are also random. Although the underlying molecular mechanisms of surprisingly high precision in biology remain not well understood, a novel theoretical picture that relies on the coupling of relevant stochastic processes has recently been proposed and applied to explain different phenomena. To illustrate this approach, in this review, we discuss two systems that exhibit precision control: spatial regulation in bacterial cell size and temporal regulation in the timing of cell lysis by λ bacteriophage. In cell-size regulation, it is argued that a balance between stochastic cell growth and cell division processes leads to a narrow distribution of cell sizes. In cell lysis, it is shown that precise timing is due to the coupling of holin protein accumulation and the breakage of the cellular membrane. The stochastic coupling framework also allows us to explicitly evaluate dynamic properties for both biological systems, eliminating the need to utilize the phenomenological concept of thresholds. Excellent agreement with experimental observations is observed, supporting the proposed theoretical ideas. These observations also suggest that the stochastic coupling method captures the important aspects of molecular mechanisms of precise cellular regulation, providing a powerful new tool for more advanced investigations of complex biological phenomena.

The B-DNA of the genome contains numerous sequences that can form various noncanonical structures including G-quadruplex (G4), formed by two or more stacks of four guanine residues in a plane, and intercalating motif (i-motif [iM]) formed by alternately arranged C-C⁺ pairs. One of the easy yet sensitive methods to study G4s and iMs is circular dichroism (CD) spectroscopy, which generates characteristic G4 and iM peaks. We have analyzed and compared the effects of various environmental factors including pH, buffer composition, temperature, flanking sequences, complimentary DNA strands, and single-stranded DNA binding protein (SSB) on the CD patterns of G4s and iMs generated by two groups of DNA molecules, one containing tandem repeats of GGGGCC and CCCCGG from the C9ORF72 gene associated with amyotrophic lateral sclerosis and frontotemporal dementia, and the second containing polyG/polyC clusters from oncogene promoter-proximal regions without such tandem repeats. Changes in pH caused drastic changes in CCCCGG-iM and GGGGCC-G4 and the changes were dependent on repeat numbers and G-C basepairing. In contrast, with the DNA sequences from the promoter-proximal regions of oncogenes, iMs disassembled upon pH changes with the peak slowly shifting to lower wavelength but the G4s did not show significant change. Complementary DNA strands and flanking DNA sequences also regulate G4 and iM formation. The SSB disassembled both G4s and iMs formed by almost all sequences suggesting an in vivo role for SSBs in the disassembly of G4s and iMs during DNA replication and other DNA transactions.

Fibroblast growth factor 21 (FGF21) is an endocrine FGF that plays a vital role in regulating essential metabolic pathways. FGF21 increases glucose uptake by cells, promotes fatty acid oxidation, reduces blood glucose levels, and alleviates metabolic diseases. However, detailed studies on its stability and biophysical characteristics have not been reported. Herein, we present the overexpression, biophysical characterization, and metabolic activity of a soluble recombinant FGF21 (rFGF21). The far-UV circular dichroism spectra of rFGF21 show a negative trough at 215 nm, indicating that the protein's backbone predominantly adopts a β sheet conformation. rFGF21 shows intrinsic tyrosine fluorescence at 305 nm. Thermal denaturation using differential scanning calorimetry reveals that rFGF21 is relatively thermally unstable, with a melting temperature of 46.8°C (±0.1°C). The urea-induced unfolding of rFGF21 is rapid, with a chemical transition midpoint of 0.4 M. rFGF21 is readily cleaved by trypsin in limited trypsin digestion assays. Isothermal titration calorimetry experiments show that rFGF21 does not bind to heparin. Interestingly, rFGF21 demonstrates proliferative activity in NIH/3T3 fibroblasts and enhances mitochondrial oxidative phosphorylation and fatty acid oxidation in 3T3-L1 adipocytes. These findings provide a crucial framework for the engineering of novel structure-based variants of FGF21 with improved stability and biological activity to treat metabolic disorders.

Transcription factor proteins bind to specific DNA promoter sequences and initiate gene transcription. These proteins often contain intrinsically disordered activation domains (ADs) that regulate their transcriptional activity. Like other disordered protein regions, ADs do not have a fixed three-dimensional structure and instead exist in an ensemble of conformations. Disordered ensembles contain sequence-encoded structural preferences that are often linked to their function. We hypothesize that this link exists between the structural preferences of AD ensembles and their ability to induce gene expression. To test this, we measured the ensemble dimensions of two ADs, HIF-1α and CITED2, in live cells using fluorescence resonance energy transfer microscopy and correlated this structural information with their transcriptional activity. We find that mutations that expanded the ensemble of HIF-1α increased transcriptional activity, while compacting mutations reduced it, highlighting the critical role of structural plasticity in regulating HIF-1α function. Conversely, CITED2 showed no correlation between ensemble dimensions and activity. Our results highlight a possible link between AD ensemble dimensions and their transcriptional activity, with implications for transcriptional regulation and dysfunction.

Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we develop a Bayesian framework (DiffMAP-GP) by placing Gaussian process (GP) priors on the family of candidate maps. For sake of computational efficiency, we leverage inducing point methods on GPs arising from the mathematical structure of the data giving rise to nonconjugate likelihood-prior pairs. We analyze both synthetic data, where ground truth is known, as well as data drawn from live-cell single-molecule imaging of membrane proteins. The resulting tool provides an unsupervised method to rigorously map diffusion coefficients continuously across membranes without data binning.

The mechanical properties of cells are closely related to function and play a crucial role in many cellular processes, including migration, differentiation, and cell fate determination. Numerous methods have been developed to assess cell mechanics under various conditions, but they often lack accuracy on biologically relevant piconewton-range forces or have limited control over the applied force. Here, we present a straightforward approach for using optically trapped polystyrene beads to accurately apply piconewton-range forces to adherent and suspended cells. We precisely apply a constant force to cells by means of a force-feedback system, allowing for quantification of deformation, cell stiffness, and creep response from a single measurement. Using drug-induced perturbations of the cytoskeleton, we show that this approach is sensitive to detecting changes in cellular mechanical properties. Collectively, we provide a framework for using optical tweezers to apply highly accurate forces to adherent and suspended cells and describe straightforward metrics to quantify cellular mechanical properties.