Biophysical reports最新文献

Membrane potential (MP) changes can provide a simple readout of bacterial functional and metabolic state or stress levels. While several optical methods exist for measuring fast changes in MP in excitable cells, there is a dearth of such methods for absolute and precise measurements of steady-state membrane potentials (MPs) in bacterial cells. Conventional electrode-based methods for the measurement of MP are not suitable for calibrating optical methods in small bacterial cells. While optical measurement based on Nernstian indicators have been successfully used, they do not provide absolute or precise quantification of MP or its changes. We present a novel, calibrated MP recording approach to address this gap. In this study, we used a fluorescence lifetime-based approach to obtain a single-cell resolved distribution of the membrane potential and its changes upon extracellular chemical perturbation in a population of bacterial cells for the first time. Our method is based on (i) a unique VoltageFluor (VF) optical transducer, whose fluorescence lifetime varies as a function of MP via photoinduced electron transfer (PeT) and (ii) a quantitative phasor-FLIM analysis for high-throughput readout. This method allows MP changes to be easily visualized, recorded and quantified. By artificially modulating potassium concentration gradients across the membrane using an ionophore, we have obtained a Bacillus subtilis-specific MP versus VF lifetime calibration and estimated the MP for unperturbed B. subtilis cells to be -65 mV (in MSgg), -127 mV (in M9) and that for chemically depolarized cells as -14 mV (in MSgg). We observed a population level MP heterogeneity of ∼6-10 mV indicating a considerable degree of diversity of physiological and metabolic states among individual cells. Our work paves the way for deeper insights into bacterial electrophysiology and bioelectricity research.

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 which are often linked to their function. We hypothesize 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 FRET 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 to 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 non-conjugate 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 exocyst is an octameric protein complex that acts as a tether for GOLGI-derived vesicles at the plasma membrane during exocytosis. It is involved in membrane expansion during axonal outgrowth. Exo70 is a major subunit of the exocyst complex and is controlled by TC10, a Rho family GTPase. How TC10 affects the dynamics of Exo70 at the plasma membrane is not well understood. There is also evidence that TC10 controls Exo70 dynamics differently in nonpolar cells and axons. To address this, we used super-resolution microscopy to study the spatially resolved effects of TC10 on Exo70 dynamics in HeLa cells and the growth cone of cortical and hippocampal neurons. We generated single-particle localization and trajectory maps and extracted mean square displacements, diffusion coefficients, and alpha coefficients to characterize Exo70 diffusion. We found that the diffusivity of Exo70 was different in nonpolar cells and the growth cone of neurons. TC10 stimulated the mobility of Exo70 in HeLa cells but decreased the diffusion of Exo70 in the growth cone of cortical neurons. In contrast to cortical neurons, TC10 overexpression did not affect the mobility of Exo70 in the axonal growth cone of hippocampal neurons. These data suggest that mainly exocyst tethering in cortical neurons was under the control of TC10.

Spatiotemporal organization of individuals within growing bacterial colonies is a key determinant of intraspecific interactions and colony-scale heterogeneities. The evolving cellular distribution, in relation to the genealogical lineage, is thus central to our understanding of bacterial fate across scales. Yet, how bacteria self-organize genealogically as a colony expands has remained unknown. Here, by developing a custom-built label-free algorithm, we track and study the genesis and evolution of emergent self-similar genealogical enclaves, whose dynamics are governed by biological activity. Topological defects at enclave boundaries tune finger-like morphologies of the active interfaces. The Shannon entropy of cell arrangements reduce over time; with faster-dividing cells possessing higher spatial affinity to genealogical relatives, at the cost of a well-mixed, entropically favorable state. Our coarse-grained lattice model demonstrates that genealogical enclaves emerge due to an interplay of division-mediated dispersal, stochasticity of division events, and cell-cell interactions. The study reports so-far hidden emergent self-organizing features arising due to entropic suppression, ultimately modulating intraspecific genealogical distances within bacterial colonies.

By identifying distance constraints, chemical cross-linking coupled with mass spectrometry (CX-MS) can be a powerful complementary technique to other structural methods by interrogating macromolecular protein complexes under native-like conditions. In this study, we developed a CX-MS approach to identify the sites of chemical cross-linking from a single targeted location within the human α1 glycine receptor (α1 GlyR) in its apo state. The human α1 GlyR belongs to the family of pentameric ligand-gated ion channel receptors that function in fast neurotransmission. A single chemically reactive cysteine was reintroduced into a Cys null α1 GlyR construct at position 41 within the extracellular domain of human α1 homomeric GlyR overexpressed in a baculoviral system. After purification and reconstitution into vesicles, methanethiosulfonate-benzophenone-alkyne, a heterotrifunctional cross-linker, was site specifically attached to Cys41 via disulfide bond formation. The resting receptor was then subjected to UV photocross-linking. Afterward, monomeric and oligomeric α1 GlyR bands from SDS-PAGE gels were trypsinized and analyzed by tandem MS in bottom-up studies. Dozens of intrasubunit and intersubunit sites of α1 GlyR cross-linking were differentiated and identified from single gel bands of purified protein, showing the utility of this experimental approach to identify a diverse array of distance constraints of the α1 GlyR in its resting state. These studies highlight CX-MS as an experimental approach to identify chemical cross-links within full-length integral membrane protein assemblies in a native-like lipid environment.

The type 2 ryanodine receptor (RyR2) is the major Ca²⁺ release channel required for Ca²⁺-induced Ca²⁺ release (CICR) and cardiac excitation-contraction coupling. The cluster organization of RyR2 at the dyad is critical for efficient CICR. Despite its central role in cardiac Ca²⁺ signaling, the mechanisms that control CICR are not fully understood. As a single RyR2 Ca²⁺ flux dictates local CICR that underlies Ca²⁺ sparks, RyR2 density in a cluster, and therefore the distance between RyR2s, should have a profound impact on local CICR. Here, we studied the effect of the RyR2 expression level ([RyR2]) on CICR activation, termination, and amplitude. The endoplasmic reticulum (ER)-targeted Ca²⁺ sensor RCEPIA-1er was used to directly measure the ER [Ca²⁺] (Ca²⁺]_ER) in the T-Rex-293 the sarco/endoplasmic reticulum Ca²⁺-ATPase (SERCA2a) stable cell line expressing human RyR2. Cells coexpressing RyR2 and SERCA2a produced periodic [Ca²⁺]_ER depletions in the form of spontaneous Ca²⁺ waves due to propagating CICR. For each studied cell, the [Ca²⁺]_ER at which Ca²⁺ waves are activated and terminated was analyzed as a function of [RyR2]. CICR parameters, such as [Ca²⁺]_ER activation, termination, and amplitude, were inversely proportional to [RyR2] at low-intermediate levels. Increasing the sensitivity of RyR2 to cytosolic Ca²⁺ lowered the [Ca²⁺]_ER at which CICR is activated and terminated. Decreasing the sensitivity of RyR2 to cytosolic Ca²⁺ had the opposite effect on CICR. These results suggest that RyR2 density in the release cluster should have a significant impact on local CICR activation and termination. Since SR Ca²⁺ load is evenly distributed throughout the SR network, clusters with higher RyR2 density would have a higher probability of initiating spontaneous CICR.

The cut-open oocyte Vaseline gap technique is a powerful electrophysiological method for the characterization of ion channels. However, traditional amplifiers for cut-open oocyte Vaseline gap are labor intensive and require significant user expertise. We introduce an innovative, open-source digital amplifier system with high-speed digitization and software-controlled electronics for computer-driven automation. This system compares well to existing commercial systems in terms of conventional specifications of step response (current peak at 25μs and decay of 36μs time constant), current noise (1.0 nA at 3-kHz bandwidth), and dynamic range (96.9 dB). Additionally, it unlocks new methods through close integration of the amplifier and software, including machine-learning techniques for tuning capacitive compensation waveforms, achieving a 100-fold suppression of mean-squared transient current, and impedance measurement methods to identify system components such as membrane capacitance and electrode resistances. For future extensions, the design has unique attributes such as real-time digital signal processing for feedback, multiple input and multiple output, and allows for user customization. By providing open-source access to the circuit board designs, control software, and field-programmable gate array code on GitHub, this approach aims to foster cross-disciplinary collaboration and facilitate instrument customization enabling previously inaccessible electrophysiology experiments.

Predicting pandemic evolution involves complex modeling challenges, typically involving detailed discrete mathematics executed on large volumes of epidemiological data. Making them physics based provides added intuition as well as predictive value. Differential equations have the advantage of offering smooth, well-behaved solutions that try to capture overall predictive trends and averages. In this paper, the canonical susceptible-infected-recovered model is simplified, in the process generating quasi-analytical solutions and fitting functions that agree well with the numerics, as well as infection data across multiple countries. The equations provide an elegant way to visualize the evolution of the pandemic spread, by drawing equivalents with the similar dynamics of a particle, whose location over time represents the growing fraction of the population that is infected. This particle slides down a potential whose shape is set by model epidemiological parameters such as reproduction rate. Potential sources of errors and their growth over time are identified, and the uncertainties are mapped into a diffusive jitter that tends to push the particle away from its minimum. The combined physical understanding and analytical expressions offered by such an intuitive drift-diffusion model sets the foundation for their eventual extension to a multi-patch model while offering practical error bounds and could thus be useful in making policy decisions going forward.

Many viruses are pleomorphic in shape and size, with pleomorphism often thought to correlate with infectivity, pathogenicity, or virus survival. For example, influenza and respiratory syncytial virus particles range in size from small spherical virions to filaments reaching many micrometers in length. We have used a pressure vessel model to investigate how the length and width of spherical and filamentous virions can vary for a given critical stress and fluorescence super-resolution microscopy along with image analysis tools to fit imaged influenza viruses to the model. We have shown that influenza virion dimensions fit within the theoretical limits of the model, suggesting that filament formation may be a way to increase an individual virus's volume without particle rupture. We have also used cryoelectron microscopy to investigate influenza and respiratory syncytial virus dimensions at the extrema of the model and used the pressure vessel model to explain the lack of alternative virus particle geometries. Our approach offers insight into the possible purpose of filamentous virus morphology and is applicable to a wide range of other biological entities, including bacteria and fungi.