Biophysical reports最新文献

Bacteria are known to interpret a range of external molecular signals that are crucial for sensing environmental conditions and adapting their behaviors accordingly. These external signals are processed through a multitude of signaling transduction networks that include the gene regulatory network (GRN). From close observation, the GRN resembles and exhibits structural and functional properties that are similar to artificial neural networks. An in-depth analysis of gene expression dynamics further provides a new viewpoint of characterizing the inherited computing properties underlying the GRN of bacteria despite being non-neuronal organisms. In this study, we introduce a model to quantify the gene-to-gene interaction dynamics that can be embedded in the GRN as weights, converting a GRN to gene regulatory neural network (GRNN). Focusing on Pseudomonas aeruginosa, we extracted the GRNN associated with a well-known virulence factor, pyocyanin production, using an introduced weight extraction technique based on transcriptomic data and proving its computing accuracy using wet-lab experimental data. As part of our analysis, we evaluated the structural changes in the GRNN based on mutagenesis to determine its varying computing behavior. Furthermore, we model the ecosystem-wide cell-cell communications to analyze its impact on computing based on environmental as well as population signals, where we determine the impact on the computing reliability. Subsequently, we establish that the individual GRNNs can be clustered to collectively form computing units with similar behaviors to single-layer perceptrons with varying sigmoidal activation functions spatio-temporally within an ecosystem. We believe that this will lay the groundwork toward molecular machine learning systems that can see artificial intelligence move toward non-silicon devices, or living artificial intelligence, as well as giving us new insights into bacterial natural computing.

Kinetic and thermodynamic models of biological systems are commonly used to connect microscopic features to system function in a bottom-up multiscale approach. The parameters of such models-free energy differences for equilibrium properties and in general rates for equilibrium and out-of-equilibrium observables-have to be measured by different experiments or calculated from multiple computer simulations. All such parameters necessarily come with uncertainties so that when they are naively combined in a full model of the process of interest, they will generally violate fundamental statistical mechanical equalities, namely detailed balance and an equality of forward/backward rate products in cycles due to Hill. If left uncorrected, such models can produce arbitrary outputs that are physically inconsistent. Here, we develop a maximum likelihood approach (named multibind) based on the so-called potential graph to combine kinetic or thermodynamic measurements to yield state-resolved models that are thermodynamically consistent while being most consistent with the provided data and their uncertainties. We demonstrate the approach with two theoretical models, a generic two-proton binding site and a simplified model of a sodium/proton antiporter. We also describe an algorithm to use the multibind approach to solve the inverse problem of determining microscopic quantities from macroscopic measurements and, as an example, we predict the microscopic ${pK}_a$ values and protonation states of a small organic molecule from 1D NMR data. The multibind approach is applicable to any thermodynamic or kinetic model that describes a system as transitions between well-defined states with associated free energy differences or rates between these states. A Python package multibind, which implements the approach described here, is made publicly available under the MIT Open Source license.

In this paper we present a transistor circuit model for cystic fibrosis transmembrane conductance regulator (CFTR) that seeks to map the functional form of CFTR both in wild type and mutants. The circuit architecture is configured so that the function, and as much as possible the form, faithfully represents what is known about CFTR from cryo-electron microscopy and molecular dynamics. The model is a mixed analog-digital topology with an AND gate receiving the input from two separate ATP-nucleotide-binding domain binding events. The analog portion of the circuit takes the output from the AND gate as its input. The input to the circuit model and its noise characteristics are extracted from single-channel patch-clamp experiments. The chloride current predicted by the model is then compared with single-channel patch-clamp recordings for wild-type CFTR. We also consider the patch-clamp recordings from CFTR with a G551D point mutation, a clinically relevant mutant that is responsive to therapeutic management. Our circuit model approach enables bioengineering approaches to CFTR and allows biophysicists to use efficient circuit simulation tools to analyze its behavior.

Thermodenaturation (melting) curves of macromolecules are used to determine folding thermodynamic parameters. Notably, this insight into RNA and DNA stability underlies nearest neighbor theory and diverse structure prediction tools. Analysis of UV-detected absorbance melting curves is complex and multivariate, requiring many data preprocessing, regression, and error analysis steps. The absorbance melting curve-fitting software MeltWin, introduced in 1996, provided a consistent and facile melting curve analysis platform used in a generation of folding parameters. Unfortunately, MeltWin software is not maintained and relies on idiosyncratic choices of baselines by the user. Herein, we provide MeltR, an open-source, curve-fitting package for analysis of macromolecular thermodynamic data. The MeltR package provides the facile conversion of melting curve data to parameters provided by MeltWin while offering additional features including global fitting of data, auto-baseline generation, and two-state melting analysis. MeltR should be a useful tool for analyzing the next generation of DNA, RNA, and nonnucleic acid macromolecular melting data.

Förster resonance energy transfer (FRET) microscopy is used in numerous biophysical and biomedical applications to monitor inter- and intramolecular interactions and conformational changes in the 2-10 nm range. FRET is currently being extended to in vivo optical imaging, its main application being in quantifying drug-target engagement or drug release in animal models of cancer using organic dye or nanoparticle-labeled probes. Herein, we compared FRET quantification using intensity-based FRET (sensitized emission FRET analysis with the three-cube approach using an IVIS imager) and macroscopic fluorescence lifetime (MFLI) FRET using a custom system using a time-gated-intensified charge-coupled device, for small animal optical in vivo imaging. The analytical expressions and experimental protocols required to quantify the product $f_{D}E$ of the FRET efficiency E and the fraction of donor molecules involved in FRET, $f_D$, are described in detail for both methodologies. Dynamic in vivo FRET quantification of transferrin receptor-transferrin binding was acquired in live intact nude mice upon intravenous injection of a near-infrared-labeled transferrin FRET pair and benchmarked against in vitro FRET using hybridized oligonucleotides. Even though both in vivo imaging techniques provided similar dynamic trends for receptor-ligand engagement, we demonstrate that MFLI-FRET has significant advantages. Whereas the sensitized emission FRET approach using the IVIS imager required nine measurements (six of which are used for calibration) acquired from three mice, MFLI-FRET needed only one measurement collected from a single mouse, although a control mouse might be needed in a more general situation. Based on our study, MFLI therefore represents the method of choice for longitudinal preclinical FRET studies such as that of targeted drug delivery in intact, live mice.

Ca²⁺ imaging provides insight into biological processes ranging from subcellular dynamics to neural network activity. Two-photon microscopy has assumed a dominant role in Ca²⁺ imaging. The longer wavelength infra-red illumination undergoes less scattering, and absorption is confined to the focal plane. Two-photon imaging can thus penetrate thick tissue ∼10-fold more deeply than single-photon visible imaging to make two-photon microscopy an exceptionally powerful method for probing function in intact brain. However, two-photon excitation produces photobleaching and photodamage that increase very steeply with light intensity, limiting how strongly one can illuminate. In thin samples, illumination intensity can assume a dominant role in determining signal quality, raising the possibility that single-photon microscopy may be preferable. We therefore tested laser scanning single-photon and two-photon microscopy side by side with Ca²⁺ imaging in neuronal compartments at the surface of a brain slice. We optimized illumination intensity for each light source to obtain the brightest signal without photobleaching. Intracellular Ca²⁺ rises elicited by one action potential had twice the signal/noise ratio with confocal as with two-photon imaging in axons, were 31% higher in dendrites, and about the same in cell bodies. The superior performance of confocal imaging in finer neuronal processes likely reflects the dominance of shot noise when fluorescence is dim. Thus, when out-of-focus absorption and scattering are not issues, single-photon confocal imaging can yield better quality signals than two-photon microscopy.

Magnetic resonance imaging (MRI) is a highly significant imaging platform for a variety of medical and research applications. However, the low spatiotemporal resolution of conventional MRI limits its applicability toward rapid acquisition of ultrahigh-resolution scans. Current aims at high-resolution MRI focus on increasing the accuracy of tissue delineation, assessments of structural integrity, and early identification of malignancies. Unfortunately, high-resolution imaging often leads to decreased signal/noise (SNR) and contrast/noise (CNR) ratios and increased time cost, which are unfeasible in many clinical and academic settings, offsetting any potential benefits. In this study, we apply and assess the efficacy of super-resolution reconstruction (SRR) through iterative back-projection utilizing through-plane voxel offsets. SRR allows for high-resolution imaging in condensed time frames. Rat skulls and archerfish samples, typical models in academic settings, were used to demonstrate the impact of SRR on varying sample sizes and applicability for translational and comparative neuroscience. The SNR and CNR increased in samples that did not fully occupy the imaging probe and in instances where the low-resolution data were acquired in three dimensions, while the CNR was found to increase with both 3D and 2D low-resolution data reconstructions when compared with directly acquired high-resolution images. Limitations to the applied SRR algorithm were investigated to determine the maximum ratios between low-resolution inputs and high-resolution reconstructions and the overall cost effectivity of the strategy. Overall, the study revealed that SRR could be used to decrease image acquisition time, increase the CNR in nearly all instances, and increase the SNR in small samples.

Cryogenic electron microscopy (cryo-EM) is among the most powerful tools available for interrogating nanoscale structure of biological materials. We recently showed that cryo-EM can be used to measure the bilayer thickness of lipid vesicles and biological membranes with subangstrom precision, resulting in the direct visualization of nanoscopic domains of different thickness in multicomponent lipid mixtures and giant plasma membrane vesicles. Despite the great potential of cryo-EM for revealing the lateral organization of biomembranes, a large parameter space of experimental conditions remains to be optimized. Here, we systematically investigate the influence of instrument parameters and image postprocessing steps on the ability to accurately measure bilayer thickness and discriminate regions of different thickness within unilamellar liposomes. This unique application of cryo-EM places particular demands on image acquisition optimization and analysis due to the facts that 1) each vesicle is a different size with different curvature, 2) the domains in each vesicle can be heterogenous in size, and 3) the random orientation of vesicles amplifies the variability of domain size in projected images. We also demonstrate a spatial autocorrelation analysis to extract additional information about lateral heterogeneity.

Neutrophils are key players of the immune system and possess an arsenal of effector functions, including the ability to form and expel neutrophil extracellular traps (NETs) in a process termed NETosis. During NETosis, the nuclear DNA/chromatin expands until it fills the whole cell and is released into the extracellular space. NETs are composed of DNA decorated with histones, proteins, or peptides, and NETosis is implicated in many diseases. Resolving the structure of the nucleus in great detail is essential to understand the underlying processes, but so far, superresolution methods have not been applied. Here, we developed an expansion-microscopy-based method and determined the spatial distribution of chromatin/DNA, histone H1, and nucleophosmin with an over fourfold improved resolution (<40-50 nm) and increased information content. It allowed us to identify the punctate localization of nucleophosmin in the nucleus and histone-rich domains in NETotic cells with a size of 54-66 nm. The technique could also be applied to components of the nuclear envelope (lamins B1 and B2) and myeloperoxidase, providing a complete picture of nuclear composition and structure. In conclusion, expansion microscopy enables superresolved imaging of the highly dynamic structure of nuclei in immune cells.

Single-cell RNA sequencing data can be modeled using Markov chains to yield genome-wide insights into transcriptional physics. However, quantitative inference with such data requires careful assessment of noise sources. We find that long pre-mRNA transcripts are over-represented in sequencing data. To explain this trend, we propose a length-based model of capture bias, which may produce false-positive observations. We solve this model and use it to find concordant parameter trends as well as systematic, mechanistically interpretable technical and biological differences in paired data sets.