Biophysical reports最新文献

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.

In vitro motility (IVM) assays allow for the examination of the basic interaction between cytoskeletal filaments with molecular motors and the influence many physiological factors have on this interaction. Examples of factors that can be studied include changes in ADP and pH that emulate fatigue, altered phosphorylation that can occur with disease, and mutations within myofilament proteins that cause disease. While IVM assays can be analyzed manually, the main limitation is the ability to extract accurate data rapidly from videos collected without individual bias. While programs have been created in the past to enable data extraction, many are now out of date or require the use of proprietary software. Here, we report the generation of a Python-based tracking program, Philament, which automatically extracts data on instantaneous and average velocities, and allows for fully automated analysis of IVM recordings. The data generated are presented in an easily accessible spreadsheet-based, comma-separated values file. Philament also contains a novel method of quantifying the smoothness of filament motion. By fitting curves to standard deviations of velocity and average velocities, the influence of different experimental conditions can be compared relative to one another. This comparison provides a qualitative measure of protein interactions where steeper slopes indicate more unstable interactions and shallower slopes indicate more stable interactions within the myofilament. Overall, Philament's automation of IVM analysis provides easier entry into the field of cardiovascular mechanics and enables users to create a truly high-throughput experimental data analysis.

The central role of RNAs in health and disease calls for robust tools to visualize RNAs in living systems through fluorescence microscopy. Live zebrafish embryos are a popular system to investigate multicellular complexity as disease models. However, RNA visualization approaches in whole organisms are notably underdeveloped. Here, we establish our RNA tagging and imaging platform Riboglow-FLIM for complex cellular imaging applications by systematically evaluating FLIM capabilities. We use adherent mammalian cells as models for RNA visualization. Additional complexity of analyzing RNAs in whole mammalian animals is achieved by injecting these cells into a zebrafish embryo system for cell-by-cell analysis in this model of multicellularity. We first evaluate all variable elements of Riboglow-FLIM quantitatively before assessing optimal use in whole animals. In this way, we demonstrate that a model noncoding RNA can be detected robustly and quantitatively inside live zebrafish embryos using a far-red Cy5-based variant of the Riboglow platform. We can clearly resolve cell-to-cell heterogeneity of different RNA populations by this methodology, promising applicability in diverse fields.

During zygotic mitosis in many species, forces generated at the cell cortex are required for the separation and migration of paternally provided centrosomes, pronuclear migration, segregation of genetic material, and cell division. Furthermore, in some species, force-generating interactions between spindle microtubules and the cortex position the mitotic spindle asymmetrically within the zygote, an essential step in asymmetric cell division. Understanding the mechanical and molecular mechanisms of microtubule-dependent force generation and therefore asymmetric cell division requires identification of individual cortical force-generating units in vivo. There is no current method for identifying individual force-generating units with high spatiotemporal resolution. Here, we present a method to determine both the location and the relative number of microtubule-dependent cortical force-generating units using single-molecule imaging of fluorescently labeled dynein. Dynein behavior is modeled to classify trajectories of cortically bound dynein according to whether they are interacting with a microtubule. The categorization strategy recapitulates well-known force asymmetries in C. elegans zygote mitosis. To evaluate the robustness of categorization, we used RNAi to deplete the tubulin subunit TBA-2. As predicted, this treatment reduced the number of trajectories categorized as engaged with a microtubule. Our technique will be a valuable tool to define the molecular mechanisms of dynein cortical force generation and its regulation as well as other instances wherein anchored motors interact with biopolymers (e.g., actin, tubulin, DNA).

Calcium ions (Ca²⁺) enter mitochondria via the mitochondrial Ca²⁺ uniporter, driven by electrical and concentration gradients. In this regard, transgenic mouse models, such as calsequestrin knockout (CSQ-KO) mice, with higher mitochondrial Ca²⁺ concentrations ([Ca²⁺]_mito), should display higher cytosolic Ca²⁺ concentrations ([Ca²⁺]_cyto). However, repeated measurements of [Ca²⁺]_cyto in quiescent CSQ-KO fibers never showed a difference between WT and CSQ-KO. Starting from the consideration that fluorescent Ca²⁺ probes (Fura-2 and Indo-1) measure averaged global cytosolic concentrations, in this report we explored the role of local Ca²⁺ concentrations (i.e., Ca²⁺ microdomains) in regulating mitochondrial Ca²⁺ in resting cells, using a multicompartmental diffusional Ca²⁺ model. Progressively including the inward and outward fluxes of sarcoplasmic reticulum (SR), extracellular space, and mitochondria, we explored their contribution to the local Ca²⁺ distribution within the cell. The model predicts Ca²⁺ concentration gradients with hot spots or microdomains even at rest, minor but similar to those of evoked Ca²⁺ release. Due to their specific localization close to Ca²⁺ release units (CRU), mitochondria could take up Ca²⁺ directly from high-concentration microdomains, thus sensibly raising [Ca²⁺]_mito, despite minor, possibly undetectable, modifications of the average [Ca²⁺]_cyto.