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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 361 records · Page 20Linked to original sources

bioRxiv2024

Predicting the DNA binding specificity of mutated transcription factors using family-level biophysically interpretable machine learning

Sequence-specific interactions of transcription factors (TFs) with genomic DNA underlie many cellular processes. High-throughput in vitro binding assays coupled with machine learning have made it possible to accurately define such molecular recognition in a biophysically interpretable way for hundreds of TFs across many structural families, providing new avenues for predicting how the sequence preference of a TF is impacted by disease-associated mutations in its DNA binding domain. We developed a method based on a reference-free tetrahedral representation of variation in base preference within a given structural family that can be used to accurately predict the effect of mutations in the protein sequence of the TF. Using the basic helix-loop-helix (bHLH) and homeodomain families as test cases, our results demonstrate the feasibility of accurately predicting the shifts ({Delta}{Delta}{Delta}G/RT) in binding free energy associated with TF mutants by leveraging high-quality DNA binding models for sets of homologous wild-type TFs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/577115v2_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@3a6674org.highwire.dtl.DTLVardef@1c4de4org.highwire.dtl.DTLVardef@3dcd6forg.highwire.dtl.DTLVardef@17a63c5_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

genomics↗

bioRxiv2024

Why is the Omicron main protease of SARS-CoV-2 less stable than its wild-type counterpart? A crystallographic, biophysical, and theoretical study of the free enzyme and its complex with inhibitor 13b-K

During the continuing evolution of SARS-CoV-2, the Omicron variant of concern emerged in the second half of 2021 and has been dominant since November that year. Along with its sublineages, it has maintained a prominent role ever since. The Nsp5 main protease (Mpro) of the Omicron virus is characterized by a single dominant mutation, P132H. Here we determined the X-ray crystal structures of the P132H mutant (or O-Mpro) as free enzyme and in complex with the Mpro inhibitor, the alpha-ketoamide 13b-K, and we conducted enzymology, biophysical as well as theoretical studies to characterize the O-Mpro. We found that O-Mpro has a similar overall structure and binding with 13b-K; however, it displays lower enzymatic activity and lower thermal stability compared to the WT-Mpro (with "WT" referring to the original Wuhan-1 strain). Intriguingly, the imidazole ring of His132 and the carboxylate plane of Glu240 are in a stacked configuration in the X-ray structures determined here. The empirical folding free energy calculations suggest that the O-Mpro dimer is destabilized relative to the WT-Mpro due to the less favorable van der Waals interactions and backbone conformation in the individual protomers. The all-atom continuous constant pH molecular dynamics (MD) simulations reveal that His132 and Glu240 display coupled titration. At pH 7, His132 is predominantly neutral and in a stacked configuration with respect to Glu240 which is charged. In order to examine whether the Omicron mutation eases the emergence of further Mpro mutations, we also determined crystal structures of the relatively frequent P132H+T169S double mutant but found little evidence for a correlation between the two sites.

biochemistry↗

bioRxiv2024

Structural and biophysical characterisation of ubiquitin variants that specifically inhibit the ubiquitin conjugating enzyme Ube2d2

The ubiquitin conjugating E2 enzymes play a central role in ubiquitin transfer. Disruptions to the ubiquitin system are implicated in multiple diseases and as a result, molecules that modulate the activity of the ubiquitin system are of interest. E2 enzyme function is reliant on interactions with partner proteins and disruption of these is an effective way of modulating activity. Here, we report the discovery of ubiquitin variants (UbVs) that inhibit the E2 enzyme, Ube2d2 (UbcH5b). The six UbVs identified inhibit ubiquitin chain building, and structural and biophysical characterisation of two of these demonstrate they bind to Ube2d2 with low micromolar affinity and high specificity within the Ube2d family of E2 enzymes. Both characterised UbVs bind at a site that overlaps with E1 binding, while the more inhibitory UbV blocks a critical non-covalent ubiquitin binding site on the E2 enzyme. The discovery of novel protein-based ubiquitin derivatives that inhibit protein-protein interactions is an important step towards discovery of small molecules that inhibit the activity of E2 enzymes. Furthermore, the specificity of the UbVs within the Ube2d family suggests that it may be possible to develop tools to selectively inhibit highly related E2 enzymes.

biochemistry↗

bioRxiv2024

Biophysical Profiling of Red Blood Cells from Thin-film Blood Smears using Deep Learning

Microscopic inspection of thin-film blood smears is widely used to identify red blood cell (RBC) pathologies, including malaria parasitism and hemoglobinopathies, such as sickle cell disease and thalassemia. Emerging research indicates that non-pathologic changes in RBCs can also be detected in images, such as deformability and morphological changes resulting from the storage lesion. In transfusion medicine, cell deformability is a potential biomarker for the quality of donated RBCs. However, a major impediment to the clinical translation of this biomarker is the difficulty associated with performing this measurement. To address this challenge, we developed an approach for biophysical profiling of RBCs based on cell images in thin-film blood smears. We hypothesize that subtle cellular changes are evident in blood smear images, but this information is currently undetectable by human cognition. To test this hypothesis, we developed a deep learning strategy to analyze Giemsa-stained blood smears to assess the subtle morphologies indicative of RBC deformability and storage-based degradation. Specifically, we prepared thin-film blood smears from 27 RBC samples (9 donors evaluated at 3 storage timepoints) and imaged them using high-resolution microscopy. Using this dataset, we trained a convolutional neural network to evaluate image-based morphological features related to cell deformability. The prediction of donor deformability is strongly correlated to the microfluidic scores and can be used to categorize images into specific deformability groups with high accuracy. We also used this model to evaluates differences in RBC morphology resulting from cold storage. Together, our results demonstrate that deep learning models can exceed the limits of human cognition to detect subtle cellular differences in morphology resulting from deformability and cold storage. This result suggests the potential to assess donor blood quality from thin-film blood smears, which can be acquired ubiquitously in clinical workflows.

bioengineering↗

bioRxiv2024

Utilising activity patterns of a complex biophysical network model to optimise intra-striatal deep brain stimulation

AO_SCPLOWBSTRACTC_SCPLOWIn this study, we develop a large-scale biophysical network model for the isolated striatal body to optimise potential intrastriatal deep brain stimulation applied in, e.g. obsessive-compulsive disorder by using spatiotemporal patterns produced by the network. The model uses modified Hodgkin-Huxley models on small-world connectivity, while the spatial information, i.e. the positions of neurons, is obtained from a detailed human atlas. The model produces neuronal activity patterns segregating healthy from pathological conditions. Three indices were used for the optimisation of stimulation protocols regarding stimulation frequency, amplitude and localisation: the mean activity of the entire network, the mean activity of the ventral striatal area (emerging as a defined community using modularity detection algorithms), and the frequency spectrum of the entire network activity. By minimising the deviation of the aforementioned indices from the normal state, we guide the optimisation of deep brain stimulation parameters regarding position, amplitude and frequency.

neuroscience↗

bioRxiv2024

Recurrent Interneuron Connectivity does not Support Synchrony in a Biophysical Dentate Gyrus Model

Synchronous activity of neuronal networks is found in many brain areas and correlates with cognition and behavior. Gamma synchrony is particularly strong in the dentate gyrus, which is thought to process contextual information in the hippocampus. Several network mechanisms for synchrony generation have been proposed and studied computationally. One such mechanism relies solely on recurrent inhibitory interneuron connectivity, but it requires a large enough number of synapses. Here, we incorporate connectivity data of the dentate gyrus into a biophysical computational model to test its ability to generate synchronous activity. We find that recurrent interneuron connectivity is insufficient to induce a synchronous network state. This applies to an interneuron ring network and the broader dentate gyrus circuitry. In the asynchronous state, recurrent interneuron connectivity can have small synchronizing effects but can also desynchronize the network for specific types of synaptic input. Our results show that synchronizing mechanisms relying solely on interneurons are unlikely to be biologically plausible in the dentate gyrus.

neuroscience↗

bioRxiv2024

A synthetic method to assay polycystin ion channel biophysics

Ion channels are biological transistors that control ionic flux across cell membranes to regulate electrical transmission and signal transduction. They are found in all biological membranes and their conductive state kinetics are frequently disrupted in human diseases. Organelle ion channels are among the most resistant to functional and pharmacological interrogation. Traditional channel protein reconstitution methods rely upon exogenous expression and/or purification from endogenous cellular sources which are frequently contaminated by resident ionophores. Here we describe a fully synthetic method to assay functional properties of polycystin channels that natively traffic to primary cilia and endoplasmic reticulum organelles. Using this method, we characterize their oligomeric assembly, membrane integration, orientation and conductance while comparing these results to their endogenous channel properties. Outcomes define a novel synthetic approach that can be applied broadly to investigate channels resistant to biophysical analysis and pharmacological characterization.

synthetic biology↗

bioRxiv2024

Contributions of action potentials to scalp EEG: theory and biophysical simulations

Differences in the apparent 1/f component of neural power spectra require correction depending on the underlying neural mechanisms, which remain incompletely understood. Past studies suggest that neuronal spiking produces broadband signals and shapes the spectral trend of invasive macroscopic recordings, but it is unclear to what extent action potentials (APs) influence scalp EEG. Here, we combined biophysical simulations with statistical modelling to examine the amplitude and spectral content of scalp potentials generated by the electric fields from spiking activity. We found that under physiological conditions, synchronized aperiodic spiking can account for at most 1% of the spectral density observed in EEG recordings, suggesting that the EEG spectral trend reflects only external noise at high frequencies. Indeed, by analyzing previously published data from pharmacologically paralyzed subjects, we confirmed that the EEG spectral trend is entirely explained by synaptic timescales and electromyogram contamination. We also investigated rhythmic EEG generation, finding that APs can generate narrowband power between approximately 60 and 600 Hz, thus reaching frequencies much faster than the timescales of excitatory synaptic currents. Our results imply that different spectral detrending strategies are required for high frequency oscillations compared to slower synaptically generated EEG rhythms.

neuroscience↗

bioRxiv2024

Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling

The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.

neuroscience↗

bioRxiv2024

Ultra-fast variant effect prediction using biophysical transcription factor binding models

Sequence variation within TF binding sites can significantly affect TF-DNA interactions, influencing gene expression and contributing to disease susceptibility or phenotypic traits. Despite recent progress in deep sequence-to-function models that predict functional output from sequence data, these methods perform inadequately on some variant effect prediction tasks, especially with common genetic variants. This limitation underscores the importance of leveraging biophysical models of TF binding to enhance interpretability of variant effect scores and facilitate mechanistic insights. We introduce motifDiff, a novel computational tool designed to quantify variant effects using mono and dinucleotide position weight matrices. motifDiff offers several key advantages, including scalability to score millions of variants within minutes, implementation of statistically rigorous normalization strategy critical for optimal performance, and support for both dinucleotide and mononucleotide models. We demonstrate motifDiff s efficacy by evaluating it across diverse ground truth datasets that quantify the effects of common variants in vivo, thereby establishing robust benchmarks for the predictive value of variant effect calculations. Finally, we show that our tool provides unique insights when interpreting human accelerated regions. motifDiff is available as a standalone Python application at https://github.com/rezwanhosseini/MotifDiff.

bioinformatics↗

bioRxiv2024

Stochastic Modeling of Biophysical Responses to Perturbation

Recent advances in high-throughput, multi-condition experiments allow for genome-wide investigation of how perturbations affect transcription and translation in the cell across multiple biological entities or modalities, from chromatin and mRNA information to protein production and spatial morphology. This presents an unprecedented opportunity to unravel how the processes of DNA and RNA regulation direct cell fate determination and disease response. Most methods designed for analyzing large-scale perturbation data focus on the observational outcomes, e.g., expression; however, many potential transcriptional mechanisms, such as transcriptional bursting or splicing dynamics, can underlie these complex and noisy observations. In this analysis, we demonstrate how a stochastic biophysical modeling approach to interpreting high-throughout perturbation data enables deeper investigation of the how behind such molecular measurements. Our approach takes advantage of modalities already present in data produced with current technologies, such as nascent and mature mRNA measurements, to illuminate transcriptional dynamics induced by perturbation, predict kinetic behaviors in new perturbation settings, and uncover novel populations of cells with distinct kinetic responses to perturbation.

genomics↗

bioRxiv2024

Biophysical Simulation Enables Multi-Scale Segmentation and Atlas Mapping for Top-Down Spatial Omics of the Nervous System

Spatial omics (SO) has produced high-definition mapping of subcellular molecules (like transcripts or proteins) within tissue samples. Mapping transcripts to anatomical regions requires segmentation, but even segmenting nuclei remains challenging for tissues like nerve cross sections, let alone for larger regions such as within the spinal cord. Neural networks could address this but need extensive human annotations--a bottleneck. We present SiDoLa-NS (Simulate, Dont Label - Nervous System), an image-driven (top-down) approach to SO analysis in the nervous system. We utilize biophysical properties of tissue architectures to design synthetic images mimicking tissue samples. With these in silico samples, we train supervised instance segmentation convolutional neural networks (CNNs) for nucleus segmentation, achieving precision and F1-scores > 0.95. We take this a step further with generalizable CNNs that can identify macroscopic tissue structures in the mouse brain (mAP50 = 0.869), spinal cord (mAP50 = 0.96), and pig sciatic nerve (mAP50 = 0.995). Short SummaryThe SiDoLa-NS micro-, meso-, and macro-scale models are generalizable, supervised CNNs for neuronal segmentation in cell to tissue-level contexts. SiDoLa-NS is novel in its combination of three core ideas: it is top-down (image first), trained solely on synthetic images, and it is multi-scale. The tool is validated on brain, spinal cord, and sciatic nerve for advanced segmentation tasks.

neuroscience↗

bioRxiv2024

There is no Biophysical Distinction between Temporal Interference Stimulation and Direct kHz Stimulation for Actuation of Peripheral Nerves

Temporal interference stimulation (TIS) has attracted increasing attention as a promising noninvasive electrical stimulation method. Despite positive results and optimistic expectations, the TIS field has been beset by misunderstandings concerning its mechanism of action and efficacy in safely targeting deep neural structures. Various studies posit that TIS exploits the low-frequency "beat" frequency produced by interference of multiple kHz carriers to essentially deliver low-frequency stimulation at the intersection of the carriers, thereby circumventing limitations associated with tissue impedance and depth penetration. Due to the documented electrophysiological effects of kHz-range stimuli, such a picture is an oversimplification, and at present a critical open question for TIS is how much stimulation thresholds practically differ for amplitude-modulated versus unmodulated kHz stimuli. This paper presents experimental evidence supporting the conclusion that TIS is driven by the kHz carrier itself, and that amplitude modulation frequency has little relatively effect on thresholds. We test TIS and different modulated/unmodulated kHz waveforms on peripheral nerve targets in an invertebrate model (Locusta migratoria), and on sensory and motor stimulation in human subjects. We find that modulated (TIS) and unmodulated sine carrier frequencies in the range 0.5 - 12.5 kHz in humans, and further up to 100 kHz in locusts, demonstrate overlapping strength-frequency (s-f) dependence across all stimulation types. Since all stimulation waveforms are governed by the same s-f curve, this implicates a common underlying biophysical mechanism. This equivalence challenges the notion that the TIS modulated "envelope" frequency can uniquely facilitate neural engagement via other mechanisms. We test amplitude-modulation frequency (AMF) dependence, confirming the resonance effect predicted by kHz rectification theory, and we evaluate the regions of tonic (unmodulated) and phasic (amplitude-modulated) stimulation regions inherent when using TIS. Our results help to resolve the mechanistic debate, at least for suprathreshold TIS. We explain how the findings can be extrapolated to subthreshold stimulation, and further suggest possible advantages of using 2-electrode amplitude-modulated kHz waveforms over the multielectrode TIS configurations.

neuroscience↗

bioRxiv2024

Covalent on-cell conjugation of biomaterials through oxidative phenolic coupling regulates stem cell fate via intracellular biophysical programming

Mechanotransduction is widely used to guide cell fate in hydrogels. Traditionally, hydrogels contain adhesive ligands that dynamically bond with cells to stimulate biochemical signalling axis such as YAP-TAZ. However, the molecular toolbox to achieve mechanotransduction has remained virtually limited to non-covalent bonds, which limits our ability to program engineered living matter. Here, we demonstrate that on-cell chemistry can be leveraged to covalently tether biomaterials directly onto cells, and reveal that mechanotransduction is enabled via intracellular biophysical programming. Specifically, droplet microfluidics produced single-cell microgels in which individual stem cells were extracellularly conjugated to either soft or stiff hydrogels via on-cell oxidative phenolic coupling, which allowed for investigation of mechanotransduction at single-cell resolution. Interestingly, this altered intracellular molecular crowding, calcium signalling, and chromatin organization by regulating cytoplasmic and nuclear volume in a stiffness-dependent yet YAP/TAZ-independent manner. Notably, addition of conventional dynamic adhesive ligands such as RGDs decreased chondrogenic commitment indicating that covalent cell-material tethering is both efficient and sufficient for programming cell fate. Encoding biomaterials with a novel form of mechanotransduction in the form of covalent on-cell chemistry, such as oxidative phenolic coupling, expands our ability to guide cellular behaviour, which can accelerate development of drug-screening models, lab-grown meat, and engineered tissues.

bioengineering↗

bioRxiv2024

In vitro-generated inflammatory fibroblasts secrete extracellular matrix with biochemical and biophysical properties similar to tissue-remodelling fibroblasts

In solid cancers, inflammation and viral infections, two main fibroblast subtypes have been identified: myofibroblast-like fibroblasts and inflammatory fibroblasts. In the tumour microenvironment (TME), these cancer-associated fibroblast (CAF) subtypes are known as myCAFs, which generate a stiffened fibrotic extracellular matrix (ECM), and iCAFs, which secrete inflammatory cytokines to locally modulate the immune response. Yet, whether iCAFs contribute to shaping the ECM biochemical and biophysical properties remains unknown, mainly because robust in vitro models to generate fibroblast subtypes are lacking. Here, we established an in vitro cell culture system based on murine NIH3T3 fibroblasts and stimulation by TGF{beta} and IL1, alone or in combination, to induce fibroblast subtypes. Gene expression analysis of well-documented myCAF (Acta2/Tagln) and iCAF (Ccl2/Il6/Lif) markers revealed that TGF{beta} induced a myCAF-like phenotype, while a combination of TGF{beta} and IL1 induced an iCAF-like phenotype. We compared these in vitro subtypes to myCAFs and iCAFs from publicly available scRNAseq data of tumour tissues from cancer patients. We found that, similar to myCAFs, both tumour-associated and in vitro iCAFs express Acta2/Tagln as well as genes encoding for typical ECM proteins, which correlated in vitro with the ability to contract collagen. Furthermore, fluorescence microscopy and atomic force microscopy revealed that in vitro both subtypes generate thick, layered and stiff matrices with highly aligned ECM, demonstrating for the first time that iCAFs may also contribute to a pathological ECM. Finally, matrices generated from these in vitro fibroblast subtypes, but not from uninduced or IL1-only stimulated fibroblasts, enhanced the expression of the immune suppression marker Arg1 in co-cultured macrophages. Our study provides new insights in the contribution of inflammatory fibroblasts to ECM deposition and remodelling and puts forward a well-defined in vitro model to generate different fibroblast subtypes for future in-depth mechanistic studies of their roles in cancer and other pathologies.

cell biology↗

bioRxiv2024

Expression screen of TNFR1 R347A, MyD88, IRAK4 death domains in E. coli followed by purification and biophysical characterization of TNFR1 R347A death domain

Death domains play a crucial role in signaling pathways related to inflammation and programmed cell death, rendering them promising targets for therapeutic interventions. However, their expression as recombinant proteins often pose challenges. Here, we present expression screening of TNFR1, IRAK4, and MyD88 death domains in E. coli, followed by the biophysical characterization of TNFR1 death domain after subsequent construct optimization. The study also discusses the influence of pH and ionic strength on TNFR1R347A stability, providing statistical models to predict optimal conditions of the buffer to achieve the highest protein stability. HighlightsO_LIOptimization of expression conditions for TNFR1R347A, MyD88, IRAK4 death domains in E. coli BL21(DE3) cells. C_LIO_LIHigh-yield production of soluble monomeric TNFR1R347A death domain. C_LI

biochemistry↗

bioRxiv2025

Modelling symplasmic transport: from biophysical basics to steeper root gradients

Development and spatial pattern formation are inherently linked. The coordinated determination of cell fates is crucial in any developmental process and requires extensive intercellular communication. Plants cells exchange many molecular signals via the symplasmic pathway, i.e., via plasmodesmata: narrow channels connecting the cytoplasm of neighbouring cells. Regulation of symplasmic transport is vital for normal plant development, and mutations that disrupt this regulation are often embryo or seedling lethal. In many tissues, symplasmic transport of small molecules is diffusion driven, resulting in a non-selective and bidirectional transport, although net directionality could arise from gradients. This has led to the (dogmatic) belief that symplasmic transport can only be detrimental to pattern formation, because signalling molecules cannot be confined, and gradients would fade. Here, we develop a detailed biophysical description of symplasmic transport to explore how plasmodesmata affect gradients in a linear tissue. We then apply the model in more complex tissue contexts, observing and explaining, e.g., that symplasmic transport may result in steeper gradients in the root apical meristem. In conclusion, our model provides a reference framework for estimating the consequences of symplasmic transport and explains how symplasmic transport can contribute to more robust developmental patterning.

plant biology↗

bioRxiv2025

Multi-output computation by single neuron biophysics in a visual system

As long anticipated (Sandberg and Bostrom 2008; Seung 2012; Szigeti et al. 2014), connectomics is providing a new foundation for brain simulation by replacing theoretical assumptions about network connectivity with solid empirical facts. Connectomics also yields detailed information about neuronal morphology, which is useful for simulating the biophysics of single neurons (Yang et al. 2016; Meier and Borst 2019; T. X. Liu et al. 2022). Here I introduce a formalism for simulating a brain as a network of synapses interacting via an effective resistance matrix. By computing this matrix for fly visual interneurons, I find evidence that some neurons may be true multi-output devices, neither well approximated as "point neurons" (Lappalainen et al. 2024; Shiu et al. 2024), nor as collections of functionally independent compartments (Meier and Borst 2019). Within a linear approximation, such a neuron is instead equivalent to a hierarchy of virtual neurons that spatially pool over multiple length scales. The computational powers of multi-output neurons may support highly sophisticated normalizations in the fly visual system (Seung 2024a).

neuroscience↗