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bioRxiv2023

Biophysical Mechanism of Allosteric Regulation of Actin Capping Protein

Actin capping protein (CP) can be regulated by steric and allosteric mechanisms. The molecular mechanism of the allosteric regulation at a biophysical level includes linkage between the binding sites for three ligands: F-actin, Capping-Protein-Interacting (CPI) motifs, and V-1/myotrophin, based on biochemical functional studies and solvent accessibility experiments. Here, we investigated the mechanism of allosteric regulation at the atomic level using single-molecule Forster resonance energy transfer (FRET) and molecular dynamics (MD) to assess the conformational and structural dynamics of CP in response to linked-binding site ligands. In the absence of ligand, both single-molecule FRET and MD revealed two distinct conformations of CP in solution; previous crystallographic studies revealed only one. CPI-motif peptide association induced conformational changes within CP that propagate in one direction, while V-1 association induced conformational changes in the opposite direction. Comparing CPI-motif peptides from different proteins, we identified variations in CP conformations and dynamics that are specific to each CPI motif. MD simulations for CP alone and in complex with a CPI motif and V-1 reveal atomistic details of the conformational changes. Analysis of the interaction of CP with wildtype (wt) and chimeric CPI-motif peptides using single-molecule FRET, isothermal calorimetry (ITC) and MD simulation indicated that conformational and affinity differences are intrinsic to the C-terminal portion of the CPI-motif. We conclude that allosteric regulation of CP involves changes in conformation that disseminate across the protein to link distinct binding-site functions. Our results provide novel insights into the biophysical mechanism of the allosteric regulation of CP.

biochemistry↗

bioRxiv2023

Exposure to aldehyde cherry e-liquid flavouring and its vape by-product disrupts pulmonary surfactant biophysical function

Over the last decade there has been a surge in vaping device usage, especially among adolescents, raising concerns for potentially related lung damage. Notoriously, there have been many e-cigarette or vaping-related lung injury (EVALI) cases resulting in hospitalisations and deaths. Although the vaping component vitamin E acetate has been linked to a large proportion of EVALI cases resulting in its widespread banning, one fifth of the cases remain unexplained. Furthermore, the overall long-term impact of vaping on respiratory health is poorly understood. A likely driver behind EVALI is pulmonary surfactant disruption, as it is the first point of contact for any inhaled toxicant in the alveoli, and abnormalities of its function are linked to some symptoms presented in EVALI cases. Aberrant biophysical function of the surfactant results in alveolar surface tension increase, causing alveolar collapse. Vaping chemicals with the potential to disrupt surfactant function must be hydrophobic molecules able to interact with surfactant components at the alveolar air-liquid interface. Recent findings have recorded the synthesis of highly hydrophobic acetal by-products of the base vaping chemical propylene glycol and common flavouring aldehydes, including the cherry flavouring benzaldehyde, not identified in preliminary e-liquid safety tests. This study provides evidence that benzaldehyde and its by-product, benzaldehyde propylene glycol acetal, have the potential to significantly disrupt surfactant biophysical function via interactions with surfactant proteins SP-B and/or SP-C, which may provide stable interactions within the surfactant film by forming associations with the sublayer of surfactant three-dimensional structure present at high lateral compression, i.e., expiration breathing. Data also suggest considerable vaping chemical loss to the experimental subphase, indicating potential further implications to the alveolar epithelial layer beneath.

pharmacology and toxicology↗

bioRxiv2023

Integrated Computational Biophysics approach for Drug Discovery against Nipah Virus

The Nipah virus (NiV) poses a pressing global threat to public health due to its high mortality rate, multiple modes of transmission, and lack of effective treatments. NiV glycoprotein G (NiV-G) emerges as a promising target for NiV drug discovery due to its essential role in viral entry and membrane fusion. Therefore, in this study we applied an integrated computational and biophysics approach to identify potential inhibitors of NiV-G within a curated dataset of Peruvian phytochemicals. Our virtual screening results indicated that these compounds could represent a natural source of potential NiV-G inhibitors with {Delta}G values ranging from -8 to -11 kcal/mol. Among them, Procyanidin B2, B3, B7, and C1 exhibited the highest binding affinities and formed the most molecular interactions with NiV-G. Molecular dynamics simulations revealed the induced-fit mechanism of NiV-G pocket interaction with these procyanidins, primarily driven by its hydrophobic nature. Non-equilibrium free energy calculations were employed to determine binding affinities, highlighting Procyanidin B3 and B2 as the ligands with the most substantial interactions. Overall, this work underscores the potential of Peruvian phytochemicals, particularly procyanidins B2, B3, B7, and C1, as lead compounds for developing anti-NiV drugs through an integrated computational biophysics approach. Key pointsO_LINipah Virus (NiV) Threat: NiV is a severe public health risk due to its high mortality rate, broad host range, multiple transmission modes, and lack of effective treatment. Outbreaks have occurred frequently in South and Southeast Asia, particularly in Bangladesh and India, leading to high fatality rates. C_LIO_LICross-Border Concerns: NiVs ability to transmit between humans and domestic animals raises concerns about its potential to cross regional borders and cause pandemics. It has been recognized as a high-priority pathogen by the World Health Organization. C_LIO_LILack of Treatment: Currently, there are no approved specific antiviral treatments or vaccines for NiV. Patients receive supportive care and some drugs used for other viruses, despite their side effects. C_LIO_LITargeting NiV Glycoprotein G: The study focuses on NiV glycoprotein G (NiV-G) as a target for potential anti-Nipah drugs due to its crucial role in viral entry. This glycoprotein mediates viral attachment and entry into host cells. C_LIO_LIComputational Drug Discovery: The research employs computational methods, including virtual screening and molecular dynamics simulations, to identify potential inhibitors of NiV-G from a dataset of Peruvian phytochemicals, particularly procyanidins B2, B3, B7, and C1. These compounds showed promising binding affinities, stable interactions, and favorable binding energies with NiV-G, making them potential lead compounds for drug development. C_LI

microbiology↗

bioRxiv2023

Biophysical modeling of frontocentral ERP generation links circuit-level mechanisms of action-stopping to a behavioral race model

Human frontocentral event-related potentials (FC-ERPs) are ubiquitous neural correlates of cognition and control, but their generating multiscale mechanisms remain mostly unknown. We used the Human Neocortical Neurosolver(HNN)s biophysical model of a canonical neocortical circuit under exogenous thalamic and cortical drive to simulate the cell and circuit mechanisms underpinning the P2, N2, and P3 features of the FC-ERP observed after Stop-Signals in the Stop-Signal task (SST). We demonstrate that a sequence of simulated external thalamocortical and cortico-cortical drives can produce the FC-ERP, similar to what has been shown for primary sensory cortices. We used this model of the FC-ERP to examine likely circuit-mechanisms underlying FC-ERP features that distinguish between successful and failed action-stopping. We also tested their adherence to the predictions of the horse-race model of the SST, with specific hypotheses motivated by theoretical links between the P3 and Stop process. These simulations revealed that a difference in P3 onset between successful and failed Stops is most likely due to a later arrival of thalamocortical drive in failed Stops, rather than, for example, a difference in effective strength of the input. In contrast, the same model predicted that early thalamocortical drives underpinning the P2 and N2 differed in both strength and timing across stopping accuracy conditions. Overall, this model generates novel testable predictions of the thalamocortical dynamics underlying FC-ERP generation during action-stopping. Moreover, it provides a detailed cellular and circuit-level interpretation that supports links between these macroscale signatures and predictions of the behavioral race model. Significance statementThe frontocentral event-related potential (FC-ERP) is an easily-measurable neural correlate of cognition and control. However, the cortical dynamics that produce this signature in humans are complex, limiting the ability of researchers to make predictions about its underlying mechanisms. In this study, we used the biophysical model included in the open-source Human Neocortical Neurosolver software to simulate and evaluate the likely cellular and circuit mechanisms that underlie the FC-ERP in the Stop-Signal task. We modeled mechanisms of the FC-ERP during successful and unsuccessful stopping, generating testable predictions regarding Stop-associated computations in human frontal cortex. Moreover, the resulting model parameters provide a starting point for simulating mechanisms of the FC-ERP and other frontal scalp EEG signatures in other task conditions and contexts.

neuroscience↗

bioRxiv2024

Biophysical Essentials - A Full Stack Open-Source Software Framework for Conserved and Advanced Analysis of Patch-Clamp Recordings

Patch-Clamp recordings allow for in depth electrophysiological characterization of single cells, their general biophysical properties as well as characteristics of voltage- and ligand-gated ionic currents. Different acquisition modes, such as whole-cell patch-clamp recordings in the current or voltage clamp configuration, capacitance measurements or single channel recordings from cultured cells as well as acute brain slices are routinely performed for these purposes. Nevertheless, multipurpose transparent and adaptable software tools to perform reproducible state-of-the-art analysis of multiple experiment types and to manage larger sets of experimental data are currently unavailable. We therefore developed Biophysical Essentials (BPE) as an open-source python software for transparent and reproducible analysis of electrophysiological recordings. While initially designed to improve time consuming and repetitive analysis steps, BPE was optimized to cover entire workflows from data acquisition, preprocessing, visualization and normalization of single recordings up to stacked calculations and statistics of multiple experiments. BPE can operate with different file formats from different amplifier systems and producers. An in-process database logs all analysis steps for later review and serves as a central storage point for recordings. Statistical testing as well as advanced analysis functions like Boltzmann-fitting and dimensional reduction methods further support the researchers needs in projects involving electrophysiology techniques. Impact statementBiophysical Essentials provides fundamental support for transparent patch-clamp analysis to improve reproducibility in electrophysiology research.

neuroscience↗

bioRxiv2024

A Biophysical Model for ATAC-seq Data Analysis

The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) can be used to identify open chromatin regions, providing complementary information to RNA-seq which measures gene expression by sequencing. Single-cell "multiome" methods offer the possibility of measuring both modalities simultaneously in cells, raising the question of how to analyze them jointly, and also the extent to which the information they provide is better than unregistered data where single-cell ATAC-seq and single-cell RNA-seq are performed on the same sample, but on different cells. We propose and motivate a biophysical model for chromatin dynamics and subsequent transcription that can be used with multiome data, and use it to assess the benefits of multiome data over unregistered single-cell RNA-seq and single-cell ATAC-seq. We also show that our model provides a biophysically grounded approach to integration of open chromatin data with other modalities.

bioinformatics↗

bioRxiv2024

Biogenic nanoparticles from liquid and solid matrices: biochemical and biophysical properties of Extracellular Vesicles-enriched samples from human plasma and skeletal muscle tissue.

AimStudies on extracellular vesicles (EVs) focused on samples enriched from liquid matrices, such as cell culture media and blood. Recent research highlights the roles of EVs derived from the extracellular matrix of solid tissues, and how investigating these specific EVs offers insights into their microenvironment and potential biological influences on surrounding cells. This study presents a shared method to separate and compare EV enriched from solid (human skeletal muscle biopsy) and liquid (human plasma) matrices, addressing technical challenges and minimizing biases in separation techniques. MethodsPlasma and skeletal muscle-EVs were obtained combining serial centrifugation steps and discontinuous sucrose density gradient. EVs characterization employed advanced analytical techniques such as Western Blot, Colorimetric Nanoplasmonic assay, Atomic Force Miscoscopy, Nanoparticle Tracking Analysis and Dynamic Light Scattering to focus on biomolecular composition, nanomechanical properties, particle yield, size distribution, and colloidal stability. ResultsThe analysis revealed distinct differences between skeletal muscle-EVs and plasma-EVs including: molecular composition, physical and nanomechanical properties, as well as particle size distribution. Skeletal muscle-EVs exhibited unique colloidal behavior compared to their plasma counterparts, suggesting tissue-specific features that may influence their biological activity and stability. ConclusionsThe findings demonstrate that EVs from skeletal muscle tissue possess unique biochemical and biophysical characteristics when compared to those derived from plasma. These differences reflect their diverse biological origins and microenvironments. Understanding these distinctions could advance the development of EV-based diagnostic tools, particularly for muscular disorders, and broaden our knowledge of EV roles across various tissue contexts. HIGHLIGHTSO_LIShared protocol to enrich and compare extracellular vesicles (EVs) from both solid (skeletal muscle biopsy) and liquid (plasma) human samples, reducing methodological bias across matrices. C_LIO_LISkeletal muscle-derived EVs differ significantly from plasma EVs in biochemical and biophysical properties such as molecular composition, size distribution, nanomechanical properties, and colloidal stability. C_LIO_LIEV heterogeneity across microenvironments emerged as a key biological feature, highlighting their potential for specific diagnostic applications. C_LI

cell biology↗

bioRxiv2024

Spectral graph model for fMRI: a biophysical, connectivity-based generative model for the analysis of frequency-resolved resting state fMRI

Resting state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brains functional organization and to examine if it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being under-utilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysical signal generation model followed by graph spectral (i.e. eigen) decomposition. We call this model a Spectral Graph Model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signals spectral and spatial features into a small number of biophysically-interpretable parameters. We expect this model-based inference of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relations in a variety of brain disorders.

neuroscience↗

bioRxiv2024

A straightforward cell culture insert model to incorporate biochemical and biophysical stromal properties into transplacental transport studies

IntroductionThe placental extracellular matrix (ECM) dynamically remodels over pregnancy and in disease. How these changes impact placental barrier function is poorly understood as there are limited in vitro models of the placenta with a modifiable stromal compartment to mechanistically investigate these extracellular factors. We developed a straightforward method to incorporate uniform hydrogels into standard cell culture inserts for transplacental transport studies. MethodsUniform polyacrylamide (PAA) gels were polymerized within cell culture inserts by (re)using the insert packaging to create a closed, controllable environmental chamber. PAA pre-polymer solution was added dropwise via a syringe to the cell culture insert and the atmosphere was purged with an inert gas. Transport and cell culture studies were conducted to validate the model. ResultsWe successfully incorporated and ECM functionalized uniform PAA gels to cell culture inserts enable cell adhesion and monolayer formation. Imaging and analyte transport studies validated gel formation and expected mass transport results and successful cell studies confirmed cell viability, monolayer formation, and that the model could be used transplacental transport studies. Detailed methods and validation protocols are included. DiscussionIt is well appreciated that ECM biophysical and biochemical properties impact cell phenotype and cell signaling in many tissues including the placenta. The incorporation of a PAA gel within a cell culture insert enables independent study of placental ECM biophysical and biochemical properties in the context of transplacental transport. These straightforward and low-cost methods to build three dimensional cellular models are readily adoptable by the wider scientific community.

physiology↗

bioRxiv2024

Structure, dynamics, coding and optimal biophysical parameters of efficient excitatory-inhibitory spiking networks

The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.

neuroscience↗

bioRxiv2024

Synergistic Biophysics and Machine Learning Modeling to Rapidly Predict Cardiac Growth Probability

AO_SCPLOWBSTRACTC_SCPLOWComputational models that can predict growth and remodeling of the heart could have important clinical applications. However, the time it takes to calibrate and run current models while considering data uncertainty and variability makes them impractical for routine clinical use. This study aims to address this need by creating a computational framework to efficiently predict cardiac growth probability. We utilized a biophysics model to rapidly simulate cardiac growth following mitral valve regurgitation (MVR). Here we developed a two-tiered Bayesian History Matching approach augmented with Gaussian process emulators for efficient calibration of model parameters to align with growth outcomes within a 95% confidence interval. We first generated a synthetic data set to assess the accuracy of our framework, and the effect of changes in data uncertainty on growth predictions. We then calibrated our model to match baseline and chronic canine MVR data and used an independent data set to successfully validate the ability of our calibrated model to accurately predict cardiac growth probability. The combined biophysics and machine learning modeling framework we proposed in this study can be easily translated to predict patient-specific cardiac growth.

bioengineering↗

bioRxiv2024

Understanding responses to multi-electrode epiretinal stimulation using a biophysical model

ObjectiveNeural interfaces are designed to evoke specific patterns of electrical activity in populations of neurons by stimulating with many electrodes. However, currents passed simultaneously through multiple electrodes often combine nonlinearly to drive neural responses, making evoked responses difficult to predict and control. This response nonlinearity could arise from the interaction of many excitable sites in each cell, any of which can produce a spike. However, this multi-site activation hypothesis is difficult to verify experimentally. ApproachWe developed a biophysical model to study retinal ganglion cell (RGC) responses to multi-electrode stimulation and validated it using data collected from ex vivo preparations of the macaque retina using a microelectrode array (512 electrodes; 30{micro}m pitch; 10{micro}m diameter). ResultsFirst, the model was validated by using it to reproduce essential empirical findings from single-electrode recording and stimulation, including recorded spike voltage waveforms at multiple locations and sigmoidal responses to injected current. Then, stimulation with two electrodes was modeled to test how the positioning of the electrodes relative to the cell affected the degree of response nonlinearity. Currents passed through pairs of electrodes positioned near the cell body or far from the axon (>40 {micro}m) exhibited approximately linear summation in evoking spikes. Currents passed through pairs of electrodes close to the axon summed linearly when their locations along the axon were similar, and nonlinearly otherwise. Over a range of electrode placements, several distinct, localized spike initiation sites were observed, and the number of these sites covaried with the degree of response nonlinearity. Similar trends were observed for three-electrode stimuli. All of these trends in the simulation were consistent with experimental observations. Significance. These findings support the multi-site activation hypothesis for nonlinear activation of neurons, providing a biophysical interpretation of previous experimental results and potentially enabling more efficient use of multi-electrode stimuli in future neural implants.

bioengineering↗

bioRxiv2024

A ChatGPT Assisted Reading Protocol for Undergraduate Research Students Engaging with Biophysics Literature

A major challenge for undergraduate students is reading scientific literature. This is especially true in biophysics, where many of the concepts may not have been covered in undergraduate courses. Students can become overwhelmed, which may lead to less overall engagement with the scientific literature. In response, we have developed a guided reading protocol that combines pre-reading strategies, structured notetaking, and ChatGPT to help students clarify unfamiliar concepts in an interactive way. To test the protocol, participants in this study were given an initial survey to determine their experience with reading scientific literature. After this they were given an abridged biophysics paper and the protocol. The ChatGPT transcripts were analyzed using open coding and the students were given a post-study survey. We found most students did not appear to regularly engage with the literature, possibly due to content barriers they encountered. Analyzing their transcripts, we observed that students asked for definitions, explanations, summaries, and simplifications. Overall, students reported that using ChatGPT was a positive experience and they expected to use ChatGPT in the future. From this work, we expect this new protocol may be a way to keep novice students from becoming discouraged when reading scientific papers and keep them engaged with the current literature.

scientific communication and education↗

bioRxiv2024

Quantifying the influence of biophysical factors in shaping brain communication through remnant functional networks

Functional connectivity (FC) reflects brain-wide communication essential for cognition, yet the role of underlying biophysical factors in shaping FC remains unclear. We quantify the influence of physical factors--structural connectivity (SC) and Euclidean distance (DC), which capture anatomical wiring and regional distance--and molecular factors--gene expression similarity (GC) and neuroreceptor congruence (RC), representing neurobiological similarity--on resting- state FC. We assess how these factors impact graph-theoretic and gradient features, capturing pairwise and higher-order interactions. By generating remnant functional networks after selectively removing connections tied to specific factors, we show that molecular factors, particularly RC, dominate graph-theoretic features, while gradient features are shaped by a mix of molecular and physical factors, especially GC and DC. SC has a surprisingly minor role. We also link FC alterations to biophysical factors in schizophrenia, bipolar disorder, and ADHD, with physical factors differentiating these groups. These insights are key for understanding FC across various applications, including task performance, development, and clinical conditions.

neuroscience↗

bioRxiv2024

Linking microscopy to diffusion MRI with degenerate biophysical models: an application of the Bayesian EstimatioN of CHange (BENCH) framework

Biophysical modelling of diffusion MRI (dMRI) is used to non-invasively estimate microstructural features of tissue, particularly in the brain. However, meaningful description of tissue requires many unknown parameters, resulting in a model that is often ill-posed. The Bayesian EstimatioN of CHange (BENCH) framework was specifically designed to circumvent parameter fitting for ill-conditioned models when one is simply interested in interpreting signal changes related to some variable of interest. To understand the biological underpinning of some observed change in MR signal between different conditions, BENCH predicts which model parameter, or combination of parameters, best explains the observed change, without having to invert the model. BENCH has been previously used to identify which biophysical parameters could explain group-wise dMRI signal differences (e.g. patients vs. controls); here, we adapt BENCH to interpret dMRI signal changes related to continuous variables. We investigate how parameters from the dMRI standard model of white matter, with an additional sphere compartment to represent glial cell bodies, relate to tissue microstructure quantified from histology. We validate BENCH using synthetic dMRI data from numerical simulations. We then apply it to ex-vivo macaque brain data with dMRI and microscopy metrics of glial density, axonal density, and axonal dispersion in the same brain. We found that (i) increases in myelin density are primarily associated with an increased intra-axonal volume fraction and (ii) changes in the orientation dispersion derived from myelin microscopy are linked to variations in the orientation dispersion index. Finally, we found that the dMRI signal is sensitive to changes in glial cell soma in the WM, but that no parameter in the extended standard model was able to explain this observed signal change, suggesting model inadequacy.

neuroscience↗

bioRxiv2024

A biophysically-detailed model of inter-areal interactions in cortical sensory processing

Mechanisms of top-down modulation in sensory perception and their relation to underlying connectivity are not completely understood. We present here a biophysically-detailed computational model of two interconnected cortical areas, representing the first steps in a cortical processing hierarchy, as a tool for potential discovery. The model integrates a large body of data from rodent primary somatosensory cortex and reproduces biological features across multiple scales: from a handful of ion channels defining a diversity of electrical types in hundreds of thousands of morphologically detailed neurons, to local and long-range networks mediated by hundreds of millions of synapses. Notably, long-range connectivity in the model incorporates target lamination patterns associated with feed-forward and feedback pathways. We use the model to study the impact of inter-areal interactions on sensory processing. First, we exhibit a cortico-cortical loop between the two model areas (X and Y), wherein sensory input to area X produces a response with two components in time, the first driven by the stimulus and the second by feedback from area Y. We perform a structural and functional characterization of this loop, finding a differential impact of layer-specific pathways in the feed-forward and feedback directions. Second, we explore stimulus discrimination by presenting four different spatially-segregate stimulus patterns. We observe well-defined temporal sequences of functional cell assembly activation, with stimulus specificity in early but not late assemblies in area X, i.e., in the stimulus-driven component of the response but not in the feedback-driven component. We also find the earliest assembly in area Y to be specific to pairs of patterns, consistent with the topography of connections. Finally, we examine the integration of bottom-up and top-down signals. When presenting a second stimulus coincident with the feedback-driven component, we observe an approximate linear superposition of responses. The implied lack of interaction is consistent with the stochastic and hence naive connectivity in the model, but provides a useful foundation for plasticity mechanisms to learn top-down influences. This work represents a first step in the study of inter-areal interactions with biophysically-detailed simulations.

neuroscience↗

bioRxiv2024

Immunologic and Biophysical Features of the BNT162b2 JN.1- and KP.2-Adapted COVID-19 Vaccines

Vaccines remain a vital public health tool to reduce the burden of COVID-19. COVID-19 vaccines that are more closely matched to circulating SARS-CoV-2 lineages elicit more potent and relevant immune responses that translate to improved real-world vaccine effectiveness. The rise in prevalence of the Omicron JN.1 lineage, and subsequent derivative sublineages such as KP.2 and KP.3, coincided with reduced neutralizing activity and effectiveness of Omicron XBB.1.5-adapted vaccines. Here, we characterized the biophysical and immunologic attributes of BNT162b2 JN.1- and KP.2-adapted mRNA vaccine-encoded spike (S) protein immunogens. Biophysical interrogations of S revealed the structural consequences of hallmark amino acid substitutions and a potential molecular mechanism of immune escape employed by JN.1 and KP.2. The vaccine candidates were evaluated for their immunogenicity when administered as fourth or fifth doses in BNT162b2-experienced mice or as a primary series in naive mice. In both vaccine-experienced and naive settings, JN.1- and KP.2-adapted vaccines conferred improved neutralizing responses over the BNT162b2 XBB.1.5 vaccine against a broad panel of emerging JN.1 sublineages, including the predominant KP.3.1.1 and emerging XEC lineages. Antigenic mapping of neutralizing responses indicated greater antigenic overlap of JN.1- and KP.2-adapted vaccine responses with currently circulating sublineages compared to an XBB.1.5-adapted vaccine. CD4+ and CD8+ T cell responses were generally conserved across all three vaccines. Together, the data support the selection of JN.1- or KP.2-adapted vaccines for the 2024-25 COVID-19 vaccine formula. ONE-SENTENCE SUMMARYThe Omicron JN.1- and KP.2-adapted BNT162b2 mRNA vaccines encoding prefusion S proteins elicit similar preclinical neutralizing antibody responses against circulating JN.1 sublineage pseudoviruses that are more potent than those elicited by past iterations of BNT162b2 licensed vaccines, thus demonstrating the importance of annual strain changes to the COVID-19 vaccine.

immunology↗

bioRxiv2024

Biophysical network modeling of temporal and stereotyped sequence propagation of neural activity in the premotor nucleus HVC

Stereotyped neural sequences are often exhibited in the brain, yet the neurophysiological mechanisms underlying their generation are not fully understood. Birdsong is a prominent model to study such behavior particularly because juvenile songbirds progressively learn from their tutors and by adulthood are able to sing stereotyped song patterns. The songbird premotor nucleus HVC coordinate motor and auditory activity responsible for learned vocalizations. The HVC comprises three neural populations that has distinct in vitro and in vivo electrophysiological responses. Typically, models that explain HVCs network either rely on intrinsic HVC circuitry to propagate sequential activity, rely on extrinsic feedback to advance the sequence or rely on both. Here, we developed a physiologically realistic neural network model incorporating the three classes of HVC neurons based on the ion channels and the synaptic currents that had been pharmacologically identified. Our model is based on a feedforward chain of microcircuits that encode for the different sub-syllabic segments (SSSs) and that interact with each other through structured feedback inhibition. The network reproduced the in vivo activity patterns of each class of HVC neurons, and unveiled key intrinsic and synaptic mechanisms that govern the sequential propagation of neural activity by highlighting important roles for the T-type Ca2+ current, Ca2+-dependent K+ current, A-type K+ current, hyperpolarization activated inward current, as well as excitatory and inhibitory synaptic currents. The result is a biophysically realistic model that suggests an improved characterization of the HVC network responsible for song production in the songbird. Significance StatementLearned motor sequences acquired through repetitive practice undergo stabilization and are integrated into cortical motor circuits through a process of consolidation. The process of mastering a complicated motor sequence requires thorough motor exploration to achieve closer alignment with the desired outcome, yet the neural mechanisms underlying sequence generation remain largely unexplored. In this study, we investigate the neural circuitry in a premotor region of the songbird brain (known as HVC) that is well-known for generating extremely precise learned sequences. We develop a biophysically realistic network model that incorporate pharmacologically identified intrinsic and synaptic currents for the three different classes of HVC neurons. The network highlights fundamental intrinsic and synaptic mechanisms regulating the sequential propagation of neural activity.

neuroscience↗