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bioRxiv2020

Model-based inference of neutralizing antibody avidities against influenza virus

To assess the response to vaccination, quantity (concentration) and quality (avidity) of neutralizing antibodies are the most important parameters. Specifically, an increase in avidity indicates germinal center formation, which is required for establishing long-term protection. For influenza, the classical hemagglutination inhibition (HI) assay, however, quantifies a combination of both, and to separately determine avidity requires high experimental effort. We developed from first principles a biophysical model of hemagglutination inhibition to infer IgG antibody avidities from measured HI titers and IgG concentrations. The model accurately describes the relationship between neutralizing antibody concentration/avidity and HI titer, and explains quantitative aspects of the HI assay, such as robustness to pipetting errors and detection limit. We applied our model to infer avidities against the pandemic 2009 H1N1 influenza virus in vaccinated patients (n=45) after hematopoietic stem cell transplantation (HSCT) and validated our results with independent avidity measurements using an enzyme-linked immunosorbent assay with urea elution. Avidities inferred by the model correlated with experimentally determined avidities ({rho}=0.54, 95% CI=[0.31, 0.70], P<10-4). The model predicted that increases in IgG concentration mainly contribute to the observed HI titer increases in HSCT patients and that immunosuppressive treatment is associated with lower baseline avidities. Since our approach requires only easy-to-establish measurements as input, we anticipate that it will help to disentangle causes for poor vaccination outcomes also in larger patient populations. This study demonstrates that biophysical modelling can provide quantitative insights into agglutination assays and complement experimental measurements to refine antibody response analyses. Author SummaryInfluenza vaccines are assessed based on the induced antibody response, where antibody quantity (concentration) and antibody binding strength (avidity) determine the potency to neutralize the virus. In addition, an increase in avidity indicates a successful germinal center reaction, which is required for establishing long-term protection. However, the hemagglutination inhibition (HI) assay - traditionally used to assess influenza vaccines - measures a combination of both antibody concentration and avidity, and to separately determine avidity requires high experimental effort. We developed a biophysical model of the HI assay, which enables the inference of antibody avidities from measured HI titers and antibody concentrations. We applied our approach to a vaccinated population of immunocompromised patients after blood stem cell transplantation and validated our results experimentally. The model predicted that vaccination induced an increase in avidity in only a few patients and that patients under immunosuppressive treatment show lower baseline avidities. Since our approach requires only easily measurable data as input, it can facilitate the investigation of vaccine responses in larger populations. This study demonstrates that biophysical modelling can complement experimental data and provide additional details on agglutination experiments and antibody responses.

immunology↗

bioRxiv2020

A unified physiological framework of transitions between seizures, status epilepticus and depolarization block at the single neuron level

The majority of seizures recorded in humans and experimental animal models can be described by a generic phenomenological mathematical model, The Epileptor. In this model, seizure-like events (SLEs) are driven by a slow variable and occur via saddle node (SN) and homoclinic bifurcations at seizure onset and offset, respectively. Here we investigated SLEs at the single cell level using a biophysically relevant neuron model including a slow/fast system of four equations. The two equations for the slow subsystem describe ion concentration variations and the two equations of the fast subsystem delineate the electrophysiological activities of the neuron. Using extracellular K+ as a slow variable, we report that SLEs with SN/homoclinic bifurcations can readily occur at the single cell level when extracellular K+ reaches a critical value. In patients and experimental models, seizures can also evolve into sustained ictal activity (SIA) and, depolarization block (DB), activities which are also parts of the dynamic repertoire of the Epileptor. Increasing extracellular concentration of K+ in the model to values found during experimental status epilepticus and DB, we show that SIA and DB can also occur at the single cell level. Thus, seizures, SIA and DB, which have been first identified as network events, can exist in a unified framework of a biophysical model at the single neuron level and exhibit similar dynamics as observed in the Epileptor. Author SummaryEpilepsy is a neurological disorder characterized by the occurrence of seizures. Seizures have been characterized in patients in experimental models at both macroscopic and microscopic scales using electrophysiological recordings. Experimental works allowed the establishment of a detailed taxonomy of seizures, which can be described by mathematical models. We can distinguish two main types of models. Phenomenological (generic) models have few parameters and variables and permit detailed dynamical studies often capturing a majority of activities observed in experimental conditions. But they also have abstract parameters, making biological interpretation difficult. Biophysical models, on the other hand, use a large number of variables and parameters due to the complexity of the biological systems they represent. Because of the multiplicity of solutions, it is difficult to extract general dynamical rules. In the present work, we integrate both approaches and reduce a detailed biophysical model to sufficiently low-dimensional equations, and thus maintaining the advantages of a generic model. We propose, at the single cell level, a unified framework of different pathological activities that are seizures, depolarization block, and sustained ictal activity.

neuroscience↗

bioRxiv2021

In vitro effects of S-Licarbazepine as a potential precision therapy on SCN8A variants causing neuropsychiatric disorders

Background and PurposeAmong genetic epilepsies, variants in sodium channel coding genes constitute a major subgroup. Variants in SCN8A, the coding gene for NaV1.6 channels, are characterized by a variety of symptoms including intractable epileptic seizures, psychomotor delay, progressive cognitive decline, and others such as autistic features, ataxia or dystonia. Standard anticonvulsant treatment has only limited impact on the course of disease. Experimental ApproachPersonalized therapeutic regimens tailored to disease-causing pathophysiological mechanisms may offer the specificity required to overcome intractability. Toward this aim, we investigated in vitro in neuroblastoma cells the effects of S-Licarbazepine, a third-generation dibenzazepine and enhancer of slow inactivation of voltage gated sodium channels, on three gain-of-function NaV1.6 variants linked to representative phenotypes of mild epilepsy (G1475R), developmental and epileptic encephalopathy (M1760I) and intellectual disability without epilepsy (A1622D). Key ResultsS-Licarbazepine strongly enhances the slow and - less pronounced - the fast inactivation of NaV1.6 wildtype channels. It acts similarly on all tested variants and irrespective of their particular biophysical dysfunction mechanism. Beyond that S-Licarbazepine has variant-specific effects including a partial reversal of pathologically slowed fast inactivation dynamics (A1622D, M1760I) and a trend to reduce the enhanced persistent Na+ current by A1622D variant channels. Conclusion and ImplicationsThese data bring out that S-Licarbazepine not only owns substance-specific effects, but also holds variant-specific effects, which can variably contribute to functional compensation of distinct channel-specific biophysical properties and thereby highlighting the role of personalized approaches, which likely will be key to improved and successful treatment not only of SCN8A-related disease. Bullet pointsO_LIWhat is already known? S-Lic strongly modulates slow and - to a less extend - fast inactivation of wild-type NaV1.6 channels. C_LIO_LIWhat this study adds? Differential modulatory effects of S-Lic extend to NaV1.6 A1622D, M1760I and G1475R variant channels irrespective of their leading biophysical mode of gain-of-function and variably contribute to variant-specific functional compensation of their altered biophysical properties. C_LIO_LIClinical significance: These data suggest therapeutic potential of S-Lic for SCN8A neuropsychiatric disorders and highlight the role of personalized approaches aimed at increasingly precise correction of underlying pathophysiological mechanisms. C_LI

pharmacology and toxicology↗

bioRxiv2021

Columnar localization and laminar origin of cortical surface electrical potentials

Electrocorticography (ECoG) methodologically bridges basic neuroscience and understanding of human brains in health and disease. However, the localization of ECoG signals across the surface of the brain and the spatial distribution of their generating neuronal sources are poorly understood. To address this gap, we recorded from rat auditory cortex using customized ECoG, and simulated cortical surface electrical potentials with a full-scale, biophysically detailed cortical column model. Experimentally, ECoG-derived auditory representations were tonotopically organized and signals were anisotropically localized to [≤]{+/-}200 m, i.e., a single cortical column. Biophysical simulations reproduce experimental findings, and indicate that neurons in cortical layers V and VI contribute [~]85% of evoked high-gamma signal recorded at the surface. Cell number and synchronicity were the primary biophysical properties determining laminar contributions to evoked ECoG signals, while distance was only a minimal factor. Thus, evoked ECoG signals primarily originate from neurons in the infragranular layers of a single cortical column. In BriefBaratham et al., investigated the localization and origins of sensory evoked ECoG responses. They experimentally found that ECoG responses were anisotropically localized [≤]{+/-}200 m, i.e., a single cortical column. Biophysically detailed simulations revealed that neurons in layers V &VI were the primary sources of evoked ECoG responses, in contrast to common thinking. HighlightsEvoked ECoG signals are localized on the surface to a cortical column. Neurons in cortical layers V and VI constitute the vast majority of the signal recorded at the surface. Different laminar contributions to ECoG signal are driven by cell density and synchronicity.

neuroscience↗

bioRxiv2021

Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation

Designing entirely new protein structures remains challenging because we do not fully understand the biophysical determinants of folding stability. Yet some protein folds are easier to design than others. Previous work identified the 43-residue {square}{beta}{beta}{square} fold as especially challenging: the best designs had only a 2% success rate, compared to 39-87% success for other simple folds (1). This suggested the {square}{beta}{beta}{square} fold would be a useful model system for gaining a deeper understanding of folding stability determinants and for testing new protein design methods. Here, we designed over ten thousand new {square}{beta}{beta}{square} proteins and found over three thousand of them to fold into stable structures using a high-throughput protease-based assay. Nuclear magnetic resonance, hydrogen-deuterium exchange, circular dichroism, deep mutational scanning, and scrambled sequence control experiments indicated that our stable designs fold into their designed {square}{beta}{beta}{square} structures with exceptional stability for their small size. Our large dataset enabled us to quantify the influence of universal stability determinants including nonpolar burial, helix capping, and buried unsatisfied polar atoms, as well as stability determinants unique to the {square}{beta}{beta}{square} topology. Our work demonstrates how large-scale design and test cycles can solve challenging design problems while illuminating the biophysical determinants of folding. SignificanceMost computationally designed proteins fail to fold into their designed structures. This low success rate is a major obstacle to expanding the applications of protein design. In previous work, we discovered a small protein fold that was paradoxically challenging to design (only a 2% success rate) even though the fold itself is very simple. Here, we used a recently developed high-throughput approach to comprehensively examine the design rules for this simple fold. By designing over ten thousand proteins and experimentally measuring their folding stability, we discovered the key biophysical properties that determine the stability of these designs. Our results illustrate general lessons for protein design and also demonstrate how high-throughput stability studies can quantify the importance of different biophysical forces.

synthetic biology↗

bioRxiv2022

Profiling skeletal muscle-derived secretome with differentiation and acute contractile activity

Extracellular vesicles (EVs) released from all cells, are essential to cellular communication, and contain biomolecular cargo that can affect recipient cell function. Studies on the effects of contractile activity (exercise) on EVs usually rely on plasma/serum-based assessments, which contain EVs from many different cells. To specifically characterize skeletal muscle-derived vesicles and the effect of acute contractile activity, we used an in vitro model where C2C12 mouse myoblasts were differentiated to form myotubes. EVs were isolated from conditioned media from muscle cells, pre-differentiation (myoblasts) and post-differentiation (myotubes), as well as from acutely stimulated myotubes (1hr @ 14V, C-Pace EM, IonOptix) using total exosome isolation reagent (TEI, ThermoFisher, referred to as extracellular particles [EPs]) and differential ultracentrifugation (dUC; EVs). Myotube-EPs (~98 nm) were 41% smaller than myoblast-EPs (~167 nm, p<0.001, N=8-10). Two-way ANOVA showed a significant main effect for size distribution of myotube vs. myoblast-EPs (p<0.01, N=10-13). Myoblast-EPs displayed a bimodal size distribution profile with peaks at <200 nm and 400-600 nm, compared to myotube-EPs that were largely 50-300 nm in size. Total protein yield from myotube-EPs was nearly 15-fold higher than myoblast-EPs, (p<0.001 N=6-9). Similar biophysical characteristics were observed when EVs were isolated using dUC: myotube-EVs (~195 nm) remained 41% smaller in average size than myoblast-EVs (~330 nm, p=0.07, N=4-6) and had comparable size distribution profiles as EPs isolated via TEI. Myotube-EVs also had 4.7-fold higher protein yield vs. myoblast EVs (p<0.05, N=4-6). Myotube-EPs had significantly decreased expression of exosomal marker proteins TSG101, CD63, ALIX and CD81 compared to myoblast-EPs (p<0.05, N=7-12). Conversely, microvesicle marker ARF6, and lipoprotein marker APO-A1was only found in the myotube-EPs (p<0.05, N=4-12). There was no effect of acute stimulation on myotube-EP biophysical characteristics (N=7), nor on expression of TSG101, ARF6 or CD81 (N=5-6). Myoblasts treated with control or acute stimulation-derived EPs (13 g/well) for 48hrs and 72hrs showed no changes in mitochondrial mass (MitoTracker Red), cell viability or cell count (N=3-4). Myoblasts treated with EP-depleted media (72hrs) had ~90% lower cell counts (p<0.01, N=3). Our data show that EVs differ in size, distribution, protein yield and expression of subtype markers pre- vs. post-skeletal muscle differentiation. There was no effect of acute stimulation on biophysical profile or protein markers in EPs. Acute stimulation-derived EPs did not alter mitochondrial mass nor cell count/viability. Further investigation into the effects of chronic contractile activity on the biophysical characteristics and cargo of skeletal muscle-specific EVs are warranted.

physiology↗

bioRxiv2023

Effects of greening-induced warming and cooling on tree phenology in temperate and boreal forests

Tree phenology, periodic biological events in trees, is highly sensitive to climate change. It has been reported that forest greening can influence the local climate by altering the seasonal surface energy budget. However, tree phenological responses to forest greening remains poorly understood at large spatial scales. Combining remote-sensing derived phenological and leaf area indices since 2001, herein we show that forest greening led to earlier spring (-1.05 {+/-} 0.17 d) and autumn phenology (-1.95 {+/-} 0.14 d) in temperate and boreal forests. Our results show that forest greening in winter and spring decreased surface albedo and thus resulted in biophysical warming that caused earlier spring leaf phenology. In contrast, forest greening in summer and autumn triggered biophysical cooling by enhancing evapotranspiration, which led to earlier autumn leaf phenology. These findings suggest that forest greening could significantly alter tree phenology through seasonal biophysical impacts. Therefore, it is essential to incorporate these complicated biophysical impacts of greening into tree phenology models to accurately predict future shifts in tree phenology under future climate change.

ecology↗

bioRxiv2024

Hierarchical Bayesian inference to model continuous phenotypical progression in Alzheimer's Disease

Throughout an organisms life, a multitude of complex and interdependent biological systems transition through biophysical processes that serve as indicators of the underlying biological states. Inferring these latent, unobserved states is a goal of modern biology and neuroscience. However, in many experimental setups, we can at best obtain discrete snapshots of the system at different times and for different individuals. This challenge is particularly relevant in the study of Alzheimers Disease (AD) progression, where we observe the aggregation of pathology in brain donors, but the underlying disease state is unknown. This paper proposes a biophysically motivated Bayesian framework (B-BIND: Biophysical Bayesian Inference for Neurode-generative Dynamics), where the disease state is modeled and continuously inferred from observed quantifications of multiple AD pathological proteins. Inspired by biophysical models, we describe pathological burden as an exponential process. The progression of AD is modeled by assigning a latent score, termed pseudotime, to each pathological state, creating a pseudotemporal order of donors based on their pathological burden. We study the theoretical properties of the model using linearization to reveal convergence and identifiability properties. We provide Markov chain Monte Carlo estimation algorithms, illustrating the effectiveness of our approach with multiple simulation studies across various data conditions. Applying this methodology to data from the Seattle Alzheimers Disease Brain Cell Atlas, we infer the pseudotime ordering of donors. Finally, we analyze the information within each pathological feature to refine the model, focusing on the most informative pathologies. This framework lays the groundwork for continuous pseudotime modeling in the analysis of neurodegenerative diseases.

neuroscience↗

bioRxiv2025

Muscular dystrophy-associated lamin variants disrupt cellular organization through a nucleolar-ribosomal axis controlling cytoplasmic macromolecular crowding

Emery-Dreifuss muscular dystrophy (EDMD) arises from mutations in nuclear lamins or emerin. Current pathological models emphasize defects in nuclear mechanics and transcription regulation. Yet these models do not fully explain the complexity of EDMD or other laminopathies. Here, we uncover an emerging pathway linking nuclear lamina defects to the reorganization of cytoplasmic biophysics, revealing how nuclear dysfunction cascades throughout the cell. Using Caenorhabditis elegans EDMD models, we demonstrate that lamin mutations dramatically alter cytoplasmic organization, reducing macromolecular crowding and increasing diffusivity of 40 nm Genetically Encoded Multimeric (GEM) nanoparticles. These striking biophysical changes coincide with nuclear positioning defects and collapsed endoplasmic reticulum architecture, mirroring phenotypes associated with ribosome depletion. We propose a mechanism where mutations in the C. elegans lamin lmn-1 disrupt nucleolar density and ribosome biogenesis, creating a nucleolar-ribosomal axis that propagates defects from the nucleus to the cytoplasm. Genetic interactions between lmn-1 and ribosomes support this regulatory relationship. While individual depletion of other nuclear envelope proteins produces minimal effects, combined loss of the functionally redundant emerin ortholog emr-1 and LEM-domain protein lem-2 phenocopied lmn-1 mutants, demonstrating that cytoplasmic biophysical disruption lies at EDMDs pathogenic core. Our findings establish a paradigm where nuclear lamina defects fundamentally rewire cellular biophysics through nucleolar-ribosomal dysfunction, opening transformative therapeutic avenues for treating laminopathies.

cell biology↗

bioRxiv2025

Decoding the physicochemical basis of taxonomy preferences in protein design models

Protein design models have transformed protein engineering by enabling computational exploration of sequence spaces far exceeding experimental capacity. However, their outputs are shaped by both the protein distributions represented in their training corpora and the information available during scoring, so the same model score may reflect backbone-compatible biophysics, taxonomic structure in sequence databases, or other learned regularities rather than protein fitness alone. Here we quantify systematic preferences across 14 protein design models that differ in data modality, training-corpus composition, and scoring context, for comparison grouped as backbone-conditioned, structure plus native-sequence context, or sequence-only. Backbone-conditioned models retain little unexplained taxonomic variance after controlling for protein family and measurable biophysical properties, with residual species variance below 3.3%. In contrast, sequence-only models retain substantial residual taxonomic dependence of 15-20%, indicating that likelihood remains strongly entangled with organism-level sequence statistics. These differences across model classes produce distinct preference landscapes. Backbone-conditioned models organise scores around compactness, packing, and charge, while sequence-only models preserve stronger within-family taxonomic effects. Redesign experiments show that these preferences propagate into generation, shifting templates toward characteristic biophysical profiles rather than uniformly sampling backbone-compatible sequence space. Continued training of ProteinMPNN on ecologically selected extremophile secretomes redirects designed surface chemistry along an acid-base axis while largely preserving structural compatibility and global taxonomic structure. These results show that systematic preferences are not a single failure mode, but separable components arising from scoring context, training-corpus composition, and learned biophysical constraints. Together, they provide a framework for disentangling the sources of model preference and linking them to both scoring behaviour and generated sequence properties.

biochemistry↗

bioRxiv2025

Direct Training of Networks of Morris-Lecar Neurons withBackprop

Spiking Neural Networks (SNNs) have the potential to replicate the brains computational efficacy by explicitly incorporating action potentials or "spikes", which is not a feature of most artificial neural networks. However, training SNNs is difficult due to the non-differentiable nature of the most common spiking models: integrate-and-fire neurons. This study investigates if some of the difficulty in training SNNs arises from the use of integrate-and-fire neurons, rather than smoother alternatives, like conductance-based neurons. To that end, we considered networks of Morris-Lecar (ML) neurons, a conductance-based neuron model which is differentiable. Networks were built using kinetic synaptic models that smoothly link presynaptic voltage dynamics directly to postsynaptic conductance changes, ensuring that all components remain fully differentiable. Switching to biophysically detailed models of synapses and neurons enabled direct end-to-end training through Backpropagation Through Time (BPTT). Biophysically detailed networks were successfully trained on image classification, regression, and time series prediction tasks. These results demonstrate the feasibility of employing biophysically detailed differentiable point neuron models to create SNNs that function as more accurate paradigms for the study of neural computations and learning. Further, this work confirms that some aspects of the difficulty in translating gradient-based learning algorithms from machine learning may arise from model choice, rather than SNNs being intrinsically difficult to train. 1. Author summaryThe brains information-processing efficiency arises in part from neurons communicating via discrete spikes. Spiking Neural Networks (SNNs) mimic this process at the neuronal level but have been difficult to train as most machine learning algorithms are not directly applicable. Most SNNs use integrate-and-fire neurons, a modelling framework that simplifies spikes into non-differentiable, abrupt voltage changes, which makes them difficult to train with powerful, standard AI training methods that use derivatives to compute gradients (e.g. Backprop). In our work, we asked if this difficulty could be overcome by considering end-to-end differentiable spiking neural networks. We used completely differentiable SNNs using the Morris-Lecar neuron, a biophysically detailed neuron model that produces smooth spikes, along with differentiable kinetic synapses. With the entire network being mathematically differentiable, we found that we could train it directly using standard backpropagation through time on different tasks (regression, classification, and chaotic time series prediction). This work demonstrates that the use of integrate-and-fire models may be limiting applications of machine learning algorithms towards understanding how learning functions in the brain.

neuroscience↗

bioRxiv2018

Analysing progress of SDG 6 in India: Past, Present, and Future

Human endeavors to meet social and economic water needs at national scale might cause negative environmental manifestations and water stress from local to global scale. So, appropriation of Sustainable Development Goals requires a comprehensive monitoring and knowledge base of the water resource availability, utilization and access. Hence, scientific research progression has a significant role to facilitate the implementation of sustainable development goals through assessment and policy implementation from global to local scales. India holds a key position among developing economies with a complex interconnected web of a fast-growing population, coupled with biophysical stress, social deprivation and economic inequality related to water and sanitation. This study addresses some of these challenges related to monitoring and implementation of the targets of the United Nations Sustainable Development Goal 6 in India. Acknowledging the contribution of society and economy in sustainability paradigm, here we have chosen 28 indicators (clustered into eleven dimensions) under two major groups, concerning biophysical and social development aspects of water and sanitation. We have shown declining level of per capita biophysical water resource and slow to rapidly developing social indicators related to Sustainable Development Goal 6 in India. From past trends, we have calculated probable scenario of biophysical consumption of India up to 2050, which shows at least 1.3 times increase. This cumulative assessment framework contributes a tool to prioritize water resource appropriation, management response and policy implementations to national level sustainability of water and sanitation in India. We also advocate the necessity of restraining threats both at source and consumption process levels in order to ensure national water security for both human and biodiversity, keeping in mind the societal and economic development scenario.

ecology↗

bioRxiv2019

Modeling the shape of synaptic spines by their actin dynamics

Dendritic spines are the morphological basis of excitatory synapses in the cortex and their size and shape correlates with functional synaptic properties. Recent experiments show that spines exhibit large shape fluctuations that are not related to activity-dependent plasticity but nonetheless might influence memory storage at their synapses. To investigate the determinants of such spontaneous fluctuations, we propose a mathematical model for the dynamics of the spine shape and analyze it in 2D -- related to experimental microscopic imagery -- and in 3D. We show that the spine shape is governed by a local imbalance between membrane tension and the expansive force from actin bundles that originates from discrete actin polymerization foci. Experiments have shown that only few such polymerization foci co-exist at any time in a spine, each having limited life time. The model shows that the momentarily existing set of such foci pushes the membrane along certain directions until foci are replaced and other directions may now be affected. We explore these relations in depth and use our model to predict shape and temporal characteristics of spines from the different biophysical parameters involved in actin polymerization. Reducing the model further to a single recursive equation we finally demonstrate that the temporal evolution of the number of active foci is sufficient to predict the size of the model-spines. Thus, our model provides the first platform to study the relation between molecular and morphological properties of the spine with a high degree of biophysical detail.\n\nAuthor summarySynaptic spines are post-synaptic contact points for pre-synaptic signals in many cortical neurons and it has been shown that synaptic transmission is correlated with spine size. However, spine size and shape can vary quite strongly on short time scales and it is currently unknown how these shape variations emerge. In this study we present a biophysical model that links spine shape fluctuations to the dynamics of the spines actin-based cytoskeleton. We show that shape fluctuations arise from the fact that fast actin polymerization in a spine is a discrete process happening at only few polymerization foci. Life and death of these foci determine from moment to moment how the membrane bulges or retracts. We provide an in-depth analysis of this effect for a large set of biophysical parameters and quantify the spatial-temporal structure of the spines. Our model, thus, provides a quantitative characterization of the link between spine morphology and the underlying molecular processes, which forms an essential step towards a better understanding of synaptic transmission during steady state but also during synaptic plasticity.

neuroscience↗

bioRxiv2019

Variation in cell surface hydrophobicity among Cryptococcus neoformans strains influences interactions with amoeba

Cryptococcus neoformans and Cryptococcus gattii are pathogenic fungi that cause significant morbidity and mortality. Cell surface hydrophobicity (CSH) is a biophysical parameter that influences the adhesion of fungal cells or spores to biotic and abiotic surfaces. C. neoformans is encased by polysaccharide capsule that is highly hydrophilic and is a critical determinant of virulence. In this study, we report large differences in the CSH of some C. neoformans and C. gattii strains. The capsular polysaccharides of C. neoformans strains differ in repeating motifs, and therefore vary in the number of hydroxyl groups, which along with higher-order structure of the capsule, may contribute to the variation in hydrophobicity that we observed. For C. neoformans, CSH correlated with phagocytosis by natural soil predator Acanthamoeba castellani. Furthermore, capsular binding of the protective antibody (18B7), but not the non-protective (13F1) antibody altered the CSH of C. neoformans strains. Variability in CSH could be an important characteristic when comparing the biological properties of cryptococcal strains. IMPORTANCEThe interaction of a microbial cell with its environment is influenced by the biophysical properties of a cell. The affinity of the cell surface for water, defined by the Cell Surface Hydrophobicity (CSH), is a biophysical parameter that varied amongst different strains of Cryptococcus neoformans. The CSH influenced the phagocytosis of the yeast by its natural predator in the soil, Amoeba. Studying variation in biophysical properties like CSH gives us insight into the dynamic host-predator interaction, and host-pathogen interaction in a damage-response framework.

microbiology↗

bioRxiv2026

Loss of dispersal via ice nucleation activity constrains microbial evolution

The ability to disperse over long distances through the atmosphere is a common trait across the tree of life, facilitating resource access and increasing long-range gene flow. Loss of dispersal mechanisms, viz. flight, can occur in animals found on islands where documented phenotypic changes like loss of wingspan impedes longer distance travel to mate with the metapopulation. Bacteria also experience atmospheric flight and descend via bioprecipitation by catalyzing the freezing of cloud droplets with protein InaZ. InaZ triggers ice nucleation at temperatures near 0{circ}C(1). This ice nucleation activity (INA), a biophysical trait, enhances bacterial deposition through precipitation. The role of InaZ-mediated ice nucleation on bacterial dispersal is well documented, but the impact of loss of INA and thus reduction or loss of atmospheric dispersal on bacterial ecology and evolution has not been described. Here we show that the loss of the ancestral inaZ gene restricts bacterial dispersal and leads to significant genetic and ecological isolation across multiple genera. Through the analysis of available complete genomes, we demonstrate that lineages lacking functional inaZ experience major gene loss events, reduced recombination rates and a marked dependence on human-mediated or insect transmission. These INA-lacking bacteria exhibit an increased ecological signature of isolation that parallels the distribution of geographically isolated animals. Our results establish InaZ as a keystone biophysical trait that defines microbial dispersal strategies. We anticipate these findings will provide a framework for understanding how shifts in biophysical traits drive niche differentiation and changes in dispersal with downstream consequences for Earth system processes. Significance StatementSome microorganisms catalyze freezing of cloud droplets near 0{degrees}C via ice nucleation activity (INA) enhancing their deposition. We determined that loss of the gene encoding the INA protein in Gammaproteobacteria restricts bacterial dispersal. Bacteria that lost this ancestral trait compared to relatives with INA experienced distinct, major gene loss events, altered gene flow and marked dependence on transmission by plant tissues or insects and an increased ecological signature of isolation paralleling the geographically isolated plants and animals. We posit that gene loss for biophysical traits such as INA is a keystone example of the consequences of a biological trait defining microbial dispersal.

evolutionary biology↗

bioRxiv2026

Physiologically realistic gamma activity produced in silico by weakening the PING attractor state

Network oscillations in the gamma (30-80 Hz) frequency range are implicated in many vital neurological functions. The seminal Pyramidal Interneuron Network Gamma (PING) mechanism produces gamma oscillations in silico with dynamics associated with a strongly attracting limit cycle. However, this activity is stronger and more stable than what is observed during in vivo gamma oscillations, which are transient and driven by sparse spiking activity. Here we describe multiple biophysically motivated changes degenerately driving PING-motivated networks to produce more realistic gamma activity. Increased heterogeneity, synapse-like noise, and a depolarized chloride reversal potential each destabilize the attractor represented by PING-driven activity: minor alterations disrupt the unrealistic organization of idealized PING oscillations, while larger changes entirely prevent this overly-synchronous network activity. This allows new dynamics to emerge: spontaneous and transient increases in gamma-band oscillatory power with excitatory cells only weakly entrained to the population rhythm. These features, better approximating the in vivo reality, mirror a systems oscillatory return to a stable focus following noise-induced perturbations. In fact, dynamical systems with both a stable focus and limit cycle are associated with Hopf bifurcations, which have been characterized in models of reciprocally connected excitatory and inhibitory neuronal populations as required for PING. We therefore propose a revised explanation for physiologically-realistic gamma activity that retains the key elements of the PING mechanism--strong reciprocal connection between excitatory and inhibitory neurons--but where biophysical phenomena bias the system towards damped oscillations around a weakly stable focus rather than a stable limit cycle. Significance StatementThe mechanisms underlying network oscillations at gamma frequencies have long been a focus of computational neuroscience. This research has produced idealized mechanisms yielding highly synchronous, active, and stable oscillations; however, in vivo gamma rhythms are transient with sparse neuronal spiking. Here, we illustrate minimal, biophysically relevant adjustments to established Pyramidal Interneuron Network Gamma (PING) networks that bridge a major divide between these traditional in silico systems and the experimental reality. Uncorrelated noisy input and a depolarized chloride reversal potential disrupt stereotyped PING rhythms and promote more realistic transient increased gamma power in network activity. This important step forward in the modeling of gamma oscillations illustrates how including biophysical detail can promote more realistic activity in computational systems.

neuroscience↗

bioRxiv2026

How to train your neuron: Developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells

Anatomically and biophysically detailed models of neurons have been widely used to study information processing in these cells. Most studies focused on understanding specific phenomena, while more general models that aim to capture various cellular processes simultaneously remain rare even though such models are required to predict neuronal behavior under more complex, natural conditions. In this study, we aimed to develop a detailed, data-driven, general-purpose biophysical model of hippocampal CA1 pyramidal neurons. We leveraged extensive morphological, biophysical and physiological data available for this cell type, and established a systematic workflow for model construction and validation that relies on our recently developed software tools. The model is based on a high-quality morphological reconstruction and includes a diverse curated set of ion channel models. After incorporating the available constraints on the distribution of ion channels, the remaining free parameters were optimized using the Neuroptimus tool to fit a variety of electrophysiological features extracted from somatic whole-cell recordings. Validation using HippoUnit confirmed the models ability to replicate key electrophysiological features, including somatic voltage responses to current input, the attenuation of synaptic potentials and backpropagating action potentials, and nonlinear synaptic integration in oblique dendrites. Our model also included active dendritic spines, modeled either explicitly or by merging their biophysical mechanisms into those of the parent dendrite. We found that many aspects of neuronal behavior were unaffected by the level of detail in modeling spines, but modeling nonlinear synaptic integration accurately required the explicit modeling of spines. Our data-driven model of CA1 pyramidal cells matching diverse experimental constraints is a general tool for the investigation of the activity and plasticity of these cells and can also be a reliable component of detailed models of the hippocampal network. Our systematic approach to building and validating general-purpose models should apply to other cell types as well. Author SummaryThe brain processes information through the activity of billions of individual neurons. To understand how these cells work, scientists build detailed computer models that reproduce their electrical behavior. These models make it possible to explore situations that are difficult or impossible to test experimentally. However, many existing neuron models were designed to explain only a few specific phenomena, which limits their usefulness in more complex settings. In this study, we developed a comprehensive computer model of a hippocampal CA1 pyramidal neuron, a cell type that plays a central role in learning and memory. We built the model using extensive experimental data and applied automated methods to ensure that it reproduces a broad range of observed neuronal behaviors. We also examined how small structures called dendritic spines--tiny protrusions where most synaptic communication occurs--affect how neurons combine incoming signals. We found that even simplified models without individual spines can capture many aspects of neuronal activity, but understanding more complex forms of signal integration requires modeling spines explicitly. Our work also supports the development of more realistic simulations of brain circuits.

neuroscience↗

bioRxiv2017

Physiological Preparation Of Hair Cells From The Sacculus Of The American Bullfrog (Rana catesbeiana)

SHORT ABSTRACTThe American bullfrogs sacculus permits direct examination of hair-cell physiology. Here we describe the dissection and preparation of the bullfrogs sacculus for biophysical studies. We show representative experiments from these hair cells, including the calculation of a bundles force-displacement relation and measurement of its spontaneous motion.\n\nLONG ABSTRACTThe study of hearing and balance rests upon insights drawn from biophysical studies of model systems. One such model, the sacculus of the American bullfrog, has become a mainstay of auditory and vestibular research. Studies of this organ have revealed how sensory cells--hair cells--can actively detect signals from the environment. Because of these studies, we now better understand the mechanical gating and localization of a hair cells transduction channels, calciums role in mechanical adaptation, and the identity of hair-cell currents. This highly accessible organ continues to provide insight into the workings of hair cells.\n\nHere we describe the preparation of the bullfrogs sacculus for biophysical studies on its hair cells. We include the complete dissection procedure and provide specific protocols for the preparation of the sacculus in specific contexts. We additionally include representative results using this preparation, including the calculation of a hair bundles force-displacement relation and measurement of a bundles spontaneous motion.

biophysics↗