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bioRxiv2021

Biophysical and Architectural Mechanisms of Subthalamic Theta under Response Conflict

The cortico-basal ganglia circuit is needed to suppress prepotent actions and to facilitate controlled behavior. Under conditions of response conflict, the frontal cortex and subthalamic nucleus [STN] exhibit increased spiking and theta band power, which are linked to adaptive regulation of behavioral output. The electrophysiological mechanisms underlying these neural signatures of impulse control remain poorly understood. To address this lacuna, we constructed a novel large-scale, biophysically principled model of the subthalamopallidal [STN-Globus Pallidus externus (GPe)] network, and examined the mechanisms that modulate theta power and spiking in response to cortical input. Simulations confirmed that theta power does not emerge from intrinsic network dynamics but is robustly elicited in response to cortical input as burst events representing action selection dynamics. Rhythmic burst events of multiple cortical populations, representing a state of conflict where cortical motor plans vacillate in the theta range, led to prolonged STN theta and increased spiking, consistent with empirical literature. Notably, theta band signaling required NMDA, but not AMPA, currents, which were in turn related to a triphasic STN response characterized by spiking, silence and bursting periods. Finally, theta band resonance was also strongly modulated by architectural connectivity, with maximal theta arising when multiple cortical populations project to individual STN "conflict detector" units, due to an NMDA-dependent supralinear response. Our results provide insights into the biophysical principles and architectural constraints that give rise to STN dynamics during response conflict, and how their disruption can lead to impulsivity and compulsivity.

neuroscience↗

bioRxiv2022

Analog of the Hutchinson equation in biophysical neurodynamics: from the Morris-Lecar model to a delay differential equation

Starting with the classical biophysical Morris-Lecar model of neuronal excitability, we introduce a functional analog of the Hutchinson equation initially obtained for population dynamics with delayed negative feedback. It is shown that the resulting equation with a fixed time delay qualitatively reproduces the dynamics of the original model upon direct current stimulation, preserving both the initial type of neuronal excitability and biophysically realistic spike shape within a wide range of the delay values. If the delay becomes very small (2 ms or less), the simplified delay-based model exhibits a distinct transition from the 1st to the 2nd excitability type.

neuroscience↗

bioRxiv2022

Rapidly evolving viral motifs target biophysically constrained binding pockets of host proteins

Evolutionary changes in the host-virus interactome can alter the course of infection, but the biophysical and regulatory constraints that shape interface evolution remain largely unexplored. Here, we focus on viral mimicry of short host-like peptide motifs that allow binding to host domains and modulation of cellular pathways. We observe that motifs from unrelated viruses preferentially target conserved, widely expressed and highly connected host proteins, enriched with regulatory and essential functions. The interface residues within these host domains are more conserved and bind a larger number of cellular proteins than similar motif-binding domains that are not known to interact with viruses. In stark contrast, rapidly evolving viral-binding human proteins form few interactions with other cellular proteins, display high tissue specificity and their interface residues have few inter-residue contacts. Our results distinguish between highly conserved and rapidly evolving host-virus interfaces, and show how regulatory, functional and biophysical factors limit host capacity to evolve, allowing for efficient viral subversion of host machineries.

microbiology↗

bioRxiv2022

Generating an in vitro gut model with physiologically relevant biophysical mucus properties

IntroductionGastrointestinal (GI) in vitro models have received lasting attention as an effective tool to model drug and nutrient absorption, study GI diseases, and design new drug delivery vehicles. A complete model of the GI epithelium should at a minimum include the two key functional components of the GI tract: mucus and the underlying epithelium. Mucus plays a key role in protecting and lubricating the GI tract, poses a barrier to orally administered therapies and pathogens, and serves as the microenvironment for the GI microbiome. These functions are reliant on the biophysical material properties of the mucus produced, including viscosity and pore size. MethodsIn this study, we generated in vitro models containing Caco-2 enterocyte-like cells and HT29-MTX goblet-like cells and determined the effects of coculture and mucus layer on epithelial permeability and biophysical properties of mucus using multiple particle tracking (MPT). ResultsWe found that mucus height increased as the amount of HT29-MTX goblet-like cells increased. Additionally, we found that increasing the amount of HT29-MTX goblet-like cells within culture corresponded to an increase in mucus pore size and mucus microviscosity, measured using MPT. When compared to ex vivo mucus samples from mice and pigs, we found that a 90:10 ratio of Caco-2:HT29-MTX coculture displayed similar mucus pore size to porcine jejunum and that the mucus produced from 90:10 and 80:20 ratios of cells shared mechanical properties to porcine jejunum and ileum mucus. ConclusionsGI coculture models are valuable tools in simulating the mucus barrier and can be utilized for a variety of applications including the study of GI diseases, food absorption, or therapeutic development. BiographyDr. Maisel joined the University of Maryland in January 2019 having done interdisciplinary training in nanotechnology, mucosal immunology, lymphatic immunology, and immunoengineering. She completed her PhD in Biomedical Engineering at the Johns Hopkins University in 2014 after which she was an NIH postdoctoral fellow at the University of Chicago in Molecular Engineering and Immunology. The Maisel Lab uses in vitro modeling, nanotechnology, and immunoengineering approaches to study and develop treatments for diseases at mucosal surfaces. They are interested in designing nanoparticles to take advantage of and study the interface between biological barriers, particularly the lymphatics, interstitial tissue, and mucosal surfaces, and nanoparticles. Dr. Maisel has won a number of awards, including NSF GRFP and NIH F32 fellowships as a trainee, the American Lung Association Dalsemer Award, LAM Foundation Career Development Award, an NSF CAREER Award, and an NIH NIGMS Maximizing Investigator Research Award. Her work has led to numerous high-impact publications, particularly in the field of drug delivery and mucosal immunoengineering, and several patents.

bioengineering↗

bioRxiv2022

Biophysical and functional characterization of K+-Cl- co-transporters from Drosophila melanogaster and Hydra vulgaris

The cation-chloride co-transporter (CCC) superfamily includes ion symporters, which co-transport monovalent cations and Cl-. CCCs have crucial roles in shaping signalling and neuronal connectivity in the vertebrate brain. K+-Cl- co-transporters (KCCs) are a subfamily of CCCs and carry out the symport of K+ and Cl- ions across the plasma membrane. The KCC proteins are involved in various physiological processes, such as cell volume regulation, transepithelial ion transport, synapse formation and signal transmission, and blood pressure regulation. Among KCCs, KCC2 has gained attention because of its unique and crucial functions in the central nervous system neuronal network. Loss of activity of this transporter has been associated with several neurological disorders including schizophrenia, epilepsy, and chronic pain. On the other hand, only a limited number of studies of KCCs have been published for invertebrates. Among invertebrate proteins, the Drosophila melanogaster KCC (DmKCC) has been studied most and suggested critical for neuronal transmission. Also Cnidarian Hydra vulgaris has been shown to have a functional KCC (HvKCC). Comparative analyses of these transporters with vertebrate ones and understanding functional and biophysical aspects of them as a model system can help understand the KCC mechanism of ion transport and its regulation and evolution broadly. In this study, we chose DmKCC and HvKCC as model systems and purified DmKCC and HvKCC from Sf9 insect cells and characterized their biophysical properties with differential scanning fluorimetry and light scattering techniques. We tested their functionality using a fluorescence assay and developed a method to measure recombinant KCC ion transport activity with flame photometry.

biochemistry↗

bioRxiv2022

Probing the biophysical constraints of SARS-CoV-2 spike N-terminal domain using deep mutational scanning

Increasing the expression level of the SARS-CoV-2 spike (S) protein has been critical for COVID-19 vaccine development. While previous efforts largely focused on engineering the receptor-binding domain (RBD) and the S2 subunit, the N-terminal domain (NTD) has been long overlooked due to the limited understanding of its biophysical constraints. In this study, the effects of thousands of NTD single mutations on S protein expression were quantified by deep mutational scanning. Our results revealed that in terms of S protein expression, the mutational tolerability of NTD residues was inversely correlated with their proximity to the RBD and S2. We also identified NTD mutations at the interdomain interface that increased S protein expression without altering its antigenicity. Overall, this study not only advances the understanding of the biophysical constraints of the NTD, but also provides invaluable insights into S-based immunogen design.

biochemistry↗

bioRxiv2022

Anatomical and biophysical characterization of intergenic graft-incompatibility within the Solanoideae

Interspecies grafting is a technique that allows beneficial shoot and root combinations from separate species to be combined into a single organism. Despite its relevance to agricultural production, little is known about the determinants of graft compatibility. One hypothesis for compatibility revolves around the taxonomic degree of relatedness between the two plants. To test how phylogenetic distance affects interspecific graft compatibility within the economically important Solanaceae sub-family, Solanoideae, we characterized the anatomical and biophysical integrity of graft junctions for graft combinations made between four species: tomato (Solanum lycopersicum), eggplant (Solanum melongena), pepper (Capsicum annuum), and groundcherry (Physalis pubescens). We analyzed the survival, growth, and junction integrity via bend tests, and imaged the cellular composition of the graft junctions in order to deduce the status of vascular connectivity across the junction. Utilizing these techniques, we were able to quantitatively assess the degree to which each interspecific combination exhibits compatibility. Despite the fact that most of our graft combinations exhibited high survival rates, we show that only tomato and eggplant heterografts are truly compatible. Unlike incompatible grafts, the formation of reconnected vascular tissue within the tomato and eggplant heterografts contributed to biophysically stable grafts that were resistant to snapping. Furthermore, we identified 10 graft combinations that show delayed incompatibility, providing a useful, economically relevant system to pursue deeper work into genetic and genomic determinants of graft compatibility. This work provides new evidence indicating that graft compatibility may be limited to intrageneric combinations within the Solanoideae subfamily. Further research using more extensive graft combinations amongst Solanaceous species can be used to test whether our hypothesis broadly applies to this family.

plant biology↗

bioRxiv2022

Biophysical control of neural trajectories under disordered brain dynamics

Neural representations rely on the ability of neuronal assemblies to display organized spiking patterns, despite being embedded within noisy networks. These structured patterns arise from attractor dynamics due to activity reverberation promoted by learnt assembly connectivity. Yet, attractor dynamics have been assessed either under low-noise conditions or in highly idealized neuronal assemblies. Here, in a spiking recurrent neural network model displaying asynchronous irregular noise, we show that realistic spike timing-dependent plasticity (STDP) imposes either low controllability of attractor recall (when STDP is strong), low stability of attractor maintenance (when STDP is low), or both. Moreover, STDP-built attractors display low independence, i.e., they perturb activity in the surrounding network. These constraints may favor self-generated representation switches essential for cognitive flexibility but dampening cognitive reliability. We reveal, by contrast, that several biophysical mechanisms alleviate these issues through a common dynamical principle, protecting excitatory-driven attractorial dynamics from inhibitory-driven spontaneous fluctuations. Amongst biophysical determinants, intrinsic properties were most efficient to increase controllability, stability and independence of attractor dynamics. Specifically, spike-triggered calcium-activated conductances improved reliability by mitigating reliance on connectivity, even at low conductance levels, i.e., in the absence of cell-autonomous intrinsic bistability. Finally, we show that the mechanisms we identify operate over a large repertoire of static (e.g., Hebbian or ring) and dynamic (e.g., sequences) attractors, with uni- and bidirectional propagation. Altogether, these results pinpoint synaptic and intrinsic synergy as a generic principle to regulate attractor reliability, as a function of the cognitive demand.

neuroscience↗

bioRxiv2022

Reduced oriens-lacunosum/moleculare (OLM)cell model identifies biophysical currentbalances for in vivo theta frequency spikingresonance

Conductance-based models have played an important role in the development of modern neuroscience. These mathematical models are powerful "tools" that enable theoretical explorations in experimentally untenable situations, and can lead to the development of novel hypotheses and predictions. With advances in cell imaging and computational power, multi-compartment models with morphological accuracy are becoming common practice. However, as more biological details are added, they make extensive explorations and analyses more challenging largely due to their huge computational expense. Here, we focus on oriens-lacunosum/moleculare (OLM) cell models. OLM cells can contribute to functionally relevant theta rhythms in the hippocampus by virtue of their ability to express spiking resonance at theta frequencies, but what characteristics underlie this is far from clear. We converted a previously developed detailed multi-compartment OLM cell model into a reduced single compartment model that retained biophysical fidelity with its underlying ion currents. We showed that the reduced OLM cell model can capture complex output that includes spiking resonance in in vivo-like scenarios as previously obtained with the multi-compartment model. Using the reduced model, we were able to greatly expand our in vivo-like scenarios. Applying spike-triggered average analyses, we were thus able to to determine that it is a combination of hyperpolarization-activated cation and muscarinic type potassium currents that specifically allow OLM cells to exhibit spiking resonance at theta frequencies. Further, we developed a robust Kalman Filtering (KF) method to estimate parameters of the reduced model in real-time. We showed that it may be possible to directly estimate conductance parameters from experiments since this KF method can reliably extract parameter values from model voltage recordings. Overall, our work showcases how the contribution of cellular biophysical current details could be determined and assessed for spiking resonance. As well, our work shows that it may be possible to directly extract these parameters from current clamp voltage recordings.

neuroscience↗

bioRxiv2022

Biophysical basis of phage liquid crystalline droplet-mediated antibiotic tolerance in pathogenic bacteria

Inoviruses are abundant filamentous phages infecting numerous prokaryotic phyla, where they can symbiotically promote host fitness and increase bacterial virulence. Due to their unique properties, inoviruses have also been utilised in biotechnology for phage display and as models for studying phase behaviour of colloidal rods. Inoviral phages secreted by bacteria can self-assemble into liquid crystalline droplets that protect bacterial cells in biofilms from antibiotics, however, factors governing the formation of such droplets and the mechanism of antibiotic protection are poorly understood. Here, we investigate the structural, biophysical, and protective properties of liquid crystalline droplets formed by Pseudomonas aeruginosa and Escherichia coli inoviral phages. We report a cryo-EM structure of the capsid from the highly studied E. coli fd phage, revealing distinct biochemical properties of fd compared to Pf4 phage from P. aeruginosa. We show that fd and Pf4 form liquid crystalline droplets with diverse morphologies governed by the underlying phage particle geometry and biophysics, rather than their surface biochemical properties. Finally, we show that these morphologically diverse droplets made of either phage can protect rod-shaped bacteria from antibiotic treatment, despite differing modes of association with cells. This study advances our understanding of phage assembly into liquid crystalline droplets, and provides insights into how filamentous molecules protect bacteria from extraneous molecules under crowding conditions, which are found in biofilms or on infected host tissues.

microbiology↗

bioRxiv2023

Biophysical modeling of the electric field magnitude and distribution induced by electrical stimulation with intracerebral electrodes

Intracranial electrodes are used clinically for diagnostic or therapeutic purposes, notably in drug-refractory epilepsy (DRE) among others. Visualization and quantification of the energy delivered through such electrodes is key to understanding how the resulting electric fields modulate neuronal excitability, i.e. the ratio between excitation and inhibition. Quantifying the electric field induced by electrical stimulation in a patient-specific manner is challenging, because these electric fields depend on a number of factors: electrode trajectory with respect to folded brain anatomy, biophysical (electrical conductivity / permittivity) properties of brain tissue and stimulation parameters such as electrode contacts position and intensity. Here, we aimed to evaluate various biophysical models for characterizing the electric fields induced by electrical stimulation in DRE patients undergoing stereoelectroencephalography (SEEG) recordings in the context of pre-surgical evaluation. This stimulation was performed with multiple-contact intracranial electrodes used in routine clinical practice. We introduced realistic 3D models of electrode geometry and trajectory in the neocortex. For the electrodes, we compared point (0D) and line (1D) sources approximations. For brain tissue, we considered three configurations of increasing complexity: a 6-layer spherical model, a toy model with a sulcus representation, replicating results from previous approaches; and went beyond the state-of-the-art by using a realistic head model geometry. Electrode geometry influenced the electric field distribution at close distances (~3 mm) from the electrode axis. For larger distances, the volume conductor geometry and electrical conductivity dominated electric field distribution. These results are the first step towards accurate and computationally tractable patient-specific models of electric fields induced by neuromodulation and neurostimulation procedures.

neuroscience↗

bioRxiv2023

Retinal ganglion cells undergo cell type-specific functional changes in a biophysically detailed model of retinal degeneration

Understanding the retina in health and disease is a key issue for neuroscience and neuroengineering applications such as retinal prostheses. During degeneration, the retinal network undergoes complex and multi-stage neuroanatomical alterations, which drastically impact the retinal ganglion cell (RGC) response and are of clinical importance. Here we present a biophysically detailed in silico model of retinal degeneration that simulates the network-level response to both light and electrical stimulation as a function of disease progression. The model is not only able to reproduce common findings about RGC activity in the degenerated retina, such as hyperactivity and increased electrical thresholds, but also offers testable predictions about the underlying neuroanatomical mechanisms. Overall, our findings demonstrate how biophysical changes associated with retinal degeneration affect retinal responses to both light and electrical stimulation, which may further our understanding of visual processing in the retina as well as inform the design and application of retinal prostheses.

neuroscience↗

bioRxiv2023

Biophysical Characterization and Interaction study of WhiB6 Protein of Mycobacterium tuberculosis with Nucleic Acid

Tuberculosis is an intractable disease because of the peculiar nature of the virulent properties of Mycobacterium tuberculosis. The WhiB6 protein, a transcriptional regulator, plays a crucial role in the virulence systems of Mtb. It regulates the expression of genes essential for the virulence pathways by binding to their promoter region; espA is one such gene. Herein, we have used biophysical methods, including steady-state intrinsic fluorescence spectroscopy, circular dichroism spectroscopy, Isothermal titration calorimetry (ITC), and Surface-Enhanced Raman Spectroscopy (SERS) to understand the interaction of WhiB6 protein with espA promoter DNA. For the first time, we report the conformational details and biophysical parameters related to the WhiB6-espA promoter DNA interaction. WhiB6 binds the DNA with moderate affinity, as revealed by ITC. CD and SERS studies suggest subtle perturbation in the secondary conformation of the protein on binding to the DNA. SERS provided detailed structural insights into the WhiB6 protein and the amino acids involved in the interaction, which could be harnessed to find suitable inhibitors of the protein-DNA interaction. Preventing the binding of WhiB6 with promoter DNA of the virulence genes can hinder the functioning of Mtb and hence can act as an effective therapeutic intervention for tuberculosis.

biochemistry↗

bioRxiv2023

Laminar Specificity of the Auditory Perceptual Awareness Negativity: A Biophysical Modeling Study

How perception of sensory stimuli emerges from brain activity is a fundamental question of neuroscience. To date, two disparate lines of research have examined this question. On one hand, human neuroimaging studies have helped us understand the large-scale brain dynamics of perception. On the other hand, work in animal models (mice, typically) has led to fundamental insight into the micro-scale neural circuits underlying perception. However, translating such fundamental insight from animal models to humans has been challenging. Here, using biophysical modeling, we show that the auditory awareness negativity (AAN), an evoked response associated with perception of target sounds in noise, can be accounted for by synaptic input to the supragranular layers of auditory cortex (AC) that is present when target sounds are heard but absent when they are missed. This additional input likely arises from cortico-cortical feedback and/or non-lemniscal thalamic projections and targets the apical dendrites of layer-V pyramidal neurons (PNs). In turn, this leads to increased local field potential activity, increased spiking activity in layer-V PNs, and the AAN. The results are consistent with current cellular models of conscious processing and help bridge the gap between the macro and micro levels of perception-related brain activity. Author SummaryTo date, our understanding of the brain basis of conscious perception has mostly been restricted to large-scale, network-level activity that can be measured non-invasively in human subjects. However, we lack understanding of how such network-level activity is supported by individual neurons and neural circuits. This is at least partially because conscious perception is difficult to study in experimental animals, where such detailed characterization of neural activity is possible. To address this gap, we used biophysical modeling to gain circuit-level insight into an auditory brain response known as the auditory awareness negativity (AAN). This response can be recorded non-invasively in humans and is associated with perceptual awareness of sounds of interest. Our model shows that the AAN likely arises from specific cortical layers and cell types. These data help bridge the gap between circuit- and network-level theories of consciousness, and could lead to new, targeted treatments for perceptual dysfunction and disorders of consciousness.

neuroscience↗

bioRxiv2023

Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories

Biophysically detailed simulations attempting to reproduce neuronal activity often rely on solving large systems of differential equations; in some models, these systems have tens of thousands of states per cell. Numerically solving these equations is computationally intensive and requires making assumptions about the initial cell states. Additional realism from incorporating more biological detail is achieved at the cost of increasingly more states, more computational resources, and more modeling assumptions. We show that for both point and morphologically-detailed cell models, the presence and timing of future action potentials is probabilistically well-characterized by the relative timings of a small number of recent synaptic events alone. Knowledge of initial conditions or full synaptic input history is not a requirement. While model time constants, etc. impact the specifics, we demonstrate that for both individual spikes and sustained cellular activity, the uncertainty in spike response decreases to the point of approximate determinism. Further, we show cellular model states are reconstructable from ongoing synaptic events, despite unknown initial conditions. We propose that a strictly event-based modeling framework is capable of representing the full complexity of cellular dynamics of the differential-equations models with significantly less per-cell state variables, thus offering a pathway toward utilizing modern data-driven modeling to scale up to larger network models while preserving individual cellular biophysics.

neuroscience↗

bioRxiv2023

Inferring parameters of pyramidal neuronexcitability in mouse models of Alzheimer'sdisease using biophysical modeling and deeplearning

Alzheimers disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We introduce a novel parameter inference technique, deep hybrid modeling (DeepHM), that combines deep learning with biophysical modeling to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model. Although mechanistic modeling and machine learning methods are by themselves powerful tools for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings by using conditional generative adversarial networks (cGANs) to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrate that DeepHM accurately infers parameter distributions of the conductance-based model and outperforms a Markov chain Monte Carlo method on several test cases using synthetic data. We then use DeepHM to estimate parameter distributions corresponding to the experimental data and infer which ion channels are altered in the Alzheimers mouse models compared to their wildtype controls at 12 and 24 months. We find that the conductances most disrupted by tauopathy, amyloidopathy, and aging are delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium, respectively.

neuroscience↗

bioRxiv2023

Can food and temperature influence regional connectivity patterns of Bivalvia in fragmented archipelagos? Evidence from biophysical modeling applied to French Polynesia

Larval dispersal and connectivity are key processes that drive marine metapopulation dynamics, and therefore should be well characterized when designing effective management strategies. While temperature and food availability can structure marine species connectivity patterns, their relative contribution has not been thoroughly investigated in highly fragmented archipelagos. We used biophysical modeling of larval dispersal to explore the connectivity patterns of species with complex life-cycles across French Polynesia (FP), a territory formed by more than a hundred small, geographically isolated islands covering an area as large as Europe. We first simulated ten years of larval dispersal to investigate the spatial and temporal (seasonal and interannual) variability in larval dispersal pathways for different hypothetical species exhibiting a range of Larval Precompetency Period (LPP) values. Then, using the black-lip pearl oyster (Pinctada margaritifera) as a model species, we accounted for variability in the LPP induced by temperature and food availability, as derived from a Dynamic Energy Budget (DEB) model. The model showed that food availability and meso-scale turbulence (eddies) in the Marquesas jointly constrained larval dispersal, reducing its potential connectivity with other archipelagos in FP. However, accounting for food and temperature effects on larval development, barely changed the connectivity pattern at regional scale due to the remoteness of this archipelago. The DEB simulations further revealed seasonal and interannual variability in connectivity driven by environmental conditions. Our results highlight the importance of considering temperature and food in biophysical models to adequately capture dispersal, connectivity and to identify appropriate management units at the regional scale.

ecology↗

bioRxiv2023

Biophysical basis for the induction of glioblastoma-like phenotype in astrocytes

While the direct biological factors underlying the progression of GBM, an aggressive form of brain cancer, have been extensively studied, emerging evidence suggests that indirect biological triggers, such as traumatic brain injury (TBI), may also have a role. Since reactive astrocytes are associated with TBI, and astroglial cells are the source of proteoglycans which contribute to changes in biophysical characteristics (stochastic topography, stiffness) of the brain, we postulated a role for stochastic nanoroughness in the induction of glioma. Using a model system to emulate such physical cues, we demonstrate that human cortical astrocytes undergo spontaneous organization into spheroids in response to nanoroughness and retain the spheroid phenotype even upon withdrawal of the physical cues. Furthermore, spheroids serve as aggregation foci for naive astrocytes; express activated MMP2, and disseminate upon implantation in mouse brain. RNA-seq revealed a tumoral phenotype with a gene expression pattern involving p53, ADAMTS proteases and fibronectin. Moreover, nanoroughness mediates a cross-talk between cancer cells and astrocytes through induced senescence. These findings implicate a role for stochastic biophysical cues in driving a potential malignant transformation of astrocytes.

neuroscience↗