Biology subjects

Rubin, J.

Publications and source records attributed to Rubin, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Dynamic network reconfigurations during task engagement following resting state in adolescent onset schizophrenia

Understanding how functional brain networks in resting state configurations reorganize to perform cognitive tasks is critical for uncovering the nature and mechanisms underlying network dysconnectivity in psychiatric disorders. We applied energy landscape analysis (ELA), a statistical physics-based computational approach, to functional MRI data from 23 adolescent-onset schizophrenia (AOS) and 44 healthy control (HC) subjects, acquired during rest followed by executive function task. ELA maps brain activity into distinct network states and quantifies how the brain transitions among them, capturing differences in stability of network states and transition complexity across conditions. AOS and HC showed markedly different condition-dependent patterns of brain state organization and dynamics. At rest, AOS exhibited reduced dynamical complexity compared to HC (7 vs. 14 stable states) that reversed during the task with more than 2-fold increase in accessible but rarely occupied brain states, while HC showed an opposite pattern. These results suggest that cognitive demands unmask latent fragmentation of the energy landscape, comprising a proliferation of accessible but rarely occupied states not apparent at rest, in AOS. State occupancy analysis revealed a small number of dominant states accounting for the majority of brain activity time, with AOS showing greater persistence in the fully-active DMN state during task performance compared to HC. These findings suggest that the rest-to-task transition features fundamentally different neural dynamics in AOS compared to HC. Combined analysis of resting fMRI and task-induced brain dynamics revealed neural factors that may contribute to cognitive dysfunction and psychiatric symptoms in schizophrenia, with important implications for development of biomarkers and treatment targets.

neuroscience↗

bioRxiv2026

Reconciling contradictory models of subthalamic nucleus contributions to basal ganglia beta oscillations

Recent computational studies of Parkinsons disease have yielded contradictory findings regarding the role of the subthalamic nucleus (STN) in pathological beta oscillations, with some models implicating STN as essential for beta generation and others suggesting that STN suppresses oscillations. This work addresses these discrepancies by systematically investigating how the specific features of the integrate-and-fire neurons used in these models influence simulated basal ganglia network dynamics. Using both rate models and spiking network simulations incorporating coupled subthalamopallidal and pallidostriatal circuits, we demonstrate that the choice between leaky integrate-and-fire (LIF) and quadratic integrate-and-fire (QIF) models to represent STN neurons fundamentally impacts the phase relationship between STN and external globus pallidus prototypical (Proto) neuron populations. QIF STN neurons establish in-phase coupling with Proto neurons, which enhances beta oscillation amplitude, while LIF STN neurons develop anti-phase relationships, which suppresses beta power. Through intervention experiments and parameter sweeps across physiologically relevant firing rates, we show that these phase-related effects persist robustly across network conditions, and we mathematically establish conditions under which these results are guaranteed to hold. Our findings reveal that the fundamental mathematical structure underlying spike generation, rather than other biophysical details, determines whether the subthalamopallidal loop acts as a beta amplifier or suppressor. This mechanistic insight reconciles contradictory findings in the literature, demonstrates that seemingly minor modeling choices can have profound consequences for understanding disease mechanisms and therapeutic targets, and offers predictions for determining which model framework reflects the biological reality. Author summarySubstantial work has explored the mechanisms underlying enhanced beta oscillations in the basal ganglia, motivated by their potential relevance to parkinsonian conditions and associated treatments. Often inferences about these mechanisms are based on simulations and reasoning that focus on features of network connectivity. We show that in fact the specific dynamical properties of the neurons in these circuits can strongly influence their emergent dynamics, with completely opposing effects arising in a given network structure depending on which neuron model is used, and we explain the factors underlying this divergence. Based on these factors, the determination of a small set of neuron properties in future biological experiments will lead to predictions about the mechanisms that can generate beta oscillations in the parkinsonian basal ganglia.

neuroscience↗

bioRxiv2026

Maximum entropy model reveals frequent brain state switching in psychotic disorders in a multiversal brain function analysis

To design network-based treatments, it is critical to understand dynamic changes in brain networks over time. We comprehensively examined network structure using regional activation-only, pairwise-coactivation-only, and systems-level maximum entropy models (MEM) applied to the Human Connectome Project-Early Psychosis (HCP-EP) resting fMRI data (patients=109, controls=56). The MEM integrates regional activation levels and pairwise correlation-based functional connectivity (FC), providing the potential for better characterization of clinically meaningful brain states and how they change over time. Using the HCP/Glasser atlas to define brain regions within the default mode (DMN) and dorsal attention (DAN) networks, group differences in regional activation, graph measures of FC, and MEM features such as transition rates between minima in an energy landscape along with correlations with cognitive and psychopathological measures were examined. Psychosis was associated with reduced activation in several regions and multiple FC graph metric alterations. MEM demonstrated a wider variety of DMN and DAN activation/deactivation configurations, with more frequent switching between them, more pronounced network configuration differences and higher basin transition rates with reduced basin dwell times suggesting temporally unstable network configurations, meaning the brain cannot rely on any particular configuration for cognitive processing. Activation of only three regions correlated with working memory, but none of the FC metrics did. MEM features, in particular basin transitions and total energy, correlated negatively with working memory. Our findings suggest that MEM provides unique information, specifically attenuated inter-network connectivity with reduced stability of DMN and DAN brain states, to characterize networks as targets and their features as markers for novel treatment development.

neuroscience↗

bioRxiv2025

Engineering SIRPα conformational plasticity to reveal a cryptic pocket suitable for structure-based drug design

The protein-protein interaction between Signal Regulatory Protein alpha (SIRP) and CD47 is a critical immune checkpoint that enables tumor immune escape, making it a key target for cancer immunotherapy. While antibody-based therapies exist, the development of small-molecule inhibitors has been hindered by the flat, featureless binding interface. Here, we report the discovery of a novel, druggable cryptic pocket within the SIRP D1 domain (the WYF pocket), revealed through a structure-based fragment screening campaign using x-ray crystallography. This pocket, defined by residues Trp38, Tyr50, and Phe74, is only accessible in a conformation that is incompatible with CD47 binding, making it a candidate for structure-based drug design and immune checkpoint inhibitor development. Through a combination of NMR spectroscopy, molecular dynamics simulations, and biophysical assays, we demonstrate that access to this cryptic site is dynamically controlled by a single "gatekeeper" residue, Gln52. The rotameric state of Gln52 dictates a conformational equilibrium between a "closed," state and a ligand-accessible "open" state. We validated this mechanism by engineering SIRP mutants to bias this equilibrium. A Q52F mutation locked the pocket in a closed state, abolishing both CD47 and fragment binding, while Q52A and Q52R mutations biased the protein toward an open state. These "open-biased" mutants not only exhibited decreased affinity for CD47 but also significantly improved binding to small-molecule fragments that inhibit the SIRP-CD47 interaction. This work reveals the intrinsic conformational plasticity of SIRP and establishes a validated structure-based roadmap for a new class of allosteric inhibitors. This flexibility-for-inhibition strategy functions by trapping a non-binding conformation and represents a broadly applicable framework for targeting this and other challenging immune checkpoints.

biochemistry↗

bioRxiv2025

Small Accelerations of the cell generate sufficient nuclear motion to modulate transcriptional activity, driving cellular response independent of matrix strain

The cells mechanical environment is a fundamental determinant of its activity. Ostensibly, the cellular response is dependent on interactions between extracellular matrix deformations and the cell adhesome. Low-intensity vibration (LIV) induces sinusoidal mechanical accelerations that stimulate mesenchymal stem cell (MSC) anabolism despite generating minimal matrix strain. In this study, we tested the hypothesis that accelerations of less than 1g cause nuclear motions relative to the cell membrane in adherent cells, resulting in elevated stresses in the cytoskeleton that promote transcriptional activity. Coupling a piezoelectric vibration platform with real-time microscopy, we applied a 0.7g, 90Hz LIV signal that oscillates the cell with displacements of up to {+/-}11 {micro}m. Live-cell tracking revealed that the sinusoidal vibrations caused the nucleus to move {+/-}1.27 {micro}m (17% of total displacement) out of phase with the cell membrane. Disruption of the LINC complex, which mechanically couples the nucleoskeleton to the cytoskeleton, doubled the magnitude of this relative motion, indicating that the nucleo-cytoskeletal configuration plays a major role in regulating nuclear motion. Consistent with a previously reported increase in nuclear stiffness caused by LIV, machine-learning-based image segmentation of confocal micrographs showed that LIV increased both apical and basal F-actin fiber numbers, generating a denser, more branched actin network near the nucleus. Following six 20 min bouts of LIV applied to MSC, RNA sequencing identified 372 differentially expressed genes. Upregulated gene sets were linked to F-actin assembly and focal adhesion pathways. Finite element simulations showed that nuclear stresses increased by LIV up to 18% were associated with nuclei flattening and a 30-50% increase in actin-generated forces. These findings demonstrate that low-intensity accelerations, independent of matrix strain, can directly activate a response of the nucleus, leading to cytoskeletal reorganization and heightened nuclear stresses. Thus, even very small oscillatory mechanical signals can markedly influence cell outcomes, establishing a mechanosensing pathway independent of extracellular strains.

bioengineering↗

bioRxiv2024

Stimulation of the Medial SNr Promotes Sustained Motor Recovery and Counteracts Parkinsonian Pathophysiology in Dopamine Depleted Mice

Dopamine loss alters the activity of neural circuits in the basal ganglia, contributing to motor symptoms of Parkinsons disease and catalepsy. Treatments that reduce basal ganglia pathophysiology alleviate motor symptoms but require maintenance. Cell-type specific interventions can reduce pathophysiology and provide sustained therapeutic benefits, but a lack of understanding of pathways involved limits translation. Here, we establish patterns of neuromodulation and electrophysiological biomarkers at the level of basal ganglia output that predict the duration of therapeutic effects. Focal activation of neurons in the ventromedial substantia nigra reticulata (SNr) engaged a gradual recovery of movement that persisted for hours after treatment, accompanied by a persistent reduction in parkinsonian pathophysiology. Global SNr inhibition, as prescribed by the classic rate model, provided only transient effects on movement and did not reverse network pathophysiology. These findings represent important steps towards developing therapeutic strategies that aim to repair, rather than simply mask, circuit dysfunction in disease.

neuroscience↗

bioRxiv2024

State modulation in spatial networks with three interneuron subtypes

Several inhibitory interneuron subtypes have been identified as critical in regulating sensory responses. However, the specific contribution of each interneuron subtype remains uncertain. In this work, we explore the contributions of cell-type specific activity and synaptic connections to dynamics of a spatially organized spiking neuron network. We find that the firing rates of the somatostatin (SOM) interneurons align closely with the level of network synchrony irrespective of the target of modulatory input. Further analysis reveals that inhibition from SOM to parvalbumin (PV) interneurons must be limited to allow gradual transitions from asynchrony to synchrony and that the strength of recurrent excitation onto SOM neurons determines the level of synchrony achievable in the network. Our results are consistent with recent experimental findings on cell-type specific manipulations. Overall, our results highlight common dynamic regimes achieved across modulations of different cell populations and identify SOM cells as the main driver of network synchrony.

neuroscience↗

bioRxiv2024

How cortico-basal ganglia-thalamic subnetworks can shift decision policies to maximize reward rate

All mammals exhibit flexible decision policies that depend, at least in part, on the cortico-basal ganglia-thalamic (CBGT) pathways. Yet understanding how the complex connectivity, dynamics, and plasticity of CBGT circuits translate into experience-dependent shifts of decision policies represents a longstanding challenge in neuroscience. Here we present the results of a computational approach to address this problem. Specifically, we simulated decisions driven by CBGT circuits under baseline, unrewarded conditions using a spiking neural network, and fit an evidence accumulation model to the resulting behavior. Using canonical correlation analysis, we then replicated the identification of three control ensembles (responsiveness, pliancy and choice) within CBGT circuits, with each of these subnetworks mapping to a specific configuration of the evidence accumulation process. We subsequently simulated learning in a simple two-choice task with one optimal (i.e., rewarded) target and found that feedback-driven dopaminergic plasticity on cortico-striatal synapses effectively manages the speed-accuracy tradeoff so as to increase reward rate over time. The learning-related changes in the decision policy can be decomposed in terms of the contributions of each control ensemble, whose influence is driven by sequential reward prediction errors on individual trials. Our results provide a clear and simple mechanism for how dopaminergic plasticity shifts subnetworks within CBGT circuits so as to maximize reward rate by strategically modulating how evidence is used to drive decisions. Author summaryThe task of selecting an action among multiple options can be framed as a process of accumulating streams of evidence, both internal and external, up to a decision threshold. A decision policy can be defined by the unique configuration of factors, such as accumulation rate and threshold height, that determine the dynamics of the evidence accumulation process. In mammals, this process is thought to be regulated by low dimensional subnetworks, called control ensembles, within the cortico-basal ganglia-thalamic (CBGT) pathways. These control ensembles effectively act by tuning specific aspects of evidence accumulation during decision making. Here we use simulations and computational analysis to show that synaptic plasticity at the cortico-striatal synapses, mediated by choice-related reward signals, adjusts CBGT control ensemble activity in a way that improves accuracy and reduces decision time to maximize the increase of reward rate during learning.

neuroscience↗

bioRxiv2024

Macroevolutionary Constraint and Selection on Anti-Bat Moth Tails

Elaborate traits evolve via intense selective pressure, overpowering ecological constraints. Hindwing tails that thwart bat attack have repeatedly originated in moon moths (Saturniidae), with longer tails having greater anti-predator effect. Here, we take a macroevolutionary approach to evaluate the evolutionary balance between predation pressure and possible limiting environmental factors on tail elongation. To trace the evolution of tail length across time and space, we inferred a time-calibrated phylogeny of the entirely tailed moth group (Actias + Argema) and performed ancestral state reconstruction and biogeographical analyses. We generated metrics of predation via estimates of bat abundance from nearly 200 custom-built species distribution models and environmental metrics via estimates of bioclimatic variables associated with individual moth observations. To access community science data, we developed a novel method for measuring wing lengths from un-scaled photos. Integrating these data into phylogenetically-informed mixed models, we find a positive association between bat predation pressure and moth tail length and body size, and a negative association between environmental factors and these morphological traits. Regions with more insectivorous bats and more consistent temperatures tend to host longer-tailed moths. Our study provides insight into tradeoffs between biotic selective pressures and abiotic constraints that shape elaborate traits across the tree-of-life.

evolutionary biology↗

bioRxiv2024

Arkypallidal neurons in the external globus pallidus can mediate inhibitory control by altering competition in the striatum

Reactive inhibitory control is crucial for survival. Traditionally, this control in mammals was attributed solely to the hyperdirect pathway, with cortical control signals flowing unidirectionally from the subthalamic nucleus (STN) to basal ganglia output regions. Yet recent findings have put this model into question, suggesting that the STN is assisted in stopping actions through ascending control signals to the striatum mediated by the external globus pallidus (GPe). Here we investigate this suggestion by harnessing a biologically-constrained spiking model of the corticobasal ganglia-thalamic (CBGT) circuit that includes pallidostriatal pathways originating from arkypallidal neurons. Through a series of experiments probing the interaction between three critical inhibitory nodes (the STN, arkypallidal cells, and indirect path-way spiny projection neurons), we find that the GPe acts as a critical mediator of both ascending and descending inhibitory signals in the CBGT circuit. In particular, pallidostriatal pathways regulate this process by weakening the direct pathway dominance of the evidence accumulation process driving decisions, which increases the relative suppressive influence of the indirect pathway on basal ganglia output. These findings delineate how pallidostriatal pathways can facilitate action cancellation by managing the bidirectional flow of information within CBGT circuits.

neuroscience↗

bioRxiv2023

Energy in functional brain states correlates with cognition in adolescent schizophrenia and healthy persons

Adolescent-onset schizophrenia (AOS) is rare, under-studied, and associated with more severe cognitive impairments and poorer outcomes than adult-onset schizophrenia. Neuroimaging has shown altered regional activations (first-order effects) and functional connectivity (second-order effects) in AOS compared to controls. The pairwise maximum entropy model (MEM) integrates first- and second-order factors into a single quantity called energy, which is inversely related to probability of occurrence of brain activity patterns. We take a combinatorial approach to study multiple brain-wide MEMs of task-associated components; hundreds of independent MEMs for various sub-systems are fit to 7 Tesla functional MRI scans. Acquisitions were collected from 23 AOS individuals and 53 healthy controls while performing the Penn Conditional Exclusion Test (PCET) for executive function, which is known to be impaired in AOS. Accuracy of PCET performance was significantly reduced among AOS compared to controls. A majority of the models showed significant negative correlation between PCET scores and the total energy attained over the fMRI. Across all instantiations, the AOS group was associated with significantly more frequent occurrence of states of higher energy, assessed with a mixed effects model. An example MEM instance was investigated further using energy landscapes, which visualize high and low energy states on a low-dimensional plane, and trajectory analysis, which quantify the evolution of brain states throughout this landscape. Both supported patient-control differences in the energy profiles. Severity of psychopathology was correlated positively with energy. The MEMs integrated representation of energy in task-associated systems can help characterize pathophysiology of AOS, cognitive impairments, and psychopathology.

neuroscience↗

bioRxiv2023

CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making

Here we introduce CBGTPy, a virtual environment for designing and testing goal-directed agents with internal dynamics that are modeled on the cortico-basal-ganglia-thalamic (CBGT) pathways in the mammalian brain. CBGTPy enables researchers to investigate the internal dynamics of the CBGT system during a variety of tasks, allowing for the formation of testable predictions about animal behavior and neural activity. The framework has been designed around the principle of flexibility, such that many experimental parameters in a decision making paradigm can be easily defined and modified. Here we demonstrate the capabilities of CBGTPy across a range of single and multi-choice tasks, highlighting the ease of set up and the biologically realistic behavior that it produces. We show that CBGTPy is extensible enough to apply to a range of experimental protocols and to allow for the implementation of model extensions with minimal developmental effort. Author summaryWe introduce a toolbox for producing biologically realistic simulations of the cortico-basal ganglia-thalamic dynamics during a variety of experimental tasks. The purpose is to foster the theory-experiment cycle, offering a tool for generating testable predictions of behavioral and neural responses that can be validated experimentally, in a framework that allows for simple updating as new experimental evidence emerges. We outline how our toolbox works and demonstrate its performance on a set of normative cognitive tasks.

neuroscience↗

bioRxiv2023

The role of Kölliker-Fuse nucleus in breathing variability

The Kolliker-Fuse nucleus (KF), which is part of the parabrachial complex, participates in the generation of eupnea under resting conditions and the control of active abdominal expiration when increased ventilation is required. Moreover, dysfunctions in KF neuronal activity are believed to play a role in the emergence of respiratory abnormalities seen in Rett syndrome (RTT), a progressive neurodevelopmental disorder associated with an irregular breathing pattern and frequent apneas. Relatively little is known, however, about the intrinsic dynamics of neurons within the KF and how their synaptic connections affect breathing pattern control and contribute to breathing irregularities. In this study, we use a reduced computational model to consider several dynamical regimes of KF activity paired with different input sources to determine which combinations are compatible with known experimental observations. We further build on these findings to identify possible interactions between the KF and other components of the respiratory neural circuitry. Specifically, we present two models that both simulate eupneic as well as RTT-like breathing phenotypes. Using nullcline analysis, we identify the types of inhibitory inputs to the KF leading to RTT-like respiratory patterns and suggest possible KF local circuit organizations. When the identified properties are present, the two models also exhibit quantal acceleration of late-expiratory activity, a hallmark of active expiration featuring forced exhalation, with increasing inhibition to KF, as reported experimentally. Hence, these models instantiate plausible hypotheses about possible KF dynamics and forms of local network interactions, thus providing a general framework as well as specific predictions for future experimental testing. Key pointsThe Kolliker-Fuse nucleus (KF), a part of the parabrachial complex, is involved in regulating normal breathing and controlling active abdominal expiration during increased ventilation. Dysfunction in KF neuronal activity is thought to contribute to respiratory abnormalities seen in Rett syndrome (RTT). This study utilizes computational modeling to explore different dynamical regimes of KF activity and their compatibility with experimental observations. By analyzing different model configurations, the study identifies inhibitory inputs to the KF that lead to RTT-like respiratory patterns and proposes potential KF local circuit organizations. Two models are presented that simulate both normal breathing and RTT-like breathing patterns. These models provide plausible hypotheses and specific predictions for future experimental investigations, offering a general framework for understanding KF dynamics and potential network interactions.

neuroscience↗

bioRxiv2023

Data driven and cell specific determination of nuclei-associated actin structure

Quantitative and volumetric assessment of filamentous actin fibers (F-actin) remains challenging due to their interconnected nature, leading researchers to utilize threshold based or qualitative measurement methods with poor reproducibility. Here we introduce a novel machine learning based methodology for accurate quantification and reconstruction of nuclei-associated F-actin. Utilizing a Convolutional Neural Network (CNN), we segment actin filaments and nuclei from 3D confocal microscopy images and then reconstruct each fiber by connecting intersecting contours on cross-sectional slices. This allowed measurement of the total number of actin filaments and individual actin filament length and volume in a reproducible fashion. Focusing on the role of F-actin in supporting nucleocytoskeletal connectivity, we quantified apical F-actin, basal F-actin, and nuclear architecture in mesenchymal stem cells (MSCs) following the disruption of the Linker of Nucleoskeleton and Cytoskeleton (LINC) Complexes. Disabling LINC in mesenchymal stem cells (MSCs) generated F-actin disorganization at the nuclear envelope characterized by shorter length and volume of actin fibers contributing a less elongated nuclear shape. Our findings not only present a new tool for mechanobiology but introduce a novel pipeline for developing realistic computational models based on quantitative measures of F- actin.

bioengineering↗

bioRxiv2022

Competition between action plans tracks with evidence accumulation during flexible decision-making

Changing your mind requires shifting the way streams of information lead to a decision. Using in silico experiments we show how the cortico-basal ganglia-thalamic (CBGT) circuits can feasibly implement shifts in the evidence accumulation process. When action contingencies change, dopaminergic plasticity redirects the balance of power, both within and between action representations, to divert the flow of evidence from one option to another. This finding predicts that when competition between action representations is highest, the rate of evidence accumulation is lowest. We then validate this prediction in a sample of homo sapiens as they perform an adaptive decision-making task while whole-brain hemodynamic responses are recorded. These results paint a holistic picture of how CBGT circuits manage and adapt the evidence accumulation process in mammals. One-sentence SummaryInteractions between cortical and subcortical circuits in the mammalian brain flexibly control the flow of information streams that drive decisions by shifting the balance of power both within and between action representations.

neuroscience↗

bioRxiv2021

Identifying control ensembles for decision-making within the cortico-basal ganglia-thalamic circuit

In situations featuring uncertainty about action-reward contingencies, mammals exhibit a high degree of flexibility in adopting strategies for decision-making that are tuned in response to the conditions that they encounter. Although the cortico-basal ganglia thalamic (CBGT) network is implicated in information processing during decisions, it features a complex synaptic architecture, comprised of multiple feed-forward, reciprocal, and feedback pathways, that complicate efforts to elucidate the roles of specific CBGT populations in the process of evidence accumulation. In this paper we apply a strategic sampling approach, based on Latin hypercube sampling, to explore how variations in CBGT network properties, including subpopulation firing rates and synaptic weights, map to variability of parameters in a normative drift diffusion model (DDM), representing algorithmic aspects of information processing during decision-making. Through the application of canonical correlation analysis, we find that this relationship can be characterized in terms of three low-dimensional control ensembles within the CBGT network that impact specific qualities of the emergent decision policy: responsiveness (associated with overall activity in corticothalamic and direct pathways), pliancy (associated largely with overall activity in components of the indirect pathway of the basal ganglia), and choice (associated with differences in direct and indirect pathways across action channels). These analyses provide key mechanistic predictions about the roles of specific CBGT network elements in tuning information processing dynamics during decisions. Author summaryMammals are continuously subjected to uncertain situations in which they have to choose among behavioral options. The cortico-basal ganglia-thalamic (CBGT) circuit is a complicated collection of interconnected nuclei believed to strongly influence the ability to adapt to environmental changes. The roles of specific CBGT components in controlling information during decisions remains unclear. At a more phenomenological, algorithmic level, drift-diffusion models have been shown to be able to reproduce behavioral data (action selection probabilities and the time needed to make a decision) obtained experimentally from mammals and to provide an abstract representation of a decision policy. In this work, we use simulated decision-making to establish a mapping from neural activity in the CBGT circuit to behavioral outcomes. This mapping illuminates the importance of three core sets of CBGT subnetworks in the action selection process and how they are involved in adapting decision policies across exploitative and exploratory situations.

neuroscience↗

bioRxiv2021

Mechanically induced nuclear shuttling of β-catenin requires co-transfer of actin

Mesenchymal stem cells (MSC) respond to environmental forces with both cytoskeletal re-structuring and activation of protein chaperones of mechanical information, {beta}-catenin and Yes-Associated Protein 1 (YAP1). To function, MSCs must differentiate between dynamic forces such as cyclic strains of extracellular matrix due to physical activity and static strains due to ECM stiffening. To delineate how MSCs recognize and respond differently to both force types, we compared effects of dynamic (200 cycles x 2%) and static (1 x 2% hold) strain on nuclear translocation of {beta}-catenin and YAP1 at 3h after force application. Dynamic strain induced nuclear accumulation of {beta}-catenin, and increased cytoskeletal actin structure and cell stiffness, but had no effect on nuclear YAP1 levels. Critically, both nuclear actin and nuclear stiffness increased along with dynamic strain-induced {beta}-catenin transport. Augmentation of cytoskeletal structure using either static strain or lysophosphatidic acid (LPA) did not increase nuclear content of {beta}-catenin or actin, but induced robust nuclear increase in YAP1. As actin binds {beta}-catenin, we considered whether {beta}-catenin, which lacks a nuclear localization signal, was dependent on actin to gain entry to the nucleus. Knockdown of cofilin-1 (Cfl1) or importin-9 (Ipo9), which co-mediate nuclear transfer of G-actin, prevented dynamic strain-mediated nuclear transfer of both {beta}-catenin and actin. In sum, dynamic strain induction of actin re-structuring promotes nuclear transport of G-actin, concurrently supporting nuclear access of {beta}-catenin via mechanisms utilized for actin transport. Thus, dynamic and static strain activate alternative mechanoresponses reflected by differences in the cellular distributions of actin, {beta}-catenin and YAP1. Significance statementCells integrate both static and dynamic mechanical signals through the actin cytoskeleton which is attached to the nuclear envelope, affecting nuclear transport of {beta}-catenin and YAP1. Dynamic strain induces nuclear translocation of {beta}-catenin, but not YAP1, while static strain causes nuclear translocation of YAP1, but not {beta}-catenin. Importantly, nuclear transport of actin is induced by dynamic but not static force. Furthermore, nuclear import of {beta}-catenin depends on cofilin/importin-9 dependent actin transport mechanisms. Thus the presence of {beta}-catenin and YAP1 in the nucleus represent specific responses to regulatory mechanical signals.

cell biology↗

bioRxiv2021

Evaluating Network Threshold Selection for Structural and Functional Brain Connectomes

IntroductionStructural and functional brain connectomes built using macroscale data collected through magnetic resonance imaging (MRI) may contain noise that contributes to false-positive edges, which can obscure structure-function relationships with implications for data interpretation. Thresholding procedures are routinely applied in practice to optimize network density by removing low-signal edges, but there is limited consensus regarding the appropriate selection of thresholds. We compare existing methods and propose a novel alternative objective function thresholding (OFT) method. MethodsThe performance of thresholding approaches, including a percolation-based approach and an objective function-based approach, is assessed by (a) computing the normalized mutual information (NMI) of community structure between a known network and a simulated, perturbed networks to which various forms of thresholding have been applied, and (b) comparing the density and the clustering coefficient (CC) between the baseline and thresholded networks. ResultsIn our analysis, the proposed objective function-based threshold exhibits the best performance in terms of high similarity between the underlying networks and their perturbed, thresholded counterparts, as quantified by NMI and CC analysis on the simulated functional networks. DiscussionExisting network thresholding methods yield widely different results when graph metrics are computed. Thresholding based on the objective function appears to maintain a set of edges such that the resulting network shares the community structure and clustering features present in the original network. This outcome provides proof-of-principle evidence that thresholding based on the objective function could offer a useful approach to reducing the network density of functional connectivity data. Impact StatementNetwork thresholding refers to removing edges between node pairs in a functional network that have weak edge-weights that may arise from unwanted variability or noise. Since edge-weight cutoffs used to generate a binary network can be sensitive to thresholding, we introduce a novel thresholding algorithm. We find that when applied to networks derived via perturbations, namely through simulated functional connectivity of a known network, this approach yields a binary network that is more similar to the known network compared to using existing thresholding approaches. Thus, our algorithm is a competitive candidate for use in thresholding of brain connectome.

neuroscience↗