Biology subjects

Baek, J.

Publications and source records attributed to Baek, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Predicting conformational changes induced by biomolecular interactions

Biomolecular interactions trigger structural changes of protein complexes essential for cellular signaling, regulation, and drug action. Modern co-folding models can now generate detailed structures of multi-component complexes directly from sequence. However, recent evaluations reveal that they frequently fail to capture specific conformational states induced by a ligand or partner. Here, we show that explicitly modeling the structural transition from an unbound to a bound state, rather than generating the bound complex alone, better resolves these interaction-induced states. Our generative learning framework, K-Fold, transforms independently predicted unbound component priors into an assembled complex through an ultrafast diffusion bridge, operating entirely without multiple sequence alignments. By eliminating co-evolutionary dependencies, K-Fold accurately recovers non-coevolved interfaces in antibody-antigen and targeted-degrader complexes, as well as ligand-directed regulatory states in GPCRs and kinases. Furthermore, by coupling this diffusion bridge with diverse monomeric equilibrium ensembles, K-Fold provides a computationally tractable foundation for sampling complex multi-state landscapes without retraining. These results demonstrate that formulating molecular assembly as an explicit structural transition moves structure prediction beyond static geometries toward capturing biomolecular dynamics.

molecular biology↗

bioRxiv2026

Local control of dopamine release in nucleus accumbens gates opioid withdrawal aversion

The opioid crisis has emphasized the need for more effective treatments for opioid use disorder (OUD)1-3, which is characterized by habitual opioid use to avoid withdrawal symptoms4,5. Both physical and affective symptoms contribute to opioid withdrawal yet whether different neural mechanisms mediate these different symptom domains and contribute distinctly to opioid relapse is unknown. While neurons expressing mu opioid receptors (MORs) gate opioids reinforcing effects6-8 by increasing dopamine (DA) release in nucleus accumbens (NAc), sharp decreases in NAc DA release are associated with withdrawal9-11, the cellular and circuit mechanisms of which are unknown. Here we describe an unusual population of evolutionarily-conserved MOR+ neurons in the NAc expressing the transcription factor Tshz1. Increased activity in these neurons is required for withdrawal aversion learning. Deletion of MORs in Tshz1 neurons prevented withdrawal-induced decreases in DA release and affective aversion, but not physical symptoms associated with withdrawal. Pharmacological activation of mGluR8, which is preferentially expressed in Tshz1 neurons, reduced withdrawal aversion. Thus, by dissociating the circuit mechanisms contributing to the physical and affective components of opioid withdrawal focusing on the critical role of Tshz1 neurons, we have identified a novel druggable target with therapeutic potential for treating key OUD withdrawal symptoms.

neuroscience↗

bioRxiv2026

Growth-adaptive spring electronics for long-term, same-neuron mapping in the developing rat brain

Neural activity reorganizes profoundly after birth, transitioning from highly synchronous population events to sparse, decorrelated firing in the mature brain. Although inhibitory maturation and shifts in excitation-inhibition balance have been implicated in this process, how individual neurons implement the transition remains unclear because rapid brain growth has prevented long-term, same-neuron mapping. Here, we introduce growth-adaptive spring electronics that provide depth-wise compliance during tissue expansion, maintaining a stable electrode-tissue interface over weeks of neonatal development. We developed a vision-language model-assisted spike processing pipeline for the developing brain that probabilistically matches units across days using high-density waveform spatial footprints, despite developmental changes in the neonatal brain. Together, these innovations enable spike-resolved mapping of the same neurons in rat visual cortex and medial prefrontal cortex from postnatal day 10 to 45. Using population coupling to quantify each neurons coordination with local population activity, we show that developmental decorrelation is driven primarily by a distinct subset of neurons that progressively shifts from strong to weak coupling during postnatal weeks 3 to 5, whereas other neurons remain stably weakly or strongly coupled throughout development. These results resolve population-level desynchronization into identifiable neuron-specific trajectories. This framework enables direct tests in neurodevelopmental disorder models, including schizophrenia and autism, of whether altered maturation reflects global circuit imbalance or selective disruption and mistiming of specific developmental programs.

bioengineering↗

bioRxiv2025

Seminal Fluid Adipokinetic Hormone Increases Insemination Refractoriness in Female Aedes aegypti

Mating often changes behavior and physiology of female insects. In many species, these changes have been attributed to receipt of seminal fluid molecules (SFMs). SFMs influence phenotypes including feeding, egg production, and response to male courtship and insemination attempts. These same phenotypes are potential targets for management of insect pests. Aedes aegypti mosquitoes are the primary vector of several pathogens including dengue, Zika, and chikungunya. Within an hour after an initial insemination, female Ae. aegypti are generally refractory to subsequent inseminations, a response attributed to SFMs. However, the specific molecules involved in inducing long-term insemination refractoriness have not been identified. In a previous study, we identified adipokinetic hormone (AKH) precursor protein as an SFM in Ae. albopictus. AKH is a well-studied insect neuropeptide that impacts phenotypes including those related to metabolism, locomotion, and reproduction. In this study, we investigated whether AKH is an SFM in Ae. aegypti and whether it impacts female re-insemination patterns. We first established that AKH is produced in the male reproductive tract and transferred to females during mating, and is, therefore, an SFM. We then created an AKH-null line which allowed us to demonstrate that seminal fluid AKH contributes to long-term insemination refractoriness of females. Together, these results have established a novel expression pattern for AKH and identified AKH as a contributor to Ae. aegypti insemination refractoriness, laying the groundwork for understanding the evolution and mode of action of novel seminal fluid proteins as well as for investigating novel pathways or approaches for mosquito control.

biochemistry↗

bioRxiv2025

Dapagliflozin mitigates hypoxia-induced metabolic stress, kidney tubular cell death and fibrosis

Sodium-glucose co-transporter 2 inhibitors (SGLT2-i) slow progression of kidney disease but therapeutic mechanisms remain elusive. Here we report the beneficial effect of dapagliflozin on hypoxia-mediated kidney tubular epithelial cell injury, a contributing factor to kidney disease progression, using a human pluripotent stem cell (hPSC)-derived hypoxic kidney organoid model. Hypoxic organoids showed increased expression of Hypoxia Inducible Factor (HIF)-associated transcriptional targets, decreased tricarboxylic acid (TCA) cycle metabolites and mitochondrial {beta}-oxidation protein expression, and activated unfolded protein response. A transcriptional signature derived from hypoxic organoids 1) identified a subgroup of individuals whose kidney disease subsequently progressed, and 2) correlated with worse tubular injury. Dapagliflozin enhanced mitochondrial stress response resulting in reversed hypoxia-induced tubular epithelial cell apoptosis, reactive oxygen species (ROS) accumulation, and organoid fibrosis. These results indicate that dapagliflozin may contribute to improved kidney disease outcomes by attenuating hypoxia-induced metabolic stress-mediated tubular epithelial cell injury.

cell biology↗

bioRxiv2025

BioLACE: unifying spatial geometry and marker priors for cohesive cell-type clustering in spatial transcriptomics

Spatial transcriptomics (ST) provides high-dimensional gene expression profiles together with spatial coordinates, enabling the reconstruction of tissue architecture at cellular resolution. While recent graph-based deep learning methods have advanced spatial clustering in ST, many rely on complex architectures that obscure interpretability and rarely integrate biological priors such as marker gene information. We introduce BioLACE, a scalable framework that unifies spatial structure, transcriptomic variation, and curated marker gene profiles within a shared Variational Autoencoder (VAE) latent space. BioLACEjointly optimizes three complementary objectives: (1) a VAE reconstruction loss to preserve transcriptional structure, (2) a graph Laplacian regularizer to enforce spatial smoothness, and (3) a temperature-scaled InfoNCE contrastive loss guided by marker-informed similarities. Applied to MERFISH hypothalamus, mouse spinal cord, and Slide-seq mouse cerebellum datasets, BioLACE achieves superior cell type clustering accuracy, well-defined biologically consistent boundaries, and interpretable latent representations, highlighting its generality and scalability for modern ST analysis. The source code and tutorials for BioLACE are publicly available at https://github.com/maiziezhoulab/BioLACE.

genomics↗

bioRxiv2025

DeviceAgent: An autonomous multimodal AI agent for flexible bioelectronics

The development of flexible bioelectronics remains a complex, multidisciplinary process that demands specialized expertise and labor-intensive efforts, limiting scalability, adaptability and accessibility. Here, we introduce DeviceAgent, an autonomous multimodal AI agent that integrates large language models (LLMs), vision-language models (VLMs), and domain-specific computational tools into a unified framework for bioelectronics research. Leveraging the emergent reasoning abilities of LLMs and VLMs, DeviceAgent enables zero and few-shot generalization, contextual learning, and flexible task execution across modalities. A multimodal context memory system orchestrates these capabilities, providing end-to-end support across the experimental pipeline-from high-level design objectives to fabrication protocol generation, visual defect inspection, and electrophysiological signal analysis, while maintaining human oversight at critical decision points. We demonstrate its capabilities through the development of stretchable mesh electronics for interfacing with human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), a representative application involving complex device architectures, heterogeneous material nanofabrication, and electrophysiology analysis. DeviceAgent autonomously (1) generates customized bioelectronic layouts; (2) creates comprehensive fabrication protocols tailored to specific materials and processes; (3) identifies microscopic defects using visual reasoning; and (4) analyzes cardiac electrophysiological recordings in an interpretable manner. By embedding LLMs and VLMs within a structured, tool-augmented architecture, DeviceAgent establishes a scalable and accessible paradigm for AI-scientist collaboration in nanofabrication and bioelectronics research.

bioengineering↗

bioRxiv2025

An AI Agent for cell-type specific brain computer interfaces

Decoding how specific neuronal subtypes contribute to brain function requires linking extracellular electrophysiological features to underlying molecular identities, yet reliable in vivo electrophysiological signal classification remains a major challenge for neuroscience and clinical brain-computer interfaces (BCI). Here, we show that pretrained, general-purpose vision-language models (VLMs) can be repurposed as few-shot learners to classify neuronal cell types directly from electrophysiological features, without task-specific fine-tuning. Validated against optogenetically tagged datasets, this approach enables robust and generalizable subtype inference with minimal supervision. Building on this capability, we developed the BCI AI Agent (BCI-Agent), an autonomous AI framework that integrates vision-based cell-type inference, stable neuron tracking, and automated molecular atlas validation with real-time literature synthesis. BCI-Agent addresses three critical challenges for in vivo electrophysiology: (1) accurate, training-free cell-type classification; (2) automated cross-validation of predictions using molecular atlas references and peer-reviewed literature; and (3) embedding molecular identities within stable, low-dimensional neural manifolds for dynamic decoding. In rodent motor-learning tasks, BCI-Agent revealed stable, cell-type-specific neural trajectories across time that uncover previously inaccessible dimensions of neural computation. Additionally, when applied to human Neuropixels recordings-where direct ground-truth labeling is inherently unavailable-BCI-Agent inferred neuronal subtypes and validated them through integration with human single-cell atlases and literature. By enabling scalable, cell-type-specific inference of in vivo electrophysiology, BCI-Agent provides a new approach for dissecting the contributions of distinct neuronal populations to brain function and dysfunction.

neuroscience↗

bioRxiv2025

Intestinal catabolism of dietary fructose promotes obesity and insulin resistance via ileal lacteal remodeling

High-fructose corn syrup (HFCS) consumption is a risk factor for obesity and metabolic syndrome, yet the underlying mechanisms are incompletely understood. Catabolism of dietary fructose primarily occurs in the small intestine and liver, with fructose breakdown in the liver being pathological, while small intestinal fructose clearance protects the liver. Here, we unexpectedly found that inhibition of fructose catabolism specifically in the small intestine mitigates fructose-induced obesity and insulin resistance. Mechanistically, blocking intestinal fructose catabolism reduces dietary fat absorption, which is associated with a decrease in the surface area of the ileal lacteals and alterations in gut microbiome. Fecal transplantation experiments revealed that such a microbiome stimulates the intestine-resident macrophages, promoting lacteal growth and boosting dietary fat absorption. Given the preclinical and clinical studies reporting the effect of fructose catabolism suppression on mitigating diet-induced obesity, our data suggest that such effects are partly mediated by intestinal lacteal remodeling. Significance StatementHere, we uncover a previously unappreciated link between intestinal fructose catabolism and ileal lacteal remodeling, suggesting the mechanisms by which fructose intake promotes obesity. Using mice lacking the fructose-processing enzyme specifically in the intestine, we show that blocking intestinal fructose metabolism protects against diet-induced obesity by reducing fat absorption. Changes in gut microbiome and immune cell interactions drive this effect.

physiology↗

bioRxiv2025

An autonomous AI agent for universal behavior analysis

Behavior analysis across species represents a fundamental challenge in neuroscience, psychology, and ethology, typically requiring extensive expert knowledge and labor-intensive processes that limit research scalability and accessibility. We introduce BehaveAgent, an autonomous multimodal AI agent designed to automate behavior analysis from video input without retraining or manual intervention. Unlike conventional methods that require manual behavior annotation, video segmentation, task-specific model training, BehaveAgent leverages the reasoning capabilities of multimodal large language models (LLM) to generalize across novel behavioral domains without need for additional training. It integrates LLMs, vision-language models (VLMs), and large-scale visual grounding modules, orchestrated through a multimodal context memory and goal-directed attention mechanism, to enable robust zero-shot visual reasoning across species and experimental paradigms, including plants, insects, rodents, primates, and humans. Upon receiving a video input, BehaveAgent autonomously identifies the correct analysis strategy and performs end-to-end behavior analysis and interpretation without human supervision. Leveraging vision-language representations, it performs general-purpose tracking, pose estimation and segmentation. We demonstrate BehaveAgents universal applicability to autonomously (1) identify the behavioral paradigm and develop an action plan specialized for the identified paradigm, (2) identify relevant subjects and objects, (3) track those features, (4) identify behavioral sequences with explicit reasoning, (5) generate and execute code for targeted analysis and (6) generate comprehensive research reports that integrate behavioral findings with relevant scientific literature. Through interpretable agentic reasoning, BehaveAgent makes its internal decision-making process transparent, clarifying why particular features are tracked or behaviors inferred. By reducing the time and expertise required for behavior analysis, BehaveAgent introduces a scalable, generalizable, and explainable paradigm for advancing biological and behavioral research.

neuroscience↗

bioRxiv2025

Spatial transcriptomics AI agent charts hPSC-pancreas maturation in vivo

Spatial transcriptomics has revolutionized our understanding of tissue organization by simultaneously capturing gene expression and spatial localization within intact tissues. However, analyzing these increasingly complex datasets requires specialized expertise across computational biology, statistics, and biological context. To address this challenge, we introduce the Spatial Transcriptomics AI Agent (STAgent), an autonomous multimodal agentic AI that integrates multimodal large language models (LLMs) with specialized computational tools to transform weeks-long analysis tasks into minutes of automated processing. Unlike conventional machine learning approaches that are limited to narrow, predefined tasks, STAgent leverages the emergent capabilities of multimodal LLMs - such as flexible reasoning, contextual understanding, and cross-modal integration - which allow it to adapt to novel data, execute multi-step analyses, and generate biologically meaningful insights with minimal human input. STAgent enables autonomous deep research through integrated capabilities, including dynamic code generation for complex analytical workflows, visual reasoning for interpreting spatial patterns, real-time retrieval of relevant peer-reviewd scientific literature, and synthesis of comprehensive, actionable reports. We applied STAgent to investigate the in vivo maturation of human stem cell-derived pancreatic cells (SC-pancreas) transplanted into immunodeficient mice. We generated single-cell spatial transcriptomics data spanning multiple developmental timepoints. STAgent autonomously (1) identified the maturation of initially scattered endocrine cells into well-defined islet-like structures, with predominantly peripheral -cells surrounding {beta}-cell cores supported by an expanding mesenchymal network; (2) revealed strengthening endocrine-endocrine cell interactions over time and, through context-aware gene set analysis, uncovered spatially resolved biological processes driving maturation; (3) unlike traditional analytical approaches, STAgent offers mechanistic explanations of spatial patterns, contextualizing findings with relevant literatures and developing cohesive insights into human pancreatic development. This agentic approach establishes a new paradigm in spatial transcriptomics analysis by substantially lowering the expertise barrier and reducing analysis time, accelerating biological and biomedical discovery.

genomics↗

bioRxiv2025

Spike sorting AI agent

Spike sorting is a fundamental process for decoding neural activity, involving preprocessing, spike detection, feature extraction, clustering, and validation. However, conventional spike sorting methods are highly fragmented, labor-intensive, and heavily reliant on expert manual curation, limiting their scalability and reproducibility. This challenge has become more pressing with advances in neural recording technology, such as high-density Neuropixels for large-scale neural recording or flexible electrodes for long-term stable recording over months to years. The volume and complexity of these datasets make manual curation infeasible, requiring an automated and scalable solution. Here, we introduce SpikeAgent, a multimodal large language model (LLM)-based AI agent that automates and standardizes the entire spike sorting pipeline. Unlike traditional approaches, SpikeAgent integrates multiple LLM backends, coding functions, and established algorithms, autonomously performing spike sorting with reasoning-based decision-making and real-time interaction with intermediate results. It generates interpretable reports, providing transparent justifications for each sorting decision, enhancing transparency and reliability. We benchmarked SpikeAgent against human experts across various neural recording technology, demonstrating its versatility and ability to achieve curation consistency that are equal to, or even higher than human experts. It also drastically reduces the expertise barrier and accelerates the curation and validation time by orders of magnitude. Moreover, it enables automated interpretability of the neural spiking data, which cannot be achieved by any conventional methods. SpikeAgent presents a paradigm shift in processing signals for neuroscience and brain-computer interfaces, while laying the ground for AI agent-augmented science across various domains.

neuroscience↗

bioRxiv2024

Corticostriatal ensemble dynamics across heroin self-administration to reinstatement

Corticostriatal projection neurons from prelimbic medial prefrontal cortex to the nucleus accumbens core critically regulate drug-seeking behaviors, yet the underlying encoding dynamics whereby these neurons contribute to drug seeking remain elusive. Here we use two-photon calcium imaging to visualize the activity of corticostriatal neurons in mice from the onset of heroin use to relapse. We find that the activity of these neurons is highly heterogeneous during heroin self-administration and seeking, with at least 8 distinct neuronal ensembles that display both excitatory and inhibitory encoding dynamics. These neuronal ensembles are particularly apparent during relapse, where excitatory responses are amplified compared to heroin self-administration. Moreover, we find that optogenetic inhibition of corticostriatal projection neurons attenuates heroin seeking regardless of the relapse trigger. Our results reveal the precise corticostriatal activity dynamics underlying drug-seeking behaviors and support a key role for this circuit in mediating relapse to drug seeking.

neuroscience↗

bioRxiv2024

Untargeted Plasma Metabolomics Unveils Distinct Metabolite Profiles in Parkinson Disease Subtypes: A Focus on idiopathic REM Sleep Behavior Disorders

BackgroundParkinsons disease (PD) is characterized by diverse clinical presentations and etiological complexities, with rapid eye movement (REM) sleep behavior disorder (RBD) serving as a prodromal marker. While extensive unbiased metabolic profiling of plasma samples from PD subjects has been conducted to identify novel PD metabolic biomarkers, comprehensive metabolic profiling of PD subtypes based on RBD status remains limited. MethodsWe conducted a comprehensive metabolic profiling of PD subtypes at disease onset, considering the presence or absence of RBD, utilizing an untargeted metabolomics approach. Plasma samples were collected from subjects with PD with and without RBD at the initial stages of disease, idiopathic RBD, and healthy controls to elucidate similarities and differences among PD subtypes. Based on ordination analysis and metabolome-wide association study (Wilcoxon rank-sum tests and generalized fold changes), we identified specific groups of metabolites enriched in the PD_Only group and RBD groups (iRBD & PD_RBD+), with few metabolites shared between groups. Furthermore, pathway enrichment analysis (hypergeometric tests) identified specific groups enriched with metabolites from specific origins and associated biospecimens, as well as disease-associated metabolites. Finally, we evaluated the biomarker potential of the identified disease metabolites by ROC curves and proposed logistic regression models of key biomarkers and clinical parameters for predicting disease status. ResultsMetabolomic analysis revealed distinct metabolic profiles between PD subtypes with and without RBD. Our analysis confirmed previously reported PD metabolic markers, such as a reduction in caffeine and urate, as well as an increase in cortisol, secondary bile acids, and p-cresol sulfate. However, our stratified analyses based on the presence of RBD discriminated RBD-associated metabolites from those associated with PD_Only (without RBD). PD patients with RBD exhibited enrichment of gut microbial-origin metabolites, including secondary bile acids and p-cresol sulfate, compared to PD patients without RBD. Conversely, metabolites associated with neuro-psychiatric diseases were enriched in PD patients without RBD. ConclusionsOur study elucidates the heterogeneous nature of PD subtypes, particularly differentiated with the presence of RBD. The metabolic features of PD with RBD subtype supports the "body-first" concept of PD pathogenesis originating from the gut.

systems biology↗

bioRxiv2024

Benchmarking clustering, alignment, and integration methods for spatial transcriptomics

Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice, and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remain challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of benchmark studies complicates the selection of methods and future method development. Here we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. Different experimental metrics and analyses, like adjusted rand index (ARI), uniform manifold approximation and projection (UMAP) visualization, layer-wise and spot-to-spot alignment accuracy, spatial coherence score (SCS), and 3D reconstruction, are meticulously designed to assess method performance as well as data quality. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics. This analysis leads to a comprehensive recommendation that covers multiple aspects for users. The code used for evaluation is available on GitHub. Additionally, we provide jupyter notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets (https://benchmarkst-reproducibility.readthedocs.io/en/latest/).

bioinformatics↗

bioRxiv2024

Substrate stiffness modulates the emergence and magnitude of senescence phenotypes

Cellular senescence is a major driver of aging and disease. Here we show that substrate stiffness modulates the emergence and magnitude of senescence phenotypes after exposure to senescence inducers. Using a primary dermal fibroblast model, we show that decreased substrate stiffness accelerates senescence-associated cell-cycle arrest and regulates the expression of conventional protein-based biomarkers of senescence. We found that the expression of these senescence biomarkers, namely p21WAF1/CIP1 and p16INK4a are mechanosensitive and are in-part regulated by myosin contractility through focal adhesion kinase (FAK)-ROCK signaling. Interestingly, at the protein level senescence-induced dermal fibroblasts on soft substrates (0.5 kPa) do not express p21WAF1/CIP1 and p16INK4a at comparable levels to induced cells on stiff substrates (4GPa). However, cells express CDKN1a, CDKN2a, and IL6 at the RNA level across both stiff and soft substrates. Moreover, when cells are transferred from soft to stiff substrates, senescent cells recover an elevated expression of p21WAF1/CIP1 and p16INK4a at levels comparable to senescence cells on stiff substrates, pointing to a mechanosensitive regulation of the senescence phenotype. Together, our results indicate that the emergent senescence phenotype depends critically on the local mechanical environments of cells and that senescent cells actively respond to changing mechanical cues.

molecular biology↗

bioRxiv2024

Dysregulation of Neuropilin-2 Expression in Inhibitory Neurons Impairs Hippocampal Circuit Development Leading to Autism-Epilepsy Phenotype

Dysregulation of development, migration, and function of interneurons, collectively termed interneuronopathies, have been proposed as a shared mechanism for autism spectrum disorders (ASDs) and childhood epilepsy. Neuropilin-2 (Nrp2), a candidate ASD gene, is a critical regulator of interneuron migration from the median ganglionic eminence (MGE) to the pallium, including the hippocampus. While clinical studies have identified Nrp2 polymorphisms in patients with ASD, whether selective dysregulation of Nrp2-dependent interneuron migration contributes to pathogenesis of ASD and enhances the risk for seizures has not been evaluated. We tested the hypothesis that the lack of Nrp2 in MGE-derived interneuron precursors disrupts the excitation/inhibition balance in hippocampal circuits, thus predisposing the network to seizures and behavioral patterns associated with ASD. Embryonic deletion of Nrp2 during the developmental period for migration of MGE derived interneuron precursors (iCKO) significantly reduced parvalbumin, neuropeptide Y, and somatostatin positive neurons in the hippocampal CA1. Consequently, when compared to controls, the frequency of inhibitory synaptic currents in CA1 pyramidal cells was reduced while frequency of excitatory synaptic currents was increased in iCKO mice. Although passive and active membrane properties of CA1 pyramidal cells were unchanged, iCKO mice showed enhanced susceptibility to chemically evoked seizures. Moreover, iCKO mice exhibited selective behavioral deficits in both preference for social novelty and goal-directed learning, which are consistent with ASD-like phenotype. Together, our findings show that disruption of developmental Nrp2 regulation of interneuron circuit establishment, produces ASD-like behaviors and enhanced risk for epilepsy. These results support the developmental interneuronopathy hypothesis of ASD epilepsy comorbidity.

neuroscience↗

bioRxiv2021

3D Microenvironment-Specific Mechanosensing Regulates Neural Stem Cell Lineage Commitment

While extracellular matrix (ECM) mechanics strongly regulate stem cell commitment, the fields mechanistic understanding of this phenomenon largely derives from simplified two-dimensional (2D) culture substrates. Here we found a three-dimensional (3D) matrix-specific mechanoresponsive mechanism for neural stem cell (NSC) differentiation. NSC lineage commitment in 3D is maximally stiffness-sensitive in the range of 0.1-1.2 kPa, a narrower and more brain-mimetic range than we had previously identified in 2D (0.75 - 75 kPa). Transcriptomics revealed stiffness-dependent upregulation of early growth response 1 (Egr1) in 3D but not in 2D. Egr1 knockdown enhanced neurogenesis in stiff ECMs by driving {beta}-catenin nuclear localization and activity in 3D, but not in 2D. Mechanical modeling and experimental studies under osmotic pressure indicate that stiff 3D ECMs are likely to stimulate Egr1 via increases in confining stress during cell volumetric growth. To our knowledge, Egr1 represents the first 3D-specific stem cell mechanoregulatory factor.

bioengineering↗