Biology subjects

Bygrave, A. M.

Publications and source records attributed to Bygrave, A. M..

9 recordsLinked to original sources

bioRxiv2026

Btbd11 regulates glutamatergic synapse organization in GABAergic inhibitory interneurons

The mechanisms that underlie glutamatergic synapse organization and function in GABAergic inhibitory interneurons (INs) are not well described, despite evidence that impaired glutamatergic excitation of INs is implicated in psychiatric disorders such as schizophrenia and anxiety. Glutamatergic synapses received by INs have unique basal transmission properties and exhibit distinct synaptic plasticity compared to those received by excitatory neurons, likely due to cell-type specific differences in postsynaptic density (PSD) composition and maintenance mechanisms. In the present study, we show that the interneuron-specific protein Btbd11 regulates excitatory synapse transmission in hippocampal interneurons through promotion of phase separation and support of postsynaptic nanoarchitecture. Btbd11 forms a phase separated protein complex with Psd-95 and TARP{gamma}2 and impacts the stability of TARP{gamma}2 and GluA1 within glutamatergic IN synapses in an expression- and phase separation-dependent manner. Using super resolution imaging, we show that Btbd11 displays nanoscale clustering properties within IN synapses that correlate with Psd-95 nanostructure. Furthermore, genetic deletion of Btbd11 decreases PSD protein expression, reduces synapse size, and disrupts Psd-95 nanocluster organization. These effects manifest as a drastic reduction in glutamatergic synaptic transmission onto INs when Btbd11 is deleted. Together, these data provide insights into a novel cell type-specific synaptic regulatory mechanism in an understudied synapse population.

neuroscience↗

bioRxiv2026

SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification

Synapses are the fundamental units of neural computation, yet quantifying their organization across circuit-level scales remains a critical bottleneck in neuroscience. While advances in fluorescent labeling and imaging can generate vast datasets, analysis is often the limiting factor. Several deep learning-based tools have been proposed to ameliorate these issues. However, existing applications primarily focus on dendritic spines and lack robust solutions for segmenting synaptic puncta in dense tissue preparations. To address this, we introduce SynAPSeg, which encompasses an open-source framework for deep learning-based analysis and, to the best of our knowledge, the first large-scale, publicly available instance segmentation dataset specifically curated for synaptic puncta. We use this dataset to train deep learning models that reach the performance of human experts across a unique benchmark dataset. SynAPSeg integrates these models into an interactive interface, with support for multi-dimensional data, enabling fully automated segmentation and quantification pipelines alongside an annotation module for refinement and validation. We demonstrate the frameworks scalability by performing the first comprehensive mapping of nearly 4 million excitatory postsynaptic PSD95 puncta within inhibitory interneurons across the dorsal hippocampus, revealing regional differences in synapse properties. Finally, we show SynAPSegs utility for 3D quantification by applying these models to study aging-associated synaptic changes in CA1 parvalbumin (PV)-positive inhibitory neurons. Through this approach, we uncover a reduction in PSD95 density along PV dendrites in the aged CA1, indicating reduced glutamatergic recruitment of PV neurons which could contribute to age-related cognitive decline. Collectively, these results demonstrate that SynAPSeg provides a scalable solution for comprehensively studying synaptic architecture in health and disease.

neuroscience↗

bioRxiv2025

Sex and experience dependent regulation of synaptic protein turnover

Synaptic transmission can be carefully tuned through plasticity mechanisms that regulate synaptic strength, structure, and number. In vivo measurements demonstrate remarkable spine dynamics, with subsets of synapses persisting for months. This correlates with the extreme longevity of certain memories, which can persist for an organisms lifetime. The molecular basis supporting the long-term stability of specific synapses and the long-term durability of memories remains unknown. At the protein level, most proteins persist for a relatively short amount of time before they are degraded and replaced with new molecules. However, recent work has identified a population of proteins, including those present at the synapse, that are exceptionally long-lived. It has been speculated that long-lived proteins (LLPs) could contribute to long-term synapse stability, function, and memory. Here, we used stable isotope labeling in mammals (SILAM) to first identify LLPs in the post synaptic density (PSD) of the hippocampus and subsequently determine if protein turnover rates varied by sex or following learning. We identified novel synaptic LLPs and found that both sex and experience can regulate synaptic protein turnover rates. We identified sex-dependent changes in protein turnover rates in autism spectrum disorder (ASD) risk genes, including increased stability of Gabrg2, a GABA-A receptor subunit, in male mice. Furthermore, we observed stabilization of a subset of PSD proteins, such as Shank3, following contextual fear conditioning. We propose that sex and experience dependent changes in protein turnover rates could help explain sex-differences in psychiatric risk and aid our understanding of the molecular mechanisms that support learning and memory.

neuroscience↗

bioRxiv2025

Dynamic extracellular interactions with AMPA receptors

Synaptic plasticity in the central nervous system enables the encoding, storing, and integrating new information. AMPA-type glutamate receptors (AMPARs) are ligand-gated ion channels that mediate most fast excitatory synaptic transmission in the brain, and plasticity of AMPARs signaling underlies the long-lasting changes in synaptic efficacy and strength important for learning and memory.1,2 Recent work has indicated that the enigmatic N-terminal domain (NTD) of AMPARs may be a critical regulator of synaptic targeting and plasticity of AMPARs. However, few synaptic proteins have been identified that regulate AMPAR plasticity through interactions with AMPAR NTDs. Moreover, the scope of AMPAR NTD interactors that are important for synaptic plasticity remains unknown. Here, we present the dynamic, extracellular interactome for AMPARs during synaptic plasticity. Using surface-restricted proximity labeling and BioSITe-based proteomics, we identified 70 proteins that were differentially labeled by APEX2-tagged AMPARs after induction of chemical Long-term potentiation of synapses (cLTP) in cultured neurons. Included in this list, were four members of the IgLON family of GPI-anchored proteins (Ntm, OBCAM/Opcml, Negr1, Lsamp). We show OBCAM and NTM directly interact with the extracellular domains of AMPARs. Moreover, overexpression of NTM significantly attenuates the mobility of surface AMPARs in dendritic spines. These data represent a significant first step at uncovering the unexplored extracellular regulation of AMPARs, with broad implications for synapse function and synaptic plasticity. Significance StatementOver the past 30 years, significant effort has been focused on understanding the mechanisms that induce long-lasting changes in synapse strength (synaptic plasticity) that drive learning and memory. While many studies have investigated intracellular mechanisms that enable plasticity, especially those acting on AMPA-type glutamate receptors (AMPARs), significantly less is known regarding extracellular mechanisms that shape changes in synapse function. Here, we identified 70 proteins that differentially associate with the extracellular region of AMPARs during chemically-induced synaptic plasticity. We show that OBCAM and NTM directly interact with the NTD of AMPARs and regulate their mobility on the surface of neurons. These data advance our understanding of extracellular AMPAR regulation, with broad implications for synapse function and synaptic plasticity.

neuroscience↗

bioRxiv2021

Btbd11 is an inhibitory interneuron specific synaptic scaffolding protein that supports excitatory synapse structure and function

Synapses in the brain exhibit cell-type-specific differences in basal synaptic transmission and plasticity. Here, we evaluated cell-type-specific differences in the composition of glutamatergic synapses, identifying Btbd11, as an inhibitory interneuron-specific synapse-enriched protein. Btbd11 is highly conserved across species and binds to core postsynaptic proteins including Psd-95. Intriguingly, we show that Btbd11 can undergo liquid-liquid phase separation when expressed with Psd-95, supporting the idea that the glutamatergic post synaptic density in synapses in inhibitory and excitatory neurons exist in a phase separated state. Knockout of Btbd11 from inhibitory interneurons decreased glutamatergic signaling onto parvalbumin-positive interneurons. Further, both in vitro and in vivo, we find that Btbd11 knockout disrupts network activity. At the behavioral level, Btbd11 knockout from interneurons sensitizes mice to pharmacologically induced hyperactivity following NMDA receptor antagonist challenge. Our findings identify a cell-type-specific protein that supports glutamatergic synapse function in inhibitory interneurons--with implication for circuit function and animal behavior.

neuroscience↗

bioRxiv2021

Experience-induced remodeling of the hippocampal post-synaptic proteome and phosphoproteome

The post synaptic density (PSD) of excitatory synapses contains a highly organized protein network with thousands of proteins and is key node in the regulation of synaptic plasticity. To gain new mechanistic insight into experience-induced changes in the PSD, we examined the global dynamics of the PSD proteome and phosphoproteome in mice following various treatments. Mice were trained using an inhibitory avoidance (IA) task and hippocampal PSD fractions were isolated for quantitative proteomic and phosphoproteomics analysis. We used a sequential enrichment strategy to explore the concurrent events of protein expression and phosphorylation in the hippocampal PSD following IA training (IA) or immediate shock (Shock). We identified more than 6,200 proteins and 3,000 phosphoproteins in the sequential strategy covering a total of 7,429 proteins. On the phosphoproteins we identified a total of 9,589 phosphosites. Strikingly, of the significantly IA-regulated proteins and phosphoproteins, a large fraction of the proteins displayed an overall decrease in phosphorylation level. Bioinformatic analysis of proteins and phosphoproteins that were regulated by IA were annotated for an involvement in regulation of glutamate receptor functionality, calcium signaling, and synaptic plasticity. We also identified synaptic kinases, phosphatases and their respective phosphosites regulated by IA training or immediate shock. Furthermore, we found that AMPA receptor surface expression was regulated by protein phosphatase, Mg2+/Mn2+ dependent 1H (Ppm1h). Together, these results unravel the dynamic remodeling of the PSD upon IA learning or immediate shock and serve as a resource for elucidating the synaptic proteome dynamics induced by experience-dependent plasticity. HighlightsO_LIThe proteome and phosphoproteome of mouse hippocampal PSD fractions were examined using quantitative phosphoproteomics and bioinformatics following inhibitory avoidance training or non-associative immediate shock. C_LIO_LIApproximately 6,200 proteins and 3,000 phosphoproteins were identified and quantified in the hippocampal PSD fractions. C_LIO_LIIA mediates widespread decreases in the abundance and phosphorylation of proteins in the hippocampal PSD fraction. C_LIO_LIKinases, phosphatases and their phosphorylation status were dynamically and significantly regulated by IA and immediate shock. C_LIO_LIFunctional validation shows that the protein phosphatase Ppm1h is linked to the regulation of synaptic plasticity in vitro and in vivo. C_LI In BriefQuantitative proteomics and phosphoproteomics combined with subcellular protein fractionation and bioinformatic analysis identifies a highly dynamic regulation of synaptic protein phosphorylation at the postsynaptic density following IA training and immediate shock.

neuroscience↗

bioRxiv2021

Highly task-specific and distributed neural connectivity in working memory revealed by single-trial decoding in mice and humans

Working memory (WM), the capacity to briefly and intentionally maintain mental items, is key to successful goal-directed behaviour and impaired in a range of psychiatric disorders. To date, several brain regions, connections, and types of neural activity have been correlatively associated with WM performance. However, no unifying framework to integrate these findings exits, as the degree of their species- and task-specificity remains unclear. Here, we investigate WM correlates in three task paradigms each in mice and humans, with simultaneous multi-site electrophysiological recordings. We developed a machine learning-based approach to decode WM-mediated choices in individual trials across subjects from hundreds of electrophysiological measures of neural connectivity with up to 90% prediction accuracy. Relying on predictive power as indicator of correlates of psychological functions, we unveiled a large number of task phase-specific WM-related connectivity from analysis of predictor weights in an unbiased manner. Only a few common connectivity patterns emerged across tasks. In rodents, these were thalamus-prefrontal cortex delta- and beta-frequency connectivity during memory encoding and maintenance, respectively, and hippocampal-prefrontal delta- and theta-range coupling during retrieval, in rodents. In humans, task-independent WM correlates were exclusively in the gamma-band. Mostly, however, the predictive activity patterns were unexpectedly specific to each task and always widely distributed across brain regions. Our results suggest that individual tasks cannot be used to uncover generic physiological correlates of the psychological construct termed WM and call for a new conceptualization of this cognitive domain in translational psychiatry.

neuroscience↗

bioRxiv2020

An Optimized CRISPR/Cas9 Approach for Precise Genome Editing in Neurons

The efficient knock-in of large DNA fragments to label endogenous proteins remains especially challenging in non-dividing cells such as neurons. We developed Targeted Knock-In with Two-guides (TKIT) as a novel CRISPR/Cas9 based approach for efficient, and precise, genomic knock-in. Through targeting non-coding regions TKIT is resistant to INDEL mutations. We demonstrate TKIT labelling of endogenous synaptic proteins with various tags, with efficiencies up to 42% in mouse primary cultured neurons. Utilizing in utero electroporation or viral injections in mice TKIT can label AMPAR subunits with Super Ecliptic pHluorin, enabling visualization of endogenous AMPARs in vivo using two-photon microscopy. We further use TKIT to assess the mobility of endogenous AMPARs using fluorescence recovery after photobleaching. Finally, we show that TKIT can be used to tag AMPARs in rat neurons, demonstrating precise genome editing in another model organism and highlighting the broad potential of TKIT as a method to visualize endogenous proteins.

neuroscience↗

bioRxiv2020

Lack of redundancy between electrophysiological measures of long-range neuronal communication

Communication between brain areas has been implicated in a wide range of cognitive and emotive functions and is impaired in numerous mental disorders. In rodent models, various functional connectivity metrics have been used to quantify inter-regional neuronal communication. However, in individual studies, typically only very few measures of coupling are reported and, hence, redundancy across such indicators is implicitly assumed. In order to test this assumption, we here comparatively assessed a broad range of directional and non-directional metrics like coherence, weighted Phase-Lag-Index (wPLI), Granger causality (GC), spike-phase coupling (SPC), cross-regional phase-amplitude coupling, amplitude cross-correlations, and others. We applied these analyses to simultaneous field recordings from the prefrontal cortex and the ventral and dorsal hippocampus in the schizophrenia-related Gria1-knockout mouse model which displays a robust novelty-induced hyperconnectivity phenotype. We find that across such measures there is a considerable lack of functional redundancy. While coherence and GC yielded similar results, other measures, especially wPLI and SPC, often produced deviating conclusions. Bivariate correlations within animals revealed that virtually none of the metrics consistently co-varied with any of the other measures across the three connections and two genotypes analysed. Parametric GC showed the qualitatively highest degree of redundancy with other metrics and would be most suitable for connectivity analysis. We conclude that analysis of multiple metrics is necessary to characterise functional connectivity.

neuroscience↗