Biology subjects

Xu, E. Y.

Publications and source records attributed to Xu, E. Y..

6 recordsLinked to original sources

bioRxiv2026

Chronic Stress Alters Dorsal Bed Nucleus of Stria Terminalis Synaptic Neurotransmission in a Dravet Syndrome Mouse Model

Stress is a commonly reported seizure precipitant and may contribute to the development of psychiatric comorbidities in epilepsy, yet how chronic stress interacts with epileptic circuits remains poorly understood. We investigated the impact of chronic restraint stress on physiological, behavioral, and synaptic outcomes in a mouse model of Dravet syndrome, specifically corticotropin-releasing factor (CRF) neurons in the bed nucleus of the stria terminalis (BNST), a stress-responsive region implicated in epilepsy patients. Chronic restraint stress produced divergent hypothalamic-pituitary-adrenal axis responses, with stressed Dravet syndrome mice exhibiting elevated corticosterone, increased mortality in females, and increased locomotion and anxiety-like behavior. Ex vivo electrophysiological recordings revealed that chronic stress increased spontaneous excitatory event frequency onto BNST CRF neurons in both genotypes and selectively increased sEPSC and sIPSC amplitude in Dravet syndrome mice. Evoked recordings demonstrated genotype-specific effects of stress on glutamatergic transmission in CRF neurons of the DS group. This suggests greater stress-dependent remodeling of spontaneous and evoked synaptic activity in DS. These findings suggest chronic stress may worsen physiological and behavioral outcomes in Dravet syndrome and promote specific maladaptive alterations in BNST CRF circuitry. More broadly, these results suggest that stress interacts with seizure vulnerability and potentially contributes to neuropsychiatric comorbidities and epilepsy.

neuroscience↗

bioRxiv2025

Predicting and Designing Red Fluorescent Protein Variants Using Sequence-to-Function Machine Learning Models

Fluorescent proteins (FPs) are widely used reporters for visualizing cellular structures and processes. Traditional wet-lab strategies for FP engineering (rational design and directed evolution) have enabled substantial improvements in photophysical performance but are limited by their requirement for deep expert knowledge or labor-intensive screening. AI-driven approaches have recently gained traction for engineering variants of green FPs, yet applications to red fluorescent proteins (RFPs) remain scarce. Here, we applied machine learning models to an RFP sequence-function dataset and trained these models to predict functional single-mutation variants of the state-of-the-art RFP mScarlet-I3. Guided by model predictions, we identified variants exhibiting red-shifted emission peaks, large Stokes shifts, or brightness comparable to the parental protein. Our findings show that even lightweight, data-efficient models can extract actionable design principles for improving RFPs. This work demonstrates the feasibility of AI-guided design for RFPs and provides a reliable benchmark for future development of more powerful AI-driven strategies for FP engineering.

molecular biology↗

bioRxiv2025

Plakophilin-2 Coordinates Energy Metabolism and Contractility in Cardiomyocytes, Revealing Its Roles beyond Desmosomes

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a familial cardiac disease associated with ventricular arrhythmias and an increased risk of sudden cardiac death. Mutations in the desmosome gene Plakophilin-2, PKP2, lead to reduction in PKP2 protein and collapse of desmosomes that is known to compromise contractility and electrical stability of cardiomyocytes. Our previous studies demonstrated the efficacy of adeno-associated virus 9 (AAV9)-mediated restoration of PKP2 expression in a cardiac specific knock-out mouse model of Pkp2 and revealed profound changes in mRNA signatures of metabolic enzymes that were reversed by the gene replacement approach. In this study, we used PKP2-deficient mouse hearts and human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to identify changes in steady-state metabolite levels associated with impaired lipid homeostasis, glycolysis, and glucose oxidation. These metabolic phenotypes align with human ARVC metabolic data and reflect an intrinsic impairment of cellular energy metabolism. Here we showed for the first time that these intracellular metabolic defects were associated specifically with poor contractility of cardiomyocytes. AAV9:PKP2 restored contractility, improved electrophysiological properties and Ca2+ transients. In contrast, we observed that treating PKP2-deficient cardiomyocytes pharmacologically with small molecule metabolic enhancers improved contractility but not electrophysiological properties and Ca2+ transients, suggesting differential sensitivity of structure-mediated functions in response to metabolic perturbance. Our study modeled and revealed a direct intracellular connection between compromised PKP2 function and metabolic impairment. We proposed that an increased risk of decoupling energy-responsive contractility from less energy-responsive electrical activities can be a new arrhythmogenic mechanism, potentially responsible for exercise-triggered cardiac adversity in ARVC disease development and progression.

cell biology↗

bioRxiv2024

In vivo cell-type and brain region classification via multimodal contrastive learning

AO_SCPLOWBSTRACTC_SCPLOWCurrent electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded without further molecular or histological analysis. Developing accurate and scalable algorithms for identifying the cell-type and brain region of recorded neurons is thus crucial for improving our understanding of neural computation. In this work, we develop a multimodal contrastive learning approach for neural data that can be fine-tuned for different downstream tasks, including inference of cell-type and brain location. We utilize this approach to jointly embed the activity autocorrelations and extracellular waveforms of individual neurons. We demonstrate that our embedding approach, Neuronal Embeddings via MultimOdal contrastive learning (NEMO), paired with supervised fine-tuning, achieves state-of-the-art cell-type classification for two opto-tagged datasets and brain region classification for the public International Brain Laboratory Brain-wide Map dataset. Our method represents a promising step towards accurate cell-type and brain region classification from electrophysiological recordings. The project page and code are available at https://ibl-nemo.github.io/.

neuroscience↗

bioRxiv2018

DAZL is a master translational regulator of murine spermatogenesis

Expression of DAZ-like (DAZL) is a hallmark of vertebrate germ cells and essential for embryonic germ cell development and differentiation, yet gametogenic function of DAZL has not been fully characterized with most of its in vivo direct targets unknown. We showed that postnatal stage-specific deletion of Dazl in mouse germ cells did not affect female fertility, but caused complete male sterility with gradual loss of spermatogonial stem cells (SSCs), meiotic arrest and spermatid arrest respectively. Using the genome-wide HITS-CLIP and mass spectrometry approach, we found that DAZL bound to a large number of testicular mRNA transcripts (at least 3008) at 3' UnTranslated Region (3' UTR) and interacted with translation proteins including PABP. In the absence of DAZL, polysome-associated target transcripts, but not their total transcripts were significantly decreased, resulting in drastic reduction of an array of spermatogenic proteins and thus developmental arrest. Thus, DAZL is a master translational regulator essential for spermatogenesis.

developmental biology↗

bioRxiv2018

Genome-wide meta-analysis of depression in 807,553 individuals identifies 102 independent variants with replication in a further 1,507,153 individuals

Major depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

genetics↗