Biology subjects

Shao, Y.

Publications and source records attributed to Shao, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

When CycloneSEQ meets Oxford Nanopore Technologies: a performance comparison of contemporary nanopore DNA sequencing platforms

Nanopore sequencing enables direct analysis of native DNA modifications, but these modifications can also introduce systematic basecalling errors. Here, we investigated how bacterial DNA methylation contributes to nanopore substitution errors using matched native whole-genome sequencing (WGS) and methylation-depleted whole-genome amplification (WGA) datasets from six bacterial species across CycloneSEQ (CS) and Oxford Nanopore Technologies platforms (ONT). Adenine-to-guanine (A2G) and guanine-to-adenine (G2A) substitutions were consistently enriched across species, nanopore platforms, sequencing configurations, and basecalling models, indicating a shared nanopore-associated substitution bias. This enrichment remained detectable in WGA reads, whereas native WGS reads showed a further increase. Using independent PacBio methylation profiling, we found that the WGS-associated substitution excess was strongly concentrated around bacterial methylation motifs and was minimal in non-motif regions. The magnitude and substitution pattern of these errors varied among motifs and sequencing platforms. At a subset of motif-associated positions, alternative-base fractions exceeded 50%, producing SNP-like signals. In ONT data, 28 WGS-specific calls survived the applied SNP-calling filters, all within PacBio-supported methylation contexts, and most showed significant strand imbalance. Together, these results identify a shared A2G/G2A substitution bias in all nanopore datasets and support a methylation-associated increase in these errors near bacterial methylation motifs, with some loci producing SNP-like artifacts.

bioinformatics↗

bioRxiv2026

Physics-Informed Neural Network Methods for Predicting Plant Height Development

Plant growth is a dynamic process affected by genes and growing environment, with all kinds of interactions between them. These complex relationships make the prediction of plant growth challenging. We propose a hybrid modelling framework that combines a logistic ordinary differential equation model with a Long Short-Term Memory (LSTM) neural network, resulting in a Physics Informed Neural Network (PINN). While PINNs have been widely applied to physical dynamical systems, their use in modelling the dynamics of plant growth systems is still largely unexplored. We illustrate the construction of a PINN on plant height data in wheat and compare its performance with alternative models for longitudinal plant data. All temporal prediction models only require time and temperature as input. Among a set of competing models, our PINN had the lowest average root mean squared error (RMSE) of prediction and the smallest standard deviation across multiple random initialisations. Therefore, we conclude that incorporating biological growth constraints into data-driven growth models can enhance prediction accuracy of longitudinal plant traits. HighlightsO_LIIntegrating plant growth equations into a temporal neural network improves plant height growth prediction over ordinary differential equations and machine learning models, especially when training data are limited. C_LI

plant biology↗

bioRxiv2025

A Genome-Based Pipeline for Digital Siderophore Typing in Pseudomonas Iron-Interaction Network

Siderophores are key mediators of microbial interactions, especially in iron-limited environments. While traditional experimental methods for siderophore typing are time-consuming and low-throughput, we present a genome-based pipeline for high-throughput digital siderophore typing in Pseudomonas. Our approach integrates profile Hidden Markov Models (pHMMs), substrate-specific motif identification, and co-evolutionary analysis to predict both the structure of pyoverdines and their corresponding uptake receptors from any given Pseudomonas genome. By this approach, we developed pHMM from 94 previously defined receptor groups and validated their accuracy and robustness across 14,230 Pseudomonas genomes. Application of our method to this large dataset demonstrated the potential of our algorithm for identifying both novel "lock-key" receptor groups and previously uncharacterized pyoverdine structures. Notably, our pipeline corrected long-term misclassifications in classical strains and proposed a new reference for the canonical Group III pyoverdine. Furthermore, interaction network analysis supports the observation of distinct siderophore utilization patterns between pathogenic and non-pathogenic strains. This standardized, user-friendly platform offers a robust tool for annotating siderophore behaviors in Pseudomonas and demonstrates the potential of digital siderophore typing in exploring iron-mediated ecology across microbes. Impact statementIron is vital for microbes. In most environments, they secrete diverse siderophores to scavenge iron. The types of siderophores a strain produces and uptakes shape its interactions, from cooperation to cheating and competition; characterizing this is called "siderophore typing." While traditional methods are limited by experimental capacities, our genome-based digital pipeline revolutionizes the process by predicting siderophore structures and receptor types directly from genomes, enabling large-scale analysis of 14,230 Pseudomonas strains. The results reveal novel groups, correct historical misclassifications, and link patterns to pathogenicity, providing a standardized software for ecological studies, antibiotic design, and bioremediation.

microbiology↗

bioRxiv2025

Prolonged Starvation Drives Epigenetic Remodeling: Insights from DNA Methylation Profiling in the Aquatic Pathogen Flavobacterium columnare

Nutrient scarcity is a common environmental stressor encountered by bacteria. Despite its significance in bacterial virulence and physiology, the underlying epigenetic mechanisms of bacterial adaptation to prolonged starvation remain poorly understood. In this study, we utilized the latest Nanopore R10.4.1 sequencing technology to comprehensively characterize the genome-wide methylation landscape of the freshwater fish pathogen Flavobacterium columnare following long-term starvation at different temperatures. Our results revealed significant methylation plasticity under starvation conditions, characterized by distinct motif-dependent patterns. Notably, demethylation of the 6mA-modified CAYNNNNNRTG motif emerged as a robust epigenetic signature of starvation adaptation, with functional enrichment of genes involved in pathways for translation and metabolism, suggesting the role of this methylase in regulating essential cellular functions under starvation stress. Additionally, another 6mA-modified GCAGA motif exhibited temperature-dependent variation, indicating its potential role in temperature-specific response. Together, this study provides novel insights into the epigenetic mechanisms underlying the bacterial adaptation to nutrient deprivation and establishes a valuable methodological reference for bacterial epigenetics analysis utilizing advanced Nanopore sequencing technologies.

microbiology↗

bioRxiv2025

NPAS2 attenuates VSMC phenotypic switching in ascending thoracic aortic aneurysm via LPCAT3/PC-PUFA2S-mediated ferroptosis

Ascending thoracic aortic aneurysm (ATAA) is a life-threatening disorder with limited therapeutic options, critically linked to vascular smooth muscle cell (VSMC) phenotypic switching. Neuronal PAS Domain Protein 2 (NPAS2), a circadian rhythm-related transcription factor, regulates diverse physiological processes. However, the biological functions and underlying regulatory mechanisms of NPAS2 in VSMCs during ATAA pathogenesis remain to be elucidated. NPAS2 expression decreased in ATAA patients, PDGF-BB-treated HASMCs and BAPN-administrated mice. NPAS2 depletion facilitated VSMC phenotype switching. VSMC-specific NPAS2 knockout (NPAS2SMKO) mice exhibited aggravated ATAA. Mechanistically, NPAS2 depletion attenuates its transcriptional repression of LPCAT3, subsequently elevating the accumulation of phosphatidylcholines with two polyunsaturated fatty acyl chains (PC-PUFA2S), thereby promoting ferroptosis-induced VSMC phenotype switching and accelerating ATAA progression. Targeting NPAS2 represents a potential therapeutic strategy for ATAA treatment.

molecular biology↗

bioRxiv2025

Parallel algorithms for phylogenetic inference under a structured coalescent approximation

While advances in molecular epidemiology and computational modeling have enhanced our capacity to track pathogen evolution, the accurate reconstruction of spatiotemporal transmission dynamics remains essential for developing epidemic preparedness frame-works and implementing outbreak response measures. Structured coalescent models offer a phylogeographic framework by restricting lineage coalescence events to geographically proximate host populations. Although the Bayesian structured coalescent approximation (BASTA) provides a tractable approach, contemporary phylogeographic analyses involving dozens of geographic localities and hundreds to thousands of viral genomes substantially exceed the computational capacity of existing implementations. The BASTA likelihood scales cubically with deme count and quadratically with sequence count due to matrix exponentiation and pairwise coalescent probability calculations. Here, we introduce a comprehensive algorithmic restructuring of the structured coalescent likelihood that eliminates redundancies, optimizes memory access, and exposes parallelization opportunities. Our approach reorganizes computations along three dimensions: (i) independent calculation of deme-transition probability matrices across time intervals; (ii) simultaneous evaluation of partial likelihood vectors within temporal slices; and (iii) concurrent aggregation of coalescent probabilities. Algorithmic restructuring cuts average coalescent likelihood computation by 7-8 fold, and parallelization further boosts performance to 10-26 fold, enabling joint phylogeographic analyses of dengue virus across 10 South American countries and H5N1 avian influenza across 20 Eurasian regions to finish in a fraction of prior time. This computational efficiency also enables comparison between backward-in-time structured coalescent approximations and forward-in-time phylogeographic methods, revealing that the former provides appropriately conservative posterior estimates, particularly at intermediate phylogenetic depths. We integrate our implementation into the popular BEAST X and BEAGLE software packages, with an accompanying interface in BEAUti X to easily set up the analyses, providing researchers with an accessible and scalable tool for real-time phylogeographic surveillance of rapidly evolving pathogens.

bioinformatics↗

bioRxiv2025

Integrative genomic and transcriptomic analyses uncover regulatory landscape of symbiotic nitrogen fixation in soybean natural population

Symbiotic nitrogen fixation (SNF) is a key trait in legume productivity, yet the genetic and regulatory basis underlying its natural variation remains poorly understood. Here, we integrated genome, transcriptome, and chromatin accessibility data from a soybean diversity panel comprising 380 accessions, including 108 wild and 272 cultivated lines. Genome-wide association studies (GWAS) detected multiple loci for SNF traits but with limited resolution due to polygenic architecture and environmental influences. Independent component analysis (ICA) identified 136 co-expression modules; ten ICs were strongly correlated with SNF phenotypes and enriched in circadian clock components (e.g., GmLHY1a/b), lipid metabolism, or defense signaling pathways. Transcriptome-wide association studies (TWAS) linked 1,453, 806, and 178 genes to NFP, NW, and NFE traits, respectively. Among TWAS hits, 185 transcription factors were identified, with 39.0% overlapping selective sweeps, suggesting regulatory evolution under domestication. To further dissect expression regulation, we performed eQTL mapping and detected 4,654 significant eQTLs, including 1,241 local (cis), 2,505 distal (trans), and 908 mixed. By integrating ATAC-seq data from sorted nodule nuclei, we found that eQTLs, particularly local eQTLs, are significantly enriched within open chromatin regions, indicating their regulatory potential. Notably, we validated the circadian clock gene GmLHY1b as a negative regulator of nodulation using CRISPR mutagenesis and CUT&Tag. Our integrative study provides comprehensive genomic and transcriptomic resources from a diverse soybean population, offering novel insights into SNF regulatory networks and a valuable foundation for future SNF research and soybean improvement.

plant biology↗

bioRxiv2025

Bifidobacterium longum subsp. nexus nov., a novel subspecies isolated from infant stool

Bifidobacterium longum is a foundational, genetically diverse member of the infant gut microbiome, yet resolving its subspecies in metagenomic data remains challenging. Here, we performed a deep phylogenetic characterization of the B. longum species utilizing 4,577 fecal metagenomes from the LLNEXT mother-infant cohort alongside a global dataset of 1,220 genomes and metagenome-assembled genomes (MAGs). We identified a novel, geographically widespread lineage: Bifidobacterium longum subsp. nexti. Using a polyphasic taxonomic analysis, including average nucleotide identity and digital DNA-DNA hybridization, confirms subspecies-level divergence. Distinct from other members of the B. longum species, BL. nexti harbors -amylopullulanase and grows on soluble starch and often pullulan, consistent with -glucan utilization. Within the LLNEXT cohort, BL. nexti is enriched in formula-fed infants and shows evidence consistent with vertical transmission. Globally, while BL. nexti is geographically widespread, it usually persists at low relative abundance across diverse populations. By resolving a major fraction of previously unclassified B. longum diversity, this study provides new insights into the specialized lineages and ecological dynamics driving the assembly of the developing infant microbiome.

microbiology↗

bioRxiv2025

Single-cell transcriptomic integrated with machine learning reveals retinal cell-specific biomarkers in diabetic retinopathy

Diabetic retinopathy (DR) remains a principal cause of vision impairment worldwide, involved complex retinal cellular pathophysiology that remains incompletely understood. To elucidate cell-type-specific molecular signatures underlying DR, we generated a high-resolution single-cell transcriptomic atlas of 297,121 retinal cells from 20 Chinese donors, including non-diabetic controls (26.4%), diabetic without retinopathy (23.4%) and DR (50.2%). Following rigorous quality control, batch-effect correction, and clustering and annotation, 10 major retinal cell populations were delineated. Differential expression analyses across disease states within each cell type yielded candidate gene sets, which were further refined via a multi-stage machine-learning pipeline combining L1-regularized logistic regression and recursive feature elimination with cross-validation, alongside bootstrap stability selection. Resulting cell-type-specific classifiers achieved high accuracy (79-95%) and AUCs (0.85-0.99) in distinguishing DR disease states. Enrichment analyses implicated immune activation, oxidative stress, neurodegeneration and synaptic dysfunction pathways across multiple cell types in retina. Integrating 567 unique marker genes from all cell types, a general multilayer perceptron classifier achieved 95.31% overall accuracy on held-out test data, demonstrating the translational potential of these signatures for non-diabetic controls, diabetic without retinopathy and DR classification. This high-resolution atlas and the accompanying analytic framework provide a robust computational framework for biomarker discovery, mechanistic insight and targeted intervention strategies in diabetic retinal diseases.

bioinformatics↗

bioRxiv2025

Cell Type Specific Aging Transcriptional Signatures of Human Retina Through Integrated Machine Learning and Single-Cell Transcriptomics

PurposeTo characterize cell type specific transcriptional changes during human retinal aging and develop machine learning model for cellular age discrimination in a Chinese cohort. DesignCross-sectional, laboratory-based observational study. ParticipantsEighteen unfrozen retinas from 12 Chinese donors (9 young, 34-55y; 9 old, 68-92 y). MethodsSingle-cell RNA sequencing (10x, v3.1) generated 223612 cells, batch-corrected with scVI; age-related signatures were defined by intersecting single-cell and pseudo-bulk differentially expressed genes, then cell-type-specific panels were rank-ordered with L1-regularised logistic regression plus recursive feature elimination and interpreted through hallmark-pathway enrichment and transcription-factor regulon mapping. Main Outcome MeasuresAge-related cellular composition shifts; cell-type-specific differentially expressed genes; machine-learning classifier accuracy and feature rankings; transcription factor regulon activity changes. ResultsEleven major retinal cell populations were identified. Aging showed declining rod-to-cone ratios, reduced bipolar cell proportions among interneurons, and increased astrocyte abundance. Muller glial cells exhibited the most pronounced transcriptional changes, followed by bipolar cells and rods. Machine-learning classifiers achieved 80-96% accuracy across cell types (microglia 96%, horizontal cells 93%, bipolar cells 91%, cones 90%, rods 89%). Shared aging signatures included mitochondrial dysfunction and inflammatory activation. Cell specific vulnerabilities emerged: mitochondria-centric stress in rods/bipolar cells, proteostasis-retinoid metabolism in cones, and structural-RNA maintenance in horizontal cells. ConclusionsThis study provides the first machine learning derived, cell-type specific aging signatures for human retina in a Chinese cohort, revealing both conserved molecular hallmarks and distinctive cellular vulnerabilities that inform targeted therapeutic strategies for retinal aging.

bioinformatics↗

bioRxiv2025

Deciphering of single-cell chromatin accessibility and transcriptome reveals the discrepancy for ex vivo human erythropoiesis

BackgroundErythroid cells can be generated from hematopoietic stem and progenitor cells (HSPC) derived from various sources; however, few studies decode ex vivo human erythropoiesis for the discrepancies at single-cell multi-omics resolution and uncover the underlying restraints for erythrocyte regeneration. ResultsWe deciphered ex vivo human erythropoiesis at two differentiation states from three sources at single-cell chromatin accessibility and transcriptomes level. We identified detrimental myeloid differentiation tendencies during early differentiation. These tendencies were linked to low glutamine activity in cord blood- and iPSC-derived erythropoiesis. The erythroid progenitor differentiation is restricted by cell cycle and hypoxia signaling deficiencies, which are pronounced in the iPSC-derived erythropoiesis. We delineated the erythroid differentiation trajectory of various ex vivo erythropoiesis systems, and revealed a distinct roadmap from HSC to orthochromatic erythroblast with unprecedented resolution. The integrative analysis of single-cell chromatin accessibility and transcriptome across developmental stages uncovered a dynamic coordination, and highlighted the pivotal role of chromatin accessibility and associated enhancers in regulating ex vivo erythropoiesis. Cell-cell communications in the ex vivo erythropoiesis system were not as well established as those in the BM, suggesting that modulating cell-cell communication signals in distinct ex vivo erythropoiesis system may facilitate erythrocyte regeneration. ConclusionsThis study comprehensively characterized the discrepancies and constraints in ex vivo human erythropoiesis at single-cell multi-omics resolution, offering novel strategies to overcome these constraints. These insights are critical for advancing functional erythrocyte generation and have significant implications for clinical applications.

developmental biology↗

bioRxiv2025

Highly sensitive chemiluminescence imaging of misfolded proteins in neurodegenerative models

Visualizing misfolded proteins would greatly facilitate early diagnosis, etiology elucidation, and therapy monitoring of neurodegeneration. Although several probes have been reported, simple and versatile detection in vivo is still challenging. We demonstrated that both generic and precise detection of misfolded proteins could be achieved with a chemiluminescence probe, ADLumin-1. For the generic aspect, ADLumin-1 was highly sensitive to various misfolded proteins, showing up to 127.73-fold higher signal-to-noise ratio than Thioflavin T. ADLumin-1 could also non-invasively visualize misfolded proteins in mouse models of Parkinsons disease, Alzheimers disease and amyotrophic lateral sclerosis. Furthermore, ADLumin-1 displayed precise detection value for -synuclein by combining with PMCA in vitro and bioorthogonal ChRET imaging technology in vivo. ADLumin-1 can selectively detect -synuclein in CSF at the femtomolar level and enables in situ monitoring of misfolded -synuclein in vivo. Combining generality and precision, our findings could be widely applied in preclinical and clinical studies of neurodegenerative diseases. TeaserA highly sensitive chemiluminescence probe enables both generic and precise detection of various misfolded proteins

biochemistry↗

bioRxiv2025

Single-cell transcriptomic atlas of human retina from Chinese donors reveals population-specific cellular diversity

The human retina exhibits complex cellular heterogeneity which is critical for visual function, yet comprehensive ethnic-specific references are scarce in ophthalmic transcriptomics. The lack of single-cell RNA sequencing (scRNA-seq) data from Asian populations particularly Chinese donors imposes significant limitations in understanding population-specific retinal biology. We constructed the first comprehensive single-cell transcriptomic atlas of the human retina from Chinese donors, generated through high-throughput scRNA-seq of [~]290,000 viable cells obtained from 18 fresh retinal specimens (living donor and post-mortem specimens). Our multi-level analysis identified 10 distinct retinal cell types, encompassing all major neuronal lineages, Muller glia, astrocytes, microglia. Detailed subcluster analyses further revealed extensive heterogeneity, identifying distinct subtypes within several cell populations such as 7 amacrine cell subtypes and 14 bipolar cell subtypes. Concurrently, through systematic analysis of delineated subtype-specific molecular programs, we mapped their associated biological signaling pathways, functions, and mechanistic processes. This analysis explained the critical involvement of these subpopulations in core biological processes including synaptic organization, neurotransmission, and phototransduction cascades, potentially governing retinal homeostatic regulation and disease mechanisms. Single-cell transcriptomic atlas of the human retina from Chinese donors describes a comprehensive cellular landscape, encompassing major cell types and subtypes including neuronal, glial, and immune populations. This ethnic-specific atlas provided an important reference for understanding retinal development, cellular interaction and disease pathogenesis in Chinese populations, addressing a longstanding gap in ophthalmic transcriptomic resources.

genomics↗

bioRxiv2025

Optogenetic restoration of neuron subtype-specific cortical activity ameliorates motor deficits in Huntington's Disease mice

Huntingtons disease (HD) is a devastating movement disorder without a current cure. Although the monogenic basis of HD is well-defined, the complex downstream effects that underlie behavioral symptoms are poorly understood. These effects include cortical dysfunctions, yet the role of specific cortical neuronal subtypes in HD symptoms remain largely unexplored. Here, we used longitudinal in vivo two-photon calcium imaging to examine the activity of two cortical inhibitory neuron (IN) subtypes and excitatory corticostriatal projection neurons (CSPNs) in the motor cortex of R6/2 HD mouse model throughout disease progression. We found that motor deficits in R6/2 mice were accompanied by neuron type-specific abnormalities in movement-related activity, including hypoactivity of vasoactive intestinal peptide (VIP)-INs and CSPNs. Optogenetic activation of VIP-INs in R6/2 mice restored healthy levels of activity in VIP-INs and their downstream CSPNs and ameliorated motor deficits in R6/2 mice. Our findings highlight cortical INs as a potential therapeutic target for HD and possibly other neurological diseases.

neuroscience↗

bioRxiv2025

Single-Cell Transcriptomics Reveals Dynamic Microglial States and Neural-Immune Networks in Human Diabetic Retinopathy

Diabetic retinopathy (DR) is a major cause of vision loss worldwide. Here, we conduct single-cell RNA sequencing of twenty human retina samples (from living and post-mortem donors) across non-diabetic, diabetic, and DR states to create a comprehensive transcriptomic atlas. We identify two stable microglial populations--homeostatic and inflammatory--that exist along a functional continuum, plus a neutrophil cluster within C1QA+ myeloid cells with dynamic transitions occurring throughout disease progression. Module-level analysis reveals divergent transcriptional trajectories: homeostatic microglia maintain energetic programs while selectively upregulating stress elements, whereas inflammatory microglia layer additional pro-inflammatory programs onto preserved biosynthetic foundations. Eleven co-expression modules organize into two major axes: an inflammatory-stress axis, and a regulatory/metabolic-motility axis, with a stable translation module persisting across disease stages. Cell communication analysis further highlights sophisticated neural-immune interactions, particularly between photoreceptors and microglia. Our findings provide insights into the complex cellular dynamics of DR progression and suggest potential therapeutic targets for early intervention.

bioinformatics↗

bioRxiv2025

Human milk oligosaccharide metabolism by Clostridium species suppresses inflammation and pathogen growth

Gut microbiome development is strongly impacted by breastmilk and human milk oligosaccharides (HMOs), with short- and long-term health implications. HMO metabolism is best characterised within bifidobacteria, but the full range of other HMO-utilising species remains unknown. This study examined HMO-utilising bacteria that colonise preterm infants (born <32 weeks gestation), their role in microbiome modulation, and their effects on intestinal barrier function. We found that infant-derived Clostridium perfringens, and three other Clostridium species, metabolise HMOs, producing short chain fatty acids, tryptophan catabolites, and other metabolites known to improve host health. C. perfringens inhibited pathobiont growth and supressed inflammation in preterm-derived intestinal organoids. These findings suggest a previously unrecognised role for C. perfringens, specifically those lacking the perfringolysin O gene, in promoting healthy gut development during infancy.

microbiology↗

bioRxiv2024

Osthole Suppresses Prostate Cancer Progression by Modulating PRLR and the JAK2/STAT3 Signaling Axis

Prostate cancer is a common malignancy in men with limited effective treatment options, highlighting the urgent need for novel therapeutic approaches. Osthole, a natural coumarin compound with antitumor properties, has demonstrated potential in targeting various cancers. This study investigates the therapeutic effects of Osthole in prostate cancer by focusing on its ability to target the Prolactin Receptor (PRLR) and modulate the JAK2/STAT3 signaling pathway. Using a combination of network pharmacology, in vitro assays, and in vivo experiments, we first employed network pharmacology to predict Ostholes potential targets, identifying 68 targets shared with prostate cancer, including AKT1, TNF, IL6, STAT3, and CTNNB1. We then confirmed these targets and assessed the effects of Osthole on cell proliferation, migration, and apoptosis using the Cell Counting Kit-8 (CCK-8) and transwell invasion assays. To further elucidate the molecular interactions and protein expression levels, we employed molecular docking and western blot analysis. Our findings revealed that Osthole significantly inhibited prostate cancer cell proliferation and migration in a dose-dependent manner and reduced tumor volume in in vivo assays. Western blot analysis also indicated that Osthole downregulated PRLR expression and decreased the phosphorylation of JAK2 and STAT3, suggesting the inhibition of the JAK2/STAT3 pathway. These results collectively highlight the therapeutic potential of Osthole in targeting prostate cancer cells through PRLR and modulating the JAK2/STAT3 pathway, warranting further clinical exploration.

cancer biology↗

bioRxiv2024

Multimodal single-cell analyses reveal molecular markers of neuronal senescence in human drug-resistant epilepsy

The histopathological neurons in the brain tissue of drug-resistant epilepsy exhibit aberrant cytoarchitecture and imbalanced synaptic circuit function. However, the gene expression changes of these neurons remain unknown, making it difficult for the diagnosis or to dissect the mechanism of drug-resistant epilepsy. By integrating whole-cell patch clamp recording and single-cell RNA sequencing approaches, we identified a transcriptionally distinct subset of cortical pyramidal neurons. These neurons highly expressed genes CDKN1A (P21), CCL2 and NFKBIA that associated with mTOR pathway, inflammatory response and cellular senescence. We confirmed the expression of senescent marker genes in a subpopulation of cortical pyramidal neurons with enlarged soma size in the brain tissue of drug-resistant epilepsy. We further revealed the expression of senescent cell markers P21, P53, COX2, {gamma}-H2AX, {beta}-Gal and reduction of nuclear integrity marker Lamin B1 in histopathological neurons in the brain tissue of drug-resistant epilepsy patients with different pathologies, but not in control brain tissue with no history of epilepsy. Additionally, chronic, but not acute, epileptic seizures induced senescent markers expression in cortical neurons in mouse models of drug-resistant epilepsy. These results provide important molecular markers for histopathological neurons and new insights into the pathophysiological mechanisms of drug-resistant epilepsy.

neuroscience↗