Biology subjects

Qiu, H.

Publications and source records attributed to Qiu, H..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Hippocampal single-cell RNA Atlas of chronic methamphetamine abuse-induced cognitive decline in mice

BackgroundChronic methamphetamine abuse leads to cognitive decline, posing a significant threat to human health and contributing to loss of productivity. However, the intricate and multifaceted mechanisms underlying methamphetamine-induced neurotoxicity have impeded the development of effective therapeutic interventions. MethodsTo establish a mouse model of cognitive decline induced by chronic methamphetamine exposure, we employed a large sample size and conducted two behavioral tests (Y-maze and novel object recognition test) at 2 and 4 weeks post-exposure. Subsequently, single-cell RNA sequencing was utilized to delineate the mRNA expression profiles of individual cells within the hippocampus. Comprehensive bioinformatics analyses, including cell clustering and identification, differential gene expression analysis, cellular communication analysis, pseudotemporal trajectory analysis, and transcription factor regulation analysis, were performed to elucidate the cellular-level changes in mRNA profiles caused by chronic methamphetamine exposure. ResultsOur findings demonstrated impairments in working memory, spatial cognition, learning, and cognitive memory. After 4 weeks of behavioral testing, we identified diverse cell types in the hippocampi of METH- and saline-treated mice through scRNA-seq, including glial cells, stromal cells, vascular cells, and immune cells. We observed that methamphetamine exerts cell-specific effects on gene expression changes associated with neuroinflammation, blood-brain barrier disruption, neuronal support dysfunction, and immune dysregulation. Furthermore, cross-talk analysis revealed extensive alterations in cellular communication patterns and signal changes within the hippocampal microenvironment induced by methamphetamine exposure. Pseudotime analysis predicted hippocampal neurogenesis disorders and identified key regulatory genes implicated in chronic methamphetamine abuse. Transcription factor analysis uncovered regulators and pathways linked to astrocyte-mediated neuroinflammation, endothelial junction integrity, microglial synaptic remodeling, and oligodendrocyte-supported neuronal cell bodies and axons. Additionally, it highlighted the role of neural precursor cells in various forms of neurodegeneration. ConclusionsThis study establishes a robust mouse model of cognitive impairment induced by chronic methamphetamine exposure. It provides valuable biological insights, characterizes the single-cell atlas of the hippocampus, and offers novel directions for investigating neurological damage associated with chronic methamphetamine-induced cognitive decline.

pharmacology and toxicology↗

bioRxiv2025

Biodegradable Poly(β-amino amide)s Enable Efficient RNA Delivery and Spleen Targeting

RNA therapeutics are emerging as transformative modalities in clinical applications and have become a key area in life science research. While lipid nanoparticles (LNPs) have become the leading platform for RNA delivery in preventive and therapeutic products, they still face significant challenges in achieving efficient extrahepatic delivery and maintaining long term shelf stability. Here we report the development of a series of biodegradable poly({beta}-amino amide) (PBAA) polymers, detailing their design, synthesis, and performance as gene delivery vehicles both in vitro and in vivo. These cationic polymers, featuring interspersed trialkylamine motifs, provide a readily tunable functional handle and facilitate complexation with gene cargo. The redox-sensitive disulfide motifs introduce a redox-responsive decomposition pathway for the polymer backbone, triggering cargo release during intracellular delivery. The results herein demonstrate that these polymers offer remarkable efficiency in encapsulating and delivering translation-competent cargos, including mRNA and CRISPR-Cas based gene editing tools. Notably, a dodecyl modified PBAA transporter has achieved over 97% gene editing efficiency in vitro, and over 97% spleen-targeting selectivity in a murine model. Additionally, it produces stable nanoparticles that maintain their physicochemical properties at 4 {degrees}C for up to two weeks without addition of excipients such as PEG, offering a cost-effective solution for RNA therapeutics supply chains. As the transformative impact of RNA and other nucleic acids continues to build within the pharmaceutical industry and beyond, it is imperative that the supporting technologies evolve in stride to maximize said impact. The tunable and biodegradable PBAA polymer designs presented herein are illustrative examples of how high-level functional performance can be acheived in conjunction with the critical targeting, formulation, and operational simplicity needed of state-of-the-art transfection and delivery technologies.

biochemistry↗

bioRxiv2025

Single Cell RNA-sequencing of BCG naive and recurrent non-muscle invasive bladder cancer reveals a CD6/ALCAM mediated immune-suppressive pathway

Non-muscle invasive bladder cancer (NMIBC) represents 70-80% patients with newly diagnosed bladder cancer, and Bacillus Calmette-Guerin (BCG) remains a cornerstone treatment for intermediate-and high-risk NMIBC to prevent disease recurrence and progression. However, many patients experience recurrence after induction BCG, posing significant challenges in the management of the disease. We conducted single cell RNA sequencing on freshly collected NMIBC samples, distinguishing between those naive to BCG treatment and those that recurred post-BCG treatment. We observed a clear activation of inflammatory pathways across cell types during recurrence, but these were not associated with canonical immune checkpoint or T cell exhaustion phenotypes. Analysis of cell-to-cell communication revealed enhanced interactions between T cells and urothelial cells in BCG-recurrence, predominantly modulated by CD6 and ALCAM. Furthermore, we found CD6hi T cells to be immunosuppressed and enriched in recurrent samples, suggesting a potential role for CD6 as an immune evasion signal in NMIBC. SIGNIFICANCEThese findings uncover a novel mechanism responsible for bladder cancer recurrence after BCG treatment. Enhanced T cell-urothelial cell communication in recurrent tumors mediated by CD6 and ALCAM leads to an immunosuppressed state. Thus, CD6 may have potential as a therapeutic target to augment BCG response in non-muscle invasive bladder cancer.

cancer biology↗

bioRxiv2025

Spatiotemporal dynamics of Hemerocallis citrina habitat suitability in China under climate change scenarios

ObjectiveHemerocailis citrina, a perennial herb belonging to the Hemerocailis genus in the Apophaceae family, has been utilized as both food and medicine for thousands of years. Additionally, it is considered a traditional dominant cash crop in China. Therefore, analyzing the spatio-temporal distribution characteristics of climate sustability of H. citrina in current and future climatic conditions is crucial for its stable industry development in China. MethodENMTools was used to screen the data of 287 distribution points of H. citrina. 37 environmental variables affecting the distribution of H. citrina were selected, the MaxEnt model and Geographic Information System (ArcGIS) were used to simulate the potential distribution patterns of H. citrina in 5 periods (current, future 2040 s, 2060 s, 2080 s, and 2100 s), and the dominant climate factors affecting the distribution of H. citrina were analyzed through contribution rate, displacement importance value, and jackknife test. Results(1) The average AUC value of 10 cross-validated fits under the ROC curve for the training dataset was 0.933, and the average AUC value of 10 cross-validated fits for the test dataset was 0.901, this indicates that the simulation stability and reliability are highest when incorporating all three types of environmental variables. (2) The main environmental factors identified affecting their distribution include the lowest temperature of the coldest month, average temperature of the coldest quarter, slope, elevation, and carbonate or lime content, as determined by the contribution rate of cutting method, displacement contribution rate, and single factor response curve. (3) The model prediction showed that H. citrina was widely distributed in 34 provincial regions of China under the current climate conditions, with a suitable area of 646.81 km2. (4) Under the future emission scenarios, the suitable habitat for H. citrina is projected to shrink and not shift to higher latitudes, indicating a negative impact of climate warming on its distribution, which will reduce the suitable habitat and narrow the ecological niche. Temperature and precipitation have been identified as important factors affecting the distribution of H. citrina. Although H. citrinas suitable habitat is expected to decrease to varying degrees in the future, the distribution area is relatively stable and there will still be a broad natural habitat in the future. ConclusionThe species distribution model was used to predict the future temporal and spatial distribution of climate-suitable areas for H. citrina in China. Based on the models simulation results, it is recommended that north and southeast China become the main planting areas for H. citrina in the future, with Sichuan and other regions as key development areas. Additionally, other regions should prioritize developing suitable cash crops based on local conditions.

bioinformatics↗

bioRxiv2024

Graph Network-Based Analysis of Disease-Gene-Drug Associations: Zero-Shot Disease-Drug Prediction and Analysis Strategies

Existing drug repurposing methods have key limitations, primarily stemming from their reliance on known direct associations between diseases and drugs for supervised learning, as well as the need for large amounts of prior disease or drug information or feature data. In practice, many disease-drug connections remain unknown, and prior information is often complex and difficult to acquire and organize, limiting the applicability of these models. Furthermore, these models generally lack interpretability, making it difficult for experts to assess the reliability of predictions based solely on standard metrics, which raises doubts about the trustworthiness of their results. To address these challenges, we propose ZS-GNT, an innovative new workflow for zero-shot drug repurposing that leverages a novel and ingenious graph data meta-path linking scheme, which does not require any known disease-drug associations or their prior features. This approach is implemented using the Graph Neural Transformer (GNT) algorithm. The method infers disease-drug relationships indirectly through gene action, utilizing disease-gene associations and gene-drug interactions. It also generates a top drug-top gene linkage map, providing clinicians with a visual tool to assess the plausibility of suggested drugs before advancing to clinical trials. Experimental results show that, under the same linking scheme, the GNT algorithm achieved interaction link prediction accuracies of 95.86%, 99.28%, and 99.54% for three diseases, surpassing four other baseline methods. In a test involving a random selection of 100 diseases for drug discovery, among the top 5 recommended drugs from the candidates identified by ZS-GNT from a pool of 33,251 total drugs, the validation rate reached 47.05%, demonstrating the models effectiveness in drug discovery.

bioinformatics↗

bioRxiv2024

Same-Slide Spatial Multi-Omics Integration Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment

The advent of spatial transcriptomics and spatial proteomics have enabled profound insights into tissue organization to provide systems-level understanding of diseases. Both technologies currently remain largely independent, and emerging same slide spatial multi-omics approaches are generally limited in plex, spatial resolution, and analytical approaches. We introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a streamlined and resource-effective approach compatible with various spatial platforms. This iterative approach first entails single-cell spatial proteomics and rapid analysis to guide subsequent spatial transcriptomics capture on the same slide without loss in RNA signal. To enable multi-modal insights not possible with current approaches, we introduce k-bandlimited Spectral Graph Cross-Correlation (SGCC) for integrative spatial multi-omics analysis. Application of IN-DEPTH and SGCC on lymphoid tissues demonstrated precise single-cell phenotyping and cell-type specific transcriptome capture, and accurately resolved the local and global transcriptome changes associated with the cellular organization of germinal centers. We then implemented IN-DEPTH and SGCC to dissect the tumor microenvironment (TME) of Epstein-Barr Virus (EBV)-positive and EBV-negative diffuse large B-cell lymphoma (DLBCL). Our results identified a key tumor-macrophage-CD4 T-cell immunomodulatory axis differently regulated between EBV-positive and EBV-negative DLBCL, and its central role in coordinating immune dysfunction and suppression. IN-DEPTH enables scalable, resource-efficient, and comprehensive spatial multi-omics dissection of tissues to advance clinically relevant discoveries.

systems biology↗

bioRxiv2024

Deep Learning Unravels Potential Antibiotic-Resistance Drugs against Klebsiella Pneumoniae

BackgroundKlebsiella pneumoniae (KP) is a critical pathogen recognized by the World Health Organization due to its high mortality rates and resistance to antibiotics. Developing effective treatments for KP is essential to mitigate the growing threat of multidrug-resistant infections. This study leverages deep learning techniques to identify and validate potential drug candidates against KP, accelerating the drug discovery process and reducing associated risks. ResultsA comprehensive dataset was constructed, comprising 3,475 drugs selected from the DrugBank database, filtered for their potential efficacy and safety. Using convolutional neural networks (CNNs) for drug-drug interaction analysis, a prediction accuracy of 72% was achieved. Evolutionary Scale Modeling (ESM) was employed to calculate molecular similarities between drugs and KP strains, identifying five promising candidates with structural similarities exceeding 85% to known KP targets. A drug repurposing knowledge graph (DRKG) further validated these findings, with the PairRE model outperforming other approaches in ranking potential drug candidates. ConclusionsThis study presents a scalable and efficient framework for identifying and validating drug candidates to combat antibiotic-resistant KP. By integrating deep learning models, molecular similarity analysis, and knowledge graph techniques, this approach shortens the drug discovery timeline from years to months. The identified candidates hold significant promise for preclinical and clinical testing, offering a pathway to effective therapies against KP and other multidrug-resistant pathogens.

bioinformatics↗

bioRxiv2024

Deep Learning with Evolutionary Scale Modeling to discover Antibiotic-Resistance Drug against Klebsiella Pneumoniae

The development of effective antibiotics is crucial in combating antibiotic-resistant pathogens such as Klebsiella pneumoniae (KP). This study utilizes deep learning, specifically Evolutionary Scale Modeling (ESM), to identify potential drug candidates. By analyzing molecular similarities between known drugs and KP strains, we aim to accelerate the discovery of new treatments and reduce the risks associated with clinical trials. This approach may shed new lights in the field of drug repositioning.

bioinformatics↗

bioRxiv2024

Decoding Liver Cancer Prognosis: From Multi-omics Subtypes, Prognostic Models to Single Cell Validation

PurposeHepatocellular carcinoma (HCC) is a highly aggressive tumor characterized by significant heterogeneity and invasiveness, leading to a lack of precise individualized treatment strategies and poor patient outcomes. This necessitates the urgent development of accurate patient stratification methods and targeted therapies based on distinct tumor characteristics. Experimental DesignBy integrating gene expression data from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO), we identified subtypes through a multi-omics consensus clustering approach amalgamated from 10 clustering techniques. Subsequently, we developed a prognostic model, employing machine learning algorithms, based on subtype classification features. Finally, by analyzing single cell sequencing data, we investigated the mechanisms driving prognostic variations among distinct subtypes. ResultsFirst, we developed a novel consensus clustering method that categorizes liver cancer patients into two subtypes, CS1 and CS2. Second, we constructed a prognostic prediction model, which demonstrated superior predictive accuracy compared to several models published in the past five years. Finally, we observed differences between CS1 and CS2 in various metabolic pathways, biological processes, and signaling pathways, such as fatty acid metabolism, hypoxia levels, PI3K-AKT and MIF signaling pathway.

bioinformatics↗

bioRxiv2024

The mechanism and consequences of amyloid-β modulating thiamine pyrophosphokinase-1 expression in microglia

Ample studies attribute cognitive decline in Alzheimers disease to amyloid-{beta} deposition 1-6. However, brain amyloid-{beta} accumulation that saturates years before the manifestation of clinical symptoms is dissociated with cognitive decline of the disease 7. It is unknown how these two processes are mechanistically linked. In this and our accompanied study, we report that thiamine pyrophosphokinase-1 (TPK) deficiency plays essential roles in both processes via distinct mechanisms. Here we describe that diminished microglia Tpk controls the propagation of amyloid-{beta} plaques. In APP/PS1 transgenic mice, microglia showed elevated Tpk expression at 2-month-old, but reduction in a plaque-centric manner at 8-month-old. Interestingly, lipopolysaccharide, but not amyloid-{beta}, induceed Tpk reduction in cultured microglia. Tpk reduction led to microglia dysfunction, showing volatile motility but reduced phagocytosis and weak response to focal tissue injury, with accumulation of intracellular lipid droplets and abnormal mitochrondria. In Alzheimers disease mice, microglia-specific knockout of Tpk caused diminished plaque coverage, exacerbated plaque burden and synaptic loss. However, increased plaques were not accompanied by the development of neurofibrillary tangles or brain atrophy, in contrast to the phenotype described in our accompanied paper with neuronal Tpk deletion. In conclusion, plaque-induced inflammation reduces Tpk in microglia, selectively exacerbating the spread of amyloid pathology.

neuroscience↗

bioRxiv2024

Pandemic Vibrio parahaemolyticus wave succession by gene loss

While spontaneous mutation and gene acquisition are well-established drivers of pathogen adaptation, the role of gene loss remains underexplored. Here, we investigated the emergence and diversification of the pandemic clone (PC) of Vibrio parahaemolyticus through large-scale phylogenomic analysis of 8,684 global isolates. The PC rapidly acquired multiple marker genes and genomic islands, subsequently diverging into successive sublineages mediating independent waves of cross-country transmission, as also observed in V. cholerae. Wave succession in the last two decades was driven by loss of putrescine utilization (Puu) genes, conferring phenotypic advantages for environmental adaptation (enhanced biofilm formation) and human transmission (increased cell adhesion/intestinal colonization, reduced virulence), consistent with the virulence trade-off hypothesis. We identified Puu-gene loss in multiple bacterial genera, with effects on biofilm and adhesion replicated in V. cholerae and Escherichia coli, suggesting convergent evolution and universal phenotypic effects. Our results highlight the indispensable role of gene loss in bacterial pathogen adaptation.

microbiology↗

bioRxiv2024

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

cancer biology↗

bioRxiv2023

EMDiffuse: a diffusion-based deep learning method augmenting ultrastructural imaging and volume electron microscopy

Electron microscopy (EM) revolutionized the way to visualize cellular ultrastructure. Volume EM (vEM) has further broadened its three-dimensional nanoscale imaging capacity. However, intrinsic trade-offs between imaging speed and quality of EM restrict the attainable imaging area and volume. Isotropic imaging with vEM for large biological volumes remains unachievable. Here we developed EMDiffuse, a suite of algorithms designed to enhance EM and vEM capabilities, leveraging the cutting-edge image generation diffusion model. EMDiffuse demonstrates outstanding denoising and super-resolution performance, generates realistic predictions without unwarranted smoothness, improves predictions resolution by [~]30%, and exhibits excellent transferability by taking only one pair of images to fine-tune. EMDiffuse also pioneers the isotropic vEM reconstruction task, generating isotropic volume similar to that obtained using advanced FIB-SEM even in the absence of isotropic training data. We demonstrated the robustness of EMDiffuse by generating isotropic volumes from six public datasets obtained from different vEM techniques and instruments. The generated isotropic volume enables accurate organelle reconstruction, making 3D nanoscale ultrastructure analysis faster and more accessible and extending such capability to larger volumes. More importantly, EMDiffuse features self-assessment functionalities and guarantees reliable predictions for all tasks. We envision EMDiffuse to pave the way for more in-depth investigations into the intricate subcellular nanoscale structures within large areas and volumes of biological systems.

cell biology↗

bioRxiv2023

MAPS: Pathologist-level cell type annotation from tissue images through machine learning

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

cancer biology↗

bioRxiv2023

ElecFeX: A user-friendly toolkit for efficient feature extraction from single-cell electrophysiological recordings

MotivationProfiling neurons by their electrophysiological phenotype is essential for understanding their roles in information coding within and beyond the nervous systems. Technological development has unleashed our power to record neurons more than ever before, yet the booming size of the dataset poses new challenges for data analysis. Current software tools require users to have either significant programming knowledge or to devote great time and effort, which impedes their prevalence and adoption among experimentalists. To address this problem, here we present ElecFeX, a MATLAB-based graphical user interface designed for a more accessible and efficient analysis of single-cell electrophysiological recordings. ElecFeX has a simple and succinct graphical layout to enable effortless handling of large datasets. This tool includes a set of customizable methods for most common electrophysiological features, and these methods can process multiple files all at once in a reliable and reproducible manner. The output is assembled in a properly formatted file which is exportable for further analysis such as statistical comparison and clustering. By providing such a streamlined and user-friendly open-sourced interface, we hope ElecFeX can benefit broader users for their studies associated with neural activity. SummaryCharacterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Recent developments in electrode technologies have enabled the collection of hundreds of neural recordings; that necessitated the development of new toolkits capable of performing feature extraction efficiently. To address this urgent need for a powerful and accessible tool, we present ElecFeX, an open-source MATLAB-based toolbox that (1) has a succinct and intuitive graphical user interface, (2) provides generalized methods for wide-ranging electrophysiological features, (3) processes large-size dataset effortlessly, and (4) yields formatted output for further analysis such as neuronal characterization and classification. We implemented the toolbox on a diverse set of neural recordings and demonstrated its functionality, efficiency, and versatility in capturing features that can well-distinguish neuronal subgroups across brain regions and species. ElecFeX is thus presented as a powerful tool to significantly promote future studies on neuronal electrical activity.

neuroscience↗

bioRxiv2023

Use of Artificial Intelligence for Automated Detection and Surveillance of Red Imported Fire Ants Nests

The red imported fire ant, Solenopsis invicta Buren is a destructive invasive species that has spread around the world. Early detection of S. invicta nests is critical for effective monitoring and control in invaded regions. This study presents a novel surveillance system for S. invicta nests combining artificial intelligence and robotic dogs. The system was designed with intelligent recognition algorithms to accurately identify S. invicta nests. With a precision rate of 95%, the system yielded efficient detection of S. invicta nests with increased sensitivity and low missing and false discovery rates, which can aid or even replace humans in locating and delivering pesticides to fire ant nests in open fields to effective control of this invasive pest.

ecology↗

bioRxiv2023

A toxic ankyrin cysteine protease effector RipBH of brown rot triggered autophagy-associated cell death

Potato brown rot, caused by Ralstonia solanacearum, is one of the most destructive diseases of potatoes. The pathogen could hide in the tuber, leading to the rotting tubers. However, few mechanisms of pathogenesis in tubers caused by brown rot were reported. Here, we identified a highly virulent type III effector RipBH, which is not only required for the pathogenesis of potato brown rot but also displays strong cell toxicity in yeast and tobacco. We found RipBH is a novel structural cysteine protease with a large ankyrin repeat domain that contains 10 ankyrin repeats, we named it as an ankyrin cysteine protease. Biochemical analysis showed that all the ankyrin repeats are required for virulence, and the first five ankyrin repeats are indispensable for auto-cleavage site recognition. Further analysis showed that RipBH triggered autophagy-associated cell death. The ankyrin cysteine protease effector existed extensively in plant and animal pathogens suggesting the ankyrin cysteine protease effectors are functionally essential for pathogen pathogenesis. Our study enhances our understanding of this type of cysteine protease and illustrates the pathogenesis of cysteine protease in potato brown rot.

microbiology↗

bioRxiv2023

Calcium buffering tunes intrinsic excitability of spinal dorsal horn parvalbumin-expressing interneurons: A computational model

Parvalbumin-expressing interneurons (PVINs) play a crucial role within the dorsal horn of the spinal cord by preventing touch inputs from activating pain circuits. After nerve injury, their output is decreased via mechanisms that are not fully understood. In this study, we show that PVINs from nerve-injured mice change their firing pattern from tonic to adaptive. To examine the ionic mechanisms responsible for this decreased output, we employed a reparametrized Hodgkin-Huxley (HH) type model of PVINs, which predicted (1) the firing pattern transition is due to an increased contribution of small conductance calcium-activated potassium (SK) channels, enabled by (2) impairment in intracellular calcium buffering systems. Analyzing the dynamics of the HH-type model further demonstrated that a generalized Hopf bifurcation differentiates the two types of state transitions observed in the transient firing of PVINs. Importantly, this predicted mechanism holds true when we embed the PVINs model within the neuronal circuit model of the spinal dorsal horn. To experimentally validate this hypothesized mechanism, we used pharmacological modulators of SK channels and demonstrated that (1) tonic firing PVINs from naive mice become adaptive when exposed to an SK channel activator, and (2) adapting PVINs from nerve-injured mice return to tonic firing upon SK channel blockade. Our work provides important insights into the cellular mechanism underlying the decreased output of PVINs in the spinal dorsal horn after nerve injury and highlights potential pharmacological targets for new and effective treatment approaches to neuropathic pain. Significant StatementParvalbumin-expressing interneurons (PVINs) exert crucial inhibitory control over A {beta} fiber- mediated nociceptive pathways at the spinal dorsal horn. The loss of their inhibitory tone leads to neuropathic symptoms, like mechanical allodynia, via mechanisms that are not fully understood. This study identifies the reduced intrinsic excitability of PVINs as a potential cause for their decreased inhibitory output in nerve-injured condition. Combining computational and experimental approaches, we predict a calcium-dependent mechanism that modulates PVINs electrical activity following nerve injury: a depletion of cytosolic calcium buffer allows for the rapid accumulation of intracellular calcium through the active membranes, which in turn potentiates SK channels and impedes spike generation. Our results therefore pinpoint SK channels as interesting therapeutic targets for treating neuropathic symptoms.

neuroscience↗