Biology subjects

Zhang, B.

Publications and source records attributed to Zhang, B..

At least 199 records · Page 11Linked to original sources

bioRxiv2025

xOmicsShiny: an R shiny application for cross-omics data analysis and pathway mapping

SummaryWe developed xOmicsShiny, a feature-rich R Shiny-powered application that enables biologists to fully explore omics datasets across experiments and data types, with an emphasis on uncovering biological insights at the pathway level. The data merging feature ensures flexible exploration of cross-omics data, such as transcriptomics, proteomics, metabolomics, and lipidomics. The pathway mapping function covers a broad range of databases, including WikiPathways, Reactome, and KEGG pathways. In addition, xOmicsShiny offers several visualization options and analytical tasks for everyday omics data analysis, namely, PCA, Volcano plot, Venn Diagram, Heatmap, WGCNA, and advanced clustering analyses. The application employs customizable modules to perform various tasks, generating both interactive plots and publication-ready figures. This dynamic, modular design overcomes the issue of slow loading in R Shiny tools and allows it to be readily expanded by the research and developer community. Availability and implementationThe R Shiny application is publicly available at: http://xOmicsShiny.bxgenomics.com. Researchers can upload their own data to the server or use the pre-loaded demo dataset. The source code, under MIT license is provided at https://github.com/interactivereport/xOmicsShiny for local installation. A full tutorial of the application is available at https://interactivereport.github.io/xOmicsShiny/tutorial/docs/index.html. Contactyuhenry.sun@biogen.com or baohong.zhang@biogen.com Supplementary dataSupplementary data are available at bioRxiv online.

bioinformatics↗

bioRxiv2025

Unsupervised Detection of Rare Events in Liquid Biopsy Assays

The use of liquid biopsies in the detection, diagnosis and treatment monitoring of different types of cancers and other diseases often requires identifying and enumerating instances of analytes that are rare. Most current techniques that aim to computationally isolate these rare instances or events first learn the signature of the event, and then scan the appropriate biological assay for this signature. While such techniques have proven to be very useful, they are limited because they must first establish what signature to look for, and only then identify events that are consistent with this signature. In contrast to this, in this study, we present an automated approach that does not require the knowledge of the signature of the rare event. It works by breaking the assay into a sequence of components, learning the probability distribution of these components, and then isolating those that are rare. This is done with the help of deep generative algorithms in an unsupervised manner, meaning without a-priori knowledge of the rare event associated with an analyte. In this study, this approach is applied to immunofluorescence microscopy images of peripheral blood, where it is shown that it successfully isolates biologically relevant events in blood from normal donors spiked with cancer-related cells and in blood from patients with late-stage breast cancer.

bioinformatics↗

bioRxiv2025

ScaleSC: A superfast and scalable single cell RNA-seq data analysis pipeline powered by GPU.

The rise of large-scale single-cell RNA-seq data has introduced challenges in data processing due to its slow speed. Leveraging advancements in GPU computing ecosystems, such as CuPy, and building on Scanpy and rapids-singlecell package, we developed ScaleSC, a GPU-accelerated solution for single-cell data processing. ScaleSC delivers over a 20x speedup through GPU computing and significantly improves scalability, handling datasets of 10-40 million cells with over 1000 batches by overcoming the memory bottleneck on a single A100 card- far surpassing rapids-singlecells capacity of processing only 1 million cells without multi-GPU support. We also resolved discrepancies between GPU and CPU algorithm implementations to ensure consistency. In addition to core optimizations, we developed new advanced tools for marker gene identification, cluster merging, and more, with GPU-optimized implementations seamlessly integrated. Designed for ease of use, the ScaleSC package is compatible with Scanpy workflows, requiring minimal adaptation from users. The ScaleSC package (https://github.com/interactivereport/ScaleSC) promises significant benefits for the single-cell RNA-seq computational community.

bioinformatics↗

bioRxiv2025

Single-AAV CRISPR editing of skeletal muscle in non-human primates with NanoCas, an ultracompact nuclease

CRISPR gene editing is a transformative technology for addressing genetic diseases, but delivery constraints have largely limited its therapeutic applications to liver-targeted and ex vivo therapies. Here, we present the discovery and engineering of NanoCas, an ultracompact CRISPR nuclease capable of extending CRISPRs reach in vivo beyond liver targets. We experimentally screened 176 ultracompact CRISPR systems found in metagenomic data and applied protein engineering approaches to enhance the editing efficiency of NanoCas. The optimized NanoCas exhibits potent editing capabilities across various cell systems and tissues in vivo when administered via adeno-associated viral (AAV) vectors. This is accomplished despite NanoCas being approximately one-third the size of conventional CRISPR nucleases. In proof-of-concept experiments, we observed robust editing with our optimized NanoCas in mouse models targeting Pcsk9, a gene involved in cholesterol regulation, and targeting exon splice sites in dystrophin to address Duchenne muscular dystrophy (DMD) mutations. We further tested the efficacy of our NanoCas system in vivo in non-human primates (NHPs) resulting in editing levels above 30% in muscle tissues. The compact size of NanoCas, in combination with robust nuclease editing, opens the door for single-AAV editing of non-liver tissues in vivo, including the use of newer editing modalities such as reverse transcriptase (RT) editing, base editing, and epigenetic editing.

bioengineering↗

bioRxiv2025

Lipid nanoparticle delivery of siRNA to dorsal root ganglion neurons to treat pain

Sensory neurons within the dorsal root ganglion (DRG) are the primary trigger of pain, relaying activity about noxious stimuli from the periphery to the central nervous system; however, targeting DRG neurons for pain management has remained a clinical challenge. Here, we demonstrate the use of lipid nanoparticles (LNPs) for effective intrathecal delivery of small interfering RNA (siRNA) to DRG neurons, achieving potent silencing of the transient receptor potential vanilloid 1 (TRPV1) ion channel that is predominantly expressed in nociceptor sensory neurons. This leads to a reversible interruption of heat-, capsaicin-, and inflammation-induced nociceptive conduction, as observed by behavioral outputs. Our work provides a proof-of-concept for intrathecal siRNA therapy as a novel and selective analgesic modality.

neuroscience↗

bioRxiv2025

Lower spatial turnover of rare fungal taxa dominantly shaped by stochastic processes in grassland soils

The spatial pattern and community assembly of soil microbial taxa have notable meanings for biodiversity shaping and maintaining mechanisms. Rare fungal taxa may exhibit distinct patterns and assembly mechanisms compared to abundant taxa, but such information is limited, especially at large scales. Here, we investigated distance-decay patterns and underlying assembly mechanisms for abundant and rare fungal taxa in 129 soil samples collected across 4,000 km in Chinese Northern grasslands, based on high-throughput sequencing data. A total of 208 abundant OTUs (relative abundance > 0.1%, 2.73% of entire OTUs) and 5,779 rare OTUs (relative abundance < 0.01%, 75.85% of entire OTUs) were identified. Both abundant and rare fungal taxa showed significant distance-decay relationships (P < 0.001), but the turnover rate for rare taxa (0.0024 per 100 km) was nearly half that of abundant taxa (0.0054 per 100 km) based on the binary Bray-Curtis distance. The lower turnover of rare fungal taxa was likely due to their community assembly mechanism dominated by stochastic processes, which were less influenced by environmental gradients. In contrast, abundant taxa assembly was dominated by deterministic factors like soil variables and plant traits, which varied significantly along the geographic distance. Consistently, rare fungal taxa were also less sensitive to environmental changes, with a lower turnover rate by environmental distance (0.0027 vs. 0.0099) than abundant taxa. In summary, our findings revealed that rare fungal taxa, shaped mainly by stochastic processes, had lower spatial turnover compared to abundant taxa, dominated by deterministic processes, enhancing our understanding of rare microbial biogeography.

ecology↗

bioRxiv2025

Mechanisms of dexamethasone-induced bone toxicity in developing bone: a single-cell perspective

AbstractGlucocorticoids, such as dexamethasone, are essential for treating severe childhood conditions, including cancer, organ transplantation, and inflammatory disorders. However, their long-term use can impair bone development, posing risks to pediatric bone health, which is vital for lifelong skeletal integrity. A mechanistic insight on how glucocorticoids negatively impact bone could improve decision-making in patient care to improve the quality of life for pediatric cancer patients and survivors. In this study, we aimed to elucidate the molecular mechanisms underlying dexamethasone-induced bone toxicity in developing bones using single-cell transcriptomics. We treated skeletally immature C57BL/6JRj mice with dexamethasone for 28 days, and assessed the bone architecture with micro-computed tomography, and characterized bone and bone marrow cells from the femurs using single-cell RNA sequencing. Our findings revealed a marked reduction in osteoblast and chondrocyte cell populations and impaired function of pre-osteoblasts. Additionally, dexamethasone adversely affected B cell subsets, significantly depleting early B cell progenitors while allowing some further developed immature B cells to persist. These cellular changes were accompanied by reduced longitudinal bone growth, compromised bone architecture, and increased bone fragility at the highest doses of dexamethasone. Interestingly, unlike observations in adults, dexamethasone did not enhance osteoclast activity in our model. Overall, our study suggests that the adverse effects of dexamethasone on bone development are primarily due to its impact on osteoblastic, chondroblastic and B cell lineages, disrupting the critical signaling crosstalk between the cells necessary for bone development and hematopoiesis. Layman summaryGlucocorticoids, like dexamethasone, are vital for treating severe childhood illnesses but can harm bone development when used long-term. This study investigated how dexamethasone affects bone health in young mice. Using advanced techniques, we found that dexamethasone reduced key bone-building cells (osteoblasts and chondrocytes) and weakened their function. It also disrupted immune cell development in the bone marrow, especially early B cells. These changes led to weaker, more fragile bones without increasing bone breakdown, unlike in adults. The findings highlight the need to carefully balance treatment benefits and risks for children to protect their bone health and overall well-being.

cancer biology↗

bioRxiv2024

Gene Conversion Directed Successive Engineering of Modular Polyketide Synthases

Modular polyketide synthases (PKSs) can produce various secondary metabolites in a collinearity fashion. Although rational engineering of modular PKS can ultimately create a diverse array of novel compounds, de novo generation of defined structures usually results in the loss or remarkable decline of productivity due primarily to the incompatibility of different elements. Here, we present a modular PKS engineering strategy driven by an evolutionary event of gene conversion to accomplish successive engineering of the modular PKS in cinnamomycin biosynthetic gene cluster (cmm BGC). By simulating the gene conversion process, cmm BGC is consecutively reprogrammed to generate a novel macrolide with predicted structural features. Moreover, in contrast to previous notion, the intra-module KS domain is demonstrated to associate with the selectivity of extender units. Collectively, the coordination between evolutionary consequence and functional manipulation of assembly line may shed a new light on modular PKS engineering. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/630704v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@11bd249org.highwire.dtl.DTLVardef@18af1a9org.highwire.dtl.DTLVardef@ffa287org.highwire.dtl.DTLVardef@10756cf_HPS_FORMAT_FIGEXP M_FIG Gene conversion process, typically occurred in KS (purple squares) and AT (blue squares), plays an integral role of structural diversity of polyketides. In this study, an unusual gene conversion was observed in cmm BGC. Subsequently, a homologous BGC was obtained through genome mining by a gene conversion-associated KS domain. Under the direction of gene conversion, the modular PKS in cmm BGC was successively reprogrammed, resulting in de novo biosynthesis of a new-to-nature polyketide. C_FIG

bioengineering↗

bioRxiv2024

Kun-peng: an ultra-memory-efficient, fast, and accurate pan-domain taxonomic classifier for all

Comprehensive metagenomic sequence classification of diverse environmental samples faces significant computing memory challenges due to exponentially expanding genome databases. Here, we present Kun-peng, featuring a unique ordered 4GB block database design for ultra-efficient resource management, faster processing, and higher accuracy. When benchmarked on mock communities (Amos HiLo, Mixed, and NIST) against Kraken2, Centrifuge, and Sylph. Kun-peng matched Sylph, achieving the highest precision and lowest false-positive rates while demonstrating superior time and memory efficiency among all tested tools. Furthermore, Kun-pengs efficient database architecture enables the practical utilization of large-scale reference databases that were previously computationally prohibitive. In comprehensive testing across 586 air, water, soil, and human metagenomic samples using an expansive pan-domain database (204,477 genomes, 4.3TB), Kun-peng classified 69.78-94.29% of reads, achieving 38-43% higher classification rates than Kraken2 with the standard database. Unexpectedly, Sylph failed to classify any reads in air samples and left > 99.85% of reads unclassified in water and soil samples. In terms of computational efficiency, Kun-peng processed each sample in 0.2[~]11.2 minutes using only 4.0[~]35.4GB peak memory. Remarkably, these processing times were comparable to Kraken2 using the standard database (81GB, 5% of the pan-domain database). Memory-wise, Kun-peng required only 35.4GB peak memory with the pan-domain database, representing a 473-fold reduction compared to Kraken2. When compared to Sylph, Kun-peng processes samples up to 46.3 times faster while using up to 20.6 times less memory. Overall, Kun-peng offers an ultra-memory-efficient, fast, and accurate solution for pan-domain metagenomic classifications.

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

A Bi-Specific T Cell-Engaging Antibody Shows Potent Activity, Specificity, and Tumor Microenvironment Remodeling in Experimental Syngeneic and Genetically Engineered Models of GBM

BackgroundBispecific T cell-engagers (BTEs) are engineered antibodies that redirect T cells to target antigen-expressing tumors. BTEs targeting tumor-specific antigens such as interleukin 13 receptor alpha 2 (IL13R2) and EGFRvIII have been developed for glioblastoma (GBM). However, there is limited mechanistic understanding of the action of BTE since prior studies were mostly conducted in immunocompromised animal models. To close this gap, the function of BTEs was assessed in the immunosuppressive glioma microenvironment (TME) of orthotopic and genetically engineered mouse models (GEMM) with intact immune systems. MethodsA BTE that bridges CD3 epsilon on murine T cells to IL13R2-positive GBM cells was developed and the therapeutic mechanism investigated in immunocompetent mouse models of GBM. Multi-color flow cytometry, single-cell RNA sequencing (scRNA-Seq), multiplex immunofluorescence, and multiparametric magnetic resonance imaging (MRI) across multiple pre-clinical models of GBM were used to evaluate the mechanism and action and response. ResultsBTE-mediated interactions between murine T cells and GBM cells triggered T cell activation and antigen-dependent killing of GBM cells. BTE treatment significantly extended the survival of mice bearing IL13R2-expressing orthotopic glioma and de novo forming GBM in the GEMM. Quantified parametric MR imaging validated the survival data showing a reduction in glioma volume and decreased glioma viability. Flow cytometric and scRNA-seq analyses of the TME revealed robust increases in activated and memory T cells and decreases in immunosuppressive myeloid cells in the brains of mice following BTE treatment. ConclusionsOur data demonstrate that the survival benefits of BTEs in preclinical models of glioma are due to the ability to engage the host immune system in direct killing, induction of immunological memory, and modulation of the TME. These findings provide a deeper insight into the mechanism of BTE actions in GBM. WHAT IS ALREADY KNOWN ABOUT THIS TOPICBi-specific T cell engaging antibodies (BTEs) targeting IL13R2 and EGFRvIII have been developed and shown to activate T cells that mediate killing of glioma cells in vitro and in in vivo. WHAT THIS STUDY ADDSBy using immune competent preclinical models, this study reveals that BTEs trigger in situ tumor immune memory within the central nervous system. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICYInsights into the mechanism of action of BTEs inform response biomarkers that should be considered for inclusion in window-of-opportunity clinical trial assessments and for the rational selection of future combinatorial strategies.

immunology↗

bioRxiv2024

The Porphyromonas gingivalis lipid A 1-phosphatase LpxE has unique features and requires a functional type IX secretion system for its activity

Porphyromonas gingivalis is a Gram-negative bacterium that plays a central role in the development of periodontal disease. It uses a type IX secretion system (T9SS) to export a range of virulence factors to the bacterial surface where they are attached to A-LPS, one of the two forms of lipopolysaccharide (LPS) produced in P. gingivalis, and then packaged into outer membrane vesicles (OMVs). We previously showed that 1-P dephosphorylation of the lipid A component of LPS is regulated by the T9SS outer membrane protein (OMP) PorV, and this is linked to membrane destabilisation and OMV blebbing/formation. In this study we aimed to extend this and investigate the role of other T9SS OMPs in OMV biogenesis. We examined gingipain activity, gingipain secretion, A-LPS production, OMV morphology, and lipid A structure in P. gingivalis W50 and T9SS OMP mutant strains, and our results support an essential role for these proteins in type IX secretion. In addition, we produced a lipid A 1-phosphatase ({Delta}lpxE) mutant and show that all T9SS OMPs are required for LpxE activity and correct vesicle formation. LpxE has a unique C-terminal extension, and we propose that a cargo protein exported by the T9SS can directly/indirectly interact with this and regulate LpxE activity. This study provides insight into a new mechanism that links type IX cargo sorting with OMV blebbing, which may also be present in other Bacteroidota that colonise the gut and oral cavity.

microbiology↗

bioRxiv2024

Parallel evolution of LRP2 gene mediating telescope-eye and celestial-eye in goldfish

After the intensive artificial selection, the development of celestial-eye in goldfish involves the protuberating and turning upwards of eyeballs, and also degeneration of the retinal. Thus, the celestial-eye goldfish provides an excellent model for both evolutionary and human ocular disease studies. Here, two mapping populations with segregating eye phenotypes in the offspring goldfish were constructed. Though whole genome sequencing using individual samples from the parents and pooled samples from the offspring, and RNA-seq for eyeball samples from pure goldfish lines, a premature stop codon in Exon 38 of LRP2 gene was identified as the top candidate mutation that is responsible for celestial-eye in goldfish. Fatty acid metabolisms and epidermal cells, especially keratocytes related functions were inhibited in the eyeballs of celestial-eye, while inflammatory reactions and extracellular matrix secreting were stimulated. These suggest the dysfunction of cornea in the celestial-eye, and same for retinal, which could be the results of the truncated LRP2 protein. Besides, evidence was provided that not all the goldfish lines share the same causal mutation for celestial-eye, while the same gene LRP2 is in charge of the similar phenotypes (celestial-eye and telescope-eye) in goldfish but no shared mutation. Therefore, those mutations and the associated phenotypes exhibit parallel evolutions in molecular level under artificial selections. Overall, the candidate mutation for celestial-eye in goldfish was identified by this study, and further analyses provide insights into the developmental and evolutionary processes of morphological changes in the eyes of goldfish. Author SummaryAs the first domesticated ornamental fish, goldfish is now worldwide spread and has undergone intensively artificial selections for morphological variations. Among them, celestial-eye is one of the most unique traits for goldfish, which consists of enlarged and protuberant eyes while the eyeballs turning upwards and thus suitable for top viewing in pots. In this study, we have identified that a single nucleotide mutation resulting in disrupted function of LRP2 gene, is responsible for the celestial-eye phenotype. Together with our transcriptome analysis, the genetic and cellular mechanisms for celestial-eye in goldfish were reported for the first time, which provides fundamental knowledge for further studies about the development of eyes in fish and also for ophthalmic diseases in humans. In the aspect of evolution, our study and previous studies reported different truncated LRP2 proteins in charge of celestial-eye and telescope-eye, respectively, which are excellent materials to understand the mechanisms of parallel evolution in molecular level, i.e., independent artificial selections resulting in mutations of the same gene.

genetics↗

bioRxiv2024

Protein Language Model Identifies Disordered, Conserved Motifs Driving Phase Separation

Intrinsically disordered regions (IDRs) play a critical role in phase separation and are essential for the formation of membraneless organelles (MLOs). Mutations within IDRs can disrupt their multivalent interaction networks, altering phase behavior and contributing to various diseases. Therefore, examining the evolutionary constraints of IDRs provides valuable insights into the relationship between protein sequences and phase separation. In this study, we utilized the ESM2 protein language model to map the residue-level mutational tolerance landscapes landscape of IDRs. Our findings reveal that IDRs, particularly those actively participating in phase separation, contain conserved amino acids. This conservation is evident through mutational constraints predicted by ESM2 and supported by direct analyses of multiple sequence alignments. These conserved, disordered amino acids include residues traditionally identified as "stickers" as well as "spacers" and frequently form continuous sequence motifs. The strong conservation, combined with their potential role in phase separation, suggests that these motifs may act as functional units under evolutionary selection to support stable MLO formation. Our findings underscore the insights into phase separations molecular grammar made possible through evolutionary analysis enabled by protein language models.

biophysics↗

bioRxiv2024

Patient-Derived Xenografts of Triple-Negative Breast Cancer Enable Deconvolution and Prediction of Chemotherapy Responses

Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significanceIntegration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.

cancer biology↗

bioRxiv2024

The Landscape of Sex- and APOE Genotype-Specific Transcriptional Changes in Alzheimer's Disease at the Single Cell Level

Alzheimers disease (AD) is the most common form of dementia, with approximately two-thirds of AD patients are females. Basic and clinical research studies show evidence supporting sex-specific differences contributing to the complexity of AD. There is also strong evidence supporting sex-specific interaction between the primary genetic risk factor of AD, APOE4 and AD-associated neurodegenerative processes. Recent studies by us and others have identified sex and/or APOE4 specific differentially expressed genes in AD based on the bulk tissue RNA-sequencing data of postmortem human brain samples in AD. However, there lacks a comprehensive investigation of the interplay between sex and APOE genotypes at the single cell level. In the current study, we systematically explore sex and APOE genotype differences in single cell transcriptomics in AD. Our work provides a comprehensive overview of sex and APOE genotype-specific transcriptomic changes across 54 high-resolution cell types in AD and highlights individual genes and brain cell types that show significant differences between sexes and APOE genotypes. This study lays the groundwork for exploring the complex molecular mechanisms of AD and will inform the development of effective sex- and APOE-stratified interventions for AD.

genomics↗

bioRxiv2024

Molecular Insights into the Regulation of GNPTAB by TMEM251

In vertebrates, newly synthesized lysosomal enzymes traffick to lysosomes through the mannose-6-phosphate (M6P) pathway. The Golgi membrane protein TMEM251 was recently discovered to regulate lysosome biogenesis by controlling the level of GlcNAc-1-phosphotransferase (GNPT). However, its precise function remained unclear. In this study, we demonstrate that TMEM251 is a two-transmembrane protein indispensable for GNPT stability, cleavage by Site-1-Protease (S1P), and enzymatic activity. We reconcile conflicting models by showing that TMEM251 enhances GNPT cleavage and prevents its mislocalization to lysosomes for degradation. We further establish that TMEM251 achieves this by interacting with GOLPH3 and retromer complexes to anchor the TMEM251-GNPT complex at the Golgi. Alanine mutagenesis identified F4XXR7 motif in TMEM251s N-tail for GOLPH3 binding. Together, our findings uncover TMEM251s multi-faceted role in stabilizing GNPT, retaining it at the Golgi, and ensuring the fidelity of the M6P pathway, thereby providing insights into its molecular function.

cell biology↗

bioRxiv2024

ARBEL: A Machine Learning Tool with Light-Based Image Analysis for Automatic Classification of 3D Pain Behaviors

A detailed analysis of pain-related behaviors in rodents is essential for exploring both the mechanisms of pain and evaluating analgesic efficacy. With the advancement of pose-estimation tools, automatic single-camera video animal behavior pipelines are growing and integrating rapidly into quantitative behavioral research. However, current existing algorithms do not consider an animals body-part contact intensity with- and distance from- the surface, a critical nuance for measuring certain pain-related responses like paw withdrawals ( flinching) with high accuracy and interpretability. Quantifying these bouts demands a high degree of attention to body part movement and currently relies on laborious and subjective human visual assessment. Here, we introduce a supervised machine learning algorithm, ARBEL: Automated Recognition of Behavior Enhanced with Light, that utilizes a combination of pose estimation together with a novel light-based analysis of body part pressure and distance from the surface, to automatically score pain-related behaviors in freely moving mice in three dimensions. We show the utility and accuracy of this algorithm for capturing a range of pain-related behavioral bouts using a bottom-up animal behavior platform, and its application for robust drug-screening. It allows for rapid objective pain behavior scoring over extended periods with high precision. This open-source algorithm is adaptable for detecting diverse behaviors across species and experimental platforms.

neuroscience↗