Biology subjects

Li, Z.

Publications and source records attributed to Li, Z..

At least 487 records · Page 27Linked to original sources

bioRxiv2025

A Comprehensive Reference Catalog of Human Skin DNA Virome Reveals Novel Viral Diversity and Microenvironmental Influences

IntroductionHuman skin serves as a dynamic habitat for a diverse microbiome, including a complex array of viruses whose diversity and roles are not fully understood. ObjectivesThis study aims to enhance our understanding of this skin viral diversity through the construction of a detailed, non-redundant DNA viral reference catalog. MethodsA total of 2,760 skin metagenomes from six published skin studies were collected. A skin virome catalog was constructed using methods independent of reference databases. Viral characteristics were identified through cross-cohort meta-analysis and used to characterize viral features across different skin environments. ResultsWe identified 20,927 viral sequences, which clustered into 2,873 viral operational taxonomic units (vOTUs). Among these, 2,610 represent previously unrecorded viral sequences, uncovering a substantial breadth of viral diversity on human skin. The results also highlight significant differences in viral communities that are associated with varying skin microenvironments. The oily skin is enriched in Papillomaviridae; the dry skin area is enriched in Autographiviridae, Inoviridae and Mitoviridae; and the moist skin is enriched in Herelleviridae, indicating the adaptive nature of viruses. We also investigated the relationship between bacteriophages and bacteria on the skin surface. We found that skin bacteria such as Pseudomonas, Klebsiella, and Staphylococcus are infected by phages from the class Caudoviricetes. ConclusionThis comprehensive skin DNA viral catalog significantly advances our understanding of the viromes role within the skin ecosystem. The findings highlight the adaptive nature of viruses to different skin microenvironments and their interactions with resident bacteria. This catalog serves as a valuable resource for further epidemiological and therapeutic research, potentially leading to better management and treatment of skin conditions influenced by the skins virome.

microbiology↗

bioRxiv2025

deepTFBS: Improving within- and cross-species prediction of transcription factor binding using deep multi-task and transfer learning

The precise prediction of transcription factor binding sites (TFBSs) is crucial in understanding gene regulation. In this study, we present deepTFBS, a comprehensive deep learning (DL) framework that builds a robust DNA language model of TF binding grammar for accurately predicting TFBSs within and across plant species. Taking advantages of multi-task DL and transfer learning, deepTFBS is capable of leveraging the knowledge learned from large-scale TF binding profiles to enhance the prediction of TFBSs under small-sample training and cross-species prediction tasks. When tested using available information on 359 Arabidopsis TFs, deepTFBS outperformed previously described prediction strategies, including position weight matrix, deepSEA and DanQ, with a 244.49%, 49.15%, and 23.32% improvement of the area under the precision-recall curve (PRAUC), respectively. Further cross-species prediction of TFBS in wheat showed that deepTFBS yielded a significant PRAUC improvement of 30.6% over these three baseline models. deepTFBS can also utilize information from gene conservation and binding motifs, enabling efficient TFBS prediction in species where experimental data availability is limited. A case study, focusing on the WUSCHEL (WUS) transcription factor, illustrated the potential use of deepTFBS in cross-species applications, in our example between Arabidopsis and wheat. deepTFBS is publically available at https://github.com/cma2015/deepTFBS. One sentence summaryThe high-performing deep learning framework deepTFBS can flexibly perform within- and cross-species prediction of TF binding, transferring the transcription regulation from model to non-model species.

bioinformatics↗

bioRxiv2025

An alternative framework for transcriptome-wide association studies to detect and decipher gene-trait associations

Transcriptome-wide association studies (TWAS) are widely used to uncover transcriptomic mechanisms underlying disease. Here, we present VINTAGE, an alternative TWAS framework designed to identify and decipher gene-trait associations through two complementary tests: a genetic variance test that generalizes TWAS and unifies it with SKAT, offering a clearer understanding of TWAS false signals when expression is not relevant to SNP-trait associations; and a local genetic correlation test that distinguishes it from TWAS by explicitly quantifying and testing the proportion of genetic effects on trait mediated through gene expression. Applied to eQTLGen and eighteen traits from UK Biobank, VINTAGE improved gene-trait association power by an average of 5% and 87% over SKAT and TWAS, respectively. VINTAGE is also the only method effective in assessing potential gene mediation effects, revealing that most genes lack detectable mediation effects (median = 12%), which explains the power advantage of VINTAGE and SKAT over TWAS. Notably, VINTAGE identified 61 genes with significant mediation, highlighting the role of expression in genetic influences on traits.

bioinformatics↗

bioRxiv2025

Rewiring of 3D chromatin topology orchestrates transcriptional reprogramming in muscle fiber-type specification and transformation

The composition of muscle fibers, characterized by distinct contractile and metabolic properties, significantly influences meat quality and glucose homeostasis. However, the mechanisms by which three-dimensional (3D) genome topology integrates with epigenetic states to regulate muscle fiber specification and transformation remain poorly understood. Here, we present an integrative analysis of the transcriptome, epigenome, and 3D genome architecture in the slow-twitch glycolytic extensor digitorum longus (EDL) and fast-twitch oxidative soleus (SOL) muscles. Global remodeling of enhancer-promoter (E-P) interactions emerged as a central driver of transcriptional reprogramming associated with muscle contraction and glucose metabolism. We identified tissue-specific super-enhancers (SEs) that regulate muscle fiber-type specification through cooperation of chromatin looping and transcription factors such as KLF5. Notably, the SE-driven activation of STARD7 facilitated the transformation of glycolytic fibers into oxidative fibers by mitigating reactive oxygen species levels and suppressing ERK MAPK signaling. This study elucidates the principles of 3D genome organization in the epigenetic regulation of muscle fiber specification and transformation, providing a foundation for novel therapeutic strategies targeting metabolic disorders and enhancing meat quality. O_FIG O_LINKSMALLFIG WIDTH=142 HEIGHT=200 SRC="FIGDIR/small/643571v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@990eecorg.highwire.dtl.DTLVardef@a2b491org.highwire.dtl.DTLVardef@1fcd142org.highwire.dtl.DTLVardef@10e4d47_HPS_FORMAT_FIGEXP M_FIG Schematic overview of the study. C_FIG

developmental biology↗

bioRxiv2025

Proteomic Analysis of Endemic Viral Infections in Neuronsoffers Insights into Neurodegenerative Diseases

Endemic viral infections with low pathogenicity are often overlooked due to their mild symptoms, yet they can exert long-term effects on cellular function and contribute to disease pathogenesis. While viral infections have been implicated in neurodegenerative disorders, their impact on the neuronal proteome remains poorly understood. Here, we differentiated human induced pluripotent stem cells (KOLF2.1J) into mature neurons to investigate virus-induced proteomic changes following infection with five neurotropic endemic human viruses: Herpes simplex virus 1 (HSV-1), Human coronavirus 229E (HCoV-229E), Epstein-Barr virus (EBV), Varicella-Zoster virus (VZV), and Influenza A virus (H1N1). Given that these viruses can infect adults and have the potential to cross the placental barrier, their molecular impact on neurons may be relevant across the lifespan. Using mass spectrometry-based proteomics with a customized library for simultaneous detection of human and viral proteins, we confirmed successful infections and identified virus-specific proteomic signatures. Notably, virus-induced protein expression changes converged on key neuronal pathways, including those associated with neurodegeneration. Gene co-expression network analysis identified protein modules correlated with viral proteins. Pathway enrichment analysis of these modules revealed associations with the nervous system, including pathways linked to Alzheimers and Parkinsons disease. Remarkably, several viral-induced proteomic alterations overlapped with changes observed in postmortem Alzheimers patient brains, suggesting a mechanistic connection between viral exposure and neurodegenerative disease progression. These findings provide molecular insights into how common viral infections perturb neuronal homeostasis and may contribute to neurodegenerative pathology, highlighting the need to consider endemic viruses as potential environmental risk factors in neurological disorders.

neuroscience↗

bioRxiv2025

Sidero-Mining: Systematic Extraction of Siderophore Biosynthetic Information Using Large Language Models

Siderophores are central mediators of microbial iron acquisition, competition, and ecological adaptation, yet their biosynthetic diversity remains difficult to resolve across species because existing sequence-based BGC comparison is strongly constrained by phylogenetic background. Here we combine large-language-model-assisted literature mining, functional-space comparison, and genome-scale analysis to resolve the global organization of siderophore biosynthesis across bacteria. We first built SideroBank, a manually curated cross-species benchmark of siderophore biosynthetic gene clusters (BGCs), and used it to show that many identical products recur across distant taxa whereas the corresponding BGCs often fail to cluster in sequence space. We then developed BGC Block Aligner, which compares BGCs as ordered systems of functionally meaningful blocks and thereby converts comparison from sequence space to functional space. Applied to 97,432 bacterial genomes, this framework produced the Siderophore Atlas, revealing that siderophore synthesis is a remarkably pervasive trait encoded by over 60% of the analyzed genomes, with certain clusters being the most widely disseminated secondary metabolites across the bacterial domain. This global landscape suggests that the adoption of specific biosynthetic strategies is predominantly driven by ecological lifestyle rather than strict phylogenetic relatedness. Furthermore, a stark macro-evolutionary dichotomy was observed between the continuous structural diversification of NRPS pathways and the standardized, HGT-driven dissemination of NIS systems, linking functional-space genomics to the global ecology and evolution of siderophore biosynthesis..

bioinformatics↗

bioRxiv2025

Red Light-Activated Reversible Inhibition of Protein Functions by Assembled Trap

Red light offers strong tissue penetration and low phototoxicity, making it an attractive option for optogenetic applications. However, the available red light-inducible optogenetic tools are rather limited. Here, we present a novel red light-induced protein clustering system, namely R-LARIAT (Red Light-Activated Reversible Inhibition by Assembled Trap), that controls protein functions in a spatiotemporal manner. Our system capitalizes on the rapid and reversible binding of LDBs (nanobodies-based dimerization binders) to a bacterial phytochrome DrBphP, which uses a mammalian endogenous biliverdin chromophore to absorb red light. An anti-GFP nanobody fused with LDBs allows this method to quickly trap a wide variety of GFP-tagged proteins into light-induced protein clusters. Strikingly, our system exhibits an excellent performance in clustering efficiency with high light sensitivity and stability, it can function even when shielded by multiple glass plates. By utilizing the R-LARIAT system to trap and sequester tubulin, cell cycle progression can be blocked in HeLa cells. Therefore, the R-LARIAT system takes advantage of red light with greater tissue penetration and holds the potential to precisely control protein functions in living organisms.

cell biology↗

bioRxiv2025

Single-dose rapamycin increases brain glucose metabolism but reduces synaptic density in Long-Evans rats: A PET imaging study

Rapamycin, an inhibitor of the mechanistic target of rapamycin (mTOR), has shown promise as a neuroprotective compound in preclinical studies. Reduced brain glucose metabolism and loss of synaptic density are key features of Alzheimers disease that can be measured in vivo using positron emission tomography (PET) imaging, allowing for assessment of treatment effects on brain function. Here, we used PET to investigate the acute effects of a single-dose of rapamycin on glucose metabolism and synaptic density in Long-Evans rats. In a repeated measures design, we quantified changes in brain glucose metabolism using [18F]FDG PET (n=13) at baseline, one day, and one week after intraperitoneal administration of rapamycin (8 mg/kg). In a separate cohort (n=6), we measured synaptic density using [18F]SynVesT-1 PET at baseline and one day after rapamycin administration. Regional standardized uptake values (SUV) were calculated for [18F]FDG while total distribution volumes were estimated for [18F]SynVesT-1 using image-derived input functions of the heart. Rapamycin induced significant increases in [18F]FDG SUV across multiple brain regions one day after administration, an effect that persisted at one-week follow-up. In contrast, [18F]SynVesT-1 binding showed significant decreases throughout the brain at 24 hours post-administration, indicating reduced synaptic density. These opposing effects on glucose metabolism and synaptic density point to multifaceted actions of rapamycin in the brain, possibly reflecting improved metabolic function occurring simultaneously with acute synaptic loss. These results show that [18F]FDG and synaptic density PET imaging could serve as useful biomarkers in human clinical trials evaluating rapamycins mechanistic and therapeutic effects in neurodegenerative disorders.

neuroscience↗

bioRxiv2025

TSHR-targeting nucleic acid aptamer treats Graves' ophthalmopathy via novel allosteric inhibition

Abnormal thyrotropin receptor (TSHR) signaling drives a spectrum of diseases affecting hundreds of millions globally, including Graves disease (GD), Graves ophthalmopathy (GO), hyperthyroidism, and thyroid cancers. Therefore, TSHR is considered an attractive therapeutic target with immense drug discovery potential. Currently, there are no clinically available TSHR inhibitors. To address this unmet medical need, we developed YC3, a functional nucleic acid aptamer targeting TSHR, via an innovative approach combining protein-targeting cell-SELEX and functional selection. YC3 exhibited nanomolar affinity with potent TSHR inhibition. Its therapeutic potential was comprehensively evaluated both in vitro and in vivo for GO. In GO patient-derived orbital fibroblasts (OFs), YC3 reversed thyroid-stimulating antibodies (TSAbs)-induced cell activation, suppressing inflammatory cytokines and extracellular matrix (ECM) secretion. In a GO mouse model, YC3 treatment markedly attenuated orbital inflammation, ECM deposition, and fibrosis, ameliorating the pathological remodeling of orbital tissue. Mechanistically, YC3 bound to a previously unidentified allosteric site within the leucine-rich repeat domain of TSHR, thereby inhibiting receptor activation. This study identifies YC3 as a promising TSHR-targeted therapy and unveils a new druggable site for inhibitor design. It also provides the first preclinical evidence supporting pharmacological TSHR inhibition for GO treatment, advancing therapeutic development for TSHR-related diseases.

biochemistry↗

bioRxiv2025

Application of machine learning to discriminate photoreceptor cell species in xenotransplanted chimeric retinas

Photoreceptor transplantation is being studied to improve visual function in retinal diseases causing blindness, such as age-related macular degeneration, hereditary eye diseases, and traumatic retinopathy, among others. Preclinical studies often involve the delivery of exogenous human photoreceptor cells into the retinas of animal models. In such experiments, a key readout is the differential frequency of donor cell somatic integration versus artificial labeling secondary to material transfer of cytosolic or nuclear labels from donor to recipient cells. For this analysis, the ability to recognize photoreceptor nuclei as being of donor (human) versus animal is key, but purely immunohistology discrimination can be challenging because of antigenic species overlap or intercellular antigen transfer. To address this challenge, we sought to develop and validate a computational technique to discriminate between photoreceptor cells of different animal species based on machine learning of nuclear morphology. Here, we aimed to evaluate the feasibility of using computer-assisted detection of separate nuclei and employing random forest classification to automate the species differentiation, among DAPI-stained photoreceptors after xeno-transplantation of human photoreceptors into the retinas of mice and pigs. Our models were trained on single-species samples and validated with mixed-species samples. We then transplanted human embryonic stem cell-derived retinal organoid cells into rodent and pig retinal degeneration models. The random forest model accurately determined cell identity post-xenotransplantation, validated by histological assessment using an anti-human nuclear antibody. Our results support the potential efficacy of employing machine learning image analysis and classification techniques that may promote experimental rigor, minimize observer bias, and enable high throughput semi-automated workflows for transplantation outcomes analysis. The methodological framework reported here may enable a more nuanced and precise analysis of the behavior of transplanted photoreceptors for the purposes of human retinal regeneration.

neuroscience↗

bioRxiv2025

Transcriptomic splicing analysis reveals a gene-independent form of TE reactivation in tumors representing unrecognized source of oncogenes and neoantigens

Transposable elements (TEs) are reactivated in tumors and serve as significant contributors to tumor transcriptomic complexity and heterogeneity. However, their repetitive nature and diverse transcriptional forms have hindered efforts to characterize TE-derived transcripts independent of host gene contexts. Here, using our self-developed splicing-junction analysis tool ASJA, we systematically interrogated TE transcription across 32 cancer types, identifying transcripts autonomously initiated from and exclusively spliced among TEs with high TE content, termed TEtrans. Our analysis revealed 5,361 TEtrans exhibiting pan-cancer prevalence, tumor-specific expression and features of mature transcripts, including canonical splicing, 5 caps and polyA tails. Mostly unannotated and enriched in intergenic regions, TEtrans are predominantly derived from primate-specific classes with conserved splice pattern. TEtrans burden demonstrates heterogeneous associations with prognosis and immune activity across cancers, while their expression can be epigenetically dual-regulated by stemness- and inflammation-associated transcription factors. Functional studies uncovered that TEtrans could act as oncogenes, exemplified by tsTE1, a HERVH-derived transcript that promotes colorectal cancer proliferation by enhancing TOP1-mediated DNA supercoil relaxation. Remarkably, [~]7.2% of TEtrans are identified to encode tumor neoantigens, including viral proteins and unannotated peptides, which are shared among patients and validated by proteogenomic analysis. These TEtrans-derived neoantigens are immunogenic both in vitro and in vivo, and exceed neoepitopes from genomic alterations in abundance per tumor, particularly in cancers with low mutational burden. Collectively, as a gene-independent form of TE-derived transcripts, TEtrans represents a unique source of oncogenes and tumor neoantigens, expanding the functional repertoire of TEs in cancer biology and offering new avenues for therapeutic targeting. HighlightsO_LITEtrans are tumor-specific transcripts with conserved splicing patterns, predominantly derived from primate TEs in intergenic regions. C_LIO_LITEtrans expression is epigenetically activated and dually regulated by stemness/inflammation transcription factors. C_LIO_LITEtrans function as oncogenes, exemplified by tsTE1 enhancing TOP1-mediated DNA relaxation. C_LIO_LITEtrans encode tumor neoantigens that elicit CD8+ T cell responses, providing targets for immunotherapy, especially in low-mutation tumors. C_LI Abstract figure O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC="FIGDIR/small/640928v1_ufig1.gif" ALT="Figure 1"> View larger version (78K): org.highwire.dtl.DTLVardef@1d2d4cforg.highwire.dtl.DTLVardef@918f14org.highwire.dtl.DTLVardef@489465org.highwire.dtl.DTLVardef@1069d17_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Repulsive interactions instruct synaptic partner matching in an olfactory circuit

Neurons exhibit extraordinary precision in selecting synaptic partners. Although cell-surface proteins (CSPs) mediating attractive interactions between developing axons and dendrites have been shown to instruct synaptic partner matching1,2, the degree to which repulsive interactions play a role is less clear. Here, using a genetic screen guided by single-cell transcriptomes3,4, we identified three CSP pairs--Toll2-Ptp10D, Fili-Kek1, and Hbs/Sns- Kirre--in mediating repulsive interactions between non-partner olfactory receptor neuron (ORN) axons and projection neuron (PN) dendrites in the developing Drosophila olfactory circuit. Each CSP pair exhibits inverse expression patterns in the select ORN-PN partners. Loss of each CSP in ORNs led to similar synaptic partner matching deficits as the loss of its partner CSP in PNs, and mistargeting phenotypes caused by overexpressing one CSP could be suppressed by loss of its partner CSP. All CSP pairs are also differentially expressed in other brain regions. Together, our data reveal that multiple repulsive CSP pairs work together to ensure precise synaptic partner matching during development by preventing neurons from forming connections with non-cognate partners.

neuroscience↗

bioRxiv2025

Rewiring an olfactory circuit by altering the combinatorial code of cell-surface proteins

Proper brain function requires the precise assembly of neural circuits during development. Despite the identification of many cell-surface proteins (CSPs) that help guide axons to their targets1,2, it remains largely unknown how multiple CSPs work together to assemble a functional circuit. Here, we used synaptic partner matching in the Drosophila olfactory circuit3,4 to address this question. By systematically altering the combination of differentially expressed CSPs in a single olfactory receptor neuron (ORN) type, which senses a male pheromone that inhibits male-male courtship, we switched its connection from its endogenous postsynaptic projection neuron (PN) type nearly completely to a new PN type that promotes courtship. From this switch, we deduced a combinatorial code including CSPs that mediate both attraction between synaptic partners and repulsion between non-partners5,6. The anatomical switch changed the odor response of the new PN partner and markedly increased male-male courtship. We generalized three manipulation strategies from this rewiring-- increasing repulsion with the old partner, decreasing repulsion with the new partner, and matching attraction with the new partner--to successfully rewire a second ORN type to multiple distinct PN types. This work demonstrates that manipulating a small set of CSPs is sufficient to respecify synaptic connections, paving ways to explore how neural systems evolve through changes of circuit connectivity.

neuroscience↗

bioRxiv2025

Protein Secretion From Liver Ex Vivo Reveals Regulation of Hepatokines by Sex and Circadian Rhythm

Here, we use an ex vivo approach compatible with the circadian timescale to interrogate protein secretion from liver, revealing several findings. Proteomic analyses in male and female mice identify hundreds of proteins that exhibit time-of-day-dependent or clock-dependent secretion involved in extracellular matrix, immune, redox, xenobiotic, and fatty acid functions. Among these, the liver secretes more endostatin, a cleavage product of collagen type XVIII alpha 1 (COL18A1), during the inactive, fasting phase of the diurnal cycle. Temporal regulation of COL18A1/endostatin is dysregulated upon loss of Bmal1 through combined effects on Col18a1 transcriptional repression and proteolytic processing. Functional experiments in vivo and in vitro reveal that endostatin suppresses mitochondrial gene expression in white adipose tissue in a time-dependent manner and reduces mitochondrial respiration in adipocytes, while enhancing lipolysis. These results support a mechanism of inter-organ crosstalk whereby hepatically derived, temporally-restricted endostatin tunes adipocytes toward metabolic activities required during the fasting phase.

molecular biology↗

bioRxiv2025

High-Parameter Spatial Multi-Omics through Histology-Anchored Integration

Spatial omics face challenges in achieving high-parameter, multi-omics co-profiling. Serial-section profiling of complementary panels mitigates technical trade-offs but introduces the spatial diagonal integration problem. To address this, we present SpatialEx and its extension SpatialEx+, computational frameworks leveraging histology as a universal anchor to integrate spatial molecular data across tissue sections. SpatialEx combines a pre-trained H&E foundation model with hypergraph and contrastive learning to predict single-cell omics from histology, encoding multi-neighborhood spatial dependencies and global tissue context. SpatialEx+ further introduces an omics cycle module that encourages cross-omics consistency via slice-invariant mappings, enabling seamless integration without co-measured training data. Extensive validations show superior H&E-to-omics prediction, panel diagonal integration, and omics diagonal integration across various biological scenarios. The frameworks scale to datasets exceeding one million cells, maintain robustness with non-overlapping or heterogeneous sections, and support unlimited omics layers in principle. Our work makes multi-modal spatial profiling broadly accessible.

bioinformatics↗

bioRxiv2025

Direct pathway bias and altered striatal neurogenesis in human iPSC models of 16p11.2 CNVs: Evidence from single-cell and functional analyses

Striatal medium spiny neurons (MSNs) control motor, cognitive, and social domains via direct (dMSNs) and indirect (iMSNs) basal ganglia pathways. Recent genomic analyses implicate striatal circuit dysfunction in neurodevelopmental disorders (NDDs) and highlight MSNs as a newly recognised cell type affected in schizophrenia, yet much NDD research still focuses on cortical interneurons and glutamatergic neurons, leaving MSN involvement understudied. Here, we use human iPSC-derived MSNs to demonstrate high-fidelity striatal development and explore 16p11.2 copy number variants (CNVs) - mutations predisposing carriers to autism spectrum disorder, schizophrenia, intellectual disability and other NDD conditions featuring basal ganglia deficits. By profiling both 16p11.2 duplication and deletion MSNs, we uncover reciprocal changes in MSN neurogenesis kinetics that converge on a dMSN fate bias. These shifts correspond to altered calcium signalling and enhanced firing upon direct-pathway activation. Our findings reveal a previously unappreciated role for MSN subtype imbalances in NDD pathogenesis and open new avenues for therapeutic intervention.

neuroscience↗

bioRxiv2025

A humanized mouse model system mimics prenatal Zika infection and reveals premature differentiation

Zika, a mosquito-borne flavivirus, has been found in 87 countries and territories. Global outbreaks peaked in 2016. Prenatal infection of Zika virus was found to be associated with microcephaly, arthrogryposis, intracranial calcifications, fetal growth restriction, and fetal demise. The most severely affected children were diagnosed with congenital Zika syndrome, which impacts thousands worldwide. With no approved treatment or preventative measures for Zika, future viral outbreaks have the potential to cause epidemic levels of prenatal brain injury, as seen over the past 70 years. Therefore, there is a great need for a reliable and clinically translational experimental system that mimics the human condition of prenatal Zika infection. To this end, we developed a humanized, immunocompetent mouse model system of virally induced brain injury from prenatal Zika infection, which ranges from mild to severe. Here, we describe the extent to which this system mirrors the human phenotypic spectrum. Using our thorough preclinical system, we find that prenatal Zika infection of mice impacts survival rate, anthropometric measurements, tissue formation, and neurological outcomes, all of which are typical of prenatal infection. Single-cell RNA sequencing of the Zika-infected cerebral cortex reveals severely disrupted transcriptome profiles and suggests that these injuries are a result of a depletion of neural stem cells. Current and future applications include the identification of genetic or environmental modifiers of brain injury, molecular or mechanistic studies of pathogenesis, and preclinical evaluation of future therapies.

neuroscience↗

bioRxiv2025

D-LMBmapX: Generalized Deep-Learning Pipeline for Automated Whole-brain Neural Circuitry Profiling Across Any Developmental Stages

Understanding whole-brain circuitry development is essential for uncovering the origins of disorders linked to abnormal neural wiring, yet progress has been limited by the lack of tools for accurate, comprehensive analysis. Brain development is highly dynamic and region-specific, with pronounced inter-individual variability during early stages, and no existing method achieves whole-brain profiling at arbitrary stages. The rapidly changing morphology of developing axons further complicates segmentation. Here, we present D-LMBmapX, a deep learning pipeline for automated whole-brain circuitry profiling across postnatal development. D-LMBmapX constructs sample-inferred atlases from flanking anchor stages, enabling accurate registration at any time point. By developing a foundation model for generalised axon and soma segmentation, it supports quantitative mesoscale developmental connectomics, demonstrated through spatial-temporal profiling of catecholaminergic projections. D-LMBmapX also enables robust cross-modality and cross-dimensional registration, including precise alignment of single 2D slices to 3D references, and offers generalizable strategies for 5D analysis of diverse biomedical spatial datasets.

neuroscience↗