Biology subjects

Zhang, L.

Publications and source records attributed to Zhang, L..

At least 163 records · Page 9Linked to original sources

bioRxiv2026

Multiscale Mechanisms of Human Memory Modulation by Deep Brain Stimulation

The highly variable effects of deep brain stimulation (DBS) on cognition highlight a fundamental gap in our understanding of how external perturbations interface with the brains ongoing, task-engaged state. The current study addressed this challenge by combining intracranial EEG recordings with hippocampal DBS stimulation in human participants during spatial sequence memory task. We found 50 Hz stimulation targeting hippocampal white matter enhanced memory performance, whereas 5 Hz stimulation targeting hippocampal gray matter impaired memory. These opposing behavioral outcomes were both governed by two dissociated mechanisms: a region-specific modulation of theta rhythms in an engagement-dependent fashion, and a global modulation of the fidelity of memory representations across the cortex. Our results suggest targeted stimulation can concurrently fine-tune specialized circuits and govern the brains global state, which not only offers a novel roadmap for understanding how external inputs shape human cognition, but also helps to design precise, state-dependent neuromodulation therapies.

neuroscience↗

bioRxiv2026

Hippocampal and Midbrain Function in Superagers Relates to Memory for Novelty and Expectation Violation

Memory performance typically declines with age, but the underlying neurobiological mechanisms remain unclear. Superagers, people over 80 years of age with episodic memory performance comparable to individuals 30 years younger, appear to resist this decline. Novelty and expectation violations are known to engage the hippocampus-midbrain system to enhance memory formation. Here, we examined whether superagers superior memory performance is supported by preserved hippocampal-midbrain function during novelty and expectation processing. We manipulated item and contextual novelty (i.e., expectation violations) during encoding to test whether superagers show greater mnemonic benefits than their age-matched peers, whether these benefits reflect enhanced hippocampal and midbrain functioning as measured by fMRI, and whether they are associated with preserved dopaminergic integrity measured with neuromelanin-sensitive MRI. Our results show that, although superagers demonstrated overall superior memory performance, both groups exhibited superior recognition of contextually unexpected items. Nevertheless, differences emerged in the processing of expectation during encoding. Superagers exhibited stronger hippocampal responses to expectation violations and habituation to expected events, irrespective of item novelty. Conversely, typical older adults exhibited reduced midbrain response when expected novelty was absent. Neuromelanin accumulation did not account for group differences in midbrain activity or memory performance. Taken together, these findings suggest superagers benefit from adaptive responses to expectation and its violation, which is therefore a candidate mechanism distinguishing exceptional from typical cognitive ageing. SIGNIFICANCE STATEMENTAlthough memory typically declines with age, superagers are individuals aged over 80 who maintain memory performance comparable to people 30 years younger. We examined whether preserved hippocampal-midbrain function during novelty and expectation processing could underlie their superior memory. Superagers exhibited adaptive hippocampal responses to expectation and its violation, with higher activation for unexpected events and habituation to expected events. In contrast, typical older adults showed hippocampal and midbrain responses oriented towards anticipated novel content, despite not showing differences in neuromelanin accumulation. These findings underscore the critical role of hippocampal function in supporting memory preservation in late life and advance our understanding of the neural mechanisms underlying healthy cognitive ageing.

neuroscience↗

bioRxiv2026

Age-associated increases in inter-individual gene expression variability across human tissues

Aging involves progressive physiological decline, yet the underlying transcriptomic patterns remain poorly understood. Although differentially expressed genes (DEGs) have been the primary focus of previous studies, here we investigate differentially variable genes (DVGs) using a novel Gene Stability Score (GSS). In 30 tissue types from nearly 1,000 individuals in the Genotype-Tissue Expression (GTEx) project, age- and sex-related DVGs account for approximately 15% of overall expression variability between samples of the same tissue, with age-related DVGs specifically contributing 7.7%. We further show that DEGs and DVGs affect distinct biological pathways, and that inter-individual instability is significantly correlated with cell-to-cell transcriptional noise. Moreover, gene regulatory network analysis reveals that this variability is not random but is shaped by local network architecture. Finally, we identify robust reference genes, including TBP, PUM1, and TMEM199, for RT-qPCR experiments in studying age-related gene expression changes in humans. Together, our findings suggest that aging involves both coordinated transcriptional programs and increased stochasticity across individuals and cells.

genomics↗

bioRxiv2026

Rapid estimation of photon measurement density functions using a deep convolutional neural network for functional near-infrared spectroscopy

The photon measurement density function (PMDF) plays a fundamental role in both pre-experimental optode arrangement and post-experimental data analysis in functional near-infrared spectroscopy (fNIRS). Conventionally, PMDFs are derived from structural MR images through tissue segmentation and photon propagation modeling (PPM), which are computationally demanding and time-consuming, thereby limiting their practical use. In this study, we propose a novel deep learning-based framework to estimate PMDFs directly from MR images and channel configurations. The proposed method supports flexible source-detector distances and eliminates the need for explicit tissue segmentation and repeated photon simulations. Specifically, a convolutional neural network is trained to predict photon fluence distributions, from which PMDFs are subsequently derived using the adjoint formulation. The trained model is evaluated on channels placed in both trained and unseen scalp regions across commonly used source-detector distances. The results demonstrate that the proposed method achieves PMDF estimations comparable to those obtained from PPM. Overall, this approach significantly reduces computational cost and has the potential to facilitate broader adoption of PMDF-based methods in the fNIRS community.

bioengineering↗

bioRxiv2026

U-Probe: universal agentic probe design for imaging-based spatial-omics

Probe design for fluorescence in situ hybridization (FISH) underpins spatial transcriptomics, three-dimensional genome studies, and clinical diagnostics, yet remains constrained by two challenges: dependence on expert knowledge for parameter selection and quality evaluation, and the inability of existing tools to accommodate the diverse probe architectures introduced by rapidly emerging methods. Here we present U-Probe, a universal and agentic probe design platform. U-Probe employs a declarative configuration system with a directed acyclic graph (DAG)-based assembly engine that supports arbitrary probe structures from established protocols such as MERFISH, seqFISH, and MiP-seq to entirely novel architectures without code modifications. Integrated LLM-based AI agents enable conversational design workflows, allowing users to specify experimental goals in natural language and receive synthesis-ready probe sequences. We validated U-Probe in three scenarios: agent-driven MiP-seq panel design for influenza-infected mouse lung tissue, genome-tiling DNA-FISH for herpesvirus detection, and a novel RCA-based ligation probe for single-nucleotide mutation discrimination. U-Probe is available as an open-source tool with CLI, Web, and agent interfaces.

bioinformatics↗

bioRxiv2026

Fruit trichome density outweighs cuticle thickness as the dominant barrier to postharvest water loss in tomato

Fruit cuticle thickness and biochemical composition have traditionally been regarded as the primary determinants of postharvest water loss in fleshy fruits. However, several reports indicate that some tomato mutants with thinner fruit cuticles or less cutin and waxes do not always show increased transpiration, suggesting that additional surface features influence postharvest water loss. Here, we s how that fruit trichome density is a previously underappreciated determinant of postharvest water loss in tomatoes. Using two independent mutants, cr-slhdziv7 and cr-slhdziv9, which exhibit reduced fruit trichome density, we found that both mutants displayed reduced water loss rates and extended shelf life during postharvest storage despite having thinner cuticles and reduced levels of key cutin monomers. Further molecular analyses, including RNA-seq, yeast one-hybrid (Y1H), and dual-luciferase reporter assays, revealed that these two HD-ZIP IV proteins not only regulate fruit trichome formation but also directly or indirectly modulate the expression of multiple cutin biosynthesis genes. Collectively, our results demonstrate that the benefit of reducing trichome-associated microchannels can outweigh the negative effects of cuticle thinning on postharvest water loss. This study establishes fruit trichome density as an important and previously underestimated target for improving postharvest fruit quality and shelf life. HighlightO_LIFruit trichome density controls tomato postharvest water loss and dominates over cuticle thickness barrier. C_LIO_LILess trichomes reduce microchannels, lowering transpiration despite thin cuticles and extending tomato fruit shelf life. C_LI

plant biology↗

bioRxiv2026

TCMCard: A High-Confidence Digital Infrastructure for Traditional Chinese Medicine Quantified by Multi-Dimensional Evidence Integration

Network pharmacology has become a widely used approach for deciphering multi-component, multi-target mechanisms of traditional Chinese medicine (TCM). Here we introduce TCMCard, a high-confidence digital infrastructure built on a Multi-Dimensional Evidence Integration (MDEI) framework. The framework integrates experimental activity data from authoritative chemical databases, literature-derived evidence, and structure-based similarity inference. Preprocessing steps include chemical structure normalization, species-specific filtering, and target quality scoring. Applied to conventional interaction datasets, this pipeline leads to the removal of over 60% of low-confidence noise. TCMCard supports network pharmacology exploration through an interactive visualization platform, and module analysis identifies functionally relevant communities that offer insights into the synergistic actions of TCM formulas. Overall, TCMCard may help move the field beyond simple data aggregation toward evidence-informed curation and quality-driven analysis. As an interactive and publicly accessible platform, it reveals an organized backbone within complex interaction networks, offering a more reliable basis for understanding multi-component synergy in TCM.

bioinformatics↗

bioRxiv2026

Continental-scale multi-omics reveals a distinct microbial-viral biome in sandy beach ecosystems structured by tidal zonation

Sandy beaches are dynamic coastal interfaces shaped by strong physical forcing and intense exchange between marine and terrestrial environments, yet their microbiomes remain poorly resolved at the genomic scale. Here we present a genome-resolved survey of microbial and viral communities across sandy beaches spanning a continental-scale latitudinal gradient along the Chinese coastline. By integrating cross-shore sampling, coastal geochemistry and large-scale multi-omics, we generated 978 metagenomes, 63 viromes and 72 metatranscriptomes, reconstructing 13,337 metagenome-assembled genomes and 38,255 viral populations. Sandy beach microbiomes exhibit exceptionally high genomic novelty, with more than 90% of species-level genomes representing previously undescribed taxa, suggesting that permeable coastal sediments constitute a distinct microbial and viral reservoir. Tidal zonation emerged as a dominant ecological driver structuring microbial diversity, metabolic strategies and virus-host interactions across cross-shore gradients. Genome-resolved analyses revealed systematic metabolic shifts from oxic heterotrophy in supratidal sediments toward increasingly chemolithotrophic and autotrophic pathways toward the low-intertidal and subtidal zone. Sandy beach microbiomes further encode broad potential for hydrocarbon and plastic transformation, together with diverse biosynthetic and antibiotic resistance repertoires that may mediate microbial chemical interactions. Together, these findings identify sandy beaches as a previously under-recognized microbial-viral biome shaped by tidal forcing, providing insight into microbiome evolution and coastal ecosystem resilience under increasing anthropogenic pressure.

microbiology↗

bioRxiv2026

Photo-downregulation of SIRT4 mitigates aging in mice by enhancing H3K9ac via fatty acid metabolism

As organisms age, mitochondrial metabolic activity declines, and disrupted gene expression regulation mediated by histone acetylation induces the emergence of senescent physiological phenotypes in tissues. In this study, we found that periodic exposure to red light significantly increased histone H3 Lys9 acetylation (H3K9ac) levels in the tissues and organs of aged mice. Following red light exposure, silent information regulation factor 4 (SIRT4) protein levels in keratinocytes were notably reduced, whereas glycolysis, fatty acid metabolism, and the tricarboxylic acid (TCA) cycle were significantly activated in keratinocytes. The reduction in mitochondrial SIRT4 levels enhances the acetylation of mitochondrial metabolic proteins, particularly malonyl-CoA decarboxylase (MCD), a potent inhibitor of the key rate-limiting enzyme carnitine palmitoyltransferase 1A (CPT1A) in fatty acid oxidation. This process promotes mitochondrial fatty acid oxidation and TCA cycle. Additionally, the decrease in SIRT4 activates SIRT1 through feedback mechanisms, thereby alleviating its inhibition on PPAR- in senescent keratinocytes and comprehensively activating the expression of genes related to lipid metabolism. This lipid metabolism activation ultimately facilitates the accumulation of acetyl-CoA within keratinocytes, increases H3K9ac levels, and reshapes the expression patterns of senescence-related genes. Eventually, cellular aging is effectively mitigated by the synergistic regulation of metabolism, inflammation, and gene expression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/717004v2_ufig1.gif" ALT="Figure 1"> View larger version (82K): org.highwire.dtl.DTLVardef@ba3865org.highwire.dtl.DTLVardef@1cc9a58org.highwire.dtl.DTLVardef@1a90e7borg.highwire.dtl.DTLVardef@1f5e88f_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical AbstractMechanism of anti-aging action of red light: Red light can reduce SIRT4 signalling in keratinocytes, thereby reactivating lipid metabolism and increasing levels of acetyl-CoA. This promotes histone acetylation, which in turn remodels the expression of age-related inflammatory factors and genes.

cell biology↗

bioRxiv2026

CPS: Mapping Physical Coordinates to High-Fidelity Spatial Transcriptomics via Privileged Multi-Scale Context Distillation

MotivationSpatial transcriptomics enables the dissection of tissue heterogeneity within native contexts, yet current platforms are inherently constrained by high sparsity and low signal-to-noise ratios that obscure fine-grained biological signals. Current efforts to recover these signals are limited by image registration dependencies or the inherent context-blindness of implicit neural representations. ResultsWe introduce the Cell Positioning System (CPS), a context-aware implicit neural representation framework designed to map physical coordinates to high-fidelity spatial transcriptomics via a privileged multi-scale context distillation strategy. CPS treats multi-scale tissue niches as privileged information, employing a teacher network equipped with a multi-scale niche attention mechanism to capture adaptive biological interactions during training. This structural knowledge is explicitly distilled into a student coordinate network, enabling the generation of context-aware expression landscapes solely from spatial coordinates during inference. Benchmarking on the DLPFC dataset demonstrates that CPS achieves state-of-the-art performance in spatial and gene expression imputation and denoising. Furthermore, CPS enables super-resolution to recover high-resolution mouse brain anatomical details and offers interpretability by identifying the scale effective size of biological interactions within human breast cancer tissues. Finally, the framework exhibits superior scalability for large-scale datasets with linear computational complexity. AvailabilitySoftware is available online at https://github.com/tju-zl/CPS.

bioinformatics↗

bioRxiv2026

Multimodal imaging reveals no evidence for magnetite-based magnetoreceptors in the mole-rat eye

Magnetoreception, the ability to perceive the geomagnetic field, is widespread across animals. The underlying sensory mechanism remains elusive, but a long-standing hypothesis proposes single-domain magnetite linked to mechanosensitive ion channels. The Ansells mole-rat (Fukomys anselli) is a subterranean rodent with a magnetic sense, and published behavioral and histological data are consistent with magnetite-based magnetoreceptors in the cornea or retina. Here, we systematically screened for magnetite in the mole-rat eye, combining iron detection via enhanced Prussian blue staining and synchrotron X-ray fluorescence microscopy (XFM) with magnetic detection via MRI quantitative susceptibility mapping (MRI-QSM) and quantum-diamond microscopy (QDM). This revealed only a few iron particles in the retina and cornea, which predominantly overlapped with titanium or chromium, indicating a non-biogenic origin. XFM showed iron-enriched lines in the cornea, but these did not show ferrimagnetic signals. Focusing on other ocular tissues, MRI-QSM revealed the highest susceptibility in the ciliary body, where iron-rich pigmented cells were identified. A TEM-screen, however, failed to detect single-domain magnetite particles in these cells. We conclude that our high-sensitivity multimodal screen provides no evidence for magnetite-based magnetoreceptors in the mole-rat eye, suggesting that mole-rat magnetoreceptors either do not reside in the eye or are based on different physical principles.

neuroscience↗

bioRxiv2026

MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization

Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires orchestrating dozens of specialized tools in multi-step workflows, yet current AI agents struggle to maintain robust performance and consistently underperform in these high-complexity scenarios. Here we present MolClaw, an autonomous agent that leads drug molecule evaluation, screening, and optimization. It unifies over 30 specialized domain resources through a three-tier hierarchical skill architecture (70 skills in total) that facilitates agent long-term interaction at runtime: tool-level skills standardize atomic operations, workflow-level skills compose them into validated pipelines with quality check and reflection, and a discipline-level skill supplies scientific principles governing planning and verification across all scenarios in the field. Additionally, we introduce MolBench, a benchmark comprising molecular screening, optimization, and end-to-end discovery challenges spanning 8 to 50+ sequential tool calls. MolClaw achieves state-of-the-art performance across all metrics, and ablation studies confirm that gains concentrate on tasks that demand structured workflows while vanishing on those solvable with ad hoc scripting, establishing workflow orchestration competence as the primary capability bottleneck for AI-driven drug discovery.

bioinformatics↗

bioRxiv2026

GPLD1 Regulates the Shedding of IZUMO1R to Block Polyspermy in Porcine Oocyte

Mammalian fertilization commences with the essential interaction between sperm IZUMO1 and its oocyte-surface receptor, JUNO. Following gamete fusion, JUNO is rapidly shed from the oocyte to establish a definitive membrane block to polyspermy, a pathological condition that remains a major hurdle in porcine in vitro fertilization (IVF). Despite its biological importance, the molecular networks driving JUNO cleavage has remained elusive. Here, by integrating proteomics, dual-color live-cell imaging, and functional perturbations, we identify the extended existence of JUNO and the GPI-specific phospholipase D1 (GPLD1) as the requisite enzyme mediating JUNO shedding in porcine oocytes. Targeted knockdown or pharmacological inhibition of GPLD1 stabilizes oocyte JUNO, prolongs the window of oocyte receptivity, and significantly exacerbates polyspermy, ultimately compromising embryonic developmental competence. Conversely, GPLD1 overexpression restricts redundant sperm adherence and enhances the efficiency of monospermic zygote formation and blastocyst development. Live-cell imaging reveals that fertilization triggers a transient, pulsed recruitment of GPLD1 in the oocyte, which precisely coincides with the biphasic kinetics of JUNO depletion. Our findings establish that the enzymatic cleavage of the GPI-anchor by GPLD1 is critical for JUNO release, defining a fundamental mechanism for the membrane-level block to polyspermy. This work provides a molecular framework for ensuring sperm-oocyte recognition and improving in vitro fertilization outcomes in mammals.

developmental biology↗

bioRxiv2026

Ceramide-rich extracellular vesicles as pathogenic biomarkers in traumatic brain injury

Extracellular vesicles (EVs) contribute to the damage caused by traumatic brain injury (TBI) and can cross the blood-brain barrier (BBB). We analyzed plasma-derived EVs from human TBI patients to identify factors potentially contributing to TBI pathology. EVs were isolated using membrane affinity (ExoEasy) and size exclusion chromatography (iZone), both yielding CD9(+) and CD63(+) EVs with minimal contamination by serum albumin and apolipoprotein. Immunoblotting detected GFAP in TBI but not control EVs, indicating astrocyte-derived EVs crossing the BBB. Proteomic analysis and immunoblotting of EVs from TBI samples identified C-reactive protein and 14-3-3 proteins, which were not detected in control EVs, indicating inflammation associated with TBI. Lipidomic analysis showed ceramide enrichment in TBI EVs, validated by anti-ceramide immunoprecipitation. In a mouse closed head-controlled cortical impact model, brain EVs similarly showed elevated ceramide, confirming ceramide-rich EV release after TBI. Immunocytochemistry localized acid sphingomyelinase (ASM), a ceramide-generating enzyme, to ependymal cilia, suggesting these sites as a potential source of EVs. This was further supported by the detection of ASM in both brain- and plasma-derived EVs, along with the ciliary marker Arl13b in the brain. To assess function, we treated murine neuronal (N2a) cells with TBI EVs. Transcriptomics and STRING analyses revealed enrichment of mitochondrial-associated transcripts. Immunoblotting showed increased p53 and voltage-dependent anion channel 1 (VDAC1), which mediate ceramide-induced apoptosis. Seahorse assays showed that TBI EVs suppressed glycolysis, as indicated by reduced ECAR, while mitochondrial respiration (OCR) remained unchanged. LDH assays further indicated that TBI EVs were more neurotoxic than control EVs. Together, these findings identify ceramide-rich EVs as plasma biomarkers of TBI-induced inflammation, potential mediators of neuronal mitochondrial dysfunction, and pharmacological targets to prevent TBI-induced damage.

neuroscience↗

bioRxiv2026

Physical continuity at biomaterial-ECM interfaces regulates fibroblast activation via NF-κB

Fibrotic responses at biomaterial-tissue interfaces limit implant integration and regenerative healing, yet how the interaction between biomaterials and the extracellular matrix (ECM) regulates fibroblast activation remains poorly understood. Granular hydrogels including microporous annealed particle scaffolds (MAP) reduce fibrosis, while chemically and mechanically matched hydrogels do not, suggesting a dominant role for scaffold architecture. In this model, MAP scaffolds allow collagen infiltration and form physically continuous composites, whereas hydrogels exclude collagen and generate interfacial slip planes. To isolate how biomaterial architecture influences extracellular matrix (ECM) integration and fibroblast activation, we developed a reductionist in vitro model that integrates collagen type I with either microporous annealed particle (MAP) scaffolds or chemically and mechanically matched bulk hydrogels. This physical integration stabilizes collagen architecture, limits fibroblast-mediated matrix compaction, suppresses contractility, and attenuates myofibroblast transition. Fibroblasts in mechanically integrated environments exhibit reduced expression and nuclear localization of NF-{kappa}B and are enriched for quiescent phenotypes. Together, these findings identify biomaterial-ECM physical continuity as a design principle for limiting fibrotic signaling.

bioengineering↗

bioRxiv2026

Novel Engineered AAV Variants Demonstrate Superior Blood-Brain Barrier Penetration and Safety in Non-Human Primates

Systemic delivery of adeno-associated virus (AAV) for gene therapy of central nervous system (CNS) disorders is limited by inefficient blood-brain barrier (BBB) penetration and dose-limiting toxicity in peripheral organs, notably the liver and dorsal root ganglia (DRG)1-5. Here, we report the development of novel AAV variants via a proprietary capsid engineering platform (REACH). In non-human primates (NHPs), intravenous administration of lead variants resulted in transgene expression levels in the brain that were 600-2000 fold higher than AAV9 at the RNA level, concomitant with a 10-50 fold reduction in liver tropism and minimal off-target exposure in the heart and DRG. These engineered capsids achieve unprecedented, pan-CNS transduction with a markedly improved safety profile, representing a transformative platform for treating a broad spectrum of neurological diseases.

neuroscience↗

bioRxiv2026

Predicting Unseen Gene Perturbation Response Using Graph Neural Networks with Biological Priors

Predicting transcriptional responses to genetic perturbations is a central challenge in functional genomics. CRISPR Perturb-seq experiments measure gene expression changes induced by targeted perturbations, yet experimentally testing all possible perturbations remains infeasible. Computational models that infer responses for unseen perturbations are therefore essential for scalable functional discovery. We introduce PerturbGraph, a biologically informed graph-learning framework for predicting transcriptional responses of unseen gene perturbations by integrating interaction networks, functional annotations, and transcriptional features. Our approach is motivated by the observation that perturbation effects propagate through molecular interaction networks and manifest as coordinated transcriptional programs. Starting from single-cell CRISPR perturbation data, we construct perturbation signatures representing expression shifts relative to control cells and project them into a compact latent program space that captures stable transcriptional variation while reducing noise. Each gene is represented using enriched biological features integrating protein-protein interaction network embeddings, network topology statistics, baseline transcriptional characteristics, and Gene Ontology annotations. A graph neural network propagates information across the interaction network to infer perturbation programs for genes whose effects are not observed during training. Across unseen-perturbation benchmarks, PerturbGraph consistently outperforms classical machine learning models, perturbation-specific deep learning approaches such as scGen and CPA, and alternative graph neural architectures. The model achieves up to 6% improvement in cosine similarity over strong tree-based baselines and more than 20% improvement over linear models while improving recovery of differentially expressed genes. These results show that integrating biological interaction networks with graph representation learning enables accurate prediction of transcriptional effects for previously unobserved genetic perturbations. Code is publicly available at https://github.com/Sajib-006/PerturbGraph.

bioinformatics↗

bioRxiv2026

LRP6-Guided Engineering of AAV9 Variants with Enhanced Blood-Brain Barrier Penetration and Reduced Liver Tropism in Non-Human Primates

The blood-brain barrier (BBB) severely restricts the delivery of systemically administered adeno-associated virus (AAV) vectors for central nervous system (CNS) gene therapy. To overcome this limitation, we engineered a library of AAV9 capsid variants through rational design focused on the low-density lipoprotein receptor-related protein 6 (LRP6), a conserved mediator of transcytosis. A multi-tiered screening strategy, encompassing human BBB endothelial cells followed by neuronal and glial target cells in vitro, identified three lead variants (QL9-21, QL9-22, and QL9-25) with markedly enhanced transduction potential. In mice, these variants achieved a 5-28 fold increase in brain-wide gene delivery compared to AAV9, without elevating hepatic tropism. Crucially, evaluation in non-human primates (NHPs) revealed that the lead variant, QL9-21, mediated a striking 3-40 fold enhancement in viral genome delivery across all examined brain regions versus AAV9, while concurrently reducing liver accumulation by 2.6 fold. Our study establishes an LRP6-guided engineering platform that yields novel AAV9 vectors capable of efficient, species-conserved BBB penetration coupled with a favorable safety profile, representing a significant advance toward clinically translatable CNS gene therapies.

neuroscience↗