Biology subjects

Zhang, Z.

Publications and source records attributed to Zhang, Z..

At least 181 records · Page 10Linked to original sources

bioRxiv2026

Deep-Plant: a supervised foundation model for plant regulatory genomics

Large-scale sequence-to-function deep learning models have demonstrated unparalleled ability to model biological sequences and have revolutionized the field of regulatory genomics. However, the majority of such efforts have centered on human and mammalian systems, leaving plant regulatory genomics comparatively underexplored. To address this gap, we introduce DO_SCPLOWEEPC_SCPLOW-PO_SCPLOWLANTC_SCPLOW, a supervised foundation model trained to predict chromatin state directly from genomic sequence. In contrast to large language models, which are trained in a selfsupervised manner using sequence alone, our model is trained to predict chromatin state across tissues and conditions. Training the model on a large collection of genome-wide experiments including DNA accessibility, transcription factor binding, and histone modifications, provides it with added biological context beyond the sequence itself. We demonstrate that the resulting model is an effective platform for developing accurate models of regulatory activity relevant to gene expression and active enhancers, exhibiting large improvements in speed, accuracy, and interpretability over the complementary approach of fine-tuning DNA language models. DO_SCPLOWEEPC_SCPLOW-PO_SCPLOWLANTC_SCPLOW models are available in Arabidopsis and rice, and work well as a building block for sequence modeling in related species such as corn. Together, these results establish supervised, chromatin-informed foundation models as a practical and effective paradigm for regulatory sequence modeling in plants.

genomics↗

bioRxiv2026

Transcription Factor Subtype Governs Response and Resistance to DLL3-Directed T-Cell Engagement in Small Cell Lung Cancer

Although small cell lung cancer (SCLC) comprises transcription factor (TF)-defined molecular subtypes (ASCL1, NEUROD1, POU2F3), the extent to which these subtypes predict response to clinically effective therapy in patients--and whether therapy can select for subtype switching--remains unknown. The recent approval of the DLL3xCD3 bispecific T-cell engager tarlatamab represents one of the first meaningful advances in relapsed small cell lung cancer (SCLC) in decades, yet responses remain heterogeneous and resistance is inevitable. Here, we inferred SCLC gene expression from circulating chromatin in prospectively collected patient plasma (46 patients; 167 samples), enabling interrogation of response and acquired resistance to tarlatamab. Parallel development of the first immunocompetent syngeneic mouse model to study tarlatamab response and resistance enabled functional validation. Across species, findings converged on a central principle: TF subtype governs both initial response and acquired resistance. Therapeutic response was significantly associated with ASCL1-subtype tumors, whereas NEUROD1-subtype tumors exhibited inferior responses and POU2F3-subtype tumors were uniformly resistant, consistent with DLL3 being a direct ASCL1 transcriptional target and most highly expressed in ASCL1-positive tumors. Strikingly, one mode of acquired resistance revealed therapeutic selection for a NEUROD1-high state with concomitant DLL3 downregulation. Other resistant tumors exhibited enrichment of regulatory and exhausted T-cell programs, highlighting tarlatamabs dual-targeting mechanism of action. Together, these results reveal that tarlatamab exerts selective pressure against ASCL1-driven lineages, facilitating resistance through loss of an antigen intrinsically linked to that state. These findings underscore the clinical relevance of TF-defined molecular subtypes in human SCLC. More broadly, they highlight the power of integrating longitudinal in vivo plasma transcriptional profiling from patient plasma with functional mouse modeling to uncover clinical and biological mechanisms of response and resistance to cell-surface-targeted therapies.

cancer biology↗

bioRxiv2026

Density-dependent facilitation of livestock by small mammal ecosystem engineers

Small mammals and large herbivores have co-evolved in grasslands for millions of years, yet how they interplay remains unclear. Although large herbivores can significantly affect the smaller ones, the reversal effect is largely unknown. On the Qinghai-Tibetan Plateau, plateau pikas (Ochotona curzoniae) are often considered pests that compete with livestock at high densities. Using field experiments, we show that the presence of pikas facilitates weight gains of yaks (Bos grunniens) by improving vegetation composition at a moderate density level. Compared to the pika-present treatment, pika removal dramatically increased cover of the poisonous Stellera forbs by two-fold, reducing the abundance and protein content of palatable grasses and sedges, yak foraging efficiency, and yak weight gain by up to 42%. Notably, we found a humped relationship between yak weight gains and pika burrow densities in the pika-present plots; the facilitative effect of pikas on yaks was highest at about 200 burrows/ha, but shifted to a competitive effect at pika densities exceeding 400 burrows/ha. These results provide the first empirical evidence that maintaining a moderate density of small mammalian herbivores can benefit growth performance of livestock by improving vegetation composition. Our study highlights the significance of moderate populations of ecosystem-engineering small mammals in sustaining pastoral productivity in rangelands.

ecology↗

bioRxiv2026

Somatic DNA methylation heterogeneity predicts extreme transgenerational epimutation hotspots in Arabidopsis

Spontaneous epimutations are stochastic and heritable changes in cytosine methylation that arise independently of DNA sequence alterations. In plants, they occur predominantly at CG sites, accumulate across generations at high and clock-like rates, and contribute substantially to constitutive epigenetic diversity. Although these features are well established, the developmental origin of spontaneous epimutations remains poorly understood. One model proposes that they arise during somatic growth in different cell layers of the shoot apical meristem, where lineage bottlenecks enable nascent epimutations to clonally propagate into developing organs. Such layer-specific dynamics are expected to generate spatial mosaics of methylation states within tissues that manifest as heterogeneity in bulk bisulfite sequencing data. Here, we quantified genome-wide CG methylation heterogeneity in single Arabidopsis leaves using read-level methylation discordance metrics adopted from cancer epigenomics. We identified thousands of highly heterogeneous loci and show that they overlap extreme transgenerational epimutation hotspots. Analysis of multiple leaves from the same individuals further revealed that methylation divergence at these loci scales with developmental distance and recapitulates the branching architecture of the shoot. Together, these results support a meristematic origin of spontaneous epimutations and highlight a shared susceptibility to methylation-maintenance errors in both developmental and transgenerational contexts.

genetics↗

bioRxiv2026

Therapeutic Stress-induced Activation of PGCC Life Cycle Drives the Resistance Acquisition and Structured Tissue Differentiation

To elucidate how cancer cells generate resistance and defined forms of tissue structure in response to therapeutic stress, we tracked the temporal dynamics of life cycle of polyploid giant cancer cells (PGCCs) induced by the mitotic destabilizer vincristine (VCR). Live-cell fluorescence imaging revealed that VCR activated a distinct endoreplication-based life cycle, which replaced canonical mitosis. PGCCs exhibited reduced proliferative activity, enhanced epithelial-mesenchymal transition (EMT), progressive acquisition of blastomere-like features, and broad multilineage differentiation potential. Both PGCC populations and single PGCC-derived progeny displayed time- and dose-dependent acquisition of malignant traits in vitro and tumorigenic capacity in vivo. PGCC-derived spheroids exhibited ability to differentiate into the cells of origin from three germ layers. Importantly, pre-budding PGCCs induced by higher VCR concentrations exhibited enhanced ability for glandular structure formation and tissue differentiation. Morphologically, the nuclei of PGCC-derived spheroids underwent growth in size, formation of luminal structure, and followed by maturation of lumen. Mechanistically, PGCCs entered a senescent state characterized by elevated senescence-associated secretory phenotype (SASP)-manifested by rich proinflammatory cytokines. Notably, silencing IL1{beta}, IL6, or IL8, or pharmacological inhibition of their receptors, suppressed PGCC formation, budding, EMT, and blastomere-like reprogramming into structured tissue. Our studies provide novel mechanistic insights into early embryogenesis and tumorigenesis at the tissue structural developmental level.

cancer biology↗

bioRxiv2026

A Haplotype-resolved Telomere-to-Telomere Pig Genome

Haplotype-resolved telomere-to-telomere (T2T) pig genomes have not been reported, but may be essential for xenotransplantation and interspecies chimerism. Here we describe fully phased paternal and maternal T2T assemblies of an Erhualian pig (Ssc_EHL), enabling chromosome-scale reconstruction at base resolution. We uncover 2,498 (paternal haplotype) and 2,577 (maternal haplotype) protein-coding loci previously unresolved in Sscrofa11.1, while maintaining high concordance for anchored gene models between haplotypes. Applying this haplotype-resolved T2T annotation to publicly available pig-to-human kidney xenograft transcriptomes further allows the resolution of xenograft cell states and reveals newly resolved, cell-type-enriched genes. Cross-species synteny and orthology analyses against human identified a subset of high-confidence pig-specific genes with limited human counterparts, thus, refining the genomic basis of interspecies divergence. By integrating these orthology features with organ transcriptomes, we further defined an Interspecies Chimerism Compatibility score to prioritize organ-enriched, highly conserved loci with minimal redundancy imbalance, providing haplotype consistent targets for engineering organ-deficient pigs. Together, this haplotype-resolved T2T pig genome provides a foundational resource for mechanistic studies and rational genome engineering in xenotransplantation and interspecies chimerism.

genomics↗

bioRxiv2026

nELAVL phosphorylation by CDKL5 regulates inter-condensates composition and communication to promote experience-dependent maturation of the visual cortex

Mutations in Cyclin-Dependent Kinase-Like 5 (CDKL5) cause CDKL5 deficiency disorder (CDD), an X-linked neurodevelopmental condition. Through a phosphoproteomic screen, we identified the neuron-specific nELAVL family of RNA-binding proteins as direct activity-dependent substrates of CDKL5. In support of this regulatory axis, single-nuclei transcriptomics of Cdkl5 knockout (KO) cortices revealed an enriched reduction in activity-dependent mRNAs. Mechanistically, we show that nELAVL proteins undergo phase separation to form biomolecular condensates, the size of which is gated by CDKL5 phosphorylation. Loss of CDKL5 leads to enlarged nELAVL condensates, which exhibit reduced binding affinity for the target mRNA Fos, resulting in its accelerated degradation. This disruption extends to inter-condensate communication: phosphodeficient nELAVL show diminished interaction with P-bodies, which themselves become enlarged in CDD mutant iNeurons. Functionally, the absence of nELAVL phosphorylation recapitulates the deficits in experience-dependent visual function observed in Cdkl5 KO mice. Our findings establish a critical molecular mechanism by which CDKL5-mediated phosphorylation governs mRNA metabolism by tuning the properties of nELAVL condensates and their communication with other biomolecular condensates, ultimately promoting experience-dependent maturation of the visual cortex.

neuroscience↗

bioRxiv2026

Novel mechanisms of chemosensory adaptation to the cave environment

Smell allows animals to find food, avoid danger, and communicate through the binding of odorants to chemosensory receptors on olfactory sensory neurons. The vision-priority hypothesis predicts an antagonistic relationship between olfaction and vision, in which olfactory ability increases as visual acuity decreases along evolutionary lineages, a tradeoff that often occurs through expansion and contraction of chemosensory receptor gene families. The Mexican tetra (Astyanax mexicanus), a fish species with both sighted surface-dwelling and blind cave-dwelling populations, presents an ideal model for exploring the mechanisms underlying this tradeoff. Here we show that although cavefish can sense odorants at lower concentrations than surface fish, they do not have an expanded repertoire of chemosensory receptors, increased sensory neuron number or density, or enhanced expression of receptors compared to surface fish. Instead, cavefish have physiological adaptations to the olfactory epithelium, including more motile cilia and decreased flow rate through the olfactory pits. Pharmacological attenuation of flow rate in the olfactory pits in surface fish increased visits to the odorant source, suggesting that the reduced flow rate in cavefish is an adaptation leading to better foraging. This unexpected evolutionary path to enhanced olfaction as a compensation for loss of vision underscores the need for mechanistic understanding of comparative genomics.

evolutionary biology↗

bioRxiv2026

Generating and navigating single cell dynamics via a geodesic bridge between nonlinear transcriptional and linear latent manifolds

Time-series single-cell RNA sequencing (scRNA-seq) captures cellular processes as sparse and unpaired snapshots, limiting our ability not only to reconstruct continuous cell state transitions, but also to navigate between states in a controlled and interpretable manner. Here we present GeoBridge, a framework modeling cellular dynamics as geodesic trajectories on the transcriptional manifold based on isometric geodesic theory. By learning the geodesic bridge mapping, the method theoretically and computationally transforms time-varying nonlinear transcriptional geodesics (original nonlinear manifold) into constant-velocity straight-line geodesics (latent linear manifold). In the learned geodesic space, continuous interpolation becomes biologically meaningful, enabling reconstruction of unobserved intermediate states and efficient navigation between distinct cellular phenotypes at a single-cell resolution. By mapping interpolated trajectories back to the original gene expression space, GeoBridge recovers smooth transcriptional programs that are robust to noise and snapshot sparsity. Leveraging the derived geodesic potentials, GeoBridge further infers pseudo-temporal trajectories with superior fidelity compared to mainstream approaches from single-snapshot scRNA-seq data without temporal annotation, and directly identifies genes that drive progression along transition paths. We demonstrate the utility of GeoBridge across diverse systems, where GeoBridge resolves EMT-MET progression in cancer stem cells and identifies stage-specific modules as well as the branching cell-fate dynamics in human pluripotent stem cells with higher reconstruction accuracy than state-of-art methods. More importantly, GeoBridge supports single-cell fate navigation in multi-target hematopoietic lineages, allowing neutrophil-biased cells to be virtually guided toward mast-cell fates along biologically plausible paths. Together, GeoBridge establishes a principled method that transforms sparse single-cell measurements into a continuous, controllable landscape for the reconstruction, navigation and manipulation of cellular state transitions.

bioinformatics↗

bioRxiv2026

HalluCodon enables species-specific codon optimization using multimodal language models

Codon optimization is widely used in transgenic crop development, plant synthetic biology, and molecular farming to improve heterologous protein expression in plant cells. Increasing availability of plant omics data now enables optimization strategies that account for species-specific sequence features. We developed HalluCodon, a customizable framework that uses multimodal language models to design coding sequences tailored to individual plant species. The framework allows users to fine tune pre-trained protein and RNA language models with their own datasets to build species-specific codon optimization models. The current implementation includes base models trained on coding sequences and proteomes from fifteen plant species. HalluCodon generates coding sequences through a hallucination-based design strategy guided by two predictive modules that evaluate coding sequence naturalness (CodonNAT) and expression potential (CodonEXP). Benchmark tests using representative proteins show that the generated sequences reproduce host-specific codon usage patterns and support high expression levels in plant systems.

bioinformatics↗

bioRxiv2026

Claw4Science: A Dataset and Platform for the OpenClaw Scientific Agent Ecosystem

Large language models have enabled a new class of scientific software in the form of AI agents that can execute research workflows across bioinformatics, drug discovery, and related domains. Among these systems, OpenClaw introduced a skill-based design that allows workflows to be expressed as structured Markdown files, lowering the barrier to contribution and enabling rapid ecosystem growth. However, this growth has led to fragmentation. Projects are distributed across independent repositories, skills vary widely in quality, naming is inconsistent, and there is no unified way to discover or compare available tools. In this work, we construct the first curated dataset of the OpenClaw scientific ecosystem. The dataset includes 91 projects organized by functional role and 2,230 skills spanning 34 scientific categories. Based on this dataset, we perform a systematic analysis of the structure, distribution, and emerging patterns of scientific agent development. To make this ecosystem accessible in practice, we further build Claw4Science, a public platform at https://claw4science.org, which is built on top of our dataset. The platform organizes projects and aggregates distributed skill repositories into a unified interface, with a focus on bioinformatics and scientific workflows, providing a practical entry point for navigating the ecosystem. Our results show that the OpenClaw ecosystem reflects a shift from isolated systems to a more modular and shareable model of scientific computation. At the same time, challenges in evaluation, reproducibility, and governance remain open. We argue that our dataset provides a foundation for future benchmark development and standardized infrastructure for scientific AI agents.

bioinformatics↗

bioRxiv2026

SSPSPredictor: A Sequence and Structure based Deep Learning Model for Predicting Phase-Separating Proteins

Liquid-liquid phase separation (LLPS) underlies the formation of biomolecular liquid condensates (also referred to membraneless organelles, MLOs), which are essential for spatially organizing various biochemical processes within cells. Proteins that play a key role in driving condensates formation are termed phase-separating proteins (PSPs). Given experimental identification of PSPs remains labor-intensive and time-consuming, multiple computational tools have been developed based on empirical features or deep learning. In this study, we propose SSPSPredictor, a novel multimodal predictive model for PSPs with folded or intrinsically disordered structures, leveraging the fusion of sequence information from a protein language model ESM-2 and structural insights from a graph neural network GVP. Compared with existing tools, SSPSPredictor achieves balanced performance in identifying endogenous PSPs, predicting relative LLPS propensities, and recognizing key regions that drive LLPS. Moreover, SSPSPredictor exhibits good interpretability in identifying driving regions along protein sequences, although no relevant supervision was provided during training. Further predictive analysis of the human proteome using SSPSPredictor reveals that the proportion of intrinsically disordered proteins (IDPs) undergoing LLPS is significantly higher than that of folded proteins. In addition, pathogenic variants, especially those located in disordered regions, exhibit higher LLPS propensity than other mutations, uncovering a link between LLPS and diseases at the amino acid level.

bioinformatics↗

bioRxiv2026

CoLa-VAE: Cell-Cell Communication-aware Variational Autoencoder with Dynamic Graph Laplacian Constraints

Single-cell RNA sequencing provides a powerful view of cellular heterogeneity, but its sparsity and dropout noise remain major obstacles for recovering biologically meaningful gene expression programs and for downstream analyses that depend on reliable expression measurements. Ligand-receptor-based cell-cell communication inference is such analysis, missing ligand or receptor expression can cause substantial false negatives in sparse single-cell data. Here, we present CoLa-VAE, a cell-cell communication-aware variational autoencoder that jointly learns latent representations and denoised expression profiles by incorporating ligand-receptor-derived communication topology through dynamic graph Laplacian regularization. Rather than treating denoising as a secondary output of representation learning, CoLa-VAE uses denoised expression to iteratively refine communication estimates and uses the resulting communication structure to guide both latent organization and expression reconstruction. In addition to improving latent space organization and producing robust denoised expression matrices, CoLa-VAE-denoised matrices also improved downstream biological analyses, including the detection of robust differential cell-cell communication programs, mitigation of batch-associated variation and enhanced spatial transcriptomic deconvolution when spatially constrained communication structure was incorporated. Together, these results establish CoLa-VAE as a communication-guided denoising and representation learning framework that recovers biologically meaningful expression signals from sparse single-cell and spatial transcriptomic data, enabling more sensitive and reliable downstream analysis.

bioinformatics↗

bioRxiv2026

Polyclonal-Monoclonal Transition in Lung Squamous Cell Carcinoma Evolution

The evolutionary trajectory of lung squamous cell carcinoma (LUSC) remains poorly defined, hindering the development of effective therapies. By integrating genomic and transcriptomic sequencing from human LUSC specimens, we delineated a polyclonal-to-monoclonal evolutionary trajectory during LUSC progression. This evolutionary pattern was corroborated by single-cell RNA sequencing, which revealed consistent tumor cell heterogeneity. Specifically, the SBS5 mutational signature was enriched and correlated with poor prognosis independent of tumor stage. By further utilizing the spontaneous LUSC mouse model to identify key genomic and genetic events in LUSC progression, we observed that the JNK pathway was inhibited and that cytoskeleton-related pathways were dysregulated during LUSC development, and identified the mutations in the JNK pathway (e.g., DACT1) and cytoskeletal regulators (e.g., KIF26A). Collectively, these findings established a polyclonal-monoclonal evolution paradigm for LUSC, potentially regulated by JNK pathways, which could benefit LUSC precision therapeutics.

genomics↗

bioRxiv2026

Temporal expectation triggers competition in working memory that leads to forgetting

Working memory (WM) provides a flexible but capacity-limited workspace for maintaining information over short intervals, whereas long-term memory (LTM) serves as a vast and enduring repository for preserving information over extended periods. Decades of research suggest that they are two distinct yet connected systems that together enable adaptive behavior. The link between WM and LTM may not be straightforward, however, as recent evidence has shown that activation-dependent competition among items in WM can weaken their representations in LTM. In the current study, we examined how dynamic competition among items for limited WM resources affects their retention in LTM. We induced competition between items by manipulating temporal expectations in a WM task with either a short (1 s) or a long (4 s) memory delay. Human participants (N = 20) initially prioritized items expected to be tested early, but shifted their priority to items expected to be tested later when the early test did not occur. Using electroencephalography (EEG) and multivariate pattern analysis (MVPA), we tracked the dynamic fluctuations in WM contents based on expected task relevance across the delay window. We linked these temporal profiles during WM with the long-term recognition performance of each item and found that forgetting was associated with a marked decrease in neural evidence for items deemed no longer relevant during the later delay period. These results demonstrate that WM representations fluctuate with temporal expectations and that the de-prioritization of items during WM maintenance is what drives their long-term forgetting.

neuroscience↗

bioRxiv2026

Vitamin D3 attenuates nitrogen mustard-induced dermal toxicity by enhancing microbial butyrate production via the intestinal VDR-α-defensin signaling pathway

Nitrogen mustard (NM)-caused severe cutaneous damage lacks effective targeted therapies. Vitamin D3 (VD3) shows promise as a therapy for NM-induced dermal toxicity; however, the underlying mechanisms remain elusive. Herein, we initially confirmed that NM induced gut flora dysbiosis, characterized by a decrease of Akkermansia muciniphila (AKK) abundance, thereby leading to butyrate reduction. Antibiotics (ABX) significantly promoted NM-induced skin injury, whereas fecal microbiota transplantation of the controls feces (HC-FMT) or AKK administration attenuated NM-induced dermal toxicity. HC-FMT or AKK significantly increased butyrate levels in feces and serum of NM-treated mice. Butyrate notably attenuated ABX-caused acceleration of NM-induced skin injury. Meanwhile, NM markedly decreased the expression of -defensins, MMP7, and VDR. NM failed to further decrease AKK abundance and BA contents in intestinal MMP7-deficient mice, which was abolished by human alpha defensin 5 (HD5) overexpression. And intestinal MMP7 deficiency enhanced NM-caused skin injury, which was markedly attenuated by HD5 overexpression, AKK transplantation, or BA supplementation. Moreover, NM also failed to further reduce MMP7 and -defensin expression, AKK abundance, and butyrate levels in intestinal VDR-silenced mice. Finally, VD3 remodeled the gut microbiome particularly enriching AKK, increased butyrate contents and promoted the expression of -defensins, MMP7, and VDR, thereby attenuating NM-induced skin damage. The protective effect of VD3 against NM-caused dermal toxicity was abolished by either ABX or intestinal-specific knockdown of MMP7 or VDR in mice; however, this impairment was reversed by butyrate or AKK. In conclusion, VD3 attenuated NM-caused dermal toxicity by promoting BA production via remodeling the gut microbiota, and this effect was partially mediated by the intestinal VDR--defensin signaling pathway. These highlight that targeting the gut flora or supplementing with BA could be potential therapies for NM-induced dermal toxicity.

molecular biology↗

bioRxiv2026

MEIsensor: a deep-learning method for mobile element insertion discovery

Mobile element insertions (MEIs) are a critical source of structural variation in the human genome, yet their accurate detection remains challenging, particularly within repetitive regions and for structurally complex insertions. While long-read sequencing enables the direct recovery of inserted sequences, current analytical pipelines rely heavily on repeat-library alignment, which can limit both computational efficiency and accuracy. Here, we present MEIsensor, a deep learning framework designed to detect and classify MEIs directly from long-read sequencing data. Unlike conventional methodologies, MEIsensor performs direct, sequence-based annotation of Alu, LINE1, and SVA insertions. Evaluated against HGSVC benchmarks using HiFi long-read datasets, MEIsensor consistently outperformed existing tools across major MEI classes while substantially improving computational efficiency, demonstrating particularly pronounced gains for structurally complex SVA insertions. Furthermore, manual curation confirmed that MEIsensor successfully identifies MEIs hidden in highly repetitive genomic regions, including centromeric higher-order repeats, that were previously absent from benchmark datasets. Together, these advancements establish MEIsensor as an efficient framework for MEI analysis that promises to significantly advance our understanding of human genomic architecture and evolution.

bioinformatics↗

bioRxiv2026

Postnatal gene restoration in succinic semialdehyde dehydrogenase deficiency (SSADHD) reveals phenotype reversibility

Succinic semialdehyde dehydrogenase deficiency (SSADHD) is a rare autosomal recessive metabolic disorder due to loss-of-function ALDH5A1 mutations impairing the catabolism of {gamma}-aminobutyric acid (GABA), the major inhibitory neurotransmitter in the brain. In SSADHD, pathologic accumulation of GABA and its metabolic by-product {gamma}-hydroxybutyrate (GHB) corresponds to a clinical syndrome dominated by developmental delay and epilepsy in half of patients with risk of sudden death in adolescence and adulthood. Brain-wide ALDH5A1 gene replacement for SSADHD is unavailable, and whether such treatment will reverse the SSADHD phenotype is unknown. We developed an inducible mouse SSADHD model, Aldh5a1lox-STOP, enabling Cre-dependent Aldh5a1 restoration to evaluate gene therapy feasibility. In the absence of SSADH, Aldh5a1lox-STOP mice exhibit hyperactivity and excessive serum GHB levels, culminating in death by [~]postnatal day 22, recapitulating the severe SSADHD condition. Systemic delivery of a blood-brain barrier (BBB)-penetrating adeno-associated virus (AAV) carrying a Cre gene to Aldh5a1lox-STOP mice leads to brain-wide SSADH restoration, serum GHB level reduction, normalization of hyperactivity, and substantial increase in survival. As a step toward clinical translation, we further assessed an AAV encompassing a functional native promoter (FLnP) of ALDH5A1 tethered to its human coding sequence, namely AAV-FLnP-hALDH5A1. Aldh5a1lox-STOP mice were effectively rescued when treated with AAV-FLnP-hALDH5A1 packaged in the blood-brain barrier (BBB)-penetrating capsid PHP.eB. These findings provide preclinical proof that SSADH gene replacement therapy is feasible and potentially effective.

neuroscience↗