Biology subjects

Hu, H.

Publications and source records attributed to Hu, H..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Treating Myotonic Dystrophy with artificial RNA endonucleases to specifically degrade toxic RNA expansions

Myotonic dystrophy type 1 (DM1), the most common autosomal dominant muscular disorder, is driven by expanded CUG repeats in the 3' UTR of DMPK gene, which sequester RNA-binding proteins and cause aberrant alternative splicing. Despite extensive study, no effective treatment exists, and current care remains limited to symptomatic management. Using a customized dual-color reporter carrying RNA repeats in different lengths, we engineered a new class of programmable artificial enzymes (artificial RNA cleavers, or ARCs) that preferentially degrade the pathogenic repeats both in cells and in a mouse model. We further examined the in vivo efficacy of ARCs by systematically delivering ARCs into the DM1 mouse model with muscle-specific adeno-associated viruses, and validated that ARCs can rescue the muscular pathology and movement ability. The treatment by ARCs showed sustained efficacy, low immunogenicity, and minimal off-target effects in animal model, which are key concerns in clinical translation of gene therapies. Collectively, these findings establish ARCs as a potent and precise RNA-targeting platform with strong clinical potential for the treatment of DM1 and other repeat expansion disorders.

synthetic biology↗

bioRxiv2025

Employing the intravesical delivery route to target on the kidney

Background and ObjectiveThere is an unmet demand for kidney-targeted non-invasive drug-delivery systems to enhance the therapeutic index and at the same time reduce extrarenal side effects. Here, we performed a proof-of-concept study to evaluate the validity and efficacy of intravesical delivery for kidney targeting in mice. MethodsProbes with a wide range of molecular sizes were tested for their efficiency to reach the kidney via intravesical delivery in female mice. The therapeutic efficacy and side effects of pazopanib in treating a renal adenocarcinoma were compared between delivery via intravesical route vs. intraperitoneal route. Intravesical delivery of empagliflozin was tested for its action on the proximal tubules. Finally, intrapelvic infusion was examined as another retrograde route for targeting the kidney. Key findings and limitationsIntravesical infusion was valid for a retrograde delivery of molecules up to 500 kDa to the kidney. Empagliflozin, an antagonist of sodium-glucose cotransporter 2 (SGLT2), could efficiently act on the proximal tubules via the intravesical route, and had an action on glucose excretion. In an orthotopic kidney carcinoma model, intravesical delivery of pazopanib was more efficacious in limiting tumor growth, accompanied with much milder adverse effects on extrarenal organs, compared to a systemic delivery when a same dose was administered. This was due to a higher intrarenal drug concentration achieved and a markedly lower level of drug leaked to the blood via intravesical delivery. Conclusions and Clinical ImplicationsThe intravesical delivery route has a great potential to benefit kidney therapeutics and research.

physiology↗

bioRxiv2025

A conditional generative model to disentangle morphological variation from batch effects in model organism imaging studies

Identifying organism-level phenotypic variation that arises from genetic variation is a longstanding problem in genetics. Model organisms such as zebrafish are frequently used for genotype to phenotype studies as they can be genetically manipulated, grown and phenotyped for morphological changes in a high throughput manner through automated systems and imaging. However, individuals from model organisms are typically grown in groups: clutches for zebrafish and frogs, and litter for rodents. These clutches act as strong confounders during image analysis, as individuals from different genotypes but grown in the same clutch tend to look more similar to each other than to individuals with the same genotype in other clutches. Existing approaches such as conditional image classification models and domain adaptation approaches perform poorly on addressing these technical batch effects. Here, we propose a conditional latent diffusion model (cLDM) that disentangles these technical batch effects from morphological features by explicitly conditioning on batch-specific variables during generation, enabling targeted separation of technical artifacts from biologically relevant data. This approach enables accurate classification of genotypes using morphological images of individual zebrafish from distinct mutant classes. Furthermore, this approach allows us to efficiently correct for batch effect and demonstrates the versatility of cLDM in tackling domain-specific problems. This work highlights the potential of cLDM to overcome batch effects and extract meaningful features.

bioinformatics↗

bioRxiv2025

Central nervous system atlas of larval zebrafish constructed using the morphology of single excitatory and inhibitory neurons

Single-neuron morphology mapping is fundamental for deciphering brain architecture and function, yet in vertebrates it remains limited in spatial coverage and cell-type information. Here we present a nervous system-wide single-neuron morphology atlas of larval zebrafish with annotated excitatory/inhibitory identity and dendrite-axon polarity. The atlas comprises >20,000 reconstructed neurons from >13,400 animals, spanning the central and peripheral nervous system and representing [~]24.5% of the total excitatory and inhibitory neurons in the brain, all integrated into a common physical space with neuroanatomical parcellations. We define >500 excitatory and inhibitory neuronal morphotypes and construct putative inter-cell-type and interregional connectomes. Projection and morphotype analyses reveal distinct laterality and directionality biases and modular innervation patterns in excitatory and inhibitory neurons, while network analyses uncover fundamental connectivity motifs, hubs, and sensorimotor pathways. Accessible through custom-built interactive platform ZExplorer, this atlas provides a foundational resource for multimodal data integration, circuit analysis, computational modeling, and hypothesis-driven research.

neuroscience↗

bioRxiv2025

DeepPGDB: A Novel Paradigm for AI-Guided Interactive Plant Genomic Database

DeepPGDB (https://www.deeppgdb.chat) is the first AI-driven plant genomics database designed to lower technical barriers in multi-omics research by enabling natural language-based data access and analysis. Integrating over 50 high-quality plant genomes, DeepPGDB combines fine-tuned large language models (LLMs) with prompt engineering to interpret user queries, generate standardized bioinformatics commands, and retrieve or visualize genomic data. Key functionalities include sequence retrieval, BLAST alignment, gene localization, expression profiling, and population genetics analysis, all presented via an intuitive conversational interface. A summarization module further enhances biological reasoning, inferring insights such as haplotype differentiation or protein properties. Benchmarking revealed that Deepseek-r1:14b optimized for short pre-prompts delivers high accuracy and speed. By bridging computational and biological expertise, DeepPGDB democratizes genomic research, fostering interdisciplinary collaboration and accelerating discoveries in agriculture and biotechnology.

bioinformatics↗

bioRxiv2025

Novel features of miRNA and isomiR-mRNA interactions

Studying the interactions between microRNAs/isomiRs and mRNAs is critical due to their fundamental roles in gene regulation and their involvement in various diseases. Although many isomiRs have been identified, the analysis of their interactions with mRNAs remains in its early stages. In this study, we compiled available human chimeric read data, each comprising a microRNA or isomiR segment paired with an mRNA fragment and identified 1,747 isomiRs and over 5 million microRNA/isomiR-mRNA interactions. We observed that microRNAs with higher adenine and thymine content, and lower cytosine content, tend to have more isomiRs and more mRNA targets. Notably, an average of 18.9% of mRNA targets were bound exclusively by isomiRs, not by their microRNA counterparts. Furthermore, isomiRs sharing the same seed sequences as their reference microRNAs may bind to different targets from their microRNAs, highlighting functional divergence. Interestingly, 20.0% of microRNAs and 8.2% of isomiRs appear to bind mRNAs independently of their seed regions. Among those that do utilize seed regions, 94.5% of microRNAs and 95.7% of isomiRs also engage non-seed regions, suggesting a broader and more complex binding behavior. Our findings offer new insights into microRNA/isomiR-mRNA interactions.

bioinformatics↗

bioRxiv2025

Deep learning inference of miRNA expression from bulk and single-cell mRNA expression

Understanding the activity of miRNA in individual cells presents a challenge due to the limitations of single-cell technologies in capturing miRNAs. To tackle this obstacle, we introduce two deep learning models: Cross-Modality (CM) and Single-Modality (SM). These models utilize encoder-decoder architectures to predict miRNA expression at the bulk and single-cell levels from mRNA data. We compared CM and SM with a state-of-the-art approach, miRSCAPE, using both bulk and single-cell datasets. We found that both CM and SM outperformed miRSCAPE in terms of accuracy. We also observed that integrating miRNA target information led to a significant enhancement in performance compared to using all genes. These models offer valuable tools for predicting miRNA expression from single-cell mRNA data.

bioinformatics↗

bioRxiv2025

Targeting Lysosomal Dysfunction to Alleviate Plaque Deposition in an Alzheimer Disease Model

Alzheimer disease (AD) is characterized by aberrant amyloid precursor protein (APP) processing and lysosomal dysfunction. This study identifies two members of the lysosomal multi-enzyme complex (LMC), neuraminidase 1 (Neu1) and protective protein/cathepsin A (PPCA), as a critical regulators of APP metabolism. Neu1 deficiency in human AD brains and 5xFAD/Neu1-/- mice leads to sialic acid retention on APP and its secretases, enhancing amyloidogenic cleavage and A{beta}42 production. Additionally, Neu1 deficiency increases lysosomal exocytosis, contributing to extracellular A{beta} release and neuroinflammation. Conversely, overexpression of PPCA in neurons or co-expression of PPCA and NEU1 normalizes sialylation patterns, reduces secretase activity, and mitigates plaque burden. These findings reveal a novel bidirectional dependency between Neu1 and PPCA, underscoring their cooperative role in maintaining lysosomal homeostasis. Additionally, AAV-mediated co-expression of NEU1 and PPCA in 5XFAD brains demonstrates therapeutic potential by reducing amyloid pathology. These findings position lysosomal dysfunction and the Neu1-PPCA axis as promising targets for therapeutic intervention in AD.

neuroscience↗

bioRxiv2025

Microengineered transplantation of human solid tumors for in vitro studies of CAR T immunotherapy

Treatment of solid malignancies using chimeric antigen receptor (CAR) T cells remains a significant challenge, but current efforts to advance this therapy are challenged by our limited capacity to probe and understand cancer-immune interactions in human solid tumors. Here, we present a microengineered platform for in vitro modeling of malignant solid tumors during CAR T therapy. This system makes it possible to vascularize human tumor explants and perfuse them with blood-borne immune cells in a controlled manner. We first present a microphysiological model of human lung adenocarcinomas infused with CAR-T cells and show how this system can be used to simulate, visualize, and interrogate tumor-directed trafficking and effector function of CAR T cells. We then demonstrate the proof-of-principle of testing a chemokine-directed CAR T cell engineering strategy in a model of malignant pleural mesothelioma and validating our in vitro assessment using a matching in vivo mouse model. Finally, we describe a potential therapeutic target discovered by single-cell RNA sequencing that can be pharmacologically modulated to increase the efficacy of CAR T cells for lung adenocarcinoma, for which we also present specific biomarkers identified by global metabolomics analysis. We believe that the bioengineering principle demonstrated here will make important contributions to developing new capabilities for preclinical studies of adoptive cell therapies for cancer and other complex diseases.

bioengineering↗

bioRxiv2025

mRNA-delivered neutralizing antibodies confer protection against SARS-CoV-2 variant in the lower and upper respiratory tract

Monoclonal antibodies (mAbs) have been developed as effective biological countermeasures against a range of human diseases. The high cost of antibody production and manufacturing limits its clinical application and widespread use. The mRNA-lipid nanoparticle (mRNA-LNP) is a versatile platform for development of vaccines and protein-replacement therapeutics. Since the COVID-19 pandemic, a number of neutralizing mAbs against SARS-CoV-2 have been identified with several being used clinically under emergency authorization. Herein, we report the design and generation of mRNA-LNPs expressing two SARS-CoV-2 neutralizing mAbs, 76E1 and LY1404, which respectively target the viral spike proteins fusion peptide (FP) epitope within the S2 subunit and the receptor-binding domain (RBD) within the S1 subunit. We show a single intramuscular administration of mRNA-LNPs results in efficient LY1404 and 76E1 mAb production in mice which is sustained for 7-14 days. Further, we evaluate the protective efficacy of mRNA-LNP formulations encoding the two antibodies in mouse and hamster models challenged with different SARS-CoV-2 viral strains. The data demonstrate that a single administration of mRNA-LNP encoding the more broadly neutralizing antibody 76E1 confers significant protection against the immune-evasive SARS-CoV-2 Omicron variant BQ.1 in both the upper and lower respiratory tract of the hamsters, indicating its potential impact on limiting both viral disease and viral acquisition. Together, our study expands the potential of the mRNA-LNP platform to deliver therapeutic antibodies for rapid prevention or treatment of pathogenic infections.

microbiology↗

bioRxiv2025

Reference-guided genome assembly at scale using ultra-low-coverage high-fidelity long-reads with HiFiCCL

Population genomics using short-read resequencing captures single nucleotide polymorphisms and small insertions and deletions but struggles with structural variants (SVs), leading to a loss of heritability in genome-wide association studies. In recent years, long-read sequencing has improved pangenome construction for key eukaryotic species, addressing this issue to some extent. Sufficient-coverage high-fidelity (HiFi) data for population genomics is often prohibitively expensive, limiting its use in large-scale populations and broader eukaryotic species and creating an urgent need for robust ultra-low coverage assemblies. However, current assemblers underperform in such conditions. To address this, we propose HiFiCCL, the first assembly framework specifically designed for ultra-low-coverage high-fidelity reads, using a reference-guided, chromosome-by-chromosome assembly approach. We demonstrate that HiFiCCL improves ultra-low-coverage assembly performance of existing assemblers and outperforms the state-of-the-art assemblers on human and plant datasets. Tested on 45 human datasets ([~]5x coverage), HiFiCCL combined with hifiasm reduces the length of misassembled contigs relative to hifiasm by an average of 21.19% and up to 38.58%. These improved assemblies enhance germline structural variant detection, reduce chromosome-level mis-scaffolding, enable more accurate pangenome graph construction, and improve the detection of rare and somatic structural variants based on the pangenome graph under ultra-low-coverage conditions.

bioinformatics↗

bioRxiv2025

Cortactin regulates metastatic dormancy of circulating tumor cells by suppressing mTOR-dependent senescence

Metastatic dormancy often refers to the stable cell cycle arrest of disseminated tumor cells (DTCs) at distant sites. However, whether circulating tumor cells (CTCs) in the blood microenvironment can enter a dormant state prior to extravasation and becoming DTCs remains unclear. Using patient-derived melanoma CTC lines and animal explant models (CDX), we identified a previously unrecognized role of the cytoskeletal regulator cortactin (encoded by CTTN) in controlling mTOR/p53-dependent senescence and metastatic dormancy. Cortactin was localized to Rab7-postive endosomes and engaged in late endosomal tethering and homeostasis. The depletion of cortactin resulted in the accumulation of aberrantly enlarged late endosomal aggregates that were positive for Rab7 and mTOR. The mTOR protein complex was accumulated and activated within these abnormal vesicular structures, leading to robust p53 activation through phosphorylation at serine (S) 15 and S33 sites. Consequently, melanoma CTCs underwent G0/G1 cell cycle arrest and entered cellular senescence. This unusual oncogene-induced senescence (OIS) mechanism was characterized by SASP upregulation, {beta}-galactosidase activity, depletion of Ki-67 and Lamin B1, and elevated mitochondrial ROS (mtROS) levels. Notably, a positive feedback loop between p53 and mtROS was essential for maintaining stable senescence in CTCs. In preclinical CDX mouse models, we developed a sequential therapeutic strategy combining cortactin depletion with anti-Bcl-xL senolytic drugs. Such "One-two punch" treatment strategy effectively eliminated viable CTCs and suppressed metastatic tumor growth in vivo. Thus, targeting cortactin to induce CTC senescence, followed by senolytic therapy, may represent a promising strategy to block CTC-mediated metastatic progression.

cancer biology↗

bioRxiv2025

Highly heterozygous Citrus changshan-huyou Y. B. Chang originated from ancient hybridization between mandarin and pummelo and displayed distinct tissue-specific allelic imbalance

The genus Citrus is characterized by a reticulate evolutionary history with frequent hybridization, making it an intriguing subject for genome evolution investigation. Citrus changshan Y. B. Chang (Huyou) is a unique landrace first discovered in Zhejiang Province, China with premium fruit quality. The evolutionary origin of Huyou has puzzled local botanists and growers. Here, we sequenced a 120-years-old "ancestral tree" of Huyou using PacBio long read and Hi-C sequencing and assembled 2 high-quality haplotype-resolved genomes HY1 and HY2. Huyou displayed a genome heterozygosity level at 3.07%, among the highest in published citrus genomes. Using a k-mer-based tracing approach, we explicitly resolved that HY1 genome contained 87.8% mandarin, 7.3% pummelo, 0.2% citron origin, whereas HY2 had 85.0% pummelo, 2.9% mandarin, 0.3% citron, implying a hybridization event between mandarin and pummelo. Phylogeny dating showed that HY1 (2.0 Mya) and HY2 (2.18 Mya) had diverged earlier than the split of Citrus clementina and Citrus reticulata, and the split of Citrus grandis and Citrus maxima, respectively. We observed clear chromosomal recombination on chr8 and chr9 in HY1, which may have occurred after the ancestral hybridization. Further transcriptome analyses in 6 tissues revealed a strong allelic dominance of HY2 over HY1 in root tissue and moderately in stem, leaf, flower, and fruits. KEGG enrichment analyses revealed that genes related to antioxidants biosynthesis and lipid metabolisms were most significantly affected by allelic imbalance. This first report of allelic imbalance in citrus species support Huyou as an interesting model to investigate genome evolution following distant hybridization.

evolutionary biology↗

bioRxiv2025

Visible-Light-Controlled Residue-Selective Cross-Linking for Deciphering Protein Complexes and Dynamic Protein-Protein Interactions in Live Cells

Cross-linking mass spectrometry (XL-MS) has emerged as an attractive technology for investigating protein complexes and protein-protein interactions (PPIs). However, commonly used cross-linking strategies present significant challenges for precise analysis of protein complexes and dynamic PPIs in native biological environments. Here we present the visible-light-controlled lysine-selective cross-linking (VL-XL) strategy for in-depth analysis of protein complexes and dynamic PPIs both in vitro and in live cells, building on light-induced primary amines and o-nitrobenzyl alcohols cyclization (PANAC) chemistry. We demonstrate that the VL-XL strategy effectively explores the dynamic dimerization of PD-L1 stimulated by exogenous modulators. Moreover, the VL-XL strategy successfully profiles the time-resolved EGF-stimulated EGFR interactome, providing valuable insights into the regulatory mechanisms of EGFR signaling and intracellular trafficking. Importantly, the VL-XL strategy efficiently deciphers the molecular glue (MG) induced dynamic PPIs and substrate profile of MG degrader, opening an innovative avenue for identifying neo-substrates. By harnessing the advantages of temporal controllability, good biocompatibility, and lysine selectivity, the VL-XL method simplifies MS data analysis and facilitates the acquisition of accurate structural information of protein complexes and the elucidation of elusive PPIs in live cells. Overall, the VL-XL strategy expands the XL-MS toolbox, and realizes in-depth analysis of protein complexes and dynamic PPIs, which will inspire innovative solutions for protein interactomes research and structural systems biology.

biochemistry↗

bioRxiv2025

Single-cell multiomics reveals disrupted glial gene regulatory programs in Alzheimer's disease via interpretable machine learning

Recent development of single-cell technology across multiple omics platforms has provided new ways to obtain holistic views of cells to study disease pathobiology. Alzheimers disease (AD) is the most common form of dementia worldwide, yet the detailed understanding of its cellular and molecular mechanisms remains limited. In this study, we analyzed paired single-cell transcriptomic (scRNA-seq) and chromatin accessibility (scATAC-seq) data from the Seattle Alzheimers Disease Brain Cell Atlas (SEA-AD) Consortium to investigate the molecular mechanisms of AD at a cell-subpopulation-specific resolution focusing on glial cells. We benchmarked various multi-omics integration methods using diverse metrics and built an analytic workflow that enabled effective batch correction and cross-modality alignment, creating a unified cell state space. Through integrative analysis of 26 human brain samples, we uncovered AD-associated gene expression and pathway changes in glial subpopulations and highlighted important transcriptomic and epigenomic signatures via functional inference and interpretable machine learning paradigms, discovering the profound involvement of the Solute Carrier proteins (SLC) family genes in multiple glial cell types. We also identified glial cell-specific regulatory programs mediated by key transcription factors such as JUN and FOSL2 in astrocytes, the Zinc Finger (ZNF) family genes in microglia, and the SOX family of transcription factors in oligodendrocytes. Our study provides a comprehensive workflow and a high-resolution view of how glial regulatory programs are disrupted in AD. Our findings offer novel insights into disease-related changes in gene regulation and suggest potential targets for further research and therapy.

genomics↗

bioRxiv2025

Bacillus velezensis 9912 enhances rice growth by triggering plant hormone modulation and complex miRNA-mRNA regulatory networks

Bacillus velezensis, a prominent member of plant growth-promoting bacteria, effectively suppresses pathogens and promotes plant growth. Despite its well-recognized efficacy, the intricate molecular mechanisms of its growth-promoting attributes remain largely unexplored. In this study, a commercial biopesticide B. velezensis 9912 significantly enhanced leaf photosynthesis and improved rice root development in the field experiment. And then, a rigorous hydroponic experiment was performed to investigate its underlying mechanism in plant growth promotion. High-throughput sequencing and bioinformatics analysis revealed 2,938 rice genes and 30 miRNAs responsive to strain 9912. Notably, differentially expressed genes encompassed key transcription factors like ERF, WRKY, and AP2. Remarkably, auxin-and ethylene-related genes were up-regulation significantly at day 1 and pectinesterase-related genes were up-regulation considerably at day 3, which revealed an unusual growth mechanism for strain 9912 to promote rice root development. In addition, DNA replication, cell cycle, cell division, and cytoskeleton organization were significantly enriched in GO and KEGG pathways. Biosynthetic pathways of secondary metabolites, such as carotenoid, lipid, diterpenoid, and brassinosteroid were also significantly enriched. An integrated analysis of the transcriptome and miRNAome identified several miRNA-mRNA regulatory networks, including miR166-Os08g14940, miR396-Os07g9320, miR529-Os12g31540, and miR6249-Os05g07880, which are involved in the process of growth promotion. In conclusion, our investigation offers insightful thoughts into the dynamic responses of rice genes and miRNAs to B. velezensis 9912, unveiling plant hormone modulation and potential miRNA-mRNA regulatory networks that are imperative in the growth promotion mechanism.

microbiology↗

bioRxiv2025

Quantitative and large-scale investigation of human TCR-HLA Cross-Reactivity

The interaction between Human leukocyte antigens (HLA) and T cell receptor (TCR) is essential for adaptive immune recognition. While it is known that one TCR can map to multiple HLA alleles, the extent of this cross-reactivity remains poorly understood. Here, we introduce THNet, a TCR-based HLA similarity inference method, and performed a comprehensive analysis of HLA-TCR cross-reactivity. This method is built upon clustering over 47 million TCRs to identify over 9 million significant HLA-TCR pairs. We created similarity networks for both class I and class II HLA alleles, illustrating how peptide cross-presentation contributes to HLA-TCR cross-reactivity. This analysis revealed novel disease-susceptibilities missed by single-HLA enrichment analyses, especially in the Black populations. Finally, we demonstrated that THNet prioritized optimal HLA mismatch candidates for organ transplantation, thereby improving patient survival rates. Our investigation of HLA-TCR cross-reactive network might provide useful insights for autoimmune risk prediction and better transplantation outcomes. One Sentence SummaryWe introduced THNet, a large-scale TCR-based HLA similarity mapping network that uncovers previously unrecognized cross-reactivity patterns across HLA alleles and provides valuable insights into their influence on disease susceptibility and graft rejection.

bioinformatics↗

bioRxiv2025

sciLaMA: A Single-Cell Representation Learning Framework to Leverage Prior Knowledge from Large Language Models

Single-cell RNA sequencing (scRNA-seq) enables high-resolution exploration of cellular diversity and gene regulation, yet analyzing such data remains challenging due to technical and methodological limitations. Existing task-specific deep generative models like Variational Auto-Encoder (VAE) and its variants struggle to incorporate external biological knowledge, while transformer-based foundational large Language Models (LLMs or large LaMs) face limitations in computational cost and applicability to tabular gene expression data. Here, we introduce sciL-aMA (single-cell interpretable Language Model Adapter), a novel representation learning frame-work that bridges these gaps by integrating static gene embeddings from multimodal LLMs with scRNA-seq tabular data through a paired-VAE architecture. Our approach generates context-aware representations for both cells and genes and outperforms state-of-the-art methods in key single-cell downstream tasks, including batch effect correction, cell clustering, and cell-state-specific gene marker and module identification, while maintaining computational efficiency. sciL-aMA offers a computationally efficient, unified framework for comprehensive single-cell data analysis and biologically interpretable gene module discovery. Source code is available at https://github.com/microsoft/sciLaMA

bioinformatics↗