Biology subjects

Hu, H.

Publications and source records attributed to Hu, H..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Loss of contractile pericytes and their impaired calcium dynamics exacerbate brain ischemic stroke of awake mice in acute and chronic phases

Ischemic stroke disrupts neurovascular uncoupling, in which neuronal activity fails to evoke appropriate microvascular blood flow responses, despite successful recanalization of upstream arteries. The cellular mechanisms underlying this dysfunction remain poorly defined. We examined Ca{superscript 2} signaling and contractile dynamics of vascular smooth muscle cells, precapillary sphincters and contractile pericytes, using two-photon microscopy and laser speckle imaging in awake mice subjected to transient middle cerebral artery occlusion. During occlusion, precapillary sphincters exhibited pronounced Ca{superscript 2} elevations and constriction, amplifying downstream capillary resistance. Following reperfusion, elevated Ca{superscript 2} signals persisted without proportional diameter changes, indicating early uncoupling between Ca{superscript 2} dynamics and vascular responses. In the chronic phase, loss of precapillary sphincters-associated contractile pericytes was associated with capillary dilation and persistent neurovascular uncoupling. Although partial recovery of pericyte coverage and Ca{superscript 2} activity occurred, stimulus-evoked vascular responses remained blunted. These findings highlight precapillary sphincters as a key contributor to ischemia-induced microvascular dysfunction.

neuroscience↗

bioRxiv2025

TUPAD: A Tn5-Mediated, Pre-amplification/Adapter-Dimer-Free Platform for Ultra-Uniform, One-Step PGT Profiling with High Stability and Broad Compatibility

High-throughput sequencing stands as a cornerstone of clinical molecular diagnostics, particularly in preimplantation genetic testing (PGT)--a key application where the selection of chromosomally normal embryos is critical to enhancing in vitro fertilization (IVF) success rates. A fundamental challenge inherent to PGT, however, lies in the ultra-low gDNA input (typically <100 pg) from embryo biopsies. To mitigate this limitation, traditional workflows rely on whole genome amplification (WGA) technologies, such as MDA, PicoPlex, and MALBAC. Yet these WGA-based protocols inherently entail pre-amplification of gDNA prior to standard library construction---a step that introduces significant coverage bias and compromised uniformity. These are intrinsic flaws irreparable by downstream library preparation or bioinformatics analysis, ultimately leading to clinical uncertainty in the interpretation of copy number variations (CNVs). In this study, we developed TUPAD (Tn5-Mediated, Uniform Pre-amplification-free and Adapter-Dimer-Free Library preparation), a transformative technology that bypasses the limitations of traditional WGA. By engineering a novel adapter-dimer-free transposon system, TUPAD enables high-efficiency library construction from as little as 1 picogram (pg) of gDNA. This breakthrough allows for a pre-amplification-free workflow, effectively eliminating the primary source of amplification bias. Compared to existing WGA standards, these advantages are fully retained in embryo biopsy and positive sample testing, TUPAD consistently generates highly uniform library data, significantly enhancing the detection limit and accuracy of CNVs and other genomic variants. Beyond these performance advantages, TUPAD also cuts wet-lab processing time by 50%. Practical validation confirms TUPAD results are stable, reliable, with proven practical utility in PGT testing.

molecular biology↗

bioRxiv2025

Towards Video-LLM Driven Workflow for Behavioral Segmentation and Scoring in Mice Performing a Skilled Water Reaching Task: An Evaluation of Recent LLM Models

SignificanceBehavior scoring is labor-intensive and subjective, introducing variability in results. Large Language Models (LLMs) capable of video understanding offer a transformative solution to manual scoring, crucial for accelerating and standardizing neuroscience workflows. AimWe sought to benchmark state-of-the-art video LLMs (Gemini 2.5 Pro, Qwen3-VL, and VideoLLaMA3) for automated behavioural segmentation and scoring of mice performing a water-reaching task. ApproachVideos of mice performing water reaching from the front view were analysed by the LLMs. Accuracy was compared across different models and against prompt adjustments within Gemini. To assess classification determinants, video fidelity was altered through pixel interpolation and key regions blurred (paws/snout-mouth). In addition, the models were asked to describe the mouses actions over time. ResultsGemini 2.5 Pro (0.74 {+/-} 0.12 accuracy) and Qwen3-VL-30B (0.67 {+/-} 0.13) exhibited ability to classify trial outcomes. Reliable classification required a minimum pixel resolution of 0.28 mm per pixel. Accuracy is significantly reduced upon obscuring the snout-mouth area. In 549/1058 of videos, Gemini 2.5 Pro also provided completely accurate frame-to-frame behaviour segmentations. ConclusionsVideo-LLMs offer potential to accelerate neuroscience by providing scalable, objective quantification of goal-directed behaviors. By producing temporal annotations, Gemini enables fast first-pass labelling that markedly streamlines manual dataset curation.

neuroscience↗

bioRxiv2025

Revised 16S rRNA V4 hypervariable region targeting primers enhance detection of Patescibacteria and other lineages across diverse environments

Primer bias in 16S rRNA gene amplicon sequencing can distort microbial diversity estimates by underrepresenting key taxa. We introduce a modified primer pair (V4-EXT) targeting the hypervariable V4 region of bacterial and archaeal 16S rRNA genes, with improved in silico taxonomic inclusivity. To benchmark performance, we analyzed 938 samples from terrestrial, aquatic, and host-associated habitats, comparing microbial community profiles derived with V4-EXT and the currently most widely used V4-targeted primers. V4-EXT substantially improved the detection of Patescibacteria and other underrepresented lineages, such as Chloroflexota and Iainarchaeota, while enhancing recovery of novel amplicon sequence variants across sample types. Overall, V4-EXT provides broader taxonomic coverage and more inclusive microbial community profiles, particularly in high-diversity ecosystems such as groundwater and soils. We propose V4-EXT as a robust successor for comprehensive microbial community analysis across diverse habitats.

microbiology↗

bioRxiv2025

In vitro Isolation and Identification of Metarhizium pinghaense Isolates and Assessment of Their Virulence against Whiteflies and Aphids

Four entomopathogenic fungal isolates were obtained from soil samples collected in Shouguang City, Shandong Province, using yellow mealworms as the baits. The result derived from the morphological and molecular identification of and assessment of the virulence against whiteflies (Bemisia tabaci) and the 4th instar aphids (Aphis gossypii) confirmed all four isolates as Metarhizium pinghaense, named SG-A, SG-B, SG-C, and SG-D, respectively. they were able to infect B. tabaci and A. gossypii with some differences in virulence and parasitic duration. The cumulative corrected mortality rates of B. tabaci and A. gossypii treated with the SG-A spore suspension (1x108 conidia/ml.) were 94.44% and 96.67%, respectively in 8 days after inoculation. The LC50 and LT50 of SG-A against B. tabaci and A. gossypii were 7.00x104, 4.21x105 (conidia/ml), 4.13 and 2.61 (days) respectively. Moreover, SG-C was more pathogenic to B. tabaci than to A. gossypii, but SG-B and SG-D were less virulent to both insects. In conclusion, M. pinghaense SG-A is highly pathogenic and greatly lethal against both A. gossypii and B. tabaci and should be used as a potential biocontrol agent to control whiteflies and aphids at their nymph stage during the vegetable production season.

microbiology↗

bioRxiv2025

Semi-parametric Empirical Bayes Method for Multiplet Detection in snATAC-seq with Probabilistic Multi-omic Integration

Multiplets, formed when multiple cells are captured in a droplet, produce hybrid molecular profiles that confound single-cell analyses. Detecting multiplets in single-nucleus ATAC-seq (snATAC-seq) data is particularly challenging due to sparsity and overdispersion of chromatin accessibility measurements. We introduce SEBULA, a semi-parametric empirical Bayes model that yields well-calibrated posterior probabilities for multiplet detection, enabling principled false discovery rate control. SEBULA also integrates probabilistic evidence with complementary signals from other modalities, such as scRNA-seq. Benchmarking on simulations and seven annotated trimodal DOGMA-seq datasets demonstrates SEBULAs superior performance. The open-source software is computationally efficient.

genomics↗

bioRxiv2025

A noncanonical TNL immune hub defines separable recognition and signaling modules for clubroot resistance

Plant immunity mediated by nucleotide-binding leucine-rich repeat (NLR) receptors often relies on canonical EDS1/NDR1 signaling, but alternative mechanisms are emerging. We uncover a novel modular, noncanonical immune hub orchestrated by Rcr1, a TIR-NLR (TNL) gene conferring clubroot resistance in Brassica napus against the root-infecting protist Plasmodiophora brassicae. Unlike typical TNLs, Rcr1 engages non-NLR partners in two separable modules: a CP1 (cysteine protease)-WRKY-based recognition module, likely monitoring a pathogen virulence target, and an AP (ankyrin-repeat protein)-ERF-based signaling module, driving jasmonic acid/ethylene-mediated defense. This architecture functions without detectable EDS1/NDR1 involvement, challenging salicylic acid-dominant models of biotrophic immunity and expanding current views of how TNLs can be wired in plant defense. Using high-throughput interactor screening and CRISPR/Cas9 knockouts, we validate these modules, while heat-inducible gene excision reveals Rcr1s critical early role (0-14 days post-inoculation). Together, our findings position Rcr1 as an exemplar of modular TNL architecture, suggesting that separable recognition and signaling branches may represent a broader principle of immune flexibility in plants. This study redefines TNL flexibility, offering a blueprint for breeding durable disease-resistant crops via modular immune engineering, with clubroot resistance as a model.

pathology↗

bioRxiv2025

Recurrent enhancer-promoter interactions across samples

Enhancer-promoter interactions (EPIs) are fundamental to gene regulation, and understanding their recurrence across diverse biological samples is key to deciphering chromatin architecture. In this study, we systematically analyzed the recurrence of EPIs across 49 Hi-C and 95 HiChIP datasets. We found that the majority of EPIs identified in a given sample were also present in other samples, regardless of the assay type (Hi-C or HiChIP) or the enhancer annotations used. Interestingly, EPIs that appeared unique to individual samples were typically surrounded by fewer neighboring EPIs, suggesting they may not represent truly sample-specific interactions. Our findings indicate that most human EPIs have already been captured and that cells primarily reuse subsets of these shared EPIs across different cell types and conditions. This study provides new insights into the pervasive and reusable nature of EPIs in the human genome, with important implications for chromatin conformation studies.

bioinformatics↗

bioRxiv2025

Predicting emergent phenotypes from single cell populations using CELLECTION

Biological systems exhibit emergent phenotypes that arise from the collective behavior of individual components, such as whole-organ functions that arise from the coordinated activity of its individual cells, or organism-level phenotypes that result from the functional interplay of collections of genes in the genome. We present CELLECTION, a deep learning framework that learns to associate subgroups of instances with different emergent phenotypes. We show CELLECTION enables interpretable predictions for heterogeneous tasks, including disease classification, identification of disease-associated cell subtypes, alignment of developmental stages between human model systems, and even predicting relative hand-wing indices across the avian lineage. CELLECTION therefore provides a scalable and flexible framework for identifying key cellular or genetic signatures underlying complex traits in development, disease, and evolution.

genomics↗

bioRxiv2025

A Multi-Disciplinary Framework for Decoding S1PR1-Selective Agonism

Sphingosine-1-phosphate (S1P), a key metabolite of sphingolipids, plays crucial roles in a wide range of physiological and pathological processes. S1P primarily exerts its functions by binding to G protein-coupled sphingosine-1-phosphate receptors (S1PRs), which include five subtypes (S1PR1-5), thereby activating these receptors and their downstream signaling pathways. Understanding the molecular determinants that govern agonist selectivity among different S1PR subtypes is vital for the rational and precise development of targeted therapeutic agents. Here, four cryo-electron microscopy structures of agonist-bound S1PR1-Gi complexes are reported. Through an integrated approach combining structural analysis, molecular dynamics simulations, and pharmacological assays, the molecular basis for the selectivity of CYM5442, HY-X-1011, Ponesimod, and SAR247799 toward S1PR1 over S1PR3 and S1PR5 is uncovered. Specifically, the selectivity arises from a combination of non-conserved residues within the ligand-binding pocket and at the Gi-protein interface, distinct curved agonist-binding modes oriented toward transmembrane helices 5-7 that cause steric clashes with S1PR3, and the presence of branched moieties at the lower part of three agonists. These features collectively enhance agonist potency and efficacy for S1PR1 while reducing activity at S1PR3 and S1PR5. These findings establish a structural framework for the rational design of next-generation S1PR1 highly selective agonists with improved therapeutic potential.

biochemistry↗

bioRxiv2025

Cross-species analysis identifies genotype-driven vulnerabilities in lung adenocarcinoma

While three major genetic alteration subsets, characterized by mutations in STK11, KRAS, and EGFR, are seminal in driving tumorigenesis in LUAD, their distinct effects on tumor cells and the tumor microenvironment are not fully understood. Here, we map critical oncogenic subset-specific vulnerabilities by identifying conserved cell-type-specific reprogrammings between human and mouse LUAD. Through harmonized scRNA-seq analysis of 57 human and 18 mouse specimens, we unveil that genetic alterations impose genotype-specific immune imprints on the tumor microenvironment: KRAS is associated with a transitional immune state, whereas STK11 and EGFR mutations define discrete and contrasting immune phenotypes. We find that STK11-mutant tumors exhibit complement and interferon-rich immune microenvironments while EGFR-mutant tumors harbor a naive T cell-rich phenotype accompanied by a global HLA downregulation, stress-responsive alveolar macrophages marked by MARCO. In the epithelial compartments, cross-species analysis reveals metabolic dependencies, including OGDH in EGFR- and PDE4D in STK11-mutant cells. Statement of SignificanceThis study delineates oncogenotype-specific molecular and immune imprints in lung adenocarcinoma, revealing STK11-, KRAS-, and EGFR-mutated tumors as transcriptionally and immunologically discrete ecosystems. By mapping epithelial vulnerabilities alongside immune imprints, we unveil genotype-imposed dependencies that inform the rational development of precision therapies with direct translational relevance.

cancer biology↗

bioRxiv2025

Mechanistic insights and in vivo HIV suppression by the BRD4-targeting small molecule ZL0580

Epigenetic suppression and durable silencing of HIV represent a promising strategy to achieve ART-free remission, consistent with the "block and lock" HIV cure paradigm. BRD4 is a host epigenetic reader and plays a critical role in HIV transcriptional regulation. We previously identified ZL0580, a first-in-class BRD4-selective small molecule distinct from the pan-BET inhibitor JQ1, which induces HIV epigenetic suppression. However, detailed molecular mechanisms, pharmacokinetics (PK), and in vivo HIV-suppressive efficacy of ZL0580 remain undefined. Here, we show that ZL0580 selectively targets BRD4 bromodomain 1 (BD1) through interaction with a key glutamic acid residue (E151), as determined by structural modeling and mutagenesis. Transcriptomic profiling by RNA-seq reveals that ZL0580 and JQ1 induce opposing gene expression programs, consistent with their distinct effects on HIV proviral transcription and latency. In a humanized mouse model of HIV infection, ZL0580 monotherapy, or in combination with ART, potently suppressed active HIV replication, reducing the plasma viremia to nearly undetectable levels, and delayed viral rebound following treatment interruption. Collectively, these findings establish ZL0580 as an epigenetic suppressor of HIV in vivo and provide proof-of-concept for its potential as a "block and lock" HIV cure candidate, warranting further optimization and development.

microbiology↗

bioRxiv2025

Defining and cataloging variants in pangenome graphs

Structural variation causes some human haplotypes to align poorly with the linear reference genome, leading to reference bias. A pangenome reference graph could ameliorate this bias by relating a sample to multiple reference assemblies. However, this approach requires a new definition of a genetic variant. We introduce a definition of pangenome variants and a method, pantree, to identify them. Our approach involves a pangenome reference tree which includes all nodes (sequences) of the pangenome graph, but only a subset of its edges; non-reference edges are variant edges. Our variants are biallelic and have well-defined positions. Analyzing the Minigraph-Cactus draft human pangenome reference graph, we identified 29.6 million genetic variants. Most variants (99.2%) are small, and most small variants (73.9%) are SNPs. 3.5 million variants (11.7%) have a reference allele which is not on GRCh38; these variants are difficult to detect without a pangenome reference, or with existing pangenome-based approaches. They tend to be embedded within tangled, multiallelic regions. We analyze two medically relevant regions, around the HLA-A and RHD genes, identifying thousands of small variants embedded within several large insertions, deletions, and inversions. We release an open-source software tool together with a VCF variant catalogue.

genomics↗

bioRxiv2025

PyMouse Lifter: Real Time 3-D Pose Estimation for Mice with Only 2-D Annotation Via Data Synthesis

Neural-network-based pose estimation models have become increasingly popular for quantitative analysis of mouse behavior, yet most recordings still use a single 2-D camera view and therefore lack the depth cues needed for accurate 3-D kinematics. Existing open-source 3-D mouse datasets for training deep-learning models cover only a narrow range of environments and do not generalize well to various laboratory settings. To overcome these limitations, we introduce PyMouse Lifter, a pipeline that automatically reconstructs 3-D mouse poses from ordinary 2-D top-view videos with minimal manual 2-D annotation. PyMouse Lifter combines (i) an anatomically realistic 3-D mouse model for automated data synthesis, (ii) a monocular depth estimation model, and (iii) a 2-D key-point estimation model, enabling accurate 3-D reconstruction (model-based 3D inference) in virtually any open-field arena without using depth or multiple camera views for reconstruction. We validate the system on multiple datasets against depth-camera ground truth and show that the lifted 3D trajectories yield improved behavior classification over 2-D data and can be implemented in real time.

neuroscience↗

bioRxiv2025

Cost-effective genomic prediction for fertility traits: A comparison of machine learning and conventional models using low-coverage sequencing in Holstein heifers

Fertility is one of the major factors affecting the efficiency of dairy herd, and genomic selection (GS) on milk yield, while ignoring fertility, has resulted in a decline in heifer fertility. Consider the cost of breeding, it is important to enhance the accuracy of GS with cost-effective way for fertility traits. This study investigates the genomic prediction (GP) of fertility traits in Holstein heifers using both machine learning (ML) and conventional methods, based on data from SNP arrays and low-coverage sequencing data. In this study, we collected 45,320 Holstein heifers with phenotype and pedigree records, from which we generated genomic data for 3,683 Holstein heifers using lcGWS. We first estimated the heritability for age at first service (AFS), gestation length (GLh) and age at first calving (AFC). We then compared the prediction performance of ML methods, kernel ridge regression (KRR), support vector regression, and random forest regression, with GBLUP, ssGBLUP and BayesR3 regarding GP accuracy and unbiasedness. Inputs for ML includes genomic relationship matrices (GRM), principal components, and SNPs. The results revealed that the heritability for the three fertility traits ranged from 0.09 to 0.48. Prediction accuracy from imputed low-coverage sequencing data was comparable to that from standard SNP chips. When both pedigree and genotypic data were used for GS, ssGBLUP yielded the highest prediction accuracy for AFS and GLh. Crucially, using only genomic data, KRR_GRM improved GP accuracy by up to 28.57% compared to GBLUP and by up to 9.46% compared to BayesR3. Our results highlight the effectiveness of low-coverage sequencing data in breeding applications and the MLs potential to enhance GP accuracy for fertility traits, offering practical insights for dairy breeding programs. ImplicationsTo improve the accuracy of GS with cost-effective way for fertility traits, this study confirms that low-coverage sequencing offers both accuracy and cost-effectiveness for fertility in Holstein heifers. The research provides a decision-making framework for breeding workers: the ssGBLUP model is optimal when combining pedigree data, while machine learning methods are superior with only genomic data. This study offers a practical tool for achieving efficient and economical genetic improvement in dairy cattle.

genomics↗

bioRxiv2025

SVPG: A pangenome-based structural variant detection approach and rapid augmentation of pangenome graphs with new samples

Breakthrough advances in long-read sequencing technologies have opened unprecedented opportunities to study genetic variations through comprehensive pangenome analysis. However, the availability of structural variant (SV) calling tools that can effectively leverage pangenome information is limited. In addition, efficient construction of pangenome graphs becomes increasingly challenging with acquisition of larger number of samples. In this study, we present SVPG, an approach that leverages haplotype-resolved pangenome reference for accurate SV detection and rapid pangenome graph augmentation from long-read sequencing data. Compared to state-of-the-art SV callers, SVPG maintained superior overall performance across different sequencing technologies and coverages. SVPG also achieved notable improvements in calling rare and individual-specific SVs on both simulated and real somatic datasets. Furthermore, in a benchmark involving 20 samples, SVPG accelerated pangenome graph augmentation by nearly 10-fold compared to traditional augmentation strategies. We believe that this novel SVPG method, has the potential to revolutionize SV detection and serve as an effective and essential tool, offering new possibilities for advancing pangenomic research.

bioinformatics↗

bioRxiv2025

Sparsening and decorrelation of granule cell activity in the dentate gyrus by noradrenaline

The dentate gyrus in the hippocampus makes important contributions to the acquisition of episodic memories by transforming synaptic inputs from the entorhinal cortex into sparse and decorrelated activity patterns of its principal neurons, the granule cells. However, the underlying mechanism remains unclear. Using a combination of electrophysiological and optical recordings, together with optogenetic and pharmacological manipulations, we demonstrate that the release of noradrenaline plays a key role in this specialization via an enhancement of feedforward inhibition generated by cholecystokinin-expressing interneurons. By imposing coincidence detection with milliseconds temporal resolution onto granule cells, this enhancement of feedforward inhibition makes granule cell activity sparser and their firing patterns decorrelated. Since decorrelation contributes to efficient memory storage during auto-associative learning, these findings reveal a circuit mechanism by which an arousal signal facilitates memory formation in the hippocampus.

neuroscience↗

bioRxiv2025

The cryo-EM-delineated mechanism underlying mimicry of CXCR4 agonism enables widespread stem cell neuroprotection in a mouse model of ALS

G-protein coupled receptors (GPCRs) are transmembrane proteins that mediate a range of signaling functions and, therefore, offer targets for a number of therapeutic interventions. Chemokine receptor CXCR4, a GPCR, plays versatile roles in normal and abnormal physiological processes. Synthetic CXCR4 antagonists have been extensively studied and approved for the clinical treatment of cancer and other diseases. We recently elucidated the structural mechanisms underlying CXCR4 antagonism using cryogenic electron microscopy (cryo-EM). CXCR4 agonism by synthetic molecules is an unanticipated therapeutic intervention we recently unveiled. The structural mechanisms underlying those actions remain poorly understood yet could help elucidate a new class of drugs. Here we demonstrate a synthetic dual-moiety strategy that combines simplified agonistic and antagonistic moieties taken from natural agonistic and antagonistic chemokines, respectively, to design de novo peptide mimics of biological function of natural CXCR4 agonist SDF-1. Two peptides so generated, SDV1a and SDVX1 were shown to mimic the action of SDF-1 in activating CXCR4 signaling pathways and cell migration. The structural mechanism of these peptides in the mimicry of CXCR4 agonism was illustrated by cryo-EM structures of CXCR4 bound and activated by the peptides in the presence of G protein, revealing common interactions with the receptor by these peptides in comparison with SDF-1 that explain their close mimicry and conformational changes leading to CXCR4 signal activation. The therapeutic benefit of one of these peptides, SDV1a, was demonstrated in the SOD1G93A mouse model of the spinal motor neuron degenerative disease, amyotrophic lateral sclerosis (ALS) wherein the success of neuroprotective actions of transplanted human neural stem cells (hNSCs) is directly correlated with the expanse of diseased neuroaxis traversed by the donor cells; SDV1a enabled broader neuroprotective coverage while also permitting a much less invasive route of cell administration for extending life. Taken together, these results provide insights into the structural determinants of therapeutic CXCR4 agonism which may allow the design of adjunctive drugs that improve cell-based treatments of central nervous system (CNS) diseases.

neuroscience↗