Biology subjects

WANG, J.

Publications and source records attributed to WANG, J..

12 recordsLinked to original sources

bioRxiv2026

Cooperative Peripheral CB1 Inhibition by Extracellular Antibody Binding and Orthosteric Antagonism

The cannabinoid receptor CB1 is an important therapeutic target for obesity and metabolic disorders, yet centrally acting CB1 antagonists are limited by neuropsychiatric side effects. Although the anti-CB1 antibody Nimacimab has advanced to Phase 2 clinical trials for metabolic disorders, the molecular mechanisms underlying peripheral CB1 inhibition remain poorly understood. Here, we report cryo-EM structures of human CB1 bound to Nimacimab alone and in complex with the orthosteric antagonists Rimonabant and JD5037. Unlike previously characterized inhibitory GPCR antibodies that block ligand access through steric occlusion or directly engage orthosteric sites, Nimacimab functions through extracellular allosteric inhibition by stabilizing an inactive-like extracellular architecture of CB1. Antibody binding restricts extracellular conformational dynamics and allosterically stabilizes inactive-state signaling microswitches. Importantly, extracellular antibody engagement is structurally compatible with orthosteric antagonism and cooperatively enhances CB1 inhibition. Structural analysis further reveals how JD5037 remodels the extracellular vestibule to achieve peripheral-selective antagonism. Together, these findings establish extracellular allosteric inhibition as a distinct mechanism of GPCR regulation and provide a structural framework for the development of peripherally selective CB1 therapeutics.

pharmacology and toxicology↗

bioRxiv2026

STR-PG: A Topology-decoupled Pangenome Framework for Scalable Short-read Genotyping of Short Tandem Repeats

Short tandem repeats (STRs) are a rich and highly polymorphic source of human genetic variation, but representing and genotyping them in pangenome graphs remains challenging. Explicitly encoding each STR allele as a separate graph path results in increasingly complex local structures as cohort diversity increases, leading to larger index sizes and requiring significant resources for graph reconstruction when new alleles are introduced. Here, we propose STR-PGa topologically decoupled genome-wide framework that separates stable locus representation from scalable STR allele content. STR-PG uses topologically fixed pointer nodes to represent each target locus, while allele sequences, repeat counts, motif annotations, and population frequency metadata are stored in an external registry. Short reads are mapped to STR loci via syncmer-based flanking anchors, and genotyping is performed within a locus-specific candidate space using allele-level alignment likelihood and Bayesian inference. Newly supported alleles can be integrated through registry-level updates without the need to rebuild the graph structure. Evaluations using simulated whole-genome sequencing data, 1000 Genomes Project (1kGP) samples, and r real whole-exome sequencing data from matched whole-blood-cell controls demonstrate that STR-PG maintains accurate genotyping results across various STR classes, reproduces expected population structures, and substantially reduces the computational cost of integrating additional alleles. STR-PG provides a compact and scalable framework for population-scale STR analysis using short-read sequencing.

bioinformatics↗

bioRxiv2025

Highly accurate protein structure prediction-based virtual docking pipeline accelerating the identification of anti-schistosomal compounds

Schistosomiasis is a major neglected tropical disease that lacks an effective vaccine and faces increasing challenges from praziquantel resistance, underscoring the urgent need for novel therapeutics. Target-based drug discovery (TBDD) is a powerful strategy for drug development. In this study, we utilized AlphaFold to predict the structures of target proteins from Schistosoma mansoni and Schistosoma japonicum, followed by virtual molecular screening to identify potential inhibitors. Among 202 potential therapeutic targets, we identified 37 proteins with high-accuracy structural predictions suitable for molecular docking with 14,600 compounds. This screening yielded 268 candidate compounds, which were further evaluated in vitro for activity against both adult and juvenile S. mansoni and S. japonicum. Seven compounds exhibited strong anti-schistosomal activity, with HY-B2171A (Carubicin hydrochloride, CH) emerging as the most potent. CH was predicted to target the splicing factor U2AF65, and knockdown of its coding gene Smp_019690 resulted in a phenotype similar to CH treatment. RNA sequencing revealed that both CH treatment and Smp_019690 RNAi disrupted splicing events in the parasites. Further studies demonstrated that CH impairs parasite viability by inhibiting U2AF65 function in mRNA splicing regulation. By integrating RNAi-based target identification with structure-based virtual screening, alongside in vitro phenotypic and molecular analyses of compound-treated schistosomes, our study provides a comprehensive framework for anti-schistosomal drug discovery and identifies promising candidates for further preclinical development. Author SummarySchistosomiasis is a debilitating disease caused by parasitic worms and affects over 230 million people worldwide. For decades, control of the disease has relied on a single drug, praziquantel, which is less effective against juvenile parasites and increasingly threatened by the risk of drug resistance. To accelerate the discovery of new treatments, we integrated advanced computational tools with molecular validation. Using AlphaFold-predicted structures of 37 key schistosome proteins, we performed large-scale virtual screening to identify promising drug candidates. Over 200 compounds were prioritized for phenotypic screening on two major human-infective schistosome species, leading to the discovery of seven with potent anti-parasitic activity. Among them, Carubicin hydrochloride showed strong efficacy by targeting a critical splicing factor, U2AF65. Inhibiting this protein--either chemically or through gene silencing-- disrupted parasite movement, suppressed cell proliferation, and altered splicing events. Our work highlights the power of integrating AI-based protein modeling with phenotypic and mechanistic validation to accelerate drug discovery for neglected tropical diseases like schistosomiasis.

pathology↗

bioRxiv2025

Exploring Population Differences in the Human Gut Microbiome: from Microbial Abundance to Single Nucleotide Polymorphisms

The human gut microbiota exhibits significant diversity across populations, influenced by factors such as geography, diet, and lifestyle, particularly between the Han Chinese and non-Chinese populations. While previous studies have predominantly focused on the taxonomic abundance of the gut microbiome, the impact of single nucleotide polymorphisms (SNPs) in driving population-specific differences remains largely underexplored. In this study, we systematically investigated gut microbial differences between the Han Chinese and non-Chinese populations using the Human Gut Microbiome Reference Genome Catalog (HGMRGC). HGMRGC includes 271,480 non-redundant genomes from 5,785 prokaryotic species, which was generated by metagenomic sequencing data from 9,320 publicly available samples across 22 countries and 3,584 newly sequenced samples from Han Chinese individuals across 29 provinces and regions in China. We observed geography was the primary driver of microbial variation of abundance and SNPs. We identified 625 novel population-specific genome clusters from HGMRGC with functional differences in carbohydrate utilization and 126 species exhibiting distinct prevalence related to vitamin biosynthesis, antibiotic resistance, and carbohydrate metabolism. Beta diversity analysis highlighted significant inter-population differences in both microbial abundance and SNPs, while alpha diversity analysis revealed that non-Chinese populations exhibited higher diversity in microbial abundance, and Han Chinese populations displayed greater diversity in SNPs. These results provide valuable insights into population-specific microbial diversity, laying the groundwork for future research on its functional and health implications.

microbiology↗

bioRxiv2023

A novel kit mutant rat enables hematopoietic stem cell engraftment without irradiation

Hematopoietic stem cell (HSC) transplantation is extensively studied in mouse models, but their limited scale presents challenges for effective engraftment and comprehensive evaluations. Rats, due to their larger size and anatomical similarity to humans, offer a promising alternative. In this study, we establish a rat model with the KitV834M mutation, mirroring KitW41 mice often used in KIT signaling and HSC research. KitV834M rats are viable and fertile, displaying anemia and mast cell depletion similar to KitW41 mice. The mutation affects myeloid cell proliferation and differentiation, as seen in the colony-forming unit granulocyte-macrophage assay. Importantly, KitV834M rats support donor rat-HSC engraftment without irradiation. Competitive transplantation assays reveal reduced reconstruction capacity in KitV834M HSCs. Leveraging the larger scale of this rat model enhances our understanding of HSC biology and transplantation dynamics, potentially advancing our knowledge in this field.

cell biology↗

bioRxiv2023

SpotLight proteomics identifies variable sequences of blood antibodies specific against deamidated human serum albumin

Spontaneous deamidation of asparaginyl residues in proteins, if not repaired or cleared, can set in motion a cascade that leads to deteriorated health. Previously, we have discovered that deamidated human serum albumin (HSA) is elevated in blood of patients with Alzheimers disease and other neurodegenerative diseases, while the level of endogenous antibodies against deamidated HSA is significantly diminished, creating an imbalance between the risk factor and the defense against it. Endogenous antibodies against deamidated proteins are still unexplored. In the current study, we employed the SpotLight proteomics approach to identify novel amino acid sequences in antibodies specific to deamidated HSA. The results provide new insights into the clearance mechanism of deamidated proteins, a possible avenue for prevention of neurodegeneration.

immunology↗

bioRxiv2022

Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data

Single-cell sequencing technology enables the simultaneous capture of multiomic data from multiple cells. The captured data can be represented by tensors, i.e., the higher-rank matrices. However, the proposed analysis tools often take the data as a collection of two-order matrices, renouncing the correspondences among the features. Consequently, we propose a probabilistic tensor decomposition framework, SCOIT, to extract embeddings from single-cell multiomic data. To deal with sparse, noisy, and heterogeneous single-cell data, we incorporate various distributions in SCOIT, including Gaussian, Poisson, and negative binomial distributions. Our framework can decompose a multiomic tensor into a cell embedding matrix, a gene embedding matrix, and an omic embedding matrix, allowing for various downstream analyses. We applied SCOIT to seven single-cell multiomic datasets from different sequencing protocols. With cell embeddings, SCOIT achieves superior performance for cell clustering compared to seven state-of-the-art tools under various metrics, demonstrating its ability to dissect cellular heterogeneity. With the gene embeddings, SCOIT enables cross-omics gene expression analysis and integrative gene regulatory network study. Furthermore, the embeddings allow cross-omics imputation simultaneously, outperforming conventional imputation methods with the Pearson correlation coefficient increased by 0.03-0.28.

bioinformatics↗

bioRxiv2022

A graph representation of gapped patterns in phage sequences for graph convolutional network

Genome sequencing technologies reveal a huge amount of genomic sequences. Neural network-based methods can be prime candidates for retrieving insights from these sequences because of their applicability to large and diverse datasets.However, the highly variable lengths of nucleic acid sequences severely impair the presentation of sequences as input to the neural network. Genetic variations further complicate tasks that involve sequence comparison or alignment. Here, we propose a graph representation of nucleic acid sequences called gapped pattern graphs. These graphs can be transformed through a Graph Convolutional Network to form lower-dimensional embeddings for downstream tasks. On the basis of the gapped pattern graphs, we implemented a neural network model and demonstrated its performance in studying phage sequences. We compared our model with equivalent models based on other forms of input in performing four tasks related to nucleic acid sequences--phage and ICE discrimination, phage integration site prediction, lifestyle prediction, and host prediction. Other state-of-the-art tools were also compared, where available. Our method consistently outperformed all the other methods in various metrics on all four tasks. In addition, our model was able to identify distinct gapped pattern signatures from the sequences.

bioinformatics↗

bioRxiv2022

Resolving single-cell copy number profiling for large datasets

The advances of single-cell DNA sequencing (scDNA-seq) enable us to characterize the genetic heterogeneity of cancer cells. However, the high noise and low coverage of scDNA-seq impede the estimation of copy number variations (CNVs). In addition, existing tools suffer from intensive execution time and often fail on large datasets. Here, we propose SeCNV, a novel method that leverages structural entropy, to profile the copy numbers. SeCNV adopts a local Gaussian kernel to construct a matrix, depth congruent map, capturing the similarities between any two bins along the genome. Then SeCNV partitions the genome into segments by minimizing the structural entropy from the depth congruent map. With the partition, SeCNV estimates the copy numbers within each segment for cells. We simulate nine datasets with various breakpoint distributions and amplitudes of noise to benchmark SeCNV. SeCNV achieves a robust performance, i.e., the F1-scores are higher than 0.95 for breakpoint detections, significantly outperforming state-of-the-art methods. SeCNV successfully processes large datasets (>50,000 cells) within four minutes while other tools failed to finish within the time limit, i.e., 120 hours. We apply SeCNV to single-nucleus sequencing (SNS) datasets from two breast cancer patients and acoustic cell tagmentation (ACT) sequencing datasets from eight breast cancer patients. SeCNV successfully reproduces the distinct subclones and infers tumor heterogeneity. SeCNV is available at https://github.com/deepomicslab/SeCNV.

bioinformatics↗

bioRxiv2022

STORM: spectral sparsification helps restore the spatial structure at single-cell resolution

Single-cell RNA sequencing thoroughly quantifies the individual cell transcriptomes but renounces the spatial structure. Conversely, recently emerged spatial transcriptomics technologies capture the cellular spatial structure but skimp cell or gene resolutions. Cell-cell affinity estimated by ligand-receptor interactions can partially reconstruct the quasi-structure of cells but falsely include the pseudo affinities between distant or indirectly interacting cells. Here, we develop a software package, STORM, to reconstruct the single-cell resolution quasi-structure from the spatial transcriptome with diminished pseudo affinities. STORM first curates the representative single-cell profiles for each spatial spot from a candidate library, then reduces the pseudo affinities in the intercellular affinity matrix by partial correlation, spectral graph sparsification, and spatial coordinates refinement. STORM embeds the estimated interactions into a low-dimensional space with the cross-entropy objective to restore the intercellular quasi-structures, which facilitates the discovery of dominant ligand-receptor pairs between neighboring cells at single-cell resolution. STORM reconstructed structures achieved shape Pearson correlations ranging from 0.91 to 0.97 on the mouse hippocampus and human organ tumor microenvironment datasets. Furthermore, STORM can solely de novo reconstruct the quasi-structures at single-cell resolution, i.e., reaching the cell-type proximity correlations 0.68 and 0.89 between reconstructed and immunohistochemistry-informed spatial structures on a human developing heart dataset and a tumor microenvironment dataset, respectively.

bioinformatics↗

bioRxiv2020

Hybrid diffuse optical monitoring of hemodynamic changes following acute cerebral ischemia in a porcine model

Rapid screening for stroke in pre-hospital settings may improve patient outcomes by allowing early deployment of thrombolytic therapies. Near-infrared hybrid diffuse optical screening devices may fill this need. This study seeks to determine whether hybrid diffuse optical measurements can measure hemodynamic changes associated with cerebral ischemia within the first few hours of the onset of acute ischemia in a large animal model. A hybrid diffuse optical device combining of near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) was fabricated to measure total hemoglobin concentration (HbT), tissue oxygen saturation (StO2) and blood flow index (BFI). Cerebral ischemia was induced by ligation of the bilateral common carotid arteries (CCA) in five miniature pigs. Additionally, a fatal stroke was induced in two pigs by injecting 5ml air emboli into both CCA. Cerebral hemodynamic parameters were monitored continuously throughout the study with the hybrid optical device. Relative changes BFI showed the good repeatability both of the ligation and fatal stroke experiments. During bilateral CCA ligation, the BFI decreased by up to about 66% of baseline values; during the fatal stroke experiment, the BFI decreased by up to about 95%, with a temporal resolution of 20 seconds. To the best of our knowledge, there are not existing methods which can measure the cerebral ischemia within the first few hours of the onset noninvasively. The MRI scanning was conducted at 24 h post injury. However, the images showed no abnormality. The results show the hybrid diffuse optical method can immediately measure the hemodynamic changes of miniature pigs in the first few hours of each single cerebral ischemia onset, and the BFI may be the promising biomarker to distinguish the cerebral ischemia and cerebral death.

neuroscience↗

bioRxiv2019

High circulating elafin levels are associated with Crohn’s disease-associated intestinal strictures

ObjectiveNearly 33% of Crohns disease (CD) patients develop intestinal strictures. Antimicrobial peptide or protein expression is associated with disease activity in inflammatory bowel disease (IBD) patients. Circulating blood cells and intestine of IBD patients have abnormal expression of elafin, a human elastase-specific protease inhibitor and antimicrobial peptide. However, the association between elafin and CD-associated intestinal stricture is unknown. We hypothesize the elafin expression in stricturing CD patients is abnormal. We determined the expression of elafin in blood, intestine, and mesenteric fat in IBD patients.\n\nMethodsHuman colonic and mesenteric fat tissues and serum samples were collected from the Cedars-Sinai Medical Center and UCLA, respectively.\n\nResultsHigh serum elafin levels were associated with a significantly elevated risk of intestinal stricture in CD patients. Machine learning algorithm using serum elafin levels and clinical data identified stricturing CD patients with high accuracy. Serum elafin levels had weak positive correlation with clinical disease activity (Partial Mayo Score and Harvey Bradshaw Index) in IBD patients. Ulcerative colitis (UC) patients had high serum elafin levels, but the increase was not associated with endoscopic Mayo score. Colonic elafin mRNA and protein expression were not associated with clinical disease activity in IBD patients, while stricturing CD patients had low colonic elafin expression. Mesenteric fat in stricturing CD patients had significantly increased elafin mRNA expression, which may contribute to high circulating elafin level.\n\nConclusionHigh serum elafin levels and adipose elafin expression are associated with intestinal strictures, which may help identify intestinal strictures in CD patients.

molecular biology↗