Biology subjects

WANG, D.

Publications and source records attributed to WANG, D..

4 recordsLinked to original sources

bioRxiv2026

An Integrated Pipeline for Cell-Type Annotation, Metabolic Profiling, and Spatial Communication Analysis in the Liver using Spatial Transcriptomics

The liver acts as a central metabolic hub, integrating systemic signals through a spatially organized pattern known as zonation, driven by the coordinated activity of diverse cell types including hepatocytes, stellate cells, Kupffer cells, endothelial cells, and immune populations. Spatial transcriptomics (ST) enables the profiling of thousands of cells with spatial resolution in a single experiment, facilitating the identification of novel gene markers, cell types, cellular states, and tissue neighborhoods across diverse tissues and organisms. By simultaneously capturing transcriptional and spatial heterogeneity, ST has become a powerful tool for understanding cellular and tissue biology. Given its advantages, there is growing demand for applying ST to uncover novel biological insights in the liver under various physiological and pathological conditions including obesity, diabetes, and metabolic dysfunction-associated steatotic liver disease (MASLD). However, to date no comprehensive and practical protocols currently exist for analyzing ST data specifically in the context of liver metabolism. Herein, we present a systematic and detailed protocol for ST data analysis using liver tissues from MASLD mouse models. This guide offers practical support for metabolic based researchers without advanced expertise in coding, mathematics and statistics enabling single-cell RNA-seq referencing for deconvolution-based annotation, curated liver cell type markers for manual annotation, and a GMT file of metabolic gene sets and flux balance analysis to analyze liver metabolic activity. This framework and integrated computational resources for decoding metabolic reprogramming and cellular heterogeneity will empower researchers to uncover novel biological pathways regulating liver metabolism in health and disease.

bioinformatics↗

bioRxiv2025

Constrained Evolutionary Funnels Shape Viral Immune Escape

Understanding how viral proteins adapt under immune pressure while preserving viability is crucial for anticipating antibody-resistant variants. We present a probabilistic framework that predicts viral escape trajectories and shows that immune evasion is channeled into a small set of viable "escape funnels" within the vast mutational space. These escape funnels arise from the combined constraints of protein viability and antibody escape, modeled using a generative model trained on homologs and deep mutational scanning data. We derive a mean-field approximation of evolutionary path ensembles, enabling us to quantify both the fitness and entropy of escape routes. Applied to SARS-CoV-2 receptor binding domain, our framework reveals convergent evolution patterns, predicts mutation sites in variants of concern, and explains differences in antibody-cocktail effectiveness. In particular, cocktails with de-correlated escape profiles slow viral adaptation by forcing longer, higher-cost escape paths. SIGNIFICANCEViruses evolve to evade our immune defenses, but with constraints. Like navigating a minefield, each step toward immune escape comes at the potential cost of structural stability and functionality. We show that despite the vast mutational space, immune escape is funneled into a small set of predictable pathways. Using a statistical-physics model grounded in antibody experiments and SARS-CoV-2 epidemiology data, we identify these escape funnels--enabling therapies designed to block them before they are ever used.

bioinformatics↗

bioRxiv2024

Discovery and Engineering of a New BvCas12a Nuclease for Mammalian Genome Editing and Nucleic Acid Detection

Cas12a is an RNA-guided endonuclease that has emerged as a powerful gene-editing tool. We have identified a novel Cas protein, BvCas12a, from Butyricimonas virosa with a 5-TYTN protospacer adjacent motif (PAM). BvCas12a exhibits double-stranded DNA cleavage activity in vitro and genome editing activity in eukaryotic cells. Though the editing efficiency of BvCas12a is marginally lower than that of AsCas12a, the editing specificity of BvCas12a in eukaryotic cells is comparable or superior to that of AsCas12a. Moreover, BvCas12a exhibits substantial collateral activity and can detect HPV DNA effectively and accurately in conjunction with isothermal amplification, highlighting its potential in nucleic acid diagnostics. Furthermore, we have engineered BvCas12a to create two variants, BvCas12a-R (N549R, T606P) and BvCas12a-RVR (N549R, K555V, C559R, T606P). These variants recognize expanded 5-YYN and 5-YN PAM in vitro, respectively. Additionally, they exhibit higher editing activity than wild-type BvCas12a and recognize 5-YYN PAM in vivo at all sites detected. In conclusion, we have identified a novel BvCas12a protein with high specificity and engineered two variants with broader PAM compatibility and improved genome editing efficiency. These findings offer a potent gene editing tool for application in scientific research, gene therapy, and nucleic acid diagnostics.

genomics↗

bioRxiv2018

MetaPGN: a pipeline for construction and graphical visualization of annotated pangenome networks

Pangenome analyses facilitate the interpretation of genetic diversity and evolutionary history of a taxon. However, there is an urgent and unmet need to develop new tools for advanced pangenome construction and visualization, especially for metagenomic data. Here we present an integrated pipeline, named MetaPGN, for construction and graphical visualization of pangenome network from either microbial genomes or metagenomes. Given either isolated genomes or metagenomic assemblies coupled with a reference genome of the targeted taxon, MetaPGN generates a pangenome in a topological network, consisting of genes (nodes) and gene-gene genomic adjacencies (edges) of which biological information can be easily updated and retrieved. MetaPGN also includes a self-developed Cytoscape plugin for layout of and interaction with the resulting pangenome network, providing an intuitive and interactive interface for full exploration of genetic diversity. We demonstrate the utility of MetaPGN by constructing Escherichia coli (E. coli) pangenome networks from five E. coli pathogenic strains and 760 human gut microbiomes respectively, revealing extensive genetic diversity of E. coli within both isolates and gut microbial populations. With the ability to extract and visualize gene contents and gene-gene physical adjacencies of a specific taxon from large-scale metagenomic data, MetaPGN provides advantages in expanding pangenome analysis to uncultured microbial taxa. MetaPGN is available at https://github.com/peng-ye/MetaPGN.

genomics↗