Biology subjects

Hung, Y.-J.

Publications and source records attributed to Hung, Y.-J..

5 recordsLinked to original sources

bioRxiv2026

IFN-γ-Dependent Macrophage Reprogramming Coordinates Inflammatory Resolution and Matrix Remodeling in Heart Regeneration

Heart regeneration requires coordinated immune activation, timely inflammatory resolution, and dynamic extracellular matrix (ECM) remodeling in addition to cardiomyocyte (CM) proliferation. However, the cytokine signals that instruct immune cell functions during cardiac repair remain incompletely understood. Here, we identify interferon-gamma (IFN-{gamma}) as a critical regulator of macrophage plasticity in zebrafish heart regeneration. IFN-{gamma} signaling components are dynamically activated following cardiac injury, with early induction of ifng1 and temporally coordinated receptor expression. Genetic ablation of ifng1 impairs myocardial regeneration, resulting in reduced CM proliferation and persistent fibrotic scarring. Temporal transcriptional profiling reveals sustained inflammatory signatures, impaired efferocytosis, and abolished reparative programs, accompanied by aberrant immune cell dynamics and retention of injury-derived debris in mutant hearts. Transcriptomic analysis of cardiac macrophages further reveals that IFN-{gamma} deficiency disrupts the transition from an inflammatory state to a reparative, ECM-remodeling phenotype, leading to reduced collagen denaturation and diminished CM protrusion at the injury border zone. Inducible- and macrophage-specific blockade of IFN-{gamma} signaling phenocopies defects in global knockout, establishing a cell-autonomous requirement for IFN-{gamma} in coordinating regenerative immune function. Collectively, our findings define an IFN-{gamma}-dependent macrophage reprogramming axis that couples inflammatory resolution to ECM remodeling in heart regeneration, elucidating how cytokine signaling actively instructs tissue repair. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/712551v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@8cb384org.highwire.dtl.DTLVardef@15dd8d6org.highwire.dtl.DTLVardef@46dcc4org.highwire.dtl.DTLVardef@47e2ac_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2025

Scube2 Modulates Coronary Vessel Formation during Cardiac Growth and Regeneration in Zebrafish

Coronary vessel formation is essential for cardiac growth and regeneration, yet the mechanisms regulating these processes remain incompletely defined. In this study, we identified and characterized Scube2 as a novel regulator in coronary vessel formation and heart regeneration. Scube2 is expressed in the epicardium covering the coronary vasculature under homeostatic conditions in adult hearts and is strongly upregulated after cardiac injury. Zebrafish scube2 mutant exhibits reduced coronary vessel coverage during cardiac growth and pronounced myocardial defects, including abnormal vessel morphology, disorganized contractile fibers, and mitochondrial damage. Following injury, scube2 mutant hearts show impaired revascularization and persistent fibrosis, indicating defective regeneration. Mechanistically, transcriptomic profiling of scube2 mutant hearts reveals attenuated expression of angiogenic-related genes, including those mediated by vascular endothelial and platelet-derived growth factors, consistent with reduced proliferation of endothelial cells and cardiomyocytes during repair. In a gain-of-function setting, SCUBE2 enhances platelet-derived growth factor signaling by promoting receptor activation in HEK293 cells. These findings support a novel role for Scube2 in cardiac growth and regeneration by modulating coronary vessel formation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/695032v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@7c5b5org.highwire.dtl.DTLVardef@451c40org.highwire.dtl.DTLVardef@141c88forg.highwire.dtl.DTLVardef@1d63765_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2025

cellSTAAR: Incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of non-coding regions

Whole genome sequencing (WGS) studies have identified hundreds of millions of rare variants (RVs) and have enabled RV association tests (RVATs) of these variants with complex traits and diseases. Analysis of non-coding variants is challenged by the considerable variability in regulatory function which candidate Cis-Regulatory Elements (cCREs) exhibit across cell types. We propose cellSTAAR, which integrates WGS data with single-cell ATAC-seq data to capture variability in chromatin accessibility across cell types via the construction of cell-type-specific functional annotations and variant sets. To reflect the uncertainty in cCRE-gene linking, cellSTAAR also links cCREs to their target genes using an omnibus framework which aggregates results from a variety of popular linking approaches. We applied cellSTAAR on Freeze 8 (N = 60,000) of the NHLBI Trans-Omics for Precision Medicine (TOPMed) consortium data to four lipids phenotypes: LDL cholesterol, a binary variable corresponding to high LDL cholesterol, HDL cholesterol, and triglycerides. We also provide replication results for all four phenotypes using UK Biobank (N = 190,000). Evidence from simulation studies and our real data analysis demonstrates that cellSTAAR boosts power and improves interpretation of RVATs of cCREs.

genetics↗

bioRxiv2023

A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

genetics↗

bioRxiv2023

Structural variation across 138,134 samples in the TOPMed consortium

Ever larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium. We describe our methodologies for SV inference resulting in high variant quality and >90% allele concordance compared to long-read de-novo assemblies of well-characterized control samples. We demonstrate utility through significant associations between SVs and important various cardio-metabolic and hemotologic traits. We have identified 690 SV hotspots and deserts and those that potentially impact the regulation of medically relevant genes. This catalog characterizes SVs across multiple populations and will serve as a valuable tool to understand the impact of SV on disease development and progression.

genomics↗