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Jun Won Park

Publications and source records attributed to Jun Won Park.

2 recordsLinked to original sources

A refined MASH-HCC model identifies macrophage Gadd45b as a key orchestrator of inflammation-driven neoplastic progression.

Metabolic dysfunction-associated steatohepatitis (MASH) is emerging as a leading driver of hepatocellular carcinoma (HCC), yet the molecular mechanisms linking metabolic stress, chronic inflammation and tumorigenesis remain poorly understood. Here we established a metabolically relevant, time-efficient MASH-to-HCC model in C57BL/6N mice by combining a MASH diet with controlled CCl4 administration, enabling stepwise recapitulation of MASH-associated neoplastic progression. Using this model, we identified growth arrest and DNA damage 45b (Gadd45b) as a novel MASH-derived protumorigenic regulator selectively activated under metabolic stress. Integrated analyses of human bulk and single-cell transcriptomic datasets and mouse transcriptomic deconvolution revealed concordant macrophage remodeling and GADD45B/Gadd45b expression dynamics during MASH-to-HCC progression. Mechanistically, fatty acids and TNFα preferentially induced Gadd45b in macrophages, where it amplified TNFα-NF-κB signaling. Macrophage-derived inflammatory signals subsequently induced Gadd45b and NF-κB activation in hepatocytes, establishing a feed-forward inflammatory loop that promoted fibrogenic and partial EMT-like programs and tumor spheroid formation. Importantly, temporal profiling during spheroid formation and progression revealed transient induction of Gadd45b during early spheroid establishment, but not during later progression, indicating that Gadd45b-mediated inflammatory signaling primarily promotes tumor initiation rather than subsequent growth. Consistent with human data, Gadd45b expression increased with disease severity and positively correlated with inflammatory factors in the MASH-HCC model, whereas pharmacological inhibition attenuated the Gadd45b-inflammation signaling axis. Collectively, our findings establish macrophage Gadd45b as a key orchestrator linking metabolic stress, chronic inflammation, and neoplastic transformation during MASH-to-HCC progression. Our refined MASH-HCC model provides a robust platform for mechanistic studies and preclinical evaluation of inflammation-targeted therapies.

Journal Article

Deep learning-assisted, pathogenesis-informed lung histopathology scoring in preclinical mouse models of SARS-CoV-2 and influenza A infection.

INTRODUCTION: SARS-CoV-2 and influenza A virus (IAV) cause viral pneumonia, yet their lung lesions evolve with distinct spatial organization and resolution-phase architecture. In preclinical murine studies, H&E histopathology is a primary endpoint, but burden-focused semiquantitative scoring can miss pathogen- and phase-specific differences in lesion topology, compartmental involvement, inflammatory organization, and repair. We aimed to define virus- and phase-specific morphologic signatures and translate them into a practical, pathogenesis-informed scoring guide, supported by whole-slide convolutional neural network (CNN) analysis with class activation mapping (CAM). METHODS: Mice were infected under standardized conditions and evaluated during the early, peak-injury, and late phases of infection, corresponding to 2~3, 5~8, and 14 days post-infection (dpi), respectively. Lungs were assessed by H&E with semiquantitative scoring and by immunostaining to map viral antigen distribution and epithelial tropism. Whole-slide CNN models were trained for virus- and phase-specific classification, and CAM localized discriminative regions. RESULTS: Dose titration established reproducible lethal and sublethal infection conditions for both viruses. Viral antigen kinetics diverged, with SARS-CoV-2 peaking early and declining toward clearance by the resolution phase, whereas IAV peaked later and declined by the resolution phase, paralleling distinct injury-repair trajectories. CNN/CAM analysis distinguished virus- and phase-specific histologic patterns across the early, peak-injury, and resolution phases of infection and highlighted spatial signatures consistent with expert review. At the peak-injury phase, SARS-CoV-2 lungs showed broad alveolar/interstitial involvement, whereas IAV exhibited bronchocentric inflammatory organization. During the resolution phase, IAV showed prominent epithelial regeneration with remodeling-forward architecture, while SARS-CoV-2 more often retained localized residual inflammatory foci. Across both infections, tissue inflammatory composition shifted over time, with higher neutrophil representation during the peak-injury phase and a relative increase in lymphocytic representation during the resolution phase. Integrating lesion topology/distribution, edema, epithelial injury-regeneration, remodeling features, and lymphocyte predominance, we proposed a pathogen-resolved, phase-informed histopathology scoring guide with recommended evaluation windows for each model. CONCLUSION: Together, these findings define virus- and phase-specific morphologic programs that inform respiratory virus pathogenesis in mice and can be translated into practical scoring criteria for preclinical respiratory virus studies.

Animals