Biomedical subjects

Ji In Park

Publications and source records attributed to Ji In Park.

2 recordsLinked to original sources

PubMed2026

Acyloxyacyl Hydrolase-Mediated Lipopolysaccharide Inactivation Limits Macrophage Endotoxin Tolerance and Promotes Inflammation and Fibrosis in Metabolic Dysfunction-Associated Steatohepatitis.

BACKGROUND & AIMS: Metabolic dysfunction-associated steatohepatitis, a chronic liver disease, is characterized by persistent low-grade inflammation, partially driven by gut-derived lipopolysaccharide. Although repeated lipopolysaccharide exposure can induce endotoxin tolerance in innate immune cells, its role in chronic liver diseases remains unclear. Acyloxyacyl hydrolase is an endogenous enzyme that inactivates lipopolysaccharide, potentially modulating this process. We aimed to investigate how acyloxyacyl hydrolase regulates endotoxin tolerance in Kupffer cells and how this affects hepatic inflammation and fibrosis during metabolic dysfunction-associated steatohepatitis progression. METHODS: Acyloxyacyl hydrolase-deficient mice and wild-type controls were subjected to multiple dietary metabolic dysfunction-associated steatohepatitis models. Inflammatory responses, fibrosis, and transcriptomic changes in liver tissues and isolated Kupffer cells were analyzed. Endotoxin tolerance was modulated through β-glucan administration or lipopolysaccharide preconditioning. Lipopolysaccharide bioactivity was assessed using Toll-like receptor 4-reporter cell assays. RESULTS: Lipopolysaccharide-preconditioned Kupffer cells exhibited reduced proinflammatory cytokine production and transcriptional suppression of inflammatory pathways, indicating tolerance. Despite slight elevation of plasma lipopolysaccharide levels in metabolic dysfunction-associated steatohepatitis, upregulation of hepatic acyloxyacyl hydrolase positively correlated with disease severity, suggesting enhanced lipopolysaccharide inactivation but impaired establishment of tolerance. In contrast, acyloxyacyl hydrolase-deficient Kupffer cells displayed reinforced endotoxin tolerance, leading to diminished hepatic inflammation and fibrosis. Reversal of tolerance using β-glucan reactivated inflammatory and fibrogenic responses in acyloxyacyl hydrolase-deficient mice, whereas tolerance induction by low-dose lipopolysaccharide preconditioning mitigated metabolic dysfunction-associated steatohepatitis pathology, supporting the protective role of macrophage tolerance in chronic liver injury. CONCLUSIONS: Endotoxin tolerance in Kupffer cells represents a protective mechanism against chronic liver inflammation and fibrosis. Acyloxyacyl hydrolase regulates this state by limiting bioactive lipopolysaccharide, thereby modulating the establishment of endotoxin tolerance and downstream inflammatory and fibrotic responses. Enhancing macrophage tolerance by utilizing lipopolysaccharide may offer a novel therapeutic avenue to control the progression of metabolic dysfunction-associated steatohepatitis.

AOAH↗

PubMed2026

Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases.

KEY POINTS: Plasma proteome profiling identified distinct signatures across biopsy-proven primary glomerular disease subtypes. An elastic net model using 93 proteins classified primary glomerular disease subtypes and controls, with external validation. Integrating proteomics with machine learning yields biologically interpretable insights in primary glomerular diseases. BACKGROUND: Primary GN is a heterogeneous group of kidney disorders where understanding of their pathophysiology remains incomplete. Despite the diagnostic potential of high-throughput proteomics, constrained proteomic depth and a reliance on binary comparisons have left the feasibility of using systemic signatures to differentiate multiple GN subtypes largely unexplored. METHODS: To identify protein signatures that noninvasively differentiate major primary glomerular disease subtypes and provide mechanistic insights, we performed large-scale systemic proteome profiling of 5416 plasma proteins via Olink Explore HT in a discovery cohort ( n =147) and an external validation cohort ( n =85) of Korean participants (mean age, 41±13 years; 46% female). The study population included patients with four GN subtypes-focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy-alongside healthy controls. We developed a machine learning (ML) model using logistic regression with elastic net regularization to classify disease groups based on proteomic profiles and evaluated its performance in the independent validation cohort. RESULTS: Plasma proteome profiles were distinct among disease subtypes, emerging as a significant source of data variation independent of conventional markers such as eGFR or proteinuria levels. The ML model performed robustly in both the discovery and validation cohorts, achieving an area under the receiver operating characteristic curve >0.8 for differentiating minimal change disease, membranous nephropathy, and IgA nephropathy. The model, even without clinical information, correctly identified 93% of minimal change disease cases (14 of 15) and 63% of IgA nephropathy cases (20 of 32), but its performance was limited for focal segmental glomerulosclerosis, with only 21% of cases (three of 14) correctly classified. Functional analysis of key proteins highlighted distinct biologic pathways, such as hemostasis in minimal change disease. CONCLUSIONS: We identified distinct systemic proteome signatures for primary glomerular diseases, where disease subtype served as a major determinant of proteomic variance alongside conventional clinical markers. ML models demonstrated robust discriminatory performance for minimal change disease, membranous nephropathy, and IgA nephropathy, underscoring the potential for proteome-based classification.

Humans↗