Biology subjects

Shin, H. Y.

Publications and source records attributed to Shin, H. Y..

2 recordsLinked to original sources

bioRxiv2025

The SAMM50 rs3761472 causes mitochondrial dysfunction and metabolic dysfunction-associated steatotic liver disease

Genome-wide association studies (GWAS) have identified the SAMM50 rs3761472 single nucleotide polymorphism (SNP) as a risk factor for metabolic dysfunction-associated steatotic liver disease (MASLD), although its in vivo functions remain unclear. SAMM50 encodes a mitochondrial outer membrane protein critical for maintaining mitochondrial structure. To investigate the biological effects of rs3761472, we generated Samm50-knock-in (KI) mice harboring a D110G substitution using CRISPR/Cas9. This variant impaired mitochondrial integrity by downregulating key regulators of mitochondrial architecture, dynamics, and quality control. This contributed to reduced ATP production and elevated oxidative stress, inflammation, and hepatocyte death. The mutation also induced insulin resistance and glucose intolerance. Samm50-KI mice fed a high-fat diet exhibited pronounced hepatic lipid accumulation and liver injury, highlighting its pathogenic role in MASLD progression. Our findings demonstrate that SAMM50 rs3761472 is a critical driver of mitochondrial dysfunction and MASLD susceptibility, supporting its potential as a therapeutic target and its relevance to precision medicine.

genetics↗

bioRxiv2024

Identification of B cell subsets based on antigen receptor sequences using deep learning

B cell receptors (BCRs) denote antigen specificity, while corresponding cell subsets indicate B cell functionality. Since each B cell uniquely encodes this combination, physical isolation and subsequent processing of individual B cells become indispensable to identify both attributes. However, this approach accompanies high costs and inevitable information loss, hindering high-throughput investigation of B cell populations. Here, we present BCR-SORT, a deep learning model that predicts cell subsets from their corresponding BCR sequences by leveraging B cell activation and maturation signatures encoded within BCR sequences. Subsequently, BCR-SORT is demonstrated to improve reconstruction of BCR phylogenetic trees, and reproduce results consistent with those verified using physical isolation-based methods or prior knowledge. Notably, when applied to BCR sequences from COVID-19 vaccine recipients, it revealed inter-individual heterogeneity of evolutionary trajectories towards Omicron-binding memory B cells. Overall, BCR-SORT offers great potential to improve our understanding of B cell responses.

bioinformatics↗