Biology subjects

Shin, S.-H.

Publications and source records attributed to Shin, S.-H..

2 recordsLinked to original sources

bioRxiv2023

Monocyte-derived microglia with Dnmt3a mutation cause motor pathology in aging mice

Microglia are established in embryogenesis forming a self-containing cellular compartment resisting seeding with cells derived from adult definitive hematopoiesis. We report that monocyte-derived macrophages (MoM{Phi}) accumulate in the brain of aging mice with distinct topology, including the nigrostriatum and medulla, but not the frontal cortex. Parenchymal MoM{Phi} adopt bona fide microglia expression profiles. Unlike microglia, these monocyte-derived microglia (MoMg) are due to their hematopoietic origin targets of clonal hematopoiesis (CH). Using a chimeric transfer model, we show that hematopoietic expression of DNMT3AR822H, a prominent mutation in human CH, renders MoMg pathogenic promoting motor deficits resembling atypical Parkinsonian disorders. Collectively, these data establish in a mouse model that MoMg progressively seed the brains of aging healthy mice, accumulate in selected areas, and, when carrying a somatic mutation associated with CH, can contribute to brain pathology.

immunology↗

bioRxiv2020

Ultra-fast Prediction of Somatic Structural Variations by Reduced Read Mapping via Pan-Genome k-mer Sets

Genome rearrangements often result in copy number alterations of cancer-related genes and cause the formation of cancer-related fusion genes. Current structural variation (SV) callers, however, still produce massive numbers of false positives (FPs) and require high computational costs. Here, we introduce an ultra-fast and high-performing somatic SV detector, called ETCHING, that significantly reduces the mapping cost by filtering reads matched to pan-genome and normal k-mer sets. To reduce the number of FPs, ETCHING takes advantage of a Random Forest classifier that utilizes six breakend-related features. We systematically benchmarked ETCHING with other SV callers on reference SV materials, validated SV biomarkers, tumor and matched-normal whole genomes, and tumor-only targeted sequencing datasets. For all datasets, our SV caller was much faster ([≥]15X) than other tools without compromising performance or memory use. Our approach would provide not only the fastest method for largescale genome projects but also an accurate clinically practical means for real-time precision medicine.

bioinformatics↗