온라인강의

Inferring metabolic pathway activity levels of Cold-Heat patterns using RNA-Seq data of individuals with Cold/Heat patterns
강사명Manseok Kim 강의시간23분 강의개설일2025-12-09
온라인강의

강의소개

Background Although Cold-Heat pattern, which is not only fundamental, whereas essential for diagnostics of Korean Medicine, is the most crucial component, the underlying functional mechanism primarily necessitating their phenotypic differences is not yet known in detail. Objectives To infer the critical underlying mechanism leading to either Cold or Heat pattern using RNA-seq datasets of Cold-Heat individuals. Methods RNA-seq datasets were obtained from the blood samples of healthy individuals with either Cold or Heat pattern. For transcriptional level, differential analyses were performed using either the whole gene list or immune associated gene list. From a metabolic perspective, comprehensive pathway analyses including pathway enrichment analyses and metabolic flux simulation were implemented. Results The whole analyses showed upregulation of lipid-associated metabolisms such as phospholipid metabolism, as well as amino acid metabolism and nucleotide metabolism on individuals with Heat pattern while maintaining relatively further activation of mitophagy and mitochondrial fission. Lysine metabolism, coenzyme A metabolism, vitamin metabolism and gluconeogenesis, however, showed downregulation on individuals with Cold pattern. Also, gene/protein clusters/modules probabilistically predicted through our custom-made pattern recognition algorithm turned out to be dependent on the metabolic alterations. Conclusions The underlying mechanisms of Cold-Heat patterns probably specifically/intimately associated with the findings from this study, and enhanced activities of target modules on each pattern may provide clues explaining the inherent/foundational differences.

강사소개

Dr. Kim is interested in "computational analysis / in-silico modeling" using multi omics. Along with high resolution views of single-cell heterogeneity, Dr. Kim's specialty lies in context-specific metabolic modeling to investigate various diseases.