Journal of translational medicine · 2024
Integrating single-cell RNA-Seq and machine learning to dissect tryptophan metabolism in ulcerative colitis.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chen Guorong, Qi Hongying, Jiang Li, Sun Shijie, Zhang Junhai, Yu Jiali, Liu Fang, Zhang Yanli, Du Shiyu
Аннотация
BACKGROUND: Ulcerative colitis (UC) is a persistent inflammatory bowels disease (IBD) characterized by immune response dysregulation and metabolic disruptions. Tryptophan metabolism has been believed as a significant factor in UC pathogenesis, with specific metabolites influencing immune modulation and gut microbiota interactions. However, the precise regulatory mechanisms and key genes involved remain unclear.
METHODS: AUCell, Ucell, and other functional enrichment algorithms were utilized to determine the activation patterns of tryptophan metabolism at the UC cell level. Differential analysis identified key genes associated with tryptophan metabolism. Five machine learning algorithms, including Random Forest, Boruta algorithm, LASSO, SVM-RFE, and GBM were integrated to identify and categorize disease-specific characteristic genes.
RESULTS: We observed significant heterogeneity in tryptophan metabolism activity across cell types in UC, with the highest activity levels in macrophages and fibroblasts. Among the key tryptophan metabolism-related genes, CTSS, S100A11, and TUBB were predominantly expressed in macrophages and significantly upregulated in UC, highlighting their involvement in immune dysregulation and inflammation. Cross-analysis with bulk RNA data confirmed the consistent upregulation of these genes in UC samples, highly indicating their relevance in UC pathology and potential as targets for therapeutic intervention.
CONCLUSIONS: This study is the first to reveal the heterogeneity of tryptophan metabolism at the single-cell level in UC, with macrophages emerging as key contributors to inflammatory processes. The identification of CTSS, S100A11, and TUBB as key regulators of tryptophan metabolism in UC underscores their potential as biomarkers and therapeutic targets.
METHODS: AUCell, Ucell, and other functional enrichment algorithms were utilized to determine the activation patterns of tryptophan metabolism at the UC cell level. Differential analysis identified key genes associated with tryptophan metabolism. Five machine learning algorithms, including Random Forest, Boruta algorithm, LASSO, SVM-RFE, and GBM were integrated to identify and categorize disease-specific characteristic genes.
RESULTS: We observed significant heterogeneity in tryptophan metabolism activity across cell types in UC, with the highest activity levels in macrophages and fibroblasts. Among the key tryptophan metabolism-related genes, CTSS, S100A11, and TUBB were predominantly expressed in macrophages and significantly upregulated in UC, highlighting their involvement in immune dysregulation and inflammation. Cross-analysis with bulk RNA data confirmed the consistent upregulation of these genes in UC samples, highly indicating their relevance in UC pathology and potential as targets for therapeutic intervention.
CONCLUSIONS: This study is the first to reveal the heterogeneity of tryptophan metabolism at the single-cell level in UC, with macrophages emerging as key contributors to inflammatory processes. The identification of CTSS, S100A11, and TUBB as key regulators of tryptophan metabolism in UC underscores their potential as biomarkers and therapeutic targets.