Metabolomics : Official journal of the Metabolomic Society · 2025
Integrative analysis of transcriptome and metabolome profiles reveals immune-metabolic alterations in pulmonary sarcoidosis.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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.
Dasgupta Sanjukta, Choudhury Priyanka, Patidar Sankalp, Joshi Mamata, Dhar Riddhiman, Roychowdhury Sushmita, Bhattacharyya Parthasarathi, Chaudhury Koel
Аннотация
BACKGROUND: Pulmonary sarcoidosis, a disease of unknown etiology, is characterized by the presence of noncaseating granulomas in lung parenchyma. This present study combines metabolomic and transcriptomic data to determine the metabolic and differentially expressed genes (DEGs) and associated pathways in sarcoidosis patients as compared to healthy controls. It is envisioned that a better understanding of the underlying mechanism will help in diagnosis and future treatment strategies.
METHODS: Using proton nuclear magnetic resonance (NMR) the altered serum metabolites were annotated in two groups of patients (discovery and validation cohorts). In addition, DEGs in blood samples were identified by analyzing a Gene Expression Omnibus (GEO) database. Next, a classification model using machine learning approach is developed to evaluate the predictive ability of these key metabotypes and DEGs. Finally, the pathways associated with these candidate metabolites and genetic features were investigated using IMPaLA version 13 tool.
RESULTS: The expression of six metabolites was found to be significantly altered in sarcoidosis patients as compared to controls. The transcriptomics analysis of microarray-based data revealed 10 DEGs to be significantly dysregulated in patients with sarcoidosis. The classification model using these key metabolites and DEGs showed the prediction ability to be 84% and 82% for metabolites and DEGs, respectively. Metabolite-DEG integrated model indicated significant association of IFN-γ signaling pathway in patients with sarcoidosis.
CONCLUSIONS: The findings of this study indicate an increased energy demand and dysregulation of inflammatory pathways in patients with sarcoidosis.
METHODS: Using proton nuclear magnetic resonance (NMR) the altered serum metabolites were annotated in two groups of patients (discovery and validation cohorts). In addition, DEGs in blood samples were identified by analyzing a Gene Expression Omnibus (GEO) database. Next, a classification model using machine learning approach is developed to evaluate the predictive ability of these key metabotypes and DEGs. Finally, the pathways associated with these candidate metabolites and genetic features were investigated using IMPaLA version 13 tool.
RESULTS: The expression of six metabolites was found to be significantly altered in sarcoidosis patients as compared to controls. The transcriptomics analysis of microarray-based data revealed 10 DEGs to be significantly dysregulated in patients with sarcoidosis. The classification model using these key metabolites and DEGs showed the prediction ability to be 84% and 82% for metabolites and DEGs, respectively. Metabolite-DEG integrated model indicated significant association of IFN-γ signaling pathway in patients with sarcoidosis.
CONCLUSIONS: The findings of this study indicate an increased energy demand and dysregulation of inflammatory pathways in patients with sarcoidosis.