Integrative analysis of transcriptome and metabolome profiles reveals immune-metabolic alterations in pulmonary sarcoidosis.

IF 3.3 3区医学 Q2 ENDOCRINOLOGY & METABOLISM

Metabolomics Pub Date : 2025-08-29 DOI:10.1007/s11306-025-02325-0

Sanjukta Dasgupta, Priyanka Choudhury, Sankalp Patidar, Mamata Joshi, Riddhiman Dhar, Sushmita Roychowdhury, Parthasarathi Bhattacharyya, Koel Chaudhury

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引用次数: 0

Abstract

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.

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转录组和代谢组的综合分析揭示了肺结节病的免疫代谢改变。

背景：肺结节病是一种病因不明的疾病，以肺实质中存在非干酪化肉芽肿为特征。本研究结合代谢组学和转录组学数据来确定结节病患者与健康对照组相比的代谢和差异表达基因（DEGs）及相关途径。据设想，更好地了解潜在的机制将有助于诊断和未来的治疗策略。方法：采用质子核磁共振（NMR）对两组患者（发现组和验证组）血清代谢物的改变进行注释。此外，通过分析基因表达综合数据库（Gene Expression Omnibus， GEO）鉴定血液样本中的deg。接下来，开发了一个使用机器学习方法的分类模型来评估这些关键代谢型和deg的预测能力。最后，使用IMPaLA version 13工具研究与这些候选代谢物和遗传特征相关的途径。结果：与对照组相比，结节病患者中六种代谢物的表达明显改变。基于微阵列数据的转录组学分析显示，结节病患者中有10个DEGs显着失调。使用这些关键代谢物和DEGs的分类模型显示，代谢物和DEGs的预测能力分别为84%和82%。代谢物- deg综合模型显示，结节病患者与IFN-γ信号通路有显著关联。结论：本研究结果表明，结节病患者能量需求增加，炎症通路失调。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Metabolomics 医学-内分泌学与代谢

CiteScore

6.60

自引率

2.80%

发文量

审稿时长

2 months

期刊介绍： Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to: metabolomic applications within man, including pre-clinical and clinical pharmacometabolomics for precision medicine metabolic profiling and fingerprinting metabolite target analysis metabolomic applications within animals, plants and microbes transcriptomics and proteomics in systems biology Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.