Evidence map›Paper›PMID 40879851›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2025

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

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

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Article in Metabolomics : Official journal of the Metabolomic Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. New Therapies for Sarcoidosis: Molecular and Pathophysiological Basis.International journal of molecular sciences · 2026
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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Sanjukta DasguptaDepartment of Biotechnology, Center for Multidisciplinary Research & Innovations, Brainware University, Kolkata, India.
Priyanka ChoudhurySchool of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India.
Sankalp PatidarDepartment of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India.
Mamata JoshiNational Facility for High-field NMR, Tata Institute of Fundamental Research, Mumbai, India.
Riddhiman DharDepartment of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, India.
Sushmita RoychowdhuryFortis Hospital Anandapur, Kolkata, India.
Parthasarathi BhattacharyyaInstitute of Pulmocare and Research, Kolkata, India.
Koel ChaudhurySchool of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India. koel@smst.iitkgp.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPulmonary 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.

methodsUsing 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.

resultsThe 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.

conclusionsThe findings of this study indicate an increased energy demand and dysregulation of inflammatory pathways in patients with sarcoidosis.

Indexed as

MetabolomeSarcoidosis, PulmonaryTranscriptomeAdultFemaleGene Expression ProfilingHumansMaleMetabolomicsMiddle AgedIntegrated omicsMachine learningMetabolomicsSarcoidosisTranscriptomics

Identifiers

PMID40879851

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