Machine-learning-based integrative –‘omics analyses reveal immunologic and metabolic dysregulation in environmental enteric dysfunction

• Published in: iScience Vol. 27; no. 6; p. 110013
• Main Authors: Zulqarnain, Fatima, Zhao, Xueheng, Setchell, Kenneth D.R., Sharma, Yash, Fernandes, Phillip, Srivastava, Sanjana, Shrivastava, Aman, Ehsan, Lubaina, Jain, Varun, Raghavan, Shyam, Moskaluk, Christopher, Haberman, Yael, Denson, Lee A., Mehta, Khyati, Iqbal, Najeeha T., Rahman, Najeeb, Sadiq, Kamran, Ahmad, Zubair, Idress, Romana, Iqbal, Junaid, Ahmed, Sheraz, Hotwani, Aneeta, Umrani, Fayyaz, Amadi, Beatrice, Kelly, Paul, Brown, Donald E., Moore, Sean R., Ali, Syed Asad, Syed, Sana
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 21.06.2024; Elsevier
• Subjects: Gastroenterology; Lipidomics; Machine learning; Medical imaging; Metabolic flux analysis; Transcriptomics; Lipidomics; Medical imaging; Transcriptomics; Metabolic flux analysis; Gastroenterology; Machine learning
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2024.110013

Environmental enteric dysfunction (EED) is a subclinical enteropathy challenging to diagnose due to an overlap of tissue features with other inflammatory enteropathies. EED subjects (n = 52) from Pakistan, controls (n = 25), and a validation EED cohort (n = 30) from Zambia were used to develop a machine-learning-based image analysis classification model. We extracted histologic feature representations from the Pakistan EED model and correlated them to transcriptomics and clinical biomarkers. In-silico metabolic network modeling was used to characterize alterations in metabolic flux between EED and controls and validated using untargeted lipidomics. Genes encoding beta-ureidopropionase, CYP4F3, and epoxide hydrolase 1 correlated to numerous tissue feature representations. Fatty acid and glycerophospholipid metabolism-related reactions showed altered flux. Increased phosphatidylcholine, lysophosphatidylcholine (LPC), and ether-linked LPCs, and decreased ester-linked LPCs were observed in the duodenal lipidome of Pakistan EED subjects, while plasma levels of glycine-conjugated bile acids were significantly increased. Together, these findings elucidate a multi-omic signature of EED. [Display omitted] •Multiomic approach to study environmental enteric dysfunction•Combines machine-learning-based image-analysis, metabolic modeling and lipidomics Gastroenterology; Medical imaging; Metabolic flux analysis; Lipidomics; Transcriptomics; Machine learning