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other·ophthalmology, dry eye, preclinical research, biomedical research·PMC10480060

Integrated multi-omics and machine learning approach reveals lipid metabolic biomarkers and signaling in age-related meibomian gland dysfunction

Computational and Structural Biotechnology Journal · 4 authors, 2 centres

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Triglycerides significantly increased with aging despite overall lipid reduction.

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Triglycerides significantly increased with aging despite overall lipid reduction. Proteomic analysis revealed differentially expressed proteins enriched in cholesterol biosynthetic pathways, including Lss, Dhcr24, and Fdft1. A machine learning model established a nine-lipid diagnostic signature, with three cholesteryl ester species—ChE(26:0), ChE(26:1), and ChE(30:1)—identified as the most promising biomarkers. Limitations include the exclusive use of murine samples, with validation in human meibum acknowledged as necessary. The study provides a plausible hypothesis for ARMGD pathogenesis but does not establish causality.

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other

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ophthalmology

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AI pipeline

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ORIGINAL PAPER

PMC10480060

Integrated multi-omics and machine learning approach reveals lipid metabolic biomarkers and signaling in age-related meibomian gland dysfunction

Computational and Structural Biotechnology Journal

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ABSTRACT

Abstract Meibomian gland dysfunction (MGD) is a prevalent inflammatory disorder of the ocular surface that significantly impacts patients’ vision and quality of life. The underlying mechanism of aging and MGD remains largely uncharacterized. The aim of this work is to investigate lipid metabolic alterations in age-related MGD (ARMGD) through integrated proteomics, lipidomics and machine learning (ML) approach. For this purpose, we collected samples of female mouse meibomian glands (MGs) dissected from eyelids at age two months (n = 9) and two years (n = 9) for proteomic and lipidomic profilings using the liquid chromatography with tandem mass spectrometry (LC-MS/MS) method. To further identify ARMGD-related lipid biomarkers, ML model was established using the least absolute shrinkage and selection operator (LASSO) algorithm. For proteomic profiling, 375 differentially expressed proteins were detected. Functional analyses indicated the leading role of cholesterol biosynthesis in the aging process of MGs. Several proteins were proposed as potential biomarkers, including lanosterol synthase (Lss), 24-dehydrocholesterol reductase (Dhcr24), and farnesyl diphosphate farnesyl transferase 1 (Fdft1). Concomitantly, lipidomic analysis unveiled 47 lipid species that were differentially expressed and clustered into four classes. The most notable age-related alterations involved a decline in cholesteryl esters (ChE) levels and an increase in triradylglycerols (TG) levels, accompanied by significant differences in their lipid unsaturation patterns. Through ML construction, it was confirmed that ChE(26:0), ChE(26:1), and ChE(30:1) represent the most promising diagnostic molecules. The present study identified essential proteins, lipids, and signaling pathways in age-related MGD (ARMGD), providing a reference landscape to facilitate novel strategies for the disease transformation. Keywords: Multi-omics, Machine learning, Meibomian gland dysfunction, Aging, Lipids, Cholesteryl esters, Therapy

CITATION EXPORT

Yuchen Cai, Siyi Zhang, Liangbo Chen et al.. Integrated multi-omics and machine learning approach reveals lipid metabolic biomarkers and signaling in age-related meibomian gland dysfunction. Computational and Structural Biotechnology Journal. (2023). https://doi.org/10.1016/j.csbj.2023.08.026

AUTHORS

4 · 2 CENTRES

Yuchen Cai, Siyi Zhang, Liangbo Chen, Yao Fu

aDepartment of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
bShanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China