Aqueous humor metabolite profile of pseudoexfoliation glaucoma is distinctive. (9th March 2020)

• Record Type: Journal Article
• Authors: Myer, Ciara; Abdelrahman, Leila; Banerjee, Santanu; Khattri, Ram B.; Merritt, Matthew E.; Junk, Anna K.; Lee, Richard K.; Bhattacharya, Sanjoy K.
• Is Part Of: Molecular omics. Volume 16:Number 5(2020)
• Page Start: 425
• Page End: 435
• Publication Date: 2020-03-09
• Subjects: Molecular biology -- Periodicals; Biochemistry -- Periodicals; Biological systems -- Periodicals; Molecular Biology; Computational Biology; Biochemistry; Biological systems; Molecular biology; Periodicals; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals
• DOI: 10.1039/c9mo00192a
• Languages: English
• ISSNs: 2515-4184

Abstract

Abstract : We identified 298 metabolites in pseudoexfoliation (PEX) glaucoma, primary open angle glaucoma (POAG) and non-glaucomatous controls. Machine learning can classify aqueous humor into the three distinct categories and presents the opportunity for future predictions. Abstract : Pseudoexfoliation (PEX) is a known cause of secondary open angle glaucoma. PEX glaucoma is associated with structural and metabolic changes in the eye. Despite similarities, PEX and primary open angle glaucoma (POAG) may have differences in the composition of metabolites. We analyzed the metabolites of the aqueous humor (AH) of PEX subjects sequentially first using nuclear magnetic resonance ( 1 H NMR: HSQC and TOCSY), and subsequently with liquid chromatography tandem mass spectrometry (LC-MS/MS) implementing isotopic ratio outlier analysis (IROA) quantification. The findings were compared with previous results for POAG and control subjects analyzed using identical sequential steps. We found significant differences in metabolites between the three conditions. Principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) indicated clear grouping based on the metabolomes of the three conditions. We used machine learning algorithms and a percentage set of the data to train, and utilized a different or larger dataset to test whether a trained model can correctly classify the test dataset as PEX, POAG or control. Three different algorithms: linear support vector machines (SVM), deep learning, and a neural network were used for prediction. They all accurately classified the test datasets based on the AH metabolome of the sample. We next compared the AH metabolome with known AH and TM proteomes and genomes in order to understand metabolic pathways that may contribute to alterations in the AH metabolome in PEX. We found potential protein/gene pathways associated with observed significant metabolite changes in PEX.