Bascom Palmer Eye Institute, University of Miami, Miami, Florida, USA.
University of Florida, Gainesville, Florida, USA.
Mol Omics. 2020 Oct 12;16(5):425-435. doi: 10.1039/c9mo00192a.
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 (1H 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.
假性剥脱（PEX）是继发性开角型青光眼的已知原因。PEX 性青光眼与眼睛的结构和代谢变化有关。尽管存在相似之处，但 PEX 和原发性开角型青光眼（POAG）在代谢物组成上可能存在差异。我们首先使用核磁共振（1H NMR：HSQC 和 TOCSY）对 PEX 患者的房水（AH）代谢物进行了顺序分析，随后使用液相色谱串联质谱（LC-MS/MS）结合同位素比值异常分析（IROA）定量分析。将这些发现与使用相同顺序步骤分析的 POAG 和对照受试者的先前结果进行了比较。我们发现三种情况下的代谢物存在显著差异。主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）表明，基于三种情况的代谢组学，存在明显的分组。我们使用机器学习算法和数据的百分比集进行训练，并使用不同或更大的数据集来测试训练好的模型是否可以正确地将测试数据集分类为 PEX、POAG 或对照。我们使用了三种不同的算法：线性支持向量机（SVM）、深度学习和神经网络进行预测。它们都根据样本的 AH 代谢组准确地对测试数据集进行了分类。接下来，我们比较了 AH 代谢组与已知的 AH 和 TM 蛋白质组和基因组，以了解可能导致 PEX 中 AH 代谢组发生变化的代谢途径。我们发现了与 PEX 中观察到的显著代谢物变化相关的潜在蛋白/基因途径。
