基于 LC/MS 的数据分析建模技术，用于区分接触紫外线辐射后血浆和尿液的代谢组学变化。

LC/MS-based discrimination between plasma and urine metabolomic changes following exposure to ultraviolet radiation by using data modelling.

机构信息

College of Medicine, University of Kerbala, Karbala, Iraq.

Strathclyde Institute of Pharmacy and Biomedical Sciences, 161, Cathedral Street, Glasgow, G4 0RE, Scotland, UK.

出版信息

Metabolomics. 2023 Feb 13;19(2):13. doi: 10.1007/s11306-023-01977-0.

DOI:10.1007/s11306-023-01977-0

PMID:36781606

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9925544/

Abstract

INTRODUCTION

This study sought to compare between metabolomic changes of human urine and plasma to investigate which one can be used as best tool to identify metabolomic profiling and novel biomarkers associated to the potential effects of ultraviolet (UV) radiation.

METHOD

A pilot study of metabolomic patterns of human plasma and urine samples from four adult healthy individuals at before (S1) and after (S2) exposure (UV) and non-exposure (UC) were carried out by using liquid chromatography-mass spectrometry (LC-MS).

RESULTS

The best results which were obtained by normalizing the metabolites to their mean output underwent to principal components analysis (PCA) and Orthogonal Partial least squares-discriminant analysis (OPLS-DA) to separate pre-from post-of exposure and non-exposure of UV. This separation by data modeling was clear in urine samples unlike plasma samples. In addition to overview of the scores plots, the variance predicted-Q2 (Cum), variance explained-R2X (Cum) and p-value of the cross-validated ANOVA score of PCA and OPLS-DA models indicated to this clear separation. Q2 (Cum) and R2X (Cum) values of PCA model for urine samples were 0.908 and 0.982, respectively, and OPLS-DA model values were 1.0 and 0.914, respectively. While these values in plasma samples were Q2 = 0.429 and R2X = 0.660 for PCA model and Q2 = 0.983 and R2X = 0.944 for OPLS-DA model. LC-MS metabolomic analysis showed the changes in numerous metabolic pathways including: amino acid, lipids, peptides, xenobiotics biodegradation, carbohydrates, nucleotides, Co-factors and vitamins which may contribute to the evaluation of the effects associated with UV sunlight exposure.

CONCLUSIONS

The results of pilot study indicate that pre and post-exposure UV metabolomics screening of urine samples may be the best tool than plasma samples and a potential approach to predict the metabolomic changes due to UV exposure. Additional future work may shed light on the application of available metabolomic approaches to explore potential predictive markers to determine the impacts of UV sunlight.

摘要

简介

本研究旨在比较人体尿液和血浆的代谢组学变化，以探讨哪一种可以作为识别与紫外线（UV）辐射潜在影响相关的代谢组学特征和新型生物标志物的最佳工具。

方法

对四名健康成年人的血浆和尿液样本在暴露（UV）和非暴露（UC）前后（S1 和 S2）进行代谢组学模式的初步研究，采用液相色谱-质谱联用（LC-MS）技术。

结果

通过将代谢物归一化为平均输出值，获得了最佳结果，然后进行主成分分析（PCA）和正交偏最小二乘判别分析（OPLS-DA），以分离暴露前后和非暴露于 UV 的样本。与血浆样本不同，这种数据建模的分离在尿液样本中非常明显。除了评分图的概述外，PCA 和 OPLS-DA 模型的交叉验证方差预测-Q2（累积）、方差解释-R2X（累积）和 p 值也表明了这种明显的分离。尿液样本 PCA 模型的 Q2（累积）和 R2X（累积）值分别为 0.908 和 0.982，OPLS-DA 模型的值分别为 1.0 和 0.914，而血浆样本的这些值分别为 Q2=0.429 和 R2X=0.660 用于 PCA 模型，Q2=0.983 和 R2X=0.944 用于 OPLS-DA 模型。LC-MS 代谢组学分析显示，许多代谢途径发生了变化，包括：氨基酸、脂质、肽、外来生物生物降解、碳水化合物、核苷酸、辅因子和维生素，这些变化可能有助于评估与 UV 阳光暴露相关的影响。