Issue 37, 2020

Metabolic profiling of rats poisoned with paraquat and treated with Xuebijing using a UPLC-QTOF-MS/MS metabolomics approach

Abstract

Xuebijing (XBJ) is a compound Chinese medicine that contains Paeoniae Radix Rubra, ChuanXiong Rhizoma, Salvia Miltiorrhiza Radix et Rhizoma, Carthami Flos, and Angelicae Sinensis Radix. It is widely used in China to treat sepsis. Previous studies have demonstrated that XBJ can decrease mortality in patients with moderate paraquat poisoning. However, the mechanism by which it exerts this effect is not completely clear. In this study, an ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS)-based metabolomics approach was used to perform a metabolic profiling analysis. Principal component analysis (PCA), random forest (RF), and partial least squares discriminant analysis (PLS-DA) were used to identify metabolites to clarify the mechanism of XBJ's activity. XBJ clearly alleviated lung injury in a Sprague Dawley (SD) rat model of paraquat (PQ) poisoning. Seven metabolites related to four pathways, including those involved in sphingolipid and phospholipid metabolism, amino acid metabolism, unsaturated fatty acid metabolism, and pantothenic acid and CoA biosynthesis, were present at different levels in PQ-poisoned rats treated with XBJ compared with untreated rats. XBJ can ameliorate the effects of PQ poisoning in SD rats. Using a metabolomics approach enabled us to gain new insight into the mechanism underlying this effect.

Graphical abstract: Metabolic profiling of rats poisoned with paraquat and treated with Xuebijing using a UPLC-QTOF-MS/MS metabolomics approach

Article information

Article type
Paper
Submitted
13 May 2020
Accepted
27 Jul 2020
First published
13 Aug 2020
This article is Open Access
Creative Commons BY-NC license

Anal. Methods, 2020,12, 4562-4571

Metabolic profiling of rats poisoned with paraquat and treated with Xuebijing using a UPLC-QTOF-MS/MS metabolomics approach

W. Liu, S. Li, Y. K. Wu, X. Yan, Y. M. Zhu, F. Y. Jiang, Y. Jiang, L. H. Zou and T. T. Wang, Anal. Methods, 2020, 12, 4562 DOI: 10.1039/D0AY00968G

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