Issue 32, 2017, Issue in Progress

High-throughput and multi-dimensional omics approach uncovers a huaxian capsule to ameliorate the dysregulated expression profiling of severe sepsis rats

Abstract

Multi-dimensional omics could be helpful to interpret the underlying mechanisms of disease. Severe sepsis (SS) is a major cause of mortality and morbidity in intensive care units and has a large burden on healthcare due to a lack of effective drugs. The underlying pathophysiology of SS is also poorly understood. A huaxian capsule (HXC) is a herbal preparation with putative effects for SS treatment. Here, we aimed to investigate the metabolic changes in SS rats by performing metabolic profiling and biomarker analysis. The phenotypic response was assessed by UPLC/MS combined with chemometrics. As a result, 12 potential metabolite biomarkers were identified and involved in multiple dysregulated metabolic pathways, such as glycerophospholipid metabolism, steroid hormone biosynthesis, and disrupted sphingolipid metabolism, etc. Then, we performed microRNA analysis to reveal that the HXC ameliorates the dysregulated expression profiling of SS. We identified 56 miRNAs that were differentially expressed in the HXC group compared with the model group, of which 20 were down-regulated and 36 were up-regulated. This study showed that microRNA and metabolic profiling is a valuable approach for exploring metabolism responses to herbal drugs, and can improve our understanding of the molecular basis of the SS treatment.

Graphical abstract: High-throughput and multi-dimensional omics approach uncovers a huaxian capsule to ameliorate the dysregulated expression profiling of severe sepsis rats

Supplementary files

Article information

Article type
Paper
Submitted
17 Dec 2016
Accepted
28 Mar 2017
First published
04 Apr 2017
This article is Open Access
Creative Commons BY license

RSC Adv., 2017,7, 19894-19903

High-throughput and multi-dimensional omics approach uncovers a huaxian capsule to ameliorate the dysregulated expression profiling of severe sepsis rats

Q. Liang, H. Liu, L. Xie, X. Li and H. Ai, RSC Adv., 2017, 7, 19894 DOI: 10.1039/C6RA28337C

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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