Basic Study
Copyright ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Feb 28, 2019; 25(8): 941-954
Published online Feb 28, 2019. doi: 10.3748/wjg.v25.i8.941
Dynamic changes of key metabolites during liver fibrosis in rats
Jiong Yu, Jian-Qin He, De-Ying Chen, Qiao-Ling Pan, Jin-Feng Yang, Hong-Cui Cao, Lan-Juan Li
Jiong Yu, Jian-Qin He, De-Ying Chen, Qiao-Ling Pan, Jin-Feng Yang, Hong-Cui Cao, Lan-Juan Li, State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University; Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, Zhejiang Province, China
Author contributions: Li LJ and Cao HC conceived and designed the study; Yu J wrote the manuscript and performed the statistical analysis; Chen DY substantially contributed to the conception and design of the study as well as the acquisition, analysis, and interpretation of the data; Yu J and Pan QL performed the immunohistochemical detection, RT-PCR, and data collection; He JQ provided technical support and revised the manuscript; all authors reviewed and approved the final version of the manuscript.
Supported by the Stem Cell and Translational Research, the National Key Research and Development Program of China, No. 2016YFA0101001; and Independent Project Fund of the State Key Laboratory for Diagnosis and Treatment of Infectious Disease (SKL DTID).
Institutional review board statement: This study was reviewed and approved by the Institutional Animal Care and Use Committee of the First Affiliated Hospital, School of Medicine, Zhejiang University.
Institutional animal care and use committee statement: All procedures involving animals were reviewed and approved by the Research Ethics Committee of the First Affiliated Hospital, School of Medicine, Zhejiang University (No. 201543).
Conflict-of-interest statement: The authors declare no conflict of interest.
Data sharing statement: No additional data are available.
ARRIVE guidelines statement: The ARRIVE Guidelines have been adopted.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Lan-Juan Li, MD, PhD, Academic Research, Doctor, Professor, Senior Researcher, State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou 310003, Zhejiang Province, China. ljli@zju.edu.cn
Telephone: +86-571-87236458 Fax: +86-571-87236459
Received: November 20, 2018
Peer-review started: November 20, 2018
First decision: December 12, 2018
Revised: January 10, 2019
Accepted: January 28, 2019
Article in press: January 28, 2019
Published online: February 28, 2019
Abstract
BACKGROUND

Fibrosis is the single most important predictor of significant morbidity and mortality in patients with chronic liver disease. Established non-invasive tests for monitoring fibrosis are lacking, and new biomarkers of liver fibrosis and function are needed.

AIM

To depict the process of liver fibrosis and look for novel biomarkers for diagnosis and monitoring fibrosis progression.

METHODS

CCl4 was used to establish the rat liver fibrosis model. Liver fibrosis process was measured by liver chemical tests, liver histopathology, and Masson’s trichrome staining. The expression levels of two fibrotic markers including α-smooth muscle actin and transforming growth factor β1 were assessed using immunohistochemistry and real-time polymerase chain reaction. Dynamic changes in metabolic profiles and biomarker concentrations in rat serum during liver fibrosis progression were investigated using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. The discriminatory capability of potential biomarkers was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

To investigate the dynamic changes of metabolites during the process of liver fibrosis, sera from control and fibrosis model rats based on pathological results were analyzed at five different time points. We investigated the association of liver fibrosis with 21 metabolites including hydroxyethyl glycine, L-threonine, indoleacrylic acid, β-muricholic acid (β-MCA), cervonoyl ethanolamide (CEA), phosphatidylcholines, and lysophosphatidylcholines. Two metabolites, CEA and β-MCA, differed significantly in the fibrosis model rats compared to controls (P < 0.05) and showed prognostic value for fibrosis. ROC curve analyses performed to calculate the area under the curve (AUC) revealed that CEA and β-MCA differed significantly in the fibrosis group compared to controls with AUC values exceeding 0.8, and can clearly differentiate early stage from late stage fibrosis or cirrhosis.

CONCLUSION

This study identified two novel biomarkers of fibrosis, CEA and β-MCA, which were effective for diagnosing fibrosis in an animal model.

Keywords: Ultra-performance liquid chromatography-mass spectrometry, Metabonomics, Liver fibrosis, Biomarker, Cervonoyl ethanolamide, β-muricholic acid

Core tip: Carbon tetrachloride induced model is stable and comparable with viral hepatitis. Metabolic changes occur during the progression of fibrosis. We investigated the association of liver fibrosis with 21 metabolites, and two of them, cervonoyl ethanolamide and β-muricholic acid, differed significantly in the fibrosis model rats compared to controls (P < 0.05) and showed prognostic value for fibrosis. The receiver operating characteristic curve analysis results showed that both metabolites had excellent diagnostic value and could be used in clinical diagnosis in the future.