The effects of acute lipopolysaccharide challenge on dairy goat liver metabolism assessed with 1HNMR metabonomics. Issue 1 (5th February 2016)

• Record Type: Journal Article
• Authors: Wang, L. F.; Jia, S. D.; Yang, G. Q.; Liu, R. Y.; Yang, G. Y.; Li, M.; Zhu, H. S.; Wang, Y. Y.; Han, L. Q.
• Is Part Of: Journal of animal physiology and animal nutrition. Volume 101:Issue 1(2017)
• Page Start: 180
• Page End: 189
• Publication Date: 2016-02-05
• Subjects: metabolomics -- lipopolysaccharide -- liver -- metabolite -- dairy goat; Animal nutrition -- Periodicals; Feeds -- Periodicals; 636.085
• DOI: 10.1111/jpn.12439
• Languages: English
• ISSNs: 0931-2439

Abstract

Summary: We investigated the mechanisms mediating hepatic metabolic responses to an acute lipopolysaccharide (LPS) challenge in goats. Guanzhong dairy goats (15) were randomly divided into three groups: control (CTL, saline, 0.2 ml/kg BW), lower dose LPS (LPS‐L, 20 μ g/kg BW) and higher dose LPS (LPS‐H, 40 μ g/kg BW). All injections were administered intraperitoneally twice with a 24‐h interval. Forty‐eight hours after the first injection, blood samples were collected to extract plasma for biochemical analysis, and liver tissues were biopsied and stored in liquid nitrogen for metabonomics analysis. We found that plasma levels of alanine aminotransferase, aspartate aminotransferase and total bilirubin increased (p < 0.05) in both LPS‐treated groups, whereas plasma triglyceride, cholesterol, very low‐density lipoprotein, low‐density lipoprotein, high‐density lipoprotein, total protein and albumin levels markedly decreased (p < 0.05). The increased activities of alanine aminotransferase (ALT) and aspartate aminotransferase (AST), levels of tumour necrosis factor α (TNF‐ α ), interleukin (IL)‐1 β, IL‐6 and IL‐8 indicated hepatic injury and metabolic dysfunction in some degree. Using proton nuclear magnetic resonance ( 1 H‐NMR) metabonomics and the Chenomx NMR suite database, 69 metabolites were detected and identified. Metabolic differences among the groups were determined with pattern recognition analyses using principal component analysis and supervised projection to latent structures discriminant analysis. Pattern recognition analysis distinguished and clustered the metabolite variables from the three groups, finding nine of 69 metabolites that differed significantly between two of the three groups: six from the LPS‐L or LPS‐H groups differed from CTL and three differed between LPS‐L and LPS‐H groups. These altered metabolites were closely connected with glucose, lipid and amino acid metabolic pathways in hepatocytes. Based on an analysis of these metabolites and their relevant pathways, the mechanisms and degree of hepatic injury were deduced. Therefore, the metabolic profile was used effectively to detect characteristic hepatic metabolites, discriminate metabolic changes induced by LPS, clarify the mechanisms for the resulting metabolic dysfunctions and provide efficient information to diagnose liver injury.