DOI: 10.5061/dryad.fttdz08q7
title: Untargeted LC–MS metabolomics reveals an adverse effect of high-fat diet on hepatic metabolism of Oreochromis niloticus

description: Hepatic steatosis commonly occurs in intensively farmed tilapia. This
 disease is harmful to fish growth and health, but knowledge of the
 metabolic changes in tilapia with fatty liver is limited. In the present
 study, we compared genetically improved farmed tilapia (GIFT, Oreochromis
 niloticus) fed a high-fat diet (HFD) with those fed a normal-fat diet
 (NFD) for 8 weeks using LC-MS-based hepatic metabolomic assays and
 traditional nutritional assessments. Juvenile GIFT fed a HFD displayed
 higher fat disposition in the liver than did those fed a NFD. The
 metabolomic analyses revealed 61 differentially accumulated metabolites
 between the groups, and these metabolites were involved in 37 signaling
 pathways. Our results illustrate the development of metabolic disorders
 related to various hepatic biological processes, including protein
 metabolism, lipid metabolism, carbohydrate metabolism, and nucleotide
 metabolism in HFD-fed GIFT. These physiological changes may be related to
 the lower growth rate of GIFT. Overall, our study reveals the metabolic
 disorders in GIFT fed HFD, and enhance our knowledge of the mechanism of
 fatty liver formation in GIFT.
description: Sample collection     GIFT were fasted for one
 day after feeding trial. Before sampling, all GIFT were anesthetized with
 100 mg L<sup>−1</sup> MS-222 and weighed, three fish were
 chosen from each container and discrete liver tissues for further LC-MS
 analysis (n=9). All liver samples were frozen immediately in liquid
 nitrogen and stored at −80°C until analysis.  LC-MS
 analysis     50mg of liver samples were homogenized in
 120 μL pre-cooled 50% methanol buffer using a high-throughput tissue lyser
 (Scientz-192, Xinzhi, NingBo, China). The solutions were incubated on ice
 for 10 min and then centrifuged at 4000 g for 20 min. The supernatants
 were collected and used for metabolomic analyses. An ultra performance
 liquid chromatography system (SCIEX, UK) equipped with an ACQUITY UPLC BEH
 Amide column (100 mm × 2.1 mm, 1.7 µm, Waters, UK) was used for metabolite
 analysis. The flow rate was 0.4 ml/min and the sample injection volume was
 4 µL. The elution gradient was as follows: solvent A (25 mM ammonium
 acetate + 25 mM NH4H2O) and solvent B (IPA:ACN=9:1 + 0.1% formic acid):
 hold at 95% B for 0.5 min, decrease linearly from 95% to 65% B (0.5–9.5
 min), decrease linearly from 65% to 40% B (9.5–10.5 min), hold at 40% B
 (10.5–12min), increase from 40% to 95% B (12–12.2min) and hold at 95% B
 until 15 min. Mass spectrometry data acquisition was performed using a
 high-resolution tandem mass spectrometer TripleTOF5600plus (SCIEX, UK).
 The Q-TOF was operated in both positive (POS) and negative (NEG) modes
 using the following parameters: curtain gas was set to 30 PSI, nebulizer
 pressure was set to 60 PSI, and the interface heater temperature was
 650°C. The detector voltage was 5 kV in positive ion mode (−4.5 kV in
 negative ion mode). Data were collected in the IDA mode and the mass
 spectra scan range was set from m/z 60 to 1200. Quality control (QC)
 samples were prepared by mixing the supernatants from all 18 individual
 liver samples and these QC samples were injected at constant intervals to
 ensure the stability of the LC-MS analysis. 2.5
 Multivariate data analysis     ProteoWizard was
 employed to convert LC−MS raw data into mzXML format. Then, XCMS software
 (http://xcmsonline.scripps.edu) was used for peak alignment, extraction,
 and quantification. The peak data were further processed by metaX toolbox
 implemented with R software (version 3.5.1). Detectable features were
 first processed to remove low-quality features (detected in less than 50%
 of QC samples or 80% of biological samples). The remaining features were
 regarded as high-quality features and were used for further analysis. PCA
 was used to visualize the dataset of the liver samples in the HFD and NFD
 groups. PLSDA was then used for cluster analysis and to build a
 discriminant model. Permutation tests (n = 200) were utilized to evaluate
 the validity of the PLSDA models. In this study, significantly different
 features were first screened based on VIP values generated from the PLSDA
 model. The VIP cut-off value was set at 1.0 and VIP values &gt; 1.0
 were selected as significant discriminatory features between the HFD and
 NFD groups. Then, these features were confirmed by a student’s t test. The
 adjusted P value was adjusted by Benjamini and Hochberg's approach.
 The fold change (FC) of each feature between these two groups was
 calculated as follows: FC = normalized mean value obtained from the HFD
 group/ normalized mean value obtained from the NFD group. Features with a
 VIP value &gt;1, FC &gt; 2 or &lt; 0.5, and an adjusted
 P-value &lt; 0.05, were selected as significantly different features
 and were used for further analyses. Significantly different features were
 further identified by comparing their m/z data and retention time (RT)
 with those in an in-house fragment spectrum library of metabolites. Some
 online databases, including ChEBI (http://www.ebi.ac.uk/chebi/init.do),
 KEGG (http://www.genome.jp/kegg/), and HMDB (http://www.hmdb/ca), were
 used to further identify and annotate each metabolite. To visualize the
 metabolic changes under a HFD, significant different metabolites were
 clustered by hierarchical clustering analysis using TigrMev 4.2 software.
 Afterwards, these metabolites were subjected to a pathway analysis against
 the zebra fish (Danio rerio) KEGG library in MetaboAnalyst 4.0
 (http://www.metaboanalyst.ca/MetaboAnalyst/).