Article Cite This: J. Proteome Res. XXXX, XXX, XXX−XXX
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LC−MS-Based Metabolomics and Lipidomics Study of High-DensityLipoprotein-Modulated Glucose Metabolism with an apoA‑I Knockout Mouse Model Jia Liu,†,§ Mingming Zhao,‡,§ Yizhang Zhu,† Xu Wang,‡ Lemin Zheng,*,‡ and Yuxin Yin*,† †
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Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China ‡ The Institute of Cardiovascular Sciences, School of Basic Medical Sciences, and Key Laboratory of Molecular Cardiovascular Sciences of Ministry of Education, Peking University Health Science Center, Beijing 100191, China S Supporting Information *
ABSTRACT: Type 2 diabetes mellitus (T2DM) has become a tremendous problem in public health nowadays. High-density lipoprotein (HDL) refers to a group of heterogeneous particles that circulate in blood, and a recent research finds that HDL acts a pivotal part of glucose metabolism. To understand systemic metabolic changes correlated with HDL in glucose metabolism, we applied LC−MS-based metabolomics and lipidomics to detect metabolomic and lipidomic profiles of plasma from apoA-I knockout mice fed a high-fat diet. Multivariate analysis was applied to differentiate apoA-I knockout mice and controls, and potential biomarkers were found. Pathway analysis demonstrated that several metabolic pathways such as aminoacyl-tRNA biosynthesis, arginine and proline metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis were dysregulated in apoA-I knockout mice. This study may provide a new insight into the underlying pathogenesis in T2DM and prove that LC−MS-based metabolomics and lipidomics are powerful approaches in finding potential biomarkers and disturbed pathways. KEYWORDS: diabetes mellitus, metabolomics, lipidomics, apoA-I, LC−MS
1. INTRODUCTION
Metabolomics (metabolic profile analysis), the study of as many small-molecule metabolites as possible in a living system at a global level, could be used to discover biomarkers differentiating phenotypes of metabolic syndrome.10−12 Lipidomics, as a branch of metabolomics, focuses on profiling, quantification, and identification of lipids in a biologic system.13 Molecular lipid species detected by lipidomics, with clear chemical constitution, display advantages over traditional biochemical measurements, which only detect each lipid class as a whole.14,15 Both metabolomics and lipidomics were increasingly applied to epidemiological cohorts of diseases including diabetes. Recent cross-sectional investigations have found different metabolite profiles in obese and lean individuals after glucose intake.16 Acylcarnitines, amino acids, and other polysaccharides display different patterns in obese compared with lean individuals.11,17 However, no such study investigated the metabolic/lipidomic profiles in apoA-I knockout mice or human-related abnormal HDL metabolism. Thus it is necessary to study and to understand changes in HDL that
Nowadays, type 2 diabetes mellitus (T2DM) has become a tremendous problem in public health.1 The pathophysiological changes in T2DM could progressively influence blood glucose and lead to vascular complications. To improve preventive strategies, it is necessary to illuminate the molecular alteration in the development of T2DM. High-density lipoprotein (HDL) has several different components including lipoproteins and lipids, in which apolipoprotein A-I (apoA-I) is a major component.2,3 It has been found that HDL exerts multiple impacts on cardiovascular disease (CVD), including antioxidant,4 anti-inflammatory,5 and antithrombotic activities.6 Besides this conventional cardiovascular protection, more and more studies have found that HDL plays an important role in glucose metabolism.7−9 For instance, Rye found that apoA-I could activate FoxO1, then increase insulin secretion and production in pancreatic β cells, and Hofmann found that HDL could modulate cellular respiration in skeletal muscle through mitochondrial bioenergetics.27,30 These findings indicated that HDL-increasing therapies may be a potential target to prevent T2DM beyond CVD. © XXXX American Chemical Society
Received: April 30, 2018
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DOI: 10.1021/acs.jproteome.8b00290 J. Proteome Res. XXXX, XXX, XXX−XXX
Article
Journal of Proteome Research
Figure 1. (A) apoA-I genotyping was confirmed using genomic DNA extracted from tails by PCR. M: marker, B: blank. (B) HDL-C was decreased in apoA-I knockout mice, whereas LDL-C was increased significantly. (C) Glucose and total cholesterol levels in plasma of apoA-I (−/−) and control mice after 8 weeks of HFD.
methods (Sigma-Aldrich kits). Mice were fasted for 16 h, then 2 g of glucose/kg of body weight was injected through the peritoneal cavity in glucose tolerance testing (GTT). Human insulin (1 mIU/g body weight; Humulin) was used for insulin tolerance testing (ITT). Plasma were gathered before and at 15, 30, 60, 90, and 120 min after the injection of glucose or insulin. Animal procedures were approved by the Ethics Committee of Animal Research, Peking University Health Science Center.
take place at the onset of T2DM using metabolic profile analysis. In this study, we investigated the differences in plasma metabolome and lipidome in apoA-I (−/−) mice for the first time using a liquid−liquid extraction method combined with nontargeted metabolomic and lipidomic approaches by UHPLC−MS/MS. The apoA-I knockout mice and controls were clearly distinguished by multivariate data analysis after 8 weeks of high-fat diet (HFD). Metabolites and lipids with significant alterations were identified with high-resolution MS and MS/MS data, and the perturbed metabolic pathways were analyzed. We detected more than 400 metabolites to determine which metabolomic profiles are associated with T2DM in apoA-I knockout mice. This approach may be helpful for further understanding the role of HDL in the molecular mechanism of diabetes.
2.2. Sample Preparation for Nontargeted LC−MS Analysis
Lipids and polar metabolites were extracted by a modified Folch method.19,20 In brief, 400 μL of methanol/HCCl3 (1:2) was added to 100 μL of plasma and centrifuged at 13 000 rpm for 20 min. The aqueous phase and the organic phase were lyophilized, respectively, in a speed vacuum. 2.3. Lipidomics
2. EXPERIMENTAL SECTION
Lipidomics analysis was conducted using an Ultimate 3000 apparatus coupled to Q-Exactive MS. Chromatographic separation was performed using an Xselect CSH C18 column (4.6 mm × 100 mm, 2.5 μm, Waters). The detailed LC parameters are summarized in Supplemental Table S3. MS was performed in both ion mode and in a top-10 scan mode. Detailed parameters: capillary temperature, 320 °C; mass range, m/z 200 to 1200; AGC target, 1 × 106 for MS1 and 1 × 105 for MS2; source voltage, 3.3 kV for positive and 2.8 kV for negative.
2.1. Mice
5 to 6 week old male apoA-I (+/+) mice and apoA-I (−/−) mice were purchased from Nanjing University Model Animal Research Center. Genomic DNA was extracted from tails and confirmed by PCR. In brief, mice tails were digested with protease K at 55 °C overnight. Then, DNA was extracted using the phenol−chloroform method. The primers are as follows: apoA-I-common: GTTCATCTTGCTGCCATACG, apoA-IWT: TCTGGTCTTCCTGACAGGTAGG, and apoA-I-KO: CCTTCTATCGCCTTCTTGACG. HFD containing 45% fat, 20% protein, and 35% carbohydrate was fed to apoA-I (−/−) mice (n = 6) and age-matched control apoA-I (+/+) mice (n = 6). Plasma were gathered before and at 4 and 8 weeks after HFD or chow. High-density lipoprotein cholesterol (HDL-C) was determined according to Roche Diagnostics kit instructions. Low-density lipoprotein cholesterol (LDL-C) was determined according to the Friedewald equation.18 Plasma cholesterol and glucose were measured using enzymatic
2.4. Polar Metabolomics
For metabolomic analysis, an Xbridge amide column (100 × 4.6 mm, 3.5 μm, Waters) was used at 30 °C and flow rate 0.5 mL/min. The mobile phase A was 5 mM NH4Ac in 95% water, 5% ACN, and mobile phase B was 5 mM NH4Ac in 95% ACN, 5% water. The linear gradient used was 90% B for 3 min, followed by a linear gradient down to 40% B for the next 12 min and down to 2% B for the next 1 min, where it was held for 2 min before it was returned to 90% B for 1 min. B
DOI: 10.1021/acs.jproteome.8b00290 J. Proteome Res. XXXX, XXX, XXX−XXX
Article
Journal of Proteome Research
Figure 2. Glucose tolerance testing (GTT) (A) and insulin tolerance testing (ITT) (B) of apoA-I knockout and control mice after 8 weeks of HFD.
Figure 3. (A) PCA score plot of metabolome from apoA-I knockout and control mice fed HFD for 8 weeks and pooled QCs. The pooled QC group (in pink) clearly demonstrates that the instrument variability is low across the run. (B) Clustering heat map by Euclidean correlation of the samples. Five groups are included.
Mass spectrometric parameters were the same as those in lipidomic analysis except that the MS1 mass range was m/z 60 to 900.
analysis were performed in MetaboAnalyst (http://www. metaboanalyst.ca/).22
2.5. Data Analysis
3. RESULTS
Data were extracted and identified using MSDIAL software.21 Molecular features not present in 80% of the data were removed from further treatment. Significance was determined by Student’s t test, and molecular features were chosen when their p value was
