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Metabolic dysfunction following weight regain compared to initial weight gain in a high-fat diet-induced obese mouse model
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ScienceDirect Journal of Nutritional Biochemistry 69 (2019) 44 – 52
Metabolic dysfunction following weight regain compared to initial weight gain in a high-fat diet-induced obese mouse model☆ Min-Sun Kim a, g, 1, Il Yong Kim b, c, 1 , Hye Rim Sung b, c , Miso Nam a, d , Youn Ju Kim b, c , Dong Soo Kyung b, c , Je Kyung Seong b, c, e,⁎, Geum-Sook Hwang a, f,⁎⁎ b
a Integrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul 120-140, Republic of Korea Laboratory of Developmental Biology and Genomics, Research Institute for Veterinary Science, and BK21 PLUS Program for Creative Veterinary Science Research, College of Veterinary Medicine, Seoul National University, Seoul 08826, Republic of Korea c Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul 08826, Republic of Korea d Department of Chemistry, Sungkyunkwan University, Suwon 16419, Republic of Korea e Interdisciplinary Program for Bioinformatics, Program for Cancer Biology and BIO-MAX/N-Bio Institute, Seoul National University, Seoul 08826, Republic of Korea f Department of Life Science, Ewha Woman's University, Seoul 120-750, Republic of Korea g Food Analysis Center, Korea Food Research Institute, Wanju, Korea
Received 22 June 2018; received in revised form 1 February 2019; accepted 28 February 2019
Abstract Diet-induced weight loss and regain leads to physiological and metabolic changes, some of which are potentially harmful. However, the specific metabolic processes and dysfunctions associated with weight regain, and how they differ from initial weight gain, remain unclear. Thus, we examined the metabolic profiles of mice following weight regain compared to initial weight gain. Mice were fed a normal diet or a high-fat diet or were cycled between the two diets to alternate between obese and lean states. Liver samples were collected and hepatic metabolites were profiled using nuclear magnetic resonance (NMR). The identified metabolites associated with weight regain were quantified using gas chromatography/mass spectrometry (GC/MS) and lipid profiles were assessed using ultra-high-performance liquid chromatography-quadrupole time-of-flight MS (UPLC-QTOF-MS). In addition, changes in expression of pro-inflammatory cytokines and gluconeogenic enzymes were investigated using polymerase chain reaction (PCR) and western blotting, respectively. Hepatic levels of several amino acids were reduced in mice during weight regain compared with initial weight gain. In addition, gluconeogenic enzyme levels were increased following weight regain, indicating an up-regulation of gluconeogenesis. Lipidomic profiling revealed that levels of ceramide and sphingomyelin, which are related to obesity-induced inflammation, were significantly increased during weight regain compared to initial weight gain. Moreover, tumor necrosis factor-α (TNF-α) and transforming growth factor-β1 (TGF-β1) levels were significantly up-regulated during weight regain. In this study, weight regains lead to an up-regulation of gluconeogenesis and aggravated inflammation. Additionally, weight regain can worsen the metabolic dysfunction associated with obesity. © 2019 Elsevier Inc. All rights reserved. Keywords: weight regain; obesity; liver; metabolism; nuclear magnetic resonance; mass spectrometry
1. Introduction The prevalence of obesity is increasing worldwide and is emerging as a serious public health problem [1,2]. Obesity is an established risk factor for several diseases, including type 2 diabetes mellitus, nonalcoholic fatty liver disease, cancer, osteoarthritis, and cardiovascular disease [1–6]. To reduce their risk for disease, many people attempt to lose weight by dieting; however, this is associated with ☆
Declarations of interest: None. ⁎ Correspondence to: J.K. Seong, Seoul National University, Seoul. Tel: +82 2 880 1259; fax: +82 2 885 8395. ⁎⁎ Correspondence to: G-S. Hwang, Korea Basic Science Institute, Seoul 120140, Republic of Korea. Tel.: +82 2 6908 6200; fax: +82 2 6908 6239. E-mail addresses: [email protected] (J.K. Seong), [email protected] (G.-S. Hwang). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.jnutbio.2019.02.011 0955-2863/© 2019 Elsevier Inc. All rights reserved.
poor long-term outcomes [2] and can result in a cycle of repeated intentional weight loss followed by regaining of the weight, known as “yo-yo dieting” [7,8]. Although the effects of yo-yo dieting are controversial [7,8], several recent studies have suggested that the regaining of weight after weight loss is associated with an increased disease risk for myocardial infarction, as well as stroke, diabetes, and coronary heart disease [9,10]. In contrast, other studies have found no harmful consequences with respect to several outcomes, including total cholesterol, high-density lipoprotein (HDL) levels, the ratio of total cholesterol to HDL, diastolic blood pressure, and cardiovascular mortality [2,7]. Understanding the metabolic and molecular processes that are affected by weight regain is a critical step towards resolving these discrepancies, as well as for developing effective measures that can prevent obesity and reduce the associated risks. One potential avenue for investigation is through metabolomics that can characterize gradual metabolic changes in biofluids or tissues resulting from dietary interventions and/or disease [11–13].
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Moreover, this technique can be applied to investigate the biochemical mechanisms related to disease pathogenesis and to discover biomarkers for disease diagnosis or prognosis [14,15]. Importantly, metabolomics studies have identified potential biomarkers for obesity and related diseases, as well as related underlying mechanisms [11,16–20]. However, while the potentially harmful effects of weight regain have been recognized, the specific metabolic processes and dysfunctions associated with weight regain remain unknown. In this study, we mimicked a yo-yo diet by cycling mice between a high-fat diet (HFD) and a normal chow diet (NCD). We hypothesized that these mice (“weight regain” group) would display an altered metabolic status when compared to mice that simply gained weight (“weight gain” group). To test this hypothesis, we characterized the metabolomes of liver tissues and measured the levels of relevant metabolites in response to weight regain using nuclear magnetic resonance (NMR). We further quantified the identified metabolites as being associated with weight regain using gas chromatography/mass spectrometry (GC/MS). Ultrahigh-performance liquid chromatography-quadrupole time-offlight mass spectrometry (UPLC-QTOF-MS) was used to generate lipid profiles for all mice to determine if levels of specific lipids in the liver tissues differed between the “weight regain” and “weight gain” groups. Our data were obtained through a combination of high-throughput technologies that allowed us to identify specific alterations in metabolic pathways that occur during weight regain. 2. Materials and methods 2.1. Chemicals High-performance liquid chromatography (HPLC)-grade organic solvents (acetonitrile, methanol, and chloroform) were purchased from Burdick & Jackson (Muskegon, MI). Methanol-d4 (99.8% v/v), deuterated chloroform (CDCl3), deuterium oxide (D2O; 99.9% v/v), and 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid sodium salt (TSP; 98% atom) were obtained from Cambridge Isotope Laboratories (Andover, MA, USA). N-tertButyldimethylsilyl-N-methyltrifluoroacetamide (MTBSTFA) with 1% tertButyldimethylchlorosilane (TBDMCS) was obtained from Sigma-Aldrich (St. Louis, MO, USA). All other chemicals and reagents were obtained from Sigma-Aldrich with the highest analytical grade available. 2.2. Animals Four-week-old, male, C57BL6/N mice were purchased from Central Lab. Animal Inc. (Seoul, Korea). Animals were housed in groups at 24±2 °C with a 12-h light/dark cycle and fed a normal diet (NIH-31; Lab Diet, Indianapolis, IN, USA) ad libitum and tap water. After 1 week of acclimatization, mice were weighed and randomly divided into three groups. The experimental period was 16 weeks and the paradigm for all three groups is shown in Fig. 1A. Mice in the “weight regain” group (designated as “regain”) were fed an HFD (60% kcal from fat, #D12492, Research Diet, New Brunswick, NJ) for 8 weeks, an NCD for 4 weeks, and an HFD for the final 4 weeks. Mice in the “weight gain” group (designated as “gain”) were fed the NCD for 12 weeks and the HFD for the last 4 weeks. We also included a “lean control” group (designated as “lean”), in which mice were fed the NCD for the full 16 weeks. All mice were weighed weekly. The amount of food intake was measured in the metabolic cage for 3 days at the end of the experiment (n=6/ group). The experimental protocol was carried out in accordance with the “Guide for Animal Experiments” (edited by the Korean Academy of Medical Sciences) and was approved by the Institutional Animal Care and Use Committee of Seoul National University (approval number: SNU-140205-2-1). Mice were euthanized by CO2 asphyxiation, and blood was removed by heart puncture. Livers and epididymal white adipose tissues were rapidly collected and weighed. The mice for body weight, fat weight, histology, and metabolic analysis were in the same experimental groups. However, mice for food intake, GTT, and fasting glucose levels tests were acquired from a separate experimental group. Numbers of mice used in the experiments are shown in Supplemental Table 1. 2.3. Histological analysis of liver tissue Liver tissue samples were collected at the end of the experimental period. Each organ was weighed and fixed with 4% paraformaldehyde (BIOSESANG, Seongnam, Korea) overnight at room temperature. Fixed tissue samples were dehydrated, cleared, and embedded in paraffin. For hematoxylin and eosin (H&E) staining, tissues were sectioned (4μm thickness), deparaffinized, rehydrated, and stained with hematoxylin for 3 min followed by a counterstaining with eosin for 1 min. For the Oil Red O staining, the liver tissues were cryoprotected by infiltration with 30% sucrose before sectioning with a cryostat (Leica, Heidelberg, Germany). Sections were washed with triple-distilled water for 5 min and once
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with 50% isopropyl alcohol and stained with an Oil Red O solution (six parts 0.5% Oil Red O powder in isopropanol with four parts water) for 20 min at 54 °C in a dry oven. Sections were rinsed with tap water and counter-stained with hematoxylin. Tissue sections were analyzed using a microscope (Olympus Co., Tokyo, Japan) equipped with a digital camera (DP71, Olympus Co.). Four to five images were captured from multiple liver tissue sections from each mouse. As there were four mice/group, a total of 15–20 images were collected for each experimental group. 2.4. Fasting glucose and glucose tolerance tests At the end of the experimental period, fasting glucose concentrations were measured in 20 μL of serum on a Hitachi 7180 biochemistry autoanalyzer (Hitachi Ltd., Tokyo, Japan). For the glucose tolerance test (GTT), mice (n=8–10/group) fasted for 16 h, a baseline tail blood sample was collected, and then mice received an intraperitoneal injection of glucose (1.5 g/kg body weight). After injection, blood samples (2 μL) were collected at 15, 30, 60, 90, and 120 min, and glucose concentrations were determined using an Accu-Chek guide meter (Roche Diagnostics, Indianapolis, IN). The mice used in this analysis consisted of a different subset of each diet group than the mice from which the liver samples were obtained for metabolic analyses. 2.5. NMR spectroscopic analysis and data processing For the metabolite extraction, 49.42±3.56 mg liver tissue (n=9–10/group) were placed into 1.5-mL microfuge tubes containing 2.8-mm zirconium oxide beads and homogenized twice at 5000 rpm with 200 μL of methanol-d4 and 200 μL of 0.2-M sodium phosphate buffer (pH 7.0±0.1) for 20 s using a Precellys 24 tissue grinder (Bertin Technologies, Ampe're Montigny-le-Bretonneux, France). After homogenization, 300 μL of methanol-d4, 150 μL of 0.2-M sodium phosphate buffer (pH 7.0±0.1), and 400 μL of CDCl3 were added to the tube. The mixture was vortexed vigorously for 1 min and allowed to separate for 15 min. Samples were centrifuged at 13,000 rpm for 10 min at 4 °C. Upper layers were transferred in 540-μL aliquots to new 1.5-mL microfuge tubes, mixed with 60 μL of 2-mM TSP dissolved in D2O, and centrifuged at 13,000 rpm for 10 min. Supernatants (600 μL) were transferred to 5-mm NMR tubes. 1 H NMR spectra of liver samples were acquired on a Bruker Avance III HD 800 MHz FT-NMR spectrometer at 298 K using a 5-mm triple-resonance inverse cryoprobe with Z-gradients (Bruker BioSpin Co., Billerica, MA). The pulse sequence used for the liver extracts was a Carr-Purcell-Meiboom-Gill pulse sequence collecting 64,000 data points with 128 transients, a spectral width of 16,025.641 Hz, a relaxation delay of 4.0 s, and an acquisition time of 2.0 s. All NMR data were phased and baseline corrected using Chenomx NMR Suite 7.1 (Chenomx, Edmonton, AB, Canada). The peaks for water and solvent were excluded, and the remaining spectral regions were divided into 0.005 ppm bins. The spectra were normalized to the total spectral area and converted to ASCII format. The ASCII files were imported into MATLAB (R2006a; MathWorks, Natick, MA) and spectra were aligned using the correlation-optimized warping (COW) method. Metabolite identities were assigned using Chenomx Profiler, a module of Chenomx NMR Suite version 7.1. Most metabolite identifications were confirmed by 2D NMR experiments and spiking experiments. 2.6. Sample preparation for mass spectrometry analysis For each mouse, a 50.77±1.49 mg liver tissue sample (n=9–10/group) was placed into a 1.5-mL tube containing 2.8-mm zirconium oxide beads and 10 μL of the labeled internal standard (2 mM 13C6-leucine). Samples were homogenized twice at 5000 rpm with 700 μL of a 1:1 (v/v) methanol:water mixture for 20 s using a Precellys 24 tissue grinder. After homogenization, 700 μL of chloroform was added and the mixture was vortexed vigorously for 1 min and centrifuged for 20 min at 13,000 rpm at 4 °C. The supernatant and chloroform layers were transferred to 1.5-mL microfuge tubes for GC/ MS and UPLC-QTOF-MS analysis, respectively. Supernatants were evaporated in a centrifugal evaporator (Thermo Scientific, Dreieich, Germany; 1000 rpm, room temperature). Tert-butyldimethylsilyl (TBDMS) derivatives of target metabolites were prepared by adding 40 μL of pyridine and 50 μL of MTBSTFA+1% TBDMCS and incubating for 120 min at 60 °C. Finally, 10 μL of the internal standard phenanthrene-d10 (2 mM) was added to the solutions. Mixtures were transferred to injection vials for GC/ MS analysis. The chloroform layer was evaporated to dryness under a gentle stream of nitrogen gas. For the UPLC-QTOF-MS analysis, liver lipid extracts were diluted with an isopropanol:acetonitrile:water mixture (2:1:1, v/v/v). 2.7. GC/MS analysis An Agilent 5977A mass spectrometer (Palo Alto, CA; electron ionization mode, 70 eV) connected to an Agilent 7890B gas chromatograph equipped with a DB-5MS capillary column (30 m×0.25 mm i.d., 0.25-μm film thickness, J&W Scientific, Folsom, CA) was used for analysis. Nitrogen was used as the carrier gas at a flow rate of 1.0 mL/ min and 1-μL samples were introduced by split-mode injection (split ratio 10:1). After sample injection, the oven temperature was held at 60 °C for 5 min, raised to 320 °C at 10 °C/min, and held for 15 min. The mass spectrometer interface temperature was set at 280 °C. The manifold temperature was maintained at 230 °C. The mass spectrometer was run in electron ionization mode with an electron energy of 70 eV and scanned a range of 50–500 amu. For sample monitoring and confirmation analyses, SIM mode was
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Fig. 1. Effect of weight regain on metabolic parameters and hepatic steatosis in mice. Male C57B1/6 mice were placed on a normal chow diet (NCD), a high-fat diet (HFD), or a combination diet for a 16-week experimental period. (A) Regain mice were fed an HFD for 8 weeks, an NCD for 4 weeks, and an HFD for the final 4 weeks. Gain mice were fed an NCD for 12 weeks and an HFD for the last 4 weeks. Lean mice were fed an NCD for the full 16 weeks. (B) Body weights were measured on a weekly basis for the duration of the study (n=9–10/ group). (C) Final body weights for each group (n=9–10/group). (D) Average food intake for each group during the experimental period (n=6/group). (E) Epididymal white adipose tissue weights at the end of the 16 weeks (n=10/group). (F) Histological analysis of hepatic fat accumulation using hematoxylin and eosin (H&E) and Oil Red O staining (n=4/group). Representative images are shown for each group (200× magnification). (G) Fasting blood glucose levels were measured for all three groups after a 16-h fast at the end of the 16-week protocol (n=8–10/group). (H) After a 16-h fast, a glucose tolerance test was performed (dose of 1.5 g glucose/kg lean mass) and measurements were taken at the indicated intervals after glucose injection (n=8–10/group). The area under the curve (AUC) was quantified for the duration of the test (inset). Data in all panels represent means ± SEM. *Pb.05 compared to lean; #Pb.05 between gain and regain. used with a dwell time of 50 ms for each ion. In GC/MS-SIM mode, two characteristic ions for each target metabolite were used for peak identification, and the most abundant ion was selected for quantification. The relative peak area was obtained by dividing the integrated area of the base peak by that of the internal standard.
2.8. UPLC-QTOF-MS analysis and data processing All UPLC-QTOF-MS analyses were performed using a UPLC system (Waters, Milford, MA) coupled with a triple TOF™ 5600 MS/MS System (AB SCIEX, Concord, ON, Canada) equipped with an electrospray ionization source. Chromatographic separation analysis was carried out using an Acquity UPLC BEH C18 column (2.1 mm×100 mm, 1.7 μm; Waters) at 40 °C; a binary gradient separation was performed at a flow rate of 0.35 mL/ min. The mobile phases consisted of solvent A (10 mM ammonium acetate in an acetonitrile:water mixture [40:60, v/v]) and solvent B (10 mM ammonium acetate in an acetonitrile:isopropanol mixture [10:90, v/v]). The gradient profile was 40–65% B over 5 min, 75% B at 20 min, 99% B at 25 min, 99% B from 25 to 27 min, and 40% B from 27.1 to 29 min to equilibrate the separation system for subsequent runs. The total run-time for each injection was 29 min and the injection volume was 3 μL. The mass spectrometer was operated in positive and negative ionization modes and acquired data in the mass range from 50 to 1100 m/z. The following parameter settings were used: ion spray voltage: 5500 V, temperature: 500 °C, curtain gas: 30 psi, declustering potential: 90 V, and collision energy: 10 V. The MS/MS analyses were acquired by automatic fragmentation, in which the five most intense mass peaks were fragmented. MS/MS experiments were run with a collision energy of 40 V and collision energy spread of 15 V. Nitrogen was used as the drying, nebulizing, and collision gas. Mass accuracy was maintained with an automated calibrant delivery system (AB SCIEX) interfaced to the second inlet of the DuoSpray source. Data acquisition and processing were performed using MarkerView software (AB SCIEX) to find peaks, perform alignments, and generate peak tables of m/z and retention times (min). Spectra were normalized to the total spectral area. To determine the
precision of detection, lipid metabolites with coefficients of variation below 20 were selected. Lipids were tentatively identified by comparing their retention times, the isotope patterns, exact mass values of the precursor, and MS/MS spectral data by searching the LIPID MAPS (www.lipidmaps.org), METLIN (metlin.scripps.edu), and Human Metabolome Database (www.hmdb.ca) databases. 2.9. Statistical analysis Statistical analyses were performed using the Kruskal-Wallis test followed by the Mann–Whitney U test using Bonferroni correction within the Statistical Package for the Social Sciences (SPSS) software, version 21.0 (SPSS Inc., Chicago, IL). The accepted level of significance was set as Pb.05. 2.10. Western blot analysis The expression levels of glucose-6-phosphatase (G6Pase), pyruvate carboxylase (PC), transforming growth factor-β1 (TGF-β1), and tumor necrosis factor-α (TNF-α) in murine liver extracts (20 μg) (n=4/group) were analyzed by western blots. Antibodies against G6Pase (ab83690), PC (ab128952), TGF-β1 (ab92486), and TNF-α (ab6671) were obtained from Abcam (Cambridge, MA). Protein extracts were prepared in radioimmunoprecipitation assay buffer with a protease inhibitor cocktail (BioVision, Mountain View, CA, USA). Proteins were harvested and centrifuged for 15 min at 4 °C. Supernatants were saved, and protein concentrations were determined using bicinchoninic acid protein assays. Proteins were separated on 10% SDSpolyacrylamide gels and transferred to polyvinylidene difluoride membranes. Membranes were blocked with 5% skim milk/Tris-buffered saline with Tween 20 (TBS-T) solution for 1 h at room temperature, and incubated with primary antibodies in 5% skim milk in TBS-T overnight at 4 °C. After washing with TBS-T three times, membranes were incubated with secondary antibodies coupled to horseradish peroxidase for 1 h at room
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temperature. The membranes were washed three times with TBS-T and immunoreactivity was detected using ECL reagents (Bio-Rad, Hercules, CA, USA) and a Fusion SL3 instrument (Vilber Lourmat, Torcy, France). Antibodies against β-actin (A5441, SigmaAldrich) were used as a loading control. 2.11. Quantitative reverse transcription-polymerase chain reaction (PCR) Total RNA was extracted from liver tissues (n=4/group) using RNeasy Mini kits (Qiagen, Valencia, CA). Quantitative PCR was performed using a StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The PCR primers used were: TGF-β1 FW, 5′-TTGCTTCAGCTCCACAGAGA-3′ and RV, 5′-TGGTTGTAGAGGGCAAGGAC3′; and TNF-α FW, 5′-CCACCACGCTCTTCTGTCTA-3′ and RV, 5′-CACTTGGTGGTTTGCTACGA-3′. mRNA levels were normalized to those of β-actin.
3. Results 3.1. Effect of weight regain on metabolic parameters and hepatic steatosis in mice The mean body weights of all mice during the experimental period are plotted in Fig. 1B. Mice placed on the HFD had a steeper increase in body weight than those on the NCD. After switching to the NCD for 4 weeks, the mice in the weight regain group lost weight and became similar in weight to the lean controls. However, after switching back to the HFD, the weight regain mice again experienced a steep increase in body weight. Both the weight regain and weight gain groups had final body weights that were significantly higher than that of the lean control group (Pb.05; Fig. 1C). There was also a significant difference in the final body weights between the weight regain and weight gain groups (Pb.05). Importantly, average food intake did not significantly differ among the three groups (Fig. 1D). To further examine the effects of diet on weight, we specifically looked at epididymal white adipose tissue weights. The epididymal white adipose tissue weights were higher in mice fed the HFD compared to those with only the lean diet (Pb.05); however, there was no difference between the gain and regain groups (Fig. 1E). Staining with H&E and Oil Red O revealed that hepatic steatosis was apparent in both HFD feeding groups (gain and regain); importantly, it was more pronounced in the weight regain mice than in the weight gain mice (Fig. 1F). To determine the effects of weight regain on serum metabolic parameters, fasting blood glucose measurements and GTTs were performed (Fig. 1G and H). Not surprisingly, the fasting blood glucose levels in the weight gain groups were elevated (Pb.05) compared to the lean controls. Furthermore, the weight regain group had even higher fasting blood glucose levels than the weight gain group (Pb.05). Conversely, while glucose tolerance was significantly reduced by HFD feeding, it did not vary between the weight gain and regain groups. The effect of weight loss on metabolic parameters in mice at 12 weeks of the study is represented in Supplemental Fig. 1. Average food intake and epididymal white adipose tissue weights did not significantly differ between the lean control and the weight regain groups (Supplementary Fig. 1A and B). Fasting blood glucose was identical in the lean control and weight regain groups at this point (Supplemental Fig. 1C). In addition, a GTT showed no difference in glucose tolerance between the two groups (Supplemental Fig. 1D). 3.2. Metabolic profiling of liver tissues using NMR To determine changes in aqueous metabolites in the liver as a function of weight regain, NMR-based metabolic profiling was performed. A representative, one-dimensional 1H NMR spectrum of an aqueous liver extract is shown in Supplemental Fig. 2. From the NMR spectrum of the liver tissue, 28 hepatic metabolites were identified (Supplemental Table 2) and compared between the experimental groups using the partial least-squares discriminant analysis (PLS-DA) model (Fig. 2A). The PLS-DA score plot showed
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significant separation in the liver samples between groups (R2Y= 0.760 and Q2=0.630; lean control n=10; weight gain, n=9; weight regain, n=10). PLS-DA models were validated using permutation tests (Fig. 2B). Several metabolites, including alanine, glycine, isoleucine, valine, leucine, phenylalanine, betaine, lactate, and glucose distinguished the weight regain group from the other groups in the loading plots and high variable importance of projection (VIP) values (VIPN1; Fig. 2C). Among those metabolites, alanine, glycine, valine, lactate, and ATP/ADP were significantly decreased in the weight regain group compared to the lean control group (Pb.05; Fig. 2D). Interestingly, some metabolites, such as alanine, glycine, isoleucine, valine, and ATP/ ADP, were decreased in the weight regain mice compared to the weight gain mice. 3.3. Quantitative analysis of metabolites using GC/MS To achieve a quantitative analysis of the amino acids and organic acids associated with weight regain, liver tissue samples from individual animals were analyzed by GC with quadrupole MS, using the selected ion monitoring (SIM) method. Derivatizations of all compounds were performed as described in the sample preparation section. In GC/MS-SIM mode, two characteristic ions for each amino acid and organic acid were used for peak identification, and the most abundant ion was selected for quantification (Supplemental Table 3). Calibration curves were generated and calculated using the method of least-squares, relating y (the peak area ratio of the metabolite to the internal standard) to x (the concentration of the metabolite in μM). The calibration curves and correlation coefficients are indicated in Supplemental Table 4. The calibration curves of the targeted metabolites generated good linearity within the given concentration ranges, with correlation coefficients (R2) greater than 0.99. A total of 12 metabolites from the liver tissues were quantitated with the described method in this paper. Supplemental Fig. 3 shows the SIM chromatograms and extracted ion chromatograms of the metabolite-TBDMS derivatives from murine liver samples. The peak abundance of 13C6-leucine was used to evaluate the recovery of the metabolites from the liver tissue sample during the analytical procedure (recoveryN83.0±5.3%). Phenanthrene-d10 was applied as an internal standard for the metabolite-TBDMS derivative. The concentrations of the metabolites in each group are shown in Supplemental Table 5. The contents of all amino acids were reduced in HFD feeding groups. Moreover, weight regain resulted in an additional decrease in amino acids in liver tissue. 3.4. Effect of weight regain on liver gluconeogenesis in mice Variations in the levels of significant metabolites in liver tissue samples are shown in Fig. 3A. Levels of several amino acids were reduced in both HFD feeding groups compared with lean controls (Pb.05); however, the levels of many of these amino acids were even lower in the weight regain group than in the weight gain group. These effects, which were enhanced in the weight regain group, may be reflective of increased amino acid utilization by gluconeogenesis pathways after HFDs. In support of this hypothesis, lactate, a major hepatic substrate for gluconeogenesis, was among the metabolites significantly reduced in the HFD groups. Conversely, pyruvate was increased in the weight regain group, compared with the weight gain group, but was significantly decreased in the weight gain group compared to lean controls. However, oxaloacetate, which is also involved in gluconeogenesis, was not significantly different between the three groups. Therefore, to investigate the effects of weight regain on gluconeogenesis, we examined the expression of G6Pase and PC in the liver tissue of all mice. Western blot analysis showed that G6Pase and PC expression was higher in the liver of both HFD groups compared to the lean controls
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Fig. 2. Metabolic profiles of liver samples from lean control (lean), weight gain (gain), and weight regain (regain) mice using NMR spectroscopy. (A) PLS-DA score plot (R2Y=0.760 and Q2=0.630). White circles represent lean, gray circles represent gain, and black circles represent regain mice. (B) Validation plots of the PLS-DA model using a permutation test. Validation plots were obtained from 100 permutation tests of the responses to the PLS-DA models. (C) Loading plot of the partial least-squares discriminant analysis (PLS-DA) model. Identified metabolites that had high variable importance of production (VIP) values (N1). (D) Altered metabolites with high VIP values are shown for all groups. Data represent means ± SEM; n=9–10/group. *Pb.05, **Pb.01, ***Pb.001 compared to lean, #Pb.05 between gain and regain.
(Pb.05, weight regain versus lean control), but was even higher in the weight regain than the weight gain mice (Fig. 3B). These results indicate that up-regulation of gluconeogenesis occurred in both HFD groups, but occurred to a greater extent in the weight regain group. 3.5. Lipidomic profiling of murine liver using UPLC-QTOF-MS To identify lipid species related to weight regain, liver samples were analyzed using UPLC-QTOF-MS. Representative chemical structures and product ion mass spectra for each group are presented in Supplemental Fig. 4. The PLS-DA score plot derived from positive and negative modes
showed clear separation across the three groups (R2Y=0.795, Q2= 0.685 for positive mode and R2Y=0.783, Q2=0.683 for negative mode; Supplemental Fig. 5). The fold change values for these species across all three groups are shown in Supplemental Table 6. Ceramide, sphingomyelin, and triglycerides were highly abundant lipids and their levels were significantly different between the weight regain and lean control groups (Pb.05). Except for ceramide (d40:1), weight regain resulted in increased ceramide and sphingomyelin levels compared with the weight gain group (Fig. 4A). Additionally, the relative levels of most triglyceride species were significantly increased in the weight regain mice compared with the weight gain mice.
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Fig. 3. Concentrations of metabolites obtained by GC/MS and enzymes involved in gluconeogenesis. (A) Metabolite concentrations in liver tissues from lean control (lean), weight gain (gain), and weight regain (regain) mice. Data represent means ± SEM (n=9–10/group). (B) Representative western blots and quantifications of glucose-6-phosphatase (G6Pase) and pyruvate carboxylase (PC) protein in murine liver tissues. Protein levels were normalized to β-actin. Data represent means ± SEM (n=4/group). *Pb.05, **Pb.01, ***Pb.001 compared to lean, #Pb.05, ## Pb.01 between gain and regain.
Sphingolipid metabolites, such as ceramide and sphingomyelin, function in regulating chronic inflammation [21,22]. Thus, we measured the mRNA levels and protein levels of the inflammatory cytokines TNF-α and TGF-β1 and found that they were increased in the liver of both the weight gain and weight regain groups compared to controls (Fig. 4B and C). In particular, the up-regulation of TNF-α mRNA expression was significantly pronounced in the weight regain group than in the weight gain group (Pb.05). Also, TNF-α protein expression was increased in the weight regain group than in the weight gain group, though the difference was statistically nonsignificant. Together, these data show that weight regain aggravates hepatic inflammation and that this process may be influenced by sphingolipids in the liver. 4. Discussion Recently, several studies have investigated the adverse health effects of weight regain following weight loss [2,7,23]. However, the
specific metabolic changes that occur with weight regain, and how these compare with initial weight gain, remained unclear. Here, we have shown that mice that were cycled between an HFD and an NCD (weight regain) had more pronounced metabolic alterations in the liver than those placed on an HFD for the first time. We performed a hepatic metabolic analysis using NMR and found that the HFD caused significant changes in amino acid levels in the liver, most notably a depletion of alanine, glycine, isoleucine, and valine, compared to a normal diet. Interestingly, these amino acids were reduced to a greater extent in the weight regain mice than in the weight gain mice. To examine further these metabolic alterations and to quantify glycogenic amino acids, we performed a targeted analysis of alanine, glycine, threonine, serine, cysteine, isoleucine, valine, methionine, and phenylalanine using GC–MS. All investigated amino acids were reduced in both HFD feeding groups (gain and regain) compared to the control group, which is consistent with previous reports and likely occurs as a result of HFD-induced gluconeogenesis, wherein some of these amino acids act as substrates [11,16,18]. In
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Fig. 4. The effect of weight regain on lipids and inflammation in murine livers. (A) The intensity of ceramide (Cer) and sphingomyelin (SM) species are shown for each group (n=9–10/ group). (B) Changes in mRNA expression of inflammatory cytokines TNF-α and TGF-β1 (n=4/group). mRNA levels were normalized to β-actin. (C) Representative western blots and quantifications of TNF-α and TGF-β1 (n=4/group) protein in murine liver tissues. Protein levels were normalized to β-actin. Data represent means ± SEM. *Pb.05, **Pb.01, ***Pb.001 compared to lean. #Pb.05, ## Pb.01, ###Pb.001 between gain and regain.
addition, we found increased levels of glucose in the blood of both HFD feeding groups, which is also indicative of enhanced gluconeogenesis and results from elevated glucose production in the liver, exacerbating hyperglycemia. Importantly, compared to weight gain, weight regain resulted in more pronounced effects with respect to both amino acid levels in the liver tissue and blood glucose levels, indicating that weight regain increases systematic metabolic dysfunction to a greater extent than does initial weight gain. We investigated transcriptional regulation of two key gluconeogenic enzymes: G6Pase and PC. Up-regulation of G6Pase expression enhances gluconeogenesis, as it catalyzes the last step in the metabolic
pathway [11,24]. Conversely, PC catalyzes the ATP-dependent carboxylation of pyruvate to oxaloacetate, which is the first regulated step in the gluconeogenic pathway of pyruvate and its precursors [25,26]. Increased protein expression of PC and G6Pase was inversely related to hepatic amino acid concentrations in obese mice and this effect was enhanced in weight regain mice. Elevated amino acid turnover may impact hepatic gluconeogenesis because of the important role of the liver in amino acid catabolism [27]. In addition, we observed a decrease in the ATP/ADP ratio in the liver of weight gain mice, and this ratio was further decreased in the weight regain mice. However, the issue whether a low ATP/ADP ratio reflects the activation
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Fig. 5. Schematic representation of hepatocyte metabolism. Metabolites, enzymes, and cytokines within this pathway that were altered by weight regain (red arrows: increased levels; blue arrows: decreased levels) as compared to weight gain are indicated.
of PC is unclear. Measurements of ADP from extracts are possible to lead to an overestimation of free ADP levels due to their low concentration and the number of intracellular binding sites [28]. Pyruvate, lactate, and oxaloacetate levels did not positively correlate with PC expression. It is likely that these metabolites are involved in very rapid processes, such as glycolysis and the TCA cycle, with turnover occurring in minutes [29,30]. Much research has focused on the link between metabolic dysfunction in obesity and chronic inflammation [31]. Recent insights suggest that sphingolipids are dysregulated under obese conditions and alter the inflammatory state in adipose tissues and immune cells [32]. Furthermore, several reports correlate HFDs with elevated sphingolipid levels in liver tissue [22,32,33]. In this study, we also observed increased levels of sphingomyelin and ceramide in both HFD groups, with levels being further elevated in the weight regain group. Therefore, given the link between sphingolipids and inflammation, we examined inflammation following weight regain. Obesity-associated inflammation is characterized by elevated levels of inflammatory cytokines [33,34]. Additionally, TNF-α expression is higher in liver tissues of obese patients with nonalcoholic steatohepatitis than in those with slight or nonexistent fibrosis [35,36]. Furthermore, TGF-β1 mRNA is elevated in patients with liver fibrosis [36]. Severe inflammation and hepatic injury also involve the up-regulation of hepatic TNF-α and TGF-β1 mRNA [37]. Our results showed increased levels of hepatic TNF-α and TGF-β1 mRNA in the weight regain group and to a lesser extent in the weight gain group compared to lean controls. Since TNF-α induces de novo ceramide production by activating sphingomyelinase [38], the enhanced obesity-induced hepatic inflammation observed in the weight regain group could be associated with higher activating TNF-α and overall ceramide levels in the liver tissue compared with the weight gain mice. To enrich our understanding of the inflammation and gluconeogenesis caused by weight regain, the metabolic responses of the adipose tissues and muscle will need further study. This study conducted metabolite profiling of mice using NMR, GC/ MS, and UPLC-QTOF-MS to examine the effects of weight regain. Weight regain decreased amino acid levels in liver tissues and increased fasting glucose levels in the blood. In addition, G6Pase and PC protein levels were higher in the weight regain group than in the weight gain group. Thus, weight regain leads to greater up-regulation of gluconeogenesis than does initial weight gain. Similarly, the weight
regain group had higher levels of ceramide and sphingomyelin in liver tissue than did the weight gain group. Furthermore, a corresponding increase in the expression of the inflammatory cytokines TNF-α and TGF-β1 was observed in the liver of the weight regain group. Taken together, our data indicate that weight regain aggravates the metabolic dysfunction associated with obesity (Fig. 5). This information increases our understanding of the metabolic pathways involved in weight regain and may be useful for the development of effective weight control measures prior to the development of obesity. Acknowledgments This study was supported by the National Research Foundation (NRF) of Korea (No. 2017M3A9C4065961 and NRF2017M3A9D5A01052449), and the Korea Basic Science Institute (C39705) to G.S.H; the Korea Mouse Phenotyping Project (2013M3A9D5072550) to J.K.S; and the Bio & Medical Technology Development Program of the National Research Foundation (2012M3A9B6055344) to I.Y.K. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jnutbio.2019.02.011. References [1] Vucenik I, Stains JP. Obesity and cancer risk: evidence, mechanisms, and recommendations. Ann N Y Acad Sci 2012;1271:37–43. [2] Lahti-Koski M, Männistö S, Pietinen P, Vartiainen E. Prevalence of weight cycling and its relation to health indicators in Finland. Obesity 2005;13:333–41. [3] Pi-Sunyer X. The medical risks of obesity. Postgrad Med 2009;121:21–33. [4] Lavie CJ, Milani RV, Ventura HO. Obesity and cardiovascular disease: risk factor, paradox, and impact of weight loss. J Am Coll Cardiol 2009;53:1925–32. [5] Sowers MR, Karvonen-Gutierrez CA. The evolving role of obesity in knee osteoarthritis. Curr Opin Rheumatol 2010;22:533–7. [6] Fabbrini E, Sullivan S, Klein S. Obesity and nonalcoholic fatty liver disease: biochemical, metabolic, and clinical implications. Hepatology 2010;51:679–89. [7] Graci S, Izzo G, Savino S, Cattani L, Lezzi G, Berselli M, et al. Weight cycling and cardiovascular risk factors in obesity. Int J Obes (Lond) 2004;28:65. [8] Montani J, Viecelli A, Prévot A, Dulloo AG. Weight cycling during growth and beyond as a risk factor for later cardiovascular diseases: the ‘repeated overshoot'theory. Int J Obes (Lond) 2006;30:S58.
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[9] Li Z, Hong K, Wong E, Maxwell M, Heber D. Weight cycling in a very low-calorie diet programme has no effect on weight loss velocity, blood pressure and serum lipid profile. Diabetes Obes Metab 2007;9:379–85. [10] Kroke A, Liese A, Schulz M, Bergmann M, Klipstein-Grobusch K, Hoffmann K, et al. Recent weight changes and weight cycling as predictors of subsequent two year weight change in a middle-aged cohort. Int J Obes (Lond) 2002;26:403. [11] An Y, Xu W, Li H, Lei H, Zhang L, Hao F, et al. High-fat diet induces dynamic metabolic alterations in multiple biological matrices of rats. J Proteome Res 2013; 12:3755–68. [12] Nam M, Jung Y, Ryu DH, Hwang GS. A metabolomics-driven approach reveals metabolic responses and mechanisms in the rat heart following myocardial infarction. Int J Cardiol 2017;227:239–46. [13] Boulangé CL, Claus SP, Chou CJ, Collino S, Montoliu I, Kochhar S, et al. Early metabolic adaptation in C57BL/6 mice resistant to high fat diet induced weight gain involves an activation of mitochondrial oxidative pathways. J Proteome Res 2013;12:1956–68. [14] Mamas M, Dunn WB, Neyses L, Goodacre R. The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch Toxicol 2011; 85:5–17. [15] Gowda GN, Zhang S, Gu H, Asiago V, Shanaiah N, Raftery D. Metabolomics-based methods for early disease diagnostics. Expert Rev Mol Diagn 2008;8:617–33. [16] Kim H, Kim JH, Noh S, Hur HJ, Sung MJ, Hwang J, et al. Metabolomic analysis of livers and serum from high-fat diet induced obese mice. J Proteome Res 2010;10: 722–31. [17] Li H, Xie Z, Lin J, Song H, Wang Q, Wang K, et al. Transcriptomic and metabonomic profiling of obesity-prone and obesity-resistant rats under high fat diet. J Proteome Res 2008;7:4775–83. [18] Xie Z, Li H, Wang K, Lin J, Wang Q, Zhao G, et al. Analysis of transcriptome and metabolome profiles alterations in fatty liver induced by high-fat diet in rat. Metabolism 2010;59:554–60. [19] Nam M, Choi M, Jung S, Jung Y, Choi J, Hwang G. Lipidomic profiling of liver tissue from obesity-prone and obesity-resistant mice fed a high fat diet. Sci Rep 2015; 5:16984. [20] Kim IY, Jung J, Jang M, Ahn YG, Shin JH, Choi JW, et al. 1 H NMR-based metabolomic study on resistance to diet-induced obesity in AHNAK knock-out mice. Biochem Biophys Res Commun 2010;403:428–34. [21] Maceyka M, Spiegel S. Sphingolipid metabolites in inflammatory disease. Nature 2014;510:58. [22] Kolak M, Westerbacka J, Velagapudi VR, Wagsater D, Yetukuri L, Makkonen J, et al. Adipose tissue inflammation and increased ceramide content characterize subjects with high liver fat content independent of obesity. Diabetes 2007;56:1960–8. [23] Stevens VL, Jacobs EJ, Sun J, Patel AV, McCullough ML, Teras LR, et al. Weight cycling and mortality in a large prospective US study. Am J Epidemiol 2012;175:785–92.
[24] van Schaftingen E, Gerin I. The glucose-6-phosphatase system. Biochem J 2002; 362:513–32. [25] Jitrapakdee S, Gong Q, MacDonald MJ, Wallace JC. Regulation of rat pyruvate carboxylase gene expression by alternate promoters during development, in genetically obese rats and in insulin-secreting cells. Multiple transcripts with 5′end heterogeneity modulate translation. J Biol Chem 1998;273:34422–8. [26] Kumashiro N, Beddow SA, Vatner DF, Majumdar SK, Cantley JL, Guebre-Egziabher F, et al. Targeting pyruvate carboxylase reduces gluconeogenesis and adiposity and improves insulin resistance. Diabetes 2013;62:2183–94. [27] Chevalier S, Burgess SC, Malloy CR, Gougeon R, Marliss EB, Morais JA. The greater contribution of gluconeogenesis to glucose production in obesity is related to increased whole-body protein catabolism. Diabetes 2006;55:675–81. [28] Koretsky AP, Brosnan MJ, Chen LH, Chen JD, Van Dyke T. NMR detection of creatine kinase expressed in liver of transgenic mice: determination of free ADP levels. Proc Natl Acad Sci U S A 1990;87:3112–6. [29] Munger J, Bennett BD, Parikh A, Feng X, McArdle J, Rabitz HA, et al. Systems-level metabolic flux profiling identifies fatty acid synthesis as a target for antiviral therapy. Nat Biotechnol 2008;26:1179. [30] Roci I, Gallart-Ayala H, Schmidt A, Watrous J, Jain M, Wheelock CE, et al. Metabolite profiling and stable isotope tracing in sorted subpopulations of mammalian cells. Anal Chem 2016;88:2707–13. [31] Bastard J, Maachi M, Lagathu C, Kim MJ, Caron M, Vidal H, et al. Recent advances in the relationship between obesity, inflammation, and insulin resistance. Eur Cytokine Netw 2006;17:4–12. [32] Kang S, Kim B, Lee S, Park T. Sphingolipid metabolism and obesity-induced inflammation. Front Endocrinol 2013;4:67. [33] Bikman BT. A role for sphingolipids in the pathophysiology of obesity-induced inflammation. Cell Mol Life Sci 2012;69:2135–46. [34] Lumeng CN, Saltiel AR. Inflammatory links between obesity and metabolic disease. J Clin Invest 2011;121:2111–7. [35] Crespo J, Cayón A, Fernández-Gil P, Hernández-Guerra M, Mayorga M, Domínguez-Díez A, et al. Gene expression of tumor necrosis factor α and TNFreceptors, p55 and p75, in nonalcoholic steatohepatitis patients. Hepatology 2001;34:1158–63. [36] Annoni G, Weiner FR, Zern MA. Increased transforming growth factor-β1 gene expression in human liver disease. J Hepatol 1992;14:259–64. [37] Tanaka N, Matsubara T, Krausz KW, Patterson AD, Gonzalez FJ. Disruption of phospholipid and bile acid homeostasis in mice with nonalcoholic steatohepatitis. Hepatology 2012;56:118–29. [38] Meyer SG, de Groot H. Cycloserine and threo-dihydrosphingosine inhibit TNF-αinduced cytotoxicity: evidence for the importance of de novo ceramide synthesis in TNF-α signaling. Biochimica et Biophysica Acta (BBA)-Molecular Cell Research 2003;1643:1–4.
ScienceDirect Journal of Nutritional Biochemistry 69 (2019) 44 – 52
Metabolic dysfunction following weight regain compared to initial weight gain in a high-fat diet-induced obese mouse model☆ Min-Sun Kim a, g, 1, Il Yong Kim b, c, 1 , Hye Rim Sung b, c , Miso Nam a, d , Youn Ju Kim b, c , Dong Soo Kyung b, c , Je Kyung Seong b, c, e,⁎, Geum-Sook Hwang a, f,⁎⁎ b
a Integrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul 120-140, Republic of Korea Laboratory of Developmental Biology and Genomics, Research Institute for Veterinary Science, and BK21 PLUS Program for Creative Veterinary Science Research, College of Veterinary Medicine, Seoul National University, Seoul 08826, Republic of Korea c Korea Mouse Phenotyping Center (KMPC), Seoul National University, Seoul 08826, Republic of Korea d Department of Chemistry, Sungkyunkwan University, Suwon 16419, Republic of Korea e Interdisciplinary Program for Bioinformatics, Program for Cancer Biology and BIO-MAX/N-Bio Institute, Seoul National University, Seoul 08826, Republic of Korea f Department of Life Science, Ewha Woman's University, Seoul 120-750, Republic of Korea g Food Analysis Center, Korea Food Research Institute, Wanju, Korea
Received 22 June 2018; received in revised form 1 February 2019; accepted 28 February 2019
Abstract Diet-induced weight loss and regain leads to physiological and metabolic changes, some of which are potentially harmful. However, the specific metabolic processes and dysfunctions associated with weight regain, and how they differ from initial weight gain, remain unclear. Thus, we examined the metabolic profiles of mice following weight regain compared to initial weight gain. Mice were fed a normal diet or a high-fat diet or were cycled between the two diets to alternate between obese and lean states. Liver samples were collected and hepatic metabolites were profiled using nuclear magnetic resonance (NMR). The identified metabolites associated with weight regain were quantified using gas chromatography/mass spectrometry (GC/MS) and lipid profiles were assessed using ultra-high-performance liquid chromatography-quadrupole time-of-flight MS (UPLC-QTOF-MS). In addition, changes in expression of pro-inflammatory cytokines and gluconeogenic enzymes were investigated using polymerase chain reaction (PCR) and western blotting, respectively. Hepatic levels of several amino acids were reduced in mice during weight regain compared with initial weight gain. In addition, gluconeogenic enzyme levels were increased following weight regain, indicating an up-regulation of gluconeogenesis. Lipidomic profiling revealed that levels of ceramide and sphingomyelin, which are related to obesity-induced inflammation, were significantly increased during weight regain compared to initial weight gain. Moreover, tumor necrosis factor-α (TNF-α) and transforming growth factor-β1 (TGF-β1) levels were significantly up-regulated during weight regain. In this study, weight regains lead to an up-regulation of gluconeogenesis and aggravated inflammation. Additionally, weight regain can worsen the metabolic dysfunction associated with obesity. © 2019 Elsevier Inc. All rights reserved. Keywords: weight regain; obesity; liver; metabolism; nuclear magnetic resonance; mass spectrometry
1. Introduction The prevalence of obesity is increasing worldwide and is emerging as a serious public health problem [1,2]. Obesity is an established risk factor for several diseases, including type 2 diabetes mellitus, nonalcoholic fatty liver disease, cancer, osteoarthritis, and cardiovascular disease [1–6]. To reduce their risk for disease, many people attempt to lose weight by dieting; however, this is associated with ☆
Declarations of interest: None. ⁎ Correspondence to: J.K. Seong, Seoul National University, Seoul. Tel: +82 2 880 1259; fax: +82 2 885 8395. ⁎⁎ Correspondence to: G-S. Hwang, Korea Basic Science Institute, Seoul 120140, Republic of Korea. Tel.: +82 2 6908 6200; fax: +82 2 6908 6239. E-mail addresses: [email protected] (J.K. Seong), [email protected] (G.-S. Hwang). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.jnutbio.2019.02.011 0955-2863/© 2019 Elsevier Inc. All rights reserved.
poor long-term outcomes [2] and can result in a cycle of repeated intentional weight loss followed by regaining of the weight, known as “yo-yo dieting” [7,8]. Although the effects of yo-yo dieting are controversial [7,8], several recent studies have suggested that the regaining of weight after weight loss is associated with an increased disease risk for myocardial infarction, as well as stroke, diabetes, and coronary heart disease [9,10]. In contrast, other studies have found no harmful consequences with respect to several outcomes, including total cholesterol, high-density lipoprotein (HDL) levels, the ratio of total cholesterol to HDL, diastolic blood pressure, and cardiovascular mortality [2,7]. Understanding the metabolic and molecular processes that are affected by weight regain is a critical step towards resolving these discrepancies, as well as for developing effective measures that can prevent obesity and reduce the associated risks. One potential avenue for investigation is through metabolomics that can characterize gradual metabolic changes in biofluids or tissues resulting from dietary interventions and/or disease [11–13].
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Moreover, this technique can be applied to investigate the biochemical mechanisms related to disease pathogenesis and to discover biomarkers for disease diagnosis or prognosis [14,15]. Importantly, metabolomics studies have identified potential biomarkers for obesity and related diseases, as well as related underlying mechanisms [11,16–20]. However, while the potentially harmful effects of weight regain have been recognized, the specific metabolic processes and dysfunctions associated with weight regain remain unknown. In this study, we mimicked a yo-yo diet by cycling mice between a high-fat diet (HFD) and a normal chow diet (NCD). We hypothesized that these mice (“weight regain” group) would display an altered metabolic status when compared to mice that simply gained weight (“weight gain” group). To test this hypothesis, we characterized the metabolomes of liver tissues and measured the levels of relevant metabolites in response to weight regain using nuclear magnetic resonance (NMR). We further quantified the identified metabolites as being associated with weight regain using gas chromatography/mass spectrometry (GC/MS). Ultrahigh-performance liquid chromatography-quadrupole time-offlight mass spectrometry (UPLC-QTOF-MS) was used to generate lipid profiles for all mice to determine if levels of specific lipids in the liver tissues differed between the “weight regain” and “weight gain” groups. Our data were obtained through a combination of high-throughput technologies that allowed us to identify specific alterations in metabolic pathways that occur during weight regain. 2. Materials and methods 2.1. Chemicals High-performance liquid chromatography (HPLC)-grade organic solvents (acetonitrile, methanol, and chloroform) were purchased from Burdick & Jackson (Muskegon, MI). Methanol-d4 (99.8% v/v), deuterated chloroform (CDCl3), deuterium oxide (D2O; 99.9% v/v), and 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid sodium salt (TSP; 98% atom) were obtained from Cambridge Isotope Laboratories (Andover, MA, USA). N-tertButyldimethylsilyl-N-methyltrifluoroacetamide (MTBSTFA) with 1% tertButyldimethylchlorosilane (TBDMCS) was obtained from Sigma-Aldrich (St. Louis, MO, USA). All other chemicals and reagents were obtained from Sigma-Aldrich with the highest analytical grade available. 2.2. Animals Four-week-old, male, C57BL6/N mice were purchased from Central Lab. Animal Inc. (Seoul, Korea). Animals were housed in groups at 24±2 °C with a 12-h light/dark cycle and fed a normal diet (NIH-31; Lab Diet, Indianapolis, IN, USA) ad libitum and tap water. After 1 week of acclimatization, mice were weighed and randomly divided into three groups. The experimental period was 16 weeks and the paradigm for all three groups is shown in Fig. 1A. Mice in the “weight regain” group (designated as “regain”) were fed an HFD (60% kcal from fat, #D12492, Research Diet, New Brunswick, NJ) for 8 weeks, an NCD for 4 weeks, and an HFD for the final 4 weeks. Mice in the “weight gain” group (designated as “gain”) were fed the NCD for 12 weeks and the HFD for the last 4 weeks. We also included a “lean control” group (designated as “lean”), in which mice were fed the NCD for the full 16 weeks. All mice were weighed weekly. The amount of food intake was measured in the metabolic cage for 3 days at the end of the experiment (n=6/ group). The experimental protocol was carried out in accordance with the “Guide for Animal Experiments” (edited by the Korean Academy of Medical Sciences) and was approved by the Institutional Animal Care and Use Committee of Seoul National University (approval number: SNU-140205-2-1). Mice were euthanized by CO2 asphyxiation, and blood was removed by heart puncture. Livers and epididymal white adipose tissues were rapidly collected and weighed. The mice for body weight, fat weight, histology, and metabolic analysis were in the same experimental groups. However, mice for food intake, GTT, and fasting glucose levels tests were acquired from a separate experimental group. Numbers of mice used in the experiments are shown in Supplemental Table 1. 2.3. Histological analysis of liver tissue Liver tissue samples were collected at the end of the experimental period. Each organ was weighed and fixed with 4% paraformaldehyde (BIOSESANG, Seongnam, Korea) overnight at room temperature. Fixed tissue samples were dehydrated, cleared, and embedded in paraffin. For hematoxylin and eosin (H&E) staining, tissues were sectioned (4μm thickness), deparaffinized, rehydrated, and stained with hematoxylin for 3 min followed by a counterstaining with eosin for 1 min. For the Oil Red O staining, the liver tissues were cryoprotected by infiltration with 30% sucrose before sectioning with a cryostat (Leica, Heidelberg, Germany). Sections were washed with triple-distilled water for 5 min and once
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with 50% isopropyl alcohol and stained with an Oil Red O solution (six parts 0.5% Oil Red O powder in isopropanol with four parts water) for 20 min at 54 °C in a dry oven. Sections were rinsed with tap water and counter-stained with hematoxylin. Tissue sections were analyzed using a microscope (Olympus Co., Tokyo, Japan) equipped with a digital camera (DP71, Olympus Co.). Four to five images were captured from multiple liver tissue sections from each mouse. As there were four mice/group, a total of 15–20 images were collected for each experimental group. 2.4. Fasting glucose and glucose tolerance tests At the end of the experimental period, fasting glucose concentrations were measured in 20 μL of serum on a Hitachi 7180 biochemistry autoanalyzer (Hitachi Ltd., Tokyo, Japan). For the glucose tolerance test (GTT), mice (n=8–10/group) fasted for 16 h, a baseline tail blood sample was collected, and then mice received an intraperitoneal injection of glucose (1.5 g/kg body weight). After injection, blood samples (2 μL) were collected at 15, 30, 60, 90, and 120 min, and glucose concentrations were determined using an Accu-Chek guide meter (Roche Diagnostics, Indianapolis, IN). The mice used in this analysis consisted of a different subset of each diet group than the mice from which the liver samples were obtained for metabolic analyses. 2.5. NMR spectroscopic analysis and data processing For the metabolite extraction, 49.42±3.56 mg liver tissue (n=9–10/group) were placed into 1.5-mL microfuge tubes containing 2.8-mm zirconium oxide beads and homogenized twice at 5000 rpm with 200 μL of methanol-d4 and 200 μL of 0.2-M sodium phosphate buffer (pH 7.0±0.1) for 20 s using a Precellys 24 tissue grinder (Bertin Technologies, Ampe're Montigny-le-Bretonneux, France). After homogenization, 300 μL of methanol-d4, 150 μL of 0.2-M sodium phosphate buffer (pH 7.0±0.1), and 400 μL of CDCl3 were added to the tube. The mixture was vortexed vigorously for 1 min and allowed to separate for 15 min. Samples were centrifuged at 13,000 rpm for 10 min at 4 °C. Upper layers were transferred in 540-μL aliquots to new 1.5-mL microfuge tubes, mixed with 60 μL of 2-mM TSP dissolved in D2O, and centrifuged at 13,000 rpm for 10 min. Supernatants (600 μL) were transferred to 5-mm NMR tubes. 1 H NMR spectra of liver samples were acquired on a Bruker Avance III HD 800 MHz FT-NMR spectrometer at 298 K using a 5-mm triple-resonance inverse cryoprobe with Z-gradients (Bruker BioSpin Co., Billerica, MA). The pulse sequence used for the liver extracts was a Carr-Purcell-Meiboom-Gill pulse sequence collecting 64,000 data points with 128 transients, a spectral width of 16,025.641 Hz, a relaxation delay of 4.0 s, and an acquisition time of 2.0 s. All NMR data were phased and baseline corrected using Chenomx NMR Suite 7.1 (Chenomx, Edmonton, AB, Canada). The peaks for water and solvent were excluded, and the remaining spectral regions were divided into 0.005 ppm bins. The spectra were normalized to the total spectral area and converted to ASCII format. The ASCII files were imported into MATLAB (R2006a; MathWorks, Natick, MA) and spectra were aligned using the correlation-optimized warping (COW) method. Metabolite identities were assigned using Chenomx Profiler, a module of Chenomx NMR Suite version 7.1. Most metabolite identifications were confirmed by 2D NMR experiments and spiking experiments. 2.6. Sample preparation for mass spectrometry analysis For each mouse, a 50.77±1.49 mg liver tissue sample (n=9–10/group) was placed into a 1.5-mL tube containing 2.8-mm zirconium oxide beads and 10 μL of the labeled internal standard (2 mM 13C6-leucine). Samples were homogenized twice at 5000 rpm with 700 μL of a 1:1 (v/v) methanol:water mixture for 20 s using a Precellys 24 tissue grinder. After homogenization, 700 μL of chloroform was added and the mixture was vortexed vigorously for 1 min and centrifuged for 20 min at 13,000 rpm at 4 °C. The supernatant and chloroform layers were transferred to 1.5-mL microfuge tubes for GC/ MS and UPLC-QTOF-MS analysis, respectively. Supernatants were evaporated in a centrifugal evaporator (Thermo Scientific, Dreieich, Germany; 1000 rpm, room temperature). Tert-butyldimethylsilyl (TBDMS) derivatives of target metabolites were prepared by adding 40 μL of pyridine and 50 μL of MTBSTFA+1% TBDMCS and incubating for 120 min at 60 °C. Finally, 10 μL of the internal standard phenanthrene-d10 (2 mM) was added to the solutions. Mixtures were transferred to injection vials for GC/ MS analysis. The chloroform layer was evaporated to dryness under a gentle stream of nitrogen gas. For the UPLC-QTOF-MS analysis, liver lipid extracts were diluted with an isopropanol:acetonitrile:water mixture (2:1:1, v/v/v). 2.7. GC/MS analysis An Agilent 5977A mass spectrometer (Palo Alto, CA; electron ionization mode, 70 eV) connected to an Agilent 7890B gas chromatograph equipped with a DB-5MS capillary column (30 m×0.25 mm i.d., 0.25-μm film thickness, J&W Scientific, Folsom, CA) was used for analysis. Nitrogen was used as the carrier gas at a flow rate of 1.0 mL/ min and 1-μL samples were introduced by split-mode injection (split ratio 10:1). After sample injection, the oven temperature was held at 60 °C for 5 min, raised to 320 °C at 10 °C/min, and held for 15 min. The mass spectrometer interface temperature was set at 280 °C. The manifold temperature was maintained at 230 °C. The mass spectrometer was run in electron ionization mode with an electron energy of 70 eV and scanned a range of 50–500 amu. For sample monitoring and confirmation analyses, SIM mode was
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Fig. 1. Effect of weight regain on metabolic parameters and hepatic steatosis in mice. Male C57B1/6 mice were placed on a normal chow diet (NCD), a high-fat diet (HFD), or a combination diet for a 16-week experimental period. (A) Regain mice were fed an HFD for 8 weeks, an NCD for 4 weeks, and an HFD for the final 4 weeks. Gain mice were fed an NCD for 12 weeks and an HFD for the last 4 weeks. Lean mice were fed an NCD for the full 16 weeks. (B) Body weights were measured on a weekly basis for the duration of the study (n=9–10/ group). (C) Final body weights for each group (n=9–10/group). (D) Average food intake for each group during the experimental period (n=6/group). (E) Epididymal white adipose tissue weights at the end of the 16 weeks (n=10/group). (F) Histological analysis of hepatic fat accumulation using hematoxylin and eosin (H&E) and Oil Red O staining (n=4/group). Representative images are shown for each group (200× magnification). (G) Fasting blood glucose levels were measured for all three groups after a 16-h fast at the end of the 16-week protocol (n=8–10/group). (H) After a 16-h fast, a glucose tolerance test was performed (dose of 1.5 g glucose/kg lean mass) and measurements were taken at the indicated intervals after glucose injection (n=8–10/group). The area under the curve (AUC) was quantified for the duration of the test (inset). Data in all panels represent means ± SEM. *Pb.05 compared to lean; #Pb.05 between gain and regain. used with a dwell time of 50 ms for each ion. In GC/MS-SIM mode, two characteristic ions for each target metabolite were used for peak identification, and the most abundant ion was selected for quantification. The relative peak area was obtained by dividing the integrated area of the base peak by that of the internal standard.
2.8. UPLC-QTOF-MS analysis and data processing All UPLC-QTOF-MS analyses were performed using a UPLC system (Waters, Milford, MA) coupled with a triple TOF™ 5600 MS/MS System (AB SCIEX, Concord, ON, Canada) equipped with an electrospray ionization source. Chromatographic separation analysis was carried out using an Acquity UPLC BEH C18 column (2.1 mm×100 mm, 1.7 μm; Waters) at 40 °C; a binary gradient separation was performed at a flow rate of 0.35 mL/ min. The mobile phases consisted of solvent A (10 mM ammonium acetate in an acetonitrile:water mixture [40:60, v/v]) and solvent B (10 mM ammonium acetate in an acetonitrile:isopropanol mixture [10:90, v/v]). The gradient profile was 40–65% B over 5 min, 75% B at 20 min, 99% B at 25 min, 99% B from 25 to 27 min, and 40% B from 27.1 to 29 min to equilibrate the separation system for subsequent runs. The total run-time for each injection was 29 min and the injection volume was 3 μL. The mass spectrometer was operated in positive and negative ionization modes and acquired data in the mass range from 50 to 1100 m/z. The following parameter settings were used: ion spray voltage: 5500 V, temperature: 500 °C, curtain gas: 30 psi, declustering potential: 90 V, and collision energy: 10 V. The MS/MS analyses were acquired by automatic fragmentation, in which the five most intense mass peaks were fragmented. MS/MS experiments were run with a collision energy of 40 V and collision energy spread of 15 V. Nitrogen was used as the drying, nebulizing, and collision gas. Mass accuracy was maintained with an automated calibrant delivery system (AB SCIEX) interfaced to the second inlet of the DuoSpray source. Data acquisition and processing were performed using MarkerView software (AB SCIEX) to find peaks, perform alignments, and generate peak tables of m/z and retention times (min). Spectra were normalized to the total spectral area. To determine the
precision of detection, lipid metabolites with coefficients of variation below 20 were selected. Lipids were tentatively identified by comparing their retention times, the isotope patterns, exact mass values of the precursor, and MS/MS spectral data by searching the LIPID MAPS (www.lipidmaps.org), METLIN (metlin.scripps.edu), and Human Metabolome Database (www.hmdb.ca) databases. 2.9. Statistical analysis Statistical analyses were performed using the Kruskal-Wallis test followed by the Mann–Whitney U test using Bonferroni correction within the Statistical Package for the Social Sciences (SPSS) software, version 21.0 (SPSS Inc., Chicago, IL). The accepted level of significance was set as Pb.05. 2.10. Western blot analysis The expression levels of glucose-6-phosphatase (G6Pase), pyruvate carboxylase (PC), transforming growth factor-β1 (TGF-β1), and tumor necrosis factor-α (TNF-α) in murine liver extracts (20 μg) (n=4/group) were analyzed by western blots. Antibodies against G6Pase (ab83690), PC (ab128952), TGF-β1 (ab92486), and TNF-α (ab6671) were obtained from Abcam (Cambridge, MA). Protein extracts were prepared in radioimmunoprecipitation assay buffer with a protease inhibitor cocktail (BioVision, Mountain View, CA, USA). Proteins were harvested and centrifuged for 15 min at 4 °C. Supernatants were saved, and protein concentrations were determined using bicinchoninic acid protein assays. Proteins were separated on 10% SDSpolyacrylamide gels and transferred to polyvinylidene difluoride membranes. Membranes were blocked with 5% skim milk/Tris-buffered saline with Tween 20 (TBS-T) solution for 1 h at room temperature, and incubated with primary antibodies in 5% skim milk in TBS-T overnight at 4 °C. After washing with TBS-T three times, membranes were incubated with secondary antibodies coupled to horseradish peroxidase for 1 h at room
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temperature. The membranes were washed three times with TBS-T and immunoreactivity was detected using ECL reagents (Bio-Rad, Hercules, CA, USA) and a Fusion SL3 instrument (Vilber Lourmat, Torcy, France). Antibodies against β-actin (A5441, SigmaAldrich) were used as a loading control. 2.11. Quantitative reverse transcription-polymerase chain reaction (PCR) Total RNA was extracted from liver tissues (n=4/group) using RNeasy Mini kits (Qiagen, Valencia, CA). Quantitative PCR was performed using a StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The PCR primers used were: TGF-β1 FW, 5′-TTGCTTCAGCTCCACAGAGA-3′ and RV, 5′-TGGTTGTAGAGGGCAAGGAC3′; and TNF-α FW, 5′-CCACCACGCTCTTCTGTCTA-3′ and RV, 5′-CACTTGGTGGTTTGCTACGA-3′. mRNA levels were normalized to those of β-actin.
3. Results 3.1. Effect of weight regain on metabolic parameters and hepatic steatosis in mice The mean body weights of all mice during the experimental period are plotted in Fig. 1B. Mice placed on the HFD had a steeper increase in body weight than those on the NCD. After switching to the NCD for 4 weeks, the mice in the weight regain group lost weight and became similar in weight to the lean controls. However, after switching back to the HFD, the weight regain mice again experienced a steep increase in body weight. Both the weight regain and weight gain groups had final body weights that were significantly higher than that of the lean control group (Pb.05; Fig. 1C). There was also a significant difference in the final body weights between the weight regain and weight gain groups (Pb.05). Importantly, average food intake did not significantly differ among the three groups (Fig. 1D). To further examine the effects of diet on weight, we specifically looked at epididymal white adipose tissue weights. The epididymal white adipose tissue weights were higher in mice fed the HFD compared to those with only the lean diet (Pb.05); however, there was no difference between the gain and regain groups (Fig. 1E). Staining with H&E and Oil Red O revealed that hepatic steatosis was apparent in both HFD feeding groups (gain and regain); importantly, it was more pronounced in the weight regain mice than in the weight gain mice (Fig. 1F). To determine the effects of weight regain on serum metabolic parameters, fasting blood glucose measurements and GTTs were performed (Fig. 1G and H). Not surprisingly, the fasting blood glucose levels in the weight gain groups were elevated (Pb.05) compared to the lean controls. Furthermore, the weight regain group had even higher fasting blood glucose levels than the weight gain group (Pb.05). Conversely, while glucose tolerance was significantly reduced by HFD feeding, it did not vary between the weight gain and regain groups. The effect of weight loss on metabolic parameters in mice at 12 weeks of the study is represented in Supplemental Fig. 1. Average food intake and epididymal white adipose tissue weights did not significantly differ between the lean control and the weight regain groups (Supplementary Fig. 1A and B). Fasting blood glucose was identical in the lean control and weight regain groups at this point (Supplemental Fig. 1C). In addition, a GTT showed no difference in glucose tolerance between the two groups (Supplemental Fig. 1D). 3.2. Metabolic profiling of liver tissues using NMR To determine changes in aqueous metabolites in the liver as a function of weight regain, NMR-based metabolic profiling was performed. A representative, one-dimensional 1H NMR spectrum of an aqueous liver extract is shown in Supplemental Fig. 2. From the NMR spectrum of the liver tissue, 28 hepatic metabolites were identified (Supplemental Table 2) and compared between the experimental groups using the partial least-squares discriminant analysis (PLS-DA) model (Fig. 2A). The PLS-DA score plot showed
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significant separation in the liver samples between groups (R2Y= 0.760 and Q2=0.630; lean control n=10; weight gain, n=9; weight regain, n=10). PLS-DA models were validated using permutation tests (Fig. 2B). Several metabolites, including alanine, glycine, isoleucine, valine, leucine, phenylalanine, betaine, lactate, and glucose distinguished the weight regain group from the other groups in the loading plots and high variable importance of projection (VIP) values (VIPN1; Fig. 2C). Among those metabolites, alanine, glycine, valine, lactate, and ATP/ADP were significantly decreased in the weight regain group compared to the lean control group (Pb.05; Fig. 2D). Interestingly, some metabolites, such as alanine, glycine, isoleucine, valine, and ATP/ ADP, were decreased in the weight regain mice compared to the weight gain mice. 3.3. Quantitative analysis of metabolites using GC/MS To achieve a quantitative analysis of the amino acids and organic acids associated with weight regain, liver tissue samples from individual animals were analyzed by GC with quadrupole MS, using the selected ion monitoring (SIM) method. Derivatizations of all compounds were performed as described in the sample preparation section. In GC/MS-SIM mode, two characteristic ions for each amino acid and organic acid were used for peak identification, and the most abundant ion was selected for quantification (Supplemental Table 3). Calibration curves were generated and calculated using the method of least-squares, relating y (the peak area ratio of the metabolite to the internal standard) to x (the concentration of the metabolite in μM). The calibration curves and correlation coefficients are indicated in Supplemental Table 4. The calibration curves of the targeted metabolites generated good linearity within the given concentration ranges, with correlation coefficients (R2) greater than 0.99. A total of 12 metabolites from the liver tissues were quantitated with the described method in this paper. Supplemental Fig. 3 shows the SIM chromatograms and extracted ion chromatograms of the metabolite-TBDMS derivatives from murine liver samples. The peak abundance of 13C6-leucine was used to evaluate the recovery of the metabolites from the liver tissue sample during the analytical procedure (recoveryN83.0±5.3%). Phenanthrene-d10 was applied as an internal standard for the metabolite-TBDMS derivative. The concentrations of the metabolites in each group are shown in Supplemental Table 5. The contents of all amino acids were reduced in HFD feeding groups. Moreover, weight regain resulted in an additional decrease in amino acids in liver tissue. 3.4. Effect of weight regain on liver gluconeogenesis in mice Variations in the levels of significant metabolites in liver tissue samples are shown in Fig. 3A. Levels of several amino acids were reduced in both HFD feeding groups compared with lean controls (Pb.05); however, the levels of many of these amino acids were even lower in the weight regain group than in the weight gain group. These effects, which were enhanced in the weight regain group, may be reflective of increased amino acid utilization by gluconeogenesis pathways after HFDs. In support of this hypothesis, lactate, a major hepatic substrate for gluconeogenesis, was among the metabolites significantly reduced in the HFD groups. Conversely, pyruvate was increased in the weight regain group, compared with the weight gain group, but was significantly decreased in the weight gain group compared to lean controls. However, oxaloacetate, which is also involved in gluconeogenesis, was not significantly different between the three groups. Therefore, to investigate the effects of weight regain on gluconeogenesis, we examined the expression of G6Pase and PC in the liver tissue of all mice. Western blot analysis showed that G6Pase and PC expression was higher in the liver of both HFD groups compared to the lean controls
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Fig. 2. Metabolic profiles of liver samples from lean control (lean), weight gain (gain), and weight regain (regain) mice using NMR spectroscopy. (A) PLS-DA score plot (R2Y=0.760 and Q2=0.630). White circles represent lean, gray circles represent gain, and black circles represent regain mice. (B) Validation plots of the PLS-DA model using a permutation test. Validation plots were obtained from 100 permutation tests of the responses to the PLS-DA models. (C) Loading plot of the partial least-squares discriminant analysis (PLS-DA) model. Identified metabolites that had high variable importance of production (VIP) values (N1). (D) Altered metabolites with high VIP values are shown for all groups. Data represent means ± SEM; n=9–10/group. *Pb.05, **Pb.01, ***Pb.001 compared to lean, #Pb.05 between gain and regain.
(Pb.05, weight regain versus lean control), but was even higher in the weight regain than the weight gain mice (Fig. 3B). These results indicate that up-regulation of gluconeogenesis occurred in both HFD groups, but occurred to a greater extent in the weight regain group. 3.5. Lipidomic profiling of murine liver using UPLC-QTOF-MS To identify lipid species related to weight regain, liver samples were analyzed using UPLC-QTOF-MS. Representative chemical structures and product ion mass spectra for each group are presented in Supplemental Fig. 4. The PLS-DA score plot derived from positive and negative modes
showed clear separation across the three groups (R2Y=0.795, Q2= 0.685 for positive mode and R2Y=0.783, Q2=0.683 for negative mode; Supplemental Fig. 5). The fold change values for these species across all three groups are shown in Supplemental Table 6. Ceramide, sphingomyelin, and triglycerides were highly abundant lipids and their levels were significantly different between the weight regain and lean control groups (Pb.05). Except for ceramide (d40:1), weight regain resulted in increased ceramide and sphingomyelin levels compared with the weight gain group (Fig. 4A). Additionally, the relative levels of most triglyceride species were significantly increased in the weight regain mice compared with the weight gain mice.
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Fig. 3. Concentrations of metabolites obtained by GC/MS and enzymes involved in gluconeogenesis. (A) Metabolite concentrations in liver tissues from lean control (lean), weight gain (gain), and weight regain (regain) mice. Data represent means ± SEM (n=9–10/group). (B) Representative western blots and quantifications of glucose-6-phosphatase (G6Pase) and pyruvate carboxylase (PC) protein in murine liver tissues. Protein levels were normalized to β-actin. Data represent means ± SEM (n=4/group). *Pb.05, **Pb.01, ***Pb.001 compared to lean, #Pb.05, ## Pb.01 between gain and regain.
Sphingolipid metabolites, such as ceramide and sphingomyelin, function in regulating chronic inflammation [21,22]. Thus, we measured the mRNA levels and protein levels of the inflammatory cytokines TNF-α and TGF-β1 and found that they were increased in the liver of both the weight gain and weight regain groups compared to controls (Fig. 4B and C). In particular, the up-regulation of TNF-α mRNA expression was significantly pronounced in the weight regain group than in the weight gain group (Pb.05). Also, TNF-α protein expression was increased in the weight regain group than in the weight gain group, though the difference was statistically nonsignificant. Together, these data show that weight regain aggravates hepatic inflammation and that this process may be influenced by sphingolipids in the liver. 4. Discussion Recently, several studies have investigated the adverse health effects of weight regain following weight loss [2,7,23]. However, the
specific metabolic changes that occur with weight regain, and how these compare with initial weight gain, remained unclear. Here, we have shown that mice that were cycled between an HFD and an NCD (weight regain) had more pronounced metabolic alterations in the liver than those placed on an HFD for the first time. We performed a hepatic metabolic analysis using NMR and found that the HFD caused significant changes in amino acid levels in the liver, most notably a depletion of alanine, glycine, isoleucine, and valine, compared to a normal diet. Interestingly, these amino acids were reduced to a greater extent in the weight regain mice than in the weight gain mice. To examine further these metabolic alterations and to quantify glycogenic amino acids, we performed a targeted analysis of alanine, glycine, threonine, serine, cysteine, isoleucine, valine, methionine, and phenylalanine using GC–MS. All investigated amino acids were reduced in both HFD feeding groups (gain and regain) compared to the control group, which is consistent with previous reports and likely occurs as a result of HFD-induced gluconeogenesis, wherein some of these amino acids act as substrates [11,16,18]. In
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Fig. 4. The effect of weight regain on lipids and inflammation in murine livers. (A) The intensity of ceramide (Cer) and sphingomyelin (SM) species are shown for each group (n=9–10/ group). (B) Changes in mRNA expression of inflammatory cytokines TNF-α and TGF-β1 (n=4/group). mRNA levels were normalized to β-actin. (C) Representative western blots and quantifications of TNF-α and TGF-β1 (n=4/group) protein in murine liver tissues. Protein levels were normalized to β-actin. Data represent means ± SEM. *Pb.05, **Pb.01, ***Pb.001 compared to lean. #Pb.05, ## Pb.01, ###Pb.001 between gain and regain.
addition, we found increased levels of glucose in the blood of both HFD feeding groups, which is also indicative of enhanced gluconeogenesis and results from elevated glucose production in the liver, exacerbating hyperglycemia. Importantly, compared to weight gain, weight regain resulted in more pronounced effects with respect to both amino acid levels in the liver tissue and blood glucose levels, indicating that weight regain increases systematic metabolic dysfunction to a greater extent than does initial weight gain. We investigated transcriptional regulation of two key gluconeogenic enzymes: G6Pase and PC. Up-regulation of G6Pase expression enhances gluconeogenesis, as it catalyzes the last step in the metabolic
pathway [11,24]. Conversely, PC catalyzes the ATP-dependent carboxylation of pyruvate to oxaloacetate, which is the first regulated step in the gluconeogenic pathway of pyruvate and its precursors [25,26]. Increased protein expression of PC and G6Pase was inversely related to hepatic amino acid concentrations in obese mice and this effect was enhanced in weight regain mice. Elevated amino acid turnover may impact hepatic gluconeogenesis because of the important role of the liver in amino acid catabolism [27]. In addition, we observed a decrease in the ATP/ADP ratio in the liver of weight gain mice, and this ratio was further decreased in the weight regain mice. However, the issue whether a low ATP/ADP ratio reflects the activation
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Fig. 5. Schematic representation of hepatocyte metabolism. Metabolites, enzymes, and cytokines within this pathway that were altered by weight regain (red arrows: increased levels; blue arrows: decreased levels) as compared to weight gain are indicated.
of PC is unclear. Measurements of ADP from extracts are possible to lead to an overestimation of free ADP levels due to their low concentration and the number of intracellular binding sites [28]. Pyruvate, lactate, and oxaloacetate levels did not positively correlate with PC expression. It is likely that these metabolites are involved in very rapid processes, such as glycolysis and the TCA cycle, with turnover occurring in minutes [29,30]. Much research has focused on the link between metabolic dysfunction in obesity and chronic inflammation [31]. Recent insights suggest that sphingolipids are dysregulated under obese conditions and alter the inflammatory state in adipose tissues and immune cells [32]. Furthermore, several reports correlate HFDs with elevated sphingolipid levels in liver tissue [22,32,33]. In this study, we also observed increased levels of sphingomyelin and ceramide in both HFD groups, with levels being further elevated in the weight regain group. Therefore, given the link between sphingolipids and inflammation, we examined inflammation following weight regain. Obesity-associated inflammation is characterized by elevated levels of inflammatory cytokines [33,34]. Additionally, TNF-α expression is higher in liver tissues of obese patients with nonalcoholic steatohepatitis than in those with slight or nonexistent fibrosis [35,36]. Furthermore, TGF-β1 mRNA is elevated in patients with liver fibrosis [36]. Severe inflammation and hepatic injury also involve the up-regulation of hepatic TNF-α and TGF-β1 mRNA [37]. Our results showed increased levels of hepatic TNF-α and TGF-β1 mRNA in the weight regain group and to a lesser extent in the weight gain group compared to lean controls. Since TNF-α induces de novo ceramide production by activating sphingomyelinase [38], the enhanced obesity-induced hepatic inflammation observed in the weight regain group could be associated with higher activating TNF-α and overall ceramide levels in the liver tissue compared with the weight gain mice. To enrich our understanding of the inflammation and gluconeogenesis caused by weight regain, the metabolic responses of the adipose tissues and muscle will need further study. This study conducted metabolite profiling of mice using NMR, GC/ MS, and UPLC-QTOF-MS to examine the effects of weight regain. Weight regain decreased amino acid levels in liver tissues and increased fasting glucose levels in the blood. In addition, G6Pase and PC protein levels were higher in the weight regain group than in the weight gain group. Thus, weight regain leads to greater up-regulation of gluconeogenesis than does initial weight gain. Similarly, the weight
regain group had higher levels of ceramide and sphingomyelin in liver tissue than did the weight gain group. Furthermore, a corresponding increase in the expression of the inflammatory cytokines TNF-α and TGF-β1 was observed in the liver of the weight regain group. Taken together, our data indicate that weight regain aggravates the metabolic dysfunction associated with obesity (Fig. 5). This information increases our understanding of the metabolic pathways involved in weight regain and may be useful for the development of effective weight control measures prior to the development of obesity. Acknowledgments This study was supported by the National Research Foundation (NRF) of Korea (No. 2017M3A9C4065961 and NRF2017M3A9D5A01052449), and the Korea Basic Science Institute (C39705) to G.S.H; the Korea Mouse Phenotyping Project (2013M3A9D5072550) to J.K.S; and the Bio & Medical Technology Development Program of the National Research Foundation (2012M3A9B6055344) to I.Y.K. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jnutbio.2019.02.011. References [1] Vucenik I, Stains JP. Obesity and cancer risk: evidence, mechanisms, and recommendations. Ann N Y Acad Sci 2012;1271:37–43. [2] Lahti-Koski M, Männistö S, Pietinen P, Vartiainen E. Prevalence of weight cycling and its relation to health indicators in Finland. Obesity 2005;13:333–41. [3] Pi-Sunyer X. The medical risks of obesity. Postgrad Med 2009;121:21–33. [4] Lavie CJ, Milani RV, Ventura HO. Obesity and cardiovascular disease: risk factor, paradox, and impact of weight loss. J Am Coll Cardiol 2009;53:1925–32. [5] Sowers MR, Karvonen-Gutierrez CA. The evolving role of obesity in knee osteoarthritis. Curr Opin Rheumatol 2010;22:533–7. [6] Fabbrini E, Sullivan S, Klein S. Obesity and nonalcoholic fatty liver disease: biochemical, metabolic, and clinical implications. Hepatology 2010;51:679–89. [7] Graci S, Izzo G, Savino S, Cattani L, Lezzi G, Berselli M, et al. Weight cycling and cardiovascular risk factors in obesity. Int J Obes (Lond) 2004;28:65. [8] Montani J, Viecelli A, Prévot A, Dulloo AG. Weight cycling during growth and beyond as a risk factor for later cardiovascular diseases: the ‘repeated overshoot'theory. Int J Obes (Lond) 2006;30:S58.
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