High-Fat Diet Induces Dynamic Metabolic Alterations in Multiple

Subscriber access provided by Linköpings universitetsbibliotek

Article

High-fat Diet Induces Dynamic Metabolic Alterations in Multiple Biological Matrices of Rats Yanpeng An, Wenxin Xu, Huihui Li, Hehua Lei, Limin Zhang, Fuhua Hao, Yixuan Duan, Xing Yan, Ying Zhao, Junfang Wu, Yulan Wang, and Huiru Tang J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr400398b • Publication Date (Web): 07 Jun 2013 Downloaded from http://pubs.acs.org on June 11, 2013

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Proteome Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Proteome Research

High-fat Diet Induces Dynamic Metabolic Alterations in Multiple Biological Matrices of Rats

Yanpeng An,†,‡ Wenxin Xu,†,‡ Huihui Li,†,‡ Hehua Lei,† Limin Zhang,† Fuhua Hao,† Yixuan Duan,†,# Xing Yan,†,‡ Ying Zhao, †,# Junfang Wu,† Yulan Wang,† and Huiru Tang*,†

†

CAS Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic

Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, 430071, P. R. China ‡

Graduate University of the Chinese Academy of Sciences, Beijing, 100049, P. R. China

#

School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan,

430074, P. R. China

*To whom all correspondence should be addressed. Huiru Tang: e-mail, [email protected]; tele,+86-27-87198430; fax, +86-27-87199291.

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT: Obesity is a condition resulting from the interactions of individual biology and environmental factors causing multiple complications. To understand the systems metabolic changes associated with the obesity development and progression, we systematically analyzed the dynamic metabonomic changes induced by high-fat-diet (HFD) in multiple biological matrices of rats using NMR and GC-FID-MS techniques. Clinical chemistry and histopathological data were obtained as complementary information. We found that HFD intakes caused systematic metabolic changes in blood plasma, liver and urine samples involving multiple metabolic pathways including glycolysis, TCA cycle and gut microbiota functions together with the metabolisms of fatty acids, amino acids, choline, B-vitamins, purines and pyrimidines. The HFD-induced metabolic variations were detectable in rat urine a week after HFD-intakes and showed clear dependence on the intake duration. B-vitamins and gut microbiota played important roles in the obesity development and progression together with changes in TCA cycle intermediates (citrate, α-ketoglutarate, succinate, and fumarate). 83-days HFD-intakes caused significant metabolic alterations in rat liver highlighted with the enhancements in lipogenesis, lipid accumulation and lipid-oxidation, suppression of glycolysis, up-regulation of gluconeogenesis and glycogenesis together with altered metabolisms of choline, amino acids and nucleotides. HFD intakes reduced the PUFA-to-MUFA ratio in both plasma and liver indicating the HFD-induced oxidative stress. These findings provided essential biochemistry information about the dynamic metabolic responses to the development and progression of HFD-induced obesity. This study also demonstrated the combined metabonomic analysis of multiple biological matrices as a powerful approach for understanding the molecular basis of pathogenesis and disease progression.

KEYWORDS: obesity, metabolism, high-fat diet, NMR, multivariate data analysis.


Page 2 of 39

Page 3 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


INTRODUCTION Obesity is a complex metabolic disorder and becoming a serious public health problem worldwide. In US, for instance, over 30% adults and one-sixth children were obese in 2009–2010 with associated costs mounting to 200 billion US dollars annually.1, 2 The developing countries are also experiencing similar problems. In 2007, for example, about 200 million people in China were obese or overweight with the prevalence rapidly rising.2 Obesity normally results from complex interactions of both the genetic3, 4 and environmental factors5 with excessive fat accumulation due to the energy intake and expenditure imbalance. Obesity is now accepted as an important risk factor for many diseases including insulin resistance (IR) and diabetes,6 reproductive system diseases,7 and even cancers8. Therefore, mechanistic aspects of obesity development became the focus of many investigations especially using systems biology approaches. Genome-wide association studies revealed that dozens of genes were related to the development of common human obesity with their functions involving hormone regulations and insulin signaling, fatty acid metabolism, energy homeostasis and inflammation.3, 4 Transcriptomic analysis also indicated that long-term high-fat diet (HFD) altered expressions for many genes related to fatty acid β-oxidation, lipogenesis,9, 10 inflammation and nicotinamide phosphorylation3, 4 in rodents. Furthermore, proteomics studies concluded that HFD feeding caused significant differential expressions of many proteins in multiple insulin target tissues of C57BL/6J mice including white adipose tissue, brown adipose tissue, muscle and liver.11 Other studies observed obesity-associated alterations in human populations involving mitochondrial, cytoskeletal and structural proteins together with TCP1 complex proteins12 and some serum proteins.13 These results imply that the development of obesity accompanies with profound changes in multiple metabolic pathways such as glycolysis, TCA cycle, fatty acid and redox metabolism.3, 9-12 It is conceivable that metabonomic analyses ought to be useful for understanding the detailed metabolic alternations associated with obesity especially the dynamic metabolic changes associated with the obesity progression. This is because metabonomics systemically detects and quantifies the metabolite composition fluctuations of an integrated biological system and its dynamic responses to the changes of both endogenous and exogenous factors.14, 15 In fact, metabonomics has already been successfully applied in understanding the biochemistry aspects of metabolic disorders16, 17, inflammatory bowel diseases18, 19, the effects of toxins,20-22 stress,23 and dietary components.24-27



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Some metabonomics studies have also been performed to investigate the obesity effects on mammalian metabolisms for Zucker (fa/fa) rats,28, 29 obese pigs,30 HFD-induced obese rodents31-33 and for human obesity.13, 34 These studies revealed that obese mammals have clearly different metabolic phenotypes (metabotypes) from lean ones in terms of the metabolism of fatty acids, amino acids, cholines together with glycolysis and TCA cycles.10, 13, 28-35 Furthermore, gut microbiota has important roles to play in HFD-induced obesity5, 36 and associated insulin resistance37 especially in terms of energy harvest5, 36 and modulations to host metabolisms.38, 39 Five inbred mouse strains showed significantly different responses to HFD intakes in terms of animal phenotypes and metabotypes.40 However, these previous works have mostly focused on the consequences of obesity and the dynamic processes of the obesity development remain to be fully elucidated especially at the pre-obese stage. Consequently, only limited information is available on the dynamic metabolic changes associated with the development of obesity even though such information is essential for understanding the obesity progression. The HFD-induced animal models are suitable for this purpose since high-fat diet is well accepted as a vital environmental factor and the HFD-induced obesity well resembles human obesity in phenotypes and complications.41 In this study, we systematically investigated the HFD-caused metabonomic changes in rat liver tissues, plasma, and urine as a function of time using the combined NMR. We also measured fatty acid compositions in blood plasma and liver using GC-FID-MS techniques. Our aims are to define the dynamic metabonomic responses of multiple biological matrices of rats to the continuous HFD intakes and to probe the dynamic urinary metabonomic changes related to HFD intakes at pre-obesity and obesity development stages.


Page 4 of 39

Page 5 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


MATERIALS AND METHODS Chemicals Analytical grade methanol, chloroform, sodium chloride, K2HPO4.3H2O, and NaH2PO4.2H2O were bought from Sinopharm Chemical Co. Ltd. (Shanghai, China) whilst sodium azide (NaN3) from Fuchen Chemical Reagent Company (Tianjin, China). Deuterium oxide (D2O, 99.9% D), deuterated chloroform (CDCl3, 99.8% D) and sodium 3-trimethylsilyl [2,2,3,3-2H4] propionate (TSP) were bought from Cambridge Isotope Laboratories Inc. (MA, U.S.A). Phosphate buffer was prepared with K2HPO4 and NaH2PO4 for their good solubility and low-temperature stability.42 For urinary metabonomic analysis, this buffer (1.5 M, pH 7.43) was prepared in D2O containing 0.1% NaN3 (wt/v) and 0.1% TSP (wt/v) whereas, for plasma analysis, such buffer (45 mM, pH 7.43) was made contained 50% D2O (v/v) and 0.9% NaCl (wt/v) to minimize inters-ample pH differences.43 For tissue extracts, a buffer (0.15 M, pH 7.43) was prepared contained 50% D2O (v/v), 0.01% NaN3 (wt/v) and 0.001% TSP (wt/v).22 Animal Experiments and Sample Collection Twenty four SPF-grade male Sprague-Dawley rats (6-weeks old, weighted 177.8 ± 18.6 g) were obtained from the Animal Experiments Center of Wuhan University (Wuhan, China). Animal experiments were carried out with a certified SPF facility in accordance with the National Guidelines for Experimental Animal Welfare (MOST of P.R. China, 2006) at the Animal Experiments Center of Wuhan University (Wuhan, China), which had full accreditation from the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC Intl). All animals were maintained in a room with temperature (20 ± 2 ℃) and relative humidity (40-70 %) regulated. An artificial 12/12 h light-dark cycle was maintained with lights on at 08:00 a.m. After acclimation for 10 days with access to low-fat diet (as a control diet in this study) and water ad libitum, rats were randomly divided into two groups (n = 12), namely, control and high-fat-diet (HFD) group. Two groups of animals were then fed with control diet and HFD, respectively, for 83 days with the diet compositions listed in Table S1. The amount of food intakes was recorded daily with rat body-weights also monitored. Twelve rat urine samples were collected for each group manually (to avoid any contamination) at time points of day 0, 7, 14, 28, 42, 56, 60, 66, 71, 76, and 81 post treatments (but not on day 83). At the end of the experiment, all animals were fasted for 12 hours and sacrificed following isoflurane anesthesia. Twelve blood serum samples were then obtained for each



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

group with standard protocol for clinical chemistry measurements. For metabonomic analysis, blood plasma samples (n=12) were also collected in a standard manner with sodium heparin as anticoagulant. Liver tissues (n=12) were immediately collected at the end of experiment (day 83) for each group and two samples from each group were fixed in 10% formalin for histopathological examinations. All remaining samples were snap-frozen with liquid nitrogen immediately after collection and stored at -80 ℃ until further analysis. Clinical Chemistry Measurements Serum biochemistry parameters were measured in the Biochemistry Laboratory of Zhongnan Hospital, Wuhan University (Wuhan, China), using a HITACHI 7080 automatic analyzer (Hitachi, Ltd). These were including alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), triglycerides (TG), blood glucose (Glc), creatinine (Crea), albumin (ALB), γ-glutamyl transferase (GGT), total bile acids (TBA), blood urea nitrogen (BUN), total protein (TP), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), 3-hydroxybutyrate (3-HB), total cholesterol (tChol), and uric acid (UA). Obtained data were analyzed with the Student’s t-test (if criteria met) or nonparametric tests as appropriate for significant inter-group differences. Histopathological Assessments The formalin-fixed liver tissues were embedded in paraffin wax, sectioned (3-4 µm), and stained with the hematoxylin and eosin (H&E) method, followed by microscopic assessments. This was carried out as a paid service by a qualified pathologist. Sample Preparation Each plasma sample (200 µL) was mixed with 400 µL phosphate buffer (45 mM, pH 7.43) into a 5 mm NMR tube and used directly for NMR analysis. Each urine sample (550 µL) was mixed with 55 µL phosphate buffer (1.5 M, pH 7.43)42 followed with 10 min centrifugation (16,099 × g, 4 ℃). The supernatant (550 µL) from each sample was then transferred into a 5 mm NMR tube and employed directly for NMR analysis. Liver tissues (about 50 mg) were extracted with 600 µL pre-cooled methanol-water mixture (2/1, v/v) using a tissue-lyser (QIAGEN TissueLyser II, Germany) as previously described.22 Supernatant for each sample was collected respectively after 10 min centrifugation (11,180 × g, 4 ℃). Such extracting procedure was further repeated twice. Three supernatants obtained for each sample were combined and


Page 6 of 39

Page 7 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


centrifuged (16,099 × g, 4 ℃) for another 10 min. The resultant supernatant for each sample was respectively lyophilized after removal of methanol in vacuo. So obtained samples are known as hydrophilic extracts of liver. The remaining solid residues from each tissue sample were transferred into a glass tube respectively and extracted with 600 µL pre-cooled chloroform by treating the mixture with 5 intermittent sonication treatments (60 s sonication-60 s break). Then the chloroform layer was carefully collected after centrifugation (1,000 × g, 4 ℃, 10 min). Such extracting procedure was further repeated twice to ensure near complete extraction. The combined extracts were collected after 10 min centrifugation (1,000 × g, 4 ℃) and chloroform was then removed in vacco. These samples are called lipophillic extracts of liver in this study. The hydrophilic extract was then individually reconstituted into 600 µL phosphate buffer (0.15 M, pH 7.43 ) and the lipophilic extract was reconstituted into 600 µL chloroform-d containing TMS. After vortex and centrifugation (16,099 × g, 4 ℃, 10 min), each supernatant (550 µL) was transferred into a 5 mm NMR tubes for NMR analysis. NMR Spectroscopic Analysis All NMR spectra were acquired at 298 K on a Bruker Advance Ⅲ 600 MHz NMR spectrometer (600.13 MHz for 1H frequency) equipped with an inverse cryogenic probe (Bruker Biospin, Germany). For each urine and liver extract, one 1H NMR spectrum was acquired with a standard NOESYGPPR1D pulse sequence (RD-G1-900-t1-900-tm-G2-900-acq) with the recycle delay (RD) of 2 s and tm of 100 ms. For each plasma sample, three spectra were collected including one with the standard NOESYPR1D sequence (RD-900-t1-900-tm-900-acq), a T2-edited spectrum with a standard Carr-Purcell-Meibom-Gill (CPMG) sequence (RD-900-(τ-1800-τ)n-acq) with τ of 350 µs and n of 100, and a diffusion-edited spectrum (with the sequence, RD-900-G1-τ-1800-G2-τ-900-∆-900-G3-τ-1800-G4-τ-900-Te-900-acq) with ∆ of 200 ms, τ of 150 µs and Te of 5 ms. Whilst noesypr1d spectra contained all detectable signals of organic metabolites, CPMG spectra contained mainly signals from small metabolites or moieties with fast motions. In contrast, the diffusion-edited spectra showed signals from mainly macromolecules such as lipid moieties in lipoproteins and acetyl-glycoproteins. The 900 pulse length was adjusted to about 10 µs for each sample and sixty-four transients were collected into 32 k data points over a spectral width of 20 ppm. For resonance assignment purposes, a series of two-dimensional (2D) NMR spectra were acquired for selected samples and processed as previously reported.44-46 These included 1H−1H correlation



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

spectroscopy (COSY), 1H−1H total correlation spectroscopy (TOCSY), J-resolved spectroscopy (JRES), 1

H−13C heteronuclear single quantum correlation (HSQC), and 1H−13C heteronuclear multiple bond

correlation (HMBC) 2D NMR spectra. NMR Data Processing and Multivariate Data Analysis All the NMR spectra were processed using the software package TOPSPIN (V3.0, Bruker Biospin, Germany). For 1H NMR spectra, an exponential window function was employed with a line broadening factor of 1 Hz and zero-filled to 128 k prior to Fourier transformation. Each spectrum was then phaseand baseline-corrected manually with the chemical shift referenced to TSP or TMS (δ 0.00) for urine and liver extracts as appropriate but to the anomeric proton signal of α-glucose (δ 5.23) for plasma samples. The spectral region of δ 0.5-9.0 for plasma, δ 0.15-11.0 for urine and δ 0.5-10.0 for liver extract samples were then integrated into bins with the width of 0.004ppm (2.4 Hz) using AMIX software package (V3.8.3, Bruker Biospin). The spectral regions containing unwanted signals were discarded prior to data normalization. These regions contained residual water signals (δ 4.30-5.17 for plasma, δ 4.50-5.07 for urine, δ 4.46-5.18 for liver hydrophilic extracts and δ 2.07-2.70 for liver lipophilic extracts), urea resonance (δ 5.64-6.04 for plasma and δ 5.53-6.25 for urine samples), and residual solvent signals (δ 3.35-3.38 for hydrophilic liver extracts and δ 7.0-7.45 for lipophilic liver extracts). The integrals of the remaining bins were normalized to the sum of total integrals for urine samples, weight of wet-tissues for liver extracts and sample volumes for plasma samples, respectively. Multivariate data analysis was performed with the software package SIMCA-P+ (12.0, Umetrics, Sweden). Principal Component Analysis (PCA) was conducted using the mean-centered data to generate an overview for group clustering and to search for possible outliers. Orthogonal Projection to Latent Structure Discriminant Analysis (OPLS-DA) was also carried out with 7-fold cross-validation using the unit-variance scaled data as X-matrix and the group information as Y-matrix. Qualities of the OPLS-DA models were assessed with R2X representing the explained variations and Q2 for the model predictabilities. All models were further tested with CV-ANOVA approach47 for significance of inter-group differentiations (with p < 0.05 as significant level). After back-transformation48, the loadings plots from OPLS-DA were generated using an in-house developed MATLAB script (MATLAB 7.1, Mathworks Inc., USA) with correlation coefficients color-coded for all variables (or metabolites). In these loadings plots, the hot-colored (e.g., red) metabolites contributed more significantly to the inter-group differences than the cold-colored (e.g., blue) ones. A cutoff value for the


Page 8 of 39

Page 9 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


correlation coefficients was chosen (depending on the number of animals used for each group) to extract metabolites having significant inter-group differences based on the significance of the Pearson’s product-moment correlation coefficients (p < 0.05).48 To obtain the dynamic metabolite changes, the ratios for metabolite concentrations in HFD and control groups were calculated similarly as reported17 and presented in the form of (CH- CC)/CC, where CH and CC denoted the average metabolite concentrations in HFD and control groups, respectively. The relative levels for different classes of fatty acids in blood plasma (mostly in the form of lipoproteins) were calculated from the diffusion-edited spectra as previously reported.17 These included the molar percentages of unsaturated fatty acids (UFA%), polyunsaturated fatty acids (PUFA%), monounsaturated fatty acids (MUFA%), the PUFA-to-MUFA ratio (PUFA/MUFA) and PUFA-to-UFA ratio (PUFA/UFA). For such parameters in liver, 1H NMR spectra of the lipophilic liver extracts were employed with quantitative data extracted from the spectral regions at δ 5.24-5.40 for UFA (–CH=CH–), δ 2.71-2.86 for PUFA (-C=C-CH2-C=C-) and δ 0.80-0.91 for methyl groups of all fatty acids (-CH3). Although free fatty acids in plasma are not included in these data, the overwhelming dominance of the levels of fatty acids in lipoproteins makes this calculation useful.

GC-FID-MS analysis of fatty acid composition for liver tissue and blood plasma. Plasma fatty acids were methylated with a previously reported method49 with some modifications. In brief, 20 µL internal standards in hexane containing 1 mg/mL methyl heptadecanoate, 0.5 mg/mL methyl tricosanate and 28mg/mL butylated hydroxytoluene (BHT) was added to a Pyrex tube followed with addition of 20 µL rat plasma and 1 mL methanol-hexane mixture (4:l, v/v). After cooling down the tubes above a self-made liquid nitrogen bath for 15 min, 100 µL pre-cooled acetyl chloride was added to the above mixture and then flushed briefly with nitrogen gas. Tubes were then screw-capped and kept at room temperature in the dark for 24 hours. The resultant mixture was cooled in an ice bath for 10 min followed with gradual addition of 2.5 mL of 6% K2CO3 solution (with shaking) to neutralize. After standing for another half an hour, 200 µL hexane was added to extract methylated fatty acids. The mixture was rested for 10 min and the top layer was transferred into a glass sample vial. This extraction process was further repeated twice and the combined supernatants were evaporated to dryness. The resultant residues were re-dissolved in 100 µL hexane followed with GC-FID-MS analysis. For tissues, about 10 mg sample was homogenized with 500 µL methanol using a TissueLyser at 20 Hz for 90 s. 100 µL of such homogenate mixture was transferred into a Pyrex tube to be methylated with the same



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

procedures described above. Methylated fatty acids were measured on a Shimadzu GC2010Plus GC-MS spectrometer (Shimadzu Scientific Instruments, USA) equipped with a mass spectrometer with an electron impact (EI) ion source and a flame ionization detector (FID). An Agilent DB-225 capillary GC column (10 m, 0.1 mm ID, 0.1 µm film thickness) was employed with sample injection volume of 1 µL and a splitter (1:60). Helium gas was used as carrier and make-up gas. The injection port and detector temperatures were both set at 230 ℃. The column temperature was set to 55 ºC for 1 min and then increased to 205 ℃ with a rate of 30 ℃/min. Colum temperature was then kept at 205℃for 3 min and increased to 230 ℃ (5 ℃ /min). The MS spectra were acquired with the EI voltage of 70 ev and the m/z range of 45-450. Methylated fatty acids were identified by comparing with a chromatogram from a mixture of 37 known standards and further confirmed with their mass spectral data. Each fatty acid was quantified with the FID data from its signal integrals and internal standards. The results were expressed as µmol fatty acids per liter of plasma and µmol per gram of tissue, respectively. The molar percentages were calculated from the above results for saturated fatty acids (SFA), unsaturated fatty acids (UFA), monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA), respectively. The amounts of n3 and n6 PUFA together with their ratio were also calculated.


Page 10 of 39

Page 11 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


RESULTS Phenotypes and clinical chemistry of animal models From day 21, rats on HFD started to show higher body-weight than control rats though statistical significance was found for such difference only after 83 days of HFD intakes (Figure S1). It is interesting to note that total energy intakes for the HFD-fed rats during 83 days was about 7% greater than that for control rats (Table 1) even though the amount of food (in weight) consumed by HFD rats were actually 13% less than that by control rats (Table 1). This is easily accountable since HFD has significantly higher levels of energy (Table S1). Both the perirenal and epididymal fat masses for the HFD-fed rats were significantly higher than these for controls (Table 1). Clinical chemistry results (Table 1) showed that compared with control diet, HFD intervention caused significant elevations for serum ALT, AST, ALP, TG, Glc, and Crea accompanied with significant decreases for AST-to-ALT ratio, ALB, and GGT. There were no significant differences for both groups in the levels of TBA, BUN, TP, LDL-C, HDL-C, 3-HB, tChol and UA (Table S2). Furthermore, larger standard deviations were observed for the AST and ALT values in the HFD group than controls (Table 1). Histopathological assessments showed that HFD-fed rats developed typical liver steatosis with lipid droplets (Figure S2) compared with the control rats. 1

H NMR Spectra of Rat Plasma, Liver Extracts and Urine Typical 1H NMR spectra of rat plasma, urine and liver extracts from both control and HFD groups

showed rich metabolite information related to 83-days differential diet interventions. Both 1H and 13C NMR data were assigned to specific 110 metabolites based on the literature data16, 27, 50-52 and in-house databases. The assignments were further confirmed individually with data from a series of 2D NMR spectra including COSY, TOCSY, JRES, HSQC and HMBC. The detectable metabolites in these spectra included organic acids, amino acids, fatty acids, carbohydrates, betaine, TMAO, creatinine, glycolysis and TCA cycle intermediates, together with some other metabolites of choline, ethanolamine, purines and pyrimidines (Table S2). Visual inspection showed that 83-days HFD caused obvious level changes for plasma glucose and lactate, urinary phenylacetylglycine and citrate together with glycogen, glucose and succinate in liver extracts compared with control rats (Figure 1). In order to obtain more details about the HFD-induced metabonomic changes, multivariate data analyses were further conducted on the NMR data for plasma, urine and liver extracts.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

HFD-Induced Metabonomic Changes PCA results showed that there were a few outliers including one in plasma sample with excessively high TG levels together with one urinary sample at day 28, 56 and 81 post treatment, respectively, due to (diet or fecael) contaminations (data not shown). These were excluded in further analysis. Subsequent PCA scores plots (Figure S3) revealed obvious differences between the control and HFD groups in urinary metabonomes from day 7-81 post treatments. Differences were also clearly visible for the metabonomes of plasma and liver extracts at day 83 post treatments. OPLS-DA was further carried out for control and HFD groups at various time points and the R2 and Q2 values confirmed good qualities for all models. Further evaluation with CV-ANOVA approach confirmed the validities of these models with p-values considerably smaller than 0.05. Corresponding loadings plots showed the metabolites having significant inter-group differences for plasma and liver (Figure 2); results were also obtained for the OPLS-DA models for urine samples (Figure S4) and the significantly changed metabolites (Table 2-3) with their correlation coefficients. The results revealed that HFD feeding for twelve-weeks induced significant level elevations for plasma TG, glucose, glutamate, alanine, cholesterol, bile acids and NAG accompanied with level declines for choline, PC, GPC, taurine and scyllo-inositol in plasma (Figure 2, Table 2). In rat liver, such HFD intervention caused significant elevations in the levels of glucose, glycogen, lactate, fumarate, succinate, alanine, uridine, TMA, TG and fatty acids together with the level decreases for acetate, glutamine, valine, lysine, glycine, phenylalanine, tyrosine, histidine, inosine, hypoxanthine, uracil, nicotinamide, choline and cholesterol (Figure 2, Table 2). Furthermore, the NMR spectra of liver lipophilic extracts and the diffusion-edited spectra of plasma were further analyzed to reveal the changes of fatty acids in these two biological matrices (Figure 3). Results indicated that compared with controls, HFD-fed rats had significantly higher levels of UFA and MUFA in plasma but lower levels of PUFA and SFA. The PUFA-to-UFA and PUFA-to-MUFA ratios were also lower in the HFD group (Figure 3). For liver, HFD-fed rats had higher levels of MUFA with lower PUFA-to-UFA and PUFA-to-MUFA ratios than the controls (Figure 3). GC-FID-MS results (Table 1 and S4) showed the detailed changes of fatty acids induced by HFD. The results for UFA, PUFA, MUFA and their ratios obtained from these data agreed well with the above observations from NMR analyses (Figure 3). Moreover, the levels of many urinary metabolites showed significant differences between the HFD


Page 12 of 39

Page 13 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


and control groups (Figure 4, Table 3). Urinary levels of nicotinamide, nicotinamide N-oxide (NMNO), N1-methyl-2-pyridone-5-carboxamide (2-PY), thiamine, pantothenate, 4-hydroxyphenylacetate, taurine, and pseudouridine were significantly higher for the HFD-fed rats than the control rats (Figure 4, Table 3). In contrast, urinary levels of citrate, α-ketoglutarate, succinate, fumarate, dimethylamine, dimetylglycine, 4-cresol glucuronide and sucrose were significantly lower for HFD rats than controls (Figure 4, Table 3). The concentration ratios for urinary metabolites in HFD rats against controls illustrated the dynamic metabolic changes as a function of HFD intakes (Figure 5). With HFD intakes, NMNO level increased for up to 80% and whereas citrate level decreased for about 60-80% and the level of 2-ketoglutarate decrease for about 20-40% (Figure 5). It is worth-noting that elevated excretions for B vitamins and suppressed urinary TCA intermediates have shown throughout HFD treatment broadly although the HFD-induced changes for gut microbiota related metabolites appeared to be less persistent.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

DISCUSSIONS This study showed that HFD intakes caused systematic metabolic changes in multiple biological matrices including blood plasma, liver and urine. The urinary metabonomic analysis revealed that the dynamic metabolic variations were associated with HFD intakes thus the obesity progression. HFD intakes induced oxidative stresses and metabolic changes involving many metabolic pathways including glycolysis/gluconeogenesis, TCA cycle and gut microbiota functions together with the metabolism of fatty acids, amino acids, choline, B vitamins, purines and pyrimidines (Figure 6). These metabolic variations started to be detectable a week after HFD intakes and showed clear dependence upon the duration of HFD intakes. High-fat-diet (HFD) intakes cause lipid accumulations in multiple organs of rats. Our results showed that 12-weeks HFD intakes caused significant increases in rat body-weights, perirenal and epididymal fat masses (Table 1) with concurrent accumulation of lipid droplets in liver (Figure S2). This is in good agreement with literature reported results for the HFD-induced obese rats10, 36, 41

with the enhanced fat-uptakes from HFD-induced excess fatty acids in circulation and de novo

lipogenesis in hepatocytes.53, 54 Such excess free fatty-acids can cause lipotoxicity-induced steatosis and hepatic dysfunction,53, 54 which is supported with our observation of HFD-induced elevations of plasma TG together with the ALT and AST levels (Table 1). Furthermore, standard deviations observed for the AST and ALT values were considerably larger in the HFD group than controls suggesting that HFD effects on the biochemistry of rats were heterogeneous. Such heterogeneity was also observable for two animals in terms of their levels of plasma fatty acids (Figure S5) and TG (data not shown) although these two animal did not show obvious differences in their body-weight and weight-gains (from the others in HFD group). We did not observe significant divisions in animal weight-gains as reported in literature32 probably due to different feeding schemes. The detailed mechanisms for the development of such heterogeneity remain to be fully understood although the obesity-prone and obesity-resistant subgroups of rats under the same high-fat diet were observed independently in previous studies.33 HFD intakes cause alterations to glucose-fatty acid cycle. Our observation of HFD-induced concurrent elevations of fatty acids, TCA cycle intermediates, glucose and glycogen in rat liver implies the HFD-induced alterations to glucose-fatty acid cycle (i.e. Randle cycle)55. On one hand, our results from both metabonomic analysis (Figure 2, S4) and


Page 14 of 39

Page 15 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


histopathological assessments (Figure S2) confirmed the findings from the clinical chemistry (Table 1) that HFD intakes caused level elevations in circulation fatty acids as well as excessive organ lipid accumulation. HFD-induced hepatic lipogenesis was consistent with previous findings that HFD intakes led to TG accumulation in liver.56 On the other hand, malonyl-CoA produced from the β-oxidation of such excess fatty acids can be readily converted into acetyl-CoA functioning as raw materials for TCA cycle and/or gluconeogenesis.55 HFD-induced significant level increases for plasma and hepatic glucose together with hepatic glycogen agreed well with what previously observed in Zucker (fa/fa) obese rats.29 However, we did not observe altered levels of betaine and methionine which was observed in this Zucker rats29 probably due to the differences between the diet-induced and genetically predisposed models for obesity. The concurrent elevation of hepatic lactate was also consistent with the HFD-induced up-regulation of LDH57 for pyruvate and lactate inter-conversion. The HFD-induced decrease of rat hepatic acetate in this study (Figure 2B) is probably associated with the elevation of hepatic glucose since acetate can be readily converted into acetyl-CoA entering gluconeogenesis and TCA cycle. HFD-induced glycolysis suppression is further supported by previous findings of down-regulation of Gck and LPK genes in high-fat feeding rats9, 10 since the former encodes glucokinase, facilitating glucose phosphorylation to form glucose-6-phosphate whereas the latter encodes L-pyruvate kinase for catalyzing the conversion of phosphoenolpyruvate to pyruvate. Moreover, HFD-caused up-regulation of G6PT (glucose 6-phosphatase)58 supports the notion of HFD-enhancements for gluconeogenesis since this enzyme catalyzes the final step in the metabolic pathways. Higher levels of glycogen observed in the HFD group was consistent with the HFD-induced up-regulation of GK in mouse liver.54 Some parts of acetyl-CoA generated from Randle cycle were diverted into TCA cycles to produce energy. Such notion is clearly supported by the HFD-induced up-regulation of genes involved in fatty acid absorption (CD36), transportation (CPT1), fatty acid metabolism (ACADs)9 and TCA cycle.58 We also observed that the hepatic cholesterol levels for HFD-fed rats (Figure 2B) declined concurrently with the promotion of gluconeogenesis and glycogenesis. This is probably due to the fact that the high mitochondrial demand of acetyl-CoA for TCA cycle58 causes decline of cholesterol biosynthesis with the shift in energy provision from glucose to fatty acids. Such notion is further supported by the HFD-induced down-regulation of the gene encoding 3-hydroxy-3-methylglutaryl-CoA synthase 1 (Hmgcs1)10 since this enzyme catalyzes the condensation of acetyl-CoA with acetoacetyl-CoA to form



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

HMG-CoA, which is a key intermediate in cholesterol biosynthesis. HFD-induced down-regulation of Ebp is also consistent with the decline of cholesterol biosynthesis since Ebp encodes another key enzyme, cholestenol ∆-isomerase, in cholesterol biosynthesis.10 HFD Induced Lipid β-oxidation and Oxidative Stress. HFD-induced significant decrease of PUFA-to-UFA ratio (Figure 3) observed in rat liver here is indicative to enhanced peroxidation of polyunsaturated fatty acids thus oxidative stress. Such peroxidation and oxidative stress can further lead to apolipoprotein B proteolysis thus impairing very low-density lipoproteins (VLDL) secretion so that exports of TG from liver will decrease contributing to TG accumulation in liver.59 The HFD-induced decrease of PUFA-to-MUFA ratio in plasma (Figure 3) is also consistent with the HFD-induced oxidation of PUFA probably in systems level.29 This lipid β-oxidation can generate large amounts of electrons entering mitochondrial respiratory chain to produce excess reactive oxygen species (ROS). HFD-induced influx of free fatty acids to peroxisomes and microsomes instead of mitochondria may generate ROS as well. This is supported by previous report that HFD induces expression enhancements for hepatic ACOX and CYP2E1.60 Whilst the latter encodes microsomal monooxygenase, the former encodes peroxisomal acyl-CoA oxidase 1 which is the first enzyme of the fatty acid β-oxidation pathway catalyzing the desaturation of acyl-CoAs to 2-trans-enoyl-CoAs and producing hydrogen peroxide. The resultant reduction of PUFA may also be related to the associations between long term HFD intakes and insulin resistance since PUFAs have preventative function from insulin resistance by increasing cell membrane fluidity and GLUT4 transport. Moreover, HFD-induced elevation of urinary allantoin, which is an end product of uric acid, in this study also supports the enhanced oxidative stress in agreement with previous studies that considered elevation of urinary allantoin as an indicator for oxidative stress.61 Our observation of HFD-induced elevation of N-acetyl-glycoproteins (NAG) further suggests that inflammation probably starts to occur during twelve-weeks HFD intakes since these proteins are well accepted biomarkers for “acute-phase” response of plasma protein synthesis to systems inflammation.62 Lower levels of urinary TCA intermediates (citrate, 2-ketoglutarate, succinate and fumarate) in HFD group were indicative of alterations in energy metabolism. A reduction of urinary citrate excretion has also been reported for obesity accompanied with insulin resistance63. However, lower level of urinary sucrose in HFD group than control rats is probably due to lower sucrose intakes in the HFD group since urinary sucrose is a


Page 16 of 39

Page 17 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


marker for sugar consumption in normal human.64 HFD Intakes Altered Choline Metabolism. The decrease of hepatic choline level was observed here in HFD group (Figure 2) compared with these in control group. This may result from either different dietary choline intakes and in choline metabolism including de novo biosynthesis. For mammals, choline is an essential nutrient since it is common knowledge that the amount of choline from de novo biosynthesis is insufficient to meet physiological demands. In this study, the average daily choline intakes for rats in the HFD group (about 49.2 mg per rat) were broadly similar to that (about 46.3 mg per rat) for controls (Table S1-S2). HFD-induced alterations to choline metabolisms are more likely for the consequences observed here, which is not surprising since choline metabolism is known for its important roles in the obesity development.37 It is also well-known that mammals can utilize dietary choline for biosynthesis of phosphorylcholine (PC) and glycerophosphorylcholine (GPC) to form phosphatidylcholines. Level declines for hepatic choline and plasma cholines (choline, PC and GPC) observed in this study (Figure 2) suggested that HFD caused inhibition to the conversion of choline to PC and GPC. This is consistent with our observation of the HFD-induced fatty-liver phenotype and is understandable since PC is normally required for biosynthesis and secretion of VLDL65; insufficient PC supplies will inevitably cause TG accumulation in liver cytosol. Choline can also be oxidized to form trimethylglycine and subsequently to form dimethylglycine (DMG), sarcosine, glycine and eventually creatinine.37 The concurrent level elevations of plasma creatinine (Table 1) and decreased excretion for urinary DMG (Figure 4) seemed to indicate that HFD intakes caused promotion of choline conversion to creatinine via DMG being excreted into urine. However, the present study showed that the urinary creatinine level was fairly constant throughout 81 days of HFD intakes. A transit elevation of urinary creatinine was only observed here a week after HFD intakes, indicating that the changes of urinary creatinine is not due to the changes of body-muscle mass either. It is worth-noting that our observation of the HFD-induced level elevation for rat hepatic choline is opposite to what has observed for a mice model.71 This probably resulted from different data-treatment approaches employed in previous and current studies. Previous study normalized their NMR data to the sum of total spectral integrals thus the level of a given metabolite represented the molar fraction of that metabolite against whole proton pools in the metabolome (i.e., percentage of the protons of a give metabolite in the whole protons pool). However, the level of a given metabolite, in this study, was the number of metabolite molecules in a



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

given weight of liver tissue (mol/mg tissue), representing the absolute metabolite concentration. HFD Intakes Affect the Gut Microbiota Functions. Our observation of the hepatic trimethylamine (TMA) elevation (Figure 2) implies that HFD intakes probably affect gut microbiota functions since choline can also be metabolized into TMA, dimethylamine (DMA) and methylamine (MA) by gut microorganisms.37 In fact, TMA is the first metabolite of dietary choline from these microorganisms and re-absorbed by mammalian microvillae penetrating the systemic circulation via the hepatic portal vein (i.e., enterohepatic recirculation).66 Such TMA accumulation is toxic to mammalian liver causing hepatic steatosis as reported previously for mice.37 However, we did not observe elevation for urinary TMA, DMA and MA. This together with the elevated hepatic TMA suggested that HFD probably induced some renal dysfunctions since the excretion of these organic amines was not substantiated. Such remains to be fully investigated. We further observed that the levels of urinary microbial-mammalian co-metabolites had dynamic changes as a function of HFD intakes. Such was highlighted by the changes of urinary phenylacetylglycine (PAG), 4-hydroxyphenylacetate (4-HPA) and 4-cresol glucuronide (Figure 4). PAG is typically produced from hepatic glycine-conjugation of phenylacetate which is a catabolic product of phenylalanine and dietary polyphenols from gut microbiota.67 4-HPA is also metabolite from the enteric bacterial catabolism of phenylalanine, tyrosine and dietary polyphenols.67 However, the effects of dietary polyphenols on the changes of these urinary metabolites can be negligible with similar levels of dietary polyphenols intakes in both groups of rats. 4-cresol glucuronide is yet another gut microbiota-related metabolite from tyrosine and phenylalanine67. In fact, p-cresol is a metabolite of 4-HPA and undergoes glucuronidation with UDP-glucuronosyltransferase in the mammalian liver prior to urinary excretion.67, 68 Furthermore, we observed that urinary urocanate level was higher in the HFD group than controls (Figure 4). Urocanate is detectable in urine as an intermediate in the conversion of histidine to glutamate in liver69 and/or a breakdown product of histidine by enteric bacteria.70 Our observed changes for this metabolite might also be related to the HFD effects on gut microbiota rather than the increased conversion of histidine in liver since there were no significant hepatic glutamate level differences for HFD and control rats (Figure 2). Our results here appeared to suggest that gut microbiota played some roles during the processes of obesity development and progression. The detailed dynamic changes for gut microbiota associated with the processes of HFD intakes clearly warrant further investigation with microecology and metagenomics approaches.


Page 18 of 39

Page 19 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


HFD Induced Alterations in Metabolism of Purines and Amino Acids. Our observations of the HFD-caused elevations of uridine and UDP together with depletion of uracil in liver (Figure 2) are probably associated with the enhancement of glycogenesis discussed earlier. This is because that uracil is the precursor for synthesizing uridine and further UDP which is needed for converting hepatic glucose into glycogen. The concurrent elevation of urinary allantoin and depletion of hepatic inosine and hypoxanthine (Figure 4) probably indicated the HFD-induced oxidative stress promoting purine catabolism as found previously.27 It is well-known that inosine is converted into hypoxanthine in rats which is further transformed into xanthine and uric acid in two steps with xanthine oxidase. Uric acid is then oxidized with urate oxidase and metabolized into allantoin. Furthermore, HFD intakes clearly caused significant level changes for many amino acids in rat liver highlighted with depletion of glutamine, valine, lysine, glycine, phenylalanine, tyrosine and histidine together with alanine elevation (Figure 2B). Some of these findings have also been reported in a previous investigation10 and are probably due to the HFD-induced gluconeogenesis with some of these amino acids acting as substrates. The HFD-induced level decreases for hepatic glycine (Figure 2) observed in this study are probably associated with the increased demands of bile acid conjugations with glycine for fat absorption in the intestine upon HFD intakes. Such notion is supported by the elevation of bile acids in both liver (Figure 2) and plasma accompanied with decrease of plasma taurine resulting from the needs of bile acid conjugations in liver. This is also consistent with the HFD-induced up-regulation of bile acid CoA: amino acid N-acyltrasferase (Baat).71 However, the elevation of hepatic alanine and lactate in the HFD group (Figure 2) may be associated with HFD-induced changes in alanine-glucose cycle which is further supported with the elevation of plasma alanine and glucose. The HFD-induced elevation of plasma glutamate and ALT supports such notion since ALT catalyzes both parts of alanine cycle in liver and muscles.72 The elevations of both ALT and AST after twelve-weeks HFD intakes indicate the HFD-induced alterations to liver functions though such changes of AST may also be concurrently involved in the changes of glutamate level. It is worth-noting that both AST and ALT functions require the coenzyme pyridoxal phosphate (the active form of vitamin B6) to convert amino acids into keto-acids. HFD Intakes Altered Metabolisms of B Vitamins Another prominent finding of this work is the HFD-induced marked elevations for urinary excretions of B vitamins (Figure 4) including thiamine (B1), nicotinamide (B3) and pantothenate (B5).



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Since food intakes were actually lower for rats in the HFD group than in control group, such observation suggested the HFD-induced excessive urinary excretions of B vitamins. It is now common knowledge that thiamine plays crucial roles in carbohydrate metabolisms since thiamine diphosphate is a coenzyme for several enzymes catalyzing dehydrogenation of α-keto acids, such as pyruvate dehydrogenase (PDH), 2-oxoglutarate dehydrogenase (OGDH) and transketolase. Whilst the cytosolic transketolase plays a key role in the pentose phosphate pathway for biosynthesis of deoxyribose and ribose, the mitochondrial PDH and OGDH are key enzymes in the ATP generating pathways. It is also common knowledge that PDH serves as the crucial link between glycolysis and TCA cycle in which the OGDH-catalyzed reaction is a rate-limiting step. Consistently elevated excretion of urinary thiamine throughout HFD intakes observed in this study (Figure 4) implies that thiamine probably has some roles to play in the obesity development which is probably related to the energy metabolism. This notion is consistent with findings that thiamine supplementation inhibits obesity and its related metabolic disorders in OLETF rats with increased hepatic PDH activity together with the reduced lipid oxidation and hepatic TG accumulation.73 Pantothenate is also well-known for its central role in energy metabolism since it is used to produce acyl-CoA involving in glycolysis/gluconeogenesis, TCA cycle and metabolisms of fatty acids and cholesterol. Previous studies showed that pantothenate intakes resulted in weight loss and its deficiency was probably prone for weight-gains.74 However, pantothenate deficiency is exceptionally rare and the elevated urinary excretion of pantothenate is probably an effect of HFD intakes although mechanisms remain unknown. The consistently elevated urinary excretion of nicotinamide N-oxide (NMNO) and sporadic elevation of urinary nicotinamide, trigonelline and N1-methyl-2-pyridone-5-carboxamide (2PY) for HFD-fed rats (Figure 4) suggest the HFD-induced excessive excretion of vitamin (B3). Nicotinamide is a component of NAD participating in intracellular respiration to oxidize fuel substrates. This vitamin B3 is readily metabolized into both NMNO (catalyzed by xanthine oxidase)75 and N1-methylnicotinamide in mammalian liver. The latter can be further oxidized to produce 2-PY76 whereas trigonelline is a metabolite of nicotinate (vitamin B3). Therefore, oxidative stress seems to be again implied here. However, the associations between deficiency of B vitamins and the obesity developments remain to be fully understood especially in terms of the underlying mechanisms.


Page 20 of 39

Page 21 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


CONCLUSIONS This study showed that metabolic responses of rats to high-fat diet intervention involved several metabolic pathways, such as increased lipid oxidation, lipogenesis and lipid accumulation, suppression of glycolysis, up-regulation of gluconeogenesis and glycogen synthesis, changed choline as well as amino acids and nucleotide metabolism (Figure 6). Furthermore, time-course urinary metabolic responses of rats to HFD intervention showed gut microbiota and B vitamins probably play vital role in the occurrence and progression of obesity. Moreover, high-fat diet intervention caused oxidative stress accompanied with decreased the PUFA/MUFA ratio both in plasma and liver. Further investigation on the metabolic responses of multiple organs and the disturbance of gut microbiota is needed to enrich our understandings of the pathogenesis and progression of obesity. This study also suggests that the mechanistic aspects of the contributions of B-vitamins towards obesity development are also an interesting topic to explore much further.

ASSOCIATED CONTENT Supporting Information Available: Table S1. Compositions and nutrient contents of diets used in this study; Table S2. Data for phenotypic characteristics, serum clinical chemistry and fatty acids in blood plasma and liver tissues from rats fed with control and high-fat diets; Table S3. 1H and 13C NMR data for metabolites assigned in plasma, urine and liver extracts; Table S4. Data for fatty acids in blood plasma and liver tissues from rats fed with control and high-fat diets; Figure S1 Changes in the body weight of rats on HFD and control diet for 83 days; Figure S2 Micrographs for the hematoxylin-and-eosin stained liver tissues from rats fed with control and high-fat diets for twelve weeks, respectively; Figure S3 PCA scores plots derived from 1H NMR data for urine, plasma, hydrophilic extracts and lipophilic extracts of liver tissue from the control group and HFD group; Figure S4 OPLS-DA scores plots and corresponding coefficient plots for 1H NMR spectra of urine obtained from the HFD and control groups at various time points. Figure S5. Scattered plots for the concentration of fatty acids in plasma of rats fed with HFD and control diet. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION Corresponding Author *

Tel: +86-(0)27-87198430. Fax: +86-(0)27-87199291. E-mail: [email protected].



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Notes. The authors declare no competing financial interest.

ACKNOWLEDGMENTS We acknowledge the financial supports from the Ministry of Science and Technology of China (2009CB118804, 2010CB912501 and 2012CB934004), National Natural Science Foundation of China (20825520, 21175149 and 21221064) and Chinese Academy of Sciences (KJCX2-YW-W13, and KSCX1-YW-02).


Page 22 of 39

Page 23 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


REFFRENCES (1) Ogden, C. L.; Carroll, M. E.; Kit, B. K.; Flegal, K. M. Prevalence of obesity in the United States, 2009-2010. NCHS Data Brief 2012, 82, 1-8. (2) Levine, J. A. Obesity in China: Causes and solutions. Chin. Med. J. 2008, 121, (11), 1043-1050. (3) Walley, A. J.; Asher, J. E.; Froguel, P. The genetic contribution to non-syndromic human obesity. Nat. Rev. Genet. 2009, 10, (7), 431-442. (4) Yang, W. J.; Kelly, T.; He, J. Genetic epidemiology of obesity. Epidemiol. Rev. 2007, 29, 49-61. (5) Backhed, F.; Ding, H.; Wang, T.; Hooper, L. V.; Koh, G. Y.; Nagy, A.; Semenkovich, C. F.; Gordon, J. I. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl. Acad. Sci. U. S. A. 2004, 101, (44), 15718-15723. (6) Kahn, S. E.; Hull, R. L.; Utzschneider, K. M. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 2006, 444, (7121), 840-846. (7) Norman, J. E. The adverse effects of obesity on reproduction. Reproduction 2010, 140, (3), 343-345. (8) Calle, E. E.; Thun, M. J. Obesity and cancer. Oncogene 2004, 23, (38), 6365-6378. (9) Vial, G.; Dubouchaud, H.; Couturier, K.; Cottet-Rousselle, C.; Taleux, N.; Athias, A.; Galinier, A.; Casteilla, L.; Leverve, X. M. Effects of a high-fat diet on energy metabolism and ROS production in rat liver. J. Hepatol. 2011, 54, (2), 348-356. (10) Xie, Z. Q.; Li, H. K.; Wang, K.; Lin, J. C.; Wang, Q.; Zhao, G. P.; Jia, W.; Zhang, Q. H. Analysis of transcriptome and metabolome profiles alterations in fatty liver induced by high-fat diet in rat. Metab.-Clin. Exp. 2010, 59, (4), 554-560. (11) Schmid, G. M.; Converset, V.; Walter, N.; Sennitt, M. V.; Leung, K. Y.; Byers, H.; Ward, M.; Hochstrasser, D. F.; Cawthorne, M. A.; Sanchez, J. C. Effect of high-fat diet on the expression of proteins in muscle, adipose tissues, and liver of C57BL/6 mice. Proteomics 2004, 4, (8), 2270-2282. (12) Hwang, H.; Bowen, B. P.; Lefort, N.; Flynn, C. R.; De Filippis, E. A.; Roberts, C.; Smoke, C. C.; Meyer, C.; Hojlund, K.; Yi, Z. P.; Mandarino, L. J. Proteomics analysis of human skeletal muscle reveals novel abnormalities in obesity and type 2 diabetes. Diabetes 2010, 59, (1), 33-42. (13) Oberbach, A.; Blueher, M.; Wirth, H.; Till, H.; Kovacs, P.; Kullnick, Y.; Schlichting, N.; Tomm, J. M.; Rolle Kampczyk, U.; Murugaiyan, J.; Binder, H.; Dietrich, A.; von Bergen, M. Combined proteomic and metabolomic profiling of serum reveals association of the complement system with obesity and Identifies novel markers of body fat mass changes. Journal of Proteome Research 2011, 10, (10), 4769-4788. (14) Nicholson, J. K.; Lindon, J. C.; Holmes, E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999, 29, (11), 1181-1189. (15) Tang, H. R.; Wang, Y. L. Metabonomics: a revolution in progress. Prog. Biochem. Biophys. 2006, 33, (5), 401-417. (16) Zhang, X. Y.; Wang, Y. L.; Hao, F. H.; Zhou, X. H.; Han, X. Y.; Tang, H. R.; Ji, L. N. Human serum metabonomic analysis reveals progression axes for glucose intolerance and insulin resistance statuses. Journal of Proteome Research 2009, 8, (11), 5188-5195. (17) Xu, W. X.; Wu, J. F.; An, Y. P.; Xiao, C. N.; Hao, F. H.; Liu, H. B.; Wang, Y. L.; Tang, H. R. Streptozotocin-induced dynamic metabonomic changes in rat biofluids. Journal of Proteome Research 2012, 11, (6), 3423-3435. (18) Marchesi, J. R.; Holmes, E.; Khan, F.; Kochhar, S.; Scanlan, P.; Shanahan, F.; Wilson, I. D.; Wang, Y. L. Rapid and noninvasive metabonomic characterization of inflammatory bowel disease. Journal of Proteome



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Research 2007, 6, (2), 546-551. (19) Bjerrum, J. T.; Nielsen, O. H.; Hao, F. H.; Tang, H. R.; Nicholson, J. K.; Wang, Y. L.; Olsen, J. Metabonomics in ulcerative colitis: diagnostics, biomarker identification, and insight into the pathophysiology. Journal of Proteome Research 2010, 9, (2), 954-962. (20) Zhang, L. M.; Ye, Y. F.; An, Y. P.; Tian, Y. A.; Wang, Y. L.; Tang, H. R. Systems responses of rats to aflatoxin B1 exposure revealed with metabonomic changes in multiple biological matrices. Journal of Proteome Research 2011, 10, (2), 614-623. (21) Zhao, X. J.; Huang, C. Y.; Lei, H. H.; Nie, X.; Tang, H. R.; Wang, Y. L. Dynamic metabolic response of mice to acute mequindox exposure. Journal of Proteome Research 2011, 10, (11), 5183-5190. (22) Jiang, L. M.; Huang, J.; Wang, Y. L.; Tang, H. R. Metabonomic analysis reveals the CCl4-induced systems alterations for multiple rat organs. Journal of Proteome Research 2012, 11, (7), 3848-3859. (23) Wang, Y. L.; Holmes, E.; Tang, H. R.; Lindon, J. C.; Sprenger, N.; Turini, M. E.; Bergonzelli, G.; Fay, L. B.; Kochhar, S.; Nicholson, J. K. Experimental metabonomic model of dietary variation and stress interactions. Journal of Proteome Research 2006, 5, (7), 1535-1542. (24) Wang, Y. L.; Lawler, D.; Larson, B.; Ramadan, Z.; Kochhar, S.; Holmes, E.; Nicholson, J. K. Metabonomic investigations of aging and caloric restriction in a life-long dog study. Journal of Proteome Research 2007, 6, (5), 1846-1854. (25) He, Q. H.; Tang, H. R.; Ren, P. P.; Kong, X. F.; Wu, G. Y.; Yin, Y. L.; Wang, Y. L. Dietary supplementation with L-arginine partially counteracts serum metabonome induced by weaning stress in piglets. Journal of Proteome Research 2011, 10, (11), 5214-5221. (26) Teague, C.; Holmes, E.; Maibaum, E.; Nicholson, J.; Tang, H. R.; Chan, Q. N.; Elliott, P.; Wilson, I. Ethyl glucoside in human urine following dietary exposure: detection by 1H NMR spectroscopy as a result of metabonomic screening of humans. Analyst 2004, 129, (3), 259-264. (27) Shi, X. H.; Xiao, C. N.; Wang, Y. L.; Tang, H. R. Gallic acid intake induces alterations to systems metabolism in rats. Journal of Proteome Research 2013, 12, (2), 991-1006. (28) Waldram, A.; Holmes, E.; Wang, Y. L.; Rantalainen, M.; Wilson, I. D.; Tuohy, K. M.; McCartney, A. L.; Gibson, G. R.; Nicholson, J. K. Top-down systems biology modeling of host metabotype-microbiome associations in obese rodents. Journal of Proteome Research 2009, 8, (5), 2361-2375. (29) Serkova, N. J.; Jackman, M.; Brown, J. L.; Liu, T.; Hirose, R.; Roberts, J. P.; Maher, J. J.; Niemann, C. U. Metabolic profiling of livers and blood from obese Zucker rats. J. Hepatol. 2006, 44, (5), 956-962. (30) He, Q. H.; Ren, P. P.; Kong, X. F.; Wu, Y. N.; Wu, G. Y.; Li, P.; Hao, F. H.; Tang, H. R.; Blachier, F.; Yin, Y. L. Comparison of serum metabolite compositions between obese and lean growing pigs using an NMR-based metabonomic approach. J. Nutr. Biochem. 2012, 23, (2), 133-139. (31) Toye, A. A.; Dumas, M. E.; Blancher, C.; Rothwell, A. R.; Fearnside, J. F.; Wilder, S. P.; Bihoreau, M. T.; Cloarec, O.; Azzouzi, I.; Young, S.; Barton, R. H.; Holmes, E.; McCarthy, M. I.; Tatoud, R.; Nicholson, J. K.; Scott, J.; Gauguier, D. Subtle metabolic and liver gene transcriptional changes underlie diet-induced fatty liver susceptibility in insulin-resistant mice. Diabetologia 2007, 50, (9), 1867-1879. (32) Kim, S. H.; Yang, S. O.; Kim, H. S.; Kim, Y.; Park, T.; Choi, H. K. 1H nuclear magnetic resonance spectroscopy-based metabolic assessment in a rat model of obesity induced by a high-fat diet. Anal. Bioanal. Chem. 2009, 395, (4), 1117-1124. (33) Li, H. K.; Xie, Z. Q.; Lin, J. C.; Song, H. G.; Wang, Q.; Wang, K.; Su, M. M.; Qiu, Y. P.; Zhao, T.; Song, K.; Wang, X. Y.; Zhou, M. M.; Liu, P.; Zhao, G. P.; Zhang, Q. H.; Jia, W. Transcriptomic and metabonomic profiling of obesity-prone and obesity-resistant rats under high fat diet. Journal of Proteome Research 2008, 7, (11), 4775-4783.


Page 24 of 39

Page 25 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(34) Kim, J. Y.; Park, J. Y.; Kim, O. Y.; Ham, B. M.; Kim, H. J.; Kwon, D. Y.; Jang, Y. S.; Lee, J. H. Metabolic profiling of plasma in overweight/obese and lean men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC-Q-TOF MS). Journal of Proteome Research 2010, 9, (9), 4368-4375. (35) Kim, H. J.; Kim, J. H.; Noh, S.; Hur, H. J.; Sung, M. J.; Hwang, J. T.; Park, J. H.; Yang, H. J.; Kim, M. S.; Kwon, D. Y.; Yoon, S. H. Metabolomic analysis of livers and serum from high-fat diet induced obese mice. Journal of Proteome Research 2011, 10, (2), 722-731. (36) Turnbaugh, P. J.; Ley, R. E.; Mahowald, M. A.; Magrini, V.; Mardis, E. R.; Gordon, J. I. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2006, 444, (7122), 1027-1031. (37) Dumas, M. E.; Barton, R. H.; Toye, A.; Cloarec, O.; Blancher, C.; Rothwell, A.; Fearnside, J.; Tatoud, R.; Blanc, V.; Lindon, J. C.; Mitchell, S. C.; Holmes, E.; McCarthy, M. I.; Scott, J.; Gauguier, D.; Nicholson, J. K. Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc. Natl. Acad. Sci. U. S. A. 2006, 103, (33), 12511-12516. (38) Martin, F. P. J.; Dumas, M. E.; Wang, Y. L.; Legido Quigley, C.; Yap, I. K. S.; Tang, H. R.; Zirah, S.; Murphy, G. M.; Cloarec, O.; Lindon, J. C.; Sprenger, N.; Fay, L. B.; Kochhar, S.; van Bladeren, P.; Holmes, E.; Nicholson, J. K. A top-down systems biology view of microbiome-mammalian metabolic interactions in a mouse model. Molecular Systems Biology 2007, 3. (39) Li, M.; Wang, B. H.; Zhang, M. H.; Rantalainen, M.; Wang, S. Y.; Zhou, H. K.; Zhang, Y.; Shen, J.; Pang, X. Y.; Zhang, M. L.; Wei, H.; Chen, Y.; Lu, H. F.; Zuo, J.; Su, M. M.; Qiu, Y. P.; Jia, W.; Xiao, C. N.; Smith, L. M.; Yang, S. L.; Holmes, E.; Tang, H. R.; Zhao, G. P.; Nicholson, J. K.; Li, L. J.; Zhao, L. P. Symbiotic gut microbes modulate human metabolic phenotypes. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 2117-2122. (40) Fearnside, J. F.; Dumas, M. E.; Rothwell, A. R.; Wilder, S. P.; Cloarec, O.; Toye, A.; Blancher, C.; Holmes, E.; Tatoud, R.; Barton, R. H.; Scott, J.; Nicholson, J. K.; Gauguier, D. Phylometabonomic patterns of adaptation to high fat diet feeding in inbred mice. Plos One 2008, 3, (2). (41) Li, S. Y.; Zhang, H. Y.; Hu, C. C.; Lawrence, F.; Gallagher, K. E.; Surapaneni, A.; Estrem, S. T.; Calley, J. N.; Varga, G.; Dow, E. R.; Chen, Y. Y. Assessment of diet-induced obese rats as an obesity model by comparative functional genomics. Obesity 2008, 16, (4), 811-818. (42) Xiao, C. N.; Hao, F. H.; Qin, X. R.; Wang, Y. L.; Tang, H. R. An optimized buffer system for NMR-based urinary metabonomics with effective pH control, chemical shift consistency and dilution minimization. Analyst 2009, 134, (5), 916-925. (43) Ding, L. N.; Hao, F. H.; Shi, Z. M.; Wang, Y. L.; Zhang, H. X.; Tang, H. R.; Dai, J. Y. Systems biological responses to chronic perfluorododecanoic acid exposure by integrated metabonomic and transcriptomic studies. Journal of Proteome Research 2009, 8, (6), 2882-2891. (44) Xiao, C. N.; Dai, H.; Liu, H. B.; Wang, Y. L.; Tang, H. R. Revealing the metabonomic variation of rosemary extracts using 1H NMR spectroscopy and multivariate data analysis. J. Agric. Food Chem. 2008, 56, (21), 10142-10153. (45) Dai, H.; Xiao, C. N.; Liu, H. B.; Tang, H. R. Combined NMR and LC-MS analysis reveals the metabonomic changes in salvia miltiorrhiza bunge induced by water depletion. Journal of Proteome Research 2010, 9, (3), 1460-1475. (46) Dai, H.; Xiao, C. N.; Liu, H. B.; Hao, F. H.; Tang, H. R. Combined NMR and LC-DAD-MS analysis reveals comprehensive metabonomic variations for three phenotypic cultivars of salvia miltiorrhiza bunge. Journal of Proteome Research 2010, 9, (3), 1565-1578. (47) Eriksson, L.; Trygg, J.; Wold, S. CV-ANOVA for significance testing of PLS and OPLS (R) models. J.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Chemometr. 2008, 22, (11-12), 594-600. (48) Cloarec, O.; Dumas, M. E.; Trygg, J.; Craig, A.; Barton, R. H.; Lindon, J. C.; Nicholson, J. K.; Holmes, E. Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1H NMR spectroscopic metabonomic studies. Anal. Chem. 2005, 77, (2), 517-526. (49) Xu, Z. D.; Harvey, K.; Pavlina, T.; Dutot, G.; Zaloga, G.; Siddiqui, R. An improved method for determining medium-and long-chain FAMEs using gas chromatography. Lipids 2010, 45, (2), 199-208. (50) Fan, W. M. T. Metabolite profiling by one-and two-dimensional NMR analysis of complex mixtures. Prog. Nucl. Magn. Reson. Spectrosc. 1996, 28, 161-219. (51) Nicholson, J. K.; Foxall, P. J. D.; Spraul, M.; Farrant, R. D.; Lindon, J. C. 750-MHz 1H and 1H-13C NMR spectroscopy of human blood plasma. Anal. Chem. 1995, 67, (5), 793-811. (52) Wu, J. F.; Xu, W. X.; Ming, Z. P.; Dong, H. F.; Tang, H. R.; Wang, Y. L. Metabolic changes reveal the development of schistosomiasis in mice. Plos Neglect. Trop. Dis. 2010, 4, (8). (53) Choi, S. S.; Diehl, A. M. Hepatic triglyceride synthesis and nonalcoholic fatty liver disease. Curr. Opin. Lipidology 2008, 19, (3), 295-300. (54) Do, G. M.; Oh, H. Y.; Kwon, E. Y.; Cho, Y. Y.; Shin, S. k.; Park, H. J.; Jeon, S. M.; Kim, E.; Hur, C. G.; Park, T. S.; Sung, M. K.; McGregor, R. A.; Choi, M. S. Long-term adaptation of global transcription and metabolism in the liver of high-fat diet-fed C57BL/6J mice. Molecular Nutrition & Food Research 2011, 55, S173-S185. (55) Randle, P. J.; Garland, P. B.; Newsholme, E. A.; Hales, C. N. Glucose fatty-acid cycle its role in insulin sensitivity and metabolic disturbances of diabetes mellitus. Lancet 1963, 1, (728), 785-&. (56) Scheja, L.; Toedter, K.; Mohr, R.; Niederfellner, G.; Michael, M. D.; Meissner, A.; Schoettler, A.; Pospisil, H.; Beisiegel, U.; Heeren, J. Liver TAG transiently decreases while PL n-3 and n-6 fatty acids are persistently elevated in insulin resistant mice. Lipids 2008, 43, (11), 1039-1051. (57) de Fourmestraux, V.; Neubauer, H.; Poussin, C.; Farmer, P.; Falquet, L.; Burcelin, R.; Delorenzi, M.; Thorens, B. Transcript profiling suggests that differential metabolic adaptation of mice to a high fat diet is associated with changes in liver to muscle lipid fluxes. J. Biol. Chem. 2004, 279, (49), 50743-50753. (58) Satapati, S.; Sunny, N. E.; Kucejova, B.; Fu, X. R.; He, T. T.; Mendez-Lucas, A.; Shelton, J. M.; Perales, J. C.; Browning, J. D.; Burgess, S. C. Elevated TCA cycle function in the pathology of diet-induced hepatic insulin resistance and fatty liver. J. Lipid Res. 2012, 53, (6), 1080-1092. (59) Pan, M. H.; Cederbaum, A. I.; Zhang, Y. L.; Ginsberg, H. N.; Williams, K. J.; Fisher, E. A. Lipid peroxidation and oxidant stress regulate hepatic apolipoprotein B degradation and VLDL production. J. Clin. Invest. 2004, 113, (9), 1277-1287. (60) Matsuzawa Nagata, N.; Takamura, T.; Ando, H.; Nakamura, S.; Kurita, S.; Misu, H.; Ota, T.; Yokoyama, M.; Honda, M.; Miyamoto, K. I.; Kaneko, S. Increased oxidative stress precedes the onset of high-fat diet-induced insulin resistance and obesity. Metab.-Clin. Exp. 2008, 57, (8), 1071-1077. (61) Grootveld, M.; Halliwell, B. Measurement of allantoin and uric acid in human body fluids. A potential index of free-radical reactions in vivo? Biochem. J. 1987, 243, (3), 803-808. (62) Bell, J. D.; Brown, J. C. C.; Nicholson, J. K.; Sadler, P. J. Assignment of resonances for 'acute-phase' glycoproteins in high resolution proton NMR spectra of human blood plasma. FEBS Lett. 1987, 215, (2), 311-315. (63) Souto, G.; Donapetry, C.; Calvino, J.; Adeva, M. M. Metabolic acidosis induced insulin resistance and cardiovascular risk. Metabolic Syndrome and Related Disorders 2011, 9, (4), 247-253. (64) Tasevska, N.; Runswick, S. A.; McTaggart, A.; Bingham, S. A. Urinary sucrose and fructose as


Page 26 of 39

Page 27 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


biomarkers for sugar consumption. Cancer Epidemiol. Biomarkers Prev. 2005, 14, (5), 1287-1294. (65) Fast, D. G.; Vance, D. E. Nascent VLDL phospholipid composition is altered when phosphatidylcholine biosynthesis is inhibited: evidence for a novel mechanism that regulates VLDL secretion. Biochimica Et Biophysica Acta-Lipids and Lipid Metabolism 1995, 1258, (2), 159-168. (66) Lang, D. H.; Yeung, C. K.; Peter, R. M.; Ibarra, C.; Gasser, R.; Itagaki, K.; Philpot, R. M.; Rettie, A. E. Isoform specificity of trimethylamine N-oxygenation by human flavin containing monooxygenase (FMO) and P450 enzymes: Selective catalysis by FMO3. Biochem. Pharmacol. 1998, 56, (8), 1005-1012. (67) Swann, J. R.; Tuohy, K. M.; Lindfors, P.; Brown, D. T.; Gibson, G. R.; Wilson, I. D.; Sidaway, J.; Nicholson, J. K.; Holmes, E. Variation in antibiotic-induced microbial recolonization impacts on the host metabolic phenotypes of rats. Journal of Proteome Research 2011, 10, (8), 3590-3603. (68) Wu, J. F.; Holmes, E.; Xue, J.; Xiao, S. H.; Singer, B. H.; Tang, H. R.; Utzinger, J.; Wang, Y. L. Metabolic alterations in the hamster co-infected with Schistosoma japonicum and Necator americanus. International Journal for Parasitology 2010, 40, (6), 695-703. (69) Hall, D. A. Histidine α-deaminase and the production of urocanic acid in the mammal. Biochem. J. 1952, 51, (4), 499-504. (70) Raistrick, H. On a new type of chemical change produced by bacteria. The conversion of histidine into urocanic acid by bacteria of the coli-typhosus group. Biochem. J. 1917, 11, (1), 71-77. (71) Rubio-Aliaga, I.; de Roos, B.; Sailer, M.; McLoughlin, G. A.; Boekschoten, M. V.; van Erk, M.; Bachmair, E.-M.; van Schothorst, E. M.; Keijer, J.; Coort, S. L.; Evelo, C.; Gibney, M. J.; Daniel, H.; Muller, M.; Kleemann, R.; Brennan, L. Alterations in hepatic one-carbon metabolism and related pathways following a high-fat dietary intervention. Physiological Genomics 2011, 43, (8), 408-416. (72) Newgard, C. B.; An, J.; Bain, J. R.; Muehlbauer, M. J.; Stevens, R. D.; Lien, L. F.; Haqq, A. M.; Shah, S. H.; Arlotto, M.; Slentz, C. A.; Rochon, J.; Gallup, D.; Ilkayeva, O.; Wenner, B. R.; Yancy, W. S., Jr.; Eisenson, H.; Musante, G.; Surwit, R. S.; Millington, D. S.; Butler, M. D.; Svetkey, L. P. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metabolism 2009, 9, (4), 311-326. (73) Tanaka, T.; Kono, T.; Terasaki, F.; Yasui, K.; Soyama, A.; Otsuka, K.; Fujita, S.; Yamane, K.; Manabe, M.; Usui, K.; Kohda, Y. Thiamine prevents obesity and obesity-associated metabolic disorders in OLETF Rats. J. Nutr. Sci. Vitaminol. 2010, 56, (6), 335-346. (74) Leung, L. H. Pantothenic acid as a weight-reducing agent：fastine without hunger, weakness and ketosis. Med. Hypotheses 1995, 44, (5), 403-405. (75) Chaykin, S.; Bloch, K. The metabolism of nicotinamide-N-oxide. Biochimica Et Biophysica Acta 1959, 31, (1), 213-216. (76) Shibata, K.; Matsuo, H. Correlation between niacin equivalent intake and urinary excretion of its metabolites,

N'-methylnicotinamide,

N'-methyl-2-pyridone-5-carboxamide,

and

N'-methyl-4-pyridone-3-carboxamide, in humans consuming a self-selected food. American Journal of Clinical Nutrition 1989, 50, (1), 114-119.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

List of Tables: Table 1. Data for rat phenotypes, serum chemistry and plasma fatty acids from these fed with control and HFD diets. Table 2. Significantly changed metabolites with corresponding correlation coefficients in plasma, hydrophilic and lipophilic extracts of liver tissues. Table 3. Significantly changed urinary metabolites with corresponding correlation coefficients between the control and HFD groups.

List of Figures: Figure 1. Typical 600 MHz 1H NMR spectra of plasma (P), urine (U), and liver aqueous extracts (L) from control (C) and HFD (H) groups. The dotted regions were vertically expanded 16, 2 and 4 times in the spectra of plasma and urine, 16 and 4 times in the spectra of liver extracts. The keys for metabolites are given in Table S2. Figure 2. OPLS-DA scores plots (left) and corresponding loadings plots (right) derived from the 1H NMR data for plasma (A), hydrophilic extracts (B) and lipophilic extracts (C) of liver from rats (n=12) fed with HFD (blue squares) and control (red squares) diets. Figure 3. Data for the percentage of unsaturated fatty acids (UFA%), polyunsaturated fatty acids (PUFA%), monounsaturated fatty acids (MUFA%), the PUFA-to-MUFA ratio (PUFA/MUFA), and the PUFA-to-UFA ratio (PUFA/UFA) calculated from (A) NMR and (B) GC-FID-MS results for blood plasma and liver of rats fed with HFD and control diets (n=12). * p

High-Fat Diet Induces Dynamic Metabolic Alterations in Multiple

Recommend Documents