Article pubs.acs.org/jpr
Comparative Nontargeted Profiling of Metabolic Changes in Tissues and Biofluids in High-Fat Diet-Fed Ossabaw Pig Kati Hanhineva,*,† Thaer Barri,‡ Marjukka Kolehmainen,† Jenna Pekkinen,† Jussi Pihlajamak̈ i,† Arto Vesterbacka,† Gloria Solano-Aguilar,§ Hannu Mykkan̈ en,† Lars Ove Dragsted,‡ Joseph F. Urban, Jr.,§ and Kaisa Poutanen†,∥ †
Institute of Public Health and Clinical Nutrition, University of Eastern Finland, P.O. Box 1627, FIN-70211 Kuopio, Finland Department of Nutrition, Exercise and Sport, University of Copenhagen, Rolighedsvej 30, DK-1958 Frederiksberg-C, Denmark § U.S. Department of Agriculture, Agricultural Research Service, Beltsville Human Nutrition Research Center, Diet, Genomics, and Immunology Laboratory, Beltsville, Maryland 20705, United States ∥ VTT Technical Research Centre of Finland, P.O. Box 1000, Tietotie 2, FI-02044 VTT, Finland ‡
S Supporting Information *
ABSTRACT: Typical clinical biomarker analyses on urine and plasma samples from human dietary interventions do not provide adequate information about diet-induced metabolic changes taking place in tissues. The aim of this study was to show how a large-scale nontargeted metabolomic approach can be used to reveal metabolite groups for generating new hypotheses of obesity-related metabolic disturbances produced in an animal model. A large spectrum of metabolites in the semipolar region, including small water-soluble molecules like betaine and dihydroxyindole, and a wide range of bile acids as well as various lipid species were detected. The high-fat diet influenced metabolic homeostasis of Ossabaw pigs, especially the lipid metabolome, throughout all the analyzed sample types, including plasma, urine, bile, liver, pancreas, brain cortex, intestinal jejunum and proximal colon. However, even dramatic metabolic changes in tissues were not necessarily observed in plasma and urine. Metabolite profiling involving multiple sample types was shown to be a feasible method for the examination of a wide spectrum of metabolic species extending from small water-soluble metabolites to an array of bile acids and lipids, thus pointing to the pathways of metabolism affected by the dietary treatment. KEYWORDS: metabolite profiling, metabolomics, high-fat diet, nutrition, pig
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INTRODUCTION Human dietary interventions have the inherent restriction in limiting analysis mostly to easily accessible biofluid samples. Although analysis of urine and/or plasma may be linked to clinical phenotypes and complications (e.g., for biomarker discovery), they are not adequate to assess the metabolic effects induced by dietary change in various tissues and organs. More studies at the tissue level could reveal effects of dietary change on metabolic homeostasis and thereafter the function of vital organs that ultimately reflect whole body health status. This can be achieved by using animal models. Large-scale metabolomic approaches offer a wider analyte spectrum than available with typical clinical biomarkers. Improvements in mass spectrometry technologies within the past decade have provided an extremely accurate, sensitive, and high-throughput approach to explore the content of small metabolites (50%), and with a peak intensity value >10. This data set (containing 141 markers) represented those metabolite signals that showed the maximum difference between the two diets at least in one of the examined sample types, and that had a high enough signal in the MS for structural elucidation by MS/MS analysis. The list of assigned metabolites is available as Supporting Information (Table S1). The marker group was clustered according to the occurrence of the metabolite signal in the different samples. The quality threshold (QT) clustering resulted in seven clusters holding markers with similar features. The clusters are shown as heat map plots in Figure 4. The largest cluster (Cluster 1) in the analysis is formed of mainly bile acids and phosphatidylcholines (PC) that are highly abundant in the bile samples, and scattered among other tissues. This cluster includes two highly abundant PCs which are present also in the plasma sample, the PC(38:4) and PC(36:4) (Figure 5). These two lipids have high intensity in the plasma, bile, and liver samples and show lower signals in pigs fed the HF diet. These PCs are also detected in smaller amounts in the other tissues with HF diet-induced lower intensity in the pancreas, jejunum, and proximal colon but higher levels in the brain cortex from pigs fed a HF diet. In addition, other phosphatidylcholines such as PC(36:5) and PC(38:6) were detected at various levels in different sample types. For example, PC(36:5) is lower and PC(38.6) higher in liver from pigs fed a HF diet. The most abundant bile acid detected in the analysis is glycocholic acid that is represented in Cluster 1 with several different ions including the pseudomolecular ion, dimer, and fragment ions after several losses of water. All of the ions are higher in the bile and jejunum samples from pigs fed a HF diet (Figure 5). Other minor metabolites classified as bile acids without further identification were detected based on the elution region, elemental composition, and fragmentation pattern and are distributed specifically among the different organs with either higher or lower signals in tissues from pigs fed a HF diet (Figure 5). Cluster 2 contains the most abundant lysophosphatidylcholines (LPC) found in all the sample types except urine, and are higher in plasma, bile, and liver from the HF-fed pigs. LPC(18:1) and LPC(18:2) are higher and LPC(16:1) and
significant (p < 0.03) increase in total fat in the HF group compared to pigs fed the basal diet (Figure 1A), as well as a
Figure 1. (A) Total body fat (kg) determined by DXA scan at week 35. (B) Body weight (kg) at week 37. Mean weight (n = 5/group) ± SEM are plotted.
significant difference in body weight at week 37 (Figure 1B). Serum total, HDL and LDL cholesterol (p < 0.001) and glucose (p < 0.01) levels in pigs fed the HF diet at week 35 differed significantly from those in pigs fed the basal diet (Table 2). Serum insulin, triglyceride, LDH, ALT, AST, and GGT levels between the two groups were not significantly different (Table 2). Table 2. Biochemical Profile in Serum from 24-h-Fasted Pigsa high fat
p-value
35.6 ± 0.6 92.20 ± 7.4
42.2 ± 1.1* 201.4 ± 22.6*
50%) for inclusion of metabolites that had a pronounced change in at least one sample type represented only ca. 2% of the total number of signals detected. Thus, only the most relevant changes were reported to demonstrate clustered effects of a HF diet on a variety of tissues and biofluids. However, smaller changes in metabolites of unknown biological relevance were widely observed, and analysis of these metabolites could provide further insight into HF diet-induced changes in metabolism. Notably, in the tissue samples we could see numerous metabolite alterations which were not detected in the fasting plasma, thus underlining the importance of animal studies which enable the inclusion of tissue samples. Our analysis pointed out the metabolite classes that were most affected by the HF diet, for example, bile acids and betaine and related metabolites, and would benefit from focused examination on multiple tissues and biofluids (including muscle and adipose tissue that were not included in this study) to provide a comprehensive view of energy metabolism and dysfunction to better understand the relationship between metabolic disorders and pathophysiological events related to obesity.
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CONCLUSION This study illustrates the impact of a high-fat diet on metabolome both at the tissue and biofluid level, and suggests that the multisample nontargeted metabolite profiling approach may be useful in analysis of the molecular mechanisms of dietary impact on whole body homeostasis. The methodology used is feasible for concomitant examination of a vast pool of metabolites from different tissues and biofluids taken from experimental animals fed different diets.
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MATERIALS AND METHODS
Maintenance of Animals and Experimentation
Ossabaw miniature female swine (sus scrofa) were obtained from the Indiana University Ossabaw Production Unit (Indiana University School of Medicine, Indianapolis, IN). According to local Animal Care and Use Committee (ACUC) recommendations, all pigs in the breeding unit were serologically tested and confirmed negative for Porcine Reproductive and Respiratory Syndrome virus, swine Influenza virus serotypes 3988
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∼30 cm from the ileal/cecal junction, jejunum at the midgut, frontal lobe of the brain cortex and were placed in cryotubes for immediate freezing in liquid nitrogen. Biofluids (urine and bile) were aseptically collected with a 18 gauge needle attached to a syringe and were frozen immediately. Blood samples were collected aseptically the day before necropsy from the right jugular fossa of 24-h-fasted pigs using a 18-gauge needle and a BD vacutainer (Franklin Lakes, NJ) for serum and a Kendall vacutainer with 7.5 mg EDTA (Tyco Healthcare, Mansfield, MA) for plasma that were separated after centrifugation and stored at −80 °C. All samples were transported with dry ice (−78.5 °C) to Denmark for metabolomics analysis and stored at −80 °C.
mented at a probe voltage of 3.2 kV. The selected m/z range was from 50 to 1000 Da. Mass spectral scan time and interscan delay were, respectively, 0.08 and 0.02 s. Leucine enkephalin was infused intermittently every 10 s and used for accurate online mass calibration. To get more structural information, a low-to-high collision energy ramp (MSE mode) was implemented for selected samples. The MSE collision energy was ramped between 10 and 35 V during each individual scan of 0.08 s with an interscan delay of 0.02 s. In some samples, MS/ MS fragmentation was performed for further confirmation of marker identity. The mass resolution for precursor and product ions scans was 8300.
Biochemical Parameters
Data Processing, Chemometrics and Statistical Analysis
Serum triglycerides (TG), total cholesterol (CHOL), lowdensity lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyltransferase (GGT), lactate dehydrogenase (LDH), glucose and insulin levels with corresponding standards were measured using a VetAce Clinical chemistry analyzer (Alfa Wasserman Diagnostic Technologies, West Caldwell, NJ).
The LC−MS data were extracted using the MarkerLynx software (Waters). Peaks were collected by automatically calculating the peak width and baseline noise. The intensity threshold for the collected peaks was set to 20, with mass tolerance of 0.05 Da in 0.05 min retention time window. Isotopic peaks were excluded. The data set was evaluated for the presence of zero values to distinguish the true absent values from the erroneous zeros caused by the software.65,66 The complete data set was first observed by ranking metabolites based on their abundance in each of the different sample types, and hierarchical clustering was performed. The heat maps were made with R 2.7.2 using heatmap.2-function from the gplotspackage. The data for the heat maps were hierarchically clustered both by markers (rows) and samples (columns). Next, the data was observed by ranking the whole data set across all the sample types based on the p-values from Student’s two-tailed t-test to each sample type (Microsoft Excel). The 2000 most differential markers throughout the entire data set were subjected to the partial least-squares discriminant analysis (PLS-DA) using the software SIMCA-P+ 12. All the data was logarithmically transformed using ten as base and pareto-scaled. The PLS-DA models were validated using SIMCA-P+’s internal 7-fold cross-validation. Lastly, the data set was reduced to contain only such metabolites that had the p-value 50%, and intensity value for the metabolite marker >10. The quality threshold (QT) cluster analysis was performed for this data set by the open-source software Multi experiment Viewer (http://www.tm4.org/http://www.tm4. org/).
Sample Preparation for Metabolite Profiling
Plasma and urine samples were prepared in 96-well Sirrocco plate as described previously. 63 Briefly, samples were sandwiched between two volumes (each 90 μL) of extraction solvent (1:1, acetonitrile/methanol). The extract was then collected and the precipitate was washed twice with two more solvent volumes. The pooled extract was evaporated to dryness and the residue was dissolved in 100 μL of MeOH/H2O (1:1). The frozen tissue samples stored in cryotubes (liver, pancreas, brain cortex, intestinal jejunum, and proximal colon) were ground to fine powder with mortar and pestle in liquid nitrogen. Aliquots of the frozen tissue were weighed to prechilled eppendorf tubes (∼100 mg aliquots) and kept frozen in liquid nitrogen until extraction. The extraction was performed in a two-step procedure as described by Masson et al., 201064 with some modifications. First, MeOH:H2O (1:1) was added in a ratio of 3 μL of solvent/mg frozen tissue (∼100 mg of tissue in each sample) to extract the polar and semipolar metabolites. The sample was sonicated at room temperature for 15 min, vortexed, centrifuged, and the supernatant was collected. Next, dichloromethane/MeOH (1:1) was added to the residue in the same ratio as in the first step to extract lipophilic metabolites. After sonication, vortexing and centrifuging, the supernatant was collected to a separate tube, and the two extracts were evaporated to dryness in a speedvac centrifuge. Both samples were redissolved in 200 μL of MeOH/H2O (1:1), and 50 μL aliquots from the two extraction steps were combined and centrifuged prior to the LC−MS analysis.
Metabolite Identification
The metabolite signals showing altered diet-dependent expression profiles were identified by retention time and mass spectra using our in-house database of authentic standards. The elemental composition of the molecular markers was calculated in the MassLynx software, and compared against common databases such as the Human Metabolome Database (http:// www.hmdb.ca), Metlin (http://metlin.scripps.edu/), SciFinder Scholar (SciFinder ScholarTM 2007), and ChemSpider (http://www.chemspider.com/). The MS/MS fragmentation spectra of the examined compounds were compared with candidate molecules found in databases, and verified with commercial standard compounds when available. The detailed list of assigned metabolic features is available as Supporting Information.
UPLC−qTOF-MS Analysis
An ultraperformance liquid chromatography (UPLC) system coupled to quadruple time-of-flight (Premier QTOF) mass spectrometer (Waters Corporation, Manchester, UK) was used for sample analysis. The analysis was performed as described previously for “Chromatography Method II”.63 In short, the chromatographic column used was HSS T3 C18 with mobile phase consisting of 0.1% formic acid in water (A) and 0.1% formic acid in “70% acetonitrile/30% methanol” (B). Positive ion acquisition mode in electrospray ionization was imple3989
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ASSOCIATED CONTENT
S Supporting Information *
Assigned metabolite list. This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*E-mail: kati.hanhineva@uef.fi. Tel: +358-40-3552364. Fax: +358-17 162 131. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The funding from the Nordforsk Nordic Centre of Excellence projects “HELGA − whole grains and health” and “SYSDIET − Systems biology in controlled dietary interventions and cohort studies” is gratefully acknowledged, as well as funding from Academy of Finland. A portion of the study was funded by USDA/ARS Project Plan 1235-51530-053-00D. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA; the USDA is an equal opportunity provider and employer.
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