Biometals (2014) 27:1137–1147 DOI 10.1007/s10534-014-9774-z
Metabolic changes associated with selenium deficiency in mice Beata Mickiewicz • Michelle L. Villemaire Linda E. Sandercock • Frank R. Jirik • Hans J. Vogel
•
Received: 25 June 2014 / Accepted: 30 June 2014 / Published online: 11 July 2014 Ó Springer Science+Business Media New York 2014
Abstract Selenium (Se), which is a central component for the biosynthesis and functionality of selenoproteins, plays an important role in the anti-oxidative response, reproduction, thyroid hormone metabolism and the protection from infection and inflammation. However, dietary Se effects have not well been established to date and the available studies often present contradictory results. To obtain a better understanding of Se intake and its influence on the metabolism of living systems, we have utilized a metabolomics approach to gain insight into the specific metabolic alterations caused by Se deficiency in mice. Serum samples were collected from two groups of C57BL/6 mice: an experimental group which was fed a Se-deficient diet and controls consuming normal chow. The samples were analyzed by 1H nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry. The resulting metabolite data were examined separately for both analytical methods and in a combined manner. By applying multivariate statistical analysis we were able
B. Mickiewicz H. J. Vogel (&) Bio-NMR-Centre, Department of Biological Sciences, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada e-mail: [email protected] M. L. Villemaire L. E. Sandercock F. R. Jirik Department of Biochemistry and Molecular Biology, University of Calgary, 3330 Hospital Dr. NW, Calgary, AB T2N 4N1, Canada
to distinguish the two groups and detect a metabolite pattern associated with Se deficiency. We found that the concentrations of 15 metabolites significantly changed in serum samples collected from Se-deficient mice when compared to the controls. Many of the perturbed biological pathways pointed towards compensatory mechanisms during Se deficiency and were associated with amino acid metabolism. Our findings show that a metabolomics approach may be applied to identify the metabolic impact of Se and reveal the most impaired biological pathways as well as induced regulatory mechanisms during Se deficiency. Keywords Selenium Biomarkers Metabolomics Multivariate data analysis
Introduction Selenium (Se), a nutritionally important microelement for all animals, is available in many foods (i.e. brazil nuts, pinto beans, tuna) and in the form of a dietary supplement (Reilly 2006). Se is known to play a critical role in reproduction, thyroid hormone metabolism, protection from oxidative damage, infection and inflammation mainly as a result of its incorporation into selenoproteins (Sunde 2012). Selenoproteins contain selenocysteine (Sec), a cysteine analogue, which is co-translationally inserted during protein synthesis (Gladyshev 2001). In humans, there are 25
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genes encoding for selenoproteins (Kryukov et al. 2003), of which 24 exist as Sec-containing proteins in mice and rats. It has been shown that selenoprotein expression is essential for life as the lack of SectRNASec, which is required for selenoprotein translation, was embryonic lethal in mice (Bosl et al. 1997). The most abundant selenoproteins in mammals are the antioxidant enzymes, such as the glutathione peroxidases (GPX) and thioredoxin reductases (TRR) (Hawkes and Alkan 2010). GPXs eliminate hydrogen peroxide and lipid hydroperoxides to reduce the destructive influence of reactive oxygen species (ROS) (Sunde 1990). Similarly, TRR regulates cellular redox status by reducing oxidized thioredoxin and its homologs in order to attenuate ROS levels in the living systems (Tamura and Stadtman 1996). Therefore, Se deficiency is associated with prolonged oxidative stress and with increased ROS concentration in the body. The beneficial antioxidant properties of Se have led to a number of epidemiological studies focusing on the relationship between Se and cancer risk (Whanger 2004), as well as on the impact of Se on neurodegenerative and cardiovascular diseases (Sanmartin et al. 2011). Several studies conducted in animals have indicated that Se can improve anticancer efficacy and is very effective in the reduction of tumor incidents (Ip 1998; Warrington et al. 2013). However, the largestever randomized placebo-controlled trial, the Selenium and Vitamin E cancer prevention trial (SELECT), conducted in 35,533 men showed that long term Se supplementation in a Se-replete population did not significantly reduce the incidence of prostate cancer (Lippman et al. 2009). Similar results were reported in a Canadian study (Fleshner et al. 2011). While different outcomes have been shown for some other cancers (Vinceti et al. 2014) the weight of the current evidence does not support that Se supplements aid in cancer prevention in humans. Additionally, it has been speculated that Se could act as an antidiabetic agent, however, recent studies have shown that exposure to higher levels of Se may actually increase insulin resistance and the risk for type 2 diabetes (Stranges et al. 2007; Zhou et al. 2013). In view of these results it is important to clearly understand the influence of dietary Se on metabolic pathways in humans and animals. Metabolomics, which is primarily focused on the identification and quantification of small molecules in
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the metabolome, provides an unbiased opportunity to look for metabolic changes in different organisms. Metabolomics relies on analytical techniques such as nuclear magnetic resonance spectroscopy (NMR) and/ or mass spectrometry (MS) (Dunn et al. 2011b; Lindon et al. 2004; Nicholson et al. 1999). The data are usually interrogated by multivariate data analysis (Eriksson et al. 2006). An advantage of NMR is that this technique is quantitative, highly reproducible and can be executed in high-throughput large subclinical settings (Lindon et al. 2005; Soininen et al. 2009). However, NMR is associated with a relatively low sensitivity and thus by using more sensitive methodologies like gas chromatography-MS (GC–MS) more metabolites/metabolic features can be detected in the sample. Metabolic features are chemical signatures identified in MS experiments but not identified in the currently available metabolite libraries. Nonetheless, it has been shown that combining different metabolic signatures and creating a larger biopattern improves the discriminatory and predictive power significantly (Garnero et al. 2000). In the present study we have therefore integrated 1H NMR and GC–MS results to describe specific metabolic changes associated with Se deficiency in a mouse model. We created a dataset for multivariate statistical analyses including unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA) (Eriksson et al. 2006). The latter approach was used to separate the metabolic variation into two parts: one component which describes the variation correlated with Se deficiency and a second one which encapsulates the metabolic variation that is not related (orthogonal) to the absence of Se in the diet. As such, OPLS-DA can be applied to identify potentially important metabolites and the most impaired metabolic pathways during Se deficiency.
Methods Animals and diets The study was approved by the University of Calgary Health Sciences Animal Care Committee and was completed within the guidelines of the Canadian Council on Animal Care. C57BL/6 mice, bred in house, were divided into two groups: experimental diet and control diet. The two diets have nearly
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identical ingredients; with the only difference being that the experimental diet lacks Se. The food was obtained from Dyets Inc., Dyet #116061 and #116062 (Bethlehem, Pennsylvania, USA). Before the mouse chow was introduced into the study, it was gamma irradiated. Bags of chow were kept frozen until needed. Items such as nestlets, hardwood bedding and water were tested for Se content. Water was tested at the Centre for Toxicology, University of Calgary (Alberta, Canada). Nestlets were tested at ALS Environmental (Calgary, Alberta, Canada) and hardwood bedding was tested by Eurofins Scientific Inc. (Calgary, Alberta, Canada). All three items were found to be free of Se. Female mice were put on either a control or Se free diet 4 weeks prior to the introduction of a stud male. Once pregnant, the studs were removed, and after weaning the litters were placed on the same diet as the mother. The mice used in this study were kept on their respective diets for a total of 32 weeks. Sample collection At 32 weeks of age, mice were euthanized using CO2 inhalation. Their final weight was recorded before euthanasia. Immediately after asphyxiation, cardiac punctures were performed and blood was taken. Blood samples were held at room temperature for 10 min to allow clotting. Blood samples were then spun at 3,500 rpm for 5 min and serum aliquots were collected and stored at -80 °C prior to analysis. Se analysis of serum samples was carried out by the London Laboratory Services Group (London, Ontario, Canada). NMR The process of NMR spectral acquisition and sample preparation has already been described in detail (Mickiewicz et al. 2014). Briefly, samples were thawed and filtered twice (3 kDa NanoSep microcentrifuge filters). The filtrates were then brought to 400 ll by adding phosphate buffer (NaH2PO4, pH 7.0), 2,2-dimethyl-2-silapentane-5-sulfonate (DSS), sodium azide and D2O. The final concentration of DSS was used as an internal standard and it was 0.5 mM for each sample. The pH of the samples was adjusted to 7.0 ± 0.04. The samples were analyzed by high resolution one-dimensional 1H NMR spectra on a 600 MHz Bruker Ultrashield Plus NMR spectrometer
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(Bruker BioSpin Ltd., Canada) using a standard Bruker 1D spectroscopy presaturation pulse sequence (noesypr1d) with optimal water suppression and a mixing time of 100 ms (Nicholson et al. 1995; Weljie et al. 2006). The spectra were manually corrected (phasing, baseline correction, referencing to the DSS peak at 0.0 ppm) and analyzed using the targeted profiling approach (Weljie et al. 2006) in the Chenomx NMR Suite 7.5 software (Chenomx Inc., Edmonton, Alberta, Canada). The Human Metabolome Database (version 2.5) and 2D NMR spectra [total correlation (TOCSY) spectra and 1H, 13C heteronuclear single quantum coherence (HSQC) spectra] were used to confirm and verify chemical shift assignments. GC–MS Samples were extracted in a two-phase mixture of chloroform/methanol to separate the polar and nonpolar fractions. For analysis of free polar metabolites the former was derivatized by using methoxyaminehydrochloride in pyridine solution and then N-methylN-(trimethylsilyl) trifluoroacetamide as a silylating agent. For fatty acid methyl ester (FAME) analysis, the aliquots of the organic phase containing the nonpolar fraction were collected in an eppendorf tube and evaporated under airflow at room temperature. Next, boron fluoride-methanol was added as an acid-catalysed esterification factor. Finally, all samples (polar fraction and FAME) were diluted with hexane, centrifuged to remove any solid particles and 200 lL of supernatant was prepared for GC injection. Pooled quality control (QC) samples were prepared in the same manner. GC–MS was performed on an Agilent chromatograph 7890 A (Agilent Technologies Canada Inc, Mississauga, Ontario, Canada) coupled with a Waters GCT mass spectrometer, using GC-TOF–MS methodology. The MS was operated in a range of 50–800 m/z. For quality assurance and to access the precision and repeatability of metabolite detection the spectra of five QC samples were acquired at the beginning of the experiment and after every fourth sample. Mass spectra of polar fraction were processed using Metabolite Detector software (version 2.06, Technische Universita¨t Carolo-Wilhelmina zu Braunschweig, Braunschweig, Germany). For metabolite identification the GOLM metabolite database (Kopka et al. 2005) and NIST database (Babushok et al. 2007) were used. FAME data analysis was performed in
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MET-IDEA version 2.08 software (Broeckling et al. 2006). Data preprocessing and statistical analysis Metabolite data obtained from the analysis of 1H NMR and GC–MS experiments were pre-processed [median fold change normalization, logarithmic transformation, centering and unit variance scaling (van den Berg et al. 2006)] and examined separately as well as in a combined manner (see Fig. 1). For the metabolites in common detected in both, the 1H NMR and GC–MS datasets (polar and FAME fraction), the mean values were calculated and incorporated into the combined dataset (Booth et al. 2011) and then the remaining 1H NMR metabolites and GC–MS metabolites/metabolic features/fatty acids were included. For quality assurance of the GC–MS data the relative standard deviation (RSD) of the QC samples was calculated and metabolites/metabolic features with an RSD of more than 30 % for the polar fraction and 50 % for FAME were excluded from further statistical analysis (Dunn et al. 2011a). All zero values were considered as missing values and all metabolites or metabolic features with more than 50 % missing values were excluded from the statistical analysis. The PCA and OPLS-DA models were performed in the SIMCAP ? 12.0.1 software (Umetrics, Sweden). The PCA method was used to summarize the variation in each dataset and highlight outliers, i.e. samples situated outside of the 95 % confidence of the Hotelling’s T-squared distribution (elliptic or spherical area in the multicomponent score scatter plots) (Eriksson et al. 2006). The OPLS-DA models were implemented for each dataset separately. For the 1H NMR data the supervised model was based on potentially important metabolites selected in 2-sample t tests with a p value of less than 0.25 as a threshold. The OPLS-DA models for GC–MS data and integrated 1H NMR/GC–MS dataset were based on metabolites/metabolic features/ fatty acids chosen in the variable importance projection (VIP). Variables with VIP values larger than one were selected (Eriksson et al. 2006). To evaluate the quality of the OPLS-DA models the R2Y (percentage of variation explained by the model) and Q2 (predictive power of the model) metrics were calculated. The difference between R2Y and Q2 indicates the goodness of fit of the OPLS-DA model. The Q2 value was estimated by threefold cross-validation (CV) (Picard
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and Cook 1984). The predictive values of Y-variables (Y-predCV) were used to calculate the sensitivity, specificity, accuracy and the area under the receiver operator curve (AUROC) in Metz ROC Software (the University of Chicago, USA). Additionally, the OPLS-DA models were evaluated by cross validated analysis of variance (CV-ANOVA) in SIMCAP ? software. In order to describe potential serum biomarkers that are associated with Se deficiency, the OPLS-DA regression coefficients were calculated for the combined dataset and only compounds with significant changes in concentration (p value less than 0.05) were considered. Next, based on the potentially important metabolites a representative metabolic pathway network was constructed to identify the most perturbed biological pathways in the Se-deficient mice [MetaboAnalyst 2.0 software (Xia and Wishart 2011)].
Results Analysis of Se levels in serum samples In total, 12 serum samples were collected for this study: six samples were obtained from Se-deficient mice and six from normal controls. We have found in previous studies with deficiency of the vitamins that this sample size is adequate (Duggan et al. 2011). Body weight was not significantly different between both groups of animals, and no behavioural abnormalities were observed in the Se-deficient animals. The average Se level in Se-deficient mice was 81.33 ± 7.09 lg/L while in the control group it was 430.33 ± 24.58 lg/L. Thus, the level of Se in blood samples collected from Se-deficient mice was *5 times lower than the level in the control group. 1
1
H NMR and GC–MS spectral analysis
H NMR experiments could be performed for all samples. However, three samples (two controls and one Se-deficient sample) had to be excluded from the GC–MS experiment due to insufficient sample volume. A total of 47 metabolites were assigned and quantified in each 1H NMR spectrum and 207 metabolites/metabolic features were detected for the polar fractions and 28 fatty acids for the non-polar fractions during GC–MS analysis
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Fig. 1 Flow chart for the metabolomics analysis used in this study
Statistical modeling After data preprocessing and quality assurance tests a total of 137 variables were retained for the multivariate statistical analysis: 46 metabolites from 1H NMR data, 73 metabolites/metabolic features from the polar fractions of the GC–MS data and 18 fatty acids from the non-polar fractions of the GC–MS data. 1
H NMR and GC–MS datasets
The PCA score scatter plots for the 1H NMR and GC– MS datasets (Fig. 2) did not show any outliers. Interestingly, the best separation between Se-deficient samples and controls was obtained for the polar fraction analyzed by GC–MS. The variation in this model was defined by three principal components, i.e. PC1 = 24.3 %, PC2 = 21.1 % and PC3 = 13.8 %. However, it should be noted that the variation between the samples found for the polar fractions of the GC– MS data was explained by the biggest number of
principal components when compared to other datasets: one and two principal components for 1H NMR, and FAME data, respectively. It is known that each principal component represents one form of the systematic variation of a dataset. Therefore, if the number of principal components is small and already sufficient to reveal the studied variation within the samples, then it indicates a strong model and good correlation between variables and observations (Eriksson et al. 2006). To better reveal the metabolic patterns related to Se deficiency in the groups of mice, supervised OPLSDA method was carried out. Figure 3 presents the OPLS-DA score scatter plots for the 1H NMR and GC–MS datasets. Both sample groups are well distinguished along the first PLS component in all datasets which indicates a very strong relationship between the metabolic data and Se deficiency. High values for the R2Y and Q2 metrics were calculated for each OPLSDA model: 0.82 and 0.74 for 1H NMR data, 0.90 and 0.81 for the polar fractions of the GC–MS data, 0.83
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and 0.75 for the FAME fractions of the GC–MS data, which indicates the good predictive ability of the models. Additional information of statistical measures for Se-deficient and control mice models based on the 1 H NMR and GC–MS data is provided in Table 1. Integrated 1H NMR/GC–MS dataset Based on reduced dataset for the variables with a VIP values larger than one we found that the combined 1H NMR/GC–MS dataset consisted of the defined metabolites only: three metabolites in common, 12 nonshared 1H NMR metabolites, 24 non-shared GC–MS metabolites and 9 fatty acids. The combined OPLSDA model clearly separates the Se-deficient group from the controls (Fig. 3d) and is characterized by high values of validation metrics, R2Y = 0.87, Q2 = 0.84 and AUROC = 1.0 (Table 1). The difference between the R2Y and Q2 parameters for this integrated model is the smallest compared to the OPLS-DA models for separate datasets which indicates a better fit for the combined OPLS-DA model. Biomarkers The supervised analysis of the integrated 1H NMR/ GC–MS dataset revealed 15 metabolites for which concentrations changed significantly in serum samples collected from the Se-deficient mice when compared to the controls (Fig. 4). It was observed that the levels of serine, threonine, phenylalanine, methionine, creatine, fructose, sorbose, sucrose, proline, glycine, isoleucine and pyruvate were increased while concentrations of stearic acid, cis-10-pentadecanoic acid and palmitic acid were decreased. These potentially important metabolites indicate the most impaired pathways during Se deficiency such as those involving glycine, serine and threonine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis and phenylalanine metabolism (Table 2). Fig. 2 The PCA score scatter plots representing the metabolomics data obtained for serum samples: a 1H NMR dataset, b polar fractions of the GC–MS dataset and c non-polar fractions of the GC–MS dataset. Samples were collected from Sedeficient mice (black dots) and normal controls (green dots)
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Discussion Although, it is known that Se deficiency affects the redox status of living systems, the actual metabolic
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Fig. 3 The OPLS-DA score scatter plots obtained for the metabolomics data for serum samples: a 1H NMR dataset, b polar fractions of the GC–MS dataset, c non-polar fractions of the GC–MS dataset and d 1H NMR/GC– MS combined dataset. Samples were collected from Se-deficient mice (black dots) and normal controls (green dots)
Table 1 Comparison of statistical measures for Se-deficient and control mice models based on the metabolomics data Metabolomics data
R2Y
Q2
P value
Sensitivity:specificity
1
0.82
0.74
2.2 9 10-3
0.83:1.0
1.0:0.86
0.92
1.0
GC–MS data
0.90
0.81
6.7 9 10-3
1.0:1.0
1.0:1:0
1.0
1.0
Polar fraction GC–MS data
0.83
0.75
1.6 9 10-2
1.0:0.80
0.83:1.0
0.90
1.0
0.87
0.84
4.0 9 10-3
1.0:1.0
1.0:1:0
1.0
1.0
H NMR dataset
PPV:NPV
ACC
AUROC
Non-polar fraction 1
H NMR/GC–MS combined dataset
R2Y variation explained by the model, Q2 predictive ability of the model, PPV positive predictive value, NPV negative predictive value, ACC accuracy, AUROC area under the receiver operating characteristic curve
changes caused by this condition have not been reported so far. Here, we have described a metabolic pattern found in serum samples collected from Sedeficient mice. Even though the difference in serum Se level between the groups was only *5-fold, we nevertheless observed significant metabolic disruption in the Se-deficient mice. The alterations in the amino acid levels suggested the presence of increased protein turnover in the Sedeficient group. It has already been reported that the lack of selenoproteins leads to increased ROS
concentrations (Steinbrenner and Sies 2009) which in turn causes oxidative stress and protein degradation (Chang et al. 2000). On the other hand, individual amino acids can also act as antioxidants themselves, and hence may play an important role in the antioxidative response (Marcuse 1960). Serine, glycine, isoleucine and methionine are well-known for their protective function against oxidative stress (Atmaca 2004; Diaz-Flores et al. 2013; Maralani et al. 2012; Zhao et al. 2013). Additionally, the increased level of methionine points to a dysregulation of the
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Fig. 4 The OPLS-DA regression coefficient plot for the combined 1H NMR/ GC–MS dataset. Positive values of coefficients (the upper part of the diagram) indicate increased metabolite concentrations in serum samples collected from Se-deficient mice (fold change [1) while negative values (the lower part of diagram) present a decrease in metabolite concentrations, as compared to normal controls (fold change\1). Only significant metabolites are shown (p \ 0.05)
Table 2 Affected metabolic pathways in Se-deficient mice Metabolic pathway
p-value
Impact
Total
Hits
Glycine, serine and threonine metabolism
8.7 9 10-4
0.51
31
Glycine, threonine, creatine, serine, pyruvate
Phenylalanine, tyrosine and tryptophan biosynthesis
0.002
0.5
4
Phenylalanine
Phenylalanine metabolism
0.002
0.41
11
Phenylalanine
Methane metabolism
0.006
0.4
9
Glycine, serine
Valine, leucine and isoleucine biosynthesis
0.05
0.33
11
Isoleucine, pyruvate
Cysteine and methionine metabolism
2.9 9 10-4
0.14
27
Methionine, serine, pyruvate
Fructose and mannose metabolism
0.03
0.13
21
Fructose
Aminoacyl-tRNA biosynthesis
0.002
0.13
69
Phenylalanine, glycine, serine, methionine, isoleucine, threonine, proline
The p value was calculated during the pathway enrichment analysis and has been adjusted by Holm-Bonferroni method while the pathway impact value was calculated from the pathway topology analysis. The Total represents the total number of compounds in the pathway and the Hits show matched metabolites detected as potentially important compounds in our study. The analysis was performed in the MetaboAnalyst 2.0 software (Xia and Wishart 2011)
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methionine-homocysteine cycle which intersects with Se metabolism (Uthus et al. 2002). It has been shown that via its effects on the methionine-homocysteine cycle, Se is able to influence redox-methylation balance and DNA methylation (Davis and Uthus 2003; Metes-Kosik et al. 2012). Moreover, the increased level of creatine that we detected in the Se-deficient mice might be linked to the synthesis of one of the key components of the methionine-homocysteine cycle, i.e. S-adenosylmethionine (adoMet). In turn, adoMet acts as an important methyl donor (Detich et al. 2003) and creatine synthesis can account for more than 70 % of the adoMet-derived methyl groups in humans (Stead et al. 2006). Since creatine is itself a good antioxidant (Sestili et al. 2011), it may provide a counter regulatory antioxidant response to Se deficiency. Significantly increased concentrations of pyruvate, fructose, sorbose and sucrose together with a major drop in the fatty acid levels (stearic acid, cis-10pentadecanoic acid and palmitic acid) likely point to expanded glycolysis and lipogenesis. It was previously reported that Se deficiency could dysregulate glucose homeostasis and impair lipid metabolism, with Se being suggested as a factor in the pathogenesis of diabetes and the metabolic syndrome (Labunskyy et al. 2011; Seale et al. 2012). The metabolic changes observed in our investigation support these observations and might confirm a potential role for Se deficiency in the development of diabetes. However, it should be noted that recent clinical trials have shown that supplemental intake of Se above adequate levels may actually increase the risk for type two diabetes mellitus (Lippman et al. 2009; Stranges et al. 2007; Zhou et al. 2013). Therefore, further studies on dietary Se levels need to be carried out to help clarify the role of Se in diabetes. Interestingly, pyruvate, for which the concentration increased in serum samples collected from Se-deficient mice in our study, has also been known to act as an anti-oxidative agent (Liu et al. 2011). During oxidative stress pyruvate is able to directly scavenge ROS through a decarboxylation reaction and mimic the function of GPX; a class of the selenoproteins required for maintenance of the glutathione system. It was reported that pyruvate could maintain the glutathione redox state by increasing the ratio of reduced glutathione versus oxidized glutathione, and was thus able to suppress myocardial damage during conditions
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of oxidative stress (Ryou et al. 2010). Taken together, our study raises the possibility that depletion of selenoproteins, as a result of Se deficiency, was being compensated for through increased concentrations of specific metabolites capable of acting as antioxidants. This study constitutes an initial step in assessing the influence of Se on metabolic regulation in living system using a metabolomics approach. Although it could be argued that this study is relatively small in scope, it is nevertheless the first combined NMR/GC– MS metabolomics study of Se deficiency reported to date. The integrated approach we employed allowed us to identify candidate metabolic pathways that were most impaired by Se deficiency and thus served to capture the impact that Se has on metabolism.
Conclusions Herein, we have demonstrated that a characteristic metabolic profile is seen in mice raised on a Sedeficient diet. Many of the metabolites that we found to be significantly altered in our study have been reported before to be able to act as oxidative stress biomarkers and/or important antioxidants. The ability to detect metabolic changes in serum in response to specific dietary modifications illustrates how metabolomics can provide a sensitive approach to help us better understand the interactions between nutrients and metabolic pathways. Acknowledgments This work was supported through the Armstrong Chair for Molecular Cancer Research, which in turn is supported by the Alberta Cancer Foundation and the University of Calgary. HJV held Scientist Award from Alberta Innovates Health Solutions. FRJ held a Canada Research Chair award.The funding agencies had no influence on the design, analysis and manuscript preparation for this study.
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Biometals (2014) 27:1137–1147 [email protected]: the golm metabolome database. Bioinformatics 21:1635–1638 Kryukov GV, Castellano S, Novoselov SV, Lobanov AV, Zehtab O, Guigo R, Gladyshev VN (2003) Characterization of mammalian selenoproteomes. Science 300: 1439–1443 Labunskyy VM, Lee BC, Handy DE, Loscalzo J, Hatfield DL, Gladyshev VN (2011) Both maximal expression of selenoproteins and selenoprotein deficiency can promote development of type 2 diabetes-like phenotype in mice. Antioxid Redox Signal 14:2327–2336 Lindon JC, Holmes E, Bollard ME, Stanley EG, Nicholson JK (2004) Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers 9:1–31 Lindon JC, Keun HC, Ebbels TM, Pearce JM, Holmes E, Nicholson JK (2005) The consortium for metabonomic toxicology (COMET): aims, activities and achievements. Pharmacogenomics 6:691–699 Lippman SM, Klein EA, Goodman PJ, Lucia MS, Thompson IM, Ford LG, Parnes HL, Minasian LM, Gaziano JM, Hartline JA, Parsons JK, Bearden JD 3rd, Crawford ED, Goodman GE, Claudio J, Winquist E, Cook ED, Karp DD, Walther P, Lieber MM, Kristal AR, Darke AK, Arnold KB, Ganz PA, Santella RM, Albanes D, Taylor PR, Probstfield JL, Jagpal TJ, Crowley JJ, Meyskens FL Jr, Baker LH, Coltman CA Jr (2009) Effect of selenium and vitamin E on risk of prostate cancer and other cancers: the Selenium and Vitamin E cancer prevention trial (SELECT). JAMA 301:39–51 Liu J, Litt L, Segal MR, Kelly MJ, Pelton JG, Kim M (2011) Metabolomics of oxidative stress in recent studies of endogenous and exogenously administered intermediate metabolites. Int J Mol Sci 12:6469–6501 Maralani MN, Movahedian A, Javanmard ShH (2012) Antioxidant and cytoprotective effects of L-Serine on human endothelial cells. Res Pharm Sci 7:209–215 Marcuse R (1960) Antioxidative effect of amino-acids. Nature 186:886–887 Metes-Kosik N, Luptak I, Dibello PM, Handy DE, Tang SS, Zhi H, Qin F, Jacobsen DW, Loscalzo J, Joseph J (2012) Both selenium deficiency and modest selenium supplementation lead to myocardial fibrosis in mice via effects on redoxmethylation balance. Mol Nutr Food Res 56:1812–1824 Mickiewicz B, Duggan GE, Winston BW, Doig C, Kubes P, Vogel HJ (2014) Metabolic profiling of serum samples by 1H nuclear magnetic resonance spectroscopy as a potential diagnostic approach for septic shock. Crit Care Med 42:1140–1149 Nicholson JK, Foxall PJ, Spraul M, Farrant RD, Lindon JC (1995) 750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma. Anal chem 67:793–811 Nicholson JK, Lindon JC, Holmes E (1999) ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29:1181–1189 Picard RR, Cook DR (1984) Cross-validation of regression models. J Am Stat Assoc 79:575–583 Reilly C (2006) Selenium in food and health, 2nd edn. Springer Science ? Business Media, LLC, New York
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1147 Tamura T, Stadtman TC (1996) A new selenoprotein from human lung adenocarcinoma cells: purification, properties, and thioredoxin reductase activity. Proc Natl Acad Sci USA 93:1006–1011 Uthus EO, Yokoi K, Davis CD (2002) Selenium deficiency in Fisher-344 rats decreases plasma and tissue homocysteine concentrations and alters plasma homocysteine and cysteine redox status. J Nutr 132:1122–1128 van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genom 7:142 Vinceti M, Dennert G, Crespi CM, Zwahlen M, Brinkman M, Zeegers MP, Horneber M, D’Amico R, Del Giovane C (2014) Selenium for preventing cancer. Cochrane Database Syst Rev 3:CD005195 Warrington JM, Kim JJ, Stahel P, Cieslar SR, Moorehead RA, Coomber BL, Corredig M, Cant JP (2013) Selenized milk casein in the diet of BALB/c nude mice reduces growth of intramammary MCF-7 tumors. BMC Cancer 13:492 Weljie AM, Newton J, Mercier P, Carlson E, Slupsky CM (2006) Targeted profiling: quantitative analysis of 1H NMR metabolomics data. Anal Chem 78:4430–4442 Whanger PD (2004) Selenium and its relationship to cancer: an update. Br J Nutr 91:11–28 Xia J, Wishart DS (2011) Metabolomic data processing, analysis, and interpretation using metaboanalyst. Curr Protoc Bioinform. doi:10.1002/0471250953.bi1410s34 Zhao J, Liu Y, Jiang J, Wu P, Jiang W, Li S, Tang L, Kuang S, Feng L, Zhou X (2013) Effects of dietary isoleucine on the immune response, antioxidant status and gene expression in the head kidney of juvenile Jian carp (Cyprinus carpio var. Jian). Fish Shellfish Immunol 35:572–580 Zhou J, Huang K, Lei XG (2013) Selenium and diabetes–evidence from animal studies. Free Radic Biol Med 65: 1548–1556
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Metabolic changes associated with selenium deficiency in mice Beata Mickiewicz • Michelle L. Villemaire Linda E. Sandercock • Frank R. Jirik • Hans J. Vogel
•
Received: 25 June 2014 / Accepted: 30 June 2014 / Published online: 11 July 2014 Ó Springer Science+Business Media New York 2014
Abstract Selenium (Se), which is a central component for the biosynthesis and functionality of selenoproteins, plays an important role in the anti-oxidative response, reproduction, thyroid hormone metabolism and the protection from infection and inflammation. However, dietary Se effects have not well been established to date and the available studies often present contradictory results. To obtain a better understanding of Se intake and its influence on the metabolism of living systems, we have utilized a metabolomics approach to gain insight into the specific metabolic alterations caused by Se deficiency in mice. Serum samples were collected from two groups of C57BL/6 mice: an experimental group which was fed a Se-deficient diet and controls consuming normal chow. The samples were analyzed by 1H nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry. The resulting metabolite data were examined separately for both analytical methods and in a combined manner. By applying multivariate statistical analysis we were able
B. Mickiewicz H. J. Vogel (&) Bio-NMR-Centre, Department of Biological Sciences, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada e-mail: [email protected] M. L. Villemaire L. E. Sandercock F. R. Jirik Department of Biochemistry and Molecular Biology, University of Calgary, 3330 Hospital Dr. NW, Calgary, AB T2N 4N1, Canada
to distinguish the two groups and detect a metabolite pattern associated with Se deficiency. We found that the concentrations of 15 metabolites significantly changed in serum samples collected from Se-deficient mice when compared to the controls. Many of the perturbed biological pathways pointed towards compensatory mechanisms during Se deficiency and were associated with amino acid metabolism. Our findings show that a metabolomics approach may be applied to identify the metabolic impact of Se and reveal the most impaired biological pathways as well as induced regulatory mechanisms during Se deficiency. Keywords Selenium Biomarkers Metabolomics Multivariate data analysis
Introduction Selenium (Se), a nutritionally important microelement for all animals, is available in many foods (i.e. brazil nuts, pinto beans, tuna) and in the form of a dietary supplement (Reilly 2006). Se is known to play a critical role in reproduction, thyroid hormone metabolism, protection from oxidative damage, infection and inflammation mainly as a result of its incorporation into selenoproteins (Sunde 2012). Selenoproteins contain selenocysteine (Sec), a cysteine analogue, which is co-translationally inserted during protein synthesis (Gladyshev 2001). In humans, there are 25
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genes encoding for selenoproteins (Kryukov et al. 2003), of which 24 exist as Sec-containing proteins in mice and rats. It has been shown that selenoprotein expression is essential for life as the lack of SectRNASec, which is required for selenoprotein translation, was embryonic lethal in mice (Bosl et al. 1997). The most abundant selenoproteins in mammals are the antioxidant enzymes, such as the glutathione peroxidases (GPX) and thioredoxin reductases (TRR) (Hawkes and Alkan 2010). GPXs eliminate hydrogen peroxide and lipid hydroperoxides to reduce the destructive influence of reactive oxygen species (ROS) (Sunde 1990). Similarly, TRR regulates cellular redox status by reducing oxidized thioredoxin and its homologs in order to attenuate ROS levels in the living systems (Tamura and Stadtman 1996). Therefore, Se deficiency is associated with prolonged oxidative stress and with increased ROS concentration in the body. The beneficial antioxidant properties of Se have led to a number of epidemiological studies focusing on the relationship between Se and cancer risk (Whanger 2004), as well as on the impact of Se on neurodegenerative and cardiovascular diseases (Sanmartin et al. 2011). Several studies conducted in animals have indicated that Se can improve anticancer efficacy and is very effective in the reduction of tumor incidents (Ip 1998; Warrington et al. 2013). However, the largestever randomized placebo-controlled trial, the Selenium and Vitamin E cancer prevention trial (SELECT), conducted in 35,533 men showed that long term Se supplementation in a Se-replete population did not significantly reduce the incidence of prostate cancer (Lippman et al. 2009). Similar results were reported in a Canadian study (Fleshner et al. 2011). While different outcomes have been shown for some other cancers (Vinceti et al. 2014) the weight of the current evidence does not support that Se supplements aid in cancer prevention in humans. Additionally, it has been speculated that Se could act as an antidiabetic agent, however, recent studies have shown that exposure to higher levels of Se may actually increase insulin resistance and the risk for type 2 diabetes (Stranges et al. 2007; Zhou et al. 2013). In view of these results it is important to clearly understand the influence of dietary Se on metabolic pathways in humans and animals. Metabolomics, which is primarily focused on the identification and quantification of small molecules in
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the metabolome, provides an unbiased opportunity to look for metabolic changes in different organisms. Metabolomics relies on analytical techniques such as nuclear magnetic resonance spectroscopy (NMR) and/ or mass spectrometry (MS) (Dunn et al. 2011b; Lindon et al. 2004; Nicholson et al. 1999). The data are usually interrogated by multivariate data analysis (Eriksson et al. 2006). An advantage of NMR is that this technique is quantitative, highly reproducible and can be executed in high-throughput large subclinical settings (Lindon et al. 2005; Soininen et al. 2009). However, NMR is associated with a relatively low sensitivity and thus by using more sensitive methodologies like gas chromatography-MS (GC–MS) more metabolites/metabolic features can be detected in the sample. Metabolic features are chemical signatures identified in MS experiments but not identified in the currently available metabolite libraries. Nonetheless, it has been shown that combining different metabolic signatures and creating a larger biopattern improves the discriminatory and predictive power significantly (Garnero et al. 2000). In the present study we have therefore integrated 1H NMR and GC–MS results to describe specific metabolic changes associated with Se deficiency in a mouse model. We created a dataset for multivariate statistical analyses including unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA) (Eriksson et al. 2006). The latter approach was used to separate the metabolic variation into two parts: one component which describes the variation correlated with Se deficiency and a second one which encapsulates the metabolic variation that is not related (orthogonal) to the absence of Se in the diet. As such, OPLS-DA can be applied to identify potentially important metabolites and the most impaired metabolic pathways during Se deficiency.
Methods Animals and diets The study was approved by the University of Calgary Health Sciences Animal Care Committee and was completed within the guidelines of the Canadian Council on Animal Care. C57BL/6 mice, bred in house, were divided into two groups: experimental diet and control diet. The two diets have nearly
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identical ingredients; with the only difference being that the experimental diet lacks Se. The food was obtained from Dyets Inc., Dyet #116061 and #116062 (Bethlehem, Pennsylvania, USA). Before the mouse chow was introduced into the study, it was gamma irradiated. Bags of chow were kept frozen until needed. Items such as nestlets, hardwood bedding and water were tested for Se content. Water was tested at the Centre for Toxicology, University of Calgary (Alberta, Canada). Nestlets were tested at ALS Environmental (Calgary, Alberta, Canada) and hardwood bedding was tested by Eurofins Scientific Inc. (Calgary, Alberta, Canada). All three items were found to be free of Se. Female mice were put on either a control or Se free diet 4 weeks prior to the introduction of a stud male. Once pregnant, the studs were removed, and after weaning the litters were placed on the same diet as the mother. The mice used in this study were kept on their respective diets for a total of 32 weeks. Sample collection At 32 weeks of age, mice were euthanized using CO2 inhalation. Their final weight was recorded before euthanasia. Immediately after asphyxiation, cardiac punctures were performed and blood was taken. Blood samples were held at room temperature for 10 min to allow clotting. Blood samples were then spun at 3,500 rpm for 5 min and serum aliquots were collected and stored at -80 °C prior to analysis. Se analysis of serum samples was carried out by the London Laboratory Services Group (London, Ontario, Canada). NMR The process of NMR spectral acquisition and sample preparation has already been described in detail (Mickiewicz et al. 2014). Briefly, samples were thawed and filtered twice (3 kDa NanoSep microcentrifuge filters). The filtrates were then brought to 400 ll by adding phosphate buffer (NaH2PO4, pH 7.0), 2,2-dimethyl-2-silapentane-5-sulfonate (DSS), sodium azide and D2O. The final concentration of DSS was used as an internal standard and it was 0.5 mM for each sample. The pH of the samples was adjusted to 7.0 ± 0.04. The samples were analyzed by high resolution one-dimensional 1H NMR spectra on a 600 MHz Bruker Ultrashield Plus NMR spectrometer
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(Bruker BioSpin Ltd., Canada) using a standard Bruker 1D spectroscopy presaturation pulse sequence (noesypr1d) with optimal water suppression and a mixing time of 100 ms (Nicholson et al. 1995; Weljie et al. 2006). The spectra were manually corrected (phasing, baseline correction, referencing to the DSS peak at 0.0 ppm) and analyzed using the targeted profiling approach (Weljie et al. 2006) in the Chenomx NMR Suite 7.5 software (Chenomx Inc., Edmonton, Alberta, Canada). The Human Metabolome Database (version 2.5) and 2D NMR spectra [total correlation (TOCSY) spectra and 1H, 13C heteronuclear single quantum coherence (HSQC) spectra] were used to confirm and verify chemical shift assignments. GC–MS Samples were extracted in a two-phase mixture of chloroform/methanol to separate the polar and nonpolar fractions. For analysis of free polar metabolites the former was derivatized by using methoxyaminehydrochloride in pyridine solution and then N-methylN-(trimethylsilyl) trifluoroacetamide as a silylating agent. For fatty acid methyl ester (FAME) analysis, the aliquots of the organic phase containing the nonpolar fraction were collected in an eppendorf tube and evaporated under airflow at room temperature. Next, boron fluoride-methanol was added as an acid-catalysed esterification factor. Finally, all samples (polar fraction and FAME) were diluted with hexane, centrifuged to remove any solid particles and 200 lL of supernatant was prepared for GC injection. Pooled quality control (QC) samples were prepared in the same manner. GC–MS was performed on an Agilent chromatograph 7890 A (Agilent Technologies Canada Inc, Mississauga, Ontario, Canada) coupled with a Waters GCT mass spectrometer, using GC-TOF–MS methodology. The MS was operated in a range of 50–800 m/z. For quality assurance and to access the precision and repeatability of metabolite detection the spectra of five QC samples were acquired at the beginning of the experiment and after every fourth sample. Mass spectra of polar fraction were processed using Metabolite Detector software (version 2.06, Technische Universita¨t Carolo-Wilhelmina zu Braunschweig, Braunschweig, Germany). For metabolite identification the GOLM metabolite database (Kopka et al. 2005) and NIST database (Babushok et al. 2007) were used. FAME data analysis was performed in
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MET-IDEA version 2.08 software (Broeckling et al. 2006). Data preprocessing and statistical analysis Metabolite data obtained from the analysis of 1H NMR and GC–MS experiments were pre-processed [median fold change normalization, logarithmic transformation, centering and unit variance scaling (van den Berg et al. 2006)] and examined separately as well as in a combined manner (see Fig. 1). For the metabolites in common detected in both, the 1H NMR and GC–MS datasets (polar and FAME fraction), the mean values were calculated and incorporated into the combined dataset (Booth et al. 2011) and then the remaining 1H NMR metabolites and GC–MS metabolites/metabolic features/fatty acids were included. For quality assurance of the GC–MS data the relative standard deviation (RSD) of the QC samples was calculated and metabolites/metabolic features with an RSD of more than 30 % for the polar fraction and 50 % for FAME were excluded from further statistical analysis (Dunn et al. 2011a). All zero values were considered as missing values and all metabolites or metabolic features with more than 50 % missing values were excluded from the statistical analysis. The PCA and OPLS-DA models were performed in the SIMCAP ? 12.0.1 software (Umetrics, Sweden). The PCA method was used to summarize the variation in each dataset and highlight outliers, i.e. samples situated outside of the 95 % confidence of the Hotelling’s T-squared distribution (elliptic or spherical area in the multicomponent score scatter plots) (Eriksson et al. 2006). The OPLS-DA models were implemented for each dataset separately. For the 1H NMR data the supervised model was based on potentially important metabolites selected in 2-sample t tests with a p value of less than 0.25 as a threshold. The OPLS-DA models for GC–MS data and integrated 1H NMR/GC–MS dataset were based on metabolites/metabolic features/ fatty acids chosen in the variable importance projection (VIP). Variables with VIP values larger than one were selected (Eriksson et al. 2006). To evaluate the quality of the OPLS-DA models the R2Y (percentage of variation explained by the model) and Q2 (predictive power of the model) metrics were calculated. The difference between R2Y and Q2 indicates the goodness of fit of the OPLS-DA model. The Q2 value was estimated by threefold cross-validation (CV) (Picard
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and Cook 1984). The predictive values of Y-variables (Y-predCV) were used to calculate the sensitivity, specificity, accuracy and the area under the receiver operator curve (AUROC) in Metz ROC Software (the University of Chicago, USA). Additionally, the OPLS-DA models were evaluated by cross validated analysis of variance (CV-ANOVA) in SIMCAP ? software. In order to describe potential serum biomarkers that are associated with Se deficiency, the OPLS-DA regression coefficients were calculated for the combined dataset and only compounds with significant changes in concentration (p value less than 0.05) were considered. Next, based on the potentially important metabolites a representative metabolic pathway network was constructed to identify the most perturbed biological pathways in the Se-deficient mice [MetaboAnalyst 2.0 software (Xia and Wishart 2011)].
Results Analysis of Se levels in serum samples In total, 12 serum samples were collected for this study: six samples were obtained from Se-deficient mice and six from normal controls. We have found in previous studies with deficiency of the vitamins that this sample size is adequate (Duggan et al. 2011). Body weight was not significantly different between both groups of animals, and no behavioural abnormalities were observed in the Se-deficient animals. The average Se level in Se-deficient mice was 81.33 ± 7.09 lg/L while in the control group it was 430.33 ± 24.58 lg/L. Thus, the level of Se in blood samples collected from Se-deficient mice was *5 times lower than the level in the control group. 1
1
H NMR and GC–MS spectral analysis
H NMR experiments could be performed for all samples. However, three samples (two controls and one Se-deficient sample) had to be excluded from the GC–MS experiment due to insufficient sample volume. A total of 47 metabolites were assigned and quantified in each 1H NMR spectrum and 207 metabolites/metabolic features were detected for the polar fractions and 28 fatty acids for the non-polar fractions during GC–MS analysis
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Fig. 1 Flow chart for the metabolomics analysis used in this study
Statistical modeling After data preprocessing and quality assurance tests a total of 137 variables were retained for the multivariate statistical analysis: 46 metabolites from 1H NMR data, 73 metabolites/metabolic features from the polar fractions of the GC–MS data and 18 fatty acids from the non-polar fractions of the GC–MS data. 1
H NMR and GC–MS datasets
The PCA score scatter plots for the 1H NMR and GC– MS datasets (Fig. 2) did not show any outliers. Interestingly, the best separation between Se-deficient samples and controls was obtained for the polar fraction analyzed by GC–MS. The variation in this model was defined by three principal components, i.e. PC1 = 24.3 %, PC2 = 21.1 % and PC3 = 13.8 %. However, it should be noted that the variation between the samples found for the polar fractions of the GC– MS data was explained by the biggest number of
principal components when compared to other datasets: one and two principal components for 1H NMR, and FAME data, respectively. It is known that each principal component represents one form of the systematic variation of a dataset. Therefore, if the number of principal components is small and already sufficient to reveal the studied variation within the samples, then it indicates a strong model and good correlation between variables and observations (Eriksson et al. 2006). To better reveal the metabolic patterns related to Se deficiency in the groups of mice, supervised OPLSDA method was carried out. Figure 3 presents the OPLS-DA score scatter plots for the 1H NMR and GC–MS datasets. Both sample groups are well distinguished along the first PLS component in all datasets which indicates a very strong relationship between the metabolic data and Se deficiency. High values for the R2Y and Q2 metrics were calculated for each OPLSDA model: 0.82 and 0.74 for 1H NMR data, 0.90 and 0.81 for the polar fractions of the GC–MS data, 0.83
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and 0.75 for the FAME fractions of the GC–MS data, which indicates the good predictive ability of the models. Additional information of statistical measures for Se-deficient and control mice models based on the 1 H NMR and GC–MS data is provided in Table 1. Integrated 1H NMR/GC–MS dataset Based on reduced dataset for the variables with a VIP values larger than one we found that the combined 1H NMR/GC–MS dataset consisted of the defined metabolites only: three metabolites in common, 12 nonshared 1H NMR metabolites, 24 non-shared GC–MS metabolites and 9 fatty acids. The combined OPLSDA model clearly separates the Se-deficient group from the controls (Fig. 3d) and is characterized by high values of validation metrics, R2Y = 0.87, Q2 = 0.84 and AUROC = 1.0 (Table 1). The difference between the R2Y and Q2 parameters for this integrated model is the smallest compared to the OPLS-DA models for separate datasets which indicates a better fit for the combined OPLS-DA model. Biomarkers The supervised analysis of the integrated 1H NMR/ GC–MS dataset revealed 15 metabolites for which concentrations changed significantly in serum samples collected from the Se-deficient mice when compared to the controls (Fig. 4). It was observed that the levels of serine, threonine, phenylalanine, methionine, creatine, fructose, sorbose, sucrose, proline, glycine, isoleucine and pyruvate were increased while concentrations of stearic acid, cis-10-pentadecanoic acid and palmitic acid were decreased. These potentially important metabolites indicate the most impaired pathways during Se deficiency such as those involving glycine, serine and threonine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis and phenylalanine metabolism (Table 2). Fig. 2 The PCA score scatter plots representing the metabolomics data obtained for serum samples: a 1H NMR dataset, b polar fractions of the GC–MS dataset and c non-polar fractions of the GC–MS dataset. Samples were collected from Sedeficient mice (black dots) and normal controls (green dots)
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Discussion Although, it is known that Se deficiency affects the redox status of living systems, the actual metabolic
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Fig. 3 The OPLS-DA score scatter plots obtained for the metabolomics data for serum samples: a 1H NMR dataset, b polar fractions of the GC–MS dataset, c non-polar fractions of the GC–MS dataset and d 1H NMR/GC– MS combined dataset. Samples were collected from Se-deficient mice (black dots) and normal controls (green dots)
Table 1 Comparison of statistical measures for Se-deficient and control mice models based on the metabolomics data Metabolomics data
R2Y
Q2
P value
Sensitivity:specificity
1
0.82
0.74
2.2 9 10-3
0.83:1.0
1.0:0.86
0.92
1.0
GC–MS data
0.90
0.81
6.7 9 10-3
1.0:1.0
1.0:1:0
1.0
1.0
Polar fraction GC–MS data
0.83
0.75
1.6 9 10-2
1.0:0.80
0.83:1.0
0.90
1.0
0.87
0.84
4.0 9 10-3
1.0:1.0
1.0:1:0
1.0
1.0
H NMR dataset
PPV:NPV
ACC
AUROC
Non-polar fraction 1
H NMR/GC–MS combined dataset
R2Y variation explained by the model, Q2 predictive ability of the model, PPV positive predictive value, NPV negative predictive value, ACC accuracy, AUROC area under the receiver operating characteristic curve
changes caused by this condition have not been reported so far. Here, we have described a metabolic pattern found in serum samples collected from Sedeficient mice. Even though the difference in serum Se level between the groups was only *5-fold, we nevertheless observed significant metabolic disruption in the Se-deficient mice. The alterations in the amino acid levels suggested the presence of increased protein turnover in the Sedeficient group. It has already been reported that the lack of selenoproteins leads to increased ROS
concentrations (Steinbrenner and Sies 2009) which in turn causes oxidative stress and protein degradation (Chang et al. 2000). On the other hand, individual amino acids can also act as antioxidants themselves, and hence may play an important role in the antioxidative response (Marcuse 1960). Serine, glycine, isoleucine and methionine are well-known for their protective function against oxidative stress (Atmaca 2004; Diaz-Flores et al. 2013; Maralani et al. 2012; Zhao et al. 2013). Additionally, the increased level of methionine points to a dysregulation of the
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Fig. 4 The OPLS-DA regression coefficient plot for the combined 1H NMR/ GC–MS dataset. Positive values of coefficients (the upper part of the diagram) indicate increased metabolite concentrations in serum samples collected from Se-deficient mice (fold change [1) while negative values (the lower part of diagram) present a decrease in metabolite concentrations, as compared to normal controls (fold change\1). Only significant metabolites are shown (p \ 0.05)
Table 2 Affected metabolic pathways in Se-deficient mice Metabolic pathway
p-value
Impact
Total
Hits
Glycine, serine and threonine metabolism
8.7 9 10-4
0.51
31
Glycine, threonine, creatine, serine, pyruvate
Phenylalanine, tyrosine and tryptophan biosynthesis
0.002
0.5
4
Phenylalanine
Phenylalanine metabolism
0.002
0.41
11
Phenylalanine
Methane metabolism
0.006
0.4
9
Glycine, serine
Valine, leucine and isoleucine biosynthesis
0.05
0.33
11
Isoleucine, pyruvate
Cysteine and methionine metabolism
2.9 9 10-4
0.14
27
Methionine, serine, pyruvate
Fructose and mannose metabolism
0.03
0.13
21
Fructose
Aminoacyl-tRNA biosynthesis
0.002
0.13
69
Phenylalanine, glycine, serine, methionine, isoleucine, threonine, proline
The p value was calculated during the pathway enrichment analysis and has been adjusted by Holm-Bonferroni method while the pathway impact value was calculated from the pathway topology analysis. The Total represents the total number of compounds in the pathway and the Hits show matched metabolites detected as potentially important compounds in our study. The analysis was performed in the MetaboAnalyst 2.0 software (Xia and Wishart 2011)
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methionine-homocysteine cycle which intersects with Se metabolism (Uthus et al. 2002). It has been shown that via its effects on the methionine-homocysteine cycle, Se is able to influence redox-methylation balance and DNA methylation (Davis and Uthus 2003; Metes-Kosik et al. 2012). Moreover, the increased level of creatine that we detected in the Se-deficient mice might be linked to the synthesis of one of the key components of the methionine-homocysteine cycle, i.e. S-adenosylmethionine (adoMet). In turn, adoMet acts as an important methyl donor (Detich et al. 2003) and creatine synthesis can account for more than 70 % of the adoMet-derived methyl groups in humans (Stead et al. 2006). Since creatine is itself a good antioxidant (Sestili et al. 2011), it may provide a counter regulatory antioxidant response to Se deficiency. Significantly increased concentrations of pyruvate, fructose, sorbose and sucrose together with a major drop in the fatty acid levels (stearic acid, cis-10pentadecanoic acid and palmitic acid) likely point to expanded glycolysis and lipogenesis. It was previously reported that Se deficiency could dysregulate glucose homeostasis and impair lipid metabolism, with Se being suggested as a factor in the pathogenesis of diabetes and the metabolic syndrome (Labunskyy et al. 2011; Seale et al. 2012). The metabolic changes observed in our investigation support these observations and might confirm a potential role for Se deficiency in the development of diabetes. However, it should be noted that recent clinical trials have shown that supplemental intake of Se above adequate levels may actually increase the risk for type two diabetes mellitus (Lippman et al. 2009; Stranges et al. 2007; Zhou et al. 2013). Therefore, further studies on dietary Se levels need to be carried out to help clarify the role of Se in diabetes. Interestingly, pyruvate, for which the concentration increased in serum samples collected from Se-deficient mice in our study, has also been known to act as an anti-oxidative agent (Liu et al. 2011). During oxidative stress pyruvate is able to directly scavenge ROS through a decarboxylation reaction and mimic the function of GPX; a class of the selenoproteins required for maintenance of the glutathione system. It was reported that pyruvate could maintain the glutathione redox state by increasing the ratio of reduced glutathione versus oxidized glutathione, and was thus able to suppress myocardial damage during conditions
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of oxidative stress (Ryou et al. 2010). Taken together, our study raises the possibility that depletion of selenoproteins, as a result of Se deficiency, was being compensated for through increased concentrations of specific metabolites capable of acting as antioxidants. This study constitutes an initial step in assessing the influence of Se on metabolic regulation in living system using a metabolomics approach. Although it could be argued that this study is relatively small in scope, it is nevertheless the first combined NMR/GC– MS metabolomics study of Se deficiency reported to date. The integrated approach we employed allowed us to identify candidate metabolic pathways that were most impaired by Se deficiency and thus served to capture the impact that Se has on metabolism.
Conclusions Herein, we have demonstrated that a characteristic metabolic profile is seen in mice raised on a Sedeficient diet. Many of the metabolites that we found to be significantly altered in our study have been reported before to be able to act as oxidative stress biomarkers and/or important antioxidants. The ability to detect metabolic changes in serum in response to specific dietary modifications illustrates how metabolomics can provide a sensitive approach to help us better understand the interactions between nutrients and metabolic pathways. Acknowledgments This work was supported through the Armstrong Chair for Molecular Cancer Research, which in turn is supported by the Alberta Cancer Foundation and the University of Calgary. HJV held Scientist Award from Alberta Innovates Health Solutions. FRJ held a Canada Research Chair award.The funding agencies had no influence on the design, analysis and manuscript preparation for this study.
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