Gut Microbiota Modulate the Metabolism of Brown ... - ACS Publications

ARTICLE pubs.acs.org/jpr

Gut Microbiota Modulate the Metabolism of Brown Adipose Tissue in Mice Renaud Mestdagh,† Marc-Emmanuel Dumas,† Serge Rezzi,‡ Sunil Kochhar,‡ Elaine Holmes,† Sandrine P. Claus,*,†,§ and Jeremy K. Nicholson*,† †

Division of Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ, London, U. K. ‡ BioAnalytical Science Department, Nestle Research Centre, Vers-Chez-Les-Blancs, Lausanne, Switzerland

bS Supporting Information ABSTRACT: A two by two experimental study has been designed to determine the effect of gut microbiota on energy metabolism in mouse models. The metabolic phenotype of germ-free (GF, n = 20) and conventional (n = 20) mice was characterized using a NMR spectroscopy-based metabolic profiling approach, with a focus on sexual dimorphism (20 males, 20 females) and energy metabolism in urine, plasma, liver, and brown adipose tissue (BAT). Physiological data of age-matched GF and conventional mice showed that male animals had a higher weight than females in both groups. In addition, conventional males had a significantly higher total body fat content (TBFC) compared to conventional females, whereas this sexual dimorphism disappeared in GF animals (i.e., male GF mice had a TBFC similar to those of conventional and GF females). Profiling of BAT hydrophilic extracts revealed that sexual dimorphism in normal mice was absent in GF animals, which also displayed lower BAT lactate levels and higher levels of (D)-3-hydroxybutyrate in liver, plasma, and BAT, together with lower circulating levels of VLDL. These data indicate that the gut microbiota modulate the lipid metabolism in BAT, as the absence of gut microbiota stimulated both hepatic and BAT lipolysis while inhibiting lipogenesis. We also demonstrated that 1H NMR metabolic profiles of BAT were excellent predictors of BW and TBFC, indicating the potential of BAT to fight against obesity. KEYWORDS: gut microbiota, body weight, total body fat content, 1H NMR metabolic profiles, brown adipose tissue, lipid metabolism

’ INTRODUCTION The mammalian gut microbiota interact constantly with the host through dynamic metabolic exchange of vitamins and nutrients such as carbohydrates and vitamin B12. These interactions are a key factor for the understanding of biological processes, such as the maintenance of the host homeostasis.1 7 It has been shown that the relationship between gut microbiota and the metabolism of the host is mutually beneficial (symbiotic), whereby the gut microbiota have been shown to play a central role in human health and disease.2,3,6 The gut microbiota positively control the intestinal and epithelial cell differentiation and proliferation through the production of short chain fatty acids and ion absorption.2 In addition to these so-called beneficial effects, the gut microbiota should be viewed as an integrated “microbial organ” involved in the symbiotic regulation of energy and metabolic homeostasis.3,4,6 Because of the extensive bidirectional mammalian microbial interaction through metabolic exchange of vitamins and dietary polysaccharides, gut microbes affect energy harvest from the diet and energy storage in the host, which suggests that intestinal microorganisms may play a direct role in the development of obesity.7 Previous studies based on the comparison of the gut microbiota of genetically obese and lean mice revealed that obesity was associated with changes in Bacteroidetes and r 2011 American Chemical Society

Firmicutes abundance, the two dominant bacterial phyla in the rodent microbiota.8 This constitutes strong evidence of the gut microbiota being an additional contributing factor in obesity development and underlines the necessity to understand the link between obesity and microbes in more depth. A recent study revealed that modulation of the gut microbial composition through norfloxacin and ampicillin improved the glucose tolerance of ob/ob mice by altering hepatic and intestinal genes and by modifying the hormonal, inflammatory, and metabolic status of this mouse model.9 A useful approach to better understand the relationship between the gut microbiota and the host metabolism is the use of germ-free (GF) mouse models. They have been intensively studied to characterize the symbiotic relationship between the host and its gut microbiota and have been validated as a model to assess the effects of the colonization of microbial species on the mammalian host and its metabolic fingerprint.10,11 GF mice display unusual gut morphology, i.e., larger cecum and thinner intestinal villi when compared to conventional animals, as well as physiological and immunological abnormalities, i.e., lower peristalsis and decreased inflammatory responses.12 In a recent study, Received: June 7, 2011 Published: November 04, 2011 620

dx.doi.org/10.1021/pr200938v | J. Proteome Res. 2012, 11, 620–630

Journal of Proteome Research

ARTICLE

the multicompartmental metabolic profiles of conventional and GF mice have been characterized using a well-established 1H NMR-based metabonomic approach.13,14 In addition, the colonization process has been characterized by integrating gut microbial data with metabolic fingerprinting, highlighting the major role of the gut microbiota on the energy metabolism of the host.15 A recent work has also demonstrated that C57Bl/6J male GF mice had a lower total body fat content (TBFC) than their conventional counterpart and that these GF mice were resistant to diet-induced obesity.7 This resistance to obesity was attributed to an elevated level of fasting-induced adipose factor (FIAF), a circulating lipoprotein lipase inhibitor, which is normally selectively suppressed in the gut epithelium by the microbiota.7 This mechanism participated in the protection against obesity induced by a Western-style, high-fat and sugar-rich diet, through the stimulation of fatty acid metabolism.7,16 However, the brown adipose tissue (BAT) metabolism, which is central in the energy expenditure regulation, has not been investigated in this context to date. Indeed, BAT produces heat by oxidizing fatty acids by uncoupling the proton gradient resulting from the turnover of the respiratory chain in the mitochondria, leading to the production of heat instead of ATP, thus contributing to thermogenesis.17 19 Finally, the lower TBFC of GF compared to conventional mice has been clearly demonstrated in C57BL/6J male mice, but the strain and/or gender specificity of these results have, so far, not been studied in depth. Investigating BAT is also particularly relevant because BAT is increasingly considered as a new target in the battle against obesity.19 21 Recent developments demonstrated its activity in humans.19 It has been shown that the amount of BAT is inversely correlated with the body mass index (BMI), especially in older people, and that BAT plays a role in the adult human metabolism, a finding which has not been further explored and which opens many possibilities for future pharmacological treatments.19,20 Furthermore, BAT and its specific uncoupling protein UCP1 have been recently linked to the development of obesity in C57Bl/6J mice ablated for the uncoupling protein 1 (UCP1).22 Another consequence of this suppression was the complete elimination of the diet-induced thermogenesis effect in this mouse strain.22 Finally, active BAT has been recently associated with triglyceride clearance, as Bartelt et al. demonstrated the regulator role that BAT plays for triglyceride-rich lipoprotein clearance and for the control of blood lipid abundance.23 These findings highlighted the potential role of BAT for reducing or eliminating the risks of metabolic syndrome and obesity developments.24 In the present study, we applied a 1H NMR metabolic profiling approach in order to investigate the influence of GF state and gender on energy metabolism in urine, plasma, liver, and BAT of C3H mice.

and described in the Supporting Information). At 8 weeks old, spot urine was collected by massaging the urinary bladder and the abdomen. Simultaneously, animals were weighed, and in vivo EchoMRI (Magnetic Resonance Imaging, Echo Medical Systems, Houston, TX) calibrated with canola oil was recorded to measure the total body fat content. Measurements were repeated twice for each individual mouse, and the average was taken as the value of total body fat content. Mice were euthanized by decapitation to collect intrascapular BAT. Samples were snap frozen in liquid nitrogen and stored at 80 °C until analysis. 1

H NMR Acquisition of Biofluids and BAT Extracts

Urine samples (25 μL) were diluted into 35 μL of a deuterated phosphate buffer solution at pH 7.4 (0.2 M NaH2PO4, Na2HPO4, 0.05% of sodium 3-(trimethylsilyl)-propionate-2,3-d4 (TSP), 70% D2O)25 before being transferred to 1.7 mm diameter capillary tubes for 1H NMR acquisition. Plasma samples (50 μL) were diluted into 450 μL of D2O before bring transferred to 5 mm diameter tubes for 1H NMR acquisition. Apolar metabolites were extracted from BAT using a binary mix of chloroform/ methanol (1:1). This protocol has been established on the basis of previous work from Folch and Bligh and Dyer on lipid extraction from biological matrices.26 29 The organic phase was washed with an equivalent volume of water to clean the lipophilic phase and extract the hydrophilic compounds. After 5 min of centrifugation at 13 000 rpm, the supernatant, containing the hydrophilic phase, was freeze-dried and resuspended in 550 μL of D2O, whereas the dried residue of the organic phase was resuspended in 600 μL of CDCl3 prior to analysis by NMR spectroscopy. 1 H NMR spectra of biofluids and BAT were acquired on a 600 MHz Bruker Avance spectrometer (Bruker Biospin, Rheinstetten, Germany) operating at 600.13 MHz and a constant temperature of 300 K using a standard 1D pulse sequence (recycle delay (RD)-90°-t1-90°-tm-90°-acquire free induction decay (FID)) with water suppression applied during RD of 2 s and mixing time (tm) of 100 ms and a 90° pulse set at 10.50 μs. Spectra were acquired using 256 scans for urine and BAT hydrophilic extracts and 128 scans for BAT lipophilic extracts into 32 000 data points with a spectral width of 12 000 Hz. The FIDs were multiplied by an exponential function corresponding to 0.3 Hz line broadening before applying Fourier transformation. High Resolution Magic Angle Spinning 1H NMR Spectroscopy of Liver 1

H NMR spectra of intact liver samples were acquired on a 600 MHz Bruker spectrometer (Bruker Biospin, Rheinstetten, Germany) operating at 600.13 MHz by high resolution magic angle spinning technique and using a Carr Purcell Meiboom Gill (CPMG) spin echo pulse sequence with water presaturation. FIDs were collected into 32 000 data points using 128 scans with a spectral width of 12 000 Hz and were multiplied by an exponential function corresponding to 1 Hz line broadening. Tissue samples were kept at 284 K during the NMR experiment to minimize timedependent degradation.

’ MATERIALS AND METHODS Animal Handling

All animal studies were carried out under the Swiss legislation on animal experimentation and appropriate national guidelines at the Nestle Research Centre (Lausanne, Switzerland). GF C3H/ Orl females (Charles River, France) were mated in isolators. At birth, half of the breed (10 males and 10 females) were transferred to a conventional environment in order to start acclimatization, whereas the other half was kept in isolators. All animals were fed with the standard chow diet A03 (produced by the company SAFE, for Scientific Animal Food and Engineering

Data Processing and Analysis

Spectra were processed using TOPSPIN 2.0 software package (Bruker Biospin, Rheinstetten, Germany). All spectra were manually phased, baseline corrected, and calibrated to TSP (δ 0.00) for urine and BAT hydrophilic extracts, to chloroform (δ 7.26) for BAT lipophilic extracts, and to glucose (δ 5.223) for 621

dx.doi.org/10.1021/pr200938v |J. Proteome Res. 2012, 11, 620–630


ARTICLE

into the model to check the robustness of the model. All models have been validated using permutation tests (n = 1000). In order to help the interpretation, the cross-validated scores plots (Tcv) were used to visualize the relationship between samples in order to reveal clustering. In addition, an O-PLS-DA coefficients plot highlighting the variables involved in the discrimination of the classes was calculated according to the method described by Trygg et al.33,34 The coefficients plots were backprojected as a pseudo-NMR spectrum to facilitate the interpretation. The line shape corresponds to the covariance of the variables. Additionally, the weights of the variables can still be compared using a color code, which relates to the correlation of class discrimination as calculated from the correlation matrix to form a heatmap. Cold colors such as blue show a weak correlation and hotter colors such as red show the most significant correlation. Metabolites were assigned using data from the literature,35,36 existing databases such as the Human Metabolome Data Base (HMDB: http://hmdb.ca/)37,38 or the Biological Magnetic Resonance Data Bank (BMRB: http://www.bmrb.wisc.edu), in-house standards, and two-dimensional (2D) NMR experiments (TOCSY, COSY, and HSQC) on selected samples.39,40

’ RESULTS Gender and Microbial Status Affected Body Weight and Total Body Fat Content

C3H mice were weighed at 8 weeks before the measurement of TBFC by MRS spectroscopy (EchoMRI). Females were significantly lighter than males in both groups (p-value < 0.05) and to the same extent (18% less in the conventional group, 22% less in the GF group). Furthermore, both male and female GF mice were significantly lighter compared to their conventional counterpart (respectively 13 and 17% less than conventional animals, Figure 1A). The TBFC was then assessed in both groups (Figure 1B). Female conventional mice had a lower amount of fat (11%) compared to conventional males. No sexual dimorphism in TBFC was observed in the GF group; no significant difference between conventional and GF females was observed. However, GF males had a significantly lower total body fat content (21%) compared to conventional animals (p-value < 0.05). Interestingly, a larger dispersion of the TBFC values was observed in the conventional group compared to the GF group, indicating a higher homogeneity in GF animals (Figure 1B).

Figure 1. Sexual dimorphism on body weight (A) and total body fat content (TBFC, B) in female (yellow box-plot) and male (green boxplot) conventional and GF mice. (n = 10/group).

plasma and liver samples. The region between δ 0.5 9 was imported into Matlab software (Version 7.5, The Mathworks Inc., Natwick, MA) for statistical analysis. For urine samples, resonances corresponding to water and urea signals (δ 4.6 5.9) were removed before data set analysis. For BAT extracts, methanol, water, and chloroform resonances were also removed before analysis of the data set (δ 2.3 2.4, δ 5.3 5.5, and δ 7 7.5, respectively). All 1H NMR spectra were aligned using an in-house algorithm.30 As described before,31 all data were analyzed with 32 000 data points and normalized to the total peak area (urinary and hydrophilic extracts of BAT) or using the probabilistic quotient (plasma, liver, and lipophilic extracts of BAT).32 Models were constructed using PLS and O-PLS-DA using unit variance (UV) scaling on Simca-P +11.5 software (Umetrics, Umea, Sweden) and Matlab 7.0.1 software (The Mathworks, Inc.) using an in-house algorithm.31 The robustness of the models was characterized using the following model parameters: Q2Y, cross-validated predicted percentage of the response; R2Y, predicted percentage of the response; and R2X, variation of X explained by the model. The validity of the models against overfitting has been tested by computing cross-validation parameters. The standard 7-fold cross-validation method repeatedly leaves out one-seventh of the sample and predicts them back

Gender was the Strongest Source of Variation of Urinary Profiles

To characterize the metabolic variations of urinary metabolic profiles associated to each factor (gender and microbial status), an O-PLS-DA model using one orthogonal component and three predictive components was carried out on all individuals. The robustness of this model was satisfactory as described by the model parameters: R2X = 0.26, R2Y = 0.56, and Q2Y = 0.44. Three urine samples in the conventional female group and two urine samples in the GF male group were not collected. A good discrimination was observed between the four groups according to the projection of the cross-validated scores on the three first components (Figure 2A). The largest source of variation was driven by gender, as the biggest discrimination was observed between male and female animals on the first component (Tcv1, Figure 2B,C). Another segregation was observed between GF and conventional animals on the second component (Tcv2, 622



ARTICLE

Figure 2. 3D O-PLS-DA scores plots derived from 1H NMR spectra from urine of conventional males (blue 9), GF males (blue 0), conventional females (red b), and GF females (red O). The strongest variation was observed between male animals in blue and female animals in red according to the first component (Tcv1). Another segregation was observed between GF (open symbols) and conventional animals (closed symbols) according to the second component (Tcv2). Finally, the third component isolated again GF from conventional animals (Tcv3).

Figure 2B,D). Finally, the third component isolated again the GF from the conventional animals but to a lesser extent than the second component (Tcv3, Figure 2C,D).

latter metabolite is known to be elevated in isovaleric acidemia, which results in the accumulation of isovaleric acid in urine and serum due to a deficiency of the specific mitochondrial enzyme isovaleryl-coA dehydrogenase. This enzyme is responsible for the dehydrogenation of isovalerate, a well-known marker of mitochondrial activity,41 and isovaleryl-CoA.

Gut Microbiota Affected a Urinary Marker of Mitochondrial Activity

Pairwise comparisons were then generated to facilitate data interpretation, and their associated loadings plots have been summarized in Table 1. Putrescine (δ 1.80 and 3.05) was observed in higher concentrations in the urinary profile of male mice compared to females in both groups. The sulfo-conjugate of m-hydroxyphenylpropionic acid (m-HPPA sulfate, δ 2.51, 2.90, 6.92, and 7.22) and 3-hydroxycinnamate (δ 6.92, 7.02, and 7.33), which both result from the metabolism of polyphenols (i.e., chlorogenic compounds) into bioavailable compounds by the gut microbiota, and phenylacetylglycine (δ 3.65 and 7.35) were observed in higher concentrations in the urinary profile of conventional compared to GF mice, whereas the level of isovaleric acid (δ 0.92 and 2.03) was higher in GF animals (Table 1 and Figures S1 S3, Supporting Information). This

Gut Microbial Urinary Metabolites were Affected by an Interplay between Microbial Status and Gender

We observed that several differences in urinary metabolite profiles that link to the interplay between microbial status (GF or conventional) and gender (female or male) as shown in Table 1. Indeed, 2-ketoisocaproate (2-kic, δ 0.94, 2.18, and 2.64) and isoleucine (δ 0.95 and 1.26) were significantly more concentrated in urine of male mice compared to female animals (Table 1 and Figures S1 S3, Supporting Information). In addition, 2-kic was found in higher concentration in the urine of GF animals compared to their conventional counterparts, whereas isoleucine was found only in higher concentrations in urine of GF females compared to conventional females. These variations were 623



ARTICLE

Table 1. Summary of Variations of Metabolite Signals in Urine Samplesa metabolites

δ 1H (ppm)

(+) F Conv vs M Conv ( )

(+) F GF vs M GF ( )

(+) M GF vs M Conv ( )

(+) F GF vs F Conv ( )

3-HCA

7.34

0.90

m-HPPA

6.92

0.91

0.88

PAG

7.38

0.82

0.83

2-kic

2.19

0.72

0.77

isoleucine

1.01

0.92

0.80

isovaleric acid

2.03

putrescine

1.80

0.85

0.78

R2X

0.40

R2Y

0.94

Q2Y

0.79

+0.82

0.75

+0.79 +0.73

+0.71

+0.86

0.35

0.48

0.30

0.93

0.96

0.99

0.47

0.67

0.73

a Correlation coefficients with the highest discriminant axis for the metabolites involved in the difference between the positive group (+) and the negative group ( ) are shown.

Figure 3. Partial 1H NMR spectra of a BAT hydrophilic fraction of a conventional male mouse (0.7 4.5 (A) and 5 9 ppm (B)) and of a GF male mouse (0.7 4.5 (C) and 5 9 ppm (D)). The regions between 5 and 9 ppm have been expanded 20 times.

specific neither to the gender nor to the microbial status of C3H mice and revealed the interplay between microorganisms and mouse gender.

male mice are shown in Figure 3. The spectra were dominated by the resonances of amino acids (alanine, glutamine, histidine, leucine, isoleucine, phenylalanine, tyrosine, and valine), organic acids ((D)-3-hydroxybutyrate, acetate, lactate), steroid derivatives, essential compounds of the membranes (choline and its phosphoderivatives phosphocholine and glycerophosphocholine), and compounds related to energy metabolism (glycerol, creatine,

Characterization of BAT Metabolic Profiles

Two examples of typical 1H NMR spectra obtained from the hydrophilic extract of BAT from 8 week old conventional and GF 624



ARTICLE

Table 2. Summary of Variations of Metabolite Signals in BAT Hydrophilic Extractsa metabolites (D)-3-hydroxybutyrate

δ 1H (ppm)

(+) F Conv vs M Conv ( )

(+) F GF vs M GF ( )

(+) M GF vs M Conv ( )

(+) F GF vs F Conv ( )

1.21

+0.65

+0.77

2.31

+0.73

+0.70

lactate

1.33

0.5

0.63

aspartate

3.90

+0.82

glutamate

2.35

+0.72

lysine

1.45

+0.78

+0.72

choline

4.05

phosphocholine glycerophosphocholine

3.21 3.22

+0.70 +0.78

myo-inositol

3.53

+0.78

+0.78 0.31

Q2Y < 0

+0.71

+0.72

R2X

0.28

0.29

Q2Y

0.4

0.6

0.4

R2Y

0.92

0.93

0.86

a

Correlation coefficients with highest the discriminant axis for the metabolites involved in the difference between the positive group (+) and the negative group ( ) are shown.

BAT were performed against BW or TBFC. We observed that 1H NMR urine metabolic profiles were weakly correlated to the BW (Q2Y/R2Y = 0.5) and even less correlated to the TBFC (Q2Y/ R2Y = 0.18) and will therefore not be further discussed. However, BAT profiles provided excellent BW and TBFC predictions as outlined below. The robustness of these models was satisfactory as described by the model parameters: R2X = 0.56, R2Y = 0.58, and Q2Y = 0.42; R2X = 0.47, R2Y = 0.51, and Q2Y = 0.39 for regression of lipophilic extracts against BW and TBFC, respectively; R2X = 0.15, R2Y = 0.68, and Q2Y = 0.40; R2X = 0.23, R2Y = 0.58, and Q2Y = 0.32 for regression of hydrophilic extracts against BW and TBFC, respectively. Random permutation tests also confirmed the validity of these models in hydrophilic extracts (p = 0.0013 and p = 0.007 for the regression against BW and TBFC, respectively) and in lipophilic extracts (p = 0.001 for both regressions against BW and TBFC, respectively). As observed in Figure 4A,C, the Y-axis, defined as the total BW, clearly discriminated conventional males, which had the highest body weight, from GF males and conventional females, having an intermediate body weight, and from GF females, having the lowest body weight. The projection of the crossvalidated scores (Tcv) obtained from the O-PLS regression of the lipophilic profiles against the BW of these mice highlighted an excellent separation between the conventional (closed symbols) and GF animals (open symbols, Figure 4A). The projection of the Tcv resulting from the O-PLS regression of the hydrophilic extracts against the total BW of these mice discriminated clearly the conventional males (blue closed symbols) from the three other groups (Figure 4C). On the basis of the pairwise comparison, this discrimination was associated with higher levels of lactate and glycerol in conventional males and (D)-3-hydroxybutyrate in GF males (Figure S6A, Supporting Information). A similar modeling approach was applied to TBFC. As displayed in Figure 4B,D, the Y-axis, defined as the TBFC, highlighted an overlapping between the conventional females and GF animals. However, conventional males displayed a higher amount of TBFC compared to the three other groups as observed previously in Figure 1B. In addition, the projection of the scores obtained from the O-PLS regression of the lipophilic extract regressed against the TBFC of these mice highlighted a

and creatinine). It is noteworthy that we did not observe any glucose in the hydrophilic extracts of BAT. This is consistent with previous findings showing that glucose enters brown adipocytes after insulin or norepinephrine activation, which allows the translocation of glucose transporters from the intracellular storage to the plasma membrane.42 In addition, one example of a typical 1 H NMR spectrum from the lipophilic extract of BAT from an 8 week old conventional male mouse is shown in Figure S4 (Supporting Information). Effect of Sexual Dimorphism and Gut Microorganisms on BAT Metabotypes

Pairwise comparisons were generated in lipophilic and hydrophilic BAT extracts. Analysis of the loadings plots from lipophilic extracts of BAT showed sexual dimorphism in conventional animals. Indeed, the level of terminal CH3 groups (δ 0.995 1.01 and δ 1.01 1.02) and of CH2 CO groups were higher in conventional females compared to males (Table S1, Supporting Information). Interestingly, no sexual dimorphism in urinary metabolic profiles was observed in GF animals. Analysis of the loadings plots from hydrophilic extracts of BAT showed metabolite variations in response to the microbial status. Indeed, GF animals presented higher levels of myo-inositol (δ 3.60), (D)-3-hydroxybutyrate (δ 1.21 and 2.31), and glutamate (δ 2.35) and lower relative levels of lactate (δ 1.33) when compared to conventional animals (Table 2 and Figure S5, Supporting Information). We also observed gender-based variations indicated by higher concentrations of glutamate and lysine in conventional females compared to conventional males (Table 2). On the opposite, we did not observe any sexual dimorphism in GF animals. Finally, higher levels of aspartate, choline, phosphocholine, and glycerophosphocholine were observed in GF males compared to conventional males. These variations were not visible in female mice, which might be due to a specific interplay between the mouse microbial status and gender (Table 2). 1

H NMR Metabolic Profiles of BAT Predicted BW and TBFC

To investigate the relationship between TBFC or BW and the rodent metabolic phenotype, regressions of the 1H NMR metabolic profiles of urine and hydrophilic and lipophilic extracts of 625



ARTICLE

Figure 4. Cross-validated scores derived from the O-PLS regression of 1H NMR spectra obtained from lipophilic extracts of BAT against the total BW (A) and the TBFC (B) and from hydrophilic extracts of BAT against the total BW (C) and the TBFC (D) of conventional and GF C3H mice (conventional males (blue 9), GF males (blue 0), conventional females (red b), and GF females (red O)). (A) Q2Y = 0.42; R2Y = 0.58; R2X = 0.56 ; 1PC; 1OC; n = 10/group. (B) Q2Y = 0.39; R2Y = 0.51; R2X = 0.47. 1PC; 1OC; n = 10/group. (C) Q2Y = 0.40; R2Y = 0.68; R2X = 0.15. 1PC; 1OC; n = 10/ group. (D) Q2Y = 0.32; R2Y = 0.58; R2X = 0.23. 1PC; 1OC; n = 10/group.

good separation between conventional males and their GF counterparts (Figure 4B). At the opposite, no discrimination was observed between female animals because of a high dispersion of conventional female scores. As observed for total BW, the projection of the scores obtained from the regression of hydrophilic extracts against the TBFC discriminated the conventional and GF individuals (Figure 4D). On the basis of the pairwise comparison, this discrimination was associated with higher levels of lactate, glycerol, and lysine in conventional males and higher levels of (D)-3-hydroxybutyrate in GF males (Figure S6B, Supporting Information). To summarize, 1H NMR metabolic profiles of BAT successfully reflected the variation in BW and TBFC in this rodent model. Individuals with the highest TBFC and BW presented higher levels of glycerol and lactate and lower levels of (D)-3hydroxybutyrate.

In GF plasma profiles, this increase of ketone body was concomitant to a remarkable decrease of VLDL but not of HDL, as shown by Figure 5. Altogether, these results demonstrate the systemic increase of (D)-3-hydroxybutyrate in GF animals.

’ DISCUSSION In the present study, we investigated the impact of sexual dimorphism on the energy metabolism of C3H mice in response to the GF status by using a 1H NMR metabolic profiling approach. Urinary, plasma, hepatic, and BAT metabolic profiles displayed marked differences between conventional and GF mice. Sexual Dimorphism and Gut Microbiota Impact on the Urinary and Bat 1H NMR Metabolic Profiles

Sexual dimorphism is extensively studied, as it plays a crucial role in the understanding and prediction of gender-based responses to xenobiotics. Indeed, it is well-known that the gender background impacts severely the host responses to pharmaceutical treatment or even disease development.43,44 In the present study, putrescine, known as a microbial cometabolite derived from the gut microbiota, was observed in higher levels in the urine of male animals when compared to females. Nevertheless, it is likely that the differential levels of putrescine observed in this study were due to an increased synthesis in males by the prostate gland through a testosterone-dependent

Hepatic and Plasma Concentration of (D)-3-Hydroxybutyrate is Affected in GF Mice

In order to decipher whether the increase of the ketone body (D)-3-hydroxybutyrate was a local response to the GF state or a systemic pattern, we also investigated the plasma and hepatic metabolic profiles. These metabotypes revealed a significant increase of (D)-3-hydroxybutyrate in both compartments in GF animals compared to their conventional counterpart (Figures 5 and S6, Supporting Information). 626



ARTICLE

Figure 5. Partial 1H NMR plasma metabolic profile derived from conventional (black) and GF (red) animals. HDL: high density lipoprotein; VLDL: very low density lipoprotein.

mechanism as previously observed in rats.45 A higher urinary excretion of m-HPPA sulfate was observed in conventional mice. This is concordant with previous publications, as this metabolite has already been reported in lower concentrations in C3H GF mice.15 Indeed, m-HPPA sulfate is a bioavailable compound obtained from the catabolism of polyphenols and chlorogenic acids by the gut microbiota; hence, the variations of m-HPPA sulfate are a reflection of gut microbial activity.15

higher correlation in older people, suggesting that BAT might be involved in metabolic disorders observed in aging.19 Recent publications also demonstrated that this tissue plays a crucial role in obesity and metabolic disorders.23,24 Active BAT helps control the triglyceride clearance in mice, as approximately 50% of ingested triglycerides are taken up by this tissue, whereas about 30% are taken up by the muscle, 15% by the liver, and 5% by the white adipose tissue (WAT). In addition, BAT is highly involved in the disposal of glucose in obese mice exposed to the cold because approximately 75% of ingested glucose is taken up by this tissue, whereas about 10% is taken up by the muscle, 8% by the heart, and the remaining 7% are equally taken up by the WAT and brain.23 Therefore, the ability of the BAT metabolic profiles to predict the BW and TBFC of C3H mice reinforces the emerging interaction of this tissue with obesity and metabolic disorders.

Brown Adipose Tissue in the Context of Obesity and Metabolic Disorders

Choline, phosphocholine, and glycerophosphocholine were observed in higher levels in GF males' BAT metabolic profiles when compared to their conventional counterparts (Table 2). These metabolites are known to be involved in the formation and maintenance of cellular membranes through structural lipids. Choline is an essential constituent of cell membranes,46 and phosphocholine and glycerophosphocholine are storage forms of choline in the cytosol, where they also act as osmoprotectants.47 Therefore, the elevated levels of choline and its phosphoderivatives may indicate a variation of brown adipocyte size between GF and conventional male animals. Indeed, conventional animals are fatter than GF animals and displayed a higher lipid storage capacity. It is therefore likely that conventional BAT contains larger mature adipocytes, whereas GF mice display a higher number of small undifferentiated preadipocytes, which could account for more total choline and its phosphoderivatives. In addition, we found a strong correlation between 1H NMR metabolic profiles of BAT and both BW and TBFC of C3H mice. We therefore highlighted a link between BAT metabolism and its possible involvement in weight management and metabolic disorders. This finding complemented recent results rehabilitating the role of this tissue in adult humans.19 As a matter of fact, BAT has long been considered as having no relevance in human physiology for several decades because it was not easily discernible and found in spots spread out in different sections of the body. However, the recent discovery of Nedergaard et al. has now demonstrated its presence and activity in adult humans, with the main depots localized in the supraclavicular and neck regions.48 Cypess et al. contributed to the rehabilitation of this adipose tissue by demonstrating that the amount of BAT was inversely correlated to BMI in both male and female humans, with an even

Gut Microbiota Affect the Regulation of β-Oxidation in BAT

Lipophilic BAT extracts were found to be significantly different between GF and conventional animals (Table S1, Supporting Information). Lipophilic extracts of BAT, WAT, and cultured brown adipocytes have been previously characterized using 1H and 13C NMR spectroscopy, allowing unambiguous assignments of fatty acids and lipid resonances.49 In addition, Zancanaro et al. characterized the fatty acid metabolism in these three matrices and showed that WAT displayed more unsaturation and polyunsaturations than BAT, whereas the level of monounsaturation was not investigated. The hydrophilic fraction of BAT was not assessed by these authors, and therefore an investigation of the impact of polar metabolites on BW and TBFC remained to be performed. In the present study, hydrophilic BAT extracts revealed higher levels of (D)-3-hydroxybutyrate and lower levels of lactate in GF animals compared to their conventional counterparts (Table 2). Interestingly, a strong elevation of (D)-3-hydroxybutyrate was also observed in plasma and liver of GF compared to conventional animals (Figures 5 and S4, Supporting Information). (D)3-Hydroxybutyrate is the major ketone body produced in the mitochondria of adipocytes and hepatocytes, initiated by the condensation of two molecules of acetyl-coA derived from the βoxidation of lipids (Figure 6).50 This metabolite is well-known to play a central role in the host energy homeostasis, as it has been reported to act on noradrenaline receptors to inhibit BAT 627



ARTICLE

Figure 6. Impact of the absence of gut microbiota on the regulation of β-oxidation in BAT. FA: fatty acid; SCFA: short chain fatty acid; TCA: tricarboxylic acid; VLDL: very low density lipoprotein.

thermogenesis and to regulate the appetite.51 53 Indeed, its appetite suppressant effect has been reported as early as two decades ago.53 Its effect, mediated by noradrenaline receptors, was demonstrated in 1998, where noradrenaline-induced thermogenesis was inhibited by (D)-3-hydroxybutyrate in lean rats but not in obese rats.52 In addition, (D)-3-hydroxybutyrate stimulated insulin secretion and thus contributed to the regulation of glycemia in the same lean rat model but not in obese animals. This illustrated a differential metabolic impact of (D)-3hydroxybutyrate according to the host fat content.52 In peripheral tissues, lactate derives from the conversion of pyruvate produced by glycolysis. The retroconversion of lactate to pyruvate is not possible in brown adipocytes, as the cellular redox potential is favorable to this reaction only in the liver (Figure 6).54 Lactate will thus travel to the liver, where it will form glucose through the gluconeogenic pathway in the fasting state. This pathway has been named after its famous discoverers, the Cori pathway. Interestingly, adipose tissue is a major site of glucose conversion to lactate, and adipose mass has been

reported as increased in obesity. Therefore, lactate overproduction could be associated with metabolic abnormalities related to obesity development.55 Taken together, the observation of higher levels of (D)-3-hydroxybutyrate and lower levels of lactate in GF animals indicates that GF BAT overactivated the catabolism of lipids compared to conventional animals. Several factors can influence the activation of lipid β-oxidation in mammals. As previously discussed, β-oxidation in the BAT is primarily induced by cold. Since all animals cages were housed in a temperature-controlled husbandry, a variation in room temperature is unlikely to be the reason of this discrepancy. The reason for such metabolic difference is thus expected to originate from the lack of gut microbiota in GF animals. In conventional animals, gut microbiota largely contribute to the host energy metabolism by providing access to indigestible nutrients via the production of short chain fatty acids (SCFAs) (i.e., acetate, butyrate, and propionate) derived from the fermentation of carbohydrates.56,57 Indeed, dietary carbohydrates such as 628


Journal of Proteome Research resistant starches and dietary fibers, defined as the nondigestible plant foods, are ideal substrates for microbial fermentation process leading to the production of SCFAs.56 Whereas butyrate is a preferred energy source for colonic epithelial cells, propionate is mainly processed by hepatocytes in the liver as a substrate for gluconeogenesis, and acetate reaches the general circulation to further enter peripheral tissues such as the brown adipocytes.58 Acetate and propionate have been reported as inhibitors of lipolysis and stimulators of lipogenesis in both adipocytes and hepatocytes.59,60 This major contribution of acetate to the host metabolism has been demonstrated both in vivo and in vitro.60 Therefore, we hypothesize that the metabolic disruptions observed in GF animals are the consequence of the missing production of SCFA by the gut microbiota, which results in the stimulation of lipolysis activity (i.e., β-oxidation) in both the liver and the BAT (increased (D)-3-hydroxybutyrate production) and in the hepatic inhibition of lipogenesis (decreased circulating VLDL levels, Figure 5). Interestingly, levels of HDL in plasma were not affected, indicating that the reverse cholesterol transport was not influenced by the lack of microbiota in this study.

ARTICLE

’ ACKNOWLEDGMENT We would like to thank the animal house technicians at Nestle Research Centre for their help regarding the animal husbanding. This study was funded by Nestle as part of the Imperial College London Nestle strategic alliance. ’ REFERENCES (1) Dethlefsen, L.; McFall-Ngai, M.; Relman, D. A. An ecological and evolutionary perspective on human microbe mutualism and disease. Nature 2007, 449 (7164), 811–8. (2) Nicholson, J. K.; Holmes, E.; Wilson, I. D. Gut microorganisms, mammalian metabolism and personalized health care. Nat. Rev. Microbiol. 2005, 3 (5), 431–8. (3) Cani, P. D.; Delzenne, N. M.; Amar, J.; Burcelin, R. Role of gut microflora in the development of obesity and insulin resistance following high-fat diet feeding. Pathol. Biol. 2008, 56 (5), 305–9. (4) Cani, P. D.; Delzenne, N. M. Gut microflora as a target for energy and metabolic homeostasis. Curr. Opin. Clin. Nutr. Metab. Care 2007, 10 (6), 729–34. (5) Velagapudi, V. R.; Hezaveh, R.; Reigstad, C. S.; Gopalacharyulu, P.; Yetukuri, L.; Islam, S. The gut microbiota modulates host energy and lipid metabolism in mice. J. Lipid Res. 2010, 51 (5), 1101–12. (6) Backhed, F.; Ley, R. E.; Sonnenburg, J. L.; Peterson, D. A.; Gordon, J. I. Host bacterial mutualism in the human intestine. Science 2005, 307 (5717), 1915–20. (7) Backhed, F.; Ding, H.; Wang, T.; Hooper, L. V.; Koh, G. Y.; Nagy, A. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl. Acad. Sci. U. S. A. 2004, 101 (44), 15718–23. (8) 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), 1027–31. (9) Membrez, M.; Blancher, F.; Jaquet, M.; Bibiloni, R.; Cani, P. D.; Burcelin, R. G. Gut microbiota modulation with norfloxacin and ampicillin enhances glucose tolerance in mice. FASEB J. 2008, 22 (7), 2416–26. (10) Gordon, H. A.; Pesti, L. The gnotobiotic animal as a tool in the study of host microbial relationships. Bacteriol. Rev. 1971, 35 (4), 390–429. (11) Gordon, J. I. A genomic view of our symbiosis with members of the gut microbiota. J. Pediatr. Gastroenterol. Nutr. 2005, 40 (Suppl 1), S28. (12) Berg, R. D. The indigenous gastrointestinal microflora. Trends Microbiol. 1996, 4 (11), 430–5. (13) 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), 1181–9. (14) Claus, S. P.; Ellero, S. L.; Berger, B.; Krause, L.; Bruttin, A.; Molina, J. Colonization-induced host gut microbial metabolic interaction. mBio 2011, 2 (2), No. doi:10.1128/mBio.00271,10. (15) Claus, S. P.; Tsang, T. M.; Wang, Y.; Cloarec, O.; Skordi, E.; Martin, F. P.; et al. Systemic multicompartmental effects of the gut microbiome on mouse metabolic phenotypes. Mol. Syst. Biol. 2008, 4, 219. (16) Backhed, F.; Manchester, J. K.; Semenkovich, C. F.; Gordon, J. I. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (3), 979–84. (17) Cannon, B.; Nedergaard, J. Brown adipose tissue: Function and physiological significance. Physiol. Rev. 2004, 84 (1), 277–359. (18) Yoshioka, K.; Yoshida, T.; Kondo, M. Brown adipose tissue thermogenesis and metabolic rate contribute to the variation in obesity among rats fed a high fat diet. Jpn. J. Physiol. 1992, 42 (4), 673–80. (19) Cypess, A. M.; Lehman, S.; Williams, G.; Tal, I.; Rodman, D.; Goldfine, A. B. Identification and importance of brown adipose tissue in adult humans. N. Engl. J. Med. 2009, 360 (15), 1509–17. (20) Lidell, M. E.; Enerback, S. Brown adipose tissue—a new role in humans? Nat Rev. Endocrinol. 2010, 6 (6), 319–25.

’ CONCLUSION We have demonstrated that the gut microbiota abolished the sexual dimorphism observed for TBFC. In addition, 1H NMR metabolic profiles of BAT from conventional and GF animals were correlated to the total BW and TBFC of C3H mice and were therefore excellent predictors of BW and TBFC in the C3H mouse strain (Figure 4). The lack of gut microbiota resulted in a higher systemic level of (D)-3-hydroxybutyrate and lower levels of circulating VLDL (Figure 6). These results indicated that the absence of gut microbiota stimulated lipolysis while it inhibited hepatic lipogenesis. We demonstrate in this study that the gut microbiota and the host metabolism interact differently according to the mouse gender, with a direct and measurable impact on the urinary, plasma, hepatic, and BAT metabotypes of these animals. The importance of studying the metabolism of BAT has been recently enhanced and appears as a key factor in terms of pharmaceutical treatment. In the context of improving the knowledge about host microbiome interaction and its link with energy metabolism, BAT could play a significant role for future research and the understanding of mechanisms and development of treatments against obesity. ’ ASSOCIATED CONTENT

bS

Supporting Information Table S1, Figures S1 S6, diet data sheet for rodent chow, and mice weights. Additional Supporting Information files correspond to the data matrices from the metabolomic analyses performed in urine, plasma, liver and brown adipose tissue (hydrophilic and lipophilic extracts). This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected] (J.K.N.), s.p.claus@reading. co.uk (S.P.C.). Present Addresses §

Department of Food and Nutritional Sciences, The University of Reading, Whiteknights, PO box 226, Reading RG6 6AP, U. K. 629



ARTICLE

intracellular stores to the plasma membrane in mouse brown adipocytes. FEBS Lett. 1992, 314 (3), 246–50. (43) Stanley, E. G.; Bailey, N. J.; Bollard, M. E.; Haselden, J. N.; Waterfield, C. J.; Holmes, E. Sexual dimorphism in urinary metabolite profiles of han wistar rats revealed by nuclear-magnetic-resonance-based metabonomics. Anal. Biochem. 2005, 343 (2), 195–202. (44) Basant, A.; Rege, M.; Sharma, S.; Sonawat, H. M. Alterations in urine, serum and brain metabolomic profiles exhibit sexual dimorphism during malaria disease progression. Malar. J. 2010, 9, 110. (45) Pegg, A. E.; Lockwood, D. H.; Williams-Ashman, H. G. Concentrations of putrescine and polyamines and their enzymic synthesis during androgen-induced prostatic growth. Biochem. J. 1970, 117 (1), 17–31. (46) Zeisel, S. H. Dietary choline: Biochemistry, physiology, and pharmacology. Annu. Rev. Nutr. 1981, 1, 95–121. (47) Rohlfs, E. M.; Garner, S. C.; Mar, M. H.; Zeisel, S. H. Glycerophosphocholine and phosphocholine are the major choline metabolites in rat milk. J. Nutr. 1993, 123 (10), 1762–8. (48) Nedergaard, J.; Bengtsson, T.; Cannon, B. Unexpected evidence for active brown adipose tissue in adult humans. Am. J. Physiol. Endocrinol. Metab. 2007, 293 (2), E444–52. (49) Zancanaro, C.; Nano, R.; Marchioro, C.; Sbarbati, A.; Boicelli, A.; Osculati, F. Magnetic resonance spectroscopy investigations of brown adipose tissue and isolated brown adipocytes. J. Lipid Res. 1994, 35 (12), 2191–9. (50) Wood, E. J. Marks’ basic medical biochemistry: A clinical approach (second edition). Biochem. Mol. Biol. Educ. 2006, 34 (5), 395. (51) De Pergola, G.; Cignarelli, M.; Corso, M.; Garruti, G.; Di Paolo, S.; Giorgino, R. D-( )-beta-hydroxybutyrate inhibits catecholaminestimulated lipolysis and decreases beta-adrenoceptors’ affinity in human fat cells but not in lymphomonocytes. Acta Endocrinol. 1990, 122 (4), 450–4. (52) Canas, X.; Fernandez-Lopez, J. A.; Remesar, X.; Alemany, M. 3-hydroxybutyrate inhibits noradrenaline-induced thermogenesis in lean but not in obese zucker rats. Int. J. Obes. Relat. Metab. Disord. 1998, 22 (8), 734–40. (53) Langhans, W.; Pantel, K.; Scharrer, E. Ketone kinetics and D-( )-3-hydroxybutyrate-induced inhibition of feeding in rats. Physiol. Behav. 1985, 34 (4), 579–82. (54) Gladden, L. B. Lactate metabolism: A new paradigm for the third millennium. J. Physiol. 2004, 558 (Pt 1), 5–30. (55) DiGirolamo, M.; Newby, F. D.; Lovejoy, J. Lactate production in adipose tissue: A regulated function with extra-adipose implications. FASEB J. 1992, 6 (7), 2405–12. (56) Bugaut, M.; Bentejac, M. Biological effects of short-chain fatty acids in nonruminant mammals. Annu. Rev. Nutr. 1993, 13, 217–41. (57) Wong, J. M.; de Souza, R.; Kendall, C. W.; Emam, A.; Jenkins, D. J. Colonic health: Fermentation and short chain fatty acids. J. Clin. Gastroenterol. 2006, 40 (3), 235–43. (58) Pomare, E. W.; Branch, W. J.; Cummings, J. H. Carbohydrate fermentation in the human colon and its relation to acetate concentrations in venous blood. J. Clin. Invest. 1985, 75 (5), 1448–54. (59) Al-Lahham, S. H.; Peppelenbosch, M. P.; Roelofsen, H.; Vonk, R. J.; Venema, K. Biological effects of propionic acid in humans; Metabolism, potential applications and underlying mechanisms. Biochim. Biophys. Acta 2010, 1801 (11), 1175–83. (60) Siler, S. Q.; Neese, R. A.; Hellerstein, M. K. De novo lipogenesis, lipid kinetics, and whole-body lipid balances in humans after acute alcohol consumption. Am. J. Clin. Nutr. 1999, 70 (5), 928–36.

(21) Virtanen, K. A.; Lidell, M. E.; Orava, J.; Heglind, M.; Westergren, R.; Niemi, T. Functional brown adipose tissue in healthy adults. N. Engl. J. Med. 2009, 360 (15), 1518–25. (22) Feldmann, H. M.; Golozoubova, V.; Cannon, B.; Nedergaard, J. UCP1 ablation induces obesity and abolishes diet-induced thermogenesis in mice exempt from thermal stress by living at thermoneutrality. Cell Metab. 2009, 9 (2), 203–9. (23) Bartelt, A.; Bruns, O. T.; Reimer, R.; Hohenberg, H.; Ittrich, H.; Peldschus, K. Brown adipose tissue activity controls triglyceride clearance. Nat. Med. 2011, 17 (2), 200–5. (24) Nedergaard, J.; Bengtsson, T.; Cannon, B. New powers of brown fat: Fighting the metabolic syndrome. Cell Metab. 2011, 13 (3), 238–40. (25) Beckonert, O.; Keun, H. C.; Ebbels, T. M.; Bundy, J.; Holmes, E.; Lindon, J. C.; et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2007, 2 (11), 2692–703. (26) Bligh, E. G.; Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 1959, 37 (8), 911–7. (27) Folch, J.; Ascoli, I.; Lees, M.; Meath, J. A.; LeBaron, N. Preparation of lipide extracts from brain tissue. J. Biol. Chem. 1951, 191 (2), 833–41. (28) Folch, J.; Lees, M.; Sloane Stanley, G. H. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 1957, 226 (1), 497–509. (29) Iverson, S. J.; Lang, S. L.; Cooper, M. H. Comparison of the Bligh and Dyer and Folch methods for total lipid determination in a broad range of marine tissue. Lipids 2001, 36 (11), 1283–7. (30) Veselkov, K. A.; Lindon, J. C.; Ebbels, T. M.; Crockford, D.; Volynkin, V. V.; Holmes, E. Recursive segment-wise peak alignment of biological (1)H NMR spectra for improved metabolic biomarker recovery. Anal. Chem. 2009, 81 (1), 56–66. (31) Cloarec, O.; Dumas, M. E.; Craig, A.; Barton, R. H.; Trygg, J.; Hudson, J. Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal. Chem. 2005, 77 (5), 1282–9. (32) Dieterle, F.; Ross, A.; Schlotterbeck, G.; Senn, H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. application in 1H NMR metabonomics. Anal. Chem. 2006, 78 (13), 4281–90. (33) Trygg JaW, S. Orthogonal projections to latent structures, (O-PLS). J. Chemom. 2002, 16 (3), 119–28. (34) Trygg JaW, S. O2-PLS, a two-block (X-Y) latent variable regression (LVR) method with an integral OSC filter. J. Chemom. 2003, 17 (1), 53–64. (35) Nicholson, J. K.; Foxall, P. J.; 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. (36) Garrod, S.; Humpher, E.; Connor, S. C.; Connelly, J. C.; Spraul, M.; Nicholson, J. K. High-resolution (1)H NMR and magic angle spinning NMR spectroscopic investigation of the biochemical effects of 2-bromoethanamine in intact renal and hepatic tissue. Magn. Reson. Med. 2001, 45 (5), 781–90. (37) Wishart, D. S.; Tzur, D.; Knox, C.; Eisner, R.; Guo, A. C.; Young, N. HMDB: The human metabolome database. Nucleic Acids Res. 2007, 35 (database issue), D521–6. (38) Wishart, D. S.; Knox, C.; Guo, A. C.; Eisner, R.; Young, N.; Gautam, B. HMDB: A knowledgebase for the human metabolome. Nucleic Acids Res. 2009, 37 (database issue), D603–10. (39) Aue, W. Two-dimenstional spectroscopy: Application to nuclear magnetic resonance. J. Chem. Phys. 1975, 64, 2229–46. (40) Bodenhausen, G. Natural abundance 15N NMR by enhanced heteronuclear spectroscopy. Chem. Phys. Lett. 1980, 69, 185–9. (41) Rhead, W. J.; Tanaka, K. Demonstration of a specific mitochondrial isovaleryl-CoA dehydrogenase deficiency in fibroblasts from patients with isovaleric acidemia. Proc. Natl. Acad. Sci. U. S. A. 1980, 77 (1), 580–3. (42) Omatsu-Kanbe, M.; Kitasato, H. Insulin and noradrenaline independently stimulate the translocation of glucose transporters from 630


Gut Microbiota Modulate the Metabolism of Brown ... - ACS Publications

Recommend Documents