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H NMR-based Metabolomic Profiling in Mice Infected with Mycobacterium tuberculosis
Ji-Hyun Shin,†,‡ Ji-Young Yang,†,§,|| Bo-Young Jeon,†,^ Yoo Jeong Yoon,§ Sang-Nae Cho,^ Yeon-Ho Kang,‡ Do Hyun Ryu,*,# and Geum-Sook Hwang*,§,|| ‡
)
Division of Bacterial Respiratory Infection, Center for Infectious Diseases, National Institute of Health, Centers for Disease Control and Prevention, Seoul 122-701, Republic of Korea § Korea Basic Science Institute, Seoul 136-701, Republic of Korea Graduate School of Analytical Science and Technology, Chungnam University, Daejeon, 305-764, Republic of Korea ^ College of Medicine, Yonsei University, Seoul l20-749, Republic of Korea # Department of Chemistry, Sungkyunkwan University, Suwon, 440-746, Republic of Korea ABSTRACT: Tuberculosis (TB) is one of three major infectious diseases, and the control of TB is becoming more difficult because of the emergence of multidrug-resistant and extensively drug-resistant strains. In this study, we explored the 1H NMRbased metabolomics of TB using an aerobic TB infection model. Global profiling was applied to characterize the responses of C57Bl/6 mice to an aerobic infection with virulent Mycobacterium tuberculosis (MTB). The metabolic changes in organs (i.e., the lung, the target organ of TB, and the spleen and liver, remote systemic organs) and in serum from control and MTB-infected rats were investigated to clarify the hostpathogen interactions in MTB-infected host systems. Principal components analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) score plots showed distinct separation between control and MTB-infected rats for all tissue and serum samples. Several tissue and serum metabolites were changed in MTB-infected rats, as compared to control rats. The precursors of membrane phospholipids, phosphocholine, and phosphoethanolamine, as well as glycolysis, amino acid metabolism, nucleotide metabolism, and the antioxidative stress response were altered based on the presence of MTB infection. This study suggests that NMR-based global metabolite profiling of organ tissues and serum could provide insight into the metabolic changes in host infected aerobically with virulent Mycobacterium tuberculosis. KEYWORDS: metabolomics, Mycobacterium tuberculosis, metabolite profiling, NMR, tissue, serum
’ INTRODUCTION Tuberculosis (TB) is a chronic wasting contagious disease that has been present in humans since immemorial antiquity. TB is one of three major infectious diseases in terms of mortality and prevalence, the others being acquired immune deficiency syndrome (AIDS) and malaria. One-third of the world’s population is infected with Mycobacterium tuberculosis (MTB), a cause of TB, which results in 9 million new TB cases and approximately 2 million deaths per year (2009 World Health Organization [WHO] Report). The situation has been worsened by the emergence of multidrug-resistant (MDR) or extensively drugresistant (XDR) strains and in coinfection with AIDS. Most people infected with MTB are asymptomatic, and approximately 1 in 10 latent infections progress to active disease, the majority of which are pulmonary TB. MTB infects several organs, causing extrapulmonary TB, which includes pleura, the central nervous system, the lymphatic system of the neck, the genitourinary system, and bones and joints. MTB has adapted to r 2011 American Chemical Society
host macrophages, which offer protection from host immunity, and in which it survives and multiplies. Mycobacterial infection induces a granulomatous inflammation in the lung, which consists of macrophages, neutrophils, T lymphocytes, B lymphocytes, and fibroblasts. T lymphocytes in the granuloma secrete cytokines such as interferon gamma (IFN-γ), IL-12, and tumor necrosis factor-alpha (TNF-R), which activate macrophages to inhibit the growth of MTB.1 However, MTB survives in the granuloma and is transmitted to other people. A decade ago, the entire genome sequence of MTB was revealed as a first pathogen.2 Proteomic and transcriptomic approaches have been applied to the study of MTB to elucidate the biological processes of MTB and establish a control strategy against TB.3 Recently, metabolomics has been applied to eubacteria,4 fungi,5 animal,6 human tissues,7 and parasite-rodent models811 Received: October 20, 2010 Published: April 01, 2011 2238
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Journal of Proteome Research to assess various metabolites in complex mixtures rapidly. Since the metabolomic approach enables an understanding of interconnected hostpathogen systems through a net flow of energy and nutrients between hosts and pathogens, studying metabolic host responses to mycobacterial infection is a potential way to understand the pathogenesis of MTB infection, thereby aiding in finding novel approaches for diagnosis and treatment. Metabolomics using nuclear magnetic resonance (NMR) spectroscopy coupled with pattern recognition has provided as powerful approach to generate high-density metabolite data and characterize metabolic changes in biological samples. In the present study, NMR-based metabolic profiling was used to characterize the responses of C57Bl/6 mice to an aerobic infection with virulent MTB. The metabolic changes in organs (i.e., the lung, the target organ of TB, and the spleen and liver, remote systemic organs) and in serum from control and MTBinfected rats were investigated to clarify the hostpathogen interactions in MTB-infected host systems. Here, this study demonstrates for the first time that the responses to an aerobic infection with virulent Mycobacterium tuberculosis can be characterized through metabolic profiles of both target organ and remote systemic organs of TB
’ MATERIALS AND METHODS Mice
Specific pathogen-free female C57Bl/6 mice at 56 weeks of age were purchased from Japan SLC (Shijuoka, Japan), maintained under barrier conditions in a biohazard animal room at Yonsei University Medical Research Center, and fed a sterile commercial mouse diet and water ad libitum. All animal experiments were performed according to the regulations of the Institutional Animal Care and Use Committee, Yonsei University Health System. MTB Infection and Bacterial Counts
Eighteen mice were challenged by aerosol exposure with MTB H37Rv (ATCC27294) using an inhalation device (Glas-Col, Terre Haute, IN) calibrated to deliver approximately 200 bacteria into the lungs, as previously described.12 For bacterial counts, five mice were killed at 4 weeks postchallenge. The numbers of viable bacteria in the lung, spleen, and liver were determined by plating serial dilutions of whole organ homogenates on Middlebrook 7H11 agar (Difco, Detroit, MI). Colonies were counted after three to four incubations at 37 C. The resulting values are represented as means of log10CFU ( standard deviation of five mice. Histopathology
All samples were collected at 4 weeks postinfection because the bacterial load in the lung reaches a peak at 4 weeks postinfection, and the granulomatous inflammation in the lung can be seen from this point in time.13 Lungs were excised from naïve and MTB-infected mice and stored in 10% formalin, then embedded and stained with hematoxylin and eosin (H&E). H&E-stained lung sections were photographed using a microscope (BX51; Olympus, Tokyo, Japan) fitted with a camera that was connected to a computer. The photos were taken at 20 magnification using DP controller software (Olympus Co.). Sample Preparation
Thirteen MTB-infected mice at 4 weeks postinfection and 10 naïve mice were sacrificed and tissue and blood samples were
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collected. Of these, ten samples each from the infected and naïve mice were analyzed. After sacrificing the mice, the lungs, spleens, and livers from 13 mice were dissected immediately, snap-frozen in liquid nitrogen, and stored at 80 C until extraction. For metabolite extraction, tissues were homogenized by a Precellys 24-bead-based homogenizer (ceramic bead; Stretton Scientific, Stretton, U.K.) containing chloroform (6 mL/g), methanol (6 mL/g), and distilled water (4 mL/g). The homogenate was vortexed and left on ice for 10 min to partition, and then centrifuged at 14 000 g at 4 C for 10 min.14 The upper polar layer was transferred into a separated vial, dried, and stored at 80 C until the NMR experiment. For the NMR experiment, each extract was dissolved in an 800-μL buffer solution (0.1 M phosphate buffer, pH 7.0), and pH was adjusted to 7.0 ( 0.1. A 60-μL volume of 1-mM sodium 2,2-dimethyl-2-silapentane-5sulfonate-d6 (DSS) in 100% D2O was added to 540 μL of tissue extracts. A 600-μL aliquot was placed in a 5-mm NMR tube (Wilmad Lab Glass, Buena, NJ) and stored at 4 C until use. After sacrificing the mice, blood was collected via heart puncture and serum samples were prepared from the blood. Each 150-μL serum aliquot was mixed with 540 μL buffer solution (0.1 M phosphate buffer, pH 7.0) and filtered with Nanosep Omega 3Kspin filters (Pall Corporation, Port Washington, NY). A 60-μL volume of 2-mM DSS in 100% D2O was added to 540 μL of filtered serum. A 600-μL aliquot was placed in a 5-mm NMR tube (Wilmad Lab Glass) and stored at 4 C until use. 1
H NMR Spectroscopic Analysis 1
H NMR spectra were acquired on a VNMRS-600 MHz NMR spectrometer (Varian Inc., Palo Alto, CA) using a triple resonance 5-mm HCN salt-tolerant cold probe. For sera and tissue extracts, the noesypresat-NOESY pulse sequence was applied to suppress the residual water signal. Free induction delays (FIDs) were collected with 1024 transients into 67 568 data points using a spectral width of 8445.9 Hz with a relaxation delay of 2 s, an acquisition time of 4 s, and a mixing time of 100 ms. All spectra were zero-filled to 128k data points, and a line-broadening of 1.5 Hz was applied. Signal assignment for representative samples was facilitated by acquisition of two-dimensional (2D) total correlation spectroscopy (TOCSY), heteronuclear single-quantum correlation (HSQC), the Human Metabolome Database (HMDB), spiking experiments, and comparisons to the literature.1517 Spectral Data Processing and Multivariate Statistical Analysis
All NMR spectra were phased and baseline-corrected, and the spectral binning data were generated using Chenomx NMR suite 6.0 (Chenomx, Edmonton, AB, Canada) software. The binning data were normalized with the total area of each spectrum by excluding the water resonance (4.555.0 ppm). The binning data files were imported into MATLAB (R2008a; MathWorks, Inc., Natick, MA). Probabilistic quotient normalization of the spectra using the median spectrum to estimate the most probable quotient was carried out,18 and the spectra were aligned using the correlation optimized warping (COW) method19 to reduce variability in the peak positions. Multivariate data analysis, principal components analysis (PCA), and orthogonal partial least-squares discriminant analysis (OPLS-DA) were performed on the binning data with mean-centered scaling using SIMCA-P (version 12; Umetrics, Umea, Sweden). PCA, an unsupervised pattern-recognition (PR) method, was performed to examine the intrinsic variation in the data set, and OPLS-DA, a supervised PR method, was also employed to maximize the separation between 2239
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Multivariate Statistic Analysis in Tissue Extracts and Sera
Figure 1. Mice were challenged via aerosol exposure with MTB and sacrificed at 4 weeks postchallenge. (A) Bacterial numbers in the lung, spleen, and liver of five mice at 4 weeks postchallenge. (B) Histopathology of lungs of naïve control or MTB-infected mice.
the control and TB-infected mice. The quality of the models was described by R2 and Q2 values. R2 is defined as the proportion of variance in the data explained by the models and indicates goodness of fit, and Q2 is defined as the proportion of variance in the data predictable by the model and indicates predictability. Statistical significance was evaluated for individual values using the t test for unequal variances in Excel (Microsoft, Redmond, WA).
’ RESULTS MTB Mouse Models and Lung Pathology
As a pulmonary TB model, mice were aerobically challenged as a natural infection route of TB with a virulent MTB H37Rv strain. After delivery of approximately 200 CFUs of MTB bacilli into the lungs of mice, bacterial CFUs were 6.4 log, 5.3 log, and 4.45 log in the lung, spleen, and liver, respectively, at 4 weeks postinfection (Figure 1A). Granulomatous inflammation was clearly seen in the lungs of MTB-infected mice at 4 weeks postinfection (Figure 1B). 1
H NMR Spectra of Aqueous Tissue Extracts and Serum
Figure 2 shows representative 1H NMR spectra of aqueous tissue extracts of the lung, spleen, liver, and serum, respectively, from naïve control and MTB-infected mice. Spectral resonances of metabolites were assigned according to the 600-MHz library from Chenomx NMR suite 6.0, 2D NMR spectra (COSY, TOCSY, and HSQC), and the literature.16,17 The ambiguous peaks caused by overlap or slight shifts were confirmed by spiking samples with the respective standard compounds. Metabolites that were commonly observed in all tissue extracts include carbohydrates such as glycogen and glucose; nucleotides such as cytosine nucleotides (CXP), uracil nucleotides (UXP), and adenine nucleotides (AXP); amino acid such as alanine, aspartate, glutamate, glutamine, glutathione, glycine, histidine, isoleucine, leucine, lysine, phenylalanine, and tyrosine; and other various metabolites such as valine, acetate, creatine, dimethylamine, formate, fumarate, lactate, oxalacetate, succinate, taurine, NADþ, NADPþ, niacinamide, choline, O-phosphocholine, glycerophosphocholine, propylene glycol, succinate, taurine, uracil, and uridine. Some distinctive metabolites exist between tissues: O-phosphoethanolamine and myo-inositol have shown clear resonance peaks in all lung and spleen tissues. Moreover, 3-hydroxybutyrate was detected in liver tissue of both control and MTB-infected groups, whereas itaconate was detected only in lung extracts of the MTB-infected group (Figure 3).
PCA was initially performed to detect outliers and examine the intrinsic differences between naïve control and MTB-infected mice. OPLS-DA was then sequentially applied to facilitate interpretation followed by PCA. PCA and OPLS-DA models of lung extracts had a higher R2 value (0.82) and Q2 value (0.98) than the liver (R2 of 0.67 and Q2 of 0.86) and spleen (R2 of 0.69 and Q2 of 0.57) extracts, which imply that these optimized models with higher R2 and Q2 values are more explainable and predictable (Table 1). Figure 4 shows the score plots of PCA and OPLS-DA models derived from the 1H NMR spectra of aqueous tissue extracts obtained from naïve control and MTB-infected mice. PCA score plots showed fairly clear differences between control and MTBinfected rats in all tissue samples, indicating significant changes in metabolism of MTB-infected rats. OPLS-DA models showed significantly improved discrimination compared to PCA models, so OPLS-DA loading plots were generated to identify the metabolites responsible for the differentiation between groups (Figure 5). Volatile metabolites, such as methylamine, dimethylamine, formate, and acetate, were removed during model generation and normalization because all aqueous tissue extracts were dried under reduced pressure and reconstituted in the sample preparation. The loadings plots of tissue extracts show the metabolites responsible for the discrimination in score plots. The foldchanges of these metabolites were generated with their relative peak intensities from the normalized binning data of 1H NMR spectra and are presented in Table 2. Even though the spectra of liver extracts contained more metabolites than the lung and spleen extracts, the selected metabolites (|fold change| > 1.2, p < 0.05) in Table 2 are similar to liver, lung, and spleen extracts. Overall, the relative intensities of a variety of amino acids (including alanine, aspartate, glutamate, leucine, lysine, isoleucine, phenylalanine, tyrosine, glutamine, glycine, and valine), PC and PE, creatine, lactate, and AMP increased, and levels of TCA-cycle intermediates, such as oxalacetate and fumarate, NADPþ, NADþ, glucose, and glycogen, decreased in the MTB-infected group, as compared to the control group. The singlet resonances at 5.37 ppm and 5.85 ppm were only observed in the TB-infected lung extracts. They were assigned itaconate with their correlation in the TOCSY spectrum and confirmed by a spiking experiment (Figure 2). Among the metabolites detected in lung tissue, 30 were quantitatively different between the naïve control and MTBinfected mice. Twenty-five metabolites were increased with infection, while five metabolites were decreased. The changes of numerous metabolites in lung tissue from MTB-infected mice indicate that the site of infection could be influenced by MTB. In the liver, the center of metabolism, 16 metabolites were increased with infection and five metabolites were decreased. Interestingly, all of the metabolites that increased in the liver tissue with infection were also elevated in the lung tissue. Eleven metabolites were increased in the spleen with infection and four metabolites were decreased. As in the liver tissue, all of the metabolites that were increased in the spleen tissues with infection were also increased in lung tissue. These results suggest that the largest change in metabolites was detected in the lung tissue, which is the target organ of M. tuberculosis, and the changes in metabolites with infection are similar among the tested organ tissue. PCA and OPLS-DA were also performed on the sera, and their scores and loading plots are displayed in Figure 6. Lactate 2240
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Figure 2. Representative 600-MHz 1H NMR spectra of aqueous tissue extracts obtained from control mice: (A) lung, (B) spleen, (C) liver, and (D) serum. Each vertical scale of the upfield region (5.09.5 ppm) was increased 4-fold for each downfield region (0.74.5 ppm). PC, phosphocholine; PE, phosphoethanolamine; GPC, glycerophosphocholine; 3-HB, 3-hydroxylbutyrate; UDPG, UDP-glucose; GSH, glutathione.
resonances in 1.32 ppm and 4.10 ppm were excluded from the binning data before normalization because no significant difference existed in the intensities of lactate peaks between groups, and the variation in lactate signal amplitudes were not systematic (results not shown). Removing these peaks and repeating the PCA revealed no significant changes in loading patterns, but improved R2 and Q2 in the discrimination analysis. In the serum loading plot (Figure 6C), the level of glucose and branched-chain amino acids such as leucine, isoleucine, valine, and phenylalanine increased in the MTB-infected group, as compared to the control; the fold-change is presented in Table 2.
’ DISCUSSION Global metabolite analysis is a powerful tool for understanding hostpathogen interactions. In this study, we explored NMRbased global metabolic profiling of TB using an aerobic TB
infection model. Global metabolite profiling can be effectively used to investigate metabolic changes in both target and remote systemic TB organs in MTB-infected models. Carbohydrate, Lipid, and Fatty Acid Metabolism
In the metabolic profiles (Table 2), glycogen and glucose levels decreased, but lactate levels increased in the MTB-infected mice, as compared to the naïve control mice, indicating a possible elevation in glycolysis. This is consistent with a previous report stating that glycolysis is elevated in granulomatous inflammation, primarily in macrophages and neutrophils, and the elevation in glycolysis is associated with an affinity of glucose receptors for deoxyglucose, which is increased by various cytokines and growth factors.20 The precursors of the membrane phospholipids PC and PE increased significantly in the lungs of MTB-infected mice, as compared to naïve control mice. The increases in PC and PE in 2241
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Table 1. Summary of Parameters for Evaluating Model Quality PCA model
OPLS-DA model
N
R X(cum)
N
R X(cum)b
R2Y(cum)b
Q2Y(cum)c
Lung
4
0.94
1Pþ1O
0.82
0.99
0.98
Spleen
3
0.80
1Pþ1O
0.69
0.88
0.57
Liver Serum
4 7
0.92 0.92
1Pþ1Od 1Pþ2O
0.67 0.71
0.90 0.92
0.86 0.74
a
b
2
a
2
a N, the number of components. b R2X(cum) and R2Y(cum) are the cumulative modeled variations in the X and Y matrices, respectively. R2 value represents the goodness of fit of the model. c Q2Y(cum) is the cumulative predictive variation in the Y matrix. A Q2 value close to 1.0 represents a higher predictive reliability. d nPþnO denotes the number of predictive and orthogonal components for establishing the OPLSDA model.
Figure 3. TOCSY spectrum of pooled lung extract of MTB-infected mouse acquired at 600 MHz in an 1H NMR spectrometer.
the lung indicate that the metabolism might be changed into β-oxidation and glyoxylate shunting, and might be due to granulomatous inflammation in the lung after MTB infection. The granulomatous inflammation might induce glucose deficiency and low oxygen levels, which divert cells to utilize lipids and fatty acids as carbon substrates via catabolic β-oxidation and glyoxylate shunting.21,22 Moreover, MTB could survive in an environment of glucose deficiency and low oxygen because MTB can divert carbon from β-oxidation of lipids into the anaplerotic glyoxylate pathway, which converts isocitrate and acetyl-CoA into succinate and malate to replenish TCA cycle intermediates.2325 Succinate increased significantly in the lung, spleen, and liver tissues of MTB-infected mice compared to naïve control mice, while no significant difference was observed in serum samples. This increase could be mediated through the methylcitrate cycle that oxidizes propionyl-CoA generated by β-oxidation of odd-chain fatty acids to pyruvate and succinate. The methylcitrate cycle might be required for the detoxification of propionate as Mu~ noz-Elías et al. described; i.e., propionate metabolism requires glyoxylate and methylcitrate cycles.26 Also, succinate may also be formed under oxidative stress, which is required to kill MTB in macrophages.27 Succinate, however, could be induced by upregulation of the glyoxylate and methylcitrate cycles of MTB. Amino Acid Metabolism
Many amino acids significantly increased in the lung, spleen, liver, and serum of MTB-infected mice. Amino acid metabolism is complex because large numbers of metabolites are involved. The perturbation of amino acid metabolism is probably related to proteolysis, oxidative catabolism, and gluconeogenesis. Free amino acids are one of the most important precursors for gluconeogenesis; they might increase when amino acids are not utilized for protein anabolism but are oxidized by impairment of protein synthesis.2831 These results are consistent with several reports stating that malnutrition and wasting, which is
referred to as “anabolic block,” were observed in patients with TB.2831 Anabolic block refers to a greater amount of ingested amino acids being oxidized rather than being utilized for protein anabolism in patients with TB. Further studies would clarify how to change MTB amino acid metabolism, which would help to efficiently treat patients with TB. The metabolic changes in serum from control and MTBinfected rats produced a distinct pattern reflecting the hostpathogen interactions in MTB-infected host systems. Several serum metabolites (such as leucine, isoleucine, valine, phenylalanine, formate, and glucose) were changed in MTB-infected rats, as compared to control rats. Of interest, we observed that amino acid levels increased in rats in response to MTB infection. The changes in the concentrations of amino acids could be due to an attempt to conserve protein by limiting the commitment of these amino acids to gluconeogenesis. There is a high demand for amino acids as substrates for energy production during infection. The high demand for amino acids is met by an enhanced proteolysis.32 Nucleotide (Purine and Pyrimidine) Metabolism
Uracil, uridine, and UDP-glucose, intermediates of pyrimidine metabolism, and ATP, AMP, inosine, allantoin, and xanthine, intermediates of purine metabolism, increased in MTB-infected mice. These results indicate that pyrimidine and purine metabolism increased after MTB infection, suggesting that cells actively divide in MTB-infected organs, especially the lungs.16,3336 Alteration of nucleotide metabolism may be highly impacted in that pathogens obtain host nucleotide precursors to multiply, even though little is known about how intracellular pathogens interact with host metabolism. Antioxidative Stress Response
Oxidative stress is defined as a large rise in cellular reduction potential or a large decrease in the reducing capacity of cellular redox couples, such as glutathione (GSH).37 Oxidative stress is produced by hypoxia, hyperoxia, xanthine/xanthine oxidase, hydroperoxide, lipid peroxidation products, oxidized low-density lipoproteins, and reactive oxygen and nitrogen species (ROS and RNS, respectively) produced by immune cells and inflammatory cytokines.3743 In our study, GSH was elevated in the lungs and spleen of MTB-infected mice. GSH is a ubiquitous and an important endogenous antioxidant providing protection against oxidative stress. Therefore, the increase in GSH/GSSH ratios 2242
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Figure 4. PCA and OPLS-DA score plots derived from the 1H NMR spectra of aqueous tissue extracts obtained from naïve control and MTB-infected mice. PCA and OPLS-DA score plots of (A and B) lung, (C and D) spleen, and (E and F) liver demonstrated clear metabolic differences between control and TB-infected groups. Each PCA model was generated with principal components (PC) and each OPLS-DA model was generated with predictive components (T) and orthogonal components (TO) to discriminate between groups.
suggests that oxidative stress might be induced by MTB infection.44 High increases in lipolysis products such as PC and PE, xanthine, and NADþ breakdown to nicotinamide, taurine, and betaine in the MTB-infected mice might be also associated with the increase in oxidative stress. Taurine is one of the most abundant free amino acids present in mammalian tissues, and betaine is a derivative of choline produced by choline oxidase. Taurine and betaine might have protective effects against oxidative stress in MTB-infected mice because these have shown hepatoprotective effects against oxidative stress such as lipid peroxidation and prooxidant status.4548 Creatine and its phosphorylated form are well recognized as key intermediates in energy metabolism, and the increase in creatine is associated with energy demand. Moreover, high levels of creatine are associated
with general cytoprotective effects toward oxidative agents.49,50 Aldehyde, one of the major end-products of lipid peroxidation, markedly increased in MTB-infected lungs, which may induce activation of the glutathione-redox cycle against oxidative stress.24 In the MTB-infected mice, high levels of succinate were detected. Succinate may be also formed by the switching of the TCA cycle to the nonenzymatic formation of succinate from Rketoglutarate under oxidative stress, which is inherent in many diseases and aging.27 These results suggest that an antioxidative stress response was induced in the MTB-infected mice. However, whether the antioxidative stress was maintained since MTB infection is not clear. Future studies should investigate whether oxidative or antioxidative stress was induced based on the stage of infection. 2243
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Table 2. The Significantly Changed Metabolites Related to MTB Infection (|| > 1.2, p < 0.05) fold changea chemical metabolite
shift (ppm)
lung
spleen
liver
serum
AMP
8.59, 6.13
2.04
1.29
1.32
b
Creatine Lactate
3.02 4.10, 1.32
2.04 2.35
1.22
1.26 1.25
Glycogen
5.40, 3.64
Glucose
5.23, 3.88, 3.71,
1.85
6.08
1.14
1.21
1.32
3.52, 3.46, 3.24 NADþ
9.33, 9.13, 8.82,
8.41, 8.16
Figure 5. OPLS-DA loading plot of aqueous extracts of the (A) lung, (B) spleen, and (C) liver from naïve control and MTB-infected mice. The loading plots represent which variables had influences on predictive components and class discrimination. PC, phosphocholine; PE, phosphoethanolamine; GPC, glycerophosphocholine; 3-HB, 3-hydroxylbutyrate; UDPG, UDP-glucose; Suc, succinate; OA, oxalacetate; NA, niacinamide; Lac, lactate; GSH, glutathione.
NADPþ
9.28, 9.09, 8.80, 8.40
2.42
4.44
1.19
Oxalacetate Fumarate
3.66 6.51
2.11 1.28
1.75 1.21
1.22
1.22
Succinate
2.39
2.45
1.30
Alanine
1.47
1.99
1.15
Aspartate
2.80, 2.66
2.37
1.63
Glutamate
2.34, 2.04
1.50
1.20
Leucine
0.95
1.92
Lysine
3.02, 1.89
1.68
1.33
Isoleucine Phenylalanine
1.00, 0.92 7.42, 7.36, 7.32
1.88 1.68
1.38
1.48 1.47
Tyrosine
7.18, 6.88
2.35
1.58
1.34
Glutamine
2.44, 2.12
1.49
1.22
1.21
PCc
4.16, 3.20
1.27
1.31
1.49
PEc
3.97
1.88
Betaine
3.89, 3.25
1.45
Niacinamide
8.93, 8.70, 7.59
1.39
Acetaldehyde Taurine
9.67 3.41, 3.25
4.45 1.31
UDP-glucose
7.93, 5.97, 4.36
Uracil
7.53, 5.80
Uridine
7.85, 5.90
1.42
Xanthine
7.91
1.75
Formate
8.44
Glutathione
3.77, 2.52, 2.15
1.40
Itaconic acid
5.85, 5.37
1.20
1.44
1.76 1.57 1.62
1.64
1.29
1.59
1.44
2.58
1.91
1.31
TB only
1.37
1.37
1.23
1.51
a
Calue represents the fold-change in metabolite levels in MTB-infected mice compared to the control. The levels were estimated from relative intensities of 1H NMR spectra following spectral normalization. b Indicates no change in the level or no significance (p > 0.05). c PC, phosphocholine; PE, phosphoethanolamine.
Itaconate in MTB-infected Lungs
Itaconate is produced from cis-aconitate and converted to pyruvate in mitochondria,51 but its pathway has been very weakly characterized in mammalian cells. Notably, itaconate increased only in MTB-infected lung tissue. This is the first report in which itaconate was detected as a metabolite in lung tissue, but why itaconate increased after MTB infection is not clear. Itaconate in prokaryotes inhibits the isocitrate lyase, a key enzyme in the glyoxylate pathway of MTB, as well as the propionyl-CoA carboxylase of Rhodospirillum rubrum.5254 Itaconate in lung tissue could originate from MTB and may inhibit isocitrate lyase in acute infection, which is known to be highly expressed in a latent state.51
We have profiled the tissue and serum metabolite response in MTB-infected mice and found distinct changes in metabolic profile with infection. These results are particularly important, given that understanding the process of M. tuberculosis infection in organs offers a potential method for elucidating the response to therapeutics. A metabolomics-based approach has the potential to allow the classification of disease states and to identify biomarkers specific for disease severity and drug efficacy. This study suggests that such a metabolite profiling approach can be applied to identify molecular signatures associated with the diagnosis of MTB infection or its progression and therapeutic efficacy. Such metabolic signatures could provide diagnostic 2244
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Figure 6. The score plots of (A) PCA and (B) OPLS-DA derived from the 1H NMR spectra of sera obtained from naïve control and MTB-infected mice. The PCA model was generated with principal components (PC), and the OPLS-DA model was generated with predictive components (T) and orthogonal components (TO) to discriminate between groups. OPLS-DA loading plot of filtered serum (C) from naïve control and MTB-infected mice. The loading plots show which variables had influences on predictive components and class discrimination.
markers for TB progression or biomarkers for the efficacy of antiTB drug treatment, and latent MTB infection. In conclusion, this study illustrates the application of metabolomics based on 1H NMR spectroscopy of various organ tissues and serum for investigating the metabolic changes in a MTB-infected model at the biochemical level. Additionally, this study suggests that global metabolite profiling could provide insight into the metabolic influence of hosts by MTB infection. Moreover, a global systems biology approach integrating global metabolite levels with genomic, transcriptional, and proteomic analysis would provide more knowledge of hostpathogen interactions, as well as efficient diagnosis and treatment of TB.
’ AUTHOR INFORMATION Corresponding Author
*G.-S.H.: tel, þ82-2-920-0737; fax, þ82-2-920-0779; e-mail,
[email protected]. D.H.R.: tel, þ82-31-290-5931; fax, þ8231-290-5976; e-mail:
[email protected]. Author Contributions †
These authors contributed equally to the work.
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