Metabolomic Signatures in Guinea Pigs Infected with Epidemic

Article pubs.acs.org/jpr

Metabolomic Signatures in Guinea Pigs Infected with Epidemic-Associated W‑Beijing Strains of Mycobacterium tuberculosis Bagganahalli S. Somashekar,†,§ Anita G. Amin,† Pratima Tripathi,§ Neil MacKinnon,§ Christopher D. Rithner,‡ Crystal A Shanley,† Randall Basaraba,† Marcela Henao-Tamayo,† Midori Kato-Maeda,∥ Ayyalusamy Ramamoorthy,*,§ Ian M. Orme,† Diane J. Ordway,† and Delphi Chatterjee*,† †

Department of Microbiology, Immunology and Pathology, Colorado State University, Campus Delivery 1682, Fort Collins, Colorado 80523-1682, United States ‡ Central Instrument Facility, Department of Chemistry, Colorado State University, Fort Collins, Colorado 80526, United States § Department of Chemistry and Biophysics, University of Michigan, Ann Arbor, Michigan 48109-1055, United States ∥ Division of Pulmonary and Critical Care Medicine, University of California, San Francisco, California 94110, United States S Supporting Information *

ABSTRACT: With the understanding that the laboratory propagated strain of Mycobacterium tuberculosis H37Rv is of modest virulence and is drug susceptible, in the present study, we performed a nuclear magnetic resonance-based metabolomic analysis of lung tissues and serum obtained from guinea pigs infected by low dose aerosol exposure to clinical isolates of Mycobacterium tuberculosis. High Resolution Magic Angle Spinning NMR coupled with multivariate statistical analysis of 159 lung tissues obtained from multiple locations of agë and 30 and 60 days of infected guinea pig lungs matched naive revealed a wide dispersal of metabolic patterns, but within these, distinct clusters of signatures could be seen that differentiated between naive control and infected animals. Several metabolites were identified that changed in concert with the progression of each infection. Major metabolites that could be interpreted as indicating host glutaminolysis were consistent with activated host immune cells encountering increasingly hypoxic conditions in the necrotic lung lesions. Moreover, glutathione levels were constantly elevated, probably in response to oxygen radical production in these lesions. Additional distinct signatures were also seen in infected serum, with altered levels of several metabolites. Multivariate statistical analysis clearly differentiated the infected from the uninfected sera; in addition, Receiver Operator Characteristic curve generated with principal component 1 scores showed an area under the curve of 0.908. These data raise optimism that discrete metabolomic signatures can be defined that can predict the progression of the tuberculosis disease process, and form the basis of an innovative and rapid diagnostic process. KEYWORDS: tuberculosis, W-Beijing strains, guinea pigs, NMR, metabolic profile, metabolomics

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A serious weakness in the field is the constant lack of surrogate markers that can rapidly predict whether a particular drug regimen is working. In many areas of the world, it will take more than “several” weeks before ineffective drug treatment is recognized.4 The same issue extends to vaccination, in the context of whether BCG (Bacillus Calmette-Guérin) or the new vaccine candidates about to be tested have generated sufficient memory immunity to resist exposure at some future date to M. tuberculosis. For obvious practical reasons, most efforts to date have focused on serum assays, usually the detection of cytokines,5

INTRODUCTION

An estimated 8 million or so people each year will become infected with Mycobacterium tuberculosis (M. tuberculosis) and can be expected to receive chemotherapy for their condition. Even in individuals that respond quickly to therapy, treatment duration is a minimum of 6−9 months.1 Some of these patients, now estimated at 650,000 by 2012,2 will turn out to have multidrug resistant tuberculosis, requiring second line drugs and may require up to two years of treatment. While there is an impressive pipeline of new drugs in development,1,3 optimization and efficacy studies of these in clinical regimens in the field will obviously take years to complete. © 2012 American Chemical Society

Received: April 9, 2012 Published: August 8, 2012 4873

dx.doi.org/10.1021/pr300345x | J. Proteome Res. 2012, 11, 4873−4884

Journal of Proteome Research

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Experimental Infections

although some new progress is being made by looking at gene signatures.4,6 A fledgling technology, which is starting to show considerable promise, is the application of metabolomics to the identification of useful diagnostic and prognostic markers.7,8 This takes advantage of nuclear magnetic resonance (NMR) and/or mass spectrometry (MS) analyses that can identify many hundreds of metabolites in biological samples and hence be used to identify complex signatures that could potentially predict the disease state, and whether or not a new drug or vaccine can shift these signatures to one that indicates a positive effect.9 We recently used High Resolution Magic Angle Spinning (HRMAS) NMR to determine the expression of metabolomics signatures of lung granulomas in guinea pigs aerosolized with the laboratory-adapted M. tuberculosis strain H37Rv.10 These initial studies showed consistent and significant up-regulation of lactate, alanine, acetate, glutamate, the oxidized and reduced forms of glutathione, aspartate, creatine, phosphocholine, glycerophosphocholine, betaine, trimethylamine N-oxide, myo-inositol, scylloinositol, and dihydroxyacetone which could be clearly identified and increased as the infection progressed. In the study reported here, we extended these investigations to explore metabolic signatures of lung tissues and serum in guinea pigs infected with four different W-Beijing strains that belong to the large sequence polymorphism based East Asian lineage and are highly pathogenic in our guinea pig model.11 Because the pathophysiology of tuberculosis in guinea pigs has multiple similarities to human disease, we also searched for signatures in serum which could be used as noninvasive diagnostic or prognostic biomarkers. To delineate the metabolic signatures, ̈ and we collected lung tissues, blood (serum) and urine from naive infected guinea pigs at 30 and 60 days of the infection. The lung tissues and serum samples were analyzed using HRMAS and solution NMR, respectively. We show here that a large number of altered metabolites are detected in the lung tissues of guinea pigs infected with these virulent isolates. Importantly, these could be well differentiated from those seen in naive controls. In the case of serum samples, more than 37 metabolites were unambiguously identified. Among these, acetate, formate, and adenosine monophosphate increased markedly by day 30 infection, and at the same time, levels of lactate, nicotinamide, glutamate, glutamine, choline, phosphocreatine, and ethanolamine decreased. This exploratory study, involving limited but statistically relevant numbers of samples, shows the potential of NMR analysis of serum to differentiate signals from animals infected with clinical isolates showing a range of immunopathology from signals seen in the naive controls.

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Four strains of M. tuberculosis were used in this study; these represented W-Beijing sublineages RD142 (strains 4619 and 4233) and RD181 (strains 4147 and 3393). These isolates were selected from a population based collection of 545 M. tuberculosis strains of East Asian lineage isolated from a clinical study in San Francisco.12 A Madison chamber aerosol generation device was used to expose animals to each isolate. This device was calibrated to deliver approximately 20 bacilli into the lungs of each guinea pig.13 Fifty guinea pigs were used in the study, in which 40 animals were grouped equally and aerosolized with each of the four M. tuberculosis strains and 10 animals set aside as naive uninfected controls. The inoculum delivered and a day 1 lung homogenate were plated on nutrient 7H11 agar and colonyforming units counted after 3 weeks incubation at 37 °C to verify the correct bacterial number delivered by all the clinical strains. No significant differences were found in bacterial numbers of the different clinical bacterial strains delivered to the guinea pigs. After euthanasia, blood samples were withdrawn directly from the heart and transferred into clean vials. For pathology analysis, the left pulmonary lobes were infused in situ with 5 mL of 10% neutral-buffered formalin and then placed into 30 mL of fixative overnight. Specimens were separately embedded in paraffin, sectioned at 4 μm slices from equivalent areas of the left cranial lung lobe (i.e., along the left primary bronchus) and stained using hematoxylin and eosin stain. The veterinary pathologist, Dr. Randall Basaraba, completed the blind scoring of lung and spleen lesions as previously published14 which is based on randomly selected sections in a representative experiment from the different animal groups at indicated times after infection to evaluate the pathogenesis. Sample Preparation

For HRMAS measurements, all the infected and uninfected lung tissues were prepared as described previously.10 Briefly, for HRMAS NMR measurements, frozen lung tissues from infected guinea pigs were briefly thawed on ice and visually identifiable granulomatous tissues were excised weighing approximately between 15 and 30 mg from various sites of the lung. However, ̈ guinea pigs, tissue sections (20− in the case of age matched naive 30 mg wet weight) were taken randomly from multiple locations in the lungs. The number of lung granulomas from each infected group and uninfected lung tissue sections used for HRMAS NMR measurements are presented in the Supporting Information Table S1. Tissue sections were rinsed with deuterium oxide (D2O, Sigma Aldrich) and inserted into a 4 mm zirconia MAS rotor (40 μL capacity) followed by the addition of ∼20 μL D2O. Excess liquid was removed while assembling the Kel-F plastic cap and the outer surface of the rotor was decontaminated, and the rotors were kept on dry ice until taken for HRMAS NMR measurements. After NMR measurements, the tissue samples were removed from the NMR rotors, fixed, embedded, sectioned and stained with Hematoxylin and Eosin and for bacterial counts. Blood samples were allowed to coagulate at room temperature for about 30 min and centrifuged (1500g, 10 min) within a short time to separate sera, and supernatants were transferred and stored at −80 °C.

METHODS AND MATERIALS

Animals

Four week old (∼300 g) specific pathogen-free albino Hartleystrain guinea pigs were purchased from the Charles River Breeding Laboratories, Inc., Wilmington, MA, and housed under barrier conditions in the Colorado State University Biosafety Level 3 biohazard facility, where they were provided sterile commercial chow in stainless-steel feeders and tap water ad libitum. The guinea pigs were maintained in a temperature- and humidity-controlled environment and exposed to a 12-h light/ dark cycle. After approximately 2 to 3 weeks to acclimate, animals were then used in these studies.

HRMAS NMR Measurements on Lung Tissues

HRMAS 1H NMR spectra were acquired on a Varian INOVA 500 spectrometer operating at 500.13 MHz for 1H observation. Though the rotor spinning frequency rate was controlled with an 4874



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accuracy of ±1.0 Hz by a MAS controller (Varian, Inc.) during measurement, spinning rate was also verified by measuring the frequency difference between the adjacent spinning side bands in the spectrum. Two sets of one-dimensional (1D) 1H NMR spectra were acquired at 298 K with a spinning frequency of 2600 Hz and using water presaturation: a standard single pulse spectrum and a Carr−Purcell−Meiboom−Gill (CPMG) spin− echo (90°x[-tau-180°±y-tau-echo]n) spectrum. The Free Induction Decays (FIDs) of the CPMG spin−echo spectra were acquired using 256 scans, a 1.98 s acquisition time of 32 000 data points, and a recycle delay of 3 s. Signals from macromolecules such as proteins were suppressed by T2 filtering using the CPMG pulse sequence; tau = 100 μs repeated 250 times resulting in a 50 ms effective echo time. The acquired data were processed using TOPSPIN 2.1 software (Bruker Biospin). Resulting data were zero filled with 32K points and Fourier transformed after multiplying by an exponential window function using a line broadening function of 0.3 Hz. All NMR spectra were referenced to CH3 signal of lactate at δ1.33.

Relative Quantitative Analysis of Serum Metabolites

Spectra were preprocessed with phase and baseline correction and removal of spectral regions of residual water (4.52− 5.10 ppm) and methanol resonances (3.34−3.37 ppm). Relative intensities of serum metabolites were calculated separately for uninfected, 30 and 60 day infected sera using aligned and total area normalized 1H CPMG NMR spectra. A representative nonoverlapping 1H signal from each metabolite was used to calculate the integral area. Statistical Analysis

All the multivariate statistical analysis was performed using Unscrambler software version 10.1 (CAMO, Oslo, Norway). Principal component analysis (PCA) on 1H CPMG spectra of lung tissues was performed as described previously.10 PCA was performed separately for (1) data of all the uninfected, 30 and 60 days infected lung tissues; (2) NMR of only 30 days infected samples, and (3) NMR of only 60 days infected samples. Before subjecting 1H CPMG spectra of the serum samples to multivariate statistical analysis, the spectral regions of water and methanol (ranging at 4.7−5.1 and 3.34−3.37 ppm) were removed, all spectra were aligned using the SpecAlign software,16 and each aligned spectrum was normalized to the total area. PCA was performed with mean-centered scaling and full cross validation. Receiver operator characteristic (ROC) curve was generated from principal component scores and area under the curve (AUC) was used to depict the predictive ability of PCA model in differentiating infected serum from that of uninfected.

Histopathological Assessment of Lung Tissues after HRMAS NMR

Immediately after acquiring 1H NMR spectra, the MAS rotors were transferred to dry ice and tissue sections were taken out of rotors and processed for histopathology as described previously.15 The lesions in the primary granuloma were further categorized based on the type of inflammation and the presence or absence of necrosis, fibrosis and mineralization. To correlate with the bacillary index, slides were subsequently stained with auramine and rhodamine.

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H NMR of Serum Metabolites

Serum samples were prepared at the time of necropsy from both naive (age control) and infected animals, and passed through a 0.2 μm filter (Part No: V259861, Pall Gelman Acrodisc Syringe filter with PTFE) to sterilize. A total of 600 μL was measured out and methanol (1.2 mL) was added to precipitate proteins and the sample was thoroughly mixed by vortex. The mixture was cooled at −20 °C for 30 min to precipitate all the serum proteins/ macromolecules. The mixture was centrifuged at 13 000 rpm for 10 min and the supernatant removed and dried. The resultant residue containing serum metabolites was reconstituted in 530 μL of D2O and transferred to 5 mm NMR tubes (WilmadLabGlass, Vineland, NJ) for NMR measurements. A coaxial capillary tube (Part No. WGS-5BL, Wilmad-LabGlass, NJ) containing 1.5 mmol of 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid sodium salt (TSP) (Sigma-Aldrich) in D2O was inserted into each NMR tube before measurements. All NMR measurements on serum samples were carried out at 298 K on a 900 MHz Bruker Avance NMR spectrometer (Bruker BioSpin, Karlsruhe, Germany) equipped with a TCI cryo-probe [5 mm 1H (13C/15N)]. 1H NMR spectra were acquired using one-pulse as well as CPMG sequences (ZGPR and CPMGPR in Bruker library); in both experiments, HDO signal was suppressed by presaturation during the relaxation delay. Typical NMR parameters used were: spectral width of 13550 Hz, 32 768 data points, 256 scans, 4 s relaxation delay and an acquisition time of 1.21 s (the total repetition time was 5.21 s). In the CPMG sequence, T2 filtering was obtained with an echo time of 800 μs repeated 80 times, resulting in an effective echo time of 64 ms. The resultant FIDs were zero-filled with 32 768 data points and multiplied by an exponential window function with a line broadening factor of 0.3 Hz prior to the Fourier transformation. The chemical shifts were referenced to the 1H signal of TSP at 0 ppm.

RESULTS

Course of Infections

The four strains used in these studies were selected from a population-based study of tuberculosis in San Francisco.17 These four strains represent two sublineages of W-Beijing/East Asian lineage described previously.12,18 The growth patterns of the four isolates used in this study are presented in Table 1. Thirty days Table 1. Clinical Strains of M. tuberculosis 4619 and 4233 Show Increased Organ Bacterial Loadsa bacterial load (log10 CFUs) (mean ± SEM) W-Beijing strains

days of infection

lung

lymph node

spleen

4619

30 60 30 60 30 60 30 60

5.20 ± 0.16 5.90 ± 0.32 5.15 ± 0.15 5.10 ± 0.36 4.70 ± 0.24 5.20 ± 0.06 4.50 ± 0.23 5.90 ± 0.28

5.49 ± 0.20 5.99 ± 0.19 5.70 ± 0.30 5.43 ± 0.07 5.73 ± 0.12 5.10 ± 0.07 3.60 ± 0.70 4.97 ± 0.14

5.31 ± 0.20 4.80 ± 0.35 5.38 ± 0.27 5.37 ± 0.16 4.75 ± 0.30 4.85 ± 0.30 2.43 ± 0.10 2.80 ± 0.45

4233 4147 3393 a

The table shows the bacterial growth in the lungs, lymph nodes and spleens from guinea pigs receiving a low dose aerosol of M. tuberculosis clinical strains 4619, 4233, 4147, and 3393, assayed on day 30 and 60 after infection. Results are expressed logarithmically as the mean Log10 bacilli colony forming units (CFU) (± SEM, n = 5).

after initial infection, the two strains 4619 and 4233 (sublineage RD142) had reached higher bacterial loads in the lungs, spleens and lymph nodes compared to the 4147 and 3393 (sublineage RD181) strains. Growth patterns by day 60 were more mixed, but this data has to be interpreted with caution due to the 4875



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Figure 1. Increased granulomatous responses from guinea pigs infected with the clinical strains of M. tuberculosis 4619, 4233, 4147, and 3393. Panels A−P show representative photomicrographs from sections of paraformaldehyde-fixed and paraffin embedded guinea pig tissues from lung which were collected on day 30 and 60 of infection with the clinical strains 4619 (A−D), 4233 (E−H), 4147 (I−L), and 3393 (M−P) of M. tuberculosis. By day 30, 4619, and 4233 showed increased pathology in primary lesions in lung denoted by extensive foci of mixed inflammation with central necrosis (B and F) compared to the 4147 and 3393 strains (J and N). In addition, the 4619 and 4233 strains showed by day 60, a severe increase number of secondary lesion progression in which multiple foci of extensive inflammation coalesced within the pulmonary parenchyma compared to the other stains tested. The increased rate of granulomatous progression with involvement of large areas of lung (C and H) compared to the 4147 and 3393 (K and O). Hematoxylin and Eosin staining, total magnification: A, E, I, M, C, G, K, O = 1000 μm and B, F, J, N, D, H, L, P = 200 μm.

virulence but all tended to eventually cause irreversible lung damage and animal mortality. A full description of these strains, their origin, molecular genetic definition, and resulting host response is described in much greater detail elsewhere.18

increasing lung damage and tissue destruction observed which can influence bacterial growth. As anticipated, the four experimental infections resulted in a range of lung lesion severity over the first sixty days of the experiment (Figure 1). In all cases, the infections resulted in severe, multifocal to coalescing mixed inflammation with extensive lesion necrosis. The lesion burden was particularly severe in the case of the two strains 4619 and 4233 (Figure 1A−H) compared to the 4147 and 3393 strains (Figure 1I−P). By day 60, the lesion severity increased in animals infected with the 4619 and 4233 strains as evidenced by numerous and extensive foci of secondary granulomatous inflammation, which was more severe for strain 4619 (Figure1A−H). As expected, residual primary lesions showed evidence of healing with foci of calcification that replaced the necrotic center (Figure. 1D,L). In contrast, infection of guinea pigs with strains 4147 (Figure 1I−L) and 3393 (Figure 1M−P) had mild primary lesions by day 30 and minimal secondary inflammation by 60 days of infection. Thus, in the guinea pig model used here, these isolates showed a range of

Metabolomic Analysis of Lung Granulomas from Guinea Pigs Infected with Virulent Clinical W-Beijing Isolates

For HRMAS NMR based metabolomic profiling, lungs both from infected and age matched naive guinea pigs were rapidly excised, immediately snap frozen in liquid nitrogen and stored at −80 °C until the tissues were removed for NMR experiments. For infected tissues, the lungs were removed, granulomas were isolated and taken for HRMAS NMR, and in the case of naive lungs, tissues were taken from random sites of the lung. For all experiments, 1H NMR spectra were acquired in 500 MHz Varian Inova as described in the Methods and Materials section. In samples that were infected with the clinical isolates, well-defined granulomas had developed by day 30 and were calcified by day 60 of infection. Typical HRMAS 1H CPMG NMR spectra 4876



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(normalized to total area) of lung tissues of 30 and 60 days of infection with four different clinical strains are shown in Figure 2, panels A and B, respectively. Assignment of 1H NMR signals of tissue metabolites was carried out using a combination of our own data10 and confirmed by 2D TOCSY spectrum. From these spectra, sixteen metabolites were unambiguously assigned. These were lactate, alanine, acetate, glutamate, reduced glutathione, aspartate, creatine, choline, phosphocholine, glycerophosphocholine, phosphatidylcholine, trimethylamine N-oxide, betaine, glycine, myo-inositol, and glucose. The chemical shift assignments of choline, phosphocholine, glycerophosphocholine, betaine and trimethylamine N-oxide were confirmed by comparing 1H NMR spectra of authentic standards obtained from BMRB (Biological Magnetic Resonance Data Bank). The 1 H signals of the metabolites aspartate, glutathione, betaine, and trimethylamine N-oxide were not detected in naive lung tissues, whereas these metabolites were present in all the infected lung tissues. Unlike our previous HRMAS study on M. tuberculosis, H37Rv infected lung tissues, relative quantification of metabolites was not performed in the present study due to the accumulation of poly unsaturated fatty acids (PUFA) in the lung tissues infected with 4619 and 4147. However, visual comparison of all NMR spectra revealed that lactate, alanine, acetate, glutathione, aspartate, creatine, phosphocholine, glycerophosphocholine, betaine/trimethylamine N-oxide and myo-inositol increased consistently in all the infected tissues compared to age matched naive controls. Qualitatively, the metabolites identified were similar to those we previously observed using the laboratory strain H37Rv.10 However, the accumulation of PUFA was unique in the lung tissues (Figure 2A) infected with 4619 and 4147 isolates. The samples were then removed from the NMR rotors, fixed, embedded, sectioned and stained (see insets in Figure 2). Only one NMR spectrum of naive lung tissue is included in Figure 2 as there were no qualitative or quantitative changes observed in naive tissues from animals caged for 30 or 60 days. We did not use nongranulomatous tissues because it was evident from our previous NMR and histopathological studies that these tissues are not really normal.10

tissues. From this observation, it was clear that 60 day infected lung tissues displayed a broad spectrum of pathology consisting of early stage to end stage granuloma, which may directly influence the metabolic fate of infected host tissues. Accordingly, the 60 day infected lung tissues that grouped in cluster 3 might very well represent end stage granuloma, but those that overlapped in cluster 2 may have pathology resembling 30 day infected tissues. To probe the association of metabolic profiles of lung tissues to pathology related features due to strain variations, unsupervised PCA was performed independently on 30 and 60 day infected tissues. Figure 3B showed 3 D PCA score plot of 30 day infected tissues, in which PC1, PC2, and PC3 explains 60%, 13%, and 6% of variations in the metabolic profile, respectively. Most of the 30 day infected tissues with less virulent strains (4147 and 3393) grouped separately from the 30 day tissues infected with more virulent strains (4619 and 4233). However, in the 3 D PCA score of 60 day infected tissues, this discrimination was not noted (Figure 3C). 1 H NMR on Infected and Naive Serum Samples from Guinea Pigs Infected with Virulent Clinical W-Beijing Isolates

Despite the presence of large number of metabolites and serum lipids, we achieved good resolution of serum metabolites on a 500 MHz NMR instrument. Differences between infected and naive samples could be visualized even without any sample processing or PCA analysis (data not shown). To obtain a better resolution of the overlapping regions, we precipitated the serum proteins with methanol and used the supernatant for the analyses of serum metabolites using Bruker 1D-1H pulse sequence on a Bruker 900 MHz instrument. Two representative spectra (normalized to the total area) of serum samples from 30 and 60 days infected guinea pigs compared to the serum from naive animals are shown in Figure 4, panels A and B, respectively. Spectral assignments were carried out using a combination of 2D 1H−1H TOCSY, BMRB, human metabolome database, and published chemical shift data of metabolites.19 Assigned metabolites in serum included branched chain amino acids (leucine, isoleucine and valine), several other amino acids (alanine, lysine, glutamine, tyrosine, histidine, tryptophan), organic acids (β-hydroxy butyrate, lactate, acetate, pyruvate, succinate, fumarate, citrate, formate, malonic acid), adenosine tri/di/mono phosphate [ATP, ADP, AMP (AXP)], inosine triphosphate, choline, creatine, phosphocreatine, creatinine, methanol and glucose. Altogether 37 metabolites were unambiguously identified. The most intense signals were due to acetate, formate, AMP, ATP, choline, ethanolamine, lactate, and phosphocreatine. There was some variability noted among the serum metabolite profiles from the guinea pigs infected with high and the low virulent strains. Visual inspection of all 1H NMR spectra of sera revealed striking difference in the metabolite levels of all the infected ones more obvious in the day 30 of infection (Figure 4).

Multivariate Statistical Analysis of HRMAS 1H CPMG NMR Spectra of Lung Tissues

To better understand the overall relationship of metabolic patterns between the naive and lung tissues infected with the four different clinical isolates, unsupervised principal component analysis (PCA) was performed on 159 tissue samples using CPMG 1H NMR spectra of naive, 30 and 60 day infected tissues (Table S1). The results were represented by means of threedimensional (3D) principal component scores plots, in which three principal components were used to represent each sample [instead of thousands of NMR data points (Variables)]. In the 3D PCA score plot shown in Figure 3A, the principal components PC1, PC2, and PC3 explained 63%, 8%, and 6% of variations in the metabolic pattern, respectively. The 3D PCA score showed a clear clustering of uninfected tissues (cluster 1), well separated from the cluster of infected tissues (clusters 2 and 3). However, infected tissues showed dispersed scores largely due to variation in the metabolic profile which can be directly correlated to the variation in the pathological features such as primary and secondary lung lesions, necrosis, calcification and bacterial burden. Furthermore, a majority of 30 day infected tissues showed a separate grouping (cluster 2) from the cluster of 60 day infected tissues (cluster 3); however, some of the 60 day infected tissues also grouped with the cluster of 30 day infected

Multivariate Statistical Analysis of Serum 1H NMR

An unsupervised PCA model was generated on the normalized spectral data acquired from samples on both M. tuberculosisinfected and naive control samples at two time points. PCA showed a clear classification among the naive and infected groups (Figure 5A), in which PC1, PC2 ,and PC3 explained 51%, 17%, and 7% of variations, respectively. However, no discrimination was observed either between the 30 and 60 day infected groups or among the samples infected with four clinical strains. This observation was in accord with the HRMAS NMR data on the 4877



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̈ guinea pig lung, compared to tissues harvested on day 30. For a better Figure 2. (A) Representative 500 MHz 1H HRMAS CPMG NMR spectra of naive presentation, the spectral region between 2.40 and 2.90 ppm was multiplied by a factor of 8 and inserted above each NMR spectrum Right Panel: ̈ lung tissue (Uninfected) compared with two high virulent Representative H&E photomicrographs of lung tissues from 30 day infection showing a naive (4619 and 4233) and two low virulent (4147 and 3393) strains infected lung tissues. A discrete lung lesion with a distinct necrotic core (arrow) surrounded by a layer of epitheloid and foamy macrophages is shown; no calcification is seen (4233, 4147 and 3393). The panel (4619) shows inflammation with lymphocytic aggregates surrounded by foamy macrophages with multifocal necrosis (arrows) and the core has become mineralized. ̈ guinea pig lung, compared to tissues harvested on day 60, nave, 4619, 4233, (B) Representative 500 MHz 1H HRMAS CPMG NMR spectra of naive 4147 and 3393. For better presentation, the spectral region between 2.40 and 2.90 ppm was multiplied by a factor of 8 and inserted above each NMR spectra. Increases in lactate, glutathione, aspartate, creatinine, phosphocholine and glycerophosphocholine was observed in all the infected tissues. Asterisks (*) represent signals from polyunsaturated fatty acids. Right Panel: Representative H&E photomicrographs of lung tissues, 60 day infection showing primary and secondary lung lesions. In 4619 and 4233 high virulent strains, the images show a primary lung lesion with extensive calcification and an acellular rim (arrow) along with the fibrous capsule containing lymphocytes and a few macrophages. Primary lung lesion with central necrosis showing dystrophic calcification (4147) and the lesion is delineated from normal lung parenchyma by a noncalcified acellular rim (arrow) in low virulent strains. Panel (3393) shows mixed inflammation representing secondary lung lesion (arrow) caused by a low virulent strain and panel (Uninfected) is ̈ lung tissue. The lesion is delineated from normal lung parenchyma by a noncalcified acellular rim (arrow). Mixed inflammation representing the naive secondary lung lesion (arrow) is shown. 4878



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Figure 3. (A) Three-dimensional PCA score plot of uninfected, 30 and 60 days M. tuberculosis infected lung tissues obtained from PCA of 1H CPMG NMR spectrum. Each point in the plot represents individual NMR spectrum of the lung tissue sample. Three principal components (PC1, PC2 and PC3) together explained 77% data variation with maximum variation explained in PC1 (63%). Clear classification between naïve and infected lung tissues was observed. Although clear separation between day 30 and 60 days infected lung tissues were not observed, 30 days infected tissues did not overlap (barring a few) with 60 days infected tissues. However, some of the 60 days infected tissues grouped with the 30 days infected tissues. (B and C) Three-dimensional PCA score plot of 30 and 60 days M. tuberculosis infected lung tissues obtained from 1H CPMG NMR spectra. The spectra of naïve tissues were removed from this analysis.

Relative Quantification of Serum Metabolites

lung tissue samples. The corresponding PC1 loadings which showed 51% variation in the data set is depicted in Figure 5B. Positive signals with high relative intensities corresponded to metabolites in increased levels in the naive controls (indicating these were reduced in the infected samples). These metabolites were lactate, creatine, phosphocreatine, nicotinamide, choline containing compounds, alanine, glutamine, and glutamate. On the other hand, signals with negative intensity arose from formate, acetate and AMPs indicating that these metabolites are significantly elevated in the serum of infected guinea pigs compared to the naive controls. Change in the glucose level was only marginal in these two time points except for the reduced level in the strain 4619 on 60 day infection. To probe the discriminating ability of the PCA model, ROC curve was generated using principal component scores. AUC values of 0.908, 0.035, and 0.706 were obtained for PC1, PC2, and PC3 scores, respectively. AUC value of 0.908 from PC1 scores (Figure 5C) revealed that the PCA was an excellent model in discriminating infected from naive controls. Partial leastsquares discriminant analysis (PLS-DA) was also performed with mean-centered scaling and full cross validation (leave-one-out design) (Figure S1 in the Supporting Information); however, it was not used as a predictive discriminative model to avoid the possibility of overfitting.

Relative intensities of metabolites were calculated separately for sera from uninfected, and day 30 and 60 infected animals using total area normalized 1H NMR spectra and depicted as mean ± standard error of the mean (SEM). A corresponding bar plot with statistical significance (Mann−Whitney) for each metabolite is presented in Figure 6. Three metabolites, namely, acetate, adenosine monophosphate, and formate, were consistently elevated, whereas phosphocreatine was down-regulated in all infected serum samples at both time points irrespective of the infecting strains. Metabolites such as adenosine triphosphate, choline, ethanolamine, glutamine, glutamate, lactate, and nicotinamide were significantly down-regulated in the majority of the 30 day infected sera. The levels of branched chain amino acids and creatine were up-regulated, whereas glucose was significantly down-regulated on day 60 in animals infected with strain 4619; however, these metabolites were unchanged in both day 30 and day 60 in the other three strains. Alanine was unchanged in all of these samples. Taken together, the significant up- or down-regulation of metabolites in sera were more pronounced on day 30 of the infections.

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DISCUSSION The results of this study showed that analysis of metabolomic data obtained from lung and serum samples of guinea pigs 4879



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Figure 4. (A) 1H NMR of 30 days infection: Representative 900 MHz 1H NMR spectra (area normalized) of serum from uninfected (bottom panel) and 30 days infected guinea pigs with four different W. Beijing strains (4619, 4233, 4147 and 3393). Spectral regions of water and methanol resonances (range 4.7−5.1 and 3.34−3.7 ppm, respectively) were removed. Thirty-seven metabolites were assigned based on NMR metabolomic database (BMRB and HMDB) and literature references. Decrease in the levels of branched chain amino acids (Leu, Ile, and Val), lactate (Lac), alanine (Ala), glutamate (Glu), glutamine (Gln), ethanolamine (Eth), choline (Cho), fumarate (Fum), quinone (Qui), histidine (His), tyrosine (Tyr), tryptophan (Trp), ATP, ADP and nicotinamide (NA), and increase in the levels of acetate (Ace) and formate (For) were observed in 30 days infected serum (acute stage) as compared to those of uninfected serum. (B) 1H NMR of 60 days infection: Representative 900 MHz 1H NMR spectra (area normalized) of serum from uninfected (bottom) and 60 days infected guinea pigs. Spectral regions of water and methanol in the range of 4.7−5.1 and 3.34−3.37 ppm, respectively, were removed. Similar to observed in the acute infection, decrease in the levels of branched chain amino acids, lactate, alanine, glutamate, glutamine, ethanolamine, choline, fumarate, quinone, histidine, tyrosine, tryptophan, ATP, ADP and nicotinamide, and increased in the levels of acetate and formate were also observed in 60 days infected samples as compared to those of uninfected serum. Alterations in serum metabolite observed during both early and late stage of infection demonstrates the possible utility of NMR based metabolomics in biofluids for biomarker discovery in tuberculosis infection.

infected with a panel of M. tuberculosis W-Beijing strains isolated from patients in San Francisco provided signatures that differed from those observed in age-matched uninfected control animals. These results thus support the growing optimism that metabolomic analysis, combining NMR and multivariate statistical analysis, can provide a powerful new way to analyze complex samples such as biological fluids and tissues affected by disease, and could be potentially further applied as

surrogate markers to predict the effects of interventions such as chemotherapy or vaccination. In the context of tuberculosis such advances are badly needed given our continuing failure to find any valid and reliable surrogate clinical markers of disease progression or resolution. In terms of practical application to tuberculosis in the clinical field, we are of the opinion that any direct application of this approach is of no value unless the actual state of disease 4880



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Figure 5. PCA Analysis of serum NMR: (A) Unsupervised principal component analysis score plot and the corresponding (B) PC1 loadings, (C) Receiver operating characteristic (ROC) curve generated from PC1 scores. Area under the curve (AUC) of 0.908 represents an excellent predictability of PCA model in differentiating infected from uninfected groups.

considerable time,15 the clinical W-Beijing strains used here rapidly cause progressive lung damage and severe necrosis in these animals and hence are far more realistic and reflective of the newly emerging high virulence isolates.21 Our central finding was that different signatures could be seen that differentiated between infected and noninfected animals. There seems no doubt therefore that these signatures could be further refined and ways found to increase the sensitivity of these signals as the basis of new diagnostic tests. In our analysis of signals in the lungs, 16 metabolites could be clearly identified [lactate, alanine, acetate, glutamate, reduced glutathione, aspartate, creatine, choline, phosphocholine, glycerophosphocholine, phosphatidylcholine, trimethylamine N-oxide, betaine, glycine, myo-inositol, and glucose] and several of these increased in expression as the infections progressed. Moreover, 1H signals for the metabolites aspartate, glutathione, betaine, and trimethylamine N-oxide metabolites were detected in the lungs of infected animals but were absent in uninfected animals. In addition, we observed that NMR profiles obtained from serum (urine data under separate publication) differed in the relative amounts of a significant number of small molecules between naive and infected animals. Overall, we found reduced levels of lactate, choline containing compounds, ethanolamine, phosphocreatine, nicotinamide and glutamate, and significantly

progression in a patient is known, and usually it is not. The premise of our studies therefore is that metabolomic signatures need to be first carefully defined in stringently controlled relevant animal models such as the guinea pig, in which timing of infection, virulence of the challenge isolate, and timing or use of interventions can be predetermined. Once these signatures are defined and validated, then clinical samples from infected individuals should be studied to see if these occur. As an example, if a signature changes significantly in guinea pigs given an active drug regimen, would a similar signature be observed in human patients who show evidence of also responding to that drug regimen. A similar approach could be used after vaccination, given that a vaccine candidate might be protective against a low virulence infection, and give a certain signature, but might be ineffective against a high virulence infection while giving a different signature. The variation in efficacy of BCG in this type of scenario has already been demonstrated in the mouse model20 and could be used to see if such signatures exist. The purpose here, however, was simply to see if signatures could be observed that clearly delineated between an infected animal and an uninfected one. We previously searched for markers in animals infected with the H37Rv strain of M. tuberculosis.10 That study found comparable signatures in the lungs,10 but no attempt was made in that study to look for similar patterns in serum. Whereas H37Rv can be clearly contained by guinea pigs for some 4881



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Figure 6. Bar plot of relative quantities of serum metabolites in normalized NMR spectra. Serum samples were treated with methanol prior to NMR analysis. Equal volumes of serum from each animal for naive and infected samples were processed for NMR; therefore, the metabolites are quantitative. P-values: *, ≤ 0.05 between naive and 30 day infected; **, ≤ 0.01 between naive and 30 day infected; •, ≤ 0.05 between naive and day 60 infected; ••, ≤ 0.01 between naive and day 60 infected; #, ≤ 0.05 between 30 day and 60 day infection with M. tuberculosis strain 4619; ○, ≤ 0.05 between 30 and 60 day infection with M. tuberculosis strain 4233; □, ≤ 0.05 between 30 and 60 day infection with strain 4147; $, ≤ 0.05 between 30 and 60 day infection with strain 3393.

were consistently elevated in the sera of infected animals, whereas phosphocreatine was consistently down-regulated. Other markers, such as alanine, did not change at all [and thus could be used as a potential baseline against which the levels of other markers could be related]. In general, the signatures observed here are consistent with our current knowledge of the pathogenesis of the virulent clinical strains tested here. As in untreated human patients, these strains rapidly induce necrosis in the lungs after low dose aerosol infection. Thus, a combination of the influx of highly activated T cells and macrophages releasing oxidant radicals, and their subsequent necrotic lysis, could explain the increases we observe in several metabolites including lactate, creatine, aspartate, alanine, and glutathione. The lesions become hypoxic,23,24 and this would explain the appearance of molecules produced under anaerobic conditions, as seen here. A further contribution to the observed signatures could be due to the Warburg Effect. It has been known for a long time that in most mammalian cells the conversion of glucose to lactate is inhibited in the presence of oxygen. This notion changed when Warburg reported that

increased amounts of formate, and acetate in the infected serum samples compared to the naive controls. To address the possibility that four clinical isolates could give four separate sets of signals, which would obviously be a major impediment to any practicality, multivariate statistical analysis of the serum 1H NMR data was performed, using an unsupervised PCA model based on the 1H CPMG spectral data acquired from the infected animals. Importantly, this analysis showed a clear classification among the naive and infected groups for both time points assayed. Equally importantly, no discrimination was observed either between the 30 and 60 day infected groups or among the samples infected with the four clinical strains. These results thus seem to support the concept that identified signatures are not likely to be significantly influenced by the timing of the infection or by differences in pathogenicity between individual clinical isolates, although this should be further rigorously tested, particularly with isolates defined as highly virulent.22 Gain/loss of signals could also be useful markers, and three metabolites [acetate, adenosine monophosphate, and formate] 4882



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48109-1055, USA; Phone: (734) 647-6572; Fax: (734) 7643323.

cancer cells show an increase in glycolytic activity in the presence of oxygen and termed this phenomenon “aerobic glycolysis”.25 Recently, the Warburg effect has also been attributed to normal proliferative tissue and has been reported as a favorable catabolic state for all rapidly proliferating mammalian cells with high glucose uptake capacity.26 Thus, the Warburg Effect is characterized by increased glucose import, increased gluconeogenesis and increased glutaminolysis for anapleurosis. Glutaminolysis involves the degradation of glutamine to produce lactate as an energy-producing pathway that utilizes part of the tricarboxylic acid cycle and is part of the malate aspartate shuttle. It is therefore of considerable interest to note that glutamate, aspartate and alanine were three of the five primary metabolites that we found consistently upregulated in granulomatous lesions in the infected animals. Moreover, as noted above, neutrophil arrival is a component of the early response in the guinea pig, and these release oxygen radicals.27 It is therefore of interest to observe in this study that levels of the tripeptide glutathione were significantly increased in the infected animals, potentially in response to the production of these radicals.23,28 This study using highly relevant newly emerging W-Beijing strains and an earlier study10 using a laboratory strain of M. tuberculosis support the hypothesis that metabolomic signatures have the potential to act as possible new surrogate markers of tuberculosis disease progression. In fact, the guinea pig model, given the similarity of the pathology of the disease process in this animal to that seen in untreated human patients, may be the model of choice to try to define specific metabolomic signatures that accurately reflect disease progression, or its halt by drug or vaccine intervention. At this point, however, available information is still very limited, and should be aggressively pursued further, given both its considerable potential and the ongoing lack of viable alternatives.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Funding for (B.S.S.) in part was supported by Bill and Melinda Gates Foundation (CSU) and in part by A.R. at U. Michigan. This study was also supported by NIH grants AI37139, AI081959, and AI092002, NIH Innovation Award 1DP2OD006450. Serum NMR was performed in the 900 MHz Biomolecular NMR facility of Michigan State University, East Lansing, MI.

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(1) Lienhardt, C.; Vernon, A.; Raviglione, M. C. New drugs and new regimens for the treatment of tuberculosis: review of the drug development pipeline and implications for national programmes. Curr. Opin. Pulm. Med. 2010, 16 (3), 186−93. (2) WHO. Anti-Tuberculosis Drug Resistance in the World, WHO/CDS/ TB 2001.287; World Health Organization: Geneva, 2008. (3) Ginsberg, A. M.; Spigelman, M. Challenges in tuberculosis drug research and development. Nat. Med. 2007, 13 (3), 290−4. (4) Wallis, R. S.; Pai, M.; Menzies, D.; Doherty, T. M.; Walzl, G.; Perkins, M. D.; Zumla, A. Biomarkers and diagnostics for tuberculosis: progress, needs, and translation into practice. Lancet 2010, 375 (9729), 1920−37. (5) Walzl, G.; Ronacher, K.; Hanekom, W.; Scriba, T. J.; Zumla, A. Immunological biomarkers of tuberculosis. Nat. Rev. Immunol. 2011, 11 (5), 343−54. (6) Fletcher, H. A.; Owiafe, P.; Jeffries, D.; Hill, P.; Rook, G. A.; Zumla, A.; Doherty, T. M.; Brookes, R. H. Increased expression of mRNA encoding interleukin (IL)-4 and its splice variant IL-4delta2 in cells from contacts of Mycobacterium tuberculosis, in the absence of in vitro stimulation. Immunology 2004, 112 (4), 669−73. (7) Waterman, D. S.; Bonner, F. W.; Lindon, J. C. Spectroscopic and statistical methods in metabonomics. Bioanalysis 2009, 1 (9), 1559−78. (8) Sansone, S. A.; Fan, T.; Goodacre, R.; Griffin, J. L.; Hardy, N. W.; Kaddurah-Daouk, R.; Kristal, B. S.; Lindon, J.; Mendes, P.; Morrison, N.; Nikolau, B.; Robertson, D.; Sumner, L. W.; Taylor, C.; van der Werf, M.; van Ommen, B.; Fiehn, O. The metabolomics standards initiative. Nat. Biotechnol. 2007, 25 (8), 846−8. (9) Lindon, J. C.; Holmes, E.; Nicholson, J. K. Metabonomics and its role in drug development and disease diagnosis. Expert Rev. Mol. Diagn. 2004, 4 (2), 189−99. (10) Somashekar, B. S.; Amin, A. G.; Rithner, C. D.; Troudt, J.; Basaraba, R.; Izzo, A.; Crick, D. C.; Chatterjee, D. Metabolic profiling of lung granuloma in Mycobacterium tuberculosis infected guinea pigs: ex vivo 1H magic angle spinning NMR studies. J. Proteome Res. 2011, 10, 4186−95. (11) Gagneux, S.; DeRiemer, K.; Van, T.; Kato-Maeda, M.; de Jong, B. C.; Narayanan, S.; Nicol, M.; Niemann, S.; Kremer, K.; Gutierrez, M. C.; Hilty, M.; Hopewell, P. C.; Small, P. M. Variable host-pathogen compatibility in Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. U.S.A. 2006, 103 (8), 2869−73. (12) Kato-Maeda, M.; Kim, E. Y.; Flores, L.; Jarlsberg, L. G.; Osmond, D.; Hopewell, P. C. Differences among sublineages of the East-Asian lineage of Mycobacterium tuberculosis in genotypic clustering. Int. J. Tuberc. Lung Dis. 2010, 14 (5), 538−44. (13) Kraft, S. L.; Dailey, D.; Kovach, M.; Stasiak, K. L.; Bennett, J.; McFarland, C. T.; McMurray, D. N.; Izzo, A. A.; Orme, I. M.; Basaraba, R. J. Magnetic resonance imaging of pulmonary lesions in guinea pigs infected with Mycobacterium tuberculosis. Infect. Immun. 2004, 72 (10), 5963−71. (14) Shang, S.; Harton, M.; Tamayo, M. H.; Shanley, C.; Palanisamy, G. S.; Caraway, M.; Chan, E. D.; Basaraba, R. J.; Orme, I. M.; Ordway, D. J. Increased Foxp3 expression in guinea pigs infected with W-Beijing strains of M. tuberculosis. Tuberculosis 2011, 91 (5), 378−85.

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CONCLUSION This study supports the potential of using NMR-based metabolomic analyses of serum, an easily accessible body fluid, for differentiating healthy subjects from individuals recently infected with tuberculosis, potentially with high sensitivity and specificity. Metabolite profiling of lung tissues and serum revealed changes that seem associated with various pathways including anaerobic glycolysis, glutaminolysis and gluconeogensis driven by the necrotic disease process. The possible use of these signatures as the basis of new diagnostics should be further investigated, including their potential use as validated surrogate markers of vaccination or therapeutic interventions.

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ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*(D.C.) E-mail: [email protected]; Department of Microbiology, Immunology and Pathology, Colorado State University, Campus Delivery 1682, Fort Collins, CO 805231682, USA; Phone: (970) 491 7495; Fax: (970) 491 1815. (A.R.; for correspondence on serum metabolites) E-mail: ramamoor@ umich.edu; Department of Chemistry and Biophysics, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 4883



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(15) Turner, O. C.; Basaraba, R. J.; Orme, I. M. Immunopathogenesis of pulmonary granulomas in the guinea pig after infection with Mycobacterium tuberculosis. Infect. Immun. 2003, 71 (2), 864−71. (16) Wong, J. W.; Cagney, G.; Cartwright, H. M. SpecAlign processing and alignment of mass spectra datasets. Bioinformatics 2005, 21 (9), 2088−90. (17) Cattamanchi, A.; Hopewell, P. C.; Gonzalez, L. C.; Osmond, D. H.; Kawamura, L. M.; Daley, C. L.; Jasmer, R. M. A 13 year molecular epidemiological analysis of tuberculosis in San Francisco. Int. J. Tuberc. Lung Dis. 2006, 10 (3), 297−304. (18) Kato-Maeda, M.; Shanley, C. A.; Jarlsberg, L. G.; Shang, S.; Obregon-Henao, A.; Marisabel, H.; Basaraba, R. J.; Henao-Tamayo, M.; Cueto Barrozo, J.; Kawamura, M.; Hopewell, P. C.; Ordway, D. J.; Orme, I. M. East-Asian (Beijing) sublineages of Mycobacterium tuberculosis differ in virulence and pathogenicity in the guinea pig. Clin. Vaccine Immunol. 2012, in press. (19) Cui, Q.; Lewis, I. A.; Hegeman, A. D.; Anderson, M. E.; Li, J.; Schulte, C. F.; Westler, W. M.; Eghbalnia, H. R.; Sussman, M. R.; Markley, J. L. Metabolite identification via the Madison Metabolomics Consortium Database. Nat. Biotechnol. 2008, 26 (2), 162−4. (20) Ordway, D. J.; Shang, S.; Henao-Tamayo, M.; Obregon-Henao, A.; Nold, L.; Caraway, M.; Shanley, C. A.; Basaraba, R. J.; Duncan, C. G.; Orme, I. M. Mycobacterium bovis BCG-mediated protection against W-Beijing strains of Mycobacterium tuberculosis is diminished concomitant with the emergence of regulatory T cells. Clin. Vaccine Immunol. 2011, 18 (9), 1527−35. (21) Palanisamy, G. S.; DuTeau, N.; Eisenach, K. D.; Cave, D. M.; Theus, S. A.; Kreiswirth, B. N.; Basaraba, R. J.; Orme, I. M. Clinical strains of Mycobacterium tuberculosis display a wide range of virulence in guinea pigs. Tuberculosis 2009, 89 (3), 203−9. (22) Ordway, D.; Henao-Tamayo, M.; Harton, M.; Palanisamy, G.; Troudt, J.; Shanley, C.; Basaraba, R. J.; Orme, I. M. The hypervirulent Mycobacterium tuberculosis strain HN878 induces a potent TH1 response followed by rapid down-regulation. J. Immunol. 2007, 179 (1), 522−31. (23) Via, L. E.; Lin, P. L.; Ray, S. M.; Carrillo, J.; Allen, S. S.; Eum, S. Y.; Taylor, K.; Klein, E.; Manjunatha, U.; Gonzales, J.; Lee, E. G.; Park, S. K.; Raleigh, J. A.; Cho, S. N.; McMurray, D. N.; Flynn, J. L.; Barry, C. E., III. Tuberculous granulomas are hypoxic in guinea pigs, rabbits, and nonhuman primates. Infect. Immun. 2008, 76 (6), 2333−40. (24) Lenaerts, A. J.; Hoff, D.; Aly, S.; Ehlers, S.; Andries, K.; Cantarero, L.; Orme, I. M.; Basaraba, R. J. Location of persisting mycobacteria in a Guinea pig model of tuberculosis revealed by r207910. Antimicrob. Agents Chemother. 2007, 51 (9), 3338−45. (25) Dakubo, G. D. The Warburg phenomenon and other metabolic alterations of cancer cells. In Mitochondrial Genetics and Cancer; Springer: Heidelberg, 2010; pp 39−66. (26) Vazquez, A.; Liu, J. X.; Zhou, Y.; Oltvai, Z. N. Catabolic efficiency of aerobic glycolysis: The Warburg effect revisited. BMC Syst. Biol. 2010, 4, 58−60. (27) Langlois, R. A.; Legge, K. L. Respiratory dendritic cells: mediators of tolerance and immunity. Immunol. Res. 2007, 39 (1−3), 128−45. (28) Venketaraman, V.; Dayaram, Y. K.; Talaue, M. T.; Connell, N. D. Glutathione and nitrosoglutathione in macrophage defense against Mycobacterium tuberculosis. Infect. Immun. 2005, 73 (3), 1886−9.

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Metabolomic Signatures in Guinea Pigs Infected with Epidemic

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