ToxSci Advance Access originally published online on August 6, 2008
Toxicological Sciences 2008 106(1):46-54; doi:10.1093/toxsci/kfn159
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Metabolomic Analyses of Body Fluids after Subchronic Manganese Inhalation in Rhesus Monkeys
* College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina 27606-1499
Cogenics, Inc. (a Division of Clinical Data), Morrisville, North Carolina 27560
1 To whom correspondence should be addressed at College of Veterinary Medicine, North Carolina State University, 4700 Hillsborough Street, Raleigh, NC 27606-1499. Fax: (919) 513-6452. E-mail: david_dorman{at}ncsu.edu.
Received June 3, 2008; accepted July 30, 2008
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONFUNDINGREFERENCES |
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Neurotoxicity is linked with high-dose manganese inhalation. There are few biomarkers that correlate with manganese exposure. Blood manganese concentrations depend upon the magnitude and duration of the manganese exposure and inconsistently reflect manganese exposure concentrations. The objective of this study was to search for novel biomarkers of manganese exposure in the urine and blood obtained from rhesus monkeys following subchronic manganese sulfate (MnSO4) inhalation. Liquid chromatography-mass spectrometry was used to identify putative biomarkers. Juvenile rhesus monkeys were exposed 5 days/week to airborne MnSO4 at 0, 0.06, 0.3, or 1.5 mg Mn/m3 for 65 exposure days or 1.5 mg Mn/m3 for 15 or 33 days. Monkeys exposed to MnSO4 at
0.06 mg Mn/m3 developed increased brain manganese concentrations. A total of 1097 parent peaks were identified in whole blood and 2462 peaks in urine. Principal component analysis was performed on a subset of 113 peaks that were found to be significantly changed following subchronic manganese exposure. Using the Nearest Centroid analysis, the subset of 113 significantly perturbed components predicted globus pallidus manganese concentrations with 72.9% accuracy for all subchronically exposed monkeys. Using the five confirmed components, the prediction rate for high brain manganese levels remained > 70%. Three of the five identified components, guanosine, disaccharides, and phenylpyruvate, were significantly correlated with brain manganese levels. In all, 27 metabolites with statistically significant expression differences were structurally confirmed by MS-MS methods. Biochemical changes identified in manganese-exposed monkeys included endpoints relate to oxidative stress (e.g., oxidized glutathione) and neurotransmission (aminobutyrate, glutamine, phenylalanine). Key Words: metabolomics; metal; neurotoxicity.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONFUNDINGREFERENCES |
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Manganese neurotoxicity is a significant public health concern associated with welding, metal smelting operations, steel production, and foundries (Levy and Nassetta, 2003
). Under conditions of high occupational exposure, excess manganese accumulates within the human striatum and globus pallidus and produces damage to dopaminergic neurons within these sites (Malecki et al., 1999; Nelson et al., 1993; Pal et al., 1999). Early manifestations of manganese neurotoxicity include fatigue, headache, muscle cramps, loss of appetite, apathy, insomnia, and diminished libido. As exposure continues and the disease progresses, patients may develop prolonged muscle contractions (dystonia), decreased muscle movement (hypokinesia), rigidity, and muscle tremors (reviewed by Pal et al., 1999).
Manganese toxicity may occur following high-dose ingestion or parenteral injection (Calne et al., 1994; Ljung and Vahter, 2007). Neurotoxicity following manganese exposure, however, is most frequently observed following inhalation of this metal. Brain delivery of manganese is higher following inhalation versus ingestion, and pharmacokinetic factors that contribute to this increased efficiency in brain manganese delivery include increased manganese absorption from the pulmonary tract, slower blood clearance of absorbed manganese, and direct delivery to the brain via the olfactory system (Andersen et al., 1999; Aschner et al., 2005). Occupational manganese neurotoxicity most commonly occurs in individuals that have been chronically exposed to aerosols or dusts that contain extremely high levels (> 1 mg Mn/m3) of manganese (ATSDR, 1992; Mergler et al., 1994; Pal et al., 1999). There is a growing concern that welders and other steel workers may be at increased risk for manganese neurotoxicity (Kim et al., 1999; Lucchini et al., 1999; McMillan, 2005).
Identification of workers with excess manganese exposure primarily relies on workplace exposure assessment studies and/or measurement of appropriate biomarkers of exposure. Biomarkers are biological parameters that can be used to identify a physiological or pathological state. There are few biomarkers that correlate reliably with excess manganese exposure (Aschner et al., 2005). Brain magnetic resonance imaging (MRI) holds promise as a biomarker of manganese exposure (Kim, 2004). Because manganese is paramagnetic, its relative distribution within the brain can be examined using MRI. Neuroimaging studies in humans have documented that either increased manganese exposure, or reduced hepatobiliary excretion of manganese, results in appreciable MRI signal hyperintensities within the pallidum and other brain regions known to accumulate manganese (see Fitsanakis et al., 2006b for review). Brain MRI evaluations also hold the potential to provide semi-quantitative estimates of brain manganese concentrations (Dorman et al., 2006b). Despite these advancements, the expense and effort associated with MRI approaches may restrict its use to focused clinical or epidemiological research studies.
There are mixed reports as to whether urinary manganese excretion is increased following occupational inhalation exposure (Ellingsen et al., 2003, 2006; Lu et al., 2005). Blood, serum, and plasma manganese concentrations depend upon the magnitude and the duration of the manganese exposure and often demonstrate only a weak association with workplace manganese exposure concentrations (Aschner et al., 2005; Ellingsen et al., 2003, 2006; Lu et al., 2005). Measurement of arginase and lymphocytic manganese superoxide dismutase activity has been touted as potential biomarkers of exposure; however, these endpoints have failed to gain broad acceptance among toxicologists (Aschner et al., 2005). The identification of suitable biomarkers of manganese exposure has seen few other recent advances.
High throughput analytical chemistry approaches provide one means of identifying novel biomarkers. "Metabolomics," the metabolite analog of genomics and proteomics (or "metabolite profiling") (Raamsdonk et al., 2001; Weckwerth and Morgenthal, 2005), is the global analysis of all metabolites in a biological sample. It is being increasingly used to monitor metabolic responses to xenobiotics or diseases. In metabolomic research, analytes of interest are separated by their physicochemical properties (partition coefficient {XlogP} and mass/charge). Developments in high-performance liquid chromatography (HPLC), gas chromatography (GC), and capillary electrophoresis (CE) when coupled with mass spectrometry (MS), that is, HPLC-MS, GC-MS, and CE-MS, have greatly contributed to recent advancements in this field. Metabolomics has been used to identify biomarkers associated with renal cancer (Perroud et al., 2006), amyotrophic lateral sclerosis (Bowser et al., 2006), myocardial ischemia (Sabatine et al., 2005), and other disease states (Oresic et al., 2006). Metabolic profiling has also been applied to toxicology with recent applications to cisplatin-induced nephrotoxicity (Portilla et al., 2006) and other types of metal toxicities (Fowler at al., 2005). These studies have demonstrated the potential of metabolomics, often coupled with proteomics, to identify new biomarkers of exposure or disease.
The goal of this study was to search for novel biomarkers of manganese exposure in biological fluids obtained from rhesus monkeys following high-dose manganese sulfate (MnSO4) inhalation. Completion of this project relied on tissue samples archived from a subchronic MnSO4 inhalation study performed in rhesus monkeys (Dorman et al., 2006a).
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONFUNDINGREFERENCES |
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Animals.
This study was conducted under federal guidelines for the care and use of laboratory animals (National Research Council, 1996
) and was approved by the CIIT Institutional Animal Care and Use Committee. Twenty-eight male rhesus monkeys purchased from Covance Research Products, Inc. (Alice, TX) were used in this study. Monkeys were 17–22 months old at the time of their arrival at CIIT. Monkeys were approximately 20–24 months old at the start of the inhalation exposure. Twenty monkeys were exposed 6 h/day, 5 days/week, for 13 weeks (65 exposure days). These monkeys were allocated as follows: 0.0 (n = 6), 0.06 (n = 6), 0.3 (n = 4), and 1.5 (n = 4) mg Mn/m3. The remaining monkeys were exposed to MnSO4 at 1.5 mg Mn/m3 for 15 (n = 4) or 33 (n = 4) exposure days and evaluated immediately thereafter. Additional details concerning these animals and their husbandry have been published (Dorman et al., 2006a).
Blood and urine collection.
Food was withheld overnight prior to necropsy. Monkeys were anesthetized with ketamine (20 mg/kg, i.m., Fort Dodge Animal Health, Fort Dodge, IA) and blood was collected from a peripheral vein using plastic syringes with hypodermic needles. A small (
50–80 µl) aliquot was used to determine the packed cell volume. Additional blood samples were collected for complete blood cell counts, routine clinical chemistries, evaluation of basal levels of luteinizing hormone, and determination of red blood cell glutathione (GSH) concentrations (Dorman et al., 2006a). Following blood collection, monkeys were euthanized with pentobarbital (80–150 mg/kg, i.v., Henry Schein, Inc., Port Washington, NY) followed by exsanguination. Following euthanasia, urine samples were collected by cystocentesis. All samples were stored in individual plastic vials or bags, frozen in liquid nitrogen, and stored at approximately –80°C until chemical analyses were performed.
Manganese exposures.
Manganese (II) sulfate monohydrate (MnSO4•H2O) was obtained from Aldrich Chemical Company, Inc. (Milwaukee, WI). Four 8-m3 stainless steel and glass inhalation exposure chambers were used. Methods describing chamber monitoring as well as generation and characterization of the MnSO4 aerosol has been previously described (Dorman et al., 2006a). Based upon optical particle sensor measurements, the overall average concentrations (± SD) for the MnSO4 atmospheres were 0.19 ± 0.01, 0.97 ± 0.06, and 4.55 ± 0.33 mg/m3 for the target concentrations of 0.18, 0.92, and 4.62 mg MnSO4/m3, respectively. The geometric mean diameter, geometric standard deviation (
g), and calculated mass median aerodynamic diameters (MMAD) of the MnSO4 aerosols were determined to be 1.04 µm (g = 1.51; MMAD = 1.73 µm), 1.07 µm (g = 1.54; MMAD = 1.89 µm), and 1.12 µm (g = 1.58; MMAD = 2.12 µm) for the target concentrations of 0.18, 0.92, and 4.62 mg MnSO4/m3, respectively.
Metabolomic analysis by LC-MS.
Metabolomic analyses were performed by Cogenics, Inc. (Morrisville, NC) and comprised liquid chromatography-mass spectrometry (LC-MS) measurement of a broad and non-selective range of low molecular weight (< 1000 amu) biochemicals including metabolites and small peptides in the urine and blood from the air- and MnSO4-exposed monkeys. Specifically, liquid chromatography time-of-flight mass spectrometry (LC-TOF-MS) was used for chromatographic separation of metabolites, followed by detection by mass. The LC-MS platform consisted of Bruker TOF instruments coupled to an internally developed HPLC method. These TOF instruments offered high sensitivity and mass accuracy (generally < 10 ppm). The ESI analysis was conducted simultaneously in positive and negative mode by splitting the HPLC column effluent into two mass specs. Measuring metabolites in simultaneous ± ESI modes enabled the identification of more metabolites than either mode alone could achieve.
Blood and urine samples were prepared for metabolomic analysis by thawing, and precipitating proteins, followed by acetonitrile extraction of the metabolites. Serum was deproteinized with aqueous acetonitrile, the precipitate spun down, and the extract dried under vacuum. The residue was reconstituted with aqueous acetonitrile. Urine was diluted with aqueous acetonitrile, and cleared by centrifugation. Up to 6 internal standards were added to the extracts for quality control (QC) purposes. The standards were chosen to cover most of the LC-MS chromatogram in terms of both retention time (RT) and m/z. Three of the standards most commonly used were D3-methionine, D5-tryptophan, and kinetin. Samples were prepared in triplicate, randomized, and analyzed in a 96-well plate format. Up to sixteen quality control standards plus blanks were plated within the 96-well plates. The QC process comprised a number of steps designed to detect and correct experimental variation. They included pre- and postflight checks at the instrument level, linearity checks, intra- and interplate reproducibility of standards in terms of both mass and intensity, and reproducibility of triplicate determinations. A coefficient of variation (CV) less than 20% was considered to be acceptable for most measurements. About 85% of all peaks generally satisfied this condition.
Typically, 10 µl of samples were injected into an Agilent 1100 series HPLC system. LC separations were performed on an Atlantis dC18, 3 µm, 2.1 x 100 mm column (Waters, MA) equilibrated with solvent A, which consists of H2O containing 5mM ammonium acetate (pH 5.5). The components were eluted at a flow rate of 0.25 ml/min with a gradient elution of 0–0.2 min: 0–0% B (CH3CN); 0.2–8 min: 0–50% B; 8–9 min: 50–50% B; 9–10 min: 50–75% B.
Data processing.
LC-MS peaks from each sample were aligned by mass to charge (m/z) ratio and RT across all samples for each matrix, and quantified using R/XCMS (xcms: LC/MS and GC/MS Data Analysis. R package, version 1.4.0. http://metlin.scripps.edu/) (Smith et al., 2005). A set of rules was developed to eliminate the most obvious mass spectrum artifacts such as isotopes, adducts, and fragments resulting from the addition or loss of water, CO2, or ammonia (among others), dimerization, or salt (acetate) addition. This was done by searching for components with identical RTs, but separated by specific mass differences (e.g., 18 for water, 44 for CO2, etc). Because this might occasionally create the risk of eliminating true, monoisotopic components, the rules were designed to be conservative, so that the final data set comprised mainly parent ions although it may also have contained an unknown number of unidentified artifacts. Intensities for each LC-MS component were normalized to that of an appropriate internal standard and averaged across the three technical replicates. Data quality was assessed by standard QC procedures including mass accuracy, reproducibility and linearity of response, and by reproducibility of the technical replicates. For this study, the intensity of each component of the triplicate samples was required to have a CV of less than 20%. Technical reproducibility was also assessed by analyzing metabolomic profiles for each subject from each tissue using hierarchical agglomerative clustering with Pearson correlation as the distance metric. Technical replicates were found to group together consistently, demonstrating that technical variation was less than biological variation.
Component identification and confirmation.
Each peak group within a biochemical profile was compared with a reference in-house database of over 700 known components to assign a probable identity using RT and m/z. Once a probable identity was assigned, it was confirmed by fragmenting the component of interest using tandem MS (MS/MS, using an HPLC system, as described above, coupled to a Thermo Finnigan LCQ-Deca ion trap with electrospray ionization) and comparing its fragmentation pattern to that of the known standard.
Tissue manganese concentrations.
Tissue manganese concentrations have been previously reported (Dorman et al., 2006a). Tissue manganese concentrations were determined by graphite furnace atomic absorption spectrometry with a Perkin Elmer Analyst 800 Atomic Absorption spectrometer equipped with AA WinLab software (version 4.1 SP) using previously published methods (Dorman et al., 2004). Samples (10–30 mg) were digested in 16M nitric acid prior to manganese analysis using a CEM MARS5 Microwave Accelerated Reaction System (CEM, Matthews, NC).
Statistical analysis.
Data were analyzed using Partek. t-Tests were conducted on each component, comparing treated samples to controls for each dose group. Additionally, for 65-day whole blood samples, an ANOVA was conducted to identify components that were significantly perturbed by treatment. For visualization of the 65-day blood results, the mean intensity for each component for each subject in the treated group was first normalized to the average of the control group. Trends between dose and time points were assessed using two techniques. First, principal components analysis (PCA) was used to visually assess biological variability. Secondly, an unbiased quantitative assessment of the separation between the subjects in each dose-time group was conducted using an unsupervised learning approach based on hierarchical agglomerative clustering of the metabolomic profiles for the subjects. Pearson correlation and Ward's minimum variance method were used to determine cluster similarities. A probability value of < 0.05 was used as the critical level of significance for all statistical tests.
To identify diagnostic or predictive biomarkers, globus pallidus manganese concentrations were categorized into 3 groups: values over 2 µg/g were considered "high," values between 1 and 2 µg/g as "medium," and values less than 1 µg/g as "low." A Nearest Centroid classification was made using all 113 metabolites showing significant changes between dose groups in the ANOVA. The success of the classification was assessed using full leave-one-out, 1-level cross-validation and a normalized correct rate that averages the correct rate for positive and negative predicted samples.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONFUNDINGREFERENCES |
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Elevated tissue manganese concentrations were observed following subchronic MnSO4 inhalation and have been reported elsewhere (Dorman et al., 2007a
). Selected mean (± SEM) manganese concentrations included
- Blood (0.010 ± 0.001, 0.015 ± 0.002, 0.022 ± 0.003, and 0.026 ± 0.003 µg Mn/g wet weight in monkeys exposed to air, 0.06, 0.3, and 1.5 mg Mn/m3, respectively). Subchronic exposure to MnSO4 at 0.3 mg Mn/m3 resulted in increased blood manganese concentrations. Increased blood manganese concentrations also occurred following exposure to MnSO4 at 1.5 mg Mn/m3 for 33 exposure days (0.022 ± 0.002 µg Mn/g wet weight).
- Urine (0.000 ± 0.000, 0.001 ± 0.000, 0.005 ± 0.003, and 0.005 ± 0.001 µg Mn/g wet weight in monkeys exposed to air, 0.06, 0.3, and 1.5 mg Mn/m3, respectively).
- Globus pallidus (0.48 ± 0.04, 0.80 ± 0.04, 1.28 ± 0.15, and 2.94 ± 0.23 µg Mn/g wet weight in monkeys exposed to air, 0.06, 0.3, and 1.5 mg Mn/m3, respectively). Subchronic exposure to MnSO4 at 0.06 mg Mn/m3 resulted in increased globus pallidus manganese concentrations. Increased globus pallidus manganese concentrations also occurred following exposure to MnSO4 at 1.5 mg Mn/m3 for 15 (1.92 ± 0.40 µg Mn/g wet weight) or 33 (2.41 ± 0.29 µg Mn/g wet weight) exposure days.
The LC-MS method used in this study detected a total of 1097 parent peaks in whole blood and 2462 peaks in urine. Using the subset of 113 peaks that were found to be significantly changed according to the ANOVA analysis, PCA of the blood samples demonstrated good separation of the three dose groups at the 65-day time point (Fig. 1). The first three principal components captured a high proportion (70%) of the total variability. The major differences were between the high-dose and lower-dose groups, and were evident in the first principle component (PC1) which contained the largest proportion (35%) of the total variability. The two lower dose groups separated from each other on the PC2 and PC3, suggesting that the changes were more subtle.
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The dendrogram and heat map produced by the hierarchical clustering analysis (Fig. 2) shows the log 2 normalized relative intensities of the individual components (peaks) for each subject. As in the PCA, subjects clustered according to dose group, and clear differences in intensities of groups of components are evident. These components, in turn, could be grouped into seven main clusters, as indicated by the dendrogram along the top of the diagram.
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Table 1 shows statistically significant findings (fold changes and p values) from the t-tests comparing treatments to control in the metabolomic analysis of the 65 day monkey blood samples. The most consistent observations were in phenylpyruvate, which was increased in both the high and mid dose groups, and in allantoin and guanosine, which were increased in the mid and low dose groups. Changes in the high-dose group at intermediate time points are shown in Table 2. Due to the absence of a matched control, these samples were compared with the 65-day vehicle group. Any apparent changes may therefore be confused by an additional time variable, limiting the value of any conclusions. The results show a high degree of internal consistency, in that decreases in the vast majority of the components were observed at both 15 and 33 days. However, there was little consistency with the 65-day results, with the notable exceptions of decreases in aminobutyric acid and glutamine.
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Observations from the urine samples are listed in Table 3. Conclusions from this data are limited because of the small number of observations in each group, but cystine appeared to be markedly depleted in both the mid and low dose groups. As above, urinary profiles at intermediate times were compared with the 65-day controls, and these results are shown in Table 4. As in the 65-day samples (Table 3), cystine appears to be markedly decreased.
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Identities of the components shown in Tables 1 and 2 were confirmed by MS-MS fragmentation and the results are listed in Table 5. Of the 38 components that matched to a reference standard, 27 identities were validated as correct, six were incorrect and three were ambiguous. Two components did not provide sufficient signal in the ion trap MS for analysis. Only blood components whose identities could be confirmed are included in the tables.
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Using the Nearest Centroid analysis, the subset of 113 significantly perturbed components predicted globus pallidus manganese concentrations with 72.9% accuracy for all treated and control subjects after 65 days exposure. Using only the five components that could be identified, the prediction rate was 70.9%. The individual profiles of these five components are shown in Figure 3. In each case the high brain levels were predicted with 100% accuracy. On an individual basis, three of the identified were significantly correlated with manganese levels, according to Pearson's linear method. The correlation coefficient for guanosine was –0.81 (p = 0.00008), for disaccharide (e.g., lactose) was –0.69 (p = 0.007), and for phenylpyruvate was 0.59 (p = 0.026).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONFUNDINGREFERENCES |
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As mentioned earlier, this study relied upon archived urine and blood samples collected at necropsy from monkeys subchronically exposed to MnSO4. We previously reported an approximately 2.5-fold increase in the amount of manganese in the blood of monkeys exposed to MnSO4 at
0.3 mg Mn/m3 for 65 exposure days (Dorman et al., 2006a). Monkeys exposed to MnSO4 at 0.3 mg Mn/m3 for 65 exposure days also developed increased manganese concentrations in the olfactory tract, caudate, pituitary gland, kidney, lung, and pancreas (Dorman et al., 2006a). Increases in pallidal manganese concentrations were appreciated by brain MRI (Dorman et al., 2006b) and confirmed by atomic absorption spectrometry analysis of the tissues (Dorman et al., 2006b). MRI changes seen in our monkey study were similar to those reported in welders that have had high manganese exposure and subsequently developed bilateral hyperintensity on T1-weighted images in the globus pallidus and other brain regions (Kim et al., 1999). Monkeys exposed to MnSO4 at the highest exposure concentration (1.5 mg Mn/m3) for 65 exposure days also had increased manganese concentrations in the frontal cortex, trigeminal nerve, liver, skeletal muscle, and parietal bone. Importantly, exposure concentrations used in this study bracket the current threshold limit value for respirable manganese of 0.2 mg Mn/m3 and are relevant to occupational exposures.
Metabolomic analysis of blood from the MnSO4-exposed monkeys revealed metabolite profiles indicative of oxidative stress. Interestingly, manganese is incorporated into several antioxidant enzymes including manganese superoxide dismutase and glutamine synthetase. Moreover, it has been hypothesized that manganese-induced cytotoxicity may be due to oxidative stress; specifically, mitochondrial oxidative stress (for review see Taylor et al., 2006). Erikson et al. (2007) have recently completed assessments of biochemical endpoints indicative of oxidative stress in brain tissues from the monkeys used in the present study. These studies revealed that monkeys exposed to the highest exposure concentration had significantly lowered caudate GSH levels but significantly higher putamen GSH levels when compared with air-exposed controls. The finding of increased levels of oxidized GSH in the blood of these exposed monkeys in the present study is consistent with this central nervous system finding. Erikson et al. (2007) have also shown that glutamine synthetase protein levels in the cerebellum and frontal cortex were significantly decreased, but were increased in the putamen of monkeys exposed to 1.5 mg Mn/m3.
Metabolomic analysis of blood from the MnSO4-exposed monkeys also revealed metabolite profiles suggesting altered neurotransmission. In particular, metabolic profiling of the manganese-exposed monkeys revealed changes in 
-glutamyl and gamma aminobutyric acid (GABA) biochemistry. GABA is the most abundant inhibitory neurotransmitter in the adult brain (Beleboni et al., 2004). Glutamate is converted to GABA by decarboxylation via glutamate decarboxylase and is degraded via GABA transaminase. In the present study, GABA and hydroxybutyric acid (a GABA degradation metabolite formed via succinic semialdehyde) had differential expression in blood from MnSO4-exposed monkeys. Other neurochemical analyses have been performed on brain tissues from the cohort of animals used in the present study. Erikson et al. (2007) showed that subchronic exposure to MnSO4 at 0.3 mg Mn/m3 resulted in decreased caudate and cerebellum protein and mRNA levels of a glutamate transporter (GLT-1). Struve et al. (2007) found marginally significant (p < 0.1) decreases in pallidal GABA and 5-hydroxyindoleacetic acid concentrations and caudate norepinephrine concentrations in monkeys exposed subchronically to MnSO4 at 1.5 mg Mn/m3 (vs. air-exposed controls). Neurochemical findings in our nonhuman primates demonstrate some concordance with results found in rodents (for review see Fitsanakis et al., 2006a). For example, Anderson et al. (2007) have shown that manganese accumulation leads to decreased GABA levels in the rat striatum apparently due to impaired uptake GABA uptake by striatal synaptosomes.
Changes in the blood metabolite profile seen in the MnSO4-exposed monkeys identified several promising biomarkers related to oxidative stress and altered GABA and glutamate neurotransmission. In addition, alterations in blood phenylpyruvate was a consistent finding being increased in monkeys exposed subchronically to 0.3 mg Mn/m3, and in blood allantoin and guanosine levels, which were increased in the mid and low dose exposure groups. Both blood phenylpyruvate and guanosine concentrations predicted globus pallidus manganese concentrations and demonstrated statistically significant correlations. Observed correlation coefficients for guanosine and phenylpyruvate were –0.81 and 0.59, respectively. Interestingly, manganese is involved in the aerobic oxidation of phenylpyruvic acid (Villablanca and Cilento, 1987). Confirmation of these potential biomarkers will require additional animal studies and/or epidemiological studies where other known biomarkers of exposure (e.g., brain MRI) have been examined.
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FUNDING |
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Afton Chemical Corporation sponsored and funded the in-life phase of this study; and Department of Defense Manganese Health Research Project (04149002) funded metabolomic analyses.
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ACKNOWLEDGMENTS |
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We would like to thank Drs Mel Andersen, Mark Sochaski, and Rusty Thomas for their critical review of this manuscript.
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