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Differentiating between Parkinson’s disease (PD) and the atypical Parkinsonian disorders of multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) is difficult clinically due to overlapping symptomatology, especially at early disease stages. Consequently, there is a need to identify metabolic markers for these diseases and to develop them into viable biomarkers. In the present investigation, solution nuclear magnetic resonance and mass spectrometry metabolomics were used to quantitatively characterize the plasma metabolomes (a total of 167 metabolites) of a cohort of 94 individuals comprising 34 PD, 12 MSA, and 17 PSP patients, as well as 31 control subjects. The distinct and statistically significant differences observed in the metabolite concentrations of the different disease and control groups enabled the identification of potential plasma metabolite markers of each disorder and enabled the differentiation between the disorders. These group-specific differences further implicate disturbances in specific metabolic pathways. The two metabolites, formic acid and succinate, were altered similarly in all three disease groups when compared to the control group, where a reduced level of formic acid suggested an effect on pyruvate metabolism, methane metabolism, and/or the kynurenine pathway, and an increased succinate level suggested an effect on the citric acid cycle and mitochondrial dysfunction.

Alzheimer’s disease (AD) is a neurodegenerative disease and presents in the accumulation of amyloid and neurofibrillary tangle. The association between modulations of gut symbiotic microbes with neurological disease via bidirectional gut-brain axis has been well documented. Bile acid (BA) pools in the enterohepatic circulation could be valuable for probing complex biochemical interactions between host and their symbiotic microbiota. Herein we investigated the levels of 28 BAs in several compartments in enterohepatic circulation (including jejunal, ileum, cecum, colon and feces, plasma and liver tissue) by employing an APP/PS1 induced transgenic AD mouse model. We found that BA profiles in AD mice were gender specific. We observed decreased levels of taurine-conjugated primary BAs (TUDCA, TCA, T-α-MCA and T-β-MCA) and increased levels of secondary BA (iso-DCA) in plasma and liver extracts for female AD transgenic mice. In contrast, increased levels of TDCA in liver extracts and decreased levels of T-β-MCA in jejunal content were noted in male AD mice. These observations suggested that perturbations of BA profiles in AD mice displayed clear gender variations. Our study highlighted the roles of gut microbiota on neurodegenerative disease, which could be gender specific.

The neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) share some common molecular deficits including disruption of protein homeostasis leading to disease-specific protein aggregation. While insoluble protein aggregates are the defining pathological confirmation of diagnosis, patient stratification based on early molecular etiologies may identify distinct subgroups within a clinical diagnosis that would respond differently in therapeutic development programs. We are developing targeted multiple reaction monitoring (MRM) mass spectrometry methods to rigorously quantify CSF proteins from known disease genes involved in lysosomal, ubiquitin-proteasomal, and autophagy pathways. Analysis of CSF from 21 PD, 21 ALS, and 25 control patients, rigorously matched for gender, age, and age of sample, revealed significant changes in peptide levels between PD, ALS, and control. In patients with PD, levels of two peptides for chromogranin B (CHGB, secretogranin 1) were significantly reduced. In CSF of patients with ALS, levels of two peptides from ubiquitin carboxy-terminal hydrolase like protein 1 (UCHL1) and one peptide each for glycoprotein non-metastatic melanoma protein B (GPNMB) and cathepsin D (CTSD) were all increased. Analysis of patients with ALS separated into two groups based on length of survival after CSF sampling revealed that the increases in GPNMB and UCHL1 were specific for short-lived ALS patients. While analysis of additional cohorts is required to validate these candidate biomarkers, this study suggests methods for stratification of ALS patients for clinical trials and identifies targets for drug efficacy measurements during therapeutic development.

Metabolomics protocols are used to comprehensively characterize the metabolite content of biological samples by exploiting cutting-edge analytical platforms, such as gas chromatography (GC) or liquid chromatography (LC) coupled to mass spectrometry (MS) assays, as well as nuclear magnetic resonance (NMR) assays. We have developed novel sample preparation procedures combined with GC-MS, LC-MS, and NMR metabolomics profiling for analyzing bronchial wash (BW) and bronchoalveolar lavage (BAL) fluid from 15 healthy volunteers following exposure to biodiesel exhaust and filtered air. Our aim was to investigate the responsiveness of metabolite profiles in the human lung to air pollution exposure derived from combustion of biofuels, such as rapeseed methyl ester biodiesel, which are increasingly being promoted as alternatives to conventional fossil fuels. Our multi-platform approach enabled us to detect the greatest number of unique metabolites yet reported in BW and BAL fluid (82 in total). All of the metabolomics assays indicated that the metabolite profiles of the BW and BAL fluids differed appreciably, with 46 metabolites showing significantly different levels in the corresponding lung compartments. Furthermore, the GC-MS assay revealed an effect of biodiesel exhaust exposure on the levels of 1-monostearylglycerol, sucrose, inosine, nonanoic acid, and ethanolamine (in BAL) and pentadecanoic acid (in BW), whereas the LC-MS assay indicated a shift in the levels of niacinamide (in BAL). The NMR assay only identified lactic acid (in BW) as being responsive to biodiesel exhaust exposure. Our findings demonstrate that the proposed multi-platform approach is useful for wide metabolomics screening of BW and BAL fluids and can facilitate elucidation of metabolites responsive to biodiesel exhaust exposure.

Introduction: Amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD) are two severe neurodegenerative disorders for which the disease mechanisms are poorly understood and reliable biomarkers are absent.

Objectives: To identify metabolite biomarkers for ALS and PD, and to gain insights into which metabolic pathways are involved in disease.

Methods: Nuclear magnetic resonance (NMR) metabolomics was utilized to characterize the metabolite profiles of cerebrospinal fluid (CSF) and plasma from individuals in three age, gender, and sampling-date matched groups, comprising 22 ALS, 22 PD and 28 control subjects.

Results: Multivariate analysis of NMR data generated robust discriminatory models for separation of ALS from control subjects. ALS patients showed increased concentrations of several metabolites in both CSF and plasma, these are alanine (CSF fold change = 1.22, p = 0.005), creatine (CSF-fc = 1.17, p = 0.001), glucose (CSF-fc = 1.11, p = 0.036), isoleucine (CSF-fc = 1.24, p = 0.002), and valine (CSF-fc = 1.17, p = 0.014). Additional metabolites in CSF (creatinine, dimethylamine and lactic acid) and plasma (acetic acid, glutamic acid, histidine, leucine, pyruvate and tyrosine) were also important for this discrimination. Similarly, panels of CSF-metabolites that discriminate PD from ALS and control subjects were identified.

Conclusions: The results for the ALS patients suggest an affected creatine/creatinine pathway and an altered branched chain amino acid (BCAA) metabolism, and suggest links to glucose and energy metabolism. Putative metabolic markers specific for ALS (e.g. creatinine and lactic acid) and PD (e.g. 3-hydroxyisovaleric acid and mannose) were identified, while several (e.g. creatine and BCAAs) were shared between ALS and PD, suggesting some overlap in metabolic alterations in these disorders.

Low-molecular-weight metabolites in human milk are gaining increasing interest in studies of infant nutrition. In the present study, the milk metabolome from a single mother was explored at different stages of lactation. Metabolites were extracted from sample aliquots using either methanol water (MeOH/H2O) extraction or ultrafiltration. Nuclear magnetic resonance (NMR) spectroscopy was used for metabolite identification and quantification, and multi- and univariate statistical data analyses were used to detect changes over time of lactation. Compared to MeOH/H2O extraction, ultrafiltration more efficiently reduced the interference from lipid and protein resonances, thereby enabling the identification and quantification of 36 metabolites. The human milk metabolomes at the early (9-24 days after delivery) and late (31-87 days after delivery) stages of lactation were distinctly different according to multi- and univariate statistics. The late lactation stage was characterized by significantly elevated concentrations of lactose, choline, alanine, glutamate, and glutamine, as well as by reduced levels of citrate, phosphocholine, glycerophosphocholine, and N-acetylglucosamine. Our results indicate that there are significant compositional changes of the human milk metabolome also in different phases of the matured lactation stage. These findings complement temporal studies on the colostrum and transitional metabolome in providing a better understanding of the nutritional variations received by an infant.

The presence of fatty acid derived oxylipins, endocannabinoids and related compounds in human milk may be of importance to the infant. Presently, clinically relevant protocols for storing and handling human milk that minimize error and variability in oxylipin and endocannabinoid concentrations are lacking. In this study, we compared the individual and combined effects of the following storage conditions on the stability of these fatty acid metabolites in human milk: state (fresh or frozen), storage temperature (4 °C, -20 °C or -80 °C), and duration (1 day, 1 week or 3 months). Thirteen endocannabinoids and related compounds, as well as 37 oxylipins were analyzed simultaneously by liquid chromatography coupled to tandem mass spectrometry. Twelve endocannabinoids and related compounds (2–111 nM) and 31 oxylipins (1.2 pM–1242 nM) were detected, with highest levels being found for 2-arachidonoylglycerol and 17(R)-hydroxydocosahexaenoic acid, respectively. The concentrations of most endocannabinoid-related compounds and oxylipins were dependent on storage condition, and especially storage at 4 °C introduced significant variability. Our findings suggest that human milk samples should be analyzed immediately after, or within one day of collection (if stored at 4 °C). Storage at -80 °C is required for long-term preservation, and storage at -20 °C is acceptable for no more than one week. These findings provide a protocol for investigating the oxylipin and endocannabinoid metabolome in human milk, useful for future milk-related clinical studies.

The study of postprandial metabolism is relevant for understanding metabolic diseases and characterizing personal responses to diet. We combined three analytical platforms – gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) – to validate a multi-platform approach for characterizing individual variation in the postprandial state. We analyzed the postprandial plasma metabolome by introducing, at three occasions, meal challenges on a usual diet, and 1.5 years later, on a modified background diet. The postprandial response was stable over time and largely independent of the background diet as revealed by all three analytical platforms. Coverage of the metabolome between NMR and GC-MS included more polar metabolites detectable only by NMR and more hydrophobic compounds detected by GC-MS. The variability across three separate testing occasions among the identified metabolites was in the range of 1.1–86% for GC-MS and 0.9–42% for NMR in the fasting state at baseline. For the LC-MS analysis, the coefficients of variation of the detected compounds in the fasting state at baseline were in the range of 2–97% for the positive and 4–69% for the negative mode. Multivariate analysis (MVA) of metabolites detected with GC-MS revealed that for both background diets, levels of postprandial amino acids and sugars increased whereas those of fatty acids decreased at 0.5 h after the meal was consumed, reflecting the expected response to the challenge meal. MVA of NMR data revealed increasing postprandial levels of amino acids and other organic acids together with decreasing levels of acetoacetate and 3-hydroxybutanoic acid, also independent of the background diet. Together these data show that the postprandial response to the same challenge meal was stable even though it was tested 1.5 years apart, and that it was largely independent of background diet. This work demonstrates the efficacy of a multi-platform metabolomics approach followed by multivariate and univariate data analysis for a broad-scale screen of the individual metabolome, particularly for studies using repeated measures to determine dietary response phenotype.