Label-free detection and quantification of ultrafine particulate matter in lung and heart of mouse and evaluation of tissue injury.

While it is known that air borne ultrafine particulate matter (PM) may pass through the pulmonary circulation of blood at the alveolar level between lung and heart and cross the air-blood barrier, the mechanism and effects are not completely clear. In this study the imaging method fluorescence lifetime imaging microscopy is adopted for visualization with high spatial resolution and quantification of ultrafine PM particles in mouse lung and heart tissues. The results showed that the median numbers of particles in lung of mice exposed to ultrafine particulate matter of diameter less than 2.5 µm was about 2.0 times more than that in the filtered air (FA)-treated mice, and about 1.3 times more in heart of ultrafine PM-treated mice than in FA-treated mice. Interestingly, ultrafine PM particles were more abundant in heart than lung, likely due to how ultrafine PM particles are cleared by phagocytosis and transport via circulation from lungs. Moreover, heart tissues showed inflammation and amyloid deposition. The component analysis of concentrated airborne ultrafine PM particles suggested traffic exhausts and industrial emissions as predominant sources. Our results suggest association of ultrafine PM exposure to chronic lung and heart tissue injuries. The current study supports the contention that industrial air pollution is one of the causative factors for rising levels of chronic pulmonary and cardiac diseases.

Alterations in Self-Aggregating Neuropeptides in Cerebrospinal Fluid of Patients with Parkinsonian Disorders.

BACKGROUND: Parkinson's disease (PD), progressive supranuclear palsy (PSP), and multiple system atrophy (MSA) present with similar movement disorder symptoms but distinct protein aggregates upon pathological examination. OBJECTIVE: Discovery and validation of candidate biomarkers in parkinsonian disorders for differential diagnosis of subgroup molecular etiologies. METHODS: Untargeted liquid chromatography (LC)-mass spectrometry (MS) proteomics was used for discovery profiling in cerebral spinal fluid (CSF) followed by LC-MS/MS based multiple reaction monitoring for validation of candidates. We compared clinical variation within the parkinsonian cohort including PD subgroups exhibiting tremor dominance (TD) or postural instability gait disturbance and those with detectable leukocytes in CSF. RESULTS: We have identified candidate peptide biomarkers and validated related proteins with targeted quantitative multiplexed assays. Dopamine-drug naïve patients at first diagnosis exhibit reduced levels of signaling neuropeptides, chaperones, and processing proteases for packaging of self-aggregating peptides into dense core vesicles. Distinct patterns of biomarkers were detected in the parkinsonian disorders but were not robust enough to offer a differential diagnosis. Different biomarker changes were detected in male and female patients with PD. Subgroup specific candidate biomarkers were identified for TD PD and PD patients with leukocytes detected in CSF. CONCLUSION: PD, MSA, and PSP exhibit overlapping as well as distinct protein biomarkers that suggest specific molecular etiologies. This indicates common sensitivity of certain populations of selectively vulnerable neurons in the brain, and distinct therapeutic targets for PD subgroups. Our report validates a decrease in CSF levels of self-aggregating neuropeptides in parkinsonian disorders and supports the role of native amyloidogenic proteins in etiologies of neurodegenerative diseases.

Prediction and early biomarkers of cognitive decline in Parkinson disease and atypical parkinsonism: a population-based study.

The progression of cognitive decline is heterogeneous in the three most common idiopathic parkinsonian diseases: Parkinson disease, multiple system atrophy and progressive supranuclear palsy. The causes for this heterogeneity are not fully understood, and there are no validated biomarkers that can accurately identify patients who will develop dementia and when. In this population-based, prospective study, comprehensive neuropsychological testing was performed repeatedly in new-onset, idiopathic parkinsonism. Dementia was diagnosed until 10 years and participants (N = 210) were deeply phenotyped by multimodal clinical, biochemical, genetic and brain imaging measures. At baseline, before the start of dopaminergic treatment, mild cognitive impairment was prevalent in 43.4% of the patients with Parkinson disease, 23.1% of the patients with multiple system atrophy and 77.8% of the patients with progressive supranuclear palsy. Longitudinally, all three diseases had a higher incidence of cognitive decline compared with healthy controls, but the types and severity of cognitive dysfunctions differed. In Parkinson disease, psychomotor speed and attention showed signs of improvement after dopaminergic treatment, while no such improvement was seen in other diseases. The 10-year cumulative probability of dementia was 54% in Parkinson disease and 71% in progressive supranuclear palsy, while there were no cases of dementia in multiple system atrophy. An easy-to-use, multivariable model that predicts the risk of dementia in Parkinson disease within 10 years with high accuracy (area under the curve: 0.86, P < 0.001) was developed. The optimized model adds CSF biomarkers to four easily measurable clinical features at baseline (mild cognitive impairment, olfactory function, motor disease severity and age). The model demonstrates a highly variable but predictable risk of dementia in Parkinson disease, e.g. a 9% risk within 10 years in a patient with normal cognition and CSF amyloid-ß42 in the highest tertile, compared with an 85% risk in a patient with mild cognitive impairment and CSF amyloid-ß42 in the lowest tertile. Only small or no associations with cognitive decline were found for factors that could be easily modifiable (such as thyroid dysfunction). Risk factors for cognitive decline in multiple system atrophy and progressive supranuclear palsy included signs of systemic inflammation and eye movement abnormalities. The predictive model has high accuracy in Parkinson disease and might be used for the selection of patients into clinical trials or as an aid to improve the prevention of dementia.

The Cerebrospinal Fluid Profile of Cholesterol Metabolites in Parkinson's Disease and Their Association With Disease State and Clinical Features.

Disordered cholesterol metabolism is linked to neurodegeneration. In this study we investigated the profile of cholesterol metabolites found in the cerebrospinal fluid (CSF) of Parkinson's disease (PD) patients. When adjustments were made for confounding variables of age and sex, 7α,(25R)26-dihydroxycholesterol and a second oxysterol 7α,x,y-trihydroxycholest-4-en-3-one (7α,x,y-triHCO), whose exact structure is unknown, were found to be significantly elevated in PD CSF. The likely location of the additional hydroxy groups on the second oxysterol are on the sterol side-chain. We found that CSF 7α-hydroxycholesterol levels correlated positively with depression in PD patients, while two presumptively identified cholestenoic acids correlated negatively with depression.

Deep mining of oxysterols and cholestenoic acids in human plasma and cerebrospinal fluid: Quantification using isotope dilution mass spectrometry.

Both plasma and cerebrospinal fluid (CSF) are rich in cholesterol and its metabolites. Here we describe in detail a methodology for the identification and quantification of multiple sterols including oxysterols and sterol-acids found in these fluids. The method is translatable to any laboratory with access to liquid chromatography - tandem mass spectrometry. The method exploits isotope-dilution mass spectrometry for absolute quantification of target metabolites. The method is applicable for semi-quantification of other sterols for which isotope labelled surrogates are not available and approximate quantification of partially identified sterols. Values are reported for non-esterified sterols in the absence of saponification and total sterols following saponification. In this way absolute quantification data is reported for 17 sterols in the NIST SRM 1950 plasma along with semi-quantitative data for 8 additional sterols and approximate quantification for one further sterol. In a pooled (CSF) sample used for internal quality control, absolute quantification was performed on 10 sterols, semi-quantification on 9 sterols and approximate quantification on a further three partially identified sterols. The value of the method is illustrated by confirming the sterol phenotype of a patient suffering from ACOX2 deficiency, a rare disorder of bile acid biosynthesis, and in a plasma sample from a patient suffering from cerebrotendinous xanthomatosis, where cholesterol 27-hydroxylase is deficient.

Polyunsaturated Fatty Acids and Their Metabolites in Hyperemesis Gravidarum.

Polyunsaturated fatty acids (PUFAs) have been studied in relation to pregnancy. However, there is limited knowledge on PUFAs and their metabolites in relation to hyperemesis gravidarum (HG), a pregnancy complication associated with nutritional deficiencies and excessive vomiting. In order to survey the field, a systematic review of the literature was performed, which also included nausea and vomiting of pregnancy (NVP) due to its close relationship with HG. In the very few published studies found, the main subjects of the research concerned free fatty acids (four records), lipid profiles (three records), and bioactive lipids (one article about prostaglandin E2 and one about endocannabinoids). The authors of these studies concluded that, although no cause-and-effect relationship can be established, HG is linked to increased sympathetic responsiveness, thermogenic activity and metabolic rate. In addition, NVP is linked to a metabolic perturbance (which lasts throughout pregnancy). The low number of retrieved records underlines the need for more research in the area of PUFAs and HG, especially with regard to the underlying mechanism for the detected effects, potentially involving growth differentiation factor 15 (GDF15) since evidence for GDF15 regulation of lipid metabolism and the role for GDF15 and its receptor in nausea and vomiting is emerging.

Targeted Multiple Reaction Monitoring Analysis of CSF Identifies UCHL1 and GPNMB as Candidate Biomarkers for ALS.

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.

General and abdominal adiposity and the risk of Parkinson's disease: A prospective cohort study.

INTRODUCTION: Due to demographic change, an increase in the frequency of Parkinson's disease (PD) patients is expected in the future and, thus, the identification of modifiable risk factors is urgently needed. We aimed to examine the associations of body mass index (BMI) and waist circumference (WC) with incident PD. METHODS: In 13 of the 23 centers of the European Prospective Investigation into Cancer and Nutrition (EPIC) study, a total of 734 incident cases of PD were identified between 1992 and 2012 with a mean follow-up of 12 years. Cox proportional hazards regression was used to calculate hazard ratios (HR) with 95% confidence intervals (CI). We modelled anthropometric variables as continuous and categorical exposures and performed subgroup analyses by potential effect modifiers including sex and smoking. RESULTS: We found no association between BMI, WC and incident PD, neither among men nor among women. Among never and former smokers, BMI and waist circumference were also not associated with PD risk. For male smokers, however, we observed a statistically significant inverse association between BMI and PD risk (HR 0.51, 95%CI: 0.30, 0.84) and the opposite for women, i.e. a significant direct association of BMI (HR 1.79, 95%CI: 1.04, 3.08) and waist circumference (HR 1.64, 95%CI: 1.03, 2.61) with risk of PD. CONCLUSION: Our data revealed no association between excess weight and PD risk but a possible interaction between anthropometry, sex and smoking.

Multi-platform mass spectrometry analysis of the CSF and plasma metabolomes of rigorously matched amyotrophic lateral sclerosis, Parkinson's disease and control subjects.

Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) are protein-aggregation diseases that lack clear molecular etiologies. Biomarkers could aid in diagnosis, prognosis, planning of care, drug target identification and stratification of patients into clinical trials. We sought to characterize shared and unique metabolite perturbations between ALS and PD and matched controls selected from patients with other diagnoses, including differential diagnoses to ALS or PD that visited our clinic for a lumbar puncture. Cerebrospinal fluid (CSF) and plasma from rigorously age-, sex- and sampling-date matched patients were analyzed on multiple platforms using gas chromatography (GC) and liquid chromatography (LC)-mass spectrometry (MS). We applied constrained randomization of run orders and orthogonal partial least squares projection to latent structure-effect projections (OPLS-EP) to capitalize upon the study design. The combined platforms identified 144 CSF and 196 plasma metabolites with diverse molecular properties. Creatine was found to be increased and creatinine decreased in CSF of ALS patients compared to matched controls. Glucose was increased in CSF of ALS patients and α-hydroxybutyrate was increased in CSF and plasma of ALS patients compared to matched controls. Leucine, isoleucine and ketoleucine were increased in CSF of both ALS and PD. Together, these studies, in conjunction with earlier studies, suggest alterations in energy utilization pathways and have identified and further validated perturbed metabolites to be used in panels of biomarkers for the diagnosis of ALS and PD.

Cerebrospinal Fluid Patterns and the Risk of Future Dementia in Early, Incident Parkinson Disease.

IMPORTANCE: Alterations in cerebrospinal fluid (CSF) have been found in Parkinson disease (PD) and in PD dementia (PDD), but the prognostic importance of such changes is not well known. In vivo biomarkers for disease processes in PD are important for future development of disease-modifying therapies. OBJECTIVE: To assess the diagnostic and prognostic value of a panel of CSF biomarkers in patients with early PD and related disorders. DESIGN, SETTING, AND PARTICIPANTS: Regional population-based, prospective cohort study of idiopathic parkinsonism that included patients diagnosed between January 1, 2004, and April 30, 2009, by a movement disorder team at a university hospital that represented the only neurology clinic in the region. Participants were 128 nondemented patients with new-onset parkinsonism (104 with PD, 11 with multiple system atrophy, and 13 with progressive supranuclear palsy) who were followed up for 5 to 9 years. At baseline, CSF from 30 healthy control participants was obtained for comparison. MAIN OUTCOMES AND MEASURES: Cerebrospinal fluid concentrations of neurofilament light chain protein, Aß1-42, total tau, phosphorylated tau, α-synuclein, and heart fatty acid-binding protein were quantified by 2 blinded measurements (at baseline and after 1 year). Follow-up included an extensive neuropsychological assessment. As PD outcome variables, mild cognitive impairment and incident PDD were diagnosed based on published criteria. RESULTS: Among the 128 study participants, the 104 patients with early PD had a different CSF pattern compared with the 13 patients with progressive supranuclear palsy (baseline area under the receiver operating characteristic curve, 0.87; P < .0001) and the 30 control participants (baseline area under the receiver operating characteristic curve, 0.69; P = .0021). A CSF biomarker pattern associated with the development of PDD was observed. In PD, high neurofilament light chain protein, low Aß1-42, and high heart fatty acid-binding protein at baseline were related to future PDD as analyzed by Cox proportional hazards regression models. Combined, these early biomarkers predicted PDD with high accuracy (hazard ratio, 11.8; 95% CI, 3.3-42.1; P = .0001) after adjusting for possible confounders. CONCLUSIONS AND RELEVANCE: The analyzed CSF biomarkers have potential usefulness as a diagnostic tool in patients with parkinsonism. In PD, high neurofilament light chain protein, low Aß1-42, and high heart fatty acid-binding protein were related to future PDD, providing new insights into the etiology of PDD.

Metabolite and peptide levels in plasma and CSF differentiating healthy controls from patients with newly diagnosed Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a progressive, multi-focal neurodegenerative disease for which there is no effective disease modifying treatment. A critical requirement for designing successful clinical trials is the development of robust and reproducible biomarkers identifying PD in preclinical stages. OBJECTIVE: To investigate the potential for a cluster of biomarkers visualized with multiple analytical platforms to provide a clinically useful tool. METHODS: Gas Chromatography-Mass Spectrometry (GC-TOFMS) based metabolomics and immunoassay-based protein/peptide analyses on samples from patients with PD diagnosed in Northern Sweden. Low molecular weight compounds from both plasma and cerebrospinal fluid (CSF) from 20 healthy subjects (controls) and 20 PD patients at the time of diagnosis (baseline) were analyzed. RESULTS: In plasma, we found a significant increase in several amino acids and a decrease in C16-C18 saturated and unsaturated fatty acids in patients as compared to control subjects. We also observed an increase in plasma levels of pyroglutamate and 2-oxoisocaproate (ketoleucine) that may be indicative of increased metabolic stress in patients. In CSF, there was a generally lower level of metabolites in PD as compared to controls, with a specific decrease in 3-hydroxyisovaleric acid, tryptophan and creatinine. Multivariate analysis and modeling of metabolites indicates that while the PD samples can be separated from control samples, the list of detected compounds will need to be expanded in order to define a robust predictive model. CSF biomarker immunoassays of candidate peptide/protein biomarkers revealed a significant decrease in the levels of Aß-38 and Aß-42, and an increase in soluble APPα in CSF of patients. Furthermore, these peptides showed significant correlations to each other, and positive correlations to the CSF levels of several 5- and 6-carbon sugars. However, combining these metabolites and proteins/peptides into a single model did not significantly improve the statistical analysis. CONCLUSIONS: Together, this metabolomics study has detected significant alterations in plasma and CSF levels of a cluster of amino acids, fatty acids and sugars based on clinical diagnosis and levels of known protein and peptide biomarkers.

Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment.

UNLABELLED: Statins are widely prescribed for reducing LDL-cholesterol (C) and risk for cardiovascular disease (CVD), but there is considerable variation in therapeutic response. We used a gas chromatography-time-of-flight mass-spectrometry-based metabolomics platform to evaluate global effects of simvastatin on intermediary metabolism. Analyses were conducted in 148 participants in the Cholesterol and Pharmacogenetics study who were profiled pre and six weeks post treatment with 40 mg/day simvastatin: 100 randomly selected from the full range of the LDL-C response distribution and 24 each from the top and bottom 10% of this distribution ("good" and "poor" responders, respectively). The metabolic signature of drug exposure in the full range of responders included essential amino acids, lauric acid (p<0.0055, q<0.055), and alpha-tocopherol (p<0.0003, q<0.017). Using the HumanCyc database and pathway enrichment analysis, we observed that the metabolites of drug exposure were enriched for the pathway class amino acid degradation (p<0.0032). Metabolites whose change correlated with LDL-C lowering response to simvastatin in the full range responders included cystine, urea cycle intermediates, and the dibasic amino acids ornithine, citrulline and lysine. These dibasic amino acids share plasma membrane transporters with arginine, the rate-limiting substrate for nitric oxide synthase (NOS), a critical mediator of cardiovascular health. Baseline metabolic profiles of the good and poor responders were analyzed by orthogonal partial least square discriminant analysis so as to determine the metabolites that best separated the two response groups and could be predictive of LDL-C response. Among these were xanthine, 2-hydroxyvaleric acid, succinic acid, stearic acid, and fructose. Together, the findings from this study indicate that clusters of metabolites involved in multiple pathways not directly connected with cholesterol metabolism may play a role in modulating the response to simvastatin treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT00451828.

Construction and completion of flux balance models from pathway databases.

MOTIVATION: Flux balance analysis (FBA) is a well-known technique for genome-scale modeling of metabolic flux. Typically, an FBA formulation requires the accurate specification of four sets: biochemical reactions, biomass metabolites, nutrients and secreted metabolites. The development of FBA models can be time consuming and tedious because of the difficulty in assembling completely accurate descriptions of these sets, and in identifying errors in the composition of these sets. For example, the presence of a single non-producible metabolite in the biomass will make the entire model infeasible. Other difficulties in FBA modeling are that model distributions, and predicted fluxes, can be cryptic and difficult to understand. RESULTS: We present a multiple gap-filling method to accelerate the development of FBA models using a new tool, called MetaFlux, based on mixed integer linear programming (MILP). The method suggests corrections to the sets of reactions, biomass metabolites, nutrients and secretions. The method generates FBA models directly from Pathway/Genome Databases. Thus, FBA models developed in this framework are easily queried and visualized using the Pathway Tools software. Predicted fluxes are more easily comprehended by visualizing them on diagrams of individual metabolic pathways or of metabolic maps. MetaFlux can also remove redundant high-flux loops, solve FBA models once they are generated and model the effects of gene knockouts. MetaFlux has been validated through construction of FBA models for Escherichia coli and Homo sapiens. AVAILABILITY: Pathway Tools with MetaFlux is freely available to academic users, and for a fee to commercial users. Download from: biocyc.org/download.shtml. CONTACT: mario.latendresse@sri.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Enteric microbiome metabolites correlate with response to simvastatin treatment.

Although statins are widely prescribed medications, there remains considerable variability in therapeutic response. Genetics can explain only part of this variability. Metabolomics is a global biochemical approach that provides powerful tools for mapping pathways implicated in disease and in response to treatment. Metabolomics captures net interactions between genome, microbiome and the environment. In this study, we used a targeted GC-MS metabolomics platform to measure a panel of metabolites within cholesterol synthesis, dietary sterol absorption, and bile acid formation to determine metabolite signatures that may predict variation in statin LDL-C lowering efficacy. Measurements were performed in two subsets of the total study population in the Cholesterol and Pharmacogenetics (CAP) study: Full Range of Response (FR), and Good and Poor Responders (GPR) were 100 individuals randomly selected from across the entire range of LDL-C responses in CAP. GPR were 48 individuals, 24 each from the top and bottom 10% of the LDL-C response distribution matched for body mass index, race, and gender. We identified three secondary, bacterial-derived bile acids that contribute to predicting the magnitude of statin-induced LDL-C lowering in good responders. Bile acids and statins share transporters in the liver and intestine; we observed that increased plasma concentration of simvastatin positively correlates with higher levels of several secondary bile acids. Genetic analysis of these subjects identified associations between levels of seven bile acids and a single nucleotide polymorphism (SNP), rs4149056, in the gene encoding the organic anion transporter SLCO1B1. These findings, along with recently published results that the gut microbiome plays an important role in cardiovascular disease, indicate that interactions between genome, gut microbiome and environmental influences should be considered in the study and management of cardiovascular disease. Metabolic profiles could provide valuable information about treatment outcomes and could contribute to a more personalized approach to therapy.