Showing posts with label metabolome. Show all posts

Urinary metabolic signatures of human adiposity

Obesity is a major public health problem worldwide. We used 24-hour urinary metabolic profiling by proton (¹H) nuclear magnetic resonance (NMR) spectroscopy and ion exchange chromatography to characterize the metabolic signatures
of adiposity in the U.S. (n = 1880) and UK (n
= 444) cohorts of the INTERMAP (International Study of Macro- and
Micronutrients and Blood Pressure) epidemiologic study.
Metabolic profiling of urine samples collected
over two 24-hour time periods 3 weeks apart showed reproducible patterns
of
metabolite excretion associated with adiposity.
Exploratory analysis of the urinary metabolome using ¹H NMR spectroscopy of the U.S. samples identified 29 molecular species, clustered in interconnecting metabolic pathways, that
were significantly associated (P = 1.5 × 10⁻⁵ to 2.0 × 10⁻³⁶) with body mass index (BMI); 25 of these species were also found in the UK validation cohort. We found multiple associations
between urinary metabolites and BMI including urinary glycoproteins and N-acetyl
neuraminate (related to renal function), trimethylamine, dimethylamine,
4-cresyl sulfate, phenylacetylglutamine and
2-hydroxyisobutyrate (gut microbial
co-metabolites), succinate and citrate (tricarboxylic acid cycle
intermediates), ketoleucine
and the ketoleucine/leucine ratio (linked to
skeletal muscle mitochondria and branched-chain amino acid metabolism),
ethanolamine
(skeletal muscle turnover), and
3-methylhistidine (skeletal muscle turnover and meat intake). We mapped
the multiple BMI-metabolite
relationships as part of an integrated systems
network that describes the connectivities between the complex pathway
and compartmental
signatures of human adiposity.

Lifestyle influences metabolism via DNA methylation

"An unhealthy lifestyle leaves traces in the DNA. These may have specific effects on metabolism, causing organ damage or disease. Scientists of Helmholtz Zentrum München have now identified 28 DNA alterations associated with metabolic traits. This world-first epigenome-wide association study (EWAS) of modified genes and metabolites has been now published in the journal Human Molecular Genetics."

Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits

Using neurolipidomics to identify phospholipid mediators of synaptic (dys)function in Alzheimer's Disease.

Not all of the mysteries of life lie in our genetic code. Some can be found buried in our membranes. These shells of fat, sculpted in the central nervous system into the cellular (and subcellular) boundaries of neurons and glia, are themselves complex systems of information. The diversity of neural phospholipids, coupled with their chameleon-like capacity to transmute into bioactive molecules, provides a vast repertoire of immediate response second messengers. The effects of compositional changes on synaptic function have only begun to be appreciated. Here, we mined 29 neurolipidomic datasets for changes in neuronal membrane phospholipid metabolism in Alzheimer's Disease (AD). Three overarching metabolic disturbances were detected. We found that an increase in the hydrolysis of platelet activating factor precursors and ethanolamine-containing plasmalogens, coupled with a failure to regenerate relatively rare alkyl-acyl and alkenyl-acyl structural phospholipids, correlated with disease severity. Accumulation of specific bioactive metabolites [i.e., PC(O-16:0/2:0) and PE(P-16:0/0:0)] was associated with aggravating tau pathology, enhancing vesicular release, and signaling neuronal loss. Finally, depletion of PI(16:0/20:4), PI(16:0/22:6), and PI(18:0/22:6) was implicated in accelerating Aβ42 biogenesis. Our analysis further suggested that converging disruptions in platelet activating factor, plasmalogen, phosphoinositol, phosphoethanolamine (PE), and docosahexaenoic acid metabolism may contribute mechanistically to catastrophic vesicular depletion, impaired receptor trafficking, and morphological dendritic deformation. Together, this analysis supports an emerging hypothesis that aberrant phospholipid metabolism may be one of multiple critical determinants required for Alzheimer disease conversion.

Identification of Altered Metabolic Pathways in Plasma and CSF in Mild Cognitive Impairment and Alzheimer's Disease Using Metabolomics.

Alzheimer's Disease (AD) currently affects more than 5 million Americans, with numbers expected to grow dramatically as the population ages. The pathophysiological changes in AD patients begin decades before the onset of dementia, highlighting the urgent need for the development of early diagnostic methods. Compelling data demonstrate that increased levels of amyloid-beta compromise multiple cellular pathways; thus, the investigation of changes in various cellular networks is essential to advance our understanding of early disease mechanisms and to identify novel therapeutic targets. We applied a liquid chromatography/mass spectrometry-based non-targeted metabolomics approach to determine global metabolic changes in plasma and cerebrospinal fluid (CSF) from the same individuals with different AD severity. Metabolic profiling detected a total of significantly altered 342 plasma and 351 CSF metabolites, of which 22% were identified. Based on the changes of >150 metabolites, we found 23 altered canonical pathways in plasma and 20 in CSF in mild cognitive impairment (MCI) vs. cognitively normal (CN) individuals with a false discovery rate <0.05. The number of affected pathways increased with disease severity in both fluids. Lysine metabolism in plasma and the Krebs cycle in CSF were significantly affected in MCI vs. CN. Cholesterol and sphingolipids transport was altered in both CSF and plasma of AD vs. CN. Other 30 canonical pathways significantly disturbed in MCI and AD patients included energy metabolism, Krebs cycle, mitochondrial function, neurotransmitter and amino acid metabolism, and lipid biosynthesis. Pathways in plasma that discriminated between all groups included polyamine, lysine, tryptophan metabolism, and aminoacyl-tRNA biosynthesis; and in CSF involved cortisone and prostaglandin 2 biosynthesis and metabolism. Our data suggest metabolomics could advance our understanding of the early disease mechanisms shared in progression from CN to MCI and to AD.

Determination of steroid metabolome as a possible tool for laboratory diagnosis of schizophrenia.

Metabolomic studies represent a promising tool for early diagnosis of schizophrenia. The aim of this study was to find differences in the steroid spectrum in patients and controls, and to assess the diagnosis of schizophrenia by building a predictive model based on steroid data. Thirty-nine serum steroids (22 neuroactive steroids and their metabolites and 17 polar conjugates) representing steroid metabolome were measured by gas chromatography-mass spectrometry in 22 drug-naive (first episode) schizophrenia patients (13 men and 9 women) before and after six-month treatment with atypical antipsychotics. The results were compared to the data from healthy subjects (22 males, 25 females). In summary the following significant differences were found: (1) In both sexes higher levels of pregnenolone sulfate and sulfated 5α- as well as 5β-saturated metabolites of C21-steroids in progesterone metabolic pathway were found in patients, pointing to decreased activity of sulfatase. (2) In a few instances decreased levels of the respective 5α-metabolites of C21 steroids were found in patients. (3) As C19 steroids concern, in both sexes there were considerably lowered levels of 5β-reduced metabolites in patients. On the other hand, with only a few exceptions, the treatment did not significantly influence most steroid levels. Further, to assess the relationships between schizophrenia status and steroid levels and to build the predictive model of schizophrenia, multivariate regression with reduction of dimensionality (the method of orthogonal projections to latent structures, OPLS) was applied. Irrespective of the small number of patients, use of this model enabled us to state the diagnosis of schizophrenia with almost 100% sensitivity. Our findings suggest that the assessment of steroid levels may become a valid and accurate laboratory test in psychiatry. A limitation of our study is the absence of subjects with a diagnosis other than schizophrenia, so we cannot conclude whether the results are specific for schizophrenia. On the other hand, steroid metabolome model may be used as a diagnostic tool for further studies.

Metabolic Perturbance in Autism Spectrum Disorders: A Metabolomics Study.

Autism spectrum disorders (ASD) are a group of biological disorders with associated metabolic derangement. This study aimed to identify a pattern of metabolic perturbance in ASD using metabolomics in urinary specimens from 48 children with ASD and 53 age matched controls. Using a combination of liquid- and gas-chromatography-based mass spectrometry, we detected the levels of 82 metabolites (53 of which were increased) that were significantly altered between the ASD and the control groups using osmolality normalized data. Pattern analysis showed that the levels of several amino acids such as glycine, serine, threonine, alanine, histidine, glutamyl amino acids and the organic acid, taurine were significantly (p≤0.05) lower in ASD children. The levels of antioxidants such as carnosine were also reduced in ASD (p=0.054). Furthermore, several gut bacterial metabolites were significantly altered in ASD children who had gastrointestinal dysfunction. Overall, this study detected abnormal amino acid metabolism, increased oxidative stress, and altered gut microbiomes in ASD. The relationship of altered gut microbial co-metabolism and the disrupted metabolisms requires further investigation.

Novel biomarkers for pre-diabetes identified by metabolomics: Molecular Systems Biology

Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4 × 10⁻⁴ to 2.1 × 10⁻¹³. Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite–protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.

Human blood metabolite timetable indicates internal body time: PNAS

A convenient way to estimate internal body time (BT) is essential for chronotherapy and time-restricted feeding, both of which use body-time information to maximize potency and minimize toxicity during drug administration and feeding, respectively. Previously, we proposed a molecular timetable based on circadian-oscillating substances in multiple mouse organs or blood to estimate internal body time from samples taken at only a few time points. Here we applied this molecular-timetable concept to estimate and evaluate internal body time in humans. We constructed a 1.5-d reference timetable of oscillating metabolites in human blood samples with 2-h sampling frequency while simultaneously controlling for the confounding effects of activity level, light, temperature, sleep, and food intake. By using this metabolite timetable as a reference, we accurately determined internal body time within 3 h from just two anti-phase blood samples. Our minimally invasive, molecular-timetable method with human blood enables highly optimized and personalized medicine.

Towards a Predictive Model of Alzheimer's Disease Progression Using Capillary Electrophoresis-Mass Spectrometry Metabolomics.

Alzheimer´s disease (AD) is the most prevalent form of dementia with an estimated worldwide prevalence of over 30 million people, its incidence is expected to increase dramatically with an increasing elderly population. Up to date, cerebrospinal fluid (CSF) has been the preferred sample to investigate central nervous system (CNS) disorders since its composition is directly related to metabolite production in brain. In this work, a non-targeted metabolomic approach based on capillary electrophoresis-mass spectrometry (CE-MS) is developed to examine metabolic differences in CSF samples from subjects with different cognitive status related to AD progression. To do this, CSF samples from 85 subjects were obtained from patients with (i) subjective cognitive impairment (SCI, i.e. control group), (ii) mild cognitive impairment (MCI) which remained stable after a follow-up period of 2 years, (iii) MCI which progressed to AD within two-year time after the initial MCI diagnostic and, (iv) diagnosed AD. A prediction model for AD progression using multivariate statistical analysis based on CE-MS metabolomics of CSF samples was obtained using 73 CSF samples. Using our model, we were able to correctly classify 97-100% of the samples in the diagnostic groups. The prediction power was confirmed in a blind small test set of 12 CSF samples, reaching a 83% of diagnostic accuracy. The obtained predictive values were higher than those reported with classical CSF AD biomarkers (A42 and tau), but need to be confirmed in larger samples cohorts. Choline, dimethylarginine, arginine, valine, proline, serine, histidine, creatine, carnitine and suberylglycine were identified as possible disease progression biomarkers. Our results suggest that CE-MS metabolomics of CSF samples can be a useful tool to predict AD progression.

PLOS ONE: Borna Disease Virus Infection Perturbs Energy Metabolites and Amino Acids in Cultured Human Oligodendroglia Cells

Borna disease virus is a neurotropic, non-cytolytic virus that has been widely employed in neuroscientific research. Previous studies have revealed that metabolic perturbations are associated with Borna disease viral infection. However, the pathophysiological mechanism underlying its mode of action remains unclear.

METHODOLOGY:

Human oligodendroglia cells infected with the human strain Borna disease virus Hu-H1 and non-infected matched control cells were cultured in vitro. At day 14 post-infection, a proton nuclear magnetic resonance-based metabonomic approach was used to differentiate the metabonomic profiles of 28 independent intracellular samples from Borna disease virus-infected cells (n = 14) and matched control cells (n = 14). Partial least squares discriminant analysis was performed to demonstrate that the whole metabonomic patterns enabled discrimination between the two groups, and further statistical testing was applied to determine which individual metabolites displayed significant differences between the two groups.

FINDINGS:

Metabonomic profiling revealed perturbations in 23 metabolites, 19 of which were deemed individually significant: nine energy metabolites (α-glucose, acetate, choline, creatine, formate, myo-inositol, nicotinamide adenine dinucleotide, pyruvate, succinate) and ten amino acids (aspartate, glutamate, glutamine, glycine, histidine, isoleucine, phenylalanine, threonine, tyrosine, valine). Partial least squares discriminant analysis demonstrated that the whole metabolic patterns enabled statistical discrimination between the two groups.

CONCLUSION:

Borna disease viral infection perturbs the metabonomic profiles of several metabolites in human oligodendroglia cells cultured in vitro. The findings suggest that Borna disease virus manipulates the host cell's metabolic network to support viral replication and proliferation.

Schizophrenia shows a unique metabolomics signature in plasma.

Schizophrenia is a severe complex mental disorder affecting 0.5-1% of the world population. To date, diagnosis of the disease is mainly based on personal and thus subjective interviews. The underlying molecular mechanism of schizophrenia is poorly understood. Using targeted metabolomics we quantified and compared 103 metabolites in plasma samples from 216 healthy controls and 265 schizophrenic patients, including 52 cases that do not take antipsychotic medication. Compared with healthy controls, levels of five metabolites were found significantly altered in schizophrenic patients (P-values ranged from 2.9 × 10(-8) to 2.5 × 10(-4)) and in neuroleptics-free probands (P-values ranging between 0.006 and 0.03), respectively. These metabolites include four amino acids (arginine, glutamine, histidine and ornithine) and one lipid (PC ae C38:6) and are suggested as candidate biomarkers for schizophrenia. To explore the genetic susceptibility on the associated metabolic pathways, we constructed a molecular network connecting these five aberrant metabolites with 13 schizophrenia risk genes. Our result implicated aberrations in biosynthetic pathways linked to glutamine and arginine metabolism and associated signaling pathways as genetic risk factors, which may contribute to patho-mechanisms and memory deficits associated with schizophrenia. This study illustrated that the metabolic deviations detected in plasma may serve as potential biomarkers to aid diagnosis of schizophrenia.

The human circadian metabolome

The circadian clock orchestrates many aspects of human physiology, and disruption of this clock has been implicated in various pathologies, ranging from cancer to metabolic syndrome and diabetes. Although there is evidence that metabolism and the circadian clockwork are intimately linked on a transcriptional level, whether these effects are directly under clock control or are mediated by the rest–activity cycle and the timing of food intake is unclear. To answer this question, we conducted an unbiased screen in human subjects of the metabolome of blood plasma and saliva at different times of day. To minimize indirect effects, subjects were kept in a 40-h constant routine of enforced posture, constant dim light, hourly isocaloric meals, and sleep deprivation. Under these conditions, we found that ∼15% of all identified metabolites in plasma and saliva were under circadian control, most notably fatty acids in plasma and amino acids in saliva. Our data suggest that there is a strong direct effect of the endogenous circadian clock on multiple human metabolic pathways that is independent of sleep or feeding. In addition, they identify multiple potential small-molecule biomarkers of human circadian phase and sleep pressure.

Genetic regulation of metabolomic biomarkers: Paths to cardiovascular diseases and type 2 diabetes

In a study to the genetic variance of human metabolism, researchers have identified thirty one regions of the genome that were associated with levels of circulating metabolites, i.e., small molecules that take part in various chemical reactions of human body. Many of the studied metabolites are biomarkers for cardiovascular disease or related disorders, thus the loci uncovered may provide valuable insight into the biological processes leading to common diseases.