Metabolome in progression to Alzheimer's disease

Metabolic Profiling and Phenotyping of Central Nervous System Diseases: Metabolites Bring Insights into Brain Dysfunctions

Metabolic phenotyping corresponds to the large-scale quantitative and qualitative analysis of the metabolome i.e., the low-molecular weight <1 KDa fraction in biological samples, and provides a key opportunity to advance neurosciences. Proton nuclear magnetic resonance and mass spectrometry are the main analytical platforms used for metabolic profiling, enabling detection and quantitation of a wide range of compounds of particular neuro-pharmacological and physiological relevance, including neurotransmitters, secondary messengers, structural lipids, as well as their precursors, intermediates and degradation products. Metabolic profiling is therefore particularly indicated for the study of central nervous system by probing metabolic and neurochemical profiles of the healthy or diseased brain, in preclinical models or in human samples. In this review, we introduce the analytical and statistical requirements for metabolic profiling. Then, we focus on key studies in the field of metabolic profiling applied to the characterization of animal models and human samples of central nervous system disorders. We highlight the potential of metabolic profiling for pharmacological and physiological evaluation, diagnosis and drug therapy monitoring of patients affected by brain disorders. Finally, we discuss the current challenges in the field, including the development of systems biology and pharmacology strategies improving our understanding of metabolic signatures and mechanisms of central nervous system diseases.

Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer's disease

There is an urgent need for the identification of Alzheimer's disease (AD) biomarkers. Studies have now suggested the promising use of associations with blood metabolites as functional intermediate phenotypes in biomedical and pharmaceutical research. The aim of this study was to use lipidomics to identify a battery of plasma metabolite molecules that could predict AD patients from controls. We performed a comprehensive untargeted lipidomic analysis, using ultra-performance liquid chromatography/mass spectrometry on plasma samples from 35 AD patients, 40 elderly controls and 48 individuals with mild cognitive impairment (MCI) and used multivariate analysis methods to identify metabolites associated with AD status. A combination of 10 metabolites could discriminate AD patients from controls with 79.2% accuracy (81.8% sensitivity, 76.9% specificity and an area under curve of 0.792) in a novel test set. Six of the metabolites were identified as long chain cholesteryl esters (ChEs) and were reduced in AD (ChE 32:0, odds ratio (OR)=0.237, 95% confidence interval (CI)=0.10-0.48, P=4.19E-04; ChE 34:0, OR=0.152, 95% CI=0.05-0.37, P=2.90E-04; ChE 34:6, OR=0.126, 95% CI=0.03-0.35, P=5.40E-04; ChE 32:4, OR=0.056, 95% CI=0.01-0.24, P=6.56E-04 and ChE 33:6, OR=0.205, 95% CI=0.06-0.50, P=2.21E-03, per (log2) metabolite unit). The levels of these metabolites followed the trend control>MCI>AD. We, additionally, found no association between cholesterol, the precursor of ChE and AD. This study identified new ChE molecules, involved in cholesterol metabolism, implicated in AD, which may help identify new therapeutic targets; although, these findings need to be replicated in larger well-phenotyped cohorts.

Aging-related Changes in Mouse Serum Glycerophospholipid Profiles

Objectives

Metabolic dysfunction is a common hallmark of the aging process and aging-related pathogenesis. Blood metabolites have been used as biomarkers for many diseases, including cancers, complex chronic diseases, and neurodegenerative diseases.

Methods

In order to identify aging-related biomarkers from blood metabolites, we investigated the specific metabolite profiles of mouse sera from 4-month-old and 21-month-old mice by using a combined flow injection analysis–tandem mass spectrometry and liquid chromatography–tandem mass spectrometry.

Results

Among the 156 metabolites detected, serum levels of nine individual metabolites were found to vary with aging. Specifically, lysophosphatidylcholine (LPC) acyl (a) C24:0 levels in aged mice were decreased compared to that in young mice, whereas phosphatidylcholine (PC) acyl-alkyl (ae) C38:4, PC ae C40:4, and PC ae C42:1 levels were increased. Three classes of metabolites (amino acids, LPCs, and PCs) differed in intraclass correlation patterns of the individual metabolites between sera from young and aged mice. Additionally, the ratio of LPC a C24:0 to PC ae C38:4 was decreased in the aged mice, whereas the ratio of PC ae C40:4 to LPC a C24:0 was increased, supporting the aging-related metabolic changes of glycerophospholipids.

Conclusion

The ratios of the individual metabolites PC and LPC could serve as potential biomarkers for aging and aging-related diseases.

Age-related alterations in the metabolic profile in the hippocampus of the senescence-accelerated mouse prone 8: a spontaneous Alzheimer's disease mouse model

Alzheimer's disease (AD), the most common age-dependent neurodegenerative disorder, produces a progressive decline in cognitive function. The metabolic mechanism of AD has emerged in recent years. In this study, we used multivariate analyses of gas chromatography-mass spectrometry measurements to determine that learning and retention-related metabolic profiles are altered during aging in the hippocampus of the senescence-accelerated mouse prone 8 (SAMP8). Alterations in 17 metabolites were detected in mature and aged mice compared to young mice (13 decreased and 4 increased metabolites), including metabolites related to dysfunctional lipid metabolism (significantly increased cholesterol, oleic acid, and phosphoglyceride levels), decreased amino acid (alanine, serine, glycine, aspartic acid, glutamate, and gamma-aminobutyric acid), and energy-related metabolite levels (malic acid, butanedioic acid, fumaric acid, and citric acid), and other altered metabolites (increased N-acetyl-aspartic acid and decreased pyroglutamic acid, urea, and lactic acid) in the hippocampus. All of these alterations indicated that the metabolic mechanisms of age-related cognitive impairment in SAMP8 mice were related to multiple pathways and networks. Lipid metabolism, especially cholesterol metabolism, appears to play a distinct role in the hippocampus in AD.

The return of metabolism: biochemistry and physiology of the pentose phosphate pathway

The pentose phosphate pathway (PPP) is a fundamental component of cellular metabolism. The PPP is important to maintain carbon homoeostasis, to provide precursors for nucleotide and amino acid biosynthesis, to provide reducing molecules for anabolism, and to defeat oxidative stress. The PPP shares reactions with the Entner–Doudoroff pathway and Calvin cycle and divides into an oxidative and non-oxidative branch. The oxidative branch is highly active in most eukaryotes and converts glucose 6-phosphate into carbon dioxide, ribulose 5-phosphate and NADPH. The latter function is critical to maintain redox balance under stress situations, when cells proliferate rapidly, in ageing, and for the ‘Warburg effect’ of cancer cells. The non-oxidative branch instead is virtually ubiquitous, and metabolizes the glycolytic intermediates fructose 6-phosphate and glyceraldehyde 3-phosphate as well as sedoheptulose sugars, yielding ribose 5-phosphate for the synthesis of nucleic acids and sugar phosphate precursors for the synthesis of amino acids. Whereas the oxidative PPP is considered unidirectional, the non-oxidative branch can supply glycolysis with intermediates derived from ribose 5-phosphate and vice versa, depending on the biochemical demand. These functions require dynamic regulation of the PPP pathway that is achieved through hierarchical interactions between transcriptome, proteome and metabolome. Consequently, the biochemistry and regulation of this pathway, while still unresolved in many cases, are archetypal for the dynamics of the metabolic network of the cell. In this comprehensive article we review seminal work that led to the discovery and description of the pathway that date back now for 80 years, and address recent results about genetic and metabolic mechanisms that regulate its activity. These biochemical principles are discussed in the context of PPP deficiencies causing metabolic disease and the role of this pathway in biotechnology, bacterial and parasite infections, neurons, stem cell potency and cancer metabolism.

Development of isotope labeling liquid chromatography mass spectrometry for mouse urine metabolomics: quantitative metabolomic study of transgenic mice related to Alzheimer's disease

Because of a limited volume of urine that can be collected from a mouse, it is very difficult to apply the common strategy of using multiple analytical techniques to analyze the metabolites to increase the metabolome coverage for mouse urine metabolomics. We report an enabling method based on differential isotope labeling liquid chromatography mass spectrometry (LC-MS) for relative quantification of over 950 putative metabolites using 20 μL of urine as the starting material. The workflow involves aliquoting 10 μL of an individual urine sample for ¹²C-dansylation labeling that target amines and phenols. Another 10 μL of aliquot was taken from each sample to generate a pooled sample that was subjected to ¹³C-dansylation labeling. The ¹²C-labeled individual sample was mixed with an equal volume of the ¹³C-labeled pooled sample. The mixture was then analyzed by LC-MS to generate information on metabolite concentration differences among different individual samples. The interday repeatability for the LC-MS runs was assessed, and the median relative standard deviation over 4 days was 5.0%. This workflow was then applied to a metabolomic biomarker discovery study using urine samples obtained from the TgCRND8 mouse model of early onset familial Alzheimer's disease (FAD) throughout the course of their pathological deposition of beta amyloid (Aβ). It was showed that there was a distinct metabolomic separation between the AD prone mice and the wild type (control) group. As early as 15-17 weeks of age (presymptomatic), metabolomic differences were observed between the two groups, and after the age of 25 weeks the metabolomic alterations became more pronounced. The metabolomic changes at different ages corroborated well with the phenotype changes in this transgenic mice model. Several useful candidate biomarkers including methionine, desaminotyrosine, taurine, N1-acetylspermidine, and 5-hydroxyindoleacetic acid were identified. Some of them were found in previous metabolomics studies in human cerebrospinal fluid or blood samples. This work illustrates the utility of this isotope labeling LC-MS method for biomarker discovery using mouse urine metabolomics.

Novel approach for analysis of bronchoalveolar lavage fluid (BALF) using HPLC-QTOF-MS-based lipidomics: lipid levels in asthmatics and corticosteroid-treated asthmatic patients

To better understand the respiratory lipid phenotypes of asthma, we developed a novel method for lipid profiling of bronchoalveolar lavage fluid (BALF) using HPLC-QTOF-MS with an internal spectral library and high-throughput lipid-identifying software. The method was applied to BALF from 38 asthmatic patients (18 patients with nonsteroid treated bronchial asthma [NSBA] and 20 patients with steroid treated bronchial asthma [SBA]) and 13 healthy subjects (NC). We identified 69 lipids, which were categorized into one of six lipid classes: lysophosphatidylcholine (LPC), phosphatidylcholine (PC), phosphatidylglycerol (PG), phosphatidylserine (PS), sphingomyelin (SM) and triglyceride (TG). Compared with the NC group, the individual quantity levels of the six classes of lipids were significantly higher in the NSBA subjects. In the SBA subjects, the PC, PG, PS, SM, and TG levels were similar to the levels observed in the NC group. Using differentially expressed lipid species (p value < 0.05, FDR < 0.1 and VIP score of PLS-DA > 1), 34 lipid biomarker candidates with high prediction performance between asthmatics and controls were identified (AUROC > 0.9). These novel findings revealed specific characteristics of lipid phenotypes in asthmatic patients and suggested the importance of future research on the relationship between lipid levels and asthma.

Cerebrospinal fluid metabolomics reveals altered waste clearance and accelerated aging in HIV patients with neurocognitive impairment

Objective(s):

HIV-associated neurocognitive disorders (HAND) remain prevalent in HIV-infected patients on antiretroviral therapy (ART), but the underlying mechanisms are unclear. Some features of HAND resemble those of age-associated cognitive decline in the absence of HIV, suggesting that overlapping mechanisms may contribute to neurocognitive impairment.

Design:

Cross-sectional analysis of cerebrospinal fluid (CSF) from 100 individuals (46 HIV-positive patients and 54 HIV-negative controls).

Methods:

Untargeted CSF metabolite profiling was performed using liquid/gas chromatography followed by mass spectrometry. Cytokine profiling was performed by Bioplex. Bioinformatic analyses were performed in Metaboanalyst and R.

Results:

Alterations in the CSF metabolome of HIV patients on ART mapped to pathways associated with neurotransmitter production, mitochondrial function, oxidative stress, and metabolic waste. Many CSF metabolites altered in HIV overlapped with those altered with advanced age in HIV-negative controls, suggesting a pattern indicative of accelerated aging. Machine learning models identified neurotransmitters (glutamate, N-acetylaspartate), markers of glial activation (myo-inositol), and ketone bodies (beta-hydroxybutyric acid, 1,2-propanediol) as top-ranked classifiers of HAND. These CSF metabolites correlated with worse neurocognitive test scores, plasma inflammatory biomarkers [interferon (IFN)-α, IFN-γ, interleukin (IL)-8, IL-1β, IL-6, IL-2Ra], and intrathecal IFN responses (IFN-γ and kynurenine : tryptophan ratio), suggesting inter-relationships between systemic and intrathecal inflammation and metabolic alterations in CSF.

Conclusions:

Alterations in the CSF metabolome of HIV patients on ART suggest that persistent inflammation, glial responses, glutamate neurotoxicity, and altered brain waste disposal systems contribute to mechanisms involved in HAND that may be augmented with aging.

Serum metabolomic biomarkers of dementia

Aims: This study compared serum metabolites of demented patients (Alzheimer's disease and vascular dementia) and controls, and explored serum metabolite profiles of nondemented individuals 5 years preceding the diagnosis. Methods: Cognitively healthy participants were followed up for 5-20 years. Cognitive assessment, serum sampling, and diagnosis were completed every 5 years. Multivariate analyses were conducted on the metabolite profiles generated by gas chromatography/time-of-flight mass spectrometry. Results: A significant group separation was found between demented patients and controls, and between incident cases and controls. Metabolites that contributed in both analyses were 3,4-dihydroxybutanoic acid, docosapentaenoic acid, and uric acid. Conclusions: Serum metabolite profiles are altered in demented patients, and detectable up to 5 years preceding the diagnosis. Blood sampling can make an important contribution to the early prediction of conversion to dementia.

Pentose Phosphate Pathway in Disease and Therapy

Pentose phosphate (PP) pathway, which is ubiquitously present in all living organisms, is one of the major metabolic pathways associated with glucose metabolism. The most important functions of this pathway includes the generation of reducing equivalents in the form of NADPH for reductive biosynthesis, and production of ribose sugars for the biosynthesis of nucleotides, amino acids, and other macromolecules required by all living cells. Under normal conditions of growth, PP pathway is important for cell cycle progression, myelin formation, and the maintenance of the structure and function of brain, liver, cortex and other organs. Under diseased conditions, such as in cases of many metabolic, neurological or malignant diseases, pathological mechanisms augment due to defects in the PP pathway genes. Adoption of alternative metabolic pathways by cells that are metabolically abnormal, or malignant cells that are resistant to chemotherapeutic drugs often plays important roles in disease progression and severity. Accordingly, the PP pathway has been suggested to play critical roles in protecting cancer or abnormal cells by providing reduced environment, to protect cells from oxidative damage and generating structural components for nucleic acids biosynthesis. Novel drugs that targets one or more components of the PP pathway could potentially serve to overcome challenges associated with currently available therapeutic options for many metabolic and non-metabolic diseases. However, careful designing of drugs is critical that takes into the accounts of cell’s broader genomic, proteomic and metabolic contexts under consideration, in order to avoid undesirable side-effects. In this review, we discuss the role of PP pathway under normal and abnormal physiological conditions and the potential of the PP pathway as a target for new drug development to treat metabolic and non-metabolic diseases.

Using the zebrafish model for Alzheimer's disease research

Rodent models have been extensively used to investigate the cause and mechanisms behind Alzheimer’s disease. Despite many years of intensive research using these models we still lack a detailed understanding of the molecular events that lead to neurodegeneration. Although zebrafish lack the complexity of advanced cognitive behaviors evident in rodent models they have proven to be a very informative model for the study of human diseases. In this review we give an overview of how the zebrafish has been used to study Alzheimer’s disease. Zebrafish possess genes orthologous to those mutated in familial Alzheimer’s disease and research using zebrafish has revealed unique characteristics of these genes that have been difficult to observe in rodent models. The zebrafish is becoming an increasingly popular model for the investigation of Alzheimer’s disease and will complement studies using other models to help complete our understanding of this disease.

Potential of metabolomics in preclinical and clinical drug development

Metabolomics is an upcoming technology system which involves detailed experimental analysis of metabolic profiles. Due to its diverse applications in preclinical and clinical research, it became an useful tool for the drug discovery and drug development process. This review covers the brief outline about the instrumentation and interpretation of metabolic profiles. The applications of metabolomics have a considerable scope in the pharmaceutical industry, almost at each step from drug discovery to clinical development. These include finding drug target, potential safety and efficacy biomarkers and mechanisms of drug action, the validation of preclinical experimental models against human disease profiles, and the discovery of clinical safety and efficacy biomarkers. As we all know, nowadays the drug discovery and development process is a very expensive, and risky business. Failures at any stage of drug discovery and development process cost millions of dollars to the companies. Some of these failures or the associated risks could be prevented or minimized if there were better ways of drug screening, drug toxicity profiling and monitoring adverse drug reactions. Metabolomics potentially offers an effective route to address all the issues associated with the drug discovery and development.

Biochemical characterization of human gluconokinase and the proposed metabolic impact of gluconic acid as determined by constraint based metabolic network analysis

The metabolism of gluconate is well characterized in prokaryotes where it is known to be degraded following phosphorylation by gluconokinase. Less is known of gluconate metabolism in humans. Human gluconokinase activity was recently identified proposing questions about the metabolic role of gluconate in humans. Here we report the recombinant expression, purification and biochemical characterization of isoform I of human gluconokinase alongside substrate specificity and kinetic assays of the enzyme catalyzed reaction. The enzyme, shown to be a dimer, had ATP dependent phosphorylation activity and strict specificity towards gluconate out of 122 substrates tested. In order to evaluate the metabolic impact of gluconate in humans we modeled gluconate metabolism using steady state metabolic network analysis. The results indicate that significant metabolic flux changes in anabolic pathways linked to the hexose monophosphate shunt (HMS) are induced through a small increase in gluconate concentration. We argue that the enzyme takes part in a context specific carbon flux route into the HMS that, in humans, remains incompletely explored. Apart from the biochemical description of human gluconokinase, the results highlight that little is known of the mechanism of gluconate metabolism in humans despite its widespread use in medicine and consumer products.

Plasma metabolite profiles of Alzheimer's disease and mild cognitive impairment

Previous studies have demonstrated altered metabolites in samples of Alzheimer's disease (AD) patients. However, the sample size from many of them is relatively small and the metabolites are relatively limited. Here we applied a comprehensive platform using ultraperformance liquid chromatography-time-of-flight mass spectrometry and gas chromatography-time-of-flight mass spectrometry to analyze plasma samples from AD patients, amnestic mild cognitive impairment (aMCI) patients, and normal controls. A biomarker panel consisting of six plasma metabolites (arachidonic acid, N,N-dimethylglycine, thymine, glutamine, glutamic acid, and cytidine) was identified to discriminate AD patients from normal control. Another panel of five plasma metabolites (thymine, arachidonic acid, 2-aminoadipic acid, N,N-dimethylglycine, and 5,8-tetradecadienoic acid) was able to differentiate aMCI patients from control subjects. Both biomarker panels had good agreements with clinical diagnosis. The 2 panels of metabolite markers were all involved in fatty acid metabolism, one-carbon metabolism, amino acid metabolism, and nucleic acid metabolism. Additionally, no altered metabolites were found among the patients at different stages, as well as among those on anticholinesterase medication and those without anticholinesterase medication. These findings provide a comprehensive global plasma metabolite profiling and may contribute to making early diagnosis as well as understanding the pathogenic mechanism of AD and aMCI.

[Nuclear magnetic resonance based metabolic phenotyping for patient evaluations in operating rooms and intensive care units]

Metabolic phenotyping consists in the identification of subtle and coordinated metabolic variations associated with various pathophysiological stimuli. Different analytical methods, such as nuclear magnetic resonance, allow the simultaneous quantification of a large number of metabolites. Statistical analyses of these spectra thus lead to the discrimination between samples and the identification of a metabolic phenotype corresponding to the effect under study. This approach allows the extraction of candidate biomarkers and the recovery of perturbed metabolic networks, driving to the generation of biochemical hypotheses (pathophysiological mechanisms, diagnostic tests, therapeutic targets…). Metabolic phenotyping could be useful in anaesthesiology and intensive care medicine for the evaluation, monitoring or diagnosis of life-threatening situations, to optimise patient managements. This review introduces the physical and statistical fundamentals of NMR-based metabolic phenotyping, describes the work already achieved by this approach in anaesthesiology and intensive care medicine. Finally, potential areas of interest are discussed for the perioperative and intensive management of patients, from newborns to adults.

The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates for therapies are under intense research. Even though there is currently no cure for AD, its effects can be managed. Today the significance of early and precise diagnosis of AD is emphasized in order to minimize its irreversible effects on the nervous system. When new drugs and therapies enter the market it is also vital to effectively identify the right candidates to benefit from these. The main objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data, electrophysiological measurements of the brain and structural, functional and molecular brain images. We also designed and built a statistical model and a framework for exploiting these biomarkers with other available patient history and background data. We were able to discover several potential novel biomarker candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials.

Evidence of altered phosphatidylcholine metabolism in Alzheimer's disease

Abberant lipid metabolism is implicated in Alzheimer's disease (AD) pathophysiology, but the connections between AD and lipid metabolic pathways are not fully understood. To investigate plasma lipids in AD, a multiplatform screen (n = 35 by liquid chromatography-mass spectrometry and n = 35 by nuclear magnetic resonance) was developed, which enabled the comprehensive analysis of plasma from 3 groups (individuals with AD, individuals with mild cognitive impairment (MCI), and age-matched controls). This screen identified 3 phosphatidylcholine (PC) molecules that were significantly diminished in AD cases. In a subsequent validation study (n = 141), PC variation in a bigger sample set was investigated, and the same 3 PCs were found to be significantly lower in AD patients: PC 16:0/20:5 (p < 0.001), 16:0/22:6 (p < 0.05), and 18:0/22:6 (p < 0.01). A receiver operating characteristic (ROC) analysis of the PCs, combined with apolipoprotein E (ApoE) data, produced an area under the curve predictive value of 0.828. Confirmatory investigations into the background biochemistry indiciated no significant change in plasma levels of 3 additional PCs of similar structure, total choline containing compounds or total plasma omega fatty acids, adding to the evidence that specific PCs play a role in AD pathology.

Biomarker Discovery and Translation in Metabolomics

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Metabolomic profiles in individuals with negative affectivity and social inhibition: a population-based study of Type D personality

BACKGROUND

Individuals with negative affectivity who are inhibited in social situations are characterized as distressed, or Type D, and have an increased risk of cardiovascular disease (CVD). The underlying biomechanisms that link this psychological affect to a pathological state are not well understood. This study applied a metabolomic approach to explore biochemical pathways that may contribute to the Type D personality.

METHODS

Type D personality was determined by the Type D Scale-14. Small molecule biochemicals were measured using two complementary mass-spectrometry based metabolomics platforms. Metabolic profiles of Type D and non-Type D participants within a population-based study in Southern Germany were compared in cross-sectional regression analyses. The PHQ-9 and GAD-7 instruments were also used to assess symptoms of depression and anxiety, respectively, within this metabolomic study.

RESULTS

668 metabolites were identified in the serum of 1502 participants (age 32-77); 386 of these individuals were classified as Type D. While demographic and biomedical characteristics were equally distributed between the groups, a higher level of depression and anxiety was observed in Type D individuals. Significantly lower levels of the tryptophan metabolite kynurenine were associated with Type D (p-value corrected for multiple testing=0.042), while no significant associations could be found for depression and anxiety. A Gaussian graphical model analysis enabled the identification of four potentially interesting metabolite networks that are enriched in metabolites (androsterone sulfate, tyrosine, indoxyl sulfate or caffeine) that associate nominally with Type D personality.

CONCLUSIONS

This study identified novel biochemical pathways associated with Type D personality and demonstrates that the application of metabolomic approaches in population studies can reveal mechanisms that may contribute to psychological health and disease.

Visualizing neurotransmitters and metabolites in the central nervous system by high resolution and high accuracy mass spectrometric imaging

The spatial localization and molecular distribution of metabolites and neurotransmitters within biological organisms is of tremendous interest to neuroscientists. In comparison to conventional imaging techniques such as immunohistochemistry, matrix-assisted laser desorption/ionization (MALDI) mass spectrometric imaging (MSI) has demonstrated its unique advantage by directly localizing the distribution of a wide range of biomolecules simultaneously from a tissue specimen. Although MALDI-MSI of metabolites and neurotransmitters is hindered by numerous matrix-derived peaks, high-resolution and high-accuracy mass spectrometers (HRMS) allow differentiation of endogenous analytes from matrix peaks, unambiguously obtaining biomolecular distributions. In this study, we present MSI of metabolites and neurotransmitters in rodent and crustacean central nervous systems acquired on HRMS. Results were compared with those obtained from a medium-resolution mass spectrometer (MRMS), tandem time-of-flight instrument, to demonstrate the power and unique advantages of HRMSI and reveal how this new tool would benefit molecular imaging applications in neuroscience.

Alcohol-induced metabolomic differences in humans

Alcohol consumption is one of the world's major risk factors for disease development. But underlying mechanisms by which moderate-to-heavy alcohol intake causes damage are poorly understood and biomarkers are sub-optimal. Here, we investigated metabolite concentration differences in relation to alcohol intake in 2090 individuals of the KORA F4 and replicated results in 261 KORA F3 and up to 629 females of the TwinsUK adult bioresource. Using logistic regression analysis adjusted for age, body mass index, smoking, high-density lipoproteins and triglycerides, we identified 40/18 significant metabolites in males/females with P-values <3.8E-04 (Bonferroni corrected) that differed in concentrations between moderate-to-heavy drinkers (MHD) and light drinkers (LD) in the KORA F4 study. We further identified specific profiles of the 10/5 metabolites in males/females that clearly separated LD from MHD in the KORA F4 cohort. For those metabolites, the respective area under the receiver operating characteristic curves were 0.812/0.679, respectively, thus providing moderate-to-high sensitivity and specificity for the discrimination of LD to MHD. A number of alcohol-related metabolites could be replicated in the KORA F3 and TwinsUK studies. Our data suggests that metabolomic profiles based on diacylphosphatidylcholines, lysophosphatidylcholines, ether lipids and sphingolipids form a new class of biomarkers for excess alcohol intake and have potential for future epidemiological and clinical studies.

Recent advances in the application of metabolomics to Alzheimer's Disease

The pathophysiological changes associated with Alzheimer's Disease (AD) begin decades before the emergence of clinical symptoms. Understanding the early mechanisms associated with AD pathology is, therefore, especially important for identifying disease-modifying therapeutic targets. While the majority of AD clinical trials to date have focused on anti-amyloid-beta (Aβ) treatments, other therapeutic approaches may be necessary. The ability to monitor changes in cellular networks that include both Aβ and non-Aβ pathways is essential to advance our understanding of the etiopathogenesis of AD and subsequent development of cognitive symptoms and dementia. Metabolomics is a powerful tool that detects perturbations in the metabolome, a pool of metabolites that reflects changes downstream of genomic, transcriptomic and proteomic fluctuations, and represents an accurate biochemical profile of the organism in health and disease. The application of metabolomics could help to identify biomarkers for early AD diagnosis, to discover novel therapeutic targets, and to monitor therapeutic response and disease progression. Moreover, given the considerable parallel between mouse and human metabolism, the use of metabolomics provides ready translation of animal research into human studies for accelerated drug design. In this review, we will summarize current progress in the application of metabolomics in both animal models and in humans to further understanding of the mechanisms involved in AD pathogenesis.

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.

Alterations in metabolic pathways and networks in Alzheimer's disease

The pathogenic mechanisms of Alzheimer's disease (AD) remain largely unknown and clinical trials have not demonstrated significant benefit. Biochemical characterization of AD and its prodromal phase may provide new diagnostic and therapeutic insights. We used targeted metabolomics platform to profile cerebrospinal fluid (CSF) from AD (n=40), mild cognitive impairment (MCI, n=36) and control (n=38) subjects; univariate and multivariate analyses to define between-group differences; and partial least square-discriminant analysis models to classify diagnostic groups using CSF metabolomic profiles. A partial correlation network was built to link metabolic markers, protein markers and disease severity. AD subjects had elevated methionine (MET), 5-hydroxyindoleacetic acid (5-HIAA), vanillylmandelic acid, xanthosine and glutathione versus controls. MCI subjects had elevated 5-HIAA, MET, hypoxanthine and other metabolites versus controls. Metabolite ratios revealed changes within tryptophan, MET and purine pathways. Initial pathway analyses identified steps in several pathways that appear altered in AD and MCI. A partial correlation network showed total tau most directly related to norepinephrine and purine pathways; amyloid-β (Ab42) was related directly to an unidentified metabolite and indirectly to 5-HIAA and MET. These findings indicate that MCI and AD are associated with an overlapping pattern of perturbations in tryptophan, tyrosine, MET and purine pathways, and suggest that profound biochemical alterations are linked to abnormal Ab42 and tau metabolism. Metabolomics provides powerful tools to map interlinked biochemical pathway perturbations and study AD as a disease of network failure.

Characterization of cerebrospinal fluid by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry

Comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) was applied in the quantification and identification of organic compounds in patient-matched human cerebrospinal fluid (CSF) and serum samples. Concentrations of 21 amino and hydroxyl acids varied from 0.04 to 77ng/μl in CSF and from 0.1 to 84ng/μl in serum. In total, 91 metabolites out of over 1200 detected were identified based on mass spectra and retention indices. The other metabolites were identified at the functional group level. The main metabolites detected in CSF were sugar and amino acid derivatives. The CSF and serum had clearly distinct metabolic profiles, with larger biological variation in the serum than in CSF. The GC×GC-TOFMS allowed detection and identification of several metabolites that have not been previously detected in CSF.

Computational tools for the secondary analysis of metabolomics experiments

Metabolomics experiments have become commonplace in a wide variety of disciplines. By identifying and quantifying metabolites researchers can achieve a systems level understanding of metabolism. These studies produce vast swaths of data which are often only lightly interpreted due to the overwhelmingly large amount of variables that are measured. Recently, a number of computational tools have been developed which enable much deeper analysis of metabolomics data. These data have been difficult to interpret as understanding the connections between dozens of altered metabolites has often relied on the biochemical knowledge of researchers and their speculations. Modern biochemical databases provide information about the interconnectivity of metabolism which can be automatically polled using metabolomics secondary analysis tools. Starting with lists of altered metabolites, there are two main types of analysis: enrichment analysis computes which metabolic pathways have been significantly altered whereas metabolite mapping contextualizes the abundances and significances of measured metabolites into network visualizations. Many different tools have been developed for one or both of these applications. In this review the functionality and use of these software is discussed. Together these novel secondary analysis tools will enable metabolomics researchers to plumb the depths of their data and produce farther reaching biological conclusions than ever before.

Obesity and psychotic disorders: uncovering common mechanisms through metabolomics

Primary obesity and psychotic disorders are similar with respect to the associated changes in energy balance and co-morbidities, including metabolic syndrome. Such similarities do not necessarily demonstrate causal links, but instead suggest that specific causes of and metabolic disturbances associated with obesity play a pathogenic role in the development of co-morbid disorders, potentially even before obesity develops. Metabolomics – the systematic study of metabolites, which are small molecules generated by the process of metabolism – has been important in elucidating the pathways underlying obesity-associated co-morbidities. This review covers how recent metabolomic studies have advanced biomarker discovery and the elucidation of mechanisms underlying obesity and its co-morbidities, with a specific focus on metabolic syndrome and psychotic disorders. The importance of identifying metabolic markers of disease-associated intermediate phenotypes – traits modulated but not encoded by the DNA sequence – is emphasized. Such markers would be applicable as diagnostic tools in a personalized healthcare setting and might also open up novel therapeutic avenues.

Plasma biomarkers for Alzheimer's disease: much needed but tough to find

Alzheimer's disease is a complex age-dependent neurodegenerative disease where definitive diagnosis is only possible after autopsy and where there is a long prodromal or preclinical phase. Biomarkers for both early diagnosis and prediction of disease progression are needed and extensive efforts to discover them have been undertaken. In this article, we have attempted to summarize the findings of current studies using proteomics and metabolomics approaches. We are also discussing how the use of emerging technologies and better study designs can support the identification of the much-needed Alzheimer's disease plasma biomarkers.

Metabolomics in the studies of islet autoimmunity and type 1 diabetes

The metabolome is sensitive to genetic and environmental factors contributing to complex diseases such as type 1 diabetes (T1D). Metabolomics is the study of biochemical and physiological processes involving metabolites. It is therefore one of the key platforms for the discovery and study of pathophysiological phenomena leading to T1D and the development of T1D-associated complications. Although the application of metabolomics in T1D research is still rare, metabolomic research has already advanced across the full spectrum, from disease progression to the development of diabetic complications. Metabolomic studies in T1D have contributed to an improved etiopathogenic understanding and demonstrated their potential in the clinic. For example, metabolomic data from recent T1D studies suggest that a specific metabolic profile, or metabotype, precedes islet autoimmunity and the development of overt T1D. These early metabolic changes are attributed to many biochemical pathways, thus suggesting a systemic change in metabolism which may be inborn. Based on this evidence, the role of the metabolome in the progression to T1D is therefore to facilitate specific biochemical processes associated with T1D, and to contribute to the development of a vulnerable state in which disease is more likely to be triggered. This may have important implications for the understanding of T1D pathophysiology and early disease detection and prevention.

Novel methodologies in metabolic profiling with a focus on molecular diagnostic applications

The metabolome contains all the biological end points of genomic, transcriptomic and proteomic perturbations, also including the influence of gut microbiota and the environment, giving a direct picture of an organism's ongoing metabolic state. Metabolomics thus has the potential to be an effective tool for early diagnosis of disease, and also to be a predictor of treatment response and survival. In recent years, the development of instrumental systems has enabled more comprehensive coverage of the metabolome. Advances in mass spectrometry and chromatography have particularly improved both the efficiency of nontargeted metabolic profiling as well as the sensitivity and reliability of targeted analyses. Mass spectrometric techniques are also increasingly becoming accepted as a routine diagnostic tool in clinical laboratories. This review summarizes the most recent advances and current challenges in metabolomics, with a focus on mass spectrometric methods utilized in biomarker research, highlighted with selected examples.

Molecular signatures from omics data: from chaos to consensus

In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.

Bayesian independent component analysis recovers pathway signatures from blood metabolomics data

Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the source matrix of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a mixing matrix, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field.

Prenatal hypoxia may aggravate the cognitive impairment and Alzheimer's disease neuropathology in APPSwe/PS1A246E transgenic mice

Most cases of Alzheimer's disease (AD) arise through interactions between genetic and environmental factors. It is believed that hypoxia is an important environmental factor influencing the development of AD. Our group has previously demonstrated that hypoxia increased β-amyloid (Aβ) generation in aged AD mice. Here, we further investigate the pathological role of prenatal hypoxia in AD. We exposed the pregnant APP(Swe)/PS1(A246E) transgenic mice to high-altitude hypoxia in a hypobaric chamber during days 7-20 of gestation. We found that prenatal hypoxic mice exhibited a remarkable deficit in spatial learning and memory and a significant decrease in synapses. We also documented a significantly higher level of amyloid precursor protein, lower level of the Aβ-degrading enzyme neprilysin, and increased Aβ accumulation in the brain of prenatal hypoxic mice. Finally, we demonstrated striking neuropathologic changes in prenatal hypoxic AD mice, showing increased phosphorylation of tau, decreased hypoxia-induced factor, and enhanced activation of astrocytes and microglia. These data suggest that although the characteristic features of AD appear later in life, hypoxemia in the prenatal stage may contribute to the pathogenesis of the disease, supporting the notion that environmental factors can trigger or aggravate AD.

Reduced platelet amyloid precursor protein ratio (APP ratio) predicts conversion from mild cognitive impairment to Alzheimer's disease

Studies have shown that platelet APP ratio (representing the percentage of 120-130 kDa to 110 kDa isoforms of the amyloid precursor protein) is reduced in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). In the present study, we sought to determine if baseline APP ratio predicts the conversion from MCI to AD dementia after 4 years of longitudinal assessment. Fifty-five older adults with varying degrees of cognitive impairment (34 with MCI and 21 with AD) were assessed at baseline and after 4 years. MCI patients were re-classified according to the conversion status upon follow-up: 25 individuals retained the diagnostic status of MCI and were considered as stable cases (MCI-MCI); conversely, in nine cases the diagnosis of dementia due to AD was ascertained. The APP ratio (APPr) was determined by the Western blot method in samples of platelets collected at baseline. We found a significant reduction of APPr in MCI patients who converted to dementia upon follow-up. These individuals had baseline APPr values similar to those of demented AD patients. The overall accuracy of APPr to identify subjects with MCI who will progress to AD was 0.74 ± 0.10, p = 0.05. The cut-off of 1.12 yielded a sensitivity of 75 % and a specificity of 75 %. Platelet APPr may be a surrogate marker of the disease process in AD, with potential implications for the assessment of abnormalities in the APP metabolism in patients with and at risk for dementia. However, diagnostic accuracy was relatively low. Therefore, studies in larger samples are needed to determine whether APPr may warrant its use as a biomarker to support the early diagnosis of AD.