The human cerebrospinal fluid metabolome

Computational strategies for metabolite identification in metabolomics

Most metabolomic data are characterized by complex spectra or chromatograms containing hundreds of peaks or features. While there are many methods for aligning or comparing these spectral features, there are few approaches for actually identifying which peaks match to which compounds. Indeed, one of the biggest unmet needs in the field of metabolomics lies in the problem of compound identification. This review describes some of the newly emerging computational strategies in metabolomics that are being used to aid in the identification of metabolites from biofluid mixtures analyzed by NMR and MS. The most successful compound-identification strategies typically involve matching spectral features of the unknown compound(s) to curated spectral databases of reference compounds. This approach is known as the identification of 'known unknowns'. However, the identification of truly novel compounds (the 'unknown unknowns') is particularly challenging and requires the use of computer-aided structure elucidation methods being applied to the purified compound. The strengths and limitations of these approaches as applied to different analytical technologies (GC-MS, LC-MS and NMR) will be discussed, as will prospects for potential improvements to existing strategies.

The human serum metabolome

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.

Qualitative metabolome analysis of human cerebrospinal fluid by 13C-/12C-isotope dansylation labeling combined with liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry

Metabolome analysis of human cerebrospinal fluid (CSF) is challenging because of low abundance of metabolites present in a small volume of sample. We describe and apply a sensitive isotope labeling LC-MS technique for qualitative analysis of the CSF metabolome. After a CSF sample is divided into two aliquots, they are labeled by (13)C-dansyl and (12)C-dansyl chloride, respectively. The differentially labeled aliquots are then mixed and subjected to LC-MS using Fourier-transform ion cyclotron resonance mass spectrometry (FTICR MS). Dansylation offers significant improvement in the performance of chromatography separation and detection sensitivity. Moreover, peaks detected in the mass spectra can be readily analyzed for ion pair recognition and database search based on accurate mass and/or retention time information. It is shown that about 14,000 features can be detected in a 25-min LC-FTICR MS run of a dansyl-labeled CSF sample, from which about 500 metabolites can be profiled. Results from four CSF samples are compared to gauge the detectability of metabolites by this method. About 261 metabolites are commonly detected in replicate runs of four samples. In total, 1132 unique metabolite ion pairs are detected and 347 pairs (31%) matched with at least one metabolite in the Human Metabolome Database. We also report a dansylation library of 220 standard compounds and, using this library, about 85 metabolites can be positively identified. Among them, 21 metabolites have never been reported to be associated with CSF. These results illustrate that the dansylation LC-FTICR MS method can be used to analyze the CSF metabolome in a more comprehensive manner.

Quantitative proteomics and metabolomics analysis of normal human cerebrospinal fluid samples

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals.

Quantitative proteomics and metabolomics analysis of normal human cerebrospinal fluid samples

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals.

A case report - Volatile metabolomic signature of malignant melanoma using matching skin as a control

Melanoma is the most serious form of skin cancer. The quest for melanoma diagnostic biomarkers is paramount since early detection of melanoma and surgical excision represent the only effective treatment of this capricious disease. Our recent study tested the hypothesis that melanoma forms a unique volatile signature that is different than control, healthy tissue. Here, we are reporting a case study, the analysis of the volatile metabolic signature of a malignant melanoma using matched, non-neoplastic skin tissue from the same patient as a control. This is a significant improvement in the methodology, since it is well known that diet, skin type, genetic background, age, sex and environment all contribute to individual variation in the skin volatile signature. In the present study, we have identified 32 volatile compounds; 9 volatile compounds were increased in melanoma when compared to normal skin and 23 volatile compounds were detected only in melanoma and not in normal skin. Out of these 32 compounds, 10 have been reported previously by our group, thus confirming our results and adding additional confidence in our untargeted metabolomics approach for detection of melanoma biomarkers.

Two routes of iron accumulation in astrocytes: ascorbate-dependent ferrous iron uptake via the divalent metal transporter (DMT1) plus an independent route for ferric iron

Astrocytes are central to iron and ascorbate homoeostasis within the brain. Although NTBI (non-transferrin-bound iron) may be a major form of iron imported by astrocytes in vivo, the mechanisms responsible remain unclear. The present study examines NTBI uptake by cultured astrocytes and the involvement of ascorbate and DMT1 (divalent metal transporter 1). We demonstrate that iron accumulation by ascorbate-deficient astrocytes is insensitive to both membrane-impermeant Fe(II) chelators and to the addition of the ferroxidase caeruloplasmin. However, when astrocytes are ascorbate-replete, as occurs in vivo, their rate of iron accumulation is doubled. The acquisition of this additional iron depends on effluxed ascorbate and can be blocked by the DMT1 inhibitor ferristatin/NSC306711. Furthermore, the calcein-accessible component of intracellular labile iron, which appears during iron uptake, appears to consist of only Fe(III) in ascorbate-deficient astrocytes, whereas that of ascorbate-replete astrocytes comprises both valencies. Our data suggest that an Fe(III)-uptake pathway predominates when astrocytes are ascorbate-deficient, but that in ascorbate-replete astrocytes, at least half of the accumulated iron is initially reduced by effluxed ascorbate and then imported by DMT1. These results suggest that ascorbate is intimately involved in iron accumulation by astrocytes, and is thus an important contributor to iron homoeostasis in the mammalian brain.

Advances in mass spectrometry applied to pharmaceutical metabolomics

Metabolomics, also referred to in the literature as metabonomics, is a relatively new systems biology tool for drug discovery and development and is increasingly being used to obtain a detailed picture of a drug's effect on the body. Metabolomics is the qualitative assessment and relative or absolute quantitative measurement of the endogenous metabolome, defined as the complement of all native small molecules (metabolites less than 1,500 Da). A metabolomics study frequently involves the comparative analysis of sample sets from a normal state and a perturbed state, where the perturbation can be of any nature, such as genetic knockout, administration of a drug, or change in diet or lifestyle. Advances in mass spectrometry (MS) technologies including direct introduction or in-line chromatographic separation modes, ionization techniques, mass analyzers, and detection methods have provided powerful tools to assess the molecular changes in the metabolome. This review focuses on advances in MS pertaining to the analytical data generation for the main metabolomics methods, namely, fingerprinting, nontargeted, and targeted approaches, as they are applied to pharmaceutical drug discovery and development.

Cerebrospinal fluid phospholipase C activity increases in migraine

BACKGROUND

Adrenaline, serotonin, cannabinoid and estrogen receptors are involved in migraine pathophysiology. The signaling of these receptors change phosphatidylcholine-specific phospholipase C (PC-PLC) activity, but there have been no reported PC-PLC studies in migraine.

METHODS

We identified PC-PLC activity in blood and cerebrospinal fluid (CSF), and quantified it in samples from ictal and interictal migraineurs without aura and healthy controls.

RESULTS

Pre-incubation with a specific PC-PLC inhibitor, D609, inhibited enzyme activity (p < .0001) and confirms its presence in CSF. PC-PLC activity was higher in the CSF from ictal migraineurs compared to controls (mean relative fluorescence unit [RFU]/µg/min [standard deviation, SD] 13.1 [3.07] vs. 9.3 [1.97]; p = .002) and, in a paired analysis, in migraineurs during ictal compared to interictal states (11.7 [1.6] vs. 7.9 [1.5]; p = .02). CSF PC-PLC activity in the ictal state correlated negatively with migraine frequency (r = -0.82). Plasma PC-PLC activity was 250-300 times less than in CSF and did not increase in migraine, implicating the brain as the source of the CSF enzyme changes.

CONCLUSION

This is the first report of PC-PLC activity in CSF and of its alteration in migraine. We propose that these PC-PLC changes in CSF reflect the overall receptor fluctuations in migraine.

1H-NMR-based metabolomic profiling of CSF in early amyotrophic lateral sclerosis

Background

Pathophysiological mechanisms involved in amyotrophic lateral sclerosis (ALS) are complex and none has identified reliable markers useful in routine patient evaluation. The aim of this study was to analyze the CSF of patients with ALS by ¹H NMR (Nuclear Magnetic Resonance) spectroscopy in order to identify biomarkers in the early stages of the disease, and to evaluate the biochemical factors involved in ALS.

Methodology

CSF samples were collected from patients with ALS at the time of diagnosis and from patients without neurodegenerative diseases. One and two-dimensional ¹H NMR analyses were performed and metabolites were quantified by the ERETIC method. We compared the concentrations of CSF metabolites between both groups. Finally, we performed principal component (PCA) and discriminant analyses.

Principal Findings

Fifty CSF samples from ALS patients and 44 from controls were analyzed. We quantified 17 metabolites including amino-acids, organic acids, and ketone bodies. Quantitative analysis revealed significantly lower acetate concentrations (p = 0.0002) in ALS patients compared to controls. Concentration of acetone trended higher (p = 0.015), and those of pyruvate (p = 0.002) and ascorbate (p = 0.003) were higher in the ALS group. PCA demonstrated that the pattern of analyzed metabolites discriminated between groups. Discriminant analysis using an algorithm of 17 metabolites revealed that patients were accurately classified 81.6% of the time.

Conclusion/Significance

CSF screening by NMR spectroscopy could be a useful, simple and low cost tool to improve the early diagnosis of ALS. The results indicate a perturbation of glucose metabolism, and the need to further explore cerebral energetic metabolism.

Survival defects of Cryptococcus neoformans mutants exposed to human cerebrospinal fluid result in attenuated virulence in an experimental model of meningitis

Cryptococcus neoformans is a fungal pathogen that encounters various microenvironments during growth in the mammalian host, including intracellular vacuoles, blood, and cerebrospinal fluid (CSF). Because the CSF is isolated by the blood-brain barrier, we hypothesize that CSF presents unique stresses that C. neoformans must overcome to establish an infection. We assayed 1,201 mutants for survival defects in growth media, saline, and human CSF. We assessed CSF-specific mutants for (i) mutant survival in both human bronchoalveolar lavage (BAL) fluid and fetal bovine serum (FBS), (ii) survival in macrophages, and (iii) virulence using both Caenorhabditis elegans and rabbit models of cryptococcosis. Thirteen mutants exhibited significant survival defects unique to CSF. The mutations of three of these mutants were recreated in the clinical serotype A strain H99: deletions of the genes for a cation ATPase transporter (ena1Δ), a putative NEDD8 ubiquitin-like protein (rub1Δ), and a phosphatidylinositol 4-kinase (pik1Δ). Mutant survival rates in yeast media, saline, and BAL fluid were similar to those of the wild type; however, survival in FBS was reduced but not to the levels in CSF. These mutant strains also exhibited decreased intracellular survival in macrophages, various degrees of virulence in nematodes, and severe attenuation of survival in a rabbit meningitis model. We analyzed the CSF by mass spectrometry for candidate compounds responsible for the survival defect. Our findings indicate that the genes required for C. neoformans survival in CSF ex vivo are necessary for survival and infection in this unique host environment.

Advances in NMR-based biofluid analysis and metabolite profiling

Significant improvements in NMR technology and methods have propelled NMR studies to play an important role in a rapidly expanding number of applications involving the profiling of metabolites in biofluids. This review discusses recent technical advances in NMR spectroscopy based metabolite profiling methods, data processing and analysis over the last three years.

Human bile as a rich source of biomarkers for hepatopancreatobiliary cancers

Metabolic profiling of biofluids is emerging as an important area with a promising number of applications in clinical medicine, including early diagnosis of numerous diseases that normally remain silent until late in the progress of disease. While blood and urine are more often used to explore biomarkers that distinguish he healthy from disease conditions, human bile is emerging as a rich source of biomarkers specifically for the cancers of the liver (hepatocellular carcinoma), bile ducts (cholangiocarcinoma), gallbladder and pancreas. This is owing to the fact that metabolites linked to the pathways of tumor cell metabolism are rich in bile by virtue of its association or proximity to the pathological source. Recent methodological developments have enabled the identification of a number of bile metabolites that have links with hepatopancreatobiliary diseases. Investigations of human bile are also considered to help the biomarker discovery process in vitro and provide avenues for translational research in detecting and following dynamic variations of biomarkers in clinical settings using noninvasive approaches, such as in vivo magnetic resonance spectroscopy. This article reviews the current status and potential applications of human bile as a source of biomarkers, with emphasis on metabolites, for early detection of cancers associated with the hepatopancreatobiliary system.

Improving metabolite knowledge in stable atherosclerosis patients by association and correlation of GC-MS and 1H NMR fingerprints

The plasma of patients with stable carotid atherosclerosis (n = 9), and healthy subjects (n = 10) have been fingerprinted with both GC-MS and (1)H NMR. Principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) have been applied to the profiles from each technique both separately and in combination. These techniques complement each other and enable a clearer picture of the biological samples to be interpreted not only for classification purposes, but also more importantly to define the metabolic state of patients with carotid atherosclerosis. The results showed at least 24 metabolites that were significantly modified in the group of atherosclerotic patients by this nontargeted procedure. Most of the changes can be associated to alterations of the metabolism characteristics of insulin resistance that can be strongly related to the metabolic syndrome. In addition, correlations among variables accounting for the classification show amino acids as variables whose changes showed a high degree of correlation. GC-MS and (1)H NMR fingerprints can provide complementary information in the identification of altered metabolic pathways in patients with carotid atherosclerosis. Moreover, correlations among the results with both techniques, instead of a single study, can provide a deeper insight into the patient state.

The complex links between dietary phytochemicals and human health deciphered by metabolomics

A large variety of phytochemicals commonly consumed with the human diet, influence health and may contribute to the prevention of diseases. However, it is still difficult to make nutritional recommendations for these bioactive compounds. Current studies of phytochemicals are generally focused on specific compounds and their effects on a limited number of markers. New approaches are needed to take into account both the diversity of phytochemicals found in the diet and the complexity of their biological effects. Recent progress in high-throughput analytical technologies and in bioinformatics now allows the simultaneous analysis of the hundreds or more metabolites constituting the metabolome in urine or plasma. These analyses give complex metabolic fingerprints characteristic of a given phenotype. The exploitation of the wealth of information it contains, in randomized controlled trials and cohort studies, should lead to the discovery of new markers of intake for phytochemicals and new markers of effects. In this paper, we briefly review the current methods used to evaluate intake of phytochemicals and their effects on health. We then describe the applications of metabolomics in this field. Recent metabolomics studies illustrate the potential of such a global approach to explore the complex relationships linking phytochemical intake and metabolism and health.

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results.

MR metabolomics of fecal extracts: applications in the study of bowel diseases

NMR-based metabolomics is becoming a useful tool in the study of body fluids and has a strong potential to contribute to disease diagnosis. While applications on urine and serum have been the focus to date, there are a number of other body fluids that are readily available and could potentially be used for metabolomics-based disease diagnosis. One such body fluid is stool or fecal extract. Given its contact with and transient stay in the colon and rectum, stool carries a lot of useful information regarding the health/disease status of both the colon and the rectum. This could be particularly useful for the non-invasive diagnosis of colorectal cancer and inflammatory bowel disease--the two bowel diseases that are very common and pose significant public health problems. Different methodological considerations including the collection of sample, the storage of sample, the preparation of sample, NMR acquisition parameters, experimental conditions and data analysis methods are discussed. Results obtained in the detection of colorectal cancer and in the differentiation of the two major forms of inflammatory bowel disease (i.e. ulcerative colitis and Crohn's disease) are presented. This is concluded with a brief discussion on the future of MR metabolomics of fecal extracts.

SMPDB: The Small Molecule Pathway Database

The Small Molecule Pathway Database (SMPDB) is an interactive, visual database containing more than 350 small-molecule pathways found in humans. More than 2/3 of these pathways (>280) are not found in any other pathway database. SMPDB is designed specifically to support pathway elucidation and pathway discovery in clinical metabolomics, transcriptomics, proteomics and systems biology. SMPDB provides exquisitely detailed, hyperlinked diagrams of human metabolic pathways, metabolic disease pathways, metabolite signaling pathways and drug-action pathways. All SMPDB pathways include information on the relevant organs, organelles, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Each small molecule is hyperlinked to detailed descriptions contained in the Human Metabolome Database (HMDB) or DrugBank and each protein or enzyme complex is hyperlinked to UniProt. All SMPDB pathways are accompanied with detailed descriptions, providing an overview of the pathway, condition or processes depicted in each diagram. The database is easily browsed and supports full text searching. Users may query SMPDB with lists of metabolite names, drug names, genes/protein names, SwissProt IDs, GenBank IDs, Affymetrix IDs or Agilent microarray IDs. These queries will produce lists of matching pathways and highlight the matching molecules on each of the pathway diagrams. Gene, metabolite and protein concentration data can also be visualized through SMPDB’s mapping interface. All of SMPDB’s images, image maps, descriptions and tables are downloadable. SMPDB is available at: http://www.smpdb.ca.

Variability analysis of human plasma and cerebral spinal fluid reveals statistical significance of changes in mass spectrometry-based metabolomics data

Analytical and biological variability are issues of central importance to human metabolomics studies. Here both types of variation are examined in human plasma and cerebrospinal fluid (CSF) using a global liquid chromatography/mass spectrometry (LC/MS) metabolomics strategy. The platform shows small analytical variation with a median coefficient of variation (CV) of 15-16% for both plasma and CSF sample matrixes when the integrated area of each peak in the mass spectra is considered. Analysis of biological variation shows that human CSF has a median CV of 35% and plasma has a median CV of 46%. To understand the difference in CV between the biofluids, we compared plasma and CSF independently obtained from different healthy humans. Additionally, we analyzed another group of patients from whom we compared matched CSF and plasma (plasma and CSF obtained from the same human subject). A similar number of features was observed in both biofluids, although the majority of features appeared with greater intensity in plasma. More than a dozen metabolites shared between the human CSF and plasma metabolomes were identified based on accurate mass measurements, retention times, and MS/MS spectra. The fold change in these metabolites was consistent with the median biological CV determined for all peaks. The measured median biological CV together with analysis of intragroup variation of healthy individuals suggests that fold changes above 2 in metabolomics studies investigating plasma or CSF are statistically relevant with respect to the inherent variability of a healthy control group. These data demonstrate the reproducibility of the global metabolomics platform using LC/MS and reveal the robustness of the approach for biomarker discovery.

Combining nuclear magnetic resonance spectroscopy and mass spectrometry in biomarker discovery

Metabolic profiling of biological specimens is emerging as a promising approach for discovering specific biomarkers in the diagnosis of a number of diseases. Amongst many analytical techniques, nuclear magnetic resonance spectroscopy and mass spectrometry are the most information-rich tools that enable high-throughput and global analysis of hundreds of metabolites in a single step. Although only one of the two techniques is utilized in a majority of metabolomics applications, there is a growing interest in combining the data from the two methods to effectively unravel the mammoth complexity of biological samples. In this article, current developments in nuclear magnetic resonance, mass spectrometry and multivariate statistical analysis methods are described. While some general applications that utilize the combination of the two analytical methods are presented briefly, the emphasis is laid on the recent applications of nuclear magnetic resonance and mass spectrometry methods in the studies of hepatopancreatobiliary and gastrointestinal malignancies.

Quantitative profiling of polar cationic metabolites in human cerebrospinal fluid by reversed-phase nanoliquid chromatography/mass spectrometry

Reversed-phase (RP) nanoliquid chromatography (LC)/mass spectrometry (MS) is widely used for proteome analysis, but hydrophilic metabolites are poorly retained on RP columns. We describe here the development and application of an efficient, robust, and quantitative nano-LC/MS method for cationic metabolome analysis in the positive ionization mode without any derivatization of analytes. Various stationary phases for nano-LC, coating of the internal wall of the capillary column, and various mobile phases were evaluated in terms of separation and peak shapes for 33 hydrophilic metabolites, including nonderivatized amino acids. Polar cationic compounds were strongly bound to mixed-functional RP with cation exchange mode resin, and the best separation was obtained with hydrophilic internal wall coating and a two-step trifluoroacetic acid (TFA) gradient in methanol as the mobile phase. Simple, but optimized, sample processing and the use of a high content of methanol allowed robust nano-LC/MS analysis. Our developed method was applied for biomarker discovery in Alzheimer's disease (AD). Several hundred peaks were detected from 10 microL of cerebrospinal fluid (CSF). In a principal component analysis (PCA) plot using peak intensities without normalization, peak separation depended on the experimental date, not disease state. Therefore, constant amounts of two stable isotope-labeled amino acids, Val and Lys, were added as internal standards (ISs) to each sample before processing. These ISs were eluted in different gradient slopes in the two-step gradient, and the normalized peak ratios using the corresponding ISs gave a unique group of PCA scores which could distinguish AD CSF samples from age-matched control CSF samples.

The correspondence problem for metabonomics datasets

In metabonomics it is difficult to tell which peak is which in datasets with many samples. This is known as the correspondence problem. Data from different samples are not synchronised, i.e., the peak from one metabolite does not appear in exactly the same place in all samples. For datasets with many samples, this problem is nontrivial, because each sample contains hundreds to thousands of peaks that shift and are identified ambiguously. Statistical analysis of the data assumes that peaks from one metabolite are found in one column of a data table. For every error in the data table, the statistical analysis loses power and the risk of missing a biomarker increases. It is therefore important to solve the correspondence problem by synchronising samples and there is no method that solves it once and for all. In this review, we analyse the correspondence problem, discuss current state-of-the-art methods for synchronising samples, and predict the properties of future methods.

The interplay between inorganic phosphate and amino acids determines zinc solubility in brain slices

Inorganic phosphate (Pi) is an important polyanion needed for ATP synthesis and bone formation. As it is found at millimolar levels in plasma, it is usually incorporated as a constituent of artificial CSF formulations for maintaining brain slices. In this paper, we show that Pi limits the extracellular zinc concentration by inducing metal precipitation. We present data suggesting that amino acids like histidine may counteract the Pi-induced zinc precipitation by the formation of soluble zinc complexes. We propose that the interplay between Pi and amino acids in the extracellular space may influence the availability of metals for cellular uptake.

Systematic NMR analysis of stable isotope labeled metabolite mixtures in plant and animal systems: coarse grained views of metabolic pathways

Background

Metabolic phenotyping has become an important ‘bird's-eye-view’ technology which can be applied to higher organisms, such as model plant and animal systems in the post-genomics and proteomics era. Although genotyping technology has expanded greatly over the past decade, metabolic phenotyping has languished due to the difficulty of ‘top-down’ chemical analyses. Here, we describe a systematic NMR methodology for stable isotope-labeling and analysis of metabolite mixtures in plant and animal systems.

Methodology/Principal Findings

The analysis method includes a stable isotope labeling technique for use in living organisms; a systematic method for simultaneously identifying a large number of metabolites by using a newly developed HSQC-based metabolite chemical shift database combined with heteronuclear multidimensional NMR spectroscopy; Principal Components Analysis; and a visualization method using a coarse-grained overview of the metabolic system. The database contains more than 1000 ¹H and ¹³C chemical shifts corresponding to 142 metabolites measured under identical physicochemical conditions. Using the stable isotope labeling technique in Arabidopsis T87 cultured cells and Bombyx mori, we systematically detected >450 HSQC peaks in each ¹³C-HSQC spectrum derived from model plant, Arabidopsis T87 cultured cells and the invertebrate animal model Bombyx mori. Furthermore, for the first time, efficient ¹³C labeling has allowed reliable signal assignment using analytical separation techniques such as 3D HCCH-COSY spectra in higher organism extracts.

Conclusions/Significance

Overall physiological changes could be detected and categorized in relation to a critical developmental phase change in B. mori by coarse-grained representations in which the organization of metabolic pathways related to a specific developmental phase was visualized on the basis of constituent changes of 56 identified metabolites. Based on the observed intensities of ¹³C atoms of given metabolites on development-dependent changes in the 56 identified ¹³C-HSQC signals, we have determined the changes in metabolic networks that are associated with energy and nitrogen metabolism.