Metabolites In Biofluids Research Articles

Diet-induced hyperinsulinemia differentially affects glucose and protein metabolism: a high-throughput metabolomic approach in rats

Metabolomics is a high-throughput tool that quantifies and identifies the complete set of biofluid metabolites. This "omics" science is playing an increasing role in understanding the mechanisms involved in disease progression. The aim of this study was to determine whether a nontargeted metabolomic approach could be applied to investigate metabolic differences between obese rats fed a high-fat sucrose (HFS) diet for 9 weeks and control diet-fed rats. Animals fed with the HFS diet became obese, hyperleptinemic, hyperglycemic, hyperinsulinemic, and resistant to insulin. Serum samples of overnight-fasted animals were analyzed by (1)H NMR technique, and 49 metabolites were identified and quantified. The biochemical changes observed suggest that major metabolic processes like carbohydrate metabolism, β-oxidation, tricarboxylic acid cycle, Kennedy pathway, and folate-mediated one-carbon metabolism were altered in obese rats. The circulating levels of most amino acids were lower in obese animals. Serum levels of docosahexaenoic acid, linoleic acid, unsaturated n-6 fatty acids, and total polyunsaturated fatty acids also decreased in HFS-fed rats. The circulating levels of urea, six water-soluble metabolites (creatine, creatinine, choline, acetyl carnitine, formate, and allantoin), and two lipid compounds (phosphatidylcholines and sphingomyelin) were also significantly reduced by the HFS diet intake. This study offers further insight of the possible mechanisms implicated in the development of diet-induced obesity. It suggests that the HFS diet-induced hyperinsulinemia is responsible for the decrease in the circulating levels of urea, creatinine, and many amino acids, despite an increase in serum glucose levels.

Biomarkers of primary dysmenorrhea and herbal formula intervention: an exploratory metabonomics study of blood plasma and urine

Primary dysmenorrhea (PDM), a common clinical endocrine disorder affecting young women, is associated with endocrinopathy and metabolic abnormalities. Although some physiological and pathological function parameters have been investigated, little information about the changes of small metabolites in biofluids has been reported, which may cause poor diagnosis and treatment for PDM. The Xiang-Fu-Si-Wu Formula (XFSWF) is a Chinese herbal formula used to treat PDM for hundreds of years. The aim of this study was to establish the metabolic profile of PDM and investigate the action mechanism of XFSWF effect. In this cross-sectional study of 25 patients with PDM and 12 healthy controls, contents of small molecular endogenous metabolites in blood plasma and urine samples were measured by ultra performance liquid chromatography (UPLC) coupled with quadrupole-time-of-flight mass spectrometry (QTOF/MS) and triple quadrupole mass spectrometry (QqQ/MS) based techniques and analyzed by multivariate statistical methods. The levels of LPCs including lypso (16 : 1), lysoPC(20 : 4), lysoPC(18 : 2), lysoPC(16 : 0), lysoPC(18 : 1), lysoPC(10 : 1), estrone, 17-hydroxyprogesterone, myristoylglycine and palmitoylglycine increased significantly (p < 0.05) in PDM, while the levels of phytosphingosine, dihydrocortisol and sphingosine decreased significantly (p < 0.05) compared with the healthy controls. These significant perturbations are involved in glycerophospholipid metabolism and sphingolipid metabolism, as well as steroid hormone biosynthesis. The metabolic deviations recovered to the normal level after XFSWF intervention. The results demonstrated that biofluids metabonomics was a powerful tool in clinical diagnosis and treatment of PDM for providing information on changes in metabolites and neural, endocrinal and immune pathways. XFSWF can be used for the treatment of PDM cases, especially for those adolescents who do not desire a contraceptive method, to reduce the risk of secondary dysmenorrhea.

MetFlexo: An Automated Simulation of Realistic H1-NMR Spectra

The development of the ‘omics’ technologies such as transcriptomics, proteomics and metabolomics has made it possible to realize some of the goals of systems biology, where biological systems are interrogated at different levels of biochemical activity (such as gene expression, protein activity and/or metabolite concentration). Metabolomics deals with the metabolome that represents the complete set of small-molecule metabolites. Even though metabolomics can be thought of as a relatively young method, it is nevertheless a rapidly growing one that has the potential to reveal the molecular mechanism of certain diseases. H1 nuclear magnetic resonance (NMR) spectroscopy is commonly used in the metabolic profiling of biofluids as it has the potential to detect all proton-containing metabolites. Metabolites in biofluids are in dynamic equilibrium with those in cells and tissues, so their metabolic profile reflects changes in the state of an organism due to disease or environmental effects. ResultsMetFlexo is as an easy-to-use C package that allows the simulation of datasets of 1H-NMR spectra in order to test data analysis techniques, hypotheses and experimental designs. The idea is based on transforming statistical parameters of metabolites (shifts, couplings, concentrations and magnetic field) to an NMR spectrum using chemical-physics theory. Our method helps in the deconvolution of NMR spectra and in a better determination of metabolite concentrations, as these concentrations are key in detecting diseases and abnormalities. Unlike others, this program generates NMR spectrum of biofluids with no limit on magnetic field or pH. Thus, our approach is able to produce complex NMR profiles with flexible conditions. It is also simple to implement in C, requires small storage, is easy to compute and uses an independent platform. It will be available in R and MATLAB soon. The algorithm is freely available upon request to the corresponding author.

Phenol-Explorer 2.0: a major update of the Phenol-Explorer database integrating data on polyphenol metabolism and pharmacokinetics in humans and experimental animals

Phenol-Explorer, launched in 2009, is the only comprehensive web-based database on the content in foods of polyphenols, a major class of food bioactives that receive considerable attention due to their role in the prevention of diseases. Polyphenols are rarely absorbed and excreted in their ingested forms, but extensively metabolized in the body, and until now, no database has allowed the recall of identities and concentrations of polyphenol metabolites in biofluids after the consumption of polyphenol-rich sources. Knowledge of these metabolites is essential in the planning of experiments whose aim is to elucidate the effects of polyphenols on health. Release 2.0 is the first major update of the database, allowing the rapid retrieval of data on the biotransformations and pharmacokinetics of dietary polyphenols. Data on 375 polyphenol metabolites identified in urine and plasma were collected from 236 peer-reviewed publications on polyphenol metabolism in humans and experimental animals and added to the database by means of an extended relational design. Pharmacokinetic parameters have been collected and can be retrieved in both tabular and graphical form. The web interface has been enhanced and now allows the filtering of information according to various criteria. Phenol-Explorer 2.0, which will be periodically updated, should prove to be an even more useful and capable resource for polyphenol scientists because bioactivities and health effects of polyphenols are dependent on the nature and concentrations of metabolites reaching the target tissues. The Phenol-Explorer database is publicly available and can be found online at http://www.phenol-explorer.eu.Database URL: http://www.phenol-explorer.eu

Metabolite profiling of small cerebrospinal fluid sample volumes with gas chromatography–mass spectrometry: application to a rat model of multiple sclerosis

Analysis of metabolites in biofluids by gas chromatography–mass spectrometry (GC–MS) after oximation and silylation is a key method in metabolomics. The GC–MS method was modified by a modified vial design and sample work-up procedure in order to make the method applicable to small volumes of cerebrospinal fluid (CSF), i.e. 10 μL, with similar coverage compared to the standard procedure using ≥100 μL of CSF. The data quality of the modified GC–MS method was assessed by analyzing a study sample set in an animal model for multiple sclerosis, including repetitively analysed quality control rat CSF samples. Automated normalization and intra- and inter-batch correction significantly improved the data quality with the majority of metabolites showing a relative standard deviation <20 %. The modified GC–MS method was successfully applied in rat model of multiple sclerosis where statistical analysis of 93 metabolites, of which 73 were (tentatively) identified, in 10 μL of rat CSF showed statistically significant differences in metabolite profiles of rats at the onset and peak of experimental autoimmune encephalomyelitis compared to rats in the control group. The modified GC–MS method presented proved to be a valid and valuable metabolomics method when only limited sample volumes are available.

Metabonomics Reveals Plasma Metabolic Changes and Inflammatory Marker in Polycystic Ovary Syndrome Patients

Polycystic ovary syndrome (PCOS) is a common, clinically heterogeneous endocrine disorder affecting women of reproductive age, associated with endocrinopathy and metabolic abnormalities. Although some metabolic parameters have been investigated, very little information has been reported on the changes of small metabolites in biofluids. The aim of this study was to establish the metabolic profile of PCOS and compare it with that of controls. In this cross-sectional study of 34 women with PCOS and 36 controls, contents of small metabolites and lipids in plasma samples were measured using nuclear magnetic resonance (NMR)-based techniques and analyzed using multivariate statistical methods. Significant decrease (P < 0.05) in the levels of amino acids (leucine, isoleucine, methionine, glutamine, and arginine), citrate, choline, and glycerophosphocholine/phosphocholine (GPC/PC), and increase (P < 0.05) in the levels of lactate, dimethylamine (DMA), creatine, and N-acetyl glycoproteins were observed in PCOS patients compared with the controls. Subgroups of patients with obesity, metabolic syndrome, or hyperandrogenism exhibited greater metabolic deviations than their corresponding subgroups without these factors. PCOS patients have perturbations in amino acid metabolism, the tricarboxylic acid (TCA) cycle, and gut microflora, as well as mild disturbances in glucose and lipid metabolism. The elevated level of N-acetyl glycoproteins demonstrates the existence of low-grade chronic inflammation in PCOS patients.

The Metabolomic Approach Identifies a Biological Signature of Low-dose Chronic Exposure to Cesium 137

Reports have described apparent biological effects of (137)Cs (the most persistent dispersed radionuclide) irradiation in people living in Chernobyl-contaminated territory. The sensitive analytical technology described here should now help assess the relation of this contamination to the observed effects. A rat model chronically exposed to (137)Cs through drinking water was developed to identify biomarkers of radiation-induced metabolic disorders, and the biological impact was evaluated by a metabolomic approach that allowed us to detect several hundred metabolites in biofluids and assess their association with disease states. After collection of plasma and urine from contaminated and non-contaminated rats at the end of the 9-months contamination period, analysis with a LC-MS system detected 742 features in urine and 1309 in plasma. Biostatistical discriminant analysis extracted a subset of 26 metabolite signals (2 urinary, 4 plasma non-polar, and 19 plasma polar metabolites) that in combination were able to predict from 68 up to 94% of the contaminated rats, depending on the prediction method used, with a misclassification rate as low as 5.3%. The difference in this metabolic score between the contaminated and non-contaminated rats was highly significant (P = 0.019 after ANOVA cross-validation). In conclusion, our proof-of-principle study demonstrated for the first time the usefulness of a metabolomic approach for addressing biological effects of chronic low-dose contamination. We can conclude that a metabolomic signature discriminated (137)Cs-contaminated from control animals in our model. Further validation is nevertheless required together with full annotation of the metabolic indicators.

"Add to subtract": a simple method to remove complex background signals from the 1H nuclear magnetic resonance spectra of mixtures.

Because of its highly reproducible and quantitative nature and minimal requirements for sample preparation or separation, (1)H nuclear magnetic resonance (NMR) spectroscopy is widely used for profiling small-molecule metabolites in biofluids. However (1)H NMR spectra contain many overlapped peaks. In particular, blood serum/plasma and diabetic urine samples contain high concentrations of glucose, which produce strong peaks between 3.2 ppm and 4.0 ppm. Signals from most metabolites in this region are overwhelmed by the glucose background signals and become invisible. We propose a simple "Add to Subtract" background subtraction method and show that it can reduce the glucose signals by 98% to allow retrieval of the hidden information. This procedure includes adding a small drop of concentrated glucose solution to the sample in the NMR tube, mixing, waiting for an equilibration time, and acquisition of a second spectrum. The glucose-free spectra are then generated by spectral subtraction using Bruker Topspin software. Subsequent multivariate statistical analysis can then be used to identify biomarker candidate signals for distinguishing different types of biological samples. The principle of this approach is generally applicable for all quantitative spectral data and should find utility in a variety of NMR-based mixture analyses as well as in metabolite profiling.

Metabolomics data and the biomarker qualification process

Since its initial applications in healthcare research over a decade ago, metabolomics has matured into a technology that can now reliably detect and quantify hundreds of metabolites in biofluids and tissue samples. With substantial improvements in analytical capabilities in recent years, metabolomics now has the potential to provide sensitive and specific biomarkers of health and disease status, drug toxicity and therapeutic efficacy. Although many of the clinical biomarkers currently in use are metabolites such as glucose, cholesterol, and creatinine, few, if any, new metabolites (or panel of metabolites) derived from metabolomic analyses have been submitted to the FDA as biomarker candidates. Under a recent FDA draft Guidance for Industry from the Center for Drug Evaluation and Research (CDER), biomarkers discovered using metabolomics, like those derived from genetic, transcriptomic, and proteomic studies, may be submitted to CDER for consideration as new drug development tools and entered into a formal biomarker qualification process. The qualification process begins with a letter of intent from the submitter, followed by discussions with FDA and then the submission of data supporting the performance of the biomarker within a specific context of use. CDER forms a Biomarker Qualification Review Team at the start of the interactive process to advise the biomarker developer on the type of information important to support the proposed context of use, and review the data that evaluates the biomarker for the proposed context of use when the biomarker development appears complete and the full supporting data are submitted. Once qualified, a biomarker can be used in drug development within the qualified context of use without requesting additional regulatory review concerning its suitability.

Development and validation of a standardized protocol to monitor human dietary exposure by metabolite fingerprinting of urine samples

Conventional tools for measuring dietary exposure have well recognized limitations. Measurement of food-derived metabolites in biofluids provides an alternative approach and our aim was to develop an experimental protocol which ensures that extraneous variability does not obscure metabolic signals from ingested foods. Healthy adults consumed a standardized meal in the evening before each test day and collected pooled overnight urine. On each test day of three different studies, urine was collected in the fasted state and at different time points after consumption of a standardized breakfast. Metabolite fingerprinting of samples using Flow Infusion Electrospray-Ionization Mass Spectrometry followed by multivariate data analysis showed strong discrimination between overnight, fasting and postprandial samples, in each study separately and when data from the three studies were pooled. Such differences were robust and highly reproducible within individuals on separate occasions. Urine volume was an efficient data normalization factor for metabolite fingerprinting data. Postprandial urines had a stable chemical composition over a period of 2–4 h after eating a standardized breakfast, suggesting that there is a flexible time window for urine collection. Fasting urine samples provided a stable baseline for universal comparisons with postprandial samples. A dietary exposure biomarker discovery protocol was validated by demonstrating that top-ranked signals discriminating between fasting and 2–4 h postprandial urine samples could be linked to metabolites abundant in some components of the standardized breakfast. We conclude that the protocol developed will have value in the search for biomarker leads of dietary exposure.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-011-0289-0) contains supplementary material, which is available to authorized users.

Modern chromatographic and electrophoretic measurements of antidepressants and their metabolites in biofluids

Depression is one of the most frequently occurring psychiatric conditions affecting the economic and social functioning of people all over the world. There are a few classes of drugs commonly used to treat patients with depression disorders. Therapeutic drug monitoring of antidepressants provides the possibility to reduce side effects and optimize the treatment of patients with depression. Hence, there is a need to develop reliable analytical methods for the determination of these agents in biological fluids. Moreover, an important part of understanding the mechanisms of action of these medicines is also associated with the recognition of the metabolites' function because of their potential influence on therapeutic benefits and/or adverse effects. Some of them possess the same primary activity as their parent compounds and may contribute to the therapeutic efficacy whereas the biochemical actions of other metabolites may be different from that of the parent drug. In this review, several validated high-performance liquid chromatographic, gas chromatographic and capillary electrophoretic methods for quantification of antidepressants and their metabolites in biofluids are compared. In addition, the review covers areas such as mechanism of actions, structure-activity relationships and metabolism of the cited antidepressants.

Highly routinely reproducible alignment of 1H NMR spectral peaks of metabolites in huge sets of urines

A method to obtain high reproducibility of 1H NMR chemical shift of peaks of biofluid metabolites, by simple acidification with HCl is evaluated. Biofluid 1H NMR analysis is indeed spoiled by a strong chemical shift dependence of metabolite peaks on parameters such as ionic strength, concentration of some earth alkali cations and, mostly, on pH of samples. The resulting chemical shift variations, as large as 0.1 ppm, generate misalignments of homogeneous peaks, artifacts and misinterpretations. Reproducible alignment is essential in 1H NMR based metabonomics, where peak misalignments prevent even very wide bins (i.e., 0.04 ppm, as elsewhere proposed) from being used to integrate spectral data for multivariate statistical analysis. Here is demonstrated that routine acidification with HCl to 1.2 ≤ pH ≤ 2.0 ensures highly reproducible peak alignment of urine 1H NMR spectra. In this respect, simple inspection of citrate peaks in the urine can be used to measure pH, as it will be extensively discussed, in that at such low pH they show no dependency on other urine components as reported at higher pH. Under these conditions, in as many as 493 urine samples, in which concentrations of Ca 2+, Mg 2+, K +, Na +, Cl −, phosphate, and creatinine and ionic strength measured by means of well standardized conventional procedures, showed very wide ranges, peaks align within a SD always lower than 0.002 ppm, thus allowing the use of integration bins at least five times narrower than 0.04 ppm.

High-Performance Isotope Labeling for Profiling Carboxylic Acid-Containing Metabolites in Biofluids by Mass Spectrometry

We have developed a new isotope labeling method, based on the use of isotope-coded p-dimethylaminophenacyl (DmPA) bromide as a reagent, combined with liquid chromatography-mass spectrometry (LC-MS) for high-performance metabolome analysis with a focus on profiling carboxylic acid-containing metabolites. Derivatization is simple, fast (1 h plus 30 min for quenching the reaction), and applicable to a wide range of carboxylic acids with a high yield and little or no side reaction products. This labeling method is demonstrated to be not only effective in introducing an isotope tag for accurate metabolite quantification but also improving the chromatographic retention of the metabolites in reversed-phase (RP) LC, enhancing ESI efficiency by 2-4 orders of magnitude, and facilitating the identification of metabolite peaks in LC-MS. In triplicate experiments of a 1:1 ratio of (13)C-/(12)C-DmPA labeled human urine, we were able to detect 2671, 2546, and 2820 ion pairs from metabolites containing one or more carboxylic acid groups.

Metabolomic Analysis in Severe Childhood Pneumonia in The Gambia, West Africa: Findings from a Pilot Study

BackgroundPneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to improve diagnostics in a range of diseases. The objective of this pilot study is to apply metabolomic analysis to childhood pneumonia to explore its potential to improve pneumonia diagnosis in a high-burden setting.Methodology/Principal FindingsEleven children with World Health Organization (WHO)-defined severe pneumonia of non-homogeneous aetiology were selected in The Gambia, West Africa, along with community controls. Metabolomic analysis of matched plasma and urine samples was undertaken using Ultra Performance Liquid Chromatography (UPLC) coupled to Time-of-Flight Mass Spectrometry (TOFMS). Biomarker extraction was done using SIMCA-P+ and Random Forests (RF). ‘Unsupervised’ (blinded) data were analyzed by Principal Component Analysis (PCA), while ‘supervised’ (unblinded) analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set. The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking. Unsupervised PCA analysis revealed good separation of pneumonia and control groups, with even clearer separation of the groups with PLS-DA and OPLS analysis. Statistically significant differences (p<0.05) between groups were seen with the following metabolites: uric acid, hypoxanthine and glutamic acid were higher in plasma from cases, while L-tryptophan and adenosine-5′-diphosphate (ADP) were lower; uric acid and L-histidine were lower in urine from cases. The key limitation of this study is its small size.Conclusions/SignificanceMetabolomic analysis clearly distinguished severe pneumonia patients from community controls. The metabolites identified are important for the host response to infection through antioxidant, inflammatory and antimicrobial pathways, and energy metabolism. Larger studies are needed to determine whether these findings are pneumonia-specific and to distinguish organism-specific responses. Metabolomics has considerable potential to improve diagnostics for childhood pneumonia.

Evaluation of standard and advanced preprocessing methods for the univariate analysis of blood serum 1H-NMR spectra

Proton nuclear magnetic resonance ((1)H-NMR)-based metabolomics enables the high-resolution and high-throughput assessment of a broad spectrum of metabolites in biofluids. Despite the straightforward character of the experimental methodology, the analysis of spectral profiles is rather complex, particularly due to the requirement of numerous data preprocessing steps. Here, we evaluate how several of the most common preprocessing procedures affect the subsequent univariate analyses of blood serum spectra, with a particular focus on how the standard methods perform compared to more advanced examples. Carr-Purcell-Meiboom-Gill 1D (1)H spectra were obtained for 240 serum samples from healthy subjects of the Asklepios study. We studied the impact of different preprocessing steps--integral (standard method) and probabilistic quotient normalization; no, equidistant (standard), and adaptive-intelligent binning; mean (standard) and maximum bin intensity data summation--on the resonance intensities of three different types of metabolites: triglycerides, glucose, and creatinine. The effects were evaluated by correlating the differently preprocessed NMR data with the independently measured metabolite concentrations. The analyses revealed that the standard methods performed inferiorly and that a combination of probabilistic quotient normalization after adaptive-intelligent binning and maximum intensity variable definition yielded the best overall results (triglycerides, R = 0.98; glucose, R = 0.76; creatinine, R = 0.70). Therefore, at least in the case of serum metabolomics, these or equivalent methods should be preferred above the standard preprocessing methods, particularly for univariate analyses. Additional optimization of the normalization procedure might further improve the analyses.

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.

Sample preparation and separation techniques for bioanalysis of morphine and related substances

In present time the use or misuse of morphine and its derivatives are monitored by assaying the presence of the drug and its metabolites in biofluids. In the present review, focus is placed on the sample preparation and on the separation techniques used in the current best practices of bioanalysis of morphine and its major metabolites. However, as methods for testing the misuse of heroin, a morphine derivative, often involve bioanalytical methods that cover a number of other illicit drug substances, such methods are also included in the review. Furthermore, the review also includes bioanalysis in a broader perspective as analysis of plant materials, cell cultures and environmental samples. The review is not intended to cover all publications that include bioanalysis of morphine but is more to be considered a view into the current best practices of bioanalysis of morphine, its metabolites and other related substances.

Metabolite Projection Analysis for Fast Identification of Metabolites in Metabonomics. Application in an Amiodarone Study

The assignment of significantly changed NMR signals, which were identified with the help of multivariate models, to individual metabolites in biofluids is a manual and tedious task requiring knowledge in chemometrics and NMR spectroscopy. Metabolite projection analysis, introduced in this work, allows automatic linking of multivariate models with metabolites by skipping the level of manual NMR signal identification. The method depends on the projection of sets of metabolite NMR spectra from a database into PCA or PLS models of NMR spectra of biofluid samples. Metabolites that are significantly changed can be identified graphically in metabolite projection plots or numerically as projected virtual concentration. The method is demonstrated together with a newly introduced algorithm for refined nonequidistant binning using a metabonomics study with amiodarone as administered drug. Amiodarone can induce phospholipidosis in the lung and liver, which is accompanied by associated organ toxicity in these organs. It is shown how metabolite projection analysis allows easy and fast tentative assignment of all structures of metabolites whose concentrations in the urine samples significantly changed upon dosage. These metabolites had also been identified previously by manually interpreting the multivariate models and spectra. Among these metabolites, phenylacetylglycine was also identified as being significantly increased. This metabolite has recently been proposed as urinary biomarker for phospholipidosis.

Malabsorption syndrome : Studies on diagnosis and pathogenesis using 1 H NMR metabonomics approach

High resolution NMR spectroscopy for the qualitative and quantitative estimation of metabolites in biofluids has become an important tool in biomedicine as it is non-destructive, doesn't require any chemical pre-treatment step, is highly selective and allows study of cellular biochemistry and metabolism. This article reviews such studies in urine and upper gut aspritate specimens of patients with Malabsorption Syndrome (MS) for the purpose of diagnosis and investigating pathogenesis of the disease. It has been demonstrated that 1 H NMR method for the diagnosis of patients with MS is more specific, highly accurate, devoid of interferences from other similar metabolites, if any, as compared to the conventional colorimetric method. The metabolic profile of upper gut aspirate provided by 1 H NMR spectroscopy demonstrates that MS patients associated with Small Intestinal Bacterial Overgrowth (SIBO) have significantly higher concentrations of unconjugated bile acids, acetate, lactate and formate as compared to MS patients without SIBO and controls. These biochemicals may damage intestinal mucosa and impair small intestinal functions in MS patients.

Desorption/Ionization on Silicon Nanowires

Dense arrays of single-crystal silicon nanowires (SiNWs) have been used as a platform for laser desorption/ionization mass spectrometry of small molecules, peptides, protein digests, and endogenous and xenobiotic metabolites in biofluids. Sensitivity down to the attomole level has been achieved on the nanowire surfaces by optimizing laser energy, surface chemistry, nanowire diameter, length, and growth orientation. An interesting feature of the nanowire surface is that it requires lower laser energy as compared to porous silicon and MALDI to desorb/ionize small molecules, therefore reducing background ion interference. Taking advantage of their high surface area and fluid wicking capabilities, SiNWs were used to perform chromatographic separation followed by mass analysis of the separated molecules providing a unique platform that can integrate separation and mass spectrometric detection on a single surface.