Mistargeting of peroxisomal EHHADH and inherited renal Fanconi's syndrome.

BACKGROUND: In renal Fanconi's syndrome, dysfunction in proximal tubular cells leads to renal losses of water, electrolytes, and low-molecular-weight nutrients. For most types of isolated Fanconi's syndrome, the genetic cause and underlying defect remain unknown. METHODS: We clinically and genetically characterized members of a five-generation black family with isolated autosomal dominant Fanconi's syndrome. We performed genomewide linkage analysis, gene sequencing, biochemical and cell-biologic investigations of renal proximal tubular cells, studies in knockout mice, and functional evaluations of mitochondria. Urine was studied with the use of proton nuclear magnetic resonance ((1)H-NMR) spectroscopy. RESULTS: We linked the phenotype of this family's Fanconi's syndrome to a single locus on chromosome 3q27, where a heterozygous missense mutation in EHHADH segregated with the disease. The p.E3K mutation created a new mitochondrial targeting motif in the N-terminal portion of EHHADH, an enzyme that is involved in peroxisomal oxidation of fatty acids and is expressed in the proximal tubule. Immunocytofluorescence studies showed mistargeting of the mutant EHHADH to mitochondria. Studies of proximal tubular cells revealed impaired mitochondrial oxidative phosphorylation and defects in the transport of fluids and a glucose analogue across the epithelium. (1)H-NMR spectroscopy showed elevated levels of mitochondrial metabolites in urine from affected family members. Ehhadh knockout mice showed no abnormalities in renal tubular cells, a finding that indicates a dominant negative nature of the mutation rather than haploinsufficiency. CONCLUSIONS: Mistargeting of peroxisomal EHHADH disrupts mitochondrial metabolism and leads to renal Fanconi's syndrome; this indicates a central role of mitochondria in proximal tubular function. The dominant negative effect of the mistargeted protein adds to the spectrum of monogenic mechanisms of Fanconi's syndrome. (Funded by the European Commission Seventh Framework Programme and others.).

MetaboNetworks, an interactive Matlab-based toolbox for creating, customizing and exploring sub-networks from KEGG.

SUMMARY: MetaboNetworks is a tool to create custom sub-networks in Matlab using main reaction pairs as defined by the Kyoto Encyclopaedia of Genes and Genomes and can be used to explore transgenomic interactions, for example mammalian and bacterial associations. It calculates the shortest path between a set of metabolites (e.g. biomarkers from a metabonomic study) and plots the connectivity between metabolites as links in a network graph. The resulting graph can be edited and explored interactively. Furthermore, nodes and edges in the graph are linked to the Kyoto Encyclopaedia of Genes and Genomes compound and reaction pair web pages. AVAILABILITY AND IMPLEMENTATION: MetaboNetworks is available from http://www.mathworks.com/matlabcentral/fileexchange/42684. CONTACT: jmp111@ic.ac.uk or j.nicholson@imperial.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Statistical spectroscopic tools for biomarker discovery and systems medicine.

Metabolic profiling based on comparative, statistical analysis of NMR spectroscopic and mass spectrometric data from complex biological samples has contributed to increased understanding of the role of small molecules in affecting and indicating biological processes. To enable this research, the development of statistical spectroscopy has been marked by early beginnings in applying pattern recognition to nuclear magnetic resonance data and the introduction of statistical total correlation spectroscopy (STOCSY) as a tool for biomarker identification in the past decade. Extensions of statistical spectroscopy now compose a family of related tools used for compound identification, data preprocessing, and metabolic pathway analysis. In this Perspective, we review the theory and current state of research in statistical spectroscopy and discuss the growing applications of these tools to medicine and systems biology. We also provide perspectives on how recent institutional initiatives are providing new platforms for the development and application of statistical spectroscopy tools and driving the development of integrated "systems medicine" approaches in which clinical decision making is supported by statistical and computational analysis of metabolic, phenotypic, and physiological data.

NMR in metabolomics and natural products research: two sides of the same coin.

Small molecules are central to biology, mediating critical phenomena such as metabolism, signal transduction, mating attraction, and chemical defense. The traditional categories that define small molecules, such as metabolite, secondary metabolite, pheromone, hormone, and so forth, often overlap, and a single compound can appear under more than one functional heading. Therefore, we favor a unifying term, biogenic small molecules (BSMs), to describe any small molecule from a biological source. In a similar vein, two major fields of chemical research,natural products chemistry and metabolomics, have as their goal the identification of BSMs, either as a purified active compound (natural products chemistry) or as a biomarker of a particular biological state (metabolomics). Natural products chemistry has a long tradition of sophisticated techniques that allow identification of complex BSMs, but it often fails when dealing with complex mixtures. Metabolomics thrives with mixtures and uses the power of statistical analysis to isolate the proverbial "needle from a haystack", but it is often limited in the identification of active BSMs. We argue that the two fields of natural products chemistry and metabolomics have largely overlapping objectives: the identification of structures and functions of BSMs, which in nature almost inevitably occur as complex mixtures. Nuclear magnetic resonance (NMR) spectroscopy is a central analytical technique common to most areas of BSM research. In this Account, we highlight several different NMR approaches to mixture analysis that illustrate the commonalities between traditional natural products chemistry and metabolomics. The primary focus here is two-dimensional (2D) NMR; because of space limitations, we do not discuss several other important techniques, including hyphenated methods that combine NMR with mass spectrometry and chromatography. We first describe the simplest approach of analyzing 2D NMR spectra of unfractionated mixtures to identify BSMs that are unstable to chemical isolation. We then show how the statistical method of covariance can be used to enhance the resolution of 2D NMR spectra and facilitate the semi-automated identification of individual components in a complex mixture. Comparative studies can be used with two or more samples, such as active vs inactive, diseased vs healthy, treated vs untreated, wild type vs mutant, and so on. We present two overall approaches to comparative studies: a simple but powerful method for comparing two 2D NMR spectra and a full statistical approach using multiple samples. The major bottleneck in all of these techniques is the rapid and reliable identification of unknown BSMs; the solution will require all the traditional approaches of both natural products chemistry and metabolomics as well as improved analytical methods, databases, and statistical tools.

Metabonomics of newborn screening dried blood spot samples: a novel approach in the screening and diagnostics of inborn errors of metabolism.

A novel, single stage high resolution mass spectrometry-based method is presented for the population level screening of inborn errors of metabolism. The approach proposed here extends traditional electrospray tandem mass spectrometry screening by introducing nanospray ionization and high resolution mass spectrometry, allowing the selective detection of more than 400 individual metabolic constituents of blood including acylcarnitines, amino acids, organic acids, fatty acids, carbohydrates, bile acids, and complex lipids. Dried blood spots were extracted using a methanolic solution of isotope labeled internal standards, and filtered extracts were electrosprayed using a fully automated chip-based nanospray ion source in both positive and negative ion mode. Ions were analyzed using an Orbitrap Fourier transformation mass spectrometer at nominal mass resolution of 100,000. Individual metabolic constituents were quantified using standard isotope dilution methods. Concentration threshold (cutoff) level-based analysis allows the identification of newborns with metabolic diseases, similarly to the traditional electrospray tandem mass spectrometry (ESI-MS/MS) method; however, the detection of additional known biomarkers (e.g., organic acids) results in improved sensitivity and selectivity. The broad range of detected analytes allowed the untargeted multivariate statistical analysis of spectra and identification of additional diseased states, therapeutic artifacts, and damaged samples, besides the metabolic disease panel.

Nephrocalcinosis (enamel renal syndrome) caused by autosomal recessive FAM20A mutations.

BACKGROUND/AIMS: Calcium homeostasis requires regulated cellular and interstitial systems interacting to modulate the activity and movement of this ion. Disruption of these systems in the kidney results in nephrocalcinosis and nephrolithiasis, important medical problems whose pathogenesis is incompletely understood. METHODS: We investigated 25 patients from 16 families with unexplained nephrocalcinosis and characteristic dental defects (amelogenesis imperfecta, gingival hyperplasia, impaired tooth eruption). To identify the causative gene, we performed genome-wide linkage analysis, exome capture, next-generation sequencing, and Sanger sequencing. RESULTS: All patients had bi-allelic FAM20A mutations segregating with the disease; 20 different mutations were identified. CONCLUSIONS: This autosomal recessive disorder, also known as enamel renal syndrome, of FAM20A causes nephrocalcinosis and amelogenesis imperfecta. We speculate that all individuals with biallelic FAM20A mutations will eventually show nephrocalcinosis.

Hierarchical alignment and full resolution pattern recognition of 2D NMR spectra: application to nematode chemical ecology.

Nuclear magnetic resonance (NMR) is the most widely used nondestructive technique in analytical chemistry. In recent years, it has been applied to metabolic profiling due to its high reproducibility, capacity for relative and absolute quantification, atomic resolution, and ability to detect a broad range of compounds in an untargeted manner. While one-dimensional (1D) (1)H NMR experiments are popular in metabolic profiling due to their simplicity and fast acquisition times, two-dimensional (2D) NMR spectra offer increased spectral resolution as well as atomic correlations, which aid in the assignment of known small molecules and the structural elucidation of novel compounds. Given the small number of statistical analysis methods for 2D NMR spectra, we developed a new approach for the analysis, information recovery, and display of 2D NMR spectral data. We present a native 2D peak alignment algorithm we term HATS, for hierarchical alignment of two-dimensional spectra, enabling pattern recognition (PR) using full-resolution spectra. Principle component analysis (PCA) and partial least squares (PLS) regression of full resolution total correlation spectroscopy (TOCSY) spectra greatly aid the assignment and interpretation of statistical pattern recognition results by producing back-scaled loading plots that look like traditional TOCSY spectra but incorporate qualitative and quantitative biological information of the resonances. The HATS-PR methodology is demonstrated here using multiple 2D TOCSY spectra of the exudates from two nematode species: Pristionchus pacificus and Panagrellus redivivus. We show the utility of this integrated approach with the rapid, semiautomated assignment of small molecules differentiating the two species and the identification of spectral regions suggesting the presence of species-specific compounds. These results demonstrate that the combination of 2D NMR spectra with full-resolution statistical analysis provides a platform for chemical and biological studies in cellular biochemistry, metabolomics, and chemical ecology.

Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery.

Ultra-performance liquid chromatography coupled to mass spectrometry (UPLC/MS) has been used increasingly for measuring changes of low molecular weight metabolites in biofluids/tissues in response to biological challenges such as drug toxicity and disease processes. Typically samples show high variability in concentration, and the derived metabolic profiles have a heteroscedastic noise structure characterized by increasing variance as a function of increased signal intensity. These sources of experimental and instrumental noise substantially complicate information recovery when statistical tools are used. We apply and compare several preprocessing procedures and introduce a statistical error model to account for these bioanalytical complexities. In particular, the use of total intensity, median fold change, locally weighted scatter plot smoothing, and quantile normalizations to reduce extraneous variance induced by sample dilution were compared. We demonstrate that the UPLC/MS peak intensities of urine samples should respond linearly to variable sample dilution across the intensity range. While all four studied normalization methods performed reasonably well in reducing dilution-induced variation of urine samples in the absence of biological variation, the median fold change normalization is least compromised by the biologically relevant changes in mixture components and is thus preferable. Additionally, the application of a subsequent log-based transformation was successful in stabilizing the variance with respect to peak intensity, confirming the predominant influence of multiplicative noise in peak intensities from UPLC/MS-derived metabolic profile data sets. We demonstrate that variance-stabilizing transformation and normalization are critical preprocessing steps that can benefit greatly metabolic information recovery from such data sets when widely applied chemometric methods are used.

Cluster analysis statistical spectroscopy using nuclear magnetic resonance generated metabolic data sets from perturbed biological systems.

We present a new approach for analysis, information recovery, and display of biological (1)H nuclear magnetic resonance (NMR) spectral data, cluster analysis statistical spectroscopy (CLASSY), which profiles qualitative and quantitative changes in biofluid metabolic composition by utilizing a novel local-global correlation clustering scheme to identify structurally related spectral peaks and arrange metabolites by similarity of temporal dynamic variation. Underlying spectral data sets are presented in a novel graphical format to represent high-dimensionality biochemical information conveying both statistical metabolite relationships and their responses to experimental perturbation simultaneously in a high-throughput and intuitive manner. The method is exemplified using multiple 600 MHz (1)H NMR spectra of rat (n = 40) urine samples collected over 160 h following the development of experimental pancreatitis induced by L-arginine (ARG) and a wider range of model toxins including acetaminophen, galactosamine, and 2-bromoethanamine. The CLASSY approach deconvolutes complex biofluid mixture spectra into quantitative fold-change metabolic trajectories and clusters metabolites by commonalities of coexpression patterns. We demonstrate that the developing pathological processes cause coordinated changes in the levels of many compounds which share similar pathway connectivities. Variability in individual responses to toxin exposure is also readily detected and visualized allowing the assessment of interanimal variability. As an untargeted, unsupervised approach, CLASSY provides significant advantages in biological information recovery in terms of increased throughput, interpretability, and robustness and has wide potential metabonomic/metabolomic applications in clinical, toxicological, and nutritional studies of biofluids as well as in studies of cellular biochemistry, microbial fermentation monitoring, and functional genomics.

Bacterial attraction and quorum sensing inhibition in Caenorhabditis elegans exudates.

Caenorhabditis elegans, a bacterivorous nematode, lives in complex rotting fruit, soil, and compost environments, and chemical interactions are required for mating, monitoring population density, recognition of food, avoidance of pathogenic microbes, and other essential ecological functions. Despite being one of the best-studied model organisms in biology, relatively little is known about the signals that C. elegans uses to interact chemically with its environment or as defense. C. elegans exudates were analyzed by using several analytical methods and found to contain 36 common metabolites that include organic acids, amino acids, and sugars, all in relatively high abundance. Furthermore, the concentrations of amino acids in the exudates were dependent on developmental stage. The C. elegans exudates were tested for bacterial chemotaxis using Pseudomonas putida (KT2440), a plant growth promoting rhizobacterium, Pseudomonas aeruginosa (PAO1), a soil bacterium pathogenic to C. elegans, and Escherichia coli (OP50), a non-motile bacterium tested as a control. The C. elegans exudates attracted the two Pseudomonas species, but had no detectable antibacterial activity against P. aeruginosa. To our surprise, the exudates of young adult and adult life stages of C. elegans exudates inhibited quorum sensing in the reporter system based on the LuxR bacterial quorum sensing (QS) system, which regulates bacterial virulence and other factors in Vibrio fischeri. We were able to fractionate the QS inhibition and bacterial chemotaxis activities, thus demonstrating that these activities are chemically distinct. Our results demonstrate that C. elegans can attract its bacterial food and has the potential of partially regulating the virulence of bacterial pathogens by inhibiting specific QS systems.

Web server suite for complex mixture analysis by covariance NMR.

Elucidation of the chemical composition of biological samples is a main focus of systems biology and metabolomics. Their comprehensive study requires reliable, efficient, and automatable methods to identify and quantify the underlying metabolites. Because nuclear magnetic resonance (NMR) spectroscopy is a rich source of molecular information, it has a unique potential for this task. Here we present a suite of public web servers (http://spinportal.magnet.fsu.edu), termed COLMAR, which facilitates complex mixture analysis by NMR. The COLMAR web portal presently consists of three servers: COLMAR covariance calculates the covariance NMR spectrum from an NMR input dataset, such as a TOCSY spectrum; COLMAR DemixC method decomposes the 2D covariance TOCSY spectrum into a reduced set of nonredundant 1D cross sections or traces, which belong to individual mixture components; and COLMAR query screens the traces against a NMR spectral database to identify individual compounds. Examples are presented that illustrate the utility of this web server suite for complex mixture analysis.

Self-consistent metabolic mixture analysis by heteronuclear NMR. Application to a human cancer cell line.

Elucidation of the chemical composition of biological samples is a main focus of systems biology and metabolomics. In order to comprehensively study these complex mixtures, reliable, efficient, and automatable methods are needed to identify and quantify the underlying metabolites and natural products. Because of its rich information content, nuclear magnetic resonance (NMR) spectroscopy has a unique potential for this task. Here we present a generalization of the recently introduced homonuclear TOCSY-based DemixC method to heteronuclear HSQC-TOCSY NMR spectroscopy. The approach takes advantage of the high resolution afforded along the (13)C dimension due to the narrow (13)C line widths for the identification of spin systems and compounds. The method combines information from both 1D (13)C and (1)H traces by querying them against an NMR spectral database using our COLMAR query web server. The complementarity of (13)C and (1)H spectral information improves the robustness of compound identification. The method is demonstrated for a metabolic model mixture and is then applied to an extract from DU145 human prostate cancer cells.

Web server based complex mixture analysis by NMR.

Comprehensive metabolite identification and quantification of complex biological mixtures are central aspects of metabolomics. NMR shows excellent promise for these tasks. An automated fingerprinting strategy is presented, termed COLMAR query, which screens NMR chemical shift lists or raw 1D NMR cross sections taken from covariance total correlation spectroscopy (TOCSY) spectra or other multidimensional NMR spectra against an NMR spectral database. Cross peaks are selected using local clustering to avoid ambiguities between chemical shifts and scalar J-coupling effects. With the use of three different algorithms, the corresponding chemical shift list is then screened against chemical shift lists extracted from 1D spectra of a NMR database. The resulting query scores produced by forward assignment, reverse assignment, and bipartite weighted-matching algorithms are combined into a consensus score, which provides a robust means for identifying the correct compound. The approach is demonstrated for a metabolite model mixture that is screened against the metabolomics BioMagResDatabank (BMRB). This NMR-based compound identification approach has been implemented in a public Web server that allows the efficient analysis of a wide range of metabolite mixtures.

2D NMR-based metabolomics uncovers interactions between conserved biochemical pathways in the model organism Caenorhabditis elegans.

Ascarosides are small-molecule signals that play a central role in C. elegans biology, including dauer formation, aging, and social behaviors, but many aspects of their biosynthesis remain unknown. Using automated 2D NMR-based comparative metabolomics, we identified ascaroside ethanolamides as shunt metabolites in C. elegans mutants of daf-22, a gene with homology to mammalian 3-ketoacyl-CoA thiolases predicted to function in conserved peroxisomal lipid ß-oxidation. Two groups of ethanolamides feature ß-keto functionalization confirming the predicted role of daf-22 in ascaroside biosynthesis, whereas α-methyl substitution points to unexpected inclusion of methylmalonate at a late stage in the biosynthesis of long-chain fatty acids in C. elegans. We show that ascaroside ethanolamide formation in response to defects in daf-22 and other peroxisomal genes is associated with severe depletion of endocannabinoid pools. These results indicate unexpected interaction between peroxisomal lipid ß-oxidation and the biosynthesis of endocannabinoids, which are major regulators of lifespan in C. elegans. Our study demonstrates the utility of unbiased comparative metabolomics for investigating biochemical networks in metazoans.

Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations.

Increasingly sophisticated measurement technologies have allowed the fields of metabolomics and genomics to identify, in parallel, risk factors of disease; predict drug metabolism; and study metabolic and genetic diversity in large human populations. Yet the complementarity of these fields and the utility of studying genes and metabolites together is belied by the frequent separate, parallel applications of genomic and metabolomic analysis. Early attempts at identifying co-variation and interaction between genetic variants and downstream metabolic changes, including metabolic profiling of human Mendelian diseases and quantitative trait locus mapping of individual metabolite concentrations, have recently been extended by new experimental designs that search for a large number of gene-metabolite associations. These approaches, including metabolomic quantitiative trait locus mapping and metabolomic genome-wide association studies, involve the concurrent collection of both genomic and metabolomic data and a subsequent search for statistical associations between genetic polymorphisms and metabolite concentrations across a broad range of genes and metabolites. These new data-fusion techniques will have important consequences in functional genomics, microbial metagenomics and disease modeling, the early results and implications of which are reviewed.

Non-negative matrix factorization of two-dimensional NMR spectra: application to complex mixture analysis.

A central problem in the emerging field of metabolomics is how to identify the compounds comprising a chemical mixture of biological origin. NMR spectroscopy can greatly assist in this identification process, by means of multi-dimensional correlation spectroscopy, particularly total correlation spectroscopy (TOCSY). This Communication demonstrates how non-negative matrix factorization (NMF) provides an efficient means of data reduction and clustering of TOCSY spectra for the identification of unique traces representing the NMR spectra of individual compounds. The method is applied to a metabolic mixture whose compounds could be unambiguously identified by peak matching of NMF components against the BMRB metabolomics database.