Identification of a new endogenous metabolite and the characterization of its protein interactions through an immobilization approach

Metabolite discovery: Biochemistry's scientific driver

Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase and the foundation of metabolism's rebirth in the 21^st century; yet, characterizing newly observed metabolites has been an enduring obstacle. Crystallography and NMR spectroscopy have been of extraordinary importance, although their applicability in resolving metabolism's fine structure has been restricted by their intrinsic requirement of sufficient and sufficiently pure materials. Mass spectrometry has been a key technology, especially when coupled with high-performance separation technologies and emerging informatic and database solutions. Even more so, the collective of artificial intelligence technologies are rapidly evolving to help solve the metabolite characterization conundrum. This perspective describes this challenge, how it was historically addressed, and how metabolomics is evolving to address it today and in the future.

Chemical Discovery in the Era of Metabolomics

Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true "novel" compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.

Efflux pumps and antimicrobial resistance: Paradoxical components in systems genomics

Efflux pumps play a major role in the increasing antimicrobial resistance rendering a large number of drugs of no use. Large numbers of pathogens are becoming multidrug resistant due to inadequate dosage and use of the existing antimicrobials. This leads to the need for identifying new efflux pump inhibitors. Design of novel targeted therapies using inherent complexity involved in the biological network modeling has gained increasing importance in recent times. The predictive approaches should be used to determine antimicrobial activities with high pathogen specificity and microbicidal potency. Antimicrobial peptides, which are part of our innate immune system, have the ability to respond to infections and have gained much attention in making resistant strain sensitive to existing drugs. In this review paper, we outline evidences linking host-directed therapy with the efflux pump activity to infectious disease.

2-Methoxyphenyl isocyanate: a chemoselective multitasking reagent for an amine protection/deprotection sequence

Organic amines protected through urea bond mediated capping.

Combined transcriptomics-metabolomics profiling of the heat shock response in the hyperthermophilic archaeon Pyrococcus furiosus

Pyrococcus furiosus is a remarkable archaeon able to grow at temperatures around 100 °C. To gain insight into how this model hyperthermophile copes with heat stress, we compared transcriptomic and metabolomic data of cells subjected to a temperature shift from 90 °C to 97 °C. In this study, we used RNA-sequencing to characterize the global variation in gene expression levels, while nuclear magnetic resonance (NMR) and targeted ion exchange liquid chromatography-mass spectrometry (LC-MS) were used to determine changes in metabolite levels. Of the 552 differentially expressed genes in response to heat shock conditions, 257 were upregulated and 295 were downregulated. In particular, there was a significant downregulation of genes for synthesis and transport of amino acids. At the metabolite level, 37 compounds were quantified. The level of di-myo-inositol phosphate, a canonical heat stress solute among marine hyperthermophiles, increased considerably (5.4-fold) at elevated temperature. Also, the levels of mannosylglycerate, UDP-N-acetylglucosamine (UDPGlcNac) and UDP-N-acetylgalactosamine were enhanced. The increase in the pool of UDPGlcNac was concurrent with an increase in the transcript levels of the respective biosynthetic genes. This work provides the first metabolomic analysis of the heat shock response of a hyperthermophile.

Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation

Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by gas chromatography-MS, 20 by NMR, and 22 by both techniques). Out of these, 47 metabolites of interest were identified: 14 metabolites were uniquely identified by NMR, and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism, leading to fatty-acid and complex-lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques: the improved detection and annotation of metabolites.

An improved method for extraction of polar and charged metabolites from cyanobacteria

A key requirement for 13C Metabolic flux analysis (13C-MFA), a widely used technique to estimate intracellular metabolic fluxes, is an efficient method for the extraction of intermediate metabolites for analysis via liquid chromatography mass spectrometry (LC/MS). The 13C isotopic labeling results in further distribution of an already sparse pool of intermediate metabolites into isotopologues, each appearing as a separate chromatographic feature. We examined some of the reported solvent systems for the extraction of polar intracellular metabolites from three strains of cyanobacteria of the genus Synechococcus, viz., Synechococcus sp. PCC 7002, Synechococcus elongatus PCC 7942, and a newly isolated Synechococcus elongatus PCC 11801 (manuscript under review). High resolution-LC/MS was used to assess the relative abundance of the extracted metabolites. The different solvent systems used for extraction led to statistically significant changes in the extraction efficiency for a large number of metabolites. While a few hundred m/z features or potential metabolites were detected with different solvent systems, the abundance of over a quarter of all metabolites varied significantly from one solvent system to another. Further, the extraction methods were evaluated for a targeted set of metabolites that are important in 13C-MFA studies of photosynthetic organisms. While for the strain PCC 7002, the reported method using methanol-chloroform-water system gave satisfactory results, a mild base in the form of NH4OH had to be used in place of water to achieve adequate levels of extraction for PCC 7942 and PCC 11801. While minor changes in extraction solvent resulted in dramatic changes in the extraction efficiency of a number of compounds, certain metabolites such as amino acids and organic acids were adequately extracted in all the solvent systems tested. Overall, we present a new improved method for extraction using a methanol-chloroform-NH4OH system. Our method improves the extraction of polar compounds such as sugar phosphates, bisphosphates, that are central to 13C-MFA studies.

Anodic oxidation of bisamides from diaminoalkanes by constant current electrolysis

In general, bisamides derived from diamines and involving 3 and 4 methylene groups as spacers between the two amide functionalities behave similar to monoamides upon anodic oxidation in methanol/LiClO₄ because both types undergo majorly mono- and dimethoxylations at the α-position to the N atom. However, in cases where the spacer contains two methylene groups only the anodic process leads mostly to CH₂-CH₂ bond cleavage to afford products of type RCONHCH₂OCH₃. Moreover, upon replacing LiClO₄ with Et₄NBF₄ an additional fragmentation type of product was generated from the latter amides, namely RCONHCHO. Also, the anodic process was found to be more efficient with C felt as the anode, and in a mixture of 1:1 methanol/acetonitrile co-solvents.

Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes

Unbiased, "nontargeted" metabolite profiling techniques hold considerable promise for biomarker and pathway discovery, in spite of the lack of successful applications to human disease. By integrating nontargeted metabolomics, genetics, and detailed human phenotyping, we identified dimethylguanidino valeric acid (DMGV) as an independent biomarker of CT-defined nonalcoholic fatty liver disease (NAFLD) in the offspring cohort of the Framingham Heart Study (FHS) participants. We verified the relationship between DMGV and early hepatic pathology. Specifically, plasma DMGV levels were correlated with biopsy-proven nonalcoholic steatohepatitis (NASH) in a hospital cohort of individuals undergoing gastric bypass surgery, and DMGV levels fell in parallel with improvements in post-procedure cardiometabolic parameters. Further, baseline DMGV levels independently predicted future diabetes up to 12 years before disease onset in 3 distinct human cohorts. Finally, we provide all metabolite peak data consisting of known and unidentified peaks, genetics, and key metabolic parameters as a publicly available resource for investigations in cardiometabolic diseases.

Identification of a novel acetylated form of branched-chain polyamine from a hyperthermophilic archaeon Thermococcus kodakarensis

Long/branched-chain polyamines are unique polycations found in thermophiles. The hyperthermophilic archaeon Thermococcus kodakarensis contains spermidine and a branched-chain polyamine, N⁴-bis(aminopropyl)spermidine, as major polyamines. The metabolic pathways associated with branched-chain polyamines remain unknown. Here, we used gas chromatography and liquid chromatography-tandem mass spectrometry analyses to identify a new acetylated polyamine, N⁴-bis(aminopropyl)-N¹-acetylspermidine, from T. kodakarensis; this polyamine was not found in other micro-organisms. The amounts of branched-chain polyamine and its acetylated form increased with temperature, indicating that branched-chain polyamines are important for growth at higher temperatures. The amount of quaternary acetylated polyamine produced was associated with the amount of N⁴-bis(aminopropyl)spermidine in the cell. The ratio of acetylated to non-acetylated forms was higher in the stationary phase than in the logarithmic growth phase under high-temperature stress condition.

A systems biology approach reveals major metabolic changes in the thermoacidophilic archaeon Sulfolobus solfataricus in response to the carbon source L-fucose versus D-glucose

Archaea are characterised by a complex metabolism with many unique enzymes that differ from their bacterial and eukaryotic counterparts. The thermoacidophilic archaeon Sulfolobus solfataricus is known for its metabolic versatility and is able to utilize a great variety of different carbon sources. However, the underlying degradation pathways and their regulation are often unknown. In this work, the growth on different carbon sources was analysed, using an integrated systems biology approach. The comparison of growth on L-fucose and D-glucose allows first insights into the genome-wide changes in response to the two carbon sources and revealed a new pathway for L-fucose degradation in S. solfataricus. During growth on L-fucose major changes in the central carbon metabolic network, as well as an increased activity of the glyoxylate bypass and the 3-hydroxypropionate/4-hydroxybutyrate cycle were observed. Within the newly discovered pathway for L-fucose degradation the following key reactions were identified: (i) L-fucose oxidation to L-fuconate via a dehydrogenase, (ii) dehydration to 2-keto-3-deoxy-L-fuconate via dehydratase, (iii) 2-keto-3-deoxy-L-fuconate cleavage to pyruvate and L-lactaldehyde via aldolase and (iv) L-lactaldehyde conversion to L-lactate via aldehyde dehydrogenase. This pathway as well as L-fucose transport shows interesting overlaps to the D-arabinose pathway, representing another example for pathway promiscuity in Sulfolobus species.

Defining the metabolome: size, flux, and regulation

Renewed interest in metabolic research over the last two decades has inspired an explosion of technological developments for studying metabolism. At the forefront of methodological innovation is an approach referred to as "untargeted" or "discovery" metabolomics. The experimental objective of this technique is to comprehensively measure the entire metabolome, which constitutes a largely undefined set of molecules. Given its potential comprehensive coverage, untargeted metabolomics is often the first choice of experiments for investigators pursuing a metabolic research question. It is important to recognize, however, that untargeted metabolomics may not always be the optimal experimental approach. Conventionally, untargeted metabolomics only provides information about relative differences in metabolite pool sizes. Therefore, depending on the specific scientific question at hand, a complementary approach involving stable isotopes (such as metabolic flux analysis) may be better suited to provide biological insights. Unlike untargeted metabolomics, stable-isotope methods can provide information about differences in reaction rates.

N-lactoyl-amino acids are ubiquitous metabolites that originate from CNDP2-mediated reverse proteolysis of lactate and amino acids

Despite technological advances in metabolomics, large parts of the human metabolome are still unexplored. In an untargeted metabolomics screen aiming to identify substrates of the orphan transporter ATP-binding cassette subfamily C member 5 (ABCC5), we identified a class of mammalian metabolites, N-lactoyl-amino acids. Using parallel protein fractionation in conjunction with shotgun proteomics on fractions containing N-lactoyl-Phe-forming activity, we unexpectedly found that a protease, cytosolic nonspecific dipeptidase 2 (CNDP2), catalyzes their formation. N-lactoyl-amino acids are ubiquitous pseudodipeptides of lactic acid and amino acids that are rapidly formed by reverse proteolysis, a process previously considered to be negligible in vivo. The plasma levels of these metabolites strongly correlate with plasma levels of lactate and amino acid, as shown by increased levels after physical exercise and in patients with phenylketonuria who suffer from elevated Phe levels. Our approach to identify unknown metabolites and their biosynthesis has general applicability in the further exploration of the human metabolome.

An untargeted metabolomic workflow to improve structural characterization of metabolites

Mass spectrometry-based metabolomics relies on MS(2) data for structural characterization of metabolites. To obtain the high-quality MS(2) data necessary to support metabolite identifications, ions of interest must be purely isolated for fragmentation. Here, we show that metabolomic MS(2) data are frequently characterized by contaminating ions that prevent structural identification. Although using narrow-isolation windows can minimize contaminating MS(2) fragments, even narrow windows are not always selective enough, and they can complicate data analysis by removing isotopic patterns from MS(2) spectra. Moreover, narrow windows can significantly reduce sensitivity. In this work, we introduce a novel, two-part approach for performing metabolomic identifications that addresses these issues. First, we collect MS(2) scans with less stringent isolation settings to obtain improved sensitivity at the expense of specificity. Then, by evaluating MS(2) fragment intensities as a function of retention time and precursor mass targeted for MS(2) analysis, we obtain deconvolved MS(2) spectra that are consistent with pure standards and can therefore be used for metabolite identification. The value of our approach is highlighted with metabolic extracts from brain, liver, astrocytes, as well as nerve tissue, and performance is evaluated by using pure metabolite standards in combination with simulations based on raw MS(2) data from the METLIN metabolite database. A R package implementing the algorithms used in our workflow is available on our laboratory website ( http://pattilab.wustl.edu/decoms2.php ).

Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database

Untargeted metabolomics provides a comprehensive platform for identifying metabolites whose levels are altered between two or more populations. By using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS), hundreds to thousands of peaks with a unique m/z ratio and retention time are routinely detected from most biological samples in an untargeted profiling experiment. Each peak, termed a metabolomic feature, can be characterized on the basis of its accurate mass, retention time and tandem mass spectral fragmentation pattern. Here a seven-step protocol is suggested for such a characterization by using the METLIN metabolite database. The protocol starts from untargeted metabolomic LC-Q-TOF-MS data that have been analyzed with the bioinformatics program XCMS, and it describes a strategy for selecting interesting features as well as performing subsequent targeted tandem MS. The seven steps described will require 2-4 h to complete per feature, depending on the compound.

Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database

Untargeted metabolomics provides a comprehensive platform for identifying metabolites whose levels are altered between two or more populations. By using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS), hundreds to thousands of peaks with a unique m/z ratio and retention time are routinely detected from most biological samples in an untargeted profiling experiment. Each peak, termed a metabolomic feature, can be characterized on the basis of its accurate mass, retention time and tandem mass spectral fragmentation pattern. Here a seven-step protocol is suggested for such a characterization by using the METLIN metabolite database. The protocol starts from untargeted metabolomic LC-Q-TOF-MS data that have been analyzed with the bioinformatics program XCMS, and it describes a strategy for selecting interesting features as well as performing subsequent targeted tandem MS. The seven steps described will require 2-4 h to complete per feature, depending on the compound.

Investigating metabolite-protein interactions: an overview of available techniques

Metabolites comprise the molar majority of chemical substances in living cells, and metabolite-protein interactions are expected to be quite common. Many interactions have already been identified and have been shown to be involved in the regulation of different types of cellular processes including signaling events, enzyme activities, protein localizations and interactions. Recent technological advances have greatly facilitated the detection of metabolite-protein interactions at high sensitivity and some of these have been applied on a large scale. In this manuscript, we review the available in vitro, in silico and in vivo technologies for mapping small-molecule-protein interactions. Although some of these were developed for drug-protein interactions they can be applied for mapping metabolite-protein interactions. Information gained from the use of these approaches can be applied to the manipulation of cellular processes and therapeutic applications.

Substrate-baited nanoparticles: a catch and release strategy for enzyme recognition and harvesting

The isolation of a single type of protein from a complex mixture is vital for the characterization of the function, structure, and interactions of the protein of interest and is typically the most laborious aspect of the protein purification process. In this work, a model system is utilized to show the efficacy of synthesizing a "baited" nanoparticle to capture and recycle enzymes (proteins that catalyze chemical reactions) from crude cell lysate. Enzyme trapping and recycling is illustrated with the carbazole 1,9a-dioxygenase (CARDO) system, an enzyme important in bioremediation and natural product synthesis. The enzymes are baited with azide-modified carbazolyl moieties attached to poly(propargyl acrylate) nanoparticles through a click transformation. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis indicates the single-step procedure to immobilize the enzymes on the particles is capable of significantly concentrating the protein from raw lysate and sequestering all required components of the protein to maintain bioactivity. These results establish a universal model applicable to concentrating and extracting known substrate-protein pairs, but it can be an invaluable tool in recognizing unknown protein-ligand affinities.

Extreme challenges and advances in archaeal proteomics

Archaea display amazing physiological properties that are of interest to understand at the molecular level including the ability to thrive at extreme environmental conditions, the presence of novel metabolic pathways (e.g. methanogenesis, methylaspartate cycle) and the use of eukaryotic-like protein machineries for basic cellular functions. Coupling traditional genetic and biochemical approaches with advanced technologies, such as genomics and proteomics, provides an avenue for scientists to discover new aspects related to the molecular physiology of archaea. This review emphasizes the unusual properties of archaeal proteomes and how high-throughput and specialized mass spectrometry-based proteomic studies have provided insight into the molecular properties of archaeal cells.

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

metaXCMS is a software program for the analysis of liquid chromatography/mass spectrometry-based untargeted metabolomic data. It is designed to identify the differences between metabolic profiles across multiple sample groups (e.g., 'healthy' versus 'active disease' versus 'inactive disease'). Although performing pairwise comparisons alone can provide physiologically relevant data, these experiments often result in hundreds of differences, and comparison with additional biologically meaningful sample groups can allow for substantial data reduction. By performing second-order (meta-) analysis, metaXCMS facilitates the prioritization of interesting metabolite features from large untargeted metabolomic data sets before the rate-limiting step of structural identification. Here we provide a detailed step-by-step protocol for going from raw mass spectrometry data to metaXCMS results, visualized as Venn diagrams and exported Microsoft Excel spreadsheets. There is no upper limit to the number of sample groups or individual samples that can be compared with the software, and data from most commercial mass spectrometers are supported. The speed of the analysis depends on computational resources and data volume, but will generally be less than 1 d for most users. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.

Mass spectrometry-based metabolomics, analysis of metabolite-protein interactions, and imaging

Our understanding of biology has been greatly improved through recent developments in mass spectrometry, which is providing detailed information on protein and metabolite composition as well as protein-metabolite interactions. The high sensitivity and resolution of mass spectrometry achieved with liquid or gas chromatography allows for detection and quantification of hundreds to thousands of molecules in a single measurement. Where homogenization-based sample preparation and extraction methods result in a loss of spatial information, mass spectrometry imaging technologies provide the in situ distribution profiles of metabolites and proteins within tissues. Mass spectrometry-based analysis of metabolite abundance, protein-metabolite interactions, and spatial distribution of compounds facilitates the high-throughput screening of biochemical reactions, the reconstruction of metabolic networks, biomarker discovery, determination of tissue compositions, and functional annotation of both proteins and metabolites.

Mass spectrometry-based technologies for high-throughput metabolomics

The metabolome is composed of a vast number of small-molecule metabolites that exhibit a diversity of physical and chemical properties and exist over a wide dynamic range in biological samples. Multiple analytical techniques, used in a complementary manner, are required to achieve high coverage of the metabolome. MS is playing a central role in metabolomics research. Herein, we present a brief overview of the MS-based technologies employed for high-throughput metabolomics. These technologies range from chromatography-MS techniques, such as GC-MS and LC-MS, to chromatography-free techniques, such as direct infusion, matrix-assisted and matrix-free laser desorption/ionization, imaging and some new ambient ionization approaches. Chemoinformatics and bioinformatics tools are widely available to facilitate successful metabolomics studies by turning the complex metabolomics data into biological information through streamlined data processing, analysis and interpretation.

metaXCMS: second-order analysis of untargeted metabolomics data

Mass spectrometry-based untargeted metabolomics often results in the observation of hundreds to thousands of features that are differentially regulated between sample classes. A major challenge in interpreting the data is distinguishing metabolites that are causally associated with the phenotype of interest from those that are unrelated but altered in downstream pathways as an effect. To facilitate this distinction, here we describe new software called metaXCMS for performing second-order ("meta") analysis of untargeted metabolomics data from multiple sample groups representing different models of the same phenotype. While the original version of XCMS was designed for the direct comparison of two sample groups, metaXCMS enables meta-analysis of an unlimited number of sample classes to facilitate prioritization of the data and increase the probability of identifying metabolites causally related to the phenotype of interest. metaXCMS is used to import XCMS results that are subsequently filtered, realigned, and ultimately compared to identify shared metabolites that are up- or down-regulated across all sample groups. We demonstrate the software's utility by identifying histamine as a metabolite that is commonly altered in three different models of pain. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.

Identification and determination of urinary acetylpolyamines in cancer patients by electrospray ionization and time-of-flight mass spectrometry

A method for the quantification of acetylpolyamines, N(1),N(12)-diacetylspermine (DiAcSpm), monoacetylspermidine (AcSpd), and N(1),N(8)-diacetylspermidine (DiAcSpd), identifying each compound simultaneously, was developed with the goal of evaluating these acetylpolyamines as potential biomarkers of cancer. The method consists of prepurification of acetylpolyamines in urine with commercially available cartridges and derivatization with heptafluorobutyric (HFB) anhydride. HFB derivatives of acetylpolyamines were determined simultaneously using (15)N-labeled acetylpolyamines as internal standards by electrospray ionization and time-of-flight mass spectrometry (ESI-TOF MS). After the method was validated, the urinary acetylpolyamines of 38 cancer patients were quantified with this method. A comparison of the concentrations of DiAcSpm with those measured by a colloidal gold aggregation method demonstrated a correlation coefficient of 0.996, showing that the two methods were equally satisfactory. Analysis of the correlation between DiAcSpd or AcSpd and DiAcSpm, performed for the first time, indicated the usefulness of DiAcSpm as a urinary biomarker of cancer. During the course of this work, two simple methods for the preparation of alpha,omega-diacetylpolyamines were developed, and a possibility to separate and determine the concentrations of the two isomers, N(1)-acetylspermidine and N(8)-acetylspermidine in AcSpd, was shown by tandem mass spectrometry (MS/MS).

Eating at the table of another: metabolomics of host-parasite interactions

The application of metabolomics, the global analysis of metabolite levels, to the study of protozoan parasites has become an important tool for understanding the host-parasite relationship and holds promise for the development of direly needed therapeutics and improved diagnostics. Research advances over the past decade have opened the door for a systems biology approach to protozoan parasites with metabolomics, providing a crucial readout of metabolic activity. In this review, we highlight recent metabolomic approaches to protozoan parasites, including metabolite profiling, integration with genomics, transcription, and proteomic analysis, and the use of metabolic fingerprints for the diagnosis of parasitic infections.

Metabolomics: moving to the clinic

Assessment of a biological system by means of global and non-targeted metabolite profiling--metabolomics or metabonomics--provides the investigator with molecular information that is close to the phenotype in question in the sense that metabolites are an ultimate product of gene, mRNA, and protein activity. Over the last few years, there has been a rapidly growing number of metabolomics applications aimed at finding biomarkers which could assist diagnosis, provide therapy guidance, and evaluate response to therapy for particular diseases. Also, within the fields of drug discovery, drug toxicology, and personalized pharmacology, metabolomics is emerging as a powerful tool. This review seeks to update the reader on analytical strategies, biomarker findings, and implications of metabolomics for the clinic. Particular attention is paid to recent biomarkers found related to neurological, cardiovascular, and cancer diseases. Moreover, the impact of metabolomics in the drug discovery and development process is examined.