Andris Jankevics

Serine is a natural ligand and allosteric activator of pyruvate kinase M2

Cancer cells exhibit several unique metabolic phenotypes that are critical for cell growth and proliferation. Specifically, they overexpress the M2 isoform of the tightly regulated enzyme pyruvate kinase (PKM2), which controls glycolytic flux, and are highly dependent on de novo biosynthesis of serine and glycine. Here we describe a new rheostat-like mechanistic relationship between PKM2 activity and serine biosynthesis. We show that serine can bind to and activate human PKM2, and that PKM2 activity in cells is reduced in response to serine deprivation. This reduction in PKM2 activity shifts cells to a fuel-efficient mode in which more pyruvate is diverted to the mitochondria and more glucose-derived carbon is channelled into serine biosynthesis to support cell proliferation.

Toward Global Metabolomics Analysis with Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry: Improved Metabolite Identification by Retention Time Prediction

Metabolomics is an emerging field of postgenomic biology concerned with comprehensive analysis of small molecules in biological systems. However, difficulties associated with the identification of detected metabolites currently limit its application. Here we demonstrate that a retention time prediction model can improve metabolite identification on a hydrophilic interaction chromatography (HILIC)-high-resolution mass spectrometry metabolomics platform. A quantitative structure retention relationship (QSRR) model, incorporating six physicochemical variables in a multiple-linear regression based on 120 authentic standard metabolites, shows good predictive ability for retention times of a range of metabolites (cross-validated R(2) = 0.82 and mean squared error = 0.14). The predicted retention times improved metabolite identification by removing 40% of the false identifications that occurred with identification by accurate mass alone. The importance of this procedure was demonstrated by putative identification of 690 metabolites in extracts of the protozoan parasite Trypanosoma brucei, thus allowing identified metabolites to be mapped onto an organism-wide metabolic network, providing opportunities for future studies of cellular metabolism from a global systems biology perspective.

PeakML/mzMatch: A File Format, Java Library, R Library, and Tool-Chain for Mass Spectrometry Data Analysis

The recent proliferation of high-resolution mass spectrometers has generated a wealth of new data analysis methods. However, flexible integration of these methods into configurations best suited to the research question is hampered by heterogeneous file formats and monolithic software development. The mzXML, mzData, and mzML file formats have enabled uniform access to unprocessed raw data. In this paper we present our efforts to produce an equally simple and powerful format, PeakML, to uniformly exchange processed intermediary and result data. To demonstrate the versatility of PeakML, we have developed an open source Java toolkit for processing, filtering, and annotating mass spectra in a customizable pipeline (mzMatch), as well as a user-friendly data visualization environment (PeakML Viewer). The PeakML format in particular enables the flexible exchange of processed data between software created by different groups or companies, as we illustrate by providing a PeakML-based integration of the widely used XCMS package with mzMatch data processing tools. As an added advantage, downstream analysis can benefit from direct access to the full mass trace information underlying summarized mass spectrometry results, providing the user with the means to rapidly verify results. The PeakML/mzMatch software is freely available at http://mzmatch.sourceforge.net, with documentation, tutorials, and a community forum.

IDEOM: An Excel interface for analysis of LC-MS based metabolomics data

SUMMARY The application of emerging metabolomics technologies to the comprehensive investigation of cellular biochemistry has been limited by bottlenecks in data processing, particularly noise filtering and metabolite identification. IDEOM provides a user-friendly data processing application that automates filtering and identification of metabolite peaks, paying particular attention to common sources of noise and false identifications generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Building on advanced processing tools such as mzMatch and XCMS, it allows users to run a comprehensive pipeline for data analysis and visualization from a graphical user interface within Microsoft Excel, a familiar program for most biological scientists. AVAILABILITY AND IMPLEMENTATION IDEOM is provided free of charge at http://mzmatch.sourceforge.net/ideom.html, as a macro-enabled spreadsheet (.xlsb). Implementation requires Microsoft Excel (2007 or later). R is also required for full functionality. CONTACT michael.barrett@glasgow.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Metabolomics to Unveil and Understand Phenotypic Diversity between Pathogen Populations

Leishmaniasis is a debilitating disease caused by the parasite Leishmania. There is extensive clinical polymorphism, including variable responsiveness to treatment. We study Leishmania donovani parasites isolated from visceral leishmaniasis patients in Nepal that responded differently to antimonial treatment due to differing intrinsic drug sensitivity of the parasites. Here, we present a proof-of-principle study in which we applied a metabolomics pipeline specifically developed for L. donovani to characterize the global metabolic differences between antimonial-sensitive and antimonial-resistant L. donovani isolates. Clones of drug-sensitive and drug-resistant parasite isolates from clinical samples were cultured in vitro and harvested for metabolomics analysis. The relative abundance of 340 metabolites was determined by ZIC-HILIC chromatography coupled to LTQ-Orbitrap mass spectrometry. Our measurements cover approximately 20% of the predicted core metabolome of Leishmania and additionally detected a large number of lipids. Drug-sensitive and drug-resistant parasites showed distinct metabolic profiles, and unsupervised clustering and principal component analysis clearly distinguished the two phenotypes. For 100 metabolites, the detected intensity differed more than three-fold between the 2 phenotypes. Many of these were in specific areas of lipid metabolism, suggesting that the membrane composition of the drug-resistant parasites is extensively modified. Untargeted metabolomics has been applied on clinical Leishmania isolates to uncover major metabolic differences between drug-sensitive and drug-resistant isolates. The identified major differences provide novel insights into the mechanisms involved in resistance to antimonial drugs, and facilitate investigations using targeted approaches to unravel the key changes mediating drug resistance.

Stable Isotope-Assisted Metabolomics for Network-Wide Metabolic Pathway Elucidation

The combination of high-resolution LC–MS-based untargeted metabolomics with stable isotope tracing provides a global overview of the cellular fate of precursor metabolites. This methodology enables detection of putative metabolites from biological samples and simultaneous quantification of the pattern and extent of isotope labeling. Labeling of Trypanosoma brucei cell cultures with 50% uniformly 13C-labeled glucose demonstrated incorporation of glucose-derived carbon into 187 of 588 putatively identified metabolites in diverse pathways including carbohydrate, nucleotide, lipid, and amino acid metabolism. Labeling patterns confirmed the metabolic pathways responsible for the biosynthesis of many detected metabolites, and labeling was detected in unexpected metabolites, including two higher sugar phosphates annotated as octulose phosphate and nonulose phosphate. This untargeted approach to stable isotope tracing facilitates the biochemical analysis of known pathways and yields rapid identification of previously unexplored areas of metabolism.

Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful and widely applied method for the study of biological systems, biomarker discovery and pharmacological interventions. LC-MS measurements are, however, significantly complicated by several technical challenges, including: (1) ionisation suppression/enhancement, disturbing the correct quantification of analytes, and (2) the detection of large amounts of separate derivative ions, increasing the complexity of the spectra, but not their information content. Here we introduce an experimental and analytical strategy that leads to robust metabolome profiles in the face of these challenges. Our method is based on rigorous filtering of the measured signals based on a series of sample dilutions. Such data sets have the additional characteristic that they allow a more robust assessment of detection signal quality for each metabolite. Using our method, almost 80% of the recorded signals can be discarded as uninformative, while important information is retained. As a consequence, we obtain a broader understanding of the information content of our analyses and a better assessment of the metabolites detected in the analyzed data sets. We illustrate the applicability of this method using standard mixtures, as well as cell extracts from bacterial samples. It is evident that this method can be applied in many types of LC-MS analyses and more specifically in untargeted metabolomics.

Metabolomics methods for the synthetic biology of secondary metabolism

Many microbial secondary metabolites are of high biotechnological value for medicine, agriculture, and the food industry. Bacterial genome mining has revealed numerous novel secondary metabolite biosynthetic gene clusters, which encode the potential to synthesize a large diversity of compounds that have never been observed before. The stimulation or “awakening” of this cryptic microbial secondary metabolism has naturally attracted the attention of synthetic microbiologists, who exploit recent advances in DNA sequencing and synthesis to achieve unprecedented control over metabolic pathways. One of the indispensable tools in the synthetic biology toolbox is metabolomics, the global quantification of small biomolecules. This review illustrates the pivotal role of metabolomics for the synthetic microbiology of secondary metabolism, including its crucial role in novel compound discovery in microbes, the examination of side products of engineered metabolic pathways, as well as the identification of major bottlenecks for the overproduction of compounds of interest, especially in combination with metabolic modeling. We conclude by highlighting remaining challenges and recent technological advances that will drive metabolomics towards fulfilling its potential as a cornerstone technology of synthetic microbiology.

mzMatch–ISO

Motivation: Stable isotope-labelling experiments have recently gained increasing popularity in metabolomics studies, providing unique insights into the dynamics of metabolic fluxes, beyond the steady-state information gathered by routine mass spectrometry. However, most liquid chromatography–mass spectrometry data analysis software lacks features that enable automated annotation and relative quantification of labelled metabolite peaks. Here, we describe mzMatch–ISO, a new extension to the metabolomics analysis pipeline mzMatch.R. Results: Targeted and untargeted isotope profiling using mzMatch–ISO provides a convenient visual summary of the quality and quantity of labelling for every metabolite through four types of diagnostic plots that show (i) the chromatograms of the isotope peaks of each compound in each sample group; (ii) the ratio of mono-isotopic and labelled peaks indicating the fraction of labelling; (iii) the average peak area of mono-isotopic and labelled peaks in each sample group; and (iv) the trend in the relative amount of labelling in a predetermined isotopomer. To aid further statistical analyses, the values used for generating these plots are also provided as a tab-delimited file. We demonstrate the power and versatility of mzMatch–ISO by analysing a 13C-labelled metabolome dataset from trypanosomal parasites. Availability: mzMatch.R and mzMatch–ISO are available free of charge from http://mzmatch.sourceforge.net and can be used on Linux and Windows platforms running the latest version of R. Contact: rainer.breitling@manchester.ac.uk . Supplementary information: Supplementary data are available at Bioinformatics online

Metabolic adaptations of Leishmania donovani in relation to differentiation, drug resistance, and drug pressure

Antimonial (sodium stibogluconate, SSG) resistance and differentiation have been shown to be closely linked in Leishmania donovani, with SSG‐resistant strains showing an increased capacity to generate infectious (metacyclic) forms. This is the first untargeted LC‐MS metabolomics study which integrated both phenomena in one experimental design and provided insights into metabolic differences between three clinical L. donovani strains with a similar genetic background but different SSG‐susceptibilities. We performed this analysis at different stages during promastigote growth and in the absence or presence of drug pressure. When comparing SSG‐resistant and SSG‐sensitive strains, a number of metabolic changes appeared to be constitutively present in all growth stages, pointing towards a clear link with SSG‐resistance, whereas most metabolic changes were only detected in the stationary stage. These changes reflect the close intertwinement between SSG‐resistance and an increased metacyclogenesis in resistant parasites. The metabolic changes suggest that SSG‐resistant parasites have (i) an increased capacity for protection against oxidative stress; (ii) a higher fluidity of the plasma membrane; and (iii) a metabolic survival kit to better endure infection. These changes were even more pronounced in a resistant strain kept under SbIII drug pressure.