Progressive peak clustering in GC-MS Metabolomic experiments applied to Leishmania parasites

Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples

Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.

Metabolic signatures of Arabidopsis thaliana abiotic stress responses elucidate patterns in stress priming, acclimation, and recovery

Temperature, water, and light are three abiotic stress factors that have major influences on plant growth, development, and reproduction. Plants can be primed by a prior mild stress to enhance their resistance to future stress. We used an untargeted metabolomics approach to examine Arabidopsis thaliana 11-day-old seedling's abiotic stress responses including heat (with and without priming), cold (with and without priming), water-deficit and high-light before and after a 2-day-recovery period. Analysis of the physiological phenotypes showed that seedlings with stress treatment resulted in a reduction in fresh weight, hypocotyl and root length but remained viable. Several stress responsive metabolites were identified, confirmed with reference standards, quantified, and clustered. We identified shared and specific stress signatures for cold, heat, water-deficit, and high-light treatments. Central metabolism including amino acid metabolism, sugar metabolism, glycolysis, TCA cycle, GABA shunt, glutathione metabolism, purine metabolism, and urea cycle were found to undergo changes that are fundamentally different, although some shared commonalities in response to different treatments. Large increases in cysteine abundance and decreases in reduced glutathione were observed following multiple stress treatments highlighting the importance of oxidative stress as a general phenomenon in abiotic stress. Large fold increases in low-turnover amino acids and maltose demonstrate the critical role of protein and starch autolysis in early abiotic stress responses.

VOCCluster: Untargeted Metabolomics Feature Clustering Approach for Clinical Breath Gas Chromatography/Mass Spectrometry Data

Metabolic profiling of breath analysis involves processing, alignment, scaling, and clustering of thousands of features extracted from gas chromatography/mass spectrometry (GC/MS) data from hundreds of participants. The multistep data processing is complicated, operator error-prone, and time-consuming. Automated algorithmic clustering methods that are able to cluster features in a fast and reliable way are necessary. These accelerate metabolic profiling and discovery platforms for next-generation medical diagnostic tools. Our unsupervised clustering technique, VOCCluster, prototyped in Python, handles features of deconvolved GC/MS breath data. VOCCluster was created from a heuristic ontology based on the observation of experts undertaking data processing with a suite of software packages. VOCCluster identifies and clusters groups of volatile organic compounds (VOCs) from deconvolved GC/MS breath with similar mass spectra and retention index profiles. VOCCluster was used to cluster more than 15 000 features extracted from 74 GC/MS clinical breath samples obtained from participants with cancer before and after a radiation therapy. Results were evaluated against a panel of ground truth compounds and compared to other clustering methods (DBSCAN and OPTICS) that were used in previous metabolomics studies. VOCCluster was able to cluster those features into 1081 groups (including endogenous and exogenous compounds and instrumental artifacts) with an accuracy rate of 96% (±0.04 at 95% confidence interval).

Untargeted Metabolomics Analysis of Eggplant (Solanum melongena L.) Fruit and Its Correlation to Fruit Morphologies

Eggplant is one of the most widely cultivated vegetables in the world and has high biodiversity in terms of fruit shape, size, and color. Therefore, fruit morphology and nutrient content become important considerations for both consumers and breeders who develop new eggplant-based products. To gain insight on the diversity of eggplant metabolites, twenty-one eggplant accessions were analyzed by untargeted metabolomics using GC-MS and LC-MS. The dataset of eggplant fruit morphologies, and metabolites specific to different eggplant fruit accessions were used for correlation analysis. Untargeted metabolomics analysis using LC-MS and GC-MS was able to detect 136 and 207 peaks, respectively. Fifty-one (51) metabolites from the LC-MS analysis and 207 metabolites from the GC-MS analysis were putatively identified, which included alkaloids, terpenes, terpenoids, fatty acids, and flavonoids. Spearman correlation analysis revealed that 14 fruit morphologies were correlated with several metabolites. This information will be very useful for the development of strategies for eggplant breeding.

Analysis of Intracellular Metabolites from Microorganisms: Quenching and Extraction Protocols

Sample preparation is one of the most important steps in metabolome analysis. The challenges of determining microbial metabolome have been well discussed within the research community and many improvements have already been achieved in last decade. The analysis of intracellular metabolites is particularly challenging. Environmental perturbations may considerably affect microbial metabolism, which results in intracellular metabolites being rapidly degraded or metabolized by enzymatic reactions. Therefore, quenching or the complete stop of cell metabolism is a pre-requisite for accurate intracellular metabolite analysis. After quenching, metabolites need to be extracted from the intracellular compartment. The choice of the most suitable metabolite extraction method/s is another crucial step. The literature indicates that specific classes of metabolites are better extracted by different extraction protocols. In this review, we discuss the technical aspects and advancements of quenching and extraction of intracellular metabolite analysis from microbial cells.

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.

Neglected diseases prioritized in Brazil under the perspective of metabolomics: A review

This review article compiles in a critical manner literature publications regarding seven neglected diseases (ND) prioritized in Brazil (Chagas disease, dengue, leishmaniasis, leprosy, malaria, schistosomiasis, and tuberculosis) under the perspective of metabolomics. Both strategies, targeted and untargeted metabolomics, were considered in the compilation. The majority of studies focused on biomarker discovery for diagnostic purposes, and on the search of novel or alternative therapies against the ND under consideration, although temporal progression of the infection at metabolic level was also addressed. Tuberculosis, followed by schistosomiasis, malaria and leishmaniasis are the diseases that received larger attention in terms of number of publications. Dengue and leprosy were the least studied and Chagas disease received intermediate attention. NMR and HPLC-MS technologies continue to predominate among the analytical platforms of choice in the metabolomic studies of ND. A plethora of metabolites were identified in the compiled studies, with expressive predominancy of amino acids, organic acids, carbohydrates, nucleosides, lipids, fatty acids, and derivatives.

Use of (13)C stable isotope labelling for pathway and metabolic flux analysis in Leishmania parasites

This protocol describes the combined use of metabolite profiling and stable isotope labelling to define pathways of central carbon metabolism in the protozoa parasite, Leishmania mexicana. Parasite stages are cultivated in standard or completely defined media and then rapidly transferred to chemically equivalent media containing a single (13)C-labelled nutrient. The incorporation of label can be followed over time or after establishment of isotopic equilibrium by harvesting parasites with rapid metabolic quenching. (13)C enrichment of multiple intracellular polar and apolar (lipidic) metabolites can be quantified using gas chromatography-mass spectrometry (GC-MS), while the uptake and secretion of (13)C-labelled metabolites can be measured by (13)C-NMR. Analysis of the mass isotopomer distribution of key metabolites provides information on pathway structure, while analysis of labelling kinetics can be used to infer metabolic fluxes. This protocol is exemplified using L. mexicana labelled with (13)C-U-glucose. The method can be used to measure perturbations in parasite metabolism induced by drug inhibition or genetic manipulation of enzyme levels and is broadly applicable to any cultured parasite stages.

WITHDRAWN: Recent advances in chemometric methods for plant metabolomics: A review

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Phosphoenolpyruvate carboxylase identified as a key enzyme in erythrocytic Plasmodium falciparum carbon metabolism

Phospoenolpyruvate carboxylase (PEPC) is absent from humans but encoded in the Plasmodium falciparum genome, suggesting that PEPC has a parasite-specific function. To investigate its importance in P. falciparum, we generated a pepc null mutant (D10^Δpepc), which was only achievable when malate, a reduction product of oxaloacetate, was added to the growth medium. D10^Δpepc had a severe growth defect in vitro, which was partially reversed by addition of malate or fumarate, suggesting that pepc may be essential in vivo. Targeted metabolomics using ¹³C-U-D-glucose and ¹³C-bicarbonate showed that the conversion of glycolytically-derived PEP into malate, fumarate, aspartate and citrate was abolished in D10^Δpepc and that pentose phosphate pathway metabolites and glycerol 3-phosphate were present at increased levels. In contrast, metabolism of the carbon skeleton of ¹³C,¹⁵N-U-glutamine was similar in both parasite lines, although the flux was lower in D10^Δpepc; it also confirmed the operation of a complete forward TCA cycle in the wild type parasite. Overall, these data confirm the CO₂ fixing activity of PEPC and suggest that it provides metabolites essential for TCA cycle anaplerosis and the maintenance of cytosolic and mitochondrial redox balance. Moreover, these findings imply that PEPC may be an exploitable target for future drug discovery.

The genome of the human malaria parasite Plasmodium falciparum encodes a protein called phosphoenolpyruvate carboxylase (PEPC) absent from the human host. PEPC is known to fix CO₂ to generate metabolites used for energy metabolism in plants and bacteria, but its function in malaria parasites remained an enigma. Our study aimed to elucidate the role and importance of PEPC in P. falciparum in its host red blood cell by generating a gene deletion mutant in P. falciparum. This was only achievable in the presence of high concentrations of malate were added to the culture medium. The mutant generated (D10^Δpepc) had a severe growth defect, which was rescued partially by malate or fumarate (but not any other downstream metabolites), suggesting that they feed into the same metabolic pathway. Using heavy isotope labelled ¹³C-U-D-glucose and ¹³C-bicarbonate we showed that PECP has an important role in intermediary carbon metabolism and is vital for the maintenance of cytosolic and mitochondrial redox balance. Together these findings imply that PEPC may be an exploitable target for future drug discovery.

CAMS-RS: Clustering Algorithm for Large-Scale Mass Spectrometry Data Using Restricted Search Space and Intelligent Random Sampling

High-throughput mass spectrometers can produce massive amounts of redundant data at an astonishing rate with many of them having poor signal-to-noise (S/N) ratio. These low S/N ratio spectra may not get interpreted using conventional spectra-to-database matching techniques. In this paper, we present an efficient algorithm, CAMS-RS (Clustering Algorithm for Mass Spectra using Restricted Space and Sampling) for clustering of raw mass spectrometry data. CAMS-RS utilizes a novel metric (called F-set) that exploits the temporal and spatial patterns to accurately assess similarity between two given spectra. The F-set similarity metric is independent of the retention time and allows clustering of mass spectrometry data from independent LC-MS/MS runs. A novel restricted search space strategy is devised to limit the comparisons of the number of spectra. An intelligent sampling method is executed on individual bins that allow merging of the results to make the final clusters. Our experiments, using experimentally generated data sets, show that the proposed algorithm is able to cluster spectra with high accuracy and is helpful in interpreting low S/N ratio spectra. The CAMS-RS algorithm is highly scalable with increasing number of spectra and our implementation allows clustering of up to a million spectra within minutes.

LC-MS alignment in theory and practice: a comprehensive algorithmic review

Liquid chromatography-mass spectrometry is widely used for comparative replicate sample analysis in proteomics, lipidomics and metabolomics. Before statistical comparison, registration must be established to match corresponding analytes from run to run. Alignment, the most popular correspondence approach, consists of constructing a function that warps the content of runs to most closely match a given reference sample. To date, dozens of correspondence algorithms have been proposed, creating a daunting challenge for practitioners in algorithm selection. Yet, existing reviews have highlighted only a few approaches. In this review, we describe 50 correspondence algorithms to facilitate practical algorithm selection. We elucidate the motivation for correspondence and analyze the limitations of current approaches, which include prohibitive runtimes, numerous user parameters, model limitations and the need for reference samples. We suggest and describe a paradigm shift for overcoming current correspondence limitations by building on known liquid chromatography-mass spectrometry behavior.

LC-MS metabolomics from study design to data-analysis - using a versatile pathogen as a test case

Thanks to significant improvements in LC-MS technology, metabolomics is increasingly used as a tool to discriminate the responses of organisms to various stimuli or drugs. In this minireview we discuss all aspects of the LC-MS metabolomics pipeline, using a complex and versatile model organism, Leishmania donovani, as an illustrative example. The benefits of a hyphenated mass spectrometry platform and a detailed overview of the entire experimental pipeline from sampling, sample storage and sample list set-up to LC-MS measurements and the generation of meaningful results with state-of-the-art data-analysis software will be thoroughly discussed. Finally, we also highlight important pitfalls in the processing of LC-MS data and comment on the benefits of implementing metabolomics in a systems biology approach.

Detection of polar metabolites through the use of gas chromatography-mass spectrometry

Gas chromatography-mass spectrometry (GC-MS) is a highly reproducible and sensitive analytical technique that has had significant use in the area of metabolite profiling. GC-MS is able to detect a wide variety of metabolites, with highly differing chemistries. In general, extracted biological samples are volatilized prior to separation on a capillary column with a stationary phase suited to the analysis of the compounds of interest. Separated compounds are eluted into a mass spectrometer equipped with an electron impact ionization source, thereby generating a quantifiable mass spectral fingerprint. This chapter describes a method for the trimethylsilyl derivatization of polar metabolites, followed by detection and relative quantification using a gas chromatograph coupled to a single quadrupole mass spectrometer. Using this method will enable the profiling of the greatest range of polar metabolites.

An ultra-fast metabolite prediction algorithm

Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy.

LeishCyc: a guide to building a metabolic pathway database and visualization of metabolomic data

The complexity of the metabolic networks in even the simplest organisms has raised new challenges in organizing metabolic information. To address this, specialized computer frameworks have been developed to capture, manage, and visualize metabolic knowledge. The leading databases of metabolic information are those organized under the umbrella of the BioCyc project, which consists of the reference database MetaCyc, and a number of pathway/genome databases (PGDBs) each focussed on a specific organism. A number of PGDBs have been developed for bacterial, fungal, and protozoan pathogens, greatly facilitating dissection of the metabolic potential of these organisms and the identification of new drug targets. Leishmania are protozoan parasites belonging to the family Trypanosomatidae that cause a broad spectrum of diseases in humans. In this work we use the LeishCyc database, the BioCyc database for Leishmania major, to describe how to build a BioCyc database from genomic sequences and associated annotations. By using metabolomic data generated in our group, we show how such databases can be utilized to elucidate specific changes in parasite metabolism.

Metabolic variation during development in culture of Leishmania donovani promastigotes

The genome sequencing of several Leishmania species has provided immense amounts of data and allowed the prediction of the metabolic pathways potentially operating. Subsequent genetic and proteomic studies have identified stage-specific proteins and putative virulence factors but many aspects of the metabolic adaptations of Leishmania remain to be elucidated. In this study, we have used an untargeted metabolomics approach to analyze changes in the metabolite profile as promastigotes of L. donovani develop during in vitro cultures from logarithmic to stationary phase. The results show that the metabolomes of promastigotes on days 3–6 of culture differ significantly from each other, consistent with there being distinct developmental changes. Most notable were the structural changes in glycerophospholipids and increase in the abundance of sphingolipids and glycerolipids as cells progress from logarithmic to stationary phase.

Leishmania infections are considered neglected tropical diseases as the parasites affect millions of people worldwide but there are limited research efforts aimed at obtaining vaccines and new drugs. Leishmania has a digenetic life cycle alternating between promastigote forms, which develop in the sand-fly, the vector of the disease, and an amastigote form, which grows in mammals after being bitten by an infected sand-fly. In vitro studies with the promastigote forms are routinely used to gain insights about the parasite's cell biology. Little is known about how the different promastigotes forms are metabolically adapted to their particular micro-environment in the host or how they are pre-adapted metabolically for infecting a mammal, thus we have undertaken a study of the metabolite profile of L. donovani promastigotes in order to gain an understanding of the changes that occur during promastigote development. The analysis has revealed that the changes in promastigotes' metabolome between days 3 and 6 take place in a progressive manner; however major differences were observed when comparing the promastigotes on days 3 and 6. An increase in lipid abundance as promastigote development occurred was notable and is likely to reflect remodelling of the parasite's surface in readiness for infecting a mammal.

An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data

Background

Nuclear magnetic resonance spectroscopy (NMR) is a powerful technique to reveal and compare quantitative metabolic profiles of biological tissues. However, chemical and physical sample variations make the analysis of the data challenging, and typically require the application of a number of preprocessing steps prior to data interpretation. For example, noise reduction, normalization, baseline correction, peak picking, spectrum alignment and statistical analysis are indispensable components in any NMR analysis pipeline.

Results

We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data.

Conclusions

The workflow performance was evaluated using a previously published dataset. Correlation maps, spectral and grey scale plots show clear improvements in comparison to other methods, and the down-to-earth quantitative analysis works well for the CluPA-aligned spectra. The whole workflow is embedded into a modular and statistically sound framework that is implemented as an R package called "speaq" ("spectrum alignment and quantitation"), which is freely available from http://code.google.com/p/speaq/.

Metabolomic analysis of trypanosomatid protozoa

Metabolomics aims to measure all low molecular weight chemicals within a given system in a manner analogous to transcriptomics, proteomics and genomics. In this review we highlight metabolomics approaches that are currently being applied to the kinetoplastid parasites, Trypanosoma brucei and Leishmania spp. The use of untargeted metabolomics approaches, made possible through advances in mass spectrometry and informatics, and stable isotope labelling has increased our understanding of the metabolism in these organisms beyond the views established using classical biochemical approaches. Set within the context of metabolic networks, predicted using genome-wide reconstructions of metabolism, new hypotheses on how to target aspects of metabolism to design new drugs against these protozoa are emerging.

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided.

Isotopomer profiling of Leishmania mexicana promastigotes reveals important roles for succinate fermentation and aspartate uptake in tricarboxylic acid cycle (TCA) anaplerosis, glutamate synthesis, and growth

Leishmania parasites proliferate within nutritionally complex niches in their sandfly vector and mammalian hosts. However, the extent to which these parasites utilize different carbon sources remains poorly defined. In this study, we have followed the incorporation of various (13)C-labeled carbon sources into the intracellular and secreted metabolites of Leishmania mexicana promastigotes using gas chromatography-mass spectrometry and (13)C NMR. [U-(13)C]Glucose was rapidly incorporated into intermediates in glycolysis, the pentose phosphate pathway, and the cytoplasmic carbohydrate reserve material, mannogen. Enzymes involved in the upper glycolytic pathway are sequestered within glycosomes, and the ATP and NAD(+) consumed by these reactions were primarily regenerated by the fermentation of phosphoenolpyruvate to succinate (glycosomal succinate fermentation). The initiating enzyme in this pathway, phosphoenolpyruvate carboxykinase, was exclusively localized to the glycosome. Although some of the glycosomal succinate was secreted, most of the C4 dicarboxylic acids generated during succinate fermentation were further catabolized in the TCA cycle. A high rate of TCA cycle anaplerosis was further suggested by measurement of [U-(13)C]aspartate and [U-(13)C]alanine uptake and catabolism. TCA cycle anaplerosis is apparently needed to sustain glutamate production under standard culture conditions. Specifically, inhibition of mitochondrial aconitase with sodium fluoroacetate resulted in the rapid depletion of intracellular glutamate pools and growth arrest. Addition of high concentrations of exogenous glutamate alleviated this growth arrest. These findings suggest that glycosomal and mitochondrial metabolism in Leishmania promastigotes is tightly coupled and that, in contrast to the situation in some other trypanosomatid parasites, the TCA cycle has crucial anabolic functions.

Evidence that intracellular stages of Leishmania major utilize amino sugars as a major carbon source

Intracellular parasites, such as Leishmania spp, must acquire suitable carbon sources from the host cell in order to replicate. Here we present evidence that intracellular amastigote stages of Leishmania exploit amino sugars in the phagolysosome of mammalian macrophages as a source of carbon and energy. L. major parasites are capable of using N-acetylglucosamine and glucosamine as primarily carbon sources and contain key enzymes required for conversion of these sugars to fructose-6-phosphate. The last step in this pathway is catalyzed by glucosamine-6-phosphate deaminase (GND), which was targeted to glycosomes via a canonical C-terminal targeting signal when expressed as a GFP fusion protein. Mutant parasites lacking GND were unable to grow in medium containing amino sugars as sole carbohydrate source and rapidly lost viability, concomitant with the hyper-accumulation of hexosamine-phosphates. Expression of native GND, but not a cytosolic form of GND, in Δgnd parasites restored hexosamine-dependent growth, indicating that toxicity is due to depletion of glycosomal pools of ATP. Non-lethal increases in hexosamine phosphate levels in both Δgnd and wild type parasites was associated with a defect in promastigote metacyclogenesis, suggesting that hexosamine phosphate levels may influence parasite differentiation. Promastigote and amastigote stages of the Δgnd mutant were unable to replicate within macrophages and were either completely cleared or exhibited reduced lesion development in highly susceptible Balb/c mice. Our results suggest that hexosamines are a major class of sugars in the macrophage phagolysosome and that catabolism of scavenged amino sugars is required to sustain essential metabolic pathways and prevent hexosamine toxicity.

Protozoan parasites belonging to the genus Leishmania are transmitted by sandfly vectors and cause a number of important diseases in humans. These parasites proliferate within mature lysosome compartments in macrophages and other phagocytic cells in the mammalian host. How intracellular stages of Leishmania survive within this hydrolytic compartment and the extent to which they utilize different carbon sources is poorly defined. Previous studies have suggested that sugar uptake is important for growth, although the nature of these sugars is unknown. In this study we show that Leishmania express all of the enzymes needed to degrade the amino sugars, glucosamine and N-acetylglucosamine. We show that a key enzyme in this pathway is sequestered within modified peroxisomes, or glycosomes, and that this localization is essential for growth on amino sugars and avoidance of amino sugar toxicity. This pathway is also required for parasite proliferation within cultured macrophages and for normal infection of highly susceptible mice. Mutant parasites are either completely eradicated or induce small lesions in Balb/c mice after an extended lag period. These findings suggest that amino sugars generated by the lysosomal breakdown of host glycoconjugates are an important carbon source for intracellular stages of Leishmania, particularly during the early stages of infection.

Towards an unbiased metabolic profiling of protozoan parasites: optimisation of a Leishmania sampling protocol for HILIC-orbitrap analysis

Comparative metabolomics of Leishmania species requires the simultaneous identification and quantification of a large number of intracellular metabolites. Here, we describe the optimisation of a comprehensive metabolite extraction protocol for Leishmania parasites and the subsequent optimisation of the analytical approach, consisting of hydrophilic interaction liquid chromatography coupled to LTQ-orbitrap mass spectrometry. The final optimised protocol starts with a rapid quenching of parasite cells to 0 °C, followed by a triplicate washing step in phosphate-buffered saline. The intracellular metabolome of 4 × 10(7) parasites is then extracted in cold chloroform/methanol/water 20/60/20 (v/v/v) for 1 h at 4 °C, resulting in both cell disruption and comprehensive metabolite dissolution. Our developed metabolomics platform can detect approximately 20% of the predicted Leishmania metabolome in a single experiment in positive and negative ionisation mode.

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.

Central carbon metabolism of Leishmania parasites

Leishmania spp. are sandfly-transmitted protozoa parasites that cause a spectrum of diseases in humans. Many enzymes involved in Leishmania central carbon metabolism differ from their equivalents in the mammalian host and are potential drug targets. In this review we summarize recent advances in our understanding of Leishmania central carbon metabolism, focusing on pathways of carbon utilization that are required for growth and pathogenesis in the mammalian host. While Leishmania central carbon metabolism shares many features in common with other pathogenic trypanosomatids, significant differences are also apparent. Leishmania parasites are also unusual in constitutively expressing most core metabolic pathways throughout their life cycle, a feature that may allow these parasites to exploit a range of different carbon sources (primarily sugars and amino acids) rapidly in both the insect vector and vertebrate host. Indeed, recent gene deletion studies suggest that mammal-infective stages are dependent on multiple carbon sources in vivo. The application of metabolomic approaches, outlined here, are likely to be important in defining aspects of central carbon metabolism that are essential at different stages of mammalian host infection.

The potential of metabolomics for Leishmania research in the post-genomics era

The post-genomics era has provided researchers with access to a new generation of tools for the global characterization and understanding of pathogen diversity. This review provides a critical summary of published Leishmania post-genomic research efforts to date, and discusses the potential impact of the addition of metabolomics to the post-genomic toolbox. Metabolomics aims at understanding biology by comprehensive metabolite profiling. We present an overview of the design and interpretation of metabolomics experiments in the context of Leishmania research. Sample preparation, measurement techniques, and bioinformatics analysis of the generated complex datasets are discussed in detail. To illustrate the concepts and the expected results of metabolomics analyses, we also present an overview of comparative metabolic profiles of drug-sensitive and drug-resistant Leishmania donovani clinical isolates.

The potential of mass spectrometry for the global profiling of parasite metabolomes

The strengths and limitations of existing mass spectrometry methods for metabolite detection and identification are discussed. A brief review is made of the methods available for quenching and extraction of cells or organisms prior to instrumental analysis. The techniques available for carrying out mass spectrometry-based profiling of metabolomes are discussed using the analysis of extracts from trypanosomes to illustrate various points regarding methods of separation and mass spectrometric analysis. The advantages of hydrophilic interaction chromatography (HILIC) for the analysis of polar metabolites are discussed. The challenges of data processing are outlined and illustrated using the example of ThermoFisher's Sieve software. The existing literature on applications of mass spectrometry to the profiling of parasite metabolomes is reviewed.

Challenges in applying chemometrics to LC–MS-based global metabolite profile data

Metabolite profiling can provide insights into the metabolic status of complex living systems through the non-targeted analysis of metabolites in any biological sample. Metabolite profiling is complementary to genomics, transcriptomics and proteomics, and its applications span epidemiology, disease diagnosis, nutrition, pharmaceutical research, and toxicology. Metabolic phenotypes are a reflection of an organism’s environment, lifestyle, diet, gut microfloral composition and are also influenced by genetic factors, with important implications in genome-wide-association studies. Specialized analytical platforms, such as NMR spectroscopy and MS, are required to interrogate such metabolic complexity. The increased sophistication of such techniques has lead to a demand for improved data analysis approaches, including preprocessing and advanced chemometric techniques. This article discusses data generation, preprocessing, multivariate analysis and data interpretation for LC-MS-based metabolite profiling, focusing on challenges encountered and potential solutions.

LeishCyc: a biochemical pathways database for Leishmania major

Background

Leishmania spp. are sandfly transmitted protozoan parasites that cause a spectrum of diseases in more than 12 million people worldwide. Much research is now focusing on how these parasites adapt to the distinct nutrient environments they encounter in the digestive tract of the sandfly vector and the phagolysosome compartment of mammalian macrophages. While data mining and annotation of the genomes of three Leishmania species has provided an initial inventory of predicted metabolic components and associated pathways, resources for integrating this information into metabolic networks and incorporating data from transcript, protein, and metabolite profiling studies is currently lacking. The development of a reliable, expertly curated, and widely available model of Leishmania metabolic networks is required to facilitate systems analysis, as well as discovery and prioritization of new drug targets for this important human pathogen.

Description

The LeishCyc database was initially built from the genome sequence of Leishmania major (v5.2), based on the annotation published by the Wellcome Trust Sanger Institute. LeishCyc was manually curated to remove errors, correct automated predictions, and add information from the literature. The ongoing curation is based on public sources, literature searches, and our own experimental and bioinformatics studies. In a number of instances we have improved on the original genome annotation, and, in some ambiguous cases, collected relevant information from the literature in order to help clarify gene or protein annotation in the future. All genes in LeishCyc are linked to the corresponding entry in GeneDB (Wellcome Trust Sanger Institute).

Conclusion

The LeishCyc database describes Leishmania major genes, gene products, metabolites, their relationships and biochemical organization into metabolic pathways. LeishCyc provides a systematic approach to organizing the evolving information about Leishmania biochemical networks and is a tool for analysis, interpretation, and visualization of Leishmania Omics data (transcriptomics, proteomics, metabolomics) in the context of metabolic pathways. LeishCyc is the first such database for the Trypanosomatidae family, which includes a number of other important human parasites. Flexible query/visualization capabilities are provided by the Pathway Tools software and its Web interface. The LeishCyc database is made freely available over the Internet .

The correspondence problem for metabonomics datasets

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

Global analysis of metabolites in rat and human urine based on gas chromatography/time-of-flight mass spectrometry

Sediment in urine may contain low-molecular-weight compounds that should be included in the analysis. To date, no systematic investigation has addressed this issue. We investigated three primary factors that influence the extraction efficiency of metabolites during preparation of urine samples for metabolomic research: centrifugation, pH, and extraction solvents. Obtained with the use of gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) technique and principal component analysis (PCA), our results indicate that (1) conventional centrifugation causes an apparent loss of some metabolites, indicating that urine samples for metabolomic research should not be centrifuged before procedures are undertaken to recover the metabolites; (2) pH adjustment has a large impact on the recovery of metabolites and is therefore not encouraged; (3) with design of experiment analysis, methanol and water yield the optimal extraction efficiency. Differences between rat and human urine were observed and are discussed. Ninety-nine metabolites identified in rat and human urine are presented. An efficient protocol is proposed for the pretreatment of urine samples.

TagFinder for the quantitative analysis of gas chromatography--mass spectrometry (GC-MS)-based metabolite profiling experiments

MOTIVATION

Typical GC-MS-based metabolite profiling experiments may comprise hundreds of chromatogram files, which each contain up to 1000 mass spectral tags (MSTs). MSTs are the characteristic patterns of approximately 25-250 fragment ions and respective isotopomers, which are generated after gas chromatography (GC) by electron impact ionization (EI) of the separated chemical molecules. These fragment ions are subsequently detected by time-of-flight (TOF) mass spectrometry (MS). MSTs of profiling experiments are typically reported as a list of ions, which are characterized by mass, chromatographic retention index (RI) or retention time (RT), and arbitrary abundance. The first two parameters allow the identification, the later the quantification of the represented chemical compounds. Many software tools have been reported for the pre-processing, the so-called curve resolution and deconvolution, of GC-(EI-TOF)-MS files. Pre-processing tools generate numerical data matrices, which contain all aligned MSTs and samples of an experiment. This process, however, is error prone mainly due to (i) the imprecise RI or RT alignment of MSTs and (ii) the high complexity of biological samples. This complexity causes co-elution of compounds and as a consequence non-selective, in other words impure MSTs. The selection and validation of optimal fragment ions for the specific and selective quantification of simultaneously eluting compounds is, therefore, mandatory. Currently validation is performed in most laboratories under human supervision. So far no software tool supports the non-targeted and user-independent quality assessment of the data matrices prior to statistical analysis. TagFinder may fill this gap.

STRATEGY

TagFinder facilitates the analysis of all fragment ions, which are observed in GC-(EI-TOF)-MS profiling experiments. The non-targeted approach allows the discovery of novel and unexpected compounds. In addition, mass isotopomer resolution is maintained by TagFinder processing. This feature is essential for metabolic flux analyses and highly useful, but not required for metabolite profiling. Whenever possible, TagFinder gives precedence to chemical means of standardization, for example, the use of internal reference compounds for retention time calibration or quantitative standardization. In addition, external standardization is supported for both compound identification and calibration. The workflow of TagFinder comprises, (i) the import of fragment ion data, namely mass, time and arbitrary abundance (intensity), from a chromatography file interchange format or from peak lists provided by other chromatogram pre-processing software, (ii) the annotation of sample information and grouping of samples into classes, (iii) the RI calculation, (iv) the binning of observed fragment ions of equal mass from different chromatograms into RI windows, (v) the combination of these bins, so-called mass tags, into time groups of co-eluting fragment ions, (vi) the test of time groups for intensity correlated mass tags, (vii) the data matrix generation and (viii) the extraction of selective mass tags supported by compound identification. Thus, TagFinder supports both non-targeted fingerprinting analyses and metabolite targeted profiling.

AVAILABILITY

Exemplary TagFinder workspaces and test data sets are made available upon request to the contact authors. TagFinder is made freely available for academic use from http://www-en.mpimp-golm.mpg.de/03-research/researchGroups/01-dept1/Root_Metabolism/smp/TagFinder/index.html.

A dynamic programming approach for the alignment of signal peaks in multiple gas chromatography-mass spectrometry experiments

Background

Gas chromatography-mass spectrometry (GC-MS) is a robust platform for the profiling of certain classes of small molecules in biological samples. When multiple samples are profiled, including replicates of the same sample and/or different sample states, one needs to account for retention time drifts between experiments. This can be achieved either by the alignment of chromatographic profiles prior to peak detection, or by matching signal peaks after they have been extracted from chromatogram data matrices. Automated retention time correction is particularly important in non-targeted profiling studies.

Results

A new approach for matching signal peaks based on dynamic programming is presented. The proposed approach relies on both peak retention times and mass spectra. The alignment of more than two peak lists involves three steps: (1) all possible pairs of peak lists are aligned, and similarity of each pair of peak lists is estimated; (2) the guide tree is built based on the similarity between the peak lists; (3) peak lists are progressively aligned starting with the two most similar peak lists, following the guide tree until all peak lists are exhausted. When two or more experiments are performed on different sample states and each consisting of multiple replicates, peak lists within each set of replicate experiments are aligned first (within-state alignment), and subsequently the resulting alignments are aligned themselves (between-state alignment). When more than two sets of replicate experiments are present, the between-state alignment also employs the guide tree. We demonstrate the usefulness of this approach on GC-MS metabolic profiling experiments acquired on wild-type and mutant Leishmania mexicana parasites.

Conclusion

We propose a progressive method to match signal peaks across multiple GC-MS experiments based on dynamic programming. A sensitive peak similarity function is proposed to balance peak retention time and peak mass spectra similarities. This approach can produce the optimal alignment between an arbitrary number of peak lists, and models explicitly within-state and between-state peak alignment. The accuracy of the proposed method was close to the accuracy of manually-curated peak matching, which required tens of man-hours for the analyzed data sets. The proposed approach may offer significant advantages for processing of high-throughput metabolomics data, especially when large numbers of experimental replicates and multiple sample states are analyzed.

Data processing for mass spectrometry-based metabolomics

Modern analytical technologies afford comprehensive and quantitative investigation of a multitude of different metabolites. Typical metabolomic experiments can therefore produce large amounts of data. Handling such complex datasets is an important step that has big impact on extent and quality at which the metabolite identification and quantification can be made, and thus on the ultimate biological interpretation of results. Increasing interest in metabolomics thus led to resurgence of interest in related data processing. A wide variety of methods and software tools have been developed for metabolomics during recent years, and this trend is likely to continue. In this paper we overview the key steps of metabolomic data processing and focus on reviewing recent literature related to this topic, particularly on methods for handling data from liquid chromatography mass spectrometry (LC-MS) experiments.

A Multivariate Screening Strategy for Investigating Metabolic Effects of Strenuous Physical Exercise in Human Serum

A novel hypothesis-free multivariate screening methodology for the study of human exercise metabolism in blood serum is presented. Serum gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) data was processed using hierarchical multivariate curve resolution (H-MCR), and orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to model the systematic variation related to the acute effect of strenuous exercise. Potential metabolic biomarkers were identified using data base comparisons. Extensive validation was carried out including predictive H-MCR, 7-fold full cross-validation, and predictions for the OPLS-DA model, variable permutation for highlighting interesting metabolites, and pairwise t tests for examining the significance of metabolites. The concentration changes of potential biomarkers were verified in the raw GC/TOFMS data. In total, 420 potential metabolites were resolved in the serum samples. On the basis of the relative concentrations of the 420 resolved metabolites, a valid multivariate model for the difference between pre- and post-exercise subjects was obtained. A total of 34 metabolites were highlighted as potential biomarkers, all statistically significant (p < 8.1E-05). As an example, two potential markers were identified as glycerol and asparagine. The concentration changes for these two metabolites were also verified in the raw GC/TOFMS data. The strategy was shown to facilitate interpretation and validation of metabolic interactions in human serum as well as revealing the identity of potential markers for known or novel mechanisms of human exercise physiology. The multivariate way of addressing metabolism studies can help to increase the understanding of the integrative biology behind, as well as unravel new mechanistic explanations in relation to, exercise physiology.

Metabolism of Leishmania: proven and predicted

The complete analysis of the genomes of three major trypanosomatid parasites has facilitated comparison of the metabolic capabilities of each, as predicted from gene sequences. Not surprisingly, there are differences but is it possible to correlate these with the lives of the parasites themselves and make further predictions of the meaning and physiological importance of the apparently parasite-specific metabolism? In this article, we relate gene predictions with the results from experimental studies. We also speculate on the key metabolic adaptations of Leishmania and reasons why it differs from Trypanosoma brucei and Trypanosoma cruzi.