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

The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites

The identification of unknown chemicals has emerged as a significant issue in untargeted metabolome analysis owing to the limited availability of purified standards for identification; this is a major bottleneck for the accumulation of reusable metabolome data in systems biology. Public resources for discovering and prioritizing the unknowns that should be subject to practical identification, as well as further detailed study of spending costs and the risks of misprediction, are lacking. As such a resource, we released databases, Food-, Plant- and Thing-Metabolome Repository (http://metabolites.in/foods, http://metabolites.in/plants, and http://metabolites.in/things, referred to as XMRs) in which the sample-specific localization of unknowns detected by liquid chromatography-mass spectrometry in a wide variety of samples can be examined, helping to discover and prioritize the unknowns. A set of application programming interfaces for the XMRs facilitates the use of metabolome data for large-scale analysis and data mining. Several applications of XMRs, including integrated metabolome and genome analyses, are presented. Expanding the concept of XMRs will accelerate the identification of unknowns and increase the discovery of new knowledge.

Using Organic Contaminants to Constrain the Terrestrial Journey of the Martian Meteorite Lafayette

A key part of the search for extraterrestrial life is the detection of organic molecules since these molecules form the basis of all living things on Earth. Instrument suites such as SHERLOC (Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals) onboard the NASA Perseverance rover and the Mars Organic Molecule Analyzer onboard the future ExoMars Rosalind Franklin rover are designed to detect organic molecules at the martian surface. However, size, mass, and power limitations mean that these instrument suites cannot yet match the instrumental capabilities available in Earth-based laboratories. Until Mars Sample Return, the only martian samples available for study on Earth are martian meteorites. This is a collection of largely basaltic igneous rocks that have been exposed to varying degrees of terrestrial contamination. The low organic molecule abundance within igneous rocks and the expectation of terrestrial contamination make the identification of martian organics within these meteorites highly challenging. The Lafayette martian meteorite exhibits little evidence of terrestrial weathering, potentially making it a good candidate for the detection of martian organics despite uncertainties surrounding its fall history. In this study, we used ultrapure solvents to extract organic matter from triplicate samples of Lafayette and analyzed these extracts via hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS). Two hundred twenty-four metabolites (organic molecules) were detected in Lafayette at concentrations more than twice those present in the procedural blanks. In addition, a large number of plant-derived metabolites were putatively identified, the presence of which supports the unconfirmed report that Lafayette fell in a semirural location in Indiana. Remarkably, the putative identification of the mycotoxin deoxynivalenol (or vomitoxin), alongside the report that the collector was possibly a student at Purdue University, can be used to identify the most likely fall year as 1919.

Depletion of a Toxoplasma porin leads to defects in mitochondrial morphology and contacts with the endoplasmic reticulum

The voltage-dependent anion channel (VDAC) is a ubiquitous channel in the outer membrane of the mitochondrion with multiple roles in protein, metabolite and small molecule transport. In mammalian cells, VDAC protein, as part of a larger complex including the inositol triphosphate receptor, has been shown to have a role in mediating contacts between the mitochondria and endoplasmic reticulum (ER). We identify VDAC of the pathogenic apicomplexan Toxoplasma gondii and demonstrate its importance for parasite growth. We show that VDAC is involved in protein import and metabolite transfer to mitochondria. Further, depletion of VDAC resulted in significant morphological changes in the mitochondrion and ER, suggesting a role in mediating contacts between these organelles in T. gondii.

This article has an associated First Person interview with the first author of the paper.

Summary: Depletion of the Toxoplasma voltage-dependent anion channel highlights the importance of endoplasmic reticulum–mitochondria membrane contact sites in maintaining organelle morphology.

TOMATOMET: A metabolome database consists of 7118 accurate mass values detected in mature fruits of 25 tomato cultivars

The total number of low-molecular-weight compounds in the plant kingdom, most of which are secondary metabolites, is hypothesized to be over one million, although only a limited number of plant compounds have been characterized. Untargeted analysis, especially using mass spectrometry (MS), has been useful for understanding the plant metabolome; however, due to the limited availability of authentic compounds for MS-based identification, the identities of most of the ion peaks detected by MS remain unknown. Accurate mass values of peaks obtained by high accuracy mass measurement and, if available, MS/MS fragmentation patterns provide abundant annotation for each peak. Here, we carried out an untargeted analysis of compounds in the mature fruit of 25 tomato cultivars using liquid chromatography-Orbitrap MS for accurate mass measurement, followed by manual curation to construct the metabolome database TOMATOMET (http://metabolites.in/tomato-fruits/). The database contains 7,118 peaks with accurate mass values, in which 1,577 ion peaks are annotated as members of a chemical group. Remarkably, 71% of the mass values are not found in the accurate masses detected previously in Arabidopsis thaliana, Medicago truncatula or Jatropha curcas, indicating significant chemical diversity among plant species that remains to be solved. Interestingly, substantial chemical diversity exists also among tomato cultivars, indicating that chemical profiling from distinct cultivars contributes towards understanding the metabolome, even in a single organ of a species, and can prioritize some desirable metabolic targets for further applications such as breeding.

Plasmodium falciparum LipB mutants display altered redox and carbon metabolism in asexual stages and cannot complete sporogony in Anopheles mosquitoes

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Apicoplast LipB deletion leads to changed antioxidant expression that precedes and coincides with accelerated differentiation.

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3D7 Plasmodium exhibits changes in glycolysis and tricarboxylic acid cycle activity after deletion of apicoplast LipB.

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When LipB is deleted from NF54 Plasmodium, the resulting parasites cannot complete their development in mosquitoes.

Malaria is still one of the most important global infectious diseases. Emergence of drug resistance and a shortage of new efficient antimalarials continue to hamper a malaria eradication agenda. Malaria parasites are highly sensitive to changes in the redox environment. Understanding the mechanisms regulating parasite redox could contribute to the design of new drugs. Malaria parasites have a complex network of redox regulatory systems housed in their cytosol, in their mitochondrion and in their plastid (apicoplast). While the roles of enzymes of the thioredoxin and glutathione pathways in parasite survival have been explored, the antioxidant role of α-lipoic acid (LA) produced in the apicoplast has not been tested. To take a first step in teasing a putative role of LA in redox regulation, we analysed a mutant Plasmodium falciparum (3D7 strain) lacking the apicoplast lipoic acid protein ligase B (lipB) known to be depleted of LA. Our results showed a change in expression of redox regulators in the apicoplast and the cytosol. We further detected a change in parasite central carbon metabolism, with lipB deletion resulting in changes to glycolysis and tricarboxylic acid cycle activity. Further, in another Plasmodium cell line (NF54), deletion of lipB impacted development in the mosquito, preventing the detection of infectious sporozoite stages. While it is not clear at this point if the observed phenotypes are linked, these findings flag LA biosynthesis as an important subject for further study in the context of redox regulation in asexual stages, and point to LipB as a potential target for the development of new transmission drugs.

Representing the Metabolome with High Fidelity: Range and Response as Quality Control Factors in LC-MS-Based Global Profiling

Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.

mzMLb: A Future-Proof Raw Mass Spectrometry Data Format Based on Standards-Compliant mzML and Optimized for Speed and Storage Requirements

With ever-increasing amounts of data produced by mass spectrometry (MS) proteomics and metabolomics, and the sheer volume of samples now analyzed, the need for a common open format possessing both file size efficiency and faster read/write speeds has become paramount to drive the next generation of data analysis pipelines. The Proteomics Standards Initiative (PSI) has established a clear and precise extensible markup language (XML) representation for data interchange, mzML, receiving substantial uptake; nevertheless, storage and file access efficiency has not been the main focus. We propose an HDF5 file format "mzMLb" that is optimized for both read/write speed and storage of the raw mass spectrometry data. We provide an extensive validation of the write speed, random read speed, and storage size, demonstrating a flexible format that with or without compression is faster than all existing approaches in virtually all cases, while with compression is comparable in size to proprietary vendor file formats. Since our approach uniquely preserves the XML encoding of the metadata, the format implicitly supports future versions of mzML and is straightforward to implement: mzMLb's design adheres to both HDF5 and NetCDF4 standard implementations, which allows it to be easily utilized by third parties due to their widespread programming language support. A reference implementation within the established ProteoWizard toolkit is provided.

Fine-tuning autophagy maximises lifespan and is associated with changes in mitochondrial gene expression in Drosophila

Increased cellular degradation by autophagy is a feature of many interventions that delay ageing. We report here that increased autophagy is necessary for reduced insulin-like signalling (IIS) to extend lifespan in Drosophila and is sufficient on its own to increase lifespan. We first established that the well-characterised lifespan extension associated with deletion of the insulin receptor substrate chico was completely abrogated by downregulation of the essential autophagy gene Atg5. We next directly induced autophagy by over-expressing the major autophagy kinase Atg1 and found that a mild increase in autophagy extended lifespan. Interestingly, strong Atg1 up-regulation was detrimental to lifespan. Transcriptomic and metabolomic approaches identified specific signatures mediated by varying levels of autophagy in flies. Transcriptional upregulation of mitochondrial-related genes was the signature most specifically associated with mild Atg1 upregulation and extended lifespan, whereas short-lived flies, possessing strong Atg1 overexpression, showed reduced mitochondrial metabolism and up-regulated immune system pathways. Increased proteasomal activity and reduced triacylglycerol levels were features shared by both moderate and high Atg1 overexpression conditions. These contrasting effects of autophagy on ageing and differential metabolic profiles highlight the importance of fine-tuning autophagy levels to achieve optimal healthspan and disease prevention.

CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network

Motivation

The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample.

Results

Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools.

Availability and implementation

https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS.

Supplementary information

Supplementary data are available at Bioinformatics online.

Tissue-Specific Sample Dilution: An Important Parameter to Optimise Prior to Untargeted LC-MS Metabolomics

When developing a sample preparation protocol for LC–MS untargeted metabolomics of a new sample matrix unfamiliar to the laboratory, selection of a suitable injection concentration is rarely described. Here we developed a simple workflow to address this issue prior to untargeted LC–MS metabolomics using pig adipose tissue and liver tissue. Bi-phasic extraction was performed to enable simultaneous optimisation of parameters for analysis of both lipids and polar extracts. A series of diluted pooled samples were analysed by LC–MS and used to evaluate signal linearity. Suitable injected concentrations were determined based on both the number of reproducible features and linear features. With our laboratory settings, the optimum concentrations of tissue mass to reconstitution solvent of liver and adipose tissue lipid fractions were found to be 125 mg/mL and 7.81 mg/mL respectively, producing 2811 (ESI+) and 4326 (ESI−) linear features from liver, 698 (ESI+) and 498 (ESI−) linear features from adipose tissue. For analysis of the polar fraction of both tissues, 250 mg/mL was suitable, producing 403 (ESI+) and 235 (ESI−) linear features from liver, 114 (ESI+) and 108 (ESI−) linear features from adipose tissue. Incorrect reconstitution volumes resulted in either severe overloading or poor linearity in our lipid data, while too dilute polar fractions resulted in a low number of reproducible features (<50) compared to hundreds of reproducible features from the optimum concentration used. Our study highlights on multiple matrices and multiple extract and chromatography types, the critical importance of determining a suitable injected concentration prior to untargeted LC–MS metabolomics, with the described workflow applicable to any matrix and LC–MS system.

Integrated genomic and metabolomic profiling of ISC1, an emerging Leishmania donovani population in the Indian subcontinent

Leishmania donovani is the responsible agent for visceral leishmaniasis (VL) in the Indian subcontinent (ISC). The disease is lethal without treatment and causes 0.2 to 0.4 million cases each year. Recently, reports of VL in Nepalese hilly districts have increased as well as VL cases caused by L. donovani from the ISC1 genetic group, a new and emerging genotype. In this study, we perform for the first time an integrated, untargeted genomics and metabolomics approach to characterize ISC1, in comparison with the Core Group (CG), main population that drove the most recent outbreak of VL in the ISC. We show that the ISC1 population is very different from the CG, both at genome and metabolome levels. The genomic differences include SNPs, CNV and small indels in genes coding for known virulence factors, immunogens and surface proteins. Both genomic and metabolic approaches highlighted dissimilarities related to membrane lipids, the nucleotide salvage pathway and the urea cycle in ISC1 versus CG. Many of these pathways and molecules are important for the interaction with the host/extracellular environment. Altogether, our data predict major functional differences in ISC1 versus CG parasites, including virulence. Therefore, particular attention is required to monitor the fate of this emerging ISC1 population in the ISC, especially in a post-VL elimination context.

A Synthetic Community System for Probing Microbial Interactions Driven by Exometabolites

Understanding microbial interactions is a fundamental objective in microbiology and ecology. The synthetic community system described here can set into motion a range of research to investigate how the diversity of a microbiome and interactions among its members impact its function, where function can be measured as exometabolites. The system allows for community exometabolite profiling to be coupled with genome mining, transcript analysis, and measurements of member productivity and population size. It can also facilitate discovery of natural products that are only produced within microbial consortia. Thus, this synthetic community system has utility to address fundamental questions about a diversity of possible microbial interactions that occur in both natural and engineered ecosystems.

Though most microorganisms live within a community, we have modest knowledge about microbial interactions and their implications for community properties and ecosystem functions. To advance understanding of microbial interactions, we describe a straightforward synthetic community system that can be used to interrogate exometabolite interactions among microorganisms. The filter plate system (also known as the Transwell system) physically separates microbial populations, but allows for chemical interactions via a shared medium reservoir. Exometabolites, including small molecules, extracellular enzymes, and antibiotics, are assayed from the reservoir using sensitive mass spectrometry. Community member outcomes, such as growth, productivity, and gene regulation, can be determined using flow cytometry, biomass measurements, and transcript analyses, respectively. The synthetic community design allows for determination of the consequences of microbiome diversity for emergent community properties and for functional changes over time or after perturbation. Because it is versatile, scalable, and accessible, this synthetic community system has the potential to practically advance knowledge of microbial interactions that occur within both natural and artificial communities.

IMPORTANCE Understanding microbial interactions is a fundamental objective in microbiology and ecology. The synthetic community system described here can set into motion a range of research to investigate how the diversity of a microbiome and interactions among its members impact its function, where function can be measured as exometabolites. The system allows for community exometabolite profiling to be coupled with genome mining, transcript analysis, and measurements of member productivity and population size. It can also facilitate discovery of natural products that are only produced within microbial consortia. Thus, this synthetic community system has utility to address fundamental questions about a diversity of possible microbial interactions that occur in both natural and engineered ecosystems.

Author Video: An author video summary of this article is available.

Discovery and resupply of pharmacologically active plant-derived natural products: A review

Medicinal plants have historically proven their value as a source of molecules with therapeutic potential, and nowadays still represent an important pool for the identification of novel drug leads. In the past decades, pharmaceutical industry focused mainly on libraries of synthetic compounds as drug discovery source. They are comparably easy to produce and resupply, and demonstrate good compatibility with established high throughput screening (HTS) platforms. However, at the same time there has been a declining trend in the number of new drugs reaching the market, raising renewed scientific interest in drug discovery from natural sources, despite of its known challenges. In this survey, a brief outline of historical development is provided together with a comprehensive overview of used approaches and recent developments relevant to plant-derived natural product drug discovery. Associated challenges and major strengths of natural product-based drug discovery are critically discussed. A snapshot of the advanced plant-derived natural products that are currently in actively recruiting clinical trials is also presented. Importantly, the transition of a natural compound from a "screening hit" through a "drug lead" to a "marketed drug" is associated with increasingly challenging demands for compound amount, which often cannot be met by re-isolation from the respective plant sources. In this regard, existing alternatives for resupply are also discussed, including different biotechnology approaches and total organic synthesis. While the intrinsic complexity of natural product-based drug discovery necessitates highly integrated interdisciplinary approaches, the reviewed scientific developments, recent technological advances, and research trends clearly indicate that natural products will be among the most important sources of new drugs also in the future.

Missing value imputation strategies for metabolomics data

The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k-means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a "gray area" and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k-means nearest neighbor and the best approximation of positioning real zeros.

Metabolomic Analyses of Leishmania Reveal Multiple Species Differences and Large Differences in Amino Acid Metabolism

Comparative genomic analyses of Leishmania species have revealed relatively minor heterogeneity amongst recognised housekeeping genes and yet the species cause distinct infections and pathogenesis in their mammalian hosts. To gain greater information on the biochemical variation between species, and insights into possible metabolic mechanisms underpinning visceral and cutaneous leishmaniasis, we have undertaken in this study a comparative analysis of the metabolomes of promastigotes of L. donovani, L. major and L. mexicana. The analysis revealed 64 metabolites with confirmed identity differing 3-fold or more between the cell extracts of species, with 161 putatively identified metabolites differing similarly. Analysis of the media from cultures revealed an at least 3-fold difference in use or excretion of 43 metabolites of confirmed identity and 87 putatively identified metabolites that differed to a similar extent. Strikingly large differences were detected in their extent of amino acid use and metabolism, especially for tryptophan, aspartate, arginine and proline. Major pathways of tryptophan and arginine catabolism were shown to be to indole-3-lactate and arginic acid, respectively, which were excreted. The data presented provide clear evidence on the value of global metabolomic analyses in detecting species-specific metabolic features, thus application of this technology should be a major contributor to gaining greater understanding of how pathogens are adapted to infecting their hosts.

Multiplexed separations for biomarker discovery in metabolomics: Elucidating adaptive responses to exercise training

High efficiency separations are needed to enhance selectivity, mass spectral quality, and quantitative performance in metabolomic studies. However, low sample throughput and complicated data preprocessing remain major bottlenecks to biomarker discovery. We introduce an accelerated data workflow to identify plasma metabolite signatures of exercise responsiveness when using multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS). This multiplexed separation platform takes advantage of customizable serial injections to enhance sample throughput and data fidelity based on temporally resolved ion signals derived from seven different sample segments analyzed within a single run. MSI-CE-MS was applied to explore the adaptive metabolic responses of a cohort of overweight/obese women (BMI > 25, n = 9) performing a 6-wk high-intensity interval training intervention using a repeated measures/cross-over study design. Venous blood samples were collected from each subject at three time intervals (baseline, postexercise, recovery) in their naïve and trained states while completing standardized cycling trials at the same absolute workload. Complementary statistical methods were used to classify dynamic changes in plasma metabolism associated with strenuous exercise and training status. Positive adaptations to exercise were associated with training-induced upregulation in plasma l-carnitine at rest due to improved muscle oxidative capacity, and greater antioxidant capacity as reflected by lower circulating glutathionyl-l-cysteine mixed disulfide. Attenuation in plasma hypoxanthine and higher O-acetyl-l-carnitine levels postexercise also indicated lower energetic stress for trained women.

A new paradigm for known metabolite identification in metabonomics/metabolomics: metabolite identification efficiency

A new paradigm is proposed for assessing confidence in the identification of known metabolites in metabonomics studies using NMR spectroscopy approaches. This new paradigm is based upon the analysis of the amount of metabolite identification information retrieved from NMR spectra relative to the molecular size of the metabolite. Several new indices are proposed including: metabolite identification efficiency (MIE) and metabolite identification carbon efficiency (MICE), both of which can be easily calculated. These indices, together with some guidelines, can be used to provide a better indication of known metabolite identification confidence in metabonomics studies than existing methods. Since known metabolite identification in untargeted metabonomics studies is one of the key bottlenecks facing the science currently, it is hoped that these concepts based on molecular spectroscopic informatics, will find utility in the field.

Development of a GC/Quadrupole-Orbitrap mass spectrometer, part II: new approaches for discovery metabolomics

Identification of unknown peaks in gas chromatography/mass spectrometry (GC/MS)-based discovery metabolomics is challenging, and remains necessary to permit discovery of novel or unexpected metabolites that may elucidate disease processes and/or further our understanding of how genotypes relate to phenotypes. Here, we introduce two new technologies and an analytical workflow that can facilitate the identification of unknown peaks. First, we report on a GC/Quadrupole-Orbitrap mass spectrometer that provides high mass accuracy, high resolution, and high sensitivity analyte detection. Second, with an "intelligent" data-dependent algorithm, termed molecular-ion directed acquisition (MIDA), we maximize the information content generated from unsupervised tandem MS (MS/MS) and selected ion monitoring (SIM) by directing the MS to target the ions of greatest information content, that is, the most-intact ionic species. We combine these technologies with (13)C- and (15)N-metabolic labeling, multiple derivatization and ionization types, and heuristic filtering of candidate elemental compositions to achieve (1) MS/MS spectra of nearly all intact ion species for structural elucidation, (2) knowledge of carbon and nitrogen atom content for every ion in MS and MS/MS spectra, (3) relative quantification between alternatively labeled samples, and (4) unambiguous annotation of elemental composition.

Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks

The rapid emergence of metabolomics has enabled system-wide measurements of metabolites in various organisms. However, advances in the mechanistic understanding of metabolic networks remain limited, as most metabolomics studies cannot routinely provide accurate metabolite identification, absolute quantification and flux measurement. Stable isotope labeling offers opportunities to overcome these limitations. Here we describe some current approaches to stable isotope-labeled metabolomics and provide examples of the significant impact that these studies have had on our understanding of cellular metabolism. Furthermore, we discuss recently developed software solutions for the analysis of stable isotope-labeled metabolomics data and propose the bioinformatics solutions that will pave the way for the broader application and optimal interpretation of system-scale labeling studies in metabolomics.

MassCascade: Visual Programming for LC-MS Data Processing in Metabolomics

Liquid chromatography coupled to mass spectrometry (LC-MS) is commonly applied to investigate the small molecule complement of organisms. Several software tools are typically joined in custom pipelines to semi-automatically process and analyse the resulting data. General workflow environments like the Konstanz Information Miner (KNIME) offer the potential of an all-in-one solution to process LC-MS data by allowing easy integration of different tools and scripts. We describe MassCascade and its workflow plug-in for processing LC-MS data. The Java library integrates frequently used algorithms in a modular fashion, thus enabling it to serve as back-end for graphical front-ends. The functions available in MassCascade have been encapsulated in a plug-in for the workflow environment KNIME, allowing combined use with e.g. statistical workflow nodes from other providers and making the tool intuitive to use without knowledge of programming. The design of the software guarantees a high level of modularity where processing functions can be quickly replaced or concatenated. MassCascade is an open-source library for LC-MS data processing in metabolomics. It embraces the concept of visual programming through its KNIME plug-in, simplifying the process of building complex workflows. The library was validated using open data.

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.

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 Sb(III) drug pressure.

Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experiments

Combining liquid chromatography-mass spectrometry (LC-MS)-based metabolomics experiments that were collected over a long period of time remains problematic due to systematic variability between LC-MS measurements. Until now, most normalization methods for LC-MS data are model-driven, based on internal standards or intermediate quality control runs, where an external model is extrapolated to the dataset of interest. In the first part of this article, we evaluate several existing data-driven normalization approaches on LC-MS metabolomics experiments, which do not require the use of internal standards. According to variability measures, each normalization method performs relatively well, showing that the use of any normalization method will greatly improve data-analysis originating from multiple experimental runs. In the second part, we apply cyclic-Loess normalization to a Leishmania sample. This normalization method allows the removal of systematic variability between two measurement blocks over time and maintains the differential metabolites. In conclusion, normalization allows for pooling datasets from different measurement blocks over time and increases the statistical power of the analysis, hence paving the way to increase the scale of LC-MS metabolomics experiments. From our investigation, we recommend data-driven normalization methods over model-driven normalization methods, if only a few internal standards were used. Moreover, data-driven normalization methods are the best option to normalize datasets from untargeted LC-MS experiments.

Determination of antiprotozoal drug mechanisms by metabolomics approaches

The discovery, development and optimal utilization of pharmaceuticals can be greatly enhanced by knowledge of their modes of action. However, many drugs currently on the market act by unknown mechanisms. Untargeted metabolomics offers the potential to discover modes of action for drugs that perturb cellular metabolism. Development of high resolution LC-MS methods and improved data analysis software now allows rapid detection of drug-induced changes to cellular metabolism in an untargeted manner. Several studies have demonstrated the ability of untargeted metabolomics to provide unbiased target discovery for antimicrobial drugs, in particular for antiprotozoal agents. Furthermore, the utilization of targeted metabolomics techniques has enabled validation of existing hypotheses regarding antiprotozoal drug mechanisms. Metabolomics approaches are likely to assist with optimization of new drug candidates by identification of drug targets, and by allowing detailed characterization of modes of action and resistance of existing and novel antiprotozoal drugs.

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.

mzMatch-ISO: an R tool for the annotation and relative quantification of isotope-labelled mass spectrometry data

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 (13)C-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.