Standard reporting requirements for biological samples in metabolomics experiments: microbial and in vitro biology experiments

Metabolomic biomarkers of multiple myeloma: A systematic review

Multiple myeloma (MM) is an incurable malignancy of clonal plasma cells. Various diagnostic methods are used in parallel to accurately determine stage and severity of the disease. Identifying a biomarker or a panel of biomarkers could enhance the quality of medical care that patients receive by adopting a more personalized approach. Metabolomics utilizes high-throughput analytical platforms to examine the levels and quantities of biochemical compounds in biosamples. The aim of this review was to conduct a systematic literature search for potential metabolic biomarkers that may aid in the diagnosis and prognosis of MM. The review was conducted in accordance with PRISMA recommendations and was registered in PROSPERO. The systematic search was performed in PubMed, CINAHL, SciFinder, Scopus, The Cochrane Library and Google Scholar. Studies were limited to those involving people with clinically diagnosed MM and healthy controls as comparators. Articles had to be published in English and had no restrictions on publication date or sample type. The quality of articles was assessed according to QUADOMICS criteria. A total of 709 articles were collected during the literature search. Of these, 436 were excluded based on their abstract, with 26 more removed after a thorough review of the full text. Finally, 16 articles were deemed relevant and were subjected to further analysis of their data. A number of promising candidate biomarkers was discovered. Follow-up studies with large sample sizes are needed to determine their suitability for clinical applications.

Non-targeted metabolomics and associations with per- and polyfluoroalkyl substances (PFAS) exposure in humans: A scoping review

OBJECTIVE

To summarize the application of non-targeted metabolomics in epidemiological studies that assessed metabolite and metabolic pathway alterations associated with per- and polyfluoroalkyl substances (PFAS) exposure.

RECENT FINDINGS

Eleven human studies published before April 1st, 2021 were identified through database searches (PubMed, Dimensions, Web of Science Core Collection, Embase, Scopus), and citation chaining (Citationchaser). The sample sizes of these studies ranged from 40 to 965, involving children and adolescents (n = 3), non-pregnant adults (n = 5), or pregnant women (n = 3). High-resolution liquid chromatography-mass spectrometry was the primary analytical platform to measure both PFAS and metabolome. PFAS were measured in either plasma (n = 6) or serum (n = 5), while metabolomic profiles were assessed using plasma (n = 6), serum (n = 4), or urine (n = 1). Four types of PFAS (perfluorooctane sulfonate(n = 11), perfluorooctanoic acid (n = 10), perfluorohexane sulfonate (n = 9), perfluorononanoic acid (n = 5)) and PFAS mixtures (n = 7) were the most studied. We found that alterations to tryptophan metabolism and the urea cycle were most reported PFAS-associated metabolomic signatures. Numerous lipid metabolites were also suggested to be associated with PFAS exposure, especially key metabolites in glycerophospholipid metabolism which is critical for biological membrane functions, and fatty acids and carnitines which are relevant to the energy supply pathway of fatty acid oxidation. Other important metabolome changes reported included the tricarboxylic acid (TCA) cycle regarding energy generation, and purine and pyrimidine metabolism in cellular energy systems.

CONCLUSIONS

There is growing interest in using non-targeted metabolomics to study the human physiological changes associated with PFAS exposure. Multiple PFAS were reported to be associated with alterations in amino acid and lipid metabolism, but these results are driven by one predominant type of pathway analysis thus require further confirmation. Standardizing research methods and reporting are recommended to facilitate result comparison. Future studies should consider potential differences in study methodology, use of prospective design, and influence from confounding bias and measurement errors.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

High-resolution metabolomics of exposure to tobacco smoke during pregnancy and adverse birth outcomes in the Atlanta African American maternal-child cohort

Exposure to tobacco smoke during pregnancy has been associated with a series of adverse reproductive outcomes; however, the underlying molecular mechanisms are not well-established. We conducted an untargeted metabolome-wide association study to identify the metabolic perturbations and molecular mechanisms underlying the association between cotinine, a widely used biomarker of tobacco exposure, and adverse birth outcomes. We collected early and late pregnancy urine samples for cotinine measurement and serum samples for high-resolution metabolomics (HRM) profiling from 105 pregnant women from the Atlanta African American Maternal-Child cohort (2014-2016). Maternal metabolome perturbations mediating prenatal tobacco smoke exposure and adverse birth outcomes were assessed by an untargeted HRM workflow using generalized linear models, followed by pathway enrichment analysis and chemical annotation, with a meet-in-the-middle approach. The median maternal urinary cotinine concentrations were 5.93 μg/g creatinine and 3.69 μg/g creatinine in early and late pregnancy, respectively. In total, 16,481 and 13,043 metabolic features were identified in serum samples at each visit from positive and negative electrospray ionization modes, respectively. Twelve metabolic pathways were found to be associated with both cotinine concentrations and adverse birth outcomes during early and late pregnancy, including tryptophan, histidine, urea cycle, arginine, and proline metabolism. We confirmed 47 metabolites associated with cotinine levels, preterm birth, and shorter gestational age, including glutamate, serine, choline, and taurine, which are closely involved in endogenous inflammation, vascular reactivity, and lipid peroxidation processes. The metabolic perturbations associated with cotinine levels were related to inflammation, oxidative stress, placental vascularization, and insulin action, which could contribute to shorter gestations. The findings will support the further understanding of potential internal responses in association with tobacco smoke exposures, especially among African American women who are disproportionately exposed to high tobacco smoke and experience higher rates of adverse birth outcomes.

A urine and serum metabolomics study of gastroesophageal reflux disease in TCM syndrome differentiation using UPLC-Q-TOF/MS

Gastroesophageal reflux disease (GERD) is a common, chronic and complex upper gastrointestinal disease. In Traditional Chinese medicine (TCM) theory, GERD is classified into two main types: stagnant heat of liver and stomach (SHLS) and deficient cold of spleen and stomach (DCSS). The discovery and evaluation of potential biomarkers for different syndrome types of GERD may contribute to comprehend specific molecular mechanism and identify new targets for diagnosis and appropriate management. In our study, 60 subjects including 40 GERD patients (20 SHLS and 20 DCSS) and 20 healthy controls were recruited, and the serum and urine metabolic profiles from untargeted liquid chromatography coupled to mass spectrometry (LC-MS) metabolomics approach were obtained. Finally 38 biomarkers associated with disease were identified and 9 metabolic pathways were enriched. The most enriched pathways were amino acid metabolism, steroid hormone biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism and TCA cycle. According to the area under curve (AUC) value, we propose a cohort of three metabolites from urine and serum samples as promising biomarkers for TCM syndrome differentiation of GERD, which are prolylhydroxyproline, glycitein-4'-O-glucuronide, capsianoside I in urine and neuAcalpha2-3Galbeta-Cer (d18:1/16:0), sphinganine, arachidonic acid in serum. The cumulative AUC value of merged biomarkers in urine and serum was 0.979 (95%CI 0.927-1) and 0.842 (95%CI 0.704-0.980), respectively. The results indicated that LC-MS based metabolomic profiling method might be an effective and promising tool on further pathogenesis discovering of GERD. The findings provided new strategy for the diagnosis of GERD TCM syndrome differentiation in clinic.

Tick-Tock Consider the Clock: The Influence of Circadian and External Cycles on Time of Day Variation in the Human Metabolome-A Review

The past decade has seen a large influx of work investigating time of day variation in different human biofluid and tissue metabolomes. The driver of this daily variation can be endogenous circadian rhythms driven by the central and/or peripheral clocks, or exogenous diurnal rhythms driven by behavioural and environmental cycles, which manifest as regular 24 h cycles of metabolite concentrations. This review, of all published studies to date, establishes the extent of daily variation with regard to the number and identity of 'rhythmic' metabolites observed in blood, saliva, urine, breath, and skeletal muscle. The probable sources driving such variation, in addition to what metabolite classes are most susceptible in adhering to or uncoupling from such cycles is described in addition to a compiled list of common rhythmic metabolites. The reviewed studies show that the metabolome undergoes significant time of day variation, primarily observed for amino acids and multiple lipid classes. Such 24 h rhythms, driven by various factors discussed herein, are an additional source of intra/inter-individual variation and are thus highly pertinent to all studies applying untargeted and targeted metabolomics platforms, particularly for the construction of biomarker panels. The potential implications are discussed alongside proposed minimum reporting criteria suggested to acknowledge time of day variation as a potential influence of results and to facilitate improved reproducibility.

Effects of harvesting and extraction methods on metabolite recovery from adherently growing mammalian cells

With the wide application of cell metabolomics in many research areas, there is a need to develop an effective procedure for adherent mammalian cell metabolomics that allows for accurate determination of intracellular metabolite levels and easy comparison between multiple studies of a similar application. Here we aimed to compare the efficiencies of different cell harvesting methods and metabolite extraction methods in sample preparation procedures, and to provide a cell sample processing protocol which focuses on maximizing metabolite recovery ranging from polar to lipidic ones. A systematical evaluation of 4 cell harvesting methods and 4 extraction methods was conducted based on human hepatoma HepG2 cells. The impact of different methods on the recoveries of 11 different categories of metabolites was further investigated. The water disruption sample harvesting method provided superior performance to the other 3 harvesting methods, trypsinization, scraping in phosphate buffered saline, and direct solvent scraping, with respect to the recoveries of polar metabolites and lipids. Among the 4 extraction methods, the novel two-phase solvent system extraction method with both methyl tert-butyl ether (MTBE) and 75% 9 : 1 methanol : chloroform showed an absolute advantage with high extraction efficiency for global metabolomics. We showed a metabolite-specific impact of the harvesting method and extraction method on metabolite concentrations. The water disruption sample collection combined with novel two-phase solvent system extraction enabled simultaneous profiling of lipids and metabolites with mixed polarity for sample preparation. Our approach may open up new perspectives toward large-scale comprehensive metabolomic analyses of adherent mammalian cell samples.

Influence of Drying Method on NMR-Based Metabolic Profiling of Human Cell Lines

Metabolic profiling of cell line and tissue extracts involves sample processing that includes a drying step prior to re-dissolving the cell or tissue extracts in a buffer for analysis by GC/LC-MS or NMR. Two of the most commonly used drying techniques are centrifugal evaporation under vacuum (SpeedVac) and lyophilization. Here, NMR spectroscopy was used to determine how the metabolic profiles of hydrophilic extracts of three human pancreatic cancer cell lines, MiaPaCa-2, Panc-1 and AsPC-1, were influenced by the choice of drying technique. In each of the three cell lines, 40-50 metabolites were identified as having statistically significant differences in abundance in redissolved extract samples depending on the drying technique used during sample preparation. In addition to these differences, some metabolites were only present in the lyophilized samples, for example, n-methyl-α-aminoisobutyric acid, n-methylnicotimamide, sarcosine and 3-hydroxyisovaleric acid, whereas some metabolites were only present in SpeedVac dried samples, for example, trimethylamine. This research demonstrates that the choice of drying technique used during the preparation of samples of human cell lines or tissue extracts can significantly influence the observed metabolome, making it important to carefully consider the selection of a drying method prior to preparation of such samples for metabolic profiling.

Secreted Metabolome of Human Macrophages Exposed to Methamphetamine

Macrophages comprise a major component of the human innate immune system that is involved in maintaining homeostasis and responding to infections or other insults. Besides cytokines and chemokines, macrophages presumably influence the surrounding environment by secreting various types of metabolites. Characterization of secreted metabolites under normal and pathological conditions is critical for understanding the complex innate immune system. To investigate the secreted metabolome, we developed a novel workflow consisting of one Reverse Phase (RP) C18 column linked in tandem with a Cogent cholesterol-modified RP C18. This system was used to compare the secreted metabolomes of human monocyte-derived macrophages (hMDM) under normal conditions to those exposed to methamphetamine (Meth). This new experimental approach allowed us to measure 92 metabolites, identify 11 of them as differentially expressed, separate and identify three hydroxymethamphetamine (OHMA) isomers, and identify a new, yet unknown metabolite with a m/z of 192. This study is the first of its kind to address the secreted metabolomic response of hMDM to an insult by Meth. Besides the discovery of novel metabolites secreted by macrophages, we provide a novel methodology to investigate metabolomic profiling.

Use cases, best practice and reporting standards for metabolomics in regulatory toxicology

Metabolomics is a widely used technology in academic research, yet its application to regulatory science has been limited. The most commonly cited barrier to its translation is lack of performance and reporting standards. The MEtabolomics standaRds Initiative in Toxicology (MERIT) project brings together international experts from multiple sectors to address this need. Here, we identify the most relevant applications for metabolomics in regulatory toxicology and develop best practice guidelines, performance and reporting standards for acquiring and analysing untargeted metabolomics and targeted metabolite data. We recommend that these guidelines are evaluated and implemented for several regulatory use cases.

Metabolomics of Pichia pastoris: impact of buffering conditions on the kinetics and nature of metabolite loss during quenching

Mass spectrometry-based metabolomic profiling is a powerful strategy to quantify the concentrations of numerous primary metabolites in parallel. To avoid distortion of metabolite concentrations, quenching is applied to stop the cellular metabolism instantly. For yeasts, cold methanol quenching is accepted to be the most suitable method to stop metabolism, while keeping the cells intact for separation from the supernatant. During this treatment, metabolite loss may occur while the cells are suspended in the quenching solution. An experiment for measuring the time-dependent loss of selected primary metabolites in differently buffered quenching solutions was conducted to study pH and salt concentration-dependent effects. Molecular properties of the observed metabolites were correlated with the kinetics of loss to gain insight into the mechanisms of metabolite leakage. Size and charge-related properties play a major role in controlling metabolite loss. We found evidence that interaction with the cell wall is the main determinant to retain a molecule inside the cell. Besides suggesting an improved quenching protocol to keep loss at a minimum, we could establish a more general understanding of the process of metabolite loss during quenching, which will allow to predict optimal conditions for hitherto not analysed metabolites.

Experimental design and reporting standards for metabolomics studies of mammalian cell lines

Metabolomics is an analytical technique that investigates the small biochemical molecules present within a biological sample isolated from a plant, animal, or cultured cells. It can be an extremely powerful tool in elucidating the specific metabolic changes within a biological system in response to an environmental challenge such as disease, infection, drugs, or toxins. A historically difficult step in the metabolomics pipeline is in data interpretation to a meaningful biological context, for such high-variability biological samples and in untargeted metabolomics studies that are hypothesis-generating by design. One way to achieve stronger biological context of metabolomic data is via the use of cultured cell models, particularly for mammalian biological systems. The benefits of in vitro metabolomics include a much greater control of external variables and no ethical concerns. The current concerns are with inconsistencies in experimental procedures and level of reporting standards between different studies. This review discusses some of these discrepancies between recent studies, such as metabolite extraction and data normalisation. The aim of this review is to highlight the importance of a standardised experimental approach to any cultured cell metabolomics study and suggests an example procedure fully inclusive of information that should be disclosed in regard to the cell type/s used and their culture conditions. Metabolomics of cultured cells has the potential to uncover previously unknown information about cell biology, functions and response mechanisms, and so the accurate biological interpretation of the data produced and its ability to be compared to other studies should be considered vitally important.

Compliance with minimum information guidelines in public metabolomics repositories

The Metabolomics Standards Initiative (MSI) guidelines were first published in 2007. These guidelines provided reporting standards for all stages of metabolomics analysis: experimental design, biological context, chemical analysis and data processing. Since 2012, a series of public metabolomics databases and repositories, which accept the deposition of metabolomic datasets, have arisen. In this study, the compliance of 399 public data sets, from four major metabolomics data repositories, to the biological context MSI reporting standards was evaluated. None of the reporting standards were complied with in every publicly available study, although adherence rates varied greatly, from 0 to 97%. The plant minimum reporting standards were the most complied with and the microbial and in vitro were the least. Our results indicate the need for reassessment and revision of the existing MSI reporting standards.

Metabolomic Analysis of Schizosaccharomyces pombe: Sample Preparation, Detection, and Data Interpretation

Metabolomics is a modern field of chemical biology that strives to simultaneously quantify hundreds of cellular metabolites. Techniques for metabolomic analysis in Schizosaccharomyces pombe have only recently been developed. Here we introduce methods that provide a complete workflow for metabolomic analysis in S. pombe Based on available literature, we estimate the yeast metabolome to comprise on the order of several thousand different metabolites. We discuss the feasibility of extraction and detection of such a large number of metabolites, and the influences of various parameters on the results. Among the parameters addressed are cell cultivation conditions, metabolite extraction techniques, and detection and quantification methods. Further, we provide recommendations on data management and data processing for metabolomic experiments, and describe possible pitfalls regarding the interpretation of metabolomic data. Finally, we briefly discuss potential future developments of this technique.

Mass spectrometry as a quantitative tool in plant metabolomics

Metabolomics is a research field used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include the analysis of a wide range of chemical species with very diverse physico-chemical properties, and therefore powerful analytical tools are required for the separation, characterization and quantification of this vast compound diversity present in plant matrices. In this review, challenges in the use of mass spectrometry (MS) as a quantitative tool in plant metabolomics experiments are discussed, and important criteria for the development and validation of MS-based analytical methods provided.This article is part of the themed issue 'Quantitative mass spectrometry'.

Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress

Metabolomics is one omics approach that can be used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include analysis of a wide range of chemical species with diverse physical properties, from ionic inorganic compounds to biochemically derived hydrophilic carbohydrates, organic and amino acids, and a range of hydrophobic lipid-related compounds. This complexitiy brings huge challenges to the analytical technologies employed in current plant metabolomics programs, and powerful analytical tools are required for the separation and characterization of this extremely high compound diversity present in biological sample matrices. The use of mass spectrometry (MS)-based analytical platforms to profile stress-responsive metabolites that allow some plants to adapt to adverse environmental conditions is fundamental in current plant biotechnology research programs for the understanding and development of stress-tolerant plants. In this review, we describe recent applications of metabolomics and emphasize its increasing application to study plant responses to environmental (stress-) factors, including drought, salt, low oxygen caused by waterlogging or flooding of the soil, temperature, light and oxidative stress (or a combination of them). Advances in understanding the global changes occurring in plant metabolism under specific abiotic stress conditions are fundamental to enhance plant fitness and increase stress tolerance. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:620-649, 2016.

Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools

The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund's Metabolomics Program, provides data from the Common Fund's Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world.

Metabolomics in chemical ecology

Chemical ecology elucidates the nature and role of natural products as mediators of organismal interactions. The emerging techniques that can be summarized under the concept of metabolomics provide new opportunities to study such environmentally relevant signaling molecules. Especially comparative tools in metabolomics enable the identification of compounds that are regulated during interaction situations and that might play a role as e.g. pheromones, allelochemicals or in induced and activated defenses. This approach helps overcoming limitations of traditional bioassay-guided structure elucidation approaches. But the power of metabolomics is not limited to the comparison of metabolic profiles of interacting partners. Especially the link to other -omics techniques helps to unravel not only the compounds in question but the entire biosynthetic and genetic re-wiring, required for an ecological response. This review comprehensively highlights successful applications of metabolomics in chemical ecology and discusses existing limitations of these novel techniques. It focuses on recent developments in comparative metabolomics and discusses the use of metabolomics in the systems biology of organismal interactions. It also outlines the potential of large metabolomics initiatives for model organisms in the field of chemical ecology.

COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access

Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

A role for vaccinia virus protein C16 in reprogramming cellular energy metabolism

Vaccinia virus (VACV) is a large DNA virus that replicates in the cytoplasm and encodes about 200 proteins of which approximately 50 % may be non-essential for viral replication. These proteins enable VACV to suppress transcription and translation of cellular genes, to inhibit the innate immune response, to exploit microtubule- and actin-based transport for virus entry and spread, and to subvert cellular metabolism for the benefit of the virus. VACV strain WR protein C16 induces stabilization of the hypoxia-inducible transcription factor (HIF)-1α by binding to the cellular oxygen sensor prolylhydroxylase domain-containing protein (PHD)2. Stabilization of HIF-1α is induced by several virus groups, but the purpose and consequences are unclear. Here, ¹H-NMR spectroscopy and liquid chromatography-mass spectrometry are used to investigate the metabolic alterations during VACV infection in HeLa and 2FTGH cells. The role of C16 in such alterations was examined by comparing infection to WT VACV (strain WR) and a derivative virus lacking gene C16L (vΔC16). Compared with uninfected cells, VACV infection caused increased nucleotide and glutamine metabolism. In addition, there were increased concentrations of glutamine derivatives in cells infected with WT VACV compared with vΔC16. This indicates that C16 contributes to enhanced glutamine metabolism and this may help preserve tricarboxylic acid cycle activity. These data show that VACV infection reprogrammes cellular energy metabolism towards increased synthesis of the metabolic precursors utilized during viral replication, and that C16 contributes to this anabolic reprogramming of the cell, probably via the stabilization of HIF-1α.

KiMoSys: a web-based repository of experimental data for KInetic MOdels of biological SYStems

BACKGROUND

The kinetic modeling of biological systems is mainly composed of three steps that proceed iteratively: model building, simulation and analysis. In the first step, it is usually required to set initial metabolite concentrations, and to assign kinetic rate laws, along with estimating parameter values using kinetic data through optimization when these are not known. Although the rapid development of high-throughput methods has generated much omics data, experimentalists present only a summary of obtained results for publication, the experimental data files are not usually submitted to any public repository, or simply not available at all. In order to automatize as much as possible the steps of building kinetic models, there is a growing requirement in the systems biology community for easily exchanging data in combination with models, which represents the main motivation of KiMoSys development.

DESCRIPTION

KiMoSys is a user-friendly platform that includes a public data repository of published experimental data, containing concentration data of metabolites and enzymes and flux data. It was designed to ensure data management, storage and sharing for a wider systems biology community. This community repository offers a web-based interface and upload facility to turn available data into publicly accessible, centralized and structured-format data files. Moreover, it compiles and integrates available kinetic models associated with the data.KiMoSys also integrates some tools to facilitate the kinetic model construction process of large-scale metabolic networks, especially when the systems biologists perform computational research.

CONCLUSIONS

KiMoSys is a web-based system that integrates a public data and associated model(s) repository with computational tools, providing the systems biology community with a novel application facilitating data storage and sharing, thus supporting construction of ODE-based kinetic models and collaborative research projects.The web application implemented using Ruby on Rails framework is freely available for web access at http://kimosys.org, along with its full documentation.

An extended dynamic model of Lactococcus lactis metabolism for mannitol and 2,3-butanediol production

Biomedical research and biotechnological production are greatly benefiting from the results provided by the development of dynamic models of microbial metabolism. Although several kinetic models of Lactococcus lactis (a Lactic Acid Bacterium (LAB) commonly used in the dairy industry) have been developed so far, most of them are simplified and focus only on specific metabolic pathways. Therefore, the application of mathematical models in the design of an engineering strategy for the production of industrially important products by L. lactis has been very limited. In this work, we extend the existing kinetic model of L. lactis central metabolism to include industrially relevant production pathways such as mannitol and 2,3-butanediol. In this way, we expect to study the dynamics of metabolite production and make predictive simulations in L. lactis. We used a system of ordinary differential equations (ODEs) with approximate Michaelis-Menten-like kinetics for each reaction, where the parameters were estimated from multivariate time-series metabolite concentrations obtained by our team through in vivo Nuclear Magnetic Resonance (NMR). The results show that the model captures observed transient dynamics when validated under a wide range of experimental conditions. Furthermore, we analyzed the model using global perturbations, which corroborate experimental evidence about metabolic responses upon enzymatic changes. These include that mannitol production is very sensitive to lactate dehydrogenase (LDH) in the wild type (W.T.) strain, and to mannitol phosphoenolpyruvate: a phosphotransferase system (PTS(Mtl)) in a LDH mutant strain. LDH reduction has also a positive control on 2,3-butanediol levels. Furthermore, it was found that overproduction of mannitol-1-phosphate dehydrogenase (MPD) in a LDH/PTS(Mtl) deficient strain can increase the mannitol levels. The results show that this model has prediction capability over new experimental conditions and offers promising possibilities to elucidate the effect of alterations in the main metabolism of L. lactis, with application in strain optimization.

LC-MS-based metabolomics

Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of a LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges.

Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains

The field of systems biology is often held back by difficulties in obtaining comprehensive, high-quality, quantitative data sets. In this paper, we undertook an interlaboratory effort to generate such a data set for a very large number of cellular components in the yeast Saccharomyces cerevisiae, a widely used model organism that is also used in the production of fuels, chemicals, food ingredients and pharmaceuticals. With the current focus on biofuels and sustainability, there is much interest in harnessing this species as a general cell factory. In this study, we characterized two yeast strains, under two standard growth conditions. We ensured the high quality of the experimental data by evaluating a wide range of sampling and analytical techniques. Here we show significant differences in the maximum specific growth rate and biomass yield between the two strains. On the basis of the integrated analysis of the high-throughput data, we hypothesize that differences in phenotype are due to differences in protein metabolism.

Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy

The study of biological systems in a holistic manner (systems biology) is increasingly being viewed as a necessity to provide qualitative and quantitative descriptions of the emergent properties of the complete system. Systems biology performs studies focussed on the complex interactions of system components; emphasising the whole system rather than the individual parts. Many perturbations to mammalian systems (diet, disease, drugs) are multi-factorial and the study of small parts of the system is insufficient to understand the complete phenotypic changes induced. Metabolomics is one functional level tool being employed to investigate the complex interactions of metabolites with other metabolites (metabolism) but also the regulatory role metabolites provide through interaction with genes, transcripts and proteins (e.g. allosteric regulation). Technological developments are the driving force behind advances in scientific knowledge. Recent advances in the two analytical platforms of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have driven forward the discipline of metabolomics. In this critical review, an introduction to metabolites, metabolomes, metabolomics and the role of MS and NMR spectroscopy will be provided. The applications of metabolomics in mammalian systems biology for the study of the health-disease continuum, drug efficacy and toxicity and dietary effects on mammalian health will be reviewed. The current limitations and future goals of metabolomics in systems biology will also be discussed (374 references).

Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping

Analytical errors caused by suboptimal performance of the chosen platform for a number of metabolites and instrumental drift are a major issue in large-scale metabolomics studies. Especially for MS-based methods, which are gaining common ground within metabolomics, it is difficult to control the analytical data quality without the availability of suitable labeled internal standards and calibration standards even within one laboratory. In this paper, we suggest a workflow for significant reduction of the analytical error using pooled calibration samples and multiple internal standard strategy. Between and within batch calibration techniques are applied and the analytical error is reduced significantly (increase of 25% of peaks with RSD lower than 20%) and does not hamper or interfere with statistical analysis of the final data.

Analytical and statistical approaches to metabolomics research

Metabolomics, the global profiling of metabolites in different living systems, has experienced a rekindling of interest partially due to the improved detection capabilities of the instrumental techniques currently being used in this area of biomedical research. The analytical methods of choice for the analysis of metabolites in search of disease biomarkers in biological specimens, and for the study of various low molecular weight metabolic pathways include NMR spectroscopy, GC/MS, CE/MS, and HPLC/MS. Global metabolite analysis and profiling of two different sets of data results in a plethora of data that is difficult to manage or interpret manually because of their subtle differences. Multivariate statistical methods and pattern-recognition programs were developed to handle the acquired data and to search for the discriminating features between data acquired from two sample sets, healthy and diseased. Metabolomics have been used in toxicology, plant physiology, and biomedical research. In this paper, we discuss various aspects of metabolomic research including sample collection, handling, storage, requirements for sample analysis, peak alignment, data interpretation using statistical approaches, metabolite identification, and finally recommendations for successful analysis.

Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum.