MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks

Pathos: a web facility that uses metabolic maps to display experimental changes in metabolites identified by mass spectrometry

This work describes a freely available web-based facility which can be used to analyse raw or processed mass spectrometric data from metabolomics experiments and display the metabolites identified--and changes in their experimental abundance--in the context of the metabolic pathways in which they occur. The facility, Pathos (http://motif.gla.ac.uk/Pathos/), employs Java servlets and is underpinned by a relational database populated from the Kyoto Encyclopaedia of Genes and Genomes (KEGG). Input files can contain either raw m/z values from experiments conducted in different modes, or KEGG or MetaCyc IDs assigned by the user on the basis of the m/z values and other criteria. The textual output lists the KEGG pathways on an XHTML page according to the number of metabolites or potential metabolites that they contain. Filtering by organism is also available. For metabolic pathways of interest, the user is able to retrieve a pathway map with identified metabolites highlighted. A particular feature of Pathos is its ability to process relative quantification data for metabolites identified under different experimental conditions, and to present this in an easily comprehensible manner. Results are colour-coded according to the degree of experimental change, and bar charts of the results can be generated interactively from either the text listings or the pathway maps. The visual presentation of the output from Pathos is designed to allow the rapid identification of metabolic areas of potential interest, after which particular results may be examined in detail.

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.

Metabolomics and its application to studying metal toxicity

Here we explain the omics approach of metabolomics and how it can be applied to study a physiological response to toxic metal exposure. This review aims to educate the metallomics field to the tool of metabolomics. Metabolomics is becoming an increasingly used tool to compare natural and challenged states of various organisms, from disease states in humans to toxin exposure to environmental systems. This approach is key to understanding and identifying the cellular or biochemical targets of metals and the underlying physiological response. Metabolomics steps are described and overviews of its application to metal toxicity to organisms are given. As this approach is very new there are yet only a small number of total studies and therefore only a brief overview of some metal metabolomics studies is described. A frank critical evaluation of the approach is given to provide newcomers to the method a clear idea of the challenges and the rewards of applying metabolomics to their research.

Path finding methods accounting for stoichiometry in metabolic networks

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks.

From models to crop species: caveats and solutions for translational metabolomics

Although plant metabolomics is largely carried out on Arabidopsis it is essentially genome-independent, and thus potentially applicable to a wide range of species. However, transfer between species, or even between different tissues of the same species, is not facile. This is because the reliability of protocols for harvesting, handling and analysis depends on the biological features and chemical composition of the plant tissue. In parallel with the diversification of model species it is important to establish good handling and analytic practice, in order to augment computational comparisons between tissues and species. Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is one of the powerful approaches for metabolite profiling. By using a combination of different extraction methods, separation columns, and ion detection, a very wide range of metabolites can be analyzed. However, its application requires careful attention to exclude potential pitfalls, including artifactual changes in metabolite levels during sample preparation under variations of light or temperature and analytic errors due to ion suppression. Here we provide case studies with two different LC-MS-based metabolomics platforms and four species (Arabidopsis thaliana, Chlamydomonas reinhardtii, Solanum lycopersicum, and Oryza sativa) that illustrate how such dangers can be detected and circumvented.

Proteomic analysis of acute kidney injury: biomarkers to mechanisms

Acute kidney injury (AKI) is a devastating clinical condition, both in terms of mortality and costs, and is occurring with increasing incidence. Despite better clinical care, the outcomes of AKI have changed little in the last 50 years. This lack of progress is due in part to a lack of early diagnostic biomarkers and a poor understanding of the disease mechanisms. This review will focus on the rapid progress being made in both the understanding of AKI and the promising panel of early biomarkers for AKI that have come out of both direct proteomic analysis of body fluids of AKI patients and more targeted proteomic approaches using clues from other methods such as transcriptomics. This review concludes with a discussion of the future of proteomics and personalized medicine in AKI and the challenges presented in translating these exciting proteomic results to the clinic.