Kathleen M. Carroll

Development of a Robust and Repeatable UPLC-MS Method for the Long-Term Metabolomic Study of Human Serum

A method for performing untargeted metabolomic analysis of human serum has been developed based on protein precipitation followed by Ultra Performance Liquid Chromatography and Time-of-Flight mass spectrometry (UPLC-TOF-MS). This method was specifically designed to fulfill the requirements of a long-term metabolomic study, spanning more than 3 years, and it was subsequently thoroughly evaluated for robustness and repeatability. We describe here the observed drift in instrumental performance over time and its improvement with adjustment of the length of analytical block. The optimal setup for our purpose was further validated against a set of serum samples from 30 healthy individuals. We also assessed the reproducibility of chromatographic columns with the same chemistry of stationary phase from the same manufacturer but from different production batches. The results have allowed the authors to prepare SOPs for fit for purpose long-term UPLC-MS metabolomic studies, such as are being employed in the HUSERMET project. This method allows the acquisition of data and subsequent comparison of data collected across many months or years.

Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics

The chemical identification of mass spectrometric signals in metabolomic applications is important to provide conversion of analytical data to biological knowledge about metabolic pathways. The complexity of electrospray mass spectrometric data acquired from a range of samples (serum, urine, yeast intracellular extracts, yeast metabolic footprints, placental tissue metabolic footprints) has been investigated and has defined the frequency of different ion types routinely detected. Although some ion types were expected (protonated and deprotonated peaks, isotope peaks, multiply charged peaks) others were not expected (sodium formate adduct ions). In parallel, the Manchester Metabolomics Database (MMD) has been constructed with data from genome scale metabolic reconstructions, HMDB, KEGG, Lipid Maps, BioCyc and DrugBank to provide knowledge on 42,687 endogenous and exogenous metabolite species. The combination of accurate mass data for a large collection of metabolites, theoretical isotope abundance data and knowledge of the different ion types detected provided a greater number of electrospray mass spectrometric signals which were putatively identified and with greater confidence in the samples studied. To provide definitive identification metabolite-specific mass spectral libraries for UPLC-MS and GC-MS have been constructed for 1,065 commercially available authentic standards. The MMD data are available at http://dbkgroup.org/MMD/.

PEDRo: A database for storing, searching and disseminating experimental proteomics data

BackgroundProteomics is rapidly evolving into a high-throughput technology, in which substantial and systematic studies are conducted on samples from a wide range of physiological, developmental, or pathological conditions. Reference maps from 2D gels are widely circulated. However, there is, as yet, no formally accepted standard representation to support the sharing of proteomics data, and little systematic dissemination of comprehensive proteomic data sets.ResultsThis paper describes the design, implementation and use of a P roteome E xperimental D ata R epo sitory (PEDRo), which makes comprehensive proteomics data sets available for browsing, searching and downloading. It is also serves to extend the debate on the level of detail at which proteomics data should be captured, the sorts of facilities that should be provided by proteome data management systems, and the techniques by which such facilities can be made available.ConclusionsThe PEDRo database provides access to a collection of comprehensive descriptions of experimental data sets in proteomics. Not only are these data sets interesting in and of themselves, they also provide a useful early validation of the PEDRo data model, which has served as a starting point for the ongoing standardisation activity through the Proteome Standards Initiative of the Human Proteome Organisation.

A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes

We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom‐up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.

Absolute Quantification of the Glycolytic Pathway in Yeast: DEPLOYMENT OF A COMPLETE QconCAT APPROACH

The availability of label-free data derived from yeast cells (based on the summed intensity of the three strongest, isoform-specific peptides) permitted a preliminary assessment of protein abundances for glycolytic proteins. Following this analysis, we demonstrate successful application of the QconCAT technology, which uses recombinant DNA techniques to generate artificial concatamers of large numbers of internal standard peptides, to the quantification of enzymes of the glycolysis pathway in the yeast Saccharomyces cerevisiae. A QconCAT of 88 kDa (59 tryptic peptides) corresponding to 27 isoenzymes was designed and built to encode two or three analyte peptides per protein, and after stable isotope labeling of the standard in vivo, protein levels were determined by LC-MS, using ultra high performance liquid chromatography-coupled mass spectrometry. We were able to determine absolute protein concentrations between 14,000 and 10 million molecules/cell. Issues such as efficiency of extraction and completeness of proteolysis are addressed, as well as generic factors such as optimal quantotypic peptide selection and expression. In addition, the same proteins were quantified by intensity-based label-free analysis, and both sets of data were compared with other quantification methods.

Systematic integration of experimental data and models in systems biology

BackgroundThe behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources.ResultsTaverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis.ConclusionsDistributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.

Guidelines for reporting the use of column chromatography in proteomics.

1Department of Comparative Molecular Medicine, School of Veterinary Science, The University of Liverpool, Liverpool, UK. 2Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, UK. 3Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, UK. 4National Institute for Biological Standards and Guidelines for reporting the use of column chromatography in proteomics

Quantification of the Proteins of the Bacterial Ribosome Using QconCAT Technology

The bacterial ribosome is a complex of three strands of RNA and approximately 55 proteins. During protein synthesis, the ribosome interacts with other proteins, numbered in the hundreds, forming some stable and some transient complexes. The stoichiometries of these complexes and of partially assembled ribosomes are often unknown. We describe the development of a flexible standard for the determination of stoichiometries of ribosomal particles and complexes. A core QconCAT, an artificial protein consisting of concatenated signature peptides derived from the ribosomal proteins L2, L4, L13, S4, S7, and S8, was developed. The core QconCAT DNA construct incorporates restriction sites for the insertion of cassettes encoding signature peptides from additional proteins under study. Two cassettes encoding signature peptides from the remaining 30S and 50S ribosomal proteins were prepared, and the resulting QconCATs were expressed, digested, and analyzed by mass spectrometry. The majority of Escherichia coli ribosomal proteins are small and basic; therefore, tryptic digestion alone yields insufficient signature peptides for quantification of all of the proteins. The ribosomal QconCATs therefore rely on a dual-enzyme strategy: endoproteinase Lys-C digestion and analysis followed by trypsin digestion and further analysis. The utility of technology was demonstrated by a determination of the effect of gentamicin on the protein composition of the E. coli ribosome.

A QconCAT informatics pipeline for the analysis, visualization and sharing of absolute quantitative proteomics data.

Absolute protein concentration determination is becoming increasingly important in a number of fields including diagnostics, biomarker discovery and systems biology modeling. The recently introduced quantification concatamer methodology provides a novel approach to performing such determinations, and it has been applied to both microbial and mammalian systems. While a number of software tools exist for performing analyses of quantitative data generated by related methodologies such as SILAC, there is currently no analysis package dedicated to the quantification concatamer approach. Furthermore, most tools that are currently available in the field of quantitative proteomics do not manage storage and dissemination of such data sets.

Quantification of Proteins and Their Modifications Using QconCAT Technology

Building a mathematical model of a biological system requires input of experimental data for each networked component, ultimately generating a model that can be used to test scientific hypotheses. A fundamental requirement in the computation of these systems is that the total amount of each component can be specified precisely. An added level of complexity occurs because a vast number of protein posttranslational modifications modulate protein function. Each of these modified forms therefore needs to be considered as a separate system component, and must therefore be quantified individually. In this chapter, we describe how designer QconCAT proteins can be used to determine the absolute amounts of both the polypeptide components and their covalently modified derivatives in both yeast and mammalian extracts derived from living cell populations.