Marie Brown

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography–mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography–MS (GC-MS) and ultraperformance liquid chromatography–MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control–based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

Growth control of the eukaryote cell: a systems biology study in yeast

BACKGROUND Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive studies at the transcriptional, proteomic and metabolic levels under defined controlled conditions are currently lacking. RESULTS Metabolic control analysis is being exploited in a systems biology study of the eukaryotic cell. Using chemostat culture, we have measured the impact of changes in flux (growth rate) on the transcriptome, proteome, endometabolome and exometabolome of the yeast Saccharomyces cerevisiae. Each functional genomic level shows clear growth-rate-associated trends and discriminates between carbon-sufficient and carbon-limited conditions. Genes consistently and significantly upregulated with increasing growth rate are frequently essential and encode evolutionarily conserved proteins of known function that participate in many protein-protein interactions. In contrast, more unknown, and fewer essential, genes are downregulated with increasing growth rate; their protein products rarely interact with one another. A large proportion of yeast genes under positive growth-rate control share orthologs with other eukaryotes, including humans. Significantly, transcription of genes encoding components of the TOR complex (a major controller of eukaryotic cell growth) is not subject to growth-rate regulation. Moreover, integrative studies reveal the extent and importance of post-transcriptional control, patterns of control of metabolic fluxes at the level of enzyme synthesis, and the relevance of specific enzymatic reactions in the control of metabolic fluxes during cell growth. CONCLUSION This work constitutes a first comprehensive systems biology study on growth-rate control in the eukaryotic cell. The results have direct implications for advanced studies on cell growth, in vivo regulation of metabolic fluxes for comprehensive metabolic engineering, and for the design of genome-scale systems biology models of the eukaryotic cell.

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/.

Metabolic profiling of serum using Ultra Performance Liquid Chromatography and the LTQ-Orbitrap mass spectrometry system

Advances in analytical instrumentation can provide significant advantages to the volume and quality of biological knowledge acquired in metabolomic investigations. The interfacing of sub-2 microm liquid chromatography (UPLC ACQUITY) and LTQ-Orbitrap mass spectrometry systems provides many theoretical advantages. The applicability of the interfaced systems was investigated using a simple 11-component metabolite mix and a complex mammalian biofluid, serum. Metabolites were detected in the metabolite mix with signals that were linear with their concentration over 2.5-3.5 orders of magnitude, with correlation coefficients greater than 0.993 and limits of detection less than 1 micromol L(-1). Reproducibility of retention time (RSD<3%) and chromatographic peak area (RSD<15%) and a high mass accuracy (<2 ppm) were observed for 14 QC serum samples interdispersed with other serum samples, analysed over a period of 40 h. The evaluation of a single deconvolution software package (XCMS) was performed and showed that two parameters (snthresh and bw) provided significant changes to the number of peaks detected and the peak area reproducibility for the dataset used. The data were used to indicate possible biomarkers of pre-eclampsia and showed both the instruments and XCMS to be applicable to the reproducible and valid detection of disease biomarkers present in serum.

Women into educational leadership and management: international differences?

The under‐representation of women in positions of senior management within educational institutions continues to be a matter of some concern, particularly as the teaching force is largely dominated, nationally and internationally, by women. Studies on gender and leadership have revealed a number of barriers to women seeking educational leadership and management positions. This paper is based on narratives drawn from women aspiring to leadership and management in different educational contexts, from very different parts of the world. The study examines the “glass ceilings” and “glass walls”; that is, horizontal and vertical barriers faced by each of the women within their cultures and environments.

A metabolome pipeline: from concept to data to knowledge

Metabolomics, like other omics methods, produces huge datasets of biological variables, often accompanied by the necessary metadata. However, regardless of the form in which these are produced they are merely the ground substance for assisting us in answering biological questions. In this short tutorial review and position paper we seek to set out some of the elements of “best practice” in the optimal acquisition of such data, and in the means by which they may be turned into reliable knowledge. Many of these steps involve the solution of what amount to combinatorial optimization problems, and methods developed for these, especially those based on evolutionary computing, are proving valuable. This is done in terms of a “pipeline” that goes from the design of good experiments, through instrumental optimization, data storage and manipulation, the chemometric data processing methods in common use, and the necessary means of validation and cross-validation for giving conclusions that are credible and likely to be robust when applied in comparable circumstances to samples not used in their generation.

A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols

BACKGROUND The stability of mammalian serum and urine in large metabolomic investigations is essential for accurate, valid and reproducible studies. The stability of mammalian serum and urine, either processed immediately by freezing at -80 degrees C or stored at 4 degrees C for 24 h before being frozen, was compared in a pilot metabolomic study of samples from 40 separate healthy volunteers. METHODS Metabolic profiling with GC-TOF-MS was performed for serum and urine samples collected from 40 volunteers and stored at -80 degrees C or 4 degrees C for 24 h before being frozen at -80 degrees C. Subsequent Wilcoxon rank sum test and Principal Components Analysis (PCA) methods were used to assess whether differences in the metabolomes were detected between samples stored at 4 degrees C for 0 or 24 h. RESULTS More than 700 unique metabolite peaks were detected, with over 200 metabolite peaks detected in any one sample. PCA and Wilcoxon rank sum tests of serum and urine data showed as a general observation that the variance associated with the replicate analysis per sample (analytical variance) was of the same magnitude as the variance observed between samples stored at 4 degrees C for 0 or 24 h. From a functional point of view the metabolomic composition of the majority of samples did not change in a statistically significant manner when stored under two different conditions. CONCLUSIONS Based on this small pilot study, the UK Biobank sampling, transport and fractionation protocols are considered suitable to provide samples, which can produce scientifically robust and valid data in metabolomic studies.

Metabolic footprinting as a tool for discriminating between brewing yeasts

The characterization of industrial yeast strains by examining their metabolic footprints (exometabolomes) was investigated and compared to genome‐based discriminatory methods. A group of nine industrial brewing yeasts was studied by comparing their metabolic footprints, genetic fingerprints and comparative genomic hybridization profiles. Metabolic footprinting was carried out by both direct injection mass spectrometry (DIMS) and gas chromatography time‐of‐flight mass spectrometry (GC–TOF–MS), with data analysed by principal components analysis (PCA) and canonical variates analysis (CVA). The genomic profiles of the nine yeasts were compared by PCR–restriction fragment length polymorphism (PCR–RFLP) analysis, genetic fingerprinting using amplified fragment length polymorphism (AFLP) analysis and microarray comparative genome hybridizations (CGH). Metabolomic and genomic analysis comparison of the nine brewing yeasts identified metabolomics as a powerful tool in separating genotypically and phenotypically similar strains. For some strains discrimination not achieved genomically was observed metabolomically. Copyright

Further developments towards a genome-scale metabolic model of yeast

BackgroundTo date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity.ResultsWe have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites - significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions.ConclusionsWe report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.

Is Serum or Plasma More Appropriate for Intersubject Comparisons in Metabolomic Studies? An Assessment in Patients with Small-Cell Lung Cancer

In clinical analyses, the most appropriate biofluid should be analyzed for optimal assay performance. For biological fluids, the most readily accessible is blood, and metabolomic analyses can be performed either on plasma or serum. To determine the optimal agent for analysis, metabolic profiles of matched human serum and plasma were assessed by gas chromatography/time-of-flight mass spectrometry and ultrahigh-performance liquid chromatography mass spectrometry (in positive and negative electrospray ionization modes). Comparison of the two metabolomes, in terms of reproducibility, discriminative ability and coverage, indicated that they offered similar analytical opportunities. An analysis of the variation between 29 small-cell lung cancer (SCLC) patients revealed that the differences between individuals are markedly similar for the two biofluids. However, significant differences between the levels of some specific metabolites were identified, as were differences in the intersubject variability of some metabolite levels. Glycerophosphocholines, erythritol, creatinine, hexadecanoic acid, and glutamine in plasma, but not in serum, were shown to correlate with life expectancy for SCLC patients, indicating the utility of metabolomic analyses in clinical prognosis and the particular utility of plasma in relation to the clinical management of SCLC.