ORCID Profile
0000-0003-0775-9581
Current Organisation
Edith Cowan University
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Medical and Health Sciences not elsewhere classified | Medical Biochemistry and Metabolomics | Structural Chemistry and Spectroscopy | Biochemistry and Cell Biology not elsewhere classified | Medical Biochemistry and Metabolomics not elsewhere classified | Chemical Characterisation of Materials | Physical Chemistry (Incl. Structural)
Expanding Knowledge in the Biological Sciences | Expanding Knowledge in the Chemical Sciences | Expanding Knowledge in the Agricultural and Veterinary Sciences | Expanding Knowledge in the Medical and Health Sciences |
Publisher: Royal Society of Chemistry (RSC)
Date: 2009
DOI: 10.1039/B901179J
Abstract: 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 s les (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 s les 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 dbkgroup.org/MMD/.
Publisher: MDPI AG
Date: 31-03-2020
Abstract: Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is ided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.
Publisher: Elsevier BV
Date: 07-2004
Publisher: Public Library of Science (PLoS)
Date: 05-12-2012
Publisher: Elsevier BV
Date: 08-1997
Publisher: Informa UK Limited
Date: 10-04-2012
DOI: 10.3109/08977194.2012.674035
Abstract: An imbalance between anti-angiogenic factors (e.g. soluble vascular endothelial growth factor receptor-1 (s-FLT1) and soluble endoglin (s-Eng)) and pro-angiogenic factors (e.g. placental growth factor (PlGF)) as well as increased oxidized low-density lipoprotein (ox-LDL) concentrations have been associated with preecl sia (PE). Risk factors associated with the development of PE, however, are known to be different between developed and developing countries. The aim of the study was to determine the levels of s-FLT1, s-Eng, PIGF, and ox-LDL in women with PE from a developing country. A multi-center case-control study was conducted. One hundred and forty three women with PE were matched by age and parity with 143 healthy pregnant women without cardiovascular or endocrine diseases. Before delivery, blood s les were taken and serum was stored until analysis. Women with PE had lower concentrations of PIGF (p<0.0001) and higher concentrations of s-Eng (p=0.001) than healthy pregnant women. There were no differences between the groups regarding ox-LDL or s-FLT1. Women with early onset PE had higher s-FLT1 concentrations (p=0.0004) and lower PIGF concentrations (p<0.0001) than their healthy pregnant controls. Women with late onset PE had higher concentrations of s-Eng (p=0.005). Women with severe PE had higher concentrations of s-Eng (p=0.0008) and ox-LDL (p=0.01), and lower concentrations of PIGF (p<0.0001). Women with PE from a developing country demonstrated an angiogenic imbalance and an increased rate of LDL oxidation. Findings from this study support the theory that PE is a multifactorial disease, and understanding differences in these subpopulations may provide a better target to approach future therapies.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 10-2010
DOI: 10.1161/HYPERTENSIONAHA.110.157297
Abstract: Preecl sia is a pregnancy-specific syndrome that causes substantial maternal and fetal morbidity and mortality. The etiology is incompletely understood, and there is no clinically useful screening test. Current metabolomic technologies have allowed the establishment of metabolic signatures of preecl sia in early pregnancy. Here, a 2-phase discovery/validation metabolic profiling study was performed. In the discovery phase, a nested case-control study was designed, using s les obtained at 15±1 weeks’ gestation from 60 women who subsequently developed preecl sia and 60 controls taking part in the prospective Screening for Pregnancy Endpoints cohort study. Controls were proportionally population matched for age, ethnicity, and body mass index at booking. Plasma s les were analyzed using ultra performance liquid chromatography-mass spectrometry. A multivariate predictive model combining 14 metabolites gave an odds ratio for developing preecl sia of 36 (95% CI: 12 to 108), with an area under the receiver operator characteristic curve of 0.94. These findings were then validated using an independent case-control study on plasma obtained at 15±1 weeks from 39 women who subsequently developed preecl sia and 40 similarly matched controls from a participating center in a different country. The same 14 metabolites produced an odds ratio of 23 (95% CI: 7 to 73) with an area under receiver operator characteristic curve of 0.92. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of preecl sia offers insight into disease pathogenesis and offers the tantalizing promise of a robust presymptomatic screening test.
Publisher: Springer Science and Business Media LLC
Date: 07-2005
Publisher: American Chemical Society (ACS)
Date: 18-10-2018
Publisher: Elsevier BV
Date: 05-2015
DOI: 10.1016/J.CLINBIOCHEM.2015.02.004
Abstract: Metabolomics is defined as the comprehensive study of all low molecular weight biochemicals, (metabolites) present in an organism. Using a systems biology approach, metabolomics in umbilical cord blood (UCB) may offer insight into many perinatal disease processes by uniquely detecting rapid biochemical pathway alterations. In vitro haemolysis is a common technical problem affecting UCB s ling in the delivery room, and can h er metabolomic analysis. The extent of metabolomic alteration which occurs in haemolysed s les is unknown. Visual haemolysis was designated by the laboratory technician using a standardised haemolysis index colour chart. The metabolomic profile of haemolysed and non-haemolysed UCB serum s les from 69 healthy term infants was compared using both (1)H-NMR and targeted DI and LC-MS/MS approach. We identified 43 metabolites that are significantly altered in visually haemolysed UCB s les, acylcarnitines (n=2), glycerophospholipids (n=23), sphingolipids (n=7), sugars (n=3), amino acids (n=4) and Krebs cycle intermediates (n=4). This information will be useful for researchers in the field of neonatal metabolomics to avoid false findings in the presence of haemolysis, to ensure reproducible and credible results.
Publisher: American Chemical Society (ACS)
Date: 26-01-2009
DOI: 10.1021/AC8019366
Publisher: Elsevier BV
Date: 03-2022
DOI: 10.1016/J.PRRV.2021.06.002
Abstract: Asthma is the most common chronic lung disease in childhood. There has been a significant worldwide effort to develop tools/methods to identify children's risk for asthma as early as possible for preventative and early management strategies. Unfortunately, most childhood asthma prediction tools using conventional statistical models have modest accuracy, sensitivity, and positive predictive value. Machine learning is an approach that may improve on conventional models by finding patterns and trends from large and complex datasets. Thus far, few studies have utilized machine learning to predict asthma in children. This review aims to critically assess these studies, describe their limitations, and discuss future directions to move from proof-of-concept to clinical application.
Publisher: European Respiratory Society (ERS)
Date: 25-11-2021
DOI: 10.1183/13993003.01733-2021
Abstract: Asthma is a heterogeneous disease with poorly defined phenotypes. Patients with severe asthma often receive multiple treatments including oral corticosteroids (OCS). Treatment may modify the observed metabotype, rendering it challenging to investigate underlying disease mechanisms. Here, we aimed to identify dysregulated metabolic processes in relation to asthma severity and medication. Baseline urine was collected prospectively from healthy participants (n=100), patients with mild-to-moderate asthma (n=87) and patients with severe asthma (n=418) in the cross-sectional U-BIOPRED cohort 12–18-month longitudinal s les were collected from patients with severe asthma (n=305). Metabolomics data were acquired using high-resolution mass spectrometry and analysed using univariate and multivariate methods. A total of 90 metabolites were identified, with 40 significantly altered (p .05, false discovery rate .05) in severe asthma and 23 by OCS use. Multivariate modelling showed that observed metabotypes in healthy participants and patients with mild-to-moderate asthma differed significantly from those in patients with severe asthma (p=2.6×10 −20 ), OCS-treated asthmatic patients differed significantly from non-treated patients (p=9.5×10 −4 ), and longitudinal metabotypes demonstrated temporal stability. Carnitine levels evidenced the strongest OCS-independent decrease in severe asthma. Reduced carnitine levels were associated with mitochondrial dysfunction via decreases in pathway enrichment scores of fatty acid metabolism and reduced expression of the carnitine transporter SLC22A5 in sputum and bronchial brushings. This is the first large-scale study to delineate disease- and OCS-associated metabolic differences in asthma. The widespread associations with different therapies upon the observed metabotypes demonstrate the need to evaluate potential modulating effects on a treatment- and metabolite-specific basis. Altered carnitine metabolism is a potentially actionable therapeutic target that is independent of OCS treatment, highlighting the role of mitochondrial dysfunction in severe asthma.
Publisher: American Society for Microbiology
Date: 03-2004
DOI: 10.1128/AEM.70.3.1583-1592.2004
Abstract: Silage quality is typically assessed by the measurement of several in idual parameters, including pH, lactic acid, acetic acid, bacterial numbers, and protein content. The objective of this study was to use a holistic metabolic fingerprinting approach, combining a high-throughput microtiter plate-based fermentation system with Fourier transform infrared (FT-IR) spectroscopy, to obtain a snapshot of the s le metabolome (typically low-molecular-weight compounds) at a given time. The aim was to study the dynamics of red clover or grass silage fermentations in response to various inoculants incorporating lactic acid bacteria (LAB). The hyperspectral multivariate datasets generated by FT-IR spectroscopy are difficult to interpret visually, so chemometrics methods were used to deconvolute the data. Two-phase principal component-discriminant function analysis allowed discrimination between herbage types and different LAB inoculants and modeling of fermentation dynamics over time. Further analysis of FT-IR spectra by the use of genetic algorithms to identify the underlying biochemical differences between treatments revealed that the amide I and amide II regions (wavenumbers of 1,550 to 1,750 cm −1 ) of the spectra were most frequently selected (reflecting changes in proteins and free amino acids) in comparisons between control and inoculant-treated fermentations. This corresponds to the known importance of rapid fermentation for the efficient conservation of forage proteins.
Publisher: Springer Science and Business Media LLC
Date: 30-07-2014
Publisher: Springer Science and Business Media LLC
Date: 14-09-2019
DOI: 10.1007/S11306-019-1588-0
Abstract: A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist however, they tend to be inflexible and rely on the user to adequately report their methods and results. To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work. To ensure broad use within the community such a framework also needs to be inclusive and intuitive for both computational novices and experts alike. To encourage metabolomics researchers from all backgrounds to take control of their own data science, mould it to their personal requirements, and enthusiastically share resources through open science. This tutorial introduces the concept of interactive web-based computational laboratory notebooks. The reader is guided through a set of experiential tutorials specifically targeted at metabolomics researchers, based around the Jupyter Notebook web application, GitHub data repository, and Binder cloud computing platform.
Publisher: American Physiological Society
Date: 10-2018
DOI: 10.1152/JAPPLPHYSIOL.00499.2018
Abstract: Although complex in nature, a number of metabolites have been implicated in the onset of exercise-induced fatigue. The purpose of this study was to identify changes in the plasma metabolome and specifically, to identify candidate metabolites associated with the onset of fatigue during prolonged cycling. Eighteen healthy and recreationally active men (mean ± SD age: 24.7 ± 4.8 yr mass 67.1 ± 6.1 kg body mass index: 22.8 ± 2.2 peak oxygen uptake: 40.9 ± 6.1 ml·kg −1 ·min −1 ) were recruited to this study. Participants performed a prolonged cycling time-to-exhaustion (TTE) test at an intensity corresponding to a fixed blood lactate concentration (3 mmol/l). Plasma s les collected at 10 min of exercise, before fatigue (last s le before fatigue min before fatigue), immediately after fatigue (point of exhaustion), and 20 min after fatigue were assessed using a liquid chromatography-mass spectrometry-based metabolomic approach. Eighty metabolites were putatively identified, with 68 metabolites demonstrating a significant change during the cycling task (duration: ~80.9 ± 13.6 min). A clear multivariate structure in the data was revealed, with the first principal component (36% total variance) describing a continuous increase in metabolite concentration throughout the TTE trial and recovery, whereas the second principal component (14% total variance) showed an increase in metabolite concentration followed by a recovery trajectory, peaking at the point of fatigue. Six clusters of correlated metabolites demonstrating unique metabolite trajectories were identified, including significant separation in the metabolome between prefatigue and postfatigue time points. In accordance with our hypothesis, free-fatty acids and tryptophan contributed to differences in the plasma metabolome at fatigue. NEW & NOTEWORTHY Metabolites have long been implicated in the onset of fatigue. This study applied a metabolomic approach to track 80 plasma-borne metabolites during a cycle to fatigue task. Of these, 68 metabolites demonstrated significant change, with the plasma metabolome at fatigue being clearly distinguishable from other time points. Six unique clusters of metabolites were identified, and free fatty acids were strongly associated with fatigue onset therein lending support to the central fatigue hypothesis.
Publisher: Springer Science and Business Media LLC
Date: 10-06-2014
Abstract: Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computational workflows to maintain data quality. This dataset represents a systematic evaluation of the reproducibility of a multi-batch DIMS metabolomics study of cardiac tissue extracts. It comprises of twenty biological s les (cow vs. sheep) that were analysed repeatedly, in 8 batches across 7 days, together with a concurrent set of quality control (QC) s les. Data are presented from each step of the workflow and are available in MetaboLights. The strength of the dataset is that intra- and inter-batch variation can be corrected using QC spectra and the quality of this correction assessed independently using the repeatedly-measured biological s les. Originally designed to test the efficacy of a batch-correction algorithm, it will enable others to evaluate novel data processing algorithms. Furthermore, this dataset serves as a benchmark for DIMS metabolomics, derived using best-practice workflows and rigorous quality assessment.
Publisher: Springer Science and Business Media LLC
Date: 05-2021
Publisher: S. Karger AG
Date: 2016
DOI: 10.1159/000446556
Abstract: b i Background: /i /b A sup /sup H-NMR-derived metabolomic index based on early umbilical cord blood alterations of succinate, glycerol, 3-hydroxybutyrate and O-phosphocholine has shown potential for the prediction of hypoxic-ischaemic encephalopathy (HIE) severity. b i Objective: /i /b To evaluate whether this metabolite score can predict 3-year neurodevelopmental outcome in infants with perinatal asphyxia and HIE, compared with current standard biochemical and clinical markers. b i Methods: /i /b From September 2009 to June 2011, infants at risk of perinatal asphyxia were recruited from a single maternity hospital. Cord blood was drawn and biobanked at delivery. Neonates were monitored for development of encephalopathy both clinically and electrographically. Neurodevelopmental outcome was assessed at 36-42 months using the Bayley Scales of Infant and Toddler Development, ed. III (BSID-III). Death and cerebral palsy were also considered as abnormal end points. b i Results: /i /b Thirty-one infants had both metabolomic analysis and neurodevelopmental outcome at 36-42 months. No child had a severely abnormal BSID-III result. The metabolite index significantly correlated with outcome (& #x03C1 sup /sup = 0.30, p 0.01), which is robust to predict both severe outcome (area under the receiver operating characteristic curve: 0.92, p i /i .01) and intact survival (0.80, p = 0.01). There was no correlation between the index score and performance in the in idual BSID-III subscales (cognitive, language, motor). b i Conclusions: /i /b The metabolite index outperformed other standard biochemical markers at birth for prediction of outcome at 3 years, but was not superior to EEG or the Sarnat score.
Publisher: Elsevier BV
Date: 02-2021
Publisher: Springer Science and Business Media LLC
Date: 04-12-2012
Publisher: Hindawi Limited
Date: 2003
DOI: 10.1002/CFG.302
Abstract: We sought to test the hypothesis that mutant bacterial strains could be discriminated from each other on the basis of the metabolites they secrete into the medium (their ‘metabolic footprint’), using two methods of ‘global’ metabolite analysis (FT–IR and direct injection electrospray mass spectrometry). The biological system used was based on a published study of Escherichia coli tryptophan mutants that had been analysed and discriminated by Yanofsky and colleagues using transcriptome analysis. Wild-type strains supplemented with tryptophan or analogues could be discriminated from controls using FT–IR of 24 h broths, as could each of the mutant strains in both minimal and supplemented media. Direct injection electrospray mass spectrometry with unit mass resolution could also be used to discriminate the strains from each other, and had the advantage that the discrimination required the use of just two or three masses in each case. These were determined via a genetic algorithm. Both methods are rapid, reagentless, reproducible and cheap, and might beneficially be extended to the analysis of gene knockout libraries.
Publisher: Springer Science and Business Media LLC
Date: 28-11-2006
Publisher: Oxford University Press (OUP)
Date: 31-07-2006
DOI: 10.1093/BIOINFORMATICS/BTL416
Abstract: Summary: We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. Availability: Contact: Roger.Jarvis@manchester.ac.uk or admin@pychem.org.uk Supplementary information: Further information is available from the project home page at whilst details of data generation are available at
Publisher: SAGE Publications
Date: 27-01-2014
Abstract: Metabolomics enables the provision of sensitive bio-markers of disease. We performed 800 MHz 1 H-nuclear magnetic resonance (NMR) spectroscopic analyses of cerebrospinal fluid (CSF) specimens to identify biomarkers of multiple sclerosis (MS), yielding reproducible detection of 15 metabolites from MS ( n=15) and non-MS ( n=17) patients. Mean levels of choline, myo-inositol and threonate were increased, whereas 3-hydroxybutyrate, citrate, phenylalanine, 2-hydroxyisovalerate and mannose were decreased in MS-derived CSF ( p .05), suggesting alterations to energy and phospholipid metabolism. Multivariate hierarchal cluster analysis indicated a high correlation within the metabolite profiles, significantly clustering s les into the two clinical groups, which was corroborated using principal components analysis. CSF metabolomics have the capacity to yield quantitative biomarkers and insights into the pathogenesis of MS.
Publisher: Oxford University Press (OUP)
Date: 02-05-2012
DOI: 10.1093/NAR/GKS374
Publisher: American Society for Microbiology
Date: 06-2002
DOI: 10.1128/AEM.68.6.2822-2828.2002
Abstract: Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show here that this technique can be used directly on the surface of food to produce biochemically interpretable “fingerprints.” Spoilage in meat is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial least-squares regression and allowed accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 10 7 bacteria·g −1 the main biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial loads of food s les accurately and rapidly in 60 s, directly from the s le surface. We believe this approach will aid in the Hazard Analysis Critical Control Point process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels.
Publisher: Humana Press
Date: 2014
DOI: 10.1007/978-1-62703-986-4_4
Abstract: High-throughput sequencing is an increasingly accessible tool for cataloging gene complements of plant pathogens and their hosts. It has had great impact in plant pathology, enabling rapid acquisition of data for a wide range of pathogens and hosts, leading to the selection of novel candidate effector proteins, and/or associated host targets (Bart et al., Proc Nat Acad Sci U S A doi:10.1073 nas.1208003109, 2012 Agbor and McCormick, Cell Microbiol 13:1858-1869, 2011 Fabro et al., PLoS Pathog 7:e1002348, 2011 Kim et al., Mol Plant Pathol 2:715-730, 2011 Kimbrel et al., Mol Plant Pathol 12:580-594, 2011 O'Brien et al., Curr Opin Microbiol 14:24-30, 2011 Vleeshouwers et al., Annu Rev Phytopathol 49:507-531, 2011 Sarris et al., Mol Plant Pathol 11:795-804, 2010 Boch and Bonas, Annu Rev Phytopathol 48:419-436, 2010 Mcdermott et al., Infect Immun 79:23-32, 2011).Identification of candidate effectors from genome data is not different from classification in any other high-content or high-throughput experiment. The primary aim is to discover a set of qualitative or quantitative sequence characteristics that discriminate, with a defined level of certainty, between proteins that have previously been identified as being either "effector" (positive) or "not effector" (negative). Combination of these characteristics in a mathematical model, or classifier, enables prediction of whether a protein is or is not an effector, with a defined level of certainty. High-throughput screening of the gene complement is then performed to identify candidate effectors this may seem straightforward, but it is unfortunately very easy to identify seemingly persuasive candidate effectors that are, in fact, entirely spurious.The main sources of danger in this area of statistical modeling are not entirely independent of each other, and include: inappropriate choice of classifier model poor selection of reference sequences (known positive and negative ex les) poor definition of classes (what is, and what is not, an effector) inadequate training s le size poor model validation and lack of adequate model performance metrics (Xia et al., Metabolomics doi:10.1007/s11306-012-0482-9, 2012). Many studies fail to take these issues into account, and thereby fail to discover anything of true significance or, worse, report spurious findings that are impossible to validate. Here we summarize the impact of these issues and present strategies to assist in improving design and evaluation of effector classifiers, enabling robust scientific conclusions to be drawn from the available data.
Publisher: American Chemical Society (ACS)
Date: 25-10-2003
DOI: 10.1021/AC034669A
Abstract: Optimizing experimental conditions for the effective analysis of intact proteins by mass spectrometry is challenging, as many analytical factors influence the spectral quality, often in very different ways for different proteins and especially with complex protein mixtures. We show that genetic search methods are highly effective in this kind of optimization and that it was possible in 6 generations with a total of <500 experiments out of some 10(14) to find good combinations of experimental variables (electrospray ionization mass spectral settings) that would not have been detected by optimizing each variable alone (i.e., the search space is epistatic). Moreover, by inspecting the evolution of the variables to be optimized using genetic programming, we discovered an important relationship between two of the mass spectrometer settings that accounts for much of this success. Specifically, the conditions that were evolved included very low values of skimmer 1 voltage (the s le cone) and a skimmer 2 voltage (extraction cone) above a threshold that would nevertheless minimize the potential difference between the s le and extraction skimmers. The discovery of this relationship demonstrates the hypothesis-generating ability of genetic search in optimization processes where the size of the search space means that little or no a priori knowledge of the optimal conditions is available.
Publisher: American Chemical Society (ACS)
Date: 29-06-2011
DOI: 10.1021/PR2002897
Abstract: Being born small for gestational age (SGA) confers increased risks of perinatal morbidity and mortality and increases the risk of cardiovascular complications and diabetes in later life. Accumulating evidence suggests that the etiology of SGA is usually associated with poor placental vascular development in early pregnancy. We examined metabolomic profiles using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) in three independent studies: (a) venous cord plasma from normal and SGA babies, (b) plasma from a rat model of placental insufficiency and controls, and (c) early pregnancy peripheral plasma s les from women who subsequently delivered a SGA baby and controls. Multivariate analysis by cross-validated Partial Least Squares Discriminant Analysis (PLS-DA) of all 3 studies showed a comprehensive and similar disruption of plasma metabolism. A multivariate predictive model combining 19 metabolites produced by a Genetic Algorithm-based search program gave an Odds Ratio for developing SGA of 44, with an area under the Receiver Operator Characteristic curve of 0.9. Sphingolipids, phospholipids, carnitines, and fatty acids were among this panel of metabolites. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of SGA offers insight into disease pathogenesis and offers the promise of a robust presymptomatic screening test.
Publisher: Elsevier BV
Date: 08-2008
DOI: 10.1016/J.JCHROMB.2008.03.021
Abstract: 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 s les interdispersed with other serum s les, 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-ecl sia and showed both the instruments and XCMS to be applicable to the reproducible and valid detection of disease biomarkers present in serum.
Publisher: BMJ
Date: 06-2020
DOI: 10.1136/BMJOPEN-2019-035930
Abstract: The effect of early and sustained administration of daily probiotic therapy on patients admitted to the intensive care unit (ICU) remains uncertain. The Restoration Of gut microflora in Critical Illness Trial (ROCIT) study is a multicentre, randomised, placebo-controlled, parallel-group, two-sided superiority trial that will enrol 220 patients in five ICUs. Adult patients who are within 48 hours of admission to an ICU and are expected to require intensive care beyond the next calendar day will be randomised in a 1:1 ratio to receive early and sustained Lactobacillus plantarum 299v probiotic therapy in addition to usual care or placebo in addition to usual care. The primary endpoint is days alive and out of hospital to day 60. ROCIT has been approved by the South Metropolitan Health Service Human Research Ethics Committee (ref: RGS00000004) and the St John of God Health Care Human Research Ethics Committee (ref: 1183). The trial results will be submitted for publication in a peer-reviewed journal. Australian and New Zealand Clinical Trials Registry (ANZCTR12617000783325) Pre-results.
Publisher: Royal Society of Chemistry (RSC)
Date: 2011
DOI: 10.1039/B906712B
Abstract: 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 in idual 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).
Publisher: Springer Science and Business Media LLC
Date: 06-03-2017
Publisher: Springer Science and Business Media LLC
Date: 12-05-2003
DOI: 10.1038/NBT823
Publisher: Springer Science and Business Media LLC
Date: 25-08-2007
Publisher: Springer Science and Business Media LLC
Date: 09-2016
Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 02-2016
DOI: 10.1016/J.CYTO.2015.11.018
Abstract: Recently, differences in the levels of various chemokines and cytokines were reported in patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) as compared with controls. Moreover, the analyte profile differed between chronic ME/CFS patients of long duration versus patients with disease of less than 3years. In the current study, we measured the plasma levels of 34 cytokines, chemokines and growth factors in 100 chronic ME/CFS patients of long duration and in 79 gender and age-matched controls. We observed highly significant reductions in the concentration of circulating interleukin (IL)-16, IL-7, and Vascular Endothelial Growth Factor A (VEGF-A) in ME/CFS patients. All three biomarkers were significantly correlated in a multivariate cluster analysis. In addition, we identified significant reductions in the concentrations of fractalkine (CX3CL1) and monokine-induced-by-IFN-γ (MIG CXCL9) along with increases in the concentrations of eotaxin 2 (CCL24) in ME/CFS patients. Our data recapitulates previous data from another USA ME/CFS cohort in which circulating levels of IL-7 were reduced. Also, a reduced level of VEGF-A was reported previously in sera of patients with Gulf War Illness as well as in cerebral spinal fluid s les from a different cohort of USA ME/CFS patients. To our knowledge, we are the first to test for levels of IL-16 in ME/CFS patients. In combination with previous data, our work suggests that the clustered reduction of IL-7, IL-16 and VEGF-A may have physiological relevance to ME/CFS disease. This profile is ME/CFS-specific since measurement of the same analytes present in chronic infectious and autoimmune liver diseases, where persistent fatigue is also a major symptom, failed to demonstrate the same changes. Further studies of other ME/CFS and overlapping disease cohorts are warranted in future.
Publisher: The Institute of Brewing & Distilling
Date: 2006
Publisher: Wiley
Date: 11-2018
DOI: 10.1113/EP087159
Publisher: Oxford University Press (OUP)
Date: 08-2018
DOI: 10.1373/CLINCHEM.2018.287045
Abstract: The metabolome of any given biological system contains a erse range of low molecular weight molecules (metabolites), whose abundances can be affected by the timing and method of s le collection, storage, and handling. Thus, it is necessary to consider the requirements for preanalytical processes and biobanking in metabolomics research. Poor practice can create bias and have deleterious effects on the robustness and reproducibility of acquired data. This review presents both current practice and latest evidence on preanalytical processes and biobanking of s les intended for metabolomics measurement of common biofluids and tissues. It highlights areas requiring more validation and research and provides some evidence-based guidelines on best practices. Although many researchers and biobanking personnel are familiar with the necessity of standardizing s le collection procedures at the axiomatic level (e.g., fasting status, time of day, “time to freezer,” s le volume), other less obvious factors can also negatively affect the validity of a study, such as vial size, material and batch, centrifuge speeds, storage temperature, time and conditions, and even environmental changes in the collection room. Any biobank or research study should establish and follow a well-defined and validated protocol for the collection of s les for metabolomics research. This protocol should be fully documented in any resulting study and should involve all stakeholders in its design. The use of s les that have been collected using standardized and validated protocols is a prerequisite to enable robust biological interpretation unhindered by unnecessary preanalytical factors that may complicate data analysis and interpretation.
Publisher: Elsevier BV
Date: 03-2003
DOI: 10.1016/S0031-9422(02)00722-7
Abstract: The aim of this study was to adopt the approach of metabolic fingerprinting through the use of Fourier transform infrared (FT-IR) spectroscopy and chemometrics to study the effect of salinity on tomato fruit. Two varieties of tomato were studied, Edkawy and Simge F1. Salinity treatment significantly reduced the relative growth rate of Simge F1 but had no significant effect on that of Edkawy. In both tomato varieties salt-treatment significantly reduced mean fruit fresh weight and size class but had no significant affect on total fruit number. Marketable yield was however reduced in both varieties due to the occurrence of blossom end rot in response to salinity. Whole fruit flesh extracts from control and salt-grown tomatoes were analysed using FT-IR spectroscopy. Each s le spectrum contained 882 variables, absorbance values at different wavenumbers, making visual analysis difficult and therefore machine learning methods were applied. The unsupervised clustering method, principal component analysis (PCA) showed no discrimination between the control and salt-treated fruit for either variety. The supervised method, discriminant function analysis (DFA) was able to classify control and salt-treated fruit in both varieties. Genetic algorithms (GA) were applied to identify discriminatory regions within the FT-IR spectra important for fruit classification. The GA models were able to classify control and salt-treated fruit with a typical error, when classifying the whole data set, of 9% in Edkawy and 5% in Simge F1. Key regions were identified within the spectra corresponding to nitrile containing compounds and amino radicals. The application of GA enabled the identification of functional groups of potential importance in relation to the response of tomato to salinity.
Publisher: Springer Science and Business Media LLC
Date: 10-02-2021
Publisher: American Society for Microbiology
Date: 10-2004
DOI: 10.1128/AEM.70.10.6157-6165.2004
Abstract: Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their “metabolic footprints” by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.
Publisher: BMJ
Date: 04-2019
DOI: 10.1136/BMJOPEN-2018-024872
Abstract: A potential link exists between prostate cancer (PCa) disease and treatment and increased inflammatory levels from gut dysbiosis. This study aims to examine if exercise favourably alters gut microbiota in men receiving androgen deprivation therapy (ADT) for PCa. Specifically, this study will explore whether: (1) exercise improves the composition of gut microbiota and increases the abundance of bacteria associated with health promotion and (2) whether gut health correlates with favourable inflammatory status, bowel function, continence and nausea among patients participating in the exercise intervention. A single-blinded, two-armed, randomised controlled trial will explore the influence of a 3-month exercise programme (3 days/week) for men with high-risk localised PCa receiving ADT. Sixty patients will be randomly assigned to either exercise intervention or usual care. The primary endpoint (gut health and function assessed via feacal s les) and secondary endpoints (self-reported quality of life via standardised questionnaires, blood biomarkers, body composition and physical fitness) will be measured at baseline and following the intervention. A variety of statistical methods will be used to understand the covariance between microbial ersity and metabolomics profile across time and intervention. An intention-to-treat approach will be utilised for the analyses with multiple imputations followed by a secondary sensitivity analysis to ensure data robustness using a complete cases approach. Ethics approval was obtained from the Human Research Ethics Committee of Edith Cowan University (ID: 19827 NEWTON). Findings will be reported in peer-reviewed publications and scientific conferences in addition to working with national support groups to translate findings for the broader community. If exercise is shown to result in favourable changes in gut microbial ersity, composition and metabolic profile, and reduce gastrointestinal complications in PCa patients receiving ADT, this study will form the basis of a future phase III trial. ANZCTR12618000280202.
Publisher: Springer Science and Business Media LLC
Date: 08-12-2015
DOI: 10.1038/BJC.2015.414
Publisher: Springer Science and Business Media LLC
Date: 25-07-2014
Publisher: Royal Society of Chemistry (RSC)
Date: 2005
DOI: 10.1039/B511484E
Abstract: Muscle foods are an integral part of the human diet and during the last few decades consumption of poultry products in particular has increased significantly. It is important for consumers, retailers and food regulatory bodies that these products are of a consistently high quality, authentic, and have not been subjected to adulteration by any lower-grade material either by accident or for economic gain. A variety of methods have been developed for the identification and authentication of muscle foods. However, none of these are rapid or non-invasive, all are time-consuming and difficulties have been encountered in discriminating between the commercially important avian species. Whilst previous attempts have been made to discriminate between muscle foods using infrared spectroscopy, these have had limited success, in particular regarding the closely related poultry species, chicken and turkey. Moreover, this study includes novel data since no attempts have been made to discriminate between both the species and the distinct muscle groups within these species, and this is the first application of Raman spectroscopy to the study of muscle foods. S les of pre-packed meat and poultry were acquired and FT-IR and Raman measurements taken directly from the meat surface. Qualitative interpretation of FT-IR and Raman spectra at the species and muscle group levels were possible using discriminant function analysis. Genetic algorithms were used to elucidate meaningful interpretation of FT-IR results in (bio)chemical terms and we show that specific wavenumbers, and therefore chemical species, were discriminatory for each type (species and muscle) of poultry s le. We believe that this approach would aid food regulatory bodies in the rapid identification of meat and poultry products and shows particular potential for rapid assessment of food adulteration.
Publisher: Wiley
Date: 18-04-2002
DOI: 10.1002/BIT.10226
Abstract: Two rapid vibrational spectroscopic approaches (diffuse reflectance-absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A erse range of unprocessed, industrial fed-batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-mean-square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as "black box" methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation-based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on-line analysis.
Publisher: Springer Science and Business Media LLC
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 15-11-2019
DOI: 10.1007/S11306-019-1612-4
Abstract: Metabolomics is increasingly being used in the clinical setting for disease diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly important in the construction of multivariate metabolite prediction. Historically, partial least squares (PLS) regression has been the gold standard for binary classification. Nonlinear machine learning methods such as random forests (RF), kernel support vector machines (SVM) and artificial neural networks (ANN) may be more suited to modelling possible nonlinear metabolite covariance, and thus provide better predictive models. We hypothesise that for binary classification using metabolomics data, non-linear machine learning methods will provide superior generalised predictive ability when compared to linear alternatives, in particular when compared with the current gold standard PLS discriminant analysis. We compared the general predictive performance of eight archetypal machine learning algorithms across ten publicly available clinical metabolomics data sets. The algorithms were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks. There was only marginal improvement in predictive ability for SVM and ANN over PLS across all data sets. RF performance was comparatively poor. The use of out-of-bag bootstrap confidence intervals provided a measure of uncertainty of model prediction such that the quality of metabolomics data was observed to be a bigger influence on generalised performance than model choice. The size of the data set, and choice of performance metric, had a greater influence on generalised predictive performance than the choice of machine learning algorithm.
Publisher: Wiley
Date: 26-06-2006
Publisher: Elsevier BV
Date: 11-2009
DOI: 10.1016/J.PLACENTA.2009.08.008
Abstract: Pre-ecl sia (PE) is a multi-system disorder thought to be mediated by circulating factors released from damaged placental villous trophoblast. There is extensive evidence of changes in the villous tissue in PE, some of which may be replicated by culturing villous tissue in hypoxic conditions. Metabolic footprinting offers a hypothesis-generating strategy to investigate factors released from this tissue in vitro. This study investigated differences in the factors released from villous trophoblast from uncomplicated pregnancies (n=6) and those with PE (n=6). In both cases, explanted placental villous fragments were cultured for 96 h in 1% O(2) (hypoxia) or 6% O(2) (placental normoxia). Metabolites consumed from and released into serum-conditioned culture medium were analysed by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS). The relative concentration of 154 features of the metabolic footprint were observed to change in culture medium from uncomplicated pregnancies cultured in normoxic and hypoxic conditions (p<0.00005). 21 and 80 features were also different in culture medium from PE versus uncomplicated pregnancies cultured in hypoxic and normoxic conditions, respectively (p<0.00005). When comparing all 4 groups, 47 metabolic features showed a similar relative concentration in PE-derived media cultured in normoxic conditions to conditioned media from normal villous tissue cultured in hypoxic conditions. These data suggest that hypoxia may have a role in the placental pathogenesis of PE. Three areas of metabolism were highlighted for systems biology investigation glutamate and glutamine, tryptophan metabolism and leukotriene or prostaglandin metabolism.
Publisher: American Association for Cancer Research (AACR)
Date: 08-2009
DOI: 10.1158/1940-6207.CAPR-09-0008
Abstract: Dietary energy restriction (DER) reduces risk of spontaneous mammary cancer in rodents. In humans, DER in premenopausal years seems to reduce risk of postmenopausal breast cancer. Markers of DER are required to develop acceptable DER regimens for breast cancer prevention. We therefore examined markers of DER in the breast, adipose tissue, and serum. Nineteen overweight or obese women at moderately increased risk of breast cancer (lifetime risk, 1 in 6 to 1 in 3) ages between 35 and 45 were randomly allocated to DER [liquid diet, 3,656 kJ/d (864 kcal/d) n = 10] or asked to continue their normal eating patterns (n = 9) for one menstrual cycle. Biopsies of the breast and abdominal fat were taken before and after the intervention. RNA was extracted from whole tissues and breast epithelium (by laser capture microdissection) and hybridized to Affymetrix GeneChips. Longitudinal plasma and urine s les were collected before and after intervention, and metabolic profiles were generated using gas chromatography-mass spectrometry. DER was associated with significant reductions in weight [−7.0 (±2.3) kg] and in alterations of serum biomarkers of breast cancer risk (insulin, leptin, total and low-density lipoprotein cholesterol, and triglycerides). In both abdominal and breast tissues, as well as isolated breast epithelial cells, genes involved in glycolytic and lipid synthesis pathways (including stearoyl-CoA desaturase, fatty acid desaturase, and aldolase C) were significantly down-regulated. We conclude that reduced expressions of genes in the lipid metabolism and glycolytic pathways are detectable in breast tissue following DER, and these may represent targets for DER mimetics as effective chemoprophylactic agents.
Publisher: American Chemical Society (ACS)
Date: 21-08-2013
DOI: 10.1021/PR400617M
Abstract: Neonatal hypoxic ischemic encephalopathy (HIE) is a severe consequence of perinatal asphyxia (PA) that can result in life-long neurological disability. Disease mechanisms, including the role and interaction of in idual metabolic pathways, remain unclear. As hypoxia is an acute condition, aerobic energy metabolism is central to global metabolic pathways, and these metabolites are detectable using 1H NMR spectroscopy, we hypothesized that characterizing the NMR-derived umbilical cord serum metabolome would offer insight into the consequences of PA that lead to HIE. Fifty-nine at-risk infants were enrolled, together with 1:1 matched healthy controls, and stratified by disease severity (n=25, HIE n=34, non-HIE PA). Eighteen of 37 reproducibly detectable metabolites were significantly altered between study groups. Acetone, 3-hydroxybutyrate, succinate, and glycerol were significantly differentially altered in severe HIE. Multivariate data analysis revealed a metabolite profile associated with both asphyxia and HIE. Multiple-linear regression modeling using 4 metabolites (3-hydroxybutyrate, glycerol, O-phosphocholine, and succinate) predicted HIE severity with an adjusted R2 of 0.4. Altered ketones suggest that systemic metabolism may play a critical role in preventing neurological injury, while altered succinate provides a possible explanation for hypoxia-inducible factor 1-α (HIF-1α) stabilization in HI injury.
Publisher: Royal Society of Chemistry (RSC)
Date: 2018
DOI: 10.1039/C8AY00830B
Abstract: A Monte Carlo simulation technique is used to accurately measure metabolite concentrations in urine.
Publisher: Springer Science and Business Media LLC
Date: 10-2020
Publisher: Wiley
Date: 18-01-2012
DOI: 10.1111/J.1471-0528.2011.03245.X
Abstract: Obstetric cholestasis (OC) is a liver disorder characterised by pruritus and elevated serum bile acids (SBA) that affects one in 200 pregnant women. It is associated with adverse perinatal outcomes such as premature delivery and stillbirth. Mild OC is defined as SBA levels of 10-39 μmol/l, and severe OC is defined by levels >40 μmol/l. SBA levels in normal pregnancy have not been investigated. We aimed to establish reference values for SBA in healthy pregnant women across different trimesters of pregnancy. Cross-sectional analysis of SBA levels. A large tertiary referral university teaching maternity hospital. Healthy pregnant women with a singleton pregnancy and a body mass index (BMI) < 40, excluding women with significant alcohol intake, history of liver disease, prior cholecystectomy and OC. Cross-sectional analysis of SBA levels at 12, 20, 28 and 36 weeks of gestation, and on days 1-3 postpartum. SBA levels in μmol/l. A total of 219 women attending for antenatal care were recruited, and SBA levels were assayed at 12, 20, 28 and 36 weeks of gestation, and up to 72 hours postpartum (n = 44-49 cases at each stage). The majority were white European women, with a median age of 30 years (range 17-46 years) and median BMI of 25 (range 18-38). Values of SBA ranged from 0.3 to 9.8 μmol/l in 216 women, with only three measurements outside this range. There were no significant changes throughout pregnancy. SBA values in uncomplicated pregnancies are consistent, regardless of gestation, and are not elevated in pregnancy. The current reference values for the diagnosis of OC appear to be appropriate.
Publisher: Informa UK Limited
Date: 17-10-2012
DOI: 10.3109/01443615.2012.714017
Abstract: Cholesterol is monitored in the non-pregnant adult population, where normal values are established. Although reported to be elevated in pregnancy, cholesterol is neither routinely measured nor treated. We aimed to investigate cholesterol levels throughout pregnancy and to establish reference values for cholesterol in healthy pregnant women. This was a cross-sectional analysis of serum cholesterol in healthy women with an uncomplicated singleton pregnancy. Pregnant women attending for antenatal care were recruited and cholesterol levels assayed at 12, 20, 28 and 36 weeks' gestation and on day 1-3 postpartum. A total of 222 women were recruited. The majority (95%) were white Irish, with a median age of 31 years (range 16-46). Median BMI was 25.9 kg/m2 (range 18-40) and 16% were smokers. Cholesterol levels were elevated in all trimesters of pregnancy, with median values from 1st trimester raised outside the non-pregnant adult range. High-density lipoprotein (HDL) levels ranged from 0.9 to 3.7 mmol/l and low-density lipoprotein (LDL) levels ranged from 1.3 to 6.1 mmol/l. Fasting, smoking and obesity did not have any significant effects on results. Total and LDL-cholesterol levels were raised throughout pregnancy. Levels were above non-pregnant adult ranges as early as the 1st trimester. The implications of this on fetus and mother are undetermined and deserve further investigation.
Publisher: Elsevier BV
Date: 10-2010
DOI: 10.1016/J.PLACENTA.2010.07.002
Abstract: Being born small for gestational age (SGA) confers significantly increased risks of perinatal morbidity and mortality. Accumulating evidence suggests that an SGA fetus results from a poorly perfused and abnormally developed placenta. Some of the placental features seen in SGA, such as abnormal cell turnover and impaired nutrient transport, can be reproduced by culture of placental explants in hypoxic conditions. Metabolic footprinting offers a hypothesis-generating strategy to investigate factors absorbed by and released from this tissue in vitro. Previously, metabolic footprinting of the conditioned culture media has identified differences in placental explants cultured under normoxic and hypoxic conditions and between normal pregnancies and those complicated by pre-ecl sia. In this study we aimed to examine the differences in the metabolic footprint of placental villous explants cultured at different oxygen (O(2)) tensions between women who deliver an SGA baby (n = 9) and those from normal controls (n = 8). Placental villous explants from cases and controls were cultured for 96 h in 1% (hypoxic), 6% (normoxic) and 20% (hyperoxic) O(2). Metabolic footprints were analysed by Ultra Performance Liquid Chromatography coupled to an electrospray hybrid LTQ-Orbitrap Mass Spectrometry (UPLC-MS). 574 metabolite features showed significant difference between SGA and normal at one or more of the oxygen tensions. SGA explant media cultured under hypoxic conditions was observed, on a univariate level, to exhibit the same metabolic signature as controls cultured under normoxic conditions in 49% of the metabolites of interest, suggesting that SGA tissue is acclimatised to hypoxic conditions in vivo. No such behaviour was observed under hyperoxic culture conditions. Glycerophospholipid and tryptophan metabolism were highlighted as areas of particular interest.
Publisher: Royal Society of Chemistry (RSC)
Date: 2016
DOI: 10.1039/C5MB00889A
Abstract: Metabolomics is used to understand the physiological response of Pseudomonas putida to exposure with the human beta-blocker propranolol.
Publisher: American Chemical Society (ACS)
Date: 16-07-2009
DOI: 10.1021/AC9011599
Abstract: A method for the preparation and GC-TOF-MS analysis of human serum s les has been developed and evaluated for application in long-term metabolomic studies. Serum s les were deproteinized using 3:1 methanol/serum, dried in a vacuum concentrator, and chemically derivatized in a two-stage process. S les were analyzed by GC-TOF-MS with a 25 min analysis time. In addition, quality control (QC) s les were used to quantify process variability. Optimization of chemical derivatization was performed. Products were found to be stable for 30 h after derivatization. An assessment of within-day repeatability and within-week reproducibility demonstrates that excellent performance is observed with our developed method. Analyses were consistent over a 5 month period. Additional method testing, using spiked serum s les, showed the ability to define metabolite differences between s les from a population and s les spiked with metabolites standards. This methodology allows the continuous acquisition and application of data acquired over many months in long-term metabolomic studies, including the HUSERMET project (www.husermet.org/).
Publisher: Springer Science and Business Media LLC
Date: 2001
DOI: 10.1038/83496
Abstract: A large proportion of the 6,000 genes present in the genome of Saccharomyces cerevisiae, and of those sequenced in other organisms, encode proteins of unknown function. Many of these genes are "silent, " that is, they show no overt phenotype, in terms of growth rate or other fluxes, when they are deleted from the genome. We demonstrate how the intracellular concentrations of metabolites can reveal phenotypes for proteins active in metabolic regulation. Quantification of the change of several metabolite concentrations relative to the concentration change of one selected metabolite can reveal the site of action, in the metabolic network, of a silent gene. In the same way, comprehensive analyses of metabolite concentrations in mutants, providing "metabolic snapshots," can reveal functions when snapshots from strains deleted for unstudied genes are compared to those deleted for known genes. This approach to functional analysis, using comparative metabolomics, we call FANCY-an abbreviation for functional analysis by co-responses in yeast.
Publisher: Wageningen Academic Publishers
Date: 02-04-2007
Publisher: The Endocrine Society
Date: 10-2015
DOI: 10.1210/JC.2015-2503
Publisher: Oxford University Press (OUP)
Date: 08-02-2008
DOI: 10.1093/IJE/DYM281
Abstract: 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 s les from 40 separate healthy volunteers. Metabolic profiling with GC-TOF-MS was performed for serum and urine s les 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 s les stored at 4 degrees C for 0 or 24 h. More than 700 unique metabolite peaks were detected, with over 200 metabolite peaks detected in any one s le. 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 s le (analytical variance) was of the same magnitude as the variance observed between s les stored at 4 degrees C for 0 or 24 h. From a functional point of view the metabolomic composition of the majority of s les did not change in a statistically significant manner when stored under two different conditions. Based on this small pilot study, the UK Biobank s ling, transport and fractionation protocols are considered suitable to provide s les, which can produce scientifically robust and valid data in metabolomic studies.
Publisher: Springer Science and Business Media LLC
Date: 03-2013
DOI: 10.1007/S00216-013-6856-7
Abstract: Direct infusion mass spectrometry (DIMS)-based untargeted metabolomics measures many hundreds of metabolites in a single experiment. While every effort is made to reduce within-experiment analytical variation in untargeted metabolomics, unavoidable sources of measurement error are introduced. This is particularly true for large-scale multi-batch experiments, necessitating the development of robust workflows that minimise batch-to-batch variation. Here, we conducted a purpose-designed, eight-batch DIMS metabolomics study using nanoelectrospray (nESI) Fourier transform ion cyclotron resonance mass spectrometric analyses of mammalian heart extracts. First, we characterised the intrinsic analytical variation of this approach to determine whether our existing workflows are fit for purpose when applied to a multi-batch investigation. Batch-to-batch variation was readily observed across the 7-day experiment, both in terms of its absolute measurement using quality control (QC) and biological replicate s les, as well as its adverse impact on our ability to discover significant metabolic information within the data. Subsequently, we developed and implemented a computational workflow that includes total-ion-current filtering, QC-robust spline batch correction and spectral cleaning, and provide conclusive evidence that this workflow reduces analytical variation and increases the proportion of significant peaks. We report an overall analytical precision of 15.9%, measured as the median relative standard deviation (RSD) for the technical replicates of the biological s les, across eight batches and 7 days of measurements. When compared against the FDA guidelines for biomarker studies, which specify an RSD of <20% as an acceptable level of precision, we conclude that our new workflows are fit for purpose for large-scale, high-throughput nESI DIMS metabolomics studies.
Publisher: Springer Science and Business Media LLC
Date: 07-2008
Abstract: In a previous study, the ability of gas chromatography time-of-flight mass spectrometry to detect potential metabolic biomarkers in preecl sia was demonstrated. In this study, the authors sought to validate their preliminary findings in an entirely different patient cohort using a complementary, novel, and powerful combination of analytical tools (ultra performance liquid chromatography and LTQ Orbitrap mass spectrometry system). Eight metabolites that appeared in the authors' previous patient cohort were identified as being statistically significant (P < .01) as discriminatory biomarkers. The chemical identities of these 8 metabolites were established using authentic chemical standards. They included uric acid, 2-oxoglutarate, glutamate, and alanine. This is the first study to identify, in an unbiased manner, a series of small-molecular-weight metabolites that effectively detect preecl sia in plasma. The identity of these metabolites provides new insights into the pathology of this condition and raises the possibility of the development of a predictive test.
Publisher: Springer Science and Business Media LLC
Date: 30-06-2011
Abstract: 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 s le. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human s les 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, s le 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.
Publisher: Springer Science and Business Media LLC
Date: 28-06-2007
Publisher: Springer Science and Business Media LLC
Date: 21-01-2020
DOI: 10.1007/S11306-020-1640-0
Abstract: Metabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods. We hypothesise that standardised optimisation, visualisation, evaluation and statistical inference techniques commonly used by metabolomics researchers for PLS-DA can be migrated to a non-linear, single hidden layer, ANN. We compared a standardised optimisation, visualisation, evaluation and statistical inference techniques workflow for PLS with the proposed ANN workflow. Both workflows were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks on GitHub. The migration of the PLS workflow to a non-linear, single hidden layer, ANN was successful. There was a similarity in significant metabolites determined using PLS model coefficients and ANN Connection Weight Approach. We have shown that it is possible to migrate the standardised PLS-DA workflow to simple non-linear ANNs. This result opens the door for more widespread use and to the investigation of transparent interpretation of more complex ANN architectures.
Publisher: American Chemical Society (ACS)
Date: 15-12-2006
DOI: 10.1021/AC061443+
Abstract: Metabolomics seeks to measure potentially all the metabolites in a biological s le, and consequently, we need to develop and optimize methods to increase significantly the number of metabolites we can detect. We extended the closed-loop (iterative, automated) optimization system that we had previously developed for one-dimensional GC-TOF-MS (O'Hagan, S. Dunn, W. B. Brown, M. Knowles, J. D. Kell, D. B. Anal. Chem. 2005, 77, 290-303) to comprehensive two-dimensional (GCxGC) chromatography. The heuristic approach used was a multiobjective version of the efficient global optimization algorithm. In just 300 automated runs, we improved the number of metabolites observable relative to those in 1D GC by some 3-fold. The optimized conditions allowed for the detection of over 4000 raw peaks, of which some 1800 were considered to be real metabolite peaks and not impurities or peaks with a signal/noise ratio of less than 5. A variety of computational methods served to explain the basis for the improvement. This closed-loop optimization strategy is a generic and powerful approach for the optimization of any analytical instrumentation.
Publisher: Springer Science and Business Media LLC
Date: 18-05-2018
Publisher: Springer Science and Business Media LLC
Date: 18-10-2019
DOI: 10.1007/S11306-019-1608-0
Abstract: Metabolomics data, with its complex covariance structure, is typically modelled by projection-based machine learning (ML) methods such as partial least squares (PLS) regression, which project data into a latent structure. Biological data are often non-linear, so it is reasonable to hypothesize that metabolomics data may also have a non-linear latent structure, which in turn would be best modelled using non-linear equations. A non-linear ML method with a similar projection equation structure to PLS is artificial neural networks (ANNs). While ANNs were first applied to metabolic profiling data in the 1990s, the lack of community acceptance combined with limitations in computational capacity and the lack of volume of data for robust non-linear model optimisation inhibited their widespread use. Due to recent advances in computational power, modelling improvements, community acceptance, and the more demanding needs for data science, ANNs have made a recent resurgence in interest across research communities, including a small yet growing usage in metabolomics. As metabolomics experiments become more complex and start to be integrated with other omics data, there is potential for ANNs to become a viable alternative to linear projection methods. We aim to first describe ANNs and their structural equivalence to linear projection-based methods, including PLS regression. We then review the historical, current, and future uses of ANNs in the field of metabolomics. Is metabolomics ready for the return of artificial neural networks?
Publisher: Future Science Ltd
Date: 09-2012
DOI: 10.4155/BIO.12.204
Abstract: The metabolic investigation of the human population is becoming increasingly important in the study of health and disease. The phenotypic variation can be investigated through the application of metabolomics to provide a statistically robust investigation, the study of hundreds to thousands of in iduals is required. In untargeted and MS-focused metabolomic studies this once provided significant hurdles. However, recent innovations have enabled the application of MS platforms in large-scale, untargeted studies of humans. Herein we describe the importance of experimental design, the separation of the biological study into multiple analytical experiments and the incorporation of QC s les to provide the ability to perform signal correction in order to reduce analytical variation and to quantitatively determine analytical precision. In addition, we describe how to apply this in quality assurance processes. These innovations have opened up the capabilities to perform routine, large-scale, untargeted, MS-focused studies.
Publisher: American Chemical Society (ACS)
Date: 11-03-2008
DOI: 10.1021/AC7023409
Abstract: Metabolomics and systems biology require the acquisition of reproducible, robust, reliable, and homogeneous biological data sets. Therefore, we developed and validated standard operating procedures (SOPs) for quenching and efficient extraction of metabolites from Escherichia coli to determine the best methods to approach global analysis of the metabolome. E. coli was grown in chemostat culture so that cellular metabolism could be held in reproducible, steady-state conditions under a range of precisely defined growth conditions, thus enabling sufficient replication of s les. The metabolome profiles were generated using gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS). We employed univariate and multivariate statistical analyses to determine the most suitable method. This investigation indicates that 60% cold (-48 degrees C) methanol solution is the most appropriate method to quench metabolism, and we recommend 100% methanol, also at -48 degrees C, with multiple freeze-thaw cycles for the extraction of metabolites. However, complementary extractions would be necessary for coverage of the entire complement of metabolites as detected by GC/TOF-MS. Finally, the observation that metabolite leakage was significant and measurable whichever quenching method is used indicates that methods should be incorporated into the experiment to facilitate the accurate quantification of intracellular metabolites.
Publisher: SAGE Publications
Date: 25-08-2017
Abstract: Elucidating metabolic effects of hypoxic-ischaemic encephalopathy (HIE) may reveal early biomarkers of injury and new treatment targets. This study uses untargeted metabolomics to examine early metabolic alterations in a carefully defined neonatal population. Infants with perinatal asphyxia who were resuscitated at birth and recovered (PA group), those who developed HIE (HIE group) and healthy controls were all recruited at birth. Metabolomic analysis of cord blood was performed using direct infusion FT-ICR mass spectrometry. For each reproducibly detected metabolic feature, mean fold differences were calculated HIE vs. controls (ΔHIE) and PA vs. controls (ΔPA). Putative metabolite annotations were assigned and pathway analysis was performed. Twenty-nine putatively annotated metabolic features were significantly different in ΔPA after false discovery correction ( q 0.05), with eight of these also significantly altered in ΔHIE. Altered putative metabolites included melatonin, leucine, kynurenine and 3-hydroxydodecanoic acid which differentiated between infant groups (ΔPA and ΔHIE) and D-erythrose-phosphate, acetone, 3-oxotetradecanoic acid and methylglutarylcarnitine which differentiated across severity grades of HIE. Pathway analysis revealed ΔHIE was associated with a 50% and 75% perturbation of tryptophan and pyrimidine metabolism, respectively. We have identified perturbed metabolic pathways and potential biomarkers specific to PA and HIE, which measured at birth, may help direct treatment.
Publisher: Oxford University Press (OUP)
Date: 07-02-2006
DOI: 10.1093/BRAIN/AWL027
Abstract: There has been considerable progress recently towards developing therapeutic strategies for Huntington's disease (HD), with several compounds showing beneficial effects in transgenic mouse models. However, human trials in HD are difficult, costly and time-consuming due to the slow disease course, insidious onset and patient-to-patient variability. Identification of molecular biomarkers associated with disease progression will aid the development of effective therapies by allowing further validation of animal models and by providing hopefully more sensitive measures of disease progression. Here, we apply metabolic profiling by gas chromatography-time-of-flight-mass spectrometry to serum s les from human HD patients and a transgenic mouse model in a hypothesis-generating search for disease biomarkers. We observed clear differences in metabolic profiles between transgenic mice and wild-type littermates, with a trend for similar differences in human patients and control subjects. Thus, the metabolites responsible for distinguishing transgenic mice also comprised a metabolic signature tentatively associated with the human disease. The candidate biomarkers composing this HD-associated metabolic signature in mouse and humans are indicative of a change to a pro-catabolic phenotype in early HD preceding symptom onset, with changes in various markers of fatty acid breakdown (including glycerol and malonate) and also in certain aliphatic amino acids. Our data raise the prospect of a robust molecular definition of progression of HD prior to symptom onset, and if validated in a genuinely prospective fashion these biomarker trajectories could facilitate the development of useful therapies for this disease.
Publisher: Wiley
Date: 15-05-2014
Abstract: To establish a gestation-specific reference range for D-dimer in healthy pregnant women with a singleton pregnancy using the Auto-Dimer assay. Cross-sectional study Cork University Maternity Hospital, Ireland. Healthy pregnant women attending for routine antenatal care. Simultaneous-quantile regression was performed to construct a median, 5th percentile, and 95th percentile, model of normal pregnancy D-dimer concentration versus gestational week, ranging from week 6 to 42. Additionally, pair-wise Mann-Whitney U-tests were performed to compare distributions of D-dimer concentrations for each of the four discrete gestational s ling windows with the distribution of D-dimer concentrations 48 hours postpartum. D-dimer concentrations (ng/ml) during normal gestation (approximately week 6 to week 42). Seven hundred and sixty healthy pregnant women were investigated between gestational age week 5 and 48 hours postpartum. There was a clear steady increase in median D-dimer concentrations over the complete gestational period. Additionally, the 95th centile estimates for all gestational time-points were above the accepted non-pregnancy normal cut-off concentration (224 ng/ml). The results of the Mann-Whitney U-tests suggested that the long-term postnatal return to normal D-dimer concentrations begins in the immediate postpartum period. We found that there is a continuous increase in D-dimer concentrations across all gestations. This research is potentially beneficial to future diagnosis of venous thromboembolism (VTE) in pregnancy using the new recommended 95th centile potential cut-offs. Possible further investigation involves an observational study comparing D-dimer concentrations in women with proven DVT with those that don't, generating likelihood ratios.
Publisher: Royal Society of Chemistry (RSC)
Date: 2015
DOI: 10.1039/C4MB00739E
Abstract: GC-MS-based metabolomics illustrates the response of central metabolism metabolites upon treatment with the anti-psoriatic drug dithranol.
Publisher: SAGE Publications
Date: 24-02-2016
Abstract: To examine perinatal determinants of the antenatal levels of D-dimers. Cross-sectional study of 760 low risk pregnant women recruited into five gestational groups. Variables examined in antenatal groups included maternal age, body mass index, parity, smoking, family history venous thromboembolism (VTE) and previous use of the oral contraceptive pill (OCP). Onset of labour and mode of delivery were also examined in the post-natal group. D-dimer levels in group 4 (38–40 + 6) were significantly lower in the women with a history of taking the OCP when compared to those that had not taken it in the past ( P = 0.027). In the day 2 post-natal group, the median level of D-dimer was significantly higher in primparous when compared to multiparous women ( P = 0.015). The median D-dimer levels were significantly lower in the elective Caesarean section group in comparison to spontaneous onset ( P = 0.003) and induction of labour ( P = 0.016). When the mode of delivery was examined, the median D-dimer levels were significantly lower in those that had an elective Caesarean section when compared to normal vaginal delivery ( P = 0.008) and instrumental vaginal delivery ( P = 0.007). Women post elective Caesarean section had a significantly lower D-dimer than those after emergency Caesarean section ( P = 0.008). There are some significant differences in D-dimer levels when certain perinatal determinants are examined. This work is potentially beneficial to the future diagnosis of VTE in pregnancy as it supports previously published recommended D-dimer levels for the diagnosis of VTE in pregnancy.
Publisher: Public Library of Science (PLoS)
Date: 03-09-2014
Start Date: 05-2017
End Date: 12-2018
Amount: $2,168,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 04-2019
End Date: 04-2020
Amount: $415,000.00
Funder: Australian Research Council
View Funded Activity