ORCID Profile
0000-0001-5307-5709
Current Organisation
Murdoch University
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Publisher: American Chemical Society (ACS)
Date: 13-05-2016
DOI: 10.1021/ACS.ANALCHEM.5B04020
Abstract: We propose a novel data-driven approach aiming to reliably distinguish discriminatory metabolites from nondiscriminatory metabolites for a given spectroscopic data set containing two biological phenotypic subclasses. The automatic spectroscopic data categorization by clustering analysis (ASCLAN) algorithm aims to categorize spectral variables within a data set into three clusters corresponding to noise, nondiscriminatory and discriminatory metabolites regions. This is achieved by clustering each spectral variable based on the r(2) value representing the loading weight of each spectral variable as extracted from a orthogonal partial least-squares discriminant (OPLS-DA) model of the data set. The variables are ranked according to r(2) values and a series of principal component analysis (PCA) models are then built for subsets of these spectral data corresponding to ranges of r(2) values. The Q(2)X value for each PCA model is extracted. K-means clustering is then applied to the Q(2)X values to generate two clusters based on minimum Euclidean distance criterion. The cluster consisting of lower Q(2)X values is deemed devoid of metabolic information (noise), while the cluster consists of higher Q(2)X values is then further subclustered into two groups based on the r(2) values. We considered the cluster with high Q(2)X but low r(2) values as nondiscriminatory, while the cluster with high Q(2)X and r(2) values as discriminatory variables. The boundaries between these three clusters of spectral variables, on the basis of the r(2) values were considered as the cut off values for defining the noise, nondiscriminatory and discriminatory variables. We evaluated the ASCLAN algorithm using six simulated (1)H NMR spectroscopic data sets representing small, medium and large data sets (N = 50, 500, and 1000 s les per group, respectively), each with a reduced and full resolution set of variables (0.005 and 0.0005 ppm, respectively). ASCLAN correctly identified all discriminatory metabolites and showed zero false positive (100% specificity and positive predictive value) irrespective of the spectral resolution or the s le size in all six simulated data sets. This error rate was found to be superior to existing methods for ascertaining feature significance: univariate t test by Bonferroni correction (up to 10% false positive rate), Benjamini-Hochberg correction (up to 35% false positive rate) and metabolome wide significance level (MWSL, up to 0.4% false positive rate), as well as by various OPLS-DA parameters: variable importance to projection, (up to 15% false positive rate), loading coefficients (up to 35% false positive rate), and regression coefficients (up to 39% false positive rate). The application of ASCLAN was further exemplified using a widely investigated renal toxin, mercury II chloride (HgCl2) in rat model. ASCLAN successfully identified many of the known metabolites related to renal toxicity such as increased excretion of urinary creatinine, and different amino acids. The ASCLAN algorithm provides a framework for reliably differentiating discriminatory metabolites from nondiscriminatory metabolites in a biological data set without the need to set an arbitrary cut off value as applied to some of the conventional methods. This offers significant advantages over existing methods and the possibility for automation of high-throughput screening in "omics" data.
Publisher: Oxford University Press (OUP)
Date: 21-07-2020
DOI: 10.1093/BIOINFORMATICS/BTAA649
Abstract: Large-scale population omics data can provide insight into associations between gene–environment interactions and disease. However, existing dimension reduction modelling techniques are often inefficient for extracting detailed information from these complex datasets. Here, we present an interactive software pipeline for exploratory analyses of population-based nuclear magnetic resonance spectral data using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS) within the R-library hastaLaVista framework. Principal component analysis models are generated for a sequential series of spectral regions (blocks) to provide more granular detail defining sub-populations within the dataset. Molecular identification of key differentiating signals is subsequently achieved by implementing Statistical TOtal Correlation SpectroscopY on the full spectral data to define feature patterns. Finally, the distributions of cross-correlation of the reference patterns across the spectral dataset are used to provide population statistics for identifying underlying features arising from drug intake, latent diseases and diet. The COMPASS method thus provides an efficient semi-automated approach for screening population datasets. Source code is available at heminfo/COMPASS. Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 20-04-2008
DOI: 10.1038/NATURE06882
Publisher: American Chemical Society (ACS)
Date: 12-07-2021
Publisher: American Chemical Society (ACS)
Date: 10-02-2022
DOI: 10.1021/ACS.JPROTEOME.1C00851
Abstract: Trimethylamine (TMA) and its
Publisher: Cold Spring Harbor Laboratory
Date: 08-05-2023
DOI: 10.1101/2023.05.08.23289637
Abstract: We present compelling evidence for the existence of an extended innate viperin dependent pathway which provides crucial evidence for an adaptive response to viral agents like SARS-CoV-2. We show the in vivo biosynthesis of a family of endogenous cytosine metabolites with potential antiviral activity. Two dimensional Nuclear magnetic resonance (NMR) spectroscopy revealed a characteristic spin-system motif indicating the presence of an extended panel of urinary metabolites during the acute viral replication phase. Mass spectrometry additionally allowed the characterization and quantification of the most abundant serum metabolites showing potential diagnostic value of the compounds for viral infections. In total, we unveiled ten nucleoside (cytosine and uracil based) analogue structures, eight of which were previously unknown in humans. The molecular structures of the nucleoside analogues and their correlation with an array of serum cytokines, including IFN-α2, IFN-γ and IL-10, suggest an association with the viperin enzyme contributing to an endogenous innate immune defence mechanism against viral infection.
Publisher: Elsevier BV
Date: 2022
DOI: 10.1016/J.NUMECD.2021.09.031
Abstract: Substantial scientific evidence supports the effectiveness of a Mediterranean diet (MedDiet) in managing type 2 diabetes mellitus (T2DM). Potential benefits of time restricted feeding (TRF) in T2DM are unknown. The MedDietFast trial aims to investigate the efficacy of a MedDiet with or without TRF compared to standard care diet in managing T2DM. 120 adults aged 20-75 with a body mass index (BMI) of 20-35 kg/m The MedDietFast trial will examine the feasibility and effectiveness of a MedDiet with/without TRF in T2DM patients. Potential synergistic effects of a MedDiet with TRF will be evaluated. Future studies will generate microbiomic and metabolomic data for translation of findings into simple and effective management plans for T2DM patients. Australia and New Zealand Clinical Trials Register, ACTRN12619000246189.
Publisher: American Chemical Society (ACS)
Date: 05-05-2021
Publisher: American Chemical Society (ACS)
Date: 13-05-2014
DOI: 10.1021/AC500161K
Publisher: Springer Science and Business Media LLC
Date: 22-12-2016
DOI: 10.1038/HR.2016.164
Publisher: Informa UK Limited
Date: 09-2016
DOI: 10.2147/PPA.S112931
Publisher: Informa UK Limited
Date: 11-2016
DOI: 10.2147/PPA.S112932
Publisher: American Chemical Society (ACS)
Date: 02-11-2010
DOI: 10.1021/PR100798R
Publisher: Elsevier BV
Date: 03-2018
DOI: 10.1093/AJCN/NQX072
Abstract: Interin idual variation in the response to diet is common, but the underlying mechanism for such variation is unclear. The objective of this study was to use a metabolic profiling approach to identify a panel of urinary metabolites representing in iduals demonstrating typical (homogeneous) metabolic responses to healthy diets, and subsequently to define the association of these metabolites with improvement of risk factors for cardiovascular diseases (CVDs). 24-h urine s les from 158 participants with pre-hypertension and stage 1 hypertension, collected at baseline and following the consumption of a carbohydrate-rich, a protein-rich, and a monounsaturated fat-rich healthy diet (6 wk/diet) in a randomized, crossover study, were analyzed by proton (1H) nuclear magnetic resonance (NMR) spectroscopy. Urinary metabolite profiles were interrogated to identify typical and variable responses to each diet. We quantified the differences in absolute excretion of metabolites, distinguishing between dietary comparisons within the typical response groups, and established their associations with CVD risk factors using linear regression. Globally all 3 diets induced a similar pattern of change in the urinary metabolic profiles for the majority of participants (60.1%). Diet-dependent metabolic variation was not significantly associated with total cholesterol or low-density lipoprotein (LDL) cholesterol concentration. However, blood pressure (BP) was found to be significantly associated with 6 urinary metabolites reflecting dietary intake [proline-betaine (inverse), carnitine (direct)], gut microbial co-metabolites [hippurate (direct), 4-cresyl sulfate (inverse), phenylacetylglutamine (inverse)], and tryptophan metabolism [N-methyl-2-pyridone-5-carboxamide (inverse)]. A d ened clinical response was observed in some in iduals with variable metabolic responses, which could be attributed to nonadherence to diet (≤25.3%), variation in gut microbiome activity (7.6%), or a combination of both (7.0%). These data indicate interin idual variations in BP in response to dietary change and highlight the potential influence of the gut microbiome in mediating this relation. This approach provides a framework for stratification of in iduals undergoing dietary management. The original OmniHeart intervention study and the metabolomics study were registered at www.clinicaltrials.gov as NCT00051350 and NCT03369535, respectively.
Publisher: SAGE Publications
Date: 25-07-2013
Abstract: Pharmacometabonomics is a new branch of science, first described in 2006 and defined as ‘the prediction of the effects of a drug on the basis of a mathematical model of pre-dose metabolite profiles’. Pharmacometabonomics has been used to predict drug metabolism, pharmacokinetics (PK), drug safety and drug efficacy in both animals and humans and is complementary to both pharmacogenomics (PGx) and pharmacoproteomics. A literature review using the search terms pharmacometabonomics, pharmacometabolomics, pharmaco-metabonomics, pharmaco-metabolomics and the singular form of all those terms was conducted in October 2012 using PubMed and Web of Science. The review was updated until mid April 2013. Since the original description of pharmacometabonomics in 2006, 21 original publications and eight reviews have emerged, covering a broad range of applications from the prediction of PK to the prediction of drug metabolism, efficacy and safety in humans and animals. Pharmacometabonomics promises to be an important new approach to the delivery of personalized medicine to improve both drug efficacy and safety for patients in the future. Pharmacometabonomics is particularly powerful as it is sensitive to both genetic and environmental factors such as diet, drug intake and most importantly, a person’s microbiome. PGx is now over 50 years old and although it has not achieved as much as some hoped, it is starting to have important applications in personalized medicine. We predict that pharmacometabonomics will be equally important in the next few decades and will be both valuable in its own right and complementary to pharmacoproteomics and PGx.
Publisher: Elsevier BV
Date: 02-2020
DOI: 10.1093/AJCN/NQZ293
Publisher: American Chemical Society (ACS)
Date: 12-02-2021
Publisher: American Chemical Society (ACS)
Date: 02-06-2009
DOI: 10.1021/AC900567E
Publisher: American Chemical Society (ACS)
Date: 11-01-2021
Publisher: American Chemical Society (ACS)
Date: 27-02-2007
DOI: 10.1021/AC062305N
Publisher: American Chemical Society (ACS)
Date: 27-08-2020
Publisher: American Chemical Society (ACS)
Date: 23-01-2021
Publisher: American Chemical Society (ACS)
Date: 19-05-2021
Publisher: Elsevier BV
Date: 07-2023
Publisher: Springer Science and Business Media LLC
Date: 24-07-2020
DOI: 10.1038/S41370-020-0252-0
Abstract: Exposure-response studies and policy evaluations of household air pollution (HAP) are limited by current methods of exposure assessment which are expensive and burdensome to participants. We collected 152 dried blood spot (DBS) specimens during the heating and non-heating seasons from 53 women who regularly used biomass-burning stoves for cooking and heating. Participants were enrolled in a longitudinal study in China. Untargeted metabolic phenotyping of DBS were generated using ultra-high performance liquid chromatography coupled with mass spectrometry to exemplify measurement precision and assessment for feasibility to detect exposure to HAP, evaluated by season (high pollution vs. low pollution) and measured personal exposure to fine particulate matter <2.5 μm diameters (PM Metabolites e.g., amino acids, acyl-carnitines, lyso-phosphorylcholines, sphinganine, and choline were detected in the DBS specimens. Our approach is capable of detecting the differences in personal exposure to HAP whilst showing high analytical reproducibility, coefficient of variance (CV) <15%, meeting the U.S. Food and Drug Administration criteria. Our results provide a proof of principle that high-resolution metabolic phenotypic data can be generated using a simple DBS extraction method thus suitable for exposure studies in remote, low-resource settings where the collection of serum and plasma is logistically challenging or infeasible. The analytical run time (19 min/specimen) is similar to most global phenotyping methods and therefore suitable for large-scale application.
Publisher: Springer Science and Business Media LLC
Date: 29-08-2015
Publisher: Oxford University Press (OUP)
Date: 05-01-2012
DOI: 10.1093/AJE/KWR292
Publisher: Elsevier BV
Date: 07-2022
DOI: 10.1093/AJCN/NQAC067
Publisher: Elsevier BV
Date: 09-2010
Publisher: American Chemical Society (ACS)
Date: 17-08-2020
Publisher: American Chemical Society (ACS)
Date: 03-2022
DOI: 10.1021/ACS.ANALCHEM.1C05389
Abstract: SARS-CoV-2 infection causes a significant reduction in lipoprotein-bound serum phospholipids give rise to supramolecular phospholipid composite (SPC) signals observed in diffusion and relaxation edited
Publisher: Public Library of Science (PLoS)
Date: 29-08-2012
Publisher: MDPI AG
Date: 03-04-2023
Abstract: Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, s le storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.
Publisher: Elsevier BV
Date: 06-2023
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2009
End Date: 2011
Funder: Channel 7 Telethon Trust
View Funded ActivityStart Date: 2019
End Date: 2023
Funder: Department of Jobs, Tourism, Science and Innovation
View Funded Activity