Browsing by Author "Cooper GJS"

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    Dissecting the relationship between plasma and tissue metabolome in a cohort of women with obesity: Analysis of subcutaneous and visceral adipose, muscle, and liver
    (Wiley Periodicals LLC on behalf of Federation of American Societies for Experimental Biology, 2022-07) Wu ZE; Kruger MC; Cooper GJS; Sequeira IR; McGill A-T; Poppitt SD; Fraser K
    Untargeted metabolomics of blood samples has become widely applied to study metabolic alterations underpinning disease and to identify biomarkers. However, understanding the relevance of a blood metabolite marker can be challenging if it is unknown whether it reflects the concentration in relevant tissues. To explore this field, metabolomic and lipidomic profiles of plasma, four sites of adipose tissues (ATs) from peripheral or central depot, two sites of muscle tissue, and liver tissue from a group of nondiabetic women with obesity who were scheduled to undergo bariatric surgery (n = 21) or other upper GI surgery (n = 5), were measured by liquid chromatography coupled with mass spectrometry. Relationships between plasma and tissue profiles were examined using Pearson correlation analysis subject to Benjamini-Hochberg correction. Plasma metabolites and lipids showed the highest number of significantly positive correlations with their corresponding concentrations in liver tissue, including lipid species of ceramide, mono- and di-hexosylceramide, sphingomyelin, phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysophosphatidylethanolamine, dimethyl phosphatidylethanolamine, ether-linked PC, ether-linked PE, free fatty acid, cholesteryl ester, diacylglycerol and triacylglycerol, and polar metabolites linked to several metabolic functions and gut microbial metabolism. Plasma also showed significantly positive correlations with muscle for several phospholipid species and polar metabolites linked to metabolic functions and gut microbial metabolism, and with AT for several triacylglycerol species. In conclusion, plasma metabolomic and lipidomic profiles were reflective more of the liver profile than any of the muscle or AT sites examined in the present study. Our findings highlighted the importance of taking into consideration the metabolomic relationship of various tissues with plasma when postulating plasma metabolites marker to underlying mechanisms occurring in a specific tissue.
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    Tissue-Specific Sample Dilution: An Important Parameter to Optimise Prior to Untargeted LC-MS Metabolomics.
    (MDPI (Basel, Switzerland), 27/06/2019) Wu ZE; Kruger MC; Cooper GJS; Poppitt SD; Fraser K
    When developing a sample preparation protocol for LC-MS untargeted metabolomics of a new sample matrix unfamiliar to the laboratory, selection of a suitable injection concentration is rarely described. Here we developed a simple workflow to address this issue prior to untargeted LC-MS metabolomics using pig adipose tissue and liver tissue. Bi-phasic extraction was performed to enable simultaneous optimisation of parameters for analysis of both lipids and polar extracts. A series of diluted pooled samples were analysed by LC-MS and used to evaluate signal linearity. Suitable injected concentrations were determined based on both the number of reproducible features and linear features. With our laboratory settings, the optimum concentrations of tissue mass to reconstitution solvent of liver and adipose tissue lipid fractions were found to be 125 mg/mL and 7.81 mg/mL respectively, producing 2811 (ESI+) and 4326 (ESI-) linear features from liver, 698 (ESI+) and 498 (ESI-) linear features from adipose tissue. For analysis of the polar fraction of both tissues, 250 mg/mL was suitable, producing 403 (ESI+) and 235 (ESI-) linear features from liver, 114 (ESI+) and 108 (ESI-) linear features from adipose tissue. Incorrect reconstitution volumes resulted in either severe overloading or poor linearity in our lipid data, while too dilute polar fractions resulted in a low number of reproducible features (<50) compared to hundreds of reproducible features from the optimum concentration used. Our study highlights on multiple matrices and multiple extract and chromatography types, the critical importance of determining a suitable injected concentration prior to untargeted LC-MS metabolomics, with the described workflow applicable to any matrix and LC-MS system.
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    Untargeted metabolomics reveals plasma metabolites predictive of ectopic fat in pancreas and liver as assessed by magnetic resonance imaging: the TOFI_Asia study
    (Springer Nature Limited, 2021-08) Wu ZE; Fraser K; Kruger MC; Sequeira IR; Yip W; Lu LW; Plank LD; Murphy R; Cooper GJS; Martin J-C; Hollingsworth KG; Poppitt SD
    BACKGROUND: Excess visceral obesity and ectopic organ fat is associated with increased risk of cardiometabolic disease. However, circulating markers for early detection of ectopic fat, particularly pancreas and liver, are lacking. METHODS: Lipid storage in pancreas, liver, abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) from 68 healthy or pre-diabetic Caucasian and Chinese women enroled in the TOFI_Asia study was assessed by magnetic resonance imaging/spectroscopy (MRI/S). Plasma metabolites were measured with untargeted liquid chromatography-mass spectroscopy (LC-MS). Multivariate partial least squares (PLS) regression identified metabolites predictive of VAT/SAT and ectopic fat; univariate linear regression adjusting for potential covariates identified individual metabolites associated with VAT/SAT and ectopic fat; linear regression adjusted for ethnicity identified clinical and anthropometric correlates for each fat depot. RESULTS: PLS identified 56, 64 and 31 metabolites which jointly predicted pancreatic fat (R2Y = 0.81, Q2 = 0.69), liver fat (RY2 = 0.8, Q2 = 0.66) and VAT/SAT ((R2Y = 0.7, Q2 = 0.62)) respectively. Among the PLS-identified metabolites, none of them remained significantly associated with pancreatic fat after adjusting for all covariates. Dihydrosphingomyelin (dhSM(d36:0)), 3 phosphatidylethanolamines, 5 diacylglycerols (DG) and 40 triacylglycerols (TG) were associated with liver fat independent of covariates. Three DGs and 12 TGs were associated with VAT/SAT independent of covariates. Notably, comparison with clinical correlates showed better predictivity of ectopic fat by these PLS-identified plasma metabolite markers. CONCLUSIONS: Untargeted metabolomics identified candidate markers of visceral and ectopic fat that improved fat level prediction over clinical markers. Several plasma metabolites were associated with level of liver fat and VAT/SAT ratio independent of age, total and visceral adiposity, whereas pancreatic fat deposition was only associated with increased sulfolithocholic acid independent of adiposity-related parameters, but not age.