Journal Articles

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    Characterisation of the Plasma and Faecal Metabolomes in Participants with Functional Gastrointestinal Disorders.
    (MDPI (Basel, Switzerland), 2024-12-16) Fraser K; James SC; Young W; Gearry RB; Heenan PE; Keenan JI; Talley NJ; McNabb WC; Roy NC; Fukui H
    There is evidence of perturbed microbial and host processes in the gastrointestinal tract of individuals with functional gastrointestinal disorders (FGID) compared to healthy controls. The faecal metabolome provides insight into the metabolic processes localised to the intestinal tract, while the plasma metabolome highlights the overall perturbances of host and/or microbial responses. This study profiled the faecal (n = 221) and plasma (n = 206) metabolomes of individuals with functional constipation (FC), constipation-predominant irritable bowel syndrome (IBS-C), functional diarrhoea (FD), diarrhoea-predominant IBS (IBS-D) and healthy controls (identified using the Rome Criteria IV) using multimodal LC-MS technologies. Discriminant analysis separated patients with the 'all constipation' group (FC and IBS-C) from the healthy control group and 'all diarrhoea' group (FD and IBS-D) from the healthy control group in both sample types. In plasma, almost all multimodal metabolite analyses separated the 'all constipation' or 'all diarrhoea' group from the healthy controls, and the IBS-C or IBS-D group from the healthy control group. Plasma phospholipids and metabolites linked to several amino acid and nucleoside pathways differed (p < 0.05) between healthy controls and IBS-C. In contrast, metabolites involved in bile acid and amino acid metabolism were the key differentiating classes in the plasma of subjects with IBS-D from healthy controls. Faecal lipids, particularly ceramides, diglycerides, and triglycerides, varied (p < 0.05) between healthy controls and the 'all constipation' group and between healthy controls and 'all diarrhoea' group. The faecal and plasma metabolomes showed perturbations between constipation, diarrhoea and healthy control groups that may reflect processes and mechanisms linked to FGIDs.
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    Synergistic Responses of Tibetan Sheep Rumen Microbiota, Metabolites, and the Host to the Plateau Environment.
    (MDPI (Basel, Switzerland), 2023-10-03) Sha Y; Guo X; He Y; Li W; Liu X; Zhao S; Hu J; Wang J; Li S; Zhao Z; Hao Z; Miccheli A; Docea AO; Fukui H
    Plateau adaptation in animals involves genetic mechanisms as well as coevolutionary mechanisms of the microbiota and metabolome of the animal. Therefore, the characteristics of the rumen microbiome and metabolome, transcriptome, and serum metabolome of Tibetan sheep at different altitudes (4500 m, 3500 m, and 2500 m) were analyzed. The results showed that the rumen differential metabolites at 3500 m and 4500 m were mainly enriched in amino acid metabolism, lipid metabolism, and carbohydrate metabolism, and there was a significant correlation with microbiota. The differentially expressed genes and metabolites at middle and high altitudes were coenriched in asthma, arachidonic acid metabolism, and butanoate and propanoate metabolism. In addition, the serum differential metabolites at 3500 m and 4500 m were mainly enriched in amino acid metabolism, lipid metabolism, and metabolism of xenobiotics by cytochrome P450, and they were also related to microbiota. Further analysis revealed that rumen metabolites accounted for 7.65% of serum metabolites. These common metabolites were mainly enriched in metabolic pathways and were significantly correlated with host genes (p < 0.05). This study found that microbiota, metabolites, and epithelial genes were coenriched in pathways related to lipid metabolism, energy metabolism, and immune metabolism, which may be involved in the regulation of Tibetan sheep adaptation to plateau environmental changes.
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    Metabolome and microbiome profiling of a stress-sensitive rat model of gut-brain axis dysfunction
    (Springer Nature Limited, 2019-10-01) Bassett SA; Young W; Fraser K; Dalziel JE; Webster J; Ryan L; Fitzgerald P; Stanton C; Dinan TG; Cryan JF; Clarke G; Hyland N; Roy NC
    Stress negatively impacts gut and brain health. Individual differences in response to stress have been linked to genetic and environmental factors and more recently, a role for the gut microbiota in the regulation of stress-related changes has been demonstrated. However, the mechanisms by which these factors influence each other are poorly understood, and there are currently no established robust biomarkers of stress susceptibility. To determine the metabolic and microbial signatures underpinning physiological stress responses, we compared stress-sensitive Wistar Kyoto (WKY) rats to the normo-anxious Sprague Dawley (SD) strain. Here we report that acute stress-induced strain-specific changes in brain lipid metabolites were a prominent feature in WKY rats. The relative abundance of Lactococcus correlated with the relative proportions of many brain lipids. In contrast, plasma lipids were significantly elevated in response to stress in SD rats, but not in WKY rats. Supporting these findings, we found that the greatest difference between the SD and WKY microbiomes were the predicted relative abundance of microbial genes involved in lipid and energy metabolism. Our results provide potential insights for developing novel biomarkers of stress vulnerability, some of which appear genotype specific.
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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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    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.