Project description:Global threats to reefs require urgent efforts to resolve coral attributes that affect survival in a changing environment. Genetically different individuals of the same coral species are known to exhibit different responses to the same environmental conditions. New information on coral physiology, particularly as it relates to genotype, could aid in unraveling mechanisms that facilitate coral survival in the face of stressors. Metabolomic profiling detects a large subset of metabolites in an organism, and, when linked to metabolic pathways, can provide a snapshot of an organism's physiological state. Identifying metabolites associated with desirable, genotype-specific traits could improve coral selection for restoration and other interventions. A key step toward this goal is determining whether intraspecific variation in coral metabolite profiles can be detected for species of interest, however little information exists to illustrate such differences. To address this gap, we applied untargeted 1H-NMR and LC-MS metabolomic profiling to three genotypes of the threatened coral Acropora cervicornis. Both methods revealed distinct metabolite "fingerprints" for each genotype examined. A number of metabolites driving separation among genotypes were identified or putatively annotated. Pathway analysis suggested differences in protein synthesis among genotypes. For the first time, these data illustrate intraspecific variation in metabolomic profiles for corals in a common garden. Our results contribute to the growing body of work on coral metabolomics and suggest future work could identify specific links between phenotype and metabolite profile in corals.
Project description:ObjectivesIdiopathic inflammatory myopathies (IIM) are a class of autoimmune diseases with high heterogeneity that can be divided into different subtypes based on clinical manifestations and myositis-specific autoantibodies (MSAs). However, even in each IIM subtype, the clinical symptoms and prognoses of patients are very different. Thus, the identification of more potential biomarkers associated with IIM classification, clinical symptoms, and prognosis is urgently needed.MethodsPlasma and urine samples from 79 newly diagnosed IIM patients (mean disease duration 4 months) and 52 normal control (NC) samples were analysed by high-performance liquid chromatography of quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS)/MS-based untargeted metabolomics. Orthogonal partial least-squares discriminate analysis (OPLS-DA) were performed to measure the significance of metabolites. Pathway enrichment analysis was conducted based on the KEGG human metabolic pathways. Ten machine learning (ML) algorithms [linear support vector machine (SVM), radial basis function SVM, random forest, nearest neighbour, Gaussian processes, decision trees, neural networks, adaptive boosting (AdaBoost), Gaussian naive Bayes and quadratic discriminant analysis] were used to classify each IIM subtype and select the most important metabolites as potential biomarkers.ResultsOPLS-DA showed a clear separation between NC and IIM subtypes in plasma and urine metabolic profiles. KEGG pathway enrichment analysis revealed multiple unique and shared disturbed metabolic pathways in IIM main [dermatomyositis (DM), anti-synthetase syndrome (ASS), and immune-mediated necrotizing myopathy (IMNM)] and MSA-defined subtypes (anti-Mi2+, anti-MDA5+, anti-TIF1γ+, anti-Jo1+, anti-PL7+, anti-PL12+, anti-EJ+, and anti-SRP+), such that fatty acid biosynthesis was significantly altered in both plasma and urine in all main IIM subtypes (enrichment ratio > 1). Random forest and AdaBoost performed best in classifying each IIM subtype among the 10 ML models. Using the feature selection methods in ML models, we identified 9 plasma and 10 urine metabolites that contributed most to separate IIM main subtypes and MSA-defined subtypes, such as plasma creatine (fold change = 3.344, P = 0.024) in IMNM subtype and urine tiglylcarnitine (fold change = 0.351, P = 0.037) in anti-EJ+ ASS subtype. Sixteen common metabolites were found in both the plasma and urine samples of IIM subtypes. Among them, some were correlated with clinical features, such as plasma hypogeic acid (r = -0.416, P = 0.005) and urine malonyl carnitine (r = -0.374, P = 0.042), which were negatively correlated with the prevalence of interstitial lung disease.ConclusionsIn both plasma and urine samples, IIM main and MSA-defined subtypes have specific metabolic signatures and pathways. This study provides useful clues for understanding the molecular mechanisms, searching potential diagnosis biomarkers and therapeutic targets for IIM.
Project description:To identify metabolite patterns associated with childhood obesity, to examine relations of these patterns with measures of adiposity and cardiometabolic risk, and to evaluate associations with maternal peripartum characteristics.Untargeted metabolomic profiling was used to quantify metabolites in plasma of 262 children (6-10 years). Principal components analysis was used to consolidate 345 metabolites into 18 factors and identified two that differed between obese (BMI???95‰; n?=?84) and lean children (BMI?<?85‰; n?=?150). The relations of these factors with adiposity (fat mass, BMI, skinfold thicknesses) and cardiometabolic biomarkers (HOMA-IR, triglycerides, leptin, adiponectin, hsCRP, IL-6) using multivariable linear regression was then investigated. Finally, the associations of maternal prepregnancy obesity, gestational weight gain, and gestational glucose tolerance with the offspring metabolite patterns was examined.A branched-chain amino acid (BCAA)-related pattern and an androgen hormone pattern were higher in obese vs. lean children. Both patterns were associated with adiposity and worse cardiometabolic profiles. For example, each increment in the BCAA and androgen pattern scores corresponded with 6% (95% CI: 1, 13%) higher HOMA-IR. Children of obese mothers had 0.61 (0.13, 1.08) higher BCAA score than their counterparts.BCAA and androgen metabolites were associated with adiposity and cardiometabolic risk during mid-childhood. Maternal obesity may contribute to altered offspring BCAA metabolism.
Project description:Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.
Project description:ObjectiveDepression has been associated with metabolomic alterations. Depressive and anxiety disorders are often comorbid diagnoses and are suggested to share etiology. We investigated whether differential metabolomic alterations are present between anxiety and depressive disorders and which clinical characteristics of these disorders are related to metabolomic alterations.MethodsData were from the Netherlands Study of Depression and Anxiety (NESDA), including individuals with current comorbid anxiety and depressive disorders (N = 531), only a current depression (N = 304), only a current anxiety disorder (N = 548), remitted depressive and/or anxiety disorders (N = 897), and healthy controls (N = 634). Forty metabolites from a proton nuclear magnetic resonance lipid-based metabolomics panel were analyzed. First, we examined differences in metabolites between disorder groups and healthy controls. Next, we assessed whether depression or anxiety clinical characteristics (severity and symptom duration) were associated with metabolites.ResultsAs compared to healthy controls, seven metabolomic alterations were found in the group with only depression, reflecting an inflammatory (glycoprotein acetyls; Cohen's d = 0.12, p = 0.002) and atherogenic-lipoprotein-related (e.g., apolipoprotein B: Cohen's d = 0.08, p = 0.03, and VLDL cholesterol: Cohen's d = 0.08, p = 0.04) profile. The comorbid group showed an attenuated but similar pattern of deviations. No metabolomic alterations were found in the group with only anxiety disorders. The majority of metabolites associated with depression diagnosis were also associated with depression severity; no associations were found with anxiety severity or disease duration.ConclusionWhile substantial clinical overlap exists between depressive and anxiety disorders, this study suggests that altered inflammatory and atherogenic-lipoprotein-related metabolomic profiles are uniquely associated with depression rather than anxiety disorders.
Project description:Background and aimsGallstone disease affects ≥40 million people in the USA and accounts for health costs of ≥$4 billion a year. Risk factors such as obesity and metabolic syndrome are well established. However, data are limited on relevant metabolomic alterations that could offer mechanistic and predictive insights into gallstone disease. This study prospectively identifies and externally validates circulating prediagnostic metabolites associated with incident gallstone disease.MethodsFemale participants in Nurses' Health Study (NHS) and Nurses' Health Study II (NHS II) who were free of known gallstones (N=9960) were prospectively followed up after baseline metabolomic profiling with liquid chromatography-tandem mass spectrometry. Multivariable logistic regression and enrichment analysis were used to identify metabolites and metabolite groups associated with incident gallstone disease at PFDR<0.05. Findings were validated in 1866 female participants in the Women's Health Initiative and a comparative analysis was performed with 2178 male participants in the Health Professionals Follow-up Study.ResultsAfter multivariate adjustment for lifestyle and putative risk factors, we identified and externally validated 17 metabolites associated with incident gallstone disease in women-nine triacylglycerols (TAGs) and diacylglycerols (DAGs) were positively associated, while eight plasmalogens and cholesterol ester (CE) were negatively associated. Enrichment analysis in male and female cohorts revealed positive class associations with DAGs, TAGs (≤56 carbon atoms and ≤3 double bonds) and de novo TAG biosynthesis pathways, as well as inverse associations with CEs.ConclusionsThis study highlights several metabolites (TAGs, DAGs, plasmalogens and CE) that could be implicated in the aetiopathogenesis of gallstone disease and serve as clinically relevant markers.