Laeticia Da Silva

Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links.

Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10−8) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite associations, six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9×10−44) and lysine (rs8101881, P = 1.2×10−33), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohns disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.

Impact of breast-feeding and high- and low-protein formula on the metabolism and growth of infants from overweight and obese mothers

Background:The combination of maternal obesity in early pregnancy and high protein intake in infant formula feeding might predispose to obesity risk in later life.Methods:This study assesses the impact of breast- or formula-feeding (differing in protein content by 1.65 or 2.7 g/100 kcal) on the metabolism of term infants from overweight and obese mothers. From birth to 3 mo of age, infants received exclusively either breast- or starter formula-feeding and until 6 mo, exclusively either a formula designed for this study or breast-feeding. From 6 to 12 mo, infants received complementary weaning food. Metabonomics was conducted on the infants’ urine and stool samples collected at the age of 3, 6, and 12 mo.Results:Infant formula-feeding resulted in higher protein-derived short-chain fatty acids and amino acids in stools. Urine metabonomics revealed a relationship between bacterial processing of dietary proteins and host protein metabolism stimulated with increasing protein content in the formula. Moreover, formula-fed infants were metabolically different from breast-fed infants, at the level of lipid and energy metabolism (carnitines, ketone bodies, and Krebs cycle).Conclusion:Noninvasive urine and stool metabolic monitoring of responses to early nutrition provides relevant readouts to assess nutritional requirements for infants’ growth.

High-Resolution Quantitative Metabolome Analysis of Urine by Automated Flow Injection NMR

Metabolism is essential to understand human health. To characterize human metabolism, a high-resolution read-out of the metabolic status under various physiological conditions, either in health or disease, is needed. Metabolomics offers an unprecedented approach for generating system-specific biochemical definitions of a human phenotype through the capture of a variety of metabolites in a single measurement. The emergence of large cohorts in clinical studies increases the demand of technologies able to analyze a large number of measurements, in an automated fashion, in the most robust way. NMR is an established metabolomics tool for obtaining metabolic phenotypes. Here, we describe the analysis of NMR-based urinary profiles for metabolic studies, challenged to a large human study (3007 samples). This method includes the acquisition of nuclear Overhauser effect spectroscopy one-dimensional and J-resolved two-dimensional (J-Res-2D) 1H NMR spectra obtained on a 600 MHz spectrometer, equipped with a 120 μL flow probe, coupled to a flow-injection analysis system, in full automation under the control of a sampler manager. Samples were acquired at a throughput of ∼20 (or 40 when J-Res-2D is included) min/sample. The associated technical analysis error over the full series of analysis is 12%, which demonstrates the robustness of the method. With the aim to describe an overall metabolomics workflow, the quantification of 36 metabolites, mainly related to central carbon metabolism and gut microbial host cometabolism, was obtained, as well as multivariate data analysis of the full spectral profiles. The metabolic read-outs generated using our analytical workflow can therefore be considered for further pathway modeling and/or biological interpretation.

High-throughput and simultaneous quantitative analysis of homocysteine-methionine cycle metabolites and co-factors in blood plasma and cerebrospinal fluid by isotope dilution LC-MS/MS.

The methionine cycle is a key pathway contributing to the regulation of human health, with well-established involvement in cardiovascular diseases and cognitive function. Changes in one-carbon cycle metabolites have also been associated with mild cognitive decline, vascular dementia, and Alzheimer’s disease. Today, there is no single analytical method to monitor both metabolites and co-factors of the methionine cycle. To address this limitation, we here report for the first time a new method for the simultaneous quantitation of 17 metabolites in the methionine cycle, which are homocysteic acid, taurine, serine, cysteine, glycine, homocysteine, riboflavin, methionine, pyridoxine, cystathionine, pyridoxamine, S-adenosylhomocysteine, S-adenosylmethionine, betaine, choline, dimethylglycine, and 5-methyltetrahydrofolic acid. This multianalyte method, developed using ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS), provides a highly accurate and precise quantitation of these 17 metabolites for both plasma and cerebrospinal fluid metabolite monitoring. The method requires a simple sample preparation, which, combined with a short chromatographic run time, ensures a high sample throughput. This analytical strategy will thus provide a novel metabolomics approach to be employed in large-scale observational and intervention studies. We expect such a robust method to be particularly relevant for broad and deep molecular phenotyping of individuals in relation to their nutritional requirements, health monitoring, and disease risk management.

One-carbon metabolism, cognitive impairment and CSF measures of Alzheimer pathology: Homocysteine and beyond

BackgroundHyperhomocysteinemia is a risk factor for cognitive decline and dementia, including Alzheimer disease (AD). Homocysteine (Hcy) is a sulfur-containing amino acid and metabolite of the methionine pathway. The interrelated methionine, purine, and thymidylate cycles constitute the one-carbon metabolism that plays a critical role in the synthesis of DNA, neurotransmitters, phospholipids, and myelin. In this study, we tested the hypothesis that one-carbon metabolites beyond Hcy are relevant to cognitive function and cerebrospinal fluid (CSF) measures of AD pathology in older adults.MethodsCross-sectional analysis was performed on matched CSF and plasma collected from 120 older community-dwelling adults with (n = 72) or without (n = 48) cognitive impairment. Liquid chromatography-mass spectrometry was performed to quantify one-carbon metabolites and their cofactors. Least absolute shrinkage and selection operator (LASSO) regression was initially applied to clinical and biomarker measures that generate the highest diagnostic accuracy of a priori-defined cognitive impairment (Clinical Dementia Rating-based) and AD pathology (i.e., CSF tau phosphorylated at threonine 181 [p-tau181]/β-Amyloid 1–42 peptide chain [Aβ1–42] >0.0779) to establish a reference benchmark. Two other LASSO-determined models were generated that included the one-carbon metabolites in CSF and then plasma. Correlations of CSF and plasma one-carbon metabolites with CSF amyloid and tau were explored. LASSO-determined models were stratified by apolipoprotein E (APOE) ε4 carrier status.ResultsThe diagnostic accuracy of cognitive impairment for the reference model was 80.8% and included age, years of education, Aβ1–42, tau, and p-tau181. A model including CSF cystathionine, methionine, S-adenosyl-L-homocysteine (SAH), S-adenosylmethionine (SAM), serine, cysteine, and 5-methyltetrahydrofolate (5-MTHF) improved the diagnostic accuracy to 87.4%. A second model derived from plasma included cystathionine, glycine, methionine, SAH, SAM, serine, cysteine, and Hcy and reached a diagnostic accuracy of 87.5%. CSF SAH and 5-MTHF were associated with CSF tau and p-tau181. Plasma one-carbon metabolites were able to diagnose subjects with a positive CSF profile of AD pathology in APOE ε4 carriers.ConclusionsWe observed significant improvements in the prediction of cognitive impairment by adding one-carbon metabolites. This is partially explained by associations with CSF tau and p-tau181, suggesting a role for one-carbon metabolism in the aggregation of tau and neuronal injury. These metabolites may be particularly critical in APOE ε4 carriers.

Urinary Metabolic Phenotyping Reveals Differences in the Metabolic Status of Healthy and Inflammatory Bowel Disease (IBD) Children in Relation to Growth and Disease Activity

Background: Growth failure and delayed puberty are well known features of children and adolescents with inflammatory bowel disease (IBD), in addition to the chronic course of the disease. Urinary metabonomics was applied in order to better understand metabolic changes between healthy and IBD children. Methods: 21 Pediatric patients with IBD (mean age 14.8 years, 8 males) were enrolled from the Pediatric Gastroenterology Outpatient Clinic over two years. Clinical and biological data were collected at baseline, 6, and 12 months. 27 healthy children (mean age 12.9 years, 16 males) were assessed at baseline. Urine samples were collected at each visit and subjected to 1H Nuclear Magnetic Resonance (NMR) spectroscopy. Results: Using 1H NMR metabonomics, we determined that urine metabolic profiles of IBD children differ significantly from healthy controls. Metabolic differences include central energy metabolism, amino acid, and gut microbial metabolic pathways. The analysis described that combined urinary urea and phenylacetylglutamine—two readouts of nitrogen metabolism—may be relevant to monitor metabolic status in the course of disease. Conclusion: Non-invasive sampling of urine followed by metabonomic profiling can elucidate and monitor the metabolic status of children in relation to disease status. Further developments of omic-approaches in pediatric research might deliver novel nutritional and metabolic hypotheses.

Consensus Clustering of temporal profiles for the identification of metabolic markers of pre-diabetes in childhood (EarlyBird 73)

In longitudinal clinical studies, methodologies available for the analysis of multivariate data with multivariate methods are relatively limited. Here, we present Consensus Clustering (CClust) a new computational method based on clustering of time profiles and posterior identification of correlation between clusters and predictors. Subjects are first clustered in groups according to a response variable temporal profile, using a robust consensus-based strategy. To discover which of the remaining variables are associated with the resulting groups, a non-parametric hypothesis test is performed between groups at every time point, and then the results are aggregated according to the Fisher method. Our approach is tested through its application to the EarlyBird cohort database, which contains temporal variations of clinical, metabolic, and anthropometric profiles in a population of 150 children followed-up annually from age 5 to age 16. Our results show that our consensus-based method is able to overcome the problem of the approach-dependent results produced by current clustering algorithms, producing groups defined according to Insulin Resistance (IR) and biological age (Tanner Score). Moreover, it provides meaningful biological results confirmed by hypothesis testing with most of the main clinical variables. These results position CClust as a valid alternative for the analysis of multivariate longitudinal data.

Validation of the Brazilian Healthy Eating Index-Revised Using Biomarkers in Children and Adolescents

The Brazilian Healthy Eating Index-Revised (BHEI-R) can be used to determine overall dietary patterns. We assessed the BHEI-R scores in children and adolescents, aged from 9 to 13 years old, and associated its component scores with biomarkers of health and dietary exposure. Three 24-h recalls were used to generate BHEI-R. Biomarkers were analyzed in plasma and red blood cells. Correlation tests, agreement, and covariance analyses were used to associate BHEI-R components with biomarkers. Data from 167 subjects were used. The strongest correlations were between fruits, vegetables and legumes with omega-6 and omega-3 fatty acids, and β-carotene intakes. Milk and dairy correlated with plasma retinol and pyridoxine. All components rich in vegetable and animal protein sources correlated with plasma creatine. Total BHEI-R scores were positively associated with intakes of omega-6, omega-3, fiber and vitamin C, and inversely associated with energy and saturated fat intakes of individuals. Plasma β-carotene and riboflavin biomarkers were positively associated with total BHEI-R. An inadequate food consumption pattern was captured by both biomarkers of health and dietary exposure. BHEI-R was validated for the above dietary components and can be associated with metabolomics and nutritional epidemiological data in future pediatric studies.

Urinary metabolomics in term newborns delivered spontaneously or with cesarean section: preliminary data

Introduction: In the last years the uncritical attitude towards cesarean section (CS) has been associated with the fast emergence of ‘modern’ diseases such as early pediatric obesity, asthma, type 2 diabetes mellitus and dermatitis. Increasing evidence shows that babies born at term by vaginal delivery (VD) have a different physiology at birth, with subsequent influence on adult health. In relation to these short-term physiological changes, in the present study we aimed at assessing the influence of the mode of delivery in term newborns on the first 24 hours metabolism of neonates. Material and methods: This study was carried out on urine samples from 42 patients admitted to the Neonatal Intensive Unit and Neonatal Pathology of “S. Giovanni Calibita” Hospital Fatebenefratelli (Rome, Italy). According to the type of delivery, term neonates with similar gestational age and birthweight were divided in two groups: (1) born by spontaneous VD, (2) born by elective CS. Urine samples, collected at birth by a non-invasive method, were subjected to proton Nuclear Magnetic Resonance spectroscopy. Results: CS newborns showed lower fatty acid omega oxidation, as evidenced by lower urinary excretion of dicarboxylic acids. This metabolic signature supports current evidence that babies delivered by CS have lower body temperature and perturbed thermogenesis. CS associates also with hypoglycaemia and altered endocrine profile, which linked to changes in central energy metabolic pathways (Krebs and Cori Cycles). Lung function may be reduced in infants born by CS, primarily due to delayed clearance of lung liquid, and surfactant insufficiency, which might be reflected in different urinary excretion of myo-inositol and choline – two intermediates in lung surfactant metabolism. Conclusion: Non-invasive urine metabolic phenotyping of children born by different mode of delivery provides relevant readouts to assess metabolic requirements associated with major physiological functions during this critical period of metabolic adaptation.

ONE-CARBON METABOLISM, COGNITIVE IMPAIRMENT AND CSF MARKERS OF ALZHEIMER PATHOLOGY: HOMOCYSTEINE AND BEYOND

was a significant relationship between the number of retinal microhemorrhages and Fazekas ratings for WMH in the CAA patients (r1⁄4.853, p<.01), and this trended towards significance when examining the area of retinal micro-hemorrhages (r1⁄4.615, p1⁄4.078). Finally, there was a relationship between area (r1⁄4-.695, p<.05) and number (r1⁄4-.573, p1⁄4.107) of retinal micro-hemorrhages and performance on measures of episodic memory in the CAA group. Conclusions:There were significantly more retinal micro-hemorrhages in a group of CAA patients than a group of healthy controls, and this relationship was moderated by blood pressure. The strong association between retinal micro-hemorrhages and cerebral WMH parallels literature indicating a progressive increase in cerebral WMH with lobar hemorrhages in CAA (Chen et al., 2006). The relationship between retinal micro-hemorrhages, cerebral WMH, and cognitive performance warrants further exploration in larger samples using longitudinal design, and retinal micro-hemorrhages merit further exploration as a potential biomarker of CAA.