Urinary gas chromatography mass spectrometry metabolomics in asphyxiated newborns undergoing hypothermia: from the birth to the first month of life.

BACKGROUND: Perinatal asphyxia is a severe clinical condition affecting around four million newborns worldwide. It consists of an impaired gas exchange leading to three biochemical components: hypoxemia, hypercapnia and metabolic acidosis. METHODS: The aim of this longitudinal experimental study was to identify the urine metabolome of newborns with perinatal asphyxia and to follow changes in urine metabolic profile over time. Twelve babies with perinatal asphyxia were included in this study; three babies died on the eighth day of life. Total-body cooling for 72 hours was carried out in all the newborns. Urine samples were collected in each baby at birth, after 48 hours during hypothermia, after the end of the therapeutic treatment (72 hours), after 1 week of life, and finally after 1 month of life. Urine metabolome at birth was considered the reference against which to compare metabolic profiles in subsequent samples. Quantitative metabolic profiling in urine samples was measured by gas chromatography mass spectrometry (GC-MS). The statistical approach was conducted by using the multivariate analysis by means of principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA). Pathway analysis was also performed. RESULTS: The most important metabolites depicting each time collection point were identified and compared each other. At birth before starting therapeutic hypothermia (TH), urine metabolic profiles of the three babies died after 7 days of life were closely comparable each other and significantly different from those in survivors. CONCLUSIONS: In conclusion, a plethora of data have been extracted by comparing the urine metabolome at birth with those observed at each time point collection. The modifications over time in metabolites composition and concentration, mainly originated from the depletion of cellular energy and homeostasis, seems to constitute a fingerprint of perinatal asphyxia

Urinary metabolomics (GC-MS) reveals that low and high birth weight infants share elevated inositol concentrations at birth

Objective: Metabolomics is a new ‘‘omics’’ platform aimed at high-throughput identification, quantification and characterization of small molecule metabolites. The metabolomics approach has been successfully applied to the classification different physiological states and identification of perturbed biochemical pathways. The purpose of the current investigation is the application of metabolomics to explore biological mechanisms which may lead to the onset of metabolic syndrome in adulthood. Methods: We evaluated differences in metabolites in the urine collected within 12 hours from 23 infants with IUGR (IntraUterine Growth Restriction), or LGA (Large for Gestational Age), compared to control infants (10 patients defined AGA: Appropriate for Gestational Age). Urinary metabolites were quantified by GC-MS and used to highlight similarities between the two metabolic diseases and identify metabolic markers for their predisposition. Quantified metabolites were analyzed using a multivariate statistics coupled with receiver operator characteristic curve (ROC) analysis of identified biomarkers. Results: Urinary myo-inositol was the most important discriminant between LGA + IUGR and control infants, and displayed an area under the ROC curve¼1. Conclusion: We postulate that the increase in plasma and consequently urinary inositol may constitute a marker of altered glucose metabolism during fetal development in both IUGR and LGA newborns

The metabolomic profile of lymphoma subtypes: A pilot study

Lymphoma defines a group of different diseases. This study examined pre-treatment plasma samples from 66 adult patients (aged 20-74) newly diagnosed with any lymphoma subtype, and 96 frequency matched population controls. We used gas chromatography-mass spectrometry (GC-MS) to compare the metabolic profile by case/control status and across the major lymphoma subtypes. We conducted univariate and multivariate analyses, and partial least square discriminant analysis (PLS-DA). When compared to the controls, statistically validated models were obtained for diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), multiple myeloma (MM), and Hodgkin lymphoma (HL), but not follicular lymphoma (FL). The metabolomic analysis highlighted interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects: Important metabolites, such as hypoxanthine and elaidic acid, were more abundant in all lymphoma subtypes. The small sample size of the individual lymphoma subtypes prevented obtaining PLS-DA validated models, although specific peculiar features of each subtype were observed; for instance, fatty acids were most represented in MM and HL patients, while 2-aminoadipic acid, 2-aminoheptanedioic acid, erythritol, and threitol characterized DLBCL and CLL. Metabolomic analysis was able to highlight interesting differences between lymphoma patients and population controls, allowing the discrimination between pathologic and healthy subjects. Further studies are warranted to understand whether the peculiar metabolic patterns observed might serve as early biomarkers of lymphoma

Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives

Urinary metabolomic analysis to identify preterm neonates exposed to histological chorioamnionitis : A pilot study

Objective Chorioamnionitis is a leading cause of preterm birth worldwide, with higher incidence at lower gestational ages. An early and reliable diagnosis of histological chorioamnionitis (HCA) in preterm infants may be helpful in guiding postnatal management, especially the administration of prophylactic antibiotics to prevent early-onset sepsis. The main aim of this study was to investigate metabolomic analysis of urines collected in the first 24 hours of life as diagnostic tool of HCA. Methods Gestational age-, birth weight-, delivery mode- and sex- matched (1:2) preterm neonates (< 35 weeks\u2019 gestation) born to mothers with or without HCA were enrolled from an observational study. Gas chromatography-mass spectrometry (GC-MS)-based metabolomic analysis was performed on urine samples non-invasively collected in the first 24 hours of life. Univariate analysis, partial least square discriminant analysis (PLS-DA) and its associated variable importance in projection (VIP) score were performed. The most affected metabolic pathways were examined by Metabolite Sets Enrichment Analysis (MSEA). Results Fifteen cases (mean GA 30.2 \ub1 3.8 weeks, mean BW 1415 \ub1 471.9 grams) and 30 controls (mean GA 30.2 \ub1 2.9 weeks, mean BW 1426 \ub1 569.8 grams) were enrolled. Following univariate analysis, 29 metabolites had a significantly different concentration between cases and controls. The supervised PLS-DA model confirmed a separation between the two groups. Only gluconic acid, an oxidation product of glucose, was higher in cases than in controls. All other VIP metabolites were more abundant in the control group. Glutamate metabolism, mitochondrial electron transport chain, citric acid cycle, galactose metabolism, and fructose and mannose degradation metabolism were the most significantly altered pathways (P < 0.01). Conclusions For the first time, urinary metabolomics was able to discriminate neonates born to mothers with and without HCA. The identification of specifically altered metabolic pathways may be helpful in understanding metabolic derangement following chorioamnionitis

Metabolomics approach for the functional evaluation of a population of kids born very preterm: preliminary results with gs-ms

By metabolomic approach we were able to differentiate urinary metabolic profile between preterm and at term in childhood age. Among different hypothesis, epigenetic variations of organs development and their metabolic pathways, as the consequence of preterm birth and related events, could be take into consideration. Following this step, it is necessary to interprete the intricated preliminary results, enroll more subjects and confirm the results