F Murgia

Metabolomics in newborns with intrauterine growth retardation (IUGR): urine reveals markers of metabolic syndrome

To date, we have little knowledge on the overall metabolic status of neonates with intrauterine growth retardation (IUGR). In the last few years, the analysis of metabolomics has assumed an important clinical role in identifying “disorders” in the metabolic profile of patients. The aim of this work has been to analyze the urine metabolic profiles of neonates with IUGR and compare them with controls to define the metabolic patterns associated with this pathology. To our knowledge, this is the first study of metabolomics performed on neonates with IUGR. Recruited for the study were 26 neonates with IUGR diagnosed in the neonatal period and with weight at birth below the 10th percentile and 30 neonates of proper gestational weight at birth (controls). In the first 24 hours (prior to feeding) (T1) and about 4 days after birth (T2), a urine sample was taken non-invasively from each neonate. The samples were then frozen at −80°C up to the time of the analysis by proton nuclear magnetic resonance spectroscopy (1H-NMR). The data contained in the NMR spectra obtained from the single samples were statistically analyzed using the Principal Components Analysis and the Partial Least Squares-Discriminate Analysis. By means of a multivariate analysis of the NMR spectra obtained, it was possible to highlight the differences between the two groups (IUGRs and controls) owing to the presence of different metabolic patterns. The discriminants in the urine metabolic profiles derived essentially from significant differences in certain metabolites such as: myo-inositol, sarcosine, creatine and creatinine. The metabolomic analysis showed different urine metabolic profiles between neonates with IUGR and controls and made it possible to identify the molecules responsible for such differences.

A metabolomic study of preterm human and formula milk by high resolution NMR and GC/MS analysis: preliminary results

Objective: The aim of the present study was to investigate the metabolic profile of preterm human breast milk (HBM) by using a metabolomic approach. Methods: NMR spectroscopy and GC/MS were used to analyze the water-soluble and lipid fractions extracted from milk samples obtained from mothers giving birth at 26–36 weeks of gestation. For the sake of comparison, preterm formula milk was also studied. Results: The multivariate statistical analysis of the data evidenced biochemical variability both between preterm HBM and commercial milk and within the group of HBM samples. Conclusions: The preliminary results of this study suggest that metabolomics may provide a promising tool to study aspects related to the nutrition and health of preterm infant.

Clinical metabolomics and urinary NGAL for the early prediction of chronic kidney disease in healthy adults born ELBW

Background: Clinical metabolomics is a recent “omic” technology which is defined as a global holistic overview of the personal metabolic status (fingerprinting). This technique allows to prove metabolic differences in different groups of people with the opportunity to explore interactions such as genotype-phenotype and genotype-environment type, whether normal or pathological. Aim: To study chronic kidney injury 1) using urine metabolomic profiles of young adults born extremely low-birth weight (ELBW) and 2) correlating a biomarker of kidney injury, urinary neutrophil gelatinase-associated lipocalin (NGAL), in order to confirm the metabolomic injury profile. Method: Urine samples were collected from a group of 18 people (mean: 24-year-old, std: 4.27) who were born with ELBW and a group of 13 who were born at term appropriate for gestational age (AGA) as control (mean 25-year-old, std: 5.15). Urine samples were analyzed by 1H-nuclear magnetic resonance spectroscopy, and then submitted to unsupervised and supervised multivariate analysis. Urine NGAL (uNGAL) was measured using ARCHITECT (ABBOTT diagnostic NGAL kit). Results: With a multivariate approach and using a supervised analysis method, PLS-DA, (partial least squares discriminant analysis) we could correlate ELBW metabolic profiles with uNGAL concentration. Conversely, uNGAL could not be correlated to AGA. Conclusions: This study demonstrates the relevance of the metabolomic technique as a predictive tool of the metabolic status of exELBW. This was confirmed by the use of uNGAL as a biomarker which may predict a subclinical pathological process in the kidney such as chronic kidney disease.

A metabolomic approach in an experimental model of hypoxia-reoxygenation in newborn piglets: urine predicts outcome

Perinatal asphyxia is one of the leading causes of morbidity and mortality in the neonatal period. Response to oxygen treatment is unpredictable and the optimum concentration of oxygen in neonatal resuscitation is still a matter of debate among neonatologists. A metabolomic approach was used to characterize the metabolic profiles of newborn hypoxic-reoxygenated piglets. Urine samples were collected from newborn piglets (n = 40) undergoing hypoxia followed by resuscitation at different oxygen concentrations (ranging from 18% to 100%) and analyzed by 1H NMR spectroscopy. Despite reoxygenation 7 piglets, out of 10 which became asystolic, did not respond to resuscitation. Profiles of the 1H NMR spectra were submitted to unsupervised (principal component analysis) and supervised (partial least squares-discriminant analysis) multivariate analysis. The supervised analyses showed differences in the metabolic profile of the urine collected before the induction of hypoxia between survivors and deaths. Metabolic variations were observed in the urine of piglets treated with different oxygen concentrations comparing T0 (basal value) and end of the experiment (resuscitation). Some of the individual metabolites discriminating between these groups were urea, creatinine, malonate, methylguanidine, hydroxyisobutyric acid. The metabolomic approach appears a promising tool for investigating newborn hypoxia over time, for monitoring the response to the treatment with different oxygen concentrations, and might lead to a tailored management of the disorder.

Metabolomics network characterization of resuscitation after normocapnic hypoxia in a newborn piglet model supports the hypothesis that room air is better.

Perinatal asphyxia is attributed to hypoxia and/or ischemia around the time of birth and may lead to multiorgan dysfunction. Aim of this research article is to investigate whether different metabolomic profiles occurred according to oxygen concentration administered at resuscitation. In order to perform the experiment, forty newborn piglets were subjected to normocapnic hypoxia and reoxygenation and were randomly allocated in 4 groups resuscitated with different oxygen concentrations, 18%, 21%, 40%, and 100%, respectively. Urine metabolic profiles at baseline and at hypoxia were analysed by 1H-NMR spectroscopy and metabolites were also identified by multivariate statistical analysis. Metabolic pathways associations were also built up by ingenuity pathway analysis (IPA). Bioinformatics analysis of metabolites characterized the effect of metabolism in the 4 groups; it showed that the 21% of oxygen is the most “physiological” and appropriate concentration to be used for resuscitation. Our data indicate that resuscitation with 21% of oxygen seems to be optimal in terms of survival, rapidity of resuscitation, and metabolic profile in the present animal model. These findings need to be confirmed with metabolomics in human and, if so, the knowledge of the perinatal asphyxia condition may significantly improve.

1H-NMR analysis provides a metabolomic profile of patients with multiple sclerosis

Objective: To investigate the metabolomic profiles of patients with multiple sclerosis (MS) and to define the metabolic pathways potentially related to MS pathogenesis. Methods: Plasma samples from 73 patients with MS (therapy-free for at least 90 days) and 88 healthy controls (HC) were analyzed by 1H-NMR spectroscopy. Data analysis was conducted with principal components analysis followed by a supervised analysis (orthogonal partial least squares discriminant analysis [OPLS-DA]). The metabolites were identified and quantified using Chenomx software, and the receiver operating characteristic (ROC) curves were calculated. Results: The model obtained with the OPLS-DA identified predictive metabolic differences between the patients with MS and HC (R2X = 0.615, R2Y = 0.619, Q2 = 0.476; p < 0.001). The differential metabolites included glucose, 5-OH-tryptophan, and tryptophan, which were lower in the MS group, and 3-OH-butyrate, acetoacetate, acetone, alanine, and choline, which were higher in the MS group. The suitability of the model was evaluated using an external set of samples. The values returned by the model were used to build the corresponding ROC curve (area under the curve of 0.98). Conclusion: NMR metabolomic analysis was able to discriminate different metabolic profiles in patients with MS compared with HC. With the exception of choline, the main metabolic changes could be connected to 2 different metabolic pathways: tryptophan metabolism and energy metabolism. Metabolomics appears to represent a promising noninvasive approach for the study of MS.

1H NMR metabolite fingerprinting as a new tool for body fluid identification in forensic science

In this feasibility study, we propose, for the first time, 1H NMR spectroscopy coupled with mathematical strategies as a valid tool for body fluid (BF) trace identification in forensic science. In order to assess the ability of this approach to identify traces composed either by a single or by two different BFs, samples of blood, urine, saliva, and semen were collected from different donors, and binary mixtures were prepared. 1H NMR analyses were carried out for all samples. Spectral data of the whole set were firstly submitted to unsupervised principal component analysis (PCA); it showed that samples of the same BF cluster well on the basis of their characterizing molecular components and that mixtures exhibit intermediate characteristics among BF typologies. Furthermore, samples were divided into a training set and a test set. An average NMR spectral profile for each typology of BF was obtained from the training set and validated as representative of each BF class. Finally, a fitting procedure, based on a system of linear equations with the four obtained average spectral profiles, was applied to the test set and the mixture samples; it showed that BFs can be unambiguously identified, even as components of a mixture. The successful use of this mathematical procedure has the advantage, in forensics, of overcoming bias due to the analysts personal judgment. We therefore propose this combined approach as a valid, fast, and non‐destructive tool for addressing the challenges in the identification of composite traces in forensics. Copyright

Is the quickness of resuscitation after hypoxia influenced by the oxygen concentration? Metabolomics in piglets resuscitated with different oxygen concentrations

Perinatal asphyxia is one of the leading causes of morbidity and mortality in the neonatal period. There is an on-going debate in the literature concerning the correct oxygen concentration to be used during neonatal resuscitation. Aim of this study was to investigate whether different metabolic profiles occurred according to oxygen concentration administered and quickness of resuscitation. We tested the hypothesis that the metabolic profile may be affected by the response to the different oxygen concentration and influenced the different time of recovery. Forty male Landrace/Large newborn piglets were the subjects of the present study. As a consequence of the different time of resuscitation, a metabolomics analysis between the two classes of reoxygenated piglets with the slowest and fastest recovery was carried out: first group (4 piglets) RT 68 minutes. In addition, 1 H-NMR metabolomics study was performed showing different metabolic profiles between the two groups. The most significant metabolites were: N-phenylacetylglycine, acetoacetate, methanol, glucose, sarcosine, succinate, dimethylamine and alanine. Our results seem to indicate that the rapidity of resuscitation is influenced by the oxygen concentration. Proceedings of the 9 th International Workshop on Neonatology · Cagliari (Italy) · October 23 rd -26 th , 2013 · Learned lessons, changing practice and cutting-edge research

Metabolomic analysis identifies altered metabolic pathways in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic, demyelinating disease that affects the central nervous system and is characterized by a complex pathogenesis and difficult management. The identification of new biomarkers would be clinically useful for more accurate diagnoses and disease monitoring. Metabolomics, the identification of small endogenous molecules, offers an instantaneous molecular snapshot of the MS phenotype. Here the metabolomic profiles (utilizing plasma from patients with MS) were characterized with a Gas cromatography-mass spectrometry-based platform followed by a multivariate statistical analysis and comparison with a healthy control (HC) population. The obtained partial least square discriminant analysis (PLS-DA) model identified and validated significant metabolic differences between individuals with MS and HC (R2X=0.223, R2Y=0.82, Q2=0.562; p<0.001). Among discriminant metabolites phosphate, fructose, myo-inositol, pyroglutamate, threonate, l-leucine, l-asparagine, l-ornithine, l-glutamine, and l-glutamate were correctly identified, and some resulted as unknown. A receiver operating characteristic (ROC) curve with AUC 0.84 (p=0.01; CI: 0.75-1) generated with the concentrations of the discriminant metabolites, supported the strength of the model. Pathway analysis indicated asparagine and citrulline biosynthesis as the main canonical pathways involved in MS. Changes in the citrulline biosynthesis pathway suggests the involvement of oxidative stress during neuronal damage. The results confirmed metabolomics as a useful approach to better understand the pathogenesis of MS and to provide new biomarkers for the disease to be used together with clinical data.

Metabolomics As a Tool for the Characterization of Drug-Resistant Epilepsy

Purpose Drug resistance is a critical issue in the treatment of epilepsy, contributing to clinical emergencies and increasing both serious social and economic burdens on the health system. The wide variety of potential drug combinations followed by often failed consecutive attempts to match drugs to an individual patient may mean that this treatment stage may last for years with suboptimal benefit to the patient. Given these challenges, it is valuable to explore the availability of new methodologies able to shorten the period of determining a rationale pharmacologic treatment. Metabolomics could provide such a tool to investigate possible markers of drug resistance in subjects with epilepsy. Methods Blood samples were collected from (1) controls (C) (n = 35), (2) patients with epilepsy “responder” (R) (n = 18), and (3) patients with epilepsy “non-responder” (NR) (n = 17) to the drug therapy. The samples were analyzed using nuclear magnetic resonance spectroscopy, followed by multivariate statistical analysis. Key findings A different metabolic profile based on metabolomics analysis of the serum was observed between C and patients with epilepsy and also between R and NR patients. It was possible to identify the discriminant metabolites for the three classes under investigation. Serum from patients with epilepsy were characterized by increased levels of 3-OH-butyrate, 2-OH-valerate, 2-OH-butyrate, acetoacetate, acetone, acetate, choline, alanine, glutamate, scyllo-inositol (C < R < NR), and decreased concentration of glucose, lactate, and citrate compared to C (C > R > NR). Significance In conclusion, metabolomics may represent an important tool for discovery of differences between subjects affected by epilepsy responding or resistant to therapies and for the study of its pathophysiology, optimizing the therapeutic resources and the quality of life of patients.