Milena Lussu

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.

Urinary 1H-NMR and GC-MS metabolomics predicts early and late onset neonatal sepsis

The purpose of this article is to study one of the most significant causes of neonatal morbidity and mortality: neonatal sepsis. This pathology is due to a bacterial or fungal infection acquired during the perinatal period. Neonatal sepsis has been categorized into two groups: early onset if it occurs within 3-6 days and late onset after 4-7 days. Due to the not-specific clinical signs, along with the inaccuracy of available biomarkers, the diagnosis is still a major challenge. In this regard, the use of a combined approach based on both nuclear magnetic resonance ((1)H-NMR) and gas-chromatography-mass spectrometry (GC-MS) techniques, coupled with a multivariate statistical analysis, may help to uncover features of the disease that are still hidden. The objective of our study was to evaluate the capability of the metabolomics approach to identify a potential metabolic profile related to the neonatal septic condition. The study population included 25 neonates (15 males and 10 females): 9 (6 males and 3 females) patients had a diagnosis of sepsis and 16 were healthy controls (9 males and 7 females). This study showed a unique metabolic profile of the patients affected by sepsis compared to non-affected ones with a statistically significant difference between the two groups (p = 0.05).

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.

Urinary metabolomics of bronchopulmonary dysplasia (BPD): preliminary data at birth suggest it is a congenital disease

Abstract Objective: Bronchopulmonary dysplasia (BPD) or chronic lung disease is one of the principal causes of mortality and morbidity in preterm infants. Early identification of infants at the greater risk of developing BPD may allow a targeted approach for reducing disease severity and complications. The trigger cause of the disease comprehends the impairment of the alveolar development and the increased angiogenesis. Nevertheless, the molecular pathways characterizing the disease are still unclear. Therefore, the use of the metabolomics technique, due to the capability of identifying instantaneous metabolic perturbation, might help to recognize metabolic patterns associated with the condition. Methods: The purpose of this study is to compare urinary metabolomics at birth in 36 newborns with a gestational age below 29 weeks and birth weight <1500 g (very low birth weight – VLBW), admitted in Neonatal Intensive Care Unit (NICU) divided into two groups: the first group (18 cases) consisting of newborns who have not yet developed the disease, but who will subsequently develop it and the second group (18 controls) consisting of newborns not affected by BPD. Urine samples were collected within 24–36 h of life and immediately frozen at −80 °C. Results: The 1H-NMR spectra were analyzed using a partial least squares discriminant analysis (PLS-DA) model coupled with orthogonal Signal Correction. Using this approach it was possible with urine at birth to discriminate newborns that will be later have a diagnosis of BPD with a high statistics power. In particular, we found five important discriminant metabolites in urine in BPD newborns: lactate, taurine, TMAO, myoinositol (which increased) and gluconate (which decreased). Conclusion: These preliminary results seem to be promising for the identification of predictor’s biomarkers characterizing the BPD condition. These data may suggest that BPD is probably the result of an abnormal development (respiratory bud, vascular tree, hypodysplasia of pneumocytes) and could be considered a congenital disease (genetics plus intrauterine epigenetics). Early identification of infants at the greater risk of developing BPD may allow a targeted approach for reducing disease severity and complications.

Discovery of biochemical biomarkers for aggression: A role for metabolomics in psychiatry.

Human aggression encompasses a wide range of behaviors and is related to many psychiatric disorders. We introduce the different classification systems of aggression and related disorders as a basis for discussing biochemical biomarkers and then present an overview of studies in humans (published between 1990 and 2015) that reported statistically significant associations of biochemical biomarkers with aggression, DSM‐IV disorders involving aggression, and their subtypes. The markers are of different types, including inflammation markers, neurotransmitters, lipoproteins, and hormones from various classes. Most studies focused on only a limited portfolio of biomarkers, frequently a specific class only. When integrating the data, it is clear that compounds from several biological pathways have been found to be associated with aggressive behavior, indicating complexity and the need for a broad approach. In the second part of the paper, using examples from the aggression literature and psychiatric metabolomics studies, we argue that a better understanding of aggression would benefit from a more holistic approach such as provided by metabolomics.

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

Statistical Health Monitoring Applied to a Metabolomic Study of Experimental Hepatocarcinogenesis: An Alternative Approach to Supervised Methods for the Identification of False Positives

In a typical metabolomics experiment, two or more conditions (e.g., treated versus untreated) are compared, in order to investigate the potential differences in the metabolic profiles. When dealing with complex biological systems, a two-class classification is often unsuitable, since it does not consider the unpredictable differences between samples (e.g., nonresponder to treatment). An approach based on statistical process control (SPC), which is able to monitor the response to a treatment or the development of a pathological condition, is proposed here. Such an approach has been applied to an experimental hepatocarcinogenesis model to discover early individual metabolic variations associated with a different response to the treatment. Liver study was performed by nuclear magnetic resonance (NMR) spectroscopy, followed by multivariate statistical analysis. By this approach, we were able to (1) identify which treated samples have a significantly different metabolic profile, compared to the control (in fact, as confirmed by immunohistochemistry, the method correctly classified 7 responders and 3 nonresponders among the 10 treated animals); (2) recognize, for each individual sample, the metabolites that are out of control (e.g., glutathione, acetate, betaine, and phosphocholine). The first point could be used for classification purposes, and the second point could be used for a better understanding of the mechanisms underlying the early phase of carcinogenesis. The statistical control approach can be used for diagnosis (e.g., healthy versus pathological, responder versus nonresponder) and for generation of an individual metabolic profile, leading to a better understanding of the individual pathological processes and to a personalized diagnosis and therapy.