Searching molecular biomarkers correlating with BSID-III at 24 months in infants with neonatal hypoxic-ischemic encephalopathy.

An early prediction of outcomes of neonatal hypoxic-ischemic encephalopathy (NE) is of key importance in reducing neonatal mortality and morbidity. The objectives were (i) to analyze the characteristics of miRNA expression and metabolic patterns of neonates with NE and (ii) to assess their predictive performance for neurodevelopmental outcomes. Plasma samples from moderate/severe NE patients (N = 92) of the HYPOTOP study were collected before, during, and after therapeutic hypothermia (TH) and compared to a control group (healthy term infants). The expression of miRNAs and concentrations of metabolites (hypoxia-related and energy, steroid, and tryptophan metabolisms) were analyzed. Neurodevelopmental outcomes were evaluated at 24 months postnatal age using Bayley Scales of Infant Development, ed. III, BSID-III. Differences in miRNA and metabolic profiles were found between NE vs. control infants, abnormal (i.e., mildly and moderately abnormal and severe) vs. normal, and severe vs. non-severe (i.e., normal and mildly and moderately abnormal) BSID-III. 4-Androstene-3,17-dione, testosterone, betaine, xanthine, and lactate were suitable for BSID-III outcome prediction (receiver operating characteristic areas under the curve (AUCs) ≥ 0.6), as well as 68 miRNAs (AUCs of 0.5-0.9). Significant partial correlations of xanthine and betaine levels and the expression of several miRNAs with BSID-III sub-scales were found. Conclusion: We have identified metabolites/miRNAs that might be useful to support the prediction of middle-term neurodevelopmental outcomes of NE. What is known and what is new: • The early prediction of outcomes of neonatal hypoxic-ischemic encephalopathy (NE) is of key importance in reducing neonatal mortality and morbidity. • Alterations of the metabolome and miRNAs had been observed in NE. • We performed miRNA sequencing and quantified selected metabolites (i.e., lactate, pyruvate, ketone bodies, Krebs cycle intermediates, tryptophan pathway, hypoxia-related metabolites, and steroids) by GC- and LC-MS. • Specific miRNAs and metabolites that allow prediction of middle-term neurodevelopmental outcomes of newborns with NE undergoing hypothermia treatment were identified.

Exploring Individual Variability in Drug-Induced Liver Injury (DILI) Responses through Metabolomic Analysis.

Drug-induced liver injury (DILI) is a serious adverse hepatic event presenting diagnostic and prognostic challenges. The clinical categorization of DILI into hepatocellular, cholestatic, or mixed phenotype is based on serum alanine aminotransferase (ALT) and alkaline phosphatase (ALP) values; however, this classification may not capture the full spectrum of DILI subtypes. With this aim, we explored the utility of assessing changes in the plasma metabolomic profiles of 79 DILI patients assessed by the RUCAM (Roussel Uclaf Causality Assessment Method) score to better characterize this condition and compare results obtained with the standard clinical characterization. Through the identification of various metabolites in the plasma (including free and conjugated bile acids and glycerophospholipids), and the integration of this information into predictive models, we were able to evaluate the extent of the hepatocellular or cholestatic phenotype and to assign a numeric value with the contribution of each specific DILI sub-phenotype into the patient's general condition. Additionally, our results showed that metabolomic analysis enabled the monitoring of DILI variability responses to the same drug, the transitions between sub-phenotypes during disease progression, and identified a spectrum of residual DILI metabolic features, which can be overlooked using standard clinical diagnosis during patient follow-up.

Longitudinal perturbations of plasma nuclear magnetic resonance profiles in neonatal encephalopathy.

BACKGROUND: Neonatal encephalopathy (NE) is a major cause of mortality and severe neurological disability in the neonatal period and beyond. We hypothesized that the degree of brain injury is reflected in the molecular composition of peripheral blood samples. METHODS: A sub-cohort of 28 newborns included in the HYPOTOP trial was studied. Brain injury was assessed by magnetic resonance imaging (MRI) once per patient and neurodevelopment at 24 months of age was evaluated using the Bayley III Scales of Infant and Toddler Development. The nuclear magnetic resonance (NMR) profile of 60 plasma samples collected before, during, and after cooling was recorded. RESULTS: In total, 249 molecular features were quantitated in plasma samples from newborns and postnatal age showed to affect detected NMR profiles. Lactate, beta-hydroxybutyrate, pyruvate, and three triglyceride biomarkers showed the ability to discern between different degrees of brain injury according to MRI scores. The prediction performance of lactate was superior as compared to other clinical and biochemical parameters. CONCLUSIONS: This is the first longitudinal study of an ample compound panel recorded by NMR spectroscopy in plasma from NE infants. The serial determination of lactate confirms its solid position as reliable candidate biomarker for predicting the severity of brain injury. IMPACT: The use of nuclear magnetic resonance (NMR) spectroscopy enables the simultaneous quantitation of 249 compounds in a small volume (i.e., 100 µL) of plasma. Longitudinal perturbations of plasma NMR profiles were linked to magnetic resonance imaging (MRI) outcomes of infants with neonatal encephalopathy (NE). Lactate, beta-hydroxybutyrate, pyruvate, and three triglyceride biomarkers showed the ability to discern between different degrees of brain injury according to MRI scores. Lactate is a minimally invasive candidate biomarker for early staging of MRI brain injury in NE infants that might be readily implemented in clinical guidelines for NE outcome prediction.

Plasma serotonergic biomarkers are associated with hypoxemia events in preterm neonates.

BACKGROUND: Hypoxemia is a physiological manifestation of immature respiratory control in preterm neonates, which is likely impacted by neurotransmitter imbalances. We investigated relationships between plasma levels of the neurotransmitter serotonin (5-HT), metabolites of tryptophan (TRP), and parameters of hypoxemia in preterm neonates. METHODS: TRP, 5-HT, 5-hydroxyindoleacetic acid (5-HIAA), and kynurenic acid (KA) were analyzed in platelet-poor plasma at ~1 week and ~1 month of life from a prospective cohort of 168 preterm neonates <31 weeks gestational age (GA). Frequency of intermittent hypoxemia (IH) events and percent time hypoxemic (<80%) were analyzed in a 6 h window after the blood draw. RESULTS: At 1 week, infants with detectable plasma 5-HT had fewer IH events (OR (95% CI) = 0.52 (0.29, 0.31)) and less percent time <80% (OR (95% CI) = 0.54 (0.31, 0.95)) compared to infants with undetectable 5-HT. A similar relationship occurred at 1 month. At 1 week, infants with higher KA showed greater percent time <80% (OR (95% CI) = 1.90 (1.03, 3.50)). TRP, 5-HIAA or KA were not associated with IH frequency at either postnatal age. IH frequency and percent time <80% were positively associated with GA < 29 weeks. CONCLUSIONS: Circulating neuromodulators 5-HT and KA might represent biomarkers of immature respiratory control contributing to hypoxemia in preterm neonates. IMPACT: Hypoxemia events are frequent in preterm infants and are associated with poor outcomes. Mechanisms driving hypoxemia such as immature respiratory control may include central and peripheral imbalances in modulatory neurotransmitters. This study found associations between the plasma neuromodulators serotonin and kynurenic acid and parameters of hypoxemia in preterm neonates. Imbalances in plasma biomarkers affecting respiratory control may help identify neonates at risk of short- and long-term adverse outcomes.

Enhancing the accuracy of mid-infrared spectroscopy-based liver steatosis quantification using digital image analysis as a reference.

The assessment of liver steatosis is crucial in both hepatology and liver transplantation (LT) surgery. Steatosis can negatively impact the success of LT. Steatosis is a factor for excluding donated organs for LT, but the increasing demand for transplantable organs has led to the use of organs from marginal donors. The current standard for evaluating steatosis is a semi-quantitative grading based on the visual examination of a hematoxylin and eosin (H&E)-stained liver biopsy, but this method is time-consuming, subjective, and lacks reproducibility. Recent research has shown that infrared (IR) spectroscopy could be used as a real-time quantitative tool to assess steatosis during abdominal surgery. However, the development of IR-based methods has been hindered by the lack of appropriate quantitative reference values. In this study, we developed and validated digital image analysis methods for the quantitation of steatosis in H&E-stained liver sections using univariate and multivariate strategies including linear discriminant analysis (LDA), quadratic DA, logistic regression, partial least squares-DA (PLS-DA), and support vector machines. The analysis of 37 tissue samples with varying grades of steatosis demonstrates that digital image analysis provides accurate and reproducible reference values that improve the performance of IR spectroscopic models for steatosis quantification. A PLS model in the 1810-1052 cm-1 region using first derivative ATR-FTIR spectra provided RMSECV = 0.99%. The gained improvement in accuracy critically enhances the applicability of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) to support an objective graft evaluation at the operation room, which might be especially relevant in cases of marginal liver donors to avoid unnecessary graft explantation.

Comparing the direct assessment of steatosis in liver explants with mid- and near-infrared vibrational spectroscopy, prior to organ transplantation.

A fast and accurate assessment of liver steatosis is crucial during liver transplantation surgery as it can negatively impact its success. Recent research has shown that near-infrared (NIR) and attenuated total reflectance-Fourier transform mid-infrared (ATR-FTIR) spectroscopy could be used as real-time quantitative tools to assess steatosis during abdominal surgery. Here, in the frame of a clinical study, we explore the performance of NIR and ATR-FTIR spectroscopy for the direct assessment of steatosis in liver tissues. Results show that both NIR and ATR-FTIR spectroscopy are able to quantify the % of steatosis with cross-validation errors of 1.4 and 1.6%, respectively. Furthermore, the two portable instruments used both provided results within seconds and can be placed inside an operating room evidencing the potential of IR spectroscopy for initial characterization of grafts in liver transplantation surgery. We also evaluated the complementarity of the spectral ranges through correlation spectroscopy.

Fast profiling of primary, secondary, conjugated, and sulfated bile acids in human urine and murine feces samples.

Bile acids (BAs) are a complex class of metabolites that have been described as specific biomarkers of gut microbiota activity. The development of analytical methods allowing the quantification of an ample spectrum of BAs in different biological matrices is needed to enable a wider implementation of BAs as complementary measures in studies investigating the functional role of the gut microbiota. This work presents results from the validation of a targeted ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method for the determination of 28 BAs and six sulfated BAs, covering primary, secondary, and conjugated BAs. The analysis of 73 urine and 20 feces samples was used to test the applicability of the method. Concentrations of BAs in human urine and murine feces were reported, ranging from 0.5 to 50 nmol/g creatinine and from 0.012 to 332 nmol/g, respectively. Seventy-nine percent of BAs present in human urine samples corresponded to secondary conjugated BAs, while 69% of BAs present in murine feces corresponded to primary conjugated BAs. Glycocholic acid sulfate (GCA-S) was the most abundant BA in human urine samples, while taurolithocholic acid was the lowest concentrated compound detected. In murine feces, the most abundant BAs were α-murocholic, deoxycholic, dehydrocholic, and ß-murocholic acids, while GCA-S was the lowest concentrated BA. The presented method is a non-invasive approach for the simultaneous assessment of BAs and sulfated BAs in urine and feces samples, and the results will serve as a knowledge base for future translational studies focusing on the role of the microbiota in health.

Metabolomics-based strategy to assess drug hepatotoxicity and uncover the mechanisms of hepatotoxicity involved.

Toxicity studies, among them hepatotoxicity, are key throughout preclinical stages of drug development to minimise undesired toxic effects that might eventually appear in the course of the clinical use of the new drug. Understanding the mechanism of injury of hepatotoxins is essential to efficiently anticipate their potential risk of toxicity in humans. The use of in vitro models and particularly cultured hepatocytes represents an easy and robust alternative to animal drug hepatotoxicity testing for predicting human risk. Here, we envisage an innovative strategy to identify potential hepatotoxic drugs, quantify the magnitude of the alterations caused, and uncover the mechanisms of toxicity. This strategy is based on the comparative analysis of metabolome changes induced by hepatotoxic and non-hepatotoxic compounds on HepG2 cells, assessed by untargeted mass spectrometry. As a training set, we used 25 hepatotoxic and 4 non-hepatotoxic compounds and incubated HepG2 cells for 24 h at a low and a high concentration (IC10 and IC50) to identify mechanism-related and cytotoxicity related metabolomic biomarkers and to elaborate prediction models accounting for global hepatotoxicity and mechanisms-related toxicity. Thereafter, a second set of 69 chemicals with known predominant mechanisms of toxicity and 18 non-hepatotoxic compounds were analysed at 1, 10, 100 and 1000 µM concentrations from which and based on the magnitude of the alterations caused as compared with non-toxic compounds, we defined a "toxicity index" for each compound. In addition, we extracted from the metabolome data the characteristic signatures for each mechanism of hepatotoxicity. The integration of all this information allowed us to identify specific metabolic patterns and, based on the occurrence of that specific metabolome changes, the models predicted the likeliness of a compound to behave as hepatotoxic and to act through a given toxicity mechanism (i.e., oxidative stress, mitochondrial disruption, apoptosis and steatosis) for each compound and concentration.

A Novel UPLC-MS Metabolomic Analysis-Based Strategy to Monitor the Course and Extent of iPSC Differentiation to Hepatocytes.

Typical protocols to differentiate induced pluripotent stem cells (iPSCs) from hepatocyte-like cells (HLCs) imply complex strategies that include transfection with key hepatic transcription factors and the addition to culture media of nutrients, growth factors, and cytokines. A main constraint to evaluate the hepatic phenotype achieved arises from the way the grade of differentiation is determined. Currently, it relies on the assessment of the expression of a limited number of hepatic gene transcripts, less frequently by assessing certain hepatic metabolic functions, and rarely by the global metabolic performance of differentiated cells. We envisaged a new strategy to assess the extent of differentiation achieved, based on the analysis of the cellular metabolome along the differentiation process and its quantitative comparison with that of primary human hepatocytes (PHHs). To validate our approach, we examined the changes in the metabolome of three iPSC progenies (transfected with/without key transcription factors), cultured in three differentiation media, and compared them to PHHs. Results revealed consistent metabolome changes along differentiation and evidenced the factors that more strongly promote changes in the metabolome. The integrated dissimilarities between the PHHs and HLCs retrieved metabolomes were used as a numerical reference for quantifying the degree of iPSCs differentiation. This newly developed metabolome-analysis approach evidenced its utility in assisting us to select a cell's source, culture conditions, and differentiation media, to achieve better-differentiated HLCs.

Noninvasive monitoring of evolving urinary metabolic patterns in neonatal encephalopathy.

BACKGROUND: Infants with moderate and severe neonatal encephalopathy (NE) frequently suffer from long-term adverse outcomes. We hypothesize that the urinary metabolome of newborns with NE reflects the evolution of injury patterns observed with magnetic resonance imaging (MRI). METHODS: Eligible patients were newborn infants with perinatal asphyxia evolving to NE and qualifying for therapeutic hypothermia (TH) included in the HYPOTOP trial. MRI was employed for characterizing brain injury. Urine samples of 55 infants were collected before, during, and after TH. Metabolic profiles of samples were recorded employing three complementary mass spectrometry-based assays, and the alteration of detected metabolic features between groups was assessed. RESULTS: The longitudinal assessment revealed significant perturbations of the urinary metabolome. After 24 h of TH, a stable disease pattern evolved characterized by the alterations of 4-8% of metabolic features related to lipid metabolism, metabolism of cofactors and vitamins, glycan biosynthesis and metabolism, amino acid metabolism, and nucleotide metabolism. Characteristic metabolomic fingerprints were observed for different MRI injury patterns. CONCLUSIONS: This study shows the potential of urinary metabolic profiles for the noninvasive monitoring of brain injury of infants with NE during TH. IMPACT: A comprehensive approach for the study of the urinary metabolome was employed involving a semi-targeted capillary electrophoresis-time-of-flight mass spectrometry (TOFMS) assay, an untargeted ultra-performance liquid chromatography (UPLC)-quadrupole TOFMS assay, and a targeted UPLC-tandem MS-based method for the quantification of amino acids. The longitudinal study of the urinary metabolome identified dynamic metabolic changes between birth and until 96 h after the initiation of TH. The identification of altered metabolic pathways in newborns with pathologic MRI outcomes might offer the possibility of developing noninvasive monitoring approaches for personalized adjustment of the treatment and for supporting early outcome prediction.

Quantitative Prediction of Steatosis in Patients with Non-Alcoholic Fatty Liver by Means of Hepatic MicroRNAs Present in Serum and Correlating with Hepatic Fat.

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent form of chronic liver disease worldwide, but a reliable non-invasive method to quantify liver steatosis in primary healthcare is not available. Circulating microRNAs have been proposed as biomarkers of severe/advanced NAFLD (steatohepatitis and fibrosis). However, the use of circulating miRNAs to quantitatively assess the % of liver fat in suspected NAFLD patients has not been investigated. We performed global miRNA sequencing in two sets of samples: human livers from organ donors (n = 20), and human sera from biopsy-proven NAFLD patients (n = 23), both with a wide range of steatosis quantified in their liver biopsies. Partial least squares (PLS) regression combined with recursive feature elimination (RFE) was used to select miRNAs associated with steatosis. Moreover, regression models with only 2 or 3 miRNAs, with high biological relevance, were built. Comprehensive microRNA sequencing of liver and serum samples resulted in two sets of abundantly expressed miRNAs (418 in liver and 351 in serum). Pearson correlation analyses indicated that 18% of miRNAs in liver and 14.5% in serum were significantly associated with the amount of liver fat. PLS-RFE models demonstrated that 50 was the number of miRNAs providing the lowest error in both liver and serum models predicting steatosis. Comparison of the two miRNA subsets showed 19 coincident miRNAs that were ranked according to biological significance (guide/passenger strand, relative abundance in liver and serum, number of predicted lipid metabolism target genes, correlation significance, etc.). Among them, miR-10a-5p, miR-98-5p, miR-19a-3p, miR-30e-5p, miR-32-5p and miR-145-5p showed the highest biological relevance. PLS regression models with serum levels of 2−3 of these miRNAs predicted the % of liver fat with errors <5%.

Analysis of the Association between Fatigue and the Plasma Lipidomic Profile of Inflammatory Bowel Disease Patients.

Inflammatory bowel disease (IBD) is a chronic, relapsing noninfectious inflammatory condition of the intestinal tract with two main phenotypes, ulcerative colitis (UC) and Crohn's disease (CD), and globally increasing incidence and prevalence. Nearly 80% of the IBD patients with active disease and 50% of those with inactive disease suffer fatigue with significant impairment of their quality of life. Fatigue has been associated with multiple factors in IBD patients but, in most cases, no direct cause can be identified, and risk factors in clinically quiescent IBD are contradictory. Furthermore, as the assessment of fatigue is subjective, there is an unmet clinical need for fatigue biomarkers. In this explorative study, we analyzed the plasma lipidomic profiles of 47 quiescent UC and CD patients (23 fatigued, 24 nonfatigued) using ultraperformance liquid chromatography-time-of-flight mass spectrometry (UPLC-TOFMS). The results showed changes in lipids associated with fatigue and IBD. Significantly decreased levels of phosphatidylcholines, plasmanyls, sphingomyelins, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, phosphatidylserines, and eicosanoids were observed in patients with fatigue. Network and metabolic pathway analysis indicated a dysregulation of the arachidonic acid and glycerophospholipid metabolisms and the sphingolipid pathway. The protein-metabolite interaction network showed interactions between functionally related metabolites and proteins, displaying 40 disease-associated hidden proteins including ABDH4, GLTP, and LCAT.

Metabolomic analysis to discriminate drug-induced liver injury (DILI) phenotypes.

Drug-induced liver injury (DILI) is an adverse toxic hepatic clinical reaction associated to the administration of a drug that can occur both at early clinical stages of drug development, as well after normal clinical usage of approved drugs. Because of its unpredictability and clinical relevance, it is of medical concern. Three DILI phenotypes (hepatocellular, cholestatic, and mixed) are currently recognized, based on serum alanine aminotransferase (ALT) and alkaline phosphatase (ALP) values. However, this classification lacks accuracy to distinguish among the many intermediate mixed types, or even to estimate the magnitude and progression of the injury. It was found desirable to have additional elements for better evaluation criteria of DILI. With this aim, we have examined the serum metabolomic changes occurring in 79 DILI patients recruited and monitored using established clinical criteria, along the course of the disease and until recovery. Results revealed that free and conjugated bile acids, and glycerophospholipids were among the most relevant metabolite classes for DILI phenotype characterization. Using an ensemble of PLS-DA models, metabolomic information was integrated into a ternary diagram to display the disease phenotype, the severity of the liver damage, and its progression. The modeling implemented and the use of such compiled information in an easily understandable and visual manner facilitates a straightforward DILI phenotyping and allow to monitor its progression and recovery prediction, usefully complementing the concise information drawn out by the ALT and ALP classification.

Toward Rapid Screening of Liver Grafts at the Operating Room Using Mid-infrared Spectroscopy.

The estimation of steatosis in a liver graft is mandatory prior to liver transplantation, as the risk of graft failure increases with the level of infiltrated fat. However, the assessment of liver steatosis before transplantation is typically based on a qualitative or semiquantitative characterization by visual inspection and palpation and histological analysis. Thus, there is an unmet need for transplantation surgeons to have access to a diagnostic tool enabling an in situ fast classification of grafts prior to extraction. In this study, we have assessed an attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopic method compatible with the requirements of an operation room for the evaluation of the lipid contents in human livers. A set of 20 human liver biopsies obtained from organs intended for transplantation were analyzed by expert pathologists, ATR-FTIR spectroscopy, lipid biochemical analysis, and UPLC-ESI(+/-)TOFMS for lipidomic profiling. Comparative analysis of multisource data showed strong correlations between ATR-FTIR, clinical, and lipidomic information. Results show that ATR-FTIR captures a global picture of the lipid composition of the liver, along with information for the quantification of the triradylglycerol content in liver biopsies. Although the methodology performance needs to be further validated, results support the applicability of ATR-FTIR for the in situ determination of the grade of liver steatosis at the operation room as a fast, quantitative method, as an alternative to the qualitative and subjective pathological examination.

Urine metabolomic analysis for monitoring internal load in professional football players.

INTRODUCTION: The design of training programs for football players is not straightforward due to intra- and inter-individual variability that leads to different physiological responses under similar training loads. OBJECTIVE: To study the association between the external load, defined by variables obtained using electronic performance tracking systems (EPTS), and the urinary metabolome as a surrogate of the metabolic adaptation to training. METHODS: Urine metabolic and EPTS data from 80 professional football players collected in an observational longitudinal study were analyzed by ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry and assessed by partial least squares (PLS) regression. RESULTS: PLS models identified steroid hormone metabolites, hypoxanthine metabolites, acetylated amino acids, intermediates in phenylalanine metabolism, tyrosine, tryptophan metabolites, and riboflavin among the most relevant variables associated with external load. Metabolic network analysis identified enriched pathways including steroid hormone biosynthesis and metabolism of tyrosine and tryptophan. The ratio of players showing a deviation from the PLS model of adaptation to exercise was higher among those who suffered a muscular lesion compared to those who did not. CONCLUSIONS: There was a significant association between the external load and the urinary metabolic profile, with alteration of biochemical pathways associated with long-term adaptation to training. Future studies should focus on the validation of these findings and the development of metabolic models to identify professional football players at risk of developing muscular injuries.

Metabolomic profiling in neuroblastoma.

BACKGROUND AND OBJECTIVES: Previous studies on several cancer types show that metabolomics provides a potentially useful noninvasive screening approach for outcome prediction and accurate response to treatment assessment. Neuroblastoma (NB) accounts for at least 15% of cancer-related deaths in children. Although current risk-based treatment approaches in NB have resulted in improved outcome, survival for high-risk patients remains poor. This study aims to evaluate the use of metabolomics for improving patients' risk-group stratification and outcome prediction in NB. DESIGN AND METHODS: Plasma samples from 110 patients with NB were collected at diagnosis prior to starting therapy and at the end of treatment if available. Metabolomic analysis of samples was carried out by ultra-performance liquid chromatography-time of flight mass spectrometry (UPLC-MS). RESULTS: The metabolomic analysis was able to identify different plasma metabolic profiles in high-risk and low-risk NB patients at diagnosis. The metabolic model correctly classified 16 high-risk and 15 low-risk samples in an external validation set providing 84.2% sensitivity (60.4-96.6, 95% CI) and 93.7% specificity (69.8-99.8, 95% CI). Metabolomic profiling could also discriminate high-risk patients with active disease from those in remission. Notably, a plasma metabolomic signature at diagnosis identified a subset of high-risk NB patients who progressed during treatment. CONCLUSIONS: To the best of our knowledge, this is the largest NB study investigating the prognostic power of plasma metabolomics. Our results support the potential of metabolomic profiling for improving NB risk-group stratification and outcome prediction. Additional validating studies with a large cohort are needed.

On-Capillary Surface-Enhanced Raman Spectroscopy: Determination of Glutathione in Whole Blood Microsamples.

Oxidative stress monitoring in the neonatal period supports early outcome prediction and treatment. Glutathione (GSH) is the most abundant antioxidant in most cells and tissues, including whole blood, and its usefulness as a biomarker has been known for decades. To date, the available methods for GSH determination require laborious sample processing and the use of sophisticated laboratory equipment. To the best of our knowledge, no tools suitable for point-of-care (POC) sensing have been reported. Surface-enhanced Raman spectroscopy (SERS), performed in a microvolume capillary measurement cell, is proposed in this study as a robust approach for the quantification of GSH in human whole blood samples. The use of a silver colloid allowed a highly selective signal enhancement for GSH providing analytical enhancement factors of 3 to 4 orders of magnitude. A highly accurate determination of GSH in whole blood samples with recoveries ranging from 99 to 107% and relative standard deviations less than or equal to 18% were achieved by signal normalization with the intensity of an isotopically labeled internal standard. GSH concentrations were retrieved within 4 min using small-volume blood samples (2 µL). The developed procedure was applied to the analysis of blood of 20 healthy adults and 36 newborns, obtaining comparable results between literature and those found by SERS and a reference method. The characteristics of this novel tool are suitable for its implementation in a portable optical sensor device enabling POC testing of oxidative stress levels in newborns.

Metabolomic Analysis of Gastric Cancer Progression within the Correa's Cascade Using Ultraperformance Liquid Chromatography-Mass Spectrometry.

Gastric cancer (GC) is among the most common cancers worldwide. Gastric carcinogenesis is a multistep and multifactorial process beginning with chronic gastritis induced by Helicobacter pylori (H. pylori) infection. This process is often described via a sequence of events known as Correas's cascade, a stepwise progression from nonactive gastritis, chronic active gastritis, precursor lesions of gastric cancer (atrophy, intestinal metaplasia, and dysplasia), and finally adenocarcinoma. Our aim was to identify a plasma metabolic pattern characteristic of GC through disease progression within the Correa's cascade. This study involved the analysis of plasma samples collected from 143 patients classified in four groups: patients with nonactive gastritis and no H. pylori infection, H. pylori infected patients with chronic active gastritis, infected or noninfected patients with precursor lesions of gastric cancer, and GC. Independent partial least-squares-discriminant binary models of UPLC-ESI(+)-TOFMS metabolic profiles, implemented in a decision-directed acyclic graph, allowed the identification of tryptophan and kynurenine as discriminant metabolites that could be attributed to indoleamine-2,3-dioxygenase upregulation in cancer patients leading to tryptophan depletion and kynurenine metabolites generation. Furthermore, phenylacetylglutamine was also classified as a discriminant metabolite. Our data suggest the use of tryptophan, kynurenine, and phenylacetylglutamine as potential GC biomarkers.

Changes of the plasma metabolome of newly born piglets subjected to postnatal hypoxia and resuscitation with air.

BACKGROUND: Perinatal hypoxic-ischemic brain damage is a major cause of mortality and morbidity in the neonatal period. Currently, limited ranges of biochemical tests assessing the intensity and duration of hypoxia are ready for clinical use. However, the need to initiate hypothermia therapy early after the clinical suspicion of hypoxic-ischemic encephalopathy requires the availability of early and reliable hypoxia markers. We have sought these biomarkers in an experimental model of hypoxia reoxygenation. METHODS: Hypoxia and hypotension were induced in newborn piglets following a standardized model and reoxygenation was carried out using room air (RA). An untargeted liquid chromatography-time of flight mass spectrometry (LC-TOFMS) approach was used to assess changes in the metabolomic profile of plasma samples after intense hypoxia and upon reoxygenation. RESULTS: At the end of hypoxia, the plasma metabolome showed an increased plasma concentration of analytes reflecting a metabolic adaptation to prolonged anaerobiosis. However, after resuscitation, metabolite levels returned to the starting values. CONCLUSION: Severe hypoxia induces early, significant, and transient changes of specific metabolites in the plasma metabolome, which represent a snapshot of the biochemical adaptation of mammals to intense hypoxia. These metabolites could have applicability in predicting the severity of hypoxia in the clinical setting.

Surface enhanced Raman spectroscopic direct determination of low molecular weight biothiols in umbilical cord whole blood.

Biothiols play an essential role in a number of biological processes in living organisms including detoxification and metabolism. Fetal to neonatal transition poses a pro-oxidant threat for newborn infants, especially those born prematurely. A reliable and rapid tool for the direct determination of thiols in small volume whole blood (WB) samples would be desirable for its application in clinical practice. This study shows the feasibility of Surface Enhanced Raman Spectroscopy (SERS) using a silver colloid prepared by reduction of silver nitrate using hydroxylamine, as the SERS substrate for the quantification of thiols in WB samples after a simple precipitation step for protein removal. Bands originating from biothiols (790, 714 and 642 cm(-1)) were enhanced by the employed SERS substrate and the specificity of the detected SERS signal was tested for molecules presenting -SH functional groups. A statistically significant correlation between the obtained SERS signals and the thiol concentration measured using a chromatographic reference method in umbilical cord WB samples could be demonstrated. Using WB GSH concentrations obtained from the chromatographic reference procedure, a Partial Least Squares (PLS) regression model covering GSH concentrations from 13 to 2200 µM was calculated obtaining a root mean square error of prediction (RMSEP) of 381 µM when applied to an external test set. The developed approach uses small blood sample volumes (50 µL), which is important for clinical applications, especially in the field of neonatology. This feasibility study shows that the present approach combines all the necessary characteristics for its potential application in clinical practice.