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.

Monitoring of system conditioning after blank injections in untargeted UPLC-MS metabolomic analysis.

Ultra-performance liquid chromatography - mass spectrometry (UPLC-MS) is widely used for untargeted metabolomics in biomedical research. To optimize the quality and precision of UPLC-MS metabolomic analysis, evaluation of blank samples for the elimination of background features is required. Although blanks are usually run either at the beginning or at the end of a sequence of samples, a systematic analysis of their effect of the instrument performance has not been properly documented. Using the analysis of two common bio-fluids (plasma and urine), we describe how the injection of blank samples within a sequence of samples may affect both the chromatographic and MS detection performance depending on several factors, including the sample matrix and the physicochemical properties of the metabolites of interest. The analysis of blanks and post-blank conditioning samples using t-tests, PCA and guided-PCA provides useful information for the elimination of background UPLC-MS features, the identification of column carry over and the selection of the number of samples required to achieve a stable performance.

Plasma sample based analysis of gastric cancer progression using targeted metabolomics.

Gastric carcinogenesis is a multifactorial process described as a stepwise progression from non-active gastritis (NAG), chronic active gastritis (CAG), precursor lesions of gastric cancer (PLGC) and gastric adenocarcinoma. Gastric cancer (GC) 5-year survival rate is highly dependent upon stage of disease at diagnosis, which is based on endoscopy, biopsy and pathological examinations. Non-invasive GC biomarkers would facilitate its diagnosis at early stages leading to improved GC prognosis. We analyzed plasma samples collected from 80 patients diagnosed with NAG without H. pylori infection (NAG-), CAG with H. pylori infection (CAG+), PLGC and GC. A panel of 208 metabolites including acylcarnitines, amino acids and biogenic amines, sphingolipids, glycerophospholipids, hexoses, and tryptophan and phenylalanine metabolites were quantified using two complementary quantitative approaches: Biocrates AbsoluteIDQ®p180 kit and a LC-MS method designed for the analysis of 29 tryptophan pathway and phenylalanine metabolites. Significantly altered metabolic profiles were found in GC patients that allowing discrimination from NAG-, CAG+ and PLGC patients. Pathway analysis showed significantly altered tryptophan and nitrogen metabolic pathways (FDR P < 0.01). Three metabolites (histidine, tryprophan and phenylacetylglutamine) discriminated between non-GC and GC groups. These metabolic signatures open new possibilities to improve surveillance of PLGC patients using a minimally invasive blood analysis.

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.

Intra-batch effect correction in liquid chromatography-mass spectrometry using quality control samples and support vector regression (QC-SVRC).

Instrumental developments in sensitivity and selectivity boost the application of liquid chromatography-mass spectrometry (LC-MS) in metabolomics. Gradual changes in the LC-MS instrumental response (i.e. intra-batch effect) are often unavoidable and they reduce the repeatability and reproducibility of the analysis, decrease the power to detect biological responses and hinder the interpretation of the information provided. Because of that, there is interest in the development of chemometric techniques for the post-acquisition correction of batch effects. In this work, the use of quality control (QC) samples and support vector regression (QC-SVRC) and a radial basis function kernel is proposed to correct intra-batch effects. The repeated analysis of a single sample is used for the assessment of both the correction accuracy and the effect of the distribution of QC samples throughout the batch. The QC-SVRC method is evaluated and compared with a recently developed method based on QC samples and robust cubic smoothing splines (QC-RSC). The results show that QC-SVRC slightly outperformed QC-RSC and allows a straightforward fitting of the SVRC parameters to the instrument performance by using the ε-insensitive loss parameter.

Green determination of the presence of volatile organic compounds in vehicle repair shops through passive sampling.

A simple, fast and green direct analytical methodology has been developed to evaluate the concentration level of volatile organic compounds (VOCs) in indoor areas of vehicle repair shops using membrane devices as passive samplers. VOCs retained in the samplers were directly determined without any sample pre-treatment and avoiding the use of solvents by head space (HS) coupled to gas chromatography-mass spectrometry (GC-MS) in only 20 min. Benzene, toluene, tetrachloroethene, m,p-xylene and o-xylene were found at concentration levels from 0.1 to 11.2 mg m(-3).

Determination of indoor air quality of a phytosanitary plant.

A new methodology has been developed to determine volatile organic compounds (VOCs) and pesticides in ambient air using membrane based devices as passive samplers. Pesticides were determined by gas chromatography with mass spectrometry detection (GC-MS) after their microwave-assisted extraction (MAE) from the passive sampler and the required clean-up. On the other hand, VOCs were also caught with the same samplers and directly determined by head space (HS) coupled to GC-MS. The use of samplers filled with florisil and activated carbon allows us to catch with a simple device both, VOCs and pesticides, with a high vapor pressure. Results obtained in the deployment of samplers in different sites of a phytosanitary plant evidenced the presence of high quantities of chlorpyriphos at thousand ng m(3) and also metalaxyl, oxyfluorphen and lindane at ng m(3) together with carbon disulfide, 2,2,4-trimethylpentane, ethylbenzene and xylene.