No abstract

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.

Preterm infants have an immature antioxidant system; however, they frequently require supplemental oxygen. Oxygen-free radicals cause both pulmonary and systemic inflammation, and they are associated with increased morbidity and mortality. Consequently, screening of metabolite profiles representing the amount of lipid peroxidation is considered of great relevance for the evaluation of in vivo oxidative stress and derived inflammation and damage. Ranges for total relative contents of isoprostanes (IsoPs), isofurans (IsoFs), neuroprostanes (NeuroPs), and neurofurans (NeuroFs) within targeted SpO2 ranges were determined in urine samples of 254 preterm infants<32 weeks of gestation within the frame of two randomized, controlled, and blinded clinical trials employing ultra-performance liquid chromatography-tandem mass spectrometry. A total of 536 serial urine samples collected during the first 4 weeks after birth in recruited infants who did not develop free radical associated conditions were analyzed. A reference range for lipid peroxidation byproducts, including isoprostanes, isofurans, neuroprostanes, and neurofurans, was calculated and possible correlations with neonatal conditions were investigated. Urinary elimination of isofurans in the first 4 days after birth correlated with later development of bronchopulmonary dysplasia. Our observations lead to the hypothesis that early urinary determination of lipid peroxidation byproducts, especially isofurans, is relevant to predict development of chronic lung conditions.

An MS-based metabolomics strategy including variable selection and PLSDA analysis has been assessed as a tool to discriminate between non-steatotic and steatotic human liver profiles. Different chemometric approaches for uninformative variable elimination were performed by using two of the most common software packages employed in the field of metabolomics (i.e., MATLAB and SIMCA-P). The first considered approach was performed with MATLAB where the PLS regression vector coefficient values were used to classify variables as informative or not. The second approach was run under SIMCA-P, where variable selection was performed according to both the PLS regression vector coefficients and VIP scores. PLSDA models performance features, such as model validation, variable selection criteria, and potential biomarker output, were assessed for comparison purposes. One interesting finding is that variable selection improved the classification predictiveness of all the models by facilitating metabolite identification and providing enhanced insight into the metabolic information acquired by the UPLC-MS method. The results prove that the proposed strategy is a potentially straightforward approach to improve model performance. Among others, GSH, lysophospholipids and bile acids were found to be the most important altered metabolites in the metabolomic profiles studied. However, further research and more in-depth biochemical interpretations are needed to unambiguously propose them as disease biomarkers.

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.

Non Muscle Invasive Bladder Cancer (NMIBC) is among the most frequent malignant cancers worldwide. NMIBC is treated by transurethral resection of the bladder tumor (TURBT) and intravesical therapies, and has the highest recurrence rate among solid tumors. It requires a lifelong patient monitoring based on repeated cystoscopy and urinary cytology, both having drawbacks that include lack of sensitivity and specificity, invasiveness and care costs. We conducted an investigative clinical study to examine changes in the urinary metabolome of NMBIC patients before and after TURBT, as well during the subsequent surveillance period. Adjusting by prior probability of recurrence per risk, discriminant analysis of UPLC-MS metabolic profiles, displayed negative predictive values for low, low-intermediate, high-intermediate and high risk patient groups of 96.5%, 94.0%, 92.9% and 76.1% respectively. Detailed analysis of the metabolome revealed several candidate metabolites and perturbed phenylalanine, arginine, proline and tryptophan metabolisms as putative biomarkers. A pilot retrospective analysis of longitudinal trajectories of a BC metabolic biomarkers during post TURBT surveillance was carried out and the results give strong support for the clinical use of metabolomic profiling in assessing NMIBC recurrence.

No abstract

A nondestructive analytical method for peroxide-based explosives determination in solid samples is described. Reversed-phase high-performance liquid chromatography in combination with on-line Fourier transform infrared (FT-IR) detection is used for the analysis of triacetonetriperoxide (TATP) and hexamethylenetriperoxide diamine (HMTD). In contrast to other liquid chromatographic methods with optical detection, no derivatization or decomposition of the peroxides is required. The peroxides are identified and quantified via their characteristic absorption spectra in the mid-infrared range of the electromagnetic spectrum. The detection limit of 0.5 mmol L-1 for HMTD and 1 mmol L-1 for TATP allows the identification of the explosives in complex matrixes.