Metabolomics applied to urine samples in childhood asthma; differentiation between asthma phenotypes and identification of relevant metabolites

Asthma is a heterogeneous disorder and one of the most common chronic childhood diseases. An improved characterization of asthma phenotypes would be invaluable for the understanding of the pathogenic mechanisms and the correct treatment of this disease. The aim of this pilot study was to explore the potential of metabolomics applied to urine samples in characterizing asthma, and to identify the most representative metabolites. Urine samples of 41 atopic asthmatic children (further subdivided in sub-groups according to the symptoms) and 12 age-matched controls were analyzed. Untargeted metabolic profiles were collected by LC-MS, and studied by multivariate analysis. The group of the asthmatics was differentiated by a model that proved to be uncorrelated with the chronic assumption of controller drugs by part of the patients. The distinct sub-groups were also appropriately modeled. Further investigations revealed a reduced excretion of urocanic acid, methyl-imidazoleacetic acid and of a metabolite resembling the structure of an Ile-Pro fragment in the asthmatics. The meaning of these findings was discussed and mainly correlated with the modulation of immunity in asthma. Metabolic profiles from urines have revealed the potential to characterize asthma and enabled the identification of metabolites which may have a role in the underlying inflammation.JRC.I.6-Systems toxicolog

Different Real Time PCR Approaches for the Fine Quantification of SNP's Alleles in DNA Pools: Assays Development, Characterization and Pre-validation

n/

Common and Distinct Components in Data Fusion

In many areas of science multiple sets of data are collected pertaining to the same system. Examples are food products which are characterized by different sets of variables, bio-processes which are on-line sampled with different instruments, or biological systems of which different genomics measurements are obtained. Data fusion is concerned with analyzing such sets of data simultaneously to arrive at a global view of the system under study. One of the upcoming areas of data fusion is exploring whether the data sets have something in common or not. This gives insight into common and distinct variation in each data set, thereby facilitating understanding the relationships between the data sets. Unfortunately, research on methods to distinguish common and distinct components is fragmented, both in terminology as well as in methods: there is no common ground which hampers comparing methods and understanding their relative merits. This paper provides a unifying framework for this subfield of data fusion by using rigorous arguments from linear algebra. The most frequently used methods for distinguishing common and distinct components are explained in this framework and some practical examples are given of these methods in the areas of (medical) biology and food science.Comment: 50 pages, 12 figure

Untargeted metabolic profiling of saliva by liquid chromatography-mass spectrometry for the identification of potential diagnostic biomarkers of asthma

Current clinical tests employed to diagnose asthma are inaccurate and limited by their invasive nature. New metabolite profiling technologies offer an opportunity to improve asthma diagnosis using non-invasive sampling. A rapid analytical method for metabolite profiling of saliva is reported using ultra-high performance liquid chromatography combined with high resolution time-of-flight mass spectrometry (UHPLC-MS). The only sample pre-treatment required was protein precipitation with acetonitrile. The method has been applied to a pilot study of saliva samples obtained by passive drool from well phenotyped patients with asthma and healthy controls. Stepwise data reduction and multivariate statistical analysis was performed on the complex dataset obtained from the UHPLC-MS analysis to identify potential metabolomic biomarkers of asthma in saliva. Ten discriminant features were identified that distinguished between moderate asthma and healthy control samples with an overall recognition ability of 80% during training of the model and 97% for model cross-validation. The reported method demonstrates the potential for a non-invasive approach to the clinical diagnosis of asthma using mass spectrometry-based metabolic profiling of saliva

The impact of chemometrics on food traceability

In the last decades, mankind has become totally aware about the importance of food quality: nowadays authentication and traceability are words of general use. Food authentication verifies how much a food is in accordance with its label description and law and it could be considered a further guarantee for the quality and safety of a foodstuff. The traceability of food could be considered an essential element in ensuring safety and high quality of food. The synergistic use of instrumental analytical techniques and chemometrics represents a promising way to obtain trustworthy results in the development of authenticity and traceability models. This chapter deals with the potentialities of chemometrics tools in resolving some real issues related to food traceability and authenticity. Particular attention will be paid to the use of some exploratory, classification and discrimination techniques. In the first part of this chapter, a briefly description of European regulations (Authenticity and Traceability: the European Union point of view), and traceability and authenticity markers (Authenticity and Traceability: a scientific point of view) is reported. The second part is split into two sections: namely Food Authenticity and Food Traceability applications, where the main features and advantages of some chemometrics approaches are presented

Novel Quantitative Real-Time LCR for the Sensitive Detection of SNP Frequencies in Pooled DNA: Method Development, Evaluation and Application

BACKGROUND: Single nucleotide polymorphisms (SNP) have proven to be powerful genetic markers for genetic applications in medicine, life science and agriculture. A variety of methods exist for SNP detection but few can quantify SNP frequencies when the mutated DNA molecules correspond to a small fraction of the wild-type DNA. Furthermore, there is no generally accepted gold standard for SNP quantification, and, in general, currently applied methods give inconsistent results in selected cohorts. In the present study we sought to develop a novel method for accurate detection and quantification of SNP in DNA pooled samples. METHODS: The development and evaluation of a novel Ligase Chain Reaction (LCR) protocol that uses a DNA-specific fluorescent dye to allow quantitative real-time analysis is described. Different reaction components and thermocycling parameters affecting the efficiency and specificity of LCR were examined. Several protocols, including gap-LCR modifications, were evaluated using plasmid standard and genomic DNA pools. A protocol of choice was identified and applied for the quantification of a polymorphism at codon 136 of the ovine PRNP gene that is associated with susceptibility to a transmissible spongiform encephalopathy in sheep. CONCLUSIONS: The real-time LCR protocol developed in the present study showed high sensitivity, accuracy, reproducibility and a wide dynamic range of SNP quantification in different DNA pools. The limits of detection and quantification of SNP frequencies were 0.085% and 0.35%, respectively. SIGNIFICANCE: The proposed real-time LCR protocol is applicable when sensitive detection and accurate quantification of low copy number mutations in DNA pools is needed. Examples include oncogenes and tumour suppressor genes, infectious diseases, pathogenic bacteria, fungal species, viral mutants, drug resistance resulting from point mutations, and genetically modified organisms in food

Prenatal exposures and exposomics of asthma

This review examines the causal investigation of preclinical development of childhood asthma using exposomic tools. We examine the current state of knowledge regarding early-life exposure to non-biogenic indoor air pollution and the developmental modulation of the immune system. We examine how metabolomics technologies could aid not only in the biomarker identification of a particular asthma phenotype, but also the mechanisms underlying the immunopathologic process. Within such a framework, we propose alternate components of exposomic investigation of asthma in which, the exposome represents a reiterative investigative process of targeted biomarker identification, validation through computational systems biology and physical sampling of environmental medi