Assessment of infant outgrowth of cow's milk allergy in relation to the faecal microbiome and metaproteome.

Previous studies provide evidence for an association between modifications of the gut microbiota in early life and the development of food allergies. We studied the faecal microbiota composition (16S rRNA gene amplicon sequencing) and faecal microbiome functionality (metaproteomics) in a cohort of 40 infants diagnosed with cow's milk allergy (CMA) when entering the study. Some of the infants showed outgrowth of CMA after 12 months, while others did not. Faecal microbiota composition of infants was analysed directly after CMA diagnosis (baseline) as well as 6 and 12 months after entering the study. The aim was to gain insight on gut microbiome parameters in relation to outgrowth of CMA. The results of this study show that microbiome differences related to outgrowth of CMA can be mainly identified at the taxonomic level of the 16S rRNA gene, and to a lesser extent at the protein-based microbial taxonomy and functional protein level. At the 16S rRNA gene level outgrowth of CMA is characterized by lower relative abundance of Lachnospiraceae at baseline and lower Bacteroidaceae at visit 12 months.

diXa: a data infrastructure for chemical safety assessment.

MOTIVATION: The field of toxicogenomics (the application of '-omics' technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available. RESULTS: The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal. AVAILABILITY AND IMPLEMENTATION: http://www.dixa-fp7.eu CONTACT: d.hendrickx@maastrichtuniversity.nl; info@dixa-fp7.eu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reverse engineering of metabolic networks, a critical assessment.

Inferring metabolic networks from metabolite concentration data is a central topic in systems biology. Mathematical techniques to extract information about the network from data have been proposed in the literature. This paper presents a critical assessment of the feasibility of reverse engineering of metabolic networks, illustrated with a selection of methods. Appropriate data are simulated to study the performance of four representative methods. An overview of sampling and measurement methods currently in use for generating time-resolved metabolomics data is given and contrasted with the needs of the discussed reverse engineering methods. The results of this assessment show that if full inference of a real-world metabolic network is the goal there is a large discrepancy between the requirements of reverse engineering of metabolic networks and contemporary measurement practice. Recommendations for improved time-resolved experimental designs are given.