Maturation of the gut microbiome and risk of asthma in childhood

The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.

Children who grow up in environments that afford them a wide range of microbial exposures, such as traditional farms, are protected from childhood asthma and atopy. In previous studies, markers of microbial exposure have been inversely related to these conditions. In two cross-sectional studies, we compared children living on farms with those in a reference group with respect to the prevalence of asthma and atopy and to the diversity of microbial exposure. In one study--PARSIFAL (Prevention of Allergy-Risk Factors for Sensitization in Children Related to Farming and Anthroposophic Lifestyle)--samples of mattress dust were screened for bacterial DNA with the use of single-strand conformation polymorphism (SSCP) analyses to detect environmental bacteria that cannot be measured by means of culture techniques. In the other study--GABRIELA (Multidisciplinary Study to Identify the Genetic and Environmental Causes of Asthma in the European Community [GABRIEL] Advanced Study)--samples of settled dust from children's rooms were evaluated for bacterial and fungal taxa with the use of culture techniques. In both studies, children who lived on farms had lower prevalences of asthma and atopy and were exposed to a greater variety of environmental microorganisms than the children in the reference group. In turn, diversity of microbial exposure was inversely related to the risk of asthma (odds ratio for PARSIFAL, 0.62; 95% confidence interval [CI], 0.44 to 0.89; odds ratio for GABRIELA, 0.86; 95% CI, 0.75 to 0.99). In addition, the presence of certain more circumscribed exposures was also inversely related to the risk of asthma; this included exposure to species in the fungal taxon eurotium (adjusted odds ratio, 0.37; 95% CI, 0.18 to 0.76) and to a variety of bacterial species, including Listeria monocytogenes, bacillus species, corynebacterium species, and others (adjusted odds ratio, 0.57; 95% CI, 0.38 to 0.86). Children living on farms were exposed to a wider range of microbes than were children in the reference group, and this exposure explains a substantial fraction of the inverse relation between asthma and growing up on a farm. (Funded by the Deutsche Forschungsgemeinschaft and the European Commission.).

Real-time polymerase chain reaction (PCR) is a highly sensitive method that can be used for the detection and quantification of microbial populations without cultivating them in anaerobic processes and environmental samples. This work was conducted to design primer and probe sets for the detection of methanogens using a real-time PCR with the TaqMan system. Six group-specific methanogenic primer and probe sets were designed. These sets separately detect four orders (Methanococcales, Methanobacteriales, Methanomicrobiales, and Methanosarcinales) along with two families (Methanosarcinaceae and Methanosaetaceae) of the order Methanosarcinales. We also designed the universal primer and probe sets that specifically detect the 16S rDNA of prokaryotes and of the domain Bacteria and Archaea, and which are fully compatible with the TaqMan real-time PCR system. Target-group specificity of each primer and probe set was empirically verified by testing DNA isolated from 28 archaeal cultures and by analyzing potential false results. In general, each primer and probe set was very specific to the target group. The primer and probe sets designed in this study can be used to detect and quantify the order-level (family-level in the case of Methanosarcinales) methanogenic groups in anaerobic biological processes and various environments.