Development of Asthma in Inner-City Children: Possible Roles of MAIT Cells and Variation in the Home Environment.

Humans have populations of innate-like T lymphocytes with an invariant TCR α-chain that recognize nonpeptide Ags, including invariant NKT (iNKT) cells and mucosal-associated invariant T (MAIT) cells. iNKT cell involvement in human asthma is controversial, whereas there has been little analysis of MAIT cells. Using peripheral blood cells from 110 participants from the Urban Environment and Childhood Asthma (URECA) birth cohort study, these cells were analyzed for number and function. We determined whether iNKT cell or MAIT cell frequency at 1 y is correlated with the cytokine polarization of mainstream CD4+ T cells and/or the development of asthma by age 7 y. Dust samples from 300 houses were tested for iNKT cell antigenic activity. Our results show that a higher MAIT cell frequency at 1 y of age was associated with a decreased risk of asthma by age 7 y. The frequency of MAIT cells was associated with increased production of IFN-γ by activated CD4+ T cells from the URECA cohort. iNKT cell antigenic activity in bedroom dust samples was associated with higher endotoxin concentration and also with reduced risk of asthma. In conclusion, MAIT cell frequency at 1 y may reflect the tendency of the immune system toward Th1 responses and is associated with protection from asthma. Additionally, iNKT cell antigenic activity may be a marker of houses with increased microbial exposures and therefore also with protection from asthma.

Dimension reduction techniques for the integrative analysis of multi-omics data.

State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput 'omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.