Microbial diversity within the airway microbiome in chronic pediatric lung diseases.

The study of the airway microbiome in children is an area of emerging research, especially in relation to the role microbial diversity may play in acute and chronic inflammation. Three such pediatric airway diseases include cystic fibrosis, asthma, and chronic lung disease of prematurity. In cystic fibrosis, the presence of Pseudomonas spp. is associated with decreased microbial diversity. Decreasing microbial diversity is also associated with poor lung function. In asthma, early viral infections appear to drive changes in bacterial diversity which may be associated with asthma risk. Premature infants with Ureaplasma spp. are at higher risk for chronic lung disease due to inflammation. Microbiome changes due to prematurity also appear to affect the inflammatory response to viral infections post-natally. Importantly, microbial diversity can be measured using metataxonomic (e.g., 16S rRNA sequencing) and metagenomic (e.g., shotgun sequencing) approaches. A metagenomics approach may be preferable as it can provide further granularity of the sample composition, identifying the bacterial species or strain, information on additional microbial components, including fungal and viral components, information about functional genomics of the microbiome, and information about antimicrobial resistance mutations. Future studies of pediatric airway diseases incorporating these techniques may provide evidence for new treatment approaches for these vulnerable patient populations.

Overexpressed somatic alleles are enriched in functional elements in Breast Cancer.

Asymmetric allele content in the transcriptome can be indicative of functional and selective features of the underlying genetic variants. Yet, imbalanced alleles, especially from diploid genome regions, are poorly explored in cancer. Here we systematically quantify and integrate the variant allele fraction from corresponding RNA and DNA sequence data from patients with breast cancer acquired through The Cancer Genome Atlas (TCGA). We test for correlation between allele prevalence and functionality in known cancer-implicated genes from the Cancer Gene Census (CGC). We document significant allele-preferential expression of functional variants in CGC genes and across the entire dataset. Notably, we find frequent allele-specific overexpression of variants in tumor-suppressor genes. We also report a list of over-expressed variants from non-CGC genes. Overall, our analysis presents an integrated set of features of somatic allele expression and points to the vast information content of the asymmetric alleles in the cancer transcriptome.