Microbiome and its impact on gastrointestinal atopy.

The prevalence of allergic conditions has continuously increased in the last few decades in Westernized countries. A dysbiotic gut microbiome may play an important role in the development of allergic diseases. Genetic, environmental, and dietary factors may alter the commensal microbiota leading to inflammatory dysregulation of homeostasis. Murine and human studies have begun to elucidate the role of the microbiota in the pathogenesis of atopic diseases including asthma, atopic dermatitis, and food allergies. However, the role of the microbiome in most eosinophilic gastrointestinal diseases (EGIDs) is not yet known. This review provides an overview of what is currently known about the development of tolerance from both molecular and clinical standpoints. We also look at the gut-specific microbiome and its role in atopic conditions with the hope of applying this knowledge to the understanding, prevention, and treatment of EGIDs, particularly EoE.

Zero-inflated negative binomial mixed model: an application to two microbial organisms important in oesophagitis.

Altered microbial communities are thought to play an important role in eosinophilic oesophagitis, an allergic inflammatory condition of the oesophagus. Identification of the majority of organisms present in human-associated microbial communities is feasible with the advent of high throughput sequencing technology. However, these data consist of non-negative, highly skewed sequence counts with a large proportion of zeros. In addition, hierarchical study designs are often performed with repeated measurements or multiple samples collected from the same subject, thus requiring approaches to account for within-subject variation, yet only a small number of microbiota studies have applied hierarchical regression models. In this paper, we describe and illustrate the use of a hierarchical regression-based approach to evaluate multiple factors for a small number of organisms individually. More specifically, the zero-inflated negative binomial mixed model with random effects in both the count and zero-inflated parts is applied to evaluate associations with disease state while adjusting for potential confounders for two organisms of interest from a study of human microbiota sequence data in oesophagitis.