Genome-wide association analysis of eosinophilic esophagitis provides insight into the tissue specificity of this allergic disease

Eosinophilic esophagitis (EoE) is a chronic inflammatory disorder associated with allergic hypersensitivity to food. We interrogated >1.5 million genetic variants in European EoE cases and subsequently in a multi-site cohort with local and out-of-study control subjects. In addition to replication of the 5q22 locus (meta-analysis p = 1.9×10−16), we identified association at 2p23 (encoding CAPN14, p = 2.5×10−10). CAPN14 was specifically expressed in the esophagus, dynamically upregulated as a function of disease activity and genetic haplotype and after exposure of epithelial cells to IL-13, and located in an epigenetic hotspot modified by IL-13. There was enriched esophageal expression for the genes neighboring the top 208 EoE sequence variants. Multiple allergic sensitization loci were associated with EoE susceptibility (4.8×10−2 < p < 5.1×10−11). We propose a model that elucidates the tissue specific nature of EoE that involves the interplay of allergic sensitization with an EoE-specific, IL-13–inducible esophageal response involving CAPN14

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

In vitro model for studying esophageal epithelial differentiation and allergic inflammatory responses identifies keratin involvement in eosinophilic esophagitis.

Epithelial differentiation is an essential physiological process that imparts mechanical strength and barrier function to squamous epithelia. Perturbation of this process can give rise to numerous human diseases, such as atopic dermatitis, in which antigenic stimuli can penetrate the weakened epithelial barrier to initiate the allergic inflammatory cascade. We recently described a simplified air-liquid interface (ALI) culture system that facilitates the study of differentiated squamous epithelia in vitro. Herein, we use RNA sequencing to define the genome-wide transcriptional changes that occur within the ALI system during epithelial differentiation and in response to allergic inflammation. We identified 2,191 and 781 genes that were significantly altered upon epithelial differentiation or dysregulated in the presence of interleukin 13 (IL-13), respectively. Notably, 286 genes that were modified by IL-13 in the ALI system overlapped with the gene signature present within the inflamed esophageal tissue from patients with eosinophilic esophagitis (EoE), an allergic inflammatory disorder of the esophagus that is characterized by elevated IL-13 levels, altered epithelial differentiation, and pro-inflammatory gene expression. Pathway analysis of these overlapping genes indicated enrichment in keratin genes; for example, the gene encoding keratin 78, an uncharacterized type II keratin, was upregulated during epithelial differentiation (45-fold) yet downregulated in response to IL-13 and in inflamed esophageal tissue from patients. Thus, our findings delineate an in vitro experimental system that models epithelial differentiation that is dynamically regulated by IL-13. Using this system and analyses of patient tissues, we identify an altered expression profile of novel keratin differentiation markers in response to IL-13 and disease activity, substantiating the potential of this combined approach to identify relevant molecular processes that contribute to human allergic inflammatory disease

In vitro ALI differentiation replicates gene signature of healthy tissue ex vivo.

<p>Venn diagram depicting the number of genes expressed in healthy esophageal tissue (FPKM > 2, n = 9,649) and the number of genes altered during air-liquid interface (ALI) differentiation (day 14 compared to day 8, n = 2,191) (<i>P</i> < 0.05, fold change > 2.0) (A). Clustered heatmap showing the log₂ FPKM values of the 1,610 genes in common to both data sets. Also shown are the 20 most dysregulated genes in cluster 1 (induced at day 14) (B). Expression (FPKM) correlation for the 1,610 genes in common between the healthy esophageal tissue and genes altered during ALI differentiation (C).</p

The ALI culture system.

<p>Schematic diagram depicting the air-liquid interface (ALI) culturing protocol (A). Representative images (40X) of hematoxylin- and eosin-stained sections at various time points and/or treatments during the ALI protocol (B). The clear semi-permeable membrane can be seen in the bottom of all images. Untx, untreated.</p

Fold change of top 10 genes induced and top 10 genes inhibited in the ALI after prolonged IL-13 exposure and altered in EoE.

<p>¹Fold change at day 14 untreated as compared to day 8 (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127755#pone.0127755.g002" target="_blank">Fig 2</a>)</p><p>²Fold change at day 14 + IL-13 as compared to day 14 untreated (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127755#pone.0127755.g003" target="_blank">Fig 3</a>)</p><p>³Fold change in active EoE as compare to healthy (NL) controls (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127755#pone.0127755.g003" target="_blank">Fig 3</a>)</p><p>Fold change of top 10 genes induced and top 10 genes inhibited in the ALI after prolonged IL-13 exposure and altered in EoE.</p