A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis

Abstract

Background: Eosinophilic esophagitis (EoE) is a chronic immune-mediated rare disease, characterized by esophageal dysfunctions. It is likely to be primarily activated by food antigens and is classified as a chronic disease for most patients. Therefore, a deeper understanding of the pathogenetic mechanisms underlying EoE is needed to implement and improve therapeutic lines of intervention and ameliorate overall patient wellness.

Methods: RNA-seq data of 18 different studies on EoE, downloaded from NCBI GEO with faster-qdump ( https://github.com/ncbi/sra-tools ), were batch-corrected and analyzed for transcriptomics and metatranscriptomics profiling as well as biological process functional enrichment. The EoE TaMMA web app was designed with plotly and dash. Tabula Sapiens raw data were downloaded from the UCSC Cell Browser. Esophageal single-cell raw data analysis was performed within the Automated Single-cell Analysis Pipeline. Single-cell data-driven bulk RNA-seq data deconvolution was performed with MuSiC and CIBERSORTx. Multi-omics integration was performed with MOFA.

Results: The EoE TaMMA framework pointed out disease-specific molecular signatures, confirming its reliability in reanalyzing transcriptomic data, and providing new EoE-specific molecular markers including CXCL14, distinguishing EoE from gastroesophageal reflux disorder. EoE TaMMA also revealed microbiota dysbiosis as a predominant characteristic of EoE pathogenesis. Finally, the multi-omics analysis highlighted the presence of defined classes of microbial entities in subsets of patients that may participate in inducing the antigen-mediated response typical of EoE pathogenesis.

Conclusions: Our study showed that the complex EoE molecular network may be unraveled through advanced bioinformatics, integrating different components of the disease process into an omics-based network approach. This may implement EoE management and treatment in the coming years.

Keywords: Esophagus; Microbiota; Transcriptomics; Web app.

Fig. 1

EoE TaMMA overview. A Sankey plot showing the relationships between different metadata. B Table listing the details of the studies analyzed within the EoE TaMMA web app. C, D Sample distribution by multidimensional scaling of the human whole transcriptome by UMAP from patients with EoE, GERD, IBD, and healthy controls, where the closer the samples are, the higher the similarity between their transcriptomes

Fig. 2

EoE TaMMA confirms EoE-specific traits. A MA plots showing the differential gene expression results expressed as log2(fold change) in the indicated comparisons as a function of log2(average gene expression). Red dots represent genes being differentially expressed with high statistical significance (false discovery rate (FDR) < 1 × 10⁻⁵). The number of differentially expressed genes and their trends are indicated in red and blue for the up and down-regulated genes, respectively. B Violin plots showing differential normalized expression of the indicated genes among EoE, GERD, and control esophagi. C, D GO plot showing modulation of biological processes related to epithelial cell proliferation, smooth muscle cell migration, proliferation, differentiation, extracellular matrix remodeling, and chemotaxis between EoE and control (C) and EoE and GERD (D). E, F Violin plots showing differential normalized expression of the indicated genes among EoE, GERD, and control esophagi. The asterisks indicate FDR < 1 × 10⁻⁵. G Pearson correlation analysis between CAPN14 and DSG1 expression levels expressed as log2(fold change)

Fig. 3

Computational deconvolution of EoE-TaMMA bulk transcriptomic. A, B Bar plots showing the differential proportion of the indicated cell populations in Control (A), EoE (B), and GERD (C). D, E GO plots showing modulation of biological processes related to the transforming growth factor beta between EoE and control (D) and EoE and GERD (E)

Fig. 4

A, B MA plots showing the differential abundances expressed as log2(fold change) between the indicated comparisons. Red dots represent bacterial species being differentially expressed with high statistical significance (P < 0.05). The number of differentially expressed genes and their trends are indicated in red and blue for the up and down-regulated genes, respectively. C Violin plots showing differential normalized expression (log2 fold change) of the indicated bacterial species among EoE, GERD, and control esophagi. The asterisks indicate P < 0.05. D Violin plots showing the Shannon and Simpson indices among EoE, GERD, and healthy esophagi. The asterisks indicate P < 0.05

Fig. 5

Multi-omic analysis in EoE TaMMA. A Heatmaps showing the omics categories explaining the highest amount of variance for each factor found by MOFA in EoE and control. B Violin plots showing composite molecular signature scores within conditions. C–F Needle plots showing weights representing the variance explained by each feature for the indicated factors and layers (C–F) and violin plots showing the relative abundance of top features within conditions (C′–F′)

Fig. 6

EoE TaMMA reveals specific microbiota composition in EoE. A Heatmap showing the different Staphylococcus species colonizing EoE and control esophagi and blood. B Violin plots showing the differential normalized abundance of the Proteus vulgaris among EoE, and control esophagi and blood. The asterisks indicate P < 0.05