Assessment of local and systemic signature of eosinophilic esophagitis (EoE) in children through multi-omics approaches

Abstract

Background: Eosinophilic oesophagitis (EoE) is a chronic food allergic disorder limited to oesophageal mucosa whose pathogenesis is still only partially understood. Moreover, its diagnosis and follow-up need repeated endoscopies due to absence of non-invasive validated biomarkers. In the present study, we aimed to deeply describe local immunological and molecular components of EoE in well-phenotyped children, and to identify potential circulating EoE-biomarkers.

Methods: Blood and oesophageal biopsies were collected simultaneously from French children with EoE (n=17) and from control subjects (n=15). Untargeted transcriptomics analysis was performed on mRNA extracted from biopsies using microarrays. In parallel, we performed a comprehensive analysis of immune components on both cellular and soluble extracts obtained from both biopsies and blood, using flow cytometry. Finally, we performed non-targeted plasma metabolomics using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Uni/multivariate supervised and non-supervised statistical analyses were then conducted to identify significant and discriminant components associated with EoE within local and/or systemic transcriptomics, immunologic and metabolomics datasets. As a proof of concept, we conducted multi-omics data integration to identify a plasmatic signature of EoE.

Results: French children with EoE shared the same transcriptomic signature as US patients. Network visualization of differentially expressed (DE) genes highlighted the major dysregulation of innate and adaptive immune processes, but also of pathways involved in epithelial cells and barrier functions, and in perception of chemical stimuli. Immune analysis of biopsies highlighted EoE is associated with dysregulation of both type (T) 1, T2 and T3 innate and adaptive immunity, in a highly inflammatory milieu. Although an immune signature of EoE was found in blood, untargeted metabolomics more efficiently discriminated children with EoE from control subjects, with dysregulation of vitamin B6 and various amino acids metabolisms. Multi-blocks integration suggested that an EoE plasma signature may be identified by combining metabolomics and cytokines datasets.

Conclusions: Our study strengthens the evidence that EoE results from alterations of the oesophageal epithelium associated with altered immune responses far beyond a simplistic T2 dysregulation. As a proof of concept, combining metabolomics and cytokines data may provide a set of potential plasma biomarkers for EoE diagnosis, which needs to be confirmed on a larger and independent cohort.

Keywords: Eosinophilic oesophagitis; children; food allergy; immune response; metabolomics; multi-omics signature; transcriptomics.

Figure 1

Transcriptomic signature of EoE in children from a French cohort. (A) Heatmap of the 50 most significantly upregulated (red) or downregulated (blue) transcripts obtained from biopsies of 11 CT and 13 EoE patients. Expression values are centered on the mean. The magnitude of the gene changes is proportional to the darkness of the color. Each column represents a separate individual and each line a DE gene. (B) Non-supervised multivariate analysis (PCA) of all transcriptomic data obtained from the 20 selected biopsies (CT: blue, n = 10; EoE: red, n = 10), showing natural separation of EoE versus controls. (C) Significantly enriched pathways in EoE patients (n = 10) relative to controls (n = 10), identified through GSEA and considering all analyzed genes. The 19 MSigDB gene sets that were enriched by a FDR q-value < 0.05 are shown. (D). Correlation of the fold changes of 300 DE genes described in (6) (x-axis) compared to our study (y-axis). For functionally organized pathway network identified with ClueGO using the 4,767 identified DE genes, see Data Sheet 2.

Figure 2

Non-supervised and supervised analysis of cellular and soluble immune constituents in oesophageal biopsies. (A) Non-supervised ACP (left) and AHC (right) analysis of all data obtained from EoE patients (red, n = 13 of 17; four have to be excluded from ACP due to missing values) and CT (blue, n = 10 of 15; five excluded due to missing values). Endoscopy observations (erythema: eryth, furrows: furr, rings, narrowing and/or stenosis) and mean eosinophil counts (mEo/hpf) obtained from the three analysed biopsies (upper, middle, and lower third) are indicated for each EoE patient. For some patients, EoE was restricted to the upper (up), middle (mid), and/or lower (low) third part of the oesophagus as indicated. (B) PLS-DA modelling constructed with all immune data available (missing values are ignored) from the 17 EoE patients (red) and 15 controls (blue), and model characteristics. Misclassified EoE patients (n=2) and CT (n=1) are shown using framed boxes in the graph. (C) Graph of the VIP values obtained on the first component of PLS-DA modelling (VIP_comp1) x P values obtained following Mann Whitney tests (p < 0.05, without post-test correction) and selection of the significant and discriminant variables (red shaded area). Some named components highly contributed in PLS-DA modelling but showed high P value (upper right) whereas other poorly contributed in PLS-DA modelling but showed P values <0.05 (lower left).

Figure 3

Cellular and soluble components in biopsies that significantly (p < 0.05) discriminate (VIP > 1) between EoE patients (red bars) and controls (blue bars): (A) ILC absolute counts, and frequencies of ILC1-IFNγ⁺ and ILC2. ILC1 and ILC2 were identified within live FSC^lowCD45⁺ singlet cells as lin^-CD127⁺ populations, and then depending on intracellular Tbet and GATA3 expression, respectively. Activated ILC1 were further identified as IFNγ⁺ within ILC1 cells. Activated ILC1 and ILC2 are expressed as % of live FSC^lowCD45⁺ singlet cells. Total IgE (B), Matrix Metalloprotease 1 and 2 (MMP1/2, (C), cytokines (D) and chemokines (E) concentrations in supernatants obtained from biopsies. P values obtained through Mann-Whitney tests are indicated.

Figure 4

Immune soluble components in plasma that significantly (p < 0.05) or trend (0.05 < p < 0.1) to discriminate (VIP > 1) between EoE patients (red bars) and controls (blue bars): Total IgE, IgG1 and IgG4 (A), cytokines (B) and chemokines (C) concentrations in plasma. IgE/IgG1 ratio is also shown in (A) P values obtained through Mann-Whitney tests are indicated.

Figure 5

Discrimination of EoE versus control based on plasma metabolome: (A) Graph of individuals after PLS-DA modelling of metabolomics data from EoE patients not under medication (EoE-PPI: n=9; red) versus that from CT not under PPI medication (CT-PPI: n=8; blue). Model characteristics: R²Ycum=0.905, R²Xcum =0.333, AUC=1, 100% sensitivity and specificity. (B) Discriminant metabolites showing significant differences when comparing EoE patients (red) to controls (blue).

Figure 6

Association between circulating cytokine, antibody, and metabolite levels and local EoE transcriptomic signature. To obtain a statistically robust model, 341 genes were selected, corresponding to the highest and most robust DE expressed genes between EoE patients and controls (|FC| > 1.5, p < 0.01; n = 10 per group, corresponding to individuals with a clear transcriptomics EoE signature and no missing data). All cytokines, antibodies, and metabolites (full names in Supplementary Table 4 ) measured in plasma were considered and all data were scaled before integration. (A) Diagnostic test: a three-component model was selected based on the total error (ER) and balanced error rate (BER). (B) The individual plots for each dataset (transcriptomics, metabolomics, cytokines and antibodies) showed good separation of EoE patients (red) from controls (blue). (C) Pairwise correlations among the different datasets and corresponding distribution of the individuals [same colour code as in (B)]. (D) Circos plot showing all variables selected by the final DIABLO model for each block. Associations higher than >|0.7| are indicated with blue (negative) or red (positive) lines. Please see Data Sheet 1 for better resolution.