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Clinical Trial
. 2015 May 28;10(5):e0128346.
doi: 10.1371/journal.pone.0128346. eCollection 2015.

Esophageal Microbiome in Eosinophilic Esophagitis

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Free PMC article
Clinical Trial

Esophageal Microbiome in Eosinophilic Esophagitis

J Kirk Harris et al. PLoS One. .
Free PMC article

Abstract

Objective: The microbiome has been implicated in the pathogenesis of a number of allergic and inflammatory diseases. The mucosa affected by eosinophilic esophagitis (EoE) is composed of a stratified squamous epithelia and contains intraepithelial eosinophils. To date, no studies have identified the esophageal microbiome in patients with EoE or the impact of treatment on these organisms. The aim of this study was to identify the esophageal microbiome in EoE and determine whether treatments change this profile. We hypothesized that clinically relevant alterations in bacterial populations are present in different forms of esophagitis.

Design: In this prospective study, secretions from the esophageal mucosa were collected from children and adults with EoE, Gastroesophageal Reflux Disease (GERD) and normal mucosa using the Esophageal String Test (EST). Bacterial load was determined using quantitative PCR. Bacterial communities, determined by 16S rRNA gene amplification and 454 pyrosequencing, were compared between health and disease.

Results: Samples from a total of 70 children and adult subjects were examined. Bacterial load was increased in both EoE and GERD relative to normal subjects. In subjects with EoE, load was increased regardless of treatment status or degree of mucosal eosinophilia compared with normal. Haemophilus was significantly increased in untreated EoE subjects as compared with normal subjects. Streptococcus was decreased in GERD subjects on proton pump inhibition as compared with normal subjects.

Conclusions: Diseases associated with mucosal eosinophilia are characterized by a different microbiome from that found in the normal mucosa. Microbiota may contribute to esophageal inflammation in EoE and GERD.

Conflict of interest statement

Competing Interests: Glenn T. Furuta: Enterotrack, LLC – Co-Founder, UpToDate-consultant, Steven J. Ackerman: Enterotrack, LLC – Co-Founder. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. The esophageal bacterial load is increased in subjects with EoE and GERD: Analysis by 16S Q-PCR.
The esophageal bacterial load was captured using the EST as previously described [5]. A. The copy number of bacteria per ng of DNA is significantly increased in subjects with EoE, and GERD as compared to Normal. B. Treatments (Trt) do not significantly change the load of bacteria in each disease. C. The disease activity or response to treatment does not significantly change the load of bacteria in EoE. D. The effective number of taxa is similar in Normal, EoE and GERD. The mean ± SEM are shown. Normal n = 25, EoE n = 37, GERD n = 8. Mean and SEM, two-sample t-test * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001.
Fig 2
Fig 2. Esophageal phyla are similar in EoE compared to normal esophagus, and treatment affects the genus abundance in EoE and GERD.
Abundance of esophageal phyla and genera as captured using the EST. A. Bar graphs present the aggregate of the relative phylum abundance on the ESTs of Normal subjects (n = 25), EoE subjects untreated (n = 11), treated (n = 26), and GERD subjects untreated (n = 4), PPI treated (n = 4). Each phylum is indicated in a different color. The width of the bar corresponds to the relative phylum abundance. B. Bar graphs present the aggregate of the relative genus abundance on the ESTs of Normal subjects (n = 25), EoE subjects untreated (n = 11), treated (n = 26), and GERD subjects untreated (n = 4), treated (n = 4). Each genus is indicated in a different color. The width of the bar corresponds to the relative genus abundance. The Other category includes taxa with <1% relative abundance in all samples.
Fig 3
Fig 3. Principal Components Analysis (PCA) of Normal and EoE samples.
PCA using normal esophagus (n = 25) and EoE samples (n = 11 untreated and n = 26 treated). The biplot displays the first two principal components which explains 29% of the variability across genera, as well as vectors corresponding to the weight and direction of the loadings for the highest weighted genera. Vectors pointing in the same direction are genera that are positively correlated; those going in opposite directions are negatively correlated.
Fig 4
Fig 4. Manhattan plot of the two part statistical analysis of normal (n = 25) versus untreated EoE (n = 11).
The plot shows the negative log10 p-value for each taxon identified. The single significant genus identified was Haemophilus, which was elevated in EoE subjects.
Fig 5
Fig 5. Comparison of Haemophilus across all subject groups.
The relative abundance of Haemophilus for each group is plotted. Box represents median and 25th and 75th percentile (interquartile range, IQR) and whiskers represent 1.5x IQR. Individual samples outside the 1.5x IQR are marked as open circles.
Fig 6
Fig 6. Manhattan plot of the two part statistical analysis of Aurora, CO (n = 17) versus Chicago, IL (n = 20) microbiome samples.
The plot shows the negative log10 p-value for each taxon identified. There are six significant taxa identified. The peaks correspond to 1. Actinomyces (p = 0.048), 2. Prevotella (p = 0.016), 3. Streptococcus (p = 0.016), 4. Parvimonas (p = 0.022), 5. Fusobacterium (p = 0.009) and 6. Aggregatibacter (p = 0.027). All significantly different taxa were elevated in the Aurora relative to Chicago group except for Streptococcus.

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