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. 2018;35(4):354-362.
doi: 10.36141/svdld.v35i4.7061. Epub 2020 Mar 9.

A common microbial signature is present in the lower airways of interstitial lung diseases including sarcoidosis

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A common microbial signature is present in the lower airways of interstitial lung diseases including sarcoidosis

Valeria D'Argenio et al. Sarcoidosis Vasc Diffuse Lung Dis. 2018.

Abstract

Background: The etiology of pulmonary sarcoidosis is not well established. Although the mechanism triggering pulmonary sarcoidosis remains to be established, inflammatory reactions seem to play an important role in this process. Objectives: The aim of this study was to define the composition of the lower airway microbiota in the bronchoalveolar lavage (BAL) of patients affected by interstitial lung diseases, including sarcoidosis, to determine whether the bacterial signature differs among these diseases. Methods: Ten patients affected by pulmonary sarcoidosis and 9 patients affected by other interstitial lung diseases were enrolled. 16S rRNA next-generation sequencing was used to study BAL microbial composition of these patients, and were also compared with already published microbial content in higher airways of such diseases. Results: Four phyla dominated the lower airway microbiota, Bacteroidetes being the most abundant phylum in both groups (56.9%). Diversity analysis showed no significant differences between the various diseases, particularly between pulmonary sarcoidosis and other interstitial lung diseases affecting lower airways. Conclusions: Our data indicate that the bacterial lower airways microbiota share the same signature and, therefore, cannot be used as a diagnostic tool to discriminate among different interstitial lung diseases, including sarcoidosis, while microbial diversity is present when considering lower or higher respiratory airways. (Sarcoidosis Vasc Diffuse Lung Dis 2018; 35: 354-362).

Keywords: airway microbiota; bronchoalveolar lavage (BAL); interstitial lung diseases; next generation sequencing; pulmonary sarcoidosis.

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Figures

Fig. 1.
Fig. 1.
Microbial communities identified in the 18 subjects at phylum level using 16S rRNA pyrosequencing and QIIME taxonomic assignment. The specific bacterial distribution of each study subject at phylum level; each bar represents a study subject. Different phyla are assigned to specific colors; Bacteroidetes phylum results the most represented one in the whole set of subjects
Fig. 2.
Fig. 2.
The composition of the lower airway microbiota identified in interstitial lung diseases (ILD) and pulmonary sarcoidosis (PS) patients, from phylum to genus level (from center to peripheral ring). Krona plots show the hierarchical taxonomic distribution in each ring from phylum to family. Taxonomic assignment shows no significant differences between the two studied groups. Bacteroidetes was the most represented phylum with an average relative abundance of 70% in the entire population both in ILD (A) and PS (B) subjects. Percentages are normalized relative abundances of bacteria observed only in the most abundant phyla (>1%)
Fig. 3.
Fig. 3.
Genus-level comparison of BAL microbial communities among the two study groups showed no differences in microbial composition among interstitial lung diseases (ILD) and pulmonary sarcoidosis (PS) patients. At genus level, we found the prevalence of members of the Bacteroidetes phylum being Prevotella (42.5%) and [Prevotella] (8.4%) the most abundant genera. Members of the Firmicutes and Proteobacteria phyla were also represented in both groups; Streptococcus (Firmicutes), Neisseria and Haemophilus (both within Proteobacteria) genera were respectively the most abundant within these two groups of patients, even if at a rather low level
Fig. 4.
Fig. 4.
Lower airway microbiota diversity analysis. Alpha diversity of microbial communities was measured using Faith’s phylogenetic diversity index (A) and the number of observed OTUs (B). The error bars represent standard error of the mean. In addition, beta diversity was also computed using the Principal Coordinates Analysis (PCoA) with both unweighted (C) and weighted (D) UniFrac distances. No statistical significance was observed when samples were grouped by disease status, which also showed a very low effect size on the microbiome composition.

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