Longitudinal profiling of the lung microbiome in the AERIS study demonstrates repeatability of bacterial and eosinophilic COPD exacerbations

Abstract

Background: Alterations in the composition of the lung microbiome associated with adverse clinical outcomes, known as dysbiosis, have been implicated with disease severity and exacerbations in COPD.

Objective: To characterise longitudinal changes in the lung microbiome in the AERIS study (Acute Exacerbation and Respiratory InfectionS in COPD) and their relationship with associated COPD outcomes.

Methods: We surveyed 584 sputum samples from 101 patients with COPD to analyse the lung microbiome at both stable and exacerbation time points over 1 year using high-throughput sequencing of the 16S ribosomal RNA gene. We incorporated additional lung microbiology, blood markers and in-depth clinical assessments to classify COPD phenotypes.

Results: The stability of the lung microbiome over time was more likely to be decreased in exacerbations and within individuals with higher exacerbation frequencies. Analysis of exacerbation phenotypes using a Markov chain model revealed that bacterial and eosinophilic exacerbations were more likely to be repeated in subsequent exacerbations within a subject, whereas viral exacerbations were not more likely to be repeated. We also confirmed the association of bacterial genera, including Haemophilus and Moraxella, with disease severity, exacerbation events and bronchiectasis.

Conclusions: Subtypes of COPD have distinct bacterial compositions and stabilities over time. Some exacerbation subtypes have non-random probabilities of repeating those subtypes in the future. This study provides insights pertaining to the identification of bacterial targets in the lung and biomarkers to classify COPD subtypes and to determine appropriate treatments for the patient.

Trial registration number: Results, NCT01360398.

Keywords: Copd exacerbations; Copd ÀÜ mechanisms; respiratory infection.

Figure 1

Flow chart of subject enrolment, sputum sampling and selection samples for microbiome analysis for AERIS (Acute Exacerbation and Respiratory InfectionS in COPD).

Figure 2

Microbiome differences in disease severity and stable or exacerbation visits. (A) The Shannon Diversity Index and relative abundances of bacteria labelled at the phylum and genus level of samples grouped by COPD disease severity. The bar graphs show the mean relative abundance at the subject level after averaging for multiple measures for that subject. Significant differences in relative abundances between groups are labelled with arrows indicating the relative change in abundance; *P<0.05 (Mann-Whitney). (B) The same alpha diversity and relative abundances grouped by stable or exacerbation status showed fewer differences overall except for Moraxella; *P<0.05 (linear mixed-effects model). (C) Paired analysis of changes in relative abundances of key genera between matched stable and subsequent exacerbation events; *P<0.05 (paired Student’s t-test).

Figure 3

Lung microbiome stability. (A) Weighted UniFrac distances measured within and between subjects and comparing stable and exacerbation events after randomly dividing individuals into equal-sized subsets to ensure independence; *P<0.05, **P<0.01 (one-way analysis of variance (ANOVA)). (B) Unweighted UniFrac distances measured within and between subjects and comparing stable and exacerbation events on the same subsets; **P<0.01 (one-way ANOVA). (C) Weighted UniFrac distances for all within-subject samples as a function of exacerbation frequency defined by the number of exacerbation events and the fraction of samples within an individual taken during an exacerbation. (D) Paired weighted UniFrac distances between an exacerbation sample and its previous stable sample from that subject. Exacerbation subtypes labelled as B (bacterial), V (viral), E (eosinophilic), other or mixed. There was not a significant difference in UniFrac distances between these groupings of stable-to-exacerbation transitions (P=0.38, one-way ANOVA). EXA, exacerbation.

Figure 4

Markov chain analysis of transitions between exacerbation states. (A) Markov chain analysis from longitudinal exacerbation sampling within individuals identifies non-random transition probabilities for bacterial and eosinophilic exacerbations, but not viral. The size of each node is proportional to abundance of that exacerbation type, and the width of the edges is proportional to the transition probabilities. (B) Markov chain analysis of the bacterial exacerbation identifies significantly different transition probabilities for bacterial exacerbations that were positive or negative for the presence of Haemophilus influenzae (HI).