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.

Conflict of interest statement

Competing interests: TMW has received reimbursement for travel and meeting attendance from Boehringer Ingelheim and AstraZeneca, outside of the submitted work. SC received a grant from Pfizer, outside of the submitted work. KJS received grants from Asthma UK (08/026) and BMA HC Roscoe Award, outside of the submitted work, and he has a patent PCT/GB2010/050821 ’Ex Vivo Modelling of Therapeutic Interventions' pending. BEM, CL, DFS, DM, GS, J-MD, JRB, ND, MM-S, RS, RT-S, SVH and VW are employees of the GSK group of companies. RP was an employee of the GSK group of companies at the time the study was conducted. BEM, JRB, J-MD, ND, RT-S and VW own shares/restricted shares in the GSK group of companies. KJS, VK, KO, ACT, SC and TMW received an institutional grant from the GSK group of companies to conduct this study.

© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Figures

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; *PMoraxella; *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

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).