DOI: 10.5061/dryad.rfj6q57ff
title: Lower airway microbiota in COPD and healthy controls

description: The lower airway microbiota in patients with chronic obstructive pulmonary
 disease (COPD) are likely altered compared with the microbiota in healthy
 individuals. Information on how the microbiota is affected by smoking, the
 use of inhaled corticosteroids (ICS), and COPD severity is still
 scarce. In the MicroCOPD Study, participant characteristics were
 obtained through standardised questionnaires and clinical measurements at
 a single centre from 2012 to 2015. Protected bronchoalveolar lavage
 samples from 97 patients with COPD and 97 controls were paired-end
 sequenced with the Illumina MiSeq System. Data were analysed in QIIME 2
 and R. Alpha-diversity was lower in patients with COPD than
 controls (Pielou evenness: COPD=0.76, control=0.80, p=0.004; Shannon
 entropy: COPD=3.98, control=4.34, p=0.01). Beta-diversity differed with
 smoking only in the COPD cohort (weighted UniFrac: permutational analysis
 of variance R2=0.04, p=0.03). Nine genera were differentially abundant
 between COPD and controls. Genera enriched in COPD belonged to the
 Firmicutes phylum. Pack years were linked to differential abundance of
 taxa in controls only (ANCOM-BC (Analysis of Compositions of Microbiomes
 with Bias Correction) log-fold difference/q-values: Haemophilus
 -0.05/0.048; Lachnoanaerobaculum -0.04/0.03). Oribacterium was absent in
 smoking patients with COPD compared with non-smoking patients (ANCOM-BC
 log-fold difference/q-values: -1.46/0.03). We found no associations
 between the microbiota and COPD severity or ICS. The lower airway
 microbiota is equal in richness in patients with COPD to controls, but
 less even. Genera from the Firmicutes phylum thrive particularly in COPD
 airways. Smoking has different effects on diversity and taxonomic
 abundance in patients with COPD compared with controls. COPD severity and
 ICS use were not linked to the lower airway microbiota.
description: # Lower airway microbiota in COPD and healthy controls #### This note was
 written by Solveig Tangedal, on Nov 29th, 2023, and edited on Dec 17th,
 2024. We have submitted deidentified participant data (metadata) in .xlsx
 and .txt format(**COPDvCMetadata.xlsx COPDvCMetadata.txt**), Amplicon
 sequencing variants (ASV) tables in qza format for all ASVs and for ASVs
 eligible for differential abundance analyses (**COPDvC_ASV.qza
 COPDvCAncomASV.qza**) Representative sequences tables in qza format for
 all ASVs (**COPDvCRepSeq.qza**), Rooted phylogenetic trees in qza format
 (**COPDvC_rootedtree.qza**), Taxonomy tables in qza format
 (**COPDvC_Taxonomy.qza**), QIIME 2 code in txt format
 (**COPDvsControl_Q2_code.txt**), R code in RScript format
 (**COPDvControl_Rcommand.R**), and the manuscript abstract in docx format
 (**ABSTRACT.docx**) The MicroCOPD study is a large study, with amplicon
 (16S rRNA) sequencing of more than 3000 samples over &gt; 30 RUNs. The
 airways samples number &gt; 2500, and are spread over 30 RUNs. Data
 cleanup and curation were performed for all 30 RUNs together to best
 utilize a shared protocol and the best use of our negative \#control
 samples (explained in detail in the supplement of the published paper
 "The airway microbiota and exacerbations of COPD" by Leiten et
 al.). The fastq files alone way override our DRYAD storage capacity, and
 since this study is on a sub-set of the study \#participants only, and
 looking only at one sample type (BAL), we have chosen to deposit the data
 matching the stripped-down metadata file. The FeatureData file is named
 COPDvC_RepSeq.qza The FeatureTable files are named COPDvC_ASV.qza and
 COPDvCAncomASV.qza. Both are curated, but only the latter was filtered
 according to the advice. \#for Ancom-BC analyses. The curation is
 described in the Supplement of the current manuscript. ##### Taxonomy was
 classified using the q2 feature-classifier classify-sklearn command,
 providing COPDvC\_Taxonomy.qza file Phylogeny was made using the q2
 phylogeny align-to-tree-mafft-fasttree command, providing a
 COPDvC_rooted-tree.qza file \#Metadata are given as xlsx and txt files as
 both file types are required for different import to R as can be seen in
 the R code. \#Code: All needed information for obtaining data and editing
 metadata is written out in the code files. Metadata is explained and
 \#re-coded in the QIIME 2 and R documents. ## Description metadata *
 *sampleid*: Unique sample identifier * *RUN*: Sequencing run in which the
 samples were analysed * *diagnose*: Classification of participants into
 COPD or Control * *diagnose_NonSmk1yr*: Classification of participants
 into smokers having smoked within the last 12 months before inclusion
 (COPD_S Control_S_) or non-smokers having not smoked the last 12 months
 before inclusion (COPD_NS Control_NS_) * *Sex*: Male or female sex *
 *Agecat*: Categorised age groups: &lt;60 60-69.99 70+ (years) *
 *NonSmk1yr*: Dichotomised smoking variable: Non-smoking = no smoking the
 last 12 months before inclusion, Smoking: Smoked within the last 12 months
 before inclusion * *Packyear*: 20 cigarettes daily for 1 year equals 1
 packyear * *fev1fvcratio*: Forced expiratory volume 1 second/Forced
 expiratory capacity (Spirometry) * *fev1prcpred*: Forced expiratory volume
 1 second as percent of predicted value (Spirometry) * *fvcprcpred*: Forced
 expiratory capacity as percent of predicted value (Spirometry) *
 *gold_3cat*: Classification of participants into GOLD stages GOLD I/II,
 GOLD III/IV and Control * *ICS*: Dichotomised inhaled corticosteroid (ICS)
 use variable for COPD only: Yes No * *Freqexac*: Dichotomised exacerbation
 variable for COPD only: Yes No * *COPDvC_ASV.qza COPDvCAncomASV.qza
 COPDvCRepSeq.qza* The Illumina amplicon sequences were quality- and
 chimera-filtered using the microbiota pipeline Quantitative Insights Into
 Microbial Ecology 2 (QIIME 2, v. 2018.8), the Divisive Amplicon Denoising
 Algorithm 2 (DADA2), and VSEARCH. DADA2 assigns sequences to amplicon
 sequence variants (ASVs) using denoising methods that generate an error
 model based on the quality of the sequencing run. The model is then used
 to discriminate probable biological variations and probable sequencing
 errors. Sequences interpreted as truly biologically similar are assigned
 to the same ASV. Having negative control samples, we could apply the
 prevalence-based method in the Decontam algorithm in R to identify
 amplicon sequences that were interpreted as contamination of the samples.
 Our research group has published both an evaluation of Decontam and a
 detailed methodological consideration discussing sample curation. *
 *COPDvC_rootedtree.qza* After de-novo alignment with MAFFT, a phylogenetic
 tree for diversity analyses was built using FastTree. *
 *COPDvC_Taxonomy.qza* ASVs created by DADA2 were assigned taxonomy using a
 self-trained Naive Bayes classifier and the Silva database v.138.1. It was
 processed through rescript in QIIME 2. The chosen dereplication mode was
 “majority”, meaning that if identical sequences were present with
 inconsistent annotation, the annotations seen in the majority of cases
 were chosen for all sequences. Only reads derived from sequencing of the
 V3-V4 region of the 16S rRNA gene were considered. ## Code/Software R is
 required to run *COPDvControl_Rcommand.R* Code: All needed information for
 obtaining data and editing metadata is written out in the code files.
 Metadata is explained and recoded in the QIIME 2 and R documents.