These analyses from repeated bronchoscopies showed that the lower airway microbiota vary over time, and more so in the second BAL fraction and protected specimen brushes, than the first fraction of protected BAL. But, there were also distinct within-person similarities, perhaps pointing to a more stable part or fraction of the individual airway microbiota.
To our knowledge, this is the first report on the stability of the airway microbiome derived from repeated bronchoscopies in an observational study. Three intervention studies have examined the effects of antibiotics [4], interferon gamma [6], and highly active anti retroviral therapy (HAART) on the microbiome [5], and were not aiming to present changes at the individual level. The study by Segal et al. was based on an RCT where azitromycin was given to smokers with emphysema [4]. There was a control group of 10 individuals, where no changes were seen in alpha diversity (Wilcoxon), beta diversity (procrustes) or taxonomy (LEfSE). Wang et al. studied the effect of IFN-gamma on the microbiome of BAL samples in 10 IPF-patients. All participants received the intervention [6]. No significant changes were detected in alpha/beta-diversity, and there was also little signal in their LEfSE-analyses. Finally, Twigg reported a study on the effect of HAART to HIV patients, and compared with baseline samples in a cohort of 22 subjects without HIV. However, repeated measurements were only available for those subjects that received the intervention [5].
Sinha and colleagues investigated the variability of the microbiome in sputum samples at intervals of 2 days and 9 months in, respectively, 4 and 9 COPD patients [8]. Although obvious variation in both diversity and composition was observed, the authors concluded that they could demonstrate short time stability and a larger variability as sampling interval increased. However, the sample size was very low, and differences would have to be considerable to reach statistical significance. The increased variability with time could also be a result of for instance altered environmental (laboratory) contamination during sampling. Finally, no negative controls were sequenced, and no measures were taken to bioinformatically detect contaminant OTUs.
Mayhew and colleagues had a larger sample size with sputum samples from 101 COPD patients sampled in both stable and exacerbation states [7]. As in our study, they found that beta diversity within an individual was lower than between different individuals. However, they did not find significant changes in diversity from stable condition to exacerbation within the same individual, but in subjects with more exacerbations the microbiome seemed to be more unstable.
In the current study, we saw a trend that the diversity between the two procedures was higher in the airway samples than in the oral samples, and more so in the protected brush samples than in the BAL samples. This might be due to the airways microbiota being more transient than the oral microbiota, but could also be a result of the low-biomass nature of the lower airways that could make sampling more susceptible to random variation. Intercurrent events led to no consistent effect on diversity between procedures, but the heterogeneity of the population and also the heterogeneous nature of these events might have made it hard to trace such effects.
The current study adds to the existing knowledge by showing a degree of microbiome stability both in COPD patients and in individuals without known lung disease. A recently proposed hypothesis by Dickson et al., suggest the airway microbiota is the result of a constant influx and clearance, rather than a stable lung-residing microbiota [20]. Our findings represents a nuanced view, where albeit there is indeed great variability in time supporting the changing hypothesis, there are also signs of a small stable residing microbiome in the lower airways.
As in other studies, samples were dominated by Firmicutes ASVs as well as Actinobacteria, Bacteriodetes and Proteobacteriae [21,22,23,24,25]. At the genus level, the dominating ASVs were Streptococci, Veillonella, Prevotella, Rothia and Haemophilus, which also bears resemblance to observations made by previous authors [22, 23, 26]. No significance testing was made on the differences between COPD cases and controls in taxonomy analyses, as the full dataset of the MicroCOPD study including 249 study subjects, will provide better power.
This is the only airways microbiota study that has had as a primary objective to examine within-individual variation over time. The study was well powered with 131 bronchoscopies, given additional statistical power by the paired analyses. Both procedure and laboratory contamination were handled by using protected BAL and protected specimen brushes, as well as extensive negative control sampling and application of bioinformatic tools to identify potentially contaminating sequences.
Nevertheless, some methodological weaknesses deserve mentioning. First of all, the variation in sampling interval was considerable, making time a potential bias to the comparison. In our multivariate analyses of diversity, we could not detect an effect of adding the length of this interval as a covariate. Also, the ideal time interval is unknown, but the range of days between the first and second examination was from 88 to 349 days, and at least implies some degree of long-term stability. Second, it might be premature to draw firm conclusions based on analyses of only two timepoints in 62 individuals. But as far as we have been able to find, this is by far the largest repeated bronchoscopy study of the airways microbiome. Third, no consensus exist to date on when a difference in microbial composition between any two samples is factually clinically or statistically substantially different. Fourth, we have applied quite strict filtering and contaminant criteria in our bioinformatic analyses, reducing the number of ASVs from more than 27,000 to 551 ASVs, to avoid spurious inclusion of sequencing errors. We did not have mock community included in the earlier sequencing runs, and could thus not benchmark this approach. However, we have based our approach on previous publications, and it does seem highly unlikely that samples from 60 individuals should encompass more than 20,000 different microbial entities. Finally, having measurements of bacterial load (e.g. quantitative PCR) would have enabled analyses by amount of bacterial DNA in the original samples. Due to logistical and financial restrictions, qPCR was only performed for a small number of participants in the MicroCOPD study, but these analyses did show that the bacterial load differed by sampling modality—with higher bacterial load in OW and BAL samples than the protected specimen brushes [1].
In conclusion, the airways microbiota seem to vary over time. However, there is compositional microbiota stability within a person beyond that of pure chance, pointing to the possible existence of an indivudual core airways-residing microbiota.