Exploring the Cystic Fibrosis Lung Microbiome: Making the Most of a Sticky Situation

Abstract

Chronic lower respiratory tract infections are a leading contributor to morbidity and mortality in persons with cystic fibrosis (pwCF). Traditional respiratory tract surveillance culturing has focused on a limited range of classic pathogens; however, comprehensive culture and culture-independent molecular approaches have demonstrated complex communities highly unique to each individual. Microbial community structure evolves through the lifetime of pwCF and is associated with baseline disease state and rates of disease progression including occurrence of pulmonary exacerbations. While molecular analysis of the airway microbiome has provided insight into these dynamics, challenges remain including discerning not only "who is there" but "what they are doing" in relation to disease progression. Moreover, the microbiome can be leveraged as a multi-modal biomarker for both disease activity and prognostication. In this article, we review our evolving understanding of the role these communities play in pwCF and identify challenges in translating microbiome data to clinical practice.

Keywords: Pseudomonas aeruginosa; biomarker; bronchiectasis; lung; microbiota; review.

Figure 1.

Establishing the structure and composition of the CF microbiome. (A) A range of respiratory sample types can be assessed, each of which differs with respect to its ease of collection, sensitivity, specificity, and relevance to the lower airways. (B) Samples can be assessed using routine and augmented culture protocols to identify specifically targeted organisms (which allow for pathogen characterization) or an agnostic approach in which next-generation sequencing is used to define the entirety of community constituents. (C) After DNA extraction, microbial communities can be defined based on establishing their gene content either using amplicon (16S ribosomal RNA) amplification or shotgun sequencing (±host DNA depletion strategies) enabling downstream analysis. Figure created with Biorender. Abbreviation: CF, cystic fibrosis.

Figure 2.

The core constituents of the CF microbiome by lung disease stage. Data presented correspond to the prevalence (%) and dominance frequency (%) of canonical CF pathogens and other members of the CF microbiota found in a multicenter cohort of 297 pwCF respiratory samples from the study described by Cuthbertson et al [26]. pwCF and their microbiota are stratified by stage of lung disease: early (percentage predicted (ppFEV₁) > 70) (n = 57), intermediate (ppFEV₁ 40-70) (n = 139), and advanced (ppFEV₁ < 40) (n = 101). Prevalence for each taxon was defined as the proportion of patients in which a given taxon was detected for each stage of lung disease. Dominance frequency was defined as the percentage of samples that had a particular taxon as the most abundant. Size of the different taxa shown represents the median relative abundance (RA) across the samples for each stage with the lowest value corresponding to RA = 0 and maximum of RA = 40. Both prevalence and dominance frequency are on a log10 axis. Abbreviations: CF, cystic fibrosis; pwCF persons with cystic fibrosis.

Figure 3.

Mechanisms by which respiratory microbiota influence CF lung disease. (A) Members of the CF microbiota have been associated with risk of infection in the airways. Colonization of commensal microbiota, such as Porphyromonas catoniae was found as a biomarker associated with a lower risk of P. aeruginosa (PA) early infection in CF [53]. In contrast, infection of Streptococcus milleri/anginosus group (SMG) at the onset of pulmonary exacerbations (PEx) is associated with symptomatic deterioration in clinical status, whereas its relative reduction is associated with symptom resolution (unlike canonical pathogens, such as P. aeruginosa) [3, 54]. (B) Commensal bacteria may negatively or positively influence the virulence of CF pathogens. Co-infection models in human epithelial cell lines with P. aeruginosa and commensal CF microbiota have shown that different strains of Streptococcus mitis reduce P. aeruginosa-induced inflammation through reduction of interleukin 8 (IL-8) production and neutrophil extracellular trap (NET) formation. The mechanism of action is still unknown but thought to be through modification of the micro-environment by metabolism adjustment by the commensal bacteria [55]. In contrast, some oral commensal streptococci enhance P. aeruginosa pathogenicity by increasing its virulence factor expression (eg, pyocyanin and elastase) [4, 56, 57]. (C) The CF microbiota contain bacteria with immunomodulatory activity that may alter host inflammatory response, which in turn could influence the progression of lung disease. Rothia mucilaginosa potentially mitigates host inflammation through the inhibition of the IL-8 production and NF-κB pathway activation in a human lung epithelial cell line [58]. Conversely, Prevotella intermedia was reported to be able to contribute to disease progression by secretion of cytotoxic extracellular toxins that induce the influx of macrophages and neutrophils in the airway lumen [59]. (D) CF microbiota influence disease through the modulation of extrinsic therapies. Extended-spectrum β-lactamases (ESBLs)-producing Prevotella isolates were reported to influence pathogenesis in vitro by shielding pathogens, such as P. aeruginosa from the action of β-lactam antibiotics [51]. (E) CF microbiota may be affected by a range of external factors, including pollution, diet, and viruses. Pollution may play a role in triggering PEx, leading to microbiota changes and further airway irritation and injury, which consequently could affect the extent of respiratory infections [60]. In the gut-lung axis, diet plays an important role in shaping the composition of the gut microbiota. Metabolites produced by the gut microbiota not only modulate gastrointestinal immunity but also impact immune responses in the lung [61, 62]. Bacteriophages may impact the fitness of members of the CF microbiota through horizontal gene transfer (HGT) of antimicrobial resistance genes [63]. Additionally, the progression of lung disease is influenced by infection with respiratory viruses which could indirectly promote community changes and host response [64]. Figure created with Biorender. Abbreviations: CF, cystic fibrosis; NF-κB, nuclear factor kappa B.