Deciphering the Ecology of Cystic Fibrosis Bacterial Communities: Towards Systems-Level Integration
Authors: Annamaria Bevivino, Giovanni Bacci, Pavel Drevinek, Maria T. Nelson, Lucas Hoffman, Alessio Mengoni
Journal: Trends in Molecular Medicine (2019)

The analysis of CF respiratory microbiome data is rapidly increasing our understanding of the microbiological correlates of lung disease, fueling hope for rational design of therapeutics that target the microbiota or its behavior to improve the health of the CF lung.Recent work using metagenomic techniques has not only identified variable taxonomic composition of microbiota, but also a conserved set of functional microbial genes in patients with similar disease severity, suggesting functional redundancy that may present opportunities for treatments. These findings have led to harnessing the sputum microbiome composition as predictive over disease progression.Among the members of the microbiota, agonistic and antagonistic interactions have been disclosed, allowing the hypothesis of interventions based on the restoration of healthy ecological relationships inside the CF microbiome. Despite over a decade of cystic fibrosis (CF) microbiome research, much remains to be learned about the overall composition, metabolic activities, and pathogenicity of the microbes in CF airways, limiting our understanding of the respiratory microbiome's relation to disease. Systems-level integration and modeling of host–microbiome interactions may allow us to better define the relationships between microbiological characteristics, disease status, and treatment response. In this way, modeling could pave the way for microbiome-based development of predictive models, individualized treatment plans, and novel therapeutic approaches, potentially serving as a paradigm for approaching other chronic infections. In this review, we describe the challenges facing this effort and propose research priorities for a systems biology approach to CF lung disease. Despite over a decade of cystic fibrosis (CF) microbiome research, much remains to be learned about the overall composition, metabolic activities, and pathogenicity of the microbes in CF airways, limiting our understanding of the respiratory microbiome's relation to disease. Systems-level integration and modeling of host–microbiome interactions may allow us to better define the relationships between microbiological characteristics, disease status, and treatment response. In this way, modeling could pave the way for microbiome-based development of predictive models, individualized treatment plans, and novel therapeutic approaches, potentially serving as a paradigm for approaching other chronic infections. In this review, we describe the challenges facing this effort and propose research priorities for a systems biology approach to CF lung disease. CF (OMIM 219700) is the most common life-shortening Mendelian disease in the white populations of Europe and North America, with an incidence of approximately one in 2500–6000 live births and a prevalence of more than 70 000 people living with CF worldwide [1Ratjen F. Doring G. Cystic fibrosis.Lancet. 2003; 361: 681-689Abstract Full Text Full Text PDF PubMed Scopus (778) Google Scholar, 2Jackson A.D. Goss C.H. Epidemiology of CF: how registries can be used to advance our understanding of the CF population.J. Cyst. Fibros. 2018; 17: 297-305Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]. Disease is caused by mutations in the CF transmembrane conductance regulator (cftr) gene, which encodes an anion channel (CFTR) found in both secretory and absorbing epithelia. This defect results in abnormal sodium, chloride, and bicarbonate transport across these epithelia, altering the composition of secretions in the lung, gastrointestinal tract, pancreas, biliary ducts, and other secretory glands. In the airways, absent or dysfunctional CFTR results in thick and tenacious mucus that compromises mucociliary clearance. This condition predisposes individuals to chronic bacterial infections and airway inflammation. During chronic infection, bacterial pathogens adapt to the microenvironment of CF airways [3Döring G. et al.Differential adaptation of microbial pathogens to airways of patients with cystic fibrosis and chronic obstructive pulmonary disease.FEMS Microbiol. Rev. 2011; 35: 124-146Crossref PubMed Scopus (0) Google Scholar, 4Faure E. et al.Pseudomonas aeruginosa in chronic lung infections: how to adapt within the host?.Front. 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These infections are key drivers of a pathophysiological cascade that leads to progressive and irreversible airway damage [8Gibson R.L. et al.Pathophysiology and management of pulmonary infections in cystic fibrosis.Am. J. Respir. Crit. Care Med. 2003; 168: 918-951Crossref PubMed Scopus (1134) Google Scholar]. Affected individuals consistently maintain high bacterial loads in their airways both during periods of clinical stability and episodic increases in symptoms known as pulmonary exacerbations (PEx), (see Glossary). Standard clinical microbiology of CF, both during stability and PEx, usually identifies a relatively low number of bacterial species that are often thought to be important for driving PEx (Table 1), occasionally also including nontuberculous mycobacteria (NTM) and fungi, such as Candida spp., Aspergillus spp., and Scedosporium spp. [9Salsgiver E.L. et al.Changing epidemiology of the respiratory bacteriology of patients with cystic fibrosis.Chest. 2016; 149: 390-400Abstract Full Text Full Text PDF PubMed Scopus (79) Google Scholar, 10Williams C. et al.Pathogenesis of fungal infections in cystic fibrosis.Curr. Fungal Infect. Rep. 2016; 10: 163-169Crossref PubMed Scopus (0) Google Scholar].Table 1Traditional CF Pathogens (Left) and Taxa Commonly Detected by Molecular Methods (Right) in CF Respiratory SecretionsTraditional CF pathogensCF respiratory secretion-associated generaPseudomonas aeruginosacContains/is an aerobic species.Staphylococcus aureusbContains/is a facultative anaerobic species.Stenotrophomonas maltophiliacContains/is an aerobic species.Burkholderia cepacia complexcContains/is an aerobic species.Haemophilus influenzaebContains/is a facultative anaerobic species.Nontuberculous mycobacteriacContains/is an aerobic species.Achromobacter xylosoxidanscContains/is an aerobic species.NeisseriacContains/is an aerobic species.RothiacContains/is an aerobic species.AtopobiumaContains/is an anaerobic species.CapnocytophagaaContains/is an anaerobic species.LeptotrichiaaContains/is an anaerobic species.PorphyromonasaContains/is an anaerobic species.PrevotellaaContains/is an anaerobic species.VeillonellaaContains/is an anaerobic species.ActinobacillusaContains/is an anaerobic species., bContains/is a facultative anaerobic species.ActinomycesaContains/is an anaerobic species., bContains/is a facultative anaerobic species.AbiotrophiabContains/is a facultative anaerobic species.GemellabContains/is a facultative anaerobic species.GranulicatellabContains/is a facultative anaerobic species.LactobacillusbContains/is a facultative anaerobic species.StreptococcusbContains/is a facultative anaerobic species.Detected with classical cultureDetected with molecular methodsTypically identified down to the species levelTypically, only identified to the genus levelOften remain stable; decrease in relative abundance prior to exacerbationMay increase in relative abundance with exacerbationHigh relative abundance associated with more advanced diseaseHigh relative abundance associated with healthier lung functionMany associated with faster lung function declinea Contains/is an anaerobic species.b Contains/is a facultative anaerobic species.c Contains/is an aerobic species. Open table in a new tab Subsequent studies utilizing culture-independent analysis demonstrated that a complex mix of bacteria, fungi, and viruses form the airway microbiota (i.e., the communities of microorganisms that inhabit a given environment) of chronically infected individuals with CF [11LiPuma J.J. The changing microbial epidemiology in cystic fibrosis.Clin. Microbiol. Rev. 2010; 23: 299-323Crossref PubMed Scopus (444) Google Scholar]. Pioneering studies by Rogers and colleagues [12Rogers G.B. et al.Bacterial diversity in cases of lung infection in cystic fibrosis patients: 16S ribosomal DNA (rDNA) length heterogeneity PCR and 16S rDNA terminal restriction fragment length polymorphism profiling.J. Clin. Microbiol. 2003; 41: 3548-3558Crossref PubMed Scopus (135) Google Scholar, 13Rogers G.B. et al.Characterization of bacterial community diversity in cystic fibrosis lung infections by use of 16s ribosomal DNA terminal restriction fragment length polymorphism profiling.J. Clin. Microbiol. 2004; 42: 5176-5183Crossref PubMed Scopus (218) Google Scholar] identified bacterial species not previously associated with CF airways in many CF sputum samples, expanding the list of potential CF pathogens and chronic airway colonizers, and establishing the polymicrobial nature of most CF respiratory infections (Table 1). Research during the past decade using next-generation sequencing confirmed and broadened this list of CF respiratory microbiota [14Huang Y.J. LiPuma J.J. The microbiome in cystic fibrosis.Clin. Chest Med. 2016; 37: 59-67Abstract Full Text Full Text PDF PubMed Google Scholar] and added to the list of potential 'atypical CF pathogens' both obligate and facultative anaerobes, such as Gemella, Rothia, and members of the Streptococcus anginosus group [15Tunney M.M. et al.Detection of anaerobic bacteria in high numbers in sputum from patients with cystic fibrosis.Am. J. Respir. Crit. Care Med. 2008; 177: 995-1001Crossref PubMed Scopus (274) Google Scholar, 16Caverly, L.J. et al. Cystic fibrosis lung microbiome: opportunities to reconsider management of airway infection. Pediatr. Pulmonol. 50 (Suppl. 40), S31–S38Google Scholar]. Recent metagenomic and metatranscriptomic studies on sputum samples suggested a role for these taxa in the CF airway microbiome (i.e., all microorganisms colonizing a defined environment as well as their genomic content and the surrounding environmental conditions) [17Quinn R.A. et al.Biogeochemical forces shape the composition and physiology of polymicrobial communities in the cystic fibrosis lung.MBio. 2014; 5e00956-13Crossref PubMed Scopus (0) Google Scholar]. Finally, next-generation sequencing has also identified diverse fungi (including novel taxa from Candida spp. and Aspergillus spp.) [18Bouchara J.P. et al.Development of an oligonucleotide array for direct detection of fungi in sputum samples from patients with cystic fibrosis.J. Clin. Microbiol. 2009; 47: 142-152Crossref PubMed Scopus (0) Google Scholar] in the airways of people with CF, although their involvement in respiratory disease remains controversial and largely understudied [10Williams C. et al.Pathogenesis of fungal infections in cystic fibrosis.Curr. Fungal Infect. Rep. 2016; 10: 163-169Crossref PubMed Scopus (0) Google Scholar, 19Tomazin R. Matos T. Fungal infections in patients with cystic fibrosis.Rev. Med. Microbiol. 2007; 18: 11-16Crossref Scopus (35) Google Scholar, 20Zhang I. et al.Fungal microbiota in chronic airway inflammatory disease and emerging relationships with the host immune response.Front. Microbiol. 2017; 8: 1-7Crossref PubMed Scopus (8) Google Scholar]. Metagenomic studies have also revealed an impact of respiratory viral infections in CF airway damage [21Willner D. et al.Case studies of the spatial heterogeneity of DNA viruses in the cystic fibrosis lung.Am. J. Respir. Cell Mol. Biol. 2012; 46: 127-131Crossref PubMed Scopus (0) Google Scholar, 22Billard L. et al.Viruses in cystic fibrosis patients' airways.Crit. Rev. Microbiol. 2017; 43: 690-708Crossref PubMed Scopus (4) Google Scholar, 23Willner D. et al.Metagenomic analysis of respiratory tract DNA viral communities in cystic fibrosis and non-cystic fibrosis individuals.PLoS One. 2009; 4e7370Crossref PubMed Scopus (0) Google Scholar], as well as the presence of bacterial viruses (phages) in CF airways [24Willner D. Furlan M. Deciphering the role of phage in the cystic fibrosis airway.Virulence. 2010; 1: 309-313Crossref PubMed Scopus (10) Google Scholar, 25Lim Y.W. et al.Metagenomics and metatranscriptomics: windows on CF-associated viral and microbial communities.J. Cyst. Fibros. 2013; 12: 154-164Abstract Full Text Full Text PDF PubMed Scopus (81) Google Scholar]. Phages may play roles in infection dynamics, perhaps by limiting specific bacterial populations and facilitating the adaptation of bacteria to CF airway conditions and/or the spread of antimicrobial resistance (e.g., through transduction) [26Fancello L. et al.Bacteriophages and diffusion of genes encoding antimicrobial resistance in cystic fibrosis sputum microbiota.J. Antimicrob. Chemother. 2011; 66: 2448-2454Crossref PubMed Scopus (42) Google Scholar], as well as providing a potential therapeutic avenue. However, due to the lack of universal gene markers for viruses (as opposed to bacterial 16S ribosomal RNA gene), the overwhelming majority of investigations of CF microbiota have focused on bacteria [22Billard L. et al.Viruses in cystic fibrosis patients' airways.Crit. Rev. Microbiol. 2017; 43: 690-708Crossref PubMed Scopus (4) Google Scholar]. Consequently, we will mainly review studies on the bacterial fraction of the CF airway microbiome, beginning with an overview of the CF microbiota and the factors that potentially affect its community structure, composition, and diversity, then moving on to the important role of the functional interaction patterns in development of precision medicine tailored to the microbiome of individual people with CF. Both longitudinal and cross-sectional study designs have been applied to investigate CF respiratory microbiota. Each of these approaches offers distinct advantages and disadvantages, providing complementary insights into CF respiratory microbiology (Figure 1). In longitudinal studies, individuals act as their own controls, allowing for a more fine-tuned analysis of how individual taxa correlate with different clinical and patient factors. Longitudinal studies have demonstrated, for instance, interpatient heterogeneity in CF respiratory microbiota to be higher than intrapatient heterogeneity; these studies have also identified great variation in the rate of microbiota change within patients [27Zhao J. et al.Decade-long bacterial community dynamics in cystic fibrosis airways.Proc. Natl. Acad. Sci. U. S. A. 2012; 109: 5809-5814Crossref PubMed Scopus (280) Google Scholar, 28Stressmann F.A. et al.Long-term cultivation-independent microbial diversity analysis demonstrates that bacterial communities infecting the adult cystic fibrosis lung show stability and resilience.Thorax. 2012; 67: 867-873Crossref PubMed Scopus (87) Google Scholar]. The complexity of performing longitudinal studies, however, often limits sample size and, therefore, generalizability [29Lim Y.W. et al.Clinical insights from metagenomic analysis of sputum samples from patients with cystic fibrosis.J. Clin. Microbiol. 2014; 52: 425-437Crossref PubMed Scopus (57) Google Scholar]. Conversely, cross-sectional studies tend to be larger, helping to mitigate the confounding effects of interpatient heterogeneity. Cross-sectional studies have also identified substantial interpatient variation in microbiota composition, albeit generally at single time points and without providing a view of intrapatient variation. However, the relatively large sizes of many cross-sectional studies have facilitated the distinction of common CF respiratory microbes from uncommon ones (i.e., core and satellite taxa) [30Van Der Gast C.J. et al.Partitioning core and satellite taxa from within cystic fibrosis lung bacterial communities.ISME J. 2011; 5: 780-791Crossref PubMed Scopus (124) Google Scholar]. These large sample sizes have allowed several investigators to identify important differences in average microbiota with varying age and disease state [31Muhlebach M.S. et al.Initial acquisition and succession of the cystic fibrosis lung microbiome is associated with disease progression in infants and preschool children.PLoS Pathog. 2018; 14e1006798Crossref PubMed Scopus (0) Google Scholar]. Collectively, both longitudinal and cross-sectional CF respiratory microbiota studies have identified important correlations with patient characteristics, treatments, and outcomes (Table 2). While it is difficult to either predict or demonstrate causality (i.e., whether microbiology drives disease, or whether the diseased airway environment or its treatments drive microbiology), the relative dynamics of both microbiota and clinical characteristics from observational trials over time can help to suggest clinically relevant relationships. Many longitudinal studies have identified remarkable stability among CF respiratory microbiota even during clinical changes [27Zhao J. et al.Decade-long bacterial community dynamics in cystic fibrosis airways.Proc. Natl. Acad. Sci. U. S. A. 2012; 109: 5809-5814Crossref PubMed Scopus (280) Google Scholar, 32Tunney M.M. et al.Use of culture and molecular analysis to determine the effect of antibiotic treatment on microbial community diversity and abundance during exacerbation in patients with cystic fibrosis.Thorax. 2011; 66: 579-584Crossref PubMed Scopus (113) Google Scholar, 33Fodor A.A. et al.The adult cystic fibrosis airway microbiota is stable over time and infection type, and highly resilient to antibiotic treatment of exacerbations.PLoS One. 2012; 7e45001Crossref PubMed Scopus (191) Google Scholar]. In some studies, PEx onset was associated with transient changes in abundance of a number of taxa, such as anaerobes [34Carmody L.A. et al.Changes in cystic fibrosis airway microbiota at pulmonary exacerbation.Ann. Am. Thorac. Soc. 2013; 10: 179-187Crossref PubMed Scopus (85) Google Scholar] and Gemella [35Carmody L.A. et al.Fluctuations in airway bacterial communities associated with clinical states and disease stages in cystic fibrosis.PLoS One. 2018; 13e0194060Crossref PubMed Scopus (0) Google Scholar]. However, studies have also shown that sputum microbiota within patients exhibited remarkable resilience, returning to their baseline states after treatment ceased [27Zhao J. et al.Decade-long bacterial community dynamics in cystic fibrosis airways.Proc. Natl. Acad. Sci. U. S. A. 2012; 109: 5809-5814Crossref PubMed Scopus (280) Google Scholar, 28Stressmann F.A. et al.Long-term cultivation-independent microbial diversity analysis demonstrates that bacterial communities infecting the adult cystic fibrosis lung show stability and resilience.Thorax. 2012; 67: 867-873Crossref PubMed Scopus (87) Google Scholar, 32Tunney M.M. et al.Use of culture and molecular analysis to determine the effect of antibiotic treatment on microbial community diversity and abundance during exacerbation in patients with cystic fibrosis.Thorax. 2011; 66: 579-584Crossref PubMed Scopus (113) Google Scholar, 33Fodor A.A. et al.The adult cystic fibrosis airway microbiota is stable over time and infection type, and highly resilient to antibiotic treatment of exacerbations.PLoS One. 2012; 7e45001Crossref PubMed Scopus (191) Google Scholar, 36Heirali A.A. et al.The effects of inhaled aztreonam on the cystic fibrosis lung microbiome.Microbiome. 2017; 5: 51Crossref PubMed Google Scholar, 37Carmody L.A. et al.The daily dynamics of cystic fibrosis airway microbiota during clinical stability and at exacerbation.Microbiome. 2015; 3: 12Crossref PubMed Scopus (57) Google Scholar]. Interestingly, there is little evidence that the density of canonical CF pathogen Pseudomonas aeruginosa changes appreciably during exacerbation [38Stressmann F.A. et al.Does bacterial density in cystic fibrosis sputum increase prior to pulmonary exacerbation?.J. Cyst. Fibros. 2011; 10: 357-365Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar], and the degree to which antibiotics reduce P. aeruginosa levels following treatment has not been correlated with therapeutic success [39Regelmann W.E. et al.Reduction of sputum Pseudomonas aeruginosa density by antibiotics improves lung function in cystic fibrosis more than do bronchodilators and chest physiotherapy alone.Am. Rev. Respir. Dis. 1990; 141: 914-921Crossref PubMed Google Scholar]. Transient microbiological changes with antibiotic therapy are often most pronounced among aerobic taxa, most notably in P. aeruginosa [32Tunney M.M. et al.Use of culture and molecular analysis to determine the effect of antibiotic treatment on microbial community diversity and abundance during exacerbation in patients with cystic fibrosis.Thorax. 2011; 66: 579-584Crossref PubMed Scopus (113) Google Scholar, 33Fodor A.A. et al.The adult cystic fibrosis airway microbiota is stable over time and infection type, and highly resilient to antibiotic treatment of exacerbations.PLoS One. 2012; 7e45001Crossref PubMed Scopus (191) Google Scholar], a common target of antibiotics in CF. However, these changes in sputum microbiota do not necessarily signify the mechanism of the clinical effects of these antibiotics [36Heirali A.A. et al.The effects of inhaled aztreonam on the cystic fibrosis lung microbiome.Microbiome. 2017; 5: 51Crossref PubMed Google Scholar, 40Zemanick E.T. et al.Inflammation and airway microbiota during cystic fibrosis pulmonary exacerbations.PLoS One. 2013; 8e62917Crossref PubMed Scopus (0) Google Scholar]. Thus, it is important to be conservative when assigning any change in microbial community constituency as causally related to therapeutic success or lung function decline.Table 2Effects of Patient Characteristics on Airway Secretion MicrobiotaEffectorEffect on microbiotaAgeRelatively sterile airways in infancyGradual rise in total microbial abundance throughout childhood and early adulthoodTransition from dominance by oral anaerobes to 'traditional' pathogens (S. aureus, P. aeruginosa), generally during childhoodDecreased community diversity and dominance of traditional CF pathogens in adulthoodPulmonary exacerbations (PEx)Stability and resilience: few microbiota changes, often involving shifts to higher anaerobe abundances, preceding or during exacerbations. Where changes occur, community often rebounds afterwards to pre-exacerbation statePersistent communities (climax microbiomes) during periods of clinical stabilityTransient communities (attack microbiomes) associated with PExAntibiotic exposureStability and resilience: few changes with antibiotic therapy for PEx. Where changes occur, community often rebounds afterwards to pretreatment stateGradual decrease in microbial diversity over individuals' lifetimes correlates with cumulative antibiotic exposure loadHost immunityLimited and contradictory dataDecreased inflammation associated with increased α-diversityIncreased inflammation associated with specific anaerobes, traditional pathogens, and increased absolute bacterial abundanceGenetic backgroundLimited data, more investigation neededTrends towards higher P. aeruginosa prevalence with F508del CFTR mutationF508del CFTR mutation associated with loss of airway bacterial diversity and conserved community compositionDisease state/Lung function declineStability over months and decades, less stability during periods of rapid clinical declineDecrease in α-diversity, increase in absolute and relative abundance of traditional pathogens with advancing diseaseDecreased diversity early in life associated with faster rate of decline Open table in a new tab While stability and resilience are core features of the CF respiratory microbiome, both cross-sectional and long-term longitudinal studies have identified a number of host and therapeutic factors that are associated with differences (cross-sectional) and changes (longitudinal) in microbiota, many of which vary over time, not only within individuals, but also in CF populations, as care evolves (Table 2) [41Cystic Fibrosis Foundation Patient Registry 2016 Annual Data Report. Cystic Fibrosis Foundation, 2017Google Scholar]. Studies have demonstrated that, on average, CF respiratory communities are usually dominated by oral anaerobes early in life, after which α-diversity (diversity within sample; i.e., the number and/or evenness, and types of taxa within a local community) usually decreases due to increasing abundances (relative and absolute) of 'traditional' CF pathogens [42Coburn B. et al.Lung microbiota across age and disease stage in cystic fibrosis.Sci. Rep. 2015; 510241Crossref PubMed Scopus (114) Google Scholar, 43Brown P.S. et al.Directly sampling the lung of a young child with cystic fibrosis reveals diverse microbiota.Ann. Am. Thorac. Soc. 2014; 11: 1049-1055Crossref PubMed Scopus (39) Google Scholar, 44Klepac-Ceraj V. et al.Relationship between cystic fibrosis respiratory tract bacterial communities and age, genotype, antibiotics and Pseudomonas aeruginosa.Environ. Microbiol. 2010; 12: 1293-1303Crossref PubMed Scopus (0) Google Scholar, 45Zemanick E.T. et al.Airway microbiota across age and disease spectrum in cystic fibrosis.Eur. Respir. J. 2017; 501700832Crossref PubMed Scopus (33) Google Scholar]. These changes are associated both with decreasing lung function and also increasing antibiotic exposure [30Van Der Gast C.J. et al.Partitioning core and satellite taxa from within cystic fibrosis lung bacterial communities.ISME J. 2011; 5: 780-791Crossref PubMed Scopus (124) Google Scholar, 46Flight W.G. et al.Rapid detection of emerging pathogens and loss of microbial diversity associated with severe lung disease in cystic fibrosis.J. Clin. Microbiol. 2015; 53: 2022-2029Crossref PubMed Scopus (26) Google Scholar]. Treatment with CFTR modulators, which address the underlying defect in CF, was recently shown to be followed by an initial decrease in absolute and relative abundances of P. aeruginosa and with an accompanying increase in relative abundances of Streptococcus and Prevotella spp. These changes resulted in increased overall α-diversity, all of which accompanied an increase in lung function [47Hisert K.B. et al.Restoring cystic fibrosis transmembrane conductance regulator function reduces airway bacteria and inflammation in people with cystic fibrosis and chronic lung infections.Am. J. Respir. Crit. Care Med. 2017; 195: 1617-1628Crossref PubMed Scopus (0) Google Scholar]. Interestingly, pre-exacerbation α-diversity and disease severity also appear to predict the degree of community perturbation at exacerbation [34Carmody L.A. et al.Changes in cystic fibrosis airway microbiota at pulmonary exacerbation.Ann. Am. Thorac. Soc. 2013; 10: 179-187Crossref PubMed Scopus (85) Google Scholar, 35Carmody L.A. et al.Fluctuations in airway bacterial communities associated with clinical states and disease stages in cystic fibrosis.PLoS One. 2018; 13e0194060Crossref PubMed Scopus (0) Google Scholar], and decreased microbial diversity early in life has also been associated with a faster rate of lung function decline [48Acosta N. et al.Sputum microbiota is predictive of long-term clinical outcomes in young adults with cystic fibrosis.Thorax. 2018; 73: 1016-1025Crossref PubMed Scopus (8) Google Scholar]. Such findings underscore the importance of systems-level approaches that more fully reflect the complexity of these microbiota. Additionally, agonistic and antagonistic interactions among members of the CF airway microbiota can occur, influencing community composition dynamics, as suggested by various reports and confirmed on some microbial species under in vitro conditions [49Bragonzi A. et al.Modelling co-infection of the cystic fibrosis lung by Pseudomonas aeruginosa and Burkholderia cenocepacia reveals influences on biofilm formation and host response.PLoS One. 2012; 7e52330Crossref PubMed Scopus (0) Google Scholar, 50Quinn R.A. et al.Ecological networking of cystic fibrosis lung infections.NPJ Biofilms Microbiomes. 2016; 2: 4Crossref PubMed Scopus (19) Google Scholar, 51Homa M. et al.In vitro interactions of Pseudomonas aeruginosa with Scedosporium species frequently associated with cystic fibrosis.Front. 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