Model Systems to Study the Chronic, Polymicrobial Infections in Cystic Fibrosis: Current Approaches and Exploring Future Directions.

A recent workshop titled "Developing Models to Study Polymicrobial Infections," sponsored by the Dartmouth Cystic Fibrosis Center (DartCF), explored the development of new models to study the polymicrobial infections associated with the airways of persons with cystic fibrosis (CF). The workshop gathered 35+ investigators over two virtual sessions. Here, we present the findings of this workshop, summarize some of the challenges involved with developing such models, and suggest three frameworks to tackle this complex problem. The frameworks proposed here, we believe, could be generally useful in developing new model systems for other infectious diseases. Developing and validating new approaches to study the complex polymicrobial communities in the CF airway could open windows to new therapeutics to treat these recalcitrant infections, as well as uncovering organizing principles applicable to chronic polymicrobial infections more generally.

Lung function and microbiota diversity in cystic fibrosis.

BACKGROUND: Chronic infection and concomitant airway inflammation is the leading cause of morbidity and mortality for people living with cystic fibrosis (CF). Although chronic infection in CF is undeniably polymicrobial, involving a lung microbiota, infection surveillance and control approaches remain underpinned by classical aerobic culture-based microbiology. How to use microbiomics to direct clinical management of CF airway infections remains a crucial challenge. A pivotal step towards leveraging microbiome approaches in CF clinical care is to understand the ecology of the CF lung microbiome and identify ecological patterns of CF microbiota across a wide spectrum of lung disease. Assessing sputum samples from 299 patients attending 13 CF centres in Europe and the USA, we determined whether the emerging relationship of decreasing microbiota diversity with worsening lung function could be considered a generalised pattern of CF lung microbiota and explored its potential as an informative indicator of lung disease state in CF. RESULTS: We tested and found decreasing microbiota diversity with a reduction in lung function to be a significant ecological pattern. Moreover, the loss of diversity was accompanied by an increase in microbiota dominance. Subsequently, we stratified patients into lung disease categories of increasing disease severity to further investigate relationships between microbiota characteristics and lung function, and the factors contributing to microbiota variance. Core taxa group composition became highly conserved within the severe disease category, while the rarer satellite taxa underpinned the high variability observed in the microbiota diversity. Further, the lung microbiota of individual patient were increasingly dominated by recognised CF pathogens as lung function decreased. Conversely, other bacteria, especially obligate anaerobes, increasingly dominated in those with better lung function. Ordination analyses revealed lung function and antibiotics to be main explanators of compositional variance in the microbiota and the core and satellite taxa. Biogeography was found to influence acquisition of the rarer satellite taxa. CONCLUSIONS: Our findings demonstrate that microbiota diversity and dominance, as well as the identity of the dominant bacterial species, in combination with measures of lung function, can be used as informative indicators of disease state in CF. Video Abstract.

Bacterial dominance is due to effective utilisation of secondary metabolites produced by competitors.

Interactions between bacteria govern the progression of respiratory infections; however, the mechanisms underpinning these interactions are still unclear. Understanding how a bacterial species comes to dominate infectious communities associated with respiratory infections has direct relevance to treatment. In this study, Burkholderia, Pseudomonas, and Staphylococcus species were isolated from the sputum of an individual with Cystic Fibrosis and assembled in a fully factorial design to create simple microcosms. Measurements of growth and habitat modification were recorded over time, the later using proton Nuclear Magnetic Resonance spectra. The results showed interactions between the bacteria became increasingly neutral over time. Concurrently, the bacteria significantly altered their ability to modify the environment, with Pseudomonas able to utilise secondary metabolites produced by the other two isolates, whereas the reverse was not observed. This study indicates the importance of including data about the habitat modification of a community, to better elucidate the mechanisms of bacterial interactions.

A novel microbiota stratification system predicts future exacerbations in bronchiectasis.

RATIONALE: Although airway microbiota composition correlates with clinical measures in non-cystic fibrosis bronchiectasis, these data are unlikely to provide useful prognostic information at the individual patient level. A system enabling microbiota data to be applied clinically would represent a substantial translational advance. OBJECTIVES: This study aims to determine whether stratification of patients according to the predominant microbiota taxon can provide improved clinical insight compared with standard diagnostics. METHODS: The presence of bacterial respiratory pathogens was assessed in induced sputum from 107 adult patients by culture, quantitative PCR, and, in 96 samples, by ribosomal gene pyrosequencing. Prospective analysis was performed on samples from 42 of these patients. Microbiological data were correlated with concurrent clinical measures and subsequent outcomes. MEASUREMENTS AND MAIN RESULTS: Microbiota analysis defined three groups: Pseudomonas aeruginosa dominated (n = 26), Haemophilus influenzae dominated (n = 34), and other taxa dominated (n = 36). Patients with P. aeruginosa- and H. influenzae-dominated communities had significantly worse lung function, higher serum levels of C-reactive protein (CRP), and higher sputum levels of IL-8 and IL-1ß. Predominance of P. aeruginosa, followed by Veillonella species, was the best predictor of future exacerbation frequency, with H. influenzae-dominated communities having significantly fewer episodes. Detection of P. aeruginosa was associated with poor lung function and exacerbation frequency, irrespective of analytical strategy. Quantitative PCR revealed significant correlations between H. influenzae levels and sputum IL-8, IL-1ß, and serum CRP. Genus richness was negatively correlated with 24-hour sputum weight, age, serum CRP, sputum IL-1ß, and IL-8. CONCLUSIONS: Stratification of patients with non-cystic fibrosis bronchiectasis on the basis of predominant bacterial taxa is more clinically informative than either conventional culture or quantitative PCR-based analysis. Further investigation is now required to assess the mechanistic basis of these associations.

A relationship between Pseudomonal growth behaviour and cystic fibrosis patient lung function identified in a metabolomic investigation.

Chronic polymicrobial lung infections in adult cystic fibrosis patients are typically dominated by high levels of Pseudomonas aeruginosa. Determining the impact of P. aeruginosa growth on airway secretion composition is fundamental to understanding both the behaviour of this pathogen in vivo, and its relationship with other potential colonising species. We hypothesised that the marked differences in the phenotypes of clinical isolates would be reflected in the metabolite composition of spent culture media. 1H NMR spectroscopy was used to characterise the impact of P. aeruginosa growth on a synthetic medium as part of an in vitro CF lower airways model system. Comparisons of 15 CF clinical isolates were made and four distinct metabolomic clusters identified. Highly significant relationships between P. aeruginosa isolate cluster membership and both patient lung function (FEV1) and spent culture pH were identified. This link between clinical isolate growth behaviour and FEV1 indicates characterisation of P. aeruginosa growth may find application in predicting patient lung function while the significant divergence in metabolite production and consumption observed between CF clinical isolates suggests dominant isolate characteristics have the potential to play both a selective role in microbiota composition and influence pseudomonal behaviour in vivo.