Molecular Methods to Detect and Quantify Fungal Communities in Cystic Fibrosis Airway Specimens

Fungal pathogens are frequently detected in the cystic fibrosis (CF) airways; however, limitations exist with culture-based detection methods. Here, we apply a novel next generation sequencing (NGS) technique to identify fungal taxa in airway samples from CF and non-CF disease controls (DC), comparing the results to traditional culture.

Bronchoalveolar lavage fluid (BALF) and oropharyngeal (OP) swabs were collected concurrently from children; expectorated sputum (ES) was collected from adults with CF. Total fungal load (TFL) was quantified via quantitative polymerase chain reaction (qPCR). Fungal communities were determined using NGS (V6/V8) and assessed for relative abundance, diversity, and richness. Wilcoxon rank-sum tests compared TFL and diversity measures.

Seventy-six samples (23 paired BALF-OP swabs, 30 ES samples) from 52 individuals (69% CF) were collected as part of two clinical research studies. Sequencing was successful in 90% of samples, including 78% of OP swabs, and identified more diverse fungal communities than culture. Predominant taxa included Agaricomycetes, Aspergillus, Candida-Lodderomyces-clade, and Cladosporium. Aspergillus was detected in all sequenced samples. Fungal richness and diversity varied by sample type with no notable difference between BALF and ES samples by culture result. Fungal community concordance, as assessed by beta diversity, between BALF-OP swab specimen pairs was low, though richness and diversity were similar.

NGS identified more distinct fungi in the airways of pwCF and OP swabs show potential for reliably detecting fungi in the airway, mitigating the need for ES. Future work will assess fungal-bacterial interactions and associations with clinical outcomes.