Improved characterization of medically relevant fungi in the human respiratory tract using next-generation sequencing

Fungi are important pathogens but challenging to enumerate using next-generation sequencing because of low absolute abundance in many samples and high levels of fungal DNA from contaminating sources.

Here, we analyze fungal lineages present in the human airway using an improved method for contamination filtering. We use DNA quantification data, which are routinely acquired during DNA library preparation, to annotate output sequence data, and improve the identification and filtering of contaminants. We compare fungal communities and bacterial communities from healthy subjects, HIV+ subjects, and lung transplant recipients, providing a gradient of increasing lung impairment for comparison. We use deep sequencing to characterize ribosomal rRNA gene segments from fungi and bacteria in DNA extracted from bronchiolar lavage samples and oropharyngeal wash. Comparison to clinical culture data documents improved detection after applying the filtering procedure.

We find increased representation of medically relevant organisms, including Candida, Cryptococcus, and Aspergillus, in subjects with increasingly severe pulmonary and immunologic deficits. We analyze covariation of fungal and bacterial taxa, and find that oropharyngeal communities rich in Candida are also rich in mitis group Streptococci,a community pattern associated with pathogenic polymicrobial biofilms. Thus, using this approach, it is possible to characterize fungal communities in the human respiratory tract more accurately and explore their interactions with bacterial communities in health and disease.