Informed interpretation of metagenomic data by StrainPhlAn enables strain retention analyses of the upper airway microbiome

Abstract

Abstract Background: Shotgun metagenomic sequencing has the potential to provide bacterial strain-level resolution which is of key importance to tackle a host of clinical questions. While bioinformatics tools that achieve strain-level resolution are available, thorough benchmarking is needed to validate their use for less investigated and low biomass microbiomes like those from the upper respiratory tract. Methods: We analyzed a previously published dataset of longitudinally collected nasopharyngeal samples from Bangladeshi infants (Microbiota & Health study) and a novel dataset of oropharyngeal samples from Swiss children with cystic fibrosis. Data from bacterial cultures were used for benchmarking the parameters of StrainPhlAn 3, a bioinformatic tool designed for strain-level resolution. In addition, StrainPhlAn 3 results were compared to metagenomic assemblies and whole-genome sequencing data of S. aureus strains. Finally, strain retention analyses were performed. Results: After optimizing the analytical parameters, we compared our results to culture gold standard methods and achieved sensitivity values of 87% (S. pneumoniae), 80% (M. catarrhalis), 75% (H. influenzae) and 57% (S. aureus) for 420 nasopharyngeal and 75% (H. influenzae) and 46% (S. aureus) for 260 oropharyngeal samples. Comparing the phylogenetic tree of the core genome of 50 S. aureus isolates with a corresponding marker gene tree generated by StrainPhlAn 3 revealed a striking similarity in tree topology for all but three samples indicating adequate strain resolution. Quantitative analyses of longitudinally collected samples revealed clear signatures of strain retention for the four analyzed species. Conclusions: Comparison of StrainPhlAn 3 results to data from bacterial cultures revealed that strain-level tracking of the respiratory microbiome is feasible despite high content of host DNA when parameters are carefully optimized to fit low biomass microbiomes. This allowed to perform strain retention analyses applied to relevant pathobionts which will help to better understand the longitudinal dynamics of the upper respiratory microbiome during health and disease.