Lower respiratory tract infections (LRTI) are a major cause for morbidity and mortality worldwide. A precise and time-effective pathogen identification is crucial for the successful treatment of patients with LRTI. At present, culture is the gold standard for routine diagnostics. However, in around half of the cases, pathogens remain undetected. Next-generation sequencing (NGS) represents a promising approach for an unbiased microbial detection and could provide additional information for clinical practice. In this descriptive work, bacterial microbiome profiles of 144 bronchoalveolar lavage fluid samples were generated with Illumina V4 16S rRNA gene sequencing and compared to the corresponding culture results. 21 samples were selected for Nanopore long-read sequencing, to reach a higher taxonomic resolution. Additionally, manual mechanical (Zymo) and automated enzymatic (eMAG) extraction methods were compared.
The results of the two extraction methods showed similarities in some respects (number of sequenced reads and the DNA yield), but variations in others (contamination and α-diversity). After the removal of the contamination genera, Zymo and eMAG presented with a similar community composition. The most abundant genera were Streptococcus, Staphylococcus, Pseudomonas, Prevotella 7, Veillonella and Rothia. The cultured bacteria could be retraced by Illumina sequencing (Zymo: 91.5%; eMAG: 85.6%), however, not always appearing among the top sequenced genera of the microbiome profiles. In 76.5 %, Nanopore sequencing result corresponded with the cultured species. In two cases, Nanopore sequencing detected a potential pathogen (H. influenzae and Str. pneumoniae), which was not reported in culture. A higher DNA yield could be observed in samples with a potential pathogen in culture. α-diversity did not vary significantly between the different culture results.
In conclusion, NGS results showed a partial overlap with culture as the current diagnostic gold standard in LRTI. NGS could represent a valuable extension to increase the microbial detection rate since it captures a broad spectrum of bacteria. Furthermore, NGS reveals additional microbial features of the complex ecosystem in the lungs.
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