Evaluation of V3-V4 and FL-16S rRNA amplicon sequencing approach for microbiota community analysis of tracheostomy aspirates

Respiratory infections pose a significant risk for people requiring prolonged mechanical ventilation, yet limited information exists regarding the complex microbiome dynamics of people with tracheostomies during chronic critical illness. Oxford Nanopore Technologies (ONT) long-read sequencing allows for full-length 16S rRNA amplicon sequencing, providing enhanced species-level understanding of the respiratory microbiome. We validated ONT-based FL-16S amplicon sequencing for microbial insights from tracheal aspirates by comparing results with those of Illumina V3-V4 amplicon sequencing. Comparisons were made on a standardized microbial community and tracheal aspirates using multiple DNA extraction kits. Conventional short-read bioinformatic pipelines are suboptimal for processing longer, error-prone ONT reads. The Emu bioinformatics pipeline, specifically designed for ONT FL-16S reads, enhances the accuracy but necessitates validation for tracheal aspirates. In this study, we compared the analysis of FL-16S reads using Emu to the standardized V3-V4 read analysis with QIIME2. Our findings demonstrate that at the same sequencing read depth, FL-16S sequencing analysis with Emu yields comparable alpha and taxonomic diversity metrics, while providing superior species-level resolution compared to V3-V4 amplicon sequencing of tracheal aspirates. Our results show that tracheal aspirates during chronic critical illness are low-diversity samples, with most pathogenic genera represented by a single species. However, members of the oral microbiota <i>Streptococcus</i> and <i>Prevotella</i> are represented by multiple species.<h4>Importance</h4>The role of the respiratory microbiome in shaping outcomes for patients with chronic critical illness undergoing prolonged mechanical ventilation via a tracheostomy remains poorly understood, despite its potential to drive infections and complicate recovery. Current methods, such as short-read 16S rRNA sequencing, lack taxonomic resolution to track pathogens at the species level, limiting clinical insights. Our study addresses this gap by validating ONT-based full-length (FL)-16S rRNA sequencing, a method that achieves species-level taxonomic precision critical for analyzing complex respiratory microbiomes. We benchmarked the microbiome composition of tracheal aspirates from ONT FL-16S rRNA workflows against Illumina V3-V4 data to demonstrate that long-read sequencing delivers comparable diversity profiles while resolving species-level diversity of clinically relevant species and microbes associated with the oral microbiome.