Methodological Impacts on Microbiome Structure and Indicator Status in the Human Lower Respiratory Tract

RationaleLung cancer is the leading cause of cancer-related death globally, and rising incidence among traditionally low-risk individuals intensifies the need for improved early-detection methods that the lower-airway microbiome may inform. ObjectivesTo evaluate how respiratory tract sampling method shapes inferred microbiome structure, and whether bronchial brushing recovers a microbial community ecologically distinct from BAL and oral rinse. MethodsProspective cohort of 33 participants (8 lung cancer, 25 non-cancer controls) underwent oral rinse, bilateral bronchoalveolar lavage (BAL), and bronchial brushing. Microbiome structure was characterized by small subunit ribosomal rRNA gene amplicon sequence variant (ASV) profiling, with indicator-species analysis and SPIEC-EASI correlation-network mapping used to identify ASVs associated with sample type or cancer status. Measurements and Main ResultsSampling method was the dominant axis of variation. BAL communities closely resembled oral rinse (65 jointly indicative ASVs; none shared with brushing), whereas bronchial brushing yielded a distinct but low-biomass signal. After excluding host-derived sequences, two brush-specific indicator ASVs affiliated with Sphingomonadaceae and an uncultured Steroidobacteraceae were identified that co-localized within a single co-occurrence module. Cancer-status effects were not detectable in this pilot cohort, consistent with limited statistical power. ConclusionsSampling method is the primary determinant of inferred respiratory microbiome structure. Bronchial brushing recovers a distinct but low-biomass signal that is obscured when BAL is used in isolation. However, overlap between this low-biomass signal with host and contaminant sequences indicates that confidently resolving a discrete lower-airway community will require deeper sequencing and dedicated contamination controls. These methodological findings directly inform the design of future cancer-and disease-association studies.