Longitudinal Analysis of Sinonasal Microbiome Dynamics in Chronic Rhinosinusitis

Poster Abstract: Sylwia Bożek, Junior Research Specialist, Sano – Centre for Computational Personalised Medicine International Research Foundation 

Abstract

Purpose: The sinuses are inhabited by various microbes. Most studies on the sinonasal microbiota were conducted at a single time point and therefore the temporal dynamics of the microbial community in this niche has not been sufficiently explored. Chronic rhinosinusitis (CRS) is one of the most common and complex inflammatory diseases affecting the sinuses.

Methods: To investigate whether microbiome fluctuations contribute to CRS development and exacerbations, we collected longitudinal samples from individuals diagnosed with CRS at a minimum of four time points. Additionally, samples from healthy individuals were obtained to characterize the microbiome of unaffected sinuses. Advances in molecular techniques, particularly third-generation and long-read sequencing, now enable species-level resolution in microbiome profiling. In this study, we applied full-length 16S rRNA gene sequencing using Oxford Nanopore Technologies. We also compared sequencing-based results with findings from hospital-standard microbiological cultures. 

Conclusions: Our analysis demonstrates that the sinonasal microbiome of CRS patients and healthy individuals exhibits high inter-individual variability, without consistent taxonomic patterns distinguishing the groups. Furthermore, the CRS cohort is heterogeneous, and in some individuals, alterations in microbiome composition and temporal dynamics seem to coincide with episodes of symptom worsening or clinical decline. Future work will investigate the effects of systemic antibiotic therapy on the sinonasal microbiome, including changes in microbes composition and the presence of antibiotic-resistant bacteria. We also aim to integrate taxonomic data with clinical parameters and microbial metadata derived from bioinformatic analyses, using machine learning approaches to better understand disease complexity and identify meaningful patterns.