Dataset Information

Microbiotyping the Sinonasal Microbiome.

ABSTRACT: This study offers a novel description of the sinonasal microbiome, through an unsupervised machine learning approach combining dimensionality reduction and clustering. We apply our method to the International Sinonasal Microbiome Study (ISMS) dataset of 410 sinus swab samples. We propose three main sinonasal "microbiotypes" or "states": the first is Corynebacterium-dominated, the second is Staphylococcus-dominated, and the third dominated by the other core genera of the sinonasal microbiome (Streptococcus, Haemophilus, Moraxella, and Pseudomonas). The prevalence of the three microbiotypes studied did not differ between healthy and diseased sinuses, but differences in their distribution were evident based on geography. We also describe a potential reciprocal relationship between Corynebacterium species and Staphylococcus aureus, suggesting that a certain microbial equilibrium between various players is reached in the sinuses. We validate our approach by applying it to a separate 16S rRNA gene sequence dataset of 97 sinus swabs from a different patient cohort. Sinonasal microbiotyping may prove useful in reducing the complexity of describing sinonasal microbiota. It may drive future studies aimed at modeling microbial interactions in the sinuses and in doing so may facilitate the development of a tailored patient-specific approach to the treatment of sinus disease in the future.

SUBMITTER: Bassiouni A

PROVIDER: S-EPMC7156599 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Microbiotyping the Sinonasal Microbiome.

Bassiouni Ahmed A Paramasivan Sathish S Shiffer Arron A Dillon Matthew R MR Cope Emily K EK Cooksley Clare C Ramezanpour Mahnaz M Moraitis Sophia S Ali Mohammad Javed MJ Bleier Benjamin S BS Callejas Claudio C Cornet Marjolein E ME Douglas Richard G RG Dutra Daniel D Georgalas Christos C Harvey Richard J RJ Hwang Peter H PH Luong Amber U AU Schlosser Rodney J RJ Tantilipikorn Pongsakorn P Tewfik Marc A MA Vreugde Sarah S Wormald Peter-John PJ Caporaso J Gregory JG Psaltis Alkis J AJ

Frontiers in cellular and infection microbiology 20200408

This study offers a novel description of the sinonasal microbiome, through an unsupervised machine learning approach combining dimensionality reduction and clustering. We apply our method to the International Sinonasal Microbiome Study (ISMS) dataset of 410 sinus swab samples. We propose three main sinonasal "microbiotypes" or "states": the first is <i>Corynebacterium</i>-dominated, the second is <i>Staphylococcus</i>-dominated, and the third dominated by the other core genera of the sinonasal m ...[more]

PMID: 32322561

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Project description:The chronic inflammatory nature of chronic rhinosinusitis (CRS) makes it a morbid condition for individuals with the disease and one whose pathogenesis is poorly understood. To date, proteomic approaches have been applied successfully in a handful of CRS studies. In this study we use a multifaceted approach, including proteomics (iTRAQ labeling) and microbiome (bacterial 16S rRNA gene sequencing) analyses of middle meatus swabs, as well as immune cell analysis of the underlying tissue, to investigate the host-microbe interaction in individuals with CRS (n = 10) and healthy controls (n = 9). Of the total 606 proteins identified in this study, seven were significantly (p < 0.05) more abundant and 104 were significantly lower in the CRS cohort compared with healthy controls. The majority of detected proteins (82% of proteins identified) were not significantly correlated with disease status. Elevated levels of blood and immune cell proteins in the CRS cohort, together with significantly higher numbers of B-cells and macrophages in the underlying tissue, confirmed the inflammatory status of CRS individuals. Protein PRRC2C and Ras-related protein (RAB14) (two of the seven elevated proteins) showed the biggest fold difference between the healthy and CRS groups. Validation of the elevated levels of these two proteins in CRS samples was provided by immunohistochemistry. Members of the bacterial community in the two study cohorts were not associated with PRRC2C, however members of the genus Moraxella did correlate with RAB14 (p < 0.0001, rho = -0.95), which is a protein involved in the development of basement membrane. In addition, significant correlations between certain members of the CRS bacterial community and 33 lower abundant proteins in the CRS cohort were identified. Members of the genera Streptococcus, Haemophilus and Veillonella were strongly correlated with CRS and were significantly associated with a number of proteins with varying functions. The results from this study reveal a strong association between the host and microbes in the sinonasal cavity. Proteins identified as associated with CRS could be new targets for drug therapies and biomarkers for assessment of treatment efficacy.

Project description:BackgroundPan-bacterial 16S rRNA microbiome surveys performed with massively parallel DNA sequencing technologies have transformed community microbiological studies. Current 16S profiling methods, however, fail to provide sufficient taxonomic resolution and accuracy to adequately perform species-level associative studies for specific conditions. This is due to the amplification and sequencing of only short 16S rRNA gene regions, typically providing for only family- or genus-level taxonomy. Moreover, sequencing errors often inflate the number of taxa present. Pacific Biosciences' (PacBio's) long-read technology in particular suffers from high error rates per base. Herein, we present a microbiome analysis pipeline that takes advantage of PacBio circular consensus sequencing (CCS) technology to sequence and error correct full-length bacterial 16S rRNA genes, which provides high-fidelity species-level microbiome data.ResultsAnalysis of a mock community with 20 bacterial species demonstrated 100% specificity and sensitivity with regard to taxonomic classification. Examination of a 250-plus species mock community demonstrated correct species-level classification of > 90% of taxa, and relative abundances were accurately captured. The majority of the remaining taxa were demonstrated to be multiply, incorrectly, or incompletely classified. Using this methodology, we examined the microgeographic variation present among the microbiomes of six sinonasal sites, by both swab and biopsy, from the anterior nasal cavity to the sphenoid sinus from 12 subjects undergoing trans-sphenoidal hypophysectomy. We found greater variation among subjects than among sites within a subject, although significant within-individual differences were also observed. Propiniobacterium acnes (recently renamed Cutibacterium acnes) was the predominant species throughout, but was found at distinct relative abundances by site.ConclusionsOur microbial composition analysis pipeline for single-molecule real-time 16S rRNA gene sequencing (MCSMRT, https://github.com/jpearl01/mcsmrt ) overcomes deficits of standard marker gene-based microbiome analyses by using CCS of entire 16S rRNA genes to provide increased taxonomic and phylogenetic resolution. Extensions of this approach to other marker genes could help refine taxonomic assignments of microbial species and improve reference databases, as well as strengthen the specificity of associations between microbial communities and dysbiotic states.

Dataset Information

Microbiotyping the Sinonasal Microbiome.

Publications

Microbiotyping the Sinonasal Microbiome.

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