Evidence of microbiota dysbiosis in chronic rhinosinusitis

Hoggard, Michael; Biswas, Kristi; Zoing, Melissa; Mackenzie, Brett Wagner; Taylor, Michael W.; Douglas, Richard

doi:10.1002/alr.21871

Cited by 151 publications

(179 citation statements)

References 39 publications

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“…Sequencing data from manuscripts that met these criteria and were published before December 31, 2015 were included in this meta-analysis. Supporting Information Table S1 provides a summary of the data that were included in this study (Stephenson et al, 2010;Feazel et al, 2012;Aurora et al, 2013;Liu et al, 2013;Ramakrishnan et al, 2013;Choi et al, 2014;Bassiouni et al, 2015;Biswas et al, 2015;Hauser et al, 2015;Joss et al, 2016;Kaspar et al, 2015;Kim et al, 2015;Liu et al, 2015;Merkley et al, 2015;, as well as relevant sequencing studies that were published after the inclusion date (Cleland et al, 2016;Hauser et al, 2016;Hoggard et al, 2016;Jain et al, 2016;Wos-Oxley et al, 2016) (Supporting Information Table S1B).…”

Section: Data Acquisitionmentioning

confidence: 99%

Bacterial community collapse: a meta‐analysis of the sinonasal microbiota in chronic rhinosinusitis

Mackenzie

Waite

Hoggard

et al. 2017

Environmental Microbiology

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Chronic rhinosinusitis (CRS) is a common, debilitating condition characterized by long-term inflammation of the nasal cavity and paranasal sinuses. The role of the sinonasal bacteria in CRS is unclear. We conducted a meta-analysis combining and reanalysing published bacterial 16S rRNA sequence data to explore differences in sinonasal bacterial community composition and predicted function between healthy and CRS affected subjects. The results identify the most abundant bacteria across all subjects as Staphylococcus, Propionibacterium, Corynebacterium, Streptococcus and an unclassified lineage of Actinobacteria. The meta-analysis results suggest that the bacterial community associated with CRS patients is dysbiotic and ecological networks fostering healthy communities are fragmented. Increased dispersion of bacterial communities, significantly lower bacterial diversity, and increased abundance of members of the genus Corynebacterium are associated with CRS. Increased relative abundance and diversity of other members belonging to the phylum Actinobacteria and members from the genera Propionibacterium differentiated healthy sinuses from those that were chronically inflamed. Removal of Burkholderia and Propionibacterium phylotypes from the healthy community dataset was correlated with a significant increase in network fragmentation. This meta-analysis highlights the potential importance of the genera Burkholderia and Propionibacterium as gatekeepers, whose presence may be important in maintaining a stable sinonasal bacterial community.

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Section: Data Acquisitionmentioning

confidence: 99%

Bacterial community collapse: a meta‐analysis of the sinonasal microbiota in chronic rhinosinusitis

Mackenzie

Waite

Hoggard

et al. 2017

Environmental Microbiology

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163

167

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“…The V3-V4 region of the bacterial 16S rRNA gene was amplified using primers 341F and 806R (47), with Nextera DNA library Prep Kit adapters attached. PCR reactions, amplification conditions and purifications were carried out as previously described (12). In brief, genomic DNA (~100 ng) from each sample was amplified in duplicate PCR reactions of 35 cycles then pooled to a final volume of 50 μL.…”

Section: Methodsmentioning

confidence: 99%

“…Sequences were merged and quality filtered in USEARCH (version 8.0) with default settings as previously described (12). OTU clustering based on a 97% 16S rRNA gene sequence similarity threshold was performed using the UCLUST algorithm in USEARCH (48).…”

Section: Methodsmentioning

confidence: 99%

“…Each distinct microbial state was dominated by one bacterial family and associated with a unique clinical and host immune response. Previous studies have found that large interpersonal variations in the sino-nasal microbiome of CRS patients (subtyped based on clinical diagnosis) can make classification difficult (12). Accordingly, a distinct advantage of stratifying patients based on microbial community profiles is that it will help to resolve the microbial heterogeneity of CRS and is a step towards implementation of a precision medicine approach.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of subtyping approaches and the underlying drivers of microbial signatures for chronic rhinosinusitis

Biswas

Cavubati

Gunaratna

et al. 2018

Preprint

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23Chronic rhinosinusitis (CRS) is a heterogeneous condition characterised by persistent sinus 24 inflammation and microbial dysbiosis. This study aimed to identify clinically relevant sub-25 groups of CRS patients based on distinct microbial signatures, with a comparison to the 26 commonly used phenotypic subgrouping approach. The underlying drivers of these distinct 27 microbial clusters were also investigated, together with associations with epithelial barrier 28 integrity. 29Sinus biopsies were collected from CRS patients (n=23), and disease controls (n=8). 30 Expression of 42 tight junction genes was evaluated using quantitative PCR, together with 31 microbiota analysis and immunohistochemistry for measuring mucosal integrity and 32 inflammation. 33 CRS patients clustered into two distinct microbial sub-groups using probabilistic modelling 34 Dirichlet (DC) multinomial mixtures. DC1 exhibited significantly reduced bacterial diversity, 35 increased dispersion, and was dominated by Pseudomonas, Haemophilus, and 36Achromobacter. DC2 had significantly elevated B-cells, incidence of nasal polyps, and 37 higher numbers of Anaerococcus, Megasphaera, Prevotella, Atopobium, and 38Propionibacterium. In addition, each DC exhibited distinct tight junction gene and protein 39 expression profiles compared with controls. Stratifying CRS patients based on clinical 40 phenotypic subtypes (absence or presence of nasal polyps (CRSsNP or CRSwNP 41 respectively) or with cystic fibrosis (CRSwCF)) did account for a larger proportion of the 42 variation in the microbial dataset compared with DC groupings. However, no significant 43 differences between CRSsNP and CRSwNP cohorts were observed for inflammatory 44 markers, beta-dispersion and alpha diversity measures. 453In conclusion, both approaches used for stratifying CRS patients had benefits and pitfalls, but 46 DC clustering did provide greater resolution when studying tight junction impairment. Future 47 studies in CRS should give careful consideration into the patient subtyping approach used. 48 49 50 Importance 51Chronic rhinosinusitis (CRS) is a major human health problem that significantly reduces 52 quality of life. While various microbes have been implicated, there is no clear understanding 53 of the role they play in CRS pathogenesis. Another equally important observation made for 54 CRS patients is that the epithelial barrier in the sino-nasal cavity is defective. Finding a 55 robust approach to subtype CRS patients would be the first step towards unravelling the 56 pathogenesis of this heterogeneous condition. Previous work has explored stratification based 57 on clinical presentation of the disease (with or without polyps), inflammatory markers, 58 pathology, or microbial composition. Comparing between the different stratification 59 approaches used in these studies has not been possible due to different cohorts, analytical 60 methods, or sample sites used. In this study, two approaches of subtyping CRS patients were 61 compared and the underlying drivers of the hetero...

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“…For the medical treatment of CRS, antibiotics such as penicillins/betalactams and macrolides were prescribed in about 70% of CRS visits(Smith et al 2013). However, most of previous metagenomic analyses(Aurora et al 2013; Choi et al 2014; Biswas et al 2015; Ramakrishnan et al 2015; Biswas et al 2017; Cope et al 2017; Hoggard et al 2017; Jain et al 2017; Lal et al 2017; Ramakrishnan et al 2017; Chalermwatanachai et al 2018; Copeland et al 2018; Koeller et al 2018; Mahdavinia et al 2018; Biswas et al 2019; Gan et al 2019; Paramasivan et al 2019; Rom et al 2019) examining CRS excluded patients who had taken antibiotics within approximately one month prior to sampling. Here, we performed metagenomics and proteomics analyses using nasal secretions in healthy control and CRS patients.…”

Section: Introductionmentioning

confidence: 99%

Antibiotic-dependent relationships between nasal microbiome and secreted proteome in chronic rhinosinusitis and nasal polyps

Kim

et al. 2020

Preprint

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BackgroundAntibiotics are commonly prescribed to treat chronic rhinosinusitis (CRS); however, the effects of antibiotics on the microbiome and secreted proteome remain unknown in regard to CRS.ObjectiveWe analyzed the effects of antibiotics on the nasal microbiome and secreted proteome in the context of CRS using multi-omic analysis.MethodsNasal secretions were collected from 29 control, 30 CRS patients without nasal polyps (CRSsNP), and 40 CRS patients with nasal polyps (CRSwNP). A total of 99 subjects were divided into two groups that included subjects who had taken antibiotics 3 months prior to sampling (ABX) and those who had not (NABX). We performed 16S rDNA sequence analyses and Orbitrap mass spectrometry-based proteomic analyses in data-independent acquisition (DIA) on the nasal secretions. Spearman correlation was used to assess the correlations between the nasal microbiome and secreted proteome.ResultsWe observed a strong association between the nasal microbiome and secreted proteome according to disease status. Antibiotic use reduced differences in the microbial community and secreted proteome according to disease status. Interestingly, in nasal polyp (NP) patients, antibiotics exhibited strong effects not only on the nasal microbiome and the secreted proteome but also on their associations. Additionally, their correlations were strengthened in subjects who had taken antibiotics.ConclusionIntegrative analyses revealed that the correlations between the microbiome and the secreted proteome could be altered and strengthened in subjects who used antibiotics. These findings provide novel insight into the effects of antibiotics on the nasal environment and the host responses in CRS.

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Evidence of microbiota dysbiosis in chronic rhinosinusitis

Cited by 151 publications

References 39 publications

Bacterial community collapse: a meta‐analysis of the sinonasal microbiota in chronic rhinosinusitis

Bacterial community collapse: a meta‐analysis of the sinonasal microbiota in chronic rhinosinusitis

Comparison of subtyping approaches and the underlying drivers of microbial signatures for chronic rhinosinusitis

Antibiotic-dependent relationships between nasal microbiome and secreted proteome in chronic rhinosinusitis and nasal polyps

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