Variability and Diversity of Nasopharyngeal Microbiota in Children: A Metagenomic Analysis

Department of Paediatric Infectious Diseases and Immunology, University Medical Center Utrecht-Wilhelmina Children's Hospital, Utrecht, The Netherlands.
PLoS ONE (Impact Factor: 3.23). 02/2011; 6(2):e17035. DOI: 10.1371/journal.pone.0017035
Source: PubMed


The nasopharynx is the ecological niche for many commensal bacteria and for potential respiratory or invasive pathogens like Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis. Disturbance of a balanced nasopharyngeal (NP) microbiome might be involved in the onset of symptomatic infections with these pathogens, which occurs primarily in fall and winter. It is unknown whether seasonal infection patterns are associated with concomitant changes in NP microbiota. As young children are generally prone to respiratory and invasive infections, we characterized the NP microbiota of 96 healthy children by barcoded pyrosequencing of the V5–V6 hypervariable region of the 16S-rRNA gene, and compared microbiota composition between children sampled in winter/fall with children sampled in spring. The approximately 1000000 sequences generated represented 13 taxonomic phyla and approximately 250 species-level phyla types (OTUs). The 5 most predominant phyla were Proteobacteria (64%), Firmicutes (21%), Bacteroidetes (11%), Actinobacteria (3%) and Fusobacteria (1,4%) with Moraxella, Haemophilus, Streptococcus, Flavobacteria, Dolosigranulum, Corynebacterium and Neisseria as predominant genera. The inter-individual variability was that high that on OTU level a core microbiome could not be defined. Microbiota profiles varied strongly with season, with in fall/winter a predominance of Proteobacteria (relative abundance (% of all sequences): 75% versus 51% in spring) and Fusobacteria (absolute abundance (% of children): 14% versus 2% in spring), and in spring a predominance of Bacteroidetes (relative abundance: 19% versus 3% in fall/winter, absolute abundance: 91% versus 54% in fall/winter), and Firmicutes. The latter increase is mainly due to (Brevi)bacillus and Lactobacillus species (absolute abundance: 96% versus 10% in fall/winter) which are like Bacteroidetes species generally related to healthy ecosystems. The observed seasonal effects could not be attributed to recent antibiotics or viral co-infection.
The NP microbiota of young children is highly diverse and appears different between seasons. These differences seem independent of antibiotic use or viral co-infection.

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Available from: Debby Bogaert
    • "Amplicon library preparation. Generation of a PCR amplicon library was performed by amplification of the 16S ribosomal RNA gene V5–V7 hypervariable region as previously described (Bogaert et al., 2011), except templates containing ⩽ 10 pg μl − 1 DNA were cycled 35 times instead of 30 times for adequate amplicon recovery. The library was sequenced in three runs with the 454 GS-FLX-Titanium Sequencer "
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    ABSTRACT: Bacterial pneumonia is a major cause of morbidity and mortality in elderly. We hypothesize that dysbiosis between regular residents of the upper respiratory tract (URT) microbiome, that is balance between commensals and potential pathogens, is involved in pathogen overgrowth and consequently disease. We compared oropharyngeal microbiota of elderly pneumonia patients (n = 100) with healthy elderly (n = 91) by 16S-rRNA-based sequencing and verified our findings in young adult pneumonia patients (n = 27) and young healthy adults (n = 187). Microbiota profiles differed significantly between elderly pneumonia patients and healthy elderly (PERMANOVA, Po0.0005). Highly similar differences were observed between microbiota profiles of young adult pneumonia patients and their healthy controls. Clustering resulted in 11 (sub)clusters including 95% (386/405) of samples. We observed three microbiota profiles strongly associated with pneumonia (Po0.05) and either dominated by lactobacilli (n = 11), Rothia (n = 51) or Streptococcus (pseudo)pneumoniae (n = 42). In contrast, three other microbiota clusters (in total n=183) were correlated with health (Po0.05) and were all characterized by more diverse profiles containing higher abundances of especially Prevotella melaninogenica, Veillonella and Leptotrichia. For the remaining clusters (n = 99), the association with health or disease was less clear. A decision tree model based on the relative abundance of five bacterial community members in URT microbiota showed high specificity of 95% and sensitivity of 84% (89% and 73%, respectively, after cross-validation) for differentiating pneumonia patients from healthy individuals. These results suggest that pneumonia in elderly and young adults is associated with dysbiosis of the URT microbiome with bacterial overgrowth of single species and absence of distinct anaerobic bacteria. Whether the observed microbiome changes are a cause or a consequence of the development of pneumonia or merely coincide with disease status remains a question for future research.
    No preview · Article · Jul 2015 · The ISME Journal
    • "Amplicon library preparation. Generation of a PCR amplicon library was performed by amplification of the 16S ribosomal RNA gene V5–V7 hypervariable region as previously described (Bogaert et al., 2011), except templates containing ⩽ 10 pg μl − 1 DNA were cycled 35 times instead of 30 times for adequate amplicon recovery. The library was sequenced in three runs with the 454 GS-FLX-Titanium Sequencer "
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    ABSTRACT: Respiratory tract infections are a major global health concern, accounting for high morbidity and mortality, especially in young children and elderly individuals. Traditionally, highly common bacterial respiratory tract infections, including otitis media and pneumonia, were thought to be caused by a limited number of pathogens including Streptococcus pneumoniae and Haemophilus influenzae. However, these pathogens are also frequently observed commensal residents of the upper respiratory tract (URT) and form-together with harmless commensal bacteria, viruses and fungi-intricate ecological networks, collectively known as the 'microbiome'. Analogous to the gut microbiome, the respiratory microbiome at equilibrium is thought to be beneficial to the host by priming the immune system and providing colonization resistance, while an imbalanced ecosystem might predispose to bacterial overgrowth and development of respiratory infections. We postulate that specific ecological perturbations of the bacterial communities in the URT can occur in response to various lifestyle or environmental effectors, leading to diminished colonization resistance, loss of containment of newly acquired or resident pathogens, preluding bacterial overgrowth, ultimately resulting in local or systemic bacterial infections. Here, we review the current body of literature regarding niche-specific upper respiratory microbiota profiles within human hosts and the changes occurring within these profiles that are associated with respiratory infections. © 2015 The Author(s) Published by the Royal Society. All rights reserved.
    No preview · Article · Jul 2015 · Philosophical Transactions of The Royal Society B Biological Sciences
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    • "Functioning as positive controls, these data were essential to develop a robust pipeline for pathogen identification. Each sample was composed of human, bacterial, and viral sequences mimicking the microbiota found in sequencing data from nasopharyngeal samples during a respiratory tract infection [31, 32]. Specifically, 10 million 100-base reads were generated for each sample with 90% of reads originating from the host transcriptome (human RNA), 9% from bacterial genomes, and 1% from viral genomes. "
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    ABSTRACT: BackgroundThe use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens.ResultsHere we present Clinical PathoScope, a pipeline to rapidly and accurately remove host contamination, isolate microbial reads, and identify potential disease-causing pathogens. We have accomplished three essential tasks in the development of Clinical PathoScope. First, we developed an optimized framework for pathogen identification using a computational subtraction methodology in concordance with read trimming and ambiguous read reassignment. Second, we have demonstrated the ability of our approach to identify multiple pathogens in a single clinical sample, accurately identify pathogens at the subspecies level, and determine the nearest phylogenetic neighbor of novel or highly mutated pathogens using real clinical sequencing data. Finally, we have shown that Clinical PathoScope outperforms previously published pathogen identification methods with regard to computational speed, sensitivity, and specificity.ConclusionsClinical PathoScope is the only pathogen identification method currently available that can identify multiple pathogens from mixed samples and distinguish between very closely related species and strains in samples with very few reads per pathogen. Furthermore, Clinical PathoScope does not rely on genome assembly and thus can more rapidly complete the analysis of a clinical sample when compared with current assembly-based methods. Clinical PathoScope is freely available at: supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-262) contains supplementary material, which is available to authorized users.
    Full-text · Article · Aug 2014 · BMC Bioinformatics
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