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Additional file 1: Table S1. List of 106.66 capsule switch mutants used in experiments [1]. Figure S1. Capsule thicknesses of strains 106.66 and 51,114 with and without capsule in BHI + FCS. Figure S2. Growth pattern of S. pneumoniae wild type strain 106.66 and 106.66 capsule switch mutants representing high carriage serotypes (A, C) and low carriage serotypes (D, B) in BHI + FCS1 (A, B) and hCSF2 (C, D) over 40 h. Figure S3. Growth of S. pneumoniae wild type strain 106.66 in BHI + FCS, CDM3 [2], hCSF and a 1:1 mix of hCSF and BHI + FCS over 40 h. Figure S4. Colony forming units (CFU) after 6 h of growth in human CSF (hCSF) for South African strain 51,114 L (serotype 19F) and its spontaneous capsule loss mutant 51,114 S. Figure S5. Maximum OD values of wild type 106.66 and capsule switch mutants in BHI + FCS. Figure S6. Colony forming units (CFU) after 6 h of growth in human CSF (hCSF).

Results of haemolysis assay for four different serotype 1 clinical isolates. Figure S2. Showing no effect on viability of pneumococcal strain 106.66 by 1mg of liposomes after 30, 120 and 240 minutes of culture. (DOC 439 kb)

Interspecies interactions of the nasopharyngeal microbiota are likely to be involved in the pathogenesis of acute otitis media (AOM). Capturing the breadth of microbial interactions requires a detailed description of the microbiota during health and AOM.

Background Understanding the composition and dynamics of the upper respiratory tract microbiota in healthy infants is a prerequisite to investigate the role of the microbiota in patients with respiratory diseases. This is especially true in early life, when the immune system is in development. Objective We sought to describe the dynamics of the upper respiratory tract microbiota in healthy infants within the first year of life. Methods After exclusion of low-quality samples, microbiota characterization was performed by using 16S rDNA pyrosequencing of 872 nasal swabs collected biweekly from 47 unselected infants. Results Bacterial density increased and diversity decreased within the first year of life (R2 = 0.95 and 0.73, respectively). A distinct profile for the first 3 months of life was found with increased relative abundances of Staphlyococcaceae and Corynebacteriaceae (exponential decay: R2 = 0.94 and 0.96, respectively). In addition, relative bacterial abundance and composition differed significantly from summer to winter months. The individual composition of the microbiota changed with increasing time intervals between samples and was best modeled by an exponential function (R2 = 0.97). Within-subject dissimilarity in a 2-week time interval was consistently lower than that between subjects, indicating a personalized microbiota. Conclusion This study reveals age and seasonality as major factors driving the composition of the nasal microbiota within the first year of life. A subject's microbiota is personalized but dynamic throughout the first year. These data are indispensable to interpretation of cross-sectional studies and investigation of the role of the microbiota in both healthy subjects and patients with respiratory diseases. They might also serve as a baseline for future intervention studies., Journal of Allergy and Clinical Immunology, 135 (4), ISSN:0091-6749, ISSN:1097-6825

The Journal of Infectious Diseases, 205 (7), ISSN:0022-1899, ISSN:1537-6613

Previous studies have suggested a significantly higher diversity in the nasal microbiota of pig farmers compared to people having no contact with farm animals. However, the fate of this nasal microbiota specificity after farmers stop being in contact with the pig farm environment is unknown. The aim of this study was to investigate the change in the nasal microbiota of pig-farmers after the change of occupation. Methods Anterior and posterior nasal swabs were collected from seven people during employment on pig farms, and again after a period of at least 50 days after leaving the pig farm. Illumina MiSeq sequencing of 16S rRNA was conducted to characterize the dynamics of the nasal microbiota. The microbiota of actively working pig farmers was compared to microbiota after they had stopped working (ex-pig-farmers) and to control groups (cow farmers and non-exposed individuals). Results Following a prolonged period without exposure to pigs, ��-diversity of both anterior and posterior cavities dropped significantly. The composition of the microbiota of pig-farmers had a low inter-similarity with the non-exposed group while ex-pig-farmers were more similar to cow-farmers and the non-exposed group than to their own microbiota during pig farming.

Adjusted analysis of the association of different variables with the vaccine era using linear mixed effect models. Table S2. Oligotyping Output and Adjusted Analysis of the Association of Oligotype Abundances and the Vaccine Era Using Negative Binomial Regression and Linear Mixed Effect Models. Table S3. Shannon Diversity Index of Clusters. (DOCX 23 kb)

A novel non-culture based 16S rRNA Terminal Restriction Fragment Length Polymorphism (T-RFLP) method using the restriction enzymes Tsp509I and Hpy166II was developed for the characterization of the nasopharyngeal microbiota and validated using recently published 454 pyrosequencing data. 16S rRNA gene T-RFLP for 153 clinical nasopharyngeal samples from infants with acute otitis media (AOM) revealed 5 Tsp509I and 6 Hpy166II terminal fragments (TFs) with a prevalence of >10%. Cloning and sequencing identified all TFs with a prevalence >6% allowing a sufficient description of bacterial community changes for the most important bacterial taxa. The conjugated 7-valent pneumococcal polysaccharide vaccine (PCV-7) and prior antibiotic exposure had significant effects on the bacterial composition in an additive main effects and multiplicative interaction model (AMMI) in concordance with the 16S rRNA 454 pyrosequencing data. In addition, the presented T-RFLP method is able to discriminate S. pneumoniae from other members of the Mitis group of streptococci, which therefore allows the identification of one of the most important human respiratory tract pathogens. This is usually not achieved by current high throughput sequencing protocols. In conclusion, the presented 16S rRNA gene T-RFLP method is a highly robust, easy to handle and a cheap alternative to the computationally demanding next-generation sequencing analysis. In case a lot of nasopharyngeal samples have to be characterized, it is suggested to first perform 16S rRNA T-RFLP and only use next generation sequencing if the T-RFLP nasopharyngeal patterns differ or show unknown TFs.

Suplementary Figure. (PDF 1873 kb)

Adjusted analysis of the association of different variables with the vaccine era using linear mixed effect models. Table S2. Oligotyping Output and Adjusted Analysis of the Association of Oligotype Abundances and the Vaccine Era Using Negative Binomial Regression and Linear Mixed Effect Models. Table S3. Shannon Diversity Index of Clusters. (DOCX 23 kb)

It has been hypothesized that the environment can influence the composition of the nasal microbiota. However, the direct influence of pig farming on the anterior and posterior nasal microbiota is unknown. Using a cross-sectional design, pig farms (n=28) were visited in 2014-2015 and nasal swabs from 43 pig farmers and 56 pigs as well as 27 air samples taken in the vicinity of pig enclosure were collected. As controls, nasal swabs from 17 cow farmers and 26 non-animal exposed individuals were also included. Analyses of the microbiota were performed based on 16S rRNA amplicon sequencing and the DADA2 pipeline to define sequence variants (SVs). We found that pig farming is strongly associated with specific microbial signatures (including alpha- and beta-diversity), which are reflected in the microbiota of the human nose. Furthermore, the microbial communities were more similar within the same farm as compared to between the different farms, indicating a specific microbiota pattern for each pig farm. In total, there were 82 SVs that occurred significantly more abundantly in samples from pig farms than from cow farmers and non-exposed (i.e. the core pig farm microbiota). Of those, nine SVs were significantly associated with the posterior part of the humans' nose. The results strongly indicate that pig farming is associated with a distinct human nose microbiota. Finally, the community structures derived by the DADA2 pipeline showed an excellent agreement with the outputs of the mothur pipeline which was revealed by procrustes analyses.Importance The knowledge about the influence of animal keeping on the human microbiome is important. Previous research shows that pets are significantly affecting the microbial communities of humans. However, the effect of animal farming on the human microbiome is less clear although it is known that the air in farms, and in particular pig farms, is charged with high amounts of dust, bacteria and fungi. In this study we have simultaneously investigated the nasal microbiota of pigs, humans and the environment in pig farms. We reveal an enormous impact of pig farming on the human nasal microbiota which is far more pronounced as compared to cow farming. In addition, we have analyzed the airborne microbiota and found significant associations suggesting an animal-human transmission of the microbiota within pig farms. We also reveal that microbial patterns are farm-specific suggesting that the environment influences animals and humans in a similar manner.

Background Bacterial colonization of the upper airways is a prerequisite for subsequent invasive disease. With the introduction of the 7- and 13-valent pneumococcal conjugate vaccines (PCV7 and PCV13), changes in pneumococcal upper airway colonization have been described. It is, however, less evident whether the vaccines lead to compositional changes of the upper airway microbiota. Here, we performed a case–control study using samples from a longitudinal infant cohort from Switzerland. We compared pneumococcal carriage and the nasal microbiota within the first year of life of healthy infants vaccinated with either PCV7 (n = 20, born in 2010) or PCV13 (n = 21, born between 2011 and 2013). Nasal swabs were collected every second week (n = 763 in total). Pneumococcal carriage was analyzed by quantitative PCR of the pneumococcal-specific lytA gene. Analysis of the bacterial core microbiota was performed based on 16S rRNA sequencing and subsequent oligotyping. We exclusively performed oligotyping of the core microbiota members, which were defined as the five most abundant bacterial families (Moraxellaceae, Streptococcaceae, Staphylococcaceae, Corynebacteriaceae, and Pasteurellaceae). Linear mixed effect (LME) and negative binomial regression models were used for statistical analyses. Results We found a higher number of samples positive for pneumococcal carriage in PCV7- compared to PCV13-vaccinated infants (LME model; P = 0.01). In contrast, infants vaccinated in the PCV13 era had an increased alpha diversity as measured by the richness and the Shannon Diversity Index (LME model; P = 0.003 and P = 0.01, respectively). Accordingly, the PCV13 era was associated with clusters of a higher diversity than PCV7-associated clusters. Furthermore, infants vaccinated with PCV13 had a higher binary-based within-subject microbiota similarity, as well as a decreased Jensen–Shannon distance over time as compared to PCV7-vaccinated infants, indicating a higher microbiota stability in the PCV13 era (LME model and t test; P = 0.06 and P = 0.03, respectively). Conclusions We hypothesize that the higher diversity and stability of the upper airway microbiota in the PCV13 era is the result of the lower pneumococcal carriage rate. This seems to indicate that the nasal bacterial microbiota of infants has changed in recent years as compared to the beginning of this study., Microbiome, 5, ISSN:2049-2618

Suplementary Figure. (PDF 1873 kb)