Little is known regarding the nasal microbiome in early childhood and the impact of respiratory infection on the infants’ nasal microbial composition. Here we investigated the temporal dynamics and diversity of the bacterial composition in the anterior nares in children attending daycare centers. For our investigation, we considered 76 parental-taken nasal swabs of 26 children (aged 13 to 36 months) collected over a study period of 3 months. Overall, there was no significant age-specific effect or seasonal shift in the nasal bacterial community structure. In a sub-sample of 14 healthy children the relative abundance of individual taxa as well as the overall diversity did not reveal relevant changes, indicating a stable community structure over the entire study period. Moreover, the nasal bacterial profiles clustered subject-specific with Bray-Curtis similarities being elevated in intra-subject calculations compared to between-subject calculations. The remaining subset of 12 children provided samples taken during picornavirus infection (PVI) and either before or after a PVI. We detected an association between the relative abundance of members of the genus Streptococcus and PV when comparing both (i) samples taken during PVI with samples out of 14 healthy children and (ii) samples taken during PVI with samples taken after PVI within the same individual. In addition, the diversity was higher during PVI than after infection. Our findings suggest that a personalized structure of the nasal bacterial community is established already in early childhood and could be detected over a timeframe of 3 months. Studies following infants over a longer time with frequent swab sampling would allow investigating whether certain parameter of the bacterial community, such as the temporal variability, could be related to viral infection.

The opportunistic pathogen Pseudomonas aeruginosa is capable of establishing severe and persistent infections in various eukaryotic hosts. It encodes a wide array of virulence factors and employs several strategies to evade immune detection. In the present study we screened the Harvard Medical School transpsoson mutant library of P. aeruginosa PA14 for bacterial factors that modulate interleukin-8 responses in A549 human airway epithelial cells. We found that in addition to the previously identified alkaline protease AprA, the elastase LasB is capable of degrading exogenous flagellin under calcium replete conditions and prevents flagellin-mediated immune recognition. Our results indicate that the production of two proteases with anti-flagellin activity provides a failsafe mechanism for P. aeruginosa to ensure the maintenance of protease-dependent immune modulating functions.

Metastases are the major cause of death from cancer. Thus, understanding the regulation of metastatic processes is of utmost importance. Here we show that mice with impaired type I IFN signaling (Ifnar1^-/-) develop more lung metastases in the 4T1 mammary and LLC lung carcinoma model, compared to control mice. In Ifnar1^-/- mice, higher metastasis load is accompanied by massive neutrophil accumulation in lungs. Elevated G-CSF levels in serum and enhanced CXCR2 expression on neutrophils are most likely responsible for this phenomenon. Lung infiltrating neutrophils facilitate an improved pre-metastatic niche formation, supporting more efficient tumor cell extravasation and proliferation in this organ. This is due to the enhanced expression of pro-metastatic proteins, like Bv8, MMP9, S100A8 and S100A9. Development of pre-metastatic niche together with reduced neutrophil cytotoxicity against tumor cells results in enhanced metastatic processes in Ifnar1^-/- mice. Overall, our findings describe a novel role for IFN during metastasis development and suggest that new treatment strategies should be considered for prevention of metastasis formation in patients.

The oral microbiome plays a key role for caries, periodontitis, and systemic diseases. A method for rapid, high-resolution, robust taxonomic profiling of subgingival bacterial communities for early detection of periodontitis biomarkers would therefore be a useful tool for individualized medicine. Here, we used Illumina sequencing of the V1-V2 and V5-V6 hypervariable regions of the 16S rRNA gene. A sample stratification pipeline was developed in a pilot study of 19 individuals, 9 of whom had been diagnosed with chronic periodontitis. Five hundred twenty-three operational taxonomic units (OTUs) were obtained from the V1-V2 region and 432 from the V5-V6 region. Key periodontal pathogens like Porphyromonas gingivalis , Treponema denticola , and Tannerella forsythia could be identified at the species level with both primer sets. Principal coordinate analysis identified two outliers that were consistently independent of the hypervariable region and method of DNA extraction used. The linear discriminant analysis (LDA) effect size algorithm (LEfSe) identified 80 OTU-level biomarkers of periodontitis and 17 of health. Health- and periodontitis-related clusters of OTUs were identified using a connectivity analysis, and the results confirmed previous studies with several thousands of samples. A machine learning algorithm was developed which was trained on all but one sample and then predicted the diagnosis of the left-out sample (jackknife method). Using a combination of the 10 best biomarkers, 15 of 17 samples were correctly diagnosed. Training the algorithm on time-resolved community profiles might provide a highly sensitive tool to detect the onset of periodontitis.

Worldwide approximately 7,000 rare diseases have been identified. Accordingly, 4 million individuals live with a rare disease in Germany. The mean time to diagnosis is about 6 years and patients receive several incorrect diagnoses during this time. A multiplicity of factors renders diagnosing a rare disease extremely difficult. Detection of shared phenomena among individuals with different rare diseases could assist the diagnostic process. In order to explore the demand for diagnostic support and to obtain the commonalities among patients, a nationwide Delphi survey of centers for rare diseases and patient groups was conducted. A two-step Delphi survey was conducted using web-based technologies in all centers for rare diseases in Germany. Moreover, the leading patient support group, the German foundation for rare diseases (ACHSE), was contacted to involve patients as experts in their disease. In the survey the experts were invited to name rare diseases with special need for diagnostic improvement. Secondly, communal experiences of affected individuals were collected. 166 of 474 contacted experts (35%) participated in the first round of the Delphi process and 95 of 166 (57%) participated in the second round. Metabolic (n = 74) and autoimmune diseases (n = 39) were ranked the highest for need for diagnostic support. For three diseases (i.e. scleroderma, Pompe’s disease, and pulmonary arterial hypertension), a crucial need for diagnostic support was explicitly stated. A typical experience of individuals with a rare disease was stigmatization of having psychological or psychosomatic problems. In addition, most experts endured an ‘odyssey’ of seeing many different medical specialists before a correct diagnosis (n = 38) was confirmed. There is need for improving the diagnostic process in individuals with rare diseases. Shared experiences in individuals with a rare disease were observed, which could possibly be utilized for diagnostic support in the future.