Host and environmental factors shape upper airway microbiota and respiratory health across the human lifespan.

Our understanding of the normal variation in the upper respiratory tract (URT) microbiota across the human lifespan and how these relate to host, environment, and health is limited. We studied the microbiota of 3,104 saliva (<10 year-olds)/oropharynx (≥10 year-olds) and 2,485 nasopharynx samples of 3,160 Dutch individuals 0-87 years of age, participating in a cross-sectional population-wide study (PIENTER-3) using 16S-rRNA sequencing. The microbiota composition was strongly related to age, especially in the nasopharynx, with maturation occurring throughout childhood and adolescence. Clear niche- and age-specific associations were found between the microbiota composition and host/environmental factors and health outcomes. Among others, social interaction, sex, and season were associated with the nasopharyngeal microbial community. By contrast, the oral microbiota was more related to antibiotics, tobacco, and alcohol use. We present an atlas of the URT microbiota across the lifespan in association with environment and health, establishing a baseline for future research.

Detection of Neisseria meningitidis in saliva and oropharyngeal samples from college students.

Carriage of Neisseria meningitidis is an accepted endpoint in monitoring meningococcal vaccines effects. We have assessed N. meningitidis and vaccine-type genogroup carriage prevalence in college students at the time of MenACWY vaccine introduction in the Netherlands, and evaluated the feasibility of saliva sampling for the surveillance of carriage. For this, paired saliva and oropharyngeal samples collected from 299 students were cultured for meningococcus. The DNA extracted from all bacterial growth was subjected to qPCRs quantifying meningococcal and genogroup-specific genes presence. Samples negative by culture yet positive for qPCR were cultured again for meningococcus. Altogether 74 (25%) of students were identified as meningococcal carrier by any method. Sixty-one students (20%) were identified as carriers with qPCR. The difference between number of qPCR-positive oropharyngeal (n = 59) and saliva (n = 52) samples was not significant (McNemar's test, p = 0.07). Meningococci were cultured from 72 students (24%), with a significantly higher (p < 0.001) number of oropharyngeal (n = 70) compared with saliva (n = 54) samples. The prevalence of genogroups A, B, C, W, and Y was none, 9%, 1%, 1% and 6%, respectively, and 8% of students carried MenACWY vaccine-type genogroup meningococci. Saliva is easy to collect and when combined with qPCR detection can be considered for meningococcal carriage studies.

Benchmarking laboratory processes to characterise low-biomass respiratory microbiota.

The low biomass of respiratory samples makes it difficult to accurately characterise the microbial community composition. PCR conditions and contaminating microbial DNA can alter the biological profile. The objective of this study was to benchmark the currently available laboratory protocols to accurately analyse the microbial community of low biomass samples. To study the effect of PCR conditions on the microbial community profile, we amplified the 16S rRNA gene of respiratory samples using various bacterial loads and different number of PCR cycles. Libraries were purified by gel electrophoresis or AMPure XP and sequenced by V2 or V3 MiSeq reagent kits by Illumina sequencing. The positive control was diluted in different solvents. PCR conditions had no significant influence on the microbial community profile of low biomass samples. Purification methods and MiSeq reagent kits provided nearly similar microbiota profiles (paired Bray-Curtis dissimilarity median: 0.03 and 0.05, respectively). While profiles of positive controls were significantly influenced by the type of dilution solvent, the theoretical profile of the Zymo mock was most accurately analysed when the Zymo mock was diluted in elution buffer (difference compared to the theoretical Zymo mock: 21.6% for elution buffer, 29.2% for Milli-Q, and 79.6% for DNA/RNA shield). Microbiota profiles of DNA blanks formed a distinct cluster compared to low biomass samples, demonstrating that low biomass samples can accurately be distinguished from DNA blanks. In summary, to accurately characterise the microbial community composition we recommend 1. amplification of the obtained microbial DNA with 30 PCR cycles, 2. purifying amplicon pools by two consecutive AMPure XP steps and 3. sequence the pooled amplicons by V3 MiSeq reagent kit. The benchmarked standardized laboratory workflow presented here ensures comparability of results within and between low biomass microbiome studies.