Benchmarking laboratory processes to characterise low-biomass respiratory microbiota

Abstract 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

Nasopharyngeal Microbiota Profiles in Rural Venezuelan Children Are Associated With Respiratory and Gastrointestinal Infections

BACKGROUND: Recent research suggests that the microbiota affects susceptibility to both respiratory tract infections (RTIs) and gastrointestinal infections (GIIs). In order to optimize global treatment options, it is important to characterize microbiota profiles across different niches and geographic/socioeconomic areas where RTI and GII prevalences are high. METHODS: We performed 16S sequencing of nasopharyngeal swabs from 209 Venezuelan Amerindian children aged 6 weeks-59 months who were participating in a 13-valent pneumococcal conjugate vaccine (PCV13) study. Using random forest models, differential abundance testing, and regression analysis, we determined whether specific bacteria were associated with RTIs or GIIs and variation in PCV13 response. RESULTS: Microbiota compositions differed between children with or without RTIs (P = .018) or GIIs (P = .001). Several species were associated with the absence of infections. Some of these health-associated bacteria are also observed in developed regions, such as Corynebacterium (log2(fold change [FC]) = 3.30 for RTIs and log2(FC) = 1.71 for GIIs), while others are not commonly observed in developed regions, such as Acinetobacter (log2(FC) = 2.82 and log2(FC) = 5.06, respectively). Klebsiella spp. presence was associated with both RTIs (log2(FC) = 5.48) and GIIs (log2(FC) = 7.20). CONCLUSIONS: The nasopharyngeal microbiota of rural Venezuelan children included several bacteria that thrive in tropical humid climates. Interestingly, nasopharyngeal microbiota composition not only differed in children with an RTI but also in those with a GII, which suggests a reciprocal interplay between the 2 environments. Knowledge of region-specific microbiota patterns enables tailoring of preventive and therapeutic approaches

Maturation of the infant respiratory microbiota, environmental drivers and health consequences: a prospective cohort study

Rationale: Perinatal and postnatal influences are presumed important drivers of the early-life respiratory microbiota composition. We hypothesized that the respiratory microbiota composition and development in infancy is affecting microbiota stability and thereby resistance against respiratory tract infections (RTIs) over time. Objectives: To investigate common environmental drivers, including birth mode, feeding type, antibiotic exposure, and crowding conditions, in relation to respiratory tract microbiota maturation and stability, and consecutive risk of RTIs over the first year of life. Methods: In a prospectively followed cohort of 112 infants, we characterized the nasopharyngeal microbiota longitudinally from birth on (11 consecutive sample moments and the maximum three RTI samples per subject; in total, n = 1,121 samples) by 16S-rRNA gene amplicon sequencing. Measurements and Main Results: Using a microbiota-based machine-learning algorithm, we found that children experiencing a higher number of RTIs in the first year of life already demonstrate an aberrant microbial developmental trajectory from the first month of life on as compared with the reference group (0-2 RTIs/yr). The altered microbiota maturation process coincided with decreased microbial community stability, prolonged reduction of Corynebacterium and Dolosigranulum, enrichment of Moraxella very early in life, followed by later enrichment of Neisseria and Prevotella spp. Independent drivers of these aberrant developmental trajectories of respiratory microbiota members were mode of delivery, infant feeding, crowding, and recent antibiotic use. Conclusions: Our results suggest that environmental drivers impact microbiota development and, consequently, resistance against development of RTIs. This supports the idea that microbiota form the mediator between early-life environmental risk factors for and susceptibility to RTIs over the first year of life

Interaction between the nasal microbiota and S. pneumoniae in the context of live-attenuated influenza vaccine

Streptococcus pneumoniae is the main bacterial pathogen involved in pneumonia. Pneumococcal acquisition and colonization density is probably affected by viral co-infections, the local microbiome composition and mucosal immunity. Here, we report the interactions between live-attenuated influenza vaccine (LAIV), successive pneumococcal challenge, and the healthy adult nasal microbiota and mucosal immunity using an experimental human challenge model. Nasal microbiota profiles at baseline are associated with consecutive pneumococcal carriage outcome (non-carrier, low-dense and high-dense pneumococcal carriage), independent of LAIV co-administration. Corynebacterium/Dolosigranulum-dominated profiles are associated with low-density colonization. Lowest rates of natural viral co-infection at baseline and post-LAIV influenza replication are detected in the low-density carriers. Also, we detected the fewest microbiota perturbations and mucosal cytokine responses in the low-density carriers compared to non-carriers or high-density carriers. These results indicate that the complete respiratory ecosystem affects pneumococcal behaviour following challenge, with low-density carriage representing the most stable ecological state

Адаптация гидравлической модели водостока к бассейнам рек Дунай и Днестр

Гидравлическая модель водостока адаптирована к бассейну рек Дунай и Днестр. По данным орографии, атмосферных осадках или поверхностном стоке она позволяет рассчитывать объемы, расходы и уровни воды с пространственным разрешением 1 км. В модели возможно использование данные об экосистемах на земной поверхности, типах почвы. По данным наблюдений стока оценены среднемесячные величины расходов рек, которые соответствуют наблюдениям, что позволяет применять модель в дальнейших оценках стока, наносов и т.д.Hydraulic model of water inflow is adapted to the Danube and the Dniester rivers basin. According to the orography, precipitation and surface inflow data it permits to calculate water volumes, discharges and levels with spatial resolution 1 km. It is possible to use the data on ecosystems on the ground surface, types of soil in the model. According to the observations data of the inflow the average monthly values of river discharges corresponding to the observations are estimated. It permits to apply the model in the further estimations of inflow, alluvia e t.c