The nasopharyngeal microbiota of African children and lower respiratory tract infection

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Center for Research on the Respiratory Microbiota of African Children (ReMAC) Research Project 2: The nasopharyngeal microbiota of African children and lower respiratory tract infection Specific aims The nasopharynx (NP) is the reservoir for many key bacterial pathogens responsible for lower respiratory tract infection (LRTI) in children, is the portal of entry for respiratory viruses and also harbors potentially pathogenic fungi. Data from our groups and others have demonstrated patterns of dysbiosis that may be associated with the development of respiratory tract infections in children, however this has not been studied comprehensively, and accounting for all components of the microbiota. There is also evidence that the bacterial microbiota may modulate the severity of viral LRTI. We therefore propose to study bacterial, viral and fungal components of the NP microbiota in order to determine whether the composition of the NP microbiota in the period before LRTI and at the time of LRTI is different between children who developed vs. did not develop LRTI and different between children who developed severe LRTI vs. non-severe LRTI. We also wish to explore whether the composition of the NP microbiota after LRTI may be associated with recurrent LRTI and with the presence of antimicrobial resistant bacteria in the NP. We will utilize existing NP specimens and metadata from a frequently sampled cohort in South Africa, with a very high incidence of LRTI, as well as specimens from cross-sectional case control studies of LRTI in The Gambia and Malawi. We will use amplicon sequencing to define the bacterial and fungal components of the microbiota, targeted nucleic acid amplification testing for the viral component, and shotgun metagenomic sequencing to complement, validate and extend our findings and identify novel targets. We will do selective culture to identify antimicrobial resistant bacteria in NP specimens. We will apply statistical models such as Bayesian state space models and Dirichlet multinomial count models to compare the mixture of microorganisms and shift over time in each of the identified comparison groups. Predictor importance will be evaluated by using a variety of machine learning models. An improved understanding of the association of the NP microbiota with LRTI may lead to the development of new tools for diagnosis or risk prediction. This could include tools to predict which children are at high risk of LRTI, identify children at risk of progression to severe LRTI and children at risk of recurrent LRTI. In addition, understanding the possible role of the NP in the pathogenesis of LRTI or recurrent LRTI may lead to the identification of novel strategies to prevent LRTI, progression to severe LRTI or recurrent LRTI. These could include targeted probiotic therapy, novel vaccines or vaccine strategies and new therapeutics to treat LRTI.