The nasopharyngeal microbiota of African children and lower respiratory tract infection

非洲儿童呼吸道微生物群研究中心（ReMAC） 研究项目2：非洲儿童鼻咽微生物群和下呼吸道 感染 具体目标 鼻咽部（NP）是许多导致下呼吸道疾病的关键细菌病原体的储存库， 儿童下呼吸道感染（LRTI）是呼吸道病毒的入口，也具有潜在的致病性 真菌来自我们小组和其他人的数据表明，生态失调的模式可能与 儿童呼吸道感染的发展，但这还没有得到全面的研究， 并解释了微生物群的所有组成部分。也有证据表明，细菌微生物群可能 调节病毒性LRTI的严重程度。因此，我们建议研究细菌，病毒和真菌成分的 NP微生物群，以确定在LRTI之前的时期内NP微生物群的组成是否 在LRTI的时间是不同的儿童谁开发与没有开发LRTI和不同的 发生严重LRTI与非严重LRTI的儿童之间的差异。我们还希望探讨 LRTI后NP微生物群的组成可能与复发性LRTI和LRTI的存在有关。 NP中的抗微生物剂抗性细菌。我们将利用现有的NP标本和元数据， 南非的抽样队列，LRTI的发病率非常高，以及来自横断面的标本 冈比亚和马拉维的LRTI病例对照研究。 我们将使用扩增子测序来确定微生物群的细菌和真菌组分，靶向 病毒成分的核酸扩增测试，以及鸟枪宏基因组测序， 补充，验证和扩展我们的研究结果，并确定新的目标。我们会做选择性培养 NP标本中的抗菌药物耐药菌。我们将应用统计模型，如贝叶斯状态空间 模型和Dirichlet多项式计数模型，以比较微生物的混合物和随时间推移的变化， 每个识别的比较组。预测因子的重要性将通过使用各种机器进行评估 学习模式 对NP微生物群与LRTI的关联的更好理解可能会导致 诊断或风险预测的新工具。这可能包括预测哪些儿童有高风险的工具。 LRTI，识别有进展为严重LRTI风险的儿童和有复发LRTI风险的儿童。此外，本发明还提供了一种方法， 了解NP在LRTI或复发性LRTI发病机制中的可能作用， 鉴定新的策略以预防LRTI、进展为严重LRTI或复发性LRTI。这些可能 包括靶向益生菌疗法、新型疫苗或疫苗策略以及治疗LRTI新疗法。

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.