The nasopharyngeal microbiota of African children and lower respiratory tract infection

非洲儿童呼吸道微生物区系研究中心 研究项目2：非洲儿童和下呼吸道的鼻咽微生物区系 感染 具体目标 鼻咽(Np)是许多引起下呼吸道的主要细菌病原体的储存库。 儿童感染(LRTI)是呼吸道病毒进入的门户，也有潜在的致病性 真菌。来自我们小组和其他人的数据已经证明了生物失调的模式，这可能与 儿童呼吸道感染的发展，然而这还没有得到全面的研究， 并占微生物区系的所有组成部分。也有证据表明，细菌微生物群可能 调节病毒LRTI的严重程度。因此，我们建议研究细菌、病毒和真菌的成分 NP微生物区系，以确定在LRTI之前的时期NP微生物区系的组成 而在LRTI发生时，发生LRTI的儿童与未发生LRTI的儿童之间存在差异 发生严重下呼吸道感染和非严重下呼吸道感染的儿童之间的差异。我们还想探讨一下， LRTI后NP微生物区系的组成可能与复发的LRTI有关，并与 NP中的抗菌素耐药性细菌。我们将利用现有的NP样本和元数据，这些样本来自 南非样本队列，LRTI发生率非常高，以及横断面样本 冈比亚和马拉维的下呼吸道感染病例对照研究。 我们将使用扩增子测序来确定微生物区系的细菌和真菌成分，有针对性的 病毒组分的核酸扩增测试和鸟枪式元基因组测序 补充、验证和扩展我们的调查结果，并确定新的目标。我们会做选择性培养以确定 肺炎支原体标本中的耐药细菌。我们将应用统计模型，如贝叶斯状态空间 模型和Dirichlet多项式计数模型比较微生物的混合物和随时间的变化 每个已识别的比较组。预报器的重要性将通过使用各种机器来评估 学习模型。 对NP微生物区系与LRTI相关性的更好理解可能会导致 用于诊断或风险预测的新工具。这可能包括预测哪些儿童患心脏病风险较高的工具。 LRTI，识别有发展为严重LRTI风险的儿童和有复发LRTI风险的儿童。此外, 了解NP在LRTI或复发LRTI发病机制中的可能作用可能导致 确定预防下呼吸道感染、进展为严重下呼吸道感染或复发下呼吸道感染的新策略。这些都有可能 包括有针对性的益生菌疗法、新的疫苗或疫苗战略以及治疗下呼吸道感染的新疗法。

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.