The nasopharyngeal microbiome and respiratory disease in African children

描述(由申请人提供)：通过对发达国家儿童的微生物组的特定成分的分析，证明了婴儿鼻咽(NP)微生物组在肺炎或喘息性疾病发展中的核心作用。还没有对NP微生物组进行全面的纵向研究，也没有与这些结果衡量标准相关的研究。我们将应用基于序列、培养和特定PCR的方法来定义从出生到2岁的婴儿NP微生物组的组成和动态，并确定NP微生物组与非洲婴儿肺炎或喘息之间的关联。我们将进一步调查肺炎或喘息的危险因素与NP微生物群之间的关系。 研究将嵌套在现有的、有资金支持的出生队列研究中。队列中登记的所有500名儿童将在出生时、6周和头两年每月进行NP抽样。 此外，将每隔两周对300名一岁以上的儿童进行NP抽样。样本将被存档，以供以后在嵌套病例对照研究中检索和分析。所有肺炎、首发喘息或反复喘息的病例都将被前瞻性地识别和调查病因。将对肺炎或喘息的儿童进行病例对照研究。与年龄、诊所地点和艾滋病毒状况相匹配的对照将从相同的研究人群中选择。将检索病例和对照的存档NP样本，以对NP微生物组的组成和动态进行详细评估。对于肺炎病例，我们将重点关注肺炎前3个月收集的样本，以确定与近期进展相关的变化。对于反复喘息，我们将重点研究出生时、6周、6个月和12个月大时NP微生物组的组成与其相关性。用于确定微生物组的技术将包括常见呼吸道病原体的培养，33种不同病毒、真菌和细菌病原体的特殊多重分子检测，以及细菌微生物的基于序列的微生物组分析。 分析将使用时间序列方法，这将允许对季节变化进行调整和评估多样性的演变。为了识别肺炎或喘息在每个时间点的决定因素，我们将使用加权广义岭回归方法，该方法能够在高维设置中选择变量。我们将进一步评估由此确定为时间函数的预测值的强度。为了整合病毒和细菌数据，微生物组的两个组成部分都将包括在SARIMA模型中。 能力建设是重点。特别是，我们建议在南非境内进行完整的微生物组管道，包括高通量测序。非常重视培训，包括来往JCVI的培训访问、培训讲习班和对研究生的培训。通过重点培训和确保在南非进行完整的管道(从序列生成到最终关联分析)，我们的目标是在这一领域建立独立和可持续的能力。

DESCRIPTION (provided by applicant): The central role of the infant nasopharyngeal (NP) microbiome in the development of pneumonia or wheezing illness has been demonstrated by analysis of specific components of the microbiome in children in developed countries. No comprehensive, longitudinal study of the NP microbiome has yet been undertaken, nor related to these outcome measures. We will apply sequence-, culture- and specific PCR-based approaches to define the composition and dynamics of the infant NP microbiome from birth to 2 years of age and to determine the association between the NP microbiome and pneumonia or wheezing in African infants. We will further investigate the association between risk factors for pneumonia or wheezing and the NP microbiome. Studies will be nested within an existing, funded birth cohort study. All 500 children enrolled in the cohort will have NP sampling at birth, 6 weeks and 6-monthly for the first 2 years. In addition, NP sampling will be done at two-week intervals in a sub-group of 300 children over the first year of life. Samples will be archived for later retrieval and analysis in nested case-control studies. All cases of pneumonia, first episode wheezing or recurrent wheezing will be prospectively identified and investigated for etiology. Case-control studies of children with pneumonia or wheezing will be done. Controls matched by age, clinic site and HIV status will be selected from the same study population. Archived NP samples from cases and controls will be retrieved to perform detailed assessment of the composition and dynamics of the NP microbiome. For pneumonia cases we will focus on the samples collected in the 3 months preceding pneumonia to identify changes associated with near-term progression. For recurrent wheezing we will focus on the association with the composition of the NP microbiome at birth, 6 weeks, 6 months and 12 months of age. Techniques used to define the microbiome will include culture for common respiratory pathogens, specific multiplex molecular detection of 33 different viral, fungal and bacterial pathogens as well as sequence-based microbiome analysis for bacterial microorganisms. Analysis will use a time-series approach which will allow adjustment for seasonal variation and assessment of the evolution of diversity. In order to identify determinants of pneumonia or wheezing at each time point we will use weighted generalized ridge regression methods, which are able to select variables in a high dimension setting. We will further gauge the strength of the predictors thus identified as a function of time. In order to integrate viral ad bacterial data, both components of the microbiome will be included in the SARIMA model. Capacity building is a major focus. In particular, we propose to perform the complete microbiome pipeline within South Africa, including high throughput sequencing. Substantial emphasis is placed on training including training visits both to and from JCVI, a training workshop and training of postgraduate students. By focusing on training and ensuring that the complete pipeline (from sequence generation to final association analysis) is performed in South Africa we aim to build independent and sustainable capacity in this field.