The nasopharyngeal microbiome and respiratory disease in African children

描述（由申请人提供）：通过分析发达国家儿童的微生物组的特定组分，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。尚未对NP微生物组进行全面的纵向研究，也没有与这些结果指标相关。我们将应用基于序列、培养和特异性PCR的方法来确定从出生到2岁婴儿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。我们将进一步研究肺炎或喘息的风险因素与NP微生物组之间的关系。 研究将嵌套在现有的资助出生队列研究中。入组队列的所有500名儿童将在出生时、前2年的6周和6个月进行NP采样。 此外，在出生后第一年内，将在300名儿童的亚组中每隔两周进行NP采样。将对样本进行存档，以便在巢式病例对照研究中进行后续检索和分析。将前瞻性识别所有肺炎、首次发作喘息或复发性喘息病例，并研究其病因。将对肺炎或喘息儿童进行病例对照研究。将从相同的研究人群中选择年龄、临床试验机构和HIV状态匹配的对照。将检索病例和对照的存档NP样本，以详细评估NP微生物组的组成和动力学。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。对于复发性喘息，我们将重点关注出生时、6周龄、6个月和12个月大时NP微生物组组成的相关性。用于定义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异性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。 分析将采用时间序列方法，以便根据季节变化进行调整，并评估多样性的演变情况。为了确定每个时间点肺炎或喘息的决定因素，我们将使用加权广义岭回归方法，该方法能够在高维环境中选择变量。我们将进一步衡量作为时间函数的预测因子的强度。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。 能力建设是一个重点。特别是，我们建议在南非进行完整的微生物组管道，包括高通量测序。重点主要放在培训上，包括往返于委员会的培训访问、一个培训讲习班和对研究生的培训。通过专注于培训和确保完整的管道（从序列生成到最终关联分析）在南非进行，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.