Towards a mechanistic understanding of the role of gut microbiota in postnatal growth impairment

项目摘要 肠道微生物群的精确工程需要对微生物如何与微生物相互作用的机制进行理解。 宿主生理途径，以产生期望的健康结果。在发育不良的儿童中， 微生物与异常的宿主炎症和生长障碍有关， 人们对这些关联的基础知之甚少。小鼠模型已经提出了因果关系，但他们没有 概括了人类粘膜免疫系统的动态和人类肠道的复杂性 微生物群本项目将在一个超过1500名来自农村的儿童队列中对这些问题进行活体调查。 孟加拉国有发育迟缓的风险，为此从0-3岁纵向收集生物标本， 年龄从这些儿童收集的>3700份粪便样品中初步分析16 S rRNA基因序列 已经鉴定出与肠道炎症高度相关的双歧杆菌序列变体， 随后的增长放缓。在目标1中，我将确定可能介导这些的菌株特异性微生物基因。 观察协会。在目标2中，我将通过评估微生物的小- 粪便和血液中与高水平双歧杆菌和并发肠道和/或 14个月大的儿童全身性炎症。在目标3中，我将使用高级潜变量统计建模 确定相关微生物组（分类、宏基因组和代谢）的重要性， 宿主（肠道和全身炎症）特征对未来生长的影响。我也会估计 通过理论上100%有效的微生物群控制，可实现儿童生长的改善 干预，提供与其他干预替代方案进行比较的预期效应量。这项工作将 增加我们对早期肠道菌群与异常肠道菌群之间关系的机械理解， 肠道/全身性炎症以及未来的增长步履蹒跚，产生新的选择，可预见的 操纵肠道微生物群以减轻不良健康后果。拟议项目将提供一个 在肠道微生物群，微生物代谢物，多元统计等领域的严格培训经验， 在一组科学专家的指导下，我将学习儿科胃肠病学，并将使我掌握 成为一名独立的研究员。

PROJECT SUMMARY Precision engineering of the gut microbiota requires a mechanistic understanding of how microbes interact with host physiological pathways in order to produce desired health outcomes. In stunted children, commensal gut microbes have been correlated with aberrant host inflammation and growth impairment, but mechanisms underlying these associations are poorly understood. Mouse models have suggested causality, but they fail to recapitulate the dynamics of the mucosal immune system in humans and the complexity of the human gut microbiota. This project will interrogate these questions in vivo in a cohort of >1500 children from rural Bangladesh at risk for stunting, for which biological specimens were collected longitudinally from 0-3 years of age. Preliminary analysis of 16S rRNA gene sequences from >3700 fecal samples collected from these children has identified a Bifidobacterium sequence variant that is highly correlated with intestinal inflammation and subsequent growth faltering. In Aim 1, I will identify strain-specific microbial genes that might mediate these observed associations. In Aim 2, I will interrogate the mechanistic underpinnings by evaluating microbial small- molecule metabolites in feces and blood associated with high levels of Bifidobacterium and concurrent gut and/or systemic inflammation in children 14 months old. In Aim 3, I will use advanced latent variable statistical modeling to determine the importance of associated groups of microbial (taxonomic, metagenomic, and metabolic) and host (gut and systemic inflammation) features on future growth faltering. I will also estimate the maximum achievable improvement in child growth from a theoretical, 100% efficacious microbiota-manipulation intervention, providing an expected effect size for comparison with other intervention alternatives. This work will increase our mechanistic understanding of the associations between early life gut microbiota and aberrant intestinal/systemic inflammation as well as future growth faltering, producing new options for predictably manipulating the gut microbiota to mitigate adverse health outcomes. The proposed project will provide a rigorous training experience in the fields of gut microbiota, microbial metabolites, multivariate statistics, and pediatric gastroenterology under the mentorship of a group of scientific experts, and will equip me with the skills necessary to become an independent researcher.