Defining stable microbial communities through functional interrogation of microbiomes

通过微生物组的功能询问来定义稳定的微生物群落

• 批准号: RGPIN-2019-06852
• 负责人: Parkinson, John
• 金额: $ 3.64万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716501
• 关键词: Defining; stable; microbial; communities; through

The gastrointestinal tract (GIT) is home to a complex, yet stable, community of microbes conferring numerous benefits to their host. Over the past 80 years, microbial communities in the GITs of livestock have been augmented through dietary supplementation of sub-therapeutic doses of antibiotics (antibiotic growth promotants; AGPs), enhancing production efficiency and reducing enteric infections. However, due to concerns over the rise of anti-microbial resistance (AMR), AGPs are being phased out; alternatives are urgently required. With a need to engraft and persist in the GIT, allied to the success of fecal microbiome transplants, attention has shifted from the use of single strain probiotics, to synthetic microbial communities. In this five-year research program, we propose to develop effective genomic and computational pipelines to identify stable microbial communities that can be used to seed the developing GIT of poultry and help establish a healthy and productive microbiome. Three aims are proposed: AIM1. Develop innovative computational tools for accurately inferring taxonomic and functional compositions of microbiomes. Metatranscriptomics datasets have the power to yield mechanistic insights into microbiome function. To fully exploit their potential, we will develop next generation tools, based on ensemble methods, in which functional and taxonomic annotations are reciprocally bootstrapped to deliver accurate classifications. AIM2. Construct systems-based models to reveal functional dependencies within microbiomes. To better understand microbiome functionality, using annotations derived from Aim1, we will generate predictive models of microbial community metabolism. Applied in the context of poultry, these models will allow us to rapidly predict the functional impact of altering community composition, together with any consequence in nutrient acquisition by the host. AIM3. Design novel community probiotics to improve poultry health. Cliques of stably associating microbes, identified through community meta-analyses, will be investigated through predictive metabolic models established in Aim2, to identify synthetic microbial communities that promote poultry gut health. The efficacy of these communities will be validated through established strategic partnerships. Supporting this program of research is a talented team of computational biologists, including undergraduate and graduate students, together with postdoctoral fellows. Developing complementary skills in high-performance computing, microbiome analysis and systems biology, trainees in this program will receive a diverse training experience, preparing them for a multitude of career opportunities in academia, as well as industry. Together with established collaborations with experts in poultry health, this program will deliver fundamental insights into the molecular interactions that drive microbiome dynamics and establish new synthetic communities that promote poultry health.

胃肠道(GIT)是一个复杂而稳定的微生物群落的家园，这些微生物为宿主带来了许多好处。在过去的80年里，通过在饲料中添加亚治疗剂量的抗生素(抗生素生长促进剂；AGPs)，提高了生产效率，减少了肠道感染，从而扩大了牲畜Git中的微生物群落。然而，由于对抗菌素耐药性(AMR)上升的担忧，AGPs正在被逐步淘汰；迫切需要替代品。由于需要嫁接并在粪便微生物组移植成功的情况下持续存在，人们的注意力已从单一菌株益生菌的使用转移到合成微生物群落上。在这项为期五年的研究计划中，我们建议开发有效的基因组和计算管道来识别稳定的微生物群落，这些微生物群落可以用来为家禽发育中的GIT播种，并帮助建立一个健康和多产的微生物群。提出了三个目标： AIM1.开发创新的计算工具，以准确推断微生物群的分类和功能组成。 元转录组学数据集有能力产生对微生物组功能的机械性见解。为了充分挖掘它们的潜力，我们将开发基于集成方法的下一代工具，其中功能注释和分类注释相互引导以提供准确的分类。 AIM2.构建基于系统的模型，以揭示微生物体内的功能依赖关系。 为了更好地了解微生物组的功能，使用来自Aim1的注释，我们将生成微生物群落新陈代谢的预测模型。应用于家禽的环境中，这些模型将使我们能够快速预测改变群落组成的功能影响，以及宿主获取营养的任何后果。 AIM3.设计新型社区益生菌以改善家禽健康。 通过群落荟萃分析确定的稳定结合的微生物集团，将通过在AIM2中建立的预测代谢模型进行调查，以确定促进家禽肠道健康的合成微生物群落。这些社区的效力将通过已建立的战略伙伴关系加以验证。 支持这一研究计划的是一支才华横溢的计算生物学家团队，包括本科生和研究生，以及博士后研究员。通过开发高性能计算、微生物组分析和系统生物学方面的互补技能，该项目的受训人员将获得多样化的培训经验，为他们在学术界和工业界获得多种职业机会做好准备。连同与家禽健康专家建立的合作，该项目将提供对驱动微生物组动态的分子相互作用的基本见解，并建立促进家禽健康的新的合成社区。