EDGE CMT: Predicting bacteriophage susceptibility from Escherichia coli genotype

EDGE CMT：根据大肠杆菌基因型预测噬菌体敏感性

• 批准号: 2220735
• 负责人: Adam Arkin
• 金额: $ 205.52万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220735&HistoricalAwards=false
• 关键词: EDGE; CMT; Predicting; bacteriophage; susceptibility

This award to the University of California-Berkeley is made to support investigations of host-pathogen interactions using bacteria and phage as a tractable experimental system. The bacterial virome, the collection of viruses that parasitize the microbes, is a critical feature of microbial community dynamics, activity and adaptation. As part of these community dynamic, bacterial viruses (bacteriophage or phages) attack exceptionally specific bacterial hosts, much like other viruses infect only very-specific plants or animals. However, the mechanisms underlying this specificity are deeply under-characterized and studies have largely focused on a handful of individual bacterium-phage systems. The lack of insights into phage specificity and the breadth of bacterial responses to different phages has limited our ability to build models that can predict which phages have the potential to infect specific bacterial strains. Research supported by this award will help to fill this knowledge gap for an environmentally and medically important species of bacteria and its phage by exploiting an extensive collection of thousands of non-model Escherichia coli (e. coli) strains originating from hundreds of environmental and animal reservoirs. The researchers will use a correspondingly diverse collection of phages alongside high-throughput genetics and measurement to map the susceptibility of these bacteria to infection and create models to predict, given the genome of a new strain of E. coli, which phage might be most effective at targeting it. Graduate students and postdoctoral trainees from under-represented groups will be supported by this award, and the researchers will use data from these studies for data science training efforts available to larger groups. Results of this effort may eventually lead to effective bio-control options to manage bacterial populations, potentially reducing the need for antimicrobial use in a wide range of application areas including agriculture, sanitation, industrial processes, and biomedical environments.Since their discovery 100 years ago, our knowledge of phage abundance, diversity, modes of infectivity and their contribution to horizontal gene transfer, microbiome structure and functional traits is limited to few environmental contexts and individual bacterium-phage systems. Research supported by this award will create a machine-learning-driven experimental workflow that exploits a natural genetic variation in bacterial strains and associated phages, scalable susceptibility assays and high throughput genetics to create a predictive model connecting bacterial genotype to phage susceptibility phenotype. The researchers will leverage an extensive collection of non-model e. coli strains originating from hundreds of reservoirs and geographic locations representative of agricultural, medical and environmentally important species. The researchers will employ high-throughput genomics and genetics to gain a mechanistic understanding of thousands of phage-host interactions necessary to build predictive models linking bacterial genotype to phage susceptibility phenotype. The success of this project will advance our understanding of complex susceptibility phenotypes and could enable development of rational phage-cocktail formulations to treat drug resistance infections, implement biocontrol measures and enable precision microbiome engineering applications. The results of the studies will be presented at scientific meetings and published in peer-reviewed journals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

加州大学伯克利分校的这一奖项是为了支持使用细菌和噬菌体作为易处理的实验系统进行宿主-病原体相互作用的研究。 细菌病毒组是寄生在微生物上的病毒的集合，是微生物群落动态、活性和适应的关键特征。作为这些群落动态的一部分，细菌病毒（噬菌体或噬菌体）攻击非常特定的细菌宿主，就像其他病毒只感染非常特定的植物或动物一样。 然而，这种特异性背后的机制是深不足的特点和研究主要集中在少数个别细菌噬菌体系统。由于缺乏对噬菌体特异性和细菌对不同病原体反应的广度的了解，限制了我们建立模型的能力，这些模型可以预测哪些病原体有可能感染特定的细菌菌株。该奖项支持的研究将有助于填补这一知识空白的环境和医学上重要的细菌物种及其噬菌体，通过利用广泛收集的数千个非模型大肠杆菌（e。大肠杆菌）菌株。 研究人员将使用相应的多样化的细菌集合以及高通量遗传学和测量来绘制这些细菌对感染的易感性，并创建模型来预测，考虑到一种新的大肠杆菌菌株的基因组。来自代表性不足群体的研究生和博士后学员将获得该奖项的支持，研究人员将使用这些研究的数据进行更大群体的数据科学培训工作。 这一努力的结果可能最终导致有效的生物控制选择来管理细菌种群，潜在地减少在广泛的应用领域（包括农业、卫生、工业过程和生物医学环境）中使用抗菌剂的需要。自从100年前发现噬菌体以来，我们对噬菌体丰度、多样性、感染性模式及其对水平基因转移的贡献的知识，微生物组结构和功能特性仅限于少数环境背景和单个细菌-噬菌体系统。该奖项支持的研究将创建一个机器学习驱动的实验工作流程，该工作流程利用细菌菌株和相关噬菌体的自然遗传变异、可扩展的敏感性测定和高通量遗传学来创建将细菌基因型与噬菌体敏感性表型联系起来的预测模型。研究人员将利用广泛收集的非模型e。大肠杆菌菌株来源于数百个水库和代表农业、医疗和环境重要物种的地理位置。研究人员将采用高通量基因组学和遗传学来获得对数千种噬菌体-宿主相互作用的机械理解，这些相互作用是建立将细菌基因型与噬菌体易感性表型联系起来的预测模型所必需的。该项目的成功将促进我们对复杂易感性表型的理解，并能够开发合理的噬菌体鸡尾酒制剂来治疗耐药性感染，实施生物控制措施并实现精确的微生物组工程应用。 研究结果将在科学会议上发表，并发表在同行评审的期刊上。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。