NSF Postdoctoral Fellowship in Biology FY 2022: The role of genomic preadaptation and physiological state in modulating bacterial invasion success
NSF Postdoctoral Fellowship in Biology FY 2022: The role of genomic preadaptation and physiological state in modulating bacterial invasion success
批准号:
2209151
负责人:
Elijah Mehlferber
金额:
$13.8万
依托单位:
依托单位国家:
美国
项目类别:
Fellowship Award
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-09-01 至 2024-08-31
中文摘要
该行动资助了美国国家科学基金会2022财年生物学博士后研究奖学金,研究基因组,环境和表型之间相互作用的生命规则的综合研究。该奖学金支持将以创新方式对生活规则领域作出贡献的研究员的研究和培训。在这项工作中,研究员将研究遗传学和环境之间复杂的相互作用,使细菌物种能够成功入侵新环境。细菌通常不能入侵宿主并引起疾病,但有时它们会成功,并在入侵时引起严重的感染。这些细菌所具有的特性以及帮助它们实现这种入侵的条件尚不清楚,特别是因为研究通常只关注成功的入侵,而不比较失败的入侵。该研究员将量化从不同环境中分离出来的各种细菌的入侵成功,确定生长条件的影响,并开发一种机器学习方法,将入侵结果与遗传学联系起来。该项目旨在预测未经测试的菌株的入侵成功。该研究员还将通过招募和培训本科生来扩大对科学的参与,具体目标是提供公平获得研究机会的机会。本项目旨在通过研究不同系统发育条件致病菌铜绿假单胞菌菌株入侵成功的差异,更好地了解致病性转变背后的遗传和环境相互作用。研究员将首先通过量化细菌在包含竞争物种的新环境中建立的能力来确定这种入侵结果的变化,并将评估系统发育相关性和基因组因素在预测成功中的作用。接下来,他们将量化细菌在进入新环境之前的生长条件所引起的不同生理状态对入侵结果的影响。最后,该研究员将整合这些数据,比较一般和特定的预测模型,以量化未来在未经测试的菌株中入侵成功的潜力。在整个项目中,研究员将获得细菌遗传学,量化细菌特征的技能,并将学习开发机器学习模型,以预测基因组信息的表型结果。除了指导本科生外,该研究员还将从事旨在促进对科学的理解和欣赏的科学推广工作,特别关注历史上服务不足的机构。该奖项反映了美国国家科学基金会的法定使命,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。
英文摘要
This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2022, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. In this work, the Fellow will investigate the complex interactions between genetics and the environment that allow a bacterial species to successfully invade a new environment. Bacteria usually fail to invade a host and cause disease, but sometimes they succeed and can cause severe infections when they do. The traits these bacteria have, and conditions which help them achieve this invasion are not well-understood, especially because studies usually only focus on successful invasion without comparing to failed invasions. The Fellow will quantify invasion success across a diverse set of bacteria isolated from different environments, determine the influence of growth conditions, and develop a machine-learning approaches to link invasion outcomes to genetics. The project aims to predict invasion success in un-tested strains. The Fellow will also broaden participation in science through the recruitment and training of undergraduate students, specifically targeted towards providing equitable access to research opportunities.This project seeks to better understand the genetic and environmental interactions underlying transitions to pathogenicity by studying the variation in invasion success across strains of the phylogenetically diverse opportunistic pathogen Pseudomonas aeruginosa. The Fellow will first determine this variation in invasion outcomes by quantifying the bacteria’s ability to establish in a novel environment containing a competitor species and will assess the role of phylogenetic relatedness and genomic factors in predicting that success. Next, they will quantify the impact of different physiological states, induced by the bacteria’s growth conditions before introduction to their novel environment, on invasion outcomes. Finally, the Fellow will integrate these data to compare general and specific predictive models to quantify the potential for future invasion success in un-tested strains. Throughout the project, the Fellow will gain skills in bacterial genetics, quantifying bacterial traits, and will learn to develop machine learning models to predict phenotypic outcomes from genomic information. In addition to mentoring undergraduate students, the Fellow will engage in scientific outreach aimed at fostering understanding and appreciation for science, with a specific focus on historically underserved institutions.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.
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