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 2022 财年 NSF 生物学博士后研究奖学金,旨在调查基因组、环境和表型之间相互作用的生命规则的综合研究。该奖学金支持研究员的研究和培训,以创新的方式为生活规则领域做出贡献。在这项工作中,研究员将研究遗传学与环境之间复杂的相互作用,这些相互作用使细菌物种能够成功侵入新环境。细菌通常无法侵入宿主并引起疾病,但有时它们会成功并导致严重感染。这些细菌具有的特征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与失败的入侵进行比较。 该研究员将量化从不同环境中分离出的多种细菌的入侵成功率,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率。该研究员还将通过招募和培训本科生扩大对科学的参与,特别是旨在提供公平的研究机会。该项目旨在通过研究系统发育多样化的机会性病原体铜绿假单胞菌菌株之间入侵成功的差异,更好地了解致病性转变背后的遗传和环境相互作用。该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入侵结果的这种变化,并将评估系统发育相关性和基因组因素在预测成功方面的作用。接下来,他们将量化细菌在引入新环境之前的生长条件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来比较一般和特定的预测模型,以量化未经测试的菌株未来入侵成功的潜力。在整个项目中,该研究员将获得细菌遗传学、量化细菌性状方面的技能,并将学习开发机器学习模型以根据基因组信息预测表型结果。除了指导本科生外,该研究员还将参与科学推广活动,旨在促进对科学的理解和欣赏,特别关注历史上服务不足的机构。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力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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