Collaborative Research: EDGE FGT: Development of a Comprehensive Selection Library to Reconcile Core Metabolic Knowledge Gaps

合作研究：EDGE FGT：开发综合选择库以弥合核心代谢知识差距

• 批准号: 2319732
• 负责人: Brian Pfleger
• 金额: $ 62.15万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319732&HistoricalAwards=false
• 关键词: Collaborative; Research; EDGE; FGT; Development

This project is motivated by the large number of genes of unknown function found in sequenced genomes. Genome sequencing and bioinformatics has transformed understanding of the capabilities of living organisms, leading to broad impacts on biotechnology, agriculture, health, and the environment. Despite these advances, the functions of many genes remain unknown, even for well-studied model organisms such as Escherichia coli. In non-model organisms, most functional assignments are not based on direct experimental evidence, but instead are based on sequence homology and/or shared patterns with known genes across datasets. Automated bioinformatics algorithms have increased the rate of genome annotation, but unfortunately fail to assign functions to 40-60% of all new gene sequences, and worse, exhibit a high rate of mis-annotation. These omissions and propagated errors complicate efforts to computationally model, understand, and engineer organisms and higher living systems. For this reason, it is critical to develop tools for rapidly identifying functions of genes in non-model organisms to better understand their genotype-phenotype relationships. This project will generate such tools and resources (algorithms, strains, and plasmids), share them with the broader scientific community, and provide related training to help reduce these gaps in knowledge and accelerate linking genotypes to phenotypes on a large scale. The project will also create systems and synthetic biology research opportunities for undergraduate and graduate students, and involve outreach efforts to engage K-12 students and the general public.This project will address the fundamental problem described above for all biological systems by developing a gene annotation pipeline (GAP) toolbox to identify genotypes and media conditions that select for genes encoding enzymes and transporters that catalyze a metabolic reaction of interest. Synthetic biology methods will then be employed to assemble a library of microbial strains that can select for key metabolic reactions using a small set of non-permissive conditions. Together these tools will be used to close the metabolic knowledge gaps in two important rhizosphere microbes as test cases for the GAP toolbox. The resulting links between genes and reactions will deepen understanding of metabolism in the rhizosphere and enable future basic research on soil as well as novel agricultural biotechnologies. More broadly, improved annotations resulting from this work will be propagated to other sequenced genomes where homologs exist, increasing global understanding of metabolism in other biological systems as well.This project is co-funded by the Genetic Mechanisms program of the Molecular and Cellular Biosciences Division in the Biological Sciences Directorate.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.

该项目的动机是在测序的基因组中发现了大量功能未知的基因。基因组测序和生物信息学改变了对生物体能力的理解，对生物技术、农业、健康和环境产生了广泛影响。尽管取得了这些进展，许多基因的功能仍然未知，即使对于大肠杆菌等经过充分研究的模式生物也是如此。在非模式生物中，大多数功能分配不是基于直接的实验证据，而是基于序列同源性和/或与跨数据集的已知基因的共享模式。自动化生物信息学算法提高了基因组注释率，但不幸的是未能为所有新基因序列的 40-60% 分配功能，更糟糕的是，错误注释率很高。这些遗漏和传播的错误使计算建模、理解和工程有机体和高等生命系统的工作变得复杂。因此，开发快速识别非模式生物中基因功能的工具以更好地了解其基因型-表型关系至关重要。该项目将生成此类工具和资源（算法、菌株和质粒），与更广泛的科学界分享，并提供相关培训，以帮助缩小这些知识差距并加速大规模将基因型与表型联系起来。该项目还将为本科生和研究生创造系统和合成生物学研究机会，并涉及吸引 K-12 学生和公众参与的外展工作。该项目将通过开发基因注释来解决上述所有生物系统的基本问题管道（GAP）工具箱，用于识别基因型和培养基条件，选择编码催化感兴趣的代谢反应的酶和转运蛋白的基因。然后将采用合成生物学方法来组装微生物菌株库，该库可以使用一小组非许可条件来选择关键代谢反应。这些工具将共同用于缩小两种重要根际微生物的代谢知识差距，作为 GAP 工具箱的测试用例。由此产生的基因和反应之间的联系将加深对根际新陈代谢的理解，并使未来的土壤基础研究以及新型农业生物技术成为可能。更广泛地说，这项工作产生的改进注释将传播到存在同源物的其他已测序基因组，从而增加全球对其他生物系统代谢的理解。该项目由分子和细胞生物科学部遗传机制计划共同资助该奖项反映了 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。