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

这个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人们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的影响。尽管取得了这些进展，但许多基因的功能仍然未知，即使是对大肠杆菌等被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/或与数据集中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，无法为所有新基因序列分配40-60%的功能，更糟糕的是，表现出很高的错误注释率。这些遗漏和传播错误使计算模型、理解和工程生物体和高等生命系统的努力复杂化。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它们的基因型-表型关系至关重要。该项目将产生这些工具和资源（算法、菌株和质粒），与更广泛的科学界共享，并提供相关培训，以帮助缩小这些知识差距，并加速大规模地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的机会，并涉及到K-12学生和公众的推广工作。该项目将通过开发一个基因注释管道（GAP）工具箱来解决上述所有生物系统的基本问题，以确定基因型和培养基条件，选择编码酶和转运体的基因，催化感兴趣的代谢反应。然后将采用合成生物学方法来组装一个微生物菌株库，这些菌株可以在一小组非许可条件下选择关键的代谢反应。这些工具将共同用于填补两种重要根际微生物的代谢知识空白，作为GAP工具箱的测试案例。由此产生的基因和反应之间的联系将加深对根际代谢的理解，并使未来的土壤基础研究和新的农业生物技术成为可能。更广泛地说，这项工作所产生的改进注释将传播到存在同源物的其他测序基因组，从而增加对其他生物系统代谢的全球理解。该项目由生物科学理事会分子和细胞生物科学部的遗传机制项目共同资助。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。

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.