High-throughput discovery of plant metabolic enzyme function using integrative approaches

多样性是地球上生命最显著的特征之一，自文明诞生以来一直是人类着迷的源泉。这种多样性的一个方面是不同的有机化合物，它们具有毒物、引诱剂、驱避剂、信使、储能分子等不同的功能。植物在所有分类群中产生100多万种不同和复杂的代谢物，特别是以其产生的化合物的特殊多样性而闻名。代谢酶促进了这种多样性的出现，其中许多酶是由串联或全基因组复制产生的大型酶家族的一部分。这些基因家族的成员具有共同的蛋白质结构域、功能冗余、底物专一性低、混杂以及基因复制后快速的功能分化等特点。由于这些原因，用计算机预测酶家族成员的功能一直是一项困难的工作。例如，在拟南芥和茄子中，超过80%的基因是基因家族的成员，其中许多成员的注释很差。这种糟糕的注释造成了理解植物表型多样性的起源和利用合理的方法来设计用于经济目的的新的植物性状的障碍。本研究的总体目标是开发用于预测未知功能的酶的假定底物的计算和湿实验室方法。虽然将对多种酶家族进行生物信息学分析，但我计划将重点放在BAHD家族作为一个用于计算建模的模型酶家族。我将利用比较基因组学的力量，首先汇编有关植物中多种BAHD酶的生化知识，然后开发基于系统发育的预测模型，根据底物相似性预测酶底物。然后，我将确定两条互补的证据线-通过RNA-SEQ和代谢组学分析获得的转录本-代谢物相关性，以及酶家族基因的过度表达表型-是否有助于补充和验证所开发的计算模型。拟议的实验将尝试使用新的、多学科的技术来解决植物生物学中的一个长期存在的问题。这些实验的结果将为理解蛋白质结构的进化和重复基因的进化奠定基础。预测酶功能的能力可以促进通过RNA-SEQ、QTL定位或GWA获得的候选代谢基因的注释，使非常广泛的植物科学界受益。这样的功能发现可以帮助合理设计作物和设计天然产物合成的合成途径。最后，建议的方法将为我自己的多学科培训以及在美国和德国的合作和网络创造重要机会，以进一步推进我的职业生涯。

Diversity is one of the most remarkable features of life on earth and has been a source of fascination for mankind since the birth of civilization. One aspect of this diversity are the different organic compounds fulfilling various functions as poisons, attractants, repellents, messengers, energy storage molecules, and more. Plants, producing over a million diverse and complex metabolites across all taxa, especially are renowned for their exceptional diversity of produced compounds. The emergence of this diversity is facilitated by metabolic enzymes, many of which are part of large enzyme families generated by tandem or whole genome duplications. Members of these gene families are characterized by shared protein domains, functional redundancy, low substrate specificity, promiscuity, and rapid functional divergence after gene duplication. For these reasons, predicting functions of enzyme family members computationally has been a difficult endeavor. For example, in Arabidopsis thaliana and Solanum lycopersicum, more than 80% of all genes are members of genes families, and many of these members are poorly annotated. Such poor annotation creates obstacles in understanding the origins of plant phenotypic diversity and in utilizing rational approaches to engineer novel plant traits for economic purposes.The overall aim of this study is to develop computational and wet-lab approaches for predicting putative substrates of enzymes with unknown function. Although multiple enzyme families will be analyzed bioinformatically, I plan to focus on the BAHD family as a model enzyme family for computational modeling. I will utilize the power of comparative genomics, by first compiling biochemical knowledge about multiple BAHD enzymes characterized in plants, followed by developing phylogeny-guided predictive models for enzyme substrate prediction by substrate similarity. I will then determine if two complementary lines of evidences – transcript-metabolite correlations obtained through RNA-seq and metabolomic analyses, and overexpression phenotypes of enzyme family genes – help in supplementing as well as validating the computational models developed.The proposed experiments will attempt solving a long-standing problem in plant biology using novel, multi-disciplinary technologies. Results of these experiments will create a foundation for understanding the evolution of protein structure and evolution of duplicate genes. The ability to predict enzyme function can boost annotation of candidate metabolic genes obtained through RNA-seq, QTL mapping or GWAS, benefiting a very broad plant science community. Such functional discovery can aid rational engineering of crops and design of synthetic pathways for natural product synthesis. Finally, the proposed approaches will generate significant opportunities for my own multi-disciplinary training and for collaborations and networking in USA and Germany to further advance my career.