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

• Applicant: Dr. Lars Hendrik Kruse
• Subject Area: Plant Biochemistry and Biophysics ; Organismic Interactions, Chemical Ecology and Microbiomes of Plant Systems ; Plant Physiology
• Term: from 2018 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 411255989

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.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor Dr. Gaurav Dilip Moghe