Elucidating Gene Network Modules Regulating Inter-specific Diversity in Plant Leaf Shape

University Of California-Davis, Davis CA

Investigators

Abstract

Conversion of solar energy to chemical energy in land plants is the source of agricultural productivity. This process occurs in leaves but how different types of leaves differ in their ability to produce carbon compounds is not well understood. Nor do we understand how genes act and interact to produce leaves of different shapes and sizes. This project will utilize analysis of how genes are regulated to generate gene products that interact to create cascades of effects eventually leading to the formation of different forms of leaves. This understanding will allow us to manipulate gene expression to generate more efficient leaf forms. This project will also disseminate knowledge of gene regulation and leaf function, and provide hands on research experience to high school students from a predominantly Hispanic school district. How morphological diversity has arisen is a key question in biology. Angiosperms exhibit a great diversity in leaf shape and leaf development has been characterized in several species, making leaves ideal targets to understand the mechanism behind morphological natural variation. Leaves are also functionally significant for generating biomass and leading to agricultural yield. This research is based upon a deduced gene co-expression network underlying leaf development in tomato and its relatives. Molecular experiments and hypothesis testing validated the bioinformatically predicted gene regulatory network (GRN) and identified key components, such as BLADE-ON-PETIOLE (BOP), within the gene network module regulating leaf shape. Alteration in BOP expression by transgenic experiments in tomato, S. pennellii and S. habrochaites, can recreate naturally occurring leaf phenotypes in the tomato species complex. The investigators will identify BOP targets, look at BOP co-expressed genes to integrate network information across three species (Aim 1), detect differential interactions (DiffCorr) between genes in the GRN across species (Aim 2), and generate transcriptomes and build GRNs to identify GRN rewiring across selected eudicots (Aim 3).