CoPIs: Mark Hall, Daisuke Kihara, Jun Xie (Purdue University)
There is a strong need for systems-level data sets that enable a more efficient translation of basic knowledge into improved crop traits. Information on protein complex composition is one such example, but resources for crop plants are lacking. This project proposes a novel mass spectrometry-based method to solve endogenous protein complexes in soybean. The results will serve as hypothesis-generating machines for the reverse-genetic analysis of a major crop. The approach couples multiple chromatography separations of cytosolic extracts with quantitative label-free mass spectrometry of the column fractions. The relative abundance of the proteins across the purification scheme will be used to cluster co-purifying proteins into groups that reveal protein complex composition. Unlike other large-scale, protein interaction techniques, this new approach is simple to experimentally validate using existing mutant collections. This inexpensive technique does not require gene replacement technology, large-scale gene cloning, or targeted purification strategies. To establish the effectiveness of this method, the investigators will pursue three research objectives: 1) to predict and validate the composition of 100 endogenous protein complexes isolated from leaf cytosol; 2) to use bioinformatics and existing "omics" data sets to predict and evaluate models for protein complex composition; and 3) to determine if the compositions of cytosolic protein complexes are conserved in Glycine max.
Although mass spectrometry has been used extensively in proteomics research, to the investigators' knowledge the proposed strategy for large-scale protein complex prediction has not been done. Successful completion of this work will provide broadly useful results, databases, and reagents that will reveal the composition of endogenous protein complexes. This deep analysis of the cytosolic proteome will provide an important data set that enables crop scientists to further modify proteins and pathways with the goal to improve crop productivity.
The Purdue Initiative on Plant Protein Interactions (PIPPI) will provide broad impact by reaching diverse audiences through such activities as research opportunities for undergraduate and graduate students and postdoctoral fellows; and a website providing data on plant protein complexes for researchers. Students will gain skills collaborating in diverse university research environments, be exposed to cutting-edge research in plant proteomics, and develop leadership and organizational abilities by assisting with project management, research design, and data release. By partnering with various campus diversity programs and offices, all aspects of the project will involve underrepresented undergraduate and graduate students in STEM disciplines. Proteomic data sets will be available to the public at https://proteomecommons.org. It is expected that this novel project will help prepare the next generation of plant geneticists that utilize diverse types of data to drive crop improvement strategies.