University of Missouri-Columbia
115 Business Loop 70 W
PLANT GENOME RESEARCH RESOURCE
Program Reference Code(s):
1329, 9109, 9179, BIOT, 9178
Program Element Code(s):
PI: Jay Thelen (University of Missouri-Columbia)
CoPIs: Dong Xu, Dmitry Korkin (University of Missouri-Columbia)
Seeds are carbon- and nitrogen-dense structures that serve as the (plant) propagule and the (human) socioeconomic foundation for modern agriculture. Genetics and environmental conditions dictate the developmental program that ultimately determines both seed yield and composition, although the underlying regulatory proteins and mechanisms are only now beginning to be understood. Regulation of seed development has recently focused on transcriptional and epigenetic control of both embryogenesis and maturation. From a biochemical perspective, few regulatory processes take place within a cell without direct or indirect involvement of proteins, and protein-protein interactions. The nucleus and cytosol are the major locations for transcriptional, translational, and post-translational regulation. A quantitative inventory of the proteins involved in these processes and their interactome network, would be a valuable resource to develop testable models for regulatory control of embryo maturation. The main focus of this project is the parallel characterization of nuclear and cytosolic protein interactomes from developing embryos of soybean and rapeseed using a combination of biochemical, proteomic, and bioinformatic approaches. The major objectives of this project are to: 1) systematically and quantitatively analyze protein-protein interactions from cytosolic and nuclear fractions of developing embryos from soybean and rapeseed using a multi-dimensional biochemical strategy that includes protein crosslinking, co-sedimentation, and quantitative mass spectrometry; 2) perform bioinformatic and statistical analyses of potential interacting proteins using data from aim 1 for comparison between soybean and rapeseed, and develop generic protein complex identification tools for such data; and 3) disseminate software, proteomic and protein interaction data by deposition into extant databases (PRIDE, http://www.ebi.ac.uk/pride/; Database of Interacting Proteins, DIP, http://dip.doe-mbi.ucla.edu/; Soybean Knowledge Base, http://soykb.org) and develop a new web database.
While the research project targets the protein interactome in developing soybean and rapeseed embryos, it has broader significance for studying plants (especially crops) in general. This study will greatly enrich the data describing plant protein interactions, which is currently limited. These data can be used as a template for studying protein interactomes of other plants. Besides the data and their analyses, the techniques and bioinformatic tools that will be developed can also be applied to study interactomes of other species; in particular, experimental protocols and bioinformatic tools (including source code) will be made freely available to the public. The project will provide critical interdisciplinary training in plant biology, proteomics, and bioinformatics for undergraduates, graduate students, and postdoctoral scholars. Current discipline-based education provides very limited training on integrating biology, biotechnology, and bioinformatics, which is often needed in large-scale biological projects. This project provides a problem-based, interdisciplinary environment to provide training in this regard. Students and postdocs will be mentored in scientific writing, scientific presentations, publishing, grant applications, constructive peer review, project management, time management, and personnel management in an effort to direct the next generation of scientists towards the realistic and professional expectations of modern academic or commercial research. Efforts will be made to recruit people with diverse backgrounds to work on this project. Undergraduate researchers in life sciences and journalism will have opportunities to gain unique experience in computational biology and scientific communication through support from a recently awarded Howard Hughes Medical Institute grant.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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Chao Zhang, Kristina Hanspers, Allan Kuchinsky, Nathan Salomonis, Dong Xu and Alexander R.. "Mosaic: making biological sense of complex networks," Bioinformatics, v.29, 2012, p. 1943.
Zhao N, Han JG, Shyu CR, Korkin D. "Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning," PLOS Computatiobnal Biology, v.10, 2014.
Qiuming Yao, Huangyi Ge, Shangquan Wu, Ning Zhang, Wei Chen, Chunhui Xu, Jianjiong Gao, Jay J. Thelen, and Dong Xu. "P3DB 3.0: From plant phosphorylation sites to protein networks," Nucleic Acids Research, v.1, 2014, p. D1206.
Zhang J, Xu D. "Fast algorithm for population-based protein structural model analysis.," Proteomics, v.13, 2013, p. 221.
Dhroso A, Korkin D, Conant G,. "The yeast protein interaction network has a capacity for self?organization," FEBS Journal, v.281, 2014, p. 3420.
He Z, Alazmi M, Zhang J, Xu D. "Protein structural model selection by combining consensus and single scoring methods.," PLoS One, v.8, 2013, p. e74006.
Rao R, Xu D, Thelen JJ, Miernyk JA. "Circles within circles: crosstalk between protein Ser/Thr/Tyr-phosphorylation and Met oxidation.," BMC Bioinformatics, v.S14, 2013.
Qiuming Yao, Jianjiong Gao, Curtis Bollinger, Jay J. Thelen, and Dong Xu. "Predicting and analyzing protein phosphorylation sites in plants using Musite," Front. Plant Sci., v.3, 2012, p. 186.
Moretti R1, Fleishman SJ, Agius R, Torchala M, Bates PA, Kastritis PL, Rodrigues JP, Trellet M, Bonvin AM, Cui M, Rooman M, Gillis D, Dehouck Y, Moal I, Romero-Durana M, Perez-Cano L, Pallara C, Jimenez B, Fernandez-Recio J, Flores S, Pacella M, Praneeth. "Community-wide Evaluation of Methods for Predicting the Effect of Mutations on Protein-Protein Interactions," Proteins, v.81, 2013, p. 1980.
Dhroso A, Korkin D, Conant G. "The yeast protein interaction network has a capacity for self-organization," FEBS Journal, v.281, 2014, p. 3420.
Kuang X, Dhroso A, Han JG, Shyu CR, Korkin D. "DOMMINO 2.0: Integrating structurally resolved protein-, RNA-, and DNA-mediated macromolecular interactions," PLOS One, 2015.
Qiuming Yao, Curtis Bollinger, Jianjiong Gao, Dong Xu, and Jay J. Thelen. "P3DB: an integrated database for plant protein phosphorylation," Front. Plant Sci., v.3, 2012, p. 206.
Zhao N, Han JG, Shyu CR, Korkin. "Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.," PLOS Computatiobnal Biology, v.10, 2014, p. e1003592.
Qiuming Yao, Huangyi Ge, Shangquan Wu, Ning Zhang, Wei Chen, Chunhui Xu, Jianjiong Gao, Jay J. Thelen, and Dong Xu. "P3DB 3.0: From plant phosphorylation sites to protein networks.," Nucleic Acids Research, v.1, 2014, p. 1206.