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Updated: Jan 20, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
A systematic proteomic study of seed filling in soybean. Establishment of high-resolution two-dimensional reference
Martin Hajduch1, Ashwin Ganapathy, Joel W Stein
1Department of Biochemistry, Life Sciences Center, University of Missouri, Columbia, Missouri 65211, USA.
Abstract:
A high-throughput proteomic approach was employed to determine the expression profile and identity of hundreds of proteins during seed filling in soybean (Glycine max) cv Maverick. Soybean seed proteins were analyzed at 2, 3, 4, 5, and 6 weeks after flowering using two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight mass spectrometry. This led to the establishment of high-resolution proteome reference maps, expression profiles of 679 spots, and corresponding matrix-assisted laser desorption ionization time-of-flight mass spectrometry spectra for each spot. Database searching with these spectra resulted in the identification of 422 proteins representing 216 nonredundant proteins. These proteins were classified into 14 major functional categories. Proteins involved in metabolism, protein destination and storage, metabolite transport, and disease/defense were the most abundant. For each functional category, a composite expression profile is presented to gain insight into legume seed physiology and the general regulation of proteins associated with each functional class. Using this approach, an overall decrease in metabolism-related proteins versus an increase in proteins associated with destination and storage was observed during seed filling. The accumulation of unknown proteins, sucrose transport and cleavage enzymes, cysteine and methionine biosynthesis enzymes, 14-3-3-like proteins, lipoxygenases, storage proteins, and allergenic proteins during seed filling is also discussed. A user-intuitive database (http://oilseedproteomics.missouri.edu) was developed to access these data for soybean and other oilseeds currently being investigated.
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