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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Soybean Genomics & Improvement Laboratory » Research » Publications at this Location » Publication #291603
Title: Proteomics: an efficient tool to analyze nematode proteins

Author
item Matthews, Benjamin - Ben
item Natarajan, Savithiry - Savi

Submitted to: Journal of Plant Production Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/5/2013
Publication Date: 5/1/2013
Citation: Matthews, B.F., Natarajan, S.S. 2013. Proteomics: an efficient tool to analyze nematode proteins. Journal of Plant Production Science. 3(2):61-64.

Interpretive Summary: The soybean cyst nematode (SCN) is the most devastating pest of soybean in the United States. To improve soybean yields by increasing the level of plant resistance to targeted pests, it is important to understand the protein composition of the SCN. To do this, we are using a “proteomics“ approach. In this study, we separated SCN proteins and determined the quantities of total amounts of protein required to obtain optimal separation and identification of individual high and low abundance SCN proteins. The separated proteins were further identified using a device called a “mass spectrometer”. We found that the optimum protein concentration for anlaysis of SCN high and low abundance proteins was 400ug. This study will be useful to scientists who wish to develop nematode resistant soybeans.

Technical Abstract: Proteomic technologies have been successfully used to analyze proteins structure and characterization in plants, animals, microbes and humans. We used proteomics methodologies to separate and characterize soybean cyst nematode (SCN) proteins. Optimizing the quantity of proteins required to separate SCN proteins by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) is critical for conducting proteomic research. In this study, we used four different amounts of SCN proteins, such as 100, 200, 400 and 600 µg for 2D-PAGE analysis. In 100 and 200 µg concentration some low abundant proteins (area 5 and 10) are not evident. Based on the protein spot distribution, we selected 400 µg protein concentrations as an optimum to analyze SCN proteins. In order to identify proteins, we have isolated proteins spots randomly which includes both abundant and low abundant proteins. Spots were isolated and manually removed and digested with trypsin. The digested protein spots were analyzed by mass spectrometry (MS).