|Natarajan, Savithiry - Savi|
|TAVAKOLAN, MONA - Towson University|
|ALKHAROUR, NADIM - Towson University|
|Matthews, Benjamin - Ben|
Submitted to: Bioinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/16/2014
Publication Date: 9/30/2014
Citation: Natarajan, S.S., Tavakolan, M., Alkharour, N.W., Matthews, B.F. 2014. SoyProLow: A protein database enriched in low abudndant soybean proteins. Bioinformation. 10(9): 598-601.
Interpretive Summary: The soybean cyst nematode (SCN) is the most devastating pest of soybean around the world. To improve soybean yields through an increased level of plant resistance to targeted pests, it is important to understand the protein composition of the SCN. To that end, we have developed and made publicly available the SCN protein database called “SCNProDB”. This database houses all the identified SCN proteins. The results of this study will be useful to scientists to understand the SCN protein composition and to develop nematode resistant soybean varieties through genetic manipulation and breeding efforts.
Technical Abstract: Soybean cyst nematode (Heterodera glycines, SCN) is the most destructive pathogen of soybean around the world. Crop rotation and the use of resistant cultivars are used to mitigate the damage of SCN, but these approaches are not completely successful because of the varied SCN populations. Thus, the limitations of these practices with soybean dictate investigation of other avenues of protection of soybean against SCN, and the prospect of genetically engineering broad resistance to SCN. For better understanding of the consequences of genetic manipulation, elucidation of SCN protein composition at the subunit level is necessary. We have conducted studies to determine the composition of SCN proteins using a proteomics approach in our laboratory using two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) to separate SCN proteins and to characterize the proteins further using mass spectrometry. Our analysis resulted in the identification of several hundred proteins. In this investigation, we developed a web based database (SCNProDB) containing protein information obtained from our previous published studies. This database will be useful to scientists who wish to develop SCN resistant soybean varieties through genetic manipulation and breeding efforts. The database is freely accessible from: http://bioinformatics.towson.edu/Soybean_SCN_proteins_2D_Gel_DB/Gel1.aspx