In silico screening for pathogenesis related-2 gene candidates in Vigna uguiculata transcriptome

Item Type:	Article
Title:	In silico screening for pathogenesis related-2 gene candidates in Vigna uguiculata transcriptome
Creators Name:	Wanderley-Nogueira, A.C. and Da Mota Soares-Cavalcanti, N. and Belarmino, L.C. and Barbosa-Silva, A. and Kido, E.A. and Do Monte, S.J.H. and Pandolfi, V. and Calsa-Junior, T. and Benko-Iseppon, A.M.
Abstract:	Plants evolved diverse mechanisms to struggle against pathogen attack, for example the activity of Pathogenesis-Related (PR) genes. Within this category PR-2 encodes a Beta-glucanase able to degrade the polysaccharides present in the pathogen cell wall. The aim of this work was to screen the NordEST database to identify PR-2 members in cowpea transcriptome and analyze the structure of the identified sequences as compared with data from public databases. After search for PR-2 sequences in NordEST; CLUSTALx and MEGA4 were used to align PR-2 orthologs and generate a dendrogram. CLUSTER program revealed the expression pattern trough differential display. A new tool was developed aiming to identify plant PR-2 proteins based in the HMMER analysis. Among results, a complete candidate from cowpea could be identified. Higher expression included all libraries submitted to biotic (cowpea severe mosaic virus, CPSMV) stress, as well as wounded and salinity stressed tissues, confirming PR expression under different kind of stresses. Dendrogram analysis showed two main clades, the outgroup and Magnoliopsida where monocots and dicots organisms were positioned as sister groups. The developed HMM model could identify PR-2 also in other important plant species, allowing the development of a bioinformatic routine that may help the identification not only of pathogenesis related genes but any other genes, classes that present similar conserved domains and motifs.
Keywords:	Bioinformatics, Pathogenesis-Related, Beta-Glucanases, Biotic Stress, Cowpea
Source:	Lecture Notes in Computer Science
Series Name:	Lecture Notes in Computer Science
ISSN:	0302-9743
ISBN:	978-3-642-14570-4
Publisher:	Springer
Volume:	6160
Page Range:	70-81
Date:	2010
Official Publication:	https://doi.org/10.1007/978-3-642-14571-1_6

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