A Computational Approach to Understand Arabidopsis Thaliana and Soybean Resistance to Fusarium Solani (Fsg)
In this study, we reported the analysis of Arabidopsis thaliana microarray gene expression profile of root tissues after the plant was challenged with fungal pathogen Fusarium solani f. sp. glycines (Fsg). Our microarray analysis showed that the infection caused 130 transcript abundances (TAs) to increase by more than 2 fold and 32 out of 130 TAs were increased by more than 3 fold in the root tissues. However, only nineteen ESTs were observed with a decrease in TAs by more than 2 fold and 13 of them went down more than 3 fold due to the pathogen infection. In addition, the number of the up-regulated genes was nearly seven times more than that of down-regulated genes. The coordinate regulation of adjacent genes was detected and the distance distribution of the nearest neighbor genes was less likely to be randomly scattered in genome. The results of this study enabled us to decipher the resistance mechanism to Fsg through an integrated computational approach.
MSU Digital Commons Citation
Yuan, Jiazheng; Zhu, Michelle; Iqbal, M. Javed; Yang, Jack Y.; and Lightfoot, David A., "A Computational Approach to Understand Arabidopsis Thaliana and Soybean Resistance to Fusarium Solani (Fsg)" (2007). Department of Computer Science Faculty Scholarship and Creative Works. 19.