Use of Bioinformatics to Investigate Abiotic Stress in Arabidopsis and to Design Primers for Pathogen Detection

Abstract

The focus of the work has been on computational approaches to solving biological problems. First, microarray analysis was used to study the role of PLDα1 in drought stress in Arabidopsis. Second, a tool for designing and in-silico testing of primers for PCR-based pathogen detection will be discussed. Phospholipase D (PLD) has been implicated in a variety of stresses including osmotic stress and wounding. PLDα 1-derived phosphatidic acid interacts with ABI1 phosphatase 2C and promotes abscisic acid signaling. Plants with abrogated PLDα 1 show insensitivity to ABA and impaired stomatal conductance. My goal is to identify PLDα-mediated downstream events in response to progressive drought stress in Arabidopsis. Arabidopsis thaliana (Col-0) and antisense-PLDα 1 (Anti-PLDα) were drought stressed by withholding water. Anti-PLDα experienced severe water stress at the same time period that Col-0 experienced less water stress. Diurnal leaf water potential (LWP) measurements showed that Anti-PLDα had lower LWP than Col-0 under drought stress conditions. qRT-PCR revealed up to 18-fold lower values for PLDα transcripts in stressed Anti-PLDα plants when compared to stressed Col-0. Microarray expression profiles revealed distinct gene expression patterns in Col-0 and Anti-PLDα. ROP8, PLDδ and lipid transfer proteins were among the differentially expressed genes between the two genotypes. Different microarray analyses methods (TM4 and Expresso) were also compared on two different data sets. The results obtained from Expresso analysis were more accurate when compared with quantitative RT-PCR data. Rapid diagnosis of disease-causing agents is extremely important since delayed diagnosis can result in disease spread and delayed prophylaxis. It is even more important in an era where disease-causing agents are used as bioterrorism agents. Rapid advances in sequencing technology have resulted in the sequencing of thousands of microorganisms in recent years. Availability of genomic sequences has made it possible to identify and characterize microorganisms at the molecular level. PCR-based detection is powerful for pathogen diagnostics since it is rapid and sensitive. We have developed a tool, PathPrime, that can design primers, computationally test them against target genes, and potential contaminant sequences, and identify a minimum set of primers that can unambiguously detect a given list of sequences.