DISSECTION OF STRESS RESPONSE NETWORKS REGULATING MULTIPLE STRESSES IN RICE

Important food crops like rice are constantly exposed to various stresses that can have devastating effect on their survival and productivity. Being sessile, these highly evolved organisms have developed elaborate molecular machineries to sense a mixture of stress signals and elicit a precise response to minimize the damage. However, recent discoveries revealed that the interplay of these stress regulatory and signaling molecules is highly complex and remains largely unknown. In this work, we conducted large scale analysis of differential gene expression using advanced computational methods to dissect regulation of stress response which is at the heart of all molecular changes leading to the observed phenotypic susceptibility. One of the most important stress conditions in terms of loss of productivity is drought. We performed genomic and proteomic analysis of epigenetic and miRNA mechanisms in regulation of drought responsive genes in rice and found subsets of genes with striking properties. Overexpressed genesets included higher number of epigenetic marks, miRNA targets and transcription factors which regulate drought tolerance. On the other hand, underexpressed genesets were poor in above features but were rich in number of metabolic genes with multiple co-expression partners contributing majorly towards drought resistance. Identification and characterization of the patterns exhibited by differentially expressed genes hold key to uncover the synergistic and antagonistic components of the cross talk between stress response mechanisms. We performed meta-analysis on drought and bacterial stresses in rice and Arabidopsis, and identified hundreds of shared genes. We found high level of conservation of gene expression between these stresses. Weighted co-expression network analysis detected two tight clusters of genes made up of master transcription factors and signaling genes showing strikingly opposite expression status. To comprehensively identify the shared stress responsive genes between multiple abiotic and biotic stresses in rice, we performed meta-analyses of microarray studies from seven different abiotic and six biotic stresses separately and found more than thirteen hundred shared stress responsive genes. Various machine learning techniques utilizing these genes classified the stresses into two major classes' namely abiotic and biotic stresses and multiple classes of individual stresses with high accuracy and identified the top genes showing distinct patterns of expression. Functional enrichment and co-expression network analysis revealed the different roles of plant hormones, transcription factors in conserved and non-conserved genesets in regulation of stress response.

A REPORT ON THE MITEP PROGRAM AND ITS IMPACT ON MY TEACHING

This report has two major objectives. First, the results of an action research project conducted at my high school concerning the use of graphic organizers and their effects on students' written expression abilities. The findings from this action research project indicate that the use of graphic organizers can prove beneficial to students. The second major objective of this report is to provide a reflection and evaluation of my experiences as a participant in the Michigan Teacher Excellence Program (MiTEP). This program provided middle and high school science teachers with an opportunity to develop research based pedagogy techniques and develop the skill necessary to serve as leaders within the public school science community. The action research project described in the first chapter of this report was a collaborative project I participated in during my enrollment in ED 5705 at Michigan Technological University. I worked closely with two other teachers in my building - Brytt Ergang and James Wright. We met several times to develop a research question, and a procedure for testing our question. Each of us investigated how the use of graphic organizers by students in our classroom might impact their performance on writing assessments. We each collected data from several of our classes. In my case I collected data from 2 different classes over 2 different assignments. Our data was collected and the results analyzed separately from classroom to classroom. After the individual classroom data and corresponding analysis was compiled my fellow collaborators and I got together to discuss our findings. We worked together to write a conclusion based on our combined results in all of our classes.