BIOCHEMICAL AND CELLULAR MECHANISMS OF RETINA AND RETINAL PIGMENT EPITHELIUM APOPTOSIS

Oxidative stress, intense light exposure and oxygen imbalances such as hypoxic or hyperoxic conditions perturb mitochondria, nuclear function and further lead to cellular damage of retina and retinal pigment epithelial (RPE) cells. Our major aim is to understand the various biochemical and proteomic events that occur during the progression of retina and RPE cell death. The comprehensive objectives of this dissertation are to understand the functional aspects of protein expression, posttranslational modifications, protein or lipid binding changes, phenotypic, morphological alterations and their regulation during the retina and RPE apoptosis under oxidative stress. The entire study is divided into four chapters Chapter 1 contains introduction and background on apoptotic signaling in retina and RPE cells. In chapter 2, we demonstrated that the oxidative stress biomarker prohibitin shuttles between mitochondria and nucleus as an anti-apoptotic molecule and acts as a transcriptional regulator by altering its lipid binding affinity and by posttranslational modifications during oxidative damage to the retina and RPE. In chapter 3, we demonstrated that oxidative and photo-oxidative stress induced nitric oxide regulates the RPE apoptosis by altering serine/threonine protein phosphatase 2A (PP2A) catalytic subunit, vimentin phosphorylation and Bcl xL expression regulation in the RPE cells in vitro. In chapter 4, we further analyzed the differential expression of prohibitin in the retina and RPE during oxidative stress, diabetic retinopathy (DR) and age-related macular degeneration (AMD) condition. Our analysis of postmortem retinas reveals that prohibitin is significantly increased in aged and AMD retina, and decreased in retinas of human diabetic retinopathy and RPE of AMD. Our study demonstrates that prohibitin levels determine the apoptotic signaling in the retina and RPE during retinal degenerative disease progression.

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.