ROLE OF MICRORNA LET-7 IN PANCREATIC BETA CELLS

MicroRNAs (miRNAs) are small non-coding RNAs that inhibit gene expression at transcriptional or post-transcriptional level. Let-7 family is among the first identified human miRNAs and regulates multiple cellular processes including glucose metabolism in multiple organs. It has been reported that overexpression of let-7 resulted in insulin resistance and impaired glucose tolerance through repressing insulin signaling pathway in both muscle and liver. However, the role and mechanism underlying let-7 function in pancreatic beta-cells have yet to be elucidated. Let-7 family contains nine members, which poses a significant challenge in complete deletion of this miRNA family. To study the function of let-7 and to overcome the functional redundancies of various let-7 members in pancreatic beta-cells, the highly expressed let-7a and let-7b were blocked simultaneously using short tandem target mimic (STTM) approach developed in our laboratory. Introducing STTM-let7 into beta-cells markedly increased the expression of Caspase 3, a direct target of let-7, confirming a sufficient functional knockdown of let-7a/b by STTM-let7. STTM-let7 enhanced apoptotic cell death induced by cytokine, indicating that let-7a/b is able to protect from apoptosis through attenuating Caspase 3 expression in pancreatic beta-cells. In contrast to the previous observation that let-7 silencing increases insulin signaling in muscle and liver, inhibition of let-7 with STTM-let7 significantly repressed glucose-stimulated insulin signaling in pancreatic beta-cells, leading to impaired insulin secretion and reduced beta-cell proliferation. Taken together, an appropriate level of let-7 is essential in maintaining beta-cell function and viability. Dysregulation of let-7 may contribute to the pathogenesis of type 2 diabetes.

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.