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Seminar abstract

Transcriptome analysis for identifying stress-inducible microRNAs

Weixiong Zhang
Washington University in St. Louis

Abstract :

MicroRNAs (miRNAs) are ~21nt non-coding RNAs that regulate gene expression at the post-transcriptional level. Plant miRNAs regulate many genes that are involved in development and stress response. Although a large number of miRNAs have been identified and studied, most of them remain to be functionally annotated. Experimental functional analysis is laborious and costly. It is, therefore, desirable to develop computational approaches to support and complement experimental approaches for miRNA functional analysis. In this talk I will describe a novel, machine learning/datamining approach for identifying microRNA genes in plants that are responsive to environmental stresses. Our overall approach consists of a new computational method for identifying cis-regulatory DNA sequences (motifs) from the promoters of mRNA genes, a method for predicting core promoters of miRNA genes, a new transcriptome-based gene expression modeling method, and experimental verification of mature miRNAs and miRNA precursors. We applied our approach to study cold-responsive microRNA genes in Arabidospsis thaliana. We predicted nineteen individual microRNAs in twelve miRNA families to be up-regulated in Arabidopsis seedlings under cold stress. Our experimental validation showed that among the twelve microRNA families, eight were differentially induced by cold and three were constantly expressed under cold stimulus. A promoter analysis also showed that these cold-inducible microRNA genes contain many known stress-related cis-regulatory elements in their promoters. I will also discuss putative transcriptional down-regulation pathways triggered by the induction of these microRNA genes. Particularly, our result indicated that auxin signaling pathways in Arabidopsis seedlings may be mediated by many microRNAs.


Weixiong Zhang is a professor of Computer Science and of Genetics at Washington University in St. Louis, Missouri, USA. He received his B.S. and M.S. in computer engineering from Tsinghua University, Beijing, China, and his M.S. and Ph.D. in computer science from University of California at Los Angeles (UCLA). Professor Zhang's research interests include computational systems biology and genomics, artificial intelligence, data mining, and combinatorial optimization. He has published more than 100 papers in these areas and is the author of a research monograph, State-Space Search: Algorithms, Complexity, Extensions and Applications, published by Springer in 1999. He is currently associate editors of PLoS Computational Biology, J. Alzheimer's Disease, Artificial Intelligence, and AI Communications - The European Journal on Artificial Intelligence.
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