Transcriptome analysis foridentification of
stress-responsivemicroRNAs in plants
Weixiong Zhang
Department of Computer Science and Engineering
Washington University in St. Louis
Department of Genetics
Washington University School of Medicine
St. Louis, MO 63130, USA
Abstract: MicroRNAsare ~21nt non-coding RNAs thatare key post-transcriptional gene regulators in eukaryotes. MicroRNAs in plantsregulate many genes that are involved in development andstress response. Although a large number of microRNAs 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 formicroRNA functional analysis. Inthis talk I will describe a novel,machine learning/datamining approach for identifying microRNA genes in plantsthat 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 microRNA genes, a new transcriptome-based gene expression modeling method, and experimental validation of mature microRNAs and microRNA precursors.We applied our approach to studycold-responsive microRNA genes in Arabidospsis thaliana. We predicted nineteen individual microRNAs intwelve microRNA families to be up-regulated under cold stress in Arabidopsis seedlings. Our experimental validation showed that among thetwelve microRNA families, eight were differentially induced by coldand three were constantly expressed under cold stimulus. A promoteranalysis also revealed that these cold-inducible microRNA genes containmany known stress-related cis-regulatory elements intheir promoters. I will also discuss putative down-regulation pathways triggered by the induction of these microRNAgenes. Particularly, our result indicated that Auxin signalingpathways in Arabidopsis seedlings areregulated by many microRNAs under cold stress.
Short bio of the speaker: Dr. Weixiong Zhang is a full professor of Computer Science and of Genetics at Washington University in St. Louis, USA. He received his BS and MS in Computer Engineering from Tsinghua University and MS and PhD in Computer Science from University of California at Los Angeles (UCLA). His main research interests are in the areas of molecular biology, genomics, genetics, machine learning and datamining. He has published more than 130 research papers in journals and peer-reviewed conferences in these areas, including Genome Research, Molecular Systems Biology, American J. Human Genetics,Genome Biology, Nucleic Acid Research, J. Virology and J. Alzheimer’s Disease. His research has been supported by NIH, NSF, USDA, DARPA, the Alzheimer’s Association and Monsanto Company. In recent years, he has been focusing on developing computational methods and bioinformatic tools for analyzing large scale biological data for transcriptome modeling and analyzing noncoding small RNA gene regulation, as well as their applications to complex human diseases, such as Alzheimer’s disease and psoriasis, viral infection, and plant stress responses. He is currently Deputy Editor of PLoS Computational Biology and Associate Editors of J. of Alzheimer’s Disease and Artificial Intelligence.