Knockdown of Long Noncoding RNAs in Breast Cancer
Poster Mar 31, 2015
1 Jennii Luu, 2 Jesper Maag, 1 Yanny Handoko, 3 Richard Redvers, 3,4 Robin L. Anderson, 5 Maren M. Gross , 2 Marcel E. Dinger, and 1,3 Kaylene J. Simpson 1 Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre; 2 Genome Informatics, The Kinghorn Cancer Centre, The Garvan Institute of Medical Research; 3 Metastasis Research Laboratory, Peter MacCallum Cancer Centre, 4 Sir Peter MacCallum Department of Oncology, University of Melbourne;
Traditionally genetics has held a protein centric view with RNA seen as an intermediate step between DNA and protein. Recently, the emerging evidence of pervasive transcription throughout the genome has challenged this view1,2. Long noncoding RNAs (lncRNA) are selectively expressed during different cell cycles3 as well as transcribed differently in specific cell types4, which emphasizes their importance in regulating cell specification. lncRNAs can work on every stage of transcription from chromatin remodeling, controlling transcription to post-transcriptional processing through various mechanisms such as directly binding to transcription activation sites, working as decoys for transcript suppressors/activators or as guiding/scaffold molecules for chromatin remodeling complexes5.
Increasing numbers of studies have associated disease with lncRNAs. However, such studies have typically only focused on exploring the function of individual lncRNAs. In preliminary studies, we investigated the functional consequences of lncRNA knockdown in the breast cell lines MCF 10A and MDA-MB-231 using cell viability and morphology as readouts. Using high throughput siRNA screening protocols established in the Victorian Centre for Functional Genomics, we have knocked down all targets in the Dharmacon™ Lincode™ siRNA Library collection (currently 2,231) in both cell lines and quantitated changes using high content imaging. Here we report the functional consequences of lncRNA knockdown in breast cell lines and correlate with patient tumor data.
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