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shRNA Validation ProjectA Cooperative Project Between the CGAP and the ICBP
Purpose
The purpose of this project was to design and test a set of shRNAs for validation as tools to target high priority cancer genes. After validation, the target sequences of the shRNAs were deposited into the NCI's Cancer Genome Anatomy Project (CGAP) open access, public database. All shRNA constructs are commercially available. The validated shRNAs are designed to serve as tools for researchers to target and modulate the expression of well-known cancer-associated genes. Background RNA interference (RNAi) is an essential tool to study gene function and it is especially powerful in advancing cancer research (1-3). The shRNA Validation Project expands researchers' ability to exploit RNAi to better understand gene expression and cancer. In this project, shRNAs for 136 high priority cancer-related genes were tested and annotated for their effect(s) on gene expression. The testing and annotation of these shRNA constructs in a lentiviral vector system constituted the validation of these shRNAs. For each of the 136 cancer-related genes, three shRNAmir constructs were designed, tested and annotated. Materials and Methods Cell Lines Results The following figure and tables summarize the data generated to produce a collection of cancer-relevant shRNAs.
Effect of shRNAmirs: Most of the 136 Cancer-Related Genes have at Least 1 shRNAmir that Represses Gene Expression ≥50%.Summary and Conclusions A set of 393 GIPZ lentiviral shRNAmirs, targeted to 132 high priority cancer-related genes and validated in MCF-7 and OVCAR-8 cells are now available as a research resource. The majority of the hairpins led to a reduction in gene expression. In the OVCAR-8 cell line, 224 shRNAmirs (or 68% of the shRNAmirs) resulted in at least a 50% reduction in gene expression. In MCF-7 cells, 119 of the 337 hairpins resulted in at least a 50% reduction in gene expression. Overall, 52% of the shRNAmirs tested were able to knockdown gene expression by at least 50%. Additionally, for 74% of the genes tested in OVCAR-8 cells, there are at least two shRNAmirs that reduced gene expression by at least 50%; in 44% of genes tested in OVCAR-8, there were two hairpins that knocked down gene expression by 70% or more. (Table 1-A and Table 1-B). While these results are promising, the effectiveness of the shRNAmir constructs can likely be increased further. Knockdown efficiency of shRNAmirs is highly dependent on the number of functional integrations within the cell. For these experiments, cell populations were transduced at low MOIs resulting in low to single copy integrations. Therefore, increasing the transducing MOIs will increase functional integrations and thus potentially increase the knockdown efficiencies of the hairpins. Additionally, transductions outlined herein were done in the presence of serum. Serum inhibits transduction efficiency and thus knockdown efficiency (unpublished data). Transducing cell populations at higher MOIs in the absence of serum would likely increase the knockdown efficiency for the shRNAmirs. A comprehensive view of the hairpins' effects shows a range of gene expression modulation (see Figure 1). In most cases there were cell line specific differences. Generally, greater knockdown effects were observed in the OVCAR-8 cell line than in the MCF-7 cell line. For example, 68% of the hairpins tested in OVCAR-8 resulted in 50% or greater knockdown, while just over 35% did so in MCF-7. These differences were in part due to the cell lines' ability to be transduced. MCF-7 was found to have a lower transduction efficiency than OVCAR-8 (data not shown). These data suggest that the selection of cell lines to study gene expression modulation is important and should be considered in the interpretation of results. The data also indicate that the level of basal gene expression should also be considered. Table 2 includes data for 85% of the 393 possible data points that would be generated for each of the 132 genes targeted in the two cell lines. However, data was not generated for 15% of total possible data points. In the majority of the samples that did not generate data, the 18S internal control was detected within the expected range suggesting much of the 15% attrition was likely due to undetectable levels of target gene expression or primer and probe failure. In some instances there is strong evidence suggesting that losses were more likely due to lack of gene expression. For example, the receptor tyrosine kinase AXL was measured in OVCAR-8 demonstrating that the primer and probe set functions. However, all of the MCF-7 AXL samples failed detection. This is consistent with expression levels too low to detect in one cell line but not the other. These data indicate that basal levels of gene expression between different cell lines are an important consideration in the selection, design and interpretation of gene expression analysis. Additionally, 73 hairpins (10% of all shRNAmirs) resulted in increased gene expression, i.e. expression levels above that of the non-silencing control. More gene expression activation was seen in MCF-7 cell populations than in OVCAR-8, i.e. 13.3% versus 8.5%, respectively. There are examples in the literature of duplexed RNA inducing transcriptional activation of genes (8-10). It is possible that the results seen here are related to this mechanism, however current data are inconclusive. In summation, there is now a validated shRNAmir clone set for genes directly relevant to cancer research. The availability of this set and the accompanying knockdown efficiency data will enable investigators to apply RNAi technologies immediately to their research efforts with greater confidence.
1. M. Allen et al., Nat. Genet. 35, 258 (2003). 2. J. Downward, Oncogene 23, 8334 (2004). 3. J. Silva, et al., Oncogene 23, 8401 (2004). 4. Algorithm design, Greg Hannon at Cold Spring Harbor. http://www.cshl.edu/public/SCIENCE/hannon.html 5. J. M. Silva et al., Nat. Genet. 37, 1281 (2005). 6. P. J. Paddison et al., Nat. Methods 1, 163 (2004). 7. F. Stegmeier, et al., Proc. Natl. Acad. Sci. 102, 13212 (2005). 8. J. J. Rossi, Nat. Chem. Biol. 3, 136 (2007). 9. L. C. Li et al., Proc. Natl. Acad. Sci. 103, 17337 (2006). 10. B. A. Janowski et al., Nat. Chem. Biol. 3, 166 (2007).
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