RNA interference (RNAi) mediated by small interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs) has become a powerful tool for gene knockdown studies. However, the levels of knockdown vary greatly. Here, we examine the effect of target disruption energy, a novel measure of target accessibility, along with other parameters that may affect RNAi efficiency. Based on target secondary structures predicted by the Sfold program, the target disruption energy represents the free energy cost for local alteration of the target structure to allow target binding by the siRNA guide strand. In analyses of 100 siRNAs and 101 shRNAs targeted to 103 endogenous human genes, we find that the disruption energy is an important determinant of RNAi activity and the asymmetry of siRNA duplex asymmetry is important for facilitating the assembly of the RNA-induced silencing complex (RISC). We estimate that target accessibility and duplex asymmetry can improve the target knockdown level significantly by nearly 40% and 26%, respectively. In the RNAi pathway, RISC assembly precedes target binding by the siRNA guide strand. Thus, our findings suggest that duplex asymmetry has significant upstream effect on RISC assembly and target accessibility has strong downstream effect on target recognition. The results of the analyses suggest criteria for improving the design of siRNAs and shRNAs.
BackgroundRNA interference (RNAi) mediated by small interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs) has become a powerful technique for eukaryotic gene knockdown. siRNA GC-content negatively correlates with RNAi efficiency, and it is of interest to have a convincing mechanistic interpretation of this observation. We here examine this issue by considering the secondary structures for both the target messenger RNA (mRNA) and the siRNA guide strand.ResultsBy analyzing a unique homogeneous data set of 101 shRNAs targeted to 100 endogenous human genes, we find that: 1) target site accessibility is more important than GC-content for efficient RNAi; 2) there is an appreciable negative correlation between GC-content and RNAi activity; 3) for the predicted structure of the siRNA guide strand, there is a lack of correlation between RNAi activity and either the stability or the number of free dangling nucleotides at an end of the structure; 4) there is a high correlation between target site accessibility and GC-content. For a set of representative structural RNAs, the GC content of 62.6% for paired bases is significantly higher than the GC content of 38.7% for unpaired bases. Thus, for a structured RNA, a region with higher GC content is likely to have more stable secondary structure. Furthermore, by partial correlation analysis, the correlation for GC-content is almost completely diminished, when the effect of target accessibility is controlled.ConclusionThese findings provide a target-structure-based interpretation and mechanistic insight for the effect of GC-content on RNAi efficiency.
Identification of potential anticancer drug targets through the selection of growth-inhibitory genetic suppressor elementsIn this article by Primiano et al. (Cancer Cell 4,, the authors inadvertently indicated the incorrect concentration of BrdU on page 50 in the second paragraph of the second column; this should read 50 M BrdU.
To identify human genes required for tumor cell growth, transcriptome-scale selection was used to isolate genetic suppressor elements (GSEs) inhibiting breast carcinoma cell growth. Growth-inhibitory GSEs (cDNA fragments that counteract their cognate gene) were selected from 57 genes, including known positive regulators of cell growth or carcinogenesis as well as genes that have not been previously implicated in cell proliferation. Many GSE-cognate genes encode transcription factors (such as STAT and AP-1) and signal transduction proteins. Monoclonal antibodies against a cell surface protein identified by GSE selection, neural cell adhesion molecule L1CAM, strongly inhibited the growth of several tumor cell lines but not of untransformed cells. Hence, selection for growth-inhibitory GSEs allows one to find potential targets for new anticancer drugs.
As a general strategy for function-based gene identification, an shRNA library containing ≈150 shRNAs per gene was enzymatically generated from normalized (reduced-redundance) human cDNA. The library was constructed in an inducible lentiviral vector, enabling propagation of growth-inhibiting shRNAs and controlled activity measurements. RNAi activities were measured for 101 shRNA clones representing 100 human genes and for 201 shRNAs derived from a firefly luciferase gene. Structure-activity analysis of these two datasets yielded a set of structural criteria for shRNA efficacy, increasing the frequencies of active shRNAs up to 5-fold relative to random sampling. The same library was used to select shRNAs that inhibit breast carcinoma cell growth by targeting potential oncogenes. Genes targeted by the selected shRNAs were enriched for 10 pathways, 9 of which have been previously associated with various cancers, cell cycle progression, or apoptosis. One hundred nineteen genes, enriched through this selection and represented by two to six shRNAs each, were identified as potential cancer drug targets. Short interfering RNAs against 19 of 22 tested genes in this group inhibited cell growth, validating the efficiency of this strategy for high-throughput target gene identification.
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