shERWOOD shRNA design + Optimized shRNA Processing = Superior Knockdown
shERWOOD-UltramiR shRNA reagents are next generation vector-based RNAi triggers designed using the proprietary shERWOOD algorithm developed and validated in Dr. Gregory Hannon’s laboratory at Cold Spring Harbor Laboratory (see Knott et al 2014). An alternate microRNA scaffold "UltramiR" has been optimized for increased shRNA processing and potency based on new information on the key determinants for primary microRNA processing (Auyeung et al 2013).
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shERWOOD-UltramiR shRNA - Lentiviral, inducible lentiviral and retroviral vector options with a choice of promoters for optimal expression in your target cell linesMammalian promoters may differ in expression level or be silenced over time depending on the target cell line. Variation in expression level can also affect fluorescent marker expression as well as knockdown efficiency. shERWOOD UltramiR shRNA are offered in a choice of promoters for optimal expression, and can be delivered by transfection or transduction.
The mouse CMV promoter expresses efficiently in a number of human and mouse cell lines and is standard in the ZIP lentiviral vector target gene sets. However, for cell lines where a different promoter may be optimal, the ZIP promoter testing kit (TLN0005) is available to quickly test for optimal expression in your target cell line. Simply use the provided pre-packaged viral particles from a panel of vectors expressing ZsGreen from the different promoters to easily detect expression efficiency.
shERWOOD-UltramiR hairpin cartoonshERWOOD shRNA are expressed with the optimized ultramiR scaffold. The figure below shows the shRNA secondary structure and highlights the sequences that are included in the mature RNAi trigger bound to the targeted mRNA.
Superior Knockdown and Specificity with shERWOOD-UltramiR shRNA
shERWOOD-UltramiR shRNA designs are stringently selected by the shERWOOD algorithm and expressed from an optimized microRNA scaffold for increased small RNA processing. These designs outperform early generation shRNA libraries providing more efficient knockdown when compared at the individual gene level or in pooled shRNA screens.
Consistent knockdown efficiency relative to early generation shRNA designsThe combination of the shERWOOD shRNA designs and UltramiR scaffold consistently produces potent shRNA even when expressed from a single integration in the genome. Knockdown efficiencies of shERWOOD-UltramiR hairpins were benchmarked against existing TRC and GIPZ shRNAs targeting three different genes. shERWOOD-UltramiR designs produced very potent and consistent knockdown at single copy relative to available TRC and GIPZ hairpins targeting the same genes (Knott et al 2014).
The consistent performance seen with shERWOOD-UltramiR shRNA provides the clearest results for single gene interrogation and is essential for optimal deconvolution in pooled shRNA screening and greater confidence in results.
Figure 1. Individual shRNA from the shERWOOD-UltramiR, Hannon-Elledge V.3 and TRC targeting the mouse genes Mgp, Slpi and Serpine2 were compared based on knockdown efficiency by measuring knockdown at the mRNA level. Dotted line represents 70% knockdown. Mouse 4T1 cells were transduced at single copy and knockdown was tested following selection. shRNA from the TRC and Hannon-Elledge V.3 were expressed from the pLKO.1 and GIPZ lentiviral vectors (respectively) and the shERWOOD-UltramiR shRNA are expressed from the LMN retroviral vector. (Data adapted from Knott et al. 2014)
Improved specificity versus classic stem loop shRNAOn target specificity of the shERWOOD-UltramiR shRNA shown in Figure 1 was compared to that of TRC shRNA that showed potent single copy knockdown. RNA-seq analysis was performed on cell lines expressing shRNA targeting Slpi and Mgp. The graphic above shows a heat map of the number of genes with differential expression (fold change > 2 and FDR <0.05) from each of the pairwise comparisons. shERWOOD-UltramiR shRNA showed relatively few differences (less than 25 genes) while TRC designs show approximately 250 genes altered in cells expressing shRNA targeting Slpi, and over 500 in the line expressing the Mgp shRNA. The two TRC shRNA selected for the comparison were the only two targeting those genes which provided significant knockdown (see shRNA in Figure 1: TRC-Mgp-1 and TRC-Slpi-1 versus all four shERWOOD-UltramiR shRNA for each gene. No shRNA targeting Serpine2 were compared due to the lack of a TRC shRNA producing significant knockdown for that gene.)
Figure 2. Heat map showing the number of differentially expressed genes (> 2-fold change and FDR <0.05) identified through pairwise comparisons of the cell lines corresponding to (A) Mgp and (B) Slpi knockdown by the shERWOOD-UltramiR shRNA and the only TRC shRNAs showing significant knockdown for the two genes (TRC-Mgp-1 and TRC-Slpi-1). (Adapted from Knott et al. 2014)
This data is consistent with other publications showing classic stem loop shRNA can cause significant off-target effects and toxicity. Several reports (Beer et al 2010, Castanatto et al 2007, Pan et al 2011, Baek et al 2014, Knott et al 2014) have shown that off-target effects can be ameliorated by expressing the same targeting sequence in a primary microRNA scaffold (shRNA-miR).
Figure 3. Percentage of shRNA targeting essential genes that depleted in a pooled shRNA survival screen. shRNA designed from the TRC, Hannon-Elledge V.3, shERWOOD or shERWOOD-UltramiR libraries were expressed from the same vector with compared. shERWOOD-UltramiR shows the greatest number of shRNA per gene for genes that are depleted in the screen. (Adapted from Knott et al. 2014)
shERWOOD UltramiR shRNA Design
The shRNA-specific shERWOOD algorithm designs combine an optimized UltramiR microRNA scaffold provide increased and consistent knockdown efficiency (Knott, et al., 2014). An unbiased screen (“Sensor Assay”) of 270,000 shRNA sequences was used to train the shERWOOD algorithm. Of these, only ~2% of the sequences tested showed extremely potent knockdown at single copy and this data set was used to train the shERWOOD algorithm. In addition, optimization of the microRNA scaffold provides increased microRNA processing. The figures below outline the screen and provide examples of efficient knockdown and increased processing.
Figure 1. Schematic showing the sensor assay used to validate 270,000 sequences and train the shERWOOD algorithm.
High-throughput sensor assay used to train the shERWOOD algorithmThe Sensor Screen tested shRNA knockdown efficiency using sequences inserted into a primary miR-30 scaffold so as to undergo microRNA pathway processing. shRNA expression was under the control of a doxycycline-inducible promoter in a viral vector that contained the target of the shRNA fused to a green fluorescent reporter gene (Venus). Fluorescence could then be used as a “Sensor” to separate cells expressing with efficient shRNA from those with inefficient shRNA, Figure 1 (Knott et al 2014).
Over 250,000 shRNA targeting all genes in the human genome were functionally tested in a Sensor screen. By analyzing the dropout rate of shRNA-mir at each step of microRNA processing (primary, precursor and mature microRNA) Fellmann et al 2011 showed that each shRNA processing had specific sequence biases that impacted both the rate and accuracy of processing and therefore potency of the hairpin.
Sequence analysis and thermodynamic information from the shRNA was used to train the shERWOOD shRNA design algorithm (Knott et al 2014).
• The first shRNA-specific design algorithm
• Optimized to predict designs based on potent single copy knockdown
• Designs target all transcripts of the gene
• Includes filters to minimize off target effects
shERWOOD shRNA designs provide potent knockdown even at single copy
Figure 2. Western blot (A) and graph (B) showing protein knockdown produced by several shERWOOD predicted hairpins targeting 3 genes. Cells were transduced at single copy (MOI=0.3) in HEK293T (A) or U2OS (B) cells. Dotted line represents 70% knockdown.
shERWOOD designs provide knockdown even when expressed from a single integration in the target cell. The figure above shows knockdown at the protein level in HEK293T or U2OS cells after single copy transductions targeting PTEN, FANCA or FANCI. Top ranked hairpins targeting each gene produced effective and consistent protein knockdown.
Figure 3. Relative abundances of processed guide sequences for two shRNA as determined by small RNA cloning and NGS analysis when cloned into traditional miR-30 and UltramiR scaffolds. Values represent log-fold enrichment of shRNA guides with respect to sequences corresponding to the top 10 most highly expressed endogenous microRNA.
Optimized scaffold for increased small RNA processingPrevious generation microRNA-adapted shRNA libraries have alterations in conserved regions of the mir-30 scaffold that were suboptimal for small RNA processing and consistency of knockdown. The alternate miR scaffold called UltramiR has been optimized based on recent knowledge of the key determinants for optimal primary microRNA processing (Auyeung et al. 2013). This new scaffold increases shRNA processing presumably by improving biogenesis. When shRNA were placed into the UltramiR scaffold, mature small RNA levels were increased roughly two fold relative to levels observed using the standard miR-30 scaffold (Knott et al., 2014).
Figure 4. Example target regions for a single transcript gene (top) and two multiple transcript genes (middle and bottom). For the middle gene, a target region (composed of >250 bp present in >80% of transcripts) was identified on the first algorithm iteration. For the bottom gene, a second algorithm iteration was required, where the smallest transcript was not considered. C) Example shRNA off-target algorithm implementation. In case A, all rank 1-4 are non-multimappers, so no shuffling occurs. In case B, all rank 1-8 shRNAs are multimappers (indicting that the gene is a paralogue), so no shuffling occurs. In case C, some but not all rank 1-4 and rank 5-8 shRNAs are multimappers and shuffling occurs to select a set of 4 shRNAs that include the highest scoring non-multimappers. (Adapted from Knott et al 2014)