Below is a figure showing the reduced off-target effects with a siPOOL (3 nM) after 48 hours in HeLa cells:
Transcriptome-wide profiling revealed a single siRNA can induce numerous off-targets (red dots) while a siPOOL against the same target gene (green dot), and containing the non-specific siRNA, had greatly reduced off-target effects.
Low hit validation rate for Dharmacon siGENOME screens
Good experimental design is important when validating hits from RNAi screens. Off-target effects from single siRNAs and low-complexity siRNA pools (e.g. Dharmacon siGENOME) result in high false-positive rates that must be sorted out in validation experiments.
Dharmacon siGENOME pools (SMARTpools) have 4 siRNAs, and the most common form of validation is to test the pool siRNAs individually (deconvolution).
Rather than deconvoluting the pool, a better approach is to test with independent reagents. Should the phenotype be due to the seed effects of an siRNA in the siGENOME pool, the new designs (with presumably different seed sequences) should not show them. (Note that because they have their own potential complicating off-targets, an even better option would be to use a reagent like siPOOLs that minimises the likelihood of off-target effects).
Independent validation reagents was the approach used by Li et al. in a screen looking for enhancers of antiviral protein ZAP activity.
They first did a genome-wide (18,200 genes) screen with siGENOME pools, looking for pools that increased viral infection rate.
The biggest effect was with the positive control, ZAP (aka ZC3HAV1). Several other pools also stood out as giving large increases in viral infection (Fig 1B):
They identified 90 non-control genes with reproducible Z-scores above 3 in their replicate experiments (~0.5% of screened genes).
These 90 genes were then tested with 3 Ambion Silencer siRNAs. (They also included a few genes in the validation round based on pathway information and off-target analysis– more on this below.)
Of the 90 candidate hit genes, only 11 could be confirmed (Fig 2B, note that ZC3HAV1/ZAP is the positive control and JAK1 was added to the validation round based on pathway info. A gene was considered confirmed if 2 of 3 siRNAs had a Z-score > 3.):
We also see that only 1 of the 7 top hits from the first round (blue genes in the first figure) was confirmed. This is a common observation in RNAi screens: the strongest phenotypes are mostly due to off-target effects.
Off-target effects are difficult to interpret, even using advanced analysis programs like Haystack or GESS. The authors tested 4 genes identified by Haystack as targets for seed-based off-targeting. None of those genes could be confirmed in the validation round.
Genetic compensation by transcriptional adaptation is a process whereby knocking out a gene (e.g by CRISPR or TALEN) results in the deregulation of genes that make up for the loss of gene function.
A 2015 study by Rossi et al. (discussed previously) alerted researchers that CRISPR/TALEN knock-out experiments may be subject to such effects.
Genetic adaption or compensation had been well known to mouse researchers creating knock-out lines. In fact, one of our company founders also ran into this when trying to confirm an RNAi phenotype in a knock-out mouse line. The knock-out mice, though not completely healthy, did not confirm the RNAi phenotype.
A paper published a couple years before the Rossi paper also showed clearly that knock-outs can create off-target effects via transcriptional adaptation.
Hall et al. showed with an siRNA screen that the centrosomal protein Azi1 was required for ciliogenesis in mouse fibroblasts, confirming previous work in zebrafish and fly.
Their Azi1 siRNA targeted the 3′ UTR, and they were able to rescue the phenotype with a plasmid expressing just the CDS (bar at far right), confirming that their phenotype was due to on-target knockdown:
However, knock-out mouse embryonic fibroblast cells (created by gene trapping) did not show any differences in in the number of cilia, centrosomes, or centrioles compared to wildtype (+/+ is wild type, Gt/Gt is the homozygous knock-out):
The one phenotypic difference they observed was that male knock-out mice were infertile, due to defective formation of sperm flagella. Female mice had normal fertility. Both were compensating, but only one showed a visible phenotype.
The authors note the benefits of RNAi in comparison to knock-out screening:
Discrepancies between the phenotypic severity observed with siRNA knock-down versus genetic deletion has previously been attributed to the acute nature of knock-down, allowing less time for compensation to occur
The excitement surrounding CRISPR should not diminish the continued value of RNAi screening.
Pooling only 4 siRNAs increases off-target effects
In a previous post, we showed how siRNA pools with small numbers of siRNAs can exacerbate off-target effects.
Low-complexity pools (with 4 siRNAs per gene) should thus lead to overall stronger off-target effects than single siRNAs.
This phenomenon was addressed in a bioinformatics paper a few years back. The authors created a model to predict gene phenotypes based on the combined on-target and off-target effects of siRNAs.
The siRNAs were screened either individually (Ambion and Qiagen), or in pools of four (Dharmacon siGENOME), in 3 different batcterial-infection assays (B. abortus, B. henselae, and S. typhimurium).
The model assumed that each siRNA silenced its on-target gene to the same level. For off-target silencing, they used the predictions from TargetScan, a program for calculating seed-based knockdown by miRNAs or siRNAs.
In order to assess model quality, they checked how similar the gene phenotype predictions were when using different reagents types in the same pathogen-infection screen.
The following figure shows the rank-biased overlap (a measure of how similar lists are with regards to top- and bottom-ranked items), when estimating siGENOME off-target knockdown in one of 2 ways:
A) using the maximum TargetScan score for any of the 4 siRNAs in the siGENOME pool
B) using the mean TargetScan score for the 4 siRNAs
If low-complexity pooling increases the degree of off-target effects, we would expect the maximum TargetScan score to produce better model concordance.
And that is what the authors found. (the two plots show the rank-biased overlap for the top and bottom of the phenotype ranked lists, respectively)
The off-target effect of a 4-siRNA, low-complexity pool is best described by the strongest off-target effect of any of the individual siRNAs.
As discussed in our NAR paper, pooling a minimum of 15 siRNAs is required to reliably prevent off-target effects.
Our 2014 Nucleic Acids Research paper provides an excellent overview of the siPOOL technology. Google Scholar shows that our paper has been cited 64 times.
To put this into perspective, the 2012 PLoS One paper on C911 controls by Buehler et al. has 72 citations. C911 controls are probably the most effective way to determine whether a single-siRNA phenotype is due to an off-target effect.
These citation numbers show that siPOOLs have good mind share when researchers consider the issue of RNAi off-target effects.
We have noticed, however, that in some cases our NAR paper is cited to justify approaches that we do not endorse.
For example, two recent papers (1, 2) cite our paper as support for the use of Dharmacon ON-TARGETplus 4-siRNA pools to reduce the potential for off-target effects.
Our paper shows, however, that high-complexity siRNA pools (> 15 siRNAs) are needed to reliably reduce off-target effects.
This blogpost describes issues encountered in target validation and how to safeguard against poor reproducibility in RNAi experiments.
The importance of target validation
More than half of all clinical trials fail from a lack of drug efficacy. One of the major reasons for this is inadequate target validation.
Target validation involves verifying whether a target (protein/nucleic acid) merits the development of a drug (small molecule/biologic) for therapeutic application.
Failing to adequately validate a target can burden a pharma with roughly 800 million to 1.4 billion in drug development costs. Impact is not only monetary as largesite closures often result as companies struggle to save costs and a reduced production effort deprives patients of new medicines.
Performing target validation well
Special attention should therefore be given to performing target validation techniques well.
Many of these techniques involve inhibiting target expression to establish its relevance in a cellular or animal disease model. This can be performed with chemical probes, RNA interference (RNAi), genetic knock-outs, and even targeted protein degradation.
The reproducibility of these techniques however has been an issue of concern for drug developers. Less than half of all findings from peer-reviewed scientific publications was reported to be successfully reproduced.
Cellular phenotypes caused by a chemical or genetic perturbant should be considered to be off-target until proved otherwise, especially when the phenotypes were detected in a down assay and therefore could reflect a nonspecific loss of cellular fitness. It is only by performing rescue experiments that one can formally address whether the effects of a perturbant are on-target.
The comment highlights the issue of reagent non-specificity as a notable contribution towards poor reproducibility.
Certainly, for RNAi the wide-spread off-target effects of siRNAs has been observed in numerous publications. The mechanism being well-established to be based on microRNA-like seed-based recognition of non-target genes. The effect dominates over on-target effects in many large RNAi screens, illustrating the depth of the problem.
Reagent non-specificity is not restricted to RNAi. There have been multiple reports of non-specificity for gene editing technique, CRISPR, which can be read about in detail here, here and here. Recent publications continue to shed more light on its potential off-targets as we learn more about this relatively new technique.
Even chemical probes may have multiple targets. It is hence imperative that more than one target validation technique be used to avoid confirmation bias.
Target validation – a story from Pharma
Back in 2013, when siTOOLs was just starting out, a pharma approached us with a target validation problem.
They were obtaining different results with 3 different siRNAs in a cellular proliferation assay. Despite all 3 siRNAs potently downregulating the target gene, they produced different effects on cell viability.
Which siRNA tool to trust?
A whole-transcriptome expression analysis performed for the 3 siRNAs and a siPOOL designed against the same target revealed the reason for the large variability.
Despite all siRNA tools affecting the same target, the difference in extent of gene deregulation was astounding. With the greatest number of off-target effects, it was not surprising that siRNA 3 showed an impact on cell proliferation.
In contrast, siPOOLs had 5 to 25X less differentially expressed genes compared to the 3 commercial siRNAs against the same target. An expression analysis carried out for another gene target showed similar results i.e. siPOOLs having far less off-targets.
The target was dropped from development. A great example where failing early is a good thing, though it was not without costs from validating the multiple siRNAs.
The recommended target validation tool
Functioning like a pack of wolves, siPOOLs increase the chances of capturing large and difficult prey, while making full use of group diversity to compensate for individual weakness.
siPOOLs efficiently counter RNAi off-target effects by high complexity pooling of sequence-defined siRNAs. This enables individual siRNAs to be administered at much lower concentrations, below the threshold for stimulating significant off-target gene deregulation. Due to having multiple siRNAs against the same target gene, target gene knock-down is maintained and in fact becomes more efficient.
We still recommend using multiple target validation techniques. As a first evaluation however, siPOOLs are quick, easy and most of all, reliable.
Summary:Low-complexity siRNA pooling (e.g. Dharmacon siGENOME SMARTpools) does not prevent siRNA off-targets. It may in fact exacerbate off-target effects. Only high-complexity pooling (siPOOLs) can reliably ensure on-target phenotypes.
Low-complexity pooling increases the number of siRNA off-targets
One of the claims often made in favour of low-complexity pooling (e.g Dharmacon siGENOME SMARTpools) is that this pooling reduces the number of seed-based off-target effects compared to single siRNAs.
If this were true, we would expect different low-complexity siRNA pools for the same gene to give similar phenotypes. But this is not the case.
Published expression data shows that low-complexity pooling actually increases the number of off-targets.
Kittler et al. (2007) looked at the effect of combining differing number of siRNAs in low to medium complexity siRNA pools (siRNA pools sizes were: 1, 3, 5, 9, and 12).
Their work showed that the number of down-regulated genes (50% or greater silencing) actually increases when small numbers of siRNAs are combined. Only when larger numbers of siRNAs are combined does the number of off-targets start to drop:
[The figure is based on data from GEO dataset GSE6807. Down-regulated genes are those whose expression is reduced by 50% or more. Note that the orange point is taken from our 2014 NAR paper, as we are not aware of other published expression datasets with this many pooled siRNAs. A few caveats with combining these datasets are that they use different target genes, siRNA concentrations, and the data comes from a different expression platform.]
Low-complexity pooling: a bad solution for siRNA off-targets
Low-complexity pooling does not get rid of the main problem associated with single siRNAs: seed-based off-target effects. Based the above analysis, it can make it even worse. It also prevents use of the most effective computational measures against seed effects.
Redundant siRNA Activity (RSA) is a common on-target hit analysis method for single-siRNA screens. It checks how over-represented the siRNAs for a gene are at the top of a ranked screening list. If a gene has 2 or more siRNAs near the top of the list, it will score better than a gene that only has a single siRNA near the top of the list. This is one way to reduce the influence of strong off-target siRNAs.
Correcting single siRNA values by seed medians has also been shown to be an effective way to increase the on-target signal in screens. This correction is not effective for low-complexity pools, since each pool can contain 3-4 different seeds.
Off-target based hit detection algorithms (e.g. Haystack and GESS) are also only effective for single-siRNA screens. The advantage of these algorithms is that it permits the detection of hit genes that were not screened with on-target siRNAs. These algorithms are not effective for low-complexity pool screens.
Our recommendation: do not convert single siRNAs into low-complexity pools, rather use high-complexity siPOOLs to confirm hits
We do not recommend that screeners combine their single siRNA libraries into low-complexity pools (e.g. combining 3 Silencer Select siRNAs for the same target gene). If possible, it is better to screen the siRNAs individually and then apply seed-based correction, RSA and seed-based hit-detection algorithms.
The time saved by only screening one well per target may prove illusory when the deconvolution experiments show that the individual siRNAs have divergent phenotypes.
It is probably better to deal with off-target effects up front (by screening single siRNAs) than to be surprised by them later in the screen (during pool deconvolution).
Summary: Conventional siRNAs have a high probability of giving off-target phenotypes. siRNA off-target effects can be reduced by using more specific reagents or narrowing the assay focus (to reduce the number of relevant genes). Even when the assay is relatively focused, more specific reagents significantly increase the probability of observing on-target effects.
Probability of siRNA off-target phenotype depends on reagent specificity and assay biology
The probability of getting an off-target effect from an siRNA depends on several factors, the main ones being reagent specificity and assay biology. If an siRNA down-regulates a large number of genes, or if an assay phenotype can be induced by a large number of genes, the probability of observing an off-target phenotype increases.
siRNAs can down-regulate many off-target genes
Garcia et al. (2011) compiled 164 different microarray experiments measuring gene expression following transfection with siRNAs. The mean number of down-regulated genes in these experiments was 132 and the median was 68 (down-regulated genes were silenced by 50% or more).
As noted in earlier studies of gene expression following siRNA treatment (e.g. Jackson et al. 2003), few of the down-regulated genes are shared between siRNAs with the same target gene. This suggests that the down-regulated genes are not the downstream result of target gene knockdown (i.e. they are mostly off-target).
High-complexity pooling of siRNAs (e.g. with siPOOLs) can reduce the number of down-regulated genes.
The following figure, based on data from Hannus et al. 2014, shows the difference between the gene expression changes caused by a single siRNA (left) and a high-complexity siRNA pool (siPOOL, right), which also includes that same single siRNA:
Estimating the probability of siRNA off-target phenotypes
Assuming different numbers of down-regulated genes (off-target) and different numbers of potent genes involved in assay pathways, we can try to estimate the probability of an siRNA giving an off-target effect.
The following plot shows the probability of getting an off-target effect when:
down-regulated means that gene expression is reduced by 50% or more
in the Garcia paper dataset, the mean is 132 and median is 68
assuming different numbers of assay-potent genes
an assay-potent gene is one whose down-regulation by 50% or more is sufficient to produce a hit phenotype
for assays with more general phenotypes (e.g. cell count) we would expect more assay-potent genes
We can see that even if there are only 20 assay-potent genes, there’s a nearly 10% chance of getting an off-target phenotype when siRNAs down-regulate 100 off-target genes (which is close to the average observed in the Garcia dataset).
In a genome-wide screen of 20,000 genes with 3 siRNAs per gene, we would thus expect 2,000 off-target siRNAs.
In contrast, a more specific reagent that only down-regulates 5 off-target genes only has a 0.5% change of producing an off-target phenotype. For the above-mentioned genome-wide RNAi screen, we would expect only 100 off-target siRNAs (a 20-fold reduction).
The importance of RNAi reagent specificity
The above analysis demonstrates the importance of using specific siRNA reagents.
Changing an assay to make the phenotypic readout narrower (to reduce the number of genes capable of inducing a phenotype) is one way to reduce the risk of off-target phenotypes. But this may be a lot of work and is not necessarily desirable or even possible.
A more ideal solution is the use of a specific RNAi reagent, like siPOOLs.
As the number of assay-potent genes increases, the probability of getting an off-target phenotype approaches one.
The following plot (same format as the one above) shows the distribution
The p-values were calculated using the hypergeometric distribution, assuming a population size of 20,000 (the approximate number of protein-coding genes in the human genome).
Note that one of the major simplifying assumptions of the above analysis is that all siRNAs have the same number of down-regulated off-target genes.
Novel anti-cancer mechanism identified by shRNA/siRNA off-target effects
siRNA off-target effects takes an interesting turn for cancer research as reported in eLIFE by Putzbach et. al. Research unveiled a specific group of survival genes in cancer cells thanks to the off-target effects of siRNAs/shRNAs.
CD95 is a death receptor that mediates apoptosis when bound to its ligand, CD95L or FasL. Known for its multiple tumour-promoting activities, it was not surprising that silencing both molecules by RNAi produced cancer cell death.
What was surprising – death induced by C95/CD95L siRNAs/shRNAs did not work through CD95/CD95L at all. Three observations contributed to this conclusion:
The toxicity correlated with siRNA/shRNA concentration
Using siRNAs at 0.1nM or expressing the shRNA in a miR-30 backbone (developed to reduce off-targets by expressing shRNAs at reduced levels) did not induce the same toxicity
Removing the target did not affect siRNA/shRNA-induced toxicity
Excising the siRNA/shRNA binding sites on CD95/CD95L with CRISPR did not protect cells from toxicity induced by these siRNAs/shRNAs
Restoring expression of the target did not rescue cells from siRNA/shRNA-induced toxicity
Introducing recombinant CD95/CD95L proteins or expressing siRNA-resistant versions of CD95/95L, did not rescue cells from toxicity induced by their siRNA/shRNAs
The case of shRNA/siRNA off-target effects
The evidence was pretty convincing that the toxic effects of the CD95/CD95L siRNA/shRNAs stemmed from off-target effects.
1. Step-wise mutations showed toxicity derived from the seed sequence
Substituting each base of the tox-inducing siRNA (siL3) with the non-toxic siRNA (siScr) sequence in a step-wise cumulative manner either from the seed end or the non-seed end, highlighted toxicity derived from the seed sequence. The seed sequence is a 6-base sequence at position 2 to 7 of the guide RNA strand and is responsible for defining the off-target profile of an siRNA (read this technote for more information)
2. Off-target survival genes identified by RNA-seq
An RNA-seq analysis of CD95 or CD95L shRNA-treated cells identified twelve genes with significantly altered expression levels – 11 downregulated, 1 upregulated:
It turns out that many of the downregulated genes were important for survival. Additionally, two recent genome-wide lethality screens independently identified six of these genes (highlighted in red). The authors therefore termed this form of CD95/CD95L siRNA/shRNA-induced cell death Death Induced by Survival Gene Elimination (DISE). Don’t we all love acronyms! As depicted, these genes mostly interfered with apoptosis, cell cycle, autophagy and senescence.
3. Survival genes targeted by miRNA-like activity of CD95/CD95L siRNA/shRNAs
The seed sequence is what microRNAs (miRNAs) use to recognize and downregulate target genes. siRNAs/shRNAs can behave like miRNAs, contributing to the off-target activity. As shown by Sylamer plots, the seed sequence of toxic shRNAs (shL3 and shR6) were enriched in highly downregulated genes. The identified survival genes also contained multiple seed matches over their 3’ UTRs. That leaves little doubt that the CD95/CD95L shRNAs were hitting these genes through miRNA-like off-target activity.
Once again, we see how siRNA off-target effects can impact experimental results. Though in this case, it actually helped identify relevant survival genes! Notably, siRNA off-target effects likely influence cell viability/proliferation data to a greater extent than other readouts since it is regulated by so many genes.
This is not an isolated report of siRNA off-targeting in cancer. Targets such as STK33 and MELK, identified with RNAi to be important in cancer progression, failed to show the same effects in experiments performed by different groups or alternative techniques. The controversy continues however as their effects on cancer activity continue to be reported.
How to avoid siRNA off-target effects
siPOOLs were developed to avoid siRNA off-target effects through high complexity pooling and optimized design. Phenotypes are therefore more clearly and reliably ascribed to loss-of-function of the target gene. The new siPOOL Cancer Toolbox now provides cancer researchers the ability to disrupt multiple genes reliably, with reduced risk of siRNA off-targets, in an affordable toolkit solution.
We scoured the published literature for the most highly cited genes involved in multiple forms of cancer. Choose your target genes from our list of top 100 cancer-associated genes to build your own siPOOL Cancer Toolbox. Notably, CD95, MELK and STK33 did not make the cut!
The most common way to validate hits from Dharmacon siGENOME screens is to test the individual siRNAs from candidate pool hits (siGENOME reagents are low-complexity pools of 4 siRNAs). In this deconvolution round, we normally see that the individual siRNAs for genes behave very differently and seed effects dominate (discussed here and here).
One could argue that deconvolution is not the correct way to validate candidate hits (even though it’s the method recommended by Dharmacon), as testing the siRNAs individually will result in seed effects that are suppressed when the siRNAs are pooled. One problem with this argument is that low-complexity pooling does not get rid of off-target effects (e.g. Fig 5 in this paper), something that is better done via high-complexity pooling. But assuming it were true, validating with a second Dharmacon pool would be better.
Tejedor et al. (2015) performed a genome-wide Dharmacon siGENOME screen for regulators of Fas/CD95 alternative splicing. ~1500 genes were identified by a deep-sequencing approach. ~400 of those were confirmed by high-throughput capillary electrophoresis (HTCE, LabChip). They then retested those ~400 genes (again by HTCE) using Dharmacon ON-TARGETplus pools.
The following plot shows the values for the siGENOME and ON-TARGETplus pools for the same genes (i.e. each point corresponds to 1 gene).
What’s measured is the percent of splice variants that include exon 6 following siRNA treatment. That was compared to the values for a plate negative control (untransfected wells) and converted to a robust Z-score. This is the main readout from the paper.
The Pearson correlation improves if the strong outlier at -150 for siGENOME is removed (R = 0.25), while the Spearman correlation is unchanged.
We see that a fairly small number of genes are giving reproducibly strong phenotypes (e.g. 13 of 400 have robust Z-scores less than -15 for both siGENOME and ON-TARGETplus reagents).
If we remove those 13 strong hit genes, the correlation approaches zero:
Even if the strong outlier for siGENOME is removed, the correlation is still near zero:
Although using a second Dharmacon pool removes some of the arbitrariness of defining validated hits (e.g. saying that 3 of 4 siRNAs must exceed a Z-score cut-off of X, or 2 of 4 siRNAs must exceed a Z-score cut-off of Y), the end result is similar: A few strong genes show reproducible phenotypes, while many of the strongest screening hits show inconsistent results. The main problem, off-target effects in the main screen, is not fixed.
Tejedor et al. say that 200 genes were confirmed by ON-TARGETplus validation. They consider a gene confirmed if the absolute value of the robust Z-score is greater than 2. The Z-score is calculated using the median for untransfected plate controls. I suspect that a significant proportion of randomly selected genes would also have passed this cut-off.
In table S3 (which has the ON-TARGETplus validation results), there are actually only 177 genes (including 2 controls) that meet this cutoff. The supplementary methods state: Genes for which Z was >2 or <-2 were considered as positive, and a total number of 200 genes were finally selected as high confidence hits.
Which suggests that genes outside the cut-off were chosen to bring the number up to 200.
But if we look at the Excel sheet with the ‘200 hit genes’, it has 200 rows, but only 199 genes. The header was included in the count.
This type of off-by-one error is probably not that uncommon. In a case like this, it does not matter so much.
One case where it did matter was in the Duke/Potti scandal. The forensic bioinformatics work of the heroes of the Duke scandal found that, when trying to reproduce the results from published software, one of the input files caused problems because of an off-by-one error created by a column header. That was one of many difficulties in reproducing the Potti paper’s results which eventually led to its exposure.
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