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Category: genomic screening

Low complexity pooling does not prevent siRNA off-targets

Low complexity pooling does not prevent siRNA off-targets

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).

Reliable high-complexity siPOOLs, as independent on-target reagents, can then be used to confirm screening hits.

siTOOLs also now has RNAi screening libraries available.  Please contact us for more information.

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What is the probability of an siRNA off-target phenotype?

What is the probability of an siRNA off-target phenotype?

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:

  • assuming RNAi reagents down-regulate varying numbers of off-target genes (5, 25, 50, 100)
    • 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.

postscript

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.

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5 factors to consider in multi-gene targeting RNAi screens

5 factors to consider in multi-gene targeting RNAi screens

Summary: Effective functional genomic screening depends on a variety of factors that need to be simultaneously addressed to obtain meaningful results. A recent Cell Reports paper demonstrates this by taking a holistic approach to siRNA screening with the use of multi-isoform/multi-gene targeting to address redundant paralogs and pathways in cancer cells.

The case for multi-gene targeting

Many RNAi screens use arrayed single gene knockdowns to find genes that play an important role in a biological process. The idea is that a single bullet is enough to take down its target leaving a gaping hole that one cannot fail to notice. In some cases, this is true, and is certainly relied upon by drug developers seeking to create specific mono-target drugs.  However, in complex diseases like cancer, cells have evolved fail-safe mechanisms to make them more resistant to external assaults. A single bullet is simply not enough.

Take for example oncogenic protein RAF or Rapidly Accelerated Fibrosarcoma, a tyrosine kinase effector that is a component of the MAPK signalling pathway (Ras-Raf-MEK-ERK). RAF has three isoforms – ARAF, BRAF and RAF1 (also called CRAF). Studies in mouse embryonic development show they all share some form of functional redundancy as knocking out two isoforms produces more severe effects than knocking out each isoform alone.

Screens that target single genes/isoforms therefore tends to bias results towards genes that have no paralogs or only have single isoforms. This was indeed the reason why classical Ras effectors were not identified in previous screens.

Factors to consider in a multi-gene targeting RNAi screen

Determining gene combinations that make sense

The authors of the study did a focussed siRNA screen on 41 RAS effector nodes represented by 84 genes. Out of the 41 nodes, 25 of them had 2-4 functional paralogs where combinatorial gene silencing was carried out with multiple siRNAs. 5 nodes knocked down multiple members of a protein complex. 5 nodes had siRNAs targeting multiple steps within a pathway. Only 6 nodes silenced single genes (highlighted).

Multi-gene targeting screen design

The only caveat with designing such a screen is the requirement for prior knowledge to perform meaningful gene silencing combinations. In this instance, many of the Ras effector pathways are characterized sufficiently to do this well however in other less studied fields, this could be a challenge. Useful tools that would help in designing gene knockdown combinations would include pathway or phenotype databases such as KEGG, REACTOME or Wikipathways. The Phenovault which siTOOLs Biotech is developing, is yet another potentially useful tool.. more details to come!

Number and types of phenotypes

The authors also highlight how a screen that reads only one phenotype might miss other important gene functions. Many RNAi screens sadly still stick to measuring cell proliferation as their only read-out which is greatly influenced by siRNA off-target effects. Here, 5 different phenotypes were measured (cell size, proliferation, apoptosis, reactive oxygen species [ROS], and viability). It was noted that silencing of Cdc42 had little effect on cell viability yet a prominent effect on ROS levels.

To take this up a notch, analysis was also performed at the single-cell level in cells expressing uniform levels of GFP and co-transfected with GFP siRNA. This allowed authors to correlate phenotypes with levels of gene knockdown, generating dose-response curves. How clever!

A lot more work, but adds to data robustness especially when using single siRNAs that are known to be rather variable.

Heterogeneity of cell lines

Many reports and our own observations attest to the heterogenous response of different cell lines to the same treatment. In cancer especially, the large heterogeneity necessitates the use of multiple cell lines. Not doing so would be failing to account for the large genetic diversity observed in the clinic. The authors screened 92 cell lines derived from lung, pancreas and colorectal tissue.

Despite seeing heterogenous responses to node knockdowns, phenotypic responses could be distinguished into  several groups based on effector engagement.  A major group dependended on RAF through direct binding with KRAS, a second major group worked via RSK p90 S6 kinases to drive RSK-mTOR signalling. And a third minor group was dependent on RalGDS. They went on to focus on the first two major groups, naming them KRAS-type and RSK-type respectively.

Reagents – choosing siRNAs and siRNA concentrations

The authors used previously characterized siRNAs to select for more potent siRNAs. This involved an RNAi sensor reporter-based assay that required the generation of 20,000 clones. The reporter was also shRNA-based. Due to heterogeneity in Dicer-mediated cleavage of shRNA, its uncertain if knockdown potency is accurately reflected when translated to siRNAs (read about the difference between shRNAs and siRNAs).

siRNA off-target effects are concentration-dependent

In any case, its a lot of work to characterize all siRNAs to be used in a screen. Furthermore, off-target effects are not addressed.

The authors stuck to a maximal concentration of 12 nM where 2 nM of siRNA was applied per gene. At 2 nM per siRNA, one still risks deregulating other genes. One of the first papers by Aimee Jackson et al., demonstrated an siRNA targeting MAPK14 deregulated many other genes even at concentrations of 1-4 nM.

An important consideration is to ensure total siRNA concentrations are kept constant. In which case, a negative control siRNA has to match or follow the maximal siRNA concentration used. Using different levels of siRNAs runs the risk of biasing off-target effects towards sequences present at higher concentrations.

To learn what the causes, extent and consequences of siRNA off-target effects are, read siTOOLs Technote 1)

Validating results

As with all scientific hypothesis, it helps to arrive at the same conclusion with different approaches.

The two different effector response subgroups identified also responded differently to small molecules. The KRAS-type lines being more sensitive to EGFR and ERK inhibition while the RSK-type lines more sensitive to inhibitors of PDK1, RSK, MTOR, S6K1 and DNA repair enzymes. This was attributed to the latter’s higher basal metabolic activity manifested in larger investments towards oxidative phosphorylation and mitochondrial ribosome maintenance.

By also projecting signatures obtained from cell lines into patient samples (in The Cancer Genome Atlas, TCGA), the subtypes were also effective at predicting differential sensitivity to multiple drug treatments. This highlights the importance in designing effective drug combinations in cancer.

Interestingly, the authors also performed CRISPR pooled screens in parallel. However, due to the restraints of being only able to knockout 1 gene at a time, smaller effects were seen due to gene redundancy. However, they did go on to use CRISPR as well to mutate key genes to affirm the pathway relationships established.

siPOOLs have been used successfully for multi-gene targeting for up to 4 genes, and potentially more. They also safely address off-target effects by high complexity pooling, enabling each siRNA to be applied at picomolar concentrations. For more articles on multi-gene targeting, read an older blogpost:

Understanding gene networks with combinatorial gene knockdown

 

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Is it important to avoid microRNA binding sites during siRNA design?

Is it important to avoid microRNA binding sites during siRNA design?

Summary: To address the question of whether one should avoid microRNA binding sites during siRNA design, we examined whether removing siRNAs that share seeds with native microRNAs would reduce the dominance of seed-based off-target effects in RNAi screening.

siRNA design and native microRNA target sites

Recently, we discussed a review of genomics screening strategies.  The authors state:

RNAi screens are powerful and readily implemented discovery tools but suffer from shortcomings arising from their high levels of false negatives and false positives (OTEs) as can be seen when comparing the low concordance among the candidate genes detected in different screens using the same species of virus, e.g., HIV-1, HRV, or IAV (Booker et al., 2011; Bushman et al., 2009; Hao et al., 2013; Perreira et al., 2015; Zhu et al., 2014).

To address these concerns, improvements in the design and synthesis of next-gen RNAi library reagents have been implemented including the elimination of siRNAs with seed sequences that are complementary to microRNA binding sites.

Given that off-target effects via microRNA-like binding are the main source of RNAi screening phenotypes, avoiding native microRNA sites during siRNA design seems like a reasonable strategy.  But does it make much difference in actual RNAi screens?

Hasson et al. 2013 performed a mitophagy screen using the Silencer Select siRNA library.  About 12% of the ~65,000 screened siRNAs have a 7-mer seed shared by a miRBase microRNA.

The screen’s main phenotypic readout, % Parkin translocation (PPT), is strongly affected by seed effects.   The intra-class correlation for siRNAs with the same seed is ~.51 (versus ~.06 for siRNAs with the same target gene).  There appears to be no difference between how siRNAs with or without microRNA seeds behave:

Is it important to avoid microRNA binding sites during siRNA design?

The same thing is found if we look at a less specific phenotype like cell count (which should be more broadly susceptible to off-target effects, as more genes should affect this phenotype):

Is it important to avoid microRNA binding sites during siRNA design?

And if we look at seeds that are enriched at the top of the screening list (sorted by descending PPT), we also don’t see much difference between siRNAs with or without native microRNA seeds.  (Note that the seed p-value is calculated in a similar way to RSA, based on how over-represented a seed is towards the top of a ranked list)

Is it important to avoid microRNA binding sites during siRNA design?

We also examined a general phenotypic readout (cell viability) in a dozen large-scale RNAi screens.

For some screens, we do see a slight shift in the values for siRNAs with or without native microRNA seeds.

For example, a genome-wide screen of Panda et al. 2017 (also using the Silencer Select library) shows a slight decrease in viability for siRNAs with native microRNA seeds:

Is it important to avoid microRNA binding sites during siRNA design?

Removing those siRNAs does not change the dominance of seed-base off-targets.

The intra-class correlation (ICC) for siRNAs with the same 7-mer seed is ~.53, with or without the inclusion of siRNAs with native microRNA seeds, while ICC for siRNAs with the same target gene is only  ~.06.

Coming back to the quote from the review article on genomic screening, next-gen RNAi library reagents that avoid native microRNA seeds are not expected to be much better than siRNAs that include them.

The most effective way to avoid seed-based off-target effects is to use high-complexity siRNA pools (siPOOLs). Learn more about siPOOLs

 

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Correcting seed-based off-target effects in RNAi screens

Correcting seed-based off-target effects in RNAi screens

Summary: Correcting for seed-based off-targets can improve the results from RNAi screening.  However, the correlation between siRNAs for the same gene is still poor and the strongest screening hits remain difficult to interpret.

Seed-based off-target correction has little effect on reagent reproducibility

Given that seed-based off-targets are the main cause of phenotypes in RNAi screening, trying to correct for those effects makes good sense.

The dominance of seed-based off-targets means that independent siRNAs for the same gene usually show poor correlation.

If one could correct for the seed effect, the correlation between siRNAs targeting the same gene may improve.

One straightforward way to do seed correction is to subtract the ‘seed median’ from each siRNA.  (The seed median is the median for all siRNAs having the given seed.)

This was the approach used by Grohar et al. in a recent genome-wide survey of EWS-FLI1 splicing (involved in Ewing sarcoma).  They used the Silencer Select library, which has 3 siRNAs per target gene.

After seed correction, there is only minor improvement in the correlation between siRNAs targeting the same gene.  The intra-class correlation (ICC) improves from 0.031 to 0.037.  The ICC for siRNAs with the same 7-mer seed decreases from 0.576 to 0.261.

Although we have reduced the seed-based signal, it has not resulted in a correspondingly large improvement in the gene-based signal.

More sophisticated seed correction can improve reagent correlation

Grohar et al. used a simple seed-median subtraction method to correct their screening results.

A more sophisticated method (scsR) was developed by Franceshini et al. for seed-based correction of screening data.  It corrects using the mean value for siRNAs with the same seed, and weighs the correction using the standard deviation the values.  This allows seeds with a more consistent effect to contribute more to the data normalisation.

Applying the scsR method to the Grohar data, ICC for siRNAs targeting the same gene increases from 0.031 to 0.041.  It is better than the increase with seed-median subtraction (0.037), but is still only a fairly minor improvement (plot created using random selection of 10,000 pairs of siRNAs that target the same gene):

 

Off-target correction increases double-hit rate in top siRNAs of RNAi screen

The following plot shows the count for single-hit and double-hit genes as we go through the top 1000 siRNAs (of ~60K screened in total).  Double-hit means that the gene is covered by 2 (or more) hit siRNAs.

Despite the small improvement in reagent correlation, the double-hit rate is essentially the same using simple seed-median subtraction or the more advanced scsR method.

Furthermore, the number of double-hits is higher than what we’d expect by chance.

This shows that, despite the noise from off-target effects, there is some on-target signal that can be detected.

siRNAs with the strongest phenotypes remain difficult to interpret

Despite the fact that the double-hit count is higher than expected by change, most of the genes targeted by the strongest siRNAs are single-hits.  siRNAs with the strongest phenotypes remain difficult to interpret.

Seed correction is best suited for single-siRNA libraries.  Low-complexity pools, like siGENOME or ON-TARGETplus, are less amenable to effective seed correction since there are (usually) 4 different seeds per pool.  This reduces the effectiveness of seed-based correction, even though seed-based off-target effects remain the primary determinant of observed phenotypes (as discussed here, here , and here).

The best way to correct for seed-based off-targets is to avoid them in the first place.  Using more specific reagents, like high-complexity siPOOLs, is the key to generating interpretable RNAi screening results.

For help with seed correction or other RNAi screening data analysis with the Phenovault, contact us at info@sitools.de

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Making sense of siGENOME deconvolution

Making sense of siGENOME deconvolution

As discussed previously, deconvoluted Dharmacon siGENOME pools often give surprising results.  (Deconvolution is the process of testing the 4 siRNAs in a pool individually.  This is usually done in the validation phase of siRNA screens.)

One way to compare the relative contribution of target gene and off-target effects is to calculate the correlation between reagents having the same target gene or the same seed sequence.  One of the first things we do when analysing single siRNA screens is to calculate a robust form of the intraclass correlation (rICC, see discussion at bottom for more about this).

Recently we were analysing deconvolution data from Adamson et al. (2012) and calculated the following rICC’s.  (The phenotype measured was relative homologous recombination.)

Grouping variable  rICC    95% confidence interval

Target gene        0.040   -0.021-0.099
Antisense 7mer     0.383   0.357-0.413
Sense 7mer         0.093   0.054-0.129

Besides the order of magnitude difference between target gene and antisense seed correlation (which is commonly observed in RNAi screens), what stands out is the ~2-fold difference between the correlation by target gene and sense seed.

Very little of the the sense strand should be loaded into RISC, if the siRNAs were designed with appropriate thermodynamic considerations (the 5′ antisense end should be less stable than the 5′ sense end, to ensure that the antisense strand is preferentially loaded into RISC).

The above correlations suggest that some not insubstantial amount of sense strand is making it into the RISC complex.

Here is the distribution of delta-delta-G for siPOOLs and siGENOME siRNAs targeting the same 500 human kinases (see bottom of post for discussion of calculation).  A positive delta-delta G means that the sense end is more thermodynamically stable than the antisense end, favouring the loading of the antisense strand into RISC.

 

 

This discrepancy in delta-delta G is also consistent with comparison of mRNA knockdown:

The siGENOME knockdown data comes from 774 genes analysed by qPCR in Simpson et al. (2008).  The siPOOL knockdown data is from 223 genes where we have done qPCR validation.

Of note, the siGENOME pools were tested at 100 nM, whereas siPOOLs were tested at 1 nM.

(It should be mentioned that, although consistent with the observed differences in ddG, this is only an indirect comparison, and delta-delta G is not the only determinant of functional siRNAs.)

 

Notes on intraclass correlation

Intraclass correlation measures the agreement between multiple measurements (in this case, multiple siRNAs with the same target gene, or multiple siRNAs with the same seed sequence).   One could also pair off all the repeated measures and calculate correlation using standard methods (parametrically using Pearson’s method, or non-parametrically using Spearman’s method).  The main problem with such an approach is that there is no natural way to determine which measure goes in the x or y column.  Correlations are normally between different variables (e.g. height and weight).  In a case of repeated measures, there is no natural order, so the intraclass correlation (ICC) is the more correct way to measure the similarity of within-group measurements.  As ICC depends on a normal distribution, datasets must first be examined, and if necessary, transformed beforehand.

Robust methods have the advantage of permitting the use of untransformed data, which is especially useful when running scripts across hundreds of screening dataset features.  The algorithm we use calculates a robust approximation of the ICC by combining resampling and non-parametric correlation.

Here is the algorithm, in a nutshell:

  1. Group observations (e.g. cell count) by the grouping variable (e.g. target gene or antisense seed)
  2. Randomly assign one value of each group to the x or y column (groups with one 1 observation are skipped)
    • for example, if the grouping variable is target gene and siRNAs targeting PLK1 had the values 23, 30, 37, 45, the program would randomly choose 1 of the values for the x column and another for the y column
  3. Calcule Spearman’s rho (non-parametric measure of correlation)
  4. Repeat steps 1-3 a set number of times (e.g. 300) and store the calculated rho’s
  5. Calculate mean of the rho values from 4.  This is the robust approximation of the ICC (rICC).
    • Values from 4 are also used to calculate confidence intervals.

The program that calculates this is available upon request.

Notes on calculating delta-delta G

Delta-delta G was calculated using the Vienna RNA package, as detailed here: https://www.biostars.org/p/58979/ (in answer by Brad Chapman).

The delta-delta G was calculated using 3 terminal bps.  We found that that ddG of the terminal 3 bps had the strongest correlation with observed knockdown.  Others (e.g. Schwarz et al., 2003 and Khvorova et al., 2003) have also used the terminal 4 bps.

 

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The final RNAiL?

The final RNAiL?

A recent article in The Scientist asks whether, in light of a paper by Lin et al. showing phenotypic discrepancies between RNAi and CRISPR, this is not ‘the last nail in the coffin for RNAi as a screening tool’?

The paper in question found that a gene (MELK) that had been shown by many RNAi-based studies to be critical for several cancer types shows no effect when knocked out via CRISPR.  They also report that in relevant published genome-wide screens, MELK was not at the top of the hit lists.

Does this mean that the papers that used RNAi were unlucky and off-target effects were responsible for their observed phenotypes?

Gray et al. identified MELK as a gene of interest based on microarray experiments.  They then designed RNAi experiments to test its role in proliferation.  Assuming that this study and the subsequent ones followed good RNAi experimental design (using reagents with varying seed sequences, testing the correlation between gene knockdown and phenotypic strength, etc.), we can be fairly confident that MELK is involved in proliferation.  It might not be the most essential player, which would explain why it is not at the top of screening hit lists.  And screening lists have the draw-back of enriching for off-target hits.

Another possibility is that Lin et al. have observed a known complicating feature of knock-out screens: genetic compensation.  Although they undertake experiments to address this issue, it could be that compensation takes place too quickly for their experiments to rule it out.  Furthermore, they could have addressed this issue by testing knock-down reagents themselves, and checking whether genes they hypothesise as responsible for the supposed off-target effect in the published RNAi work are in fact down-regulated.  C911 reagents could also be used to test for off-target effects.  This is extra work, but given that they are disputing the results in many published studies, this seems justified.

As regards the role of RNAi in screening, The Scientist concludes with the following (suggesting that their answer to the question of whether this is the final nail is also No):

In the meantime, one obvious solution to the problem of target identification and validation is to use both CRISPR and RNAi to validate a target before it moves into clinical research, rather than relying on a single method. “We have CRISPR and short hairpin reagents for every gene in the human genome,” said Bernards. “So when we see a phenotype with CRISPR, we validate with short hairpin, and the other way around. I think that would be ideal.”

Although we agree that validating CRISPR hits with RNAi reagents is important (especially if drugability is a concern), one has to be careful with RNAi reagents, like single siRNAs/shRNAs or low-complexity pools, that are susceptible to seed-based off-target effects.  For validating CRISPR screening hits, siPOOLs provide the best protection against unwanted off-target effects, saving you time, money, and disappointment during the validation phase.

 

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Where’s the beef?

Where’s the beef?

In our last blog entry, we discussed a classic RNAi screening paper from 2005 that showed that the top 3 screening hits were were due to off-target effects.

In this post, we analyse a more recent genome-wide RNAi screen by Hasson et al., looking in more detail at what proportion of top screening hits are due to on- vs. off-target effects.

Hasson et al. used the Silencer Select library, a second-generation siRNA library designed to optimise on-target knock down, and chemically modified to reduce off-target effects.  Each gene is covered by 3 different siRNAs.

To begin the analysis, we ranked the screened siRNAs in descending order of % Parkin translocation, the study’s main readout.

We then performed a hypergeometric test on all genes covered by the ranked siRNAs.  For example, if gene A has three siRNAs that rank 30, 44, and 60, we calculate a p-value for the likelihood of having siRNAs that rank that highly (more details provided at bottom of this post).  It’s the underlying principle of the RSA algorithm, widely used in RNAi screening hit selection.  If the 3 siRNAs for gene B have a ranking of 25, 1000, and 1500, the p-value will be higher (worse) than for gene A.

The same type of hypergeometric testing was done for the siRNA seeds in the ranked list.  For example, if the seed ATCGAA was found in siRNAs having ranks of 11, 300, 4000, and 6000, we would calculate the p-value for those rankings.  Seeds are over-represented in siRNAs at the top of the ranked list will have lower p-values.

After doing these hypergeometric tests, we had a gene p-value and a seed p-value for each row in the ranked list.  We could then look at each row in the ranked list estimate whether the phenotypic is due to an on- or off-target effect by comparing the gene and seed p-values.  [As a cutoff, we said that the effect is due to one of either gene or seed if the difference in p-value is at least two orders of magnitude.  If the difference is less than this, the cause was considered ambiguous.]

After assigning the effect as gene/seed/ambiguous, we then calculated the cumulative percent of hits by effect at each position in the ranked list.   Those fractions were then plotted as a stacked area chart (here, looking at the top 200 siRNAs from the screen):

 

The on-target effect is sandwiched between the massive ‘bun’ of off-target effects and ambiguous cause.  We are reminded of these classic commercials from the 80s:

 

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Note on p-value calculations:

P-values were calculated using the cumulative hyper-geometric test (tests the probability of finding that many or more instances of members belonging to the particular group, in our case a particular gene or seed sequence).  The p-value associated with a gene or seed is the best p-value for all the performed tests.  For example, assume a gene had siRNAs with the following ranks: 5, 20, 1000.  The first test calculates the p-value for finding 1 (of the 3) siRNAs when taking a sample of 5 siRNAs.  The next test calculates the p-value for finding 2 (of 3) siRNAs when taking a sample of 20 siRNAs.  And the last is the probability of getting 3 (of 3) siRNAs when taking a sample of 1000.  If the best p-value came from the second test (2 of 3 siRNAs found in a sample size of 20), that is the p-value that the gene receives.  This is also the approach used by the RSA (redundant siRNA activity) algorithm.  One advantage of RSA is that it can compensate for variable knock down efficiency of the siRNAs covering a gene (e.g. if 1 of 3 gives little knockdown).

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Classic Papers Series: Lin et al. show RNAi screen dominated by seed effects

Classic Papers Series: Lin et al. show RNAi screen dominated by seed effects

Over the coming months, we will highlight a number of seminal papers in the RNAi field.

The first such paper is from 2005 by Lin et al. of Abbott Laboratories, who showed that the top hits from their RNAi screen were due to seed-based off-target effects, rather than the intended (and at that time, rather expected) on-target effect.

The authors screened 507 human kinases with 1 siRNA per gene, using a HIF-1 reporter assay to identify genes regulating hypoxia-induced HIF-1 response.

In the validation phase of their screen, they tested new siRNAs for hit genes, but found that they failed to reproduce the observed effect, even when using siRNAs that had a better on-target knock down than the pass 1 siRNAs.

Figure 1A.  Left panel shows on-target knock down of pass 1 siRNA for GRK4 (O) and the new design (N).  Centre panel shows Western blot of protein  levels  Right panel shows HIF-1 reporter activity for positive control (HIF1A) and the original (O) and new (N) siRNAs.

The on-target knock down is much-improved for the new design, yet its reporter activity is indistinguishable from negative control.  Yet the pass 1 siRNA with poor knock down gives almost as strong a result as HIF1A (positive control).

By qPCR, they then showed that GRK4(O) and another one of the top 3 siRNAs silence HIF1A (the positive control gene).  Using a number of different target constructs they also nicely show that it was due to seed-based targeting in the 3′ UTR.

Although the authors screened at a high initial concentration (100 nM), the observed off-targets persisted at 5 nM, suggesting that just screening at lower concentrations would not have improved their results.

The authors conclude:

In addition, due to the large percentage of the off- target hits generated in the screening, using a redundant library without pooling in the primary screen could significantly reduce the efforts required to eliminate off-target false positives and therefore, will be a more efficient design than using a pooled library.

This is true for low-complexity pools, but high-complexity pools can overcome this problem by providing a single reliable result for each screened gene.

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The nasty, ugly fact of off-target effects

The nasty, ugly fact of off-target effects

Once upon a time, it was imagined that siRNAs specifically knock down the intended target gene.

Unfortunately, this turned out to be wrong.

The disappointing results from siRNA screening following the initial high hopes brings to mind T.H. Huxley‘s famous quote about a beautiful theory being killed by an ugly, nasty little fact.

As pointed out by S.J. Gould in Eight Little Pigs, the origin of the quote is given in the autobiography of Sir Francis Galton.

Galton’s autobiography is inspirational.  As one reviewer put it, “there is a feeling of calmness and awe that comes from knowing that a person of his genius, wisdom and versatility actually existed.”  He was 70, and had already made significant contributions to genetics, statistics, meteorology, and geography, when he published his first major work on fingerprints, which would become the basis for modern forensic fingerprint analysis.

His work on fingerprints also provides the context for the famous Huxley quote:

Much has been written, but the last word has not 
been said, on the rationale of these curious papillary 
ridges ; why in one man and in one finger they form 
whorls and in another loops. I may mention a 
characteristic anecdote of Herbert Spencer in con- 
nection with this. He asked me to show him my 
Laboratory and to take his prints, which I did. Then 
I spoke of the failure to discover the origin of these 
patterns, and how the fingers of unborn children had 
been dissected to ascertain their earliest stages, and so 
forth. Spencer remarked that this was beginning in 
the wrong way ; that I ought to consider the purpose 
the ridges had to fulfil, and to work backwards. 
Here, he said, it was obvious that the delicate mouths 
of the sudorific glands required the protection given 
to them by the ridges on either side of them, and 
therefrom he elaborated a consistent and ingenious 
hypothesis at great length. 

I replied that his arguments were beautiful and 
deserved to be true, but it happened that the mouths 
of the ducts did not run in the valleys between the crests, 
but along the crests of the ridges themselves. He 
burst into a good-humoured and uproarious laugh, and 
told me the famous story which I have heard from 
each of the other two who were present on the 
occurrence. Huxley was one of them. Spencer, 
during a pause in conversation at dinner at the 
Athenaeum, said, "You would little think it, but I 
once wrote a tragedy." Huxley answered promptly, 
" I know the catastrophe." Spencer declared it was 
impossible, for he had never spoken about it before 
then. Huxley insisted. Spencer asked what it was. 
Huxley replied, "A beautiful theory, killed by a 
nasty, ugly little fact."  

Memories of My Life, pp 257-258
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