Browsed by
Tag: RNAi off-targets

Performing target validation well

Performing target validation well

Summary

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 large site 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.

target validation techniques
Overview of target validation techniques (Lindsay, Nat Review Drug Discovery, 2013)

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.

Dismal rates of reproducibility from several pharma-led cancer-focused studies ranged from 11% (Amgen) to 25% (Bayer). A review by William Kaelin Jr sums up the common pitfalls of preclinical cancer target validation. One of his key points:

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?

target validation siRNA vs siPOOL pharma story
Three different siRNAs against the same target were tested in a cell proliferation assay. Despite all 3 siRNAs showing potent target gene silencing, effect on cell proliferation differed greatly.

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.

target validation expression analysis siRNA vs siPOOL pharma story
How many genes can you affect with an siRNA? Whole transcriptome analysis by microarray was performed and number and % of up and down-regulated genes are shown over total number of genes assayed (18567).

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.

siRNA vs siPOOL rtqPCR knock-down efficiency
siPOOLs increase targeting efficiency, avoiding knock-down variability. Figure shows rtqPCR quantification of target RNA levels when two siPOOLs vs two siRNAs against 36 genes were tested.

We still recommend using multiple target validation techniques. As a first evaluation however, siPOOLs are quick, easy and most of all, reliable.

Rescue experiments can also be performed with siPOOL-resistant rescue constructs.

Find out more

Follow us or share this post:
Little correlation between Dharmacon siGENOME and ON-TARGETplus reagents

Little correlation between Dharmacon siGENOME and ON-TARGETplus reagents

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.

postscript

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.

Follow us or share this post:
siRNA vs shRNA – applications and off-targeting

siRNA vs shRNA – applications and off-targeting

Short interfering RNA (siRNA) and short hairpin RNA (shRNA) are both used in RNAi-mediated gene silencing. In this blogpost, we explore the differences in applications of siRNA and shRNA and compare their capacity for off-targeting.

For a summary of their properties, please refer to Table 1 at the end of  the post.

In what situations should we use siRNA or shRNA?

In terms of application, siRNAs are commonly applied for rapid and transient knockdown of gene expression.

It is performed in cell lines amenable to transfection by liposomes/electroporation and effects typically last from 3-7 days though retransfection can be performed to extend the effect.

The amount of siRNA introduced can be highly controlled and efficiency of gene knockdown is dependent on the levels of siRNA in the cell which is influenced by transfection efficiency and siRNA stability. Knockdown is also influenced by characteristics of the gene. A gene that is highly transcribed for example, may experience less siRNA-mediated downregulation compared to a gene where lesser copies of RNA are produced over time. In addition, a gene which expresses a protein with a very long half-life, may require extended periods of siRNA application to see a knockdown effect.

Due to the transient effect of siRNAs, shRNAs were developed to be used for prolonged knockdown of genes.

As they are introduced by viral vectors, cells that are more difficult to transfect are better targeted with shRNA. Furthermore, promoter-driven expression allows for inducible expression of the shRNA. Depending on the viral vector used – refer to Labome’s post that covers siRNA/shRNA delivery in greater detail – the shRNA may be integrated into the host genome, allowing it to be propagated into daughter cells. This maintains a consistent gene knockdown over several generations. However, knockdown efficiency can decline over time. This is mainly due to varying levels of uptake of the shRNA among cells, with a cell population having lower shRNA expression being over-represented with time.

 

What about RNAi screening?

siRNAs and shRNAs are both used in RNAi screening to identify genes of interest in a studied phenotype. These are performed with siRNA/shRNA libraries that target a large variety of genes. There are two RNAi screening formats commonly used – arrayed and pooled.

siRNAs and shRNAs can both be used in an arrayed screening format. This means that the siRNA(s)/shRNA(s) against each gene is tested in distinct cell populations. Arrayed screens have the advantage of being compatible with various phenotypic readouts and do not suffer from possible reagent cross-talk or challenges associated with deconvoluting data. However, they are more energy and resource-intensive to perform. (See Fig. 2)

The pooled screening format in contrast, applies only with shRNAs. Here, all shRNAs (e.g. a whole-genome shRNA library) are introduced to a single cell population. As low titers of viral vectors are used, each cell in the population is expected to take up one shRNA vector.

With pooled screening, only readouts linked to cell number can be assessed. These include measurements for cell viability or altered expression of a cell surface marker assessed by fluorescence activated-cell sorting. shRNAs targeting genes which impact these readouts are expected to skew the cell population, such that only cells affected by the relevant shRNAs can be identified. This is either through negative selection, where lost cell populations are noted, or positive selection, where cells with certain shRNAs become over-represented.

The resulting cell population is then assessed by PCR, microarray hybridization or next generation sequencing to measure which shRNAs are highly or lowly-represented. The shRNAs are identified usually by means of a DNA barcode present in the vector sequence. Of note, pooled screens take up less resources to perform but require longer assay times to allow for significant changes in the overall cell population to occur.

Fig. 2 Simplified workflow for arrayed and pooled RNAi screening formats

 

Off-target effects with shRNAs?

The use of siRNAs are known to produce several off-target effects but what about shRNAs? Given they are processed the same way as siRNAs, shRNAs are also subject to microRNA-like off-target effects. In addition, because they are expressed from DNA and rely on endogenous machinery to be processed into siRNA, several variations may be introduced not found with introducing siRNA directly. Some potential sources of off-target effects for shRNAs include:

1. Promoter-driven expression. shRNAs are typically controlled with a U6 promoter which drives high levels of transcription via RNA polymerase III. The high shRNA expression levels may saturate endogenous RNAi machinery, contributing to off-target effects. To counter this, shRNAs can be expressed in a context mimicking miRNAs, utilizing RNA polymerase II for transcription instead. This has been found by several groups to reduce the incidence of off-target effects (Grimm et al., 2006, Kampman et al., 2015)

2. Dicer-mediated hairpin processing. shRNAs undergo Dicer-mediated cleavage in the cytosol to remove its hairpin loop. Gu et al., 2012 reported that Dicer cleaves with sufficient heterogeneity to generate multiple sequences. This factor was reported to generate the higher noise levels unique to shRNA screens (Bhinder and Djaballah, 2013). As specificity of Dicer cleavage is influenced by neighbouring loop and bulge structures, care should be taken in shRNA design.

3. Multiple shRNA uptake. During viral transduction, the viral titer is minimized to increase the probability that cells take up a single shRNA vector. However, this does not guarantee that multiple shRNA uptake will not occur. In this event, a combinatorial gene knockdown ensues resulting in a mixed phenotype that may generate false hits.

4. Differences in genomic integration between shRNAs. Varying efficiencies in transfection and genome integration between shRNAs may skew results to over-represent certain shRNAs over others, especially in pooled screens. Furthermore, integration into the host genome may disrupt the function of certain genes, producing more off-targets.

Studies comparing results from siRNA and shRNA screens have found extremely poor overlap, both between and within the reagent-specific screens. Bhinder and Djaballah’s (2013) analysis of results from 30 published RNAi screens (16 siRNA, 14 shRNA) searching for genes that impact cell viability saw no common genes identified across the board. Furthermore, different genes were identified depending on whether the screen used siRNA or shRNA. PLK1 for example, was a prominent hit for siRNA screens but was only marginally represented in shRNA screens. In contrast, KRAS was a top hit among shRNA screens.

Fig. 3 Reagent format of RNAi screens analysed in Bhinder and Djaballah, 2013 Screens were performed either with genome-wide (GW) or focused (FD) siRNA/shRNA libraries. For siRNA screens, Pooled refers to pools of 3 siRNAs applied together compared to Singles where a single siRNA duplex was applied. For shRNA screens, Pooled refers to a pooled format screen (Fig. 2) where ~50, 000 shRNAs were applied to a single cell population. Arrayed refers to arrayed format screen where shRNAs were applied individually (Fig. 2).

Fig. 4 Overlap of hits among genome-wide (left) and focused (right) siRNA screens (Bhinder and Djaballah, 2013) Only 4 common hits detected across the 2 lethal gene lists from genome-wide siRNA screens. In focused siRNA screens, a greater overlap was detected but still limited across the 22 lethal gene lists. PLK1 detected in 9 out of 22 gene lists.

Fig. 5 Overlap of hits among genome-wide (left) and focused (right) shRNA screens (Bhinder and Djaballah, 2013) KRAS was a top hit in shRNA GW screens, appearing in 5 out of 9 lists. In focused shRNA screens, KRAS was present in 15 out of 31 lists. 

Worryingly, an enrichment of gene candidates exclusive to pooled shRNA screens was observed as opposed to arrayed shRNA or siRNA screens. Most of the overlap seen in gene lists (80% global overlaps, 60% after stringent filtering) were specific to pooled shRNA screens. Exclusion of data from pooled shRNA screens would have reduced overlap to a mere 27%. This indicates gene targets obtained from shRNA pooled screens is specific to the technique as opposed to specific gene downregulation.

Furthermore, a greater number of hits were obtained from shRNA screens – 6664 candidates from 40 shRNA gene lists – as opposed to 1525 candidates from 24 siRNA gene lists. This indicates a generally noisier dataset associated with shRNA screens.

Bhinder and Djaballah later performed a head-to-head comparison of an arrayed siRNA and shRNA screen and reported similarly dismal results. Despite using a gain-of-function assay, which tends to yield clearer results, only a 29 hit overlap was seen between siRNA and shRNA libraries which shared 15,068 common genes. Based on a known set of positive controls, siRNAs identified 8 known regulators as opposed to shRNA which only identified 3. Furthermore, predicted siRNA sequences obtained after Dicer-processing of shRNA which corresponded to exactly the same siRNA sequence from the siRNA library yielded different phenotypes. The authors highlight that differential intracellular processing of the shRNA contributes significantly to the discrepancies observed.

It is evident that shRNAs are at risk to greater number of off-target effects than siRNAs. Much care should be taken towards the interpretation of pooled shRNA screens in particular. Secondary validation of gene hits plays an increasingly important role. It is recommended to validate gene hits with siPOOLs (high-complexity, defined siRNA pools) which have a lower off-target profile than single siRNAs or low complexity siRNA pools of 3-4. siPOOL-resistant rescue constructs enable further affirmation that the loss-of-function phenotype is attributed to the target gene. Alternative tools such as compounds, antibodies or gene knockout technologies are also highly recommended.

Want to receive regular blog updates? Sign up for our siTOOLs Newsletter:

Table. 1 Comparison of properties between siRNA and shRNA

siRNA shRNA
Structure 20-25 nucleotide long double-stranded RNA (dsRNA) with 2 nucleotide overhangs at the 3’ end

~57-58 nucleotide long RNA sequence with a dsRNA region linked by non-pairing nucleotides to form a stem-loop structure

Delivery RNA itself with liposome/electroporation-mediated delivery into cells Usually delivered to cells via viral vectors. DNA may be incorporated into host genome depending on viral vector used.
Processing In the cytosol, guide or antisense strand* (shown in blue in Fig. 1) is incorporated into RNA induced silencing complex (RISC). RISC is guided towards RNA transcripts with the complementary sequence to mediate cleavage and subsequent degradation of the transcript.

 

*Note that the sense strand may also load into RISC and mediate off-targeting but incidence of this is reduced by designing siRNA with  appropriate thermodynamic properties (refer to previous blogpost on siRNA design)

In the nucleus, shRNA is transcribed from DNA by either RNA polymerase I or III, depending on the promoter.

Drosha, a member of the ribonuclease III family, processes the RNA transcript of its long flanking single-stranded RNA sequences and the resultant shRNA is exported out of the nucleus by Exportin-5.

 

In the cytosol, the enzyme Dicer cuts off the hairpin loop of the shRNA and releases the functional active siRNA which follows the same downstream processing as siRNAs.

 

Length of expression Varies from 3-7 days. Affected by degradation of siRNA within cell and dilution of effect upon cell division. Expression can be reinstated by re-transfecting the siRNA. If the DNA is stably integrated in the host genome, knock-down is theoretically permanent.
Control of knockdown Easily controlled by varying amount of siRNA introduced. Magnitude of knockdown harder to control as determined by promoter-driven efficiency and shRNA vector uptake. Expression however can be made inducible with Tet-on/off systems.

 

Follow us or share this post:

Like what you see? Mouse over icons to Follow / Share