Category: General

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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CRISPR – what can go wrong and how to deal with it

CRISPR – what can go wrong and how to deal with it

CRISPR is a gene editing technique based on tools and principles learnt from the bacterial immune system. Gaining immense popularity world-wide, many are trying to establish CRISPR in their favourite model systems to study gene function. Here, we highlight issues to be aware of when using CRISPR and what one can do to counter or manage them.

To simplify matters, we have classified what could go wrong while performing CRISPR into three main categories, accompanied by associated exclamations one may hear in the process:

  1. “Hmm… I don’t see anything.” – Absence of phenotype
  2. “This is taking wayyy too long.” – Inefficient editing
  3. “What the *@#?!” – Unexpected phenotypes

First, some key terms…

Cas9: The bacterial RNA-guided endonuclease that mediates cutting of the DNA. The most commonly used Cas9 ortholog is from Streptococcus Pyogenes and can be introduced into cells in the form of DNA, mRNA, or protein.

sgRNA: single guide RNA composed of a 17-20 base long guide RNA (gRNA) which hybridizes to its complementary DNA sequence on the genome, defining  the target site. This is often joined to a ~70-80 base long transactivating crRNA (tracrRNA), a constant region that mediates recruitment of Cas9. sgRNAs can be introduced as one unit or in its separate components – gRNA and tracRNA – as DNA or RNA.

PAM: protospacer adjacent motif, a trinucleotide sequence 3’ adjacent to the gene editing site required for Cas9 to bind and mediate cleavage. Sequence is NGG for Cas9 from Streptococcus Pyogenes though NAG is often recognized as well. PAM sequences differ between various forms of Cas enzymes.

 

  1. “Hmm… I don’t see anything.” – Absence of phenotype

The anti-climax of a null result may stem from adaptation where the cell or organism alters other gene pathways to compensate for the loss-of-function of the target gene.

This problem is most visible to those maintaining Drosophila stocks as strength of phenotype typically decreases over multiple generations. The phenomenon is also well-documented in other models such as yeast (Teng X et al., 2013), zebrafish (Rossi et al., 2016, covered in a previous blogpost) and mice (Babaric et al., 2007). A notable Developmental Cell paper recently reported adaptation in cells (Cerikan et al., 2016) where prolonged knock-down (KD) or knock-out (KO) yielded no visible phenotype as opposed to acute KD by RNAi.

Multiple cell passages increase genetic drift, providing opportunities for the system to adapt to counter the disruptive effects of a gene knock-out. It is therefore prudent to preserve early passages of clones during clonal selection and limit multiple passages prior to assay measurement.

Besides adaptation, redundancy may also account for an absence of phenotype. Paralogous genes (i.e. genes closely related in structure or function) often exist in model systems that can fully or partially compensate for the loss-of-function of the target gene. About 50% of mouse genes and at least 17% of human genes have paralogues that may mask loss-of-function phenotypes.

One can find paralogous genes arising from gene duplication with this database and by checking existing literature. If they do exist, a co-knock-out/knock-down approach may be necessary.

 

  1. “This is taking wayyy too long.” – Inefficient editing

Despite the high efficiency of Cas9-mediated cleavage, obtaining the desired gene knock-out can still be a tedious and time-consuming process, with wide-ranging overall efficiencies of 1-79% (Unniyampurath et al., 2016).

These challenges often stem from issues associated with the cell line of choice. Due to many standard cell lines being polyploid (containing multiple copies of chromosomes), every copy of the gene has to be disrupted to ensure a complete knock-out. A process aggravated by the need for a homozygous knock-out. Transfection efficiencies, how well the cell line tolerates clonal selection and the impact of the gene modification on cell viability can also impact outcomes. If performing homology directed repair (HDR) to introduce a new sequence at the cut site, clone screening efforts have to be amplified due to the lower frequency of HDR events compared to indels.

Understanding the characteristics of your cell line and ensuring sufficient numbers of clones are screened is essential to avoid mindless weeks repeating experiments!

Editing efficiency may also be hindered by genomic accessibility. gRNAs targeting transcriptional start sites or promoters were found to be more efficient than intergenic sites due to the open chromatin structure in these areas (Liu X et al., 2016). Numerous design criteria have been recommended to ensure high cutting efficiency but performance of gRNAs may still vary. Therefore it is advisable to use at least 3 sgRNAs per gene to increase chances of success.

Sidenote: Looking for someone who can design CRISPR sgRNAs for you? siTOOLs Biotech’s CRISPR sgRNA design service couples our long-standing experience in off-target filtering with published gRNA design criterion to generate reliable gRNA sequences. Send us your enquiry and we will get back to you.

 

  1. “What the *@#?!” – Unexpected phenotypes

Unexpected results can stem from off-target effects or in some cases, may be a real effect that requires some brain rattling to make sense of.

Off-target effects are still a cause of concern for CRISPR and vary widely with different gRNA sequences ranging from 0 to up to 150 in one report (Tsai et al., 2015). In another study, ~10 to > 1000 off-target binding sites were found that varied with sgRNA sequence (Kuscu et al., 2014).

Toxicity correlated with increased off-targeting (Morgens et al., 2017) and the use of safe-targeting controls (i.e. where gRNAs are directed towards sites where cleavage is predicted to have minimal impact) was recommended. This served as a more appropriate measure of nuclease-induced toxicity as opposed to non-targeting controls that might not lead to cleavage.

Some other strategies to minimize off-targets:

  • Use the Cas9 recombinant protein/mRNA rather than a plasmid or keep DNA transfection amounts low (plasmid-driven prolonged Cas9 expression increased off-targeting events as reported by Liang et al., 2015)
  • Use truncated gRNAs of 17-18 nucleotides
  • Use D10A Cas9 nickase and paired gRNAs
  • Use a Cas9 ortholog with a longer PAM requirement

Despite our efforts to predict off-target effects, two reported sources of potential off-targets make prediction challenging:

a) Single nucleotide variants from clonal heterogeneity

b) Cas9 effects on mRNA translation

 

a) Single nucleotide variants from clonal heterogeneity

Table 1: Spontaneous SNVs and indels generated over clonal selection in human pluripotent stem cells.

Two studies (Smith et al., 2014Veres et al., 2014) carried out in pluripotent stem cells to detect off-targets saw a higher specificity of Cas9 in these cells compared to cancer cell lines but shockingly, rather large clonal heterogeneity (Table 1).  Each clone generated from the parental cell line had on average 100 unique SNVs per clone and 2-5 indels not induced by the gene modification but arising spontaneously during cell culture.

Target and off-target indel frequencies
Number of mismatches Number of genomic sites Cas9 targeting efficiency
0 1 53.9%
1 0
2 0 → 1 36.7%
3 32 ~0.15% per site

Table 2: Editing efficiencies at off-target sites with 0-3 mismatches. Condition of SNV enhancing editing efficiency shown in bold.

Yang et al., 2014 then goes on to demonstrate how an SNV at the wrong place at the wrong time can produce a high-efficiency off-target site. The said SNV corrected a mismatch at an off-target site, reducing mismatch number from 3 to 2, which increased Cas9 –mediated indel frequency to ~37%!

To manage clonal heterogeneity, we recommend performing deep sequencing to fully characterize the knock-out clone and its parental wild-type cell line. Once the locations of SNVs are identified, these can be aligned with potential off-target gRNA binding sites to check for interference. Check locations of identified unique SNVs or indels to see if they are impacting genes that may play a relevant role in your studied phenotype.

b) Cas9 effects on mRNA translation

A Scientific Reports study (Liu Y et al., 2016) reported a worrying finding that Cas9 could be recruited by gRNAs to mRNAs and block their translation. Neither PAM sequences nor Cas9 enzyme activity was required for this and the effect varied with gRNA sequence. Cas9-mediated mRNA translation suppression produced a 30-60% decrease in protein levels, sufficient to impact downstream phenotypes. For example, a gRNA targeting VEGFA with an off-target binding site to the mRNA of oncogene, B3GNT8, produced a nearly 50% drop in B3GNT8 protein levels with a corresponding drop in cell viability. This was partially rescued by overexpressing B3GNT8 with a vector.

It is still unclear to what extent this phenomenon occurs. There have been limited reports on this mechanism so far, but if true, would have a far-ranging impact. The study found gRNAs with single base mismatches at position 8-20 were still able to carry out Cas9-mediated translation repression. This low hybridization stringency requirement would make off-targets impossible to predict.

CRISPR is no doubt a powerful technology, but it still brings many unknowns. After its discovery in the 1990s, RNAi experienced a similar exponential uptake and use by the scientific community. It took several years for the problem of siRNA off-targets to become visible. Unfortunately by that time, enormous resources and energy had been sunk into large RNAi screens, which yielded numerous false hits and difficult-to-interpret data.

Figure 1. Pubmed Citations (1999-2015) with CRISPR or RNAi in Title/Abstract/Summary

Thankfully we now have  siPOOLs, or high-complexity defined siRNA pools (from siTOOLs Biotech). These custom-designed pools of 30 unique siRNAs counter the off-target effects often seen with single siRNAs or low complexity siRNA pools of 3-4 siRNAs (Marine et al., 2012, Hannus et al., 2014). Efficient at 1 nM in standard cell lines, it is the optimal RNAi reagent for highly specific, efficient and robust gene knock-down.

In order not to repeat past mistakes, it is imperative to proceed with caution and use multiple methods to establish gene function.

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References:

Barbaric, I., Miller, G. & Dear, T. N. Appearances can be deceiving: Phenotypes of knockout mice. Briefings Funct. Genomics Proteomics 6, 91–103 (2007).

Cerikan, B. et al. Cell-Intrinsic Adaptation Arising from Chronic Ablation of a Key Rho GTPase Regulator. Dev. Cell 39, 28–43 (2016).

Kuscu, C., Arslan, S., Singh, R., Thorpe, J. & Adli, M. Genome-wide analysis reveals characteristics of off-target sites bound by the Cas9 endonuclease. Nat Biotechnol 32, 677–683 (2014).

Hannus, M. et al. siPools: highly complex but accurately defined siRNA pools eliminate off-target effects. Nucleic Acids Res. 42, 8049–61 (2014).

Liang, X. et al. Rapid and highly efficient mammalian cell engineering via Cas9 protein transfection. J. Biotechnol. 208, 44–53 (2015).

Liu, X. et al. Sequence features associated with the cleavage efficiency of CRISPR/Cas9 system. Sci. Rep. 6, 19675 (2016).

Liu, Y. et al. Targeting cellular mRNAs translation by CRISPR-Cas9. Nat. Publ. Gr. 2–10 (2016). doi:10.1038/srep29652

Marine, S., Bahl, A., Ferrer, M. & Buehler, E. Common seed analysis to identify off-target effects in siRNA screens. J. Biomol. Screen. 17, 370–8 (2012).

Rossi, A. et al. Genetic compensation induced by deleterious mutations but not gene knockdowns. Nature 524, 230–233 (2015).

Smith, C. et al. Whole-Genome Sequencing Analysis Reveals High Specificity of CRISPR/Cas9 and TALEN-Based Genome Editing in Human iPSCs. doi:10.1016/j.stem.2014.06.011

Teng, X. et al. Genome-wide Consequences of Deleting Any Single Gene. Mol. Cell 52, 485–494 (2017).

Tsai, S. Q. et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat Biotech 33, 187–197 (2015).

Unniyampurath, U., Pilankatta, R. & Krishnan, M. N. RNA Interference in the Age of CRISPR : Will CRISPR Interfere with RNAi ? (2016). doi:10.3390/ijms17030291

Veres, A. et al. Low incidence of Off-target mutations in individual CRISPR-Cas9 and TALEN targeted human stem cell clones detected by whole-genome sequencing. Cell Stem Cell 15, 27–30 (2014).

Yang, L. et al. Targeted and genome-wide sequencing reveal single nucleotide variations impacting specificity of Cas9 in human stem cells. Nat. Commun. 5, 1–6 (2014).

Further helpful reading:

Housden, B. E. et al. Loss-of-function genetic tools for animal models: cross-species and cross-platform differences. Nat. Publ. Gr. (2016). doi:10.1038/nrg.2016.118

 

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).

Tips for optimizing RNA affinity purification

Tips for optimizing RNA affinity purification

RNA affinity purification (RAP) experiments enable the isolation and analysis of interacting molecules with an RNA of interest. Often performed to gain insight into RNA function, it is gaining popularity in the study of lncRNAs but can also be applied to coding RNAs.

The general workflow involves preserving nucleic acid and protein interactions with a cross-linking reagent, followed by lysis and sonication to shear nucleic acids to sizes amenable for pulldown. Biotin-labelled DNA probes (which we offer, see raPOOLs) are added to the lysates and hybridize with the RNA of interest. This enables the isolation of the RNA and its associated molecules through the high affinity biotin-streptavidin interaction between biotinylated probes within the complex and streptavidin-coated magnetic beads. The complexes are then disrupted and individual components analysed by various methods: western blotting/mass spectrometry (for proteins), sequencing/northern and southern blots/PCR detection (for nucleic acids). A detailed protocol can be found here.

Despite the seemingly simple workflow, there are a number of parameters requiring optimization when applying the protocol to your RNA of interest. We break them down into the following sections:

1. Input material

If trying to detect protein partners of the target RNA, sufficient amounts of target RNA have to be isolated as unlike nucleic acids, proteins cannot be amplified. It is always advisable to determine the number of copies of the target RNA per cell to examine how much input material is required. As a guide, XIST is expressed at < 2000 copies/cell and required 50-250 million cells (depending on cell type) per pulldown condition to visualize proteins by immunoblotting/MS (Chu et al., 2015). For some lowly expressed lncRNAs, up to 1 billion cells may be required per pulldown! If your RNA is too lowly expressed and RAP is not feasible, other methods involving FISH and immunofluorescence imaging or protein arrays might be explored.

2. Cross-linking

Cross-linking reagents produce covalent bonds between nucleic acids and proteins in close proximity with each other, stabilizing these interactions for subsequent analysis. Formaldehyde (CH2O) and glutaraldehyde (C5H8O2) are most commonly used and cross-link direct and indirect associations between proteins-proteins, nucleic acids- nucleic acids and nucleic acids-proteins. Compared to formaldehyde, glutaraldehyde has two reactive groups, making it a stronger cross-linker. It also fixes long range interactions compared to formaldehyde, a zero-length cross-linker. Formaldehyde fixation can be reversed by heating at 65◦C for 6 h, while glutaraldehyde fixation is irreversible. Alternatively, UV irradiation produces irreversible cross-links only between nucleic acids and proteins.

There are a whole range of other cross-linking chemicals one could use in combination that can cross-link different reactive groups and at different ranges, but take note that the activity of cross-linking chemicals is highly determined by pH and buffer conditions, so be sure to follow given protocols closely and ensure reagents used are fresh. Note also that the longer cross-linking is carried out, the harder it is to sonicate nucleic acids down to smaller fragments.

3. Sonication and lysis

Sonication is an effective way to randomly shear nucleic acids to increase efficiency of their pulldown, hence improving detection sensitivity. Ideally, fragments should be sheared down to ~100-500 bases. If using PCR detection, be aware that your amplicon size should be small enough to lie within these sheared fragments.

Factors affecting sonication efficiency include volume of sample, sonication strength, frequency and duration and if using probe sonicators, probe position/depth. Follow the instrument manufacturer’s instructions closely and optimize duration of sonication by collecting samples at time-points and studying the extent of fragmentation. If using probe sonicators, ensure the probe is inserted into the lysate deep enough as frothing tends to occur during the sonication process. Always make sure to wipe the probe clean carefully each time. Due to the length of the protocol, take the usual precautions to avoid RNA degradation i.e. use RNase inhibitors, filter tips, and ensure temperatures are kept < 10°C. Lysates should be non-sticky and clear after sonication, which may take several hours. Pauses between sonications should be incorporated to avoid overheating of the sample.

If you are more focused on isolating chromatin, take note that lysis buffer conditions may vary to ensure efficient lysis of nuclei. In this case, swelling buffers may be incorporated and additional lysis steps might be required. Refer Chu et al., 2011.

4. Probe hybridization

The amount of biotinylated probes (raPOOL) should exceed the copy number of target RNA present in the lysate. With a recommended addition of 100 pmol of raPOOL per ml of lysate,

the number of copies of raPOOL =100 x 10^-12 * 6×10^23 (Avogadro’s constant) = 6 x 10^13 copies

and the number of copies of each probe (30 probes/raPOOL) = 6 x 10^13/30 = 2 x 10^12 copies

which is usually more than sufficient. However one can perform the hybridization with a range of probe concentrations to determine the optimal condition. Hybridization temperatures can also be varied with higher temperatures known to increase stringency of probe association.

5. Elution of components

As mentioned before, detection sensitivity is lowest for proteins, hence aliquot the beads in a proportion where most are used for protein analsis with a small proportion for nucleic acid detection. Benzonase is used to remove nucleic acids leaving proteins intact which can then be precipitated with TCA in the presence of deoxycholate. An acetone wash usually follows to remove the deoxycholate. Alternatively, biotin elution can be performed to compete out streptavidin binding sites and release complexes. It is recommended to separate the isolated proteins by PAGE to simplify the sample for MS analysis.

6. How much pulldown is enough?

The efficiency of RNA enrichment is determined by the difference in amounts of target RNA in the pulldown fraction as compared to the input fraction (i.e. lysate post-sonication and prior to probe hybridization). Be sure to account for the fraction of input used in the analysis. For a calculation guide, download this sheet. Fold enrichment is obtained by comparing against a negative/background condition. This may be performed using a negative control raPOOL which targets sequences not found in the cell. Non-target genes such as GAPDH/Cyclophilin A can also be analysed in the pulldown and input conditions to indicate pulldown is specific for the target RNA.

As a guide to how much pulldown is enough, Chu et al. retrieved ~60% of their target RNA and were able to analyse isolated proteins by western blotting and MS. One could also measure target RNA in lysates that have been subjected to pulldown, and a corresponding depletion of the target RNA should be observed.

We hope this helps you in your RAP optimization. For more questions and raPOOL requests, please feel free to email us at info@sitools.de

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The limits of chemical modification

The limits of chemical modification

In addition to potential reductions in on-target efficiency, chemically modifying siRNAs will not necessarily eliminate seed-based off-target effects.

Rasmussen et al. found that a chemically-modified siRNA can still have substantial seed effects.

They examined the expression data for 3 siRNAs from Jackson et al. and showed that for one of them the seed is still active following chemical modification.

The algorithm of Rasmussen et al. (cWords) looks through a ranked list of 3′ UTR sequences (in this case, ranked by deregulation as measured on a microarray) and finds words that are enriched towards the top of the list.

The seed target sequence of the unmodified Pik3ca siRNA is strongly enriched (B panel), as expected, but is also strongly enriched after chemical modification (C panel):

screen-shot-2016-12-02-at-15-22-03

 

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Deconvoluted SMARTpools are like a box of chocolates

Deconvoluted SMARTpools are like a box of chocolates

 

Falkenberg et al. (2014) performed a synthetic lethal  RNAi screen to identify genes which, when knocked down in combination with drug treatment, induced apoptosis in drug-resistant cells.

The first screening pass covered over 18,000 protein-coding genes using Dharmacon’s siGENOME, probably the most widely used library for genome-wide RNAi screens.

siGENOME is based on low-complexity pooling, with 4 siRNAs pooled per gene.  In the first pass, hits are identified based on the pooled siRNA result.  In the second pass, siRNAs for hit genes are tested individually (deconvoluted).

In Falkenberg et al.’s pass 2, they examine 450 hit genes, split across 2 phenotypes of interest (Caspase-Glo 3/7 as a measure of apoptosis and Cell Fluor Titre, CTF, as a measure of general viability).  They choose 317 genes for Caspase-Glo 3/7 and 150 genes for CTF (adds to 467 because 17 genes are hits for both phenotypes).

If the pooled result from pass 1 is due to an on-target effect, and if the siRNAs give consistent knockdown, the 4 siRNAs should give similar phenotypes (and they should also be similar to the pooled phenotype from pass 1).

Is that what Falkenberg et al. show?   Or is the pool more like Gump’s proverbial box of chocolates?  The latter looks to be the case, as the following plots show.  For the 2 phenotypes of interest, the top 20 genes (based on the pass 1 pool phenotype) were plotted with the pass 1 (pooled) and pass 2 (single siRNA) results.

Falkenberg_deconvolution_plots 6 Falkenberg_deconvolution_plots 5

With a few exceptions, the siRNA results are widely divergent.

(note that the pass 2 phenotypic results are weaker than pass 1, and the authors discuss the reduction in dynamic range in the second pass– however, the divergence between individual siRNAs is still pronounced)

As suggested by the above plots, the correlation between different siRNAs for the same gene is very weak:

Falkenberg_deconvolution_plots 8Falkenberg_deconvolution_plots 7

Note that R’s of 0.15 and 0.12 mean that only 2.3% and 1.4%, respectively, of the screening variance can be explained by on-target effects of the reagent.

In light of this very weak on-target signal in pass 2, it’s surprising that Dharmacon reported to the authors (after analysing their data) that there were no seed-based off-target effects.  Given these results, and the known preponderance of seed-based off-target signal and the necessity of high-complexity pools to overcome seed effects, that statement must be treated very skeptically.

Overall, it looks like we can be quite confident about the top screening hit, but beyond that it really difficult to know what is causing the observed phenotype (probably not on-target knockdown effects).

To paraphrase the wisdom of Forest Gump, deconvoluted Dharmacon pools are like a box of chocolates: you never know what you’re going to get!

 

Additional info:

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Intronic off-target effects with antisense oligos

Intronic off-target effects with antisense oligos

Undecided on whether to use silencing RNAs (siRNAs) or antisense oligos (ASOs) for your RNA interference experiments? Read on.

ASOs are single-stranded DNA oligonucleotides which downregulate specific RNA by hybridizing with it, forming a heteroduplex recognizable by RNase H1. RNase H1 is found in the nucleus, hence ASOs are often used to target nuclear-localized RNAs such as non-coding RNA.

A recent paper however highlighted that since RNase H1 is found in the nucleus, intronic sequences may also fall prey to ASO-mediated silencing, exposing ASOs to a much larger array of off-target sequences. Indeed, a quantitative PCR analysis of chemically modified ASOs (marketed as GapmeRs) targeting a gene called BACH1 showed a surprisingly high number of positive off-target effects even with transcripts of low sequence identity (PHF6 – 4 nt gap with exon; 3 mismatches with intron).

blogpostfig1

The dose and time-dependent nature of ASO-mediated RNA downregulation (measured by qPCR) in off-target genes of two ASOs targeting BACH1 is seen here in various cell lines.

ASO-mediated off-target effects were detected against both exonic and intronic regions, though intronic regions proved more susceptible. This is illustrated below where greater off-target activity was seen in chart C (intron-based OTEs) as opposed to chart B (exon-based OTEs).

blogpostfig2

Graphical summary of 26 OTEs predicted and tested for GSK2910546A (ASO). Total (A), exonic (B) and intronic (C) off-target effects are grouped by their potency relative to EC50 of intended target (BACH1, 0.3 μM).

Nuclear-localized RNAs are thought to be less susceptible to siRNA-mediated degradation due to a lower expression level of RNA-induced silencing machinery (RISC) components in the nucleus. That is not to say they are not thereSilencing of nuclear-localized RNAs such as 7SK by siRNA also occurs efficiently, indicating RISC-mediated RNAi can take place in the nucleus.

siPOOLs address hybridization-dependent toxicities and have been shown to work with nuclear-localized RNAs. Contact us to try out siPOOLs today.

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References:

Kamola, P. J., Kitson, J. D. A., Turner, G., Maratou, K., Eriksson, S., Panjwani, A., … Parry, J. D. (n.d.). In silico and in vitro evaluation of exonic and intronic off-target effects form a critical element of therapeutic ASO gapmer optimization. Nucleic Acids Research , 43 (18 ), 8638–8650. Retrieved from https://nar.oxfordjournals.org/content/43/18/8638.abstract

Gagnon, K. T., Li, L., Chu, Y., Janowski, B. A., & Corey, D. R. (2016). RNAi Factors Are Present and Active in Human Cell Nuclei. Cell Reports, 6(1), 211–221. doi:10.1016/j.celrep.2013.12.013

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