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Month: August 2018

Clearly compensating

Clearly compensating

Genetic compensation by transcriptional adaptation is a process whereby knocking out a gene (e.g by CRISPR or TALEN) results in the deregulation of genes that make up for the loss of gene function.

A 2015 study by Rossi et al. (discussed previously) alerted researchers that CRISPR/TALEN knock-out experiments may be subject to such effects.

Genetic adaption or compensation had been well known to mouse researchers creating knock-out lines.  In fact, one of our company founders also ran into this when trying to confirm an RNAi phenotype in a knock-out mouse line.  The knock-out mice, though not completely healthy, did not confirm the RNAi phenotype.

A paper published a couple years before the Rossi paper also showed clearly that knock-outs can create off-target effects via transcriptional adaptation.

Hall et al. showed with an siRNA screen that the centrosomal protein Azi1 was required for ciliogenesis in mouse fibroblasts, confirming previous work in zebrafish and fly.

Their Azi1 siRNA targeted the 3′ UTR, and they were able to rescue the phenotype with a plasmid expressing just the CDS (bar at far right), confirming that their phenotype was due to on-target knockdown:

However, knock-out mouse embryonic fibroblast cells (created by gene trapping) did not show any differences in in the number of cilia, centrosomes, or centrioles compared to wildtype (+/+ is wild type, Gt/Gt is the homozygous knock-out):

The one phenotypic difference they observed was that male knock-out mice were infertile, due to defective formation of sperm flagella.  Female mice had normal fertility.  Both were compensating, but only one showed a visible phenotype.

The authors note the benefits of RNAi in comparison to knock-out screening:

Discrepancies between the phenotypic severity observed with siRNA knock-down versus genetic deletion has previously been attributed to the acute nature of knock-down, allowing less time for compensation to occur

The excitement surrounding CRISPR should not diminish the continued value of RNAi screening.

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Pooling only 4 siRNAs increases off-target effects

Pooling only 4 siRNAs increases off-target effects

In a previous post, we showed how siRNA pools with small numbers of siRNAs can exacerbate off-target effects.

Low-complexity pools (with 4 siRNAs per gene) should thus lead to overall stronger off-target effects than single siRNAs.

This phenomenon was addressed in a bioinformatics paper a few years back.  The authors created a model to predict gene phenotypes based on the combined on-target and off-target effects of siRNAs.

The siRNAs were screened either individually (Ambion and Qiagen), or in pools of four (Dharmacon siGENOME), in 3 different batcterial-infection assays (B. abortus, B. henselae, and S. typhimurium).

The model assumed that each siRNA silenced its on-target gene to the same level.  For off-target silencing, they used the predictions from TargetScan, a program for calculating seed-based knockdown by miRNAs or siRNAs.

In order to assess model quality, they checked how similar the gene phenotype predictions were when using different reagents types in the same pathogen-infection screen.

The following figure shows the rank-biased overlap (a measure of how similar lists are with regards to top- and bottom-ranked items), when estimating siGENOME off-target knockdown in one of 2 ways:

A) using the maximum TargetScan score for any of the 4 siRNAs in the siGENOME pool

B) using the mean TargetScan score for the 4 siRNAs

If low-complexity pooling increases the degree of off-target effects, we would expect the maximum TargetScan score to produce better model concordance.

And that is what the authors found.  (the two plots show the rank-biased overlap for the top and bottom of the phenotype ranked lists, respectively)

The off-target effect of a 4-siRNA, low-complexity pool is best described by the strongest off-target effect of any of the individual siRNAs.

As discussed in our NAR paper, pooling a minimum of 15 siRNAs is required to reliably prevent off-target effects.

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Citations of our Nucleic Acids Research Paper

Citations of our Nucleic Acids Research Paper

Our 2014 Nucleic Acids Research paper provides an excellent overview of the siPOOL technology.  Google Scholar shows that our paper has been cited 64 times.

To put this into perspective, the 2012 PLoS One paper on C911 controls by Buehler et al. has 72 citations.  C911 controls are probably the most effective way to determine whether a single-siRNA phenotype is due to an off-target effect.

These citation numbers show that siPOOLs have good mind share when researchers consider the issue of RNAi off-target effects.

We have noticed, however, that in some cases our NAR paper is cited to justify approaches that we do not endorse.

For example, two recent papers (1, 2) cite our paper as support for the use of Dharmacon ON-TARGETplus 4-siRNA pools to reduce the potential for off-target effects.

Our paper shows, however, that high-complexity siRNA pools (> 15 siRNAs) are needed to reliably reduce off-target effects.

We have also discussed how low-complexity siRNA pools can in fact exacerbate off-target effects.

There’s an old saying that any publicity is good publicity, and we are certainly thankful that these authors have referenced our paper, even if we don’t agree with the interpretations.

And we are especially grateful to all the researchers who have purchased siPOOLs and referred to our products in their publications.

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