Deconvoluted Dharmacon pools are like a box of chocolates

… you never know what you’re going to get!

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 quite amazing 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, these statements from Dharmacon 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).

 

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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.

 

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 http://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

Robb, G. B., Brown, K. M., Khurana, J., & Rana, T. M. (2005). Specific and potent RNAi in the nucleus of human cells. Nat Struct Mol Biol, 12(2), 133–137. Retrieved from http://dx.doi.org/10.1038/nsmb886

 

Simplicity is the ultimate sophistication

The beauty of the siPOOL strategy is its simplicity.

In this presentation from the  (relatively) early days of Apple, Steve Jobs says that his company’s goal is to serve the one-on-one relationship between a user and his/her computer.

 

Similarly, siPOOLs, are designed to serve the one-on-one relationship between a scientist and his/her RNAi results.

By providing an interpretable result without the need for extensive follow-up work and off-target corrections, siPOOLs make it possible for a scientist to use a single gene list to gain insight into biological function.

We believe, as stated in the brochure to market the Apple II,  that  Simplicity is the ultimate sophistication.

(Note: this quote is popularly, though apparently falsely, attributed to Leonardo da Vinci) .

Seed effects persist in hyperdimensional space

Screen Shot 2015-12-16 at 17.42.26

Work from the Carpenter lab suggests that attempts to shake seed-based off-targets by going to  ‘phenotypic hyperspace’ will not work.

They performed a high-content assay with 315 shRNAs covering 41 genes.  A 1301-dimensional profile was created for each well, and compressed to 205 principal components that captured  99% of the variance.

The hope would be that by examining a wider phenotypic space, the gene-specific effects of RNAi reagents would become more prominent.

However, the profiles between shRNAs targeting the same gene are only slightly better than those between random shRNAs, while shRNAs sharing the same seed sequence have much more similar profiles.

Screen Shot 2015-12-15 at 14.27.51

(figure shows percent of significant profile correlations for different pairings)

Off-target phenotypes can only be escaped by using a reagent that exclusively knocks down the target gene.

Knocking out the phenotype

Consistent with the work of Rossi et al. (discussed previously),  another recent paper shows a lack of phenotypic response when knocking out a gene that gives a phenotypic response when knocked down.

Knocking out klf2a does not result in any discernible difference from wild-type (whereas knock-down has been shown to produce a range of cardiovascular phenotypes).

The authors conclude:

In summary, our work shows that even in the face of clear evidence of a potentially disruptive mutation induced in a gene of interest, it is currently very difficult to be certain that this leads to loss-of-function, and hence to be confident about the role of the gene in embryonic development.

Using a knock-down reagent that prevents off-target effects is the best way to be confident about your phenotypes.

Genetic compensation

Recent work by Rossi et al. show that an unintended consequence of gene knockout may be genetic compensation that mitigates phenotypes.

Knockdown in zebrafish of egfl7, an endothelial extracellular gene, causes severe vascular defects:

Screen Shot 2015-09-25 at 21.44.15

However, following knockout of eglf7, there was no visible effect on vascular development, even after application of the knockdown reagent (demonstrating that the knockdown phenotype was not due to an off-target and that the knockout’s normal vascular development was not due some minor levels of egfl7):

Screen Shot 2015-09-25 at 21.56.06

The authors found that in egfl7 mutants, Emilin genes were upregulated.  Like egfl7, these genes are involved in elastogenesis, and thus their up regulation could be compensating for missing Egfl7.  (It seems that humans are also able to compensate for loss of Egfl7).

Work by Kok et al. also reported discrepancies between the phenotypes elicited by knockdown versus knockout experiments.  They found that the vast majority of phenotypes from knockdown experiments were not confirmed by knockout experiments.  They concluded:

Based on these results, we suggest that mutant phenotypes become the standard metric to define gene function in zebrafish, after which Morpholinos [knockdown reagent] that recapitulate respective phenotypes could be reliably applied for ancillary analyses.

People are understandably wary of knockdown phenotypes, given the prevalence of off-target effects.  But the work by Rossi et al. suggests that gene inactivation may give misleading results about gene function.  The best metric for defining gene function will be gene knockdown experiments using reagents that prevent off-target effects.

 

Russian Roulette, RNAi style

Which bases should you choose for the seed region of a single siRNA?

It’s like Russian Roulette on full-automatic, where a specific seed will result in dozens or hundreds of down-regulated genes.

If you’re lucky, none of the off-targets results in a false-positive phenotype.   But odds are that you won’t be so lucky.

A recent paper suggests that taking single siRNA drugs may be closer to real Russian Roulette than anyone would hope.

The authors show that an siRNA designed to knock down human Huntington protein (HTT), and which had shown no adverse effects in Rhesus monkeys, was toxic when administered to mice.  The siRNA had been designed to produce minimal off-target effects in human, macaques, and mice, confirming the extreme difficulty in predicting off-target effects.

By modifying a single nucleotide in the siRNA seed region, the authors were able to create a version not toxic for mice.  Whether it will also be non-toxic for macaques?  That experiment must be repeated.  Single siRNAs create a lot of work.

Using miRNA target identification algorithms, the authors identify a number of potential off-target genes that may be responsible for toxicity.  For 3 of 4 genes, they show by qPCR using brain tissue that there is significant off-target silencing by the HTT siRNA (HDS1) compared to control (Ctl):

 

Monteys_Fig2D

But when they try to confirm these results in an immortalised mouse neuronal striatal cell line, only one gene (Bcl2) is still being silenced (U6 is an additional vector control):

Montey_Fig2E

The authors comment:

In contrast, Sdf4 and Map2k6 expression was not reduced by overexpression of miHDS1 (Figure 2D and E), suggesting that these genes may not be direct off-targets in vivo, and may reflect indirect effects of Htt suppression over time or off-target suppression in non-neuronal cells, although AAV2/1 transduces primarily neurons.

Another possibility is that Sdf4 and Map2k6 are regulated by one or more genes that are off-targets of HDS1, but which are differentially expressed in live animal cells and the immortalised cell line.

The pool of accessible off-targets (and the resulting off-target footprint) differs with each transcriptional profile.

One should not assume that single siRNAs used in different cell types, or even under different experimental conditions, will behave the same way.

Using single siRNAs as normalisation controls may be especially problematic.

Why take risks with your RNAi experiments?  Use siPools for consistent, robust phenotypes.

The beauty of the siPool

The siPool strategy is beautifully simple:

By having many on-target siRNAs, each with a different seed sequence, you maintain on-target efficiency while diluting out off-target effects.

One analogy is the beauty of composite faces.

Which of these faces do you find most attractive?

composite_faces

If you’re like most people, you will have chosen the last face, which is actually a composite of the other 5 faces (source).

Each individual face has its flaw(s).  Spock ears, mildly everted lips, incongruous eyebrows, etc.  No face is ideal.  But when combined, these flaws are evened out.

Same thing with siRNAs.  Each individual siRNA has its own ugly off-target signature.  But combined in a high-complexity siPool, the off-target warts are removed, and you’re left with a beautiful on-target phenotype.

And not all facial averages are equal.  Perrett et al. found that a composite face created from faces pre-selected as being more attractive was preferred  over a composite created from all available faces.

Likewise, siPools are composed of the best available individual siRNAs.

Being selective about what goes into a siPool is important, as we will discuss more in future posts.

This amusing anectdote from the autobiography of Francis Galton is à propos.  (Galton pioneered composite facial analysis, in addition to finger print analysis, as discussed previously)

I could not make good composites of lunatics ; 
their features are apt to be so irregular in different 
ways that it was impossible to blend them. I took a 
photographer with me to Hanwell, where it was 
arranged that the patients should sit two at a time on 
a bench. One of them was to be led forward and 
posted in front of the camera, while his place on the 
bench was filled by the second patient moving up 
into it, whose previous place was to be occupied by a 
third patient It happened that the second of the 
pair who were the first to occupy the bench considered 
himself to be a very mighty man, I forget whom, but 
let us say Alexander the Great. He boiled with 
internal fury at not being given precedence, and when 
the photographer had his head well under the velvet 
cloth, with his body bent, in the familiar attitude of 
photographers while focusing, Alexander the Great 
slid swiftly to his rear and administered a really good 
bite to the unprotected hinder end of the poor 
photographer, whose scared face emerging from 
under the velvet cloth rises vividly in my memory as 
I write this. The photographer guarded his rear 
afterwards by posting himself in a corner of the 
room.

Memories of My Life, pp 262-263

Don’t get bitten by bad RNAi reagents.

Please see our website for more information on siPools technology.

 

Notes:

See faceresearch.org for lots of information and interesting tools for facial analysis.

See this article for composite faces from different nations.

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.

What, in theory, should have been the ultimate functional genomics tool has turned out to be, if not dead, then perhaps merely undead or delinquent.

The failure of 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

Don’t swallow the fly

There was a PI who screened one s i [RNA],

Oh, I don’t know why they screened one s i …

siRNA screens have a high false positive rate, due to pervasive off-target effects.

Confirming ‘hits’ from single-siRNA screens is a lot of work.  For low-complexity pool screens, it’s even worse (and, as we will discuss in a later post, less likely to result in true genes of interest).

Progressively, one accumulates a nearly indigestible set of experiments and analyses.

On the in vitro side:

  • Screen additional siRNAs for ‘hit’ genes.
  • Do quantitative PCR.  Single siRNAs vary significantly in their effectiveness, so look for correlation between knock-down and phenotypic strength.
  • Create C911 versions of hit siRNAs as off-target controls.  To rule out a confounding on-target effect, do qPCR.
  • Screen additional siRNAs with the same seed sequence as off-target controls (as done in a recent paper).
  • For low-complexity pools, test the siRNAs individually.

On the in silico side:

  • For each hit siRNA, look at plots of phenotypic effects of siRNAs with same seed sequence.
  • Adjust phenotypic scores based on predicted off-target effects for seeds.
  • Run off-target hit selection tools (like Haystack or GESS), to see if hit genes also show up as strong off-targets.

Does it really have to be so complicated?

Wouldn’t you prefer being able to trust your phenotypic readout?

Better yet, how about hits that don’t turn out to be mostly false-positives?

There is a simpler, better way.

siPools maximize the separation between on-target signal and off-target noise, making interpretation of RNAi phenotypes as clear as possible.