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Month: February 2018

Disrupting lncRNA function with siPOOLs (RNAi), antisense oligos and CRISPR

Disrupting lncRNA function with siPOOLs (RNAi), antisense oligos and CRISPR

Summary

This blogpost covers methods used in the disruption of lncRNA function. Specifically focusing on RNA interference (with siPOOLs), antisense oligos, and CRISPR approaches. Challenges faced with these approaches are addressed.

Long non-coding RNAs (lncRNAs) make up a major subgroup of RNAs and are defined as over 200 nucleotides long with limited protein-coding potential. There are three times as many genes producing lncRNAs as opposed to proteins. Numerous studies have described functional roles of lncRNAs in development and disease. This has stimulated major global interest and intense efforts to decode lncRNA function.

Disrupting lncRNA function

One way to find out what a lncRNA does is to decrease its expression, thereby disrupting its function. Current methods of downregulating lncRNA expression include knockdown approaches with siRNA and antisense oligos (ASOs), or knockout approaches with CRISPR, TALENs and other techniques involving DNA nucleases.

As we have mentioned before, knockdown and knockout approaches employ different mechanisms and as a result sometimes yield different results. Hence it is highly recommended to employ both techniques when possible to thoroughly validate lncRNA function.

LncRNA functional knockdown – RNAi and antisense approaches

LncRNA knockdown involves the transient downregulation of lncRNAs at the RNA level. This typically involves RNA degradation mediated by the RNA interference (RNAi) machinery for siRNAs, or with RNase H for ASOs.

Disrupting lncRNA function - How ASOs and siRNAs downregulate RNA
How ASOs and siRNAs downregulate RNA

Figure from Watts, J. K. & Corey, D. R. Silencing disease genes in the laboratory and the clinic. J. Pathol. 226, 365–79 (2012).

Some challenges that both technologies face when targeting lncRNAs:

  • low endogenous expression of lncRNA may limit efficiency of knockdown
  • accessibility of siRNA/ASO to lncRNA may be limited by secondary structure (created by folding of the lncRNA and self-base pairing)
  • accessibility to siRNA/ASO to lncRNA may be limited by bound proteins
  • off-target effects

Does cellular localization matter when disrupting lncRNA function?

Cellular localization of lncRNAs was reported to account for differences in knockdown efficiency by ASOs compared with siRNAs. Although there have been observations that RNAi factors are present in the nuclei, siRNAs were reportedley less efficient than ASOs for modulating nuclear-localized lncRNAs (Lennox and Behlke, Nucleic Acids Res, 2016).

This does not appear to apply to all cases as using siPOOLs (high complexity pooled siRNA) or ASOs led to similar downregulation of NEAT1, a nucleus-localized lncRNA:

Disrupting lncRNA function - Downregulation of lncRNA NEAT1 with siPOOLs and ASOs
Downregulation of lncRNA NEAT1 with siPOOLs and ASOs

NEAT1 lncRNA has two isoforms, 3.7kb NEAT1_1 and longer 21.7kb NEAT1_2. MCF7 cells were transfected with either LNA GapmeRs (ASOs) or siPOOLs that target both isoforms (N1) or the long form only (N1_2). RNA levels of both isoforms (NEAT1) or only the long isoform (NEAT1_2) were quantified after 24h. (Adriaens et al., Nat Med, 2016) 

siPOOLs also worked well for XIST and MALAT1 (~80% KD at 1 nM), both nuclear-localized lncRNA. Notably however, cytosolic-localized lncRNAs such as H19 were much more efficiently targeted with the high complexity siRNA pools (> 95% KD at 1 nM).

Disrupting lncRNA function - siTOOLs data lncRNA gene knockdown with siPOOLs, 1-3 nM
siPOOL knockdown efficiency of lncRNAs

siTOOLs Biotech in-house data showing knockdown efficiencies of siPOOLs against 16 lncRNAs tested at 1 or 3 nM in standard cell lines (MCF7, A549, Huh7). Assayed by real-time quantitative PCR after 24h.

Compared to coding genes, the above-mentioned factors do limit efficiencies of knockdown approaches. But with siPOOLs, the greater diversity of siRNA sequences is expected to increase chances of association with the target RNA. In-house data shows 12 of 16 tested lncRNAs showed good knockdown efficiencies of > 70% with siPOOLs.

Importantly, siPOOLs efficiently counter off-target effects commonly associated with siRNA. Off-target effects have also been reported to occur with ASOs, especially since they are also exposed to intronic regions. Hepatotoxicity related to certain sequence motifs on LNA-modified ASOs have also been reported (Burdick et al., 2014)

lncRNA functional knockout with CRISPR

The genomic distribution of lncRNA loci is rather complex. They are typically categorized in relation to their proximity with protein coding genes.

Types of lncrna
Types of lncrna

Figure showing lncRNA loci in green and protein-coding loci in purple. Arrows indicate direction of transcription. Figure and description below from McManus lncRNA presentation: http://mcmanuslab.ucsf.edu/node/251

  • Sense – The lncRNA sequence overlaps with the sense strand of a protein coding gene.
  • Antisense – The lncRNA sequence overlaps with the antisense strand of a protein coding gene.
  • Bidirectional – The lncRNA sequence is located on the opposite strand from a protein coding gene whose transcription is initiated less than 1000 base pairs away.
  • Intronic – The lncRNA sequence is derived entirely from within an intron of another transcript. This may be either a true independent transcript or a product of pre-mRNA processing
  • Intergenic – The lncRNA sequence is not located near any other protein coding loci.

Hence disrupting lncRNAs with DNA nucleases can be a challenging affair that runs the risk of affecting neighbouring genes.

How many lncRNAs can be CRISPRed?

Goyal et al. 2017 performed a genome-wide “CRISPRability” analysis to evaluate the risks and utility of CRISPR for disrupting lncRNA function.

Introducing mutations with CRISPR is generally not applicable for lncRNAs. Mainly due to difficulty predicting active functional domains and the fact that some lncRNAs exert phenotypes through the act of transcription per se.

Deleting the entire lncRNA is an option but not when it overlaps with other genes. Hence, the major approach is to target lncRNA promoters. But then we run into the problem of affecting neighbouring genes that share promoters.

So they came up with three “CRISPRability” rules to avoid potential effects on neighbouring genes:

Rule 1: Sense, antisense and intergenic lncRNAs are considered “non-CRISPRable” when transcribed from bidirectional promoters, defined by presence of another promoter present 2000bp upstream/downstream of lncRNA start.

LncRNA with bidirectional promoter
LncRNA with bidirectional promoter

Rule 2: Sense, antisense and intergenic lncRNAs are considered “non-CRISPRable” when the start of the lncRNA is located closer than 2000p to the start of the neighbouring gene, excluding lncRNAs transcribed from bidirectional promoters – termed “proximal promoters“.

LncRNA with proximal promoter
LncRNA with proximal promoter

Rule 3: Sense and antisense lncRNAs are considered “non-CRISPRable” when transcribed from internal promoters, where the start of the lncRNA falls within the gene body of another coding/non-coding transcript. This would include intronic lncRNAs.

LncRNA with internal promoter
LncRNA with internal promoter

After applying “CRISPRability” rules, only 38% of all lncRNAs were suitable for CRISPR-based functional disruption

CRISPRability of lncRNAs
CRISPRability of lncRNAs

 

Figure from Goyal et al., 2017 showing proportion of lncRNAs that fall within the 3 rules of “CRISPRability”

The study went on to corroborate the relevance of the classification by testing effects of CRISPR/Cas9 compared to ASOs/siRNA on their targets and neighbouring genes.

 

HOTAIR downregulation by CRISPR and siPOOL
HOTAIR downregulation by CRISPR and siPOOL

An example involved lncRNA HOTAIR that arises from the HOXC locus which regulates expression of several genes including HOXC11. They found that dCas9-KRAB , which produces CRISPR-based transient inhibition (CRISPRi) by blocking transcription, caused knockdown of HOXC11 when designed to target HOTAIR. This occurred for all 3 independent sgRNAs. siPOOL-mediated knockdown of HOTAIR, in contrast, did not affect HOXC11.

Similar scenarios were seen with coding genes, in particular for well-known tumour suppressor TP53, where neighbouring gene WRAP53alpha tended to be downregulated by dCas9-KRAB. This effect was absent with siPOOLs targeting TP53.

It therefore pays to carefully note the genomic neighbourhood of lncRNAs when using CRISPR for disruption. A careful scientist would also monitor the expression of neighbouring/overlapping genes in parallel to the target gene. Orthogonal methods such as RNAi (with siPOOLs), or rescue experiments that restore expression of the lncRNA, is recommended to fully evaluate lncRNA function.

Learn more about siPOOLs!

Featured blog image from lncRNA blog, photo credit autism.am

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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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