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5 factors to consider in multi-gene targeting RNAi screens

5 factors to consider in multi-gene targeting RNAi screens

Summary: Effective functional genomic screening depends on a variety of factors that need to be simultaneously addressed to obtain meaningful results. A recent Cell Reports paper demonstrates this by taking a holistic approach to siRNA screening with the use of multi-isoform/multi-gene targeting to address redundant paralogs and pathways in cancer cells.

The case for multi-gene targeting

Many RNAi screens use arrayed single gene knockdowns to find genes that play an important role in a biological process. The idea is that a single bullet is enough to take down its target leaving a gaping hole that one cannot fail to notice. In some cases, this is true, and is certainly relied upon by drug developers seeking to create specific mono-target drugs.  However, in complex diseases like cancer, cells have evolved fail-safe mechanisms to make them more resistant to external assaults. A single bullet is simply not enough.

Take for example oncogenic protein RAF or Rapidly Accelerated Fibrosarcoma, a tyrosine kinase effector that is a component of the MAPK signalling pathway (Ras-Raf-MEK-ERK). RAF has three isoforms – ARAF, BRAF and RAF1 (also called CRAF). Studies in mouse embryonic development show they all share some form of functional redundancy as knocking out two isoforms produces more severe effects than knocking out each isoform alone.

Screens that target single genes/isoforms therefore tends to bias results towards genes that have no paralogs or only have single isoforms. This was indeed the reason why classical Ras effectors were not identified in previous screens.

Factors to consider in a multi-gene targeting RNAi screen

Determining gene combinations that make sense

The authors of the study did a focussed siRNA screen on 41 RAS effector nodes represented by 84 genes. Out of the 41 nodes, 25 of them had 2-4 functional paralogs where combinatorial gene silencing was carried out with multiple siRNAs. 5 nodes knocked down multiple members of a protein complex. 5 nodes had siRNAs targeting multiple steps within a pathway. Only 6 nodes silenced single genes (highlighted).

Multi-gene targeting screen design

The only caveat with designing such a screen is the requirement for prior knowledge to perform meaningful gene silencing combinations. In this instance, many of the Ras effector pathways are characterized sufficiently to do this well however in other less studied fields, this could be a challenge. Useful tools that would help in designing gene knockdown combinations would include pathway or phenotype databases such as KEGG, REACTOME or Wikipathways. The Phenovault which siTOOLs Biotech is developing, is yet another potentially useful tool.. more details to come!

Number and types of phenotypes

The authors also highlight how a screen that reads only one phenotype might miss other important gene functions. Many RNAi screens sadly still stick to measuring cell proliferation as their only read-out which is greatly influenced by siRNA off-target effects. Here, 5 different phenotypes were measured (cell size, proliferation, apoptosis, reactive oxygen species [ROS], and viability). It was noted that silencing of Cdc42 had little effect on cell viability yet a prominent effect on ROS levels.

To take this up a notch, analysis was also performed at the single-cell level in cells expressing uniform levels of GFP and co-transfected with GFP siRNA. This allowed authors to correlate phenotypes with levels of gene knockdown, generating dose-response curves. How clever!

A lot more work, but adds to data robustness especially when using single siRNAs that are known to be rather variable.

Heterogeneity of cell lines

Many reports and our own observations attest to the heterogenous response of different cell lines to the same treatment. In cancer especially, the large heterogeneity necessitates the use of multiple cell lines. Not doing so would be failing to account for the large genetic diversity observed in the clinic. The authors screened 92 cell lines derived from lung, pancreas and colorectal tissue.

Despite seeing heterogenous responses to node knockdowns, phenotypic responses could be distinguished into  several groups based on effector engagement.  A major group dependended on RAF through direct binding with KRAS, a second major group worked via RSK p90 S6 kinases to drive RSK-mTOR signalling. And a third minor group was dependent on RalGDS. They went on to focus on the first two major groups, naming them KRAS-type and RSK-type respectively.

Reagents – choosing siRNAs and siRNA concentrations

The authors used previously characterized siRNAs to select for more potent siRNAs. This involved an RNAi sensor reporter-based assay that required the generation of 20,000 clones. The reporter was also shRNA-based. Due to heterogeneity in Dicer-mediated cleavage of shRNA, its uncertain if knockdown potency is accurately reflected when translated to siRNAs (read about the difference between shRNAs and siRNAs).

siRNA off-target effects are concentration-dependent

In any case, its a lot of work to characterize all siRNAs to be used in a screen. Furthermore, off-target effects are not addressed.

The authors stuck to a maximal concentration of 12 nM where 2 nM of siRNA was applied per gene. At 2 nM per siRNA, one still risks deregulating other genes. One of the first papers by Aimee Jackson et al., demonstrated an siRNA targeting MAPK14 deregulated many other genes even at concentrations of 1-4 nM.

An important consideration is to ensure total siRNA concentrations are kept constant. In which case, a negative control siRNA has to match or follow the maximal siRNA concentration used. Using different levels of siRNAs runs the risk of biasing off-target effects towards sequences present at higher concentrations.

To learn what the causes, extent and consequences of siRNA off-target effects are, read siTOOLs Technote 1)

Validating results

As with all scientific hypothesis, it helps to arrive at the same conclusion with different approaches.

The two different effector response subgroups identified also responded differently to small molecules. The KRAS-type lines being more sensitive to EGFR and ERK inhibition while the RSK-type lines more sensitive to inhibitors of PDK1, RSK, MTOR, S6K1 and DNA repair enzymes. This was attributed to the latter’s higher basal metabolic activity manifested in larger investments towards oxidative phosphorylation and mitochondrial ribosome maintenance.

By also projecting signatures obtained from cell lines into patient samples (in The Cancer Genome Atlas, TCGA), the subtypes were also effective at predicting differential sensitivity to multiple drug treatments. This highlights the importance in designing effective drug combinations in cancer.

Interestingly, the authors also performed CRISPR pooled screens in parallel. However, due to the restraints of being only able to knockout 1 gene at a time, smaller effects were seen due to gene redundancy. However, they did go on to use CRISPR as well to mutate key genes to affirm the pathway relationships established.

siPOOLs have been used successfully for multi-gene targeting for up to 4 genes, and potentially more. They also safely address off-target effects by high complexity pooling, enabling each siRNA to be applied at picomolar concentrations. For more articles on multi-gene targeting, read an older blogpost:

Understanding gene networks with combinatorial gene knockdown

 

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Understanding Gene Networks with Combinatorial Gene Knockdown

Understanding Gene Networks with Combinatorial Gene Knockdown

Genes hardly ever work alone, functioning instead in complex gene networks.  Increasing advances in genomics and proteomics and corresponding developments in computational analysis, has really put this into perspective. A recent large scale RNAi study by Novartis found this hairball of a gene network in cancer cells:

A gene “hairball”

As such, the standard approach of disrupting the expression of a single gene to study loss-of-function phenotypes may not accurately reveal its genetic function. The highly redundant nature of signalling pathways often allows cells to respond robustly to single-strike manipulations. A combinatorial gene disruption approach, where one disrupts several genes in a single setting, is therefore more effective at elucidating signalling networks.

Types of redundancy

Redundancy can occur across signalling pathways or within a signalling pathway. Paralogous genes arising from gene duplication (B’) may also contribute to redundancy. In the figure above, gene B is disrupted but phenotype remains unaffected if other genes (A or B’) can perform similar functions. Within a single pathway, genetic interactions (A → C) may exist that make B redundant.

In addition to countering redundancy, combinatorial gene disruptions also uncover interesting epistatic or synergistic interactions. An epistatic or synergistic interaction occurs when the effect of disrupting two genes differs from the additive effect expected from disrupting the genes individually. This reveals the nature of genetic interactions and identifies interesting functional networks that play relevant roles in complex diseases. In cancer for example, a combinatorial approach is useful for identifying genes that confer drug resistance and to explore multi-treatment approaches that achieve synthetic lethality of cancer cells.

Combinatorial gene disruption has been successfully applied to yeast where multiple knockouts are easy to generate (Fiedler et al., 2009). However, multiple knockouts are harder to perform in higher organisms and may also not represent the full picture. RNAi, due to its ease of application, has been used for combinatorial disruptions in Drosphila (Nir et al., 2010, Horn et al., 2011), C. Elegans (Tischler et al., 2006) and human cells (Laufer et al., 2013). Its dose-dependency and transient effect also mimics the use of drugs and allows researchers to determine the quantitative nature of functional interactions.

Studies have found that predictions of genetic interactions made based on double gene knockdowns showed greater sensitivity than predictions based on single gene knockdowns. Nir et al analysed cell morphological changes under RNAi knockdown of RhoGAPs in a RhoGTPase overexpression condition. Using single/double knockdowns to validate 5 biologically validated interactions and 3 non-interactions, they found the double knockdowns were far more sensitive in detecting genetic interactions.

 

Double gene knockdowns (KD) improve sensitivity of genetic interaction detection

Relying on the fact that GAPs deactivate GTPases, a screen using single/double RNAi KDs was performed on RhoGAPs in Drosophila cells. The table above shows validated biological interactions (5 interactions, 3 non-interactions) and prediction success from the single/double KD experiments.

 

Larger scale studies looking at looking at 50 000 to 70 000 pairwise perturbations of signalling factors in both Drosophila (Horn et al., 2011) and human cells (Laufer et al., 2013) saw similar results. A higher sensitivity was afforded by the double knockdowns and phenotypes obtained from single knockdowns often differed from double knockdowns.

Some challenges highlighted from these large combinatorial RNAi studies:

  • Inconsistent phenotypes from single siRNAs that target the same gene either due to off-target effects or poor knockdown (KD) efficiencies. This is a known problem with siRNAs that siPOOLs were developed to counter. In Laufer et al (2013), an additional quality control step had to be taken to remove inconsistent siRNAs and choose siRNAs that provided good KD.
  • Need for large sample sizes. When Laufer et al. reduced the number of cells analysed from 7100 to 1775, the number of genetic interactions detected decreased from 5262 to 1022 indicating reduced sensitivity. This is naturally assay dependent as well with larger, robust phenotypes requiring smaller sample sizes. A multiparametric (measuring multiple parameters of cell behaviour/morphology) approach is often encouraged to increase data robustness.
  • Differences between model organisms. Knockdown efficiencies in human cells were lower compared to Drosophila cells and off-target effects more widespread. This is a factor for consideration as additional computational analysis and reagent pre-evaluation may be necessary.
  • Greater resources required performing double/triple knockdowns compared to single gene knockdowns. Furthermore, measuring these phenotypes in multiple cell lines are often recommended to affirm phenotypes. Therefore, a focussed approach looking at interesting subsets of genes is recommended.
  • Risk of toxicity increases with increasing concentrations of siRNA used.

 

The use of siPOOLs counters some of the challenges faced in combinatorial RNAi knockdowns. Due to the low effective working concentration, multiple siPOOLs can be used together with reduced risk of toxicity. The lowered off-target profile and high reproducibility and robustness of on-target knockdown demonstrated with siPOOLs also add to greater data reliability and eliminates the need for siRNA pre-evaluation.

Dr. Derek Welsbie et al. from the University of California, San Diego, recently published in Neuron the use of siPOOLs in a combinatorial knockdown approach. A synergistic relationship between Leucine Zipper Kinase (LZK) and Dual Leucine Zipper Kinase (DLK) was identified to promote survival in an axon degeneration model with primary mouse retinal ganglion cells (RGCs).

A large high-throughput functional genomic screen where cells were first subject to DLK knockdown was performed to sensitize them to other kinase siRNAs that promote RGC survival. In this way LZK was identified and the synergistic relationship was verified with siPOOLs:

LZK and DLK synergize to promote retinal ganglion cell survival

Knockdown of LZK alone produced no visible effect but siPOOL-mediated knockdown of both LZK and DLK produced a synergistic effect on cell survival.

 

An additional screen was performed where LZK siPOOL was used to sensitize RGCs to protective effects afforded by DLK and potential novel DLK pathway members. Screening performed with 16 698 low complexity pools of 4 siRNAs each, identified 6 novel hits. Though these failed to be verified following siRNA deconvolution (learn why here and here), Haystack analysis to account for seed-based off-targets verified certain hits and additionally identified new hits such as Sox11.

siPOOL-mediated combinatorial knockdown of four identified genes – Sox11, Mef2a, Jun and Atf2 – highly promoted RGC survival under colchicine-induced injury. The survival-promoting synergistic effects of all four transcription factors was comparable to that of the DLK/LZK interaction.

Notably, these effects were verified with CRISPR sgRNA knockouts.

Combinatorial gene disruption allows us to learn more about gene networks and the nature of genetic interactions. Complementing gene knockout approaches, RNAi is an easy method of performing combinatorial gene disruptions in the transient setting.

siPOOLs afford the added advantage of increased efficiency and reliability, removing the need for siRNA pre-evaluation and increasing ease of data analysis.

 

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