Category: General

The Hidden World of Microbiomes and Their Impact on Our Lives

The Hidden World of Microbiomes and Their Impact on Our Lives

Microbiomes are the diverse communities of microorganisms that inhabit different parts of our bodies, as well as the environment around us. In recent years, research has revealed the vast and complex hidden world of microbiomes and their impact on our lives, from influencing our digestion and immune system to potentially affecting our mood and behavior. Advances in technology have enabled scientists to study microbiomes in unprecedented detail, leading to new insights into their diversity and functions. Understanding the microbiome and its role in human health and disease has the potential to transform how we approach medicine, nutrition, and the environment.

Staph, can be both good and bad for humans

With high diversity, you also get a combination of characters, the human microbiome is consequently no stranger to the good, the bad and the ugly.

There are good microorganisms, then nasty ones, and then good ones that might turn into bad ones.

One of the most famous good/bad bacteria is Staphylococcus aureus commonly known as Staph. It’s generally found on the skin and in the nasal passages of healthy individuals, where it can play a beneficial role in preventing colonization by other, potentially harmful bacteria. However, S. aureus can also cause a range of infections, including skin infections, pneumonia, bloodstream infections, and heart infections. Some strains of S. aureus are antibiotic-resistant, making them particularly difficult to treat. Thus, understanding what triggers the switch from a peaceful commensal bacterium inhabiting our noses to a virulent pathogen is key to identifying potential therapeutic targets.

A study by Wittekind et al. (2022) provided further insight into the mechanisms behind the expression of virulence genes in S. aureus. The research describes the discovery of a novel protein, ScrA (which stands for S. aureus clumping regulator A), in Staphylococcus aureus (SaeRS). ScrA interacts with the SaeRS two-component system (TCS), which is known to regulate the expression of virulence genes in S. aureus. The results show that ScrA plays a key role in the regulation of virulence gene expression by the SaeRS system, and that deletion of the ScrA gene results in a significant decrease in virulence in a mouse infection model. Thus, ScrA could be a promising target for the development of new therapies to treat S. aureus infections.

One of the key methods in Wittekind et al. (2022)  experiment was RNA-sequencing to get a glimpse of the gene expression profile of S. aureus. The global view provided by RNA-Seq helped pinpoint one of the S. aureus two-component systems that showed higher expression when ScrA was overexpressed.

Since rRNA accounts for 80-90% of the transcriptome limiting the detection efficiency of desired RNAs by RNA-Seq. The removal of ribosomal RNA (rRNA) before RNA-Seq greatly improves and economizes RNA-Seq. In this study, ribosomal RNA depletion was performed using the Staphylococcus aureus– specific riboPOOL rRNA removal kit.

Marcus busy in the lab. 👨🏻‍🔬

A Brief Interview with Dr. Marcus Wittekind

To have further insight into the process, challenges of studying human microbiomes, and the most interesting findings related to small RNAs (sRNAs) we interviewed Dr. Marcus Wittekind.

Marcus is a research scientist at Ohio University and is a member of Dr. Ronan Carroll’s Lab. His research is focused on bacterial pathogenesis and the role RNA molecules play in the bacterial cell. Meet one of the scientists behind the research:

  1. What inspired you to pursue research on human microbiomes?

I have always had an interest in how microbes interact with their host. Staphylococcus aureus is particularly interesting to me in that it is found in ~30% of the population as a human commensal and just sits in the nose without any issues. Yet, when S. aureus migrates to other areas you can get devastating disease. It’s fascinating how S. aureus is able to make this transition and switch from a relatively passive existence to a virulent pathogen. Along with S. aureus, it’s astounding how little we actually know about the microbiome and how it influences our health. It’s exciting to live during a time when we’re uncovering these connections.

  1. What are the most interesting findings from your latest research on the commensal bacteria Staphylococcus aureus?

My findings about S. aureus have focused primarily on a single small protein ScrA. Although my research has been focused on a single protein, I think it can serve as an example of just how much we have left to learn. I found ScrA to act as a sort of link between two well-studied regulatory systems in S. aureus. While this is an interesting subject in its own right, I think where this story comes from is particularly interesting. My mentor Ronan Carroll originally identified the scrA gene, which was at the time called tsr37, as a small non-coding RNA. However, we later came to find out that some of these small RNAs actually encoded small proteins. Now this isn’t surprising, we already know of a toxin encoded on a small RNA. However, it makes me wonder how many more proteins are we overlooking as being just small RNAs? Some of my studies also suggest that ScrA is really only important when S. aureus is infecting the heart. In laboratory conditions we don’t really see any changes when we delete scrA, which would normally lead to us just moving on without discerning the function of ScrA. Only due to marked phenotypes when we overexpress ScrA did we even become interested in its function. How many more genes play a vital role in virulence but are being overlooked because we can’t see anything in the lab? I think ScrA serves as a reminder of how unassuming genes can actually have a larger role than what we see on the benchtop.

  1. What are some of the biggest challenges researchers face in the field of microbiomes?

The sheer complexity of the interactions between pathogens and their host. For me, this has manifested as finding the exact conditions in which ScrA is activated and carries out its function. All I really know is that scrA plays a role in infecting the heart. However, the question still remains as to what triggers scrA production. Nutrient abundance? Immune system components? Temperature? Host signals? At this point, I can only guess. For me I only have to focus on a single organism. The complexity drastically increases when you consider environments with multiple organisms such as the digestive system, skin, or wounds. While the complexity is fascinating it is also difficult to wrap your head around exactly what is taking place.

  1. What technologies and methods are key for your research?

There are many different technologies and methods that are essential for my work. However, a few stand out to me. I went into this project with no idea what was causing the phenotypes. So, we decided to cast a wide net and see what was being altered in the cell. RNA-sequencing actually gave us our first hint of what was going on. We saw global changes in gene expression; however, we were able to pick out one system in particular that showed promise. One of the two-component systems in S. aureus (SaeRS) showed higher expression when we overexpressed ScrA. Thanks to the global view we can get by using RNA-seq we were able to identify a potential mechanism with one experiment as opposed to screening individual regulators.

On the same note, mass spectrometry allowed us to get a global view of protein changes. This was particularly useful when we were identifying what host factors were being bound when we overexpress or delete scrA. We were able to “shave” the surface of the cells with immobilized trypsin and identify the exact proteins present, and more importantly what proteins could be accessed by the trypsin. Being able to quickly sort through all the different components was essential to forming a working model for ScrA mediated aggregation.

Finally, we can’t ignore how essential animal models are for studying virulence. While it would be great and I look forward to a day when we no longer need to perform animal experiments, right now they are absolutely vital to understanding these pathogens. We utilized a mouse model of systemic infection to determine if scrA was essential for virulence. Not only was I able to show that scrA is needed for virulence, but I was also able to show that scrA is primarily needed for heart infections. This is something we wouldn’t have known without animal models. When we delete scrA and use it in our in vitro experiments, we see limited effects and only under specific conditions. However, we saw a drastic decrease in virulence in a mouse model.

  1. What are some potential applications of your research on human health?

One of the primary reasons I want to understand S. aureus virulence is to identify potential therapeutic targets. It’s well known that antibiotic resistance is on the rise and at some point, we are going to need alternative treatments. S. aureus is interesting because in most cases it just sits in the nose and doesn’t cause disease. If we can understand what triggers that switch from a passive carry to an aggressive infection, we might be able to force S. aureus to stay in a passive state or at least limit its virulence. I’ve shown ScrA is needed for effective heart infection by S. aureus. It may be possible to target ScrA and inactivate it, reducing its ability to infect the heart. This could be useful in people undergoing heart surgeries, especially in cases with indwelling medical devices, which may introduce S. aureus into the heart.

  1. What advice would you give to someone interested in pursuing a career in Bacteriology?

Bacteriology is a wide field, take your time to explore different aspects and find something that really interests you. The sheer volume of information can be overwhelming when you get started, but as time goes on it becomes more familiar. The best way to see what really interests you is to get involved in research. Reach out to people whose research interests you and find opportunities to get involved. I know how intimidating this idea can be (I started researching as an undergraduate) but many professors are happy to have interested people join their lab regardless of experience. Most importantly don’t feel obligated to stick with the first thing you start studying. One of the things I love about bacteriology is how much there is to learn. If you don’t like what you’re studying, there is always something else you can try. It’s important to find your niche and what you enjoy. Being passionate about your work is an important part of this field.

Biocabulary:

Two-component systems (TCSs) are signaling pathways that allow bacteria to sense and respond to changes in their environment. A TCS consists of two proteins: a sensor histidine kinase and a response regulator. The sensor histidine kinase detects a specific environmental signal and transfers a phosphate group to the response regulator protein, which then activates or represses the expression of specific genes.

Small RNAs (sRNAs) are short, non-coding RNA molecules that typically range in size from 50 to 500 nucleotides. They are important regulators of gene expression in bacteria, archaea, and eukaryotes, and play diverse roles in cellular processes such as stress response, metabolism, and virulence.

Similar seed effects in independent siRNA screens

Similar seed effects in independent siRNA screens

A 2013 study on Parkin translocation used genome-wide siRNA libraries from Ambion (single Silencer Select siRNAs) and Dharmacon (pools of 4 siGENOME siRNAs).

The correlation between results for the same on-target gene from the two libraries was very low (R = 0.09). (Each point in the following plot is for a gene.)

% Parkin Translocation (PPT) for Ambion vs. Dharmacon siRNAs grouped by same 7mer seed

The correlation between results for the same 7mer seed were higher (0.26), providing another example of the Iron Law of RNAi Screening. (Each point in the following plot is for a 7mer seed.)

It is also worth noting that the seed-based correlation would likely have been much higher, had the Dharmacon siRNAs been screened individually (see details below).

Conclusion

The only effective way to avoid off-target effects in RNAi screening is to use high-complexity reagents like siPOOLs, which dilute away off-target effects while maintaining strong on-target silencing.

Analysis details

To calculate the Ambion by-gene value, the mean PPT value was taken for the 3 on-target siRNAs for the gene. (The Dharmacon pooled library only has 1 value per gene, so no further calculation is necessary.)

To calculate the Ambion by-seed value, the mean PPT value was taken for all siRNAs with the 7mer. For Dharmacon, the pool value was assigned to each siRNA, and then siRNAs were grouped by their 7mer seed in order to calculate the seed mean. This means that the Dharmacon siRNA seed value is actually the average from 4 different siRNAs (with different seeds). Had the Dharmacon siRNAs been screened individually, the correlation with Ambion seed results would have been higher.

The Iron Law of RNAi Screening

The Iron Law of RNAi Screening

This is the lead singer of a band called Iron Law. He looks like a researcher experiencing massive frustration after discovering what we call the Iron Law of RNAi Screening.

This law states that in any screen with low-complexity reagents (single siRNAs like Silencer Selects, or mini-pools like Dharmacon SMARTpools), off-target effects will predominate.

Given that the average lone siRNA will down-regulate nearly 100 off-target genes, but has only a single on-target gene, it is not hard to see how this comes about.

The only effective way to break this law is to use high-complexity reagents like siPOOLs, which dilute away off-target effects while maintaining strong on-target silencing.

Below is a figure showing the reduced off-target effects with a siPOOL (3 nM) after 48 hours in HeLa cells:

Reduce Off Targets effect with siPOOLs

Transcriptome-wide profiling revealed a single siRNA can induce numerous off-targets (red dots) while a  siPOOL against the same target gene (green dot), and containing the non-specific siRNA, had greatly reduced off-target effects.

Low hit validation rate for Dharmacon siGENOME screens

Low hit validation rate for Dharmacon siGENOME screens

Good experimental design is important when validating hits from RNAi screens.  Off-target effects from single siRNAs and low-complexity siRNA pools (e.g. Dharmacon siGENOME) result in high false-positive rates that must be sorted out in validation experiments.

Dharmacon siGENOME pools (SMARTpools) have 4 siRNAs, and the most common form of validation is to test the pool siRNAs individually (deconvolution).

Unfortunately, the results of such deconvolution screening rounds are difficult to interpret.

The pool phenotype could be due to the off-target effects of any single siRNA, or even synthetic off-target effects from pooled siRNAs.

Rather than deconvoluting the pool, a better approach is to test with independent reagents.  Should the phenotype be due to the seed effects of an siRNA in the siGENOME pool, the new designs (with presumably different seed sequences) should not show them.  (Note that because they have their own potential complicating off-targets, an even better option would be to use a reagent like siPOOLs that minimises the likelihood of off-target effects).

Independent validation reagents was the approach used by Li et al. in a screen looking for enhancers of antiviral protein ZAP activity.

They first did a genome-wide (18,200 genes) screen with siGENOME pools, looking for pools that increased viral infection rate.

The biggest effect was with the positive control, ZAP (aka ZC3HAV1).   Several other pools also stood out as giving large increases in viral infection (Fig 1B):

They identified 90 non-control genes with reproducible Z-scores above 3 in their replicate experiments (~0.5% of screened genes).

These 90 genes were then tested with 3 Ambion Silencer siRNAs.  (They also included a few genes in the validation round based on pathway information and off-target analysis– more on this below.)

Of the 90 candidate hit genes, only 11 could be confirmed (Fig 2B, note that ZC3HAV1/ZAP is the positive control and JAK1 was added to the validation round based on pathway info.  A gene was considered confirmed if 2 of 3 siRNAs had a Z-score > 3.):

 

We also see that only 1 of the 7 top hits from the first round (blue genes in the first figure) was confirmed.  This is a common observation in RNAi screens: the strongest phenotypes are mostly due to off-target effects.

Off-target effects are difficult to interpret, even using advanced analysis programs like Haystack or GESS.  The authors tested 4 genes identified by Haystack as targets for seed-based off-targeting.  None of those genes could be confirmed in the validation round.

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.

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.

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.

Performing target validation well

Performing target validation well

Summary

This blogpost describes issues encountered in target validation and how to safeguard against poor reproducibility in RNAi experiments.

The importance of target validation

More than half of all clinical trials fail from a lack of drug efficacy. One of the major reasons for this is inadequate target validation.

Target validation involves verifying whether a target (protein/nucleic acid) merits the development of a drug (small molecule/biologic) for therapeutic application.

Failing to adequately validate a target can burden a pharma with roughly 800 million to 1.4 billion in drug development costs. Impact is not only monetary as large site closures  often result as companies struggle to save costs and a reduced production effort deprives patients of new medicines.

Performing target validation well

Special attention should therefore be given to performing target validation techniques well.

target validation techniques
Overview of target validation techniques (Lindsay, Nat Review Drug Discovery, 2013)

Many of these techniques involve inhibiting target expression to establish its relevance in a cellular or animal disease model. This can be performed with chemical probes, RNA interference (RNAi), genetic knock-outs, and even targeted protein degradation.

The reproducibility of these techniques however has been an issue of concern for drug developers. Less than half of all findings from peer-reviewed scientific publications was reported to be successfully reproduced.

Dismal rates of reproducibility from several pharma-led cancer-focused studies ranged from 11% (Amgen) to 25% (Bayer). A review by William Kaelin Jr sums up the common pitfalls of preclinical cancer target validation. One of his key points:

Cellular phenotypes caused by a chemical or genetic perturbant should be considered to be off-target until proved otherwise, especially when the phenotypes were detected in a down assay and therefore could reflect a nonspecific loss of cellular fitness. It is only by performing rescue experiments that one can formally address whether the effects of a perturbant are on-target.

The comment highlights the issue of reagent non-specificity as a notable contribution towards poor reproducibility.

Certainly, for RNAi the wide-spread off-target effects of siRNAs has been observed in numerous publications. The mechanism being well-established to be based on microRNA-like seed-based recognition of non-target genes. The effect dominates over on-target effects in many large RNAi screens, illustrating the depth of the problem.

Reagent non-specificity is not restricted to RNAi. There have been multiple reports of non-specificity for gene editing technique, CRISPR, which can be read about in detail here, here and here. Recent publications continue to shed more light on its potential off-targets as we learn more about this relatively new technique.

Even chemical probes may have multiple targets. It is hence imperative that more than one target validation technique be used to avoid confirmation bias.

Target validation – a story from Pharma

Back in 2013, when siTOOLs was just starting out, a pharma approached us with a target validation problem.

They were obtaining different results with 3 different siRNAs in a cellular proliferation assay. Despite all 3 siRNAs potently downregulating the target gene, they produced different effects on cell viability.

Which siRNA tool to trust?

target validation siRNA vs siPOOL pharma story
Three different siRNAs against the same target were tested in a cell proliferation assay. Despite all 3 siRNAs showing potent target gene silencing, effect on cell proliferation differed greatly.

A whole-transcriptome expression analysis performed for the 3 siRNAs and a siPOOL designed against the same target revealed the reason for the large variability.

target validation expression analysis siRNA vs siPOOL pharma story
How many genes can you affect with an siRNA? Whole transcriptome analysis by microarray was performed and number and % of up and down-regulated genes are shown over total number of genes assayed (18567).

Despite all siRNA tools affecting the same target, the difference in extent of gene deregulation was astounding. With the greatest number of off-target effects, it was not surprising that siRNA 3 showed an impact on cell proliferation.

In contrast, siPOOLs had 5 to 25X less differentially expressed genes compared to the 3 commercial siRNAs against the same target. An expression analysis carried out for another gene target showed similar results i.e. siPOOLs having far less off-targets.

The target was dropped from development. A great example where failing early is a good thing, though it was not without costs from validating the multiple siRNAs.

The recommended target validation tool

Functioning like a pack of wolves, siPOOLs increase the chances of capturing large and difficult prey, while making full use of group diversity to compensate for individual weakness.

siPOOLs efficiently counter RNAi off-target effects by high complexity pooling of sequence-defined siRNAs. This enables individual siRNAs to be administered at much lower concentrations, below the threshold for stimulating significant off-target gene deregulation. Due to having multiple siRNAs against the same target gene, target gene knock-down is maintained and in fact becomes more efficient.

siRNA vs siPOOL rtqPCR knock-down efficiency
siPOOLs increase targeting efficiency, avoiding knock-down variability. Figure shows rtqPCR quantification of target RNA levels when two siPOOLs vs two siRNAs against 36 genes were tested.

We still recommend using multiple target validation techniques. As a first evaluation however, siPOOLs are quick, easy and most of all, reliable.

Rescue experiments can also be performed with siPOOL-resistant rescue constructs.

Find out more

Low complexity pooling does not prevent siRNA off-targets

Low complexity pooling does not prevent siRNA off-targets

Summary: Low-complexity siRNA pooling (e.g. Dharmacon siGENOME SMARTpools) does not prevent siRNA off-targets.  It may in fact exacerbate off-target effects.  Only high-complexity pooling (siPOOLs) can reliably ensure on-target phenotypes.

Low-complexity pooling increases the number of siRNA off-targets

One of the claims often made in favour of low-complexity pooling (e.g Dharmacon siGENOME SMARTpools) is that this pooling reduces the number of seed-based off-target effects compared to single siRNAs.

If this were true, we would expect different low-complexity siRNA pools for the same gene to give similar phenotypes.  But this is not the case.

Published expression data shows that low-complexity pooling actually increases the number of off-targets.

Kittler et al. (2007) looked at the effect of combining differing number of siRNAs in low to medium complexity siRNA pools (siRNA pools sizes were: 1, 3, 5, 9, and 12).

Their work showed that the number of down-regulated genes (50% or greater silencing) actually increases when small numbers of siRNAs are combined.  Only when larger numbers of siRNAs are combined does the number of off-targets start to drop:

 

 

[The figure is based on data from GEO dataset GSE6807.  Down-regulated genes are those whose expression is reduced by 50% or more.  Note that the orange point is taken from our 2014 NAR paper, as we are not aware of other published expression datasets with this many pooled siRNAs.  A few caveats with combining these datasets are that they use different target genes, siRNA concentrations, and the data comes from a different expression platform.]

Low-complexity pooling: a bad solution for siRNA off-targets

Low-complexity pooling does not get rid of the main problem associated with single siRNAs: seed-based off-target effects.   Based the above analysis, it can make it even worse.  It also prevents use of the most effective computational measures against seed effects.

Redundant siRNA Activity (RSA) is a common on-target hit analysis method for single-siRNA screens.  It checks how over-represented the siRNAs for a gene are at the top of a ranked screening list.  If a gene has 2 or more siRNAs near the top of the list, it will score better than a gene that only has a single siRNA near the top of the list.  This is one way to reduce the influence of strong off-target siRNAs.

Correcting single siRNA values by seed medians has also been shown to be an effective way to increase the on-target signal in screens.  This correction is not effective for low-complexity pools, since each pool can contain 3-4 different seeds.

Off-target based hit detection algorithms (e.g. Haystack and GESS) are also only effective for single-siRNA screens.  The advantage of these algorithms is that it permits the detection of hit genes that were not screened with on-target siRNAs.  These algorithms are not effective for low-complexity pool screens.

Our recommendation: do not convert single siRNAs into low-complexity pools, rather use high-complexity siPOOLs to confirm hits

We do not recommend that screeners combine their single siRNA libraries into low-complexity pools (e.g. combining 3 Silencer Select siRNAs for the same target gene).  If possible, it is better to screen the siRNAs individually and then apply seed-based correction, RSA and seed-based hit-detection algorithms.

The time saved by only screening one well per target may prove illusory when the deconvolution experiments show that the individual siRNAs have divergent phenotypes.

It is probably better to deal with off-target effects up front (by screening single siRNAs) than to be surprised by them later in the screen (during pool deconvolution).

Reliable high-complexity siPOOLs, as independent on-target reagents, can then be used to confirm screening hits.

siTOOLs also now has RNAi screening libraries available.  Please contact us for more information.

What is the probability of an siRNA off-target phenotype?

What is the probability of an siRNA off-target phenotype?

Summary:   Conventional siRNAs have a high probability of giving off-target phenotypes.  siRNA off-target effects can be reduced by using more specific reagents or narrowing the assay focus (to reduce the number of relevant genes).  Even when the assay is relatively focused, more specific reagents significantly increase the probability of observing on-target effects.

Probability of siRNA off-target phenotype depends on reagent specificity and assay biology

The probability of getting an off-target effect from an siRNA depends on several factors, the main ones being reagent specificity and assay biology.  If an siRNA down-regulates a large number of genes, or if an assay phenotype can be induced by a large number of genes, the probability of observing an off-target phenotype increases.

siRNAs can down-regulate many off-target genes

Garcia et al. (2011) compiled 164 different microarray experiments measuring gene expression following transfection with siRNAs.  The mean number of down-regulated genes in these experiments was 132 and the median was 68 (down-regulated genes were silenced by 50% or more).

As noted in earlier studies of gene expression following siRNA treatment (e.g. Jackson et al. 2003), few of the down-regulated genes are shared between siRNAs with the same target gene.  This suggests that the down-regulated genes are not the downstream result of target gene knockdown (i.e. they are mostly off-target).

High-complexity pooling of siRNAs (e.g. with siPOOLs) can reduce the number of down-regulated genes.

The following figure, based on data from Hannus et al. 2014, shows the difference between the gene expression changes caused by a single siRNA (left) and a high-complexity siRNA pool (siPOOL, right), which also includes that same single siRNA:

 

Estimating the probability of siRNA off-target phenotypes

Assuming different numbers of down-regulated genes (off-target) and different numbers of potent genes involved in assay pathways, we can try to estimate the probability of an siRNA giving an off-target effect.

The following plot shows the probability of getting an off-target effect when:

  • assuming RNAi reagents down-regulate varying numbers of off-target genes (5, 25, 50, 100)
    • down-regulated means that gene expression is reduced by 50% or more
    • in the Garcia paper dataset, the mean is 132 and median is 68
  • assuming different numbers of assay-potent genes
    • an assay-potent gene is one whose down-regulation by 50% or more is sufficient to produce a hit phenotype
    • for assays with more general phenotypes (e.g. cell count) we would expect more  assay-potent genes

 

We can see that even if there are only 20 assay-potent genes, there’s a nearly 10% chance of getting an off-target phenotype when siRNAs down-regulate 100 off-target genes (which is close to the average observed in the Garcia dataset).

In a genome-wide screen of 20,000 genes with 3 siRNAs per gene, we would thus expect 2,000 off-target siRNAs.

In contrast, a more specific reagent that only down-regulates 5 off-target genes only has a 0.5% change of producing an off-target phenotype.  For the above-mentioned genome-wide RNAi screen, we would expect only 100 off-target siRNAs (a 20-fold reduction).

The importance of RNAi reagent specificity

The above analysis demonstrates the importance of using specific siRNA reagents.

Changing an assay to make the phenotypic readout narrower (to reduce the number of genes capable of inducing a phenotype) is one way to reduce the risk of off-target phenotypes.  But this may be a lot of work and is not necessarily desirable or even possible.

A more ideal solution is the use of a specific RNAi reagent, like siPOOLs.

postscript

As the number of assay-potent genes increases, the probability of getting an off-target phenotype approaches one.

The following plot (same format as the one above) shows the distribution

 

The p-values were calculated using the hypergeometric distribution, assuming a population size of 20,000 (the approximate number of protein-coding genes in the human genome).

Note that one of the major simplifying assumptions of the above analysis is that all siRNAs have the same number of down-regulated off-target genes.

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