Although we talk a lot about off-targets, one of the main advantages of siPOOLs (complex siRNA pools) compared to single siRNAs or mini-pools (Dharmacon) is that they provide near optimum silencing of target genes. Two siPOOLs for the same gene give very similar knock down levels, and their silencing is around the best of any single siRNA. Given how many candidate siRNAs there are for a gene, and how difficult it is to accurately predict silencing levels, this makes siPOOLs the best choice for gene silencing.
The following plot, comparing independent siPOOLs and siRNAs for the same target gene, shows that siPOOLs for the same gene give more similar silencing than do siRNAs (these are Ambion Silencer Select siRNAs).
We see that the correlation for independent siPOOLs is nearly twice that for independent siRNAs.
(Note that for siRNAs we are doing all pairwise comparisons for 3 siRNAs per target gene. Randomly selecting 2 siRNAs per gene gives similar R values.)
In the above plot, we removed 3 siRNAs that did not work, for the gene TRIB1. TRIB1 has some association with the nucleus and has a short mRNA half life, both of which are factors associated with poor gene silencing.
The following plot shows the TRIB1 siPOOLs and siRNAs.
Note that including these non-functional siRNAs actually improves the reagent correlation, though not for a good reason!
We also see that independent TRIB1 siPOOLs give very similar silencing and it’s much better than for the siRNAs. In our experience, if a siPOOL does not work well for a gene, designing a second siPOOL does not substantially improve things, as the poor silencing is normally a feature of the target gene itself. ~50% silencing is probably about the best one can expect for this gene.
Just because siRNAs do not give any on-target silencing, this does not mean they can’t show up as hits in screening assays. Because most of the downregulation is in off-target genes (due to the seed effect), each of those TRIB1 siRNAs may silence nearly 100 genes.
We looked at a genome-wide RNAi screen that included these 3 Silencer Select siRNAs. We see that one of them gives a fairly strong phenotype (Z-score < -2 for cell count), even though the siRNAs do not silence their on-target gene.
Screening with siPOOLs is the smarter alternative, as you can be confident that they provide near optimal on-target silencing and have less off-target effects.
The C911 siRNA control generated a lot of excitement in the RNAi world when it emerged ~11 years ago. A former colleague, who was a pioneer in the commercialisation of RNAi, described it then as the biggest breakthrough in the last 10 years of RNAi research.
The idea of the C911 control is to get rid of the on-target effect of the siRNA by using the complement of bases 9-11, while retaining any off-target (seed-based) effects of the siRNA, which are mostly dictated by the bases in positions 2-8.
If the observed phenotype of the siRNA is due to an off-target effect (rather than silencing of the on-target gene), the C911 version will show the same phenotype. i.e., because it is not silencing the target gene, the phenotype must come from an off-target effect.
Despite the initial excitement, the C911 approach did not become that widely used. There are a number of drawbacks to the strategy, perhaps foremost being that new reagents must be ordered and the assay set up to run again. We’ve compared the validation of low-complexity RNAi reagents to the old lady who swallowed a fly.
The best strategy is to avoid getting entangled in off-targets in the first place. And that seems to be the approach preferred by the research community.
The following plot shows Google Scholar citations for siTOOLs (i.e., papers using our reagents) and the C911 method paper.
We see that after an initial adoption period, use of C911s tapered off and it has levelled out in recent years.
None of this suggests that C911s are bad. For single siRNAs or Dharmacon pools, they are indeed an effective control. But the inconvenience of the method has probably hindered its adoption.
The convenience and robustness of the siPOOL are its great advantages. The siPOOL approach ensures maximum on-target silencing and a minimum of off-target effects. We look forward to supporting more great research in the coming years.
At siTOOLs we are celebrating RNA day (Aug. 1st) the entire month with a focus on all things RNA and a promotion on siPOOLs and riboPOOLs.
RNA day is celebrated the 1st of August, since the RNA codon that initiates protein synthesis is made of the following nucleotides: adenine (A), uracil (U), and guanine (G). AUG codes for the amino acid methionine (Met) in eukaryotes and formyl methionine (fMet) in prokaryotes.
RNA is one of the most versatile biomolecules in existence and the focus of siTOOLs research, so we asked the siTOOLs team which is their favorite RNA molecule.✨
1. Dr. Michael Hannus – Founder and Managing Director:
2. Dr. Nicola Conci – RNA-Seq Specialist & Bioinformatician:
3. Matthias Weiss- Head of Production:
4. Kevin Wobedo – Scientific Sales & Marketing:
5. Dr. Laura Leiva – Scientific Marketing:
6. Jessie Midgley – Junior Sales Manager:
RNAi vs CRISPR: RNAi even better at finding essential genes
The best answer to this question, like so many others is, it depends.
If cells can adapt and compensate for loss of the gene, or you want to titrate gene levels (important in drug discovery), then RNAi will be better.
If a gene’s transcripts have lots of secondary structure and must be silenced to 99.9% in order to see an assay phenotype, then CRISPR may be better.
We have used two large datasets to attempt to answer the following question: is RNAi or CRISPR better at identifying essential genes?
The first dataset is the BROAD Institute’s Dependency Map (DepMap). It has both RNAi (shRNA) and CRISPR (Cas9) screens from over 700 human cell lines, using hundreds of thousands of reagents. Both types of reagents were used to do pooled screening for cell viability.
The second dataset, also from the BROAD Institute, is called gnomAD. It has genome and exome sequencing for over 100K humans. Based on how frequently mutations are found in the sequenced genomes/exomes (and what type of mutations are preferred), an essentiality score can be assigned to every human gene. It’s the ultimate test (within ethical limits) of whether a gene is essential to humans.
Our approach was as follows:
for each gene, get the median DepMap viability score across the 700+ cell lines
done separately for RNAi and CRISPR screens
for each gene, retrieve the gnomdAD pLI score (probability that loss-of-function not tolerated)
higher values means the gene is considered more essential
genes with pLI > 0.9 are classified by gnomAD as essential
If we look at the top 200 genes in each of the RNAi and CRISPR datasets (note: 70 genes are common to both lists), we see that the top 200 genes from RNAi screening are more essential (as measured by pLI) than are the top 200 genes from CRISPR screening. (note that the curves show the running mean for 30 genes)
Eventually the curves do converge, but for the top genes, we see that those found by RNAi are more essential.
Alternatively, if we group the genes from the CRISPR and RNAi screens into deciles for cell viability score, we again see that the results from the RNAi screens are more consistent with gnomAD.
In the following plots, we look at the number of gnomAD essential genes (defined as pLI > 0.9) in each of the deciles. Decile 1 has the top 10% of genes for reducing cell viability (most essential), whereas Decile 10 has the bottom 10% (least essential).
For CRISPR screens, we see that the top 2 deciles show markedly more gnomAD essential genes. But after that, the counts flatten out. There is little difference in the number of gnomAD essential genes in deciles 3 through 10.
The results from RNAi screening show a fairly steady decline in gnomAD essential genes in deciles 1 through 10. Which is what one would expect. Genes that increase cell count should tend to be less essential. i.e., decile 10 should have the smallest number of essential genes. That is what we see with the RNAi screens, but not with the CRISPR screens.
Conclusion
RNAi and CRISPR screens can both pull out genes found to be essential in the gnomAD dataset.
However, the top genes from RNAi screening tend to be a bit more essential in real-life experiments (i.e., the humans from the gnomAD dataset).
Furthermore, the trend for gnomAD essential gene counts through the ranked datasets makes more sense for RNAi screens than for CRISPR screens.
CRISPR may be a newer technology, but that does not necessarily make it better than RNAi.
Both have their advantages and disadvantages, which we will discuss more in future blog posts.
It should also be noted that two of the main disadvantages of RNAi screening (seed-based off-target effects, and variability in silencing between different siRNAs) have been addressed by siPOOLs.
In an upcoming blog post, we will take a closer look at genes that gave different results in the DepMap RNAi and CRISPR screens.
Chemical modifications only shift the siRNA seed profile
In the last post, we saw that chemically modified ON-TARGETplus siRNAs still have a strong seed effect.
The seed-based off-target effects (measured by correlation of reagents with the same 7mer seed) were as strong for chemically modified ON-TARGETplus (R = 0.50) and Silencer Select (R = 0.59) as what we typically see with unmodified siRNAs (Qiagen, siGENOME, or Silencer).
Chemical modification must not prevent seed-based target recognition, because RISC uses the seed to scan the transcriptome for target sites. Because of how RISC presents the guide strand seed region for target scanning, the binding energy for finding an on-target site (19-base complementarity) versus an off-target site (6/7-base complementarity) is nearly the same. It’s not like a microarray oligo, where more extensive complementarity leads to stronger binding. The seed is driving this site recognition, so any modification that eliminates its binding will make the siRNA ineffective.
The chemical modifications added by Ambion and Dharmacon do not prevent seed binding, but instead change the efficiency of different bases at certain positions, in effect changing the seed profile of off-target sites.
The following heatmap shows the cell viability scores from 9 genome-wide siRNA screens. The average viability score for all siRNAs with a specific base at a specific position was calculated (shown are guide positions 1-9). If the value is red, it means siRNAs with the base at that position tend to be more lethal.
The first 4 columns are from screens using chemically modified, Silencer Select siRNAs (S+). The next 2 columns are from screens using unmodified, Silencer siRNAs (S). And the last 3 are from screens using unmodified, Qiagen siRNAs (Q).
We see that for some bases (e.g. 2C, top row), siRNAs tend to be non-toxic regardless of whether or not they are chemically modified (S+, S, and Q all show deep blue).
But there are other positions where the chemically modified siRNAs are very different from the unmodified siRNAs.
For example, the bottom row shows that 6G tends to be very toxic in unmodified siRNAs, but is not toxic in Silencer Select (chemically modified) siRNAs. On the other hand, 6U (towards the middle row) looks to be toxic for Silencer Select siRNAs but have the opposite effect for unmodified siRNAs.
Whatever the chemical modification for Silencer Select is (has not been made public), it appears to make seed off-targets stronger when position 6 is a U, and weaker when position 6 is a G.
If we compare the effect on cell viability of Silencer Select vs ON-TARGETplus siRNAs from the Tan and Martin screen (subject of last post), we also see strong differences in the effect of having a U or a G at position 6.
The following plot shows the toxicity rank of seed bases in Silencer Select siRNAs vs ON-TARGETplus siRNAs. Bases towards the origin (e.g., 2C) tend to make siRNAs non-toxic for both types, whereas bases towards the top right (e.g., 2G) tend make make siRNAs toxic for both types. Bases that fall off the diagonal tend to be toxic for one type and non-toxic for the other.
We see that 6U is toxic for Silencer Select siRNAs (as also seen in the heat map) and ON-TARGETplus siRNAs, like the unmodified siRNAs from the heat map, tend to be non-toxic. And the effect is similar to the heat map for 6G: toxic for Silencer Select and non-toxic for ON-TARGETplus (and unmodified in heat map).
Conclusion
Chemical modification does not get rid of seed effects, as evidenced by the strong phenotypic correlation of modified siRNAs with the same seed sequence. Rather, modifications tend to change the effectiveness for specific bases in eliciting seed-based silencing.
One suggestion would be to design a chemically modified siRNA library that avoids bases that tend to be toxic (e.g., 6U for Silencer Select).
However, there are a few problems:
The heat maps and scatterplot only show tendencies. There is still variation within those positions. While 2G tends to be non-toxic for Silencer Select, there are still lots of toxic siRNAs with that sequence.
Bases that reduce toxicity may be doing so because they tend to reduce target recognition. For example, 2C is also associated with poorer on-target silencing. Using only 2C for siRNAs could thus result in a library that is not as efficient at on-target silencing.
Finally, these suppliers have already produced their siRNA libraries. i.e., those bases have already been used.
The only reliable way to both reduce the off-target effect (via dilution of seeds) and maintain robust on-target silencing is by using siRNA pools (siPOOLs).
ON-TARGETplus siRNAs have strong off-target effects (despite chemical modification)
Chemical modification has long been proposed as a way to limit the off-target effects of siRNAs.
The earliest siRNAs from the two main commercial suppliers (siGENOME from Dharmacon/Horizon Discovery, and Silencer from Ambion/ThermoFisher) were quickly replaced with new chemically-modified siRNAs (ON-TARGETplus from Dharmacon, and Silencer Select from Ambion).
The phenotypic correlation between siGENOME (unmodified) and ON-TARGETplus (chemically modified) low-complexity (4-siRNA) pools for the same gene was shown to be very poor.
However, showing a direct seed effect of ON-TARGETplus siRNAs using published data is not straightforward, since Dharmacon (unlike Ambion) has not made their siRNA sequences publicly available.
Here, for the first time, we show massive seed-based off-target effects from ON-TARGETplus siRNAs.
Seed off-target effects from ON-TARGETplus siRNAs
Tan and Martin (2016) provide a dataset that includes 4 different ON-TARGETplus siRNAs for nearly 700 genes, screened for their effect on nuclear area.
We were also able to find a paper that provides sequences for ON-TARGETplus siRNAs. Those sequences were assigned to the siRNAs from the Tan and Martin screen (details on sequence assignment provided at end of post).
The intraclass correlation (ICC) is a measure of reproducibility of measures of the same group, e.g. siRNAs with the same target gene, or siRNAs with the same 7mer seed.
The ICC for ON-TARGETplus siRNAs with the same gene was only 0.09.
However, the ICC for ON-TARGETplus siRNAs with the same 7mer seed was much higher: 0.50.
Despite chemical-modification, the phenotype of ON-TARGETplus siRNAs is still mostly driven by off-target seed effects.
To show these ICCs graphically, here is a plot with pairs of siRNAs for the same target gene (2 of 4 siRNAs chosen randomly for each gene). [ note that some outliers were removed to assist comparison with same-seed siRNAs]
And here is the plot with pairs of siRNAs with the same 7mer seed:
Conclusion
Chemical modification does not get rid of seed-based off-target effects.
The only effective way to robustly eliminate these effects is with high-complexity (30+ siRNA) pools (siPOOLs).
Technical notes
In order to determine the sequence of the ON-TARGETplus siRNAs from the Tan and Martin screen, the sequences from the supplementary materials of Kim et al. were assigned in order to the siRNAs sorted by catalog number. It is possible that some of the sequences thus assigned were not correct (e.g. Tan and Martin may have used different siRNAs from those listed in Kim et al. for some of the genes), in which case the observed seed effect is actually underestimated.
A journey into the gut microbial control center: small RNA’s influence on Bacteroides thetaiotaomicron’s metabolism
The gut model organism Bacteroides thetaiotaomicron
Bacteroides thetaiotaomicron is a commensal bacterium that inhabits primarily the human large intestine and is considered one of the most important members of this microbial community. B. thetaiotaomicron is a highly versatile microbe, capable of utilizing a wide range of carbohydrates including those that are indigestible by human enzymes. It breaks down complex polysaccharides from plant cell walls and other dietary sources, producing short-chain fatty acids (SCFAs) that are an important energy source for humans. Furthermore, it has also been shown to play a crucial role in our immune system development, through the production of regulatory T-cells that help prevent autoimmune disorders.
It’s no wonder Bacteroides thetaiotaomicron has been chosen as a model representative of the gut microbiota. B. thetaiotaomicron is widely spread in human populations and relatively easy to grow and study under laboratory conditions. In a study by Ryan et al. (2020), differential RNA sequencing (dRNA-Seq) was used to generate a single-nucleotide resolution transcriptome map of B. thetaiotaomicron. High-resolution RNA-sequencing served as a tool to explore the role of small RNA molecules in regulating metabolism in the gut bacterium Bacteroides thetaiotaomicron.
By comparing different laboratory growth conditions, the researchers identified various small RNA molecules that exhibited differential expression patterns. These small RNAs were found to be associated with the regulation of key metabolic pathways in the bacterium. The authors suggest that these findings could have implications for understanding the interactions between gut microbes and the host and for the development of new therapies for metabolic diseases. Overall, the study highlights the importance of transcriptome mapping in uncovering novel regulatory mechanisms in bacterial metabolism. Furthermore, the results shed light on the intricate regulatory networks within this gut microbe and provide insights into its adaptation to different nutrient environments.
Note: Our Pan-Prokaryote riboPOOL played a small but significant role in this study. Prior to RNA sequencing, the Pan-Prokaryote riboPOOLs kit was used for ribosomal RNA depletion. Our Pan-riboPOOLs are a versatile solution that allows for simple mono- and multitranscriptomic studies using a single-step rRNA depletion for a phylogenetic group (e.g., bacteria, fungi, or mammals).
A Brief Interview with Dr. Daniel Ryan
Dr. Daniel Ryan, postdoc at the Helmholtz Institute for RNA-based Infection Research
We interviewed the first author of the study: “A high-resolution transcriptome map identifies small RNA regulation of metabolism in the gut microbe Bacteroides thetaiotaomicron”. Dr. Daniel Ryan is a postdoc at the Helmholtz Institute for RNA-based Infection Research located in Würzburg, Germany. He is a member of the Westermann Lab and his research focuses on non-coding RNAs and RNA-binding proteins in the human gut commensal Bacteroides thetaiotaomicron.
He further explained the process of generating a high-resolution transcriptome for Bacteroides thetaiotaomicron and the exciting parts of being an RNA research scientist.
Can you briefly explain the main findings of your research and what motivated you to study small RNA regulation in Bacteroides thetaiotaomicron?
The gut microbiota has recently attracted significant attention from the scientific community due to its impact on human health and physiology. Various diseases, including inflammatory bowel disease (IBD), diabetes, colon cancer, and depression, have been linked to an imbalanced microbiota, also known as dysbiosis. Furthermore, a healthy gut microbiota plays a crucial role in preventing invasive pathogens from gaining a foothold and establishing infections. My research at the Westermann Lab aims to understand the diverse interactions between gut microbes and their host. To achieve this goal, we utilize the gut model organism Bacteroides thetaiotaomicron (B. theta), an anaerobic, non-spore forming predominant member of the healthy gut microbiota.
My foray into small RNA biology started during my Master’s thesis work at the KU Leuven, Belgium where I investigated the regulatory networks of sRNAs in E. coli. I came to appreciate the immense regulatory potential of these non-coding molecules in governing rapid responses to diverse environmental cues. A few years later, during my PhD research at KIIT University, India, I had the opportunity to delve into the roles of sRNAs in regulating acid stress survival and virulence programs in Salmonella, the pathogen responsible for causing typhoid. Having gained extensive experience in studying sRNAs and their intricate regulatory networks, I shifted gears to the gut microbiota as the focus of my postdoctoral research. After more than a decade of involvement in the field of sRNAs, I remain highly enthusiastic about uncovering novel sRNAs and investigating their intricate interactions within diverse organisms. Ultimately, my aim is to reveal regulatory cascades and pathways that can be harnessed to improve human health.
What tools were necessary to create a high-resolution transcriptome map for Bacteroides thetaiotaomicron, and what challenges did you encounter during this process?
In order to construct a high-resolution transcriptome map of B. theta, it is crucial to extract RNA of high quality from representative and diverse conditions that effectively stimulate gene expression. This is easier said than done, since one of the main challenges of working with gut microbes is their anaerobic nature and often cumbersome culture conditions. To ensure optimal anaerobic conditions, all media and equipment used for bacterial culture must be degassed to remove oxygen, which can be toxic and inhibit growth. Once robust and reproducible growth can be achieved, RNA is extracted, sequenced and analyzed to obtain a single nucleotide resolution of the transcriptome. I then employed a suite of bio-informatics tools to annotate the transcriptome and subsequently manually validate and edit each feature. Although this final step is time-consuming and labor-intensive, it is essential for obtaining a high-quality and reliable result.
Were there any unexpected or surprising findings in your research?
I was delighted to discover that the number of potential sRNAs in B. theta was similar to that of other model organisms, such as E. coli and Salmonella. Moreover, the vast majority of these sRNAs had no known homologs in these well-known species. This suggests that B. theta has undergone functional adaptations specific to its niche, which is primarily the human large intestine. Consequently, I anticipate a wealth of novel biological insights, potentially revealing new modes of interaction and target regulation.
Are there any potential applications or implications of your research for human health, such as developing targeted therapies or interventions for gut-related disorders?
In order to develop effective targeted therapies, it is crucial to first and foremost “know your target”. Going in blind is never a good strategy and this is where I see the potential of this work. With this high-resolution transcriptome and the “Theta-Base” browser, we have provided a framework to discover and identify novel genes and sRNAs that can further be investigated as potential targets to regulate or modulate activity. These newly identified targets whether coding or non-coding can be exploited to modulate B. theta to achieve specific functions for instance, they could be used to exclusively metabolize a particular carbon source or prevent the consumption of a specific metabolite. While this example is simplistic, several laboratories are already conducting pilot studies in this area, offering promise for the future of targeted medicine.
What would you say are some of the challenges or gaps in knowledge that need to be addressed in the field of gut microbiota research?
One of the overarching challenges in gut microbiota research is distinguishing between correlative and causative effects. It is therefore imperative to develop protocols and methodologies that delve into various phenomena at a detailed level before drawing reliable conclusions.
There are also specific challenges to address, particularly regarding the creation of microbial consortia that accurately reflect the composition of the gut microbiota. Â This is not easy considering the vast numbers of bacteria and their complex interactomes that make up a healthy microbiota. Moreover, models representing the human intestinal niche, which harbor these diverse microbial communities, need further refinement to better reflect this complex environment.
Finally, what is your favorite part of being an RNA research scientist?
As an RNA research scientist, what I find most fascinating is the varied range of roles that this molecule can play. From intricate structural scaffolds to subtle enzymatic and regulatory functions, RNA displays a multitude of capabilities, and witnessing these firsthand is truly captivating.
Biocabulary
Differential RNA sequencing (dRNA-Seq) is a technique used to identify transcriptome features and define overall transcriptomic architecture, such as transcription start sites, terminators, non-coding RNAs, coding RNAs, promoters, etc.
A transcriptome map is a comprehensive profile or catalog of all the RNA molecules (transcripts) produced by an organism or a specific cell type under particular conditions. It provides a snapshot of the active genes and their expression levels within the cells or tissues being studied.
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. aureusclumping 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) Â 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:
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.
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.
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.
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.
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.
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.
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.)
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.
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:
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.
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