Studying And Fighting Pathogenic Bacteria with the Help of Crispr
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Transcript: English(auto-generated)
00:17
So, thank you very much for the opportunity to present my work in this conference that
00:24
I've really enjoyed so far. So I'm going to show you what we've been doing with system CRISPR in bacteria, and how we're using them both to develop tools to study and to fight pathogenic bacteria. So I first want to give you a bit of history about these CRISPR systems.
00:48
So they basically were first discovered, but not understood, in 1987 by this Japanese group who was just sequencing this IAP gene, and just downstream of the gene they found this
01:06
interesting sequence where you had these repeats that were interspaced by this sequence that looked sort of random. And the size of the repeats and of these variable regions was very well-conserved.
01:22
At the time, of course, they didn't understand at all what these sequences were. And if you look at the citation report for that paper, basically it was published in 1987, but it got almost no citations until 2007. So 2015 hasn't been updated, but it's probably up there now.
01:42
So what happened in 2007 is basically this paper. So that's a landmark paper in the field of CRISPR. It's basically the first experimental evidence that this CRISPR loci actually provide acquired
02:00
immunity against bacteriophages. And what they did in that paper, they worked in this strain of streptococcus thermophilus. The interesting thing also to note is that this paper didn't come out from an academy group,
02:21
but rather from the industry and from a company called Vanisco that was later bought by DuPont that was developing ferments for lactic fermentation, for yogurt production, cheese production. And these people, as other speakers before have noted, have big problem with viral
02:40
contamination that can ruin the batch production. So they always try to select strains that are resistant to bacteriophages. And when doing so, basically they noticed that the CRISPR locus was changing and that it was capturing new sequences coming from the phages that were used in the challenge. And so when a CRISPR captures a specific spacer, so the sequence coming from a phage,
03:07
it becomes resistant to the phage that matches this sequence. So that was a very interesting discovery. So since 2007, the field has moved at a quite incredible pace.
03:21
And we now understand a lot about these CRISPR systems. And this is basically the picture of how they work. So first I should also say that CRISPR itself stands for Clustered Regularly Interspaced Short Palindromic Repeats. So that's a very complex acronym and that was coined actually by bioinformaticians.
03:43
So that explains very well the structure of this loci where you have these clusters of repeats that are regularly interspaced and the repeats themselves frequently contain short palindromes. So hence this long CRISPR name. And the idea is that when you have a bacteriophage injecting its DNA in the cell,
04:02
at some frequency the CRISPR system, thanks to some proteins known as CAS for CRISPR-associated, is able to capture a piece of DNA from the phage and integrate it along with the new repeats in the CRISPR locus. Once it's captured this information in this adaptation phase,
04:21
it's then able to use this information to fight infection by similar phages. And the way it does that in this immunity phase is by transcribing the CRISPR locus in the first precursor RNAs that then process into some small CRISPR RNA. And those small CRISPR RNA are actually in complex with CAS proteins
04:43
that are going to be able to cut and degrade sequences homologous to the CRISPR RNA. So basically the CRISPR is building a memory of past infection and then using that memory to fight infection by similar phages. I want to go just a bit more in detail about one specific CRISPR system.
05:04
So that's the type 2c CRISPR system from Streptococcus pyogenes. And there's a good reason for that is this system is the one that is not the most used for biotechnological application of CRISPR. And you see that this CRISPR system contains actually four Cas gene,
05:22
Cas9, Cas1, Cas2, CSN2. It has a CRISPR array with actually six space certain repeats. And the interesting part here is that it also has another small RNA involved in this process that's called a tracer RNA that stands for transactivating CRISPR RNA. And these three Cas genes here, Cas1, Cas2, CSN2 are actually involved
05:45
in the capture of new sequences for the CRISPR system. And the Cas9 protein itself is able to carry out the whole immunity step from the systems. So the role of this tracer RNA is actually also very important in this system.
06:03
What it does is that it's complementary to the sequence of the repeat. So when you transcribe the primary transcript here from the CRISPR array, the tracer RNA can then hybridize making duplex RNA with each of the repeats. And that duplex RNA is then recognized by Cas9 and by the host RNA3
06:24
and that processes the primary transcript into the smaller CRISPR RNA. And after this process, you basically end up with this complex as Cas9 and these two RNAs, the CRISPR RNA and the tracer RNA and all the elements of this complex are absolutely essential for the function of this system.
06:43
So once you have generated this complex, this nucleoprotein complex, what happens is that it's going to basically look for possible targets. So it acts as a surveillance complex that's constantly scanning the DNA in the cell, looking for homologies with the CRISPR RNA.
07:02
But first, what the complex is actually scanning for, it's looking for a small motif. In this case, the motif is NGG and that's known as the PAM motif for protospacer-adjacent motif. And once Cas9 finds this GG motif, then it starts to unwind the double helix and pair it with the CRISPR RNA.
07:22
If there is some match, then it can completely pair the full length of the CRISPR RNA that triggers a conformational shift in the protein that brings two catalytic domains in contact with the double helix, introducing a double strand break in the target DNA. And that can then lead of course to the degradation of the target DNA.
07:46
So the way this system works has basically been very well described by the group of Emmanuel Charpentier and Jennifer Dodna and that got them to get this breakthrough prize last year
08:02
and mingle with some Hollywood stars like Cameron Diaz and the CEO of Twitter here. So it's good to know that if you do exciting science, you can meet your movie stars. So what have we been doing with this system?
08:22
So we're a microbiology group and we work on both understanding the way this CRISPR system works in bacteria, but also developing technologies. And today I'm going to mostly talk about the technological aspects. And the first technology we developed using this is a strategy to edit the genome of bacteria.
08:42
And the strategy here that we employed, so I'm showing here what we did for E. coli, we introduced a plasmid carrying Cas9 in the cell and then we can electroporate together another plasmid that is programmed with a CRISPR that will target here a gene we want to modify.
09:03
At the same time, we introduce a specific mutation we want by electroporating a single-stranded oligo that carries a mutation we want to introduce. And we don't do that just in any strain. We do that here in this HME63 strain from the lab of Donald Court that expresses a lambda-red recombination machinery
09:24
that allows to basically recombine this oligo at a high frequency. And then the CRISPR system can basically select this mutation by killing all the cells that did not introduce this mutation. And that works relatively well.
09:42
So at the same time that we were doing that work, there has been a lot of groups starting to work on CRISPR systems and people have called that the CRISPR craze. And this is just a tiny, tiny sample of all the papers that have been published describing the development of CRISPR tools for genome editing in a very broad range of organisms.
10:04
The global idea of all this tool work is, in all cases, you start with programming Cas9 to cut at a specific position in the genome that you want to modify. And once you generate this double strain break,
10:20
then the cell can deal with it in different ways. So either you can provide a template for homologous recombination and that way you can control maybe the exact point mutation you want to produce, gene insertion, deletions, etc. If you don't provide a template for recombination, you can maybe rely on endogenous repair systems of the cell
10:43
such as non-homologous enjoining and HEJ. And in that case, the cell is able to just join together the broken ends, but it frequently makes mistakes doing so. And like this, you can introduce small indels at the target position and that's used a lot to introduce knockouts. And the last possible outcome is if the cell is not able to repair the break,
11:04
then this will, of course, lead to the death of the cells. And when you look at the literature, the picture that we have is that these two pathways seem to work pretty well in eukaryotic systems, actually very well in eukaryotic systems, to the point that the technology has spread super fast
11:20
and now CRISPR editing has become really a standard technology to introduce mutation in eukaryotic systems. And in bacteria, it looks like CRISPR system is actually pretty good at killing the cells. And you can still use that for editing purpose but more as a selection tool than a way to trigger the introduction of a specific mutation.
11:45
And the fact that you can actually use CRISPR to kill the cell, you can think that maybe you can even use that as an antimicrobial strategy. And like the very basic experiments you can do is if you have a plasmid carrying Cas9 and a CRISPR that you program, for instance, here
12:02
to target a kanamycin-resistant gene, so that's worked on in Staphylococcus aureus, that plasmid you can transform it very well in the cell if the target is not in the chromosome. But if the target is in the chromosome, then you recover very little transformants and the idea is that the CRISPR is actually going to kill the cells. Like this, you can specifically kill bacteria
12:20
that carry, for instance, antibiotic-resistant genes or virulence genes. But of course, you need to be able to deliver the system to a whole population of cells. And the way we're doing that is by using phage as a vector. So phage are naturally able to package not only their own DNA
12:42
but other DNA present in the cell and that's called transduction. It turns out you can actually increase the frequency of transduction really a lot by simply cloning, for instance, the packaging signal present on the phage DNA. If you put that on a plasmid, then you can have the plasmid being packaged at a very high frequency in the phage capsid.
13:01
And then you can use this system to inject a plasmid in an whole population of cells. So we're using this strategy to inject a CRISPR system consisting of Cas9 and a CRISPR that we programmed to target an antibiotic-resistance gene
13:21
present in the chromosome of Staphylococcus aureus cell. And the idea is that this should kill the bacteria. So this is just to show you the specificity of it. So what you see is lungs of cell on a plate and it's actually either a strain that carries the kanamycin-resistance gene in the chromosome or that does not.
13:41
And then we program the CRISPR either to target the kanamycin-resistance gene or with a spacer that doesn't target anything. And then we just put a drop of our CRISPR phage mid preparation on top of the bacterial lung and we see that it's clearing the lung only when the CRISPR is programmed to target the gene
14:01
and the gene is present in the chromosome. So this is a very specific anti-microbial. So you might wonder why do we want to make antimicrobials that are so specific? What's the purpose of this? And the idea is that if you're able to specifically
14:21
eliminate strains that carry antibiotic resistance, for instance, then you can take advantage of the competition with other strains that do not carry this resistance gene to more effectively eliminate the threat. And to demonstrate this idea, we did this very simple experiment
14:40
where we just make a co-culture of two Staphylococci strains, one that is resistant to kanamycin and has this APH gene in the genome and another that's sensitive to kanamycin. And what we did too is that we put GFP plus mid in the kanamycin-resistant so that we can easily follow both populations in a co-culture.
15:01
And so this is what happens in the control experiments. So the cells are mixed one to one. We follow both the optical density and the dashed line is the fluorescence in the culture. And so in the control experiment, you see that about half of the population is the kanamycin-resistant cells. Now what would happen if you use, for instance,
15:21
kanamycin as an antibiotic? So if you had an infection like that, it would be a very bad antibiotic choice. And here what you see is that, of course, you kill all the sensitive ones and you will only end up with the fluorescence Staphylococci. If you make a slightly better antibiotic choice, so for instance, streptomycin, here what happens is that you basically will kill
15:41
both populations of cells. But after a little while, you would select resistance in both populations and you would still end up with about half of the population being the bad guys. But now if you actually treat with a CRISPR system that specifically targets the kanamycin-resistant gene, so that's the purple lines, you see that the fluorescent signal here is not recovered.
16:04
That doesn't mean that we killed all the kanamycin-resistant cells, but what it means is that we killed enough of them and we let the other population grow in the culture. You see that by following the OD curve and these cells now occupy the niche and prevent any possible survivors from coming again.
16:25
So in this specific scenario, using a CRISPR antimicrobial can actually be more efficient than using antibiotic treatments to eliminate bad bugs.
16:40
So we showed that we can not only do that in laboratory strain of Staphylococcus aureus, but also with some really pathogenic and problematic strains like the meticillin-resistant USA300 strains that are a big problem in the US right now. And we show that if we make a mixed population
17:01
with this MRSA strain and some non-pathogenic Staphylococci, we can specifically eliminate the MRSA strains by targeting the MEK-A-resistant gene. So I also want to mention that there is actually a very interesting side effect
17:20
so to speak of this strategy, which is when we treat a population of bacteria, we inject the CRISPR system not only in the bad bugs, but also in bacteria of the same species that might not carry the target. And those are going to survive, and if our CRISPR is carried on a replicative plasmid, they're going to keep that CRISPR.
17:41
And now they are basically immune to horizontal gene transfer of the targeted genes. So in this case, we show for instance that we can immunize a population of Staphylococci against the acquisition of tetracycline resistance by injecting this CRISPR system. And then we show that if we control that
18:02
as basically a non-working CRISPR system, we can do a transduction experiment to recover tetracycline resistance, but if we immunize, we cannot do it. So that's also an interesting possibility. So we went now to do also some animal experiments with this ID,
18:21
and this is a skin colonization model in the mice. What we do is we shave the back of the mice, and then we colonize with a mixture of resistant and non-resistant Staphylococci, and then we treat with our phagemid preparation that targets specifically the kanamycin resistant one. And the resistant one are also here fluorescent, so we can nicely follow them after treatment.
18:43
And here we show that we're able to specifically decolonize the antibiotic resistant bugs. So it's not as efficient as in vitro, but it also works in a more complex environment like the skin. I also want to mention that this work is now being pursued
19:00
by a startup company called Elego Bioscience, of which I'm a co-founder, and that's hosted at the Institute Pasteur. So basically, we have this picture right now where Cas9 is very useful to make genome editing in eukaryotes.
19:21
In bacteria, it tends to rather kill the bacteria, and you could ask the question why, and is this really always the case? Will CRISPR really always kill the bacteria, and why does it kill bacteria? And not eukaryotes. So some experiments that we've done recently to address this question is simply to do a very easy assay
19:42
where we still have a Cas9 in the cell, and we transform a CRISPR that we program to target many different positions across the genome of E. coli. And then we see what happens. We basically just count how many colonies do we get when we do this transformation. And this is a result that we got that was very surprising at first,
20:02
because we really expected the CRISPR to kill E. coli at any target position, but what we realized is that some target positions, we don't recover colonies, meaning that the CRISPR kills the cells, but other positions, we actually recover just as many colonies as a non-targeting CRISPR control.
20:20
And what we saw that was interesting is that for some of them, the one where I put a star, the colonies that we recover are actually some pretty small colonies, indicating that the bacteria here are pretty stressed. And this points to the idea that probably, first, not all targets are equal, some targets are better than others, and some target position might be tolerated by E. coli,
20:43
and E. coli might be able to constantly repair the breaks introduced by Cas9 at this target. And we could confirm this idea by simply repeating this experiment in a RecA mutant, so I think you might not see very well the white bar here, but that's the control. If we don't target anything,
21:00
we, of course, recover a lot of transformers, but all of those, there is no white bar, basically, because if you're in a mutant of the repair system of E. coli, RecA, then all these targets are going to kill the cells. But that still doesn't tell us
21:20
why does it kill the cell when it actually does. And the idea is that it probably does so because when CRISPR is going to cut in the genome, it's going to cut all copies of the genome at the same position at the same time. And it turns out that most bacteria strictly rely on homologous recombination to repair breaks.
21:42
So if you don't have a sister chromosome to do homologous recombination with, then basically you're dead. And we believe that this is the kind of damage that CRISPR does. And to demonstrate this, what we did is very simply putting a template for recombination on a plasmid that is not going to be cut by the CRISPR.
22:00
And the idea is that if our hypothesis is right, this should rescue the cells. And then when we transform our CRISPR system that targets this gene here, what we see is that without the repair template, we do see a lot of deaths when we target the gene. But no way if we add the repair template,
22:21
we see that we rescue a lot of the cells. So this confirms that the cells die because they don't have a template for repair. Then what we also noticed is that whenever we transform this CRISPR system to target in the position whether it kills or not,
22:43
we were wondering if this could trigger mutation at the target. And because when it kills, it usually does not kill all the bacteria. You might have some that survive. And this is the case for this LaxZ2 target here where you have some bacteria surviving. But here when we plate this on X-gal plate,
23:00
that allows us to see if the LaxZ gene is intact or not, we see that we recover about half blue colonies and half white colonies. So that suggests that the CRISPR actually led to the introduction of mutation at this target position. And when we checked what kind of mutation we obtained, what we see is that we obtained a lot of very large deletions around the target.
23:23
Deletions up to about 40 kB. And in most cases, what was interesting is that these deletions involve these rep elements of E. coli. So I had no idea what these rep elements were before we studied these mutants. But those are basically repeat elements.
23:41
So there are a lot of them in the genome of E. coli. I don't remember the exact number, between 200 and 300, that are scattered throughout the genome. And basically the idea is that they have an ophthalmology between them, that if you introduce a double-strand break, they can recombine together to repair the break and the cells might survive, if of course there were no essential genes
24:01
in the deleted region. So then you can try to make the hypothesis, why do eukaryotic cells survive CRISPR breaks so much better than bacteria? And one idea you could have is that,
24:20
bacteria, most bacteria don't have non-homologous enjoining, but eukaryotic cells do. So maybe they are able to repair the carcinoid break with NHTJ and that allows them to survive. So then we said, okay, so let's try to introduce an NHTJ system in E. coli and see if that can rescue the cells.
24:40
So that's what we did. And we took the NHTJ system from mycobacterium tuberculosis. So I told you that most bacteria don't have NHTJ. Actually, there are a few bacterial species that do. And then we repeat the same experiment where we transform a CRISPR system that's going to cut in the chromosome. So in the control that doesn't target anything, we recover a lot of bacteria,
25:01
but here, whether we have or we don't have the CRISPR system, the CRISPR system still kills most of the bacteria. So it does kill most of the bacteria, but what we noticed is that when we play it on this X-gal plate that allows us to see if there was a mutation introduced in like Z, we see that we start to recover more white colonies with the NHTJ system than without.
25:22
And if we PCR the target position, we start to see small variation in size at the target position that are very typical of repairs made by NHTJ. And so we can map this small deletion and we see that when we have NHTJ present in the cell, we obtain a lot of deletion ranging from six bases
25:42
to about 300 bases that are very variable in size, but that are still very different from the big deletions we obtain without NHTJ in the cells. And something that was interesting is that these deletions were always very variable on one side, but always within basically three bases on the other side.
26:02
And that basically led to the idea that probably Cas9 might remain bound more strongly to this end of the break and might protect it from nucleases, which might not be the case of the other side explaining the asymmetry in the repair. So to sum up this part,
26:20
basically, Cas9 when you're introducing bacteria, if it's able to cut all copies of the chromosome at the same position at the same time and the cells cannot do homologous combination, it's going to die. But some bacteria might be able to survive by making, for instance, big deletions. And the other thing that we found out is that at least with the setup,
26:41
we had NHTJ is not able to rescue the cells. It can make some repairs, but very, very low efficiency. And efficiency is basically even too low to be able to use it really as a tool to introduce indels in bacteria. So now I want to talk about a slightly different thing you can do
27:01
with this CRISPR system, which is to use the catalytic dead mutants of the Cas9 protein. And that's a very interesting mutant because it's still able to find its target position and bind to it very strongly, but it doesn't cut anymore. And the thing is that it buys strongly enough,
27:21
for instance, to block transcription and so that you can use it to silence gene. So that's something that we demonstrated. The group of Sanlei Csi also published a very similar thing to this work. And what you see here is just a GFP reporter gene,
27:41
and this is a relative fluorescence measured, and then it's just a position we target with these guide RNAs, either within the gene or in the promoter of the gene and the fluorescent level we obtain. So you see that if you target in the promoter sequence, you obtain very strong repression, in some cases really barely detectable expression of GFP.
28:01
If you target inside of the gene, you can still get very strong repression up to a hundred fold repression, but then it really depends on the orientation of the target in the gene. If you target the coding strand, you can get good repression. If you target the template strand, you can only have a much weaker repression. And to understand what's happening here,
28:20
you can do a simple Northern blot to see this is basically in the control, the full-length transcript of the GFP. If you target the promoter region, you don't see any transcripts, so basically you block the initiation of transcription. If you target within the gene, but in the wrong orientation, you start to see a smaller transcript here appearing, that the size of that transcript matches
28:41
exactly what you would expect if the transcript is just stopped at the Cas9 binding position. And if you target now the coding strand here, you don't see any more of the full-length transcript, so you have very good repression and you produce this shorter transcript here. So basically this dCas9 protein
29:01
is able either to block initiation of transcription or to block even the elongation of transcription and it can be very easily reprogrammed to bind any place you want in the genome, so you can use that to silence genes in a very convenient way. And then you can start to do even more things with this dCas9. You can fuse protein domains to it
29:22
to introduce functions at a specific position in the genome. And here, for instance, what we did is to fuse it with the omega subunit of the RNA polymerase in order to turn it into a transcription activator. And then we target it to bind upstream of weak promoter sequence and we should see activation of the downstream gene.
29:43
And so we targeted it to a different position upstream of the weak promoter, either on one strand or on the other strand, and we see that at a very specific distance from the promoter and in the right orientation, we can get up to a 23-fold activation of the GFP.
30:02
So I also just want to mention work by other groups related to these very interesting applications of dCas9, for instance, including in imaging, if you fuse a GFP to dCas9, and here, for instance, they targeted it to bind to telomere sequences. You can see here in this picture, every little dot is a different telomere in the cells,
30:22
and here you don't have to fix the cell or anything, the cell is alive, so you can follow the dynamics of different loci in the cell and it's a very powerful tool for people studying the architecture, chromosome organization and dynamics. So the last application I want to talk about
30:40
is something that we're really focusing on at the moment, is to develop screens, functional screens, based on CRISPR. And this comes from the fact that you can so easily reprogram this CRISPR system that you can construct libraries of CRISPR of guide RNAs that are going, for instance, to target up to 10 to the 5 position
31:01
here in the genome of E. coli, and then we can introduce this library of guide RNAs in our cells, and then you can perform a functional assay of interest. You might be interested in studying sub-MIC antibiotic concentrations, some different stresses, you might want to combine it with some query gene knockout, etc.
31:21
And the readout of this experiment is going to be done through deep sequencing. What you do is you sequence the CRISPR library both before you perform the experiment and after you perform the experiment, and by comparing all the proportion of each guide RNA changes during the course of the evolution, you can basically compute the fitness of each mutant in the population.
31:48
And in theory, there are two ways you can try to perform these CRISPR screens. You can either think of doing them using Cas9 to introduce knockouts in combination with NHG,
32:00
and that's something that people have already published and are doing in eukaryotic systems where you have this endogenous NHG in the cells and these screens work very well. And you can also think about doing screens based on dCas9 where here you're going not to introduce knockouts but to knock down and silence the target position.
32:21
And I explained to you that NHG repair, at least in E. coli, is very inefficient, so we cannot do this, but we can do this type of screens. But ideally, we might be interested in both, and both type of screens have actually different properties that might be complementary. So for dCas9 screen, you can think that you would only have partial silencing and polar effect, but you actually know what the polar effects are.
32:40
For instance, in an operon, if you knock down targeted gene early in the operon, it will also block expression of all the downstream genes. A big advantage of dCas9 knockdowns is that it's actually both inducible and reversible. So knockouts with Cas9 and HEJ. Here, the good thing is that if you really have a frame shift, you can hope to have complete deletion, but the problem is that the edits made are going to be quite unpredictable,
33:03
and you don't really understand what the polar effects will be. So I just want to give you a flavor of what kind of data we can get with this type of screen. And this is just a very raw data showing you the number of reads we get for each guide in the library, both before the experiment and after the experiment.
33:22
So this is a control where we don't induce the expression of dCas9, so everything folds more or less nicely along the diagonal, meaning that the number of guides in the library didn't change between before and after the experiment. But now if you start to induce the expression of dCas9, you see a lot of points either going down or points going up.
33:41
And points going down here means that basically those guides provide a fitness defect to the cells in the population, and the guides going up will provide a fitness advantage to guides in the population. And so then you can of course focus on some specific points and try to see what's going on. And here for instance, it's the exact same figure, except that I just kept the targets in this mRNA gene,
34:03
that's an essential gene in E. coli involved in cell wall synthesis. And what you see is that the control experiment, everything is still nicely along the diagonal, but now when you induce, you see that here all these pink points basically go down and the green one, blue one stay there. And what these points highlight is these blue points
34:23
actually target the template strand, and if you remember from a few slides before, I explained that targeting the template strand gets you only very weak repression. But if you target the coding strand, those are these red points, here you have very good repression and you see depletion of this guide in the library,
34:41
which is expected because it's an essential gene. So like this, you can very easily basically figure out what are all the essential genes in the cells, and of course if you do that in specific conditions where you want to do functional screens, you can also get more interesting information. Something that we think is also very interesting and that's linked to the fact that this is something that you can induce
35:01
at a precise moment, you can actually follow the evolution of each guide over time. And here we sequence, for instance, this library after 20 generation of culture or after 40 generation of culture, and this is targets in the Mercy or SecD gene. So here I kept the same color code, the blue ones target the coding strand,
35:21
so good repression, the red one targets template strain, weak repression, so you see that all the red lines are basically straight horizontal lines, and the blue lines here, both these genes are essential genes, but it turns out that if you target Mercy, the guides are depleted much much faster in the population than if you target SecD.
35:41
So both genes are essential, but maybe you can start to see some degrees in essentiality if you want to say. So where we're going with this basically is that this single knockdown or knockout screens are actually very cool, and in some ways actually also pretty similar
36:00
to what people have been doing already in the past, for instance with transposon, tag, metagenesis, TNC kind of approaches, but what we think we can do that's very exciting with this is now go after genetic interaction by doing multiple knockdowns simultaneously in the cells. And so that's something that we're really excited
36:21
in doing right now, and we're just starting to be able to construct these double knockdown screens and study them in the same way. So with this, I'd like to thank people in my group. So we have a very nice lab space at the Institute Pasteur we just started a year and a half ago,
36:40
and some of my collaborators and funding. Thank you very much. We have time for a couple questions. Out of curiosity, what's known about the CRISPR and dino-cocci? So dino-cocci constitute a film bacteria
37:01
that are able to sustain thousands of DNA double breaks and repair them. They typically have no less than four copies minimum of their genome per cell. And so I was wondering whether some studies have been done on other designs. Not to my knowledge. It would be super easy to check whether dino-cocci tend to have or not CRISPR system.
37:23
It turns out that something like 50% of bacteria have CRISPR systems. So there is this very nice CRISPR database on lines that we could look into. I never checked for dino-cocci, I don't know. Any questions? A few slides back in your mercy data
37:40
for the different generation times, some of the lines on the graph went up in 40 generations. Is that just an artifact of the way you're measuring or is there some recovery? So we're not quite sure yet. We're just starting to look into this type of things.
38:01
So the answer is I don't know. I suspect that it might actually be some mutant guide RNAs that might survive and then have a regular fitness or recover a bit or something like that. But honestly, I'm not quite sure. So you showed that the CRISPR is killing these cells unless you provide a template for repair.
38:22
That is what I realized. Which means it would be an even better mutagenesis to a target insertion. Yeah. So you're putting your finger on a very, very good point. And the answer is that we first believed so as well. And it actually turns out not to be the case. And we were lucky to have this experiment
38:41
where actually the template is able to rescue the cells efficiently. But it turns out that this works only if what we introduce is a point mutation and only for a specific homology length of your homology arms to need to be very specific. And as soon as you want to, for instance, introduce a small deletion, efficiency drops dramatically
39:02
and et cetera. So we're and we don't understand really why yet. And this is something that we're investigating as well. David, that was really nice. But I think I'm just not quite understanding the CRISPR screen. So it's similar to TNC, transposon-seq in some ways.
39:22
So one, I was wondering if you can compare and contrast the methods. And then the second is, how are you preventing death in this case? So here it's using the dCas9. So we're not cutting the genome. It's just going to silence expression of the target positions. So we're not killing.
39:40
And then, but you're perfectly right that it's very interesting to compare this type of method with TNC. And we're actually doing that at the moment. So we're conducting in parallel a dCas9 and a TNC screen. And we're going to sequence them and analyze the results very soon. Following up on that, how do you construct the library? Do you design your 20 base pair RNAs?
40:02
And then, or is that random? So you can design them. So now you have these companies, including, I think, probably the ones that is most commonly used is oligo arrays. So I don't have any shares in them whatsoever. I'm just saying the ones that we're using. They provide, you can order a pool of oligo.
40:22
And you can precisely determine the sequence of up to 10 to the 5 different oligos you want to be in this pool. And they just generate it for you. So the technologies that they're using are very similar to the technology used to generate the microarrays, except that then they just cleave it off the chip and recover it in a pool. And you get that.
40:41
And then you have this pool of oligos. And you just use it in a standard cloning procedure. And what's the role of the PAM sequence? You showed this one promoter where you had nicely spaced target RNAs. And then what is about the time? So you absolutely need a PAM motif
41:01
to be able to Cas9 to bind. So Cas9 is really first looking for the PAM motif. If there is no PAM motif, it's not going to bind. And so in the experiments where I show that, for instance, we tested many different positions along a promoter, we specifically engineered a promoter to contain many PAM motifs so that we can test
41:21
precisely different positions. So would that be a problem if you want to target a gene and then you would want to target the plus 1 or the minus 10 or something? But there is no PAM, so you cannot? So the answer to that is that you have quite a broad range
41:44
of position you can target. Even if what you want to block is initiation of transcription, you see here it's 100 base per range where you are still very good at blocking the initiation. So a GG motif is very frequent. It's randomly every eight bases. One last question.
42:01
Sorry. I was just wondering, you were talking about immunizing without CRISPR when you're treating a population to kill the bad guys. So how long does that immunization last? So it should last as long as the cells keep the CRISPR system. Then how long will they keep? It will depend on the stability of the plasmids
42:21
that you inject and many other things. So we haven't really investigated. And the cells in themselves? How long do they persist? The cells? Which effect of your resistance? The cells were, I'm not sure I understand which cells you're talking about.
42:41
Well, previous question was about the period of the existence of the resistance. And you refer this period to the cell survival. So cells, how long do they survive in your system? I mean, if they're not killed by the CRISPR system, they're going to survive indefinitely.
43:02
There's apparently one question over here. Just a question about the natural CRISPR system. How many repeats does the natural material maintain? So that's very, very, very variable. It's basically from one to 600.
43:22
And it depends on different bacterial species. And yeah, yeah, so this is really not understood why some species of bacteria tend to have shorter CRISPR while some have larger CRISPR arrays. Yeah, I cannot really speak to that. It's really still a mystery. I think there'll be an opportunity
43:41
to ask more questions during the discussion. Thank you, David. Thanks.