Probing the dynamics of complex microbial communities using DNA tridimensional contacts
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Koszul, Romain
Probing the dynamics of complex microbial communities using DNA tridimensional contacts A fundamental question in biology is the extent to which physiological and environmentally-acquired information can be ransmitted from an animal to its descendants. I will present an example of trans-generational epigenetic inheritance where a temperature-induced change in gene expression lasts for less than 10 generations. I will also present an example of an inter-generational effect whereby the physiological state of an animal (it’s age) has a large influence on the characteristics of the next generation.
 So first I want to thank you for this invitation. The first time I come here I used to live next door. So it's in Gifte Rivette on Orsay. So yeah, I'm a local but I never set foot in the institute so I'm very pleased to be here today. Thank you for this opportunity. So today I'm going to talk about mostly what is actually a technique that we have developed in my team. My team being mostly interested into the functional folding of genomes, mostly bacterial genomes, yeast genomes. But by working on these topics we ended up finding out that we can actually exploit the quantitative measurement of physical collisions between DNA molecules to solve genomics or metagenomics limitations that exist in these fields. So we are not coming from the genomics or metagenomics field but now part of my team is now working on these approaches by developing new methods. And so I will show you today how this works. So something common between all ecosystems in the world, whether it is the gut of a mouse or the mangrove in the French Caribbean, are the fact that they are colonized by complex heterogeneous populations of microorganisms, mostly bacteria but also yeast, worms, I mean a lot of different species. And these communities of organisms have important roles in many fields. For instance, the production of oxygen, recycling matters, depollution, bioenergy, etc., so they can have some industrial importance or just biological relevance. And therefore it is quite interesting or important for a lot of people to try to decipher the content of these communities to find out which bacteria is there, what are the genomes in order to understand the maintenance of the equilibrium of the ecosystem. I'm going to switch here because there are more people here than there. So to understand these ecosystems, so a lot of people are interested to describe them the more precisely possible to the full extent of their complexity. So you have many species coexisting together. You can also imagine you have phages, viruses, mobile elements between this that can be exchanged between some of these species. So it's quite important to be able to describe the structure to the full extent of its complexity. And this is where the field of metagenomic emerge at the convergence between genomes and sequencing, so very technical fields, how do you explore genomes from the structure down to the sequence, so high throughput sequencing, to the question what are the diversity of microbes in the wild. So metagenomics is a field that consists in sequencing DNA from the environment and trying to find out what species coexist with each other and trying to find out how they co-evolve or coexist in overtime. So these studies provide some hints about the genetic content of an ecosystem and also some hints, therefore, about the balance or the imbalance of the community. And then you can actually work about many things with these approaches. The way it is done so far is by extracting DNA from the population, from the ecosystem you are looking at. So you basically just extract DNA molecules that belong to different genomes, different organisms. And you do genome sequencing on this mix of DNA molecules. So you sequence a lot of short reads or even longer reads. So you end up with a big picture of the DNA present in the population. And to improve things a bit, because you don't know which molecule belongs to which, you can actually try to extend these short DNA molecules into longer DNA molecules that we are calling, that are called contigs. And still that don't represent full genomes because of the limitation of current assembly programs, because also some of these species share some identical sequences. So the program just failed to isolate the individual molecules from the original population. And because it's a very complex problem, you have to imagine some of these communities contain hundreds of species, therefore hundreds of genomes. And so it's quite impossible to assemble these DNA sequences into full genomes that provide you a good insight on the original community. So what people used to do is try to pull these contigs that are stretch of DNA of a few thousands of bases into pools of contigs that group these DNA molecules according to, for instance, covariance in different experiments. You look at covariance between the amount of these contigs you obtain after sequencing. So you can pull these guys together. You can look at the GC content, codon usage, different heterogeneous information present along these molecules that help you to pull these contigs according to specific features. It's a very imperfect process, and usually what you end up getting is many more pools of contigs that you have original sequences in the community. So here, for instance, you have six, for instance, let's say you have six bacteria. In the end, you will end up with nine communities of contigs, which is there is a discrepancy between the number of pools of contigs and the number of genomes you expect in the end. So there is actually a strong inability in this field to reach at a comprehensive genomic structure of complex communities, and therefore this limits the investigation of the dynamics and equilibrium of these ecosystems. So my team used to work on, I mean, is working on genome folding. And so what we noticed, and I will show you what I mean by that, is that obviously each sequence, DNA sequence in 1D has a unique 3D signature in the wild. So we can actually exploit this 3D signature to reverse, to go back to the 1D structure. So here is how we do it. So we use mostly an assay, which is called a chromosome conformation capture assay, which has been developed by Job Decker 15 years ago, that aims at trapping physical collisions between DNA segments along a genome, according to their collision frequencies inside a population of cells. So how you do that is you freeze the folding of DNA in each cell by adding a fixating chemical agent such as formaldehyde, that is going to generate covalent bridges between proteins and proteins and proteins on DNA. And therefore if you have a DNA molecule inside a cell that is folded like this with a protein complex here, you will generate covalent bridges between these proteins on the DNA, and therefore you will freeze the folding of this molecule in this cell. So if you have billions of cells or millions of cells, you have a population of frozen structures like this in your mix where you add this chemical agent. Then the trick was to digest these DNA molecules. Therefore you end up with restriction fragments, so short pieces of DNA fragments that are again fixed with each other according to their frequency of collision inside this population. And then when you add an enzyme called ligase that is going to religate these two restriction fragments together, you will end up with a molecule that is chimerae with respect to the original genome, but that reflects the fact that these two fragments, red and black, were actually close to each other in the 3D space. Okay? So then what you do is you purify all the DNA molecules, and you end up with a library of DNA molecules where restriction fragments, so DNA segments that were close to each other in 3D. How big a segment is your restriction? So it depends on the enzyme you use, but they can be between, let's say, 10 base pairs up to 2KB, 3KB. So if you use a frequent cutter, it will be mostly between 20 base pairs and 200 base pairs. If you use a 6-cutter, an enzyme that recognizes 6 base pairs, it will be between 500 base pairs on the 3KB, and it depends also on the GC content of the genome, so there are a lot of parameters there at play, but usually it's between 1KB and 1KB, let's say, on average. So for a long time, the limitation in this field was to quantify the respective amount of these events, because if you are able to have a global overview of the respective amount of all the religation events between all the DNA segments in the original population, then you would have a global overview of the average folding of the genome. That was the theory. And that was actually solved with the advent of high throughput sequencing, like Illumina sequencing, where basically you just plug sequencing adapters to the edges of these DNA molecules here, and you sequence one end of the molecule on the other end, and now you only count how many times you found in the library, for instance, the green fragment religated with the blue fragment, and actually with all of the other fragments of the genome. And therefore, you have the respective contact frequencies of religation between all the restriction fragments of the genome with each other. And this allows you to generate heat maps, contact maps, that reflect this respective ratio. So this is an example for the bacterial Vibrio cholerae, which contains two circular genomes, some approximately a four megabase genome. So here are the genomes, the chromosomes represented here under their linear form. And when you plot the contact between all the DNA fragments along chromosome one with themselves, you end up with this heat map here. So that's an intracromosomal contact map. This is intracromosomal contact map of chromosome two. Then you can also plot the contact between chromosome one and chromosome two. And here you have intracromosomal contact map. So this heat map is quite representative of what you get when you do this on any species so far, whether it is mammalian cells or bacteria or yeast. You have first a very strong diagonal that reflect the fact that the DNA molecule is a polymer. And therefore, two DNA fragments that are close to each other along this polymer are going to be related more frequently than to DNA fragments that are far apart from each other. Therefore, you have this strong DNA contact that DNA, sorry, this strong contact. You know, to really get the polymerized DNA, because it won't be close to the father, you have to coincide. Sorry? You have to be able to get the sticky ends must coincide more or less, yeah? Yes, it's okay. When the different ones are close to the father, they will be the same. So the contact frequency is more or less a power low. And so the further you go along, the further you will increase the distance between two de-reduction fragments, and it's going to drop very quickly. And this is actually a log scale. But the ligation depends on the sequences of the ends, yeah? Yeah, so you have some biases at this level, a few biases which are kind of characterized, and they are not strong enough to kind of affect the outcome of this result. You may have issues if you have very high GC rich genomes. Actually, the sequencing is going to be affected as well. Like the Illumina sequencing may be problematic on highly rich GC or so. So there are biases, but overall, it will not affect the general trend, which is that two fragments close to each other along this polymer are going to be frequently in contact and two fragments far apart. Like here, I'm not going to be frequently in contact. So here, the color of scale reflect the fact that rare contact are in white, frequent contact are in red. But it also depends on the stage and the cell cycle, yeah, how they're positioned. Yes, so that's exactly, yeah. So this will modify, for instance, the width of this diagonal. If you go into metaphase, you will increase the width. If you are doing replication, it will look like this. So indeed, there are some functional events that you can actually identify in this contact map. And this is something we are working on in different species. For instance here, so the chromosomes are circular, meaning this position here is adjacent to that one. That's why you have this contact in the edges of the maps. And also, also related to functional events, you can see here, for instance, you have contact, it's very weak, but you have contact between the middle of this chromosome two and the middle of chromosome one. And this corresponds to the termination of replication position of these two chromosomes. And you can actually ask why do these chromosomes see each other in space. You also have one specific contact here between the origin of chromosome two and the position of chromosome one. And that relates to an original control of the firing of replication of un-chromosome two by the progression of replication fork around a long chromosome one, which seems actually mechanically related. So this is something we are working on in collaboration with DJ Mazel at the start. But today what I want to insist on is that in all genomes you have this very strong diagonal that reflect the fact that these two chromosomes are polymers and therefore in the space, in the cellular space behave as relatively individualized entities. So the question we asked at some point when we were working on this was, okay, so what happens if we do this same experiment on a mix of species? Will DNA fragment from genome one is going to be in contact by accident, by experimental procedure biases, be related frequently with the genome of fragment of genome two or genome three? Or is this relagation events are going to be sufficiently low? No, because the restriction on time it cuts the same site, so you have a cohesive site. So if you use a cohesive end it will relagate very efficiently. If you use a blunt cut it's not so efficient. So the question we asked was whether or not these restriction fragments are going to be relagated often with each other from the different genomes and therefore blur the signal. So what we did was to do a preliminary experiment where we took three different species, again viviocollerae, H.C. and basilis subtilis. We grew them independently and we mixed them together, the cells together and then we performed this genome 3C experiment directly onto the mix of species. Until then we take the reads from the parent reads and we align the reads against the reference genomes of these three species. And then we look at the contact pattern between these genomes and within these genomes. And when we did that we were quite pleased to see that actually there is very little background between the different species. So that means that it's very rare, I mean it's relatively rare that one fragment from species one is going to be relagated by accident with a fragment of species two. And the relagation may occur just because when you lyse the cell and you just process the cell biochemically you may just disrupt the genome and have a fragment floating in the solution that will just relagate spontaneously with each other. So this doesn't happen very often. And therefore you have well individualized squares in the main diagonal that correspond first to the genome of basilis subtilis. Then you have this square here that is the genome of E.coli. You can actually see that there is also a tiny square there that correspond to a plasmid that sees a lot the genome of E.coli. And so that tells you that this plasmid probably shares the same cellular space as the genome of E.coli. And here you have again the two chromosomes of vibriocholary that also see each other in 3D very often and therefore you can actually pull them together inside the same cellular space. So we can be convolved in a sense the genomes of these three different species from this heat map. Why is E.coli different qualitatively than the other? So the coverage may be at play here and it was a very preliminary experiment. So there may be some differences in the amount of cells we put even though we thought we put the similar amount of cells maybe we just did some little mistakes. And also basilis is a gram plus bacteria. These are gram minus bacteria and it seems to be slightly easier to extract DNA from gram minus bacteria than gram plus. So therefore the coverage of basilis is lower in this experiment than these two guys here. So you're attributing this spread of the... The thin line here. So is the diagonal is much more... The diagonal is also a matter of color scale here. It's the same color scale for the entire map and therefore you kind of blur. And it was again a very early experiment. So basilis doesn't look like this. We did work on that. I will show you maps. It's much nicer than this one. Okay. So let's keep that. So what does the thickness of the diagonal mean? So it means that it can be interpreted somehow as a condensation level. It's not totally true but you can actually visualize it as a kind of measure of condensation of the chromosome. So if condensation is high to DNA fragment that are far apart are going to be more frequently connected with each other than if it's very decondensed. That's one way to see it. So what we did then was to do the same kind of experiment with 11 species. Therefore 11 genomes and these are yeast genomes. So each genome now is composed of multiple chromosomes. So we pulled all these species together inside a mix and we did the metagenomic assay that I know sorry the metatricy assay as we call it directly on the mix. So when we aligned the reads again against the reference genomes of these species we were pleased to observe that there are indeed 11 big squares in the main diagonal that correspond to the genomes of these species. Some of them are not as well covered as others. Again you can see differences but this is not very significant. But again here we aligned the genomes against we aligned the reads against the reference genomes so we kind of cheat. We know the answer. So what we wanted to do was to do a design an assay that would allow us to start. Excuse me but for these you don't have any common sequences or any common placemates or any kind of... No here there are no placemates. We don't have placemates. But each of these big squares is composed of smaller squares that correspond to the individual chromosomes present in each species. On the dot also just so you know in yeast... You heard it yesterday so you probably talk about that maybe but centromeres are collocalized in the nucleus and therefore the DNA next to the centromere is colliding with the other DNA quite often. Or you can see clusters of centromeres which are these dots in the contact maps. In the case of cerevisiae you would have the tumor complacemate. Yes but we don't align it. So there may be a placemate there but it depends on the strain I guess. For this strain certainly. Is there cerevisiae? Yes. Can you count the number of chromosomes and it will be... Yes so I will show you what we do now which is actually exactly that. So what we wanted to do was because this is kind of nice but it's kind of useless if you want to explore something very from the wild where you don't know the content in genomes. You cannot align the reads against reference genomes if you don't have the reference genomes. So what we wanted to do is to design a protocol, a computational protocol where we would start from the reads. So only the parent reads and we would like to see whether we can go down to the genome of all of these species. So we start here only with the raw sequences which are the parent reads that reflect the contact frequencies between all DNA in the population. So what we can do is we can first try to increase the size of this small DNA reads so we can use standard assemblers like DBAUD that are going to increase the size of the chunks of DNA we have. Therefore we have a set of contigs that we can work on. The other information we have besides the DNA sequence in this set of reads are the parent information which correspond to the contact between the DNA molecules from the design of the 3C experiment. So now what we can do is we can also align the contact information on this set of contigs. And that gives us a network of contact between this set of contigs. So we have this network which is pretty big and so a nice way to analyze network is to use the Louvain algorithm which is going to partition the network to optimize the modularity of the network partitioning and look for communities of contigs that see each other very much inside this big network and don't see the rest of the other communities very often. And so we work with this Louvain algorithm, we design a protocol that allows us to actually segregate these pools of contigs into 11 communities of contigs. Just by using the standard algorithm we ended up getting 11 communities which is actually the number of yeast species that we originally put into the mix. We also had a contaminant like a Nicolai genome here, 1%, which is probably coming from one of the cultures of yeast we did at the same time. So that's okay. So most of these contigs just see each other within the same community and they don't see the contigs of the other communities very much. Can you please repeat the nature of the ages in your interaction network? The ages are the contigs and the number of contacts between the contigs are going to be the nodes. So the age, you put an age between this and another contig in case of what? But we know that the number of contacts is going to reflect the collocalization in the same cell. So we just segregate. And so that works relatively well but here we have pools of contigs and if we take one of these communities, we look at the contact between these contigs. This is what we get. So here we have 1000 contigs. And of course we don't sort them according to their position along the genome because we don't have the reference genome. So when we just align the contigs, the reads against the contigs and we generate the contact map, this is what we get. It's a very messy contact map compared to what I showed you before. And so here I will introduce another algorithm we designed at the same time that allows us to reorder these contigs accordingly to their contact frequencies and therefore try to resort the, to rescap the genomes of these species. So this is a program that we call the GRAL that was designed by a disfiant in my group and that, this is like an interlude, that aims at scaffolding contigs based on their contact in 3D. So the situation we have here is the same as if we had the contact frequency between a non-fully assembled genome. Most genomes are not fully assembled. And therefore when you align the reads of a contact experiment against the reference genomes, you have this messy contact map where, because you don't know here that the green contig is adjacent to the green one here, you will not position these two guys next to each other along the axis. And therefore you will have this strong intercontic contact here in the contact map that is actually incongruent with what we know about polymer physics of the polymer property of the DNA. So on time for the red chromosome here which is split into three contigs here, there and there, you will have this incongruent contact signal at far, I mean long distance in the contact map. So what you want to do is to solve this puzzle. So here it's quite simple. You can solve it by hand. So for instance here you can take the green contig there. You look at the contact made by this position and you see it makes a lot of contact, the white position here with this guy. So you will position this edge next to that one. And now you have a longer scaffold and it looks good on the map. But you may have long distance interactions. Yes, so you may have long distance interactions which are maybe functional, for instance like the centromeres in yeast. But this long distance functional contact are always much smaller than the very strong polymer behavior of the chromatin. So this diagonal contacts are always two fold, two log higher than the contact you see at longer distances. And then what you want to do for instance with the blue guy, you take this contig here and you see it sees a lot this edge there. And therefore you will have a red guy. Anyway, you get the idea. You just resort the contigs until you get only squares in the diagonal. And you minimize the signal outside the main diagonal. So this is easy to do by hand, but it's actually more tricky to do with real data and with a very big complex genomes. So what we did was to design a program that used this contact, which are very quantitative contacts on polymer physics prediction to explore the space of genome structures and identify the genome structure in 1D that reflects the 3D data with the highest likelihood. So that's what they did. So again, this is for instance the power law and we noticed so if you know the contact frequencies between two DNA fragments and you have the contact decrease according to the genomic distance along the genome, for a genome, if you know the contact frequency, you can actually guess with a very high accuracy the distance that separates the two loci you are looking at. So here for instance you have a contact around 100 and then you can say, OK, so these two guys are, no sorry, contact would be like 50 and then you say, OK, if I have a contact of 50 between two segments, it means they are separated with a certain likelihood by 100 KB in 1D. So Hervé designed this program which starts only with the 3D data and is initialized with a polymer model because at first we don't have contigs big enough to compute this power law, this distribution, which changes depending on genomes slightly. So at first we start with a polymer model on the data on the original genome. So the set of contigues that is not fully scaffolded or that has been published or that we have generated. And then it's going to screen through the genome structures in 1D trying to improve the likelihood of the structure by fitting the 3D data on it and computing a new measurement of the fit between the data and the 1D genome structure. So we are going to sample the space of genome structure in 1D and compute for each time the new likelihood. So this is the likelihood on the wide axis. This is again here and this is the increase of likelihood according to the iteration of events. So we basically make small changes in the 1D structure and for each small change we compute the likelihood and then we take the best likelihood structure, I mean the most likely structure and then we converge towards the situation where we are actually oscillating around one D genome structure and this is just a movie that illustrates this process. So this is a genome that is actually that was published as a set of 76 contigs. So it's scaffolded. So 76 big pieces of DNA. And so fungi and we know that this species doesn't have 76 chromosomes. So there are more chromosomes and also more contigs published than number of chromosomes. And when you do the 3D data you can see clearly that indeed something is wrong with this contact map, it doesn't look right. And here we have illustrated the 76 contigs that were published. So what we want to do is to reorder this DNA fragment according to the 3D contact and converge towards the most likely 1D structure. So the first part of the program is actually splitting these big contigs into smaller pieces because we actually assume that there are some assembly errors in the first place that were published. And so therefore we cut this big DNA fragment into pieces of 10 kb and we just start to reassort them according to their collision frequencies. So the first part of the movie is just doing that and randomizing it for… So we just split this DNA fragment into tiny pieces and then here's the likelihood and that's the number of contigs. And we start to explore 1D structures and improve it in likelihood. And in the end we are starting to converge towards a state where we have 11 big chromosomes, I mean big chunks of DNA. And we have a contact map now that accordingly represents something that looks like yeast or fungi genome. So here when we stop the program, sorry not 11 but 7 chromosomes, yes, right, it didn't go down to the end. Okay, it stopped. Yeah, it stopped, one of them should be, but it doesn't matter. Well, anyway. So when we stop the program, which is oscillating forever around the same structure, we actually get this genome structure, 7 chromosomes. We have contact that looks very much like inter-centromeric contact, which is totally expected for a fungi at this stage and like this dot here, we can actually identify the centromeres by looking at the position of these dots along the sequence, etc. And so we can actually scaffold this genome pretty efficiently and this is actually the real genome now, we checked it independently. And we can do that actually for other species and so this is just an example of unpublished data. It doesn't just work on big, I mean on small genomes like this fungi, but this is the parasitoid wasp. This is a collaboration with Jean-Michel Dresden in Angers, where when we do the 3D contact between the chromosomes of these wasps, we have this very messy contact map. This is a published genome made out of thousands of contigs. You can see them here. Each of the contigs is making a square, but then you have all these inter-chromosomal contact contacts that we want to solve. And the program is also able to do that pretty efficiently, so I'm just going to go quickly. But again, we are just resorting the chunks of DNA according to their collision frequencies and this gives us a relatively robust scaffold in the end. So it takes a bit longer. Here, so when we do that, we end up getting relatively nice scaffolds that actually correspond to what is expected from cytology data. So we were able, by exploiting the collision frequencies between these DNA fragments, to rescapable these genomes in a way that matches the expected cytological data. So now our collaborators can look at their favorite genome sequences, such as viral sequences that contribute to the parasitoid process of the wasp. The wasp disrupts the host immune system to, I don't know if you say pond, but to send the eggs inside the caterpillar it is infecting. So these viral sequences are important for the reproduction of the wasp. Anyway, so going back to this metagenomic assay, we did this program, we run this program on this 1000 contigs and to get it short, we end up to get something that looks like a yeast-genome contact map. And when we compare this contact map with the reference genome, we have 95% of the reference genome covered by this heat map. So in one single experiment, we were able to actually isolate 11 genomes from this mix of species and rescapable them with approximately 90% accuracy compared to what was published. So that's what was quite promising. And here I'm going to show you the real experiment. This is not a control mix. It is done on the gut of a mouse from the Pasteur Institute animal facility. It's not a very exciting sample because, again, we were not microbiologists at this time, but so we took this one single species of this mice and we performed the metatrissi assay, the contact assay directly onto this single species. So we were able therefore to assemble this DNA reads into contigs. And here it's a very big network. We have 400,000 contigs. It represents approximately 600 megabays of DNA. The N50 is around 4KB. So the contigs are relatively small. Again we can, so when you look at the contact map of these contigs, well, okay. Again we can use the parent information to bridge these contigs according to their contact frequencies. And we have this network of contigs that contain 45 million contacts. So it's a very complex network. And this is how it looks like when it's not sorted. What we did again was to partition this network using a Louvain clustering algorithm. And when we saw this network using Louvain, we were able to get nice communities of contigs that present more contacts inside each other compared to outside with each other. So each of these squares correspond to a set of DNA sequences that collide more frequently in space compared to the other ones. Okay, I'm going to skip that. Okay. So what we did next was to investigate the content of these communities. And first what we can do is to align the genome annotation against the DNA present in all of these communities. So the communities are sorted by size. So of course we have small communities of a few contigs. Start from one up to one of the contigs. But most of the DNA is being actually pulled into the large communities of contigs that contain above 1,000 contigs. So they are like more than one megabase of DNA. And so of course since most of the DNA is present in the large communities, most of the gene annotation is going to be present to correspond to the large communities. So therefore this is what you see here. Most of the genes are actually present in the biggest communities. This is not very interesting. If we look at bacterial essential genes, again, most of the bacterial genes, essential genes are present inside these large communities, suggesting that these large communities correspond to bacterial DNA, which is again not totally unexpected. The interesting thing was when we look at phage-related DNA or plasmid DNA or conjugative DNA, we actually see an enrichment as well inside the smaller communities. As if, of course, the phages are in contact with the bacterial genomes, the bacterial genes, because some of the phages are integrated in the bacterial genomes like prophages or some phages are maybe infecting the bacterial genomes. But also some phages behave as individualized entities that in space are being actually trapped on themselves or not with another molecule. So that was quite interesting. So we decided to analyze these two sets of communities independently. First the large ones and then the smaller ones. And so what we observe, for instance, if we take one of these large communities, so this is a 68. Again we have this messy contact pattern. We can improve this pattern by working with the program we designed, Graal. And in the end we end up with a scaffold of approximately four megabays of DNA, a single scaffold that presents this kind of pattern. So you have here the large diagonal. And interestingly you have also a secondary diagonal. And I will show you why this is interesting. It's because at the same time we are working on basillus subtilis on this kind of 3D folding of bacterial genome. It's typical of bacteria like basillus subtilis. So first this origin of replication is positioned at the crossing between this secondary diagonal and the main diagonal. You have more DNA around the origin of replication compared to the termination of replication which corresponds to a bacteria that is actively dividing. So you have more DNA at the or that at the termination of replication. And the secondary diagonal reflects the fact that the two chromosome arms, even though they are bridged there, they are actually colliding with each other more frequently because they are being bridged by cohesines as we actually showed in basillus subtilis. So when you start replication you bridge the two chromosome arms and they are actually maintained in close contact with each other during the cell cycle. And this is what is reflected by this secondary diagonal. So this is actually typical of the 3D structure of bacterial genomes like subtilis. So these are another community, another one and so on. This is more like the E. coli bacterial chromosome where we don't have the secondary diagonal. So this is like basillus, this is like E. coli. But we get DNA structures that all look like bacterial scaffold genomes, 3D structures. So in one single experiment we are able to recover dozens of bacterial scaffolds which is actually, I mean until now it was not, you cannot do that in this field. You need to have multiple samples and you need to do a lot of correlation analysis. But here in one single experiment we end up getting 100 of bacterial genomes, relatively complete. And at the same time what we did was to analyze the content of these small communities which were the phages, I mean that looked like phage. So here we took 72 small communities that contain mostly gene that are phage genes. And we reprocessed the data a little bit. And we look at the sequences we obtain for these 82 communities. And so these are different examples of these 82 communities. So one of them for instance is a 235KB genome that looks like a FKZ genome, it's a phage. These guys here correspond to two molecules of 25KB which are also good size for a phage genome. This is 50KB like a Codovirales genome, etc. So we have genomes of phages in this community of species that we can now actually try to relate with the potential host which are the bacterial genomes. So we have in one hand bacterial genomes and in the other hand we have the phage genomes. And so now what we can do is to plot the contact in 3D between the phage and the bacterial genomes. And this is what we do here, we have the 82 phage sequences and the 100 something bacterial sequences. And this is what we get now, we have a good overview of the infection spectrum, global of infection spectrum in the community. So this is quite important because now what we can do is to do this kind of experiment over time and look at the propagation for instance of a virus in different bacterial species or closely related bacterial. We can also look at genes of interest, for instance an antibiotic resistant gene which is present in one of the species and then if we treat one of the community with, we give antibiotics to a patient, then we can look at the propagation of this phage, sorry, of this gene, antibiotic resistant gene inside the population of bacterial species and see if at some point it will induce some dramatic stress in the population of bacteria. We can look at the activation of profages depending on stress, etc. So it opens a broad range of studies which until now were quite difficult to address because there was no good way to bridge the virum, so the phage sequences, to the microbiome which is the bacterial sequences. And also we did not have the full genomes of this bacteria so it's difficult to look at interplay between metabolic networks in this complex communities. So this is, I don't know how long, what time is it? Five more minutes. So this is just another little observation is that we also have profages inside the bacterial genomes so that don't correspond to this individual, necessarily correspond to these communities of phage sequences but we can actually observe also some phage sequences in the bacterial chromosomes that correspond to profages most likely. And so here in red this is the annotation of the phage sequences and you can see clusters of phage annotation in this bacterial chromosome that actually correspond to this weird pattern in the 3D contact. And so these are typical of phage sequences inside these bacterial genomes, maybe differences in AT composition which affect the outcome of this experiment. And what we showed is that for instance in bacillus subtilis, this is a phage here, SPB beta, which also present this weird pattern. So the AT composition of this sequence is different from the rest of the chromosomes so the restriction is going to be affected and therefore the contact pattern is going to be affected. And what we showed is that if we stress this bacteria, the phage now is actively dividing and is actually very prominent in the contact data. So this is again in controlled experiment of bacillus subtilis and we can actually observe also the increase in sequence coverage of this prophage here. And so that tells us that this prophage is actually actively dividing in this bacteria, is actually liaising probably the bacteria. And when we go back to our unknown data from the world, we can actually observe similar patterns. For instance here we have a prophage that doesn't seem to be activated, the coverage is actually flat compared to the rest of the chromosomes. But here this guy there seems to be actually actively dividing, so maybe this prophage is active, this one is not active. And so we wonder if we may actually guess the activity state of the prophage sequences inside the bacteria again in this single experiment. So that would be also interesting. So this is the experiment we are doing now along with others, either with wild type mouse or with mouse with controlled mixes of species like 13 species or more. And we can for instance treat some of these mice with antibiotics. We can change the composition of the gut by adding for instance a species with an antibiotic resistant gene, etc. And so now the idea is to sample the species of this mouse at different times and track it in 3D, in 4D actually, so in 3D over time, the propagation of genes, the activation of prophages and reach at a comprehensive picture of the dynamics of the population. And so this is where I'm going to stop. This is just a very preliminary data where we observe that the lines here correspond to phage, let's say the 5 phages present in this bacteria, which is one of the 120 bacteria present in the gut in this mice. So this is one of the bacteria on the phage behavior is represented with this line, the 5 phage that present there. And actually one of the phage actually seems to behave very differently from the rest of the DNA fragments and over amplified so that may point out when we treat with antibiotics. The activation of this prophage that may play a role in the lysing of the bacteria or the dynamics of the population. So now we are trying to integrate all of this data. It's quite complex. I mean for us it's complex because we are not used to work on these things, but to try to integrate all of this data together to get at this comprehensive picture. So again, so what I showed you was that this contact frequencies can be used of course to analyze the biology of the chromosome, but we can also develop this alternative approaches that helps us to breed the microbiome with the biome and get this improved understanding of ecosystems, complex ecosystems over time. So these are people who did the work. Most of the experiments were done by postdoc in my lab, Martial, who now got a scenario as position two years ago, excel an engineer in my team and a Liam PhD student as well as a former PhD student for the GRAL program and an engineer as a physicist we work with at UPMC, but he didn't contribute to this work, but a very good collaborator as well. I thank you for your attention.
 So first I want to thank you for this invitation.  The first time I come here I used to live next door.  So it's in Gifte Rivette on Orsay.  So yeah, I'm a local but I never set foot in the institute so I'm very pleased to be  here today.  Thank you for this opportunity.  So today I'm going to talk about mostly what is actually a technique that we have developed  in my team.  My team being mostly interested into the functional folding of genomes, mostly bacterial genomes,  yeast genomes.  But by working on these topics we ended up finding out that we can actually exploit the  quantitative measurement of physical collisions between DNA molecules to solve genomics or  metagenomics limitations that exist in these fields.  So we are not coming from the genomics or metagenomics field but now part of my team  is now working on these approaches by developing new methods.  And so I will show you today how this works.  So something common between all ecosystems in the world, whether it is the gut of a mouse  or the mangrove in the French Caribbean, are the fact that they are colonized by complex  heterogeneous populations of microorganisms, mostly bacteria but also yeast, worms, I mean  a lot of different species.  And these communities of organisms have important roles in many fields.  For instance, the production of oxygen, recycling matters, depollution, bioenergy, etc., so  they can have some industrial importance or just biological relevance.  And therefore it is quite interesting or important for a lot of people to try to decipher the  content of these communities to find out which bacteria is there, what are the genomes in  order to understand the maintenance of the equilibrium of the ecosystem.  I'm going to switch here because there are more people here than there.  So to understand these ecosystems, so a lot of people are interested to describe them  the more precisely possible to the full extent of their complexity.  So you have many species coexisting together.  You can also imagine you have phages, viruses, mobile elements between this that can be exchanged  between some of these species.  So it's quite important to be able to describe the structure to the full extent of its complexity.  And this is where the field of metagenomic emerge at the convergence between genomes  and sequencing, so very technical fields, how do you explore genomes from the structure  down to the sequence, so high throughput sequencing, to the question what are the diversity of  microbes in the wild.  So metagenomics is a field that consists in sequencing DNA from the environment and  trying to find out what species coexist with each other and trying to find out how they  co-evolve or coexist in overtime.  So these studies provide some hints about the genetic content of an ecosystem and also  some hints, therefore, about the balance or the imbalance of the community.  And then you can actually work about many things with these approaches.  The way it is done so far is by extracting DNA from the population, from the ecosystem  you are looking at.  So you basically just extract DNA molecules that belong to different genomes, different  organisms.  And you do genome sequencing on this mix of DNA molecules.  So you sequence a lot of short reads or even longer reads.  So you end up with a big picture of the DNA present in the population.  And to improve things a bit, because you don't know which molecule belongs to which, you  can actually try to extend these short DNA molecules into longer DNA molecules that we  are calling, that are called contigs.  And still that don't represent full genomes because of the limitation of current assembly  programs, because also some of these species share some identical sequences.  So the program just failed to isolate the individual molecules from the original population.  And because it's a very complex problem, you have to imagine some of these communities  contain hundreds of species, therefore hundreds of genomes.  And so it's quite impossible to assemble these DNA sequences into full genomes that provide  you a good insight on the original community.  So what people used to do is try to pull these contigs that are stretch of DNA of a few thousands  of bases into pools of contigs that group these DNA molecules according to, for instance,  covariance in different experiments.  You look at covariance between the amount of these contigs you obtain after sequencing.  So you can pull these guys together.  You can look at the GC content, codon usage, different heterogeneous information present  along these molecules that help you to pull these contigs according to specific features.  It's a very imperfect process, and usually what you end up getting is many more pools  of contigs that you have original sequences in the community.  So here, for instance, you have six, for instance, let's say you have six bacteria.  In the end, you will end up with nine communities of contigs, which is there is a discrepancy  between the number of pools of contigs and the number of genomes you expect in the end.  So there is actually a strong inability in this field to reach at a comprehensive genomic  structure of complex communities, and therefore this limits the investigation of the dynamics  and equilibrium of these ecosystems.  So my team used to work on, I mean, is working on genome folding.  And so what we noticed, and I will show you what I mean by that, is that obviously each  sequence, DNA sequence in 1D has a unique 3D signature in the wild.  So we can actually exploit this 3D signature to reverse, to go back to the 1D structure.  So here is how we do it.  So we use mostly an assay, which is called a chromosome conformation capture assay, which  has been developed by Job Decker 15 years ago, that aims at trapping physical collisions  between DNA segments along a genome, according to their collision frequencies inside a population  of cells.  So how you do that is you freeze the folding of DNA in each cell by adding a fixating chemical  agent such as formaldehyde, that is going to generate covalent bridges between proteins  and proteins and proteins on DNA.  And therefore if you have a DNA molecule inside a cell that is folded like this with  a protein complex here, you will generate covalent bridges between these proteins on  the DNA, and therefore you will freeze the folding of this molecule in this cell.  So if you have billions of cells or millions of cells, you have a population of frozen  structures like this in your mix where you add this chemical agent.  Then the trick was to digest these DNA molecules.  Therefore you end up with restriction fragments, so short pieces of DNA fragments that are again  fixed with each other according to their frequency of collision inside this population.  And then when you add an enzyme called ligase that is going to religate these two restriction  fragments together, you will end up with a molecule that is chimerae with respect to  the original genome, but that reflects the fact that these two fragments, red and black,  were actually close to each other in the 3D space.  Okay?  So then what you do is you purify all the DNA molecules, and you end up with a library  of DNA molecules where restriction fragments, so DNA segments that were close to each other  in 3D.  How big a segment is your restriction?  So it depends on the enzyme you use, but they can be between, let's say, 10 base pairs  up to 2KB, 3KB.  So if you use a frequent cutter, it will be mostly between 20 base pairs and 200 base pairs.  If you use a 6-cutter, an enzyme that recognizes 6 base pairs, it will be between 500 base pairs  on the 3KB, and it depends also on the GC content of the genome, so there are a lot of parameters  there at play, but usually it's between 1KB and 1KB, let's say, on average.  So for a long time, the limitation in this field was to quantify the respective amount  of these events, because if you are able to have a global overview of the respective amount  of all the religation events between all the DNA segments in the original population, then  you would have a global overview of the average folding of the genome.  That was the theory.  And that was actually solved with the advent of high throughput sequencing, like Illumina  sequencing, where basically you just plug sequencing adapters to the edges of these  DNA molecules here, and you sequence one end of the molecule on the other end, and now  you only count how many times you found in the library, for instance, the green fragment  religated with the blue fragment, and actually with all of the other fragments of the genome.  And therefore, you have the respective contact frequencies of religation between all the  restriction fragments of the genome with each other.  And this allows you to generate heat maps, contact maps, that reflect this respective  ratio.  So this is an example for the bacterial Vibrio cholerae, which contains two circular genomes,  some approximately a four megabase genome.  So here are the genomes, the chromosomes represented here under their linear form.  And when you plot the contact between all the DNA fragments along chromosome one with  themselves, you end up with this heat map here.  So that's an intracromosomal contact map.  This is intracromosomal contact map of chromosome two.  Then you can also plot the contact between chromosome one and chromosome two.  And here you have intracromosomal contact map.  So this heat map is quite representative of what you get when you do this on any species  so far, whether it is mammalian cells or bacteria or yeast.  You have first a very strong diagonal that reflect the fact that the DNA molecule is a  polymer.  And therefore, two DNA fragments that are close to each other along this polymer are  going to be related more frequently than to DNA fragments that are far apart from each  other.  Therefore, you have this strong DNA contact that DNA, sorry, this strong contact.  You know, to really get the polymerized DNA, because it won't be close to the father,  you have to coincide.  Sorry?  You have to be able to get the sticky ends must coincide more or less, yeah?  Yes, it's okay.  When the different ones are close to the father, they will be the same.  So the contact frequency is more or less a power low.  And so the further you go along, the further you will increase the distance between two  de-reduction fragments, and it's going to drop very quickly.  And this is actually a log scale.  But the ligation depends on the sequences of the ends, yeah?  Yeah, so you have some biases at this level, a few biases which are kind of characterized,  and they are not strong enough to kind of affect the outcome of this result.  You may have issues if you have very high GC rich genomes.  Actually, the sequencing is going to be affected as well.  Like the Illumina sequencing may be problematic on highly rich GC or so.  So there are biases, but overall, it will not affect the general trend, which is that  two fragments close to each other along this polymer are going to be frequently in contact  and two fragments far apart.  Like here, I'm not going to be frequently in contact.  So here, the color of scale reflect the fact that rare contact are in white, frequent contact  are in red.  But it also depends on the stage and the cell cycle, yeah, how they're positioned.  Yes, so that's exactly, yeah.  So this will modify, for instance, the width of this diagonal.  If you go into metaphase, you will increase the width.  If you are doing replication, it will look like this.  So indeed, there are some functional events that you can actually identify in this contact  map.  And this is something we are working on in different species.  For instance here, so the chromosomes are circular, meaning this position here is adjacent  to that one.  That's why you have this contact in the edges of the maps.  And also, also related to functional events, you can see here, for instance, you have contact,  it's very weak, but you have contact between the middle of this chromosome two and the  middle of chromosome one.  And this corresponds to the termination of replication position of these two chromosomes.  And you can actually ask why do these chromosomes see each other in space.  You also have one specific contact here between the origin of chromosome two and the position  of chromosome one.  And that relates to an original control of the firing of replication of un-chromosome  two by the progression of replication fork around a long chromosome one, which seems  actually mechanically related.  So this is something we are working on in collaboration with DJ Mazel at the start.  But today what I want to insist on is that in all genomes you have this very strong diagonal  that reflect the fact that these two chromosomes are polymers and therefore in the space, in  the cellular space behave as relatively individualized entities.  So the question we asked at some point when we were working on this was, okay, so what  happens if we do this same experiment on a mix of species?  Will DNA fragment from genome one is going to be in contact by accident, by experimental  procedure biases, be related frequently with the genome of fragment of genome two or genome  three?  Or is this relagation events are going to be sufficiently low?  No, because the restriction on time it cuts the same site, so you have a cohesive site.  So if you use a cohesive end it will relagate very efficiently.  If you use a blunt cut it's not so efficient.  So the question we asked was whether or not these restriction fragments are going to be  relagated often with each other from the different genomes and therefore blur the signal.  So what we did was to do a preliminary experiment where we took three different species, again  viviocollerae, H.C.  and basilis subtilis.  We grew them independently and we mixed them together, the cells together and then we performed  this genome 3C experiment directly onto the mix of species.  Until then we take the reads from the parent reads and we align the reads against the reference  genomes of these three species.  And then we look at the contact pattern between these genomes and within these genomes.  And when we did that we were quite pleased to see that actually there is very little  background between the different species.  So that means that it's very rare, I mean it's relatively rare that one fragment from  species one is going to be relagated by accident with a fragment of species two.  And the relagation may occur just because when you lyse the cell and you just process  the cell biochemically you may just disrupt the genome and have a fragment floating in  the solution that will just relagate spontaneously with each other.  So this doesn't happen very often.  And therefore you have well individualized squares in the main diagonal that correspond  first to the genome of basilis subtilis.  Then you have this square here that is the genome of E.coli.  You can actually see that there is also a tiny square there that correspond to a plasmid  that sees a lot the genome of E.coli.  And so that tells you that this plasmid probably shares the same cellular space as the genome  of E.coli.  And here you have again the two chromosomes of vibriocholary that also see each other  in 3D very often and therefore you can actually pull them together inside the same cellular  space.  So we can be convolved in a sense the genomes of these three different species from this  heat map.  Why is E.coli different qualitatively than the other?  So the coverage may be at play here and it was a very preliminary experiment.  So there may be some differences in the amount of cells we put even though we thought we  put the similar amount of cells maybe we just did some little mistakes.  And also basilis is a gram plus bacteria.  These are gram minus bacteria and it seems to be slightly easier to extract DNA from  gram minus bacteria than gram plus.  So therefore the coverage of basilis is lower in this experiment than these two guys here.  So you're attributing this spread of the...  The thin line here.  So is the diagonal is much more...  The diagonal is also a matter of color scale here.  It's the same color scale for the entire map and therefore you kind of blur.  And it was again a very early experiment.  So basilis doesn't look like this.  We did work on that.  I will show you maps.  It's much nicer than this one.  Okay.  So let's keep that.  So what does the thickness of the diagonal mean?  So it means that it can be interpreted somehow as a condensation level.  It's not totally true but you can actually visualize it as a kind of measure of condensation  of the chromosome.  So if condensation is high to DNA fragment that are far apart are going to be more frequently  connected with each other than if it's very decondensed.  That's one way to see it.  So what we did then was to do the same kind of experiment with 11 species.  Therefore 11 genomes and these are yeast genomes.  So each genome now is composed of multiple chromosomes.  So we pulled all these species together inside a mix and we did the metagenomic assay that  I know sorry the metatricy assay as we call it directly on the mix.  So when we aligned the reads again against the reference genomes of these species we  were pleased to observe that there are indeed 11 big squares in the main diagonal that correspond  to the genomes of these species.  Some of them are not as well covered as others.  Again you can see differences but this is not very significant.  But again here we aligned the genomes against we aligned the reads against the reference  genomes so we kind of cheat.  We know the answer.  So what we wanted to do was to do a design an assay that would allow us to start.  Excuse me but for these you don't have any common sequences or any common placemates  or any kind of...  No here there are no placemates.  We don't have placemates.  But each of these big squares is composed of smaller squares that correspond to the individual  chromosomes present in each species.  On the dot also just so you know in yeast...  You heard it yesterday so you probably talk about that maybe but centromeres are collocalized  in the nucleus and therefore the DNA next to the centromere is colliding with the other  DNA quite often.  Or you can see clusters of centromeres which are these dots in the contact maps.  In the case of cerevisiae you would have the tumor complacemate.  Yes but we don't align it.  So there may be a placemate there but it depends on the strain I guess.  For this strain certainly.  Is there cerevisiae?  Yes.  Can you count the number of chromosomes and it will be...  Yes so I will show you what we do now which is actually exactly that.  So what we wanted to do was because this is kind of nice but it's kind of useless if you  want to explore something very from the wild where you don't know the content in genomes.  You cannot align the reads against reference genomes if you don't have the reference genomes.  So what we wanted to do is to design a protocol, a computational protocol where we would start  from the reads.  So only the parent reads and we would like to see whether we can go down to the genome  of all of these species.  So we start here only with the raw sequences which are the parent reads that reflect the  contact frequencies between all DNA in the population.  So what we can do is we can first try to increase the size of this small DNA reads so we can  use standard assemblers like DBAUD that are going to increase the size of the chunks of  DNA we have.  Therefore we have a set of contigs that we can work on.  The other information we have besides the DNA sequence in this set of reads are the  parent information which correspond to the contact between the DNA molecules from the  design of the 3C experiment.  So now what we can do is we can also align the contact information on this set of contigs.  And that gives us a network of contact between this set of contigs.  So we have this network which is pretty big and so a nice way to analyze network is to  use the Louvain algorithm which is going to partition the network to optimize the modularity  of the network partitioning and look for communities of contigs that see each other  very much inside this big network and don't see the rest of the other communities very  often.  And so we work with this Louvain algorithm, we design a protocol that allows us to actually  segregate these pools of contigs into 11 communities of contigs.  Just by using the standard algorithm we ended up getting 11 communities which is actually  the number of yeast species that we originally put into the mix.  We also had a contaminant like a Nicolai genome here, 1%, which is probably coming from one  of the cultures of yeast we did at the same time.  So that's okay.  So most of these contigs just see each other within the same community and they don't see  the contigs of the other communities very much.  Can you please repeat the nature of the ages in your interaction network?  The ages are the contigs and the number of contacts between the contigs are going to  be the nodes.  So the age, you put an age between this and another contig in case of what?  But we know that the number of contacts is going to reflect the collocalization in the  same cell.  So we just segregate.  And so that works relatively well but here we have pools of contigs and if we take one  of these communities, we look at the contact between these contigs.  This is what we get.  So here we have 1000 contigs.  And of course we don't sort them according to their position along the genome because  we don't have the reference genome.  So when we just align the contigs, the reads against the contigs and we generate the contact  map, this is what we get.  It's a very messy contact map compared to what I showed you before.  And so here I will introduce another algorithm we designed at the same time that allows us  to reorder these contigs accordingly to their contact frequencies and therefore try to resort  the, to rescap the genomes of these species.  So this is a program that we call the GRAL that was designed by a disfiant in my group  and that, this is like an interlude, that aims at scaffolding contigs based on their  contact in 3D.  So the situation we have here is the same as if we had the contact frequency between  a non-fully assembled genome.  Most genomes are not fully assembled.  And therefore when you align the reads of a contact experiment against the reference  genomes, you have this messy contact map where, because you don't know here that the green  contig is adjacent to the green one here, you will not position these two guys next  to each other along the axis.  And therefore you will have this strong intercontic contact here in the contact map that is actually  incongruent with what we know about polymer physics of the polymer property of the DNA.  So on time for the red chromosome here which is split into three contigs here, there and  there, you will have this incongruent contact signal at far, I mean long distance in the  contact map.  So what you want to do is to solve this puzzle.  So here it's quite simple.  You can solve it by hand.  So for instance here you can take the green contig there.  You look at the contact made by this position and you see it makes a lot of contact, the  white position here with this guy.  So you will position this edge next to that one.  And now you have a longer scaffold and it looks good on the map.  But you may have long distance interactions.  Yes, so you may have long distance interactions which are maybe functional, for instance like  the centromeres in yeast.  But this long distance functional contact are always much smaller than the very strong  polymer behavior of the chromatin.  So this diagonal contacts are always two fold, two log higher than the contact you see at  longer distances.  And then what you want to do for instance with the blue guy, you take this contig here  and you see it sees a lot this edge there.  And therefore you will have a red guy.  Anyway, you get the idea.  You just resort the contigs until you get only squares in the diagonal.  And you minimize the signal outside the main diagonal.  So this is easy to do by hand, but it's actually more tricky to do with real data and with  a very big complex genomes.  So what we did was to design a program that used this contact, which are very quantitative  contacts on polymer physics prediction to explore the space of genome structures and  identify the genome structure in 1D that reflects the 3D data with the highest likelihood.  So that's what they did.  So again, this is for instance the power law and we noticed so if you know the contact  frequencies between two DNA fragments and you have the contact decrease according to  the genomic distance along the genome, for a genome, if you know the contact frequency,  you can actually guess with a very high accuracy the distance that separates the two loci you  are looking at.  So here for instance you have a contact around 100 and then you can say, OK, so these two  guys are, no sorry, contact would be like 50 and then you say, OK, if I have a contact  of 50 between two segments, it means they are separated with a certain likelihood by  100 KB in 1D.  So Hervé designed this program which starts only with the 3D data and is initialized with  a polymer model because at first we don't have contigs big enough to compute this power  law, this distribution, which changes depending on genomes slightly.  So at first we start with a polymer model on the data on the original genome.  So the set of contigues that is not fully scaffolded or that has been published or that  we have generated.  And then it's going to screen through the genome structures in 1D trying to improve  the likelihood of the structure by fitting the 3D data on it and computing a new measurement  of the fit between the data and the 1D genome structure.  So we are going to sample the space of genome structure in 1D and compute for each time  the new likelihood.  So this is the likelihood on the wide axis.  This is again here and this is the increase of likelihood according to the iteration of  events.  So we basically make small changes in the 1D structure and for each small change we compute  the likelihood and then we take the best likelihood structure, I mean the most likely structure  and then we converge towards the situation where we are actually oscillating around one  D genome structure and this is just a movie that illustrates this process.  So this is a genome that is actually that was published as a set of 76 contigs.  So it's scaffolded.  So 76 big pieces of DNA.  And so fungi and we know that this species doesn't have 76 chromosomes.  So there are more chromosomes and also more contigs published than number of chromosomes.  And when you do the 3D data you can see clearly that indeed something is wrong with this contact  map, it doesn't look right.  And here we have illustrated the 76 contigs that were published.  So what we want to do is to reorder this DNA fragment according to the 3D contact and converge  towards the most likely 1D structure.  So the first part of the program is actually splitting these big contigs into smaller pieces  because we actually assume that there are some assembly errors in the first place that were  published.  And so therefore we cut this big DNA fragment into pieces of 10 kb and we just start to  reassort them according to their collision frequencies.  So the first part of the movie is just doing that and randomizing it for…  So we just split this DNA fragment into tiny pieces and then here's the likelihood and  that's the number of contigs.  And we start to explore 1D structures and improve it in likelihood.  And in the end we are starting to converge towards a state where we have 11 big chromosomes,  I mean big chunks of DNA.  And we have a contact map now that accordingly represents something that looks like yeast  or fungi genome.  So here when we stop the program, sorry not 11 but 7 chromosomes, yes, right, it didn't  go down to the end.  Okay, it stopped.  Yeah, it stopped, one of them should be, but it doesn't matter.  Well, anyway.  So when we stop the program, which is oscillating forever around the same structure, we actually  get this genome structure, 7 chromosomes.  We have contact that looks very much like inter-centromeric contact, which is totally  expected for a fungi at this stage and like this dot here, we can actually identify the  centromeres by looking at the position of these dots along the sequence, etc.  And so we can actually scaffold this genome pretty efficiently and this is actually the  real genome now, we checked it independently.  And we can do that actually for other species and so this is just an example of unpublished  data.  It doesn't just work on big, I mean on small genomes like this fungi, but this is the  parasitoid wasp.  This is a collaboration with Jean-Michel Dresden in Angers, where when we do the 3D contact  between the chromosomes of these wasps, we have this very messy contact map.  This is a published genome made out of thousands of contigs.  You can see them here.  Each of the contigs is making a square, but then you have all these inter-chromosomal  contact contacts that we want to solve.  And the program is also able to do that pretty efficiently, so I'm just going to go quickly.  But again, we are just resorting the chunks of DNA according to their collision frequencies  and this gives us a relatively robust scaffold in the end.  So it takes a bit longer.  Here, so when we do that, we end up getting relatively nice scaffolds that actually correspond  to what is expected from cytology data.  So we were able, by exploiting the collision frequencies between these DNA fragments, to  rescapable these genomes in a way that matches the expected cytological data.  So now our collaborators can look at their favorite genome sequences, such as viral sequences  that contribute to the parasitoid process of the wasp.  The wasp disrupts the host immune system to, I don't know if you say pond, but to send  the eggs inside the caterpillar it is infecting.  So these viral sequences are important for the reproduction of the wasp.  Anyway, so going back to this metagenomic assay, we did this program, we run this program  on this 1000 contigs and to get it short, we end up to get something that looks like  a yeast-genome contact map.  And when we compare this contact map with the reference genome, we have 95% of the reference  genome covered by this heat map.  So in one single experiment, we were able to actually isolate 11 genomes from this mix  of species and rescapable them with approximately 90% accuracy compared to what was published.  So that's what was quite promising.  And here I'm going to show you the real experiment.  This is not a control mix.  It is done on the gut of a mouse from the Pasteur Institute animal facility.  It's not a very exciting sample because, again, we were not microbiologists at this  time, but so we took this one single species of this mice and we performed the metatrissi  assay, the contact assay directly onto this single species.  So we were able therefore to assemble this DNA reads into contigs.  And here it's a very big network.  We have 400,000 contigs.  It represents approximately 600 megabays of DNA.  The N50 is around 4KB.  So the contigs are relatively small.  Again we can, so when you look at the contact map of these contigs, well, okay.  Again we can use the parent information to bridge these contigs according to their contact  frequencies.  And we have this network of contigs that contain 45 million contacts.  So it's a very complex network.  And this is how it looks like when it's not sorted.  What we did again was to partition this network using a Louvain clustering algorithm.  And when we saw this network using Louvain, we were able to get nice communities of contigs  that present more contacts inside each other compared to outside with each other.  So each of these squares correspond to a set of DNA sequences that collide more frequently  in space compared to the other ones.  Okay, I'm going to skip that.  Okay.  So what we did next was to investigate the content of these communities.  And first what we can do is to align the genome annotation against the DNA present in all  of these communities.  So the communities are sorted by size.  So of course we have small communities of a few contigs.  Start from one up to one of the contigs.  But most of the DNA is being actually pulled into the large communities of contigs that  contain above 1,000 contigs.  So they are like more than one megabase of DNA.  And so of course since most of the DNA is present in the large communities, most of the  gene annotation is going to be present to correspond to the large communities.  So therefore this is what you see here.  Most of the genes are actually present in the biggest communities.  This is not very interesting.  If we look at bacterial essential genes, again, most of the bacterial genes, essential genes  are present inside these large communities, suggesting that these large communities correspond  to bacterial DNA, which is again not totally unexpected.  The interesting thing was when we look at phage-related DNA or plasmid DNA or conjugative  DNA, we actually see an enrichment as well inside the smaller communities.  As if, of course, the phages are in contact with the bacterial genomes, the bacterial genes,  because some of the phages are integrated in the bacterial genomes like prophages or  some phages are maybe infecting the bacterial genomes.  But also some phages behave as individualized entities that in space are being actually trapped  on themselves or not with another molecule.  So that was quite interesting.  So we decided to analyze these two sets of communities independently.  First the large ones and then the smaller ones.  And so what we observe, for instance, if we take one of these large communities, so this  is a 68.  Again we have this messy contact pattern.  We can improve this pattern by working with the program we designed, Graal.  And in the end we end up with a scaffold of approximately four megabays of DNA, a single  scaffold that presents this kind of pattern.  So you have here the large diagonal.  And interestingly you have also a secondary diagonal.  And I will show you why this is interesting.  It's because at the same time we are working on basillus subtilis on this kind of 3D folding  of bacterial genome.  It's typical of bacteria like basillus subtilis.  So first this origin of replication is positioned at the crossing between this secondary diagonal  and the main diagonal.  You have more DNA around the origin of replication compared to the termination of replication  which corresponds to a bacteria that is actively dividing.  So you have more DNA at the or that at the termination of replication.  And the secondary diagonal reflects the fact that the two chromosome arms, even though  they are bridged there, they are actually colliding with each other more frequently because  they are being bridged by cohesines as we actually showed in basillus subtilis.  So when you start replication you bridge the two chromosome arms and they are actually  maintained in close contact with each other during the cell cycle.  And this is what is reflected by this secondary diagonal.  So this is actually typical of the 3D structure of bacterial genomes like subtilis.  So these are another community, another one and so on.  This is more like the E. coli bacterial chromosome where we don't have the secondary diagonal.  So this is like basillus, this is like E. coli.  But we get DNA structures that all look like bacterial scaffold genomes, 3D structures.  So in one single experiment we are able to recover dozens of bacterial scaffolds which  is actually, I mean until now it was not, you cannot do that in this field.  You need to have multiple samples and you need to do a lot of correlation analysis.  But here in one single experiment we end up getting 100 of bacterial genomes, relatively  complete.  And at the same time what we did was to analyze the content of these small communities which  were the phages, I mean that looked like phage.  So here we took 72 small communities that contain mostly gene that are phage genes.  And we reprocessed the data a little bit.  And we look at the sequences we obtain for these 82 communities.  And so these are different examples of these 82 communities.  So one of them for instance is a 235KB genome that looks like a FKZ genome, it's a phage.  These guys here correspond to two molecules of 25KB which are also good size for a phage  genome.  This is 50KB like a Codovirales genome, etc.  So we have genomes of phages in this community of species that we can now actually try to  relate with the potential host which are the bacterial genomes.  So we have in one hand bacterial genomes and in the other hand we have the phage genomes.  And so now what we can do is to plot the contact in 3D between the phage and the bacterial  genomes.  And this is what we do here, we have the 82 phage sequences and the 100 something bacterial  sequences.  And this is what we get now, we have a good overview of the infection spectrum, global  of infection spectrum in the community.  So this is quite important because now what we can do is to do this kind of experiment  over time and look at the propagation for instance of a virus in different bacterial  species or closely related bacterial.  We can also look at genes of interest, for instance an antibiotic resistant gene which  is present in one of the species and then if we treat one of the community with, we  give antibiotics to a patient, then we can look at the propagation of this phage, sorry,  of this gene, antibiotic resistant gene inside the population of bacterial species and see  if at some point it will induce some dramatic stress in the population of bacteria.  We can look at the activation of profages depending on stress, etc.  So it opens a broad range of studies which until now were quite difficult to address  because there was no good way to bridge the virum, so the phage sequences, to the microbiome  which is the bacterial sequences.  And also we did not have the full genomes of this bacteria so it's difficult to look  at interplay between metabolic networks in this complex communities.  So this is, I don't know how long, what time is it?  Five more minutes.  So this is just another little observation is that we also have profages inside the bacterial  genomes so that don't correspond to this individual, necessarily correspond to these communities  of phage sequences but we can actually observe also some phage sequences in the bacterial  chromosomes that correspond to profages most likely.  And so here in red this is the annotation of the phage sequences and you can see clusters  of phage annotation in this bacterial chromosome that actually correspond to this weird pattern  in the 3D contact.  And so these are typical of phage sequences inside these bacterial genomes, maybe differences  in AT composition which affect the outcome of this experiment.  And what we showed is that for instance in bacillus subtilis, this is a phage here, SPB  beta, which also present this weird pattern.  So the AT composition of this sequence is different from the rest of the chromosomes  so the restriction is going to be affected and therefore the contact pattern is going  to be affected.  And what we showed is that if we stress this bacteria, the phage now is actively dividing  and is actually very prominent in the contact data.  So this is again in controlled experiment of bacillus subtilis and we can actually observe  also the increase in sequence coverage of this prophage here.  And so that tells us that this prophage is actually actively dividing in this bacteria,  is actually liaising probably the bacteria.  And when we go back to our unknown data from the world, we can actually observe similar  patterns.  For instance here we have a prophage that doesn't seem to be activated, the coverage  is actually flat compared to the rest of the chromosomes.  But here this guy there seems to be actually actively dividing, so maybe this prophage  is active, this one is not active.  And so we wonder if we may actually guess the activity state of the prophage sequences  inside the bacteria again in this single experiment.  So that would be also interesting.  So this is the experiment we are doing now along with others, either with wild type mouse  or with mouse with controlled mixes of species like 13 species or more.  And we can for instance treat some of these mice with antibiotics.  We can change the composition of the gut by adding for instance a species with an antibiotic  resistant gene, etc.  And so now the idea is to sample the species of this mouse at different times and track  it in 3D, in 4D actually, so in 3D over time, the propagation of genes, the activation of  prophages and reach at a comprehensive picture of the dynamics of the population.  And so this is where I'm going to stop.  This is just a very preliminary data where we observe that the lines here correspond to  phage, let's say the 5 phages present in this bacteria, which is one of the 120 bacteria  present in the gut in this mice.  So this is one of the bacteria on the phage behavior is represented with this line, the  5 phage that present there.  And actually one of the phage actually seems to behave very differently from the rest of  the DNA fragments and over amplified so that may point out when we treat with antibiotics.  The activation of this prophage that may play a role in the lysing of the bacteria or the  dynamics of the population.  So now we are trying to integrate all of this data.  It's quite complex.  I mean for us it's complex because we are not used to work on these things, but to try  to integrate all of this data together to get at this comprehensive picture.  So again, so what I showed you was that this contact frequencies can be used of course to  analyze the biology of the chromosome, but we can also develop this alternative approaches  that helps us to breed the microbiome with the biome and get this improved understanding  of ecosystems, complex ecosystems over time.  So these are people who did the work.  Most of the experiments were done by postdoc in my lab, Martial, who now got a scenario  as position two years ago, excel an engineer in my team and a Liam PhD student as well  as a former PhD student for the GRAL program and an engineer as a physicist we work with  at UPMC, but he didn't contribute to this work, but a very good collaborator as well.  I thank you for your attention.
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