 Hi, everyone. Welcome to the Elife Evolutionary Medicine Symposium. Thank you so much for joining us. We have four speakers today, and these speakers are going to be presenting their work, which are papers that are included in a special issue that is out in Elife, and I encourage you to check out and to keep checking, keep coming back to check out further, because there are 37 published articles so far, but more in the works that will continue to be published. I'm George Perry, a senior editor at Elife and associate professor at Penn State University in the United States, and we developed this special issue with the excited understanding of the importance of using evolutionary theory to advance our understandings of modern human health. And there's a number of different areas of research in which this is done, for example, studying antibiotic resistance or considering the evolutionary history of humans in terms of how it affects modern human health and even dynamics of cancer progression within the lifetime of an individual. So again, please check out the special issue. We're here today to celebrate some emerging scholars who led some amazing papers as part of that special issue, and I hope that you enjoy the talks. So our speakers today are Stephanie Yan from Johns Hopkins University, Geronimo Rodriguez Beltran from Ramoni Kahal University Hospital, Josh Deeth from Imperial College London, and Juan Manuel Vasquez from the University of California Berkeley. Hello, everyone. My name is Maria, and I'm part of the Elife executive staff team. I would like to welcome you all to today's session. So on behalf of Elife, I'm very excited to hear today's presentation and to see so many of you here joining us today. Before we start, I would just like to go through some participation guidelines. So we want everyone to enjoy today's meeting. So to ensure that that happens, we will ask all participants to abide by Elife's code of conduct. So some examples of behavior that contribute positively to our communities, including showing empathy and kindness towards others, as well as being respectful of any different opinions, viewpoints and experiences that we may hear today. And acceptable behaviors will include making it difficult for others to speak or participate, for example, through repeated interruptions or disruptions. At the end of each talk, there will be roughly five minutes for Q&A. So you can submit your questions through the chat function at any point, and we'll do our best to address all the questions within the allocated time. We'd appreciate it if you're asking any questions to also indicate your name and affiliation after your question and indicate the dispute you're asking the question to. For your reference, this session is being recorded and will be available to view after the event. A live transcription is also available by the autor.ai app. Throughout the session, if you experience any technical difficulties, please message my colleague, Ania Starrs of the Lifestyle Team. Without further ado, I'll end it over to George Schrinter, who's also speaking. Absolutely. Thank you, Maria. And thank you, Maria and Ania, for all the work that you have done to put this symposium together. And thank you again to our speakers for presenting their work here. And our first speaker today is Stephanie Yen from Johns Hopkins University, and her title is Local Adaptation and Archaic Interagression at Human Structural Variant Loci. All right. I'll get started. Okay. Hello, everyone. I'm Steph. I'm a PhD student at Johns Hopkins. I'm in the lab of Rajiv McCoy, and today I'm excited to tell you about this project. So studying patterns of genetic variation is a useful way for us to get insights into human evolutionary history. And so from this research, we know that anatomically modern humans originated in Africa. And then from Africa, they migrated through the world where they encountered new environments, and then they experienced selection for variants that were adapted to these environments. So one really common form of this local adaptation phenomenon in human populations is selections on alleles that are related to immunity. So one really famous example is positive selection on sickle cell alleles that confirm malaria resistance in humans. And so another key element of human evolutionary history is that during these migrations of human groups out of Africa, they encounter groups of archaic hominins like the Neanderthals and Denisobens, and they interbred with these archaic hominin populations. So this admixture event left all of the descendants of these migrating human populations with a small amount, about two to four percent per person of intergressed archaic DNA. So most of the research on these events in human evolution has focused just on one form of genetic variation, which is single nucleotide variants or SNPs. And people tend to focus on SNPs because these are the easiest kinds of variants to discover with short read sequencing. But it also means that we're overlooking the impacts of all other forms of genetic variation. So in this talk, I'll be focusing on structural variants or SVs, which are any larger insertions, deletions, or inversions that are also widespread throughout the genome. So the reason why SVs are usually not as studies is because they're hard to find with short read sequencing. So short reads are usually shorter than the length of an actual structural variant. And so short reads are relying almost completely on abnormalities and alignment to detect these kinds of variants. So for example, short reads tend to be especially bad at discovering insertions, where most of the insertion sequence doesn't actually align to the reference. So you just don't see most of these insertion reads. And then they're also, they also find it difficult to look for variants in repetitive regions of the genome that are generally more difficult to align to accurately. So because of these limitations, we've only been able to comprehensively study structural variants using long read sequencing methods. So here the reads are actually long enough that you can sequence the full length of most of these SVs. But the main drawback of long reads is that they are much lower throughput and a lot more costly than short read sequencing is. And this means that we still don't really have data about structural variants and population scales. And as a result, the role of these variants in human evolution is still not well understood. So in our study, we overcame this challenge by looking by using a hybrid approach where we discover structural variants using long read sequencing. And then we genotype these variants in short read sequence datasets. And so we used a method for this called graph genotyping that was implemented in this tool called paragraph that was developed by Lumina. And how this works is that instead of aligning your sequencing reads to a linear reference, like you normally would do, you construct these local graph representations of known structural variant loci. So at these sites, the graph has one path that had the SV in it and then one path without the SV. And you can align your reads to the graph along the path the best fit. And unless you get accurate genotypes for your sample at all of these SV loci. So you can scale this up, run it on thousands of samples, and you can genotype them for all of these SVs that you've discovered with long reads. So in our study, we started with 15 long read sequence samples from this paper by Evan Eichler's lab. And we used the samples to discover about 100,000 structural variants in these 15 individuals. And then we used graph genotyping to genotype these variants in 2,500 individuals from the 1,000 genomes project. So this is a really large, diverse human data set that includes 26 populations from around the world. And so we did some quality filtering, and this left us with about 92,000 SVs that we had genotyped in these individuals. So we then used the set of genotype structural variants to look for variants that were under selection in populations. So to do this, we used the method called Ohana. This was developed by Rasmus Nielsen's lab. And the first step of this method involves modeling ancestry in your data set by representing all of the individuals that you have as combinations of some number of ancestry components. So the actual number of components that you choose is a little bit arbitrary, but here we chose eight components total, which lets us look at more continental scale patterns. And so here I'm showing these admixture results for all of the individuals in the data set. So you can see that this is nicely separating out individuals into the five continents that are represented in the data. And it's also showing us known instances of admixture like these two African American populations in the upper left, which have both African ancestry in purple and also European ancestry in these pink and green lines. So this first ancestry step allows this method to work effectively with admixt samples like these African American populations. And it also means that we're not relying on population labels that were manually assigned to individuals when maybe their actual ancestry is a little bit more complicated than just a label. So the second step of this method is that Ohana looks for SVs that were under selection by finding variants that have extreme allele frequencies in one ancestry component. So this pattern should occur with locally adaptive variants because if something is undergoing selection in just one or two populations, they're going to have a higher allele frequency in that population specifically. And so I'm representing this process visually with this tree here, where this one in particular is showing a genome wide average distribution of allele frequencies, which we are assuming to be a null distribution where there's no selection that's occurring. And then there's a hypothetical model where a variant is under selection in just one ancestry component. So here I'm showing ancestry component three where it has this longer branch here. So you can compare these models, this genome wide null and this selection model to the variants actual allele frequencies that you observe. And this lets you get a selection likelihood statistic. So we use this approach and we found 220 unique SVs in our dataset that exhibit the significant deviation from genome wide patterns of allele frequency. So today I'll be talking about just one of these variants. So on this plot, I am showing the selection results for the ancestry component that corresponds to the Chinese dye and the Vietnamese populations of the status that we used. So the x-axis is the log scaled length of all the structural variants that we're looking at. The y-axis is this likelihood statistic that I just talked about. And so the strongest signal of selection in this population that we found is this short insertion in an intron of gene called IgHG4. So this gene codes for the constant domain of an immunoglobulin protein. So this was really exciting for us. We know that immune-related genes are common targets of local adaptation of humans. So this is a really cool result that we ended up finding. So these immunoglobulins, they immediate the adaptive immune response. And they have a variable domain, which is in gray here. So in B cells, this variable domain undergoes somatic recombination and hypermutation to generate a diversity of epitopes that can recognize antigens. And then these immunoglobulins also have a constant domain, which is in blue and this domain determines what type of immunoglobulin it is. So this insertion SP that we identified is in one of the genes that codes for this constant domain. And another thing that's interesting is that this region of the genome that these immunoglobulin constant domain genes are in is at the very end of chromosome 14. And it's a pretty repetitive region. So we did a lot of kind of internal validations to sanity check or genotyping. And I won't describe them in a lot of detail, but this is a really hard region to study just with short reads. And we think that it's a case where graph genotyping or similar approaches can really make a big difference in these kinds of situations. Okay. So this IGHG4 insertion SP shows dramatic differences in allele frequency between populations. So you can see this from this map where I'm showing these pie charts of allele frequency where the insertion SP is in purple. And then the alternative no insertion allele is in yellow. And so the insertion is almost fixed in the Chinese population. It's a really high frequency in the Vietnamese population. The other Asian populations have it at an intermediate frequency, and then it's almost absent in populations outside of Asia. So we then started looking through the literature to look for other examples of research on these immunoglobulin constant domain genes. And we found this paper by Sharon Browning that reported a haplotype in this region of the genome that was introgressed from Neanderthals into humans. And so then we wanted to check whether the adaptive haplotype that we had identified in our study was this same Neanderthal introgress sequence. So what we did is that we looked for allele matching between Neanderthals and humans. So on this plot, the variants are ordered by position on the y-axis. And then on the x-axis are individuals from a bunch of different modern human populations. And then we also have three Neanderthals and one Denisovan. And so the sequences for every individual are these vertical lines. And they're colored white at sites where they carry the reference allele and then black at sites where they carry the alternative allele. So from this, you can see that all three of the Neanderthals, but interestingly not the Denisovan, carry this haplotype of almost all alternative alleles in black. And the haplotype is also shared by the majority of individuals in this Chinese side population and also the Vietnamese population. It's at lower frequency in the rest of Asia. And then it's almost absent outside of Asia. So this is matching the pattern that we see for the insertion SV that we found. And this really high level of allele matching between the Neanderthal sequence and the human adaptive haplotype is confirming that the sequence was actually Neanderthal introgressed. So the last thing that we did was that we used some simulations to further investigate the selection event on this immunoglobulin insertion SV. So we used a five population demographic model that was based on these two papers over here to try to infer the time that selection began and also the selection coefficient. So with these simulations, we inferred really strong and recent selection on this SV, where the selection coefficient that we inferred is actually on a comparable magnitude, selection on traits like the sickle cell allele that confers merely our resistance. And then we also infer that the onset of selection occurred within this gray range over here, which is right after this die and Vietnamese population kind of split off from the rest of the populations in Asia that are in the simulation. And so we aren't actually including the Neanderthal introgression event in the simulation. This is both for simplicity and also because the introgression would have occurred like around here. So that's before the simulation begins and it would have mostly served to to just introduce this SV allele into the human populations. But it's interesting to note that based on the selection timing that we've inferred from the data, it suggests that this adaptive haplotype was introgressed into humans from Neanderthals like many, many years ago during these out of Africa migrations. And then the haplotype stuck around at some low frequency for tens of thousands of years. But then when these two populations in Southeast Asia migrated into a region where this haplotype was now advantageous and rapidly swept almost to fixations. And so possibly Neanderthals that were living in Eurasia had already undergone some forms of adaptation to these, to this trait. And then they contributed these adaptive alleles back into the human populations during atmosphere. So in conclusion, we use graph based methods, the genotype structural variants of population scales. And we use these methods to identify and adaptively introgressed Neanderthal haplotype in the immunoglobulin locus. And this is really interesting because it kind of suggests that there are multiple levels of selection that are acting to increase diversity on these immunoglobulin genes. So they have this variable that means that is undergoing mutation recombination semantically. And then also a constant domain that we've shown undergo selection to maintain diversity, not only between different human populations, but also between different hominin groups. And so together this is demonstrating that structural variants are unexplored potential contributors to human local adaptation. And that we can hopefully begin to unlock the evolutionary impacts of these variants now with long read sequencing. So in conclusion, I'd like to thank my lab, especially Rajeve, my advisor, all the people here in bold who've contributed directly to this work, and then our collaborators. And thank you all for listening. And I'm happy to answer any questions. Thank you so much, Steph. And yes, if anybody has questions, please enter them in the chat. While people are potentially typing their questions, I'll get us started Steph. So are there any theories from that cell paper or from your further work in terms of the functional significance of the variant that's been under selection? Yeah, that's a great question. It's something that we're really interested in as well. We've tried to answer this in a lot of ways, and we haven't come to any real satisfying conclusions, which is the short answer. But we've looked at things like Juvatis has a bunch of RNA sequencing data for some 1000 genomes individuals. So you try to look for expression associations. But we're running into this issue where there is not a ton of phenotype data for East Asian populations. A lot of this RNA seek data phenotype data is for mostly Europeans. And so we just don't really have the sample size to be able to like satisfactorily try to get a phenotype. But if anyone has like any data sets that they recommend for this or are working on, we are super interested in exploring this further. But yeah, it's a great question. No real concrete answers yet, though we were really curious about it as well. Thank you. So Stephanie, a question came in. Two questions. One is, why do we see these geographically distinct genetic structural variants? And can this be because of the genomic manifestations of environmental selection? Yeah, so I think these structural variants are, I'll pull up the allele frequency chart again. So I think these structural variants, they are all on like haplotypes of variants that are, you know, undergoing like both neutral drift and then selection in some populations. And so I think in this case, like we through simulations and other like other forms of data, we've concluded that this is like almost definitively a selection of it and not just neutral drift. We also have in our model, our selection scan like method, because it is look is also incorporating like the neutral allele frequency distributions that you just see genome wide. This is kind of like inherently accounting for allele frequency differences that just result from drift between these populations, because you're comparing it to like all the variants where you don't expect selection on all of them and you're comparing just like one variance distribution. And so hopefully that answers the question. And another question is, so what are your next steps with this work? And I guess, I guess we can ask both on like this specific locus that you're highlighting, but then the broader, you know, worldwide findings. Yeah, so I think for this specific locus, I didn't have time to go into it in a lot of detail here, but it actually has a really interesting pattern of allele frequencies between populations. And so I'll show this plot that I have my extra slides. And so the adaptive haplotype that I was talking about most of the time is this like kind of yellow region in these like East Southeast Asian populations up here. And it's, it looks really complicated here because there's like a bunch of different kind of forms of this haplotype and some of them are kind of reaching higher allele frequencies in some of the European populations as well. And so it's really interesting. We also see a lot of recombination in this region, like at least three recombination events that have kind of split up this haplotype at different places. And so it's really interesting. We've been thinking about doing like more complicated simulations that are trying to, that would maybe be able to explain how we get this really interesting different patterns of haplotypes at different frequencies of populations. So that's definitely one follow up that we're interested in doing. And then on the broader data set. Yeah. So I think more broadly, we're also interested in using this data to just more broadly look for evidence of introgressed structural variant alleles. And so like when we look for introgression in human populations, we are again, mostly just focusing on SNPs. But I think it's also really interesting to ask like for SVs, like, are the distributions of introgression like different for these variants? Like potentially like, are there inversion SVs that are inhibiting recombination? And so they're like reduced for introgression at those sites. And so I think like using this, this set of population wide SV allele frequencies to ask questions about structural variant introgression in addition to just like SNP and indel introgression would be really interesting. Awesome. I agree. Okay. All right. So one more question. And the question is, thank you. Well, first a statement, which I concur with, thank you for a great talk. What kind of medical implications does this have? And for example, would the findings impact drug discovery or personalized medicine tactics? Yeah, so I think the medical implications really depend on being able to get at a phenotype from this genotype data somehow. And so either like, you know, like really directed like what lab studies with like cell lines that carry the insertion versus without or like some more phenotype based study of like associations where you have a data set that does have a lot of representation from meet East Asian and Southeast Asian individuals that have this insertion. And then you can kind of start to ask, like, what kind of phenotypic effects does this have? Like, does it increase like resistance to some specific form of infection or something? We did actually look for like, if there are any reports of like Southeast Asian epidemics, like 66,000 years ago or something couldn't really find anything. But I think any future like downstream medical implications will have to get at the get at the phenotype associated with this haplotype first. Okay, thank you so much, Stephanie. Great talk and great paper and thank you for being here and presenting your work. We will now proceed to our next speaker who is Geronimo Rodriguez Beltran from Ramoni Cajal University Hospital in Spain. And his talk is collateral sensitivity associated with antibiotic resistance, resistance plasmids. Okay, so thank you very much, George. Thank you for having me. It's a real pleasure to be here. Today I'm going to present our work characterizing the collateral sensitivity responses associated with the acquisition of antibiotic resistance plasmids. But first, a little bit of introduction, antibiotic resistance is one of the most pressing problems facing humanity. Indeed, recent estimations predict that by 2050 antibiotic resistance might become the living cause of death in the world with an staggering death toll of one person every three seconds. So these figures highlight that we need to act urgently to tackle this pressing problem. But how do bacteria become resistant to antibiotics in the first place? Well, as most of you know, the development of antibiotic resistance is a simple example of Darwinian evolution. So in every large bacterial population, bacterial resistance to a given antibiotic may emerge through mutation just by chance. And this bacteria will take over the population once antibiotics are introduced into the environment. This bacteria will take over because sensitive bacteria will be eliminated or killed by the antibiotic or if the concentration of the antibiotic is slow, it will at least select for this resistant bacteria that we will not grow the sensitive bacteria. So this is the classical Darwinian selection, but a crucial factor for the development of antibiotic resistance is that bacteria can transfer antibiotic resistant genes through a process known as horizontal gene transfer that I'm sure you heard about. So this allows antibiotic resistance to rapidly spread through initially sensitive bacterial populations. And it's one of the main problems we face when trying to control antibiotic resistance. So one of the main vehicles for this horizontal transfer of antibiotic resistant genes are plasmids. That as you know, are self replicative DNA molecules that are able to coexist with the bacterial chromosome. And crucially, plasmids are able to move between different bacteria through a process known as conjugation that I'm showing you here in this beautiful picture. So plasmids are one of the main drivers of antibiotic resistance. And therefore the strategies that we may come up with to fight the antibiotic resistant crisis should also work against plasmids. One such strategy is collateral sensitivities, the use of collateral sensitivity. So collateral sensitivity, the sensitivity occurs when the acquisition of resistant to one antibiotic causes an increased susceptibility to a second antibiotic. So let's see an example because perhaps it's easier to understand here. So imagine this blue bacteria here, which is resistant to antibiotic A, where's my my here, to antibiotic A, but it's somewhat sensitive to antibiotic B. Right. So collateral sensitivity occurs when the evolution of resistant towards antibiotic B makes this bacteria less resistant to antibiotic A. So in some sense, this bacteria cannot become resistant to both A and B antibiotics because of this collateral sensitivity. So this concept or this theory behind collateral sensitivities will interest him because it could be used as a new therapeutic approach to fight antibiotic resistance. Because in principle, one could use a couple of antibiotics showing collateral sensitivity responses and design treatment treatments that cycle through both antibiotics to prevent or at least reduce the emergence of resistance. However, although there is a huge body of literature of paper showing these collateral sensitivity effects, most of them are focused on chromosomal mutations. And as I told you before, to be useful, these approaches should work for the main mechanisms for the spread of antibiotic resistance, which are of course plasmids. And also a second factor that we need for these approaches to be useful is that they should work in phylogenetically diverse bacteria and not only laboratory bacteria. So in this work, we decided to test these two ideas. And to that end, we took these six natural plasmids that you can see here. These are plasmids that were isolated from different interbacteria that were isolated from patients. There are clinical interbacteria. These plasmids all encode antibiotic resistant genes that you can see here, colored in yellow. And these plasmids had different sizes from 8,000 base pairs to 147,000 base pairs. They had different different mobility. They were conjugative, mobility stable or even not transmissible. They have different incompatibility groups. So this diverse set of six plasmids kind of represents the diversity of plasmids that we found in hospitals. So we introduced these plasmids as a model of regular plasmids into E. coli, a laboratory strain of E. coli, which is called E. coli MG 1655. And we just performed those response experiments with 13 antibiotics. So in these experiments, for those of you who are not familiar with these kind of experiments, we measured the level of resistant of a given bacteria by recording the minimum antibiotic concentration that is able to kill that bacteria. This concentration is called the minimal inhibitory concentration or MIC. So for instance, in this example, this will be the MIC. We have the antibiotic being diluted here in this multiple plate. And here, the bacteria is unable to grow. So this will be the MIC, the minimal inhibitory concentration. So as I was saying, we determined this MIC concentration, this minimal inhibitory concentration using this method for the wild day bacteria and bacteria carrying each of the six plasmids that I showed you before. So in this heat map, you can see the results. And here in every column represents the results from one plasmid that you can see here. And every row represents one antibiotic of these 13 different antibiotics that we use from eight different class families of antibiotics. And the results are that that we show here is the fault change in MIC between the plasmid carrying and the plasmid free bacteria. So red colors represent an increase in resistance. And as I told you, I told you before, the plasmids were carrying resistant genes. So it's not unexpected that the bacteria showed some resistance to some antibiotics like, for instance, the Botaxim. And but crucially, we found six instances of collateral sensitivity response to different antibiotics, which are represented here in blue and also with these asterisks, right? So for instance, the P oxa 48 plasmid, which is a really clinically relevant plasmid, which spreads epidemically and confers resistant to last resort antibiotics. So the acquisition of this plasmid induced collateral sensitivity to two antibiotics, which are acetromycin and colistin, right? Or in other words, when bacteria acquire this antibiotic resistant plasmid, they become less resistant to other antibiotics, which are in this case, acetromycin and colistin. So we validated these results using different methods that I'm not going to show you here today. But at the end, we concluded from this part of the experiment that indeed, plasmid acquisition can induce collateral sensitivity, opening the door for for developing these strategies. So next, we move to the second step of the talk, which is trying to, for these strategies to work, we needed that they were that this response was conserved across the diverse phylogeny, right? So what we need is to take this P oxa 48 plasmid and we introduce them, introduce it into nine E. coli strains of E. coli distributed that are distributed across the E. coli phylogeny, right? So here you have a phylogenetic of E. coli and I have a ledger here, the E. coli strains that we used. So as you can see, they are quite diverse and they are distributed across the phylogeny, right? So then we are just as before, we measure the levels of resistance of the plasmid free and the strains carrying this P oxa 48 plasmid by using this diffusion assay. So these assays are a little bit different and it's really simple. We just put, we just placed an antibiotic containing this on the top of an agar plate and we just let the antibiotic to diffuse from the disc into the agar, right? So at some point, the antibiotic concentration is low enough so that bacteria can grow, which is what we see here with this white lounge of bacteria. So the diameter of the inhibition halo is proportional to the level of resistance. So here in this example, you can see that the plasmid free bacteria produces a smaller halo than the plasmid carrying bacteria or the bacteria that carries the P oxa 48, right? So we use this technique for the nine strains that I showed you before and I know there's too much information in this plot, but let me walk you through it. So first regarding acetromycin, which was one of the antibiotics in which we found this collateral sensitivity, sorry, there you go. So I have highlighted here in gray the instances where this acetromycin collateral sensitivity response was conserved and you can see that in eight out of the nine strains, the introduction of the plasmid P oxa 48 produces collateral sensitivity to acetromycin, right? So this response is remarkably conserved. On the contrary, collateral sensitivity to colistin was only conserved into cases, which is not that good, but nevertheless, all strains show collateral sensitivity to at least one antibiotic, which suggests that collateral sensitivity can be used to selectively kill plasmid carrying bacteria. So next we move to test that idea if we can selectively remove plasmid carrying bacteria, antibiotic resistant bacteria from mixed populations using this experiment that I'm going to explain you right now. So we designed for treatments, for antibiotic treatments in which we, in two of them, we maintain the antibiotic constant. So during the course of today we treated the bacteria with acetromycin or acetromycin, yeah, or only with colistin over the course of two days. But we also designed two treatments in which the antibiotic cycles, so we started with acetromycin and the second day we changed the antibiotic to colistin and the other way around. So we propagated a number of bacterial populations in these four antibiotic treatments and just recorded the number of populations that were able to survive, right? So here you have the results. So for the plasmid-free bacteria, which are highlighted here with these gray lines, right? And the plasmid carry on the bacteria carrying the BIOXA48 plasmid, which are these red lines. So you can see that in all four treatments for this particular strain, the four treatments were able to kill preferentially or selectively kill or kill more bacteria carrying the BIOXA48 plasmid than the plasmid-free bacteria, right? In some cases, both bacteria, both the plasmid-free and the BIOXA48 plasmid bacteria died, but in most cases, the bacteria carrying the plasmid died faster. And this is also true for, we tried, we repeated this experiment with six or five strains of those representing the E. coli phylogeny. And we found the same result for, so that we can selectively kill plasmid-carry in bacteria in 11 out of the 20 treatment strain combinations. So suggesting that effectively coli phylogeny sensitivity strategies can be used to selectively eliminate plasmid-carry in bacteria. So this was the paper, so it was really nice. It's a little bit preliminary in the sense that we don't think that these responses are ready to be used in the clinic, but for sure there are stepping stone in the development of these strategies to counteract the plasmid-mediated antibiotic resistance. So well, just to conclude, the take-home message would be that the acquisition of antibiotic resistant plasmid induces coli phylogeny sensitivity and that these coli phylogeny sensitivity conserved, at least for the BIOXA48 plasmid, and that we can use this coli phylogeny sensitivity to selectively kill this plasmid-carry in bacteria. So this was the paper, and I just wanted to thank the members of my group, and particularly Cristina Adencias and Alvaro Sambillán, who were the main authors of this paper. So thank you very much, and I'll gladly thank you for the questions. Outstanding, thank you. Yes, please again put any questions that you have in the chat box, and then I will read them a lot. And while people might be typing, I'll go ahead and ask. So in terms of, I know you said that this is preliminary in terms of application, but what would a broader strategy look like from the point of identification of an antibiotic resistant strain to then testing to then implementation? What theoretically would the process be? Well, I think the ideal situation would be that to identify just one antibiotic that works with as many plasmids as we can find, right? So in our study, we didn't find that, so the coli phylogeny sensitivity responses seem to be quite specific. So one plasmid promotes coli phylogeny sensitivity to one antibiotic, but we don't find general patterns there. So for this to be applicable and useful, we would need something like that. But in any case, we could design specific treatments for specific plasmids like the P-OXA48 plasmid that I've shown you here for concerning plasmids. And then what we would need to do is first, well, what we are doing actually is trying to understand why this coli phylogeny sensitivity happens, what's the molecular mechanisms behind these coli phylogeny sensitivity patterns that we observe. And if we can identify general patterns there, we can maybe find drugs, specific drugs that will enhance the killing or make the differences larger. So I think that we, the way to go with these approaches, because otherwise you are stuck with just the searching for a specific plasmid, which antibiotics will give this coli phylogeny sensitivity, and then that may be a little bit too burdensome to be effective in the clinic. Okay, let's thank Geronimo and the great work here. And again, thank you for being here to give the presentation and for also submitting a paper to the special issue. And congratulations. Thank you very much. Yeah, thank you. Okay, Josh. Our next speaker is Josh Deeth from the Imperial College London. And his talk is interspecies spread of AMR elements via recombination within pneumococci. Great. Thanks, George. Do you see my slides, all right? We can. Thank you. Great. Yeah, so hello. Good afternoon, everyone, from London. My name is Josh and I'm a PhD student at Imperial College London. And today I'll be talking about my paper looking into antibiotic-resistant pneumococcus neonges and how they evolved resistance. So a bit of background first. So streptococcus pneumoniae is a grand positive proteinistic pathogen that is a member of the permacutes, or now to be known as the basalota, is a leading cause of mortality worldwide, especially among infants. More than ever, numerous multi-drug-resistant neuges that have spread globally. This has led to the pneumococcus being one of the WHO's priority pathogens for research, for further research on antimicrobial resistance. So in general, resistance in the pneumococci can be gained by alterations to the core genes, such as the person binding protein genes, KPPs, the person resistance, or through acquisition of gene cosets on mobile elements, which I'll focus on in this talk. So in the pneumococcus, there is a limited plasmid repertoire. So integrative-contractive elements, ICES, drive much of the resistance gene spread. And these can integrate within the host chromosome, but they can also encode their own conjugation machinery to spread between cells. And the first identified ICES, and one of the most abundant, is the TN916 element, shown here, both in the, it's linear and circularized forms. And this encodes the tetracycline resistance by the tetem gene present. So TN916 can integrate within the host chromosome, but it can also excise and circularize to move by a conjugation, as depicted here, and as John and Mary want to over as well. So conjugation is whereby a pore is linked, is formed between two cells, and this allows for a stable transfer of an element into a new host. And this element can then integrate into the host chromosome via site-specific recombination in the case of ICES. And so while there is evidence for TN916 and ICES moving between species via conjugation, there's no in vitro evidence, however, for conjugation between pneumococci, which leads to a question of how these common elements disseminate within a pneumococcal population. So the other mechanism which these MGEs might be using to spread among pneumococci is transformation. Transformation is the update of exogenous DNA into a cell that is incorporated into the host genome via homologous recombination. And this requires cells to be in a competent state, and also to plea favours the import of short sequence length and its bias towards sequences and closely related cells, which is due to the physical structuring of bacterial populations and the nature of homologous recombination. The pneumococcus is a naturally transformable species that exerts tight control over the regulation of its competence machinery. Transformation is key for major adaptations gained by the pneumococcus with serotype switching to evade vaccine response mediated through gene exchange via transformation and homologous recombination. Also common penicillin resistance is also gained by transformation with numerous studies, somehow I did here, showing resistant PVP genes in the pneumococcus often originate from other streptococcal species, with mosaic resistant fragments imported by transformation and homologous recombination. We also observe this in our paper with resistant PVP genes in the global collection of pneumococcus we look into, often originating from other commensal streptococcal species. But today I just want to focus primarily on the transfer of MGEs between cells, where there is less evidence of large MGEs moving via transformation. So the aims of this work were to firstly try and understand how ICES move within pneumococcal populations, secondly to try and get an idea for how widespread these genes are in the large global collection of pneumococcal tenomes, and then lastly to assess where these genes were inserting from if they were reconstructed to have inserted pneumococcus in the host chromosome. So the results. So we looked at the two common MGEs among the global collection of pneumococcal, TNi6, which I've just mentioned in the introduction, and TN127.1, which is shown here. This is a shorter seven kilobase elements, not an ICES, but a transposon, which includes macrolide resistance, fiber meth-A, eflux pump shown in the middle. Both TN127.1 and TNi6 elements can often be found together in a larger construct, such as the TN192 and 2010 elements shown here, or even even larger elements, sometimes up to 50 or 60 kilobases long, but can also contain other resistance genes. So the dataset we looked into was the global pneumococcal sequencing project dataset, also known as the DTPS dataset, and this has over 20,000 genomes collected and sequenced mainly in the last decade, and the majority of these, 65 percent of which come from Africa and Asia are seen in the map on the right. These ICES have been subdivided and clustered by Pop-Punk into 621 different global pneumococcal sequence clusters, which are sort of akin to bacterial lignatures. So in terms of present absence of the MDs under investigation, TNi6 was much more present in the collection than TN127.1. It was detected in over 5,000 ICES in 134 different TPSCs. TN127.1, on the other hand, was present in over 2,000 ICES across 64 different TPSCs. TN116 was also present on its own in 3,860 ICES, or TN127.1 was present in 635 ICES. TN127.1 was actually more frequently observed as part of a larger TN116 element, such as the TN2010 or TN1900 in the show previously, this large construct occurring in 1,150 ICES. So you can see here that these resistance and cutting elements are reasonably common among a large global collection of pneumococci. But to understand how these elements move between ICES and where they come from, you need to identify how often these elements insert within the image and where in the chromosome they tend to insert. So to do this, we came up with an insertion analysis pipeline, which looks mostly to subdivide MGE insertions based on where they insert within the genome and how large they are to try and identify unique insertion types. And once these are identified, we go across all the TPSCs within MGE present and reconstruct the number of insertions. These have been checked against predictions of recombination events across the genome to see if these MGEs are likely insert by homologous recombination transformation as opposed to site-specific recombination and propagation. And then the insertions within recombination blocks then have their thanking regions extracted to test which species these insertions likely came from. So here's the reconstruction of all the different insertion sites for the TN127.1 element. And so this circle represents the pneumococcus genome. The genes with hits proxies are identified. This is a reference genome without any MGEs present within. And in total, 93% of the hits were able to be reconstructed, although some hits were in smaller contigs. So thanking regions weren't able to be reconstructed. But in total, there were 50 unique insertion types across 27 different insertion loci around the genome. These inserting independently across the collection over 200 times. The element was most commonly present within TN116 element, but not shown on the graph. But the second most common was within the tag gene. An example of that insertion is shown here with TN127.1, the pink here at the bottom, compared to the reference genome on the top. And the bars in between the two genomes represent the sequence of the density between the hits. So this insertion appears to split the tag gene, which is a methyl-asinine glycosylate involved in DNA-based repair. And this depicts how MGEs can carry useful cargo genes, such as macular resistance in the case of TN127.1, but here they can also be disruptive to host cells upon insertion. For TN916, there was a much larger diversity in insertion type of over 400 unique types, over 100 different loci in the genome. It was most frequently observed appearing as a TN2010 element, inserting near the FTSE gene, which is involved in the cell division. We reconstructed over 1,000 independent insertions across the collection, with the most frequent insertion being TN916, present within a larger 40 kilobase element, which inserted close to the ZoomA gene, which was a metalloprotease gene, and that occurred 29 times across the collection. So once we had these insertion types, the next step was to try to identify the MGEs inserting via recombination. So on the right, we have the distributions of the length of recombination events, identified through the governs, and also the SNP density for these combination events, those events that imported the MGE highlighted in blue, against the background of the rest of recombination events for the GPS collection. And so for TN127.155% of the insertions, while within putative recombination events, while for TN906, this dropped down to only 8% of insertions. So this probably reflects TN916, coding its own conjugation machinery as a way to move between cells. These MGE importing recombination events, highlighted in blue here, tended to be much longer and also much more SNP dense, which suggests these imports might be arriving from non-neumococcal species, as they're slightly more divergent from pneumococcal donors. So to test this, we looked at the flanking regions for these insertions, and so here we plot the closest species match for the flanking regions and solid lines of control regions, which are orthologous regions around insertion from an isolate with no MGE present, and these are represented by dotted lines. So coral here represents matches to the pneumococcus, whereas green represents streptococcus mitis, blue or rhalus and purple pseudomaniac. And these are all common oesopharyngeal commensals, which are closely related to the pneumococcus. And so we can see that TN127.1 here, the upstream regions in particular, the top left hand of the graph, match much more closely to mitis here, than compared to control, while for TN916, the top hit is also mitis at early flanking lengths, but this soon converges back into pneumococci. But yeah, so this likely indicates that these elements are still inserting into pneumococcal populations from other non-pathogenic species. And in the paper, we highlight one really interesting example of interspecies transfer of TN127.1, and this occurs within the MDR P-bin9 age shown here, which is a subset of the GPS data set. And so the tree in this some associated metadata here. So in particular, we look at the German clades of isotopes for the industry, and this is so named because the majority of isotopes come from the German reference center for striptocopy. These all have TN127.1 insertion. They're all of serotype 14, which is included in the PCV7 vaccine, and they're all generally penicillins susceptible. And so when we look further into the insertion site, TN127.1 in this clade, we saw it was splitting the COMI-CG, shown here in sort of shiny blue on a control, and then we see the TN127.1 insertion up here in pink in the M200 genome. This gene is necessary for cells to enter a competent state in order to uptake DNA through through transformation. So this insertion prevents these cells from acquiring DNA virus method. And this seems sort of deleterious. The clades in Germany is quite large. It's the largest clade within PM9, coming in at 250 isotopes. And so we wondered what could be causing the spread of this clades in Germany. So Germany, especially over the periods of sort of the late 1990s and early 2000s when these isotopes were collected, tended to have a low rate of antibiotic consumption compared to the rest of Europe. However, the ratio of its rates of macular consumption, which TN127.1 would compare resistance to, and person consumption, which these isotopes are susceptible to are quite high, and generally the highest in Western Europe over the first time period. So we wondered if this unique pattern of antibiotic consumption could explain the relative success of this clade during the period. So to answer this, we formed some phylotonic mechanities focusing on the German isotopes. And so we reconstructed the effective population size through time, shown in this middle plot, while incorporating the ratio of macular to beta-lactam consumption into the reconstructions. And so we observed that there was a significant effect of this ratio on growth rate of lineage, which is shown in the bottom right here. So this is the growth rate of the effective population size over time. And we see that this peaks around the same time as the peak in macular to beta-lactam consumption for this lineage. So this could explain how TN116 might have been able to grow in Germany during this period. However, we see that these isotopes all start to decline towards 2005, and they're not really present in Germany after 2006-2007. And in 2006, the PCV7 vaccine was widely introduced in Germany, and that covers serotype 14, which is all of these isotopes. And so given that these isotopes could not acquire DNA via transformation due to the insertion of TN127.1, they could not switch serotypes to avoid the effects of the vaccine. So this could explain their loss from the population. So in conclusion, we can see that these resistance and creating elements are quite common in this numerical population that they insert often. We've also seen that TN116 appears to not move by transformation that often, whereas some uncertainty in this mild TN127.1 often moves through this pathway. We've also seen that while interspecies transformations are rare, section pressure from antibiotic consumption can enable the cross-species spread of resistance, creating elements, which has important implications in terms of preventing the spread of antibiotic resistance among important pathogenic species. And finally, we've also seen that the case of TAG and the COMI-C insertions, these MGs inserting can often be deleterious to hosts, but again, such as the strength of suction for antibiotic resistance, these insertions can often spread within the population. So thank you for listening. I'd just like to thank all my co-authors, particularly my supervisor, Imperial Nick Croucher, and my funder, The Welcome Trust, as well. And I'm happy to take any of your questions. Thank you, Josh, so much. And first, this goes to all the all the speakers, just so impressed and so inspiring to see you as early career scholars doing such impressive work and helping to leave these papers. So just really, really great, really great moment to see. Okay, there is one question already. And if anybody else has other questions, please enter them as comments in the chat. This question is regarding the large diversity of insertion sites. Could insertional gene and activation events be leading to adaptation independently of antibiotic resistance phenotype? So could you repeat the question again? I can. And I think it's also visible to you in the chat if it would help to read it, but yeah, I'll read it for others as well. So regarding the large diversity of insertion sites, could insertional gene and activation events, so could it be insertional gene and activation events that are leading to adaptation independently of the antibiotic resistance phenotype? That's a good question. Yeah, we haven't really looked into that in terms of how, yeah, so lots of insertion types we didn't get a proper chance to properly dig into in terms of such a great diversity as you can see in TNR16. So we can only pick up the big examples for TNR17.1 in terms of the obvious disruptions of COMEC in terms of competence, which prevents transformation. Well, yeah, that's something definitely that we could look into in further work in terms of trying to understand sort of the interplay between MGEs, both in terms of how they can be beneficial to a host in terms of containing antibiotic resistance genes, but then also the costs of our hosts that they incur in terms of disrupting metabolic pathways and disrupting core genes as well. And that's a good question, yeah, it's something we definitely need to look into more. I agree. Thank you. So, you know, fascinating work, including on the connection to the history of vaccines and so on in Germany. So you have some timing information on these events. My question was, is the timing inference only from the years at which data were collected or based on, do you have resolution to estimate certain events based on the phylogenetic tree and so on? Yeah, that's a good question. So we have the Germany, I think with our earliest Eisterts collected in 1992, and then most recent Eisterts was collected in 2007. So we have like a 15-year span. And so from that, we've been able to sort of look at the effect of population size over time, going back into the origin of this clade, which was around the 1960s. So we can sort of see where it first emerged. And we're lucky in the case that we had this data from Germany because it's all been sort of systematically sampled. It's all from the National Reference Centre. So we were able to properly employ these sort of phylogenetic methods and have a reasonable guess at the dates of emergence and things like that. And this was just quite a nice story that jumped out in terms of this sort of this linking between antibiotic consumption and the growth of this clade. And we did look at sort of how just-person consumption affected the growth rate of this clade, and that sort of had some negative effects as well, which is nice to see in macular resistance as well, which had the positive effect, which is what we do expect. But this sort of ratio had the largest effect. So yeah, that was always nice to try and reconstruct this data. Yeah, things like the comments about antibiotic consumption variation. That's something I lived. My daughter was born in Germany, and then we moved back to the United States. Until you experience it is remarkable just the kind of cultural medicine differences worldwide. And then that's very informative with respect to these patterns and evolutionary processes and evolutionary medicine. So well done. Yeah, it's quite useful. I think yeah, especially on a population level, there's lots of studies that you know, even in Western Europe, lots of heterogeneity in terms of prescribing rates and how that's affected local rates of resistance as well. But it's nice to see that link evolutionary wise. Yeah. Okay, we will move to our final speaker, who is Juan Manuel Vasquez from the University of California, Berkeley. And his talk is of mice and elephants, tradeoffs of tumor suppressor duplication and body size evolution in Afro theria. Take it away. All right, thank you very much. So today I'll be talking to you about a different kind of evolutionary problem. So this is a problem that's intrinsic to all of life. It's a problem of all of multi cellular life. And it's a question of how we actually grow and develop like our body size. But more importantly, how we avoid cancer. And so of mice and elephants is because of the differences in size between mice and elephants. And in order to understand this problem, we need to understand cancer. And the story of cancer starts with a single cell. So all of our bodies are comprised of various different cells. And every cell has an intrinsic risk of becoming cancerous. And this happens as cells acquire different mutations, which leads to them becoming this pre carcinogenic, and eventually like a car, like a cancer cell, which informs tumors. And this is described by the multi stage model of cancer formation, which was originally described by Armata Jindal in 20 and sorry, in 1954. And what this model basically dictates is that if you take any cell and you give it some arbitrary cancer risk, there's it will acquire mutations over a period of time. And eventually you'll end up in this pre cancerous state, where it has all the mutations necessary minus one before it becomes cancerous. And then with that one extra mutation, it will become a cancer cell which can divide indefinitely. And what this model says is that there's two real things that dictates your cancer risk given all else equal, it's number of cells you have that can become cancerous. And the time you have in order to develop cancer. And so if I walk you through it, the number of cells is because between individuals, the size of a cell is dictated by its function. So liver cells between humans are all the same size, heart cells are all the same size. So the difference in height, for example, between a short individual and a tall individual isn't actually because of different size cells, it's because of the different number of cells in your body. And so that means that over periods of time, every time they acquire these mutations, there's more cells in the tall person, which can acquire mutations and more likelihood, there's a higher likelihood that any one of those cells requires a pro cancerous mutation, which means over the course of the lifetime of the short person, a tall person, a tall person is expected then to have a higher likelihood of any given cell in their body acquiring all the mutations necessary to actually form two. And if you were to look at a meta analysis, you would expect to see that there's a meta analysis of cancers, you would expect to see this positive correlation between the cancer rates and the body sizes. And that's actually exactly what you see. So in this study shown here, there was a meta analysis of various different cancer cells across the world. And what you can see is that the hazard ratio relative to height is generally greater than one for all types of cancers. In other words, with your increasing height in a population, you have an increased risk of cancer. And the overall mean tends to be 1.12, which means that a tall person has a 12% greater likelihood of cancer relative to someone below. I think in this study, it was like the median height was like 5.5. Sorry to all the tall people in the clock. So the other thing I talked about is time, right? And this one's a little bit more intuitive. When you're born, you're kind of a fresh slate. And over time, then you acquire more mutations that can then lead to cancer. If you die relatively young, you're not likely to have the time necessary to actually develop cancer. On the other hand, in old age, you do have that timeframe. And so many of us are sadly familiar with this in that older people have a higher incidence rate of cancer than younger people. This is data from the National Cancer Institute's CER study, which then shows you that at different age cohorts, the incidence rates of all cancers increases in humans with age. So as I've just said, either being long-lived as a human or being taller as a human kind of leads to this increased cancer risk relative to your peers. But this isn't just a human phenomena. This has also been shown to be true in mice. It's been shown to be true in cats and dogs. And even in elephants, there's some data suggesting this is true. So this is one of those like phenomena that within every species, it holds real very well. So the question is, what happens between species, right? Because if you look at different species of mammals, for example, there's a huge range of body sizes and lifespans that we see. So this is data taken from the Hagar database of all the published information of body size and lifespans for all this mammal species they can get their hands on. And so you can see that you have mice and bats over here. You have dogs and other carnivores kind of in the middle. And then you have humans as a giant outlier, elephants and whales being enormous. And compounding this problem is this positive correlation between body size and lifespans that you see like across all mammals. So if you stop and think, well, okay, what's the cancer risk that we expect here, right? It should be higher on the right side, higher on the top side. So that means that if you were to try to imagine what the data showing that cancer and body size correlates in these species, right? You'd expect to see a graph kind of like this, where if you were to be able to take animals and then just look at them at time of death to find tumors in their bodies, right? So you're looking at percent necropsies with tumors. And you look to see how that correlates to body size and lifespan. You would expect to see this kind of positive correlation, right? It's where like mice and small short-lived species kind of fall over here. And then there are positive and correlated cancer risk. So the elephants and whales are just constantly dying cancer. But of course, I wouldn't be here today if that was actually true. As it turns out, this is data. Now it's a little old from 2015, but there's now more convincing data from this group published in 2020. Basically showing that if you actually take real-life necropsy samples both in the wild and captivity from all these different species plotted here, from all these different orders, there's actually no correlation between your cancer risk and your body size and lifespan. So this paradox has become known as PEDO's paradox named after Richard, Sir Richard PEDO who talked about this between mice and humans of mice and man in 1975. And so the paradox has kind of trivially true solution. Obviously over the course of evolutionary history, evolution works to decouple the cancer risk between body size and lifespan. And so large species evolve countermeasures to the increased cancer risk that comes with their size. However, we still don't know what the mechanism is. And that's kind of where, like did mystery lives. Of course, there's already a genetic tool set common to all mammals of tumor suppressor genes, which serve to reduce your cancer risk. And so this works in a variety of different ways either by killing off the precancerous cells, repairing them so they become not so precancerous and fixing mutations before they arise, and or just arresting the cell and preventing them from dividing any further and propagating these mutations. So evolution can act on this in one of two ways, either through sequence evolution, why like change the wheel just to fix the wheel, make it better. Or if you're carrying a heavier load, have more wheels, right? So the 18 wheeler model is basically duplicates the genes that are tumor suppressors, and then in order to reduce your cancer risk. So in this study, when I started my PhD, we really started focusing on this gene duplication model as a kind of way of reducing your cancer risk in a very quick period of time. And so this is important in some species where suddenly there's like a large individual that emerges, right? Such as, for example, elephants. So this here is the phylogenetic tree of all the members of Atlanta, Genado, which includes, for example, elephants. And what many of you may not know is that the closest relatives to the elephants shown here are manatees and the rock hyrax. And so for a sense of scale, elephants are almost five metric tons, whereas like manatees are actually quite large, even though if you've never seen a manatee before, you may not have thought about it. They're also like 300 kilograms. And then the rock hyrax is about the size of a medium sized dog. So this is a huge diversity in lifespan that just occurs over here in about like maybe 50 million years of divergence. If you then look at the rest of the tree, the rest of the tree is also quite small actually. So there are an aardvark over here labeled. And then like all these other species like the lesser hedgehog, tenric, goat, cape, elephant, shrew, and then over here you have sloth and armadillos. And so what's nice about this clade is that, you know, it seems like they're all kind of small, right? We have genomes for some of these. So like later on, like we'll be able to do genetic analyses. And importantly, we have ancient genomes of various extinct elephant species like mammoths and mastodons. But if you look at this tree, you kind of think, oh, well, you know, elephants are large and everyone else is small. So like it was a sudden shift towards body size and elephants. And so that's kind of why we started on looking at gene duplications. However, you know, we need to find out whether or not this is actually true. And as it turns out, this monstrosity right here, the titano hyrax is the ancestor of the current tiny hyrax. And likewise, you all are probably familiar with like the giant sloth, which you still like, basically dominates like the forces of South Africa, America. And it's the common ancestor of the current sloth, right? So clearly, not historically speaking, it hasn't always been the case that the elephant is the only giant ant species. Actually, historically speaking, all these different clades have undergone a seem to have large bodies of ancestors. So we really set out to see like, how does this actually look like on phylogenetic tree? If you actually look to see an ancestral reconstruct body sizes, what are the ancestral body sizes in this tree? Is it just that the elephants were always large or the elephants tiny? And as it turns out, that's kind of what ends up happening. So here I have the phylogenetic tree. Now the branch size blends have been rescaled for how quickly body size has changed. And then the color intensity is how big they became on a log scale. So as you can see here, there was this large shift in, for example, the sloths, where like, suddenly you have like the giant sloth. And that was a change that happened relatively quickly in evolutionary timescale. And likewise, you used to have like these giant armadillos as well, which happened. But for the Tendrix and for this Afrosorcida clan, there were some individuals that were much larger that evolved large body sizes suddenly. But as a general rule of thumb, they stayed small. And if some of them became large, it became large quite like, you know, towards the end of fossil record. Then you have over here, like, you know, like this big shift in pseudo-ngolada. So the common ancestor of all the species shown here, where they suddenly became quite large. And then some of them, like, for example, the Ardvark became smaller afterwards. But then you had another big shift in body size at pinongui lengths. So the common ancestor of the rock hyrax, manatee, and then the elephant. And it kind of makes sense if you think about it, because they all, I showed you, they all have large body size, like ancestors. I did not show you a stellar sea cow, which was an elephant-sized manatee that used to live back in the day. And it's that branch here that's like very dark. And so what you can see is that there's like this accordion, right? Like the body size is increase and decrease and increase and decrease. And the cool thing is that if the common ancestor of elephants, they became about the size of large dogs. And that is kind of like the common ancestor to all elephants. And we know that there were tiny elephants. We have evidence of like these dwarf evidence, these dwarf elephants. But the cool thing is that there was like two accordionings that happened in a very short period of time. We have these different large body elephants that suddenly evolved. But in a very small period of time, it kept accordioning. And so we kind of wanted to know, okay, well, how does this influence the cancer risk, right? As I've already talked to you about, there's a positive correlation between body size and lifespan. And the model predicts that your cancer risk is proportional to those two. We have ancestral reconstructed body sizes. So we can now use this relationship between body size and lifespan and extend lifespan information to basically run a linear of phylogenetic linear regression to predict the lifespan for all these nodes as well. And so we can also then see how cancer risk evolves in this tree. And so shown here, it's a little bit not intuitive. But cancer risk is predicted to decrease as body size increases, right? Because the animal needs to exist. So even though the risk, the real risk increases, the cancer risk must decrease in order to compensate for the increases in body size and lifespans. So you can see that every time you have this dark long branch here, there's a corresponding white branch here of the increase of like decreasing cancer risk and need to occur. And then you have an intensification of cancer risk in some species, which then suddenly decrease in size relative to how they were before. And so this kind of gives us a bunch of nodes, which we can look at and be like, okay, well, if we were to predict some evolutionary response to cancer risk, it should occur at these nodes where there was a large increase in cancer risk. Therefore, there must have been, because of body size and lifespan, therefore, there must have been a compensatory decrease in cancer risk overall due to like in order for the animals to survive. And so in order to identify these, we basically searched the different genomes that I had shown before in this clade in order to see how many copies of tumor suppressor genes they had. And so in order to do this, we use the reciprocal best hit black method that I developed, a pipeline that I developed, the method is actually quite well done into the literature. And so basically, the pipeline automates this process of taking your favorite gene and taking protein sequence of your favorite gene and looking in all these other genomes for all instances that you have that gene pop up. For all those forward hits, you didn't want to ask the question, is this really the gene that I searched for, or is it a close relative of the gene I searched for? Is it P53 or is it P63, for example? And so you then take all those forward hits and you search back the genome you started with. So in this case, we used the human genome and the human genome proteins as a reference and we searched all these other genomes with the collection of human protein coding genes. And then for each hit that we found, we searched the human genome back again. We only called it a real copy of our gene of interest if the reverse search also returned our gene of interest as the top hits. And using this method, we found 13,700 so genes that were conserved that we were able to identify across all the different plates. And so it's worth noting here that it seems like increase in a stepwise fashion. Obviously, there's not that many copies that were duplicated at the common ancestor and various different genes duplicated across the tree. However, you'll notice two things about this tree. The first one is that it seems like there's a reasonable number of duplications over here. And then suddenly, there's like 2000 are duplicated in the Cape Elephant Shrew. What we find is actually that the quality of these genomes over here were lesser than the quality of the genomes in the elephant. So there was already like a bit of a confounding factor where we can't really say much about these gene duplications because of the first off the number of duplications that we have. But also the fact that relative to the tree I showed you before, we don't have very many species here. So this really serves to polarize the changes that we see on this part of the tree, the aphorveria. And so this is where we directed most of our attention. It's also where we saw most of the body size accordioning occur. So we looked at them to see what we have all these gene duplications, right? What does that actually mean for us? We want to actually see what pathways these genes are involved in and to look to see in these gene pathways what's actually being enriched for. Are we enriching for tumor suppressor genes or for random other genes? And so we developed this upset plot, which I'll introduce step-wise. First, here is a histogram of the different number of genes that are enriched according to in different pathways, a number of pathways that were enriched according to the genes that they had duplicated using the reactome pathway like compendium. And so in black is just all the pathways and in purple are the cancer specific pathways. And so you can see is that for a lot of these body sizes where I mentioned there was an accordioning, there was an increase, there was a decrease, you do see that there is an enrichment for cancer like genes associated to cancer pathways. For example, in the African elephant over half of the genes that are like the pathways that are enriched in the duplicated gene set for African elephants are enriched for cancer pathways. Likewise for teffytheria, which is the common ancestor of elephants and manatees, and penongulata then is the common ancestor of elephants, manatees, and the rock hyrax. When you then look to see how these different different reactome pathways are enriched across the different clades. So for example, this line would indicate like pathways that are duplicated where genes are duplicated both in the African elephant as well as in the art bark as well as in teffytheria. And you didn't just ask the question, okay, well, what are what is the like right like how many of these have enriched cancer pathway intersections. You can see that a couple of these pathways, for example, per bosevea has the most enriched cancer pathways amongst all of its groups amongst all of its duplications. And likewise, then everything that involves the African elephant seems to actually have the majority like you know they have many of these cancer pathway intersections that are in common and shared between these different groups. When you look to see what that looks like, you find you get this word cloud, where everything in purple is a gene duplication is a pathway that is enriched only in that clade in this case per bosevea. And then green is everything that's enriched only in per bosevea and one other species. And blue is everything that's enriched only in teffytheria so only between elephants and manatees. And so you can see these are all cancer pathway genes. These are not like enriched for anything like I did not do any like exclusion for other things. So it's just cancer associated genes that are located in these gene sets, which means that gene duplications that occurred in elephants and in common large common ancestors of elephants were all enriched in these pathways involving cancer resistance. Of course, this is all historical. Here I'm showing you a subset of the one of the genes that we've found in elephants. And where basically you can see the duplications occur throughout the gene. Many of these duplications occurred very recently in the African elephant, which underwent a sudden increase from the common ancestor, which was small to like the current large African elephants. But there are some duplications that also occur in the manatees, some duplications that also occurred at the roots. And now these can just be artifacts, right? So you could have the retro genes, which are no longer functional, you could have like your premature stop codons and such. But what we found actually is that if you look at gene expression data in the elephants, these gene like duplicates are actually all expressed in the elephants in modern day elephants. And therefore they need to have some functional significance, whether it's pro to fully protein coding gene duplications, or maybe they're truncated to mRNA mutations. And now as evidence has suggested P 53 function more as like syncs for mRNA level regulation of the master gene. And so all this together does suggest that like not these things that we're finding, they're not just different skeletons and like fossil record of like genes that used to be functional, but rather they're actually currently functioning gene duplications in the elephants. So in conclusion, right, I've shown you that body size enhanced risk has increased throughout Atlanta, Atlanta genotins shown here, but especially in this tepitheorian lineage red and green, red and yellow. And while these gene duplications events are relatively common and scattered across the tree, elephant linear specific duplicates are actually the ones that are enriched into cancer related pathways, and they're conserved amongst like elephants, and they're still functional and conserved in modern day elephants, indicating they're still functional in these elephants. So there's a big open question to this, which is what my current work is now focusing on and what I kind of want to like leave you guys think about for the rest of the day. These gene duplications are in tumor suppressor genes, which by definition inhibit growth. Yet I'm talking about animals which are gigantic. So there's this kind of like new paradox which is kind of formed, which is you somehow managed to become the size of the whale while working as hard as possible to make sure that your cells don't divide. And so many of these other genes that are proepoptotic, the elephants, GP53 duplications have been shown to be purely like a working by cell death. And we know from like current model organism studies that these duplications of like these genes I'm showing you are duplicated in the elephants. They all have very negative like effects in mouse models when you overexpress them, which indicates that there's other whole other side of this paradox that we're not looking at, which is genes that evolved in response to genes which evolved in response to the body size, right? So it's this kind of like arms race between the pro growth pro like like the pro growth and pro large animal genes and these anti growth anti tumor suppressor genes. And so whether or not like how this they like orchestrate this dance for you to develop into a large animal. And then when you see your adulthood walk down any further growth is kind of an open ended question which I want to leave you as future directions for this project. And with that I want to acknowledge my PI who may be on this call from my grad student work and he's currently at University of Buffalo Vincent Lynch. This work was done when I was a grad student at University of Chicago and I'd like to thank my current funding from NSF. And with that I'll take any questions. Outstanding. Okay we have two questions so far and anyone else you are please welcome to enter questions in the chat then I'll read them out loud. The first question is in the presence of cell death, the number of mutations does not necessarily correlate. The number of mutations does not necessarily correlate with the number of cells. How does this affect the argument? The presence of cell death. So I'm going to interpret this as like as mutation can occur which then like kills off like the cells. So like that is true but it's still in this case right there's like a term that I did not talk about at all in like this like which is just the C term right to proportionality constant. If you have cell death right you can underestimate the number of somatic mutations but that doesn't preclude the fact that there will be cells that survive that actually hold all these mutations and that's the more important key part here right. Even if you have an insanely high cell death rates as with the elephants for example that can help deal with this problem. So actually what Vincent's group had shown before right when I joined the lab and then when I worked on my PhD is the elephants do kind of increase cell death turnover in order to kind of get rid of all these mutations and that is a considered like row like or rather an anti-cancer mechanism right. So you just kind of get rid of any cells with any mutations regardless of how severe it is because you're just like really putting a lot of weight on the double-strand break and like any mutations and like shifting the response from repair to just apoptosis. I think that kind of answers the question. Thank you. Okay next question. Do you think there could be gene loss events in addition to gene duplications that might have conferred cancer resistance in these species? That's the question that like kept me up at nights. So the problem is that in these genomes if we have very I was not comfortable myself at like considering gene losses because we're not talking about like human quality genomes we're talking about genomes where I had to like correct for like fragmentation and like fake heterozygous like duplications to the heterozygosia and many other things which I did not get into in this talk. So we focused specifically on gene gain because it was a lot easier to parse apart than gene loss. Did we not find it because the gene was just too different from the mother gene so we wouldn't have identified it as like a duplicate or real duplicates or was it right it would have failed like the percent identity thresholds or did we not find it because the genome was just awful or did we not find it because it literally isn't there. My guess is that yes gene losses are going to be very important as well. It's just that with the current state of affairs we don't have the power but I'd love to look into that as like VGP and many other projects are making better genomes for these species. Thank you. Okay next question. Wonderful to see emphasis on the trade off of cancer suppression mechanisms. Any further ideas about how to study these perhaps by looking at subsequent genetic changes associated with gene deprecations? Yeah so one thing that I kind of like you know finished my PhD right when because of the pandemic I actually really wanted to look into like sub-functionalization, neo-functionalization in these gene sets right to kind of get that question like there's additional gene like changes that then result from these duplications. Whether like I think the next step for a lot of this work is more like doing like ex vivo work so like working with elf and cells to actually like characterize these in like multiple like massively like parallelized assays or using organoid models to actually like see for example this is very broad but like I said and I think there's a new question it's kind of getting into this. There's a very strong developmental aspect to this that needs to be addressed as well and so I think looking at the gene regulatory like the gene regulatory context of these duplications and how they avoid just shutting down the animal massively are probably the next steps here. Thank you. Okay and regarding the the new paradox that you've presented do tumor suppression genes inhibit growth or do they regulate growth? If it is their job to regulate growth then fire regulation is not necessarily a paradoxical right? Kind of yes and no right so like these genes kind of operate on a spectrum where I've shown that like one of them these duplications the duplication of the chemian inhibitory factor that happens in the elephants is kind of like a kill switch that gets turned on by p53 it's a zombie gene it was like it was a pseudo gene that got refunctionalized by the evolution of p53 binding sites and it just so happens that the new version of the zombie gene when it gets turned on by p53 instantaneously kills the cells so that would be straight up like not either inhibiting nor regulating growth it would just increase cell death right? But there are other genes that I looked at in my PhD and I'm trying to wrap up the analyses now over Christmas break so stay tuned. Essentially what happens is that their cell cycle genes which are duplicated and then they like if you express the gene duplicates in any other cell type like show cells and human cells in like other species related species cells it just shuts down growth like the cells instantaneously like you know within 18 hours they undergoes an essence in that sense. So it seems like it's more of like this it's like going to be gene by gene the specific and there's definitely going to be some cases where it's just a regulation of growth in which case how they regulate growth and how strictly they do that it's going to be the bigger question than like you know do they straight up stop growth? I think that like regulation is like the key word there like we need to this is showing that the repertoire of genetics exists for like evolution act on now the question is how is it actually working? Alright thank you another question it would seem that the advantage of duplication to anti-cancer genes isn't to double the effect but to provide extra assurance against loss of function if there were loss of function mutation in anti-cancer genes i.e. the duplication is more of an insurance policy reasonable? So I was tempted to agree with you at the beginning of my PhD but that this graph I show here is kind of like the counter-arguments so if I were to show you like differential expression data between these genes and other like like close related species that like for example the levels of cast-9 being expressed in these cells is much higher than like if you when you add up these two copies than the levels of cast-9 in like other species so it's if it were like just to be a redundancy you would expect that the species would either would either equalize two levels to like normal like of the expression of the two genes so that way like you know if you duplicate it then you have expression of both of them so you don't end up with this like overdose of the gene but in this case there seems to be for many of these genes a straight-up overdose in this is like data this is gene expression data from like PBMCs for example and like other like genes like um and other like tissues from elephants so we're also not talking about cell culture models we're talking about relevance like in vivo elephants like gene expression so there is probably going to be like a like insurance policy aspect to this story but there's probably going to be more of a functional like there's an active like risk because it's also right like the risk of cancer isn't a passive one that sometimes pops up due to mutation mutation occurs every time you have a cell division so but when you go from like you know the zygote all the way to a giant elephant or giant whale there needs to be like this constant surveillance which i think is the more key part here okay outstanding thank you for the questions and the discussion um yeah fascinating fascinating subject and i think there's a lot of opportunity to integrate your comparative species findings here with within species patterns of variation and effects on cancer risk where we see you know a different pattern and then we've got all the population genetic tools as well to to consider so so great work um yeah i i want to you know quickly wrap up the entire symposium and and again just thank our thank our speakers thank all the authors you know other authors of papers in the special issue on evolutionary medicine in life or in the audience yeah thank you for your participation i think you know it's a fascinating time with you know we in terms of being able to bring a lot of tools to evolutionary theory evolutionary data evolutionary genomics experimental tools to bear on you know integrating evolutionary concepts with medicine and and human health and you saw different examples here today of how we go about that from studying antibiotic resistance to studying human evolutionary history in the context of human health studying cancer processes and also you know in in wands talk the comparative you know the kind of comparative long-term evolutionary biology model there so you know one thing i'm excited about in the in the field writ large is to see these different groups and different types of studies come together and integrate in terms of methods and practices because you know their human evolutionary history is affecting also a lot of dynamics going on with antibiotic resistance and so on right so you know that that's something that that i'm excited and i'm excited about our special issue which again you know please please check out there's 37 articles published so far but more coming through the through the pipeline over the the next the next few months and yeah thanks everyone again for participating today really exciting to see you as early career scholars doing such great work really inspiring for me and i'm sure others on the on the call so thank you thank you again marie has a few final final words thank you very much church i wanted to thank our speaker for the great presentations today our audience at the time during the the busy period before christmas to to join us and listen to these thoughts and and pj for the the great chairing thank you so much and equally for for leading the work with the life to promote evolutionary medicine as a topic of interest to the community so we do hope that by signaling with the special issue that elive is interested in kind of work that the people on the call will consider sending us their research for for consideration at your life and we will be sharing the recording of the session online in a few days and this will also be shared with you by email so without further ado i wanted to thank you all for for the time to join today and i hope you have a lovely holiday break thank you so much