 Hey, good afternoon everyone in the room and online and it's a pleasure to be back here in Lipset for the NHGRI seminar series and welcome any of you to come in the future. So, I'm Julie Sugri and I'm here to introduce Christina Cuomo. It's a real pleasure to welcome Christina to the NIH. Christina is a long time colleague, although it has taken me a while to get close enough to be able in fields to be able to work with Christina. So it's a bit of a background Christina has her PhD in genetics from Harvard and then did a postdoc at UCSF. And then joined the Whitehead Institute and started to focus on the human genome and joined the human genome closure team. So, was part of closing chromosomes 8, 11, 15, 17 remember when we used to do chromosomes one at a time that was in 2005 2006. But really at the same time Christina was developing an independent research project, becoming a leader in fungal genomics, and in 2007 published one of the first fungal genomes for fusarium. Grammarium. This is always my problem with microbiology is I can't pronounce any of these things, but really went on to sort of be the pioneer and be the leader in the genomic studies about fungi and sort of how would genomic information reveal really cool biology about so for example in 2009 a study on the evolution of the pathogenesis and the sexual reproduction and candidate genomes. And also, so while Christina's research has focused mostly on human pathogens she's taken also a larger view of human health and thought about environmental health and was one of the first people who talked to me about the the Kittred fungi that were affecting frogs and is really just very well versed in the different fungal genomes and works with so many people and is seen as really the person who is leading fungal genomic studies. So today Christina's talks going to focus on population genomics and the evolution of Ireland's traits and cryptococcus neoflormins so welcome Christina and good to have you here thanks. Thank you Julie for that introduction and thank you also to Diana Proctor for the invitation to come. So our focus today just on one area of what we work on is talking about cryptococcus and the priority that we've given to craft to caucus is been highlighted by this recent WHO report. This came out just within the last few months where the WHO prioritized. This time, this this some of the most health threatening fungi. And they did this because of the on the increasing threat of fungal infections and the rise of anti fungal resistance and is highlighted in these quotes that they're because of that they're becoming resistant to the treatments that we have. On the top of the list of the most important threats they highlighted this critical priority group that includes four species that we work on in my group, and one of these is cryptococcus neoflormins. Cryptococcus is a global disease causing meningitis, and it's been estimated to cause over 180,000 deaths per year, but it is of particular burden in Sub Saharan Africa. Because of the large population of HIV positive individuals there, and in that cryptococcus is causing disease and immunocompromised individuals. But because of that also it affects people worldwide in the United States causing disease in people who are immunocompromised or living with cancers or who are undergoing things like organ transplants. And this picture just highlights the annual incidents of cryptococcus around the world. I wanted to also highlight that, you know, while I highlighted a lot of the some of the other fungi that are human pathogens, including Canada and aspergillus that cryptococcus is very distantly related from them. Here in this phylogeny produced by Antonos rocus in a paper last year. This is highlighting in red some of the fungal pathogens within the human within the fungal kingdom. Cryptococcus is in a phylum called the city of my coda that contains a genus of fungi that are commensals and cause dandruff called malassezia. But otherwise, this is not where you find human fungal pathogens. You mostly find them more around four or five o'clock in this phylogeny where you find Canada. And then dimorphic fungi down around six o'clock. So it's not very closely genetically related to other human pathogens so some of our knowledge is really going to be need to be newly discovered about what makes it a pathogen. And within Cryptococcus there are multiple species, how many species has been a topic that has been pretty widely debated. And wanted to just recognize that there are these two large subdivisions called Cryptococcus gatti and Cryptococcus neoforments. I won't be talking about Cryptococcus gatti but just wanted to mention that this is really different in that it affects immediate competent hosts, and has been found within the United States particularly causing an outbreak in the Pacific Northwest, which is subdivided into these lineages or species that are on the top of this tree. But the group I'll be talking about is Cryptococcus neoforments that I've mentioned, the fact that we have compromised hosts. It has environmental reservoirs in different types of trees in urban environments in pigeon guano and also in soil. And as I know it has a global distribution. So let's discuss how it has been broken down further into these distinct genetics of groups. I'm here denoted VN one, VNB and VN two that have distinct global distributions. And I'll mention further that there's a related species called Cryptococcus de neoforments. And then these two can be distinguished serologically. While Cryptococcus is an environmental pathogen, it's been demonstrated that isolates from the environment and isolates from the clinic are very different in virulence. So first is to review its mode of infection. As I mentioned, there are these environmental reservoirs that from which spores can be released and infection then can start as being inhaled and began as a pulmonary inhaled into the lungs and began as a pulmonary infection. It can then disseminate to the central nervous system and cause meningitis. And those infections can be diagnosed by a positive culture. And if you compare these clinical isolates that are recovered from patients from those recovered from environmental sources. They're died by side in a mouse model in this paper from Anna live and save a they're very different in how they affect the mice, where the environmental isolates as a group in this study did not cause the mice to die but the clinical isolates did. So although this is an environmentally acquired pathogen to suggest that there are isolates that are very predisposed to causing clinical infection, which seems unusual for an opportunistic pathogen but perhaps there could be things in the environment that are predisposing it to cause disease. It's been hypothesized that this could include interactions with other eukaryotes such as amoeba. But we'd like to understand what, what are the factors that that explains these kinds of differences. So to do this. We launched a large genomic study to understand how natural variation contributes to violence. And we began this by trying to better understand the population structure of both clinical and environmental isolates and taking this global perspective. We looked at recombination and hybridization and these populations to lay the foundation for doing genome wide association studies that then we tried to use to look at genetic traits associated with clinical isolates. And then we married the whole genome sequencing to phenotypic analysis that I'll describe. That was either a range of things from in vitro phenotyping to using patient metadata to try and associate traits with the genome variants. So to start with the picture of the population structure. We did this using phylogenetic analysis from the whole genome sequence. And this revealed that while initially there were described these three population subdivisions are analysis showed very clear support for four major populations, not shown in this phylogeny, and that we recovered this new split at the top of the tree of the VNB group separating into two lineages. And that the colors around the external circle. The blue and the red at the innermost circle are showing clinical and environmental origin. And what I hope you can observe even at a distance that that's that there's a good interspersed pattern of these two colors so there aren't any big pockets of being a clinical isolate or an environmental isolate, they're fairly interspersed. So this suggests that there's not any one or two or three major clinical lineages, but that these these patterns are interspersed suggesting again that a association approach seems like a valid way to proceed. So just to provide a little more context of the type of initial work we did, we did find some additional substructure within one of the lineages called VN one, where it split very cleanly into three separate claims that we gave additional names to. We found that these were also separated by geography, where one of them that we call VN one C, that is in the principal components analysis that is on the right part of the slide was entirely composed of isolates from Botswana, which were collected from all collected from the southern represented the most southern part of Africa that we collected isolates from. So this whereas in contrast the other two groups in this tree were and in the PCA work were globally collected. So while being one is thought of as a global lineage the shows that the the finer scale picture is a little bit more refined where one of the groups is actually specific to Sub-Saharan Africa. So then to pivot now once now that we have a clear understanding of the population structure into carrying out genotype phenotype association wanted to just give a picture of what kind of data we were thinking about for some of the phenotypes. We wanted to be able to test phenotypes that we're going to be as relevant as possible for thinking about virulence traits. So for Cryptococcus two of the big ones were production of melanin, which is a virulence property. As well as these other ones that are listed here to do with temperature growth resistance to oxidative stress and drug which I won't have time to talk about. And then I will talk about as well some of the clinical metadata as I go through the talk to carry out the genome my association because we saw such strong population division. We decided initially to separately analyze the lineages and and carry out separate analyses for these like being one or being separately. And we then subset the variants for their functional impact but we also because we know that we are looking for probably rare variants to be able to look for things associated for virulence. We decided to collapse signals by gene to be able to find rare variants. And then the test that we run was called Gemma, which is a linear mixed model. So, on the first test that we ran was for loss of melanization which is an assay that you can just run on a plate and measure for each isolate the level of melanin. And the top hit that we got from the G was was a loss of function mutation in a transcription factor called B zip for. And we had four different mutations that were associated with this transcription factor and four different clinical isolates. And then what they actually look like is shown on this plate. The three at the bottom and then the one the top row on the right is a little more melanized. And then there's some controls of a wild type isolate that is melanized and a mutant that is not. And then this is just some reproducibility of the assay. This was a gene that in a transcription factor screen by another group was shown to affect melanization so this was already validated not by us. So this was a nice kind of positive control that we were able to pull out a real signal. And then we had the other hits that were coming out in this analysis were all many of them were in or next to a serine 3 and 8 phosphatase that we haven't looked into as much because they were very common variants. In this first analysis of G was we also then took a very, I guess aggressive swing at trying to look for variants associated with just being a clinical isolate so asking, are there snips associated with clinical origin, compared to environmental. And when we did that, we found that, yes, you can find snips and they are, if you look at the top of the list, what you see is a lot of genes that are linked to virulence factors and genes involved in the oxidative stress response. But many of these mutations are either common or in genes and some of them are kind of in just linked to the genes in intergenic regions. There's nothing directly yet to follow up on but we're, I think we took this as a positive sign that this analysis look like it was going to be able to find some signal. But we definitely recognize that just comparing a clinical to an environmental isolate may not reflect the level of virulence. And what we wanted to move to next was to be able to look a little closer to reflect the level of a strains virulence by moving to metadata of the, how the patients that the isolates came from on fared on during the course of their treatment. And we did this by partnering with the clinicians who were involved in this clinical trial called active. This was a trial of patients from hospitals in Malawi and it was a trial for antifungal regimen. The patients were all HIV positive and had developed meningitis and the trial was comparing two different antifungal regimens. So we sequenced about 300 Cryptococcus isolates. These now are from a different lineage than the previous analysis these are mostly vn one. And with this clinical with this study, working with the clinicians we selected this metadata that the clinicians thought might be useful for us to think about how they were doing, including the fungal burden, how fast the infection was cleared, and then things to do with the, the power patient fared during the infection the mortality, how long they survived and things to do with their mental status reflecting the meningitis. Again, one of our preliminary assessments was plotting some of these features on the phylogeny to ensure to evaluate if there was any jackpotting again of phenotypes of genetically related isolates, which again we did not observe suggesting that there's no again kind of single genetic lineages representing strong phenotypes. So the other initial look we took at the metadata was just comparing it to determine what had the strongest correlation just with itself. And in doing this we found a very strong correlation between the fungal burden or the the colony forming units coming from the CSF and the patient mortality. And when we looked at the fungal burden in the CSF, there's a very wide range that was observed in in the counts that came out. So this plot is showing for isolates from these different vn groups and a few from vnb that there's a very wide dynamic range here plotted on a log scale of these CFU counts. So from this we thought this was going to be a good place to start of a GWAS with the level of fungal burden because it was such a it looked like it was a good measure of death and it had a wide dynamic range. So we ran the GWAS with the fungal burden and we were able to find that there was a very strong association for variants with genes involved in virulence. And this provided some interesting hits, including that one of the top hits was a gene that is the target of azol drugs, Serg 11. This included multiple genes involved in capsule, which is the polysaccharide coach that is on the outer part of Cryptococcus and also considered a virulence factor. There's a gene called SGF 29 that I'll mention because I'll come back to that is involved in histone acetylation and also melanization and virulence and other genes involved in thermotolerance or iron. So this is a pretty encouraging list, you can see, they're, they're definitely kind of single hits that come up on this Manhattan plot. I'm showing here just the 14 chromosomes of Cryptococcus and the p values of the individual SNPs. So in terms of follow up, we selected the most highly significant loss of function mutation and working with John Perfect's lab at Duke. They tested this gene in a rabbit model. This was a loss of function mutation mutation in a phosphofructokinase that is involved in glycolysis. When this gene was deleted in the lab strain H99 in a mouse model it did not affect the survival of mice in an infection model. But when they deleted when they tested this deletion in a rabbit model that has some advantages for recapitulating what infection looks like in a human. Is it significantly reduced the fungal load that you measure from CSF in the plot on the right, and that is the phenotype that we were associating on. So this was was recapitulated. I also want to highlight importantly that while we find many interesting hits from the GWAS, a lot of the top hits, a lot of the top genes that are highlighted now with this version of the slide and noted with HPs in the, the, in the cloud above the within the cloud plots are hypothetical proteins and in fact, as noted here nearly a third of the most highly significant variants are these genes with very little or no predicted functional annotation. So, there's a lot of a lot that we have yet to let to learn about Cryptococcus gene function kind of as I alluded to in the slide of phylogeny. Cryptococcus is again just a lot of phylogenetic distance away from other known fungi that from which we can infer function. So to start to do that for one gene, again, we took what was the most highly significant GWAS association, a frame shift in an unknown protein. And we took two approaches we looked at the difference in virulence of two natural isolates and tested them side by side in a mouse model on the left. And in that model, we were able to show that there was a significant difference in the survival of mice, just under a significant p value. However, when we go and delete this gene in the lab strain h 99, we do not see a significant difference in in the probability of the mice survival. So to us this suggests that there might be other factors going on in these natural isolates that we have not yet uncovered that either might interplay with these genes or or contribute to their virulence. We also are taking advantage of all these sequence genomes to look at what larger other forces may be at play and looking at selection across all of these lineages that I've mentioned. And when we do this using a test for selection across the entire genome. What we're able to highlight is genes that have undergone selective sweeps in each of the lineages. And one major signal that this has highlighted is the importance of sugar transporters. Carbohydrate transporters and inocital glucose transporters in particular in VN one and VNB lineages. And here I'm just showing a more detailed phylogeny of those transporters where we've done a more detailed analysis of selection and highlighted in red the the the individual genes that are under selection and different lineages. So inocital and xylose are are compounds that are abundant in plants which might then explain why they've been selected is that they're important in the environment. But why it might be important for the pathogen is that inocital is really abundant in the human central nervous system so that might be a real benefit for cryptococcus as a pathogen. And it's been shown that it's actually required both for virulence and mating in different studies. So this is helping to highlight how this, you know, really strong selection in the environment probably is an example of coincidental adaptation for the human central nervous system. Lastly, we are would really like to be able to of course be able to zero back to looking at how the an isolate from the environment then moves into a human and adapts but there. It's extraordinarily rare if there's only been one case published of tracing the exact origin of a clinical case back to it's an environmental source for the other, you know, many, many, many cases. We don't have that exact mapping. And so for the, the populations that we've looked at for VN one have only had very few environmental isolates collected, but we have now gone back and tried to for VN be match geographically around these hospital locations where we've been sequencing the clinical sets of isolates, go back and collect environmental isolates in the same location and see if we can come more close to matching isolates. So that was done. And in this, and then we repeated this study with him. The collecting was done by Daniel Loedo and poppy Sefton Clark and my group then repeated the G was and the hit that I wanted to talk about from this analysis was the gene I mentioned one of the genes I mentioned earlier called sgf 29 that is very strongly associated with clinical isolates when we do this test. So this is in this now collection we have a 650 vnb isolates, and we see this mutation really exclusively coming up in clinical isolates that we see loss of function mutation occurring of this gene called sgf 29. This is as I've mentioned is involved in chromatin modification. So might have a lot of pleotropic effects that looks very, very specific for these clinical isolates. And what's interesting about it is that a recent paper has also described it occurring within this, this interesting series of isolates that are linked to the lab strain called h 99, as it's been passage by different groups. And in one of those passages, moving on the right hand side of the slide from this h 99 zero, as it's becoming hyper virulent. It has had this, this paper noted it there's a loss of function mutation of this gene that occurred. And so as I've noted here, this gene is involved in histone acetylation. And so we expect that there might be a consequence of a lot of different epigenetic changes as a consequence of a bit complicated to look into. But definitely intriguing that this might be a common change that's occurring in clinical isolates. One other common mechanism we've uncovered that's occurring in clinical isolates is the aneuploidy, which is very common and has been known about in Cryptococcus work that we've published previously using genomics has has found there's a lot of changes you can detect from using genomics by looking at read. Depth analysis. And of course, landmark analysis from June Quan Chung's group demonstrating that aneuploidy of chromosome one will allow resistance to as all drugs to emerge. We have also identified a case where you could get targeted duplication of genes that can confer drug resistance. And this is an extreme example, where, in this case, you could get 13 times 13 x amplification just of the drug target in this one clinical isolate. So we went back and looked at first just at the active clinical isolates that I described. For ones that display aneuploidy. I want to focus on first the ones in these plots in dark blue that have an entire chromosome that's the aneuploid. So in the phylogeny they're noted with the dark blue circles around the tree. So they're of diverse origin. In the chromosome plot they are their most commonly chromosome 12 and nine but they are multiple chromosomes. The light blue is is partial chromosome aneuploidies which can be of more different chromosomes. And then on the right most part of the slide demonstrates growth data on that showing that the aneuploid grow more slowly. There's a doubling time and both at 30 and 37. There's a significantly slower growth that you observe for the aneuploid cells. And it has also been work from Neil Stone into Hanabek Hanek demonstrating that in a series of isolates from the same patient that kind of echoing what the Kwong Chung lab has shown that you can pick up an extra copy of chromosome one, but this is this is unstable and is confers hetero-resistance to asal drugs over the course of recurrent infections. We do not see that there is very much chromosome one aneuploidy in these act isolates but most of them were taken at the beginning of the trial. So the patients have not yet undergone their full asal therapy yet so that might not be surprising. So most of the isolates were capable of growth on fluconazole only one had this chromosome one aneuploidy and there's some others that were not aneuploid that could grow on fluconazole. But what we see a much bigger picture is in larger cohorts is just a huge bias for clinical isolates to be aneuploid compared to environmental isolates. So here looking at VNB as a whole this group of 650 isolates that we've sequenced we see that 13% of isolates that are clinical are aneuploid compared to 3% of environmental. And again, we see this kind of the same signal of maybe chromosome four and 12 being the most frequent clinically environmental it looks like also chromosome four being is very common but but as much smaller numbers. And we also looked back at a larger set of VN one isolates looking back not to start at our data but other published data so 1400 isolates. And here also we see a very big difference 12% of clinical compared to only 3% of environmental. Again chromosome 12 comes up as very frequent, but now gone different on other chromosomes. And I just wanted to highlight one other piece of data which is that I mean Cryptococcus will will tolerate all the other kinds of unusual karyotype behavior in that it can form hybrids with its related sister species DNA formants. And when it does so it can it, it undergoes widespread aneuploidy and loss of heterozygosity. So this is the example of this is a single genome now of two different a hybrid of both a and D. Looking across the y axis, both these genomes are in a single cell, but in some isolates there's their extra copies of one or the other chromosomes. And in the bottom one wonder the other chromosome might have been lost or gained. And yet when we analyze these at the genetic level, all of these isolates that are shown at the top in terms of SNPs are nearly identical. And if you look at a finer scale at some of these ad hybrids, this is one that has an example of one that has resolved itself to a close to haploid level, but has undergone crossing over between these a and D haplotypes. So rather than this, just this part of the some of these unusual features I just wanted to highlight the level of genome instability that cryptococcus can undergo. That appears very higher, much higher in clinical isolates and question if it could be due to stress or provide some kind of advantage to which we don't yet know the answer. And while we know there's an advantage to chromosome one because of the role in drug resistance. It does seem that other chromosomes are more frequent and whether or not that's just because they're tolerated or if they might provide an advantage. I think it's yet to be understood we know that in Canada. There are particular triploities that provide an advantage for living in certain niches so it seems possible there could be those things to find in cryptococcus. And then I think it's an open question of how when it's, how can cryptococcus can tolerate this level of aneuploidy when it's clearly such a growth disadvantage for example. And then to wrap up the stream of wine scans that I talked to you about. We've begun to show that clinical isolates can be used to find variation linked to melanization and clinical origin and I didn't show data on drug resistance but it's in some of our papers. And the direction we're moving for this work is to increase our power where we're definitely underpowered for the types of variants we're trying to find. And we've also would really like to just measure virulence directly by pooling isolates to be able to kind of side by side be able to associate on violence. We're sequencing additional isolates and working on those additional phenotypes. So at some of the time there remains I wanted to talk about newer work that's ongoing that I thought might be of interest to a genomic audience and it's a comparative genomic project, stepping back thinking about how cryptococcus has evolved and and kind of an unusual finding that came out of that. So again I kind of I started out some of the talk, talking about how cryptococcus is not very closely related to other pathogens but now I'm going to kind of zoom in around it and look at what's kind of immediately outside of it. And it's within this group of species listed here. The cryptococcus neoformins and Gaddii are related to these other species, some other cryptococcus and some other quantiella, some of the names have have changed I'll show on the next slide that are mostly suprobic taxa that have been isolated from different types of arthropods from other types of plants, there's some that are micro parasites or soil. And so we working with Joe Hyman's group at Duke, thought that if we sequence these species related to cryptococcus that might give us a view into how the human pathogens differ. And if we could just zoom into their genomes. So we initially had started this with a luminous sequencing, but we have since moved to completing all of these genomes with long range sequencing and have now this kind of complete view of this cryptococcus in the sister gum genus quantiella at the bottom, and the top group in red encompasses the pathogens. And then there's several cryptococcus below it that are not pathogenic. And then all of the quantiella are just related to suprobic species. So anyway, the pathogens differs that their genomes are a little bit smaller. That's the next. That's the first column. They're about 18 up to 19 meg, the genomes of the other species are a little bit bigger. We have slightly fewer genes as well, but in evaluation of the conserved gene content, all of these look pretty complete in terms of our efforts at predicting genes. So we looked at gene conservation by building orthologs across all of these species and looking at conservation patterns and this is work from Marco Quello at Duke and Sage McGinley Smith, who was an undergrad in my group. And we looked initially at patterns to see what was the predominant patterns but then have have focused in initially on the groups that were unique to some of the pathogens. And this is this group on the those just found in the pathogenic species. And what's striking just for starters is how few there are of them. There's only 53 orthologs, and they include some genes that are of potentially unknown function, like fossil hydrolase and transcription factors, oxydorovid, I say, a whole lot of genes of unknown function. But what they do not include is all of the many of the, and all of the canonical virulence factor pathways, which are the, which are found in all of these species. So when you think about all of the genes required to produce capsule or melanin which we think about as cryptococcus virulence factors, those are found in all species. What we focused on next, that was the surprise that came out of this project had to do with karyotype. When we got to the point of complete genomes, and that while most of these, many of these genomes and all of the pathogens had 14 chromosomes, chromosome number varied from three chromosomes to 14. And that you could infer based on some of the ones with 14 and two of these species that appears that we would estimate the ancestral chromosomes were, that there were 14 chromosomes that we could align. We could paint over the 14 chromosomes between these two species, between these two groups and they look fairly synthetic. But if we look now across the quantiella where the chromosome number varies so widely. And we start painting from one of the ones that has 14 now across the other species. What I hope you can appreciate is that there's been a really unusual pattern of chromosome fusion that has occurred in these species. And in particular, with these the species that have three or five or six chromosomes. There's mainly been one chromosome that's been the object of all the chromosome fusion. And this is just a little more detail on what has taken place. The two species and the top two rows that have multiple chromosomes. What has occurred is a series of fusions where there's been an inversion that is has occurred next to the centromere, which is then lost the centromeres lost in then the fusion chromosome that is then present at the bottom. So this is not some, I'll show a model on the next slide. But in the in the process of all of these fusions it's not just simply chromosomes joining together is a series of inversions linked to centromeres. So all of this this then process probably inactivated all of the centromeres so you end up just with the one centromere that is the ancestral one. One of the thing that we have followed up on is that the centromere length is is pretty different in all of these species. We can predict this computationally by looking at this class of transposons they are. I'm just showing an example on the right hand slide of what centromeres will look like in Cryptococcus neiformans. It's not exactly the same. It's not the same elements in all species but we can identify similar elements at all of the centromeres in the species and further. Marcia Parra in the Heitman lab has has proven these are centromeres using chromatin IP methods. But what is striking is that the centromere size has changed in these species, but it's it's not that the the species with three chromosomes have the the the tiniest centromeres but definitely the Tiniela as a group have very small small centromeres compared to the Cryptococcus. So we're this is a bit of a whiteboard slide to end this part of the talk we're trying to think about how this has occurred. Because we need to join things together while making inversions and inactivating centromeres. This could occur by fusing centromeres and by fusing telomeres and then and then inverting or the other way around. I think some things to add layers into the the ideas we think about is that we we don't see any loss of telomeric genes so there there haven't been telomeres getting chewed away that are kind of instigating the process. And neither really do we see kind of centromere genes kind of adjacent to centromeres being lost. So it's, it's, it's really a fine scale process that kind of initiates this kind of fusion events, and definitely unusual that they're all being joined to a single chromosome. So just to wrap this part of the talk and this is the end of this will be the end is that we are thinking about kind of the evolution of Cryptococcus as you know that a lot of the canonical virulence traits for how what we think about is pathogen are already present in a lot of the sap robes, a lot of the capacity to be a pathogen. So thinking about gene content might already be there, and then we have to understand specialization at a finer scale to think about how the pathogens have adapted to become pathogens. And that the last story is what I just talked about is is thinking about carry type evolution and how this unusual mechanism occurred. I want to acknowledge a large number of people who contributed to the work I talked about. In particular I'll call out poppy September Clark, who did a lot of the Cryptococcus population genomics and sage who's worked on this comparative project. At Duke we worked very closely both with john perfect and Joe Heyman's group, and Jenny and Marco and Marcia in their labs, and then the active clinical trial was a close collaboration with time to be conic and Tom Harrison's groups, and we'd be happy to pause here and take any questions. Thank you Christina and people can go to the microphones in the aisles and also post questions online and we will be monitoring those. And I see people already going to the mics but I'm going to ask the question anyway. I've got a couple. But, so I'll start with one about the the transmissions and and your thoughts that they are coming from the environment into patients. And I wonder, I mean that was originally the model with pseudomonas and cystic fibrosis kids, and then it was shown that on top of that there actually was a fair amount of transmission occurring in the hospitals. And so I guess I wonder if there are any strains like that have that you're seeing repeatedly that maybe would suggest that there is some patient to patient transmission within the facilities and some strains that maybe have optimized a fitness landscape for colonizing human hosts. We are working with a data set now that so the, the, the data that we worked with for the active trial was from these just three hospitals from Malawi but we are working now with a data set that's from a fourth location. And that data set, we definitely have strains that look like they are suspiciously very closely related. And so we have a call next week to talk to the people about this because yes it does look suspicious that it could be, you would you would think it could be transmission but I wouldn't. I've never seen anything like this before, but yes, it does look suspicious, I think that they're, you know, we, we kind of went down this road when there was in the news. Pre pandemic a few years before the pandemic there was a supposed cryptococcus outbreak in this hospital in the UK where they thought they had contamination in like a garden inside the hospital. And so we worked with Andy Borman and a couple other people there and sequenced isolates from patients and isolates they collected from. They tried to collect isolates from the garden and they didn't even get any cryptococcus and so they just collect that they collected a couple other isolates from nearby. The big the long story short nothing was related in that case. So, a lot of these cases there's just a lot of diversity. We do have this one case of this fourth, this other site in Africa where it looks like there could be some genetic relationships. Great, great, great talk. So the the aneuploid sort of lower fitness yet. More common in the isolates sort of locked in a question. Have you ever tried competing a virulent and non virulent in an immunocompromised mouse or rabbit, because I'm wondering if, if it's not just calling I get better at immunizing people but the the immunocompromised environment is a specific niche, and if these these mutations are only useful in that context and actually reduce vigor, and even a wild type or a normal immune, there's no normal person I guess. That's definitely a direction we want to go in that we are putting barcodes into strains so we can start competing them. And I, we had some of our work done I can't cryptococcus but on Canada, we had seen some nice differences in strains in the gut but versus the blood that, yeah, maybe we would start to see things in in certain types of patients I mean we don't. I think for. Yeah, we haven't worked in a. I have to think of I'll have to talk to john perfect about if there's a good, how we're, how we're accounting for the immunocompromised part in the animals. Yeah, that's not. I was wondering about this massive fusion event that you're seeing across chromosomes. Yeah, could that somehow be caused by some sort of sport competition and in myosas where the larger chromosome spores and hurting larger chromosomes or somehow getting advantage over the other spores or if there might be like a selfish element like a spore killer gene that somehow all the chromosomes want to be on that on the chromosome that has the spore killer gene or something like that. Interesting idea we would take any ideas like that because we've been trying to with we've I think we've been stuck figuring like why it seems like it would just be more of a disadvantage to be dragging around that big chromosome during my hostess so yes thank you for the idea. I had an idea and then we'll go to the online and or I had a question sorry to know an idea. And do you think that these could be used as a model for chromosome fusion or karyotype evolution I mean you've got it now but are you seeing this. Are you seeing this happening or could you use these these, you know, species to try to force karyotype evolution and learn more about the process. Well Joe has done an experiment where he has used CRISPR to induce a break and then sees what happens in the aforementioned so I guess you can think about doing those types of things right if your hypothesis is something was initiated by a break. There is, it seems that in the aforementioned that centromere centromeres are also favored sites of rearrangements. So I think that's just part of the story is that that's. I don't know if it's because of proximity if if such centromere clustering is part of what's going on and that's initiating some you've just starting initiating rearrangements, and then something goes awry. Okay, we have a question from Andrew Hazley, or has Lee. Sorry, I don't know how to pronounce your last name. And he asks, could there be an underlying relationship between the anapyloid intolerance and the unique karyotipic evolution in the environmental strains tie between any ploying tolerance and the unique karyotipic evolution in the environmental strains. Well, there seems to be less any ploying in the environmental strains. I think that I mean we've always more worried about like when are we just losing any ploying when we're culturing things, because is obviously since it's, since it's a growth disadvantage we've all we've, we've, we've had some concerns were biased just by just by by culturing we might be losing things. But it seems to be more of a factor in clinical strains. I think that the, yeah, this, the, we haven't, I haven't seen as much of, I think I'm beginning to come convinced that there's a, both the frequency is higher in clinical strains and that there's a specific chromosome. The biggest signals chromosome 12 on in clinical strains. Okay, and then I actually had a question which was, did you look at the bird poop isolate separately from the environmental isolates. I mean I wonder whether passage through a host actually. I mean we know for candidate albicans that passage through a mammalian host increases the mutation rate. I wonder if it's something specific about the mammalian environment or I need so they tend to be more of the vn one isolates that are that are more of the vn one isolates that came from birds as well as vn two. And also, we had this, this, the one case that's been linked to the environment came from a bird, it's, it came from a cockatoo someone had a pet cockatoo and they got cryptococcus from their cockatoo. But there wasn't a series of isolates unfortunately from the cockatoo is just a single isolate. Great, we've got our last question. Yeah. I'm quick question about the select this week in your in your slide. So do you have in your isolate are they all apple type or do you have deep deployed also. My question is, do you have a selective swipe or could you have also clonal interference in your data set. There are some diploid cryptococcus that we we we did look at that separately in the genetics paper that we published but there I didn't talk about any of those here. Okay, thanks so much Christina for a wonderful talking for visiting us and NIH.