 Good. Thank you. So thanks for the organizers for inviting me and I am fully aware of the fact that my talk which is going to be half an hour exactly is Between you and getting lunch. I know it's been a long session. So I want to Tell a little bit about a work that we have been doing over the last five years. My lab was part of the roadmap project So if you've been working on this just a way of an introduction. I'm actually a physician scientist just like The first speaker so I am an aphrologist. So what I do on a daily basis, I have patients who are on dialysis So we have about half a million patients in the United States and they spend an excessive amount of time over there so it's four hours three times a week and It's not the best to have it so in general just a way of introduction to the kidney the kidney is basically You know a paired organ right at the retroperitoneum and on a microscopic basis it consists of I think you can see this so it consists of the structure, which is called which is called glomerulus where you basically Filter your blood It's actually you filter a lot. So it's about a hundred cc per minute So you filter about one coffee every two minutes And then you as you probably noted you don't pee out 10 buckets Actually 18 buckets of water on a every day so that's because you have these long and Convoluted and different parts of a tubal system which basically reabsorbs the water and the electrolytes and then there's some Some form of a secretion so the the function of the kidney is measured by the the filtering function of this glomerulus and and This is a fibrotic kidney So in kidney disease that we study and cause offensive general disease is basically you get this Scarring of the organ where you lose the epithelial cells and then the glomerulus as well so the function is measured how much you filter and Nephrologists are really simple people. So you filter hundred cc per minute, you know, we like that round number hundred And that's how we measure it So I know you many of the people have this notion that well why to care about kidney disease You have dialysis and transplantation Indeed we do have it But I just want to tell you that if you have end stage kidney disease and you are on dialysis You have about 20 25 percent chance of living through five years And that's just a little bit better than getting lung cancer of AML And then it's actually largely worse than many of the common cancers and Just a way of putting renal cell cancer on this bar on this graph as well So actually the survivor of you know, so cancer is slightly better than being on dialysis. It's not a trivial problem And also it costs about 30 billion dollars a year Which is actually 10% of the Medicare budget despite these patients actually I think consists only 1% of the Of the total population of it So it's quite costly you do better if you get a transplant, but very few people are able to get a transplant So why do people develop kidney disease and how could this always and that's what my lab is trying to understand so as Nasi cox kind of introduced this so it's a complex treat we have Contribution of genetic contribution and then we have these numbers for heritability And now you could see that this is ranges point point three to point seven right now we believe the heritability of GFR among Europeans is somewhere around point three the point seven comes in African Americans For NCH kidney disease and I'm gonna show an example what could explain that actually very high heritability And then in a bunch of environmental factors aging That's why most people It contributes very strongly for kidney disease development Diabetes and smoking and then here you are with kidney disease So how to understand the genetics of kidney disease we have GWAS and I think people have kind of talked about this quite extensively This is the data for the most updated GWAS paper from CKD gen that my lab collaborates quite Significantly and there is a new one in the pipeline this one has about 67,000 participants in it and the new one is gonna have about more than a hundred thousand Cases of European descent and what you see here that some of the loci of these Come out with high significance and then we were able to increase the significance and I actually don't know how many on this part this Graph but right now we have about 67 curated loci that we've worked on that has shown reproducible association in people with European descent in chronic kidney disease development I will talk a little bit about this top locus over here on chromosome 16 and As you know, I you know, we all love geneticists. They already gave a name So we don't have nothing to do with that, you know after that They know what the genes that cause kidney disease Indeed as it was explained in a very beginning We really don't know whether these are the actual genes that underlie the association or cause it related to disease development So as just for many other traits for kidney disease Also, these SNPs are in the non-coding region of the genome So 80% of our non-coding and then we have the questions that have been discussed before that How do these lips actually lead to kidney disease development? So we would just like to know which one is the causal SNP which one is the target cell type Really because I'm a cell biologist mostly So we really would like to know the target genes and then maybe the mode of this regulation would not be as bad as well So what my lab also? This is the framework the way I we think about it And then I think many of the people on the audio thinks about this of how we could understand and Make sense of this G was data So we think that this causal variant somehow localized the regulatory region in a disease-relevant cell type I'm gonna give data and there are papers from John Stam and Brad Ross and also looking at the Kidney associated traits that we believe that actually these cell types somewhere localized in the kidney It's not really an immune phenotype That's what we thought about before as well So the variant should alter the target gene expression in this disease-relevant cell type via Most likely altering transcription factor binding although we could maybe accept other mechanisms What we add to this is that we believe that the target expression Should the target should be expressed than in the kidney And then we also think that the target expression should change in disease states And then we would like to have a correlation how the genotype and the disease state Changes the target expression. So if we the the risk allele is increases the target expression We hope that we find the same kind of correlation if you look at samples from patients with chronic kidney disease and Obviously the target expression should somehow cause kidney disease and therefore should be functional So I will go through a couple of examples So the first one is that this should be localized in the regulatory region in the kidney So to understand that my lab physically Started to develop this fairly large kidney bank. So we have more than a thousand samples at the moment 1200 on the last count and then what we have is Slightly similar for what other G was a day. So this is actually updated with clinical data in real time So we mostly these are collected for unaffected part of tumor nephrectomies and those patients The disease kidney disease incidence is fairly high 20% of them And since the common conditions that caught kidney disease is diabetes hypertension So these are actually quite highly prevalent conditions and people who are getting nephrectomies who are, you know, the usual 58 year old You know males are humans And but what we have built in now that is this data is is updates itself. So we have not just the static Clinical update but it updates over the years as so we have information for functional decline We have done a fairly detailed histopathological examination Which is not just that whether you have a disease or you don't have disease But we use 20 parameters that are we hope to use as maybe as endophenotype So we score different things that people under the microscopes can score of The differentiation of epithelial cells is scarring the inflammatory cells and so on just by visually looking So we have a large efforts to do transcriptome analysis And I think we are about 500 samples that we have done already And because I told you that there are two different segments in the kidney One is this glomerulus, which is the filter and the tubules that kind of process the filtrate So these we micro dissect all samples to glomeruli and tubuli We have epigenome analysis mostly methylation and we are working on what I will show data to isolate different cell types out of the kidney and Make chip-seq based chromatin annotation for them And then we have genotyped all the samples that we have processed using a biobank chip Because it's much cheaper and then obviously we try to integrate all that together to figure out what's causing kidney disease So so the causal variants should be somewhere in the kidney So to do that we get this Kind of organ transplanted kidneys Where we use just the kidney cortex itself or we separate different cell types out of it and using the encode based chromatin on it. I mean chip-seq marks the H3K27S Atylation and K4 monomethylation is an enhancer marks and K4 trimethylation as promoters and K36 trimethylation is as transcribed regions to annotate regions in different cell types So now if you look at the SNPs So we could look at in the kidney and this is just an so-called adult kidney So what do you find is what we find and that's fairly similar what's published is that a large percentage of these SNPs Of the 67 of Locus are actually localized enhancers So this is actually there are several ways to do this This is mapping just the leading SNP that it's published in the paper and then we can kind of enhance this to about 65% if you take all the tagging SNPs in the LD block and then you accept that if one of the LD is actually in an enhancer then you call it as an enhancer, but not more than that For the kidney and that's there is a significant enrichment if we compare it to Like H1 stem cell and the fibroblast This is actually encode data and then we looked at multiple encode cell types. So indicating that the kidney disease associated polymorphisms are localized to Enhancer region in the kidney So now we can do a little bit better than that because We have now these multiple cell types that we make out of the kidney and then we made the maps for these cell types as well And then we can also say that this is actually nurse just somewhere in the kidney But maybe in some enrichment although I would take this with a grain of salt But we see an enrichment that it's somewhere in the cubal epithelium from all the places when we compare it to other cell types that's in the kidney of Glomerular epithelial cells endothelial fibroblasts and misangiosas that seems to be the cell type where where we see kind of more clustering of these CKD associated polymorphisms So that's very nice, but that's computational and then obviously my lab is very interested in the mechanism So we have to actually do the hard work So we have to screen through these enhancers and then show that they are actually Localized to and then act as a regulatory region in the kidney. So to do that we actually use the zebrafish system and this Very nice reporter system where you have a damn cherry Flank by two tall two sites and then we can do large-scale Cloning into it which we got with Shannon Fisher who have helped us quite a bit. So we clone all these so-called putative Enhancers over here and then we use a fish where we have it's a transgenic fish where we label the cubal So the zebrafish has actually just one filter by two little tubes on the side So we label this with green and therefore if we clone in the m cherry We could see that whether it's in the you could screen fairly efficiently whether you see that So here is in the real life. So this is the cube Which is green and this is the m cherry of this is actually that chromosome 16 locus Which we are working on dissecting which had the highest peak on the g was and then we are dissecting into multiple regions And you see that that actually localizes again to the tubal so The histone based on a station and now a validation coincided both of them this region Somewhere in this region is able to drive expression to the kidney. So it's a kidney specific regulatory element So that's very nice the question is obviously which Because we are more cell biology based. What are the target genes of these variants? So this is nice that it's in regulatory region But you know, what are the target transcripts and to do that? We toyed a little bit with the in vitro Transfaction on Luciferous consequence and looking at them But many of these genes actually or putative targets are not expressed in these cell lines that we can easily Transfect so we mostly use looking at Working through this using eqtls which have been introduced before so basically you're looking at the genetic variations and the transcript expression And then so we have because we have a lot of kidneys that are genotype and we have transcript level data then we can Use now a kidney specific data to annotate the variants so So it's it depending on the genotype you see variation in gene expression So this is a result So this is 100 of the kidneys that we have because this is of a more homogeneous See you the scent We feel that's important and then you find you know large number of so-called e genes that are genes that have There are snips that are associated the transcript level changes in the kidney So just to I probably should have introduced that that somehow the kidney is left out of all of these big efforts So GTACs is not very good at collecting kidneys in that big science paper that just came out They had three kidneys although I have to say that they made a major conclusion out of that I'm not 100% sure and I think kidney is being transplanted so it's hard to collect them So I think it's actually a quite useful a unique resource and also in road map John and Brad Bernstein had some kidney data here and there But it was really not well represented even in the roadmap data and it's not part of really encode so Maybe in a way of advertising could be included and yes So this is a so I feel that these efforts are actually quite important So we have number of e genes which is quite consistent of what GTACs is finding and then many of them are seem to be Coal-clad shared genes about one third of this is shared what's now published in GTACs So this is the CCQ to our plot so with hundred samples. We cannot really do trans So this is the SNP location. This is the transcript location and each spot is Represented here if that SNP as significantly regulates a target gene expression and in real life It looks like this. This is I think one of the best EQTL plus that we have so this is this particular variant which could be CC or CT and CT and then you see that this Solid carriers, you know the tubules are mainly, you know, express high number of solid carriers because that's what it function It has to reabsorb salt and water and you see that this variance has a very nice strong effect on on the transcript level of this particular solid carrier and then there is this is another one I showed this because this being proposed by the CKD gen consortium this And they did functional studies indicating that this variance actually Influences the level of this gene. They did not have EQTL data in the paper. What they did is they did a Morpholino based knockdown of this gene in that showed a phenotype, but indeed Looking at the EQTL now. This effect is not as great as this one I guarantee you but there is an association between the genotype of this and the target gene of this and that Seems to validate what it's out there. So doing this obviously If you can see very small fraction of overlap with the CKDG was hits And then you what you could do is obviously you can just look at the G was nips Whether you can find an association for any type of target gene. So to be very transparent Right now, I think we have three or four where we have good statistical significance and then hopefully we will have more maybe by dissection or Other methods that we are doing just in a way of introducing indeed these e-snips are enriched And they are more on enhancers and specifically this is an overlap Of of the tubal cell line h3k4 monometallation and the e-snip location And this is the e-snip these are control slips and you see an enrichment and that is not there If you use other type of regulatory marks and then actually this is also not there if you look at other cell types in the kidney So this is glomerular epithelial cells and midzangial cells. So again somehow Indicating that the tubal epithelial cells may be the important cell type for the kidney and disease development So I'm going to show you an example of that so this is that top hit on chromosome 16 and what you see is this is the The the the snips that are showing the highest Significance and then these are the genes under here similarly that have been shown previously by the other speakers and Well, you probably saw the first Plot under this is something called U mod U mod has a urinary Gene has the name Urine in it so it has something to do with the kidney So that's why this path is actually was labeled with a big sign U mod in the kidney and that's believed to be this snip is actually seems to increase the expression of this gene by Some studies and but we know that the gene expression actually decreases in disease development So the snip should increase the expression of this gene, but in disease the gene expression goes down so we look at this locus again because Now we have Qtl data, but you see is this actually quite broader. So there are a couple of other genes around it as well So this is the locus again. So these are the snips here. This is that U mod These are the other genes over here, and then here is how the Qtl looks. So this is the Transcript expression of the U mod genes there is a little trend for increased expression What has been described in the literature, but it didn't reach statistical significance in our data Then you're looking at the next gene over here, which is actually a gene family a CSM Something to do with asset till co a medium chain I really it's not very well annotated in the literature, but there are five of them and they are right here together and This one did not show a change, but this one if you look at it, there is a very nice Change between the genotype and an expression of this gene and actually the RPKM values for this gene is fairly decent Showing as an e-gene This one did not and this one again shows some association here is not as nice as for this one and the expression of this Gene is actually much lower. So Indicating that for us when we look at this snip which was associated with this gene as a target gene now Maybe one gene away is where we find a significant effect on gene expression So we included two additional card area that the target should be expressed in the disease-relevant issue in the kidney So this is actually an Illumina body map RAC data and then what you see is the expression of these genes of that area in the kidney What you see is this gene U mod that's been proposed to be as highly expressed but our target is also fairly nicely expressed in the kidney and Maybe some expression in the liver, but it's indeed very nicely expressed And then if you look at the protein expression indeed again, it's fairly nicely expressed in the kidney as well Now we also added that the target expression should change in kidney disease development So because we have a thousand samples we can actually look at the correlation of the gene and kidney function because that's a Kidney function really are the changes so going from hundred to zero you see that there is a quite nice R-square and correlation and then that's not just the RNA expression But we can pick random samples from the top on the bottom and then the protein expression correlate with disease development as well So alteration of the target can cause kidney disease. So the target should be functional in the kidney So for this we again use the zebrafish system and the morpholine or knockdown So as I discussed the function of the kidneys to get rid of salt and water if the kidney doesn't function You don't get rid of salt and water and that's represented in the fish as having an edema So they puff up and then they have a lot of it's pericardial edema So they have salt and water in excess and that's what you see if you knock down the the orthologue of this ACSM gene in zebrafish so in Kind of and that's kind of the proposed function of this ACSM It's something to do with acetyl-CoA and fatty acid metabolism Somewhere not much known in the literature So in a conclusion, so we have this roadmap to understand G was associated here I think human tissue samples and especially large number of human tissue samples are really critical to get to this We use the epigenome maps to identify regulatory regions model algorithms to validate the causal variants EQTL maps for targeting Targeting identification and then we look at in addition to that We also look at the correlation of the genes with kidney function because we feel that should be also present and Then use model organisms and the zebrafish has seems to be a fairly quick Screening tool to to figure this out and then I showed you this out of the tree that we have as a hit But mainly this is limited by the EQTLs because right now this is These identify I think just very few variants with significant effect because our sample size is small and Couple of other issues with that so that's this gene and maybe that has to do something with fatty acid metabolism I Don't know about how about with time, but I have few other things that I wanted to share So I will go through that quickly. So, you know that these snips actually explain 2% of the Heritability and then we have about 30 to 70% so what about the others? So these variants, you know explained very little so where is the missing heritability and then there are several things to To think about this more samples deeper sequencing Ethnic groups and epigenetics. I'll show example for two of these One is I think it's absolutely tangential to the meeting, but I think it's a beautiful example of genetics so I cannot Skip that so and that's about different ethnic groups So the first slide that I show you G was was Europeans and then you had these 67 regions each of them adding together maybe explaining 2% of heritability Now if you do the same at mixtures study in black population for kidney disease You get this one and only beautiful big hit on chromosome 22 one hit and That turns out to be a variant a coding region variants in a gene called a poor one So that's very very rare for any kind of complex trade and that turns out to be that there was as evolutionary pressure To maintain that coding region variant because that variant protects people from Tripanosome Isis, which is the African sleeping sickness So I guess shows similarities to malaria and sickle cell rate So this is the same exact story the heterozygote form of this variant protects you from tripanosome And then this is the lysis of the tripanosome By this G1 variant, but if you have two copies of this variant You get kidney disease and then the odds ratios for kidney diseases is not insignificant Go from 2 to 100 X and actually if you get HIV on top of getting this variant It's almost like sure to develop disease with these two Alleles so just in a way of that So we my lab contributed to this by making a mouse model for the variant and indeed if we put these variants into Specific cell type in the kidney which is these grimerulapithelias as you get disease development So indicating that indeed this coding region variant is is is disease causing so That's one way of finding those rare variants with large effects size going into a different population but As part of the roadmap for five years, we were looking at whether epigenetic differences could explain this missing caritability So This is actually just this part of the my talk is pretty much published So we looked at samples of a hundred micro dissected human patient samples kidney samples with different conditions of kidney disease And then this is was my squad dissected and then we looked at Changes in this tubular epithelial cells that we micro dissected from patient samples of a hundred kidneys and with a genome-wide methylation analysis using a Method I would say it's a some something like an MRE chip like a methylation Sensitive isoschism air digestion was developed by John Greeley at Einstein and of course this Illumina 4 to 50k arrays and what we find is that Indeed you can identify this Epigenetic changes in healthy and disease kidneys that are able to cluster normal and disease samples Client nicely at separately and if you look at Validation cohort again, you see that these methylation differences cluster and different in control samples and disease samples But I just would like to show some of the other things. So we get Fantastic p values with even with fairly small samples But what you see is the difference in methylation Differences in absolute values is fairly small. So what you see in kidney disease And I think I see that in multiple other disease conditions. There are changes. They are very Consistent changes we can replicate it in different samples the same changes But the absolute difference in methylation level is fairly small unlike in cancer When you can see a difference going from zero methylation to hundred percent methylation these methylation differences are small and Of course the future should tell whether they are actually significant going through that route we looked at Whether these methylation differences are randomly distributed to the genome or they are maybe on promoters There is a lot of data on promoter methylation differences influencing gene expression But when we looked at by just rap see mapping these Differentially methylated regions were depleted on promoter regions We could hardly find any methylation difference on a promoter and when we looked at by chip seek based annotation Where they are they were actually on enhancers and they were on kidney specific enhancers when we were able to We looked at the nine encode cell lines again So these are small differences on enhancers Therefore we could look at whether they could potentially influence transcription factor binding So we looked at the same computational analysis and we find that they influence several transcription factors one of them was for example 62 and then we found a bunch of others and then I I'm probably very few of them are Nephrologists probably on the audience, but this is actually a very important kidney development or transcription factor So as these two others so it seems that there was some sort of an enrichment on these enhancers that they can computationly bind kidney specific Developmental transcription factors over here Now looking at the other way of whether these differential methylation is actually functional We looked at gene expression by mapping them to the nearby genes and indeed we find correlation between Differential methylation and transcript level differences So maybe these differential methylation actually drive gene expression and if they drive gene expression Maybe they are of course important in disease development. So we had some of like But 40% of them were correlating with gene expression And this is going to be my last slide and they were also again enriched for developmental processes the same you find it when you do Enhancers for HTK for monomatolations again, they are enriched for developmental processes So that correlates with some of the data in the literature that kidney disease may be Developmentally programmed. This is a slide. I borrowed it from Francine Einstein from Einstein So if you feed rats and the control diets and look at the pups versus if you feed rats in a calorie restriction Diet, then you look at these pops what you see is that these pops with a calorie restricted diet develop You know one measure of kidney disease Which they leak albumin in there and that correlates with differences in their epigenome in cytosine methylation level So indicating that maybe indeed they are programmed somewhere early on so this Second set of conclusion is that we find small but highly consistent cytosine methylation changes in kidney disease Tubal samples. They are isolated the methylation changes are enriched on kidney specific enhancers and then they are Enriched on fibrosis and developmental genes are affected more Commonly and maybe that's consistent that somehow this kidney disease has some sort of development or origin Which is being proposed in the literature in the past And I would like to say that most of the work has been done by really talented graduate student He-Yang Ko. She will be here tomorrow and Huigang Yi who is a Informatics person in the lab and the second half of the project is published and that was part of this Roadmap genomics project and we have lots of collaborators who helped us with In the GWAS studies or EQT analysis and many of the other work that we have been doing. Thanks so much