Quantitative Morphology in Kidney Research
February 13-14, 2012 Conference Videos

Genetic Variation in Podocyte Number Transcript: Lessons From Mice
Erwin Bottinger, Mount Sinai Hospital School of Medicine

Video Transcript

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KEVIN LEMLEY: Our next speaker is Erwin Bottinger from Mount Sinai. He’s Professor of Medicine and he’s Director of the Charles

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Bronfman Institute for Personalized Medicine and he’s going to talk about some work I find fascinating, and that’s what determines podocyte

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number and I think this is something that’s going to help make sense of what maybe we can measure and maybe we can’t but is a very

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important factor we have to keep in consideration. Erwin?

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ERWIN BOTTINGER: Wow. Good morning. I would like to thank Kevin for inviting me to talk to

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you and I feel profoundly humble that will become apparent as I go through the next 20 or 30 minutes with you, apparently simple-minded, and

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so therefore I want to disclaimer. What I’m going to tell you about is not intended to describe methods to take to the FDA as surrogate markers

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or methods that would allow you to assess the clinical condition in a patient and then actually make recommendations about the management of

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the patient. So, we are interested and have been interested for many years in the processes that underlie the degeneration of the kidney and in

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particular, the glomerulus, and so therefore we have in many of our studies recited to experimental animal models and have modeled the

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process of chronic kidney disease and glomerulosclerosis over time in many of these models with the understanding that the key is

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intervening in certain pathways to find out the relevance of these pathways and the progression of disease and therefore, hopefully,

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to identify potentially new targets to prevent or reverse the progression of the disease. So with this preamble let me get straight to the starting

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point when I began to think about some approaches to modeling the fate, I shouldn’t say fate, but modeling podocyte number in our

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experimental animal models. I was struck by these repeatedly appearing cross-sectional cartoon renderings of an artist in Wilhelm Kriz’s

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group where clearly about 15 years ago or so Wilhelm Kriz impressed many of us with a model of the progression of glomerulosclerosis where,

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based on the study of many experimental model systems and very careful morphometry, he basically condensed his view of the progression

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of glomerulosclerosis to a two-dimensional view and a cross-sectional view and showed, again here as a cartoon, but showed a progressive

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loss of podocytes and from largely the periphery, as Kevin just mentioned of the glomerular tuft and where the number of podocytes declined past a

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certain threshold, which I’m sure we will hear subsequent talk by Roger Wiggins, can be determined experimentally. So once the decline is

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going beyond a certain threshold there will be collapse of the underlying capillary unit and sclerotic process with ensue. So about 12 years

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ago or so when I first read these papers I was impressed with this particular process and the rendering of the process and I figured well, you

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know, animal models…can we in a very efficient way apply again such a cross-sectional analysis to understand in those models where this has not

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been looked at, at the time? What happens to the podocyte numbers? So we applied this to the TGF-beta of one transgenic animal model that

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was initially generated in Snorri Thorgeirsson’s lab at the NCI and then extensively characterized by Jeffrey Kopp who is here and his group at

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NIDDK, and what Mario Schiffer and Markus Bitzer in my lab could show in a very crude cross-sectional approach staining sections of

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kidneys from these animals with WT1 that indeed the number of WT1 positive nuclei per cross-section was declining compared to wild type and

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the progressively declining coincident with disease progression. The gray bar here is that, in this context, we can assess the appearance of

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the molecular marker, in this case Smad7, in podocytes and you can see that in the wild-type healthy condition a small fraction of podocytes

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show the positive signal for a marker of, in this case, TGF-beta-induced apoptosis. But as this disease progressed, virtually all podocytes

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became positive for marker of a pathway involved in apoptosis and so therefore giving us clues as to what is going on in a signaling world

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in these cells. So, these kind of relatively crude and initial studies were expanded to a number of other models including the Cd2ap knockout mouse

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model, which is another model of progressive glomerulosclerosis, not segmental I should argue but rather a global sclerosis and again here you

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can see labeling for WT1 in nuclei of podocytes and then counting a large number of glomerular profiles in the large number of animals we could

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again demonstrate that as disease progresses there is a loss of podocyte compared to controls and the important aspect is that here the time

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factor, as in all models that we’ve reported, is showing that this is a dynamic process because in the same genotype Cd2ap knockout at a pre-

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-albuminuric stage the number of podocytes is virtually identical or indistinguishable from the wild-type controls, and only after onset of

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proteinuria do we actually see…[phone rings]…Oh, that’s my dean calling. He says, “What are you doing there?” No; just a little joke. This is

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clearly observed very consistently in a number of animal models. Others include the [---] podocyte-specific knockout, TGF-B receptor 1,

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overexpression in podocytes, etc., etc. Let me move on now to observations that were reported again and have been cited here already, also

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over a decade ago by people—some of them are here in the room—that relate to diabetic glomerular injury and clearly established in a

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clinical cohort setting that depletion of podocytes is, in fact, a rather early feature in diabetics that develop subsequent albuminuria and progress to

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glomerular diabetic injury. So are you all familiar with these studies, at least some of them, the earlier ones, and this applies to both Type I as

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well as Type II diabetes; Mike Steffes in Type I and then Pagtalunan and Meyer in the Type II context. Now, at the time I was participating in

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the NIDDK-funded Animal Models of Diabetic Complications Consortium and what Tom Coffman and Matt Breyer, as other members in the

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consortium, had shown is that certain features of diabetic glomerular injury in mouse models are highly dependent on the genetic context. That is

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in two papers that I’m listing here. It was shown that, in particular, the DBA/2J inbred strain was quite susceptible in terms of development of

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albuminuria as well as mesangial expansion and sclerosis to experimentally induced diabetes, whereas the black 6c57black/6 inbred

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background proved rather resistant. At the time these papers had looked at albuminuria, matrix score, etc., etc., but had not done the careful

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analyses of cell numbers, so we in our group hypothesized perhaps the increase susceptibility in the DBA/2 mice is related to an inherent

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difference in the podocyte number, and so the hypothesis at the time was that perhaps in the inbred strain that’s susceptible, they start out

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with a lower podocyte number per glomerulus and so the environmental injury imposed by diabetes then moves to a threshold of podocyte

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insufficiency and point-of-no-return more quickly and rapidly. So this was the underlying hypothesis and we understood that we needed

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to be more precise in our analysis as compared to the initial studies, and we came up with a revised protocol that is fully automated, that is

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microscopy-based acquisition of glomerular profile images from fluorescence-labeled sections that is then coupled with a software

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implemented in the MetaMorph Suite that determines the number of WT1 positive and WT1 negative cells per glomerular sections. So, here

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what we do is WT1 label as well as synaptopodin label and DAPI label; so DAPI for all nuclei, WT1 podocytes and then synaptopodin

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gives us an estimate of the shape and outline of the glomerular tuft. So, then the software is programmed that when there’s a steep drop in

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the synaptopodin signal plus adding a few microns, that will determine again in an automated way the shape and outline of the tuft that is then

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also used as the surface area for the tuft and then the cells are counted within that outline. So, here is a fake color-coding. You see the

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podocytes and then when DAPI again is blue on the fluorescence but in the software converts it to fake color coding to red, so you can determine

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quite readily the nuclei of glomerular cells and count them and also you can impose parameters with regard to nuclear time dimension, etc., which

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we did. And so, based on these kind of tuning of this method we developed, I think, a fairly robust, absolutely automated and efficient method of

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estimating glomerular cell numbers WT1-negative and WT1-positive cells. This was validated by manual counting and showed a rather decent

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consistency between the automated method as well as the manual counting. Now, here came the big surprise. We felt all good that we would be

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able to show that DBA/2 susceptible strain would have lower podocyte numbers, but in fact we observed just the opposite. I should refrain from

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calling it podocyte numbers; it’s just an estimate. What we’ve clearly shown is that in the susceptible strain there’s a significantly

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increased presence of WT1-positive cells per profile compared to the resistant strain. This was a bit puzzling and we needed to then move on

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and understand, first of all, the factors that underlie the differences in podocyte number, and secondly, what else could account for the

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increase susceptibility as it is relating to podocytes in increased susceptibility to diabetic glomerular injury. So before I go on, we had at

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that time been engaged in an extensive genetic mapping approach using mouse genetic tools with recombinant inbred strains, which I will

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describe in a little while, to try to map the genomic regions for the differences in the podocyte numbers, but I have to say that as we did these

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experiments and repeated the experiments multiple times, initially having significant signals on a genome scan for loci that control podocyte

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numbers, on repeated experiments this was not reproducible. So, that was a disappointment but it compelled us even more to look in a different

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direction, and that was perhaps that podocytes and the DBA/2 strain more susceptible to diabetic injury compared to the resistant podocytes in the

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resistant BL/6 strain. We performed the very initial pilot experiment, Kremena Star at the time in my lab, where she imposed STZ-induced Type I

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diabetes for six weeks on inbred BL/6 and DBA/2 animals, and as you can see in the controls, the estimate for podocytes per glomerular section

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was increased compared to wild type BL/6 as shown before and in BL/6, indeed, there was very little—at least no significant—change in

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WT1-positive cells, whereas in the DBA/2 there was a profound and highly significant loss of WT1-positive cells. So, that led us then to the

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hypothesis that there are factors that we can perhaps approach with a genetic mapping strategy that would allow us to understand

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podocyte depletion as a factor in the development of diabetic glomerular injury. And Haiying Qi, another graduate student in the lab, picked up on

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these initial observations and made her thesis out of this, first confirming what Coffman and Breyer had previously shown, that in fact when we look

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at albuminuria in two models of Type I diabetes STZ as well as AKITA genetically driven model of Type I diabetes over time—here are controls, 3

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weeks, 6 weeks, 12 weeks of diabetes—this compared to resistant BL/6 very significant and rather impressive increase in albuminuria in the

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susceptible D2 strain. Indeed, when we then applied our method of estimating podocytes or glomerular cell numbers, we observed that the

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increased albuminuria susceptibility was also associated with the susceptibility to lose podocytes over time in Type I diabetes and

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already at three weeks of age we could see significant drop in the WT1-positive podocytes. Interestingly, this reached a level of up to 31% at

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12 weeks and I’m sure we will hear some more from Roger Wiggins later on thresholds that would then compel a glomerulus to go down to

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complete sclerosis, but 31% loss of podocytes here at a histopathological level was not consistent or was not associated with a sclerotic

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appearance, I should point out. So having this tool, having established this basic difference in susceptibility to podocyte depletion induced by

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diabetes in these two strains, we recited again to a very powerful mouse genetic model which is the BXD recombinant inbred strain, and just to

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introduce you very briefly to the model system, from the parental strains BL/6 and DBA/2 through intercrossing over many generations suddenly an

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axis of 20 generations in the crossing of sib links or sib pairs in each generation, one essentially creates a fixed inbred genome in these strains

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derived from these initial matings where the genome is a mosaic of the initial parental strains in a fixed configuration. When one applies the

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markers that distinguish the parental strains, one can determine exactly or precisely which region on which chromosome was derived from the

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parental D2 strain or the parental B6 strain and then this goes into the classic approaches to quantitative trait loci mapping; one can then

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assess which of these regions segregate with phenotypic parameters. I should point out that a terrific tool just for reference is the WebQTL

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system developed by Rob Williams and colleagues, and I refer you to Google this and take a look if you’re interested in this type of

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approach. So together with Matt Breyer and Rob Williams, we set out to undertake an ambitious experiment where we subjected 70 BXD strains

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and 5 animals in each strain and the parental strains—you can do the math, it’s quite extensive in terms of animal work—to a long-term diabetes

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protocol where STZ-induced Type I diabetes was sustained for 6 months in each animal, and then animals were sacrificed under perfusion protocol

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and the intent for our group was to apply our methods to quantitate podocytes as well as take a look at the glomerular tuft surface area after

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long-term diabetes. So here you can see, just in the parental strains that were examined in the protocol, we certainly reproduced our initial

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findings that, after long-term diabetes, there’s about a 30-40% loss of or reduction in WT1-positive cells in the D2 strain whereas in the B6

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strain, even after long-term diabetes, there’s very little change in the WT1-positive cell number. Interestingly, this was associated with an

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increase in the tuft area in the susceptible D2 animals. Now, this is a quite illustrative cartoon of the actual phenotype in each one of these

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strains, so here you have the parental strains—Black/6, DBA/2—and then here you have the various recombinant inbred strains, and you can

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see as you would expect that the phenotype of podocyte number of podocyte per glomerular section tracks or segregate, is quite distinct, as

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you would expect, for a genetic model with the two across the strains. So in other words, in most strains or in some strains it’s close to the

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Black/6 and in other stains close to the DBA/2 line, and so that is an indication that, indeed, genetics underlie the loss of podocytes in long-

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term diabetes. With the computational tools available on the WebQTL online system we were indeed able to map two QTLs, rather narrow

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regions on chromosome 13 as well as chromosome 17 in the mouse. You can see the two statistical peaks of the association of

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markers with the phenotype and so this discriminates here that these markers point to regions of the D2 genome underlying the

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phenotype and is statistically enriched in animals with susceptibility to podocyte depletion. So, here is the location and the locus markers. So, there

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are two loci with about 20 genes mapping to each one of those loci. Interestingly, when we applied the same analysis with the change in

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glomerular surface area, again based on a rather crude estimate, we find an identical overlapping peak statistically significant on chromosome 13.

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So, it appears that one locus on chromosome 13 underlies both expansion of glomerular tuft area as well as loss of podocytes, whereas the locus

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on chromosome 17 underlies specifically the loss of podocytes in this animal model system. So to illustrate that even further, we can take a close

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up view of the chromosome 17 locus and you see here—although I apologize, this comes out quite small—that here somewhere these are

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ranked by phenotype, so from very low podocyte numbers in the 7-8-9 range here to rather quite normal podocyte numbers in the 12-13 range

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here you can see that here in green depicts the DBA genome, red depicts the Black/6 genome; you get a very good segregation of DBA genome

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with low podocyte numbers at this locus. So, suggesting some dependency of glomerular section area expansion and loss of podocyte

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numbers, we see that around the susceptible DBA/2 strain, parental strain value for podocyte number or podocyte estimate as well as surface

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area, there’s a cluster of susceptible animals all with the DBA/2 genome content on this locus, whereas in the Black/6 resistant strain there’s a

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cluster of animals that have the Black/6 genome at this locus. So, we are in the process of characterizing the genes that are mapping to

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these loci. Up here you see there’s, again, about 20 genes. There are some that prioritized to underlie this phenotype that have to do with

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control of oxidative stress or generation of oxidative stress, but we really don’t have any conclusive experimental data yet to present. In

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keeping with the theme of this meeting, what I want to summarize and leave you with here is that methods that apply nuclear labeling of WT1

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as a podocyte marker on a two-dimensional cross-sectional glomerulus section prove, when done in sufficient numbers—and I’ll give you the

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numbers below—as robust and enable implementation of high volume experimental protocols that are designed to compare baseline

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and change in cell numbers between experimental groups, specifically dependent on intervention or genetic backgrounds and certainly

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when observed over time. In our case we find it typically sufficient for statistical analysis to have 50 glomerular profiles in each animal, in 5-10

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animals per experimental group, in particular when you are interested in change over time. And so with the method that we have applied,

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we can determine WT1-positive, WT1-negative cells per glomerular section area and give a rough estimate of the glomerular section area in

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terms of micron-square. I’ve shown you a number of the biological results that we obtained from the application of this approach and we

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have identified loci and are in the process of identifying the underlying genes that are related to phenotypic traits in this experimental system. I

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want to close with acknowledging people who have done the work, former lab members on the earlier reports were: Mario Schiffer and Markus

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Bitzer, and the recent work was done that I described to you in large part by Haiying Qi, a graduate student who graduated successfully

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and with honors last fall. Thanks for your attention.

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JOHN BASGEN: John Basgen, Charles Drew University, Los Angeles. Well, I think as you heard I wouldn’t report the results as podocytes but

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number of podocyte profiles per glomerular cross-section area. But my question is or comment, in your early slides where you showed

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the fluorescent immunolabel-like staining and then made the comment that you were surprised that there wasn’t a decrease in the number of

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podocyte profiles in the knock-out or whatever—the experimental animals versus the control—and I want to remind people of what John Bertram

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said yesterday, that when you’re counting profiles and single sections, the chances of hitting big things are greater than little things and

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I’m wondering if the experimental animal was larger in what you were trying to stain and that was the reason why you were seeing

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surprisingly more of them and not that there really were more of them if they were bigger.

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ERWIN BOTTINGER: Thanks for bringing this up. I think those are good points. Clarification in the particular animal model is [---] knockout model,

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what I was referring to was that a point in life of the knockout animals pre-onset albuminuria the appearance of podocyte profiles, if we can

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agree on this term, per glomerular section was indistinguishable between the wild-type age-matched mice, as well as the knock-out mice, and

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only with onset of the process that is indicative of glomerular injury that is reflected by albuminuria did we actually see a change in the

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knockout, but there was no change in the wild-type. So the time component and the dynamic component in these experimental model systems,

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that’s what I tried to point out with regard to the volume and particular characteristics of each podocyte profile in the calibration of the method

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subsequently, then extensive analysis looking at the distribution of nuclear diameter in various models and we found a certain range of the

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nuclear diameter that covered most nuclei on the cross-section, then this diameter was specified; there was a lower diameter and then an upper

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diameter specified in the software that then automatically analyzed all sections. So, there was a specified range of nuclear size.

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JOHN BASGEN: But that data only came from two-dimensional sections.

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ERWIN BOTTINGER: That’s correct. JOHN BASGEN: That, I think, can lead to

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misinterpretations. ERWIN BOTTINGER: That’s correct.

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ROGER WIGGINS: Roger Wiggins, Ann Arbor. Really, the same question. So, have you tried to make a calculation making some assumptions

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about whether…because the mice with the bigger area had the fewer podocytes, so whether that could be accounted for just by the

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change in volume in the glomerular tuft, in other words, the fact that you were counting fewer podocyte nuclei, given the assumptions that

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you’ve made, could you actually be looking at increased glomerular tuft volume? Because one of your variables…actually they were both

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covered by the reduced podocyte number and increased glomerular tuft size were covered by the same marker and just one explanation would

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be that either one or the other is correct. ERWIN BOTTINGER: So the question is whether,

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just by virtue of having expanding into capillary surface—capillary and matrix, whatever constitutes that—as well as increasing

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non-podocyte cell types, potentially does one decrease the podocyte number? I would argue “not” because when we just take podocyte

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number…if you argue that increasing size of the glomerular tuft would reduce podocyte numbers then you should actually see a decrease…

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ROGER WIGGINS: It would reduce measured podocyte numbers, counted podocyte numbers.

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ERWIN BOTTINGER: Yeah, counted podocyte number. Then you should actually see a decrease in the podocyte number with increasing size, but

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when we look at the parental strains that is not the case, right? When we look at the parental strains the increased size of the glomerular tuft is

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associated with larger numbers of podocytes. Am I not making that clear?

00:34:08,066 --> 00:34:10,632
ROGER WIGGINS: Yeah, all right. ERWIN BOTTINGER: So, the starting point is that

00:34:10,633 --> 00:34:24,466
you have more podocyte profiles and a somewhat larger tuft in the susceptible strain and what happens over time in diabetes is that the

00:34:24,466 --> 00:34:39,032
size of the tuft is increasing whereas the number of podocytes over time is decreasing. So, if you explain the decrease in podocyte number just as

00:34:39,033 --> 00:34:48,333
a function of increasing tuft size, where do the podocyte nuclei go? They shouldn’t disappear because we are just doing cross-sections, right?

00:34:48,333 --> 00:34:53,566
ROGER WIGGINS: But you’re not doing serial cross sections, you’re just doing a single cross-section.

00:34:53,566 --> 00:34:58,299
ERWIN BOTTINGER: Right. JOHN BERTRAM: Tough crowd. It is a tough

00:34:58,300 --> 00:35:14,833
problem and I think you’re really actually close to a really smart solution. So I guess the questions John Basgen was raising and Roger is raising is

00:35:14,833 --> 00:35:25,533
this issue of what you’re counting of podocyte nuclear profiles as distinct from podocyte nuclei. I guess what you don’t know and maybe you want

00:35:25,533 --> 00:35:35,033
to know is the number of podocytes per glomerulus and I think I’m like, perhaps, the situation in the biopsies that Kevin talked about

00:35:35,033 --> 00:35:46,666
this morning. In these animal studies you can cut sections of any thickness you like. You’ve got lots of ability to get tissue, you can cut thin sections,

00:35:46,666 --> 00:35:52,332
thick sections using beautiful fluorescent markers and it’s really impressive that you’re using something like MetaMorph to start to do this

00:35:52,333 --> 00:36:02,833
automatically, which is fantastic. I would think you’re close to be able to with thicker sections and you’re making the sections, you’ve got the

00:36:02,833 --> 00:36:09,233
immuno working beautifully, you’ve got MetaMorph doing it automatically in 2D, which is fantastic. With thicker sections you could use an

00:36:09,233 --> 00:36:20,033
optical fractionator approach that I mentioned briefly yesterday and probably even get your technique to count nuclei in three-dimensional

00:36:20,033 --> 00:36:27,533
space even automatically because you’ve got such specific labeling with your WT1 antibody—I think it is to the podocyte nuclei—and then you

00:36:27,533 --> 00:36:36,433
could get, even if you couldn’t do it automatically, you could easily count the number per glomerulus and then Basgen and Wiggins and I don’t ask you

00:36:36,433 --> 00:36:46,866
these kind of questions and we’d all come to you and ask you to use your technique. So I think you’re nearly there, but at the moment you’re not

00:36:46,866 --> 00:36:54,499
actually getting that number of podocytes per glom and I think if you knew that you could do that, all these issues of “did the glom get bigger,

00:36:54,500 --> 00:36:59,933
did the glom get smaller, did the density change,” they’re gone. Those issues don’t matter.

00:36:59,933 --> 00:37:07,233
ERWIN BOTTINGER: Well, I’m glad I came here and I can pick up those key insights from experts to improve the method. I should point out that as

00:37:07,233 --> 00:37:19,499
long as we can apply an improved method that would get closer to the stringency that you guys are applying to more than 10,000 glomerular

00:37:19,500 --> 00:37:30,033
profile, which is what the mapping experiment entailed, as long we can apply it to that with a reasonable effort I think I would welcome any of

00:37:30,033 --> 00:37:32,666
these… JOHN BERTRAM: I think you’re very close

00:37:32,666 --> 00:37:40,699
because the hard part of the labeling and the whole complexity and sophistication of the genetics approach is incredibly powerful. But I

00:37:40,700 --> 00:37:48,933
think with not a lot more development you could probably do this really nicely, whether you could do it automatically, you know push a button and

00:37:48,933 --> 00:37:57,333
five minutes later it goes “bing” and it says there’s 156 podocytes, I don’t know; that’s our dream. We’re actually trying to do that but I think you’re

00:37:57,333 --> 00:38:00,733
further advanced than we are, but I’d be happy to talk to you about that.

00:38:00,733 --> 00:38:07,866
ERWIN BOTTINGER: Thank you. PAUL KIMMEL: Very pretty data, Erwin. So, just

00:38:07,866 --> 00:38:19,032
to continue the theme of clinic relevance which we’ve been marking for the last day, I just want to know whether the loci you identified were

00:38:19,033 --> 00:38:28,666
associated with renal functional aspects in the animals, such as any kind of estimate of the glomerular filtration rate or proteinuria, to be able

00:38:28,666 --> 00:38:36,732
to link the podocyte changes to the disease? You’d expect them to be co-segregated.

00:38:36,733 --> 00:38:50,333
ERWIN BOTTINGER: Yes. I can say we just finished a pilot study where we used an inhibitor to the top candidate on chromosome 17 and…

00:38:50,333 --> 00:39:00,833
well, let me get back to that. First, the top candidate we considered on chromosome 17 was also increased, a gene product was

00:39:00,833 --> 00:39:11,266
increased or a modified gene product was increased in the circulation in the susceptible animals. When we use an inhibitor we prevent

00:39:11,266 --> 00:39:25,699
albuminuria and loss of podocyte profiles, and it has to do strictly with reactive oxygen species.

00:39:25,700 --> 00:39:36,500
PAUL KIMMEL: But does proteinuria in the animals associate with the loci that you showed us or is it significant only for…?

00:39:36,500 --> 00:39:37,533

00:39:37,533 --> 00:39:42,366
ERWIN BOTTINGER: Yes. This is data from…we were not measuring the proteinuria, this was

00:39:42,366 --> 00:39:47,266
measured by Matt Breyer. He was taking on that phenotype===.

00:39:47,266 --> 00:39:47,899
PAUL KIMMEL: But you can see they’re links to the same loci?

00:39:47,900 --> 00:39:48,600

00:39:48,600 --> 00:39:51,100
PAUL KIMMEL: Very nice. Thank you.

Date Last Updated: 10/3/2012

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