 Let's get into the show everyone. It is time to start off with our interview. We're going to be speaking with Dr. Kathleen Lutz, the head of the mouse models repository at the Jackson laboratory. She is also senior research scientist and in her laboratory she focuses on modeling human neurodegenerative disease in mice emphasizing optimum use and best practices for research and preclinical drug testing. She works with the NIH and multiple disease foundations to improve existing mouse models, identify modifier genes and generate new models that will facilitate therapeutic development. Perfect person to be speaking with. Kat, thank you so much for joining us. Thank you for having me. It's nice to be here. You are so welcome. So I reached out to the Jackson laboratory because Justin here said we talk about lab mice all the time. We're constantly talking about what about either stress in lab mice or results of experiments with lab mice, lab rats, etc. And a world lab day came up and we thought what a great opportunity to actually try and elucidate some of these processes and procedures that go on kind of that seem like magic to everyone who benefits from the research that comes out of it. So how did you get into working on mouse genetics in the first place? Oh boy, that's going back many many years. I really did cut my teeth on working with mouse models of different diseases, but in a very different way than we do today. You know 25 years ago when I was a student, we didn't necessarily have a lot of the genetic engineering techniques that we have now. So we used to rely on spontaneous mutations in the very large mouse colonies at the Jackson laboratory. So when you're breeding all of these mice, eventually you'll have a spontaneous mutation that occurs just like sometimes it happens in a human population and you'll usually be able to see that visibly. So sometimes the mice would have a neurological disorder. They might be a little ataxic. They may be waffling. They may have a wasting disorder. A lot of them were the ones that we could visibly see. You know their tails were crooked, their ears were bigger or smaller, there was a co-color mutation and so all of those things that we could see and at the time what we could do is we could look at that, the phenotype of those mice, but what we could see and you know sometimes they were really related to the diseases that we were studying, maybe the mice were obese, for example. And then we would sort of work backwards from the phenotype and say, okay, can we identify the gene? And that took a long time. I think that actually was a large part of my PhD thesis and took the better part of six years and now you can probably do that in a summer. You know, a couple weeks with a good summer student. Is that all because of technology now? Everything is just so much, it's computerized or standardized. Technology, I think with any area, whether it's biology or science or computers, there's always some very big disruptive innovations. You know things that happen in the field, discoveries, inventions, technologies, that completely change the way we do our work and we monitor our progress and the way we do science. And for a long time, it was you know, this is sort of the genetics approaches, those spontaneous mutations that I was just telling you about. And then in the early 90s, genetic engineering really changed with embryonic stem cells and the ability to do genetically engineered mice in a way that you could target a particular gene, any particular gene that you wanted. And that was huge. And for a long time we used those embryonic stem cells and those genetic engineering techniques and mice to really drive forward and manipulate the genes that we wanted to interrogate. So if we knocked out that gene, what would happen? If we made that particular gene a null or introduced a point mutation, what would happen? And then we could study the disease mechanisms that way. Right. Yeah, when I was in grad school in the 90s or late 90s, early 2000s, there was a lot of the knock-in and knock-out work that was being done. And one of the questions there that I've always found really interesting is the, I guess, the off-target effects on the phenotype. So you knock out a gene expecting to have a certain, say, cognitive effect or behavioral effect. But then, you know, what other effects does stuff like that have on the health of a mouse? Right. So that's a really good question and it's a really good point. Because mice aren't humans and humans aren't mice and there's a lot of years of evolution between mice and humans. And so while the genes are conserved and the proteins are conserved and we learn a lot about you know, maybe the regulation of a particular gene, the pathways and the proteins that it interacts with, you know, there are sometimes where we get results in mice that we don't quite expect. Sometimes that actually is very enlightening because if you're always looking under the street lamp, you're only going to find what's under the street lamp. Right. And so when we do make a knock-out or a mutation in a mouse and it doesn't necessarily do what you think it's going to do, that's just, you know, really, you know, sometimes people would think, well, that's really unfortunate. But in the end of the day, it really just tells us more about that gene or more about that protein or more about that disease that we just didn't know before. So it's a big, it's discovery. I mean, and that's what science is. Yeah. So that's an interesting way to think of it. I come from the behavior side of things. And so it's always the, oh, well, that didn't have the behavioral effect that we wanted. Behavior in mice is hard. You know, it's an art. I like to think of it. It's not as straightforward as people, you know, like to think. And there's all kinds of things that affect your experiments, as you know. Absolutely. So I guess from a historical perspective, we started out, as you said, kind of going just from genetic, natural genetic mutations and using those animals. And into this manipulation of embryonic stem cells, knocking in, knocking out. How has this affected how many strains of mice there are? I sort of a picture, especially with the CRISPR-Cas9 that now there's orders pouring in from everywhere to get the specific thing. And I sort of pictured like, well, do they have to open another warehouse? Because there's these changes that they have to keep putting up everywhere. And I suppose some of it is like that. Although I guess the ability to do knockouts and do specific things and not have to sort of wait for the manifestation of an obese mouse to just sort of present itself within the population and start to do the breeding that way. You at the same time can get all these other papers that researchers would have loved to have worked on if they had a model, but now there's a way to get that model. And you still have to do a fair amount of breeding and sort of still isolating that from that breeding pool, if I'm correct. It's not as if it's like, okay, we've knocked out the gene and these two mice and now they'll start the population and off it goes, all those mice are exactly what we want. How does that work? How does that process of you start with we're going to knock out a gene and how do we find the samples that we actually want to send to the researcher? Well, I think the interesting thing, since you brought up the CRISPR-Cas9, I'll just touch on that briefly because that technology is the destructive innovation that we were just talking about. It completely changes the way we do our work, how we approach science, how we approach therapeutic testing as well. So it's orders of magnitude easier with CRISPR-Cas9 than it ever was with embryonic stem cells. Embryonic stem cells, you know, were good, but you lost efficiency along the way. You lost efficiency in the targeting process. You lost efficiency in the germline transmission and the percentage of chimeras. And then if you were so lucky as to get a correctly targeted mouse, then you would have to breed out the selection markers and a lot of other things that went with it. So with CRISPR-Cas9, you can really have your founder lines in one generation instead of a year. And so the time that it takes to do those experiments has been greatly reduced. And so of course the cost has been greatly reduced and now you can do a lot more with the same amount of money when it comes to the targeting. So that's really, really exciting. And I also think it opens up more possibilities because with embryonic stem cells, you know, we were really restricted a lot by the genetic backgrounds that we could work with, because we just never got passed with embryonic stem cells that, you know, there are just particular strains of mice where you could make embryonic stem cells that would be germline confident, but others would not. And, you know, we just never got over that efficiency. But now with CRISPR-Cas9, we can go into any genetic background of any mouse that we want and make these, you know, very, very efficiently. So that's, you know, extremely exciting. And so of course now this whole playground opens up of all those things that you wanted to do, but, you know, either didn't have the technology to do or the time where the money was a little consuming. But, you know, in that respect, I think that you still have to think very carefully about the models that you're doing and what you're making because, you know, sometimes you'll find your answer in a different model organism. Maybe you'll use zebrafish or flies or even maybe even do a cell-based assay. That can provide you with a lot of information. So you have to think about the temptation about going right into the mammalian system, right into the mouse. It's there, and it's easy to do, and it's great. But, you know, there will be a bottleneck down the road, and that will be the space. You know, you mentioned we have to build more warehouses. I think we do. But it's also money, too, because you still have to obtain a type of mice. You still have to be really thorough investigation about exactly what's going on in that system because you don't want to miss anything, and you don't want to jump to a lot of conclusions. And as we all know, we produce ability and being able to replicate these results are incredibly important. So you do have to, you know, think very thoughtfully about what it is you're doing and what the goal is, especially in light of, you know, this huge, like I said, almost a playground in front of you. There's too many things and too many experiments you want to do. So is it, are the orders up? I mean, are you getting many, many, many more requests for, I mean, I can imagine that there's a laundry list of researchers out there who had a study they would love to have done if it was possible to have the model. But now that it is, are they getting funded? Are they getting to go at the green light to do their research, the grants, and then calling you? So that is going. It's a definite uptick. So I think, yeah, it definitely has. There's a lot more interest than we might have had before because of this new technology. But then the other part of it is you pair it with the other innovative technology and that's genome sequencing. And not that the sequencing of a genome is necessarily innovative, but because now the cost of sequencing a genome or even a whole exome sequencing has gone down, that means now we can start to interrogate these patient populations in ways that we didn't before. You know, think about diseases like Lou Gehrig's disease, ALS, Alzheimer's Parkinson's. All of these diseases where we used to have a loved one diagnosed with that disease. You know, we would do our best to care for that patient and that family member. But it wasn't necessarily the case that clinicians would try to gather these patients and try to sequence their genomes or their exomes to try to understand exactly what the pathogenic variant in their genome would be. And now they're doing that and they're doing that with a great deal of success. And so once you've identified the pathogenic variant in the human population, of course, modeling that particular mutation in another mammalian system that you can manipulate more is very powerful. Yeah, it just unlocks all of the ability to actually research and look for things that are preventative measures. Now it's not even the question so much of how is this happening? How do we actually now take that next step and prevent or deal with it or create a therapy? Can you tell me a little bit about the Jackson Laboratory Rare and Orphan Disease Center? Sure. So this is a part of the organization that we thought very hard about, especially because of these two different technologies. Most of rare and orphan diseases are monogenic in nature and that usually just means that they are caused by a single gene. And so if we do have the ability now to do this kind of genome editing for these single gene mutations, we now have the ability to help a population that may have gone essentially with an unmet need, a huge unmet need because the patient population is so small. And there have been some other really interesting developments in rare and orphan diseases, one is that the FDA and the government have gotten together and really have put some incentives in front of biotech and pharmaceutical companies to allow them to hold patents for longer to get accelerated approval for therapeutics. And when you're a biotech company, especially having those kinds of intellectual property in those patents is going to be what makes or breaks you as a company and allows you to move forward. So being able to, I don't want to use the word capitalize because I don't think that that's the right word, but so much investment goes into biotech and pharmaceutical companies. And most of that at the end of the day is not a widget or a gadget, it's intellectual property. And so they need to be able to profit from that in some way that they can go back and reinvest those dollars in the next disease that they want to. They want to look for therapies for. And so I think that the rare and orphan disease center at JAX, what we really want to be able to do is help those disease populations, those patients, those groups with this huge unmet need where we can provide those resources to the biomedical community. We can work with pharmaceutical companies, we can work with biotech companies so that at least they have the starting materials in the form of the mice with the proper genetic mutations and the precise genetic mutations as well. And then they can start to interrogate whether or not their therapeutic is really going to have an effect or not. And then once they get a preclinical indication that gives them and their investors and the FDA a lot more confidence in moving forward with the clinical trial. And then not that maybe I misheard, but you're not also raising orphans for research. But that's got to be a huge obstacle. If there's only a thousand or two thousand or ten thousand, it could be even a hundred thousand, really. And the global population that's affected at any given time by a particular mutation or disease, you're not going to get a biotech to pour billions of dollars into just those people. So there does need to be a way for research to be done than that to see if there's already a therapy out there. There could already be a therapy out there that's already existing that's effective on it. So that's a brilliant benefit, Jax, is applying that. And I think the other thing that while we seem to think of these diseases, their rare and orphan diseases, maybe they only affect a couple hundred people in the population or maybe a couple thousand people in the population at any one given time, a lot of these diseases will have very common underlying mechanisms and very common therapeutic strategies. And so while you're only going after one particular gene or one particular disease, the underlying mechanisms or the application of a particular therapeutic, like an anti-sensit with a nucleotide or gene therapy, while you're only solving maybe that one particular disease today, you're opening yourself up for a whole suite of different diseases that may have an application with your drug or therapeutic. And a quick question that reminds me too, like when we were talking about how the stem cell use was like, it was hit and miss but you learned other things from sort of that process. Is CRISPR giving us that same sort of look or is it not giving us much of an idea of what else is being affected in parallel? I think it's giving us a huge look. And I think that, so we mentioned therapeutics that might be able to cure a disease, but we also talked about things that could prevent a disease. And so one of the things that we're always interested in when we make a mouse model, so for example we have some mouse models of Lou Gehrig's disease, for example, where the mice are very susceptible to that disease and they get motor neuron degeneration. You know, maybe behave classically like we would consider a motor neuron and we think, well that's a really great disease model because then we can test therapeutics in it. But what's even more interesting is the mouse, when you put that same mutation in a different strain or a different genetic background, it doesn't get the disease. And so now you're saying, okay, why is this mouse succumbing to the disease but this other mouse isn't? And it's the same thing for the patient population, right? It may be, or a mutation may be necessary but not sufficient to cause the disease and there could be, of course, environmental triggers but when you're looking at a mouse model, the mice are genetically identical, the patients aren't, obviously. And they're also all in this basically identical laboratory mouse environment. We control the environment, yeah. I mean, I don't want to preempt this part but this is something that's been coming up more and more is actually the environment in which mice are kept and affecting the outcomes of research. Yeah, I was going to ask what temperature you keep your mice at since we talked about that last week. I mean, if a lab has a better result because they have classical music playing, it seems like that's actually in itself, it sounds like, oh great, they got a better result but it's actually kind of disastrous to think about if they're the only ones doing it and it's giving an artificially high result from that lab. Uncontrolled variables. Uncontrolled variables. So how much effort is going in to controlling what you were just saying, keeping that environment controlled? Yeah, so we're very aware of those kinds of effects and how they can really derail everybody's research and confuse the literature and, you know, was that a real result or was it? And, you know, I think the important thing to remember is that in cases where you can't reproduce somebody's results, it's not necessarily the case. In fact, it's very, you know, often not the case or very rare that that person is just being fraudulent, right? They're not making up their data. They're not, you know, trying to get a nature paper, you know, come hell or high water. You know, I do believe, I think we're all in agreement that the results they got at the time that they got them, you know, were real. And so there's a lot of things that can, it's the environment. It could be the reagents that they were using at the time. So we get mice into the Jackson laboratory. If it's a, for example, a behavioral phenotype or something that we may think may be highly variable or a little bit persnickety, you know, depending on, you know, what person's hands they're in, we'll do a second site validation. You know, we'll get those mice in and we'll say, did we see the same exact thing? So most of the time we do in some very subtle phenotypes. We may not necessarily see it, but we allow people to understand what we saw. And of course this all comes out in the literature eventually anyway. If it doesn't come out at, you know, people at meetings just talking about, oh, I couldn't reproduce that part, but I could reproduce this part. And I think at the end of the day, those phenotypes and the things that are going to be the most robust, you know, are going to sort of rise to the top. And we may have to just let some of those very variable portions go. But I think the consideration to try to have the most reproducible results that you can have, you know, right from the beginning, making sure that your experiments are powered the way that they should be, that they're statistically being conducted and blinded and controlled, just making sure that we're not introducing that variability in ways that are not appropriate. Great. Did you see the story last week about temperatures with lab animals and that maybe these mice were too cold? Did you have any ideas about that? Well, I mean, certainly there's a lot of anecdotal information out there from, you know, the sex of the animal caretaker to the temperature of the mouse room to really, you know, what noise is going on in the mouse room, which probably makes a little bit more sense. But then there's also variables, you know, some people will say, well, don't put the cages on the top rack if you really want them to breed because there could be, you know, something about the lights. So there's a lot of different, you know, but there are so many different variables when it comes to that. But I do think there is a lot more room for those kind of animal husbandry types of experiments. You know, just because we're comfortable at that temperature doesn't mean the mice are comfortable at that temperature. And there's also, again, a strain by strain case that some of the mice actually like to be a little crowded. You know, they actually do better when there's more mice in the cage. But for other strains, they don't like that at all. And they do a little bit worse if you're measuring reproductive performance. So you have these instructions then. If somebody is going to be hoarding your mice, like, you know, do not overcrowd this one. This is going to change sort of the viability of your experiment. I mean, do they get like a little list? Don't feed them after midnight. They never get them wet. Do they get this sort of rundown of what to and not to do? Well, I think for most people, their animal husbandry is dictated by their animal care and use committee. And so their veterinarians and their staff will dictate what they think is an appropriate housing condition. And there are, you know, international guidelines along these types. But other people will say, oh, you know, as soon as the female mouse has a litter, you should remove the male because it stresses them out. Right. Well, you know, maybe in some strains, maybe not in others. But we just find that if you do that, you just miss the next estrus cycle and you've just lost a round of breeding and so. But other people feel, again, you know, do you provide enrichment to the mouse? Do you give them toys to play with? Those are the kind of things that actually really introduce those environmental factors that can really change your results. And that's my concern, too, is that if we don't have some sort of a standard in place. And there's international standards, but I'm not really talking about the just basic animal husbandry of the situation, but something like this wheel that you introduce for the mouse. Something like this. And if you have found out that if you keep it cold and there's no wheel, you'll get a much lower results, which you might then use if you're trying to prove the negative. And then, hey, if you really want the high number result, let's get the wheel in here. Let's crank the temperature up to 72 degrees. Let's put the classical music on in the background and we'll avoid the top cage. It seems like the standard needs to be something like this. If they're all going to be in a cold room, let's keep them all in a cold room and that's the standard or whatever that is. Make it a minimum. There should be a maximum standard, too, of the type of gear and entertainment they're getting. Otherwise, it does seem like you're tweaking your result, whether unintentionally or not. I think that, again, so you can control those things and I do think that most people, for the most part, they do. We have, like I said, the ALAC guide for animal care and husbandry that basically sort of set the norm. So if you have a singly housed animal, you need to provide them with enrichment. That's pretty standard in uniform across all institutions, as is the way that we handle the mice and the cage changing and things like that. But the other part of it is there are other factors that are likely to be much larger than that that we can't necessarily control for. So maybe it is your reagents. Maybe it is the food that you're feeding, the animals. Even though in the chow that we give our animals, the protein and the fat content is controlled for what's going on with the way that wheat was stored or that corn was stored that year could introduce a higher or lower level of ribothelavins or aflatoxins or something along those lines. And then let's not forget the microbiome and the pathogens and the opportunistics that if you try to exclude every single organism and have just this pathogen-free animal, and we're not talking about bad pathogens. They would be dead. They would all die. They'd be unable to digest their food. I really think those microbiome experiments are really going to be interesting. So we can control for, and we should control for, the environment as much as we possibly can and standardize. But at the end of the day, I think there are going to be a multitude of factors and we're just going to have to do our best to control for it and make sure that we're running the proper controls in our experiments because you may not be able to get exactly the same result that the other person got. But between your experimental group and your control group, you should be seeing the same, maybe not value, but the same end result anyway. And is it more on the researchers who want to run particular studies to determine the exact strain of mice that they want to use and then based on that strain determine all these factors that we're talking about, whether or not they should be housed in a group or housed individually? Yeah, I think, you know, for the genetically engineered animals, we've been a little bit, as I mentioned, constrained to maybe C57 Black 6 or 129 or the common inbred strains. And so in a lot of ways, you know, we have a small number of genetic backgrounds with a large number of mutations that are introduced onto them. And so by and large, you know, they can change from one mutation to the other, but most C57 Black 6 mice, regardless of the mutations that they're carrying, and that's a huge generality. Obviously, it's not a rule. You know, we'll give you the same litter size. You know, the females still don't like to be disturbed. You know, just leave them alone, let them have their babies and do their job, and you know, you'll have a lot more mice at the end of the day. You know, whereas if you keep, you know, poking your head in the cage and looking to see how they're doing, they're going to get a little stressed out and not like that. Some models and some strains are much more sensitive to those kinds of variables than others. Yeah. So, yeah, you just have to, you have to know your mice, and we certainly provide as much information as we can. You know, we have a whole database and a whole group of technical information support specialists, you know, who will tell you, you know, if your experiments aren't working or your mice aren't breeding or things aren't happening the way you think that they should, you know, we'll give you the benefit of, you know, the 75 or eight years or more of experience that we've accumulated at the lab and pass that on. Someone in the chat room, Ed from Connecticut is asking, to what degree could modeling, computer modeling even replace working with live animals? Is this something that as computer models become more and more sophisticated? Is this a direction that Jackson Lab is Jackson Lab working on this stuff as well? Oh, yeah. I mean, I think that to not take advantage of the computational sciences and the computational network biology, we know so much more about the systems and pathways than we did before. So to not be using that kind of computational biology, you know, I don't really know, you know, the institutions or universities that don't incorporate that, you know, into their science. It's just, you know, another way of doing science. It's computer modeling and you certainly still have to test those hypotheses at some level. And you also still have to fill in all that data that they're modeling in the first place. I mean, this is, I think what his point is, this actually becomes more useful if you're considering running some research and would be able to go into a database and find that not only has this been modeled or this experiment been run once or twice or three times and you might still want to run it yourself. But the computer model can show you what those results are. But the computer model can show you what those results are based on. Three or four disparate studies that all sort of touched on the same gene. But you absolutely have to have done the actual real-world research in the first place. Otherwise, you have nothing to create that model on. That's very true. But I also think, you know, just to add to that, you do have, you know, these databases that have, you know, all of these really great computational software behind them. So for example, if I wanted to make a particular mutation because I thought, well, we saw this, you know, allelic variant in humans and we think it's going to be pathogenic. Hey, let's make it in mouse. Well, before I do that, you know, I'm going to look at the level of the sequence and the genomics and the transcriptome. And I'm going to see if there is cryptic splice variants that exist in the mouse or the human that don't exist, you know, in the other organism or vice versa. And so, you know, you have to take into account, I think, a lot of those changes. And, you know, certainly the network, the networking of genes and proteins and pathways that you can get is really, really interesting. And again, though you're right at, you know, sometimes it'll tell you the experiment, not to do, sometimes it'll tell you the experiment that you think is going to strengthen your hypothesis, but then, you know, you really do have to do the experiment as well. Yeah. And I think there's a lot of computational genomics that is predictive as well. So being able to give you a better guess as to where to look than you would have otherwise. Yeah. And that's really a lot of what the Jackson Laboratory and the Connecticut facility. So we're talking a lot about the mice and the genetic engineering that goes on in the Bar Harbor campus for sure. But we have facilities in Sacramento and Connecticut is our latest campus, which we just opened, that really takes advantage of the genomics and leverages what we know about the sequence and the genomics to really do science in ways that are much different than we used to do before. So from your position, working in neurodegenerative diseases and also in these rare and orphan diseases and kind of seeing the crisper technology come in and disrupt, do you have any thoughts on where you think lab mouse genetics is going to be going in the near future? Yeah. I think it's going to be better. Having just worked in spinal muscular atrophy for the last decade or so, working with those mouse models from the ground up, doing a lot of that work in embryonic stem cells and now seeing at what pace we can accelerate that, now that we have the models and now that those resources are more accessible to the scientific community, I think a lot of that preclinical work will go faster. I think as a community, we'll be more inclined to collaborate and form consortia that will help us solve these problems faster because I think the days of one mouse in one lab, the lab that studies this particular mouse mutation for the last five years, those days are over and that's good. I think it's a really good thing because we need to move beyond just creating the resources. We need to be moving towards using those resources and the applications that we need to get for these therapeutics that we just really, and they're there and they're possible. I mean, for spinal muscular atrophy, I never really thought when I started working in that 10 years ago that we would have drugs and clinical trials that really were as powerful as they look to be at this point, but they're there and I think with this new technology for these other rare and orphan diseases, I think we're just going to get there much, much faster and that's an optimistic, and I am optimistic about it because I do think it's going to happen but it doesn't necessarily mean that there's not a lot of work to do still behind it. It just makes the job a little bit easier but there's still a lot to do. Yeah, and then I guess finally to ask as we end the interview, so World Lab Animal Day, we can take a moment to think about all the animals that help us to come up with our therapeutic treatments, to come up with our understanding of the human body, of the animal body systems, how it all works and how we can fix it when it's broken. I mean, do you see a day? I mean, do you think at any point in the future will we know enough to get rid of the animal models or is the lab animal, is it something that we should respect and understand and accept that we're going to be working with it for a long time to come? Well, I think for sure we'll be using animals and research, I think, just from the standard that we're not ethically going to conduct these experiments in humans. We're just not. But to your point, I think the idea of, and we do this now, I mean, I think every researcher I know in the United States and Europe and around the world are incredibly cognizant of the care of the animals, how many animals we're using. We certainly, you know, the three R's with the reduction of the animals is very much in the forefront of our minds. We don't want to be using animals unnecessarily in the research, whether they're flies or mice or anything else for that matter. So that really goes back to, you know, incorporating all the tools that we have. You know, we may do a cell-based assay and make those mutations in a cell line before we even think about going into mice. We'll do the computational work behind the scenes to make sure that we're not just, you know, taking a wild shot in the dark on a hypothesis. They're going to be well thought out. And I think that that's where we have, you know, good regulatory systems in place, not only in the way that we use animals and house animals, but in the way that we do our peer-reviewed work on our grants. You know, if you're really not putting together a good hypothesis and there's laws in your experimental design, you better believe your review. Committee will tell you that, you know, we're not going to fund you because this is why we think you're wrong. And you know, here are all of our suggestions and you can go back and sort of re-sustain that. And I can picture a day, perhaps a hundred thousand, perhaps sooner, perhaps much longer from now, when a group of, the ancestors of a group of escaped transgenic doogie mice with their accelerated learning and memory discover the archive of hominid research through their archaeologists and discover that everything they need to know about curing any disease that befalls them is there in that archive. I think they'll very much appreciate the work that's been done. Yeah, we're doing a really good job at curing mice of all sorts of things. I think, you know, that's a good point too because, you know, I think in oncology, you know, nothing, that statement is more true than anywhere else where, you know, again, mice aren't humans and humans aren't mice and just because you get a positive finding in a mouse, whether it's a neurodegenerative disease or a metabolic disease or an oncology indication, you know, you really have to think about mechanism. You really have to think about your outcome measures and look carefully because the mice are telling you information, they're not going to, you know, tell you whether or not you have a cure in front of you. And for that, you know, the preclinical model is a way to give you confidence to move forward and the more data that you have and the more thoroughly you can interrogate it and be your worst critic on what exactly does this mean. You know, are the same biomarkers there? Are the mice dying of the same things or suffering from the same ailments of the human patient population? So those are the things that you really have to look carefully and be your own critic on that. And then I think that we'll get to the point where we really believe that, you know, the mice do translate and, you know, and they will translate if you're watching and listening closely to yourself being, you know, maybe overly optimistic or if it's just not the right, you know, area to be interrogating or the right disease to be modeling, then, you know, you may have to go with something a little different. Thank you so much for your time tonight. It's just been wonderful to talk with you about the work you've been doing and just in general how this all works and the, you know... Well, I'm happy to have been a part of the conversation and thank you so much for having me. This was fun. This was great. And for everyone out there, if you want to find out more about the Jackson Laboratory, you can go to jax.org. That is their website. And they're also on Twitter as Jackson Lab. And Kat, I don't know if you're on Twitter or anything, but it was wonderful once again. And thank you for staying up late with us. My pleasure. Thank you. Thank you.