From Mammoths to Humans: The Case for Gene Editing with Eriona Hysolli

(Intro Narrator at 00:00:00) Today, we're catching up with past guest and esteemed biologist, Eriona Hysolli, about what gene editing technology means for the future of human health. You're listening to Joel Beasley, Modern CTO.

(Joel Beasley at 00:00:17) Hello, Eriona. How are you?

(Eriona Hysolli at 00:00:19) Hi, Joel. Nice to see you.

(Joel Beasley at 00:00:21) It's been a minute.

(Eriona Hysolli at 00:00:23) It has.

(Joel Beasley at 00:00:24) How did the whole Jurassic Park thing go?

(Eriona Hysolli at 00:00:27) Oh, at that time, I wasn't even considering Jurassic Park. As you know, I was very much into mammals, and therefore, the correct term is Pleistocene Park.

(Joel Beasley at 00:00:37) So I'm excited to catch up with you. Last time we had you on, you were at Colossal, and you were working on resurrecting the woolly mammoth with DNA you found in the permafrost. Is that correct?

(Eriona Hysolli at 00:00:50) Yeah. DNA that pretty much a lot of people had found previously in the permafrost, including myself, when I actually traveled to Siberia back in 2018. Those samples actually turned out to be amazing. They had probably the longest DNA sequences, at least, that we could find from mammoth samples to date, which was amazing. So who knew a newcomer would actually find the best mammoth DNA in mammoth samples?

(Joel Beasley at 00:01:17) You're lucky. You're a lucky person.

(Eriona Hysolli at 00:01:20) Or maybe I just did it right.

(Joel Beasley at 00:01:23) With the process of extracting the DNA, you did that correctly? Is that what you're talking about?

(Eriona Hysolli at 00:01:27) It was very thoughtful and a little bit expensive for George at the time, for George's lab, because I did have to specifically ship it at controlled temperatures. And so that probably made some difference. And I think, yeah, perhaps it was luck. Perhaps it was my capabilities. Who knows? But suffice it to say, those specimens were amazing.

(Joel Beasley at 00:01:55) And how, just curious, like, how fast can we decode stuff now? So from the time that we talked, maybe four years ago, to right now, has there been improvements in the decoding technology?

(Eriona Hysolli at 00:02:08) What specifically? Could you maybe be more specific on what you mean by decoding? Because it could mean a lot of things.

(Joel Beasley at 00:02:16) Well, I'm so stupid. I only know it means one thing, and that's a literal interpretation of just decoding. Like, from what my basic understanding of it is that there's some computer system where you put the DNA in and then out comes the text translation or whatever of that DNA. Is that not how it happens?

(Eriona Hysolli at 00:02:35) You mean the sequencing. Sequencing, yes. Yeah. Let me walk you through a little bit of the process, but I think decoding is also right. So we'll get to decoding as well in a way. So in terms of sampling the specimen, unless it's really frozen, which in my case, when I sampled those mammoths, they were pretty much very, very hard, like rock hard frozen. So amazing because that also means that they are, hopefully, there's better preserved DNA there. So we had to drill into the specimen. So we made a lot of, like, drill holes all throughout the specimen. But it was for a good cause. So hopefully, that mammoth did not mind at all. It really increased our understanding of mammoth genomes because we could stitch longer and longer pieces together rather than super short ones that usually are found in ancient DNA, even in a really well preserved specimen like mammoths. And that one, apart from the shipping, sequencing doesn't take too long, especially if you have an in-house sequencer, probably a few hours. So that is not, it doesn't take long at all. Maybe prepping the sample just because you have to do a few more steps when it's ancient DNA, especially if you want to enrich some of the endogenous DNA, so the DNA is actually from the specimen, from the species you're looking at rather than contaminant DNA. So there are probably a few more cleanup steps, but a matter of days. And then I think the process of stitching the pieces together, building the full view of the genome, and then the best part, which is trying to compare it to a closely related relative or trying to do comparative analysis with other species. So trying to really understand what you were reading from that genome is actually the fun part. That might take longer because it's more of a discovery process at that point. It's not just about reading the code. It's about trying to understand the code. And so, yeah, that depends on what the question you're trying to address is, but that may take months, may take years. Right? That's the more the discovery and the understanding part that may take a little bit longer.

(Joel Beasley at 00:04:51) Okay. Understanding the genome itself and what it's doing versus sequencing it. Yeah. So you sequence the DNA, and that creates—

(Eriona Hysolli at 00:04:59) That is fairly routine now, even for ancient samples, unless they are very, very, unless the DNA is very, very highly degraded. But I think what's amazing is that paleogeneticists have done such a great job in the past few decades to really get to accelerate the sequencing and the readout of these genomes, even though they're ancient genomes and more degraded, more gaps. As I said, it's on par. Maybe with a few more steps, it's on par with any sort of, like, more modern genome that is more complete. So, yeah, as I mentioned, that process is pretty well established at this point. You can read it very well, and you can read a lot of them at the same time as well. So it's like read a lot of genomes simultaneously and then build them simultaneously. But then the discovery. Right? Try to understand. As I mentioned, try to get information as to what those sequences mean, what that string of letters means for that particular species and try to compare it to other species. In our case, for the mammoth, we were trying to really do a direct comparison of elephants and mammoths since they were the closest related to each other. Yeah. That process can take a little bit longer. Of course, though, nowadays, with a lot more compute power, you can do that fairly quickly as well, but it still will require a lot of the interpretation of data, which, again, will require a little bit more time. But, yeah, it was very exciting.

(Joel Beasley at 00:06:28) You just throw it into Gemini. Just put it into Gemini. It'll come out. Yeah. It's easy. Easy stuff.

(Eriona Hysolli at 00:06:33) That's right. Yeah. Yeah. That's nowadays, I think that's, or at least now and in the near future, I think that's probably the idea and the goal is we can also potentially delegate the learning as well to these very powerful programs, and I think that will accelerate discovery. As I said, probably in a from months to years, really shrink down the process to a matter of days and even more ideally, a matter of hours.

(Joel Beasley at 00:07:01) So you were talking a little bit about the DNA sequencing and having to do it multiple times because of deterioration in the—is it like recovering data from a hard drive where the old animal is like the hard drive and you have to take multiple passes to try to recover the data and you can only recover fragments from each individual sample? Or is that the way it is for everything? Like, if I just give you some blood, like, some direct live tissue DNA type stuff, are you getting a better readout from the live recent stuff, and it's a harder ask from the old frozen stuff?

(Eriona Hysolli at 00:07:39) Yeah. So there are several ways to go about it. So, again, it depends on—I think a lot of people initially, they just take the specimen and do a small readout of the sequencing or do a very quick sequencing analysis to just determine how good the sample is and then determine the next step. So let's say that sample is good enough or decent enough, you can just harvest the DNA or harvest the nucleic acid and then go through the process of making these libraries to be able to read through the sequences. And, generally, they are short reads, but there is enough of that DNA from the specimen of choice that you can just follow a more routine procedure to sequencing or to reading it. And then there are cases where the sample is either too degraded because it hasn't been stored well or it's too old, in which case you have to do a little bit more processing of the sample itself. So maybe you can use some baits, so just sequences that pull out only the sequences you want rather than the contaminants which overwhelm the system if you were to just subject it to reading it just as a population of DNA that you collected from the specimen. Right? So let's say you have, like, 99% of contaminant and only a small percentage of your desired DNA. You don't want to overwhelm the sequencer with the reads you don't want, with the sequences you don't want. So you try to enrich or you try to pick out first the sequences you want, and then you can do a deeper read. And then, of course, if you have more resources and you have more sequencing power, you can really go super deep into the sample no matter how contaminated or degraded it is because, eventually, you'll get to even the rare, those rare reads. So I'm not sure whether it makes any sense. But you do a quick quality check, quality control of the sample to see which next steps to follow into reading the full genome. And then you either try to enrich if it's very heavily degraded or there isn't a lot of it, or you can just do much, much deeper sequencing runs. So you keep sequencing and you keep sequencing, so that means you will eventually get to even these rarer sequences that come, let's say, from the mammoth. In the case of a mammoth, it's, I think, generally, specimens are so well preserved, especially Siberian specimens that have been found, that a more standard sequencing run will work well. But let's say if you find an Alaskan mammoth that perhaps doesn't have the preservation quality that a Siberian mammoth has found, you can do a little bit more of these enrichment steps or deeper sequences.

(Joel Beasley at 00:10:28) Were you guys able to make the woolly mammoth? Do we have woolly mammoths now?

(Eriona Hysolli at 00:10:33) It's a little bit of a mammoth task to make a mammoth. So I think it's still work in progress. But I think there's a lot of very exciting—there's a lot of exciting advancements that come with going after these ambitious projects because you have to build them from scratch, and you have to really do R&D or discovery for multiple disciplines at the same time. It just means that you innovate more on multiple fronts. And so interesting discoveries come from tackling each of those challenges to get to a full woolly mammoth. So I think you can tackle it multiple ways. You can go for full de-extinction right away that takes a little bit more time, let's say, because you have to build the genome from scratch or you have to make a lot more edits. Or you can do functional de-extinction, which is capture the traits that make a mammoth a mammoth and try to engineer those into the closest living relative. That will narrow down some of the number of targets or number of genes you can engineer into an elephant. And so I think that's more sort of like a short-term goal that is very achievable.

(Joel Beasley at 00:11:42) Yeah. So have they made anything? Like, I think they made a mouse or something. Right? Did they make a woolly mammoth mouse?

(Eriona Hysolli at 00:11:49) Yeah. So that's one of the things that I also worked on. When we were thinking about looking at these targets we got from a direct comparison between an elephant and a mammoth. Right? So you want to be able to validate whether those are exactly the targets you'd want that make the mammoth or that make that particular species, that particular organism cold adapted. How would you test that? So in order to shrink the process or shorten the process, we were thinking about doing this in a rodent model. So a smaller model that has a faster developmental rate. So if you compare a mouse development with an elephant slash mammoth development, it's really so much shorter. Twenty days versus twenty months or so. So you can, you are able to test a lot more of these targets, of these genes that make a species like a mammoth cold adapted into a smaller model. And so that's actually what they did eventually in that paper. So that's the embryology workflow that I was able to build in the lab where we could engineer by actually targeting early in the embryo. So doing a lot of the targets super early in the mouse embryo. So that's the workflow that was used for the woolly mouse, which is to try to get to some of these targets that make fuzzy hair and hopefully engineer the right metabolism and the right metabolism or lipid metabolism structure within the mouse such that it replicates that, potentially that of a mammoth. So those are some other things that you can do to expedite discovery and expedite research.

(Joel Beasley at 00:13:35) And then after Colossal, you went and you're at a new company now, or what's your journey been like since Colossal?

(Eriona Hysolli at 00:13:42) So I've always been interested in very ambitious synthetic biology, big synthetic biology projects. And so, back when I was a postdoctoral fellow in George's lab when I was working on the woolly mammoth project, in order to tackle projects like the woolly mammoth project, you have to build a lot of tools in order to target multiple genes at the same time. So I coined the term that George has also coined, so multiplex genome engineering or radical genome engineering. Because you don't want to just change one gene at a time. Let's say you want to really go after multiple. That's what I did when I was in his lab. In fact, my colleagues and I were the first to publish targeting multiple genes at the same time that are in different places rather than these very repetitive sequences of the DNA. So that was quite powerful, and, of course, we were using those discoveries and then to implement changes that make an elephant cell look more like a mammoth cell. In addition to that, really was thinking about how these technologies, of course, will apply to human health and disease later on. And, of course, we do use these kind of technologies, right, these gene editing technologies in current clinical trials to tackle severe debilitating disease. But what if you want to go earlier in development? So to be able to target multiple genes at the same time, but when the embryo is just a few cells old or ideally one cell old. And so that's what I had been thinking for a while, although that is a topic that is a little bit more challenging to tackle as well in public discourse because of some of the charged ethical considerations that come with that work. But either way, I really wanted to pursue it, and so I cofounded a company called Manhattan Genomics. And the goal was to be able to bring all of these radical, powerful, precise genome editing tools earlier in development in order to prevent disease rather than treat it later in adulthood. So that was the goal.

(Joel Beasley at 00:15:48) Was anyone against that?

(Eriona Hysolli at 00:15:57) So there are people who are against it. There are people in favor of it. There are people who are neutral. There are, of course, a lot of voices that are, that definitely have a very strong stance on it, and you tend to see that quite a bit more, especially if you search for the topic. Those voices that usually are opposing can be a little bit more loud. And so they—

(Joel Beasley at 00:16:18) That's how it always works across everything you could possibly do.

(Eriona Hysolli at 00:16:21) Yeah. Yeah. And they give you the impression that perhaps that's what the public opinion is. But as I said, I think just like with any topic, the public opinion is split. So there will be people who oppose it, people who favor it, and neutral.

(Joel Beasley at 00:16:36) Yeah. I don't like, I usually like to be able to, I like to look at things from different perspectives. That's what I like to do, is why I like to interview people and ask questions, and I'm curious. And it's like, man, it's hard for me because even if you have the faith-based community, no specific religion mentioned, but just the faith-based community.

(Eriona Hysolli at 00:16:56) Mhmm.

(Joel Beasley at 00:16:57) Saying, like, it's playing God. It's like, well, you know, I know a lot of people in the faith communities that would say, if you're going to prevent a disease, that's like a known disease and you're gonna prevent it, it's called germline editing. Is that how you say it?

(Eriona Hysolli at 00:17:12) Yep. Yep.

(Joel Beasley at 00:17:13) If you're gonna germline edit out, I mean, how is that not, like, feeding the hungry or, like, taking care of the poor? I mean, that is doing, that's like taking an advancement that God has given us as a society and putting it to good use. So the only people I could see that would be on the opposite side of that are people who are really hard in their ways, and they don't think deeply about it. And they're just scared of the idea versus, like, genuinely understanding it and thinking it through logically.

(Eriona Hysolli at 00:17:49) Yeah. I think here's my perspective on it as a person who favors very, very significantly and deeply opening up the gates to exploring that particular area of research. I think the onus is on us to tell people why it's a good idea and not just verbalize it, but actually provide the evidence for it. Now the evidence also is a little bit hard to gain because of the lack of funding and some of the legislation and policy obstacles related to this work. But the public will probably, you know, the public needs to be informed.

(Eriona Hysolli at 00:18:25) And I think prejudging them wherever they're coming from, whether it's a religious background, whether it's a bioethicist background. And, you know, actually, some of the loudest voices I've personally encountered are actually from bioethicists, with good reason. I think they wanna safeguard some of these, what some of these technologies can be used for, for the public. They really wanna put the right framework. But they also tend to be a little bit, there's so much use for them, but they also tend to be very, very much strongly opinionated when it comes to these topics.

(Eriona Hysolli at 00:18:58) But having said that, I've met a few that are very, very open. They wanna explore it in limited capability or they really wanna completely explore it. Generally, they are in the minority. So I would say the ethicists are probably the loudest voices that I have personally encountered. When it comes to the public, it can be for a variety of reasons.

(Eriona Hysolli at 00:19:15) Of course, the religious community has their thoughts in terms of whether we should be playing God or not, or whether we should just explore more of a natural way of doing things rather than just, like, more of a laboratory-engendered, you know, synthetic, artificial way. People have strong opinions on that. But it's also a lot of academics and researchers who just fear what these tools can be used for or how much we push the limits to these technologies. And I think what I'm advocating for is to do this with the right framework, to implement that framework so we can explore it safely. And especially if you do it in a country that has regulatory bodies and regulatory oversight.

(Eriona Hysolli at 00:20:04) I mean, that already has a framework that protects people from individuals or entities that can take this in nefarious ways. I mean, you can say that about any particular technological advancement. Right? We just do a good job trying to put framework and safety measures so that it can be explored and still preserve the safety of other individuals rather than just shut it down completely.

(Eriona Hysolli at 00:20:29) So I think it's multiple things. I think I would like to see a more concerted effort to just be open to at least having the conversation and open to more of the details of what would you like to see for this to happen. It's like, what are some of the frameworks you wanna implement? So ethical frameworks, scientific frameworks. What should be considered allowable versus not?

(Eriona Hysolli at 00:20:56) And then we can get to work and then generate some of the evidence and the data you need to show safety and feasibility. I'm a scientist. Of course, I've worked on genome engineering tools for quite a bit, so I know the power and how much more precise they are getting year to year. But that's not enough, of course. We have to convince people by generating data that that's the case.

(Eriona Hysolli at 00:21:20) And so we wanna be able to actually generate that data and generate it in such a way that people can trust that data. And, of course, there is also oversight in that. We put some restrictions on what this can be used for and what it cannot be used for. And I think a good use for it would be to prevent genetic disease and prevent debilitating disease because if there's an option, why not? Right?

(Eriona Hysolli at 00:21:43) It is for public good as you just mentioned.

(Joel Beasley at 00:21:46) Yeah. I think we have similar stuff like that with the neural implants. I think there's other areas of science where we have, like, specific use cases where we're allowed to do things like that.

(Eriona Hysolli at 00:21:56) Yeah. That's exactly right. So we're not shutting down other progress because it can be used for nefarious purposes. We're saying, this is what it can be used for. We're putting forward the use cases, and this is what is not allowed.

(Eriona Hysolli at 00:22:13) And let's see where progress takes us.

(Joel Beasley at 00:22:19) I go through moods. Some moods, I'm like, let's do it all. Just whatever we could do to do it all.

(Eriona Hysolli at 00:22:24) Do it all.

(Joel Beasley at 00:22:24) Well, yeah, maybe we should. I think my personal internal dialogue compromises on we should be able to do anything in pursuit of, like, the very difficult, debilitating diseases that cause incredibly low quality of painful life. I mean, if we're gonna innovate like, when I hear about Elon Musk Neuralink, you know, you think about the future where you can just zap around in your head, imagine, go on a vacation, do whatever you want. It's like, yeah, that would be cool. I don't think we should break eggs in pursuit of that. But if we've got someone who's paralyzed, who there's this technology where they can implant and then help them walk, yeah, let's start with that.

(Joel Beasley at 00:23:03) But some people don't even want you to do that. They're like, it's unnatural, and they just don't want you to do it.

(Eriona Hysolli at 00:23:08) Yeah. There's a way to go about it, though, and I'll give you an example because I think it's a really, really good example. So anytime you talk about embryo manipulation, which is what this would be, germline gene correction, germline gene editing, people are fearful. Right? We've already established that, you know, there are quite a few people who have concerns.

(Eriona Hysolli at 00:23:29) Yet the UK paved the way about a decade or so. They legalized a procedure called mitochondrial replacement therapy, where people who have debilitating mitochondrial disease, and mitochondria is, you know, are the powerhouses of the cell, and they contain their own little mini circles of DNA inside. Some of those DNA circles have mutations, and therefore it's just very hard to treat mitochondrial disease. And so what you can do is for future parents, they can replace those unhealthy mitochondria with healthy mitochondria found in a donor egg. So this particular procedure is banned in the US.

(Eriona Hysolli at 00:24:10) It goes by the terminology of three-parent baby. Right? Because you're using a donor egg, and then you're using the parental genomic content to create a baby. So banned in the US, almost universally banned, but the UK legalized this particular procedure for very specific use cases. People suffer from mitochondrial disease.

(Eriona Hysolli at 00:24:37) And they were able, eight healthy babies were able to be born last year, or at least the study came out last year that showed that that procedure worked and worked really well. So there was embryo manipulation. Right? In fact, not only was there embryo manipulation, the way they did the procedure was they had to fertilize a donor egg to be able to retrieve the right sac, pretty much, the egg to be used as a donor. So that means that they also destroyed an embryo in the process in order to create this healthier embryo.

(Eriona Hysolli at 00:25:12) Of course, they put the ethical framework there and it is supported legally. And healthy babies were born, and no one, to my knowledge, took up arms and said those researchers played God. Right? So they really put the right blueprint for a very controversial, or at least what was considered a controversial procedure, and it is considered a controversial procedure here in the US. And they did it right.

(Eriona Hysolli at 00:25:39) So they gave options to parents who don't want their children to have debilitating mitochondrial disease. And I think that's kind of like the approach and the attitude we could take. Once you demystify it, once you take this kind of safe approach and limited use cases to where, you know, you are actually aiming for healthy future generations, all you have to do is implement and let the very, very talented scientists and clinicians do what they do best. And, again, I think if you show that it's very safe and it has important uses, in this case, making healthier humans, no one will be, well, I mean, there will always be people who have an opinion or another, but generally, it will be widely accepted, and that's actually what happened there. So I do consider that a really, really cool case and pretty much a blueprint of how to go about some of these more controversial, currently controversial, ways to tackle future health.

(Joel Beasley at 00:26:40) Yeah. I mean, you're never gonna get 100%.

(Eriona Hysolli at 00:26:43) You're never gonna get 100%. Right?

(Joel Beasley at 00:26:44) Of course not. Some people, there are the people that will just take the opposing position because there's no one on the opposing side.

(Eriona Hysolli at 00:26:50) And I do believe that if you show that the procedure is, like, safe enough and has been used long enough, even though people have strong opinions, if there is need, they change their mind very quickly.

(Joel Beasley at 00:27:00) Mhmm. Oh, for sure. Strong opinions loosely held.

(Eriona Hysolli at 00:27:04) Yes. Strong opinions loosely held. I love that.

(Joel Beasley at 00:27:07) Yeah. Yeah. That's the only way to operate. Because when you start questioning everything and, like, you really think deeply, you realize your capacity to be wrong is so large. And so then it's like you have to have strong opinions in order to operate in reality, but you have to be able to adjust them as you get new data.

(Joel Beasley at 00:27:23) Otherwise, you'll just look stupid. Absolutely. I think, but I'm gonna tell you what I think. You don't have to agree or anything like that. I think we're doing this, like, at a government level, our government.

(Joel Beasley at 00:27:34) I think other governments. I think they're all just doing it. I think you're in this, you're in, like, the private area where you're like, what are we allowed to do in commerce? But I think that they're doing this stuff. Like, why would you not?

(Joel Beasley at 00:27:46) If I were in the government and someone came to me and, like, yeah. We can make these, like, super soldiers. We could do this. We could do that. We could make, yeah.

(Joel Beasley at 00:27:53) I'd be like, okay. Cool. Let's do it. Like, I would greenlight the project so fast.

(Eriona Hysolli at 00:27:59) I will have no comment on the matter.

(Joel Beasley at 00:28:04) I love it. I love it. You made my day.

(Eriona Hysolli at 00:28:07) On the super soldier, you know, without getting too deep into it because I'm not knowledgeable enough, but on the super soldier side, at least we've seen the power of even very cheaply made FPV drones. Why would you even have to make a super soldier? Right? I think—

(Joel Beasley at 00:28:24) That is true.

(Eriona Hysolli at 00:28:25) Just better, better use case to get the confidence. That's why.

(Joel Beasley at 00:28:26) Yeah. Yeah. We're not the eyes on the side. Yeah. Yeah. We want the eye color. We want the, if I have how many gametes, if she has how many eggs, I want the right combination.

(Joel Beasley at 00:28:36) I want, yeah. You know, I'm married, one of the reasons why. And I don't feel bad about this because I just talked to a neuroscientist yesterday who explained that this is something most people do even subconsciously. But one of the main reasons why I picked my wife was because, well, she's very beautiful. She's a tall height for a girl.

(Joel Beasley at 00:28:57) And she also, her brothers and fathers are all, like, huge. They're all, like, really tall dudes. Mhmm. And that to me was one of them. I remember having the thought of, like, okay.

(Joel Beasley at 00:29:10) I'm dating this girl. I know I wanna have kids. If I married her, they're gonna be tall. They're gonna be attractive. Like, this is gonna be a good match genetically.

(Joel Beasley at 00:29:21) And that was part of my decision making process.

(Eriona Hysolli at 00:29:24) Absolutely. I think people will have different opinions on how and why they choose their mate. And that's one area where I just feel like you should have any criteria you want without shame at all. And so, absolutely, especially if it's been ingrained in our brains evolutionarily that there are certain criteria that we would prefer over others, although that changes quite a bit because, thankfully, we're just very, very different. Each one of us is very different in what we want and desire.

(Eriona Hysolli at 00:30:00) But, absolutely, for one reason or another, people pick another person for whatever reason they have, and that should be the case. So it's in a way, it's just selective breeding. You know?

(Joel Beasley at 00:30:12) Yeah. Yeah. For sure. And you know what? When I was, I'm almost 40 now.

(Joel Beasley at 00:30:18) When I was, like, 18, I learned about, like, the concept of eugenics, but I learned about it, like, stumbling into it. I didn't understand the societal energy behind the word. But, like, I was like, why wouldn't we do this? Why wouldn't we set up the human species to be the best it could possibly be? And then I realized you can get really dark really fast with it.

(Joel Beasley at 00:30:44) And that's when I was like, oh, okay. That's why we don't do it and, or we don't talk about it, and it's not like a thing that happens a lot. But it is happening subconsciously for sure.

(Eriona Hysolli at 00:30:55) Yeah. First, I mean, first of all, it did, so I hear that term quite a bit, right, when it comes to what the cases will be for these kind of technologies in the future. And first of all, it depends on how you define it because people use some sort of selection, as we just talked about, for their future children and progeny. I think there is, however, the, so let me say this. At the individual level, people should just be free to pick whoever they want or be able to hold the thoughts that they have.

(Eriona Hysolli at 00:31:27) In terms of what is a more universal criteria, or group of criteria, that's where it gets a little bit tricky. Right? So because what is best? What are you considering best? Let's pick me, for example.

(Eriona Hysolli at 00:31:40) I have zero artistic genes in my background. I think I could be probably a much more interesting human if I had them. Alright? So when you start comparing people in terms of what they are capable of doing, it's just how do you define best given that the biodiversity of thought and the biodiversity of capabilities in the human population, that's what's exciting about it because it's just so different. So if you were to start messing with that a little bit more in traits that have nothing to do, let's say, with just well-being and health, what are we selecting for?

(Eriona Hysolli at 00:32:22) What will the future of our species look like if we then just bottleneck us to just a small number of criteria rather than the beautiful diversity we have currently? So that's why it gets really super complicated, I think, to have that kind of discussion. And I try to keep in the lane of, I know where there is almost universal consensus, which is we all want happy, healthy lives. And so for that, I think these kind of technologies are very, very powerful. In what society prefers to think of as best humans or not, I think there's a reason why we are so genetically diverse. It's made us very, really powerful as a species. And so I just don't have, I think, a good answer or an informed answer as to why we should go that route, the route of start going further than things that make us happier and healthier.

(Joel Beasley at 00:33:20) Yeah. Yeah. So I I'm definitely for, like, being like, if I have the code inside of me, like, if my wife and I together have X amount of options, like things that could happen if we procreate, the ability to just choose without modification to any of them, just to be able to choose which ones, that to me I think is very cool. I don't think there's anything weird or wrong with that. I am curious to know from this concept of, like, real time, I don't know what you call it. I'll describe it. You translate it. Okay? The idea that in my lifetime, like right now, I could have my DNA modified throughout my body to obtain a new trait. Or is that possible? Is that something even theoretically possible?

(Eriona Hysolli at 00:34:15) So let me just describe what's possible now and what the limitations are. I think when it comes to the future, everything is possible. I mean, I just know enough how we progress very quickly to say that just because the limitations are current does not mean that they will be there tomorrow or after tomorrow or a few months or a few years down the line. What is possible today? So what is possible today in a lot of diseases or a lot of targets currently with these gene modifying enzymes, gene modifying tools, pertain to diseases that are simpler, in quotation marks, which is there's a specific mutation or one specific disease that is caused by mutations or changes in one specific gene. And we understand those better because it's a direct causal relationship between that gene mutation and the disease. So that's what usually we are targeting with gene editing tools currently at the adult stage. And even there, we are limited in certain tissues we can go after just because there are delivery—we are working on it, of course, but the delivery vehicles to get that gene editing tool into the right tissue or cell, there's still some limitations to where it goes and where it goes efficiently. So that's at least currently with the current gene editing technologies are usually single gene diseases that are being targeted.

(Eriona Hysolli at 00:35:43) A lot of complex diseases and in the future complex traits, if you're going to go down the trait route, involve more than one gene or more than one target in the genome. So it involves a pathway. And pathway means that genes interact in different ways, not only to the genes itself, but like the genomic information outside of the gene regions. They interact in different ways, and those are a little bit harder to untangle. So you really need to understand them very well before you go and tackle these more complex diseases or complex traits. And that's what people are trying to understand. There's a lot of work in the discovery of, you know, what causes complex diseases like autism, Alzheimer's, schizophrenia. Right? Those are more complex diseases. And then of course traits. What makes you tall? What makes you big? What makes you, pick your trait? Those are a bit more complex because it involves more than one gene or more than one genetic region interacting with another.

(Joel Beasley at 00:36:42) What about eye color? Is that complicated?

(Eriona Hysolli at 00:36:45) I think that is a little bit more straightforward for eye color, I would say. But either way, so a lot of the physical traits are more complex to understand. And that's why I think to even begin thinking of targeting, you really need to know what you're targeting for some reason.

(Joel Beasley at 00:37:03) Really messing with that right now. We're more focused on the other side.

(Eriona Hysolli at 00:37:06) There you can. I think it's more about you want to make sure that what you're targeting is what you're targeting, right? That you're not going to end up with extra limbs, perhaps, maybe, or whatever else.

(Joel Beasley at 00:37:18) If AI mocked it up, we might, yeah.

(Eriona Hysolli at 00:37:20) As scientists, we have to be more precise, right, which is we want to control what we edit. Even if we go down that route, I don't know. Society has to pick, society has to decide. It's not just on me, thankfully. It's not just on my shoulders, you know, where we're going with these technologies. I know where we can go now, but in the future, I think everyone has to congregate and decide in some way.

(Joel Beasley at 00:37:42) What's the coolest thing we can do right now today that we, like, we know we can do it? We can just do it. It's, like, been proven, but we're not doing it.

(Eriona Hysolli at 00:37:53) I mean, you can go ahead and do it, right, if you don't have any strict consideration on the ethics or whether you're going to get any—

(Joel Beasley at 00:38:00) But what's the coolest thing we can technically do today? Can I get wings?

(Eriona Hysolli at 00:38:05) Can you get wings? Can you get you wings? I don't think the pathway for wings is fully understood, but you can try at least some of the targets that have been identified and see what you get if you want to uncover that.

(Joel Beasley at 00:38:17) Yeah. We're discovering a lot. We're, like, experimenting. We're finding targets. We're, like, oh, we think this is connected, or we think. And we're still exploring. Like, some things we know for sure. Yeah. But other things we, like, we think we understand them. Is that where we're at?

(Eriona Hysolli at 00:38:33) Yeah. I mean, there is, so don't get me wrong. So we have targets, right, for each of these interesting traits, whether it's in humans, whether it's in other animals or other species. It's more about, do you want to get to certainty level so that you know that if you were to go and modify it, you know exactly what you're going to get rather than these what are called pleiotropic effects. Like, oh, unknown, what you can get if you target these number of targets, but you cannot be quite sure whether those will guarantee a particular trait or not. So it's more about expanding or refining the findings enough so that you are certain that if you go and do that modification, that you're going to get what you predict you're going to get. And I think now, of course, with AI progress, that's actually what it can help us do. So take all of this wealth of data we have from different kind of species, and we're sequencing more, analyzing more, trying to narrow down specific gene networks or genetic region networks that are associated with anything, any phenotype you want or any disease. That is, that's going to be very, very powerful. So going from just correlated targets to a specific trait to, these are exactly the targets. And so that's going to be super powerful. And of course, even the company I used to work for is very interested in that, of course, trying to identify specific traits that make those species what they are, but even for the future of humanity, whatever that looks like. So having said that, Joel, I think if you want to become a volunteer, perhaps there are avenues.

(Joel Beasley at 00:40:08) What's the coolest target we're confident in that we could do today?

(Eriona Hysolli at 00:40:13) What is the coolest target? I mean, we know for single gene diseases, we know. And those are probably less interesting to you.

(Joel Beasley at 00:40:19) Yeah. Yeah. Those are less interesting. So single gene diseases you guys have for.

(Eriona Hysolli at 00:40:22) I think it's pretty powerful. I know.

(Joel Beasley at 00:40:25) Saving lives is great. Wings, better headlines.

(Eriona Hysolli at 00:40:28) Yeah. I think maybe we can try some hair targets for sure.

(Joel Beasley at 00:40:32) Yeah. Can—is there—

(Eriona Hysolli at 00:40:36) We definitely know what makes, so for example, there are phenotypes to making certain bovine species or bovine male bovines much, much bigger. So bigger cows, right? Because you just think, can you do that after?

(Joel Beasley at 00:40:49) They're born? Can you do it after they're born, or does it all have to be done in, like, embryo?

(Eriona Hysolli at 00:40:53) That is very interesting. I'm not sure that anyone has tried, and I cannot comment on that. But you can certainly modify before, and then you could get to, like, the super massive cows that look like they've worked out all their lives.

(Joel Beasley at 00:41:05) Yeah. Strong cows.

(Eriona Hysolli at 00:41:06) That's strong cows. Yeah.

(Joel Beasley at 00:41:08) That's what we were looking for. We wanted woolly mammoths. We got strong cows.

(Eriona Hysolli at 00:41:10) Those are pretty strong. And, you know, even from the rodent, like, the woolly mouse, it's very cute, right, because we really turned—so I think Colossal's two studies, one on the making fuzzy hair and the other one making just switching the hair color, actually pretty cool as well. Don't you think? Because maybe you can actually do that even with your, just changing the hair color to white. I think we can do that potentially in humans as well.

(Joel Beasley at 00:41:36) That is, that is pretty cool. Yeah. I love all of this technology and all of this advancement.

(Eriona Hysolli at 00:41:42) Hopefully this conversation does not get us in trouble.

(Joel Beasley at 00:41:45) It won't. It won't. We've got great editors. It's fun. It's like we're learning about life, and it's cool.

(Eriona Hysolli at 00:41:51) It's a thought experiment. Thought experiments, all of it.

(Joel Beasley at 00:41:54) Everything is a thought experiment. Everything is purely hypothetical. I'm just a curious monkey over here asking questions. And, you know, I'm interested in the genetic stuff just because of having three kids and, like, watching how different they all are. And our youngest even has Down syndrome, so that was a learning curve for us to have a special needs child. But I'm always fascinated in, like, what we can do to make the world a better place. I mean, how could you not be?

(Eriona Hysolli at 00:42:26) Yeah. Absolutely. Yeah. Very powerful. I mean, I think at the end of the day, for people who are interested in these technologies and how it benefits them and their loved ones, you still need to, as I mentioned, you need to guarantee success. Right? And there's nothing 100% guaranteed anyways even with current, let's say, medicine or current approaches or strategies, but you get as close as you can, and then you inform the individual. We're just not there with some of these more complex traits, as I mentioned, to give you. So for example, if I tell you that you're going to get this, like, amazing—although you, I think the tradeoff here, I think you've nailed that for sure. So you don't need to. But whatever else you're interested in, if I tell you there's only 50% chance you're going to get that, are you going to take that fifty-fifty shot? I'm not sure. Maybe not everyone takes that. But if you get to, like, 99%, I think then it will make you very interested. And I think for anything, regardless of what society decides, I have my goal, but again, I cannot speak for society. You at least want to get to really high level of guarantee or some prediction that tells you that's what you're going to get. And so I think that's pretty important no matter what your, where your thought process is taking you or that thought experiment is taking you.

(Joel Beasley at 00:43:42) Well, I'm excited about the future because, like, I'm excited to see all the undocumented, like, unknown genes and abilities and, like, weird little things people have that just haven't hit an occurrence rate large enough for it to make it into our databases.

(Eriona Hysolli at 00:43:58) We should sequence everyone on Earth.

(Joel Beasley at 00:44:00) Yeah. For sure. I mean, if they want to.

(Eriona Hysolli at 00:44:02) It should be standard. It should—

(Joel Beasley at 00:44:03) Definitely be, like, opt in. Yeah. It should definitely be opt in. But, you know, for a long time, there was a time when someone would say, oh, if I eat that peanut, it's going to kill me, and be like, nah, you're fine. You're fine. Proven. You're okay. You're okay. Now it's half the third grade class. You know? Yeah. Um, last thing I want to just share with you. Have you looked into the regrowth of limbs? Have you looked into where we're at right now with the regrowth of limbs?

(Eriona Hysolli at 00:44:35) Well, I mean, this is just a very, very recent anecdote, right? Because I had never experienced that before. But I was hiking in California, and just in the trail, I see this crow trying to capture a lizard. And the lizard was fighting back, but it lost, it released its tail in order to make an escape, which is very powerful. And then you could see the tail kind of circle and spookily move even with the lizard being gone for a few minutes. And the lizards just do that, obviously, as a protective defensive mechanism, and it's totally fine because they will regrow it. So why don't we, more complex mammals do that? I mean, I think in a way, we do that a little bit when our wounds heal. Obviously, there is a little bit of rejuvenation helping or rejuvenation pathways involved there. But I think, well, not an expert in the field, I think a lot of these species that do have rejuvenation powers are really good models, and I know quite a few people are studying them precisely to uncover those. I understand. It's very exciting.

(Joel Beasley at 00:45:42) Yeah. The, so I don't usually share stuff with, like, oh, this was a cool episode, but this person reminded me a lot of you, and they were studying the regrowth in the salamanders and the different animals.

(Eriona Hysolli at 00:45:56) Of course. Yeah.

(Joel Beasley at 00:45:57) It's very difficult. Yeah. Modeling is good. There was a biological signal between the cells communicating that was emitted that made the decision to either cauterize or regrow. And it's like there's a certain time frame from when the release happens where the cells actually make that decision. I think it was, like, forty-eight hours or seventy-two hours. So he recorded that signal and started playing it back to animals that did not have this, like, as a known trait, and it would grow back. And then they started moving it into human trials. They want to get it on like a—the guy I was talking to was right at the point where they made a discovery, and then he was handing it off to the team that would commercialize the discovery.

(Eriona Hysolli at 00:46:42) Mhmm. Absolutely. That is so cool.

(Joel Beasley at 00:46:44) So cool.

(Eriona Hysolli at 00:46:45) I wonder sometimes because as you grow in complexity with such as the case with complex mammals, it just takes so much longer to probably be able to rejuvenate, let's say, a lost limb. And I wonder whether because it just, it was so resource intensive that evolutionarily it was just decided it's just not worth it, alright? It just takes so much resources, especially back when we didn't have as many resources to be able to sustain an injured individual. And so, but there's no reason why we shouldn't reverse engineer rejuvenation powers of model species. I mean, I think it goes back to that, right? Really understanding genetics, epigenetics, and traits and be able to capture that and engineer them into a completely new genome. Once you understand those relationships super well, I think it becomes a much easier engineering problem. And being able to do that earlier in development would be even more amazing because it's actually more straightforward to do.

(Ariana Hysolli at 00:47:46) So I'm very excited to see where that field is going. Having done a lot of my training in stem cell biology, being able to convert one cell type to another, which happens a lot in rejuvenation, they actually change identity, which is very cool. How to do that time and time again and how to engineer that into humans will be a really super exciting field to follow.

(Joel Beasley at 00:48:06) Oh, my parents' practice, they do a lot of stem cell therapy type of work in there. So I hear about it. I'm not smart enough to talk about it.

(Ariana Hysolli at 00:48:15) To reprogram cells in vitro, but that's because we're capturing in vivo what's happening in our own bodies as well as, of course, in other species, like how they change identity from one cell type to another or reverse or go back in time. There's ways to do that, of course. But for rejuvenation, it's still been a little bit more challenging to capture how that happens. Maybe we need to study more than one organism, and people do, of course. But salamander is such a classic one, and I feel like I have to do a deeper dive as to what else is being studied so that you can try to see converged pathways to achieving the same outcome.

(Joel Beasley at 00:48:57) And then as we start to wrap up, I know your company, your latest thing, it's in stealth mode. Are you sharing anything about it, or is it just leave it at stealth mode?

(Ariana Hysolli at 00:49:08) I think my philosophy is, so people have different approaches or philosophies when they're in a kitchen, right? So some people will probably share the step by step while they're cooking something. I think my philosophy is I want to serve something more mature and more fully baked. And because we're not there yet, I think we'll leave it for another time. So it's gonna be super exciting, of course. We'll still be a lot of convergence of a lot of experiences and expertise that I've acquired and put to good use over the years. And yeah, it will just have to be a little bit more mature for public consumption.

(Joel Beasley at 00:49:51) A hundred percent understand. I think you should build something, and just because I'm biased because I grew up building this type of stuff, but you know so much. If you pair it up with a technology person to build some AI models that help progress this stuff, you would be turbocharging the entire space. But that might not be where your bias is.

(Ariana Hysolli at 00:50:12) AI-powered stuff. Nowadays, by the way, almost nothing in biology is not AI-powered.

(Joel Beasley at 00:50:18) Oh, really? It's all AI-powered.

(Ariana Hysolli at 00:50:20) It's very much facilitating a lot of the discovery process. It's really making scientists' lives much, much easier. And so I think it's almost universal at this point. Maybe there are a few. Like, if you're a botanist, maybe the AI has a trickle down. But even then, I feel like, why wouldn't you use chatbots and so on? Yeah, everyone should. So for sure that is going to be part of the equation for pretty much anything that's built in biology or anywhere else. So that's for sure an ingredient that's part of the recipe.

(Joel Beasley at 00:50:54) Yeah. Well, it's an API for intelligence, and you can apply intelligence anywhere.

(Ariana Hysolli at 00:50:59) But what I will make sure to do is, given how awesome you were at volunteering yourself for crazy ideas, I'll keep that in mind very strongly.

(Joel Beasley at 00:51:10) Thank you so much. Maybe I'll end up with—

(Ariana Hysolli at 00:51:12) We will test your resolve. We will test your resolve.

(Joel Beasley at 00:51:15) Thank you so much for listening. And if you found this episode useful, please share it with a friend or colleague who you think would get value from it. And if you have topics that you'd like to hear discussed on the podcast, either add me on LinkedIn or send me an email: joel at moderncto.io. Every time I get an email or LinkedIn message, it absolutely makes my day and inspires me to keep going.