WEBVTT - Understanding Obesity and Alzheimer’s via Epigenomics 0:00:15.356 --> 0:00:28.996 Pushkin. I'm Jacob Goldstein and this is What's Your Problem, 0:00:29.076 --> 0:00:31.196 the show where I talk to people who are trying 0:00:31.236 --> 0:00:35.716 to make technological progress. My guest today is Manola's. Kellis 0:00:36.196 --> 0:00:40.236 Manola's is a professor of computer science at MIT, and 0:00:40.276 --> 0:00:44.956 he works in computational biology. It's a field where researchers 0:00:44.996 --> 0:00:48.516 take giant data sets relating to things like genetics and 0:00:48.636 --> 0:00:52.596 health outcomes and try and understand basically what's going on, 0:00:52.916 --> 0:00:56.316 things like what are the cellular mechanisms of disease and 0:00:56.476 --> 0:00:59.476 how can we intervene to keep people healthy. In particular, 0:00:59.836 --> 0:01:04.836 Minola's research focuses on genomics and a related field called epigenomics. 0:01:05.196 --> 0:01:07.236 Here's how Manola's explains. 0:01:06.836 --> 0:01:13.276 What that means. What's extraordinary with genomics is that we 0:01:13.356 --> 0:01:18.476 can see beyond the limits of human imagination. We're talking 0:01:18.476 --> 0:01:21.916 about millions of cells across hundreds of people, across thousands 0:01:21.916 --> 0:01:25.356 of genes, and now we can now look at how 0:01:25.596 --> 0:01:29.916 the single genome manifests in every cell type of the 0:01:29.996 --> 0:01:33.516 human body in a slightly different way to create this 0:01:33.596 --> 0:01:38.836 extraordinary symphony that is the human life, that is human thought, 0:01:38.876 --> 0:01:43.556 that is human understanding, cognition, and every biological process that 0:01:44.236 --> 0:01:48.636 ability to now start understanding the building blocks of how 0:01:48.996 --> 0:01:53.476 this human genome manifests into all of these myriad of 0:01:53.516 --> 0:01:57.636 cell types and their interactions and their combinations and their 0:01:57.676 --> 0:02:02.596 coordination and their communication is what we can do for 0:02:02.636 --> 0:02:04.956 the first time. They're also giving us the entry points 0:02:05.516 --> 0:02:10.116 for understanding the basis of human variation, the basis of 0:02:10.156 --> 0:02:13.836 human disease, and the basis for reversing human disease. 0:02:14.276 --> 0:02:17.116 So that is the very big picture view of what 0:02:17.196 --> 0:02:20.436 Manola's does. In our conversation, we got into a lot 0:02:20.476 --> 0:02:23.916 more detail. For one thing, Manola's talked about his work 0:02:23.956 --> 0:02:28.196 on obesity, and that work is based on epigenomics, which 0:02:28.276 --> 0:02:31.676 is basically the way in which different genes are turned 0:02:31.836 --> 0:02:34.236 on and off, and this turns out to be a 0:02:34.276 --> 0:02:37.396 really big deal. Manola's and I also talked about his 0:02:37.556 --> 0:02:40.996 work on Alzheimer's disease. In that part of the conversation, 0:02:41.356 --> 0:02:43.556 he talked about how he and his colleagues are trying 0:02:43.596 --> 0:02:48.156 to find these key biological pathways that contribute to lots 0:02:48.196 --> 0:02:50.676 of different diseases, and how they're trying to come up 0:02:50.676 --> 0:02:55.036 with drugs to target those pathways. We started our conversation 0:02:55.396 --> 0:02:58.516 by talking about Manola's early work on the human genome, 0:02:58.676 --> 0:03:00.516 which led to the work he's doing now. 0:03:00.636 --> 0:03:03.076 So the human genome was mapped by K ninety nine 0:03:03.156 --> 0:03:05.396 or two thousand and three, depending on how you count. 0:03:05.476 --> 0:03:08.196 And then we had all of the nucleotides, all of 0:03:08.236 --> 0:03:13.676 the letters through into billion letters. Then the hard part begins, 0:03:14.036 --> 0:03:15.796 how do you make sense of that book? So that 0:03:15.876 --> 0:03:17.516 was the Book of Life. So we had all of 0:03:17.516 --> 0:03:18.916 the letters, how do you make sense of the book? 0:03:20.076 --> 0:03:26.596 My own PhD was developing evolutionary signatures for understanding systematically 0:03:26.916 --> 0:03:30.676 the human genome. So how do you recognize where are 0:03:30.716 --> 0:03:32.956 the protein coding parts? What are the parts that code 0:03:32.956 --> 0:03:34.476 for protein? We didn't even know. 0:03:34.676 --> 0:03:39.196 And just to be clear, sort of non intuitively, most 0:03:39.236 --> 0:03:43.116 of the human genome is not protein coding, right, Like 0:03:43.156 --> 0:03:46.676 there's this very basic idea that like, oh, sure the genome, 0:03:46.676 --> 0:03:48.876 that's what codes for proteins, but in fact, most of 0:03:48.916 --> 0:03:50.396 the genome is not doing that. 0:03:51.196 --> 0:03:55.156 Ninety eight percent of the human genome does not code 0:03:55.156 --> 0:03:55.676 for protein. 0:03:55.796 --> 0:03:58.876 It's wild. That is so nonintuitive, correct. 0:03:59.236 --> 0:04:02.916 So in that mysterious ninety eight percent of the genome 0:04:03.396 --> 0:04:07.836 lie control regents that are responsible for turning genes on 0:04:07.876 --> 0:04:13.756 and off. And that's where ninety three percent of the 0:04:13.876 --> 0:04:16.556 disease associated genetic variants are sitting. 0:04:17.156 --> 0:04:20.876 Huh, it's not the genes that actually code for proteins, 0:04:20.956 --> 0:04:24.476 it's the genes that control when are proteins made, when 0:04:24.476 --> 0:04:25.996 are they not made, how much are they made. 0:04:26.076 --> 0:04:26.836 That's exactly right. 0:04:26.916 --> 0:04:31.636 Okay, so I get that in a broad sense. That's 0:04:31.676 --> 0:04:34.036 sort of the state of affairs when you're coming into the. 0:04:34.036 --> 0:04:37.556 Field's exactly right. So I wrote a series of papers, 0:04:37.636 --> 0:04:40.036 both as a student and as a faculty member that 0:04:40.196 --> 0:04:44.276 sought to then uncover how to even parse the genome, 0:04:44.316 --> 0:04:46.636 how to even start understanding reading that book of life. 0:04:47.076 --> 0:04:49.796 So that's one part. The second part is where the 0:04:49.836 --> 0:04:53.076 regulatory motifs are. What are regulatory motifs. They are the 0:04:53.116 --> 0:04:57.556 short words of the language of DNA that are bound 0:04:58.116 --> 0:05:02.196 by regulators to turn genes on and off. So there's 0:05:02.236 --> 0:05:06.716 these regulatory regions, and within these regions lie these words 0:05:07.676 --> 0:05:09.556 which are the regulatory mode. 0:05:09.716 --> 0:05:13.756 And just to be clear, the regulatory motifs are part 0:05:13.756 --> 0:05:17.396 of what determine sort of when and how much different 0:05:17.436 --> 0:05:19.116 genes express different proteins. 0:05:19.196 --> 0:05:21.636 That's exactly right, that's exactly right. And that's where the 0:05:21.716 --> 0:05:24.476 human epigenome comes in. So what we needed to now 0:05:24.556 --> 0:05:28.156 understand is how that genome turns to life. So you 0:05:28.196 --> 0:05:30.756 can think of the epigenome as the living genome, as 0:05:30.796 --> 0:05:33.836 the genome. There's the genome itself is static. It's just 0:05:33.876 --> 0:05:36.316 the book the tablets, if you wish that Moses brought 0:05:36.356 --> 0:05:40.076 down from the mountain, and then the epigenome is the 0:05:40.276 --> 0:05:43.796 music that gets played from the orchestra. The epigenome tells 0:05:43.836 --> 0:05:47.116 you which parts are active in the brain and the liver, 0:05:47.236 --> 0:05:49.236 and the heart and the muscle and so and so forth. 0:05:49.796 --> 0:05:53.156 So your work on the epigenome is really interesting to me. 0:05:53.676 --> 0:05:56.356 And I know you've done some work on obesity, and 0:05:56.396 --> 0:05:58.996 the epigenome tell me about that. 0:05:59.436 --> 0:06:02.796 The strongest genetic association with obesity sits in one gene 0:06:03.476 --> 0:06:09.676 called FTO, and FTO was renamed fat and obesity associated 0:06:09.716 --> 0:06:13.716 after that discovery, and it remained mysterious for seven years. 0:06:14.036 --> 0:06:16.436 People had no idea how that gene works. 0:06:16.836 --> 0:06:17.876 You just saw correlate. 0:06:17.956 --> 0:06:18.676 There was a correlation. 0:06:18.836 --> 0:06:19.516 There was a correlation. 0:06:19.836 --> 0:06:22.316 Just the problem of genetics and the beauty of genetics. 0:06:22.356 --> 0:06:24.716 The beauty of genetics is that it tells you what 0:06:24.996 --> 0:06:28.516 region is responsible for disease. Regardless of how it functions. 0:06:29.156 --> 0:06:32.036 The downside is that it after he tells. 0:06:31.756 --> 0:06:32.836 You it's the same thing. 0:06:33.596 --> 0:06:36.076 After it tells you that he has a role, you 0:06:36.116 --> 0:06:40.596 have no idea how it functions. And what we showed 0:06:41.196 --> 0:06:45.956 in our work is that that region doesn't affect the 0:06:46.076 --> 0:06:47.276 FTO gene at all. 0:06:47.356 --> 0:06:50.556 So like in the middle of a gene, there is 0:06:50.596 --> 0:06:56.316 this whatever series of nucleotides, but those those nucleotides are 0:06:56.356 --> 0:06:58.396 just randomly in the middle of that gene and actually 0:06:58.436 --> 0:07:00.156 have nothing to do with that gene. I didn't even 0:07:00.156 --> 0:07:01.276 know you could do that. 0:07:01.236 --> 0:07:07.156 Fairly, you can't. So there are eighty nine differences, eighty 0:07:07.236 --> 0:07:11.876 nine common variants, common genetic variants that are all coinherited. 0:07:12.236 --> 0:07:14.596 If you get a here, you get all of the 0:07:14.636 --> 0:07:19.116 other you know, actage, you get that passage. If you 0:07:19.116 --> 0:07:22.116 get that package, it spans fifty thousand letters. But there 0:07:22.156 --> 0:07:25.956 are only eighty nine differences in these fifty thousand letters. Wow, 0:07:26.396 --> 0:07:32.116 and these will increase your body weight by one standard deviation, 0:07:33.676 --> 0:07:35.676 which is like how much it's like nine pounds, Like 0:07:35.756 --> 0:07:38.636 it's a lot, okay. So so basically what that does 0:07:39.196 --> 0:07:42.756 is that it functions somehow to increase your risk for 0:07:42.756 --> 0:07:46.516 a basits, it's like the strongest genetic association before. And 0:07:47.276 --> 0:07:50.356 what we reason is, how could it be acting. It 0:07:50.396 --> 0:07:52.596 could be acting in your brain to decide whether you 0:07:52.716 --> 0:07:55.116 like sweets or salting. It could be acting your muscle 0:07:55.156 --> 0:07:57.196 to make you more fit or less fit. It could 0:07:57.236 --> 0:08:00.916 be asking in your digestives. So we basically said, okay, 0:08:00.996 --> 0:08:03.716 well that's speculation. Let's look at the data. And we 0:08:03.796 --> 0:08:05.716 looked at the data and we found that there was 0:08:05.796 --> 0:08:11.316 this massive control region that was active in mesenchymal stem 0:08:11.356 --> 0:08:13.916 cells what are mesimo cells and sells. They are the 0:08:13.956 --> 0:08:21.036 progenitors of brown fat and white fat. Now, white fat 0:08:21.396 --> 0:08:24.836 is white because it's full of lipids. That's where all 0:08:24.876 --> 0:08:28.276 the calories are stored. Brown fat is brown because of 0:08:28.316 --> 0:08:31.076 all of the iron in the mitochondria. That's where the 0:08:31.076 --> 0:08:34.156 calories are burned. So it turns out that our fat 0:08:34.196 --> 0:08:38.076 cells make a developmental decision in their first three days 0:08:38.076 --> 0:08:41.756 of differentiation to go down the white path lineage or 0:08:41.916 --> 0:08:45.396 the brown path lineage. And what the white fat does 0:08:45.636 --> 0:08:51.796 is it stores energies and brown burns energies. So it 0:08:51.836 --> 0:08:55.156 turns out that I'm actually homozygous risk for the store 0:08:55.196 --> 0:08:58.796 calories position, which is the obesity risk. 0:08:58.996 --> 0:09:00.476 So you have the obesity. 0:09:00.596 --> 0:09:03.036 I have two copies of the obesity risk. My wife 0:09:03.076 --> 0:09:06.276 has zero. I can tell you, you know, we look 0:09:06.356 --> 0:09:12.196 very different. Fair So we basically realize that it sits 0:09:12.196 --> 0:09:16.036 in the progenitor cells of white and brown flat and 0:09:16.116 --> 0:09:19.836 then we could show that the true target was not 0:09:19.956 --> 0:09:23.676 the ftogene at all. It was instead two other genes 0:09:23.716 --> 0:09:28.116 that are sitting one point two million letters away from 0:09:28.116 --> 0:09:31.796 this region and six hundred thousand letters away, and those 0:09:31.836 --> 0:09:37.756 genes turned out to be master controllers of thermogenesis. They 0:09:37.796 --> 0:09:44.116 are basically switching your metabolic state. So my cells are 0:09:44.236 --> 0:09:48.516 stuck on the store position and my wife cells are 0:09:48.556 --> 0:09:49.716 stuck on the burn position. 0:09:50.596 --> 0:09:53.676 And so what is the relationship between the genes that 0:09:53.716 --> 0:09:59.116 are acting here and this this you know, package variant 0:09:59.116 --> 0:10:00.636 that is far away from them. 0:10:00.756 --> 0:10:05.196 It comes back to the epigena. So our DNA is 0:10:05.316 --> 0:10:10.596 stored inside a tiny little space. The way that gene 0:10:10.596 --> 0:10:13.476 regulation works is that you have these control regions that 0:10:13.516 --> 0:10:17.716 are scattered throughout the region of every gene that are 0:10:17.796 --> 0:10:21.076 linked together to that gene in three dimensions. So they 0:10:21.156 --> 0:10:22.836 do around and. 0:10:22.756 --> 0:10:24.796 So it's it's far away. If you think of it 0:10:24.836 --> 0:10:27.636 as a strand but in three dimensional space, right there, 0:10:27.996 --> 0:10:32.276 three dimension pats right, Ah, that's satisfying. 0:10:32.396 --> 0:10:36.036 And when we took these genes and we modulated them, 0:10:36.636 --> 0:10:41.436 we show that you can turn off one gene in mouse, 0:10:42.276 --> 0:10:46.956 in specifically the adipocytes of mouse with a dominant negative 0:10:46.996 --> 0:10:51.316 cus of fat cells with a dominant negative construct, and 0:10:51.476 --> 0:10:56.236 that turned the mouse fifty percent leaner. They eat the 0:10:56.276 --> 0:10:59.556 same amount, they exercise the same amount, but they burn 0:10:59.636 --> 0:11:03.756 calories when they're awake and they burn calories when they're sleeping. 0:11:05.036 --> 0:11:08.116 And what's really fascinated with that story is that the 0:11:08.276 --> 0:11:13.116 variant associated with obesity is at two percent frequency in Africa, 0:11:13.996 --> 0:11:17.716 but forty two percent frequency in Europe and forty four 0:11:17.756 --> 0:11:22.276 percent frequency in Southeast Asia. So it rose from two 0:11:22.316 --> 0:11:27.196 percent to forty four percent maybe because of positive selection. 0:11:27.996 --> 0:11:31.076 Maybe it was beneficial to be able to store every 0:11:31.116 --> 0:11:31.676 kind of. 0:11:31.556 --> 0:11:34.276 Places where food is, where you have food is scarce 0:11:34.316 --> 0:11:36.436 in moments of famine, exactly. 0:11:36.036 --> 0:11:38.276 In the out of Africa event, this may have been 0:11:38.276 --> 0:11:40.916 selected for. Or in the you know, ice ages, it 0:11:40.956 --> 0:11:44.316 may have been selected for. And it's only after World 0:11:44.316 --> 0:11:49.036 War two that this variant became associated with obesity. 0:11:48.636 --> 0:11:51.036 Because food became so abundant. 0:11:50.556 --> 0:11:54.436 And we stopped exercising as much. So it's fascinating to 0:11:54.436 --> 0:11:58.196 see how the environmental shift led to a new genetic 0:11:58.196 --> 0:12:02.956 association which is now plaguing our society, and of course 0:12:02.996 --> 0:12:07.316 the hope that by understanding the circuit systematically, we can 0:12:07.396 --> 0:12:13.676 now solve so many different circuits and ultimately so many 0:12:13.676 --> 0:12:17.076 different pathways and ultimately so many different disorders. 0:12:19.636 --> 0:12:22.956 In a minute, Manola's describes how he and his colleagues 0:12:22.996 --> 0:12:26.476 are trying to turn their genomic research into new medicines. 0:12:35.476 --> 0:12:36.436 That's the end of the ads. 0:12:36.876 --> 0:12:38.036 Now we're going back to the show. 0:12:39.036 --> 0:12:41.396 Another area where Manola's and his colleagues have done a 0:12:41.436 --> 0:12:44.796 lot of work is on Alzheimer's disease. They looked at 0:12:44.836 --> 0:12:48.876 a common genetic variant called apo E four. People with 0:12:48.956 --> 0:12:51.556 two copies of this variant have a much much higher 0:12:51.636 --> 0:12:54.956 risk of getting Alzheimer's, and Manola's and his colleagues were 0:12:54.956 --> 0:12:57.996 trying to figure out why. They found that having this 0:12:58.116 --> 0:13:02.436 Apoe four variant was linked to problems with moving cholesterol 0:13:02.636 --> 0:13:07.956 around in the brain, a process called cholesterol transport. Then 0:13:08.156 --> 0:13:11.356 they did experiments and mice that found that drugs that 0:13:11.436 --> 0:13:17.036 restore cholesterol transport actually restored cognition in the mice. Now 0:13:17.156 --> 0:13:21.316 that's in mice, and Alzheimer's is a notoriously difficult disease 0:13:21.396 --> 0:13:25.316 to treat in humans. So I asked Minolas what it 0:13:25.356 --> 0:13:28.116 will take to move his research from mice to humans, 0:13:28.476 --> 0:13:31.956 and his answer was really interesting. It pointed not only 0:13:31.956 --> 0:13:35.276 two ideas about treating Alzheimer's, but to bigger ideas about 0:13:35.316 --> 0:13:37.116 treating human disease more generally. 0:13:38.236 --> 0:13:39.916 The way that I'm thinking about this, the way that 0:13:39.956 --> 0:13:43.116 our team is thinking about these, is how do we 0:13:43.276 --> 0:13:49.476 enable personalized medicine and precision medicine. Namely, Alzheimer's is not 0:13:49.476 --> 0:13:51.956 going to be only about transport. It's going to be 0:13:51.996 --> 0:13:56.276 a combination. Every person has some combination of these regulations. 0:13:56.716 --> 0:14:00.596 A point four is the strongest genetic risk, but there 0:14:00.596 --> 0:14:03.676 are many others. And the question is how do we 0:14:03.996 --> 0:14:07.476 now systematically take a person with Alzheimer's, or take a 0:14:07.516 --> 0:14:12.036 family with risk, develop treatments that are either directly addressing 0:14:12.036 --> 0:14:17.196 the root causes rather than treating the symptoms, and are 0:14:17.356 --> 0:14:22.556 not only preventative but adapted to every family and every person. 0:14:22.876 --> 0:14:25.116 And just to be clear, like having you know, two 0:14:25.196 --> 0:14:30.196 copies of the APO four lil is neither necessary nor 0:14:30.196 --> 0:14:32.796 sufficient to get Alzheimer's. Right, that's exactly both of them 0:14:32.796 --> 0:14:34.436 and not get it. You can have neither of them 0:14:34.476 --> 0:14:37.756 and get it. So it's exactly so complicated hard. 0:14:37.836 --> 0:14:41.676 So, as with everything with human disease, genetics is not destiny. 0:14:42.076 --> 0:14:45.836 Genetics is a predisposition, and there are environmental factors. There 0:14:45.876 --> 0:14:50.316 are behavioral factors, there are nutritional exercise factors, there are 0:14:50.356 --> 0:14:52.836 socio economic factors. There's so many other factors that are 0:14:52.876 --> 0:14:58.836 affecting how your genetics will manifest ultimately into disease. But 0:14:59.076 --> 0:15:02.316 now the question is for every person, how do we 0:15:02.676 --> 0:15:06.636 create a drug? And it's not going to be feasible 0:15:06.756 --> 0:15:11.276 economically or in any other way to create one pill 0:15:11.316 --> 0:15:13.716 for each person. The way that we're going to enable 0:15:13.716 --> 0:15:17.916 personalized medicine is by understanding what are the hallmarks of disease, 0:15:18.316 --> 0:15:20.916 what are the hallmarks of Alzheimer's, the wholemarks of obesity, 0:15:20.996 --> 0:15:23.916 the whole moods of diabetes, the hallmarks of cardiac disorders, 0:15:24.276 --> 0:15:28.436 and develop therapeutics for every one of those hallmarks. So 0:15:28.516 --> 0:15:31.836 think of it as an arsenal of twelve or twenty 0:15:32.076 --> 0:15:35.076 different drugs for Alzheimer's that you're going to be taking 0:15:35.076 --> 0:15:37.156 a combination of it. 0:15:37.196 --> 0:15:41.156 Seems like oncology is already some way down that road, right, 0:15:41.196 --> 0:15:44.876 I mean, you know her two positive breast cancers have 0:15:44.996 --> 0:15:47.036 certain drugs that target them that sort of thing, right, 0:15:47.116 --> 0:15:47.836 is that the model? 0:15:48.596 --> 0:15:51.876 That's exactly the model. So the hallmarks of cancer have 0:15:52.036 --> 0:15:55.116 been the way of thinking about cancer for twenty plus 0:15:55.236 --> 0:15:58.716 years now. And the difference in cancer is the following. 0:15:59.476 --> 0:16:04.356 Cancer is subject to positive selection. What does that mean? 0:16:04.716 --> 0:16:09.236 That means that because it's a replicative disorder where the cell, 0:16:09.356 --> 0:16:12.836 the cancer cells make more of themselves. If a cell 0:16:12.916 --> 0:16:17.636 acquires a mutation that allows it to replicate faster, you 0:16:17.716 --> 0:16:21.396 will have more of that cell. So you are subject 0:16:21.476 --> 0:16:25.716 to positive selection where the bad mutations are increasing in 0:16:25.756 --> 0:16:32.556 frequency in every generation of the cancer. By contrast, most 0:16:32.596 --> 0:16:37.556 complex disorders are subject to purifying selection, where the mutations 0:16:37.596 --> 0:16:40.916 that are responsible for them are maintained at low frequency 0:16:40.916 --> 0:16:41.516 by evolution. 0:16:42.556 --> 0:16:42.836 Huh. 0:16:43.356 --> 0:16:47.556 So it's working at the opposite ends of the evolutionary spectrum. 0:16:47.836 --> 0:16:50.796 So cancer has a small number of genes that drive 0:16:50.956 --> 0:16:55.236 the disorder. Complex traits have thousands of genes that are 0:16:55.356 --> 0:16:59.396 maintained at low frequency or at weak effects. 0:16:59.836 --> 0:17:03.276 Except that sounds much harder. It's harder to figure out 0:17:03.316 --> 0:17:04.116 what's going on harder. 0:17:05.236 --> 0:17:08.076 But the saving grace is that even though you have 0:17:08.196 --> 0:17:12.356 extreme heterogeneity in the number of drivers, for every one 0:17:12.356 --> 0:17:19.476 of these disorders, they coalesce, they cluster, they converge in 0:17:19.556 --> 0:17:24.276 a small number of recurrent pathways, and these are the hallmarks. 0:17:24.676 --> 0:17:24.876 Huh. 0:17:25.396 --> 0:17:28.716 So you can find multiple genes associated with lipid transport, 0:17:28.996 --> 0:17:32.036 you can find multiple genes associated with new inflammation with 0:17:32.156 --> 0:17:33.156 DNA damage, so. 0:17:33.116 --> 0:17:35.876 You target the sort of pathways where they converge. 0:17:35.956 --> 0:17:37.876 That's exactly right. So we're not going to make a 0:17:38.036 --> 0:17:40.916 drug for Alzheimer's that we might make a drug for 0:17:41.036 --> 0:17:44.716 DNA damage, a drug for lipid metabolism, a drug for 0:17:44.796 --> 0:17:48.076 cholesterol transport, et cetera. And that's what we're working. 0:17:48.356 --> 0:17:50.916 That's satisfying. That's a satisfying explanation. 0:17:51.316 --> 0:17:54.876 It basically says that it is a limited number. There's 0:17:54.916 --> 0:17:57.076 a billion people in the planet. We're not going to 0:17:57.116 --> 0:17:59.356 have a billion drugs. What we're going to have it's 0:17:59.396 --> 0:18:02.796 a small number of drugs, one for each pathway, and 0:18:02.916 --> 0:18:07.276 these are sometimes going to be actually reused between different disorders. 0:18:07.836 --> 0:18:10.796 So we work on cardie disorders, we're finding the same 0:18:10.916 --> 0:18:16.196 genes underlying Alzheimer's, and specifically the lipid and cholesterol component 0:18:16.676 --> 0:18:20.196 are in fact reused in the heart disease. And again 0:18:20.236 --> 0:18:25.356 it's about lipids. It's about saturation of the fat stores 0:18:25.396 --> 0:18:28.036 of an individual and now the lipid escaping into the 0:18:28.036 --> 0:18:32.036 blacks into the bloodstream, forming these plaques that will then 0:18:32.196 --> 0:18:35.756 cause heart you know, failure and heart damage and so 0:18:35.796 --> 0:18:38.836 and so forth. So that's where we're at. 0:18:39.036 --> 0:18:43.236 So is there. I mean, the dream is that there 0:18:43.316 --> 0:18:46.476 is some dysfunction that is common to all these different 0:18:46.516 --> 0:18:51.156 diseases that you could target, right, Like, I mean, the 0:18:51.276 --> 0:18:53.956 naive dream is find the cure for everything, or not everything, 0:18:53.996 --> 0:18:55.596 but find the cure for a lot of things, or 0:18:55.636 --> 0:18:59.316 at least find a drug that will reduce risks of 0:18:59.396 --> 0:19:02.916 many different bad things, right, I mean, is that plausible 0:19:02.996 --> 0:19:05.476 or am I just naive in going there? From what 0:19:05.516 --> 0:19:05.996 you're saying. 0:19:06.276 --> 0:19:11.796 So you're right that some of the time these pathways 0:19:11.796 --> 0:19:14.796 that we're finding are going to be helping in multiple frauds, 0:19:15.636 --> 0:19:18.476 And then that's absolutely the dream. We should basically start 0:19:18.596 --> 0:19:21.036 not with what is the worst disease, but maybe what 0:19:21.116 --> 0:19:23.596 is the best pathway that if we fix that one, 0:19:23.676 --> 0:19:26.156 we're going to have an impact on most diseases. 0:19:25.956 --> 0:19:28.636 Right, like the highest return on investments for example. 0:19:28.676 --> 0:19:30.556 Like, Yeah, that's a great way to think about it. 0:19:31.956 --> 0:19:35.276 But the way that I would say is that for 0:19:35.636 --> 0:19:38.236 each person, this might be a different molecule. 0:19:39.876 --> 0:19:42.396 So now I'm not hopeful. 0:19:43.596 --> 0:19:47.036 But that with a small number of these molecules, say 0:19:47.076 --> 0:19:48.956 one hundred, one hundred and fifty two hundred molecules. 0:19:48.956 --> 0:19:50.436 When you say molecule, you mean drug. 0:19:50.356 --> 0:19:52.836 I mean trust, might I mean drust. Yeah, Basically that 0:19:52.876 --> 0:19:54.956 there's going to be a small number of pathways and 0:19:55.036 --> 0:19:59.396 a small number of these modulators, and that those are 0:19:59.396 --> 0:20:01.876 going to be mixed and matched in each person to 0:20:02.116 --> 0:20:04.996 then target a communatorially large number of people. 0:20:05.076 --> 0:20:07.556 Yeah, it just got hard. I know, I know biology 0:20:07.676 --> 0:20:10.196 is hard, but I got up to for a second. 0:20:10.836 --> 0:20:12.596 There's not going to be a single silver bullet for 0:20:13.076 --> 0:20:15.276 all of those. In fact, for any one of these diseases, 0:20:15.276 --> 0:20:17.996 there's no silver bullet. But the moment you build your 0:20:18.076 --> 0:20:20.956 panelbly of fifty silver bullets, then you're going to be 0:20:20.996 --> 0:20:24.196 hitting two hundred diseases. That's the beauty of it. 0:20:24.396 --> 0:20:26.996 Fifty bronze bo there's no silver bullet, but maybe. 0:20:26.836 --> 0:20:28.236 You can find it for hearts exactly right. 0:20:30.356 --> 0:20:43.916 We'll be back in a minute with the lightning round. Now, 0:20:43.956 --> 0:20:46.516 let's get back to the show. I read that you 0:20:46.676 --> 0:20:48.876 have been an author on more than two hundred and 0:20:48.956 --> 0:20:52.156 thirty papers, which is a lot. Which one was the 0:20:52.156 --> 0:20:52.636 most fun? 0:20:52.756 --> 0:20:54.196 Oh? You know what, don't I tell you about my 0:20:54.316 --> 0:20:54.876 very first one? 0:20:54.916 --> 0:20:55.196 Sure? 0:20:56.756 --> 0:20:59.876 And the very first paper was published in c graph 0:21:00.036 --> 0:21:02.196 and it now has like two thousand citations, And it 0:21:02.236 --> 0:21:05.916 was about how do we reconstruct the surface of an 0:21:05.956 --> 0:21:09.836 object from a cloud of points? So you can basically 0:21:09.916 --> 0:21:12.476 use laser scanning to sort of figure out points in 0:21:12.516 --> 0:21:14.636 three D and then the question is what is the 0:21:14.676 --> 0:21:17.516 surface that goes between them. I've always been fascinated with 0:21:17.556 --> 0:21:19.716 three D space, so it was very fun for me 0:21:19.796 --> 0:21:22.036 to just like you know, as a kid, basically as 0:21:22.116 --> 0:21:25.636 as a freshman at to work on such a project 0:21:25.716 --> 0:21:28.916 and then showing up at the conference. He was in Disneyland, 0:21:29.196 --> 0:21:30.876 so it was my first time in Disneyland as an 0:21:30.916 --> 0:21:31.996 author of a vapor. 0:21:31.836 --> 0:21:35.156 Sounds relevant for motion capture, not knowing anything about it. 0:21:35.196 --> 0:21:38.636 When I think of, like, you know, people, the way 0:21:38.636 --> 0:21:40.636 they make movies now exactly as they put a bunch 0:21:40.676 --> 0:21:42.876 of censors on people and they move around and then 0:21:42.916 --> 0:21:45.276 you can turn them into a dragon or whatever you want. 0:21:45.316 --> 0:21:48.636 Yeah, that's exactly right. So you know that paper has 0:21:48.676 --> 0:21:50.756 been quite influential and used for a lot of a 0:21:50.796 --> 0:21:51.676 lot of different things. 0:21:52.076 --> 0:21:54.276 What's the most overrated Greek island? 0:21:54.316 --> 0:21:55.756 Oh my god, I can tell you about the most 0:21:55.836 --> 0:21:59.676 underrated Santorini. Definitely not overrated tons of people, but worth 0:21:59.836 --> 0:22:02.276 every time. I can tell you about my first day 0:22:02.316 --> 0:22:05.516 in Santorini, which is I walked out on this balcony 0:22:05.796 --> 0:22:07.716 and I asked the owner of the restaurant if I 0:22:07.756 --> 0:22:09.036 can take a look at the view and I'm not 0:22:09.436 --> 0:22:12.996 order anything. He said, please be my guest, and I 0:22:13.036 --> 0:22:15.196 walked out, and ten minutes later, I'm like, I can't leave. 0:22:15.236 --> 0:22:18.556 I'm gonna have to order. He tells me, ten years ago, 0:22:18.596 --> 0:22:19.836 I came here to look at the view. 0:22:19.916 --> 0:22:21.756 I want you to throw a little bit of shade. 0:22:21.836 --> 0:22:23.556 I want you to get in a little bit of drug. 0:22:23.596 --> 0:22:23.996 Can't. 0:22:24.116 --> 0:22:26.636 What's one place in Greece I should not cannot. 0:22:28.356 --> 0:22:32.676 It's not possible. I mean, you know, if you keep insisting, 0:22:32.676 --> 0:22:34.836 I'll give you another twenty amazing places to visit. 0:22:35.236 --> 0:22:38.116 Well, that's fair, that's fair. I did what I could do. 0:22:38.596 --> 0:22:41.396 If everything goes well, what problem will you be trying 0:22:41.396 --> 0:22:43.036 to solve in five years? 0:22:43.676 --> 0:22:46.836 I think what I'm trying to solve now of actually 0:22:47.756 --> 0:22:53.156 creating these drugs in such a modular, AI driven, personalized, 0:22:53.796 --> 0:22:58.076 reusable way, centered on pathways. That's going to keep me 0:22:58.116 --> 0:23:01.076 busy for a long time. And I hope that in 0:23:01.116 --> 0:23:05.876 five years we have actually sold a big chunk of 0:23:05.916 --> 0:23:10.036 the platform and that we have a few drugs in 0:23:10.076 --> 0:23:13.076 clinical trials. So you know, my dream needs to take 0:23:13.116 --> 0:23:16.196 all of these circuits that we have uncovered and make 0:23:16.236 --> 0:23:18.316 a difference for humanity, make a difference for you know, 0:23:18.356 --> 0:23:21.116 my fellow beings. That's my big goal. 0:23:21.596 --> 0:23:23.596 Great, it's fun to talk to you. 0:23:23.996 --> 0:23:26.516 I learned a lot, such a pleasure, thank you, and 0:23:26.556 --> 0:23:29.516