Manolis Kellis: Biology of Disease | Lex Fridman Podcast #133
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Manolis Kellis is a computational biologist at MIT. Please support this podcast by checking out our sponsors:
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EPISODE LINKS:
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OUTLINE:
0:00 - Introduction
2:49 - Molecular basis for human disease
26:48 - Deadliest diseases
32:31 - Genetic component of diseases
41:22 - Genetic understanding of disease
57:09 - Unified theory of human disease
1:03:10 - Genome circuitry
1:28:13 - CRISPR
1:39:50 - Mitochondria
1:47:54 - Future of biology research
2:17:30 - The genetic circuitry of disease

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 The following is a conversation with Manolis Kellis, his third time on the podcast. He is a professor at MIT and head of the MIT Computational Biology Group. This time we went deep on the science, biology and genetics. So this is a bit of an experiment. Manolis went back and forth between the basics of biology to the latest state of the art in the research. He's a master at this. So I just sat back and enjoyed the ride. This conversation happened at 7 a.m. So it's yet another podcast episode after an all-nighter for me. And once again, since the universe has a sense of humor, this one was a tough one for my brain to keep up, but I did my best and I never shy away from good challenge. Quick mention of each sponsor, followed by some thoughts related to the episode. First is SEMrush, the most advanced SEO optimization tool I've ever come across. I don't like looking at numbers, but someone probably should. It helps you make good decisions. Second is Pessimist Archive. They're back, one of my favorite history podcasts on why people resist new things from recorded music to umbrellas, to cars, chess, coffee and the elevator. Third is 8 Sleep, a mattress that cools you and cools itself, measures heart rate variability, has an app and has given me yet another reason to look forward to sleep, including the all-important Power Nap. And finally, BetterHelp, online therapy when you want to face your demons with a licensed professional, not just by doing the David Goggins-like physical challenges like I seem to do on occasion. Please check out these sponsors in the description to get a discount and to support this podcast. As a side note, let me say that biology in the brain and in the various systems of the body, fill me with awe every time I think about how such a chaotic mess, coming from its humble origins in the ocean, was able to achieve such incredibly complex and robust mechanisms of life that survived despite all the forces of nature that want to destroy it. It is so unlike the computing systems we humans have engineered that it makes me feel that in order to create artificial general intelligence and artificial consciousness, we may have to completely rethink how we engineer computational systems. If you enjoy this thing, subscribe on YouTube, review it with five stars on Apple Podcast, follow on Spotify, support on Patreon, or connect with me on Twitter at Lex Friedman. And now here's my conversation with Manolis Kallis. So your group at MIT is trying to understand the molecular basis of human disease. What are some of the biggest challenges in your view? Don't get me started. I mean, understanding human disease is the most complex challenge in modern science. So because human disease is as complex as the human genome, it is as complex as the human brain, and it is in many ways even more complex because the more we understand disease complexity, the more we start understanding genome complexity and epigenome complexity and brain circuitry complexity and immune system complexity and cancer complexity and so on and so forth. So traditionally, human disease was following basic biology. You would basically understand basic biology in model organisms like mouse and fly and yeast. You would understand sort of mammalian biology and animal biology and eukaryotic biology in sort of progressive layers of complexity, getting closer to human phylogenetically. And you would do perturbation experiments in those species to see if I knock out a gene, what happens. And based on the knocking out of these genes, you would basically then have a way to drive human biology because you would sort of understand the functions of these genes. And then if you find that a human gene, locus, something that you've mapped from human genetics to that gene is related to a particular human disease, you take, aha, now I know the function of the gene from the model organisms. I can now go and understand the function of that gene in human. But this is all changing. This is dramatically changed. So that was the old way of doing basic biology. You would start with the animal models, the eukaryotic models, the mammalian models, and then you would go to human. Human genetics has been so transformed in the last decade or two that human genetics is now actually driving the basic biology. There is more genetic mutation information in the human genome than there will ever be in any other species. What do you mean by mutation information? So perturbations is how you understand systems. So an engineer builds systems, and then they know how they work from the inside out. A scientist studies systems through perturbations. You basically say, if I poke that balloon, what's gonna happen? And I'm gonna film it in super high resolution, understand, I don't know, air dynamics or fluid dynamics if it's filled with water, et cetera. So you can then make experimentation by perturbation, and then the scientific process is sort of building models that best fit the data, designing new experiments that best test your models and challenge your models and so on and so forth. That's the same thing with science, basically. If you're trying to understand biological science, you basically want to do perturbations that then drive the models. So how do these perturbations allow you to understand disease? So if you know that a gene is related to disease, you don't wanna just know that it's related to the disease. You wanna know what is the disease mechanism because you wanna go and intervene. So the way that I like to describe it is that traditionally, epidemiology, which is basically the study of disease, sort of the observational study of disease, has been about correlating one thing with another thing. So if you have a lot of people with liver disease who are also alcoholics, you might say, well, maybe the alcoholism is driving the liver disease, or maybe those who have liver disease self-medicate with alcohol. So the connection could be either way. With genetic epidemiology, it's about correlating changes in genome with phenotypic differences, and then you know the direction of causality. So if you know that a particular gene is related to the disease, you can basically say, okay, perturbing that gene in mouse causes the mice to have X-phenotype. So perturbing that gene in human causes the humans to have the disease. So I can now figure out what are the detailed molecular phenotypes in the human that are related to that organismal phenotype in the disease. So it's all about understanding disease mechanism, understanding what are the pathways, what are the tissues, what are the processes that are associated with the disease so that we know how to intervene. You can then prescribe particular medications that also alter these processes. You can prescribe lifestyle changes that also affect these processes, and so on and so forth. That's such a beautiful puzzle to try to solve. Like what kind of perturbations eventually have this ripple effect that leads to a disease across the population? And then you study that for animals, a mice first, and then see how that might possibly connect to humans. How hard is that puzzle of trying to figure out how little perturbations might lead to, in a stable way, to a disease? In animals, we make the puzzle simpler because we perturb one gene at a time. That's the beauty of it, it's the power of animal models. You can basically decouple the perturbations. You only do one perturbation, and you only do strong perturbations at a time. In human, the puzzle is incredibly complex because, I mean, obviously you don't do human experimentation, you wait for natural selection and natural genetic variation to basically do its own experiments, which it has been doing for hundreds and thousands of years in the human population, and for hundreds of thousands of years across the history leading to the human population. So you basically take this natural genetic variation that we all carry within us. Every one of us carries six million perturbations. So I've done six million experiments on you, six million experiments on me, six million experiments on everyone of seven billion people on the planet. What's the six million correspond to? Six million unique genetic variants that are segregating the human population. Every one of us carries millions of polymorphic sites, polymanymorph forms. Polymorphic means many forms, variants. That basically means that every one of us has single nucleotide alterations that we have inherited from mom and from dad that basically can be thought of as tiny little perturbations. Most of them don't do anything, but some of them lead to all of the phenotypic differences that we see between us. The reason why two twins are identical is because these variants completely determine the way that I'm gonna look at exactly 93 years of age. How happy are you with this kind of data set? Is it large enough of the human population of Earth? Is that too big, too small? Yeah, so is it large enough is a power analysis question. And in every one of our grants, we do a power analysis based on what is the effect size that I would like to detect and what is the natural variation in the two forms. So every time you do a perturbation, you're asking, I'm changing form A into form B. Form A has some natural phenotypic variation around it, and form B has some natural phenotypic variation around it. If those variances are large and the differences between the mean of A and the mean of B are small, then you have very little power. The further the means go apart, that's the effect size, the more power you have, and the smaller the standard deviation, the more power you have. So basically, when you're asking, is that sufficiently large? Certainly not for everything, but we already have enough power for many of the stronger effects in the more tight distributions. So that's a hopeful message that there exists parts of the genome that have a strong effect, that has a small variance. That's exactly right. Unfortunately, those perturbations are the basis of disease in many cases. So it's not a hopeful message. Sometimes it's a terrible message. It's basically, well, some people are sick, but if we can figure out what are these contributors to sickness, we can then help make them better and help many other people better who don't carry that exact mutation, but who carry mutations on the same pathways. And that's what we like to call the allelic series of a gene. You basically have many perturbations of the same gene in different people, each with a different frequency in the human population and each with a different effect on the individual that carries them. So you said in the past, there would be these small experiments on perturbations in animal models. What does this puzzle solving process look like today? So we basically have something like seven billion people in the planet, and every one of them carries something like six million mutations. You basically have an enormous matrix of genotype by phenotype, by systematically measuring the phenotype of these individuals. And the traditional way of measuring this phenotype has been to look at one trait at a time. You would gather families and you would sort of paint the pedigrees of a strong effect, what we'd like to call Mendelian mutation. So a mutation that gets transmitted in a dominant or a recessive, but strong effect form, where basically one locus plays a very big role in that disease. And you could then look at carriers versus non-carriers in one family, carriers versus non-carriers in another family, and do that for hundreds, sometimes thousands of families, and then trace these inheritance patterns, and then figure out what is the gene that plays that role. Is this the matrix that you're showing in talks or lectures? So that matrix is the input to the stuff that I show in talks. So basically that matrix has traditionally been strong effect genes. What the matrix looks like now is instead of pedigrees, instead of families, you basically have thousands and sometimes hundreds of thousands of unrelated individuals, each with all of their genetic variants and each with their phenotype, for example, height, or lipids, or whether they're sick or not for a particular trait. That has been the modern view. Instead of going to families, going to unrelated individuals with one phenotype at a time. And what we're doing now, as we're maturing in all of these sciences, is that we're doing this in the context of large medical systems or enormous cohorts that are very well phenotyped across hundreds of phenotypes, sometimes with our complete electronic health record. So you can now start relating, not just one gene segregating one family, not just thousands of variants segregating with one phenotype, but now you can do millions of variants versus hundreds of phenotypes. And as a computer scientist, I mean, deconvolving that matrix, partitioning it into the layers of biology that are associated with every one of these elements is a dream come true. It's like the world's greatest puzzle. And you can now solve that puzzle by throwing in more and more knowledge about the function of different genomic regions and how these functions are changed across tissues and in the context of disease. And that's what my group and many other groups are doing. We're trying to systematically relate this genetic variation with molecular variation at the expression level of the genes, at the epigenomic level of the gene regulatory circuitry, and at the cellular level of what are the functions that are happening in those cells at the single cell level using single cell profiling, and then relate all that vast amount of knowledge computationally with the thousands of traits that each of these of thousands of variants are perturbing. I mean, this is something we talked about, I think last time. So there's these effects at different levels that happen. You said at a single cell level, you're trying to see things that happen due to certain perturbations. And then it's not just like a puzzle of perturbation and disease. It's perturbation then effect at a cellular level, then at an organ level, at a body, like how do you disassemble this into like what your group is working on? You're basically taking a bunch of the hard problems in the space. How do you break apart a difficult disease and break it apart into problems that you, into puzzles that you can now start solving? So there's a struggle here. Computer scientists love hard puzzles. And they're like, oh, I wanna build a method that just deconvolves the whole thing computationally. And that's very tempting and it's very appealing, but biologists just like to decouple that complexity experimentally, to just like peel off layers of complexity experimentally. And that's what many of these modern tools that my group and others have both developed and used. The fact that we can now figure out tricks for peeling off these layers of complexity by testing one cell type at a time, or by testing one cell at a time. And you could basically say, what is the effect of these genetic variants associated with Alzheimer's on human brain? Human brain sounds like, oh, it's an organ, of course, just go one organ at a time. But human brain has of course, dozens of different brain regions. And within each of these brain regions, dozens of different cell types. And every single type of neuron, every single type of glial cell, between astrocytes, oligodendrocytes, microglia, between all of the neural cells and the vascular cells, and the immune cells that are co-inhabiting the brain between the different types of excitatory and inhibitory neurons that are sort of interacting with each other between different layers of neurons in the cortical layers. Every single one of these has a different type of function to play in cognition, in interaction with the environment, in maintenance of the brain, in energetic needs, in feeding the brain with blood, with oxygen, in clearing out the debris that are resulting from the super high energy production of cognition in humans. So all of these things are basically potentially deconvolvable computationally, but experimentally, you can just do single cell profiling of dozens of regions of the brain across hundreds of individuals, across millions of cells. And then now you have pieces of the puzzle that you can then put back together to understand that complexity. I mean, first of all, the cells in the human brain are the most, maybe I'm romanticizing it, but cognition seems to be very complicated. So separating into the function, breaking Alzheimer's down to the cellular level seems very challenging. Is that basically you're trying to find a way that some perturbation in genome results in some obvious major dysfunction in the cell? You're trying to find something like that. Exactly. So what does human genetics do? Human genetics basically looks at the whole path from genetic variation all the way to disease. So human genetics has basically taken thousands of Alzheimer's cases and thousands of controls matched for age, for sex, for environmental backgrounds and so on and so forth. And then looked at that map where you're asking what are the individual genetic perturbations and how are they related all the way to Alzheimer's disease. And that has actually been quite successful. So we now have more than 27 different loci, these are genomic regions that are associated with Alzheimer's at this end to end level. But the moment you sort of break up that very long path into smaller levels, you can basically say from genetics, what are the epigenomic alterations at the level of gene regulatory elements where that genetic variant perturbs the control region nearby. That effect is much larger. You mean much larger in terms of this down the line impact? Or it's much larger in terms of the measurable effect, this A versus B variance is actually so much cleanly defined when you go to the shorter branches. Because for one genetic variant to affect Alzheimer's, that's a very long path. That basically means that in the context of millions of these 6 million various that every one of us carries, that one single nucleotide has a detectable effect all the way to the end. I mean, it's just mind boggling that that's even possible. But indeed, yeah, but indeed there are such effects. So the hope is, or the most scientifically speaking, the most effective place where to detect the alteration that results in disease is earlier on in the pipeline, as early as possible. It's a trade off. If you go very early on in the pipeline, now each of these epigenomic alterations, for example, this enhancer control region is active maybe 50% less, which is a dramatic effect. Now you can ask, well, how much does changing one regulatory region in the genome, in one cell type change disease? Well, that path is now long. So if you instead look at expression, the path between genetic variation and the expression of one gene goes through many enhancer regions, and therefore it's a subtler effect at the gene level, but then now you're closer because one gene is acting on, you know, in the context of only 20,000 other genes, as opposed to one enhancer acting in the context of two million other enhancers. So you basically now have genetic, epigenomic, the circuitry, transcriptomic, the gene expression level, and then cellular, where you can basically say, I can measure various properties of those cells. What is the calcium influx rate when I have the genetic variation? What is the synaptic density? What is the electric impulse conductivity? And so on and so forth. So you can measure things along this path to disease, and you can also measure endophenotypes. You can basically measure, you know, your brain activity. You can do imaging in the brain. You can basically measure, I don't know, the heart rate, the pulse, the lipids, the amount of blood secreted, and so on and so forth. And then through all of that, you can basically get at the path to causality, the path to disease. And is there something beyond cellular? So you mentioned lifestyle interventions or changes as a way to, or like be able to prescribe changes in lifestyle. Like what about organs? What about like the function of the body as a whole? Yeah, absolutely. So basically when you go to your doctor, they always measure, you know, your pulse. They always measure your height. They always measure your weight, you know, your BMI. So basically these are just very basic variables. But with digital devices nowadays, you can start measuring hundreds of variables for every individual. You can basically also phenotype cognitively through tests, Alzheimer's patients. There are cognitive tests that you can measure, that you typically do for cognitive decline, these mini mental, you know, observations that you have specific questions to. You can think of sort of enlarging the set of cognitive tests. So in the mouse, for example, you do experiments for how do they get out of mazes? How do they find food? Whether they recall a fear? Whether they shake in a new environment and so on and so forth. In the human, you can have much, much richer phenotypes where you can basically say, not just imaging at the, you know, organ level, but, and all kinds of other activities at the organ level, but you can also do at the organism level, you can do behavioral tests. And how did they do on empathy? How did they do on memory? How did they do on long-term memory versus short-term memory? And so on and so forth. I love how you're calling that phenotype. I guess it is. It is. But like your behavior patterns that might change over a period of a life, your ability to remember things, your ability to be, yeah, empathetic or emotionally, or your intelligence perhaps even. Yeah, but intelligence has hundreds of variables. You can be your math intelligence, your literary intelligence, your puzzle solving intelligence, your logic. It could be like hundreds of things. And all of that, we're able to measure that better and better. So all of that could be connected to the entire pipeline. We used to think of each of these as a single variable, like intelligence. I mean, that's ridiculous. It's basically dozens of different genes that are controlling every single variable. You can basically think of, imagine us in a video game where every one of us has measures of strength, stamina, energy left, and so on and so forth. But you could click on each of those like five bars that are just the main bars. And each of those will just give you then hundreds of bars. And you can basically say, okay, great for my machine learning task. I want someone who, a human, who has these particular forms of intelligence. I require now these 20 different things. And then you can combine those things and then relate them to, of course, performance in a particular task. But you can also relate them to genetic variation that might be affecting different parts of the brain. For example, your frontal cortex versus your temporal cortex versus your visual cortex and so on and so forth. So genetic variation that affects expression of genes in different parts of your brain can basically affect your music ability, your auditory ability, your smell, your, just dozens of different phenotypes can be broken down into hundreds of cognitive variables and then relate each of those to thousands of genes that are associated with them. So somebody who loves RPGs, role playing games, there's too few variables that we can control. So I'm excited if we're in fact living in a simulation and this is a video game, I'm excited by the quality of the video game. The game designer did a hell of a good job. So we're impressed. Oh, I don't know. The sunset last night was a little unrealistic. Yeah, yeah, the graphics. Exactly. Come on Nvidia. To zoom back out, we've been talking about the genetic origins of diseases, but I think it's fascinating to talk about what are the most important diseases to understand. And especially as it connects to the things that you're working on. So it's very difficult to think about important diseases to understand. There's many metrics of importance. One is lifestyle impact. I mean, if you look at COVID, the impact on lifestyle has been enormous. So understanding COVID is important because it has impacted the wellbeing in terms of ability to have a job, ability to have an apartment, ability to go to work, ability to have a mental circle of support. And all of that for millions of Americans, like huge, huge impact. So that's one aspect of importance. So basically mental disorders, Alzheimer's has a huge importance in the wellbeing of Americans. Whether or not it kills someone for many, many years, it has a huge impact. So the first measure of importance is just wellbeing. Like impact on the quality of life. Impact on the quality of life, absolutely. The second metric, which is much easier to quantify is deaths. What is the number one killer? The number one killer is actually heart disease. It is actually killing 650,000 Americans per year. Number two is cancer with 600,000 Americans. Number three, far, far down the list is accidents. Every single accident combined. So basically, you read the news, accidents, like there was a huge car crash all over the news. But the number of deaths, number three by far, 167,000. Lower respiratory disease. So that's asthma, not being able to breathe and so on and so forth, 160,000. Alzheimer's, number five with 120,000. And then stroke, brain aneurysms and so on and so forth. That's 147,000. Diabetes and metabolic disorders, et cetera. That's 85,000. The flu is 60,000. Suicide, 50,000. And then overdose, et cetera. You know, goes further down the list. So of course, COVID has creeped up to be the number three killer this year with more than 100,000 Americans and counting. But if you think about sort of what do we use, what are the most important diseases, you have to understand both the quality of life and the sheer number of deaths and just numbers of years lost, if you wish. And each of these diseases you can think of as, and also including terrorist attacks and school shootings, for example, things which lead to fatalities, you can look at as problems that could be solved. And some problems are harder to solve than others. I mean, that's part of the equation. So maybe if you look at these diseases, if you look at heart disease or cancer or Alzheimer's or just like schizophrenia and obesity, like not necessarily things that kill you, but affect the quality of life, which problems are solvable, which aren't, which are harder to solve, which aren't? I love your question because it puts it in the context of a global effort rather than just the local effort. So basically, if you look at the global aspect, exercise and nutrition are two interventions that we can as a society make a much better job at. So if you think about sort of the availability of cheap food, it's extremely high in calories, it's extremely detrimental for you, like a lot of processed food, et cetera. So if we change that equation, and as a society, we made availability of healthy food much, much easier and charged a burger at McDonald's, the price that it costs on the health system, then people would actually start buying more healthy foods. So basically that's sort of a societal intervention, if you wish. In the same way, increasing empathy, increasing education, increasing the social framework and support would basically lead to fewer suicides, it would lead to fewer murders, it would lead to fewer deaths overall. So that's something that we as a society can do. You can also think about external factors versus internal factors. So the external factors are basically communicable diseases like COVID, like the flu, et cetera. And the internal factors are basically things like cancer and Alzheimer's where basically your genetics will eventually drive you there. And then of course, with all of these factors, every single disease has both a genetic component and environmental component. So heart disease, huge genetic contribution, Alzheimer's, it's like 60% plus genetic. So I think it's like 79% heritability. So that basically means that genetics alone explains 79% of Alzheimer's incidents. And yes, there's a 21% environmental component where you could basically enrich your cognitive environment, enrich your social interactions, read more books, learn a foreign language, go running, sort of have a more fulfilling life. All of that will actually decrease Alzheimer's, but there's a limit to how much that can impact because of the huge genetic footprint. So this is fascinating. So each one of these problems have a genetic component and an environment component. And so like when there's a genetic component, what can we do about some of these diseases? What have you worked on? What can you say that's in terms of problems that are solvable here or understandable? So my group works on the genetic component, but I would argue that understanding the genetic component can have a huge impact even on the environmental component. Why is that? Because genetics gives us access to mechanism. And if we can alter the mechanism, if we can impact the mechanism, we can perhaps counteract some of the environmental components. Oh, interesting. So understanding the biological mechanisms leading to disease is extremely important in being able to intervene. But when you can intervene, the analogy that I like to give is, for example, for obesity. Think of it as a giant bathtub of fat. There's basically fat coming in from your diet and there's fat coming out from your exercise. That's an in-out equation. And that's the equation that everybody's focusing on. But your metabolism impacts that bathtub. Basically, your metabolism controls the rate at which you're burning energy. It controls the rate at which you're storing energy. And it also teaches you about the various valves that control the input and the output equation. So if we can learn from the genetics, the valves, we can then manipulate those valves. And even if the environment is feeding you a lot of fat and getting a little fat out, you can just poke another hole at the bathtub and just get a lot of the fat out. Yeah, that's fascinating. Yeah, so we're not just passive observers of our genetics. The more we understand, the more we can come up with actual treatments. And I think that's an important aspect to realize when people are thinking about strong effect versus weak effect variants. So some variants have strong effects. We talked about these Mendelian disorders where a single gene has a sufficiently large effect, pan and trans expressivity, and so on and so forth, that basically you can trace it in families with cases and not cases, cases, not cases, and so on and so forth. But even the, you know, but so these are the genes that everybody says, oh, that's the genes we should go after because that's a strong effect gene. I like to think about it slightly differently. These are the genes where genetic impacts that have a strong effect were tolerated because every single time we have a genetic association with disease, it depends on two things. Number one, the obvious one, whether the gene has an impact on the disease. Number two, the more subtle one is whether there is genetic variation standing and circulating and segregating in the human population that impacts that gene. Some genes are so darn important that if you mess with them, even a tiny little amount, that person's dead. So those genes don't have variation. You're not gonna find a genetic association if you don't have variation. That doesn't mean that the gene has no role. It simply that the gene, it simply means that the gene tolerates no mutations. So that's actually a strong signal when there's no variation. That's so fascinating. Exactly. Genes that have very little variation are hugely important. You can actually rank the importance of genes based on how little variation they have. And those genes that have very little variation but no association with disease, that's a very good metric to say, oh, that's probably a developmental gene because we're not good at measuring those phenotypes. So it's genes that you can tell evolution has excluded mutations from, but yet we can't see them associated with anything that we can measure nowadays. It's probably early embryonic lethal. What are all the words you just said? Early embryonic what? Lethal. Meaning? Meaning that if you don't have that, that embryo will die. Okay. There's a bunch of stuff that is required for a stable functional organism across the board. Exactly. For entire species, I guess. If you look at sperm, it expresses thousands of proteins. Does sperm actually need thousands of proteins? No, but it's probably just testing them. So my speculation is that misfolding of these proteins is an early test for failure. So that out of the millions of sperm that are possible, you select the subset that are just not grossly misfolding thousands of proteins. So it's kind of an assert that this is folded correctly. Correct. Yeah, this just, because if this little thing about the folding of a protein isn't correct, that probably means somewhere down the line there's a bigger issue. That's exactly right. So fail fast. So basically if you look at the mammalian investment in a newborn, that investment is enormous in terms of resources. So mammals have basically evolved mechanisms for fail fast. Where basically in those early months of development, I mean, it's horrendous, of course, at the personal level when you lose your future child. But in some ways, there's so little hope for that child to develop and sort of make it through the remaining months that sort of fail fast is probably a good evolutionary principle. Yeah, from an evolutionary perspective. For mammals. And of course, humans have a lot of medical resources that you can sort of give those children a chance. And we have so much more success in sort of giving folks who have these strong carrier mutations a chance. But if they're not even making it through the first three months, we're not gonna see them. So that's why when we say, what are the most important genes to focus on? The ones that have a strong effect mutation or the ones that have a weak effect mutation. Well, the jury might be out. Because the ones that have a strong effect mutation are basically not mattering as much. The ones that only have weak effect mutations, by understanding through genetics that they have a weak effect mutation and understanding that they have a causal role on the disease, we can then say, okay, great. Evolution has only tolerated a 2% change in that gene. Pharmaceutically, I can go in and induce a 70% change in that gene. And maybe I will poke another hole at the bathtub that was not easy to control in many of the other sort of strong effect genetic variants. So there's this beautiful map of across the population of things that you're saying strong and weak effects. So stuff with a lot of mutations and stuff with little mutations, with no mutations. And you have this map and it's the latest data that lays out the puzzle. Yeah, so when I say strong effect, I mean at the level of individual mutations. So basically genes where, so you have to think of first the effect of the gene on the disease. Remember how I was sort of painting that map earlier from genetics all the way to phenotype. That gene can have a strong effect on the disease, but the genetic variant might have a weak effect on the gene. So basically when you ask what is the effect of that genetic variant on the disease, it could be that that genetic variant impacts the gene by a lot, and then the gene impacts the disease by a little, or it could be that the genetic variants impacts the gene by a little, and then the gene impacts the disease by a lot. So what we care about is genes that impact the disease a lot, but genetics gives us the full equation. And what I would argue is if we couple the genetics with expression variation, to basically ask what genes change by a lot, and which genes correlate with disease by a lot, even if the genetic variants change them by a little, then those are the best places to intervene. Those are the best places where, pharmaceutically, if I have even a modest effect, I will have a strong effect on the disease. Whereas those genetic variants that have a huge effect on the disease, I might not be able to change that gene by this much without affecting all kinds of other things. Interesting. So yeah, okay, so that's what we're looking at. Then what have we been able to find in terms of which disease could be helped? Again, don't get me started. This is, we have found so much. Our understanding of disease has changed so dramatically with genetics. I mean, places that we had no idea would be involved. So one of the worst things about my genome is that I have a genetic predisposition to age-related molecular degeneration, AMD. So it's a form of blindness that causes you to lose the central part of your vision progressively as you grow older. My increased risk is fairly small. I have an 8% chance. You only have a 6% chance. I'm an average. Yeah. By the way, when you say my, you mean literally yours. You know this about you. I know this about me. Yeah. Which is kind of, I mean, philosophically speaking is a pretty powerful thing to live with. Maybe that's, so we agreed to talk again, by the way, for the listeners to where we're gonna try to focus on science today and a little bit of philosophy next time. But it's interesting to think about the more you're able to know about yourself from the genetic information in terms of the diseases, how that changes your own view of life. Yeah. So there's a lot of impact there and there's something called genetic exceptionalism, which basically thinks of genetics as something very, very different than everything else as a type of determinism. And, you know, let's talk about that next time. So basically- That's a good preview. Yeah. So let's go back to AMD. So basically with AMD, we have no idea what causes AMD. You know, it was a mystery until the genetics were worked out. And now the fact that I know that I have a predisposition allows me to sort of make some life choices, number one. But number two, the genes that lead to that predisposition give us insights as to how does it actually work. And that's a place where genetics gave us something totally unexpected. So there's a complement pathway, which is an immune function pathway that was in, you know, most of the loci associated with AMD. And that basically told us that, wow, there's an immune basis to this eye disorder that people had just not expected before. If you look at complement, it was recently also implicated in schizophrenia. And there's a type of microglia that is involved in synaptic pruning. So synapses are the connections between neurons. And in this whole use it or lose it view of mental cognition and other capabilities, you basically have microglia, which are immune cells that are sort of constantly traversing your brain and then pruning neuronal connections, pruning synaptic connections that are not utilized. So in schizophrenia, there's thought to be a change in the pruning that basically, if you don't prune your synapses the right way, you will actually have an increased role of schizophrenia. This is something that was completely unexpected for schizophrenia. Of course, we knew it has to do with neurons, but the role of the complement complex, which is also implicated in AMD, which is now also implicated in schizophrenia was a huge surprise. What's the complement complex? So it's basically a set of genes, the complement genes, that are basically having various immune roles. And as I was saying earlier, our immune system has been co-opted for many different roles across the body. So they actually play many diverse roles. And somehow the immune system is connected to the synaptic pruning process. Exactly. The process. Exactly. So immune cells were co-opted to prune synapses. How did you figure this out? How does one go about figuring this intricate connection, like pipeline of connections out? Yeah. Let me give you another example. So Alzheimer's disease, the first place that you would expect it to act is obviously the brain. So we had basically this roadmap epigenomics consortium view of the human epigenome, the largest map of the human epigenome that has ever been built across 127 different tissues and samples with dozens of epigenomic marks measured in hundreds of donors. So what we've basically learned through that is that you basically can map what are the active gene regulatory elements for every one of the tissues in the body. And then we connected these gene regulatory active maps of basically what regions of the human genome are turning on in every one of different tissues. We then can go back and say, where are all of the genetic loci that are associated with disease? This is something that my group, I think was the first to do back in 2010 in this Ernst Nature Biotech paper. But basically we were for the first time able to show that specific chromatin states, specific epigenomic states, in that case enhancers, were in fact enriched in disease-associated variants. We pushed that further in the Ernst Nature paper a year later, and then in this roadmap epigenomics paper, a few years after that. But basically that matrix that you mentioned earlier was in fact the first time that we could see what genetic traits have genetic variants that are enriched in what tissues in the body. And a lot of that map made complete sense. If you looked at a diversity of immune traits like allergies and type 1 diabetes and so on and so forth, you basically could see that they were enriching, that the genetic variants associated with those traits were enriched in enhancers, in these gene regulatory elements, active in T cells and B cells and hematopoietic stem cells and so on and so forth. So that basically gave us confirmation in many ways that those immune traits were indeed enriching in immune cells. If you looked at type 2 diabetes, you basically saw an enrichment in only one type of sample, and it was pancreatic islets. And we know that type 2 diabetes sort of stems from the dysregulation of insulin in the beta cells of pancreatic islets. And that sort of was spot on, super precise. If you looked at blood pressure, where would you expect blood pressure to occur? You know, I don't know, maybe in your metabolism and ways that you process coffee or something like that. Maybe in your brain, the way that you stress out and increases your blood pressure, et cetera. What we found is that blood pressure localized specifically in the left ventricle of the heart. So the enhancers of the left ventricle in the heart contained a lot of genetic variants associated with blood pressure. If you look at height, we found an enrichment specifically in embryonic stem cell enhancers. So the genetic variants predisposing you to be taller or shorter are in fact acting in developmental stem cells. Makes complete sense. If you looked at inflammatory bowel disease, you basically found inflammatory, which is immune, and also bowel disease, which is digestive. And indeed we saw a double enrichment both in the immune cells and in the digestive cells. So that basically told us that, ah, this is acting in both components. There's an immune component to inflammatory bowel disease and there's a digestive component. And the big surprise was for Alzheimer's. We had seven different brain samples. We found zero enrichment in the brain samples for genetic variants associated with Alzheimer's. And this is mind boggling. Our brains were literally hurting. What is going on? And what is going on is that the brain samples are primarily neurons, oligodendrocytes, and astrocytes in terms of the cell types that make them up. So that basically indicated that genetic variants associated with Alzheimer's were probably not acting in oligodendrocytes, astrocytes, or neurons. So what could they be acting in? Well, the fourth major cell type is actually microglia. Microglia are resident immune cells in your brain. Oh, nice. The immune, oh, wow. And they are CD14+, which is this sort of cell surface markers of those cells. So they're CD14+, just like microphages that are circulating in your blood. The microglia are resident monocytes that are basically sitting in your brain. They're tissue specific monocytes. And every one of your tissues, like your fat, for example, has a lot of macrophages that are resident. And the M1 versus M2 macrophage ratio has a huge role to play in obesity. And so basically, again, these immune cells are everywhere, but basically what we found through this completely unbiased view of what are the tissues that likely underlie different disorders, we found that Alzheimer's was humongously enriched in microglia, but not at all in the other cell types. So what are we supposed to make that if you look at the tissues involved, is that simply useful for indication of propensity for disease, or does it give us somehow a pathway of treatment? It's very much the second. If you look at the way to therapeutics, you have to start somewhere. What are you gonna do? You're gonna basically make assays that manipulate those genes and those pathways in those cell types. So before we know the tissue of action, we don't even know where to start. We basically are at a loss, but if you know the tissue of action, and even better if you know the pathway of action, then you can basically screen your small molecules, not for the gene, you can screen them directly for the pathway in that cell type. So you can basically develop a high throughput multiplexed robotic system for testing the impact of your favorite molecules that you know are safe, efficacious, and sort of hit that particular gene, and so on and so forth. You can basically screen those molecules against either a set of genes that act in that pathway, or on the pathway directly by having a cellular assay. And then you can basically go into mice and do experiments and basically sort of figure out ways to manipulate these processes that allow you to then go back to humans and do a clinical trial that basically says, okay, I was able indeed to reverse these processes in mice, can I do the same thing in humans? So the knowledge of the tissues gives you the pathway to treatment, but that's not the only part. There are many additional steps to figuring out the mechanism of disease. I mean, so that's really promising. Maybe to take a small step back, you've mentioned all these puzzles that were figured out with the Nature paper for, I mean, you've mentioned a ton of diseases, from obesity to Alzheimer's, even schizophrenia, I think you mentioned. And it's just, what is the actual methodology of figuring this out? So indeed, I mentioned a lot of diseases, and my lab works on a lot of different disorders. And the reason for that is that if you look at the, if you look at biology, it used to be, you know, zoology departments and botanology departments, and, you know, virology departments and so on and so forth. And MIT was one of the first schools to basically create a biology department, like, oh, we're gonna study all of life suddenly. Why was that even a case? Because the advent of DNA and the genome and the central dogma of DNA makes RNA makes protein, in many ways, unified biology. You could suddenly study the process of transcription in viruses or in bacteria, and have a huge impact on yeast and fly, and maybe even mammals, because of this realization of these common underlying processes. And in the same way that DNA unified biology, genetics is unifying disease studies. So you used to have, you know, I don't know, cardiovascular disease department, and, you know, neurological disease department, and neurodegeneration department, and, you know, basically immune and cancer and so on and so forth. And all of these were studied in different labs, you know, because it made sense, because basically the first step was understanding how the tissue functions, and we kind of knew the tissues involved in cardiovascular disease and so on and so forth. But what's happening with human genetics is that all of these walls and edifices that we had built are crumbling. And the reason for that is that genetics is in many ways revealing unexpected connections. So suddenly we now have to bring the immunologists to work on Alzheimer's. They were never in their room. They were in another building altogether. The same way for schizophrenia, we now have to sort of worry about all these interconnected aspects. For metabolic disorders, we're finding contributions from brain. So suddenly we have to call the neurologist from the other building and so on and so forth. So in my view, it makes no sense anymore to basically say, oh, I'm a geneticist studying immune disorders. I mean, that's ridiculous because, I mean, of course, in many ways you still need to sort of focus. But what we're doing is that we're basically saying, we'll go wherever the genetics takes us. And by building these massive resources, by working on our latest map is now 833 tissues, sort of the next generation of the epigenomics roadmap, which we're now called epiMAP, is 833 different tissues. And using those, we've basically found enrichments in 540 different disorders. Those enrichments are not like, oh, great, you guys work on that and we'll work on this. They're intertwined amazingly. So of course there's a lot of modularity, but there's these enhancers that are sort of broadly active and these disorders that are broadly active. So basically some enhancers are active in all tissues and some disorders are enriching in all tissues. So basically there's these multifactorial and these other class, which I like to call polyfactorial diseases, which are basically lighting up everywhere. And in many ways, it's sort of cutting across these walls that were previously built across these departments. And the polyfactorial ones were probably the previous structure of departments wasn't equipped to deal with those. I mean, again, maybe it's a romanticized question, but there's in physics, there's a theory of everything. Do you think it's possible to move towards an almost theory of everything of disease from a genetic perspective? So if this unification continues, is it possible that, do you think in those terms, trying to arrive at a fundamental understanding of how disease emerges, period? That unification is not just foreseeable, it's inevitable. I see it as inevitable. We have to go there. You cannot be a specialist anymore if you're a genomicist. You have to be a specialist in every single disorder. And the reason for that is that the fundamental understanding of the circuitry of the human genome that you need to solve schizophrenia, that fundamental circuitry is hugely important to solve Alzheimer's. And that same circuitry is hugely important to solve metabolic disorders. And that same exact circuitry is hugely important for solving immune disorders and cancer and every single disease. So all of them have the same sub-task. And I teach dynamic programming in my class. Dynamic programming is all about sort of not redoing the work. It's reusing the work that you do once. So basically for us to say, oh, great, you guys in the immune building, go solve the fundamental circuitry of everything. And then you guys in the schizophrenia building, go solve the fundamental circuitry of everything separately, it's crazy. So what we need to do is come together and sort of have a circuitry group, the circuitry building that sort of tries to solve the circuitry of everything. And then the immune folks who will apply this knowledge to all of the disorders that are associated with it associated with immune dysfunction. And the schizophrenia folks will basically interact with both the immune folks and with the neuronal folks. And all of them will be interacting with the circuitry folks and so on and so forth. So that's sort of the current structure of my group, if you wish. So basically what we're doing is focusing on the fundamental circuitry. But at the same time, we're the users of our own tools by collaborating with many other labs in every one of these disorders that we mentioned. We basically have a hard focus on cardiovascular disease, coronary artery disease, heart failure, and so on and so forth. We have an immune focus on several immune disorders. We have a cancer focus on metastatic melanoma and immunotherapy response. We have a psychiatric disease focus on schizophrenia, autism, PTSD, and other psychiatric disorders. We have an Alzheimer's and neurodegeneration focus on Huntington's disease, ALS, and AD-related disorders, like frontotemporal dementia and Lewy body dementia. And of course, a huge focus on Alzheimer's. We have a metabolic focus on the role of exercise and diets and sort of how they're impacting metabolic organs across the body and across many different tissues. And all of them are interfacing with the circuitry and the reason for that is another computer science principle of eat your own dog food. If everybody ate their own dog food, dog food would taste a lot better. The reason why Microsoft Excel and Word and PowerPoint was so important and so successful is because the employees that were working on them were using them for their day-to-day tasks. You can't just simply build a circuitry and say, here it is guys, take the circuitry, we're done, without being the users of that circuitry because you then go back. And because we span the whole spectrum from profiling the epigenome, using comparative genomics, finding the important nucleotides in the genome, building the basic functional map of what are the genes in the human genome? What are the gene regulatory elements of the human genome? I mean, over the years, we've written a series of papers on how do you find human genes in the first place, using comparative genomics. How do you find the motifs that are the building blocks of gene regulation using comparative genomics? How do you then find how these motifs come together and act in specific tissues using epigenomics? How do you link regulators to enhancers and enhancers to their target genes using epigenomics and regulatory genomics? So through the years, we've basically built all this infrastructure for understanding what I like to say, every single nucleotide of the human genome and how it acts in every one of the major cell types and tissues of the human body. I mean, this is no small task. This is an enormous task that takes the entire field. And that's something that my group has taken on, along with many other groups. And we have also, and that sort of a thing sets my group perhaps apart, we have also worked with specialists in every one of these disorders to basically further our understanding all the way down to disease. And in some cases, collaborating with pharma to go all the way down to therapeutics because of our deep, deep understanding of that basic circuitry and how it allows us to now improve the circuitry, not just treat it as a black box, but basically go and say, okay, we need a better cell type specific wiring that we now have at the tissue specific level. So we're focusing on that because we're understanding the needs from the disease front. So you have a sense of the entire pipeline. I mean, one, maybe you can indulge me. One nice question to ask would be how do you, from the scientific perspective, go from knowing nothing about the disease to going, you said, to go into the entire pipeline and actually have a drug or a treatment that cures that disease? So that's an enormously long path and an enormously great challenge. And what I'm trying to argue is that it progresses in stages of understanding rather than one gene at a time. The traditional view of biology was you have one postdoc working on this gene and another prostitute working on that gene. And they'll just figure out everything about that gene. And that's their job. What we've realized is how polygenic the diseases are. So we can't have one postdoc per gene anymore. We now have to have these cross-cutting needs. And I'm gonna describe the path to circuitry along those needs. And every single one of these paths we are now doing in parallel across thousands of genes. So the first step is you have a genetic association. And we talked a little bit about sort of the Mendelian path and the polygenic path to that association. So the Mendelian path was looking through families to basically find gene regions and ultimately genes that are underlying particular disorders. The polygenic path is basically looking at unrelated individuals in this giant matrix of genotype by phenotype. And then finding hits where particular variant impacts disease all the way to the end. And then we now have a connection, not between a gene and a disease, but between a genetic region and a disease. And that distinction is not understood by most people. So I'm gonna explain it a little bit more. Why do we not have a connection between a gene and a disease, but we have a connection between a genetic region and a disease? The reason for that is that 93% of genetic variants that are associated with disease don't impact the protein at all. So if you look at the human genome, there's 20,000 genes. There's 3.2 billion nucleotides. Only 1.5% of the genome codes for proteins. The other 98.5% does not code for proteins. If you now look at where are the disease regions located, 93% of them fall in that outside the gene's portion. Of course, genes are enriched, but they're only enriched by a factor of three. That means that still 93% of genetic variants fall outside the proteins. Why is that difficult? Why is that a problem? The problem is that when a variant falls outside the gene, you don't know what gene is impacted by that variant. You can't just say, oh, it's near this gene. Let's just connect that variant to the gene. And the reason for that is that the genome circuitry is very often long range. So you basically have that genetic variant that could sit in the intron of one gene. And intron is sort of the place between the exons that code for proteins. So proteins are split up into exons and introns, and every exon codes for a particular subset of amino acids, and together they're spliced together, and then make the final protein. So that genetic variant might be sitting in an intron of a gene. It's transcribed with the gene, it's processed and then excised, but it might not impact this gene at all. It might actually impact another gene that's a million nucleotides away. So it's just riding along, even though it has nothing to do with this nearby neighborhood. That's exactly right. Let me give you an example. The strongest genetic association with obesity was discovered in this FTO gene, fat and obesity associated gene. So this FTO gene was studied ad nauseum. People did tons of experiments on it. They figured out that FTO is in fact, RNA methylation transferase. It basically, it sort of impacts something that we know, that we call the epi transcriptome. Just like the genome can be modified, the transcriptome, the transcript of the genes can be modified. And we basically said, oh great, that means that epi transcriptomics is hugely involved in obesity because that gene FTO is clearly where the genetic locus is at. My group studied FTO in collaboration with a wonderful team led by Melina Klausnitzer. And what we found is that this FTO locus, even though it is as associated with obesity, does not implicate the FTO gene. The genetic variance, it's in the first intron of the FTO gene, but it controls two genes, IRX3 and IRX5 that are sitting 1.2 million nucleotides away, several genes away. Oh boy. What am I supposed to feel about that? Because isn't that like super complicated then? So the way that I was introduced at a conference a few years ago was, and here's Manolis Kellis who wrote the most depressing paper of 2015. And the reason for that is that the entire pharmaceutical industry was so comfortable that there was a single gene in that locus. Because in some loci, you basically have three dozen genes that are all sitting in the same region of association. And you're like, oh gosh, which ones of those is it? But even that question of which ones of those is it, is making the assumption that it is one of those, as opposed to some random gene just far, far away, which is what our paper showed. So basically what our paper showed is that you can't ignore the circuitry. You have to first figure out the circuitry, all of those long range interactions, how every genetic variant impacts the expression of every gene in every tissue imaginable across hundreds of individuals. And then you now have one of the building blocks, not even all of the building blocks, for then going and understanding disease. So, okay, so embrace the wholeness of the circuitry. Correct. But what, so back to the question of starting, knowing nothing to the disease and going to the treatment. So what are the next steps? So you basically have to first figure out the tissue, and then describe how you figure out the tissue. You figure out the tissue by taking all of these non-coding variants that are sitting outside proteins, and then figuring out what are the epigenomic enrichments. And the reason for that, thankfully, is that there is convergence, that the same processes are impacted in different ways by different loci. And that's a saving grace for our field. The fact that if I look at hundreds of genetic variants associated with Alzheimer's, they localize in a small number of processes. Can you clarify why that's hopeful? So like they show up in the same exact way in the specific set of processes? Yeah, so basically there's a small number of biological processes that underlie, or at least that play the biggest role in every disorder. So in Alzheimer's, you basically have, you know, maybe 10 different types of processes. One of them is lipid metabolism. One of them is immune cell function. One of them is neuronal energetics. So these are just a small number of processes, but you have multiple lesions, multiple genetic perturbations that are associated with those processes. So if you look at schizophrenia, it's excitatory neuron function, it's inhibitory neuron function, it's synaptic pruning, it's calcium signaling, and so on and so forth. So when you look at disease genetics, you have one hit here and one hit there and one hit there and one hit there, completely different parts of the genome, but it turns out all of those hits are calcium signaling proteins. Oh, cool. You're like, aha, that means that calcium signaling is important. So those people who are focusing on one focus at a time cannot possibly see that picture. You have to become a genomicist. You have to look at the omics, the om, the holistic picture to understand these enrichments. But you mentioned the convergence thing. So the, whatever the thing associated with the disease shows up. So let me explain convergence. Convergence is such a beautiful concept. So you basically have these four genes that are converging on calcium signaling. So that basically means that they are acting each in their own way, but together in the same process. But now in every one of these loci, you have many enhancers controlling each of those genes. That's another type of convergence where this regulation of seven different enhancers might all converge on this regulation of that one gene, which then converges on calcium signaling. And in each one of those enhancers, you might have multiple genetic variants distributed across many different people. Everyone has their own different mutation, but all of these mutations are impacting that enhancer. And all of these enhancers are impacting that gene. And all of these genes are impacting this pathway. And all of these pathways are acting in the same tissue. And all of these tissues are converging together on the same biological process of schizophrenia. And you're saying the saving grace is that that convergence seems to happen for a lot of these diseases. For all of them. Basically that for every single disease that we've looked at, we have found an epigenomic enrichment. How do you do that? You basically have all of the genetic variants associated with the disorder. And then you're asking for all of the enhancers active in a particular tissue. For 540 disorders, we've basically found that indeed there is an enrichment. That basically means that there is commonality. And from the commonality, we can just get insights. So to explain in mathematical terms, we're basically building an empirical prior. We're using a Bayesian approach to basically say, great, all of these variants are equally likely in a particular locus to be important. So in a genetic locus, you basically have a dozen variants that are co-inherited. Because the way that inheritance works in the human genome is through all of these recombination events during meiosis. You basically have, you know, you inherit maybe three, chromosome three, for example, in your body. It's inherited from four different parts. One part comes from your dad, another part comes from your mom, another part comes from your dad, another part comes from your mom. So basically, the way that it... Sorry, from your mom's mom. So you basically have one copy that comes from your dad and one copy that comes from your mom. But that copy that you got from your mom is a mixture of her maternal and her paternal chromosome. And the copy that you got from your dad is a mixture of his maternal and his paternal chromosome. So these break points that happen when chromosomes are lining up are basically ensuring through these crossover events, they're ensuring that every child cell during the process of meiosis, where you basically have one spermatozoid that basically couples with one ovule to basically create one egg, to basically create the zygote. You basically have half of your genome that comes from dad and half your genome that comes from mom. But in order to line them up, you basically have these crossover events. These crossover events are basically leading to co-inheritance of that entire block coming from your maternal grandmother and that entire block coming from your maternal grandfather. Over many generations, these crossover events don't happen randomly. There's a protein called PRDM9 that basically guides the double-stranded breaks and then leads to these crossovers. And that protein has a particular preference to only a small number of hotspots of recombination, which then leads to a small number of breaks between these co-inheritance patterns. So even though there are 6 million variants, there are 6 million loci, this variation is inherited in blocks. And every one of these blocks has like two dozen genetic variants that are all associated. So in the case of FTO, it wasn't just one variant, it was 89 common variants that were all humongously associated with obesity. Which one of those is the important one? Well, if you look at only one locus, you have no idea. But if you look at many loci, you basically say, aha, all of them are enriching in the same epigenomic map. In that particular case, it was mesenchymal stem cells. So these are the progenitor cells that give rise to your brown fat and your white fat. Progenitor is like the early on developmental stem cells. So you start from one zygote and that's a totipotent cell type, it can do anything. That cell divides, divides, divides. And then every cell division is leading to specialization where you now have a mesodermal lineage and ectodermal lineage and endodermal lineage that basically leads to different parts of your body. The ectoderm will basically give rise to your skin. Ecto means outside, derm is skin. So ectoderm, but it also gives rise to your neurons and your whole brain. So that's a lot of ectoderm. Mesoderm gives rise to your internal organs, including the vasculature and your muscle and stuff like that. So you basically have this progressive differentiation. And then if you look further, further down that lineage, you basically have one lineage that will give rise to both your muscle and your bone, but also your fat. And if you go further down the lineage of your fat, you basically have your white fat cells. These are the cells that store energy. So when you eat a lot, but you don't exercise too much, there's an excess set of calories, excess energy. What do you do with those? You basically create, you spend a lot of that energy to create these high energy molecules, lipids, which you can then burn when you need them on a rainy day. So that leads to obesity if you don't exercise and if you overeat, because your body is like, oh, great, I have all these calories. I'm gonna store them. Ooh, more calories. I'm gonna store them too. Ooh, more calories. And the 42% of European chromosomes have a predisposition to storing fat, which was selected probably in the food scarcity periods. Like basically as we were exiting Africa, before and during the ice ages, there was probably a selection to those individuals who made it north to basically be able to store energy, a lot more energy. So you basically now have this lineage that is deciding whether you want to store energy in your white fat or burn energy in your beige fat. Turns out that your fat is, we have such a bad view of fat. Fat is your best friend. Fat can both store all these excess lipids that would be otherwise circulating through your body and causing damage, but it can also burn calories directly. If you have too much energy, you can just choose to just burn some of that as heat. So basically when you're cold, you're burning energy to basically warm your body up and you're burning all these lipids and you're burning all these caters. So what we basically found is that across the board, genetic variants associated with obesity across many of these regions were all enriched repeatedly in mesenchymal stem cell enhancers. So that gave us a hint as to which of these genetic variants was likely driving this whole association. And we ended up with this one genetic variant called RS142-1085. And that genetic variant out of the 89 was the one that we predicted to be causal for the disease. So going back to those steps, first step is figure out the relevant tissue based on the global enrichment. Second step is figure out the causal variant among many variants in this linkage disequilibrium in this co-inherited block between these recombination hotspots, these boundaries of these inherited blocks. That's the second step. The third step is once you know that causal variant, try to figure out what is the motif that is disrupted by that causal variant. Basically how does it act? Variants don't just disrupt elements, they disrupt the binding of specific regulators. So basically the third step there was how do you find the motif that is responsible, like the gene regulatory word, the building block of gene regulation that is responsible for that dysregulatory event. And the fourth step is finding out what regulator normally binds that motif and is now no longer able to bind. And then once you have the regulator, can you then try to figure out how to, what, after it developed, how to fix it? That's exactly right. You now know how to intervene. You have basically a regulator, you have a gene that you can then perturb and you say, well, maybe that regulator has a global role in obesity. I can perturb the regulator. Just to clarify, when we say perturb, like on the scale of a human life, can a human being be helped? Of course, of course. Yeah, I guess understanding is the first step. Exactly. No, no, but perturbed basically means you now develop therapeutics, pharmaceutical therapeutics against that, or you develop other types of intervention that affect the expression of that gene. What do pharmaceutical therapeutics look like when your understanding's on a genetic level? Yeah. Sorry if it's a dumb question. No, no, no, it's a brilliant question, but I wanna save it for a little bit later when we start talking about therapeutics. Perfect. We've talked about the first four steps. There's two more. So basically the first step is figure out, I mean, the zero step, the starting point is the genetics. The first step after that is figure out the tissue of action. The second step is figuring out the nucleotide that is responsible or set of nucleotides. The third step is figure out the motif and the upstream regulator, number four. Number five and six is what are the targets? So number five is great. Now I know the regulator, I know the motif, I know the tissue, and I know the variant. What does it actually do? So you have to now trace it to the biological process and the genes that mediate that biological process. So knowing all of this can now allow you to find the target genes. How? By basically doing perturbation experiments or by looking at the folding of the epigenome or by looking at the genetic impact of that genetic variant on the expression of genes. And we use all three. So let me go through them. Basically, one of them is physical links. This is the folding of the genome onto itself. How do you even figure out the folding? It's a little bit of a tangent, but it's a super awesome technology. Think of the genome as, again, this massive packaging that we talked about of taking two meters worth of DNA and putting it in something that's a million times smaller than two meters worth of DNA, that's a single cell. You basically have this massive packaging, and this packaging basically leads to the chromosome being wrapped around in sort of tie-tied ways in ways, however, that are functionally capable of being reopened and reclosed. So I can then go in and figure out that folding by sort of chopping up the spaghetti soup, putting glue, and ligating the segments that were chopped up but nearby each other, and then sequencing through these ligation events to figure out that this region of this chromosome, that region of the chromosome were near each other, that means they were interacting, even though they were far away on the genome itself. So that chopping up, sequencing, and re-gluing is basically giving you folds of the genome that we call. Sorry, can you backtrack? Of course. How does cutting it help you figure out which ones were close in the original folding? So you have a bowl of noodles. Go on. And in that bowl of noodles, some noodles are near each other. So throw in a bunch of glue. You basically freeze the noodles in place. Throw in a cutter that chops up the noodles into little pieces. Now throw in some ligation enzyme that lets those pieces that were free re-ligate near each other. In some cases, they're re-ligate what you had just got, but that's very rare. Most of the time, they will re-ligate in whatever was proximal. You now have glued the red noodle that was crossing the blue noodle to each other. You then reverse the glue, the glue goes away, and you just sequence the heck out of it. Most of the time, you'll find red segment with, you know, red segment, but you can specifically select for ligation events that have happened that were not from the same segment by sort of marking them in a particular way, and then selecting those, and then you sequence and you look for red with blue matches of sort of things that were glued that were not immediate proximal to each other. And that reveals the linking of the blue noodle and the red noodle. You're with me so far? Yeah. Good. So we've done these experiments. That's the physical. That's the physical. That's step one of the physical. And what the physical revealed is topologically associated domains, basically big blocks of the genome that are topologically, you know, connected together. That's the physical. The second one is the genetic links. It basically says, across individuals that have different genetic variants, how are their genes expressed differently? Remember before I was saying that the path between genetics and disease is enormous, but we can break it up to look at the path between genetics and gene expression. So instead of using Alzheimer's as a phenotype, I can now use expression of IRX3 as the phenotype, expression of gene A. And I can look at all of the humans who contain a G at that location and all the humans who contain a T at that location and basically say, wow, turns out that the expression of each gene is higher for the T humans than for the G humans at that location. So that basically gives me a genetic link between a genetic variant, a locus, a region, and the expression of nearby genes. Good on the genetic link? I think so. Awesome. So the third link is the activity link. What's an activity link? It basically says, if I look across 833 different epigenomes, whenever this enhancer is active, this gene is active. That gives me an activity link between this region of the DNA and that gene. And then the fourth one is perturbations where I can go in and blow up that region and see what are the genes that change in expression. Or I can go in and over activate that region and see what genes change in expression. So I guess that's similar to activity? Yeah, yeah. So that's basically similar to activity. I agree, but it's causal rather than correlational. Again, I'm a little weird. No, no, you're 100% on. It's exactly the same as activity. But the perturbations. The perturbation. Where I go and intervene, I basically take a bunch of cells. So you know CRISPR, right? CRISPR is this genome guidance and cutting mechanism. It's what George Church likes to call genome vandalism. So you basically are able to. You can basically take a guide RNA that you put into the CRISPR system. And the CRISPR system will basically use these guide RNAs, scan the genome, find wherever there's a match, and then cut the genome. So I digress, but it's a bacterial immune defense system. So basically bacteria are constantly attacked by viruses, but sometimes they win against the viruses and they chop up these viruses and remember as a trophy inside their genome, they have this loci, this CRISPR loci, that basically stands for clustered repeats, interspersed, et cetera. So basically it's an interspersed repeats structure where basically you have a set of repetitive regions and then interspersed were these variable segments that were basically matching viruses. So when this was first discovered, it was basically hypothesized that this is probably a bacterial immune system that remembers the trophies of the viruses that managed the kill. And then the bacteria pass on, you know, they sort of do lateral transfer of DNA and they pass on these memories so that the next bacterium says, ooh, you killed that guy. When that guy shows up again, I will recognize him. And the CRISPR system was basically evolved as a bacterial adaptive immune response to sense foreigners that should not belong and to just go and cut their genome. So it's an RNA guided, RNA cutting enzyme or an RNA guided DNA cutting enzyme. So there's different systems. Some of them cut DNA, some of them cut RNA, but all of them remember this sort of viral attack. So what we have done now as a field is, you know, through the work of, you know, Jennifer Doudna, Emmanuel Carpentier, Feng Zhang and many others is co-opted that system of bacterial immune defense as a way to cut genomes. You basically have this guiding system that allows you to use an RNA guide to bring enzymes to cut DNA at a particular locus. That's so fascinating. So this is like already a natural mechanism, a natural tool for cutting that was useful in this particular context. And we're like, well, we can use that thing to actually, it's a nice tool that's already in the body. Yeah, yeah, it's not in our body, it's in the bacterial body. It was discovered by the yogurt industry. They were trying to make better yogurts and they were trying to make their bacteria in their yogurt cultures more resilient to viruses. And they were studying bacteria and they found that, wow, this CRISPR system is awesome. It allows you to defend against that. And then it was co-opted in mammalian systems that don't use anything like that as a targeting way to basically bring these DNA cutting enzymes to any locus in the genome. Why would you want to cut DNA to do anything? The reason is that our DNA has a DNA repair mechanism where if a region of the genome gets randomly cut, you'll basically scan the genome for anything that matches and sort of use it by homology. So the reason why we're deployed is because we now have a spare copy. As soon as my mom's copy is deactivated, I can use my dad's copy. And somewhere else, if my dad's copy is deactivated, I can use my mom's copy to repair it. So this is called homologous-based repair. So all you have to do is the cutting and you don't have to do the fixing. That's exactly right. You don't have to do the fixing. Because it's already built in. That's exactly right. But the fixing can be co-opted by throwing in a bunch of homologous segments that instead of having your dad's version, have whatever other version you'd like to use. So you then control the fixing by throwing in a bunch of other stuff. That's exactly right. And that's how you do genome editing. So that's what CRISPR is. That's what CRISPR is. In popular culture, people use the term. I've never, wow, that's brilliant. That's an awesome explanation. Genome vandalism followed by a bunch of band-aids that have the sequence that you'd like. And you can control the choices of band-aids. Correct. And of course, there's new generations of CRISPR. There's something that's called prime editing that was sort of very much in the press recently. That basically instead of sort of making a double-stranded break, which again is genome vandalism, you basically make a single stranded break. You basically just nick one of the two strands, enabling you to sort of peel off without sort of completely breaking it up and then repair it locally using a guide that is coupled to your initial RNA that took you to that location. Dumb question, but is CRISPR as awesome and cool as it sounds, I mean, technically speaking, in terms of like as a tool for manipulating our genetics in the positive meaning of the word manipulating? Or is there downsides, drawbacks in this whole context of therapeutics that we're talking about or understanding and so on? So when I teach my students about CRISPR, I show them articles with the headline, genome editing tool revolutionizes biology. And then I show them the date of these articles and they're 2004, like five years before CRISPR was invented. And the reason is that they're not talking about CRISPR. They're talking about zinc finger enzymes that are another way to bring these cutters to the genome. It's a very difficult way of sort of designing the right set of zinc finger proteins, the right set of amino acids that will now target a particularly long stretch of DNA because for every location that you want to target, you need to design a particular regulator, a particular protein that will match that region well. There's another technology called tailings, which are basically just a different way of using proteins to sort of guide these cutters to a particular location of the genome. These require a massive team of engineers, of biological engineers to basically design a set of amino acids that will target a particular sequence of your genome. The reason why CRISPR is amazingly awesomely revolutionary is because instead of having this team of engineers design a new set of proteins for every location that you want to target, you just type it in your computer and you just synthesize an RNA guide. The beauty of CRISPR is not the cutting, it's not the fixing, all of that was there before. It's the guiding. And the only thing that changes is that it makes the guiding easier by sort of just typing in the RNA sequence, which then allows the system to sort of scan the DNA to find that. So the coding, the engineering of the cutter is easier in terms of SP. That's kind of similar to the story of deep learning versus old school machine learning. Some of the challenging parts are automated. Okay, so, but CRISPR is just one cutting technology. And then that's part of the challenges and exciting opportunities of the field is to design different cutting technologies. Yeah, yeah. So now, this was a big parenthesis on CRISPR, but now, when we were talking about perturbations, you basically now have the ability to not just look at correlation between enhancers and genes, but actually go and either destroy that enhancer and see if the gene changes in expression, or you can use the CRISPR targeting system to bring in not vandalism and cutting, but you can couple the CRISPR system with, and the CRISPR system is called usually CRISPR-Cas9, because Cas9 is the protein that will then come and cut, but there's a version of that protein called dead Cas9 where the cutting part is deactivated. So you basically use D-Cas9, dead Cas9, to bring in an activator or to bring in a repressor. So you can now ask, is this enhancer changing that gene by taking this modified CRISPR, which is already modified from the bacteria to be used in humans, that you can now modify the Cas9 to be dead Cas9, and you can now further modify to bring in a regulator, and you can basically turn on or turn off that enhancer and then see what is the impact on that gene. So these are the four ways of linking the locus to the target gene, and that's step number five, okay? Step number five is find the target gene, and step number six is what the heck does that gene do? You basically now go and manipulate that gene to basically see what are the processes that change, and you can basically ask, well, in this particular case, in the FTO locus, we found mesenchymal stem cells that are the progenitors of white fat and brown fat, or beige fat. We found the RS1421085 nucleotide variant as the causal variant. We found this large enhancer, this master regulator. I like to call it OB1 for obesity one, like the strongest enhancer associated with. And OB1 was kind of chubby as the actor, I don't know if you remember him. Yeah. So you basically are using this JEDA mind trick to basically find out the. Thank you. The location of the genome that is responsible, the enhancer that harbors it, the motif, the upstream regulator, which is ARID5B for AT-rich interacting domain 5B. That's a protein that sort of comes and binds normally. That protein is normally a repressor. It represses this super enhancer, this massive 12,000 nucleotide master regulatory control region, and it turns off IRX3, which is a gene that's 600,000 nucleotides away, and IRX5, which is 1.2 million nucleotides away. So those. And what's the effect of turning them off? That's exactly the next question. So step six is what do these genes actually do? So we then ask, what does IRX3 and IRX5 do? The first thing we did is look across individuals for individuals that had higher expression of IRX3 or lower expression of IRX3. And then we looked at the expression of all of the other genes in the genome. And we look for simply correlation. And we found that IRX3 and IRX5 were both correlated positively with lipid metabolism and negatively with mitochondrial biogenesis. You're like, what the heck does that mean? Doesn't sound related to obesity. Not at all, superficially. But lipid metabolism should, because lipids is these high energy molecules that basically store fat. So IRX3 and IRX5 are negatively correlated with lipid metabolism. So that basically means that when they turn on, lipid metabolism, oh, sorry, positively, when they turn on, they turn on lipid metabolism. And they're negatively correlated with mitochondrial biogenesis. What do mitochondria do in this whole process? Again, small parenthesis, what are mitochondria? Mitochondria are little organelles. They arose, they only are found in eukaryotes. Ewe means good, karyo means nucleus. So truly like a true nucleus. So eukaryotes have a nucleus. Prokaryotes are before the nucleus. They don't have a nucleus. So eukaryotes have a nucleus. Compartmentalization. Eukaryotes have also organelles. Some eukaryotes have chloroplasts. These are the plants. They photosynthesize. Some other eukaryotes, like us, have another type of organelle called mitochondria. These arose from an ancient species that we engulfed. This is an endosymbiosis event. Symbiosis, bio means life, sim means together. So symbiotes are things that live together. Endosymbiosis, endo means inside. So endosymbiosis means you live together, holding the other one inside you. So the pre-eukaryotes engulfed an organism that was very good at energy production. And that organism eventually shed most of its genome to now have only 13 genes in the mitochondrial genome. And those 13 genes are all involved in energy production. The electron transport chain. So basically, electrons are these massive, super energy-rich molecules. We basically have these organelles that produce energy. And when your muscle exercises, you basically multiply your mitochondria. You basically sort of use more and more mitochondria. And that's how you get beefed up. So basically, the muscle sort of learns how to generate more energy. So basically, every single time, your muscles will over night regenerate and sort of become stronger and amplify their mitochondria and so forth. So what does mitochondria do? The mitochondria use energy to sort of do any kind of task. When you're thinking, you're using energy. This energy comes from mitochondria. Your neurons have mitochondria all over the place. Basically, this mitochondria can multiply as organelles and they can be spread along the body of your muscle. Some of your muscle cells have actually multiple nuclei, they're polynucleated, but they also have multiple mitochondria to basically deal with the fact that your muscle is enormous. You can sort of span this super, super long length and you need energy throughout the length of your muscle. So that's why you have mitochondria throughout the length and you also need transcription through the length. So you have multiple nuclei as well. So these two processes, lipids store energy. What do mitochondria do? So there's a process known as thermogenesis. Thermo heat, genesis generation. Thermogenesis is generation of heat. Remember that bathtub with the in and out? That's the equation that everybody's focused on. So how much energy do you consume? How much energy do you burn? But in every thermodynamic system, there's three parts to the equation. There's energy in, energy out, and energy lost. Any machine has loss of energy. How do you lose energy? You emanate heat. So heat is energy loss. So there's- Which is where the thermogenesis comes in. Thermogenesis is actually a regulatory process that modulates the third component of the thermodynamic equation. You can basically control thermogenesis explicitly. You can turn on and turn off thermogenesis. And that's where the mitochondria comes into play. Exactly. So IRX-10 and IRX-5 turn out to be the master regulators of a process of thermogenesis versus lipogenesis, generation of fat. So IRX-10 and IRX-5 in most people burn heat, burn calories as heat. So when you eat too much, just burn it off in your fat cells. So that bathtub has basically a sort of dissipation knob that most people are able to turn on. I am unable to turn that on because I am a homozygous carrier for the mutation that changes a T into a C in the RS1421-085 allele, a locus, a snip. I have the risk allele twice from my mom and from my dad. So I'm unable to thermogenize. I'm unable to turn on thermogenesis through IRX-3 and IRX-5 because the regulator that normally binds here, IRX-5B can no longer bind because it's an AT-rich interacting domain. And as soon as I change the T into a C, it can no longer bind because it's no longer AT-rich. But doesn't that mean that you're able to use the energy more efficiently? You're not generating heat or is that- That means I can eat less and get around just fine. Yes. Yeah, so- That's a feature actually. It's a feature in a food scarce environment. Yeah, but- If we're all starving, I'm doing great. If we all have access to massive amounts of food, I'm obese basically. That's taken us to the entire process of then understanding that why mitochondria and then the lipids are both even though distant are somehow involved. Exactly, different sides of the same coin. And you basically choose to store energy or you can choose to burn energy. And that all of that is involved in the puzzle of obesity. And that's what's fascinating, right? Here we are in 2007, discovering the strongest genetic association with obesity and knowing nothing about how it works for almost 10 years. For 10 years, everybody focused on this FTO gene. And they were like, oh, it must have to do something with, you know, RNA modification. And it's like, no, it has nothing to do with the function of FTO, it has everything to do with all of these other process. And suddenly the moment you solve that puzzle, which is a multi-year effort, by the way, a tremendous effort by Melina and many, many others. So this tremendous effort basically led us to recognize this circuitry. You went from having some 89 common variants associated in that region of the DNA sitting on top of this gene to knowing the whole circuitry. When you know the circuitry, you can now go crazy. You can now start intervening at every level. You can start intervening at the iRx5b level. You can start intervening with CRISPR-Cas9 at the single SNP level. You can start intervening at iRx3 and iRx5 directly there. You can start intervening at the thermogenesis level because you know the pathway. You can start intervening at the differentiation level where the decision to make either white fat or beige fat, the energy burning beige fat, is made developmentally in the first three days of differentiation of your adipocytes. So as they're differentiating, you basically can choose to make fat burning machines or fat storing machines. And sort of that's how you populate your fat. You basically can now go in pharmaceutical and do all of that. And in our paper, we actually did all of that. We went in and manipulated every single aspect. At the nucleotide level, we use CRISPR-Cas9 genome editing to basically take primary adipocytes from risk and non-risk individuals and show that by editing that one nucleotide out of 3.2 billion nucleotides in the human genome, you could then flip between an obese phenotype and a lean phenotype like a switch. You can basically take micelles that are non-thermogenizing and just flip into thermogenizing cells by changing one nucleotide. It's mind boggling. It's so inspiring that this puzzle could be solved in this way and it feels within reach to then be able to crack the problem of some of these diseases. What are, so 2007, you mentioned, what are the technologies, the tools that came along that made this possible? Like what are you excited about? Maybe if we just look at the buffet of things that you've kind of mentioned, is there, is there, what's involved? What should we be excited about? What are you excited about? I love that question because there's so much ahead of us. There's so, so much. There's, so basically solving that one locus required massive amounts of knowledge that we have been building across the years through the epigenome, through the comparative genomics to find out the causal variant and the controller regulatory motif through the conserved circuitry. It required knowing this regulatory genomic wiring. It required high C of these sort of topologically associated domains to basically find these long range interaction. It required EQTLs of this sort of genetic perturbation of these intermediate gene phenotypes. It required all of the arsenal of tools that I've been describing was put together for one locus. And this was a massive team effort, huge investment in time, energy, money, effort, intellectual, everything. You're referring to, I'm sorry, just for the obesity one. This one paper. Yeah, this one paper. This one single paper. This one single locus. I like to say that this is a paper about one nucleotide in the human genome, about one bit of information, C versus T in the human genome. That's one bit of information and we have 3.2 billion nucleotides to go through. So how do you do that systematically? I am so excited about the next phase of research because the technologies that my group and many other groups have developed allows us to now do this systematically, not just one locus at a time, but thousands of loci at a time. So let me describe some of these technologies. The first one is automation and robotics. So basically, you know, we talked about how you can take all of these molecules and see which of these molecules are targeting each of these genes and what do they do. So you can basically now screen through millions of molecules, through thousands and thousands and thousands of plates, each of which has thousands and thousands and thousands of molecules, every single time testing, you know, all of these genes and asking which of these molecules perturb these genes. So that's technology number one, automation and robotics. Technology number two is parallel readouts. So instead of perturbing one locus and then asking if I use CRISPR-Cas9 on this enhancer to basically use dCas9 to turn on or turn off the enhancer, or if I use CRISPR-Cas9 on the SNP to basically change that one SNP at a time, then what happens? But we have 120,000 disease-associated SNPs that we wanna test. We don't wanna spend 120,000 years doing it. So what do we do? We've basically developed this technology for massively parallel reporter assays, MPRA. So in collaboration with Tarzan Mikkelsen, Eric Lander, I mean, Jason Dury's group has done a lot of that. So there's a lot of groups that basically have developed technologies for testing 10,000 genetic variants at a time. How do you do that? We talked about microarray technology, the ability to synthesize these huge microarrays that allow you to do all kinds of things like measure gene expression by hybridization, by measuring the genotype of a person, by looking at hybridization with one version with a T versus the other version with a C, and then sort of figuring out that I am a risk carrier for obesity based on these hybridization differential hybridization in my genome that says, oh, you seem to only have this allele or you seem to have that allele. Microarrays can also be used to systematically synthesize small fragments of DNA. So you can basically synthesize these 150 nucleotide long fragments across 450,000 spots at a time. You can now take the result of that synthesis, which basically works through all of these sort of layers of adding one nucleotide at a time. You can basically just type it into your computer and order it. And you can basically order 10,000 or 100,000 of these small DNA segments at a time. And that's where awesome molecular biology comes in. You can basically take all these segments, have a common start and end barcode or sort of like Gator, like just like pieces of a puzzle, you can make the same end piece and the same start piece for all of them. And you can now use plasmids, which are these extra chromosomal, small DNA circular segments that are basically inhabiting all our genomes. We basically have plasmids floating around. I mean, bacteria use plasmids for transferring DNA. And that's where they put a lot of antibiotic resistance genes. So they can easily transfer them from one bacterium to the other. So one bacterium evolves a gene to be resistant to a particular antibiotic. It basically says to all its friends, hey, here's that sort of DNA piece. We can now co-opt these plasmids into human cells. We can basically make a human cell culture and add plasmids to that human cell culture that contain the things that you want to test. You now have this library of 450,000 elements. You can insert them each into the common plasmid and then test them in millions of cells in parallel. And the common plasmid is all the same before you add it. Exactly, the rest of the plasmid is the same. So it's called an episomal reporter assay. Epizome means not inside the genome, it's sort of outside the chromosomes. So it's an episomal assay that allows you to have a variable region where you basically test 10,000 different enhancers. And you have a common region which basically has the same reporter gene. You now can do some very cool molecular biology. You can basically take the 450,000 elements that you've generated and you have a piece of the puzzle here, piece of the puzzle here, which is identical. So they're compatible with that plasmid. You can chop them up in the middle to separate a barcode reporter from the enhancer. And in the middle, put the same gene, again, using the same pieces of the puzzle. You now can have a barcode readout of what is the impact of 10,000 different versions of an enhancer on gene expression. So we're not doing one experiment, we're doing 10,000 experiments. And those 10,000 can be 5,000 of different loci and each of them in two versions, risk or non-risk. I can now test tens of thousands. These are little hypotheses. Exactly. And then you can do 10,000. You can test 10,000 hypotheses at once. How hard is it to generate those 10,000? Trivial, trivial. But it's by biology. No, no, generating the 10,000 is trivial because you basically add, it's by technology. You basically have these arrays that add one nucleotide at a time at every spot. Oh, and so it's printing. Exactly. So you're able to control. Yeah. Super costly, is it? 10,000 bucks. Oh, so this isn't millions? 10,000 bucks for 10,000 experiments. Sounds like the right, you know. I mean, so that's super, that's exciting because you don't have to do one thing at a time. You can now use that technology, these massively parallel reporter assays to test 10,000 locations at a time. We've made multiple modifications of that technology. One was sharper MPRA, which stands for, you know, basically getting a higher resolution view by tiling these elements. So you can see where along the region of control are they acting. And we made another modification called Hydra for high, you know, definition regulatory annotation or something like that, which basically allows you to test 7 million of these at a time by sort of cutting them directly from the DNA. So instead of synthesizing, which basically has the limit of 450,000 that you can synthesize at a time, we basically said, hey, if we wanna test all accessible regions of the genome, let's just do an experiment that cuts accessible regions. Let's take those accessible regions, put them all with the same end joints of the puzzles, and then now use those to create a much, much larger array of things that you can test. And then tiling all of these regions, you can then pinpoint what are the driver nucleotides, what are the elements, how are they acting across 7 million experiments at a time. So basically this is all the same family of technology where you're basically using these parallel readouts of the barcodes. And then, you know, to do this, we used a technology called Starseq for self-transcribing reporter assays, a technology developed by Alex Stark, my former postdoc who's now a PI over in Vienna. So we basically coupled the Starseq, the self-transcribing reporters, where the enhancer can be part of the gene itself. So instead of having a separate barcode, that enhancer basically acts to turn on the gene and is transcribed as part of the gene. So you don't have to have the two separate parts. Exactly, so you can just read them directly. So there's a constant improvement in this whole process. By the way, generating all these options, is it basically brute force? How much human intuition is? Oh gosh, of course it's human intuition and human creativity and incorporating all of the input data sets. Because again, the genome is enormous, 3.2 billion. You don't wanna test that. Instead, you basically use all of these tools that I've talked about already. You generate your top favorite 10,000 hypothesis, and then you go and test all 10,000. And then from what comes out, you can then go to the next step. So that's technology number two. So technology number one is robotics automation, where you have thousands of wells and you constantly test them. The second technology is instead of having wells, you have these massively parallel readouts in sort of these pooled assays. The third technology is coupling CRISPR perturbations with these single cell RNA readouts. So let me make another parenthesis here to describe now single cell RNA sequencing. So what does single cell RNA sequencing mean? So RNA sequencing is what has been traditionally used, oh well traditionally, the last 20 years, ever since the advent of next generation sequencing. So basically before, RNA expression profiling was based on these microarrays. The next technology after that was based on sequencing. So you chop up your RNA and you just sequence small molecules, just like you would sequence a genome, basically reverse transcribe the small RNAs into DNA, and you sequence that DNA in order to get the number of sequencing reads corresponding to the expression level of every gene in the genome. You now have RNA sequencing. How do you go to single cell RNA sequencing? That technology also went through stages of evolution. The first was microfluidics. You basically had these, even chambers, you basically had these ways of isolating individual cells, putting them into a well for every one of these cells. So you have 384 well plates, and you now do 384 parallel reactions to measure the expression of 384 cells. That sounds amazing, and it was amazing, but we wanna do a million cells. How do you go from these wells to a million cells? You can't. So what the next technology was after that is instead of using a well for every reaction, you now use a lipid droplet for every reaction. So you use micro droplets as reaction chambers to basically amplify RNA. So here's the idea. You basically have microfluidics where you basically have every single cell coming down one tube in your microfluidics, and you have little bubbles getting created in the other way with specific primers that mark every cell with its own barcode. You basically couple the two, and you end up with little bubbles that have a cell and tons of markers for that cell. You now mark up all of the RNA for that one cell with the same exact barcode, and you then lyse all of the droplets, and you sequence the heck out of that, and you have for every RNA molecule, a unique identifier that tells you what cell was it on. That is such good engineering, microfluidics, and using some kind of primer to put a label on the thing. I mean, you're making it sound easy. I assume it's- It's beautiful, right? It's quite challenging, but it's gorgeous, yeah. So there's the next generation. That's great engineering, yeah. So that's the second generation. Next generation is, forget the microfluidics altogether. Just use big bottles. How can you possibly do that with big bottles? So here's the idea. You dissociate all of your cells or all of your nuclei from complex cells like brain cells that are very long and sticky, so you can't do that. So if you have blood cells or if you have neuronal nuclei or brain nuclei, you can basically dissociate, let's say, a million cells. You now want to add a unique barcode, a unique barcode in each one of a million cells using only big bottles. How can you possibly do that? Sounds crazy, but here's the idea. You use a hundred of these bottles. You randomly shuffle all your million cells and you throw them into those hundred bottles, randomly, completely randomly. You add one barcode out of a hundred to every one of the cells. You then, you now take them all out, you shuffle them again, and you throw them again into the same hundred bottles. But now in a different randomization, and you add a second barcode. So every cell now has two barcodes. You take them out again, you shuffle them, and you throw them back in. Another third barcode is adding, randomly, from the same hundred barcodes. You've now labeled every cell probabilistically based on the unique path that he took of which of a hundred bottles did he go for the first time, which of a hundred bottles the second time, and which of a hundred bottles the third time. A hundred times a hundred times a hundred is a million unique barcodes in every single one of these cells. You can now have these cells without ever using microfluidics. It's very clever. It's beautiful, right? From a computer science perspective, that's very clever. Yeah. So you now have the single cell sequencing technology. You can use the wells, you can use the bubbles, or you can use the bottles. And you have ways- The bubbles still sound pretty damn cool. The bubbles are awesome. And that's basically the main technology that we're using. So the bubbles is the main technology. So there are kits now that companies just sell to basically carry out single cell RNA sequencing that you can basically for $2,000, you can basically get 10,000 cells from one sample. And for every one of those cells, you basically have the transcription of thousands of genes. And of course the data for any one cell is noisy, but being computer scientists, we can aggregate the data from all of the cells together across thousands of individuals together to basically make very robust inferences. Okay, so the third technology, basically single cell RNA sequencing that allows you to now start asking not just what is the brain expression level difference of that genetic variant, but what is the expression difference of that one genetic variant across every single subtype of brain cell. How is the variance changing? You can't just, you know, with a brain sample, you can just ask about the mean. What is the average expression? If I instead have 3,000 cells that are neurons, I can ask not just what is the neuronal expression, I can say for layer five excitatory neurons of which I have, I don't know, 300 cells, what is the variance that this genetic variant has? So suddenly it's amazingly more powerful. I can basically start asking about this middle layer of gene expression at unprecedented levels. And when you look at the average, it washes out some potentially important signal that corresponds to ultimately the disease. Completely. Yeah. So that, I can do that at the RNA level, but I can also do that at the DNA level for the epigenome. So remember how before I was telling you about all these technologies that we're using to probe the epigenome? One of them is DNA accessibility. So what we're doing in my lab is that from the same dissociation of say a brain sample, where you now have all these tens of thousands of cells floating around, you basically take half of them to do RNA profiling and the other have to do epigenome profiling both at the single cell level. So that allows you to now figure out what are the millions of DNA enhancers that are accessible in every one of tens of thousands of cells. And computationally, we can now take the RNA and the DNA readouts and group them together to basically figure out how is every enhancer related to every gene. And remember these sort of enhancer gene linking that we were doing across 833 samples? 833 is awesome, don't get me wrong, but 10 million is way more awesome. So we can now look at correlated activity across 2.3 million enhancers and 20,000 genes in each of millions of cells to basically start piecing together the regulatory circuitry of every single type of neuron, every single type of astrocytes, oligodendrocytes, microglial cell inside the brains of 1,500 individuals that we sample across multiple different brain regions across both DNA and RNA. So that's the data set that my team generated last year alone. So in one year, we basically generated 10 million cells from human brain across a dozen different disorders across schizophrenia, Alzheimer's, frontotemporal dementia, Lewy body dementia, ALS, Huntington's disease, post-traumatic stress disorder, autism, bipolar disorder, healthy aging, et cetera. So it's possible that even just within that data set lie a lot of keys to understanding these diseases and then be able to like directly leads to the treatment. Correct, correct. So basically we are now- Motivating. Yeah, so our computational team is in heaven right now and we're looking for people. I mean, if you have listeners who are super smart- How much is this? So this is a very interesting kind of side question. How much of this is biology? How much of this is computation? So you have the computational biology group, but how much of, should you be comfortable with biology to be able to solve some of these problems? If you just find, if you put several of the hassle you were on fundamentally, are you thinking like a computer scientist here? You have to, this is the only way. As I said, we are the descendants of the first digital computer. We're trying to understand the digital computer. We understand, we're trying to understand the circuitry, the logic of this digital core computer and all of these analog layers surrounding it. So the case that I've been making is that you cannot think one gene at a time. The traditional biology is dead. There's no way you cannot solve disease with traditional biology. You need it as a component. Once you figured out RX3 and RX5, you now can then say, hey, have you guys worked on those genes with your single gene approach? We'd love to know everything you know. And if you haven't, we now know how important these genes are. Let's now launch a single gene program to dissect them and understand them. But you cannot use that as a way to dissect disease. You have to think genomically. You have to think from the global perspective and you have to build these circuits systematically. So we need numbers of computer scientists who are interested and willing to dive into these data, you know, fully, fully in and sort of extract meaning. We need computer science people who can understand sort of machine learning and inference and sort of, you know, decouple these matrices, come up with super smart ways of sort of dissecting them. But we also need computer scientists who understand biology, who are able to design the next generation of experiments. Because many of these experiments, no one in their right mind would design them without thinking of the analytical approach that you would use to deconvolve the data afterwards. Because it's massive amounts of ridiculously noisy data. And if you don't have the computational pipeline in your head before you even design the experiment, you would never design the experiment that way. That's brilliant. So in designing the experiment, you have to see the entirety of the computational pipeline. That drives the design. That even drives the necessity for that design. Basically, you know, if you didn't have a computer scientist way of thinking, you would never design these hugely combinatorial, massively parallel experiments. So that's why you need interdisciplinary teams. You need teams. And I wanna sort of clarify that. What do we mean by computational biology group? The focus is not on computational. The focus is on the biology. So we are a biology group. What type of biology? Computational biology. That's the type of biology that uses the whole genome. That's the type of biology that designs experiments, genomic experiments, that can only be interpreted in the context of the whole genome. Right, so it's philosophically looking at biology as a computer. Correct, correct. So which is, in the context of the history of biology, is a big transformation. Yeah, yeah. You can think of the name as what do we do? Only computation. That's not true. But how do we study it? Only computationally, that is true. So all of these single cell sequencing can now be coupled with the technology that we talked about earlier for perturbation. So here's the crazy thing. Instead of using these wells and these robotic systems for doing one drug at a time, or for perturbing one gene at a time in thousands of wells, you can now do this using a pool of cells and single cell RNA sequencing. How? You basically can take these perturbations using CRISPR, and instead of using a single guide RNA, you can use a library of guide RNAs generated exactly the same way using this array technology. So you synthesize a thousand different guide RNAs. You now take each of these guide RNAs and you insert them in a pool of cells where every cell gets one perturbation. And you use CRISPR editing or CRISPR, so with either CRISPR Cas9 to edit the genome with these thousand perturbations. Or the activation one. Or with the activation or with the repression. And you now can have a single cell readout where every single cell has received one of these modifications. And you can now in massively parallel ways couple the perturbation and the readout in a single experiment. How are you tracking which perturbations each cell received? So there's ways of doing that, but basically one way is to make that perturbation an expressible vector so that part of your RNA reading is actually that perturbation itself. So you can basically put it in an expressible part so you can self drive it. So the point that I wanna get across is that the sky's the limit. You basically have these tools, these building blocks of molecular biology. You have these massive data sets of computational biology. You have this huge ability to sort of use machine learning and statistical methods and linear algebra to sort of reduce the dimensionality of all these massive data sets. And then you end up with a series of actionable targets that you can then couple with pharma and just go after systematically. So the ability to sort of bring genetics to the epigenomics, to the transcriptomics, to the cellular readouts using these sort of high throughput perturbation technology that I'm talking about, and ultimately to the organismal through the electronic health record endophenotypes and ultimately the disease battery of assays at the cognitive level, at the physiological level and every other level. There is no better or more exciting field in my view to be a computer scientist then, or to be a scientist then period. Basically this confluence of technologies, of computation, of data, of insights and of tools for manipulation is unprecedented in human history. And I think this is what's shaping the next century to really be a transformative century for our species and for our planet. So you think the 21st century will be remembered for the big leaps in understanding and alleviation of biology? If you look at the path between discovery and therapeutics, it's been on the order of 50 years, it's been shortened to 40, 30, 20, and now it's on the order of 10 years. But the huge number of technologies that are going on right now for discovery will result undoubtedly in the most dramatic manipulation of human biology that we've ever seen in the history of humanity in the next few years. Do you think we might be able to cure some of the disease that we started this conversation with? Absolutely, absolutely. It's only a matter of time. Basically, the complexity is enormous and I don't want to underestimate the complexity, but the number of insights is unprecedented and the ability to manipulate is unprecedented and the ability to deliver these small molecules and other non-traditional medicine perturbations. There's a lot of sort of new, there's a new generation of perturbations that you can use at the DNA level, at the RNA level, at the microRNA level, at the epigenomic level. There's a battery of new generations of perturbations. If you couple that with cell type identifiers that can basically sense when you are in the right cell based on the specific combination and then turn on that intervention for that cell, you can now think of combinatorial interventions where you can basically sort of feed a synthetic biology construct to someone that will basically do different things in different cells. So basically for cancer, this is one of the therapeutics that our collaborator Ron Weiss is using to basically start sort of engineering the circuits that will use microRNA sensors of the environment to sort of know if you're in a tumor cell or if you're in an immune cell or if you're in a stromal cell and so forth and basically turn on particular interventions there. You can sort of create constructs that are tuned to only the liver cells or only the heart cells or only the brain cells and then have these new generations of therapeutics coupled with this immense amount of knowledge on the sort of which targets to choose and what biological processes to measure and how to intervene. My view is that disease is gonna be fundamentally altered and alleviated as we go forward. Next time we talk, we'll talk about the philosophical implications of that and the effect of life, but let's stick to biology for just a little longer. We did pretty good today, we stuck to the science. What are you excited in terms of the future of this field, the technologies in your own group, in your own mind, you're leading the world at MIT in the science and the engineering of this work. So what are you excited about here? I could not be more excited. We are one of many, many teams who are working on this. In my team, the most exciting parts are manyfold. So basically we've now assembled this battery of technologies, we've assembled these massive, massive data sets and now we're really sort of in the stage of our team's path of generating disease insights. So we are simultaneously working on a paper on schizophrenia right now, that is basically using the single cell profiling technologies, using this editing and manipulation technologies to basically show how the master regulators underlying changes in the brain that are sort of found in schizophrenia are in fact affecting excitatory neurons and inhibitory neurons in pathways that are active both in synaptic pruning but also in neurodevelopment. We've basically found this set of four regulators that are connecting these two processes that were previously separate in schizophrenia in sort of having a sort of more unified view across those two sides. The second one is in the area of metabolism. We basically now have a beautiful collaboration with the Goodyear lab that's basically looking at multi-tissue perturbations in six or seven different tissues across the body in the context of exercise and in the context of nutritional interventions using both mouse and human, where we can basically see what are the cell-to-cell communications that are changing across them and what we're finding is this immense role of both immune cells as well as adipocyte stem cells in sort of reshaping that circuitry of all of these different tissues and that's sort of painting to a new path for a therapeutical intervention there. In Alzheimer's, it's this huge focus on microglia and now we're discovering different classes of microglial cells that are basically either synaptic or immune and these are playing vastly different roles in Alzheimer's versus in schizophrenia and what we're finding is this immense complexity as you go further and further down of how in fact there's 10 different types of microglia each with their own sort of expression programs. We used to think of them as, oh yeah, they're microglia but in fact now we're realizing just even in that sort of least abundant of cell types, there's this incredible diversity there. The differences between brain regions is another sort of major, major insight. Again, one would think that, oh, astrocytes are astrocytes no matter where they are, but no, there's incredible region-specific differences in the expression patterns of all of the major brain cell types across different brain regions. So basically there's the neocortical regions that are sort of the recent innovation that makes us so different from all other species. There's the sort of reptilian brain sort of regions that are sort of much more, very extremely distinct. There's the cerebellum. There's each of those basically is associated in a different way with disease. And what we're doing now is looking into pseudo temporal models for how disease progresses across different regions of the brain. If you look at Alzheimer's, it basically starts in this small region called the enterinol cortex and then it spreads through the brain and, you know, through the hippocampus and, you know, ultimately affecting the neocortex. And with every brain region that it hits, it basically has a different impact on the cognitive and, you know, memory aspects, orientation, short-term memory, long-term memory, et cetera, which is, you know, dramatically affecting the cognitive path that the individuals go through. So what we're doing now is creating this computational model for ordering the cells and the regions and the individuals according to their ability to predict Alzheimer's disease. So we can have a cell level predictor of pathology that allows us to now create a temporal time course that tells us when every gene turns on along this pathology progression, and then trace that across regions and pathological measures that are region specific, but also cognitive measures and so on and so forth. So that allows us to now sort of, for the first time, look at can we actually do early intervention for Alzheimer's where we know that the disease starts manifesting for 10 years before you actually have your first cognitive loss. Can we start seeing that path to build new diagnostics, new prognostics, new biomarkers for this sort of early intervention in Alzheimer's? The other aspect that we're looking at is mosaicism. We talked about the common variants and the rare variants, but in addition to those rare variants, as your initial cell that forms the zygote divides and divides and divides, with every cell division, there are additional mutations that are happening. So what you end up with is your brain being a mosaic of multiple different types of genetic underpinnings. Some cells contain a mutation that other cells don't have. So every human has the common variants that all of us carry to some degree, the rare variants that your immediate tree of the human species carries, and then there's the somatic variants, which is the tree that happened after the zygote that sort of forms your own body. So these somatic alterations is something that has been previously inaccessible to study in human postmortem samples. But right now, with the advent of single-cell RNA sequencing, in this particular case, we're using the well-based sequencing, which is much more expensive, but gives you a lot richer information about each of those transcripts. So we're using now that richer information to infer mutations that have happened in each of the thousands of genes that sort of are active in these cells, and then understand how the genome relates to the function, this genotype-phenotype relationship that we usually build in GWAS, between genome-wide association studies between genetic variation and disease. We're now building that at the cell level, where for every cell, we can relate the unique specific genome of that cell with the expression patterns of that cell and the predicted function, using these predictive models that I mentioned before, on dysregulation for cognition, for pathology, in Alzheimer's, at the cell level. And what we're finding is that the genes that are altered, and the genetic regions that are altered in common variants versus rare variants versus somatic variants, are actually very different from each other. The somatic variants are pointing to neuronal energetics and oligodendrocyte functions that are not visible in the genetic regions that you find for the common variants, probably because they have too strong of an effect, that evolution is just not tolerating them on the common side of the allele frequency spectrum. So the somatic one, that's the variation that happens after the zygote, after you individual. I mean, it's a dumb question, but there's mutation and variation, I guess, that happens there. And you're saying that through this, if we focus in on individual cells, we're able to detect the story that's interesting there, and that might be a very unique kind of important variability that arises for, you said neuronal or something. Energetics. Energetics. Energetics, sounds like a cool term. So, I mean, the metabolism of humans is dramatically altered from that of nearby species. We talked about that last time, that basically we are able to consume meat that is incredibly energy rich, and that allows us to sort of have functions that are meeting this humongous brain that we have. Basically, on one hand, every one of our brain cells is much more energy efficient than our neighbors, than our relatives. Number two, we have way more of these cells. And number three, we have this new diet that allows us to now feed all these needs. That basically creates a massive amount of damage, oxidative damage from this huge, super powered factory of ideas and thoughts that we carry in our skull. And that factory has energetic needs, and there's a lot of sort of biological processes underlying that, that we are finding are altered in the context of Alzheimer's disease. That's fascinating that, so you have to consider all of these systems if you want to understand even something like diseases that you would maybe traditionally associate with just the particular cells of the brain. Yeah. The immune system. The metabolic system. The metabolic system. And these are all the things that makes us uniquely human. So our immune system is dramatically different from that of our neighbors. Our societies are so much more clustered. The history of infection that have plagued the human population is dramatically different from every other species. The way that our society and our population has sort of exploded, has basically put unique pressures on our immune system, and our immune system has both coped with that density and also been shaped by, as I mentioned, the vast amount of death that has happened in the Black Plague and other sort of selective events in human history, famines, ice ages, and so forth. So that's number one on the sort of immune side. On the metabolic side, again, we are able to sort of run marathons. I don't know if you remember the sort of human versus horse experiment where the horse actually tires out faster than the human, and the human actually wins. So on the metabolic side, we're dramatically different. On the immune side, we're dramatically different. On the brain side, again, no need to sort of, it's a no brainer of how our brain is just enormously more capable. And then in the side of cancers, so basically the cancers that humans are having, the exposures, the environmental exposures is again, dramatically different. And the lifespan, the expansion of human lifespan is unseen in any other species in recent evolutionary history. And that now leads to a lot of new disorders that are starting to manifest late in life. So Alzheimer's is one example where basically these vast energetic needs over a lifetime of thinking can basically lead to all of these debris and eventually saturate the system and lead to Alzheimer's in the late life. But there's just such a dramatic set of frontiers when it comes to aging research that will, so what I often like to say is that if you want to engineer a car to go from 70 miles an hour to 120 miles an hour, that's fine. You can basically fix a few components. If you want it to now go at 400 miles an hour, you have to completely redesign the entire car because the system has just not evolved to go that far. Basically our human body has only evolved to live to, I don't know, 120. Maybe we can get to 150 with minor changes. But if, as we start pushing these frontiers for not just living, but well living, the F-zine that we talked about last time. So to basically push F-zine into the 80s and 90s and 100s and much further than that, we will face new challenges that have never been faced before in terms of cancer, the number of divisions, in terms of Alzheimer's and brain related disorders, in terms of metabolic disorders, in terms of regeneration. There's just so many different frontiers ahead of us. So I am thrilled about where we're heading. So basically I see this confluence in my lab and many other labs of AI, the next frontier of AI for drug design. So basically these sort of graph neural networks on specific chemical designs that allow you to create new generations of therapeutics. These molecular biology tricks for intervening at the system at every level. These personalized medicine prediction, diagnosis and prognosis using the electronic health records and using these polygenic risk scores, weighted by the burden, the number of mutations that are accumulating across common, rare and somatic variants, the burden converging across all of these different molecular pathways, the delivery of specific drugs and specific interventions into specific cell types. And again, you've talked with Bob Langer about this. There's many giants in that field. And then the last concept is not intervening at the single gene level. I want you to sort of conceptualize the concept of an on target side effect. What is an on target side effect? An off target side effect is when you design a molecule to target one gene and instead it targets another gene and you have side effects because of that. An on target side effect is when your molecule does exactly what you were expecting, but that gene is pleiotropic. Pleio means many, tropos means ways, many ways. It acts in many ways. It's a multifunctional gene. So you find that this gene plays a role in this, but as we talked about the wiring of genes to phenotypes is extremely dense and extremely complex. So the next stage of intervention will be intervening not at the gene level, but at the network level, intervening at the set of pathways and the set of genes with multi-input perturbations to the system, multi-input modulations, pharmaceutical or other interventional. And that basically allow you to now work at the sort of full level of understanding, not just in your brain, but across your body, not just in one gene, but across the set of pathways and so on and so forth for every one of these disorders. So I think that we're finally at the level of systems medicine of basically instead of sort of medicine being at the single gene level, medicine being at the systems level, where it can be personalized based on the specific set of genetic markers and genetic perturbations that you are either born with or that you have developed during your lifetime, your unique set of exposures, your unique set of biomarkers and your unique set of, current set of conditions through your EHR and other ways. And the precision component of intervening extremely precisely in the specific pathways and the specific combinations of genes that should be modulated to sort of bring you from the disease state to the physiologically normal state or even to physiologically improved state through this combination of interventions. So that's in my view, the field where basically computer science comes together with artificial intelligence, statistics, all of these other tools, molecular biology technologies and biotechnology and pharmaceutical technologies that are sort of revolutionary and the way of intervention. And of course, this massive amount of molecular biology and data gathering and generation perturbation in massively parallel ways. So there's no better way, there's no better time, there's no better place to be sort of, you know, looking at this whole confluence of ideas. And I'm just so thrilled to be a small part of this amazing, enormous ecosystem. It's exciting to imagine what humans of 100, 200 years from now, what their life experience is like, because these ideas seem to have potential to transform the quality of life. That when they look back at us, they probably wonder how we were put up with all the suffering in the world. Manolis, it's a huge honor. Thank you for spending this early Sunday morning with me. I deeply appreciate it. See you next time. Sounds like a plan. Thank you, Lex. Thanks for listening to this conversation with Manolis Kellis. And thank you to our sponsors, SEMrush, which is an SEO optimization tool, Pessimist Archive, which is one of my favorite history podcasts, 8Sleep, which is a self-cooling mattress with smart sensors and an app, and finally, BetterHelp, which is an online therapy service. Please check out these sponsors in the description to get a discount and to support this podcast. If you enjoy this thing, subscribe on YouTube, review it with Five Stars and Apple Podcasts, follow on Spotify, support it on Patreon, or connect with me on Twitter at Lex Friedman. And now, let me leave you with some words from Haruki Murakami. Human beings are ultimately nothing but carriers, passageways for genes. They ride us into the ground like racehorses from generation to generation. Genes don't think about what constitutes good or evil. They don't care whether we're happy or unhappy. We're just means to an end for them. The only thing they think about is what is most efficient for them. Thank you for listening and hope to see you next time.
 The following is a conversation with Manolis Kellis,
 his third time on the podcast.
 He is a professor at MIT
 and head of the MIT Computational Biology Group.
 This time we went deep on the science, biology and genetics.
 So this is a bit of an experiment.
 Manolis went back and forth between the basics of biology
 to the latest state of the art in the research.
 He's a master at this.
 So I just sat back and enjoyed the ride.
 This conversation happened at 7 a.m.
 So it's yet another podcast episode
 after an all-nighter for me.
 And once again, since the universe has a sense of humor,
 this one was a tough one for my brain to keep up,
 but I did my best and I never shy away from good challenge.
 Quick mention of each sponsor,
 followed by some thoughts related to the episode.
 First is SEMrush, the most advanced SEO optimization tool
 I've ever come across.
 I don't like looking at numbers, but someone probably should.
 It helps you make good decisions.
 Second is Pessimist Archive.
 They're back, one of my favorite history podcasts
 on why people resist new things from recorded music
 to umbrellas, to cars, chess, coffee and the elevator.
 Third is 8 Sleep, a mattress that cools you
 and cools itself, measures heart rate variability,
 has an app and has given me yet another reason
 to look forward to sleep,
 including the all-important Power Nap.
 And finally, BetterHelp, online therapy
 when you want to face your demons
 with a licensed professional,
 not just by doing the David Goggins-like physical challenges
 like I seem to do on occasion.
 Please check out these sponsors in the description
 to get a discount and to support this podcast.
 As a side note, let me say that biology in the brain
 and in the various systems of the body,
 fill me with awe every time I think about
 how such a chaotic mess,
 coming from its humble origins in the ocean,
 was able to achieve such incredibly complex
 and robust mechanisms of life that survived
 despite all the forces of nature that want to destroy it.
 It is so unlike the computing systems
 we humans have engineered that it makes me feel
 that in order to create artificial general intelligence
 and artificial consciousness,
 we may have to completely rethink
 how we engineer computational systems.
 If you enjoy this thing, subscribe on YouTube,
 review it with five stars on Apple Podcast,
 follow on Spotify, support on Patreon,
 or connect with me on Twitter at Lex Friedman.
 And now here's my conversation with Manolis Kallis.
 So your group at MIT is trying to understand
 the molecular basis of human disease.
 What are some of the biggest challenges in your view?
 Don't get me started.
 I mean, understanding human disease
 is the most complex challenge in modern science.
 So because human disease is as complex as the human genome,
 it is as complex as the human brain,
 and it is in many ways even more complex
 because the more we understand disease complexity,
 the more we start understanding genome complexity
 and epigenome complexity and brain circuitry complexity
 and immune system complexity and cancer complexity
 and so on and so forth.
 So traditionally, human disease was following basic biology.
 You would basically understand basic biology
 in model organisms like mouse and fly and yeast.
 You would understand sort of mammalian biology
 and animal biology and eukaryotic biology
 in sort of progressive layers of complexity,
 getting closer to human phylogenetically.
 And you would do perturbation experiments in those species
 to see if I knock out a gene, what happens.
 And based on the knocking out of these genes,
 you would basically then have a way to drive human biology
 because you would sort of understand the functions
 of these genes.
 And then if you find that a human gene, locus,
 something that you've mapped from human genetics
 to that gene is related to a particular human disease,
 you take, aha, now I know the function of the gene
 from the model organisms.
 I can now go and understand the function
 of that gene in human.
 But this is all changing.
 This is dramatically changed.
 So that was the old way of doing basic biology.
 You would start with the animal models,
 the eukaryotic models, the mammalian models,
 and then you would go to human.
 Human genetics has been so transformed
 in the last decade or two that human genetics
 is now actually driving the basic biology.
 There is more genetic mutation information
 in the human genome than there will ever be
 in any other species.
 What do you mean by mutation information?
 So perturbations is how you understand systems.
 So an engineer builds systems,
 and then they know how they work from the inside out.
 A scientist studies systems through perturbations.
 You basically say, if I poke that balloon,
 what's gonna happen?
 And I'm gonna film it in super high resolution,
 understand, I don't know, air dynamics or fluid dynamics
 if it's filled with water, et cetera.
 So you can then make experimentation by perturbation,
 and then the scientific process is sort of building models
 that best fit the data, designing new experiments
 that best test your models and challenge your models
 and so on and so forth.
 That's the same thing with science, basically.
 If you're trying to understand biological science,
 you basically want to do perturbations
 that then drive the models.
 So how do these perturbations allow you
 to understand disease?
 So if you know that a gene is related to disease,
 you don't wanna just know that it's related to the disease.
 You wanna know what is the disease mechanism
 because you wanna go and intervene.
 So the way that I like to describe it
 is that traditionally, epidemiology,
 which is basically the study of disease,
 sort of the observational study of disease,
 has been about correlating one thing with another thing.
 So if you have a lot of people with liver disease
 who are also alcoholics, you might say,
 well, maybe the alcoholism is driving the liver disease,
 or maybe those who have liver disease
 self-medicate with alcohol.
 So the connection could be either way.
 With genetic epidemiology,
 it's about correlating changes in genome
 with phenotypic differences,
 and then you know the direction of causality.
 So if you know that a particular gene
 is related to the disease, you can basically say,
 okay, perturbing that gene in mouse
 causes the mice to have X-phenotype.
 So perturbing that gene in human
 causes the humans to have the disease.
 So I can now figure out
 what are the detailed molecular phenotypes
 in the human that are related
 to that organismal phenotype in the disease.
 So it's all about understanding disease mechanism,
 understanding what are the pathways,
 what are the tissues, what are the processes
 that are associated with the disease
 so that we know how to intervene.
 You can then prescribe particular medications
 that also alter these processes.
 You can prescribe lifestyle changes
 that also affect these processes, and so on and so forth.
 That's such a beautiful puzzle to try to solve.
 Like what kind of perturbations
 eventually have this ripple effect
 that leads to a disease across the population?
 And then you study that for animals, a mice first,
 and then see how that might possibly connect to humans.
 How hard is that puzzle of trying to figure out
 how little perturbations might lead to,
 in a stable way, to a disease?
 In animals, we make the puzzle simpler
 because we perturb one gene at a time.
 That's the beauty of it, it's the power of animal models.
 You can basically decouple the perturbations.
 You only do one perturbation,
 and you only do strong perturbations at a time.
 In human, the puzzle is incredibly complex
 because, I mean, obviously you don't do
 human experimentation, you wait for natural selection
 and natural genetic variation
 to basically do its own experiments,
 which it has been doing for hundreds and thousands of years
 in the human population, and for hundreds of thousands
 of years across the history leading to the human population.
 So you basically take this natural genetic variation
 that we all carry within us.
 Every one of us carries six million perturbations.
 So I've done six million experiments on you,
 six million experiments on me,
 six million experiments on everyone
 of seven billion people on the planet.
 What's the six million correspond to?
 Six million unique genetic variants
 that are segregating the human population.
 Every one of us carries millions of polymorphic sites,
 polymanymorph forms.
 Polymorphic means many forms, variants.
 That basically means that every one of us
 has single nucleotide alterations
 that we have inherited from mom and from dad
 that basically can be thought of
 as tiny little perturbations.
 Most of them don't do anything,
 but some of them lead to all of the phenotypic differences
 that we see between us.
 The reason why two twins are identical
 is because these variants completely determine
 the way that I'm gonna look at exactly 93 years of age.
 How happy are you with this kind of data set?
 Is it large enough of the human population of Earth?
 Is that too big, too small?
 Yeah, so is it large enough is a power analysis question.
 And in every one of our grants,
 we do a power analysis based on what is the effect size
 that I would like to detect
 and what is the natural variation in the two forms.
 So every time you do a perturbation,
 you're asking, I'm changing form A into form B.
 Form A has some natural phenotypic variation around it,
 and form B has some natural phenotypic variation around it.
 If those variances are large
 and the differences between the mean of A
 and the mean of B are small,
 then you have very little power.
 The further the means go apart,
 that's the effect size, the more power you have,
 and the smaller the standard deviation,
 the more power you have.
 So basically, when you're asking,
 is that sufficiently large?
 Certainly not for everything,
 but we already have enough power
 for many of the stronger effects
 in the more tight distributions.
 So that's a hopeful message
 that there exists parts of the genome
 that have a strong effect, that has a small variance.
 That's exactly right.
 Unfortunately, those perturbations
 are the basis of disease in many cases.
 So it's not a hopeful message.
 Sometimes it's a terrible message.
 It's basically, well, some people are sick,
 but if we can figure out
 what are these contributors to sickness,
 we can then help make them better
 and help many other people better
 who don't carry that exact mutation,
 but who carry mutations on the same pathways.
 And that's what we like to call
 the allelic series of a gene.
 You basically have many perturbations
 of the same gene in different people,
 each with a different frequency in the human population
 and each with a different effect
 on the individual that carries them.
 So you said in the past, there would be
 these small experiments on perturbations in animal models.
 What does this puzzle solving process look like today?
 So we basically have something
 like seven billion people in the planet,
 and every one of them carries
 something like six million mutations.
 You basically have an enormous matrix
 of genotype by phenotype,
 by systematically measuring the phenotype
 of these individuals.
 And the traditional way of measuring this phenotype
 has been to look at one trait at a time.
 You would gather families
 and you would sort of paint the pedigrees
 of a strong effect,
 what we'd like to call Mendelian mutation.
 So a mutation that gets transmitted
 in a dominant or a recessive, but strong effect form,
 where basically one locus plays a very big role
 in that disease.
 And you could then look at carriers versus non-carriers
 in one family,
 carriers versus non-carriers in another family,
 and do that for hundreds, sometimes thousands of families,
 and then trace these inheritance patterns,
 and then figure out what is the gene that plays that role.
 Is this the matrix that you're showing in talks or lectures?
 So that matrix is the input
 to the stuff that I show in talks.
 So basically that matrix has traditionally
 been strong effect genes.
 What the matrix looks like now is instead of pedigrees,
 instead of families,
 you basically have thousands and sometimes hundreds
 of thousands of unrelated individuals,
 each with all of their genetic variants
 and each with their phenotype, for example, height,
 or lipids, or whether they're sick or not
 for a particular trait.
 That has been the modern view.
 Instead of going to families,
 going to unrelated individuals
 with one phenotype at a time.
 And what we're doing now,
 as we're maturing in all of these sciences,
 is that we're doing this in the context
 of large medical systems or enormous cohorts
 that are very well phenotyped across hundreds of phenotypes,
 sometimes with our complete electronic health record.
 So you can now start relating,
 not just one gene segregating one family,
 not just thousands of variants segregating
 with one phenotype,
 but now you can do millions of variants
 versus hundreds of phenotypes.
 And as a computer scientist, I mean,
 deconvolving that matrix, partitioning it
 into the layers of biology that are associated
 with every one of these elements is a dream come true.
 It's like the world's greatest puzzle.
 And you can now solve that puzzle
 by throwing in more and more knowledge
 about the function of different genomic regions
 and how these functions are changed across tissues
 and in the context of disease.
 And that's what my group and many other groups are doing.
 We're trying to systematically relate
 this genetic variation with molecular variation
 at the expression level of the genes,
 at the epigenomic level of the gene regulatory circuitry,
 and at the cellular level of what are the functions
 that are happening in those cells
 at the single cell level using single cell profiling,
 and then relate all that vast amount of knowledge
 computationally with the thousands of traits
 that each of these of thousands of variants are perturbing.
 I mean, this is something we talked about,
 I think last time.
 So there's these effects at different levels that happen.
 You said at a single cell level,
 you're trying to see things that happen
 due to certain perturbations.
 And then it's not just like a puzzle
 of perturbation and disease.
 It's perturbation then effect at a cellular level,
 then at an organ level,
 at a body, like how do you disassemble this
 into like what your group is working on?
 You're basically taking a bunch of the hard problems
 in the space.
 How do you break apart a difficult disease
 and break it apart into problems that you,
 into puzzles that you can now start solving?
 So there's a struggle here.
 Computer scientists love hard puzzles.
 And they're like, oh, I wanna build a method
 that just deconvolves the whole thing computationally.
 And that's very tempting and it's very appealing,
 but biologists just like to decouple
 that complexity experimentally,
 to just like peel off layers of complexity experimentally.
 And that's what many of these modern tools
 that my group and others have both developed and used.
 The fact that we can now figure out tricks
 for peeling off these layers of complexity
 by testing one cell type at a time,
 or by testing one cell at a time.
 And you could basically say,
 what is the effect of these genetic variants
 associated with Alzheimer's on human brain?
 Human brain sounds like, oh, it's an organ, of course,
 just go one organ at a time.
 But human brain has of course,
 dozens of different brain regions.
 And within each of these brain regions,
 dozens of different cell types.
 And every single type of neuron,
 every single type of glial cell,
 between astrocytes, oligodendrocytes, microglia,
 between all of the neural cells and the vascular cells,
 and the immune cells that are co-inhabiting the brain
 between the different types of excitatory
 and inhibitory neurons that are sort of interacting
 with each other between different layers of neurons
 in the cortical layers.
 Every single one of these has a different type of function
 to play in cognition, in interaction with the environment,
 in maintenance of the brain, in energetic needs,
 in feeding the brain with blood, with oxygen,
 in clearing out the debris that are resulting
 from the super high energy production of cognition in humans.
 So all of these things are basically
 potentially deconvolvable computationally,
 but experimentally, you can just do single cell profiling
 of dozens of regions of the brain
 across hundreds of individuals, across millions of cells.
 And then now you have pieces of the puzzle
 that you can then put back together
 to understand that complexity.
 I mean, first of all, the cells in the human brain
 are the most, maybe I'm romanticizing it,
 but cognition seems to be very complicated.
 So separating into the function,
 breaking Alzheimer's down to the cellular level
 seems very challenging.
 Is that basically you're trying to find a way
 that some perturbation in genome results
 in some obvious major dysfunction in the cell?
 You're trying to find something like that.
 Exactly.
 So what does human genetics do?
 Human genetics basically looks at the whole path
 from genetic variation all the way to disease.
 So human genetics has basically taken thousands
 of Alzheimer's cases and thousands of controls
 matched for age, for sex, for environmental backgrounds
 and so on and so forth.
 And then looked at that map where you're asking
 what are the individual genetic perturbations
 and how are they related all the way to Alzheimer's disease.
 And that has actually been quite successful.
 So we now have more than 27 different loci,
 these are genomic regions that are associated
 with Alzheimer's at this end to end level.
 But the moment you sort of break up that very long path
 into smaller levels, you can basically say from genetics,
 what are the epigenomic alterations
 at the level of gene regulatory elements
 where that genetic variant perturbs
 the control region nearby.
 That effect is much larger.
 You mean much larger in terms of this down the line impact?
 Or it's much larger in terms of the measurable effect,
 this A versus B variance is actually so much cleanly defined
 when you go to the shorter branches.
 Because for one genetic variant to affect Alzheimer's,
 that's a very long path.
 That basically means that in the context of millions
 of these 6 million various that every one of us carries,
 that one single nucleotide has a detectable effect
 all the way to the end.
 I mean, it's just mind boggling that that's even possible.
 But indeed, yeah, but indeed there are such effects.
 So the hope is, or the most scientifically speaking,
 the most effective place where to detect the alteration
 that results in disease is earlier on in the pipeline,
 as early as possible.
 It's a trade off.
 If you go very early on in the pipeline,
 now each of these epigenomic alterations,
 for example, this enhancer control region
 is active maybe 50% less, which is a dramatic effect.
 Now you can ask, well, how much does changing
 one regulatory region in the genome,
 in one cell type change disease?
 Well, that path is now long.
 So if you instead look at expression,
 the path between genetic variation
 and the expression of one gene
 goes through many enhancer regions,
 and therefore it's a subtler effect at the gene level,
 but then now you're closer because one gene is acting on,
 you know, in the context of only 20,000 other genes,
 as opposed to one enhancer acting in the context
 of two million other enhancers.
 So you basically now have genetic, epigenomic, the circuitry,
 transcriptomic, the gene expression level,
 and then cellular, where you can basically say,
 I can measure various properties of those cells.
 What is the calcium influx rate
 when I have the genetic variation?
 What is the synaptic density?
 What is the electric impulse conductivity?
 And so on and so forth.
 So you can measure things along this path to disease,
 and you can also measure endophenotypes.
 You can basically measure, you know, your brain activity.
 You can do imaging in the brain.
 You can basically measure, I don't know,
 the heart rate, the pulse, the lipids,
 the amount of blood secreted, and so on and so forth.
 And then through all of that,
 you can basically get at the path to causality,
 the path to disease.
 And is there something beyond cellular?
 So you mentioned lifestyle interventions or changes
 as a way to, or like be able to prescribe changes
 in lifestyle.
 Like what about organs?
 What about like the function of the body as a whole?
 Yeah, absolutely.
 So basically when you go to your doctor,
 they always measure, you know, your pulse.
 They always measure your height.
 They always measure your weight, you know, your BMI.
 So basically these are just very basic variables.
 But with digital devices nowadays,
 you can start measuring hundreds of variables
 for every individual.
 You can basically also phenotype cognitively
 through tests, Alzheimer's patients.
 There are cognitive tests that you can measure,
 that you typically do for cognitive decline,
 these mini mental, you know, observations
 that you have specific questions to.
 You can think of sort of enlarging
 the set of cognitive tests.
 So in the mouse, for example,
 you do experiments for how do they get out of mazes?
 How do they find food?
 Whether they recall a fear?
 Whether they shake in a new environment
 and so on and so forth.
 In the human, you can have much, much richer phenotypes
 where you can basically say, not just imaging at the,
 you know, organ level, but,
 and all kinds of other activities at the organ level,
 but you can also do at the organism level,
 you can do behavioral tests.
 And how did they do on empathy?
 How did they do on memory?
 How did they do on long-term memory
 versus short-term memory?
 And so on and so forth.
 I love how you're calling that phenotype.
 I guess it is.
 It is.
 But like your behavior patterns
 that might change over a period of a life,
 your ability to remember things,
 your ability to be, yeah, empathetic or emotionally,
 or your intelligence perhaps even.
 Yeah, but intelligence has hundreds of variables.
 You can be your math intelligence,
 your literary intelligence,
 your puzzle solving intelligence, your logic.
 It could be like hundreds of things.
 And all of that,
 we're able to measure that better and better.
 So all of that could be connected to the entire pipeline.
 We used to think of each of these as a single variable,
 like intelligence.
 I mean, that's ridiculous.
 It's basically dozens of different genes
 that are controlling every single variable.
 You can basically think of,
 imagine us in a video game
 where every one of us has measures of strength,
 stamina, energy left, and so on and so forth.
 But you could click on each of those like five bars
 that are just the main bars.
 And each of those will just give you then hundreds of bars.
 And you can basically say,
 okay, great for my machine learning task.
 I want someone who, a human,
 who has these particular forms of intelligence.
 I require now these 20 different things.
 And then you can combine those things
 and then relate them to, of course,
 performance in a particular task.
 But you can also relate them to genetic variation
 that might be affecting different parts of the brain.
 For example, your frontal cortex
 versus your temporal cortex
 versus your visual cortex and so on and so forth.
 So genetic variation that affects expression of genes
 in different parts of your brain
 can basically affect your music ability,
 your auditory ability, your smell, your,
 just dozens of different phenotypes
 can be broken down into hundreds of cognitive variables
 and then relate each of those to thousands of genes
 that are associated with them.
 So somebody who loves RPGs, role playing games,
 there's too few variables that we can control.
 So I'm excited if we're in fact living in a simulation
 and this is a video game,
 I'm excited by the quality of the video game.
 The game designer did a hell of a good job.
 So we're impressed.
 Oh, I don't know.
 The sunset last night was a little unrealistic.
 Yeah, yeah, the graphics.
 Exactly.
 Come on Nvidia.
 To zoom back out,
 we've been talking about the genetic origins of diseases,
 but I think it's fascinating to talk about
 what are the most important diseases to understand.
 And especially as it connects to the things
 that you're working on.
 So it's very difficult to think about important diseases
 to understand.
 There's many metrics of importance.
 One is lifestyle impact.
 I mean, if you look at COVID,
 the impact on lifestyle has been enormous.
 So understanding COVID is important
 because it has impacted the wellbeing
 in terms of ability to have a job,
 ability to have an apartment, ability to go to work,
 ability to have a mental circle of support.
 And all of that for millions of Americans,
 like huge, huge impact.
 So that's one aspect of importance.
 So basically mental disorders,
 Alzheimer's has a huge importance
 in the wellbeing of Americans.
 Whether or not it kills someone for many, many years,
 it has a huge impact.
 So the first measure of importance is just wellbeing.
 Like impact on the quality of life.
 Impact on the quality of life, absolutely.
 The second metric,
 which is much easier to quantify is deaths.
 What is the number one killer?
 The number one killer is actually heart disease.
 It is actually killing 650,000 Americans per year.
 Number two is cancer with 600,000 Americans.
 Number three, far, far down the list is accidents.
 Every single accident combined.
 So basically, you read the news, accidents,
 like there was a huge car crash all over the news.
 But the number of deaths, number three by far,
 167,000.
 Lower respiratory disease.
 So that's asthma, not being able to breathe
 and so on and so forth, 160,000.
 Alzheimer's, number five with 120,000.
 And then stroke, brain aneurysms and so on and so forth.
 That's 147,000.
 Diabetes and metabolic disorders, et cetera.
 That's 85,000.
 The flu is 60,000.
 Suicide, 50,000.
 And then overdose, et cetera.
 You know, goes further down the list.
 So of course, COVID has creeped up
 to be the number three killer this year
 with more than 100,000 Americans and counting.
 But if you think about sort of what do we use,
 what are the most important diseases,
 you have to understand both the quality of life
 and the sheer number of deaths
 and just numbers of years lost, if you wish.
 And each of these diseases you can think of as,
 and also including terrorist attacks
 and school shootings, for example,
 things which lead to fatalities,
 you can look at as problems that could be solved.
 And some problems are harder to solve than others.
 I mean, that's part of the equation.
 So maybe if you look at these diseases,
 if you look at heart disease or cancer or Alzheimer's
 or just like schizophrenia and obesity,
 like not necessarily things that kill you,
 but affect the quality of life,
 which problems are solvable,
 which aren't, which are harder to solve, which aren't?
 I love your question because it puts it in the context
 of a global effort rather than just the local effort.
 So basically, if you look at the global aspect,
 exercise and nutrition are two interventions
 that we can as a society make a much better job at.
 So if you think about sort of the availability
 of cheap food, it's extremely high in calories,
 it's extremely detrimental for you,
 like a lot of processed food, et cetera.
 So if we change that equation, and as a society,
 we made availability of healthy food much, much easier
 and charged a burger at McDonald's,
 the price that it costs on the health system,
 then people would actually start buying more healthy foods.
 So basically that's sort of a societal intervention,
 if you wish.
 In the same way, increasing empathy, increasing education,
 increasing the social framework and support
 would basically lead to fewer suicides,
 it would lead to fewer murders,
 it would lead to fewer deaths overall.
 So that's something that we as a society can do.
 You can also think about external factors
 versus internal factors.
 So the external factors are basically communicable diseases
 like COVID, like the flu, et cetera.
 And the internal factors are basically things like cancer
 and Alzheimer's where basically your genetics
 will eventually drive you there.
 And then of course, with all of these factors,
 every single disease has both a genetic component
 and environmental component.
 So heart disease, huge genetic contribution,
 Alzheimer's, it's like 60% plus genetic.
 So I think it's like 79% heritability.
 So that basically means that genetics alone explains
 79% of Alzheimer's incidents.
 And yes, there's a 21% environmental component
 where you could basically enrich your cognitive environment,
 enrich your social interactions, read more books,
 learn a foreign language, go running,
 sort of have a more fulfilling life.
 All of that will actually decrease Alzheimer's,
 but there's a limit to how much that can impact
 because of the huge genetic footprint.
 So this is fascinating.
 So each one of these problems have a genetic component
 and an environment component.
 And so like when there's a genetic component,
 what can we do about some of these diseases?
 What have you worked on?
 What can you say that's in terms of problems
 that are solvable here or understandable?
 So my group works on the genetic component,
 but I would argue that understanding the genetic component
 can have a huge impact even on the environmental component.
 Why is that?
 Because genetics gives us access to mechanism.
 And if we can alter the mechanism,
 if we can impact the mechanism,
 we can perhaps counteract
 some of the environmental components.
 Oh, interesting.
 So understanding the biological mechanisms
 leading to disease is extremely important
 in being able to intervene.
 But when you can intervene,
 the analogy that I like to give is, for example, for obesity.
 Think of it as a giant bathtub of fat.
 There's basically fat coming in from your diet
 and there's fat coming out from your exercise.
 That's an in-out equation.
 And that's the equation that everybody's focusing on.
 But your metabolism impacts that bathtub.
 Basically, your metabolism controls
 the rate at which you're burning energy.
 It controls the rate at which you're storing energy.
 And it also teaches you about the various valves
 that control the input and the output equation.
 So if we can learn from the genetics,
 the valves, we can then manipulate those valves.
 And even if the environment is feeding you a lot of fat
 and getting a little fat out,
 you can just poke another hole at the bathtub
 and just get a lot of the fat out.
 Yeah, that's fascinating.
 Yeah, so we're not just passive observers of our genetics.
 The more we understand,
 the more we can come up with actual treatments.
 And I think that's an important aspect to realize
 when people are thinking about strong effect
 versus weak effect variants.
 So some variants have strong effects.
 We talked about these Mendelian disorders
 where a single gene has a sufficiently large effect,
 pan and trans expressivity, and so on and so forth,
 that basically you can trace it in families
 with cases and not cases,
 cases, not cases, and so on and so forth.
 But even the, you know, but so these are the genes
 that everybody says, oh, that's the genes we should go after
 because that's a strong effect gene.
 I like to think about it slightly differently.
 These are the genes where genetic impacts
 that have a strong effect were tolerated
 because every single time we have a genetic association
 with disease, it depends on two things.
 Number one, the obvious one,
 whether the gene has an impact on the disease.
 Number two, the more subtle one is whether there is
 genetic variation standing and circulating and segregating
 in the human population that impacts that gene.
 Some genes are so darn important that if you mess with them,
 even a tiny little amount, that person's dead.
 So those genes don't have variation.
 You're not gonna find a genetic association
 if you don't have variation.
 That doesn't mean that the gene has no role.
 It simply that the gene,
 it simply means that the gene tolerates no mutations.
 So that's actually a strong signal
 when there's no variation.
 That's so fascinating.
 Exactly.
 Genes that have very little variation are hugely important.
 You can actually rank the importance of genes
 based on how little variation they have.
 And those genes that have very little variation
 but no association with disease,
 that's a very good metric to say,
 oh, that's probably a developmental gene
 because we're not good at measuring those phenotypes.
 So it's genes that you can tell evolution
 has excluded mutations from,
 but yet we can't see them associated with anything
 that we can measure nowadays.
 It's probably early embryonic lethal.
 What are all the words you just said?
 Early embryonic what?
 Lethal.
 Meaning?
 Meaning that if you don't have that,
 that embryo will die.
 Okay.
 There's a bunch of stuff that is required
 for a stable functional organism
 across the board. Exactly.
 For entire species, I guess.
 If you look at sperm, it expresses thousands of proteins.
 Does sperm actually need thousands of proteins?
 No, but it's probably just testing them.
 So my speculation is that misfolding of these proteins
 is an early test for failure.
 So that out of the millions of sperm that are possible,
 you select the subset that are just not grossly
 misfolding thousands of proteins.
 So it's kind of an assert that this is folded correctly.
 Correct.
 Yeah, this just, because if this little thing
 about the folding of a protein isn't correct,
 that probably means somewhere down the line
 there's a bigger issue.
 That's exactly right.
 So fail fast.
 So basically if you look at the mammalian investment
 in a newborn, that investment is enormous
 in terms of resources.
 So mammals have basically evolved mechanisms for fail fast.
 Where basically in those early months of development,
 I mean, it's horrendous, of course, at the personal level
 when you lose your future child.
 But in some ways, there's so little hope
 for that child to develop and sort of make it
 through the remaining months that sort of fail fast
 is probably a good evolutionary principle.
 Yeah, from an evolutionary perspective.
 For mammals.
 And of course, humans have a lot of medical resources
 that you can sort of give those children a chance.
 And we have so much more success in sort of giving folks
 who have these strong carrier mutations a chance.
 But if they're not even making it
 through the first three months, we're not gonna see them.
 So that's why when we say,
 what are the most important genes to focus on?
 The ones that have a strong effect mutation
 or the ones that have a weak effect mutation.
 Well, the jury might be out.
 Because the ones that have a strong effect mutation
 are basically not mattering as much.
 The ones that only have weak effect mutations,
 by understanding through genetics
 that they have a weak effect mutation
 and understanding that they have a causal role
 on the disease, we can then say, okay, great.
 Evolution has only tolerated a 2% change in that gene.
 Pharmaceutically, I can go in
 and induce a 70% change in that gene.
 And maybe I will poke another hole at the bathtub
 that was not easy to control
 in many of the other sort of strong effect genetic variants.
 So there's this beautiful map of across the population
 of things that you're saying strong and weak effects.
 So stuff with a lot of mutations
 and stuff with little mutations, with no mutations.
 And you have this map and it's the latest data
 that lays out the puzzle.
 Yeah, so when I say strong effect,
 I mean at the level of individual mutations.
 So basically genes where,
 so you have to think of first the effect
 of the gene on the disease.
 Remember how I was sort of painting that map earlier
 from genetics all the way to phenotype.
 That gene can have a strong effect on the disease,
 but the genetic variant might have a weak effect on the gene.
 So basically when you ask what is the effect
 of that genetic variant on the disease,
 it could be that that genetic variant
 impacts the gene by a lot,
 and then the gene impacts the disease by a little,
 or it could be that the genetic variants
 impacts the gene by a little,
 and then the gene impacts the disease by a lot.
 So what we care about is genes that impact the disease a lot,
 but genetics gives us the full equation.
 And what I would argue is if we couple the genetics
 with expression variation,
 to basically ask what genes change by a lot,
 and which genes correlate with disease by a lot,
 even if the genetic variants change them by a little,
 then those are the best places to intervene.
 Those are the best places where,
 pharmaceutically, if I have even a modest effect,
 I will have a strong effect on the disease.
 Whereas those genetic variants
 that have a huge effect on the disease,
 I might not be able to change that gene by this much
 without affecting all kinds of other things.
 Interesting.
 So yeah, okay, so that's what we're looking at.
 Then what have we been able to find
 in terms of which disease could be helped?
 Again, don't get me started.
 This is, we have found so much.
 Our understanding of disease has changed so dramatically
 with genetics.
 I mean, places that we had no idea would be involved.
 So one of the worst things about my genome
 is that I have a genetic predisposition
 to age-related molecular degeneration, AMD.
 So it's a form of blindness that causes you
 to lose the central part of your vision
 progressively as you grow older.
 My increased risk is fairly small.
 I have an 8% chance.
 You only have a 6% chance.
 I'm an average.
 Yeah.
 By the way, when you say my, you mean literally yours.
 You know this about you.
 I know this about me.
 Yeah.
 Which is kind of, I mean, philosophically speaking
 is a pretty powerful thing to live with.
 Maybe that's, so we agreed to talk again, by the way,
 for the listeners to where we're gonna try to focus
 on science today and a little bit of philosophy next time.
 But it's interesting to think about
 the more you're able to know about yourself
 from the genetic information in terms of the diseases,
 how that changes your own view of life.
 Yeah.
 So there's a lot of impact there
 and there's something called genetic exceptionalism,
 which basically thinks of genetics
 as something very, very different than everything else
 as a type of determinism.
 And, you know, let's talk about that next time.
 So basically-
 That's a good preview.
 Yeah.
 So let's go back to AMD.
 So basically with AMD, we have no idea what causes AMD.
 You know, it was a mystery until the genetics were worked out.
 And now the fact that I know that I have a predisposition
 allows me to sort of make some life choices, number one.
 But number two, the genes that lead to that predisposition
 give us insights as to how does it actually work.
 And that's a place where genetics
 gave us something totally unexpected.
 So there's a complement pathway,
 which is an immune function pathway
 that was in, you know,
 most of the loci associated with AMD.
 And that basically told us that, wow,
 there's an immune basis to this eye disorder
 that people had just not expected before.
 If you look at complement,
 it was recently also implicated in schizophrenia.
 And there's a type of microglia
 that is involved in synaptic pruning.
 So synapses are the connections between neurons.
 And in this whole use it or lose it view
 of mental cognition and other capabilities,
 you basically have microglia, which are immune cells
 that are sort of constantly traversing your brain
 and then pruning neuronal connections,
 pruning synaptic connections that are not utilized.
 So in schizophrenia, there's thought to be a change
 in the pruning that basically,
 if you don't prune your synapses the right way,
 you will actually have an increased role of schizophrenia.
 This is something that was completely unexpected
 for schizophrenia.
 Of course, we knew it has to do with neurons,
 but the role of the complement complex,
 which is also implicated in AMD,
 which is now also implicated in schizophrenia
 was a huge surprise.
 What's the complement complex?
 So it's basically a set of genes, the complement genes,
 that are basically having various immune roles.
 And as I was saying earlier,
 our immune system has been co-opted
 for many different roles across the body.
 So they actually play many diverse roles.
 And somehow the immune system is connected
 to the synaptic pruning process.
 Exactly. The process.
 Exactly.
 So immune cells were co-opted to prune synapses.
 How did you figure this out?
 How does one go about figuring this intricate connection,
 like pipeline of connections out?
 Yeah. Let me give you another example.
 So Alzheimer's disease,
 the first place that you would expect it to act
 is obviously the brain.
 So we had basically this
 roadmap epigenomics consortium view of the human epigenome,
 the largest map of the human epigenome
 that has ever been built
 across 127 different tissues and samples
 with dozens of epigenomic marks
 measured in hundreds of donors.
 So what we've basically learned through that
 is that you basically can map
 what are the active gene regulatory elements
 for every one of the tissues in the body.
 And then we connected these gene regulatory active maps
 of basically what regions of the human genome
 are turning on in every one of different tissues.
 We then can go back and say,
 where are all of the genetic loci
 that are associated with disease?
 This is something that my group,
 I think was the first to do back in 2010
 in this Ernst Nature Biotech paper.
 But basically we were for the first time able to show
 that specific chromatin states,
 specific epigenomic states, in that case enhancers,
 were in fact enriched in disease-associated variants.
 We pushed that further in the Ernst Nature paper
 a year later, and then in this roadmap epigenomics paper,
 a few years after that.
 But basically that matrix that you mentioned earlier
 was in fact the first time that we could see
 what genetic traits have genetic variants
 that are enriched in what tissues in the body.
 And a lot of that map made complete sense.
 If you looked at a diversity of immune traits
 like allergies and type 1 diabetes and so on and so forth,
 you basically could see that they were enriching,
 that the genetic variants associated with those traits
 were enriched in enhancers, in these gene regulatory elements,
 active in T cells and B cells
 and hematopoietic stem cells and so on and so forth.
 So that basically gave us confirmation in many ways
 that those immune traits were indeed
 enriching in immune cells.
 If you looked at type 2 diabetes,
 you basically saw an enrichment in only one type of sample,
 and it was pancreatic islets.
 And we know that type 2 diabetes
 sort of stems from the dysregulation of insulin
 in the beta cells of pancreatic islets.
 And that sort of was spot on, super precise.
 If you looked at blood pressure,
 where would you expect blood pressure to occur?
 You know, I don't know, maybe in your metabolism
 and ways that you process coffee or something like that.
 Maybe in your brain, the way that you stress out
 and increases your blood pressure, et cetera.
 What we found is that blood pressure localized specifically
 in the left ventricle of the heart.
 So the enhancers of the left ventricle in the heart
 contained a lot of genetic variants
 associated with blood pressure.
 If you look at height,
 we found an enrichment specifically
 in embryonic stem cell enhancers.
 So the genetic variants predisposing you
 to be taller or shorter are in fact acting
 in developmental stem cells.
 Makes complete sense.
 If you looked at inflammatory bowel disease,
 you basically found inflammatory, which is immune,
 and also bowel disease, which is digestive.
 And indeed we saw a double enrichment
 both in the immune cells and in the digestive cells.
 So that basically told us that,
 ah, this is acting in both components.
 There's an immune component to inflammatory bowel disease
 and there's a digestive component.
 And the big surprise was for Alzheimer's.
 We had seven different brain samples.
 We found zero enrichment in the brain samples
 for genetic variants associated with Alzheimer's.
 And this is mind boggling.
 Our brains were literally hurting.
 What is going on?
 And what is going on is that the brain samples
 are primarily neurons, oligodendrocytes, and astrocytes
 in terms of the cell types that make them up.
 So that basically indicated that genetic variants
 associated with Alzheimer's were probably not acting
 in oligodendrocytes, astrocytes, or neurons.
 So what could they be acting in?
 Well, the fourth major cell type is actually microglia.
 Microglia are resident immune cells in your brain.
 Oh, nice.
 The immune, oh, wow.
 And they are CD14+, which is this sort of cell surface
 markers of those cells.
 So they're CD14+, just like microphages
 that are circulating in your blood.
 The microglia are resident monocytes
 that are basically sitting in your brain.
 They're tissue specific monocytes.
 And every one of your tissues, like your fat, for example,
 has a lot of macrophages that are resident.
 And the M1 versus M2 macrophage ratio
 has a huge role to play in obesity.
 And so basically, again, these immune cells are everywhere,
 but basically what we found through this completely unbiased
 view of what are the tissues that likely underlie
 different disorders, we found that Alzheimer's
 was humongously enriched in microglia,
 but not at all in the other cell types.
 So what are we supposed to make that
 if you look at the tissues involved,
 is that simply useful for indication
 of propensity for disease,
 or does it give us somehow a pathway of treatment?
 It's very much the second.
 If you look at the way to therapeutics,
 you have to start somewhere.
 What are you gonna do?
 You're gonna basically make assays
 that manipulate those genes and those pathways
 in those cell types.
 So before we know the tissue of action,
 we don't even know where to start.
 We basically are at a loss,
 but if you know the tissue of action,
 and even better if you know the pathway of action,
 then you can basically screen your small molecules,
 not for the gene, you can screen them directly
 for the pathway in that cell type.
 So you can basically develop a high throughput multiplexed
 robotic system for testing the impact
 of your favorite molecules that you know are safe,
 efficacious, and sort of hit that particular gene,
 and so on and so forth.
 You can basically screen those molecules
 against either a set of genes that act in that pathway,
 or on the pathway directly by having a cellular assay.
 And then you can basically go into mice and do experiments
 and basically sort of figure out ways to manipulate
 these processes that allow you to then go back to humans
 and do a clinical trial that basically says,
 okay, I was able indeed to reverse these processes in mice,
 can I do the same thing in humans?
 So the knowledge of the tissues gives you the pathway
 to treatment, but that's not the only part.
 There are many additional steps
 to figuring out the mechanism of disease.
 I mean, so that's really promising.
 Maybe to take a small step back,
 you've mentioned all these puzzles that were figured out
 with the Nature paper for,
 I mean, you've mentioned a ton of diseases,
 from obesity to Alzheimer's, even schizophrenia,
 I think you mentioned.
 And it's just, what is the actual methodology
 of figuring this out?
 So indeed, I mentioned a lot of diseases,
 and my lab works on a lot of different disorders.
 And the reason for that is that if you look at the,
 if you look at biology, it used to be, you know,
 zoology departments and botanology departments,
 and, you know, virology departments and so on and so forth.
 And MIT was one of the first schools
 to basically create a biology department,
 like, oh, we're gonna study all of life suddenly.
 Why was that even a case?
 Because the advent of DNA and the genome
 and the central dogma of DNA makes RNA makes protein,
 in many ways, unified biology.
 You could suddenly study the process of transcription
 in viruses or in bacteria,
 and have a huge impact on yeast and fly,
 and maybe even mammals,
 because of this realization
 of these common underlying processes.
 And in the same way that DNA unified biology,
 genetics is unifying disease studies.
 So you used to have, you know, I don't know,
 cardiovascular disease department,
 and, you know, neurological disease department,
 and neurodegeneration department,
 and, you know, basically immune and cancer
 and so on and so forth.
 And all of these were studied in different labs,
 you know, because it made sense,
 because basically the first step was understanding
 how the tissue functions,
 and we kind of knew the tissues involved
 in cardiovascular disease and so on and so forth.
 But what's happening with human genetics is that
 all of these walls and edifices
 that we had built are crumbling.
 And the reason for that is that genetics
 is in many ways revealing unexpected connections.
 So suddenly we now have to bring the immunologists
 to work on Alzheimer's.
 They were never in their room.
 They were in another building altogether.
 The same way for schizophrenia,
 we now have to sort of worry about
 all these interconnected aspects.
 For metabolic disorders,
 we're finding contributions from brain.
 So suddenly we have to call the neurologist
 from the other building and so on and so forth.
 So in my view, it makes no sense anymore
 to basically say,
 oh, I'm a geneticist studying immune disorders.
 I mean, that's ridiculous because,
 I mean, of course, in many ways
 you still need to sort of focus.
 But what we're doing is that we're basically saying,
 we'll go wherever the genetics takes us.
 And by building these massive resources,
 by working on our latest map is now 833 tissues,
 sort of the next generation of the epigenomics roadmap,
 which we're now called epiMAP,
 is 833 different tissues.
 And using those, we've basically found enrichments
 in 540 different disorders.
 Those enrichments are not like, oh, great,
 you guys work on that and we'll work on this.
 They're intertwined amazingly.
 So of course there's a lot of modularity,
 but there's these enhancers that are sort of broadly active
 and these disorders that are broadly active.
 So basically some enhancers are active in all tissues
 and some disorders are enriching in all tissues.
 So basically there's these multifactorial
 and these other class,
 which I like to call polyfactorial diseases,
 which are basically lighting up everywhere.
 And in many ways, it's sort of cutting across these walls
 that were previously built across these departments.
 And the polyfactorial ones were probably
 the previous structure of departments
 wasn't equipped to deal with those.
 I mean, again, maybe it's a romanticized question,
 but there's in physics, there's a theory of everything.
 Do you think it's possible to move towards
 an almost theory of everything of disease
 from a genetic perspective?
 So if this unification continues, is it possible that,
 do you think in those terms, trying to arrive
 at a fundamental understanding
 of how disease emerges, period?
 That unification is not just foreseeable, it's inevitable.
 I see it as inevitable.
 We have to go there.
 You cannot be a specialist anymore if you're a genomicist.
 You have to be a specialist in every single disorder.
 And the reason for that is that the fundamental understanding
 of the circuitry of the human genome
 that you need to solve schizophrenia,
 that fundamental circuitry is hugely important
 to solve Alzheimer's.
 And that same circuitry is hugely important
 to solve metabolic disorders.
 And that same exact circuitry is hugely important
 for solving immune disorders and cancer
 and every single disease.
 So all of them have the same sub-task.
 And I teach dynamic programming in my class.
 Dynamic programming is all about sort of not redoing the work.
 It's reusing the work that you do once.
 So basically for us to say, oh, great,
 you guys in the immune building,
 go solve the fundamental circuitry of everything.
 And then you guys in the schizophrenia building,
 go solve the fundamental circuitry of everything separately,
 it's crazy.
 So what we need to do is come together
 and sort of have a circuitry group, the circuitry building
 that sort of tries to solve the circuitry of everything.
 And then the immune folks who will apply this knowledge
 to all of the disorders that are associated with it
 associated with immune dysfunction.
 And the schizophrenia folks will basically
 interact with both the immune folks
 and with the neuronal folks.
 And all of them will be interacting with the circuitry
 folks and so on and so forth.
 So that's sort of the current structure of my group,
 if you wish.
 So basically what we're doing is focusing
 on the fundamental circuitry.
 But at the same time, we're the users of our own tools
 by collaborating with many other labs
 in every one of these disorders that we mentioned.
 We basically have a hard focus on cardiovascular disease,
 coronary artery disease, heart failure, and so on and so forth.
 We have an immune focus on several immune disorders.
 We have a cancer focus on metastatic melanoma
 and immunotherapy response.
 We have a psychiatric disease focus on schizophrenia,
 autism, PTSD, and other psychiatric disorders.
 We have an Alzheimer's and neurodegeneration focus
 on Huntington's disease, ALS, and AD-related disorders,
 like frontotemporal dementia and Lewy body dementia.
 And of course, a huge focus on Alzheimer's.
 We have a metabolic focus on the role of exercise and diets
 and sort of how they're impacting metabolic organs
 across the body and across many different tissues.
 And all of them are interfacing with the circuitry
 and the reason for that is another computer science principle
 of eat your own dog food.
 If everybody ate their own dog food,
 dog food would taste a lot better.
 The reason why Microsoft Excel and Word and PowerPoint
 was so important and so successful
 is because the employees that were working on them
 were using them for their day-to-day tasks.
 You can't just simply build a circuitry and say,
 here it is guys, take the circuitry, we're done,
 without being the users of that circuitry
 because you then go back.
 And because we span the whole spectrum
 from profiling the epigenome, using comparative genomics,
 finding the important nucleotides in the genome,
 building the basic functional map
 of what are the genes in the human genome?
 What are the gene regulatory elements of the human genome?
 I mean, over the years, we've written a series of papers
 on how do you find human genes in the first place,
 using comparative genomics.
 How do you find the motifs
 that are the building blocks of gene regulation
 using comparative genomics?
 How do you then find how these motifs come together
 and act in specific tissues using epigenomics?
 How do you link regulators to enhancers
 and enhancers to their target genes
 using epigenomics and regulatory genomics?
 So through the years, we've basically built
 all this infrastructure for understanding
 what I like to say, every single nucleotide
 of the human genome and how it acts
 in every one of the major cell types
 and tissues of the human body.
 I mean, this is no small task.
 This is an enormous task that takes the entire field.
 And that's something that my group has taken on,
 along with many other groups.
 And we have also, and that sort of a thing
 sets my group perhaps apart,
 we have also worked with specialists
 in every one of these disorders
 to basically further our understanding
 all the way down to disease.
 And in some cases, collaborating with pharma
 to go all the way down to therapeutics
 because of our deep, deep understanding
 of that basic circuitry
 and how it allows us to now improve the circuitry,
 not just treat it as a black box,
 but basically go and say, okay,
 we need a better cell type specific wiring
 that we now have at the tissue specific level.
 So we're focusing on that because we're understanding
 the needs from the disease front.
 So you have a sense of the entire pipeline.
 I mean, one, maybe you can indulge me.
 One nice question to ask would be
 how do you, from the scientific perspective,
 go from knowing nothing about the disease
 to going, you said, to go into the entire pipeline
 and actually have a drug or a treatment
 that cures that disease?
 So that's an enormously long path
 and an enormously great challenge.
 And what I'm trying to argue is that
 it progresses in stages of understanding
 rather than one gene at a time.
 The traditional view of biology was
 you have one postdoc working on this gene
 and another prostitute working on that gene.
 And they'll just figure out everything about that gene.
 And that's their job.
 What we've realized is how polygenic
 the diseases are.
 So we can't have one postdoc per gene anymore.
 We now have to have these cross-cutting needs.
 And I'm gonna describe the path to circuitry
 along those needs.
 And every single one of these paths
 we are now doing in parallel across thousands of genes.
 So the first step is you have a genetic association.
 And we talked a little bit about sort of the Mendelian path
 and the polygenic path to that association.
 So the Mendelian path was looking through families
 to basically find gene regions
 and ultimately genes that are underlying
 particular disorders.
 The polygenic path is basically looking at
 unrelated individuals in this giant matrix
 of genotype by phenotype.
 And then finding hits where particular variant
 impacts disease all the way to the end.
 And then we now have a connection,
 not between a gene and a disease,
 but between a genetic region and a disease.
 And that distinction is not understood by most people.
 So I'm gonna explain it a little bit more.
 Why do we not have a connection between a gene
 and a disease, but we have a connection
 between a genetic region and a disease?
 The reason for that is that 93% of genetic variants
 that are associated with disease
 don't impact the protein at all.
 So if you look at the human genome, there's 20,000 genes.
 There's 3.2 billion nucleotides.
 Only 1.5% of the genome codes for proteins.
 The other 98.5% does not code for proteins.
 If you now look at where are the disease regions
 located, 93% of them fall in that outside the gene's portion.
 Of course, genes are enriched,
 but they're only enriched by a factor of three.
 That means that still 93% of genetic variants
 fall outside the proteins.
 Why is that difficult?
 Why is that a problem?
 The problem is that when a variant falls outside the gene,
 you don't know what gene is impacted by that variant.
 You can't just say, oh, it's near this gene.
 Let's just connect that variant to the gene.
 And the reason for that is that the genome circuitry
 is very often long range.
 So you basically have that genetic variant
 that could sit in the intron of one gene.
 And intron is sort of the place between the exons
 that code for proteins.
 So proteins are split up into exons and introns,
 and every exon codes for a particular subset of amino acids,
 and together they're spliced together,
 and then make the final protein.
 So that genetic variant might be sitting
 in an intron of a gene.
 It's transcribed with the gene,
 it's processed and then excised,
 but it might not impact this gene at all.
 It might actually impact another gene
 that's a million nucleotides away.
 So it's just riding along,
 even though it has nothing to do
 with this nearby neighborhood.
 That's exactly right.
 Let me give you an example.
 The strongest genetic association with obesity
 was discovered in this FTO gene,
 fat and obesity associated gene.
 So this FTO gene was studied ad nauseum.
 People did tons of experiments on it.
 They figured out that FTO is in fact,
 RNA methylation transferase.
 It basically, it sort of impacts something that we know,
 that we call the epi transcriptome.
 Just like the genome can be modified,
 the transcriptome, the transcript of the genes
 can be modified.
 And we basically said, oh great,
 that means that epi transcriptomics
 is hugely involved in obesity
 because that gene FTO is clearly
 where the genetic locus is at.
 My group studied FTO in collaboration
 with a wonderful team led by Melina Klausnitzer.
 And what we found is that this FTO locus,
 even though it is as associated with obesity,
 does not implicate the FTO gene.
 The genetic variance, it's in the first intron
 of the FTO gene, but it controls two genes,
 IRX3 and IRX5 that are sitting 1.2 million nucleotides away,
 several genes away.
 Oh boy.
 What am I supposed to feel about that?
 Because isn't that like super complicated then?
 So the way that I was introduced at a conference
 a few years ago was, and here's Manolis Kellis
 who wrote the most depressing paper of 2015.
 And the reason for that is that the entire
 pharmaceutical industry was so comfortable
 that there was a single gene in that locus.
 Because in some loci, you basically have three dozen genes
 that are all sitting in the same region of association.
 And you're like, oh gosh, which ones of those is it?
 But even that question of which ones of those is it,
 is making the assumption that it is one of those,
 as opposed to some random gene just far, far away,
 which is what our paper showed.
 So basically what our paper showed is that
 you can't ignore the circuitry.
 You have to first figure out the circuitry,
 all of those long range interactions,
 how every genetic variant impacts the expression
 of every gene in every tissue imaginable
 across hundreds of individuals.
 And then you now have one of the building blocks,
 not even all of the building blocks,
 for then going and understanding disease.
 So, okay, so embrace the wholeness of the circuitry.
 Correct.
 But what, so back to the question of starting,
 knowing nothing to the disease and going to the treatment.
 So what are the next steps?
 So you basically have to first figure out the tissue,
 and then describe how you figure out the tissue.
 You figure out the tissue by taking all of these
 non-coding variants that are sitting outside proteins,
 and then figuring out what are the epigenomic enrichments.
 And the reason for that, thankfully,
 is that there is convergence,
 that the same processes are impacted in different ways
 by different loci.
 And that's a saving grace for our field.
 The fact that if I look at hundreds of genetic variants
 associated with Alzheimer's,
 they localize in a small number of processes.
 Can you clarify why that's hopeful?
 So like they show up in the same exact way
 in the specific set of processes?
 Yeah, so basically there's a small number
 of biological processes that underlie,
 or at least that play the biggest role in every disorder.
 So in Alzheimer's, you basically have, you know,
 maybe 10 different types of processes.
 One of them is lipid metabolism.
 One of them is immune cell function.
 One of them is neuronal energetics.
 So these are just a small number of processes,
 but you have multiple lesions,
 multiple genetic perturbations
 that are associated with those processes.
 So if you look at schizophrenia,
 it's excitatory neuron function,
 it's inhibitory neuron function, it's synaptic pruning,
 it's calcium signaling, and so on and so forth.
 So when you look at disease genetics,
 you have one hit here and one hit there
 and one hit there and one hit there,
 completely different parts of the genome,
 but it turns out all of those hits
 are calcium signaling proteins.
 Oh, cool.
 You're like, aha, that means that calcium signaling
 is important.
 So those people who are focusing on one focus at a time
 cannot possibly see that picture.
 You have to become a genomicist.
 You have to look at the omics, the om,
 the holistic picture to understand these enrichments.
 But you mentioned the convergence thing.
 So the, whatever the thing associated with the disease
 shows up.
 So let me explain convergence.
 Convergence is such a beautiful concept.
 So you basically have these four genes
 that are converging on calcium signaling.
 So that basically means that they are acting each
 in their own way, but together in the same process.
 But now in every one of these loci,
 you have many enhancers controlling each of those genes.
 That's another type of convergence
 where this regulation of seven different enhancers
 might all converge on this regulation of that one gene,
 which then converges on calcium signaling.
 And in each one of those enhancers,
 you might have multiple genetic variants
 distributed across many different people.
 Everyone has their own different mutation,
 but all of these mutations are impacting that enhancer.
 And all of these enhancers are impacting that gene.
 And all of these genes are impacting this pathway.
 And all of these pathways are acting in the same tissue.
 And all of these tissues are converging together
 on the same biological process of schizophrenia.
 And you're saying the saving grace is that
 that convergence seems to happen for a lot of these diseases.
 For all of them.
 Basically that for every single disease that we've looked at,
 we have found an epigenomic enrichment.
 How do you do that?
 You basically have all of the genetic variants
 associated with the disorder.
 And then you're asking for all of the enhancers
 active in a particular tissue.
 For 540 disorders, we've basically found that
 indeed there is an enrichment.
 That basically means that there is commonality.
 And from the commonality, we can just get insights.
 So to explain in mathematical terms,
 we're basically building an empirical prior.
 We're using a Bayesian approach to basically say,
 great, all of these variants are equally likely
 in a particular locus to be important.
 So in a genetic locus, you basically have a dozen variants
 that are co-inherited.
 Because the way that inheritance works in the human genome
 is through all of these recombination events
 during meiosis.
 You basically have, you know,
 you inherit maybe three, chromosome three,
 for example, in your body.
 It's inherited from four different parts.
 One part comes from your dad,
 another part comes from your mom, another part comes
 from your dad, another part comes from your mom.
 So basically, the way that it...
 Sorry, from your mom's mom.
 So you basically have one copy that comes from your dad
 and one copy that comes from your mom.
 But that copy that you got from your mom
 is a mixture of her maternal and her paternal chromosome.
 And the copy that you got from your dad is a mixture
 of his maternal and his paternal chromosome.
 So these break points that happen
 when chromosomes are lining up
 are basically ensuring through these crossover events,
 they're ensuring that every child cell
 during the process of meiosis,
 where you basically have one spermatozoid
 that basically couples with one ovule
 to basically create one egg,
 to basically create the zygote.
 You basically have half of your genome that comes from dad
 and half your genome that comes from mom.
 But in order to line them up,
 you basically have these crossover events.
 These crossover events are basically leading to
 co-inheritance of that entire block coming from
 your maternal grandmother
 and that entire block coming from your maternal grandfather.
 Over many generations,
 these crossover events don't happen randomly.
 There's a protein called PRDM9
 that basically guides the double-stranded breaks
 and then leads to these crossovers.
 And that protein has a particular preference
 to only a small number of hotspots of recombination,
 which then leads to a small number of breaks
 between these co-inheritance patterns.
 So even though there are 6 million variants,
 there are 6 million loci,
 this variation is inherited in blocks.
 And every one of these blocks has like
 two dozen genetic variants that are all associated.
 So in the case of FTO, it wasn't just one variant,
 it was 89 common variants
 that were all humongously associated with obesity.
 Which one of those is the important one?
 Well, if you look at only one locus, you have no idea.
 But if you look at many loci,
 you basically say, aha,
 all of them are enriching in the same epigenomic map.
 In that particular case, it was mesenchymal stem cells.
 So these are the progenitor cells that give rise
 to your brown fat and your white fat.
 Progenitor is like the early on developmental stem cells.
 So you start from one zygote
 and that's a totipotent cell type, it can do anything.
 That cell divides, divides, divides.
 And then every cell division is leading to specialization
 where you now have a mesodermal lineage
 and ectodermal lineage and endodermal lineage
 that basically leads to different parts of your body.
 The ectoderm will basically give rise to your skin.
 Ecto means outside, derm is skin.
 So ectoderm, but it also gives rise to your neurons
 and your whole brain.
 So that's a lot of ectoderm.
 Mesoderm gives rise to your internal organs,
 including the vasculature and your muscle
 and stuff like that.
 So you basically have this progressive differentiation.
 And then if you look further, further down that lineage,
 you basically have one lineage that will give rise
 to both your muscle and your bone, but also your fat.
 And if you go further down the lineage of your fat,
 you basically have your white fat cells.
 These are the cells that store energy.
 So when you eat a lot, but you don't exercise too much,
 there's an excess set of calories, excess energy.
 What do you do with those?
 You basically create, you spend a lot of that energy
 to create these high energy molecules, lipids,
 which you can then burn when you need them on a rainy day.
 So that leads to obesity if you don't exercise
 and if you overeat, because your body is like,
 oh, great, I have all these calories.
 I'm gonna store them.
 Ooh, more calories.
 I'm gonna store them too.
 Ooh, more calories.
 And the 42% of European chromosomes
 have a predisposition to storing fat,
 which was selected probably in the food scarcity periods.
 Like basically as we were exiting Africa,
 before and during the ice ages,
 there was probably a selection to those individuals
 who made it north to basically be able to store energy,
 a lot more energy.
 So you basically now have this lineage
 that is deciding whether you want to store energy
 in your white fat or burn energy in your beige fat.
 Turns out that your fat is,
 we have such a bad view of fat.
 Fat is your best friend.
 Fat can both store all these excess lipids
 that would be otherwise circulating through your body
 and causing damage, but it can also burn calories directly.
 If you have too much energy,
 you can just choose to just burn some of that as heat.
 So basically when you're cold,
 you're burning energy to basically warm your body up
 and you're burning all these lipids
 and you're burning all these caters.
 So what we basically found is that across the board,
 genetic variants associated with obesity
 across many of these regions were all enriched repeatedly
 in mesenchymal stem cell enhancers.
 So that gave us a hint as to which of these genetic variants
 was likely driving this whole association.
 And we ended up with this one genetic variant
 called RS142-1085.
 And that genetic variant out of the 89
 was the one that we predicted to be causal for the disease.
 So going back to those steps,
 first step is figure out the relevant tissue
 based on the global enrichment.
 Second step is figure out the causal variant
 among many variants in this linkage disequilibrium
 in this co-inherited block
 between these recombination hotspots,
 these boundaries of these inherited blocks.
 That's the second step.
 The third step is once you know that causal variant,
 try to figure out what is the motif that is disrupted
 by that causal variant.
 Basically how does it act?
 Variants don't just disrupt elements,
 they disrupt the binding of specific regulators.
 So basically the third step there was
 how do you find the motif that is responsible,
 like the gene regulatory word,
 the building block of gene regulation
 that is responsible for that dysregulatory event.
 And the fourth step is finding out
 what regulator normally binds that motif
 and is now no longer able to bind.
 And then once you have the regulator,
 can you then try to figure out how to,
 what, after it developed, how to fix it?
 That's exactly right.
 You now know how to intervene.
 You have basically a regulator,
 you have a gene that you can then perturb
 and you say, well, maybe that regulator
 has a global role in obesity.
 I can perturb the regulator.
 Just to clarify, when we say perturb,
 like on the scale of a human life,
 can a human being be helped?
 Of course, of course.
 Yeah, I guess understanding is the first step.
 Exactly.
 No, no, but perturbed basically means
 you now develop therapeutics,
 pharmaceutical therapeutics against that,
 or you develop other types of intervention
 that affect the expression of that gene.
 What do pharmaceutical therapeutics look like
 when your understanding's on a genetic level?
 Yeah.
 Sorry if it's a dumb question.
 No, no, no, it's a brilliant question,
 but I wanna save it for a little bit later
 when we start talking about therapeutics.
 Perfect.
 We've talked about the first four steps.
 There's two more.
 So basically the first step is figure out,
 I mean, the zero step, the starting point is the genetics.
 The first step after that is figure out the tissue of action.
 The second step is figuring out the nucleotide
 that is responsible or set of nucleotides.
 The third step is figure out the motif
 and the upstream regulator, number four.
 Number five and six is what are the targets?
 So number five is great.
 Now I know the regulator, I know the motif,
 I know the tissue, and I know the variant.
 What does it actually do?
 So you have to now trace it to the biological process
 and the genes that mediate that biological process.
 So knowing all of this
 can now allow you to find the target genes.
 How?
 By basically doing perturbation experiments
 or by looking at the folding of the epigenome
 or by looking at the genetic impact
 of that genetic variant on the expression of genes.
 And we use all three.
 So let me go through them.
 Basically, one of them is physical links.
 This is the folding of the genome onto itself.
 How do you even figure out the folding?
 It's a little bit of a tangent,
 but it's a super awesome technology.
 Think of the genome as, again,
 this massive packaging that we talked about
 of taking two meters worth of DNA
 and putting it in something that's a million times smaller
 than two meters worth of DNA, that's a single cell.
 You basically have this massive packaging,
 and this packaging basically leads to the chromosome
 being wrapped around in sort of tie-tied ways
 in ways, however, that are functionally capable
 of being reopened and reclosed.
 So I can then go in and figure out that folding
 by sort of chopping up the spaghetti soup,
 putting glue, and ligating the segments
 that were chopped up but nearby each other,
 and then sequencing through these ligation events
 to figure out that this region of this chromosome,
 that region of the chromosome were near each other,
 that means they were interacting,
 even though they were far away on the genome itself.
 So that chopping up, sequencing, and re-gluing
 is basically giving you folds of the genome that we call.
 Sorry, can you backtrack?
 Of course.
 How does cutting it help you figure out
 which ones were close in the original folding?
 So you have a bowl of noodles.
 Go on.
 And in that bowl of noodles,
 some noodles are near each other.
 So throw in a bunch of glue.
 You basically freeze the noodles in place.
 Throw in a cutter that chops up the noodles
 into little pieces.
 Now throw in some ligation enzyme
 that lets those pieces that were free
 re-ligate near each other.
 In some cases, they're re-ligate what you had just got,
 but that's very rare.
 Most of the time, they will re-ligate
 in whatever was proximal.
 You now have glued the red noodle
 that was crossing the blue noodle to each other.
 You then reverse the glue, the glue goes away,
 and you just sequence the heck out of it.
 Most of the time, you'll find red segment with,
 you know, red segment,
 but you can specifically select for ligation events
 that have happened that were not from the same segment
 by sort of marking them in a particular way,
 and then selecting those,
 and then you sequence and you look for red with blue matches
 of sort of things that were glued
 that were not immediate proximal to each other.
 And that reveals the linking of the blue noodle
 and the red noodle.
 You're with me so far?
 Yeah.
 Good.
 So we've done these experiments.
 That's the physical.
 That's the physical.
 That's step one of the physical.
 And what the physical revealed
 is topologically associated domains,
 basically big blocks of the genome
 that are topologically, you know, connected together.
 That's the physical.
 The second one is the genetic links.
 It basically says,
 across individuals that have different genetic variants,
 how are their genes expressed differently?
 Remember before I was saying
 that the path between genetics and disease is enormous,
 but we can break it up to look at the path
 between genetics and gene expression.
 So instead of using Alzheimer's as a phenotype,
 I can now use expression of IRX3 as the phenotype,
 expression of gene A.
 And I can look at all of the humans
 who contain a G at that location
 and all the humans who contain a T at that location
 and basically say, wow,
 turns out that the expression of each gene is higher
 for the T humans than for the G humans at that location.
 So that basically gives me a genetic link
 between a genetic variant, a locus, a region,
 and the expression of nearby genes.
 Good on the genetic link?
 I think so.
 Awesome.
 So the third link is the activity link.
 What's an activity link?
 It basically says,
 if I look across 833 different epigenomes,
 whenever this enhancer is active, this gene is active.
 That gives me an activity link
 between this region of the DNA and that gene.
 And then the fourth one is perturbations
 where I can go in and blow up that region
 and see what are the genes that change in expression.
 Or I can go in and over activate that region
 and see what genes change in expression.
 So I guess that's similar to activity?
 Yeah, yeah.
 So that's basically similar to activity.
 I agree, but it's causal rather than correlational.
 Again, I'm a little weird.
 No, no, you're 100% on.
 It's exactly the same as activity.
 But the perturbations.
 The perturbation.
 Where I go and intervene,
 I basically take a bunch of cells.
 So you know CRISPR, right?
 CRISPR is this genome guidance and cutting mechanism.
 It's what George Church likes to call genome vandalism.
 So you basically are able to.
 You can basically take a guide RNA
 that you put into the CRISPR system.
 And the CRISPR system will basically use these guide RNAs,
 scan the genome, find wherever there's a match,
 and then cut the genome.
 So I digress, but it's a bacterial immune defense system.
 So basically bacteria are constantly attacked by viruses,
 but sometimes they win against the viruses
 and they chop up these viruses
 and remember as a trophy inside their genome,
 they have this loci, this CRISPR loci,
 that basically stands for clustered repeats,
 interspersed, et cetera.
 So basically it's an interspersed repeats structure
 where basically you have a set of repetitive regions
 and then interspersed were these variable segments
 that were basically matching viruses.
 So when this was first discovered,
 it was basically hypothesized
 that this is probably a bacterial immune system
 that remembers the trophies of the viruses
 that managed the kill.
 And then the bacteria pass on,
 you know, they sort of do lateral transfer of DNA
 and they pass on these memories
 so that the next bacterium says, ooh, you killed that guy.
 When that guy shows up again, I will recognize him.
 And the CRISPR system was basically evolved
 as a bacterial adaptive immune response
 to sense foreigners that should not belong
 and to just go and cut their genome.
 So it's an RNA guided, RNA cutting enzyme
 or an RNA guided DNA cutting enzyme.
 So there's different systems.
 Some of them cut DNA, some of them cut RNA,
 but all of them remember this sort of viral attack.
 So what we have done now as a field is, you know,
 through the work of, you know, Jennifer Doudna,
 Emmanuel Carpentier, Feng Zhang and many others
 is co-opted that system of bacterial immune defense
 as a way to cut genomes.
 You basically have this guiding system
 that allows you to use an RNA guide
 to bring enzymes to cut DNA at a particular locus.
 That's so fascinating.
 So this is like already a natural mechanism,
 a natural tool for cutting that was useful
 in this particular context.
 And we're like, well, we can use that thing to actually,
 it's a nice tool that's already in the body.
 Yeah, yeah, it's not in our body, it's in the bacterial body.
 It was discovered by the yogurt industry.
 They were trying to make better yogurts
 and they were trying to make their bacteria
 in their yogurt cultures more resilient to viruses.
 And they were studying bacteria and they found that,
 wow, this CRISPR system is awesome.
 It allows you to defend against that.
 And then it was co-opted in mammalian systems
 that don't use anything like that as a targeting way
 to basically bring these DNA cutting enzymes
 to any locus in the genome.
 Why would you want to cut DNA to do anything?
 The reason is that our DNA has a DNA repair mechanism
 where if a region of the genome gets randomly cut,
 you'll basically scan the genome for anything that matches
 and sort of use it by homology.
 So the reason why we're deployed
 is because we now have a spare copy.
 As soon as my mom's copy is deactivated,
 I can use my dad's copy.
 And somewhere else, if my dad's copy is deactivated,
 I can use my mom's copy to repair it.
 So this is called homologous-based repair.
 So all you have to do is the cutting
 and you don't have to do the fixing.
 That's exactly right.
 You don't have to do the fixing.
 Because it's already built in.
 That's exactly right.
 But the fixing can be co-opted
 by throwing in a bunch of homologous segments
 that instead of having your dad's version,
 have whatever other version you'd like to use.
 So you then control the fixing
 by throwing in a bunch of other stuff.
 That's exactly right.
 And that's how you do genome editing.
 So that's what CRISPR is.
 That's what CRISPR is.
 In popular culture, people use the term.
 I've never, wow, that's brilliant.
 That's an awesome explanation.
 Genome vandalism followed by a bunch of band-aids
 that have the sequence that you'd like.
 And you can control the choices of band-aids.
 Correct.
 And of course, there's new generations of CRISPR.
 There's something that's called prime editing
 that was sort of very much in the press recently.
 That basically instead of sort of making
 a double-stranded break, which again is genome vandalism,
 you basically make a single stranded break.
 You basically just nick one of the two strands,
 enabling you to sort of peel off
 without sort of completely breaking it up
 and then repair it locally using a guide
 that is coupled to your initial RNA
 that took you to that location.
 Dumb question, but is CRISPR as awesome
 and cool as it sounds, I mean, technically speaking,
 in terms of like as a tool for manipulating our genetics
 in the positive meaning of the word manipulating?
 Or is there downsides, drawbacks
 in this whole context of therapeutics
 that we're talking about or understanding and so on?
 So when I teach my students about CRISPR,
 I show them articles with the headline,
 genome editing tool revolutionizes biology.
 And then I show them the date of these articles
 and they're 2004, like five years before CRISPR was invented.
 And the reason is that they're not talking about CRISPR.
 They're talking about zinc finger enzymes
 that are another way to bring these cutters to the genome.
 It's a very difficult way of sort of designing
 the right set of zinc finger proteins,
 the right set of amino acids that will now target
 a particularly long stretch of DNA
 because for every location that you want to target,
 you need to design a particular regulator,
 a particular protein that will match that region well.
 There's another technology called tailings,
 which are basically just a different way of using proteins
 to sort of guide these cutters
 to a particular location of the genome.
 These require a massive team of engineers,
 of biological engineers to basically design a set
 of amino acids that will target a particular sequence
 of your genome.
 The reason why CRISPR is amazingly awesomely revolutionary
 is because instead of having this team of engineers
 design a new set of proteins
 for every location that you want to target,
 you just type it in your computer
 and you just synthesize an RNA guide.
 The beauty of CRISPR is not the cutting,
 it's not the fixing, all of that was there before.
 It's the guiding.
 And the only thing that changes
 is that it makes the guiding easier
 by sort of just typing in the RNA sequence,
 which then allows the system to sort of scan the DNA
 to find that.
 So the coding, the engineering of the cutter is easier
 in terms of SP.
 That's kind of similar to the story of deep learning
 versus old school machine learning.
 Some of the challenging parts are automated.
 Okay, so, but CRISPR is just one cutting technology.
 And then that's part of the challenges
 and exciting opportunities of the field
 is to design different cutting technologies.
 Yeah, yeah.
 So now, this was a big parenthesis on CRISPR,
 but now, when we were talking about perturbations,
 you basically now have the ability
 to not just look at correlation between enhancers and genes,
 but actually go and either destroy that enhancer
 and see if the gene changes in expression,
 or you can use the CRISPR targeting system
 to bring in not vandalism and cutting,
 but you can couple the CRISPR system with,
 and the CRISPR system is called usually CRISPR-Cas9,
 because Cas9 is the protein that will then come and cut,
 but there's a version of that protein called dead Cas9
 where the cutting part is deactivated.
 So you basically use D-Cas9, dead Cas9,
 to bring in an activator or to bring in a repressor.
 So you can now ask, is this enhancer changing that gene
 by taking this modified CRISPR,
 which is already modified from the bacteria
 to be used in humans,
 that you can now modify the Cas9 to be dead Cas9,
 and you can now further modify to bring in a regulator,
 and you can basically turn on or turn off that enhancer
 and then see what is the impact on that gene.
 So these are the four ways of linking the locus
 to the target gene, and that's step number five, okay?
 Step number five is find the target gene,
 and step number six is what the heck does that gene do?
 You basically now go and manipulate that gene
 to basically see what are the processes that change,
 and you can basically ask, well, in this particular case,
 in the FTO locus, we found mesenchymal stem cells
 that are the progenitors of white fat and brown fat,
 or beige fat.
 We found the RS1421085 nucleotide variant
 as the causal variant.
 We found this large enhancer, this master regulator.
 I like to call it OB1 for obesity one,
 like the strongest enhancer associated with.
 And OB1 was kind of chubby as the actor,
 I don't know if you remember him.
 Yeah.
 So you basically are using this JEDA mind trick
 to basically find out the.
 Thank you.
 The location of the genome that is responsible,
 the enhancer that harbors it, the motif,
 the upstream regulator, which is ARID5B
 for AT-rich interacting domain 5B.
 That's a protein that sort of comes and binds normally.
 That protein is normally a repressor.
 It represses this super enhancer,
 this massive 12,000 nucleotide
 master regulatory control region,
 and it turns off IRX3, which is a gene
 that's 600,000 nucleotides away,
 and IRX5, which is 1.2 million nucleotides away.
 So those.
 And what's the effect of turning them off?
 That's exactly the next question.
 So step six is what do these genes actually do?
 So we then ask, what does IRX3 and IRX5 do?
 The first thing we did is look across individuals
 for individuals that had higher expression of IRX3
 or lower expression of IRX3.
 And then we looked at the expression
 of all of the other genes in the genome.
 And we look for simply correlation.
 And we found that IRX3 and IRX5 were both correlated
 positively with lipid metabolism
 and negatively with mitochondrial biogenesis.
 You're like, what the heck does that mean?
 Doesn't sound related to obesity.
 Not at all, superficially.
 But lipid metabolism should,
 because lipids is these high energy molecules
 that basically store fat.
 So IRX3 and IRX5 are negatively correlated
 with lipid metabolism.
 So that basically means that when they turn on,
 lipid metabolism, oh, sorry, positively,
 when they turn on, they turn on lipid metabolism.
 And they're negatively correlated
 with mitochondrial biogenesis.
 What do mitochondria do in this whole process?
 Again, small parenthesis, what are mitochondria?
 Mitochondria are little organelles.
 They arose, they only are found in eukaryotes.
 Ewe means good, karyo means nucleus.
 So truly like a true nucleus.
 So eukaryotes have a nucleus.
 Prokaryotes are before the nucleus.
 They don't have a nucleus.
 So eukaryotes have a nucleus.
 Compartmentalization.
 Eukaryotes have also organelles.
 Some eukaryotes have chloroplasts.
 These are the plants.
 They photosynthesize.
 Some other eukaryotes, like us,
 have another type of organelle called mitochondria.
 These arose from an ancient species that we engulfed.
 This is an endosymbiosis event.
 Symbiosis, bio means life, sim means together.
 So symbiotes are things that live together.
 Endosymbiosis, endo means inside.
 So endosymbiosis means you live together,
 holding the other one inside you.
 So the pre-eukaryotes engulfed an organism
 that was very good at energy production.
 And that organism eventually shed most of its genome
 to now have only 13 genes in the mitochondrial genome.
 And those 13 genes are all involved in energy production.
 The electron transport chain.
 So basically, electrons are these massive,
 super energy-rich molecules.
 We basically have these organelles that produce energy.
 And when your muscle exercises,
 you basically multiply your mitochondria.
 You basically sort of use more and more mitochondria.
 And that's how you get beefed up.
 So basically, the muscle sort of learns
 how to generate more energy.
 So basically, every single time,
 your muscles will over night regenerate
 and sort of become stronger
 and amplify their mitochondria and so forth.
 So what does mitochondria do?
 The mitochondria use energy to sort of do any kind of task.
 When you're thinking, you're using energy.
 This energy comes from mitochondria.
 Your neurons have mitochondria all over the place.
 Basically, this mitochondria can multiply as organelles
 and they can be spread along the body of your muscle.
 Some of your muscle cells have actually multiple nuclei,
 they're polynucleated, but they also have multiple mitochondria
 to basically deal with the fact that your muscle is enormous.
 You can sort of span this super, super long length
 and you need energy throughout the length of your muscle.
 So that's why you have mitochondria throughout the length
 and you also need transcription through the length.
 So you have multiple nuclei as well.
 So these two processes, lipids store energy.
 What do mitochondria do?
 So there's a process known as thermogenesis.
 Thermo heat, genesis generation.
 Thermogenesis is generation of heat.
 Remember that bathtub with the in and out?
 That's the equation that everybody's focused on.
 So how much energy do you consume?
 How much energy do you burn?
 But in every thermodynamic system,
 there's three parts to the equation.
 There's energy in, energy out, and energy lost.
 Any machine has loss of energy.
 How do you lose energy?
 You emanate heat.
 So heat is energy loss.
 So there's-
 Which is where the thermogenesis comes in.
 Thermogenesis is actually a regulatory process
 that modulates the third component
 of the thermodynamic equation.
 You can basically control thermogenesis explicitly.
 You can turn on and turn off thermogenesis.
 And that's where the mitochondria comes into play.
 Exactly.
 So IRX-10 and IRX-5 turn out to be the master regulators
 of a process of thermogenesis
 versus lipogenesis, generation of fat.
 So IRX-10 and IRX-5 in most people burn heat,
 burn calories as heat.
 So when you eat too much, just burn it off
 in your fat cells.
 So that bathtub has basically a sort of dissipation knob
 that most people are able to turn on.
 I am unable to turn that on
 because I am a homozygous carrier for the mutation
 that changes a T into a C in the RS1421-085 allele,
 a locus, a snip.
 I have the risk allele twice from my mom and from my dad.
 So I'm unable to thermogenize.
 I'm unable to turn on thermogenesis through IRX-3 and IRX-5
 because the regulator that normally binds here,
 IRX-5B can no longer bind because it's an AT-rich
 interacting domain.
 And as soon as I change the T into a C,
 it can no longer bind because it's no longer AT-rich.
 But doesn't that mean that you're able to use the energy
 more efficiently?
 You're not generating heat or is that-
 That means I can eat less and get around just fine.
 Yes.
 Yeah, so-
 That's a feature actually.
 It's a feature in a food scarce environment.
 Yeah, but-
 If we're all starving, I'm doing great.
 If we all have access to massive amounts of food,
 I'm obese basically.
 That's taken us to the entire process of then understanding
 that why mitochondria and then the lipids are both
 even though distant are somehow involved.
 Exactly, different sides of the same coin.
 And you basically choose to store energy
 or you can choose to burn energy.
 And that all of that is involved in the puzzle of obesity.
 And that's what's fascinating, right?
 Here we are in 2007, discovering the strongest
 genetic association with obesity and knowing nothing
 about how it works for almost 10 years.
 For 10 years, everybody focused on this FTO gene.
 And they were like, oh, it must have to do something
 with, you know, RNA modification.
 And it's like, no, it has nothing to do with the function
 of FTO, it has everything to do with all
 of these other process.
 And suddenly the moment you solve that puzzle,
 which is a multi-year effort, by the way,
 a tremendous effort by Melina and many, many others.
 So this tremendous effort basically led us
 to recognize this circuitry.
 You went from having some 89 common variants associated
 in that region of the DNA sitting on top of this gene
 to knowing the whole circuitry.
 When you know the circuitry, you can now go crazy.
 You can now start intervening at every level.
 You can start intervening at the iRx5b level.
 You can start intervening with CRISPR-Cas9
 at the single SNP level.
 You can start intervening at iRx3 and iRx5
 directly there.
 You can start intervening at the thermogenesis level
 because you know the pathway.
 You can start intervening at the differentiation level
 where the decision to make either white fat or beige fat,
 the energy burning beige fat, is made developmentally
 in the first three days of differentiation
 of your adipocytes.
 So as they're differentiating, you basically can choose
 to make fat burning machines or fat storing machines.
 And sort of that's how you populate your fat.
 You basically can now go in pharmaceutical
 and do all of that.
 And in our paper, we actually did all of that.
 We went in and manipulated every single aspect.
 At the nucleotide level, we use CRISPR-Cas9 genome editing
 to basically take primary adipocytes from risk
 and non-risk individuals and show that by editing
 that one nucleotide out of 3.2 billion nucleotides
 in the human genome, you could then flip
 between an obese phenotype and a lean phenotype
 like a switch.
 You can basically take micelles that are non-thermogenizing
 and just flip into thermogenizing cells
 by changing one nucleotide.
 It's mind boggling.
 It's so inspiring that this puzzle could be solved
 in this way and it feels within reach to then be able
 to crack the problem of some of these diseases.
 What are, so 2007, you mentioned, what are the technologies,
 the tools that came along that made this possible?
 Like what are you excited about?
 Maybe if we just look at the buffet of things
 that you've kind of mentioned, is there,
 is there, what's involved?
 What should we be excited about?
 What are you excited about?
 I love that question because there's so much ahead of us.
 There's so, so much.
 There's, so basically solving that one locus
 required massive amounts of knowledge
 that we have been building across the years
 through the epigenome, through the comparative genomics
 to find out the causal variant and the controller
 regulatory motif through the conserved circuitry.
 It required knowing this regulatory genomic wiring.
 It required high C of these sort of topologically
 associated domains to basically find
 these long range interaction.
 It required EQTLs of this sort of genetic perturbation
 of these intermediate gene phenotypes.
 It required all of the arsenal of tools
 that I've been describing was put together for one locus.
 And this was a massive team effort,
 huge investment in time, energy, money, effort,
 intellectual, everything.
 You're referring to, I'm sorry,
 just for the obesity one. This one paper.
 Yeah, this one paper.
 This one single paper.
 This one single locus.
 I like to say that this is a paper
 about one nucleotide in the human genome,
 about one bit of information, C versus T in the human genome.
 That's one bit of information
 and we have 3.2 billion nucleotides to go through.
 So how do you do that systematically?
 I am so excited about the next phase of research
 because the technologies that my group
 and many other groups have developed
 allows us to now do this systematically,
 not just one locus at a time,
 but thousands of loci at a time.
 So let me describe some of these technologies.
 The first one is automation and robotics.
 So basically, you know,
 we talked about how you can take all of these molecules
 and see which of these molecules are targeting
 each of these genes and what do they do.
 So you can basically now screen through
 millions of molecules,
 through thousands and thousands and thousands of plates,
 each of which has thousands and thousands
 and thousands of molecules,
 every single time testing, you know, all of these genes
 and asking which of these molecules perturb these genes.
 So that's technology number one, automation and robotics.
 Technology number two is parallel readouts.
 So instead of perturbing one locus
 and then asking if I use CRISPR-Cas9 on this enhancer
 to basically use dCas9 to turn on or turn off the enhancer,
 or if I use CRISPR-Cas9 on the SNP
 to basically change that one SNP at a time,
 then what happens?
 But we have 120,000 disease-associated SNPs
 that we wanna test.
 We don't wanna spend 120,000 years doing it.
 So what do we do?
 We've basically developed this technology
 for massively parallel reporter assays, MPRA.
 So in collaboration with Tarzan Mikkelsen, Eric Lander,
 I mean, Jason Dury's group has done a lot of that.
 So there's a lot of groups that basically have
 developed technologies for testing
 10,000 genetic variants at a time.
 How do you do that?
 We talked about microarray technology,
 the ability to synthesize these huge microarrays
 that allow you to do all kinds of things
 like measure gene expression by hybridization,
 by measuring the genotype of a person,
 by looking at hybridization with one version with a T
 versus the other version with a C,
 and then sort of figuring out that I am a risk carrier
 for obesity based on these hybridization
 differential hybridization in my genome that says,
 oh, you seem to only have this allele
 or you seem to have that allele.
 Microarrays can also be used to systematically synthesize
 small fragments of DNA.
 So you can basically synthesize these 150 nucleotide
 long fragments across 450,000 spots at a time.
 You can now take the result of that synthesis,
 which basically works through all of these sort of layers
 of adding one nucleotide at a time.
 You can basically just type it into your computer
 and order it.
 And you can basically order 10,000 or 100,000
 of these small DNA segments at a time.
 And that's where awesome molecular biology comes in.
 You can basically take all these segments,
 have a common start and end barcode or sort of like Gator,
 like just like pieces of a puzzle,
 you can make the same end piece
 and the same start piece for all of them.
 And you can now use plasmids,
 which are these extra chromosomal,
 small DNA circular segments
 that are basically inhabiting all our genomes.
 We basically have plasmids floating around.
 I mean, bacteria use plasmids for transferring DNA.
 And that's where they put a lot
 of antibiotic resistance genes.
 So they can easily transfer them
 from one bacterium to the other.
 So one bacterium evolves a gene to be resistant
 to a particular antibiotic.
 It basically says to all its friends,
 hey, here's that sort of DNA piece.
 We can now co-opt these plasmids into human cells.
 We can basically make a human cell culture
 and add plasmids to that human cell culture
 that contain the things that you want to test.
 You now have this library of 450,000 elements.
 You can insert them each into the common plasmid
 and then test them in millions of cells in parallel.
 And the common plasmid is all the same before you add it.
 Exactly, the rest of the plasmid is the same.
 So it's called an episomal reporter assay.
 Epizome means not inside the genome,
 it's sort of outside the chromosomes.
 So it's an episomal assay
 that allows you to have a variable region
 where you basically test 10,000 different enhancers.
 And you have a common region
 which basically has the same reporter gene.
 You now can do some very cool molecular biology.
 You can basically take the 450,000 elements
 that you've generated
 and you have a piece of the puzzle here,
 piece of the puzzle here, which is identical.
 So they're compatible with that plasmid.
 You can chop them up in the middle
 to separate a barcode reporter from the enhancer.
 And in the middle, put the same gene,
 again, using the same pieces of the puzzle.
 You now can have a barcode readout
 of what is the impact of 10,000 different versions
 of an enhancer on gene expression.
 So we're not doing one experiment,
 we're doing 10,000 experiments.
 And those 10,000 can be 5,000 of different loci
 and each of them in two versions, risk or non-risk.
 I can now test tens of thousands.
 These are little hypotheses.
 Exactly.
 And then you can do 10,000.
 You can test 10,000 hypotheses at once.
 How hard is it to generate those 10,000?
 Trivial, trivial.
 But it's by biology.
 No, no, generating the 10,000 is trivial
 because you basically add, it's by technology.
 You basically have these arrays
 that add one nucleotide at a time at every spot.
 Oh, and so it's printing.
 Exactly.
 So you're able to control.
 Yeah.
 Super costly, is it?
 10,000 bucks.
 Oh, so this isn't millions?
 10,000 bucks for 10,000 experiments.
 Sounds like the right, you know.
 I mean, so that's super, that's exciting
 because you don't have to do one thing at a time.
 You can now use that technology,
 these massively parallel reporter assays
 to test 10,000 locations at a time.
 We've made multiple modifications of that technology.
 One was sharper MPRA, which stands for, you know,
 basically getting a higher resolution view
 by tiling these elements.
 So you can see where along the region of control
 are they acting.
 And we made another modification called Hydra
 for high, you know, definition regulatory annotation
 or something like that,
 which basically allows you to test 7 million of these
 at a time by sort of cutting them directly from the DNA.
 So instead of synthesizing, which basically has the limit
 of 450,000 that you can synthesize at a time,
 we basically said, hey, if we wanna test
 all accessible regions of the genome,
 let's just do an experiment that cuts accessible regions.
 Let's take those accessible regions,
 put them all with the same end joints of the puzzles,
 and then now use those to create a much, much larger array
 of things that you can test.
 And then tiling all of these regions,
 you can then pinpoint what are the driver nucleotides,
 what are the elements, how are they acting
 across 7 million experiments at a time.
 So basically this is all the same family of technology
 where you're basically using these parallel readouts
 of the barcodes.
 And then, you know, to do this,
 we used a technology called Starseq
 for self-transcribing reporter assays,
 a technology developed by Alex Stark,
 my former postdoc who's now a PI over in Vienna.
 So we basically coupled the Starseq,
 the self-transcribing reporters,
 where the enhancer can be part of the gene itself.
 So instead of having a separate barcode,
 that enhancer basically acts to turn on the gene
 and is transcribed as part of the gene.
 So you don't have to have the two separate parts.
 Exactly, so you can just read them directly.
 So there's a constant improvement in this whole process.
 By the way, generating all these options,
 is it basically brute force?
 How much human intuition is?
 Oh gosh, of course it's human intuition
 and human creativity and incorporating
 all of the input data sets.
 Because again, the genome is enormous, 3.2 billion.
 You don't wanna test that.
 Instead, you basically use all of these tools
 that I've talked about already.
 You generate your top favorite 10,000 hypothesis,
 and then you go and test all 10,000.
 And then from what comes out,
 you can then go to the next step.
 So that's technology number two.
 So technology number one is robotics automation,
 where you have thousands of wells
 and you constantly test them.
 The second technology is instead of having wells,
 you have these massively parallel readouts
 in sort of these pooled assays.
 The third technology is coupling CRISPR perturbations
 with these single cell RNA readouts.
 So let me make another parenthesis here to describe now
 single cell RNA sequencing.
 So what does single cell RNA sequencing mean?
 So RNA sequencing is what has been traditionally used,
 oh well traditionally, the last 20 years,
 ever since the advent of next generation sequencing.
 So basically before, RNA expression profiling
 was based on these microarrays.
 The next technology after that was based on sequencing.
 So you chop up your RNA
 and you just sequence small molecules,
 just like you would sequence a genome,
 basically reverse transcribe the small RNAs into DNA,
 and you sequence that DNA
 in order to get the number of sequencing reads
 corresponding to the expression level
 of every gene in the genome.
 You now have RNA sequencing.
 How do you go to single cell RNA sequencing?
 That technology also went through stages of evolution.
 The first was microfluidics.
 You basically had these, even chambers,
 you basically had these ways of isolating individual cells,
 putting them into a well for every one of these cells.
 So you have 384 well plates,
 and you now do 384 parallel reactions
 to measure the expression of 384 cells.
 That sounds amazing, and it was amazing,
 but we wanna do a million cells.
 How do you go from these wells to a million cells?
 You can't.
 So what the next technology was after that
 is instead of using a well for every reaction,
 you now use a lipid droplet for every reaction.
 So you use micro droplets as reaction chambers
 to basically amplify RNA.
 So here's the idea.
 You basically have microfluidics
 where you basically have every single cell
 coming down one tube in your microfluidics,
 and you have little bubbles getting created
 in the other way with specific primers
 that mark every cell with its own barcode.
 You basically couple the two,
 and you end up with little bubbles that have a cell
 and tons of markers for that cell.
 You now mark up all of the RNA for that one cell
 with the same exact barcode,
 and you then lyse all of the droplets,
 and you sequence the heck out of that,
 and you have for every RNA molecule,
 a unique identifier that tells you what cell was it on.
 That is such good engineering, microfluidics,
 and using some kind of primer to put a label on the thing.
 I mean, you're making it sound easy.
 I assume it's-
 It's beautiful, right?
 It's quite challenging, but it's gorgeous, yeah.
 So there's the next generation.
 That's great engineering, yeah.
 So that's the second generation.
 Next generation is, forget the microfluidics altogether.
 Just use big bottles.
 How can you possibly do that with big bottles?
 So here's the idea.
 You dissociate all of your cells
 or all of your nuclei from complex cells like brain cells
 that are very long and sticky, so you can't do that.
 So if you have blood cells
 or if you have neuronal nuclei or brain nuclei,
 you can basically dissociate, let's say, a million cells.
 You now want to add a unique barcode,
 a unique barcode in each one of a million cells
 using only big bottles.
 How can you possibly do that?
 Sounds crazy, but here's the idea.
 You use a hundred of these bottles.
 You randomly shuffle all your million cells
 and you throw them into those hundred bottles,
 randomly, completely randomly.
 You add one barcode out of a hundred
 to every one of the cells.
 You then, you now take them all out,
 you shuffle them again,
 and you throw them again into the same hundred bottles.
 But now in a different randomization,
 and you add a second barcode.
 So every cell now has two barcodes.
 You take them out again, you shuffle them,
 and you throw them back in.
 Another third barcode is adding,
 randomly, from the same hundred barcodes.
 You've now labeled every cell probabilistically
 based on the unique path that he took
 of which of a hundred bottles did he go for the first time,
 which of a hundred bottles the second time,
 and which of a hundred bottles the third time.
 A hundred times a hundred times a hundred
 is a million unique barcodes
 in every single one of these cells.
 You can now have these cells
 without ever using microfluidics.
 It's very clever.
 It's beautiful, right?
 From a computer science perspective, that's very clever.
 Yeah.
 So you now have the single cell sequencing technology.
 You can use the wells, you can use the bubbles,
 or you can use the bottles.
 And you have ways-
 The bubbles still sound pretty damn cool.
 The bubbles are awesome.
 And that's basically the main technology that we're using.
 So the bubbles is the main technology.
 So there are kits now that companies just sell
 to basically carry out single cell RNA sequencing
 that you can basically for $2,000,
 you can basically get 10,000 cells from one sample.
 And for every one of those cells,
 you basically have the transcription of thousands of genes.
 And of course the data for any one cell is noisy,
 but being computer scientists,
 we can aggregate the data from all of the cells together
 across thousands of individuals together
 to basically make very robust inferences.
 Okay, so the third technology,
 basically single cell RNA sequencing
 that allows you to now start asking
 not just what is the brain expression level difference
 of that genetic variant,
 but what is the expression difference
 of that one genetic variant
 across every single subtype of brain cell.
 How is the variance changing?
 You can't just, you know, with a brain sample,
 you can just ask about the mean.
 What is the average expression?
 If I instead have 3,000 cells that are neurons,
 I can ask not just what is the neuronal expression,
 I can say for layer five excitatory neurons
 of which I have, I don't know, 300 cells,
 what is the variance that this genetic variant has?
 So suddenly it's amazingly more powerful.
 I can basically start asking about this middle layer
 of gene expression at unprecedented levels.
 And when you look at the average,
 it washes out some potentially important signal
 that corresponds to ultimately the disease.
 Completely.
 Yeah.
 So that, I can do that at the RNA level,
 but I can also do that at the DNA level for the epigenome.
 So remember how before I was telling you
 about all these technologies
 that we're using to probe the epigenome?
 One of them is DNA accessibility.
 So what we're doing in my lab is that
 from the same dissociation of say a brain sample,
 where you now have all these tens of thousands
 of cells floating around, you basically take half of them
 to do RNA profiling and the other have to do epigenome
 profiling both at the single cell level.
 So that allows you to now figure out what are the millions
 of DNA enhancers that are accessible
 in every one of tens of thousands of cells.
 And computationally, we can now take the RNA
 and the DNA readouts and group them together
 to basically figure out how is every enhancer related
 to every gene.
 And remember these sort of enhancer gene linking
 that we were doing across 833 samples?
 833 is awesome, don't get me wrong,
 but 10 million is way more awesome.
 So we can now look at correlated activity
 across 2.3 million enhancers and 20,000 genes
 in each of millions of cells
 to basically start piecing together the regulatory circuitry
 of every single type of neuron,
 every single type of astrocytes, oligodendrocytes,
 microglial cell inside the brains of 1,500 individuals
 that we sample across multiple different brain regions
 across both DNA and RNA.
 So that's the data set that my team generated
 last year alone.
 So in one year, we basically generated 10 million cells
 from human brain across a dozen different disorders
 across schizophrenia, Alzheimer's, frontotemporal dementia,
 Lewy body dementia, ALS, Huntington's disease,
 post-traumatic stress disorder, autism,
 bipolar disorder, healthy aging, et cetera.
 So it's possible that even just within that data set
 lie a lot of keys to understanding these diseases
 and then be able to like directly leads to the treatment.
 Correct, correct.
 So basically we are now-
 Motivating.
 Yeah, so our computational team is in heaven right now
 and we're looking for people.
 I mean, if you have listeners who are super smart-
 How much is this?
 So this is a very interesting kind of side question.
 How much of this is biology?
 How much of this is computation?
 So you have the computational biology group,
 but how much of, should you be comfortable with biology
 to be able to solve some of these problems?
 If you just find, if you put several of the hassle
 you were on fundamentally,
 are you thinking like a computer scientist here?
 You have to, this is the only way.
 As I said, we are the descendants
 of the first digital computer.
 We're trying to understand the digital computer.
 We understand, we're trying to understand the circuitry,
 the logic of this digital core computer
 and all of these analog layers surrounding it.
 So the case that I've been making
 is that you cannot think one gene at a time.
 The traditional biology is dead.
 There's no way you cannot solve disease
 with traditional biology.
 You need it as a component.
 Once you figured out RX3 and RX5,
 you now can then say, hey, have you guys worked
 on those genes with your single gene approach?
 We'd love to know everything you know.
 And if you haven't, we now know how important
 these genes are.
 Let's now launch a single gene program
 to dissect them and understand them.
 But you cannot use that as a way to dissect disease.
 You have to think genomically.
 You have to think from the global perspective
 and you have to build these circuits systematically.
 So we need numbers of computer scientists
 who are interested and willing to dive into these data,
 you know, fully, fully in and sort of extract meaning.
 We need computer science people who can understand
 sort of machine learning and inference
 and sort of, you know, decouple these matrices,
 come up with super smart ways of sort of dissecting them.
 But we also need computer scientists
 who understand biology, who are able to design
 the next generation of experiments.
 Because many of these experiments,
 no one in their right mind would design them
 without thinking of the analytical approach
 that you would use to deconvolve the data afterwards.
 Because it's massive amounts
 of ridiculously noisy data.
 And if you don't have the computational pipeline
 in your head before you even design the experiment,
 you would never design the experiment that way.
 That's brilliant.
 So in designing the experiment,
 you have to see the entirety of the computational pipeline.
 That drives the design.
 That even drives the necessity for that design.
 Basically, you know, if you didn't have
 a computer scientist way of thinking,
 you would never design these hugely combinatorial,
 massively parallel experiments.
 So that's why you need interdisciplinary teams.
 You need teams.
 And I wanna sort of clarify that.
 What do we mean by computational biology group?
 The focus is not on computational.
 The focus is on the biology.
 So we are a biology group.
 What type of biology?
 Computational biology.
 That's the type of biology that uses the whole genome.
 That's the type of biology that designs experiments,
 genomic experiments, that can only be interpreted
 in the context of the whole genome.
 Right, so it's philosophically looking at biology
 as a computer.
 Correct, correct.
 So which is, in the context of the history of biology,
 is a big transformation.
 Yeah, yeah.
 You can think of the name as what do we do?
 Only computation.
 That's not true.
 But how do we study it?
 Only computationally, that is true.
 So all of these single cell sequencing
 can now be coupled with the technology
 that we talked about earlier for perturbation.
 So here's the crazy thing.
 Instead of using these wells and these robotic systems
 for doing one drug at a time,
 or for perturbing one gene at a time in thousands of wells,
 you can now do this using a pool of cells
 and single cell RNA sequencing.
 How?
 You basically can take these perturbations using CRISPR,
 and instead of using a single guide RNA,
 you can use a library of guide RNAs
 generated exactly the same way using this array technology.
 So you synthesize a thousand different guide RNAs.
 You now take each of these guide RNAs
 and you insert them in a pool of cells
 where every cell gets one perturbation.
 And you use CRISPR editing or CRISPR,
 so with either CRISPR Cas9 to edit the genome
 with these thousand perturbations.
 Or the activation one.
 Or with the activation or with the repression.
 And you now can have a single cell readout
 where every single cell has received
 one of these modifications.
 And you can now in massively parallel ways
 couple the perturbation and the readout
 in a single experiment.
 How are you tracking which perturbations
 each cell received?
 So there's ways of doing that,
 but basically one way is to make that perturbation
 an expressible vector
 so that part of your RNA reading
 is actually that perturbation itself.
 So you can basically put it in an expressible part
 so you can self drive it.
 So the point that I wanna get across
 is that the sky's the limit.
 You basically have these tools,
 these building blocks of molecular biology.
 You have these massive data sets of computational biology.
 You have this huge ability to sort of use machine learning
 and statistical methods and linear algebra
 to sort of reduce the dimensionality
 of all these massive data sets.
 And then you end up with a series of actionable targets
 that you can then couple with pharma
 and just go after systematically.
 So the ability to sort of bring genetics to the epigenomics,
 to the transcriptomics, to the cellular readouts
 using these sort of high throughput perturbation technology
 that I'm talking about,
 and ultimately to the organismal
 through the electronic health record endophenotypes
 and ultimately the disease battery of assays
 at the cognitive level, at the physiological level
 and every other level.
 There is no better or more exciting field in my view
 to be a computer scientist then,
 or to be a scientist then period.
 Basically this confluence of technologies,
 of computation, of data, of insights
 and of tools for manipulation
 is unprecedented in human history.
 And I think this is what's shaping the next century
 to really be a transformative century for our species
 and for our planet.
 So you think the 21st century will be remembered
 for the big leaps in understanding
 and alleviation of biology?
 If you look at the path between discovery and therapeutics,
 it's been on the order of 50 years,
 it's been shortened to 40, 30, 20,
 and now it's on the order of 10 years.
 But the huge number of technologies
 that are going on right now for discovery
 will result undoubtedly in the most dramatic manipulation
 of human biology that we've ever seen
 in the history of humanity in the next few years.
 Do you think we might be able to cure some of the disease
 that we started this conversation with?
 Absolutely, absolutely.
 It's only a matter of time.
 Basically, the complexity is enormous
 and I don't want to underestimate the complexity,
 but the number of insights is unprecedented
 and the ability to manipulate is unprecedented
 and the ability to deliver these small molecules
 and other non-traditional medicine perturbations.
 There's a lot of sort of new,
 there's a new generation of perturbations
 that you can use at the DNA level, at the RNA level,
 at the microRNA level,
 at the epigenomic level.
 There's a battery of new generations of perturbations.
 If you couple that with cell type identifiers
 that can basically sense when you are in the right cell
 based on the specific combination
 and then turn on that intervention for that cell,
 you can now think of combinatorial interventions
 where you can basically sort of feed
 a synthetic biology construct to someone
 that will basically do different things in different cells.
 So basically for cancer, this is one of the therapeutics
 that our collaborator Ron Weiss is using
 to basically start sort of engineering the circuits
 that will use microRNA sensors of the environment
 to sort of know if you're in a tumor cell
 or if you're in an immune cell
 or if you're in a stromal cell and so forth
 and basically turn on particular interventions there.
 You can sort of create constructs
 that are tuned to only the liver cells
 or only the heart cells or only the brain cells
 and then have these new generations of therapeutics
 coupled with this immense amount of knowledge
 on the sort of which targets to choose
 and what biological processes to measure
 and how to intervene.
 My view is that disease is gonna be fundamentally altered
 and alleviated as we go forward.
 Next time we talk, we'll talk about
 the philosophical implications of that
 and the effect of life, but let's stick to biology
 for just a little longer.
 We did pretty good today, we stuck to the science.
 What are you excited in terms of the future of this field,
 the technologies in your own group, in your own mind,
 you're leading the world at MIT in the science
 and the engineering of this work.
 So what are you excited about here?
 I could not be more excited.
 We are one of many, many teams who are working on this.
 In my team, the most exciting parts are manyfold.
 So basically we've now assembled this battery of technologies,
 we've assembled these massive, massive data sets
 and now we're really sort of in the stage
 of our team's path of generating disease insights.
 So we are simultaneously working on a paper
 on schizophrenia right now,
 that is basically using the single cell profiling technologies,
 using this editing and manipulation technologies
 to basically show how the master regulators
 underlying changes in the brain
 that are sort of found in schizophrenia
 are in fact affecting excitatory neurons
 and inhibitory neurons in pathways
 that are active both in synaptic pruning
 but also in neurodevelopment.
 We've basically found this set of four regulators
 that are connecting these two processes
 that were previously separate in schizophrenia
 in sort of having a sort of more unified view
 across those two sides.
 The second one is in the area of metabolism.
 We basically now have a beautiful collaboration
 with the Goodyear lab that's basically looking at
 multi-tissue perturbations
 in six or seven different tissues across the body
 in the context of exercise
 and in the context of nutritional interventions
 using both mouse and human,
 where we can basically see what are the cell-to-cell
 communications that are changing across them
 and what we're finding is this immense role
 of both immune cells as well as adipocyte stem cells
 in sort of reshaping that circuitry
 of all of these different tissues
 and that's sort of painting to a new path
 for a therapeutical intervention there.
 In Alzheimer's, it's this huge focus on microglia
 and now we're discovering different classes
 of microglial cells that are basically either synaptic
 or immune and these are playing vastly different roles
 in Alzheimer's versus in schizophrenia
 and what we're finding is this immense complexity
 as you go further and further down
 of how in fact there's 10 different types of microglia
 each with their own sort of expression programs.
 We used to think of them as, oh yeah, they're microglia
 but in fact now we're realizing just even in that sort of
 least abundant of cell types,
 there's this incredible diversity there.
 The differences between brain regions
 is another sort of major, major insight.
 Again, one would think that, oh, astrocytes are astrocytes
 no matter where they are, but no,
 there's incredible region-specific differences
 in the expression patterns of all of the major brain cell
 types across different brain regions.
 So basically there's the neocortical regions
 that are sort of the recent innovation
 that makes us so different from all other species.
 There's the sort of reptilian brain sort of regions
 that are sort of much more, very extremely distinct.
 There's the cerebellum.
 There's each of those basically is associated
 in a different way with disease.
 And what we're doing now is looking
 into pseudo temporal models for how disease progresses
 across different regions of the brain.
 If you look at Alzheimer's, it basically starts
 in this small region called the enterinol cortex
 and then it spreads through the brain and, you know,
 through the hippocampus and, you know,
 ultimately affecting the neocortex.
 And with every brain region that it hits,
 it basically has a different impact on the cognitive
 and, you know, memory aspects, orientation,
 short-term memory, long-term memory, et cetera,
 which is, you know, dramatically affecting
 the cognitive path that the individuals go through.
 So what we're doing now is creating
 this computational model for ordering the cells
 and the regions and the individuals
 according to their ability to predict Alzheimer's disease.
 So we can have a cell level predictor of pathology
 that allows us to now create a temporal time course
 that tells us when every gene turns on
 along this pathology progression,
 and then trace that across regions
 and pathological measures that are region specific,
 but also cognitive measures and so on and so forth.
 So that allows us to now sort of, for the first time,
 look at can we actually do early intervention for Alzheimer's
 where we know that the disease starts manifesting
 for 10 years before you actually have
 your first cognitive loss.
 Can we start seeing that path to build new diagnostics,
 new prognostics, new biomarkers
 for this sort of early intervention in Alzheimer's?
 The other aspect that we're looking at is mosaicism.
 We talked about the common variants and the rare variants,
 but in addition to those rare variants,
 as your initial cell that forms the zygote
 divides and divides and divides,
 with every cell division,
 there are additional mutations that are happening.
 So what you end up with is your brain being a mosaic
 of multiple different types of genetic underpinnings.
 Some cells contain a mutation that other cells don't have.
 So every human has the common variants
 that all of us carry to some degree,
 the rare variants that your immediate tree
 of the human species carries,
 and then there's the somatic variants,
 which is the tree that happened after the zygote
 that sort of forms your own body.
 So these somatic alterations is something
 that has been previously inaccessible
 to study in human postmortem samples.
 But right now, with the advent of single-cell RNA sequencing,
 in this particular case,
 we're using the well-based sequencing,
 which is much more expensive,
 but gives you a lot richer information
 about each of those transcripts.
 So we're using now that richer information
 to infer mutations that have happened
 in each of the thousands of genes
 that sort of are active in these cells,
 and then understand how the genome
 relates to the function, this genotype-phenotype relationship
 that we usually build in GWAS,
 between genome-wide association studies
 between genetic variation and disease.
 We're now building that at the cell level,
 where for every cell,
 we can relate the unique specific genome of that cell
 with the expression patterns of that cell
 and the predicted function,
 using these predictive models that I mentioned before,
 on dysregulation for cognition, for pathology,
 in Alzheimer's, at the cell level.
 And what we're finding is that the genes that are altered,
 and the genetic regions that are altered in common variants
 versus rare variants versus somatic variants,
 are actually very different from each other.
 The somatic variants are pointing to neuronal energetics
 and oligodendrocyte functions
 that are not visible in the genetic regions
 that you find for the common variants,
 probably because they have too strong of an effect,
 that evolution is just not tolerating them
 on the common side of the allele frequency spectrum.
 So the somatic one, that's the variation that happens
 after the zygote, after you individual.
 I mean, it's a dumb question,
 but there's mutation and variation,
 I guess, that happens there.
 And you're saying that through this,
 if we focus in on individual cells,
 we're able to detect the story that's interesting there,
 and that might be a very unique kind of important variability
 that arises for, you said neuronal or something.
 Energetics. Energetics.
 Energetics, sounds like a cool term.
 So, I mean, the metabolism of humans
 is dramatically altered from that of nearby species.
 We talked about that last time,
 that basically we are able to consume meat
 that is incredibly energy rich,
 and that allows us to sort of have functions
 that are meeting this humongous brain that we have.
 Basically, on one hand, every one of our brain cells
 is much more energy efficient than our neighbors,
 than our relatives.
 Number two, we have way more of these cells.
 And number three, we have this new diet
 that allows us to now feed all these needs.
 That basically creates a massive amount of damage,
 oxidative damage from this huge,
 super powered factory of ideas and thoughts
 that we carry in our skull.
 And that factory has energetic needs,
 and there's a lot of sort of biological processes
 underlying that, that we are finding are altered
 in the context of Alzheimer's disease.
 That's fascinating that,
 so you have to consider all of these systems
 if you want to understand even something like diseases
 that you would maybe traditionally associate
 with just the particular cells of the brain.
 Yeah.
 The immune system.
 The metabolic system.
 The metabolic system.
 And these are all the things that makes us uniquely human.
 So our immune system is dramatically different
 from that of our neighbors.
 Our societies are so much more clustered.
 The history of infection that have plagued
 the human population is dramatically different
 from every other species.
 The way that our society and our population
 has sort of exploded,
 has basically put unique pressures on our immune system,
 and our immune system has both coped with that density
 and also been shaped by, as I mentioned,
 the vast amount of death that has happened
 in the Black Plague and other sort of selective events
 in human history, famines, ice ages, and so forth.
 So that's number one on the sort of immune side.
 On the metabolic side,
 again, we are able to sort of run marathons.
 I don't know if you remember
 the sort of human versus horse experiment
 where the horse actually tires out faster than the human,
 and the human actually wins.
 So on the metabolic side, we're dramatically different.
 On the immune side, we're dramatically different.
 On the brain side, again, no need to sort of,
 it's a no brainer of how our brain
 is just enormously more capable.
 And then in the side of cancers,
 so basically the cancers that humans are having,
 the exposures, the environmental exposures
 is again, dramatically different.
 And the lifespan, the expansion of human lifespan
 is unseen in any other species
 in recent evolutionary history.
 And that now leads to a lot of new disorders
 that are starting to manifest late in life.
 So Alzheimer's is one example where basically
 these vast energetic needs over a lifetime of thinking
 can basically lead to all of these debris
 and eventually saturate the system
 and lead to Alzheimer's in the late life.
 But there's just such a dramatic set of frontiers
 when it comes to aging research that will,
 so what I often like to say is that
 if you want to engineer a car to go from 70 miles an hour
 to 120 miles an hour, that's fine.
 You can basically fix a few components.
 If you want it to now go at 400 miles an hour,
 you have to completely redesign the entire car
 because the system has just not evolved
 to go that far.
 Basically our human body has only evolved to live to,
 I don't know, 120.
 Maybe we can get to 150 with minor changes.
 But if, as we start pushing these frontiers
 for not just living, but well living,
 the F-zine that we talked about last time.
 So to basically push F-zine into the 80s and 90s and 100s
 and much further than that,
 we will face new challenges that have
 never been faced before in terms of cancer,
 the number of divisions, in terms of Alzheimer's
 and brain related disorders,
 in terms of metabolic disorders, in terms of regeneration.
 There's just so many different frontiers ahead of us.
 So I am thrilled about where we're heading.
 So basically I see this confluence in my lab
 and many other labs of AI,
 the next frontier of AI for drug design.
 So basically these sort of graph neural networks
 on specific chemical designs
 that allow you to create new generations of therapeutics.
 These molecular biology tricks
 for intervening at the system at every level.
 These personalized medicine prediction,
 diagnosis and prognosis
 using the electronic health records
 and using these polygenic risk scores,
 weighted by the burden,
 the number of mutations that are accumulating
 across common, rare and somatic variants,
 the burden converging across all of these different
 molecular pathways,
 the delivery of specific drugs and specific interventions
 into specific cell types.
 And again, you've talked with Bob Langer about this.
 There's many giants in that field.
 And then the last concept is
 not intervening at the single gene level.
 I want you to sort of conceptualize the concept of an
 on target side effect.
 What is an on target side effect?
 An off target side effect is when you design a molecule
 to target one gene and instead it targets another gene
 and you have side effects because of that.
 An on target side effect is when your molecule does
 exactly what you were expecting,
 but that gene is pleiotropic.
 Pleio means many, tropos means ways, many ways.
 It acts in many ways.
 It's a multifunctional gene.
 So you find that this gene plays a role in this,
 but as we talked about the wiring of genes to phenotypes
 is extremely dense and extremely complex.
 So the next stage of intervention will be intervening
 not at the gene level, but at the network level,
 intervening at the set of pathways and the set of genes
 with multi-input perturbations to the system,
 multi-input modulations,
 pharmaceutical or other interventional.
 And that basically allow you to now work at the sort of
 full level of understanding, not just in your brain,
 but across your body, not just in one gene,
 but across the set of pathways and so on and so forth
 for every one of these disorders.
 So I think that we're finally at the level of systems
 medicine of basically instead of sort of medicine
 being at the single gene level,
 medicine being at the systems level,
 where it can be personalized based on the specific
 set of genetic markers and genetic perturbations
 that you are either born with or that you have developed
 during your lifetime, your unique set of exposures,
 your unique set of biomarkers and your unique set of,
 current set of conditions through your EHR and other ways.
 And the precision component of intervening
 extremely precisely in the specific pathways
 and the specific combinations of genes
 that should be modulated to sort of bring you
 from the disease state to the physiologically normal state
 or even to physiologically improved state
 through this combination of interventions.
 So that's in my view, the field where basically
 computer science comes together with artificial intelligence,
 statistics, all of these other tools,
 molecular biology technologies and biotechnology
 and pharmaceutical technologies that are sort of revolutionary
 and the way of intervention.
 And of course, this massive amount of molecular biology
 and data gathering and generation perturbation
 in massively parallel ways.
 So there's no better way, there's no better time,
 there's no better place to be sort of, you know,
 looking at this whole confluence of ideas.
 And I'm just so thrilled to be a small part
 of this amazing, enormous ecosystem.
 It's exciting to imagine what humans of 100,
 200 years from now, what their life experience is like,
 because these ideas seem to have potential
 to transform the quality of life.
 That when they look back at us,
 they probably wonder how we were put up
 with all the suffering in the world.
 Manolis, it's a huge honor.
 Thank you for spending this early Sunday morning with me.
 I deeply appreciate it.
 See you next time.
 Sounds like a plan.
 Thank you, Lex.
 Thanks for listening to this conversation
 with Manolis Kellis.
 And thank you to our sponsors, SEMrush,
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 or connect with me on Twitter at Lex Friedman.
 And now, let me leave you with some words
 from Haruki Murakami.
 Human beings are ultimately nothing but carriers,
 passageways for genes.
 They ride us into the ground like racehorses
 from generation to generation.
 Genes don't think about what constitutes good or evil.
 They don't care whether we're happy or unhappy.
 We're just means to an end for them.
 The only thing they think about
 is what is most efficient for them.
 Thank you for listening and hope to see you next time.