Manolis Kellis: Biology of Disease | Lex Fridman Podcast #133

Lex Fridman 0:00 The following is a conversation with Manolis Kellis, his third time on the podcast. He's 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 7:00 AM (laughs) , 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 (laughs) to keep up, but I did my best, and I never shy away from a 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 Eight Sleep, a mattress that 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, uh, 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 Podcasts, follow on Spotify, support on Patreon, or connect with me on Twitter @lexfridman. And now, here's my conversation with Manolis Kellis.

Lex Fridman 2:49 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?

Manolis Kellis 2:57 Don't get me started.

Lex Fridman 2:59 (laughs)

Manolis Kellis 2:59 I mean-

Lex Fridman 2:59 Here we go again.

Manolis Kellis 2:59 ... understanding human disease is the most complex, uh, 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, you know, 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, 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'd say, "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, 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 gene- genome than there will ever be in any other species.

Lex Fridman 5:06 What do you mean by mutation information?

Manolis Kellis 5:08 So, 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, that 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, etcetera. 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.

Lex Fridman 5:54 So how do these perturbations allow you to understand disease?

Manolis Kellis 5:58 So if, 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, uh, epidemiology, which is basically the study of disease, you know, sort of the observational study of disease, has been about correlating one thing with another thing. So, if you, 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, 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.

Lex Fridman 7:38 That's such a beautiful puzzle to try to solve, like what kind of perturbations eventually have this ripple effect that leads to disease across a population, and then you study that for animals or 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?

Manolis Kellis 8:04 In animals, we make the puzzle h- simpler because we perturb one gene at a time. That's the beauty and 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, you know, the, 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 every one of seven billion people on the planet.

Lex Fridman 9:06 What's the six million correspond to?

Manolis Kellis 9:08 Six million unique genetic variants that are segregating the human population. Every one of us carries millions of polymorphic sites. Poly, many, morph, 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 b- 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.

Lex Fridman 9:52 How happy are you with this kind of data set? Is it, uh, large enough of-

Manolis Kellis 9:57 It's-

Lex Fridman 9:57 ... the human population of Earth? Is that too big, too small?

Manolis Kellis 10:01 Yeah, so, so the h- the, is it, 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 genetic varia- 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.

Lex Fridman 11:01 So that's a hopeful message that there exists parts of the genome that ha- ha- that have a strong effect, that has a small, uh, variance.

Manolis Kellis 11:13 That's exactly right. Unfortunately, those perturbations are the basis of disease in many cases. So it's not a, you know, hopeful message. Sometimes it's a terrible message. It's basically, well, some people are sick, but if when, 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 who carries them.

Lex Fridman 11:56 So you said, uh, in the past there would be these small experiments on perturbations and animal models. What does this puzzle-solving process look like today?

Manolis Kellis 12:08 So we basically have, you know, 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 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 can then look at carriers versus non-carriers in one family, carriers ver- v- 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.

Lex Fridman 13:09 Is this the matrix that you show in, in, uh, in talks or lectures?

Manolis Kellis 13:14 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 variance and each with their phenotype. For example, height or lipids, or, you know, 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 a 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, 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, are 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.

Lex Fridman 15:31 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, at a single-cell level, you're trying to see things that happen due to certain perturbations, and then... S- it's not just like a puzzle of, um, perturbation and disease. It's perturbation then affect at a cellular level, then at an organ level, the body... Like, how do you disassemble this into, like, what your group is working on? Y- you're basically taking a bunch of the hard problems in space. How do you break apart a difficult disease, uh, and break it apart into problems that you... Into puzzles that you can now start solving?

Manolis Kellis 16:15 Yeah. So there's a struggle here. Computer scientists love hard puzzles, and they're like, "Oh, I want to, you know, build a method that just deconvolves the whole thing computationally." And, you know, 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, you know, 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 this genetic variant 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, you know, all of the neural cells and the vascular cells and the immune cells that are co-inhabiting the, 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, in humans. So all of these things are basically, um, 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.

Lex Fridman 18:33 I mean, first of all, I mean, the human brain, the cells in the human brain are the most, okay, maybe I'm romanticizing it, but cognition seems to be very complicated. So, uh, separating into the f- the function, breaking Alzheimer's down to s- the cellular level seems very challenging. S- 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- you're trying to find something like that?

Manolis Kellis 19:13 Uh, e- exactly. So, 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, you know, uh, 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 to all the way to Alzheimer's disease?" And that has actually been quite successful. So we now have, you know, 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.

Lex Fridman 20:21 You mean much larger in terms of its down-the-line impact? Or...

Manolis Kellis 20:25 It's much larger in terms of the measurable effect, this-

Lex Fridman 20:28 Okay.

Manolis Kellis 20:28 ... A versus B variance-

Lex Fridman 20:30 Got it.

Manolis Kellis 20:30 ... 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.

Lex Fridman 20:40 Yeah.

Manolis Kellis 20:40 That basically means that in the context of millions, of these six million variants 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-

Lex Fridman 20:55 That's crazy.

Manolis Kellis 20:55 ... yeah, but indeed-

Lex Fridman 20:56 But-

Manolis Kellis 20:56 ... there are such effects.

Lex Fridman 20:57 So the hope is, or the most, scientifically speaking, the, the most effective place were to detect the alteration that results in disease is ear- earlier on in the pipeline-

Manolis Kellis 21:09 So, so-

Lex Fridman 21:09 ... as early as possible.

Manolis Kellis 21:10 ... it's a, it's, 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, uh, 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 this 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, y- you know, um, 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.

Lex Fridman 22:55 And is there something beyond cellular? So you mentioned lifestyle i- interventions or changes as a way to, um, or like be able to prescribe changes in lifestyle. Like what, what about organs? What about like the function of the body as a whole?

Manolis Kellis 23:13 Yeah, absolutely.

Lex Fridman 23:14 Which-

Manolis Kellis 23:14 So basically when you go to your doctor, they always m- measure, you know, your pulse, they always measure your height, they always measure your weight, your, 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, uh, Alzheimer's patients. There are cognitive tests that you can mea- that you, that you typically do for, uh, cognitive decline, these mini-mental, you know, observations that, 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, and all kinds of other activities at the organ level, but you can also do at the, uh, 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?

Lex Fridman 24:26 I love how you're calling that phenotype. I guess it is-

Manolis Kellis 24:29 It is. (laughs)

Lex Fridman 24:30 (laughs) But, like, your behavior patterns that might change over, over, uh, over a period of a life. Yeah, your ability to remember things, your ability to be, yeah, em- empathetic or emotionally, your intelligence, perhaps, even?

Manolis Kellis 24:44 Yeah, but intelligence has hundreds of variables.

Lex Fridman 24:47 Yeah.

Manolis Kellis 24:47 It can be your math intelligence, your literary intelligence, your puzzle-solving intelligence, your logic. It could be, like, hundreds of things.

Lex Fridman 24:52 And all of that is, it's, we're able to measure that better and better.

Manolis Kellis 24:56 So we used to-

Lex Fridman 24:56 And all of that could be connecting to the entire pipeline somehow.

Manolis Kellis 24:58 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, you know, imagine us in a video game, where every one of us has measures of, you know, strength, stamina, you know, 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 e- each of those will just give you then hundreds of bars.

Lex Fridman 25:26 Yeah.

Manolis Kellis 25:27 And you can basically say, "Okay, great. For my, you know, machine learning task, I want someone who, um, a human, who has these particular forms of intelligence. I require now these, you know, 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, you know, music ability, your auditory ability, your smell, your, you know, just dozens of different phenotypes can be broken down into, you know, hundreds of cognitive variables and then relate each of those to thousands of genes that are associated with them.

Lex Fridman 26:20 So somebody who loves RPGs, role-playing games, there's, uh, there's too few variables that we can control. So I'm excited if we're in fact living in a simulation, if this is a video game, I'm excited by the quality of, of the video game. The, the, the, the, the game designer did a hell of a good job, so I'm impressed.

Manolis Kellis 26:41 Oh, I don't know, the sunset last night was a little unrealistic

Narrator 26:44 (laughs)

Manolis Kellis 26:44 ...as a video.

Lex Fridman 26:45 (laughs)

Manolis Kellis 26:45 Yeah.

Lex Fridman 26:45 Yeah, yeah. The graphics.

Manolis Kellis 26:47 Exactly.

Lex Fridman 26:47 Come on, NVIDIA.

Lex Fridman 26:48 To zoom back out, we've been talking about the genetic origins of diseases, but I think it's fascinating to, um, talk about what are the most important diseases to understand? And especially as it connects to the things that you're working on.

Manolis Kellis 27:05 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 well-being 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, uh, all of that for, you know, 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 well-being of Americans, whether or not it die, it kills someone, for many, many years, it has a huge impact. So the first measure of importance is just well-being. Like-

Lex Fridman 27:52 The impact on the quality of life.

Manolis Kellis 27:53 Impact on the quality of life, absolutely. The second metric, which is much easier to quantify, is deaths.

Lex Fridman 28:00 What is the number one killer?

Manolis Kellis 28:01 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, you know, you read the news, accidents like, you know, 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 fi- number five, with 120,000. And then stroke, brain aneurysms and so on and so forth, that's 147,000. Diabetes and metabolic disorders, etc., that's 85,000. The flu is 60,000. Suicide, 50,000. And then overdose, etc., you know, goes further down the list. So of course, COVID has creeped up to be the number three killer this year with, you know, more than 100,000 Americans and counting. Um, and, you know, but, 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 just sheer number of deaths, and just numbers of years lost, if you wish.

Lex Fridman 29:25 And, and, uh, each of these diseases you can think of as, uh, 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, uh, like, schizophrenia and obesity, diabe- 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?

Manolis Kellis 30:07 I lo- I love your question, because it puts it in the context of a global, um, e- effort, rather than just a 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, etc. 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, uh, 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, you know, deaths overall. Um, so, you know, that's something that we as a society can do. You can th- you can also think about external factors versus internal factors. So the external factors are basically communicable diseases like COVID, like the flu, etc., and the internal factors are basically things like, you know, cancer and Alzheimer's, where basically your, your genetics will eventually, you know, drive you there. Um, and then, of course, with all of these factors, every single disease has both a genetic component and an environmental component. So heart disease, you know, huge genetic contribut- contribution. Alzheimer's, it's like, you know, 60%, uh, plus genetic. So I think it's like 79% heritability. So that basically means that genetics alone explains 79% of Alzheimer's incidents.

Lex Fridman 32:05 Uh-huh.

Manolis Kellis 32:06 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, you know, sort of have a more fulfilling life. All of that will actually decrease Alzheimer's, but there's a limit to how much that, that can impact because of the huge genetic footprint.

Lex Fridman 32:31 So this is fascinating. So s- uh, uh, 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, what have you worked on? What can you say that's, uh, in terms of problems that are solvable here, or understandable?

Manolis Kellis 32:50 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.

Lex Fridman 33:11 Oh, interesting.

Manolis Kellis 33:12 So understanding the biological mechanisms leading to disease is extremely important in being able to intervene. But when you can intervene, and what... Uh, you know, the analogy that I like to gay- to, to give is, for example, for obesity. You know, 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, okay? That's an in-out equation, and that's the equation that everybody's focusing on. But your metabolism impacts that, you know, bathtub. Basically, your metabolism controls the rate at which you're burning energy, it controls the way, 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 of that out, you can just poke another hole at the bathtub-

Lex Fridman 34:18 (laughs) .

Manolis Kellis 34:18 ... and just get a lot of the fat out.

Lex Fridman 34:19 Yeah. That's fascinating. Yeah, so the... We're not just passive observers of our genetics. Um, the more we understand, the more we can come up with actual treatments.

Manolis Kellis 34:29 And I think that's an important, uh, aspect to realize when people are thinking about, uh, 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, um, trace it in families with cases and not cases, cases, not cases, and so on and so forth. But even the... But, you know, but... So, 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 in- 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 ass- association if you don't have variation. That doesn't mean that the gene has no role. It's simply that the gene... It simply means that the gene tolerates no mutations.

Lex Fridman 35:59 So that's actually a strong signal when there's no variation. That's so fascinating (laughs) .

Manolis Kellis 36:02 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.

Lex Fridman 36:10 Yeah.

Manolis Kellis 36:10 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.

Lex Fridman 36:34 What are all the words you just said? Early embryonic what?

Manolis Kellis 36:37 Lethal.

Lex Fridman 36:38 Meaning?

Manolis Kellis 36:39 Meaning that-

Lex Fridman 36:39 If you don't have that, then-

Manolis Kellis 36:40 ... that embryo will die.

Lex Fridman 36:41 Yeah, okay. There's a bunch of stuff that, um, is required for a stable, functional organism-

Manolis Kellis 36:48 Exactly.

Lex Fridman 36:48 ... across the board.

Manolis Kellis 36:49 Exactly.

Lex Fridman 36:50 For our entire, uh, for- for an entire species, I guess.

Manolis Kellis 36:53 If you look at sperm, it expresses thousands of proteins. Does sperm actually need thousands of proteins? No. But it- it's probably just testing them.

Lex Fridman 37:05 (laughs) Early on.

Manolis Kellis 37:05 So my speculation is that misfolding of these proteins is an early test for failure, so that out of the, you know, millions of sperm that are possible, you select the subset-

Lex Fridman 37:18 Right.

Manolis Kellis 37:18 ... that are just not grossly misfolding thousands of proteins.

Lex Fridman 37:21 So it's kind of a- an assert, uh, that this is folded-

Manolis Kellis 37:24 It's an assert.

Lex Fridman 37:24 ... correctly.

Manolis Kellis 37:25 Correct.

Lex Fridman 37:26 Yeah, this, uh, just the... Because if this little thing about the folding of a protein is incorrect-

Manolis Kellis 37:31 Correct.

Lex Fridman 37:32 ... that probably means, somewhere down the line, there's a bigger issue.

Manolis Kellis 37:35 That's exactly right.

Lex Fridman 37:36 Yeah. (laughs)

Manolis Kellis 37:36 So fail fast. So basically if you look at the mammalian investment in a newborn, that investment is enormous in terms of resources.

Lex Fridman 37:47 Yeah.

Manolis Kellis 37:47 So mammals have basically evolved mechanisms for fail fast, where basically in those early months of development... I mean, it's- it's horrendous, of course, at the personal level, when you lose a- a- a- a, you know, 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-

Lex Fridman 38:16 Yeah, from an evolutionary perspective.

Manolis Kellis 38:17 ... for mammals. And, of course, humans have a lot of medical resources that you can sort of give those children a chance. And, you know, 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- 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, you know, the jury might be out, because the ones that have a strong effect mutation are basically, you know, 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, you know, many of the other sort of strong effect genetic variants.

Lex Fridman 39:35 So, okay, so there's this beautiful map of, uh, across the population of things that ha- You're saying strong and weak effects, so stuff with a lot of mutations and stuff with little mutations, with n- n- no mutations, and you have this map, and it's- it lays out the puzzle.

Manolis Kellis 39:51 Yeah. So- so when I say strong effect, I mean at the level of individual mutations. So- so basically genes where... So- so you have to think of, first, the effect of the gene on the disease. Remember how I was sort of pa- 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 variance im- 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, you know, which genes correlate with disease by a lot, even if the genetic variants change them by a little, then that- 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.

Lex Fridman 41:22 Interesting. So you're... 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?

Manolis Kellis 41:32 Again, (laughs) don't- don't get me started.

Lex Fridman 41:34 (laughs)

Manolis Kellis 41:35 This is, um... We- we- 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 macular degeneration, AMD. So it's a form of blindness that causes you to ch- 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.

Lex Fridman 42:09 You- Um, I'm an average-

Manolis Kellis 42:11 Yeah.

Lex Fridman 42:12 By the way, when you say my, you mean literally yours? You know this about you?

Manolis Kellis 42:16 I know this about me. Yeah.

Lex Fridman 42:17 Which is kind of a... I mean, uh, philosophically speaking, is a pretty powerful thing-

Manolis Kellis 42:24 So-

Lex Fridman 42:25 ... to live with. I mean, m- maybe that's, uh... So we agreed to talk again, by the way, for the-

Manolis Kellis 42:30 (laughs)

Lex Fridman 42:30 ... listeners, to where we're gonna try to focus on science today and, uh, a little bit of, uh, philosophy next time. But-It's, uh, 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.

Manolis Kellis 42:48 Yeah. So there's, there's a lot of impact there, and there's, uh, something called genetic exceptionalism, which basically thinks of genetics as something very, very different than everything else, as a type of determinism. And, um, you know, let's talk about that next time.

Lex Fridman 43:07 (laughs)

Manolis Kellis 43:07 So basically-

Lex Fridman 43:07 That's a good preview.

Manolis Kellis 43:08 Yeah (laughs) . So let's go back to AMD. So basically-

Lex Fridman 43:12 Okay.

Manolis Kellis 43:12 ... with AMD, we have no idea what causes AMD, you know? It was, it was a mystery u- un- 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 insight 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, uh, 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.

Lex Fridman 45:06 What's the complement complex?

Manolis Kellis 45:08 So it's basically a set of genes, the complement genes-

Lex Fridman 45:11 Mm-hmm.

Manolis Kellis 45:11 ... 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-

Lex Fridman 45:21 So

Narrator 45:21 the-

Manolis Kellis 45:21 ... actually play many diverse roles.

Lex Fridman 45:23 And somehow, the immune system is connected to the synaptic pruning process.

Manolis Kellis 45:28 Exactly.

Lex Fridman 45:28 The process of it (laughs) .

Manolis Kellis 45:29 Exactly. So immune cells were co-opted to prune synapses.

Lex Fridman 45:33 How did you figure this out (laughs) ?

Manolis Kellis 45:35 (laughs) .

Narrator 45:35 (laughs) .

Lex Fridman 45:35 How does one go about figuring this intricate connection, uh, uh, like pipeline of connections out?

Manolis Kellis 45:42 Yeah. Let me give you another example. So, so Alzheimer's disease, the first place that you would expect it to act is obviously the brain. So we had basically this, um, roadmap epigenomics consortium view of the human epigenome, the, 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, you know, hundreds of donors. So what we've basically learned through that is that you, 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, that basically we were, for the first time, able to show that specific chromatin states, specific epigenomic states, in that case enhancers, were in fact enrich, enriched in disease-associated variants. We pushed that further in the Ernst, uh, Nature paper a year later, and then in this roadmap epigenomics paper i- you know, a few, 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, uh, confirmation, in many ways, that those immune traits were inste- indeed enriching the immune cells. If you look-

Lex Fridman 47:59 Well... Go ahead.

Manolis Kellis 48:00 ... if you, 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 in, you know, sort of stems from the dysregulation of insulin in the beta cells of pancreatic islets. And that sort of was, you know, 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, in ways that you process coffee or something like that. It may be in your brain, the way that you stress out increases your blood pressure, etc. 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 contain 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, aha, 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.

Lex Fridman 50:04 Mm-hmm.

Manolis Kellis 50:04 So what could they be acting in? Well, the fourth major cell type is actually microglia. Microglia are resident immune cells in your brain.

Lex Fridman 50:13 Oh, nice.

Manolis Kellis 50:14 (laughs)

Lex Fridman 50:15 They immune... Oh, wow. Okay.

Manolis Kellis 50:17 And they are CD14+, which is this sort of cell surface markers, uh, of those cells. So they're CD14+ cells. Just like macrophages 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, your, your fat, for example, has a lot of macrophages that are resident.

Lex Fridman 50:43 Mm-hmm.

Manolis Kellis 50:43 And the M1 versus M2 macrophage ratio has a huge role to play in obesity. And, you know, 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.

Lex Fridman 51:09 So, what, what are we supposed to make that if, if you look at the tissues involved, is that simply useful for indication of, uh, propensity for disease? Or does it give us somehow a pathway of treatment?

Manolis Kellis 51:24 It's very much the second. If you look at the, um, the, 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, you know, robotic system for testing the impact of your favorite molecules that you know are safe, efficacious, and, you know, 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 to 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 that, the, 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.

Lex Fridman 52:57 I mean, so that's really promising. Maybe, uh, uh, ta- take a small step back. You've, you've mentioned all these puzzles that were figured out with the Nature paper for... I mean, you, you've me- you've mentioned a ton of diseases, uh, m- from obesity to Alzheimer's, even schizophrenia, I think you mentioned.

Manolis Kellis 53:15 Mm-hmm.

Lex Fridman 53:16 And just, um, what is the actual methodology of figuring this out?

Manolis Kellis 53:20 So, indeed, I mentioned a lot of diseases. And, 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 botany 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 the 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, um... you used to have, uh, you know, I don't know, um, cardiovascular disease department and, uh, you know, neurological disease department and neurodegeneration department and, uh, you know, um, 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 if we- 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 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 the room. They were in another building all together. 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 neurologists 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- that's ridiculous, because, I mean, yeah, of course, in many ways, you still need to sort of focus. But what- what- 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- the- the next generation of the epigenomics roadmap, which we now call 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." No. 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 this other class which I like to call polyfactorial diseases, which are basically lighting up everywhere. And in many ways, it's, you know, sort of cr- cutting across these walls that were previously built across these departments.

Lex Fridman 57:01 And the polyfactorial ones were probably the previous s- structure of departments wasn't equipped to deal with those. I mean,

Lex Fridman 57:10 again, maybe it's a romanticized question, but you know, there's, in physics, there's a theory of everything. Do you think it's possible to move towards an almost, uh, theory of everything of disease from a genetic perspective? So if this unification continues, is it possible that... Like do you think in those terms? Like trying to arrive at a fundamental understanding of how disease emerges, period?

Manolis Kellis 57:35 That unification is not just foreseeable, it's inevitable.

Lex Fridman 57:41 Mm.

Manolis Kellis 57:41 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, uh, hugely important for solving immune disorders and cancer, and you know, 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 know, 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," is 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 immune dysfunction. And the schizophrenia folks who are basically interacting 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 heart focus on cardiovascular disease, coronary artery disease, heart failure and so on and so forth. We have an immune focus on, uh, 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 you know, 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 diet, and sort of how they are impacting metabolic, uh, you know, 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.

Lex Fridman 1:00:41 Mm-hmm.

Manolis Kellis 1:00:42 If everybody ate their own dog food, dog food would taste a lot better.

Lex Fridman 1:00:47 Mm-hmm.

Manolis Kellis 1:00:47 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, I think, 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. They basically go and say, "Okay, we need a better cell type-specific wiring that we now have tiss- at this tissue-specific level." So, we're focusing on that because we're understanding, you know, the needs from the disease front.

Lex Fridman 1:03:01 So, you have a sense of the entire pipeline. I mean, o- one... M- maybe you can indulge me. One nice question to ask would be,

Lex Fridman 1:03:10 how do you, from the scientific perspective, go from knowing nothing about the disease to going, you said, uh, to, to going through the entire pipeline and actually have a drug or, or a treatment that cures that disease?

Manolis Kellis 1:03:26 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 post-doc working on this gene and another post-doc 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 post-doc 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 a 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 e- explain it a little bit more. Why do, 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 variants located, 93% of them fall in that outside the genes portion. Of course, genes are enriched, but on- 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 an- an intron is sort of the, 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 a 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.

Lex Fridman 1:07:01 So, it's just riding along, even though it has nothing to do with, uh, with its, uh, nearby neighborhood.

Manolis Kellis 1:07:05 That's exactly right.Let me give you an example.

Lex Fridman 1:07:08 Oh, man.

Manolis Kellis 1:07:09 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 in, on it. They figured out that FTO is in fact, uh, RNA methylationtransferase. It basically cre- it, it sort of impacts something that we know, that we call the epitranscriptome. Just like the genome can be modified, the transcriptome, the transcripts of the genes can be modified. And we basically said, "Oh, great, that means that, that epitranscriptomics is hugely involved in obesity, because that, that gene, FTO, is, you know, uh, clearly where the genetic locus is at." My group studied FTO in collaboration with, you know, a wonderful team, uh, led by Melina Claussnitzer, 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 variant sits 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.

Lex Fridman 1:08:33 Oh, boy.

Manolis Kellis 1:08:34 (laughs)

Lex Fridman 1:08:34 Uh, so what am I supposed to feel about that? 'Cause isn't that, like, super complicated then?

Manolis Kellis 1:08:38 Uh, so, so the way that I was introduced at a conference a few years ago was, uh, "And here's Manolis Kellis who wrote the most depressing paper of 2015."

Lex Fridman 1:08:47 (laughs)

Manolis Kellis 1:08:48 (laughs) 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.

Lex Fridman 1:09:39 So, okay. So embrace the, the wholeness of the circuitry.

Manolis Kellis 1:09:44 Correct.

Lex Fridman 1:09:45 But what ... So ba- back to the question of, of starting knowing nothing to the disease and, and going to the treatment-

Manolis Kellis 1:09:51 So, so you-

Lex Fridman 1:09:52 ... what, what are the next steps?

Manolis Kellis 1:09:53 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 noncoding variants that are sitting outside proteins, and then figuring out what are the epigenomic enrichments. And the reason for that, you know, 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.

Lex Fridman 1:10:31 Can you clarify why that's hopeful? So, like, they show up in the same exact way in the, in the specific set of processes that we-

Manolis Kellis 1:10:40 Yeah, so-

Lex Fridman 1:10:40 ... were saying?

Manolis Kellis 1:10:40 ... so basically there's a small number of biological processes that underlie, or at least that play the, 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 hi- hits are calcium signaling proteins.

Lex Fridman 1:11:32 Oh, cool.

Manolis Kellis 1:11:33 You're like, "Aha, that means that calcium signaling is important." So those people who are focusing on one locus 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.

Lex Fridman 1:11:51 But you, you mentioned the convergence thing. So the-

Manolis Kellis 1:11:55 So-

Lex Fridman 1:11:55 ... what-, whatever the thing associated with the disease shows up-

Manolis Kellis 1:11:58 So let me explain convergence.

Lex Fridman 1:11:59 Yeah.

Manolis Kellis 1:11:59 Convergence is such a beautiful concept.

Lex Fridman 1:12:02 (laughs)

Manolis Kellis 1:12:03 (laughs) 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.

Lex Fridman 1:12:19 Mm-hmm.

Manolis Kellis 1:12:19 But now in every one of these loci, you have many enhancers controlling each of those genes. That's another type of convergence, where dysregulation of seven different enhancers might all converge on dysregulation 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 these pathways are acting in the same tissue, and all of these tissues are converging together on the same biological process of schizophrenia.

Lex Fridman 1:13:04 And, uh, and, uh, you're saying the saving grace is that that convergence seems to happen for a lot of these diseases?

Manolis Kellis 1:13:11 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, 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." In a ge- 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, uh, you, you inherit maybe three, chromosome 3, for example, in your, in your body, is inherited from four different parts. One part comes from your m- dad, another part comes from your mom, another co- part comes from your dad, and another part comes from your mom. So basically, the way that it, um, sorry, from your mom's mom. So, um, 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 breakpoints that happen when chromosomes are lighting up, are lining up, are basically ensuring th- through these crossover events, they're ensuring that every, um, child cell during the process of m- of meiosis, where you basically have, you know, 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 of your genome that comes from mom. But in order to line u- not line them up, you basically have these crossover events. These crossover ve- events are basically leading to co-inheritance of that entire block coming from the, your maternal grandmother, and that entire block coming from your maternal grand- 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 six million variants, there are six million loci. There, there's, you know, 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 ones 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.

Lex Fridman 1:16:50 Progenitor is like the early on developmental st- st- stems cells?

Manolis Kellis 1:16:54 So you start from one zygote, and that's a totipotent cell type. It can do anything. You then differ- you, you know, that cell divides, divides, divides, and then every cell division is leading to specialization, where you now have a mesodermal lineage, an ectodermal lineage, an endodermal lineage that basically leads to different parts of your d- of your, 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, you know, 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, uh, set of calories, like 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.

Lex Fridman 1:18:19 Mm-hmm.

Manolis Kellis 1:18:19 So that leads to obesity if you don't exercise and if you overeat because your body's 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, you know, 42% of European chromosomes have a predisposition to storing fat which was selected probably in the, you know, food scarcity periods. Like basically as we were exiting Africa, you know, before and during the ice ages-

Lex Fridman 1:18:52 Mm-hmm.

Manolis Kellis 1:18:52 ... you know, there was probably a selection to those individuals who made it north to basically be able to store energy, you know, 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. It turns out that your fat is, you know, like, we, um, 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, you know, m-... body and causing damage. But it can also burn calories directly. If you have too much of 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 calories. 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 RS1421085, and that genetic variant, out of the 89, was the one that we predicted to be causal for the disease.

Lex Fridman 1:20:21 Wow.

Manolis Kellis 1:20:21 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.

Lex Fridman 1:20:58 Mm-hmm.

Manolis Kellis 1:20:59 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.

Lex Fridman 1:21:19 And then once you have the regulator, can you then try to figure out how to, what, uh, after it developed, how to fix it?

Manolis Kellis 1:21:27 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."

Lex Fridman 1:21:40 Just to clarify, when we say perturb, like, on the scale of a human life-

Manolis Kellis 1:21:45 Mm-hmm.

Lex Fridman 1:21:46 ... can a human being be helped-

Manolis Kellis 1:21:48 Of course.

Lex Fridman 1:21:48 ... by-

Manolis Kellis 1:21:49 Of course. Yeah, so perturbed-

Lex Fridman 1:21:50 Yeah, I guess understanding is the first step. Understand-

Manolis Kellis 1:21:52 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.

Lex Fridman 1:22:03 What do, uh, pharmaceutical therapeutics look like when your understanding is on a genetic level?

Manolis Kellis 1:22:11 Yeah.

Lex Fridman 1:22:11 Sorry if it's a dumb question.

Manolis Kellis 1:22:12 So, no, no, no, it's a brilliant question, but I want to save it for a little bit later when we start talking about therapeutics.

Lex Fridman 1:22:17 Perfect.

Manolis Kellis 1:22:18 We've talked about the first four steps. There's two more. So basically the first step is figure out... I mean, the zeroth step, the starting point is the genetics.

Lex Fridman 1:22:26 Mm-hmm.

Manolis Kellis 1:22:26 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 figuring out the motif and the upstreaming 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 tight, tight 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 and 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-

Lex Fridman 1:24:43 Sorry, can you backtrack?

Manolis Kellis 1:24:44 Of course.

Lex Fridman 1:24:44 How does cutting it f- help you figure out which ones were close in the original folding?

Manolis Kellis 1:24:50 So you have a bowl of noodles.

Lex Fridman 1:24:53 Go on.

Manolis Kellis 1:24:54 And in that bowl of noodles, some, some noodles are, are near each other.

Lex Fridman 1:24:59 Yes.

Manolis Kellis 1:24:59 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-ligate what you had just cut, 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 n- 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, b- 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 with me so far?

Lex Fridman 1:26:09 Yeah.

Manolis Kellis 1:26:09 Good. So we, y- you know, we've done these experiments-

Lex Fridman 1:26:11 That's the physical. That's the-

Manolis Kellis 1:26:13 That's the physical.

Lex Fridman 1:26:14 ... yeah.

Manolis Kellis 1:26:14 That's step one of the physical. And what, what the physical revealed is topologically associated domains, basically big blocks of the genome that are topologically dom- 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 G- all of the h- humans who contain a G at that location and all the humans that contain a T at that location, and basically say, "Wow, turns out that the expression of this 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?

Lex Fridman 1:27:21 I think so.

Manolis Kellis 1:27:21 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, you know, 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.

Lex Fridman 1:27:54 Uh, so I guess that's similar to activity?

Manolis Kellis 1:27:57 Yeah, yeah. So that's per- pertur- basically, it's similar to activity, I agree, but it's causal rather than correlational.

Lex Fridman 1:28:03 Again, I'm, I'm a little weir- uh, like, uh...

Manolis Kellis 1:28:05 No, no. You're, you're 100% on.

Lex Fridman 1:28:07 The, the, but, but the perturbations-

Manolis Kellis 1:28:08 It's exactly the same as activity, but perturbation, where I go and intervene-

Lex Fridman 1:28:11 Yes.

Manolis Kellis 1:28:11 ... I basically take a se- a bunch of cells.

Manolis Kellis 1:28:13 So you know CRISPR, right? CRISPR is this genome guidance and cutting mechanism. It's what George Church like to, likes to call genome vandalism. So you basically are able to... (laughs)

Lex Fridman 1:28:26 Good line.

Manolis Kellis 1:28:28 You can basically take a guide RNA that you put into the CRISPR, uh, system, and the CRISPR system will basically use this guide RNA, scan the genome, find wherever there's a match, and then cut the genome. So, um, you know, it's- 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 these loci, these CRISPR loci. That basically stands for clustered repeats interspersed, et cetera. So basically, it's, it's an intersperse repeats, uh, 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 it managed to kill, and then the bacteria pass on, you know, they, 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, uh, sort of viral attack. So what we have done now as a field is, you know, through the work of, you know, uh, Jennifer Doudna, Emmanuelle Charpentier, 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.

Lex Fridman 1:30:37 That's so fascinating. Just, uh, so this is like already a natural mechanism-

Manolis Kellis 1:30:41 Mm-hmm.

Lex Fridman 1:30:41 ... a natural tool for cutting that was useful-

Manolis Kellis 1:30:45 Mm-hmm.

Lex Fridman 1:30:45 ... in this particular context.

Manolis Kellis 1:30:46 Yeah.

Lex Fridman 1:30:47 And we're like, "Well, we can use that thing to actually..." it's a nice tool that's already in the body.

Manolis Kellis 1:30:51 Yeah, yeah.

Lex Fridman 1:30:52 And then we-

Manolis Kellis 1:30:52 It's not in our body. It's in the bacterial body. It was discovered by the, by the yogurt industry.

Lex Fridman 1:30:59 (laughs)

Manolis Kellis 1:30:59 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 ba- as a, 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 diploid 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.

Lex Fridman 1:31:59 So, all you have to do is the- the cutting and-

Manolis Kellis 1:32:02 That's exactly right.

Lex Fridman 1:32:02 ... you don't have to do the fixing? (laughs)

Manolis Kellis 1:32:04 That's exactly right. You don't have to do the fixing.

Lex Fridman 1:32:05 (laughs) 'Cause it's already built in.

Manolis Kellis 1:32:07 That's exactly right. But the fixing can be co-opted-

Lex Fridman 1:32:10 That's awesome.

Manolis Kellis 1:32:10 ... by throwing in a bunch of homologous segments-

Lex Fridman 1:32:14 Oh.

Manolis Kellis 1:32:14 ... that instead of having your dad's version-

Lex Fridman 1:32:16 Interesting.

Manolis Kellis 1:32:16 ... have whatever other version you'd like to use.

Lex Fridman 1:32:19 Aha. So the fi- so you c- you then control the fixing by throwing in a bunch of other stuff.

Manolis Kellis 1:32:23 That's exactly right.

Lex Fridman 1:32:24 Doesn't work.

Manolis Kellis 1:32:24 And that's how you do genome editing.

Lex Fridman 1:32:26 So, that's what CRISPR is.

Manolis Kellis 1:32:27 That's what CRISPR is.

Lex Fridman 1:32:28 For when- when in popular culture people use the term. I've never... Wow, that's brilliant.

Manolis Kellis 1:32:32 Yeah. So, so CRISPR is-

Lex Fridman 1:32:33 That's just an awesome explanation.

Manolis Kellis 1:32:34 ... genome vandalism followed by a bunch of Band-Aids that have the sequence that you'd like.

Lex Fridman 1:32:39 And you could control the- the choices of, uh, Band-Aids, uh, yeah.

Manolis Kellis 1:32:42 Correct. And, of course, there's new generations of s- of CRISPR. There's something that's called prime editing, that was sort of very- 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