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

The following is a conversation with Manolis Kellis, his third time on the podcast. He is a professor at MIT and head of the MIT Computational Biology Group. This time we went deep on the science, biology and genetics, so this is a bit of an experiment. Manois 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 7am so it's yet another podcast episode after an all nighter for me. And once again, since the universe has a sense of humor, this one was a tough one for my brain to keep up. But I did my best and I never shy away from a good challenge. Quick mention of each sponsor followed by some thoughts related to the 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 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 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 lexfriedman. And now here's my conversation with Manolis Kalis. So your group at MIT is trying to understand the molecular basis of human disease.

What are some of the biggest challenges in your view?

Don't get me started. I mean, understanding human disease is the most complex challenge in modern science. So because human disease is as complex as the human genome, it is as complex as the human brain, and it is in many ways even more complex because the more we understand disease complexity, the more we start understanding genome complexity and epigenome complexity and brain circuitry complexity and immune system complexity and cancer complexity, and so on and so forth. So traditionally, human disease was following basic biology. You would basically understand basic biology and model organisms like mouse and fly and yeast. You would understand sort of mammalian biology and animal biology and eukaryotic biology in sort of progressive layers of complexity, getting closer to human phylogenetically. And you would do perturbation experiments in those species to see if I knock out a gene, what happens. And based on the knocking out of these genes, you would basically then have a way to drive human biology because you would sort of understand the functions of these genes. And then if you find that a human gene locus, something that you've mapped from human genetics to that gene, is related to a particular human disease, you'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. That was the old way of doing basic biology. You would start with the animal models, the eukaryotic models, the mammalian models, and then you would go to human. Human genetics has been so transformed in the last decade or two that human genetics is now actually driving the basic biology. There is more genetic mutation information in the human genome than there will ever be in any other species.

What do you mean by mutation information?

So perturbation is how you understand systems. So an engineer builds systems and then they know how they work from the inside out. A scientist study systems through perturbations. You basically say, if I poke that balloon, what's going to happen? And I'm going to film it in super high resolution, understand, I don't know, air dynamics or fluid dynamics, if it's filled with water, etc. So you can then make experimentation by perturbation. And then the scientific process is sort of building models that best fit the data, designing new experiments that best test your models and challenge your models and so on and so forth. That's the same thing with science, basically. If you're trying to understand biological science, you basically want to do perturbations that then drive the models.

So how do these perturbations allow you to understand disease?

So if you know that a gene is related to disease, you don't want to just know that it's related to the disease. You want to know what is the disease mechanism? Because you want to go and intervene. So the way that I like to describe it is that traditionally epidemiology, which is basically the study of disease, sort of the observational study of disease, has been about correlating one thing with another thing. So if you have a lot of people with liver disease who are also alcoholics, you might say, well, maybe the alcoholism is driving the liver disease, or maybe those who have liver disease self medicate with alcohol. So the connection could be either way. With genetic epidemiology, it's about correlating changes in the 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, so forth.

That's such a beautiful puzzle to try to solve, like what kind of perturbations eventually have this ripple effect that leads to disease across the 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.

In animals, we make the puzzle 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 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 6 million perturbations. So I've done 6 million experiments on you. 6 million experiments on me. 6 million experiments on every one of 7 billion people on the planet.

What's the 6 million correspond to?

6 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 between us. The reason why two twins are identical is because these variants completely determine the way that I'm going to look at exactly 93 years of age.

How happy are you with this kind of data set? Is it large enough of the human population of Earth? Is that too big? Too small, yeah.

So is it large enough? Is a power analysis question. And in every one of our grants, we do a power analysis based on what is the effect size that I would like to detect and what is the natural variation in the two forms. So every time you do a perturbation, you're asking, I'm changing form A into form B. Form A has some natural genetic, some natural phenotypic variation around it, and form B has some natural phenotypic variation around it. If those variances are large and the differences between the mean of A and the mean of B are small, then you have very little power. The further the means go apart, that's the effect size. The more power you have and the smaller the standard deviation, the more power you have. So basically, when you're asking, is that sufficiently large? Certainly not for everything, but we already have enough power for many of the stronger effects in the more tight distributions.

So that's a hopeful message, that there exists parts of the genome that have a strong effect that has a small variance.

That's exactly right. Unfortunately, those perturbations are the basis of disease in many cases. So it's not a hopeful message. Sometimes it's a terrible message. It's basically, well, some people are sick, but if we can figure out what are these contributors to sickness, we can then help make them better and help many other people better who don't carry that exact mutation, but who carry mutations on the same pathways. And that's what we like to call the allelic series of a gene. You basically have many perturbations of the same gene in different people, each with a different frequency, each different human population, and each with a different effect on the individual vocarism.

So you said in the past there would be these small experiments on perturbations in animal models. What does this puzzle solving process look like today?

So we basically have something like 7 billion people in the planet, and every one of them carries something like 6 million mutations. You basically have an enormous matrix of, 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 could then look at carriers versus non carriers in one family, carriers versus non carriers in another family, and do that for hundreds, sometimes thousands of families, and then trace these inheritance patterns and then figure out what is the gene that plays that role.

Is this the matrix that you're shown in and talks or lectures?

So that matrix is the input to the stuff that I show in talks. So basically, that matrix has traditionally been strong effect genes. What the matrix looks like now is instead of pedigrees, instead of families, you basically have thousands and sometimes hundreds of thousands of unrelated individuals, each with all of their genetic variants and each with their phenotype, for example, height or lipids, or, 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 their complete electronic health record. So you can now start relating not just one gene segregating one family, not just thousands of variants segregating with one phenotype. But now you can do millions of variants versus hundreds of phenotypes. And as a computer scientist, I mean, deconvolving that matrix, partitioning it into the layers of biology that are associated with every one of these elements, is a dream come true. It's like the world's greatest puzzle. And you can now solve that puzzle by throwing in more and more Knowledge about the function of different genomic regions and how these functions are changed across tissues and in the context of disease. And that's what my group and many other groups are doing. We're trying to systematically relate this genetic variation with molecular variation at the expression level of the genes, at the epigenomic level of the gene regulatory circuitry, and at the cellular level of what are the functions that are happening in those cells at the single cell level, using single cell profiling, and then relayed all that vast amount of knowledge computationally with the thousands of traits that each of these of thousands of variants are perturbing.

I mean, this is something we talked about, I think, last time. So there's these effects at different levels that happen. You said at a single cell level, you're trying to see things that happen due to certain perturbations. And then it's not just like a puzzle of perturbation and disease is perturbation then effect at a cellular level, then at an organ level, a body. Like, how do you disassemble this into like what your group is working on? You're basically taking a bunch of the hard problems in the space. How do you break apart a difficult disease and break it apart into problems that you. Into puzzles that you can now start solving?

So there's a struggle here. Computer scientists love hard puzzles. And they're like, oh, I want to build a method that just deconvolves the whole thing computationally. And that's very tempting and it's very appealing. But biologists just like to decouple that complexity experimentally, to just peel off layers of complexity experimentally. And that's what many of these modern tools that my group and others have both developed and used. The fact that we can now figure out tricks for peeling off these layers of complexity by testing one cell type at a time, or by testing one cell at a time. And you could basically say, what is the effect of 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 all of the neural cells and the vascular cells and the immune cells that are co inhabiting the brain, between the different types of excitatory and inhibitory neurons that are sort of interacting with each other, between different layers of neurons in the cortical layers. Every single one of these has a different type of function to play in cognition. In interaction with the environment, in maintenance of the brain, in energetic needs, in feeding the brain with blood, with oxygen, in clearing out the debris that are resulting from the super high energy production of cognition in humans. So all of these things are basically potentially deconvolvable computationally, but experimentally, you can just do single cell profiling of dozens of regions of the brain, across hundreds of individuals, across millions of cells. And then now you have pieces of the puzzle that you can then put back together to understand that complexity.

I mean, first of all, the cells in the human brain are the most. Okay, maybe I'm romanticizing it, but cognition seems to be very complicated. So separating into the function, breaking Alzheimer's down to the cellular level seems very challenging. Is that basically you're trying to find a way that some perturbation in the genome results in some obvious major dysfunction in the cell? You're trying to find something like that.

Exactly. So what does human genetics do? Human genetics basically looks at the whole path from genetic variation all the way to disease. So human genetics has basically taken thousands of Alzheimer's cases and thousands of controls matched for age, for sex, for environmental backgrounds, and so on, so forth, and then looked at that map where you're asking what are the individual genetic perturbations and how are they related all the way to Alzheimer's disease? And that has actually been quite successful. So we now have more than 27 different loci. These are genomic regions that are associated with Alzheimer's at this end to end level. But the moment you break up that very long path into smaller levels, you can basically say from genetics, what are the epigenomic alterations at the level of gene regulatory elements where that genetic variant perturbs the control region nearby, that effect is much larger.

You mean much larger in terms of its down the line impact, or it's.

Much larger in terms of the measurable effect. This A versus B variance is actually so much cleanly defined when you go to the shorter branches, because for one genetic variant to affect Alzheimer's, that's a very long path. That basically means that in the context of millions of these 6 million 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 yeah, but indeed there are such effects.

So the hope is, or the most scientifically speaking, the, the most Effective place where to detect the alteration that results in disease is earlier on in the pipeline, as early as possible.

It's a, it's, it's a trade. 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 in the context of only 20,000 other genes, as opposed to one enhancer acting in the context of 2 million other enhancers. You basically now have genetic, epigenomic, the circuitry, transcriptomic, the gene expression level, and then cellular, where you can basically say, I can measure this 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, you know, your brain activity, you can do imaging in the brain. You can basically measure, I don't know, the heart rate, the pulse, the lipids, the amount of blood secreted and so on, so forth. And then through all of that, you can basically get at the path to causality, the path to disease.

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

Yeah, absolutely. So basically when you go to your doctor, they always measure, you know, your pulse, they always measure your height, they always measure your weight, your, you know, your BMIs. Basically these are just very basic variables. But with digital devices nowadays, you can start measuring hundreds of variables for every individual. You can basically also phenotype cognitively through tests. Alzheimer's patients, there are cognitive tests that you can measure that you typically do for cognitive decline. These mini mental observations that you have specific questions to. You can think 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 organ level and all kinds of other activities at the organ level, but you can also do at the organism level, you can do behavioral tests. And how did they do on empathy, how did they do on memory, how did they do on long term memory versus short term memory and so on, so forth.

I love how you're calling that phenotype. I guess it is, it is. But like your behavior patterns that might change over, over, over a period of a life. Yeah. Your ability to remember things, your ability to be. Yeah. Empathetic or emotionally. Your intelligence, perhaps even.

Yeah, but intelligence has hundreds of variables. You can be your math intelligence, your literary intelligence, your puzzle solving intelligence, your logic. It could be like hundreds.

And all of that, we're able to measure that better and better. And all of that could be connected to the entire pipeline.

We used to think of each of these as a single variable, like intelligence. I mean, that's ridiculous. It's basically dozens of different genes that are controlling every single variable. You can basically think of imagine us in a video game where every one of us has measures of strength, stamina, energy left, and so on and so forth. But you could click on each of those, like five bars that are just the main bars and each of those will just give you then hundreds of bars. And you can basically say, okay, great. For my machine learning task, I want someone who, a human who has these particular forms of intelligence. I require now these 20 different things. And then you can combine those things and then relate them to, of course, performance in a particular task. But you can also relate them to genetic variation that might be affecting different parts of the brain. For example, your frontal cortex versus your temporal cortex versus your visual cortex and so on, so forth. So genetic variation that affects expression of genes in different parts of your brain can basically affect your music ability, your auditory ability, your smell, your. Just dozens of different phenotypes can be broken down into, you know, hundreds of cognitive variables and then relate each of those to thousands of genes that are associated with them.

So somebody who loves RPGs, role playing.

Games.

There'S too few variables that we can control. So I'm excited if we're in fact living in a simulation and this is.

A video game, I'm excited by the.

Quality of the video game. The game designer did a hell of a good job. So we're impressed.

Oh, I don't know. The sunset last night was a little unrealistic.

Yeah, yeah. The graphics.

Exactly.

Come on, Nvidia to zoom back out. We've been talking about the genetic origins of diseases, but I think it's fascinating to talk about what are the most important diseases to understand and especially as it connects to the things that you're working on.

So it's very difficult to think about important diseases to understand. There's many metrics of importance. One is lifestyle impact. I mean, if you look at Covid, the impact on lifestyle has been enormous. So understanding Covid is important because it has impacted the 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 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 kills someone for many, many years, it has a huge impact. So the first measure of importance is just well being.

Impact on the quality of life.

Impact on the quality of life, absolutely. The second metric which is much easier to quantify is deaths.

What is the number one killer?

The number one killer is actually heart disease. It is actually killing 650,000Americans per year. Number two is cancer with 600,000Americans. Number three, far, far down the list is accidents, Every single accident combined. So basically 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 so forth, 160,000. Alzheimer's, number five with 120,000 and then stroke, brain aneurysms and so on, 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 100,000Americans and counting. 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.

And each of these diseases you can think of as, and also including terrorist attacks and school shootings, for example, things which lead to fatalities, you can look at as problems that could be solved. And some problems are harder to solve than others. I Mean, that's part of the equation. So maybe if you look at these diseases, if you look at heart disease or cancer or Alzheimer's, or just like schizophrenia and obesity debut, like not necessarily things that kill you, but affect the quality of life, which problems are solvable, which aren't, which are harder to solve, which aren't.

I love your question because it puts it in the context of a global effort rather than just 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 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. So, you know, that's something that we as a society can do. You can, you can also think about external factors versus internal factors. So the external factors are basically communicable diseases like Covid, like the flu, et cetera. And the internal factors are basically things like, you know, cancer and Alzheimer's, where basically your, your genetics will eventually, you know, drive you there. And then of course, with all of these factors, every single disease has both a genetic component and environmental component. So heart disease, you know, huge genetic contributor contribution, Alzheimer's, it's like, you know, 60% plus genetic. So I think it's like 79% heritability. So that basically means that genetics alone explains 79% of Alzheimer's incidence. 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.

So this is fascinating. So each one of these problems have a genetic component and an environment component. And so like, when there's a genetic component, what can we do about some of these diseases? What, what have you worked on? What can you say that's in Terms of problems that are solvable here or understandable.

So my group works on the genetic component. But. 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.

Interesting.

So understanding the biological mechanisms leading to disease is extremely important in being able to intervene. But when you can intervene. The analogy that I like to give is, for example, for obesity. Think of it as a giant bathtub of fat. There's basically fat coming in from your diet and there's fat coming out from your exercise. That's an in, out equation, and that's the equation that everybody's focusing on. But your metabolism impacts that, 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 that out, you can just poke another hole at the bathtub and just get a lot of the fat out.

Yeah, that's fascinating. Yeah. So that we're not just passive observers of our genetics. The more we understand, the more we can come up with actual treatments.

And I think that's an important aspect to realize when people are thinking about strong effect versus weak effect variants. So some variants have strong effects. We talked about these mendelian disorders where a single gene has a sufficiently large effect pan and trans expressivity and so on so forth, that basically you can trace it in families with cases and not cases, cases, not cases, and so on, so forth. But even the, 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 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 is dead. So those genes don't have variation. You're not going to find a genetic 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.

So that's actually a strong signal when there's no variation. That's so fascinating.

Exactly. Genes that have very little variation are hugely important. You can actually rank the importance of genes based on how little variation they have. And those genes that have very little variation but no association with disease, that's a very good metric to say, oh, that's probably a developmental gene because we're not good at measuring those phenotypes. It's genes that you can tell evolution has excluded mutations from, but yet we can't see them associated with anything that we can measure nowadays. It's probably early embryonic lethal.

What are all the words you just said? Early embryonic what?

Lethal.

Meaning?

Meaning that you don't have to die.

Okay. There's a bunch of stuff that is required for a stable functional organism across the board.

Exactly.

For an entire species, I guess if.

You look at sperm, it expresses thousands of proteins. Does sperm actually need thousands of proteins? No, but it's probably just testing them. So my speculation is that misfolding of these proteins is an early test for failure. So that out of the millions of sperm that are possible, you select the subset that are just not grossly misfolding thousands of proteins.

So it's kind of an assert that this is folded correctly.

Correct.

Yeah. This just because if this little thing about the folding of a protein is incorrect.

Correct.

That probably means somewhere down the line there's a bigger issue.

That's exactly right. So fail fast. So basically, if you look at the mammalian investment in a newborn, that investment is enormous in terms of resources. So mammals have basically evolved mechanisms for fail fast where basically in those early months of development, I mean, it's. It's horrendous, of course, at the personal level, when you lose 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 from an evolutionary perspective for mammals. And of course, humans have a lot of medical resources that you can sort of give those children a chance. And, 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 going to see them. So that's why? When we say what are the most important genes to focus on, the ones that have a strong effect mutation or the ones that have a weak effect mutation? Well, 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 many of the other strong effect genetic variants.

Okay, so there's this beautiful map of, across the population of things that you're saying strong and weak effects. So stuff with a lot of mutations and stuff with little mutations with no mutations. And you have this map and it lays out the puzzle.

Yeah, so. So when I say strong effect, I mean at the level of individual mutations. So basically, genes where. So you have to think of first the effect of the gene on the disease. Remember how I was sort of painting that map earlier from genetics all the way to phenotype? That gene can have a strong effect on the disease, but the genetic variant might have a weak effect on the gene. So basically, when you ask what is the effect of that genetic variant on the disease, it could be that that genetic variant impacts the gene by a lot and then the gene impacts the disease by a little. Or it could be that the genetic variance 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.

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?

Again, don't get me started. This is. We have found so much our Understanding of disease has changed so dramatically with genetics. I mean, places that we had no idea would be involved. So one of the worst things about my genome is that I have a genetic predisposition to age related macular degeneration, amd. So it's a form of blindness that causes you to lose the central part of your vision progressively as you grow older. My increased risk is fairly small. I have an 8% chance. You only have a 6% chance.

I'm an average.

Yeah.

By the way, when you say my, you mean literally yours?

You know this about you, I know this about me. Yeah.

Which is kind of, I mean, philosophically speaking is a pretty powerful thing to live with. Maybe that's. So we agreed to talk again, by the way, for the listeners to where we're going to try to focus on science today and a little bit of philosophy next time. But it's interesting to think about the more you're able to know about yourself from the genetic information in terms of the diseases, how that changes your own view of life. Yeah.

So there's a lot of impact there. And there's something called genetics exceptionalism, which basically thinks of genetics as something very, very different than everything else, as a type of determinism. And you know, let's talk about that next time. So basically that's a good preview. Yeah. So let's go back to amd. So basically with amd we have no idea what causes amd. You know, it was a mystery until the genetics were worked out. And now the fact that I know that I have a predisposition allows me to sort of make some life choices, number one. But number two, the genes that lead to that predisposition give us insights as to how does it actually work. And that's a place where genetics gave us something totally unexpected. So there's a complement pathway, which is an immune function pathway that was in, you know, most of the loci associated with amd. And that basically told us that, wow. There's an immune basis to this eye disorder that people had just not expected before. If you look at complement, it was recently also implicated in schizophrenia. And there's a type of microglia that is involved in synaptic pruning. So synapses are the connections between neurons. And in this whole use it or lose it view of mental cognition and other capabilities, you basically have microglia, which are immune cells that are constantly traversing your brain and then pruning neuronal connections, pruning synaptic connections that are not utilized. So in schizophrenia, there's Thought to be a change in the pruning that basically if you don't prune your synapses the right way, you will actually have an increased role of schizophrenia. This is something that was completely unexpected for schizophrenia. Of course we knew it has to do with neurons, but the role of the complement complex, which is also implicated in amd, which is now also implicated in schizophrenia, was a huge surprise.

What's the complement complex?

So it's basically a set of genes, the complement genes that are basically having various immune roles. And as I was saying earlier, our immune system has been co opted for many different roles across the body. So they actually play many diverse roles.

And somehow the immune system is connected to the synaptic pruning process. The process.

Exactly. So immune cells were co opted to prune synapses.

How did you figure this out? How does one go about figuring this intricate connection, like pipeline of connections out?

Yeah, let me give you another example. So Alzheimer's disease, the first place that you would expect it to act is obviously the brain. So we had basically this roadmap epigenomics consortium view of the human epigenome. The largest map of the human epigenome that has ever been built across 127 different tissues and samples with dozens of epigenomic marks measured in, you know, hundreds of donors. So what we've basically learned through that is that you basically can map what are the active gene regulatory elements for every one of the tissues in the body. And then we connected these gene regulatory active maps of basically what regions of the human genome are turning on in every one of different tissues. We then can go back and say, where are all of the genetic loci that are associated with disease? This is something that my group, I think was the first to do back in 2010 in this Ernst Nature Biotech paper. But basically we were for the first time able to show that specific chromatin states, specific epigenomic states, in that case enhancers were in fact enriched in disease associated variants. We pushed that further in the Ernst Nature paper a year later and then in this roadmap epigenomics paper a few years after that. But basically that matrix that you mentioned earlier was in fact the first time that we could see what genetic traits have genetic variants that are enriched in what tissues in the body. And a lot of that map made complete sense. If you looked at a diversity of immune traits like allergies and type 1 diabetes and so on 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, so forth. So that basically gave us confirmation in many ways that those immune traits were indeed enriching in immune cells. If you look, 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 sort of stems from the dysregulation of insulin in the beta cells of pancreatic islets. And that sort of was spot on, super precise. If you looked at blood pressure, where would you expect blood pressure to occur? I don't know, maybe in your metabolism, in ways that you process coffee or something like that. Maybe in your brain. The way that you stress out increases your blood pressure, et cetera. What we found is that blood pressure localized specifically in the left ventricle of the heart. So the enhancers of the left ventricle in the heart 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. This is mind boggling. Our brains were literally hurting. What is going on? 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. That basically indicated that genetic variants associated with Alzheimer's were probably not acting in oligodendrocytes, astrocytes, or neurons. So what could they be acting in? Well, the fourth major cell type is actually microglia. Microglia are resident immune cells in your brain.

Oh, nice. The immune. Oh, wow.

And they are CD14 plus, which is these sort of cell surface markers of those cells. So they're CD14 plus 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 fat, for example, has a lot of macrophages that are resident. And the M1 versus M2 macrophage ratio has a huge role to play in obesity. And so basically, again, these immune cells are everywhere. But basically what we found through this completely unbiased view of what are the tissues that likely underlie different disorders, we found that Alzheimer's was humongously enriched in microglia, but not at all in the other cell types.

What are we supposed to make that, if you look at the tissues involved, is that simply useful for indication of propensity for disease or does it give us somehow a pathway of treatment?

It's very much the second. If you look at the, the, the way to therapeutics, you have to start somewhere. What are you going to do? You're going to 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 so forth. You can basically screen those molecules against either a set of genes that act in that pathway, or on the pathway directly by having a cellular assay. And then you can basically go into mice and do experiments and basically sort of figure out ways to manipulate these processes that allow you to then go back to humans and do a clinical trial that basically says, okay, I was able indeed to reverse these processes in mice. Can I do the same thing in humans? So the knowledge of the tissues gives you the pathway to treatment. But that's not the only part. There are many additional steps to figuring out the mechanism of disease.

So that's really promising. Maybe to take a small step back, you've mentioned all these puzzles that were figured out with the Nature paper for. I mean, you've mentioned a ton of diseases from obesity to Alzheimer's, even schizophrenia. I think you mentioned. And just what is the actual methodology of figuring this out?

So indeed I mentioned a lot of diseases and my lab works on a lot of different disorders. And the reason for that is that if you look at the, if you look at biology, it used to be zoology departments and botanology departments and 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 going to 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 these realization of these common underlying processes. And in the same way that DNA unified biology, genetics is unifying disease studies. So you used to have you used to have, you know, I don't know, cardiovascular disease department and, you know, neurological disease department and neurodegeneration department, and, you know, basically.