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OUTLINE:
0:00 - Introduction
2:49 - Molecular basis for human disease
26:48 - Deadliest diseases
32:31 - Genetic component of diseases
41:22 - Genetic understanding of disease
57:09 - Unified theory of human disease
1:03:10 - Genome circuitry
1:28:13 - CRISPR
1:39:50 - Mitochondria
1:47:54 - Future of biology research
2:17:30 - The genetic circuitry of disease

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The following is a conversation with Manolis Kellis, his third time on the podcast.

He's a professor at MIT and head of the MIT Computational Biology Group.

This time we went deep on the science, biology and genetics.

Manolis went back and forth between the basics of biology to the latest state of the art in the research.

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 1 was a tough 1 for my brain to keep up, but I did my best and I never shy away from good challenge.

Quick mention of each sponsor, followed by some thoughts related to the episode.

First is SEMrush, the most advanced SEO optimization tool I've ever come across.

I don't like looking at numbers, but someone probably should.

1 of my favorite history podcasts on why people resist new things from recorded music to umbrellas, to cars, chess, coffee, and the elevator.

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.

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 the 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 5 Stars and Apple Podcasts, follow on Spotify, support on Patreon, or connect with me on Twitter at Lex Friedman.

And Now, here's my conversation with Manolis Kalas.

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?

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.

So that was the old way of doing basic biology.

You would start with the animal models, the eukaryotic models, the mammalian models, and then you would go to human.

Human genetics has been so transformed in the last decade or 2, that human genetics is now actually driving the basic biology.

There is more genetic mutation information in the human genome than there will ever be in any other species.

What do you mean by mutation information?

So perturbations is how you understand systems.

So an engineer builds systems and then they know how they work from the inside out.

A scientist studies systems through perturbations.

You basically say if I poke that balloon what's 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 wanna just know that it's related to the disease.

You wanna know what is the disease mechanism because you wanna go and intervene.

So the way that I like to describe it is that traditionally, Epidemiology, which is basically the study of disease, you know, sort of the observational study of disease, has been about correlating 1 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.

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 and so forth.

That's such a beautiful puzzle to try to solve.

Like what kind of perturbations eventually have this ripple effect that leads to 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 1 gene at a time.

That's the beauty of it, it's the power of animal models.

You can basically decouple the perturbations.

You only do 1 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 1 of us carries 6000000 perturbations.

So I've done 6000000 experiments on you, 6000000 experiments on me, 6000000 experiments on everyone of 7000000000 people on the planet.

6000000 unique genetic variants that are segregating the human population.

Every 1 of us carries millions of polymorphic sites.

That basically means that every 1 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 2 twins are identical is because these variants completely determine the way that I'm gonna look at exactly 93 years of age.

How happy are you with this kind of data set?

Yeah, so is it large enough is a power analysis question.

And in every 1 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 2 forms.

So every time you do a perturbation you're asking I'm changing form A into form B.

Form A has some natural phenotypic variation around it and form B has some natural phenotypic variation around it.

If those variances are large and the differences between the mean of A and the mean of B are small, then you have very little power.

The further the means go apart, that's the effect size, the more power you have, and the smaller the standard deviation, the more power you have.

So basically, when you're asking, is that sufficiently large?

Certainly not for everything, but we already have enough power for many of the stronger effects in the more tight distributions.

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

Unfortunately, those perturbations are the basis of disease in many cases.

So it's not a hopeful message, sometimes it's a terrible message.

It's basically, well, some people are sick, but if we can figure out what are these contributors to sickness, we can then help make them better and help many other people better who don't carry that exact mutation, but who carry mutations on the same pathways.

And that's what we like to call the allelic series of a gene.

You basically have many perturbations of the same gene in different people, each with a different frequency in the human population and each with a different effect on the individual that carries them.

said in the past, there would be these small experiments on perturbations and animal models.

What does this puzzle-solving process look like today?

So we basically have something like 7000000000 people on the planet.

And every 1 of them carries something like 6000000 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 1 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 1 locus plays a very big role in that disease.

And you can then look at carriers versus non-carriers in 1 family, carriers versus non-carriers in another family, and do that for hundreds, sometimes thousands of families, and then trace these inheritance patterns and then figure out what is the gene that plays that role.

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

So That matrix is the input to the stuff that I saw in talks.

So basically that matrix has traditionally been strong effect genes.

What the matrix looks like now is instead of pedigrees, instead of families, you basically have thousands and sometimes hundreds of thousands of unrelated individuals, each with all of their genetic variants, and each with their phenotype, for example, height, or lipids, or whether they're sick or not for a particular trait.

Instead of going to families, going to unrelated individuals with 1 phenotype at a time.

And what we're doing now, as we're maturing in all of these sciences, is that we're doing this in the context of large medical systems or enormous cohorts that are very well phenotyped across hundreds of phenotypes, sometimes with our complete electronic health record.

So you can now start relating not just 1 gene segregating 1 family, not just thousands of variants segregating with 1 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 1 of these elements is a dream come true.

And you can now solve that puzzle by throwing in more and more knowledge about the function of different genomic regions and how these functions are changed across tissues and in the context of disease.

And that's what my group and many other groups are doing.

We're trying to systematically relate this genetic variation with molecular variation at the expression level of the genes, at the epigenomic level of the gene regulatory circuitry, and at the cellular level of what are the functions that are happening in those cells, at the single cell level using single cell profiling, and then relate all that vast amount of knowledge computationally with the thousands of traits that each of these of thousands of variants are perturbing.

I mean, this is something we talked about, I think, last time.

So there's these effects at different levels that happen.

You said at a single cell level, you're trying to see things that happen due to certain perturbations.

And then, it's not just like a puzzle of perturbation and disease.

It's perturbation, then effect at a cellular level, then at an organ level, 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 puzzles that you can now start solving?

And they're like, ooh, I wanna build a method that just deconvolves the whole thing computationally.

And that's very tempting and it's very appealing, but biologists just like to decouple that complexity experimentally, to just like peel off layers of complexity experimentally.

And that's what many of these modern tools that, 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 1 cell type at a time, or by testing 1 cell at a time.

And you could basically say, what is the effect of these genetic variants associated with Alzheimer's on human brain?

Human brain sounds like, oh, it's an organ, of course, just go 1 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 1 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, I mean, 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 genome results in some obvious major dysfunction in the cell.

You're trying to find something like that.

Human genetics basically looks at the whole path from genetic variation all the way to disease.

So human genetics has basically taken thousands of Alzheimer's cases and thousands of controls matched for age, for sex, for environmental backgrounds and so on and so forth.

And then looked at that map where you're asking what are the individual genetic perturbations and how are they related to all the way to Alzheimer's disease.

And that has actually been quite successful.

So we now have more than 27 different loci, these are genomic regions, that are associated with Alzheimer's at this end-to-end level.

But the moment you sort of break up that very long path into smaller levels, you can basically say from genetics, what are the epigenomic alterations at the level of gene regulatory elements where that genetic variant perturbs the control region nearby.

You mean much larger in terms of this down the line impact?

Or- It's much larger in terms of the measurable effect.

This A versus B variance is actually so much cleanly defined when you go to the shorter branches.

Because for 1 genetic variant to affect Alzheimer's, that's a very long path.

That basically means that in the context of millions of these 6000000 variants that every 1 of us carries, that 1 single nucleotide has a detectable effect all the way to the end.

I mean, it's just mind boggling that that's even possible, but indeed, yeah, but indeed there are such effects.

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

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 1 regulatory region in the genome, in 1 cell type, change disease?

So if you instead look at expression, the path between genetic variation and the expression of 1 gene goes through many enhancer regions, and therefore it's a subtler effect at the gene level, but then now you're closer because 1 gene is acting in the context of only 20,000 other genes as opposed to 1 enhancer acting in the context of 2 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 electric impulse conductivity?

So you can measure things along this path to disease And you can also measure endophenotypes.

You can basically measure your brain activity.

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.

So you mentioned lifestyle interventions or changes as a way to, or like be able to prescribe changes in lifestyle?

What about like the function of the body as a whole?

So basically When you go to your doctor, they always measure your pulse, they always measure your height, they always measure your weight, your BMI.

So basically these are just very basic variables.

But with digital devices nowadays, you can start measuring hundreds of variables for every individual.

You can basically also phenotype cognitively through tests, Alzheimer's patients.

There are cognitive tests that you can measure, that you typically do for cognitive decline.

These mini mental observations that you have specific questions to.

You can think of sort of enlarging the set of cognitive tests.

So in the mouse, for example, you do experiments for how do they get out of mazes?

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 long-term memory versus short-term memory?

I love how you're calling that phenotype.

But like your behavior patterns that might change over a period of a life, your ability to remember things, your ability to be 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.

And all of that, we're able to measure that better and better.

And all of that could be connected to the entire pipelines.

We used to think of each of these as a single variable, like intelligence.

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 1 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 5 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.

And then you can combine those things and then relate them to, of course, performance in a particular task, but you can also relate them to genetic variation that might be affecting different parts of the brain.

For example, your frontal cortex versus your temporal cortex versus your visual cortex and so on and so forth.

So genetic variation that affects expression of genes in different parts of your brain can basically affect your music ability, your auditory ability, your smell, your just dozens of different phenotypes can be broken down into hundreds of cognitive variables and then relate each of those to thousands of genes that are associated with them.

So somebody who loves RPGs, role-playing games, there's too few variables that we can control.

So I'm excited if we're in fact living in a simulation and this is a video game, I'm excited by the quality of the video game.

The game designer did a hell of a good job, so we're impressed.

So I don't know, the sunset last night was a little unrealistic.

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.

I mean, if you look at COVID, the impact on lifestyle has been enormous.

So understanding COVID is important because it has impacted the wellbeing in terms of ability to have a job, ability to have an apartment, ability to go to work, ability to have a mental circle of support, and all of that for millions of Americans, like huge, huge impact.

So basically mental disorders, Alzheimer's has a huge importance in the wellbeing of Americans.

Whether or not it kills someone, for many, many years, it has a huge impact.

So the first measure of importance is just well-being.

Impact on the quality of life, absolutely.

The second metric which is much easier to quantify is deaths.

The number 1 killer is actually heart disease.

Number 3, far, far down the list is accidents.

So basically you read the news, accidents, like there was a huge car crash, all over the news.

But the number of deaths, number 3 by far,

Lower respiratory disease, so that's asthma, not being able to breathe and so on and so forth,

And then stroke, brain aneurysms and so on and so forth, that's 147,000.

Diabetes and metabolic disorders, et cetera, that's 85,000, the flu is 60,000, suicide

and then overdose, et cetera, goes further down the list.

So of course COVID has creeped up to be the number 3 killer this year with more than 100,000 Americans and counting.

But if you think about sort of what do we use, what are the most important diseases, you have to understand both the quality of life and the just sheer number of deaths and just numbers of years lost, if

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.

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, 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.

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