Manolis Kellis: Biology of Disease
Guests: Manolis Kellis
Full transcript · Read the full conversation in order, with its original context.
Contents18 chapters
§01Transcript · 00:00–09:08§02Transcript · 09:11–18:03§03Transcript · 18:06–26:47§04Transcript · 26:48–36:06§05Transcript · 36:08–45:21§06Transcript · 45:23–54:27§07Transcript · 54:28–1:03:54§08Transcript · 1:03:56–1:13:07§09Transcript · 1:13:09–1:22:17§10Transcript · 1:22:18–1:30:55§11Transcript · 1:30:58–1:39:51§12Transcript · 1:39:56–1:48:48§13Transcript · 1:48:51–1:57:49§14Transcript · 1:57:52–2:06:24§15Transcript · 2:06:27–2:15:22§16Transcript · 2:15:26–2:24:17§17Transcript · 2:24:20–2:33:18§18Transcript · 2:33:21–2:34:51§01Transcript · 00:00–09:08
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 a.m.
So it's yet another podcast episode
after an all-nighter for me.
And once again, since the universe has a sense of humor,
this one was a tough one for my brain to keep up,
but I did my best and I never shy away from good challenge.
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As a side note, let me say that biology in the brain
and in the various systems of the body fill me with awe
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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
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and artificial consciousness,
we may have to completely rethink
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And now, here's my conversation with Manolis Callas.
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.
So that was the old way of doing basic biology.
You would start with the animal models,
the eukaryotic models, the mammalian models,
and then you would go to human.
Human genetics has been so transformed
in the last decade or two that human genetics
is now actually driving the basic biology.
There is more genetic mutation information
in the human genome than there will ever be
in any other species.
- What do you mean by mutation information?
- So perturbations is how you understand systems.
So an engineer builds systems,
and then they know how they work from the inside out.
A scientist studies systems through perturbations.
You basically say, if I poke that balloon,
what's gonna happen?
And I'm gonna film it in super high resolution,
understand, I don't know, air dynamics or fluid dynamics,
if it's filled with water, et cetera.
So you can then make experimentation by perturbation.
And then the scientific process is sort of building models
that best fit the data, designing new experiments
that best test your models and challenge your models
and so on and so forth.
That's the same thing with science.
Basically, if you're trying to understand
biological science, you basically want to do perturbations
that then drive the models.
- So how do these perturbations
allow you to understand disease?
- So if you know that a gene is related to disease,
you don't wanna just know that it's related to the disease.
You wanna know what is the disease mechanism
because you wanna go and intervene.
So the way that I like to describe it
is that traditionally, epidemiology,
which is basically the study of disease,
sort of the observational study of disease,
has been about correlating one thing with another thing.
So if you have a lot of people with liver disease
who are also alcoholics, you might say,
well, maybe the alcoholism is driving the liver disease,
or maybe those who have liver disease
self-medicate with alcohol.
So the connection could be either way.
With genetic epidemiology,
it's about correlating changes in 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 one gene at a time.
That's the beauty of it, it's the power of animal models.
You can basically decouple the perturbations.
You only do one perturbation,
and you only do strong perturbations at a time.
In human, the puzzle is incredibly complex
because, I mean, obviously,
you don't do human experimentation.
You wait for natural selection
and natural genetic variation
to basically do its own experiments,
which it has been doing for hundreds and thousands of years
in the human population,
and for hundreds of thousands of years
across the history leading to the human population.
So you basically take this natural genetic variation
that we all carry within us.
Every one of us carries six million perturbations.
So I've done six million experiments on you,
six million experiments on me,
six million experiments on every one
of seven billion people on the planet.
- What's the six million correspond to?
- Six million unique genetic variants
§02Transcript · 09:11–18:03
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 gonna look at exactly 93 years of age.
- How happy are you with this kind of data set?
Is it large enough of the human population of Earth?
Is that too big, too small?
- Yeah, so is it large enough is a power analysis question.
And in every one of our grants,
we do a power analysis based on what is the effect size
that I would like to detect
and what is the natural variation in the two forms.
So every time you do a perturbation,
you're asking I'm changing form A into form B.
Form A has some natural phenotypic variation around it
and form B has some natural phenotypic variation around it.
If those variances are large
and the differences between the mean of A
and the mean of B are small,
then you have very little power.
The further the means go apart, that's the effect size,
the more power you have,
and the smaller the standard deviation,
the more power you have.
So basically when you're asking is that sufficiently large,
certainly not for everything,
but we already have enough power
for many of the stronger effects
in the more tight distributions.
- So that's a hopeful message
that there exists parts of the genome
that have a strong effect that has a small variance.
- That's exactly right.
Unfortunately, those perturbations
are the basis of disease in many cases.
So it's not a hopeful message.
Sometimes it's a terrible message.
It's basically, well, some people are sick,
but if we can figure out
what are these contributors to sickness,
we can then help make them better
and help many other people better
who don't carry that exact mutation,
but who carry mutations on the same pathways.
And that's what we like to call the allelic series of a gene.
You basically have many perturbations
of the same gene in different people,
each with a different frequency in the human population
and each with a different effect
on the individual that carries them.
- So you said in the past,
there would be these small experiments
on perturbations in animal models.
What does this puzzle-solving process look like today?
- So we basically have something
like seven billion people in the planet,
and every one of them carries
something like six million mutations.
You basically have an enormous matrix
of genotype by phenotype
by systematically measuring the phenotype
of these individuals.
And the traditional way of measuring this phenotype
has been to look at one trait at a time.
You would gather families,
and you would sort of paint the pedigrees
of a strong effect,
what we like to call Mendelian mutation.
So a mutation that gets transmitted
in a dominant or a recessive,
but strong effect form,
where basically one locus plays a very big role
in that disease.
And you could then look at carriers versus non-carriers
in one family,
carriers versus non-carriers in another family,
and do that for hundreds, sometimes thousands of families,
and then trace these inheritance patterns,
and then figure out what is the gene that plays that role.
- Is this the matrix that you're showing
in talks or lectures?
- So that matrix is the input
to the stuff that I show in talks.
So basically that matrix
has traditionally been strong effect genes.
What the matrix looks like now
is instead of pedigrees, instead of families,
you basically have thousands,
and sometimes hundreds of thousands
of unrelated individuals,
each with all of their genetic variants,
and each with their phenotype,
for example, height, or lipids,
or whether they're sick or not for a particular trait.
That has been the modern view.
Instead of going to families,
we're going to unrelated individuals
with one phenotype at a time.
And what we're doing now,
as we're maturing in all of these sciences,
is that we're doing this in the context
of large medical systems,
or enormous cohorts that are very well phenotyped
across hundreds of phenotypes,
sometimes with our complete electronic health record.
So you can now start relating
not just one gene, segregating one family,
not just thousands of variants,
segregating with one phenotype,
but now you can do millions of variants
versus hundreds of phenotypes.
And as a computer scientist,
I mean, deconvolving that matrix,
partitioning it into the layers of biology
that are associated with every one of these elements
is a dream come true.
It's like the world's greatest puzzle.
And you can now solve that puzzle
by throwing in more and more knowledge
about the function of different genomic regions
and how these functions are changed across tissues
and in the context of disease.
And that's what my group and many other groups are doing.
We're trying to systematically relate
this genetic variation with molecular variation
at the expression level of the genes,
at the epigenomic level of the gene regulatory circuitry,
and at the cellular level
of what are the functions that are happening in those cells,
at the single cell level, using single cell profiling,
and then relate all that vast amount of knowledge
computationally with the thousands of traits
that each of these of thousands of variants are perturbing.
- I mean, this is something we talked about,
I think, last time.
So there's these effects at different levels that happen.
You said at a single cell level,
you're trying to see things that happen
due to certain perturbations.
And then, it's not just like a puzzle
of perturbation and disease.
It's perturbation, then effect at a cellular level,
then at an organ level, at a body,
like how do you disassemble this
into 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?
- So there's a struggle here.
Computer scientists love hard puzzles.
And they're like, "Oh, I wanna build a method
that just deconvolves the whole thing computationally."
And that's very tempting and it's very appealing,
but biologists just like to decouple
that complexity experimentally,
to just like peel off layers of complexity experimentally.
And that's what many of these modern tools
that my group and others have both developed and used.
The fact that we can now figure out tricks
for peeling off these layers of complexity
by testing one cell type at a time,
or by testing one cell at a time.
And you could basically say,
what is the effect of these genetic variants
associated with Alzheimer's on human brain?
Human brain sounds like, oh, it's an organ, of course,
just go one organ at a time.
But human brain has, of course,
dozens of different brain regions.
And within each of these brain regions,
dozens of different cell types.
And every single type of neuron,
every single type of glial cell,
between astrocytes, oligodendrocytes, microglia,
between all of the neural cells and the vascular cells
and the immune cells that are co-inhabiting the brain
between the different types of excitatory
and inhibitory neurons that are sort of interacting
with each other between different layers of neurons
in the cortical layers.
Every single one of these has a different type of function
to play in cognition, in interaction with the environment,
in maintenance of the brain, in energetic needs,
in feeding the brain with blood, with oxygen,
in clearing out the debris that are resulting
from the super high energy production
of cognition in humans.
§03Transcript · 18:06–26:47
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 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 separating into the function,
breaking Alzheimer's down to the cellular level
seems very challenging.
Is that basically you're trying to find a way
that some perturbation in genome results
in some obvious major dysfunction in the cell?
You're trying to find something like that.
- Exactly.
So what does human genetics do?
Human genetics basically looks at the whole path
from genetic variation all the way to disease.
So human genetics has basically taken thousands
of Alzheimer's cases and thousands of controls
matched for age, for sex, for environmental backgrounds
and so on and so forth.
And then looked at that map where you're asking
what are the individual genetic perturbations
and how are they related 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.
That effect is much larger.
- You mean much larger in terms of
this down the line impact?
- 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 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.
- That's crazy. - But indeed, yeah.
But indeed, there are such effects.
- So the hope is, or the most, scientifically speaking,
the most effective place where to detect
the alteration that results in disease
is earlier on in the pipeline, as early as possible.
- It's a trade off.
If you go very early on in the pipeline,
now each of these epigenomic alterations,
for example, this enhancer control region,
is active maybe 50% less, which is a dramatic effect.
Now you can ask, well, how much does changing
one regulatory region in the genome
in one cell type change disease?
Well, that path is now long.
So if you instead look at expression,
the path between genetic variation
and the expression of one gene
goes through many enhancer regions,
and therefore it's a subtler effect at the gene level.
But then now you're closer because one gene is acting
in the context of only 20,000 other genes,
as opposed to one 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 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 your brain activity.
You can do imaging in the brain.
You can basically measure, I don't know,
the heart rate, the pulse, the lipids,
the amount of blood secreted, and so on and so forth.
And then through all of that,
you can basically get at the path to causality,
the path to disease.
- And is there something beyond cellular?
So you mentioned lifestyle interventions
or changes as a way to,
or like be able to prescribe changes in lifestyle.
Like what about organs?
What about like the function of the body as a whole?
- Yeah, absolutely.
So basically, when you go to your doctor,
they always measure 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?
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,
but, and all kinds of other activities at the organ level,
but you can also do at the organism level,
you can do behavioral tests.
And how did they do on empathy?
How did they do on memory?
How did they do on long-term memory
versus short-term memory?
And so on and so forth.
- I love how you're calling that phenotype.
I guess it is. - It is.
- But like your behavior patterns
that might change over a period of a life.
Your ability to remember things,
your ability to be, yeah, empathetic or emotionally,
or your intelligence, perhaps even.
- Yeah, but intelligence has hundreds of variables.
You can be your math intelligence,
your literary intelligence,
your puzzle-solving intelligence, your logic.
It could be like hundreds of things.
- And all of that, we're able to measure that
better and better.
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 five bars
that are just the main bars,
and each of those will just give you then hundreds of bars.
And you can basically say,
"Okay, great, for my machine learning task,
"I want someone who, a human,
"who has these particular forms of intelligence.
"I require now these 20 different things."
And then you can combine those things
and then relate them to, of course,
performance in a particular task.
But you can also relate them to genetic variation
that might be affecting different parts of the brain.
For example, your frontal cortex
versus your temporal cortex
versus your visual cortex, and so on and so forth.
So genetic variation that affects expression of genes
in different parts of your brain
can basically affect your music ability,
your auditory ability, your smell,
your, 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.
- 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,
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.
(both laughing)
- Yeah, yeah, the graphics.
- Exactly.
- Come on, Nvidia.
§04Transcript · 26:48–36:06
To zoom back out, we've been talking about
the genetic origins of diseases,
but I think it's fascinating to talk about
what are the most important diseases to understand,
and especially as it connects to the things
that you're working on.
- So it's very difficult to think about
important diseases to understand.
There's many metrics of importance.
One is lifestyle impact.
I mean, if you look at COVID,
the impact on lifestyle has been enormous.
So understanding COVID is important
because it has impacted the wellbeing
in terms of ability to have a job,
ability to have an apartment,
ability to go to work,
ability to have a mental circle of support,
and all of that for millions of Americans,
like huge, huge impact.
So that's one aspect of importance.
So basically mental disorders,
Alzheimer's has a huge importance
in the wellbeing of Americans.
Whether or not it kills someone,
for many, many years, it has a huge impact.
So the first measure of importance is just wellbeing.
- Impact on the quality of life.
- Impact on the quality of life, absolutely.
The second metric, which is much easier to quantify,
is deaths.
- What is the number one killer?
- The number one killer is actually heart disease.
It is actually killing 650,000 Americans per year.
Number two is cancer, with 600,000 Americans.
Number three, far, far down the list, is accidents.
Every single accident combined.
So basically you read the news, accidents,
like there was a huge car crash, all over the news.
But the number of deaths, number three by far, 167,000.
Lower respiratory disease, so that's asthma,
not being able to breathe, and so on and so forth, 160,000.
Alzheimer's, number five, with 120,000.
And then stroke, brain aneurysms, and so on and so forth,
that's 147,000.
Diabetes and metabolic disorders, et cetera, that's 85,000.
The flu, a 60,000.
Suicide, 50,000.
And then overdose, et cetera,
you know, goes further down the list.
So of course, COVID has creeped up
to be the number three killer this year,
with more than 100,000 Americans, and counting.
And, you know, 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,
that'd be 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, et cetera.
So if we change that equation, and as a society,
we made availability of healthy food much, much easier,
and charged a burger at McDonald's,
the price that it costs on the health system,
then people would actually start buying more healthy foods.
So basically, that's sort of a societal intervention,
if you wish.
In the same way, increasing empathy, increasing education,
increasing the social framework and support
would basically lead to fewer suicides,
it would lead to fewer murders,
it would lead to fewer deaths overall.
So that's something that we as a society can do.
You can also think about external factors
versus internal factors.
So the external factors are basically communicable diseases,
like COVID, like the flu, et cetera.
And the internal factors are basically things like cancer
and Alzheimer's, where basically your genetics
will eventually drive you there.
And then of course, with all of these factors,
every single disease has both a genetic component
and environmental component.
So heart disease, huge genetic contribution.
Alzheimer's, it's like 60% plus genetic.
So I think it's like 79% heritability.
So that basically means that genetics alone
explains 79% of Alzheimer's 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,
sort of have a more fulfilling life.
All of that will actually decrease Alzheimer's,
but there's a limit to how much that can impact
because of the huge genetic footprint.
- So this is fascinating.
So each one of these problems have a genetic component
and an environment component.
And so when there's a genetic component,
what can we do about some of these diseases?
What have you worked on?
What can you say that's in terms of problems
that are solvable here or understandable?
- So my group works on the genetic component,
but I would argue that understanding the genetic component
can have a huge impact even on the environmental component.
Why is that?
Because genetics gives us access to mechanism.
And if we can alter the mechanism,
if we can impact the mechanism,
we can perhaps counteract
some of the environmental components.
- Oh, interesting.
- So understanding the biological mechanisms
leading to disease is extremely important
in being able to intervene.
But when you can intervene,
the analogy that I like to give is, for example, for obesity.
Think of it as a giant bathtub of fat.
There's basically fat coming in from your diet
and there's fat coming out from your exercise.
That's an in-out equation.
And that's the equation that everybody's focusing on.
But your metabolism impacts that bathtub.
Basically, your metabolism controls
the rate at which you're burning energy.
It controls the rate at which you're storing energy.
And it also teaches you about the various valves
that control the input and the output equation.
So if we can learn from the genetics, the valves,
we can then manipulate those valves.
And even if the environment is feeding you a lot of fat
and getting a little of 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 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,
penetrance, expressivity, and so on and so forth,
that basically you can trace it in families
with cases and not cases, cases not cases,
and so on and so forth.
But even the, you know, but,
so these are the genes that everybody says,
oh, that's the genes we should go after
because that's a strong effect gene.
I like to think about it slightly differently.
These are the genes where genetic impacts
that have a strong effect were tolerated.
Because every single time we have a genetic association
with disease, it depends on two things.
Number one, the obvious one,
whether the gene has an impact on the disease.
Number two, the more subtle one,
is whether there is genetic variation
standing and circulating and segregating
in the human population that impacts that gene.
Some genes are so darn important
that if you mess with them even a tiny little amount,
that person's dead.
So those genes don't have variation.
You're not gonna find a genetic association
if you don't have variation.
That doesn't mean that the gene has no role.
It simply that the gene,
it simply means that the gene tolerates no mutations.
- So that's actually a strong signal
when there's no variation.
That's so fascinating. - Exactly.
Genes that have very little variation are hugely important.
You can actually rank the importance of genes
§05Transcript · 36:08–45:21
based on how little variation they have.
And those genes that have very little variation
but no association with disease,
that's a very good metric to say,
oh, that's probably a developmental gene
because we're not good at measuring those phenotypes.
So it's genes that you can tell evolution
has excluded mutations from,
but yet we can't see them associated
with anything that we can measure nowadays.
It's probably early embryonic lethal.
- What are all the words you just said?
Early embryonic what?
- Lethal.
- Meaning?
- Meaning that--
- If you don't have that, then--
- If you don't have that, you'll die.
- Okay.
There's a bunch of stuff that is required
for a stable functional organism--
- Exactly. - Across the board.
- Exactly. - For our entire,
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-- - Early on.
- Is that misfolding of these proteins
is an early test for failure.
So that out of the millions of sperm that are possible,
you select the subset that are just not
grossly misfolding thousands of proteins.
- So it's kind of an assert that this is folded correctly.
- Correct.
- Yeah, this, just because if this little thing
about the folding of a protein isn't correct,
that probably means somewhere down the line
there's a bigger issue.
- That's exactly right, so fail fast.
So basically if you look at the mammalian investment
in a newborn, that investment is enormous
in terms of resources.
So mammals have basically evolved mechanisms for fail fast.
Where basically in those early months of development,
I mean, it's horrendous, of course, at the personal level
when you lose your future child,
but in some ways there's so little hope
for that child to develop and sort of make it
through the remaining months that sort of fail fast
is probably a good evolutionary principle.
- Yeah, from an evolutionary perspective.
- For mammals, and of course humans have a lot
of medical resources that you can sort of give
those children a chance.
And we have so much more success in sort of giving folks
who have these strong carrier mutations a chance,
but if they're not even making it
through the first three months, we're not gonna see them.
So that's why when we say what are the most important genes
to focus on, the ones that have a strong effect mutation
or the ones that have a weak effect mutation,
well, the jury might be out,
because the ones that have a strong effect mutation
are basically not mattering as much.
The ones that only have weak effect mutations,
by understanding through genetics
that they have a weak effect mutation
and understanding that they have a causal role
on the disease, we can then say, okay, great,
evolution has only tolerated a 2% change in that gene.
Pharmaceutically, I can go in and induce a 70% change
in that gene.
And maybe I will poke another hole at the bathtub
that was not easy to control in many of the studies
in many of the other strong effect genetic variants.
- So there's this beautiful map 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 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 variant
impacts the gene by a little,
and then the gene impacts the disease by a lot.
So what we care about is genes that impact the disease a lot,
but genetics gives us the full equation.
And what I would argue is if we couple the genetics
with expression variation,
to basically ask what genes change by a lot,
and which genes correlate with disease by a lot,
even if the genetic variants change them by a little,
then those are the best places to intervene.
Those are the best places where pharmaceutically,
if I have even a modest effect,
I will have a strong effect on the disease.
Whereas those genetic variants
that have a huge effect on the disease,
I might not be able to change that gene by this much
without affecting all kinds of other things.
- Interesting.
So yeah, okay, so that's what we're looking at.
And 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.
- You, I'm an average.
- Yeah.
- By the way, when you say my, you mean literally yours.
You know this about you.
- I know this about me, yeah.
- Which is kind of, I mean, philosophically speaking
is a pretty powerful thing to live with.
Maybe that's, so we agreed to talk again, by the way,
for the listeners to where we're gonna try to focus
on science today and a little bit of philosophy next time.
But it's interesting to think about
the more you're able to know about yourself
from the genetic information in terms of the diseases,
how that changes your own view of life.
- Yeah, so there's a lot of impact there.
And there's something called genetic exceptionalism,
which basically thinks of genetics
as something very, very different than everything else
as a type of determinism.
And, you know, let's talk about that next time.
So basically-- - That's a good preview.
- Yeah.
So let's go back to AMD.
So basically with AMD, we have no idea what causes AMD.
You know, it was a mystery
until the genetics were worked out.
And now the fact that I know that I have a predisposition
allows me to sort of make some life choices, number one.
But number two, the genes that lead to that predisposition
give us insights as to how does it actually work.
And that's a place where genetics
gave us something totally unexpected.
So there's a complement pathway,
which is an immune function pathway
that was in, you know,
most of the loci associated with AMD.
And that basically told us that, wow,
there's an immune basis to this eye disorder
that people had just not expected before.
If you look at complement,
it was recently also implicated in schizophrenia.
And there's a type of microglia
that is involved in synaptic pruning.
So synapses are the connections between neurons.
And in this whole use it or lose it view
of mental cognition and other capabilities,
you basically have microglia, which are immune cells
that are sort of constantly traversing your brain
and then pruning neuronal connections,
pruning synaptic connections that are not utilized.
So in schizophrenia,
there's thought to be a change in the pruning
that basically if you don't prune your synapses
the right way,
you will actually have an increased role of schizophrenia.
This is something that was completely unexpected
for schizophrenia.
Of course, we knew it has to do with neurons,
but the role of the complement complex,
which is also implicated in AMD,
which is now also implicated in schizophrenia,
was a huge surprise.
- What's the complement complex?
- So it's basically a set of genes, the complement genes,
that are basically having various immune roles.
And as I was saying earlier,
our immune system has been co-opted
for many different roles across the body.
So they actually play many diverse roles.
§06Transcript · 45:23–54:27
- And somehow the immune system is connected
to the synaptic pruning process.
- Exactly. - The process of it.
- Exactly.
So immune cells were co-opted to prune synapses.
- How did you figure this out?
(laughing)
How does one go about figuring this intricate connection,
like pipeline of connections out?
- Yeah, let me give you another example.
So Alzheimer's disease,
the first place that you would expect it to act
is obviously the brain.
So we had basically this
roadmap epigenomics consortium view of the human epigenome,
the largest map of the human epigenome
that has ever been built,
across 127 different tissues and samples
with dozens of epigenomic marks
measured in hundreds of donors.
So what we've basically learned through that
is that you basically can map
what are the active gene regulatory elements
for every one of the tissues in the body.
And then we connected these gene regulatory active maps
of basically what regions of the human genome
are turning on in every one of different tissues.
We then can go back and say,
where are all of the genetic loci
that are associated with disease?
This is something that my group,
I think was the first to do back in 2010
in this Ernst Nature Biotech paper.
But basically we were for the first time able to show
that specific chromatin states,
specific epigenomic states,
in that case enhancers,
were in fact enriched in disease associated variants.
We pushed that further
in the Ernst Nature paper a year later,
and then in this roadmap epigenomics paper,
a few years after that.
But basically that matrix that you mentioned earlier
was in fact the first time that we could see
what genetic traits have genetic variants
that are enriched in what tissues in the body.
And a lot of that map made complete sense.
If you looked at a diversity of immune traits,
like allergies and type 1 diabetes and so on and so forth,
you basically could see that they were enriching,
that the genetic variants associated with those traits
were enriched in enhancers,
in these gene regulatory elements,
active in T cells and B cells
and hematopoietic stem cells and so on and so forth.
So that basically gave us a confirmation in many ways
that those immune traits were indeed
enriching in immune cells.
If you looked at type 2 diabetes,
you basically saw an enrichment in only one type of sample,
and it was pancreatic islets.
And we know that type 2 diabetes
sort of stems from the dysregulation of insulin
in the beta cells of pancreatic islets.
And that sort of was spot on, super precise.
If you looked at blood pressure,
where would you expect blood pressure to occur?
You know, I don't know, maybe in your metabolism,
in ways that you process coffee or something like that,
maybe in your brain, the way that you stress out
and increases your blood pressure, et cetera.
What we found is that blood pressure localized specifically
in the left ventricle of the heart.
So the enhancers of the left ventricle in the heart
contained a lot of genetic variants
associated with blood pressure.
If you look at height,
we found an enrichment specifically
in embryonic stem cell enhancers.
So the genetic variants predisposing you
to be taller or shorter
are in fact acting in developmental stem cells.
Makes complete sense.
If you looked at inflammatory bowel disease,
you basically found inflammatory, which is immune,
and also bowel disease, which is digestive.
And indeed, we saw a double enrichment,
both in the immune cells and in the digestive cells.
So that basically told us that,
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.
So what could they be acting in?
Well, the fourth major cell type is actually microglia.
Microglia are resident immune cells in your brain.
- Oh, nice.
The immune, oh, wow.
- And they are CD14+, which is this sort of
cell surface markers of those cells.
So they're CD14+ cells, just like microphages
that are circulating in your blood.
The microglia are resident monocytes
that are basically sitting in your brain.
They're tissue-specific monocytes.
And every one of your tissues, like your fat, for example,
has a lot of macrophages that are resident.
And the M1 versus M2 macrophage ratio
has a huge role to play in obesity.
And so basically, again, these immune cells are everywhere,
but basically what we found,
through this completely unbiased view
of what are the tissues that likely
underlie different disorders,
we found that Alzheimer's was humongously enriched
in microglia, but not at all in the other cell types.
- So what are we supposed to make of that
if you look at the tissues involved?
Is that simply useful for indication
of propensity for disease?
Or does it give us somehow a pathway of treatment?
- It's very much the second.
If you look at the way to therapeutics,
you have to start somewhere.
What are you gonna do?
You're gonna basically make assays
that manipulate those genes and those pathways
in those cell types.
So before we know the tissue of action,
we don't even know where to start.
We basically are at a loss.
But if you know the tissue of action,
and even better, if you know the pathway of action,
then you can basically screen your small molecules,
not for the gene, you can screen them directly
for the pathway in that cell type.
So you can basically develop
a high throughput multiplexed robotic system
for testing the impact of your favorite molecules
that you know are safe, efficacious,
and sort of hit that particular gene, and so on and so forth.
You can basically screen those molecules
against either a set of genes that act in that pathway,
or on the pathway directly by having a cellular assay.
And then you can basically go into mice and do experiments
and basically sort of figure out ways
to manipulate these processes
that allow you to then go back to humans
and do a clinical trial that basically says,
okay, I was able indeed to reverse these processes in mice,
can I do the same thing in humans?
So the knowledge of the tissues
gives you the pathway to treatment.
But that's not the only part.
There are many additional steps
to figuring out the mechanism of disease.
- I mean, so that's really promising.
Maybe to take a small step back,
you've mentioned all these puzzles
that were figured out with the Nature paper for,
I mean, you've mentioned a ton of diseases,
from obesity to Alzheimer's, even schizophrenia,
I think you mentioned.
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 gonna study all of life suddenly.
Why was that even a case?
Because the advent of DNA and the genome
and the central dogma of DNA makes RNA makes protein,
in many ways, unified biology.
You could suddenly study the process of transcription
in viruses or in bacteria
and have a huge impact on yeast and fly
and maybe even mammals,
because of this realization
of these common underlying processes.
And in the same way that DNA unified biology,
genetics is unifying disease studies.
So you used to have,
§07Transcript · 54:28–1:03:54
you used to have, I don't know,
cardiovascular disease department
and neurological disease department
and neurodegeneration department
and basically immune and cancer and so on and so forth.
And all of these were studied in different labs
because it made sense,
because basically the first step
was understanding how the tissue functions
and we kind of knew the tissues involved
in cardiovascular disease and so on and so forth.
But what's happening with human genetics
is that all of 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 altogether.
The same way for schizophrenia,
we now have to sort of worry about
all these interconnected aspects.
For metabolic disorders,
we're finding contributions from brain.
So suddenly we have to call the neurologist
from the other building and so on and so forth.
So in my view, it makes no sense anymore
to basically say, oh, I'm a geneticist
studying immune disorders.
I mean, that's ridiculous because,
I mean, of course, in many ways,
you still need to sort of focus.
But what we're doing is that we're basically saying,
we'll go wherever the genetics takes us.
And by building these massive resources,
by working on our latest maps, now 833 tissues,
sort of the next generation of the epigenomics roadmap,
which we're now called EpiMap, is 833 different tissues.
And using those, we've basically found enrichments
in 540 different disorders.
Those enrichments are not like, oh, great,
you guys work on that and we'll work on this.
They're intertwined amazingly.
So of course there's a lot of modularity,
but there's these enhancers that are sort of broadly active
and these disorders that are broadly active.
So basically some enhancers are active in all tissues
and some disorders are enriching in all tissues.
So basically there's these multifactorial
and these other class,
which I like to call polyfactorial diseases,
which are basically lighting up everywhere.
And in many ways, it's sort of cutting across these walls
that were previously built across these departments.
- And the polyfactorial ones were probably
the previous structure departments
wasn't equipped to deal with those.
I mean, again, maybe it's a romanticized question,
but there's in physics, there's a theory of everything.
Do you think it's possible to move towards
an almost theory of everything of disease
from a genetic perspective?
So if this unification continues, is it possible that,
like, do you think in those terms,
like trying to arrive at a fundamental understanding
of how disease emerges, period?
- That unification is not just foreseeable, it's inevitable.
I see it as inevitable.
We have to go there.
You cannot be a specialist anymore if you're a genomicist,
you have to be a specialist in every single disorder.
And the reason for that is that the fundamental understanding
of the circuitry of the human genome
that you need to solve schizophrenia,
that fundamental circuitry is hugely important
to solve Alzheimer's.
And that same circuitry is hugely important
to solve metabolic disorders.
And that same exact circuitry is hugely important
for solving immune disorders and cancer
and every single disease.
So all of them have the same sub task.
And I teach dynamic programming in my class.
Dynamic programming is all about sort of
not redoing the work, it's reusing the work that you do once.
So basically for us to say, oh, great,
you guys in the immune building,
go solve the fundamental circuitry of everything.
And then you guys in the schizophrenia building
go solve the fundamental circuitry
of everything separately, 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 will basically interact
with both the immune folks and with the neuronal folks.
And all of them will be interacting
with the circuitry folks and so on and so forth.
So that's sort of the current structure of my group,
if you wish.
So basically what we're doing is focusing
on the fundamental circuitry.
But at the same time, we're the users of our own tools
by collaborating with many other labs
in every one of these disorders that we mentioned.
We basically have a heart focus on cardiovascular disease,
coronary artery disease, heart failure, and so on and so forth.
We have an immune focus on several immune disorders.
We have a cancer focus on metastatic melanoma
and immunotherapy response.
We have a psychiatric disease focus on schizophrenia,
autism, PTSD, and other psychiatric disorders.
We have an Alzheimer's and neurodegeneration focus
on Huntington's disease, ALS, and AD-related disorders
like frontotemporal dementia and Lewy body dementia.
And of course, a huge focus on Alzheimer's.
We have a metabolic focus on the role of exercise and diet
and sort of how they're impacting metabolic organs
across the body and across many different tissues.
And all of them are interfacing with the circuitry.
And the reason for that is another computer science principle
of eat your own dog food.
If everybody ate their own dog food,
dog food would taste a lot better.
The reason why Microsoft Excel and Word and PowerPoint
was so important and so successful
is because the employees that were working on them
were using them for their day-to-day tasks.
You can't just simply build a circuitry and say,
"Here it is, guys, take the circuitry, we're done,"
without being the users of that circuitry
because you then go back.
And because we span the whole spectrum
from profiling the epigenome, using comparative genomics,
finding the important nucleotides in the genome,
building the basic functional map
of what are the genes in the human genome,
what are the gene regulatory elements of the human genome.
I mean, over the years, we've written a series of papers
on how do you find human genes in the first place,
using comparative genomics.
How do you find the motifs that are the building blocks
of gene regulation, using comparative genomics?
How do you then find how these motifs come together
and act in specific tissues, using epigenomics?
How do you link regulators to enhancers and enhancers
to their target genes,
using epigenomics and regulatory genomics?
So through the years, we've basically built
all this infrastructure for understanding
what I like to say, every single nucleotide
of the human genome and how it acts in every one
of the major cell types and tissues of the human body.
I mean, this is no small task.
This is an enormous task that takes the entire field.
And that's something that my group has taken on,
along with many other groups.
And we have also, and that sort of, 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,
but basically go and say, okay,
we need a better cell type specific wiring
that we now have at the tissue specific level.
So we're focusing on that because we're understanding
the needs from the disease front.
- So you have a sense of the entire pipeline.
I mean, one, maybe you can indulge me,
one nice question to ask would be,
how do you, from the scientific perspective,
go from knowing nothing about the disease
to going, you said, to going through the entire pipeline
and actually have a drug or a treatment
that cures that disease?
- So that's an enormously long path
and an enormously great challenge.
And what I'm trying to argue is that
it progresses in stages of understanding
rather than one gene at a time.
The traditional view of biology was
you have one postdoc working on this gene
and another postdoc 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.
§08Transcript · 1:03:56–1:13:07
So we can't have one postdoc per gene anymore.
We now have to have these cross-cutting needs.
And I'm gonna describe the path to circuitry
along those needs.
And every single one of these paths,
we are now doing in parallel across thousands of genes.
So the first step is you have a genetic association.
And we talked a little bit about sort of the Mendelian path
and the polygenic path to that association.
So the Mendelian path was looking through families
to basically find gene regions and ultimately genes
that are underlying particular disorders.
The polygenic path is basically looking at
unrelated individuals in this giant matrix
of genotype by phenotype,
and then finding hits where 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 explain it a little bit more.
Why do we not have a connection between a gene
and a disease, but we have a connection
between a genetic region and a disease?
The reason for that is that 93% of genetic variants
that are associated with disease
don't impact the protein at all.
So if you look at the human genome, there's 20,000 genes.
There's 3.2 billion nucleotides.
Only 1.5% of the genome codes for proteins.
The other 98.5% does not code for proteins.
If you now look at where are the disease variants located,
93% of them fall in that outside the genes portion.
Of course, genes are enriched,
but they're only enriched by a factor of three.
That means that still 93% of genetic variants
fall outside the proteins.
Why is that difficult?
Why is that a problem?
The problem is that when a variant falls outside the gene,
you don't know what gene is impacted by that variant.
You can't just say, "Oh, it's near this gene.
Let's just connect that variant to the gene."
And the reason for that is that the genome circuitry
is very often long range.
So you basically have that genetic variant
that could sit in the intron of one gene.
And an intron is sort of the place
between the exons that code for proteins.
So proteins are split up into exons and introns,
and every exon codes for a particular subset
of amino acids, and together they're spliced together,
and then make the final protein.
So that genetic variant might be sitting
in an intron of a gene.
It's transcribed with the gene,
it's processed and then excised,
but it might not impact this gene at all.
It might actually impact another gene
that's a million nucleotides away.
- So it's just riding along,
even though it has nothing to do
with this nearby neighborhood.
- That's exactly right.
Let me give you an example.
The strongest genetic association with obesity
was discovered in this FTO gene,
fat and obesity associated gene.
So this FTO gene was studied ad nauseum.
People did tons of experiments on it.
They figured out that FTO is in fact
RNA methylation trans rays.
It basically, 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 epitranscriptomics
is hugely involved in obesity
because that gene FTO is clearly
where the genetic locus is at.
My group studied FTO in collaboration
with a wonderful team led by Melina Klausnitzer.
And what we found is that this FTO locus,
even though it is 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.
- Oh boy.
When am I supposed to feel about that?
Isn't that like super complicated then?
- So the way that I was introduced at a conference
a few years ago was, and here's Manolis Kellis
who wrote the most depressing paper of 2015.
(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.
- So okay, so embrace the wholeness of the circuitry.
- Correct.
- But what, so back to the question of starting
knowing nothing to the disease and going to the treatment.
So what are the next steps?
- So you basically have to first figure out the tissue
and then describe how you figure out the tissue.
You figure out the tissue by taking
all of these non-coding variants
that are sitting outside proteins,
and then figuring out what are the epigenomic enrichments.
And the reason for that, thankfully,
is that there is convergence,
that the same processes are impacted in different ways
by different loci.
And that's a saving grace for our field.
The fact that if I look at hundreds of genetic variants
associated with Alzheimer's,
they localize in a small number of processes.
- Can you clarify why that's hopeful?
So they show up in the same exact way
in the specific set of processes?
- Yeah, so basically there's a small number
of biological processes that underlie,
or at least that play the biggest role in every disorder.
So in Alzheimer's, you basically have
maybe 10 different types of processes.
One of them is lipid metabolism.
One of them is immune cell function.
One of them is neuronal energetics.
So these are just a small number of processes,
but you have multiple lesions,
multiple genetic perturbations
that are associated with those processes.
So if you look at schizophrenia,
it's excitatory neuron function,
it's inhibitory neuron function,
it's synaptic pruning, it's calcium signaling,
and so on and so forth.
So when you look at disease genetics,
you have one hit here and one hit there
and one hit there and one hit there,
completely different parts of the genome.
But it turns out all of those hits
are calcium signaling proteins.
- Oh, cool.
- You're like, aha, that means
that calcium signaling is important.
So those people who are focusing on one docus at a time
cannot possibly see that picture.
You have to become a genomicist.
You have to look at the omics, the om,
the holistic picture to understand these enrichments.
- But you mentioned the convergence thing.
So whatever the thing associated
with the disease shows up.
- So let me explain convergence.
Convergence is such a beautiful concept.
(Zubin 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.
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 of these pathways are acting in the same tissue,
and all of these tissues are converging together
on the same biological process of schizophrenia.
- And you're saying the saving grace
is that that convergence seems to happen
§09Transcript · 1:13:09–1:22:17
for a lot of these diseases.
- For all of them.
Basically that for every single disease
that we've looked at,
we have found an epigenomic enrichment.
How do you do that?
You basically have all of the genetic variants
associated with the disorder,
and then you're asking for all of the enhancers
active in a particular tissue.
For 540 disorders,
we've basically found that indeed there is an enrichment.
That basically means that there is commonality,
and from the commonality, we can just get insights.
So to explain in mathematical terms,
we're basically building an empirical prior.
We're using a Bayesian approach to basically say,
"Great, all of these variants are equally likely
"in a particular locus to be important."
So in a genetic locus,
you basically have a dozen variants that are co-inherited,
because the way that inheritance works in the human genome
is through all of these recombination events during meiosis.
You basically have, you know,
you inherit maybe three, chromosome three, for example,
in your body.
It's inherited from four different parts.
One part comes from your dad,
another part comes from your mom,
another part comes from your dad,
another part comes from your mom.
So basically, the way that it,
sorry, from your mom's mom.
So you basically have one copy that comes from your dad
and one copy that comes from your mom.
But that copy that you got from your mom
is a mixture of her maternal and her paternal chromosome.
And the copy that you got from your dad
is a mixture of his maternal and his paternal chromosome.
So these breakpoints that happen
when chromosomes are lining up
are basically ensuring, through these crossover events,
they're ensuring that every child cell
during the process of meiosis,
where you basically have, 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 your genome that comes from mom.
But in order to line them up,
you basically have these crossover events.
These crossover events are basically leading
to co-inheritance of that entire block
coming from your maternal grandmother
and that entire block coming from your maternal grandfather.
Over many generations,
these crossover events don't happen randomly.
There's a protein called PRDM9
that basically guides the double-stranded breaks
and then leads to these crossovers.
And that protein has a particular preference
to only a small number of hotspots of recombination,
which then lead to a small number of breaks
between these co-inheritance patterns.
So even though there are 6 million variants,
there are 6 million loci,
this variation is inherited in blocks.
And every one of these blocks has like two dozen
genetic variants that are all associated.
So in the case of FTO, it wasn't just one variant,
it was 89 common variants
that were all humongously associated with obesity.
Which 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.
- Progenitor is like the early on developmental stem cells?
- So you start from one zygote,
and that's a totipotent cell type, it can do anything.
You then, 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 body.
The ectoderm will basically give rise to your skin.
Ecto means outside, derm is skin.
So ectoderm, but it also gives rise to your neurons
and your whole brain, so that's a lot of ectoderm.
Mesoderm gives rise to your internal organs,
including the vasculature and your muscle
and stuff like that.
So you basically have this progressive differentiation,
and then if you look further, further down that lineage,
you basically have one lineage that will give rise
to both your muscle and your bone, but also your fat.
And if you go further down the lineage of your fat,
you basically have your white fat cells.
These are the cells that store energy.
So when you eat a lot, but you don't exercise too much,
there's an excess set of calories, excess energy.
What do you do with those?
You basically create, you spend a lot of that energy
to create these high-energy molecules, lipids,
which you can then burn when you need them on a rainy day.
So that leads to obesity if you don't exercise
and if you overeat, because your body'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 42% of European chromosomes
have a predisposition to storing fat,
which was selected probably in the food scarcity periods.
Like basically as we were exiting Africa
before and during the ice ages,
there was probably a selection to those individuals
who made it north to basically be able to store energy,
a lot more energy.
So you basically now have this lineage
that is deciding whether you want to store energy
in your white fat or burn energy in your beige fat.
Turns out that your fat is,
we have such a bad view of fat.
Fat is your best friend.
Fat can both store all these excess lipids
that would be otherwise circulating through your body
and causing damage, but it can also burn calories directly.
If you have too much 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.
So going back to those steps,
first step is figure out the relevant tissue
based on the global enrichment.
Second step is figure out the causal variant
among many variants in this linkage disequilibrium
in this co-inherited block
between these recombination hotspots,
these boundaries of these inherited blocks.
That's the second step.
The third step is once you know that causal variant,
try to figure out what is the motif
that is disrupted by that causal variant.
Basically, how does it act?
Variants don't just disrupt elements,
they disrupt the binding of specific regulators.
So basically the third step there was
how do you find the motif that is responsible,
like the gene regulatory word,
the building block of gene regulation
that is responsible for that dysregulatory event.
And the fourth step is finding out
what regulator normally binds that motif
and is now no longer able to bind.
- And then once you have the regulator,
can you then try to figure out how to,
what, after it developed, how to fix it?
- That's exactly right.
You now know how to intervene.
You have basically a regulator,
you have a gene that you can then perturb.
And you say, well, maybe that regulator
has a global role in obesity.
I can perturb the regulator.
- Just to clarify, when we say perturb,
like on the scale of a human life,
can a human being be helped?
- Of course, of course.
Yeah, so perturb. - I guess understanding
is the first step.
- Exactly.
No, no, but perturbed basically means
you now develop therapeutics,
pharmaceutical therapeutics against that.
Or you develop other types of intervention
that affect the expression of that gene.
- What do pharmaceutical therapeutics look like
when your understanding's on a genetic level?
- Yeah. - Sorry if it's
a dumb question. - No, no, no.
It's a brilliant question,
but I wanna save it for a little bit later
when we start talking about therapeutics.
- Perfect. - We've talked about
§10Transcript · 1:22:18–1:30:55
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.
The first step after that is figure out
the tissue of action.
The second step is figuring out the nucleotide
that is responsible or set of nucleotides.
The third step is figure out the motif
and the upstream regulator, number four.
Number five and six is what are the targets?
So number five is great.
Now I know the regulator, I know the motif,
I know the tissue, and I know the variant.
What does it actually do?
So you have to now trace it to the biological process
and the genes that mediate that biological process.
So knowing all of this can now allow you
to find the target genes.
How?
By basically doing perturbation experiments
or by looking at the folding of the epigenome
or by looking at the genetic impact
of that genetic variant on the expression of genes.
And we use all three.
So let me go through them.
Basically, one of them is physical links.
This is the folding of the genome onto itself.
How do you even figure out the folding?
It's a little bit of a tangent,
but it's a super awesome technology.
Think of the genome as, again,
this massive packaging that we talked about
of taking two meters worth of DNA
and putting it in something that's a million times smaller
than two meters worth of DNA, that's a single cell.
You basically have this massive packaging,
and this packaging basically leads to the chromosome
being wrapped around in sort of tie-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,
that region of the chromosome were near each other,
that means they were interacting,
even though they were far away on the genome itself.
So that chopping up, sequencing, and re-gluing
is basically giving you folds of the genome that we call.
- Sorry, can you backtrack?
- Of course. - How does cutting it
help you figure out which ones were close
in the original folding?
- So you have a bowl of noodles.
- Go on.
- And in that bowl of noodles,
some noodles are near each other.
So throwing a bunch of glue,
you basically freeze the noodles in place,
throwing a cutter that chops up the noodles
into little pieces,
now throwing 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 blue noodle to each other.
You then reverse the glue, the glue goes away,
and you just sequence the heck out of it.
Most of the time, you'll find red segment with,
you know, a 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 a particular way,
and then selecting those,
and then you sequence and you look for red with blue matches
of sort of things that were glued
that were not immediate proximal to each other,
and that reveals the linking of the blue noodle
and the red noodle.
You're with me so far? - Yeah.
- Good.
So we've done these experiments.
- That's the physical.
- That's the physical.
That's step one of the physical,
and what the physical revealed
is topologically associated domains,
basically big blocks of the genome
that are topologically, you know, connected together.
That's the physical.
The second one is the genetic links.
It basically says, across individuals
that have different genetic variants,
how are their genes expressed differently?
Remember before I was saying
that the path between genetics and disease is enormous,
but we can break it up to look at the path
between genetics and gene expression.
So instead of using Alzheimer's as the phenotype,
I can now use expression of IRX3 as the phenotype,
expression of gene A,
and I can look at all of the humans
who contain a G at that location
and all the humans 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?
- I think so. - Awesome.
So the third link is the activity link.
What's an activity link?
It basically says,
if I look across 833 different epigenomes,
whenever this enhancer is active,
this gene is active.
That gives me an activity link
between this region of the DNA and that gene.
And then the fourth one is perturbations
where I can go in and blow up that region
and see what are the genes that change in expression,
or I can go in and over-activate that region
and see what genes change in expression.
- So I guess that's similar to activity?
- Yeah, yeah, so that's basically similar to activity.
I agree, but it's causal rather than correlational.
- Again, I'm a little weird.
- No, no, you're 100% on.
It's exactly the same as activity,
but perturbation. - But the perturbations.
- Where I go and intervene,
I basically take a bunch of cells.
So you know CRISPR, right?
CRISPR is this genome guidance and cutting mechanism.
It's what George Church likes to call genome vandalism.
So you basically are able to, (laughs)
you can basically take a guide RNA
that you put into the CRISPR 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 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 an interspersed repeats structure,
where basically you have a set of repetitive regions,
and then interspersed where 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 manage the kill.
And then the bacteria pass on,
you know, they sort of do lateral transfer of DNA,
and they pass on these memories
so that the next bacterium says,
"Ooh, you killed that guy.
"When that guy shows up again, I will recognize him."
And the CRISPR system was basically evolved
as a bacterial adaptive immune response
to sense foreigners that should not belong,
and to just go and cut their genome.
So it's an RNA guided, RNA cutting enzyme,
or an RNA guided DNA cutting enzyme.
So there's different systems.
Some of them cut DNA, some of them cut RNA,
but all of them remember this sort of viral attack.
So what we have done now as a field is, you know,
through the work of, you know, Jennifer Doudna,
Manuel Carpentier, Feng Zhang, and many others,
is co-opted that system of bacterial immune defense
as a way to cut genomes.
You basically have this guiding system
that allows you to use an RNA guide
to bring enzymes to cut DNA at a particular locus.
- That's so fascinating.
So this is like already a natural mechanism,
a natural tool for cutting that was useful
in this particular context.
And we're like, well, we can use that thing to actually,
it's a nice tool that's already in the body.
- Yeah, yeah.
It's not in our body, it's in the bacterial body.
It was discovered by the yogurt industry.
§11Transcript · 1:30:58–1:39:51
They were trying to make better yogurts,
and they were trying to make their bacteria
in their yogurt cultures more resilient to viruses.
And they were studying bacteria,
and they found that, wow, this CRISPR system is awesome.
It allows you to defend against that.
And then it was co-opted in mammalian systems
that don't use anything like that as a targeting way
to basically bring these DNA-cutting enzymes
to any locus in the genome.
Why would you want to cut DNA to do anything?
The reason is that our DNA has a DNA repair mechanism,
where if a region of the genome gets randomly cut,
you will 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.
- So all you have to do is the cutting,
and you don't have to do the fixing.
- That's exactly right.
You don't have to do the fixing.
- 'Cause it's already built in.
- That's exactly right.
But the fixing can be co-opted
by throwing in a bunch of homologous segments
that instead of having your dad's version,
have whatever other version you'd like to use.
- So you then control the fixing
by throwing in a bunch of other stuff.
- That's exactly right.
And that's how you do genome editing.
- So that's what CRISPR is.
- That's what CRISPR is.
- In popular culture, people use the term.
I've never, wow, that's brilliant.
That's just an awesome explanation.
- Genome vandalism followed by a bunch of Band-Aids
that have the sequence that you'd like.
- And you can control the choices of Band-Aids.
- Correct.
And of course, there's new generations of CRISPR.
There's something that's called prime editing
that was sort of very, very much in the press recently,
that basically instead of sort of making
a double-stranded break, which again is genome vandalism,
you basically make a single-stranded break.
You basically just nick one of the two strands,
enabling you to sort of peel off
without sort of completely breaking it up,
and then repair it locally using a guide
that is coupled to your initial RNA
that took you to that location.
- Dumb question, but is CRISPR as awesome
and cool as it sounds?
I mean, technically speaking, in terms of like,
as a tool for manipulating our genetics
in the positive meaning of the word manipulating,
or is there downsides, drawbacks,
in this whole context of therapeutics
that we're talking about, or understanding and so on?
- So when I teach my students about CRISPR,
I show them articles with the headline,
Genome Editing Tool Revolutionizes Biology,
and then I show them the date of these articles,
and they're 2004, like five years
before CRISPR was invented.
And the reason is that they're not talking about CRISPR.
They're talking about zinc finger enzymes
that are another way to bring these cutters to the genome.
It's a very difficult way of sort of designing
the right set of zinc finger proteins,
the right set of amino acids
that will now target a particular long stretch of DNA,
because for every location that you want to target,
you need to design a particular regulator,
a particular protein that will match that region well.
There's another technology called talons,
which are basically just a different way of using proteins
to sort of guide these cutters
to a particular location in the genome.
These require a massive team of engineers,
of biological engineers,
to basically design a set of amino acids
that will target a particular sequence of your genome.
The reason why CRISPR is amazingly, awesomely revolutionary
is because instead of having this team of engineers
design a new set of proteins
for every locus that you want to target,
you just type it in your computer,
and you just synthesize an RNA guide.
The beauty of CRISPR is not the cutting.
It's not the fixing.
All of that was there before.
It's the guiding, and the only thing that changes
is that it makes the guiding easier
by sort of just typing in the RNA sequence,
which then allows the system to sort of scan the DNA
to find that.
- So the coding, the engineering of the cutter is easier
in terms of SV. - Exactly.
- That's kind of similar to the story of deep learning
versus old school machine learning,
is some of the challenging parts are automated.
Okay, so, but CRISPR's just one cutting technology.
And then there's, that's part of the challenges
and exciting opportunities of the field
is to design different cutting technologies.
- So now, this was a big parenthesis on CRISPR,
but now, when we were talking about perturbations,
you basically now have the ability
to not just look at correlation between enhancers and genes,
but actually go and either destroy that enhancer
and see if the gene changes in expression,
or you can use the CRISPR targeting system
to bring in not vandalism and cutting,
but you can couple the CRISPR system with,
and the CRISPR system is called usually CRISPR-Cas9
because Cas9 is the protein that will then come and cut.
But there's a version of that protein called dead Cas9
where the cutting part is deactivated.
So you basically use dCas9, dead Cas9,
to bring in an activator or to bring in a repressor.
So you can now ask, is this enhancer changing that gene?
By taking this modified CRISPR,
which is already modified from the bacteria
to be used in humans,
that you can now modify the Cas9 to be dead Cas9,
and you can now further modify to bring in a regulator,
and you can basically turn on or turn off that enhancer
and then see what is the impact on that gene.
So these are the four ways of linking the locus
to the target gene, and that's step number five.
Step number five is find the target gene,
and step number six is what the heck does that gene do?
You basically now go and manipulate that gene
to basically see what are the processes that change.
And you can basically ask, well, in this particular case,
in the FTO locus, we found mesenchymal stem cells
that are the progenitors of white fat and brown fat,
or beige fat.
We found the RS1421085 nucleotide variant
as the causal variant.
We found this large enhancer, this master regulator.
I like to call it OB1 for obesity one,
like the strongest enhancer associated with it.
And OB1 was kind of chubby as the actor,
I don't know if you remember him.
(laughing)
- Yeah.
- So you basically are using this Jedi mind trick
to basically find out the--
- Thank you.
(laughing)
- The location of the genome that is responsible,
the enhancer that harbors it, the motif,
the upstream regulator, which is ARID5B,
for AT-rich interacting domain 5B.
That's a protein that sort of comes and binds normally.
That protein is normally a repressor.
It represses this super enhancer,
this massive 12,000 nucleotide
master regulatory control region,
and it turns off IRX3,
which is a gene that's 600,000 nucleotides away,
and IRX5, which is 1.2 million nucleotides away.
So those--
- And what's the effect of turning them off?
- That's exactly the next question.
So step six is, what do these genes actually do?
So we then ask, what does IRX3 and IRX5 do?
The first thing we did is look across individuals
for individuals that had higher expression of IRX3
or lower expression of IRX3.
And then we looked at the expression
of all of the other genes in the genome.
And we looked for simply correlation.
And we found that IRX3 and IRX5
were both correlated positively with lipid metabolism
and negatively with mitochondrial biogenesis.
You're like, what the heck does that mean?
- Doesn't sound related to obesity.
- Not at all, superficially.
But lipid metabolism should,
because lipids is these high energy molecules
that basically store fat.
So IRX3 and IRX5 are negatively correlated
with lipid metabolism.
So that basically means that when they turn on,
lipid metabolism, or positively,
when they turn on, they turn on lipid metabolism.
And they're negatively correlated
with mitochondrial biogenesis.
What do mitochondria do in this whole process?
Again, small parenthesis, what are mitochondria?
Mitochondria are little organelles.
§12Transcript · 1:39:56–1:48:48
They arose, they only are found in eukaryotes.
Euk means good, karyote means nucleus.
So truly, like a true nucleus.
So eukaryotes have a nucleus.
Prokaryotes are before the nucleus.
They don't have a nucleus.
So eukaryotes have a nucleus.
Hmm, compartmentalization.
Eukaryotes have also organelles.
Some eukaryotes have chloroplasts.
These are the plants, they photosynthesize.
Some other eukaryotes, like us,
have another type of organelle called mitochondria.
These arose from an ancient species that we engulfed.
This is an endosymbiosis event.
Symbiosis, bio means life, sym means together.
So symbiotes are things that live together.
Endosymbiosis, endo means inside,
so endosymbiosis means you live together,
holding the other one inside you.
So the pre-eukaryotes engulfed an organism
that was very good at energy production,
and that organism eventually shed most of its genome
to now have only 13 genes in the mitochondrial genome.
And those 13 genes are all involved in energy production,
the electron transport chain.
So basically, electrons are these massive,
super energy-rich molecules.
We basically have these organelles that produce energy,
and when your muscle exercises,
you basically multiply your mitochondria.
You basically sort of use more and more mitochondria,
and that's how you get beefed up.
So basically, the muscle sort of learns
how to generate more energy.
So basically, every single time,
your muscles will, you know, overnight regenerate
and sort of become stronger
and amplify their mitochondria and so forth.
So what do the mitochondria do?
The mitochondria use energy to sort of do any kind of task.
When you're thinking, you're using energy.
This energy comes from mitochondria.
Your neurons have mitochondria all over the place.
Basically, this mitochondria can multiply its organelles,
and they can be spread along the body of your muscle.
Some of your muscle cells have actually multiple nuclei.
They're polynucleated,
but they also have multiple mitochondria
to basically deal with the fact
that your muscle is enormous.
You can sort of span this super, super long length,
and you need energy throughout the length of your muscle.
So that's why you have mitochondria throughout the length,
and you also need transcription through the length,
so you have multiple nuclei as well.
So these two processes, lipids store energy.
What do mitochondria do?
So there's a process known as thermogenesis,
thermal heat genesis generation.
Thermogenesis is the generation of heat.
Remember that bathtub with in and out?
That's the equation that everybody's focused on.
So how much energy do you consume?
How much energy do you burn?
But in every thermodynamic system,
there's three parts to the equation.
There's energy in, energy out, and energy lost.
Any machine has loss of energy.
How do you lose energy?
You emanate heat.
So heat is energy loss.
So there's--
- Which is where the thermogenesis comes in.
- Thermogenesis is actually a regulatory process
that modulates the third component
of the thermodynamic equation.
You can basically control thermogenesis explicitly.
You can turn on and turn off thermogenesis.
- And that's where the mitochondria comes into play.
- Exactly.
So RX3 and RX5 turn out to be the master regulators
of a process of thermogenesis
versus lipogenesis, generation of fat.
So RX3 and RX5 in most people burn heat,
burn calories as heat.
So when you eat too much,
just burn it off in your fat cells.
So that bathtub has basically a sort of dissipation knob
that most people are able to turn on.
I am unable to turn that on
because I am a homozygous carrier
for the mutation that changes a T into a C
in the RS1421085 allele, a locus, a SNP.
I have the risk allele twice,
from my mom and from my dad.
So I'm unable to thermogenize.
I'm unable to turn on thermogenesis through RX3 and RX5
because the regulator that normally binds here,
RX5B, can no longer bind
because it's an AT-rich interacting domain.
And as soon as I change the T into a C,
it can no longer bind because it's no longer AT-rich.
- But doesn't that mean
that you're able to use the energy more efficiently?
You're not generating heat?
Or is that--
- That means I can eat less and get around just fine.
- Yes.
- Yeah, so--
- That's a feature, actually.
- It's a feature in a food-scarce environment.
- Yeah, but--
- If we're all starving, I'm doing great.
If we all have access to massive amounts of food,
I'm obese, basically.
- That's taken us through the entire process
of then understanding why mitochondria and then the lipids
are both, even though distant, are somehow involved.
- Different sides of the same coin.
You basically choose to store energy
or you can choose to burn energy.
- And that all of that is involved in the puzzle of obesity.
- And that's what's fascinating, right?
Here we are in 2007,
discovering the strongest genetic association with obesity
and knowing nothing about how it works for almost 10 years.
For 10 years, everybody focused on this FTO gene.
And they were like, "Oh, it must have to do something
"with RNA modification."
And it's like, "No, it has nothing to do
"with the function of FTO.
"It has everything to do with all of this other process."
And suddenly, the moment you solve that puzzle,
which is a multi-year effort, by the way,
a tremendous effort by Melina and many, many others.
So this tremendous effort basically led us
to recognize this circuitry.
You went from having some 89 common variants associated
in that region of the DNA, sitting on top of this gene,
to knowing the whole circuitry.
When you know the circuitry, you can now go crazy.
You can now start intervening at every level.
You can start intervening at the RX5B level.
You can start intervening with CRISPR-Cas9
at the single SNP level.
You can start intervening at RX3 and RX5, directly there.
You can start intervening at the thermogenesis level
because you know the pathway.
You can start intervening at the differentiation level,
where the decision to make either white fat or beige fat,
the energy-burning beige fat,
is made developmentally in the first three days
of differentiation of your adipocytes.
So as they're differentiating, you basically can choose
to make fat-burning machines or fat-storing machines,
and sort of that's how you populate your fat.
You basically can now go in pharmaceutically
and do all of that.
And in our paper, we actually did all of that.
We went in and manipulated every single aspect.
At the nucleotide level,
we use CRISPR-Cas9 genome editing
to basically take primary adipocytes
from risk and non-risk individuals,
and show that by editing that one nucleotide
out of 3.2 billion nucleotides in the human genome,
you could then flip between an obese phenotype
and a lean phenotype like a switch.
You can basically take micelles that are non-thermogenizing
and just flip into thermogenizing cells
by changing one nucleotide.
It's mind-boggling.
- It's so inspiring that this puzzle
could be solved in this way,
and it feels within reach to then be able
to crack the problem of some of these diseases.
What are, so it's 2007 you mentioned,
what are the technologies, the tools that came along
that made this possible?
Like what are you excited about,
maybe if we just look at the buffet of things
that you've kind of mentioned.
Is there, what's involved, what should we be excited about,
what are you excited about?
- I love that question because there's so much ahead of us.
There's so, so much.
There's, so basically solving that one locus
required massive amounts of knowledge
that we have been building across the years
through the epigenome, through the comparative genomics
to find out the causal variant
and the controller regulatory motif
through the conserved circuitry.
It required knowing this regulatory genomic wiring.
It required high C of these sort of topologically
associated domains to basically find
this long range interaction.
It required EQTLs of this sort of genetic perturbation
of these intermediate gene phenotypes.
§13Transcript · 1:48:51–1:57:49
It required all of the arsenal of tools
that I've been describing was put together for one locus.
And this was a massive team effort,
huge investment in time, energy, money,
effort, intellectual, everything.
- You're referring to, I'm sorry,
just for the obesity one. - This one paper, yeah.
This one paper. - This one single paper.
- This one single locus.
I like to say that this is a paper
about one nucleotide in the human genome,
about one bit of information,
C versus T in the human genome.
That's one bit of information
and we have 3.2 billion nucleotides to go through.
So how do you do that systematically?
- I am so excited about the next phase of research
because the technologies that my group
and many other groups have developed
allows us to now do this systematically,
not just one locus at a time,
but thousands of loci at a time.
So let me describe some of these technologies.
The first one is automation and robotics.
So basically, we talked about how you can take
all of these molecules and see
which of these molecules are targeting each of these genes
and what do they do.
So you can basically now screen
through millions of molecules,
through thousands and thousands and thousands of plates,
each of which has thousands and thousands
and thousands of molecules,
every single time testing all of these genes
and asking which of these molecules perturb these genes.
So that's technology number one, automation and robotics.
Technology number two is parallel readouts.
So instead of perturbing one locus
and then asking if I use CRISPR-Cas9 on this enhancer
to basically use dCas9 to turn on or turn off the enhancer,
or if I use CRISPR-Cas9 on the SNP
to basically change that one SNP at a time,
then what happens?
But we have 120,000 disease-associated SNPs
that we wanna test.
We don't wanna spend 120,000 years doing it.
So what do we do?
We basically develop this technology
for massively parallel reporter assays, MPRA.
So in collaboration with Tarjan Michelson, Eric Lander,
I mean Jason Durey's group has done a lot of that.
So there's a lot of groups
that basically have developed technologies
for testing 10,000 genetic variants at a time.
How do you do that?
We talked about microarray technology,
the ability to synthesize these huge microarrays
that allow you to do all kinds of things
like measure gene expression by hybridization,
by measuring the genotype of a person,
by looking at hybridization with one version with a T
versus the other version with a C,
and then sort of figuring out
that I am a risk carrier for obesity
based on these hybridization,
differential hybridization in my genome that says,
oh, you seem to only have this allele
or you seem to have that allele.
Microarrays can also be used
to systematically synthesize small fragments of DNA.
So you can basically synthesize
these 150 nucleotide long fragments
across 450,000 spots at a time.
You can now take the result of that synthesis,
which basically works through all of these sort of layers
of adding one nucleotide at a time.
You can basically just type it into your computer
and order it, and you can basically order 10,000
or 100,000 of these small DNA segments at a time.
And that's where awesome molecular biology comes in.
You can basically take all these segments,
have a common start and end barcode or sort of ligator,
like just like pieces of a puzzle,
you can make the same end piece
and the same start piece for all of them.
And you can now use plasmids,
which are these extra chromosomal,
small DNA circular segments
that are basically inhabiting all our genomes.
We basically have plasmids floating around.
I mean, bacteria use plasmids for transferring DNA,
and that's where they put a lot
of antibiotic resistance genes.
So they can easily transfer them
from one bacterium to the other.
So one bacterium evolves a gene to be resistant
to a particular antibiotic.
It basically says to all its friends,
"Hey, here's that sort of DNA piece."
We can now co-opt these plasmids into human cells.
You can basically make a human cell culture
and add plasmids to that human cell culture
that contain the things that you want to test.
You now have this library of 450,000 elements.
You can insert them each into the common plasmid
and then test them in millions of cells in parallel.
- And the common plasmid is all the same
before you add any-- - Exactly.
The rest of the plasmid is the same.
So it's called an epizomal reporter assay.
Epizome means not inside the genome.
It's sort of outside the chromosomes.
So it's an epizomal assay
that allows you to have a variable region
where you basically test 10,000 different enhancers,
and you have a common region
which basically has the same reporter gene.
You now can do some very cool molecular biology.
You can basically take the 450,000 elements
that you've generated,
and you have a piece of the puzzle here,
piece of the puzzle here, which is identical,
so they're compatible with that plasmid.
You can chop them up in the middle
to separate a barcode reporter from the enhancer,
and in the middle, put the same gene,
again, using the same pieces of the puzzle.
You now can have a barcode readout
of what is the impact of 10,000 different versions
of an enhancer on gene expression.
So we're not doing one experiment.
We're doing 10,000 experiments.
And those 10,000 can be 5,000 of different loci,
and each of them in two versions, risk or non-risk.
I can now test tens of thousands--
- These are little hypotheses.
- Exactly.
- And then you can do 10,000, and wait--
- You can test 10,000 hypotheses at once.
- How hard is it to generate those 10,000?
- Trivial, trivial.
- But it's biology.
- No, no, generating the 10,000 is trivial
because you basically add, it's by technology.
You basically have these arrays
that add one nucleotide at a time at every spot.
- So it's printing, and so you're able to control.
- Yeah.
- Super costly, is it?
- 10,000 bucks.
- So this is in millions?
- 10,000 bucks for 10,000 experiments.
Sounds like the right, you know.
- I mean, so that's super, that's exciting,
'cause you don't have to do one thing at a time.
- You can now use that technology,
these massively parallel reporter assays,
to test 10,000 locations at a time.
We've made multiple modifications to that technology.
One was SHARPER MPRA, which stands for, you know,
basically getting a higher resolution view
by tiling these elements.
So you can see where along the region of control
are they acting.
And we made another modification called HYDRA
for high, you know, definition, regulatory annotation
or something like that,
which basically allows you to test 7 million of these
at a time by sort of cutting them directly from the DNA.
So instead of synthesizing,
which basically has the limit of 450,000
that you can synthesize at a time,
we basically said, "Hey, if we wanna test
all accessible regions of the genome,
let's just do an experiment that cuts accessible regions.
Let's take those accessible regions,
put them all with the same end joints of the puzzles,
and then now use those to create a much, much larger array
of things that you can test.
And then tiling all of these regions,
you can then pinpoint what are the driver nucleotides,
what are the elements, how are they acting
across 7 million experiments at a time.
So basically, this is all the same family of technology
where you're basically using these parallel readouts
of the barcodes.
And then, you know, to do this,
we used a technology called StarSeq
for self-transcribing reporter assays,
a technology developed by Alex Stark,
my former postdoc, who's now a PI over in Vienna.
So we basically coupled the StarSeq,
the self-transcribing reporters,
where the enhancer can be part of the gene itself.
So instead of having a separate barcode,
that enhancer basically acts to turn on the gene
and is transcribed as part of the gene.
- So you don't have to have the two separate parts.
- Exactly, so you can just read them directly.
- So there's a constant improvement in this whole process.
§14Transcript · 1:57:52–2:06:24
By the way, generating all these options,
is it basically brute force?
How much human intuition is--
- Oh gosh, of course it's human intuition
and human creativity and incorporating
all of the input data sets,
because again, the genome is enormous, 3.2 billion.
You don't wanna test that.
Instead, you basically use all of these tools
that I've talked about already.
You generate your top favorite 10,000 hypotheses,
and then you go and test all 10,000.
And then from what comes out,
you can then go to the next step.
So that's technology number two.
So technology number one is robotics, automation,
where you have thousands of wells
and you constantly test them.
The second technology is instead of having wells,
you have these massively parallel readouts
in sort of these pooled asses.
The third technology is coupling CRISPR perturbations
with these single-cell RNA readouts.
So let me make another parenthesis here
to describe now single-cell RNA sequencing.
So what does single-cell RNA sequencing mean?
So RNA sequencing is what has been traditionally used,
or well, traditionally, the last 20 years,
ever since the advent of next-generation sequencing.
So basically, before, RNA expression profiling
was based on these microarrays.
The next technology after that was based on sequencing.
So you chop up your RNA
and you just sequence small molecules,
just like you would sequence a genome,
basically reverse transcribe the small RNAs into DNA,
and you sequence that DNA
in order to get the number of sequencing reads
corresponding to the expression level
of every gene in the genome.
You now have RNA sequencing.
How do you go to single-cell RNA sequencing?
That technology also went through stages of evolution.
The first was microfluidics.
You basically had these, or even chambers,
you basically had these ways of isolating individual cells,
putting them into a well for every one of these cells.
So you have 384 well plates,
and you now do 384 parallel reactions
to measure the expression of 384 cells.
That sounds amazing, and it was amazing,
but we wanna do a million cells.
How do you go from these wells to a million cells?
You can't.
So what the next technology was after that
is instead of using a well for every reaction,
you now use a lipid droplet for every reaction.
So you use micro droplets as reaction chambers
to basically amplify RNA.
So here's the idea.
You basically have microfluidics,
where you basically have every single cell
coming down one tube in your microfluidics,
and you have little bubbles getting created in the other way
with specifical primers that mark every cell
with its own barcode.
You basically couple the two,
and you end up with little bubbles that have a cell
and tons of markers for that cell.
You now mark up all of the RNA for that one cell
with the same exact barcode,
and you then lyse all of the droplets,
and you sequence the heck out of that,
and you have for every RNA molecule
a unique identifier that tells you what cell was it on.
- That is such good engineering, microfluidics,
and using some kind of primer to put a label on the thing.
I mean, you're making this sound easy.
I assume it's-- - It's beautiful, right?
- But it's gorgeous, yeah.
- So there's the next generation.
So that's the second generation.
Next generation is, forget the microfluidics altogether.
Just use big bottles.
How can you possibly do that with big bottles?
So here's the idea.
You dissociate all of your cells,
or all of your nuclei from complex cells like brain cells
that are very long and sticky, so you can't do that.
So if you have blood cells,
or if you have neuronal nuclei or brain nuclei,
you can basically dissociate, let's say, a million cells.
You now want to add a unique barcode,
a unique barcode in each one of a million cells
using only big bottles.
How can you possibly do that?
Sounds crazy, but here's the idea.
You use 100 of these bottles.
You randomly shuffle all your million cells,
and you throw them into those 100 bottles,
randomly, completely randomly.
You add one barcode out of 100 to every one of the cells.
You then, you now take them all out,
you shuffle them again,
and you throw them again into the same 100 bottles,
but now in a different randomization,
and you add a second barcode.
So every cell now has two barcodes.
You take them out again,
you shuffle them, and you throw them back in.
Another third barcode is adding,
randomly, from the same 100 barcodes.
You've now labeled every cell probabilistically
based on the unique path that it took
of which of 100 bottles did it go for the first time,
which of 100 bottles the second time,
and which of 100 bottles the third time.
100 times 100 times 100 is a million unique barcodes
in every single one of these cells
without ever using microfluid.
- Very clever.
- It's beautiful, right? - From a computer science
perspective, that's very clever.
- Yeah, so you now have the single cell
sequencing technology.
You can use the wells, you can use the bubbles,
or you can use the bottles.
You have ways--
- The bubbles still sound pretty damn cool.
- The bubbles are awesome,
and that's basically the main technology that we're using.
So the bubbles is the main technology.
So there are kits now that companies just sell
to basically carry out single cell RNA sequencing
that you can basically, for $2,000,
you can basically get 10,000 cells from one sample,
and for every one of those cells,
you basically have the transcription of thousands of genes.
And of course, the data for any one cell is noisy,
but being computer scientists,
we can aggregate the data from all of the cells together
across thousands of individuals together
to basically make very robust inferences.
So the third technology is basically single cell
RNA sequencing that allows you to now start asking
not just what is the brain expression level difference
of that genetic variant,
but what is the expression difference
of that one genetic variant
across every single subtype of brain cell?
How is the variance changing?
You can't just, you know, with a brain sample,
you can just ask about the mean.
What is the average expression?
If I instead have 3,000 cells that are neurons,
I can ask not just what is the neuronal expression,
I can say for layer five excitatory neurons,
of which I have, I don't know, 300 cells,
what is the variance that this genetic variant has?
So suddenly, it's amazingly more powerful.
I can basically start asking
about this middle layer of gene expression
at unprecedented levels.
- And when you look at the average,
it washes out some potentially important signal
that corresponds to ultimately the disease.
- Completely.
- Yeah.
- So that, I can do that at the RNA level,
but I can also do that at the DNA level for the epigenome.
So remember how before I was telling
about all this technology that we're using
to probe the epigenome?
One of them is DNA accessibility.
So what we're doing in my lab is that
from the same dissociation of, say, a brain sample,
where you now have all these tens of thousands
of cells floating around,
you basically take half of them to do RNA profiling,
and the other half to do epigenome profiling,
both at the single cell level.
So that allows you to now figure out
what are the millions of DNA enhancers
that are accessible in every one
of tens of thousands of cells.
And computationally, we can now take the RNA
and the DNA readouts and group them together
to basically figure out how is every enhancer
related to every gene.
And remember these sort of enhancer gene linking
that we were doing across 833 samples?
833 is awesome, don't get me wrong,
but 10 million is way more awesome.
So we can now look at correlated activity
across 2.3 million enhancers and 20,000 genes
in each of millions of cells
to basically start piecing together
the regulatory circuitry of every single type of neuron,
every single type of astrocytes, oligodendrocytes,
microglial cell inside the brains
§15Transcript · 2:06:27–2:15:22
of 1,500 individuals that we've sampled
across multiple different brain regions
across both DNA and RNA.
So that's the dataset that my team generated last year alone.
So in one year, we've basically generated 10 million cells
from human brain across a dozen different disorders,
across schizophrenia, Alzheimer's,
frontotemporal dementia, Lewy body dementia, ALS,
Huntington's disease, post-traumatic stress disorder,
autism, bipolar disorder, healthy aging, et cetera.
- So it's possible that even just within that dataset
lie a lot of keys to understanding these diseases
and then be able to directly lead to then treatment.
- Correct, correct.
So basically we are now-- - Motivating.
- Yeah, so our computational team is in heaven right now
and we're looking for people.
I mean, if you have listeners who are--
- They inspire, yeah, yeah.
- Super smart computational people.
- So this is a very interesting kind of side question.
How much of this is biology, how much of this is computation?
So you had the computational biology group,
but how much of, should you be comfortable with biology
to be able to solve some of these problems?
If you just find, if you put several of the hats
that you wear on, fundamentally,
are you thinking like a computer scientist here?
- You have to.
This is the only way.
As I said, we are the descendants
of the first digital computer.
We're trying to understand the digital computer.
We're trying to understand the circuitry,
the logic of this digital core computer
and all of these analog layers surrounding it.
So the case that I've been making
is that you cannot think one gene at a time.
The traditional biology is dead.
There's no way, you cannot solve disease
with traditional biology.
You need it as a component.
Once you figured out RX3 and RX5,
you now can then say, hey,
have you guys worked on those genes
with your single gene approach?
We'd love to know everything you know.
And if you haven't,
we now know how important these genes are.
Let's now launch a single gene program
to dissect them and understand them.
But you cannot use that as a way to dissect disease.
You have to think genomically.
You have to think from the global perspective
and you have to build these circuits systematically.
So we need numbers of computer scientists
who are interested and willing to dive into these data,
you know, fully, fully in and sort of extract meaning.
We need computer science people
who can understand sort of machine learning and inference
and sort of, you know, decouple these matrices,
come up with super smart ways of sort of dissecting them.
But we also need computer scientists who understand biology,
who are able to design the next generation of experiments.
Because many of these experiments,
no one in their right mind would design them
without thinking of the analytical approach
that you would use to deconvolve the data afterwards.
Because it's massive amounts of ridiculously noisy data.
And if you don't have the computational pipeline
in your head before you even design the experiment,
you would never design the experiment that way.
- That's brilliant, so in designing the experiment,
you have to see the entirety of the computational pipeline.
- That drives the design.
That even drives the necessity for that design.
Basically, you know, if you didn't have
a computer scientist way of thinking,
you would never design these hugely combinatorial,
massively parallel experiments.
So that's why you need interdisciplinary teams.
You need teams.
And I wanna sort of clarify that,
what do we mean by computational biology group?
The focus is not on computational,
the focus is on the biology.
So we are a biology group.
What type of biology?
Computational biology.
That's the type of biology that uses the whole genome.
That's the type of biology that designs experiments,
genomic experiments, that can only be interpreted
in the context of the whole genome.
- Right, so it's philosophically
looking at biology as a computer.
- Correct, correct.
- So which is, in the context of the history of biology,
is a big transformation.
- Yeah, yeah, you can think of the name as,
what do we do?
Only computation, that's not true.
But how do we study it?
Only computationally, that is true.
So all of these single cell sequencing
can now be coupled with the technology
that we talked about earlier for perturbation.
So here's the crazy thing.
Instead of using these wells and these robotic systems
for doing one drug at a time,
or for perturbing one gene at a time in thousands of wells,
you can now do this using a pool of cells
and single cell RNA sequencing.
How?
You basically can take these perturbations using CRISPR,
and instead of using a single guide RNA,
you can use a library of guide RNAs
generated exactly the same way using this array technology.
So you synthesize a thousand different guide RNAs.
You now take each of these guide RNAs
and you insert them in a pool of cells
where every cell gets one perturbation.
And you use CRISPR editing or CRISPR,
so with either CRISPR-Cas9 to edit the genome
with these thousand perturbations.
- Or the activation one. - Or with the activation
or with the repression.
And you now can have a single cell readout
where every single cell has received
one of these modifications.
And you can now, in massively parallel ways,
couple the perturbation and the readout
in a single experiment.
- How are you tracking which perturbations
each cell received?
- So there's ways of doing that,
but basically one way is to make that perturbation
an expressible vector so that part of your RNA reading
is actually that perturbation itself.
So you can basically put it in an expressible part,
so you can self-drive it.
So the point that I wanna get across
is that the sky's the limit.
You basically have these tools,
these building blocks of molecular biology.
You have these massive datasets of computational biology.
You have this huge ability to sort of use machine learning
and statistical methods and linear algebra
to sort of reduce the dimensionality
of all these massive datasets.
And then you end up with a series of actionable targets
that you can then couple with pharma
and just go after systematically.
So the ability to sort of bring genetics to the epigenomics,
to the transcriptomics, to the cellular readouts
using these sort of high-throughput perturbation technologies
that I'm talking about,
and ultimately to the organismal
through the electronic health record endophenotypes,
and ultimately the disease battery of assays
at the cognitive level, at the physiological level,
and every other level,
there is no better or more exciting field, in my view,
to be a computer scientist then
or to be a scientist in period.
Basically, this confluence of technologies,
of computation, of data, of insights,
and of tools for manipulation
is unprecedented in human history.
And I think this is what's shaping the next century
to really be a transformative century
for our species and for our planet.
- So you think the 21st century will be remembered
for the big leaps in understanding
and alleviation of biology?
- If you look at the path
between discovery and therapeutics,
it's been on the order of 50 years.
It's been shortened to 40, 30, 20,
and now it's on the order of 10 years.
But the huge number of technologies
that are going on right now for discovery
will result undoubtedly
in the most dramatic manipulation of human biology
that we've ever seen in the history of humanity
in the next few years.
- Do you think we might be able to cure some of the diseases
we started this conversation with?
- Absolutely, absolutely.
It's only a matter of time.
Basically, the complexity is enormous,
and I don't want to underestimate the complexity,
but the number of insights is unprecedented,
and the ability to manipulate is unprecedented,
and the ability to deliver these small molecules
and other non-traditional medicine perturbations.
There's a lot of sort of new,
there's a new generation of perturbations
that you can use at the DNA level, at the RNA level,
at the microRNA level, the epigenomic level.
There's a battery of new generations of perturbations.
§16Transcript · 2:15:26–2:24:17
If you couple that with cell type identifiers
that can basically sense when you are in the right cell
based on a specific combination
and then turn on that intervention for that cell,
you can now think of combinatorial interventions
where you can basically sort of feed
a synthetic biology construct to someone
that will basically do different things in different cells.
So basically for cancer, this is one of the therapeutics
that our collaborator Ron Weiss is using
to basically start sort of engineering the circuits
that will use microRNA sensors of the environment
to sort of know if you're in a tumor cell,
or if you're in an immune cell,
or if you're in a stromal cell, and so on and so forth,
and basically turn on particular interventions there.
You can sort of create constructs
that are tuned to only the liver cells,
or only the heart cells, or only the brain cells,
and then have these new generations of therapeutics
coupled with this immense amount of knowledge
on the sort of which targets to choose
and what biological processes to measure
and how to intervene.
My view is that disease is gonna be fundamentally altered
and alleviated as we go forward.
- Next time we talk,
we'll talk about the philosophical implications of that
and the effect of life,
but let's stick to biology for just a little longer.
We did pretty good today.
We stuck to the science.
What are you excited in terms of the future of this field,
the technologies in your own group, in your own mind?
You're leading the world at MIT in the science
and the engineering of this work,
so what are you excited about here?
- I could not be more excited.
We are one of many, many teams who are working on this.
In my team, the most exciting parts are many folds.
So basically, we've now assembled
these battery of technologies.
We've assembled these massive, massive data sets,
and now we're really sort of in the stage
of our team's path of generating disease insights.
So we are simultaneously working on a paper
on schizophrenia right now
that is basically using
the single-cell profiling technologies,
using this editing and manipulation technologies
to basically show how the master regulators
underlying changes in the brain
that are sort of found in schizophrenia
are in fact affecting excitatory neurons
and inhibitory neurons in pathways
that are active both in synaptic pruning,
but also in early development.
We've basically found this set of four regulators
that are connecting these two processes
that were previously separate in schizophrenia
in sort of having a sort of more unified view
across those two sides.
The second one is in the area of metabolism.
We basically now have a beautiful collaboration
with the Goodyear Lab
that's basically looking at multi-tissue perturbations
in six or seven different tissues across the body
in the context of exercise
and in the context of nutritional interventions
using both mouse and human,
where we can basically see
what are the cell-to-cell communications
that are changing across them.
And what we're finding is this immense role
of both immune cells,
as well as adipocyte stem cells
in sort of reshaping that circuitry
of all of these different tissues,
and that's sort of painting to a new path
for therapeutical intervention there.
In Alzheimer's, it's this huge focus on microglia,
and now we're discovering different classes
of microglial cells
that are basically either synaptic or immune,
and these are playing vastly different roles
in Alzheimer's versus in schizophrenia.
And what we're finding is this immense complexity
as you go further and further down
of how, in fact, there's 10 different types of microglia,
each with their own sort of expression programs.
We used to think of them as, oh, yeah, they're microglia,
but in fact, now we're realizing
just even in that sort of least abundant of cell types,
there's this incredible diversity there.
The differences between brain regions
is another sort of major, major insight.
Again, one would think that,
oh, astrocytes are astrocytes no matter where they are,
but no, there's incredible region-specific differences
in the expression patterns
of all of the major brain cell types
across different brain regions.
So basically there's the neocortical regions
that are sort of the recent innovation
that makes us so different from all other species.
There's the sort of reptilian brain sort of regions
that are sort of much more,
very extremely distinct.
There's the cerebellum.
Each of those basically is associated
in a different way with disease,
and what we're doing now is looking into
pseudotemporal models for how disease progresses
across different regions of the brain.
If you look at Alzheimer's,
it basically starts in this small region
called the entorhinal cortex,
and then it spreads through the brain,
and through the hippocampus,
and ultimately affecting the neocortex.
And with every brain region that it hits,
it basically has a different impact on the cognitive
and memory aspects, orientation,
short-term memory, long-term memory, et cetera,
which is dramatically affecting the cognitive path
that the individuals go through.
So what we're doing now is creating
these computational models for ordering the cells
and the regions and the individuals
according to their ability to predict Alzheimer's disease.
So we can have a cell-level predictor of pathology
that allows us to now create a temporal time course
that tells us when every gene turns on
along this pathology progression,
and then trace that across regions
and pathological measures that are region-specific,
but also cognitive measures, and so on and so forth.
So that allows us to now sort of, for the first time,
look at, can we actually do early intervention
for Alzheimer's, where we know that the disease
starts manifesting for 10 years
before you actually have your first cognitive loss?
Can we start seeing that path to build new diagnostics,
new prognostics, new biomarkers
for this sort of early intervention in Alzheimer's?
The other aspect that we're looking at is mosaicism.
We talked about the common variants and the rare variants,
but in addition to those rare variants,
as your initial cell that forms the zygote
divides and divides and divides,
with every cell division,
there are additional mutations that are happening.
So what you end up with is your brain being a mosaic
of multiple different types of genetic underpinnings.
Some cells contain a mutation that other cells don't have.
So every human has the common variants
that all of us carry to some degree,
the rare variants that your immediate tree
of the human species carries,
and then there's the somatic variant,
which is the tree that happened after the zygote
that sort of forms your own body.
So these somatic alterations
is something that has been previously inaccessible
to study in human post-mortem samples.
But right now,
with the advent of single-cell RNA sequencing,
in this particular case,
we're using the well-based sequencing,
which is much more expensive,
but gives you a lot richer information
about each of those transcripts.
So we're using now that richer information
to infer mutations that have happened
in each of the thousands of genes
that sort of are active in these cells,
and then understand how the genome relates to the function,
this genotype-phenotype relationship
that we usually build in GWAS,
between, in genome-wide association studies,
between genetic variation and disease.
We're now building that at the cell level,
where for every cell,
we can relate the unique specific genome of that cell
with the expression patterns of that cell,
and the predicted function,
using these predictive models that I mentioned before,
on dysregulation for cognition,
for pathology in Alzheimer's, at the cell level.
And what we're finding is that the genes that are altered,
and the genetic regions that are altered in common variants
versus rare variants versus somatic variants,
are actually very different from each other.
The somatic variants are pointing to neuronal energetics,
and oligodendrocyte functions
that are not visible in the genetic legions
that you find for the common variants,
probably because they have too strong of an effect
that evolution is just not tolerating them
on the common side of the allele frequency spectrum.
§17Transcript · 2:24:20–2:33:18
- So the somatic one,
that's the variation that happens after the zygote,
after a new individual. - Correct.
- I mean, this is a dumb question,
but there's mutation and variation, I guess,
that happens there,
and you're saying that through this,
if we focus in on individual cells,
we're able to detect a story that's interesting there,
and that might be a very unique
kind of important variability that arises for,
you said neuronal, or something,
that would sound-- - Energetics.
- Energetics,
that's not a really cool term. - Energetics.
So you're, I mean, the metabolism of humans
is dramatically altered from that of nearby species.
You know, we talked about that last time,
that basically we are able to consume meat
that is incredibly energy-rich,
and that allows us to sort of have functions
that are, you know,
meeting this humongous brain that we have.
So basically, on one hand,
every one of our brain cells is much more energy-efficient
than our neighbors, than our relatives.
Number two, we have way more of these cells,
and number three, we have, you know,
this new diet that allows us to now feed all these needs.
That basically creates a massive amount of damage,
oxidative damage, from this huge,
super-powered factory of ideas and thoughts
that we carry in our skull.
And that factory has energetic needs,
and there's a lot of sort of biological processes
underlying that, that we are finding are altered
in the context of Alzheimer's disease.
- That's fascinating that,
so you have to consider all of these systems
if you wanna understand even something like diseases
that you would maybe traditionally associate
with just the particular cells of the brain.
- Yeah.
- The immune system.
- The metabolic system.
- The metabolic system.
- And these are all the things that makes us uniquely human.
So our immune system is dramatically different
from that of our neighbors.
Our societies are so much more clustered.
The history of infections that have plagued
the human population is, you know,
dramatically different from every other species.
The way that our society and our population
has sort of exploded has basically put unique pressures
on our immune system, and our immune system
has both coped with that density and also been shaped by,
as I mentioned, the vast amount of death
that has happened in the Black Plague
and other sort of selective events in human history,
famines, ice ages, and so forth.
So that's number one on the sort of immune side.
On the metabolic side, you know, again,
we are able to sort of run marathons.
You know, I don't know if you remember
the sort of human versus horse experiment
where the horse actually tires out faster than the human,
and the human actually wins.
So on the metabolic side, we're dramatically different.
On the immune side, we're dramatically different.
On the brain side, again, you know,
no need to sort of, you know, it's a no-brainer
of how our brain is like just enormously more capable.
And then, you know, in the side of cancer,
so basically the cancers that humans are having,
the exposures, the environmental exposures,
is again dramatically different.
And the lifespan, the expansion of human lifespan
is unseen in any other species
in, you know, recent evolutionary history.
And that now leads to a lot of new disorders
that are starting to, you know, manifest late in life.
So, you know, Alzheimer's is one example
where basically, you know, these fast energetic needs
over a lifetime of thinking
can basically lead to all of these debris
and eventually saturate the system
and lead to, you know, Alzheimer's in the late life.
But there's, you know, there's just such a dramatic
set of frontiers when it comes to aging research
that, you know, will, so what I often like to say
is that if you want to engineer a car
to go from 70 miles an hour to 120 miles an hour,
that's fine, you can basically, you know,
fix a few components.
If you want it to now go at 400 miles an hour,
you have to completely redesign the entire car.
Because the system has just not evolved to go that far.
Basically, our human body has only evolved
to live to, I don't know, 120.
Maybe we can get to 150 with minor changes.
But if, you know, as we start pushing these frontiers
for not just living, but well living,
the F-Zine that we talked about last time.
So to basically push F-Zine into the 80s and 90s
and 100s and, you know, much further than that,
we will face new challenges that have, you know,
never been faced before.
In terms of cancer, the number of divisions,
in terms of Alzheimer's and brain related disorders,
in terms of metabolic disorders, in terms of regeneration.
There's just so many different frontiers ahead of us.
So I am thrilled about where we're heading.
So basically I see this confluence in my lab
and many other labs of AI, of, you know,
sort of, you know, the next frontier of AI for drug design.
So basically these sort of graph neural networks
on specific chemical designs that allow you to create
new generations of therapeutics.
These molecular biology tricks for intervening
at the system at every level.
These personalized medicine prediction, diagnosis,
and prognosis using the electronic health records
and using these polygenic risk scores,
weighted by the burden, the number of mutations
that are accumulating across common, rare
and somatic variants, the burden converging
across all of these different molecular pathways,
the delivery of specific drugs and specific interventions
into specific cell types.
And again, you've talked with Bob Langer about this.
There's, you know, many giants in that field.
And then the last concept is not intervening
at the single gene level.
And I want you to sort of conceptualize the concept
of an on target side effect.
What is an on target side effect?
An off target side effect is when you design a molecule
to target one gene and instead it targets another gene
and you have side effects because of that.
An on target side effect is when your molecule
does exactly what you were expecting,
but that gene is pleiotropic.
Pleio means many, tropos means ways, many ways.
It acts in many ways.
It's a multifunctional gene.
So you find that this gene plays a role in this,
but as we talked about, the wiring of genes to phenotypes
is extremely dense and extremely complex.
So the next stage of intervention will be intervening,
not at the gene level, but at the network level.
Intervening at the set of pathways and the set of genes
with multi input perturbations to the system,
multi input modulations, pharmaceutical
or other interventional.
That basically allow you to now work
at the sort of full level of understanding,
not just in your brain, but across your body,
not just in one gene, but across the set of pathways
and so on and so forth for every one of these disorders.
So I think that we're finally at the level of systems
medicine of basically instead of sort of medicine
being at a single gene level,
medicine being at the systems level,
where it can be personalized based on a specific set
of genetic markers and genetic perturbations
that you are either born with,
or that you have developed during your lifetime,
your unique set of exposures,
your unique set of biomarkers,
and your unique set of current set of conditions
through your EHR and other ways.
And the precision component of intervening
extremely precisely in the specific pathways
and in specific combinations of genes
that should be modulated to sort of bring you
from the disease state to the physiologically normal state,
or even to physiologically improved state
through this combination of interventions.
So that's in my view, the field
where basically computer science comes together
with artificial intelligence, statistics,
all of these other tools,
molecular biology technologies and biotechnology
and pharmaceutical technologies
that are sort of revolutionary in the way of intervention.
And of course, this massive amount of molecular biology
and data gathering and generation and perturbation
in massively parallel ways.
So there's no better way, there's no better time,
there's no better place to be sort of,
you know, looking at this whole confluence of ideas.
And I'm just so thrilled to be a small part
of this amazing, enormous ecosystem.
- It's exciting to imagine what humans of 100,
200 years from now, what their life experience is like,
because these ideas seem to have potential
to transform the quality of life.
And I think that when they look back at us,
they probably wonder how we were put up
§18Transcript · 2:33:21–2:34:51
with all the suffering in the world.
Manolis, it's a huge honor.
Thank you for spending this early Sunday morning with me.
I deeply appreciate it.
See you next time.
- Sounds like a plan.
Thank you, Lex.
Thanks for listening to this conversation
with Manolis Kellis, and thank you to our sponsors.
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And now, let me leave you some words from Haruki Murakami.
Human beings are ultimately nothing
but carriers, passageways for genes.
They ride us into the ground like racehorses
from generation to generation.
Genes don't think about what constitutes good or evil.
They don't care whether we're happy or unhappy.
We're just means to an end for them.
The only thing they think about
is what is most efficient for them.
Thank you for listening, and hope to see you next time.
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