The following is a conversation with Manolis Kellis, his third

time on the podcast. He is a professor at MIT and head of the

MIT Computational Biology Group. This time, we went deep on the

science, biology and genetics. So this is a bit of an experiment.

Manolis went back and forth between the basics of biology

to the latest state of the art in the research. He's a master

at this. So I just sat back and enjoyed the ride. This

conversation happened at 7am. So it's yet another podcast

episode after an all nighter for me. And once again, since the

universe has a sense of humor, this one was a tough one for my

brain to keep up. But I did my best and I never shy away from

good challenge. Quick mention of a sponsor followed by some

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biology in the brain and in the various systems of the body

fill me with awe. Every time I think about how such a

chaotic mess coming from its humble origins in the ocean

was able to achieve such incredibly complex and robust

mechanisms of life that survived despite all the forces of

nature that want to destroy it. It is so unlike the computing

systems we humans have engineered that it makes me feel

that in order to create artificial general intelligence

and artificial consciousness, we may have to completely

rethink how we engineer computational systems. If you

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Patreon, or connect with me on Twitter at Lex Freedman. And

now here's my conversation with Manolis Callis. So your group

at MIT is trying to understand the molecular basis of human

disease. What are some of the biggest challenges in your

view? Don't get me started. I mean understanding human

disease is the most complex challenge in modern science.

So because human disease is as complex as the human genome,

it is as complex as the human brain. And it is in many ways

even more complex because the more we understand disease

complexity, the more we start understanding genome

complexity and epigenome complexity and brain

circuitry complexity and immune system complexity and

cancer complexity and so on and so forth. So traditionally,

human disease was following basic biology. You would

basically understand basic biology in model organisms

like, you know, mouse and fly and yeast. You would

understand sort of mammalian biology and animal

biology and eukaryotic biology in sort of progressive layers

of complexity getting closer to human phylogenetically.

And you would do perturbation experiments in those species

to see if I knock out a gene, what happens.

And based on the knocking out of these genes, you would

basically then have a way to drive human biology

because you would you would sort of understand the functions of these

genes and then if you find that a human gene

locus, something that you've mapped from human genetics

to that gene is related to a particular human disease, you

say, aha, now I know the function of the gene from the model organisms.

I can now go and understand the function of that gene

in human. But this is all changing. This is

dramatically changed. So that that was the old way of doing basic biology. You

would start with the animal models, the eukaryotic models,

the mammalian models, and then you would go to human.

Human genetics has been so transformed in the last

decade or two that human genetics is now actually

driving the basic biology. There is more genetic mutation information

in the human genome than there will ever be in any other species.

What do you mean by mutation information? So so perturbations is how you

understand systems. So an engineer builds systems

and then they know how they work from the inside out. A scientist

studies systems through perturbations. You basically say if I poke that balloon

what's going to happen and I'm going to film it in super high resolution

understand I don't know air dynamics or fluid dynamics if it's filled with water

etc. So you can then make experimentation by

perturbation and then the scientific process is sort of

building models that best fit the data designing you

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 if you know that

a gene is related to disease you don't want to just know that it's related to

the disease. You want to know what is the disease mechanism because you want to

go and intervene. So the way that I like to describe it is that

traditionally epidemiology which is basically the study of disease

you know sort of the observational study of disease

has been about correlating one thing with another thing.

So if you if you have a lot of people with liver disease who are also

alcoholics you might say well maybe the alcoholism is

driving the liver disease or maybe those who have liver disease

self medicate with alcohol so that the connection could be either way.

With genetic epidemiology it's about correlating changes in genome

with phenotypic differences and then you know the direction of causality.

So if you know that a particular gene is related to the disease

you can basically say okay perturbing that gene in mouse

causes the mice to have x phenotype. So perturbing that gene in human causes

the humans to have the disease so I can now figure out what are the

detailed molecular phenotypes in the human that are related to

that organismal phenotype in the disease. So it's all

about understanding disease mechanism, understanding what are the pathways,

what are the tissues, what are the processes

that are associated with the disease so that we know how to intervene.

You can then prescribe particular medications that also alter these processes.

You can prescribe lifestyle changes that also affect these processes

and so on 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, a mice first and then see how that might possibly

connect to humans. How hard is that puzzle of

trying to figure out how little perturbations might lead to

in a stable way to a disease? In animals we make the puzzle

simpler because we perturb one gene at a time.

That's the beauty and that'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 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.

You basically take this natural genetic variation

that we all carry within us. Every one of us carries

six million perturbations. 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 that are segregating in the human

population. Every one of us carries millions of

polymorphic sites, poly, many morph forms. Polymorphic means many forms

variants. That basically means that every one of us has

single nucleotide alterations that we have inherited from mom and from dad

that basically can be thought of as tiny little perturbations.

Most of them don't do anything but some of them

lead to all of the phenotypic differences that we see between us.

The reason why two twins are identical is because these variants

completely determine the way that I'm going to look at exactly 93 years of age.

How happy are you with this kind of data set? Is it large enough

of the human population of Earth? Is that too big, too small?

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

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. 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 and animal models.

What does this puzzle solving process look like today?

So we basically have you know something like seven billion people in the

planet and every one of them carries something like six million

mutations. You basically have an enormous

matrix of genotype by phenotype by systematically measuring

the phenotype of these individuals and the traditional way of measuring this

phenotype has been to look at one trait at a time.

You would gather families and you would sort of paint

the pedigrees of a strong effect what we like to call

Mendelian mutation. So a mutation that gets transmitted

in a dominant or a recessive but strong effect form

where basically one locus plays a very big role

in that disease and you could then look at carriers versus non carriers

in one family, carries versus non carries 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 you know whether they're

sick or not for a particular trait. That has been the modern view

instead of going to families going to unrelated individuals

with one phenotype at a time and what we're doing now

as we're maturing in all of these sciences

is that we're doing this in the context of large medical systems

or enormous cohorts that are very well phenotyped across

hundreds of phenotypes sometimes with their complete electronic health record.

So you can now start relating not just one gene segregating one family

not just thousands of variants segregating with one phenotype

but now you can do millions of variants versus hundreds of phenotypes

and as a computer scientist I mean deconvolving that matrix partitioning it

into the layers of biology that are associated with every one of these

elements is a dream come true it's 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 so it's not just like a puzzle of

perturbation and disease it's perturbation then

effect at the cellular level then at an organ level

that by like how do you disassemble this into like what your group is working on

you're basically taking a bunch of the hard problems in the space

how do you break apart a difficult disease

and break it apart into problems that you into puzzles that you can now start

solving. So there's a struggle here computer

scientists love hard puzzles and they're like oh I want to

you know build a method that just deconvolves the whole thing computationally

and you know that's very tempting and it's very appealing

but biologists just like to decouple that complexity experimentally

to just like peel off layers of complexity experimentally

and that's what many of these modern tools that you know my group and others

have both developed and used the fact that we can now

figure out tricks for peeling off these layers of complexity

by testing one cell type at a time or by testing one cell at a time

and you could basically say what is the effect of this genetic variant associated

with Alzheimer's on human brain. Human brain

sounds like oh it's an organ of course just go one organ at a time

but human brain has of course dozens of different brain regions

and within each of these brain regions dozens of different cell types

and every single type of neuron every single type of glial cell

between astrocytes oligodendrocytes microglia

between you know all of the mural cells and the vascular cells and the immune

cells that are coinhabiting the the brain between the

different types of excitatory and inhibitory neurons

that are sort of interacting with each other between different layers

of neurons in the cortical layers every single one of these

has a different type of function to play in cognition

in interaction with the environment in maintenance

of the brain in energetic needs in feeding the brain with blood with oxygen

in clearing out the debris that are resulting from the super high energy

production of cognition in humans so all of these things are basically

potentially deconvolvable computationally but experimentally

you can just do single cell profiling of dozens of regions of the brain across

hundreds of individuals across millions of cells

and then now you have pieces of the puzzle

that you can then put back together to understand

that complexity i mean first of all i mean the 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 so what does human

genetics do human genetics basically looks at the whole path

from genetic variation all the way to disease

so human genetics has basically taken thousands of

Alzheimer's cases and thousands of controls

matched for age for sex for you know environmental

backgrounds and so on so forth and then looked at that map

where you're asking what are the individual genetic perturbations

and how are they related to all the way to Alzheimer's disease

and that has actually been quite successful so we now have

you know more than 27 different loci these are genomic regions

that are associated with Alzheimer's at this end to end level but the moment

you sort of break up that very long path into smaller levels

you can basically say from genetics what are the

epigenomic alterations at the level of gene regulatory elements

where that genetic variant perturbs the control region nearby

that effect is much larger you mean much larger in terms of his

down the line impact or it's much larger in terms of the

measurable effect this a versus b variants 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 but indeed yeah but indeed there are such

effects so the hope is or the most scientifically

speaking the the most effective place where to detect

the alteration that results in disease is earlier on in the pipeline

as early as possible it's it's it's a trade off

if you go very early on in the pipeline now each of these

epigenomic alterations for example this enhancer control region

is active maybe 50 less which is a dramatic effect

now you can ask well how much just changing one regulatory region in the

genome in one cell type change disease well that path is now

long so if you instead look at expression

the path between genetic variation and the expression of one gene goes through

many enhancer regions and therefore it's a subtler effect at the gene

level but then now you're closer because

one gene is acting on you know in the context of only 20 000 other genes

as opposed to one enhancer acting in the context of two million other enhancers

so you basically now have genetic epigenomic the circuitry

transcriptomic the gene expression level and then cellular where you can

basically say I can measure various properties of those cells

what is the calcium influx rate when I have these genetic variation

what is the synaptic density what is the electric

impulse conductivity and so on so forth so you can measure

things along this path to disease and you can also measure

endophenotypes you can basically measure you know

your brain activity you can do imaging in the brain

you can basically measure I don't know the heart rate the pulse the lipids

the amount of blood secreted and so on so forth and then through all of that

you can basically get at the path to causality the path to disease

and is there something beyond cellular so you mentioned lifestyle

interventions or changes as a way to uh or like be able to prescribe changes in

lifestyle like what about organs what about like

the function of the body as a whole yeah absolutely so basically

when you go to your doctor they always measure you know your pulse they always

measure your height those measure your weight

your you know your bmi so basically these are just very basic variables

but with digital devices nowadays you can start measuring hundreds of variables

for every individual you can basically also phenotype

cognitively through tests uh Alzheimer's patients

there are cognitive tests that you can measure that you that you typically do

for cognitive decline these mini mental you know observations that that you have

specific questions too 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 so forth in the human you can

have much much richer phenotypes where you can basically say

not just imaging at the you know organ level but

and in all kinds of other activities at the organ level but you can also do at

the organism level you can do behavioral tests

and how did they do on empathy how did they do on memory

how did they do on long term memory versus short term memory

and so on so forth i love how you're calling that phenotype

i guess it is it is but like your behavior patterns that might change over

over a period of a life yet your ability to remember

things your ability to be yeah empathetic or emotionally

your intelligence perhaps even yeah but intelligence has hundreds of variables

you can be your math intelligence your literary intelligence your puzzle solving

intelligence your logic it could be like hundreds of things and all that

it's we were able to measure that better and better so all that could be

connected to the entire pipeline so we used to think of each of these as a

single variable like intelligence i mean that's ridiculous

it's basically dozens of different genes that are controlling

every single variable you can basically think of

you know imagine us in a video game where every one of us has

measures of you know strength stamina you know energy left and so on so forth

but you could click on each of those like five bars that are just the main bars

and each of those will just give you then hundreds of bars

yeah and you basically say okay great for my you know machine learning task

i want someone who i'm a human who has

these particular forms of intelligence i require now these

you know 20 different things and then you can combine those things

and then relate them to of course performance in particular task

but you can also relate them to genetic variation

that might be affecting different parts of the brain

for example your frontal cortex versus your temporal cortex versus your visual

cortex and so on so forth so genetic variation that affects

expression of genes in different parts of your brain

can basically affect your you know music ability your auditory ability your

smell your you know just dozens of different

phenotypes can be broken down into you know hundreds of cognitive variables

and then relate each of those to thousands of genes that are associated

with them so somebody who loves RPGs role playing

games there's uh there's too few variables that

we can control so i'm excited if we're in fact living in a simulation

this is a video game i'm excited by the quality of the video game

the the the game designer did a hell of a

good job so we're impressed so i don't know at the sunset last

night was a little unrealistic yeah yeah the graphics

exactly come on in video to zoom back out we've been talking about the

genetic origins of diseases but i think it's fascinating to

talk about what are the most important diseases to understand

and especially as it connects to the things that you're working on

so it's very difficult to think about important diseases to understand there's

many metrics of importance one is lifestyle impact

i mean if you look at covid the impact on lifestyle has been enormous

so understanding covid is important because it has impacted

the well being in terms of ability to have a job

ability to have an apartment ability to go to work ability to have a mental

circle of support and uh all of that for you know millions of

americans like huge huge impact so that's one aspect of

importance so basically mental disorders alzheimer's has a huge

importance in the well being of americans

whether or not it die it kills someone for many many years it has a huge impact

so the first measure of importance is just well being

like 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 thousand americans per

year number two is cancer with 600

thousand americans number three far far down the

list is accidents every single accident combined so

basically you you know you read the news accidents like you know there was a

huge car crash all over the news but the number of deaths

number three by far 167 thousand lower respiratory disease so that's asthma

not being able to breathe and so on so forth 160 000

alzheimer's number four number five with 120 000

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

diabetes and metabolic disorders etc that's 85 000

the flu is 60 000 suicide 50 000 and then overdose etc

you know goes further down the list so of course covid has creeped up to be the

number three killer this year with you know

more than 100 000 americans and counting um and you know but but if you think

about sort of what do we use what are the most important diseases you have to

understand both the quality of life and the just sheer number of

deaths and just numbers of years lost if you wish and and uh each of these

diseases you can think of as uh and also including terrorist

attacks and school shootings for example

things which lead to fatalities you can look at

as problems that could be solved and some problems are harder to solve than

others i mean that's part of the equation so maybe if you

look at these diseases if you look at heart disease or cancer

or alzheimer's or just uh like schizophrenia and

obesity debut like not necessarily things that kill you but

affect the quality of life which problems are solvable

which aren't which are harder to solve which aren't

i love your question because you put it in the context of a global

um effort rather than just a local effort so basically

if you look at the global aspect exercise and nutrition are two

interventions that we can as a society make a much better job at

so if you think about sort of the availability of cheap food

it's extremely high in calories it's extremely detrimental for you like a

lot of processed food etc so if we change that equation

and as a society we made availability of healthy food

much much easier and charged a burger at mcdonald's the price that it costs

on the health system then people would actually start buying more

healthy uh foods so basically that's sort of a

societal intervention if you wish in the same way

increasing empathy increasing education increasing the social

framework and support would basically lead to fewer suicides

it would lead to fewer murders it would lead to fewer

you know deaths overall um so you know that's something that we as a society

can do you can you can also think about external factors versus

internal factors so the external factors are basically communicable

diseases like covid like the flu etc and the internal

factors are basically things like you know cancer and Alzheimer's where

basically your your genetics will eventually you know drive you there

um and then of course with all of these

factors every single disease has both a genetic component

and environmental component so heart disease you know huge genetic

contributor contribution alzheimer's it's like you know

60 plus genetic so i think it's like 79 percent

heritability so that basically means that genetics alone

explains 79 percent of alzheimer's incidents and yes there's a 21

percent environmental component where you could basically

enrich your cognitive environment enrich your social interactions

read more books learn a foreign language go running

you know sort of have a more fulfilling life all of that will actually decrease

alzheimer's but there's a limit to how much that that can

impact because of the huge genetic footprint so this is fascinating so

each one of these problems have a genetic component

and an environment component and so like when there's a genetic component

what can we do about some of these diseases what what have you worked on

what can you say that's uh 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 all

interesting so understanding the biological mechanisms leading to

disease is extremely important in being able to

intervene but when you can intervene what you know the analogy that i like to

give is for example for obesity you know think

of it as a giant bathtub of fat there's basically fat coming in from your diet

and there's fat coming out from your exercise

okay that's an in out equation and that's the equation that everybody's

focusing on but your metabolism impacts that you know bathtub

basically your metabolism controls the rate at which

you're burning energy it controls the way the rate at which you're storing

energy and it also teaches you about the various

valves that control the input and the output equation

so if we can learn from the genetics the valves

we can then manipulate those valves and even if the environment is feeding you

a lot of fat and getting a little that out

you can just poke another hole at the bathtub and just get a lot of the fat

out yeah that's fascinating yeah so that we're not just

passive observers of our genetics the more we understand the more we can come

up with actual treatments and i think that's an important

aspect to realize when people are thinking about

strong effect versus weak effect variants so some variants have strong

effects we talked about these Mendelian disorders where a single

gene has a sufficiently large effect pen and trans expressivity and so on so

forth that basically you can trace it in families with

cases and not cases cases not cases and so 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 is dead

so those genes don't have variation you're not going to find a genetic

association if you don't have variation that doesn't mean that the gene has no

role it 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 fast exactly

genes that have very little variation are hugely important you can actually

rank the importance of genes based on how little variation they have

and those genes that have very little variation but no association

with disease that's a very good metric to say oh that's probably a developmental

gene because we're not good at measuring those phenotypes

so it's genes that you can tell evolution has excluded

mutations from but yet we can't see them associated with anything that we can

measure nowadays it's probably early embryonic lethal

what are all the words you just said early embryonic what lethal

meaning meaning that if you don't have that okay there's a bunch of stuff that

is required for a stable functional organism

across the board exactly our entire for for entire species I guess if you look

at sperm it expresses thousands of proteins

does sperm actually need thousands of proteins no

but it's probably just testing them so my speculation

is that misfolding of these proteins is an early

test for failure so that out of the you know 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 insert

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 we're basically in those

early months of development I mean it's it's horrendous of course at the

personal level when you lose a you know your future child

but in some ways there's so little hope for that child to develop

and sort of make it through the remaining months that sort of fail fast is

probably a good evolutionary principle from an evolutionary

for mammals and of course humans have a lot of medical resources

that you can sort of give those children a chance

and you know we have so much more success instead of giving

folks who have these strong carrier mutations a chance but if they're not

even making it through the first three months we're not going to see them

so that's why when we when we say what are the most important genes to focus on

the ones that have a strong effect mutation or the ones that have a weak

effect mutation well you know the jury might be out

because the ones that have a strong effect mutation

are basically you know not mattering that 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 two percent

change in that gene pharmaceutically I can go in and induce a 70 percent

change in that gene and maybe I will poke another

hole at the bathtub that was not easy to control

in you know many of the other sort of strong effect genetic variants

so okay so there's this beautiful map of across the population of things that

you're saying strong and weak effects so stuff with a lot of mutations and stuff

with little mutations with no mutations any of this

map and it lays out the puzzle yeah so so when I say strong effect I mean at

the level of individual mutations so so basically genes where

so so you have to think of first the effect of the gene on the disease

remember how I was sort of painting that map earlier

from genetics all the way to phenotype that gene

can have a strong effect on the disease but the genetic variant

might have a weak effect on the gene so basically

when you ask what is the effect of that genetic variant

on the disease it could be that that genetic variant impacts the gene by a

lot and then the gene impacts the disease by

a little or it could be that the genetic variants in

impacts the gene by a little and then the gene impacts the disease by a lot

so what we care about is genes that impact the disease a lot

but genetics gives us the full equation and what I would argue

is if we couple the genetics with expression variation

to basically ask what genes change by a lot

and you know which genes correlate with disease by a lot

even if the genetic variants change them by a little

then that those are the best places to intervene

those are the best places where pharmaceutical if I have

even a modest effect I will have a strong effect on the disease

whereas those genetic variants that have a huge effect on the disease

I might not be able to change that gene by this much without affecting all

kinds of other things interesting so yeah okay so that's what

we're looking at then what have we been able to find in terms of

which disease could be helped again and 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 to lose the central part of your vision

progressively as you grow older my increased risk

is fairly small I have an eight percent chance you only have a six percent

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

so to live with I mean maybe that's so we agreed to talk again by the way for

the listeners to where we're going to try to focus on science today and a

little bit of philosophy next time but it's interesting

to think about the more you're able to know about yourself from the genetic

information in terms of the diseases how that changes

your own view of life yeah so there's there's a lot of impact there

and there's a 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 a good preview yeah

so let's go back to AMD so basically with AMD

we have no idea what causes an AMD you know

it was 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 coopted

for many different roles across the body so they actually play many diverse

roles somehow the immune system is connected to the

synaptic pruning process exactly so immune cells were coopted

to prune synapse how did you figure this out

how does one go off figuring this intricate connection

like pipeline of connections out yeah let me give you another example

so so Alzheimer's disease the first place that you would expect it to act is

obviously the brain so we had basically this

roadmap epigenomics consortium view of the human epigenome the largest map of

the human epigenome that has ever been built

across 127 different tissues and samples

with dozens of epigenomic marks measured in you know

hundreds of donors so what we've basically learned through that

is that you 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 enriched in disease associated variants

we pushed that further in the Ernst Nature paper a year later

and then in this roadmap epigenomics paper

you know a few years after that but basically that matrix

that you mentioned earlier was in fact the first time that we could see

what genetic traits have genetic variants that are enriched

in what tissues in the body and a lot of that map made complete

sense if you looked at a diversity of immune

traits like allergies and type 1 diabetes and so on so forth

you basically could see that they were enriching

that the genetic variants associated with those traits were enriched

in enhancers in these gene regulatory elements

active in T cells and B cells and hematopoietic stem cells and so on so forth

so that basically gave us uh confirmation in many ways that those

immune traits were indeed enriching immune cells

if you look if you if you looked at type 2 diabetes

you basically saw an enrichment in only one type of sample

and it was pancreatic islets and we know that type 2 diabetes

you know sort of stems from the dysregulation of insulin

in the beta cells of pancreatic islets and that sort of was

you know spot on super precise if you looked at blood pressure

where would you expect blood pressure to occur

you know I don't know maybe in your metabolism in ways that you process

coffee or something like that maybe in your brain the way that you stress out

and increases your blood pressure etc what we found is that blood pressure

localized specifically in the left ventricle

of the heart so the enhancers of the left rectum 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 show a double

enrichment both in the immune cells and in the digestive

cells so that basically told us that I have this is acting in both components

there's an immune component in commentary 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 plus which is this

sort of cell surface markers of those cells so they're cd14 plus 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 your your fat for example

has a lot of macrophages that are resin and the m1 versus m2 macrophage ratio

has a huge role to play in obesity and you know so basically again these

immune cells are everywhere but basically what we found

through this completely unbiased view of what are the tissues that likely

underlie different disorders we found that Alzheimer's

was humongously enriched in microglia but not at all in the other cell types

so what what are we supposed to make that if you look at the

tissues involved is that simply useful for indication

of propensity for disease or does it give us somehow a pathway of treatment

it's very much the second if you look at the

um the the way to therapeutics you have to start somewhere

what are you going to do you're going to basically make assays

that manipulate those genes and those pathways in those cell types so

before we know the tissue of action we don't even know where to start

we basically are at a loss but if you know the tissue of action

and even better if you know the pathway of action

then you can basically screen your small molecules

not for the gene you can screen them directly for the pathway

in that cell type so you can basically develop a high throughput multiplexed

you know robotic system for testing the impact of your favorite molecules

that you know are safe efficacious and you know sort of hit that particular

gene and so and so forth you can basically screen those molecules

against either a set of genes that act in that pathway

or on the pathway directly by having a cellular assay

and then you can basically go into mice and do experiments and basically sort

of figure out ways to manipulate these processes

that allow you to then to go back to humans and do a clinical trial that

basically says okay i was able indeed to reverse these processes in mice

can i do the same thing in humans so that the the knowledge of the tissues

gives you the pathway to treatment but that's not the only part

there are many additional steps to figuring out the mechanism of disease

i mean so that's really promising maybe uh to take a small step back you've

mentioned all these puzzles that were figured out with the nature paper

for me you've mentioned a ton of diseases

from obesity to Alzheimer's even schizophrenia i think you mentioned

is just uh what is the actual methodology of figuring this out

so indeed i mentioned a lot of diseases and and my lab works on a lot of

different disorders and the reason for that

is that if you look at the

if you look at biology it used to be you know zoology departments and

botanology departments and you know virology departments and so on and so

forth and MIT was one of the first schools to basically create a biology

department like oh we're gonna study all of life suddenly

why was that even a case because the advent of DNA

and the genome and the central dogma of DNA makes RNA makes protein

in many ways unified biology you could suddenly

study the process of transcription in viruses or in bacteria

and have a huge impact on yeast and fly and maybe even mammals

because of this realization of these common underlying processes

and in the same way that DNA unified biology

genetics is unifying disease studies so you used to have

um you used to have uh you know i don't know um

cardiovascular disease department and uh you know neurological disease

department and you know the generation department and

you know um basically immune and cancer and so on so forth

and all of these were studied in different labs

you know because it made sense because we basically the first step was

understanding how the tissue functions and we kind of knew the tissues

involved in cardiovascular disease and so on 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 their room

they were in another building altogether the same way for schizophrenia

we now have to sort of worry about all these interconnected

aspects for metabolic disorders we're finding contributions from brain

so suddenly we have to call the neurologists from the other building

and so on so forth so in my view it makes no sense anymore

to basically say oh i'm a geneticist studying immune disorders

i mean that's that's ridiculous because i mean yeah of course in many ways

you still need to sort of focus but what what what we're doing is that we're

basically saying we'll go wherever the genetics takes us

and by building these massive resources by working on

our latest map is now 833 tissues sort of the the next generation of

the epitomics 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 old tissues

and some disorders are enriching in old tissues so basically there's these

multifactorial and this other class which i like to call

polyfactorial diseases which are basically lighting up everywhere

and in many ways it's you know sort of 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 you know 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 you know every single disease so all of them

have the same sub task and i teach dynamic programming in my class

dynamic program is all about sort of not redoing the work

it's reusing the work that you do once so basically for us to say oh great

you know you guys in the immune building go solve the fundamental circuitry of

everything and then you guys in the schizophrenia building go

solve the fundamental circuitry of everything separately

is crazy so what we need to do is come together

and sort of have a circuitry group the circuitry building that sort of tries

to solve the circuitry of everything and then the immune folks

who will apply this knowledge to all of the disorders that are

associated with immune dysfunction and the schizophrenia folks

will basically interacting with both the immune folks and with the neuronal

folks and all of them will be interacting with the

circuitry folks and so on 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 forth we have an immune focus on

several immune disorders we have a cancer focus

on uh 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 hunting to disease ALS and you know

AD related disorders like frontal temporal dementia and louis body

dementia and of course a huge focus on Alzheimer's

we have a metabolic focus on the role of exercise

and diets and sort of how they're impacting metabolic

you know organs across the body and across many different tissues

and all of them are interfacing with the circuitry

and the reason for that is another computer science principle

of eat your own dog food 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 nucleotide in the genome

building the basic functional map of what are the genes in the human genome

what are the gene regulatory elements of the human genome

i mean over the years we've written a series of papers on how do you find human

genes in the first place using comparative genomics

how do you find the motifs that are the building blocks of gene regulation

using comparative genomics how do you then find how these motifs come together

and act in specific tissues using epigenomics how do you link regulators

to enhancers and enhancers to their target genes

using epigenomics and regulatory genomics so

through the years we've basically built all this infrastructure

for understanding what i like to say every single nucleotide of the human genome

and how it acts in every one of the major cell types and tissues of the human body

i mean this is no small task this is an enormous task that takes the entire

field and that's something that my group has taken on

along with many other groups and we have also

and that sort of a thing sets my group perhaps apart we have also worked

with specialists in every one of these disorders

to basically further our understanding all the way down to disease

and in some cases collaborating with pharma to go all the way down to

therapeutics because of our deep deep understanding

of that basic circuitry and how it allows us to now improve the

circuitry not just treat it as a black box but

basically go and say okay we need a better cell type specific wiring

that we now have at the tissue specific level so we're focusing on that

because we're understanding you know the needs from the disease front

so you have a sense of the entire pipeline i mean one

maybe you can indulge me one nice question to ask would be

how do you from the scientific perspective go from knowing nothing

about the disease to going you said to go into the

entire pipeline and actually have a drug or 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 so we can't have one postdoc per

gene anymore we now have to have these cross cutting

needs and i'm going to 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 going to 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 percent 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 percent of the genome

codes for proteins the other 98.5 percent does not code for proteins

if you now look at where are the disease variants located

93 percent of them fall in that outside the gene's portion

of course genes are enriched but they're only enriched by a factor of three

that means that still 93 percent 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

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 the

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 nauseam people did tons of experiments on it

they figured out that FTO is in fact a RNA methylation

transferase 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

that epitranscriptomics is hugely involved in obesity because that

that gene FTO is you know clearly where the genetic locus is at

my group studied FTO in collaboration with you know 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 intran 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 uh what am i supposed to feel about that because isn't that like super

complicated then uh so so the way that i was introduced at a

conference a few years ago was uh and here's Manolis Kelis who

wrote the most depressing paper of 2015

and the reason for that is that the entire pharmaceutical industry was so

comfortable that there was a single gene in that locus

because in some loci you basically have three dozen genes that are all sitting

in the same region of association and you're like oh gosh which ones of those

is it but even that question of which ones of

those is it is making the assumption that it is

one of those as opposed to some random gene just far far away

which is what our paper showed so basically what our paper showed is that

you can't ignore the circuitry you have to first figure out the circuitry

all of those long range interactions how every genetic variant impacts the

expression of every gene in every tissue imaginable

across hundreds of individuals and then you now have

one of the building blocks not even all of the building blocks for then going

and understanding disease so okay so embrace the the

wholeness of the circuitry correct but what so back to the question of

starting knowing nothing to the disease and and go into 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

noncoding variants that are sitting outside proteins

and then figuring out what are the epigenomic enrichments

and the reason for that you know thankfully is that there is convergence

that the same processes are impacted in different ways by different loci

and that's a saving grace for our field the fact that if I look at hundreds of

genetic variants associated with Alzheimer's they localize

in a small number of processes can you clarify why that's hopeful so like they

show up in the same exact way in the in the specific set of

processes yeah so so basically there's a small

number of biological processes that underlie or at least that play them the

biggest role in every disorder so in Alzheimer's

you basically have you know maybe 10 different types of processes

one of them is lipid metabolism one of them is immune cell function

one of them is neuronal energetics so these are just a small number of

processes but you have multiple lesions

multiple genetic perturbations that are associated with those processes

so if you look at schizophrenia it's excitatory neuron function it's

inhibitory neuron function it's synaptic pruning it's calcium

signaling and so on 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 he

hits are calcium signaling proteins oh cool you're like

aha that means that calcium signaling is important

so those people who are focusing on one focus at a time cannot possibly

see that picture you have to become a genomicist you have to

look at the omics the om the holistic picture

to understand these enrichments but you you mentioned the convergence thing so

the whatever the thing associated with the disease shows up

so let me explain convergence yeah convergence is such a beautiful concept

so you basically have these four genes

that are converging on calcium signaling

so that basically means that they are acting each in their own way

but together in the same process but now

in every one of these low side you have many

enhancers controlling each of those genes that's another type of convergence

where dysregulation of seven different enhancers might all converge

on dysregulation of that one gene which then converges

on calcium signaling and in each one of those enhancers

you might have multiple genetic variants distributed across

many different people everyone has their own different mutation

but all of these mutations are impacting that enhancer and all of these

enhancers are impacting that gene and all of these genes are impacting this

pathway and all these pathways are acting the same tissue

and all of these tissues are converging together on the same biological process

of schizophrenia and you're saying the saving grace

is that that convergence seems to happen for a lot of these diseases

for all of them basically that for every single disease that we've looked at

we have found an epigenomic enrichment how do you do that

you basically have all of the genetic variants associated with the disorder

and then you're asking for all of the enhancers active in a particular tissue

for 540 disorders we've basically found that

indeed there is an enrichment that basically means that

there is commonality and from the commonality we can just

get insights so to explain in the mathematical terms

we're basically building an empirical prior

we're using a Bayesian approach to basically say great all of these variants

are equally likely in a particular locus to be important

in a genetic 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

he's inherited from four different parts one part comes from your

dad another part comes from your mom another part comes from your dad and

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

one spermatozoid that basically couples with one ovule to basically create one egg to basically

create the zygote. You basically have half of your genome that comes from dad and half your

genome that comes from mom, but in order to line them up, you basically have these crossover

events. These crossover events are basically leading to co inheritance of that entire block

coming from your maternal grandmother and that entire block coming from your maternal grandfather.

Over many generations, these crossover events don't happen randomly. There's a protein called

PRDM9 that basically guides the double stranded breaks and then leads to these crossovers

and that protein has a particular preference to only a small number of hotspots of recombination

which then leads to a small number of breaks between these co inheritance patterns. So even

though there are six million variants, there are six 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.

Progenitors like the early on developmental stem cells.

So you start from one zygote and that's a tautipotent cell type. It can do anything.

You then, you know, that cell divides, divides, divides, and then every cell division is leading

to specialization where you now have a mesodermal lineage and ectodermal lineage and endodermal

lineage that basically leads to different parts of your body. The ectoderm will basically give

rise to your skin. Ecto means outside, derm is skin. So ectoderm, but it also gives rise to

your neurons and your whole brain. So that's a lot of ectoderm. Mesoderm gives rise to your

internal organs, including the vasculature and, you know, your muscle and stuff like that. So

you basically have this progressive differentiation. And then if you look further, further down that

lineage, you basically have one lineage that will give rise to both your muscle and your bone,

but also your fat. And if you go further down the lineage of your fat, you basically have your

white fat cells. These are the cells that store energy. So when you eat a lot, but you don't

exercise too much, there's an excess set of calories, excess energy. What do you do with those?

You basically create, you spend a lot of that energy to create these high energy molecules,

lipids, which you can then burn when you need them on a rainy day. So that leads to obesity

if you don't exercise and if you overeat, because your body is like, oh, great, I have all these

calories. I'm going to store them. Ooh, more calories. I'm going to store them too. Ooh,

more calories. And the, you know, 42% of European chromosomes have a predisposition to storing fat,

which was selected probably in the, you know, food scarcity periods. Like basically as we were

exiting Africa, you know, before and during the ice ages, you know, there was probably a selection

to those individuals who made it north to basically be able to store energy, you know, a lot more

energy. So you basically now have this lineage that is deciding whether you want to store energy

in your white fat or burn energy in your beige fat. Turns out that your fat is, you know, like

we, we have such a bad view of fat. Fat is your best friend. Fat can both store all these excess

lipids that would be otherwise circulating through your, you know, 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 caters.

Burning all these caters. So what we basically found is that across the board, genetic variants

associated with obesity across many of these regions were all enriched repeatedly in mesenchymal,

stem cell enhancers. So that gave us a hint as to which of these genetic variants was likely

driving this whole association. And we ended up with this one genetic variant called RS142,

1085. And that genetic variant out of the 89 was the one that we predicted to be causal for the

disease. So going back to those steps, first step is figure out the relevant tissue based on the

global enrichment. Second step is figure out the causal variant among many variants in this

linkage disequilibrium in this co inherited block between these recombination hotspots,

these boundaries of these inherited blocks. That's the second step. The third step is once you know

that causal variant, try to figure out what is the motif that is disrupted by that causal variant.

Basically, how does it act? Variants don't just disrupt elements, they disrupt the binding of

specific regulators. So basically the third step there was how do you find the motif that is responsible

like the gene regulatory word, the building block of gene regulation that is responsible for that

dysregulatory event. And the fourth step is finding out what regulator normally binds that motif

and is now no longer able to bind. And then once you have the regulator, can you then try to figure

out how to what after 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 perturbed, on the scale of a human life, can a human being be helped?

Of course. Of course. Yeah. I guess understanding is the first step.

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 is on a genetic level?

Yeah. Sorry if it's a dumb question. No, no, no. It's a brilliant question,

but I want to save it for a little bit later when we start talking about therapeutics.

Perfect. We've talked about the first four steps. There's two more. So basically the first step is

figure out the zero step. The starting point is the genetics. The first step after that is figure

out the tissue of action. The second step is figuring out the nucleotide that is responsible

or set of nucleotides. The third step is figuring 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 tight, tight ways. In ways, however,

that are functionally capable of being reopened and reclosed. So I can then go in and figure out

that folding by sort of chopping up the spaghetti soup, putting glue and ligating the segments that

were chopped up but nearby each other, and then sequencing through these ligation events to figure

out that this region of this chromosome, 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 regluing 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. Yes.

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,

religate near each other. In some cases, they're religate what you had just got,

but that's very rare. Most of the time, they will religate in whatever was proximal. You now have

glued the red noodle that was crossing the blue noodle to each other. You then reverse the glue,

the glue goes away, and you just sequence the heck out of it. Most of the time,

you'll find red segment with, you know, red segment, but you can specifically select for

ligation events that have happened that were not from the same segment by sort of marking

them in a particular way. And then selecting those, and then you sequence and you look for

red with blue matches of sort of things that were glued that were not immediate proximal to

each other. And that reveals the linking of the blue noodle and the red noodle. You're with me

so far? Yeah. Good. So we, you know, we've done these experiments. That's physical. That's 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 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 will contain a T at that location.

And basically say, wow, turns out that the expression of the 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

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 perturbation where I go and intervene.

Yes. I basically take a bunch of cells. So you know CRISPR, right? CRISPR is this

genome guidance and cutting mechanism. It's what George George likes to call genome vandalism.

So you basically are able to, you can basically take a guide RNA that you put into the CRISPR

system and the CRISPR system will basically use this guide RNA, scan the genome, find wherever

there's a match and then cut the genome. So, you know, I digress, but it's a bacterial immune

defense system. So basically bacteria are constantly attacked by viruses, but sometimes they win

against the viruses and they chop up these viruses and remember as a trophy inside their genome,

they have this low side, this CRISPR low side 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, oh, 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 Donner, Emmanuel Carpentier, Fang Zhang and many

others is coopted 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 those 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, by the yogurt industry. They were trying to

make better yogurts and they were trying to make their bacteria in their yogurt cultures

more resilient to viruses. And they were studying bacteria and they found that, wow,

this CRISPR system is awesome. It allows you to defend against that. And then it was coopted in

mammalian systems that don't use anything like that as a, as a, as a targeting way to basically

bring these DNA cutting enzymes to any locus in the genome. Why would you want to cut DNA

to do anything? The reason is that our DNA has a DNA repair mechanism where if a region of the

genome gets randomly cut, you will basically scan the genome for anything that matches

and sort of use it by homology. So the reason why we're deployed is because we now have a spare

copy. As soon as my mom's copy is deactivated, I can use my dad's copy. And somewhere else,

if my dad's copy is deactivated, I can use my mom's copy to repair it. So this is called homologous

based repair. So all you have to do is the cutting and you don't have to do the fixing.

That's exactly right. You don't have to do the fixing because it's already built in.

That's exactly right. But the fixing can be coopted 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 the, so you, 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 in popular culture, people use the term. I've never, wow, that's brilliant.

So CRISPR is genome vandalism followed by a bunch of band aids that have the sequence that you'd

like. And you can control the, 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 tools,

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 codders 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, you know, for every location that you want to target,

you need to design a particular regulator, a particular protein that will match that region

well. There's another technology called tailings, which are basically, you know, just a different

way of using proteins to sort of, you know, guide these codders to a particular location of the

genome. These require a massive team of engineers, of biological engineers to basically design a set

of amino acids that will target a particular sequence of your genome. The reason why CRISPR is

amazingly, awesomely revolutionary is because instead of having this team of engineers

design a new set of proteins for every location that you want to target, you just type it in

your computer and you just synthesize an RNA guide. The beauty of CRISPR is not the cutting,

it's not the fixing. All of that was there before. It's the guiding, and the only thing that changes

that it makes the guiding easier by sort of, you know, 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 on the, in terms of, that's kind of similar

to the story of deep learning versus old school machine learning. Some of the challenging parts

are automated. Okay, so, but CRISPR is just one cutting technology. And then there's,

that's part of the challenges and exciting opportunities of the field is to design different

cutting technologies. Yeah. So now, we, you know, this was a big parenthesis on CRISPR.

But now you, you know, when we were talking about perturbations, you basically now have the ability

to not just look at correlation between enhancers and genes, but actually go and either

destroy that enhancer and see if the gene changes in expression, or you can use the CRISPR targeting

system to bring in not vandalism and cutting, but you can couple the CRISPR system with,

and the CRISPR system is called usually CRISPR Cas9, because Cas9 is the protein that will then come

and cut. But there's a version of that protein called dead Cas9, where the cutting part is

deactivated. So you basically use D Cas9, dead Cas9, to bring in an activator or to bring in a

repressor. So you can now ask, is this enhancer changing that gene by taking this modified CRISPR,

which is already modified from the bacteria to be used in humans, that you can now modify the Cas9,

to be dead Cas9, and you can now further modify to bring in a regulator. And you can basically

turn on or turn off that enhancer and then see what is the impact on that gene. So these are the four

ways of linking the locus to the target gene. And that's step number five. Okay, step number five

is find the target gene. And step number six is what the heck does that gene do? You basically now

go and manipulate that gene to basically see what are the processes that change. And you can

basically ask, well, you know, in this particular case, in the FTO locus, we found mesenchymal stem

cells that are the progenitors of white fat and brown fat, or beige fat. We found the RS1421085

nucleotide variant as the causal variant. We found this large enhancer, this master regulator,

I like to call it OB1 for obesity one, like the strongest enhancer associated with and OB1 was

kind of chubby as the actor. I don't know if you remember him. So you basically are using this

Jedi mind trick to basically find out the location of the genome that is responsible, the enhancer

that harbors it, the motif, the upstream regulator, which is arid 5B 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 RX3 and RX5 do? The first thing we did is look across individuals

for individuals that had higher expression of RX3 or lower expression of RX3. 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 RX5 were both correlated positively with lipid metabolism

and negatively with mitochondrial biogenesis. You're like, what the heck does that mean?

It 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 RX5 are

negatively correlated with lipid metabolism. So that basically means that when they turn on

lipid metabolism, positively, when they turn on, they turn on lipid metabolism. And they're

negatively correlated with mitochondrial biogenesis. What do mitochondria do in this whole

process? Again, small parenthesis. What are mitochondria?

Mitochondria are little organelles. They arose. They only are found in eukaryotes.

U 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.

Compartmentalization. Eukaryotes have also organelles. Some eukaryotes have chloroplasts.

These are the plants. They photosynthesize. Some other eukaryotes, like us, have another

type of organelle called mitochondria. These arose from an ancient species that we engulfed.

This is an endosymbiosis event. Symbiosis, bio means life. Sim means together. So symbiotes

are things that live together. Endosymbiosis, endo means inside. So endosymbiosis means you live

together, holding the other one inside you. So the pre eukaryotes engulfed an organism that

was very good at energy production. And that organism eventually shed most of its genome

to now have only 13 genes in the mitochondrial genome. And those 13 genes are all involved in

energy production, the electron transport chain. So basically, electrons are these massive super

energy rich molecules. We basically have these organelles that produce energy. And when your

muscle exercises, you basically multiply your mitochondria. You basically sort of, you know,

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 on and so

forth. So what do mitochondria do? The mitochondria use energy to sort of do any kind of task. When

you're thinking, you're using energy. This energy comes from mitochondria. Your neurons have

mitochondria all over the place. Basically, this mitochondria can multiply as organelles

and they can be spread along the body of your muscle. Some of your muscle cells have actually

multiple nuclei, they're polynucleated, but they also have multiple mitochondria to basically

deal with the fact that your muscle is enormous. You can sort of span these super, super long

length. And you need energy throughout the length of your muscle. So that's why you have

mitochondria throughout the length. And you also need transcription through the length. So you

have multiple nuclei as well. So these two processes, lipids, store energy, what do mitochondria do?

So there's a process known as thermogenesis, thermo heat, genesis generation, thermogenesis

generation of heat. Remember that bathtub with the in and out? That's the equation that everybody's

focused on. So how much energy do you consume? How much energy do you burn? But in every thermodynamic

system, there's three parts to the equation. There's energy in, energy out, and energy lost.

Any machine has loss of energy. How do you lose energy? You emanate heat. So heat is energy loss.

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 when the mitochondria comes into play. Exactly. So IRIX 3 and RX 5 turn out to be the

master regulators of a process of thermogenesis versus lipogenesis generation of fat. So IRIX

3 and RX 5 in most people burn heat, burn calories as heat. So when you eat too much,

just burn it off in your fat cells. So with 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 RS 1 4 2 1 0 8 5 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 IRIX 3 and RX 5 because the regulator

that normally binds here, IRIX 5B, can no longer bind because it's an AT rich interacting domain.

And as soon as I change the T into a C, it can no longer bind because it's no longer AT rich.

But doesn't that mean that you're able to use the energy more efficiently?

You're not generating heat or is that... That means I can eat less and get around just fine.

Yes. So that's a feature actually. It's a feature in a food scarce environment.

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 that why mitochondria and then the lipids are both even though distant or 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. 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 arid 5B level. You can start intervening with CRISPR cast 9 at the single

SNP level. You can start intervening at IRX3 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

pharmaceutical and do all of that. And in our paper, we actually did all of that. We went in and

manipulated every single aspect. At the nucleotide level, we use CRISPR cast 9 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 the technologies, the tools that came along that made this possible? What are you excited

about maybe if we just look at the buffet of things that you've kind of mentioned? 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. 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 these regulatory genomic wiring. It required high C

of the 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.

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, you know, everything. You're referring to, I'm sorry, this one paper? Yeah, this one

single paper. This one single locus. I like to say that this is a paper about one nucleotide

in the human genome, about one bit of information, C versus T in the human genome. That's one bit of

information and we have 3.2 billion nucleotides to go through. So how do you do that systematically?

I am so excited about the next phase of research because the technologies that my group and many

other groups have developed allows us to now do this systematically, not just one locus at a time,

but thousands of loci at a time. So let me describe some of these technologies.

The first one is automation and robotics. So basically, you know, we talked about how you

can take all of these molecules and see which of these molecules are targeting each of these

genes and what do they do. So you can basically now screen through millions of molecules,

through thousands and thousands and thousands of plates, each of which has thousands and thousands

and thousands of molecules, every single time testing, you know, all of these genes and asking

which of these molecules perturb these genes. So that's technology number one, automation and

robotics. Technology number two is parallel readouts. So instead of perturbing one locus

and then asking if I use CRISPR Cas9 on this enhancer to basically use D Cas9 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 happened? But we have 120,000 disease associated SNPs that we want to test.

We don't want to spend 120,000 years doing it. So what do we do? We've basically developed this

technology for massively parallel reporter assays, MPRA. So in collaboration with Tarzan

Mickelson, Eric Lander, I mean, Jason Durie'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.

Okay. How do you do that? You know, 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 all our genomes. We basically have, you know,

plasmids floating around. 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 coopt these plasmids

into human cells. We can basically make a human cell culture and add plasmids to that human cell

culture that contain the things that you want to test. You now have this library of 450,000

elements. You can insert them each into the common plasmid and then test them in millions of cells

in parallel. And the common plasmid is all the same before you add it. Exactly. The rest of the

plasmid is the same. So it's called an 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 piece 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 10s

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 biotechnology. You basically have these arrays that add one nucleotide at a

time at every spot. And yet, so it's printing in it. So you're able to control. Super costly.

10,000 bucks. So this isn't millions? 10,000 bucks for 10,000 experiments. Sounds like the right,

you know. So that's super, that's exciting because you don't have to do one thing at a time.

You can now use that technology, these massively parallel reporter assays to test 10,000 locations

at a time. We've made multiple modifications 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, basically said, hey, if we want to 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 assets, 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 he's transcribed as part of

the gene. You don't have to have the two separate parts. Exactly. So you can just read them directly.

So there's a constant improvements in this whole process. By the way, generating all these options

is a 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 want to test that. Instead, you basically use all

of these tools that I've talked about already, you generate your top favorite 10,000 hypothesis,

and then you go and test all 10,000. And then from what comes out, you can then go to the next step.

So that's technology number two. So technology number one is robotics, automation, where you

have thousands of wells, and you constantly test them. The second technology is instead

of having wells, you have these massively parallel readouts in sort of these pooled 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,

well, traditionally the last 20 years, ever since the advent of next generation sequencing.

So basically before RNA expression profiling was based on this 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 want to do a million cells. How do you go from these wells

to a million cells? You can't. So what the next technology was after that is instead of using

a well for every reaction, you now use a lipid droplet for every reaction. So you use micro

droplets as reaction chambers to basically amplify RNA. So here's the idea. You basically have

microfluidics where you basically have every single cell coming down one tube in your microfluidics,

and you have little bubbles getting created in the other way with

specific primers that mark every cell with its own barcode. You basically couple the two,

and you end up with little bubbles that have a cell and tons of markers for that cell.

You now mark up all of the RNA for that one cell with the same exact barcode,

and you then lyse all of the droplets, and you sequence the heck out of that,

and you have for every RNA molecule a unique identifier that tells you what cell was it on.

That is such good engineering, microfluidics, and using some kind of primer to put a label

on the thing. I mean, you're making it sound easy. I assume it's beautiful, but it's gorgeous.

So there's the next generation. So that's the second generation. Next generation is forget

the microfluidics all together. 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 in each one of a million cells

using only big bottles. How can you possibly do that? Sounds crazy, but here's the idea.

You use a hundred of these bottles. You randomly shuffle all your million cells,

and you throw them into those hundred bottles randomly, completely random. You add one barcode

out of a hundred to every one of the cells. You then you now take them all out, you shuffle them

again, and you throw them again into the same hundred bottles, but now in a different randomization,

and you add a second barcode. So every cell now has two barcodes. You take them out again,

you shuffle them, and you throw them back in. Another third barcode is adding randomly from

the same hundred barcodes. You've now labeled every cell probabilistically based on the unique

path that it took of which of a hundred bottles did it go for the first time, which of a hundred

bottles the second time, and which of a hundred bottles the third time. A hundred times a hundred

times a hundred is a million unique barcodes in every single one of these cells without ever using

microfluid. It's beautiful, right? From a computer science perspective, it's very clever. 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. The bottles still sound pretty damn good. The bottles are awesome,

and that's basically the main technology that we're using. So the bottles is the main technology.

So there are kids 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 you about all these technologies that

we're using to probe the epigenome, one of them is DNA accessibility. So what we're doing in my lab

is that from the same dissociation of say a brain sample, where you now have all these tens of thousands

of cells floating around, you basically take half of them to do RNA profiling. And the other have to

do epigenome profiling, both at the single cell level. So that allows you to now figure out what

are the millions of DNA enhancers that are accessible in every one of tens of thousands of cells.

And computationally, we can now take the RNA and the DNA readouts and group them together

to basically figure out how is every enhancer related to every gene. And remember these sort

of enhancer gene linking that we were doing across 833 samples. 833 is awesome. Don't get me wrong.

But 10 million is way more awesome. So we can now look at correlated activity across 2.3 million

enhancers and 20,000 genes in each of millions of cells to basically start piecing together the

regulatory circuitry of every single type of neuron, every single type of astrocytes, oligodendrocytes,

microglial cell, inside the brains of 1,500 individuals that we've sampled across multiple

different brain regions across both DNA and RNA. So that's the data set that my team generated last

year alone. So in one year, we've basically generated 10 million cells from human brain across

a dozen different disorders across ketophenia, Alzheimer's, frontal temporal dementia,

Lewy body dementia, ALS, you know, Huntington's disease, post traumatic stress disorder, autism,

like, you know, bipolar disorder, healthy aging, etc. So it's possible that even just within that

data set lie a lot of keys to understanding these diseases and then be able to, like, directly leads

to 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

super smart. 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

I should, should you be comfortable with biology to be able to solve some of these problems?

If you just find if you put several of the hassle you were on,

fundamentally, are you thinking like a computer scientist here?

You have to. This is the only way. As I said, we are the descendants of the first digital

computer. We're trying to understand the digital computer, understand, we're trying to understand

the circuitry, the logic of this digital, you know, core computer and all of these analog layers

surrounding it. So you, you know, 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 this 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 by all computer scientists who understand biology,

who are able to design the next generation of experiments. Because many of these experiments,

no one in the right mind would design them without thinking of the analytical approach