Kind: captions
Language: en
the following is a conversation with
manolas 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 manolas went back and
forth
between the basics of biology to the
latest state of the art
and the research he's a master at this
so i just said back
and enjoyed the ride this conversation
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at lex friedman and now here's my
conversation
with manolis kalis so your group at mit
is trying to understand the molecular
basis of human disease
what are some of the biggest challenges
in your view don't
get me started i mean irregularities
standing human disease
is the most complex challenge
in modern science so because human
disease
is as complex as the human genome
it is as complex as the human brain
and it is in many ways even more complex
because the more we understand disease
complexity
the more we start understanding genome
complexity and
epigenome complexity and brain circuitry
complexity
and immune system complexity and cancer
complexity and so on and so forth
so traditionally
human disease was following basic
biology
you would basically understand basic
biology and model organisms
like 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
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 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
aerodynamics or fluid dynamics if it's
filled with water etc so you can then
make experimentation by perturbation and
then the scientific process is sort of
building models that
best fit the data designing new
experiments that best test your models
and challenge your models and so 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 the genome
with phenotypic differences and then you
know the direction of causality
so if you know that a particular gene is
related
to the disease you can basically say
okay perturbing that gene in mouse
causes the mice to have x phenotype
so perturbing that gene in human causes
the humans to have the disease
so i can now figure out what are the
detailed molecular phenotypes
in the human that are related to that
organismal
phenotype in the disease so it's all
about understanding
disease mechanism understanding what are
the pathways what are the tissues
what are the processes that are
associated with the disease so that we
know how to intervene
you can then prescribe particular
medications that also alter these
processes
you can prescribe lifestyle changes that
also affect these processes
and so 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 of it's the power of
animal models you can basically decouple
the perturbations you only do one
perturbation and you only do strong
perturbations at a time
in human the puzzle is incredibly
complex
because i mean obviously you don't do
human experimentation
you wait for natural selection and
natural genetic variation
to basically do its own experiments
which it has been doing for hundreds
and thousands of years in the human
population
and for hundreds of thousands of years
across
you know the the history leading to the
human population
so you basically take this natural
genetic variation
that we all carry within us every one of
us carries
six million perturbations so i've done
six million experiments on you
six million experiments for 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 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 that
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 uh
large enough of the human population of
earth is that too big
too small yeah so so the the is it is it
large enough
is a power analysis question and in
every one of our grants we do a power
analysis
based on what is the effect size that i
would like to detect
and what is the natural variation
in the two forms so every time you do a
perturbation you're asking i'm changing
form a into for
b form a has some natural genetic vary
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 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 you know hopeful message
sometimes it's a terrible message
it's basically well some people are sick
but if when
if we can figure out what are these
contributors to sickness
we can then help make them better and
help many other people better
who don't carry that exact mutation but
who carry
mutations on the same pathways
and that's what we like to call the
allelic series of a gene
you basically have many perturbations
of the same gene in different people
each with a different
frequency in the human population and
each with a different effect
on the individual charism so you said uh
in the past
there would be these small experiments
on perturbations and animal models
what does this puzzle solving process
look like today
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-carriers in another family
and do that for hundreds sometimes
thousands of families
and then trace these inheritance
patterns and then figure out what is the
gene
that plays that role is this the matrix
that you're showing
in in talks or lectures
so that matrix is the
input to the stuff that i saw in talks
so basically that matrix has
traditionally been strong effect genes
what the matrix looks like now is
instead of pedigrees instead of families
you basically have thousands and
sometimes
hundreds of thousands of unrelated
individuals
each with all of their genetic variants
and each with their
phenotype for example height or lipids
or 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 our complete
electronic health record
so you can now start relating not just
one gene segregating one family
not just thousands of variants
segregating with one phenotype
but now you can do millions of variants
versus hundreds of phenotypes
and as a computer scientist i mean
deconvolving that matrix partitioning it
into the layers of biology
that are associated with every one of
these elements
is a dream come true it's 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
um perturbation and disease
it's perturbation then effect at a
cellular level
at an organ level a body like
how do you disassemble this into like
what your group is working on
you're basically taking a bunch of the
hard problems in the space
how do you break apart a difficult
disease
uh 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 neural cells
and the vascular cells and the immune
cells
that are co-inhabiting the the brain
between the different types of
excitatory and inhibitory neurons that
are sort of interacting with each other
between different layers
of neurons in the cortical layers every
single one of these
has a different type of function
to play in cognition in
interaction with the environment in
maintenance
of the brain in energetic needs
in feeding the brain with blood with
oxygen
in clearing out the debris that are
resulting from the super high energy
production of cognition in in humans
so all of these things are basically um
potentially deconvolvable
computationally but experimentally
you can just do single cell profiling of
dozens of regions of the brain across
hundreds of individuals
across millions of cells and then now
you have
pieces of the puzzle that you can then
put back together
to understand that complexity
i mean first of all i mean the human
brain the cells in the human brain are
the most
okay maybe i'm romanticizing it but
cognition seems to be very complicated
so uh separating into the 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 and 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 forth and then looked
at that map
where you're asking what are the
individual genetic persuasions
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 this
down the line impact
or it's much larger in terms of the
measurable effect this a versus b
variance is actually so much cleanly
defined
when you go to the shorter branches
because for 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 a trade-off if you go very early on
in the pipeline
now each of these epigenomic alterations
for example this enhancer control region
is active maybe 50 less which is a
dramatic effect
now you can ask well how much does
changing one regulatory region in the
genome
in one cell type change disease well
that path is now long
so if you instead look at expression
the path between genetic variation 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 this 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
forth
and then through all of that you can
basically get at
the path to causality the path to
disease
and is there something beyond cellular
so you mentioned lifestyle
interventions or changes as a way to
or like be able to prescribe changes in
life style
like what what about organs what about
like
the function of the body as a whole yeah
absolutely so basically
when you go to your doctor they always
measure you know your pulse they always
measure your height those measure your
weight
your you know your bmis basically these
are just very basic variables
but with digital devices nowadays you
can start measuring hundreds of
variables for every individual
you can basically also phenotype
cognitively
through tests uh alzheimer's patients
there are cognitive tests that you can
imagine that you that you typically do
for uh cognitive decline these
minimental
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 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 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 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 uh
over a period of a life
it's yeah your ability to remember
things your ability to be
yeah empathetic or emotionally your
intelligence
perhaps even yeah but intelligence has
hundreds of variables
you can be your math intelligence your
literary intelligence your
puzzle-solving intelligence your logic
it could be like hundreds of things
and all of that is it's we were able to
measure that better and better so and
all that could be connected to the
entire pipeline
we used to think of each of these as a
single variable like intelligence i mean
that's ridiculous
it's basically dozens of different genes
that are controlling every single
variable you can basically think of 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 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 can
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 a particular task
but you can also relate them to genetic
variation
that might be affecting different parts
of the brain
for example your frontal cortex versus
your temporal cortex versus your visual
cortex
and so 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
and this is a video game
i'm excited by the quality of of the
video game the
the the the game designer did a hell of
a good job
so we're impressed oh i don't know the
sunset last night was a little
unrealistic
yeah yeah the graphics exactly come on
nvidia
to zoom back out we've been talking
about the
genetic origins of diseases but i think
it's fascinating to
talk about what are the most important
diseases to understand
and especially as it connects to the
things that you're working on
so it's very difficult to think about
important diseases to understand there's
many metrics of importance
one is lifestyle impact i mean if you
look at kovid the impact on lifestyle
has been enormous so understanding kovid
is important because it has impacted the
well-being
in terms of ability to have a job
ability to have an apartment ability to
go to work
ability to have a mental circle of
support
and all of that for you know millions of
americans like huge huge
impact so that's one aspect of
importance so basically mental disorders
alzheimer's has a huge importance in the
well-being of americans
whether or not it 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
650 000 americans per year
number two is cancer with 600 000
americans
number three far far down the list is
accidents
every single accident combined so
basically you you know you read the news
accidents like you know there was a huge
car crash all over the news
but the number of deaths number three by
far
167 000 lower respiratory disease so
that's
asthma not being able to breathe and so
forth 160 000
alzheimer's number four number five with
000 and then stroke brain aneurysms and
so forth that's 147
000 diabetes and metabolic disorders etc
that's 85 000.
the flu is 60 000 suicide
50 000 and then overdose et cetera
you know goes further down the list so
of course kovit 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
the 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
like schizophrenia and obesity w like
not necessarily things that kill you but
affect the quality of life
which problems are solvable which aren't
which are harder to solve which aren't i
love your question because it puts it in
the context
of a global 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
foods so basically that's sort of a
societal intervention if you wish
in the same way increasing empathy
increasing education increasing the
social
framework and support would basically
lead to fewer suicides
it would lead to fewer murders it would
lead to fewer
you know deaths overall so
you know that's something that we as a
society can do you can you can also
think about external factors versus
internal factors so the external factors
are basically communicable diseases
like covid like the flu 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 then they
contribute
contribution alzheimer's it's like you
know
60 plus genetic
so i think it's like 79 heritability
so that basically means that genetics
alone explains
79 of alzheimer's incidence
and yes there's a 21 environmental
component
where you could basically enrich your
cognitive environment enrich your social
interactions read more books learn a
foreign language
go running you know sort of have a more
fulfilling life
all of that will actually decrease
alzheimer's but there's a limit to how
much that
that can impact because of the huge
genetic footprint so this is fascinating
so
each one of these problems have a
genetic component
and an environment component and so like
when there's a genetic component
what can we do about some of these
diseases what what have you worked on
what can you say that's 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
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 gay
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 away 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 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 uh
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 so
forth that basically
you can um trace it in families with
cases and not cases cases not cases and
so forth
but even the you know but
so so these are the genes that everybody
says oh that's the genes we should go
after
because that's a strong effect gene i
like to think about it slightly
differently
these are the genes where genetic
impacts that have a strong effect were
tolerated
because every single time we have a
genetic association with disease
it depends on two things number one the
obvious one
whether the gene has an impact on the
disease number two the more subtle one
is whether there is genetic in variation
standing and circulating and segregating
in the human population
that impacts that gene some genes
are so darn important that if you mess
with them
even a tiny little amount that person is
dead
so those genes don't have variation
you're not going to find the genetic
association if you don't have variation
that doesn't mean that the gene has no
role
it's simply that the gene it simply
means that the gene tolerates no
mutations
so that's actually a strong signal when
there's no variation that's so 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
earlier in brionic what
lethal meaning meaning that if you don't
have it
okay there's a bunch of stuff that um
is required for a stable functional
organism
exactly across the board for 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 assert uh that this
is followed
correctly correct yeah this uh just uh
because
if this little thing about the folding
of a protein is incorrect
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 new born that investment is enormous
in terms of resources
so mammals have basically evolved
mechanisms
for fail fast where basically in those
early
months of development i mean it's it's
horrendous of course at the personal
level
when you lose a uh you know your future
child
but in some ways
there's so little hope for that child to
develop
and sort of make it through the
remaining months that sort of fail fast
is probably
a good evolutionary principle from an
evolutionary perspective for
mammals and of course humans
have a lot of medical resources that you
can sort of give those children a chance
and you know we have so much more
success in sort of giving folks who have
these strong carrier mutations a chance
but if they're not even making it
through the first three months
we're not going to see them so that's
why when we
when we say what are the most important
genes to focus on the ones that have a
strong effect
mutation or the ones that have a weak
effect mutation
well you know the jury might be out
because the ones that have a strong
effect mutation
are basically you know not mattering as
much
the ones that only have weak effect
mutations
by understanding through genetics that
they have a weak effect mutation
and understanding that they have a
causal role on the disease
we can then say okay great evolution has
only tolerated a two percent
change in that gene pharmaceutically
i can go in and induce a 70 change in
that gene
and maybe i will poke another hole at
the bathtub
that was not easy
to control in you know many of the other
sort of strong effect genetic variants
so okay so there's this beautiful map
of uh across the population of things
that
you're saying strong and weak effects so
stuff with a lot of
mutations and stuff with little
mutations with
no mutations and you have this map and
it's 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
variant
impacts the gene by a little and then
the gene impacts the disease by a lot
so what we care about is genes that
impact the disease a lot
but genetics gives us the full equation
and what i would argue
is if we couple the genetics
with expression variation to basically
ask what
genes change by a lot
and you know which genes correlate with
disease by a lot
even if the genetic variants change them
by a little
then that those are the best places to
intervene
those are the best places where
pharmaceutically if i have
even a modest effect i will have a
strong effect on the disease
whereas those genetic variants that have
a huge effect on the disease i might not
be able to change that gene by this much
without affecting all kinds of other
things
interesting so yeah okay so that's what
we're looking at then
what have we been able to find in terms
of
which disease could be helped again
don't get me started this is um
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 on 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 uh
i mean uh philosophically speaking is a
pretty powerful thing
so to live with i mean maybe that's uh
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 uh interesting to think about
the more you're able to know about
yourself from the genetic information in
terms of the diseases
how that changes your own view of life
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 um you know let's talk about that
next time
so basically it's a good preview yeah so
let's go back to amd so basically with
amd
we have no idea what causes amd you know
it was 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 uh microglia which
are immune cells that are sort of
constantly traversing your brain
and then pruning neuronal connections
pruning synaptic connections
that are not utilized so
in schizophrenia there's thought to be
a change in the pruning that basically
if you don't prune your synapses the
right way
you will actually have an increased role
of schizophrenia this is something that
was completely unexpected
for schizophrenia of course we knew it
has to do with neurons but the role of
the complement complex
which is also implicated in amd which is
now also implicating schizophrenia was a
huge surprise what's the complement
complex
so it's basically a set of genes the
complement genes
that are basically having various immune
roles and as i was saying earlier our
immune system has been co-opted
for many different roles across the body
so they actually play
many diverse roles and somehow the
immune system
is connected to the synaptic pruning
process exact process
exactly so immune cells were co-opted to
prune synapses how did you figure this
out
how does one go about figuring this
intricate connection
uh 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
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
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 forth
so that basically gave us a
confirmation in many ways that those
immune traits were instead
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
eyelids
and we know that type 2 diabetes in you
know sort of stems from the
dysregulation of insulin
in the beta cells of pancreatic eyelids
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
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 technology in
the heart
contain a lot of genetic variants
associated with blood pressure
if you look at height we found an
enrichment specifically
in embryonic stem cell enhancers so the
genetic variants predisposing you to be
taller or shorter
are in fact acting in developmental stem
cells makes
complete sense if you looked at
inflammatory bowel disease
you basically found inflammatory which
is immune
and also bowel disease which is
digestive
and indeed we saw a double enrichment
both in the immune cells
and in the digestive cells so that
basically told us that
this is acting in both components
there's an immune component to
inflammatory bowel disease
and there's a digestive component and
the big surprise was for alzheimer's
we had seven different brain samples
we found zero enrichment in the brain
samples
for genetic variants associated with
alzheimer's and this is mind-boggling
our brains were literally hurting what
is going on
and what is going on is that the brain
samples are primarily
neurons oligodendrocytes and astrocytes
in terms of the cell types that make
them up
so that basically indicated that genetic
variants associated with alzheimer's
were probably not acting in
oligodendrocytes astrocytes or neurons
so what could they be acting in well the
fourth major cell type is actually
microglia
microglia are resident immune cells in
your brain
oh nice the immune oh wow
and they are cd14 plus which is this
sort of cell surface markers uh of those
cells
so their cd14 plus cells just like
macrophages that are circulating
in your blood the microglia are
resident monocytes that are basically
sitting in your brain they're
tissue-specific
monocytes and every one of your tissues
like your 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 uh
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 way to therapeutics you have to
start somewhere
what are you gonna do you're gonna
basically make assays
that manipulate those genes
and those pathways in those cell types
so before we know the tissue of action
we don't even know where to start
we basically are at a loss but if you
know the tissue of action and even
better if you know the pathway of action
then you can basically screen your small
molecules
not for the gene you can screen them
directly for the pathway
in that cell type so you can basically
develop a high throughput multiplexed
you know robotic system for testing
the impact of your favorite molecules
that you know are safe efficacious and
you know sort of
hit that particular gene and so 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 you've
mentioned all these puzzles that were
figured out with the nature paper
for i mean you've mentioned a ton of
diseases
from obesity to alzheimer's even
schizophrenia i think you mentioned
and just what is the actual methodology
of figuring this out
so indeed i mentioned a lot of diseases
and 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 in both technology
departments and you know virology
departments and so on so forth and mit
was one of the first schools to
basically create a biology department
like oh we're going to study
all of life suddenly why was that even
the case
because the advent of dna and the genome
and the central dogma of dna makes rna
mixed protein
in many ways unified biology you could
suddenly
study the process of transcription in
viruses
or in bacteria and have a huge impact on
yeast and fly and maybe even mammals
because of this realization of these
common underlying processes
and in the same way that dna unified
biology
genetics is unifying disease
studies so you used to have
um you used to have
uh you know i don't know um
cardiovascular disease department
and uh you know neurological disease
department
and neurodegeneration department and uh
you know um basically immune and cancer
and so forth
and all of these were studied in
different labs
you know because it made sense because
basically the first step was
understanding how the tissue functions
and we kind of knew the tissues involved
in cardiovascular disease and so forth
but what's happening with human genetics
is that all of that
all of these walls and edifices that we
had built are
crumbling and the reason for that is
that
genetics is in many ways revealing
unexpected connections so suddenly we
now have to bring the immunologists
to work on alzheimer's they were never
in the room they were in another
building altogether
the same way for schizophrenia we now
have to sort of worry about
all these interconnected aspects for
metabolic disorders we're finding
contributions from brain
so suddenly we have to call the
neurologist from the other building and
so 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
epigenomics roadmap which we're now
called epimap
is 833 different tissues and using those
we've basically found enrichments in 540
different disorders
those enrichments are not like oh great
you guys work on that and we'll work on
this
they're intertwined amazingly so of
course there's a lot of modularity
but there's these enhancers that are
sort of broadly active and these
disorders that are broadly active
so basically some enhancers are active
in on tissues and some disorders are
enriching
in all tissues so basically there's
these multifactorial
and this other class which i like to
call polyfactorial diseases
which are basically li