(piano riff)
- [Man] We are the paradoxical ape,
bipedal, naked, large brained,
long the master of fire,
tools, and language,
but still trying to understand ourselves.
Aware that death is inevitable,
yet filled with optimism,
we grow up slowly,
we hand down knowledge,
we empathize,
and deceive.
We shape the future from our
shared understanding of the past.
CARTA brings together experts
from diverse disciplines
to exchange insights on who we are,
and how we got here.
An exploration made possible
by the generosity of humans like you.
(electronic riff)
- I'm gonna give you a little
bit of a break from the brain,
and focus on the face.
So, a central, if in fact
not the central question
in modern biology is to understand
the genotype-to-phenotype connection.
In other words how does
information encoded by the genome
reveals itself through
the developmental process
to produce specific phenotypes.
And the flip side to this question
is to understand how
variation between the genome
during evolution produces
phenotypic variation,
either between species,
or between individuals
within the same species,
and how come such common genetic variants
can determine disease susceptibility
in complex human diseases.
I'm sure most of you
heard by now that most
complex disease and
trait associated variants
map to the non-coding parts of the genome,
so not to our genes per se,
the protein coding parts of the genome
but to the vast non-coding
space that comprises
over 98% of the genome.
Moreover, evidence begins to emerge
that these variants are enriched within
cis-regulatory elements,
but especially within
a specific class of regulatory
elements called enhancers.
So, these are modular genetic sequences
that can activate gene
expression at large distances,
and are typically specialized
to activate gene expression
in a specific tissue context.
And the idea here is that
this genetic variance,
or single nucleotide
polymorphisms within enhancers
can produce either weak or
strong enhancer alleles,
which in turn can lead to
quantitative differences
in gene expression, and
influence downstream traits.
We learned from the EVO-DEVO community
working on model organisms that
starting this regulatory
landscape evolution of closely
related species can be an
extremely powerful tool
in uncovering genotype
to phenotype connections.
So, for example elegant
work from Sean Carrol,
David Kingsley, and others,
unequivocally demonstrated the change
in enhancer sequence can
dictate change in phenotypes
such as wing pattern in Drosophila,
or skeletal changes in stickleback fish.
And we were very interested
in expanding this type
of EVO-DEVO thinking into
humans and other primates.
But of course, as it's
already been mentioned,
some of the key developmentally,
and evolutionary important cell types
cannot be studied directly
in embryonic context
from humans and other great apes,
for obvious ethical reasons.
But it has already been introduced
that now with induced birth,
but in stem cell models
we can study cellular,
and molecular mechanisms
underlying phenotypic divergence
in higher primates.
A few years back we referred
to this general strategy
as cellular anthropology
So, one of the phenotypes
that we are most interested in
is human face, and also human
craniofacial development.
We know that craniofacial
variation both within and between
species is largely genetically encoded,
so we have unique facial
features that are distinct
from those of other great apes,
but also there is a lot
of individual variation
in facial form, both within the humans,
but also in our close
evolutionary relatives.
So, the question is what
are the cellular origins
of facial variation?
It turns out that most
of our face and head
is derived from the single cell type
called cranial neural crest cells.
These are very unique cells
that form early in gestation,
at three to five weeks,
they're induced in dorsal
anterior part of the neural tube,
and then they migrate long distances,
and their destination
they can differentiate
to a large variety of cell
types including bone cartilage,
connective tissue, cranial
nerve, and pigment cells,
and as a result in fact, most
of the craniofacial complex
is derived from the neural crest.
Moreover, similar work
from Nicole LeDuarin,
and others showed through
cross species transplantation
experiments in avian
embryos that these cells
autonomously carry much of the information
on species specific facial morphology.
So, these cells are important,
but how can we access them
from humans, and other primates?
As you already probably realized,
this is a tricky proposition,
so there's a transient
embryonic cell types
forming at three to
five weeks of gestation,
we cannot access them
directly from the embryo
but thanks to the IPS and ES technologies
we can derive them in vitro,
and indeed that's what my lab has done
over the last several years,
established protocols
to obtain in vitro cells
with bonafide characteristics
of the cranial neural crest cells.
More recently Sara Prescott,
former graduate student
in the lab, working
collaboration with Rusty Gage
extended this model to a chimpanzee,
and here really were a few key features,
were to reduce heterogeneity
of the population,
and properly stage human and chimp cells,
because unless you do that,
the developmental differences
are gonna dominate over
species differences.
Moreover, we also went to great lengths
to ensure that not only
molecular signatures,
but also cellular
properties of these cells
represent bonafide neural crests cells,
so for example, we can
transplant these cells
to the chick embryo neural tube,
and they will engraft, migrate,
and assume correct positional
identities in the embryo,
and they also retain this
very broad differentiation
potential characteristic
of the crest.
So, now with this model in hand,
we used epigenomic strategy
to systematically annotate
regulatory elements.
So, just to give you an
idea for those of you
who are not so familiar with epigenomics,
one active enhancer is to share
certain common chromatin features,
and we can use technology
like or ChIP-seq or ATAC-seq
to map these features genome wide
in an unbiased manner
in a specific cell type.
Then we can correlate
these changes in epigenomic
patterns with changes in gene expression
determined by RNA-seq.
So, to do this, Sara took in-vitro derived
neural crest cells from
multiple independent
chimp and human individuals,
and performed mapping of
transcription factors,
and general co-activators,
analysis of hypersensitivity
by ATAC-seq, and also analyze
certain chemical types
or histone modifications that
are present on nucleosomes,
when regulatory elements are active.
And based on the simple
chromatin signatures,
she was able to map
enhancers that are active
in the neural crest cells,
and also distinguish them
from other classes of regulatory elements
such as promoters, and
then ultimately link to
differences in gene expression.
One thing I really need to mention
is that not only we can map
these regulatory elements,
but that quantitative
comparisons of chromatin
signatures at these elements
can be used to infer
differences in their activity.
So, it's not only where
these enhancers are,
but we can compare quantitatively
tags of chromatin signatures
to infer how different
they are in their activity
between the species.
So, this forms the basis
of what I refer to as
comparative epigenomics,
an idea here is simple,
that we're comparing in the same cell,
that between two closely related species,
so most of these epigenomic patterns
that we recover should
be invariant between
the two species.
But the idea is also that
we will be able to see
some differences, quantitative differences
in those chemical tags
that mark active chromatin,
which will be indicative of the difference
in regulatory activity,
which then hopefully will also be able to
link to differences in gene expression.
So, that's the idea, how does
the actual data look like?
Well indeed most of
the epigenomic patterns
are invariant between human and chimp.
But we also see at the smaller subset
of regions we see differences,
as indicated here in
this chimp-biased region,
or indicated here in this
human-biased enhancer region.
So, we can quantify
them, these differences
across the whole genome,
and compare different
individuals of the same species,
so different humans or different chimps,
and that variation is shown here in red,
or we can compare between the species
between human and chimp,
and as you can see there is more variation
between the species, shown here in blue,
than within the species,
and we'll refer to those falling outside
as either human-biased or chimp-biased
candidate enhancer regions.
Importantly, we can also
see these regulatory changes
with changes in gene expression,
so in other words, genes near
the human-biased enhancers
tend to be human-biased in expression,
and those near chimp-biased enhancers
are chimp-biased in expression,
and that's of course important
because gene expression
differences ultimately matters
for exerting phenotypic differences.
Moreover, these are not
just some random genes,
but in fact when we do ontology annotation
for these biased genes,
we see that they're
associated with development
and malformation of various
craniofacial structures,
including those structures that
are actually quite divergent
between human and chimp.
But now an important question to ask
is where do these interspecies differences
in enhancer signatures come from?
Are they due to differences
in the trans-regulatory
environments of the human
and chimp neural crests
or different in transcription
factor networks,
things like that?
Or are they due to differences in enhancer
sequence itself, so
cis-regulatory changes?
And to distinguish between
these two possibilities,
we designed the following assay.
We synthesized the library
of orthologous regions
that were either
human-biased, chimp-biased,
or invariant in our epigenomic data.
And then we cloned this
library to a so called
self transcribing enhancer reporter vector
forming a basis of the STARR-seq
as he described two years
ago by Alex Stark's lab.
And which relies on
the ability of enhancer
to activate it's own
expression at a distance,
and then drive it's own expression,
and serve as it's own barcode.
And we took this library,
and introduced it to either chimp,
or human neural crest cells.
From this, we learned
two important things,
first of all, bias in epigenomic signature
translates into the bias in the ability
of these sequences to
drive gene expression,
in other words what we
predicted as human-biased,
which you should be seeing
here was human-biased,
and what we predicted as chimp-biased,
was chimp-biased in general
in ability to drive gene expression.
And the second important conclusion
was the direction of
the bias was the same,
whether we tested our library
in the context of human,
or chimp neural crest cells,
and what this tells us is that the bias
is encoded by the sequence
of enhancers themselves,
rather than imposed by the differences
in trans-regulatory environments.
So, if it's in the sequence,
and the trans-regulatory
environments are more conserved,
we can go as far as to the
trans-regulatory environment
of the mouse to gain some
insight when and where
during development these
enhancers maybe active,
and here I'm showing a couple of examples
of this, where we're looking
at the human-biased enhancer,
and we're cloning orthologous sequence
from either human or chimp
to the LacZ enhancer reporter vector,
and testing in the context
of the mouse embryo.
And why there is a shared expression
in the olfactory placode associated gain
in multiple domains
within the head and face.
And here is another example,
again a human-biased enhancer,
this one is more pan-neural crest,
and again you see the gain of activity
for the human sequence,
and this one is actually
quite blazing in the face,
whereas the corresponding
chimp sequence is not active.
So, this is interesting because it shows
that we can systematically
identify enhancers
that change their regulatory activity
during recent human evolution,
and we can even learn something about
their spacial-temporal
activity during development.
But what this type of
work will not tell us,
is whether these enhancers
are actually responsible
for driving variation
in facial morphology.
And while we can model
some of these aspects
of variation in the mouse,
again mouse is not an ideal system
for studying human facial variation.
So, to try addressing this question,
we thought about harnessing
glorious normal-range
human facial variation,
which you can see just
looking around this room,
and our ability to know evasively,
and quite precisely quantified.
And to do this we teamed up
with a group of anthropologists,
engineers, and human geneticists
who developed a very novel methodology
for unbiased facial phenotyping,
and I just want to mention
at least three names,
Peter Claes, Mark Shriver,
and Seth Weinberg.
And what Peter has done is developed
this very precise method
to quantify facial shape,
which relies on 3D facial scans,
that are then mirror imaged,
and then remapped into the dense
mesh of 10,000 coordinates.
So, essentially, in other words,
each face is translated
into 10,000 coordinates,
then averaged to remove deviations
from bilateral symmetry,
and then aligned across
many, many participants
in the study to establish correspondence
of these 10,000 coordinates.
And this allows for
unbiased facial phenotyping,
and quantifying shape variation
over global-to-local facial segments,
starting from very global
effects on facial variation
to effects on very specific
aspects of the facial shape,
as highlighted here in yellow.
And this type of analysis has been done
on many, many participants
over 2,000 participants
replicated on the independent cohort
of another 2,000 participants,
and having genotype information
for these participants
allowed us to perform genome
wide association studies
to identify candidate variants
that may be associated
with different aspects
of facial variation.
While I don't have time to go
into the details of that study,
I just want to show you one example
that I think is really quite interesting.
So, this example of one
such genetic variant,
the lead SNP on Chromosome two,
that we linked to variation
in lower face morphology,
particularly the jaw shape.
So, when we look underneath this SNP
at our epigenomic data,
we see that it falls smack in the middle
of an active neuron crest enhancer,
but what is really interesting about it,
is that the same enhancer
is actually chimp-biased
in activity.
Moreover, the very same SNP was associated
by another group earlier this year
with susceptibility to
non-syndromic cleft lip,
and palate in Europeans as shown here.
So, we were really quite
excited by examples like that,
because what this suggests that perhaps
overlapping set of regulatory elements
can influence variation
at multiple levels,
both between the species,
within the species,
and then when combined with either
environmental pertubation
or other variants
influencing disease susceptibility
in common disease like cleft lip palate.
So, we're starting to think that
it's going to be about the
quantitative differences,
and about the combinatorial effect
which really will ultimately
separate those three scenarios,
the differences between within-species,
and disease states.
And with this thought,
I want to leave you,
and thank people involved.
In particular Sara Prescott,
who now moved onto Harvard as a post-doc
was an extremely talented and
open minded graduate student
who took on, and started all
the evolutionary projects
in my lab.
I want to acknowledge Tomek
for most of the genomic data anlaysis
that I've shown you today,
and really thinking about
how we can quantitatively
compare human and chimp epigenomic data.
I'm also grateful to
Rusty, and Carol Marchetto,
about whom you already heard today,
for really sharing those chimp IPS cells
very early in the game.
Our mouse genetics
collaborator, Licia Selleri,
and also a human
phenotyping and GWAS team,
Peter Claes, Seth
Weinberg, and Mark Shriver.
Thank you very much.
(applause)
(electronic riff)
