(light music)
- Hi, my name is Wang.
I'm a PhD student from Mike Beer's group
from Johns Hopkins University.
Today, I'm going to talk about
DNA communication in the nucleus.
So let's first talk about DNA
which is the essential genetic material
that determines our appearance
and behaviors as living organisms.
So what does DNA look like?
You might think of it as a strand of double helix
as Watson and Crick are also thinking very hard about here
or you might also think of
the famous x-ray diagram from Rosalind Franklin.
But if you ever looked at the nucleus
of an interphase cell under a microscope,
the chromatin of it looks just like a hairball.
And in fact, in each human cell
a total of two meter long DNA which is the length of a bed
is packed inside this one-micron, tiny little nucleus.
So it is super crowded there
and instead of acting independently,
there are always extensive interactions
and communications across different parts of the chromatin,
and this complex 3D structure
is also known as chromatin organization.
So chromatin organization has
important implications in common diseases.
For example, when normal cell becomes a malignant tumor cell
not only its shape become irregular,
but also the chromatin organization
gets loose and disrupted.
And my research interest
is to find out the pattern and mechanism
behind the seemingly random chromatin organization.
And personally I'm interested in figuring out
the rules in biology.
In other fields, for example,
mathematicians have their formulas
and physicists have their theories.
The formulas and theories could be very simple and powerful
and in biology things are usually a bit more complicated.
But in this case I do believe
that there must exist some rules
because the chromatin organization
are tightly associated with regulation of gene expression.
Recalling from the central dogma
which is probably the most famous rule in molecular biology,
it says DNA is transcribed into RNA
and then translated into proteins,
but none of them are simple, one-step processes.
There are lots of regulations
to ensure that the right amount of RNA
and proteins are being generated at the correct time.
The expression level of the same gene
across cell types can be dramatically different
and in our genome,
the coding region of genes only constitutes 2% of it.
The remaining 98% of our genome
contains a lot of regulatory elements.
For example, here for RNA transcription
when the gene needs to activated during development
in a specific cell type,
it is going to spatially interact with a distal DNA element
called enhancer that controls its expression.
The activity of enhances are highly cell type specific
and to help enhancers
find their target genes faster or easier,
a pair of protein called CTCF form a loop with each other
to bring the enhancer and gene inside them into proximity
and therefore activating gene expression.
So CTCF is the type of barrier protein
that acts as loops anchors
at the boundaries of active transcription domains.
As you can see,
CTCF loop is a critical structure unit
of mammalian chromatin organization.
And what I'm trying to understand here
is how these CTCF loops are formed.
This is a critical problem
because not all the CTCFs
bound to the chromatin can interact.
There are around 50K CTCF binding sites in the genome
which corresponds to over one million CTCF pairs.
However only less than 5% of them
are identified as interacting
or looping by experiment.
So is there a generic rule for CTCF interaction?
Well, we're not completely sure about that,
but we do have
a compelling hypothesis called loop extrusion.
It says that a ring-shaped protein cohesin
is loaded onto the genome
in between a pair of CTCF binding sites
and due to its motor activity,
cohesin translocates along the chromatin fiber
and extrudes DNA out of it,
and cohesin gets blocked when it meets CTCF at both ends.
So there has been in vitro experimental evidences
that validates the loop extrusion model.
In this example, both ends of a piece of DNA
is tethered on the surface.
And when cohesin and cofactors
are put together into the medium with the DNA,
single-molecule imaging shows that a loop is generated
and enlarged in real time from this U-shape DNA
as the white arrow is pointing at here.
This direct visualization provides very strong evidence
of the loop extrusion model,
but there are two remaining questions.
The first one is how do we validate
this loop extrusion model for all the CTCF loops?
It is extremely difficult to do so experimentally
simply because there's just so many CTCF loops
and we're not able to trace them
all at once at the same time.
And the second is can we use the loop extrusion model
to explain why some CTCFs are interacting
and the others are not
or the CTCF interaction specificity?
So the way I come up with to answer these two questions
is to develop a mathematic model of loop extrusion
and then apply it to predict CTCF interaction specificity.
If the predictions from the loop extrusion model
are consistent with experimentally detected CTCF loops,
then the model itself also gets validated.
Understanding the formation of CTCF loops
again, will add to our knowledge
of a mammalian chromatin organization,
as well as transcriptional regulation
and of course with applications
to developing treatments of genetic diseases.
So to do that, let's first revisit the model.
A couple of steps needs to be done
before a loop can be formed.
First, we have two CTCF binding motifs
that are in convergence orientation towards each other
because this is a preferred configuration.
And second, CTCF protein dynamically binds
on those two binding sites.
And third, cohesin is loaded
and extrudes the DNA for a long distance.
And last but not least,
an existing loop can block the formation
of a neighboring loop in 3D space.
After breaking this model down,
we can use the language of math
to describe each of these processes.
And what we're doing here
is to predict interaction probability
between a pair of CTCF binding sites.
First, a scalar W is used as the contribution
of motif orientation to loop stability.
It represents how much
this convergent orientation is preferred
and a bigger W means that this orientation
is more preferred than tandem or divergent orientation.
The CTCF binding intensity is described
by the binding chemical equilibrium equation.
It depends on local CTCF concentration
which can be different among CTCF binding sites,
as well as the dissociation constant, Kd.
The activity of cohesin also depends on distance
as cohesin can stochastically fall off when translocating
along the chromatin fiber as distance gets larger,
so we used an exponential decay term to describe it.
And we also considered
all neighboring CTCF binding sites for loop competition.
So our final probability of any CTCF pair
interacting with each other is given by the product
of those four components.
Orientation, binding intensity, distance,
as well as loop competition.
And among those CTCF pairs,
all four components can be different
and that can result in the difference
in loop stability or interaction probability.
And CTCF pairs with high interaction probability
are predicted to be CTCF loops.
We then compare those predicted CTCF loops
with those actual loops that are experimentally detected
to see how accurate our predictions are.
And the experimental data
are coming from a technique called ChIA-PET
or Chromatin Interaction Analysis of Paired-End Tag.
It belongs to a class
of chromatin conformation capture assay
and in brief, the chromatin is cross-linked
using paraformaldehyde and sonicated
and then chromatin complexes are put down
using a specific antibody
against a particular protein factor.
For example, CTCF here.
Then DNA from distinct genomic loci
are ligated into the same fragment.
And it allows us to use sequencing to detect DNA fragments
that are linearly apart but spatially close
and each of those arcs is an output of ChIA-PET
that represents a spatial interaction or a loop.
So here's an example
of relatively complicated chromatin organizations.
Each of those peaks on the first track
is a CTCF binding event
and each of those arcs from the second track
is a loop detected by ChIA-PET.
The distance component of our model
is simply linear genomic distance
between each of those peaks
and any overlapping loops
are in loop competition with each other.
As you can see, there are many overlapping and nested loops,
so this is quite complicated.
However most of those loops
are correctly predicted by our computational model
as indicated on the third track.
We then applied the comparison to the whole genome
and looked at each individual features of our model,
as well as combinations of them.
And the evaluation metric we used here
is an accuracy score called AUPRC
which is between zero and one.
As we can see, none of the four features
alone can accurately predict CTCF interaction specificity.
But when we combine them with each other,
the accuracy increases
and the best model is given by the one
composed of CTCF binding intensity, orientation,
as well as loop competition.
The best model reached AUPRC about .6
which is quite good considering
it is a very difficult task
to distinguish 100K CTCF loops
from over one million non-interacting CTCF pairs.
And the result means that loop extrusion model
can indeed accurately predict CTCF interaction.
So this model itself
is also likely to be the true underlying mechanism
for CTCF loop formation.
And among those four features,
loop competition and distance
are highly correlated with each other.
This is very natural to understand
because as distance gets larger,
the number of competing loop also increases.
But to further understand this model,
we wanna know which one of them is the causal factor
that really determines loop formation probability.
And the way we do it
is like whenever we have two free variables
that we wanna study,
we fix one of them and then study the other.
So for each interacting CTCF loop we first sampled
a distance-matched, non-interacting CTCF pair.
In other words, in between those two datasets,
binding intensity, orientation, and distance
are all being matched.
As you would imagine,
it would be much more difficult to do classification here
because loop competition is the only feature
that you could use.
But with loop competition alone,
we still reached accuracy about .75
meaning that about 75% of the cases
are being correctly classified
and that indicates there are
some unique information to loop competition
that determines CTCF interactions specificity.
However when we do the same thing
to control loop competition,
we found that distance itself is not predictive at all.
Therefore we conclude that loop competition
is the more important component than distance in this model.
And finally, we use the predicted CTCF loop
to show that enhancers and promoters
are more likely to contact with each other
when CTCF interaction is strong.
On the figure on the right,
on the x-axis are the contact frequency
of CTCF loops from weak to strong
and on the y-axis are the enrichment of interactions
between enhancers and promoters inside those CTCF loops.
And this result implies that gene expression
is indeed regulated by chromatin organization
and facilitated inside CTCF loops.
So to summarize, we developed a mathematic model
that is able to accurately predict CTCF interaction
based on the loop extrusion process.
We found that loop competition is a more important component
in this model than distance
and we also validated the role of CTCF loops
in constraining active transcription domains.
This study shows that mathematic modeling
can be really powerful
in understanding biological mechanisms.
We expect our loop extrusion computational model
to be useful for further explorations of both the features
and mechanisms of chromatin packing,
as well as its impact on gene regulation.
I wanna thank everyone from the Mike Beer group
for their support of my work,
as well as my funding resources
includes Johns Hopkins University,
as well as the Encode Consortium, thanks for listening.
(light music)