Pavel Sumazin: The title is a little bit different but it's
the same idea.
You'll notice that this talk is a bit different than the other talks we've heard today.
We are using TCGA data to infer new knowledge but not doing the primary analysis.
Specifically I will speak today about our efforts to TCGA samples to infer post-transcriptional
regulation in cancer.
I'm mostly interested in regulation of and by microRNAs.
MicroRNAs or miRs [spelled phonetically] [unintelligible] post-transcriptionally regulate their targets
through multiple mechanisms including transcript [spelled phonetically] degradation that is
shown in the cartoon above.
They can act as tumor suppressors and they can act as oncomeres.
We and other groups like us are interested in building integrated networks that include
both transcriptional interactions and interactions that involve microRNAs.
In my opinion, in order to do this, we need to address at least -- or at least initially
address the following four aims.
We need to be able to predict transcriptional and post transcriptional regulation [spelled
phonetically] of microRNAs, their targets, and to identify regulators of their miR activity.
TCGA samples and their profiles are exactly the kind of data that is useful that we can
use to at least make initial attempts to address these four aims.
Now today I'm going to touch on two of these.
And these two are probably the least -- they receive the least amount of attention, both
in the system to biology [spelled phonetically] community and the scientific community at
large.
These are post-transcriptional regulation of miRs and regulation of miR activity.
I hope to convince you today that these types of interactions are really worth looking at.
Now, microRNAs are very tightly post-transcriptionally regulated and changes in this type of regulation
can lead to significant swings in the expression of mature microRNAs.
MicroRNAs are transcribed -- you can see the cartoon on the right -- they are transcribed
to produce transcriptional units.
They are then processed to produce pri- [spelled phonetically], pre- and finally mature microRNAs.
The processing machinery includes canonical biogenesis regulators, droshan [spelled phonetically],
dyser [spelled phonetically].
These are non-sequence specific regulators but they also include sequence specific regulators.
We believe that these are in the hundreds, but only about 10 of them are known.
The effect of these biogenesis regulators can be seen in the visualization of deep sequencing
data of a short RNA library and what you see there are two tracks on the UCSC genome browser.
On the bottom in red we visualize fragments -- these are RNA fragments -- less than 30
bases long.
And on the top in black, fragments between 30 and 150 basis.
On the bottom in red, we can estimate the expression of mature microRNAs while at the
top in black we're estimating intermediary products of microRNAs, pre- and pri-.
Looking at this cluster -- very well-studied cluster -- of oncomeres [spelled phonetically],
the distance between miR 17 and miR 19A on the genome is less than 400 basis.
Yet even though they're co-transcribed, the expression of the mature miR 18A is almost
20 times less than that of miR 17.
Looking at the black, the top track, we can see evidence for the accumulation of pre-
and pri- product, intermediary product.
Intermediary product of the miR suggesting that the reason that miR 18A is poorly expressed
is that it's biogenesis is not as efficient as the biogenesis of its flanking microRNAs.
These are exactly -- this is exactly the kind of thing we're looking at when we examine
TCGA samples; we're looking for deviations in the expression of co-transcribed mature
microRNAs.
Genes whose expression correlate with this kind of deviation would be for us candidate
biogenesis regulators.
We can do the same thing for hosted [spelled phonetically] miRs.
MiR 218 sits in an intron of slit 3 [spelled phonetically].
If slit 3 is expressed, so would be miR 218.
We expect to see an increase in the expression of the mature 218 if slit 3 increases and
we see that on the top right.
When the an expression of another gene, DDX10, is high, an increase in the expression of
slit 3 is followed by an increase in the expression of miR 218 but this does not happen on the
top left where the expression of DDX10 is low.
This, to us, would be an indicator where the DDX10 is somehow regulating the biogenesis
of miR 218 and indeed upregulation of DDX10 in the glioblastoma cell line SNB19 led to
upregulation of both the pre- and the mature 218.
Now doing this in a little bit of a higher throughput, we identified a set of microRNAs
of interest, designed probes that measure the expression of mature, pre- and pri- products,
downregulated, silenced [spelled phonetically] DDX10, and measured the response of these
miRs to the downregulation of DDX10.
And what you see here is a panel of miRs where the mature microRNA responded to the downregulation
of DDX10 while the intermediary products are relatively unchanged.
Looking at even higher throughput way [spelled phonetically], we measured the expression,
the response, of about 800 microRNAs to silencing of predicted biogenesis regulators.
What you see in the heat map on the right, every road [spelled phonetically] describes
the response of a single microRNA to silencing of every one of the genes listed at the top.
We selected two positive controls.
These were predicted to downregulate microRNA biogenesis, to change microRNA biogenesis
in glioblastoma cell lines and are also well known biogenesis regulators.
So DDX5, DDX17, positive control.
We assembled a panel of predicted biogenesis regulators and a panel of negative controls.
These are proteins that are similar to the predicted biogenesis regulators, but are not
predicted to regulate biogenesis in glioblastoma.
There are two things I'd like to point out in this heat map on the right.
First, the response of the predicted biogenesis regulators is more similar to that of positive
control than negative control and second, looking at every row in this heat map you
can see that microRNAs are responding differently to the silencing of different regulators,
which is in line with the assertion that these are sequence specific.
Going back to DDX10, we collected the microRNAs that respond to DDX10 inhibition and identified
an enriched motif and the precursors of these miRs.
Drilling back down again, we could even identify biogenesis regulators that are very different
on the protein level but have very similar signatures in terms of microRNA response.
And now we're looking to see if these kinds of interactions are synergistic.
I'm going to switch to regulation of miR activity.
For operational reasons, we distinguish between two types of regulators of miR activity or
modulators of miR activity.
Oh, what's happening?
Okay.
Sponge [spelled phonetically] regulators compete for microRNA programs that regulate other
RNAs and non-sponge regulators activate or suppress [unintelligible] risk media deregulation
[spelled phonetically] of target RNAs.
I'll give you an example of each one of these.
P10P1 [spelled phonetically] is probably the well known -- the most well known sponge regulator.
P10P1 is a pseudo gene of P10, a known tumor suppressor.
P10P1 and P10 share high homology in their three prime UTRs [spelled phonetically] and
are targeted by many of the same microRNAs.
What [inaudible] argued was that P10P1 over-expression attracts microRNAs that would be otherwise
free to target P10 and therefore increase P10 expression.
And similarly, downregulation of P10P1 releases microRNAs that would otherwise target P10P1.
These are now free to target P10, reducing its expression.
TNRC6A is an example of a non-sponge regulator.
It is required for MI [spelled phonetically] risk function.
Targets and mutations in this protein have been shown to contribute to miR genesis.
So we are interested to identify these things on a genome-wide basis.
And this cartoon, I hope, would illustrate the main idea of our method.
So looking across TCGA samples, each sample in every column, we're looking at genes identifying
microRNA programs but target them and what we would hope to see is that the expression,
the total expression of these microRNA programs is inversely correlated with the expression
of their target.
When we do a genome-wide screen, we are looking for genes that change this correlation and
in this cartoon you can see the miR program at the top when the modulator is high, our
candidate's modulator of miR expression is high, the miR program is inversely correlated
with target.
On the bottom when it's slow, this correlation is weaker.
To us this would be an indication that the modulator is a modulator of the activity of
this miR program.
We can look for modulators on miR programs that are target specific, miR specific in
all combinations.
When we did a genome-wide scan, we were amazed to see a huge number of interactions.
On the left is a visualization of the network that we predicted, about a quarter million
interactions involving about 7,000 genes.
Not only is this RNA-RNA [spelled phonetically] network, which we call the NPR [spelled phonetically]
network, not only is it large, the forces exerted by these interactions are also unusually
-- well, unexpectedly large.
On the right you can see that these forces depend on the size of the neighborhood.
As the number of regulators for a gene increases, so does the correlation between the regulators
and the target and on the right there we are approaching Pierson [spelled phonetically]
correlation of about 0.6.
P10, which we mentioned before is way at the right.
Inside this network, we identified a small subnetwork.
It's very dense, a lot of edges, and it includes six well-known glioblastoma drivers.
To see what response the expression of each of these genes has to changes in frequent
UTRs [spelled phonetically], we transpected [spelled phonetically] the cell with P10 3'
UTR and so in upregulation, roughly about 50 percent in every one of these genes.
Now, this is not very strong upregulation, but it is consistent and it is significant.
Silencing dyser and drosha [spelled phonetically] which are canonical, non-sequence specific
biogenesis regulators of microRNAs completely abrogated this response.
Moreover, as I said in a previous slide, the regulation is communitorial and so transpecting
with pairs of UTRs instead of a single UTR, even though we're doing it with the same quantity,
increased the response dramatically.
We went from 0.5 to about twofold change here.
Not only is this affecting gene expression, it's also affecting phenotypical things we
can observe about the cell.
So this cartoon here, this diagram here, shows what happens to the cell when we silence P10,
that's the purple line at the top.
The cell growth rate increases when you silence P10.
Transpect with P10 3' UTR and cell growth decreases.
Transpect with P10C DNA, you have forever increased [spelled phonetically].
This is what we expect to see.
Now looking at regulators of P10, and we specifically were interested in regulators that have -- whose
locus is deleted in samples where P10 locus was intact, we compared the effects on cell
growth rate of silencing P10 and silencing each of these regulators.
I didn't include this, but silencing each of these regulators led to downregulation
of P10 and what we see here is the consequence of that.
You can see that in blue, silencing these regulators leads to a similar if not the same
reaction in cell growth.
These genes have never been associated with cancer before but their effect on P10 is leading
to the same effect as silencing of P10 itself.
Each one of these genes may be deleted in very few tumors, but there are 500 of these
and combinations of deletions at each of these locus will have the same effect.
Some with this I'll end.
I'd like to acknowledge Ho Shang [spelled phonetically] who is sitting here and has
a poster outside, Wei Jian [spelled phonetically] and Cheri [spelled phonetically] and this
work was done in the [unintelligible] lab with help from [unintelligible] lab.
Thank you for listening.
I'll take some questions.
[applause]
Male Speaker: Good stuff.
Are there questions for Pavel?
Question here?
Male Speaker: Very interesting approach.
So with ARACNE you can make predict -- paralyzed [spelled phonetically] predictions on transcriptional
regulatory interactions just through correlations between gene expression patterns and then
the sort of sponge mRNA interactions can be predicted from sequence homology.
So do you see when you when you look at those two independent ways of drawing networks of
gene regulatory interactions, do you see that an edge that's predicted by ARACNE tends to
be more -- at least between these two genes -- is enriched in those predicted sequence
homologies that would suggest a sponge regulator?
Pavel Sumazin: I didn't quite understand, but I'll give you
my interpretation.
I think you're asking whether the two methods are predicting the same interactions, right?
Male Speaker: Roughly.
Pavel Sumazin: The answer is no, they're mutually exclusive,
almost entirely.
So for example, we use ARACNE to understand why genes are downregulated when we silence
a transcription factor.
Looking at data of transcription factor silencing, and trying to understand why genes are reacting
to the silencing, you'll find that this kind of a method -- we're talking about miR activity
regulation; these are indirect RNA/RNA interactions -- this method explains an entirely different
set of genes that are explained by ARACNE.
The size is about the same.
There's almost nothing that is common between the two methods.
Male Speaker: Interesting.
Male Speaker: All right, thanks again.
[applause]