Karen Adelman: So I want [unintelligible]
Aviv Regev: [unintelligible]
Karen Adelman: -- for the opportunity to be here and to talk
a little bit about how I see sort of past and future interplays between questions of
basic biology, fundamental mechanisms and, sort of, unbiased approaches to collecting
genomic data.
All good?
Male Speaker: Yeah.
Karen Adelman: [unintelligible]
Male Speaker: Let’s get that right here.
Then just turn your microphone so they can hear you.
Karen Adelman: Okay.
And so -- and I --
Male Speaker: [unintelligible]
Karen Adelman: -- I want to note that a lot of the things
that, sort of, I want to talk about today are things that come from not only my head
but also discussions with John Lis who, unfortunately, cannot be here today.
But where I, sort of, want to make a plug for the ideas that he and I would probably
both be presenting here during this -- these discussions.
So basically from the context of basic biology one of the things that we’ve heard again
and again is that we -- as we’ve started to describe what these functional elements
are, or these regulatory elements are, is going to be important to understand really
how do they function.
And I think we have heard a few examples of where we can’t assume that all enhancers
or all silencers are the same, that they function identically, and that the logic of the grammar
underlying them is going to be identical.
And so one of the things that I think we’ve learned as of, you know, the past few years
is that these early studies showing that enhancer regions were actually regions of transcription,
that’s not just the locust control region for beta globin, that’s actually a really
generalize able thing that we can say about enhancer regions and these regulatory elements
is they’re not just sites where transcription factors bind and only function at a distance,
but there are local events taking place, there’s local transcription.
And that’s something that I think really needs to function, to get its way into our
catalog of these regulatory elements is to understand the transcription of non-coding
RNAs that’s happening locally.
And so I think that this really begs the question of what new readouts and what new techniques
we should be developing, we should be focusing on.
And so I think we’ve heard a decent amount about possible readouts for regulatory elements,
things like everything from reporter assays to some really lovely, sort of, sophisticated
phenotypic examples.
And so I think that we really need to think hard about if we’re going to attack function
how are we going to define function in specific contexts.
But I do think that it’s important in our description of these elements to take the
step beyond chromatin state, beyond histone modifications, and to start asking questions
about how these things are affecting gene expression and, in fact, how the expression
of these elements, how these non-coding RNAs, is being regulated.
And so I make a, sort of, push for not just doing this by thinking that expression is
summed up in an RNA-seq experiment.
You know, that tells you about processed, stable, PolyAdenylated RNAs and then they’re
really cheap, really easy experiments that you can do that tell you just so much more.
And so I’m going to, sort of, argue that we start -- need to start looking at nascent
transcription, nascent RNA species, so that we can study the direct and proximal effects
of enhancers.
So we’ve heard arguments that enhancer effects could be either amplified or suppressed by
everything that functions downstream of that, right?
If you’re specifically looking at transcription, added enhancer, and at nearby genes then you
don’t have to worry as much about processing, or stability, or localization, or translation
of the protein, et cetera.
And so I really think that we need to come at this from both angles.
I think we need better assays of the proteome and I think we need better assays with the
nascent transcriptome to really understand how these two things interface with each other,
there’s a whole world in between there that we’re not -- we don’t fully understand.
Okay, so I want to also make the argument that doing so just doesn’t just tell us
something about basic biology.
But if you can figure out a way of taking, let’s say, human derived IPS lines or some
sort of differentiated tissue, and looking at nascent RNA you can quantitatively and
simultaneously measure the activity from an enhancer and all the local genes in that region,
as well as understanding how changing expression in that region affects all the other genes
in the genome.
And so I think you get basically a quantitative effect of how changing, for example, sequence
or deletion of an enhancer affects not only local targets genes but the entire genome,
and I think then you start to get some insights into networks of interactions.
Okay, so again, I’m going to sort of suggest that we need to look at more sensitive measures
of transcription, and how it’s affected by regulatory elements.
And then, again, not just identifying the direct target in the local region but also,
sort of, getting at these big networks.
And I want to just point out that if you can identify where transcription takes place in
an enhancer, then understanding the transcription factors that are regulating that is easier.
And I’ll give you examples of how identifying where enhancer transcription takes place pinpoints
a spot, a nucleotide, around which you can look for transcription factors.
And that gives you a lot more focus, a lot higher resolution than looking at a 5KB region
that has some histone modification.
All right and so also one of the things that we heard from Joe yesterday is that ultimately
to understand how these things function, how these regulatory elements function, we need
to start to get a handle on how these might be affecting gene expression at the level
of initiation or elongation, RNA processing, RNA stability.
And by defining exactly where transcription starts and ends then you start to put a bound
on what these non-coding RNAs do.
If they’re really short, if they’re long, can you find hidden ORFs by better defining
the actual extent of that transcript.
And so I think that we need to work harder to find assays that can be high throughput,
those that require a small number of cells.
And ideally they would be able to yield meaningful data in the sort of cheap way, meaning that
you can get some information without having to sequence incredibly deeply, which is an
issue with some of the histone modifications so that we could actually multiplex and evaluate
different time points, different conditions, multiple either patient samples or deletions
and SNPs.
Okay, so this is my main point is I think that in a next iteration of ENCODE we can
look more closely at RNA and not just a full stable RNA, but also nascent and newly produced
species, because I think that this is an efficient and direct way to look at function.
And I just want to give a couple examples of how unbiased genomic data collection has
already interfaced with fundamental biological questions, and how they have both really led
to new techniques that have then fed back and foster.
So I don’t see, again, as Aviv pointed out, these aren’t two sides where if one gives
the other has to take.
I actually see these things really as functioning together.
Okay, so I want to -- the example I want to give derives from the studies of John Lis.
So for many years he studied a couple of genes in the fly.
And he had discovered this phenomenon called promoter proximal pausing where the preliminaries
would transcribe a short RNA and then just sort of sit in the promoter proximal region
waiting for a signal, in this case heat shock, to come and release that pause signal and
let the preliminaries go rapidly into the gene.
And so he studied this, sort of in isolation, for quite a long time before we had the ability
to look at things genomically.
And so even though a few other genes had been demonstrated to be regulated in a similar
fashion, before we could do unbiased data collection we had no idea how broad this fundamental
biological process really was, right?
And so we thought this was a rare phenomenon, we thought this was something happening in
a handful of genes.
And, you know, so this is a pathetic example of an experiment I did 10 years ago in John’s
lab and this was like the state of the art, I did some chiP and I had some primer pairs
and I said, look there’s a peak of preliminaries at the promoter of this gene.
That’s where we were.
And fast forward, you know, to being able to do a chiP chiP and a chiP-seq so, you know,
Ginger Muse in my lab did these experiments in Drosophila as two cells and looked across
the entire genome for are there other genes in the genome that are regulated using this
same strategy.
So fundamental biological questions identified a strategy but we needed genomics, we needed
unbiased data collection to ask how broad is this strategy, how broadly used is it in
cells.
And so just as an example of this here I’m just showing some chiP chiP and all 16,000
Drosophila genes is a heat map where I think you can see that the intensity of signal for
every gene that has preliminaries at the promoter, basically the intensity of the signal is really
enriched right around the promoter so that pausing isn’t something that happens at
five genes, but it’s something that’s really a regulatory strategy that now we have
to integrate into the way that we think about how genes are regulated.
And so I really think that that’s where genomics enabled basic biology to make a leap.
But then basic biology had a question it said well here’s Pol II and yeah it’s sitting
there at the promoter, but is it engaged in transcription?
Are -- is this actually the same thing that John Lis had been talking about for three
decades?
And so both John, both Leighton Core in John’s lab, and Sergey Nachaev in my lab, wanted
to develop techniques to look at the nascent RNA generated by either -- specifically by
pause Pol II or by elongation competent transcription complexes, again, across the genome.
And so, as many of you know, Leighton developed this global run-on-seq technology where you
isolate nuclei and then you feed them a labeled nucleotide and you then purify RNA containing
that labeled nucleotide so you can identify very specifically, with very beautiful signal-to-noise,
where transcription complexes were sitting in the genome that are competent to elongate.
So this just gives such nicer estimation or evaluation of the actual transcription rate
in a cell at a specific time then you’re going to get from RNA-seq or from Pol II chip-seq,
or from anything of the like.
It really tells you something about where engaged elongation complexes are.
So the strategy that we took this, which was just focusing on transcription start sites,
doesn’t give you information about the level of transcription going through the entire
gene.
Oh, so anyway the basic biology question that this answered was that we could find nascent
RNAs almost everywhere we could find Pol II.
All right so that was the answer to the basic biology.
But what John and I realized, what a number of people have realized, is that these techniques
have a life beyond pausing, right?
So they have a life way beyond what our geeky little initial question got, you know, got
us into.
And now I think these techniques should be more broadly utilized to address genomic questions.
And so just to, sort of, say something about our technique, which is a start-seq and John
now is doing a 5’ cap, a version of GRO-seq.
They really focus on the 5’ end of these nascent transcripts and so this gives you
single nucleotide resolution of exactly where transcription is starting, right?
And so we pull these either from the chromatin fraction or out of the nucleus, we get them
when they’re short and still associated with the preliminaries by and large, and we
purify them by use of the fact that they have a cap so we can decap, we can identify that
first nucleotide.
And the nice thing about doing something like the start-seq is that you don’t require
the preliminaries to put another nucleotide in there.
And so you can get these RNAs even if these preliminaries are not stably paused, even
if they’re just actively transcribing through, if they’re arrested in transcription, or
if they’re in the process of termination.
So you can get these short RNAs from wherever transcription is initiating.
And the, again, the thing is that all these reads get focused at the 5’ end so that
you don’t need a billion reads to cover your sequence space, because you’re really
just looking at exactly where transcription is initiating.
And so I’ll give you some examples of that.
Okay, so here’s just an example of what data looks like so you have the Start-RNA-seq.
The same thing can be said for the 5’ GRO cap.
And so you can get sense strand reads that line up to transcription start sites, and
you can get anti-sense reads that tell you something about divergent transcription.
So this is a surprise, again, something that people would have never guessed at but genomics
told us it was there.
So in mammals the preliminaries goes in both directions at many coding genes, and in the
antisense upstream antisense direction makes a non-coding transcript.
And so, again, we know this because of these genomic assays because of unbiased data collection
and that -- but then this raises questions that basic biology can delve into further.
All right and so we can use these techniques to specifically say well, you know, I think
the start site of this is slightly different from annotation in this cell type.
We can use this to annotate exactly where the antisense transcription is originating
and, again, we can also use these to define the end of that transcription.
So now here’s just an example of what the data can be used for is you can start to get
a handle on promoter structure.
So if you align -- this is -- these are in mouse macrophages, you look a line all the
data by the Start-RNA sense strand and you sort of rank order the genes by how far away
the antisense transcription start site is from the sense.
You can see, as Frank Pugh showed for yeast, that you actually have two distinct Pol II
complexes going in opposite directions.
And you have -- and you can see TBP is there, this is his TauT binding protein.
And you can see that you have this big nucleus underprived region in between these two sites.
And so now we have an understanding of promoter structure that we didn’t have before we
could precisely identify where the anti-sense, where this divergent transcription was coming
from.
And again this is, you know, this is new information from a technique that is being repurposed,
I would say, to do it more unbiased search of the genome.
Okay, so this is not just something that can be used to look at promoters or divergent
non-coding transcription around promoters but it can also give us insight into some
of these unstable non-coding RNAs that are generated enhancers.
And so what I want to point out here is that enhancers are described often by the presence
of DNAs hypersensitivity, and a number of histone modifications.
And while this is really beautiful and it’s given us lots of sites to look at, one thing
I should note is that the regions defined are awfully broad.
And so here I’m just showing an example from the cell stem cell paper where they were
identifying the enhancer regions in embryonic stem cells, and then later after those stem
cells were differentiated into epiblast -- or iPSCs.
Okay, so the things that are labeled here in blue are the things that they defined as
enhancers in mouse embryonic stem cells.
And you notice that a lot of these key enhancers right around this KDM5B gene are actually
lost in the iPSCs and so you lose the monomethol DNAs, H3K27 subtle, et cetera.
And then they would argue that there are new enhancers that are coming up.
And so, again, here’s a situation where the perturbation, or in this case the time
course of development, is informative because you see these things changing.
Okay so is there a way, then, of better defining enhancers and asking whether or not everything
in this region is identical, or are there hotspots, are there hubs or specific loci
within this enhancer that are potentially more relevant to their function.
And so one thing, you know, one way you can think about this is in this big region, you
know, where is the action?
And so I just want to, sort of, show our work that we have done in collaboration with Joanna
Wysocka, characterizing the same transition from ESCs to iPSCs.
And this is super preliminary data, we’ve done one lane on a miSeq of our start-seq
data.
And here, in the mouse embryonic stem cells, this is the forward strand and then this is
the divergent transcription on the reverse strand.
And you can see that, in fact, in these loci that are described as enhancers you can see
both forward and reverse transcription in the embryonic stem cells that’s totally
gone in the iPSCs.
So, again, in a lane of miSeq you can sort of identify where transcription is taking
place within these regions.
And one thing I just wanted to note is that the alignment of these reads is really beautifully,
sort of, lined up with the DNAs which, sort of, I think, tells us that there’s a focal
region within these enhancers that we need to be thinking about where the transcription
and the DNAs are pinpointing the exact same region within this sort of multi KB locust.
Okay, so the only -- the thing I want to point out here is that, again, in the lane on the
miSeq we can easily monitor changes in the enhancer activity in these altered contexts,
okay?
And then, again, because it’s a high-resolution definition of 5’ ends we’ve looked around
these 5’ ends and tried to understand which transcription factors are present and absent
in the mESCs versus the iPSCs to try to get a handle on this.
This is easier to do if you’re looking in 200 base pairs upstream of a promoter than
in a 6KB region.
Okay, so I just want to say that what we have done is, sort of -- what I’ve tried to get
at here is that you can take techniques that are developed to get at a very specific biological
problem, and use these techniques.
And I have focused on GRO-seq and Start-seq but I also am in love with this metabolic
labeling 4sU-seq than Aviv has done a lot of because it gives you this broad, sort of,
agnostic data collection possibility and yet you can use it for everything from annotating
functional elements to answering very specific biological questions.
So I would argue that NHGRI should continue supporting technology development even if
it seems like it might be set out to answer a specific question, almost everything that
we’ve -- that is done in ENCODE was started that way and then it can be repurposed to
then ask the same kinds of questions, how broad are they, in a bigger context.
And I would just like to say that I think that investing in new technologies is important
but I also wonder if some of the large consortia projects could be structured in a way so that
they could flexibly integrate new kinds of techniques, new sorts of data analysis, you
know, add on a new collaboration to a consortia to integrate some of these new data sets.
And I think that these are things that are already in the works but I think in the next
phase doing even more of that would be great.
Okay, so these are the questions that I was asked.
I just want to say that I think we need better assays of direct consequences and better readouts
of phenotype and function of some of these elements.
And I think that there are ways of doing this in an efficient manner that can be then easily
scalable and multiplexed across many perturbations, many time points, many conditions which, I
think, ultimately is a goal that I hear, sort of, reiterated here is to understand the functional
genome, you sometimes have to push it a little bit and ask how it responds, but we can’t
do that with 800 different assays.
And so there are probably some low hanging fruit and I would just argue that RNA should
be among them.
That’s it.
Aviv Regev: Thank you.
We are running --
[applause]
We’re running behind schedule so we’re going to leave the questions to the discussion
session and move on straight to Anjana Rao, who’ll talk about analyzing site design
modifications in genomic DNA.
[end of transcript]