Franklin Pugh: Okay, I’m kind of an outsider here, don’t
work with ENCODE at all, but I think about the problems that ENCODE also faces in my
own work.
My background is actually -- I spent 30 years training in biochemistry and studying genome,
or gene regulation from a mechanistic point of view, and the past 15 years doing it on
a genomic scale.
And as I was listening here, some of the things that really resonated with me was -- I think
Joe may have mentioned this, but it was sort of the PI vision.
It’s really, you know, the how of each of these elements.
I think that when we talk about, “How do these elements work?” we really mean mechanistically.
How are they pushing proteins around?
How are they working with each other, the whole conglomerate of factors?
And when we think about the whole conglomerate of factors, there’s sort of one thing that’s
kind of, you know, missing from this whole thing, the elephant in the room.
And that is, if you get beyond the functional elements themselves, there’s a world of
factors there.
And beyond the chromatin, beyond the sequence specific factors, there’s the chromatin
regulators.
And I know some of these things are covered under ENCODE but, at least from my perspective,
they seem to be afterthoughts.
And it’s really, you know, the primary focus is on sequence specific factors.
But you have these vast number of regulators, you have the initiation machinery, the transcription
machinery, elongation, and all these guys need to be working together as one massive
complex.
And I think it’s really important to understand how all of these guys work together.
So that’s the entirety of it.
So when you think about mechanisms -- here’s a mechanism, a car, a steering mechanism.
And we can think about our bodies as mechanisms.
And when things break down, we need a mechanic for the car, but for us we need drugs.
And how do we get drugs?
Well, we need to understand mechanisms.
And how do we get mechanisms?
But it brings us all the way back to the top and the concept of how do we take all this
genomic data to get to these mechanisms?
And the first phase really in this process is correlations, and we’ve been doing a
lot of that, and that works really well.
But correlations is like saying, okay you have a steering wheel, a rack, a pinion, here’s
the tires.
They’re all kind of correlated with each other.
They’re all in the same place but that doesn’t mean we understand what they do.
So we need to go to that next phase, and there’s three parts to it that I see, because correlation
is not causation.
We have to determine causation, and structure, and dynamics.
And the way I think about it is, causation really comes down to making genetic perturbations
in the system.
So over here, if you took this rack away, well, what happens?
Well, these wheels won’t turn, but the steering wheel will turn, and the drive shaft and the
gear will turn.
So we know parts of it will turn, other parts won’t.
So that’s causation and we’ll know what the wheel can do up to this point and the
steering wheel, but not beyond that, all right?
The second is high-resolution assays.
If you look at this structure here, you can pretty much guess what it does.
So if you have really high resolution view, and I’m talking about base pair resolution
view of the genome, you can just get a visual on what it is and that’s going to be quite
helpful.
And finally the time course.
You can get a better sense of how things work when you see them moving and changing with
time.
But I’d emphasize that I don’t think time courses are necessarily causation.
They can be along very parallel paths, but not necessarily causation.
And really, the whole process of knocking things out and looking down the stream of
that may be more effective means than causation.
I put a red X next to functional assays and states because, at least for me, these concepts,
you know, histone, chromatin states, and things like that weren’t necessarily resonating
with me because, in part, they’re either out of context or they’re rather low resolution.
If we get into things like causation, structure, and dynamics, I think these things fall out
from that.
You’ll get functions, and you’ll get more detailed definition, and therefore states
go away.
So when I think about states, I think about resolution.
Here’s sort of an aerial image of actually a schoolyard, and you can get lots of things
from it.
But at low resolution, I kind of think of ChIP-seq as this kind of resolution, you get
certain types of information, it can be quite useful about where things are, but the details
are kind of lost.
You get much higher resolution, you can get into the details and change the questions
that you’re asking.
You can get much more specific, you can see how all the parts fit together.
So that was the whole visualization component that I was mentioning.
You have the details, and one concrete example of that is FoxA2 transcription factor.
We were doing some of this work with Ken Zaret’s lab and essentially, you know, it’s a ChIP-exo
assay.
You can use the exonucleases to chew up the ChIP data and chew up the borders.
You can get pretty high resolution.
And with Ken’s lab we found 35,000 sites for that and each one of them with a binding
motif.
And if you blow that region up, you can see that you can get the ChIP-exo sort of midpoint
between the two key pairs of the exonuclease stock.
And each of these columns is a single base pair.
You get really base pair resolution.
You can line this up with the structure of the proteins and in fact you can just line
up the sequence there for each of these columns.
Do they line up the nucleotides for these three green things there: T, T, T, you see
at the end of these genes the gold, and then finally this midpoint between the blue and
the red is the cross section point.
So you can find it right here.
You can get pretty -- we use this, actually, for structural information.
It’s great when you have the actual crystal structure because you really need it to interpret
the data.
So the charge that I had here was about coming up with a proposal, a proposed set of work,
all right?
And so this is what I want to describe to you.
And it really comes down to, you know, like a proposal you’d need to have some background,
and then you’d need to propose an experiment, and things like that, and feasibility.
So for this cartoon purpose these monikers will be the genomes ACGT, the epigenome sits
on top of that, that’s the blue box, and then the environment.
And to me there’s really two major factors that one could change in the system, it’s
either the genetic change or you can -- environmental change.
That’s really all there is if you’re talking about doing high-throughput sequencing, to
look at the epigenome.
So there’s these two types of perturbations.
Of course each one of those creates an epigenome change and the epigenome change goes on and
creates phenotypes.
So this is kind of the framework that I think about, think, and I want to just use one example
here.
You know, as in a proposal, you might have some preliminary results, this is our sort
of CHiP-exo assay of a protein, it doesn’t matter what it is.
The window here is about 100 base pair from here to here, just to give you a sense of
framework.
And this is promoter regions of a set of genes where this protein is found.
And if you elicit a genetic change -- it turns out, a protein that regulates this thing -- the
confirmation changes but it’s only at a subset of genes to which this protein is more
broadly spread.
So through these mutations we can get a sense of what’s happening here mechanistically.
Now there’s a lot more to this I want to show you in a much more descriptive manner.
And I’m going to take this data here which you’re all familiar with as heat maps, I’m
going to turn it into an abstraction, and then I want to start layering other data sets
over it and how it informs of us mechanisms.
And this is what you see here.
So for example, there’s about 100 genes here, okay, and then it’s just average data
groups of these one set of genes, and another set of genes, and so on.
And you have a blue protein bound, and a red protein spread across here, and these green
and the black.
And if you actually go through this genotypic change where you actually remove the blue,
the red collapses, the green expands out.
So this is spatially what happens.
But instead if you went and did an environmental change, the blue also goes away but the red
stays broad, and the green goes away and then this black thing moves in.
So there’s all sorts of reasons these microscopical [spelled phonetically] or base pair level
changes that take place when you go through, either knock out a factor and ask what downstream
things happened, or you create an environmental change and you ask what downstream event happened.
But really what that takes is having a look at the individual proteins at high spatial
resolutions so you can separate them all from each other.
So in terms of a proposal, the idea would be simply -- and this is, anybody could think
of this because it’s fairly straight forward -- you have all your various cell systems,
I don’t care what they are because -- my perspective, we work in yeast, we work in
flies, we work in mouse, we work in human, but I don’t really care what the organism
is, I just care about mechanisms.
I care to understand how all these proteins work together and fundamentally from yeast
to humans, those mechanisms you can think of them irregardless [spelled phonetically]
of the organism.
So you take your cell type and now you do your various, throw your various tools at
it that you have.
Your dynamics is going to be time point, you can do you CRISPR Cas9-based deletions and
then plus or minus perturbation.
And you go through this and you run through your assays and you stick to your quality
control metrics.
Okay, so part of any proposal requires a budget.
And so Ross came up with 800 million.
Did you come up with a budget for that, Ross?
[laughter]
Ross Hardison: I’m still working on it.
Franklin Pugh: Not enough money in the universe.
So get feasible about it, and this is just sort of a down payment on Ross’s proposal,
is to say, “Okay, well if you took 10 cell types and you took three time points through
that and applied deletions of let’s say the sequence specific transcription factors,
and you were just trying to look at 22 factors in particular, of course you’d need to do
replicates.
I put three down, you want two to hold: one in case it doesn’t work for that assay.
So you add that up, you get 22 of these factors right here.
And that’s about 10,000 assays right there.
That seems rather simplistic, but if you do the multiplication and it’s about 10,000
assays, if you can get it down to $200 an assay, plus some fixed cost, you’re up to
about $3 million just in this project.
So, you know, these are fictitious numbers, but they’re hopefully reasonable.
That’s the kind of project that may be feasible, you know, for one proposal I would guess.
Okay, so now this gets down to sort of my last slide really.
And that is how to actually make this thing happen.
And this is kind of -- some of this I think -- again, I’m sort of an outsider looking
at ENCODE, so I don’t fully understand the details of it, but it strikes me, you know,
really what you need and have already is just basically a pipeline: data production, and
processing pipeline.
So you make it and then you analyze the data.
One idea would be that community samples come in so they can be anybody who’s interested
in this or perhaps even to an RFA, come in.
And I think what really is key for me here is this, is a sort of single monolithic pipeline,
if you will, that the data comes through, that the samples come in, they get processed,
and they get pushed all the way through, and then it finally comes out the other end.
This is the rapid release that the community can do analysis on.
And the reason why I’m kind of keen on perhaps a monolith, at least for the purpose discussion,
is because of standards, all right.
I think really what’s happened over the past many years has been a compromise, okay.
Standards were hard to come by.
They were years in negotiation, they finally happened, but really they were work in progress.
And as we go on I think we need to continue to improve those standards, but we also need
to somewhat lock them down and hold different labs accountable that may be processing samples
through the various assays.
So some of the enabling components -- I’m going to get back to this in just a second
-- but this is what I think that whatever runs the pipeline can control.
For example, you have affinity capture.
We talked about this today, antibodies.
Antibodies can be a real challenge, heard from Mike that 75 percent of them in sort
of a random test aren’t very good.
So we need to get a hold of that and maintain strict standards.
There’s noise minimization, there’s spatial resolution that’s important.
You can use the exonuclease, MNase, DNase, Tn5, the mapping 5’ 3’ ends of RNA.
These are all some important high resolution methods that will give you a spatial picture
of what’s going on.
I think it’s important to look at these things in a native context rather than in
reporter context because the native is going to be really the proper context in which this
thing will be ultimately functioning in.
High-throughput, that’s going to be critical but I also think of it as true production
versus variable compliance.
True productivity or processing through, you might think of a car being manufactured in
a plant.
There’s absolute standards, everything is done the same way.
It isn’t like, you know, one plant might manufacture a car slightly different than
a different plant of the same model.
So the question is variable compliance.
I think that’s going to be important.
And perhaps finally this is, this is something I actually gave some thought and I’ve really
waffled on.
Should the DCC be maybe something more of a line item?
What I mean is, should it be open for funding, you know, determining whether it should exist
or not?
Or should it already be determined that this is what we need, and it’s going to exist?
And really what the RFA should be about is how to improve it, how to adjust it, how to
tweak it, how to do all sorts of things with it, but not question its existence, all right?
Because I don’t think we can really -- we need something like this and I don’t think
that will ever go away.
And I don’t know if it’s a good thing to have it passed around maybe from one academic
site to another, it seems to be maybe we really want to lock it into place.
Okay.
And I’ll just turn it over to this -- these are some questions that I think Elise sent
over, asked me to identify, and I’ll just leave the answers there and answer any questions
that you might have.
[applause] Paul Sternberg:
So, Frank, you’re proposing this, sort of this sequencing center model for this, right?
Franklin Pugh: Well --
Paul Sternberg: I mean that you have -- that you say you --
Franklin Pugh: Yeah, I actually -- well one thing I would
really --
Paul Sternberg: Which is really different than your 1 million,
you know, your $3 million project.
Franklin Pugh: Yeah, actually, so a sequencing center, but
not necessarily just one.
You might have different pipelines here for different assays.
I mean I think ENCODE already does that, right?
There’s -- different labs have different specialties, and so you’d use that.
But I really see this central pipeline as the DCC coordinating not all of it but also
setting the standards, both in terms of report -- every aspect of it, a lot more concentralized
[spelled phonetically] control.
Paul Sternberg: Also, I bought, when I bought, the last time
I bought a Toyota I got the one that was built in Japan, not the U.S. because it was better,
because the doors close differently.
[laughter]
Franklin Pugh: Was phantom [spelled phonetically] better?
Male Speaker 1: You know, Frank, I would say most that already
exists or is in place.
Franklin Pugh: Right.
Male Speaker 1: Okay.
So maybe I missed what you’re proposing.
I guess just to continue doing what we’re doing
Franklin Pugh: Yeah, no, no that’s exactly the point.
I mean a lot of these things are already exist, it’s just from my outside point, these are
just independently what I think should happen.
They already are there.
And the point really is to have detail.
You know, improving it, tweaking it, and how it might be centralized, you know.
Male Speaker 2: Wasn’t the community sample input, wasn’t
that a change?
Franklin Pugh: Yeah, well that was part of it.
Male Speaker 2: Yeah, right, that’s what I -- yeah.
[end of transcript]