John O’Shea: All right, so I’m the last speaker, and
I think what you will find is most of the things I have to say have been said by other
speakers.
So I’ll try to be quick, but I think I have, maybe, a little difference perspective from
most of you here.
Number one -- I’m not asking for any money.
I’m in the intermural program.
You can’t even give me money if you wanted to.
That’s number one.
Number two -- I am, I’ll say, I definitely am addicted to ENCODE.
I’m an abuser of ENCODE for sure, and haven’t contributed in any way.
But maybe we’ll get to that in the end -- as to whether people have small lives like mine,
but end up doing big data, sort of, that’s just, sort of, how the technology works now
-- whether that will be important.
And the other part is that I’m, I guess, one of the few people here who has an M.D.
after my name, and I think I’m the only rheumatologist, so let me give you that sort
of perspective.
So some of the things that I wanted to focus on here were, you know, what NHGRI can do
for me.
Thank you very much, Eric.
I really appreciate you, you know, advancing the goals of my lab.
But what does the draft genome really mean for rank-and-file physicians who are trying
to engage in precision medicine?
And then, sort of practically thinking about here, at a place like the NIH, where we have
a big research hospital, when a patient comes in how can ENCODE and Roadmap help us?
And conversely, though, how can ENCODE and Roadmap in animal models help us better understand
disease pathogenesis in humans, and think about new therapies?
So, you know, this is personalized medicine from my perspective.
We’re sort of a “mom and pop” operation -- picture my lab in the early ‘90s.
And, you know, this is ENCODE.
And the question, then, for me is how does a little mom and pop operation -- how do we
benefit from ENCODE?
And I think there are a lot of labs that are in my position.
So, I mean, this is in -- I can summarize my life in 20 years or so in one slide here.
You know, in the old days, we cloned, like probably many of you, started off cloning
a single gene when that was hard and took a long time.
So again, I’m not really sure whether I’m a geneticist or a biochemist or an immunologist,
but in essence, we clone one gene.
We sort of help figure out this pathway, which it turns out to be a very tractable pathway
in terms of understanding a lot of the things people have talked about today -- how you
have an environmental signal, ultimately, all through gene expression.
We related this to a rare disease, in this case severe combined immune deficiency, and
then used this as a basis to collaborate with industry to get a new class of drugs that
are now FDA approved.
But obviously, the goal of ENCODE is not to have this take 20 years, but to do this a
little quicker.
And obviously, I think that certainly is the case now.
So to me, this is the question that I’d like to sort of pose in my talk -- how does
Costco relate to this mom and pop corner store that I have?
And so one thing we certainly have done with the advent of massive parallel sequencing,
and this sort of thing, is we’ve certainly benefited.
This pathway, as I alluded to, is very amenable to thinking about where does the signal induce
a transcription factor, like stats, to deposit within the genome.
And then, how does this relate?
This is a paper that just is coming out in Nature shortly, it’s online.
And we’re very interested in taking assays we did on a sort of a small level and integrating
them into, sort of, a genomic scale.
So the one thing, though, that I did want to emphasize thinking about, you know, how
can ENCODE be used by rank-and-file clinicians and scientists is getting back to, sort of,
the ease in which they can use the data.
So for genes, you know, it’s pretty easy.
You put in your favorite -- everybody’s favorite gene, or my favorite at least, JAK3
in PubMed, and you get a whole bunch of hits.
And that’s pretty accessible for people who have very limited computational skills.
It’s very intuitive, et cetera, et cetera.
It’s like, sort of, shopping on iTunes or Amazon.
You don’t need a master’s degree in computational biology to get the data you need.
Similarly, you know, on your iPhone, you have your ImmGen app, and whenever we’re sitting
in any meetings and somebody says something about a gene that I don’t know about, I
put it into my iPhone, and right away I go, “Oh, that’s sort of interesting.”
Now, if you put JAK3 into Epigenomics: no hits, you know.
And, I mean, I know why that is.
You, obviously, if you put JAK3 into the ENCODE Roadmap site and you get the UCSC browser
view and everything -- but I sort of wonder, in long-term -- I know, in one level, if you
try to make everything accessible to everybody, it would collapse the system.
At the same time, I think it gets to the point that not all users are the same.
There’s some users who really need to know the details, and then, you know, for clinician
scientists, they probably don’t need all the details.
And Eric, I think, had -- I forget, the tool that you use, it sound like crying, Lachrymose
or something.
But that sounds like a great idea.
But I would certainly have NHGRI think about the end users, how this can be most effectively
used for the greatest number of people.
So let’s see, I think I covered a lot of this -- integration, individualization.
And that was the other part, is that how do you visualize -- I’m definitely with you,
Russ.
I’d like to put on my, you know, virtual, you know, and sort of be just like you see
in, you know, movies and stuff, and figure out my favorite enhancer for my favorite gene,
you know.
Ideally on my iPhone, though.
[laughter]
Cross-integration of cross species, epigenomics, we talked a lot about that -- user-friendly.
Obviously, more and more we’re using epigenomic therapies, and that’s obviously going to
be useful.
Because if you put a patient on a drug that targets the epigenome, well, what are we going
to measure?
And I think of diseases like Lupus, which really disorders the epigenetic homeostasis.
Now, again, whose problem is this?
Maybe it’s not your problem in NHGRI.
Maybe it’s NCBI, Analamb [spelled phonetically], et cetera, and some of that has been alluded
to before.
What technological breakthroughs -- we talked a lot about identification of rare variants
in regulatory elements.
And so, it really is a challenge to understand when dealing with disorders of the regulome,
and it will be a challenge when in a place like the NIH, now, we have an interesting
patient come in.
They get the whole exome sequencing.
If we don’t see anything, they get whole genome sequencing.
And, understanding and defining pathological regulome mutation, as been alluded to, is
obviously a challenge and trying to understand, you know, which of these are actionable and,
you know, are you going to do a bone-marrow transplant on a patient or something because
they have a regulome mutation.
And I think we’ve talked about a high through-put assays for this.
So we talked about how many different cells are there in the body -- 400.
Well, this is a Sami-speaking Laplander, which, tell me, if I get any of this wrong, correct
me.
And my understanding is is that Laplanders have 2,000 different words for reindeer.
Now, we have one word for reindeer, but for this guy here, his livelihood really depends
on understanding the subtleties of reindeer, and probably has evolved over many millennia,
et cetera, et cetera.
So when you’re thinking, “Well, how many cells do we have to do?”
I think it all depends.
If you’re, you know, if you’re in North America, then you’d say, “You only need
one, you need to do ‘reindeer’.”
But if you ask this guy here, whose livelihood really depends on understanding all the subtle
differences between reindeer, then he would say, you know, “What are you, nuts?
You need to do all 2,000.”
So you know, if he was bad at this, if he didn’t really distinguish all the subtle
differences between all these reindeer, he might, you know, end up having -- working
in an epigenetics lab or something like that, rather than, you know, herding reindeer.
We wouldn’t want that.
Now, from an immunologist’s point of view, if you ask an immunologist, “How many cells
are there in the body?”
You know, I think we would say, “There’s probably 400 immune cells.”
You know, this is an experiment we’ve done, and many people have done this as well.
This is an ATAC-seq experiment, and we’ve looked at all these different cells and all
these difference states of cells.
I think there are 80 different regulomes expressed here.
But that’s when it gets to the point that I don’t know that it’s ENCODE’s problem
or NHGRI’s problem to look through all of the amazingly interesting subsets of CD4 T-cells
that Anjana Rao and I are so, you know, enthralled with, just like the Sami-speaking reindeer
herder.
So, you know, how many different type of cells?
It depends upon who you’re asking.
It depends upon the state of activation versus differentiation.
It depends upon how you activate the cells by diverse receptors and different types of
signals.
It depends upon what stage of the cell cycle they’re in.
Immunologists now have discovered metabolism and all its effects on immune cells.
In essence, we just talked about that, about exhausted cells.
And how do all these data sets get integrated into ENCODE Roadmap?
And this brings up the issue -- Mike Pazin sent me a paper thinking about this, about,
you know, so-called long tail data that, how do you incorporate small data sets produced
by individual scientists, which really do represent the vast majority of scientific
data, with, you know, these big data initiatives.
And so my response, in a way, is, “You know, what am I, chopped liver?
You know, we do ChIP-seq.
We think it’s pretty good.
I mean, maybe it’s not up to your standards, but, you know, I -- you know, I think it’s
pretty good.”
So the question you have to ask yourself is, I think, you know, you obviously don’t want
garbage in, garbage out.
That was alluded to.
But we’ve also talked a lot here about when is good enough good enough?
And, you know, our ChIP-seq data is good enough for me, but, you know, a bit -- you know,
that will be your choice and something to think about.
And so, on one level I guess you’d have to think about the lost opportunities by not
incorporating other data sets, and the ability to access this Sami-speaking reindeer herder
when he has done epigenetics on the 2,000 different types of reindeer, versus the garbage
in, garbage out problem.
So that’s sort of, I think, the challenge.
I think the other thing that if you’re going to deposit all this information, we do really
need a better history of the cells that are acquired.
If they’re from humans, what diseases people have, allergies, et cetera, et cetera, infectious
disease, geography, whether they smoke, and all these things.
Again, I alluded to -- well, I’ll come to it in a moment with Lupus.
We talked a lot about epigenomics of single cells.
We’ve talked a lot about dynamic views.
And for me, it’s really exciting, the idea of how signal transduction really meets chromatin
biology with these epigenetic changes.
And so, better understanding of how these changes occur dynamically and how quickly
they change, and what changes quickly and what doesn’t, I think, is really important
going forward.
We talked about different ways of viewing these changes and other speakers, and understanding
this, really in real time by imaging, et cetera, et cetera.
At the same time, one can think about this from a different point of view, which is longitudinal
views: studying the epigenome in humans over their lifetime.
And I would point out again, from our business, with a disease like Lupus, that autoantibodies
precede the disease by many decades, and so that we can study.
We can see how the disease evolves.
Again, a disease like Lupus is really quite interesting because there’s only 25% concordance
in twins, and so this is really a disease of the epigenome.
This has been brought up as well using model animals, but I remind you that species like
the mice, we have, actually, lots of different mouse strains, and people like Chris Glass.
This is taking a page from Chris Glass’ book of using a variation in mice to understand
the regulome.
Primates have been mentioned equally.
People have done large scale ENU mutagenesis projects, and one wonders whether you can
benefit from that as well.
Another thing that immunologists think about is that we can do a lot with cells, but we
need to do some of the experiments in vivo.
And there have been examples where people have transplanted, essentially, large segments
of the human genome into mice.
And in many cases, these regions really do behave themselves quite well, not just in
cells, but in vivo -- large regions that are highly regulated by diverse cells in diverse
ways, like the IL10 locus.
And again, this helps moving from the association of causality that we talked about.
And again, others have emphasized evolutionary approaches.
And I’m done.
And I think I’m done two minutes quicker than.
I saved two minutes.
Thanks.
Aviv Regev: So we almost recovered back our lost time.
And I think we can hit off on the discussion.
Do we want to pull back the questions we had before?
I have no -- I simply don’t know where they are.
Male Speaker: I’ll get them.
Aviv Regev: So we’ll pull them out, and we can start
with Ricky.
Richard Meyers: This is just a small point, the “is your
ChIP data good enough?”
I think it’s not quality as much as interoperability question.
So having some kind of standardizations -- I think that probably has to be worked on first.
So for instance, duplicates -- so, there’s a good argument for it.
Not everybody wants to, it depends on the cells themselves.
So I don’t think it’s as much quality for many of these as it is just being able
to mix the data.
I don’t know all the issues in that, but these guys do, so.
Aviv Regev: I -- just before we -- oh, to respond to --
Mike Snyder: Yeah, I was just going to comment on that.
I mean, we are doing some of this as part of RegulomeDB, so there is -- but it’s true,
it’s not probably as systematic as it should.
I think we’re getting a fair amount of the data in, so.
John O’Shea: Okay, that’s a good point.
I mean, if -- I think most people were aware that to get their data deposited into ENCODE,
they had to do duplicates.
That would sort of be a reason.
We usually do duplicates.
But, you know, I know your standards are out there, but that may be something to think
about, is maybe make sure that everyone understands that that’s the price of getting into this
data set.
Aviv Regev: I will point out in this context, reproducibility
is an incredibly important piece, but when you start thinking about working with specialty
cells types coming from in vivo, coming from cells that are difficult to grow, and so on,
there is also a huge premium on the experiment being conducted by labs that actually does
the cell work well.
So there is huge value.
If there were an incentive to the right labs with the right biology expertise to also do
the genomics part with the standards of a genomic community, I think great things would
happen, which would be very difficult to just siphon off to somebody else doing the experiment.
Before we delve in deeper into that, I just wanted to -- because many of the speakers
didn’t have a Q&A due to time constraints -- I wanted to just give a very quick bird’s
eye of what I think are the key points that each one made, so that people have them in
their minds as they start discussing.
[end of transcript]