Anjana Rao: Oh yeah.
Aviv Regev: [unintelligible] yeah.
Anjana Rao: Good.
So I’m very glad to have come to this meeting because I’ve learned an enormous amount.
I’m not in ENCODE and I don’t do ENCODE-type experiments so it’s actually been very useful.
I’ve used ENCODE and it’s been very useful to use it and to see how people are thinking
about using it.
So I think I’ll be talking about a much more focused topic than many of you have talked
about.
It’s going to be about DNA methylation, DNA modifications, and I hope that I can bring
some perspectives to what I think, so far, has been a very small fraction of what ENCODE
has been doing.
So that -- now how do I do this?
Sorry.
[unintelligible dialogue]
Anjana Rao: How to move the --
Male Speaker: There’s the slide.
Anjana Rao: Yeah, just this?
Male Speaker: Okay.
This would go forward then --
Anjana Rao: Oh okay --
Male Speaker: -- go back.
Anjana Rao: -- perfect.
Thank you.
So as probably all of you know this is a very short introduction to DNA methylation.
Methylation tends to be -- oops -- in CpG contexts and symmetrically methylated; that’s
about 90 percent of the -- or more, of the CpGs in differentiated cells in the mammalian
genome, probably saving neurons, which Joe Ecker has done a lot of work on non-CpG methylation.
Anyway the methylated CpGs are in the major groove available for binding by various kinds
of methyl-binding proteins.
And the way -- the standard way for DNA methylation to be erased is, in fact, just DNA replication
which replaces the methylated cytosine on the replicated strand, or the plain cytosine,
then DNMT1 puts it back, DNMT UHF R1 complex.
And basically if you prevent DNMT1 from putting the methyl group back what you get is a slow
progressive replication dependent loss of DNA methylation.
Now since the Tets were discovered in our lab about six years ago now, it’s been clear
that while DNMTs put on the methylcytosine, another mechanism for removing DNA -- the
DNA methylation mark, is -- involves just progressive oxidation of methylcytosine by
Tet proteins to 5-hydroxymethyl, 5-Formyl, and 5-Carboxylcytosine.
And these are all -- five, 10 -- they’re all epigenetic marks and, of course, they
can come in combinations so that what you have is -- you can have -- this is terrible
-- what you can have is different kinds of methylation marks here so there are 25 possible
combinations for epigenetic readers of different kinds to look at.
Now all of these marks, if they’re opposite from the methylated cytosine, tend to prevent
DNMT1 from doing the maintenance methylation.
So they would, in fact, interfere with maintenance DNA methylation and be a way of facilitating
replication dependent DNA demethylation.
And there are also some methods that involve base excision repair proteins like thymine
DNA glycosylase, which normally takes a base -- a mismatched TG dinucleotide and excises
it.
But in this case can also recognize a perfectly well base fed 5-Formyl and 5-Carboxylcytosine
and excise those and then the -- those are converted back to cytosine by the basic scission
repair machinery.
So there are a couple of other mechanisms that have been suggested for demethylation
that I won’t go into because they’re not as well validated.
So where is 5hmC in the genome?
It’s highest in gene bodies of the most highly transcribed genes, and I think you
can see that over here, that’s the top 25 top quartile of 35 percent of most highly
expressed genes.
And you can see the unexpressed genes are really not enriched for 5hmC in this standard
DNA affinity enrichment method.
But -- and the lowest expressed genes that you can see are slightly enriched but not
highly.
It’s the most highly expressed genes that are the most highly enriched.
And if you look at enhancers marked by K4me1 and K27 acetylation, the calibrating gives
you how much 5hmC there is in these enhancers and you can see that it’s the most active
enhancers as defined by high levels of both marks that have the highest levels of 5hmC.
And these are all highly transcribed versions of the genome -- gene bodies as well as active
enhancers as you just heard from -- I’m blanking on who just spoke, sorry.
It’s all right.
And it -- this ping that you’ve shown here in T cells is -- has, in fact, been shown
for neurons and during spermatogenesis and during erythroid differentiations, are exactly
true in many different subtypes.
So this is also shown in this RNA-seq 5hmC picture where you can see, just call them
cell type one and cell type two, they’re just two different kinds of cells in the thymus.
And you can see that the 5hmC levels and the RNA levels match very nicely so these two
are expressed in cell type two, and the -- this one is expressed in cell type one.
So basically what we were trying to do was to understand the functions of Tet in mammals
and for the purposes of this talk I’ll try to relate it to what we know about DNA methylation
changes.
So there are three Tet enzymes.
We’ve made mice with conditional floxed alleles of all of them and we’ve found that
the individual mutations give you a relatively mild phenotypes, depending on whether you’re
looking at embryonic development or particular tissues.
And what we do know is that double and triple mutations, and we made different types in
different cell types, actually give you fairly striking phenotypes such as embryonic lethality
and ablation of specific lineages over -- in stem cell.
When we delete type two and type three in hematopoietic cells we lose their red blood
cells, we lose B cells, depending on what Cre we use.
And what I’m really going to focus on today is the relation to cancer and I’ll go through
it very swiftly.
So at about the same time that we were identifying the enzymatic activity of Tet2, a different
group in France showed by looking at a very small deletion of this 4q24 region, which
is often mutated in mild or dysplastic syndromes and so on, that one small deletion contained
only the Tet2 genes.
And this was followed by a flurry of whole exome sequencing that basically said the mutations
were all across the Tet genes and we and others have shown that these are very likely all
loss of function mutations of Tet proteins -- of Tet2.
Since then there have been any number of papers relating all sorts of cancers, including solid
cancers, to mutations in Tet1 and Tet2, or to loss of 5-hydroxymethylcytosine which is
a readout for all Tet activity.
And so the question became for us, we worked with model organisms, not so much to relate
the mutations in human cancers to what’s going on but to see whether we could induce
an acute cancer of different cell types in a model organism by acutely deleting Tet proteins.
And this, in fact, turned out to be true.
Basically we asked the question is acute loss of Tet functions associated with cancer, and
the answer is yes.
We can’t delete just one Tet protein we have to delete two and haven’t yet been
able to do it with three because of the embryonic lethality, but two model -- two Tet proteins
definitely would -- Tet2 and Tet3 insofar as what we’ve tested because we are present
to the highest extents in differentiated cells.
And we’ve taken it out, taken out Tet2 and Tet3 together with -- either with Mx1Cre followed
by double stranded RNA injection, or with Cre-ERT2 tamoxifen injection.
And the effects of deletion if we do it either way, which is an acute deletion, is first
seen in the hematopoietic stem cell compartment and what we get is an aggressive myeloid leukemia.
If we delete Tet2 and Tet3 with CD4-Cre in T cells what we get is an aggressive antigen
driven T cell leukemia.
In both of these cases the cancers are cell intrinsic, polyclonal, transmissible indefinitely
to recipient mice, and the cancer develops really rapidly.
And so we have to assume that the initiating event in each one of these cases is the acute
and profound loss of Tet function, which derives from losing this hydroxymethylcytosine and
other oxidized methylcytosines in the genome, or maybe changes in DNA methylation.
So that was one of the questions we asked.
We took LSK cells from hematopoietic stem precursor cells, LSK cells, from mice that
had either been -- either control mice or mice that had been before deletion, or mice
after the acute tamoxifen or pre-rT2 treatment, and did whole genome bisulfite sequencing
on it.
And now I’m a complete newcomer to this field so one of the things I’m going to
be really presenting this data for is to solicit your opinion whether we’re doing it right.
And basically what we’ve done is taken three control samples, three belashka [spelled phonetically]
replicas, of control LSK cells and three knockouts, and sequenced to fairly high depth so that’s
200 million reads, most of which are mapable.
And the coverage in each case is about seven fold so that we have an overall coverage of
about 21 -- 20 fold in each one of those cases.
And that translates to pretty good coverage of CpGs in the genome.
And that was validated -- our choice of the seven fold and so on, it was validated by
this paper which is an ancient methods paper, I believe, that tells us that you need at
least two replicates and you need a certain amount of depth of coverage in order to get
a sequencing.
So what did we find out?
We find out that the canyons of DNA methylation is -- that’s a terminology is by Peggy Goodell,
or which what Ben Grant has called valleys of DNA methylation.
Or some of these canyons and valleys correspond to CpG in the genome do, in fact, if we look
at the control versus Tet knockouts, do in fact shrink in terms of getting extra methylation
at the edges.
And this has been called CpG -- methylation of CpG island shores, for what it’s worth.
And that’s true of both of these genes, and if we look at what both of these genes
do, both of them are down regulated in Tet knockout cells relative to wild type.
The promoter regions of both of these genes are actually present within the valleys and
I think that’s shown here.
And so one could take this handful of genes which shows this behavior and say, well, we’ve
shown that there are methylation associated gene expression changes and, therefore, this
sort of explains the phenotype.
I would rather say that if you look at a whole genome level, however, we -- the changes that
we see are really mild.
So, for instance, here are three replicates so those are three lines of the controls and
three lines of the Tet knockouts.
And what you can see over here is all across the genome you see an increase in methylation,
but it’s not really very striking at this average level at all.
And that’s also true for active enhances, it’s a little higher there, but basically
the level of DNA methylation that we see is not -- change in DNA methylation is not very
high.
We can also look at this -- in the previous slide I was looking at it, at all genes, so
that’s 21,000 genes, a lot of those are not expressed.
And we then focused our attention on the differentially expressed genes in both of these subtypes,
and you can see once again there’s a minor change in the average level of DNA methylation.
So Edahi Avalos and Lukas Chavez are -- my colleagues did was basically to sum up the
methylation at the transcription job site and in the three quarters of the end of the
-- this model gene body, and then plot those values in sort of a dot plot where each dot
here then represents one region of the genome.
And what you can see over here is if you look at each one of these dots there’s as the
trend is towards increased methylation in the Tet knockouts, but only about a 20 percent
change in methylation overall.
And if you look at up-regulated genes and down-regulated genes, there are about twice
as many up-regulated as down-regulated genes and you can see that in both cases they gain
methylation.
So we’re not seeing that methylation’s making any change, either at the transcription
start site or in the gene body to overall gene methylation.
There are some genes that follow the pattern.
Over here are genes that are, goodness, up-regulated and lose methylation and here are genes that
are down regulated and gain methylation.
But overall there’s really no particular pattern.
So what are we to make of this?
I don’t really know at this point.
Are we to focus on the small subset of genes that does show the kinds of changes and quote/unquote
“expect?”
Or are we to look at the entire pattern and say that we may be changing the metabolic
state of the cell but we don’t really know.
However, that’s probably a reasonable question for later.
But what I would like to point out to you is that, in fact, the measurements that we’re
making with this whole genome bisulfite sequencing don’t distinguish 5mC to 5hfC, because what
happens is that neither of these gets deanimated upon sodium bisulfate treatment.
Similarly, CfC and caC all do get deanimated and turned into D on sodium bisulfate treatment.
And, therefore, these are all read as C after amplification and conversion.
And really since the numbers of fC and caC, at least in mouse ES cells are very low, what
we should at least be trying to do is distinguish 5mC from 5hfC.
So the recommendations for this first part of the talk are basically to include oxi-mC,
or at least 5hmC measurements, in any of the DNA methylation analyses that ENCODE might
be contemplating doing in the future.
So as I told you 5mC and 5hmC can be distinguished by certain techniques such as TAB-seq and
oxi-mC, but these are really too expensive to do and -- on a routine basis.
They require about 10 times as much coverage as simply whole genome bisulfite mapping so
one would have to do it at an applicon [spelled phonetically] level.
And also remember that we are talking about five new cytosine basis unmodified by mC 5hmC
5fC and 5caC, but the change is binary whereas what we’re looking at when we see a change
in methylation level is a change of -- at the population level.
And there’s some undefined proportion have change state.
So the other thing we probably ought to do is include perturbations in kinetic measurements.
These changes seem to be much more dynamic than we had previously appreciated -- oops
-- and they may happen on very rapid time scales.
And, finally, I think if one really should encourage the development of new sequencing
methods to map modified cytosines in unamplified genomic DNA.
And that would include, ideally, a long read so as to allow unambiguous mapping of repetitive
regions of the genome.
[unintelligible dialogue]
Anjana Rao: Am I finished?
[unintelligible dialogue]
Anjana Rao: One small section but --
[unintelligible dialogue]
Anjana Rao: One minute?
Okay.
So I want to bring up the idea that one should also be looking at primary cells that are
available in small numbers.
And in order to do that what we did was look at a phenomenon called T cell exhaustion,
which is observed in chronic viral infections and in tumor infiltrating cells and it appears
to result from prolonged exposure to antigen and is important in the context of certain
immunodeficiencies.
And what I’m just showing you here is some ATAC-seq data that are looking at naive cells,
effector cells, and exhausted cells in a region.
And what you can see over here looking at these tiny numbers of cells, only about five
times [unintelligible] four cells is that there are, in fact, regions that are expressible
to exhausted cells in vivo in the chronic viral infection context, and do not appear
when you stimulate the T cells in vitro.
So I think something like this should be incorporated into the flow for ATAC-seq.
And you can see over here that the same peaks show up in cells that are tumor infiltrating
and also exposed to antigen and don’t show up in bystander T cells in the tumor.
And so I’ll skip forward and just tell you about my collaborators.
I think Aravind, actually, is a very important resources at -- on the NIH campus.
And these are the people who did the work on the hematopoietic cells, and this is the
person who did the work on the T cell cancer, and these are our [unintelligible] colleagues
whom I’ve intended to mention.
Thank you.
[applause]
Aviv Regev: So again, because we are still 10 minutes
away from where we need to be I guess we need a moment to do a -- to do some recalibration
of the AV.
But our next speaker will be John O’Shea, who will ask us not with -- well, ask not
what you can do for ENCODE but what ENCODE can do for you.
[end of transcript]