John, thank you for the introduction. And a lot of this work is in close collaboration

with John’s lab. So I was going to talk about how epigenetics actually may control

genetics. So throughout this meeting there are multiple talks pointing to the importance

of genetic variation in understanding of a genetic landscape. So there is a lot of justifiable

interest in how genetics controls epigenetics. So briefly all the studies can be summarized

as, pick a favorite epigenetic feature. QTL studies, right? So you take -- you have eQTLs,

methylation QTLs, chromatin accessibility QTLs; all types of QTLs. And of course we

believe that understanding the epigenetic variation on epigenetic features can allow

us to go a long way in understanding the mechanism of the [unintelligible] association, the biology,

and so forth.

However, we’re interested in the inverse problem, is, what is the fact of epigenetic

landscape on genetics? And one of these effect is how a genomic landscape controls mutation,

right? Because the source of variation is mutation. So what we’ve been doing, we’ve

been looking at data on mutations, both in germ line context -- so we are now sequencing

data for multiple trios and quads; I wouldn’t be talking about this today -- and somatic

mutations, coinco-somatic [spelled phonetically] mutations, where -- so these are differences

between parents and children, where changes are happening in the DNA, and here differences

are between usually blots and control cell type and cancer cell.

So this is the idea, and the idea is to see, what are the facts of epigenetic variables

on these changes in the DNA sequence? Why are we interested? So we’re interested for

multiple reasons. One interest is in statistical and medical genetics fields because understand

of mutation rate models would inform methods for gene mapping, and I’ll talk about that

in a second. Another big interest of ours is evolutionary biology, and there are two

reasons we care from an evolutionary biology perspective. One is that mutation rate is

a key parameter in a lot of evolutionary models, right? If we want to infer selection, if we

want to understand differences between populations, differences between species, date speciation

advance, we have to have some understanding of mutation rate. The other interest is evolution

of mutation rate itself, right? Because cell controls mutational events, and mutation rate

is one phenotype which is under selection. So the question now is not only why mutation

rate is what it is, but -- so not only what is mutation rate but why is mutation rate

what it is? Also there is of course interest from biology perspective -- biology of DNA

repair and biology of DNA replication.

So for maybe couple minutes talk about statistical genetics piece of work. So there is a growing

interest in gene mapping using de novo mutations. There are two areas specifically; it’s genetics

of neuropsychiatric diseases and cancer genomics. And the idea here is to map genes involved

in disease progression, or cancer driver genes, using recurrents. So this is not your classic

genetic mapping. For example, LD-based association or linkage; this is mapping using mutations,

and this is the only mapping strategy which is possible to learn in sectional systems.

The idea is very simple: you find different patients carrying mutations in the same gene,

collapse them by gene, and you can make an inference that this is a significantly mutated

gene, right? There are more mutations than you expect. Now the big question is, what

do you actually expect, right?

Because in these studies you cannot run case control. You cannot really look at how many

mutations in this gene happens in cases versus how many in controls because you would lack

the statistical power to do so. So the idea is to do some sort of model. So for example,

the simplest approach -- and this is -- was used in early papers on the subject -- you

take some estimate of genomic mutation rate using independent samples, then you evaluate

from an ability to observe recurrent events in a given gene, correct for multiple testing,

right? So why this is not the correct strategy because if you have heterogeneity among samples,

especially problem in cancer genomics, where you have one -- some samples basically filled

with mutations; others have much lower mutation densities -- you will make flushes, inferences,

and this mapping will generate a lot of false positives.

So there is another strategy; another strategy is the following. So you take -- look at your

real data and just permute data around. Look at permutation expanse, multiple permutations,

and you can evaluate how frequently you see these do mutations independently in the same

gene. And the problem here, of course, is mutation rate variation, because if mutation

rate is heterogeneous along the genome, this may simply be a mutational hot spot which

you don’t know about. So what we need -- we need careful model of local mutation rate.

And the problem in cancer is that, because of accessibility to specific mutagens or specific

genetic changes in repair systems, and I’ll be talking about that. You may have a situation

where this mutation rate heterogeneity is patient-specific, not just cancer type-specific

but specific to individual patients. Now, over five years ago, we -- again, collaboration

with John’s lab -- made an observation that a bold density of human SNPs and human-chimpanzee

diversions is increased in later replicating regions of the genome, compared to earlier

replication regions of the genome. So we have certain epigenomic variables that control,

potentially, mutation rate, so this is stratification of S-phase of cell cycle into four regions.

And we’ve seen increase in both the versions and polymorphous.

So this fueled our interest in the question. And it turned out that the same effect is

observed in cancer genomics, so this in collaboration with [unintelligible]’s lab. We see that

the risk affect the replication timing in pretty much every single cancer type we analyzed,

so there is increase of mutation density later replication compared to earlier replication.

And some genes that are located in later-replicating regions are sort of usual false positives

of mutation mapping and in cancers. There is another variable, which is level of gene

expression. Genes that are expressed at high levels have less mutations in cancer genomes.

And the standard idea as the culprit is, the transcription coupled repair mechanism. And

I’ll show you the pathway because I’ll be -- and I’ll show it then again because

I’ll be talking about this pathway throughout the talk. So the idea is the following: in

the resolution in DNA, one of the mechanisms is nucleic acid scission repair, which starts

with the FDH, which is helicase on one’s DNA. There is precision step in bold direction,

there is a resynthesis using the other strand as the template. Now, this mechanism -- this

is a very accurate repair mechanism which can be recruited in two different ways. So

one way is stalled RNA polymerase, so if the resolution and DNA transcription cannot proceed

forward. And polymerase recruits nucleic acid scission repair systems downstream. The other

mechanism is what we call global genome repair, is active search by the SPC complex for lesions

in DNA. So first thing we decided to check is, “Okay, we think that this mechanism

leads to reduction of mutation density in actively transcribed genes. What happens in

active regulatory elements?” We decided to look within DNA’s one hypersensitive

sites; I don’t have to introduce them for this audience. You’re all familiar with

that. My naïve expectation was that mutation density may be elevated because these sites

are not protected by nucleosomes; maybe they are more accessible to some sort of damage

and so forth. So when we looked at multiple cell types -- this was published last year

-- multiple myeloma, colon cancer, melanoma, lung cancer, CLL, and this scale depends on

number of samples we had -- we see reduction in every single cell type, reduction of mutation

density within regions of open chromatin. Now, what’s important -- the effect is very

well localized. I’m not talking mega-basis or hundreds of KB; this is one kilobase resolution,

right? And the reduction is compared to immediate flank, and I’m not going through many regression

models, how to take into account effective location, effective nucleotide composition,

mutational spectrum in this cancer type, and so forth.

Okay, so what can be behind this effect? So we decided to look at one system specifically

in melanoma, and there are several reasons. One is, there are multiple samples available;

it’s high mutation-rate cancer; and, most importantly, we know the mutation source.

We have a signature, and we believe this signature corresponds to UV damage of DNA. And we know

that the major repair mechanism acting on this signature is nucleic acid scission repair,

so we can make some biological hypotheses from looking at this system. Okay, so now

it’s little more coincitative [spelled phonetically] presentation on the same data. These are intergenic

regions; these are intronic regions; we have mutation density and we have chromatin accessibility

in coincitative fashion. This is just number of mapped DNase1 cleavages. So what we think

is this is the action of transcription coupled repair of the difference between intergenic

and intronic regions. However, within each of those there is very strong dependency on

chromatin accessibility.

Okay, why is this happening? So there are many possibilities. One is that what we’re

seeing is purifying selection in regulatory elements, so maybe mutations are happening

but negative selection purges them, and we’re not seeing them. So I don’t have time to

discuss this in detail, but as somebody who unsuccessfully spent now almost three years

looking for signatures of purifying selection in cancers, I don’t believe in that, right?

So in order to assume that this is the case, selection must be dramatically stronger than

encoding regions of the genome; we never observe that. Another possibility is this -- is association

with replication timing or other epigenetic feature, not necessarily specifically with

chromatin accessibility. So we test it in two ways; you can run multiple variation regression

models and see that this is not the case, and also the scale of the effect is very different,

right? So there’s a very localized phenomenon. Okay, so another possibility is the accessibility

to DNA repair. And here, what the hypothesis is, XPC in global genome repair is the large

bulky complex, like DNase1, right? With footprint which is much larger than a distance between

nucleosomes. So it has to work in -- with chromatinized DNA, and there is active mechanism

to assist nucleic scission repair to work on chromatinized DNA. And if you look through

experimental literature, the access of DNA repair to naked DNA is always much faster.

So again, the idea is that global genome repair may work more efficiently in open DNA compared

to chromatinized DNA and recruit the same nucleic acid scission repair machinery downstream.

Now, even as bioinformaticists, we can test the hypothesis without running any experiments

because cancer genomic data -- when you look at mutations, you have phenotype and genotype

in the same dataset, right? So I have a phenotype, “What is the drop of mutation density in

DNA’s hypersensitive regions?” and I have genotype of a tumor. And I have a hypothesis

that nucleic acid scission repair is involved. So we can stratify all our melanoma samples

into those where we do not see any change in nucleic acid scission repair -- which are

marked green -- or samples where we do observe potentially deactivating mutation anywhere

in nucleic acid scission repair pathway. And we see that there is statistically significant

enrichment of samples with potentially deactivated nucleic acid scission repair among samples

where the drop in mutation density is associated where chromatin accessibility is very small.

We can further exploit the structure of the pathway because, if mutations deactivating

nucleic acid scission repair happen downstream, and actual repair part of the pathway, then

we should [unintelligible] both facts dependency of mutation density on transcription -- so

correlation with expression level -- and correlation with chromatin. So as we see here, these three

samples, for example, where mutations happen downstream, in these genes, in core repair

part of the pathway, they have very small or no decrease in mutation density associated

with either transcription or chromatin accessibility.

Unfortunately, we had only one sample -- this is sample number four -- upstream specifically

with mutations specifically in global genome repair. And this beats the hypothesis, but

I probably wouldn’t really make very strong inference from a single sample. So, concluding

this part of the talk, we think that mutation density -- what we think, we know -- we observed

that mutation density is remarkably reduced in regulatory regions marked by DNase hypersensitive

sides. And the fact is, like limited by global genome repair, as can be shown by association

of this effect with presence of intact nucleic acid scission repair pathway in the same.

Okay, so this is very focal. So what we learned so far -- we learned that mutation density

in cancers is shifted towards later replicating regions, regions cancer don’t really -- doesn’t

really need, because most of expressed genes and active elements are located in earlier

replicating domains. We observed that mutation density in cancers is reduced in actively

transcribed genes, in genes cancer needs, versus genes cancer doesn’t need. And we

also learned that mutation density is reduced in actively [unintelligible] regulatory elements,

right? So this is kind of the thing. So these are primarily observations especially on expression

and DNase1 accessibility, with -- specifically within functional -- potentially functional

elements. So what happens if we change resolution and we’ll look at the mega-base scale, and

we use the data collected by the Epigenome Roadmap Consortium from multiple cell types

and multiple epigenomic variables. So first, again, looking at -- looking at variation

in DNase1 hypersensitivity, just density of picks per mega-base, versus number of mutations.

Again, I use melanoma as an example and I use classic UBE-induced mutation density;

there’s pretty good correlation.

However, one interested feature we noted is the following. So I can look at three different

skin cell types -- melanocytes, fiber blocks, and keratinocytes. And I see that there is

decrease in mutation density associated with density of open chromatin regions in each

of the three cell types. However, in melanocytes, this decrease is much more profound. Right?

The correlation coefficient -- negative correlation coefficient is much greater. The general phenomenon,

again, is that activating works are negatively related with mutation density, and repressive

marks are positively related with mutation density; again, places where cancer doesn’t

need functional genes to work have reduced density of mutations. And I’ll come back

to that point. Now, back to specific cell types; so if I take, for example, mutations

in liver cancers and information about non-methylation marks in liver and information about melanocytes