Name: 自己免疫疾患における制御不能遺伝子の同定 - Chris Cotsapas (Identifying Dysregulated Genes in Autoimmune Disease - Chris Cotsapas)
Uploaded: 2021-01-14T05:48:32.000Z
Duration: 34 min 3 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

Chris Cotsapas: I’d like to thank the organizers for the

opportunity to come talk to you guys about what we’ve been thinking about in my lab.

様態の副詞

So, what I’m going to talk about primarily is stuff that’s going on. So, all of this

is unpublished. Feel free to think about it, share it, whatever. But it’s very much work

in progress. Some of it is hot off the press. So, do take it with a pinch of salt. So, what

we think about a lot is autoimmune diseases in my lab. And we kind of want to think about

which genes go wrong in disease, and we think about these regulatory genes. But actually

what we’re interested in are the causal genes. And my pointer doesn’t work. I can

use this pointer. It’s all coming up Chris today.

So, we’re thinking more about causality than anything else. So, when we say dysregulation,

we’re interested in pathogenesis, right? That’s ultimately what we’re after. And

so, just a 30,000 foot view of the immune system. If you remember, you start with a

stem cell. You have two major lineages in the immune system, that the lymphoid and the

myeloid lineages. So, things like macrophages are all the way down here. And your T cells

and B cells are all the way down here. If you think of you think of them as adaptive

versus innate. And what happens is every now and then, this goes wrong. So, the immune

system’s primary function is to protect the body from things that are foreign. And

so it’s got this amazing capacity to tell the difference between your cells and the

rest of world. And it’s really good at this, but occasionally it screws up. And it kind

of -- what happens is that it starts attacking certain tissues.

So, if it doesn’t like myelin, you get multiple sclerosis. The immune systems manage to go

into the brain and attack the myelin sheath [spelled phonetically] very specifically around

neurons, chew it up, and you get lesions into your brain. You can get things like skin attacks

which give you Sjogren’s syndrome, scleroderma, you can get type 1 diabetes, which we now

know is an immune disease. If it doesn’t like aspects of the GI tract, you wind up

with Crohn’s disease, ulcerative colitis, or celiac disease if it doesn’t like the

epithelia joint; specific joint dislikes, should we say, give you rheumatoid arthritis

or ankylosing spondylitis. And if it just doesn’t like DNA, if it doesn’t like nucleic

acid, it attacks everything, then you wind up something called lupus, right? What’s

really interesting is that these are very, very, specific dislikes. So, MS is not rheumatoid

arthritis. It’s a very specific attack against myelin. It’s not a specific attack against

And what we really want to understand is what these diseases are. So, something’s going

wrong with the immune system. We don’t really understand what it is. What we do know is

that all of these diseases are common. They’re complex genetic diseases. There’s a large

portion of heritability. They track in families. But they’re not Mendelian. It’s not one

catastrophic mutation, right? And, of course, as GWAS came along, I’m going to talk about

multiple sclerosis, which is something that I work on. But you can take this as read for

any immune disease. As GWAS came along, we hadn’t really gotten a lot of traction on

the genetics of these diseases. And then, sort of we barely managed to identify two

loci in the genome in one of the first GWAS studies. Then a little while later, we managed

to get another one. A meta-analysis of these two sets of studies from international consortia

kind of gave six new hits, and we’re starting to climb this power curve of discovery.

Then a further meta-analysis with more markers and a few more samples gave us an additional

three new hits. Even more samples gave us another 25 new hits. The immunochip gave us

47. That took us up to 100. And our current studies, which are about 16,000 cases, 26,000

controls and replication in another 36,000 samples, we’ve got another 100 odd new hits.

So, we’re standing at around 200 loci right now in GWAS, right? That explains -- including

the HLA -- it explains about 55 percent of the heritability. We estimate that in the

common space there’s probably another 600 to 800 loci that we don’t know about yet.

We kind of do know about them. They’re not genome-wide significant yet. But we know they’re

there. And we know the approximate complexity of the disease is about 1,000 independent

And so, when ENCONDE came along and we did -- we were a very small part of this paper

from John Stam sort of showing that in Crohn’s disease and in multiple sclerosis, there is

strong enrichment of the risk SNPs on regulatory regions active in very specific subsets of

the immune cells. And in multiple sclerosis in particular, you can see CD3 cells, CD19s,

B lymphocytes, and CD14s, which is interesting. There’s a lot of pathogenesis coming out

of T cells as well. But these are more B cell like. And so, dysregulation in multiple sets

of immune cells seems to be an issue here. But this kind of sends us chasing down this

idea that is now extremely common. And this is one of the great right, right? So, 10 years

ago GWAS wasn’t going to work. And five years ago, everyone was asking why we haven’t

solved disease yet. Five years ago, everything was coding. And now, everything is now regulatory.

And it seems really obvious. But even two, three years ago, this was not that obvious.

And so, this chases us down -- starts us chasing down this rabbit hole of which genes are getting

dysregulated and how does that cause disease. And so, that’s what we are going to talk

about today -- further evidence that in specific immune cells, you get dysregulation that maps

into specific transcription factor binding sites as is from Kyle Farh and Brad Bernstein

showing that the MS SNPs are particularly enriched for NF-kappa B transcription factor

ChIP-seq peaks for instance. And so, there’s something that’s fairly specific dysregulation

in immune cells, which is great in bulk, hard when you actually want to identify specific

effects on specific genes in specific cells. And so, that’s the task at hand. And so,

when you look at some of the loci, you know, you put up a GWAS locus. Here’s a classic

locus in MS. Well, there’s NF-kappa B one and mannose-binding protein A. And you could

sort of make a case for mannose-binding protein A, but really everyone’s going to assume

that NF-kappa B one is one is the appropriate gene. And it turns out that that’s right

for various reasons. And so you can start working on that because you kind of are reasonably

When you look at another locus of course, that gets a lot more difficult. You’ve got

this big association peak. There’s a bunch of genes in here, and the problem isn’t

that they’re not good candidates. There’s a bunch of good candidates in here. ORMDL3

is here. IKZF3, which is Helios, which is a transcription factor that controls T regulatory

cell differentiation is there. A bunch of other immune cells. And so, you’re kind

of going, “What’s going on here?” So, we kind of thought, “Okay. If there is regulation,

and we have SNPS, how do we unite the genetics with the epigenomics?” And a lot of people

are thinking about this. You’re going to hear a lot more stories about this. You’re

already heard some. Here’s how we’ve been thinking about it.

So, we’re kind of amateur math geeks, and so we thinking about how we can transfer some

of this probability and do some functional fine mapping. So, you have a set of SNPs in

the genome. We’re going to talk about hypersensitive sites now. But instead of DHS, you can think

of any regulatory mark. We’ve been working a lot with hypersensitive sites because we

like them. They’re stable. They’re nice. They tell you a lot. We’re going to expand

this to the other sets. But think about DHS for now. And you’ve a gene in the locus.

So, this is my like tiered view of a locus.

So, each of these guys is associated to disease. And -- oh, this is going to chop off my -- thanks.

Oh well. So, what that says is posterior probability of association or PPA, okay? So, when you

do a GWAS for each of these SNPs, you get a P value of whether it’s associated to

disease or not. You can convert that simple P value into basically a posterior probability

which tells you, what is the likelihood that this SNPs is the one driving the signal, okay?

We’re not going to talk about the math magic that underlies that. I’ll bore you with