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Good morning. Welcome back.
So, the Red Sox won, it's pretty

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convincing, yeah,
very good. Yay Red Sox.

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So, as you can also tell,
I have something of a cold,

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so I'll see if I, if my voice makes
it through, but what I wanted to do

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today, if the voice allows,
was to talk about genomics.

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Now, this is a little bit different
than what we normally do in the

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class because, I
work on genomics,

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it's something I'm
extremely interested in.

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And so, what I wanted to do today,
and I'll do it one more time before

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the end of the term, is to
talk about research that's

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going on in genomics, give
you a sense of what's really

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going on. I can assure you that
what I say is not going to be in the

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text book, or any other text book.
And, I'm not entirely sure how this

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might appear on an exam, so
don't ask, because I'm really

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just going to talk about
research that's going on today.

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And part of the purpose in doing
that is to a, show you that it's

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possible for you to understand the
kind of research that's going on in

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this field, and b, to excite
you about what's going on

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in this field. So each
year I pick different

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things to talk about, and
I've picked a few things,

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and we'll see. So feel
free to interrupt and to ask

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questions, and all of that, but
this is very much more, sort of

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the edge of genomics,
including stuff that's going on,

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you know, right now as we
speak. So, we'll fire away.

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So a little introductory stuff.
I call this, we can actually keep

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the lights up,
I think people,

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can people read that?
Yeah, it's fine, good,

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so we'll leave the lights
up and I can see people.

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So, I think the thing that sets
apart this revolution of biology

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that we're looking through right
now, is the transformation of biology,

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not just from being the
study of living organisms,

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to the study of chemicals and
enzymes, to the study of molecules,

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but to the study of biology
as information. That is what's

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distinctive about this decade,
is the idea that the information

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sciences have begun to merge with
biology, or biology merged with

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information sciences, and
that it's having a profound

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effect on driving biomedicine. In
both of the two talks I'll give,

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this one and near the end of the
term, that will be the common theme,

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because I think that's the most
important thing that's going on

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right now. Now,
just to remind you,

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of course, the idea that biology
is about information is an old one,

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it goes back to my hero, Gregor
Mendel, with the recognition that

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information was passed from parent
to offspring, according to rules.

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And, as you know, the
history of biology in the 20th

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century can be read as the
development of biology's information.

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The first quarter of the 20th
century was the development of the

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idea that the information lives
in chromosomes. The next quarter of

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the 20th century, the idea
that the information of the

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chromosomes resides in the DNA
double-helix, and that information

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was contained in this molecule,
and somehow in it's sequence, and

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you know all of this. And
the next quarter of the 20th

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century, basically from 1950 to
1975, understanding how it is that the

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cell reads out that information,
from DNA to RNA to protein, how it

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uses a genetic code to
translate RNA's into proteins,

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and the development of the tools
of recombinant DNA that made it

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possible for us to read out the
information that the cell reads out.

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So that brought us ¾ of the
way through the 20th century,

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with the ability to read out genetic
information, at least in little ways,

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but they were little ways.
You could write a PhD thesis,

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around that time, for
sequencing 200 letters of DNA.

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That would be, you know,
considered amazingly exciting PhD

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thesis. The next quarter of the
20th century, the last quarter of

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the 20th century, was
characterized by a veracious

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appetite to read as much of
this information as possible.

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It started, first, with trying to
read out the sequence of individual

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genes, then sets of genes,
then genomes of small organisms'

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bacteria, medium-sized
organisms. And then, you know,

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in a wonderful closure to the 20th
century, the reading out of the

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nearly complete genetic information
of the human being in the closing

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weeks of the 20th century. When
you remember that, that Mendel

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was rediscovered in January of 1900,
that's when the papers rediscovering

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Mendel came out, and you
figure you've got perfect

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bookends from the rediscovery
of Mendel in January of 1900,

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to the sequencing of the
human genome in around 2000.

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You realize what a century can do.
It's not bad, as centuries go, you

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know, to accomplish all that, and
it gives you know, as students,

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you get a point estimate in
time of what science knows,

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but you guys aren't old enough yet
and haven't lived long enough yet,

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to measure the derivative, and
see how rapidly it's changing.

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But just look at what happened
over the course of that century,

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and then just project forward
to what that can mean for

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the next century. So what
that's done is it's brought

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us to the next picture. I
have a picture in my head,

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of biology as a vast library
of information, a library of

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information in which evolution
has been taking patient notes.

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Evolution is a very
good experimentalist,

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and it's a very patient note taker.
It's notes, of course, are written

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in the genomes, and
everyday evolution wakes up,

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changes a few nucleotides,
sees how the organism works,

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if it was an improvement,
evolution keeps the notes,

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if it was disadvantageous,
evolution discards the notes.

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That, by the way, for those
of you working in labs,

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is no longer considered
appropriate laboratory practice.

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You're obliged to keep your
laboratory notes from failed

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experiments, as well, but
evolution got into this before

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those rules were codified, and
so it discards the notes from

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unsuccessful experiments,
and keeps the notes from the

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successful experiments. But
nonetheless, we have all the

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notes from the successful
experiments, and we can learn a

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tremendous amount from it.
There's a volume on the shelf

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corresponding to each species on
the planet. There's a volume on the

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shelf corresponding to each
individual within each species,

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to each tissue within each
individual within each species,

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and there's information
there about the DNA sequence,

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about the RNA readouts, about
the protein expression levels,

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and in principle, even if not yet
in practice, we can pull down any

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volume we want,
and interrogate it,

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and compare it for related species,
for individuals within a species,

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some of whom might have a disease,
some of whom might not, for

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different kinds of tissues
treated in different ways.

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That is, I think, going
to be a tremendous theme of

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biology going forward, and
that's why it's a particular

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pleasure to teach biology at MIT,
where you guys understand what that

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could mean, that fusion could
mean. Now, this idea of extracting

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genomic information in large-scale,
is a relatively new one. In the

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mid-1980's, the scientific community
began debating what was a pretty

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radical idea, sequencing
the human genome.

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This was floated in a couple of
places, in 1984 at one meeting,

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somebody raised the idea, you've got
to realize that sequencing itself,

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that sequencing DNA, only
came from the late 70's,

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so within six, seven years of
being able to sequence anything,

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people were now saying,
let's sequence everything.

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That was a reasonably audacious
thing to do, and it was

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controversial. There
were many people who felt

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that the human genome
project was a terrible idea,

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and with good reason, because
the initial version of the

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human genome project was, kind
of, a blunderbuss approach.

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It was, let's immediately mount a
massive factory and start sequencing

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the human genome with the just
horrible technologies of the

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mid-80's, with radioactive
sequencing gels,

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and you know, lots and
lots of people doing stuff.

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And so, you know, many people in
science were, were concerned that an

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entire generation of students
would need to be chained to the

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bench, sequencing
DNA. Sydney Brenner,

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a great molecular biologist,
proposed the whole thing be done at

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institutions [LAUGHTER],
because you know, people could be

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sentenced to, 20 million bases,
with time off for accuracy, or

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things like that [LAUGHTER].
And so what happened was, the

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scientific community came
together well, in it's best form.

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Group, a group was put together by
the National Academy of Sciences,

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who said, well look, this
is a really good idea,

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but we also need a carefully
thought-through program to do it.

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We need intermediate goals that will
get us things that will advance the

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science along the way, we need
to improve the technologies,

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and laid out a plan. The goals of
that plan, to develop a genetic map,

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a map showing the locations
of DNA polymorphisms,

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sites of variation, genetic
markers, just like Sturdiman

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did with fruit flies,
but to do it with humans,

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and with DNA sequence differences,
to be used to trace inheritance.

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That, that genetic map could
be used to map human diseases,

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and if all you accomplish was,
got a human map of the human being,

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that would be a good thing. Then
you could get a physical map of

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the human being, all the
pieces of DNA overlapping

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each other, so that you would know
if you had a genetic marker linked

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to cystic fibrosis, you
would be able to get the piece

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of DNA that contains the gene.
Then, if we managed to pull that

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off, we could get a sequence of
the human genome, all three billion

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nucleotides, on the web, so
that you could go to just any

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place on the genome,
double-click, and up would pop the

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sequence. Now,
you guys of course,

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don't laugh at that, but about eight
years ago, when I would give talks

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about this, I would speak about,
oh you'll be able to go double-click

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and up will pop the sequence,
and of course, everybody thought

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that was really funny, and
that, that was something people

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laughed at. But of course,
you can just do that today, if

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anybody has a wireless you can just
double-click, and up will pop the

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sequence. And then, of
course, a complete inventory of

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all the genes within that sequence.
And a very importantly, and from

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the very beginning, the notion
that all this information

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should be completely,
freely available to anybody,

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regardless of where they were,
whether in academia, or industry,

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in first world, third world
countries, that everybody should

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have free and unrestricted
access to that information.

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So a plan was laid out, I
won't go into the details here,

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but the plan was laid out that
involved work constructing genetic

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maps, physical maps,
sequence maps, in the human,

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the mouse, and some model organisms,
including the bacteria yeast, fruit

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flies, worms. And, quite
remarkably, it largely went

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according to plan, over the
course of about 15 years.

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A lot of people in the scientific
community came together and took up

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different tasks. I should
say, with some pride,

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that MIT was by far, one of the
leading contributors to this effort,

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having been involved in
essentially every stage of this,

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the genetic mapping of human and
mouse, the physical mapping of human

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and mouse, and the
sequencing of human and mouse,

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and having been the leading
contributor to the latter,

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and it's not an accident because
MIT's a marvelous environment in

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which to undertake
this kind of research.

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It involved changing the way we
do biology. Back in the mid-80's,

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when we sequenced DNA, we
did it with radioactivity,

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remember I taught you how to
sequence using radioactive label of

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a gel, and all that.
That's how we did it,

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stood behind this plastic shield,
and you loaded the gels. Of course,

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now it's done in a highly automated
fashion. This is the production

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floor at the Broad Institute,
which is here at MIT, where robots

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prepare all the DNA samples, so
E. coli's grown up, and then you

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have to crack open the cells,
purify the DNA, purify the plasmid,

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do a sequencing reaction, etc.,
etc. it's all done robotically there,

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and this is capable of processing,
and does process, in a given day,

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about 200,000 samples per day.
They then go, and this is all

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equipment designed by people here at
MIT, and then commercially built for

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us. They then go to the
back room where, actually,

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these are the previous
generation of DNA sequencers,

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commercial detectors, those
capillary detectors that have

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little lasers on them, there's
a whole farm of them that

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sit there, and are
able to get data out.

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In the course of a single day, we
can now generate about 40 billion

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bases, I'm sorry, in the
course of a single year we

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can generate about 40
billion bases of DNA sequence.

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The genome project itself, was
a collaboration involving 20

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different groups around the world,
groups in the United States, United

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Kingdom, France,
Germany, and Japan,

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and China. They were of different
sizes, they used different

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approaches, but everybody was
committed to one common cause of

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producing this information,
and making it freely available,

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and everybody worked together.
And for the rest of my life,

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when it comes to Friday, at 11
o'clock, I will always think genome

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project, because we had a weekly
conference call of all the groups in

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the world working on this Fridays,
at eleven, and it was a fascinating

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experience, there were many,
many years of that. So a draft

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sequence, a rough draft
sequence of the human genome,

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was published in the year,
in February of 2001, it was

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announced with some fanfare in June
of 2000, but the real scientific

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paper came out in
February of 2001.

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This was not a perfect
sequence of the human genome,

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by any means. We discovered about
90% of the sequence of the human

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genome. It still had about 150,
00 gaps in it, it had errors. But,

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it still did have 90% of the
sequence of the human genome.

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For the next three years,
people worked very hard,

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and, as of last April, a
finished sequence of the human

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genome was produced, and was
published a couple weeks ago,

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and it contains, our
best guess, about 99.

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% of the human genome, and
it still has about 343 gaps,

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they're, we know what they are,
we know where they are, but they're

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not sequence able with
current technology.

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That's the “finished human genome”.
What is it like? Well, this is a

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picture of the genome,
do we have a pointer, yes,

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I see here we do have a pointer.
This is your genome here, this is

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chromosome number 11, and
I'll call attention to some

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interesting bits. So
these colored lines here,

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00:14:34,000 --> 00:14:38,000
represent genes, or
gene-predictions, based on both,

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00:14:38,000 --> 00:14:42,000
sequencing of the DNA, and
mapping them back to the genome,

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00:14:42,000 --> 00:14:46,000
as well as computer programs
that analyze the genome.

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And, right here, you have
a big pileup of lots of

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genes, very few genes of here.
Lots of genes, few genes. Notice

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00:14:52,000 --> 00:14:55,000
the places where there are lots
of genes, match up with these

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00:14:55,000 --> 00:14:58,000
light-grey bands, which
are the light-grey bands of

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the microscope, on
chromosomes. The places with

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very few genes match up with
the dark bands in the chromosome.

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Do you know why that is, that
the gene-rich regions are these

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light bands, and the gene-poor
regions are the chromosome dark

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bands? Me neither. Nobody
has a clue. It's really,

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it's really just one of these things.
We had no reason to expect that

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we'd see these striking patterns,
and other genomes, e-coli, doesn't

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have this dense, urban
cluster, and these big,

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rural plains that are gene-poor.
This is very weird, and it's

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distinctive to mammals.
You'll also notice that the

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00:15:35,000 --> 00:15:38,000
gene-rich regions, here,
are rich in G's and C's,

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they have different distributions
of some repeat elements,

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it's all sorts of weirdness that
comes from just looking at the

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00:15:43,000 --> 00:15:46,000
genome. The biggest weirdness
was the number of genes,

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the count of genes is,
our best guess, about 22,

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00:15:49,000 --> 00:15:52,000
00 genes, if I had to pick a number
today, it would be our count of

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genes, and of course,
that's down from the 100,

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00:15:55,000 --> 00:15:58,000
00 that was in some textbooks,
and it's down from even 30 to 40,

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00:15:58,000 --> 00:16:01,000
00 that was in the genome
paper of February, 2001.

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Our best guess is that it's
really just about that range.

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Genes, themselves,
are very interesting.

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When you look at, you know, if
we only have 22,000 genes we know

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00:16:11,000 --> 00:16:15,000
of, how do we manage to run a
human being with so few genes?

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It is, by the way, probably
fewer genes than the mustard weed,

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00:16:19,000 --> 00:16:22,000
or Arabidopsis thaliana. So,
what do we do? Well, humans, one

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00:16:22,000 --> 00:16:26,000
thing we may take comfort in,
is that we, although we only have

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00:16:26,000 --> 00:16:30,000
about 22,000 genes,
there's a lot of alternative

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00:16:30,000 --> 00:16:34,000
splicing, on average the
typical gene, on average,

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00:16:34,000 --> 00:16:38,000
has about two alternative
splice products.

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00:16:38,000 --> 00:16:41,000
Some have many, some
have few, but probably,

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00:16:41,000 --> 00:16:45,000
when you're all done, those 22,
00 genes may encode 70-80,000

279
00:16:45,000 --> 00:16:48,000
different proteins, and
it could be more than that

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because we don't know all the
alternative splice products,

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00:16:52,000 --> 00:16:55,000
and what they do. But, if you ask,
humans get credit for being really

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inventive or creative, for
having lots of new genes that

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00:16:59,000 --> 00:17:03,000
make us human,
the answer is, no.

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Not only are humans not different
in their gene complement from other

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00:17:06,000 --> 00:17:10,000
mammals, mammals, as a
group, really haven't invented

286
00:17:10,000 --> 00:17:14,000
that much, when you get down to it.
Most of the recognizable sub-domains

287
00:17:14,000 --> 00:17:18,000
of proteins, proteins are
built up of sub-domains,

288
00:17:18,000 --> 00:17:22,000
recognizable sequences that have
certain motifs that fold up in

289
00:17:22,000 --> 00:17:26,000
certain ways, or carry out
certain enzymatic functions.

290
00:17:26,000 --> 00:17:30,000
And it looks like our genomes,
our genes, are mixed-and-matched

291
00:17:30,000 --> 00:17:34,000
combinations of many domains that
were invented a long time ago,

292
00:17:34,000 --> 00:17:38,000
in invertebrates and before, and
that most of evolutionary innovation

293
00:17:38,000 --> 00:17:42,000
in the more complex,
multi-cellular animals,

294
00:17:42,000 --> 00:17:46,000
has simply been mixing-and-matching
these domains in new ways,

295
00:17:46,000 --> 00:17:50,000
to get slightly
different functions.

296
00:17:50,000 --> 00:17:54,000
You don't get a lot of points for
creativity, but it does seem to work.

297
00:17:54,000 --> 00:17:58,000
By far, the most derivative of all,
and what characterizes our genome

298
00:17:58,000 --> 00:18:02,000
tremendously is,
when a gene works,

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00:18:02,000 --> 00:18:07,000
make extra copies of it,
and let it diverge slightly,

300
00:18:07,000 --> 00:18:11,000
and take up new functions. Really,
your genome is just characterized by

301
00:18:11,000 --> 00:18:15,000
large expansions of families,
immunoglobulin-like genes,

302
00:18:15,000 --> 00:18:20,000
intermediate filament proteins
holding together the cytoskeleton.

303
00:18:20,000 --> 00:18:23,000
There are 111 different
keratin-like genes in your genome.

304
00:18:23,000 --> 00:18:26,000
They're all different,
they do different things,

305
00:18:26,000 --> 00:18:29,000
but they all came from one
gene that was copied, copied,

306
00:18:29,000 --> 00:18:33,000
copied, at random, randomly
duplicated, and then diverged to

307
00:18:33,000 --> 00:18:36,000
take up new functions. Growth
factors, flies and worms

308
00:18:36,000 --> 00:18:39,000
managed to get by just fine,
thank you, with two growth factors

309
00:18:39,000 --> 00:18:43,000
of the TGF beta-class,
whatever that is. You have 42

310
00:18:43,000 --> 00:18:46,000
growth factors of this TGF
beta-class, all of which help

311
00:18:46,000 --> 00:18:50,000
communicate, cells
communicate, in different ways.

312
00:18:50,000 --> 00:18:53,000
And then, of course, all
the olfactory receptors.

313
00:18:53,000 --> 00:18:57,000
In your genome, you have about 1,
00 genes for olfactory, for smell

314
00:18:57,000 --> 00:19:00,000
receptors. This is what Richard
Axel and Linda Buck won a Nobel

315
00:19:00,000 --> 00:19:04,000
Prize for this year, was
their work on the olfactory

316
00:19:04,000 --> 00:19:07,000
receptors. Sad to say though, out
of all your olfactory receptors,

317
00:19:07,000 --> 00:19:11,000
genes, most of them are broken.
They're most pseudo-genes.

318
00:19:11,000 --> 00:19:15,000
It's not true in dogs and mice,
who keep their olfactory receptor

319
00:19:15,000 --> 00:19:18,000
genes in pretty fine-working order,
but it's very clear that in primates

320
00:19:18,000 --> 00:19:22,000
with color vision, our
olfactory receptor genes have

321
00:19:22,000 --> 00:19:25,000
been going to seed. They've
been piling up mutations,

322
00:19:25,000 --> 00:19:28,000
and there's no selective
pressure to keep many of them.

323
00:19:28,000 --> 00:19:32,000
And, in fact, we've now shown, in
a paper that will come out soon,

324
00:19:32,000 --> 00:19:35,000
that this process is accelerating
dramatically in the last 7 million

325
00:19:35,000 --> 00:19:38,000
years since we diverged from
chimps. And so, humans have almost

326
00:19:38,000 --> 00:19:42,000
completely lost interest in smell,
that's not totally true, some of

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00:19:42,000 --> 00:19:45,000
these olfactory receptors surely
matter for various processes,

328
00:19:45,000 --> 00:19:48,000
but most of them are
probably irrelevant right now.

329
00:19:48,000 --> 00:19:52,000
And so, anyway, that's the
nature of the genes there.

330
00:19:52,000 --> 00:19:56,000
Anyway, another interesting fact
that's worth mentioning about your

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00:19:56,000 --> 00:20:01,000
genome is half of your genome
consists of transposable elements,

332
00:20:01,000 --> 00:20:05,000
elements that simply duplicate
themselves, and hop around the

333
00:20:05,000 --> 00:20:10,000
genome. Elements that are
like viruses, they make a copy,

334
00:20:10,000 --> 00:20:14,000
sometimes in RNA, the RNA is copied
back into DNA and slammed elsewhere

335
00:20:14,000 --> 00:20:19,000
in your genome.
These elements,

336
00:20:19,000 --> 00:20:24,000
well the, there
are four classes.

337
00:20:24,000 --> 00:20:27,000
Alo elements, Line elements,
Retro-Virus like elements, all these

338
00:20:27,000 --> 00:20:30,000
go through RNA intermediates,
and use reverse transcription.

339
00:20:30,000 --> 00:20:34,000
And then there's certain DNA
transposons, that go through DNA

340
00:20:34,000 --> 00:20:37,000
intermediate. The number of
copies of the aloe element,

341
00:20:37,000 --> 00:20:40,000
the aloe element that's
hopped around your genome,

342
00:20:40,000 --> 00:20:44,000
you have about a million, you
have a million fossils of this

343
00:20:44,000 --> 00:20:47,000
element. You say, why is
it there, and the answer is,

344
00:20:47,000 --> 00:20:50,000
because it's there. Because
anything that knows how to make a

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00:20:50,000 --> 00:20:54,000
copy of itself, and insert
it itself in it's genome,

346
00:20:54,000 --> 00:20:57,000
you can't get rid of.
You can consider it,

347
00:20:57,000 --> 00:21:00,000
if you wish, an infection, but
half of your genome consists of

348
00:21:00,000 --> 00:21:03,000
an infection, with these
kinds of transposable elements.

349
00:21:03,000 --> 00:21:12,000
Now that's
it, yes?

350
00:21:12,000 --> 00:21:16,000
Well, it's very interesting,
what's the effect? Well, they do,

351
00:21:16,000 --> 00:21:20,000
some of them are transcribed
and, it's very interesting.

352
00:21:20,000 --> 00:21:24,000
Sometimes it's bad, one of them
will hop into a gene and mutate it,

353
00:21:24,000 --> 00:21:28,000
and that's bad, that person
will have a lethal mutation,

354
00:21:28,000 --> 00:21:32,000
but the genome has probably begun
to use them, and count on their being

355
00:21:32,000 --> 00:21:36,000
there. So, when a bunch,
when a transposable goes in,

356
00:21:36,000 --> 00:21:40,000
and creates a spacing, if you,
for example, if an engineering

357
00:21:40,000 --> 00:21:44,000
committee came in and cleaned up
the genome by getting rid of all the

358
00:21:44,000 --> 00:21:48,000
transposable elements,
it would surely not work.

359
00:21:48,000 --> 00:21:51,000
Because we have evolutionarily
come to count on the spacing there.

360
00:21:51,000 --> 00:21:55,000
It's sort of like, if in some very,
some very messy attic, you put a cup

361
00:21:55,000 --> 00:21:58,000
of coffee down on top of a stack of
papers, those papers may be utterly

362
00:21:58,000 --> 00:22:02,000
irrelevant, but now they're holding
up that cup of coffee that you put

363
00:22:02,000 --> 00:22:06,000
down on it. And if you were to just,
poof, magically get rid of them,

364
00:22:06,000 --> 00:22:10,000
the cup of coffee would
come crashing to the ground.

365
00:22:10,000 --> 00:22:13,000
So, you know it,
they're just there,

366
00:22:13,000 --> 00:22:17,000
taking up space. Now sometimes,
even more than that, a few of them

367
00:22:17,000 --> 00:22:20,000
have actually been co-opted
into being human genes.

368
00:22:20,000 --> 00:22:24,000
We know that a few of these
transposable elements have mutated

369
00:22:24,000 --> 00:22:27,000
into being our genes
that do something for us.

370
00:22:27,000 --> 00:22:31,000
And others of them may do things in
affecting the general neighborhood

371
00:22:31,000 --> 00:22:35,000
with regard to transcription,
and so, instead of it being a

372
00:22:35,000 --> 00:22:38,000
parasite, think of them as a
symbiont, that's a genomic symbiont,

373
00:22:38,000 --> 00:22:42,000
which takes some advantage of us,
and we may, you know, have worked

374
00:22:42,000 --> 00:22:46,000
out a compromise to take
some advantage of it.

375
00:22:46,000 --> 00:22:49,000
Every time a copy is made of these,
and it hops in the genome, some

376
00:22:49,000 --> 00:22:53,000
mutations may happen in the master
element, but when it lands in the

377
00:22:53,000 --> 00:22:56,000
new place, we have a record of
that hop. And if you reconstruct the

378
00:22:56,000 --> 00:23:00,000
sequence of the million AluI
elements, you can see which ones are

379
00:23:00,000 --> 00:23:04,000
very close relatives of each other,
and had to have hopped recently, and

380
00:23:04,000 --> 00:23:08,000
which ones are somewhat
more distant relatives.

381
00:23:08,000 --> 00:23:11,000
And you can build an evolutionary
tree connecting all of the repeat

382
00:23:11,000 --> 00:23:14,000
elements that have hopped around
your genome, and thereby attaching a

383
00:23:14,000 --> 00:23:17,000
date to each of them,
as to when they hopped.

384
00:23:17,000 --> 00:23:20,000
So it really is a fossil record,
and you can figure out how many of

385
00:23:20,000 --> 00:23:23,000
them have been hopping at
different times over history.

386
00:23:23,000 --> 00:23:27,000
And we can even make a plot
of that, this is long ago,

387
00:23:27,000 --> 00:23:30,000
sometime here, some 30 million years
ago, there was a huge explosion and

388
00:23:30,000 --> 00:23:33,000
in transposion,
transposons, in our genome.

389
00:23:33,000 --> 00:23:36,000
We don't know why that happened,
but it's very interesting, it does

390
00:23:36,000 --> 00:23:40,000
correspond to very interesting
periods of primate evolution.

391
00:23:40,000 --> 00:23:43,000
And then, interestingly,
there's been a huge crash,

392
00:23:43,000 --> 00:23:46,000
and transposition has dropped
dramatically. We have no clue why

393
00:23:46,000 --> 00:23:49,000
this is, but we have a whole
fossil record here of the rate of

394
00:23:49,000 --> 00:23:52,000
transposition of different kinds of
repeat elements around our genome,

395
00:23:52,000 --> 00:23:55,000
and people are now starting to try
to figure out what in the world this

396
00:23:55,000 --> 00:23:58,000
means. So all this is sort of
there, inherent in the sequence,

397
00:23:58,000 --> 00:24:01,000
and if you want the sequence, as
I say, you can go to the web and

398
00:24:01,000 --> 00:24:04,000
pull all this stuff now.
So how do we understand the

399
00:24:04,000 --> 00:24:07,000
sequence? Well, I've told
you a little bit about it,

400
00:24:07,000 --> 00:24:10,000
from the simple things that we've
done, but there's a lot more that

401
00:24:10,000 --> 00:24:13,000
needs to be learned about the
sequence, so what I really want to

402
00:24:13,000 --> 00:24:16,000
turn to, is how we're extracting
information out of this sequence.

403
00:24:16,000 --> 00:24:19,000
So, DNA sequence is long and
boring, it's only marginally more

404
00:24:19,000 --> 00:24:23,000
interesting than reading your hard
disk, because it has four letters,

405
00:24:23,000 --> 00:24:27,000
instead of ones and zeros, but it's,
you know, well, it's pretty really

406
00:24:27,000 --> 00:24:30,000
boring if you take a look at it.
How do you attach meaning to all

407
00:24:30,000 --> 00:24:34,000
this stuff? One of the most
powerful ways is by comparison with

408
00:24:34,000 --> 00:24:38,000
other genomes. And so,
comparing the human genome

409
00:24:38,000 --> 00:24:42,000
to the mouse genome is very
informative in many ways.

410
00:24:42,000 --> 00:24:45,000
So, as soon as the human genome
was far along, a portion of the

411
00:24:45,000 --> 00:24:49,000
international consortium, set
to work getting a sequence of

412
00:24:49,000 --> 00:24:52,000
the mouse genome. And that
was published in December

413
00:24:52,000 --> 00:24:56,000
of 2002. We have a nice map of the
mouse genome, with all these things,

414
00:24:56,000 --> 00:24:59,000
it, too, shows these gene-rich
regions, gene-poor regions,

415
00:24:59,000 --> 00:25:03,000
all sorts of funny things. And if
we look closely at a portion of the

416
00:25:03,000 --> 00:25:06,000
human genome over here, I've
picked about a million bases of

417
00:25:06,000 --> 00:25:10,000
the human genome, and we
take any little spot in that

418
00:25:10,000 --> 00:25:14,000
million bases of the human
genome, let's say over here.

419
00:25:14,000 --> 00:25:17,000
And we take half the DNA sequence
corresponding to this spot,

420
00:25:17,000 --> 00:25:20,000
and we run it in the computer
against the mouse genome,

421
00:25:20,000 --> 00:25:23,000
and ask where in the mouse genome
do we get the best match for this,

422
00:25:23,000 --> 00:25:26,000
the best match to this is here.
Now let's do it for this piece,

423
00:25:26,000 --> 00:25:29,000
here. The best match anywhere in
the mouse genome lands in the same

424
00:25:29,000 --> 00:25:32,000
million bases here as
the mouse genome. In fact,

425
00:25:32,000 --> 00:25:36,000
for every single sequence that we
pull out from this million bases in

426
00:25:36,000 --> 00:25:39,000
the human genome, the best
match is in this million

427
00:25:39,000 --> 00:25:42,000
bases of the mouse genome.
That's very interesting. Why is

428
00:25:42,000 --> 00:25:45,000
that? Sorry? No,
people do know.

429
00:25:45,000 --> 00:25:49,000
It, it was a good try, though.
[LAUGHTER]. This million bases in

430
00:25:49,000 --> 00:25:52,000
the mouse genome, and this
million bases in the human

431
00:25:52,000 --> 00:25:56,000
genome, represent the evolutionary
descendents of a common million

432
00:25:56,000 --> 00:25:59,000
bases that occurred in our common
ancestor 75-million years ago.

433
00:25:59,000 --> 00:26:03,000
This is a clear evidence
of the evolution here,

434
00:26:03,000 --> 00:26:06,000
because we can see that this is
a segment of DNA from our common

435
00:26:06,000 --> 00:26:10,000
ancestor that really hasn't
undergone much rearrangement,

436
00:26:10,000 --> 00:26:14,000
and we can just line up
the sequences and see.

437
00:26:14,000 --> 00:26:17,000
In fact, we can build a whole map
across the mouse genome like this.

438
00:26:17,000 --> 00:26:20,000
For any bit of the mouse genome,
I don't know, here's a bit on mouse

439
00:26:20,000 --> 00:26:24,000
chromosome 17, this whole
stretch corresponds to a

440
00:26:24,000 --> 00:26:27,000
portion of human chromosome
number eight. This stretch here,

441
00:26:27,000 --> 00:26:30,000
I don't know, this green color
here on chromosome number six,

442
00:26:30,000 --> 00:26:34,000
corresponds to chromosome
four in the human. And so,

443
00:26:34,000 --> 00:26:37,000
we can build a look-up table that
says, for any portion of the human

444
00:26:37,000 --> 00:26:40,000
genome, what's the corresponding
portion of the mouse genome that

445
00:26:40,000 --> 00:26:44,000
came from the same ancestor, has
basically the same complement of

446
00:26:44,000 --> 00:26:47,000
genes in it. And there's
only about 330 such

447
00:26:47,000 --> 00:26:50,000
regions that we need to
cut-and-paste the human genome order

448
00:26:50,000 --> 00:26:53,000
to the mouse genome order,
roughly speaking. There's a lot of

449
00:26:53,000 --> 00:26:56,000
little local rearrangements,
but at this gross level. So now,

450
00:26:56,000 --> 00:26:59,000
if we go back more closely and
we look at this, and we say,

451
00:26:59,000 --> 00:27:03,000
OK, so now we look at this region,
we now know these two regions

452
00:27:03,000 --> 00:27:06,000
descend from a common ancestor,
if we do a careful evolutionary

453
00:27:06,000 --> 00:27:09,000
analysis, lining up all
the sequences, and see how

454
00:27:09,000 --> 00:27:12,000
well-preserved the sequences are,
some are much better preserved than

455
00:27:12,000 --> 00:27:16,000
others. Evolution
has been much more

456
00:27:16,000 --> 00:27:20,000
lovingly conserving other
sequences than others, and so,

457
00:27:20,000 --> 00:27:24,000
so let's now zoom-in on a gene,
this is a gene that goes by the name,

458
00:27:24,000 --> 00:27:28,000
PP-Gama, I'm fond of this gene but,
it doesn't matter. If we look, I've

459
00:27:28,000 --> 00:27:32,000
indicated all the regions here, in
which there's a heightened degree

460
00:27:32,000 --> 00:27:36,000
of conservation. The sequence
is well-conserved here,

461
00:27:36,000 --> 00:27:40,000
here, here, here, here,
here, here, and here,

462
00:27:40,000 --> 00:27:44,000
here, here, here, here, here.
These correspond to the exons of

463
00:27:44,000 --> 00:27:48,000
the PPR-Gama gene, they
encode the protein of the gene,

464
00:27:48,000 --> 00:27:52,000
then the splicing goes like this, OK?
These things here do not correspond

465
00:27:52,000 --> 00:27:56,000
to the exons. People have
no idea what they are,

466
00:27:56,000 --> 00:28:00,000
in fact, this is not supposed to be
here. The official textbook picture

467
00:28:00,000 --> 00:28:04,000
says, the vast majority
of what matters for a gene,

468
00:28:04,000 --> 00:28:09,000
what evolution should preserve,
is the exons plus the promoter.

469
00:28:09,000 --> 00:28:13,000
Here's the promoter. But in
fact, what we found is that

470
00:28:13,000 --> 00:28:17,000
an awful lot more is being
preserved. In fact, across the genome,

471
00:28:17,000 --> 00:28:21,000
our best estimate is there are about
500,000 conserved elements across

472
00:28:21,000 --> 00:28:26,000
the genome, and only 1/3 of
them are protein-coding exons.

473
00:28:26,000 --> 00:28:30,000
That means 2/3 of the stuff
evolution has been interested in,

474
00:28:30,000 --> 00:28:34,000
is not protein-coding exons, and the
truth is, we do not know what it is,

475
00:28:34,000 --> 00:28:38,000
this was a very radical finding,
when this mouse paper came out,

476
00:28:38,000 --> 00:28:43,000
about a year and a half,
about two years ago now.

477
00:28:43,000 --> 00:28:47,000
What it must be, I
think, but we're guessing,

478
00:28:47,000 --> 00:28:51,000
are regulatory signals, the
structural elements in chromosomes,

479
00:28:51,000 --> 00:28:56,000
RNA genes, but there's an awful
lot more of it than we had imagined.

480
00:28:56,000 --> 00:28:59,000
And we've, now we're in
this fascinating situation,

481
00:28:59,000 --> 00:29:03,000
where computational analysis has
told us what's on evolution's mind,

482
00:29:03,000 --> 00:29:07,000
and now we have to go to the lab and
figure out what in the world it does.

483
00:29:07,000 --> 00:29:10,000
But there's no doubt that it must
do something, because evolution has

484
00:29:10,000 --> 00:29:14,000
preserved it quite well. Now,
I oversimplified greatly in

485
00:29:14,000 --> 00:29:18,000
this discussion,
let me first say,

486
00:29:18,000 --> 00:29:21,000
and I'll come back to that. We
do know, if we take some of those

487
00:29:21,000 --> 00:29:24,000
elements, here's one, there's
a 481 base-pair elements

488
00:29:24,000 --> 00:29:27,000
that's 84% identical between human
and mouse. You could write yourself

489
00:29:27,000 --> 00:29:31,000
a little statistical model to say
that's way unusual to have something

490
00:29:31,000 --> 00:29:34,000
that's so well preserved. When
Eddie Ruben and his colleagues

491
00:29:34,000 --> 00:29:37,000
from Berkley made a knockout
mouse that deleted that segment,

492
00:29:37,000 --> 00:29:40,000
this knockout mouse loses regulation
of three different genes in the

493
00:29:40,000 --> 00:29:43,000
neighborhood, saying that this
must be a regulatory sequence that

494
00:29:43,000 --> 00:29:47,000
affects multiple genes
in the neighborhood. That,

495
00:29:47,000 --> 00:29:50,000
that's one, with about 300, 00
such elements to go, in order to

496
00:29:50,000 --> 00:29:54,000
attach meaning to them. So
doing this entirely by knocking

497
00:29:54,000 --> 00:29:58,000
out mice will be a slow process,
one's going to need other ways to be

498
00:29:58,000 --> 00:30:02,000
able to attach meaning,
but there's no doubt. Now,

499
00:30:02,000 --> 00:30:06,000
there's some other interesting
papers where people have knocked

500
00:30:06,000 --> 00:30:10,000
some of these things out, and
they've seen no effect on the

501
00:30:10,000 --> 00:30:14,000
mouse. They get a totally viable
mouse. Can you conclude from that,

502
00:30:14,000 --> 00:30:18,000
that they have no function? Why
not? The knockout mouse is viable.

503
00:30:18,000 --> 00:30:22,000
Could be redundant, it
could even not be redundant,

504
00:30:22,000 --> 00:30:26,000
but yes, it could be redundant,
but you couldn't knock out both of

505
00:30:26,000 --> 00:30:29,000
two things. It turns
out, suppose knocking it

506
00:30:29,000 --> 00:30:33,000
out affected the mouse's viability
by part, ten to the third,

507
00:30:33,000 --> 00:30:37,000
it was only 99.9% as fertile,
would you be able to see that in the

508
00:30:37,000 --> 00:30:41,000
laboratory? No. Would
that matter to evolution?

509
00:30:41,000 --> 00:30:44,000
It would be lethal, in
an evolutionary sense.

510
00:30:44,000 --> 00:30:48,000
Such mutation could never
propagate through a population.

511
00:30:48,000 --> 00:30:52,000
One part, and ten to the third,
is massive selection against, from

512
00:30:52,000 --> 00:30:56,000
an evolutionary point of view,
but almost undetectable in a

513
00:30:56,000 --> 00:31:00,000
laboratory batch. Evolution
has a far more sensitive

514
00:31:00,000 --> 00:31:04,000
assay than we do. Now,
I won't go into detail,

515
00:31:04,000 --> 00:31:09,000
but for the mathematically inclined
here, showing that there really were

516
00:31:09,000 --> 00:31:13,000
about 5% of the human genome under,
under evolutionary selection, it was

517
00:31:13,000 --> 00:31:18,000
a complicated affair,
because with only two genomes,

518
00:31:18,000 --> 00:31:23,000
what we really had to do, and if
this doesn't make sense, ignore it.

519
00:31:23,000 --> 00:31:26,000
We looked at the background
distribution of conservation of the

520
00:31:26,000 --> 00:31:29,000
genome in unimportant elements,
in those repeat elements that we

521
00:31:29,000 --> 00:31:32,000
knew to be functionally
broken. We looked at the overall

522
00:31:32,000 --> 00:31:35,000
conservation of the genome, and
found that the overall genome

523
00:31:35,000 --> 00:31:38,000
has this rightward tail, by
subtracting the distributions we

524
00:31:38,000 --> 00:31:41,000
were able to see how much
excess conservation there was.

525
00:31:41,000 --> 00:31:44,000
That's because we only had two
genomes, we had to draw inferences.

526
00:31:44,000 --> 00:31:47,000
If we had more genomes,
like the mouse and the rat,

527
00:31:47,000 --> 00:31:50,000
and the dog and the-this-and-the-that,

528
00:31:50,000 --> 00:31:54,000
we would be able to
extract signal from noise.

529
00:31:54,000 --> 00:31:57,000
We would be able to see right away,
which bits were well-conserved, and

530
00:31:57,000 --> 00:32:01,000
we wouldn't have to do this as
a sensitive statistical analysis.

531
00:32:01,000 --> 00:32:05,000
So, in fact, we need more mammalian
genomes, so, so right now there's

532
00:32:05,000 --> 00:32:09,000
been a sequence of the rat
genome in the past year or so,

533
00:32:09,000 --> 00:32:12,000
there's a sequence of the dog genome,
we're writing up that paper now,

534
00:32:12,000 --> 00:32:16,000
but it's on the web already.
There's a sequence of the chimpanzee

535
00:32:16,000 --> 00:32:20,000
genome we're writing up a paper
on that, in collaboration with our

536
00:32:20,000 --> 00:32:24,000
friends in the
genome-sequencing community.

537
00:32:24,000 --> 00:32:27,000
We're currently sequencing
a variety of other organisms,

538
00:32:27,000 --> 00:32:30,000
as well. And if you had enough
organisms, you ought to be able to

539
00:32:30,000 --> 00:32:34,000
just line it up and say,
what has evolution preserved,

540
00:32:34,000 --> 00:32:37,000
and figure out exactly
which nucleotides matter,

541
00:32:37,000 --> 00:32:40,000
and which nucleotides don't, are
allowed to drift freely, at the

542
00:32:40,000 --> 00:32:44,000
background rate. How far
could you go with this?

543
00:32:44,000 --> 00:32:47,000
Well, we decided to try
an interesting experiment.

544
00:32:47,000 --> 00:32:50,000
We said, since mammals are very big,
then we're going to need a lot of

545
00:32:50,000 --> 00:32:54,000
genome sequences, how about
we try a small organism,

546
00:32:54,000 --> 00:32:58,000
like yeast? What if we
were to try to do this,

547
00:32:58,000 --> 00:33:02,000
this kind of evolutionary, genomic
analysis on something like the yeast

548
00:33:02,000 --> 00:33:06,000
genome? And so, this is
work that I'll describe,

549
00:33:06,000 --> 00:33:10,000
that was between a bunch of people
here at MIT who do genome-sequencing,

550
00:33:10,000 --> 00:33:14,000
and a student in computer science,
Manolis Kellis, was PhD student in

551
00:33:14,000 --> 00:33:18,000
computer science, he now
just joined the faculty here

552
00:33:18,000 --> 00:33:21,000
at MIT in computer science. But
it was a really great example of

553
00:33:21,000 --> 00:33:25,000
how biology and computer
science could come together.

554
00:33:25,000 --> 00:33:28,000
So, the genome-sequencing folks
sequenced three related species,

555
00:33:28,000 --> 00:33:32,000
through our friend, the baker's
yeast, Saccharomyces cerevisiae,

556
00:33:32,000 --> 00:33:35,000
workhorse of geneticist. These
three different species are

557
00:33:35,000 --> 00:33:39,000
separated by different evolutionary
distances, from Saccharomyces

558
00:33:39,000 --> 00:33:42,000
cerevisiae. When you line up their
genomes, just like with human and

559
00:33:42,000 --> 00:33:46,000
mouse, you find the genes
occur largely in the same order,

560
00:33:46,000 --> 00:33:49,000
and it's not hard to pick out,
oh there's this gene there, there,

561
00:33:49,000 --> 00:33:53,000
it's all lined up, you've got
these evolutionary segments,

562
00:33:53,000 --> 00:33:56,000
and very few rearrangements have
occurred across these species,

563
00:33:56,000 --> 00:34:00,000
despite the fact that they're about
20 million years apart in history.

564
00:34:00,000 --> 00:34:05,000
But here's an interesting
thing. When the yeast genome,

565
00:34:05,000 --> 00:34:11,000
Saccharomyces cerevisiae,
was first published in 1995,

566
00:34:11,000 --> 00:34:16,000
the paper describing it reported
6, 00 genes. Now, how did they know

567
00:34:16,000 --> 00:34:22,000
there were 6,200 genes? They
ran a computer program looking

568
00:34:22,000 --> 00:34:28,000
for open reading frames. Any
open reading frame, consecutive

569
00:34:28,000 --> 00:34:34,000
codons without a stop sufficiently
long, was called a gene.

570
00:34:34,000 --> 00:34:37,000
But statistically,
you could, by chance,

571
00:34:37,000 --> 00:34:41,000
just have a long stretch of
codons without a stop codon.

572
00:34:41,000 --> 00:34:44,000
And so, if I saw 100 codons
in a row, without a stop,

573
00:34:44,000 --> 00:34:48,000
they called it a gene, but
it might just be chance.

574
00:34:48,000 --> 00:34:52,000
And they knew that, of course,
they wrote that in the paper, but

575
00:34:52,000 --> 00:34:55,000
for many years,
people then had 6,

576
00:34:55,000 --> 00:34:59,000
00 open reading frames,
which were the yeast's genes.

577
00:34:59,000 --> 00:35:02,000
Could evolution now tell us which
one of them were real and which

578
00:35:02,000 --> 00:35:06,000
weren't? Well, it turns
out that evolution was

579
00:35:06,000 --> 00:35:10,000
tremendously powerful
in doing that.

580
00:35:10,000 --> 00:35:14,000
If you take something that's
a well-known gene that has been

581
00:35:14,000 --> 00:35:19,000
extensively studied by yeast
geneticists, you line it up across

582
00:35:19,000 --> 00:35:23,000
all four species, you
almost never see deletions.

583
00:35:23,000 --> 00:35:28,000
And when you do see the lesions,
here in grey, they're always a

584
00:35:28,000 --> 00:35:33,000
multiple of three. Why are
they a multiple of three?

585
00:35:33,000 --> 00:35:37,000
They preserve the reading frame.
By contrast, if I take some clear,

586
00:35:37,000 --> 00:35:42,000
intergenetic DNA, that's
not protein-coding,

587
00:35:42,000 --> 00:35:47,000
and I compare it across these four
species, I see lots and lots of

588
00:35:47,000 --> 00:35:52,000
frame shifting
deletions that occur,

589
00:35:52,000 --> 00:35:54,000
Evolution tolerates frame shifting
deletions, and if I juts write down

590
00:35:54,000 --> 00:35:57,000
the rates, frame shifting deletions
are 75x more common in intergenic

591
00:35:57,000 --> 00:36:00,000
DNA, than genic DNA. This
provides a very powerful test.

592
00:36:00,000 --> 00:36:03,000
Run this test across the genome,
looking for the density of frame

593
00:36:03,000 --> 00:36:06,000
shifting deletions, any
place that doesn't tolerate

594
00:36:06,000 --> 00:36:09,000
frame shifting deletions is probably
a real gene, anything that does

595
00:36:09,000 --> 00:36:12,000
tolerate it is probably not.
When you sorted through all this,

596
00:36:12,000 --> 00:36:15,000
it turned out that 528 of the
official yeast genes were clearly

597
00:36:15,000 --> 00:36:18,000
not real, not real genes. They
were just chock-a-block full

598
00:36:18,000 --> 00:36:22,000
of these frame shifting deletions.
And, and a bunch of others could be

599
00:36:22,000 --> 00:36:26,000
confirmed. So the yeast gene
count, and I won't tell you all the

600
00:36:26,000 --> 00:36:30,000
experimental and other
that shows this is right,

601
00:36:30,000 --> 00:36:34,000
but the yeast genome has now
been revised downward to 5,

602
00:36:34,000 --> 00:36:38,000
00 genes, and we have great
confidence that almost all of those

603
00:36:38,000 --> 00:36:42,000
are real genes, there
are 20 whose origins that

604
00:36:42,000 --> 00:36:46,000
we're not sure of, and new
genes could be found in this

605
00:36:46,000 --> 00:36:50,000
way. Here's a really
audacious thing.

606
00:36:50,000 --> 00:36:51,000
This graduate student in
computer science said, I think,

607
00:36:51,000 --> 00:36:53,000
based on these other species,
there was a mistake made in the

608
00:36:53,000 --> 00:36:55,000
sequencing of the first yeast, and
that the reason these things are

609
00:36:55,000 --> 00:36:57,000
called two separate genes, is
that somebody made a sequencing

610
00:36:57,000 --> 00:36:58,000
error that got a stop codon here,
but I think these are really part of

611
00:36:58,000 --> 00:37:00,000
one gene. And so, somebody
went back and re-sequenced

612
00:37:00,000 --> 00:37:02,000
some of these,
and sure enough,

613
00:37:02,000 --> 00:37:04,000
he had correctly predicted that
there had been a mistake made at

614
00:37:04,000 --> 00:37:06,000
that letter, and that these
were in fact, a single gene.

615
00:37:06,000 --> 00:37:11,000
The computational analysis was
incredibly powerful in this regard,

616
00:37:11,000 --> 00:37:17,000
it could go further than this, you
could ask, could I also figure out

617
00:37:17,000 --> 00:37:23,000
the way genes are regulated in
this fashion, could I work out the

618
00:37:23,000 --> 00:37:29,000
intergenic signals in the
promoter regions? Remember that lac

619
00:37:29,000 --> 00:37:35,000
repressor to a certain operator
site, well, all of these regulatory

620
00:37:35,000 --> 00:37:41,000
proteins bind to different sequences,
could we figure out what the

621
00:37:41,000 --> 00:37:46,000
sequences were, computational?
Well, if we look closely at a genic,

622
00:37:46,000 --> 00:37:50,000
intergenic region, here's one where
there's two genes being transcribed

623
00:37:50,000 --> 00:37:54,000
in opposite directions, gal-1
and gal-10, both involved in

624
00:37:54,000 --> 00:37:58,000
galactose metabolism, and
there's a particular protein,

625
00:37:58,000 --> 00:38:03,000
a transcription factor here,
called Gal-4, in this region,

626
00:38:03,000 --> 00:38:07,000
and it has a particular
sequence that it likes,

627
00:38:07,000 --> 00:38:11,000
CCG, 11 bases, GGC.
So, that Gal-4 we see,

628
00:38:11,000 --> 00:38:16,000
is very well preserved
across all of the species.

629
00:38:16,000 --> 00:38:20,000
So, in no regulatory
sequence is well-preserved,

630
00:38:20,000 --> 00:38:24,000
now let's look at that closely.
This Gal-4 binding site is a measly,

631
00:38:24,000 --> 00:38:29,000
crummy, six nucleotides
of information. At random,

632
00:38:29,000 --> 00:38:33,000
it's going to occur in many
places in the yeast genome,

633
00:38:33,000 --> 00:38:38,000
but not be a real, important Gal-4,
right? Some of them matter, some of

634
00:38:38,000 --> 00:38:42,000
them don't. How do we figure out
which of these occurrences are real

635
00:38:42,000 --> 00:38:46,000
Gal-4, well, if we look across all
four species, what we find is that

636
00:38:46,000 --> 00:38:51,000
those occurrences that
occur in promoter regions,

637
00:38:51,000 --> 00:38:55,000
are much more likely to be
conserved by evolution than those

638
00:38:55,000 --> 00:39:00,000
that don't. So there's
a special property here,

639
00:39:00,000 --> 00:39:04,000
conservation of the motif
and the motor regions.

640
00:39:04,000 --> 00:39:08,000
In fact, this particular sequence
is four times more likely to be

641
00:39:08,000 --> 00:39:12,000
preserved when it occurs
in a promoter region,

642
00:39:12,000 --> 00:39:16,000
than when it occurs in a coded
region. And for a typical control

643
00:39:16,000 --> 00:39:20,000
region, the opposite is true.
Since genes, since coding sequences

644
00:39:20,000 --> 00:39:24,000
are better preserved in general,
for a randomly chosen sequence, I

645
00:39:24,000 --> 00:39:28,000
don't know, ATGGCAT, it's
more likely to be preserved in

646
00:39:28,000 --> 00:39:32,000
coding regions than
non-coding regions.

647
00:39:32,000 --> 00:39:35,000
So this Gal-4 motif has a
very funky property that,

648
00:39:35,000 --> 00:39:38,000
on average, it's 12x more
likely than background,

649
00:39:38,000 --> 00:39:41,000
to be preserved when it
occurs in a promoter. Now,

650
00:39:41,000 --> 00:39:44,000
that's a test you apply to
another motif, and another motif.

651
00:39:44,000 --> 00:39:47,000
In fact, you could, by computer,
test all possible motifs, and ask

652
00:39:47,000 --> 00:39:50,000
which ones have that property?
Make a scatter plot, most motifs

653
00:39:50,000 --> 00:39:53,000
are better conserved when
they occur in promoter regions,

654
00:39:53,000 --> 00:39:56,000
than when they occur in
coding regions, some however,

655
00:39:56,000 --> 00:40:00,000
are better preserved in promoter
regions than in coding regions.

656
00:40:00,000 --> 00:40:04,000
Our friend, Gal-4, is up
there, but there are a lot

657
00:40:04,000 --> 00:40:09,000
more things like it, that
are better preserved by

658
00:40:09,000 --> 00:40:14,000
evolution than promoters are.
You can make a list of them. You

659
00:40:14,000 --> 00:40:19,000
can get about 72 well-conserved,
regulatory motifs and it turns out

660
00:40:19,000 --> 00:40:24,000
that 20 years of yeast work produced
knowledge about things like the

661
00:40:24,000 --> 00:40:29,000
Gal-4 site, and other sites.
Almost all the known regulatory

662
00:40:29,000 --> 00:40:34,000
sites that had been discovered
over the course of 20 years of

663
00:40:34,000 --> 00:40:39,000
experimental work appear on this
list that falls out of the computer

664
00:40:39,000 --> 00:40:44,000
analysis of evolutionary
comparison of genomes.

665
00:40:44,000 --> 00:40:48,000
You can actually go a step further,
I'll hesitate to tell you, but I'll

666
00:40:48,000 --> 00:40:53,000
try anyway. If you wanted to find
out, without knowing in advance,

667
00:40:53,000 --> 00:40:57,000
what these motifs were doing,
what their biological function was,

668
00:40:57,000 --> 00:41:02,000
you can do that informationally,
too. It turns out that if I take my

669
00:41:02,000 --> 00:41:06,000
motif, Gal-4, and I ask, which
chains does it occur in front

670
00:41:06,000 --> 00:41:11,000
of? Well, across Saccharomyces
cerevisiae, you find this crummy

671
00:41:11,000 --> 00:41:15,000
little motif in many, many
places because, as I said,

672
00:41:15,000 --> 00:41:20,000
most of it's just noise. But
if I ask, which genes have this

673
00:41:20,000 --> 00:41:24,000
motif in all four species, these
genes, there's a huge overlap

674
00:41:24,000 --> 00:41:28,000
with a class of genes involved
in carbohydrate metabolism.

675
00:41:28,000 --> 00:41:33,000
So, if I didn't know in advance
that the Gal-4 motif was involved in

676
00:41:33,000 --> 00:41:37,000
regulating genes in carbohydrate
metabolism, I could tell,

677
00:41:37,000 --> 00:41:41,000
just from the fact that the
genes that'd conserved it,

678
00:41:41,000 --> 00:41:46,000
are genes involved in
carbohydrate metabolism.

679
00:41:46,000 --> 00:41:50,000
You can do that using all sorts
of tricks, expression of genes,

680
00:41:50,000 --> 00:41:54,000
protein mass spec, blah, blah,
blah, and the short answer is, for

681
00:41:54,000 --> 00:41:58,000
almost all of those motifs that
you can find in the computer,

682
00:41:58,000 --> 00:42:02,000
by consulting public data bases of
sets of genes that are co-expressed,

683
00:42:02,000 --> 00:42:06,000
or have similar properties and all
that, the computer can also offer

684
00:42:06,000 --> 00:42:10,000
you a pretty good hypothesis about
what that motif is associated with.

685
00:42:10,000 --> 00:42:14,000
You can even go a step further
than that. You can begin to look at

686
00:42:14,000 --> 00:42:18,000
pairs of motifs, you can
say, if I have a certain

687
00:42:18,000 --> 00:42:23,000
regulatory sequence, number
one, and a second regulatory

688
00:42:23,000 --> 00:42:27,000
sequence, number two, do
they tend to be preserved in

689
00:42:27,000 --> 00:42:31,000
front of the same genes as each
other? Is their conservation

690
00:42:31,000 --> 00:42:36,000
correlated? And you can
build a map of these two

691
00:42:36,000 --> 00:42:40,000
guys tend, when this guy's
correlated, this guy tends to be

692
00:42:40,000 --> 00:42:44,000
correlated. And you can say, oh
those proteins must be talking to

693
00:42:44,000 --> 00:42:48,000
each other, and you can read that
off from the patterns of evolution,

694
00:42:48,000 --> 00:42:52,000
as well. There are two
regulators, one called Sterile 12,

695
00:42:52,000 --> 00:42:57,000
one called Tec1. This computational
analysis shows that they tend to

696
00:42:57,000 --> 00:43:01,000
co-occur in a conserved fashion,
far more often then you'd expect by

697
00:43:01,000 --> 00:43:05,000
chance. And when you do the
analysis, you find that those genes

698
00:43:05,000 --> 00:43:09,000
that just have a conserved
Sterile 12, those genes tend to

699
00:43:09,000 --> 00:43:13,000
be involved in mating. Genes
that just have a conserved

700
00:43:13,000 --> 00:43:16,000
instance of Tec1 tend to be
involved in the budding of the yeast,

701
00:43:16,000 --> 00:43:20,000
and those genes that have conserved
the occurrences of both tend to be

702
00:43:20,000 --> 00:43:23,000
involved in fillamentation.
Now all that can be read out,

703
00:43:23,000 --> 00:43:26,000
which is way cool, this is not
the way we used to do biology.

704
00:43:26,000 --> 00:43:30,000
Now don't get me wrong, there's
a ton of experiments that underlay

705
00:43:30,000 --> 00:43:33,000
creating these databases, and
there's a ton of experiments

706
00:43:33,000 --> 00:43:36,000
that have to be done to check any
of these things. But what we have is

707
00:43:36,000 --> 00:43:40,000
one of the most powerful
hypothesis generators that's ever

708
00:43:40,000 --> 00:43:44,000
been seen here. Evolution,
by telling us what to

709
00:43:44,000 --> 00:43:48,000
focus on, is giving us, on
a silver platter, hundreds of

710
00:43:48,000 --> 00:43:52,000
hypothesis about who's interacting
with whom, and sending us back to

711
00:43:52,000 --> 00:43:56,000
the lab then, to test
these hypotheses. Now,

712
00:43:56,000 --> 00:44:00,000
what are the implications of
all of this for the human genome?

713
00:44:00,000 --> 00:44:04,000
Could we do this for
the human genome? Well,

714
00:44:04,000 --> 00:44:08,000
these species,
Saccharomyces cerevisiase, S.

715
00:44:08,000 --> 00:44:12,000
paradoxus, S. mikatae and S.
bayanus, are they a good model for

716
00:44:12,000 --> 00:44:15,000
mammals? Well it
turns out that their

717
00:44:15,000 --> 00:44:19,000
evolutionary distance from each
other is the same as the distance of

718
00:44:19,000 --> 00:44:23,000
human to lemur,
to dog, to mouse.

719
00:44:23,000 --> 00:44:27,000
So they were chosen with a purpose.
Those are actually fairly good

720
00:44:27,000 --> 00:44:30,000
models for the human. So
could we do exactly the same

721
00:44:30,000 --> 00:44:34,000
analysis for the human,
for the entire human genome?

722
00:44:34,000 --> 00:44:38,000
If we had, human, lemur, dog,
and mouse, are basically four

723
00:44:38,000 --> 00:44:42,000
species, human,
mouse, rat, and dog.

724
00:44:42,000 --> 00:44:46,000
Well, there's one little fly in the
ointment. The human genome is 20x

725
00:44:46,000 --> 00:44:50,000
bigger than the yeast genome.
If I want to analyze the whole

726
00:44:50,000 --> 00:44:54,000
human genome, I have a
problem of signal-to-noise.

727
00:44:54,000 --> 00:44:58,000
The genome is 20x bigger, I've
got 20x as much noise to get

728
00:44:58,000 --> 00:45:03,000
rid of. I won't walk you through
it, but I need more evolutionary

729
00:45:03,000 --> 00:45:07,000
information to get rid of all that
noise. And, you can do a simple

730
00:45:07,000 --> 00:45:11,000
calculation that says, my
evolutionary tree needs to be

731
00:45:11,000 --> 00:45:15,000
bigger, it's branch length needs to
be bigger by about the natural log

732
00:45:15,000 --> 00:45:20,000
of 20, to get rid of
20 fold more noise.

733
00:45:20,000 --> 00:45:24,000
And that would mean I'd need more
species, I'd need about 16 species,

734
00:45:24,000 --> 00:45:28,000
or something like that to be
able to do that. But if I built an

735
00:45:28,000 --> 00:45:32,000
evolutionary tree that had
a branch length of four,

736
00:45:32,000 --> 00:45:36,000
that is, four substitutions per
base across this evolutionary tree,

737
00:45:36,000 --> 00:45:40,000
as indicated by these colored lines
here, I should have enough power to

738
00:45:40,000 --> 00:45:44,000
analyze the entire human genome,
the way we just did the yeast genome.

739
00:45:44,000 --> 00:45:48,000
So we currently have human,
chimp, mouse, rat, dog. As of this

740
00:45:48,000 --> 00:45:52,000
fall, during in fact, right
at the beginning of this term,

741
00:45:52,000 --> 00:45:56,000
the National Institute of Health
signed off on the sequencing of

742
00:45:56,000 --> 00:46:00,000
these additional eight mammals.
These mammals are now in process,

743
00:46:00,000 --> 00:46:04,000
and in fact, the elephant is done,
and the armadillo is in process,

744
00:46:04,000 --> 00:46:08,000
and the tree shrew, I think,
is being caught at the moment.

745
00:46:08,000 --> 00:46:12,000
[LAUGHTER]. The ten-,
don't talk about the tree

746
00:46:12,000 --> 00:46:18,000
shrews. The tenrec is actually
being tested right now,

747
00:46:18,000 --> 00:46:24,000
etc, and all this is going
on right now, as we speak,

748
00:46:24,000 --> 00:46:29,000
and I think that by next summer,
we should have much of, and by

749
00:46:29,000 --> 00:46:35,000
certainly, by a year from now, we
should have all this information

750
00:46:35,000 --> 00:46:41,000
to do such an analysis.
That said, we're of course,

751
00:46:41,000 --> 00:46:47,000
very impatient people, you
could just take the human,

752
00:46:47,000 --> 00:46:51,000
the mouse, the rat, and the dog.
And I said that's not enough if you

753
00:46:51,000 --> 00:46:55,000
wanted to analyze the whole genome,
but suppose you just wanted to

754
00:46:55,000 --> 00:46:59,000
analyze a portion of the genome,
maybe about a yeast-size piece of

755
00:46:59,000 --> 00:47:03,000
the genome, well let's see,
at 20,000 genes, I don't know,

756
00:47:03,000 --> 00:47:06,000
suppose I take, I don't know,
two kilo bases around each 20,

757
00:47:06,000 --> 00:47:10,000
00 genes, well that's you know,
40 mega bases of DNA, it's only a

758
00:47:10,000 --> 00:47:14,000
couple-fold more than yeast.
Maybe, if I just focus on a limited

759
00:47:14,000 --> 00:47:18,000
region around each promoter,
I could start reading out these

760
00:47:18,000 --> 00:47:22,000
regulatory signals,
with just four species.

761
00:47:22,000 --> 00:47:26,000
So in fact, the post-doctorate
fellow is, has been working on this

762
00:47:26,000 --> 00:47:30,000
problem over the summer, and
a little bit, too, through the

763
00:47:30,000 --> 00:47:34,000
spring and summer, together
with Manolis Kellis,

764
00:47:34,000 --> 00:47:38,000
who's now in the computer science
department. And I think we have a

765
00:47:38,000 --> 00:47:42,000
preliminary list for the human
genome that's fallen out over the

766
00:47:42,000 --> 00:47:46,000
course of the past couple of months,
and we're in the process, right now,

767
00:47:46,000 --> 00:47:50,000
of finishing up a paper that we're
hoping to get submitted by Friday,

768
00:47:50,000 --> 00:47:54,000
with a preliminary list of
regulatory signals in the human

769
00:47:54,000 --> 00:47:58,000
genome, read out from evolution
of human, mouse, rat, and dog.

770
00:47:58,000 --> 00:48:01,000
It won't be everything, we
don't have full power to pick up

771
00:48:01,000 --> 00:48:04,000
all possible signals, but
we're picking up a lot of the

772
00:48:04,000 --> 00:48:08,000
signals, we're picking up a
very large fraction of previously

773
00:48:08,000 --> 00:48:11,000
discovered signals, and
lots more new signals,

774
00:48:11,000 --> 00:48:14,000
as well, are falling out
of that analysis. So anyway,

775
00:48:14,000 --> 00:48:18,000
I can assure you that that's
not in the textbooks because,

776
00:48:18,000 --> 00:48:21,000
actually, it hasn't been submitted
yet. This other stuff I've

777
00:48:21,000 --> 00:48:25,000
described about the yeast analysis,
this, you do want to look it up,

778
00:48:25,000 --> 00:48:28,000
there's a paper in nature
about a year and change ago,

779
00:48:28,000 --> 00:48:32,000
Kellis et. al. describes this
yeast work. This is what's going on.

780
00:48:32,000 --> 00:48:36,000
This is what's fun about teaching
at MIT, as I can tell you this stuff,

781
00:48:36,000 --> 00:48:41,000
and you guys have a sense for the
convergence that's going on in our

782
00:48:41,000 --> 00:48:45,000
field. Much of what I've
tried to make the biology,

783
00:48:45,000 --> 00:48:50,000
you know, in making the biology
clear, I've talked about how the

784
00:48:50,000 --> 00:48:54,000
different directions,
genetics, biochemistry,

785
00:48:54,000 --> 00:48:59,000
have converged together. What
we're really seeing now is

786
00:48:59,000 --> 00:49:03,000
information sciences converging with
that as well, and I've got to say,

787
00:49:03,000 --> 00:49:08,000
it's a tremendous amount of
fun. See you on Monday, good

788
00:49:08,000 --> 00:49:13,000
luck on
the quiz.