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Professor Jacks is out of town so I
am going to tell you about

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Recombinant DNA 3,
then he's going to come back and

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tell you about Cell Biology,
and then you will have finished the

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foundations part of the course.
And we'll move onto things that

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build on the foundation,
the Formation Module and the part of

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the Systems Module,
which I'll be teaching you for the

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next few weeks,
but today is Recombinant DNA 3.

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And, as you've been hearing for the
last couple of lectures,

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this is one of the How-To Modules
that we've put in the course.

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How to make use of the information
that you have been learning in

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Molecular Biology and in
Biochemistry and in Genetics to use

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these disciplines or these pieces of
information to do something useful.

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And recombinant DNA is really an
extraordinary set of technologies

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that just keeps getting more and
more extraordinary.

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And the way one can manipulate
biological systems now is really

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very exciting.
And it continues to be exciting.

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When I was a beginning graduate
student we were able to clone the

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first pieces of DNA.
And now we can really do a lot more

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than just clone DNA.
So I want to tell you about some of

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the things that are really essential
to understand about this technology,

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and then take you through some of
the forefronts of where recombinant

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DNA technology is now.
We're going to cover three things

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in this lecture.

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DNA sequencing,
using genetic polymorphisms for

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various genotyping analyses,
and then I'm going to try to touch

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on, and we'll have to see how we do
here, making animals that are

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so-called transgenic.
So transgenic technology.

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And I'm going to use PowerPoint
pretty much for most of the lecture,

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so you have most of the relevant
stuff in front of you.

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I'm going to frame this in terms of
a human disease, familial

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hypercholesterolemia.
So you may remember way back when in

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biochemistry we talked about
cholesterol.  Anyone remember what

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class of macromolecules cholesterol
belongs to?  Lipids.

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Thank you.  Lipids.  OK.
I'm not even going to give a frog

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for that.  And we have this sense of
cholesterol being a really bad kind

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of molecule but,
in fact, cholesterol is an essential

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lipid.  It's extremely important.
Without cholesterol you'd die and

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you need it for many things.
Not only for building membranes in

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your cells but also,
if you think way back,

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you may remember me telling you that
cholesterol was part of or had a

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chemical structure that was very
similar to the steroid hormone

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family.  And steroid hormones,
and we'll discuss this more in the

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future, are very important molecules
that tell one part of the body what

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to do, that regulate what different
parts of the body are doing.

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So cholesterol is part of this
whole signaling system.

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And really it's not actually
understood all of what cholesterol

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does, but it's very important.
However, too much of it is not good.

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And it's probably not good because,
but it's not actually clear.  I'll

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tell you what happens if you have
too much cholesterol,

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but actually why it happens is not
that clear.  So let me talk about

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this slide up here,
and then we'll talk about what too

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much cholesterol does for you.
So familial hypercholesterolemia is

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an inherited disease,
and it's caused by mutations in a

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gene called the LDL receptor,
that encodes for something called

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the LDL receptor.
Now, LDL stands for low density

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lipoprotein.  And you had this in a
previous lecture because I'd been

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mentioned these to you.
Low density lipoproteins.

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And these bind to various lipids,
including cholesterol, and are taken

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up into the cell.
And some of them are OK,

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you probably need some LDLs,
but too much LDL is bad.  And if you

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have too much LDL receptor,
the thing that actually binds to the

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LDLs, you get too much LDL taken up
into the cell.

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So this LDL receptor,
you'll talk more about this in cell

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biology, this LDL receptor,
and you've already had some of this,

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the LDL receptor is a protein that
binds to these LDLs,

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takes them into the cell,
and then your cell gets full of LDLs.

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OK?  And as a consequence of this,
your cholesterol levels go way up.

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Now, you can be heterozygote or
homozygote for familiar

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hypercholesterolemia,
for the LDL receptor gene.

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OK?  For the familiar
hypercholesterolemia gene.

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Try to say that one quickly.
All right.

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So if you're heterozygote,
you have an increased risk of heart

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disease.  In particular for this
thing called atherosclerosis I'll

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talk more about in a moment.
If you are homozygote, so you have

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two copies of a mutated LDL receptor
gene, you get severe heart symptoms

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and you die early.  OK?
What is atherosclerosis?

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Atherosclerosis is a disease that
occurs because you get these

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buildups of stuff in the blood
vessels.  And the stuff is fat and

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it's proteins,
and it basically makes a big lump

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that eventually occludes or blocks
the blood vessel.

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And so atherosclerosis is bad
because impedes blood flow.

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And if you impede blood flow,
eventually your heart will seize up

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and you will have a heart attack,
and that can have, obviously, very

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severe consequences.
So atherosclerosis occurs because

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you have high levels of LDL.
And it's really, the actual

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etiology of atherosclerosis
is not really clear.

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Part it may be that there's just too
much fat around and that starts

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actually getting deposited out of
solution, but it's much more

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complicated than that.
And there seems to be a very

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complicated chain of events by which
you get these atherosclerosis

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plaques sitting on the lining of
blood vessels and impeding blood

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flow.  OK.  So there is a lot of
interest medically in

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atherosclerosis,
particularly in countries such as

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ours where food is plentiful and
people tend to have too much.

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And obesity is a problem anyway
because that is part of the set of

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risk factors for atherosclerosis.
So here are the risk factors.  High

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levels of LDL,
high blood pressure,

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diabetes, cigarette smoke and so on.
And familial hypercholesterolemia

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is contributory to high levels of
LDL and atherosclerosis.  OK.

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So one of the things I want to do is
to keep thinking about this disorder

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and walk you through how you figure
out who's got  FH.

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OK.  What you can do is to get
blood cells from people at-risk,

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and you can actually examine the LDL
receptor gene in the blood cells of

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people who are at-risk for familial
hypercholesterolemia.

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And what I tell you about is how you
can actually sequence the gene,

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the FH gene, see if you can find the
mutation and see whether or not you

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can then identify people who are
at-risk for the disorder.

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So the first thing I want to tell
you about today is DNA sequencing.

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DNA sequencing.  What is DNA
sequencing?  Does someone care to

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give me a definition or think about
what I might mean by

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DNA sequencing?
In particular,

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what part of the DNA are we
sequencing?  Thank you,

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Jamie.  You want to say it louder?
The bases.  Yes.  So in DNA

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sequencing, and maybe I even wrote
this, what is this,

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what you want to do is to determine
the base sequence of the DNA.

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OK?  You want to determine the
sequence of AGCT along

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a DNA fragment.
This technique is powerful beyond

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almost anything else.
It's an extraordinary technique.

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The ability to sequence DNA is
extraordinary.

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And it's extraordinary because you
can get out of it information that

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is absolutely essential for
understanding life.

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What you can get from DNA
sequencing is an understanding of

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the coding capacity of a gene.
So, just like you did in your exam,

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we gave you a string of DNA and you
conceptually translated

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it into the protein.
Well, you can do that in real life

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by looking through the genome,
the human genome and finding

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stretches of DNA and conceptually
turning them into RNA and into

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protein and saying,
OK, is this is a gene?

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Does it code for something?
And what does it code for?  So you

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can figure out the coding capacity
of a gene.  Part of that is actually

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identifying is a gene a gene?
So we've sequenced the entire human

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genome.  And I've told you
previously that only about 5% of the

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genome is actually genes and the
rest is other stuff.

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So one of the things you want to do
with DNA sequencing is to identify

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genes.  And that's actually very
difficult to do it turns out.

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But that's one of the things you
can do with DNA sequencing.

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I'll talk more about identifying
genes that are associated with

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disease, that are causative
of disease.

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And particularly alleles that are
associated with disease such as in

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the case of familial
hypercholesterolemia.

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One can figure out evolutionary
relationships between organisms.

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So you've probably heard for years
about how similar we are to

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chimpanzees or how similar we are to
dogs or to dolphins or whatever.

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But, actually, we didn't really
know.  Now we can sequence a human

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genome, we can sequence a chimp
genome, a dog genome,

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a dolphin genome, and we can
actually look and see

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how similar we are.
And we can try to figure out,

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in evolutionary time, what's changed
between the dolphin and ourselves

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and what makes a dolphin a dolphin
and ourselves ourselves.

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It's a very tough question,
but DNA sequencing is essential for

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trying to answer that kind of
question.  And then one can ask

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about the genome is other ways.
Can one find the promoters of all

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the different genes?
Remember promoters that make genes

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be transcribed?
The centromeres,

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the middle of chromosomes.
Various other elements in the genome

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that are essential for its function.
So I'm going to spend quite some

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time talking about DNA sequencing
and tell you that DNA sequencing,

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most of the DNA sequencing we do
uses a trick.  And it's a terrific

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trick.  It really is.
So this DNA sequencing,

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I'll write it because I don't think
I have this on one of

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your PowerPoints.
The method of DNA sequencing I'm

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going to tell you about was devised
by a scientist called Fred Sanger.

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So I'll tell you about it.  It's
called dideoxy,

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it's also called chain termination,
and it's also called Sanger

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sequencing.
Professor Sanger is a British

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scientist who received two Nobel
Prizes.  The first was for figuring

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out how proteins,
how to sequence proteins,

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and the second was for figuring out
how to sequence DNA.

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When I was a student,
I heard Professor Sanger talk.

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And he gave a lecture which was
really memorable.

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It was packed, a packed auditorium.
And he spoke the entire time like

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this.  I don't think he looked up
once.  He gave the entire lecture

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like this, and he was
barely audible.

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But at the end of the lecture he got
a standing ovation from everybody

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because really what he's done,
figuring out how to sequence

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proteins and how to sequence DNA was
really an extraordinary

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accomplishment.
So that's the method I'll tell you

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about.  And it uses a cool trick.
So you know now that the sugar in

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DNA has a 3 prime hydroxyl group,
and that hydroxyl group is the group

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unto which the phosphate
gets added.

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Right?  And without that hydroxyl
group you could not add on the next

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nucleotide, right?
It's a question.  Think about it.

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OK?  I don't mean it to be
rhetorical.  I want you to really be

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thinking, OK, about this,
because otherwise you won't

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understand the method.
So here's the 3 prime hydroxyl on

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regular deoxyribose.  OK?
In the Sanger or dideoxy method one

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uses in the reaction mix,
and I'll go through this with you in

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a moment, a sugar or nucleotide
that's a dideoxy nucleotide.

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In other words, on both the 2 prime
and the 3 prime of the sugar,

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of the ribose there is no hydroxyl
group.  There are just

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those hydrogens.
Now, a dideoxy nucleotide such as

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this one can get incorporated into
DNA just fine because this phosphate,

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the triphosphate here can react with
a regular nucleotide that's got a 3

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prime hydroxyl.
However, once it's been

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incorporated you cannot elongate the
chain anymore because there is no

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reactive hydroxyl group.
OK.  So based on this principle let

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me explain.
I've got one of your handouts here.

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OK.  So here we go.  Revision, your
template, your primer,

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here's your template strand,
always goes 3 prime to 5 prime.

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Here's your 5 prime to 3 prime
primer.  If you add nucleotides,

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deoxynucleotide triphosphates and
DNA polymerase,

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you will polymerize the whole
fragment.

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If you add, however,
to the mix of dNTPs and DNA

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polymerase a low-level of dideoxy
nucleotide triphosphates,

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every time you add on a nucleotide
the polymerase can either use a

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regular nucleotide triphosphate,
in which case the chain can elongate

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subsequently, or it can use a
dideoxy nucleotide triphosphate.

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If it uses one of the dideoxy NTPs
the chain will terminate.

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It cannot be elongated any further.
So you get something like this.

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And the trick here is really this
low-level of ddNTPs.

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OK?  So if you have your template
and your primer and you do a

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reaction with your dNTPs at a
reasonable level and you spike the

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reaction with a low-level of dideoxy
NTPs, you get a whole bunch of

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different length chains
polymerized.

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Because there is some probability,
at every position, that you're

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either going to get a ddNTP
incorporated, in which case the

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chain terminates,
or you're going to get a regular

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nucleotide incorporated in which
case the chain can continue for a

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bit.  OK?  So that is paramount to
dideoxy sequencing.

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00:17:56,000 --> 00:18:01,000
So let's continue now by looking at
a specific polymer and following

221
00:18:01,000 --> 00:18:06,000
through exactly what happens.
So here I've given you a template

222
00:18:06,000 --> 00:18:10,000
and a primer.  And we're going to do
the same reaction that we just did

223
00:18:10,000 --> 00:18:15,000
conceptually.  We're going to do it
again conceptually except with

224
00:18:15,000 --> 00:18:19,000
letters.  We're going to mix
together.  And we're going to do,

225
00:18:19,000 --> 00:18:24,000
and I see a mistake up here already,
but that's OK.  You'll bear with me.

226
00:18:24,000 --> 00:18:29,000
What I've done here is to put in
some dideoxy ATP.

227
00:18:29,000 --> 00:18:33,000
And I meant to say here I've got
dATP at high levels.

228
00:18:33,000 --> 00:18:37,000
And I've got all the other
nucleotides here,

229
00:18:37,000 --> 00:18:41,000
too, at high levels.
OK?  That's my error and I will

230
00:18:41,000 --> 00:18:45,000
correct it.  You should correct it
now in your handout.

231
00:18:45,000 --> 00:18:49,000
So where it says dATP high,
that should actually say dNTPs high,

232
00:18:49,000 --> 00:18:53,000
not just dATP.  OK?  All right.  So
let's look and see what happens to

233
00:18:53,000 --> 00:18:57,000
this reaction.
And I've noted here that this

234
00:18:57,000 --> 00:19:02,000
dideoxy ATP can be radioactive
or florescent.

235
00:19:02,000 --> 00:19:06,000
Or actually it doesn't have to work
that way but let's just leave it

236
00:19:06,000 --> 00:19:10,000
that way for now.
OK.  That actually is not

237
00:19:10,000 --> 00:19:15,000
necessarily true.
So let's just focus on the ddATP

238
00:19:15,000 --> 00:19:19,000
plus the high dNTPs,
and let's see what happens.

239
00:19:19,000 --> 00:19:24,000
OK.  So one thing that can happen
is that, here's your primer in red

240
00:19:24,000 --> 00:19:28,000
and here's the polymerized DNA in
blue, you get a bit

241
00:19:28,000 --> 00:19:33,000
of DNA polymerase.
Now here's an A.

242
00:19:33,000 --> 00:19:38,000
See?  It goes GAGTAA.
And I've given you a reaction where

243
00:19:38,000 --> 00:19:42,000
the first two As use regular dATP.
And so the chain will continue

244
00:19:42,000 --> 00:19:47,000
after that.  All right?
So here we go, GAGTA.  And then the

245
00:19:47,000 --> 00:19:52,000
next A that's put in is a dideoxy A.
And that's the end of that

246
00:19:52,000 --> 00:19:57,000
polymerization reaction,
and the fragments you're going to

247
00:19:57,000 --> 00:20:02,000
get out of it is this little red and
blue composite there.

248
00:20:02,000 --> 00:20:06,000
You can do the same thing where you
say actually in some molecules you

249
00:20:06,000 --> 00:20:10,000
get polymerization past the second A,
and you keep going until you get to

250
00:20:10,000 --> 00:20:15,000
the next A.  And at that point,
by chance, you get a dideoxy ATP

251
00:20:15,000 --> 00:20:19,000
added to some molecules.
That is the end of polymerization

252
00:20:19,000 --> 00:20:24,000
for those molecules.
The chain terminates.

253
00:20:24,000 --> 00:20:28,000
For some molecules, however,
you'll put in a regular dATP and the

254
00:20:28,000 --> 00:20:33,000
chain will continue.
But it will terminate,

255
00:20:33,000 --> 00:20:38,000
excuse me, at the next A that's put
in because you put a dideoxy A in.

256
00:20:38,000 --> 00:20:43,000
So in different molecules you're
going to land up with a spectrum of

257
00:20:43,000 --> 00:20:47,000
elongated products of different
length.  All right?

258
00:20:47,000 --> 00:20:52,000
And what's crucial here is that the
length of the molecules that chain

259
00:20:52,000 --> 00:20:57,000
terminate, because they incorporated
dideoxy nucleotide,

260
00:20:57,000 --> 00:21:02,000
correspond to the position of that
particular nucleotide

261
00:21:02,000 --> 00:21:07,000
along the chain.
So you're only going to get a

262
00:21:07,000 --> 00:21:13,000
molecule chain terminating with A
when there was a T on the template

263
00:21:13,000 --> 00:21:18,000
strand.  OK?  And so you can map the
positions of the T on the template

264
00:21:18,000 --> 00:21:23,000
or the A on the elongated strand by
the length of the elongated products

265
00:21:23,000 --> 00:21:29,000
that come out of this reaction.
I'm going to assume you're with me

266
00:21:29,000 --> 00:21:33,000
here.
OK.  So the point is the polymerized

267
00:21:33,000 --> 00:21:37,000
fragments terminate where dideoxy A
incorporates.  Now,

268
00:21:37,000 --> 00:21:40,000
you've got to do four reactions to
determine the sequence of something.

269
00:21:40,000 --> 00:21:44,000
OK.  And I've noted here.  And the
length of the terminated fragment

270
00:21:44,000 --> 00:21:48,000
indicates the position of A.
You may need to go and work with

271
00:21:48,000 --> 00:21:51,000
this a bit.  OK?
It's a very clever method but it

272
00:21:51,000 --> 00:21:55,000
may not be something that's
immediately apparent,

273
00:21:55,000 --> 00:21:59,000
so go and work with it if you need
to.

274
00:21:59,000 --> 00:22:03,000
So the length of the terminated
fragments indicates the positions of

275
00:22:03,000 --> 00:22:07,000
A in the elongated strand,
or if you want in T of the template

276
00:22:07,000 --> 00:22:11,000
strand.  In order to get the
positions of all the different

277
00:22:11,000 --> 00:22:16,000
nucleotides along that DNA fragment
you have to do four separate

278
00:22:16,000 --> 00:22:20,000
reactions. One that includes dideoxy
ATP, one that includes dideoxy CTP,

279
00:22:20,000 --> 00:22:25,000
one dideoxy GTP and one dideoxy TTP.

280
00:22:25,000 --> 00:22:29,000
And you do those separately so that
you can monitor the positions of

281
00:22:29,000 --> 00:22:33,000
each of those four nucleotides by
the position of chain terminating as

282
00:22:33,000 --> 00:22:38,000
you're going along.
OK.  So assuming that you guys are

283
00:22:38,000 --> 00:22:42,000
with me here at this point,
are you?  No.  That's an honest

284
00:22:42,000 --> 00:22:46,000
answer.  Raise your hands if you're
with me.  OK.  If you're not with me,

285
00:22:46,000 --> 00:22:51,000
don't worry about.
You have to go work with it.

286
00:22:51,000 --> 00:22:55,000
It's not intuitive.  It's very
clever.  I mean there's a reason

287
00:22:55,000 --> 00:23:00,000
this guy got the Nobel
Prize for this.  OK?

288
00:23:00,000 --> 00:23:03,000
It's a really clever method.
OK.  So the deal is this.  So now

289
00:23:03,000 --> 00:23:07,000
what you get out of this is a whole
mix of fragments of different

290
00:23:07,000 --> 00:23:11,000
lengths that have terminated at
positions of particular nucleotides,

291
00:23:11,000 --> 00:23:14,000
depending on how you've spiked the
reaction.  And you've got to

292
00:23:14,000 --> 00:23:18,000
separate them from one another
somehow to figure out what those

293
00:23:18,000 --> 00:23:22,000
positions are.
And you can do this in a couple of

294
00:23:22,000 --> 00:23:26,000
ways.
You can use gel electrophoresis,

295
00:23:26,000 --> 00:23:31,000
which was discussed with you
previously, where you separate the

296
00:23:31,000 --> 00:23:36,000
DNA on the basis of size where the
DNA migrates in a gel in an electric

297
00:23:36,000 --> 00:23:40,000
field and long fragments stay near
the top of the gel and short

298
00:23:40,000 --> 00:23:45,000
fragments go to the bottom of the
gel because they migrate quickly.

299
00:23:45,000 --> 00:23:50,000
And what you can do on a gel, and
you've somehow labeled,

300
00:23:50,000 --> 00:23:55,000
don't worry about this right now,
but somehow you're able to detect

301
00:23:55,000 --> 00:24:00,000
each of the fragments that has come
out of your mix.

302
00:24:00,000 --> 00:24:04,000
OK?  So remember you're doing the
sequencing reaction on millions and

303
00:24:04,000 --> 00:24:08,000
millions or billions of molecules.
And so you've got this kind of

304
00:24:08,000 --> 00:24:12,000
stochastic mix of molecules of
different lengths.

305
00:24:12,000 --> 00:24:16,000
And you want to separate this mix
of molecules of different lengths.

306
00:24:16,000 --> 00:24:20,000
OK.  So what you can end up with,
once you've separated all these

307
00:24:20,000 --> 00:24:24,000
different molecules,
is in your dideoxy A reaction mix a

308
00:24:24,000 --> 00:24:28,000
series of one,
two, three, four,

309
00:24:28,000 --> 00:24:33,000
five different sized fragments.
In your ddG mix,

310
00:24:33,000 --> 00:24:37,000
you got out of that also a series of
five different sized fragments.

311
00:24:37,000 --> 00:24:42,000
And notice that they're different
in size from the ones in the ddA

312
00:24:42,000 --> 00:24:47,000
lane, the ones in the ddC lane and
the ones in the ddT lane.

313
00:24:47,000 --> 00:24:51,000
And the reason they're different in
size is because their size indicates

314
00:24:51,000 --> 00:24:56,000
the position of where a particular
nucleotide is in the DNA fragment or

315
00:24:56,000 --> 00:25:01,000
particular bases in
the DNA fragment.

316
00:25:01,000 --> 00:25:06,000
And then the trick is you could look
at this gel and you could read off

317
00:25:06,000 --> 00:25:11,000
the sequence.  So the shortest
fragments that you're going to get

318
00:25:11,000 --> 00:25:16,000
are the ones that are nearest the
beginning of that molecule you made,

319
00:25:16,000 --> 00:25:21,000
nearest the 5 prime end.  So the
bottom one is G,

320
00:25:21,000 --> 00:25:26,000
here's the band in the ddG lane.
Then up above it there is this band

321
00:25:26,000 --> 00:25:32,000
indicating a fragment
in the ddA lane.

322
00:25:32,000 --> 00:25:38,000
Above it there's one in the G lane
again.  Above it there's one in the

323
00:25:38,000 --> 00:25:44,000
T lane.  So the sequence goes
G-A-G-T, and then you can keep

324
00:25:44,000 --> 00:25:50,000
reading A-A-C-G-G-T-A-T-G-C-A.
OK?  Literally like that on a gel.

325
00:25:50,000 --> 00:25:56,000
OK?  So you can do that on a gel.
It's really fantastic.

326
00:25:56,000 --> 00:26:02,000
And this is what old sequencing
gels look like.

327
00:26:02,000 --> 00:26:05,000
And, actually,
I used to run them.

328
00:26:05,000 --> 00:26:09,000
I used to spend hours and hours
running these gels.

329
00:26:09,000 --> 00:26:13,000
They're very, very thin.
They're about a millimeter thick

330
00:26:13,000 --> 00:26:16,000
acrylamide so that you can resolve
the fragments that are one

331
00:26:16,000 --> 00:26:20,000
nucleotide different in size.
Think about that.  OK?  Each of

332
00:26:20,000 --> 00:26:24,000
these fragments,
indicated by a band,

333
00:26:24,000 --> 00:26:28,000
is one nucleotide different in size.
Otherwise, you couldn't get the one

334
00:26:28,000 --> 00:26:32,000
nucleotide resolution.
So you do that by running very,

335
00:26:32,000 --> 00:26:37,000
very thin gels so that you can
resolve the fragments well,

336
00:26:37,000 --> 00:26:42,000
and then you read off the bottom.
OK?  I've thrown out all my old

337
00:26:42,000 --> 00:26:46,000
sequencing gels.
And the reason that I have is that

338
00:26:46,000 --> 00:26:51,000
there is new technology where you
don't use this kind of display

339
00:26:51,000 --> 00:26:56,000
anymore.  This is a display where
your fragments were labeled with

340
00:26:56,000 --> 00:27:01,000
radioactivity and you exposed them
to x-ray film and you read the

341
00:27:01,000 --> 00:27:06,000
sequence after exposure.
Nowadays this is done by machine.

342
00:27:06,000 --> 00:27:11,000
And the dideoxy nucleotides are
labeled fluorescently.

343
00:27:11,000 --> 00:27:15,000
OK?  So they're not labeled with
radioactivity.

344
00:27:15,000 --> 00:27:20,000
They're literally labeled with
labels that fluoresce with different

345
00:27:20,000 --> 00:27:25,000
colors when you put UV light on them.
And you do your dideoxy reaction

346
00:27:25,000 --> 00:27:30,000
and you run a gel.  Again,
it's a gel.

347
00:27:30,000 --> 00:27:35,000
It's actually a very thin tube of a
gel mostly, but your run your gel.

348
00:27:35,000 --> 00:27:41,000
And, again, it's the same idea.
You resolve fragments at single base

349
00:27:41,000 --> 00:27:46,000
resolution, single nucleotide
resolution, and they keep,

350
00:27:46,000 --> 00:27:52,000
the gel keeps running and running.
And single fragments actually run

351
00:27:52,000 --> 00:27:57,000
off the bottom of the gel.
And as they're passing down the gel

352
00:27:57,000 --> 00:28:03,000
they are detected by a laser.
A laser excites the fluorochrome.

353
00:28:03,000 --> 00:28:06,000
And the detector,
there is a detector which will

354
00:28:06,000 --> 00:28:10,000
detect whether or not it's yellow,
orange, blue or green.  OK?  And

355
00:28:10,000 --> 00:28:14,000
that will tell you which base is
being, has been incorporated at that

356
00:28:14,000 --> 00:28:18,000
position.  So you get things that
come out.  It's kind of small but

357
00:28:18,000 --> 00:28:22,000
you can go back and look,
where instead of getting a gel with

358
00:28:22,000 --> 00:28:26,000
those bands that I showed you,
you get these peaks and valleys that

359
00:28:26,000 --> 00:28:30,000
are different colors.
And that's what current DNA

360
00:28:30,000 --> 00:28:35,000
sequencing readout looks like.
And, in fact, there are machines.

361
00:28:35,000 --> 00:28:40,000
What did I do?  Lots of primers.
Well, it depends.

362
00:28:40,000 --> 00:28:45,000
Many copies of the same primer,
right.  Yes.  Dr. Gardel is pointing

363
00:28:45,000 --> 00:28:50,000
out that there are many copies of
the same primer in a reaction mix.

364
00:28:50,000 --> 00:28:55,000
Certainly there are.  There are
billions of molecules in the

365
00:28:55,000 --> 00:29:00,000
reaction mix, and so there are
billions of primers.

366
00:29:00,000 --> 00:29:03,000
OK, so you have to have a primer for
each molecule.

367
00:29:03,000 --> 00:29:06,000
OK.  And each band,
you should realize, is not a single

368
00:29:06,000 --> 00:29:09,000
molecule.  It's a composite of many,
many molecules, many thousands of

369
00:29:09,000 --> 00:29:12,000
molecules that have all chain
terminated at the same position.

370
00:29:12,000 --> 00:29:15,000
So what I want to point out here is
that this is what today's readout

371
00:29:15,000 --> 00:29:19,000
looks like.  And,
in fact, nowadays you just get a

372
00:29:19,000 --> 00:29:22,000
printout from the company or from
the machine that tells

373
00:29:22,000 --> 00:29:26,000
you a DNA sequence.
And it's this improvement in

374
00:29:26,000 --> 00:29:31,000
technology, but that basically uses
this chain termination method,

375
00:29:31,000 --> 00:29:36,000
that has allowed one to sequence,
rapidly enough to sequence the human

376
00:29:36,000 --> 00:29:41,000
genome and to sequence multiple
human genomes in multiple animals.

377
00:29:41,000 --> 00:29:46,000
OK.  So let's see.  Actually, I
have a movie.  I guess we can take

378
00:29:46,000 --> 00:29:51,000
the time to watch this movie.
Let's see if it will work.  All

379
00:29:51,000 --> 00:29:56,000
right.  So primer template.
Four reactions, each with lots of

380
00:29:56,000 --> 00:30:01,000
molecules, each with their primer.
DNA polymerase,

381
00:30:01,000 --> 00:30:05,000
dNTPs, dATP, dGTP,
dCTP, dTTP, dCTP, excuse me.

382
00:30:05,000 --> 00:30:09,000
OK.  They're your four reactions.
OK.  I think is a less dorky movie

383
00:30:09,000 --> 00:30:13,000
than some.  OK.
So here we go.  Here's your primer

384
00:30:13,000 --> 00:30:17,000
and your template,
and here's polymerization.

385
00:30:17,000 --> 00:30:21,000
And, ah, there we go, chain
termination, dideoxy nucleotide

386
00:30:21,000 --> 00:30:25,000
incorporation,
and you cannot get elongation.

387
00:30:25,000 --> 00:30:30,000
The poor G is thwarted in its
desire to elongate.  OK?

388
00:30:30,000 --> 00:30:34,000
So you land up with this mix,
just like I showed you, and you land

389
00:30:34,000 --> 00:30:38,000
up with a set of four reactions,
each with molecules of different

390
00:30:38,000 --> 00:30:42,000
lengths in them.
And here's your gel,

391
00:30:42,000 --> 00:30:47,000
and you load them on your gel,
and they migrate through your

392
00:30:47,000 --> 00:30:51,000
electric field.
And there you have your things,

393
00:30:51,000 --> 00:30:55,000
you have your fragments.  This is a
piece of x-ray film you put on top.

394
00:30:55,000 --> 00:31:00,000
There are your little bands, your
radioactive bands, and here we go.

395
00:31:00,000 --> 00:31:04,000
GT, you can read it.
OK.  Enough.  Enough.

396
00:31:04,000 --> 00:31:09,000
OK.  You can go and look at this
yourself.  This is an old gel

397
00:31:09,000 --> 00:31:13,000
apparatus that one used to do DNA
sequencing on.

398
00:31:13,000 --> 00:31:18,000
This was the first generation of
machine that you could do the

399
00:31:18,000 --> 00:31:22,000
fluorescent sequencing on.
This is a room full of sequencing

400
00:31:22,000 --> 00:31:27,000
machines of the kind that was used
to sequence the human genome.

401
00:31:27,000 --> 00:31:30,000
In fact, many rooms of machines
going all day and all night

402
00:31:30,000 --> 00:31:34,000
sequencing and sequencing and
sequencing.  We have a lot of

403
00:31:34,000 --> 00:31:37,000
nucleotides.  And it takes a long
time to sequence.

404
00:31:37,000 --> 00:31:41,000
Although, in retrospect it's not
such a long time.

405
00:31:41,000 --> 00:31:45,000
And now all the sequencing machines
that sequence the human genome are

406
00:31:45,000 --> 00:31:48,000
sitting around looking for other
work because they all exist.

407
00:31:48,000 --> 00:31:52,000
And so that is why we are
sequencing things like dolphins and

408
00:31:52,000 --> 00:31:56,000
dogs and multiple strains of dogs,
multiple breeds, excuse me, of dogs

409
00:31:56,000 --> 00:32:00,000
because we have all these sequencing
machines sitting around.

410
00:32:00,000 --> 00:32:04,000
OK.  Honestly,
I think that's true,

411
00:32:04,000 --> 00:32:08,000
not that it's not useful.
All right.  So I'm going to move on

412
00:32:08,000 --> 00:32:13,000
here.  This is Professor Jack's joke
that I decided to use also.

413
00:32:13,000 --> 00:32:17,000
OK.  This is something about DNA
sequencing and the implications of

414
00:32:17,000 --> 00:32:21,000
being able to use DNA sequencing for
genotyping.  So I'm going to use

415
00:32:21,000 --> 00:32:26,000
that.  You can go and read that on
your thing.  I'm going to move on

416
00:32:26,000 --> 00:32:30,000
right to talking about familial
hypercholesterolemia and the notion

417
00:32:30,000 --> 00:32:35,000
of a disease allele.
So here's part of the normal FH gene,

418
00:32:35,000 --> 00:32:40,000
the LDL receptor gene,
and here it is.  And there is a T

419
00:32:40,000 --> 00:32:45,000
here in red.  And here is the mutant
gene sequence and there is an A.

420
00:32:45,000 --> 00:32:50,000
So if you're wild type you have a T
at this position that's arrowed and

421
00:32:50,000 --> 00:32:55,000
if you're a mutant you have an A.
And if you do your conceptual

422
00:32:55,000 --> 00:33:00,000
protein translation here you get
your amino acid, part of

423
00:33:00,000 --> 00:33:05,000
the amino acid chain.
Obviously it's not at the beginning.

424
00:33:05,000 --> 00:33:09,000
And obviously this is DNA and this
is protein, so we've removed the RNA

425
00:33:09,000 --> 00:33:14,000
here, the RNA step.
And you can see here is the amino

426
00:33:14,000 --> 00:33:19,000
acid of your wild type,
the sequence of your wild type gene.

427
00:33:19,000 --> 00:33:23,000
And in your LDL receptor mutant
there is a stop codon at this

428
00:33:23,000 --> 00:33:28,000
position that terminates the LDL
receptor.  And so the receptor gene

429
00:33:28,000 --> 00:33:33,000
is mutant and does not function
as it should.

430
00:33:33,000 --> 00:33:38,000
OK.  All right.
So let me move onto the next thing

431
00:33:38,000 --> 00:33:43,000
I want to talk about,
which is this question of

432
00:33:43,000 --> 00:33:48,000
polymorphisms.  What
is a polymorphism?

433
00:33:48,000 --> 00:34:03,000
Anyone.  All right.

434
00:34:03,000 --> 00:34:07,000
I'll tell you what a polymorphism
is.  A polymorphism is defined as

435
00:34:07,000 --> 00:34:12,000
some kind of variation
in DNA sequence.

436
00:34:12,000 --> 00:34:23,000
And it's defined as a variation in

437
00:34:23,000 --> 00:34:27,000
DNA sequence at a particular
position.

438
00:34:27,000 --> 00:34:40,000
So our DNA, all of us have very

439
00:34:40,000 --> 00:34:45,000
similar DNA.  If we were to sequence
me and we were to sequence you and

440
00:34:45,000 --> 00:34:49,000
we were to sequence you,
we would find that our DNA was

441
00:34:49,000 --> 00:34:54,000
greater than 99% identical.
If we lined up our three times ten

442
00:34:54,000 --> 00:34:59,000
to the ninth base pairs in a very
long line, we would find

443
00:34:59,000 --> 00:35:04,000
it was very similar.
There was about 1% difference in

444
00:35:04,000 --> 00:35:10,000
sequence between each of us.
And most of that, some of that

445
00:35:10,000 --> 00:35:15,000
corresponds to disease gene alleles.
We all are supposed to carry about

446
00:35:15,000 --> 00:35:20,000
a thousand bad genes,
or a thousand genes that if

447
00:35:20,000 --> 00:35:26,000
homozygous would give us something
bad, and sometimes do.

448
00:35:26,000 --> 00:35:31,000
And some of those correspond to
changes in differences in DNA

449
00:35:31,000 --> 00:35:37,000
sequence that are not
directly in genes.

450
00:35:37,000 --> 00:35:41,000
All of these differences between
different individuals are called

451
00:35:41,000 --> 00:35:46,000
polymorphisms,
DNA sequence variation.

452
00:35:46,000 --> 00:35:50,000
And you can use these to help
figure out whether or not someone

453
00:35:50,000 --> 00:35:55,000
has a particular disease allele,
and also you can use it to figure

454
00:35:55,000 --> 00:35:59,000
out where the DNA from a sample
comes from me or from you

455
00:35:59,000 --> 00:36:04,000
or from Dr. Gardel.
OK?  And I'll talk about this,

456
00:36:04,000 --> 00:36:08,000
using polymorphisms to map genotype.
I'm going to talk about a

457
00:36:08,000 --> 00:36:12,000
particular kind of polymorphism,
and these are called SNPs which is

458
00:36:12,000 --> 00:36:17,000
pronounced ìsnipî.
This stands for single nucleotide

459
00:36:17,000 --> 00:36:21,000
polymorphisms.
So I've said again that human

460
00:36:21,000 --> 00:36:25,000
genomes are 99% identical,
but there are throughout the genome

461
00:36:25,000 --> 00:36:30,000
changes, differences
between regions.

462
00:36:30,000 --> 00:36:34,000
Single nucleotide polymorphisms are
variations in one region.

463
00:36:34,000 --> 00:36:38,000
Here's a sample sequence I made up.
Here's a G in one individual and an

464
00:36:38,000 --> 00:36:42,000
A in another individual.
And if you take the population,

465
00:36:42,000 --> 00:36:47,000
you find very often that there just
is a choice of two,

466
00:36:47,000 --> 00:36:51,000
sometimes more, but often just a
choice of two nucleotides in one

467
00:36:51,000 --> 00:36:55,000
position.  Most of the genomes are
identical, but you find these little

468
00:36:55,000 --> 00:36:59,000
regions where in many individuals of
a population there are

469
00:36:59,000 --> 00:37:04,000
these variations.
In fact, these variations have to be

470
00:37:04,000 --> 00:37:08,000
present in more than 1% of the
population for this thing to be

471
00:37:08,000 --> 00:37:12,000
called a SNP.  This is a definition
that humans have given but it's a

472
00:37:12,000 --> 00:37:16,000
useful definition as a genetic tool.
So if there is a polymorphism

473
00:37:16,000 --> 00:37:20,000
present in about 1% of the
population, whereby I might have an

474
00:37:20,000 --> 00:37:24,000
A here, excuse me,
and Dr. Gardel has a G at that

475
00:37:24,000 --> 00:37:28,000
position, that would be a SNP,
and we would be polymorphic for that

476
00:37:28,000 --> 00:37:32,000
SNP.
In fact, my two chromosomes,

477
00:37:32,000 --> 00:37:38,000
OK, that are homologous chromosomes
might on one copy carry an A and on

478
00:37:38,000 --> 00:37:43,000
the other copy carry a G.
Now, these different bases are

479
00:37:43,000 --> 00:37:49,000
present at different frequencies.
So, for example, it might be very

480
00:37:49,000 --> 00:37:54,000
common to have a G at this position
in the sequence and it might be very

481
00:37:54,000 --> 00:38:00,000
rare to have an A at that
position.  All right?

482
00:38:00,000 --> 00:38:04,000
And that's useful because you can
use the frequency of these different

483
00:38:04,000 --> 00:38:09,000
nucleotides, these different bases
to help you use the SNP to genotype.

484
00:38:09,000 --> 00:38:13,000
And I want to point out that
usually SNPs occur outside coding

485
00:38:13,000 --> 00:38:18,000
regions because 95%,
actually more than that,

486
00:38:18,000 --> 00:38:22,000
99% of the genome is not coding per
se.  95% is not genes,

487
00:38:22,000 --> 00:38:27,000
but then if you remove all the
introns and promoters and so on,

488
00:38:27,000 --> 00:38:32,000
99% does not code for any protein.

489
00:38:32,000 --> 00:38:36,000
OK.  So usually these SNPs are
present outside coding regions.

490
00:38:36,000 --> 00:38:40,000
So here's to explore this a bit
more.  You can find lots of these

491
00:38:40,000 --> 00:38:44,000
SNPs.  There are about three million
SNPs in the human genome,

492
00:38:44,000 --> 00:38:49,000
and a very large percentage of those
SNPs has been identified by DNA

493
00:38:49,000 --> 00:38:53,000
sequencing.  So you can get the idea.
You have to sequence DNA from lots

494
00:38:53,000 --> 00:38:57,000
and lots of individuals to identify
these SNPs, but people

495
00:38:57,000 --> 00:39:02,000
have done it.
And we know now more than a million

496
00:39:02,000 --> 00:39:06,000
SNPs in the human genome that are
located all over different

497
00:39:06,000 --> 00:39:10,000
chromosomes, and we know where
they're located on different

498
00:39:10,000 --> 00:39:14,000
chromosomes.  And so you can use
these SNPs to make kind of a map,

499
00:39:14,000 --> 00:39:19,000
I'll tell you in a moment.  So here
are some possible genotypes.

500
00:39:19,000 --> 00:39:23,000
I've given you a choice of two for
each of these.

501
00:39:23,000 --> 00:39:27,000
OK?  So, for example,
for this red SNP here you can be AA,

502
00:39:27,000 --> 00:39:32,000
AC or CC on the two homologous
chromosomes.

503
00:39:32,000 --> 00:39:36,000
All right.  So let's keep going with
this thread.  So because you have

504
00:39:36,000 --> 00:39:41,000
these SNPs all over your genome and
you know where they are,

505
00:39:41,000 --> 00:39:46,000
you can use them to make a map of
your entire genome.

506
00:39:46,000 --> 00:39:51,000
That doesn't depend on the genes.
It just depends on the sequence.

507
00:39:51,000 --> 00:39:56,000
And knowing these SNPs is a lot
easier to work with than having to

508
00:39:56,000 --> 00:40:01,000
sequence the entire genome of
somebody every time you

509
00:40:01,000 --> 00:40:06,000
want some information.
So you can use these SNPs to

510
00:40:06,000 --> 00:40:11,000
identify each person.
So I have a SNP map of all these

511
00:40:11,000 --> 00:40:16,000
hundreds of thousands of SNPs,
or up to a million.  The usual maps

512
00:40:16,000 --> 00:40:20,000
presently used are about 300,
00 SNPs per genome.  I have a map of

513
00:40:20,000 --> 00:40:25,000
300,000 SNPs where there are
different, actually,

514
00:40:25,000 --> 00:40:30,000
I don't, but I could,
where there are different alleles at

515
00:40:30,000 --> 00:40:35,000
different frequencies,
different bases present at different

516
00:40:35,000 --> 00:40:40,000
frequencies at specific positions.
And we could pick any one of you and

517
00:40:40,000 --> 00:40:44,000
make a SNP map for you.
And it would look really different

518
00:40:44,000 --> 00:40:48,000
from mine, not because the SNPs
themselves are different,

519
00:40:48,000 --> 00:40:53,000
they'd be the same SNPs, but the
actual bases and the combination of

520
00:40:53,000 --> 00:40:57,000
bases between all these different
SNPs would be different between

521
00:40:57,000 --> 00:41:01,000
different individuals.
And this SNP-type map is the basis

522
00:41:01,000 --> 00:41:05,000
for DNA fingerprinting that is used
in forensics and to figure out

523
00:41:05,000 --> 00:41:09,000
disease alleles.
I'll talk more about this in a

524
00:41:09,000 --> 00:41:13,000
second.  I want to point out that
there are other kinds of

525
00:41:13,000 --> 00:41:16,000
polymorphisms that are used in
genotyping, restriction fragment

526
00:41:16,000 --> 00:41:20,000
length polymorphisms and things
called simple repeat polymorphisms.

527
00:41:20,000 --> 00:41:24,000
And you can look in your book for
these restriction fragment length

528
00:41:24,000 --> 00:41:28,000
polymorphisms, but let's
talk more about SNPs.

529
00:41:28,000 --> 00:41:32,000
So SNP genotyping,
here's a whole list,

530
00:41:32,000 --> 00:41:36,000
but the ones I'm going to focus on
are disease gene mapping and

531
00:41:36,000 --> 00:41:41,000
forensics.  Also,
you use SNP genotyping for paternity

532
00:41:41,000 --> 00:41:45,000
suits.  OK?  So if someone comes and,
you know, if someone says it's my

533
00:41:45,000 --> 00:41:50,000
kid and the other one says it's my
kid, you can figure out very easily

534
00:41:50,000 --> 00:41:54,000
whose it is by looking at these
various SNPs and figuring out what

535
00:41:54,000 --> 00:41:59,000
pattern of SNPs is present
in the offspring.  OK.

536
00:41:59,000 --> 00:42:02,000
So let me actually consider,
let me not deal with genotyping for

537
00:42:02,000 --> 00:42:06,000
disease alleles at this point.
Let me talk about forensics a bit

538
00:42:06,000 --> 00:42:09,000
because it's kind of interesting.
So how do you do this?  Let's look

539
00:42:09,000 --> 00:42:13,000
through this slide.
You have it as a handout.

540
00:42:13,000 --> 00:42:17,000
Here are SNPs.  And I've just given
you two chromosomes each with two

541
00:42:17,000 --> 00:42:20,000
SNPs.  OK?  And different people
will have different bases at these

542
00:42:20,000 --> 00:42:24,000
particular SNPs,
or they'll have different

543
00:42:24,000 --> 00:42:28,000
combinations of these bases.
So here's the spot of blood at the

544
00:42:28,000 --> 00:42:32,000
crime scene.
OK?  Our red blood cells do not have

545
00:42:32,000 --> 00:42:37,000
nuclei so you cannot get DNA from
those, but there are enough white

546
00:42:37,000 --> 00:42:42,000
blood cells that do have nuclei so
you can.  And,

547
00:42:42,000 --> 00:42:47,000
actually, you know from PCR now that
you need very little to amplify

548
00:42:47,000 --> 00:42:52,000
something up by PCR.
One cell is sufficient,

549
00:42:52,000 --> 00:42:58,000
right?  It's pushing the technology,
but you can really use one cell.

550
00:42:58,000 --> 00:43:03,000
So there are plenty of cells in a
spot of blood at a crime scene to

551
00:43:03,000 --> 00:43:08,000
isolate the DNA and to PCR amplify
the regions surrounding the SNP.

552
00:43:08,000 --> 00:43:13,000
So you're not just dealing with
these two nucleotides or the choice

553
00:43:13,000 --> 00:43:19,000
of these two nucleotides at the SNP.
You've got a little piece of DNA

554
00:43:19,000 --> 00:43:24,000
that's usually maybe 20 or so bases
that includes this choice of single

555
00:43:24,000 --> 00:43:29,000
nucleotide polymorphism.
So you amplify the SNP region,

556
00:43:29,000 --> 00:43:34,000
OK, a region that's constant, that
includes the nucleotide polymorphism,

557
00:43:34,000 --> 00:43:39,000
and you determine the sequence at
the different single nucleotide

558
00:43:39,000 --> 00:43:44,000
polymorphism regions.
So you might get someone who,

559
00:43:44,000 --> 00:43:49,000
at the red position you an be A or C,
at the green you can be G.

560
00:43:49,000 --> 00:43:54,000
OK, let's have an example here.
You can get genotypes where at red

561
00:43:54,000 --> 00:43:59,000
you're A or C,
green you're G or G,

562
00:43:59,000 --> 00:44:04,000
purple GT, and yellow you can be A
or C.

563
00:44:04,000 --> 00:44:07,000
And here the example is C and C.
So here are the four suspects,

564
00:44:07,000 --> 00:44:11,000
numbers one to four.
OK.  And here are their genotypes.

565
00:44:11,000 --> 00:44:15,000
OK.  And here is the spot of blood
at the crime scene that actually has

566
00:44:15,000 --> 00:44:18,000
this genotype.
OK.  So let me go back here.

567
00:44:18,000 --> 00:44:22,000
This is the genotype in the blood
at the crime scene.

568
00:44:22,000 --> 00:44:26,000
OK.  So the red sequence on one
chromosome is an A,

569
00:44:26,000 --> 00:44:30,000
on the other is a C,
so you have AC.

570
00:44:30,000 --> 00:44:34,000
On the other, the green sequence you
have GG, purple you have GT,

571
00:44:34,000 --> 00:44:38,000
and yellow CC.  So you're looking to
see whether or not any of the

572
00:44:38,000 --> 00:44:43,000
suspect genotypes map up with a spot
of blood, right?

573
00:44:43,000 --> 00:44:47,000
So we're assuming that a spot of
blood, you know,

574
00:44:47,000 --> 00:44:52,000
comes from one of the suspects that
was attacked by the person who was

575
00:44:52,000 --> 00:44:56,000
the victim.  OK.
So you have a victim with scratch.

576
00:44:56,000 --> 00:45:01,000
Someone has a spot of blood.
And you see whether or not,

577
00:45:01,000 --> 00:45:05,000
or you can use semen samples,
you can see whether or not the DNA

578
00:45:05,000 --> 00:45:09,000
in the human tissue that is believed
to come from the attacker is

579
00:45:09,000 --> 00:45:13,000
matching of any of the suspects'
genotypes.  So there are a lot of

580
00:45:13,000 --> 00:45:17,000
assumptions there,
right?  You have to have tissue at

581
00:45:17,000 --> 00:45:21,000
the crime scene that you believe to
come from the attacker.

582
00:45:21,000 --> 00:45:25,000
And then, once you have that,
you can determine its genotype and

583
00:45:25,000 --> 00:45:30,000
compare it to the genotypes
of the suspects.

584
00:45:30,000 --> 00:45:35,000
And you find, for example,
here that, let's see, yeah,

585
00:45:35,000 --> 00:45:40,000
so I believe the suspect number
three has the same genotype as the

586
00:45:40,000 --> 00:45:45,000
DNA that was in the spot of blood at
the crime scene.

587
00:45:45,000 --> 00:45:50,000
And that would be some evidence
that this suspect number three was

588
00:45:50,000 --> 00:45:55,000
the person who did it.
Now, in actual fact, you do this

589
00:45:55,000 --> 00:46:00,000
not just for four SNPs,
you do it for thousands of SNPs.

590
00:46:00,000 --> 00:46:04,000
You don't usually do this for 300,
00 SNPs because that's expensive and

591
00:46:04,000 --> 00:46:08,000
it's a lot of work.
And forensics doesn't put that much

592
00:46:08,000 --> 00:46:12,000
money into this.
However, the more SNPs you use for

593
00:46:12,000 --> 00:46:17,000
genotyping the more sure you are of
the suspect's identity.

594
00:46:17,000 --> 00:46:21,000
OK?  Because it's really a matter
of frequency of whether or not

595
00:46:21,000 --> 00:46:25,000
you're going to get the same
combination of these different SNP

596
00:46:25,000 --> 00:46:30,000
bases in different potential
suspects.

597
00:46:30,000 --> 00:46:33,000
So the greater the spectrum of SNPs
you look at, the more sure you are

598
00:46:33,000 --> 00:46:37,000
of the suspect's identity.
Now, in some cases this has been

599
00:46:37,000 --> 00:46:41,000
very, very useful.
And there are a number of people on

600
00:46:41,000 --> 00:46:45,000
Death Row who have been exonerated
by going back to DNA recovered from

601
00:46:45,000 --> 00:46:49,000
the crime scene sometimes years ago,
doing SNP mapping and showing that

602
00:46:49,000 --> 00:46:53,000
they really couldn't have done it
because the genotypes did not match

603
00:46:53,000 --> 00:46:57,000
up.  Usually these were rape cases
and the semen genotype just did not

604
00:46:57,000 --> 00:47:01,000
match up with the semen genotype of
the person on Death Row.

605
00:47:01,000 --> 00:47:05,000
So this is very valuable technology.
OK.  It was used in the O.J.

606
00:47:05,000 --> 00:47:10,000
Simpson trial,
but not as well as it could have

607
00:47:10,000 --> 00:47:14,000
been which lead to equivocation
there.  OK.  So time is fleeting.

608
00:47:14,000 --> 00:47:19,000
I'm going to mention a technology
to you in the last couple of minutes,

609
00:47:19,000 --> 00:47:24,000
and then we'll come back to it as we
go on through later parts of the

610
00:47:24,000 --> 00:47:29,000
course.  So I've talked today about
DNA sequencing.

611
00:47:29,000 --> 00:47:33,000
I've talked about using
polymorphisms to genotype people

612
00:47:33,000 --> 00:47:38,000
either, well, for disease alleles I
focused on who-done-its.

613
00:47:38,000 --> 00:47:43,000
Something else that I want to throw
out at you at this point is the

614
00:47:43,000 --> 00:47:48,000
notion of transgenic technology.
And I'm going to tell you what

615
00:47:48,000 --> 00:47:52,000
transgenic organisms are as part of
completing the Recombinant DNA

616
00:47:52,000 --> 00:47:57,000
Module.  And then we'll come back in
future modules and talk more about

617
00:47:57,000 --> 00:48:02,000
how you make these things.
But I want to have this as part of

618
00:48:02,000 --> 00:48:07,000
your compendium now.
A transgenic animal or transgenic

619
00:48:07,000 --> 00:48:12,000
organism is an organism where you
have manipulated its genome in some

620
00:48:12,000 --> 00:48:17,000
way, where you've either inserted
extra DNA into its genome or you've

621
00:48:17,000 --> 00:48:22,000
removed DNA from its genome or
you've done something to its genome

622
00:48:22,000 --> 00:48:27,000
such that it was not the organism
that you started off with.

623
00:48:27,000 --> 00:48:32,000
Genetically modified organisms.
The food that you eat that is

624
00:48:32,000 --> 00:48:36,000
genetically modified has had its
genome tampered with.

625
00:48:36,000 --> 00:48:40,000
This type of transgenic technology
is very, very useful,

626
00:48:40,000 --> 00:48:44,000
not only for creating genetically
modified foods,

627
00:48:44,000 --> 00:48:49,000
but it's very, very useful for
creating disease models of animals.

628
00:48:49,000 --> 00:48:53,000
And I'll tell you now that there is
a mouse model of human familial

629
00:48:53,000 --> 00:48:57,000
hypercholesterolemia that has been
created by making a specific

630
00:48:57,000 --> 00:49:02,000
mutation, that T to A mutation in
the mouse LDL receptor gene.

631
00:49:02,000 --> 00:49:06,000
Another thing that is extremely
useful about transgenic animals is

632
00:49:06,000 --> 00:49:10,000
that you can get them to make
specific proteins.

633
00:49:10,000 --> 00:49:14,000
So, for example, there are goats
that have had inserted into their

634
00:49:14,000 --> 00:49:18,000
genomes genes that encode for
particular medications,

635
00:49:18,000 --> 00:49:22,000
for particular drugs.  And you can
get these drugs out of the milk of

636
00:49:22,000 --> 00:49:26,000
the goats usually or out of the
serum of the goats because they are

637
00:49:26,000 --> 00:49:30,000
constitutively producing them
because you've put various genes

638
00:49:30,000 --> 00:49:35,000
into their genome.
So I'm going to leave it there and

639
00:49:35,000 --> 00:49:38,000
we'll talk about how to make
transgenics in a future lecture.