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00:00:00,000 --> 00:00:04,000
There was a little confusion with
dideoxies in one sense,

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00:00:04,000 --> 00:00:08,000
and some of these things like the
PCR you're going to have to sort of

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00:00:08,000 --> 00:00:12,000
sit down and actually think about it,
but the principle of the dideoxies,

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00:00:12,000 --> 00:00:17,000
if we were making a chain of beads
that had a hook on one end and a

5
00:00:17,000 --> 00:00:21,000
little hole on the other,
and we were joining these things

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00:00:21,000 --> 00:00:25,000
together, we could make obviously,
a chain that went on.  And then we

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00:00:25,000 --> 00:00:30,000
could hook another one
in, and so on.

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00:00:30,000 --> 00:00:34,000
And if we had a bunch of beads like
this every now and then we threw in

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00:00:34,000 --> 00:00:39,000
a very small number that didn't have
the hook on the end,

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any time this particular chain were
elongating and we put on one of

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these things, the chain would stop
because you haven't got any where to

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00:00:49,000 --> 00:00:54,000
join onto it.  You added dideoxy
into a polymerase reaction.

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00:00:54,000 --> 00:00:59,000
A chain that gets the dideoxy
doesn't have anything to join in the

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00:00:59,000 --> 00:01:04,000
end, and that will stop.
If we only added this,

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00:01:04,000 --> 00:01:10,000
the entire reaction would stop,
and everyone would come at the first

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00:01:10,000 --> 00:01:16,000
time a dideoxy got incorporated.
The trick is to put a little bit in.

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00:01:16,000 --> 00:01:21,000
So, a few of the molecules stop.
Everything keeps going.  The next

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00:01:21,000 --> 00:01:27,000
time a dideoxy gets incorporated,
the chain will stop there.  And out

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00:01:27,000 --> 00:01:33,000
of this, you will generate a family
of polymers that are of

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00:01:33,000 --> 00:01:38,000
different lengths.
Each one will terminate with a

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00:01:38,000 --> 00:01:43,000
dideoxy nucleotide,
and if the dideoxy nucleotide we

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00:01:43,000 --> 00:01:49,000
used in that particular reaction was,
let's say, dideoxy ATP,

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00:01:49,000 --> 00:01:54,000
that means that an A was the last
nucleotide added to every one of

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00:01:54,000 --> 00:02:00,000
those.  And we can separate these on
the basis of size.

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00:02:00,000 --> 00:02:04,000
And if I ran them out on a gel,
I'd see something like that.  And

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00:02:04,000 --> 00:02:09,000
that would tell me that when that
polymerase was coming along,

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00:02:09,000 --> 00:02:13,000
that was the first time it saw an A.
A few stopped there, polymerized a

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few more.  Then it put in another A,
put in some other things, and so on.

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00:02:18,000 --> 00:02:22,000
And that by itself wouldn't tell us
the sequence.  But if I did that

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00:02:22,000 --> 00:02:27,000
reaction four times in a row,
then I could tell.

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00:02:27,000 --> 00:02:31,000
In the old days,
they didn't used to use dyes.

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00:02:31,000 --> 00:02:35,000
We just did P32 on it as a label,
and then you'd run the four

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00:02:35,000 --> 00:02:39,000
reactions side-by-side.
And this would be with dideoxy ATP.

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00:02:39,000 --> 00:02:43,000
You'd see a pattern like that, and
maybe with dideoxy TTP,

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00:02:43,000 --> 00:02:47,000
you'd see something like this.
And when you got the rest of them,

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00:02:47,000 --> 00:02:51,000
you'd kind of end up working out
what the sequence was by looking

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00:02:51,000 --> 00:02:55,000
across the four lanes.
This business of using the dye is

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00:02:55,000 --> 00:02:59,000
just one more step up in the
engineering side that enables the

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00:02:59,000 --> 00:03:03,000
thing to be done automatically.
And it's pretty well explained in

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00:03:03,000 --> 00:03:07,000
your textbooks.
OK, PCR, someone was confused as to

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00:03:07,000 --> 00:03:11,000
why we didn't just let the cell do
it.  Well, the cell does a great job,

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00:03:11,000 --> 00:03:16,000
but if you are a molecular biologist
trying to understand the basis of

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00:03:16,000 --> 00:03:20,000
life or if you're a biological
engineer, and you want to produce

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00:03:20,000 --> 00:03:25,000
something, you need to get hold of a
particular piece of DNA.

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00:03:25,000 --> 00:03:29,000
Or, if you're a forensic
investigator, and you've got a tiny,

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00:03:29,000 --> 00:03:33,000
tiny sample of human DNA, and you
want to know whose it is,

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00:03:33,000 --> 00:03:38,000
you have to make more of it.
And, that's what PCR is all about.

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00:03:38,000 --> 00:03:42,000
So I'm going to switch just over to
the net for a minute.

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00:03:42,000 --> 00:03:46,000
I think this first site,
I just want to show you something,

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00:03:46,000 --> 00:03:51,000
how somebody functions in a lab now
with all these genes out there.

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00:03:51,000 --> 00:03:55,000
And then, I'm going to show you a
little animation for PCR that will

52
00:03:55,000 --> 00:03:59,000
help.  So, if you just go to Google
and type NCBI,

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00:03:59,000 --> 00:04:04,000
that's the National Center for
Biotechnology Information.

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00:04:04,000 --> 00:04:10,000
And, the Dolan Learning Center is a
center that Cold Spring Harbor

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00:04:10,000 --> 00:04:16,000
Laboratory has set up to teach
people about DNA.

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00:04:16,000 --> 00:04:22,000
So let me just see here.
So, let's just go to, OK,

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00:04:22,000 --> 00:04:28,000
let's use, whoops, this is going to
seize on us.  OK, let's

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00:04:28,000 --> 00:04:43,000
find how it happens.

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00:04:43,000 --> 00:04:47,000
OK, so here's this National Center
for Biotechnology Information.

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00:04:47,000 --> 00:04:51,000
There's all sorts of things you can
search for, and I'm not expecting

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00:04:51,000 --> 00:04:55,000
you to know the site.
I just want to sort of give you a

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00:04:55,000 --> 00:04:59,000
demo.  If I was sitting in my office,
this is the sort of thing I can do

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00:04:59,000 --> 00:05:03,000
easily.  Rather than sequence
looking for DNA sequence,

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00:05:03,000 --> 00:05:07,000
I'm going to look for the translated
sequence of the protein that's

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00:05:07,000 --> 00:05:11,000
encoded by the gene where the
computer's gone through and used the

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00:05:11,000 --> 00:05:15,000
genetic codes tell me the sequence
of a protein.  I told you about

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00:05:15,000 --> 00:05:19,000
sequencing a mismatch repair gene
back in the 80s.

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00:05:19,000 --> 00:05:23,000
It was called MutS,
and I'll put in Walker GC,

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00:05:23,000 --> 00:05:27,000
and probably hopefully get us to the
thing.

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00:05:27,000 --> 00:05:31,000
And there, the very first hit is DNA
repair.  Protein MutS salmonella

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00:05:31,000 --> 00:05:36,000
typhimurium, that's the one I
sequenced.  So I'll just go to that.

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00:05:36,000 --> 00:05:41,000
It has various ways of displaying
the sequence.  I'm going to switch

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00:05:41,000 --> 00:05:45,000
to Fasta, which is a very easy way
to see it.  Now what you see is the

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00:05:45,000 --> 00:05:50,000
sequence of the protein using a one
letter code, or one letter stands

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00:05:50,000 --> 00:05:55,000
for each amino acid.
K is lysine.  A is alanine,

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00:05:55,000 --> 00:06:00,000
and so on.  I'm just going to copy
that, that piece of sequence.

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00:06:00,000 --> 00:06:03,000
OK so that's the bacterial gene for
mismatch repair.

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00:06:03,000 --> 00:06:06,000
At the time I put that in the
database, there wasn't anything else

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00:06:06,000 --> 00:06:09,000
like it, except for the gene that
was Streptococcus pneumonia.

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00:06:09,000 --> 00:06:13,000
But I found out, someone else is
sequencing by phoning around in the

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00:06:13,000 --> 00:06:16,000
field.  I'm going to go back to the
main site and I'm going to use a

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00:06:16,000 --> 00:06:19,000
program called Blast,
which lets you search the entire

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00:06:19,000 --> 00:06:23,000
database.  I'll use a protein blast.
I'm going to take a protein

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00:06:23,000 --> 00:06:26,000
sequence, and I'm going to ask what
else is out there in terms

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00:06:26,000 --> 00:06:32,000
of protein sequences?
I'll paste in this bacterial

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00:06:32,000 --> 00:06:40,000
sequence, and then I'm going to,
if I can, manage this thing.  Let's

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00:06:40,000 --> 00:06:49,000
see if I can get myself down here.
OK, over here I'll probably do, OK,

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00:06:49,000 --> 00:06:57,000
so I'm going to limit it, let's just
search the human genome.

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00:06:57,000 --> 00:07:05,000
That's all we've got to do.
And what did I have to do to get

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00:07:05,000 --> 00:07:13,000
this thing to fit?  Which button?
Go to the right.

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00:07:13,000 --> 00:07:19,000
Can you just come up here for a
second to help me get this set up?

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00:07:19,000 --> 00:07:25,000
I'm computer limited here
apparently.  OK,

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00:07:25,000 --> 00:07:31,000
that one, OK, great,
so why do we not try this again?

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00:07:31,000 --> 00:07:51,000
PAUSE]
Sorry about this.

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00:07:51,000 --> 00:08:01,000
We'll see if we can get this thing
to go.  I have what?

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00:08:01,000 --> 00:08:15,000
Yeah, that's OK though.
That should be fine.  Try again.

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00:08:15,000 --> 00:08:30,000
Let's see if I can get the thing to
work.

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00:08:30,000 --> 00:08:35,000
OK, so it's got it now.
It'll tell me.  It's searching all

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00:08:35,000 --> 00:08:40,000
the sequence that's out there.
There's just an unbelievable amount

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00:08:40,000 --> 00:08:45,000
of sequence.  That's just how long
it took.  It's showing me here a

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00:08:45,000 --> 00:08:51,000
diagrammatic representation of the
things.  Then I can see that the

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00:08:51,000 --> 00:08:56,000
very first it was MutS homolog three
for humans.  And if I go down here,

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00:08:56,000 --> 00:09:01,000
we can actually see on alignment of
the bacterial gene on the top line,

104
00:09:01,000 --> 00:09:07,000
and the lines below is the sequence
of that particular human homolog.

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00:09:07,000 --> 00:09:10,000
And you can see in between all the
things that are in common,

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00:09:10,000 --> 00:09:13,000
and particularly down at the
C-terminus of the protein,

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00:09:13,000 --> 00:09:17,000
you can see there's very strong
conservation.  You may not think

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00:09:17,000 --> 00:09:20,000
that that's impressive,
but remember for every one of those

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00:09:20,000 --> 00:09:24,000
positions, there's 20 possibilities.
So, if you get that many in a row,

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00:09:24,000 --> 00:09:27,000
that's the same gene basically.
And when you take the structure,

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00:09:27,000 --> 00:09:31,000
the structure's going to be very,
very similar.

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00:09:31,000 --> 00:09:38,000
And it does mismatch repair in both.
Just to try and give you an idea of

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how you do sequence now,
because with all these genomes done,

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00:09:45,000 --> 00:09:52,000
you do the vast majority of it by
computer, rather than some other way.

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I want to take you and show you
this.  DNA learning: if you go there,

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00:09:59,000 --> 00:10:06,000
the second thing is a
set of animations.

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00:10:06,000 --> 00:10:11,000
Go to the animations.
There's one on polymerase chain

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00:10:11,000 --> 00:10:16,000
reaction.  And,
I'm going to just show you this

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00:10:16,000 --> 00:10:21,000
because this is a nice little,
let's see if we can get this thing

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00:10:21,000 --> 00:10:26,000
to center.  OK we'll have to see
whether this is going to work.

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00:10:26,000 --> 00:10:31,000
OK, so this is the principle of,
you can go do this at your leisure,

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00:10:31,000 --> 00:10:37,000
but the idea is to heat the DNA up,
the strands come apart.

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00:10:37,000 --> 00:10:40,000
Then, were going to take these two
little primers,

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00:10:40,000 --> 00:10:43,000
not promoters, which I think someone
was confused about,

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00:10:43,000 --> 00:10:46,000
a little piece of DNA that
complimentary,

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00:10:46,000 --> 00:10:50,000
and anneal them.
Than we added DNA polymerase.

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00:10:50,000 --> 00:10:53,000
You know what happens then.  We
extend those primers.

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00:10:53,000 --> 00:10:56,000
That was the first cycle.
Do the same thing again.  Go to the

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second cycle.  This is what I was
drawing on the board the other day.

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00:11:00,000 --> 00:11:06,000
Now are going to denature the DNA.
The strands come apart.  Let's let

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00:11:06,000 --> 00:11:13,000
the polymerase extend them.
Let's go to the third cycle,

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00:11:13,000 --> 00:11:19,000
denature the DNA, anneal the primers,
extend the primers,

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00:11:19,000 --> 00:11:26,000
and now for the first time,
we've got what we were shooting for.

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00:11:26,000 --> 00:11:35,000
We have a double-stranded copy of

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00:11:35,000 --> 00:11:45,000
just the DNA that was defined
between those primers.

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00:11:45,000 --> 00:11:55,000
OK, I think this actually,
I'm going to go back one.  Oops,

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00:11:55,000 --> 00:12:02,000
OK.
If you go, then,

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00:12:02,000 --> 00:12:06,000
to the amplification graph,
what they're doing here is they're

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00:12:06,000 --> 00:12:11,000
showing you what happens as you do
successive cycles.

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00:12:11,000 --> 00:12:16,000
So, at the first, oh,
it's down here.  Just a minute.

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00:12:16,000 --> 00:12:20,000
If we do the first cycle, we end up
with two DNA copies.

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00:12:20,000 --> 00:12:25,000
That's just plotting what I showed
you.  The next one: we have four.

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00:12:25,000 --> 00:12:30,000
We haven't yet got to this target
sequence.

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00:12:30,000 --> 00:12:36,000
By the next cycle,
we now have two copies of the target

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00:12:36,000 --> 00:12:43,000
sequence plus these other things.
But if you keep going, let's say by

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00:12:43,000 --> 00:12:50,000
the time we're at seven cycles,
the number of targets is up to 114.

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00:12:50,000 --> 00:12:56,000
The number of DNA copies is 128.
But, if we keep going like this,

148
00:12:56,000 --> 00:13:03,000
we'll find out that the target
copies become the vast majority of

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00:13:03,000 --> 00:13:09,000
the sequences that are in there.
So, by the time that you're up in

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00:13:09,000 --> 00:13:13,000
the 30 cycles,
or something like that,

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00:13:13,000 --> 00:13:18,000
there's only a handful of the
original things,

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00:13:18,000 --> 00:13:22,000
or almost all, and that,
I hope will help some of you who

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00:13:22,000 --> 00:13:27,000
might have had problems with
understanding the PCR.

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00:13:27,000 --> 00:13:32,000
So, what I'm going to do is tell you
a few more things about what you can

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00:13:32,000 --> 00:13:37,000
do with recombinant DNA,
this recombinant DNA technology,

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00:13:37,000 --> 00:13:42,000
because it's just so powerful.  And
I can only sort of give you a few

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00:13:42,000 --> 00:13:47,000
ideas, and show you a few variations.
But, most of these things are just

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00:13:47,000 --> 00:13:53,000
taking principles that you've
already learned as part of the basic

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00:13:53,000 --> 00:13:58,000
biology I'm trying to tell you,
and then using them like an engineer

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00:13:58,000 --> 00:14:03,000
to achieve some applied purpose.
For example suppose I wanted to

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00:14:03,000 --> 00:14:07,000
produce a human protein,
and try and produce it in a

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00:14:07,000 --> 00:14:11,000
bacterium.  That would be great.
I could take the one gene.  I could

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00:14:11,000 --> 00:14:15,000
grow a fermenter load of E.
coli, and if I got it right,

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00:14:15,000 --> 00:14:19,000
then I'd be able to make a lot of
this protein instead of trying to

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00:14:19,000 --> 00:14:23,000
isolate it from some human source or
something like that.

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00:14:23,000 --> 00:14:27,000
There's a couple of problems.
We talked about them.  One is the

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00:14:27,000 --> 00:14:32,000
problem of promoters.
Another one is that human DNA would

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00:14:32,000 --> 00:14:36,000
have introns in it.
And, bacteria doesn't recognize the

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00:14:36,000 --> 00:14:41,000
human promoters.
It wouldn't start to make an RNA in

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00:14:41,000 --> 00:14:46,000
the right place,
and it doesn't know what to do about

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00:14:46,000 --> 00:14:50,000
splicing out the intron.
So, let's address the intron first.

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00:14:50,000 --> 00:14:55,000
There is a way of handling that
that's quite easy.

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00:14:55,000 --> 00:15:00,000
And that's what's called to make
cDNA library.

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00:15:00,000 --> 00:15:08,000
So, if we have DNA,
and then we get the RNA,

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00:15:08,000 --> 00:15:17,000
we get the RNA copy [SOUND OFF/THEN
ON] including these intron sequences.

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00:15:17,000 --> 00:15:25,000
And then what happens,
this is RNA splicing that we talked

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00:15:25,000 --> 00:15:34,000
about.  And what we get out of that
would be an mRNA,

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00:15:34,000 --> 00:15:43,000
in which the introns have been
removed.

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00:15:43,000 --> 00:15:47,000
So, eukaryotic cells,
my cells, know how to express the

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00:15:47,000 --> 00:15:51,000
gene, so they make the RNA,
they know how to get rid of the

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00:15:51,000 --> 00:15:55,000
introns.  So, if I were to isolate a
messenger RNA.

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00:15:55,000 --> 00:15:59,000
That's been spliced from me,
or you, or anything, what we would

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00:15:59,000 --> 00:16:03,000
have is a population of RNA
molecules that don't have

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00:16:03,000 --> 00:16:10,000
introns anymore.
Anybody remember any way we could

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00:16:10,000 --> 00:16:19,000
get from RNA back to DNA?
Reverse transcriptase.  So,

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00:16:19,000 --> 00:16:28,000
if we used reverse transcriptase,
that protein that David Baltimore

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00:16:28,000 --> 00:16:37,000
discovered and viruses,
and which retroviruses use,

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00:16:37,000 --> 00:16:46,000
now we would have a signle stranded
DNA copy of the mRNA.

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00:16:46,000 --> 00:16:49,000
And then, we could use an ordinary
DNA polymerase to get ourselves to

190
00:16:49,000 --> 00:16:53,000
double-stranded DNA.
We'd be doing, in essence,

191
00:16:53,000 --> 00:16:57,000
exactly what one of these
retroviruses does.

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00:16:57,000 --> 00:17:03,000
This would give us what's known as a
cDNA library, where the genes don't

193
00:17:03,000 --> 00:17:09,000
have introns anymore.
So, if I wanted to get at one of my

194
00:17:09,000 --> 00:17:15,000
proteins, one of my genes,
and think about expressing it in E.

195
00:17:15,000 --> 00:17:21,000
coli, what I would do is go looking
in a cDNA library using a sort of

196
00:17:21,000 --> 00:17:27,000
approach as we've done,
trying to find my gene of interest,

197
00:17:27,000 --> 00:17:33,000
because if I use the cDNA library
now it would just be like

198
00:17:33,000 --> 00:17:37,000
a bacterial gene.
You could see the ATG start.

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00:17:37,000 --> 00:17:41,000
You could get out your handy little
genetic code, and you could walk

200
00:17:41,000 --> 00:17:44,000
along, and read out the sequence of
the protein.  So,

201
00:17:44,000 --> 00:17:48,000
that's part of what you need to do
if you wanted to make,

202
00:17:48,000 --> 00:17:52,000
say, a protein inside of a bacterium.
The other one which we talked about

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00:17:52,000 --> 00:17:55,000
was since the promoters are not a
universal language,

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00:17:55,000 --> 00:17:59,000
what E. coli RNA polymerase sees is
different than what human RNA

205
00:17:59,000 --> 00:18:04,000
polymerase sees as a start site.
I would have to add in a promoter

206
00:18:04,000 --> 00:18:10,000
that would drive the expression of
this open reading frame if I wanted

207
00:18:10,000 --> 00:18:17,000
it to work in E.
coli.  And that's fairly easily done,

208
00:18:17,000 --> 00:18:23,000
too.  A general thing that's for
this is called expression cloning.

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00:18:23,000 --> 00:18:30,000
And it would be more or less the
same idea.

210
00:18:30,000 --> 00:18:34,000
We'd have a vector that had a
cloning site.  It has an origin of

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00:18:34,000 --> 00:18:39,000
replication, and maybe there's a
selectable marker such as the drug

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00:18:39,000 --> 00:18:44,000
resistance.  That's the basic kind
of factor that I talked about before.

213
00:18:44,000 --> 00:18:49,000
However, if I clone in a piece of
DNA into that,

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00:18:49,000 --> 00:18:54,000
it has to have a promoter that can
be read in the organism working with,

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00:18:54,000 --> 00:18:59,000
because it's just out whatever
nature gave it,

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00:18:59,000 --> 00:19:04,000
whatever promoter that would be in
front of that.

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00:19:04,000 --> 00:19:09,000
But, if I were to now into this
vector put an E.

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00:19:09,000 --> 00:19:15,000
coli promoter right there,
now, if I just downstream of that

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00:19:15,000 --> 00:19:21,000
put any open reading frame,
human protein lets say, which we've

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00:19:21,000 --> 00:19:27,000
gotten rid of the introns,
the human genes minus its introns,

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00:19:27,000 --> 00:19:33,000
which you got from the cDNA library,
now when the bacterial polymerase

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00:19:33,000 --> 00:19:39,000
came along, it would be copying,
making it a messenger RNA for human

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00:19:39,000 --> 00:19:45,000
protein, and we could
get it out of that.

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00:19:45,000 --> 00:19:51,000
And the beauty of that,
suppose we took the front part of

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the Lac operator,
we would have a regulated promoter.

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00:19:57,000 --> 00:20:03,000
It would be just everything we
studied about Lac if we

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00:20:03,000 --> 00:20:08,000
were to starve it for,
you know, we have to get rid of

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00:20:08,000 --> 00:20:12,000
glucose, and then if we added
lactose or some kind of synthetic

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00:20:12,000 --> 00:20:16,000
inducer, we can turn the promoter on
and off.  So, you could grow an

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00:20:16,000 --> 00:20:20,000
entire fermenter load of bacteria
without expressing the gene.

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00:20:20,000 --> 00:20:24,000
And then, once you had the bacteria
all grown up, you could throw in

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00:20:24,000 --> 00:20:28,000
something that would normally induce
the expression of the Lac

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00:20:28,000 --> 00:20:32,000
regulatory system.
And now, instead of making beta

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00:20:32,000 --> 00:20:36,000
galactosidase,
instead it would make the protein

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00:20:36,000 --> 00:20:40,000
that you are interested in,
you with me?  It's very pretty.

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00:20:40,000 --> 00:20:44,000
And in fact, so much of what you
can see in this is,

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00:20:44,000 --> 00:20:47,000
these really basic studies,
since the Lac system was one of the

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00:20:47,000 --> 00:20:51,000
first to really be worked out in
detail, we use its parts.

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00:20:51,000 --> 00:20:55,000
And, there are many vectors around
now that have exactly that.

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00:20:55,000 --> 00:20:59,000
They have the Lac promoter,
and you can turn things on and off

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00:20:59,000 --> 00:21:04,000
in a regulated way,
so not only provide a promoter that

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00:21:04,000 --> 00:21:11,000
works in the organism,
but it also gives you a measure of

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00:21:11,000 --> 00:21:19,000
control.  There's another very cute
trick, and what we've done sort of

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00:21:19,000 --> 00:21:26,000
here is we took,
say, the promoter for Lac in the

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00:21:26,000 --> 00:21:32,000
regulatory region.
I'll use R to stand for regulatory

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00:21:32,000 --> 00:21:37,000
region, and then this would be the
LacZ sequence.

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00:21:37,000 --> 00:21:41,000
What we've really done,
is we've taken a gene from somewhere

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00:21:41,000 --> 00:21:46,000
else, let's call it gene X that had
a promoter from gene X.

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00:21:46,000 --> 00:21:51,000
And, in essence, is cutting each of
them here.  And,

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00:21:51,000 --> 00:21:56,000
now we take the regulatory promoter
region from Lac and we put down

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00:21:56,000 --> 00:22:00,000
below it gene X.
And, now we've got this gene whose

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00:22:00,000 --> 00:22:05,000
products we're interested in
producing in a fermenter under the

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00:22:05,000 --> 00:22:10,000
control of the Lac operon.
Well, there's another kind of thing

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00:22:10,000 --> 00:22:16,000
we can do.  We can do the other way
around.  We could take LacZ,

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00:22:16,000 --> 00:22:21,000
which makes beta galactosidase.
We could put it under the promoter

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00:22:21,000 --> 00:22:26,000
regulatory region of gene X.
Well, what will happen then?

257
00:22:26,000 --> 00:22:32,000
If that construct is sitting in a
cell, anytime that the cell decides

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00:22:32,000 --> 00:22:37,000
to make gene X,
instead it will make beta

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00:22:37,000 --> 00:22:43,000
galactosidase, which is really
easy to assay for.

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00:22:43,000 --> 00:22:51,000
And, this sort of strategy,
you'd use something like LacZ as a

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00:22:51,000 --> 00:23:00,000
reporter.
In this case beta galactosidase

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00:23:00,000 --> 00:23:11,000
synthesis, which you can assay for,
reports when is the promoter of gene

263
00:23:11,000 --> 00:23:23,000
X is functioning.
This reporter gene now has the

264
00:23:23,000 --> 00:23:34,000
regulatory characteristics that are
imposed upon it by that

265
00:23:34,000 --> 00:23:42,000
particular promoter.
So, there's this picture that I've

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00:23:42,000 --> 00:23:46,000
showed you, this little movie I
showed you early on.

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00:23:46,000 --> 00:23:50,000
You've seen it a couple of times.
In this case, the reporter is green

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00:23:50,000 --> 00:23:55,000
fluorescent protein.
What Barbara Meyer, who made this

269
00:23:55,000 --> 00:23:59,000
particular construct,
did was they took the gene for green

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00:23:59,000 --> 00:24:04,000
florescent protein which started out
in a jellyfish as you may remember.

271
00:24:04,000 --> 00:24:09,000
And, the protein folds up,
and ends up being fluorescent.

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00:24:09,000 --> 00:24:15,000
So, we can tell when it's expressed
very easily.  And in this case,

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00:24:15,000 --> 00:24:21,000
you'll notice not all of the genes
in the whole worm isn't glowing.

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00:24:21,000 --> 00:24:27,000
And so, it's under the control of
the promoter regulatory region that

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00:24:27,000 --> 00:24:33,000
is expressed only in specific
body parts.

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00:24:33,000 --> 00:24:36,000
And so, you can see where that
promoter is working by just looking

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00:24:36,000 --> 00:24:39,000
at the worm.  In the case of
something like the mouse that we

278
00:24:39,000 --> 00:24:43,000
talked about, it's a pretty uniform
expression at least in the skin.

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00:24:43,000 --> 00:24:46,000
So, that was probably, in that case,
the green fluorescent protein was

280
00:24:46,000 --> 00:24:50,000
probably put in a promoter that's
expressed in probably most of the

281
00:24:50,000 --> 00:24:53,000
cells in the body,
at least certainly all the ones in

282
00:24:53,000 --> 00:24:57,000
the mouse cell.
I don't know the details of that.

283
00:24:57,000 --> 00:25:01,000
Ditto over here.
It was probably something that was

284
00:25:01,000 --> 00:25:07,000
expressed in most of the body cells,
but you also could have put

285
00:25:07,000 --> 00:25:12,000
something that was just expressed in
some very little bits.

286
00:25:12,000 --> 00:25:18,000
So, depending on how you do the
construct, there are a lot of

287
00:25:18,000 --> 00:25:24,000
different things that people can do
in this sort of thing.

288
00:25:24,000 --> 00:25:29,000
OK, one more category that comes
out of the sort of thing,

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00:25:29,000 --> 00:25:35,000
is if we have a gene of some type,
I don't know what it does but I'd

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00:25:35,000 --> 00:25:40,000
like to find out.
You know, at least budding

291
00:25:40,000 --> 00:25:44,000
geneticists, know what we'd like to
do.  We'd probably just like to

292
00:25:44,000 --> 00:25:48,000
disable that gene very specifically,
and then look at the live organism

293
00:25:48,000 --> 00:25:53,000
to see what happens.
And, the principle is the same

294
00:25:53,000 --> 00:25:57,000
whether you're doing it in E.
coli or a mouse.  It gets a little

295
00:25:57,000 --> 00:26:02,000
more complicated for technical
reasons doing it in a mouse.

296
00:26:02,000 --> 00:26:06,000
But the idea is exactly the same.
And here's the strategy.  So, we'll

297
00:26:06,000 --> 00:26:11,000
just take a piece of DNA from the
organism.  And sitting at here is

298
00:26:11,000 --> 00:26:15,000
this open reading frame that we've
seen.  We don't know what its

299
00:26:15,000 --> 00:26:20,000
function is.  We think if I could
knock it out, get rid of its

300
00:26:20,000 --> 00:26:24,000
function, I'll look at the organism.
Maybe I can make a guess then.  So,

301
00:26:24,000 --> 00:26:29,000
if we were to cut the gene somewhere
with a restriction site,

302
00:26:29,000 --> 00:26:33,000
and then we were to take,
for example, a gene encoding a drug

303
00:26:33,000 --> 00:26:38,000
resistance or something like that,
and insert it at that point, what we

304
00:26:38,000 --> 00:26:43,000
would end up with is this piece of
the organism's DNA.

305
00:26:43,000 --> 00:26:51,000
The first part of gene X,
then a drug resistance, then the

306
00:26:51,000 --> 00:26:59,000
last part of gene X,
and some more sequence from the

307
00:26:59,000 --> 00:27:06,000
organism.
Now, this would be,

308
00:27:06,000 --> 00:27:11,000
we'd have this in a test tube.
We could do it by the kind of

309
00:27:11,000 --> 00:27:16,000
recombinant DNA manipulations that
we have.  And what would happen if I

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00:27:16,000 --> 00:27:21,000
were to put, now,
let's keep it with bacteria where

311
00:27:21,000 --> 00:27:26,000
it's easy to see.
If I were to take that piece of DNA,

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00:27:26,000 --> 00:27:31,000
put it inside a living cell,
what's going to happen?

313
00:27:31,000 --> 00:27:38,000
Well, let's make this,
say, here's the end of it.

314
00:27:38,000 --> 00:27:45,000
That's all we've got.  Well,
inside the living cell, we of course

315
00:27:45,000 --> 00:27:52,000
have the entire genome.
And then we come to this part.

316
00:27:52,000 --> 00:28:00,000
We have gene X.  Then we'd have
this going.

317
00:28:00,000 --> 00:28:04,000
That would be the whole thing.
Well, this particular piece doesn't

318
00:28:04,000 --> 00:28:09,000
have an origin of replication.
It's not joined to a vector.  It's

319
00:28:09,000 --> 00:28:13,000
just sitting there.
So, if the cell divides,

320
00:28:13,000 --> 00:28:18,000
it's not going to get replicated.
So, if I select for a drug

321
00:28:18,000 --> 00:28:22,000
resistance that's on that piece of
DNA, unless something happens I'm

322
00:28:22,000 --> 00:28:27,000
not going to get a drug-resistant
bacteria.  But you do know a way

323
00:28:27,000 --> 00:28:32,000
that this thing could join to an
origin of replication.

324
00:28:32,000 --> 00:28:36,000
It could join to the origin of
replication that's on the bacteria

325
00:28:36,000 --> 00:28:41,000
chromosome.  And,
the way to do it would be by

326
00:28:41,000 --> 00:28:46,000
undergoing genetic recombination
over here, because this DNA is

327
00:28:46,000 --> 00:28:51,000
exactly the same as on that side,
and over here it's the same thing.

328
00:28:51,000 --> 00:28:56,000
This DNA is the same as that side.
So, if that genetic exchange

329
00:28:56,000 --> 00:29:01,000
happened, what would happen,
even if it happened rarely, was this

330
00:29:01,000 --> 00:29:06,000
piece of DNA would replace the piece
of DNA that's in there.

331
00:29:06,000 --> 00:29:10,000
I'd be able to tell it was there
because I'd just select for drug

332
00:29:10,000 --> 00:29:14,000
resistance.  And even if it only
happened only one in 500,

333
00:29:14,000 --> 00:29:18,000
00 cells, it wouldn't matter because
up would growth the colony that now

334
00:29:18,000 --> 00:29:22,000
has the drug in the middle of gene X.
Gene X is gone,

335
00:29:22,000 --> 00:29:26,000
and I could look at the organism if
it's alive and see if

336
00:29:26,000 --> 00:29:31,000
it has a phenotype.
If it's an essential gene,

337
00:29:31,000 --> 00:29:35,000
that strategy obviously won't work,
and when people do the more

338
00:29:35,000 --> 00:29:40,000
complicated thing of doing this kind
of experiment to make a transgenic

339
00:29:40,000 --> 00:29:44,000
mouse, it takes about a year to go
from our DNA manipulation all the

340
00:29:44,000 --> 00:29:49,000
way to the live mouse with a
disrupted gene.

341
00:29:49,000 --> 00:29:53,000
And sometimes what they find after
spending half of your PhD.

342
00:29:53,000 --> 00:29:58,000
is that that was an essential gene.
And, there's no live mouse, or it

343
00:29:58,000 --> 00:30:03,000
made it two days into being an
embryo and it tanked at that point.

344
00:30:03,000 --> 00:30:07,000
But, this again,
you could see, we talked about going

345
00:30:07,000 --> 00:30:11,000
back and forth between gene,
protein, and trying to figure out

346
00:30:11,000 --> 00:30:15,000
function.  All I can sort of do is
give you the flavor of what's going

347
00:30:15,000 --> 00:30:19,000
on.  But one sort of overarching
thing I hope you remember going

348
00:30:19,000 --> 00:30:23,000
through this is DNA sequencing,
PCR, all these kinds of

349
00:30:23,000 --> 00:30:27,000
manipulations we're talking about
are just exploiting these basic

350
00:30:27,000 --> 00:30:31,000
cellular components that we learned
about studying, how does

351
00:30:31,000 --> 00:30:36,000
DNA replicate?
How is information coded?

352
00:30:36,000 --> 00:30:40,000
How do genes get expressed?
How does genetic information gets

353
00:30:40,000 --> 00:30:45,000
sorted between cells?
It's simply applying those

354
00:30:45,000 --> 00:30:50,000
relatively well understood tools,
or sort of biological principles and

355
00:30:50,000 --> 00:30:54,000
parts that we learned about,
and now using them as tools in an

356
00:30:54,000 --> 00:30:59,000
engineering way,
and have just completely transformed

357
00:30:59,000 --> 00:31:04,000
the way biology has been done in the
last couple decades.

358
00:31:04,000 --> 00:31:11,000
And it's just,
as I say, things are changing so,

359
00:31:11,000 --> 00:31:18,000
so fast.  It's almost breathtaking.
So, the last little bit of sort of

360
00:31:18,000 --> 00:31:25,000
technique oriented stuff,
I just want to at least make sure

361
00:31:25,000 --> 00:31:32,000
I've mentioned what are called
microarrays.  You often hear these

362
00:31:32,000 --> 00:31:38,000
referred to as DNA chips.
The principle here is this is a way

363
00:31:38,000 --> 00:31:42,000
that lets you ask,
not only whether one gene is being

364
00:31:42,000 --> 00:31:46,000
expressed or not under a particular
condition, whether its RNA is being

365
00:31:46,000 --> 00:31:50,000
made, and in most cases that means
making protein,

366
00:31:50,000 --> 00:31:54,000
or whether it's off,
or whether it's at some intermediate

367
00:31:54,000 --> 00:31:58,000
level.  A microarray lets you do
that experiment with many,

368
00:31:58,000 --> 00:32:02,000
many, many genes at once.  And
here's the principle.

369
00:32:02,000 --> 00:32:11,000
You take some surface,
and there will be a bunch of,

370
00:32:11,000 --> 00:32:20,000
if you will, sites on the surface,
on this chip or whatever.  And, what

371
00:32:20,000 --> 00:32:30,000
will go to be attached here would be
a little piece of DNA

372
00:32:30,000 --> 00:32:39,000
from gene one.
I mean, let's say,

373
00:32:39,000 --> 00:32:47,000
maybe a hundred nucleotides: that
would be far more than enough to

374
00:32:47,000 --> 00:32:55,000
make it absolutely specific that
they could only hybridize to a

375
00:32:55,000 --> 00:33:03,000
messenger RNA from gene one,
and not from anything else.  And,

376
00:33:03,000 --> 00:33:11,000
this one, then, would have from gene
two, this one, from gene

377
00:33:11,000 --> 00:33:22,000
three, and so on.
Then, if we were to take a messenger

378
00:33:22,000 --> 00:33:32,000
RNA preparation from,
say, an organism if it's a little

379
00:33:32,000 --> 00:33:37,000
one, or maybe a tissue,
or something like that, anywhere you

380
00:33:37,000 --> 00:33:41,000
could isolate RNA from.
And then, we'll label it in some

381
00:33:41,000 --> 00:33:46,000
kind of way, and we can label it
radioactivity,

382
00:33:46,000 --> 00:33:51,000
we can label it with dyes.
It's usually done with dyes,

383
00:33:51,000 --> 00:33:55,000
and there are a variety of
variations on this.

384
00:33:55,000 --> 00:34:00,000
Those are sort of technical details
how to do it.  But here's

385
00:34:00,000 --> 00:34:05,000
the principle.
Let's just, for the moment,

386
00:34:05,000 --> 00:34:11,000
just consider that it's got a label
on it.  So if we take the messenger

387
00:34:11,000 --> 00:34:17,000
RNA, and take this little chip that
has samples in the extreme,

388
00:34:17,000 --> 00:34:22,000
it could be a sample of every single
gene that's in the genome of that

389
00:34:22,000 --> 00:34:28,000
organism, we take this labeled RNA,
actually what we would usually do is

390
00:34:28,000 --> 00:34:34,000
to use this to make a labeled cDNA
preparation, which would be a copy

391
00:34:34,000 --> 00:34:39,000
of each one of these things.
That the technically easy way to get

392
00:34:39,000 --> 00:34:43,000
label into it.
But what we do have is if the gene

393
00:34:43,000 --> 00:34:47,000
was on, its messenger RNA would be
on, and we'd have a bunch of stuff

394
00:34:47,000 --> 00:34:52,000
corresponding to gene one that had
label on it.  And if we give it a

395
00:34:52,000 --> 00:34:56,000
chance, that will,
then, hybridize here.

396
00:34:56,000 --> 00:35:01,000
And there would be some way of
detecting this label.

397
00:35:01,000 --> 00:35:05,000
If gene two was off in that sample,
there won't be any hybridization.

398
00:35:05,000 --> 00:35:10,000
There won't be any signal.
You can sort of see in principle

399
00:35:10,000 --> 00:35:14,000
what you're doing is you're
interrogating each gene in the

400
00:35:14,000 --> 00:35:19,000
extreme, each gene in the organism
under some condition.

401
00:35:19,000 --> 00:35:23,000
Is it on?  Is it off?
If you did various samples,

402
00:35:23,000 --> 00:35:28,000
and you could see maybe it's in an
intermediate level, and so on.

403
00:35:28,000 --> 00:35:32,000
So, the chips look like that,
50-100,000 genes perhaps, something

404
00:35:32,000 --> 00:35:37,000
like that.  These things are really
small.  Here's sort of a display of

405
00:35:37,000 --> 00:35:41,000
a simple one, and this is one where
they're taking RNA.

406
00:35:41,000 --> 00:35:46,000
The samples are from two conditions.
One's labeled with a dye that's

407
00:35:46,000 --> 00:35:50,000
green, and one's labeled with a dye
that's red.  And,

408
00:35:50,000 --> 00:35:55,000
if you get equal amounts,
it looks yellow.  So, they mix the

409
00:35:55,000 --> 00:35:59,000
two things together,
and if the gene is the same under

410
00:35:59,000 --> 00:36:04,000
two conditions it would be yellow.
Under one condition,

411
00:36:04,000 --> 00:36:08,000
if the gene was on in condition one
than it would be green,

412
00:36:08,000 --> 00:36:12,000
and off in two, and back and forth.
So, without trying to get lost in

413
00:36:12,000 --> 00:36:16,000
the technical details right now,
which doesn't matter, the principle

414
00:36:16,000 --> 00:36:20,000
of this thing is that you can take,
you can sort of, by making a

415
00:36:20,000 --> 00:36:24,000
preparation of RNA,
then you can use these DNA chips and

416
00:36:24,000 --> 00:36:28,000
say, is each gene on and off?
Or if I switch conditions, who

417
00:36:28,000 --> 00:36:31,000
comes on and off?
So, it's a little like,

418
00:36:31,000 --> 00:36:35,000
I think of it this way.  It's like
having, all right,

419
00:36:35,000 --> 00:36:39,000
who's on today?  And a number of
hands go up, or something.

420
00:36:39,000 --> 00:36:43,000
And the rest of you would be off.
But, come back on Monday, and I say

421
00:36:43,000 --> 00:36:46,000
to the something or other,
and a different set of you would put

422
00:36:46,000 --> 00:36:50,000
up hands.  And what I'm kind of
looking at are the changes between

423
00:36:50,000 --> 00:36:54,000
that.  And the sort of thing where
this has been so powerful,

424
00:36:54,000 --> 00:36:58,000
for example, is there are kind of
cancers for which there is a

425
00:36:58,000 --> 00:37:02,000
treatment, but it was
only 20% successful.

426
00:37:02,000 --> 00:37:07,000
And, when people started to study
these cancers and then looked to see

427
00:37:07,000 --> 00:37:12,000
what genes were on,
what they realized was even though

428
00:37:12,000 --> 00:37:17,000
physicians had given these cancers a
particular name,

429
00:37:17,000 --> 00:37:22,000
if you looked at which genes were
being expressed,

430
00:37:22,000 --> 00:37:27,000
they fell into two classes,
class A and class B.

431
00:37:27,000 --> 00:37:35,000
And what they then realized was that
the treatment they were using was

432
00:37:35,000 --> 00:37:43,000
100% effective of tumors of class A,
and wasn't doing anything for the

433
00:37:43,000 --> 00:37:51,000
tumors of class B.
The physician couldn't tell the

434
00:37:51,000 --> 00:38:00,000
difference between these two types
of tumors, but a microarray can

435
00:38:00,000 --> 00:38:05,000
So, again we have so little time in
this class.  I could go on basically

436
00:38:05,000 --> 00:38:11,000
for ages.  There's the output of the
real sort of DNA chip.

437
00:38:11,000 --> 00:38:17,000
You can see things are very dense,
and the great cleverness in doing

438
00:38:17,000 --> 00:38:23,000
these things, people now use the
technology that goes with LaserJet

439
00:38:23,000 --> 00:38:29,000
printers to actually synthesize
little pieces of either DNA or

440
00:38:29,000 --> 00:38:35,000
proteins starting on a little spot
on each membrane, or on

441
00:38:35,000 --> 00:38:39,000
the chip, or whatever.
You put it on one nucleotide and

442
00:38:39,000 --> 00:38:43,000
then you put it on the next,
and the next, and the next.  You

443
00:38:43,000 --> 00:38:47,000
could sequence it using technology
that's already around for inkjet

444
00:38:47,000 --> 00:38:51,000
printers at that kind of thing.
So here you've seen a fusion of

445
00:38:51,000 --> 00:38:55,000
different types of engineering.
OK, the last thing I'm going to

446
00:38:55,000 --> 00:38:59,000
tell you about is a little bit about
the immune system.

447
00:38:59,000 --> 00:39:03,000
We've run into this.
This is a movie that some of you

448
00:39:03,000 --> 00:39:08,000
liked, got the biggest aw I think of
the last part of the course anyway.

449
00:39:08,000 --> 00:39:13,000
But what we are seeing here is a
white blood cell pushing aside from

450
00:39:13,000 --> 00:39:18,000
red blood cells,
which are stationary,

451
00:39:18,000 --> 00:39:22,000
and chasing a bacterium.
It's obvious that it can trace it.

452
00:39:22,000 --> 00:39:27,000
It's able to recognize some things,
and at some point, then, it took it

453
00:39:27,000 --> 00:39:32,000
up.  The principle of what happened
there was this white blood cell had

454
00:39:32,000 --> 00:39:37,000
a capacity to recognize the bacteria,
then bind to it.

455
00:39:37,000 --> 00:39:43,000
And then, its membrane,
this is the membrane, and this is a

456
00:39:43,000 --> 00:39:50,000
white blood cell.
There's many types of these,

457
00:39:50,000 --> 00:39:56,000
and it pinches the membrane off.
So you have the bacterium.  This is

458
00:39:56,000 --> 00:40:02,000
the bacterium.
And, it's inside a little membrane

459
00:40:02,000 --> 00:40:06,000
compartment, as if the bacteria is
in a little soap bubble.

460
00:40:06,000 --> 00:40:11,000
And the principle of what happens
is the white blood cell has another

461
00:40:11,000 --> 00:40:15,000
soap bubble that's full of poison.
And, if you took two soap bubbles

462
00:40:15,000 --> 00:40:19,000
and push them together,
you know what happens.  They'll fuse,

463
00:40:19,000 --> 00:40:24,000
and you'll get a bigger soap bubble.
And that's, in essence, how these

464
00:40:24,000 --> 00:40:28,000
white blood cells normally kill
bacteria.  They would bring together

465
00:40:28,000 --> 00:40:33,000
these two compartments.
Now you have a bacteria and a poison

466
00:40:33,000 --> 00:40:38,000
within a white blood cell at the
bacterium, would get killed.

467
00:40:38,000 --> 00:40:43,000
And we talked about how bacteria
fought back.  That was a

468
00:40:43,000 --> 00:40:48,000
streptococcus that has a capsule.
And the capsule, by having

469
00:40:48,000 --> 00:40:53,000
polysaccharide on the outside,
prevent the white blood cell from

470
00:40:53,000 --> 00:40:58,000
being able to grab hold of some
feature of the bacteria,

471
00:40:58,000 --> 00:41:04,000
that starts this process of killing
it.

472
00:41:04,000 --> 00:41:08,000
And when I told you the story of how
DNA was found,

473
00:41:08,000 --> 00:41:12,000
it was people studying pneumonia.
If you remember, it was

474
00:41:12,000 --> 00:41:17,000
streptococcus.
If the streptococcus had a capsule,

475
00:41:17,000 --> 00:41:21,000
and the people would get very sick.
And after five or six days,

476
00:41:21,000 --> 00:41:26,000
there would be a crisis where they
either lived, or they died.

477
00:41:26,000 --> 00:41:30,000
And what would happen in that time
is that, the last thing I'll tell

478
00:41:30,000 --> 00:41:35,000
you about, what's called the
adaptive immune system would have

479
00:41:35,000 --> 00:41:39,000
generated special recognition
molecules called antibodies that

480
00:41:39,000 --> 00:41:44,000
would have learns to recognize the
capsule at that bacterium.

481
00:41:44,000 --> 00:41:49,000
And once those were there,
now those white blood cells would be

482
00:41:49,000 --> 00:41:54,000
able to capture the bacterium,
because the antibodies give it a

483
00:41:54,000 --> 00:41:59,000
hand in recognizing there was
something there that needed

484
00:41:59,000 --> 00:42:04,000
to be killed.
The way this adaptive immune system

485
00:42:04,000 --> 00:42:09,000
works, it's almost like science
fiction.  And I'll tell you the

486
00:42:09,000 --> 00:42:14,000
molecular basis of it.
The key insight came from Susumu

487
00:42:14,000 --> 00:42:19,000
Tonegawa, another member of the MIT
faculty biology,

488
00:42:19,000 --> 00:42:24,000
and also runs Picower Center,
who got a Nobel Prize for

489
00:42:24,000 --> 00:42:30,000
understanding the basis of the
diversity of the immune system.

490
00:42:30,000 --> 00:42:35,000
What I just want to do for the
moment is just sort of point out the

491
00:42:35,000 --> 00:42:40,000
key features of what's called the
adaptive immune system.

492
00:42:40,000 --> 00:42:45,000
And this is one of the reasons that
we are able to live.

493
00:42:45,000 --> 00:42:50,000
And even though we get sick from
time to time, and we've all had one

494
00:42:50,000 --> 00:42:55,000
thing or another get us for a little
while during the semester,

495
00:42:55,000 --> 00:43:00,000
the reason we aren't sick all the
time, and the reason we recover when

496
00:43:00,000 --> 00:43:06,000
we get sick, as we have what's
called an adaptive immune system.

497
00:43:06,000 --> 00:43:10,000
What happens when people get
infected with HIV virus is the cells

498
00:43:10,000 --> 00:43:15,000
that it lives in and destroys are
key players in your adaptive immune

499
00:43:15,000 --> 00:43:19,000
system.  And to some people don't
die from the H1V infection itself,

500
00:43:19,000 --> 00:43:24,000
they died because lots of things
that we just, bacteria or fungi,

501
00:43:24,000 --> 00:43:29,000
whatever things we just have on us
and we live with all the time

502
00:43:29,000 --> 00:43:33,000
suddenly become killers because you
lack the immune system

503
00:43:33,000 --> 00:43:39,000
that fights them off.
So the so-called adaptive immune

504
00:43:39,000 --> 00:43:47,000
system is absolutely amazing.
So several features, it's got an

505
00:43:47,000 --> 00:43:55,000
incredible diversity,
there is a general word that's used

506
00:43:55,000 --> 00:44:03,000
to describe some sort of all sorts
of chemical entities, and

507
00:44:03,000 --> 00:44:09,000
it's called an antigen.
So anyway, this can recognize many,

508
00:44:09,000 --> 00:44:13,000
many what are called antigens.  And
at the moment I think you can just

509
00:44:13,000 --> 00:44:17,000
think of them as some kind of
chemical entity.

510
00:44:17,000 --> 00:44:22,000
It could be a carbohydrate.
It could be a little piece of an

511
00:44:22,000 --> 00:44:26,000
organic molecule.
It could be a few amino acids on a

512
00:44:26,000 --> 00:44:30,000
protein.  But it's something that's
potentially capable of being

513
00:44:30,000 --> 00:44:35,000
recognized by your immune system.
So, there are many,

514
00:44:35,000 --> 00:44:40,000
many, many things.  And the amazing
thing is I can go into a lab and

515
00:44:40,000 --> 00:44:45,000
synthesize a molecule that's never
been seen on this Earth before and

516
00:44:45,000 --> 00:44:49,000
challenge somebody with it,
and you'll produce an immune

517
00:44:49,000 --> 00:44:54,000
response that will be mounted
against that even though it's never

518
00:44:54,000 --> 00:44:59,000
been on Earth before.
It's also the specificity.

519
00:44:59,000 --> 00:45:05,000
It is completely amazing.
If I were to take a protein,

520
00:45:05,000 --> 00:45:12,000
and then, let's say, put on a phenyl
ring with a methyl there,

521
00:45:12,000 --> 00:45:19,000
inject it into someone, the immune
system would figure out how to

522
00:45:19,000 --> 00:45:27,000
recognize this thing with the phenyl
ring, and the methyl.

523
00:45:27,000 --> 00:45:32,000
But, the response it generated,
it would see this but not, let's say,

524
00:45:32,000 --> 00:45:38,000
that if I wanted to get an immune
system, something with methyl here,

525
00:45:38,000 --> 00:45:44,000
I'd have to put that into the
organism and let the immune system

526
00:45:44,000 --> 00:45:50,000
figure out a response.
So, the specificity is at the same

527
00:45:50,000 --> 00:45:56,000
kind of level that you are used to
here.  If restriction enzymes can

528
00:45:56,000 --> 00:46:02,000
read different sequences in DNA or
protein can tell one optical isomer

529
00:46:02,000 --> 00:46:08,000
of a small molecule from another,
it's this fitting of complementary

530
00:46:08,000 --> 00:46:13,000
shapes.
So, with the immune system is all

531
00:46:13,000 --> 00:46:17,000
about is figuring out how to get a
complementary shape somehow that's

532
00:46:17,000 --> 00:46:21,000
able to recognize essentially any
kind of chemical shape and structure

533
00:46:21,000 --> 00:46:26,000
you can think of.
It's just mind blowing.

534
00:46:26,000 --> 00:46:30,000
And, you could already see right
from the beginning,

535
00:46:30,000 --> 00:46:35,000
where the fundamental problem people
could see from the beginning.

536
00:46:35,000 --> 00:46:39,000
It sounds like we would need a
genome that's infinitely big,

537
00:46:39,000 --> 00:46:43,000
full of things that are ready to
recognize anything.

538
00:46:43,000 --> 00:46:47,000
And so, one of the real surprises
is, and now we know,

539
00:46:47,000 --> 00:46:51,000
is there is 20,000 genes are so in
the human genome.

540
00:46:51,000 --> 00:46:55,000
There can't possibly be a zillion
genes, each one specific for one of

541
00:46:55,000 --> 00:46:59,000
these structures.
There had to be some underlying

542
00:46:59,000 --> 00:47:04,000
principle that we had to learn.
And that was one of the big

543
00:47:04,000 --> 00:47:09,000
challenges in the immune system for
a long time.  Another one was that

544
00:47:09,000 --> 00:47:14,000
if you have an organism that has
this capacity,

545
00:47:14,000 --> 00:47:19,000
and you could recognize it,
why don't you do yourself in?

546
00:47:19,000 --> 00:47:24,000
Because you yourself are full of
entities that could,

547
00:47:24,000 --> 00:47:29,000
in principle, generate an immune
system.

548
00:47:29,000 --> 00:47:35,000
So, one of the other things the
immune system had to deal with was

549
00:47:35,000 --> 00:47:41,000
avoiding self recognition.
If you're able to recognize

550
00:47:41,000 --> 00:47:48,000
anything, how do I not avoid killing
my own selves?

551
00:47:48,000 --> 00:47:54,000
So, that is another really
fundamental problem in this immune

552
00:47:54,000 --> 00:48:00,000
system.  This is exciting.
We can all stop and watch,

553
00:48:00,000 --> 00:48:05,000
but I think I'll just try and keep
soldiering along for the last minute

554
00:48:05,000 --> 00:48:10,000
or so.  So, one other feature that
was interesting about the immune

555
00:48:10,000 --> 00:48:14,000
system, is it has a memory.
And I'll tell you more about

556
00:48:14,000 --> 00:48:19,000
antibodies at the beginning of next
lecture.  But,

557
00:48:19,000 --> 00:48:24,000
these are the kinds of molecule
that's able to recognize these

558
00:48:24,000 --> 00:48:29,000
different entities.
And you've all heard the term in

559
00:48:29,000 --> 00:48:34,000
your ordinary life.
But, if we look at the level of

560
00:48:34,000 --> 00:48:39,000
antibodies that are
made in the body,

561
00:48:39,000 --> 00:48:48,000
if the first exposure to an antigen
that would get some kind of response

562
00:48:48,000 --> 00:48:57,000
that comes up upon the first
exposure, and this is time here.

563
00:48:57,000 --> 00:49:03,000
If we let there be some delay,
it could be even into years, and

564
00:49:03,000 --> 00:49:09,000
then we get a second exposure,
the antigen, the response is much

565
00:49:09,000 --> 00:49:15,000
higher.  And this could be a log
scale.  So, it could be dramatically

566
00:49:15,000 --> 00:49:21,000
higher.  So, what was the basis of
that?  How does it work?

567
00:49:21,000 --> 00:49:27,000
You see right there the principle
of vaccination in the sense that if

568
00:49:27,000 --> 00:49:33,000
you ever got chickenpox as a kid,
your body has learned how to make

569
00:49:33,000 --> 00:49:37,000
antibodies.
So if you ever see it again,

570
00:49:37,000 --> 00:49:41,000
it mounts a really big immune
response.  If you want to have a

571
00:49:41,000 --> 00:49:45,000
disease something like tetanus that
you haven't seen,

572
00:49:45,000 --> 00:49:48,000
you go to the doctor and they squirt
and a bit of the stuff that doesn't

573
00:49:48,000 --> 00:49:52,000
make you sick,
but it gives you the initial immune

574
00:49:52,000 --> 00:49:56,000
response.  Then,
if you ever step on a rusty nail,

575
00:49:56,000 --> 00:50:00,000
you get a very powerful response
against tetanus.

576
00:50:00,000 --> 00:50:04,000
And that sort of the underlying
principle of vaccination is this

577
00:50:04,000 --> 00:50:09,000
concept of memory.
We'll pick that up on Wednesday.

578
00:50:09,000 --> 00:50:12,000
So, have a great Patriots Day
weekend.