1
00:00:01,000 --> 00:00:05,000
Hello, everybody.
Can we get started?

2
00:00:05,000 --> 00:00:10,000
So my name is Andrew Chess. And
I'm lecturing today replacing

3
00:00:10,000 --> 00:00:15,000
Eric Lander for the day.
He had to be out of town,

4
00:00:15,000 --> 00:00:20,000
something that he could not
reschedule. He really tries to

5
00:00:20,000 --> 00:00:25,000
arrange his very busy
schedule so that he is here,

6
00:00:25,000 --> 00:00:30,000
but this was something that
could not be rearranged. Anyway.

7
00:00:30,000 --> 00:00:34,000
So I am a professor at
Harvard Medical School,

8
00:00:34,000 --> 00:00:38,000
but I have a long history here at
MIT, including being on the faculty

9
00:00:38,000 --> 00:00:42,000
here for a number of years. I
used to teach undergraduates.

10
00:00:42,000 --> 00:00:46,000
And even going back further than
that, I actually took this class.

11
00:00:46,000 --> 00:00:50,000
It was called 7.01 without
any extra number at that time.

12
00:00:50,000 --> 00:00:54,000
Now it is what, 7.012 or 7.
13? 0-1-2. Anyway. So it was

13
00:00:54,000 --> 00:00:58,000
called 7.01. And it was an
extremely interesting introduction

14
00:00:58,000 --> 00:01:01,000
to biology back then. It was
some time over 20 years ago.

15
00:01:01,000 --> 00:01:05,000
I'm not sure exactly how many years.
I kind of stopped counting at 20.

16
00:01:05,000 --> 00:01:09,000
So when Eric called me, when
Professor Lander called me up to ask

17
00:01:09,000 --> 00:01:12,000
me to give this lecture, I
talked to him for a while and I

18
00:01:12,000 --> 00:01:16,000
agreed to do it. And then
I was thinking about what

19
00:01:16,000 --> 00:01:20,000
to present. And I went over the
material that he has presented to

20
00:01:20,000 --> 00:01:23,000
earlier this week. And I
discussed with him some of

21
00:01:23,000 --> 00:01:27,000
the things that he likes to do for
the third lecture on neurobiology.

22
00:01:27,000 --> 00:01:31,000
And I also thought about some of
the things that I would like to do.

23
00:01:31,000 --> 00:01:34,000
And so one of the things
that occurred to me,

24
00:01:34,000 --> 00:01:38,000
so it's probably occurred to
you from hearing Eric talk about

25
00:01:38,000 --> 00:01:42,000
neurobiology earlier this week that
he is very enthusiastic about the

26
00:01:42,000 --> 00:01:45,000
subject. He always talks about it
as being one of the driving forces

27
00:01:45,000 --> 00:01:49,000
that led him to enter biology from
the realm of mathematics where he

28
00:01:49,000 --> 00:01:53,000
started his academic career.
Anyway. So he's always talked to

29
00:01:53,000 --> 00:01:57,000
me, in the years I've known him,
about how he loves neurobiology.

30
00:01:57,000 --> 00:02:01,000
And I was thinking about
it. It is, in some ways,

31
00:02:01,000 --> 00:02:05,000
kind of ironic that Eric loves
neurobiology so much because in some

32
00:02:05,000 --> 00:02:09,000
ways he's been a big
trouble-maker for neurobiology.

33
00:02:09,000 --> 00:02:13,000
Let me explain. So Eric,
Professor Lander is, of course,

34
00:02:13,000 --> 00:02:17,000
as you all know, was an instrumental
driving force behind the sequencing

35
00:02:17,000 --> 00:02:21,000
of the human genome.
Before all those efforts,

36
00:02:21,000 --> 00:02:25,000
over the last decade, people used
to go around, biologists used to go

37
00:02:25,000 --> 00:02:30,000
around to each other
and they would talk.

38
00:02:30,000 --> 00:02:33,000
And they would say there are around
100,000 genes in the human genome,

39
00:02:33,000 --> 00:02:36,000
or 100,000 genes in a mouse genome.
Mammalian genomes have around

40
00:02:36,000 --> 00:02:39,000
100,000 genes. Sometimes
some people would say 90,

41
00:02:39,000 --> 00:02:42,000
00 genes. Sometimes they
would say 110,000 genes.

42
00:02:42,000 --> 00:02:45,000
But generally it was around
100, 00 genes. And everybody was

43
00:02:45,000 --> 00:02:48,000
comfortable with that. In
fact, right around the turn of

44
00:02:48,000 --> 00:02:52,000
the century when the stock
market was going way up,

45
00:02:52,000 --> 00:02:55,000
the Internet bubble and
biotech bubble and everything,

46
00:02:55,000 --> 00:02:58,000
estimates of the number of
genes in a human genome actually

47
00:02:58,000 --> 00:03:02,000
went higher also. They
went up as high as 120,

48
00:03:02,000 --> 00:03:06,000
00, 150,000. And this, I think,
was because various companies were

49
00:03:06,000 --> 00:03:10,000
competing to have the most genes
on their kind of micro array or

50
00:03:10,000 --> 00:03:14,000
whatever they were selling. They
wanted to say we have the most,

51
00:03:14,000 --> 00:03:18,000
and so they kept saying more.
The academic scientist usually

52
00:03:18,000 --> 00:03:22,000
still stayed around 100, 00
in terms of their thinking.

53
00:03:22,000 --> 00:03:26,000
OK. The other thing
that was of a lot of

54
00:03:26,000 --> 00:03:30,000
use to neurobiologists in terms of
them feeling that their problem of

55
00:03:30,000 --> 00:03:34,000
trying to figure out how the brain
is set up as a tractable problem was

56
00:03:34,000 --> 00:03:38,000
that they thought there were going
to be lots of genes available.

57
00:03:38,000 --> 00:03:42,000
So there were 100,000 genes in the
genome, and around half of them,

58
00:03:42,000 --> 00:03:46,000
people would say, are
probably brain-specific.

59
00:03:46,000 --> 00:03:50,000
So there were a variety of pieces
of evidence that people would think

60
00:03:50,000 --> 00:03:54,000
that a lot of the genes in the
genome would be brain-specific.

61
00:03:54,000 --> 00:03:58,000
And that still remains the case.
But, as you know from Eric Landers

62
00:03:58,000 --> 00:04:02,000
and other people's work sequencing
the genome and mouse genome and

63
00:04:02,000 --> 00:04:06,000
other genomes now, it looks
like mammals now have only

64
00:04:06,000 --> 00:04:10,000
around 30,000 to 40,000 genes.
Now, if Eric told you a different

65
00:04:10,000 --> 00:04:16,000
number listen to his number.
What did he say? [20,000 to 25,

66
00:04:16,000 --> 00:04:22,000
00?]. OK. Anyway. So many
fewer than 100,000. OK?

67
00:04:22,000 --> 00:04:28,000
A small number. A fly is thought
to have only around 15,000 genes.

68
00:04:28,000 --> 00:04:31,000
So Lander is now saying that humans
don't have that many more genes than

69
00:04:31,000 --> 00:04:34,000
flies. So this presented a
problem for neurobiologists,

70
00:04:34,000 --> 00:04:37,000
because even if you have
half of them brain-specific,

71
00:04:37,000 --> 00:04:41,000
you still don't have nearly as
many genes to play with to make the

72
00:04:41,000 --> 00:04:44,000
complex structure of the brain
as you did when there were 100,

73
00:04:44,000 --> 00:04:47,000
00 genes in the genome.
So the brain, of course,

74
00:04:47,000 --> 00:04:51,000
is an extremely complicated
structure. There are thought to be

75
00:04:51,000 --> 00:04:54,000
somewhere between 100 billion and
a trillion different neurons in the

76
00:04:54,000 --> 00:04:58,000
brain, and they also fall into
many, many different neural types.

77
00:04:58,000 --> 00:05:01,000
And so the developmental process,
developmental biology is something

78
00:05:01,000 --> 00:05:05,000
that some of you will study in
future courses and you'll have had

79
00:05:05,000 --> 00:05:09,000
some introduction to here,
that's how you get from a single

80
00:05:09,000 --> 00:05:13,000
fertilized egg, a single
cell to the complex

81
00:05:13,000 --> 00:05:16,000
organism. In the brain it's a
particularly difficult problem

82
00:05:16,000 --> 00:05:20,000
because you have so many different
types of cells and so many cells.

83
00:05:20,000 --> 00:05:24,000
And each cell then makes all
of these complex connections.

84
00:05:24,000 --> 00:05:28,000
A given neuron might connect to 1,
00 or a few thousand different other

85
00:05:28,000 --> 00:05:32,000
cells as a normal process. So
forming all these different kinds

86
00:05:32,000 --> 00:05:36,000
of neurons and wiring up
is a very daunting problem.

87
00:05:36,000 --> 00:05:40,000
So what I thought I would do today
would be to focus on two examples

88
00:05:40,000 --> 00:05:44,000
where in each case, starting
with either one gene or a

89
00:05:44,000 --> 00:05:48,000
small number of genes you
get a lot of complexity.

90
00:05:48,000 --> 00:05:52,000
So this would then allow the
smaller number of total gene number

91
00:05:52,000 --> 00:05:56,000
in the human genome to allow a lot
of different kinds of proteins and

92
00:05:56,000 --> 00:06:00,000
maybe provide some explanations
for certain parts of the complexity

93
00:06:00,000 --> 00:06:04,000
of the brain. Now, by
no means am I going to

94
00:06:04,000 --> 00:06:08,000
attempt to explain all
of how the brain develops.

95
00:06:08,000 --> 00:06:12,000
That would take, well,
at least one course,

96
00:06:12,000 --> 00:06:16,000
more likely a few different courses
to actually get a good appreciation

97
00:06:16,000 --> 00:06:20,000
of that, but I'm going to go through
a couple of very intriguing examples

98
00:06:20,000 --> 00:06:24,000
that are approachable at
the level of this class.

99
00:06:24,000 --> 00:06:43,000
OK. So the
standard way that we

100
00:06:43,000 --> 00:06:49,000
think about how genetic information
gets made into proteins is that

101
00:06:49,000 --> 00:06:55,000
there is DNA and then RNA and then
proteins. I hope at this point in 7.

102
00:06:55,000 --> 00:07:01,000
1 this is all familiar to you.
Good. OK. So the DNA sequence

103
00:07:01,000 --> 00:07:06,000
gets transcribed into an RNA,
and then there's a splicing event

104
00:07:06,000 --> 00:07:12,000
which takes bits and pieces
of the RNA, puts them together,

105
00:07:12,000 --> 00:07:18,000
and then there's an area of the
RNA that tells the ribosome to start

106
00:07:18,000 --> 00:07:24,000
making protein. And
so you get the protein

107
00:07:24,000 --> 00:07:29,000
synthesis. Everything
is following from the

108
00:07:29,000 --> 00:07:33,000
blueprint that was in the DNA.
So what I'm going to talk about

109
00:07:33,000 --> 00:07:37,000
today are two different examples.
One of them involves alternative

110
00:07:37,000 --> 00:07:49,000
splicing.

111
00:07:49,000 --> 00:07:54,000
And that will be causes where
instead of there being a static

112
00:07:54,000 --> 00:07:59,000
always reproduced way of going
from DNA to RNA to a protein,

113
00:07:59,000 --> 00:08:04,000
that there are different alternative
splicing events that can occur.

114
00:08:04,000 --> 00:08:08,000
And this can allow one gene to make
multiple different protein products.

115
00:08:08,000 --> 00:08:13,000
And then I'm going to go over
another way that you can violate

116
00:08:13,000 --> 00:08:18,000
this central dogma,
this DNA, RNA to protein,

117
00:08:18,000 --> 00:08:22,000
which is something called RNA
editing. So, as you might imagine

118
00:08:22,000 --> 00:08:27,000
from the common usage of the word
editing, by editing what we mean is

119
00:08:27,000 --> 00:08:32,000
that the RNA sequence itself is
actually changed so that it no

120
00:08:32,000 --> 00:08:37,000
longer reflect the exact
nucleotide sequence of the DNA.

121
00:08:37,000 --> 00:08:41,000
This can also add diversity to the
number of potential encoded proteins.

122
00:08:41,000 --> 00:08:46,000
I want to make sure that
I, I'm going to talk about

123
00:08:46,000 --> 00:08:50,000
neural-specific examples, but
I want to mention that these

124
00:08:50,000 --> 00:08:55,000
processes, alternative splicing and
RNA editing are used also by other

125
00:08:55,000 --> 00:09:00,000
parts of the developing animal,
and also in other plants and other

126
00:09:00,000 --> 00:09:05,000
organisms, but not just by the
brain to generate diversity.

127
00:09:05,000 --> 00:09:08,000
So these are mechanisms that are
widely used. But some of the most

128
00:09:08,000 --> 00:09:12,000
striking examples, as you'll
see from my lecture and in

129
00:09:12,000 --> 00:09:16,000
further reading that you might
do in the future, some of the most

130
00:09:16,000 --> 00:09:19,000
striking examples come
from the nervous system.

131
00:09:19,000 --> 00:09:23,000
And that's not surprising given the
complexity of the nervous system and

132
00:09:23,000 --> 00:09:27,000
the fact that there are so many
genes out there ready to help with

133
00:09:27,000 --> 00:09:31,000
this complexity. So
the first I'll turn to

134
00:09:31,000 --> 00:09:44,000
alternative splicing.

135
00:09:44,000 --> 00:09:47,000
So first, before getting into the
extremely complex case that I'm

136
00:09:47,000 --> 00:09:51,000
going to focus on, I'm
going to just briefly,

137
00:09:51,000 --> 00:09:55,000
by way of introduction, go
over a standard alternative

138
00:09:55,000 --> 00:10:15,000
splicing scenario.

139
00:10:15,000 --> 00:10:23,000
OK. So in a gene for which
there is no alternative splicing,

140
00:10:23,000 --> 00:10:31,000
if I draw the exons as boxes
and the introns as lines,

141
00:10:31,000 --> 00:10:40,000
what winds up happening is you have
the first exon spliced to the second

142
00:10:40,000 --> 00:10:48,000
one, second to the third, third
to the forth. And so what you

143
00:10:48,000 --> 00:11:00,000
wind up with is
a messenger RNA.

144
00:11:00,000 --> 00:11:03,000
So here is the messenger RNA which
has been spliced from the primary

145
00:11:03,000 --> 00:11:07,000
transcript. The primary transcript,
of course, reflects the actual

146
00:11:07,000 --> 00:11:11,000
structure of the DNA in
terms of sequence also,

147
00:11:11,000 --> 00:11:14,000
because in the genomic DNA you'd
also have areas that are going to be

148
00:11:14,000 --> 00:11:18,000
exon, intron, exon intron exactly
like this. So this is just a

149
00:11:18,000 --> 00:11:22,000
general example of alternative
splicing, I'm sorry,

150
00:11:22,000 --> 00:11:26,000
of regular splicing. So then
alternative splicing would involve

151
00:11:26,000 --> 00:11:32,000
something like this.
You'd have 1, 2,

152
00:11:32,000 --> 00:11:41,000
3A, 3B, 4. So then what happens is
you have normal splicing from 1 to 2.

153
00:11:41,000 --> 00:11:51,000
And then 2 could either go to 3A,
which will then go to 4 leaving 3B

154
00:11:51,000 --> 00:12:00,000
out. Or the alternative is
that 2 can skip 3A, go to 3B,

155
00:12:00,000 --> 00:12:07,000
which will then splice to 4. So
this allows then two different

156
00:12:07,000 --> 00:12:33,000
messengers
to be formed.

157
00:12:33,000 --> 00:12:37,000
So this 3A and 3B might encode
a slightly different sequence and

158
00:12:37,000 --> 00:12:41,000
might then allow two distinct
proteins with different functions to

159
00:12:41,000 --> 00:12:45,000
be forwarded from one message.
So this is an example, a simple

160
00:12:45,000 --> 00:12:49,000
example, a general example
of alternative splicing.

161
00:12:49,000 --> 00:12:54,000
So from one gene you
have two proteins.

162
00:12:54,000 --> 00:13:02,000
The example that
I'm going to focus

163
00:13:02,000 --> 00:13:07,000
on today, instead of going
from one gene to two proteins,

164
00:13:07,000 --> 00:13:13,000
allows you to go from one gene
to 38, 00 different possibilities.

165
00:13:13,000 --> 00:13:18,000
It's actually 38,016 to be exact,
and I'll explain to you why, but 38,

166
00:13:18,000 --> 00:13:23,000
00 will have occurred to you that
this is larger than the number of

167
00:13:23,000 --> 00:13:29,000
genes that Eric Lander says
are in the human genome.

168
00:13:29,000 --> 00:13:34,000
It's certainly larger than the
number of genes in the fly genome.

169
00:13:34,000 --> 00:13:39,000
And this example I'm giving you is
from a single gene in the fruit fly.

170
00:13:39,000 --> 00:13:45,000
This one gene can come
in 38,000 different forms.

171
00:13:45,000 --> 00:13:59,000
The gene is called
drosophila DSCAM.

172
00:13:59,000 --> 00:14:03,000
It's named for a human gene which
was cloned first and characterized

173
00:14:03,000 --> 00:14:08,000
first which was called just plain
DSCAM. What DSCAM stands for is

174
00:14:08,000 --> 00:14:13,000
Down syndrome cell
adhesion molecule.

175
00:14:13,000 --> 00:14:33,000
And let me just
explain briefly why

176
00:14:33,000 --> 00:14:36,000
it has this name. I don't
think that this name is

177
00:14:36,000 --> 00:14:39,000
actually relevant so much to
the biology, and it certainly not

178
00:14:39,000 --> 00:14:42,000
relevant to the alternative splicing
because the human gene and the mouse

179
00:14:42,000 --> 00:14:45,000
gene, neither of them have a
lot of alternative splicing.

180
00:14:45,000 --> 00:14:48,000
This is something that
is particular to the fly.

181
00:14:48,000 --> 00:14:51,000
That's something I will
return to later in lecture.

182
00:14:51,000 --> 00:14:54,000
This name Down syndrome cell
adhesion molecule came about because

183
00:14:54,000 --> 00:14:57,000
this was cloned first from
human and it's located on human

184
00:14:57,000 --> 00:15:05,000
chromosome 21.

185
00:15:05,000 --> 00:15:10,000
Chromosome 21 is normally present
in two copies in every individual.

186
00:15:10,000 --> 00:15:15,000
In individuals who wind up with
three copies of chromosome 21,

187
00:15:15,000 --> 00:15:21,000
something called trisomy 21,
trisomy 21 causes Down syndrome,

188
00:15:21,000 --> 00:15:26,000
which is a syndrome that has some
brain manifestations like mental

189
00:15:26,000 --> 00:15:32,000
retardation, and also has a
number of other problems associated

190
00:15:32,000 --> 00:15:37,000
with it. When the people
who found this gene

191
00:15:37,000 --> 00:15:42,000
found it they named it. So
they gave the first part of its

192
00:15:42,000 --> 00:15:47,000
name, the DS comes from Down
syndrome. Cell adhesion molecule

193
00:15:47,000 --> 00:15:52,000
comes from the fact that this gene
is similar in structure to a lot of

194
00:15:52,000 --> 00:15:57,000
known cell adhesion molecules
that are encoded by many different

195
00:15:57,000 --> 00:16:02,000
loci in the genome. And
they initially thought that

196
00:16:02,000 --> 00:16:06,000
perhaps, and this gene is expressed
in the brain. And they initially

197
00:16:06,000 --> 00:16:10,000
thought that perhaps having an
extra copy, a third copy of this gene

198
00:16:10,000 --> 00:16:14,000
might be what's causing a
lot of the brain phenotypes.

199
00:16:14,000 --> 00:16:19,000
Subsequent work has not provided
further evidence for that.

200
00:16:19,000 --> 00:16:23,000
So at this point what I would say
is that the name of the gene is Down

201
00:16:23,000 --> 00:16:27,000
syndrome cell adhesion molecule,
DSCAM. It's on human chromosome 21.

202
00:16:27,000 --> 00:16:32,000
It may play a role in Down
syndrome but there isn't --

203
00:16:32,000 --> 00:16:37,000
The name is really the best
evidence that it plays a role,

204
00:16:37,000 --> 00:16:42,000
just the fact that they named it
that. OK. But in the fly this is

205
00:16:42,000 --> 00:16:47,000
an extremely interesting
molecule because, as I mentioned,

206
00:16:47,000 --> 00:16:52,000
it can come in 38,000 different
forms. OK. So as for why you would

207
00:16:52,000 --> 00:16:57,000
have a cell adhesion molecule
in the brain, I just want to

208
00:16:57,000 --> 00:17:02,000
mention briefly. So Professor
Lander went over with

209
00:17:02,000 --> 00:17:06,000
you the structure of a neuron.
That neurons have cell bodies and

210
00:17:06,000 --> 00:17:10,000
axons and growth cones which allow
them to get to wherever they're

211
00:17:10,000 --> 00:17:14,000
supposed to connect. One of
the types of molecules that

212
00:17:14,000 --> 00:17:18,000
allows an axon as it's growing,
the growth cone to lead an axon

213
00:17:18,000 --> 00:17:22,000
along a complex path is to interact
with various structures that it

214
00:17:22,000 --> 00:17:26,000
encounters. And so cell adhesion
molecule is one of the kinds of

215
00:17:26,000 --> 00:17:30,000
molecules that can allow the growth
cone and then the rest of the axon

216
00:17:30,000 --> 00:17:34,000
to interact with various other cells
or other extracellular substrates,

217
00:17:34,000 --> 00:17:38,000
proteins that have been deposited
by other kinds of cells as they make

218
00:17:38,000 --> 00:17:43,000
their way and make the appropriate
connections in the brain.

219
00:17:43,000 --> 00:17:46,000
So cell adhesion molecules
are one of the mechanisms.

220
00:17:46,000 --> 00:17:50,000
There are also mechanisms that
allow cells to respond to gradients

221
00:17:50,000 --> 00:17:54,000
of chemical signaling messengers.
Question? Yes. Could you explain

222
00:17:54,000 --> 00:17:58,000
what a growth cone is?
Oh, I'm sorry. That was not

223
00:17:58,000 --> 00:18:02,000
covered? OK. So the
neuron has a cell body

224
00:18:02,000 --> 00:18:06,000
with a nucleus and all the other
stuff that's in regular cells that

225
00:18:06,000 --> 00:18:10,000
you learned about. It
then has an axon which then

226
00:18:10,000 --> 00:18:14,000
allows it to connect. And
this connection could be very

227
00:18:14,000 --> 00:18:19,000
far away. In the case of, for
example, a motor neuron in the

228
00:18:19,000 --> 00:18:23,000
spinal cord that's intermating
a muscle in the foot,

229
00:18:23,000 --> 00:18:27,000
that single cell would have its cell
body in the spinal cord and its axon

230
00:18:27,000 --> 00:18:32,000
go all the way out to the foot.
OK? So that's just an example of

231
00:18:32,000 --> 00:18:36,000
one very long neuron.
Some of them are very long.

232
00:18:36,000 --> 00:18:40,000
Some of them are shorter. So this
is the axon. At the tip of the axon

233
00:18:40,000 --> 00:18:44,000
is this thing which looks sort of
like my son's mitten when it's cold

234
00:18:44,000 --> 00:18:48,000
outside. But this is the growth
cone. And basically what's going on

235
00:18:48,000 --> 00:18:52,000
is as the axon is growing out in
this direction it's feeling its way.

236
00:18:52,000 --> 00:18:56,000
And there might be cell adhesion
molecules on these different

237
00:18:56,000 --> 00:19:00,000
protrusions that if they
attach really well --

238
00:19:00,000 --> 00:19:06,000
Like let's say that this area over
here is stickier for this particular

239
00:19:06,000 --> 00:19:12,000
growth cone than this area over
here. Then this growth axon is more

240
00:19:12,000 --> 00:19:18,000
likely to grow in that direction.
OK. So cell adhesion molecules are

241
00:19:18,000 --> 00:19:24,000
well known to play important
roles in axon guidance.

242
00:19:24,000 --> 00:19:30,000
It's how axons grow in
different directions.

243
00:19:30,000 --> 00:19:34,000
So I will tell you know about the
DSCAM gene, and that will give some

244
00:19:34,000 --> 00:19:39,000
insight into how the 38, 00
different forms might be used

245
00:19:39,000 --> 00:19:43,000
because they're going to provide
different kinds of stickiness.

246
00:19:43,000 --> 00:19:54,000
Let me explain.

247
00:19:54,000 --> 00:20:00,000
So this is a drawing of the genomic
organization of DSCAM that allows

248
00:20:00,000 --> 00:20:13,000
the extensive
alternative splicing.

249
00:20:13,000 --> 00:20:17,000
So this diagram is similar to the
diagram that I drew by hand for the

250
00:20:17,000 --> 00:20:21,000
more simple cases. But
basically in DSCAM what you

251
00:20:21,000 --> 00:20:26,000
start out with is exon 1 gets
spliced to exon 2 gets spliced to

252
00:20:26,000 --> 00:20:30,000
exon 3, and then when you reach
exon 4 there are 12 distinct

253
00:20:30,000 --> 00:20:35,000
possibilities. And only
one of the 12 is chosen.

254
00:20:35,000 --> 00:20:39,000
In this case the diagram shows
it choosing, I don't know,

255
00:20:39,000 --> 00:20:43,000
the ninth one perhaps. Then exon
5 is regular so that always gets

256
00:20:43,000 --> 00:20:47,000
included. And exon 6 there
are 48 distinct choices.

257
00:20:47,000 --> 00:20:51,000
And again only one is chosen.
Here, in this example, this one has

258
00:20:51,000 --> 00:20:55,000
been chosen at the expense
of all of these other ones.

259
00:20:55,000 --> 00:20:59,000
Exon 7 and 8 are normal. And
exon 9 there are 33 choices.

260
00:20:59,000 --> 00:21:03,000
Exon 17 there are two choices. And
if you multiple 2 x 33 x 48 x 12

261
00:21:03,000 --> 00:21:07,000
you wind up with 38, 16.
There is evidence from a number

262
00:21:07,000 --> 00:21:12,000
of different types of studies,
including coning and sequencing,

263
00:21:12,000 --> 00:21:17,000
lots of different messenger RNAs
that are already spliced that

264
00:21:17,000 --> 00:21:21,000
basically almost all of these forms
can be made. So what this structure

265
00:21:21,000 --> 00:21:26,000
allows is for there to be diversity
generated in important areas of the

266
00:21:26,000 --> 00:21:31,000
cell adhesion part
of this molecule.

267
00:21:31,000 --> 00:21:53,000
The DSCAM molecule
starts out with a

268
00:21:53,000 --> 00:21:59,000
number of domains which are
called immunoglobulin-like domains.

269
00:21:59,000 --> 00:22:08,000
Immunoglobulin
domains are named for

270
00:22:08,000 --> 00:22:12,000
immunoglobulins which is
another name for antibodies.

271
00:22:12,000 --> 00:22:16,000
Antibodies help you fight infection.
I don't know if that's been covered.

272
00:22:16,000 --> 00:22:20,000
Has it been covered? Yes.
And the particular fold that

273
00:22:20,000 --> 00:22:24,000
they form allows recognition of
foreign antigens and it also allows

274
00:22:24,000 --> 00:22:28,000
stickiness of molecules in general.
So cell adhesion molecules often

275
00:22:28,000 --> 00:22:33,000
have these immunoglobulin
domains. The DSCAM starts out with,

276
00:22:33,000 --> 00:22:39,000
from the N-terminus towards
the C-terminus it starts out,

277
00:22:39,000 --> 00:22:45,000
the first nine domains are these Ig
type domains. That's then followed

278
00:22:45,000 --> 00:22:51,000
by another kind of domain called
a fiberonectin type domain,

279
00:22:51,000 --> 00:22:57,000
which that's not important.
All of the diversity is in this

280
00:22:57,000 --> 00:23:02,000
nine immunoglobulin domain.
The exon 4 diversity allows

281
00:23:02,000 --> 00:23:08,000
diversity of the second of the
nine. The exon 6 alternative splicing

282
00:23:08,000 --> 00:23:14,000
affects the third out of the
nine immunoglobulin folds.

283
00:23:14,000 --> 00:23:19,000
And the exon 9 diversity affects
the seventh. So of these nine

284
00:23:19,000 --> 00:23:25,000
domains of immunoglobulin folds
that allow for different kinds of

285
00:23:25,000 --> 00:23:31,000
stickiness, a lot
of them are the same.

286
00:23:31,000 --> 00:23:35,000
One is the same. 4,
5 and 6 are the same.

287
00:23:35,000 --> 00:23:40,000
And 8 and 9 are the same.
But 2, 3 and 7 have these

288
00:23:40,000 --> 00:23:44,000
differences which are encoded
by this striking kind of genomic

289
00:23:44,000 --> 00:24:11,000
structure and
alternative splicing.

290
00:24:11,000 --> 00:24:29,000
So how is this
diversity used?

291
00:24:29,000 --> 00:24:34,000
So the early models for how DSCAM
would be used stipulated that

292
00:24:34,000 --> 00:24:40,000
individual different kinds of
neurons might express vastly reduced

293
00:24:40,000 --> 00:24:45,000
subsets out of the 38,
00. So let's say that one

294
00:24:45,000 --> 00:24:51,000
particular neuron type, of
which there might be many

295
00:24:51,000 --> 00:24:57,000
different neurons, might
express maybe ten out of the

296
00:24:57,000 --> 00:25:02,000
38,000 or even one out of 38,000.
These were the different kinds of

297
00:25:02,000 --> 00:25:06,000
models that were tossed around by
people who were thinking about this

298
00:25:06,000 --> 00:25:11,000
problem. But then people started to
study it. And what turned out to be

299
00:25:11,000 --> 00:25:16,000
the case is that it looks like every
kind of neuron population at first

300
00:25:16,000 --> 00:25:20,000
approximately expresses almost
all of the different forms.

301
00:25:20,000 --> 00:25:25,000
OK? So these are wrong, these
models. And each different

302
00:25:25,000 --> 00:25:30,000
neuron type expresses a
slightly different repertoire.

303
00:25:30,000 --> 00:25:35,000
But at first approximation over 10,
00 or 20,000 forms are possible for

304
00:25:35,000 --> 00:25:41,000
each different neuron type.
So that then caused people to

305
00:25:41,000 --> 00:25:47,000
scratch their heads and wonder,
well, how is this used then? How is

306
00:25:47,000 --> 00:25:53,000
this used to make different kinds
of neurons different from one another

307
00:25:53,000 --> 00:25:59,000
or anything in the
function of them?

308
00:25:59,000 --> 00:26:05,000
So the answer to this question has
emerged in part from analyses of

309
00:26:05,000 --> 00:26:12,000
individual single cells. So
it turns out that an individual

310
00:26:12,000 --> 00:26:18,000
cell, and I'm using the word cell
and neuron interchangeably because

311
00:26:18,000 --> 00:26:25,000
neurons are cells. And not
all cells are neurons but

312
00:26:25,000 --> 00:26:32,000
all neurons are cells. So
for one cell or one neuron it

313
00:26:32,000 --> 00:26:39,000
makes somewhere in the
range of 10 to 50 forms.

314
00:26:39,000 --> 00:26:44,000
These are randomly chosen,
apparently from the data that's

315
00:26:44,000 --> 00:26:49,000
available, from the tens of
thousands of forms that are possible.

316
00:26:49,000 --> 00:26:54,000
So you can imagine that two
neighboring cells that are otherwise

317
00:26:54,000 --> 00:26:59,000
identical, that each are picking,
let's say ten just to make it easy,

318
00:26:59,000 --> 00:27:04,000
ten different forms of DSCAM, are
going to wind up with very different

319
00:27:04,000 --> 00:27:10,000
repertoires of DSCAM
than an adjacent cell.

320
00:27:10,000 --> 00:27:13,000
So what this allows is each
individual cell to have a unique

321
00:27:13,000 --> 00:27:17,000
identity. The whole idea that
individual neurons might need to

322
00:27:17,000 --> 00:27:21,000
have a unique identity actually
is a new concept that's really been

323
00:27:21,000 --> 00:27:24,000
enlightened by this molecule.
Because the way that people used to

324
00:27:24,000 --> 00:27:28,000
think of neurons is that they would
wind up with unique identities based

325
00:27:28,000 --> 00:27:32,000
on the connections or experience,
what they were exposed to in terms

326
00:27:32,000 --> 00:27:36,000
of different stimuli. But what
this indicates is that from

327
00:27:36,000 --> 00:27:42,000
the splicing of an individual gene
and the fact that each time this

328
00:27:42,000 --> 00:27:48,000
gene gets spliced you can wind up
with a different form that at any

329
00:27:48,000 --> 00:27:54,000
given time each cell will have
a unique set of messenger RNAs,

330
00:27:54,000 --> 00:28:00,000
and therefore proteins
encoding this DSCAM gene.

331
00:28:00,000 --> 00:28:05,000
OK. So I mentioned earlier that
the human DSCAM does not have

332
00:28:05,000 --> 00:28:10,000
alternative splicing. We all
like to think of ourselves,

333
00:28:10,000 --> 00:28:15,000
humans and other mammals as having
brains that are on the level of

334
00:28:15,000 --> 00:28:21,000
complexity, at least on par with
the fly. And so it's odd to think of

335
00:28:21,000 --> 00:28:26,000
all this complexity that's there for
flies and other insects but why is

336
00:28:26,000 --> 00:28:31,000
it not there for humans? Well,
it turns out that there are

337
00:28:31,000 --> 00:28:36,000
other kinds of genes that do have
extensive alternative splicing in

338
00:28:36,000 --> 00:28:40,000
mammals. So one of them is called
neurexins. These are genes that are

339
00:28:40,000 --> 00:28:45,000
involved in synapse, how
the different kinds of cells

340
00:28:45,000 --> 00:28:50,000
communicate with each other at the
interface. There are genes called

341
00:28:50,000 --> 00:28:55,000
protocadherins. And there
are also other kinds of

342
00:28:55,000 --> 00:29:00,000
genes that all have extensive
alternative splicing in mammals.

343
00:29:00,000 --> 00:29:04,000
Interestingly, these
genes tend not to have

344
00:29:04,000 --> 00:29:08,000
extensive alternative splicing in
flies. It's as if in each lineage

345
00:29:08,000 --> 00:29:12,000
certain genes have been chosen
to get a lot of diversity by this

346
00:29:12,000 --> 00:29:16,000
mechanism of alternative splicing
and other genes are left with their

347
00:29:16,000 --> 00:29:20,000
just standard single
function where it's one gene,

348
00:29:20,000 --> 00:29:24,000
one RNA, one protein. OK.
So I'm going to switch now to

349
00:29:24,000 --> 00:29:29,000
the second example
which is RNA editing.

350
00:29:29,000 --> 00:29:41,000
So, as I
mentioned earlier,

351
00:29:41,000 --> 00:29:45,000
RNA editing involves an actual
change in the RNA sequence so that

352
00:29:45,000 --> 00:29:49,000
it no longer reflects the
exact DNA sequence. Now,

353
00:29:49,000 --> 00:29:53,000
this is different than splicing.
Splicing takes different pieces of

354
00:29:53,000 --> 00:29:57,000
RNA and splices them together
leaving out intervening sequences or

355
00:29:57,000 --> 00:30:01,000
introns. But in RNA editing you
actually change the nucleotide

356
00:30:01,000 --> 00:30:05,000
sequence so that it no longer
is identical to the DNA.

357
00:30:05,000 --> 00:30:10,000
This is used in a number of parts of
the brain. Most of the examples are

358
00:30:10,000 --> 00:30:15,000
brain-specific. There are
some non-brain specific

359
00:30:15,000 --> 00:30:20,000
parts. Most of the time the
editing event changes in adenosine,

360
00:30:20,000 --> 00:30:25,000
an A in the ACGT
nomenclature, into an inosine.

361
00:30:25,000 --> 00:30:34,000
This is read
differently by the

362
00:30:34,000 --> 00:30:40,000
ribosome than the adenosine. So
this leads, for example, in an

363
00:30:40,000 --> 00:30:45,000
important kind of channel
called a glutamate receptor.

364
00:30:45,000 --> 00:30:51,000
And the specific subtype that I'm
talking about is something called an

365
00:30:51,000 --> 00:30:56,000
AMPA glutamate receptor. That's
for a chemical ligand that

366
00:30:56,000 --> 00:31:02,000
activates this particular
kind of glutamate receptor.

367
00:31:02,000 --> 00:31:10,000
This leads to an important change
of a glutamine to an arginine in the

368
00:31:10,000 --> 00:31:19,000
protein. So let me draw a quick
diagram of what the protein looks

369
00:31:19,000 --> 00:31:27,000
like so we can see what the
importance of this glutamine to

370
00:31:27,000 --> 00:31:36,000
arginine switch in this
glutamate receptor is.

371
00:31:36,000 --> 00:31:47,000
So in the absence
of detailed

372
00:31:47,000 --> 00:31:51,000
structural information about
different kinds of neurotransmitter

373
00:31:51,000 --> 00:31:55,000
receptors people often draw a
diagram like this where this is the

374
00:31:55,000 --> 00:31:59,000
outside of the cell, this
is the inside of the cell,

375
00:31:59,000 --> 00:32:05,000
this represents the cell membrane.
And here's the amino terminus.

376
00:32:05,000 --> 00:32:12,000
And then they draw a transmembrane
portion. And this is what's called

377
00:32:12,000 --> 00:32:18,000
a reentrant loop. It doesn't
quite pass through the

378
00:32:18,000 --> 00:32:25,000
membrane, but then it passes
the membrane again. And here's

379
00:32:25,000 --> 00:32:33,000
the carboxy terminus. The
glutamine to arginine change is

380
00:32:33,000 --> 00:32:41,000
here. It's in this area which
is involved in making the pore.

381
00:32:41,000 --> 00:32:50,000
So the pore of the
channel has this change.

382
00:32:50,000 --> 00:33:04,000
Glutamine to
arginine change.

383
00:33:04,000 --> 00:33:11,000
This vastly changes the
properties of the channel,

384
00:33:11,000 --> 00:33:18,000
a channel that doesn't
undergo this editing event.

385
00:33:18,000 --> 00:33:25,000
So let me just state that
for the GluR2 AMPA receptor,

386
00:33:25,000 --> 00:33:33,000
which is one of four different
genes, it is 99% edited in adults.

387
00:33:33,000 --> 00:33:37,000
So where over 99% of the time this
adenosine is made into an inosine

388
00:33:37,000 --> 00:33:42,000
which leads to a glutamine
becoming an arginine in the protein.

389
00:33:42,000 --> 00:33:47,000
What this does to channel is
it changes its permeability.

390
00:33:47,000 --> 00:33:52,000
So this is a kind of channel that
is mostly designed to let sodium in,

391
00:33:52,000 --> 00:33:57,000
but if it doesn't get edited,
if the glutamine is there it also

392
00:33:57,000 --> 00:34:02,000
lets calcium in. So whether
or not calcium gets into

393
00:34:02,000 --> 00:34:06,000
the cell is very important because
they're both, both sodium and

394
00:34:06,000 --> 00:34:10,000
calcium are cat ions and can lead
to membrane, potential disturbances

395
00:34:10,000 --> 00:34:14,000
like you learned about earlier this
week, leading to an action potential.

396
00:34:14,000 --> 00:34:18,000
But calcium also has other effects.
It can lead ultimately to the turn

397
00:34:18,000 --> 00:34:22,000
on and off of genes and
phosphorylation of various proteins

398
00:34:22,000 --> 00:34:26,000
to other kinds of effects in
the neuron. So it has to be

399
00:34:26,000 --> 00:34:30,000
regulated very tightly. So
these channels are designed to

400
00:34:30,000 --> 00:34:34,000
just let sodium through, to
be involved in the transmission

401
00:34:34,000 --> 00:34:38,000
of an action potential
from one neuron to the next.

402
00:34:38,000 --> 00:34:42,000
So if you had a perturbation in
this process you would then also let

403
00:34:42,000 --> 00:34:46,000
calcium in because the glutamine
containing channel lets calcium in.

404
00:34:46,000 --> 00:34:50,000
In fact, early in development it's
probably true that you don't edit

405
00:34:50,000 --> 00:34:55,000
100% and you let a
little bit of calcium in.

406
00:34:55,000 --> 00:35:00,000
And there are also other glutamate
receptors that are not part of the

407
00:35:00,000 --> 00:35:05,000
AMPA family but they are related.
And they're encoded by genes called,

408
00:35:05,000 --> 00:35:11,000
so this GluR1 through 4 encodes AMPA
receptors, a gene called GluR5 and 6,

409
00:35:11,000 --> 00:35:16,000
they have editing which is more
regulated. And by regulated I mean

410
00:35:16,000 --> 00:35:22,000
that the editing is sometimes
present and sometimes not.

411
00:35:22,000 --> 00:35:27,000
So even within a given neuron you
might have some channels that have

412
00:35:27,000 --> 00:35:33,000
the glutamine and some
channels that have the arginine.

413
00:35:33,000 --> 00:35:41,000
There are also
other sites.

414
00:35:41,000 --> 00:35:45,000
I've talked about the main site.
This is the one that has the most

415
00:35:45,000 --> 00:35:49,000
profound impact on the function of
the protein. There are also other

416
00:35:49,000 --> 00:35:53,000
sites in the molecule that are
edited. And then they also have

417
00:35:53,000 --> 00:35:57,000
important but slightly less
prominent roles in the regulation of

418
00:35:57,000 --> 00:36:02,000
these channels. So what
leads this gene to become

419
00:36:02,000 --> 00:36:07,000
edited? Why do most genes not
become edited and this gene becomes

420
00:36:07,000 --> 00:36:12,000
edited? Well, people are
starting to pursue that

421
00:36:12,000 --> 00:36:17,000
kind of mechanisms. And
one of the mechanisms that's

422
00:36:17,000 --> 00:36:23,000
become very clear is that, for
example, in the Q to R change in

423
00:36:23,000 --> 00:36:28,000
the pore, or the adenosine to
inosine change in the messenger RNA

424
00:36:28,000 --> 00:36:33,000
that leads to the Q to R change
in the pore, if you look at

425
00:36:33,000 --> 00:36:38,000
the exon where that --
I'll draw it as an A.

426
00:36:38,000 --> 00:36:44,000
Where that A is present.
There's an area, and then here's

427
00:36:44,000 --> 00:36:49,000
the intron, of the intronic sequence
which actually loops back and then

428
00:36:49,000 --> 00:36:55,000
allows base pairing to form between
the messenger RNA and the intron.

429
00:36:55,000 --> 00:37:01,000
And then the enzymes that are
involved in mediating this adenosine

430
00:37:01,000 --> 00:37:07,000
to inosine change recognize the base
pairing of this short area and some

431
00:37:07,000 --> 00:37:13,000
sequence specificity to the
RNA sequence that's around here.

432
00:37:13,000 --> 00:37:19,000
It's not just any base pairing,
but the base pairing is critical.

433
00:37:19,000 --> 00:37:25,000
And then the enzymes, which
are called adenosine deaminase,

434
00:37:25,000 --> 00:37:32,000
can mediate this change of
an adenosine to an inosine.

435
00:37:32,000 --> 00:37:37,000
So this gene has been selected
to have parts of its intron,

436
00:37:37,000 --> 00:37:42,000
in addition to allowing splicing
to occur, to actual be able to base

437
00:37:42,000 --> 00:37:47,000
pair and allow this editing function
to happen. And so this allows an

438
00:37:47,000 --> 00:37:52,000
individual gene then to make
more than one form of protein.

439
00:37:52,000 --> 00:37:57,000
OK. One other example that I'll
tell you about of RNA editing

440
00:37:57,000 --> 00:38:03,000
involves the serotonin system.
It involves a serotonin receptor

441
00:38:03,000 --> 00:38:10,000
which has RNA editing. And
this is a serotonin receptor

442
00:38:10,000 --> 00:38:18,000
whose name is serotonin receptor 2C,
so it's often written as 5-HT or 5

443
00:38:18,000 --> 00:38:25,000
hydroxtryptamine 2C serotonin
receptor. This receptor is a member

444
00:38:25,000 --> 00:38:33,000
of the G protein-coupled
receptor super family.

445
00:38:33,000 --> 00:38:39,000
G protein-coupled receptors are
7-transmembrane domain receptors.

446
00:38:39,000 --> 00:38:46,000
And the editing of the serotonin
receptor occurs in the second

447
00:38:46,000 --> 00:38:53,000
intracellular loop and affects
the coupling to G protein.

448
00:38:53,000 --> 00:39:00,000
So the way that these G
protein-coupled receptors --

449
00:39:00,000 --> 00:39:04,000
Did they do this already?
G protein-coupled receptors

450
00:39:04,000 --> 00:39:08,000
transduce the signal through a G
protein which often binds to the

451
00:39:08,000 --> 00:39:12,000
intracellular loops,
particularly the second loop.

452
00:39:12,000 --> 00:39:16,000
And the editing event which
changes adenosines to inosines,

453
00:39:16,000 --> 00:39:20,000
there are actually a few
of them in this region here,

454
00:39:20,000 --> 00:39:24,000
a few different sites which can
get changed. That affects the

455
00:39:24,000 --> 00:39:28,000
efficiency of the transduction,
when serotonin is present the

456
00:39:28,000 --> 00:39:33,000
transduction of a
signal inside the cell.

457
00:39:33,000 --> 00:39:37,000
There are other serotonin receptors
that are ligand gated channels but

458
00:39:37,000 --> 00:39:41,000
they don't appear to have the same
kind of editing as this serotonin

459
00:39:41,000 --> 00:39:46,000
receptor that is of the G
protein-coupled variety has.

460
00:39:46,000 --> 00:39:59,000
So I'll end with
just one final note

461
00:39:59,000 --> 00:40:03,000
about this serotonin receptor which
is that a drug called fluoxetine or

462
00:40:03,000 --> 00:40:07,000
Prozac, did Eric go over
that this year? Prozac?

463
00:40:07,000 --> 00:40:11,000
No? He mentioned it. OK,
he mentioned it. So it's

464
00:40:11,000 --> 00:40:16,000
mostly known as something which
blocks reuptake of serotonin.

465
00:40:16,000 --> 00:40:20,000
So one cell releases serotonin,
which is a neurotransmitter.

466
00:40:20,000 --> 00:40:24,000
Another cell responds to it.
If you block the reuptake the

467
00:40:24,000 --> 00:40:28,000
neurotransmitter is present in the
synapse for a longer amount of time

468
00:40:28,000 --> 00:40:33,000
leading to increased signaling.
Prozac is widely thought to have its

469
00:40:33,000 --> 00:40:37,000
main effect, and that probably is
its main effect to just block the

470
00:40:37,000 --> 00:40:41,000
reuptake leading to
increased serotonin signaling.

471
00:40:41,000 --> 00:40:45,000
Someone has studied the serotonin
receptor in individuals who are

472
00:40:45,000 --> 00:40:49,000
taking Prozac versus individuals
who are not taking Prozac.

473
00:40:49,000 --> 00:40:53,000
And what they found was that there
were differences in the amount of

474
00:40:53,000 --> 00:40:57,000
editing of various sites in this
key area in individuals taking Prozac

475
00:40:57,000 --> 00:41:02,000
versus individuals
not taking Prozac.

476
00:41:02,000 --> 00:41:06,000
And the direction of the difference,
whether it's switching from edited

477
00:41:06,000 --> 00:41:10,000
to unedited or back, and
it varies for the different

478
00:41:10,000 --> 00:41:15,000
sites, the direction was the
opposite of what was seen in a

479
00:41:15,000 --> 00:41:19,000
comparison of brains of victims
of suicide versus brains of other

480
00:41:19,000 --> 00:41:24,000
accident victims. So it
looks like Prozac is having

481
00:41:24,000 --> 00:41:28,000
an effect which is in the opposite
effect to the skewing of editing

482
00:41:28,000 --> 00:41:33,000
that one sees in certain
cases of depression.

483
00:41:33,000 --> 00:41:38,000
So I bring that example up because
it's important to know that,

484
00:41:38,000 --> 00:41:43,000
you know, if you can impact on some
of these subtle differences between

485
00:41:43,000 --> 00:41:48,000
different kinds of messages,
like whether it's edited or not or

486
00:41:48,000 --> 00:41:53,000
whether the splicing is more towards
one kind of alternative splicing or

487
00:41:53,000 --> 00:41:58,000
another kind of alternative splicing,
these might provide very interesting

488
00:41:58,000 --> 00:42:03,000
pharmacologic targets for therapies
that might impact on a variety of

489
00:42:03,000 --> 00:42:08,000
different human diseases. So
what I've hoped to do is give you

490
00:42:08,000 --> 00:42:12,000
a sense of a couple of different
examples where you can take a single

491
00:42:12,000 --> 00:42:17,000
gene and make more than one protein,
and this can lead to increases in

492
00:42:17,000 --> 00:42:21,000
the diversity of neurons,
and therefore increases in the

493
00:42:21,000 --> 00:42:26,000
complexity of the brain.
Thank you. [APPLAUSE] Are there

494
00:42:26,000 --> 00:42:31,000
questions?