WEBVTT 00:00:01.000 --> 00:00:05.000 Hello, everybody. Can we get started? 00:00:05.000 --> 00:00:10.000 So my name is Andrew Chess. And I'm lecturing today replacing 00:00:10.000 --> 00:00:15.000 Eric Lander for the day. He had to be out of town, 00:00:15.000 --> 00:00:20.000 something that he could not reschedule. He really tries to 00:00:20.000 --> 00:00:25.000 arrange his very busy schedule so that he is here, 00:00:25.000 --> 00:00:30.000 but this was something that could not be rearranged. Anyway. 00:00:30.000 --> 00:00:34.000 So I am a professor at Harvard Medical School, 00:00:34.000 --> 00:00:38.000 but I have a long history here at MIT, including being on the faculty 00:00:38.000 --> 00:00:42.000 here for a number of years. I used to teach undergraduates. 00:00:42.000 --> 00:00:46.000 And even going back further than that, I actually took this class. 00:00:46.000 --> 00:00:50.000 It was called 7.01 without any extra number at that time. 00:00:50.000 --> 00:00:54.000 Now it is what, 7.012 or 7. 13? 0-1-2. Anyway. So it was 00:00:54.000 --> 00:00:58.000 called 7.01. And it was an extremely interesting introduction 00:00:58.000 --> 00:01:01.000 to biology back then. It was some time over 20 years ago. 00:01:01.000 --> 00:01:05.000 I'm not sure exactly how many years. I kind of stopped counting at 20. 00:01:05.000 --> 00:01:09.000 So when Eric called me, when Professor Lander called me up to ask 00:01:09.000 --> 00:01:12.000 me to give this lecture, I talked to him for a while and I 00:01:12.000 --> 00:01:16.000 agreed to do it. And then I was thinking about what 00:01:16.000 --> 00:01:20.000 to present. And I went over the material that he has presented to 00:01:20.000 --> 00:01:23.000 earlier this week. And I discussed with him some of 00:01:23.000 --> 00:01:27.000 the things that he likes to do for the third lecture on neurobiology. 00:01:27.000 --> 00:01:31.000 And I also thought about some of the things that I would like to do. 00:01:31.000 --> 00:01:34.000 And so one of the things that occurred to me, 00:01:34.000 --> 00:01:38.000 so it's probably occurred to you from hearing Eric talk about 00:01:38.000 --> 00:01:42.000 neurobiology earlier this week that he is very enthusiastic about the 00:01:42.000 --> 00:01:45.000 subject. He always talks about it as being one of the driving forces 00:01:45.000 --> 00:01:49.000 that led him to enter biology from the realm of mathematics where he 00:01:49.000 --> 00:01:53.000 started his academic career. Anyway. So he's always talked to 00:01:53.000 --> 00:01:57.000 me, in the years I've known him, about how he loves neurobiology. 00:01:57.000 --> 00:02:01.000 And I was thinking about it. It is, in some ways, 00:02:01.000 --> 00:02:05.000 kind of ironic that Eric loves neurobiology so much because in some 00:02:05.000 --> 00:02:09.000 ways he's been a big trouble-maker for neurobiology. 00:02:09.000 --> 00:02:13.000 Let me explain. So Eric, Professor Lander is, of course, 00:02:13.000 --> 00:02:17.000 as you all know, was an instrumental driving force behind the sequencing 00:02:17.000 --> 00:02:21.000 of the human genome. Before all those efforts, 00:02:21.000 --> 00:02:25.000 over the last decade, people used to go around, biologists used to go 00:02:25.000 --> 00:02:30.000 around to each other and they would talk. 00:02:30.000 --> 00:02:33.000 And they would say there are around 100,000 genes in the human genome, 00:02:33.000 --> 00:02:36.000 or 100,000 genes in a mouse genome. Mammalian genomes have around 00:02:36.000 --> 00:02:39.000 100,000 genes. Sometimes some people would say 90, 00:02:39.000 --> 00:02:42.000 00 genes. Sometimes they would say 110,000 genes. 00:02:42.000 --> 00:02:45.000 But generally it was around 100, 00 genes. And everybody was 00:02:45.000 --> 00:02:48.000 comfortable with that. In fact, right around the turn of 00:02:48.000 --> 00:02:52.000 the century when the stock market was going way up, 00:02:52.000 --> 00:02:55.000 the Internet bubble and biotech bubble and everything, 00:02:55.000 --> 00:02:58.000 estimates of the number of genes in a human genome actually 00:02:58.000 --> 00:03:02.000 went higher also. They went up as high as 120, 00:03:02.000 --> 00:03:06.000 00, 150,000. And this, I think, was because various companies were 00:03:06.000 --> 00:03:10.000 competing to have the most genes on their kind of micro array or 00:03:10.000 --> 00:03:14.000 whatever they were selling. They wanted to say we have the most, 00:03:14.000 --> 00:03:18.000 and so they kept saying more. The academic scientist usually 00:03:18.000 --> 00:03:22.000 still stayed around 100, 00 in terms of their thinking. 00:03:22.000 --> 00:03:26.000 OK. The other thing that was of a lot of 00:03:26.000 --> 00:03:30.000 use to neurobiologists in terms of them feeling that their problem of 00:03:30.000 --> 00:03:34.000 trying to figure out how the brain is set up as a tractable problem was 00:03:34.000 --> 00:03:38.000 that they thought there were going to be lots of genes available. 00:03:38.000 --> 00:03:42.000 So there were 100,000 genes in the genome, and around half of them, 00:03:42.000 --> 00:03:46.000 people would say, are probably brain-specific. 00:03:46.000 --> 00:03:50.000 So there were a variety of pieces of evidence that people would think 00:03:50.000 --> 00:03:54.000 that a lot of the genes in the genome would be brain-specific. 00:03:54.000 --> 00:03:58.000 And that still remains the case. But, as you know from Eric Landers 00:03:58.000 --> 00:04:02.000 and other people's work sequencing the genome and mouse genome and 00:04:02.000 --> 00:04:06.000 other genomes now, it looks like mammals now have only 00:04:06.000 --> 00:04:10.000 around 30,000 to 40,000 genes. Now, if Eric told you a different 00:04:10.000 --> 00:04:16.000 number listen to his number. What did he say? [20,000 to 25, 00:04:16.000 --> 00:04:22.000 00?]. OK. Anyway. So many fewer than 100,000. OK? 00:04:22.000 --> 00:04:28.000 A small number. A fly is thought to have only around 15,000 genes. 00:04:28.000 --> 00:04:31.000 So Lander is now saying that humans don't have that many more genes than 00:04:31.000 --> 00:04:34.000 flies. So this presented a problem for neurobiologists, 00:04:34.000 --> 00:04:37.000 because even if you have half of them brain-specific, 00:04:37.000 --> 00:04:41.000 you still don't have nearly as many genes to play with to make the 00:04:41.000 --> 00:04:44.000 complex structure of the brain as you did when there were 100, 00:04:44.000 --> 00:04:47.000 00 genes in the genome. So the brain, of course, 00:04:47.000 --> 00:04:51.000 is an extremely complicated structure. There are thought to be 00:04:51.000 --> 00:04:54.000 somewhere between 100 billion and a trillion different neurons in the 00:04:54.000 --> 00:04:58.000 brain, and they also fall into many, many different neural types. 00:04:58.000 --> 00:05:01.000 And so the developmental process, developmental biology is something 00:05:01.000 --> 00:05:05.000 that some of you will study in future courses and you'll have had 00:05:05.000 --> 00:05:09.000 some introduction to here, that's how you get from a single 00:05:09.000 --> 00:05:13.000 fertilized egg, a single cell to the complex 00:05:13.000 --> 00:05:16.000 organism. In the brain it's a particularly difficult problem 00:05:16.000 --> 00:05:20.000 because you have so many different types of cells and so many cells. 00:05:20.000 --> 00:05:24.000 And each cell then makes all of these complex connections. 00:05:24.000 --> 00:05:28.000 A given neuron might connect to 1, 00 or a few thousand different other 00:05:28.000 --> 00:05:32.000 cells as a normal process. So forming all these different kinds 00:05:32.000 --> 00:05:36.000 of neurons and wiring up is a very daunting problem. 00:05:36.000 --> 00:05:40.000 So what I thought I would do today would be to focus on two examples 00:05:40.000 --> 00:05:44.000 where in each case, starting with either one gene or a 00:05:44.000 --> 00:05:48.000 small number of genes you get a lot of complexity. 00:05:48.000 --> 00:05:52.000 So this would then allow the smaller number of total gene number 00:05:52.000 --> 00:05:56.000 in the human genome to allow a lot of different kinds of proteins and 00:05:56.000 --> 00:06:00.000 maybe provide some explanations for certain parts of the complexity 00:06:00.000 --> 00:06:04.000 of the brain. Now, by no means am I going to 00:06:04.000 --> 00:06:08.000 attempt to explain all of how the brain develops. 00:06:08.000 --> 00:06:12.000 That would take, well, at least one course, 00:06:12.000 --> 00:06:16.000 more likely a few different courses to actually get a good appreciation 00:06:16.000 --> 00:06:20.000 of that, but I'm going to go through a couple of very intriguing examples 00:06:20.000 --> 00:06:24.000 that are approachable at the level of this class. 00:06:24.000 --> 00:06:43.000 OK. So the standard way that we 00:06:43.000 --> 00:06:49.000 think about how genetic information gets made into proteins is that 00:06:49.000 --> 00:06:55.000 there is DNA and then RNA and then proteins. I hope at this point in 7. 00:06:55.000 --> 00:07:01.000 1 this is all familiar to you. Good. OK. So the DNA sequence 00:07:01.000 --> 00:07:06.000 gets transcribed into an RNA, and then there's a splicing event 00:07:06.000 --> 00:07:12.000 which takes bits and pieces of the RNA, puts them together, 00:07:12.000 --> 00:07:18.000 and then there's an area of the RNA that tells the ribosome to start 00:07:18.000 --> 00:07:24.000 making protein. And so you get the protein 00:07:24.000 --> 00:07:29.000 synthesis. Everything is following from the 00:07:29.000 --> 00:07:33.000 blueprint that was in the DNA. So what I'm going to talk about 00:07:33.000 --> 00:07:37.000 today are two different examples. One of them involves alternative 00:07:37.000 --> 00:07:49.000 splicing. 00:07:49.000 --> 00:07:54.000 And that will be causes where instead of there being a static 00:07:54.000 --> 00:07:59.000 always reproduced way of going from DNA to RNA to a protein, 00:07:59.000 --> 00:08:04.000 that there are different alternative splicing events that can occur. 00:08:04.000 --> 00:08:08.000 And this can allow one gene to make multiple different protein products. 00:08:08.000 --> 00:08:13.000 And then I'm going to go over another way that you can violate 00:08:13.000 --> 00:08:18.000 this central dogma, this DNA, RNA to protein, 00:08:18.000 --> 00:08:22.000 which is something called RNA editing. So, as you might imagine 00:08:22.000 --> 00:08:27.000 from the common usage of the word editing, by editing what we mean is 00:08:27.000 --> 00:08:32.000 that the RNA sequence itself is actually changed so that it no 00:08:32.000 --> 00:08:37.000 longer reflect the exact nucleotide sequence of the DNA. 00:08:37.000 --> 00:08:41.000 This can also add diversity to the number of potential encoded proteins. 00:08:41.000 --> 00:08:46.000 I want to make sure that I, I'm going to talk about 00:08:46.000 --> 00:08:50.000 neural-specific examples, but I want to mention that these 00:08:50.000 --> 00:08:55.000 processes, alternative splicing and RNA editing are used also by other 00:08:55.000 --> 00:09:00.000 parts of the developing animal, and also in other plants and other 00:09:00.000 --> 00:09:05.000 organisms, but not just by the brain to generate diversity. 00:09:05.000 --> 00:09:08.000 So these are mechanisms that are widely used. But some of the most 00:09:08.000 --> 00:09:12.000 striking examples, as you'll see from my lecture and in 00:09:12.000 --> 00:09:16.000 further reading that you might do in the future, some of the most 00:09:16.000 --> 00:09:19.000 striking examples come from the nervous system. 00:09:19.000 --> 00:09:23.000 And that's not surprising given the complexity of the nervous system and 00:09:23.000 --> 00:09:27.000 the fact that there are so many genes out there ready to help with 00:09:27.000 --> 00:09:31.000 this complexity. So the first I'll turn to 00:09:31.000 --> 00:09:44.000 alternative splicing. 00:09:44.000 --> 00:09:47.000 So first, before getting into the extremely complex case that I'm 00:09:47.000 --> 00:09:51.000 going to focus on, I'm going to just briefly, 00:09:51.000 --> 00:09:55.000 by way of introduction, go over a standard alternative 00:09:55.000 --> 00:10:15.000 splicing scenario. 00:10:15.000 --> 00:10:23.000 OK. So in a gene for which there is no alternative splicing, 00:10:23.000 --> 00:10:31.000 if I draw the exons as boxes and the introns as lines, 00:10:31.000 --> 00:10:40.000 what winds up happening is you have the first exon spliced to the second 00:10:40.000 --> 00:10:48.000 one, second to the third, third to the forth. And so what you 00:10:48.000 --> 00:11:00.000 wind up with is a messenger RNA. 00:11:00.000 --> 00:11:03.000 So here is the messenger RNA which has been spliced from the primary 00:11:03.000 --> 00:11:07.000 transcript. The primary transcript, of course, reflects the actual 00:11:07.000 --> 00:11:11.000 structure of the DNA in terms of sequence also, 00:11:11.000 --> 00:11:14.000 because in the genomic DNA you'd also have areas that are going to be 00:11:14.000 --> 00:11:18.000 exon, intron, exon intron exactly like this. So this is just a 00:11:18.000 --> 00:11:22.000 general example of alternative splicing, I'm sorry, 00:11:22.000 --> 00:11:26.000 of regular splicing. So then alternative splicing would involve 00:11:26.000 --> 00:11:32.000 something like this. You'd have 1, 2, 00:11:32.000 --> 00:11:41.000 3A, 3B, 4. So then what happens is you have normal splicing from 1 to 2. 00:11:41.000 --> 00:11:51.000 And then 2 could either go to 3A, which will then go to 4 leaving 3B 00:11:51.000 --> 00:12:00.000 out. Or the alternative is that 2 can skip 3A, go to 3B, 00:12:00.000 --> 00:12:07.000 which will then splice to 4. So this allows then two different 00:12:07.000 --> 00:12:33.000 messengers to be formed. 00:12:33.000 --> 00:12:37.000 So this 3A and 3B might encode a slightly different sequence and 00:12:37.000 --> 00:12:41.000 might then allow two distinct proteins with different functions to 00:12:41.000 --> 00:12:45.000 be forwarded from one message. So this is an example, a simple 00:12:45.000 --> 00:12:49.000 example, a general example of alternative splicing. 00:12:49.000 --> 00:12:54.000 So from one gene you have two proteins. 00:12:54.000 --> 00:13:02.000 The example that I'm going to focus 00:13:02.000 --> 00:13:07.000 on today, instead of going from one gene to two proteins, 00:13:07.000 --> 00:13:13.000 allows you to go from one gene to 38, 00 different possibilities. 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, 00:13:18.000 --> 00:13:23.000 00 will have occurred to you that this is larger than the number of 00:13:23.000 --> 00:13:29.000 genes that Eric Lander says are in the human genome. 00:13:29.000 --> 00:13:34.000 It's certainly larger than the number of genes in the fly genome. 00:13:34.000 --> 00:13:39.000 And this example I'm giving you is from a single gene in the fruit fly. 00:13:39.000 --> 00:13:45.000 This one gene can come in 38,000 different forms. 00:13:45.000 --> 00:13:59.000 The gene is called drosophila DSCAM. 00:13:59.000 --> 00:14:03.000 It's named for a human gene which was cloned first and characterized 00:14:03.000 --> 00:14:08.000 first which was called just plain DSCAM. What DSCAM stands for is 00:14:08.000 --> 00:14:13.000 Down syndrome cell adhesion molecule. 00:14:13.000 --> 00:14:33.000 And let me just explain briefly why 00:14:33.000 --> 00:14:36.000 it has this name. I don't think that this name is 00:14:36.000 --> 00:14:39.000 actually relevant so much to the biology, and it certainly not 00:14:39.000 --> 00:14:42.000 relevant to the alternative splicing because the human gene and the mouse 00:14:42.000 --> 00:14:45.000 gene, neither of them have a lot of alternative splicing. 00:14:45.000 --> 00:14:48.000 This is something that is particular to the fly. 00:14:48.000 --> 00:14:51.000 That's something I will return to later in lecture. 00:14:51.000 --> 00:14:54.000 This name Down syndrome cell adhesion molecule came about because 00:14:54.000 --> 00:14:57.000 this was cloned first from human and it's located on human 00:14:57.000 --> 00:15:05.000 chromosome 21. 00:15:05.000 --> 00:15:10.000 Chromosome 21 is normally present in two copies in every individual. 00:15:10.000 --> 00:15:15.000 In individuals who wind up with three copies of chromosome 21, 00:15:15.000 --> 00:15:21.000 something called trisomy 21, trisomy 21 causes Down syndrome, 00:15:21.000 --> 00:15:26.000 which is a syndrome that has some brain manifestations like mental 00:15:26.000 --> 00:15:32.000 retardation, and also has a number of other problems associated 00:15:32.000 --> 00:15:37.000 with it. When the people who found this gene 00:15:37.000 --> 00:15:42.000 found it they named it. So they gave the first part of its 00:15:42.000 --> 00:15:47.000 name, the DS comes from Down syndrome. Cell adhesion molecule 00:15:47.000 --> 00:15:52.000 comes from the fact that this gene is similar in structure to a lot of 00:15:52.000 --> 00:15:57.000 known cell adhesion molecules that are encoded by many different 00:15:57.000 --> 00:16:02.000 loci in the genome. And they initially thought that 00:16:02.000 --> 00:16:06.000 perhaps, and this gene is expressed in the brain. And they initially 00:16:06.000 --> 00:16:10.000 thought that perhaps having an extra copy, a third copy of this gene 00:16:10.000 --> 00:16:14.000 might be what's causing a lot of the brain phenotypes. 00:16:14.000 --> 00:16:19.000 Subsequent work has not provided further evidence for that. 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 00:16:23.000 --> 00:16:27.000 syndrome cell adhesion molecule, DSCAM. It's on human chromosome 21. 00:16:27.000 --> 00:16:32.000 It may play a role in Down syndrome but there isn't -- 00:16:32.000 --> 00:16:37.000 The name is really the best evidence that it plays a role, 00:16:37.000 --> 00:16:42.000 just the fact that they named it that. OK. But in the fly this is 00:16:42.000 --> 00:16:47.000 an extremely interesting molecule because, as I mentioned, 00:16:47.000 --> 00:16:52.000 it can come in 38,000 different forms. OK. So as for why you would 00:16:52.000 --> 00:16:57.000 have a cell adhesion molecule in the brain, I just want to 00:16:57.000 --> 00:17:02.000 mention briefly. So Professor Lander went over with 00:17:02.000 --> 00:17:06.000 you the structure of a neuron. That neurons have cell bodies and 00:17:06.000 --> 00:17:10.000 axons and growth cones which allow them to get to wherever they're 00:17:10.000 --> 00:17:14.000 supposed to connect. One of the types of molecules that 00:17:14.000 --> 00:17:18.000 allows an axon as it's growing, the growth cone to lead an axon 00:17:18.000 --> 00:17:22.000 along a complex path is to interact with various structures that it 00:17:22.000 --> 00:17:26.000 encounters. And so cell adhesion molecule is one of the kinds of 00:17:26.000 --> 00:17:30.000 molecules that can allow the growth cone and then the rest of the axon 00:17:30.000 --> 00:17:34.000 to interact with various other cells or other extracellular substrates, 00:17:34.000 --> 00:17:38.000 proteins that have been deposited by other kinds of cells as they make 00:17:38.000 --> 00:17:43.000 their way and make the appropriate connections in the brain. 00:17:43.000 --> 00:17:46.000 So cell adhesion molecules are one of the mechanisms. 00:17:46.000 --> 00:17:50.000 There are also mechanisms that allow cells to respond to gradients 00:17:50.000 --> 00:17:54.000 of chemical signaling messengers. Question? Yes. Could you explain 00:17:54.000 --> 00:17:58.000 what a growth cone is? Oh, I'm sorry. That was not 00:17:58.000 --> 00:18:02.000 covered? OK. So the neuron has a cell body 00:18:02.000 --> 00:18:06.000 with a nucleus and all the other stuff that's in regular cells that 00:18:06.000 --> 00:18:10.000 you learned about. It then has an axon which then 00:18:10.000 --> 00:18:14.000 allows it to connect. And this connection could be very 00:18:14.000 --> 00:18:19.000 far away. In the case of, for example, a motor neuron in the 00:18:19.000 --> 00:18:23.000 spinal cord that's intermating a muscle in the foot, 00:18:23.000 --> 00:18:27.000 that single cell would have its cell body in the spinal cord and its axon 00:18:27.000 --> 00:18:32.000 go all the way out to the foot. OK? So that's just an example of 00:18:32.000 --> 00:18:36.000 one very long neuron. Some of them are very long. 00:18:36.000 --> 00:18:40.000 Some of them are shorter. So this is the axon. At the tip of the axon 00:18:40.000 --> 00:18:44.000 is this thing which looks sort of like my son's mitten when it's cold 00:18:44.000 --> 00:18:48.000 outside. But this is the growth cone. And basically what's going on 00:18:48.000 --> 00:18:52.000 is as the axon is growing out in this direction it's feeling its way. 00:18:52.000 --> 00:18:56.000 And there might be cell adhesion molecules on these different 00:18:56.000 --> 00:19:00.000 protrusions that if they attach really well -- 00:19:00.000 --> 00:19:06.000 Like let's say that this area over here is stickier for this particular 00:19:06.000 --> 00:19:12.000 growth cone than this area over here. Then this growth axon is more 00:19:12.000 --> 00:19:18.000 likely to grow in that direction. OK. So cell adhesion molecules are 00:19:18.000 --> 00:19:24.000 well known to play important roles in axon guidance. 00:19:24.000 --> 00:19:30.000 It's how axons grow in different directions. 00:19:30.000 --> 00:19:34.000 So I will tell you know about the DSCAM gene, and that will give some 00:19:34.000 --> 00:19:39.000 insight into how the 38, 00 different forms might be used 00:19:39.000 --> 00:19:43.000 because they're going to provide different kinds of stickiness. 00:19:43.000 --> 00:19:54.000 Let me explain. 00:19:54.000 --> 00:20:00.000 So this is a drawing of the genomic organization of DSCAM that allows 00:20:00.000 --> 00:20:13.000 the extensive alternative splicing. 00:20:13.000 --> 00:20:17.000 So this diagram is similar to the diagram that I drew by hand for the 00:20:17.000 --> 00:20:21.000 more simple cases. But basically in DSCAM what you 00:20:21.000 --> 00:20:26.000 start out with is exon 1 gets spliced to exon 2 gets spliced to 00:20:26.000 --> 00:20:30.000 exon 3, and then when you reach exon 4 there are 12 distinct 00:20:30.000 --> 00:20:35.000 possibilities. And only one of the 12 is chosen. 00:20:35.000 --> 00:20:39.000 In this case the diagram shows it choosing, I don't know, 00:20:39.000 --> 00:20:43.000 the ninth one perhaps. Then exon 5 is regular so that always gets 00:20:43.000 --> 00:20:47.000 included. And exon 6 there are 48 distinct choices. 00:20:47.000 --> 00:20:51.000 And again only one is chosen. Here, in this example, this one has 00:20:51.000 --> 00:20:55.000 been chosen at the expense of all of these other ones. 00:20:55.000 --> 00:20:59.000 Exon 7 and 8 are normal. And exon 9 there are 33 choices. 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 00:21:03.000 --> 00:21:07.000 you wind up with 38, 16. There is evidence from a number 00:21:07.000 --> 00:21:12.000 of different types of studies, including coning and sequencing, 00:21:12.000 --> 00:21:17.000 lots of different messenger RNAs that are already spliced that 00:21:17.000 --> 00:21:21.000 basically almost all of these forms can be made. So what this structure 00:21:21.000 --> 00:21:26.000 allows is for there to be diversity generated in important areas of the 00:21:26.000 --> 00:21:31.000 cell adhesion part of this molecule. 00:21:31.000 --> 00:21:53.000 The DSCAM molecule starts out with a 00:21:53.000 --> 00:21:59.000 number of domains which are called immunoglobulin-like domains. 00:21:59.000 --> 00:22:08.000 Immunoglobulin domains are named for 00:22:08.000 --> 00:22:12.000 immunoglobulins which is another name for antibodies. 00:22:12.000 --> 00:22:16.000 Antibodies help you fight infection. I don't know if that's been covered. 00:22:16.000 --> 00:22:20.000 Has it been covered? Yes. And the particular fold that 00:22:20.000 --> 00:22:24.000 they form allows recognition of foreign antigens and it also allows 00:22:24.000 --> 00:22:28.000 stickiness of molecules in general. So cell adhesion molecules often 00:22:28.000 --> 00:22:33.000 have these immunoglobulin domains. The DSCAM starts out with, 00:22:33.000 --> 00:22:39.000 from the N-terminus towards the C-terminus it starts out, 00:22:39.000 --> 00:22:45.000 the first nine domains are these Ig type domains. That's then followed 00:22:45.000 --> 00:22:51.000 by another kind of domain called a fiberonectin type domain, 00:22:51.000 --> 00:22:57.000 which that's not important. All of the diversity is in this 00:22:57.000 --> 00:23:02.000 nine immunoglobulin domain. The exon 4 diversity allows 00:23:02.000 --> 00:23:08.000 diversity of the second of the nine. The exon 6 alternative splicing 00:23:08.000 --> 00:23:14.000 affects the third out of the nine immunoglobulin folds. 00:23:14.000 --> 00:23:19.000 And the exon 9 diversity affects the seventh. So of these nine 00:23:19.000 --> 00:23:25.000 domains of immunoglobulin folds that allow for different kinds of 00:23:25.000 --> 00:23:31.000 stickiness, a lot of them are the same. 00:23:31.000 --> 00:23:35.000 One is the same. 4, 5 and 6 are the same. 00:23:35.000 --> 00:23:40.000 And 8 and 9 are the same. But 2, 3 and 7 have these 00:23:40.000 --> 00:23:44.000 differences which are encoded by this striking kind of genomic 00:23:44.000 --> 00:24:11.000 structure and alternative splicing. 00:24:11.000 --> 00:24:29.000 So how is this diversity used? 00:24:29.000 --> 00:24:34.000 So the early models for how DSCAM would be used stipulated that 00:24:34.000 --> 00:24:40.000 individual different kinds of neurons might express vastly reduced 00:24:40.000 --> 00:24:45.000 subsets out of the 38, 00. So let's say that one 00:24:45.000 --> 00:24:51.000 particular neuron type, of which there might be many 00:24:51.000 --> 00:24:57.000 different neurons, might express maybe ten out of the 00:24:57.000 --> 00:25:02.000 38,000 or even one out of 38,000. These were the different kinds of 00:25:02.000 --> 00:25:06.000 models that were tossed around by people who were thinking about this 00:25:06.000 --> 00:25:11.000 problem. But then people started to study it. And what turned out to be 00:25:11.000 --> 00:25:16.000 the case is that it looks like every kind of neuron population at first 00:25:16.000 --> 00:25:20.000 approximately expresses almost all of the different forms. 00:25:20.000 --> 00:25:25.000 OK? So these are wrong, these models. And each different 00:25:25.000 --> 00:25:30.000 neuron type expresses a slightly different repertoire. 00:25:30.000 --> 00:25:35.000 But at first approximation over 10, 00 or 20,000 forms are possible for 00:25:35.000 --> 00:25:41.000 each different neuron type. So that then caused people to 00:25:41.000 --> 00:25:47.000 scratch their heads and wonder, well, how is this used then? How is 00:25:47.000 --> 00:25:53.000 this used to make different kinds of neurons different from one another 00:25:53.000 --> 00:25:59.000 or anything in the function of them? 00:25:59.000 --> 00:26:05.000 So the answer to this question has emerged in part from analyses of 00:26:05.000 --> 00:26:12.000 individual single cells. So it turns out that an individual 00:26:12.000 --> 00:26:18.000 cell, and I'm using the word cell and neuron interchangeably because 00:26:18.000 --> 00:26:25.000 neurons are cells. And not all cells are neurons but 00:26:25.000 --> 00:26:32.000 all neurons are cells. So for one cell or one neuron it 00:26:32.000 --> 00:26:39.000 makes somewhere in the range of 10 to 50 forms. 00:26:39.000 --> 00:26:44.000 These are randomly chosen, apparently from the data that's 00:26:44.000 --> 00:26:49.000 available, from the tens of thousands of forms that are possible. 00:26:49.000 --> 00:26:54.000 So you can imagine that two neighboring cells that are otherwise 00:26:54.000 --> 00:26:59.000 identical, that each are picking, let's say ten just to make it easy, 00:26:59.000 --> 00:27:04.000 ten different forms of DSCAM, are going to wind up with very different 00:27:04.000 --> 00:27:10.000 repertoires of DSCAM than an adjacent cell. 00:27:10.000 --> 00:27:13.000 So what this allows is each individual cell to have a unique 00:27:13.000 --> 00:27:17.000 identity. The whole idea that individual neurons might need to 00:27:17.000 --> 00:27:21.000 have a unique identity actually is a new concept that's really been 00:27:21.000 --> 00:27:24.000 enlightened by this molecule. Because the way that people used to 00:27:24.000 --> 00:27:28.000 think of neurons is that they would wind up with unique identities based 00:27:28.000 --> 00:27:32.000 on the connections or experience, what they were exposed to in terms 00:27:32.000 --> 00:27:36.000 of different stimuli. But what this indicates is that from 00:27:36.000 --> 00:27:42.000 the splicing of an individual gene and the fact that each time this 00:27:42.000 --> 00:27:48.000 gene gets spliced you can wind up with a different form that at any 00:27:48.000 --> 00:27:54.000 given time each cell will have a unique set of messenger RNAs, 00:27:54.000 --> 00:28:00.000 and therefore proteins encoding this DSCAM gene. 00:28:00.000 --> 00:28:05.000 OK. So I mentioned earlier that the human DSCAM does not have 00:28:05.000 --> 00:28:10.000 alternative splicing. We all like to think of ourselves, 00:28:10.000 --> 00:28:15.000 humans and other mammals as having brains that are on the level of 00:28:15.000 --> 00:28:21.000 complexity, at least on par with the fly. And so it's odd to think of 00:28:21.000 --> 00:28:26.000 all this complexity that's there for flies and other insects but why is 00:28:26.000 --> 00:28:31.000 it not there for humans? Well, it turns out that there are 00:28:31.000 --> 00:28:36.000 other kinds of genes that do have extensive alternative splicing in 00:28:36.000 --> 00:28:40.000 mammals. So one of them is called neurexins. These are genes that are 00:28:40.000 --> 00:28:45.000 involved in synapse, how the different kinds of cells 00:28:45.000 --> 00:28:50.000 communicate with each other at the interface. There are genes called 00:28:50.000 --> 00:28:55.000 protocadherins. And there are also other kinds of 00:28:55.000 --> 00:29:00.000 genes that all have extensive alternative splicing in mammals. 00:29:00.000 --> 00:29:04.000 Interestingly, these genes tend not to have 00:29:04.000 --> 00:29:08.000 extensive alternative splicing in flies. It's as if in each lineage 00:29:08.000 --> 00:29:12.000 certain genes have been chosen to get a lot of diversity by this 00:29:12.000 --> 00:29:16.000 mechanism of alternative splicing and other genes are left with their 00:29:16.000 --> 00:29:20.000 just standard single function where it's one gene, 00:29:20.000 --> 00:29:24.000 one RNA, one protein. OK. So I'm going to switch now to 00:29:24.000 --> 00:29:29.000 the second example which is RNA editing. 00:29:29.000 --> 00:29:41.000 So, as I mentioned earlier, 00:29:41.000 --> 00:29:45.000 RNA editing involves an actual change in the RNA sequence so that 00:29:45.000 --> 00:29:49.000 it no longer reflects the exact DNA sequence. Now, 00:29:49.000 --> 00:29:53.000 this is different than splicing. Splicing takes different pieces of 00:29:53.000 --> 00:29:57.000 RNA and splices them together leaving out intervening sequences or 00:29:57.000 --> 00:30:01.000 introns. But in RNA editing you actually change the nucleotide 00:30:01.000 --> 00:30:05.000 sequence so that it no longer is identical to the DNA. 00:30:05.000 --> 00:30:10.000 This is used in a number of parts of the brain. Most of the examples are 00:30:10.000 --> 00:30:15.000 brain-specific. There are some non-brain specific 00:30:15.000 --> 00:30:20.000 parts. Most of the time the editing event changes in adenosine, 00:30:20.000 --> 00:30:25.000 an A in the ACGT nomenclature, into an inosine. 00:30:25.000 --> 00:30:34.000 This is read differently by the 00:30:34.000 --> 00:30:40.000 ribosome than the adenosine. So this leads, for example, in an 00:30:40.000 --> 00:30:45.000 important kind of channel called a glutamate receptor. 00:30:45.000 --> 00:30:51.000 And the specific subtype that I'm talking about is something called an 00:30:51.000 --> 00:30:56.000 AMPA glutamate receptor. That's for a chemical ligand that 00:30:56.000 --> 00:31:02.000 activates this particular kind of glutamate receptor. 00:31:02.000 --> 00:31:10.000 This leads to an important change of a glutamine to an arginine in the 00:31:10.000 --> 00:31:19.000 protein. So let me draw a quick diagram of what the protein looks 00:31:19.000 --> 00:31:27.000 like so we can see what the importance of this glutamine to 00:31:27.000 --> 00:31:36.000 arginine switch in this glutamate receptor is. 00:31:36.000 --> 00:31:47.000 So in the absence of detailed 00:31:47.000 --> 00:31:51.000 structural information about different kinds of neurotransmitter 00:31:51.000 --> 00:31:55.000 receptors people often draw a diagram like this where this is the 00:31:55.000 --> 00:31:59.000 outside of the cell, this is the inside of the cell, 00:31:59.000 --> 00:32:05.000 this represents the cell membrane. And here's the amino terminus. 00:32:05.000 --> 00:32:12.000 And then they draw a transmembrane portion. And this is what's called 00:32:12.000 --> 00:32:18.000 a reentrant loop. It doesn't quite pass through the 00:32:18.000 --> 00:32:25.000 membrane, but then it passes the membrane again. And here's 00:32:25.000 --> 00:32:33.000 the carboxy terminus. The glutamine to arginine change is 00:32:33.000 --> 00:32:41.000 here. It's in this area which is involved in making the pore. 00:32:41.000 --> 00:32:50.000 So the pore of the channel has this change. 00:32:50.000 --> 00:33:04.000 Glutamine to arginine change. 00:33:04.000 --> 00:33:11.000 This vastly changes the properties of the channel, 00:33:11.000 --> 00:33:18.000 a channel that doesn't undergo this editing event. 00:33:18.000 --> 00:33:25.000 So let me just state that for the GluR2 AMPA receptor, 00:33:25.000 --> 00:33:33.000 which is one of four different genes, it is 99% edited in adults. 00:33:33.000 --> 00:33:37.000 So where over 99% of the time this adenosine is made into an inosine 00:33:37.000 --> 00:33:42.000 which leads to a glutamine becoming an arginine in the protein. 00:33:42.000 --> 00:33:47.000 What this does to channel is it changes its permeability. 00:33:47.000 --> 00:33:52.000 So this is a kind of channel that is mostly designed to let sodium in, 00:33:52.000 --> 00:33:57.000 but if it doesn't get edited, if the glutamine is there it also 00:33:57.000 --> 00:34:02.000 lets calcium in. So whether or not calcium gets into 00:34:02.000 --> 00:34:06.000 the cell is very important because they're both, both sodium and 00:34:06.000 --> 00:34:10.000 calcium are cat ions and can lead to membrane, potential disturbances 00:34:10.000 --> 00:34:14.000 like you learned about earlier this week, leading to an action potential. 00:34:14.000 --> 00:34:18.000 But calcium also has other effects. It can lead ultimately to the turn 00:34:18.000 --> 00:34:22.000 on and off of genes and phosphorylation of various proteins 00:34:22.000 --> 00:34:26.000 to other kinds of effects in the neuron. So it has to be 00:34:26.000 --> 00:34:30.000 regulated very tightly. So these channels are designed to 00:34:30.000 --> 00:34:34.000 just let sodium through, to be involved in the transmission 00:34:34.000 --> 00:34:38.000 of an action potential from one neuron to the next. 00:34:38.000 --> 00:34:42.000 So if you had a perturbation in this process you would then also let 00:34:42.000 --> 00:34:46.000 calcium in because the glutamine containing channel lets calcium in. 00:34:46.000 --> 00:34:50.000 In fact, early in development it's probably true that you don't edit 00:34:50.000 --> 00:34:55.000 100% and you let a little bit of calcium in. 00:34:55.000 --> 00:35:00.000 And there are also other glutamate receptors that are not part of the 00:35:00.000 --> 00:35:05.000 AMPA family but they are related. And they're encoded by genes called, 00:35:05.000 --> 00:35:11.000 so this GluR1 through 4 encodes AMPA receptors, a gene called GluR5 and 6, 00:35:11.000 --> 00:35:16.000 they have editing which is more regulated. And by regulated I mean 00:35:16.000 --> 00:35:22.000 that the editing is sometimes present and sometimes not. 00:35:22.000 --> 00:35:27.000 So even within a given neuron you might have some channels that have 00:35:27.000 --> 00:35:33.000 the glutamine and some channels that have the arginine. 00:35:33.000 --> 00:35:41.000 There are also other sites. 00:35:41.000 --> 00:35:45.000 I've talked about the main site. This is the one that has the most 00:35:45.000 --> 00:35:49.000 profound impact on the function of the protein. There are also other 00:35:49.000 --> 00:35:53.000 sites in the molecule that are edited. And then they also have 00:35:53.000 --> 00:35:57.000 important but slightly less prominent roles in the regulation of 00:35:57.000 --> 00:36:02.000 these channels. So what leads this gene to become 00:36:02.000 --> 00:36:07.000 edited? Why do most genes not become edited and this gene becomes 00:36:07.000 --> 00:36:12.000 edited? Well, people are starting to pursue that 00:36:12.000 --> 00:36:17.000 kind of mechanisms. And one of the mechanisms that's 00:36:17.000 --> 00:36:23.000 become very clear is that, for example, in the Q to R change in 00:36:23.000 --> 00:36:28.000 the pore, or the adenosine to inosine change in the messenger RNA 00:36:28.000 --> 00:36:33.000 that leads to the Q to R change in the pore, if you look at 00:36:33.000 --> 00:36:38.000 the exon where that -- I'll draw it as an A. 00:36:38.000 --> 00:36:44.000 Where that A is present. There's an area, and then here's 00:36:44.000 --> 00:36:49.000 the intron, of the intronic sequence which actually loops back and then 00:36:49.000 --> 00:36:55.000 allows base pairing to form between the messenger RNA and the intron. 00:36:55.000 --> 00:37:01.000 And then the enzymes that are involved in mediating this adenosine 00:37:01.000 --> 00:37:07.000 to inosine change recognize the base pairing of this short area and some 00:37:07.000 --> 00:37:13.000 sequence specificity to the RNA sequence that's around here. 00:37:13.000 --> 00:37:19.000 It's not just any base pairing, but the base pairing is critical. 00:37:19.000 --> 00:37:25.000 And then the enzymes, which are called adenosine deaminase, 00:37:25.000 --> 00:37:32.000 can mediate this change of an adenosine to an inosine. 00:37:32.000 --> 00:37:37.000 So this gene has been selected to have parts of its intron, 00:37:37.000 --> 00:37:42.000 in addition to allowing splicing to occur, to actual be able to base 00:37:42.000 --> 00:37:47.000 pair and allow this editing function to happen. And so this allows an 00:37:47.000 --> 00:37:52.000 individual gene then to make more than one form of protein. 00:37:52.000 --> 00:37:57.000 OK. One other example that I'll tell you about of RNA editing 00:37:57.000 --> 00:38:03.000 involves the serotonin system. It involves a serotonin receptor 00:38:03.000 --> 00:38:10.000 which has RNA editing. And this is a serotonin receptor 00:38:10.000 --> 00:38:18.000 whose name is serotonin receptor 2C, so it's often written as 5-HT or 5 00:38:18.000 --> 00:38:25.000 hydroxtryptamine 2C serotonin receptor. This receptor is a member 00:38:25.000 --> 00:38:33.000 of the G protein-coupled receptor super family. 00:38:33.000 --> 00:38:39.000 G protein-coupled receptors are 7-transmembrane domain receptors. 00:38:39.000 --> 00:38:46.000 And the editing of the serotonin receptor occurs in the second 00:38:46.000 --> 00:38:53.000 intracellular loop and affects the coupling to G protein. 00:38:53.000 --> 00:39:00.000 So the way that these G protein-coupled receptors -- 00:39:00.000 --> 00:39:04.000 Did they do this already? G protein-coupled receptors 00:39:04.000 --> 00:39:08.000 transduce the signal through a G protein which often binds to the 00:39:08.000 --> 00:39:12.000 intracellular loops, particularly the second loop. 00:39:12.000 --> 00:39:16.000 And the editing event which changes adenosines to inosines, 00:39:16.000 --> 00:39:20.000 there are actually a few of them in this region here, 00:39:20.000 --> 00:39:24.000 a few different sites which can get changed. That affects the 00:39:24.000 --> 00:39:28.000 efficiency of the transduction, when serotonin is present the 00:39:28.000 --> 00:39:33.000 transduction of a signal inside the cell. 00:39:33.000 --> 00:39:37.000 There are other serotonin receptors that are ligand gated channels but 00:39:37.000 --> 00:39:41.000 they don't appear to have the same kind of editing as this serotonin 00:39:41.000 --> 00:39:46.000 receptor that is of the G protein-coupled variety has. 00:39:46.000 --> 00:39:59.000 So I'll end with just one final note 00:39:59.000 --> 00:40:03.000 about this serotonin receptor which is that a drug called fluoxetine or 00:40:03.000 --> 00:40:07.000 Prozac, did Eric go over that this year? Prozac? 00:40:07.000 --> 00:40:11.000 No? He mentioned it. OK, he mentioned it. So it's 00:40:11.000 --> 00:40:16.000 mostly known as something which blocks reuptake of serotonin. 00:40:16.000 --> 00:40:20.000 So one cell releases serotonin, which is a neurotransmitter. 00:40:20.000 --> 00:40:24.000 Another cell responds to it. If you block the reuptake the 00:40:24.000 --> 00:40:28.000 neurotransmitter is present in the synapse for a longer amount of time 00:40:28.000 --> 00:40:33.000 leading to increased signaling. Prozac is widely thought to have its 00:40:33.000 --> 00:40:37.000 main effect, and that probably is its main effect to just block the 00:40:37.000 --> 00:40:41.000 reuptake leading to increased serotonin signaling. 00:40:41.000 --> 00:40:45.000 Someone has studied the serotonin receptor in individuals who are 00:40:45.000 --> 00:40:49.000 taking Prozac versus individuals who are not taking Prozac. 00:40:49.000 --> 00:40:53.000 And what they found was that there were differences in the amount of 00:40:53.000 --> 00:40:57.000 editing of various sites in this key area in individuals taking Prozac 00:40:57.000 --> 00:41:02.000 versus individuals not taking Prozac. 00:41:02.000 --> 00:41:06.000 And the direction of the difference, whether it's switching from edited 00:41:06.000 --> 00:41:10.000 to unedited or back, and it varies for the different 00:41:10.000 --> 00:41:15.000 sites, the direction was the opposite of what was seen in a 00:41:15.000 --> 00:41:19.000 comparison of brains of victims of suicide versus brains of other 00:41:19.000 --> 00:41:24.000 accident victims. So it looks like Prozac is having 00:41:24.000 --> 00:41:28.000 an effect which is in the opposite effect to the skewing of editing 00:41:28.000 --> 00:41:33.000 that one sees in certain cases of depression. 00:41:33.000 --> 00:41:38.000 So I bring that example up because it's important to know that, 00:41:38.000 --> 00:41:43.000 you know, if you can impact on some of these subtle differences between 00:41:43.000 --> 00:41:48.000 different kinds of messages, like whether it's edited or not or 00:41:48.000 --> 00:41:53.000 whether the splicing is more towards one kind of alternative splicing or 00:41:53.000 --> 00:41:58.000 another kind of alternative splicing, these might provide very interesting 00:41:58.000 --> 00:42:03.000 pharmacologic targets for therapies that might impact on a variety of 00:42:03.000 --> 00:42:08.000 different human diseases. So what I've hoped to do is give you 00:42:08.000 --> 00:42:12.000 a sense of a couple of different examples where you can take a single 00:42:12.000 --> 00:42:17.000 gene and make more than one protein, and this can lead to increases in 00:42:17.000 --> 00:42:21.000 the diversity of neurons, and therefore increases in the 00:42:21.000 --> 00:42:26.000 complexity of the brain. Thank you. [APPLAUSE] Are there 00:42:26.000 --> 00:42:31.000 questions?