1 00:00:16,710 --> 00:00:21,570 ADAM MARTIN: All right, so today, I'm trying something 2 00:00:21,570 --> 00:00:23,430 different this semester. 3 00:00:23,430 --> 00:00:27,300 I wanted to tell you guys about one way 4 00:00:27,300 --> 00:00:30,900 that you can discover things in biology. 5 00:00:30,900 --> 00:00:37,380 And this is going to fall in the genetics cluster of lectures. 6 00:00:37,380 --> 00:00:41,580 I want to show you how you can go from being interested 7 00:00:41,580 --> 00:00:47,580 in some property of an organism, or even its behavior-- 8 00:00:47,580 --> 00:00:50,100 how would you go from there to identifying 9 00:00:50,100 --> 00:00:54,150 genes and mechanisms that are responsible for that type 10 00:00:54,150 --> 00:00:56,950 of behavior or appearance? 11 00:00:56,950 --> 00:01:02,280 OK, so today, we're going to go from some type of phenotype, 12 00:01:02,280 --> 00:01:06,510 or process that you're interested in, such as maybe 13 00:01:06,510 --> 00:01:09,240 the appearance of an organism. 14 00:01:09,240 --> 00:01:11,500 But we're even going to go more abstract 15 00:01:11,500 --> 00:01:13,110 than that because at the end, I'm 16 00:01:13,110 --> 00:01:16,740 going to tell you if you're interested in behavior, 17 00:01:16,740 --> 00:01:19,710 you can try to figure out the genes and mechanisms that 18 00:01:19,710 --> 00:01:24,870 are involved in determining the behavior of an organism. 19 00:01:24,870 --> 00:01:28,410 So the question is, how do you go from something 20 00:01:28,410 --> 00:01:31,590 you're interested in learning about an organism to actually 21 00:01:31,590 --> 00:01:37,260 identifying genes and mechanisms that are important for that? 22 00:01:41,480 --> 00:01:45,440 And on my title slide here, I have three fruit 23 00:01:45,440 --> 00:01:49,370 fly mutant phenotypes that you can see, 24 00:01:49,370 --> 00:01:53,330 and each of these mutants defined 25 00:01:53,330 --> 00:01:58,790 genes that were subsequently found to be present-- 26 00:01:58,790 --> 00:02:02,270 or homologous genes were present in humans 27 00:02:02,270 --> 00:02:07,850 and were shown to play important roles in human biology. 28 00:02:07,850 --> 00:02:10,130 So later on in the lecture, hall kind 29 00:02:10,130 --> 00:02:13,610 explain what each of these phenotypes is. 30 00:02:13,610 --> 00:02:16,040 But first, I want to just highlight 31 00:02:16,040 --> 00:02:18,590 the importance of model organisms 32 00:02:18,590 --> 00:02:21,380 and their use in biology. 33 00:02:21,380 --> 00:02:23,990 You've been seeing them already. 34 00:02:23,990 --> 00:02:25,580 I've talked a bit about flies, we 35 00:02:25,580 --> 00:02:28,550 talked about Mendel's pea plants. 36 00:02:28,550 --> 00:02:31,460 I just now have a compendium of model organisms 37 00:02:31,460 --> 00:02:34,100 that I'm going to throw up to tell you about. 38 00:02:34,100 --> 00:02:38,510 So we've talked about bacteria and the importance of bacteria 39 00:02:38,510 --> 00:02:41,930 in elucidating the flow of information from DNA 40 00:02:41,930 --> 00:02:44,630 to RNA to protein. 41 00:02:44,630 --> 00:02:49,070 Bacteria we're also extremely important for elucidating 42 00:02:49,070 --> 00:02:54,230 the initial mechanisms of gene regulation. 43 00:02:54,230 --> 00:02:57,470 I mentioned yeast a little bit in the last lecture when 44 00:02:57,470 --> 00:03:01,970 I mentioned tetrad analysis, but we'll talk about yeast a lot 45 00:03:01,970 --> 00:03:03,710 more later, when we start talking 46 00:03:03,710 --> 00:03:07,970 about the regulation of cell division and the cell cycle, 47 00:03:07,970 --> 00:03:12,350 because yeast played a pivotal role in identifying 48 00:03:12,350 --> 00:03:16,190 the gene that's critical for this decision of a cell whether 49 00:03:16,190 --> 00:03:19,400 or not to divide. 50 00:03:19,400 --> 00:03:23,030 Also, one terrific model organism in plants 51 00:03:23,030 --> 00:03:25,520 is a Arabidopsis Thaliana. 52 00:03:25,520 --> 00:03:28,130 And Arabidopsis has also played an important role 53 00:03:28,130 --> 00:03:31,610 in elucidating mechanisms of development, but also, 54 00:03:31,610 --> 00:03:36,170 in making advances in scientific research that 55 00:03:36,170 --> 00:03:42,050 relate to agriculture, such as disease resistance and pathogen 56 00:03:42,050 --> 00:03:43,160 interactions. 57 00:03:45,800 --> 00:03:48,830 So the two heroes of today's lecture 58 00:03:48,830 --> 00:03:53,600 will be the roundworm, Caenorhabditis elegans, 59 00:03:53,600 --> 00:03:56,930 and the fruit fly, Drosophila melanogaster. 60 00:03:56,930 --> 00:04:00,800 But before getting into study is in those organisms, 61 00:04:00,800 --> 00:04:04,100 I wanted to highlight a couple important vertebrate-model 62 00:04:04,100 --> 00:04:05,150 organisms-- 63 00:04:05,150 --> 00:04:08,420 the zebrafish and the mouse. 64 00:04:08,420 --> 00:04:12,140 And so you can see the mouse is our lab mascot, 65 00:04:12,140 --> 00:04:14,660 but it's also an important genetic model. 66 00:04:14,660 --> 00:04:16,820 In particular, there are many models 67 00:04:16,820 --> 00:04:20,190 in mice that mimic cancer. 68 00:04:20,190 --> 00:04:22,430 And so these have been useful for elucidating 69 00:04:22,430 --> 00:04:25,680 mechanisms of cancer. 70 00:04:25,680 --> 00:04:29,180 So it's really unethical for us to do 71 00:04:29,180 --> 00:04:32,270 a lot of different types of experiments on humans, 72 00:04:32,270 --> 00:04:35,330 but I will mention that there are human cell 73 00:04:35,330 --> 00:04:38,060 lines, such as the HeLa cell line, 74 00:04:38,060 --> 00:04:42,020 and these also play an important role in biology research. 75 00:04:42,020 --> 00:04:44,960 But these cell lines are taken out of context. 76 00:04:44,960 --> 00:04:49,950 They're not functioning in the context of an entire organism. 77 00:04:49,950 --> 00:04:53,060 So human cell lines are important, 78 00:04:53,060 --> 00:04:56,750 but you have to understand that they're 79 00:04:56,750 --> 00:04:59,270 sort of an in-vitro system, that they're out 80 00:04:59,270 --> 00:05:03,560 of the context of a functioning organism. 81 00:05:03,560 --> 00:05:08,160 OK, so why is it that we research these model organisms? 82 00:05:08,160 --> 00:05:11,400 So there are some practical reasons. 83 00:05:11,400 --> 00:05:13,250 Most of them are fairly small, and they're 84 00:05:13,250 --> 00:05:16,760 easy to house large numbers of them in a lab. 85 00:05:16,760 --> 00:05:22,310 They're often cheap to house in the lab and work with. 86 00:05:22,310 --> 00:05:25,670 Also, they develop fast. 87 00:05:25,670 --> 00:05:28,550 And especially when we consider genetics, 88 00:05:28,550 --> 00:05:31,340 the rate-limiting step in genetics research 89 00:05:31,340 --> 00:05:34,610 is the time it takes from the conception 90 00:05:34,610 --> 00:05:37,370 of an organism to the time that that organism can 91 00:05:37,370 --> 00:05:39,800 reproduce sexually. 92 00:05:39,800 --> 00:05:41,870 So these model organisms-- 93 00:05:41,870 --> 00:05:44,670 most of them reproduce very quickly, 94 00:05:44,670 --> 00:05:49,160 and so that accelerates the pace of research. 95 00:05:49,160 --> 00:05:52,220 But maybe most importantly is the fact 96 00:05:52,220 --> 00:05:55,940 that we are related to each of these model organisms 97 00:05:55,940 --> 00:06:01,550 through evolution because we all arose from a common ancestor. 98 00:06:01,550 --> 00:06:05,180 And just to highlight this example, using the fruit fly, 99 00:06:05,180 --> 00:06:11,070 the fruit fly has 17,000 genes when compared to our 20,000 100 00:06:11,070 --> 00:06:16,520 genes, so we have roughly the same order of magnitude number 101 00:06:16,520 --> 00:06:18,870 of genes as the fruit fly. 102 00:06:18,870 --> 00:06:22,880 And so let's think about important genes. 103 00:06:22,880 --> 00:06:24,980 Let's think about just genes that are 104 00:06:24,980 --> 00:06:27,320 associated with human diseases. 105 00:06:27,320 --> 00:06:30,860 If we just consider human-disease causing genes, 106 00:06:30,860 --> 00:06:35,060 75% of the human-disease causing genes 107 00:06:35,060 --> 00:06:38,540 have a homologous gene in the fruit fly. 108 00:06:38,540 --> 00:06:42,020 So we're similar to these model organisms, such as the fruit 109 00:06:42,020 --> 00:06:46,700 fly, in particular, genes that are important for understanding 110 00:06:46,700 --> 00:06:47,315 human disease. 111 00:06:50,460 --> 00:06:54,050 So I came across this quote when preparing for the lecture, 112 00:06:54,050 --> 00:06:54,860 and I liked it. 113 00:06:54,860 --> 00:06:58,550 It's from John Rinn, who is a contemporary of ours. 114 00:06:58,550 --> 00:07:01,160 And John Rinn said, "Genetic approaches 115 00:07:01,160 --> 00:07:05,270 are as fundamental to biology as math is to physics." 116 00:07:05,270 --> 00:07:09,950 And I think this is an apt quote because both genetics and math 117 00:07:09,950 --> 00:07:12,920 themselves are scientific disciplines, 118 00:07:12,920 --> 00:07:17,780 but they can be leveraged to learn things about biology 119 00:07:17,780 --> 00:07:21,710 and physics respectively. 120 00:07:21,710 --> 00:07:25,820 So genetics really plays a fundamental role in biology 121 00:07:25,820 --> 00:07:30,520 and the discovery of new biological mechanisms. 122 00:07:30,520 --> 00:07:35,180 OK, so now, I want to just briefly take you through, 123 00:07:35,180 --> 00:07:39,140 how is it that we discover things using genetics? 124 00:07:39,140 --> 00:07:41,780 And I'm going to take you through a type of approach 125 00:07:41,780 --> 00:07:43,880 that's called a "forward genetic screen." 126 00:07:48,910 --> 00:07:51,800 And I'll get to the orchestra in just a minute, 127 00:07:51,800 --> 00:07:53,990 but I briefly just want to define what 128 00:07:53,990 --> 00:07:56,630 a forward genetic screen is. 129 00:07:56,630 --> 00:08:00,500 So a forward genetic screen is type of approach 130 00:08:00,500 --> 00:08:03,230 you would do if you don't know the genes that 131 00:08:03,230 --> 00:08:05,180 are involved in a specific process, 132 00:08:05,180 --> 00:08:08,340 but you want to identify them. 133 00:08:08,340 --> 00:08:10,520 So in a forward genetic screen, you 134 00:08:10,520 --> 00:08:13,880 don't know the genes and mechanisms involved. 135 00:08:17,415 --> 00:08:19,040 So you don't know what these genes are. 136 00:08:19,040 --> 00:08:22,110 You might not even have a genome sequence of the organism. 137 00:08:22,110 --> 00:08:23,900 You don't need a genome sequence in order 138 00:08:23,900 --> 00:08:27,800 to do a forward genetic screen because you don't know 139 00:08:27,800 --> 00:08:31,970 the genes, but you're interested in a particular aspect 140 00:08:31,970 --> 00:08:36,919 of development, or of organismal function, 141 00:08:36,919 --> 00:08:38,750 and you want to identify the genes that 142 00:08:38,750 --> 00:08:40,520 are important for that. 143 00:08:40,520 --> 00:08:44,059 So when starting a forward genetic screen, you have to, 144 00:08:44,059 --> 00:08:47,600 then, infer what a possible phenotype would look 145 00:08:47,600 --> 00:08:52,190 like if you broke genes that were involved in that process. 146 00:08:52,190 --> 00:08:55,580 So the mantra of geneticists is that we 147 00:08:55,580 --> 00:09:00,020 are going to break genes and then look at the result 148 00:09:00,020 --> 00:09:03,740 and see if it gives the phenotype we're interested in. 149 00:09:03,740 --> 00:09:15,080 So in a forward genetic screen, you're looking for a phenotype 150 00:09:15,080 --> 00:09:22,550 that you would expect if you affected a certain process, 151 00:09:22,550 --> 00:09:29,720 if you disrupt a process. 152 00:09:29,720 --> 00:09:33,830 So think about this orchestra up here. 153 00:09:33,830 --> 00:09:35,870 Let's say you are interested in what 154 00:09:35,870 --> 00:09:40,940 regulates each of these sections in the orchestra. 155 00:09:40,940 --> 00:09:46,700 What is the master regulator of this orchestra, if you will? 156 00:09:46,700 --> 00:09:51,290 And so conceptually, what a genetic screen would involve 157 00:09:51,290 --> 00:09:53,660 is taking hundreds, maybe thousands, 158 00:09:53,660 --> 00:09:57,050 of orchestras like this one, and just shooting 159 00:09:57,050 --> 00:09:59,660 an individual in this orchestra, and removing them 160 00:09:59,660 --> 00:10:01,820 from the orchestra. 161 00:10:01,820 --> 00:10:07,500 And so let's say you remove-- 162 00:10:07,500 --> 00:10:09,710 let's say you remove this guy right here, 163 00:10:09,710 --> 00:10:13,150 then maybe nothing really happens because they are, like, 164 00:10:13,150 --> 00:10:16,130 30 or 40 other violinists, and that's 165 00:10:16,130 --> 00:10:19,250 not the major control circuit. 166 00:10:19,250 --> 00:10:22,730 So then you would infer that this gene is not 167 00:10:22,730 --> 00:10:25,430 important for the regulation of the orchestra. 168 00:10:25,430 --> 00:10:28,340 But let's say you took out Bernstein, 169 00:10:28,340 --> 00:10:31,520 and then you listen to the orchestra 170 00:10:31,520 --> 00:10:36,270 and find that the sections are uncoordinated, 171 00:10:36,270 --> 00:10:39,840 the bassists start doing crazy things on their own. 172 00:10:39,840 --> 00:10:43,910 So then the logic of Drosophila genetics, 173 00:10:43,910 --> 00:10:46,550 you would then name that gene uncoordinated 174 00:10:46,550 --> 00:10:49,760 and infer that that gene has some important role 175 00:10:49,760 --> 00:10:56,030 in coordinating the different sections of the orchestra. 176 00:10:56,030 --> 00:11:01,010 So the goal in genetics is to identify a mutation that 177 00:11:01,010 --> 00:11:04,160 alters a gene function that gives you a phenotype 178 00:11:04,160 --> 00:11:06,120 that you're interested in. 179 00:11:06,120 --> 00:11:08,240 And rather than taking a gun and shooting 180 00:11:08,240 --> 00:11:11,570 members of the orchestra, in genetics, 181 00:11:11,570 --> 00:11:13,340 you try to identify mutations. 182 00:11:13,340 --> 00:11:17,360 So you try to induce mutations. 183 00:11:17,360 --> 00:11:19,070 So we're looking for mutations. 184 00:11:22,220 --> 00:11:26,720 And these mutations could be spontaneous mutations, 185 00:11:26,720 --> 00:11:30,860 meaning you didn't do anything to induce it, 186 00:11:30,860 --> 00:11:35,850 but they just appear as a variant in the population. 187 00:11:35,850 --> 00:11:37,520 And so we've talked about Thomas Hunt 188 00:11:37,520 --> 00:11:41,260 Morgan and the white mutant, and the white gene. 189 00:11:41,260 --> 00:11:44,830 So the white mutant was a spontaneous mutation. 190 00:11:44,830 --> 00:11:46,900 Morgan's lab didn't do anything special. 191 00:11:46,900 --> 00:11:49,600 They just looked at lots of flies, and over the decades, 192 00:11:49,600 --> 00:11:51,760 they identified this one special male 193 00:11:51,760 --> 00:11:55,090 fly that they could work on. 194 00:11:55,090 --> 00:12:01,400 But nowadays, researchers have a way to accelerate this process. 195 00:12:01,400 --> 00:12:03,610 And so we can induce mutations. 196 00:12:10,120 --> 00:12:12,300 In the way we can induce mutations is 197 00:12:12,300 --> 00:12:15,360 by using some type of mutagen. 198 00:12:15,360 --> 00:12:19,350 So for example, you could have some sort of chemical mutagen 199 00:12:19,350 --> 00:12:24,420 that increases the error rate in DNA replication, 200 00:12:24,420 --> 00:12:28,920 or you could use radiation to induce DNA damage, 201 00:12:28,920 --> 00:12:31,710 and that essentially accelerates the frequency 202 00:12:31,710 --> 00:12:35,280 of mutations that occur in the genome of an organism. 203 00:12:38,580 --> 00:12:42,960 And so the process of mutagenizing an organism 204 00:12:42,960 --> 00:12:44,640 isn't specific to genes. 205 00:12:44,640 --> 00:12:46,800 You're just inducing random mutations 206 00:12:46,800 --> 00:12:52,140 across the genome of the individual. 207 00:12:52,140 --> 00:12:55,990 Let's say this piece I'm drawing here is part of a chromosome, 208 00:12:55,990 --> 00:13:00,840 and these boxes are genes. 209 00:13:00,840 --> 00:13:03,630 You're inducing random mutations, 210 00:13:03,630 --> 00:13:07,020 and maybe one mutation hits this gene. 211 00:13:07,020 --> 00:13:09,660 So that's a mutation that might affect 212 00:13:09,660 --> 00:13:12,060 the function of an organism. 213 00:13:12,060 --> 00:13:16,350 You might have other mutations that are outside genes 214 00:13:16,350 --> 00:13:20,130 and may have no effect, or you could 215 00:13:20,130 --> 00:13:22,380 have a different kind of mutation which 216 00:13:22,380 --> 00:13:24,390 isn't in the coding region of the gene, 217 00:13:24,390 --> 00:13:30,600 but maybe affects the regulation of this blue gene over here. 218 00:13:30,600 --> 00:13:32,340 So this is a random process. 219 00:13:32,340 --> 00:13:36,690 If you're feeding an organism some chemical mutagen, 220 00:13:36,690 --> 00:13:40,260 you're inducing random mutations in different places, 221 00:13:40,260 --> 00:13:45,060 and you don't know which are the ones that you want until you 222 00:13:45,060 --> 00:13:47,065 look at their phenotypes and try to find 223 00:13:47,065 --> 00:13:48,190 the needle in the haystack. 224 00:13:51,600 --> 00:13:54,080 So let me show you some examples. 225 00:13:54,080 --> 00:13:58,440 So let's say we were interested in our body pattern. 226 00:13:58,440 --> 00:14:00,000 We all have a body. 227 00:14:00,000 --> 00:14:01,710 Our head is in our head position, 228 00:14:01,710 --> 00:14:03,810 our ass is in our ass position, we 229 00:14:03,810 --> 00:14:05,760 have arms that are in a right position, 230 00:14:05,760 --> 00:14:07,890 our legs are in the right position. 231 00:14:07,890 --> 00:14:10,230 Let's say we're interested in figuring out 232 00:14:10,230 --> 00:14:13,380 what genes were responsible for that body pattern. 233 00:14:13,380 --> 00:14:15,540 What kind of a mutant might you look for? 234 00:14:19,761 --> 00:14:20,340 Anyone? 235 00:14:20,340 --> 00:14:20,840 Rachel. 236 00:14:20,840 --> 00:14:22,738 AUDIENCE: [INAUDIBLE] 237 00:14:22,738 --> 00:14:23,780 ADAM MARTIN: What's that? 238 00:14:23,780 --> 00:14:27,050 A mutant with his body parts in the wrong place. 239 00:14:27,050 --> 00:14:31,280 Maybe you have like a leg where your arm should be coming out, 240 00:14:31,280 --> 00:14:33,800 or maybe you have two heads, or something like that. 241 00:14:33,800 --> 00:14:36,740 So you look for some sort of defect 242 00:14:36,740 --> 00:14:39,480 in the pattern formation. 243 00:14:39,480 --> 00:14:44,150 And so this would be, obviously, unethical to do in humans, 244 00:14:44,150 --> 00:14:47,120 but in model organisms, we can actually 245 00:14:47,120 --> 00:14:49,370 find these types of mutations. 246 00:14:49,370 --> 00:14:51,650 I'm just going to highlight a couple mutants. 247 00:14:51,650 --> 00:14:52,910 This one's an obvious one. 248 00:14:52,910 --> 00:14:55,760 So this fly has two wings. 249 00:14:55,760 --> 00:14:58,970 You can see them folded over each other right here. 250 00:14:58,970 --> 00:15:01,130 But there's a specific class of mutant 251 00:15:01,130 --> 00:15:02,810 that was called "wingless"-- 252 00:15:02,810 --> 00:15:05,600 where the flies have fewer than two wings. 253 00:15:05,600 --> 00:15:09,320 This particular fly has only one wing. 254 00:15:09,320 --> 00:15:12,560 And this wingless mutant defined a gene 255 00:15:12,560 --> 00:15:16,760 that became known as wingless, and it's 256 00:15:16,760 --> 00:15:20,550 a gene that has a homologous gene in humans. 257 00:15:20,550 --> 00:15:25,220 And this particular gene defines an entire signaling pathway, 258 00:15:25,220 --> 00:15:26,660 which-- 259 00:15:26,660 --> 00:15:30,650 whoops, sorry-- which is important in stem-cell biology 260 00:15:30,650 --> 00:15:34,010 and is also over activated in cancer. 261 00:15:34,010 --> 00:15:35,840 But obviously, we don't have wings, 262 00:15:35,840 --> 00:15:38,810 so this gene didn't get discovered in humans. 263 00:15:38,810 --> 00:15:40,940 It was discovered in flies, and then 264 00:15:40,940 --> 00:15:43,460 only later on, it was inferred-- or it 265 00:15:43,460 --> 00:15:47,010 was discovered that there is a related gene in humans. 266 00:15:47,010 --> 00:15:50,150 OK, so that's one example. 267 00:15:50,150 --> 00:15:53,960 One of the other phenotypes I showed you is called "notch." 268 00:15:53,960 --> 00:15:57,380 Normally, a fly wing has a nice, smooth margin. 269 00:15:57,380 --> 00:16:01,880 But notch mutants have wings that have this chunk taken out 270 00:16:01,880 --> 00:16:04,340 of them at the end. 271 00:16:04,340 --> 00:16:07,760 So again, the gene became known as "notch" because of this fly 272 00:16:07,760 --> 00:16:08,410 phenotype. 273 00:16:08,410 --> 00:16:10,430 But again, there's a human notch, 274 00:16:10,430 --> 00:16:13,580 and human notch, again, is involved in human diseases, 275 00:16:13,580 --> 00:16:15,230 such as cancer. 276 00:16:15,230 --> 00:16:17,780 OK, so that's two examples. 277 00:16:17,780 --> 00:16:20,810 But now, I want to talk to you about how one might find 278 00:16:20,810 --> 00:16:22,160 the needle in the haystack. 279 00:16:22,160 --> 00:16:25,340 How can you have a concerted effort 280 00:16:25,340 --> 00:16:31,388 to identify genes that have that function in a given process? 281 00:16:31,388 --> 00:16:32,930 I'm going to tell you about work done 282 00:16:32,930 --> 00:16:36,530 by Eric Wieschaus and Christiane Nusslein-Volhard 283 00:16:36,530 --> 00:16:39,530 because they did one of the more famous genetic screens that 284 00:16:39,530 --> 00:16:43,400 had been done, and they won the Nobel Prize for their results 285 00:16:43,400 --> 00:16:45,860 in 1995. 286 00:16:45,860 --> 00:16:48,710 So I mean I take you through this classic genetic screen. 287 00:16:51,600 --> 00:16:54,950 In this screen, they're going to induce mutations. 288 00:16:54,950 --> 00:16:56,780 So I'm going to take a parental generation. 289 00:16:56,780 --> 00:17:00,650 And this screen was done using fruit flies. 290 00:17:00,650 --> 00:17:04,579 And they took male fruit flies and treated the males 291 00:17:04,579 --> 00:17:07,940 with a mutagen to induce mutations 292 00:17:07,940 --> 00:17:12,470 in the gametes these male flies that would then be passed 293 00:17:12,470 --> 00:17:16,040 to subsequent generations. 294 00:17:16,040 --> 00:17:22,430 And they mated the mutagenized males to females, 295 00:17:22,430 --> 00:17:24,650 and then they went on to look at-- 296 00:17:24,650 --> 00:17:28,710 to isolate individual F1 progeny. 297 00:17:28,710 --> 00:17:33,290 So we're going to look at individual F1 298 00:17:33,290 --> 00:17:35,400 progeny in this generation. 299 00:17:37,950 --> 00:17:41,990 So in the F1 progeny have the potential 300 00:17:41,990 --> 00:17:45,860 to get one mutagenized chromosome from their father 301 00:17:45,860 --> 00:17:50,630 and will get a normal chromosome from the mother. 302 00:17:50,630 --> 00:17:53,020 So I'll just drawn this like this, 303 00:17:53,020 --> 00:17:57,290 where independent mutations are going to be different colors. 304 00:17:57,290 --> 00:18:02,870 So that has maybe a mutation on one of its chromosomes. 305 00:18:02,870 --> 00:18:05,120 Another fly, because this is random, 306 00:18:05,120 --> 00:18:08,840 might have a different mutation on the same 307 00:18:08,840 --> 00:18:10,070 or a different chromosome. 308 00:18:12,790 --> 00:18:13,820 I'll draw a couple more. 309 00:18:13,820 --> 00:18:17,240 And maybe one of these flies doesn't have 310 00:18:17,240 --> 00:18:19,640 a mutation that's been induced. 311 00:18:19,640 --> 00:18:22,550 And then I'll draw just one independent mutation 312 00:18:22,550 --> 00:18:24,320 right there. 313 00:18:24,320 --> 00:18:25,650 So again, this is random. 314 00:18:25,650 --> 00:18:29,930 So to see random mutations, you have to take individual flies. 315 00:18:29,930 --> 00:18:32,420 Now, are you're going to see a phenotype in this F1 316 00:18:32,420 --> 00:18:33,110 generation? 317 00:18:36,410 --> 00:18:40,140 So Miles-- is it the Malik or Miles? 318 00:18:40,140 --> 00:18:40,640 Sorry. 319 00:18:40,640 --> 00:18:41,150 AUDIENCE: Miles. 320 00:18:41,150 --> 00:18:41,480 ADAM MARTIN: Miles. 321 00:18:41,480 --> 00:18:41,980 Good. 322 00:18:41,980 --> 00:18:42,565 OK. 323 00:18:42,565 --> 00:18:47,610 AUDIENCE: Most likely not because any mutation 324 00:18:47,610 --> 00:18:52,142 [INAUDIBLE] spontaneous would be overshadowed 325 00:18:52,142 --> 00:18:56,545 by the direct gene, the female. 326 00:18:56,545 --> 00:18:57,420 ADAM MARTIN: Exactly. 327 00:18:57,420 --> 00:19:00,840 So what Miles just said is that-- 328 00:19:00,840 --> 00:19:02,910 basically, he said that these mutations 329 00:19:02,910 --> 00:19:05,460 if their loss-of-function mutations are going to be 330 00:19:05,460 --> 00:19:06,720 recessive-- 331 00:19:06,720 --> 00:19:08,940 because they're going to be overshadowed 332 00:19:08,940 --> 00:19:13,530 by a normal functioning gene on the other chromosome. 333 00:19:13,530 --> 00:19:16,980 So if you're looking for a loss-of-function mutation, 334 00:19:16,980 --> 00:19:19,380 that's likely going to be recessive, 335 00:19:19,380 --> 00:19:22,980 and you would not see it in the F1 generation. 336 00:19:22,980 --> 00:19:27,420 So in order to look for organisms 337 00:19:27,420 --> 00:19:30,600 that have incorrect body patterning, 338 00:19:30,600 --> 00:19:33,510 you need to get a situation where 339 00:19:33,510 --> 00:19:38,760 each of the mutated chromosomes is homozygous recessive. 340 00:19:38,760 --> 00:19:40,605 And to do that, you have to do more crosses. 341 00:19:44,450 --> 00:19:46,700 So what the researchers did was to take 342 00:19:46,700 --> 00:19:53,180 independent individual organisms, and cross them all, 343 00:19:53,180 --> 00:19:57,170 again, to non-mutated flies. 344 00:19:57,170 --> 00:20:00,830 And remember, flies don't self-cross. 345 00:20:00,830 --> 00:20:03,080 So they need a male and a female that 346 00:20:03,080 --> 00:20:06,590 has the same mutated chromosome mating to each other 347 00:20:06,590 --> 00:20:10,190 in order to see a recessive phenotype. 348 00:20:10,190 --> 00:20:12,800 So because you only have an individual fly 349 00:20:12,800 --> 00:20:16,010 for each of these, you need to get more than one fly 350 00:20:16,010 --> 00:20:18,500 with the same mutation. 351 00:20:18,500 --> 00:20:24,980 So they mate to a normal fly. 352 00:20:24,980 --> 00:20:28,370 But now, you can get an F2-- 353 00:20:28,370 --> 00:20:35,480 multiple F2 males and females that 354 00:20:35,480 --> 00:20:39,980 will have the same mutated chromosome. 355 00:20:39,980 --> 00:20:41,540 So here, let's draw these. 356 00:20:48,460 --> 00:20:50,810 They're still heterozygous. 357 00:20:50,810 --> 00:20:52,200 At this point. 358 00:20:52,200 --> 00:20:55,420 But at this point, you just generated multiple organisms, 359 00:20:55,420 --> 00:20:59,990 male and female, that have the same chromosome that 360 00:20:59,990 --> 00:21:04,972 arose from, essentially, the same chromosome and the father, 361 00:21:04,972 --> 00:21:10,150 or the male gamete and the parental generation. 362 00:21:10,150 --> 00:21:11,280 Oh, I did green. 363 00:21:11,280 --> 00:21:11,780 OK. 364 00:21:18,170 --> 00:21:23,600 OK, so now we have males and females, both of which 365 00:21:23,600 --> 00:21:25,700 are heterozygous. 366 00:21:25,700 --> 00:21:30,490 And I'm skipping some of the details of this cross. 367 00:21:30,490 --> 00:21:32,240 You'll want to take my word for the fact 368 00:21:32,240 --> 00:21:35,920 that they can keep track of which chromosome is which. 369 00:21:35,920 --> 00:21:40,190 And I'm not going to tell you how they did that because it's 370 00:21:40,190 --> 00:21:42,932 fly genetics, and it's not terribly important 371 00:21:42,932 --> 00:21:43,640 that you know it. 372 00:21:47,510 --> 00:21:50,600 So now, you have males and females. 373 00:21:50,600 --> 00:21:52,940 And now you can look an F3. 374 00:21:52,940 --> 00:21:55,980 And from each of these lines-- 375 00:21:55,980 --> 00:21:58,130 each of these lines, you're going to cross siblings 376 00:21:58,130 --> 00:21:58,730 to each other. 377 00:22:02,390 --> 00:22:03,980 So you're doing a sibling-cross. 378 00:22:06,620 --> 00:22:10,250 What fraction of the progeny for each of these sibling crosses 379 00:22:10,250 --> 00:22:12,873 should be homozygous for the mutant chromosome? 380 00:22:16,120 --> 00:22:16,770 Yes, Steven. 381 00:22:16,770 --> 00:22:17,805 AUDIENCE: One-fourth. 382 00:22:17,805 --> 00:22:19,430 ADAM MARTIN: One-fourth, exactly right. 383 00:22:19,430 --> 00:22:21,890 So Steven's exactly right. 384 00:22:21,890 --> 00:22:26,540 A quarter of the progeny should get two copies 385 00:22:26,540 --> 00:22:28,610 of the mutated chromosome. 386 00:22:28,610 --> 00:22:34,070 So 25% should be homozygous recessive. 387 00:22:38,240 --> 00:22:42,170 And so you can screen this F3 progeny 388 00:22:42,170 --> 00:22:44,330 for each of these independent lines, 389 00:22:44,330 --> 00:22:48,350 and look for flies at some stage of development that 390 00:22:48,350 --> 00:22:51,222 are defective in patterning. 391 00:22:51,222 --> 00:22:53,840 And so I'll show you how-- 392 00:22:53,840 --> 00:22:54,970 where'd my clicker go? 393 00:22:58,180 --> 00:23:01,867 All right, so they're looking for incorrect body pattern. 394 00:23:01,867 --> 00:23:03,700 Yes, your question-- what's your name again? 395 00:23:03,700 --> 00:23:04,420 AUDIENCE: Georgia. 396 00:23:04,420 --> 00:23:05,130 ADAM MARTIN: Georgia, yeah. 397 00:23:05,130 --> 00:23:06,838 AUDIENCE: Would there be some [INAUDIBLE] 398 00:23:06,838 --> 00:23:11,490 in the F2 [INAUDIBLE] normal? 399 00:23:11,490 --> 00:23:15,200 [INAUDIBLE] 400 00:23:15,200 --> 00:23:16,040 ADAM MARTIN: Yes. 401 00:23:16,040 --> 00:23:19,480 So they're able to select for the mutated chromosome, yeah. 402 00:23:19,480 --> 00:23:21,740 And I'm skipping over how they did that because it's 403 00:23:21,740 --> 00:23:23,192 kind of esoteric. 404 00:23:23,192 --> 00:23:23,900 But you're right. 405 00:23:23,900 --> 00:23:25,400 Some would be normal. 406 00:23:25,400 --> 00:23:28,280 But I'm just telling you they're able to figure out 407 00:23:28,280 --> 00:23:30,650 the ones that have the mutant chromosome, 408 00:23:30,650 --> 00:23:34,040 and they made sure to keep those in the next generation. 409 00:23:34,040 --> 00:23:35,750 Good question, Georgia. 410 00:23:35,750 --> 00:23:40,880 All right, so they can screen F3 for a phenotype. 411 00:23:40,880 --> 00:23:42,660 It turns out all of the mutations 412 00:23:42,660 --> 00:23:44,400 they're interested in are lethal, 413 00:23:44,400 --> 00:23:47,450 so they have to look at the larval stages for ones that 414 00:23:47,450 --> 00:23:49,370 have a defect in patterning. 415 00:23:49,370 --> 00:23:52,140 So this is what a Drosophila larvae looks like. 416 00:23:52,140 --> 00:23:55,520 And you can see it has a segmental pattern here. 417 00:23:55,520 --> 00:23:58,610 This is the head of the larvae, the tail of the larvae. 418 00:23:58,610 --> 00:24:00,770 But you see there are these segments that 419 00:24:00,770 --> 00:24:05,150 alternate between smooth cuticle and hairy cuticle. 420 00:24:05,150 --> 00:24:07,350 They have things called "denticles," 421 00:24:07,350 --> 00:24:09,650 which are these hairlike projections, which 422 00:24:09,650 --> 00:24:13,490 are essentially, what the maggot uses for traction so it 423 00:24:13,490 --> 00:24:16,100 can crawl around. 424 00:24:16,100 --> 00:24:18,170 So they're basically just looking at maggots here 425 00:24:18,170 --> 00:24:23,180 and looking at the pattern of segments in the maggots. 426 00:24:23,180 --> 00:24:25,260 But what they found is a mutant. 427 00:24:25,260 --> 00:24:27,140 And here's a mutant here which just 428 00:24:27,140 --> 00:24:30,260 has these hairlike projections all across the cuticle 429 00:24:30,260 --> 00:24:35,570 without these intervening regions of naked cuticle. 430 00:24:35,570 --> 00:24:38,570 And because there's a lot of hairlike projections 431 00:24:38,570 --> 00:24:41,900 here, it reminded the researchers of a hedgehog, 432 00:24:41,900 --> 00:24:45,050 and so this mutant became known as "hedgehog." 433 00:24:45,050 --> 00:24:49,220 And the hedgehog gene was the founding member 434 00:24:49,220 --> 00:24:52,100 of an entire signaling pathway, known as the "hedgehog 435 00:24:52,100 --> 00:24:56,600 pathway," that plays important roles in human development 436 00:24:56,600 --> 00:24:59,190 and also, human cancer. 437 00:24:59,190 --> 00:25:02,510 So the human gene for a hedgehog, 438 00:25:02,510 --> 00:25:04,490 or one of the most important ones in humans, 439 00:25:04,490 --> 00:25:06,500 is known as sonic hedgehog. 440 00:25:06,500 --> 00:25:12,680 So you see that geneticists have kind of an odd sense of humor. 441 00:25:12,680 --> 00:25:16,940 But the sonic-hedgehog gene is important in cancer. 442 00:25:16,940 --> 00:25:19,160 And actually, there are now a number 443 00:25:19,160 --> 00:25:20,930 of drugs that are being developed 444 00:25:20,930 --> 00:25:23,600 to target the hedgehog pathway. 445 00:25:23,600 --> 00:25:27,500 And one was approved back in 2012 for use 446 00:25:27,500 --> 00:25:29,900 in treating basal-cell carcinoma. 447 00:25:29,900 --> 00:25:31,850 And there's currently another drug 448 00:25:31,850 --> 00:25:34,940 that's in phase-II clinical trials for treating 449 00:25:34,940 --> 00:25:37,460 some forms of leukemia. 450 00:25:37,460 --> 00:25:40,700 So this is a story that goes from identifying 451 00:25:40,700 --> 00:25:44,600 this weird fly mutant all the way to clinical trials, 452 00:25:44,600 --> 00:25:49,010 developing drugs, whose purpose is 453 00:25:49,010 --> 00:25:52,340 to inhibit this signaling pathway, which hedgehog 454 00:25:52,340 --> 00:25:54,070 is the signal-- 455 00:25:54,070 --> 00:25:56,150 the extracellular ligand for. 456 00:25:59,300 --> 00:26:02,240 OK, so now, I'm going to switch gears and tell you 457 00:26:02,240 --> 00:26:04,340 about another story. 458 00:26:04,340 --> 00:26:10,010 It's a bit like this one, except this one has an MIT connection. 459 00:26:10,010 --> 00:26:17,920 So I'm going to tell you about work done at MIT in the worm 460 00:26:17,920 --> 00:26:19,620 Caenorhabditis elegans. 461 00:26:22,910 --> 00:26:24,470 And this work was done in the lab 462 00:26:24,470 --> 00:26:33,260 of Robert Horvitz, who is a member of our biology 463 00:26:33,260 --> 00:26:34,610 department. 464 00:26:34,610 --> 00:26:37,970 And Robert Horvitz, for his work, 465 00:26:37,970 --> 00:26:40,560 won the Nobel Prize, along with John Sulston 466 00:26:40,560 --> 00:26:44,240 and Sydney Brenner for their work 467 00:26:44,240 --> 00:26:48,590 on how cells decide what fates they give rise to. 468 00:26:48,590 --> 00:26:53,690 And one thing that the Horvitz Lab has done 469 00:26:53,690 --> 00:26:58,160 is to elucidate the mechanisms that determine whether or not 470 00:26:58,160 --> 00:27:01,680 a cell lives or dies during development. 471 00:27:04,430 --> 00:27:07,940 So if we think about the development of an organism, 472 00:27:07,940 --> 00:27:10,310 we all start from a single cell. 473 00:27:10,310 --> 00:27:14,990 And the cell divides into different cells, 474 00:27:14,990 --> 00:27:17,210 but different cells in our body give rise 475 00:27:17,210 --> 00:27:20,150 to different cell fates. 476 00:27:20,150 --> 00:27:23,330 So if we consider just a generic cell division here, 477 00:27:23,330 --> 00:27:26,060 you have a parent cell, A. It might 478 00:27:26,060 --> 00:27:29,780 divide into cell B and cell C. 479 00:27:29,780 --> 00:27:33,420 And maybe cell B might have a particular fate. 480 00:27:33,420 --> 00:27:36,290 It might go on to develop into a neuron, or a muscle 481 00:27:36,290 --> 00:27:37,700 cell, whatever. 482 00:27:37,700 --> 00:27:39,290 It doesn't matter. 483 00:27:39,290 --> 00:27:46,310 But one fate that Horvitz, Sulston, and Brenner saw 484 00:27:46,310 --> 00:27:48,110 is that sometimes, cells-- 485 00:27:48,110 --> 00:27:50,810 their fate was to just die. 486 00:27:50,810 --> 00:27:52,700 And this was very stereotypical. 487 00:27:52,700 --> 00:27:53,600 You get a death. 488 00:27:56,710 --> 00:28:00,200 And this was defined as "programmed cell death" 489 00:28:00,200 --> 00:28:06,020 because it followed a very stereotypic pattern, where 490 00:28:06,020 --> 00:28:10,820 the same cell, each time, would undergo cell death, 491 00:28:10,820 --> 00:28:14,440 so it seems like there's a program for it. 492 00:28:14,440 --> 00:28:19,140 This also is called "apoptosis." 493 00:28:19,140 --> 00:28:27,060 So what really enabled this work is the biology of C. elegans. 494 00:28:27,060 --> 00:28:30,810 And, here, I'm showing you C. elegans development. 495 00:28:30,810 --> 00:28:33,190 This is the C. elegans zygote. 496 00:28:33,190 --> 00:28:35,920 This cell divides into two cells that are different. 497 00:28:35,920 --> 00:28:39,390 One is called AB, one is called P1. 498 00:28:39,390 --> 00:28:42,660 And we know exactly what the fates for the descendants 499 00:28:42,660 --> 00:28:46,980 of both these cells are based on the work of Sulston, Brenner, 500 00:28:46,980 --> 00:28:48,570 and Horvitz. 501 00:28:48,570 --> 00:28:50,940 Now, you get a four-cell stage where 502 00:28:50,940 --> 00:28:55,600 AB divides into two cells, and P1 divides into two cells. 503 00:28:55,600 --> 00:28:59,370 And again, we know the fates of all these cells. 504 00:28:59,370 --> 00:29:02,010 And what C. elegans researchers have 505 00:29:02,010 --> 00:29:07,300 done, because C. elegans is a relatively simple organism-- 506 00:29:07,300 --> 00:29:12,390 there are only 947 cells in every individual worm, 507 00:29:12,390 --> 00:29:15,405 so there are 947 somatic cells. 508 00:29:18,480 --> 00:29:20,220 And this is stereotypic. 509 00:29:20,220 --> 00:29:24,330 Every worm has these 947 cells. 510 00:29:24,330 --> 00:29:25,670 How many cells do you have? 511 00:29:29,690 --> 00:29:32,960 More than or less than 1,000? 512 00:29:32,960 --> 00:29:35,210 You have more than 1,000 cells, right? 513 00:29:35,210 --> 00:29:38,240 That makes you much more complicated. 514 00:29:38,240 --> 00:29:41,420 So really, the practical aspect of C. elegans 515 00:29:41,420 --> 00:29:45,620 is it has a much simpler composition, 516 00:29:45,620 --> 00:29:48,920 and they can track what happens to every single one 517 00:29:48,920 --> 00:29:50,420 of these cells. 518 00:29:50,420 --> 00:29:52,490 They know when every cell divides 519 00:29:52,490 --> 00:29:56,000 and what the daughter cells of that division will turn into. 520 00:29:56,000 --> 00:30:00,530 In other words, they know the entire lineage of this animal. 521 00:30:00,530 --> 00:30:02,240 So this is a picture of the lineage. 522 00:30:02,240 --> 00:30:05,360 That's showing you what happens to every single cell 523 00:30:05,360 --> 00:30:09,230 in the development of an adult worm. 524 00:30:09,230 --> 00:30:14,960 And so what particularly interested Robert Horvitz 525 00:30:14,960 --> 00:30:20,300 is 131 cells, during the development of this animal, 526 00:30:20,300 --> 00:30:23,950 underwent programmed cell death. 527 00:30:23,950 --> 00:30:27,350 And this is not random cell death. 528 00:30:27,350 --> 00:30:29,630 It's the same cells every time. 529 00:30:29,630 --> 00:30:32,570 They know what happens to every single cell in the development 530 00:30:32,570 --> 00:30:35,600 of this organism, so they know when there's a division, 531 00:30:35,600 --> 00:30:40,700 and one specific cell dies every time in the organism. 532 00:30:40,700 --> 00:30:42,590 So it's really a unique scenario, 533 00:30:42,590 --> 00:30:48,020 where you can really see what becomes of every cell that 534 00:30:48,020 --> 00:30:51,320 is present in the organism. 535 00:30:51,320 --> 00:30:56,330 OK, so now, let's talk a little bit about the death process. 536 00:30:56,330 --> 00:30:59,990 So I'll make the cell-death process simple. 537 00:30:59,990 --> 00:31:03,990 You start with a live cell. 538 00:31:03,990 --> 00:31:07,340 So you have a live cell. 539 00:31:07,340 --> 00:31:11,810 That live cell dies such that you then have a dead cell. 540 00:31:17,180 --> 00:31:20,510 And then after the cell dies-- 541 00:31:20,510 --> 00:31:23,900 after the cell dies, the remnants of that dead cell 542 00:31:23,900 --> 00:31:25,940 are engulfed by neighboring cells, such 543 00:31:25,940 --> 00:31:29,480 that you no longer see that cell. 544 00:31:29,480 --> 00:31:32,645 So the last step in this process is engulfment. 545 00:31:37,400 --> 00:31:42,290 Now, a key aspect of one of Horvitz's screens 546 00:31:42,290 --> 00:31:47,630 is that other researchers had identified a mutation that 547 00:31:47,630 --> 00:31:50,480 affected cell death that specifically blocked 548 00:31:50,480 --> 00:31:51,695 this engulfment process. 549 00:31:54,830 --> 00:31:58,070 And this gene is called ced-1. 550 00:31:58,070 --> 00:32:01,640 There's the first cell-death mutant that was identified. 551 00:32:01,640 --> 00:32:12,990 "Ced" stands for "Cell Death abnormal" 552 00:32:12,990 --> 00:32:16,770 so C-E-D is short for "ced." 553 00:32:16,770 --> 00:32:19,760 So researchers in the C. elegans field 554 00:32:19,760 --> 00:32:23,480 started isolating these cell-death-abnormal mutants, 555 00:32:23,480 --> 00:32:26,870 where something happened in the cell-death process that 556 00:32:26,870 --> 00:32:29,360 was abnormal. 557 00:32:29,360 --> 00:32:33,110 And I'll tell you how this was leveraged by the Horvitz lab 558 00:32:33,110 --> 00:32:36,410 to identify, basically, a pathway of genes 559 00:32:36,410 --> 00:32:39,930 that were involved in cell death. 560 00:32:39,930 --> 00:32:42,320 So this is now a worm. 561 00:32:42,320 --> 00:32:43,700 And what you see in this worm are 562 00:32:43,700 --> 00:32:45,470 these bubble-like structures that 563 00:32:45,470 --> 00:32:49,400 are cells that are dead but haven't been engulfed. 564 00:32:49,400 --> 00:32:53,420 So these dead cells, because they're not engulfed, 565 00:32:53,420 --> 00:32:55,700 are visible in the adult worm. 566 00:32:55,700 --> 00:32:59,060 And so you can basically just look at adult worms, 567 00:32:59,060 --> 00:33:02,180 and see whether or not these bubble-like structures are 568 00:33:02,180 --> 00:33:05,150 present in a ced- mutant. 569 00:33:05,150 --> 00:33:07,340 In a wild-type worm, you don't see this, 570 00:33:07,340 --> 00:33:09,260 and so it's harder to see. 571 00:33:09,260 --> 00:33:13,250 But this provided a visual assay to look 572 00:33:13,250 --> 00:33:17,510 for mutations that block the cell-death process 573 00:33:17,510 --> 00:33:22,070 because if you block the process upstream of ced-1 574 00:33:22,070 --> 00:33:24,140 in an engulfment, you no longer will see 575 00:33:24,140 --> 00:33:27,740 these bubble-like structures. 576 00:33:27,740 --> 00:33:31,760 And so that is what the Horvitz Lab did. 577 00:33:31,760 --> 00:33:36,380 They started with ced-1 mutant worms. 578 00:33:36,380 --> 00:33:39,350 So they're starting with the ced-1 mutants. 579 00:33:39,350 --> 00:33:45,540 And they then mutagenized these ced-1 mutants. 580 00:33:45,540 --> 00:33:46,770 Sorry-- mutagenized. 581 00:33:50,930 --> 00:33:54,500 And so they're essentially, looking for second mutants 582 00:33:54,500 --> 00:33:58,010 in this animal that will affect the death process. 583 00:34:02,150 --> 00:34:06,240 OK, I'll take you through the logic of this. 584 00:34:06,240 --> 00:34:08,540 So for the remainder of the generations 585 00:34:08,540 --> 00:34:11,180 I'm going to tell you about, they're all ced-1 mutant. 586 00:34:15,139 --> 00:34:19,429 Ced-1 is homozygous, in this case. 587 00:34:19,429 --> 00:34:23,150 And the worms are hermaphrodites, 588 00:34:23,150 --> 00:34:26,030 meaning they are both male and female sex organs. 589 00:34:26,030 --> 00:34:28,449 And therefore, they can self-fertilize. 590 00:34:28,449 --> 00:34:29,830 So this is a self-cross. 591 00:34:32,920 --> 00:34:36,429 And so ced-1 remains homozygous mutant 592 00:34:36,429 --> 00:34:39,880 throughout all these crosses. 593 00:34:39,880 --> 00:34:45,100 Now similar to the fly screen, they can look for individual-- 594 00:34:45,100 --> 00:34:49,206 or you get individual worms here. 595 00:34:49,206 --> 00:34:54,727 Let's get some colors so we can compare different chromosomes. 596 00:34:54,727 --> 00:34:58,280 All right, so this chromosome might have one mutant, 597 00:34:58,280 --> 00:35:01,030 this chromosome could have another mutant, 598 00:35:01,030 --> 00:35:03,420 and this chromosome could have another mutant, 599 00:35:03,420 --> 00:35:07,210 and maybe this one doesn't have a mutant. 600 00:35:07,210 --> 00:35:11,560 OK, but again, these are heterozygous animals, 601 00:35:11,560 --> 00:35:14,920 so if you're looking for a loss-of-function mutant, 602 00:35:14,920 --> 00:35:19,600 you would not see the phenotype at this stage. 603 00:35:19,600 --> 00:35:23,590 So what was done in this screen is, because these are worms 604 00:35:23,590 --> 00:35:26,620 and they're hermaphrodites, they basically 605 00:35:26,620 --> 00:35:31,810 allowed each of these worms to self-cross. 606 00:35:31,810 --> 00:35:34,090 So now, were talking just about a self-cross. 607 00:35:38,290 --> 00:35:44,350 And because a single worm has just one of these chromosomes, 608 00:35:44,350 --> 00:35:48,580 when it undergoes a self-cross, a quarter of the progeny 609 00:35:48,580 --> 00:35:51,670 will be homozygous recessive for the mutation. 610 00:35:54,850 --> 00:35:58,160 OK, so here, we have the F1 generation. 611 00:35:58,160 --> 00:36:01,470 And now, they can just look at the F2 generation, 612 00:36:01,470 --> 00:36:06,430 and they're looking for some fraction of the worms resulting 613 00:36:06,430 --> 00:36:10,570 from each individual that has a phenotype that 614 00:36:10,570 --> 00:36:14,080 looks like a cell-death mutant phenotype. 615 00:36:20,655 --> 00:36:22,030 And so they're essentially, going 616 00:36:22,030 --> 00:36:25,270 to screen through the F2 generation, 617 00:36:25,270 --> 00:36:28,360 and look for worms that fail to have 618 00:36:28,360 --> 00:36:33,130 these bubble-like structures in the adult world. 619 00:36:33,130 --> 00:36:37,780 So they're looking for a loss of these refractile structures. 620 00:36:37,780 --> 00:36:40,640 And they got one. 621 00:36:40,640 --> 00:36:44,650 This is now a double mutant between ced-1 and ced-3, 622 00:36:44,650 --> 00:36:46,690 and you see how you no longer have 623 00:36:46,690 --> 00:36:49,180 these big bubble-like structures in the worm. 624 00:36:52,810 --> 00:36:56,440 So they identified a mutant, and thus, a gene, 625 00:36:56,440 --> 00:37:02,680 that's called "ced-3," which basically causes a failure 626 00:37:02,680 --> 00:37:05,290 of the cells to undergo cell death. 627 00:37:05,290 --> 00:37:09,130 Now given what I've given what I've shown you, 628 00:37:09,130 --> 00:37:13,060 is this necessarily a mutant that's involved in cell death? 629 00:37:13,060 --> 00:37:15,130 Can you think of an alternative explanation 630 00:37:15,130 --> 00:37:24,060 for why you would lose these sort of bubble-like structures? 631 00:37:24,060 --> 00:37:25,890 What else could be happening? 632 00:37:25,890 --> 00:37:27,810 When you do a screen like this, and when 633 00:37:27,810 --> 00:37:30,523 you do science in general, you have 634 00:37:30,523 --> 00:37:32,190 to think through all the different types 635 00:37:32,190 --> 00:37:33,270 of possibilities. 636 00:37:33,270 --> 00:37:34,303 Yes, Georgia. 637 00:37:34,303 --> 00:37:37,622 AUDIENCE: [INAUDIBLE] 638 00:37:37,622 --> 00:37:38,580 ADAM MARTIN: Excellent. 639 00:37:38,580 --> 00:37:42,540 Georgia said you could have re-mutated ced-1. 640 00:37:42,540 --> 00:37:47,760 So one possibility is that you isolated a ced-1 revertant, 641 00:37:47,760 --> 00:37:52,590 which means you changed its DNA sequence so that now, 642 00:37:52,590 --> 00:37:55,470 the ced-1 gene is functional. 643 00:37:55,470 --> 00:37:58,170 So one scenario here is you could 644 00:37:58,170 --> 00:38:03,420 have some type of revertant, or you could have a suppressor 645 00:38:03,420 --> 00:38:04,080 mutation. 646 00:38:04,080 --> 00:38:08,970 Maybe you have a mutation that bypasses 647 00:38:08,970 --> 00:38:12,870 the function of ced-1 such that now, the cells 648 00:38:12,870 --> 00:38:14,840 can engulf the dead cell. 649 00:38:14,840 --> 00:38:17,052 So you could also have a suppressor. 650 00:38:20,500 --> 00:38:23,940 Or the alternative scenario, the one that we want, 651 00:38:23,940 --> 00:38:26,340 is that we've affected the cell-death process 652 00:38:26,340 --> 00:38:32,730 and identified something that is a bona-fide cell-death mutant. 653 00:38:32,730 --> 00:38:34,860 How would you differentiate between these two? 654 00:38:39,185 --> 00:38:39,685 Any ideas? 655 00:38:43,110 --> 00:38:44,650 Should this have an extra cell? 656 00:38:49,210 --> 00:38:49,960 No. 657 00:38:49,960 --> 00:38:51,370 Stevens says no. 658 00:38:51,370 --> 00:38:52,450 I agree. 659 00:38:52,450 --> 00:38:57,280 There should be no extra cell here 660 00:38:57,280 --> 00:39:00,020 because if you restore the phagocytic process, 661 00:39:00,020 --> 00:39:02,020 then the cell should die, it should be engulfed, 662 00:39:02,020 --> 00:39:04,640 and there shouldn't be an extra cell. 663 00:39:04,640 --> 00:39:08,500 But if this is a bona-fide cell-death mutant, 664 00:39:08,500 --> 00:39:12,010 then you should have extra cells. 665 00:39:12,010 --> 00:39:15,250 And it turns out the ced-3 mutant blocks 666 00:39:15,250 --> 00:39:20,890 all 131 of these cell deaths so that you have 131 extra cells. 667 00:39:20,890 --> 00:39:25,090 Because they know the entire cell lineage of this worm 668 00:39:25,090 --> 00:39:28,240 and can see whether or not cells are present or not, 669 00:39:28,240 --> 00:39:30,650 they can tell if there are extra cells or not. 670 00:39:30,650 --> 00:39:33,130 And therefore, that allowed them to infer 671 00:39:33,130 --> 00:39:35,560 that they had an actual mutant that 672 00:39:35,560 --> 00:39:41,230 was affecting the mechanism that promotes the cell to die. 673 00:39:41,230 --> 00:39:45,180 And this shows that the cell death-- 674 00:39:45,180 --> 00:39:48,640 it shows the cell death is an active process. 675 00:39:48,640 --> 00:39:51,710 It's not just some random event that's happening. 676 00:39:51,710 --> 00:39:57,010 It's an active process that is controlled by genes. 677 00:39:57,010 --> 00:39:59,860 So there's an active mechanism involved in the cell death. 678 00:40:03,150 --> 00:40:07,180 Any questions about how these screens go in the crosses 679 00:40:07,180 --> 00:40:08,080 before I move on? 680 00:40:11,160 --> 00:40:15,210 OK, so I have one more story to tell you about, 681 00:40:15,210 --> 00:40:18,005 and this relates to behavior. 682 00:40:18,005 --> 00:40:19,755 So now, I want to tell you about behavior. 683 00:40:22,630 --> 00:40:27,880 We all behave, some of us better than others. 684 00:40:27,880 --> 00:40:30,490 So how is it we can go from something 685 00:40:30,490 --> 00:40:35,230 as abstract as behavior to specific genes and mechanisms 686 00:40:35,230 --> 00:40:38,000 that control this? 687 00:40:38,000 --> 00:40:41,410 And I'm going to tell you about work that was awarded the Nobel 688 00:40:41,410 --> 00:40:43,480 Prize last year. 689 00:40:43,480 --> 00:40:45,700 Two of these researchers-- 690 00:40:45,700 --> 00:40:48,400 their careers are at Brandeis. 691 00:40:48,400 --> 00:40:53,290 And so what they discovered is the mechanism that controls 692 00:40:53,290 --> 00:40:56,380 circadian rhythm in organisms-- 693 00:40:56,380 --> 00:40:59,350 so circadian rhythm. 694 00:40:59,350 --> 00:41:02,860 So circadian rhythm is a behavior. 695 00:41:02,860 --> 00:41:07,450 We are awake during certain parts of the day 696 00:41:07,450 --> 00:41:09,685 and are asleep at night. 697 00:41:13,810 --> 00:41:15,910 If you're hidden from the light-dark cycle, 698 00:41:15,910 --> 00:41:19,870 you continue this cycle for some amount of time. 699 00:41:19,870 --> 00:41:22,540 So there's something intrinsic in our system 700 00:41:22,540 --> 00:41:29,170 such that we want to exist on this 24-hour wake-sleep cycle. 701 00:41:29,170 --> 00:41:30,820 That's why it's a pain in the ass 702 00:41:30,820 --> 00:41:34,270 to travel to the other side of the world. 703 00:41:39,380 --> 00:41:43,240 So we want to identify what controls this behavior. 704 00:41:43,240 --> 00:41:45,520 What, then, might be a phenotype you might look for? 705 00:41:51,420 --> 00:41:53,850 What would happen if you break circadian rhythm? 706 00:41:56,680 --> 00:41:57,250 Yeah, Rachel. 707 00:41:57,250 --> 00:41:59,400 AUDIENCE: [INAUDIBLE] 708 00:41:59,400 --> 00:42:03,070 ADAM MARTIN: It wouldn't have a nice, clear wake-sleep cycle. 709 00:42:03,070 --> 00:42:04,980 Exactly. 710 00:42:04,980 --> 00:42:07,860 So this screen-- what they did is 711 00:42:07,860 --> 00:42:11,820 to look for flies where they didn't have 712 00:42:11,820 --> 00:42:15,330 this robust wake-sleep cycle. 713 00:42:15,330 --> 00:42:19,860 So you basically look for flies that are awake at night, 714 00:42:19,860 --> 00:42:27,100 and identify the mutations that cause this to happen. 715 00:42:27,100 --> 00:42:30,390 OK, I'll show you an example of what the data looks like. 716 00:42:30,390 --> 00:42:34,500 So what each of these lines is each tick mark 717 00:42:34,500 --> 00:42:38,040 is a measurement of fly activity. 718 00:42:38,040 --> 00:42:41,490 So what you see are flies asleep, they're awake, 719 00:42:41,490 --> 00:42:44,110 they're asleep, they're awake. 720 00:42:44,110 --> 00:42:47,190 And researchers identified mutants that 721 00:42:47,190 --> 00:42:49,870 didn't show this 24-hour cycle. 722 00:42:49,870 --> 00:42:52,350 So some are just totally arrhythmic. 723 00:42:52,350 --> 00:42:55,040 Other mutants alter the period of this cycle 724 00:42:55,040 --> 00:42:58,440 so that it's either longer or shorter. 725 00:42:58,440 --> 00:43:00,690 So this is a really elegant way to look 726 00:43:00,690 --> 00:43:04,335 for genes that are important for circadian rhythm. 727 00:43:04,335 --> 00:43:06,225 And I'll take you through the genetic screen. 728 00:43:10,890 --> 00:43:15,465 This was done by Konopka and Benzer. 729 00:43:18,320 --> 00:43:20,880 And they did a nice screen. 730 00:43:20,880 --> 00:43:26,440 It involves a genetic trick which I'll show you. 731 00:43:26,440 --> 00:43:30,660 There's a type of chromosome called "attached-X" in flies, 732 00:43:30,660 --> 00:43:33,120 where two X chromosomes are fused to each other-- 733 00:43:36,610 --> 00:43:38,370 so two X chromosomes fused. 734 00:43:44,820 --> 00:43:48,240 And if a fly has two X chromosomes, it's female. 735 00:43:48,240 --> 00:43:51,030 If it just has one X chromosome, it's male. 736 00:43:51,030 --> 00:43:57,035 So that's determines fly gender. 737 00:43:59,760 --> 00:44:04,010 So then these researchers took males and mutagenized them. 738 00:44:09,360 --> 00:44:13,420 And they crossed these males to attached X females. 739 00:44:13,420 --> 00:44:17,670 And when you cross males to attached X females, 740 00:44:17,670 --> 00:44:21,090 you do something clever, which is half of the progeny 741 00:44:21,090 --> 00:44:23,280 dies because you need an X chromosome 742 00:44:23,280 --> 00:44:26,130 and you can't have three X chromosomes. 743 00:44:26,130 --> 00:44:30,690 But your females are all attached XY. 744 00:44:30,690 --> 00:44:33,060 So the females actually get their X chromosomes 745 00:44:33,060 --> 00:44:35,820 from their mom, which is the opposite of the way it normally 746 00:44:35,820 --> 00:44:37,050 works. 747 00:44:37,050 --> 00:44:40,650 And males get their X chromosome from dad 748 00:44:40,650 --> 00:44:45,060 because the attached X strain has a Y chromosome. 749 00:44:45,060 --> 00:44:47,520 So this is a little bit of a genetic trick. 750 00:44:47,520 --> 00:44:50,760 And the fly-- the reason it was done, in this case, is they 751 00:44:50,760 --> 00:44:53,130 wanted to mutate the X chromosome 752 00:44:53,130 --> 00:44:56,670 and then have the fathers pass on their X chromosome 753 00:44:56,670 --> 00:44:59,940 to their sons because there's only one copy of the X 754 00:44:59,940 --> 00:45:00,550 chromosome. 755 00:45:00,550 --> 00:45:03,360 So if you get a mutation on X, you 756 00:45:03,360 --> 00:45:06,120 don't have to homozygous it because there's 757 00:45:06,120 --> 00:45:08,310 only one copy of it. 758 00:45:08,310 --> 00:45:10,290 So it's a little bit of a genetic trick that, 759 00:45:10,290 --> 00:45:12,210 in this case, served the researchers 760 00:45:12,210 --> 00:45:15,350 a generation on their screen. 761 00:45:15,350 --> 00:45:21,240 OK, so you take this, and then you identify males now 762 00:45:21,240 --> 00:45:24,120 that have a mutated X chromosome, 763 00:45:24,120 --> 00:45:25,860 and they have only one X chromosomes 764 00:45:25,860 --> 00:45:30,120 so you should be able to observe the behavior even here. 765 00:45:32,640 --> 00:45:36,870 But to establish a line of flies that have this mutation, 766 00:45:36,870 --> 00:45:41,280 they then took these F1 mutated males, 767 00:45:41,280 --> 00:45:45,270 and crossed them, again, to attached X flies. 768 00:45:47,800 --> 00:45:50,880 And again, in doing this, all the males from this cross 769 00:45:50,880 --> 00:45:53,260 are mutant. 770 00:45:53,260 --> 00:45:57,160 So now, you have multiple males, all of which are mutant. 771 00:45:57,160 --> 00:46:02,730 So you can start with a single male F1. 772 00:46:02,730 --> 00:46:07,050 And by crossing it to this strain here, 773 00:46:07,050 --> 00:46:10,560 you get a lot of males now that have the mutant chromosome, 774 00:46:10,560 --> 00:46:12,600 and you can look at their behavior 775 00:46:12,600 --> 00:46:17,400 to determine whether you've affected circadian rhythm. 776 00:46:17,400 --> 00:46:21,150 Does everyone understand how the attached X works here? 777 00:46:21,150 --> 00:46:22,980 It's kind of a little bit of a trick, 778 00:46:22,980 --> 00:46:26,000 but flies have these tools that you can use 779 00:46:26,000 --> 00:46:30,990 to save your time in the lab. 780 00:46:30,990 --> 00:46:37,770 OK, so these mutants that the Benzer Lab identified had 781 00:46:37,770 --> 00:46:40,530 altered period of the sleep-wake cycle, 782 00:46:40,530 --> 00:46:44,100 and therefore, the gene was named "period." 783 00:46:44,100 --> 00:46:50,340 So this screen identified a gene called "period." 784 00:46:52,950 --> 00:46:54,310 This is a gene. 785 00:46:54,310 --> 00:46:56,655 And there's a hammer log of the period gene in humans. 786 00:46:59,310 --> 00:47:01,770 And the gene in humans is associated 787 00:47:01,770 --> 00:47:04,920 with familial advanced sleep-phase syndrome. 788 00:47:04,920 --> 00:47:07,410 So defects in the genes that were identified 789 00:47:07,410 --> 00:47:12,720 in Drosophila actually are relevant to human sleep 790 00:47:12,720 --> 00:47:13,410 disorders. 791 00:47:17,400 --> 00:47:20,240 All right, I'm all set.