1 00:00:00,960 --> 00:00:03,270 The following content is provided under a Creative 2 00:00:03,270 --> 00:00:04,630 Commons license. 3 00:00:04,630 --> 00:00:07,140 Your support will help MIT Open Courseware 4 00:00:07,140 --> 00:00:11,470 continue to offer high quality educational resources for free. 5 00:00:11,470 --> 00:00:14,100 To make a donation or view additional materials 6 00:00:14,100 --> 00:00:18,050 from hundreds of MIT courses, visit MIT Open Courseware 7 00:00:18,050 --> 00:00:19,000 at ocw.mit.edu. 8 00:00:25,440 --> 00:00:28,160 JOANNE STUBBE: Because we haven't gotten that far 9 00:00:28,160 --> 00:00:31,220 in class to understand what this protein is 10 00:00:31,220 --> 00:00:35,150 that's the focus of the paper, I still 11 00:00:35,150 --> 00:00:37,450 think the paper is straightforward to understand. 12 00:00:37,450 --> 00:00:39,960 I'm just going to put it into context. 13 00:00:39,960 --> 00:00:45,110 So I was having trouble trying to decide what to do. 14 00:00:45,110 --> 00:00:46,680 And maybe I shouldn't have done this. 15 00:00:46,680 --> 00:00:49,370 But the fact is that this technology 16 00:00:49,370 --> 00:00:51,830 we're going to be focusing on in a very sort 17 00:00:51,830 --> 00:00:56,770 of simple way, CRISPR-Cas, has taken the world by storm. 18 00:00:56,770 --> 00:00:59,820 And that's the take home message from this. 19 00:00:59,820 --> 00:01:02,420 So you can sort of get what it does. 20 00:01:02,420 --> 00:01:03,890 But really to look at the details, 21 00:01:03,890 --> 00:01:05,209 you have to go in and study it. 22 00:01:05,209 --> 00:01:09,859 And every time you pick up another journal, 23 00:01:09,859 --> 00:01:11,525 you look at Google Journals or something 24 00:01:11,525 --> 00:01:15,470 like-- there's another 100, 200, 300 papers published on this. 25 00:01:15,470 --> 00:01:22,160 So this is a current technology that has taken off really 26 00:01:22,160 --> 00:01:24,320 since 2012. 27 00:01:24,320 --> 00:01:29,780 And so very rarely is technology successful in that short period 28 00:01:29,780 --> 00:01:32,280 of time. 29 00:01:32,280 --> 00:01:34,730 And it happens to have been applied 30 00:01:34,730 --> 00:01:37,520 to one of the key enzymes that people are now 31 00:01:37,520 --> 00:01:40,760 focused on in terms of controlling cholesterol 32 00:01:40,760 --> 00:01:43,080 levels, which is what we're talking about. 33 00:01:43,080 --> 00:01:46,640 So I use it as an opportunity to just show you 34 00:01:46,640 --> 00:01:48,065 what this technology is. 35 00:01:48,065 --> 00:01:50,790 Have any of you ever done this technology? 36 00:01:50,790 --> 00:01:53,310 Nobody in the last class had done the technology either. 37 00:01:53,310 --> 00:01:56,270 But my niece is a sophomore here. 38 00:01:56,270 --> 00:01:58,580 She spent a whole EUROP doing this technology. 39 00:01:58,580 --> 00:02:02,112 So this technology has moved into the lab. 40 00:02:02,112 --> 00:02:03,320 My lab hasn't used it either. 41 00:02:03,320 --> 00:02:06,510 So I probably can't answer any of the details. 42 00:02:06,510 --> 00:02:09,500 But it's one of these things that it 43 00:02:09,500 --> 00:02:10,949 is extremely complicated. 44 00:02:10,949 --> 00:02:13,730 I think I can give you a cartoon overview of how it works. 45 00:02:13,730 --> 00:02:16,310 But if you're going to use it, just like every tool, 46 00:02:16,310 --> 00:02:18,150 you have to study it in more detail. 47 00:02:18,150 --> 00:02:18,650 OK. 48 00:02:18,650 --> 00:02:20,730 So I am going to ask you questions. 49 00:02:20,730 --> 00:02:22,730 And this is going to be different from the one I 50 00:02:22,730 --> 00:02:26,090 did on Thursday, because I spent too much time talking 51 00:02:26,090 --> 00:02:27,790 about this article. 52 00:02:27,790 --> 00:02:29,460 And then I'll come back to-- 53 00:02:29,460 --> 00:02:31,720 how many of you read these articles? 54 00:02:31,720 --> 00:02:35,534 How many of you didn't read these articles? 55 00:02:35,534 --> 00:02:36,390 OK. 56 00:02:36,390 --> 00:02:38,750 So your class is much worse than the other one. 57 00:02:38,750 --> 00:02:40,430 The other one had read all the articles. 58 00:02:40,430 --> 00:02:40,929 OK. 59 00:02:40,929 --> 00:02:43,610 So we won't have a very good discussion about this. 60 00:02:43,610 --> 00:02:46,070 And I'll tell you why I think you should read that, 61 00:02:46,070 --> 00:02:48,400 but I'm not going to focus on that till we end. 62 00:02:48,400 --> 00:02:49,010 OK. 63 00:02:49,010 --> 00:02:53,810 So the paper we are going to focus on is this one. 64 00:02:53,810 --> 00:02:57,260 And this is the gene product, the protein that 65 00:02:57,260 --> 00:03:01,670 has become a focus of attention of many people 66 00:03:01,670 --> 00:03:04,980 in terms of controlling cholesterol levels 67 00:03:04,980 --> 00:03:11,280 and as an alternative to statins or maybe better than statins, 68 00:03:11,280 --> 00:03:12,640 but we haven't gotten there yet. 69 00:03:12,640 --> 00:03:13,139 OK. 70 00:03:13,139 --> 00:03:17,820 So this is still on the drawing board in there. 71 00:03:17,820 --> 00:03:20,090 Many people focused on clinical trials 72 00:03:20,090 --> 00:03:22,620 targeting this particular protein. 73 00:03:22,620 --> 00:03:26,330 And so one of the questions that this paper 74 00:03:26,330 --> 00:03:27,740 focused on and asked-- 75 00:03:27,740 --> 00:03:29,390 hopefully, you all have read the paper. 76 00:03:29,390 --> 00:03:33,220 It was only three pages, so it wasn't very hard to read. 77 00:03:33,220 --> 00:03:36,320 Is this protein important in terms 78 00:03:36,320 --> 00:03:38,780 of controlling cholesterol levels? 79 00:03:38,780 --> 00:03:41,840 And they did experiments in tissue culture and in mice 80 00:03:41,840 --> 00:03:45,470 to try to address that issue using CRISPR-Cas 81 00:03:45,470 --> 00:03:49,850 as a way of destroying the gene, the gene which then destroys 82 00:03:49,850 --> 00:03:50,590 the protein. 83 00:03:50,590 --> 00:03:51,400 OK. 84 00:03:51,400 --> 00:03:55,190 So this other article sort of gives you 85 00:03:55,190 --> 00:03:59,510 an overview of the kinds of things we need to think about 86 00:03:59,510 --> 00:04:02,140 to make the technology better. 87 00:04:02,140 --> 00:04:04,670 And when technology is introduced-- 88 00:04:04,670 --> 00:04:07,370 just like if you look at unnatural amino acids 89 00:04:07,370 --> 00:04:10,280 that you guys looked at with the Schultz technology. 90 00:04:10,280 --> 00:04:14,540 I mean, first 15 years Peter was doing that, he collaborated 91 00:04:14,540 --> 00:04:15,080 with my lab. 92 00:04:15,080 --> 00:04:17,540 We never published a single paper, OK? 93 00:04:17,540 --> 00:04:19,810 Because the technology was not good. 94 00:04:19,810 --> 00:04:22,670 And so now, the technology is still not good. 95 00:04:22,670 --> 00:04:26,070 But it's getting there, and it's improved greatly. 96 00:04:26,070 --> 00:04:27,380 So you often see something. 97 00:04:27,380 --> 00:04:29,450 It looks, oh my goodness, you know, 98 00:04:29,450 --> 00:04:31,350 this is going to be fantastic. 99 00:04:31,350 --> 00:04:33,860 But the devil is in the details, OK? 100 00:04:33,860 --> 00:04:36,380 And that's one of the take-home messages from this course 101 00:04:36,380 --> 00:04:36,990 anyhow. 102 00:04:36,990 --> 00:04:37,490 OK. 103 00:04:37,490 --> 00:04:39,080 So what I want to do is just give you 104 00:04:39,080 --> 00:04:42,800 a very brief overview of what I had 105 00:04:42,800 --> 00:04:47,810 hoped to get to by the end of lecture today 106 00:04:47,810 --> 00:04:50,840 and didn't quite get there. 107 00:04:50,840 --> 00:04:54,320 And so we did get to the fact that we made LDL particles. 108 00:04:54,320 --> 00:04:58,370 And LDL is transferred in the blood 109 00:04:58,370 --> 00:05:00,630 and is a major carrier of cholesterol. 110 00:05:00,630 --> 00:05:03,360 So it takes cholesterol from our diet. 111 00:05:03,360 --> 00:05:07,400 And it's going to deliver it into different kinds of cells, 112 00:05:07,400 --> 00:05:09,840 and it does so. 113 00:05:09,840 --> 00:05:14,010 There's a receptor on the surface of the cell. 114 00:05:14,010 --> 00:05:18,290 And these little things here, these little flags, 115 00:05:18,290 --> 00:05:20,460 are at the receptors and the receptors. 116 00:05:20,460 --> 00:05:23,450 That's what we're going to talk about next lecture 117 00:05:23,450 --> 00:05:27,600 is low density lipoprotein receptor. 118 00:05:27,600 --> 00:05:28,100 OK. 119 00:05:28,100 --> 00:05:30,560 And this is the basic. 120 00:05:30,560 --> 00:05:35,060 Brown and Goldstein figured out that genetic mutations 121 00:05:35,060 --> 00:05:37,700 in this receptor and other steps associated 122 00:05:37,700 --> 00:05:42,260 with getting the receptor to the surface of the plasma membrane 123 00:05:42,260 --> 00:05:46,205 are responsible for children for the disease familial 124 00:05:46,205 --> 00:05:52,820 hypercholesterolemia where kids die at age 7 heart attacks, 125 00:05:52,820 --> 00:05:56,720 because of inability to control cholesterol levels. 126 00:05:56,720 --> 00:05:57,260 OK. 127 00:05:57,260 --> 00:05:59,480 So I need to just sort of briefly 128 00:05:59,480 --> 00:06:02,420 walk you through the model, because that model is 129 00:06:02,420 --> 00:06:05,790 related to the effect of this protein 130 00:06:05,790 --> 00:06:08,830 you were reading about in the paper 131 00:06:08,830 --> 00:06:10,610 that you were supposed to read for today. 132 00:06:10,610 --> 00:06:11,109 OK. 133 00:06:11,109 --> 00:06:16,910 So this lipoprotein can bind to the receptor. 134 00:06:16,910 --> 00:06:18,750 This is a plasma membrane. 135 00:06:18,750 --> 00:06:20,210 You see there are three receptors. 136 00:06:20,210 --> 00:06:25,595 The receptors have to cluster to be successful at somehow, 137 00:06:25,595 --> 00:06:29,990 by mechanisms that are moderately well-understood, 138 00:06:29,990 --> 00:06:35,530 can engulf the LDL particle and form a little vesicle. 139 00:06:35,530 --> 00:06:39,361 And the little vesicle is coated with a protein called clathrin. 140 00:06:39,361 --> 00:06:39,860 OK. 141 00:06:39,860 --> 00:06:43,160 And we'll see over the course of the rest of the semester this 142 00:06:43,160 --> 00:06:46,000 is used over and over again-- so is clathrin-- 143 00:06:46,000 --> 00:06:48,990 as a way of taking up nutrients into the cell. 144 00:06:48,990 --> 00:06:51,770 So this is a major mechanism of doing that. 145 00:06:51,770 --> 00:06:57,140 And then what happens is the clathrin is removed. 146 00:06:57,140 --> 00:07:00,020 Biochemically, it's removed enzymatically. 147 00:07:00,020 --> 00:07:03,860 And what you're left with is a vesicle that 148 00:07:03,860 --> 00:07:06,200 then fuses with an endosome. 149 00:07:06,200 --> 00:07:10,790 And so that's a little organelle with lipid membranes 150 00:07:10,790 --> 00:07:13,320 that is acidic. 151 00:07:13,320 --> 00:07:17,180 And when the LDL protein gets into the interior 152 00:07:17,180 --> 00:07:21,290 of this little vesicle and the pH is lower than the normal pH, 153 00:07:21,290 --> 00:07:27,350 goes from 7 and 1/2 to 5, the LDL receptor 154 00:07:27,350 --> 00:07:31,760 dissociates from the LDL particle. 155 00:07:31,760 --> 00:07:35,330 And so then what happens, by mechanisms that are really 156 00:07:35,330 --> 00:07:38,450 incompletely understood, the receptors 157 00:07:38,450 --> 00:07:40,430 can recycle to the surface. 158 00:07:40,430 --> 00:07:41,330 OK? 159 00:07:41,330 --> 00:07:44,210 And what happens is that, when some of them 160 00:07:44,210 --> 00:07:48,680 recycle to the surface, you're left with an LDL particle 161 00:07:48,680 --> 00:07:53,890 that fuses with another organelle called the lysosome. 162 00:07:53,890 --> 00:07:56,720 And then this LDL particle goes into the lysosome. 163 00:07:56,720 --> 00:07:59,630 The lysosome is sort of like a proteasome. 164 00:07:59,630 --> 00:08:02,720 It's a bag of proteases and lipases. 165 00:08:02,720 --> 00:08:06,770 So it just degrades everything in there-- amino acids, fats, 166 00:08:06,770 --> 00:08:08,000 everything-- 167 00:08:08,000 --> 00:08:11,690 allowing you to produce amino acids and cholesterol, 168 00:08:11,690 --> 00:08:12,860 free cholesterol. 169 00:08:12,860 --> 00:08:16,420 And then cholesterol in the liver is often stored. 170 00:08:16,420 --> 00:08:17,500 It gets esterified. 171 00:08:17,500 --> 00:08:20,510 And it's stored as triacylglycerol. 172 00:08:20,510 --> 00:08:23,180 Fatty acids esterify to cholesterols. 173 00:08:23,180 --> 00:08:23,870 OK? 174 00:08:23,870 --> 00:08:27,980 So the process, of course, of getting the LDL receptor 175 00:08:27,980 --> 00:08:32,360 to the surface is done in the rough endoplasmic reticulum. 176 00:08:32,360 --> 00:08:35,570 Because it's a membrane protein, it's 177 00:08:35,570 --> 00:08:39,270 transferred by things called little coated vesicles. 178 00:08:39,270 --> 00:08:42,140 And then somehow these little coated vesicles 179 00:08:42,140 --> 00:08:45,690 deliver the receptor to the protein. 180 00:08:45,690 --> 00:08:46,190 OK? 181 00:08:46,190 --> 00:08:48,710 So this is a very complicated process. 182 00:08:48,710 --> 00:08:53,990 And, in fact, mutations that are responsible for heart attacks 183 00:08:53,990 --> 00:08:55,950 occur in every step in this process. 184 00:08:55,950 --> 00:08:57,650 It's not just the LDL receptor. 185 00:08:57,650 --> 00:09:00,120 We'll see that in class next time. 186 00:09:00,120 --> 00:09:00,710 OK. 187 00:09:00,710 --> 00:09:03,200 So the key thing you need to know for today 188 00:09:03,200 --> 00:09:09,080 is that you have LDL receptors that interact with LDL. 189 00:09:09,080 --> 00:09:12,312 And that's key to taking the cholesterol into the cell. 190 00:09:12,312 --> 00:09:13,520 That's the take-home message. 191 00:09:13,520 --> 00:09:14,990 That's fairly easy to understand. 192 00:09:14,990 --> 00:09:15,920 OK. 193 00:09:15,920 --> 00:09:22,820 So the protein we're focused on today is this guy, PCSK9-- 194 00:09:22,820 --> 00:09:24,950 horrible acronym which I've written down. 195 00:09:24,950 --> 00:09:26,090 I can't even remember it. 196 00:09:26,090 --> 00:09:34,730 But it stands for Proprotein Convertase Subtilisin/Kexin 9. 197 00:09:34,730 --> 00:09:35,300 OK. 198 00:09:35,300 --> 00:09:38,210 So the important thing is subtilisin. 199 00:09:38,210 --> 00:09:40,160 Has anyone ever heard of subtilisin? 200 00:09:40,160 --> 00:09:43,410 So that's like [INAUDIBLE]. 201 00:09:43,410 --> 00:09:46,360 So it evolved convergently. 202 00:09:46,360 --> 00:09:50,930 And so it is a protease that has a serine, a histidine, 203 00:09:50,930 --> 00:09:53,810 and aspartic acid, like you learned in protein media 204 00:09:53,810 --> 00:09:58,480 degradation in the first part of the module. 205 00:09:58,480 --> 00:09:59,965 OK. 206 00:09:59,965 --> 00:10:02,660 And this protein was discovered-- 207 00:10:02,660 --> 00:10:03,710 we'll see in a minute-- 208 00:10:03,710 --> 00:10:05,250 again, because of patients. 209 00:10:05,250 --> 00:10:05,750 OK. 210 00:10:05,750 --> 00:10:09,080 So the patients presented themselves in a funny way. 211 00:10:09,080 --> 00:10:11,690 That's how the LDL receptor was discovered. 212 00:10:11,690 --> 00:10:13,940 If you've read Brown and Goldstein's article, which 213 00:10:13,940 --> 00:10:16,260 was one of the things I asked you to read, 214 00:10:16,260 --> 00:10:18,140 you've already gone through that. 215 00:10:18,140 --> 00:10:22,370 That was the thing that got Brown and Goldstein 216 00:10:22,370 --> 00:10:23,480 excited about this. 217 00:10:23,480 --> 00:10:25,970 What's going on? 218 00:10:25,970 --> 00:10:29,210 Why do these kids have heart attacks at such an early age? 219 00:10:29,210 --> 00:10:32,060 Can we figure out what's wrong? 220 00:10:32,060 --> 00:10:34,970 And can we do something to fix it? 221 00:10:34,970 --> 00:10:39,080 And so, here, what happens is this protein 222 00:10:39,080 --> 00:10:42,260 is made as a proprotein just like any kind 223 00:10:42,260 --> 00:10:44,870 of serine protease. 224 00:10:44,870 --> 00:10:46,800 Lots of times you are pre-proproteins. 225 00:10:46,800 --> 00:10:49,370 And they process, they usually self-process, 226 00:10:49,370 --> 00:10:51,306 into an active form. 227 00:10:51,306 --> 00:10:52,970 And why do they have that? 228 00:10:52,970 --> 00:10:57,910 Why does a protease have a pre-pro sequence on it? 229 00:10:57,910 --> 00:11:00,480 AUDIENCE: So you have, like, spatial temporal control 230 00:11:00,480 --> 00:11:01,299 of the sectors? 231 00:11:01,299 --> 00:11:02,840 JOANNE STUBBE: Yeah, over activities. 232 00:11:02,840 --> 00:11:04,310 So you're controlling the activity. 233 00:11:04,310 --> 00:11:06,830 Because if you produce a protease, 234 00:11:06,830 --> 00:11:09,270 nobody could ever overproduce proteases. 235 00:11:09,270 --> 00:11:09,770 Why? 236 00:11:09,770 --> 00:11:11,270 What happens inside the cell? 237 00:11:11,270 --> 00:11:12,580 Everything gets degraded. 238 00:11:12,580 --> 00:11:13,080 OK. 239 00:11:13,080 --> 00:11:14,810 Because proteins have specificity. 240 00:11:14,810 --> 00:11:17,570 But if you overproduce them, all your proteins have degraded. 241 00:11:17,570 --> 00:11:21,530 So it's not trivial to overproduce proteases. 242 00:11:21,530 --> 00:11:23,546 And so they have a mechanism-- 243 00:11:23,546 --> 00:11:24,920 hopefully, you learned about that 244 00:11:24,920 --> 00:11:28,450 in introductory biochemistry course-- that makes it inactive 245 00:11:28,450 --> 00:11:29,720 till you're ready to use it. 246 00:11:29,720 --> 00:11:31,820 And then it cleaves itself. 247 00:11:31,820 --> 00:11:33,560 Something triggers it, it cleaves itself. 248 00:11:33,560 --> 00:11:34,667 And then it's ready to go. 249 00:11:34,667 --> 00:11:35,750 And that's true here, too. 250 00:11:35,750 --> 00:11:38,990 So here you have this little purple worm 251 00:11:38,990 --> 00:11:42,890 that has to auto process to become active. 252 00:11:42,890 --> 00:11:47,930 And in some way, it's going to end up extracellularly. 253 00:11:47,930 --> 00:11:49,640 And so it's got to go through membranes. 254 00:11:49,640 --> 00:11:51,830 So it goes through the Golgi stacks, 255 00:11:51,830 --> 00:11:56,180 just like I just showed you with a cholesterol, the LDL 256 00:11:56,180 --> 00:11:57,140 receptor. 257 00:11:57,140 --> 00:12:00,470 And it gets extruded extracellularly. 258 00:12:00,470 --> 00:12:01,610 And that's where it is. 259 00:12:01,610 --> 00:12:02,250 It's out there. 260 00:12:02,250 --> 00:12:02,780 OK. 261 00:12:02,780 --> 00:12:05,470 It's processed from the original version of it. 262 00:12:05,470 --> 00:12:08,890 And so the working hypothesis is-- 263 00:12:08,890 --> 00:12:11,120 and this was based on a patient. 264 00:12:11,120 --> 00:12:19,100 They found a patient where the LDL levels were elevated, OK. 265 00:12:19,100 --> 00:12:26,570 And the child that had this had early coronary disease. 266 00:12:26,570 --> 00:12:28,490 That is heart attacks at an earlier age. 267 00:12:28,490 --> 00:12:30,770 And they studied this in some detail. 268 00:12:30,770 --> 00:12:33,330 And they found out that what this protein does-- 269 00:12:33,330 --> 00:12:35,570 I'm not sure we really understand the details of what 270 00:12:35,570 --> 00:12:36,770 the protein does-- 271 00:12:36,770 --> 00:12:39,995 was that it could bind to the LDL receptor. 272 00:12:39,995 --> 00:12:42,050 OK, so this little orange thing is what you just 273 00:12:42,050 --> 00:12:43,870 saw on the previous slide. 274 00:12:43,870 --> 00:12:47,600 And this little blue thing is LDL. 275 00:12:47,600 --> 00:12:51,770 So, now, what happens is instead of having just LDL, low density 276 00:12:51,770 --> 00:12:54,500 lipoprotein and the receptor, you've 277 00:12:54,500 --> 00:12:56,760 now got another protein stuck to this. 278 00:12:56,760 --> 00:12:57,530 OK. 279 00:12:57,530 --> 00:13:01,200 And so when this protein is bound, 280 00:13:01,200 --> 00:13:04,550 it also undergoes receptor mediated-- 281 00:13:04,550 --> 00:13:06,050 they don't show any steps here. 282 00:13:06,050 --> 00:13:09,110 I'm not sure if it's been studied in detail. 283 00:13:09,110 --> 00:13:12,660 It also undergoes receptor mediated endocytosis. 284 00:13:12,660 --> 00:13:15,060 So it's taken into the cells. 285 00:13:15,060 --> 00:13:17,750 And normally, remember, with the LDL receptor, 286 00:13:17,750 --> 00:13:19,600 the receptor gets recycled. 287 00:13:19,600 --> 00:13:20,720 Here, what happens? 288 00:13:20,720 --> 00:13:23,840 Something changes because of this complex. 289 00:13:23,840 --> 00:13:27,230 And so now this complex is in the endosome. 290 00:13:27,230 --> 00:13:30,830 But the LDL particle, which has a cholesterol, 291 00:13:30,830 --> 00:13:33,890 doesn't associate from the LDL receptor. 292 00:13:33,890 --> 00:13:35,960 The receptor doesn't recycle. 293 00:13:35,960 --> 00:13:40,400 But, instead, the whole gemisch, the protein, the receptor, 294 00:13:40,400 --> 00:13:43,430 and the LDL particle, fuse with the lysosome, 295 00:13:43,430 --> 00:13:44,740 which is a bag of proteases. 296 00:13:44,740 --> 00:13:46,080 And it's degraded. 297 00:13:46,080 --> 00:13:48,620 So what are the consequences of that? 298 00:13:48,620 --> 00:13:51,740 The consequences of that are that you 299 00:13:51,740 --> 00:13:57,010 lower concentrations of the LDL receptor 300 00:13:57,010 --> 00:13:58,673 on the plasma membrane. 301 00:13:58,673 --> 00:13:59,600 OK? 302 00:13:59,600 --> 00:14:03,160 And if you lower the concentrations of the LDL 303 00:14:03,160 --> 00:14:07,450 receptor on the plasma membrane, what happens to the low density 304 00:14:07,450 --> 00:14:09,135 lipoprotein concentrations? 305 00:14:09,135 --> 00:14:10,010 AUDIENCE: [INAUDIBLE] 306 00:14:10,010 --> 00:14:11,290 JOANNE STUBBE: Yeah, it increases. 307 00:14:11,290 --> 00:14:12,610 And so then you're in trouble. 308 00:14:12,610 --> 00:14:13,300 OK? 309 00:14:13,300 --> 00:14:16,590 So that's the model. 310 00:14:16,590 --> 00:14:19,550 Again, I haven't read a lot of papers on this. 311 00:14:19,550 --> 00:14:23,950 The discovery was made of this of patients that 312 00:14:23,950 --> 00:14:26,500 had that phenotype in 2003. 313 00:14:26,500 --> 00:14:32,450 But they also found patients that had a loss of function. 314 00:14:32,450 --> 00:14:34,450 And they found out that some of these patients-- 315 00:14:34,450 --> 00:14:36,009 they're different kinds of patients. 316 00:14:36,009 --> 00:14:37,300 They have different phenotypes. 317 00:14:37,300 --> 00:14:40,770 But they had a single mutation. 318 00:14:40,770 --> 00:14:44,230 And these patients with single mutation 319 00:14:44,230 --> 00:14:48,420 had reduced LDL cholesterol. 320 00:14:48,420 --> 00:14:52,630 And they had the same amounts or elevated amounts 321 00:14:52,630 --> 00:14:55,240 of the LDL receptor. 322 00:14:55,240 --> 00:14:59,990 And because they had LDL receptor, 323 00:14:59,990 --> 00:15:04,300 they had lower cholesterol and more LDL receptor 324 00:15:04,300 --> 00:15:07,420 to take up the cholesterol, they had 325 00:15:07,420 --> 00:15:09,980 reduction in coronary disease. 326 00:15:09,980 --> 00:15:10,480 OK? 327 00:15:10,480 --> 00:15:12,850 Everybody get that? 328 00:15:12,850 --> 00:15:14,050 Why do we care about that? 329 00:15:14,050 --> 00:15:14,710 OK. 330 00:15:14,710 --> 00:15:16,510 So can somebody tell me from the paper 331 00:15:16,510 --> 00:15:18,730 what was the take-home message from the paper? 332 00:15:18,730 --> 00:15:20,320 Why do we care about that? 333 00:15:20,320 --> 00:15:24,400 What's unique about this particular protein 334 00:15:24,400 --> 00:15:28,175 protein, PCSK9, compared to using statins, for example? 335 00:15:33,350 --> 00:15:35,881 Did you guys read the paper? 336 00:15:35,881 --> 00:15:36,380 OK. 337 00:15:36,380 --> 00:15:38,650 So the paper was pretty short. 338 00:15:38,650 --> 00:15:40,930 Even if you didn't understand all the details, 339 00:15:40,930 --> 00:15:43,270 I thought the paper was pretty easy to understand. 340 00:15:46,090 --> 00:15:47,599 So why do we care about? 341 00:15:47,599 --> 00:15:48,890 What was the take-home message? 342 00:15:48,890 --> 00:15:52,470 Why are we targeting this? 343 00:15:52,470 --> 00:15:53,898 AUDIENCE: To change the expression 344 00:15:53,898 --> 00:15:56,754 of proteins that create new-- 345 00:15:59,260 --> 00:16:00,010 JOANNE STUBBE: PC. 346 00:16:00,010 --> 00:16:00,790 AUDIENCE: Yeah. 347 00:16:00,790 --> 00:16:01,623 JOANNE STUBBE: Yeah. 348 00:16:01,623 --> 00:16:03,870 So but why do we want to do that? 349 00:16:03,870 --> 00:16:04,950 We have statins. 350 00:16:04,950 --> 00:16:09,570 Statins, you know, everybody's gobbling statins a lot. 351 00:16:09,570 --> 00:16:12,770 I mean, you probably know 20 people that take statins. 352 00:16:12,770 --> 00:16:14,840 I know many, many people that take statins. 353 00:16:14,840 --> 00:16:18,510 So it's a wonder drug in many ways. 354 00:16:18,510 --> 00:16:22,248 But when do you start giving statins? 355 00:16:22,248 --> 00:16:25,029 When do people start taking-- 356 00:16:25,029 --> 00:16:26,570 I'm probably not allowed to ask that. 357 00:16:26,570 --> 00:16:28,611 So you don't have to answer if you don't want to. 358 00:16:28,611 --> 00:16:31,490 But are any of you taking statins? 359 00:16:31,490 --> 00:16:32,240 No. 360 00:16:32,240 --> 00:16:32,770 OK. 361 00:16:32,770 --> 00:16:34,990 But there could be people that have, you know, 362 00:16:34,990 --> 00:16:35,790 high cholesterol. 363 00:16:35,790 --> 00:16:37,120 I mean, a lot of it is genetic. 364 00:16:37,120 --> 00:16:38,920 I eat McDonald's hamburgers all the time. 365 00:16:38,920 --> 00:16:40,480 And I eat huge amounts of ice cream. 366 00:16:40,480 --> 00:16:44,290 And I have extremely low cholesterol levels. 367 00:16:44,290 --> 00:16:45,070 OK? 368 00:16:45,070 --> 00:16:47,020 And it's genetic. 369 00:16:47,020 --> 00:16:47,690 OK. 370 00:16:47,690 --> 00:16:49,760 Other people might not eat any of that stuff, 371 00:16:49,760 --> 00:16:53,540 and they might have extremely high cholesterol. 372 00:16:53,540 --> 00:16:57,010 So when you see people, maybe your parents, 373 00:16:57,010 --> 00:16:58,900 basically, they're taking this. 374 00:16:58,900 --> 00:17:01,710 And it's after you have some issue, right? 375 00:17:01,710 --> 00:17:03,190 You have coronary heart problem. 376 00:17:03,190 --> 00:17:04,492 You have chest pains, whatever. 377 00:17:04,492 --> 00:17:06,700 So they start looking for what could be causing that. 378 00:17:06,700 --> 00:17:08,075 And the first thing they look for 379 00:17:08,075 --> 00:17:11,470 is clogging of the arteries. 380 00:17:11,470 --> 00:17:15,430 And that's when they start some kind of therapy like statins. 381 00:17:15,430 --> 00:17:20,119 The beauty of this is, if this model is correct 382 00:17:20,119 --> 00:17:23,020 that I just showed you, if you could figure out 383 00:17:23,020 --> 00:17:26,829 how to remove or greatly reduce that protein, then 384 00:17:26,829 --> 00:17:29,710 that would automatically, you know, 385 00:17:29,710 --> 00:17:33,700 prevent the normal function of this protein, 386 00:17:33,700 --> 00:17:40,360 which is to degrade the LDL receptor in the lysosome. 387 00:17:40,360 --> 00:17:42,320 And I'll get to that in the very end. 388 00:17:42,320 --> 00:17:44,320 So if you could figure out how to treat, 389 00:17:44,320 --> 00:17:47,500 you could diagnose the predisposition 390 00:17:47,500 --> 00:17:49,480 to having elevated cholesterol levels 391 00:17:49,480 --> 00:17:51,580 and start treating it much earlier. 392 00:17:51,580 --> 00:17:55,960 You have a much higher propensity for success 393 00:17:55,960 --> 00:17:58,960 compared if you take statins halfway through your life. 394 00:17:58,960 --> 00:18:01,900 I mean, there's really good epidemiological data 395 00:18:01,900 --> 00:18:02,860 that support that. 396 00:18:02,860 --> 00:18:06,436 So people are extremely interested in figuring out-- 397 00:18:06,436 --> 00:18:07,810 I don't think we know the details 398 00:18:07,810 --> 00:18:09,684 of the function of what this protein is-- but 399 00:18:09,684 --> 00:18:11,080 lowering this protein. 400 00:18:11,080 --> 00:18:14,440 Because the consequences of that are lowering 401 00:18:14,440 --> 00:18:16,371 cholesterol in the plasma. 402 00:18:16,371 --> 00:18:16,870 OK? 403 00:18:16,870 --> 00:18:18,880 So are we all on the same page? 404 00:18:18,880 --> 00:18:20,740 Everybody understand that? 405 00:18:20,740 --> 00:18:26,430 Because that's key to thinking about the paper. 406 00:18:26,430 --> 00:18:27,340 OK. 407 00:18:27,340 --> 00:18:31,660 So the reason I picked this is people 408 00:18:31,660 --> 00:18:35,300 these people in this paper wanted to understand 409 00:18:35,300 --> 00:18:38,240 is this protein really important. 410 00:18:38,240 --> 00:18:41,590 And so what they did was they decided 411 00:18:41,590 --> 00:18:45,460 they were going to knock out the gene 412 00:18:45,460 --> 00:18:47,320 or do something to greatly reduce 413 00:18:47,320 --> 00:18:51,680 the gene, which then would reduce the amount of protein, 414 00:18:51,680 --> 00:18:54,640 which then would allow you to analyze 415 00:18:54,640 --> 00:18:56,360 the phenotypic consequences. 416 00:18:56,360 --> 00:18:56,990 OK? 417 00:18:56,990 --> 00:19:00,871 And what was the analysis they used in this paper? 418 00:19:00,871 --> 00:19:02,980 They used two different kinds of analysis. 419 00:19:02,980 --> 00:19:04,710 Well, we're not in detail. 420 00:19:04,710 --> 00:19:07,810 Globally, what did they use? 421 00:19:07,810 --> 00:19:10,640 What were their model systems? 422 00:19:10,640 --> 00:19:12,930 AUDIENCE: [INAUDIBLE] 423 00:19:12,930 --> 00:19:15,294 JOANNE STUBBE: You need to talk louder because I'm deaf. 424 00:19:15,294 --> 00:19:17,670 AUDIENCE: For [INAUDIBLE] they used a surveyor as-- 425 00:19:17,670 --> 00:19:19,878 JOANNE STUBBE: Yeah, so that used a surveyor on what, 426 00:19:19,878 --> 00:19:20,436 though? 427 00:19:20,436 --> 00:19:21,690 So that's too detailed. 428 00:19:21,690 --> 00:19:23,050 I want a bigger picture. 429 00:19:23,050 --> 00:19:23,790 So you're right. 430 00:19:23,790 --> 00:19:26,072 They used surveyor cell assays. 431 00:19:26,072 --> 00:19:27,780 That's more detail than I want right now. 432 00:19:27,780 --> 00:19:33,130 So they looked at it two ways if you look at the figures. 433 00:19:33,130 --> 00:19:35,610 So what were the assays? 434 00:19:35,610 --> 00:19:38,506 In the surveyor assay, what were they assaying? 435 00:19:38,506 --> 00:19:39,782 AUDIENCE: The blood samples. 436 00:19:39,782 --> 00:19:41,490 JOANNE STUBBE: The blood samples of mice. 437 00:19:41,490 --> 00:19:43,430 So that's one of the things from the liver. 438 00:19:43,430 --> 00:19:43,930 OK. 439 00:19:43,930 --> 00:19:45,690 So they took liver cells from mice. 440 00:19:45,690 --> 00:19:47,490 So they were using animal models. 441 00:19:47,490 --> 00:19:48,130 OK. 442 00:19:48,130 --> 00:19:50,980 So one of the questions that, if you read the paper carefully, 443 00:19:50,980 --> 00:19:52,490 you should be asking yourself-- 444 00:19:52,490 --> 00:19:54,629 and this is always a question when 445 00:19:54,629 --> 00:19:55,920 you're looking at therapeutics. 446 00:19:55,920 --> 00:19:58,331 Is this animal model any good? 447 00:19:58,331 --> 00:19:58,830 OK. 448 00:19:58,830 --> 00:20:02,250 And then the other way that they were looking at this 449 00:20:02,250 --> 00:20:05,020 was with tissue culture cells. 450 00:20:05,020 --> 00:20:07,950 Because, in general, when you start studying something, 451 00:20:07,950 --> 00:20:09,630 you don't start on humans, or you 452 00:20:09,630 --> 00:20:11,640 don't start on whole animals. 453 00:20:11,640 --> 00:20:14,500 You need to start on something simpler. 454 00:20:14,500 --> 00:20:16,170 And we haven't gotten to this yet, 455 00:20:16,170 --> 00:20:19,520 but Brown and Goldstein, if you've read the reading, 456 00:20:19,520 --> 00:20:21,730 have used fibroblast cells. 457 00:20:21,730 --> 00:20:26,200 And they showed fibroblast cells behave like liver. 458 00:20:26,200 --> 00:20:28,930 And it turns out it had great predictive power. 459 00:20:28,930 --> 00:20:30,520 It might not have, but it does. 460 00:20:30,520 --> 00:20:32,260 So you need some kind of a model system. 461 00:20:32,260 --> 00:20:34,560 And so they used both of those systems 462 00:20:34,560 --> 00:20:37,740 to try to test the idea that, if you could get rid 463 00:20:37,740 --> 00:20:41,040 of this protein, you could alter in a way 464 00:20:41,040 --> 00:20:46,350 that these patients, these loss of function patients, 465 00:20:46,350 --> 00:20:48,785 behaved in terms of the levels of cholesterol. 466 00:20:48,785 --> 00:20:49,410 So that was it. 467 00:20:49,410 --> 00:20:52,036 And so how did they decide to do this? 468 00:20:52,036 --> 00:20:54,810 And so I would say, in general, we 469 00:20:54,810 --> 00:20:59,350 don't talk about this kind of stuff very much in 508. 470 00:20:59,350 --> 00:21:01,350 But if you're ever going to be a biochemist, 471 00:21:01,350 --> 00:21:03,510 you can't do biochemistry without being 472 00:21:03,510 --> 00:21:06,200 able to do gene knockouts inside the cell. 473 00:21:06,200 --> 00:21:11,180 So 25 years ago, that was tough, OK? 474 00:21:11,180 --> 00:21:16,140 In the mid-1980s, you could first do that well in bacteria. 475 00:21:16,140 --> 00:21:18,180 We still do a lot of that in my lab. 476 00:21:18,180 --> 00:21:20,330 It takes four months, three, four months. 477 00:21:20,330 --> 00:21:21,480 It's not easy. 478 00:21:21,480 --> 00:21:23,620 With the older technology, it works. 479 00:21:23,620 --> 00:21:25,459 But it's a rare event, and you've 480 00:21:25,459 --> 00:21:27,000 got to screen through a lot of things 481 00:21:27,000 --> 00:21:29,620 to find the ones that are interesting. 482 00:21:29,620 --> 00:21:33,420 And this technology, CRISPR-Cas, allows you 483 00:21:33,420 --> 00:21:34,890 to do this in a couple of days. 484 00:21:34,890 --> 00:21:37,200 It's revolutionized what you can do. 485 00:21:37,200 --> 00:21:40,290 So you might be studying something really complicated 486 00:21:40,290 --> 00:21:41,640 in the test tube. 487 00:21:41,640 --> 00:21:43,260 But the question is is what you're 488 00:21:43,260 --> 00:21:46,880 studying relevant to what's happening in the cell. 489 00:21:46,880 --> 00:21:49,590 And so if you're asking a chemical question, 490 00:21:49,590 --> 00:21:51,400 a mechanistic question, like how does 491 00:21:51,400 --> 00:21:55,350 isopentenyl pyrophosphate do its chemistry, 492 00:21:55,350 --> 00:21:57,330 you don't need to do that in a cell. 493 00:21:57,330 --> 00:21:58,920 You can do that in a test tube. 494 00:21:58,920 --> 00:22:01,270 If you're asking how things are regulated, 495 00:22:01,270 --> 00:22:05,520 which is what we're doing now, you must be in the cell. 496 00:22:05,520 --> 00:22:07,830 And the issues within the cell are 497 00:22:07,830 --> 00:22:09,630 that people overproduce stuff. 498 00:22:09,630 --> 00:22:12,390 You know, they have to mess around, 499 00:22:12,390 --> 00:22:13,810 so they can see something. 500 00:22:13,810 --> 00:22:16,350 And whenever they do that, they change everything. 501 00:22:16,350 --> 00:22:19,890 So the future, for anybody that's 502 00:22:19,890 --> 00:22:23,520 interested in biochemistry biology interface, 503 00:22:23,520 --> 00:22:26,320 is you've got to be able to do both. 504 00:22:26,320 --> 00:22:29,820 And so this technology, I guarantee, 505 00:22:29,820 --> 00:22:33,240 in some form you will be using if you 506 00:22:33,240 --> 00:22:39,650 pursue a career in doing biochemical and biological 507 00:22:39,650 --> 00:22:40,280 studies. 508 00:22:40,280 --> 00:22:40,940 OK. 509 00:22:40,940 --> 00:22:44,060 So the question really is we want 510 00:22:44,060 --> 00:22:46,470 to do manipulation of a gene. 511 00:22:46,470 --> 00:22:46,970 OK? 512 00:22:46,970 --> 00:22:51,420 And so people have wanted to do this forever. 513 00:22:51,420 --> 00:22:53,300 So you might want to delete the gene 514 00:22:53,300 --> 00:22:55,400 and see what the phenotypic consequences are. 515 00:22:55,400 --> 00:22:56,280 You can do that. 516 00:22:56,280 --> 00:22:59,030 You know, there's been technology around. 517 00:22:59,030 --> 00:23:01,970 They won the Nobel Prize for the technology in 1983. 518 00:23:01,970 --> 00:23:03,710 But, again, it takes months. 519 00:23:03,710 --> 00:23:06,230 And you have to screen through millions of cells 520 00:23:06,230 --> 00:23:08,630 to be able to figure out which one 521 00:23:08,630 --> 00:23:12,080 has your gene deleted or another gene inserted in place 522 00:23:12,080 --> 00:23:13,580 of the gene of interest where you've 523 00:23:13,580 --> 00:23:16,580 modified the gene of interest, which then gives you 524 00:23:16,580 --> 00:23:20,300 information about the function of the protein. 525 00:23:20,300 --> 00:23:24,830 And so having technology that can turn around rapidly 526 00:23:24,830 --> 00:23:26,620 is important. 527 00:23:26,620 --> 00:23:29,750 And so I'm just going to show you what the state of the art 528 00:23:29,750 --> 00:23:32,060 has been up until two years ago. 529 00:23:32,060 --> 00:23:34,890 And, really, they also work by the same mechanisms. 530 00:23:34,890 --> 00:23:38,690 It's just the CRISPR-Cas, even though it's really still 531 00:23:38,690 --> 00:23:40,900 early days, works much more efficiently. 532 00:23:40,900 --> 00:23:41,600 OK. 533 00:23:41,600 --> 00:23:47,240 So the idea is you have a piece of DNA that you care about, 534 00:23:47,240 --> 00:23:49,840 and you want to cleave it. 535 00:23:49,840 --> 00:23:53,570 And all of these cleavages are double-strand breaks. 536 00:23:53,570 --> 00:23:56,456 So double-stranded breaks are lethal to the cell, 537 00:23:56,456 --> 00:23:57,580 so you have to repair them. 538 00:23:57,580 --> 00:23:58,070 OK. 539 00:23:58,070 --> 00:23:59,819 And you have to have a way to repair them. 540 00:23:59,819 --> 00:24:01,580 And I'll show you what those two ways are. 541 00:24:01,580 --> 00:24:03,740 You've all seen it in some form. 542 00:24:03,740 --> 00:24:07,760 But you want to have cleavage at a specific site. 543 00:24:07,760 --> 00:24:09,770 And then when you have cleavage, the question, 544 00:24:09,770 --> 00:24:12,230 if you repair that, can you delete 545 00:24:12,230 --> 00:24:16,010 part of that gene which would make the entire gene inactive? 546 00:24:16,010 --> 00:24:20,420 Or, can you replace, in this cleavage site, 547 00:24:20,420 --> 00:24:23,551 a gene of interest with a mutation in it, et cetera? 548 00:24:23,551 --> 00:24:24,050 OK. 549 00:24:24,050 --> 00:24:28,040 You can do many, many, many genetic engineering projects, 550 00:24:28,040 --> 00:24:32,530 which are sort of covered in review articles. 551 00:24:32,530 --> 00:24:34,760 The more creative people become, the more things 552 00:24:34,760 --> 00:24:35,840 you can actually do. 553 00:24:35,840 --> 00:24:36,380 OK. 554 00:24:36,380 --> 00:24:37,940 So how do you do that? 555 00:24:37,940 --> 00:24:41,120 So what they do in the case of the zinc fingers, 556 00:24:41,120 --> 00:24:43,860 does anybody know what a zinc finger is? 557 00:24:43,860 --> 00:24:48,560 Has anybody seen a zinc finger before? 558 00:24:48,560 --> 00:24:52,690 So a zinc finger is a little small protein, I don't know, 559 00:24:52,690 --> 00:24:56,830 maybe 70, 80 amino acids that combine zinc 560 00:24:56,830 --> 00:25:00,650 and that its sequence specifically binds DNA. 561 00:25:00,650 --> 00:25:01,270 OK. 562 00:25:01,270 --> 00:25:05,121 That's a major way of regulating transcription inside the cell. 563 00:25:05,121 --> 00:25:05,620 OK. 564 00:25:08,500 --> 00:25:10,330 And there's not just one zinc finger. 565 00:25:10,330 --> 00:25:12,010 There are many, many zinc fingers. 566 00:25:12,010 --> 00:25:12,680 OK. 567 00:25:12,680 --> 00:25:16,150 So what people have done is taken these little motifs that 568 00:25:16,150 --> 00:25:21,560 combine zinc and designed these motifs, 569 00:25:21,560 --> 00:25:24,222 so they recognize a sequence. 570 00:25:24,222 --> 00:25:26,940 So these guys, these little zinc fingers, 571 00:25:26,940 --> 00:25:31,800 now are targeting the DNA that you want to cleave. 572 00:25:31,800 --> 00:25:36,580 So they're targeting it here, and the targeting it here. 573 00:25:36,580 --> 00:25:38,340 So what that means is every time you 574 00:25:38,340 --> 00:25:41,230 want to do an experiment like this, 575 00:25:41,230 --> 00:25:43,330 you have to make a little zinc finger. 576 00:25:43,330 --> 00:25:45,640 String them together to get enough binding affinity, 577 00:25:45,640 --> 00:25:46,930 so you get specificity. 578 00:25:46,930 --> 00:25:49,764 That's really key. 579 00:25:49,764 --> 00:25:51,820 And you could do it. 580 00:25:51,820 --> 00:25:55,220 We're pretty good at this, but it takes months. 581 00:25:55,220 --> 00:25:58,120 And so what they do is, once they have these binders, 582 00:25:58,120 --> 00:26:02,090 then they attach a nuclease. 583 00:26:02,090 --> 00:26:02,590 OK. 584 00:26:02,590 --> 00:26:04,420 So Fok1 is the nuclease. 585 00:26:04,420 --> 00:26:06,520 So that just means you're cleaving a phosodiester 586 00:26:06,520 --> 00:26:08,930 bond over your nucleic acid. 587 00:26:08,930 --> 00:26:10,164 And you cleave on one strand. 588 00:26:10,164 --> 00:26:12,580 And on the other strand, you have a double-standard break. 589 00:26:12,580 --> 00:26:17,160 And these enzymes work by giving you blunt-ended cleavage. 590 00:26:17,160 --> 00:26:19,970 There's no overhangs in the DNA cleavage. 591 00:26:19,970 --> 00:26:22,380 So people have used this for a long time. 592 00:26:22,380 --> 00:26:28,630 In fact, Carl Pabo at MIT, who's an X-ray crystallographer that 593 00:26:28,630 --> 00:26:31,000 studied regulation by zinc finger transcription, 594 00:26:31,000 --> 00:26:33,760 was one of the people that founded the companies that got 595 00:26:33,760 --> 00:26:36,430 this technology off the ground. 596 00:26:36,430 --> 00:26:37,400 But it's hard. 597 00:26:37,400 --> 00:26:37,900 OK. 598 00:26:37,900 --> 00:26:40,120 So the second technology which I think 599 00:26:40,120 --> 00:26:42,480 is much more widely used-- 600 00:26:42,480 --> 00:26:44,770 but I think it'll be completely displaced. 601 00:26:44,770 --> 00:26:46,400 I might be wrong. 602 00:26:46,400 --> 00:26:48,729 You can buy a kit Golden Gate, TALEN kit. 603 00:26:48,729 --> 00:26:49,270 That's right. 604 00:26:49,270 --> 00:26:50,940 You can buy it from some company. 605 00:26:50,940 --> 00:26:52,570 And it's the same idea. 606 00:26:52,570 --> 00:26:56,770 So, I mean, I don't know anything about this in detail. 607 00:26:56,770 --> 00:26:59,770 But it turns out that these little proteins, which 608 00:26:59,770 --> 00:27:03,910 are 34 amino acids, you can actually 609 00:27:03,910 --> 00:27:10,270 look at a sequence of DNA and design 34 amino acid 610 00:27:10,270 --> 00:27:15,081 repeats in a way that it can bind to double-stranded DNA. 611 00:27:15,081 --> 00:27:15,580 OK. 612 00:27:15,580 --> 00:27:18,800 So this is like, so you have a double-stranded DNA helix. 613 00:27:18,800 --> 00:27:22,630 You string a bunch of these little domains together. 614 00:27:22,630 --> 00:27:25,690 And you can actually design these little domains, 615 00:27:25,690 --> 00:27:27,270 the sequence of these little domains. 616 00:27:27,270 --> 00:27:30,010 And it forms a super helix around 617 00:27:30,010 --> 00:27:31,710 the double-stranded helix. 618 00:27:31,710 --> 00:27:38,020 So the protein forms a helix around the nucleic acid helix. 619 00:27:38,020 --> 00:27:41,930 And what it does is it targets the nuclease for cleavage. 620 00:27:41,930 --> 00:27:42,970 So it's the same idea. 621 00:27:42,970 --> 00:27:47,920 It's just the mechanisms of targeting are different. 622 00:27:47,920 --> 00:27:51,370 And so, I mean, they have structures of these things. 623 00:27:51,370 --> 00:27:54,400 It's sort of really an interesting problem 624 00:27:54,400 --> 00:27:58,160 in molecular recognition if any of you are interested. 625 00:27:58,160 --> 00:27:59,890 But I would say, if you want to use 626 00:27:59,890 --> 00:28:03,880 this to do something biochemical and biological, 627 00:28:03,880 --> 00:28:07,310 you probably want to go to CRISPR-Cas system now. 628 00:28:07,310 --> 00:28:07,810 OK. 629 00:28:07,810 --> 00:28:08,950 So both of these are the same. 630 00:28:08,950 --> 00:28:11,140 They have a nuclease and something that targets it 631 00:28:11,140 --> 00:28:14,860 to a DNA sequence of interest. 632 00:28:14,860 --> 00:28:19,080 And if you've read the paper on the PCSK9, 633 00:28:19,080 --> 00:28:20,740 that's exactly what they're doing. 634 00:28:20,740 --> 00:28:24,970 They're targeting a sequence for double-strand cleavage. 635 00:28:24,970 --> 00:28:25,560 OK. 636 00:28:25,560 --> 00:28:31,590 So this then brings us into the CRISPR-Cas system. 637 00:28:31,590 --> 00:28:34,080 And I've given you a hand out of this which 638 00:28:34,080 --> 00:28:36,120 is, again, a simplification. 639 00:28:36,120 --> 00:28:40,470 Now, I think there are six different moderately 640 00:28:40,470 --> 00:28:42,270 well-studied CRISPR-Cas systems. 641 00:28:42,270 --> 00:28:43,480 They're all different. 642 00:28:43,480 --> 00:28:47,280 So they all have different numbers of proteins. 643 00:28:47,280 --> 00:28:50,110 Although, the idea of how they work is pretty similar, 644 00:28:50,110 --> 00:28:53,340 I think this has turned out to be the best behaved in terms 645 00:28:53,340 --> 00:28:57,430 of biochemically putting it back together and having it work. 646 00:28:57,430 --> 00:28:57,930 OK. 647 00:28:57,930 --> 00:28:59,110 So what do we have here? 648 00:28:59,110 --> 00:29:03,240 So, hopefully, you all know now that what you need 649 00:29:03,240 --> 00:29:05,520 for this to work is a Cas9. 650 00:29:05,520 --> 00:29:06,075 What's Cas9? 651 00:29:09,228 --> 00:29:11,570 AUDIENCE: The CRISPR associated with [INAUDIBLE].. 652 00:29:11,570 --> 00:29:14,060 JOANNE STUBBE: So a CRISPR-- 653 00:29:14,060 --> 00:29:15,242 is that what the acronym is? 654 00:29:15,242 --> 00:29:16,450 AUDIENCE: ...it's a nuclease. 655 00:29:16,450 --> 00:29:17,283 JOANNE STUBBE: Yeah. 656 00:29:17,283 --> 00:29:18,420 It's a nuclease. 657 00:29:18,420 --> 00:29:19,010 OK. 658 00:29:19,010 --> 00:29:21,654 And what's special about this nuclease? 659 00:29:21,654 --> 00:29:23,502 AUDIENCE: Sequence-specific. 660 00:29:23,502 --> 00:29:24,888 That's like-- 661 00:29:24,888 --> 00:29:26,055 JOANNE STUBBE: It has what? 662 00:29:26,055 --> 00:29:27,596 AUDIENCE: A guide RNA that makes it-- 663 00:29:27,596 --> 00:29:28,180 JOANNE STUBBE: No. 664 00:29:28,180 --> 00:29:30,804 So just the nuclease, we're just talking about the protein now. 665 00:29:30,804 --> 00:29:32,780 We do have to worry about that, yeah. 666 00:29:32,780 --> 00:29:39,420 So if you look at the Cas9 sequence, what do you find out? 667 00:29:39,420 --> 00:29:40,700 That's not in the paper, but-- 668 00:29:40,700 --> 00:29:43,075 AUDIENCE: So it's got two different regions that can bind 669 00:29:43,075 --> 00:29:44,380 the two different strands-- 670 00:29:44,380 --> 00:29:44,711 JOANNE STUBBE: Right 671 00:29:44,711 --> 00:29:45,856 AUDIENCE: --and, like, [INAUDIBLE] in a different 672 00:29:45,856 --> 00:29:46,330 [INAUDIBLE]. 673 00:29:46,330 --> 00:29:46,585 JOANNE STUBBE: Yeah. 674 00:29:46,585 --> 00:29:48,910 So you have two different nuclease domains. 675 00:29:48,910 --> 00:29:50,390 OK. 676 00:29:50,390 --> 00:29:52,690 I mean, this is not necessarily given. 677 00:29:52,690 --> 00:29:54,154 One is going to go to one strand. 678 00:29:54,154 --> 00:29:55,945 And one is going to go to the other strand. 679 00:29:55,945 --> 00:29:56,445 OK. 680 00:29:56,445 --> 00:29:58,180 And we'll talk a little bit about that. 681 00:29:58,180 --> 00:30:03,420 And then, as you were saying, what's unique about this? 682 00:30:03,420 --> 00:30:05,830 In this picture, what's wrong with this picture? 683 00:30:05,830 --> 00:30:11,080 If you read the original discoveries in the bacterial 684 00:30:11,080 --> 00:30:14,830 system, what's unusual about this particular-- well, 685 00:30:14,830 --> 00:30:16,080 I guess-- 686 00:30:16,080 --> 00:30:17,070 OK, no. 687 00:30:17,070 --> 00:30:17,810 It's OK. 688 00:30:17,810 --> 00:30:18,310 OK. 689 00:30:18,310 --> 00:30:19,351 So what do you have here? 690 00:30:19,351 --> 00:30:21,240 What is this part? 691 00:30:21,240 --> 00:30:23,380 This should be tracr. 692 00:30:23,380 --> 00:30:24,550 What's tracr? 693 00:30:24,550 --> 00:30:26,245 AUDIENCE: It's the transactivator. 694 00:30:26,245 --> 00:30:28,120 JOANNE STUBBE: Yeah, so it's transactivating. 695 00:30:28,120 --> 00:30:28,619 OK. 696 00:30:28,619 --> 00:30:31,660 And then what's the gRNA? 697 00:30:31,660 --> 00:30:32,734 AUDIENCE: The guide. 698 00:30:32,734 --> 00:30:34,150 JOANNE STUBBE: So that's the guide 699 00:30:34,150 --> 00:30:37,824 that is part of this bigger piece of DNA 700 00:30:37,824 --> 00:30:39,490 that we're going to look at in a second. 701 00:30:39,490 --> 00:30:40,000 OK. 702 00:30:40,000 --> 00:30:41,880 So what you need, although this isn't 703 00:30:41,880 --> 00:30:43,930 what people use now for the technology, 704 00:30:43,930 --> 00:30:46,090 is you need two pieces of RNA. 705 00:30:46,090 --> 00:30:49,540 And you need the target for double-strand cleavage. 706 00:30:49,540 --> 00:30:52,440 And you only need a single nuclease. 707 00:30:52,440 --> 00:30:52,940 OK? 708 00:30:52,940 --> 00:30:56,640 And the key question is how do you make them assemble. 709 00:30:56,640 --> 00:30:57,140 OK. 710 00:30:57,140 --> 00:30:59,440 And how do you make it as simple as possible, 711 00:30:59,440 --> 00:31:01,880 so that you can use this in bacteria, 712 00:31:01,880 --> 00:31:06,820 but also use it in humans which is what Eric Lander focuses on. 713 00:31:06,820 --> 00:31:09,520 So CRISPR, and we'll look at this in a [INAUDIBLE],, 714 00:31:09,520 --> 00:31:12,160 has this horrible name, Clustered Regulatory 715 00:31:12,160 --> 00:31:15,700 Interspaced Short Palindrome Repeat. 716 00:31:15,700 --> 00:31:16,390 OK? 717 00:31:16,390 --> 00:31:18,950 So that's the name. 718 00:31:18,950 --> 00:31:23,009 And so this just summarizes-- and we're 719 00:31:23,009 --> 00:31:25,300 going to come back to this in a minute-- that all three 720 00:31:25,300 --> 00:31:29,700 of these methods, the zinc fingers, the towels, 721 00:31:29,700 --> 00:31:32,820 and the Cas9 system, all do the same thing. 722 00:31:32,820 --> 00:31:35,640 They somehow recognize double-stranded DNA 723 00:31:35,640 --> 00:31:37,380 and cleave it, OK? 724 00:31:37,380 --> 00:31:41,380 And so they all give you a break in the DNA, which 725 00:31:41,380 --> 00:31:42,880 is lethal if you don't figure out 726 00:31:42,880 --> 00:31:44,110 how to deal with that break. 727 00:31:44,110 --> 00:31:44,720 OK. 728 00:31:44,720 --> 00:31:47,410 And there are two ways to deal with that break. 729 00:31:47,410 --> 00:31:50,162 There are two ways of repairing the break that we're not 730 00:31:50,162 --> 00:31:52,120 going to talk about in detail, but you probably 731 00:31:52,120 --> 00:31:53,760 have heard about somewhere. 732 00:31:53,760 --> 00:31:59,530 So what's the way that they deal with this double-stranded break 733 00:31:59,530 --> 00:32:00,220 in the paper? 734 00:32:00,220 --> 00:32:03,192 Did anybody read the paper carefully enough? 735 00:32:03,192 --> 00:32:05,380 And how do they know? 736 00:32:05,380 --> 00:32:08,420 So, somehow, you've got to put these things back together. 737 00:32:08,420 --> 00:32:10,700 Otherwise, your organism is completely dead, 738 00:32:10,700 --> 00:32:14,870 which is the goal of having this CRISPR locus for the bacteria. 739 00:32:14,870 --> 00:32:16,730 They want to kill the invading virus. 740 00:32:16,730 --> 00:32:18,530 OK. 741 00:32:18,530 --> 00:32:25,495 But in this particular paper, which one of these two methods 742 00:32:25,495 --> 00:32:29,310 did they show or did they propose from the data 743 00:32:29,310 --> 00:32:33,210 that they talked about was involved in repairing 744 00:32:33,210 --> 00:32:35,310 the double-stranded break? 745 00:32:35,310 --> 00:32:40,180 If you look at this paper, they describe non-homologous end 746 00:32:40,180 --> 00:32:40,680 joining. 747 00:32:40,680 --> 00:32:43,830 Because in the end, if you looked at the paper 748 00:32:43,830 --> 00:32:46,140 carefully, when they were trying to tell whether they 749 00:32:46,140 --> 00:32:48,360 successfully got a double-stranded cleavage, 750 00:32:48,360 --> 00:32:51,030 they did a lot of polymerase chain reactions 751 00:32:51,030 --> 00:32:54,161 to figure out whether they got specific or non-specific 752 00:32:54,161 --> 00:32:54,660 cutting. 753 00:32:54,660 --> 00:32:56,970 And when they did the sequencing on this, 754 00:32:56,970 --> 00:32:59,370 they could tell, because of the different mechanisms 755 00:32:59,370 --> 00:33:02,190 between these two, that most of the damage 756 00:33:02,190 --> 00:33:05,790 was repaired by non-homologous end joining. 757 00:33:05,790 --> 00:33:08,470 So what happens with this approach? 758 00:33:08,470 --> 00:33:10,350 What happens with this approach is 759 00:33:10,350 --> 00:33:15,360 that the repair is putting the things back together. 760 00:33:15,360 --> 00:33:17,970 When you have blunt ends, you've lost the information 761 00:33:17,970 --> 00:33:18,780 from the sequence. 762 00:33:18,780 --> 00:33:20,170 And you have a disconnect and, if you 763 00:33:20,170 --> 00:33:22,620 got a couple of cleavage sites putting them back together, 764 00:33:22,620 --> 00:33:24,380 is really tough. 765 00:33:24,380 --> 00:33:26,130 And so when you put them back together, 766 00:33:26,130 --> 00:33:27,330 you might have an insertion. 767 00:33:27,330 --> 00:33:28,510 You might have a deletion. 768 00:33:28,510 --> 00:33:30,030 You might have a frameshift. 769 00:33:30,030 --> 00:33:32,180 You get a mess. 770 00:33:32,180 --> 00:33:34,890 But then when you look at the very ends of your gene 771 00:33:34,890 --> 00:33:36,960 using the polymerase chain reaction, what happens 772 00:33:36,960 --> 00:33:38,130 is you get a mixture of things. 773 00:33:38,130 --> 00:33:40,296 And you can sequence them, so you can tell something 774 00:33:40,296 --> 00:33:43,890 about how the repair happened at the double-stranded break. 775 00:33:43,890 --> 00:33:48,300 So if you have a double-stranded break, 776 00:33:48,300 --> 00:33:53,200 OK, so the question is here do you have a deletion, 777 00:33:53,200 --> 00:33:54,840 so it's a little bit shorter. 778 00:33:54,840 --> 00:33:56,760 Or, do you have an insertion? 779 00:33:56,760 --> 00:33:58,410 Or, do you have an rearrangement? 780 00:33:58,410 --> 00:34:02,730 And what you do then is sequence these things using PCR. 781 00:34:02,730 --> 00:34:06,520 And then you can get information about the mechanism of repair. 782 00:34:06,520 --> 00:34:07,020 OK? 783 00:34:07,020 --> 00:34:09,150 So the alternative mechanism-- and this 784 00:34:09,150 --> 00:34:11,070 is really important if you want to replace 785 00:34:11,070 --> 00:34:13,920 one gene with another gene, a whole gene, 786 00:34:13,920 --> 00:34:16,139 rather than just removing the gene, which 787 00:34:16,139 --> 00:34:17,530 is what happens here. 788 00:34:17,530 --> 00:34:21,250 Here, you've made a cut in the middle of this chain. 789 00:34:21,250 --> 00:34:23,070 You've removed a few amino acids. 790 00:34:23,070 --> 00:34:24,810 Or, you a removed amino acid, and it's 791 00:34:24,810 --> 00:34:27,060 rearranged a little bit, so the protein 792 00:34:27,060 --> 00:34:29,620 is never going to get formed. 793 00:34:29,620 --> 00:34:33,510 Here, what you're doing with the homologous repair is 794 00:34:33,510 --> 00:34:34,960 you have a template. 795 00:34:34,960 --> 00:34:35,460 OK. 796 00:34:35,460 --> 00:34:38,248 So if you don't know anything about homologous DNA repair, 797 00:34:38,248 --> 00:34:40,664 you need to go back and look into it, your basic textbook, 798 00:34:40,664 --> 00:34:43,150 and at least read the definition of what's going on. 799 00:34:43,150 --> 00:34:44,250 But you have a template. 800 00:34:44,250 --> 00:34:50,400 Once you have a template, you can copy that template 801 00:34:50,400 --> 00:34:52,830 and replace one gene with another gene. 802 00:34:52,830 --> 00:34:57,990 So this template becomes really key in replacing, 803 00:34:57,990 --> 00:35:01,380 site specifically, one gene with another gene 804 00:35:01,380 --> 00:35:05,100 and, as a consequence, one enzyme or protein 805 00:35:05,100 --> 00:35:06,960 of interest with another one. 806 00:35:06,960 --> 00:35:07,530 OK. 807 00:35:07,530 --> 00:35:14,030 So this was taken from an article by Jay Keasling. 808 00:35:14,030 --> 00:35:18,710 And Jay Keasling is interested in synthetic biology. 809 00:35:18,710 --> 00:35:20,310 He's an Artemisinin in fame. 810 00:35:20,310 --> 00:35:22,140 We talked about that in class. 811 00:35:22,140 --> 00:35:24,060 That's the anti-malarial agent. 812 00:35:24,060 --> 00:35:26,700 He's also been a major player in trying to figure out 813 00:35:26,700 --> 00:35:29,700 how to make bacteria use mevalonic acid pathway, which 814 00:35:29,700 --> 00:35:32,890 is what we're talking about in class, to make jet fuel. 815 00:35:32,890 --> 00:35:33,390 OK. 816 00:35:33,390 --> 00:35:36,430 So how do you make hydrocarbons that 817 00:35:36,430 --> 00:35:41,160 are really energy efficient compared to ethanol or butanol? 818 00:35:41,160 --> 00:35:44,150 And so his whole lab is focused on figuring out 819 00:35:44,150 --> 00:35:48,060 how to use CRISPR-Cas to engineer genes 820 00:35:48,060 --> 00:35:49,740 from many different organisms back 821 00:35:49,740 --> 00:35:52,380 into the organism of choice. 822 00:35:52,380 --> 00:35:54,720 And this technology, apparently, allows 823 00:35:54,720 --> 00:35:58,030 you to do five or six genes simultaneously 824 00:35:58,030 --> 00:35:59,650 once you figure out how to do it. 825 00:35:59,650 --> 00:36:01,860 And so you can do a lot of manipulation 826 00:36:01,860 --> 00:36:04,510 in a really fast time compared to the months 827 00:36:04,510 --> 00:36:06,545 it used to take before. 828 00:36:06,545 --> 00:36:07,980 And so what does this tell us? 829 00:36:07,980 --> 00:36:10,000 I mean, I think this is the most amazing thing. 830 00:36:12,720 --> 00:36:16,680 If you read the Eric Lander historical perspective 831 00:36:16,680 --> 00:36:20,880 on the discovery of CRISPR-Cas, there 832 00:36:20,880 --> 00:36:26,010 was a guy in the late 1980s that lived in Spain 833 00:36:26,010 --> 00:36:28,410 and did all his research in a salt marsh. 834 00:36:28,410 --> 00:36:29,190 OK. 835 00:36:29,190 --> 00:36:32,160 And he got really interested in these archaebacteria, 836 00:36:32,160 --> 00:36:33,750 really weird bacteria. 837 00:36:33,750 --> 00:36:35,670 I don't think they're weird, but most people 838 00:36:35,670 --> 00:36:37,086 don't really think about they have 839 00:36:37,086 --> 00:36:38,930 really interesting chemistry. 840 00:36:38,930 --> 00:36:42,150 And when he was sequencing part of this, for some reason, 841 00:36:42,150 --> 00:36:48,390 he found palindromic repeats, many palindromic repeats. 842 00:36:48,390 --> 00:36:50,497 And that's those purple spacers. 843 00:36:50,497 --> 00:36:52,830 And he says, well, what that heck is going on with that? 844 00:36:52,830 --> 00:36:53,740 What is this? 845 00:36:53,740 --> 00:36:54,330 OK. 846 00:36:54,330 --> 00:36:58,270 So he had discovered this locus in the genome. 847 00:36:58,270 --> 00:36:58,770 OK. 848 00:36:58,770 --> 00:37:00,940 Now, bioinformatics over the years, 849 00:37:00,940 --> 00:37:03,230 if you read the history of this, played a huge role. 850 00:37:03,230 --> 00:37:05,790 So you go back, and you look at all of these sequences. 851 00:37:05,790 --> 00:37:13,920 You find even an E. coli, you have these little spacer 852 00:37:13,920 --> 00:37:17,390 repeats over and over again that are palindromic. 853 00:37:17,390 --> 00:37:21,150 And so then the community got really 854 00:37:21,150 --> 00:37:26,181 interested in why you would have a locus that looks like this. 855 00:37:26,181 --> 00:37:27,680 And what they found is, if you start 856 00:37:27,680 --> 00:37:30,740 looking at the genes on either side of it, 857 00:37:30,740 --> 00:37:33,310 you found genes that were conserved, 858 00:37:33,310 --> 00:37:36,980 that coded for the Cas9 protein. 859 00:37:36,980 --> 00:37:39,020 In this case, S. pyogenes is the one 860 00:37:39,020 --> 00:37:43,050 that was used in this paper, which is the nuclease. 861 00:37:43,050 --> 00:37:47,840 And they also found this transactivating RNA. 862 00:37:47,840 --> 00:37:50,360 And what's interesting about the transactivating 863 00:37:50,360 --> 00:37:55,040 RNA is it has a sequence that's homologous to one 864 00:37:55,040 --> 00:37:58,980 of the sequences in the spacer. 865 00:37:58,980 --> 00:37:59,480 OK. 866 00:37:59,480 --> 00:38:02,750 So they've got to be able to hybridize to each other. 867 00:38:02,750 --> 00:38:03,470 OK. 868 00:38:03,470 --> 00:38:04,530 So that started. 869 00:38:04,530 --> 00:38:06,140 It became very interesting. 870 00:38:06,140 --> 00:38:08,360 And the question was focused on how 871 00:38:08,360 --> 00:38:10,040 is this editing going to happen when 872 00:38:10,040 --> 00:38:12,130 you get cleavage of your gene. 873 00:38:12,130 --> 00:38:14,220 Or does this act like, for example, 874 00:38:14,220 --> 00:38:18,410 in SI or an SH RNA in controlling levels of gene 875 00:38:18,410 --> 00:38:19,110 expression? 876 00:38:19,110 --> 00:38:23,000 And so there were many people that contributed over the years 877 00:38:23,000 --> 00:38:26,810 to figure out how this locus is used. 878 00:38:26,810 --> 00:38:28,940 And that's what I'm going to briefly describe. 879 00:38:28,940 --> 00:38:30,410 So the idea is the following. 880 00:38:30,410 --> 00:38:32,760 And I think that discovery, in my opinion, 881 00:38:32,760 --> 00:38:36,550 is really a seminal discovery by some guy who 882 00:38:36,550 --> 00:38:41,810 was working in a marsh working on some bizarre archae, 883 00:38:41,810 --> 00:38:44,810 made this discovery, followed it through for the next 15 years, 884 00:38:44,810 --> 00:38:49,407 and discovered that bacteria have adaptive immunity systems. 885 00:38:49,407 --> 00:38:51,240 I mean, that's really sort of mind-boggling. 886 00:38:51,240 --> 00:38:54,716 I remember when this paper was published, 887 00:38:54,716 --> 00:38:56,090 a first paper was published where 888 00:38:56,090 --> 00:38:57,654 they knew this was happening. 889 00:38:57,654 --> 00:38:59,570 Somebody in my lab gave a group meeting on it. 890 00:38:59,570 --> 00:39:02,000 And my mouth just dropped to the floor, 891 00:39:02,000 --> 00:39:03,720 because nobody predicted this at all. 892 00:39:03,720 --> 00:39:07,910 This is what I would call a revolutionary discovery. 893 00:39:07,910 --> 00:39:10,190 And what they found was-- and, again, this 894 00:39:10,190 --> 00:39:12,920 is the bioinformatics data analysis now, 895 00:39:12,920 --> 00:39:15,230 which we can do better and better and better. 896 00:39:15,230 --> 00:39:17,670 What they discovered-- they were looking for what's 897 00:39:17,670 --> 00:39:21,650 in between these spacers, OK? 898 00:39:21,650 --> 00:39:23,479 So what's in between these repeats? 899 00:39:23,479 --> 00:39:24,770 I'm calling it the wrong thing. 900 00:39:24,770 --> 00:39:27,020 These little purple things are the repeats. 901 00:39:27,020 --> 00:39:29,030 Again, they are the palindromic sequences 902 00:39:29,030 --> 00:39:30,470 over and over and over again. 903 00:39:30,470 --> 00:39:33,870 What is in between the repeats of the spacers? 904 00:39:33,870 --> 00:39:34,370 OK. 905 00:39:34,370 --> 00:39:36,470 Where do the spacers come from? 906 00:39:36,470 --> 00:39:37,550 Well, they didn't know. 907 00:39:37,550 --> 00:39:38,050 OK. 908 00:39:38,050 --> 00:39:39,950 But when they started looking at sequences 909 00:39:39,950 --> 00:39:42,320 of many of these things, what they found was 910 00:39:42,320 --> 00:39:44,570 they came from phage, viruses. 911 00:39:44,570 --> 00:39:45,080 OK. 912 00:39:45,080 --> 00:39:48,320 So, here, they have a bacteria, and they have phage DNA. 913 00:39:48,320 --> 00:39:50,630 Because people would sequence a lot of, at that time, 914 00:39:50,630 --> 00:39:52,930 phage DNA. 915 00:39:52,930 --> 00:39:54,950 And so what happens is the virus, 916 00:39:54,950 --> 00:40:00,680 or it could be a plasmid born piece of DNA where information 917 00:40:00,680 --> 00:40:04,050 is transferred from one bacteria to another, 918 00:40:04,050 --> 00:40:05,360 they get into the cell. 919 00:40:05,360 --> 00:40:07,790 And then they have proteins. 920 00:40:07,790 --> 00:40:09,590 And these, again, are Cas genes. 921 00:40:09,590 --> 00:40:11,630 And people are still studying these 922 00:40:11,630 --> 00:40:14,810 that take the viral DNA or the plasmid DNA 923 00:40:14,810 --> 00:40:19,010 and cut it into little pieces and somehow insert it 924 00:40:19,010 --> 00:40:20,820 between these repeats. 925 00:40:20,820 --> 00:40:25,280 So all of these spacers are different sequences of DNA 926 00:40:25,280 --> 00:40:28,310 that come from the invading species, the virus, 927 00:40:28,310 --> 00:40:33,620 or a piece of plasmid that you got from another bacteria. 928 00:40:33,620 --> 00:40:35,850 And so that became really exciting. 929 00:40:35,850 --> 00:40:36,620 OK? 930 00:40:36,620 --> 00:40:38,510 And so then the question is, how do you 931 00:40:38,510 --> 00:40:41,360 take all this information and convert it into something 932 00:40:41,360 --> 00:40:43,070 that can kill the idea-- 933 00:40:43,070 --> 00:40:44,990 if you have adaptive immunity, how 934 00:40:44,990 --> 00:40:47,550 do you use this information to kill the virus? 935 00:40:47,550 --> 00:40:48,230 OK. 936 00:40:48,230 --> 00:40:54,410 So what we now know happens is that the DNA 937 00:40:54,410 --> 00:40:56,680 can be transcribed into RNA. 938 00:40:56,680 --> 00:40:57,260 OK? 939 00:40:57,260 --> 00:40:59,870 And so you have this piece of RNA 940 00:40:59,870 --> 00:41:03,670 with the repeat and the spacer. 941 00:41:03,670 --> 00:41:10,790 And then you can also transcribe the transactivating RNA. 942 00:41:10,790 --> 00:41:13,731 And they form stem loop structures. 943 00:41:13,731 --> 00:41:15,230 That's what those little things are. 944 00:41:15,230 --> 00:41:17,313 So they have palindromic sequences-- so, you know, 945 00:41:17,313 --> 00:41:21,030 the base pair, that's why they draw the picture like that-- 946 00:41:21,030 --> 00:41:22,601 and Cas9. 947 00:41:22,601 --> 00:41:23,100 OK. 948 00:41:23,100 --> 00:41:29,430 And so what we know now is that this strand of RNA, 949 00:41:29,430 --> 00:41:36,520 this pre-CRISPR RNA can interact with the transactivating RNA. 950 00:41:36,520 --> 00:41:37,020 OK? 951 00:41:37,020 --> 00:41:39,180 So they have a way of hybridizing to each other. 952 00:41:39,180 --> 00:41:40,930 And that's what you see here. 953 00:41:40,930 --> 00:41:43,940 So you see this little purple repeat. 954 00:41:43,940 --> 00:41:47,890 And you see the hybridization there. 955 00:41:47,890 --> 00:41:53,361 And these two pieces of RNA can bind to Cas9. 956 00:41:53,361 --> 00:41:53,860 OK. 957 00:41:53,860 --> 00:41:56,594 So Cas9 is the nuclease. 958 00:41:56,594 --> 00:42:01,590 And in this particular type of CRISPR, 959 00:42:01,590 --> 00:42:04,590 there's a ribonuclease, RNase III, 960 00:42:04,590 --> 00:42:06,610 which takes off all this stuff. 961 00:42:06,610 --> 00:42:10,980 So you only have a single spacer that's actually 962 00:42:10,980 --> 00:42:12,220 going to be recognized. 963 00:42:12,220 --> 00:42:12,900 OK. 964 00:42:12,900 --> 00:42:15,015 So you typically could do this. 965 00:42:15,015 --> 00:42:19,470 You could do this again with different pieces of DNA 966 00:42:19,470 --> 00:42:22,890 and make many of these things, OK, and do many cuts. 967 00:42:22,890 --> 00:42:24,522 And so that's why people in engineering 968 00:42:24,522 --> 00:42:25,480 are excited about this. 969 00:42:25,480 --> 00:42:27,330 You can do more than one cut at once. 970 00:42:27,330 --> 00:42:32,010 But we're just going to focus on a single set of cleavages, 971 00:42:32,010 --> 00:42:34,230 double-strand cleavage, with one spacer. 972 00:42:34,230 --> 00:42:36,770 And in this case, the spacer is brown. 973 00:42:36,770 --> 00:42:37,350 OK? 974 00:42:37,350 --> 00:42:38,550 So we trim it. 975 00:42:38,550 --> 00:42:39,410 OK. 976 00:42:39,410 --> 00:42:44,190 And so this is our machine, two pieces of RNA and a protein. 977 00:42:44,190 --> 00:42:46,500 And then it goes searching for what happens. 978 00:42:46,500 --> 00:42:48,390 The virus invades. 979 00:42:48,390 --> 00:42:53,340 The virus has this sequence somewhere in its genome. 980 00:42:53,340 --> 00:42:57,930 Somehow, the bacteria knows the virus has invaded. 981 00:42:57,930 --> 00:42:59,670 It makes this machinery. 982 00:42:59,670 --> 00:43:02,900 It goes searching for this sequence. 983 00:43:02,900 --> 00:43:06,000 This sequence then is recognized, 984 00:43:06,000 --> 00:43:13,130 because it can hybridize to one of the two strands of the DNA. 985 00:43:13,130 --> 00:43:13,950 OK. 986 00:43:13,950 --> 00:43:18,370 And the nuclease then simply cuts it in two pieces. 987 00:43:18,370 --> 00:43:19,900 So the idea is simple. 988 00:43:19,900 --> 00:43:23,250 I mean, obviously, this is an extremely complex process where 989 00:43:23,250 --> 00:43:27,660 it's going to be regulated at every step along the way. 990 00:43:27,660 --> 00:43:33,090 But, somehow, bacteria have figured out that, you know, 991 00:43:33,090 --> 00:43:36,990 if you have a virus that infects the bacteria, what 992 00:43:36,990 --> 00:43:41,140 often happens is the virus causes cells to lyse. 993 00:43:41,140 --> 00:43:42,994 And the bacteria is dead. 994 00:43:42,994 --> 00:43:43,860 OK? 995 00:43:43,860 --> 00:43:44,640 So that happens. 996 00:43:44,640 --> 00:43:48,340 So to save yourself, you want to get rid of the virus. 997 00:43:48,340 --> 00:43:49,170 OK. 998 00:43:49,170 --> 00:43:52,830 And so this is a way that bacteria 999 00:43:52,830 --> 00:43:56,670 have evolved to be able to kill this invading virus that 1000 00:43:56,670 --> 00:44:01,740 would otherwise kill that, which is what adaptive immunity is 1001 00:44:01,740 --> 00:44:02,591 all about. 1002 00:44:02,591 --> 00:44:03,090 OK. 1003 00:44:03,090 --> 00:44:05,090 So this is the model. 1004 00:44:05,090 --> 00:44:08,250 And so then what people have been focusing on 1005 00:44:08,250 --> 00:44:11,861 and what was focused on in this paper is Cas9. 1006 00:44:11,861 --> 00:44:12,360 OK. 1007 00:44:12,360 --> 00:44:14,700 So the idea's easy. 1008 00:44:14,700 --> 00:44:17,850 It cleaves double-stranded DNA and gives you blunt ends. 1009 00:44:17,850 --> 00:44:24,790 Furthermore, it knows where that occurs relative to a P-A-M 1010 00:44:24,790 --> 00:44:26,820 site, a PAM activating site. 1011 00:44:26,820 --> 00:44:28,740 It cuts in a certain region. 1012 00:44:28,740 --> 00:44:30,141 So they've studied all of that. 1013 00:44:30,141 --> 00:44:31,140 They know where it cuts. 1014 00:44:31,140 --> 00:44:34,210 I'll show you that in a minute. 1015 00:44:34,210 --> 00:44:40,600 And then if you want to target any gene inside the cell, 1016 00:44:40,600 --> 00:44:44,400 you now can put in the right spacer. 1017 00:44:44,400 --> 00:44:45,240 OK? 1018 00:44:45,240 --> 00:44:47,400 Then you put the whole thing together. 1019 00:44:47,400 --> 00:44:51,330 Now, the key issue is getting all of this stuff, the protein 1020 00:44:51,330 --> 00:44:53,500 and the two pieces of RNA. 1021 00:44:53,500 --> 00:44:57,690 They are going to go in as DNA, OK, getting them into the cell. 1022 00:44:57,690 --> 00:44:58,860 And how did they get-- 1023 00:44:58,860 --> 00:45:00,270 because if you can't get it into the cell, 1024 00:45:00,270 --> 00:45:02,050 you can't do the double-stranded cleavage. 1025 00:45:02,050 --> 00:45:03,883 So how did they get this into the cell then? 1026 00:45:03,883 --> 00:45:05,965 Anybody notice that in the paper? 1027 00:45:05,965 --> 00:45:06,840 AUDIENCE: Adenovirus. 1028 00:45:06,840 --> 00:45:08,298 JOANNE STUBBE: Yeah, so adenovirus. 1029 00:45:08,298 --> 00:45:10,980 So people are trying to do gene replacements 1030 00:45:10,980 --> 00:45:12,130 using all kinds of methods. 1031 00:45:12,130 --> 00:45:14,040 None of this is trivial. 1032 00:45:14,040 --> 00:45:17,860 In this case, they're working on a mouse liver. 1033 00:45:17,860 --> 00:45:20,820 And adenovirus, I don't know very much about adenovirus. 1034 00:45:20,820 --> 00:45:23,830 But, apparently, it likes to live in the liver. 1035 00:45:23,830 --> 00:45:25,830 So that's one of the reasons they 1036 00:45:25,830 --> 00:45:27,870 chose looking at the mouse liver, 1037 00:45:27,870 --> 00:45:30,870 but it happens to also be where all the cholesterol 1038 00:45:30,870 --> 00:45:35,070 metabolism or the predominant cholesterol metabolism 1039 00:45:35,070 --> 00:45:36,250 occurs as well. 1040 00:45:36,250 --> 00:45:38,236 And so they wanted to try other things. 1041 00:45:38,236 --> 00:45:39,860 In the end, you're probably never going 1042 00:45:39,860 --> 00:45:41,310 to be able to use adenovirus. 1043 00:45:41,310 --> 00:45:43,518 People have been trying to do that for years for gene 1044 00:45:43,518 --> 00:45:45,000 replacement without success. 1045 00:45:45,000 --> 00:45:47,100 So a key issue is going to be how do you get this 1046 00:45:47,100 --> 00:45:48,675 into the cell, I mean. 1047 00:45:48,675 --> 00:45:51,420 And so that's what a lot of people 1048 00:45:51,420 --> 00:45:52,770 are trying to focus on now. 1049 00:45:52,770 --> 00:45:54,330 But to do this in tissue culture, 1050 00:45:54,330 --> 00:45:56,070 you can do it without any problems. 1051 00:45:56,070 --> 00:45:59,700 There are ways of getting it into the cell. 1052 00:45:59,700 --> 00:46:00,660 OK. 1053 00:46:00,660 --> 00:46:03,200 So this is what we were talking about. 1054 00:46:03,200 --> 00:46:06,100 Once you get the cleavage, you know, 1055 00:46:06,100 --> 00:46:09,130 you can repair the cleavage by this method. 1056 00:46:09,130 --> 00:46:11,620 If you want to read about this, you can go read about it. 1057 00:46:11,620 --> 00:46:16,120 But what this does is gives you a deletion of your gene. 1058 00:46:16,120 --> 00:46:18,910 Or, if you have a template, then you 1059 00:46:18,910 --> 00:46:23,530 can use this template to remake a protein 1060 00:46:23,530 --> 00:46:25,610 with a mutation in it, for example, 1061 00:46:25,610 --> 00:46:27,820 or with a tag on the end, so you can purify it 1062 00:46:27,820 --> 00:46:30,600 by affinity column, chromatography. 1063 00:46:30,600 --> 00:46:35,111 And so then the question is in almost all cells-- 1064 00:46:35,111 --> 00:46:36,610 and it depends on the organism-- you 1065 00:46:36,610 --> 00:46:38,690 have both mechanisms of repair. 1066 00:46:38,690 --> 00:46:40,900 And so one of the issues is how do you 1067 00:46:40,900 --> 00:46:42,640 tweak the repair depending on what 1068 00:46:42,640 --> 00:46:46,600 function you want to use the technology for to do this 1069 00:46:46,600 --> 00:46:47,440 or to do that. 1070 00:46:47,440 --> 00:46:49,023 And a lot of people are studying that. 1071 00:46:49,023 --> 00:46:50,920 That's one of the focuses of many labs 1072 00:46:50,920 --> 00:46:54,400 if you look at what are the issues, where are we going. 1073 00:46:54,400 --> 00:46:55,700 OK. 1074 00:46:55,700 --> 00:47:00,880 So if you look here, we were just talking about Cas9. 1075 00:47:00,880 --> 00:47:03,370 So the blue is one nuclease. 1076 00:47:03,370 --> 00:47:07,550 And the green is another nuclease. 1077 00:47:07,550 --> 00:47:13,240 And in this case, this is the double-stranded DNA target 1078 00:47:13,240 --> 00:47:14,826 they're after. 1079 00:47:14,826 --> 00:47:19,750 And this little piece here, this TGG, is called the PAM site. 1080 00:47:19,750 --> 00:47:22,580 And that's required for recognition 1081 00:47:22,580 --> 00:47:24,120 by the Cas9 protein. 1082 00:47:24,120 --> 00:47:26,480 And do we understand the basis of that? 1083 00:47:26,480 --> 00:47:27,729 The answer is yes. 1084 00:47:27,729 --> 00:47:28,270 I don't know. 1085 00:47:28,270 --> 00:47:30,670 Did any of you hear Jennifer Doudna talk? 1086 00:47:30,670 --> 00:47:31,170 Yeah. 1087 00:47:31,170 --> 00:47:33,410 So, I mean, she's the one that discovered that. 1088 00:47:33,410 --> 00:47:38,779 She just published two weeks ago the structure of this complex. 1089 00:47:38,779 --> 00:47:40,820 And so, you know, I haven't had time to study it. 1090 00:47:40,820 --> 00:47:42,680 And this is the kind of thing that, if you really 1091 00:47:42,680 --> 00:47:44,721 want to use this, you got to roll up your sleeves 1092 00:47:44,721 --> 00:47:47,030 and get in there and study it. 1093 00:47:47,030 --> 00:47:49,700 But what they did was instead of having 1094 00:47:49,700 --> 00:47:53,150 the guide RNA and the transactivating RNA, 1095 00:47:53,150 --> 00:47:55,710 they put them together. 1096 00:47:55,710 --> 00:47:58,939 And so that makes the genetic engineering simpler. 1097 00:47:58,939 --> 00:48:00,980 But the question is how do you put them together. 1098 00:48:00,980 --> 00:48:02,250 OK-- not trivial. 1099 00:48:02,250 --> 00:48:02,900 OK. 1100 00:48:02,900 --> 00:48:06,190 And the whole Eric Lander article 1101 00:48:06,190 --> 00:48:08,870 about the history of this process, 1102 00:48:08,870 --> 00:48:12,140 the Doudna, Charpentier group, figured out 1103 00:48:12,140 --> 00:48:15,250 how to do this extremely efficiently for bacteria-- 1104 00:48:15,250 --> 00:48:17,900 doesn't work in humans. 1105 00:48:17,900 --> 00:48:19,940 So Lander's article is focusing on, 1106 00:48:19,940 --> 00:48:22,320 you know, humans is much more important. 1107 00:48:22,320 --> 00:48:26,690 And so Zhang, who is at MIT, had figured out another way. 1108 00:48:26,690 --> 00:48:30,380 You can't just use these two little pieces of RNA. 1109 00:48:30,380 --> 00:48:33,920 You need something much bigger to have the Cas9 work 1110 00:48:33,920 --> 00:48:35,472 successfully inside the cell. 1111 00:48:35,472 --> 00:48:36,680 And what's the basis of that? 1112 00:48:36,680 --> 00:48:37,221 I don't know. 1113 00:48:37,221 --> 00:48:38,990 But they did a lot of experiments 1114 00:48:38,990 --> 00:48:43,770 to figure out how you could get this to work most efficiently. 1115 00:48:43,770 --> 00:48:46,380 So these are the two partners. 1116 00:48:46,380 --> 00:48:47,990 And the question is what's going on. 1117 00:48:47,990 --> 00:48:50,360 And, you, know, frankly I haven't even 1118 00:48:50,360 --> 00:48:52,430 had time to digest. 1119 00:48:52,430 --> 00:48:54,250 My lab hasn't used this technology. 1120 00:48:54,250 --> 00:48:56,670 I haven't had time to digest it. 1121 00:48:56,670 --> 00:48:57,627 But what you see-- 1122 00:48:57,627 --> 00:48:59,210 let me just point out one other thing. 1123 00:48:59,210 --> 00:49:01,960 There are two domains. 1124 00:49:01,960 --> 00:49:05,600 So you have the nuclease domain, which are not contiguous. 1125 00:49:05,600 --> 00:49:07,960 And then you have a helical domain. 1126 00:49:07,960 --> 00:49:12,710 And Doudna used Cryo-EM, which we've talked about at 25, 1127 00:49:12,710 --> 00:49:16,130 30 angstroms resolution-- not particularly good-- 1128 00:49:16,130 --> 00:49:18,890 to show that when you started with Cas9, 1129 00:49:18,890 --> 00:49:21,980 but you added the two pieces of RNA, 1130 00:49:21,980 --> 00:49:23,780 you got a change in conformation. 1131 00:49:23,780 --> 00:49:26,870 They could see that in the Cryo-EM, because it was huge. 1132 00:49:26,870 --> 00:49:27,930 OK. 1133 00:49:27,930 --> 00:49:31,580 And then when you add the targeting DNA, what happens 1134 00:49:31,580 --> 00:49:36,350 is the nuclease domains change tremendously. 1135 00:49:36,350 --> 00:49:38,960 The conformation of the protein changes tremendously. 1136 00:49:38,960 --> 00:49:42,650 Putting everything together, you have the double-stranded DNA. 1137 00:49:42,650 --> 00:49:45,290 So here you have the double-stranded DNA. 1138 00:49:45,290 --> 00:49:51,650 Here, it's hybridizing to the guide RNA. 1139 00:49:51,650 --> 00:49:56,300 And this is the tracr transactivating RNA. 1140 00:49:56,300 --> 00:49:59,120 And the Cas9 simply surrounds this whole thing. 1141 00:49:59,120 --> 00:50:04,190 That's what she talk about in the lecture this past week 1142 00:50:04,190 --> 00:50:06,620 or past two weeks. 1143 00:50:06,620 --> 00:50:08,240 So this was at 30 angstroms. 1144 00:50:08,240 --> 00:50:09,390 And they had that model. 1145 00:50:09,390 --> 00:50:13,190 And this was the result of an atomic resolution structure 1146 00:50:13,190 --> 00:50:16,000 that just came out a week ago. 1147 00:50:16,000 --> 00:50:17,930 And it just shows you, I've told you, 1148 00:50:17,930 --> 00:50:19,940 you know, you have to separate the strands. 1149 00:50:19,940 --> 00:50:21,670 I've given you a cartoon of that. 1150 00:50:21,670 --> 00:50:23,780 And you need to stare at this a long time. 1151 00:50:23,780 --> 00:50:25,310 But green is one nuclease. 1152 00:50:25,310 --> 00:50:28,280 Blue is another nuclease. 1153 00:50:28,280 --> 00:50:31,559 One can see that the blue and the purple are the DNA. 1154 00:50:31,559 --> 00:50:33,350 And you can see the two strands separating, 1155 00:50:33,350 --> 00:50:36,680 because they need hybridize to two 1156 00:50:36,680 --> 00:50:39,770 different parts of the guide and the tracr RNA, 1157 00:50:39,770 --> 00:50:43,010 which is in orange. 1158 00:50:43,010 --> 00:50:44,810 And so what they're doing is looking 1159 00:50:44,810 --> 00:50:46,610 at a model for how this works. 1160 00:50:46,610 --> 00:50:47,280 OK. 1161 00:50:47,280 --> 00:50:51,170 So the key issues, all of which were 1162 00:50:51,170 --> 00:50:55,940 covered in this paper in some form, are shown here. 1163 00:50:55,940 --> 00:50:58,400 And these are the key issues that everybody is facing. 1164 00:50:58,400 --> 00:51:00,650 And I'm already over. 1165 00:51:00,650 --> 00:51:05,330 But the delivery method into the cell, OK, we talked about that. 1166 00:51:05,330 --> 00:51:07,590 They use adenovirus in this paper. 1167 00:51:07,590 --> 00:51:10,430 So I would suggest you go back and you look at what they did. 1168 00:51:10,430 --> 00:51:11,570 OK. 1169 00:51:11,570 --> 00:51:14,260 Off target effects, did they look at that? 1170 00:51:14,260 --> 00:51:16,360 Does anybody know? 1171 00:51:16,360 --> 00:51:19,100 Did they look at that in this particular paper? 1172 00:51:19,100 --> 00:51:20,275 Yeah, they did. 1173 00:51:20,275 --> 00:51:24,410 And so how did they figure out what's going to be off target? 1174 00:51:24,410 --> 00:51:26,815 How did they choose what to look for? 1175 00:51:26,815 --> 00:51:31,390 I mean, you know, you got a billion base pairs, right? 1176 00:51:31,390 --> 00:51:33,260 So how did they tell what to look for? 1177 00:51:42,260 --> 00:51:44,690 One of the first things they did is what? 1178 00:51:44,690 --> 00:51:47,985 How did they target the PCSK9? 1179 00:51:47,985 --> 00:51:49,610 How did they figure out what to target? 1180 00:51:49,610 --> 00:51:50,780 Can anybody tell me that? 1181 00:51:54,756 --> 00:51:57,900 All right, nobody can tell me that? 1182 00:51:57,900 --> 00:52:00,410 I guarantee you're going to have this on the first exam. 1183 00:52:00,410 --> 00:52:02,785 You're going to have something on this in the first exam. 1184 00:52:02,785 --> 00:52:04,800 We'll see if you go back and you read this. 1185 00:52:04,800 --> 00:52:08,550 So the whole paper, really the whole first couple figures, 1186 00:52:08,550 --> 00:52:12,960 is focused on how do you decide what to target in the PCSK9. 1187 00:52:12,960 --> 00:52:14,100 And they looked of exon 1. 1188 00:52:14,100 --> 00:52:15,570 And they looked at exon 2. 1189 00:52:15,570 --> 00:52:18,090 And then they did experiments to try to look at that. 1190 00:52:18,090 --> 00:52:21,090 That's how they got this idea about what 1191 00:52:21,090 --> 00:52:23,100 the mechanism of repair was. 1192 00:52:23,100 --> 00:52:26,670 And the sequence they targeted, they then 1193 00:52:26,670 --> 00:52:30,570 look for other sequences that were three or four base pairs 1194 00:52:30,570 --> 00:52:31,570 different. 1195 00:52:31,570 --> 00:52:35,070 And then they also did PCR reactions on all those genes 1196 00:52:35,070 --> 00:52:37,510 to see if you got cleavage or not. 1197 00:52:37,510 --> 00:52:40,860 So if this is ever going to be used technologically 1198 00:52:40,860 --> 00:52:44,010 in humans, which is the goal of this paper-- you know, 1199 00:52:44,010 --> 00:52:45,460 we're very far removed from that. 1200 00:52:45,460 --> 00:52:48,240 We have a lot of ethical questions. 1201 00:52:48,240 --> 00:52:50,610 The bottom line is you need to remove 1202 00:52:50,610 --> 00:52:53,520 all the off-target sites. 1203 00:52:53,520 --> 00:52:57,250 You need to control, as we've already talked about, 1204 00:52:57,250 --> 00:53:00,370 the two methods of repair of the double-stranded breaks. 1205 00:53:00,370 --> 00:53:04,270 And I think now that we have structure, 1206 00:53:04,270 --> 00:53:07,510 we ought to be able to even better design 1207 00:53:07,510 --> 00:53:10,970 these three pieces to make more efficient chemistry 1208 00:53:10,970 --> 00:53:11,470 of cleavage. 1209 00:53:11,470 --> 00:53:13,303 I mean, it's amazing how efficient this was. 1210 00:53:13,303 --> 00:53:16,400 So they did the whole thing in four or five days. 1211 00:53:16,400 --> 00:53:18,280 I mean, that was really quite amazing. 1212 00:53:18,280 --> 00:53:22,120 And so what I suggest you do now in the rest of the paper is, 1213 00:53:22,120 --> 00:53:24,310 I think, straightforward. 1214 00:53:24,310 --> 00:53:26,920 It just tests this model by looking 1215 00:53:26,920 --> 00:53:30,910 for what happens to low density lipoprotein cholesterol. 1216 00:53:30,910 --> 00:53:33,070 What happens to the receptor? 1217 00:53:33,070 --> 00:53:35,110 What happens to cholesterol levels? 1218 00:53:35,110 --> 00:53:38,680 What happens to triacylglycerol levels? 1219 00:53:38,680 --> 00:53:41,620 And does it conform to the model that people 1220 00:53:41,620 --> 00:53:45,970 have for the function of this protein in controlling 1221 00:53:45,970 --> 00:53:47,030 cholesterol levels? 1222 00:53:47,030 --> 00:53:49,900 So what I would suggest you do is you go back now. 1223 00:53:49,900 --> 00:53:52,420 Hopefully, you're now interested in this a little more. 1224 00:53:52,420 --> 00:53:55,280 And go back and read this. 1225 00:53:55,280 --> 00:53:56,780 And if anybody has any questions, 1226 00:53:56,780 --> 00:53:59,430 they can come back and talk to me.