1 00:00:08,440 --> 00:00:08,719 Speaker 1: Coming. 2 00:00:11,880 --> 00:00:14,720 Speaker 2: This is on Crisper, the Story of Jennifer DOWDNA. I'm 3 00:00:14,760 --> 00:00:18,040 Speaker 2: Evan Ratliffe. For our final episode, We're bringing you something 4 00:00:18,079 --> 00:00:20,840 Speaker 2: a bit different. It's a conversation that Walter Isaacson and 5 00:00:20,960 --> 00:00:23,720 Speaker 2: Jennifer Dowdna had at the New Orleans Book Festival at 6 00:00:23,760 --> 00:00:29,040 Speaker 2: Tulane University earlier this year. It's a fascinating exchange that 7 00:00:29,120 --> 00:00:32,240 Speaker 2: shows how, four years after the book's publication, the medical 8 00:00:32,240 --> 00:00:36,120 Speaker 2: breakthroughs brought on by Crisper have only multiplied. Isaacson and 9 00:00:36,240 --> 00:00:39,000 Speaker 2: Downa also touch upon how recent cuts and science funding 10 00:00:39,240 --> 00:00:42,760 Speaker 2: and researcher visas have shaken the field, putting at risk 11 00:00:42,840 --> 00:00:45,400 Speaker 2: the very kind of work that created Crisper and is 12 00:00:45,440 --> 00:00:49,040 Speaker 2: now saving lives. If you've listened and enjoyed the series 13 00:00:49,080 --> 00:00:51,600 Speaker 2: so far, thank you. We would be grateful if you 14 00:00:51,600 --> 00:00:53,599 Speaker 2: could take a moment to rate and review the podcast 15 00:00:53,640 --> 00:00:56,560 Speaker 2: on your platform of choice. Really helps us reach more people. 16 00:00:57,280 --> 00:00:58,160 Speaker 2: Here's the conversation. 17 00:01:00,520 --> 00:01:05,520 Speaker 1: Thank you, thank you, thank you, and Jennifer, thank you 18 00:01:05,560 --> 00:01:08,160 Speaker 1: so much for being here. Somebody who's written biographies, I 19 00:01:08,280 --> 00:01:11,040 Speaker 1: tried people saying who's the nicest one. I said, there's 20 00:01:11,080 --> 00:01:15,840 Speaker 1: only one I've written about who well, Jennifer is the 21 00:01:15,920 --> 00:01:19,039 Speaker 1: intersection of being a good person and a brilliant scientist, 22 00:01:19,680 --> 00:01:24,280 Speaker 1: and probably the person most defining a future with biotech. 23 00:01:27,560 --> 00:01:29,959 Speaker 1: When you were a kid, you were an Ilo Hawaii. 24 00:01:30,319 --> 00:01:33,080 Speaker 1: I think you had a guidance counselor who once said 25 00:01:33,400 --> 00:01:37,240 Speaker 1: girls don't do science. How did you end up thinking, Okay, 26 00:01:37,400 --> 00:01:41,039 Speaker 1: there are women scientists. I can do this, I. 27 00:01:41,000 --> 00:01:43,679 Speaker 3: Think when I you know, I think back to my upbringing. 28 00:01:43,800 --> 00:01:47,280 Speaker 3: My father was a literature professor. He gave me a 29 00:01:47,319 --> 00:01:51,640 Speaker 3: copy of The Double Helix, a book about the discovery 30 00:01:51,680 --> 00:01:54,040 Speaker 3: of the structure of DNA, when I was probably eleven 31 00:01:54,120 --> 00:01:58,640 Speaker 3: or twelve years old, and that book really showed me 32 00:01:58,800 --> 00:02:01,880 Speaker 3: that science is a process of discovery. And I was 33 00:02:01,920 --> 00:02:06,760 Speaker 3: fascinated by that description of how scientists could figure out 34 00:02:06,760 --> 00:02:11,000 Speaker 3: the mystery of something in biology by doing experiments. And 35 00:02:11,040 --> 00:02:15,400 Speaker 3: then I learned about Marie Cure's work and was inspired 36 00:02:15,760 --> 00:02:20,000 Speaker 3: by her story, and together I think those two ideas 37 00:02:20,080 --> 00:02:23,080 Speaker 3: really spurred me on to thinking about a career in science. 38 00:02:23,240 --> 00:02:26,200 Speaker 1: One of the things in James Watson's Book of the 39 00:02:26,240 --> 00:02:31,200 Speaker 1: Double Helix is he kind of minimizes and dismisses dismissively 40 00:02:31,280 --> 00:02:34,800 Speaker 1: a bit rosalind Franklin, who did the great photograph that 41 00:02:34,880 --> 00:02:40,040 Speaker 1: helped watching and quick understand the structure, calls her Rosie. 42 00:02:40,360 --> 00:02:42,600 Speaker 1: Most people when they read that book, they say, oh, 43 00:02:42,680 --> 00:02:44,960 Speaker 1: you know, it was dismissive. But when you read the book, 44 00:02:44,960 --> 00:02:45,920 Speaker 1: what did you think? 45 00:02:46,120 --> 00:02:49,720 Speaker 3: When I read the book, I thought that's ridiculous. Of 46 00:02:49,760 --> 00:02:53,000 Speaker 3: course she was doing important experiments. 47 00:02:52,600 --> 00:02:55,040 Speaker 1: And that a woman could be a scientist. Yes, which 48 00:02:55,320 --> 00:02:57,160 Speaker 1: you told me at one point that up until then 49 00:02:57,200 --> 00:03:00,760 Speaker 1: you hardly knew that there were women science. That's true exactly. 50 00:03:00,960 --> 00:03:05,880 Speaker 1: So from there you decide to go to not be 51 00:03:05,960 --> 00:03:10,079 Speaker 1: a French teacher, but go into biochemistry and chemistry mainly. 52 00:03:10,720 --> 00:03:15,880 Speaker 1: Why did you choose that that's not a usual path 53 00:03:15,960 --> 00:03:16,680 Speaker 1: on chemistry? 54 00:03:16,760 --> 00:03:19,440 Speaker 3: Well, I think it started with my chemistry teacher in 55 00:03:19,560 --> 00:03:23,320 Speaker 3: tenth grade. She was a Miss Wong in Helo. She 56 00:03:23,560 --> 00:03:28,200 Speaker 3: taught us kids that science is about solving puzzles and 57 00:03:28,360 --> 00:03:31,280 Speaker 3: not memorizing facts and textbooks. And I thought that was 58 00:03:31,760 --> 00:03:35,800 Speaker 3: so interesting, and I thought to myself, wouldn't it be 59 00:03:35,840 --> 00:03:39,360 Speaker 3: amazing to understand the chemistry of life? So that was 60 00:03:39,440 --> 00:03:42,200 Speaker 3: really the first inkling I had that really what I 61 00:03:42,200 --> 00:03:44,400 Speaker 3: wanted to do was work, be working right at that 62 00:03:44,440 --> 00:03:46,920 Speaker 3: intersection between chemistry and biology. 63 00:03:47,400 --> 00:03:50,280 Speaker 1: Now on the power of books, your father leaves on 64 00:03:50,320 --> 00:03:52,200 Speaker 1: your bed, the double Heelix. I think it was the 65 00:03:52,240 --> 00:03:59,640 Speaker 1: old vintage paperback Penguin Blood. Sorry, And if I remember, 66 00:03:59,720 --> 00:04:03,360 Speaker 1: you thought it was a detective story because it looked 67 00:04:03,440 --> 00:04:07,400 Speaker 1: like that, right, and then you find out it wasn't, 68 00:04:07,440 --> 00:04:10,000 Speaker 1: And then you found out it was Yep. 69 00:04:09,840 --> 00:04:12,040 Speaker 3: That's right. I thought it was a detective story of 70 00:04:12,080 --> 00:04:13,960 Speaker 3: one type, and in fact, when I read the book, 71 00:04:14,000 --> 00:04:17,040 Speaker 3: I realized it is a detective story. It's just about 72 00:04:17,080 --> 00:04:18,799 Speaker 3: something very different than I expected. 73 00:04:19,160 --> 00:04:21,679 Speaker 1: Tell me. Growing up, you're taking hikes and you're seeing 74 00:04:22,080 --> 00:04:24,560 Speaker 1: things in Hawaii that we kind of have here too, 75 00:04:24,640 --> 00:04:27,760 Speaker 1: out in the you know, which is weird grasses that 76 00:04:27,800 --> 00:04:31,359 Speaker 1: if you touch them they curl up. And one of 77 00:04:31,400 --> 00:04:34,200 Speaker 1: the things I like about you, Leonardo da Vinci and 78 00:04:34,279 --> 00:04:38,760 Speaker 1: others is a curiosity about things that we all see 79 00:04:38,800 --> 00:04:41,960 Speaker 1: every day, but we don't go, oh my god, why 80 00:04:42,080 --> 00:04:44,560 Speaker 1: is that? And when you touched I can't remember the 81 00:04:44,640 --> 00:04:47,960 Speaker 1: name of the grass and it curled, you just become 82 00:04:47,960 --> 00:04:50,159 Speaker 1: fixated on how does that happen? Right? 83 00:04:50,960 --> 00:04:54,200 Speaker 3: Yeah, you know, I think when I was in Hawaii, 84 00:04:54,440 --> 00:04:57,680 Speaker 3: I was amazed by all the plants and animals that 85 00:04:57,720 --> 00:05:01,400 Speaker 3: had adapted to that island environment. So sleeping grass, that's 86 00:05:01,440 --> 00:05:04,440 Speaker 3: one of the organisms that I was fascinated by. But 87 00:05:04,800 --> 00:05:07,760 Speaker 3: we also had blind spiders that lived in lava tubes, 88 00:05:07,920 --> 00:05:10,520 Speaker 3: and this was, you know, the something that I just 89 00:05:10,839 --> 00:05:14,120 Speaker 3: I found myself drawn to that question of why. 90 00:05:15,160 --> 00:05:18,400 Speaker 1: When we get you get to Berkeley after a while, 91 00:05:18,480 --> 00:05:22,799 Speaker 1: so along thank through Harvard, and but when you're there, 92 00:05:23,560 --> 00:05:28,680 Speaker 1: you weren't doing RNA qusper type stuff. I mean, it 93 00:05:28,800 --> 00:05:33,960 Speaker 1: was not yet a full field. How did you end 94 00:05:34,040 --> 00:05:37,039 Speaker 1: up starting to study that? I mean, who called you 95 00:05:37,080 --> 00:05:38,280 Speaker 1: and said let's do something? 96 00:05:38,680 --> 00:05:40,880 Speaker 3: Well, you know, this is the great thing about doing 97 00:05:40,920 --> 00:05:44,440 Speaker 3: research is that ideas come out of all sorts of directions. 98 00:05:44,480 --> 00:05:48,360 Speaker 3: So in our case with crisper, the first indication that 99 00:05:48,400 --> 00:05:51,920 Speaker 3: there was something very interesting going on in bacteria known 100 00:05:52,000 --> 00:05:55,440 Speaker 3: as as crisper systems was the work of Jill Banfield, 101 00:05:55,480 --> 00:05:58,960 Speaker 3: who studies bacteria in their native environment. And I think 102 00:05:59,000 --> 00:06:01,120 Speaker 3: she was really one of the first people that noticed 103 00:06:01,480 --> 00:06:06,479 Speaker 3: that bacteria can acquire immunity to viruses that infect them 104 00:06:06,600 --> 00:06:10,000 Speaker 3: in real time. And she wondered how that works and why, 105 00:06:10,080 --> 00:06:15,719 Speaker 3: And she had a hypothesis that involved molecules of RNA, 106 00:06:15,800 --> 00:06:20,920 Speaker 3: which are the chemical cousins of DNA. She googled who 107 00:06:20,920 --> 00:06:23,520 Speaker 3: at Berkeley works on RNA. My name popped up and 108 00:06:23,560 --> 00:06:25,480 Speaker 3: she called me and that's literally how we got together. 109 00:06:25,520 --> 00:06:28,800 Speaker 1: And there's why great research university is so good, because 110 00:06:29,320 --> 00:06:32,159 Speaker 1: you have a lot of people together and somebody says, okay, 111 00:06:32,200 --> 00:06:36,720 Speaker 1: I need to know about this molecule, and you form 112 00:06:36,720 --> 00:06:37,359 Speaker 1: a partnership. 113 00:06:37,400 --> 00:06:38,719 Speaker 3: Right, that's right, collaboration. 114 00:06:39,360 --> 00:06:43,760 Speaker 1: And one of the problems nowadays is so much of 115 00:06:43,839 --> 00:06:51,120 Speaker 1: that is funding of basic research, not applied research, just 116 00:06:51,279 --> 00:06:56,080 Speaker 1: funding for curiosity's sake. That the federal government under Veneva Bush, 117 00:06:56,080 --> 00:06:59,320 Speaker 1: starting in nineteen forty five, made part of what we 118 00:06:59,400 --> 00:07:04,600 Speaker 1: as a nation do, which is curiosity driven basic science. 119 00:07:05,000 --> 00:07:09,640 Speaker 1: So when Jill Banfi, Jillian Banfield calls you up and says, 120 00:07:09,840 --> 00:07:12,280 Speaker 1: I got this molecule. I know you're interested in it, 121 00:07:12,320 --> 00:07:14,720 Speaker 1: but maybe it has something to do with the sequences 122 00:07:14,720 --> 00:07:18,120 Speaker 1: in bacteria. Were you all thinking of an applied application 123 00:07:18,240 --> 00:07:19,720 Speaker 1: or were you just basic research? 124 00:07:19,800 --> 00:07:23,840 Speaker 3: Oh? Certainly not. It was pure you know, curiosity driven science. 125 00:07:24,280 --> 00:07:26,520 Speaker 1: And what was it you were trying to figure out? 126 00:07:26,800 --> 00:07:29,640 Speaker 3: Well, the question at that time for me was, well, 127 00:07:29,640 --> 00:07:31,880 Speaker 3: first of all, I guess I was amazed that bacteria 128 00:07:31,960 --> 00:07:34,880 Speaker 3: would have an adaptive immune system. We're all familiar with 129 00:07:35,000 --> 00:07:38,160 Speaker 3: our own bodies working that way, but nobody had any 130 00:07:38,160 --> 00:07:41,320 Speaker 3: inkling that bacteria could do something like that. So I 131 00:07:41,400 --> 00:07:44,400 Speaker 3: was fascinated by that possibility and also by the role 132 00:07:44,920 --> 00:07:47,320 Speaker 3: of molecules of RNA, which are thought to be some 133 00:07:47,360 --> 00:07:50,600 Speaker 3: of the most ancient molecules on our planet and perhaps 134 00:07:50,640 --> 00:07:52,040 Speaker 3: even the source of life on. 135 00:07:52,600 --> 00:07:56,240 Speaker 1: Earth, well source of life. So we're all trying to 136 00:07:56,240 --> 00:07:59,000 Speaker 1: figure out how did life begin? You had a great 137 00:07:59,560 --> 00:08:02,760 Speaker 1: Tom what was his name, right, or who who did 138 00:08:02,800 --> 00:08:05,920 Speaker 1: the origins of life and figured the RNA world thesis 139 00:08:05,960 --> 00:08:06,680 Speaker 1: that you worked on. 140 00:08:06,800 --> 00:08:09,480 Speaker 3: Well, Tom check was one person who I worked with 141 00:08:09,520 --> 00:08:12,440 Speaker 3: in the past, but also my graduate advisor, Jack Shawstak, 142 00:08:12,520 --> 00:08:14,480 Speaker 3: that's very interested in this question. 143 00:08:15,040 --> 00:08:18,760 Speaker 1: And it was he said, asked big questions, right, he did, 144 00:08:18,800 --> 00:08:19,880 Speaker 1: And what was the big question? 145 00:08:20,400 --> 00:08:22,480 Speaker 3: Well, the big question was where did life come from? 146 00:08:22,480 --> 00:08:24,200 Speaker 3: And can we study it in the lab? 147 00:08:25,680 --> 00:08:30,120 Speaker 1: And why does it? The answer is yes, but you 148 00:08:30,200 --> 00:08:32,960 Speaker 1: have to do it chicken and egg riddle to get there, right, 149 00:08:33,120 --> 00:08:36,840 Speaker 1: which is we thought it was DNA that becomes the 150 00:08:36,880 --> 00:08:42,160 Speaker 1: code for replicating species, but you can't have How did 151 00:08:42,200 --> 00:08:43,840 Speaker 1: you figure out that chicken and egg thing? 152 00:08:44,200 --> 00:08:46,840 Speaker 3: Well, you know, the fundamental question is DNA is the 153 00:08:46,840 --> 00:08:52,000 Speaker 3: code of life, yet it is replicated by proteins that 154 00:08:52,040 --> 00:08:55,520 Speaker 3: are encoded by DNA, So sort of sets up this 155 00:08:55,720 --> 00:08:59,440 Speaker 3: conundrum of you know, which came first. And some scientists 156 00:08:59,480 --> 00:09:02,600 Speaker 3: think that in fact neither one. It was really RNA 157 00:09:02,679 --> 00:09:05,880 Speaker 3: molecules that might have had the ability to both encode information, 158 00:09:06,040 --> 00:09:10,760 Speaker 3: which they do, but also copy information originally, and that 159 00:09:10,760 --> 00:09:14,440 Speaker 3: that union of activities in one molecule could have given 160 00:09:14,559 --> 00:09:16,280 Speaker 3: rise to early life. 161 00:09:16,559 --> 00:09:18,240 Speaker 1: Yeah. I mean, the main thing is to be able 162 00:09:18,280 --> 00:09:21,200 Speaker 1: to replicate itself, which I guess is what distinguishes a 163 00:09:21,320 --> 00:09:26,000 Speaker 1: rock from life, right. Yeah. And so one of the 164 00:09:26,000 --> 00:09:28,160 Speaker 1: things I say in the book, but you've pushed back on, 165 00:09:28,280 --> 00:09:32,760 Speaker 1: but I'll let you do it again, is when you 166 00:09:32,880 --> 00:09:35,280 Speaker 1: look at when you were doing that, say in the 167 00:09:35,400 --> 00:09:40,800 Speaker 1: nineteen nineties, approximately all the men, including dear Francis Collins 168 00:09:40,880 --> 00:09:43,319 Speaker 1: I think I see here and others. They're doing the 169 00:09:43,440 --> 00:09:51,400 Speaker 1: Human Genome project, Eric Lander. Anyway, it's a very alpha 170 00:09:51,440 --> 00:09:55,240 Speaker 1: male thing to figure out DNA and to sequence it 171 00:09:55,280 --> 00:09:59,520 Speaker 1: by two thousand and yet if I look, there were 172 00:09:59,679 --> 00:10:02,480 Speaker 1: almost there's no women on that project, but women like 173 00:10:02,559 --> 00:10:07,440 Speaker 1: you Jillian Banfield Emano Chopantier. All are focusing on the 174 00:10:07,600 --> 00:10:11,800 Speaker 1: sibling or cousin molecule RNA. Why is that? And was 175 00:10:11,840 --> 00:10:14,640 Speaker 1: that a gender thing or just happenstance? 176 00:10:14,760 --> 00:10:15,840 Speaker 3: No, happenstance. 177 00:10:15,960 --> 00:10:20,120 Speaker 1: Okay, but you played soccer and you said you always 178 00:10:20,200 --> 00:10:22,240 Speaker 1: knew to run where the ball was going, not where 179 00:10:22,240 --> 00:10:25,160 Speaker 1: the ball was and I figured that was part of 180 00:10:25,240 --> 00:10:26,640 Speaker 1: what you got you an RNA. 181 00:10:28,120 --> 00:10:32,840 Speaker 3: Hmmm. I think what got me into RNA, just frankly, 182 00:10:32,960 --> 00:10:36,920 Speaker 3: was just curiosity and this question about its role possibly 183 00:10:37,080 --> 00:10:41,360 Speaker 3: in evolution that I found so fascinating. Also in the 184 00:10:41,440 --> 00:10:43,040 Speaker 3: Crisper pathway. 185 00:10:42,800 --> 00:10:46,920 Speaker 1: Well, now that we're talking about Crisper, I'll start to 186 00:10:47,000 --> 00:10:50,160 Speaker 1: give it, which means it's clustered repeated sequences that are 187 00:10:50,200 --> 00:10:52,480 Speaker 1: in the back Chiera, You almost got it right. Yeah, 188 00:10:52,480 --> 00:10:56,000 Speaker 1: well I'm not going to do it clustered repeated inter's 189 00:10:56,120 --> 00:11:00,480 Speaker 1: verse palandropic. By the way, it was a whatever it's 190 00:11:00,480 --> 00:11:03,080 Speaker 1: called where somebody comes up with a name and then 191 00:11:03,160 --> 00:11:04,880 Speaker 1: tries to come up with the words that will spell 192 00:11:04,920 --> 00:11:07,560 Speaker 1: the name. Wasn't it the true that he is said, Okay, 193 00:11:07,559 --> 00:11:09,240 Speaker 1: I'm going to call it Crisper, and then he had 194 00:11:09,280 --> 00:11:11,679 Speaker 1: to figure out what with the cr I we want 195 00:11:11,679 --> 00:11:14,319 Speaker 1: a nice acronym. Yeah, it was a nice acronym. It's 196 00:11:14,360 --> 00:11:17,480 Speaker 1: called a backronym or something where you go backwards to 197 00:11:17,520 --> 00:11:21,880 Speaker 1: get the acronym. But what it is is repeated sequences 198 00:11:22,600 --> 00:11:29,680 Speaker 1: in the genetics of a bacteria. Explain why bacteria would 199 00:11:29,720 --> 00:11:32,000 Speaker 1: waste a lot of time repeating sequences. 200 00:11:32,720 --> 00:11:36,400 Speaker 3: Well, what's interesting is that it's it's really as you said, 201 00:11:36,400 --> 00:11:40,840 Speaker 3: it's a it's a series of repeated sequences of DNA. 202 00:11:40,880 --> 00:11:43,280 Speaker 3: So you probably all know that DNA is a four 203 00:11:43,400 --> 00:11:47,800 Speaker 3: letter code and it is the you know, spelling out 204 00:11:47,840 --> 00:11:52,520 Speaker 3: all kinds of molecular information that are required for cells 205 00:11:52,520 --> 00:11:56,800 Speaker 3: to function. But how to cells mark a particular set 206 00:11:56,840 --> 00:11:59,360 Speaker 3: of sequences so they know what to do with them. 207 00:11:59,520 --> 00:12:02,959 Speaker 3: And what this is What happens in crisper sequences is 208 00:12:03,000 --> 00:12:07,000 Speaker 3: that there's a repetitive region in the DNA that tells 209 00:12:07,040 --> 00:12:10,400 Speaker 3: the cell this part is special. This is where I'm 210 00:12:10,440 --> 00:12:13,839 Speaker 3: storing information about viruses that are infecting me over time. 211 00:12:14,200 --> 00:12:16,439 Speaker 3: So it creates a genetic vaccination card. 212 00:12:17,280 --> 00:12:21,400 Speaker 1: It's a little bit like bugshots. They say, hey, this 213 00:12:21,440 --> 00:12:25,080 Speaker 1: one attacked me before exactly, and we didn't quite know 214 00:12:25,240 --> 00:12:28,920 Speaker 1: we might need that as a human species. While you 215 00:12:28,960 --> 00:12:31,440 Speaker 1: were doing it right, I mean we were going to 216 00:12:31,440 --> 00:12:32,959 Speaker 1: be hit by viruses that way. 217 00:12:33,679 --> 00:12:35,840 Speaker 3: Well, we always get hit by viruses, of course, but 218 00:12:36,280 --> 00:12:40,520 Speaker 3: humans don't have a crisper system. They do immunity differently. 219 00:12:40,600 --> 00:12:43,640 Speaker 3: But in bacteria, this is a very effective way in 220 00:12:43,720 --> 00:12:46,880 Speaker 3: real time for cells to acquire immunity to viruses and 221 00:12:46,920 --> 00:12:48,560 Speaker 3: then use it to protect themselves. 222 00:12:48,960 --> 00:12:51,480 Speaker 1: Well, they've been at it longer than we have, meaning 223 00:12:51,720 --> 00:12:55,199 Speaker 1: bacteria have been fighting viruses for four billion years or 224 00:12:55,240 --> 00:12:58,199 Speaker 1: so roughly. Yeah, and so is this an evolutionary thing 225 00:12:58,240 --> 00:13:00,000 Speaker 1: that the smart bacteria figured out? 226 00:13:00,160 --> 00:13:02,040 Speaker 3: Yeah? 227 00:13:02,080 --> 00:13:06,520 Speaker 1: And uh so where did so? Jillian Banfield calls you up? 228 00:13:06,920 --> 00:13:08,000 Speaker 1: Take the story from there. 229 00:13:09,080 --> 00:13:11,000 Speaker 3: Yeah, she called me on the phone. This was in 230 00:13:11,000 --> 00:13:18,600 Speaker 3: the days before we were all, you know, texting each other. Yeah. Yeah, 231 00:13:17,880 --> 00:13:21,000 Speaker 3: And we met at the Free Speech Movement Cafe at 232 00:13:21,040 --> 00:13:25,040 Speaker 3: Berkeley in a quintessential place, and she arrived with a big, 233 00:13:25,120 --> 00:13:27,439 Speaker 3: you know, stack of papers and she said, Jennifer, I've 234 00:13:27,440 --> 00:13:32,800 Speaker 3: got we just noticed something fascinating in these bacterial genomic 235 00:13:32,880 --> 00:13:35,200 Speaker 3: sequences and we don't know what it means. But she 236 00:13:35,360 --> 00:13:40,960 Speaker 3: showed me these signatures of repetitive DNA elements that flanked 237 00:13:41,320 --> 00:13:44,920 Speaker 3: unique sequences that came from viruses, and so the question 238 00:13:45,080 --> 00:13:48,839 Speaker 3: was why why would bacteria be storing little pieces of 239 00:13:49,000 --> 00:13:54,040 Speaker 3: viral DNA in their genome? That was the question. And 240 00:13:54,120 --> 00:13:56,640 Speaker 3: she was so passionate and so excited about this that 241 00:13:56,679 --> 00:13:58,400 Speaker 3: I couldn't help but you know, be drawn to it. 242 00:13:58,840 --> 00:14:02,360 Speaker 1: And how did that start leading to a ged editing tool. 243 00:14:03,320 --> 00:14:07,720 Speaker 3: Well, that led to a whole project that initiated in 244 00:14:07,760 --> 00:14:11,800 Speaker 3: our lab biochemically to figure out how these sequences might 245 00:14:11,840 --> 00:14:14,679 Speaker 3: be protecting bugs. And what we figured out, and this 246 00:14:14,720 --> 00:14:17,040 Speaker 3: is the royal we with other people working in the 247 00:14:17,080 --> 00:14:21,760 Speaker 3: field as well, is that these crisper sequences encode molecules 248 00:14:21,760 --> 00:14:26,200 Speaker 3: of RNA that provide the molecular zip codes that tell 249 00:14:26,240 --> 00:14:29,560 Speaker 3: proteins that are also part of the crisper pathway where 250 00:14:29,600 --> 00:14:32,280 Speaker 3: to go and what to cut. And so what they 251 00:14:32,360 --> 00:14:35,360 Speaker 3: do in these cells is they cut up viral DNA 252 00:14:35,480 --> 00:14:38,640 Speaker 3: that gets into the cell and prevent it from causing 253 00:14:38,680 --> 00:14:39,240 Speaker 3: an infection. 254 00:14:39,840 --> 00:14:43,760 Speaker 1: So these are proteins sometimes called enzymes in this case, right, 255 00:14:44,040 --> 00:14:46,560 Speaker 1: that know how to cut. They're just like scissors, but 256 00:14:46,600 --> 00:14:49,800 Speaker 1: they're made up molecules, right, And so you see it 257 00:14:49,840 --> 00:14:52,840 Speaker 1: cuts DNA. When does it occur to you? Oh? Wait, 258 00:14:53,120 --> 00:14:58,320 Speaker 1: if I can cut and paste DNA, I can edit genes. 259 00:14:58,640 --> 00:15:01,760 Speaker 3: Well, you know, Walter, I remember this morning in sitting 260 00:15:01,760 --> 00:15:04,720 Speaker 3: in my office in Berkeley, when Martin Yeneck, who was 261 00:15:04,760 --> 00:15:08,400 Speaker 3: the scientist in Berkeley working on this project, came into 262 00:15:08,440 --> 00:15:11,000 Speaker 3: my office and he said, Jennifer, you know, we figured 263 00:15:11,040 --> 00:15:15,480 Speaker 3: out that this protein called Crisper CAST nine is an 264 00:15:15,640 --> 00:15:20,600 Speaker 3: RNA guided enzyme that has the ability to recognize viral 265 00:15:20,800 --> 00:15:24,360 Speaker 3: DNA that matches the little letter sequence in these RNA 266 00:15:24,400 --> 00:15:27,800 Speaker 3: molecules and make a double stranded DNA cut just like 267 00:15:27,840 --> 00:15:30,640 Speaker 3: you would cut a rope. And when we looked at 268 00:15:30,640 --> 00:15:34,920 Speaker 3: the data, we realized that we had in our hands 269 00:15:34,960 --> 00:15:39,160 Speaker 3: the knowledge of how to reprogram these cast nine proteins 270 00:15:39,160 --> 00:15:41,920 Speaker 3: so they would cut DNA where we wanted. And if 271 00:15:41,920 --> 00:15:45,040 Speaker 3: that one could do that, you could trigger DNA repair 272 00:15:45,720 --> 00:15:48,320 Speaker 3: in other cell types like plant or animal or even 273 00:15:48,360 --> 00:15:52,160 Speaker 3: human cells to make targeted changes in the genome. And 274 00:15:52,200 --> 00:15:54,680 Speaker 3: this is you know, it was really the synthesis of 275 00:15:54,720 --> 00:15:57,480 Speaker 3: a lot of other scientists work in the field. But 276 00:15:57,640 --> 00:16:01,040 Speaker 3: realizing putting all of those pieces together with our knowledge 277 00:16:01,040 --> 00:16:04,640 Speaker 3: of this Crisper enzyme made us recognize that we were 278 00:16:04,680 --> 00:16:07,040 Speaker 3: probably sitting on a very powerful technology. 279 00:16:07,320 --> 00:16:10,640 Speaker 1: And what did you think at first that this ability 280 00:16:10,720 --> 00:16:16,000 Speaker 1: to edit DNA DNA and humans would be good for. 281 00:16:17,400 --> 00:16:19,840 Speaker 3: All kinds of things. I mean, people were already able 282 00:16:20,120 --> 00:16:25,120 Speaker 3: to use earlier forms of genome engineering to make targeted 283 00:16:25,240 --> 00:16:28,680 Speaker 3: changes in DNA. So imagine that you could, you know, 284 00:16:28,840 --> 00:16:32,320 Speaker 3: perturb a gene and understand its function, or maybeturb a 285 00:16:32,320 --> 00:16:35,320 Speaker 3: whole set of genes. But even beyond that, what if 286 00:16:35,320 --> 00:16:39,080 Speaker 3: you could actually change a DNA sequence to correct a 287 00:16:39,120 --> 00:16:42,080 Speaker 3: disease causing mutation. I think that was really one of 288 00:16:42,120 --> 00:16:44,040 Speaker 3: the things that first attracted our attention. 289 00:16:44,280 --> 00:16:47,800 Speaker 1: Well give us, I think when maybe the simplest which 290 00:16:47,840 --> 00:16:51,680 Speaker 1: is sickle sell anemia, is just a one letter mess 291 00:16:51,760 --> 00:16:56,920 Speaker 1: up right, a typo, and it's you, Now your technology 292 00:16:57,000 --> 00:16:58,080 Speaker 1: has done what with that? 293 00:16:58,800 --> 00:17:01,440 Speaker 3: Right? Well, this is a disease that's been characterized for 294 00:17:01,680 --> 00:17:04,639 Speaker 3: understood for a long time at the genetic level, but 295 00:17:05,280 --> 00:17:09,920 Speaker 3: it was impossible to cure it certainly, and not trivial 296 00:17:09,920 --> 00:17:12,040 Speaker 3: to treat. And of course, if you know anyone with 297 00:17:12,119 --> 00:17:16,760 Speaker 3: sickle cell disease, you know that it's a terrible disorder 298 00:17:16,800 --> 00:17:20,520 Speaker 3: that causes repetitive cycles of crisis where patients have to 299 00:17:20,520 --> 00:17:23,479 Speaker 3: get blood transfusions. That's really the only way they can 300 00:17:23,520 --> 00:17:27,400 Speaker 3: be treated up until till Crisper came along. But with Crisper, 301 00:17:27,720 --> 00:17:32,000 Speaker 3: it's now possible to override that mutation and give patients 302 00:17:32,040 --> 00:17:35,880 Speaker 3: back a normal blood supply, which means that they're free 303 00:17:35,920 --> 00:17:37,720 Speaker 3: of these repetitive crises. 304 00:17:37,960 --> 00:17:41,440 Speaker 1: How big of a deal is it to cross the 305 00:17:41,480 --> 00:17:44,600 Speaker 1: line between doing that and a patient and doing that 306 00:17:44,840 --> 00:17:51,000 Speaker 1: in the I'll say, the inheritable genetics of a patient, 307 00:17:51,160 --> 00:17:54,919 Speaker 1: so that the children and grandchildren will have been edited. 308 00:17:55,359 --> 00:17:58,439 Speaker 3: Well, now you're talking about something that I think is 309 00:17:58,560 --> 00:18:02,640 Speaker 3: really interesting and fundamental about a technology like Crisper, which 310 00:18:02,680 --> 00:18:06,359 Speaker 3: is that it enables making targeted changes in the DNA 311 00:18:06,440 --> 00:18:09,639 Speaker 3: of an individual, as is being done currently for sickle 312 00:18:09,680 --> 00:18:12,800 Speaker 3: cell treatments, but in principle it could also be done 313 00:18:12,880 --> 00:18:16,399 Speaker 3: in embryos, where it creates a change in DNA that 314 00:18:16,440 --> 00:18:19,280 Speaker 3: can be passed on to future generations. We call that 315 00:18:19,359 --> 00:18:22,440 Speaker 3: a heritable change, and to me, that's really kind of 316 00:18:22,440 --> 00:18:23,440 Speaker 3: in a different category. 317 00:18:23,800 --> 00:18:26,440 Speaker 1: One guy who has done it in China, Hejuang Ki, 318 00:18:26,600 --> 00:18:30,720 Speaker 1: who visited some of your seminars out at Cold Spring Harbor. 319 00:18:31,720 --> 00:18:35,920 Speaker 1: He's the only person who has crossed that line, right, 320 00:18:36,440 --> 00:18:38,040 Speaker 1: or the only person we know that we know of, 321 00:18:38,400 --> 00:18:42,359 Speaker 1: and even China punished him. It's like, okay, because you 322 00:18:42,520 --> 00:18:47,120 Speaker 1: help get a consensus around the world, let's not cross 323 00:18:47,200 --> 00:18:53,480 Speaker 1: the line of inheritance or heritable gene editing. Do you 324 00:18:53,560 --> 00:18:57,440 Speaker 1: think that should tell me about that line and how 325 00:18:57,480 --> 00:18:59,320 Speaker 1: it can hold well? 326 00:18:59,320 --> 00:19:02,040 Speaker 3: I think the you know, the current state of the field, 327 00:19:02,119 --> 00:19:05,520 Speaker 3: this is true even now, is that there's very little 328 00:19:05,560 --> 00:19:11,199 Speaker 3: information about how genomediting would actually work in embryos, to 329 00:19:11,240 --> 00:19:14,959 Speaker 3: the point where it's really not I think, technically safe 330 00:19:15,040 --> 00:19:19,280 Speaker 3: to use it in that setting. So many scientists think 331 00:19:19,320 --> 00:19:22,159 Speaker 3: that it's irresponsible to proceed with that kind of an 332 00:19:22,160 --> 00:19:26,600 Speaker 3: application until we have really vetted the technology and also 333 00:19:26,720 --> 00:19:30,560 Speaker 3: determined under what circumstances, in which conditions would it really 334 00:19:30,600 --> 00:19:32,320 Speaker 3: be the right way to proceed. 335 00:19:32,960 --> 00:19:36,120 Speaker 1: But in some ways that begs the larger moral question, 336 00:19:36,600 --> 00:19:40,280 Speaker 1: which is suppose it was something you could technically do, 337 00:19:40,520 --> 00:19:43,200 Speaker 1: as you can easily imagine that in five or ten years, 338 00:19:43,560 --> 00:19:46,600 Speaker 1: we'll be able to do it without you know, mistakes 339 00:19:46,760 --> 00:19:51,320 Speaker 1: or hallucinations as we call them. An ai a lot 340 00:19:51,320 --> 00:19:52,960 Speaker 1: to let me tell you a story. When I was 341 00:19:53,000 --> 00:19:57,480 Speaker 1: doing it, the book about You and gene editing, there 342 00:19:57,560 --> 00:19:59,720 Speaker 1: was a young kid I'm blanking on. His name is 343 00:19:59,760 --> 00:20:03,159 Speaker 1: Pick in the book, and he loved playing basketball, except 344 00:20:03,160 --> 00:20:05,280 Speaker 1: for when he crumpled over on the floor because he 345 00:20:05,320 --> 00:20:09,120 Speaker 1: had sickle self. David Sanchez, David Sanchez, And so he's 346 00:20:09,200 --> 00:20:13,480 Speaker 1: working in the Bay Area being treated and one of 347 00:20:13,560 --> 00:20:15,199 Speaker 1: them says, you know, we'll be able to edit this 348 00:20:15,280 --> 00:20:18,800 Speaker 1: out someday. In fact, we'll edit it out so that 349 00:20:19,560 --> 00:20:23,240 Speaker 1: neither you nor your children or anybody grandchildren will ever 350 00:20:23,320 --> 00:20:25,879 Speaker 1: have it. And he said, that's great. And then he 351 00:20:25,920 --> 00:20:28,919 Speaker 1: comes back and he says, well, wait, shouldn't that be 352 00:20:29,040 --> 00:20:33,040 Speaker 1: my child's decision. They said, well, didn't you hate happening? 353 00:20:33,080 --> 00:20:37,080 Speaker 1: He said, yeah, but there were things I learned, including 354 00:20:37,240 --> 00:20:40,200 Speaker 1: resilience and getting up off the floor when I fell down. 355 00:20:40,920 --> 00:20:46,880 Speaker 1: That maybe we should be careful about editing for future generations. 356 00:20:47,280 --> 00:20:50,320 Speaker 1: So that seems to me the larger issue than can 357 00:20:50,359 --> 00:20:51,600 Speaker 1: we technically get it right. 358 00:20:52,480 --> 00:20:55,920 Speaker 3: Yeah, I agree. It's a really moving section in the 359 00:20:56,560 --> 00:20:58,720 Speaker 3: story of David. You know, David. His story was told 360 00:20:58,760 --> 00:21:03,959 Speaker 3: in the film to Nature documentary, and it's it's a 361 00:21:04,000 --> 00:21:06,960 Speaker 3: fascinating reflection that he has, even as a young boy, 362 00:21:07,240 --> 00:21:10,760 Speaker 3: about what is it that truly makes us individuals, makes 363 00:21:10,840 --> 00:21:13,800 Speaker 3: us who we are? And I think he, you know, 364 00:21:14,400 --> 00:21:17,840 Speaker 3: really appreciated the fact that, you know, his disease had 365 00:21:19,000 --> 00:21:21,480 Speaker 3: you know, it was a terrible, terrible disease, and I'm 366 00:21:21,480 --> 00:21:24,560 Speaker 3: sure he wouldn't wish it on anyone. But he also 367 00:21:24,680 --> 00:21:27,720 Speaker 3: reflected that it had helped to shape him as a 368 00:21:27,880 --> 00:21:30,439 Speaker 3: as a person and that he would be different without it. 369 00:21:30,480 --> 00:21:32,920 Speaker 3: So you know, it raises an interesting challenge. 370 00:21:33,280 --> 00:21:36,080 Speaker 1: Yeah, but we've always had those child I mean, I'm 371 00:21:36,119 --> 00:21:40,119 Speaker 1: not sure Jonas Sack or Saban people would say, wait, 372 00:21:40,359 --> 00:21:43,639 Speaker 1: if Franklin Roosevelt hadn't gotten polio, he would remain to 373 00:21:43,640 --> 00:21:47,720 Speaker 1: an Upper East Side playboy. So let's keep polio. 374 00:21:49,600 --> 00:21:50,640 Speaker 3: Interesting example. 375 00:21:50,760 --> 00:21:53,800 Speaker 1: Yeah, and from Darris Khan's good one, who's one of 376 00:21:53,840 --> 00:21:57,480 Speaker 1: my next interviews here, But because she did the polio's 377 00:21:57,520 --> 00:22:04,080 Speaker 1: effect on Franklin Roosevelt up. But do you let's go 378 00:22:04,160 --> 00:22:08,000 Speaker 1: back to sickle cell. You can change the letter so 379 00:22:08,119 --> 00:22:11,880 Speaker 1: the cells aren't sickled in theory. I know we haven't 380 00:22:11,880 --> 00:22:14,720 Speaker 1: done in prices. You could probably change it so that 381 00:22:14,760 --> 00:22:19,720 Speaker 1: the cells carry more oxygen rather than less more oxygen 382 00:22:19,880 --> 00:22:23,000 Speaker 1: than on average. And you could edit so that your 383 00:22:23,080 --> 00:22:28,400 Speaker 1: children would win the Olympics or be sprinters. Is that 384 00:22:28,760 --> 00:22:30,120 Speaker 1: morally acceptable? 385 00:22:31,119 --> 00:22:34,320 Speaker 3: Well, let's just first point out that you could only 386 00:22:34,359 --> 00:22:36,560 Speaker 3: do those things if you knew which genes to edit 387 00:22:36,600 --> 00:22:39,440 Speaker 3: and which mutations to make. But you know, if we say, 388 00:22:39,480 --> 00:22:41,800 Speaker 3: for the sake of argument, suppose you did know those things, 389 00:22:42,880 --> 00:22:46,159 Speaker 3: I think you're right. It brings up a very important 390 00:22:46,440 --> 00:22:48,880 Speaker 3: question we all have to grapple with, because I think 391 00:22:48,920 --> 00:22:51,800 Speaker 3: this technology will be capable of making those kinds of 392 00:22:51,960 --> 00:22:55,080 Speaker 3: changes in embryos in the not probably not distant future, 393 00:22:55,640 --> 00:22:58,200 Speaker 3: and we have to decide is that are we okay 394 00:22:58,200 --> 00:22:58,520 Speaker 3: with them? 395 00:22:58,880 --> 00:23:00,879 Speaker 1: And one of the things I had tired about Jennifer 396 00:23:00,880 --> 00:23:03,800 Speaker 1: and why I wanted to pick her as the subject. 397 00:23:03,840 --> 00:23:05,560 Speaker 1: I didn't know you're going to win the Nobel Prize, 398 00:23:05,560 --> 00:23:12,040 Speaker 1: which helped the book a bit, but was that once 399 00:23:12,440 --> 00:23:16,399 Speaker 1: you do this and you have this tool, you start 400 00:23:16,640 --> 00:23:19,840 Speaker 1: worrying about these questions. And in the history of science 401 00:23:20,640 --> 00:23:25,000 Speaker 1: we have so many examples, Oppenheimer. The movie is somewhat 402 00:23:25,040 --> 00:23:28,679 Speaker 1: about it, which is the Prometheus problem, and that we 403 00:23:28,760 --> 00:23:31,960 Speaker 1: have snatched a technology from the gods and who knows 404 00:23:32,080 --> 00:23:35,480 Speaker 1: what we're going to do with it, and early on 405 00:23:35,720 --> 00:23:39,520 Speaker 1: in biotechnology, I'll call it a bioengineering. There was a 406 00:23:39,560 --> 00:23:44,240 Speaker 1: group called the Asilomar Group in California, that said okay, 407 00:23:44,320 --> 00:23:47,160 Speaker 1: this could be dangerous, and they met a few times 408 00:23:47,400 --> 00:23:49,439 Speaker 1: and said, we don't want government to regulate it. We 409 00:23:49,480 --> 00:23:51,400 Speaker 1: don't want to let the genie out of the bottle, 410 00:23:51,880 --> 00:23:55,440 Speaker 1: and they did that process. And what impressed me about 411 00:23:55,520 --> 00:24:01,080 Speaker 1: you is that when you got this discovery and technology right, 412 00:24:01,600 --> 00:24:05,800 Speaker 1: you almost replicated right the Asilomar process. So you said, okay, 413 00:24:05,880 --> 00:24:08,439 Speaker 1: let's try it again for this Well. 414 00:24:08,480 --> 00:24:11,960 Speaker 3: I really admired that scientists in the seventies had grappled 415 00:24:12,040 --> 00:24:17,280 Speaker 3: with these sort of fundamental ethical questions about biotechnology. In 416 00:24:17,280 --> 00:24:20,600 Speaker 3: that case, they were looking at examples of modification of 417 00:24:20,680 --> 00:24:23,000 Speaker 3: bugs that live, you know, bacteria that live in the 418 00:24:23,040 --> 00:24:27,520 Speaker 3: human body, and wondering whether there could be health risks 419 00:24:28,000 --> 00:24:30,920 Speaker 3: to making those kinds of modifications that had become possible. 420 00:24:31,520 --> 00:24:35,680 Speaker 3: So we actually contacted Paul Berg and David Baltimore, two 421 00:24:35,720 --> 00:24:39,080 Speaker 3: of the scientists who were some of the leaders of 422 00:24:39,119 --> 00:24:42,920 Speaker 3: those original group discussions in the nineteen seventies, and they 423 00:24:42,960 --> 00:24:45,480 Speaker 3: came to an early conference we had and I think 424 00:24:45,520 --> 00:24:50,640 Speaker 3: it was twenty fourteen, to discuss the ethics of crisper 425 00:24:50,760 --> 00:24:52,520 Speaker 3: and how we should think about it and how we 426 00:24:52,560 --> 00:24:55,040 Speaker 3: should proceed as a scientific community. 427 00:24:54,680 --> 00:24:57,520 Speaker 1: And how did you enlist it, because if it was 428 00:24:57,600 --> 00:25:02,359 Speaker 1: only US scientists could tail it, then we'd fall behind, 429 00:25:02,359 --> 00:25:04,560 Speaker 1: as like the AI argument, let's I could tail it 430 00:25:04,600 --> 00:25:07,520 Speaker 1: here because China will do it. How did you try 431 00:25:07,560 --> 00:25:08,800 Speaker 1: to make it international? 432 00:25:09,080 --> 00:25:11,600 Speaker 3: Well, we reached out to scientists and other countries, including 433 00:25:11,600 --> 00:25:16,800 Speaker 3: in China. We got the scientific academies involved, and this 434 00:25:17,040 --> 00:25:20,320 Speaker 3: was I think really critical to bringing together a global 435 00:25:20,359 --> 00:25:24,480 Speaker 3: community of people who could think together about how to proceed. 436 00:25:24,359 --> 00:25:28,800 Speaker 1: And what lines have you sketched out or drawn on 437 00:25:28,960 --> 00:25:32,280 Speaker 1: the use of this technology that have been agreed to 438 00:25:32,520 --> 00:25:33,800 Speaker 1: at least a consensus. 439 00:25:33,960 --> 00:25:36,359 Speaker 3: Well, I think one of the real challenges with something 440 00:25:36,440 --> 00:25:38,320 Speaker 3: like this is that it's I mean, you know, we 441 00:25:38,359 --> 00:25:41,439 Speaker 3: didn't say this earlier, but maybe folks here understand this already. 442 00:25:41,440 --> 00:25:44,360 Speaker 3: But you know, the thing that's so powerful about crisper 443 00:25:44,400 --> 00:25:48,439 Speaker 3: really is that it's not difficult to use, and so 444 00:25:48,520 --> 00:25:51,000 Speaker 3: that's meant that it could you know, take off very 445 00:25:51,080 --> 00:25:54,159 Speaker 3: quickly as a powerful tool. But the flip side is 446 00:25:54,200 --> 00:25:58,159 Speaker 3: that it's you know, it's kind of readily deployable for 447 00:25:58,200 --> 00:26:02,480 Speaker 3: these other purposes. And in the case of the meeting 448 00:26:02,520 --> 00:26:05,800 Speaker 3: that we had in California to discuss the you know, 449 00:26:05,880 --> 00:26:08,200 Speaker 3: kind of the early days of Crisper and the ethics 450 00:26:08,200 --> 00:26:11,560 Speaker 3: of it. We really wanted to make sure that scientists 451 00:26:11,560 --> 00:26:14,600 Speaker 3: would get on board with the you know, kind of 452 00:26:14,359 --> 00:26:17,720 Speaker 3: the responsibility that we thought we all had, and so 453 00:26:17,880 --> 00:26:22,240 Speaker 3: our approach has been to publish articles about this, to 454 00:26:22,320 --> 00:26:26,800 Speaker 3: get the World Health Organization involved in creating a registry 455 00:26:26,840 --> 00:26:30,040 Speaker 3: where scientists can be very transparent about work that they're doing, 456 00:26:30,040 --> 00:26:34,440 Speaker 3: and also to get the scientific journals involved in ensuring 457 00:26:34,520 --> 00:26:38,480 Speaker 3: that work that gets submitted for publication is reviewed with 458 00:26:38,560 --> 00:26:40,600 Speaker 3: a lens on ethics. 459 00:26:42,440 --> 00:26:45,080 Speaker 2: Coming up after a break, Isaacson and down to discuss 460 00:26:45,119 --> 00:26:47,639 Speaker 2: what it takes to keep doing scientific research in a 461 00:26:47,680 --> 00:26:51,240 Speaker 2: fraud political moment, and how AI and Crisper are joining 462 00:26:51,240 --> 00:26:52,840 Speaker 2: forces stay with us. 463 00:27:01,080 --> 00:27:03,399 Speaker 1: How worried are you or is there a reason to 464 00:27:03,480 --> 00:27:07,960 Speaker 1: worry about what's happening now in Washington, both with Robert 465 00:27:08,040 --> 00:27:12,120 Speaker 1: Kennedy Junior at h JASS and the government in terms 466 00:27:12,200 --> 00:27:16,840 Speaker 1: of first of all, let's talk about regulations and science. 467 00:27:17,960 --> 00:27:19,800 Speaker 3: Well, it's you know, it's an interesting thing. I mean, 468 00:27:19,880 --> 00:27:23,080 Speaker 3: you know, there's a there's kind of two sides to 469 00:27:23,480 --> 00:27:26,119 Speaker 3: that coin. On the one hand, I think we appreciate 470 00:27:26,160 --> 00:27:29,320 Speaker 3: the importance of regulations and especially when we think about 471 00:27:29,640 --> 00:27:32,840 Speaker 3: approving drugs that are going to be used in US 472 00:27:32,920 --> 00:27:34,960 Speaker 3: or our kids. We want them to be safe, we 473 00:27:35,000 --> 00:27:37,480 Speaker 3: want them to be effective, and that's the job of 474 00:27:37,520 --> 00:27:41,040 Speaker 3: the Food and Drug Administration in the US. The flip 475 00:27:41,080 --> 00:27:44,400 Speaker 3: side is that if you have too many regulations or 476 00:27:44,600 --> 00:27:47,040 Speaker 3: regulations that don't really make sense, then that can slow 477 00:27:47,080 --> 00:27:49,399 Speaker 3: down the process. And I think we all I certainly 478 00:27:49,400 --> 00:27:52,080 Speaker 3: have seen, you know, both sides of that. So it's 479 00:27:52,359 --> 00:27:54,159 Speaker 3: you know, it's a it's a delicate balance. How do 480 00:27:54,160 --> 00:27:57,760 Speaker 3: you get the regulation regulations right so that they do 481 00:27:57,800 --> 00:27:59,719 Speaker 3: what you want them to do and protect us but 482 00:27:59,800 --> 00:28:01,200 Speaker 3: not impede progress. 483 00:28:01,680 --> 00:28:04,400 Speaker 1: And give me some examples where you've taken it from 484 00:28:04,400 --> 00:28:08,720 Speaker 1: the lab to the bedside in a way by creating 485 00:28:08,800 --> 00:28:11,240 Speaker 1: commercial companies to develop drugs. 486 00:28:11,400 --> 00:28:13,240 Speaker 3: Well, this is the thing. I mean, companies play a 487 00:28:13,400 --> 00:28:17,679 Speaker 3: very important role in that pipeline. Academic scientists are great 488 00:28:17,680 --> 00:28:20,560 Speaker 3: at innovation. We love our students coming in with their 489 00:28:20,640 --> 00:28:24,040 Speaker 3: ideas and having the freedom to pursue things. And that's 490 00:28:24,080 --> 00:28:27,040 Speaker 3: really I think what gave rise to something like Crisper. 491 00:28:27,680 --> 00:28:32,240 Speaker 3: But when it comes to expanding on an idea to 492 00:28:32,320 --> 00:28:37,440 Speaker 3: the point where it can be globally accessible, academic labs 493 00:28:37,480 --> 00:28:39,320 Speaker 3: are really not appropriate for that. We don't have the 494 00:28:39,320 --> 00:28:41,400 Speaker 3: funding to do it, we don't have the resources and 495 00:28:41,440 --> 00:28:44,640 Speaker 3: the personnel to do it. This is where companies are necessary. 496 00:28:44,880 --> 00:28:46,920 Speaker 3: So I've long believed that, you know, there's a really 497 00:28:47,000 --> 00:28:50,520 Speaker 3: important partnership between academics and companies, and we have to 498 00:28:50,520 --> 00:28:52,400 Speaker 3: figure out how to forge that effectively. 499 00:28:52,600 --> 00:28:57,520 Speaker 1: And Berkeley is very good at allowing people to commercialize 500 00:28:57,680 --> 00:29:02,960 Speaker 1: and to start companies with the intellectual property. Right. No, Okay, 501 00:29:02,960 --> 00:29:06,160 Speaker 1: they're not as good as Chilane, I mean, but yeah, 502 00:29:06,160 --> 00:29:10,680 Speaker 1: oh yeah. 503 00:29:06,560 --> 00:29:11,680 Speaker 3: Yeah, they could be better. But you know, well, I 504 00:29:11,720 --> 00:29:14,840 Speaker 3: think I think there are a number of challenges. You know, 505 00:29:14,920 --> 00:29:17,480 Speaker 3: as we just said, universities are not set up to 506 00:29:17,520 --> 00:29:20,680 Speaker 3: be companies and that means that intrinsically. And I'll just 507 00:29:20,720 --> 00:29:22,840 Speaker 3: speak for my own institution. I don't know about Tulane. 508 00:29:22,880 --> 00:29:25,360 Speaker 3: To Lane maybe much better at this, but you know, 509 00:29:25,560 --> 00:29:32,000 Speaker 3: really figuring out, really figuring out how to foster that connection. 510 00:29:32,200 --> 00:29:35,560 Speaker 3: How do you, for example, how do you train scientists 511 00:29:35,640 --> 00:29:38,120 Speaker 3: that are coming out of academic labs, that are trained 512 00:29:38,160 --> 00:29:41,800 Speaker 3: as academics to be good business people? You know, how 513 00:29:41,800 --> 00:29:44,440 Speaker 3: do you do that? There's not there's no sort of 514 00:29:44,480 --> 00:29:47,680 Speaker 3: easy answer to that. How do you de risk an 515 00:29:47,760 --> 00:29:51,120 Speaker 3: idea so that investors are now willing to put money 516 00:29:51,160 --> 00:29:53,520 Speaker 3: into it where they think they're going to get a return. 517 00:29:53,640 --> 00:29:56,080 Speaker 3: That's also not not easy. So and these are not 518 00:29:56,160 --> 00:29:58,800 Speaker 3: unique to to anyone institution, of course, but you know, 519 00:29:58,840 --> 00:30:01,600 Speaker 3: this was part of the motive for us to establish 520 00:30:01,600 --> 00:30:05,280 Speaker 3: the Innovative Genomics Institute ten years ago, which is a 521 00:30:05,320 --> 00:30:09,120 Speaker 3: partnership between different campuses of the University of California that's 522 00:30:09,160 --> 00:30:14,360 Speaker 3: expressly focused on this kind of smooth pipeline between discovery 523 00:30:14,560 --> 00:30:15,240 Speaker 3: and application. 524 00:30:16,120 --> 00:30:18,600 Speaker 1: That's something that's happened in a few places over the 525 00:30:18,640 --> 00:30:21,760 Speaker 1: past fifty sixty years. I mean, I teach a history 526 00:30:21,760 --> 00:30:25,600 Speaker 1: of technology course here, which is when MIT and Harvard 527 00:30:25,720 --> 00:30:31,000 Speaker 1: resisted the commercialization of the things. Stanford University under Frederick Turman, 528 00:30:31,040 --> 00:30:35,680 Speaker 1: who was the provost, encouraged graduate students starting with Hewlett 529 00:30:35,680 --> 00:30:38,560 Speaker 1: and Packard and ending with Larry Page and so Gay 530 00:30:38,640 --> 00:30:41,960 Speaker 1: Brenn that if you had a good idea, form a company, 531 00:30:42,960 --> 00:30:49,920 Speaker 1: to what extent do you think that process can be improved? 532 00:30:50,880 --> 00:30:53,680 Speaker 3: Well, again, I think that we need to do better. 533 00:30:54,000 --> 00:30:56,000 Speaker 3: I'm speaking to myself here, really, you know, we need 534 00:30:56,040 --> 00:30:58,680 Speaker 3: to do better at giving our students the training that 535 00:30:58,720 --> 00:31:02,479 Speaker 3: they need to be effected in business. And you know, 536 00:31:02,640 --> 00:31:04,560 Speaker 3: one thing that's very interesting is that this is I 537 00:31:04,560 --> 00:31:06,680 Speaker 3: see this in my own lab that people that are 538 00:31:06,680 --> 00:31:09,720 Speaker 3: coming out of our labs, some of them are very 539 00:31:09,760 --> 00:31:13,200 Speaker 3: focused on the science and they want to stay that way. 540 00:31:13,320 --> 00:31:17,160 Speaker 3: Others want to, you know, take a different lens to it. 541 00:31:17,200 --> 00:31:19,960 Speaker 3: They are willing to have or maybe even happy to 542 00:31:20,040 --> 00:31:22,480 Speaker 3: let other people do the actual science. What they really 543 00:31:22,520 --> 00:31:24,480 Speaker 3: want to do is they want to think about the 544 00:31:24,520 --> 00:31:27,040 Speaker 3: business model around it. How do you how do you 545 00:31:27,280 --> 00:31:29,840 Speaker 3: how do you expand it? How do you develop it 546 00:31:29,840 --> 00:31:32,720 Speaker 3: in ways that will solve real world problems? And frankly 547 00:31:32,720 --> 00:31:33,400 Speaker 3: you need both. 548 00:31:33,960 --> 00:31:36,040 Speaker 1: I ask all this because we are at two lane 549 00:31:36,040 --> 00:31:40,160 Speaker 1: trying to BUYO Innovation Zone, a two lane innovation institute. 550 00:31:40,240 --> 00:31:44,320 Speaker 1: All of this is happening now. And when you discovered this, 551 00:31:44,440 --> 00:31:47,000 Speaker 1: you had two or three really great graduates. I think 552 00:31:47,120 --> 00:31:49,680 Speaker 1: Lucas was one of O Mecca and you said, Okay, 553 00:31:49,680 --> 00:31:51,960 Speaker 1: we're going to just call a company Mammoth and we're 554 00:31:51,960 --> 00:31:54,800 Speaker 1: going to make T shirts. And they knew how to 555 00:31:54,840 --> 00:31:56,920 Speaker 1: form a company. Last time I was coming in from 556 00:31:56,960 --> 00:32:00,320 Speaker 1: the San Francisco Apple there's a huge building that's Mammath 557 00:32:00,360 --> 00:32:04,360 Speaker 1: pharmacut So explain how you picked the graduate students and said, 558 00:32:04,440 --> 00:32:06,840 Speaker 1: you can form a company and I'll be I guess 559 00:32:06,920 --> 00:32:07,960 Speaker 1: scientific advisor. 560 00:32:08,280 --> 00:32:10,920 Speaker 3: Well, they kind of are self selecting, you know. These 561 00:32:10,920 --> 00:32:14,080 Speaker 3: are often the students who recognize that that's their interest 562 00:32:14,520 --> 00:32:15,880 Speaker 3: and that's what they want to do, and I feel 563 00:32:15,920 --> 00:32:19,320 Speaker 3: like my job is to help them get there. And 564 00:32:19,840 --> 00:32:22,880 Speaker 3: I love, you know, working with people in the lab, 565 00:32:23,040 --> 00:32:25,560 Speaker 3: helping them figure out what they're really good at and 566 00:32:25,600 --> 00:32:27,880 Speaker 3: then do more of that. And so in the case 567 00:32:27,880 --> 00:32:30,640 Speaker 3: that you mentioned with Mammoth Biosciences, that was a wonderful 568 00:32:31,040 --> 00:32:33,720 Speaker 3: situation where there were two graduate students in the lab, 569 00:32:33,800 --> 00:32:37,160 Speaker 3: Lucas Harrington and Janis Chen, who were working together on 570 00:32:37,200 --> 00:32:40,720 Speaker 3: a project. They both recognized that there was an opportunity 571 00:32:40,840 --> 00:32:43,640 Speaker 3: to commercialize it. They wanted to be part of that, 572 00:32:44,320 --> 00:32:47,440 Speaker 3: and they teamed up with another a third student coming 573 00:32:47,440 --> 00:32:51,200 Speaker 3: out of Stanford. Maybe the only history of a Stanford 574 00:32:51,200 --> 00:32:54,280 Speaker 3: Berkeley partnership successful. 575 00:32:55,040 --> 00:32:56,600 Speaker 1: Yeah, right, and be done. 576 00:32:56,440 --> 00:33:00,959 Speaker 3: Can be done but rare, Yeah, And they started Mammoth 577 00:33:01,040 --> 00:33:03,440 Speaker 3: Biosciences and they're going strong. 578 00:33:04,200 --> 00:33:07,880 Speaker 1: Getting back to the policy challenges we have now, we 579 00:33:08,040 --> 00:33:12,640 Speaker 1: talked a little bit about regulation and trying to get 580 00:33:12,720 --> 00:33:16,000 Speaker 1: the balance right. The more pressing ones. I'll start, well, 581 00:33:16,040 --> 00:33:17,520 Speaker 1: there are two of them, I think, but I'll start 582 00:33:17,520 --> 00:33:24,760 Speaker 1: with this NIH funding being cut radically and other NSF 583 00:33:25,080 --> 00:33:29,120 Speaker 1: funding being cut. Is that destroying the seed corn for 584 00:33:29,160 --> 00:33:31,800 Speaker 1: the future inventions like crisper? 585 00:33:32,240 --> 00:33:49,960 Speaker 3: It's not a good idea, you know. I mean, you're. 586 00:33:44,040 --> 00:33:49,360 Speaker 1: Not quite as forceful as Tony Fauci was stronger language 587 00:33:49,360 --> 00:33:50,520 Speaker 1: than not a good idea. 588 00:33:50,560 --> 00:33:54,880 Speaker 3: Well, let me, let me, let me expand so you 589 00:33:54,920 --> 00:33:58,640 Speaker 3: may appreciate that in the United States we are a 590 00:33:58,760 --> 00:34:02,960 Speaker 3: leader around the world world right now in science and technology. 591 00:34:03,200 --> 00:34:08,120 Speaker 3: Why is that? It's because taxpayer money for decades has 592 00:34:08,160 --> 00:34:11,120 Speaker 3: gone into funding the kind of science that we're talking 593 00:34:11,160 --> 00:34:16,000 Speaker 3: about here, you know, curiosity driven science that is asking 594 00:34:16,120 --> 00:34:19,120 Speaker 3: questions about how nature works and then you know, taking 595 00:34:19,160 --> 00:34:21,600 Speaker 3: those key insights that come out of that kind of 596 00:34:21,640 --> 00:34:26,520 Speaker 3: work and turning them into applications. Companies aren't going to 597 00:34:26,600 --> 00:34:29,520 Speaker 3: do that. Why not? It's too risky, right, It's just 598 00:34:29,840 --> 00:34:32,840 Speaker 3: the companies are not going to be able to invest 599 00:34:33,239 --> 00:34:37,360 Speaker 3: in the kind of curiosity driven science that does provide 600 00:34:37,400 --> 00:34:40,080 Speaker 3: that pipeline, but does so in a way that is, 601 00:34:40,160 --> 00:34:44,000 Speaker 3: you know, kind of open ended. And if we cut 602 00:34:44,000 --> 00:34:46,719 Speaker 3: that off, I guarantee that we're going to see a 603 00:34:46,760 --> 00:34:49,800 Speaker 3: big change not only in this country but around the world, 604 00:34:49,840 --> 00:34:54,120 Speaker 3: because right now the United States really drives the discovery 605 00:34:54,160 --> 00:34:56,600 Speaker 3: of all of the not all, but many of the 606 00:34:56,760 --> 00:34:59,040 Speaker 3: of the medicines that we take and the kinds of 607 00:34:59,080 --> 00:35:01,360 Speaker 3: technologies that have had such a huge benefit. 608 00:35:01,960 --> 00:35:04,120 Speaker 1: I would think that if you were an enemy of 609 00:35:04,160 --> 00:35:07,200 Speaker 1: the United States and you wanted to destroy its future, 610 00:35:07,640 --> 00:35:10,160 Speaker 1: one thing you would be doing is say, you know, 611 00:35:10,239 --> 00:35:12,400 Speaker 1: they did the Internet, they did all these things at 612 00:35:12,440 --> 00:35:16,680 Speaker 1: AI all because of these science grant even Larry Page 613 00:35:16,680 --> 00:35:20,359 Speaker 1: and so Gay Brenn On National Science Foundation grants when 614 00:35:20,360 --> 00:35:24,640 Speaker 1: they were graduates. And you say, let's pull all these 615 00:35:24,680 --> 00:35:27,640 Speaker 1: away so that China can be doing it. Do you 616 00:35:27,800 --> 00:35:31,200 Speaker 1: worry that competition a country like China will end up 617 00:35:31,200 --> 00:35:32,920 Speaker 1: being in the foe if we keep this path. Oh? 618 00:35:32,960 --> 00:35:35,760 Speaker 3: I not only worry about it, it's already happening. 619 00:35:35,800 --> 00:35:38,440 Speaker 1: I mean it's well, explain. Give me some example. 620 00:35:38,560 --> 00:35:41,799 Speaker 3: Well, I think we're already seeing scientists being recruited to 621 00:35:41,880 --> 00:35:45,080 Speaker 3: other countries. They've been very, very proactive already about reaching 622 00:35:45,120 --> 00:35:48,720 Speaker 3: out even to people in my lab about job opportunities. 623 00:35:49,239 --> 00:35:52,360 Speaker 3: We're seeing that some universities in the US are already 624 00:35:52,400 --> 00:35:56,320 Speaker 3: cutting back on their graduate training programs due to NIH 625 00:35:56,400 --> 00:35:59,759 Speaker 3: cuts or anticipated cuts, and it's not going to get 626 00:35:59,760 --> 00:36:03,120 Speaker 3: better unless there's a real change in the approach in Washington. 627 00:36:03,320 --> 00:36:06,719 Speaker 1: And one of the related things is this, i'll call 628 00:36:06,760 --> 00:36:10,960 Speaker 1: it cracked down on visas and people who are on 629 00:36:11,120 --> 00:36:15,840 Speaker 1: student visas sometimes getting over I mean, so having foreign 630 00:36:15,960 --> 00:36:21,640 Speaker 1: students studying here, that's going to be harder for them. 631 00:36:21,800 --> 00:36:24,360 Speaker 1: Have you seen any problem with that yet? 632 00:36:24,640 --> 00:36:28,120 Speaker 3: Well, you know, science is really international, and it's international, 633 00:36:28,560 --> 00:36:30,480 Speaker 3: not just in the sense that there are people all 634 00:36:30,520 --> 00:36:34,640 Speaker 3: over the world working on scientific problems, but it's international 635 00:36:34,719 --> 00:36:36,719 Speaker 3: here in the United States in the sense that we 636 00:36:36,760 --> 00:36:40,920 Speaker 3: recruit many of our scientists and our trainees from other countries. 637 00:36:41,239 --> 00:36:43,560 Speaker 3: Why is that, Well, again, it's because the US has 638 00:36:43,560 --> 00:36:46,120 Speaker 3: been a real magnet for them, right, It's attracted them 639 00:36:46,120 --> 00:36:49,440 Speaker 3: to come here because of the wonderful opportunities that they 640 00:36:49,480 --> 00:36:53,279 Speaker 3: have had, and if we stifle that, it's going to 641 00:36:53,280 --> 00:36:54,120 Speaker 3: be a disaster. 642 00:36:54,480 --> 00:36:58,280 Speaker 1: Have you seen stories of researchers who nail are afraid 643 00:36:58,400 --> 00:37:02,080 Speaker 1: because they're not they're visas that they may lose. 644 00:37:02,480 --> 00:37:04,799 Speaker 3: Well, sure, I think that's happening all over and we're 645 00:37:04,800 --> 00:37:08,160 Speaker 3: seeing some frightening examples of students even being pulled off 646 00:37:08,160 --> 00:37:10,000 Speaker 3: the street, which is really shocking. 647 00:37:10,880 --> 00:37:13,360 Speaker 1: And does that have a ripple effect even at Berkeley? 648 00:37:13,480 --> 00:37:16,280 Speaker 3: Oh sure, I mean I think it, you know, creates 649 00:37:16,320 --> 00:37:17,360 Speaker 3: an atmosphere of fear. 650 00:37:20,760 --> 00:37:24,279 Speaker 1: What would you do to try to make sure we 651 00:37:24,400 --> 00:37:29,960 Speaker 1: became a magnet for the best around the world became 652 00:37:30,280 --> 00:37:35,279 Speaker 1: you mean, yeah, well, yeah, we gain make sure we 653 00:37:35,360 --> 00:37:36,600 Speaker 1: stay a magnet. 654 00:37:37,000 --> 00:37:40,360 Speaker 3: Well, I wouldn't be proceeding the way we are currently 655 00:37:40,400 --> 00:37:41,840 Speaker 3: as a country. I mean, I think we have to 656 00:37:41,880 --> 00:37:45,120 Speaker 3: be welcoming to people from other countries. We have to 657 00:37:45,160 --> 00:37:51,480 Speaker 3: be willing to support science with taxpayer funding in ways 658 00:37:51,520 --> 00:37:53,960 Speaker 3: that have been so valuable in the past. I didn't 659 00:37:53,960 --> 00:37:57,720 Speaker 3: mention before Walter, but you know, our very first grant 660 00:37:57,960 --> 00:38:01,040 Speaker 3: that supported crisper research in my lab was actually from 661 00:38:01,040 --> 00:38:06,280 Speaker 3: the National Science Foundation. The NSF supported our work long 662 00:38:06,320 --> 00:38:08,839 Speaker 3: before anybody appreciated that there was going to be human 663 00:38:08,880 --> 00:38:10,120 Speaker 3: health value to it. 664 00:38:10,840 --> 00:38:14,759 Speaker 1: And they did that just out of curiosity. Yes, yes, 665 00:38:21,280 --> 00:38:24,439 Speaker 1: going back to Chrisper, we talked about sickle cell. There's 666 00:38:24,480 --> 00:38:31,600 Speaker 1: many other applications. Tell me in humans first, I know 667 00:38:31,680 --> 00:38:35,080 Speaker 1: there's for agriculture, climate and other things, but in humans, 668 00:38:35,600 --> 00:38:40,760 Speaker 1: how will it be applied maybe even in cancer research. 669 00:38:41,560 --> 00:38:43,359 Speaker 3: Right. Well, you know, I think one of the things 670 00:38:43,400 --> 00:38:47,200 Speaker 3: that's very interesting about CRISPER is that as the first 671 00:38:47,440 --> 00:38:51,640 Speaker 3: applications are coming to the fore we mentioned sickle cell disease, 672 00:38:51,719 --> 00:38:55,120 Speaker 3: but there are also several therapies for liver diseases that 673 00:38:55,160 --> 00:38:58,400 Speaker 3: are already in the last sort of third phase of 674 00:38:58,400 --> 00:39:02,239 Speaker 3: clinical trial testing that are looking very promising. These are 675 00:39:02,280 --> 00:39:06,400 Speaker 3: all for genetic diseases that are relatively rare in the population. 676 00:39:06,560 --> 00:39:08,359 Speaker 3: But I think that what we're going to see over 677 00:39:08,400 --> 00:39:12,680 Speaker 3: the next decade of CRISPER is increasingly this technology being 678 00:39:12,719 --> 00:39:18,680 Speaker 3: deployed to prevent disease and to cure diseases that affect 679 00:39:18,719 --> 00:39:21,360 Speaker 3: many people. You mentioned cancer. I think they're you know, 680 00:39:21,400 --> 00:39:25,319 Speaker 3: we're looking at opportunities with programming the immune system in 681 00:39:25,400 --> 00:39:30,680 Speaker 3: ways that allow targeted cancer therapies, and also thinking about 682 00:39:30,680 --> 00:39:34,759 Speaker 3: ways that we can provide preventative changes in DNA that 683 00:39:34,800 --> 00:39:36,160 Speaker 3: will protect us from disease. 684 00:39:36,880 --> 00:39:41,160 Speaker 1: Are you suggesting something that can to a cancer vaccines. 685 00:39:42,640 --> 00:39:43,839 Speaker 3: I think that's a possibility. 686 00:39:44,000 --> 00:39:45,320 Speaker 1: Yeah, And how would that work. 687 00:39:45,600 --> 00:39:49,279 Speaker 3: Well, the idea would be to program immune cells in 688 00:39:49,320 --> 00:39:53,080 Speaker 3: a person so that those cells could find and destroy 689 00:39:53,239 --> 00:39:57,560 Speaker 3: tumor cells before they form a tumor or before they metastasize. 690 00:39:57,840 --> 00:40:06,360 Speaker 1: Amazing, Yeah, and explain it works. Let's say messenger RNA 691 00:40:06,560 --> 00:40:11,120 Speaker 1: and guide RNA. The guide RNA is what you did 692 00:40:11,160 --> 00:40:14,000 Speaker 1: for gene editing. Messenger RNA is what we use for 693 00:40:14,040 --> 00:40:18,480 Speaker 1: the vaccines. But it tends to tell our cell make 694 00:40:18,640 --> 00:40:22,640 Speaker 1: this protein or something. What are the implications of that 695 00:40:23,120 --> 00:40:27,719 Speaker 1: of saying, okay, let's have let's code our molecules the 696 00:40:27,719 --> 00:40:29,240 Speaker 1: way we code microchips. 697 00:40:29,920 --> 00:40:34,200 Speaker 3: Well, what's interesting about using RNA to do that kind 698 00:40:34,239 --> 00:40:37,440 Speaker 3: of therapy is that it's a transient thing. That means 699 00:40:37,440 --> 00:40:41,680 Speaker 3: that it happens briefly. And so with Crisper, if we 700 00:40:41,680 --> 00:40:44,919 Speaker 3: were to use mRNA, for example, just as was used 701 00:40:44,960 --> 00:40:48,840 Speaker 3: in the COVID vaccine to deliver Crisper molecules, then you 702 00:40:48,880 --> 00:40:54,040 Speaker 3: could imagine a short term production of the genomeediting molecules 703 00:40:54,080 --> 00:40:58,000 Speaker 3: that could make targeted changes and then go away, which 704 00:40:58,040 --> 00:41:00,319 Speaker 3: is kind of ideal. So then you'd have the the 705 00:41:00,360 --> 00:41:04,760 Speaker 3: protective change made the editor goes away and a duration 706 00:41:05,400 --> 00:41:06,320 Speaker 3: a lasting treatment. 707 00:41:06,920 --> 00:41:10,880 Speaker 1: But people looking at the mRNA vaccines, who are the 708 00:41:10,960 --> 00:41:14,160 Speaker 1: anti vax people and whatever, and some of them in 709 00:41:14,239 --> 00:41:19,960 Speaker 1: government now have been implying that a messenger RNA or 710 00:41:20,000 --> 00:41:23,319 Speaker 1: some guide a thing like that will totally change your 711 00:41:23,440 --> 00:41:27,759 Speaker 1: DNA and is a permanent thing. How do you how 712 00:41:27,800 --> 00:41:32,280 Speaker 1: could one get across the fact that no, RNA doesn't 713 00:41:32,320 --> 00:41:35,200 Speaker 1: even go into the nucleus of the cell if it's 714 00:41:35,200 --> 00:41:38,879 Speaker 1: building a protein, it just programs the outer I mean, 715 00:41:38,960 --> 00:41:43,920 Speaker 1: it's complicated to make people believe that they're not getting reprogrammed. 716 00:41:44,560 --> 00:41:47,160 Speaker 3: I think this is where, you know, we scientists have 717 00:41:47,200 --> 00:41:50,560 Speaker 3: to do better at explaining our findings. Right now, there's 718 00:41:50,960 --> 00:41:54,680 Speaker 3: zero evidence that there's any permanent changes that are made 719 00:41:54,800 --> 00:41:59,080 Speaker 3: with mRNA use. So there's just no data that would 720 00:41:59,080 --> 00:42:00,640 Speaker 3: support that conclusion. 721 00:42:00,680 --> 00:42:02,600 Speaker 1: Yeah, you just said something interesting to me, which is 722 00:42:02,680 --> 00:42:05,120 Speaker 1: we scientists are not good. I mean, one of the 723 00:42:05,160 --> 00:42:07,839 Speaker 1: reasons I wrote this book and others do is, wait, 724 00:42:08,000 --> 00:42:13,480 Speaker 1: let's explain. Scientists used to be better at being public intellectuals, 725 00:42:13,520 --> 00:42:17,120 Speaker 1: explaining from the old days of Carl Sagan and others. 726 00:42:17,760 --> 00:42:21,719 Speaker 1: What should science instead of blaming on the people who 727 00:42:21,800 --> 00:42:25,359 Speaker 1: don't get it, to what extent are scientists should they 728 00:42:25,400 --> 00:42:26,879 Speaker 1: be doing more to communicate? 729 00:42:26,960 --> 00:42:30,040 Speaker 3: Oh, it's critical. I think it's incredibly important. I tell 730 00:42:30,080 --> 00:42:34,960 Speaker 3: my students this regularly, and I'm sure you do too 731 00:42:35,040 --> 00:42:37,879 Speaker 3: in your class, right, you really have to. We have 732 00:42:37,960 --> 00:42:41,160 Speaker 3: to be educating students to be not only great at 733 00:42:41,200 --> 00:42:43,480 Speaker 3: what they do in the lab, but also thinking about 734 00:42:43,480 --> 00:42:46,080 Speaker 3: how they explain the importance of what they do. I 735 00:42:46,160 --> 00:42:48,000 Speaker 3: tell my students, I want you to be able to say, 736 00:42:48,000 --> 00:42:50,960 Speaker 3: in one sentence to your grandmother, you know why you're 737 00:42:50,960 --> 00:42:52,640 Speaker 3: doing what you do and why it matters. 738 00:42:53,360 --> 00:42:56,080 Speaker 1: In one sentence to your grandmother, what are you doing 739 00:42:56,120 --> 00:42:57,200 Speaker 1: now and why does it matter? 740 00:43:00,400 --> 00:43:04,440 Speaker 3: Thank you? Rewriting the code of life to protect us 741 00:43:04,480 --> 00:43:05,480 Speaker 3: from disease. 742 00:43:05,560 --> 00:43:06,400 Speaker 1: And you're doing it. 743 00:43:09,960 --> 00:43:13,399 Speaker 3: I do, okayday. 744 00:43:13,520 --> 00:43:16,360 Speaker 1: And what about rewriting it to protect us from climate change? 745 00:43:17,040 --> 00:43:20,560 Speaker 3: Well, I'd like to do that too. Well. You know, 746 00:43:20,640 --> 00:43:24,120 Speaker 3: here's the thing. So you know, CRISPER is a powerful 747 00:43:24,160 --> 00:43:29,120 Speaker 3: technology in part because it works across all of biology. 748 00:43:29,239 --> 00:43:31,839 Speaker 3: We know that it works in bacteria, but it also 749 00:43:31,920 --> 00:43:34,479 Speaker 3: works in humans, as we've been discussing, and it works 750 00:43:34,480 --> 00:43:37,799 Speaker 3: in plants because you know, fundamentally they are all using 751 00:43:37,960 --> 00:43:43,000 Speaker 3: DNA to encode their properties. And so we realized in 752 00:43:43,080 --> 00:43:47,480 Speaker 3: thinking about that fact that CRISPER could actually be used 753 00:43:47,640 --> 00:43:51,400 Speaker 3: to make changes in plants, but also frankly in the 754 00:43:51,440 --> 00:43:56,960 Speaker 3: microbes that support agriculture. That will be beneficial in terms 755 00:43:57,000 --> 00:44:00,279 Speaker 3: of protecting the climates. I'll give you an example. So 756 00:44:00,360 --> 00:44:07,920 Speaker 3: cattle are harboring microbes in their gut, in their roomen 757 00:44:08,680 --> 00:44:11,520 Speaker 3: that are important for digestion, but they also produce a 758 00:44:11,560 --> 00:44:14,040 Speaker 3: lot of methane. So methane is one of the most 759 00:44:14,040 --> 00:44:16,759 Speaker 3: powerful greenhouse gases. And it turns out that when you 760 00:44:16,800 --> 00:44:21,040 Speaker 3: look at methane produced from animal farming around the world, 761 00:44:21,040 --> 00:44:24,160 Speaker 3: it's about a third of the global methane that's released 762 00:44:24,160 --> 00:44:28,239 Speaker 3: around the world. Imagine that we could reprogram those microbes 763 00:44:28,880 --> 00:44:31,640 Speaker 3: to not produce methane and in fact to use that 764 00:44:31,840 --> 00:44:35,960 Speaker 3: energy to make more meat or more milk. Great for farmers, 765 00:44:36,200 --> 00:44:39,000 Speaker 3: great economically, and the right thing to do for the climate. 766 00:44:39,080 --> 00:44:40,120 Speaker 3: So that's what we're working on. 767 00:44:40,360 --> 00:44:43,279 Speaker 1: And tell me how close you are and how that 768 00:44:43,280 --> 00:44:43,920 Speaker 1: would happen. 769 00:44:44,640 --> 00:44:48,120 Speaker 3: Well, this is where we brought on board a partner, 770 00:44:48,200 --> 00:44:51,720 Speaker 3: a third partner campus partner at the Innovative Genomics Institute 771 00:44:51,800 --> 00:44:56,720 Speaker 3: University California, Davis, one of the world's great agricultural universities, 772 00:44:57,040 --> 00:45:00,960 Speaker 3: with experts working on this methaneroblem in cattle, and they 773 00:45:00,960 --> 00:45:04,239 Speaker 3: had shown that you could change the cow diet to 774 00:45:04,360 --> 00:45:07,960 Speaker 3: control methane production, but it wasn't It was clearly not 775 00:45:08,080 --> 00:45:11,640 Speaker 3: an affordable or sustainable solution to the problem. So we 776 00:45:11,719 --> 00:45:14,480 Speaker 3: got together and we said, look, let's take your knowledge 777 00:45:14,520 --> 00:45:20,040 Speaker 3: of cattle and ruman microbiology and combine it with the 778 00:45:20,120 --> 00:45:25,560 Speaker 3: Crisper technology for reprogramming and make changes in the microbiome 779 00:45:25,680 --> 00:45:29,800 Speaker 3: of cattle that could be permanent and could reduce the 780 00:45:30,160 --> 00:45:32,399 Speaker 3: release of methane. And that's what we're working on right now. 781 00:45:32,960 --> 00:45:39,719 Speaker 1: How do you feel, in this current climate, not just 782 00:45:39,800 --> 00:45:44,839 Speaker 1: the politics in Washington, about saying all right, we're going 783 00:45:44,880 --> 00:45:50,239 Speaker 1: to use RNA guided things to edit the biomes of 784 00:45:50,280 --> 00:45:54,000 Speaker 1: our cows, etc. Do you think there would be a 785 00:45:54,200 --> 00:45:57,360 Speaker 1: backlash or you're going to have trouble getting people to 786 00:45:57,400 --> 00:46:01,200 Speaker 1: understand that. It seems like it would be deemedized right away. 787 00:46:01,680 --> 00:46:03,600 Speaker 3: Well, I think we have to be proactive. I mean this. 788 00:46:03,680 --> 00:46:06,240 Speaker 3: We have a big public impact team at the Innovative 789 00:46:06,239 --> 00:46:09,799 Speaker 3: Generalmics Institute to work on the communications about this, to 790 00:46:09,920 --> 00:46:12,799 Speaker 3: explain the technology, to show the data that we have 791 00:46:12,960 --> 00:46:16,359 Speaker 3: for the technology, and to really invite a partnership. You know, 792 00:46:16,400 --> 00:46:20,560 Speaker 3: you talked about scientists needing to be better kind of ambassadors, 793 00:46:20,560 --> 00:46:23,240 Speaker 3: and I think that has to be not through lecturing. 794 00:46:23,280 --> 00:46:27,000 Speaker 3: It has to be through real partnership with our communities. 795 00:46:28,680 --> 00:46:30,560 Speaker 1: I'm going to talk about myself for a second. What 796 00:46:30,719 --> 00:46:33,080 Speaker 1: is it like? I mean, I had to trail you 797 00:46:33,160 --> 00:46:35,120 Speaker 1: for a couple of years. I was in your lab 798 00:46:35,200 --> 00:46:36,920 Speaker 1: all the time, in your hair all the time, so 799 00:46:37,040 --> 00:46:39,640 Speaker 1: to speak, or Rubbert gloves, trying to learn how to 800 00:46:39,719 --> 00:46:42,879 Speaker 1: do things. What's it like to have books and other 801 00:46:42,920 --> 00:46:47,560 Speaker 1: things written about you? Does that you're you're not an 802 00:46:47,600 --> 00:46:50,399 Speaker 1: out there person trying to get publicity. 803 00:46:51,000 --> 00:46:54,520 Speaker 3: Well, I'm still stunned that it got done. Do you 804 00:46:54,520 --> 00:46:57,600 Speaker 3: remember Walter that you know you called me so just 805 00:46:57,840 --> 00:47:00,279 Speaker 3: It's kind of an interesting backstory because you know, Walter 806 00:47:00,320 --> 00:47:02,879 Speaker 3: and I had met at the Aspen Ideas Festival where 807 00:47:02,920 --> 00:47:05,840 Speaker 3: we did a chat like this, and you know, a 808 00:47:05,880 --> 00:47:10,520 Speaker 3: few generations ago. Now it feels like and and Walter 809 00:47:11,120 --> 00:47:13,160 Speaker 3: a few months later called me up one day and 810 00:47:13,200 --> 00:47:15,920 Speaker 3: he said, you know, I'm thinking about writing a book. 811 00:47:16,280 --> 00:47:18,399 Speaker 3: And I said, oh, that sounds great. You're always writing books. 812 00:47:18,440 --> 00:47:20,520 Speaker 3: And he said, no, I mean about you. And I said, 813 00:47:21,040 --> 00:47:25,279 Speaker 3: I said, well, that'll never happen. I couldn't imagine that 814 00:47:25,360 --> 00:47:27,640 Speaker 3: it would come to pass. But you know, Walter is 815 00:47:28,239 --> 00:47:33,879 Speaker 3: very very uh you know, persistent and one thing led 816 00:47:33,880 --> 00:47:36,920 Speaker 3: to another. And I think what's been great about the book, 817 00:47:36,960 --> 00:47:39,440 Speaker 3: Walter is that I think you did a wonderful job 818 00:47:39,640 --> 00:47:42,480 Speaker 3: of telling a compelling story. It's a you know, it's 819 00:47:42,520 --> 00:47:44,200 Speaker 3: kind of a bit of a you know, it could 820 00:47:44,200 --> 00:47:46,120 Speaker 3: be a tone, but it's not, you know, it's it's 821 00:47:46,200 --> 00:47:48,200 Speaker 3: it's a it's a kind of a page turner, actually, 822 00:47:48,760 --> 00:47:51,200 Speaker 3: And you did a great job of interviewing a lot 823 00:47:51,200 --> 00:47:53,520 Speaker 3: of the people who were involved in the story telling 824 00:47:53,520 --> 00:47:56,760 Speaker 3: their sides of it, talking about the way that science 825 00:47:56,920 --> 00:48:00,320 Speaker 3: really works, the way it really gets done, and they're 826 00:48:00,200 --> 00:48:05,040 Speaker 3: there's competition, there's collaboration that both plays into the things 827 00:48:05,040 --> 00:48:08,319 Speaker 3: that actually happen in the laboratory. So I think it's 828 00:48:08,320 --> 00:48:10,960 Speaker 3: a great way for people to try to, you know, 829 00:48:11,080 --> 00:48:14,960 Speaker 3: really understand the science that goes into a new technology 830 00:48:14,960 --> 00:48:16,920 Speaker 3: that you might read headlines about but you don't have 831 00:48:16,960 --> 00:48:18,359 Speaker 3: any idea where it emerged from. 832 00:48:18,560 --> 00:48:21,799 Speaker 1: I mean, you have that in history with great you know, 833 00:48:22,400 --> 00:48:26,480 Speaker 1: advances in science. The Double Helix being whatever you may 834 00:48:26,520 --> 00:48:31,040 Speaker 1: think of Jim Watson just a wonderfully written book. I mean, 835 00:48:31,080 --> 00:48:36,440 Speaker 1: it is colorful, even if it's maybe too colorful at times. 836 00:48:38,239 --> 00:48:42,439 Speaker 1: Do you see a role at Igi, Berkeley, Tulane, whatever 837 00:48:42,480 --> 00:48:46,439 Speaker 1: it may be, of just training science communicators, not people 838 00:48:46,520 --> 00:48:49,600 Speaker 1: going to be great scientists. But when people ask me 839 00:48:49,640 --> 00:48:51,560 Speaker 1: how do I go into journalism whatever, I say, it's 840 00:48:51,600 --> 00:48:53,880 Speaker 1: a tough time to go into journalism. But pick a 841 00:48:53,920 --> 00:48:58,640 Speaker 1: particular feel like maybe science. Do you think Berkeley and 842 00:48:58,680 --> 00:49:02,439 Speaker 1: others should have as science communication programs? I do. 843 00:49:02,560 --> 00:49:04,960 Speaker 3: I think that's very important. I also think that it's 844 00:49:05,000 --> 00:49:09,400 Speaker 3: important to encourage people that you know, we're coming into 845 00:49:09,400 --> 00:49:13,480 Speaker 3: contact with to pursue those ideas. I mean, I think that. 846 00:49:13,880 --> 00:49:15,759 Speaker 3: I mean one one example from my own lab is 847 00:49:16,200 --> 00:49:19,920 Speaker 3: a scientist named Sam Sternberg who was a former graduate student. 848 00:49:19,960 --> 00:49:23,799 Speaker 3: You know Sam. You've interviewed Sam and when Sam was 849 00:49:23,800 --> 00:49:26,080 Speaker 3: finishing up his PhD. He's a wonderful scientist, you know, 850 00:49:26,160 --> 00:49:28,560 Speaker 3: incredibly talented. I asked him, you know, what do you 851 00:49:28,560 --> 00:49:30,160 Speaker 3: want to do next in your career and he said, well, 852 00:49:30,160 --> 00:49:31,880 Speaker 3: you know what, I think I want to write a book. 853 00:49:32,400 --> 00:49:34,160 Speaker 3: And I said, really, you want to write a book 854 00:49:34,160 --> 00:49:35,640 Speaker 3: and he said, yeah, I want to write a book 855 00:49:35,680 --> 00:49:40,040 Speaker 3: about the work that was that went into the discovery 856 00:49:40,040 --> 00:49:42,960 Speaker 3: of crisper because I've lived through it in your lab 857 00:49:43,120 --> 00:49:45,920 Speaker 3: and I think it's just an extraordinary story. And so 858 00:49:45,960 --> 00:49:48,400 Speaker 3: again I sort of thought, well that'll that'll probably not happen, 859 00:49:48,480 --> 00:49:49,680 Speaker 3: but it did. You know he did. 860 00:49:49,800 --> 00:49:52,399 Speaker 1: It's called the Crack in Creation and you should buy it, right, 861 00:49:52,480 --> 00:49:54,840 Speaker 1: Oh no, it's you're talking about a cracking creator. 862 00:49:54,880 --> 00:49:55,120 Speaker 3: Yeah. 863 00:49:55,200 --> 00:49:55,560 Speaker 2: Yeah. 864 00:49:55,600 --> 00:49:59,400 Speaker 3: And so he took a year off from his research 865 00:49:59,480 --> 00:50:04,080 Speaker 3: and he spent time, hold up, you know, writing this story. 866 00:50:04,200 --> 00:50:05,520 Speaker 3: And it was a struggle. 867 00:50:05,560 --> 00:50:09,360 Speaker 1: I mean writing is tough, you know, and editing genes 868 00:50:09,440 --> 00:50:13,160 Speaker 1: is tough. Writing it. I've done both. 869 00:50:13,480 --> 00:50:16,480 Speaker 3: I think writing I've done both too, and it's I 870 00:50:16,520 --> 00:50:19,279 Speaker 3: think writing is very hard either way. 871 00:50:19,400 --> 00:50:24,080 Speaker 1: But I did edit. I think it was human kidney cells, right, Yes, 872 00:50:24,960 --> 00:50:27,439 Speaker 1: I was able to add it in her lab, these 873 00:50:27,480 --> 00:50:31,879 Speaker 1: cells so that they would phosphorus or blow in the dark. 874 00:50:31,960 --> 00:50:36,279 Speaker 1: As I'm not a scientist, and I thought, okay, I'm 875 00:50:36,320 --> 00:50:39,600 Speaker 1: now doctor Frank. And they made sure that we poured 876 00:50:39,719 --> 00:50:44,279 Speaker 1: large amounts of chlorine and killed it. So it's a 877 00:50:44,360 --> 00:50:47,520 Speaker 1: type of thing. Though it would be better if labs 878 00:50:47,680 --> 00:50:50,160 Speaker 1: like yours or here or whatever could say the kids 879 00:50:50,200 --> 00:50:54,719 Speaker 1: come in and just go to the bench and have 880 00:50:54,760 --> 00:50:56,440 Speaker 1: a pipe and it's an experience. 881 00:50:56,719 --> 00:50:59,960 Speaker 3: Maybe, like you said, they don't have to be professional scientists. 882 00:51:00,080 --> 00:51:02,719 Speaker 3: In the future, but understanding a little bit about how 883 00:51:02,760 --> 00:51:05,319 Speaker 3: science actually works. I think it's very valuable, and then 884 00:51:05,360 --> 00:51:07,480 Speaker 3: communicating that to people is critical. 885 00:51:07,840 --> 00:51:12,240 Speaker 1: Yeah, we have an anti science movement seeming to happen now, 886 00:51:12,560 --> 00:51:17,560 Speaker 1: but it also comes at a time when uh, humanists 887 00:51:17,840 --> 00:51:22,200 Speaker 1: are intimidated by science. You know science, there's you know 888 00:51:23,520 --> 00:51:28,360 Speaker 1: Twobe cultures system that I've been written about. How important 889 00:51:28,400 --> 00:51:32,440 Speaker 1: is it to sort of connect the sciences and the humanities. 890 00:51:33,120 --> 00:51:35,600 Speaker 3: I think it's very important again for the same reasons 891 00:51:35,600 --> 00:51:37,760 Speaker 3: I think, you know, these are these are there fundamental 892 00:51:37,840 --> 00:51:40,200 Speaker 3: ideas that we're all grappling with. How do we how 893 00:51:40,200 --> 00:51:42,799 Speaker 3: do we use technologies? And we haven't brought a BAI yet, 894 00:51:42,800 --> 00:51:45,319 Speaker 3: but you know, artificial intelligence, I think is the same 895 00:51:45,400 --> 00:51:48,759 Speaker 3: kind of thing where it's you know, it's powerful, it's complicated. 896 00:51:48,880 --> 00:51:52,880 Speaker 3: You know, really understanding how these models, like large language 897 00:51:52,920 --> 00:51:56,799 Speaker 3: models are actually working is non trivial. And then to evaluate, 898 00:51:56,960 --> 00:51:59,520 Speaker 3: you know, what's the what's the safety of these things, 899 00:51:59,560 --> 00:52:03,080 Speaker 3: what the appropriate way to regulate them? These are non 900 00:52:03,280 --> 00:52:06,120 Speaker 3: trivial things to figure out, and so I just think 901 00:52:06,160 --> 00:52:10,160 Speaker 3: that it's going to require a better effort between scientists 902 00:52:10,239 --> 00:52:14,120 Speaker 3: and technologists and then the rest of us to work 903 00:52:14,160 --> 00:52:14,600 Speaker 3: that out. 904 00:52:14,640 --> 00:52:17,520 Speaker 1: But I feel that humanists who care about the morale 905 00:52:17,560 --> 00:52:20,120 Speaker 1: and they're going to be left out of the equation 906 00:52:20,520 --> 00:52:23,239 Speaker 1: if they don't make the effort to learn some of 907 00:52:23,280 --> 00:52:26,960 Speaker 1: the science. That if you're clueless about the science, is 908 00:52:27,000 --> 00:52:30,160 Speaker 1: going to be hard to discuss should we do arritable 909 00:52:30,200 --> 00:52:30,840 Speaker 1: gene editing? 910 00:52:31,080 --> 00:52:33,000 Speaker 3: And that's why I love that you asked me if 911 00:52:33,000 --> 00:52:35,399 Speaker 3: you could come to the lab and work with Chris Burd. 912 00:52:35,440 --> 00:52:37,240 Speaker 3: You know, it is great. 913 00:52:38,239 --> 00:52:42,360 Speaker 1: The two great historic advances of our time in science, 914 00:52:43,719 --> 00:52:45,320 Speaker 1: just like you know one hundred years ago it was 915 00:52:45,360 --> 00:52:48,839 Speaker 1: the age of electricity and then the digital revolution. We're 916 00:52:48,840 --> 00:52:51,880 Speaker 1: seeing two revolutions happen at once that I think are 917 00:52:51,920 --> 00:52:54,960 Speaker 1: going to be the most transformative of the past five 918 00:52:55,040 --> 00:52:59,200 Speaker 1: hundred years. The life science is revolution, meaning gene editing 919 00:52:59,440 --> 00:53:03,840 Speaker 1: at the core, and the AI revolution meaning artificial intelligence. 920 00:53:04,000 --> 00:53:07,520 Speaker 1: We saw the Nobel Prize this year being awarded both 921 00:53:07,560 --> 00:53:12,440 Speaker 1: in physics and in chemistry to AI because that combination 922 00:53:12,920 --> 00:53:15,759 Speaker 1: tell me what happens in your lab and your work 923 00:53:15,800 --> 00:53:19,680 Speaker 1: and in your thought when you combine the power of 924 00:53:19,760 --> 00:53:24,000 Speaker 1: the AI revolution to the power of the genetic revolution. 925 00:53:24,560 --> 00:53:27,600 Speaker 3: Well, when the work was done that was recognized by 926 00:53:27,600 --> 00:53:31,399 Speaker 3: the chemistry Nobel this year, which is a program called 927 00:53:31,480 --> 00:53:36,480 Speaker 3: alpha fold that allows prediction of protein three dimensional structures 928 00:53:36,480 --> 00:53:39,640 Speaker 3: in a very accurate way. Our lab and many many 929 00:53:39,680 --> 00:53:44,040 Speaker 3: others began using it almost immediately because it instantly provides 930 00:53:44,200 --> 00:53:47,560 Speaker 3: a tool that we can use to predict the functions 931 00:53:47,560 --> 00:53:50,920 Speaker 3: of proteins, how they might interact with other molecules, and 932 00:53:50,920 --> 00:53:54,200 Speaker 3: that's very valuable. Used to be incredibly time consuming to 933 00:53:54,320 --> 00:53:58,799 Speaker 3: work out individual shapes of proteins experimentally, and we don't. 934 00:53:59,200 --> 00:54:00,799 Speaker 3: We still do that, but we don't have to do 935 00:54:00,840 --> 00:54:06,080 Speaker 3: it nearly to the extent that was required previously. And 936 00:54:06,560 --> 00:54:09,880 Speaker 3: as a result, it accelerates the pace of science. And 937 00:54:09,920 --> 00:54:12,319 Speaker 3: we're seeing this more and more with other kinds of 938 00:54:12,880 --> 00:54:18,319 Speaker 3: AI driven approaches in technology approaches, is that we can 939 00:54:18,360 --> 00:54:22,600 Speaker 3: do experiments faster, we can increasingly predict the right experiments 940 00:54:22,640 --> 00:54:24,839 Speaker 3: to do and not waste time on the others. And 941 00:54:24,880 --> 00:54:26,759 Speaker 3: I think we're just going to continue to see this 942 00:54:27,000 --> 00:54:30,640 Speaker 3: acceleration of the pace of discovery. It's very, very exciting, 943 00:54:30,640 --> 00:54:33,320 Speaker 3: but it's also it's a little bit scary. 944 00:54:33,320 --> 00:54:36,400 Speaker 1: To give me a very specific concrete way we get 945 00:54:36,440 --> 00:54:40,279 Speaker 1: ahead of round and maybe take vaccine, where you use 946 00:54:40,400 --> 00:54:46,240 Speaker 1: AI to totally say, handle a huge database that humans 947 00:54:46,280 --> 00:54:50,239 Speaker 1: could never have coped with and discover something that could 948 00:54:50,239 --> 00:54:51,280 Speaker 1: be a vaccine. 949 00:54:51,880 --> 00:54:55,080 Speaker 3: Well, it means that you can quickly evaluate all the 950 00:54:55,160 --> 00:54:58,120 Speaker 3: molecules that are being produced by a virus or a 951 00:54:58,200 --> 00:55:02,200 Speaker 3: bacterium that's infectious and try to figure out what are 952 00:55:02,200 --> 00:55:03,680 Speaker 3: the ways to neutralize it. 953 00:55:04,040 --> 00:55:06,160 Speaker 1: And how might it work with cant or something. 954 00:55:06,239 --> 00:55:09,360 Speaker 3: Well, similarly with cancer, same thing. You know, cancer cells 955 00:55:09,400 --> 00:55:12,680 Speaker 3: often produce molecules on their surface that are not found 956 00:55:12,680 --> 00:55:15,440 Speaker 3: on normal tissues. So imagine that you could figure out 957 00:55:15,480 --> 00:55:17,279 Speaker 3: what those are and what they look like, and then 958 00:55:17,320 --> 00:55:18,160 Speaker 3: how to target them. 959 00:55:18,600 --> 00:55:21,880 Speaker 1: One of the problems with crisper is that it costs 960 00:55:21,920 --> 00:55:27,440 Speaker 1: a whole lot I mean doing sickle cell I mean millions, 961 00:55:27,560 --> 00:55:31,840 Speaker 1: so you can't really do it. What is the reason 962 00:55:31,920 --> 00:55:35,200 Speaker 1: the cost is so high and what could you do 963 00:55:35,239 --> 00:55:37,640 Speaker 1: with delivery systems to get that cost down? 964 00:55:37,719 --> 00:55:39,759 Speaker 3: Yeah, thanks for bringing that up, because that's a very 965 00:55:39,760 --> 00:55:42,759 Speaker 3: important point. So right now, there's a drug castev that's 966 00:55:42,760 --> 00:55:45,560 Speaker 3: approved by the FDA we mentioned earlier for sickle cell 967 00:55:45,600 --> 00:55:50,560 Speaker 3: disease and it's extraordinary. I've met one of the patients 968 00:55:50,600 --> 00:55:53,840 Speaker 3: who is treated in the first trial using that therapy 969 00:55:53,960 --> 00:55:58,960 Speaker 3: and it's completely changed her life in a very positive way. 970 00:55:59,320 --> 00:56:02,440 Speaker 3: So why and everybody with sickle cell disease able to 971 00:56:02,560 --> 00:56:04,600 Speaker 3: get this if they want it? And the reason, at 972 00:56:04,680 --> 00:56:07,040 Speaker 3: least in part is the cost. So it's about two 973 00:56:07,040 --> 00:56:11,960 Speaker 3: million dollars of patient right now for this therapeutic Yeah good, 974 00:56:12,080 --> 00:56:16,400 Speaker 3: not good? And why is that? Well, it's it's again 975 00:56:16,480 --> 00:56:20,719 Speaker 3: in large part, it's for technical reasons. It's because it's 976 00:56:20,840 --> 00:56:24,920 Speaker 3: not easy to get those genomeeditors into the cells that 977 00:56:25,000 --> 00:56:27,960 Speaker 3: need editing, namely the cells and the bone marrow that 978 00:56:28,200 --> 00:56:30,960 Speaker 3: are the source of our blood supply and our bodies. 979 00:56:31,520 --> 00:56:34,239 Speaker 3: So imagine that you had a way to do that 980 00:56:34,360 --> 00:56:38,520 Speaker 3: kind of targeted delivery into blood stem cells in the 981 00:56:38,520 --> 00:56:42,040 Speaker 3: bone marrow by a simple injection or even maybe someday 982 00:56:42,040 --> 00:56:45,120 Speaker 3: it's a pill that somebody could take. That would be 983 00:56:45,160 --> 00:56:48,440 Speaker 3: incredibly valuable and we change the whole field. And it 984 00:56:48,480 --> 00:56:51,600 Speaker 3: would also make it possible to use crisper for lots 985 00:56:51,640 --> 00:56:54,640 Speaker 3: of other types of diseases. So that's really one of 986 00:56:54,680 --> 00:57:01,120 Speaker 3: the core mission goals of the IG is to figure 987 00:57:01,160 --> 00:57:05,799 Speaker 3: out how to change the technology around genomediting delivery so 988 00:57:05,840 --> 00:57:08,239 Speaker 3: those kinds of applications become possible. 989 00:57:09,040 --> 00:57:13,360 Speaker 1: So final question, you're at Pomona College. You're thinking of 990 00:57:13,440 --> 00:57:17,280 Speaker 1: being a French teacher maybe, but you're also holding the 991 00:57:17,360 --> 00:57:20,560 Speaker 1: chemistry things and it's kind of fascinating you, and you 992 00:57:20,640 --> 00:57:24,200 Speaker 1: figure out a path that takes you to the Nobel Prize. 993 00:57:24,320 --> 00:57:28,600 Speaker 1: For my students here, what should they be doing that 994 00:57:28,680 --> 00:57:31,800 Speaker 1: will get them, if not a path to a Nobel 995 00:57:32,040 --> 00:57:33,840 Speaker 1: a path to helping our society. 996 00:57:33,960 --> 00:57:35,360 Speaker 3: Well, all I can say is when I ask my 997 00:57:35,440 --> 00:57:38,680 Speaker 3: French teacher about switching my major from chemistry to French, 998 00:57:38,720 --> 00:57:44,680 Speaker 3: she said, no, stay with chemistry. So it's probably good advice, 999 00:57:45,080 --> 00:57:48,080 Speaker 3: but no. I always tell my students you have to 1000 00:57:48,080 --> 00:57:51,280 Speaker 3: figure out what you're really passionate about and pursue it, 1001 00:57:51,480 --> 00:57:55,000 Speaker 3: just sort of doggedly, and not be dissuaded by naysayers. 1002 00:57:55,440 --> 00:57:59,240 Speaker 3: You have to be able to identify what you really 1003 00:57:59,480 --> 00:58:01,640 Speaker 3: want to and your time on and then and then 1004 00:58:01,680 --> 00:58:04,240 Speaker 3: go after it wholeheartedly. And I really see this over 1005 00:58:04,280 --> 00:58:07,040 Speaker 3: and over in my own lab, is that when students 1006 00:58:07,080 --> 00:58:09,080 Speaker 3: do that, they are they are successful. 1007 00:58:09,960 --> 00:58:16,600 Speaker 1: Jennifer dowdno nowse both codes on Crisper. 1008 00:58:16,640 --> 00:58:19,160 Speaker 2: The Story of Jennifer Downa is a production of Kaleidoscope 1009 00:58:19,200 --> 00:58:21,480 Speaker 2: and iHeart This show is based on the writing and 1010 00:58:21,520 --> 00:58:24,640 Speaker 2: reporting of Walter Isaacson. It's hosted by me Evan Ratliffe 1011 00:58:24,680 --> 00:58:27,960 Speaker 2: and produced by Adrianna Tavia with assistance from Alex Joneveld. 1012 00:58:28,400 --> 00:58:31,000 Speaker 2: It was mixed by Kyle Murdoch and our studio engineer 1013 00:58:31,040 --> 00:58:34,240 Speaker 2: was Thomas Walsh. Our executive producers are Kate Osborne and 1014 00:58:34,240 --> 00:58:38,200 Speaker 2: my Guesttigador from Kaleidoscope and Katrina Norvell from iHeart Podcasts. 1015 00:58:38,840 --> 00:58:41,800 Speaker 2: If you enjoy hearing stories about visionaries and science and technology, 1016 00:58:42,040 --> 00:58:44,320 Speaker 2: check out our other seasons based on the biographies that 1017 00:58:44,360 --> 00:58:47,600 Speaker 2: Walter Isaacson has written. On Musk for an intimate dive 1018 00:58:47,640 --> 00:58:51,000 Speaker 2: into all facets of Elon Musk and on Benjamin Franklin 1019 00:58:51,040 --> 00:58:54,240 Speaker 2: to understand how his scientific curiosity shape society as we 1020 00:58:54,280 --> 00:58:56,000 Speaker 2: know it. Thank you for listening. 1021 00:59:00,120 --> 00:59:00,760 Speaker 1: No