WEBVTT 00:00.000 --> 00:02.000 You 00:30.000 --> 00:56.000 I'm afraid that the latest beta tells us that we're feeling with essentially a worst case scenario I'm afraid that the latest beta tells us that we're dealing with essentially a worst case scenario 01:00.000 --> 01:30.000 The latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the 01:30.000 --> 02:00.000 latest beta says that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that the latest beta tells us that 02:00.000 --> 02:07.000 The rules protect yourself at all 02:07.000 --> 02:08.000 time. 02:08.000 --> 02:10.000 Follow my instructions. 02:10.000 --> 02:11.000 Keep it clean. 02:11.000 --> 02:12.000 Tough gloves if you wish. 02:12.000 --> 02:13.000 Let's do it. 02:13.000 --> 02:14.000 Sweaty bombs. 02:14.000 --> 02:15.000 This is so crazy. 02:15.000 --> 02:16.000 Like who's boss? 02:16.000 --> 02:17.000 This is so crazy. 02:17.000 --> 02:18.000 I feel so nervous. 02:18.000 --> 02:21.000 Like what in the world, man? 02:30.000 --> 02:40.000 All right. 02:40.000 --> 02:41.000 I like to hear that. 02:41.000 --> 02:46.000 I like to hear the fact that people like the time. 02:46.000 --> 02:49.000 It works really well for the fam family. 02:49.000 --> 02:51.000 For me to do it in the afternoon and be available at 02:51.000 --> 02:53.000 dinner time and be available at bread time. 02:53.000 --> 02:54.000 It's really great. 02:54.000 --> 02:56.000 And then maybe do a late show. 02:56.000 --> 02:58.000 So we'll see what we do. 02:58.000 --> 02:59.000 Welcome to the stream everybody. 02:59.000 --> 03:04.000 Thank you very much for joining me. 03:04.000 --> 03:07.000 The way this stream works is that if you've been here for a 03:07.000 --> 03:10.000 while, you're here at the top of the wave with us where we 03:10.000 --> 03:12.000 are staying focused on the biology. 03:12.000 --> 03:15.000 We aren't taking the bait on TV and social media and we are 03:15.000 --> 03:16.000 loving our neighbors. 03:16.000 --> 03:19.000 We're trying to save the skilled TV watchers in our 03:19.000 --> 03:20.000 lives. 03:20.000 --> 03:23.000 And the way it's done is that people share this work every 03:23.000 --> 03:26.000 week and more importantly, for my family, there are people 03:26.000 --> 03:28.000 who support us every month. 03:28.000 --> 03:32.000 And so I want to give a shout out to those people as I always 03:32.000 --> 03:34.000 do. 03:34.000 --> 03:36.000 But more importantly, this is where you find me at 03:36.000 --> 03:39.000 GigaOMbiological.com and GigaOM.bio. 03:39.000 --> 03:42.000 You can also find a confession of mine at the link 03:42.000 --> 03:43.000 named Scooby there. 03:43.000 --> 03:47.000 You can also find a one time support link at GigaOM. 03:47.000 --> 03:50.000 And then if you scroll down, you can find a schedule and a 03:50.000 --> 03:52.000 place to subscribe. 03:52.000 --> 03:55.000 And the list of subscribers is actually slowly growing. 03:55.000 --> 03:59.000 I'm a little bit optimistic that we might be able to keep this 03:59.000 --> 04:03.000 up because the list of subscribers and people that is 04:03.000 --> 04:05.000 supporting the work is growing. 04:05.000 --> 04:10.000 And so there are people who are making one time donations. 04:10.000 --> 04:13.000 There are people that are also subscribing and making one 04:13.000 --> 04:14.000 time. 04:14.000 --> 04:17.000 It's really all hands on deck and I'm really impressed and 04:17.000 --> 04:20.000 thankful from the bottom of my heart as so is my rest of my 04:20.000 --> 04:21.000 family. 04:21.000 --> 04:23.000 Smooth out a little bit. 04:23.000 --> 04:26.000 I'm going to put a little bit of footage in there. 04:26.000 --> 04:27.000 Dog on it. 04:27.000 --> 04:28.000 Smooth. 04:28.000 --> 04:29.000 Smooth. 04:29.000 --> 04:30.000 Make it smoother. 04:30.000 --> 04:31.000 Where is the smooth? 04:31.000 --> 04:32.000 There's smoothness. 04:32.000 --> 04:34.000 Doesn't really like all those names. 04:34.000 --> 04:37.000 So let's get more names on the list and make it even more 04:37.000 --> 04:42.000 jumpy as it scrolls those names by. 04:42.000 --> 04:43.000 Yeah. 04:43.000 --> 04:47.000 So this is a presentation of the independent bright web. 04:47.000 --> 04:48.000 What is that? 04:48.000 --> 04:49.000 I don't really know. 04:49.000 --> 04:52.000 But it's not the independent and it's not the rather 04:52.000 --> 04:53.000 the intellectual dark web. 04:53.000 --> 04:56.000 It's the independent bright web. 04:56.000 --> 05:00.000 And I guess that's all I can really say. 05:00.000 --> 05:03.000 This is Giga Own Biological High Resistance Low Noise 05:03.000 --> 05:05.000 Information Brief brought to you by biologists. 05:05.000 --> 05:09.000 This is the 15th of February 2024. 05:09.000 --> 05:11.000 It's another study hall. 05:11.000 --> 05:15.000 I'm not going to belabor you with a bunch of let's say 05:15.000 --> 05:20.000 overarching explanations where I can rehab the hypothesis today 05:20.000 --> 05:22.000 that we are going to pay attention to the fact that we 05:22.000 --> 05:25.000 have been consciously manipulated. 05:25.000 --> 05:27.000 Our habits have been organized. 05:27.000 --> 05:30.000 Our opinions have been manipulated over the course of 05:30.000 --> 05:32.000 decades since we were a kid. 05:32.000 --> 05:34.000 Since we were kids. 05:34.000 --> 05:37.000 And once you start to understand that then you can start 05:37.000 --> 05:40.000 to see how we were fooled when we were children when there 05:40.000 --> 05:44.000 was only three newspapers and a few TV channels. 05:44.000 --> 05:47.000 And what's happening to our children now when they 05:47.000 --> 05:49.000 already have a cell phone. 05:49.000 --> 05:53.000 When they already have a tick tock if you haven't been very 05:53.000 --> 05:54.000 vigilant. 05:54.000 --> 05:59.000 So that's where I think we are. 05:59.000 --> 06:02.000 Ooh out of focus. 06:02.000 --> 06:03.000 Out of focus. 06:03.000 --> 06:04.000 Ding, ding, ding. 06:04.000 --> 06:05.000 Hello. 06:05.000 --> 06:06.000 Hello. 06:06.000 --> 06:07.000 Good evening. 06:07.000 --> 06:08.000 Good morning. 06:08.000 --> 06:09.000 Good afternoon. 06:09.000 --> 06:13.000 It is one twenty in the afternoon today at Pittsburgh 06:13.000 --> 06:16.000 in Pittsburgh, Pennsylvania coming to you live from my garage. 06:16.000 --> 06:18.000 It is a very unusual. 06:18.000 --> 06:20.000 I've got the blinds closed. 06:20.000 --> 06:21.000 That's too bad. 06:21.000 --> 06:22.000 There is some daylight out there. 06:22.000 --> 06:24.000 It's not nighttime. 06:24.000 --> 06:26.000 And yeah, we are still sorry. 06:26.000 --> 06:29.000 I had my had my echo on there. 06:29.000 --> 06:31.000 Refocus this one more time. 06:31.000 --> 06:34.000 Come on, Sony, you can do it without crashing. 06:34.000 --> 06:38.000 We are still fighting this unseen mechanism of society, 06:38.000 --> 06:41.000 but we're starting to, I think, acknowledge that it might be 06:41.000 --> 06:43.000 more in our face than we thought. 06:43.000 --> 06:46.000 And that actually this is how informed consent has been 06:46.000 --> 06:48.000 ignored for the duration of the pandemic. 06:48.000 --> 06:52.000 And it is these people in cooperation with the national 06:52.000 --> 06:56.000 security operation and in cooperation likely with weaponized 06:56.000 --> 07:00.000 piles of money in cooperation with likely pharmaceutical companies 07:00.000 --> 07:03.000 and other manufacturers and things and contractors that were 07:03.000 --> 07:07.000 involved in this have all been involved in making sure that none 07:07.000 --> 07:10.000 of us can exercise informed consent that everybody accepted 07:10.000 --> 07:13.000 the worst case scenario and that we all tried our best to 07:13.000 --> 07:14.000 comply. 07:14.000 --> 07:16.000 And that was all part of the plan. 07:16.000 --> 07:18.000 That's what I'm trying to argue. 07:18.000 --> 07:19.000 That's what I'm trying to explain. 07:19.000 --> 07:22.000 They fooled us into solving a mystery by first keeping us at 07:22.000 --> 07:25.000 home and making us feel lost on a road all by ourselves and 07:25.000 --> 07:28.000 then getting picked up by this team worst case scenario told us 07:28.000 --> 07:32.000 crazy stories for a year about how they were going to break 07:32.000 --> 07:35.000 all the rules to make sure that we didn't die and that the 07:35.000 --> 07:37.000 worst case scenario was avoided. 07:37.000 --> 07:40.000 But if the worst case scenario came and people would didn't 07:40.000 --> 07:44.000 conform to the public health authority of the way that they 07:44.000 --> 07:48.000 needed to if people didn't take the necessary precautions we 07:48.000 --> 07:50.000 could have a disaster. 07:50.000 --> 07:56.000 And there were people behind the scenes in social media, 07:56.000 --> 08:01.000 mainstream media whose job was to do this to make sure that the 08:01.000 --> 08:05.000 worst case scenario was not considered to be a few thousand 08:05.000 --> 08:07.000 people a little worse than SARS one. 08:07.000 --> 08:10.000 No, the worst case scenario could be billions. 08:10.000 --> 08:16.000 And this worst case scenario was used to motivate us number one 08:16.000 --> 08:20.000 to conform to the lockdowns and conform to the school closures 08:20.000 --> 08:23.000 and conform to the masking in public. 08:23.000 --> 08:26.000 But it also tricked us into believing that there was a 08:26.000 --> 08:27.000 mystery to solve. 08:27.000 --> 08:29.000 That there was a crime being covered up. 08:29.000 --> 08:32.000 That there were lies being told. 08:33.000 --> 08:35.000 And this. 08:36.000 --> 08:38.000 What is running still? 08:39.000 --> 08:41.000 I can't see what's running still. 08:41.000 --> 08:43.000 Oh, I've just faded it out, didn't I? 08:43.000 --> 08:44.000 Okay, sorry. 08:45.000 --> 08:49.000 I feel as though it's really important that we keep repeating 08:49.000 --> 08:52.000 this over and over because every college kid that sees this for 08:52.000 --> 08:55.000 the first time is going to say, wow, wait, what did he say? 08:56.000 --> 09:00.000 Because when they were sent back to college in the fall of 2020, 09:00.000 --> 09:04.000 this was already really going on in the background in social 09:04.000 --> 09:05.000 media. 09:05.000 --> 09:07.000 This debate about a lab leak and whether they were covering it up 09:07.000 --> 09:10.000 and what those emails meant and what they were yelling about in 09:10.000 --> 09:12.000 the Senate with Rand Paul and Tony Fauci. 09:12.000 --> 09:13.000 This is all real. 09:13.000 --> 09:16.000 And those college kids were going to their, going to their 09:16.000 --> 09:18.000 parties at night talking about that stuff. 09:19.000 --> 09:22.000 Wondering whether the adults were going to solve the mystery 09:22.000 --> 09:25.000 and whether anyone's going to cop to the, to the responsibility 09:25.000 --> 09:26.000 of this. 09:27.000 --> 09:29.000 And that was in 2020. 09:30.000 --> 09:32.000 And in 2021. 09:33.000 --> 09:36.000 When we could have had people on the internet and on social 09:36.000 --> 09:39.000 media elevated spontaneously because they had the right message, 09:39.000 --> 09:43.000 which was that natural immunity was better than anything that 09:43.000 --> 09:46.000 the government would provide in the form of a vaccine, that 09:46.000 --> 09:50.000 natural immunity to previous encounters with RNAs like this 09:50.000 --> 09:51.000 might be relevant. 09:52.000 --> 09:55.000 And the shocking thing was is that there were people on all 09:55.000 --> 09:58.000 sides of that narrative pulling us away from that foundational 09:58.000 --> 10:01.000 truth that was that we could trust our bodies and our 10:01.000 --> 10:05.000 natural immunity and our previous health condition likely 10:05.000 --> 10:09.000 better than we could risk it on a novel countermeasure. 10:12.000 --> 10:16.000 And so I never got to this idea until 2023 because again, I was 10:16.000 --> 10:21.000 also swimming in this, in this muddy water filled with people 10:21.000 --> 10:24.000 who had all kinds of crazy stories and conjecture about what 10:24.000 --> 10:27.000 this had to be and why it had to be that. 10:30.000 --> 10:32.000 And it is not random. 10:33.000 --> 10:37.000 That people like Kevin McCare and in Charles Rixie and George 10:37.000 --> 10:43.000 Webb and Paul Cottrell and, and, and I mean, the list is just 10:43.000 --> 10:44.000 endless. 10:45.000 --> 10:48.000 Of people who were there at the beginning of the pandemic. 10:51.000 --> 10:54.000 Seating this worst case scenario narrative instead of 10:54.000 --> 10:58.000 amplifying the idea that, Holy cow, maybe wait a minute, 10:58.000 --> 11:01.000 wait a minute, maybe we should not trust the public health 11:01.000 --> 11:04.000 system that's given us this, this vaccine schedule that lots of 11:04.000 --> 11:07.000 people have known has been sketchy for years that everybody's 11:07.000 --> 11:08.000 been ignoring. 11:08.000 --> 11:11.000 Maybe we should slow down and hold, hold the phone for a minute. 11:11.000 --> 11:14.000 No, nobody with that voice was elevated. 11:15.000 --> 11:20.000 Until the end of the year in 2020 in Germany where Robert F. 11:20.000 --> 11:24.000 Kennedy Jr. spoke and did he speak about public health and 11:24.000 --> 11:26.000 about lockdowns and about how the, yes, he did. 11:26.000 --> 11:28.000 He spoke against all of it. 11:30.000 --> 11:31.000 Was he elevated? 11:31.000 --> 11:35.000 No, he was lambasted by his own wife. 11:37.000 --> 11:39.000 And what did they distort it into that? 11:39.000 --> 11:43.000 He compared it to something to do with the Nazi regime and 11:43.000 --> 11:45.000 something to do with World War II. 11:45.000 --> 11:47.000 And that was just unacceptable. 11:47.000 --> 11:49.000 It is unacceptable to make that parallel. 11:49.000 --> 11:53.000 Even though now we have someone as, as. 11:54.000 --> 12:00.000 Courageous as Vera Sharav making that very comparison and doing it 12:00.000 --> 12:03.000 with, with success. 12:03.000 --> 12:06.000 Also, nowhere on the Internet, nowhere to be found. 12:06.000 --> 12:09.000 Five part movie, nowhere to be found, nowhere to be heard, 12:09.000 --> 12:13.000 not on mainstream media, not on social media, just vanished. 12:13.000 --> 12:14.000 Gone. 12:17.000 --> 12:20.000 And so understand very clearly that this is the main message of 12:20.000 --> 12:24.000 giga-owned biological anything else that somebody says that 12:24.000 --> 12:28.000 we're off on is ignoring these basic messages, which is that 12:28.000 --> 12:31.000 intramuscular injection of any combination of substances with 12:31.000 --> 12:34.000 the intent of augmenting the immune system is dumb, which is 12:34.000 --> 12:38.000 the transfection is in healthy humans was criminally 12:38.000 --> 12:39.000 negligent. 12:39.000 --> 12:42.000 And there are thousands, if not hundreds of thousands of 12:42.000 --> 12:48.000 academic biologists and academic doctors, MedMDs, that 12:48.000 --> 12:51.000 should have known because they've used transfection in the past 12:51.000 --> 12:55.000 that this technique, this methodology would have been wholly 12:55.000 --> 12:58.000 inappropriate for augmenting the immune response of any healthy 12:58.000 --> 12:59.000 human. 12:59.000 --> 13:00.000 They should have known it. 13:00.000 --> 13:01.000 They did know it. 13:01.000 --> 13:03.000 They just chose not to know it. 13:03.000 --> 13:07.000 And that's because they have grown up and developed as 13:07.000 --> 13:11.000 career adults inside of a system, which makes them look the other 13:11.000 --> 13:16.000 way and stick right to their own little sliver of knowledge, 13:16.000 --> 13:18.000 whatever that might be. 13:18.000 --> 13:21.000 And not to step on the toes of any other academic edition that 13:21.000 --> 13:24.000 might have it wrong because that's not my expertise. 13:24.000 --> 13:25.000 That's theirs. 13:25.000 --> 13:29.000 I'll let their grant committee figure that out. 13:29.000 --> 13:33.000 And so I was the only person at the University of Pittsburgh 13:33.000 --> 13:37.000 School of Medicine that took the time to speak out against 13:37.000 --> 13:38.000 transfection. 13:38.000 --> 13:45.000 The only one of 135 members of the faculty of neurobiology that 13:45.000 --> 13:51.000 spoke out one of hundreds of faculty members that did not. 13:51.000 --> 13:55.000 I'm the only one that did in that entire med school, as far as I 13:55.000 --> 13:57.000 know, because no one's contacted me. 13:57.000 --> 14:03.000 No one said, Hey man, I totally agree with you. 14:03.000 --> 14:05.000 Think about that. 14:05.000 --> 14:08.000 And how many med schools are there in the United States where people 14:08.000 --> 14:12.000 have the professional expertise to have known that 14:12.000 --> 14:16.000 transfection and healthy humans is criminally negligent, to have 14:16.000 --> 14:20.000 known and could know now that Peter Cullis's admission that they 14:20.000 --> 14:24.000 can't control where the lipid nanoparticle goes is a very big 14:24.000 --> 14:25.000 problem. 14:26.000 --> 14:27.000 They could have known. 14:27.000 --> 14:28.000 They could know. 14:28.000 --> 14:34.000 They could come out and say it and be heroes. 14:34.000 --> 14:39.000 Nobody's doing it because it's guess not glorious enough to admit 14:39.000 --> 14:41.000 you had it wrong for three years. 14:41.000 --> 14:43.000 So nobody has the courage to do it. 14:43.000 --> 14:46.000 But I'll gladly admit it. 14:46.000 --> 14:53.000 But I will also claim with much vigor that when I was still 14:53.000 --> 14:57.000 consulting for Bobby on that book and CHD asked me to write a 14:57.000 --> 15:00.000 letter to the no virus people I wrote a letter which was inviting 15:00.000 --> 15:04.000 them to come to the table and talk about the molecular biological 15:04.000 --> 15:08.000 explanation I had where they might be right and some other 15:08.000 --> 15:12.000 molecular biologists might think they're right because they 15:12.000 --> 15:16.000 don't understand coronavirus biology and molecular biology 15:16.000 --> 15:19.000 well enough to understand how they've been bamboozled by PCR 15:19.000 --> 15:23.000 and sequencing but no. 15:23.000 --> 15:26.000 The no virus people would have nothing of it and went with 15:26.000 --> 15:30.000 nothing but insults and asked for only citations that had to do 15:30.000 --> 15:33.000 with isolation and purification and otherwise we don't want to 15:33.000 --> 15:36.000 talk to you. 15:36.000 --> 15:39.000 And those same no virus people are now starting to promote 15:39.000 --> 15:43.000 Denny Rankor as though he's realizing that virology is on 15:43.000 --> 15:48.000 thin ice and continuing to call me a gatekeeper. 15:48.000 --> 15:54.000 Even though I presented with them with the opportunity to win. 15:54.000 --> 15:58.000 They could have essentially gone on a stream with anybody from 15:58.000 --> 16:03.000 CHD including me and talked about how infectious clones or 16:03.000 --> 16:06.000 synthetic RNA and DNA could have been used to create the illusion 16:06.000 --> 16:11.000 of a pandemic that never was and is used to create the illusion 16:11.000 --> 16:15.000 of RNA virology in laboratories all around the world. 16:16.000 --> 16:21.000 To create this illusion of fidelity this illusion of it not 16:21.000 --> 16:27.000 being a ghost but something real RNA viruses are everywhere. 16:30.000 --> 16:33.000 Ladies and gentlemen we are going to break this but we're only 16:33.000 --> 16:38.000 going to break it when we stop the spread of these bad ideas. 16:38.000 --> 16:41.000 And so I've been guilty of spreading these bad ideas and all 16:41.000 --> 16:44.000 we've got to do is understand how we've all been guilty of 16:44.000 --> 16:49.000 these bad ideas and come to the humble realization that we 16:49.000 --> 16:52.000 bought into this illusion of consensus that the worst case 16:52.000 --> 16:55.000 scenario is again a function laboratory bioweapon and then 16:55.000 --> 16:56.000 now reject it. 16:56.000 --> 17:00.000 It's okay. 17:00.000 --> 17:02.000 It's the way you get out of a cult. 17:02.000 --> 17:04.000 It's the way you get out of a bad idea. 17:04.000 --> 17:09.000 It's the way you get out of you know it's the way you grow up. 17:09.000 --> 17:13.000 You admit mistakes and you move on. 17:13.000 --> 17:17.000 And so we've really got to strive to push for this to 17:17.000 --> 17:20.000 continue to be an understanding where people see this team 17:20.000 --> 17:24.000 worst case scenario and try to actively get off of it. 17:24.000 --> 17:27.000 Whether you were wittingly or unwittingly a part of this team 17:27.000 --> 17:30.000 we need to encourage people to get off of the team. 17:30.000 --> 17:35.000 Not join our team get off of the team where you think that 17:35.000 --> 17:38.000 bad cave viruses are real where you think that the diffuse 17:38.000 --> 17:41.000 proposal of putting fear and cleavage sites in bad cave 17:41.000 --> 17:45.000 viruses is real where you think that passage in ferrets can 17:45.000 --> 17:48.000 result in a pandemic virus where you think that they can 17:48.000 --> 17:51.000 stitch this stuff together and create something that can go 17:51.000 --> 17:52.000 around the world for five years. 17:52.000 --> 17:55.000 You've got to get off this team. 17:55.000 --> 17:57.000 That's all you've got to do. 17:57.000 --> 18:00.000 Once you're off this team we can all talk about what the best 18:00.000 --> 18:02.000 course forward is for our children. 18:02.000 --> 18:05.000 We can all talk about it. 18:05.000 --> 18:08.000 And we can all start to rebuild a model of our biology and our 18:08.000 --> 18:11.000 ecology and our place in the world that doesn't have to do 18:11.000 --> 18:13.000 with these fairy tales. 18:13.000 --> 18:15.000 Early, what I want is high morbidity. 18:15.000 --> 18:17.000 I want people to complain. 18:17.000 --> 18:18.000 So what do I do? 18:18.000 --> 18:19.000 I go to Des Moines. 18:19.000 --> 18:20.000 Ladies and gentlemen. 18:20.000 --> 18:23.000 Because we have reason to believe that this has been 18:23.000 --> 18:27.000 thought of, that faking it has been thought of, that exaggerating 18:27.000 --> 18:31.000 it has been thought of, that creating the illusion of it has 18:31.000 --> 18:32.000 been thought of. 18:32.000 --> 18:35.000 It has been planned and it has been gained out. 18:35.000 --> 18:38.000 And so that is the most likely thing that has occurred here. 18:38.000 --> 18:43.000 And it jives very well with the idea of who is objecting to it. 18:43.000 --> 18:48.000 Who has stepped forward repeatedly as a supposed 18:48.000 --> 18:52.000 dissident, supposedly people speaking out. 18:52.000 --> 18:56.000 And some of the most extraordinary people to speak out are people 18:56.000 --> 19:00.000 who are tied all the way back to HIV, the original virology 19:00.000 --> 19:01.000 illusion. 19:02.000 --> 19:05.000 And yet somehow or another, after three years of waking up, 19:05.000 --> 19:08.000 some of these people still haven't even bothered to think about 19:08.000 --> 19:09.000 that possibility. 19:11.000 --> 19:14.000 Haven't even bothered to entertain the possibility that the 19:14.000 --> 19:18.000 people who objected to the HIV PCR testing in the 80s were 19:18.000 --> 19:19.000 right. 19:23.000 --> 19:25.000 So yesterday we watched this video. 19:25.000 --> 19:27.000 And I thought it was really interesting. 19:27.000 --> 19:28.000 And I think you should watch it again. 19:29.000 --> 19:31.000 If you haven't seen it, you can download it, share it with your 19:31.000 --> 19:32.000 friends. 19:32.000 --> 19:33.000 I think it's really important today. 19:33.000 --> 19:36.000 I wanted to watch a video, which is kind of a shout out to my 19:36.000 --> 19:40.000 friend, Mark Koolack, who has been doing a lot of work researching 19:40.000 --> 19:45.000 the human genome project and what led up to it, what people were 19:45.000 --> 19:48.000 involved and what was really accomplished there. 19:48.000 --> 19:52.000 And he did a couple of videos, one video from a day or two ago 19:52.000 --> 19:53.000 about Eric Lander. 19:53.000 --> 19:55.000 And I just found it so extraordinary. 19:55.000 --> 19:58.000 Some of the admissions that Eric Lander met and made. 19:58.000 --> 20:01.000 And I went looking for other videos. 20:01.000 --> 20:06.000 And I found this Broad at 15 talk series, where the Broad 20:06.000 --> 20:09.000 Institute is 15 years old right at the start of the pandemic in 20:09.000 --> 20:10.000 2019. 20:10.000 --> 20:14.000 And the founder and director of the Broad Institute, none other 20:14.000 --> 20:21.000 than Eric Lander himself, the former supervisor and mentor of 20:22.000 --> 20:27.000 our friend, Kevin McCurnan, gave a presentation in 2019 kind of 20:27.000 --> 20:30.000 talking about the history of the human genome project. 20:30.000 --> 20:34.000 And I wanted to kind of visit some of the things that Mark had 20:34.000 --> 20:38.000 revealed or was able to find out in his video and cover them here 20:38.000 --> 20:41.000 kind of spontaneously see where this takes us. 20:41.000 --> 20:44.000 So hopefully this will be an entertaining video. 20:44.000 --> 20:48.000 I don't know if it is, but I assume it is because I think this 20:48.000 --> 20:50.000 guy is a really honest dude. 20:50.000 --> 20:51.000 He is a mathematician. 20:51.000 --> 20:55.000 He, his own story, by his own words, he says he didn't just kind 20:55.000 --> 20:58.000 of stumble in and ask the question and then they invited him to 20:58.000 --> 20:59.000 help. 20:59.000 --> 21:03.000 And the thing that I find most compelling about what Mark 21:03.000 --> 21:06.000 played yesterday and what I hope I am going to see here is that 21:06.000 --> 21:12.000 he is very forthright in talking about how the accomplishment of 21:12.000 --> 21:16.000 the human genome project was kind of exaggerated. 21:17.000 --> 21:26.000 I think a lot of people think that what they mean is if this 21:26.000 --> 21:31.000 book is a chromosome, then the human genome project, what they 21:31.000 --> 21:36.000 did was they took each chromosome and they took and read all 21:36.000 --> 21:40.000 the pages in the chromosome and then they put that on the 21:40.000 --> 21:44.000 internet and that is what the human genome project is. 21:45.000 --> 21:49.000 And there are 23 chromosomes and so 23 books of genes were 21:49.000 --> 21:52.000 read from beginning to end and published on the internet. 21:52.000 --> 21:57.000 That is what you might think happened, but that is not what 21:57.000 --> 21:58.000 happened. 21:58.000 --> 22:06.000 One of the, one of the concepts that you need to understand 22:06.000 --> 22:11.000 with respect to molecular biology is called a restriction map. 22:12.000 --> 22:16.000 And just to give you a brief idea of what a restriction map is, 22:16.000 --> 22:24.000 hopefully this pen will write. 22:24.000 --> 22:27.000 So a restriction map is really kind of an interesting idea 22:27.000 --> 22:30.000 because remember we are talking about genomes for which we have no 22:30.000 --> 22:31.000 idea what is really there. 22:31.000 --> 22:34.000 We don't know where the genes start and where they end. 22:34.000 --> 22:38.000 We call, we used to call these things introns and exons, but 22:39.000 --> 22:42.000 like it is a very, very subtle map. 22:42.000 --> 22:44.000 And then of course there might be reading frames. 22:44.000 --> 22:49.000 And so we don't really know what all these bases really mean 22:49.000 --> 22:50.000 together. 22:50.000 --> 22:53.000 And when we say we sequence the human genome, it is a very 22:53.000 --> 22:57.000 amorphous, it is a very amorphous claim. 22:57.000 --> 22:59.000 So what is a restriction map? 22:59.000 --> 23:07.000 You might remember the idea that if you have a set of 23:08.000 --> 23:13.000 nucleotides, what I am doing here is not using A's and T's and 23:13.000 --> 23:17.000 C's and G's because I am chicken to make a mistake while I do it 23:17.000 --> 23:18.000 and then look like a fool. 23:18.000 --> 23:22.000 But anyway, the point is that restriction enzymes oftentimes 23:22.000 --> 23:26.000 will cut a DNA, say like this. 23:26.000 --> 23:29.000 So if there was X's over here still. 23:29.000 --> 23:34.000 So what you end up creating is two strands of DNA that have kind 23:34.000 --> 23:39.000 of an end where there are bases hanging off and the hanging 23:39.000 --> 23:44.000 off bases end up being an end that will stick together, right? 23:44.000 --> 23:47.000 So this is kind of a sticky end here that will come together. 23:47.000 --> 23:52.000 And so these restriction enzymes all cut at a particular base 23:52.000 --> 23:55.000 combination, like a particular word. 23:55.000 --> 24:00.000 So this restriction enzyme might cut at AT, T, G, C or something 24:00.000 --> 24:02.000 like that and make this shape. 24:02.000 --> 24:06.000 Now the point is really cool, is that what you can do is you 24:06.000 --> 24:11.000 can take a whole genome and now if this was the whole genome of 24:11.000 --> 24:19.000 the human and you lined it all out in a line, right? 24:19.000 --> 24:26.000 And imagine that the restriction enzyme site that it 24:26.000 --> 24:29.000 recognizes, I hope you're following, the restriction enzyme 24:29.000 --> 24:32.000 site that it recognizes is regularly spaced throughout the 24:32.000 --> 24:33.000 genome. 24:33.000 --> 24:37.000 Then when you do a restriction map with that restriction enzyme, 24:37.000 --> 24:40.000 what you're going to do is you're going to cut the whole genome up 24:40.000 --> 24:43.000 using this specific enzyme. 24:43.000 --> 24:47.000 And when you do that, you end up getting all fragments of the same 24:47.000 --> 24:48.000 length. 24:48.000 --> 24:52.000 Now what's curious about this is that you might take the same 24:52.000 --> 24:56.000 restriction enzyme and you might cut up another person's genome 24:56.000 --> 24:59.000 and what you might find is that one of these fragments is 24:59.000 --> 25:06.000 missing instead, you might have a longer fragment. 25:06.000 --> 25:11.000 And what that means is that that restriction site, which in the 25:11.000 --> 25:15.000 control patient was equally spaced across the genome, is 25:15.000 --> 25:18.000 actually missing here and therefore when you make a 25:18.000 --> 25:24.000 restriction map of that person's genome, you get this many regular 25:24.000 --> 25:28.000 spaced fragments and then you get one double sized fragment. 25:28.000 --> 25:34.000 And so by using a restriction map, you can find mutations in a 25:34.000 --> 25:37.000 genome without knowing what you're finding, without knowing 25:37.000 --> 25:40.000 whether there's a gene there or not, without knowing what really 25:40.000 --> 25:44.000 that means, other than that sequence, which is regular in most of 25:44.000 --> 25:47.000 the genomes, is missing in this one. 25:47.000 --> 25:51.000 Now what I want you to conceive is the possibility that if you had 25:51.000 --> 25:54.000 a bunch of these enzymes that cut in a bunch of different ways, 25:54.000 --> 25:59.000 that you could make a bunch of different restriction enzyme maps 25:59.000 --> 26:05.000 of the genome and then wherever those places showed up, you would 26:05.000 --> 26:09.000 get a unique fragment, which would somehow allow you to 26:09.000 --> 26:12.000 characterize a certain portion of the genome, right? 26:12.000 --> 26:14.000 Depending on where those fragments were and how they were 26:14.000 --> 26:18.000 spaced, you would start to understand that in these places 26:18.000 --> 26:23.000 there exist these sequences and so the more of these enzymes 26:23.000 --> 26:27.000 with more different sequences you have, the more places on the genome 26:27.000 --> 26:31.000 you can map out by finding where they cut, right? 26:31.000 --> 26:34.000 Because every one of these places, if you zoomed into it, would 26:34.000 --> 26:37.000 actually look like this. 26:37.000 --> 26:38.000 You see? 26:38.000 --> 26:40.000 Is it starting to make sense? 26:40.000 --> 26:44.000 And so a restriction map was one of the first ways that the human 26:44.000 --> 26:49.000 genome project actually worked is that they would digest a part 26:49.000 --> 26:54.000 of the genome with a certain restriction enzyme and then map 26:54.000 --> 26:57.000 the fragment lengths that are created and then that told them 26:57.000 --> 27:00.000 where the cut sites were. 27:00.000 --> 27:04.000 And so it was kind of a cheat way of sequencing, but not really 27:04.000 --> 27:08.000 sequencing because then if in every genome there's this 27:08.000 --> 27:11.000 found, but then in this genome, maybe this fragment is only this 27:11.000 --> 27:15.000 long and so maybe that tells them something is missing in that 27:15.000 --> 27:18.000 genome or maybe it tells them that there's an insertion here or 27:18.000 --> 27:21.000 something like that and they can start to figure that stuff out. 27:21.000 --> 27:24.000 And so I'm not giving you all of the tricks of the trade. 27:24.000 --> 27:28.000 I'm trying to help you understand how a restriction map based on 27:28.000 --> 27:33.000 different restriction enzymes could be used to create a proxy of 27:33.000 --> 27:39.000 a map of the human genome that you could use to compare across 27:39.000 --> 27:40.000 genomes. 27:40.000 --> 27:46.000 How varied is the restriction map to this enzyme when we compare 27:46.000 --> 27:50.000 Chinese people to American people, when we compare people with 27:50.000 --> 27:52.000 schizophrenia to people who are normal. 27:52.000 --> 27:57.000 That's the kind of genome fishing kind of thing you do with a 27:57.000 --> 27:59.000 restriction enzyme map. 27:59.000 --> 28:03.000 And so when he starts to talk in this video about the mapping of the 28:03.000 --> 28:06.000 genome and he's probably going to talk about it, he's likely going to 28:06.000 --> 28:09.000 mention restriction enzymes and restriction maps. 28:09.000 --> 28:14.000 And if he doesn't, this is part of the methodology that was used to 28:14.000 --> 28:17.000 complete the mapping of the genome. 28:17.000 --> 28:22.000 But in reality, if they had many, many restriction maps of the human 28:22.000 --> 28:26.000 genome, they still haven't sequenced the human genome, but they kind of 28:26.000 --> 28:29.000 called it like they had sequenced the human genome. 28:29.000 --> 28:33.000 And the reason why is because that gave them enough markers within 28:33.000 --> 28:38.000 the genome to find marker locations that were associated with 28:38.000 --> 28:39.000 some diseases. 28:39.000 --> 28:42.000 And so it felt like they were already making forward progress. 28:42.000 --> 28:46.000 Man, I hope I'm doing okay and not ruining and losing the whole 28:46.000 --> 28:54.000 stream here. 28:54.000 --> 28:59.000 I was going to do, sorry, I'll pause that, I was going to do 28:59.000 --> 29:03.000 an interview with Ryan Christian of the last Vagabond today at 29:03.000 --> 29:06.000 noon, but it got rescheduled so I'm doing it at the same time on 29:06.000 --> 29:24.000 Monday. 29:24.000 --> 29:27.000 Oh yeah, there's going to be a little dedication to a teacher, 29:27.000 --> 29:31.000 a really smart mathematician that worked with him for a little 29:31.000 --> 29:35.000 while named Alana Hetcher. 29:35.000 --> 29:38.000 I don't want to belittle her memory. 29:38.000 --> 29:40.000 Good evening, everybody. 29:40.000 --> 29:42.000 I'm going to skip over it though. 29:42.000 --> 29:44.000 She was really smart. 29:44.000 --> 29:47.000 She was 17 years old when she went to college. 29:47.000 --> 29:51.000 And she was a mathematician, there she is. 29:51.000 --> 29:55.000 And she was a Rhodes Scholar and then she went to Oxford and then 29:55.000 --> 29:58.000 went to MIT and Harvard and somehow she's gone now. 29:58.000 --> 30:00.000 I don't know what happened to her, she's dead. 30:00.000 --> 30:03.000 Anyway, I'm going to move forward and then he's going to give a 30:03.000 --> 30:09.000 little tiny talk about her and then we're going to start. 30:09.000 --> 30:16.000 The last 15 years, this broke at 15 lecture series. 30:16.000 --> 30:17.000 Here we go. 30:17.000 --> 30:22.000 Is really a way of celebrating what has been an incredible... 30:23.000 --> 30:28.000 I've been getting that a lot on the recording but I don't hear it here on the show. 30:28.000 --> 30:31.000 We could go back a little before that 25 years. 30:31.000 --> 30:33.000 This period of science has been extraordinary. 30:33.000 --> 30:37.000 We thought that to celebrate the 15th anniversary of the Broad, 30:37.000 --> 30:42.000 we would ask a bunch of people in the Broad community to give talks about 30:42.000 --> 30:47.000 the past, the present and looking ahead into the future. 30:47.000 --> 30:50.000 It's a lot of fun to do that. 30:50.000 --> 30:53.000 It's a kind of step back day to day at the bench. 30:53.000 --> 30:59.000 One's always mindful of the fact that progress doesn't always go so fast. 30:59.000 --> 31:04.000 But if you integrate over a year or several years or a decade, 31:04.000 --> 31:07.000 it's just stunning what has been going on. 31:07.000 --> 31:09.000 And so this is a chance for us to look. 31:09.000 --> 31:12.000 It's not only about things that have gone on at the Broad, 31:12.000 --> 31:16.000 but topics that the Broad cares a lot about and works in a lot. 31:16.000 --> 31:20.000 We kind of review this whole sleep of history. 31:20.000 --> 31:29.000 One of the problems of agreeing that we should have a Broad at 15 lecture series 31:29.000 --> 31:35.000 was that when the question came, would I give the first talk in the Broad at 15 lecture series? 31:35.000 --> 31:37.000 I couldn't really say no. 31:37.000 --> 31:40.000 And so here I am to kick off this series. 31:40.000 --> 31:50.000 But it is a special lecture in this series because it is also, as was mentioned, 31:50.000 --> 31:53.000 the Eliana Hector Memorial lecture. 31:53.000 --> 31:55.000 I'm annoyed though. 31:55.000 --> 32:01.000 I want to pause this and just acknowledge that I hear the static when I play it back on my phone as well. 32:01.000 --> 32:07.000 And I've been trying to find it for a while, but I don't hear it on this end. 32:07.000 --> 32:12.000 Like, for example, is it gone now if I drop that one or if I drop that one? 32:12.000 --> 32:16.000 And if you still hear it, then I don't know where it's coming from. 32:16.000 --> 32:21.000 It's not gone, right? Yeah. So that's still super annoying. 32:21.000 --> 32:31.000 If I lower this one, is it better? I mean, does it go down when I lower my voice? 32:31.000 --> 32:36.000 I appreciate you guys trying to help me troubleshoot this because I find it also really annoying 32:36.000 --> 32:42.000 because I know that my sound in my ears is really good. 32:42.000 --> 32:44.000 So we're going to have to troubleshoot this. 32:44.000 --> 32:47.000 I'm not going to troubleshoot it today, but I am well, not with you now. 32:47.000 --> 32:52.000 But I do think we could even do a stream later tonight where we just come on and we troubleshoot it 32:52.000 --> 32:56.000 and then delete the stream afterward. I think it would be really worthwhile. 32:56.000 --> 33:00.000 It's a remarkable person. 33:00.000 --> 33:06.000 She worked for significant time in my lab. I collaborated with her scientifically, 33:06.000 --> 33:10.000 so this is particularly meaningful for me. 33:10.000 --> 33:17.000 And before her untimely passing, she was magna cum laude, 33:17.000 --> 33:24.000 graduate of the University of Washington at the age of 18, with a degree in mathematics, 33:24.000 --> 33:31.000 and went on to become the second youngest Rhodes Scholar in history. 33:31.000 --> 33:34.000 And there was a long history, by the way, of Rhodes Scholars. 33:34.000 --> 33:41.000 She then continued her studies in medical school at Harvard and at MIT. 33:41.000 --> 33:46.000 But beyond those things, she was also a remarkably gifted writer. 33:46.000 --> 33:51.000 She wrote short stories. 33:51.000 --> 33:57.000 She wrote science, and in everything she wrote, she did it with tremendous care 33:57.000 --> 34:03.000 and tremendous thought. She's the only person with whom I have ever engaged in a new discussion. 34:03.000 --> 34:05.000 Oh, sorry. I thought this was done. 34:05.000 --> 34:07.000 On the difference between a lot. 34:07.000 --> 34:14.000 I'm sure she's really nice. I'm sure she's really nice. 34:14.000 --> 34:17.000 I'm not being a creep. I just don't want to. We can do that. 34:17.000 --> 34:25.000 This doesn't in the future. I get to do the human genomic revolution, which has really been remarkable. 34:25.000 --> 34:32.000 I'm going to tell you a lot of science, but not require that you know lots of science to start. 34:32.000 --> 34:39.000 But I'm not going to pull the punches, because I'm going to really try to tell you this amazing arc that has happened. 34:39.000 --> 34:44.000 And there are going to be times I'm going to tell you really interesting, complicated things, 34:44.000 --> 34:52.000 because I think that's the way to best appreciate the journey. But don't worry. 34:52.000 --> 34:56.000 It's not like people didn't wonder about the causes of human diseases. 34:56.000 --> 35:02.000 People have been wondering about the causes of diseases that ran in families for centuries. 35:02.000 --> 35:08.000 People knew that certain diseases, certain traits ran in families. 35:08.000 --> 35:14.000 But actually painting down what was the basis of those diseases was really hard. 35:14.000 --> 35:23.000 It could be done sometimes. For example, sickle cell anemia was worked out because it was obviously a disease of red blood cells. 35:23.000 --> 35:29.000 And red blood cells basically are bags of hemoglobin. They don't have a nucleus anymore. 35:29.000 --> 35:33.000 There's the bag of hemoglobin. If there's a problem with the red blood cell, it's probably the hemoglobin. 35:33.000 --> 35:37.000 It's a pretty good guess. And it was worked out a long time ago that it was. 35:37.000 --> 35:42.000 It was a single change in a building block and amino acid of the hemoglobin protein. 35:42.000 --> 35:46.000 And that was worked out of its molecular mechanism. 35:46.000 --> 35:54.000 Then there's phenylketonuria. It used to be a leading cause of mental retardation. 35:54.000 --> 36:03.000 But today it's not anymore because people realize that phenylketonuria was caused by the inability to break down a nutrient, 36:03.000 --> 36:11.000 the amino acid phenylalanine. And now every baby in the United States gets a heel stick at about eight days of age 36:11.000 --> 36:20.000 to test and find the one in a million kids who has phenylketonuria so that they go on a low phenylalanine diet 36:20.000 --> 36:25.000 who don't develop that mental retardation. It's a brilliant question. 36:25.000 --> 36:31.000 In fact, if you look on a diet coke can, next time you get a diet coke can, there's a warning to phenylketonurics 36:31.000 --> 36:36.000 that it contains significant amounts of phenylalanine because aspartame has phenylalanine. 36:36.000 --> 36:41.000 So there's even a story about genetic disease on every diet coke can. 36:41.000 --> 36:46.000 Well, but these are great. But the thing about both of these is that they were ad hoc. 36:46.000 --> 36:51.000 They were not general strategies for finding disease genes. 36:51.000 --> 36:56.000 They required inspired guessing. And there's nothing wrong with inspired guessing. 36:56.000 --> 37:03.000 I don't know how to do it most of the time. And one would rather have a systematic approach. 37:03.000 --> 37:12.000 So the story I'm going to tell you is how science moved on to take on diseases where the genetic cause of the protein, 37:12.000 --> 37:17.000 the mechanism in the cells were not obvious, where they were not candidate genes. 37:17.000 --> 37:25.000 But when I first came into the story in the 1980s, everything was about you had to have a candidate gene. 37:25.000 --> 37:32.000 How would you get it? And there was a big fat book, Victor McCusick's catalog of Mendelian diseases, 37:32.000 --> 37:36.000 single gene diseases that you could tell by the way they were inherited in families. 37:36.000 --> 37:41.000 And there were 4,000 of these things and there weren't good candidates for more than a handful. 37:41.000 --> 37:49.000 And then what about things that weren't single gene diseases that transmitted in families like Mendel's rules? 37:49.000 --> 37:56.000 Height, weight, schizophrenia, heart disease. 37:56.000 --> 38:02.000 How in the world are you going to find out that it's not a single gene, multiple genes that might be contributing? 38:02.000 --> 38:05.000 We didn't even know how many genes might be contributing to that. 38:05.000 --> 38:11.000 I'm just going to tell you the story of how that happened, where we are today and where we're going. 38:11.000 --> 38:24.000 Do you think it's odd that somewhere along the line, disease as infectious disease and the word disease for genetic disease got kind of mixed together? 38:25.000 --> 38:38.000 Why they chose to start calling it genetic disease as opposed to genetic disorders or genetic anomalies or whatever is a choice, a conscious choice? 38:38.000 --> 38:46.000 And I don't think you should underestimate how important that choice is because they want to put them under the same rubric of public health. 38:46.000 --> 38:53.000 They want you to think that this is part of the same sort of motivation and impetus and morality. 38:53.000 --> 39:00.000 And that's the magic of this. That's part of the magic that's happening here already, where these people think that nothing has happened. 39:00.000 --> 39:15.000 We're already there, only eight minutes into the talk, and he's already convinced them that genetic disease is something that needs to be cured and can be fixed if we could just find the gene. 39:15.000 --> 39:22.000 It's impressive, but just think about how impressive this enchantment is that it's been going on for a couple of three decades. 39:22.000 --> 39:36.000 Now to do that, I'm going to briefly take you back, not 15 years, but a lot earlier than that, to 1865, because if we're going to talk genetics, I have to at least mention the founding of genetics. 39:36.000 --> 39:55.000 In 1865 by Gregor Mendel, Gregor Mendel was a remarkable scientist, and you know, you always tell the story in the high schools about how Gregor Mendel was this monk working in a monastery who did these experiments with pea plants. 39:56.000 --> 40:16.000 They don't tell you he actually studied math and physics and was hired by the abbot of the monastery who was basically running a small biotech incubator in the city of Brno, and they deliberately were working on this stuff in order to improve agriculture to make the world better. 40:16.000 --> 40:24.000 So this was not some isolated scientist. He was in, you know, kind of a Cambridge, Massachusetts of his time in some way. 40:26.000 --> 40:37.000 Now, he worked at this beautiful mathematical model, from which you can infer that there must be particles of inheritance, what would later come to be called genes. 40:38.000 --> 40:54.000 And it was so beautiful and so mathematical, and it explains so much that it was completely ignored for the next 35 years, because biologists don't like abstract mathematical things. 40:55.000 --> 41:00.000 If you're inferring the existence of genes, they say, I want to see the gene, where's the gene, what is it? 41:00.000 --> 41:15.000 And an abstraction doesn't really help anybody. So it was largely when dormant for about 35 years, until the year 1900, when genetics was rediscovered by three different groups simultaneously in the year 1900. 41:16.000 --> 41:26.000 And this field got underway in a serious fashion, studying genetics, studying mutants, studying organisms that lack a particular gene. 41:27.000 --> 41:30.000 And that causes a particular trait, what we'll call phenotype. 41:31.000 --> 41:53.000 Now, almost exactly at the same moment, really just a couple of years earlier, a parallel field got started, biochemistry, grinding up organisms and purifying a particular component that could carry out a function, like a chemical reaction, digesting sugars, grinding up yeast to figure out how can they digest sugars and purifying components. 41:54.000 --> 42:04.000 And these two complementary methods, studying an organism minus a component, studying a component taken away from the organism, were almost perfectly dual to each other. 42:05.000 --> 42:13.000 They were both powerful, and for almost 50 years, they had almost nothing to say to each other, because nobody understood what those components were. 42:14.000 --> 42:21.000 And then the really interesting intellectual advance in the 20th century was the recognition that they were connected. 42:22.000 --> 42:29.000 The components of the geneticist study, genes, actually encoded the instructions for proteins. 42:30.000 --> 42:37.000 And that recognition, that these were two sides of the same coin, really gave rise to this field of molecular biology. 42:38.000 --> 42:45.000 The recognition that DNA, its double helical structure, let it replicate, that it encoded the instructions for proteins. 42:46.000 --> 42:50.000 And we now had this amazing triangle on the intellectual unification. 42:51.000 --> 42:56.000 And by the 1960s, people had even worked out the genetic code. 42:57.000 --> 43:05.000 They knew which three letters of DNA specified which building blocks of proteins, a lookup table of three letter codons to amino acids. 43:07.000 --> 43:17.000 They were so pleased with themselves that they declared victory, said that molecular biology was now largely solved, and let's go on and study the brain. 43:19.000 --> 43:22.000 And some people left the field because they figured it was done. 43:23.000 --> 43:26.000 And then as so often happens, a younger generation arose. 43:27.000 --> 43:33.000 And the younger generation said, well, not so fast, we actually can't even read a single gene yet. 43:34.000 --> 43:38.000 We know an abstract what the genetic code is, but we can't read one gene. 43:39.000 --> 43:40.000 Maybe we're not finished yet. 43:41.000 --> 43:47.000 And they gave rise to amazing technologies in the 1970s of recombinant DNA. 43:48.000 --> 43:59.000 The recombinant DNA revolution gave us the ability to purify genes away from each other by putting them into little pieces of DNA that could replicate in bacteria. 43:59.000 --> 44:02.000 And each bacteria would pick up a different piece of human DNA and copy it. 44:03.000 --> 44:05.000 And then you could get purified forms of individual genes. 44:06.000 --> 44:11.000 And then they worked out how to study those genes and even to the level of reading out their DNA letters. 44:12.000 --> 44:27.000 Now, keep in mind that what he's saying here basically is that the use of recombinant DNA and reverse molecular genetics, the next major jump in molecular biology occurred. 44:28.000 --> 44:30.000 So keep in mind what we're talking about here. 44:31.000 --> 44:37.000 We are talking about in the 1970s when Ralph Barracks says he started working on coronavirus in 1984. 44:38.000 --> 44:43.000 In the 1970s, they started using recombinant DNA and cloning to look at sequencing genes. 44:44.000 --> 44:52.000 They used recombinant DNA to make more DNA, which opened up a whole world of exploration. 44:53.000 --> 45:02.000 And a whole new palette of techniques that involved restriction enzymes and ligation methods using restriction enzymes. 45:04.000 --> 45:06.000 So they've been baking like this for a long time. 45:08.000 --> 45:18.000 When Ralph Barrack came on the scene and wanted to use baking techniques with coronaviruses, it wasn't like he had to figure out how bread and cakes were done. 45:19.000 --> 45:27.000 Do you see what I'm trying to say here? I hope you see that this history is long and rich and not starting in North Carolina. 45:30.000 --> 45:41.000 And that's why I estimate that a lot of people have trouble with me pointing out that RNA virology is enabled by recombinant DNA technology and reverse genetics. 45:42.000 --> 45:52.000 There's no other way that we would even have anything remotely like the cartoon virology that we now have if it wasn't for this technology in the 70s. 45:53.000 --> 45:59.000 And then it's ever increasing application, ever increasing fidelity and ever increasing cheapness. 46:03.000 --> 46:04.000 I hope you can see it. 46:05.000 --> 46:07.000 It's sequencing genes. 46:08.000 --> 46:12.000 And you could use that to find the gene where you knew the protein insulin. 46:13.000 --> 46:15.000 If you knew insulin, you could find the gene for insulin. 46:16.000 --> 46:19.000 You could clone the gene for insulin, make tons of insulin, and give it to diabetics. 46:20.000 --> 46:27.000 If you wanted to find the gene for growth hormone, something that had been discovered, you could look for the gene for growth hormone that encoded that particular protein. 46:27.000 --> 46:36.000 So why in the world if you're studying an RNA virus that barely replicates, you can't get any infectious material of that the vast majority of the particles are replication and competent. 46:36.000 --> 46:38.000 Why would you ever start with RNA then? 46:44.000 --> 46:51.000 Infectious clones, ladies and gentlemen, they are ignoring it. They ruined my life because of it. They threw me out of it, be out of CHD because of it. 46:52.000 --> 46:54.000 It's all the same story. It's all the same thing. 46:54.000 --> 47:08.000 It's the one thing that we have to ignore. It's the one thing we have to ignore that RNA virology is an illusion that is sustained by, or possible by, just simple synthetic molecular biology done. 47:09.000 --> 47:17.000 Produce recombinant growth hormone and hemoglobin and onward as long as you knew what you were looking for. 47:18.000 --> 47:30.000 And yet to come back to what I said at the beginning, there was a problem. For most diseases, we didn't know what we were looking for. 47:31.000 --> 47:42.000 So while this technology was super cool and led to the entire biotech industry, it could produce recombinant proteins, but it couldn't find the gene for cystic fibrosis. 47:43.000 --> 47:44.000 I see. 47:45.000 --> 47:47.000 And this is the story I want to tell. 47:48.000 --> 47:49.000 This is the story. 47:50.000 --> 47:57.000 The principles first for how you could map genes when you didn't actually know what you were looking for. 47:58.000 --> 48:06.000 Sometimes it's hard to find something when you know what you're looking for, but it's much harder to find something when you don't know what you're looking for, because how would you find it? 48:07.000 --> 48:33.000 Well, the answer was that a brilliant MIT professor and colleague, David Botstein in 1980, proposed an extremely simple, elegant idea that had actually been developed in fruit flies back in 1911 and had been used for fruit fly genetics and corn genetics and other things, which is that when genomes, when chromosomes are transmitted in families 48:34.000 --> 48:43.000 and across the fruit flies or across of maize or other organisms, things that are nearby on the chromosome. 48:44.000 --> 49:02.000 So I'll pause it right here. Remember when I talked about at the beginning of this little lecture here that says, can you see my arrow, that says construction of a genetic linkage map in man using restriction fragment length polymorphisms. 49:03.000 --> 49:10.000 What are restriction fragments? Restriction fragments are these things that we just talked about right here. 49:11.000 --> 49:19.000 These are the fragments that are cut up when you cut up a whole sample of DNA using one enzyme that makes a specific cut at a specific base combination. 49:20.000 --> 49:45.000 Those enzymes will make a predictable set of fragments out of the DNA sample, and polymorphisms, meaning alternatives or variation in these fragment lengths, can give them clues as to where in the genome these specific Mendelian traits are being inherited from. 49:46.000 --> 49:57.000 And if you do enough of these restriction enzyme maps using different restriction enzymes, you get a pretty good idea of how the variation in the genome occurs and where the anomalies are. 49:58.000 --> 50:14.000 That's what he's talking about when he says, and when these shows this little paper here saying a genetic linkage map in man using restriction fragment length polymorphisms, and that's what he also means by saying you can look for things without knowing what you're looking for. 50:15.000 --> 50:37.000 Because in these family trees, if the disease segregates with any of these polymorphisms in what should be a pretty uniform restriction enzyme map, or restriction enzyme maps, then the people know where, the genealogists know where on the chromosome and what chromosome to look at. 50:37.000 --> 50:40.000 Does that make sense? I hope this makes sense. 50:41.000 --> 50:59.000 Other organisms. Things that are nearby on the chromosome tend to travel together. Sometimes they genetically recombine, but the closer they are, the more often this form of the gene and that form of the gene that's that are bound on the same chromosome would continue to travel together. 51:00.000 --> 51:13.000 And so in fruit flies, what they did was if you had a trait that you didn't know what the gene was, you could look at how it was inherited in a family of fruit flies relative to lots of other genetic markers. 51:14.000 --> 51:26.000 Genetic markers that controlled white eyes, curly wings, funny hairs, other things, and they built genetic maps of all these funny traits and the funny flies, and then they mapped a new thing relative to that. 51:26.000 --> 51:40.000 The problem with doing this in humans was we haven't got wings, we haven't got these funny white eyes and also you're not allowed to set up crosses. 51:40.000 --> 51:46.000 I mean you're allowed to set up crosses with somebody, you have to talk about it and all that. 51:47.000 --> 52:01.000 And then it turns out you've never actually produced a statistically significant number of children because those of you who have children know that reaching statistical significance is not compatible with raising the kids. 52:02.000 --> 52:09.000 So the question is, how are you going to do this for the human population? That's what David Botstein worked out. He said, we don't need to worry about it. 52:10.000 --> 52:21.000 Just think natural families. There's inherent genetic variation in all of us. And let's just pretend that all that genetic variation we put there deliberately and just use it to trace inheritance in families. 52:22.000 --> 52:35.000 So really you only need the genes. You just need the whole genome and then we can make restriction maps and we can use algorithms to find where the polymorphisms are correlated with the traits we're looking at. 52:35.000 --> 52:39.000 And then we can look across large numbers and voila. 52:42.000 --> 52:47.000 You don't even need hypothesis anymore. You just wait for the computer to spit it out. 52:47.000 --> 53:04.000 Can you argue any better for the hypothesis of Mark Hewson-Tonic, Mark Koolack, and myself really, I'm on board as well with the idea that the primary goal here is to shift our understanding of our sovereignty 53:04.000 --> 53:17.000 so that we think it's our duty to hand over our medical data and our genetic data with the hopes that someday a future AI will be able to mine that database and make us live forever. 53:19.000 --> 53:28.000 And they need us. They need us because they need our genome from the very beginning and they need lots of measurements of it so that they can monitor all the epigenetic changes that occur. 53:29.000 --> 53:42.000 And so they need us to understand as little of this as possible. They need us to think it is as simple as a sample and then that's fine and we're done and everything else is going to be great. 53:43.000 --> 53:51.000 This is a mythology that they've been telling each other and claiming in theory is going to work for three decades and they aren't any closer to this happening. 53:52.000 --> 54:01.000 Some of these methodologies have become more powerful. Some of these methodologies have become cheaper. Some of these methodologies have become faster. 54:02.000 --> 54:17.000 But none of them have enabled a useful understanding of the genome and none of them have enabled a useful understanding of the complexity of the pattern integrity that is any one system in the human existence in the human organism. 54:18.000 --> 54:21.000 Never mind how these systems work together in concert. 54:23.000 --> 54:36.000 But again, we are listening to how this mythology was developed and how this enchantment has evolved over 30 years to really encompass almost all of us. 54:37.000 --> 54:47.000 And so we said find lots of genetic variation and trace inheritance with it. It was a brilliant idea and it actually worked. 54:48.000 --> 54:57.000 I met David in the early 1980s and we began talking about what would you do if you didn't have a single gene trait and you didn't have families? 54:58.000 --> 55:02.000 Could you do this even if you didn't have families and the genetics was more complicated? 55:02.000 --> 55:26.000 We actually worked out ways to do this for common diseases that might be polygenic, might have multiple genes, by instead of tracing things slavishly in a family, treating an entire population as if it was one big family and using lots of genetic markers to recognize segments of the genome that had come from a common ancestor. 55:27.000 --> 55:32.000 Common ancestral segments. So you could pretend like it was a big family. I just happened to have laughed. 55:33.000 --> 55:40.000 Please understand that they are still using the very basic principle that I told you in the beginning of the talk. 55:41.000 --> 55:56.000 They are using restriction maps and restriction enzyme polymorphisms. Restriction fragment polymorphisms. So a restriction fragment is simply a piece of DNA that results from the cutting of a DNA sample with a single restriction enzyme. 55:58.000 --> 56:07.000 And so because each restriction enzyme cuts at a very specific site, you can imagine, for example, here, I'll give you a very good example of it. Are you ready? 56:08.000 --> 56:09.000 And then you'll understand. 56:12.000 --> 56:19.000 Here's a book you should read if you haven't read it. It's by Soren Kierkegaard. It's called Sickness unto Death. If you haven't read it, please read it. 56:20.000 --> 56:38.000 If we were to cut this book with a restriction enzyme for the English language and we were to cut the and we were to choose a restriction enzyme that cut between the words. 56:39.000 --> 56:51.000 Let's say between the words just and because. And so anywhere in this book where the words just and because show up, the text will be cut in half right there. 56:52.000 --> 57:01.000 And so let's say it shows up in five places. Then the book will be divided in five places with a known number of pages between each of those cuts. 57:02.000 --> 57:11.000 If we had another restriction enzyme would have cut at another different combination of words, then we would get another set of segments of this book cut where those two words are. 57:12.000 --> 57:30.000 And if we compare this book to another book using the same two enzymes that cut between just because and between therefore, then what we would find are that each book could be characterized by the way that it is cut by those two enzymes. 57:31.000 --> 57:38.000 And the more of those enzymes we have, the more of those maps of a book we could make and then the more comparisons we could make across books. 57:40.000 --> 57:56.000 Now very similar, but more importantly, books that are all related like people's genetics will cut in very similar places with very similar enzymes and where they don't cut will be a clue as to what's different or that there. 57:57.000 --> 58:01.000 That there exists a polymorphism at that location. 58:02.000 --> 58:15.000 Hope this is still making sense. Hope I'm not beating any dead horses or anything like that. I just want everybody to understand that it's not that they're reading the whole book from beginning to end and then searching the index or searching the the genome word by word. 58:16.000 --> 58:31.000 They have made maps using these restriction enzymes and then they're looking for patterns that are off in people that have the gene or the combination of disease traits that they're looking for. Hope this is clear. 58:32.000 --> 58:36.000 The previous hundred generations, but if I had a lot of genetic markers, I could still see that. 58:37.000 --> 58:53.000 Now this was all great. It was used in the 1980s to clone a couple of genes, the cystic fiber. I remember the day David Botstein crossed the street, called me into his office, closed the door, locks it and says, the gene for cystic fibrosis is on chromosome seven. 58:54.000 --> 58:57.000 He was really excited about this. This was pretty cool. 58:58.000 --> 59:13.000 Now at the time, the genetic marker that was linked to the cystic fibrosis gene, it traveled together 90% of the time, which sounds good, but it's actually terrible because it meant it was 10 million base pairs of DNA away. 59:14.000 --> 59:25.000 And in those ancient days, traveling a distance of 10 million bases to find the gene was really hard. Anyway, it took five years of work. It took tens of millions of dollars, probably a hundred people were. 59:26.000 --> 59:39.000 So what he's explaining to you is that what they found was the polymorphism linked to cystic fibrosis on a chromosome, but then they were still millions of bases away from where the gene ended up being. 59:40.000 --> 59:49.000 And so what again, what they do is they had to then take this region and do the same kind of study more focused on this region to eventually get in there. 59:50.000 --> 01:00:05.000 But I hope you can understand how, again, we're looking for markers that are segregating with other markers. And so we're looking for a needle in a haystack, and then we find the right haystack, but we still need to find the needle in it. 01:00:06.000 --> 01:00:18.000 And that, at some point, was considered the human genome project scoring a touchdown. You see? You see how amazing it is? They still haven't done it. 01:00:19.000 --> 01:00:25.000 And by 1989, they found the gene for cystic fibrosis. And when they found it, it looked like this. 01:00:26.000 --> 01:00:39.000 It had a lot of A's, T's, T's, G's. But I call your attention to the little red box there because those three letters, TTT, were missing in most of the people who had cystic fibrosis. 01:00:40.000 --> 01:00:48.000 A deletion of three letters deleted one building block, one amino acid, phenylalanine in position 508, and that was a major cause of cystic fibrosis. 01:00:49.000 --> 01:01:04.000 Not only that, so that means you can do genetic testing and see who's got that. It'd be very easy. Not only that, you could toss the sequence of the protein in the computer and ask the computer to compare it to every other protein anybody else had sequenced to that date and ask, was it similar to anything? 01:01:05.000 --> 01:01:14.000 And they did that, and they found, oh, my goodness, it's similar to a whole bunch of other proteins that sit in membranes and transport things. 01:01:15.000 --> 01:01:25.000 Isn't that weird? So when they found the gene and they had the whole sequence, they couldn't really tell what it was without comparing it to other proteins that had already been sequenced. 01:01:26.000 --> 01:01:36.000 They couldn't even compare it to genes. They had to compare it to proteins that had already been sequenced. And when they did that, they found that it was a transporter, an inter... 01:01:37.000 --> 01:01:44.000 A membrane-bound transporter, sorry, why did that take me so long to fight? 01:01:45.000 --> 01:02:01.000 So that's pretty interesting, because it shows you again, even when they find the segment in the genome, they can't just look at it and say, oh, wow, that's a seven transmembrane spanning ion channel related to the nicotinic acetylcholine receptors. 01:02:02.000 --> 01:02:08.000 They couldn't see that right away, still can't see that right away without going back to the protein data. 01:02:11.000 --> 01:02:24.000 Because there are, of course, enhancers and other sequences, extemporaneous outside of the actual coding sequence of the protein that are involved in that gene as well. 01:02:25.000 --> 01:02:34.000 That we barely even have a grasp on understanding, but certainly epigenetics have something to do with enhancers and whatever, and whether they're activated or not, promoters. 01:02:35.000 --> 01:02:53.000 These are all regulatory mechanisms we barely even scratch the surface of. And so again, you can see here stumbling in the dark, looking for a piece of DNA in a restriction map that correlates with a disease state, and then looking at that fragment and hoping that somewhere in that fragment is something that makes sense. 01:02:54.000 --> 01:02:59.000 And after looking and looking and looking, they found a protein that seems to resemble, maybe it's like that. 01:03:02.000 --> 01:03:05.000 So the human genome project is not all they cracked it up to be. 01:03:06.000 --> 01:03:15.000 You must, the cystic fibrosis gene must be a transporter of ions, in fact. That's amazing. You didn't have to do any experiments. 01:03:16.000 --> 01:03:25.000 You sequenced this stuff. You could do genetic diagnostics, and you could find out that the function was probably this. You have to do an experiment to confirm it. 01:03:26.000 --> 01:03:38.000 But being handed a hypothesis on a silver platter was pretty amazing. So this was great. Everybody was very excited that we could find genes without knowing what they were. There was only one problem. 01:03:39.000 --> 01:03:53.000 I mentioned like lots of years, lots of money, lots of people, and that was just one of thousands and thousands of problems you'd want to apply it to. 01:03:54.000 --> 01:04:06.000 The problem is the human genome was a very big place. It was three billion bases long, and a typical gene might only have 3,000 letters of coding information, and the mutation might be one or two or three letters. 01:04:07.000 --> 01:04:11.000 And how would you find this needle in a haystack, actually? 01:04:12.000 --> 01:04:33.000 And so three billion bases mapped with restriction fragment maps. Three billion bases mapped with restriction enzyme mapping. That's it. Just cutting fragments, looking for places where the fragments don't show up in the same pattern, and then zeroing in on that part of the genome. 01:04:33.000 --> 01:04:51.000 Can you understand it now? I hope you can see it. It's really not sequencing the whole book and putting it out there. It's not, you know, here's an ion channel, here's an ion channel, here's a G protein coupled receptor, here's a sodium channel, here's a 01:04:52.000 --> 01:04:57.000 myelin protein, here's, it's not like that. It's not like that at all. 01:04:57.000 --> 01:05:13.000 Calculation correctly, it's somewhat worse than a needle in a haystack. Needles make up more than one three billionth of a haystack if you calculate correctly. So somewhat worse than needles in a haystack. 01:05:14.000 --> 01:05:19.000 Well, that is what led us as a field to the idea that we needed to action. 01:05:20.000 --> 01:05:31.000 Nice photograph. I know that photograph. Don't you know that photograph? I know that photograph here. Let me play a video of that photograph. I know that photograph. Holy cow. That's pretty cool. 01:05:32.000 --> 01:05:41.000 I know that photograph. Let me see if I can play a video with that photograph in it. I think I can play a video with a photograph of that in it. 01:05:42.000 --> 01:05:45.000 Here's a video of that photograph. Let's see. 01:05:46.000 --> 01:05:55.000 So I got my, my star in this field on the Human Genome Project building, what you can see in my backdrop here, which is a DNA sequencing pipeline at the Whitehead Institute. 01:05:56.000 --> 01:06:04.000 This is meant for purifying sometimes phage out of bacteria, sometimes plasma is out of bacteria, mostly coli sequencing those at very high fruit. 01:06:05.000 --> 01:06:12.000 And then I built spent some time building PCR tests to look for HIV to look for a variety of viruses at a company called Agencourt that we started. 01:06:13.000 --> 01:06:26.000 And that also built a DNA sequencer known as a solid sequencer. There you go. So HIV testing and a sequencing machine right after the Human Genome Project using that same picture. I knew I saw that picture somewhere. That's pretty funny. Okay, let's go. 01:06:27.000 --> 01:06:41.000 We map the human genome. We're going to have to make this much easier to do. So a plan was laid out. If we were going to have these genetic markers to trace inheritance, we'd build a genetic map of lots of genetic landmarks. 01:06:42.000 --> 01:06:51.000 We then needed to fill in all the DNA in between so we could cover that distance. So instead of having to tediously cover the distance, we could just, you know, look it up. 01:06:51.000 --> 01:07:00.000 We would have a whole sequence, narcissistic fibrosis. They spent a long time finding the region's sequencing. We want to just be able to double click on it and that pops the sequence. 01:07:00.000 --> 01:07:08.000 We want to have a list of genes and importantly we want that to be completely freely available to everybody around the world so anybody can do stuff like this. 01:07:10.000 --> 01:07:18.000 The Human Genome Project got going. It got finished. I'm not going to say that much about it. I lived through all of this. 01:07:18.000 --> 01:07:34.000 In fact, here the Broad grew out of the Human Genome Center that existed here in Kendall Square and a beer and popcorn warehouse, not far from here, just a few blocks away where the Human Genome got a third of the Human Genome sequence was done. 01:07:34.000 --> 01:07:42.000 But it was an international collaboration involving groups in six different countries, 16 different centers, everybody putting their data freely available on the web every day. 01:07:43.000 --> 01:08:03.000 Yeah, it cost three billion bucks. But, you know, thanks to the American Congress and the four-sidedness of the government saying we should build these things as public infrastructure, it got done and made freely available and three billion bucks turned out to be only one percent of the national institute's health budget. 01:08:04.000 --> 01:08:12.000 So it sounds like a lot, but when you divide it over the 15 or so years and you compare it to the NIH budget, it was a mighty good investment. 01:08:13.000 --> 01:08:22.000 So it got finished. There was a draft by the year 2000, a finished sequence by April 25th, 2003. That was not an accident. 01:08:22.000 --> 01:08:32.000 It was 50 years to the day after the Watson Crick paper and everybody pulled all nighters to make sure we could get done by the 50th anniversary of the Watson Crick paper. 01:08:32.000 --> 01:08:39.000 So we had a sequence of the Human Genomes. Now the question was, could we map disease genes in a simple way like falling off a log? 01:08:39.000 --> 01:08:47.000 And for these, remember we had this rare Mendelian tracemin families, common disease look across populations. 01:08:47.000 --> 01:08:57.000 Well, for the rare Mendelian diseases, the single gene diseases that transmit cleanly in families, it worked like a charm. 01:08:58.000 --> 01:09:06.000 By, in the 1980s, I said cystic fibrosis to five years, lots of people, Huntington's disease was worse. It took 10 years. 01:09:06.000 --> 01:09:20.000 But by 2003, when the Human Genome Project was finishing, all these tools being made available meant that more than 1,700 Mendelian diseases had been solved to the extent that the gene was found. 01:09:20.000 --> 01:09:31.000 By today, 5,000 disease genes for different Mendelian disorders are known, and that's the vast majority of Mendelian diseases we know. 01:09:31.000 --> 01:09:42.000 There are very rare ones where people are still looking and there are, you know, things still to do, but it's pretty fair to say we kind of know how to do this, and it works really well. 01:09:42.000 --> 01:09:44.000 And they haven't fixed one of them. 01:09:44.000 --> 01:09:56.000 And what used to take tens of millions of dollars, it's not a bad rotation project for a student today if you have the samples, which is what progress is in science to see that happen. 01:09:56.000 --> 01:10:00.000 But for common diseases, it was very disappointing. 01:10:00.000 --> 01:10:10.000 People tried to do the same thing, pretending that common diseases were really a bunch of single gene Mendelian disorders, and it just failed. 01:10:10.000 --> 01:10:15.000 Looking within families for co-inheritance. 01:10:15.000 --> 01:10:22.000 And so now by 2003, there were about 8 significant discoveries across all of medicine. 01:10:22.000 --> 01:10:29.000 Now we're really getting into this extraordinary place where he's starting to assume that there is a genetic cause for everything. 01:10:29.000 --> 01:10:37.000 That there is a genetic cause for Alzheimer's, a genetic cause for Crohn's, and a genetic cause for immune disorders, a genetic cause for everything. 01:10:38.000 --> 01:10:42.000 Can you hear the shift? It suddenly happened. 01:10:42.000 --> 01:10:48.000 Now they're coming up with the idea that, okay, maybe we can't find these genes because there's too many of them. 01:10:48.000 --> 01:11:00.000 But we've completely discounted the possibility that our pattern integrity is interacting with its environment in a maladaptive way, and that's resulting in the disease state. 01:11:01.000 --> 01:11:13.000 I think it's pretty spectacular that this talk is so myopic that it doesn't acknowledge that the United States healthcare system is starting out by spending the most money and getting the worst results. 01:11:13.000 --> 01:11:26.000 It's coming up on a crisis because we're about to spend $500,000 per six months on every aging out boomer that we have going into retirement. 01:11:27.000 --> 01:11:44.000 But he's still talking about diseases and the genetic causes of them and trying to get into the complicated genetic causes of them without, while ignoring the entire lifestyle and medical history that is all these people and blaming it all on their genes. 01:11:44.000 --> 01:11:47.000 It's really extraordinary. 01:11:48.000 --> 01:11:55.000 And even they were kind of weird because unlike Mendelian disorders, we had single genes of huge effects. 01:11:55.000 --> 01:12:04.000 The cystic fibrosis gene, if you inherit it, well, having a single copy dramatically increases your risk of disease because if you get another copy, you're definitely going to get it. 01:12:04.000 --> 01:12:06.000 It's like a 500-fold effect. 01:12:06.000 --> 01:12:12.000 By contrast, the handful of things that were found increase your risk by two-fold, three-fold, four-fold. 01:12:12.000 --> 01:12:15.000 It just looked totally different. 01:12:15.000 --> 01:12:20.000 Increasing your risk by one-fold or two-fold is not a significant finding. 01:12:20.000 --> 01:12:21.000 It is a correlation. 01:12:21.000 --> 01:12:27.000 But he's still talking like this data is real, and we've got to figure out how to understand what it means. 01:12:30.000 --> 01:12:43.000 That's what's extraordinary here is that you're listening to this paparian iterative science illusion where P values have created facts and that he can't ignore. 01:12:44.000 --> 01:12:52.000 Including correlations between genetic markers and disease is too hard for him to ignore because the P values are there. 01:12:52.000 --> 01:12:54.000 Think about this. 01:12:54.000 --> 01:12:57.000 And it wasn't working. 01:12:57.000 --> 01:13:01.000 So that was that second approach I was telling you. 01:13:01.000 --> 01:13:06.000 Look for ancestral segments that were common across a population. 01:13:06.000 --> 01:13:14.000 Collect lots of people who have a disease and ask, why are there some ancestral segments that are shared amongst those people? 01:13:14.000 --> 01:13:20.000 Now, of course, to do this, you would need to have documented all the common genetic variation in the population. 01:13:20.000 --> 01:13:22.000 So let's put the refrigerator list up. 01:13:22.000 --> 01:13:25.000 Find 10 million genetic variants. 01:13:25.000 --> 01:13:27.000 Catalog the correlations. 01:13:27.000 --> 01:13:29.000 He needs a genetic basis. 01:13:29.000 --> 01:13:33.000 He needs a genetic database before any of these things work. 01:13:33.000 --> 01:13:40.000 Genome wide association studies require as many genomes as possible. 01:13:40.000 --> 01:13:46.000 So they have been telling each other in the back rooms and in secret meetings that we just need more data. 01:13:46.000 --> 01:13:49.000 How does this sound any different than we need more chess games? 01:13:49.000 --> 01:13:52.000 We need more go games? 01:13:52.000 --> 01:14:01.000 How is this any different than the AI needed lots and lots of images before it could ever decide whether a motorcycle was coming closer or not? 01:14:01.000 --> 01:14:05.000 And it still can't decide. 01:14:05.000 --> 01:14:09.000 It's not any different. 01:14:09.000 --> 01:14:11.000 It's exactly the same thing. 01:14:11.000 --> 01:14:13.000 It's many iterations of the game. 01:14:13.000 --> 01:14:22.000 It's many iterations of the genome fed into an AI that's going to indicate correlations and pick the most significant ones and then we're supposed to go from there. 01:14:22.000 --> 01:14:24.000 This is the best case scenario. 01:14:24.000 --> 01:14:29.000 They don't have a plan beyond this. 01:14:29.000 --> 01:14:34.000 And think very carefully about what charlatans are right now involved in us making sure that we don't see through this. 01:14:34.000 --> 01:14:38.000 That we don't see through this illusion. 01:14:38.000 --> 01:14:48.000 That we don't see how we've been bamboozled by recombinant DNA technology being used against us in absolutely trivial ways. 01:14:48.000 --> 01:14:55.000 To create a mythology that they want us to pass on to our children so that they can never free themselves. 01:14:55.000 --> 01:14:58.000 We are at a crucial moment in human history. 01:14:58.000 --> 01:15:00.000 I know it sounds ridiculous. 01:15:00.000 --> 01:15:03.000 It sounds ridiculous to me every time I say it. 01:15:03.000 --> 01:15:17.000 Because what it essentially means is that some guy in the back of his garage in Pittsburgh is one of the last sort of outposts of sacred biology left on earth. 01:15:17.000 --> 01:15:22.000 How dark and schizophrenic is that idea? 01:15:22.000 --> 01:15:26.000 How do I explain that as my conception of reality to my kids every night? 01:15:26.000 --> 01:15:29.000 I don't. 01:15:29.000 --> 01:15:32.000 Dad just streams for a living. I'm a teacher on the internet. That's all I can say. 01:15:32.000 --> 01:15:35.000 But in reality I'm trying to save their grandkids from this. 01:15:35.000 --> 01:15:47.000 I'm trying to save my sons and my daughters from their own schools and their own fellow students. 01:15:47.000 --> 01:15:49.000 Just like you are. 01:15:49.000 --> 01:15:53.000 I'm trying to build a structure across the entire genome of which things go with which things. 01:15:53.000 --> 01:16:08.000 Be able to genotype in, you know, 10,000 people, a million different genetic variants which is like 100 billion genotypes, like 100 million data points. 01:16:08.000 --> 01:16:13.000 And then have rigorous analytical methods. So this is kind of like insane. 01:16:13.000 --> 01:16:16.000 And so we got to work. 01:16:16.000 --> 01:16:28.000 We got done. It got done about six years. It got done because when you put your mind to it and a community comes together, we went from knowing almost none of the common genetic variations in the population and knowing tens of millions of them. 01:16:28.000 --> 01:16:35.000 We went from knowing almost nothing about the correlation structure to having an international project to define the entire correlation structure. 01:16:35.000 --> 01:16:45.000 We went from being able to genotype one genetic marker at a time to being able to do a million genetic markers simultaneously at a time. 01:16:45.000 --> 01:16:52.000 I leave out some details there. It was an exhilarating period because we were building the infrastructure. 01:16:52.000 --> 01:16:56.000 We as a community here. The Brody's played a huge role in this. 01:16:56.000 --> 01:16:59.000 But a whole community came together to do this. 01:16:59.000 --> 01:17:05.000 Also, I should say that with regard to sequencing, the cost of sequencing plummeted. 01:17:05.000 --> 01:17:09.000 That sequence I told you about the first human sequence was 3 billion bucks. 01:17:09.000 --> 01:17:12.000 That is not as they say scalable. 01:17:12.000 --> 01:17:25.000 If you wanted to do like 4,000 people at 3 billion bucks, roughly US GDP, which is just like very hard to write a grant for. 01:17:25.000 --> 01:17:36.000 So instead, it's plummeted. What used to be 3 billion dollars, I can get you if you're willing to buy in bulk for 600 bucks. 01:17:37.000 --> 01:17:45.000 I think it will drop to 100 bucks. That is like mind-boggling. It's three times faster than Moore's law, the cost of sequence. 01:17:45.000 --> 01:17:50.000 You want a genome sequence? 600 bucks today in bulk. 01:17:50.000 --> 01:17:57.000 So we now come to the situation of can we find those genes for common genetic diseases? 01:17:58.000 --> 01:18:03.000 Well, like I tell you, they were very slow pace of discovery. 01:18:03.000 --> 01:18:08.000 Each year, we got just a handful of discoveries that were coming along. 01:18:08.000 --> 01:18:14.000 And one, two, one, one, one. And then the tools began to fall into place. 01:18:14.000 --> 01:18:17.000 In 2005, four discoveries. 01:18:18.000 --> 01:18:22.000 2006, eight discoveries worldwide. 01:18:22.000 --> 01:18:28.000 2007, a lot of discoveries as all these tools kicked in. 01:18:28.000 --> 01:18:35.000 Now, you might think everybody's really excited that we got all of these genetic. 01:18:35.000 --> 01:18:38.000 We're not getting really excited because it's starting to become more complicated. 01:18:38.000 --> 01:18:44.000 We're just identifying a whole host of genes that are involved in a whole host of things. 01:18:45.000 --> 01:18:49.000 And some of these genes start to become sort of anchors or pivots in a network 01:18:49.000 --> 01:18:55.000 where they're related to lots of different genetic disorders 01:18:55.000 --> 01:18:58.000 or lots of different genetic predispositions. 01:18:58.000 --> 01:19:01.000 And so it becomes grayer and grayer. 01:19:01.000 --> 01:19:08.000 The closer they approach the irreducible complexity that really is the human animal. 01:19:08.000 --> 01:19:17.000 And as long as they stay at extreme examples of maladaptive proteins or disease states, 01:19:17.000 --> 01:19:19.000 they get a pretty clear signal. 01:19:19.000 --> 01:19:24.000 But as they approach ever closer to what is essentially a healthy human, 01:19:24.000 --> 01:19:30.000 they have no signals anymore. 01:19:30.000 --> 01:19:36.000 And so this whole strategy is only going to get us so far as disease states 01:19:36.000 --> 01:19:41.000 can allow us to have insight into a healthy state. 01:19:41.000 --> 01:19:47.000 And yes, some of those insights are very powerful, but most of them aren't. 01:19:47.000 --> 01:19:54.000 And so understanding how to augment the healthy animal or the almost perfect mammal starts to become 01:19:54.000 --> 01:20:01.000 a very, very daunting task when you can only work on extreme signals like this right now. 01:20:01.000 --> 01:20:07.000 I want to keep a perspective on it so you see where he's going versus where I'm still trying to hold us back 01:20:07.000 --> 01:20:12.000 and not buying this as a touchdown that's already been accomplished. 01:20:12.000 --> 01:20:15.000 All of these genetic discoveries that were coming along. 01:20:15.000 --> 01:20:20.000 However, scientists have an amazing ability to be grumpy. 01:20:20.000 --> 01:20:29.000 And so people said, yeah, okay, it's nice, but, you know, most of the genetic effects you found 01:20:29.000 --> 01:20:32.000 have only small magnitude. 01:20:32.000 --> 01:20:38.000 They might increase your risk by twofold or even just 50 percent or 20 percent. 01:20:38.000 --> 01:20:39.000 I'm not impressed. 01:20:39.000 --> 01:20:42.000 But then what is he talking about? 01:20:42.000 --> 01:20:47.000 He just told us a minute ago this was all done, but if it's all done and yet we haven't getting got out of it, 01:20:47.000 --> 01:20:50.000 how are we done with it then? 01:20:50.000 --> 01:20:51.000 Remember what we're talking about. 01:20:51.000 --> 01:20:53.000 We're talking about markers. 01:20:53.000 --> 01:20:55.000 We're talking about known sections. 01:20:55.000 --> 01:20:58.000 We're talking about accepted restriction fragment maps. 01:20:58.000 --> 01:21:07.000 We're talking about kind of a consensus understanding of what's present in these chromosomes. 01:21:07.000 --> 01:21:10.000 But we're not talking about understanding what's all there. 01:21:10.000 --> 01:21:14.000 We're not talking about knowing where all the ion channels are and where all the subunits are 01:21:14.000 --> 01:21:20.000 and where all the transporters are and all the dyin molecules and all that. 01:21:20.000 --> 01:21:23.000 We're not talking about any of that. 01:21:23.000 --> 01:21:33.000 We're talking about understanding how these chapters are organized and how the sub-sections are organized a little bit 01:21:33.000 --> 01:21:39.000 across people based on marker maps. 01:21:39.000 --> 01:21:43.000 And we can do whole genome screens and we can do whole genome sequencing. 01:21:43.000 --> 01:21:44.000 Sure, we can read that. 01:21:44.000 --> 01:21:49.000 We can make a photocopy of the whole book, but it doesn't mean we can read it. 01:21:49.000 --> 01:21:55.000 It doesn't mean we understand how chapter one relates to chapter four. 01:21:55.000 --> 01:21:59.000 And so we're still hand-waving over this thing if we say that, 01:21:59.000 --> 01:22:03.000 well, we made all these discoveries now like you did on that previous screen. 01:22:03.000 --> 01:22:06.000 All these discoveries. 01:22:06.000 --> 01:22:09.000 But these discoveries haven't carried any diseases either. 01:22:09.000 --> 01:22:14.000 Even the ones that were made in 2007 when he was all, you know, look at this huge list. 01:22:14.000 --> 01:22:18.000 They have only small magnitude. 01:22:18.000 --> 01:22:23.000 They might increase your risk by two-fold or even just 50 percent or 20 percent. 01:22:23.000 --> 01:22:24.000 I'm not impressed. 01:22:24.000 --> 01:22:27.000 And they didn't land in the coding instructions for genes. 01:22:27.000 --> 01:22:32.000 They landed in a non-coding dark matter. 01:22:32.000 --> 01:22:35.000 We don't know what it did kind of regions of the human genome. 01:22:35.000 --> 01:22:41.000 And it didn't explain an awful lot of the total heritability and how could you interpret it anyway biologically? 01:22:41.000 --> 01:22:46.000 And so people immediately said, maybe these are just statistical artifacts or something else, 01:22:46.000 --> 01:22:51.000 or maybe it's caused by rare variants like Mendelian diseases. 01:22:51.000 --> 01:22:55.000 Even though we, of course, found that it wasn't caused by that, but nonetheless, 01:22:55.000 --> 01:22:58.000 the pendulum was swinging back and forth. 01:22:58.000 --> 01:23:04.000 But a perfectly plausible answer was that the sample sizes were actually rather small. 01:23:04.000 --> 01:23:08.000 And maybe if we just looked at more people, we'd see more stuff. 01:23:08.000 --> 01:23:10.000 And that turned out to be the answer. 01:23:10.000 --> 01:23:18.000 It turned out a study that was done here at the Broad on schizophrenia looked at 3,000 cases with schizophrenia, 01:23:18.000 --> 01:23:23.000 3,000 controls, and it found absolutely nothing. 01:23:23.000 --> 01:23:27.000 Not a single thing across the threshold of statistical significance, 01:23:27.000 --> 01:23:31.000 which, if you're looking at 6,000 people and looking at a million genetic variants, 01:23:31.000 --> 01:23:35.000 and I'm in spent a lot of money on it, you are somewhat disappointed. 01:23:36.000 --> 01:23:39.000 However, if you're mathematical, you look at the dating and you say, 01:23:39.000 --> 01:23:41.000 yeah, but it's not like randomly distributed. 01:23:41.000 --> 01:23:47.000 There's still some saying, there's a signal, and it's sort of whispering, 01:23:47.000 --> 01:23:54.000 increase the sample size, and we'll give you something, and sure enough, 01:23:54.000 --> 01:23:59.000 oh my gosh, here he goes, 5 significant regions of the genome. 01:23:59.000 --> 01:24:01.000 Wow, unbelievable. 01:24:01.000 --> 01:24:04.000 And then schizophrenia was cured. 01:24:04.000 --> 01:24:09.000 At 25,000 and 25,000 went up to 62 significantly. 01:24:09.000 --> 01:24:11.000 Wow, and then schizophrenia was cured. 01:24:11.000 --> 01:24:17.000 And then at 82,000 and 82,000 went up to 245 regions of the human genome 01:24:17.000 --> 01:24:23.000 that were unambiguously, strongly, statistically, significantly associated. 01:24:23.000 --> 01:24:29.000 And now, with larger and larger samples across more and more traits, 01:24:29.000 --> 01:24:36.000 there are more than 100,000 associations between regions of the genome 01:24:36.000 --> 01:24:38.000 and particular common diseases. 01:24:38.000 --> 01:24:43.000 Wow, and all those genomes, all those genes, 01:24:43.000 --> 01:24:46.000 and all those understandings have cured nothing. 01:24:46.000 --> 01:24:48.000 Absolutely nothing. 01:24:51.000 --> 01:24:52.000 It's impressive. 01:24:52.000 --> 01:24:57.000 Pre-examples in type 2 diabetes, 273 in inflammatory bowel disease, 01:24:57.000 --> 01:25:01.000 166 in coronary artery disease, the 245 in schizophrenia. 01:25:01.000 --> 01:25:05.000 245 significant correlations. 01:25:05.000 --> 01:25:07.000 Well, we're almost there then. 01:25:07.000 --> 01:25:11.000 I'm sure that that's enough of a whittle down of the significant genes 01:25:11.000 --> 01:25:15.000 that we can figure out what pattern integrity is screwed up and fix it. 01:25:15.000 --> 01:25:17.000 Why not? 01:25:18.000 --> 01:25:25.000 I think it comes down to one of 245 different parts in the laptop 01:25:25.000 --> 01:25:30.000 that is burnt out or broken or shorted or mismanufactured. 01:25:30.000 --> 01:25:36.000 And so if we can figure out how those 245 parts are interacting maladaptively, 01:25:36.000 --> 01:25:38.000 we can fix the laptop. 01:25:41.000 --> 01:25:45.000 What in the hell are we even talking about here, ladies and gentlemen? 01:25:45.000 --> 01:25:53.000 What in the world are they even talking about 403 associations with type 2 diabetes? 01:25:53.000 --> 01:25:57.000 I thought type 2 diabetes was something to do with sugar. 01:26:02.000 --> 01:26:05.000 That's what common variants, and now as people are doing more sequencing, 01:26:05.000 --> 01:26:08.000 they're finding rare variants and suddenly the floodgates are open. 01:26:08.000 --> 01:26:14.000 It is possible to find lots of regions of the genome in the human population 01:26:14.000 --> 01:26:20.000 that have variants that are higher frequencies in people with a disorder than not. 01:26:20.000 --> 01:26:24.000 Each of them makes a small contribution, but together... 01:26:24.000 --> 01:26:29.000 There's a huge assumption that each one of those makes a small contribution. 01:26:29.000 --> 01:26:34.000 It couldn't be that our associations are just random measurements that just happen to show up. 01:26:34.000 --> 01:26:39.000 Nope, the end numbers are high enough. This has to be a signal. 01:26:40.000 --> 01:26:43.000 See? Do you see where we are? 01:26:45.000 --> 01:26:54.000 Without any basis for saying that these associations are biologically significant, 01:26:54.000 --> 01:26:59.000 he makes the argument that statistical significance... 01:27:02.000 --> 01:27:11.000 Rather, he presupposes that statistical significance is an indication of biological significance, 01:27:11.000 --> 01:27:14.000 and that assumption is a bad model. 01:27:14.000 --> 01:27:20.000 It's causing them to make bad models of irreducible complexity like schizophrenia, 01:27:20.000 --> 01:27:26.000 and then working and spending billions of dollars trying to understand an inadequate model 01:27:26.000 --> 01:27:32.000 of schizophrenia based on one or two of these 400 or 200 genes. 01:27:32.000 --> 01:27:36.000 One or two that make the most plausible sense to this particular investigator 01:27:36.000 --> 01:27:40.000 or that particular investigator to make my animal model of. 01:27:41.000 --> 01:27:46.000 So we can pull things out of this hat all day long and keep cutting this sausage into a million pieces, 01:27:46.000 --> 01:27:48.000 and we're never going to get anywhere. 01:27:51.000 --> 01:27:55.000 The only question is, is it possible that these people think this is progress? 01:27:55.000 --> 01:27:57.000 Do they really believe it? 01:27:58.000 --> 01:28:00.000 Do they really believe this is progress? 01:28:00.000 --> 01:28:07.000 Or do they really believe this is like the creation of more and more and more out of the same amount of something? 01:28:08.000 --> 01:28:11.000 The slicing of the same amount of bread ever thinner? 01:28:12.000 --> 01:28:13.000 The lot. 01:28:14.000 --> 01:28:16.000 What can we get from this? 01:28:17.000 --> 01:28:22.000 It turns out that by looking at these many, many genetic variants, 01:28:22.000 --> 01:28:26.000 it's not as easy as cystic fibrosis with this one gene you toss it in the computer, 01:28:26.000 --> 01:28:28.000 and the computer will tell you, here's what this thing does. 01:28:28.000 --> 01:28:29.000 It's a transporter. 01:28:29.000 --> 01:28:35.000 You've got to look at lots of regions of the human genome, but already discoveries are coming. 01:28:35.000 --> 01:28:37.000 Let me come back to schizophrenia. 01:28:37.000 --> 01:28:42.000 For schizophrenia, there's one region that had a screaming big signal, 01:28:42.000 --> 01:28:44.000 and I've pointed to it there with an arrow. 01:28:44.000 --> 01:28:49.000 That region lies in a critical part of the human genome that affects immunity 01:28:49.000 --> 01:28:52.000 called the major histocompatibility complex. 01:28:52.000 --> 01:28:58.000 And that led people to say, oh, maybe it's an infectious agent that's causing schizophrenia, 01:28:58.000 --> 01:29:04.000 and there's even this article in the Atlantic about how you could get schizophrenia from your cat 01:29:04.000 --> 01:29:10.000 because it would transmit this bacteria, toxoplasma, gandii. 01:29:10.000 --> 01:29:16.000 I have no evidence that any of that is correct, but it was a very amusing article in the Atlantic. 01:29:16.000 --> 01:29:22.000 But it did raise what was going on in this region of the human genome related to immunity. 01:29:22.000 --> 01:29:29.000 To make a long, long story short, investigators here at the Broad found the gene. 01:29:29.000 --> 01:29:32.000 It was really hard, and I wish I could tell you all the details, 01:29:32.000 --> 01:29:34.000 because it's a beautiful story. 01:29:34.000 --> 01:29:41.000 But it turned out to be a gene with the boring name C4, complement component four. 01:29:41.000 --> 01:29:46.000 Now, complement component four works in the immune system. 01:29:46.000 --> 01:29:49.000 How's it going to affect schizophrenia? 01:29:49.000 --> 01:29:53.000 Well, because another scientist working in this community 01:29:53.000 --> 01:29:57.000 happened to also be working on the complement system. 01:29:57.000 --> 01:30:05.000 The gene knew that it didn't just play in a role in the immune system of marking bacteria 01:30:05.000 --> 01:30:08.000 to be destroyed by immune cells. 01:30:08.000 --> 01:30:11.000 It also had a second job in the body. 01:30:11.000 --> 01:30:14.000 If you hear a car alarm, it's not in my house, it's in this video. 01:30:14.000 --> 01:30:22.000 It marked synapses in the brain to be destroyed by certain things called microglia, 01:30:22.000 --> 01:30:25.000 a certain set of cells that clean things up in the brain. 01:30:25.000 --> 01:30:31.000 And thus the idea that this was working by affecting the pruning of synapses. 01:30:31.000 --> 01:30:35.000 And sure enough, the genetic variants that were associated with schizophrenia 01:30:35.000 --> 01:30:39.000 were associated with greater pruning of synapses. 01:30:39.000 --> 01:30:42.000 And suddenly, the pieces started clicking into place, 01:30:42.000 --> 01:30:48.000 because there had been this old observation that post-mortem analysis of brains 01:30:48.000 --> 01:30:53.000 from people with schizophrenia revealed that fewer synapses. 01:30:53.000 --> 01:31:01.000 And the time of greatest pruning of synapses in the brain is late adolescence, 01:31:01.000 --> 01:31:06.000 early adulthood, which is exactly the time of onset of schizophrenia. 01:31:06.000 --> 01:31:09.000 And then when you start looking at those 245 genes, you say, 01:31:09.000 --> 01:31:12.000 oh, there are some other genes that are also involved in pruning, 01:31:12.000 --> 01:31:16.000 and some other things involved in pruning, and suddenly there's hypothesis. 01:31:16.000 --> 01:31:21.000 And maybe schizophrenia may someday be treated by finding chemicals, 01:31:21.000 --> 01:31:26.000 by finding drugs that will turn down overactive pruning. 01:31:26.000 --> 01:31:30.000 Now, this is just one example of that list of 245. 01:31:30.000 --> 01:31:35.000 So think about how, as a neurobiologist, I can't help but just want to say, 01:31:35.000 --> 01:31:38.000 dude, you sound like a clown, okay? 01:31:38.000 --> 01:31:43.000 Because what you're suggesting is that one gene or a combination of genes 01:31:43.000 --> 01:31:48.000 and a mechanism of pruning, if it was just a knob in the brain, 01:31:48.000 --> 01:31:52.000 if you could just turn that knob a little bit, everything would be fixed. 01:31:52.000 --> 01:31:59.000 Even though you just told us that there are 246 genes associated with schizophrenia, 01:31:59.000 --> 01:32:00.000 this is one of them. 01:32:00.000 --> 01:32:04.000 And there are some other genes that are related to, I guess, similar cascades 01:32:04.000 --> 01:32:11.000 or similar mechanistic molecular chains that lead to synaptic pruning. 01:32:11.000 --> 01:32:16.000 But what else in neurobiology do we know about synapses and their pruning? 01:32:16.000 --> 01:32:21.000 What else do we know about synapses and their growth and strengthening in neurobiology? 01:32:21.000 --> 01:32:27.000 Well, we know a hell of a lot. 01:32:27.000 --> 01:32:34.000 And so this idea that he thinks that there's just one knob, one drug, 01:32:34.000 --> 01:32:38.000 that if we just give it to these kids, that their brains will develop normally, 01:32:38.000 --> 01:32:42.000 that if that knob wouldn't have been changed, that those synapses would be there 01:32:42.000 --> 01:32:43.000 and everything would be fine. 01:32:43.000 --> 01:32:50.000 It couldn't be that those synapses not being there are just like a side effect 01:32:50.000 --> 01:32:56.000 of what caused schizophrenia, what causes, it couldn't possibly be that. 01:32:56.000 --> 01:33:00.000 It couldn't possibly be that the immune system plays some role 01:33:00.000 --> 01:33:04.000 in the physiological change that results in schizophrenia. 01:33:04.000 --> 01:33:06.000 Instead, we have to blame it on the immune system now. 01:33:06.000 --> 01:33:09.000 Do you see how inverted this is? 01:33:09.000 --> 01:33:17.000 If you start to exclusively model your entire biology based on cause and effect of genes, 01:33:17.000 --> 01:33:22.000 you're essentially using a ball in a box that's half white and half black, 01:33:22.000 --> 01:33:27.000 has a model of daytime and then trying to understand the seasons. 01:33:27.000 --> 01:33:33.000 If you're starting to try and understand the complex pattern integrity of an adult human 01:33:33.000 --> 01:33:38.000 by thinking about it as being related to genes, 01:33:38.000 --> 01:33:43.000 you're never going to get there. 01:33:43.000 --> 01:33:49.000 And this presentation in 2019 is still as backwards as has it would have been 01:33:49.000 --> 01:33:52.000 if it was given in 1970. 01:33:52.000 --> 01:33:56.000 Because they're still trapped in this idea that most or everything can be explained 01:33:56.000 --> 01:34:00.000 by these sequences. 01:34:00.000 --> 01:34:04.000 And that's just not possible. 01:34:05.000 --> 01:34:10.000 We are not a book incarnate. 01:34:10.000 --> 01:34:12.000 We are much more than that. 01:34:12.000 --> 01:34:16.000 We are a pattern integrity, a very complex set of interacting pattern 01:34:16.000 --> 01:34:23.000 integrity that sustains itself over as long as a hundred years. 01:34:23.000 --> 01:34:26.000 So you're not going to be able to distill it down to a couple of genes 01:34:26.000 --> 01:34:31.000 or a couple knobs that just need tweaking with a pharmaceutical. 01:34:31.000 --> 01:34:34.000 But according to Eric Lander, they're there with a million different of these. 01:34:34.000 --> 01:34:35.000 They're almost there. 01:34:35.000 --> 01:34:36.000 We're almost there. 01:34:36.000 --> 01:34:38.000 Things that are there. 01:34:38.000 --> 01:34:42.000 There are tons more examples of discoveries that are coming from being able to take 01:34:42.000 --> 01:34:45.000 an unbiased look to find without knowing what you're looking for. 01:34:45.000 --> 01:34:49.000 For heart disease, we all know that our lipid levels are very important, 01:34:49.000 --> 01:34:54.000 but genetics teaches us that 80% of the gene regions that have been found so far 01:34:54.000 --> 01:34:56.000 play no role in lipids. 01:34:56.000 --> 01:34:59.000 And therefore, there are a lot of other things going on 01:34:59.000 --> 01:35:01.000 that we have been thinking about in the heart disease. 01:35:01.000 --> 01:35:02.000 Wow. 01:35:02.000 --> 01:35:03.000 And you all have heard that. 01:35:03.000 --> 01:35:04.000 Surprise, surprise. 01:35:04.000 --> 01:35:06.000 There's a lot more going on than we thought. 01:35:06.000 --> 01:35:07.000 It's more than one gene. 01:35:07.000 --> 01:35:08.000 Holy man. 01:35:08.000 --> 01:35:10.000 HDL is protective. 01:35:10.000 --> 01:35:12.000 Your LDL is bad. 01:35:12.000 --> 01:35:13.000 HDL is good. 01:35:13.000 --> 01:35:14.000 You might have heard. 01:35:14.000 --> 01:35:17.000 It turns out HDL is not good or bad. 01:35:17.000 --> 01:35:21.000 It turns out HDL is not the causal factor at all. 01:35:21.000 --> 01:35:25.000 It turns out something that's negatively correlated with HDL is really the causal factor, 01:35:25.000 --> 01:35:26.000 triglycerides. 01:35:26.000 --> 01:35:28.000 And you can figure that out. 01:35:28.000 --> 01:35:32.000 And had you known that, actually, you would have saved the pharmaceutical industry $5 billion 01:35:32.000 --> 01:35:35.000 developing drugs to increase your HDL levels. 01:35:35.000 --> 01:35:36.000 Holy shit. 01:35:36.000 --> 01:35:38.000 What did he just say? 01:35:38.000 --> 01:35:41.000 That we would have saved the pharmaceutical industry. 01:35:41.000 --> 01:35:42.000 How much money? 01:35:42.000 --> 01:35:50.000 Is he actually suggesting that the pharmaceutical industry loses money in order to bring us cures? 01:35:50.000 --> 01:35:57.000 Who is this Joe Rogan talking to Alex Berenson? 01:35:58.000 --> 01:36:01.000 That's the most ridiculous thing I've heard him say yet. 01:36:01.000 --> 01:36:02.000 Holy shit. 01:36:02.000 --> 01:36:08.000 The pharmaceutical company hasn't lost a dime saving anyone. 01:36:08.000 --> 01:36:10.000 Glycerides. 01:36:10.000 --> 01:36:11.000 And you can figure that out. 01:36:11.000 --> 01:36:19.000 And had you known that, actually, you would have saved the pharmaceutical industry $5 billion developing drugs to increase your HDL levels. 01:36:19.000 --> 01:36:23.000 Which all failed because it turns out it's not the causal factor. 01:36:23.000 --> 01:36:28.000 So it turns out knowing things makes a big difference in developing drugs. 01:36:28.000 --> 01:36:32.000 In fact, you're not a thing. 01:36:32.000 --> 01:36:35.000 You're decreasing LDL class law. 01:36:35.000 --> 01:36:38.000 You're doing so effective. 01:36:38.000 --> 01:36:41.000 So increasing HDL must be effective. 01:36:41.000 --> 01:36:44.000 But they skipped over the point of, was it causal or just the correlation? 01:36:44.000 --> 01:36:47.000 The genetics actually told you it was just the correlation. 01:36:48.000 --> 01:36:55.000 Beautiful work of, say, Kath Riesen and his colleagues here in the Broad community who demonstrated the power of this kind of thing. 01:36:55.000 --> 01:36:57.000 Alzheimer's disease. 01:36:57.000 --> 01:37:04.000 We have seen failed clinical trials for Alzheimer's disease because everybody is going after neurons. 01:37:04.000 --> 01:37:06.000 Obviously it makes sense. 01:37:06.000 --> 01:37:09.000 Alzheimer's, neurons, that makes sense. 01:37:09.000 --> 01:37:16.000 But it turns out that if you ask the genetics, 80% of the genetic regions that have been found 01:37:16.000 --> 01:37:20.000 by doing these human genetic studies don't play a role in neurons. 01:37:20.000 --> 01:37:23.000 They play a role in those microglia I was telling you about. 01:37:23.000 --> 01:37:25.000 The things that clean up damage in the brain. 01:37:25.000 --> 01:37:27.000 And I think it's going to become clear. 01:37:27.000 --> 01:37:29.000 Clean up damage in the brain. 01:37:29.000 --> 01:37:32.000 They don't clean up damage in the brain. 01:37:32.000 --> 01:37:34.000 Like that's one thing they do. 01:37:34.000 --> 01:37:38.000 What do we have damage in the brain all the time? 01:37:38.000 --> 01:37:43.000 It's extraordinary what's going on here. 01:37:43.000 --> 01:37:45.000 This is 2019. 01:37:45.000 --> 01:37:47.000 It's not an old video. 01:37:47.000 --> 01:37:55.000 This is essentially state of the art Eric Lander right before the pandemic. 01:37:55.000 --> 01:38:08.000 Arguing that we need more genomes collected right before universities started taking weekly samples all around the world. 01:38:08.000 --> 01:38:16.000 That there is a neuroimmune access where the immune cells of the brain may be the primary driving force for Alzheimer's. 01:38:16.000 --> 01:38:25.000 And you would never know except if you just ask in an utterly unbiased way and have genetics tell you here are the things that seem to matter. 01:38:25.000 --> 01:38:26.000 Not only- 01:38:26.000 --> 01:38:36.000 I'm not suggesting that that particular observation about Alzheimer's being connected to microglia is a bad idea or that the genes pointing to it are a bad. 01:38:36.000 --> 01:38:44.000 The reason why neurobiology wasn't studying microglia up until then was because there was no funding for it. 01:38:44.000 --> 01:38:52.000 Because the grant calls ignored microglia even though the glial cells outnumber the neurons like three to one. 01:38:52.000 --> 01:38:58.000 Glial cell people have been saying that for decades in neuron people have been ignoring them. 01:38:58.000 --> 01:39:05.000 So maybe in some ways sometimes these genome-wide studies are effective but the guys were already saying it. 01:39:05.000 --> 01:39:12.000 The Alzheimer's people working on microglia were already saying it before the genome screen showed them. 01:39:12.000 --> 01:39:18.000 Just that the grant calls and NIH were ignoring them. 01:39:18.000 --> 01:39:32.000 And for every sort of signpost that we get from these genome-wide screens we get as he said hundreds of signposts that are irrelevant. 01:39:32.000 --> 01:39:38.000 And we waste just as much time on those if not more. 01:39:38.000 --> 01:39:44.000 That you can use these millions of genetic markers to build risk scores for people. 01:39:44.000 --> 01:39:48.000 Polygenic risk scores. Who's at more risk and who's at less risk? 01:39:48.000 --> 01:39:52.000 And it turns out beautiful work over the past couple of years has shown. 01:39:52.000 --> 01:39:58.000 We can identify 8% of the population. It's a threefold higher risk for cardiovascular disease. 01:39:58.000 --> 01:40:01.000 Maybe they should take those lipid lowering statins earlier. 01:40:01.000 --> 01:40:03.000 Couple of percent of the population. 01:40:03.000 --> 01:40:07.000 Yeah! Can you believe it? 01:40:07.000 --> 01:40:15.000 I mean, can you believe anyone's to search the population's genetics to find the people that should take statins? 01:40:15.000 --> 01:40:19.000 Population aren't much higher risk, threefold higher risk for breast cancer. 01:40:19.000 --> 01:40:22.000 And it's not the rare BRCA genes. 01:40:22.000 --> 01:40:25.000 It's a general polygenic risk for breast cancer. 01:40:25.000 --> 01:40:29.000 Maybe for such individuals the age of mammography should be different. 01:40:29.000 --> 01:40:33.000 Maybe patients aren't all the same. And looking at their genetic profiles 01:40:33.000 --> 01:40:37.000 and correlating it for these different diseases may lead to 01:40:37.000 --> 01:40:40.000 different recommendations for different people. 01:40:40.000 --> 01:40:42.000 So all right, this is all great. 01:40:42.000 --> 01:40:45.000 Summary, Mendelian disease, lots of progress, single gene diseases. 01:40:45.000 --> 01:40:48.000 We've found most of the genetic disorders that are known. 01:40:48.000 --> 01:40:52.000 The work is now on how do you cure gene therapies and things like that. 01:40:52.000 --> 01:40:55.000 Common disease, we know the architecture. 01:40:55.000 --> 01:41:00.000 If there are 240 genes involved in a disease, how is gene therapy going to help? 01:41:00.000 --> 01:41:04.000 Gene therapy is just the knob. You just turn the knob up and then stuff happens. 01:41:04.000 --> 01:41:08.000 And if I had to put more reverb on, everything would have been fine. 01:41:08.000 --> 01:41:13.000 So I'll just turn the reverb knob and then this whole pattern integrity will be fixed. 01:41:13.000 --> 01:41:15.000 There isn't one knob to turn. 01:41:15.000 --> 01:41:19.000 And that's what gene therapy is. 01:41:19.000 --> 01:41:23.000 It's turning a knob and hoping that the system will go back to balance. 01:41:23.000 --> 01:41:25.000 It's absurd. 01:41:25.000 --> 01:41:27.000 Highly polygenic. 01:41:27.000 --> 01:41:30.000 These modest effects, 100,000 mapped. 01:41:30.000 --> 01:41:33.000 Here's the problem. 01:41:33.000 --> 01:41:35.000 I keep talking about these gene regions. 01:41:35.000 --> 01:41:38.000 I haven't told you we know all the genes because we don't know all the genes. 01:41:38.000 --> 01:41:44.000 We now have 100,000 gene regions that have been pinned down and they're highly significant. 01:41:44.000 --> 01:41:45.000 Highly significant. 01:41:45.000 --> 01:41:48.000 The work of moving from a genetic region that is linked to the disease 01:41:48.000 --> 01:41:52.000 is actually what the gene is, what cell type it works in, what process it works. 01:41:52.000 --> 01:41:55.000 And is incredibly hard. 01:41:55.000 --> 01:42:01.000 And there are heroic efforts and heroic papers of people doing this one at a time. 01:42:01.000 --> 01:42:05.000 But 100,000 is a big number. Doing this one at a time is not going to work. 01:42:05.000 --> 01:42:11.000 We're going to need to supplement those heroic efforts with systematic, 01:42:11.000 --> 01:42:16.000 comprehensive efforts, just like happens in every other stage of the story I told you. 01:42:16.000 --> 01:42:18.000 The road ahead. 01:42:18.000 --> 01:42:22.000 The road ahead is to take the same approach that has worked before. 01:42:22.000 --> 01:42:24.000 Lots of numbers. 01:42:24.000 --> 01:42:26.000 Build the infrastructure. 01:42:26.000 --> 01:42:28.000 Make it broadly available. 01:42:28.000 --> 01:42:34.000 I will just briefly tell you in the closing minutes about these remarkable, 01:42:34.000 --> 01:42:37.000 powerful, driving scientific forces. 01:42:37.000 --> 01:42:38.000 Yes. 01:42:38.000 --> 01:42:40.000 We're going to drive the next decade or 15 years. 01:42:40.000 --> 01:42:43.000 The mar remarkable scientific forces. 01:42:43.000 --> 01:42:44.000 Human forces. 01:42:44.000 --> 01:42:47.000 Like a family with a disease. 01:42:47.000 --> 01:42:51.000 But now the United Kingdom goes out and gets half a million people 01:42:51.000 --> 01:42:54.000 to participate in the UK Biobank. 01:42:54.000 --> 01:42:57.000 Or Estonia has 200,000 people sign up. 01:42:57.000 --> 01:43:01.000 Or the United States has launched all of this project with a million people. 01:43:01.000 --> 01:43:06.000 Well, these very large cohorts are remarkable. 01:43:06.000 --> 01:43:08.000 And also, across psychiatric diseases. 01:43:08.000 --> 01:43:13.000 In a disease-focused way, there's a worldwide psychiatric genetics consortium. 01:43:13.000 --> 01:43:15.000 I said we needed big numbers. 01:43:15.000 --> 01:43:17.000 82,000 people. 01:43:17.000 --> 01:43:19.000 It's between 82,000 people without. 01:43:19.000 --> 01:43:25.000 Well, I believe we are moving to a world where genomics is going to get used in medicine. 01:43:25.000 --> 01:43:30.000 And the number of people relevant are everybody. 01:43:30.000 --> 01:43:35.000 That there will be a point in the not-so-distant future where it's pretty routine to simply 01:43:35.000 --> 01:43:37.000 sequence your genome. 01:43:37.000 --> 01:43:41.000 If I say it's 100 bucks, 100 bucks not so expensive. 01:43:41.000 --> 01:43:45.000 You know, amortize it over a life around the life to 100 years, 100 bucks. 01:43:45.000 --> 01:43:52.000 Like a buck a year, it's less than Starbucks, one Starbucks a year. 01:43:52.000 --> 01:43:54.000 We're going to get the information. 01:43:54.000 --> 01:43:57.000 The real question is what can we learn from it? 01:43:57.000 --> 01:43:59.000 And how can we share it securely? 01:43:59.000 --> 01:44:02.000 And there are all of these different projects going on. 01:44:02.000 --> 01:44:04.000 The world is thinking about how we're going to do this. 01:44:04.000 --> 01:44:07.000 Also, other powerful driving scientific forces. 01:44:07.000 --> 01:44:09.000 Sequencing costs I said would fall to 100 bucks. 01:44:09.000 --> 01:44:12.000 And also, single-cell analysis. 01:44:12.000 --> 01:44:17.000 If you keep coming to the Brode of 50 talks, you will hear a talk about single-cell biology, 01:44:17.000 --> 01:44:23.000 another one of these revolutions that has gone on, which many people, but I think prominently, 01:44:23.000 --> 01:44:26.000 many, many people in the Brode community have played a critical role, 01:44:26.000 --> 01:44:29.000 that are going to let us analyze millions and billions of cells, 01:44:29.000 --> 01:44:33.000 and understand the genetic programs of cells in a single cell level. 01:44:33.000 --> 01:44:37.000 And then the ability to edit DNA, if you come to the Brode of 15 talks, 01:44:37.000 --> 01:44:40.000 you'll also hear about CRISPR genome editing. 01:44:40.000 --> 01:44:46.000 And how that allows you to edit genes, activate genes, inhibit genes, 01:44:46.000 --> 01:44:48.000 and suddenly you have all of these tools. 01:44:48.000 --> 01:44:55.000 Read genomes, study the expression patterns of single cells, edit the genome, tweak the genome. 01:44:55.000 --> 01:44:57.000 And then finally, data. 01:44:57.000 --> 01:44:59.000 Data science is critical. 01:44:59.000 --> 01:45:02.000 Doing this, as has been done up until very recently, 01:45:02.000 --> 01:45:05.000 where all the data that you generate is on your computer. 01:45:05.000 --> 01:45:11.000 So what he's basically arguing for here is the idea that they need to collect data. 01:45:11.000 --> 01:45:15.000 So he argued earlier, I'll just go back a few minutes here. 01:45:15.000 --> 01:45:21.000 He's arguing that the sequencing is becoming cheaper, so there's less of an excuse. 01:45:21.000 --> 01:45:24.000 Not only that, but we're talking about CRISPR coming online. 01:45:24.000 --> 01:45:28.000 We're talking about single-cell analysis of the DNA and RNA, 01:45:28.000 --> 01:45:31.000 so they're looking at expression analysis too. 01:45:31.000 --> 01:45:36.000 And so what he's talking about is genomic and medical data for tens or hundreds of millions of people. 01:45:36.000 --> 01:45:38.000 How can we build it? How can we share it? 01:45:38.000 --> 01:45:42.000 The all of us program is the NIH program that I told you about. 01:45:42.000 --> 01:45:46.000 These other biobanks that are in the Netherlands and in Vanderbilt 01:45:46.000 --> 01:45:51.000 and in some of these other countries that have statewide medical care. 01:45:51.000 --> 01:45:54.000 All of this stuff is ahead of us. 01:45:55.000 --> 01:46:03.000 It's very important to understand that the rest of the world has been portrayed as being ahead of us. 01:46:03.000 --> 01:46:12.000 In other words, China, the UK, Denmark, anywhere where they've had a centralized database of medical data, 01:46:12.000 --> 01:46:16.000 the United States' biosecurity state thinks that they're very much ahead of us 01:46:16.000 --> 01:46:19.000 because we haven't been able to collect genetic data 01:46:19.000 --> 01:46:22.000 because we have laws protecting this privacy. 01:46:22.000 --> 01:46:27.000 We have a whole system that's fragmented into different states and different private entities. 01:46:27.000 --> 01:46:31.000 And so it's been very much more difficult for us to get something like this going. 01:46:31.000 --> 01:46:37.000 They think that we are behind the rest of the world. 01:46:37.000 --> 01:46:47.000 And so part of the impetus that is pushing this forward despite the absurdity of COVID-19 and SARS-CoV-2, the pandemic, 01:46:47.000 --> 01:46:50.000 is this impetus to catch up. 01:46:52.000 --> 01:46:57.000 And it's our freedom and our sovereignty and our individuality and our legislative laws 01:46:57.000 --> 01:47:03.000 and our system of a republic in the United States that is preventing this, that's holding us back. 01:47:05.000 --> 01:47:07.000 And these globalists don't care anymore. 01:47:07.000 --> 01:47:09.000 They don't want this system. They never have wanted it. 01:47:09.000 --> 01:47:14.000 In fact, a lot of the people that are involved in this five-eyes-centered operation 01:47:14.000 --> 01:47:20.000 I think have always been interested in bringing America down for the last 200 years of her existence. 01:47:20.000 --> 01:47:24.000 We may have never really escaped the crowd. 01:47:24.000 --> 01:47:27.000 There are many people that make that argument. 01:47:27.000 --> 01:47:32.000 And the reason why I think we are here, where we are right now, is because these higher powers, 01:47:32.000 --> 01:47:38.000 these weaponized piles of money, have decided that this is something that we need. 01:47:38.000 --> 01:47:41.000 This is the way forward. This is the future. 01:47:41.000 --> 01:47:46.000 And they decided it a long time ago. We are just starting to be able to see it much more clearly 01:47:46.000 --> 01:47:49.000 because they expose themselves with the pandemic. 01:47:49.000 --> 01:47:55.000 Another one of these revolutions that has gone on, which many people, but I think prominently, 01:47:55.000 --> 01:47:58.000 many, many people in the Broad community have played a critical role, 01:47:58.000 --> 01:48:01.000 that are going to let us analyze millions and billions of cells 01:48:01.000 --> 01:48:05.000 and understand the genetic programs of cells at the single cell level. 01:48:05.000 --> 01:48:08.000 And then the ability to edit DNA, if you come to the Broad of 15 talks, 01:48:08.000 --> 01:48:12.000 you will also hear about CRISPR genome editing 01:48:12.000 --> 01:48:17.000 and how that allows you to edit genes, activate genes, inhibit genes, 01:48:17.000 --> 01:48:24.000 and suddenly you have all these tools, read genomes, study the expression patterns of single cells, 01:48:24.000 --> 01:48:28.000 edit the genome, tweak the genome, and then finally data. 01:48:28.000 --> 01:48:30.000 Data science is critical. 01:48:30.000 --> 01:48:33.000 Doing this, as has been done, finally data. 01:48:33.000 --> 01:48:38.000 What he mentions here in this slide is the sequencing of single cells right here, 01:48:38.000 --> 01:48:42.000 single cell analysis of RNA and chromatin. I actually did that in my PhD. 01:48:42.000 --> 01:48:49.000 So I use quantitative PCR to assess the various subtypes of nicotinic acetylcholine receptors 01:48:49.000 --> 01:48:54.000 that were present in individual neurons that I pulled out of brain slices of mice. 01:48:54.000 --> 01:48:59.000 So I'm right there. 01:49:00.000 --> 01:49:04.000 I was part of this revolution. I know exactly what he's talking about. 01:49:04.000 --> 01:49:08.000 I know a lot about how these techniques are done, their limitations, et cetera. 01:49:08.000 --> 01:49:14.000 I'm not pulling this out of my pandemic training. 01:49:14.000 --> 01:49:19.000 This is unlike immunology. This I knew before the pandemic. 01:49:19.000 --> 01:49:24.000 I wouldn't call myself the expert, but I would call myself a very competent player 01:49:24.000 --> 01:49:31.000 with single cell PCR techniques, whether or not you think they're very valuable 01:49:31.000 --> 01:49:34.000 or give us a particular insight is a completely different thing. 01:49:34.000 --> 01:49:38.000 But I know that I was involved in helping to develop this methodology at a time 01:49:38.000 --> 01:49:41.000 when not very many people were doing it in the brain. 01:49:41.000 --> 01:49:45.000 And I've also done it in human slices. Thank you very much. 01:49:45.000 --> 01:49:50.000 So I just want to put that out there so that everybody doesn't... 01:49:50.000 --> 01:49:54.000 I am an armchair virologist and I am an armchair immunologist, 01:49:54.000 --> 01:49:57.000 but I am not an armchair molecular biologist. 01:49:57.000 --> 01:50:03.000 I have done these experiments myself and I published the work. 01:50:03.000 --> 01:50:09.000 I have all these tools. Read genomes, study the expression patterns of single cells, 01:50:09.000 --> 01:50:13.000 edit the genome, tweak the genome, and then finally data. 01:50:13.000 --> 01:50:15.000 Data science is critical. 01:50:15.000 --> 01:50:18.000 Doing this, as has been done up until very recently, 01:50:18.000 --> 01:50:22.000 where all the data that you generate is on your computer and somebody else's... 01:50:22.000 --> 01:50:26.000 Where is all this data being stored now for the epic database, for example? 01:50:26.000 --> 01:50:29.000 Are we storing it on Amazon servers? 01:50:29.000 --> 01:50:35.000 If we're storing it on Amazon servers, then technically speaking, is that data private? 01:50:35.000 --> 01:50:41.000 Or do certain people in that infrastructure of Amazon actually have access to it? 01:50:41.000 --> 01:50:44.000 I think that's probably the right answer. 01:50:44.000 --> 01:50:50.000 If they are stored offshore or outside of the contiguous United States, 01:50:50.000 --> 01:50:52.000 are they still United States assets? 01:50:52.000 --> 01:50:58.000 Are they still... If they're stored on a server farm in the Netherlands that's powered by solar? 01:50:58.000 --> 01:51:07.000 I mean, this is where our genetic data and its use against us becomes very, very cryptic and dangerous, 01:51:07.000 --> 01:51:10.000 because again, once it's being stored, we're kind of going back to something 01:51:10.000 --> 01:51:13.000 that somebody like Mark Koolak would understand better than me. 01:51:13.000 --> 01:51:18.000 He was working in this industry before he started streaming. 01:51:18.000 --> 01:51:24.000 But the gray area that's created, once this data is being stored and shared and analyzed, 01:51:24.000 --> 01:51:27.000 the gray area can't be underestimated. 01:51:27.000 --> 01:51:30.000 On their computer, it's just not going to work anymore. 01:51:30.000 --> 01:51:31.000 The data sets are too big. 01:51:31.000 --> 01:51:34.000 You can't even download somebody's data. 01:51:34.000 --> 01:51:36.000 Happily, we have cloud computing. 01:51:36.000 --> 01:51:41.000 And figuring out how these data can live in clouds, be accessed by people, 01:51:41.000 --> 01:51:43.000 it's a serious high-class problem. 01:51:43.000 --> 01:51:46.000 And the Broad and scientists here are very much involved in that. 01:51:46.000 --> 01:51:52.000 They developed a framework called the Data Biosphere for making open-source software 01:51:52.000 --> 01:51:56.000 and with open API as so things could all be connected together. 01:51:56.000 --> 01:52:00.000 We helped stand up together with others in the world, 01:52:00.000 --> 01:52:03.000 the Global Alliance for Genomics and Health to set standards. 01:52:03.000 --> 01:52:08.000 And a data platform called Terra is getting built that is again open-source, 01:52:08.000 --> 01:52:13.000 and hopefully the world will be able to exchange and use data, again, 01:52:13.000 --> 01:52:16.000 with real security around it. 01:52:16.000 --> 01:52:20.000 What's amazing is the surprising power of these data. 01:52:20.000 --> 01:52:22.000 Human cohorts. 01:52:22.000 --> 01:52:27.000 I could go on and on and on about how much you could learn by studying human populations. 01:52:27.000 --> 01:52:30.000 Not just who gets diabetes, but the progression rate of diabetes, 01:52:30.000 --> 01:52:32.000 the response to drugs for diabetes. 01:52:32.000 --> 01:52:36.000 Here you got a little hint about what Mark and I have been talking about with each other 01:52:36.000 --> 01:52:40.000 behind the scenes is the idea that isolated populations are useful 01:52:40.000 --> 01:52:45.000 and also sorted populations like American outbred populations are useful, 01:52:45.000 --> 01:52:48.000 but they represent different cohorts. 01:52:48.000 --> 01:52:51.000 And so America has a cohort that China wants. 01:52:51.000 --> 01:52:54.000 China has a cohort that America wants. 01:52:54.000 --> 01:52:58.000 Maybe they're even afraid that China's already got our cohort by other nefarious means. 01:52:58.000 --> 01:53:04.000 Maybe they're already afraid that China has gotten the equivalent cohort from somebody like the UK 01:53:04.000 --> 01:53:07.000 or from somewhere else. 01:53:07.000 --> 01:53:14.000 And so what we're really talking about is even the potential that's being created 01:53:14.000 --> 01:53:20.000 in bureaucratic back rooms about the idea of saying that there is a threat because Denmark 01:53:20.000 --> 01:53:24.000 and the rest of Europe have databases that we don't have, 01:53:25.000 --> 01:53:29.000 that the Chinese have been assembling a database that we don't have. 01:53:29.000 --> 01:53:33.000 The Chinese are working on a more homogenous genome set than we are working on, 01:53:33.000 --> 01:53:38.000 so they might be ahead of understanding their genome in a way that we can't get ahead of them 01:53:38.000 --> 01:53:41.000 because our genomes are more diverse. 01:53:44.000 --> 01:53:49.000 And so there's a real problem here where some progress will be made with these homogenous genomes, 01:53:49.000 --> 01:53:53.000 these isolated genomes, and then compared to the more diverse ones. 01:53:53.000 --> 01:53:58.000 And this is actually really crucial to understand that it's just not, you know, 01:53:58.000 --> 01:54:05.000 let's just get a bunch of genomes, there is going to be a very meticulous need for them 01:54:05.000 --> 01:54:09.000 to use these populations in the way that they are currently arranged 01:54:09.000 --> 01:54:11.000 and then preserve them as data sets. 01:54:11.000 --> 01:54:13.000 This is not a joke. 01:54:13.000 --> 01:54:14.000 They know that this is an issue. 01:54:14.000 --> 01:54:19.000 They know that if they let this go on, that this data will be lost. 01:54:19.000 --> 01:54:24.000 It's only going to exist for a couple more generations, so they need to invert our sovereignty 01:54:24.000 --> 01:54:28.000 to permissions so that we reside to give all of this data, or more importantly, 01:54:28.000 --> 01:54:33.000 our grandchildren's will reside to give us all of this data away. 01:54:33.000 --> 01:54:36.000 All of these are traits that are affected by genetics. 01:54:36.000 --> 01:54:38.000 You can even study behaviors. 01:54:38.000 --> 01:54:43.000 UK Biobank, because it's the UK, asks all these questions like, 01:54:43.000 --> 01:54:46.000 would you call yourself miserable? 01:54:46.000 --> 01:54:49.000 Or, and so there's a miserableness index. 01:54:49.000 --> 01:54:51.000 Are you a mourning person? 01:54:51.000 --> 01:54:53.000 They have like, you know, what do you eat for breakfast? 01:54:53.000 --> 01:54:58.000 The UK just like went to town asking questions, and it turns out you can find genetic correlates 01:54:58.000 --> 01:55:01.000 for all these interesting diet preferences and things. 01:55:01.000 --> 01:55:10.000 He's suggesting that a questionnaire combined with genetics can produce useful biological signals 01:55:10.000 --> 01:55:16.000 when he just got done explaining that of the 247 signals in schizophrenia, 01:55:16.000 --> 01:55:22.000 only one or two was really useful in terms of formulating a hypothesis about a mechanism 01:55:22.000 --> 01:55:27.000 that still didn't get us any closer to understanding how that mechanism actually resulted in schizophrenia. 01:55:27.000 --> 01:55:32.000 Now he's suggesting that this genome database can also be useful 01:55:32.000 --> 01:55:35.000 in a combination of questionnaires. 01:55:40.000 --> 01:55:41.000 Things like that. 01:55:41.000 --> 01:55:44.000 Also, the population is so wonderfully diverse. 01:55:44.000 --> 01:55:49.000 Finland is a genetic isolate where frequencies have been scattered high and low 01:55:49.000 --> 01:55:51.000 because they went through a little bottleneck. 01:55:51.000 --> 01:55:55.000 Other places, Pakistan and India have cousin marriages, 01:55:55.000 --> 01:56:01.000 and you see genes coming together, and you can see recessive traits appear much more easily. 01:56:01.000 --> 01:56:07.000 You can learn so much by looking at these plots I was showing you of genetic association 01:56:07.000 --> 01:56:08.000 across the genome. 01:56:08.000 --> 01:56:13.000 You can figure out for a given gene, what different traits does it affect? 01:56:13.000 --> 01:56:22.000 You can figure out for a given set of traits, schizophrenia and bipolar disease, 01:56:22.000 --> 01:56:24.000 manic depression, they used to call it. 01:56:24.000 --> 01:56:26.000 Do they have a similar foundation? 01:56:26.000 --> 01:56:28.000 Do they correlate with each other? 01:56:28.000 --> 01:56:32.000 You can correlate their genetic factors and ask, are there common processes? 01:56:32.000 --> 01:56:36.000 You can figure out which cell types matter by looking at the genes that are coming up 01:56:36.000 --> 01:56:41.000 and asking which cell types show those genes, and onward and onward and it just turns out 01:56:41.000 --> 01:56:46.000 that when you really get at this amount of data and you correlate it with all these other tools, 01:56:46.000 --> 01:56:48.000 you can learn so much. 01:56:48.000 --> 01:56:50.000 And this bit. 01:56:50.000 --> 01:56:53.000 And he's selling it completely as an upside only, right? 01:56:53.000 --> 01:56:55.000 There's no noise, nothing. 01:56:55.000 --> 01:56:59.000 It's really, there are examples of how this has worked out well, 01:56:59.000 --> 01:57:03.000 and there are many, many hundreds of thousands of examples of where it's produced nothing. 01:57:03.000 --> 01:57:09.000 Isn't this of these regions that are found in non-coding parts of the genome, 01:57:09.000 --> 01:57:13.000 and not knowing what the genes are, that's just plain unacceptable? 01:57:13.000 --> 01:57:15.000 We need a total lookup table. 01:57:15.000 --> 01:57:19.000 For every genetic variant in the human genome, every region of the genome, 01:57:19.000 --> 01:57:23.000 is it a regulator and what does it regulate in which tissues? 01:57:23.000 --> 01:57:28.000 We just want one big lookup table, and for every genetic change, does it affect that regulator? 01:57:28.000 --> 01:57:32.000 So this is a lot what, when we watched that video a couple months ago, 01:57:32.000 --> 01:57:38.000 what the Chan Zuckerberg Initiative purports to be doing is that they're going to make 01:57:38.000 --> 01:57:44.000 an atlas of every human cell type, and they're going to catalog all variants affecting gene expression. 01:57:44.000 --> 01:57:50.000 They're even going to make little drones that go around inside your body and report back, apparently. 01:57:50.000 --> 01:57:53.000 They're affecting this gene in what tissue. 01:57:53.000 --> 01:57:56.000 And there are amazing sets of tools that make it possible now. 01:57:56.000 --> 01:57:58.000 It's not easy, but five years. 01:57:58.000 --> 01:58:05.000 I mean, think about this, characterize many human cell types by massively parallel CRISPR inhibition of gene expression. 01:58:05.000 --> 01:58:14.000 So you're going to mess with gene expression using CRISPR to try and see how those genes work in different cell types. 01:58:14.000 --> 01:58:23.000 That sounds very much like a lesion or a knockout mouse that sometimes we have learned something from, 01:58:23.000 --> 01:58:31.000 but most of the time they're pretty useless because they disrupt the pattern integrity so much that you learn nothing about the element that you took out. 01:58:32.000 --> 01:58:48.000 Here's what you would say was impossible. And today you can say, I see a path using sequencing and single cell analysis and CRISPR and other things to make that look up. 01:58:48.000 --> 01:58:53.000 I see a path is lay terms for I can write grants. 01:58:53.000 --> 01:58:57.000 I can see a research line here that I can sustain for 20 years. 01:58:58.000 --> 01:59:16.000 And today the things that are heroic, like with everything else we said, I suspect 10 and 15 years from now, students will take it for granted that people have always known the complete lookup table of what regulates what in the human genome, just like everybody's known the periodic table or everybody's known the map of the earth. 01:59:16.000 --> 01:59:22.000 My guess is that my sons will die and we will not have what he's talking about right now. 01:59:23.000 --> 01:59:36.000 My sons are 14 and 12 and I'm pretty sure they're going to be dead before anything remotely what he's talking about a lookup table about how every every enhancer and promoter is related to every gene. 01:59:36.000 --> 01:59:39.000 That's ridiculous. That is absurd. 01:59:39.000 --> 01:59:51.000 We need that sure it'd be great if we could, you know, find one of those on a spaceship or on a on a Dead Sea Scroll somewhere, but it's so wonderful because they so quickly adjust to what the world is and assume it's always been like that. 01:59:51.000 --> 01:59:58.000 And our job is to create that world where all of that information is available and we can start looking this up. 01:59:58.000 --> 02:00:06.000 And for that reason, the community, the genetics community is coming together around a new revolution in human genetics. 02:00:06.000 --> 02:00:09.000 The timing could not be better. 02:00:09.000 --> 02:00:20.000 Last summer a group of folks met in New York to say it's time again to reset the vision and reset the projects. 02:00:20.000 --> 02:00:24.000 And then a meeting was followed up in Oxford and a hundred people came to that. 02:00:24.000 --> 02:00:39.000 And then a meeting was held in Helsinki and it was decided to create an international common disease alliance spanning continents and countries and groups working all the way from the basic human cohorts to the cellular mechanisms to the people in pharmaceutical industries. 02:00:39.000 --> 02:00:46.000 And its first official coming out party will be in Washington in September of this year. 02:00:46.000 --> 02:00:53.000 And we're seeing the same thing happen as it sustained each of these revolutions. 02:00:53.000 --> 02:00:55.000 It's a pretty remarkable thing. 02:00:55.000 --> 02:01:03.000 The human genome, human cells turn out to be this incredible library of information. 02:01:04.000 --> 02:01:07.000 This is the beautiful library and University College Dublin. 02:01:07.000 --> 02:01:10.000 And this is how I think about the genome and all these genomes. 02:01:10.000 --> 02:01:13.000 If you ask the right questions, you can learn so much. 02:01:13.000 --> 02:01:15.000 You can learn about disease and health. 02:01:15.000 --> 02:01:16.000 You can learn about evolution. 02:01:16.000 --> 02:01:17.000 You can learn about physiology. 02:01:17.000 --> 02:01:19.000 You can learn about migration. 02:01:19.000 --> 02:01:21.000 It's all there. 02:01:21.000 --> 02:01:26.000 If we can pull out the right volumes and learn to read it in the right ways. 02:01:26.000 --> 02:01:30.000 So that's the adventure story for today. 02:01:30.000 --> 02:01:37.000 When we said there was a sweep of history over the past century, really the last 15 years when this has happened. 02:01:37.000 --> 02:01:40.000 Where we stand today and where this is going. 02:01:40.000 --> 02:01:45.000 It's really fun to share with everybody how science changes. 02:01:45.000 --> 02:01:47.000 Day to day, it is frustrating. 02:01:47.000 --> 02:01:49.000 You don't feel like this day to day. 02:01:49.000 --> 02:01:55.000 But as long as you stand back, like go home for weekends, see your family and things like that and you stand back and you think, 02:01:55.000 --> 02:01:58.000 wow, where were we like two years ago? 02:01:58.000 --> 02:02:01.000 And you realize mind boggling change happens. 02:02:01.000 --> 02:02:04.000 Nowhere did I say we're curing all disease yet? 02:02:04.000 --> 02:02:05.000 We're not. 02:02:05.000 --> 02:02:10.000 But for the first time, we really can find the causes of all these Mendelian diseases. 02:02:10.000 --> 02:02:14.000 We can find so many of these things affecting common diseases. 02:02:14.000 --> 02:02:24.000 I have no doubt anymore that this will become the library of information for people who now go forward 02:02:24.000 --> 02:02:25.000 to create therapies. 02:02:25.000 --> 02:02:28.000 We see enough examples to know that this will become routine. 02:02:28.000 --> 02:02:30.000 It's not going to be easy. 02:02:30.000 --> 02:02:31.000 It's going to take a lot of hard work. 02:02:31.000 --> 02:02:33.000 It's going to take a generation. 02:02:33.000 --> 02:02:35.000 But I feel enormous confidence in it. 02:02:35.000 --> 02:02:36.000 Generation. 02:02:36.000 --> 02:02:37.000 All right. 02:02:37.000 --> 02:02:42.000 So again, my my posit would be, why are they doing this? 02:02:42.000 --> 02:02:46.000 Because we are at a time point when human diversity is at its peak for all time. 02:02:46.000 --> 02:02:49.000 And there will never be more genetic diversity available. 02:02:49.000 --> 02:02:54.000 Again, your grandchildren are what they need in order to fill this database with the data 02:02:54.000 --> 02:02:57.000 that they will eventually use to crack the human genome. 02:02:57.000 --> 02:03:02.000 To get beyond where they were, to actually complete the task that they said they completed 02:03:02.000 --> 02:03:07.000 back with that nature cover back in the early 2000s. 02:03:07.000 --> 02:03:11.000 It's all a big smoke and mirrors program. 02:03:11.000 --> 02:03:16.000 Just like when Robert Malone said that when he was cutting his teeth back in Murray Gardner's lab, 02:03:16.000 --> 02:03:20.000 he thought that he would be a geneticist at working at a hospital just like every other 02:03:20.000 --> 02:03:27.000 hospital had a geneticist using retroviruses to cure human genomes, to cure human diseases. 02:03:27.000 --> 02:03:30.000 So he's talking about exactly the same thing. 02:03:30.000 --> 02:03:34.000 Eric Landers talking about gene therapy to cure these Mendelian inherited diseases. 02:03:34.000 --> 02:03:36.000 We're still not doing it. 02:03:36.000 --> 02:03:37.000 It's still not happening. 02:03:37.000 --> 02:03:40.000 Jesse Gelsinger is still the one guy. 02:03:40.000 --> 02:03:44.000 The one example. 02:03:44.000 --> 02:03:46.000 Ladies and gentlemen, this is an illusion. 02:03:46.000 --> 02:03:47.000 This is a mythology. 02:03:47.000 --> 02:03:50.000 They've been telling each other for 20 years and keep resetting the story. 02:03:50.000 --> 02:03:52.000 It's 10 years from now. 02:03:52.000 --> 02:03:58.000 Just over the hill, the AI is going to come and solve the problem. 02:03:58.000 --> 02:04:03.000 Just over the hill, the artificial general intelligence is going to finally awaken. 02:04:03.000 --> 02:04:09.000 And as long as we do it right, and get it right the first time. 02:04:09.000 --> 02:04:14.000 Ladies and gentlemen, stop all transfections in humans because they are trying to eliminate 02:04:14.000 --> 02:04:16.000 the control group by any means necessary. 02:04:16.000 --> 02:04:20.000 This has been giga-ohm biological where intramuscular injection of any combination of substances 02:04:20.000 --> 02:04:23.000 with the intent of augmenting the immune system is gum. 02:04:23.000 --> 02:04:28.000 Transfection in healthy humans is criminally negligent and viruses are not patterned integrity. 02:04:28.000 --> 02:04:35.000 Please go to giga-ohmbiological.com and sign up to subscribe and go to giga-ohm bio 02:04:35.000 --> 02:04:39.000 and sign up to chat with us. 02:04:39.000 --> 02:04:44.000 I'm starting to get a little help from that chat group over there with videos and other 02:04:44.000 --> 02:04:45.000 hints on what to do. 02:04:45.000 --> 02:04:51.000 So if you've got an idea for a show, go to giga-ohm.bio and start posting there. 02:04:51.000 --> 02:04:54.000 There's a pretty active conversation going on. 02:04:54.000 --> 02:04:56.000 Jessica's there, Mark's there. 02:04:56.000 --> 02:04:57.000 Occasionally other people are there. 02:04:57.000 --> 02:04:58.000 I like it a lot. 02:04:58.000 --> 02:05:01.000 And go to the website and subscribe if you haven't already. 02:05:01.000 --> 02:05:03.000 We need about 900 subscribers. 02:05:03.000 --> 02:05:08.000 And we're going to keep this going forever and get as far as I want to get, which is ending 02:05:08.000 --> 02:05:14.000 the vaccine schedule in America and setting everybody on a strict liability for all pharmaceutical 02:05:14.000 --> 02:05:15.000 projects. 02:05:15.000 --> 02:05:17.000 These are the people that make giga-ohm biological possible. 02:05:17.000 --> 02:05:18.000 Thank you very much guys. 02:05:18.000 --> 02:05:19.000 I'll see you tomorrow. 02:05:31.000 --> 02:05:38.000 See you next time. 02:06:01.000 --> 02:06:08.000 Thanks for watching.