WEBVTT

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You

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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

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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

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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

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The rules protect yourself at all

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time.

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Follow my instructions.

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Keep it clean.

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Tough gloves if you wish.

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Let's do it.

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Sweaty bombs.

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This is so crazy.

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Like who's boss?

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This is so crazy.

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I feel so nervous.

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Like what in the world, man?

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All right.

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I like to hear that.

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I like to hear the fact that people like the time.

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It works really well for the fam family.

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For me to do it in the afternoon and be available at

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dinner time and be available at bread time.

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It's really great.

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And then maybe do a late show.

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So we'll see what we do.

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Welcome to the stream everybody.

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Thank you very much for joining me.

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The way this stream works is that if you've been here for a

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while, you're here at the top of the wave with us where we

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are staying focused on the biology.

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We aren't taking the bait on TV and social media and we are

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loving our neighbors.

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We're trying to save the skilled TV watchers in our

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lives.

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And the way it's done is that people share this work every

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week and more importantly, for my family, there are people

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who support us every month.

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And so I want to give a shout out to those people as I always

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do.

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But more importantly, this is where you find me at

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GigaOMbiological.com and GigaOM.bio.

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You can also find a confession of mine at the link

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named Scooby there.

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You can also find a one time support link at GigaOM.

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And then if you scroll down, you can find a schedule and a

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place to subscribe.

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And the list of subscribers is actually slowly growing.

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I'm a little bit optimistic that we might be able to keep this

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up because the list of subscribers and people that is

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supporting the work is growing.

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And so there are people who are making one time donations.

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There are people that are also subscribing and making one

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time.

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It's really all hands on deck and I'm really impressed and

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thankful from the bottom of my heart as so is my rest of my

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family.

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Smooth out a little bit.

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I'm going to put a little bit of footage in there.

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Dog on it.

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Smooth.

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Smooth.

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Make it smoother.

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Where is the smooth?

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There's smoothness.

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Doesn't really like all those names.

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So let's get more names on the list and make it even more

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jumpy as it scrolls those names by.

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Yeah.

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So this is a presentation of the independent bright web.

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What is that?

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I don't really know.

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But it's not the independent and it's not the rather

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the intellectual dark web.

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It's the independent bright web.

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And I guess that's all I can really say.

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This is Giga Own Biological High Resistance Low Noise

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Information Brief brought to you by biologists.

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This is the 15th of February 2024.

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It's another study hall.

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I'm not going to belabor you with a bunch of let's say

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overarching explanations where I can rehab the hypothesis today

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that we are going to pay attention to the fact that we

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have been consciously manipulated.

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Our habits have been organized.

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Our opinions have been manipulated over the course of

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decades since we were a kid.

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Since we were kids.

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And once you start to understand that then you can start

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to see how we were fooled when we were children when there

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was only three newspapers and a few TV channels.

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And what's happening to our children now when they

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already have a cell phone.

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When they already have a tick tock if you haven't been very

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vigilant.

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So that's where I think we are.

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Ooh out of focus.

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Out of focus.

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Ding, ding, ding.

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Hello.

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Hello.

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Good evening.

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Good morning.

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Good afternoon.

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It is one twenty in the afternoon today at Pittsburgh

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in Pittsburgh, Pennsylvania coming to you live from my garage.

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It is a very unusual.

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I've got the blinds closed.

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That's too bad.

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There is some daylight out there.

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It's not nighttime.

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And yeah, we are still sorry.

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I had my had my echo on there.

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Refocus this one more time.

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Come on, Sony, you can do it without crashing.

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We are still fighting this unseen mechanism of society,

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but we're starting to, I think, acknowledge that it might be

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more in our face than we thought.

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And that actually this is how informed consent has been

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ignored for the duration of the pandemic.

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And it is these people in cooperation with the national

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security operation and in cooperation likely with weaponized

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piles of money in cooperation with likely pharmaceutical companies

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and other manufacturers and things and contractors that were

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involved in this have all been involved in making sure that none

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of us can exercise informed consent that everybody accepted

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the worst case scenario and that we all tried our best to

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comply.

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And that was all part of the plan.

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That's what I'm trying to argue.

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That's what I'm trying to explain.

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They fooled us into solving a mystery by first keeping us at

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home and making us feel lost on a road all by ourselves and

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then getting picked up by this team worst case scenario told us

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crazy stories for a year about how they were going to break

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all the rules to make sure that we didn't die and that the

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worst case scenario was avoided.

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But if the worst case scenario came and people would didn't

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conform to the public health authority of the way that they

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needed to if people didn't take the necessary precautions we

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could have a disaster.

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And there were people behind the scenes in social media,

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mainstream media whose job was to do this to make sure that the

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worst case scenario was not considered to be a few thousand

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people a little worse than SARS one.

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No, the worst case scenario could be billions.

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And this worst case scenario was used to motivate us number one

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to conform to the lockdowns and conform to the school closures

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and conform to the masking in public.

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But it also tricked us into believing that there was a

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mystery to solve.

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That there was a crime being covered up.

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That there were lies being told.

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And this.

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What is running still?

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I can't see what's running still.

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Oh, I've just faded it out, didn't I?

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Okay, sorry.

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I feel as though it's really important that we keep repeating

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this over and over because every college kid that sees this for

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the first time is going to say, wow, wait, what did he say?

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Because when they were sent back to college in the fall of 2020,

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this was already really going on in the background in social

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media.

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This debate about a lab leak and whether they were covering it up

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and what those emails meant and what they were yelling about in

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the Senate with Rand Paul and Tony Fauci.

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This is all real.

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And those college kids were going to their, going to their

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parties at night talking about that stuff.

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Wondering whether the adults were going to solve the mystery

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and whether anyone's going to cop to the, to the responsibility

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of this.

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And that was in 2020.

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And in 2021.

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When we could have had people on the internet and on social

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media elevated spontaneously because they had the right message,

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which was that natural immunity was better than anything that

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the government would provide in the form of a vaccine, that

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natural immunity to previous encounters with RNAs like this

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might be relevant.

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And the shocking thing was is that there were people on all

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sides of that narrative pulling us away from that foundational

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truth that was that we could trust our bodies and our

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natural immunity and our previous health condition likely

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better than we could risk it on a novel countermeasure.

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And so I never got to this idea until 2023 because again, I was

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also swimming in this, in this muddy water filled with people

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who had all kinds of crazy stories and conjecture about what

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this had to be and why it had to be that.

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And it is not random.

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That people like Kevin McCare and in Charles Rixie and George

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Webb and Paul Cottrell and, and, and I mean, the list is just

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endless.

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Of people who were there at the beginning of the pandemic.

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Seating this worst case scenario narrative instead of

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amplifying the idea that, Holy cow, maybe wait a minute,

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wait a minute, maybe we should not trust the public health

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system that's given us this, this vaccine schedule that lots of

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people have known has been sketchy for years that everybody's

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been ignoring.

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Maybe we should slow down and hold, hold the phone for a minute.

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No, nobody with that voice was elevated.

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Until the end of the year in 2020 in Germany where Robert F.

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Kennedy Jr. spoke and did he speak about public health and

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about lockdowns and about how the, yes, he did.

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He spoke against all of it.

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Was he elevated?

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No, he was lambasted by his own wife.

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And what did they distort it into that?

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He compared it to something to do with the Nazi regime and

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something to do with World War II.

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And that was just unacceptable.

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It is unacceptable to make that parallel.

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Even though now we have someone as, as.

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Courageous as Vera Sharav making that very comparison and doing it

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with, with success.

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Also, nowhere on the Internet, nowhere to be found.

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Five part movie, nowhere to be found, nowhere to be heard,

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not on mainstream media, not on social media, just vanished.

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Gone.

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And so understand very clearly that this is the main message of

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giga-owned biological anything else that somebody says that

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we're off on is ignoring these basic messages, which is that

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intramuscular injection of any combination of substances with

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the intent of augmenting the immune system is dumb, which is

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the transfection is in healthy humans was criminally

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negligent.

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And there are thousands, if not hundreds of thousands of

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academic biologists and academic doctors, MedMDs, that

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should have known because they've used transfection in the past

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that this technique, this methodology would have been wholly

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inappropriate for augmenting the immune response of any healthy

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human.

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They should have known it.

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They did know it.

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They just chose not to know it.

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And that's because they have grown up and developed as

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career adults inside of a system, which makes them look the other

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way and stick right to their own little sliver of knowledge,

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whatever that might be.

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And not to step on the toes of any other academic edition that

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might have it wrong because that's not my expertise.

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That's theirs.

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I'll let their grant committee figure that out.

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And so I was the only person at the University of Pittsburgh

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School of Medicine that took the time to speak out against

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transfection.

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The only one of 135 members of the faculty of neurobiology that

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spoke out one of hundreds of faculty members that did not.

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I'm the only one that did in that entire med school, as far as I

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know, because no one's contacted me.

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No one said, Hey man, I totally agree with you.

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Think about that.

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And how many med schools are there in the United States where people

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have the professional expertise to have known that

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transfection and healthy humans is criminally negligent, to have

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known and could know now that Peter Cullis's admission that they

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can't control where the lipid nanoparticle goes is a very big

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problem.

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They could have known.

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They could know.

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They could come out and say it and be heroes.

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Nobody's doing it because it's guess not glorious enough to admit

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you had it wrong for three years.

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So nobody has the courage to do it.

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But I'll gladly admit it.

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But I will also claim with much vigor that when I was still

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consulting for Bobby on that book and CHD asked me to write a

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letter to the no virus people I wrote a letter which was inviting

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them to come to the table and talk about the molecular biological

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explanation I had where they might be right and some other

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molecular biologists might think they're right because they

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don't understand coronavirus biology and molecular biology

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well enough to understand how they've been bamboozled by PCR

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and sequencing but no.

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The no virus people would have nothing of it and went with

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nothing but insults and asked for only citations that had to do

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with isolation and purification and otherwise we don't want to

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talk to you.

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And those same no virus people are now starting to promote

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Denny Rankor as though he's realizing that virology is on

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thin ice and continuing to call me a gatekeeper.

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Even though I presented with them with the opportunity to win.

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They could have essentially gone on a stream with anybody from

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CHD including me and talked about how infectious clones or

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synthetic RNA and DNA could have been used to create the illusion

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of a pandemic that never was and is used to create the illusion

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of RNA virology in laboratories all around the world.

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To create this illusion of fidelity this illusion of it not

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being a ghost but something real RNA viruses are everywhere.

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Ladies and gentlemen we are going to break this but we're only

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going to break it when we stop the spread of these bad ideas.

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And so I've been guilty of spreading these bad ideas and all

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we've got to do is understand how we've all been guilty of

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these bad ideas and come to the humble realization that we

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bought into this illusion of consensus that the worst case

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scenario is again a function laboratory bioweapon and then

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now reject it.

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It's okay.

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It's the way you get out of a cult.

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It's the way you get out of a bad idea.

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It's the way you get out of you know it's the way you grow up.

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You admit mistakes and you move on.

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And so we've really got to strive to push for this to

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continue to be an understanding where people see this team

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worst case scenario and try to actively get off of it.

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Whether you were wittingly or unwittingly a part of this team

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we need to encourage people to get off of the team.

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Not join our team get off of the team where you think that

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bad cave viruses are real where you think that the diffuse

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proposal of putting fear and cleavage sites in bad cave

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viruses is real where you think that passage in ferrets can

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result in a pandemic virus where you think that they can

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stitch this stuff together and create something that can go

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around the world for five years.

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You've got to get off this team.

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That's all you've got to do.

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Once you're off this team we can all talk about what the best

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course forward is for our children.

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We can all talk about it.

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And we can all start to rebuild a model of our biology and our

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ecology and our place in the world that doesn't have to do

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with these fairy tales.

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Early, what I want is high morbidity.

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I want people to complain.

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So what do I do?

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I go to Des Moines.

18:19.000 --> 18:20.000
Ladies and gentlemen.

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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

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been thought of.

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It has been planned and it has been gained out.

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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

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dissident, supposedly people speaking out.

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And some of the most extraordinary people to speak out are people

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who are tied all the way back to HIV, the original virology

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illusion.

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And yet somehow or another, after three years of waking up,

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some of these people still haven't even bothered to think about

19:08.000 --> 19:09.000
that possibility.

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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.

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So yesterday we watched this video.

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And I thought it was really interesting.

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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

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the human genome project and what led up to it, what people were

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involved and what was really accomplished there.

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And he did a couple of videos, one video from a day or two ago

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about Eric Lander.

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And I just found it so extraordinary.

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Some of the admissions that Eric Lander met and made.

19:58.000 --> 20:01.000
And I went looking for other videos.

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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

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2019.

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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

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our friend, Kevin McCurnan, gave a presentation in 2019 kind of

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talking about the history of the human genome project.

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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.

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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

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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

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the human genome project was kind of exaggerated.

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I think a lot of people think that what they mean is if this

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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

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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

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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

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with respect to molecular biology is called a restriction map.

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And just to give you a brief idea of what a restriction map is,

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hopefully this pen will write.

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So a restriction map is really kind of an interesting idea

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because remember we are talking about genomes for which we have no

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idea what is really there.

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We don't know where the genes start and where they end.

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We call, we used to call these things introns and exons, but

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like it is a very, very subtle map.

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And then of course there might be reading frames.

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And so we don't really know what all these bases really mean

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together.

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And when we say we sequence the human genome, it is a very

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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

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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

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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.

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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

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like that and make this shape.

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Now the point is really cool, is that what you can do is you

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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

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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

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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

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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.

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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,

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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.

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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.

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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.

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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?

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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.

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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.

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It's impressive, but just think about how impressive this enchantment is that it's been going on for a couple of three decades.

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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.

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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.

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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.

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So this was not some isolated scientist. He was in, you know, kind of a Cambridge, Massachusetts of his time in some way.

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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.

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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.

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If you're inferring the existence of genes, they say, I want to see the gene, where's the gene, what is it?

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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.

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And this field got underway in a serious fashion, studying genetics, studying mutants, studying organisms that lack a particular gene.

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And that causes a particular trait, what we'll call phenotype.

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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.

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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.

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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.

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And then the really interesting intellectual advance in the 20th century was the recognition that they were connected.

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The components of the geneticist study, genes, actually encoded the instructions for proteins.

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And that recognition, that these were two sides of the same coin, really gave rise to this field of molecular biology.

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The recognition that DNA, its double helical structure, let it replicate, that it encoded the instructions for proteins.

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And we now had this amazing triangle on the intellectual unification.

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And by the 1960s, people had even worked out the genetic code.

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They knew which three letters of DNA specified which building blocks of proteins, a lookup table of three letter codons to amino acids.

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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.

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And some people left the field because they figured it was done.

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And then as so often happens, a younger generation arose.

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And the younger generation said, well, not so fast, we actually can't even read a single gene yet.

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We know an abstract what the genetic code is, but we can't read one gene.

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Maybe we're not finished yet.

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And they gave rise to amazing technologies in the 1970s of recombinant DNA.

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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.

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And each bacteria would pick up a different piece of human DNA and copy it.

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And then you could get purified forms of individual genes.

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And then they worked out how to study those genes and even to the level of reading out their DNA letters.

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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.

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So keep in mind what we're talking about here.

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We are talking about in the 1970s when Ralph Barracks says he started working on coronavirus in 1984.

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In the 1970s, they started using recombinant DNA and cloning to look at sequencing genes.

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They used recombinant DNA to make more DNA, which opened up a whole world of exploration.

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And a whole new palette of techniques that involved restriction enzymes and ligation methods using restriction enzymes.

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So they've been baking like this for a long time.

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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.

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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.

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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.

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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.

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And then it's ever increasing application, ever increasing fidelity and ever increasing cheapness.

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I hope you can see it.

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It's sequencing genes.

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And you could use that to find the gene where you knew the protein insulin.

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If you knew insulin, you could find the gene for insulin.

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You could clone the gene for insulin, make tons of insulin, and give it to diabetics.

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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.

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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.

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Why would you ever start with RNA then?

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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.

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It's all the same story. It's all the same thing.

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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.

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Produce recombinant growth hormone and hemoglobin and onward as long as you knew what you were looking for.

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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.

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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.

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I see.

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And this is the story I want to tell.

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This is the story.

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The principles first for how you could map genes when you didn't actually know what you were looking for.

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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?

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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

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and across the fruit flies or across of maize or other organisms, things that are nearby on the chromosome.

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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.

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What are restriction fragments? Restriction fragments are these things that we just talked about right here.

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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.

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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.

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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.

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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.

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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.

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Does that make sense? I hope this makes sense.

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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.

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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.

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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.

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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.

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I mean you're allowed to set up crosses with somebody, you have to talk about it and all that.

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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.

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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.

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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.

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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.

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And then we can look across large numbers and voila.

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You don't even need hypothesis anymore. You just wait for the computer to spit it out.

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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

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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.

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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.

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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.

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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.

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Some of these methodologies have become more powerful. Some of these methodologies have become cheaper. Some of these methodologies have become faster.

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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.

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Never mind how these systems work together in concert.

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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.

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And so we said find lots of genetic variation and trace inheritance with it. It was a brilliant idea and it actually worked.

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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?

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Could you do this even if you didn't have families and the genetics was more complicated?

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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.

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Common ancestral segments. So you could pretend like it was a big family. I just happened to have laughed.

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Please understand that they are still using the very basic principle that I told you in the beginning of the talk.

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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.

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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?

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And then you'll understand.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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That there exists a polymorphism at that location.

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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.

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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.

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The previous hundred generations, but if I had a lot of genetic markers, I could still see that.

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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.

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He was really excited about this. This was pretty cool.

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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.

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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.

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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.

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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.

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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.

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And by 1989, they found the gene for cystic fibrosis. And when they found it, it looked like this.

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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.

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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?

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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.

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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.

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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...

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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.

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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.

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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.

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And after looking and looking and looking, they found a protein that seems to resemble, maybe it's like that.

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So the human genome project is not all they cracked it up to be.

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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.

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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.

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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.

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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.

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And how would you find this needle in a haystack, actually?

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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So it got finished. There was a draft by the year 2000, a finished sequence by April 25th, 2003. That was not an accident.

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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.

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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

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projects.

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These are the people that make giga-ohm biological possible.

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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.

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Thanks for watching.

