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Speaker 1: We as humans were scared that we were going to

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Speaker 1: unleash some kind of monster. What were the consequences of

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Speaker 1: us genetically engineering things? I mean, more recently, we've had

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Speaker 1: similar debates about crispers, And maybe you've heard that there

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Speaker 1: was even a case where a Chinese scientist used crisper

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Speaker 1: to genetically engineer a baby, a human that has really

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Speaker 1: really big ethical questions, And this was happening only in bacteria,

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Speaker 1: but it was the first time it had happened, and

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Speaker 1: we were rightfully, really thinking carefully about what the consequences

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Speaker 1: could be. For example, imagine you cloned a bacteria that

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Speaker 1: contained genes that could break down petroleum, and then you

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Speaker 1: unleashed that in an oil mining operation. You could really

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Speaker 1: cause a lot of destruction. And what if that was

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Speaker 1: just impossible to control and then you destroyed huge natural

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Speaker 1: resources unintentionally intentionally for that matter. So those were the

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Speaker 1: kinds of questions people were worried about.

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Speaker 2: Or what if it helped the bactery organize and it

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Speaker 2: crawled out of the vat and like extracted vengeance for

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Speaker 2: all the brethren that we've tortured in order to extract

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Speaker 2: incident from them.

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Speaker 1: That's a great question, but I'm pretty sure about it.

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Speaker 2: That's a very polite answer to a totally bonkers questionin

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Speaker 2: you all should have seen her face.

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Speaker 1: That didn't make it to the top ten list for

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Speaker 1: discussion out of so lamar.

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Speaker 3: Hi.

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Speaker 2: I'm Daniel. I'm a particle physicist and a member of

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Speaker 2: the White Senn Research Institute in Irvine, and today I'm

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Speaker 2: going to be an extra big fan of biology.

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Speaker 3: Hello, I'm Kelly Wiener Smith. Can I be like an

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Speaker 3: adjunct at the White sin Research Institute?

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Speaker 1: Like?

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Speaker 3: Is that what friends are called?

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Speaker 2: Come be a visiting professor? No problem? Yes, all right?

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Speaker 3: Does that come with dinner?

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Speaker 2: Dinner is a bonus? Yes? Plus you can add this

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Speaker 2: as an extra affiliation on all of your papers to

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Speaker 2: make you sound really.

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Speaker 3: Smart, fantastic. I'm go to you. I'm gonna put it

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Speaker 3: right on my CV. Have you ever done a show

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Speaker 3: with your wife before?

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Speaker 2: I have never done a podcast episode with Katrina, though

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Speaker 2: I've had lots and lots of science conversations over the

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Speaker 2: dinner table, so we definitely talked a lot about science

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Speaker 2: and board our teenagers to death. But no, I don't

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Speaker 2: think it's ever been recorded. So this is great for posterity.

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Speaker 3: Well, and I'm excited because we have talked on the

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Speaker 3: show often about how, you know, since she studies microbiome stuff,

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Speaker 3: sometimes you find bags of poop in your freezer and

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Speaker 3: stuff like that, and I feel like it's really important

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Speaker 3: for people to like put a voice to those stories.

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Speaker 3: And today that's gonna happen.

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Speaker 2: You just want another poop in the freezer ally on

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Speaker 2: the show to outvote me. That's what's going on here, really,

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Speaker 2: I do.

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Speaker 3: I want the gross votes to outnumber the people who

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Speaker 3: are squeamish. And also I like, you know, pushing the

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Speaker 3: envelope in the direction of gross so that I don't

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Speaker 3: seem as gross, you know, Like, mostly I want Zach

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Speaker 3: to realize how lucky he is. Yeah, that there's not

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Speaker 3: bags of poop in the freezer like the dead bird.

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Speaker 3: Not a big deal.

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Speaker 2: All right, Well, welcome to the podcast where we pretend

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Speaker 2: to be talking about science, but really we're doing marital therapy.

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Speaker 3: Well, and on today's show, we're very lucky to have

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Speaker 3: Katrina come on to talk to us about diabetes and

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Speaker 3: the history of insulin production and the future of insulin production.

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Speaker 3: And she was amazing. She had a lot of incredible insights.

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Speaker 2: Yeah, I think a lot of people think they understand

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Speaker 2: diabetes generally, but there's a lot of really fascinating biochemistry

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Speaker 2: and a lot of nuance there, and a lot to

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Speaker 2: understand about the daily life of somebody with diabetes. And

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Speaker 2: it's kind of amazing that until about one hundred years ago,

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Speaker 2: diabetes was a death sentence. You got diabetes and you

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Speaker 2: were dead a couple years later. And now because of

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Speaker 2: amazing biologists, we can save the lives of all those kids,

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Speaker 2: and people like my wife can grow up and be

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Speaker 2: like a professor and had kids and live a full life.

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Speaker 2: It's really kind of an amazing testament to what science

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Speaker 2: can do.

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Speaker 3: And I'm not going to release any spoilers here, but

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Speaker 3: there were at least two things she talked about where

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Speaker 3: I was like, I thought I knew and I was

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Speaker 3: totally wrong. And so I think we're gonna have a

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Speaker 3: good episode of sort of dispelling rumors or myths about diabetes.

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Speaker 2: All right, Well, then, without further ado, let's welcome my

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Speaker 2: favorite scientist at the whites And Research Institute. So then

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Speaker 2: today it's my great pleasure to welcome to the podcast.

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Speaker 2: Co president of the White Sun Research Institute and winner

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Speaker 2: of the whites In Research Prize as Professor Katrina whites

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Speaker 2: In at uc Irvine Katriina, Welcome to the podcast.

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Speaker 1: Thank you.

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Speaker 3: I'm so excited you're here.

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Speaker 1: I didn't even know about those awards, but I will.

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Speaker 2: I guess I'll go update your CV right now.

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Speaker 3: Update your tenure packet, all the things.

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Speaker 2: Yeah, but we did just invite Chain onto the podcast

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Speaker 2: to joke around with her. We invited her on because

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Speaker 2: she's a deep expert in today's topic.

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Speaker 3: So today we're talking about bioengineering bacteria, we're talking about diabetes,

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Speaker 3: we're talking about lydia villa comarov and it's going to

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Speaker 3: be an amazing conversation and we're super excited to have

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Speaker 3: you here.

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Speaker 1: Thank you.

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Speaker 3: So by the end of the episode, we're going to

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Speaker 3: get to bacteria producing pharmaceutical components. But let's start by

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Speaker 3: talking about diabetes. What are the mechanisms underlying diabetes.

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Speaker 1: Well, that's a really good question, and actually even just

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Speaker 1: using the word diabetes already doesn't give you quite enough

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Speaker 1: information to be able to answer that question, because there's

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Speaker 1: two really different forms of diabetes. I guess we could

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Speaker 1: go back to ancient times when the word first arose,

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Speaker 1: and in that time we understood that sugar was involved

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Speaker 1: and that not being able to process sugar was involved.

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Speaker 1: We've actually known that for hundreds of years, so you

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Speaker 1: could think of glucose as maybe the first biomarker. There

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Speaker 1: were really interesting ways that people would detect that someone

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Speaker 1: had diabetes. It's actually diabetes melitis, and the words mean

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Speaker 1: that you're losing water due to sugar. And people would

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Speaker 1: take urine and put it out to see if the

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Speaker 1: ants were attracted to it. They would taste the urine

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Speaker 1: to see if it was sweet, and that would give

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Speaker 1: them a clue that you had this wasting disease where

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Speaker 1: your body couldn't process sugar. And like, probably the first

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Speaker 1: thing that pops into mind when you think of diabetes

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Speaker 1: in modern times is it's associated with obesity, But actually

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Speaker 1: diabetes is a wasting disease. It means that you can't

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Speaker 1: get energy from sugar and you actually starve to death

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Speaker 1: while drowning with lots of sugar in your blood. So

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Speaker 1: I think that's just actually really amazing and usually counterintuitive.

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Speaker 1: When I'm teaching, I sometimes show images of the children

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Speaker 1: who would die from diabetes before the insulin was discovered,

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Speaker 1: and they are emaciated because they're unable to get any

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Speaker 1: energy from the sugar that they're eating. So to back up,

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Speaker 1: I mean, there's two main kinds of diabetes. The first one,

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Speaker 1: type one diabetes for a while even called juvenile diabetes,

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Speaker 1: is when your immune system kills the cells in your

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Speaker 1: pancreas that make insulin, so you're no longer able to

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Speaker 1: access sugar. So you eat sugar. It gets into your blood,

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Speaker 1: but the key that allows the sugar to be used

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Speaker 1: by your cells is just totally missing.

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Speaker 2: And so you're saying, insulin is that key. Insulin is

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Speaker 2: a thing that takes sugar from your blood and into

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Speaker 2: your cells exactly.

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Speaker 1: Insulin is a protein like ham, but it's acting like

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Speaker 1: a little it's a little robot. It's a little key

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Speaker 1: that actually summons the transporters that help glucose get into

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Speaker 1: your cell up to the surface and then the gates

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Speaker 1: open and the sugar can get into the cells. Without that,

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Speaker 1: the sugar just sits in your blood and the cells starve.

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Speaker 3: Is there a toxic effect of the sugar building up

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Speaker 3: as well? Or is it just that you're starving? Is

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Speaker 3: the main problem the long term?

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Speaker 1: The sugar in your blood is super toxic. But those

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Speaker 1: are kind of like decade long problems. So if you're

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Speaker 1: dealing with starving in the course of weeks or months,

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Speaker 1: then the decade long problem of too much sugar in

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Speaker 1: your blood is just not something to worry about. But yes, hope,

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Speaker 1: too much sugar in your blood is definitely toxic. And

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Speaker 1: I think that's the part that's more famous about diabetes

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Speaker 1: right now, because most diabetes in our time, ninety percent

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Speaker 1: of diabetes is what we called type two diabetes, and

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Speaker 1: it can be lifestyle associated, but amazingly, it's actually more genetic.

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Speaker 1: It's more inherited to get type two diabetes. So about

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Speaker 1: ninety percent of the diabetes in the world right now

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Speaker 1: emerges usually later in life, and it's not because you

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Speaker 1: don't have insulin. You could actually have plenty of insulin,

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Speaker 1: it's just not working very well, so your body is

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Speaker 1: resistant to the use of the insulin. That's associated with

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Speaker 1: metabolic syndrome, but actually can be quite genetic. You can

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Speaker 1: be a very healthy, thin looking person and still get

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Speaker 1: type two diabetes. So that's also a bit of a

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Speaker 1: misnomertis always assume it's associated with lifestyle, but in the

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Speaker 1: case of type two diabetes, if it is arising because

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Speaker 1: of obesity, you can reverse it if you eat less carbs,

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Speaker 1: exercise more, and in general it don't overload the system

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Speaker 1: that depends on insulin. You can resensitize your body to

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Speaker 1: the insulin that it can make, and you can also

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Speaker 1: take medicines that make the insulin more effective.

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Speaker 2: All right, so let me recap for the non biologists.

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Speaker 2: You eat a ham sandwich, your body turns some of

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Speaker 2: that into sugar, puts it into your blood, but then

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Speaker 2: your cells and your body, like your muscles, can't access

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Speaker 2: it from your blood without insulin, this thing that takes

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Speaker 2: it from the blood into the cells. And type one

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Speaker 2: diabetes is when your body kills the cells to produce insulin,

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Speaker 2: so you just don't have it. Type two is when

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Speaker 2: you still have insulin, but because of some metabolic things,

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Speaker 2: it's not working as well, or it's just not.

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Speaker 1: As effective, or you just don't have enough. Yeah, that's

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Speaker 1: about right. There's a lot of nuances you will not

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Speaker 1: be surprised to hear. For example, your brain, which uses

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Speaker 1: about twenty percent of the glucus in your body, doesn't

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Speaker 1: really depend on insulin in the same way. So I

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Speaker 1: find that kind of amazing that your brain is just

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Speaker 1: like this constant machine that's using a lot of the

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Speaker 1: sugar in your blood. It's like the main reason it's

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Speaker 1: so important that our blod glucose levels are maintained at

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Speaker 1: a certain level, otherwise your brain doesn't function. So that's

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Speaker 1: kind of independent of the insulin. And then there's other

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Speaker 1: really cool exceptions, like your muscles, especially during exercise, they're

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Speaker 1: extra sensitive to being able to get glucose in. So

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Speaker 1: there are extreme examples, Like there's a story about a

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Speaker 1: guy one hundred years ago before there was really insulin,

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Speaker 1: who kept himself alive with like a really crazy exercise

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Speaker 1: regiment and he had like amazing muscle mass and he

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Speaker 1: kind of like extended his capacity to metabolize his diet

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Speaker 1: even though he didn't have much insulin. So it's kind

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Speaker 1: of interesting we didn't do more of that, but on average, yeah,

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Speaker 1: without insulin, your blood will be full of sugar and

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Speaker 1: your cells will be starving. And if you look at

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Speaker 1: a picture of a kid who died from type one

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Speaker 1: diabetes before nineteen twenty one, when insulin was discovered, they

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Speaker 1: look emaciated, which is just terrible so.

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Speaker 2: You're starving, but your blood is filled with sugar and

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Speaker 2: your pea is filled with sugar. You just don't have

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Speaker 2: access to it. So it's like being hungry at a buffet.

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Speaker 2: It's crazy.

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Speaker 1: Yeah, exactly.

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Speaker 3: Do you know this story of how we discovered insulin.

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Speaker 1: Yeah, I mean, there's actually a lot of really good

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Speaker 1: stories around that. It was a slow process like science

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Speaker 1: often is, because we got a lot of important clues

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Speaker 1: even maybe half a century before the discovery of insulin,

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Speaker 1: Like we knew for a long time, hundreds of years

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Speaker 1: that it was related to glucose. So then by the

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Speaker 1: eighteen eighties in Germany, there were scientists who figured out

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Speaker 1: that if you removed the pancreas from a dog, it

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Speaker 1: would cause essentially the symptoms of diabetes.

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Speaker 2: What were they doing. Were they just like taking random

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Speaker 2: organs out of dogs one at a time to see

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Speaker 2: what happens, Like, let's see what happens that dogs don't

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Speaker 2: have a heart. Oh that didn't work very well.

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Speaker 1: Yeah, I guess that's not diabetes related. Good question. I mean,

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Speaker 1: you know it could be that like some unlucky dog

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Speaker 1: got kicked by a cow or hit by There weren't

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Speaker 1: cars in those days, but hit by a horse.

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Speaker 3: I guess we did do some pretty awful things to

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Speaker 3: animals in the past. It wouldn't surprise me if there

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Speaker 3: were a series of experiments we were like, how do

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Speaker 3: they do without this? How about without that?

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Speaker 2: In the past, biologists are still doing horrible things to animals,

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Speaker 2: and the name of science.

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Speaker 3: We have to go through complicated protocols and institutions and

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Speaker 3: prove that those animals are needed for scientific discovery these days.

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Speaker 3: But that could be a whole different episode.

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Speaker 2: Unless you want to do experiments on your own children,

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Speaker 2: in which case you don't have to ask an IRB,

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Speaker 2: but you should ask your husband.

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Speaker 1: But if you're the parent, you could also ask yourself,

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Speaker 1: especially if it's like something very low risk. This is

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Speaker 1: a case where I could try to look up the

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Speaker 1: eighteen eighty nine story that I have in my brain

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Speaker 1: about the pancreas. But anyway, somehow they realized that there

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Speaker 1: was a connection between pancreas being missing and diabetes. So

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Speaker 1: then that sounds like the kind of thing where it

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Speaker 1: shouldn't have taken more than a couple of months to

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Speaker 1: go from knowing it was the pancreas to extracting insulin

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Speaker 1: from the pancreas so that you could save all these

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Speaker 1: kids who were dying from diabetes. But it wasn't until

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Speaker 1: nineteen twenty one, twenty two that we finally extracted insulin

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Speaker 1: from the pancreas. A lot of that time went to

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Speaker 1: figuring out how to purify the insulin protein away from

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Speaker 1: all the other digestive enzymes and proteins that are also

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Speaker 1: produced by the pancreas. So you know, your pancreas is

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Speaker 1: this organ that's there to help you digest stuff. One

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Speaker 1: of its main jobs is to produce proteins that break

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Speaker 1: down our food, and those are also made of protein.

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Speaker 1: So basically the pancreas is full of stuff that destroys insulin,

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Speaker 1: since insulin is also a protein, so like separating those

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Speaker 1: things away from each other. It took forty years or

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Speaker 1: thirty years. That's a really hard thing to do. Now,

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Speaker 1: that's the kind of thing we're really good at, but

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Speaker 1: in those days we had to invent a bunch of

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Speaker 1: techniques to get good at that.

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Speaker 2: It's amazing to me how recently we have like any

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Speaker 2: idea what big organs in our body do. Like before that,

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Speaker 2: we're just like we don't know this is just a

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Speaker 2: blob of meat like stuff that seems to be important.

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Speaker 1: What I think is amazing is that when we don't

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Speaker 1: know what something does, we assume it's irrelevant. So like

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Speaker 1: we're always saying that the spleen and the appendix, for example,

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Speaker 1: serve no purpose. You know, you listen to them telling

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Speaker 1: you in the hospital when you're getting your appendix out, like, hey,

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Speaker 1: you don't need this, It'll be no big deal. I

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Speaker 1: find that really funny that when we don't understand something,

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Speaker 1: we just say it's irrelevant. Like that is definitely not true.

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Speaker 1: In the case of the appendix, having your appendix there

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Speaker 1: as a reservoir for gut bacteria is like the difference

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Speaker 1: between life and death. To be able to recede your

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Speaker 1: gut microbiome and be protected from infection in the future.

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Speaker 1: That was like clearly important enough that we developed an

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Speaker 1: organ and hung onto it, you know, and.

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Speaker 3: The evidence for that is really good now, right, that

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Speaker 3: that is the appendix's role to hold onto those bacteria.

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Speaker 1: I mean, it's a hypothesis that's hard to prove, right

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Speaker 1: because it's very context dependent. But if you think about

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Speaker 1: the context of human history, that seems like a very

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Speaker 1: relevant context.

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Speaker 2: It's biology. So the answer is it depends.

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Speaker 3: It's true. It's true. So they opened up the pancreas,

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Speaker 3: they've been able to divide out the different proteins that

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Speaker 3: are in there. How do you get from art I've

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Speaker 3: divided out the proteins to actually treating the disease. Was

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Speaker 3: that a pretty easy step?

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Speaker 1: That's a real cool question. And this all happened at

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Speaker 1: a university. It was at the University of Toronto, and

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Speaker 1: there was a medical student there that summer, Best, and

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Speaker 1: he was working with a lab supervisor, Banting. So Banting

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Speaker 1: and Best are the famous team who discovered insulin that

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Speaker 1: summer in Toronto. The second they had it purified out,

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Speaker 1: they started treating a dog who had had their pancreas

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Speaker 1: taken out, and they were able to keep a dog

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Speaker 1: alive for a couple of months using the dog insulin extract,

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Speaker 1: which was pretty crude. In the meantime, they also worked

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Speaker 1: on better purifying insulin from cows and that's when they

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Speaker 1: started considering using it to treat a child. And they

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Speaker 1: actually moved to this really quickly.

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Speaker 3: Interesting.

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Speaker 1: I would say that would still happen today because it

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Speaker 1: would definitely be a life or death circumstance. We still

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Speaker 1: have these compassionate use exemptions from the FDA, the Food

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Speaker 1: and Drug Administration. In fact, I use those for our

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Speaker 1: phage therapy trials, where if somebody has no other options,

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Speaker 1: we'll allow you to do with something more experimental. But

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Speaker 1: that means that this like lab guy, a med student

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Speaker 1: who was certainly not a professional at making medicines, was

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Speaker 1: just taking this extract they made in the lab. And

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Speaker 1: the first boy they treated was a Canadian boy at

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Speaker 1: the hospital at the University of Toronto. He was fourteen

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Speaker 1: and he was wasting away because he didn't have any insulin,

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Speaker 1: and he went from being very very ill with very

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Speaker 1: high blood sugar to doing much better, like within hours,

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Speaker 1: and then really soon after that. There's an image that

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Speaker 1: I think is super iconic to anybody who knows about

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Speaker 1: the discovery of insulin, and it's an image of a

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Speaker 1: nurse with a cart and maybe a doctor next to them,

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Speaker 1: and the story goes that they went into this ward

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Speaker 1: of the hospital that had thirty comatose children with type

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Speaker 1: one diabetes, and the parents were there, and then they

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Speaker 1: injected this very experimental cocktail they had made in the

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Speaker 1: lab into those thirty kids and they all woke up,

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Speaker 1: and you know, that was a really big moment in

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Speaker 1: science and must have been amazing for the parents who

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Speaker 1: really had no reason to hope their kids had any

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Speaker 1: chance of getting better.

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Speaker 2: I mean, if your kid is wasting away and the

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Speaker 2: doctor says, hey, we're going to inject this experimental dog

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Speaker 2: organ juice into your kid, you might just be like, yes,

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Speaker 2: do anything please?

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Speaker 1: Right? Yeah, exactly.

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Speaker 2: But how did we know that dog pancreas would produce

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Speaker 2: dog insulin which humans could use. Isn't it possibility that

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Speaker 2: like dog insulin could be different from human insulin.

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Speaker 1: Yeah, that's a really good point. I mean, I guess

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Speaker 1: we tried it and learned in the moment that first

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Speaker 1: fourteen year old was injected with insulin, we figured that out.

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Speaker 1: And we now know that the insulins across animals have

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Speaker 1: really shared features. So you know, animals that have guts

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Speaker 1: also have insulin basically, so insulin is very conserved. This system,

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Speaker 1: which might seem quite delicate, is being used across the

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Speaker 1: animal kingdom, and in general, the insulins are pretty exchangeable, so.

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Speaker 2: You could like extract insulin from a hummingbird and use

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Speaker 2: it on a person.

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Speaker 1: Well, hummingbird. Wow, you'd need a lot of hummingbirds. That's

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Speaker 1: a good question, but yeah, I think so. There was

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Speaker 1: actually a Czech couple, Eva and Victor Saxel, who fled

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Speaker 1: Nazi occupied Czechoslovakia in nineteen forty. She was teaching English

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Speaker 1: in Shanghai when her symptoms of diabetes developed when she

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Speaker 1: was around nineteen or twenty years old. By then, highly

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Speaker 1: purified insulin from cows was available in the pharmacies of Shanghai,

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Speaker 1: and people were living pretty healthy lives in the nineteen

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Speaker 1: forties when they had access to insulin. But then when

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Speaker 1: the Japanese occupied, those pharmacies shut down and Eva was

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Speaker 1: in a really tough spot. How was she going to

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Speaker 1: survive without access to insulin from pharmacies. And so this

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Speaker 1: is really an amazing tale of survival because Eva and

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Speaker 1: Victor they actually figured out they got their hands on

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Speaker 1: a book showing the method that Banting and Best had

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Speaker 1: developed for purifying insulin out of the pancreas. They didn't

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Speaker 1: have dogs or cows, but they figured out that they could.

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Speaker 1: They were knitting socks and selling them to get money

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Speaker 1: to buy water buffalo pancreases, and first they figured out

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Speaker 1: how to get the water buffalo insulin for Eva, but

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Speaker 1: they actually made enough to sustain hundreds of people with

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Speaker 1: diabetes in the ghetto of Shanghai. So hundreds of people

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Speaker 1: survived for the years of World War two depending on

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Speaker 1: the insulin that Eva and her husband were purifying out

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Speaker 1: of the water buffalo pancreas. It's really amazing.

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Speaker 3: Oh that's interesting.

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Speaker 1: There's another story like that in Chile of a husband

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Speaker 1: who learned how to extract insulin from any kind of animal,

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Speaker 1: and he was helping his wife survive. And I think

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Speaker 1: the issue actually was that she would develop immunity to

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Speaker 1: one kind of insulin, and not because of the insulin itself,

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Speaker 1: but the extract is never pure, so she basically would

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Speaker 1: develop allergies to the extract. So then he would move

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Speaker 1: on to another kind of animal. And I think she

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00:19:43,720 --> 00:19:46,520
Speaker 1: didn't have access to pharmaceutical grade insulin, so he was

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Speaker 1: helping her survive that way.

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Speaker 2: Because insulin doesn't cure diabetes, right, it just helps get

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00:19:51,160 --> 00:19:53,959
Speaker 2: the sugar across the cells. Right now, you need a

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00:19:54,000 --> 00:19:56,320
Speaker 2: constant supply of insulin, right, Like, how long will the

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00:19:56,359 --> 00:19:59,840
Speaker 2: type one diabetic live if insulin supply just gets shut off.

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00:20:00,520 --> 00:20:02,640
Speaker 1: Yeah, just a couple days, And so you hear those

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Speaker 1: stories about the kids wasting away over the course of

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Speaker 1: a year or two. And that's at the beginning of

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Speaker 1: the disease when they still make some insulin. But somebody

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Speaker 1: who has developed long term type one diabetes and has

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Speaker 1: been dependent on insulin for a long time, they literally

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Speaker 1: have no insulin. And then it's not actually a matter

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Speaker 1: of starvation, like you wouldn't live long enough to starve

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00:20:22,680 --> 00:20:25,280
Speaker 1: with type one diabetis and no insulin, because your blood

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Speaker 1: sugar would go very high. And then you go into

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Speaker 1: a state that's called ketosis, where your body starts producing

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00:20:31,760 --> 00:20:35,960
Speaker 1: key tones, basically wasting away your muscles to get energy,

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00:20:36,440 --> 00:20:39,719
Speaker 1: and that process produces a lot of acid, and you

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Speaker 1: basically die from the acid in your blood. It's like

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Speaker 1: when you hit the wall in a marathon and you

420
00:20:43,640 --> 00:20:45,960
Speaker 1: don't have any more glucose from your liver and muscles.

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Speaker 1: Your liver and muscles have glycogen stores, and so then

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00:20:48,680 --> 00:20:51,000
Speaker 1: your body turns to breaking down the protein of your

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00:20:51,080 --> 00:20:54,480
Speaker 1: muscles to get energy, and that process produces keytnes, which

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Speaker 1: are acidic, and then you die from the acid in

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Speaker 1: your blood.

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Speaker 2: So we can survive if we extract insulin from these

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00:21:00,359 --> 00:21:02,800
Speaker 2: poor animals. But that's a destructive process, right You can't

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Speaker 2: extract insulin from a dog without killing the dog, or

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Speaker 2: can you?

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Speaker 1: The way it's done, as far as I know, has

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00:21:08,200 --> 00:21:10,840
Speaker 1: always been destructive. And so I mean, this could lead

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Speaker 1: us to another interesting conversation about like, well, okay, we

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Speaker 1: made this discovery, so then how did we actually produce

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Speaker 1: enough insulin to keep the diabetics of the world alive

435
00:21:18,680 --> 00:21:22,359
Speaker 1: who need it every couple hours, you know, not just days.

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Speaker 3: And that seems like a great topic to ponder, And

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Speaker 3: we'll return to it after the break and we're back.

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Speaker 3: And so we had just finished discussing how it had

439
00:21:47,040 --> 00:21:49,800
Speaker 3: been figured out how you can extract the insulin protein

440
00:21:49,960 --> 00:21:52,879
Speaker 3: from deceased animals, and now we're talking about starting to

441
00:21:52,960 --> 00:21:55,239
Speaker 3: scale up so that we can provide this in an

442
00:21:55,280 --> 00:21:56,440
Speaker 3: industrial sort of way.

443
00:21:56,600 --> 00:21:59,159
Speaker 2: I have another question before we talk about industrial production

444
00:21:59,240 --> 00:22:02,080
Speaker 2: of insulin, which is a naive question, like if it's

445
00:22:02,119 --> 00:22:04,720
Speaker 2: about the pancreas producing insulin and your pancreas is not

446
00:22:04,760 --> 00:22:07,520
Speaker 2: making one, why can't you do a pancreas transplant, like

447
00:22:07,520 --> 00:22:09,840
Speaker 2: we can do a heart transplant or a kidney transplant

448
00:22:10,320 --> 00:22:12,199
Speaker 2: or other kinds of transplants. And why can't I just

449
00:22:12,520 --> 00:22:14,840
Speaker 2: get the pancreas from a human corpse and put it

450
00:22:14,880 --> 00:22:17,399
Speaker 2: into a diabetic and cure their diabetes.

451
00:22:18,240 --> 00:22:20,679
Speaker 1: You can do that actually, and you know, Canada has

452
00:22:20,720 --> 00:22:23,320
Speaker 1: been so important in all these diabetes developments and the

453
00:22:23,480 --> 00:22:27,680
Speaker 1: Edmonton Protocol was developed also in Canada. Point is, yes,

454
00:22:27,800 --> 00:22:30,680
Speaker 1: you can transplant a pancreas into a person, but then

455
00:22:30,720 --> 00:22:34,400
Speaker 1: you have to give them immunosuppressants. In order to survive

456
00:22:34,720 --> 00:22:37,920
Speaker 1: after an organ transplant, you have to take immunosuppressive drugs,

457
00:22:38,440 --> 00:22:41,600
Speaker 1: which mean that your risk of cancer and infectious disease

458
00:22:41,680 --> 00:22:43,960
Speaker 1: become very high. So to do that to a young

459
00:22:43,960 --> 00:22:47,560
Speaker 1: person who otherwise has a healthy life expectancy is dooming

460
00:22:47,600 --> 00:22:50,000
Speaker 1: them to a less healthy and shorter life.

461
00:22:50,160 --> 00:22:51,720
Speaker 3: I assume who would you do that for.

462
00:22:51,840 --> 00:22:54,480
Speaker 1: Then there's one context where it happens a lot, which

463
00:22:54,520 --> 00:22:57,240
Speaker 1: is really cool, which is if you have kidney failure,

464
00:22:57,280 --> 00:22:59,800
Speaker 1: which is also more common in people with diabetes, and

465
00:23:00,080 --> 00:23:03,159
Speaker 1: doing a kidney transplant, you can do a tandem transplant

466
00:23:03,200 --> 00:23:05,399
Speaker 1: of kidney and pancreas, and you have to take the

467
00:23:05,440 --> 00:23:08,679
Speaker 1: immunister presence anyway for the kidney transplant, so then you

468
00:23:08,720 --> 00:23:11,679
Speaker 1: get a bonus of no longer being diabetic, which is

469
00:23:11,680 --> 00:23:13,760
Speaker 1: great because it also protects the kidney. So that's a

470
00:23:13,840 --> 00:23:17,040
Speaker 1: really cool procedure, which is pretty common that people get

471
00:23:17,080 --> 00:23:19,560
Speaker 1: a kidney and a pancreas transplant together.

472
00:23:19,880 --> 00:23:21,840
Speaker 2: All right, So if you don't get a pancreas transplant,

473
00:23:21,840 --> 00:23:24,320
Speaker 2: you need your steady supply of insulin. And we were saying,

474
00:23:24,320 --> 00:23:26,600
Speaker 2: we don't want to just keep killing dogs in order

475
00:23:26,680 --> 00:23:29,359
Speaker 2: to in order to extract the insulin from their pancreas,

476
00:23:29,680 --> 00:23:32,320
Speaker 2: or rats of China or whatever, So then tell us

477
00:23:32,320 --> 00:23:35,240
Speaker 2: about how we produce insulin without killing all of our

478
00:23:35,240 --> 00:23:36,040
Speaker 2: furry friends.

479
00:23:36,160 --> 00:23:40,040
Speaker 1: Well, for many years between around nineteen twenty something and

480
00:23:40,400 --> 00:23:44,439
Speaker 1: the nineteen eighties, we relied on animals that we produce

481
00:23:44,520 --> 00:23:47,600
Speaker 1: for food, so cows and pigs were our main source

482
00:23:47,600 --> 00:23:51,239
Speaker 1: of insulin for all those years, and so that scaled up.

483
00:23:51,240 --> 00:23:54,879
Speaker 1: In the nineteen twenties. There were two main companies that emerged,

484
00:23:54,920 --> 00:23:56,919
Speaker 1: and there's lots of stories about how the patents all

485
00:23:56,960 --> 00:24:00,720
Speaker 1: worked out, but basically Eli Lilly in Indiana became the

486
00:24:00,880 --> 00:24:04,720
Speaker 1: US leader in producing insulin, and Novo Nordisk in Denmark

487
00:24:04,800 --> 00:24:07,879
Speaker 1: became the European leader and making insulin. Currently there's a

488
00:24:07,920 --> 00:24:11,399
Speaker 1: third producer, so Nofi, they're French. Ninety percent of the

489
00:24:11,400 --> 00:24:13,800
Speaker 1: insulin in the world is still produced by those three companies.

490
00:24:13,840 --> 00:24:16,600
Speaker 1: And initially all their insulin was coming from animals, and

491
00:24:16,640 --> 00:24:22,159
Speaker 1: so you would get pig pancreas or cow pancreas from butchers.

492
00:24:22,880 --> 00:24:25,800
Speaker 1: It became a very refined process, and I think it

493
00:24:25,880 --> 00:24:30,000
Speaker 1: took you know, a crazy amount of pig pancreas. I mean,

494
00:24:30,160 --> 00:24:33,320
Speaker 1: you know, it was like a thousand pounds of pancreas

495
00:24:33,359 --> 00:24:36,320
Speaker 1: would would lead to one pound of insulin being produced.

496
00:24:36,359 --> 00:24:39,040
Speaker 1: It took a lot of purification and so and we

497
00:24:39,160 --> 00:24:42,760
Speaker 1: kept that going, and this supply was quite widespread and

498
00:24:42,880 --> 00:24:45,680
Speaker 1: a lot of people's lives were sustained with that insulin.

499
00:24:45,880 --> 00:24:48,040
Speaker 1: I don't think we could have kept scaling up. So

500
00:24:48,119 --> 00:24:51,720
Speaker 1: it's very good we had an early and amazing advance

501
00:24:51,880 --> 00:24:55,600
Speaker 1: and changing how we produced insulin around the eighties. But yeah,

502
00:24:55,640 --> 00:24:59,040
Speaker 1: for all those years we were collecting pigs and cow

503
00:24:59,160 --> 00:25:04,119
Speaker 1: pancreas from butcher an extracting insulin from there. And actually

504
00:25:04,160 --> 00:25:06,679
Speaker 1: we know a few people like Daniel, our friend Mones.

505
00:25:07,040 --> 00:25:10,639
Speaker 1: His mom she worked at Nova NOORDESK and she was

506
00:25:10,680 --> 00:25:13,359
Speaker 1: one of the people who developed those procedures, or at

507
00:25:13,400 --> 00:25:16,960
Speaker 1: least she was involved, I know in extracting insulin from pigs.

508
00:25:17,000 --> 00:25:19,159
Speaker 1: A lot of people were involved in making that happen.

509
00:25:19,320 --> 00:25:21,199
Speaker 2: I wonder if that was complicated for some folks, like

510
00:25:21,240 --> 00:25:24,840
Speaker 2: people who were vegetarians but diabet and then they had

511
00:25:24,840 --> 00:25:27,520
Speaker 2: to essentially use this animal product. Or what if you

512
00:25:27,560 --> 00:25:30,000
Speaker 2: were like Hindu and didn't want to use cow insulin,

513
00:25:30,119 --> 00:25:32,879
Speaker 2: or your Muslim and you didn't want to use pig insulin. Yeah,

514
00:25:33,000 --> 00:25:35,240
Speaker 2: could people like choose which animal it came from.

515
00:25:35,560 --> 00:25:38,159
Speaker 1: Yeah? I never had to use animal insulin myself, so

516
00:25:38,240 --> 00:25:40,480
Speaker 1: I don't know, but yes, I think you could choose

517
00:25:41,320 --> 00:25:44,600
Speaker 1: which animal source it came from. That was part of

518
00:25:44,640 --> 00:25:47,560
Speaker 1: the what you knew about the product. That's one very

519
00:25:47,560 --> 00:25:50,560
Speaker 1: big issue. Another big issue is that people would typically

520
00:25:50,600 --> 00:25:54,080
Speaker 1: develop sensitivities or allergies to it over time, and then

521
00:25:54,119 --> 00:25:56,479
Speaker 1: it would be less effective. It was a solution in

522
00:25:56,520 --> 00:25:59,200
Speaker 1: many ways, and there are people who lived for decades

523
00:25:59,520 --> 00:26:02,639
Speaker 1: taking and cow insulin, but it wasn't as easy to

524
00:26:02,720 --> 00:26:04,800
Speaker 1: keep that going your whole life because a lot of

525
00:26:04,840 --> 00:26:07,640
Speaker 1: people developed sensitivities like allergies, you mean.

526
00:26:07,480 --> 00:26:09,880
Speaker 2: Like your immune system is responding because it's like, hey,

527
00:26:09,920 --> 00:26:12,479
Speaker 2: this is a cow product, not something from inside the body.

528
00:26:12,960 --> 00:26:15,400
Speaker 1: Yeah, I don't even think it was the insulin itself.

529
00:26:15,480 --> 00:26:17,800
Speaker 1: And despite all the purification I just talked about, I

530
00:26:17,800 --> 00:26:21,320
Speaker 1: think it was hard to remove everything allergenic about it,

531
00:26:21,400 --> 00:26:23,600
Speaker 1: so then you would develop immunity.

532
00:26:23,920 --> 00:26:27,520
Speaker 3: Were there any concerns about diseases passing from pigs and

533
00:26:27,560 --> 00:26:30,320
Speaker 3: cows to people or the purification process or to cleared

534
00:26:30,359 --> 00:26:30,680
Speaker 3: that out.

535
00:26:31,080 --> 00:26:34,080
Speaker 1: That's a really cool question, and there were actually concerns.

536
00:26:34,119 --> 00:26:36,080
Speaker 1: I don't think we even knew about it at the time,

537
00:26:36,200 --> 00:26:39,400
Speaker 1: but now there's a lot of concern about preon diseases,

538
00:26:39,600 --> 00:26:43,800
Speaker 1: and so actually diabetics are sometimes excluded from blood donation

539
00:26:44,000 --> 00:26:47,639
Speaker 1: and other kind of organ transplant because of concern for

540
00:26:47,760 --> 00:26:52,000
Speaker 1: preon diseases, especially coming from cows. So yeah, people who

541
00:26:52,520 --> 00:26:54,919
Speaker 1: have been having a lot of animal products like that,

542
00:26:55,080 --> 00:26:59,080
Speaker 1: in theory could have been exposed to preon diseases, which

543
00:26:59,119 --> 00:27:01,440
Speaker 1: I think were less and at that time, also because

544
00:27:01,440 --> 00:27:04,840
Speaker 1: of agricultural practices, like I think preon diseases became more

545
00:27:04,880 --> 00:27:08,920
Speaker 1: common towards the end of the twentieth century because we

546
00:27:08,920 --> 00:27:13,719
Speaker 1: were doing all this like feed animal waste, like chopped

547
00:27:13,800 --> 00:27:17,640
Speaker 1: up animal bits to the animals themselves, and that would

548
00:27:17,720 --> 00:27:21,720
Speaker 1: kind of concentrate the chances of preon diseases developing. And

549
00:27:21,760 --> 00:27:24,120
Speaker 1: that's better now. We know not to do that now.

550
00:27:24,600 --> 00:27:27,800
Speaker 3: And so you indicated in the nineteen eighties something changed.

551
00:27:28,000 --> 00:27:29,639
Speaker 3: What was the thing that changed.

552
00:27:29,480 --> 00:27:32,439
Speaker 1: Well, it's so amazing to me that this happened as

553
00:27:32,480 --> 00:27:34,800
Speaker 1: early as it did in the eighties. But we actually

554
00:27:34,800 --> 00:27:38,680
Speaker 1: figured out how to produce human insulin by fermenting it

555
00:27:38,720 --> 00:27:43,000
Speaker 1: in bacteria and sometimes yeast by the early eighties, and

556
00:27:43,080 --> 00:27:47,280
Speaker 1: so this was connected to the first genetically engineered organisms.

557
00:27:47,960 --> 00:27:50,560
Speaker 1: In the fifties and sixties, we made the discovery of DNA.

558
00:27:50,640 --> 00:27:54,200
Speaker 1: We understood the central dogma that DNA was the storage

559
00:27:54,240 --> 00:27:58,000
Speaker 1: material of biology and that it was a blueprint for

560
00:27:58,200 --> 00:28:02,280
Speaker 1: producing RNA and then protein insulin is a protein. You know,

561
00:28:02,320 --> 00:28:05,600
Speaker 1: it was only a decade or maybe twenty years after

562
00:28:05,640 --> 00:28:08,240
Speaker 1: we understood that that we were really making use of

563
00:28:08,240 --> 00:28:11,440
Speaker 1: that information, which I think is really cool. So by

564
00:28:11,480 --> 00:28:14,399
Speaker 1: the seventies, the hotbed of a lot of this activity

565
00:28:14,480 --> 00:28:19,040
Speaker 1: was California and also Boston. People were starting to clone

566
00:28:19,320 --> 00:28:21,119
Speaker 1: and like they were starting to be able to, like,

567
00:28:21,520 --> 00:28:24,879
Speaker 1: you know, use little molecular scissors to chop out a

568
00:28:24,920 --> 00:28:28,159
Speaker 1: piece of DNA from a bacteria and replace it with

569
00:28:28,240 --> 00:28:31,320
Speaker 1: another piece of DNA that encoded something you were interested in.

570
00:28:32,200 --> 00:28:34,000
Speaker 1: And so it's kind of interesting to me to think

571
00:28:34,040 --> 00:28:37,360
Speaker 1: about how these molecular biologists who were more like theoreticians

572
00:28:37,400 --> 00:28:39,880
Speaker 1: about the biology almost these weren't like doctors who were

573
00:28:39,920 --> 00:28:42,520
Speaker 1: thinking about solutions so much. But they're like, Hey, I

574
00:28:42,520 --> 00:28:45,520
Speaker 1: wonder what a good important protein to try to clone

575
00:28:45,520 --> 00:28:47,200
Speaker 1: would be. And they're like, hey, insulin. A lot of

576
00:28:47,240 --> 00:28:50,240
Speaker 1: people need that. Let's try that. And so one of

577
00:28:50,280 --> 00:28:53,600
Speaker 1: the first things that was involved in these early cloning

578
00:28:53,640 --> 00:28:56,480
Speaker 1: projects just to demonstrate, like, hey, can we clone stuff

579
00:28:56,520 --> 00:29:00,480
Speaker 1: in bacteria was insulin and so in the seven we

580
00:29:00,520 --> 00:29:03,520
Speaker 1: started to do that kind of cloning. And then around

581
00:29:03,600 --> 00:29:07,000
Speaker 1: the mid seventies there was this moment where everyone realized, like,

582
00:29:07,080 --> 00:29:08,920
Speaker 1: oh my god, what are we doing. If I, like

583
00:29:09,000 --> 00:29:13,040
Speaker 1: complete these experiments, have I just created something that could

584
00:29:13,120 --> 00:29:15,560
Speaker 1: go on and replicate and cause a lot of destruction.

585
00:29:15,720 --> 00:29:18,680
Speaker 1: And so there was actually a meeting in a Sillamart, California.

586
00:29:19,320 --> 00:29:21,880
Speaker 1: I think it was around nineteen seventy seven. We all

587
00:29:22,000 --> 00:29:26,200
Speaker 1: know this meeting, the Silamar meeting about the safety of

588
00:29:26,640 --> 00:29:30,920
Speaker 1: genetic engineering basically, and maybe one hundred and fifty people

589
00:29:30,920 --> 00:29:34,640
Speaker 1: were there, mostly scientists, but politicians too, and they had

590
00:29:34,640 --> 00:29:37,360
Speaker 1: a real look in the mirror, like what are we

591
00:29:37,440 --> 00:29:40,320
Speaker 1: doing and is this safe? And everybody halted their work

592
00:29:40,440 --> 00:29:43,160
Speaker 1: until the meeting so that we could decide what to do.

593
00:29:44,000 --> 00:29:47,280
Speaker 1: And during that meeting there were a lot of discussion

594
00:29:47,280 --> 00:29:49,680
Speaker 1: about whether it was okay, and in the end there

595
00:29:49,680 --> 00:29:52,480
Speaker 1: were rules around what you were allowed to do, but

596
00:29:52,640 --> 00:29:55,720
Speaker 1: it was decided that you could indeed proceed with cloning,

597
00:29:56,360 --> 00:30:02,480
Speaker 1: especially under controlled circumstances. So the project for cloning insulin

598
00:30:02,600 --> 00:30:05,360
Speaker 1: was one of the ones that halted until after that meeting,

599
00:30:05,720 --> 00:30:08,800
Speaker 1: and then once the decision was made that it was

600
00:30:08,840 --> 00:30:13,480
Speaker 1: okay to proceed, then the scientists continued with their cloning projects.

601
00:30:13,480 --> 00:30:15,920
Speaker 1: And so I think it was about around nineteen seventy eight,

602
00:30:15,920 --> 00:30:18,240
Speaker 1: which is also the year I was born, that the

603
00:30:18,320 --> 00:30:20,840
Speaker 1: first insulin cloning project was completed.

604
00:30:21,320 --> 00:30:23,080
Speaker 3: So when I hear the word clone, I think of

605
00:30:23,200 --> 00:30:25,480
Speaker 3: like copying and pasting and organism.

606
00:30:25,560 --> 00:30:25,920
Speaker 1: Mm hmm.

607
00:30:26,360 --> 00:30:30,240
Speaker 3: Is it you're essentially copying and pasting bacteria that now

608
00:30:30,280 --> 00:30:32,239
Speaker 3: have the human insulin gene. Is that the right way

609
00:30:32,280 --> 00:30:32,840
Speaker 3: to think about it?

610
00:30:33,160 --> 00:30:37,320
Speaker 1: Yeah, exactly, So the human insulin gene was copied and

611
00:30:37,440 --> 00:30:41,240
Speaker 1: pasted into a bacteria, and then they asked the bacterial

612
00:30:41,320 --> 00:30:44,400
Speaker 1: cell to grow and produce the insulin. Now, I just

613
00:30:44,440 --> 00:30:46,600
Speaker 1: made it sound really simple like there was just only

614
00:30:46,640 --> 00:30:48,680
Speaker 1: one thing that had to be copied in But to

615
00:30:48,720 --> 00:30:51,480
Speaker 1: be honest, a lot of genetic engineering had to happen

616
00:30:51,680 --> 00:30:53,880
Speaker 1: so that the insulin could be produced. They had to

617
00:30:53,920 --> 00:30:56,040
Speaker 1: make sure that all the ingredients were there, that it

618
00:30:56,160 --> 00:31:00,360
Speaker 1: was in a spot that the bacterial sell again its

619
00:31:00,360 --> 00:31:03,640
Speaker 1: own needs, was producing this protein. You know, it's not

620
00:31:03,680 --> 00:31:05,960
Speaker 1: like the bacteria needed the insulin, So they had to

621
00:31:06,000 --> 00:31:08,760
Speaker 1: kind of rewire some of the metabolic pathways to force

622
00:31:08,840 --> 00:31:11,520
Speaker 1: that to happen. So, to be honest, it was a

623
00:31:11,520 --> 00:31:15,560
Speaker 1: combination of real savvy and luck that insulin could be

624
00:31:15,600 --> 00:31:18,880
Speaker 1: produced that way. A lot of bacteria don't have the

625
00:31:18,960 --> 00:31:23,720
Speaker 1: right tools for making other kinds of modifications to proteins

626
00:31:23,720 --> 00:31:26,520
Speaker 1: that are common in eukaryotic cells, and so the fact

627
00:31:26,560 --> 00:31:28,720
Speaker 1: that they could do that a little bit was luck.

628
00:31:28,920 --> 00:31:32,320
Speaker 1: And when we look back at which bacteria were initially

629
00:31:32,440 --> 00:31:36,200
Speaker 1: chosen for some of these cloning projects, they were just random,

630
00:31:36,280 --> 00:31:39,120
Speaker 1: Like the most common Ecoli, the kind of bacteria this

631
00:31:39,200 --> 00:31:42,000
Speaker 1: was done and is called ecoli assure Shia coli. And

632
00:31:42,040 --> 00:31:44,680
Speaker 1: there's this really famous strain of E. Coli ecol I

633
00:31:44,840 --> 00:31:46,880
Speaker 1: K twelve that was the one that was used in

634
00:31:46,880 --> 00:31:51,040
Speaker 1: this project. And E. Coli K twelve was isolated from

635
00:31:51,040 --> 00:31:54,760
Speaker 1: a Stanford patient who had some kind of infectious disease.

636
00:31:54,800 --> 00:31:58,960
Speaker 1: I think diphtheria just randomly taken from this person's gut,

637
00:32:00,080 --> 00:32:02,800
Speaker 1: and then it happened to have properties that were good

638
00:32:02,840 --> 00:32:05,440
Speaker 1: for cloning. Like I have equal IK twelve in my lab.

639
00:32:05,520 --> 00:32:08,240
Speaker 1: All biologists no equal IK twelve, but it's just like

640
00:32:08,320 --> 00:32:11,680
Speaker 1: a random Standford patient from the nineteen twenties ecoli.

641
00:32:12,080 --> 00:32:14,280
Speaker 2: I have some basic questions about how this works, like

642
00:32:14,320 --> 00:32:17,240
Speaker 2: why are we using bacteria? Is it just like the

643
00:32:17,320 --> 00:32:20,680
Speaker 2: simplest unit that we think we could still genetically engineer.

644
00:32:21,200 --> 00:32:24,400
Speaker 2: Why can't we genetically engineer dogs to make human insulin?

645
00:32:24,680 --> 00:32:27,480
Speaker 1: I mean, I think we were using bacteria thinking that

646
00:32:27,480 --> 00:32:30,120
Speaker 1: that would be a really great way to scale up

647
00:32:30,440 --> 00:32:34,080
Speaker 1: and not have a combination of ethics and safety and

648
00:32:34,120 --> 00:32:37,280
Speaker 1: just production. How great is it if the same way

649
00:32:37,320 --> 00:32:39,960
Speaker 1: you brew beer you can brew insulin. You know, that

650
00:32:40,120 --> 00:32:43,120
Speaker 1: was the thinking. We're good at producing things with bacteria.

651
00:32:44,040 --> 00:32:48,680
Speaker 1: Yogurt beer beer is mostly yeast, but still bread. We

652
00:32:48,800 --> 00:32:53,360
Speaker 1: have microbial fermentation for food production really down, So the

653
00:32:53,440 --> 00:32:56,760
Speaker 1: idea was to pivot that towards making medicines.

654
00:32:57,000 --> 00:33:00,360
Speaker 2: I see, so bacteria can't make cute puppy eyes, care

655
00:33:00,400 --> 00:33:03,160
Speaker 2: about growing them up and slaughtering them all for our.

656
00:33:03,040 --> 00:33:05,880
Speaker 1: Insulin, sure, I mean that's one way of looking at it.

657
00:33:05,960 --> 00:33:11,240
Speaker 1: But also just from a purely energy climate and finance perspective,

658
00:33:12,160 --> 00:33:15,800
Speaker 1: bacteria are very efficient, so you know it's going to

659
00:33:15,880 --> 00:33:17,040
Speaker 1: be quick and.

660
00:33:17,880 --> 00:33:20,920
Speaker 3: Much more short generation times.

661
00:33:20,680 --> 00:33:24,320
Speaker 1: Yes, exactly, E Coli double in twenty minutes. I still

662
00:33:24,360 --> 00:33:28,160
Speaker 1: think that a batch of insulin is not an overnight process,

663
00:33:28,240 --> 00:33:33,040
Speaker 1: like I think even in modern insulin production factories it

664
00:33:33,120 --> 00:33:35,440
Speaker 1: probably takes like a month or two to go from

665
00:33:35,680 --> 00:33:39,920
Speaker 1: the overnight culture of the bacteria producing the insulin to

666
00:33:40,080 --> 00:33:42,760
Speaker 1: like all of the different processing steps. Insulin is a

667
00:33:42,800 --> 00:33:47,520
Speaker 1: really complicated protein because it's so you know, powerful, which

668
00:33:47,720 --> 00:33:50,440
Speaker 1: you know, with great power comes great responsibility. Too much

669
00:33:50,480 --> 00:33:53,760
Speaker 1: insulin can very quickly kill you in our own bodies.

670
00:33:53,800 --> 00:33:57,280
Speaker 1: Our insulin is produced in an inactive form, and it

671
00:33:57,320 --> 00:34:01,640
Speaker 1: has to be cleaved to become active. And so the

672
00:34:01,680 --> 00:34:04,720
Speaker 1: insulin that's produced in the bacteria initially also is in

673
00:34:04,760 --> 00:34:07,320
Speaker 1: that inactive form, and then all that processing that would

674
00:34:07,320 --> 00:34:09,680
Speaker 1: normally happen in someone's body has to happen in the

675
00:34:09,680 --> 00:34:12,680
Speaker 1: factory so that the form that you inject is already active.

676
00:34:12,719 --> 00:34:15,839
Speaker 1: So there's a lot of complex steps there. And if

677
00:34:15,840 --> 00:34:17,440
Speaker 1: you were to try to take it out of puppies,

678
00:34:17,440 --> 00:34:19,640
Speaker 1: I think that would be way less effective. I mean,

679
00:34:19,880 --> 00:34:23,600
Speaker 1: the number of cows we needed to produce enough insulin

680
00:34:23,640 --> 00:34:27,919
Speaker 1: to support humanity was becoming untenable. Like there weren't enough

681
00:34:27,920 --> 00:34:29,800
Speaker 1: cows to make all the insulin, even though we have

682
00:34:29,880 --> 00:34:32,480
Speaker 1: a crazy appetite for meat and beef, but we still

683
00:34:33,040 --> 00:34:35,920
Speaker 1: couldn't probably sustain all the diabetics who needed insulin.

684
00:34:36,160 --> 00:34:38,040
Speaker 2: And why do we need to build this in a

685
00:34:38,120 --> 00:34:40,920
Speaker 2: life form anyway? Like we know how to do chemistry,

686
00:34:40,960 --> 00:34:43,200
Speaker 2: I mean I don't, but some people do. Why can't

687
00:34:43,200 --> 00:34:45,799
Speaker 2: you just like stick the atoms together and build this

688
00:34:45,880 --> 00:34:48,600
Speaker 2: thing out of little lego pieces, you know, synthesize the

689
00:34:48,640 --> 00:34:49,239
Speaker 2: thing in the lab.

690
00:34:49,719 --> 00:34:51,600
Speaker 1: Yeah, I like that idea. And there is a thing

691
00:34:51,719 --> 00:34:56,680
Speaker 1: called cell free extract production that people sometimes use these days.

692
00:34:56,760 --> 00:34:59,560
Speaker 1: I think it just is convenient and handy that these

693
00:34:59,600 --> 00:35:03,880
Speaker 1: bacteria in a way are great little factories. They already

694
00:35:03,880 --> 00:35:06,560
Speaker 1: know how to make copies of themselves. So I think

695
00:35:07,160 --> 00:35:10,200
Speaker 1: from an efficiency perspective, I mean, how great is it

696
00:35:10,280 --> 00:35:12,880
Speaker 1: that the bacteria you just give them a little glucose

697
00:35:12,920 --> 00:35:15,799
Speaker 1: and they go figure all that stuff out for themselves.

698
00:35:15,840 --> 00:35:19,440
Speaker 1: Otherwise you'd have to manufacture thousands and thousands of different

699
00:35:19,440 --> 00:35:23,239
Speaker 1: components that the bacteria otherwise just build for themselves, and.

700
00:35:23,239 --> 00:35:25,600
Speaker 2: They're self replicating, which is much more than our grad

701
00:35:25,600 --> 00:35:26,239
Speaker 2: students can do.

702
00:35:27,640 --> 00:35:29,440
Speaker 1: I mean, to be honest, to the way we produce

703
00:35:29,480 --> 00:35:32,680
Speaker 1: a lot of the things you might consider putting into

704
00:35:32,760 --> 00:35:36,920
Speaker 1: a sell free artificial manufacturing process, we'd probably get them

705
00:35:36,960 --> 00:35:39,239
Speaker 1: from microbial production to a lot of the things that

706
00:35:39,280 --> 00:35:42,880
Speaker 1: we produce that way we do with the help of microps.

707
00:35:42,640 --> 00:35:44,720
Speaker 2: Like cheese. I don't think I want to eat lab

708
00:35:44,960 --> 00:35:51,600
Speaker 2: synthesized cheese either. We actually have a friend who was

709
00:35:51,640 --> 00:35:54,640
Speaker 2: working on plant free food and he give us a

710
00:35:54,640 --> 00:35:59,080
Speaker 2: taste from like chemically synthesized butter, and it wasn't good. No,

711
00:35:59,280 --> 00:36:02,600
Speaker 2: I didn't like it. No mascrecy, but it wasn't better.

712
00:36:02,719 --> 00:36:04,840
Speaker 2: But what I have one more question, simple, which is

713
00:36:05,040 --> 00:36:08,320
Speaker 2: you talk about inserting these genes into the bacteria to

714
00:36:08,360 --> 00:36:10,319
Speaker 2: make it do its thing, which makes it feel like

715
00:36:10,320 --> 00:36:13,239
Speaker 2: the bacteria is some sort of computer and you're just

716
00:36:13,239 --> 00:36:15,000
Speaker 2: like changing the program and you're like, hey, can you

717
00:36:15,040 --> 00:36:17,920
Speaker 2: make this instead of that? Is it as simple as that?

718
00:36:17,960 --> 00:36:19,360
Speaker 2: Can you go a little bit into the detail of

719
00:36:19,440 --> 00:36:22,120
Speaker 2: like how do we know how to write this code?

720
00:36:22,160 --> 00:36:24,000
Speaker 2: And then how do we take the code and actually

721
00:36:24,080 --> 00:36:26,239
Speaker 2: put it into the genome? Right, it's not as simple

722
00:36:26,280 --> 00:36:29,160
Speaker 2: as like logging into the bacteria and editing the files.

723
00:36:29,440 --> 00:36:31,880
Speaker 2: You need to actually like put it into the DNA.

724
00:36:31,920 --> 00:36:33,120
Speaker 2: How do we know how to do any of that?

725
00:36:33,520 --> 00:36:35,960
Speaker 1: Yeah? What a good question. I'm not sure I know

726
00:36:36,040 --> 00:36:38,680
Speaker 1: all the answers, but I can tell you that one

727
00:36:38,680 --> 00:36:41,680
Speaker 1: of the first proteins that we ever even figured out

728
00:36:41,719 --> 00:36:44,520
Speaker 1: what the sequence of it was was insulin. And so

729
00:36:44,960 --> 00:36:47,440
Speaker 1: that's at the protein level, like the sequence of amino

730
00:36:47,480 --> 00:36:50,479
Speaker 1: acids that you need to make insulin, that's what makes

731
00:36:50,520 --> 00:36:55,320
Speaker 1: insulin insulin. From there, the protein sequence could have lots

732
00:36:55,320 --> 00:36:58,560
Speaker 1: of different DNA sequences that lead to the same protein

733
00:36:58,560 --> 00:37:01,840
Speaker 1: sequence because we have redund and see in the genetic code.

734
00:37:01,920 --> 00:37:06,200
Speaker 1: So usually you have three nucleotide bases encoding an amino

735
00:37:06,239 --> 00:37:10,880
Speaker 1: acid for a protein sequence, and there's many combinations of

736
00:37:10,960 --> 00:37:13,600
Speaker 1: DNA that would lead to the right sequence for the protein.

737
00:37:14,280 --> 00:37:18,320
Speaker 1: I actually don't know how they chose what DNA sequence

738
00:37:18,520 --> 00:37:21,440
Speaker 1: to initially clone into the E. Coli. In theory, it

739
00:37:21,480 --> 00:37:24,200
Speaker 1: could be that they just this is not how they

740
00:37:24,200 --> 00:37:25,640
Speaker 1: did it because they didn't have the right tools to

741
00:37:25,640 --> 00:37:27,160
Speaker 1: do this. But in theory, it could be that they

742
00:37:27,160 --> 00:37:28,920
Speaker 1: were just like, oh, well, we know what protein sequence

743
00:37:28,960 --> 00:37:32,400
Speaker 1: we want, so therefore any of these bajillion different DNA

744
00:37:32,400 --> 00:37:35,120
Speaker 1: sequences will work, So we'll just artificially synthesize one of

745
00:37:35,120 --> 00:37:38,360
Speaker 1: those and do it from there. We could do that today.

746
00:37:38,400 --> 00:37:41,560
Speaker 1: That's actually pretty easy thing to do. I've often thought

747
00:37:41,560 --> 00:37:44,840
Speaker 1: about that, Like, if you knew something you wanted to produce,

748
00:37:45,520 --> 00:37:49,000
Speaker 1: you could synthesize the piece of DNA and send it

749
00:37:49,000 --> 00:37:51,880
Speaker 1: to a yogurt manufacturer anywhere in the world, and they

750
00:37:51,880 --> 00:37:54,200
Speaker 1: could make vast quantities of a protein that you were

751
00:37:54,200 --> 00:37:56,800
Speaker 1: interested in if you could get the cells to cooperate.

752
00:37:56,840 --> 00:37:58,120
Speaker 1: I mean, there's a few things that would be hard

753
00:37:58,120 --> 00:38:00,440
Speaker 1: about it, but yeah, I think they must have known

754
00:38:00,520 --> 00:38:04,680
Speaker 1: this sequence for human DNA and then physically got in

755
00:38:04,760 --> 00:38:08,200
Speaker 1: their hands on that and chopped it out and then

756
00:38:08,320 --> 00:38:11,520
Speaker 1: like physically we use the word ligation, it just means

757
00:38:11,560 --> 00:38:15,440
Speaker 1: like pasting it into the bacteria, and it wasn't just that,

758
00:38:15,600 --> 00:38:18,040
Speaker 1: And a lot of this happened also with the company Genentech.

759
00:38:18,680 --> 00:38:21,480
Speaker 1: I'm sure that many people know more about this history

760
00:38:21,560 --> 00:38:23,920
Speaker 1: than I do. But some of it is proprietary, right

761
00:38:24,000 --> 00:38:28,680
Speaker 1: because it was happening at a company, some of it anyway.

762
00:38:28,719 --> 00:38:32,840
Speaker 1: And so they cloned the human DNA sequence, copied it

763
00:38:32,840 --> 00:38:36,239
Speaker 1: into the bacterial cell, and just fired the bacteria up

764
00:38:36,280 --> 00:38:36,960
Speaker 1: to produce it.

765
00:38:37,320 --> 00:38:39,719
Speaker 3: So now you've got the bacteria with the code, and

766
00:38:39,760 --> 00:38:41,720
Speaker 3: you figured out how to get them to be running

767
00:38:41,719 --> 00:38:44,040
Speaker 3: that code all the time, so they're making more insulin. Yeah,

768
00:38:44,160 --> 00:38:48,120
Speaker 3: is that insulin accumulating inside the bacteria or do they

769
00:38:48,160 --> 00:38:50,799
Speaker 3: excrete it? How do we get it after that?

770
00:38:51,120 --> 00:38:54,200
Speaker 1: That's a really good question, and I think there's actually

771
00:38:54,239 --> 00:38:56,400
Speaker 1: several ways you could do that, and I would not

772
00:38:56,480 --> 00:38:59,759
Speaker 1: be surprised if both of those options are happening in

773
00:39:00,000 --> 00:39:03,080
Speaker 1: various factories in the world right now. One way it

774
00:39:03,080 --> 00:39:06,279
Speaker 1: can happen often when a bacterial cell is confronted with

775
00:39:06,360 --> 00:39:08,239
Speaker 1: like a crazy amount of something it doesn't quite know

776
00:39:08,239 --> 00:39:10,920
Speaker 1: what to do with, it pumps it into a little

777
00:39:10,920 --> 00:39:13,799
Speaker 1: compartment called a vacuole, and it just like makes little

778
00:39:13,880 --> 00:39:16,799
Speaker 1: pouches of it. That's my understanding for the main thing

779
00:39:16,840 --> 00:39:19,040
Speaker 1: that happens is that you get these little pouches of

780
00:39:19,080 --> 00:39:22,320
Speaker 1: the vacuoles from the cells, and then the next step

781
00:39:22,480 --> 00:39:25,400
Speaker 1: is to pop the cells, pull out all those vacuoles,

782
00:39:25,480 --> 00:39:28,320
Speaker 1: and then get the insulin from there. So I'm pretty

783
00:39:28,320 --> 00:39:30,839
Speaker 1: sure that's the main way that it's done these days

784
00:39:30,840 --> 00:39:34,440
Speaker 1: from E. Coli. Not all insulin is made in bacteria

785
00:39:34,560 --> 00:39:37,480
Speaker 1: and ecoli. There's I think maybe twenty or thirty percent

786
00:39:37,520 --> 00:39:39,320
Speaker 1: of the insulin that's manufactured in the world is in

787
00:39:39,440 --> 00:39:42,840
Speaker 1: yeast and sachromics service. And then it's going to be

788
00:39:42,920 --> 00:39:45,439
Speaker 1: yet another process. And so yeah, theory, you could make

789
00:39:45,480 --> 00:39:49,680
Speaker 1: the cell excrete the insulin into the liquid, which might

790
00:39:49,840 --> 00:39:52,759
Speaker 1: make your process easier, but since insulin's a protein and

791
00:39:52,800 --> 00:39:56,600
Speaker 1: it can get broken down by enzymes that eat proteins,

792
00:39:56,680 --> 00:39:58,359
Speaker 1: in a way, you might be better off having it

793
00:39:58,440 --> 00:40:02,279
Speaker 1: be in a protected pouch that you could just pull

794
00:40:02,320 --> 00:40:04,359
Speaker 1: those aside and get the insulin out.

795
00:40:04,640 --> 00:40:07,760
Speaker 3: I don't think I've thought before about the complicated process

796
00:40:07,800 --> 00:40:10,319
Speaker 3: of acquiring this stuff after the bacteria has made it.

797
00:40:10,360 --> 00:40:12,480
Speaker 3: Like I guess i'd imagine, like you know, you skim

798
00:40:12,520 --> 00:40:14,440
Speaker 3: the top and there's the insulin and you stick it

799
00:40:14,480 --> 00:40:17,919
Speaker 3: into needles. But yeah, I mean extracting all of those vacules, Yeah,

800
00:40:17,920 --> 00:40:20,160
Speaker 3: and popping them doesn't sound like easy work.

801
00:40:20,320 --> 00:40:24,200
Speaker 1: No, that's a sophisticated process. And then the protein sequence

802
00:40:24,239 --> 00:40:28,560
Speaker 1: still contains extra bits that make it inactive on purpose,

803
00:40:28,680 --> 00:40:30,759
Speaker 1: so that it's not in our bodies. We don't want

804
00:40:30,760 --> 00:40:33,200
Speaker 1: it to be active exactly when it's produced. It's like

805
00:40:33,360 --> 00:40:37,719
Speaker 1: unleashed and activated very intentionally, So to get it to

806
00:40:37,760 --> 00:40:40,120
Speaker 1: be in that active form takes yet more steps.

807
00:40:40,520 --> 00:40:42,359
Speaker 3: All right, awesome, So we're going to take a break now,

808
00:40:42,360 --> 00:40:43,839
Speaker 3: and when we get back from the break, we're going

809
00:40:43,880 --> 00:40:50,160
Speaker 3: to hear about lydia Villa Komorov's contribution to this field.

810
00:41:04,200 --> 00:41:07,520
Speaker 3: All right, and we're back. So it's Women's History month.

811
00:41:07,560 --> 00:41:10,920
Speaker 3: We're trying to feature some amazing women that most people

812
00:41:10,960 --> 00:41:14,040
Speaker 3: have perhaps not heard of. And you agreed to come

813
00:41:14,080 --> 00:41:17,000
Speaker 3: on the show and talk to us about lydia Villa Comarov.

814
00:41:17,640 --> 00:41:19,360
Speaker 3: What was her contribution to this field.

815
00:41:19,800 --> 00:41:22,759
Speaker 1: Well, lydia Villa Comorov was a grad student in the

816
00:41:22,840 --> 00:41:26,280
Speaker 1: nineteen seventies during this era where we were just figuring

817
00:41:26,360 --> 00:41:29,000
Speaker 1: out that we could clone bacteria. So not only did

818
00:41:29,000 --> 00:41:32,239
Speaker 1: we understand the central dogma, the way that molecular biology

819
00:41:32,320 --> 00:41:34,840
Speaker 1: is encoded. We were now starting to be able to

820
00:41:34,920 --> 00:41:35,640
Speaker 1: manipulate it.

821
00:41:36,160 --> 00:41:38,080
Speaker 3: Interesting, So a few.

822
00:41:37,840 --> 00:41:40,640
Speaker 1: People had done anything in this area. Right around the

823
00:41:40,680 --> 00:41:44,640
Speaker 1: time that doctor Villa Komarova finished her PhD from MIT

824
00:41:44,800 --> 00:41:47,760
Speaker 1: in nineteen seventy five and she embarked on a really

825
00:41:47,840 --> 00:41:51,200
Speaker 1: bold post doc at Harvard. It was to clone the

826
00:41:51,280 --> 00:41:54,480
Speaker 1: human insulin gene into bacteria. It was so bold that

827
00:41:54,520 --> 00:41:57,320
Speaker 1: Harvard actually asked them to stop. They paused the project

828
00:41:57,400 --> 00:42:02,400
Speaker 1: for fear of ethical consideration for what the consequences of

829
00:42:02,600 --> 00:42:05,640
Speaker 1: humans cloning things could be. So she actually continued her

830
00:42:05,719 --> 00:42:08,600
Speaker 1: project at Cold Spring Harbor. There were lots of failures.

831
00:42:08,719 --> 00:42:11,040
Speaker 1: I think that was a really tough post doc, but

832
00:42:11,200 --> 00:42:14,640
Speaker 1: ultimately she succeeded. I think that was around nineteen seventy eight,

833
00:42:14,760 --> 00:42:18,440
Speaker 1: and by the early eighties there was commercially available human

834
00:42:18,440 --> 00:42:23,080
Speaker 1: insulin that had been synthesized in bacteria, and her project

835
00:42:23,400 --> 00:42:27,760
Speaker 1: was to clone the gene for insulin into E. Coli,

836
00:42:28,040 --> 00:42:32,120
Speaker 1: the bacteria. So she has a paper in PNAS, the

837
00:42:32,160 --> 00:42:35,600
Speaker 1: Proceedings of the National Academy of Science, and in that

838
00:42:35,680 --> 00:42:40,040
Speaker 1: paper she demonstrates that you can pull the insulin gene

839
00:42:40,160 --> 00:42:44,480
Speaker 1: from humans, clone it into bacteria and get the bacterial

840
00:42:44,520 --> 00:42:48,680
Speaker 1: cells to replicate. So it was a big step, not

841
00:42:48,880 --> 00:42:51,799
Speaker 1: just that this was an idea, but that it could

842
00:42:51,880 --> 00:42:55,040
Speaker 1: actually be done, and everyone had their eyes on this happening.

843
00:42:55,200 --> 00:42:59,160
Speaker 1: It was considered risky and important enough that her project

844
00:42:59,200 --> 00:43:02,520
Speaker 1: was halted as these considerations about the ethics were being

845
00:43:02,520 --> 00:43:06,880
Speaker 1: discussed at the ASILAMAR meeting, and after the ASILAMAR meeting

846
00:43:07,160 --> 00:43:10,239
Speaker 1: she was allowed to proceed. And it all happened very

847
00:43:10,280 --> 00:43:13,439
Speaker 1: fast because her paper was already published in nineteen seventy eight,

848
00:43:13,480 --> 00:43:15,760
Speaker 1: and I think the Assillamar meeting was only in nineteen

849
00:43:15,800 --> 00:43:18,680
Speaker 1: seventy seven, So I bet that was intense. I bet

850
00:43:18,719 --> 00:43:19,920
Speaker 1: she was working hard with.

851
00:43:20,040 --> 00:43:22,640
Speaker 2: What ethics concerns do we have? Because, as we said earlier,

852
00:43:22,640 --> 00:43:26,280
Speaker 2: bacteria can't make peppy eyes, so what are the ethical issues.

853
00:43:26,600 --> 00:43:29,440
Speaker 1: It was a very new idea that humans could clone things,

854
00:43:29,480 --> 00:43:33,600
Speaker 1: that we could decide what the DNA that a creature

855
00:43:33,719 --> 00:43:37,160
Speaker 1: encoded was. And in some ways, I don't think it's

856
00:43:37,200 --> 00:43:40,960
Speaker 1: different than agriculture, not just farming of crops, but I

857
00:43:41,000 --> 00:43:43,680
Speaker 1: mean dog breeding or horse breeding or anything where you

858
00:43:43,880 --> 00:43:47,640
Speaker 1: select for traits that you care about and then intentionally

859
00:43:47,760 --> 00:43:52,319
Speaker 1: push a population towards having specific traits that we had

860
00:43:52,360 --> 00:43:56,200
Speaker 1: always only done that in the context of selection, where

861
00:43:56,280 --> 00:43:59,960
Speaker 1: all of the reproducing was happening by itself, or you know,

862
00:44:00,160 --> 00:44:02,280
Speaker 1: what you might consider a natural way, in the sense

863
00:44:02,320 --> 00:44:04,759
Speaker 1: that you were just like picking out the peas that

864
00:44:04,800 --> 00:44:07,239
Speaker 1: had the color you were excited about and crossing those,

865
00:44:07,280 --> 00:44:09,480
Speaker 1: and then you get more, and after hundreds of years

866
00:44:09,520 --> 00:44:13,319
Speaker 1: you can't even recognize the organism compared to where it started.

867
00:44:12,960 --> 00:44:16,080
Speaker 2: From the way we transformed corn from like a tiny

868
00:44:16,080 --> 00:44:18,920
Speaker 2: little grain to this like hugely productive.

869
00:44:18,520 --> 00:44:21,719
Speaker 1: Food, for example, exactly, or like watermelons, or you know

870
00:44:21,760 --> 00:44:27,360
Speaker 1: something where watermelons weren't these like amazing, gicy, genormous fruits

871
00:44:27,400 --> 00:44:31,279
Speaker 1: when we first found them, right, We cultivated that, and

872
00:44:31,400 --> 00:44:35,799
Speaker 1: the consequences of the cultivation are obviously encoded in the

873
00:44:35,840 --> 00:44:39,040
Speaker 1: genome and in some ways not really different than cloning

874
00:44:39,080 --> 00:44:43,360
Speaker 1: something on purpose. In fact, we're not that good at cloning.

875
00:44:43,440 --> 00:44:47,600
Speaker 1: Like cloning requires us as humans to decide what pieces

876
00:44:47,640 --> 00:44:51,239
Speaker 1: of DNA belong in an organism, and we typically are

877
00:44:51,320 --> 00:44:54,000
Speaker 1: kind of bad at that. It's interesting. I went to

878
00:44:54,040 --> 00:44:57,840
Speaker 1: grad school in an era where rational design of proteins

879
00:44:57,920 --> 00:45:00,719
Speaker 1: was really popular and a lot of people's projects were

880
00:45:00,760 --> 00:45:03,640
Speaker 1: like looking at protein sequences and being like, oh, I

881
00:45:03,680 --> 00:45:06,040
Speaker 1: think we should put an alanine there, and that's going

882
00:45:06,080 --> 00:45:08,520
Speaker 1: to make this work better. That kind of thing, and

883
00:45:08,600 --> 00:45:11,240
Speaker 1: typically it didn't actually work better. So in some ways,

884
00:45:11,239 --> 00:45:15,040
Speaker 1: I think we've come to respect that evolution and allowing

885
00:45:15,120 --> 00:45:17,600
Speaker 1: natural selection to happen is if in some ways, a

886
00:45:17,640 --> 00:45:20,359
Speaker 1: more powerful way to pull off things you want to do.

887
00:45:21,040 --> 00:45:24,719
Speaker 1: But you would never get insulin production through evolution. I mean,

888
00:45:25,000 --> 00:45:28,120
Speaker 1: bacteria would never produce insulin. So it's actually a really

889
00:45:28,160 --> 00:45:31,200
Speaker 1: great case for genetic engineering. It was a very cool

890
00:45:31,239 --> 00:45:35,080
Speaker 1: problem that lydia Villa comorov took on because it was

891
00:45:35,160 --> 00:45:38,440
Speaker 1: so effective and it really would never have happened without cloning.

892
00:45:39,280 --> 00:45:41,880
Speaker 1: So your question was how did it come to be

893
00:45:42,000 --> 00:45:45,440
Speaker 1: that this was an ethical problem? And the real issue

894
00:45:45,560 --> 00:45:49,120
Speaker 1: was just that we as humans were scared that we

895
00:45:49,120 --> 00:45:52,239
Speaker 1: were going to unleash some kind of monster. What were

896
00:45:52,280 --> 00:45:57,520
Speaker 1: the consequences of us genetically engineering things? I mean, more recently,

897
00:45:57,560 --> 00:46:00,680
Speaker 1: we've had similar debates about crispers, and you've heard that

898
00:46:00,719 --> 00:46:04,239
Speaker 1: there is even a case where a Chinese scientist used

899
00:46:04,239 --> 00:46:08,800
Speaker 1: Crisper to genetically engineer a baby, a human that has really,

900
00:46:08,840 --> 00:46:13,120
Speaker 1: really big ethical questions, And this was happening only in bacteria,

901
00:46:13,320 --> 00:46:15,359
Speaker 1: but it was the first time it had happened, and

902
00:46:15,400 --> 00:46:19,000
Speaker 1: we were rightfully, really thinking carefully about what the consequences

903
00:46:19,000 --> 00:46:23,319
Speaker 1: could be. For example, imagine you cloned a bacteria that

904
00:46:23,480 --> 00:46:27,359
Speaker 1: contained genes that could break down petroleum, and then you

905
00:46:27,440 --> 00:46:31,480
Speaker 1: unleashed that in an oil mining operation. You could really

906
00:46:31,520 --> 00:46:33,719
Speaker 1: cause a lot of destruction. And what if that was

907
00:46:33,840 --> 00:46:39,240
Speaker 1: just impossible to control and then you destroyed huge natural

908
00:46:39,239 --> 00:46:43,040
Speaker 1: resources unintentionally or intentionally for that matter. So those were

909
00:46:43,080 --> 00:46:44,879
Speaker 1: the kinds of questions people were worried about.

910
00:46:45,120 --> 00:46:47,120
Speaker 2: Or what if it helped the bacteria organize and it

911
00:46:47,160 --> 00:46:50,120
Speaker 2: crawled out of the vat and like extracted vengeance for

912
00:46:50,239 --> 00:46:53,160
Speaker 2: all the brethren that we've tortured in order to extract

913
00:46:53,200 --> 00:46:54,000
Speaker 2: insulin from them.

914
00:46:54,440 --> 00:46:56,600
Speaker 1: That's a great question, but I'm pretty sure they didn't

915
00:46:56,600 --> 00:46:57,400
Speaker 1: talk about it.

916
00:46:57,160 --> 00:47:00,360
Speaker 2: That's a very polite answer to a totally bunkers question.

917
00:47:00,680 --> 00:47:03,760
Speaker 2: You all should have seen her face.

918
00:47:04,880 --> 00:47:07,080
Speaker 1: That didn't make it to the top ten list for

919
00:47:07,160 --> 00:47:08,280
Speaker 1: discussion out of Solomar.

920
00:47:08,520 --> 00:47:10,480
Speaker 2: I bet there were some science fiction writers there. They

921
00:47:10,480 --> 00:47:12,120
Speaker 2: would have come up with that scenario. It didn't take

922
00:47:12,120 --> 00:47:13,640
Speaker 2: me very long to think about it.

923
00:47:15,280 --> 00:47:17,799
Speaker 3: I think there was a recent ant Solomar meeting that

924
00:47:17,880 --> 00:47:20,520
Speaker 3: was sort of inspired by then prior meeting. So you

925
00:47:20,560 --> 00:47:23,719
Speaker 3: mentioned the ethical issues associated with Crisper. Yeah, but I

926
00:47:23,719 --> 00:47:26,680
Speaker 3: think they like literally had another Ansilomar meeting with like

927
00:47:26,760 --> 00:47:29,560
Speaker 3: similar goals to figure out what direction.

928
00:47:30,000 --> 00:47:32,080
Speaker 1: I think it's next month, Oh is it? Yeah? I

929
00:47:32,080 --> 00:47:33,719
Speaker 1: think it's next week. I think you guys could have

930
00:47:33,760 --> 00:47:36,359
Speaker 1: this episode coincide or we could look into that. That'd

931
00:47:36,400 --> 00:47:38,840
Speaker 1: be cool. Yeah, because my friend Jen Martini was invited,

932
00:47:38,840 --> 00:47:40,600
Speaker 1: but she couldn't go because she's teaching, and I know

933
00:47:40,680 --> 00:47:42,160
Speaker 1: she doesn't start teaching until.

934
00:47:41,880 --> 00:47:44,640
Speaker 3: Next week's I mean maybe we should have a whole

935
00:47:44,680 --> 00:47:47,879
Speaker 3: episode on the ethical implications of Crisper too. That yeah,

936
00:47:48,000 --> 00:47:48,920
Speaker 3: important and timely.

937
00:47:49,040 --> 00:47:51,080
Speaker 2: So how ballsy was it for her to take on

938
00:47:51,120 --> 00:47:53,920
Speaker 2: this project because if it didn't work, she could ended

939
00:47:54,000 --> 00:47:56,520
Speaker 2: up with basically nothing, no result. I mean, this was

940
00:47:56,560 --> 00:47:58,719
Speaker 2: like really swinging for a home run, wasn't it.

941
00:47:59,040 --> 00:48:01,640
Speaker 1: Yeah, that's a really good question. It'd be fun to

942
00:48:01,800 --> 00:48:03,960
Speaker 1: talk to the people who are around at that time.

943
00:48:04,120 --> 00:48:05,920
Speaker 1: I don't think I would give this to a student

944
00:48:05,960 --> 00:48:09,520
Speaker 1: as their only project. It does seem really risky. But yeah,

945
00:48:09,520 --> 00:48:12,120
Speaker 1: if you look at the people involved, like who her

946
00:48:12,280 --> 00:48:15,719
Speaker 1: bosses were and who their collaborators were. You know, they

947
00:48:15,760 --> 00:48:18,800
Speaker 1: were used to taking on really big projects and hitting

948
00:48:18,840 --> 00:48:22,920
Speaker 1: for home runs. So that's what you do in Boston, Yeah, exactly.

949
00:48:25,239 --> 00:48:27,480
Speaker 3: So what came for her after this? So she did

950
00:48:27,520 --> 00:48:30,520
Speaker 3: this amazing breakthrough, was the first person to do this

951
00:48:30,600 --> 00:48:32,560
Speaker 3: amazing thing. What did she do after that?

952
00:48:32,920 --> 00:48:35,359
Speaker 1: She stayed in science. Actually, she made a really good

953
00:48:35,440 --> 00:48:38,360
Speaker 1: use of her science education, and she went on to

954
00:48:38,480 --> 00:48:41,560
Speaker 1: work on not just insulin, but other hormones that are

955
00:48:41,800 --> 00:48:44,719
Speaker 1: related to insulin. Some of them are called insulin like

956
00:48:44,800 --> 00:48:49,200
Speaker 1: growth factors. It's a really complex field that I honestly

957
00:48:49,440 --> 00:48:52,160
Speaker 1: couldn't tell you too much about what all the implications are,

958
00:48:52,200 --> 00:48:55,040
Speaker 1: but I do know that she ran a lab and

959
00:48:55,120 --> 00:48:59,200
Speaker 1: spent many years studying hormones that are related to insulin.

960
00:48:59,400 --> 00:49:01,560
Speaker 1: And so she stayed in that field. And she also

961
00:49:02,280 --> 00:49:05,520
Speaker 1: really put a lot of energy into being a role

962
00:49:05,600 --> 00:49:08,440
Speaker 1: model and a leader for other people who wanted to

963
00:49:08,480 --> 00:49:14,680
Speaker 1: become scientists. She'd encountered definitely roadblocks herself, considering that most

964
00:49:14,840 --> 00:49:18,320
Speaker 1: institutions wouldn't even accept women as applicants to graduate school

965
00:49:18,360 --> 00:49:20,799
Speaker 1: when she was applying, and so she became a co

966
00:49:20,920 --> 00:49:24,480
Speaker 1: founding member of the Society for the Advancement of Chicanos

967
00:49:24,560 --> 00:49:28,600
Speaker 1: and Hispanics and Native Americans and Science or SACNAS, And

968
00:49:28,600 --> 00:49:30,800
Speaker 1: that was back in the nineteen seventies, and I actually

969
00:49:30,800 --> 00:49:33,719
Speaker 1: didn't know that. I personally have had a lot of

970
00:49:33,760 --> 00:49:37,080
Speaker 1: students who have been supported by SAKNAS to go to conferences.

971
00:49:37,120 --> 00:49:40,360
Speaker 1: They have meetings every year. I work at UC Irvine,

972
00:49:40,360 --> 00:49:43,799
Speaker 1: which is a Hispanic serving institution, and a lot of

973
00:49:43,800 --> 00:49:46,080
Speaker 1: our students have gone to those meetings and had a

974
00:49:46,120 --> 00:49:49,000
Speaker 1: really good time. So I think that's really cool she

975
00:49:49,080 --> 00:49:49,560
Speaker 1: founded that.

976
00:49:50,080 --> 00:49:52,799
Speaker 2: And why isn't she a zillionaire? I mean, I know

977
00:49:52,880 --> 00:49:56,200
Speaker 2: that this technology is the foundation of like billions of

978
00:49:56,280 --> 00:49:59,520
Speaker 2: dollars of annual profit for Nova Noordis and Eli Lilly.

979
00:50:00,040 --> 00:50:01,920
Speaker 2: Why isn't she elon Musk?

980
00:50:02,239 --> 00:50:04,799
Speaker 1: What a good question. I don't know the details of

981
00:50:04,920 --> 00:50:07,640
Speaker 1: what kind of IP they tried to get for the

982
00:50:07,840 --> 00:50:11,120
Speaker 1: technique that they developed. I also know that genen Tech

983
00:50:11,280 --> 00:50:13,600
Speaker 1: was really involved, and so actually an important thing to

984
00:50:13,600 --> 00:50:15,239
Speaker 1: say is that, you know, so her paper came out

985
00:50:15,239 --> 00:50:18,000
Speaker 1: in nineteen seventy eight, she didn't keep up with this

986
00:50:18,239 --> 00:50:22,240
Speaker 1: specific feel. I know that genen Tech was the company

987
00:50:22,280 --> 00:50:27,440
Speaker 1: that really developed the possibility of producing insulin in bacteria.

988
00:50:28,280 --> 00:50:30,840
Speaker 1: My sense is that what she did was proof of concept,

989
00:50:31,000 --> 00:50:34,560
Speaker 1: I see, which was critical because it made people ready

990
00:50:34,600 --> 00:50:37,719
Speaker 1: for the idea. But my guess is that genen Tech

991
00:50:37,800 --> 00:50:41,280
Speaker 1: went on and developed the technology in its own specific

992
00:50:41,360 --> 00:50:44,720
Speaker 1: way that could then have ip that stayed within the company.

993
00:50:44,880 --> 00:50:47,680
Speaker 2: Isn't there like fast acting and long acting with insulin stuff.

994
00:50:48,160 --> 00:50:50,440
Speaker 1: Her paper came out in nineteen seventy eight, and I

995
00:50:50,480 --> 00:50:52,919
Speaker 1: know that by nineteen eighty two the Food and Drug

996
00:50:52,920 --> 00:50:56,720
Speaker 1: Administration had approved human insulin to be given to people.

997
00:50:57,400 --> 00:50:59,640
Speaker 1: I was diagnosed with type one diabetes in nineteen eighty

998
00:50:59,680 --> 00:51:02,480
Speaker 1: four and I never took animal insulin. I only got

999
00:51:02,520 --> 00:51:05,239
Speaker 1: the human insulin at that time. It was just the

1000
00:51:05,280 --> 00:51:08,080
Speaker 1: straight up human insulin sequence. It hadn't been modified to

1001
00:51:08,120 --> 00:51:11,680
Speaker 1: have new properties. But that's pretty amazing that we scaled

1002
00:51:11,680 --> 00:51:15,600
Speaker 1: that up for humanity that quickly. And so the insulins

1003
00:51:15,600 --> 00:51:19,520
Speaker 1: that I took with like regular insulin, is still produced

1004
00:51:19,520 --> 00:51:23,880
Speaker 1: today and I used it for twelve years, like multiple

1005
00:51:23,920 --> 00:51:27,920
Speaker 1: injections daily, And now if you go to Walmart, you

1006
00:51:27,960 --> 00:51:30,560
Speaker 1: can actually buy that kind of insulin for twenty five dollars.

1007
00:51:30,600 --> 00:51:33,120
Speaker 1: This is probably the most important thing I'm saying like

1008
00:51:33,239 --> 00:51:35,560
Speaker 1: ever anytime I have an audience, I say this because

1009
00:51:35,560 --> 00:51:38,600
Speaker 1: it could really save lives that you can buy regular

1010
00:51:38,680 --> 00:51:42,280
Speaker 1: human insulin at Walmart. It's intended for cats and dogs

1011
00:51:42,280 --> 00:51:45,000
Speaker 1: who have diabetes. But it's exactly the insulin that I

1012
00:51:45,120 --> 00:51:49,080
Speaker 1: used for many years, and it cost twenty five dollars

1013
00:51:49,520 --> 00:51:51,760
Speaker 1: and you don't need a prescription. It's just over the counter.

1014
00:51:51,880 --> 00:51:54,080
Speaker 1: You can just go to Walmart and buy this insulin

1015
00:51:54,600 --> 00:51:57,000
Speaker 1: and it's exactly the same bottle as the one that

1016
00:51:57,040 --> 00:51:59,520
Speaker 1: I used in the nineteen eighties. So, starting in the

1017
00:51:59,600 --> 00:52:03,480
Speaker 1: nineteen eight e we scaled up fermentation of human insulin

1018
00:52:03,920 --> 00:52:07,160
Speaker 1: to be available for all people. I wouldn't say access

1019
00:52:07,200 --> 00:52:11,440
Speaker 1: is perfect, but it's pretty good. Like there's definitely parts

1020
00:52:11,480 --> 00:52:13,600
Speaker 1: of the world where you can buy insulin at a

1021
00:52:13,640 --> 00:52:16,279
Speaker 1: pretty reasonable cost. I'm sure you've heard a lot about

1022
00:52:16,280 --> 00:52:18,360
Speaker 1: the cost of insulin, Like if you cross the border

1023
00:52:18,360 --> 00:52:20,640
Speaker 1: into Mexico, you can buy this insulin at a very

1024
00:52:20,680 --> 00:52:24,960
Speaker 1: reasonable cost too. It wasn't until the nineties that engineered

1025
00:52:25,040 --> 00:52:27,920
Speaker 1: insulins that have different kinetics, like they can be faster

1026
00:52:28,000 --> 00:52:31,400
Speaker 1: and slower. Those came out in the nineties. The slower

1027
00:52:31,400 --> 00:52:34,680
Speaker 1: insulins were available earlier, like the insulin you can buy

1028
00:52:34,680 --> 00:52:37,560
Speaker 1: at Walmart. There's a slower one too, that's called NPH,

1029
00:52:37,560 --> 00:52:40,319
Speaker 1: and that didn't require crazy bioengineering or I don't know,

1030
00:52:40,400 --> 00:52:42,120
Speaker 1: they've somehow figured that out way earlier.

1031
00:52:42,280 --> 00:52:45,480
Speaker 3: Why would you want faster and slower insulin, Well.

1032
00:52:45,360 --> 00:52:49,160
Speaker 1: The regular insulin it takes a few hours to become active.

1033
00:52:49,320 --> 00:52:55,799
Speaker 1: So the insulin exists in little trimers around zinc molecules

1034
00:52:55,960 --> 00:52:58,799
Speaker 1: in a dimer. So there's two zinc molecules that each

1035
00:52:58,840 --> 00:53:01,439
Speaker 1: have three insulin molecules attached to them, So there's six

1036
00:53:01,560 --> 00:53:05,279
Speaker 1: insulin molecules all hanging out together. You have to wait

1037
00:53:05,320 --> 00:53:08,040
Speaker 1: for them to dissociate because the insulin is only active

1038
00:53:08,040 --> 00:53:11,880
Speaker 1: as a monomer by itself, So when you inject regular insulin,

1039
00:53:12,480 --> 00:53:15,479
Speaker 1: you have to wait one hour for it to even

1040
00:53:15,520 --> 00:53:19,879
Speaker 1: start working at all, and it doesn't peak until three hours. Now,

1041
00:53:19,880 --> 00:53:22,520
Speaker 1: when you eat your food, it doesn't come in immediately either,

1042
00:53:22,719 --> 00:53:25,600
Speaker 1: But regular insulin is too slow for an average meal,

1043
00:53:25,680 --> 00:53:28,640
Speaker 1: so most people would have to either inject their insulin early,

1044
00:53:29,120 --> 00:53:32,480
Speaker 1: but that's kind of dangerous. Imagine you inject your insulin,

1045
00:53:32,800 --> 00:53:35,360
Speaker 1: but you're like your food is late, or you're on

1046
00:53:35,400 --> 00:53:36,960
Speaker 1: a walk and you forget to eat or you know,

1047
00:53:37,000 --> 00:53:38,960
Speaker 1: something like that. If your blood seger goes too low,

1048
00:53:39,280 --> 00:53:42,840
Speaker 1: you die from low blood sugar immediately. It's like the

1049
00:53:42,880 --> 00:53:45,799
Speaker 1: most common cause of death in people with type one diabetes.

1050
00:53:46,600 --> 00:53:48,799
Speaker 1: So that's really really something you have to be careful with.

1051
00:53:48,880 --> 00:53:52,160
Speaker 1: So having your insulin kinetics not really matching when you

1052
00:53:52,200 --> 00:53:57,320
Speaker 1: need the insulin is both for convenience and basic survival,

1053
00:53:57,600 --> 00:54:01,440
Speaker 1: really really important. There was a big arms race between

1054
00:54:01,480 --> 00:54:05,960
Speaker 1: Eli Lilly and Novonordisk in the nineties to rationally design

1055
00:54:06,040 --> 00:54:09,960
Speaker 1: insulins that had different properties, and they both succeeded in

1056
00:54:10,000 --> 00:54:13,560
Speaker 1: slightly different ways. And now you can buy insulin that

1057
00:54:14,120 --> 00:54:18,560
Speaker 1: falls apart more easily. Those dimers on the trimers of

1058
00:54:18,600 --> 00:54:21,720
Speaker 1: the zinc are not as stable, so they fall apart

1059
00:54:21,920 --> 00:54:24,200
Speaker 1: more quickly. And now when you inject the insulin, it

1060
00:54:24,239 --> 00:54:28,000
Speaker 1: starts to become active within minutes and peaks within an hour,

1061
00:54:28,520 --> 00:54:33,200
Speaker 1: which matches how people eat much better. So it's safer

1062
00:54:33,480 --> 00:54:36,280
Speaker 1: and a little bit easier to keep your blood sugar

1063
00:54:36,320 --> 00:54:38,680
Speaker 1: within range. But matching the kinetics of your food with

1064
00:54:38,719 --> 00:54:41,200
Speaker 1: the kinetics of your insulin is just a major challenge.

1065
00:54:41,440 --> 00:54:43,960
Speaker 3: So is it still the case that today people with

1066
00:54:44,000 --> 00:54:46,800
Speaker 3: diabetes need to be really careful about, like what kinds

1067
00:54:46,960 --> 00:54:50,040
Speaker 3: of insulin that they're injecting. Has it gotten easier over time?

1068
00:54:50,120 --> 00:54:51,680
Speaker 3: I feel like I heard once that there are these

1069
00:54:52,160 --> 00:54:54,040
Speaker 3: things that can attach to your body that sort of

1070
00:54:54,120 --> 00:54:56,160
Speaker 3: do all the measuring for you. What is it like today.

1071
00:54:56,840 --> 00:54:59,840
Speaker 1: Well, so there's been big changes both in how we

1072
00:55:00,040 --> 00:55:04,040
Speaker 1: deliver insulin and how we monitor blood sugar. So we

1073
00:55:04,120 --> 00:55:06,719
Speaker 1: haven't talked about blood sugar monitoring at all in any

1074
00:55:06,760 --> 00:55:08,959
Speaker 1: of this, but I'll bring up that, you know, we've

1075
00:55:09,000 --> 00:55:13,080
Speaker 1: known for centuries how to detect glucose and urine, but

1076
00:55:13,120 --> 00:55:15,560
Speaker 1: that's very old information. By the time the sugar hits

1077
00:55:15,600 --> 00:55:18,480
Speaker 1: your urine, that's like happened hours ago, and you can't

1078
00:55:18,480 --> 00:55:20,520
Speaker 1: really take that sample and demand quite in the same

1079
00:55:20,520 --> 00:55:22,640
Speaker 1: way as you can take a blood sample. So we

1080
00:55:22,719 --> 00:55:25,759
Speaker 1: started poking our fingers to get blood sugar measurements. Those

1081
00:55:26,080 --> 00:55:29,240
Speaker 1: devices started being available at home in the nineteen eighties

1082
00:55:29,880 --> 00:55:32,279
Speaker 1: and then for about ten or fifteen years. We now

1083
00:55:32,280 --> 00:55:34,840
Speaker 1: have these sensors that you can put a little sensor

1084
00:55:34,880 --> 00:55:37,319
Speaker 1: on your arm and you get blood sugar information every

1085
00:55:37,360 --> 00:55:41,279
Speaker 1: five minutes. So those sensors help you see what your

1086
00:55:41,320 --> 00:55:45,160
Speaker 1: blood sugar is. We also have both injections for insulin

1087
00:55:45,239 --> 00:55:49,279
Speaker 1: and also insulin pumps. So the pump will deliver insulin

1088
00:55:49,800 --> 00:55:52,240
Speaker 1: at rates that you tell it to give you the insulin.

1089
00:55:52,719 --> 00:55:55,959
Speaker 1: But none of this is happening without a lot of thought.

1090
00:55:56,200 --> 00:55:58,080
Speaker 1: There's not like a machine that just takes care of

1091
00:55:58,120 --> 00:56:00,440
Speaker 1: it for you. A lot of people when they see

1092
00:56:00,440 --> 00:56:03,040
Speaker 1: an insulin pump imagine like, oh, that must be so

1093
00:56:03,160 --> 00:56:05,279
Speaker 1: nice that like, oh, the AI is taking care of

1094
00:56:05,280 --> 00:56:07,960
Speaker 1: your bl chicker for you, And that's not the case

1095
00:56:08,000 --> 00:56:10,160
Speaker 1: at all. Insulin pumps just do what you tell them

1096
00:56:10,200 --> 00:56:10,440
Speaker 1: to do.

1097
00:56:10,719 --> 00:56:12,799
Speaker 3: That is what I imagined. Thanks for clarifying that.

1098
00:56:13,000 --> 00:56:15,160
Speaker 1: Even my doctors, like if I go to the eye doctor,

1099
00:56:15,200 --> 00:56:17,400
Speaker 1: they're like, oh, it must be some nice having your

1100
00:56:17,440 --> 00:56:19,360
Speaker 1: pump doing that for you. But I mean, no, the

1101
00:56:19,440 --> 00:56:21,640
Speaker 1: pump is not operating independently.

1102
00:56:22,080 --> 00:56:24,640
Speaker 2: And talk per a minute about why that's a hard problem.

1103
00:56:24,680 --> 00:56:26,640
Speaker 2: I mean, people might be thinking, well, you have a

1104
00:56:26,680 --> 00:56:28,360
Speaker 2: sense of that tells you how much glucose you have,

1105
00:56:28,440 --> 00:56:31,000
Speaker 2: and you have insulin which brings the glucose down. Why

1106
00:56:31,000 --> 00:56:33,080
Speaker 2: can't you just you know, fit a straight line to

1107
00:56:33,160 --> 00:56:35,520
Speaker 2: that and descide how much insulin I have? Why do

1108
00:56:35,560 --> 00:56:38,120
Speaker 2: you need a human brain in the loop there or

1109
00:56:38,120 --> 00:56:39,280
Speaker 2: why is it a hard problem.

1110
00:56:39,560 --> 00:56:41,799
Speaker 1: There's a lot of reasons it's a hard problem. A

1111
00:56:41,840 --> 00:56:44,239
Speaker 1: big one is that the way that we're giving the

1112
00:56:44,280 --> 00:56:49,759
Speaker 1: insulin just subcutaneously means that it's slow. So you know,

1113
00:56:49,800 --> 00:56:52,879
Speaker 1: when you eat, your pancreas is exactly in the right

1114
00:56:52,880 --> 00:56:56,000
Speaker 1: place at the right time, sensing the glucose as it's

1115
00:56:56,040 --> 00:56:59,920
Speaker 1: getting released from your digestion. So then not only do

1116
00:56:59,960 --> 00:57:03,080
Speaker 1: you get real time information, but you also have the

1117
00:57:03,080 --> 00:57:05,920
Speaker 1: insulin being delivered exactly where you need it. So when

1118
00:57:05,960 --> 00:57:09,160
Speaker 1: you take insulin subcutaneously, it takes like a good half

1119
00:57:09,200 --> 00:57:13,400
Speaker 1: an hour to dissolve and become active. I guess. Another

1120
00:57:13,440 --> 00:57:17,240
Speaker 1: really important thing to say is that the insulin's activity

1121
00:57:17,320 --> 00:57:20,840
Speaker 1: is affected by your own activity. So if you're exercising,

1122
00:57:20,880 --> 00:57:23,320
Speaker 1: the insulin that you're taking will do a lot more work.

1123
00:57:23,920 --> 00:57:27,360
Speaker 1: And your own pancrease has more than one hormone. It

1124
00:57:27,400 --> 00:57:31,080
Speaker 1: has insulin and also glucagon, so it's a two component system.

1125
00:57:31,360 --> 00:57:34,720
Speaker 1: Insulin drives your blood sugar down, Glucagon drives your blood

1126
00:57:34,760 --> 00:57:38,760
Speaker 1: sugar up. So what glucagon does is it releases the

1127
00:57:38,800 --> 00:57:41,760
Speaker 1: glycogen stores in your liver and muscle to raise your

1128
00:57:41,760 --> 00:57:44,960
Speaker 1: blood sugar. So if this dangerous thing starts to happen

1129
00:57:45,000 --> 00:57:47,960
Speaker 1: that your insulin drives your blood sugar too low. Then

1130
00:57:48,000 --> 00:57:50,800
Speaker 1: the glucagon can pick it up from the floor and

1131
00:57:50,840 --> 00:57:53,120
Speaker 1: save your life so you don't die from low blood sugar.

1132
00:57:54,120 --> 00:57:56,800
Speaker 1: Our insulin pumps don't have glucagon in them. Glucagon is

1133
00:57:56,840 --> 00:58:00,360
Speaker 1: a really delicate hormone. There's about one hundred companies there

1134
00:58:00,360 --> 00:58:03,560
Speaker 1: engineering more stable glukug on. Maybe we'll have that soon,

1135
00:58:03,720 --> 00:58:06,480
Speaker 1: but right now the pumps just have insulin. There is

1136
00:58:06,520 --> 00:58:09,200
Speaker 1: a company that's trying to make a two component pump

1137
00:58:09,280 --> 00:58:12,400
Speaker 1: that also has glucagon, but anyway, that doesn't happen yet.

1138
00:58:12,760 --> 00:58:15,960
Speaker 1: So there's a number of reasons, like you could, in theory,

1139
00:58:16,040 --> 00:58:19,120
Speaker 1: use the glucose information, the old information you're getting from

1140
00:58:19,120 --> 00:58:22,240
Speaker 1: the sensor in your arm, and have that direct the

1141
00:58:22,280 --> 00:58:26,560
Speaker 1: dosing of the insulin from your pump. However, your pump

1142
00:58:26,600 --> 00:58:29,280
Speaker 1: doesn't know if you're about to go running, or if

1143
00:58:29,320 --> 00:58:31,880
Speaker 1: you are sick, or you know. The amount of insulin

1144
00:58:31,920 --> 00:58:35,280
Speaker 1: you need is affected by easily forty two factors that

1145
00:58:35,360 --> 00:58:38,400
Speaker 1: most of them can't be measured, and so you need

1146
00:58:38,480 --> 00:58:41,840
Speaker 1: your brain to synthesize all those factors and think through

1147
00:58:41,840 --> 00:58:43,840
Speaker 1: the decision of how much insulin you need, and.

1148
00:58:43,800 --> 00:58:46,040
Speaker 2: You don't trust chat GPT to make those decisions for

1149
00:58:46,080 --> 00:58:46,479
Speaker 2: you yet.

1150
00:58:46,600 --> 00:58:49,400
Speaker 1: I mean, I wouldn't mind like chatting with chat GPT

1151
00:58:49,520 --> 00:58:52,480
Speaker 1: about it and getting ideas, but I would definitely want

1152
00:58:52,520 --> 00:58:54,400
Speaker 1: to be the one who's the buck stops with me

1153
00:58:54,480 --> 00:58:55,680
Speaker 1: when it comes to that decision.

1154
00:58:55,720 --> 00:58:57,640
Speaker 2: All right, So then our last question is what do

1155
00:58:57,680 --> 00:59:00,240
Speaker 2: you see happening in the future, like in ten year

1156
00:59:00,280 --> 00:59:03,120
Speaker 2: and fifty years, in one hundred years, what's going to

1157
00:59:03,280 --> 00:59:06,920
Speaker 2: change about our treatment of diabetes or understanding of the

1158
00:59:06,920 --> 00:59:08,200
Speaker 2: biochemical processes.

1159
00:59:08,440 --> 00:59:12,560
Speaker 1: Well, there's biological and engineering fronts to talk about, and

1160
00:59:12,600 --> 00:59:14,920
Speaker 1: actually I should make a really big shout out to

1161
00:59:15,000 --> 00:59:19,040
Speaker 1: the group of people who are engineering devices. There's even

1162
00:59:19,120 --> 00:59:22,560
Speaker 1: groups of people who have built algorithms that do take

1163
00:59:22,640 --> 00:59:25,760
Speaker 1: the information from the glucose sensors and use it to

1164
00:59:25,840 --> 00:59:29,320
Speaker 1: dose the insulin. Sometimes they're called loopers. They're closing the loop.

1165
00:59:29,960 --> 00:59:32,960
Speaker 1: Many of those algorithms are open source, and people are

1166
00:59:33,040 --> 00:59:37,919
Speaker 1: having good results with better blood sugar control using those algorithms.

1167
00:59:37,960 --> 00:59:41,560
Speaker 1: It takes a really tech savvy person and somebody who

1168
00:59:42,000 --> 00:59:44,080
Speaker 1: you know. In my case, I go running every day

1169
00:59:44,520 --> 00:59:47,760
Speaker 1: and I don't think those algorithms take exercise into account

1170
00:59:47,880 --> 00:59:50,080
Speaker 1: in a way that works for me. So that's why

1171
00:59:50,120 --> 00:59:54,080
Speaker 1: I personally am not using the looping algorithms, although I'm interested.

1172
00:59:54,160 --> 00:59:56,120
Speaker 1: So if there's someone out there who's into looping, like,

1173
00:59:56,320 --> 00:59:59,400
Speaker 1: my mind is open. So there are people making really

1174
00:59:59,400 --> 01:00:02,120
Speaker 1: big progress on the algorithms, I think those are going

1175
01:00:02,160 --> 01:00:05,400
Speaker 1: to be a really big frontier that our algorithms are

1176
01:00:05,440 --> 01:00:07,840
Speaker 1: going to get better and better. Some of the major

1177
01:00:07,880 --> 01:00:11,800
Speaker 1: insulin pump companies have soft versions of those algorithms with

1178
01:00:11,880 --> 01:00:13,960
Speaker 1: a lot of safety on them, where they keep the

1179
01:00:14,480 --> 01:00:17,760
Speaker 1: average blood sugar values a little higher to give you

1180
01:00:17,800 --> 01:00:21,439
Speaker 1: a safety buffer. So that's starting to happen already. In fact,

1181
01:00:21,480 --> 01:00:24,040
Speaker 1: the pump I use has a soft algorithm like that that,

1182
01:00:24,760 --> 01:00:27,040
Speaker 1: especially for sleeping at night when there's not as many

1183
01:00:27,080 --> 01:00:29,200
Speaker 1: different changes going on, it can do a really good

1184
01:00:29,280 --> 01:00:31,640
Speaker 1: job of helping you keep your blood sugar in control.

1185
01:00:32,240 --> 01:00:35,120
Speaker 1: So that's already getting better. I'd say the engineering front

1186
01:00:35,160 --> 01:00:39,640
Speaker 1: will include better sensors, better algorithms. If we had glucagon,

1187
01:00:39,800 --> 01:00:42,440
Speaker 1: then that will also close the loop and help to

1188
01:00:42,480 --> 01:00:46,360
Speaker 1: make the measurements better overall, though, I mean, humans are

1189
01:00:46,360 --> 01:00:49,280
Speaker 1: really complex. Like one of my favorite results recently showed

1190
01:00:49,280 --> 01:00:52,760
Speaker 1: how people eating exactly the same meal one week apart

1191
01:00:53,160 --> 01:00:56,680
Speaker 1: and wearing a continuous glucose monitor had different blood sugar

1192
01:00:56,720 --> 01:00:59,840
Speaker 1: responses to the same meal, which I think highlights that

1193
01:01:00,080 --> 01:01:03,760
Speaker 1: challenge of why it has to be pretty thoughtful how

1194
01:01:03,800 --> 01:01:06,440
Speaker 1: you are dosing insulin even for the same meal and

1195
01:01:06,480 --> 01:01:08,720
Speaker 1: the same person. You can't assume it's going to work

1196
01:01:08,720 --> 01:01:12,439
Speaker 1: the same way each time. But then on the biology front,

1197
01:01:12,480 --> 01:01:16,360
Speaker 1: there's also really big progress. So we have been learning

1198
01:01:16,440 --> 01:01:21,000
Speaker 1: how to direct the program of stem cells and differentiating

1199
01:01:21,000 --> 01:01:23,360
Speaker 1: them into different kinds of cells that we can use

1200
01:01:23,400 --> 01:01:26,760
Speaker 1: for different kinds of medicine. There's a whole arm of

1201
01:01:26,800 --> 01:01:32,760
Speaker 1: research towards building pancreatic beta cells, the ones that secrete insulin,

1202
01:01:33,600 --> 01:01:37,360
Speaker 1: so that they could be transplanted. The first versions of

1203
01:01:37,400 --> 01:01:41,400
Speaker 1: these have required people to take immunosuppressants, just like for

1204
01:01:41,480 --> 01:01:45,480
Speaker 1: a pancreased transplant, so it didn't feel as exciting to me,

1205
01:01:46,200 --> 01:01:49,800
Speaker 1: but that's actually starting to get better, and there are

1206
01:01:50,000 --> 01:01:54,880
Speaker 1: efforts towards making hypoimmune, so like not giving an immune

1207
01:01:54,880 --> 01:01:59,040
Speaker 1: response cells, so that people could get transplants with these

1208
01:01:59,080 --> 01:02:01,720
Speaker 1: kind of cells and not have to take imminosuppressants and

1209
01:02:01,760 --> 01:02:05,920
Speaker 1: potentially have them work to control their blood sugar. I

1210
01:02:05,920 --> 01:02:08,240
Speaker 1: don't know, it's interesting to me to imagine trusting a

1211
01:02:08,280 --> 01:02:11,600
Speaker 1: little renegade group of cells to do that. But you know,

1212
01:02:11,720 --> 01:02:13,959
Speaker 1: that will get tested and we'll know a lot more

1213
01:02:14,080 --> 01:02:17,080
Speaker 1: those are. On the treatment front, there's another whole aspect

1214
01:02:17,200 --> 01:02:20,240
Speaker 1: to talk about, which is, you know, the way that

1215
01:02:20,360 --> 01:02:23,560
Speaker 1: type one diabetes develops, at least your immune system starts

1216
01:02:23,600 --> 01:02:27,080
Speaker 1: to attack your pancreas. This process can take a couple

1217
01:02:27,040 --> 01:02:30,320
Speaker 1: of years. A lot of people when they're diagnosed, imagine that.

1218
01:02:30,600 --> 01:02:32,200
Speaker 1: You know, oh, I got a cold and then I

1219
01:02:32,240 --> 01:02:34,440
Speaker 1: got diabetes, or I took finals and I was all

1220
01:02:34,440 --> 01:02:37,040
Speaker 1: stressed out and that caused my diabetes. But really it

1221
01:02:37,080 --> 01:02:39,440
Speaker 1: was just the straw that broke the camel's back. And

1222
01:02:39,520 --> 01:02:41,280
Speaker 1: this was a process that was going on for a

1223
01:02:41,320 --> 01:02:44,760
Speaker 1: couple of years, and then a stressful circumstance made you

1224
01:02:44,840 --> 01:02:47,920
Speaker 1: need more insulin, so then the system couldn't support you anymore.

1225
01:02:48,520 --> 01:02:52,520
Speaker 1: But now there's actually a treatment for people who are

1226
01:02:52,720 --> 01:02:55,320
Speaker 1: just starting to build the antibodies that are killing their

1227
01:02:55,320 --> 01:03:01,520
Speaker 1: pancreatic cells, and that treatment will basically target and slow

1228
01:03:01,600 --> 01:03:04,480
Speaker 1: down the immune process. It's called tea yield. And we

1229
01:03:04,520 --> 01:03:08,520
Speaker 1: actually have a friend whose son was caught early because

1230
01:03:08,520 --> 01:03:10,640
Speaker 1: he had a really savvy mom who understood what was

1231
01:03:10,720 --> 01:03:13,640
Speaker 1: going on, and she helped him get that treatment, and

1232
01:03:13,680 --> 01:03:16,320
Speaker 1: it's supposed to delay the onset of his type one

1233
01:03:16,400 --> 01:03:19,880
Speaker 1: diabetes for several years. So he's probably still on that path,

1234
01:03:20,600 --> 01:03:22,680
Speaker 1: but I could imagine us getting even better at that.

1235
01:03:22,800 --> 01:03:25,080
Speaker 1: And to be honest, the reason I became a microbiome

1236
01:03:25,160 --> 01:03:30,360
Speaker 1: scientist is that we know that the diagnosis of autoimmune

1237
01:03:30,360 --> 01:03:34,919
Speaker 1: diseases is becoming more and more frequent. EXAMA allergies, type

1238
01:03:34,920 --> 01:03:37,920
Speaker 1: one diabetes, a lot of autoimmune diseases are becoming more common,

1239
01:03:38,680 --> 01:03:42,400
Speaker 1: and we don't exactly know why. It's clearly a change

1240
01:03:42,440 --> 01:03:45,880
Speaker 1: in our immune development and the exposures we have in

1241
01:03:45,920 --> 01:03:50,720
Speaker 1: early life. And for example, most babies born in human

1242
01:03:50,840 --> 01:03:55,480
Speaker 1: history were breastfed and their guts became dominated by a

1243
01:03:55,520 --> 01:03:59,000
Speaker 1: biffidobacteria that's good at helping break down the breast milk fibers,

1244
01:03:59,840 --> 01:04:03,360
Speaker 1: and that's now missing from most people in the industrialized world.

1245
01:04:04,080 --> 01:04:06,600
Speaker 1: So there are big studies right now to try to

1246
01:04:06,640 --> 01:04:10,800
Speaker 1: reintroduce that biffodobacteria and see if it helps us reduce

1247
01:04:11,000 --> 01:04:14,920
Speaker 1: our incidence of autoimmune diseases. So the easiest disease to

1248
01:04:14,920 --> 01:04:17,720
Speaker 1: study is ezema in some ways because it emerges within

1249
01:04:17,760 --> 01:04:19,800
Speaker 1: the first year or so of life, and they're now

1250
01:04:19,920 --> 01:04:22,880
Speaker 1: are big studies using biffidobacteria to see if we can

1251
01:04:22,920 --> 01:04:26,320
Speaker 1: reduce those diseases. But there's also right now there's a

1252
01:04:26,360 --> 01:04:29,480
Speaker 1: big study in Europe across five countries with more than

1253
01:04:29,480 --> 01:04:32,200
Speaker 1: a thousand people who are from families who are a

1254
01:04:32,200 --> 01:04:34,720
Speaker 1: little bit more at risk for developing type one diabetes,

1255
01:04:35,320 --> 01:04:38,479
Speaker 1: and they're introducing that biffodobacteria. So it's going to take

1256
01:04:38,680 --> 01:04:41,000
Speaker 1: like probably ten years until we have the beginnings of

1257
01:04:41,040 --> 01:04:43,880
Speaker 1: the answer, because type one diabetes can happen in childhood

1258
01:04:43,920 --> 01:04:48,600
Speaker 1: or even adulthood. But we're gonna know if these changes

1259
01:04:48,640 --> 01:04:52,240
Speaker 1: in microbiome exposure that put us on a better immune

1260
01:04:52,240 --> 01:04:57,200
Speaker 1: development course could help to reduce the incidence of type

1261
01:04:57,200 --> 01:05:00,160
Speaker 1: one diabetes. So it might be akin to vaccination in

1262
01:05:00,200 --> 01:05:05,720
Speaker 1: the future that we intentionally develop our microbial exposures to

1263
01:05:05,760 --> 01:05:08,520
Speaker 1: direct the way our immune systems develop so we don't

1264
01:05:08,600 --> 01:05:11,240
Speaker 1: end up with all these autoimmune diseases.

1265
01:05:11,720 --> 01:05:13,240
Speaker 2: All right, Well, it sounds like a lot of potential

1266
01:05:13,240 --> 01:05:15,400
Speaker 2: progress and lots of different directions. I hope that young

1267
01:05:15,440 --> 01:05:18,600
Speaker 2: scientists out there are excited about working in all of

1268
01:05:18,640 --> 01:05:22,240
Speaker 2: these angles and taking big risks like Lydia did.

1269
01:05:22,720 --> 01:05:24,919
Speaker 3: Yeah, thanks for being on the show, kut Ri enough,

1270
01:05:25,000 --> 01:05:25,640
Speaker 3: that was awesome.

1271
01:05:25,800 --> 01:05:26,200
Speaker 1: Thank you.

1272
01:05:26,320 --> 01:05:28,200
Speaker 2: I'm going to nominate you for next year's White Sin

1273
01:05:28,320 --> 01:05:30,840
Speaker 2: Research Award as well, even that you've twenty five years ago.

1274
01:05:30,880 --> 01:05:37,880
Speaker 1: Now I'm going to nominate you. You. I mean, I

1275
01:05:37,920 --> 01:05:41,080
Speaker 1: think it's amazing that our society has produced all this

1276
01:05:41,160 --> 01:05:44,440
Speaker 1: insulin to keep so many people with diabetes alive. And

1277
01:05:44,480 --> 01:05:46,560
Speaker 1: we could do better with the access. But I mean,

1278
01:05:46,640 --> 01:05:49,840
Speaker 1: considering that it's like water for so many people to

1279
01:05:50,040 --> 01:05:52,480
Speaker 1: depend on the insulin, it's kind of amazing that we've

1280
01:05:52,560 --> 01:05:53,680
Speaker 1: kept these systems going.

1281
01:05:54,120 --> 01:05:56,640
Speaker 2: All right. Well, thanks very much, China, and thanks everybody

1282
01:05:56,760 --> 01:05:57,560
Speaker 2: for listening.

1283
01:06:04,840 --> 01:06:08,680
Speaker 3: Daniel and Kelly's Extraordinary Universe is produced by iHeartRadio. We

1284
01:06:08,720 --> 01:06:11,160
Speaker 3: would love to hear from you, We really would.

1285
01:06:11,320 --> 01:06:14,040
Speaker 2: We want to know what questions you have about this

1286
01:06:14,280 --> 01:06:15,920
Speaker 2: Extraordinary Universe.

1287
01:06:16,040 --> 01:06:19,000
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1288
01:06:19,040 --> 01:06:22,040
Speaker 3: for future shows. If you contact us, we will get

1289
01:06:22,080 --> 01:06:22,480
Speaker 3: back to you.

1290
01:06:22,680 --> 01:06:26,200
Speaker 2: We really mean it. We answer every message. Email us

1291
01:06:26,240 --> 01:06:29,400
Speaker 2: at questions at Danielandkelly dot org, or.

1292
01:06:29,320 --> 01:06:31,440
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1293
01:06:31,520 --> 01:06:35,440
Speaker 3: on x, Instagram, Blue Sky, and on all of those platforms.

1294
01:06:35,480 --> 01:06:38,440
Speaker 3: You can find us at D and Kuniverse.

1295
01:06:38,560 --> 01:06:40,080
Speaker 2: Don't be shy right to us.