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Speaker 1: Welcome back to thrilling threads. If you are joining us

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for the first time, strap in because we are about

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to pull on a loose thread of reality and see

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just how far it unravels.

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Speaker 2: And today that thread is all about speed.

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Speaker 1: Oh, it's about speed, specifically the terrifying, exhilarating velocity of

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scientific evolution.

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Speaker 2: It's moving faster than most of us can actually comprehend.

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We tend to think of scientific breakthroughs as these distinct

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finish lines. You run the marathon, you break the tape,

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you pop the champagne, and you're done. You solve the

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problem exactly.

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Speaker 1: We have this historical narrative where we think, okay, we

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map the human genome, check done, or we invented the Internet. Finished.

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But in the world of biotechnology, a breakthrough isn't a

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finish line. It's barely even the warm up.

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Speaker 2: It's a starting gun.

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Speaker 1: It is a starting gun, and the race that starts

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after that gun goes off. That is where the real

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story happens.

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Speaker 2: And nowhere is that truer than in the story of

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gene editing. We are going to be talking about a

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tool that has i mean, effectively redefined what it means

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to be human in the last decade.

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Speaker 1: It really has.

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Speaker 2: But more importantly, we are talking about why that tool,

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which we all think of as the absolute cutting edge,

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is already being treated as legacy technology by the very

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people who built it.

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Speaker 1: We are talking, of course about Crisper, the heavyweight champion

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of biotech, the celebrity, the heavy hitter, the celebrity of

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the molecular world.

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Speaker 2: For sure, it really is. I mean, it went from

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being a theoretical mechanism something we just observed in bacteria

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and argued about in you know, obscure journals, to a

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functional tool for curing a living, breathing human infant in

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just over a decade.

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Speaker 1: A decade.

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Speaker 2: That's just that is light speed in the world of medicine.

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Usually that process takes thirty, forty, maybe fifty years.

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Speaker 1: It's absolutely mind betting. I mean, we are talking about

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a tool that allows to literally cut and paste DNA.

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It sounds like science fiction, but it is science fact.

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Speaker 2: It is.

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Speaker 1: But and here is the twist, And this is why

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I was so excited to record this specific episode. The

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very people who pioneered Crisper, the people who hold the patents,

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the people who won the accolades, They aren't sitting around

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polishing their trophies.

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Speaker 2: No, they're certainly not. In fact, they're the ones most

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critically analyzing their own creation. They see the flaws more

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clearly than anyone.

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Speaker 1: They're already looking for its replacement. Yeah, they are already saying, Okay,

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Crisper is great, but what's next, what's version two point zero.

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It's not just about editing genes anymore. It's about reverse

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engineering life itself with a level of precision we didn't

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think was possible even five years ago.

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Speaker 2: And that brings us to our source material for today.

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We are taking a look at a fascinating field trip

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to the Broad Institute in Cambridge, Massachusetts. This is arguably

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the epicenter of genomic research right now. It is the

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Yankees of biotech, if you.

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Speaker 1: Will, that's a good way to put it.

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Speaker 2: And central to this story is a figure named feng Zeng.

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Speaker 1: Feng Zeng. Now, if you follow biotech, you know this name.

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He is a giant. But if you don't, let me

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just paint a picture. This guy is a titan in

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the field. He's won the National Medal of Technology and Innovation.

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He's a core member at the Broad Institute, but his

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reputation isn't just smart guy. His reputation is that he's

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the guy who doesn't just want to solve the puzzle.

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He wants to build a better puzzle solver.

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Speaker 2: That's a great way to put it. He is a

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tool builder. He's an engineer at heart who happens to

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work with biology, and.

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Speaker 1: Our mission today on Thrilling Threads is to pull apart

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the threads of his newest discovery. It's a system with

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a very cool name, Tiger. Tiger tigr like the animal,

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but cooler because it's an acronym exactly.

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Speaker 2: And we're going to find out why Tiger might just

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make Crisper look like the opening act to the main event.

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Speaker 1: Which is a bold claim considering Crisper is currently curing

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diseases in living people, a very bold claim. But before

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we get to the Tiger, we have to understand the man.

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Let's unpack thing Zang because his origin story, it really

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it explains everything of about how he approaches biology.

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Speaker 2: It really does.

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Speaker 1: He didn't start as a biology kid, did he. He

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wasn't out there with a butterfly net or a microscope

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looking upond water.

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Speaker 2: No, not at all. And this is so critical to

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understanding his success thing. Sang's parents were computer scientists, so

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just imagine that household environment, logic, code systems, debugging. As

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a preteen, he wasn't out catching frogs or looking at

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leaves under a magnifying glass. He was taking apart PCs.

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Speaker 1: I love that image. Just a kid surrounded by motherboards,

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circuit boards and hard drives.

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Speaker 2: He was stripping them down to see how they worked,

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and then using the parts to build new, better computers.

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He had an engineer's brain from day one. He sees

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the world as a series of components that can be assembled, disassembled,

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and reassembled.

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Speaker 1: He understands modularity.

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Speaker 2: Yes, exactly, the idea that you can swap out one

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part for a better one without having to rebuild the

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entire system.

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Speaker 1: So how does a computer hardware kid end up rewriting

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the code of life? I mean, how do you make

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that leap from sel licon to carbon.

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Speaker 2: Well, the story is it happened in seventh grade. It's

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such a specific pivotal moment. He went to a Saturday

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enrichment class.

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Speaker 1: It's always the Saturday classes. That's what the magic happens,

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the extra credit life, it really is.

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Speaker 2: And the topic was molecular biology. They showed a film

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you can probably picture it, one of those classic nineties

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educational films about the inner workings of the cell.

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Speaker 1: With the like the animated ribosomes chugging along a strand

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of RNA.

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Speaker 2: That's the one and for zaying the light bulb didn't

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just turn on, it exploded. He had this epiphany that

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biology is just a different kind of operating system.

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Speaker 1: Okay, let's pause on that, because that is a profound

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shift for a seventh grader. Most kids see biology as

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squishy stuff, you know, dissections, plants. He saw it his software.

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Speaker 2: It is profound because he realized that DNA is just code.

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It's the kernel of the operating system and the proteins.

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The proteins are the hardware. They're the chips, the processors,

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the physical machinery that carries out the instructions for the code.

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The cell is the machine, and.

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Speaker 1: The logic follows perfectly. If you change the code the DNA,

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you change the output of the machine.

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Speaker 2: That is such a programmer mindset. Oh the program is glitching.

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Let me check the source code. Oh there's a bug

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in line four hundred, Let me just fix that.

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Speaker 1: You don't just treat the symptom of the glitch, you

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go to the source.

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Speaker 2: And rewrite it exactly. And that realization moved him from

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soldering sirpit boards to tinkering with the building blocks of life.

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He realized that if you can learn the language of

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the code, you can program us sell to do something

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different than what it was programmed to do by nature.

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Speaker 1: He stops seeing disease as a tragedy and started seeing

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it as a syntax error.

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Speaker 2: That's a great line. It really reframes the whole problem.

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A syntax error can be fixed, it can be debugged.

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Speaker 1: And he didn't wait until his PhD to start, right.

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He was doing this stuff early, very early.

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Speaker 2: By high school, he was already volunteering at a gene

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therapy lab. And this is where he started working with

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something that's now a staple in every biology lab GFP.

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Speaker 1: GFP green fluorescent protein, the glowing jellyfish protein.

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Speaker 2: Right, it comes from the jellyfish Aquaria victoria. In nature,

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it makes the jellyfish glow this eerie green color under

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certain light. But for a molecular biologist, it's a tracking device.

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It's a biological flashlight.

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Speaker 1: So break that down for us, how do you use

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a jellyfish protein to track things in a human cell

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or a mouse.

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Speaker 2: Well, think about a virus or a cancer cell. They're

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invisible to the naked eye. You can't just watch them moving.

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Speaker 1: Through a body, right, they're too small.

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Speaker 2: But if you take the gene for that glowing protein,

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the actual sequence of DNA that says make this green

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glowing thing, and you attach it to the virus's DNA

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or the cancer cells DNA.

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Speaker 1: You're tagging it. It's like putting a neon vest on

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a construction worker, so you can see them in the dark.

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Speaker 2: Precisely. The cell reads its own DNA, sees this new

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glow green instruction, and it just it manufactures the protein,

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so the cell literally glows green under a special light.

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Speaker 1: So Zang used this during his undergen at Harvard right

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to track the flu he did.

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Speaker 2: He used it to track how the influenza virus enters cells.

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He literally lit up the path of the virus as

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it infected a cell. He could watch it happen in

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real time.

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Speaker 1: That is incredibly cool. It's like putting a GPS tracker

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ond the bad guys in a movie, so you can

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see where they're going on the map.

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Speaker 2: There is a great example from the source material about

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cancer research. Using this imagine, you want to know how

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a cancer metastasizes, how it spreads from say the lung

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to the brain, A hugely important question.

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Speaker 1: It's one of the biggest questions. So you put the

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gfpgene into a cancer cell, put that cell in a mouse,

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and then you just you look. You can use imaging

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technology to see the mouse's body. Wherever you see a

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tiny green light, that's where the cancer has spread.

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Speaker 2: That visualization is so powerful. It takes the abstract concept

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of disease progression and makes it visible data it does.

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Speaker 1: And this seems to establish his whole philosophy, doesn't it.

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Speaker 2: It really does.

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Speaker 1: He finds a tool in nature, like the jellyfish glow,

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he figures out how it works, understands its mechanism, and

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then he applies it to see things to solve a

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problem that was previously invisible or unsolvable.

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Speaker 2: And that's the core of it. It's the distinction between

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basic science and applied science. Right, Let's break that down,

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because I feel like those terms get thrown around a

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lot in academia, but they really matter here.

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Speaker 1: They do. So applied science is when you have a

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specific goal. I want to build a better rocket engine.

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I want to cure this specific flu You are playing

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existing knowledge to solve a defined problem.

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Speaker 2: You're building something you have a blueprint exactly. Basic science

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is just curiosity. It's asking how does this weird bacteria

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in a hot spring work? Why does this jellyfish glow?

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You aren't trying to sell a product, You're just trying

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to understand the universe.

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Speaker 1: But Zaying sits right in the middle, doesn't he He's

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straddling that fence.

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Speaker 2: He does. He does the basic science. He pokes at

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bacteria and viruses just to see what they do. But

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he always has that engineer's eye. He's always thinking, Okay,

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this is a cool mechanism, but what tool could this become.

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Speaker 1: He's looking for the API of nature, the application programming interface,

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the part you can plug into.

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Speaker 2: That is a perfect analogy for his mindset, which leads

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us perfectly to the tool that made him famous, the

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Big One, Crisper.

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Speaker 1: The Big One. Now we've covered Crisper on other shows,

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but we need to set the stage here to understand

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why we might need an upgrade because to the average person,

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to me, Crisper already sounds like magic. Why fix what

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isn't broken?

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Speaker 2: Well, it's a great question. And first it's so important

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to remember where crisper came from. We didn't invent it.

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It wasn't designed in a lab by humans to cure

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human diseases.

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Speaker 1: Right.

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Speaker 2: It is a bacterial immune system.

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Speaker 1: It's how bacteria fight the flu.

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Speaker 2: Essentially, yes, bacteria get attacked by viruses too. We call

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them bacterio phages or just phages, and crisper is the

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bacteria's defense mechanism. It's a way to find and chop

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up the DNA of an attacking virus. It's ancient molecular warfare.

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Speaker 1: And the mechanism is often described as molecular scissors. That's

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the go to.

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Speaker 2: Analogis that's the classic one. You have two main parts.

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You have a snippet of RNA which acts like a

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tour guide or maybe a.

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Speaker 1: Bounty hunter, I like bellonder.

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Speaker 2: It has a wanted poster of the bad DNA, the

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sequence of the virus's DNA. It floats around the cell

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and it leads a protein, usually one called CAST nine,

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to that specific spot in the DNA, and then CAST

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nine clamps down and just cuts it.

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Speaker 1: Cuggy paste or really just cut, primarily cut. The pace

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part is what the cell tries to do afterward to

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repair the damage, which is a whole other complicated story.

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Speaker 2: Now we have to acknowledge the Nobel winners Emmanuel Sharp

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Pontier and Jennifer Dobna, who did the foundational work on

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this mechanism. Absolutely, they discovered how the system works, but

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Fingzang's specific contribution was crucial. He was the one who

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proved you could take this entire system out of a

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bacteria and make it work in eukaryotic cells. Eukaryokase, that's us,

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that's us, that's mice, that's plants, anything with a cell nucleus.

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That was the game changer. Going from prokaryotes to eukaryotes

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is like going from a simple calculator to a supercomputer.

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Speaker 1: That's what turned it from a biological curiosity into a

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medical revolution. Fat a doubt, and speaking of revolution, the

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source material mentions a milestone from twenty twenty five. This

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is in science fiction anymore. This is recent history.

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Speaker 2: It's absolutely stunning. In twenty twenty five, Crisper was used

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to rewrite the DNA of a living infant to treat

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a devastating liver condition.

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Speaker 1: Just pause and think about that for a second. We

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rewrote the source code of a living human being to

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save its life. That is some Star Trek level medicine.

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Speaker 2: It is miraculous, it really is, and it worked incredibly well.

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But then here's the butt that drives people like Feng Zang.

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It's not perfect. In fact, for an engineer, it's messy.

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It's version one point zero.

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Speaker 1: Okay, So why if we can cure a liver condition

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in an infant, why do we need an upgrade? What's

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wrong with Crisper?

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Speaker 2: Several things? First, let's talk about that liver condition. Why

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was it a liver condition?

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Speaker 1: Is it because the liver is just easier to get to?

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Speaker 2: Exactly? This is what researchers call the liver bias.

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Speaker 1: The liver bias sounds like a band name, but I'm

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guessing it's about physiology.

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Speaker 2: It is. Think about how we deliver these gene editing tools.

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We usually package them inside a viral vector, basically a

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hollowed out virus that acts like a delivery.

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Speaker 1: Truck, right an AAV or something usually.

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Speaker 2: In a Dano associated mirus. Yeah, we inject that truck

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into the blookstream now the blood circulates, where does the

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blood get filtered?

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Speaker 1: The liver.

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Speaker 2: The liver. The liver is the body's detox center. It

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is designed to catch foreign things floating in the blood.

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Anatomy Wise, the liver has these massive holes in its

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blood vessels called fenestrations. It's like a biological sieve.

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Speaker 1: So the viral vectors just naturally get trapped there.

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Speaker 2: They fall right in. So it's not that we chose

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the liver because we love the liver. It's that the

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liver is where the package naturally gets delivered.

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Speaker 1: So if you want to treat the liver, nature helps you.

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But what if you want to treat the brain or

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the heart or muscle tissue.

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Speaker 2: The liver is hoarding all the medicine. It's a huge

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delivery problem. Imitation number one.

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Speaker 1: Okay, what's number two? You mentioned bluntness earlier.

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Speaker 2: Yes, the cut itself, the original and most famous crisper

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protein CAS nine cuts straight across both strands of the DNA.

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It snaps the latter in half. That's a blunt end cut.

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Speaker 1: And why is that bad? A cut is a cut,

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isn't it?

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Speaker 2: Because the cell goes into absolute panic mode? Oh no,

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my DNA is broken. This is an emergency. It rushes

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to glue the ends back together using a process called

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non homologous end joining, and that sounds messy. It is

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the messiest. It's quick and dirty. The cell often jams

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in random letters or deletes a few just to get

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the two strands reconnected. It prioritizes connection over accuracy, so.

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Speaker 1: It's like using duct tape to fix a ripped masterpiece painting.

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Speaker 2: It is. It's great if your goal is just to

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break a gene, say a gene that's producing a toxic protein.

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Breaking it stops it from working. But if your goal

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is to fix a typo, to precisely change one letter

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to another, blunt cuts are very, very risky.

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Speaker 1: You might fix the original TYPEO, but introduce three new

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errors in the process.

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Speaker 2: You absolutely could, which could be even worse. And the

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third problem is size right right, You mentioned that size

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is a huge issue. Molecularly speaking, CAS nine is a

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big protein. It's a complex piece of machinery. The viral

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vectors we used to deliver it, those AAVs are tiny.

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They have a very small cargo capacity.

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Speaker 1: It's like trying to move a grand piano in a Mini.

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Speaker 2: Cooper exactly If the CAS nine protein itself takes up

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the whole trunk, you have no room for the other

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stuff you need, like the guide RNA that tells it

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where to go, or regulatory switches to turn the system

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on and off at the right time. It's physically difficult

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to package it all up.

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Speaker 1: So Zang, being the engineer, looks at this and says, Okay,

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Version one point zero is great. It's a proof of concept.

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But it's too big, it's a bit messy, and it

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gets stuck in the liver. I need to make version

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two point zero.

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Speaker 2: He actually had an intermediate step, an upgrade called cast

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one bay. It was smaller, it only needed one RNA

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guide instead of two, and it made jagged, sticky cuts,

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which are much better for precisely inserting new DNA.

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Speaker 1: So that was like version one point five.

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Speaker 2: It was. It was an optimization. But even that wasn't enough.

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He wanted something radically different, something that solved all the

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problems at once.

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Speaker 1: And this is where the story gets really thrilling, because

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to find the next big thing, he didn't look forward

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into the future of synthetic biology. He went back to nature.

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He went back to the drawing board.

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Speaker 2: He did he started mining genomes again. He went digging

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through these massive public databases of genetic sequences from all

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corners of.

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Speaker 1: Life, looking for another paris.

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Speaker 2: Looking for another tool. And he found a new set

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of genes. But he didn't find them in bacteria this time,

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which is where Crisper was found. He found them prominently

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in transposons and viruses in bad guys. Well biology is messy,

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bad guy is relative. But yes, he found these genes

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and mobile genetic elements, things that jump around the genome.

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And he named this new system tiger.

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Speaker 1: TIGER which stands for let me check my notes here,

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tandem inners based guide RNA.

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Speaker 2: That's the one.

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Speaker 1: Okay, break that name down for us. What does it

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look like? Structurally?

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Speaker 2: It looks a lot like Crisper on the surface. It's

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these long stretches of repetitive DNA with these unique non

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repeating guide snippets in between each repeat. Tandem repeats with

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innerspace guides.

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Speaker 1: So the organization is similar, but the context is completely

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different because of where you found it.

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Speaker 2: Completely different. And this is the central mystery, right, Why

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would a virus or a transposon, which is like a

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jumping gene, why would they carry around a pair of scissors.

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Speaker 1: It seems counterintuitive.

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Speaker 2: It's a huge question. Viruses are usually minimalists. They want

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to travel light. Their whole goal is just to get

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into a cell and replicate as efficiently as possible. Why

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carry a heavy, complex gene editing system. That's extra baggage.

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Speaker 1: So what's the theory? Why do they have it?

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Speaker 2: The leading theory is biological warfare. It's an evolutionary arms race.

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Speaker 1: I love an arms race theory.

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Speaker 2: Think about it. Viruses infect bacteria. Bacteria try to kill

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viruses with their crisper system right defense. So maybe viruses

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evolved tie as a counter attack, a countermeasure. Maybe they

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use it to cut open the bacteria's genome and insert

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themselves like a burglar cutting a hole in a security fence.

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Speaker 1: Or what if the bacteria is steal the tiger from

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the virus to use it back against other viruses.

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Speaker 2: Exactly, it's a constant back and forth of stealing weapons

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and developing defenses over billions of years. It's this incredible

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invisible war happening all around.

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Speaker 1: Us and humans. We are just swooping in at the

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very last second of evolutionary time, seeing these weapons lying

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on the battlefield and saying, hey, I bet I could

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use that to cure Alzheimer's.

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Speaker 2: We are the ultimate scavengers. Ooh, nice sword.

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Speaker 1: I'll take that truly. But okay, let's get into the

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specs of this new weapon. Why is Tiger potentially better

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than Crisper? What's the technical smack down here?

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Speaker 2: Yeah, let's do the stats Tiger versus Crisper.

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Speaker 1: Give me the tail of the tape.

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Speaker 2: Okay, First major advantage the mechanism of the cut. Unlike Crisper,

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Tiger appears to read both sides of the DNA double helix.

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Speaker 1: Both sides. Okay, Why does that matter so much?

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Speaker 2: We think of it like a security check Crisper or

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cast nine. It checks one id, it binds to one

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strand of the DNA. If the sequence matches its guide,

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it cuts. But sometimes other parts of the genome look

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similar enough to fool it. There might be a sequence

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that's only one or two letters different, and Crisper might

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accidentally cut there. That leads to off target effects.

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Speaker 1: Which is the nightmare scenario in medicine. You're trying to

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fix one gene and you accidentally break another, potentially vital.

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Speaker 2: One correct Taiger by reading both strands acts like a

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two factor authentication. It asks does the left strand match

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my guide? Yes? Okay? Now does the right strand match

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my guide? Yes? Only then does it act the double

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check a double check, and this potential for a double

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check means it could be significantly more accurate, more precise.

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It ensures you were cutting exactly where you want to

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cut and nowhere else.

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Speaker 1: Okay. So that's a huge upgrade in safety and precision.

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What's the next advantage?

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Speaker 2: The second advantage is what they call the wind. When

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Crisper opens up the DNA to make an edit, it

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creates a little bubble, an unwound section of DNA that's

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about five to eight genetic letters wide. That sounds tiny

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in our world, yes, but in the molecular world, where

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a single letter can cause a disease, it's actually kind

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of wide. If you are trying to change just one letter,

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say to fix the single letter mutation that causes sickle

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cell anemia, having eight other letters exposed is risky.

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Speaker 1: It's like trying to erase a single typo in a

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book with the giant, clumsy eraser. You might smudge the

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words next to it.

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Speaker 2: That's a great analogy. You fix the A, but you

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accidentally delete the G next to it. Tager seems to

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open a much smaller window. This potentially allows for single

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letter precision. We are talking about the ability to change

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an A to a T without touching anything else. It

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is truly surgical.

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Speaker 1: And the third and perhaps the most critical advantage you

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hinted at before, size Tiger is more compact, much more compact,

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so it fits through the doggy door.

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Speaker 2: It fits through the doggy door. We're back to our

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car analogy. It fits in the Mini Cooper with plenty

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of room to.

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Speaker 1: Spare, and that changes everything regarding where we can use

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it in the body.

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Speaker 2: It's the key that could unlock everything beyond.

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Speaker 1: The liver before we get to where we can use it,

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which I know is the exciting part about the brain.

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I want to talk about how they figure this stuff

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out because the source material takes us inside the lab

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at the Broad Institute, and it's this fascinating mix of

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being incredibly mundane and yet totally science fiction at the

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same time.

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Speaker 2: It really is a study in contrasts. It demystifies that

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whole Ivory Tower idea of science.

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Speaker 1: Yeah, there's this FedEx moment described in the piece walk

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us through that because it's wild.

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Speaker 2: So imagine you are a researcher on Zeng's team. You've

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identified a genetic mutation and a patient that you want

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to target. Your first step isn't in a wet lab

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with beakers.

473
00:21:48,079 --> 00:21:48,880
Speaker 1: It's at a computer.

474
00:21:49,079 --> 00:21:51,799
Speaker 2: It's at a computer. You sit down and you design

475
00:21:51,799 --> 00:21:55,359
an RNA sequence that perfectly matches that mutation. You are

476
00:21:55,400 --> 00:21:59,200
basically typing code ATCG.

477
00:21:58,160 --> 00:21:59,519
Speaker 1: Okay, so I'm just typing it out on a screen.

478
00:21:59,559 --> 00:22:02,680
Speaker 2: You're typing out. Then you open a website a commercial vendor.

479
00:22:02,759 --> 00:22:04,920
You paste your sequence into a box, you put in

480
00:22:04,960 --> 00:22:08,440
your credit card info. The customer RNA sequence costs about twenty.

481
00:22:08,319 --> 00:22:11,400
Speaker 1: Dollars twenty bucks, I spend more lunch sometimes right.

482
00:22:11,920 --> 00:22:14,920
Speaker 2: You hit by, and a few days later, a FedEx

483
00:22:15,000 --> 00:22:18,839
envelope arrives at the lab. Inside is a tiny plastic

484
00:22:18,880 --> 00:22:21,920
tube with your custom guide RNA freeze dried into a

485
00:22:21,920 --> 00:22:22,759
little white powder.

486
00:22:23,160 --> 00:22:27,359
Speaker 1: That is just it's wild. You are ordering the fundamental

487
00:22:27,359 --> 00:22:30,440
building blocks of life online like it's a pair of

488
00:22:30,440 --> 00:22:31,680
sneakers from Zappos.

489
00:22:32,000 --> 00:22:35,759
Speaker 2: It highlights how accessible the tools of this revolution have become.

490
00:22:36,359 --> 00:22:38,880
The hard part isn't getting the materials anymore. The hard

491
00:22:38,880 --> 00:22:41,680
part is the design, the knowledge, the understanding of what

492
00:22:41,799 --> 00:22:45,920
to order. But the logistics it's just online shopping.

493
00:22:46,039 --> 00:22:48,440
Speaker 1: The commoditization of synthetic biology.

494
00:22:48,519 --> 00:22:50,920
Speaker 2: It's one of the biggest, quietest stories of our time.

495
00:22:51,160 --> 00:22:53,880
Speaker 1: But then you have the machinery. Once the FedEx package arrives,

496
00:22:53,920 --> 00:22:57,160
the work gets physical and the description of the centrifuge

497
00:22:57,160 --> 00:22:58,119
is blew my mind.

498
00:22:58,240 --> 00:23:00,559
Speaker 2: Oh yes, this is where we leave the Pewter kid

499
00:23:00,599 --> 00:23:04,039
world and enter the industrial biology world. To test these systems,

500
00:23:04,039 --> 00:23:06,839
they need to concentrate their viral vectors. They grow the

501
00:23:06,920 --> 00:23:09,759
virus in huge vats of cells. But then they need

502
00:23:09,759 --> 00:23:12,240
to separate the tiny virus particles from all the cell

503
00:23:12,279 --> 00:23:13,119
debris and liquid.

504
00:23:13,279 --> 00:23:16,759
Speaker 1: They need to purify their fleet of delivery trucks exactly.

505
00:23:17,000 --> 00:23:19,559
Speaker 2: To do that, they have to spin them down. And

506
00:23:19,599 --> 00:23:21,640
we are not talking about a salad spinner here. We

507
00:23:21,640 --> 00:23:25,759
are talking about ultracentrifuges spinning at one hundred thousand times

508
00:23:25,799 --> 00:23:27,200
the force of gravity.

509
00:23:26,880 --> 00:23:29,680
Speaker 1: One hundred thousand gs. I mean, what does that even mean?

510
00:23:30,000 --> 00:23:33,400
A fighter pilot might pull what nine g's and pass out?

511
00:23:33,759 --> 00:23:37,759
Speaker 2: Right? This is orders of magnitude beyond that. The interviewer

512
00:23:37,759 --> 00:23:40,119
in the source who is a marine scientist, tried to

513
00:23:40,160 --> 00:23:42,880
compare it to the crushing pressure at the bottom of

514
00:23:42,920 --> 00:23:45,680
the ocean, but the host at the broad laughed and said, no, no,

515
00:23:45,880 --> 00:23:47,720
this is exponentially higher than that.

516
00:23:48,039 --> 00:23:49,799
Speaker 1: So what would happen to a person at one hundred

517
00:23:49,839 --> 00:23:50,640
thousand g's.

518
00:23:50,799 --> 00:23:55,079
Speaker 2: If a human were somehow inside that centrifuge rotor, they

519
00:23:55,079 --> 00:23:58,519
wouldn't just be crushed. The structural integrity of your cells

520
00:23:58,519 --> 00:24:01,920
would fail. You're heavy proteins would separate from your lipids,

521
00:24:01,960 --> 00:24:04,480
which would separate from the water. You would be, as

522
00:24:04,519 --> 00:24:08,119
the host put it, smushed, just liquified into your component parts,

523
00:24:08,359 --> 00:24:10,279
perfectly layered based on density.

524
00:24:10,640 --> 00:24:12,640
Speaker 1: It's a terrifying amount of power just sitting there on

525
00:24:12,640 --> 00:24:14,279
a lab bench, and yet.

526
00:24:14,240 --> 00:24:16,839
Speaker 2: The virus survives because it is so small and has

527
00:24:16,880 --> 00:24:20,440
such a dense crystalline structure, it can withstand forces that

528
00:24:20,480 --> 00:24:23,400
would obliterate us. It's a reminder of how different the

529
00:24:23,400 --> 00:24:25,200
physics of the microscopic world are.

530
00:24:25,319 --> 00:24:27,559
Speaker 1: And then there are the sequencers, which are the other

531
00:24:27,559 --> 00:24:28,079
side of the coin.

532
00:24:28,240 --> 00:24:31,559
Speaker 2: The sequencers are the eyes of the entire operation. We

533
00:24:31,680 --> 00:24:35,160
used to spend years and literally billions of dollars to

534
00:24:35,279 --> 00:24:37,319
sequence the first human genome.

535
00:24:37,599 --> 00:24:40,359
Speaker 1: I remember that being a huge international project it was.

536
00:24:40,480 --> 00:24:43,599
Speaker 2: Now they have machines that look like office printers that

537
00:24:43,640 --> 00:24:47,160
can sequence a whole human genome, all three billion base

538
00:24:47,200 --> 00:24:48,440
pairs in a single day.

539
00:24:48,559 --> 00:24:49,000
Speaker 1: One day.

540
00:24:49,079 --> 00:24:53,039
Speaker 2: Its industrial scale biology, and that speed is what allows

541
00:24:53,119 --> 00:24:56,200
Zang's team to test Tegger so quickly. They can have

542
00:24:56,240 --> 00:24:59,279
an idea in the morning, order the parts, build the system,

543
00:24:59,359 --> 00:25:03,000
put it into Ea Coli bacteria, purify the protein, put

544
00:25:03,039 --> 00:25:05,519
it into human cells in a petri dish, and then

545
00:25:05,599 --> 00:25:08,119
sequence those cells the next day to see exactly what

546
00:25:08,240 --> 00:25:11,279
edits were made. So they get feedback almost instantly, almost instantly.

547
00:25:11,359 --> 00:25:14,599
It's the fail fast learn fast model of Silicon Valley

548
00:25:14,599 --> 00:25:16,240
apply to evolution itself.

549
00:25:16,119 --> 00:25:18,920
Speaker 1: And that iteration is what leads to the future. So

550
00:25:19,039 --> 00:25:21,839
let's talk about the implications. Yeah, because if Tiger is

551
00:25:21,880 --> 00:25:25,559
smaller and more precise, what does that unlock. You mentioned

552
00:25:25,559 --> 00:25:26,480
the brain earlier, and.

553
00:25:26,640 --> 00:25:29,799
Speaker 2: This is the holy grail, moving beyond the liver. As

554
00:25:29,839 --> 00:25:33,079
we discussed, the nervous system is notoriously difficult to treat.

555
00:25:33,319 --> 00:25:37,039
The cells your neurons, they don't divide, so the repair

556
00:25:37,079 --> 00:25:40,680
mechanisms we rely on in other tissues are weak or

557
00:25:40,799 --> 00:25:41,480
non existent.

558
00:25:41,839 --> 00:25:43,359
Speaker 1: And there's the blood brain barrier.

559
00:25:43,400 --> 00:25:45,960
Speaker 2: The blood brain barrier, it's like a fortress wall around

560
00:25:45,960 --> 00:25:49,200
the brain that keeps out most large molecules to protect it.

561
00:25:49,200 --> 00:25:51,599
It's great for keeping out toxins, but it's terrible for

562
00:25:51,599 --> 00:25:52,480
getting in medicine.

563
00:25:52,480 --> 00:25:54,640
Speaker 1: But Tiger, being smaller, could sneak in.

564
00:25:54,839 --> 00:25:57,359
Speaker 2: Because it is so compact, it is much easier to

565
00:25:57,400 --> 00:26:01,599
package into a delivery vehicle, like a specific engineered AAV

566
00:26:02,079 --> 00:26:03,960
that has been designed to be able to cross the

567
00:26:03,960 --> 00:26:06,920
blood brain barrier. This just it opens the door for

568
00:26:06,960 --> 00:26:09,759
treating a whole class of neurodegenerative diseases.

569
00:26:09,839 --> 00:26:13,079
Speaker 1: Okay, let's get specific. What diseases are we talking about.

570
00:26:13,200 --> 00:26:16,079
Speaker 2: We are talking about things like als lou Gerrigg's disease.

571
00:26:16,119 --> 00:26:20,640
We're talking about Huntington's disease, Parkinson's and the biggest one of.

572
00:26:20,559 --> 00:26:24,559
Speaker 1: All, Alzheimer's diseases that we have for all of human

573
00:26:24,640 --> 00:26:26,640
history been essentially helpless.

574
00:26:26,240 --> 00:26:30,240
Speaker 2: Against exactly right now. For Alzheimer's, we mostly treat symptoms.

575
00:26:30,279 --> 00:26:33,279
We try to clear out the amyloid plaque, or we

576
00:26:33,400 --> 00:26:35,680
try to give drugs to slow down the memory loss,

577
00:26:35,920 --> 00:26:38,519
but we're not fixing the root cause.

578
00:26:38,559 --> 00:26:39,559
Speaker 1: They're managing the declined.

579
00:26:39,599 --> 00:26:41,960
Speaker 2: They're managing the decline. But if you could go in

580
00:26:42,039 --> 00:26:46,480
and edit the genetic risk factors like the apoe four gene,

581
00:26:46,480 --> 00:26:49,519
which is a major risk factor for Alzheimer's, if you

582
00:26:49,559 --> 00:26:50,839
could reverse the mutation.

583
00:26:51,200 --> 00:26:53,640
Speaker 1: And that's a key phrase. You just used reverse mutation.

584
00:26:54,000 --> 00:26:55,720
That's different from what we discussed before.

585
00:26:55,839 --> 00:26:59,960
Speaker 2: Yes, this is the ultimate goal of precision gene editing.

586
00:27:00,200 --> 00:27:02,440
It's not just cutting a gene to break it, which

587
00:27:02,480 --> 00:27:03,880
is what we do when we want to stop a

588
00:27:03,880 --> 00:27:07,359
bad protein from being made, but actually rewriting the error,

589
00:27:07,400 --> 00:27:08,359
correcting the typo.

590
00:27:08,839 --> 00:27:10,319
Speaker 1: Explain the difference with an example.

591
00:27:10,359 --> 00:27:13,359
Speaker 2: Okay, a perfect example is Huntington's disease. It's caused by

592
00:27:13,400 --> 00:27:16,839
a genetic stutter in one gene. The code repeats itself

593
00:27:16,880 --> 00:27:20,759
too many times cag cagcag over and over. Current Christper

594
00:27:20,759 --> 00:27:23,680
therapies might try to just cut that whole gene to

595
00:27:23,720 --> 00:27:26,519
silence it, but that gene might be doing something else

596
00:27:26,559 --> 00:27:29,240
important in the cell. If you break it, you might

597
00:27:29,319 --> 00:27:30,400
cause other problems.

598
00:27:30,480 --> 00:27:31,119
Speaker 1: Right, you lose it.

599
00:27:31,240 --> 00:27:35,839
Speaker 2: Normal function With reverse mutation using a precise tool like Tiger,

600
00:27:36,319 --> 00:27:38,640
the goal is to go in and snip out just

601
00:27:38,759 --> 00:27:41,599
the extra repeats. You turn the stutter back into a

602
00:27:41,599 --> 00:27:42,279
clear sentence.

603
00:27:42,359 --> 00:27:45,799
Speaker 1: You're not deleting a corrupted file. You're actually fixing the

604
00:27:45,839 --> 00:27:48,799
code inside the file so it runs perfectly. Again.

605
00:27:48,839 --> 00:27:52,160
Speaker 2: That's it exactly. It is restoring function rather than just

606
00:27:52,279 --> 00:27:56,359
managing dysfunction. It's the difference between being curative and being palliative.

607
00:27:56,680 --> 00:27:59,599
Speaker 1: And it all comes back to Zaying's core philosophy about nature.

608
00:28:00,079 --> 00:28:02,240
I loved his quote about this in the Source. He said,

609
00:28:02,640 --> 00:28:03,799
nature is very wise.

610
00:28:04,319 --> 00:28:07,319
Speaker 2: Nature has been innovating for billions of years. That is

611
00:28:07,359 --> 00:28:10,839
the engineer's humility. He realizes that he doesn't need to

612
00:28:10,880 --> 00:28:13,400
invent the solution from scratch. He doesn't need to be

613
00:28:13,480 --> 00:28:15,680
smarter than four billion years of evolution.

614
00:28:16,000 --> 00:28:18,400
Speaker 1: The virus needed a way to cut DNA to survive,

615
00:28:18,960 --> 00:28:21,680
the bacteria needed a way to block it to survive.

616
00:28:22,240 --> 00:28:24,920
The solution is almost certainly already out there. We just

617
00:28:25,000 --> 00:28:25,680
have to find it.

618
00:28:25,880 --> 00:28:28,440
Speaker 2: Our job is to go and learn to be the

619
00:28:28,480 --> 00:28:31,279
student of nature, not just the master of it, to

620
00:28:31,319 --> 00:28:34,599
be a really good scavenger on that ancient battlefield.

621
00:28:34,640 --> 00:28:36,559
Speaker 1: I want to touch on the human element here before

622
00:28:36,599 --> 00:28:39,799
we wrap up. Yeah, because despite all this, the nobel

623
00:28:39,880 --> 00:28:43,480
of a work, the immense pressure, the potential to cure

624
00:28:43,559 --> 00:28:47,039
Alzheimer's Fangsang seems remarkably grounded.

625
00:28:47,240 --> 00:28:50,839
Speaker 2: He does. He talks about the thrill of everyday lab work.

626
00:28:51,200 --> 00:28:53,319
He's not just in it for the big gala dinners

627
00:28:53,400 --> 00:28:57,079
or the awards. He genuinely loves the process. He likes

628
00:28:57,079 --> 00:28:59,839
seeing the cells change color. He likes the pipette in

629
00:28:59,839 --> 00:29:00,400
his hand.

630
00:29:00,559 --> 00:29:02,799
Speaker 1: There was a beautiful moment in the Source where he

631
00:29:02,880 --> 00:29:04,559
described the feeling of discovery.

632
00:29:04,759 --> 00:29:06,880
Speaker 2: Yes, he said that when you find an answer to

633
00:29:06,920 --> 00:29:09,200
a question no one has ever asked before, when you

634
00:29:09,240 --> 00:29:11,079
see that data pop up on the screen for the

635
00:29:11,079 --> 00:29:13,599
first time, for a brief moment, you are the only

636
00:29:13,680 --> 00:29:16,960
person in the universe who knows that specific secret of

637
00:29:17,000 --> 00:29:17,759
how life works.

638
00:29:17,880 --> 00:29:20,839
Speaker 1: That gives me chills, the only person in the universe.

639
00:29:21,039 --> 00:29:24,799
Speaker 2: It's a profound intimacy with reality. You have peaked behind

640
00:29:24,799 --> 00:29:28,319
the curtain. And I think that feeling, that pure curiosity

641
00:29:28,680 --> 00:29:31,200
is what drives him. It's that same feeling of the

642
00:29:31,240 --> 00:29:34,279
seventh grader taking apart the computer just scaled up to

643
00:29:34,319 --> 00:29:36,039
the machinery of life itself.

644
00:29:36,200 --> 00:29:39,039
Speaker 1: And his optimism is infectious. I mean, he treats biology

645
00:29:39,119 --> 00:29:41,440
like broken code, and if it's just code, it can

646
00:29:41,480 --> 00:29:45,119
be debugged. It implies that sickness isn't a destiny, it's

647
00:29:45,240 --> 00:29:46,440
just a glitch in the system.

648
00:29:46,640 --> 00:29:50,440
Speaker 2: That is a very powerful and hopeful worldview. It shifts

649
00:29:50,440 --> 00:29:54,480
the entire narrative from coping with disease to solving disease.

650
00:29:54,559 --> 00:29:56,799
It's an engineer's approach to human suffering.

651
00:29:57,119 --> 00:29:59,240
Speaker 1: So what does this all mean? We've gone from a

652
00:29:59,359 --> 00:30:02,759
kid taking apart PCs in his bedroom to a man

653
00:30:02,799 --> 00:30:04,559
who might be on the verge of handing us the

654
00:30:04,599 --> 00:30:06,799
keys to the human mind. We are moving from a

655
00:30:06,839 --> 00:30:09,519
world of treating symptoms to a world of debugging the

656
00:30:09,559 --> 00:30:10,759
source code of humanity.

657
00:30:10,880 --> 00:30:13,759
Speaker 2: It raises huge, huge questions. We are getting very, very

658
00:30:13,799 --> 00:30:16,640
good at the debugging part, but tools are agnostic. A

659
00:30:16,680 --> 00:30:19,440
scalpel can be used for healing or for harming. It

660
00:30:19,480 --> 00:30:22,640
doesn't know the difference between fixing and changing right.

661
00:30:22,599 --> 00:30:24,319
Speaker 1: And Tiger seems to be the next level of that,

662
00:30:24,400 --> 00:30:28,640
a sharper, smaller, more accessible scalpel. But it leaves me

663
00:30:28,680 --> 00:30:30,559
with a thought and I want our listeners to really

664
00:30:30,599 --> 00:30:34,440
chew on this one. If Tiger works as well as predicted,

665
00:30:35,160 --> 00:30:37,440
if we can edit single letters of DNA in the

666
00:30:37,440 --> 00:30:40,960
brain with ease, where does the debugging stop and the

667
00:30:41,039 --> 00:30:41,920
enhancing begin.

668
00:30:42,319 --> 00:30:43,880
Speaker 2: That is the line we're going to have to draw

669
00:30:43,920 --> 00:30:46,920
as a society, and it's a very blurry one. Because

670
00:30:46,920 --> 00:30:50,400
if you can fix a gene for Alzheimer's to restore memory,

671
00:30:50,799 --> 00:30:53,359
can you also tweak a gene for memory retention in

672
00:30:53,400 --> 00:30:56,079
a healthy person to give them a photographic memory?

673
00:30:56,119 --> 00:30:59,400
Speaker 1: Exactly? If the tool is that precise and that easy

674
00:30:59,440 --> 00:31:03,279
to deliver, the temptation will be immense. The line between

675
00:31:03,319 --> 00:31:05,039
therapy and enhancement could vanish.

676
00:31:05,160 --> 00:31:07,359
Speaker 2: It brings us right back to the start. The breakthrough

677
00:31:07,440 --> 00:31:10,759
is just the starting gun. We're inventing these incredibly powerful

678
00:31:10,799 --> 00:31:13,200
tools faster than we are inventing the ethics and the

679
00:31:13,240 --> 00:31:14,319
wisdom to handle them.

680
00:31:14,400 --> 00:31:17,279
Speaker 1: So here is my question to you listening right now.

681
00:31:17,680 --> 00:31:19,440
If you could change one line of code in your

682
00:31:19,440 --> 00:31:22,759
own genetic makeup, race a family illness, sharpen as sense,

683
00:31:23,039 --> 00:31:26,240
maybe change a trait you've always struggled with, would you

684
00:31:26,279 --> 00:31:28,920
trust yourself to make that edit? It's a tough question,

685
00:31:29,319 --> 00:31:32,519
or is some code better left unread?

686
00:31:32,960 --> 00:31:36,119
Speaker 2: A fascinating question. I think many of us would say

687
00:31:36,200 --> 00:31:39,240
yes to the cure for a terrible disease, but would

688
00:31:39,319 --> 00:31:42,319
we be able to say no to the upgrade to

689
00:31:42,440 --> 00:31:43,720
making ourselves better than.

690
00:31:43,599 --> 00:31:46,279
Speaker 1: A Well, we want to hear from you. Leave a comment,

691
00:31:46,359 --> 00:31:49,240
tell us your stance. Would you tiger your own genome?

692
00:31:49,319 --> 00:31:50,160
What's the line for you?

693
00:31:50,279 --> 00:31:52,480
Speaker 2: I'm very curious to see what people say me too.

694
00:31:53,400 --> 00:31:55,839
Speaker 1: Thanks for joining us on this journey into the microscopic,

695
00:31:56,119 --> 00:31:58,839
and thanks for listening to thrilling threads. Until next time,

696
00:31:59,039 --> 00:32:01,279
keep pulling on those threats. You never know what you'll find.