The Yak Mutation That Could Help Repair the Brain (EP 56)

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So we've solved the delivery mechanism.
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We've not solved the targeting problem.
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Yes.
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And there is the challenge.
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How do we find a molecule that is small enough to cross the blood-brain barrier, but it only activates the gamma receptor of these stem cells, and it doesn't trigger some random catastrophic activity elsewhere in the body?
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So we got past security, but our ticket didn't get us on the right plane.
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Yeah, no, exactly.
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And that's where this current paper comes in.
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We're using nature to cheat on the test because nature's been doing the test for way longer than we have.
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Yes, yeah, yeah.
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Hello, Internet.
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This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdhury.
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After our massive two-part marathon deep dive on quantum computing in our last two episodes, we wanted to get back into our biology bag with this week's deep dive, which is going to cover a new paper published in the journal Neuron on May 20th, 2026.
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This research focuses on our quest to combat brain disease, something we've covered in a couple of our previous episodes, stemming from an unlikely inspiration, which is the genetic adaptation of high-altitude Tibetan animals.
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So today we're going to break down...
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What myelin, which is a protective layer around nerve cell fibers, actually does.
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Why MS, multiple sclerosis for those who may not know, destroys it.
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And why the brain eventually stops repairing itself.
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why our current drugs can prevent the damage but cannot reverse it, how a genetic adaptation from those high-altitude Tibetan animals has led researchers to a new pathway for rebuilding myelin, and how they worked backwards from that evolutionary clue to a potential drug strategy.
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By the end of this episode, you will be able to look at the graphical abstract from the paper and,
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and understand what exactly is happening.
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As always, we are going to talk about the science from the ground up today because this is From First Principles.
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Multiple sclerosis, leukodystrophies, and periventricular leukomalacia in premature infants.
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These are all not actually obscure conditions that are buried in the back of some medical textbook.
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Okay, this isn't like house an episode where it's like some weird thing.
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This is actually very, very...
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Prevalent in our society, roughly 2.8 million people worldwide live with MS alone.
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And for most of them, the disease is a slow, relentless war of attrition against just neurodegradation and brain damage.
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It's their own immune system attacking the nervous system.
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And today's best treatments, they can only really slow that immune assault.
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They can't really fix the problem.
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They can only slow it down, but if the damage has already been done, that's it, right?
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And that's the gap that this particular paper steps into.
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And it's a new fundamental type of research that could pave the way for rebuilding that damage that's caused by these conditions.
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And it does so, it's coming from a very unlikely source, namely the genomes of animals that are populating the Tibetan Plateau at extremely high altitudes.
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It turns out these animals have certain adaptations from evolution that we could borrow from
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to maybe fix these problems in neurodegradation.
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This would be huge.
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I mean, as you mentioned, this is something that impacts a huge population of people globally.
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And it's extremely pernicious because there's no rewind.
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Yes, exactly.
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And there's ways to slow it down, to maybe pause it, but that's the best you can do right now.
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The holy grail is to rewind neurodegradation, right?
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Nerve damage is something that is notoriously...
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a one-way street, especially if you get all the way to where the nerves themselves are damaged.
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We don't grow new neurons, right?
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But it turns out there might be a way to grow some of the scaffolding around neurons that is the problem in MS and a lot of these other disorders.
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So in this episode, we're going to go over some of the fundamental neuroscience behind what is MS and these adjacent conditions, right?
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where the current therapies are lacking, and then this new paper, which uses this emerging field of evolutionary pharmacology to propose a possible rebuilding therapy.
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And what I really like about this paper is the method by which they all figured this out.
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It's a really nice story because it starts from very large, you know, how does a yak adapt to the Tibetan plateau?
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And these researchers are honing in on that mechanism, and it's like a detective work.
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in some sense, because you're going through molecular biology, cellular biology, to try and understand what is the exact mechanism that the yak genome has developed to let these animals survive, and not only survive, but thrive in this hypoxic environment, where there's not a lot of oxygen.
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It turns out that environment is very similar to,
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to the kind of stuff that we're getting in MS. And if we can borrow that, then we could possibly have a rewind button on MS.
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So by the end of the episode, you should be able to fully understand this graphic.
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So whenever you publish in the Elsevier journals, which is like Neuron, Cell, they make you do not just a normal graph, a normal abstract,
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Every journal has an abstract, right?
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You do the abstract and then your main paper.
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There's being like a block of executive summary in text.
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Yes, in text.
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So El Sevier journals also have a graphical abstract where they've got like a pictorial...
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way of showing what exactly is happening.
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And my goal today is to explain that graphical abstract in a lot of detail.
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So by the end, you should be able to know every single one of those words, every single one of those arrows.
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Okay?
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That's the idea.
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There's a lot of words and a lot of arrows.
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Yes.
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So before we do that, let's take a brief housekeeping note.
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Yes.
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For those of you joining for the first time, welcome.
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We are very excited to have you here on the best science show on the planet.
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You can follow us across all of our socials at FFPpod.
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It's one of the best ways to support our show to get it to more people.
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A like, a share, a follow, a subscribe channel.
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helps us in the battle of the algorithm to get people to focus for more than 15 seconds on something that is actually going to help them learn about the world around them we've made some huge updates to the website at ffppod.com where you can watch all of our episodes you can find all the research papers we cover like this one we just mentioned and get to our interactive apps
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for the science R&D funding tracker, our new science transfer board, where we're looking at the migration of some of the world's top scientists in and out of the country.
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Mostly out.
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Mostly out of the country.
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Mostly out, but we're still growing the data set.
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It's not looking good so far.
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These are really great tools to get beyond the episode, whether you listen or you watch.
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And as always, you can find us on all the main platforms, YouTube and Spotify to watch.
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Apple video is coming soon, but it's currently audio only.
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We really appreciate the support of so many of you who are just as curious and love the minutiae and technical detail of science and the way in which an expert and a layman try to work through it together.
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And with that, we are going to get back into why you all have come to this episode.
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And I'm excited for this because we've been...
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on the quantum side for quite some time.
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Yeah, we've been on the physics and math side for quite some time.
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That's true, yeah, because we had the Riemann and the Jacobian before that.
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And so getting back into some bio work I think is going to be refreshing for many folks who are listening.
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Yeah, and before we get into MS, I think we should first try and understand just some basics about the nervous system and what myelin actually is.
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So in a healthy nervous system, nerve fibers, which are these axons, so a nerve cell has a cell body, it's got dendrites where all of the inputs come in, and then an axon where the output goes down.
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to post-synaptic neurons, to the neurons that this particular neuron is talking to.
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Now that axon is usually wrapped up in something called a myelin sheath.
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And it's useful to visualize the nervous system as kind of a vast network of electrical wiring.
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Neurons use electricity to talk to one another.
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That's how they're fast.
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If they just use diffusion, which is just like molecules moving around and bumping into each other, it wouldn't be as fast as the reflexes that we have.
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But there's a caveat.
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Even with electricity...
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Neurons would not be as fast as they are now.
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Like the reflexes that we have or the reflexes that Formula One drivers have, they're pretty insane.
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Fractions of a second that nerve impulses are traveling down the arm or the leg to like break and things like that.
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Right.
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Yeah.
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That's happening because of the myelin sheath.
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Okay?
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So I want to get into a little bit of the physics here because it's kind of cool biophysics.
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It's one of the first like biophysics things that I kind of learned about where I was like, this is pretty cool that like biology is using these principles.
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So here's the idea.
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You've got the axon, right?
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And you've got this myelin sheath around it.
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It's kind of analogous to the insulation around copper wire, okay, in some sense.
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The problem is that the membrane of the neuron is leaky.
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The membrane, meaning the thing that is separating the inside to the outside,
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The way the neuron discharges and fires off a signal is you've got a bunch of ions on one side and a lack of those ions on the other side.
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So when you open up little holes, little membrane channels on the membrane, the ions sort of discharge.
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It's kind of like having a capacitor and you short circuit it.
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So all of the charges flow from one place to the other.
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The visual I think about is Black Friday, the inside of the store is locked.
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Everyone's standing on the outside.
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Yeah, yeah, yeah.
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And when you open all the doors...
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It's a rush.
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It's a rush.
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It's a rush, right?
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Usually it's this potassium, sodium, calcium.
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These are the ions that we're working with.
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And because they're all positively charged, they want to get the hell away from each other, right?
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So without insulation, the signal would actually just decay.
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And it wouldn't actually travel all that far down the axon terminal.
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But because we have this insulation, you can imagine a cable, okay?
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You've got...
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Spots where there's insulation and spots where there aren't.
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Okay.
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Now, the spots where there are no insulation, those are called the nodes of Ranvier.
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Okay.
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That's where the actual ion transport is happening.
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Okay.
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So we're sort of...
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Into and out of the cell.
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We're constricting it to specific spots where the ability to go in and out is concentrated.
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They're like gates, and then there's the protective layer.
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It visually reminds me of those... At summer camp, you'd make those beads with the little plastic rope, and you'd string the beads on top of it to put around your wrist.
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Exactly.
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And the little gaps in between the beads is where the ions can actually go in and out.
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Yeah, and those would be the nodes of Ranvier.
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Okay.
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Right?
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And the point is that without...
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Without the myelin sheath, there would be ion channels everywhere along this cable.
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So the ions would come in and then they'd diffuse and then maybe they'd go out, right?
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On the other hand, if there is insulation, the ions are coming in, and now they're all jam-packed into this tiny little axon.
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So they're going to travel really fast to the next gate to open up that next channel.
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That makes total sense.
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It's kind of like, I mean, if we go back to your Black Friday analogy, right?
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It's like there's an entrance in the front, and then when all the people come in, if there's an entrance all the way in the back...
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These people are going to – they're going to diffuse way quicker than if there's entrances all over and exits all over the place.
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Then they're going to sort of meander.
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People start blocking.
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Traffic doesn't move very – no, that's – and I think the – so the idea is this physical structure –
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of how this is set up amplifies the speed at which the signal can go from one point to the next.
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Exactly.
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And so this means you need less energy because what you've actually done is, you know, for those who are electrical engineers or just like undergrad physicists, capacitors, when you put them in parallel, the capacitance decreases.
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Okay.
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Sorry.
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No, no, no.
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No.
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No.
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When you put capacitors in series, the capacitance decreases.
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When you put resistors in parallel, the resistance decreases.
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It's the 1 over C thing.
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Anyways, here you've got the axon membrane, which is kind of like a capacitor because you've got charge on one side and the other side.
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So it's storing some kind of voltage difference based on the charge.
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But if you're wrapping...
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this myelin sheath around it, that's a bunch of capacitors.
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And so you're decreasing the capacitance.
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And what that means is you need less energy to absorb and store all of that electrical charge.
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Now, why is that important?
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You can have faster charging, right?
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For the same amount of voltage, you need less ions.
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That's always good.
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And the signal travels a lot faster because of what we just said, right?
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And between these nodes of Ranvier, that's where the signal is traveling.
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This mechanism is called saltatory conduction, where it goes from one node of Ranvier to the next node of Ranvier.
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And here you can see a visualization of that.
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On the left-hand side,
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is an axon without any myelin.
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Notice the action potential is very slow.
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On the right-hand side, you've got these nodes of Ranvier in between these myelin cells.
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Yep.
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Boom, boom, boom, boom, boom.
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The signal propagates a lot faster.
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Mm-hmm.
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And so again, just as a simple way for me to think about this, the myelin helps reduce the friction.
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In some sense, yeah.
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Right?
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So things can just move.
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Yeah.
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It facilitates kind of like the charge coming in and all of that positive charge wanting to get the hell away from one another.
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There's not any exits except for like way down there.
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And ultimately, I think part of the point you're trying to bring up here is myelin is a very fundamental benefit to how neurons can work so efficiently.
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And it is a fundamental part of the effectiveness of neural communication and the signals that get sent that are such a big part of what make us incredible neurons.
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Creatures.
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Yes, 100%.
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This is why we have fast reflexes.
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This is why the antelope can like see a cheetah and be like, I'm out immediately.
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Right.
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There's no, there's no delay between its brain sensing a cheetah and its legs being like, I'm out.
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Right.
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The difference between reaching out and catching a falling glass versus like the thing falling.
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I mean, we all have myelin in our hands and in our feet.
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That is what gives us the reflexes without.
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I mean, for example, the corticospinal tract, right, which is the motor control pathway goes from the brain all the way down to, let's say, the legs.
218
00:16:26.932 --> 00:16:28.153
That is a single cell.
219
00:16:29.133 --> 00:16:29.734
That entire way.
220
00:16:29.754 --> 00:16:29.974
Okay?
221
00:16:30.254 --> 00:16:32.596
That entire thing is a single axon sometimes.
222
00:16:32.937 --> 00:16:35.178
There are single axons that go all the way down.
223
00:16:35.198 --> 00:16:35.799
Okay?
224
00:16:35.819 --> 00:16:36.480
That's incredible.
225
00:16:36.580 --> 00:16:44.987
If it's a single cell, you need this kind of compartmentalization in order to get the signal all the way down to your foot so quickly.
226
00:16:45.427 --> 00:16:45.647
Right?
227
00:16:45.727 --> 00:16:51.532
This is how Formula One drivers can have 10 milliseconds worth of reflex time.
228
00:16:51.572 --> 00:16:52.153
Have you seen those?
229
00:16:52.413 --> 00:16:56.736
By the way, have you seen those where they've got their trainer with...
230
00:16:57.617 --> 00:17:23.335
balls and like they're like releasing the balls and the and the formula one drivers have to like catch yes right before the race if you watch the races right before the race you'll see all of the formula one drivers just like doing some warm-up reflex exercises well they can do that because of the myelin sheath so you're saying that messi has a better myelin sheath than any other footballer ever yeah maybe ronaldo but you know
231
00:17:25.454 --> 00:17:27.555
I'm going to get a lot of hate for that.
232
00:17:27.755 --> 00:17:29.415
Let's see what the comments say about that.
233
00:17:29.775 --> 00:17:30.575
Hey, let me know.
234
00:17:30.595 --> 00:17:32.796
I am curious.
235
00:17:32.836 --> 00:17:35.377
How many are Ronaldo versus Messi?
236
00:17:36.177 --> 00:17:37.998
I'm curious to see what the comments say.
237
00:17:38.018 --> 00:17:42.119
Some have said the debate is over, but I'm not an expert.
238
00:17:42.539 --> 00:17:44.720
I'm certainly not an expert, but I have an opinion.
239
00:17:46.541 --> 00:17:50.749
Okay, so let's talk about the myelin now in a more cellular level.
240
00:17:50.789 --> 00:17:53.314
What is the cell that is responsible for that myelin?
241
00:17:53.715 --> 00:17:55.879
It's something called the oligodendrocyte.
242
00:17:57.482 --> 00:18:00.623
This was identified by a Spanish neuroanatomist.
243
00:18:01.464 --> 00:18:01.804
Can you?
244
00:18:02.424 --> 00:18:04.065
Pio de Río Ortega.
245
00:18:04.506 --> 00:18:04.886
Nice.
246
00:18:04.966 --> 00:18:05.466
Yeah.
247
00:18:05.646 --> 00:18:06.747
Around 1921.
248
00:18:06.807 --> 00:18:11.490
So that guy, he stained using the Golgi silver staining techniques.
249
00:18:12.150 --> 00:18:17.994
And he found that there's these cells that have a few branches that branch out and reach out to a bunch of neurons.
250
00:18:18.854 --> 00:18:25.278
A key discovery here is that a single cell can actually branch out to a bunch of neurons and wrap.
251
00:18:26.495 --> 00:18:26.658
It's
252
00:18:27.968 --> 00:18:29.729
processes around those neurons.
253
00:18:29.769 --> 00:18:32.810
So you know those myelin sheaths that I was talking about, where they're wrapping around?
254
00:18:33.170 --> 00:18:34.731
That's not a single cell.
255
00:18:35.071 --> 00:18:43.534
There's one cell that is going around and reaching out to a bunch of axons around it, kind of like Venom in Spider-Man.
256
00:18:44.534 --> 00:18:48.116
And it goes around and wraps around all of these axons nearby.
257
00:18:48.296 --> 00:18:49.556
Interesting.
258
00:18:49.636 --> 00:18:51.357
Which is kind of cool to think about.
259
00:18:51.457 --> 00:18:53.278
It's not a one-to-one sort of mapping.
260
00:18:53.638 --> 00:18:56.019
It's a single cell that's doing like 50 different...
261
00:18:56.879 --> 00:19:10.359
electrical wires as this distributed uh placement of its that oh that's actually very yeah intriguing and so this maybe foreshadows some of the stuff that we're going to talk about which is if there's a problem with these oligodendrocytes yeah
262
00:19:11.256 --> 00:19:13.638
It's like not a single neuron problem.
263
00:19:14.159 --> 00:19:16.901
It becomes a single point of failure across multiple endpoints.
264
00:19:17.001 --> 00:19:17.462
Exactly.
265
00:19:17.542 --> 00:19:19.824
And that's what we're sort of building towards.
266
00:19:19.984 --> 00:19:20.404
That makes sense.
267
00:19:20.424 --> 00:19:20.544
Okay.
268
00:19:20.885 --> 00:19:29.232
So the precise ultra structure of this myelin was only sort of revealed in the 1950s with the advent of electron microscopy.
269
00:19:29.632 --> 00:19:34.557
Here's an electron microscope image where you can see this is a cross section of a neuron.
270
00:19:35.017 --> 00:19:36.457
So that's the axon in the middle.
271
00:19:36.497 --> 00:19:37.738
There's a little mitochondria.
272
00:19:37.758 --> 00:19:38.538
You see the circle?
273
00:19:39.078 --> 00:19:40.418
That's a mitochondria.
274
00:19:40.498 --> 00:19:42.039
Neurons have a bunch of mitochondria.
275
00:19:42.059 --> 00:19:43.139
The powerhouse of the cell.
276
00:19:43.419 --> 00:19:57.762
It is the powerhouse of the cell, and neurons need a lot of powerhouses of the cell because they need to maintain this voltage difference, which means they need to pump ions against – like, you know, if there's a bunch of calcium ions on one side –
277
00:19:58.712 --> 00:20:02.617
it takes a lot of energy to pump even more calcium ions over there, right?
278
00:20:02.797 --> 00:20:04.460
Even with the myelin sheath, you still need power.
279
00:20:04.500 --> 00:20:05.641
You still need a lot of power.
280
00:20:05.661 --> 00:20:12.510
And the myelin sheath, actually, it's not just something that provides this capacitance trade-off of, like, speed.
281
00:20:13.071 --> 00:20:14.933
The myelin sheath is something that...
282
00:20:15.898 --> 00:20:21.944
provides energy for the cell and provides like metabolic waste removal from the cell.
283
00:20:22.204 --> 00:20:23.405
It's kind of like a maintainer.
284
00:20:24.066 --> 00:20:29.231
One of the key things that it does is wrap around the cell and you can see the little layers, right?
285
00:20:29.271 --> 00:20:31.193
You can see the layers of the myelin...
286
00:20:31.773 --> 00:20:53.015
sort of wrapping around to create a bunch of capacitors in parallel but it does way more than just this physics argument that that makes sense and for the those who are listening if you've ever eaten nerds rope and took a bite out of it and looked at the inside um or like the twizzler things where there's an inside that's a little bit then there's an outer layer it looks like when you've taken a bite and look at it long ways so
287
00:20:53.155 --> 00:20:53.375
Yeah.
288
00:20:53.495 --> 00:20:54.416
No, you know what I would say?
289
00:20:54.476 --> 00:20:57.718
It was, it's like a Twizzler on the inside and then fruit roll up.
290
00:20:57.738 --> 00:20:57.879
Yeah.
291
00:20:57.899 --> 00:20:58.919
On the, on the, you know, yeah.
292
00:20:58.939 --> 00:21:00.641
Like wrapped, like wrapped around.
293
00:21:00.661 --> 00:21:00.941
Yeah.
294
00:21:00.961 --> 00:21:01.822
It's like fruit roll up.
295
00:21:01.842 --> 00:21:02.042
Yeah.
296
00:21:02.122 --> 00:21:02.742
That's a great point.
297
00:21:02.802 --> 00:21:03.143
Yeah.
298
00:21:03.203 --> 00:21:03.423
Yeah.
299
00:21:03.463 --> 00:21:06.885
We should have some fruit roll up in the studio actually.
300
00:21:07.346 --> 00:21:07.686
Anyways.
301
00:21:08.046 --> 00:21:09.828
So, um, Betty Ben Garan.
302
00:21:10.548 --> 00:21:15.993
She graduated from Washington University and then later went to Harvard Medical School where she was doing a lot of her research.
303
00:21:16.033 --> 00:21:21.498
She showed in 1954 that myelin is formed from spiral wrapping of a single cell.
304
00:21:21.558 --> 00:21:25.541
So this oligodendrocyte wraps around and that's a single cell that's doing it.
305
00:21:26.202 --> 00:21:27.343
It's a living tissue.
306
00:21:27.743 --> 00:21:29.545
That's the main finding from this paper.
307
00:21:29.905 --> 00:21:31.667
Before then, it was thought that it was just like...
308
00:21:32.522 --> 00:21:35.747
It was something that maybe the neurons secreted because we don't know, right?
309
00:21:35.767 --> 00:21:36.248
You don't know.
310
00:21:36.649 --> 00:21:40.715
But she definitively showed that there's a single cell that goes around.
311
00:21:40.895 --> 00:21:43.499
This oligodendrocyte goes around and wraps around.
312
00:21:44.220 --> 00:21:47.205
And all of that is a single process that's doing it.
313
00:21:47.505 --> 00:21:55.170
The venom analogy is very visually compelling in the context of how to kind of visualize what that would mean.
314
00:21:55.371 --> 00:21:55.611
Yeah.
315
00:21:55.831 --> 00:21:55.991
Right?
316
00:21:56.011 --> 00:21:57.172
Like the movement of it.
317
00:21:57.372 --> 00:21:57.612
Anyway.
318
00:21:58.093 --> 00:21:58.533
Exactly.
319
00:21:58.653 --> 00:22:05.538
And as we started learning more about the brain, we started realizing that this dichotomy between the gray matter and the white matter...
320
00:22:06.058 --> 00:22:06.378
Right.
321
00:22:06.678 --> 00:22:08.880
That was already known from like the 1500s.
322
00:22:08.920 --> 00:22:11.981
There were like these Italian surgeons who were dissecting brains.
323
00:22:13.082 --> 00:22:14.583
Michelangelo being one of them.
324
00:22:14.903 --> 00:22:19.425
And they had already noticed that there were parts of the brain on the outside where there was gray matter.
325
00:22:19.725 --> 00:22:21.706
And then on the inside, there was this white matter.
326
00:22:22.067 --> 00:22:23.988
It turns out that gray matter, white matter.
327
00:22:24.808 --> 00:22:46.348
difference has to do with myelination okay um you've got the gray matter on the outside that's mostly where the cell bodies are okay and then the white matter are the axons that connect different parts of the brain and a lot of the myelin is in the axons because that's where the signal propagation is happening it's like that's the super highway and the gray matter are these islands
328
00:22:46.648 --> 00:22:46.888
Yeah.
329
00:22:46.948 --> 00:22:49.611
That are connected by the white matter of the superhighway system.
330
00:22:49.631 --> 00:22:49.991
Exactly.
331
00:22:50.231 --> 00:22:50.672
Exactly.
332
00:22:51.172 --> 00:22:52.774
So this is where we get into MS.
333
00:22:53.174 --> 00:22:54.155
Okay?
334
00:22:54.295 --> 00:22:54.736
1868.
335
00:22:55.036 --> 00:23:00.421
French neurologist Jean Martin Charcot, I believe.
336
00:23:00.721 --> 00:23:01.602
And people might...
337
00:23:01.662 --> 00:23:02.543
It might be Charcot.
338
00:23:02.984 --> 00:23:03.224
Yeah.
339
00:23:03.524 --> 00:23:03.824
Maybe.
340
00:23:03.844 --> 00:23:04.685
Anyways.
341
00:23:05.606 --> 00:23:06.527
He documented...
342
00:23:08.390 --> 00:23:12.012
evidence of plaques in the white matter.
343
00:23:12.892 --> 00:23:13.133
Okay?
344
00:23:13.693 --> 00:23:20.557
And he showed that there's a correlation between those plaques when he did the autopsies of these patients and the patient's conditions at the time.
345
00:23:20.857 --> 00:23:26.860
The patient's conditions at the time were abnormal eye movements, intention tremors, slurred speech.
346
00:23:26.960 --> 00:23:30.982
This became known as Charcot's or Charcot's triad.
347
00:23:31.522 --> 00:23:36.145
And he recognized that these plaques in the white matter is probably...
348
00:23:36.542 --> 00:23:39.305
What is causing these symptoms of early MS?
349
00:23:39.365 --> 00:23:48.894
This was the discovery of MS. We saw people having these conditions we could see, these physiological happenings.
350
00:23:48.934 --> 00:23:49.154
Yeah.
351
00:23:49.875 --> 00:23:55.160
When you then went and did an autopsy after the fact, there was something in the brain that looked different than everybody else.
352
00:23:55.240 --> 00:23:55.440
Yeah.
353
00:23:55.581 --> 00:23:56.802
There's a control and then there's...
354
00:23:57.242 --> 00:23:59.844
So maybe these things may be connected.
355
00:23:59.904 --> 00:24:00.465
Yeah, yeah.
356
00:24:00.565 --> 00:24:04.588
And side note, Charcot was super into hypnosis.
357
00:24:04.648 --> 00:24:06.269
That's what he's most well-known for.
358
00:24:06.289 --> 00:24:06.670
That's funny.
359
00:24:06.710 --> 00:24:08.031
If you go back to that painting.
360
00:24:09.132 --> 00:24:09.892
Yes.
361
00:24:09.992 --> 00:24:12.374
Yeah.
362
00:24:12.454 --> 00:24:15.957
He's showing a woman who's like in hysteria is what he called it.
363
00:24:16.057 --> 00:24:17.298
And then he hypnotized her.
364
00:24:17.618 --> 00:24:22.362
And then all of these medical professionals are looking on like, oh my gosh, I can't believe you could settle our head.
365
00:24:22.382 --> 00:24:23.483
I can't believe you've done this.
366
00:24:23.503 --> 00:24:25.425
Yeah, yeah, yeah.
367
00:24:25.465 --> 00:24:25.725
So...
368
00:24:26.666 --> 00:24:29.489
You know, you win some, you lose some.
369
00:24:30.590 --> 00:24:31.791
Right.
370
00:24:31.931 --> 00:24:32.752
That's so funny.
371
00:24:32.772 --> 00:24:35.095
So for nearly a century, though, this was in 1868.
372
00:24:35.776 --> 00:24:39.480
For nearly a century, MS was a disease without a mechanism.
373
00:24:39.700 --> 00:24:40.020
Yeah.
374
00:24:40.140 --> 00:24:43.504
Because we didn't actually know, okay, so there's these plaques, but, you know, that's not...
375
00:24:43.784 --> 00:24:47.365
good enough to really understand what is actually happening to create this.
376
00:24:47.885 --> 00:24:56.628
There was an autoimmune hypothesis that the immune system was attacking the myelin because maybe the immune system thought that it was some kind of foreign agent.
377
00:24:56.968 --> 00:25:05.030
And that gained traction because there was a discovery that a lot of the proteins that are around in myelin are major targets for an immune attack.
378
00:25:05.390 --> 00:25:08.971
Things like the myelin basic protein, the proteolipid protein.
379
00:25:09.011 --> 00:25:12.412
And then also with the development of MRIs, we could literally track
380
00:25:13.289 --> 00:25:20.731
the development of the white matter and the gray matter without actually doing an autopsy on a dead patient.
381
00:25:20.771 --> 00:25:26.572
Now there's a living patient, and now what we're seeing with MS is that it's kind of cycling.
382
00:25:27.372 --> 00:25:31.713
There's a remission, and then things come back to normal.
383
00:25:31.753 --> 00:25:34.393
And then there's a remission, and then there's things coming back to normal.
384
00:25:34.793 --> 00:25:35.434
What does that say?
385
00:25:35.494 --> 00:25:41.715
That says that there is a chance, there is a mechanism inside the brain that is fixing...
386
00:25:42.753 --> 00:25:44.094
this myelin degradation.
387
00:25:44.534 --> 00:25:44.735
Right.
388
00:25:44.815 --> 00:25:45.055
Right.
389
00:25:45.255 --> 00:25:45.495
Right.
390
00:25:45.555 --> 00:25:49.819
But the fixing mechanism is deteriorating with time.
391
00:25:51.100 --> 00:25:57.504
There's an ongoing battle, and the defense is losing the war of attrition.
392
00:25:57.705 --> 00:25:58.165
Exactly.
393
00:25:58.305 --> 00:25:58.505
Yeah.
394
00:25:58.866 --> 00:26:01.007
And so now that becomes kind of the focus.
395
00:26:01.568 --> 00:26:04.050
And with individuals suffering from multiple sclerosis—
396
00:26:05.335 --> 00:26:07.157
We now have some kind of mechanism.
397
00:26:07.257 --> 00:26:08.278
It is the myelin sheath.
398
00:26:08.338 --> 00:26:16.167
And you can see now stains between a normal control individual in A and someone with MS in B. Wow.
399
00:26:16.187 --> 00:26:17.908
The stains are the myelin sheaths.
400
00:26:17.928 --> 00:26:21.272
You can see in A, there's so many around every single nerve fiber.
401
00:26:21.292 --> 00:26:21.692
Yeah.
402
00:26:21.712 --> 00:26:22.934
There's a bunch of myelin sheath.
403
00:26:23.134 --> 00:26:24.695
In B, it's almost gone.
404
00:26:24.715 --> 00:26:25.076
Yeah.
405
00:26:25.096 --> 00:26:25.216
Yeah.
406
00:26:25.416 --> 00:26:28.557
And the point here is now that it's gone, it means we lose that speed.
407
00:26:28.637 --> 00:26:36.359
We lose the other efficiencies that it provides, not just the physics perspective of what you get out of having that myelin sheath be present.
408
00:26:36.459 --> 00:26:37.659
Yeah.
409
00:26:37.679 --> 00:26:42.840
And so now we're starting to – the detective work is progressing.
410
00:26:42.880 --> 00:26:45.101
We're starting to at least piece together what is going wrong.
411
00:26:45.221 --> 00:26:45.421
Yes.
412
00:26:45.461 --> 00:26:46.541
It's definitely the myelin.
413
00:26:46.641 --> 00:26:46.841
Yes.
414
00:26:47.041 --> 00:26:47.281
Okay.
415
00:26:47.321 --> 00:26:47.521
Right.
416
00:26:48.922 --> 00:26:49.802
Now let's talk about –
417
00:26:50.675 --> 00:27:13.469
that remission stuff right because you said there was noticing in the mri scans even in these early mri studies that it would go down and then it would go up then it would go down then it would go up so certainly which almost feels like an immune response right there's something that shows up and then the immune system comes and then it goes away yeah there's yeah and then the immune system maybe like stops paying attention so then comes back so then the myelin comes back
418
00:27:14.502 --> 00:27:16.243
So how is the myelin actually coming back?
419
00:27:16.743 --> 00:27:17.483
Right.
420
00:27:17.563 --> 00:27:27.026
Let's try to focus in on what is the repair mechanism that is working in healthy patients and what is sometimes working in patients with MS.
421
00:27:27.246 --> 00:27:27.446
Okay.
422
00:27:28.606 --> 00:27:33.508
There is a mechanism for spontaneous repair of the myelin, right?
423
00:27:34.108 --> 00:27:41.770
The brain and the spinal cord, they're populated by a reservoir of specialized stem cells called myelin.
424
00:27:41.990 --> 00:27:44.891
Adult oligodendrocyte precursor cells.
425
00:27:45.351 --> 00:27:54.193
These are oligodendrocyte precursor cells, meaning they are stem cells that could go on to become the oligodendrocytes that create the myelin, right?
426
00:27:55.094 --> 00:27:59.035
So you've got the OPC, and that's the abbreviation that we're going to use from now on.
427
00:27:59.195 --> 00:28:00.035
Yeah, you know me.
428
00:28:00.998 --> 00:28:01.998
Know an OPC?
429
00:28:02.459 --> 00:28:03.579
What is that from?
430
00:28:04.480 --> 00:28:05.780
It's a rap reference.
431
00:28:05.960 --> 00:28:06.600
Oh, okay, got it.
432
00:28:06.620 --> 00:28:07.101
We can continue.
433
00:28:07.121 --> 00:28:07.361
All right.
434
00:28:07.521 --> 00:28:09.201
Well, I'm going to look it up later.
435
00:28:09.221 --> 00:28:17.105
So the OPC, this oligodendrocyte precursor cell, can become a myelinating oligodendrocyte cell.
436
00:28:17.485 --> 00:28:17.665
Mm-hmm.
437
00:28:18.045 --> 00:28:19.627
And there's some pathway, right?
438
00:28:19.647 --> 00:28:30.457
There's some signaling where the stem cell, which could become a bunch of different things, it gets specific signals from its environment to be like, oh, there's presumably, there's like myelin degradation.
439
00:28:30.497 --> 00:28:31.799
There's some kind of damage.
440
00:28:32.239 --> 00:28:33.100
I need to go lock in.
441
00:28:33.180 --> 00:28:33.440
Mm-hmm.
442
00:28:34.361 --> 00:28:39.808
become an oligodendrozyte cell and wrap myself around these axons that no longer have myelin.
443
00:28:40.249 --> 00:28:40.429
Right?
444
00:28:40.469 --> 00:28:41.070
That's the idea.
445
00:28:41.190 --> 00:28:41.290
Yes.
446
00:28:41.310 --> 00:28:48.759
And that's the form of differentiation where it gets some kind of cellular signaling from its environment, and then it goes and does a specific job.
447
00:28:48.819 --> 00:28:49.280
Right.
448
00:28:49.400 --> 00:28:50.421
The idea is this is a baby.
449
00:28:50.461 --> 00:28:51.021
It's in an egg.
450
00:28:51.041 --> 00:28:51.961
It hasn't yet been born.
451
00:28:52.001 --> 00:28:53.362
It doesn't know what it's going to be yet.
452
00:28:53.402 --> 00:29:01.687
It's like a, you know, and then depending on the context of the environment, it can become one thing or another.
453
00:29:01.747 --> 00:29:01.927
Yeah.
454
00:29:02.267 --> 00:29:02.447
Yeah.
455
00:29:02.487 --> 00:29:03.488
That's what a stem cell is.
456
00:29:03.568 --> 00:29:03.748
Right.
457
00:29:03.788 --> 00:29:07.710
And in this case, it becomes this oligodendrocyte that becomes the myelon.
458
00:29:07.830 --> 00:29:09.931
It's what wraps the fruit roll up around the twister.
459
00:29:10.271 --> 00:29:11.612
And this is good.
460
00:29:11.712 --> 00:29:16.955
And so the point here being like this is the mechanism by which the body is generating.
461
00:29:19.316 --> 00:29:29.607
The thing that is going in to then wrap again is this OPC that becomes myelinating oligodendrocyte cell.
462
00:29:29.727 --> 00:29:30.267
Yes.
463
00:29:30.347 --> 00:29:32.530
And so in healthy patients, this is happening all the time.
464
00:29:32.730 --> 00:29:33.090
It's fine.
465
00:29:33.310 --> 00:29:33.511
Right?
466
00:29:33.571 --> 00:29:35.032
It's happening all the time and it's fine.
467
00:29:35.312 --> 00:29:37.154
So what's happening in MS?
468
00:29:37.355 --> 00:29:42.260
The first hypothesis would be, okay, maybe the stem cells themselves are dying.
469
00:29:42.780 --> 00:29:43.080
Okay.
470
00:29:43.120 --> 00:29:43.320
Right?
471
00:29:43.621 --> 00:29:45.542
That's a pretty easy hypothesis, right?
472
00:29:45.582 --> 00:29:47.143
There's not enough going around.
473
00:29:47.624 --> 00:29:49.285
The stem cells with age die off.
474
00:29:49.365 --> 00:29:52.688
And so you don't have them going around doing this myelinating stuff.
475
00:29:53.228 --> 00:29:56.330
Your population is decreasing because you don't have enough babies that can become adults.
476
00:29:56.350 --> 00:29:56.811
Yeah, yeah.
477
00:29:57.251 --> 00:29:58.512
That's not the case.
478
00:29:58.632 --> 00:29:58.932
Okay?
479
00:29:59.173 --> 00:30:08.440
People do studies, and they actually show that with histological studies where you can stay in certain parts, you can actually see that there are certain lesions—
480
00:30:09.468 --> 00:30:16.413
Within someone who has MS, where certain lesions in the red arrow, the lesions are fine.
481
00:30:17.233 --> 00:30:18.074
They have repaired.
482
00:30:18.494 --> 00:30:25.859
But in other lesions, like the green arrow, in the same individual, you're getting lesions of MS where there's no myelin and there's no repair.
483
00:30:25.939 --> 00:30:29.462
So within the same individual, it can't really be possible that there's...
484
00:30:33.577 --> 00:30:38.002
There's no oligodendrocytes, but somehow there's repair still happening.
485
00:30:38.022 --> 00:30:38.843
There's repair still happening.
486
00:30:38.883 --> 00:30:43.467
Because as far as we're aware in this context, that is the mechanism by which the repair happens.
487
00:30:43.487 --> 00:30:43.988
Yeah, yeah.
488
00:30:44.048 --> 00:30:46.290
So certainly there's oligodendrocytes that are working.
489
00:30:46.430 --> 00:30:46.651
Right.
490
00:30:46.811 --> 00:30:48.533
There's OPCs that are working on the red.
491
00:30:48.553 --> 00:30:48.853
Right, right.
492
00:30:49.093 --> 00:30:50.675
The pregenitor stem cells.
493
00:30:50.795 --> 00:30:51.015
Yes.
494
00:30:51.115 --> 00:30:53.778
The stem cells are working in the red area, but not in the green area.
495
00:30:53.878 --> 00:30:54.699
Right, right, right.
496
00:30:54.839 --> 00:30:59.083
And if you go and stain and actually look into it, the green areas have the stem cells.
497
00:30:59.643 --> 00:31:07.931
It's just that the stem cells, maybe they're not getting the correct type of signaling, or maybe they get the correct type of signaling, but they can't actually execute.
498
00:31:08.511 --> 00:31:16.278
And so the point here is that it's not a lack of the defense.
499
00:31:16.318 --> 00:31:17.159
Yeah, the workers.
500
00:31:17.219 --> 00:31:18.801
The workers, they're there.
501
00:31:19.001 --> 00:31:19.201
Yeah.
502
00:31:19.661 --> 00:31:19.842
Yeah.
503
00:31:20.542 --> 00:31:23.704
But something is breaking down further down in the process.
504
00:31:23.945 --> 00:31:24.405
Exactly.
505
00:31:24.805 --> 00:31:29.448
And so they're failing to mature into these myelin-producing cells.
506
00:31:29.668 --> 00:31:30.549
Stuck in adolescence.
507
00:31:30.789 --> 00:31:31.069
Yes.
508
00:31:31.370 --> 00:31:31.610
Yes.
509
00:31:31.790 --> 00:31:32.390
In some sense.
510
00:31:33.511 --> 00:31:35.032
So what's actually happening?
511
00:31:35.072 --> 00:31:38.815
Well, it could be some epigenetic barrier to differentiation.
512
00:31:38.875 --> 00:31:43.218
Here we're seeing immune cells in the yellow...
513
00:31:43.611 --> 00:31:47.497
actually attacking a myelin cell, an oligodendrocyte cell.
514
00:31:47.777 --> 00:31:51.002
And you can see that as it attacks, there's going to be a lot of cellular debris.
515
00:31:51.022 --> 00:31:52.605
There's going to be a lot of stress hormones.
516
00:31:52.905 --> 00:31:53.826
There's going to be a lot of like
517
00:31:54.954 --> 00:31:56.415
random nonsense that is happening.
518
00:31:56.735 --> 00:32:03.917
And perhaps that environment is making it hard for the oligodendrocytes that are around to come in and actually do the repair.
519
00:32:04.377 --> 00:32:04.537
Right?
520
00:32:04.617 --> 00:32:06.978
If there's like a battlefield, right?
521
00:32:07.038 --> 00:32:09.139
And there's like the...
522
00:32:09.499 --> 00:32:13.961
In some sense, there's like evidence of a battle where a bunch of your homies got knocked out.
523
00:32:13.981 --> 00:32:14.181
Yeah.
524
00:32:14.201 --> 00:32:14.401
Yeah.
525
00:32:15.090 --> 00:32:17.571
Like, you know, maybe you're like, I might go that way.
526
00:32:17.611 --> 00:32:20.091
Yeah, I might not hang out.
527
00:32:20.631 --> 00:32:27.673
Like, it's kind of strange to think about, but like even individual cells are feeling stress from the environment.
528
00:32:27.953 --> 00:32:35.635
And the macrophages that usually come in to clean up the battlefield aren't doing their jobs because maybe there's too much battlefield debris.
529
00:32:36.135 --> 00:32:38.296
And so perhaps this is the problem.
530
00:32:39.496 --> 00:32:48.761
And the consequences of this failed remethylation is what it's called, where there's no methylation and then you remethylate the brain.
531
00:32:49.481 --> 00:32:56.404
The consequences of not doing this, of not remethylating, are catastrophic, namely because...
532
00:32:57.588 --> 00:33:00.049
The neurons that you have are the neurons that you have.
533
00:33:00.749 --> 00:33:06.150
This is why it's super important to have these mechanisms in the nervous system.
534
00:33:06.410 --> 00:33:11.212
In other organ systems, the cells, by and large, can replicate.
535
00:33:11.772 --> 00:33:12.572
Neurons cannot.
536
00:33:12.652 --> 00:33:16.793
You are, by and large, born with the number of neurons that you have.
537
00:33:17.153 --> 00:33:18.874
And when a neuron dies, that's it.
538
00:33:18.894 --> 00:33:19.634
Yeah.
539
00:33:19.774 --> 00:33:24.938
And so just I want to reground myself in kind of the circumstance we've set the table for right now.
540
00:33:25.419 --> 00:33:37.248
So, you know, in part we have the neuron itself, right, and the neural pathway is like where the signal is being sent so we can do all of this communication and function the way we do.
541
00:33:37.989 --> 00:33:39.650
The myelin is this wrapping.
542
00:33:39.670 --> 00:33:39.830
Right.
543
00:33:39.950 --> 00:33:40.150
Yeah.
544
00:33:40.671 --> 00:33:44.877
Around it that has a variety of benefits.
545
00:33:44.937 --> 00:33:45.158
Yeah.
546
00:33:45.879 --> 00:33:47.762
Speed, efficiency.
547
00:33:48.823 --> 00:33:49.264
Housekeeping.
548
00:33:49.284 --> 00:33:49.985
Housekeeping.
549
00:33:50.766 --> 00:33:54.271
And in MS patients specifically –
550
00:33:55.212 --> 00:34:03.998
There is this degradation in this myelin sheath that surrounds your neurons that gives it all of these added benefits.
551
00:34:04.639 --> 00:34:13.745
We thought it might have been that the way that they're born, the myelin, the way that it was created, there's not enough resource to keep creating it.
552
00:34:14.485 --> 00:34:15.766
That did not seem to be true.
553
00:34:24.372 --> 00:34:37.477
The environment, because there's this constant ebbing and flowing of myelin degradation, and then the body recreates its myelin sheaths in healthy patients.
554
00:34:37.877 --> 00:34:50.101
But in MS patients, that repair process breaks down because of this chaos that is happening in the context of the battle that creates the myelin degradation in the first place.
555
00:34:50.121 --> 00:34:50.582
Yeah, yeah.
556
00:34:50.902 --> 00:35:04.218
And it seems that the pregenitor cells, these stem cells, that would go on to create the myelin and repair, they're either confused, they're not having the right signal, they're getting too much inflammatory signal.
557
00:35:04.579 --> 00:35:07.422
Something is happening that is preventing the present workers…
558
00:35:08.223 --> 00:35:10.706
From repairing what they should be repairing.
559
00:35:10.826 --> 00:35:12.187
And the key idea is that they're there.
560
00:35:12.267 --> 00:35:12.728
They're there.
561
00:35:12.748 --> 00:35:14.309
That's the key.
562
00:35:14.330 --> 00:35:15.230
Yeah, it's a big point.
563
00:35:15.250 --> 00:35:15.871
That's a big point.
564
00:35:16.111 --> 00:35:16.752
That's a big point.
565
00:35:16.812 --> 00:35:18.934
And that's the whole, yeah, that was the whole point of this whole thing.
566
00:35:18.994 --> 00:35:19.175
Right.
567
00:35:19.375 --> 00:35:20.376
Is that they are there.
568
00:35:20.716 --> 00:35:24.200
We just need to equip them with perhaps the right signal.
569
00:35:24.740 --> 00:35:25.021
Okay?
570
00:35:25.181 --> 00:35:25.401
Okay.
571
00:35:25.561 --> 00:35:27.824
So where do the current therapies stand?
572
00:35:28.304 --> 00:35:28.584
Well, the...
573
00:35:29.165 --> 00:35:31.166
They can prevent, but they cannot fix.
574
00:35:31.726 --> 00:35:35.128
These are called disease-modifying therapies, DMTs.
575
00:35:35.549 --> 00:35:37.450
Not to be... Not that DMT.
576
00:35:37.490 --> 00:35:38.750
Not that DMT, guys.
577
00:35:39.351 --> 00:35:41.992
This is not the Joe Rogan podcast.
578
00:35:42.272 --> 00:35:44.874
But once an axon is demyelinated...
579
00:35:45.534 --> 00:35:54.600
These disease-modifying therapies cannot force the body to remyelinate it, but they can perhaps force the body to stop the degradation.
580
00:35:54.620 --> 00:35:55.761
To stop the bleeding, effectively.
581
00:35:55.941 --> 00:36:00.264
Yeah, and by the 2000s, the field had identified a bunch of different signals.
582
00:36:00.304 --> 00:36:03.006
There's thyroid hormone, retinoids.
583
00:36:03.146 --> 00:36:05.988
But translating this into therapies proved kind of difficult.
584
00:36:06.488 --> 00:36:07.589
A big obstacle...
585
00:36:08.529 --> 00:36:09.930
is the blood-brain barrier.
586
00:36:10.530 --> 00:36:14.492
This is something that we revisit a lot on this podcast.
587
00:36:14.792 --> 00:36:22.136
The blood-brain barrier is an essential barrier between the blood and the brain because the human brain is protected by this thing.
588
00:36:22.696 --> 00:36:28.419
It's a highly selective, semi-permeable border of endothelial cells.
589
00:36:29.339 --> 00:36:35.863
Endothelial cells are just, you know, cells on the lining of blood vessels, but very specific types of endothelial cells are...
590
00:36:36.883 --> 00:36:41.247
And they prevent circulating blood from just freely mixing with the brain.
591
00:36:41.808 --> 00:36:44.851
Everywhere else it's fine, but the brain is a finicky thing.
592
00:36:45.011 --> 00:36:46.472
The neurons are a finicky thing.
593
00:36:46.792 --> 00:36:53.358
And so they want a very particular environment to actually do all of the computation that we know and love.
594
00:36:54.119 --> 00:36:58.223
This barrier has doomed a lot of different things.
595
00:36:59.075 --> 00:37:05.400
Okay, one of the most famous was this therapy from Biogen, which Biogen is a company.
596
00:37:05.980 --> 00:37:10.643
They were developing this therapy called Opicinumab.
597
00:37:10.663 --> 00:37:13.806
I love the pharmaceutical namings of stuff.
598
00:37:13.906 --> 00:37:15.567
Yeah, Opicinumab.
599
00:37:17.068 --> 00:37:26.114
It was developed as a monoclonal antibody that was going to target and block something called LINGO1, which is a protein that would inhibit this differentiation.
600
00:37:26.174 --> 00:37:29.056
So the idea is you've got the stem cell.
601
00:37:29.096 --> 00:37:30.557
The stem cell is differentiating.
602
00:37:31.137 --> 00:37:36.501
There's a protein called LINGO1 that would stop that differentiation, that would stop these workers.
603
00:37:36.841 --> 00:37:40.364
So this monoclonal antibody is going to go attack that protein.
604
00:37:40.644 --> 00:37:44.707
So then the workers are kind of free to do whatever.
605
00:37:44.807 --> 00:37:44.987
Yes.
606
00:37:45.853 --> 00:37:47.133
Works in mice.
607
00:37:48.794 --> 00:37:51.034
Human phase 2 trials, huge failure.
608
00:37:51.074 --> 00:37:54.235
Because these monoclonal antibodies are massive.
609
00:37:54.995 --> 00:37:56.476
They're complex protein structures.
610
00:37:56.776 --> 00:38:00.137
And because of their sheer size, they couldn't actually get through the blood-brain barrier.
611
00:38:00.677 --> 00:38:00.857
Right?
612
00:38:01.137 --> 00:38:03.458
In mice, the mice have a different blood-brain barrier.
613
00:38:03.778 --> 00:38:04.778
So perhaps it got through.
614
00:38:04.798 --> 00:38:05.338
Okay.
615
00:38:05.518 --> 00:38:09.841
This is going to come back later when we put an asterisk on the current paper.
616
00:38:10.221 --> 00:38:13.163
But I still think this current paper is cool because of all the methodology.
617
00:38:13.703 --> 00:38:17.385
So to bypass the blood-brain barrier, what do we need to do?
618
00:38:17.685 --> 00:38:20.167
We need to make our thingy smaller.
619
00:38:21.187 --> 00:38:24.269
Because the blood-brain barrier has extremely tiny holes in it.
620
00:38:24.509 --> 00:38:32.712
And if I can get through that hole, then perhaps I can get into the neurons, get into the astrocytes, get into these oligodendrocytes and help out where I'm trying to help out.
621
00:38:33.193 --> 00:38:38.535
So researchers have started turning to small molecule drugs, but these molecules are tiny.
622
00:38:39.683 --> 00:38:42.606
And they're lipophilic, meaning they're fat-soluble.
623
00:38:42.826 --> 00:38:45.268
Lipophilic meaning fat-loving.
624
00:38:46.129 --> 00:38:52.374
So they slip past the blood-brain barrier, but they're also going to slip past every other barrier.
625
00:38:52.394 --> 00:38:52.975
Yeah.
626
00:38:53.215 --> 00:38:53.435
Right?
627
00:38:53.716 --> 00:38:55.617
So we want target specificity.
628
00:38:56.178 --> 00:38:58.340
That's not going to happen for a lot of these chemicals.
629
00:38:58.360 --> 00:38:58.720
Right.
630
00:38:59.101 --> 00:39:02.004
So this is almost, it's the problem in the opposite direction.
631
00:39:02.024 --> 00:39:09.310
Yes, because now I'm slipping through the blood-brain barrier, but because I have to be so small to get through that, I'm also going to get to the liver.
632
00:39:09.650 --> 00:39:10.731
I'm going to get to the kidneys.
633
00:39:10.831 --> 00:39:12.152
I'm going to get all over the place.
634
00:39:12.673 --> 00:39:20.840
And one of the big, most catastrophic and educational failures in this case was the trial of BEXA rotine.
635
00:39:21.713 --> 00:39:30.737
So Bexarotin was an FDA-approved oral medication that was targeting something called the retinoid X receptors, which you see over here, the RXRs.
636
00:39:31.137 --> 00:39:35.419
These retinoid X receptors are nuclear transcription factors.
637
00:39:35.899 --> 00:39:38.200
We've talked about transcription factors a lot.
638
00:39:38.500 --> 00:39:41.221
These are the on-off switches for a bunch of genes.
639
00:39:41.621 --> 00:39:45.563
So if you can target the transcription factor, then you can influence...
640
00:39:46.043 --> 00:39:49.566
what types of genes the cell is actually getting access to.
641
00:39:49.826 --> 00:40:01.237
And there you see a bit of DNA, and the transcription factor is the protein that goes in and locks in on that piece of DNA, recruits, for example, RNA polymerase to make RNA that makes the protein that actually makes
642
00:40:01.677 --> 00:40:23.646
whatever thing that is happening but if you can target this thing then perhaps you can actually have a control of the on and off switch for something that controls whether the stem cell goes on to become a myelin that's the idea so we're going back to the factory and the instructions of how the factory generates its stuff with the right instructions it's like maybe let's just reprogram at to at the factory to factory settings
643
00:40:23.726 --> 00:40:24.146
Exactly.
644
00:40:24.166 --> 00:40:27.589
It's like maybe the factory has some instructions and I'm just going to black out.
645
00:40:27.970 --> 00:40:28.290
Yeah.
646
00:40:28.390 --> 00:40:28.570
Right.
647
00:40:28.630 --> 00:40:30.732
I'm going to like redact a bunch of instructions.
648
00:40:31.152 --> 00:40:36.416
The problem is the redaction is going to happen over all the instructions because it's not specific.
649
00:40:36.877 --> 00:40:38.138
That's the idea that I'm trying to get to.
650
00:40:38.158 --> 00:40:40.240
Because of the size problem we talked about earlier.
651
00:40:40.280 --> 00:40:40.480
Yeah.
652
00:40:40.640 --> 00:40:42.802
And so it's going to effectively start turning off.
653
00:40:43.322 --> 00:40:46.565
It's going to start impacting functionality that you were not trying to.
654
00:40:46.865 --> 00:40:47.165
Yeah.
655
00:40:47.205 --> 00:40:47.546
Change.
656
00:40:47.606 --> 00:40:47.806
Yeah.
657
00:40:47.926 --> 00:40:48.107
Yeah.
658
00:40:48.327 --> 00:40:49.308
Because it can't be specific.
659
00:40:49.368 --> 00:40:49.768
Exactly.
660
00:40:49.789 --> 00:40:50.009
Yes.
661
00:40:50.109 --> 00:40:51.911
Because it can't be specific because it's so small.
662
00:40:52.271 --> 00:40:57.097
Now, before we get into the details of that failure, a brief...
663
00:40:59.257 --> 00:41:03.761
a brief sojourn into vitamin A, because that's going to be important later on in this episode.
664
00:41:04.081 --> 00:41:09.365
Vitamin A is very important, and that RXR is very much tied to that vitamin A.
665
00:41:09.385 --> 00:41:11.827
That transcription factor is very much tied to the vitamin A.
666
00:41:12.087 --> 00:41:16.951
So vitamin A is a broad family of fat-soluble molecules, like retinol is...
667
00:41:17.751 --> 00:41:19.852
one specific molecule that belongs to that.
668
00:41:20.152 --> 00:41:23.114
And these retinoids are critical for neurodevelopment.
669
00:41:23.514 --> 00:41:24.575
Too much and it's bad.
670
00:41:24.755 --> 00:41:25.815
Too little and it's bad.
671
00:41:25.835 --> 00:41:30.758
That's why a lot of these prenatal vitamins have vitamin A.
672
00:41:30.978 --> 00:41:34.220
But they have it in the form of beta carotene and not like straight up vitamin A.
673
00:41:34.520 --> 00:41:39.544
Because beta carotene, our body can then process, and then it can decide how much vitamin A to get.
674
00:41:39.825 --> 00:41:42.967
If you just straight up inject yourself with retinol, that's really bad.
675
00:41:42.987 --> 00:41:47.191
And that's why I like the skincare products that have retinol is bad during pregnancy.
676
00:41:47.211 --> 00:41:50.194
Because then that goes into the infant, and that's going to cause trouble.
677
00:41:50.694 --> 00:42:01.101
So the active form is something called retinoic acid, and that acts as the transcription factor for these receptors.
678
00:42:01.181 --> 00:42:01.421
Yes.
679
00:42:01.662 --> 00:42:01.962
Okay?
680
00:42:02.022 --> 00:42:02.222
Right.
681
00:42:02.802 --> 00:42:06.425
So, and that regulates the gene expression later on, right?
682
00:42:06.565 --> 00:42:14.370
Now, preclinical studies showed that these pregenitor cells, the stem cells that go on to become the myelin.
683
00:42:14.410 --> 00:42:15.191
The OPCs.
684
00:42:15.331 --> 00:42:15.611
Yes.
685
00:42:16.211 --> 00:42:18.493
Those guys, they express...
686
00:42:19.897 --> 00:42:27.119
These RXRs, the receptors, but only a gamma kind.
687
00:42:27.579 --> 00:42:27.999
Okay.
688
00:42:28.019 --> 00:42:35.880
There's multiple types, and the stem cells in the brain that become myelin, they express the gamma kind of the receptor.
689
00:42:36.000 --> 00:42:36.461
Okay.
690
00:42:36.741 --> 00:42:42.762
And then when researchers knocked out that gamma kind, then the mice lost all the ability to remyelinate the receptor.
691
00:42:43.302 --> 00:42:43.962
The axons.
692
00:42:44.162 --> 00:42:50.005
So now we have a better idea of the specific receptors that the OPCs use.
693
00:42:50.025 --> 00:42:51.646
Because there's a lot of options on the table.
694
00:42:52.146 --> 00:42:56.428
And so part of maybe what was happening before was we weren't choosing the right flavor.
695
00:42:56.608 --> 00:42:56.848
Yes.
696
00:42:57.328 --> 00:43:02.890
And now we know we were making lemon pepper when we needed to be making teriyaki.
697
00:43:02.910 --> 00:43:03.090
Yeah.
698
00:43:03.391 --> 00:43:05.732
So at least we now know that we got to make the gamma.
699
00:43:05.892 --> 00:43:07.392
We got to make the teriyaki, right?
700
00:43:07.572 --> 00:43:07.732
Yeah.
701
00:43:07.873 --> 00:43:08.373
And so...
702
00:43:09.153 --> 00:43:16.915
This clinical trial in Cambridge in the UK decided that BEXAROTINE...
703
00:43:17.195 --> 00:43:19.996
Sorry, I'm really bad with these medical terminologies, to be honest.
704
00:43:20.196 --> 00:43:25.678
But BEXAROTINE was something that could target the gamma receptor and then perhaps...
705
00:43:26.298 --> 00:43:30.681
reinvigorate the stem cell differentiation from stem cell to myelin.
706
00:43:31.362 --> 00:43:35.344
The problem turns out that there are other types of receptors.
707
00:43:35.784 --> 00:43:44.650
There is the RXR gamma, which is in the brain that does the myelin stuff, but there's also RXR alpha, RXR beta, and those are in the liver and in the kidneys.
708
00:43:45.031 --> 00:43:48.073
And this thing was going around and targeting everything.
709
00:43:48.093 --> 00:43:48.233
Okay.
710
00:43:48.553 --> 00:43:51.516
So all of a sudden, the patients got hypothyroidism.
711
00:43:52.357 --> 00:43:57.263
90% of the patients developed dangerously high levels of fat circulating in the blood.
712
00:43:57.764 --> 00:44:01.147
Something called hypertriglyceridemia.
713
00:44:02.156 --> 00:44:02.837
This is the – yeah.
714
00:44:02.937 --> 00:44:16.431
And so this is – so just to double click on this, the Bexorotin, which was this potential therapeutic, right, that we were going to use, did activate RXR gamma, which is the one we know – Yeah, that's good.
715
00:44:16.771 --> 00:44:20.395
That the OPCs use to basically get to this point where they can remyelinate.
716
00:44:20.856 --> 00:44:22.638
The problem is it didn't only –
717
00:44:23.218 --> 00:44:37.948
reactivate rxr gamma it reactivated rxr gamma as well as the alpha and beta and so the all three subtypes were being activated by this therapeutic and as such it was effectively not targeted enough
718
00:44:38.528 --> 00:44:46.072
for the specific solution we were looking for, even though we solved the delivery problem of getting through the blood-brain barrier, which was the initial challenge.
719
00:44:46.092 --> 00:44:47.232
Yeah, that was the initial challenge.
720
00:44:47.252 --> 00:44:53.595
Okay, the monoclonal antibody is too big, it's not going to get through, let's make the thing smaller, well now it's getting through everywhere.
721
00:44:53.975 --> 00:44:54.175
Right.
722
00:44:54.255 --> 00:44:55.656
And targeting too many things.
723
00:44:56.016 --> 00:44:56.456
Exactly.
724
00:44:56.516 --> 00:44:59.318
So we've solved the delivery mechanism, we've not solved the targeting problem.
725
00:44:59.338 --> 00:45:01.139
Yes, and there is the challenge.
726
00:45:01.419 --> 00:45:05.661
How do we find a molecule that is small enough to cross the blood-brain barrier,
727
00:45:06.433 --> 00:45:16.020
But it only activates the gamma receptor of these stem cells, and it doesn't trigger some random catastrophic activity elsewhere in the body.
728
00:45:16.501 --> 00:45:28.470
I want to just really briefly talk about how important it is to have done this journey because we've now set the table of where the problem set exists today as these research teams are looking at this problem.
729
00:45:28.910 --> 00:45:30.692
Why is this so pernicious?
730
00:45:30.732 --> 00:45:32.813
Why is it so difficult to solve?
731
00:45:33.054 --> 00:45:34.375
Well, we first had to identify...
732
00:45:34.975 --> 00:45:37.556
the mechanisms and the mechanics of what was actually happening.
733
00:45:37.716 --> 00:45:37.916
Yeah.
734
00:45:38.176 --> 00:45:43.378
Once we've identified what's actually happening, then we have to think about different angles of attack to solve the problem.
735
00:45:43.418 --> 00:45:43.618
Yeah.
736
00:45:43.718 --> 00:45:45.859
Where in that chain do we go?
737
00:45:46.039 --> 00:45:46.259
Yeah.
738
00:45:46.539 --> 00:45:46.739
Right.
739
00:45:47.039 --> 00:45:52.221
And so then we decided, okay, let's try to see if we can reinvigorate these OPCs, which seems to be breaking down.
740
00:45:52.341 --> 00:45:52.561
Yeah.
741
00:45:52.962 --> 00:45:53.262
And then,
742
00:45:54.402 --> 00:45:55.783
We have to get to the location.
743
00:45:56.143 --> 00:45:59.604
So that was the hardest problem at first because it doesn't matter what we think if we can't get there.
744
00:45:59.624 --> 00:46:00.125
Yeah, yeah, yeah.
745
00:46:00.145 --> 00:46:01.545
Let's get past security.
746
00:46:01.745 --> 00:46:01.885
Right.
747
00:46:01.905 --> 00:46:05.107
We got to get through TSA and then we'll figure out if we can get on the plane.
748
00:46:05.387 --> 00:46:08.608
So we got past security, but our ticket didn't get us on the right plane.
749
00:46:08.628 --> 00:46:10.089
Yeah, no, it's every single plane.
750
00:46:10.129 --> 00:46:11.109
It gets us on every plane.
751
00:46:11.209 --> 00:46:13.150
And we were trying to go from London to Paris.
752
00:46:13.190 --> 00:46:14.951
And so we need to get the London to Paris ticket.
753
00:46:14.991 --> 00:46:22.214
And so that's where we are right now as we sort of transition into this current paper.
754
00:46:22.934 --> 00:46:24.676
This is the problem set.
755
00:46:25.176 --> 00:46:26.017
This is where we are.
756
00:46:26.578 --> 00:46:29.020
And now let's talk about where this current paper comes in.
757
00:46:29.540 --> 00:46:33.784
This current paper introduces the...
758
00:46:33.944 --> 00:46:41.471
This current paper basically introduces the idea of evolutionary medicine into this fray.
759
00:46:41.852 --> 00:46:45.995
Evolutionary medicine is a rapidly growing interdisciplinary field.
760
00:46:46.576 --> 00:46:47.216
Here's the idea.
761
00:46:47.257 --> 00:46:48.518
You basically apply...
762
00:46:48.938 --> 00:47:01.116
the principles of evolutionary biology to understand, prevent and treat human diseases because biology has had about 4 billion years to solve random problems.
763
00:47:01.216 --> 00:47:01.537
Okay.
764
00:47:02.017 --> 00:47:04.521
That have come up in every single niche, um,
765
00:47:05.242 --> 00:47:07.884
that life can possibly live in, right?
766
00:47:08.264 --> 00:47:10.786
Life has found a way to live everywhere somehow.
767
00:47:11.266 --> 00:47:14.728
And to do that, it has mutated its genome.
768
00:47:15.269 --> 00:47:18.751
It has figured out novel mechanisms by which to solve problems.
769
00:47:19.692 --> 00:47:30.679
The environment applies these selective pressures, and there's some way in which in order to survive, a species will optimize some molecular mechanism.
770
00:47:30.699 --> 00:47:30.819
Okay.
771
00:47:31.338 --> 00:47:35.921
We're using nature to cheat on the test because nature has been doing the test for way longer than we have.
772
00:47:35.941 --> 00:47:36.161
Yes.
773
00:47:36.281 --> 00:47:36.461
Yeah.
774
00:47:36.621 --> 00:47:36.802
Yeah.
775
00:47:36.942 --> 00:47:40.544
And in some sense, it would be foolish not to.
776
00:47:41.184 --> 00:47:41.485
Right.
777
00:47:41.645 --> 00:47:42.245
Right.
778
00:47:42.265 --> 00:47:43.586
The professor's not looking.
779
00:47:43.626 --> 00:47:44.126
Yeah.
780
00:47:44.146 --> 00:47:44.887
Look.
781
00:47:45.427 --> 00:47:49.610
In fact, the professor is like giving us all these disorders.
782
00:47:49.770 --> 00:47:50.430
Right.
783
00:47:50.510 --> 00:47:50.971
Right.
784
00:47:51.171 --> 00:47:52.292
The professor is the problem.
785
00:47:52.312 --> 00:47:52.452
Yeah.
786
00:47:52.472 --> 00:47:53.993
The professor is straight up the problem.
787
00:47:54.013 --> 00:47:55.294
The test is rigged.
788
00:47:56.234 --> 00:47:56.474
Right?
789
00:47:56.734 --> 00:48:00.696
So I'd rather try to figure out how to pass and live a good life.
790
00:48:00.996 --> 00:48:05.217
So there are a bunch of iconic successes in evolutionary pharmacology.
791
00:48:05.477 --> 00:48:10.019
The first one I want to talk about is Captopril.
792
00:48:10.639 --> 00:48:16.601
Captopril, the first ACE inhibitor, and it was discovered in snake venom.
793
00:48:16.961 --> 00:48:17.101
Mm-hmm.
794
00:48:17.708 --> 00:48:20.189
This came from the South American pit viper.
795
00:48:20.809 --> 00:48:24.350
It's an active compound that was isolated from the venom of this snake.
796
00:48:24.830 --> 00:48:32.333
And it's the first FDA-approved ACE inhibitor that revolutionized the treatment of high blood pressure and heart failure.
797
00:48:32.693 --> 00:48:34.634
And it saved millions of human lives.
798
00:48:35.174 --> 00:48:37.915
Effectively, the snake has evolved...
799
00:48:38.655 --> 00:48:42.378
a way to drop the blood pressure of its victims.
800
00:48:42.498 --> 00:48:44.260
That's how it kills people.
801
00:48:44.280 --> 00:48:49.044
Okay, when it bites things, like the venom drops the blood pressure and then the thing is dead.
802
00:48:49.124 --> 00:48:50.765
Yeah, because it can't get enough blood to it.
803
00:48:51.326 --> 00:48:52.827
Right, yeah, the heart stops.
804
00:48:52.867 --> 00:48:54.429
I mean, it's just, yeah, obvious, right?
805
00:48:55.289 --> 00:49:01.735
So then modern pharmacology diluted the thing and titrated it to the point where it's just enough
806
00:49:03.003 --> 00:49:05.644
To drop the blood pressure to healthy levels, right?
807
00:49:05.684 --> 00:49:09.485
I mean, obviously there was more into it, but that's effectively what happened.
808
00:49:09.765 --> 00:49:19.908
We figured it out in the venom and then we're like, okay, we can use this compound, things that look like this compound to then create all of the high blood pressure drugs that we see today.
809
00:49:20.523 --> 00:49:30.832
This also goes back to this idea of looking at things that feel totally orthogonal or unrelated to problems we're trying to solve are actually the location of the solution sometimes.
810
00:49:31.012 --> 00:49:31.212
Yeah.
811
00:49:31.412 --> 00:49:32.693
And this is a perfect example.
812
00:49:32.973 --> 00:49:33.314
Totally.
813
00:49:33.414 --> 00:49:41.460
Who knew watching the crocodile hunter and being inspired by snake venom as a child inspired some researcher to go and research that venom?
814
00:49:41.520 --> 00:49:41.741
Yeah.
815
00:49:41.881 --> 00:49:44.763
And then that ended up being a cure to cardiovascular disease.
816
00:49:44.783 --> 00:49:46.965
Yeah, I mean, the next one is even crazier, dude.
817
00:49:47.286 --> 00:49:52.812
The next one is Exanatide, which comes from the Gila monster lizard saliva.
818
00:49:53.232 --> 00:49:55.375
This is how we get GLP ones.
819
00:49:55.455 --> 00:49:56.756
This is how we get Ozempic.
820
00:49:56.936 --> 00:49:57.877
Oh, my goodness.
821
00:49:57.957 --> 00:50:04.404
Someone was messing around with a Gila monster venom and was like, hey, hey, this could be used against diabetes.
822
00:50:04.464 --> 00:50:04.585
Yeah.
823
00:50:05.248 --> 00:50:06.108
It's pretty crazy.
824
00:50:06.148 --> 00:50:14.010
So the groundbreaking GLP-1 receptor agonist is exanatide, and it was used to manage type 2 diabetes.
825
00:50:14.631 --> 00:50:17.111
The compound is exanidin-4.
826
00:50:17.211 --> 00:50:20.412
It was discovered in the toxic saliva of the gilla monster.
827
00:50:21.052 --> 00:50:24.193
It's this thing that eats only a few massive meals per year.
828
00:50:24.473 --> 00:50:27.474
This thing only eats like a few meals a year, right?
829
00:50:27.494 --> 00:50:31.355
So it needs a way to maintain stable blood sugar.
830
00:50:33.136 --> 00:50:35.637
Because it's only got a few, you know what I mean?
831
00:50:35.757 --> 00:50:37.797
It's in this extreme environment where it doesn't have a lot to eat.
832
00:50:37.857 --> 00:50:39.157
Yeah, yeah.
833
00:50:39.337 --> 00:50:43.458
And so humans looked at this and said, hey, this could actually be used against type 2 diabetes.
834
00:50:43.818 --> 00:50:56.141
The success of Xanatide and the pathway made scientists go in and engineer Ozempic, which is structurally, it's like basically a modified form of Xanatide.
835
00:50:56.341 --> 00:50:57.081
For the human body.
836
00:50:57.161 --> 00:50:57.901
For the human body.
837
00:50:57.961 --> 00:50:59.521
It's like optimized for the human body.
838
00:50:59.581 --> 00:51:02.402
But it came from the gillamonster venom.
839
00:51:03.221 --> 00:51:03.961
Kind of hilarious.
840
00:51:04.181 --> 00:51:11.063
If you're on Ozempic, if you're on WeGoV or any of the generics, you can thank the Gila monster.
841
00:51:11.203 --> 00:51:11.443
Yeah.
842
00:51:11.883 --> 00:51:20.385
Because without that and the research people that spent the time looking at this exotic animal in this extreme environment because the venom.
843
00:51:21.425 --> 00:51:21.905
Yeah.
844
00:51:21.945 --> 00:51:24.585
You're putting Gila monster venom derivative in your body.
845
00:51:24.946 --> 00:51:26.046
Okay.
846
00:51:26.326 --> 00:51:26.686
Yeah.
847
00:51:26.706 --> 00:51:29.266
That's effectively what we're doing.
848
00:51:29.286 --> 00:51:30.427
This is why science matters.
849
00:51:30.447 --> 00:51:31.227
It helps you lose weight.
850
00:51:31.327 --> 00:51:31.507
Right.
851
00:51:31.527 --> 00:51:31.627
Yeah.
852
00:51:32.688 --> 00:51:35.749
Yeah, this is finally how we're going to get Americans to pay attention.
853
00:51:35.769 --> 00:51:37.510
This is why basic research matters.
854
00:51:37.530 --> 00:51:40.011
That's how we got Ozepic, the Gilo monster.
855
00:51:40.412 --> 00:51:41.512
Everyone's going to be like, vote.
856
00:51:42.373 --> 00:51:42.873
Vote now.
857
00:51:44.113 --> 00:51:46.054
That's actually so crazy, though.
858
00:51:46.435 --> 00:51:47.335
Because that's so crazy.
859
00:51:48.490 --> 00:51:51.211
Do you understand how much money Ozempic is making right now and all of the GLP-1?
860
00:51:51.231 --> 00:51:51.972
I can't even imagine.
861
00:51:51.992 --> 00:51:56.374
The industry is – it's literally a driver of GDP growth.
862
00:51:56.394 --> 00:52:15.523
The CEO of one of the GLP-1 creators was like, we need to get a billion people on the planet onto GLPs because it's going to be a driver for America's GDP growth when we're struggling to find other areas of GDP growth.
863
00:52:15.823 --> 00:52:18.946
I'm just – that's a crazy thing to say.
864
00:52:19.026 --> 00:52:20.728
I'm just reminding people.
865
00:52:21.128 --> 00:52:22.349
No, that's a crazy thing to say.
866
00:52:22.369 --> 00:52:31.938
But that's literally – and then he's like, the problem is as we get more people on GLP-1s, we're destroying the GDP growth of like the food industry, right?
867
00:52:32.058 --> 00:52:32.339
Right.
868
00:52:32.419 --> 00:52:33.019
Oh, I see.
869
00:52:33.039 --> 00:52:34.481
Because people don't eat as much.
870
00:52:34.701 --> 00:52:37.483
So we have to find – this is where we're at.
871
00:52:39.553 --> 00:52:40.935
Sorry for the digression.
872
00:52:41.576 --> 00:52:43.018
Okay, that's actually hilarious.
873
00:52:44.200 --> 00:52:51.831
Well, Gila monsters, snakes, this particular paper has to do with the animals of the Tibetan plateau.
874
00:52:53.032 --> 00:52:55.075
They live at 4,000 meters on average.
875
00:52:56.268 --> 00:53:02.051
Like the yak, the humble yak, where oxygen partial pressure is roughly 40% lower than at sea level.
876
00:53:02.292 --> 00:53:04.653
I mean, we're pretty close to sea level here in Los Angeles.
877
00:53:05.013 --> 00:53:07.014
Imagine 40% less oxygen.
878
00:53:08.155 --> 00:53:20.681
These animals like the yaks, the Tibetan antelopes, snow leopards, they've spent thousands of generations evolving solutions to that environment that would kill most mammals in hours to days.
879
00:53:20.801 --> 00:53:21.022
Yeah.
880
00:53:21.042 --> 00:53:21.342
Okay.
881
00:53:21.882 --> 00:53:22.643
Including humans.
882
00:53:23.143 --> 00:53:26.986
Like chronic hypoxia, which is like low oxygen, is disastrous.
883
00:53:28.147 --> 00:53:29.648
A lot of oxidative stress.
884
00:53:30.409 --> 00:53:33.611
Very specifically, neurodevelopmental deficits.
885
00:53:34.012 --> 00:53:38.475
Particularly in white matter injury and demyelination.
886
00:53:38.756 --> 00:53:39.876
This is a known thing.
887
00:53:40.197 --> 00:53:42.639
This is the superhighway that we talked about earlier.
888
00:53:42.939 --> 00:53:43.159
Yeah.
889
00:53:43.259 --> 00:53:47.503
The white matter in the brain, which has a bunch of myelin, gets demyelinated.
890
00:53:48.163 --> 00:53:49.984
with hypoxia.
891
00:53:50.064 --> 00:53:51.665
So maybe there's a clue, right?
892
00:53:52.005 --> 00:53:52.826
Very interesting.
893
00:53:52.986 --> 00:53:56.388
I mean, the brain consumes something like 20% of the body's oxygen.
894
00:53:56.448 --> 00:54:01.331
So it makes sense that if the oxygen is low, the brain is one of the first things that's going to go, right?
895
00:54:01.391 --> 00:54:07.014
It doesn't have the raw materials to do the things it normally does, part of which is the myelination process.
896
00:54:07.234 --> 00:54:07.715
Exactly.
897
00:54:08.015 --> 00:54:10.096
And that's where this current paper comes in.
898
00:54:10.436 --> 00:54:13.498
So it's out of Shanghai, Jiao Tong University.
899
00:54:15.102 --> 00:54:30.234
It's a bunch of researchers that have published in Neuron that show that the genomes of these creatures has an amino acid change in a gene called RETSTAT, which is retinol saturase.
900
00:54:30.274 --> 00:54:31.955
We're going to get into what exactly that is.
901
00:54:31.975 --> 00:54:32.816
Right.
902
00:54:33.568 --> 00:54:37.889
That mutation in that gene is a gain-of-function mutation.
903
00:54:37.969 --> 00:54:51.474
But in this case, the gain-of-function is a natural gain-of-function that is caused by evolutionary mechanisms to let yaks and snow leopards and all of these animals survive in that hypoxic environment.
904
00:54:51.994 --> 00:54:52.774
The key thing is...
905
00:54:54.057 --> 00:54:55.679
This is across animals.
906
00:54:56.740 --> 00:54:57.601
This is conserved.
907
00:54:57.721 --> 00:55:00.564
This single mutation is conserved not just in the axe.
908
00:55:00.924 --> 00:55:02.185
It's in the Tibetan antelope.
909
00:55:03.407 --> 00:55:04.668
That's very interesting.
910
00:55:04.688 --> 00:55:06.249
Which means that there is something there.
911
00:55:06.430 --> 00:55:07.451
Yeah.
912
00:55:07.871 --> 00:55:10.574
The substrate has some universality to it.
913
00:55:10.834 --> 00:55:11.054
Yeah.
914
00:55:11.555 --> 00:55:14.480
Not the substrate, but there's a fundamental that has universality.
915
00:55:14.500 --> 00:55:14.900
Exactly.
916
00:55:14.960 --> 00:55:19.587
And it's a missense mutation, meaning it's not a mutation where the amino acid is unchanged.
917
00:55:19.988 --> 00:55:22.973
This is a different amino acid that has gone into the protein.
918
00:55:23.353 --> 00:55:26.157
So it has changed the structure of this protein.
919
00:55:26.618 --> 00:55:28.980
That is coming from the gene, right?
920
00:55:29.020 --> 00:55:30.341
So there's something that is going on.
921
00:55:30.801 --> 00:55:35.525
And historically, this gene, I mean, it's been studied before because we study genes all the time.
922
00:55:35.865 --> 00:55:40.868
This retinal saturase, it's just been studied for the context of fat storage.
923
00:55:41.809 --> 00:55:42.309
That's been it.
924
00:55:42.510 --> 00:55:42.750
Mm-hmm.
925
00:55:43.504 --> 00:55:45.365
Now there could be something else, right?
926
00:55:45.405 --> 00:55:52.026
Because now all of a sudden we found a mutation that is very specific to species that live in hypoxia.
927
00:55:52.927 --> 00:55:54.027
It's been found in yaks.
928
00:55:54.167 --> 00:55:57.348
It's been found in Tibetan antelopes, possibly others.
929
00:55:57.808 --> 00:56:04.550
So this convergent evolution, that signal is what told these researchers that there might be something going on.
930
00:56:05.150 --> 00:56:07.531
And so just to set the table one more time...
931
00:56:08.273 --> 00:56:16.156
We've identified why in multiple sclerosis, like functionally what's happening, all the research.
932
00:56:16.176 --> 00:56:16.856
Demyelination.
933
00:56:16.876 --> 00:56:17.696
Demyelination.
934
00:56:18.497 --> 00:56:25.039
All the research that led to this place of we know how we need to deliver to the source to solve the problem.
935
00:56:25.319 --> 00:56:32.882
But we've been unable to solve the problem because we can't target the therapeutic enough to not degrade other systems.
936
00:56:34.282 --> 00:56:36.163
In a separate lane from that setup.
937
00:56:36.183 --> 00:56:36.403
Yeah.
938
00:56:37.663 --> 00:56:50.550
This idea of studying the evolution across other animals on planet Earth has actually led to therapeutic solutions in humans and other use cases.
939
00:56:50.970 --> 00:57:02.977
The famous one we talked about being Ozempic, where we looked at gila monster venom and its ability to basically allow you to survive with less blood sugar, which is great if you want to manage.
940
00:57:03.177 --> 00:57:03.377
Yeah.
941
00:57:03.677 --> 00:57:06.319
Diabetes type 2 or obesity.
942
00:57:06.380 --> 00:57:07.020
Or obesity.
943
00:57:07.580 --> 00:57:15.847
And so the methodology of looking at other evolutionary examples to solve problems in humans has been wildly successful already.
944
00:57:17.410 --> 00:57:22.955
And in this environment, it's notable that this is coming out of several Chinese universities.
945
00:57:23.215 --> 00:57:23.956
Yeah, yeah.
946
00:57:23.976 --> 00:57:26.978
The main author is from Shanghai, but yeah.
947
00:57:27.439 --> 00:57:31.622
Across, it's a big team who have proximity to Tibet.
948
00:57:31.782 --> 00:57:32.683
Yep.
949
00:57:32.723 --> 00:57:38.228
So it's a context where they'll have regional reference points for this.
950
00:57:38.468 --> 00:57:43.352
And I think there's also a government sort of effort to...
951
00:57:44.653 --> 00:57:48.616
Get the genomes of all of its exotic animals.
952
00:57:48.896 --> 00:57:49.377
Which makes sense.
953
00:57:49.397 --> 00:57:49.797
In China.
954
00:57:49.977 --> 00:57:50.918
Which makes sense, totally.
955
00:57:51.118 --> 00:57:52.839
Because of exactly what we're talking about.
956
00:57:52.859 --> 00:57:55.861
Who knows what benefits come out of having the information.
957
00:57:56.261 --> 00:58:06.409
And so they've now found that in these low oxygen environments, a variety of species have been able to find a way to live.
958
00:58:06.729 --> 00:58:07.630
Yeah.
959
00:58:07.890 --> 00:58:11.032
And specifically combat white matter degradation.
960
00:58:11.352 --> 00:58:13.254
Which is related to...
961
00:58:14.234 --> 00:58:20.478
The problem with MS. And so we're trying to see, is there a bridge now between these two things?
962
00:58:20.498 --> 00:58:23.939
We've defined sort of both Lego blocks of the ideas.
963
00:58:24.660 --> 00:58:26.961
And we're going to try to see if we can build a bridge between these two things.
964
00:58:26.981 --> 00:58:27.721
Yes, exactly.
965
00:58:28.021 --> 00:58:29.162
And that's where we're going with this.
966
00:58:29.462 --> 00:58:29.722
Okay.
967
00:58:30.143 --> 00:58:35.345
So the researchers said, okay, we've got this substitution at the 247 locus.
968
00:58:37.691 --> 00:58:41.435
And this is a gain-of-function mutation because clearly this thing is doing well, right?
969
00:58:42.515 --> 00:58:54.847
This thing is causing the individuals, the species, to actually create white matter, even under environmental stress, even under the stress of low oxygen.
970
00:58:55.836 --> 00:58:57.477
First of all, how did they actually prove that?
971
00:58:57.797 --> 00:59:02.098
They actually proved that by engineering a genetic knock-in mouse model.
972
00:59:02.498 --> 00:59:13.642
So you can take these mouse models that are in the lab, and you can take the RETSTAT-YAK version and replace the RETSTAT mouse version with the RETSTAT-YAK version.
973
00:59:13.902 --> 00:59:17.724
So now these genetic mouse models have the RETSTAT-YAK version.
974
00:59:17.804 --> 00:59:20.525
You've got a control, and you've got the YAK version.
975
00:59:20.545 --> 00:59:24.086
We're basically just changing the genetic ingredients and seeing what comes out at the end.
976
00:59:24.206 --> 00:59:25.307
Yeah, yeah.
977
00:59:25.327 --> 00:59:29.870
And we're trying to see, okay, does this thing play a role in protecting brain health?
978
00:59:30.130 --> 00:59:33.872
This is like the first thing that you do if you're a grad student that's assigned to this project.
979
00:59:34.312 --> 00:59:39.055
Is there a there there that can then turn into this massive neuron paper?
980
00:59:39.716 --> 00:59:40.376
Turns out, yes.
981
00:59:40.756 --> 00:59:42.758
The way they did it was really quite simple.
982
00:59:43.098 --> 00:59:48.862
They had the wild type, which is the control, and they had the RETSTAT knock-in mice, right?
983
00:59:49.835 --> 00:59:53.539
And they exposed these knock-in mice and the control mice to hypoxia.
984
00:59:54.040 --> 01:00:03.331
They put it in a chamber where it mimicked the amount of oxygen that's there at 5,000 meters or whatever the Tibetan plateau is.
985
01:00:04.031 --> 01:00:09.236
You subject it to something called a Morris water maze, which is the...
986
01:00:09.376 --> 01:00:19.565
Imagine you've got like a circular sort of water tank, and you've got a single spot of a platform just under the water, so the mouse can't actually see where the platform is.
987
01:00:19.605 --> 01:00:22.467
But if it gets to the platform, it gets like cheese or something.
988
01:00:22.868 --> 01:00:26.030
It's got to swim around, and it's only seeing the...
989
01:00:26.866 --> 01:00:28.427
cues of the walls.
990
01:00:28.607 --> 01:00:31.788
Like there's like patterns on the walls that tell it where it is in the water maze.
991
01:00:32.208 --> 01:00:40.072
Now, pretty soon, if you're a smart mouse and everything is working, you're going to do a random walk, random swimming to figure out where the platform is.
992
01:00:40.372 --> 01:00:45.774
And then once you're on the platform, you're going to notice, given the cues on the outside, where I am.
993
01:00:46.874 --> 01:00:50.676
And then the subsequent trials, I'm going to be way faster, right?
994
01:00:50.996 --> 01:00:55.118
What they noticed was that the wild type under hypoxia did not learn.
995
01:00:56.178 --> 01:01:04.454
But, and that's why you see the wild type, as the days go by, the amount of time that it takes to get to the platform stays the constant.
996
01:01:05.015 --> 01:01:07.159
But the ones with the red stack...
997
01:01:09.107 --> 01:01:10.068
Very rapid learning.
998
01:01:10.108 --> 01:01:10.408
Yeah.
999
01:01:10.649 --> 01:01:10.849
Right?
1000
01:01:10.889 --> 01:01:13.432
So the brain is functioning normally for the ones with RETSTAT.
1001
01:01:13.652 --> 01:01:16.955
What this shows is one, the RETSTAT knock-in thing is working.
1002
01:01:16.996 --> 01:01:17.256
Yeah.
1003
01:01:17.436 --> 01:01:22.722
And two, the RETSTAT is crucial for brain functioning in low oxygen environments.
1004
01:01:22.922 --> 01:01:31.071
So we've done our job in creating a biological model that I can now use in the lab to manipulate and understand what exactly is going on here.
1005
01:01:31.131 --> 01:01:31.371
Yeah.
1006
01:01:31.551 --> 01:01:31.711
Right?
1007
01:01:31.791 --> 01:01:32.632
Yeah.
1008
01:01:32.652 --> 01:01:33.633
That's the main idea.
1009
01:01:33.653 --> 01:01:38.336
We were able to replicate what happens in nature in a lab, which means we can then make it repeatable.
1010
01:01:38.576 --> 01:01:39.277
Yeah, exactly.
1011
01:01:39.297 --> 01:01:40.017
For our own purposes.
1012
01:01:40.037 --> 01:01:40.238
Yeah.
1013
01:01:40.458 --> 01:01:44.521
Now, the second hypothesis is that the – I mean, this was all a behavioral assay.
1014
01:01:45.021 --> 01:01:45.822
What about the myelin?
1015
01:01:45.922 --> 01:01:47.903
Is it actually doing the thing that the myelin is doing?
1016
01:01:48.184 --> 01:01:53.748
And yes, you can go in and look at the brain slices and see if you stain the myelin –
1017
01:01:54.660 --> 01:02:00.526
The wild type has, in normal oxygen environments, the wild type has a certain amount of myelin.
1018
01:02:00.546 --> 01:02:00.886
Yes.
1019
01:02:01.066 --> 01:02:04.630
And if you look in the hypoxia environment, the wild type does not have that myelin.
1020
01:02:04.890 --> 01:02:06.792
So clearly the myelin is getting degraded.
1021
01:02:07.172 --> 01:02:08.293
Which is what happens in MS.
1022
01:02:08.494 --> 01:02:11.877
Which is what happens in MS and what happens in normal individuals when they go to,
1023
01:02:12.137 --> 01:02:13.718
Tibet and hang out for way too long.
1024
01:02:13.998 --> 01:02:16.280
Like if you go to Mount Everest, that's why you need oxygen.
1025
01:02:16.820 --> 01:02:18.521
It's because this is happening, right?
1026
01:02:18.982 --> 01:02:22.464
But for the ones with Rhetstat, you've got normal amounts of myelin.
1027
01:02:22.724 --> 01:02:25.386
So the second hypothesis is also making sense.
1028
01:02:25.806 --> 01:02:30.069
That the Rhetstat has something to do with the maintenance of myelin itself.
1029
01:02:30.649 --> 01:02:32.432
In low oxygen environments.
1030
01:02:32.653 --> 01:02:33.174
Is that clear?
1031
01:02:33.595 --> 01:02:34.176
Very much so.
1032
01:02:34.336 --> 01:02:34.596
Okay.
1033
01:02:34.837 --> 01:02:37.882
So now we're going to move on and understand how is RETSTAT doing that?
1034
01:02:38.022 --> 01:02:38.263
Okay.
1035
01:02:38.523 --> 01:02:42.270
So we know that the RETSTAT has something to do with myelin maintenance.
1036
01:02:42.290 --> 01:02:42.430
Okay.
1037
01:02:43.550 --> 01:02:48.194
But from what I've previously told you, that has to do with these oligodendrocytes, right?
1038
01:02:48.714 --> 01:02:52.197
And the pregenitor cells, those stem cells becoming oligodendrocytes.
1039
01:02:52.377 --> 01:02:53.218
OPCs.
1040
01:02:53.378 --> 01:02:56.520
Oh, the OPCs, yeah, becoming the myelin sheets.
1041
01:02:56.761 --> 01:02:56.961
Yes.
1042
01:02:57.341 --> 01:03:00.424
Does the RET-stat have something to do with that pathway?
1043
01:03:01.064 --> 01:03:01.685
Okay, okay.
1044
01:03:01.705 --> 01:03:02.105
Right?
1045
01:03:02.125 --> 01:03:04.187
Because that's what I love about this.
1046
01:03:04.307 --> 01:03:08.310
It's like very gradually, I was just following the figures in the main text.
1047
01:03:08.690 --> 01:03:16.495
And they're telling a story of like, first we established RETSTAT has something to do with myelin survival in hypoxia.
1048
01:03:16.955 --> 01:03:20.657
Now, does it have something to do with repairing the myelin specifically?
1049
01:03:21.458 --> 01:03:23.259
And that's where we get into these lesion studies.
1050
01:03:23.279 --> 01:03:27.461
What you can do is you can have LPC-induced demethylation.
1051
01:03:27.501 --> 01:03:31.564
Basically, you take like a kind of poison and you micro-inject it into parts of the brain.
1052
01:03:31.864 --> 01:03:33.885
And so you see like in the top row...
1053
01:03:34.645 --> 01:03:36.587
There's like a hole in the green.
1054
01:03:36.727 --> 01:03:39.030
The green is tagging the myelin.
1055
01:03:39.450 --> 01:03:41.833
And you've created like kind of a hole, right?
1056
01:03:43.455 --> 01:03:46.097
On the bottom row, there's no hole.
1057
01:03:47.058 --> 01:03:48.300
The top row is the control.
1058
01:03:49.636 --> 01:03:51.058
The hole cannot be repaired.
1059
01:03:51.338 --> 01:03:54.281
But on the bottom row, because of the RET stat, the hole was repaired.
1060
01:03:54.321 --> 01:03:59.507
This is basically, I think this was 15 days, 14 days after the initial injury.
1061
01:03:59.767 --> 01:04:05.814
So they've done some kind of injury, and you're seeing that that injury is persisting in the control under hypoxia.
1062
01:04:06.174 --> 01:04:06.355
But...
1063
01:04:07.220 --> 01:04:08.141
In the red stat.
1064
01:04:08.421 --> 01:04:08.602
Right?
1065
01:04:08.842 --> 01:04:10.704
So now we're on to it.
1066
01:04:10.924 --> 01:04:22.537
Because this is different than what we're pointing to here specifically is the ability to, for the regenerative, once you've created this problem.
1067
01:04:22.697 --> 01:04:24.438
Yeah, the lesion.
1068
01:04:24.458 --> 01:04:26.458
This is like simulating what MS would do.
1069
01:04:26.478 --> 01:04:26.878
Would do.
1070
01:04:27.438 --> 01:04:38.441
You're actually doing the thing that MS doesn't do, which is the OPCs go through their pathway to then become the ogala dentricites that can now remelanate.
1071
01:04:38.642 --> 01:04:38.822
Yeah.
1072
01:04:39.182 --> 01:04:41.382
And it's not happening in the control.
1073
01:04:41.442 --> 01:04:41.682
Yes.
1074
01:04:41.762 --> 01:04:43.503
Which is what we would see in an MS patient.
1075
01:04:43.523 --> 01:04:43.683
Mm-hmm.
1076
01:04:43.963 --> 01:04:46.146
But when we have the reset, it is happening.
1077
01:04:46.166 --> 01:04:59.246
So now we've made the connection not only to the hypoxia environment, but specifically to this idea that the stem cell can get to its adult maturity and do the repair process that we're actually trying to target.
1078
01:04:59.987 --> 01:05:03.309
Starting from this evolutionary inspiration.
1079
01:05:03.369 --> 01:05:03.849
Exactly.
1080
01:05:04.110 --> 01:05:07.572
And so we're getting down to the mechanism, but we're still not there yet.
1081
01:05:07.672 --> 01:05:07.932
Okay.
1082
01:05:08.092 --> 01:05:08.292
Right?
1083
01:05:08.512 --> 01:05:10.353
Because so far, what have we established?
1084
01:05:10.373 --> 01:05:15.497
We've established that this new gene that's in yaks, it can remyelinate.
1085
01:05:16.258 --> 01:05:43.202
lesions how is it doing it though right now we get into the process this is the detective work we're digging deeper and deeper into now trying to understand what is the exact molecular pathway that is going to get us there because that is how we get a therapy right right we're not going to get a therapy by like genetically modifying patients that's not what we're doing right we're trying to figure out what is the gene doing right and can we maybe hijack that pathway yes
1086
01:05:43.782 --> 01:05:43.982
Right?
1087
01:05:44.122 --> 01:05:44.323
Yes.
1088
01:05:44.423 --> 01:05:44.663
Okay.
1089
01:05:45.003 --> 01:05:45.924
So let's move on.
1090
01:05:46.545 --> 01:05:49.128
And this is where we get into a weird, weird discovery.
1091
01:05:49.268 --> 01:05:49.548
Okay.
1092
01:05:50.169 --> 01:05:58.498
What you would think, based on that previous photo, is that the stem cells are creating the oligodendrocytes, right?
1093
01:05:58.578 --> 01:05:59.078
Which is then...
1094
01:06:00.090 --> 01:06:01.251
populating the myelin.
1095
01:06:02.112 --> 01:06:05.637
So this has something to do with the oligodendrocytes.
1096
01:06:05.677 --> 01:06:11.023
This is an innate mechanism within the stem cells, within the oligodendrocytes that is doing the repair.
1097
01:06:11.844 --> 01:06:16.870
They did a bunch of studies, which I'm not going to show here, because, I mean, they have so many amazing figures.
1098
01:06:16.930 --> 01:06:18.912
But one of the figures that we're going to skip here is...
1099
01:06:19.702 --> 01:06:30.865
They put in the gene and expressed it only in the stem cells, the precursor cells, the oligodendrocyte precursor cells.
1100
01:06:31.065 --> 01:06:33.486
Naively, you would think, that's all I need.
1101
01:06:33.506 --> 01:06:34.466
Okay, right.
1102
01:06:34.586 --> 01:06:42.908
If the oligodendrocytes have this new gene, and those are the ones that are doing the repair, then it should be enough to do the repair.
1103
01:06:43.088 --> 01:06:43.768
Nothing happens.
1104
01:06:44.649 --> 01:06:44.929
Okay?
1105
01:06:49.550 --> 01:06:54.432
the stem cells to become myelin, but the gene has nothing to do with those stem cells.
1106
01:06:55.052 --> 01:06:56.773
There's an intermediary.
1107
01:06:57.613 --> 01:07:02.455
So you introduce it into the environment, but it's not a direct correlation.
1108
01:07:02.575 --> 01:07:06.377
If you introduce it to the entire genome, that's what they did with these mouse models, right?
1109
01:07:06.657 --> 01:07:09.478
It's like all of the cells now have this gene, it works.
1110
01:07:10.098 --> 01:07:14.040
If only the myelin sheath cells have this gene, it doesn't work.
1111
01:07:14.100 --> 01:07:18.742
But that doesn't, it's kind of weird because the myelin sheath is the thing that is doing the repair, but it's not working.
1112
01:07:19.802 --> 01:07:23.546
The next figure, we're going to see the kind of resolution here.
1113
01:07:24.847 --> 01:07:28.049
They expressed the genes only in the neurons.
1114
01:07:29.331 --> 01:07:36.617
And all of a sudden, you get a recovery of that oligodendrocytes coming back and the myelin coming back.
1115
01:07:36.777 --> 01:07:38.378
As opposed to doing it in the OPCs.
1116
01:07:38.418 --> 01:07:38.698
Yes.
1117
01:07:39.279 --> 01:07:39.879
That's weird.
1118
01:07:39.920 --> 01:07:40.540
That is weird.
1119
01:07:40.720 --> 01:07:41.161
It's weird.
1120
01:07:41.741 --> 01:07:43.523
It's like the neurons are doing something.
1121
01:07:44.533 --> 01:07:49.334
That is then telling the OPCs to come and help me out.
1122
01:07:49.694 --> 01:07:50.314
Help me.
1123
01:07:51.094 --> 01:07:51.735
Help me.
1124
01:07:52.015 --> 01:07:53.535
Yeah, that's what's happening, right?
1125
01:07:54.895 --> 01:08:07.198
But that's an interesting point, which is sometimes the site of where the solution arises, which is these OPCs that ultimately mature and then are the ones that create the myelin sheaths.
1126
01:08:09.210 --> 01:08:14.253
So it actually could be what is signaling it that's really where you need to target.
1127
01:08:14.293 --> 01:08:16.794
And this is part of, I think, what we're trying to say we're getting in here.
1128
01:08:17.294 --> 01:08:21.316
And I had told you earlier that the problem was not that the OPCs were not there.
1129
01:08:21.476 --> 01:08:21.896
Correct.
1130
01:08:21.996 --> 01:08:24.097
The problem was that the signaling was not there.
1131
01:08:24.157 --> 01:08:25.158
They didn't know what to do.
1132
01:08:25.718 --> 01:08:29.500
Perhaps, perchance, the neurons are signaling them.
1133
01:08:30.220 --> 01:08:31.322
to do this correctly.
1134
01:08:31.462 --> 01:08:39.914
And the RET-STAT genome, that gene, that mutation, is giving the neurons the ability to correctly tell the oligodendrocytes, hey, I need help.
1135
01:08:40.628 --> 01:08:46.871
The I need help signal was basically – which goes from the neurons to the OPCs.
1136
01:08:47.111 --> 01:08:48.671
There was a breakdown in communication.
1137
01:08:48.691 --> 01:08:48.851
Yep.
1138
01:08:49.552 --> 01:08:54.594
And that seems to now be where we're centering in as like the cause of the problem.
1139
01:08:54.614 --> 01:08:54.794
Yeah.
1140
01:08:55.254 --> 01:08:57.835
And so we were looking in all the wrong places before.
1141
01:08:58.135 --> 01:08:58.355
Yeah.
1142
01:08:58.475 --> 01:08:59.876
We had to do all that discovery work.
1143
01:08:59.896 --> 01:09:00.396
Yeah, yeah, yeah.
1144
01:09:00.456 --> 01:09:01.856
We had to rule things out.
1145
01:09:02.457 --> 01:09:03.997
And now it's okay.
1146
01:09:04.438 --> 01:09:08.059
Actually, the thing that is giving the proper instruction –
1147
01:09:09.221 --> 01:09:11.523
is where the degradation is actually problematic.
1148
01:09:11.543 --> 01:09:11.763
Yeah.
1149
01:09:12.203 --> 01:09:15.886
And it's in the source that needs the protection, which is the neuron.
1150
01:09:15.946 --> 01:09:16.387
Exactly.
1151
01:09:16.707 --> 01:09:16.927
Yeah.
1152
01:09:17.228 --> 01:09:20.270
And so now we're holding in on this signaling, right?
1153
01:09:20.290 --> 01:09:20.510
Yeah.
1154
01:09:20.590 --> 01:09:22.672
Perhaps it's something called paracrine signaling.
1155
01:09:22.712 --> 01:09:27.496
It's simply like a chemical message or some kind of raw material that the neuron is producing.
1156
01:09:27.876 --> 01:09:29.918
It's being secreted into the local neighborhood.
1157
01:09:30.238 --> 01:09:35.142
And then the oligodendrocytes are catching it and being like, oh, this guy needs help.
1158
01:09:35.542 --> 01:09:36.363
I need to go get there.
1159
01:09:37.629 --> 01:09:44.073
They performed, you know, because even now, what is the mechanism of this signaling?
1160
01:09:44.333 --> 01:09:44.513
Right.
1161
01:09:44.553 --> 01:09:47.194
Is the signaling neuron to oligodendrocyte straight up?
1162
01:09:47.855 --> 01:09:53.058
Or is it something more general where the neuron just like puts out a PSA on Twitter?
1163
01:09:53.278 --> 01:09:53.998
Yeah.
1164
01:09:54.138 --> 01:09:56.419
And then those who are interested come and help him.
1165
01:09:56.560 --> 01:09:56.740
Yeah.
1166
01:09:56.760 --> 01:09:56.920
Right?
1167
01:09:57.000 --> 01:09:59.601
Is it a DM or is it a full tweet?
1168
01:10:00.101 --> 01:10:01.322
Or is there an intermediary?
1169
01:10:01.422 --> 01:10:01.642
Yeah.
1170
01:10:01.802 --> 01:10:02.563
That is a secret.
1171
01:10:02.883 --> 01:10:03.884
Is it a back channel?
1172
01:10:03.944 --> 01:10:04.184
Yes.
1173
01:10:04.524 --> 01:10:04.724
Yeah.
1174
01:10:05.044 --> 01:10:05.945
These are all the possibilities.
1175
01:10:05.965 --> 01:10:06.245
Possibilities.
1176
01:10:06.505 --> 01:10:06.685
Right?
1177
01:10:06.925 --> 01:10:08.526
And we need to figure that out.
1178
01:10:08.606 --> 01:10:11.187
So how did they figure out which is what?
1179
01:10:11.387 --> 01:10:11.988
How do we know?
1180
01:10:12.228 --> 01:10:12.708
How do we know?
1181
01:10:13.208 --> 01:10:13.809
Here's what they did.
1182
01:10:13.849 --> 01:10:19.291
They took something called a conditioned medium, which is a liquid bath that surrounds the cultured cells.
1183
01:10:19.631 --> 01:10:20.292
So here's what you do.
1184
01:10:20.732 --> 01:10:25.654
You express the mutation, the RETSTAT mutation, the YAK mutation in the neurons.
1185
01:10:26.114 --> 01:10:30.297
Presumably the neurons are going to secrete something and then that's going to go to the oligodendrocytes.
1186
01:10:30.577 --> 01:10:32.858
How do we know that it's not a DM and just a...
1187
01:10:33.478 --> 01:10:36.700
just something that I'm secreting into the environment.
1188
01:10:36.720 --> 01:10:36.981
A tweet.
1189
01:10:37.281 --> 01:10:37.461
Right?
1190
01:10:37.701 --> 01:10:39.602
What I can do is I can take the medium.
1191
01:10:40.163 --> 01:10:43.785
The cells have like some, you know, the medium that they grow in.
1192
01:10:44.226 --> 01:10:52.792
And if the neurons are really just secreting into the environment and it's not a direct signal, then I can extract that medium and I can now put it into another...
1193
01:10:54.227 --> 01:11:11.410
wild type control and it would and the the precursors should see this signal this signal and start myelinating because if it was a tweet to everybody it would be it would be in the environment in the in the yes it would be and that's exactly what we see oh my goodness
1194
01:11:12.591 --> 01:11:16.677
It's a nice progressive kind of, ah, we're getting somewhere.
1195
01:11:16.697 --> 01:11:17.338
Yeah, yeah, yeah.
1196
01:11:17.398 --> 01:11:17.598
Right?
1197
01:11:17.878 --> 01:11:21.103
The story is very cool from one figure to the next.
1198
01:11:21.423 --> 01:11:27.011
And so here on the bottom, we're seeing the medium, and you're seeing a lot more green dots.
1199
01:11:27.271 --> 01:11:27.471
Mm-hmm.
1200
01:11:28.352 --> 01:11:29.414
Yep.
1201
01:11:29.694 --> 01:11:33.239
And that's from these neurons.
1202
01:11:33.919 --> 01:11:34.861
So this is so interesting.
1203
01:11:34.901 --> 01:11:42.410
So we've now identified that the challenge with the degradation of myelin—
1204
01:11:43.931 --> 01:11:55.496
which we initially thought might have been because it wasn't going from baby OPC to these oligodendrocytes that were the thing that created and fixed myelin sheaths as things happen.
1205
01:11:55.576 --> 01:11:55.776
Yeah.
1206
01:11:56.276 --> 01:11:59.237
So we thought maybe there's something in the maturity process of that that's problematic.
1207
01:11:59.638 --> 01:12:00.798
It ended up not being true.
1208
01:12:01.158 --> 01:12:05.980
We looked at this evolutionary reference point from high-altitude Tibetan animals.
1209
01:12:06.440 --> 01:12:09.862
We found something that works for hypoxia that seemed like it was connected.
1210
01:12:09.882 --> 01:12:10.222
Yeah.
1211
01:12:10.422 --> 01:12:13.744
We established that with the Restat.
1212
01:12:14.085 --> 01:12:15.465
It definitely was connected somehow.
1213
01:12:15.545 --> 01:12:17.747
It definitely was connected somehow, but we didn't quite know how.
1214
01:12:18.407 --> 01:12:27.373
And back to where we started at the beginning, it wasn't clear that the problem was that – because the OPCs were present in MS patients.
1215
01:12:27.994 --> 01:12:30.996
And so it wasn't that they weren't there anymore.
1216
01:12:32.250 --> 01:12:37.015
It seemed to be that the trigger to do the job was breaking down.
1217
01:12:37.155 --> 01:12:37.455
Exactly.
1218
01:12:37.475 --> 01:12:40.018
But we didn't know where the trigger was being triggered from.
1219
01:12:40.038 --> 01:12:40.278
Yes.
1220
01:12:40.518 --> 01:12:42.180
Who's the captain that's giving orders?
1221
01:12:42.340 --> 01:12:42.560
Yes.
1222
01:12:43.241 --> 01:12:49.366
Through this process that this team in Shanghai did, we've now found that the neuron –
1223
01:12:50.067 --> 01:13:12.206
is the captain exactly that is triggering the instruction for the opc's to get the proper instruction to mature to then go to the process to recreate the myelin sheath yeah and it's sending those instructions not in a discreet way like a dm it's just literally blasting the signal out yeah it's like i need help and so what makes sense is uh
1224
01:13:12.987 --> 01:13:25.840
There's a – the neuron is the source of the problem and it's not tweeting and it's private messaging itself or just – it sets account on private and no one can see its tweets as a crude, annoying analogy.
1225
01:13:26.481 --> 01:13:32.767
And that's the functional – so now – and we know because when we took the tweet –
1226
01:13:33.027 --> 01:13:39.793
from one environment and put it around things that were not exposed to the tweet initially, it started to do the thing.
1227
01:13:39.993 --> 01:13:41.234
So we know that that's true.
1228
01:13:41.514 --> 01:13:42.335
We know that that's true.
1229
01:13:42.415 --> 01:13:44.556
And now we just need to hone in on what the tweet is.
1230
01:13:44.616 --> 01:13:45.277
What is the tweet?
1231
01:13:45.517 --> 01:13:47.979
What is the compound that is doing this?
1232
01:13:48.039 --> 01:13:54.664
Because if we could make this compound, then maybe we could solve this problem.
1233
01:13:54.764 --> 01:13:55.005
Yes.
1234
01:13:55.425 --> 01:13:59.388
So now we've established that there is something in that medium, in that cultured medium that,
1235
01:14:00.063 --> 01:14:01.804
that we can transfer from one to the other.
1236
01:14:02.225 --> 01:14:02.825
And it works.
1237
01:14:02.885 --> 01:14:03.746
And it works.
1238
01:14:03.906 --> 01:14:05.607
That's low-key crazy.
1239
01:14:05.727 --> 01:14:06.608
Yeah.
1240
01:14:07.468 --> 01:14:10.190
I think it's really cool, just the story of this whole thing.
1241
01:14:10.410 --> 01:14:11.571
So now, what could that signal be?
1242
01:14:11.831 --> 01:14:16.915
Well, we've got retinol, which is vitamin A, and that enters cells and gets processed in two major branches.
1243
01:14:17.055 --> 01:14:22.259
One way is just the classical branch, which is retinol into retinal into retinoic acid.
1244
01:14:22.539 --> 01:14:25.261
Chemists are so annoying about this, right?
1245
01:14:25.341 --> 01:14:27.022
It's like, come on, guys.
1246
01:14:27.462 --> 01:14:27.783
Anyways.
1247
01:14:28.623 --> 01:14:32.664
The other way, which is mediated by this RET-STAT gene...
1248
01:14:32.944 --> 01:14:40.167
Which we discovered through studying these Tibetan... Well, no, actually, the RET-STAT gene is something that's been discovered before.
1249
01:14:40.247 --> 01:14:42.328
This mutation was discovered through the...
1250
01:14:42.368 --> 01:14:44.108
The specific mutation of the gene.
1251
01:14:44.388 --> 01:14:49.310
And we know that the RET-STAT gene does this, which is it takes retinol and then it...
1252
01:14:49.910 --> 01:14:55.157
puts it into something called ATDR, which is 1314 dihydroretinol.
1253
01:14:55.457 --> 01:15:03.106
It's effectively an isomer type of thing where you take the retinol, you remove some double bonds, make them single bonds and things like that.
1254
01:15:03.126 --> 01:15:08.253
And here we can see that the ATDR that...
1255
01:15:10.129 --> 01:15:15.732
the RET-stat mediated thing that is coming out of the vitamin A, that is going up.
1256
01:15:16.452 --> 01:15:16.673
Okay?
1257
01:15:16.853 --> 01:15:22.175
The concentration of this particular compound is going up because of this gene.
1258
01:15:23.096 --> 01:15:23.316
Yes.
1259
01:15:23.596 --> 01:15:23.896
Okay?
1260
01:15:23.916 --> 01:15:38.203
So we've now made the causal link between the presence of this gene mutation and the compound, this ATDR, being present, which we see in these spikes in that top section with the RESTAT and the RESTAT Q247R.
1261
01:15:38.303 --> 01:15:38.663
Yeah, yeah.
1262
01:15:38.683 --> 01:15:42.006
And you can see that the second spike is larger than the control.
1263
01:15:42.086 --> 01:15:42.266
Yeah.
1264
01:15:42.427 --> 01:15:45.589
Which means that you've got, this is that gain of function that I was talking about.
1265
01:15:45.610 --> 01:15:45.870
Yes.
1266
01:15:45.930 --> 01:15:46.170
Right.
1267
01:15:46.290 --> 01:15:46.470
Right.
1268
01:15:46.490 --> 01:15:50.374
Like this, this particular mutation is giving me more ATDR.
1269
01:15:50.534 --> 01:15:54.077
And perhaps that more signal is telling the,
1270
01:15:54.818 --> 01:16:18.985
opcs and the stem cells hey i need more myelin it's it's now loud enough yes when we have the because so actually this is a good point so the in the three top charts right the second and third the restat and the restat q24 that second the third one all the way to the right that's the that's the yak version that's the mutation yeah and then the one in the middle is just like control standard operating procedure yeah and so potentially it's the unless the atd
1271
01:16:19.465 --> 01:16:24.269
the ATDR being at a particular level might be the tipping point.
1272
01:16:24.289 --> 01:16:24.529
Yes.
1273
01:16:24.709 --> 01:16:26.110
And if it's below a particular level.
1274
01:16:26.130 --> 01:16:27.731
In hypoxia, it might be a tipping point.
1275
01:16:27.811 --> 01:16:28.252
Exactly.
1276
01:16:28.272 --> 01:16:28.412
Okay.
1277
01:16:28.592 --> 01:16:29.673
And perhaps in MS.
1278
01:16:29.993 --> 01:16:30.714
It might be a tipping point.
1279
01:16:30.734 --> 01:16:31.354
It might be a tipping point.
1280
01:16:31.374 --> 01:16:34.737
That's the connection we're trying to, that's the bridge again from before that we're trying to draw.
1281
01:16:34.777 --> 01:16:34.997
Yeah.
1282
01:16:35.277 --> 01:16:35.737
So, okay.
1283
01:16:35.757 --> 01:16:37.419
What if I just add ATDR?
1284
01:16:38.820 --> 01:16:39.020
Right.
1285
01:16:39.060 --> 01:16:39.240
Right.
1286
01:16:39.620 --> 01:16:40.401
Is that going to be enough?
1287
01:16:40.421 --> 01:16:40.561
Yeah.
1288
01:16:42.545 --> 01:16:44.286
Biology is never so kind.
1289
01:16:44.326 --> 01:16:45.147
It's never so simple.
1290
01:16:45.367 --> 01:16:45.567
Right?
1291
01:16:46.007 --> 01:16:54.593
In H, you can see that if I add the ATDR, all of those bars are about the same.
1292
01:16:54.653 --> 01:16:56.374
There's an NS on top of them.
1293
01:16:56.674 --> 01:16:58.635
What that means is not significant.
1294
01:16:58.675 --> 01:17:05.840
There is no significant difference between whether I add it and whether I don't in the control or in the restat, whatever, right?
1295
01:17:06.080 --> 01:17:08.121
So clearly ATDR is not everything.
1296
01:17:08.902 --> 01:17:10.803
But in the K...
1297
01:17:12.474 --> 01:17:20.957
If I add ATDR to the neurons and then I culture it, remember the ATDR is the stuff that came out of the RET stat.
1298
01:17:21.057 --> 01:17:22.677
The control even makes the RET stat.
1299
01:17:23.898 --> 01:17:25.338
The control even makes the ATDR.
1300
01:17:25.698 --> 01:17:28.979
If I add that to my OPCs, then all of a sudden I get this effect.
1301
01:17:29.720 --> 01:17:30.740
So there is something else.
1302
01:17:31.760 --> 01:17:36.502
This proves that ATDR is just a precursor and the neurons are doing something else.
1303
01:17:37.267 --> 01:17:38.870
With that ATDR, right?
1304
01:17:38.910 --> 01:17:39.512
Yeah, yeah, yeah.
1305
01:17:39.532 --> 01:17:39.832
Okay.
1306
01:17:40.013 --> 01:17:45.765
So not only am I getting more of ATDR, but the neurons are doing something more with that more ATDR.
1307
01:17:46.566 --> 01:17:48.029
So it's almost like it is a...
1308
01:17:50.222 --> 01:17:59.691
just by having the neurons, it's a fundamental ingredient for whatever it is the neurons are doing, but it is not the solution in and of itself.
1309
01:17:59.731 --> 01:17:59.992
Yes.
1310
01:18:00.552 --> 01:18:10.922
And one of the inclinations that these guys had, one of the inclinations that these guys had was, well, the ATDR, which is a second, you know, it's a version two of vitamin A of the retinol.
1311
01:18:11.322 --> 01:18:12.263
There's a version three.
1312
01:18:13.004 --> 01:18:15.865
which is ATDRA, it's just ATDR with an acid.
1313
01:18:16.225 --> 01:18:16.485
Okay?
1314
01:18:16.545 --> 01:18:21.466
It's the acidic version of that, that maybe this thing is maturing into.
1315
01:18:22.966 --> 01:18:23.046
Ah.
1316
01:18:23.106 --> 01:18:23.406
Okay?
1317
01:18:23.586 --> 01:18:23.906
Okay.
1318
01:18:24.126 --> 01:18:27.027
When they add that, they get a massive spike.
1319
01:18:27.867 --> 01:18:28.827
They get a massive spike.
1320
01:18:28.987 --> 01:18:35.988
So all of those columns are all of the different permutations and combinations of what I could add to my cells.
1321
01:18:36.309 --> 01:18:38.249
They were like, okay, look, I don't know.
1322
01:18:38.769 --> 01:18:39.569
Yes.
1323
01:18:39.869 --> 01:18:40.529
What's going on?
1324
01:18:40.569 --> 01:18:42.810
Let's just do all of the permutations and combinations.
1325
01:18:43.190 --> 01:18:48.271
And if you look at the fifth column, you can see a bunch of myelination happening.
1326
01:18:48.851 --> 01:18:52.412
That is with ATDRA alone, with the acidic version of this.
1327
01:18:52.952 --> 01:18:57.813
So when we introduced the ATDRA, the acidic version, things are happening.
1328
01:18:57.913 --> 01:18:58.874
Things happen now.
1329
01:18:59.394 --> 01:19:03.515
And so the ATDRA base was directionally helpful.
1330
01:19:03.555 --> 01:19:03.755
Yep.
1331
01:19:04.115 --> 01:19:05.515
And it's a slightly different permutation.
1332
01:19:05.575 --> 01:19:05.675
Yeah.
1333
01:19:09.557 --> 01:19:24.088
Yes, and the key insight here, and that's effectively the punchline, is this ATDRA, this acidic version of the vitamin A derivative, that is the selective activator.
1334
01:19:24.585 --> 01:19:25.746
Of the RXR Gamma.
1335
01:19:25.987 --> 01:19:26.447
Bingo.
1336
01:19:26.767 --> 01:19:26.988
Right?
1337
01:19:27.408 --> 01:19:27.808
Bingo.
1338
01:19:28.009 --> 01:19:29.510
The failed trial.
1339
01:19:29.830 --> 01:19:31.212
The Bexarotin from earlier.
1340
01:19:31.252 --> 01:19:36.477
Yes, the Bexarotin from earlier that was not only targeting Gamma, but also targeting everything.
1341
01:19:36.537 --> 01:19:36.777
Yes.
1342
01:19:37.218 --> 01:19:41.783
Now we've got a selective activator that is going to target only the Gamma.
1343
01:19:42.563 --> 01:19:53.987
And so this is perfect because from earlier when we talked about we had identified the RXR as like where we need it to target.
1344
01:19:54.027 --> 01:19:57.068
That's what we got to do for the therapeutic.
1345
01:19:57.328 --> 01:19:59.409
But we couldn't target it selectively enough.
1346
01:19:59.469 --> 01:20:01.510
We figured out the size problem for delivery.
1347
01:20:01.530 --> 01:20:03.070
We did not figure out the targeting problem.
1348
01:20:03.450 --> 01:20:07.692
The ATDRA is the solution to the targeting problem we defined earlier in the show.
1349
01:20:07.992 --> 01:20:09.072
Yeah, exactly.
1350
01:20:09.432 --> 01:20:11.193
And so how do we make sure?
1351
01:20:13.374 --> 01:20:17.017
And this is where we get in the translational payoff of the entire study.
1352
01:20:17.277 --> 01:20:18.518
We've established the science.
1353
01:20:18.798 --> 01:20:19.038
Yes.
1354
01:20:19.359 --> 01:20:26.945
Now, can this actually work as something that perhaps could combat MS and maybe even reverse the effects?
1355
01:20:26.985 --> 01:20:28.466
Can we deliver the actual solution?
1356
01:20:28.526 --> 01:20:28.706
Yeah.
1357
01:20:29.046 --> 01:20:30.287
So they got young mice.
1358
01:20:30.307 --> 01:20:31.828
They exposed them to low oxygen.
1359
01:20:32.229 --> 01:20:36.812
And then those young mice received injections of ATDR and ATDRA.
1360
01:20:36.832 --> 01:20:37.172
Right.
1361
01:20:38.213 --> 01:20:43.435
both molecules actually reduced that impact of hypoxia.
1362
01:20:44.036 --> 01:20:44.276
Okay?
1363
01:20:44.516 --> 01:20:46.137
So both molecules actually worked.
1364
01:20:46.417 --> 01:20:49.118
It makes sense because a mouse brain has neurons.
1365
01:20:49.638 --> 01:20:55.821
It got the ATDR, which it wasn't getting earlier because, I mean, this is in control, remember.
1366
01:20:56.201 --> 01:20:56.482
Okay?
1367
01:20:56.802 --> 01:21:00.483
This is in control that don't have that mutation.
1368
01:21:00.523 --> 01:21:05.186
So this is normal mice, but we're giving them the stuff that the mutation makes.
1369
01:21:05.326 --> 01:21:05.566
Yes.
1370
01:21:05.586 --> 01:21:06.066
Okay?
1371
01:21:06.226 --> 01:21:31.817
and it's working yeah you're getting remyelination yeah yeah okay oh my goodness and the the the killer is really the next one yes the next one is so because this was this was hypoxia now what if we straight up go in lesion right which are the two this is right because hypoxia i don't know it could be doing other things right right what if we now normal normal environment but we go ahead and we do a lesion um
1372
01:21:33.078 --> 01:21:42.066
Do we get, like in an MS-like brain, there are these things called autoimmune encephalomyelitis, EAE.
1373
01:21:42.187 --> 01:21:49.433
These types of mice are basically a standard model for multiple sclerosis because they mimic a lot of the effects.
1374
01:21:49.533 --> 01:21:51.916
And there you can see the control has not that much myelin.
1375
01:21:52.376 --> 01:22:05.501
But the ones that get ATDR, this particular type of mouse brand that mimics MS, if you give it ATDR, you don't have to give it ATDRA, just even ATDR, which is way simpler to make, actually.
1376
01:22:05.661 --> 01:22:05.941
Mm-hmm.
1377
01:22:06.772 --> 01:22:08.353
You're getting a lot more myelination.
1378
01:22:08.934 --> 01:22:10.735
The myelination volume is increased.
1379
01:22:11.035 --> 01:22:11.996
Which is what we want to see.
1380
01:22:12.517 --> 01:22:15.079
The repair process is reactivating.
1381
01:22:15.099 --> 01:22:16.400
It's becoming successful again.
1382
01:22:18.621 --> 01:22:25.627
And so now we've weaved this story, which is so, so good.
1383
01:22:26.367 --> 01:22:39.231
To this point where we've now understood the current state of play with MS, what the limiting factors were for delivering a therapeutic, which was being able to target this RXR gamma.
1384
01:22:40.832 --> 01:22:44.593
We knew we needed to target RXR, but we couldn't selectively do so.
1385
01:22:44.653 --> 01:22:45.893
Yeah, because it was going everywhere.
1386
01:22:46.333 --> 01:22:51.275
We found this other pathway through looking at the evolutionary...
1387
01:22:53.069 --> 01:22:59.472
progress of high altitude Tibetan animals and how they survive hypoxia, which are low oxygen environments.
1388
01:22:59.733 --> 01:23:13.900
They're able to survive because in low oxygen environments, you see the same depletion of your myelin sheath that you do similarly in MS. And so whatever the active ingredient to their solution for the hypoxia problem, our intuition was, you know,
1389
01:23:14.280 --> 01:23:19.104
Like other evolutionary solutions, the Gila monster venom.
1390
01:23:19.124 --> 01:23:20.765
Yeah, perhaps it's doing something.
1391
01:23:20.965 --> 01:23:24.968
Perhaps we can translate this into a biologic solution for humans.
1392
01:23:25.648 --> 01:23:33.834
We've now gotten to the point where we've found the selective targeting of RxR gamma through ATDR.
1393
01:23:33.894 --> 01:23:36.856
ATDR slash ATDRA.
1394
01:23:37.416 --> 01:23:39.398
And there's some pros and cons to both.
1395
01:23:39.418 --> 01:23:39.638
Yeah.
1396
01:23:40.782 --> 01:23:49.305
But we know how to target so that the neuron signals to the OPCs to mature, to restart this process of creating the myelin sheath.
1397
01:23:49.325 --> 01:23:49.525
Exactly.
1398
01:23:49.805 --> 01:23:55.688
And the ATDR we can just put in because the neurons can take that and create the thing they need to do.
1399
01:23:55.708 --> 01:23:56.808
The thing they need to do, right?
1400
01:23:56.868 --> 01:24:00.470
We don't need to give the OPCs the signal in itself.
1401
01:24:00.490 --> 01:24:02.410
We can just have the neurons do it.
1402
01:24:02.710 --> 01:24:03.971
Which is actually an important distinction.
1403
01:24:04.051 --> 01:24:04.611
Yeah.
1404
01:24:04.711 --> 01:24:06.612
Because the ATDR is a bit easier to make.
1405
01:24:06.752 --> 01:24:07.412
Right, right.
1406
01:24:07.632 --> 01:24:08.473
That's an important point.
1407
01:24:08.493 --> 01:24:08.613
Okay.
1408
01:24:08.633 --> 01:24:08.813
Okay.
1409
01:24:09.361 --> 01:24:09.542
Yes.
1410
01:24:09.582 --> 01:24:09.903
All right.
1411
01:24:10.124 --> 01:24:13.113
And so now we can finally understand the graphical abstract.
1412
01:24:13.674 --> 01:24:14.477
We're finally here.
1413
01:24:14.778 --> 01:24:15.099
Okay.
1414
01:24:15.119 --> 01:24:16.322
The wild type is on the left.
1415
01:24:16.342 --> 01:24:16.523
Okay.
1416
01:24:17.579 --> 01:24:19.880
The mutation is on the right.
1417
01:24:19.940 --> 01:24:22.920
You've got a Himalayas in the background.
1418
01:24:24.261 --> 01:24:28.362
Retinol goes to ATDR, goes to ATDRA.
1419
01:24:29.102 --> 01:24:33.763
But on the right-hand side, the ATDRA has an up arrow because it's upregulated.
1420
01:24:33.803 --> 01:24:35.643
That's that gain of function.
1421
01:24:35.743 --> 01:24:42.085
And when that goes to the OPCs and the oligodendrocytes, the OPCs are the green cells.
1422
01:24:42.145 --> 01:24:42.345
Yep.
1423
01:24:42.925 --> 01:24:46.718
When it goes to the OPCs, it creates myelin around my axon.
1424
01:24:46.858 --> 01:24:49.427
On the left-hand side, though, there's not enough signal.
1425
01:24:50.465 --> 01:24:53.447
to get the OPCs, those stem cells, to become myelin.
1426
01:24:54.007 --> 01:24:54.268
Okay?
1427
01:24:54.528 --> 01:24:56.849
And so on the right-hand side, we're getting healthy myelin.
1428
01:24:57.330 --> 01:24:59.431
On the left-hand side, we are not.
1429
01:24:59.731 --> 01:24:59.972
Yeah.
1430
01:25:00.852 --> 01:25:01.092
Yeah.
1431
01:25:01.252 --> 01:25:03.294
And that's the graphical abstract.
1432
01:25:03.414 --> 01:25:09.778
And from something that was totally disconnected from anything, you'd be able to understand.
1433
01:25:10.119 --> 01:25:11.660
We now have a reference point.
1434
01:25:12.120 --> 01:25:19.485
And also, like, a fundamental understanding of one of the most, you know, impactful neurodegenerative elements.
1435
01:25:19.685 --> 01:25:23.386
you know, issues we face as humanity, among others.
1436
01:25:23.486 --> 01:25:24.206
Among others, yeah.
1437
01:25:24.366 --> 01:25:25.166
Which we've discussed.
1438
01:25:25.186 --> 01:25:28.146
I mean, any progress is good progress.
1439
01:25:28.186 --> 01:25:38.348
And on the bottom, the two blue circles, on the left-hand side, you see a wild-type mouse just doing random walks to get to the cheese in the Morse water maze.
1440
01:25:38.488 --> 01:25:41.789
On the right-hand side, it's getting there very, very quickly.
1441
01:25:41.809 --> 01:25:45.289
Because it's able to repair itself and maintain its cognitive functions.
1442
01:25:45.509 --> 01:25:46.190
Very good.
1443
01:25:46.270 --> 01:25:48.670
In a way that the wild-type cannot.
1444
01:25:50.695 --> 01:25:54.400
Oh, the bio stories, some of our best stories.
1445
01:25:54.420 --> 01:25:54.700
Yeah.
1446
01:25:55.461 --> 01:26:05.675
I mean, I just, it's incredibly difficult to get this kind of granular understanding of something so fundamental.
1447
01:26:05.735 --> 01:26:05.935
Yes.
1448
01:26:06.035 --> 01:26:10.218
And it's just very cool the amount of technology that we have in order to do this.
1449
01:26:10.919 --> 01:26:14.542
Like, we know how to stain myelin extremely well.
1450
01:26:14.822 --> 01:26:22.768
We know how to make these mouse assays with any type of genetic mutation that we want.
1451
01:26:23.058 --> 01:26:30.124
Not only that, we know how to express only that genetic mutation in neurons only or oligodendrocytes only, right?
1452
01:26:30.164 --> 01:26:34.568
In order to establish it's not the oligodendrocytes, it's the neurons that are doing this.
1453
01:26:34.688 --> 01:26:39.652
We had to express that gene in only the neurons, which is kind of crazy.
1454
01:26:39.752 --> 01:26:48.059
And the way they do it is with this kind of thing that the neuron creates a type of protein, and then that protein is going to go in and trigger...
1455
01:26:49.073 --> 01:26:55.520
I mean, incredible amounts of genetic engineering, bioengineering to get this kind of access.
1456
01:26:55.941 --> 01:27:00.045
And also, it's just standard scientific method.
1457
01:27:01.186 --> 01:27:04.770
One would say they did it from first principles.
1458
01:27:04.790 --> 01:27:04.970
Yeah.
1459
01:27:05.994 --> 01:27:06.774
One would say that.
1460
01:27:06.894 --> 01:27:07.615
I certainly would.
1461
01:27:09.155 --> 01:27:11.055
And this is going to be incredible.
1462
01:27:11.075 --> 01:27:15.256
The last few things I want to touch on is where this can help us.
1463
01:27:15.777 --> 01:27:17.657
One is neonatal white matter injury.
1464
01:27:17.897 --> 01:27:24.138
So preterm birth is highly associated with massive chronic diffuse white matter injury.
1465
01:27:24.539 --> 01:27:31.240
Because when you have extremely low birth weight infants, the blood vessels in the brain are incredibly fragile.
1466
01:27:35.281 --> 01:27:38.884
stem cells, they're very vulnerable to oxidative stress.
1467
01:27:39.224 --> 01:28:01.703
And if the blood vessels are not properly in there, and now you've exposed this preterm infant to the world outside of the womb, you've got this risk factor where you've got severe cognitive delays, lifelong executive dysfunction, because these stem cells, these progenitors, are not getting enough oxygen.
1468
01:28:02.104 --> 01:28:02.304
Well,
1469
01:28:03.460 --> 01:28:08.324
maybe this ATDR-ATDRA pathway could fundamentally change that, right?
1470
01:28:08.404 --> 01:28:17.491
Maybe you could take some of this evolutionary pressure that the YACs have adapted to and make it some kind of therapeutic intervention.
1471
01:28:18.031 --> 01:28:23.416
Because in this context, the hypoxic stress is a one-to-one mapping.
1472
01:28:23.596 --> 01:28:24.376
Yeah.
1473
01:28:24.557 --> 01:28:26.218
Versus when we talked earlier about the MS, it's...
1474
01:28:26.498 --> 01:28:28.560
It's a few steps away.
1475
01:28:28.580 --> 01:28:29.681
There's a few steps away.
1476
01:28:30.061 --> 01:28:32.343
This one, it's like straight up just hypoxia.
1477
01:28:32.403 --> 01:28:37.747
The other thing with hypoxia is a type of dementia called vascular dementia and small vessel disease.
1478
01:28:38.167 --> 01:28:44.212
This happens in the elderly estimated 57 million people live with dementia worldwide.
1479
01:28:44.372 --> 01:28:47.595
Effectively, the blood vessels are getting damaged.
1480
01:28:48.460 --> 01:28:53.843
And so if the blood vessels are getting damaged, you have oxygen deprivation in your brain.
1481
01:28:54.223 --> 01:28:58.405
That's going to lead to neurodegradation, specifically in the white matter.
1482
01:28:58.885 --> 01:29:17.535
Well, if you could have some kind of therapeutic RET-STAT ATDR axis drug that mediates the same exact therapeutic axis that we've been discussing, then perhaps you have some way to slow this decay.
1483
01:29:17.813 --> 01:29:19.435
That would be so huge.
1484
01:29:19.496 --> 01:29:19.716
Yeah.
1485
01:29:20.437 --> 01:29:30.312
I think one of the points that this brings up is the connection we've made by studying these high-altitude Tibetan animals has multiple...
1486
01:29:32.051 --> 01:29:33.232
impact pathways.
1487
01:29:34.113 --> 01:29:38.617
We started and referenced MS and some of these prenatal issues.
1488
01:29:39.157 --> 01:29:47.384
But to your point, this like vascular dementia and potentially other things we're not even discussing in the scope of this episode is,
1489
01:29:48.465 --> 01:30:12.667
have some connection points to utilizing this ADTR, ATDR, neuron, OPC, myelin sheath, and other low oxygen regenerative benefit avenues that, you know, again, this is the idea that sometimes discoveries are discreet.
1490
01:30:13.387 --> 01:30:16.511
and have one application, and sometimes you have platforms.
1491
01:30:16.951 --> 01:30:19.595
A classic example I always bring up is CRISPR.
1492
01:30:20.636 --> 01:30:28.345
You have multiple use cases for one unlock, and this could be a key that unlocks several doors.
1493
01:30:28.525 --> 01:30:30.367
And another big...
1494
01:30:33.691 --> 01:30:36.295
Another aspect to this story is the source.
1495
01:30:36.595 --> 01:30:36.796
Yes.
1496
01:30:36.816 --> 01:30:36.976
Right?
1497
01:30:37.397 --> 01:30:38.318
It was in Neuron.
1498
01:30:38.678 --> 01:30:38.919
Yeah.
1499
01:30:39.319 --> 01:30:41.863
But the team is from China.
1500
01:30:42.023 --> 01:30:43.906
The team is out of China, yeah.
1501
01:30:44.106 --> 01:30:48.352
And as we've discussed several times on the pod, we're in a very different environment
1502
01:30:49.313 --> 01:30:59.957
today than five, 10, 15 years ago in how competitive some of these university institutions are in the frontier and not all spaces, but in some spaces.
1503
01:31:00.898 --> 01:31:02.999
I would say most spaces, to be honest.
1504
01:31:03.019 --> 01:31:03.239
Right.
1505
01:31:03.439 --> 01:31:14.063
And you can see that from the recruitment of some of the best talent across several frontier areas that are now arriving in the Chinese university system.
1506
01:31:14.083 --> 01:31:18.225
So we're going to continue to see, you know, it'd be a very competitive landscape.
1507
01:31:18.325 --> 01:31:18.525
Yeah.
1508
01:31:19.205 --> 01:31:20.446
at the frontier of research.
1509
01:31:20.466 --> 01:31:22.846
Yeah, I mean, and this was a phenomenal study.
1510
01:31:22.906 --> 01:31:28.388
I mean, I just, I love the narrative of it, the way the figures sort of told the story.
1511
01:31:28.868 --> 01:31:35.811
Just from an even aesthetic and like, you know, narrative point of view, I thought the paper was just very well written.
1512
01:31:36.427 --> 01:31:38.629
They know what they're doing.
1513
01:31:38.929 --> 01:31:40.830
They're not just copying us in all ways.
1514
01:31:40.850 --> 01:31:53.260
And this was, you know, and for folks who may have friends or family members that are impacted by some of these things, hopefully this may bring you just at least a better fundamental understanding of,
1515
01:31:53.700 --> 01:32:00.065
of like what the body is doing and why it is so difficult to solve this problem.
1516
01:32:00.305 --> 01:32:07.811
Again, frontier research and end therapeutics, there is a large delta between these two things.
1517
01:32:08.471 --> 01:32:10.633
And the authors themselves make that point.
1518
01:32:10.693 --> 01:32:19.920
There's a bunch of news articles where you can read quotes from the authors and they say, you know, there's a giant delta, but you got to do the science, you got to do the work.
1519
01:32:20.180 --> 01:32:21.300
We got to start somewhere.
1520
01:32:21.441 --> 01:32:28.623
And so I just want to be careful about making this people's takeaway being, oh, there's going to be a solution soon for my loved one.
1521
01:32:29.003 --> 01:32:30.564
That's not the story here.
1522
01:32:30.744 --> 01:32:30.904
No.
1523
01:32:31.144 --> 01:32:35.026
But hopefully this helps you understand how we do get to these solutions.
1524
01:32:35.046 --> 01:32:35.246
Yeah.
1525
01:32:35.606 --> 01:32:35.806
Yeah.
1526
01:32:36.206 --> 01:32:37.407
And why they do take time.
1527
01:32:38.867 --> 01:32:40.068
We've found a mechanism.
1528
01:32:40.388 --> 01:32:43.529
We've not necessarily created anything.
1529
01:32:43.909 --> 01:32:46.231
The actual end product.
1530
01:32:46.251 --> 01:32:47.853
But great story.
1531
01:32:47.953 --> 01:32:48.954
Love the bio stuff.
1532
01:32:49.434 --> 01:32:53.178
We will wrap up this week in half the time.
1533
01:32:55.107 --> 01:32:57.728
Of the last episode.
1534
01:32:57.808 --> 01:33:02.268
For those still listening, you can always catch our shows across all the main platforms.
1535
01:33:02.288 --> 01:33:04.449
YouTube, Spotify, Apple Podcasts.
1536
01:33:04.489 --> 01:33:06.369
Our clips will be on social.
1537
01:33:06.849 --> 01:33:08.409
Instagram seems to love us.
1538
01:33:08.869 --> 01:33:10.390
That's where we have our biggest audience.
1539
01:33:10.770 --> 01:33:24.252
So if you want to catch these little snacks, little bite-sized clips from this, when you can, be sure to follow us on Instagram, on Twitter, on Facebook, wherever you get your daily dose of...
1540
01:33:24.632 --> 01:33:51.562
brain rot and hopefully we can give your feed a little bit of nice brain food um again if you want to check out the website to become a donor to check out all of our resources ffppod.com is where you can go we can give the listeners a prompt to let us know if they've made it to the end some of you i do want to say in the last episode the germany the german clapback prompt the
1541
01:33:51.842 --> 01:33:53.623
Those are, some of them are brutal.
1542
01:33:53.683 --> 01:33:54.824
Some of them are very good.
1543
01:33:55.004 --> 01:33:55.304
Yeah.
1544
01:33:55.524 --> 01:33:57.185
Some of them are very good.
1545
01:33:57.245 --> 01:34:00.507
Eventually when we can do more production stuff, we'll pull them up live.
1546
01:34:00.528 --> 01:34:00.788
Yeah.
1547
01:34:00.908 --> 01:34:02.689
That's beyond the scope of what we can do right now.
1548
01:34:02.709 --> 01:34:03.289
That was pretty funny.
1549
01:34:03.509 --> 01:34:04.710
So you guys did well.
1550
01:34:04.990 --> 01:34:05.811
Do you have any thoughts?
1551
01:34:05.831 --> 01:34:06.011
Yeah.
1552
01:34:06.051 --> 01:34:16.177
Maybe I was thinking, you know, in the vein of evolutionary pharmacology, what's like one animal out there that you'd like want like an ability from?
1553
01:34:16.277 --> 01:34:20.040
Or like, you know, like if we could do evolutionary pharmacology on...
1554
01:34:20.700 --> 01:34:35.927
on an animal and like get some kind of you know ability what what what would it be for the gilla monster it was the low blood sugar for the snake it was the low blood pressure so what what do you guys think that's a great one
1555
01:34:36.847 --> 01:34:43.930
I am Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdhury.
1556
01:34:43.950 --> 01:34:53.415
We are so happy for all of you who joined us for many of the new folks who may have caught some of our last four or five episodes that were super physics and math heavy.
1557
01:34:54.175 --> 01:34:56.496
Welcome to the best general science show on earth.
1558
01:34:56.976 --> 01:35:03.179
We cover everything and it'll just ebb and flow with what's in the news and where our timing is.
1559
01:35:03.519 --> 01:35:04.820
And what we find interesting.
1560
01:35:05.280 --> 01:35:11.448
Next episode, it'll be coming up on our Labor Day weekend, so it might be a little bit more relaxed than normal.
1561
01:35:11.829 --> 01:35:18.277
So we wanted to give you one last good solid deep dive before we take a little bit of a half break.
1562
01:35:19.258 --> 01:35:21.561
And we will see you all next week.