WEBVTT

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go, I think this is going to work. Okay, so remember, we're getting into this faith in

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a novel virus, and they have faith in a novel biology, it's actually morphed into this faith

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in a novel virology, a virology, excuse me, that revolves around a gain of function RNA

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virus, it involves around particular, particular pieces of that RNA that made it extra special,

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it involves transfection working, but not working for being rushed, being adulterated

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or having chosen the wrong protein. That wrong protein is the spike protein that from

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very early on in 2020, here in the first year already, was being lambasted by many different

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people in the narrative and in the media about being a gain of function protein that the

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protein itself was the evidence. And then later the protein is a toxin later, the protein

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was causing amyloidosis, prion disease, etc. And so this whole, this whole mythology,

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this immuno mythology that they have, they have, they have seeded around the novel virus,

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has gotten quite, quite expansive. And the part that we're working on here with these

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two streams, the one that I just ended in the one I'm starting now, is understanding

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protein folding in the context of prion disease, so that we can get an idea of where they

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have exaggerated, where they have simplified, and how we can, how we can bring this back

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into a really reasonable focus where the sacred aspects of biology, the irreducible

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complexity of biology is met with a certain reverence that right now it's, it just is

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not given. So what we're really working with here is a faith in a novel biology, it's

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a mythology that covers for the expected damage as we transition into population wide testing

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of transfection technology. So they know that amyloidosis and prion disease and protein

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misfolding and this kind of damage are going to occur over time if they continue to transfect

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old people and young people alike. And they, they expect it to go up, they expect it to

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show up from already the, the people that have been transfected multiple times. And

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I believe this because it was also included in the spars pandemic narrative on page 48,

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you can find a little talk about how three years after the rollout of the, of the vaccine

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for spars, that a bunch of people develop some kind of, of, of crowds felt yock of disease

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and it was blamed on the, on the vaccine and a lot of people were upset. And it started

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to undermine the way that people felt about the public health system. It's in their little

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Rockefeller tabletop exercise called the spars pandemic, you can find it. And so without

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a doubt, what they learned from that tabletop exercise and others was that if they wanted

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to cover up for this, the easiest way to do it would be to see a lot of these potential

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worst case scenarios into the gain of function story in the beginning of the pandemic. So

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that by the time those things manifested, it would be too lost in that narrative of lab

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leak or natural virus. And the whole acceptance of a circulating novel pathogen would have

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already happened years earlier. And that's where we are right now. That's why, that's

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why we need to watch this.

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Guess it was me that was, it was clipping here. Somehow I have everything. Oh, I see it

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now. Sorry, I got it. I got it. I got it. It was a knob, a knob, a very knob that had

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been brushed by a looks like it got pulled by the headphone wire there. Now I see it. Now

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I know why my my my my levels were. So there, there was the little morbid title that I was

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going to use proteins and protein folding with a dead lady from MIT. That's this one. I'm

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going to get rid of this. And I'm going to bring up a new window from my word file here, part B.

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That would be this one. So we got the idea that maybe we could use those these cells as

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I'm sort of living test tube. And the reason why we would want to do that is there's no

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organism. So this is starting in the middle. I got to go to the front. Sorry about that. Here

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we go. So here we go. I'm going to put it at 1.5 speed. Hope you can handle it. I think that

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should be normal. Now we're used to it.

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Remember the Howard Hughes Medical Institute and I work at the Whitehead Institute at MIT. I work

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on a variety of different protein folding problems. And in my last lecture, I told you a video

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brought introduction to the problem for how it manifested. I think it's a little bit

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how it manifested in infectious diseases and more broadly how it is used by cancers to drive

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them in the living state. In this lecture, I'd like to tell you about a different aspect

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of protein pathology, another equally devastating aspect of protein folding and pathology, the

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neurodegenerative diseases, because all of these diseases are diseases of protein and

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swollen. This is a extremely vivid demonstration of the difference between the brain of a normal

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adult, a pine autopsy, versus adult who died of Alzheimer's disease. It's obviously devastating

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disease. And this is why the people who have these diseases lose their memory, lose control

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of functions. Okay, sorry. Diseases are really terrible.

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Now, this is a graph of what's happened to human longevity over the last couple hundred years.

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And it's really, I think this is the red and the black are just two different calculations.

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It's not so easy in the going back to the older days to calculate when exactly how old people

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lived on average. But these two very different ways of doing it came out with the same answer.

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And you can see that there's been the steady march of progress and it's just been amazing.

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Wait, where's the drop off for is World War I then going to be considered the flu? I thought

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the flu showed up on that one graph all by itself. It was just the flu not World War I or

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anything like it was just the flu. Remember? Holy cow. Now she says it's World War. So that's cool.

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This has been I think one of the glories of mankind to be able to do this and alter their

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own average lifespan. And it's been due to many different factors due to changes in public health,

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cleaner drinking water, due to refrigeration and preservation of food and cooking. It's due to

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the development of antibiotics, the development of vaccines, the development of anesthesia. So

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you could do surgery on people and correct illnesses that way. So anyway, this wonderful

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steady, steady progress of mankind is unfortunately in some ways of thinking about it a road to ruin

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because as we are curing these other diseases, as we're living longer and longer lives,

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we are finding that the incidents of neurodegenerative diseases are going out. These diseases used to

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be practically unheard of 100 years ago. Now there's a very large fraction of people around the world

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that are suffering from these diseases. And as we extend lifespan, it's getting worse and worse.

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There are 5 million Americans suffering from Alzheimer's disease alone. And the same increase

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in disease is occurring for all of the neurodegenerative diseases across our globe. So unfortunately,

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with respect to neurodegeneration and it being a road to ruin, this is why I say a road to ruin,

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we're headed for neurodegeneration and right now there's no exit. We do not have a single therapy

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that really fixes these problems. So these are some of the common and uncommon neurodegenerative

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diseases you might have heard about Alzheimer's disease and Parkinson's disease, frontal temporal

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dementia, Huntington's ALS and Croissphal Yacob disease. And you can see these brown blobs

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inside of these cells. And those brown blobs are aggravated proteins, like those aggregates of

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Friday I showed you earlier. And as I said, there are all these neurodegenerative diseases

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are protein folding diseases and there's not a single therapeutic strategy that cures the

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underlying protein pathology. We have some things that address some symptoms in some of these diseases,

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but for the most part, we're pretty helpless against them. So I've been working on protein

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folding for a long time and I've worked on a lot of different organisms. And the one thing

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that my studies over the years have taught me is that this problem, as I mentioned earlier,

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is common to all organisms on earth. And so we got the kind of crazy idea that considering the

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eukaryotic tree of life, you see, plants, animals and fungi actually split from each other not that

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long ago in terms of evolution. So we thought we might be able to take advantage of this similarity

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and to study some of these really difficult, really complicated diseases. Yes, we will not be able

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to study many different aspects of protein folding neurodegenerative disease in a simpler

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organism. But if we could study some aspects of the precipitating, initiating protein pathology,

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the cellular pathology, not the complexity of the disease as a whole, but just the initiating,

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precipitating pathology from those proteins in a simple organism, we might be able to move

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much more quickly than we would if we had that we can find solely to working on these more complex

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organisms. So as I mentioned, one of the things we have in common with yeast is a wide variety

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of systems for controlling the protein folding problem. So we have chaperone proteins which

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interact with highly reactive proteins that are not quite finished folding and prevent just like

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human chaperones, prevent them. Their charges from interacting inappropriately with other partners

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and until they're ready and mature, protein chaperones do the same thing. But we also have

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protein modeling factors, things that can rest those protein aggregates when they start to appear

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apart. We have osmolites, we have things called leproteosome, which degrade proteins that are

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that are not properly folded, ubiquitin, ubiquitin ligases, and that entire system is just completely

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conserved from yeast to human cells. It's not just that. Lipid biology is actually quite highly

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conserved. There certainly are differences of lipid biology at least in human cells, but for

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example cholesterol, yeast use a very closely related lipid called regastrol for exactly the

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same reason that we use cholesterol to control the fluidity of membranes and to control the movement

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and density of proteins within those membranes. And they move packages of membrane-bounded proteins

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around the cell in very highly orchestrated ways, really the same way that a nerve cell will move

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dopamine around. The yeast cells will move things like mating factors around.

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So she says that we move dopamine around in little vesicles. That's all we do is

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neurotransmitters around in little vesicles. That's really sad because we know in the brain there are

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at least vesicles of RNA that are released of the arc protein, which cause local actin skeleton

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remodeling to be possible in neighboring cells that weren't necessarily activated by the same

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genetic or neuronal signal that the postsynaptic neuron was. And so the postsynaptic neuron can

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release virus-like particles that contain the mRNA of the arc gene cause arc protein to be

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expressed locally at that synapse and cause remodeling of that local arc protein cytoskeleton

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in that neuron that was never activated by the synapse or the signal that came into the postsynaptic

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neuron. And that is just the tip of the iceberg. I'm quite certain of it. So it's really funny,

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almost to the point of being a little bit... Why did you say that? If you say that like we

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move dopamine around these yeast proteins move all kinds of stuff around in their vesicles.

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We know that there's extracellular signaling between tissue using extracellular vesicles

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called exosomes. I'm sure she knows it too. She's got to know it. And if she doesn't, it's because

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of the intense compartmentalization of biology. Sosomes and peroxosomes, these are very complex

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organelles that are involved in doing very complicated functions. Some of them are involved in degrading

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proteins. Some of them are involved in a wide variety of metabolic actions that have to be

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segregated from the normal cytoplasm. These cells have both of those. They have autophagy. This

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is a process by which the cell actually directs its degradation and eating machinery to eat up

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protein aggregates and get rid of them. Apoposis, a programmed form of cell death.

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Cell cycle, very complexly regulated cell cycle, regulated very, very differently bacteria,

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but in yeast and humans regulated in very much the same way. And in fact, studies of that cell

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cycle work extremely important for our understanding of cancer. And why cancer cells start to replicate

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uncontrollably, study them in yeast to provide its key insights. We have mitochondria, the

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energy factory of the cells, and mitochondria do amazing things in yeast and human cells.

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But they also are a place where reactive oxygen species are generated and can do a great deal

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of damage. And then there's a whole variety of signal transaction pathways. Again,

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these key pathways that control growth and development in us, but control responses to the

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environment, responses to other cells, and responses to internal and external stresses,

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those same signaling pathways have been controlled, have been preserved rather in yeast and higher

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eukaryotes. So, calcinarism, example, map kinases, G-coupled protein receptors,

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all of these were first developed long ago in eukaryotic life, and greatly greatly elaborated

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in us. We have many, many more G-coupled receptors than a yeast cell has, for example.

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But the basic machinery and the basic concepts and the basic ways in which those signaling pathways

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drive processes inside the cell are similar. So, we got the idea that maybe we could

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use those yeast cells as our living test tube, and the reason why we want to do that is there's

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no organism on Earth that we can manipulate and get to tell us its secrets better than yeast.

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It has an absolutely unrivaled toolkit, and it really derives from brewers back about 150 years

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ago wanting to make better beer, and wanting to understand that organism and how to manipulate it,

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and it's taken off from there, and it's just amazing. Massive, massive numbers of people have

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been building and developing technologies that allow us to knock out every gene in the genome,

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or overexpress every gene in the genome, make point mutants wherever we want in the genome,

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and so that's just something we can't do in any other organism at this level today.

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So, here's how we set things up. We have yeast cells that are growing on, in the top row there,

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they're growing on glucose medium. In the bottom portion of the panel, they're growing on lactose

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medium. We have a gene that will turn on whenever we give the cells lactose, and so we make a

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recombinant form of that gene that now, well, instead of making the proteins that these cells use for

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lactose utilization, they make different proteins that misfold in human diseases, like alpha-synuclein,

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A-beta, TVP-43, and TIN-TIN-FUS. And you can see that we've built, for synuclein here, we've

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shown you all three different strains that are expressed in a protein at different levels,

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and are exhibiting different levels of toxicity, just by the fact that they can't grow very well,

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and we've then done that with all of those different disease proteins, and we've matched them so that

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they have the same level of toxicity. So, same level of toxicity from different proteins,

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what I said is just some non-specific protein aggregation mess. It turns out that it's not,

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but when those proteins misfold inside of the yeast cell, they go into the cell, they interact

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with the same kinds of highly conserved constituents that they interact with in a neuron, and they do

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bad things in a very specific way. So, here's an example of a phenotype that glob over there

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is protein nitration, and it's happening all of the cells at the same level of toxicity,

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the nitration damage is happening really only in the cells that are expressing

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alpha-synuclein. That's really interesting, because in the human diseases that are known to be caused

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by the misfolding of alpha-synuclein, and that is Parkinson's disease, multiple systems atrophy,

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Lewy body, dementia, and nerve-brain iron accumulation, they too show very high levels of very specific

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protein aggregates with nitration. So, very unique and very specific cellular pathologies directly

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related to the human disease. So, here's our cells, we've got this gene that we can turn on with

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galactose, anaerobic galactose, and we've hooked it up to GFP, just so that we could see what was

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happening to it in the cells, as they were either healthy or a guy. And when we had just one or two

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copies of the protein in the cells, they would find, and the protein went out to the membrane,

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which is where it should belong. And if we had more, one extra copy, we started seeing things

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going along, and then if we had two extra copies, it went even worse, this does not look good,

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they said protein conglomerates here in aggregation, type some type of aggregation, and then those

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cells grow fine, those cells grow slowly, and those cells die. Very, very strong dosage difference,

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and what's really interesting about that, is that's true in man as well.

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So, it's really important for you to understand that there is one gigantic caveat here, which I

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find almost disturbing. Green fluorescent protein is a massive protein, native to jellyfish that

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glows in the dark green. If you attach that massive protein to alpha-synuclein, I would be

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willing to bet that that cartoon up there is wrong, the GFP is a lot bigger than alpha-synuclein.

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Could be wrong, you look it up yourself and find out if I'm right or wrong, or whether that cartoon

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is right or wrong, but the point is, is that GFP is not small, and overexpressing GFP in any cell

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line, or in any mammal tissue will result in cell death. Because that level of GFP is toxic,

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if you can see it like this, it's already a lot of molecules, a lot of molecules of GFP in order

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to see that signal. If it starts to make these kinds of, these kinds of, uh, punctate sort of

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constructs of, of, of the cell is, is putting this GFP into vesicles to get rid of it, to keep it,

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to keep it compartmentalized, so it's already beyond toxic levels. And she is very conveniently

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ignoring that fact, because there are no controls here with, with just GFP, right, to show what

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GFP toxicity looks like, but I can guarantee you, I can tell you from experience that GFP

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toxicity is real. Over expression of GFP causes toxicity is very, very real, and that is toxic

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levels already in the middle. And anybody that's used GFP to label neurons in a mouse brain could

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tell you that anybody that's used GFP as a label for anything can tell you that because if you get

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a good good signal, you also get dead cells. It's extraordinary. And this is the kind of science

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that passes for knowledge creation at this time. This is how we got here. This is, you know, how

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many years ago is this eight? Just the basic principle of this is silly because the GFP is not

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just a glow in the dark tag. It's a 10,000. It's a huge thing. Let's just, I'm just going to look

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it up because I don't know. Green, fluorescent protein, and there will probably be a Wikipedia

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page right there. Green fluorescent protein has a wavelength blah, blah, blah. Natural protein is

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238 amino acids. It's 27 kilodultans. And then let's look up alpha synuclein.

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140 amino acids. So what is it bigger?

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238. No, it's bigger. 140 by 238. So it's roughly twice the size of it, you see? So this is not the

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right, this is not the right cartoon. It's a much bigger protein. And she is pretending that that

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much bigger protein has no effect on whether the cells are healthy or not. It's all the alpha

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synuclein. That's it. That's impressive.

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Things that have just one extra copy of the wild type of synuclein protein will get early onset

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Parkinson's disease. And if they have two extra copies, they'll get even earlier, more virulent

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form of the disease. So this unusual, I mean, ask yourself, why does it need to be tied to GFP?

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If you know that alpha synuclein is being expressed by the cells, why can't you stain for it? Why do

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you need to tie it to GFP? When GFP is a gigantic toxin, a gigantic toxic protein at high fluorescent

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levels, it's extraordinary. Extreme sensitivity to exactly how much protein you're making was

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certainly, was certainly reminiscent of what was happening in man. So how can we get a better idea

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of what's going on here if there's anything really deeper involved? Well, we do something

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called screen first, you could do a control for the GFP. Meaning we screen every gene in the genome

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for what makes cells better or worse. We can take with these, we have libraries,

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every gene in the genome, we can turn them up or turn them down and see how that changes

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the disease manifestation. And in these cells that have the four copies where they're just

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playing frankly dying of the disease, we can screen for chemical compounds that might rescue them.

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And studying those compounds might tell us something about the disease mythology.

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So I love the fact that she uses mite, but it's exactly how science is done. You declare

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a rough shot experiment, a suitable model for a disease, and then you go for it.

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That's what she did just there, right? She said, this is a pretty suitable model for disease.

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Why don't we use the four copy version for a model of disease and we'll screen compounds

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on it? Holy cow, that'll pay for at least two postdocs in five years of my lab.

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Compounds that might rescue them. And studying those compounds might tell us something about

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the disease mythology. So screening is a lot like panning for gold. You go through a whole

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lot of stuff and you look through it, you look through it and you look through it and you find

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nothing for a while, and then all of a sudden you get these and then get some gold. And you get,

20:28.760 --> 20:33.000
so out of the 6,000 genes in the yeast genome that we studied, only about 60 or 70 of them in

20:33.000 --> 20:35.880
our initial studies seem to matter with respect to alpha-snooplane. And the genes that we got out

20:35.880 --> 20:38.840
of our alpha-snooplane screens were completely different than the genes we got out of our

20:38.840 --> 20:41.320
beta screens and completely different than the ones we got of our proteins in the screen.

20:41.320 --> 20:44.760
And they told us something about the biology. Because for example, the largest class of genes

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we got were genes that were involved in busgal trafficking, moving those numbering bounded,

20:47.560 --> 20:52.520
proteinaceous compartments around the cell. And so when I showed you these protein conglomerations

20:52.520 --> 20:57.800
or these aggregated forms of protein in this cellular model of the alpha-snooplane pathology,

20:57.800 --> 21:02.520
it turns out that when we got that result, the genes that saved the yeast cells from that pathology

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were genes that were involved in moving little vesicles around. We thought, well,

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gee, I wonder if those things actually have something to do with vesicle trafficking. And so

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when we look at the level of the electron microscope, which allows a much, much higher

21:12.120 --> 21:16.520
resolution of the cell, you can see that, yes, these little vesicles that are packed with proteins

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depending on how much the alpha-snooplane we're expressing, we get more and more of these protein

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aggregates. And then we did something called immuno-electron microscopy.

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This is absolutely terrible, right? You know, if this is the same cell model that she's looking

21:30.200 --> 21:36.120
at under the microscope here, under the electron microscope, she has no idea whether this is from

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the alpha-synuclein or whether it's from the overexpression of the GFP, or whether the

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combination of both would do it. I just can't, I can't really fathom how bad this is. I got to

21:52.680 --> 21:57.240
believe that these papers have some controls or something in them, but I'm scared that they don't.

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We attached a label to an antibody against the alpha-synuclein and against a protein involved

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in vesicle trafficking, and we found that they were there together. So these blobs, these green

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blobs here, are actually blobs, not just of aggregated cineuclin, but aggregated cineuclin

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enmeshed in vesicles that are not moving around the cell and getting to the places they're supposed

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to be. And when that happens in a nerve cell, it's really disastrous because that's one of the

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major ways in which a nerve cells communicate with each other. That's so good for a yeast cell

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either. Anyway, this finding that alpha-synuclein blocks vesicle trafficking has not been corroborated

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by many other laboratories. And to cut a long story short and move on to the very final stage of

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this talk, we found that there were parallel effects, we moved back and forth between these

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two neurons, and we found that there were parallel effects. We moved back and forth between yeast

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and neurons, and you've got to be very careful when biologists are claiming to be able to do things

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like that. Not just vesicle trafficking, but bursts of reactive nitrogen species, as I showed you in

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that protein block, mitochondrial dysfunction, and perturbations in middle line homeostasis. So

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at least at this early, very simple cellular level, there's a lot of similarities there.

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But we really needed to be able to show that the genes we found in yeast, and the genes that

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saved the yeast cells, those same genes would matter to a neuron. So we actually looked at,

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in a couple of different systems initially, one was this wonderful nematode system,

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it was a worm, it's a simple little worm, but it's got lots of different kinds of neurons,

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and in fact it's got the same kind of neurons, dopaminergic neurons, that are adversely affected

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in Parkinson's disease. And we could actually peer through, wire up those cells to express

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alpha-synuclein, and mire them up so that they were green, they glow green, we could actually

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study them in a living worm, and we could see that when the worms were expressing alpha-synuclein

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in those cells, you can see how some of them are disappearing over there. It's a true neurodegenerative

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model in the nematode. And our genes that rescue the yeast cells also rescue that nematode.

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And the same thing happened when we took neurons from rat brains, the midbrain region of the rat,

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which is the corresponding region that's affected Parkinson's in humans. So that was pretty encouraging.

23:40.600 --> 23:46.040
Next thing we did was to screen a chemical lab. Of course there she said that she's taking embryonic

23:46.040 --> 23:51.880
rat neurons from a particular brain region, and then she's culturing them and using them as a model as

23:51.880 --> 23:58.760
well. Just be clear, this one looks like it has a GFP-only stain, so this one looks like it's GFP

23:58.760 --> 24:03.560
only, this one looks like it might be, then alpha-synuclein, I got to get this alpha-synuclein

24:03.560 --> 24:09.800
plus GFP. I mean, it seems like that's a better experiment than the one she showed us, at least

24:09.800 --> 24:13.800
there's some evidence there was a control there. So you could assume that maybe there was a control

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back there where I was losing my mind, but I'm not sure. I don't know. I hope so.

24:19.240 --> 24:42.600
So we're doing here is taking a human protein, expressing it in C. elegans, and then having those

24:42.600 --> 24:51.240
neurons die. Is that really surprising? Alpha-synuclein would be a protein that would be foreign

24:51.240 --> 24:55.480
technically, right? However, they got it in there, however they put it in there and be interesting to see

24:57.240 --> 25:03.080
to think about, what did she say she did with this? Six hits from yeast screen validated in both

25:03.080 --> 25:07.480
nematode and neuron models, so the midbrain region of the rat. Which is the corresponding region that's

25:07.480 --> 25:11.000
affected Parkinson's in humans. So they want to be used to rescue the yeast cell, also rescued

25:11.000 --> 25:15.560
that nematode. And the same thing happened when we took neurons from rat brains in the brain region

25:15.560 --> 25:20.120
of the rat, which is left. And the same thing happened. So supposedly they expressed alpha-synuclein,

25:20.120 --> 25:24.920
and then those those, but overexpression of protein doesn't necessarily is not surprisingly

25:24.920 --> 25:30.600
toxic. That's normally toxic. So I'm still curious as to where this is going. A corresponding

25:30.600 --> 25:34.280
region that's affected Parkinson's in humans. So that was pretty encouraging. Next thing we do

25:34.280 --> 25:38.840
is to screen a chemical library. And this again is something that is so much easier to do in yeast.

25:39.480 --> 25:42.360
We asked whether we could find compounds that would fix more than one problem. I told you there

25:42.360 --> 25:45.320
lots of different things going on. There's a cascade of pathology that gets kicked off by those

25:45.320 --> 25:48.760
misfolded proteins. And can we find one that compounds that can fix more than one of those

25:48.760 --> 25:54.040
problems? So and the next question was can we use yeast genetics to find a target? So why would

25:54.040 --> 25:59.880
this matter? Well, we could screen through. Can we use yeast genetics to find the target? So you

25:59.880 --> 26:05.000
see what she's doing here. She's developing the same kind of screening techniques across genome

26:05.000 --> 26:11.720
libraries that would eventually be employed by the actual project on humans. He just

26:11.720 --> 26:18.600
need the computing power in the data facility. And this is at MIT Whitehead. It's at the same

26:18.600 --> 26:22.840
institute that developed all the stuff for the human genome project. It's at the same institute

26:22.840 --> 26:30.280
from which the patents that that Kevin McCurnan built his his many companies from came from.

26:30.600 --> 26:40.600
And so we're talking about all the same basic mythology, which is designed to make you think that

26:40.600 --> 26:46.200
on the basis of studying disease and public health that that they're they're developing

26:46.200 --> 26:51.160
techniques to help people when in reality they are developing techniques, which will ultimately

26:51.160 --> 26:57.080
be used to mine the human population for as much medical and genomic data as possible.

26:57.160 --> 27:01.080
And of course, they have to start with something simple that has all the basic parts of our

27:01.080 --> 27:06.200
cellular physiology like yeast. And if they can do it in a single-celled organism like yeast,

27:06.200 --> 27:11.480
then they can eventually expand to a multicellular organism and eventually to us. That's what this

27:11.480 --> 27:16.920
is all about. Did it that screen through 500,000 chemical compounds asking for which ones were

27:16.920 --> 27:21.960
able to rescue these cells? That kind of a screen which took us several months would take, I don't

27:22.120 --> 27:26.040
know, maybe a hundred thousand years if you were using a mouse. And also probably, I don't know,

27:26.040 --> 27:29.480
billions and billions of dollars. We did it much more cheaply, much more easily, much more rapidly

27:29.480 --> 27:33.400
in these cells. And of course, you heard Ray Kurzweiler say that we're not going to do this in mice

27:33.400 --> 27:42.680
or in people. We'll do it in synthetic computerized people, synthetic biology and in silico biology.

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The other reason why it mattered was that these cells offered, as I mentioned earlier, they are

27:47.640 --> 27:51.640
unparalleled genetics. And so we're actually able to take advantage of that genetics to figure out

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what those compounds were doing to save the cell and then go back into neurons and ask

27:55.720 --> 27:59.240
whether those same compounds would work in the neurons and would those compounds fix the same

27:59.240 --> 28:03.960
pathologies that are taking place in the neurons. And it worked. So we screened 500, 50,000 compounds.

28:03.960 --> 28:07.560
Simply asked for restoring growth. We've got a lot of genes, don't know which one is the right one

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to try to go after. We just looked for something that would have stored growth. And we've only

28:11.320 --> 28:14.200
dissected a few of these compounds so far, but they merely raid vascular trafficking defects,

28:14.200 --> 28:17.640
they merely rate mitochondrial defects, and they work the ones we've tested in nematode,

28:17.640 --> 28:22.440
react, and human neurons. So the final piece of the story is to turn towards human

28:22.440 --> 28:26.520
IPS cells made from patients that have one of these diseases. This has been one of the most

28:26.520 --> 28:30.600
exciting aspects of revolutionary aspects of biology in terms of being able to devise better

28:30.600 --> 28:35.560
treatments for patients. And so this is interesting because this is one of the technologies that I

28:35.560 --> 28:39.000
think when I was going into neuroscience, what I thought was kind of cool was that they're going

28:39.000 --> 28:43.000
to do is they're going to take cells from the patient, they're going to induce them to form

28:43.720 --> 28:49.240
the tissue that you want to study in the laboratory that you think would have in

28:49.240 --> 28:55.320
genetic similarity to the patient in question. And then you could do your screening on the

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preparation that was made from the patient's cells, but never need any patient sample other

29:00.120 --> 29:04.920
than that. I thought that was kind of a cool idea in theory.

29:04.920 --> 29:09.080
Skin cells from a patient can actually de-differentiate those skin cells into an embryonic surge state

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and then re-differentiate them into neurons. And another amazing technology that's been developed

29:13.480 --> 29:18.200
recently by many other investigators has been the ability to surgically genetically edit

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those cells such that you have corrected just the mutation that's responsible for that person's

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disease. And so you have absolutely genetically identical cell types here. The only difference

29:26.760 --> 29:32.520
between them is the difference that causes the disease. Well, there you go. That's what CRISPR

29:32.520 --> 29:38.280
now can do or they say can do with a lot of off-target side effects. But even Jessica Rose says the

29:38.280 --> 29:41.880
plan is to use CRISPR for personalized medicine. She announced it on our last

29:43.480 --> 29:48.360
substack as well as trying to speculate as to whether they're not already CRISPR-ed you in the

29:48.360 --> 29:53.880
shot. But definitely they will in the future. It was like a big announcement. But the idea that

29:53.880 --> 29:59.480
we should resist or that there was something to be stopped was not in that substack. It was actually

29:59.480 --> 30:05.160
she was sure that CRISPR was part of the plan in the future of personalized medicine and genetic

30:05.160 --> 30:11.160
modification of humans. Well, that's really great. And here it is already 10 years ago. They're

30:11.160 --> 30:14.760
talking about this stuff and they knew that this was the plan. They knew this was the way they were

30:14.760 --> 30:19.080
going to go and they slow walked us there. And I think that this MIT professor was probably part

30:19.080 --> 30:25.560
of it. They have to get you to believe that protein folding is a, you know, criticality.

30:25.560 --> 30:32.760
And it's a criticality upon which all of these pathologies depend. And then they get you to believe

30:32.760 --> 30:37.400
that because that's what they're focused on. Everything they look at has a play. And so in this

30:37.400 --> 30:42.840
case protein folding has that role. And so then you can ask whether or not you have any pathologies

30:42.840 --> 30:45.000
that are different between them and whether any of the things you've discovered earlier

30:45.000 --> 30:50.840
work against those pathologies except the cells looked pretty much identical. So how do we figure

30:50.840 --> 30:54.360
out what pathologies might be happening? Because after all these pathologies only manifest themselves

30:54.360 --> 30:57.960
in terms of human disease, even if people have these terrible mutations, they only manifest at

30:57.960 --> 31:03.480
the ages of 40, 50, 60. Yeah, so if you get all of academic biology to work on IPS cells for 10

31:03.480 --> 31:08.920
or 15 years, then at the same time you get all of biology to develop very, very, very,

31:10.120 --> 31:19.720
how do you say it homogenized and optimized techniques for using and leveraging IPS cell

31:19.720 --> 31:26.600
technologies. And that could very well include using them as medical technologies, medical

31:27.400 --> 31:32.440
interventions, even longevity, this kind of thing. And you don't have to have any academic

31:32.440 --> 31:40.840
biologists be aware of that. You just have to have the ongoing infrastructure to make sure that

31:40.840 --> 31:47.080
the remnants from all the hospitals and all the world that can produce IPS cells are always available

31:47.080 --> 31:51.560
and are always flowing and are always coordinated and are always there. And that's the way it is.

31:54.680 --> 31:59.080
None of that material goes to waste. It's way too valuable. And because it's not going to be

31:59.080 --> 32:05.080
there forever, we're not always going to have 350 million people in America and 100 million

32:05.080 --> 32:08.920
people needing a hospital every year. We're not going to have that forever. They don't plan to

32:08.920 --> 32:15.480
have that forever. So they need to gather all this data now. Seven years old. But we had the ability

32:15.480 --> 32:19.160
to go back to these cells and remember what we had learned from these cells and looked for

32:19.160 --> 32:23.400
those same pathologies arising in these cells long before they started to die. So long before

32:23.400 --> 32:26.440
this has started to die, we saw problems in the same problem in the vascular trafficking,

32:26.440 --> 32:31.800
the same problems in nitrosative damage, nitroic mitral stress. And when, and they did not happen

32:31.800 --> 32:35.240
in our Newton corrected cells, so that made us allow us to know that yes, not only were those

32:35.240 --> 32:38.920
pathologies happening, but those pathologies were due to the mutation that was causing that person's

32:38.920 --> 32:43.880
disease. And then we went back and we asked whether or not our compounds could rescue those cells

32:43.960 --> 32:48.600
and they could, at least the first few that we've tried have. And we then used yeast genetics,

32:48.600 --> 32:51.800
as I mentioned, a lot of complicated experiments I won't take you all through. But we used yeast

32:51.800 --> 32:55.560
genetics to figure out what the target of the compound was. And lo and behold, it turned out

32:55.560 --> 32:58.600
to be a very, very highly conserved ubiquitin ligase. You can see this is a cartoon of the

32:58.600 --> 33:02.920
different domains of the ubiquitin ligase in the yeast cell known as RSP5 and in human cells known

33:02.920 --> 33:08.120
as NED4. And basically every domain is conserved. And this is really an interesting kind of protein

33:08.120 --> 33:12.360
defined because the ubiquitin ligases are very large complex family in humans. There are about

33:12.360 --> 33:15.480
700 of them in humans. There are about 300 of them in yeast. And they've been very,

33:15.480 --> 33:18.680
very difficult for pharmaceutical companies to target when they take the protein out of the cell

33:18.680 --> 33:21.960
and try to do what they have done traditionally over the years to try to find chemical compounds

33:21.960 --> 33:25.240
that will alter the function of the protein in a purified system. And that is because the biology

33:25.240 --> 33:30.520
of these proteins really only manifests within the context of a living cell where all the proteins

33:30.520 --> 33:33.480
are very, very close together and crowded and moving around and changing their conformational

33:33.480 --> 33:38.120
states. That's where you see that this protein particularly matters. And this protein is very

33:38.120 --> 33:41.960
complicated when it would have been impossible to find without the kinds of simple chemical genetic

33:41.960 --> 33:45.960
methods that we use. So it really demonstrates I think pretty strongly that the power of phenotypic

33:45.960 --> 33:49.720
screens, if you're looking for compounds that really have very special properties, properties that can

33:49.720 --> 33:53.880
correct the disease pathology and the power of chemical genetics to figure out how those targets

33:53.880 --> 33:57.960
work. Now this is really only the beginning whether this will ever turn out to be a therapy or not,

33:57.960 --> 34:01.000
we don't know. But what it has done is showed us that this ubiquitin ligase plays a very key

34:01.000 --> 34:04.600
central role in the way that pathology, that cellular pathology is manifested in lots of different

34:04.600 --> 34:10.600
ways. And so it's certainly a useful tool. We're finding more and more of the genes that we found

34:10.680 --> 34:14.600
in these cells are useful tools in understanding the biology. And we hope one day maybe this will

34:14.600 --> 34:19.080
be a way of finding therapeutic compounds. But what I do believe is that we need these diseases

34:19.080 --> 34:22.840
are very, very difficult and we need to try every trick in the book. And so something as unconventional

34:22.840 --> 34:29.320
as this, maybe it could provide one key. So we're pursuing the same sort of strategy with

34:29.320 --> 34:33.800
Alzheimer's and other neurodegenerative diseases. Many other people are pursuing other very

34:33.800 --> 34:37.400
strategies. But the reason I concentrated on showing you what happens with one particular

34:37.400 --> 34:41.720
protein was that, remember that idea that maybe you could alter this HEAC response and

34:41.720 --> 34:45.000
and soup up a HEAC response in these neurodegenerative diseases and take care of the protein folding

34:45.000 --> 34:48.760
problem? Well, our work with cancer told us that might not be a good idea. It might make those

34:48.760 --> 34:52.280
the brain cells much more susceptible to cancer. So that's why we're going after individual proteins

34:52.280 --> 35:00.440
in these diseases. So the first and final thing I want to say is that this work is all thanks to

35:00.440 --> 35:04.360
the extraordinary group of people in my laboratory who've been working with their whole hearts and

35:04.360 --> 35:10.680
souls over the last. So I wonder if you can already notice that one of the shocking things for me

35:10.680 --> 35:17.240
is that we've never really got to prions. In this two part lecture, we never really got to prions

35:17.240 --> 35:23.000
and what prions are and what prion misfolding is. We never got to aggregation or this kind of

35:23.000 --> 35:27.880
thing, which is also thought to happen with some of these proteins. So it's really curious to me

35:27.880 --> 35:33.640
because over expression of alpha synuclein being some kind of toxic thing is not very surprising to

35:33.640 --> 35:40.200
me. Any over expression of a protein could potentially be toxic. I just find a lot of this

35:40.200 --> 35:47.800
stuff to be. I'm sure that there's some good science there. And I'm also sure that there's a

35:47.800 --> 35:52.520
lot of exaggeration, especially with regard to prions. So I think tomorrow we're going to take one

35:52.520 --> 35:58.040
last look at her and prions. And then we're going to move on to the main literature of the Nobel

35:58.040 --> 36:04.520
Prize by Stanley Pruser. And we're going to do our little short course on prions and protein

36:04.520 --> 36:10.120
folding and where we are then and where we are now. And see why it is, for example, that Google

36:10.120 --> 36:15.720
Fold hasn't told us how prions work yet, even though Google Fold has supposedly figured out how

36:15.720 --> 36:22.120
protein folding works or it's really, really good. It's quite shocking. And when you see it

36:22.920 --> 36:30.600
from stepping back, it's really very easy to see how overextended they are. And you know,

36:30.600 --> 36:37.160
that's the way it is. They have told us lots of stories about what artificial general intelligence

36:37.160 --> 36:43.560
is going to do. And what it really is is a story about that they tell each other in private meetings.

36:43.560 --> 36:49.160
It's a story about how they have to use this resource while it's available. And as we ramp down

36:49.640 --> 36:54.600
the reproductive rate of our global population and dissolve national borders,

36:54.600 --> 36:59.320
we want to be sure that we collect the data from all of these last generations of a crowded

36:59.320 --> 37:06.840
Earth. Ladies and gentlemen, don't let this new world order, this little lie, become something

37:06.840 --> 37:11.800
that you pass on to your children, make sure that you tell them the truth about the sacred biology

37:11.800 --> 37:16.760
of the world, the reverence that we should have for it. Do not let other men with machines control

37:16.760 --> 37:22.280
your kids and stop all transfections in humans, because they are trying to eliminate the controls

37:22.280 --> 37:28.600
with many needs necessary. I'm sorry for a little bit of disorganization today, not a lot of slides,

37:28.600 --> 37:34.200
just a couple study halls today. I cut it in the middle, hopefully so that peer tube can handle

37:34.200 --> 37:37.800
it a little better. But I think it's probably going to fail on the second one. We'll see.

37:38.760 --> 37:42.440
And then we'll have tested the system a little bit more. Thank you very much for joining me.

37:43.400 --> 37:48.360
You will see me again tomorrow. Intramuscular injection of any combination of substances

37:48.360 --> 37:52.920
with the intent of augmenting the immune system is dumb transfection and healthy humans

37:52.920 --> 37:58.360
is criminally negligent and RNA cannot pandemic.

38:05.320 --> 38:11.320
And I'm wearing my Brooklyn number 11, which is actually Kyrie Irving. Kyrie Irving one,

38:11.400 --> 38:17.560
he was on the Brooklyn. When he was on the nets, did not take the transfection,

38:17.560 --> 38:24.120
he kept himself part of the control group. So although I do think that Kyrie Irving is a jersey

38:24.120 --> 38:30.760
worth having, I actually think this is the Kyrie Irving jersey worth having, because for me this

38:30.760 --> 38:38.360
is when he was standing up for the biology. And so shout out to Kyrie and I will see you guys again

38:38.360 --> 38:40.360
very, very soon.

