WEBVTT 00:00.000 --> 00:06.360 go, I think this is going to work. Okay, so remember, we're getting into this faith in 00:06.360 --> 00:11.120 a novel virus, and they have faith in a novel biology, it's actually morphed into this faith 00:11.120 --> 00:17.160 in a novel virology, a virology, excuse me, that revolves around a gain of function RNA 00:17.160 --> 00:23.080 virus, it involves around particular, particular pieces of that RNA that made it extra special, 00:23.080 --> 00:29.120 it involves transfection working, but not working for being rushed, being adulterated 00:29.120 --> 00:33.700 or having chosen the wrong protein. That wrong protein is the spike protein that from 00:33.700 --> 00:40.320 very early on in 2020, here in the first year already, was being lambasted by many different 00:40.320 --> 00:46.840 people in the narrative and in the media about being a gain of function protein that the 00:46.840 --> 00:52.080 protein itself was the evidence. And then later the protein is a toxin later, the protein 00:52.080 --> 00:59.080 was causing amyloidosis, prion disease, etc. And so this whole, this whole mythology, 00:59.080 --> 01:05.920 this immuno mythology that they have, they have, they have seeded around the novel virus, 01:05.920 --> 01:11.460 has gotten quite, quite expansive. And the part that we're working on here with these 01:11.460 --> 01:15.480 two streams, the one that I just ended in the one I'm starting now, is understanding 01:15.480 --> 01:21.000 protein folding in the context of prion disease, so that we can get an idea of where they 01:21.040 --> 01:26.720 have exaggerated, where they have simplified, and how we can, how we can bring this back 01:26.720 --> 01:31.920 into a really reasonable focus where the sacred aspects of biology, the irreducible 01:31.920 --> 01:37.520 complexity of biology is met with a certain reverence that right now it's, it just is 01:37.520 --> 01:44.400 not given. So what we're really working with here is a faith in a novel biology, it's 01:44.400 --> 01:50.280 a mythology that covers for the expected damage as we transition into population wide testing 01:50.280 --> 01:57.040 of transfection technology. So they know that amyloidosis and prion disease and protein 01:57.040 --> 02:02.560 misfolding and this kind of damage are going to occur over time if they continue to transfect 02:02.560 --> 02:07.680 old people and young people alike. And they, they expect it to go up, they expect it to 02:07.680 --> 02:13.080 show up from already the, the people that have been transfected multiple times. And 02:13.080 --> 02:20.440 I believe this because it was also included in the spars pandemic narrative on page 48, 02:20.440 --> 02:25.920 you can find a little talk about how three years after the rollout of the, of the vaccine 02:25.920 --> 02:33.680 for spars, that a bunch of people develop some kind of, of, of crowds felt yock of disease 02:33.680 --> 02:38.040 and it was blamed on the, on the vaccine and a lot of people were upset. And it started 02:38.040 --> 02:44.880 to undermine the way that people felt about the public health system. It's in their little 02:44.880 --> 02:51.600 Rockefeller tabletop exercise called the spars pandemic, you can find it. And so without 02:51.600 --> 02:56.600 a doubt, what they learned from that tabletop exercise and others was that if they wanted 02:56.600 --> 03:02.000 to cover up for this, the easiest way to do it would be to see a lot of these potential 03:02.000 --> 03:07.360 worst case scenarios into the gain of function story in the beginning of the pandemic. So 03:07.400 --> 03:12.600 that by the time those things manifested, it would be too lost in that narrative of lab 03:12.600 --> 03:17.560 leak or natural virus. And the whole acceptance of a circulating novel pathogen would have 03:17.560 --> 03:24.240 already happened years earlier. And that's where we are right now. That's why, that's 03:24.240 --> 03:25.440 why we need to watch this. 03:30.280 --> 03:36.600 Guess it was me that was, it was clipping here. Somehow I have everything. Oh, I see it 03:36.600 --> 03:43.840 now. Sorry, I got it. I got it. I got it. It was a knob, a knob, a very knob that had 03:43.840 --> 03:50.880 been brushed by a looks like it got pulled by the headphone wire there. Now I see it. Now 03:50.880 --> 03:57.120 I know why my my my my levels were. So there, there was the little morbid title that I was 03:57.120 --> 04:02.520 going to use proteins and protein folding with a dead lady from MIT. That's this one. I'm 04:02.560 --> 04:11.880 going to get rid of this. And I'm going to bring up a new window from my word file here, part B. 04:13.560 --> 04:24.600 That would be this one. So we got the idea that maybe we could use those these cells as 04:24.600 --> 04:28.640 I'm sort of living test tube. And the reason why we would want to do that is there's no 04:28.640 --> 04:32.040 organism. So this is starting in the middle. I got to go to the front. Sorry about that. Here 04:32.040 --> 04:37.520 we go. So here we go. I'm going to put it at 1.5 speed. Hope you can handle it. I think that 04:37.520 --> 04:39.320 should be normal. Now we're used to it. 04:40.080 --> 04:44.080 Remember the Howard Hughes Medical Institute and I work at the Whitehead Institute at MIT. I work 04:44.080 --> 04:48.080 on a variety of different protein folding problems. And in my last lecture, I told you a video 04:48.240 --> 04:50.720 brought introduction to the problem for how it manifested. I think it's a little bit 04:50.720 --> 04:55.320 how it manifested in infectious diseases and more broadly how it is used by cancers to drive 04:55.320 --> 04:58.440 them in the living state. In this lecture, I'd like to tell you about a different aspect 04:58.440 --> 05:02.040 of protein pathology, another equally devastating aspect of protein folding and pathology, the 05:02.040 --> 05:05.000 neurodegenerative diseases, because all of these diseases are diseases of protein and 05:05.000 --> 05:09.960 swollen. This is a extremely vivid demonstration of the difference between the brain of a normal 05:09.960 --> 05:16.120 adult, a pine autopsy, versus adult who died of Alzheimer's disease. It's obviously devastating 05:16.120 --> 05:19.480 disease. And this is why the people who have these diseases lose their memory, lose control 05:19.480 --> 05:22.920 of functions. Okay, sorry. Diseases are really terrible. 05:23.640 --> 05:30.200 Now, this is a graph of what's happened to human longevity over the last couple hundred years. 05:30.840 --> 05:33.800 And it's really, I think this is the red and the black are just two different calculations. 05:33.800 --> 05:37.480 It's not so easy in the going back to the older days to calculate when exactly how old people 05:37.480 --> 05:40.680 lived on average. But these two very different ways of doing it came out with the same answer. 05:40.680 --> 05:43.640 And you can see that there's been the steady march of progress and it's just been amazing. 05:43.640 --> 05:52.200 Wait, where's the drop off for is World War I then going to be considered the flu? I thought 05:52.200 --> 05:57.560 the flu showed up on that one graph all by itself. It was just the flu not World War I or 05:57.560 --> 06:03.240 anything like it was just the flu. Remember? Holy cow. Now she says it's World War. So that's cool. 06:03.880 --> 06:07.160 This has been I think one of the glories of mankind to be able to do this and alter their 06:07.160 --> 06:11.160 own average lifespan. And it's been due to many different factors due to changes in public health, 06:11.160 --> 06:16.040 cleaner drinking water, due to refrigeration and preservation of food and cooking. It's due to 06:16.040 --> 06:20.360 the development of antibiotics, the development of vaccines, the development of anesthesia. So 06:20.360 --> 06:23.960 you could do surgery on people and correct illnesses that way. So anyway, this wonderful 06:23.960 --> 06:30.840 steady, steady progress of mankind is unfortunately in some ways of thinking about it a road to ruin 06:30.840 --> 06:35.160 because as we are curing these other diseases, as we're living longer and longer lives, 06:35.720 --> 06:39.960 we are finding that the incidents of neurodegenerative diseases are going out. These diseases used to 06:39.960 --> 06:45.480 be practically unheard of 100 years ago. Now there's a very large fraction of people around the world 06:45.480 --> 06:49.400 that are suffering from these diseases. And as we extend lifespan, it's getting worse and worse. 06:49.480 --> 06:53.560 There are 5 million Americans suffering from Alzheimer's disease alone. And the same increase 06:53.560 --> 06:58.680 in disease is occurring for all of the neurodegenerative diseases across our globe. So unfortunately, 06:58.680 --> 07:03.000 with respect to neurodegeneration and it being a road to ruin, this is why I say a road to ruin, 07:03.960 --> 07:08.280 we're headed for neurodegeneration and right now there's no exit. We do not have a single therapy 07:08.280 --> 07:12.280 that really fixes these problems. So these are some of the common and uncommon neurodegenerative 07:12.280 --> 07:15.560 diseases you might have heard about Alzheimer's disease and Parkinson's disease, frontal temporal 07:15.560 --> 07:19.880 dementia, Huntington's ALS and Croissphal Yacob disease. And you can see these brown blobs 07:19.880 --> 07:24.200 inside of these cells. And those brown blobs are aggravated proteins, like those aggregates of 07:24.200 --> 07:29.880 Friday I showed you earlier. And as I said, there are all these neurodegenerative diseases 07:29.880 --> 07:33.800 are protein folding diseases and there's not a single therapeutic strategy that cures the 07:33.800 --> 07:39.000 underlying protein pathology. We have some things that address some symptoms in some of these diseases, 07:39.000 --> 07:43.320 but for the most part, we're pretty helpless against them. So I've been working on protein 07:43.320 --> 07:46.440 folding for a long time and I've worked on a lot of different organisms. And the one thing 07:46.440 --> 07:49.800 that my studies over the years have taught me is that this problem, as I mentioned earlier, 07:49.800 --> 07:55.400 is common to all organisms on earth. And so we got the kind of crazy idea that considering the 07:55.400 --> 08:01.640 eukaryotic tree of life, you see, plants, animals and fungi actually split from each other not that 08:01.640 --> 08:07.080 long ago in terms of evolution. So we thought we might be able to take advantage of this similarity 08:07.080 --> 08:12.280 and to study some of these really difficult, really complicated diseases. Yes, we will not be able 08:12.280 --> 08:16.440 to study many different aspects of protein folding neurodegenerative disease in a simpler 08:16.440 --> 08:20.760 organism. But if we could study some aspects of the precipitating, initiating protein pathology, 08:20.760 --> 08:24.040 the cellular pathology, not the complexity of the disease as a whole, but just the initiating, 08:24.040 --> 08:27.720 precipitating pathology from those proteins in a simple organism, we might be able to move 08:27.720 --> 08:31.000 much more quickly than we would if we had that we can find solely to working on these more complex 08:31.000 --> 08:37.240 organisms. So as I mentioned, one of the things we have in common with yeast is a wide variety 08:37.240 --> 08:41.080 of systems for controlling the protein folding problem. So we have chaperone proteins which 08:41.720 --> 08:46.280 interact with highly reactive proteins that are not quite finished folding and prevent just like 08:46.280 --> 08:50.680 human chaperones, prevent them. Their charges from interacting inappropriately with other partners 08:50.680 --> 08:55.160 and until they're ready and mature, protein chaperones do the same thing. But we also have 08:55.160 --> 08:58.200 protein modeling factors, things that can rest those protein aggregates when they start to appear 08:58.200 --> 09:02.280 apart. We have osmolites, we have things called leproteosome, which degrade proteins that are 09:02.280 --> 09:06.040 that are not properly folded, ubiquitin, ubiquitin ligases, and that entire system is just completely 09:06.040 --> 09:11.240 conserved from yeast to human cells. It's not just that. Lipid biology is actually quite highly 09:11.240 --> 09:14.680 conserved. There certainly are differences of lipid biology at least in human cells, but for 09:14.680 --> 09:19.240 example cholesterol, yeast use a very closely related lipid called regastrol for exactly the 09:19.240 --> 09:23.320 same reason that we use cholesterol to control the fluidity of membranes and to control the movement 09:23.320 --> 09:28.520 and density of proteins within those membranes. And they move packages of membrane-bounded proteins 09:28.520 --> 09:32.840 around the cell in very highly orchestrated ways, really the same way that a nerve cell will move 09:32.840 --> 09:36.280 dopamine around. The yeast cells will move things like mating factors around. 09:38.040 --> 09:43.240 So she says that we move dopamine around in little vesicles. That's all we do is 09:43.240 --> 09:49.240 neurotransmitters around in little vesicles. That's really sad because we know in the brain there are 09:49.240 --> 09:55.800 at least vesicles of RNA that are released of the arc protein, which cause local actin skeleton 09:55.800 --> 10:01.240 remodeling to be possible in neighboring cells that weren't necessarily activated by the same 10:01.880 --> 10:09.480 genetic or neuronal signal that the postsynaptic neuron was. And so the postsynaptic neuron can 10:09.480 --> 10:15.240 release virus-like particles that contain the mRNA of the arc gene cause arc protein to be 10:15.240 --> 10:22.600 expressed locally at that synapse and cause remodeling of that local arc protein cytoskeleton 10:22.600 --> 10:28.520 in that neuron that was never activated by the synapse or the signal that came into the postsynaptic 10:29.160 --> 10:34.200 neuron. And that is just the tip of the iceberg. I'm quite certain of it. So it's really funny, 10:34.200 --> 10:40.760 almost to the point of being a little bit... Why did you say that? If you say that like we 10:40.760 --> 10:47.720 move dopamine around these yeast proteins move all kinds of stuff around in their vesicles. 10:49.480 --> 10:55.560 We know that there's extracellular signaling between tissue using extracellular vesicles 10:55.560 --> 11:01.160 called exosomes. I'm sure she knows it too. She's got to know it. And if she doesn't, it's because 11:01.160 --> 11:07.000 of the intense compartmentalization of biology. Sosomes and peroxosomes, these are very complex 11:07.000 --> 11:09.880 organelles that are involved in doing very complicated functions. Some of them are involved in degrading 11:09.880 --> 11:13.320 proteins. Some of them are involved in a wide variety of metabolic actions that have to be 11:13.320 --> 11:17.640 segregated from the normal cytoplasm. These cells have both of those. They have autophagy. This 11:17.640 --> 11:22.200 is a process by which the cell actually directs its degradation and eating machinery to eat up 11:22.200 --> 11:25.720 protein aggregates and get rid of them. Apoposis, a programmed form of cell death. 11:26.600 --> 11:30.760 Cell cycle, very complexly regulated cell cycle, regulated very, very differently bacteria, 11:30.760 --> 11:33.640 but in yeast and humans regulated in very much the same way. And in fact, studies of that cell 11:33.640 --> 11:37.240 cycle work extremely important for our understanding of cancer. And why cancer cells start to replicate 11:37.240 --> 11:41.320 uncontrollably, study them in yeast to provide its key insights. We have mitochondria, the 11:41.320 --> 11:44.760 energy factory of the cells, and mitochondria do amazing things in yeast and human cells. 11:44.760 --> 11:48.040 But they also are a place where reactive oxygen species are generated and can do a great deal 11:48.040 --> 11:51.400 of damage. And then there's a whole variety of signal transaction pathways. Again, 11:51.400 --> 11:56.280 these key pathways that control growth and development in us, but control responses to the 11:56.280 --> 11:59.720 environment, responses to other cells, and responses to internal and external stresses, 11:59.720 --> 12:04.680 those same signaling pathways have been controlled, have been preserved rather in yeast and higher 12:04.680 --> 12:10.040 eukaryotes. So, calcinarism, example, map kinases, G-coupled protein receptors, 12:10.040 --> 12:13.880 all of these were first developed long ago in eukaryotic life, and greatly greatly elaborated 12:13.880 --> 12:17.080 in us. We have many, many more G-coupled receptors than a yeast cell has, for example. 12:17.080 --> 12:20.360 But the basic machinery and the basic concepts and the basic ways in which those signaling pathways 12:21.400 --> 12:25.000 drive processes inside the cell are similar. So, we got the idea that maybe we could 12:25.000 --> 12:28.760 use those yeast cells as our living test tube, and the reason why we want to do that is there's 12:28.760 --> 12:34.200 no organism on Earth that we can manipulate and get to tell us its secrets better than yeast. 12:34.200 --> 12:39.160 It has an absolutely unrivaled toolkit, and it really derives from brewers back about 150 years 12:39.160 --> 12:42.520 ago wanting to make better beer, and wanting to understand that organism and how to manipulate it, 12:42.520 --> 12:47.560 and it's taken off from there, and it's just amazing. Massive, massive numbers of people have 12:47.560 --> 12:52.120 been building and developing technologies that allow us to knock out every gene in the genome, 12:52.120 --> 12:55.480 or overexpress every gene in the genome, make point mutants wherever we want in the genome, 12:55.480 --> 12:58.440 and so that's just something we can't do in any other organism at this level today. 12:59.160 --> 13:05.320 So, here's how we set things up. We have yeast cells that are growing on, in the top row there, 13:05.320 --> 13:09.000 they're growing on glucose medium. In the bottom portion of the panel, they're growing on lactose 13:09.000 --> 13:14.360 medium. We have a gene that will turn on whenever we give the cells lactose, and so we make a 13:14.360 --> 13:18.840 recombinant form of that gene that now, well, instead of making the proteins that these cells use for 13:18.840 --> 13:24.200 lactose utilization, they make different proteins that misfold in human diseases, like alpha-synuclein, 13:24.200 --> 13:29.480 A-beta, TVP-43, and TIN-TIN-FUS. And you can see that we've built, for synuclein here, we've 13:29.480 --> 13:32.680 shown you all three different strains that are expressed in a protein at different levels, 13:32.680 --> 13:35.960 and are exhibiting different levels of toxicity, just by the fact that they can't grow very well, 13:36.040 --> 13:38.920 and we've then done that with all of those different disease proteins, and we've matched them so that 13:38.920 --> 13:43.320 they have the same level of toxicity. So, same level of toxicity from different proteins, 13:43.320 --> 13:47.640 what I said is just some non-specific protein aggregation mess. It turns out that it's not, 13:47.640 --> 13:51.400 but when those proteins misfold inside of the yeast cell, they go into the cell, they interact 13:51.400 --> 13:55.160 with the same kinds of highly conserved constituents that they interact with in a neuron, and they do 13:55.160 --> 13:59.640 bad things in a very specific way. So, here's an example of a phenotype that glob over there 14:00.520 --> 14:04.680 is protein nitration, and it's happening all of the cells at the same level of toxicity, 14:04.680 --> 14:08.200 the nitration damage is happening really only in the cells that are expressing 14:08.200 --> 14:11.960 alpha-synuclein. That's really interesting, because in the human diseases that are known to be caused 14:11.960 --> 14:15.480 by the misfolding of alpha-synuclein, and that is Parkinson's disease, multiple systems atrophy, 14:15.480 --> 14:20.520 Lewy body, dementia, and nerve-brain iron accumulation, they too show very high levels of very specific 14:20.520 --> 14:24.600 protein aggregates with nitration. So, very unique and very specific cellular pathologies directly 14:24.600 --> 14:29.800 related to the human disease. So, here's our cells, we've got this gene that we can turn on with 14:29.800 --> 14:33.160 galactose, anaerobic galactose, and we've hooked it up to GFP, just so that we could see what was 14:33.160 --> 14:37.480 happening to it in the cells, as they were either healthy or a guy. And when we had just one or two 14:37.480 --> 14:40.440 copies of the protein in the cells, they would find, and the protein went out to the membrane, 14:40.440 --> 14:45.560 which is where it should belong. And if we had more, one extra copy, we started seeing things 14:45.560 --> 14:49.400 going along, and then if we had two extra copies, it went even worse, this does not look good, 14:49.400 --> 14:53.640 they said protein conglomerates here in aggregation, type some type of aggregation, and then those 14:53.640 --> 14:58.520 cells grow fine, those cells grow slowly, and those cells die. Very, very strong dosage difference, 14:59.080 --> 15:01.720 and what's really interesting about that, is that's true in man as well. 15:02.040 --> 15:07.480 So, it's really important for you to understand that there is one gigantic caveat here, which I 15:07.480 --> 15:17.880 find almost disturbing. Green fluorescent protein is a massive protein, native to jellyfish that 15:17.880 --> 15:24.520 glows in the dark green. If you attach that massive protein to alpha-synuclein, I would be 15:24.520 --> 15:29.960 willing to bet that that cartoon up there is wrong, the GFP is a lot bigger than alpha-synuclein. 15:32.040 --> 15:36.760 Could be wrong, you look it up yourself and find out if I'm right or wrong, or whether that cartoon 15:36.760 --> 15:43.480 is right or wrong, but the point is, is that GFP is not small, and overexpressing GFP in any cell 15:43.480 --> 15:52.280 line, or in any mammal tissue will result in cell death. Because that level of GFP is toxic, 15:52.280 --> 15:58.280 if you can see it like this, it's already a lot of molecules, a lot of molecules of GFP in order 15:58.280 --> 16:05.400 to see that signal. If it starts to make these kinds of, these kinds of, uh, punctate sort of 16:05.400 --> 16:13.960 constructs of, of, of the cell is, is putting this GFP into vesicles to get rid of it, to keep it, 16:13.960 --> 16:22.440 to keep it compartmentalized, so it's already beyond toxic levels. And she is very conveniently 16:22.520 --> 16:30.200 ignoring that fact, because there are no controls here with, with just GFP, right, to show what 16:30.200 --> 16:35.560 GFP toxicity looks like, but I can guarantee you, I can tell you from experience that GFP 16:35.560 --> 16:43.400 toxicity is real. Over expression of GFP causes toxicity is very, very real, and that is toxic 16:43.400 --> 16:50.440 levels already in the middle. And anybody that's used GFP to label neurons in a mouse brain could 16:50.440 --> 16:55.160 tell you that anybody that's used GFP as a label for anything can tell you that because if you get 16:55.160 --> 17:04.440 a good good signal, you also get dead cells. It's extraordinary. And this is the kind of science 17:04.440 --> 17:10.040 that passes for knowledge creation at this time. This is how we got here. This is, you know, how 17:10.040 --> 17:19.960 many years ago is this eight? Just the basic principle of this is silly because the GFP is not 17:19.960 --> 17:27.720 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 17:27.720 --> 17:37.800 it up because I don't know. Green, fluorescent protein, and there will probably be a Wikipedia 17:37.800 --> 17:48.680 page right there. Green fluorescent protein has a wavelength blah, blah, blah. Natural protein is 17:48.680 --> 17:55.800 238 amino acids. It's 27 kilodultans. And then let's look up alpha synuclein. 18:08.840 --> 18:11.720 140 amino acids. So what is it bigger? 18:12.520 --> 18:21.560 238. No, it's bigger. 140 by 238. So it's roughly twice the size of it, you see? So this is not the 18:21.560 --> 18:27.720 right, this is not the right cartoon. It's a much bigger protein. And she is pretending that that 18:27.720 --> 18:33.160 much bigger protein has no effect on whether the cells are healthy or not. It's all the alpha 18:33.720 --> 18:43.000 synuclein. That's it. That's impressive. 18:43.640 --> 18:48.360 Things that have just one extra copy of the wild type of synuclein protein will get early onset 18:48.360 --> 18:51.320 Parkinson's disease. And if they have two extra copies, they'll get even earlier, more virulent 18:51.320 --> 18:56.840 form of the disease. So this unusual, I mean, ask yourself, why does it need to be tied to GFP? 18:56.840 --> 19:01.400 If you know that alpha synuclein is being expressed by the cells, why can't you stain for it? Why do 19:01.400 --> 19:09.480 you need to tie it to GFP? When GFP is a gigantic toxin, a gigantic toxic protein at high fluorescent 19:09.480 --> 19:16.200 levels, it's extraordinary. Extreme sensitivity to exactly how much protein you're making was 19:16.200 --> 19:19.800 certainly, was certainly reminiscent of what was happening in man. So how can we get a better idea 19:19.800 --> 19:22.840 of what's going on here if there's anything really deeper involved? Well, we do something 19:22.840 --> 19:28.600 called screen first, you could do a control for the GFP. Meaning we screen every gene in the genome 19:28.600 --> 19:31.720 for what makes cells better or worse. We can take with these, we have libraries, 19:31.720 --> 19:34.520 every gene in the genome, we can turn them up or turn them down and see how that changes 19:34.520 --> 19:38.520 the disease manifestation. And in these cells that have the four copies where they're just 19:38.520 --> 19:42.120 playing frankly dying of the disease, we can screen for chemical compounds that might rescue them. 19:42.840 --> 19:45.640 And studying those compounds might tell us something about the disease mythology. 19:45.640 --> 19:52.920 So I love the fact that she uses mite, but it's exactly how science is done. You declare 19:53.720 --> 20:00.040 a rough shot experiment, a suitable model for a disease, and then you go for it. 20:00.040 --> 20:04.360 That's what she did just there, right? She said, this is a pretty suitable model for disease. 20:04.360 --> 20:10.600 Why don't we use the four copy version for a model of disease and we'll screen compounds 20:10.600 --> 20:14.840 on it? Holy cow, that'll pay for at least two postdocs in five years of my lab. 20:14.840 --> 20:18.280 Compounds that might rescue them. And studying those compounds might tell us something about 20:18.280 --> 20:23.160 the disease mythology. So screening is a lot like panning for gold. You go through a whole 20:23.160 --> 20:25.960 lot of stuff and you look through it, you look through it and you look through it and you find 20:25.960 --> 20:28.760 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 20:44.760 --> 20:47.560 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 21:02.520 --> 21:05.080 were genes that were involved in moving little vesicles around. We thought, well, 21:05.080 --> 21:09.080 gee, I wonder if those things actually have something to do with vesicle trafficking. And so 21:09.080 --> 21:12.120 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 21:17.160 --> 21:20.840 depending on how much the alpha-snooplane we're expressing, we get more and more of these protein 21:20.840 --> 21:23.560 aggregates. And then we did something called immuno-electron microscopy. 21:23.560 --> 21:30.200 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 21:36.120 --> 21:40.040 the alpha-synuclein or whether it's from the overexpression of the GFP, or whether the 21:40.040 --> 21:52.680 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. 21:58.040 --> 22:02.280 We attached a label to an antibody against the alpha-synuclein and against a protein involved 22:02.280 --> 22:05.160 in vesicle trafficking, and we found that they were there together. So these blobs, these green 22:05.160 --> 22:09.720 blobs here, are actually blobs, not just of aggregated cineuclin, but aggregated cineuclin 22:09.720 --> 22:13.720 enmeshed in vesicles that are not moving around the cell and getting to the places they're supposed 22:13.720 --> 22:16.680 to be. And when that happens in a nerve cell, it's really disastrous because that's one of the 22:16.680 --> 22:19.960 major ways in which a nerve cells communicate with each other. That's so good for a yeast cell 22:19.960 --> 22:24.600 either. Anyway, this finding that alpha-synuclein blocks vesicle trafficking has not been corroborated 22:24.600 --> 22:29.080 by many other laboratories. And to cut a long story short and move on to the very final stage of 22:29.080 --> 22:32.360 this talk, we found that there were parallel effects, we moved back and forth between these 22:32.360 --> 22:37.400 two neurons, and we found that there were parallel effects. We moved back and forth between yeast 22:37.400 --> 22:42.280 and neurons, and you've got to be very careful when biologists are claiming to be able to do things 22:42.280 --> 22:46.440 like that. Not just vesicle trafficking, but bursts of reactive nitrogen species, as I showed you in 22:46.440 --> 22:50.840 that protein block, mitochondrial dysfunction, and perturbations in middle line homeostasis. So 22:50.840 --> 22:54.040 at least at this early, very simple cellular level, there's a lot of similarities there. 22:55.720 --> 22:58.440 But we really needed to be able to show that the genes we found in yeast, and the genes that 22:58.440 --> 23:02.040 saved the yeast cells, those same genes would matter to a neuron. So we actually looked at, 23:02.040 --> 23:05.480 in a couple of different systems initially, one was this wonderful nematode system, 23:05.480 --> 23:08.520 it was a worm, it's a simple little worm, but it's got lots of different kinds of neurons, 23:08.520 --> 23:13.000 and in fact it's got the same kind of neurons, dopaminergic neurons, that are adversely affected 23:13.000 --> 23:16.760 in Parkinson's disease. And we could actually peer through, wire up those cells to express 23:16.760 --> 23:19.960 alpha-synuclein, and mire them up so that they were green, they glow green, we could actually 23:19.960 --> 23:23.800 study them in a living worm, and we could see that when the worms were expressing alpha-synuclein 23:23.800 --> 23:27.240 in those cells, you can see how some of them are disappearing over there. It's a true neurodegenerative 23:27.240 --> 23:31.880 model in the nematode. And our genes that rescue the yeast cells also rescue that nematode. 23:31.880 --> 23:36.440 And the same thing happened when we took neurons from rat brains, the midbrain region of the rat, 23:36.440 --> 23:40.600 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 24:13.800 --> 24:19.160 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. 27:43.480 --> 27:47.640 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 27:51.640 --> 27:55.720 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 28:07.560 --> 28:11.320 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 28:55.320 --> 29:00.120 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 29:09.080 --> 29:13.480 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 29:18.200 --> 29:22.040 those cells such that you have corrected just the mutation that's responsible for that person's 29:22.040 --> 29:26.760 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.