From 485dc54a9428f142e340af888240586f93f642ee Mon Sep 17 00:00:00 2001 From: Soothspider Date: Wed, 17 Apr 2024 12:36:19 -0700 Subject: [PATCH] AI Captions. April 17. Protein Folding Part II. --- ...ng Part II STUDY HALL -- (17 Apr 2024).vtt | 1367 +++++++++++++++++ .../README.md | 5 + 2 files changed, 1372 insertions(+) create mode 100755 twitch/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024)/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024).vtt create mode 100644 twitch/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024)/README.md diff --git a/twitch/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024)/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024).vtt b/twitch/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024)/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024).vtt new file mode 100755 index 0000000..d1b2c84 --- /dev/null +++ b/twitch/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024)/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024).vtt @@ -0,0 +1,1367 @@ +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. + diff --git a/twitch/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024)/README.md b/twitch/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024)/README.md new file mode 100644 index 0000000..c68a22f --- /dev/null +++ b/twitch/2122375903 (2024-04-17) - Protein Folding Part II STUDY HALL -- (17 Apr 2024)/README.md @@ -0,0 +1,5 @@ +# Protein Folding Part II STUDY HALL -- (17 Apr 2024) -- Gigaohm Biological High Resistance Low Noise Information Brief + +## Streams +- https://twitch.tv/videos/2122375903 +