WEBVTT 00:00.000 --> 00:02.000 You 00:30.000 --> 00:32.000 You 01:00.000 --> 01:02.000 You 01:30.000 --> 01:32.000 You 02:00.000 --> 02:02.000 I 02:07.320 --> 02:10.280 Yes, sir. I'm back again. We got to finish this video 02:13.360 --> 02:19.400 And basketball is over one of the boys didn't want to play king of the court, so we're back a little early 02:20.520 --> 02:23.800 And that's okay. That's okay. I don't mind at all 02:23.800 --> 02:28.560 Welcome back to the show 02:29.520 --> 02:34.880 This is gigo and biological a high-resistance low-noise information brief brought to you by a biologist 02:34.880 --> 02:40.920 We've been trying to review free on protein biology and in so reviewing 02:41.400 --> 02:47.760 We have been listening to one Susan Linquist as well as one Stanley Prouzner as 02:48.080 --> 02:54.320 Well as some other people in the current COVID pandemic narrative like Stephanie Sinev 02:56.640 --> 03:02.080 Who have been speaking out rather vocally about the potential for the spike protein to 03:03.160 --> 03:05.160 generate pre-on like 03:06.040 --> 03:08.840 situations or neurodegenerative disorders and 03:10.080 --> 03:16.000 Amyloidogenesis as well has been tossed around as something that is possible so 03:16.360 --> 03:18.360 We've been listening to Susan Linquist 03:19.080 --> 03:26.360 Because she's very interesting from the perspective of her model of the disease her model of the disease is actually yeast 03:26.800 --> 03:30.360 Which has many of the same cellular components that we do 03:30.640 --> 03:38.880 But let's keep in mind that yeast is a single cellular organism and so there's a lot there that's not gonna be there as well and 03:39.320 --> 03:41.160 there are gonna be 03:41.200 --> 03:50.040 machinery and communicative devices and communicative mechanisms that aren't there and our cells are the analog or homolog rather will be 03:50.200 --> 03:52.200 will be different 03:52.200 --> 03:58.520 And so what she has identified are heat shock proteins which seem to be proteins which are activated 04:00.360 --> 04:04.800 By acute exposure to warmer temperatures of yeast and other 04:05.760 --> 04:08.600 bacteria and plants in culture and 04:09.360 --> 04:14.160 She has extrapolated this to the role of chaperones 04:14.160 --> 04:19.760 so proteins that prevent protein misfolding and protein aggregation and so she's 04:20.360 --> 04:26.320 Trying to use yeast as a model system for when chaperone proteins prevent 04:27.000 --> 04:29.000 the aggregation of 04:29.680 --> 04:34.920 Misfolded proteins and those could be amyloid proteins. They could be pre-on proteins 04:34.960 --> 04:38.640 They could be whatever protein that we decide to look at so here 04:39.360 --> 04:43.960 SUP 35 is is titled a self-assembling amyloid 04:44.160 --> 04:48.880 But we haven't actually defined what an amyloid is and if you go back to the previous 04:49.560 --> 04:53.880 Discussion where we started with the first 13 minutes. Oh, we're not defining what amyloid is 04:54.000 --> 04:59.000 We're not defining what pre-ion is except for the fact that there is an amyloid beta 04:59.800 --> 05:04.760 protein in the brain and there is a pre-on protein in the brain and 05:04.920 --> 05:10.440 But other than that, we're not talking about whether SUP 35 is also an amyloid 05:11.200 --> 05:16.840 Or whether we're just calling it that and it's unfortunate that we're not talking about it with more precision 05:16.840 --> 05:20.400 But here we go right where we left off. Thank you very much for coming back 05:21.120 --> 05:22.680 Not just a mess of aggregates 05:22.680 --> 05:27.760 They were very highly organized amyloid filaments and they were organized in a very interesting way because the central spine of that filament 05:27.760 --> 05:33.200 Was that region of the protein that previously been shown to be essential for the inheritance of the white trait? 05:33.920 --> 05:37.920 And the functional part of the protein that's normally functioning in translation is stuck out on the outside 05:37.920 --> 05:41.120 So when this protein gets get into assembled into this this amyloid fiber 05:41.120 --> 05:43.400 It can no longer get to the ribosome and there's no longer functioning 05:43.400 --> 05:49.920 So that's a lot of protein there that instead of being a sub component to a ribosome is now 05:50.840 --> 05:55.680 assembling into a a fiber which is which is an interesting positive and 05:56.360 --> 06:01.240 We were then able to actually monitor the assembly kinetics of this protein and we found that 06:01.800 --> 06:06.760 The protein initially assembles in a test tube very very inefficiently and very very slow 06:06.760 --> 06:08.680 We took hours and hours and hours with protein to assemble 06:08.680 --> 06:11.240 But now the question is is this pure protein? 06:11.240 --> 06:14.400 Is that the way that the protein exists in the cell? 06:14.560 --> 06:19.640 Probably not so this is again an in vitro preparation looking at pure protein and its behavior 06:20.160 --> 06:25.640 Might be different the key thing that I think explains the ability of this protein to serve as an element of genetic inheritance 06:26.040 --> 06:29.240 Is that lunch? Oh if you take a very small amount of this 06:29.880 --> 06:33.560 Preassembled protein and add it to the start of a new assembly reaction this one right here 06:33.560 --> 06:40.960 But you see is that boom the preassembled fibers have the capacity to completely very very rapidly convert all the other protein 06:40.960 --> 06:43.920 That same state and so this is where the idea comes from 06:44.680 --> 06:47.160 the idea comes from yeast and 06:47.800 --> 06:55.480 Whether or not pre on protein and other examples of this kind of phenomenon in protein folding exist in nature and underlie these 06:55.560 --> 06:58.120 Other disease states is completely 06:58.920 --> 07:06.560 Probably not not founded in experimental evidence, but found it in conjecture based on what is done in yeast so pay attention carefully 07:07.320 --> 07:09.160 so 07:09.160 --> 07:15.120 Now you can begin to see how this could explain the inheritance of this trait because if you have a soluble protein that's functional in the cells are 07:15.120 --> 07:20.320 Red and an insoluble protein that's not functional so that the cells are white you make those cells together and 07:20.640 --> 07:22.720 Then you sporeulate and segregate the genomes 07:22.720 --> 07:26.240 Well, the protein has served as a template to change the protein in the other cell type 07:26.240 --> 07:30.400 And so that when this cells spore late all of the progeny are going to be white is Brian Cox 07:30.400 --> 07:37.520 I'd shown and it doesn't segregate with the DNA doesn't matter with which which cells get which chromosome because the trait is not based upon the inheritance of a DNA change 07:37.600 --> 07:42.680 It's based upon the inheritance of a protein with an altered conformational state and how we just be one of four figures in this 07:42.680 --> 07:44.120 Is that but if the altered 07:44.560 --> 07:48.320 Confirmational state is requires the presence of the other 07:48.800 --> 07:54.880 Confirmational state and the other conformational state induces that conformational state and its aggregate 07:56.120 --> 08:05.480 It doesn't sound like a really nice kind of inheritance. You're not inheriting like a function or a are these proteins now serving as stop codons 08:05.480 --> 08:10.320 Or are they actually interrupting you see that's that's what's interesting. We're doing now 08:10.360 --> 08:17.720 We're we're using this as a model of genetic inheritance that doesn't use a nucleic acid, but we're not really 08:18.680 --> 08:20.680 using a 08:20.720 --> 08:29.200 Natural example of it. We're using an in vitro example. It happens in a laboratory and we're extrapolating to 08:30.120 --> 08:33.160 Natural systems and is it really possible that? 08:33.960 --> 08:35.360 Let's go on 08:35.840 --> 08:40.920 104 controls that amyloid state I told you that it saves cells from heat shock by now 08:40.920 --> 08:43.640 He just called it she just called it an amyloid state 08:43.640 --> 08:47.680 So I'm not sure that that has been substantiated as a term yet 08:48.160 --> 08:53.080 Aggregating proteins it also can disaggregate those amyloids. So in fact, it can cut them a little bits and pieces 08:53.080 --> 08:57.400 So here are fibers of that protein amyloid fibers very tough difficult to 08:57.960 --> 08:59.400 dissolve or 08:59.400 --> 09:01.600 Chemically you have to really go through incredible lengths to get them to dissolve 09:01.600 --> 09:08.240 But here's what H2O4 doesn't it chops them until tiny little pieces and that allows them to be inherited and to form the template 09:08.240 --> 09:11.760 That goes into the daughter cell and allows the daughter cells proteins to change 09:12.800 --> 09:16.960 So the whole thing putting that together then looks like this you have a reds. I don't understand the 09:17.360 --> 09:20.040 but the fibers don't exist, right, so 09:21.160 --> 09:27.760 When do they chop up? It's weird cells that are carrying a particular gene and when ribosomes do what they're supposed to do the translation 09:27.760 --> 09:32.820 Termination factors in a soluble state it tells the ribosomes to stop when the ribosomes see the stop codon 09:33.760 --> 09:37.680 But that protein can assemble into this self-repetuating amyloid and when it does 09:37.680 --> 09:39.560 There's not very much of the translation terminations we have to round 09:39.560 --> 09:45.680 And so quite a few of the ribosomes wind up reading through that stop codon ignoring it that changes the cells from having a red pigment 09:45.680 --> 09:49.560 They're having a white pigment and so that's the only stop codon that gets affected 09:50.520 --> 09:54.440 Aren't there other stop codons for other proteins that are made by the yeast 09:55.080 --> 09:57.760 Doesn't it have a lethal effect on other 09:59.200 --> 10:06.320 Proteins that don't stop where they're supposed to stop see that's the part about this model that doesn't make sense already to me 10:06.760 --> 10:10.520 If this is a stop codon, is it particular for one protein? 10:10.920 --> 10:15.520 It's a stop terminator protein assembly protein for 10:16.360 --> 10:22.200 Ribosomes that's only for one protein or is it for all pro if it's for all proteins and all proteins will have an altered I 10:23.160 --> 10:25.160 Don't know I don't know 10:27.440 --> 10:29.440 Now 10:29.800 --> 10:39.560 Cells turn white and the next key the inheritance of this is that the self-templating amyloid protein is actually cut by HSP 104 to allow 10:39.560 --> 10:44.880 It's orderly segregation into daughter cells for inheritance and in fact with Helen Sable 10:44.880 --> 10:47.880 We overexpress this protein to a point where it made massive 10:48.440 --> 10:52.640 Assemblies amyloid assemblies in the cell and and use some very very sophisticated imaging techniques 10:53.680 --> 10:55.760 demography 10:55.760 --> 11:01.200 To suggest that just like polytene chromosomes larger assembly the polytene chromosomes give us our first view of 11:01.560 --> 11:04.520 The organization of DNA and in chromosomes 11:04.840 --> 11:08.840 We think these larger assemblies are giving us the first view of the way in which the an attribute 11:08.840 --> 11:12.000 I was sophisticated mitotic apparatus that breaks these fibers apart is working 11:12.000 --> 11:12.720 But in any case 11:12.720 --> 11:15.440 It's we want to force key to it, but it's not the only key to it 11:15.440 --> 11:20.320 There are multiple other proteins that are involved in helping these so those are only there during mitosis 11:20.320 --> 11:23.360 She said mitotic processes so during cell division 11:24.040 --> 11:28.400 those those which she calls amyloid fibers are broken up by 11:29.440 --> 11:33.000 HSP 104 but not in the normal state in the normal state 11:33.000 --> 11:40.480 They stay like that and there's no terminator and so they read through it's a very interesting model of what's going on without a whole 11:40.480 --> 11:43.360 Lot of explanation for why it would do this other than a color 11:45.080 --> 11:52.480 These amyloid fibers to be partitioned to be ourselves in a very orderly way sophisticated mitotic apparatus with Helen 11:52.720 --> 12:01.480 Sable hmm sophisticated mitotic apparatus sounds like voodoo guaranteeing the inheritance of the trait that's caused when this protein changes its conformational state 12:04.120 --> 12:08.720 So this provides a completely coherent biochemical explanation for this that is called 12:09.000 --> 12:14.240 insistence in science completely coherent mechanism. There's nothing wrong with my model 12:14.400 --> 12:21.240 My model has no holes in it. My model is seamless. Wow. What a weird statement. Is it really responsible? 12:21.240 --> 12:22.720 Is it the only thing that's going on? 12:22.720 --> 12:27.040 We did a lot of things we had mutations in the prion that didn't assemble very well 12:27.040 --> 12:31.880 And they could not inherit the trait and get ones that caused it to assemble more and they more like they didn't have the trade 12:31.880 --> 12:32.440 We did all sorts of things 12:32.440 --> 12:35.760 But let me just tell you about one line of evidence that that was particularly fun 12:35.760 --> 12:39.680 We figured that well if this really was responsible for that that new trait 12:39.800 --> 12:44.960 This this protein assembly process and we should be able to use that knowledge to create a new prion all of our own 12:45.120 --> 12:50.480 And so what we did was to take the sub 35 translation termination factor and in the genome 12:50.480 --> 12:53.560 We deleted the prion element from that strength is we didn't want it to interfere with our new prion 12:53.560 --> 12:56.040 We're going to be making and then we took glucocorticoid receptor 12:56.040 --> 13:02.200 Which is a steroid hormone receptor from rap and asked whether or not we could monitor its activity in a yeast cell and see it 13:02.200 --> 13:07.600 Change in its activity state when we fused it to that domain, which we now call the prion domain of this protein 13:07.600 --> 13:10.480 Which is responsible for this by stable state and sure enough 13:10.480 --> 13:13.560 We had a recorder in there that when the glucocorticoid receptor was working 13:13.560 --> 13:19.200 It would turn the cells blue when the glucocorticoid receptor was assembled into a self-repetuating aggregated template 13:19.200 --> 13:22.320 If the cells became white because it was no longer working and moreover 13:22.320 --> 13:25.000 They passed that white state onto their project in a very very stable way 13:25.000 --> 13:27.680 So they could either pass on the blue state or they could pass on the white state 13:27.720 --> 13:32.320 So the whitesmen led another wonderful experiment. They took the cell walls off of yeast cells and 13:33.160 --> 13:37.840 They did a protein-only transformation assembling the protein into those fibers that I told you that we need earlier 13:37.840 --> 13:39.280 They made them under a couple of different conditions 13:39.280 --> 13:42.880 And they used those fibers that stuck them into the yeast cells 13:42.880 --> 13:49.920 And they were able to show that the cells turn from red to white in a heritable way. So that protein only transformation 13:50.800 --> 13:55.680 Really was another nail establishing the coffin establishing that this another nail in the coffin 13:55.680 --> 14:03.200 She means that they're establishing the possibility that proteins as they fold differently can also be inherited and that 14:03.480 --> 14:10.160 Inherited folding difference can be passed on to generations whether or not it's causing other proteins to fold 14:10.960 --> 14:16.000 Differently is not always the center of their model and they go back and forth between that that is 14:16.480 --> 14:24.080 Sometimes required sometimes assumed but they are doing it in the context of highly manipulated in vitro models of 14:24.240 --> 14:31.280 yeast and yeast without cell walls and yeast with over expression of these proteins and yiddie yada yada 14:31.840 --> 14:35.840 So keep in mind everything here needs to take be taken with a teaspoon of salt 14:36.560 --> 14:39.680 Genetic trait was due to the inheritance of a protein with an altered transformation 14:40.320 --> 14:44.560 Now here we have this pre-owned protein as I mentioned, it's a translation termination factor 14:44.800 --> 14:49.600 Really important factor in this other determines when ribosomes will interpret stop codons properly 14:50.240 --> 14:53.680 Now the question is that that could be the n-terminal domain 14:54.720 --> 15:01.280 Of the pre-owned protein that that prisoner's studying. I'm not really sure she hasn't made that really clear to me yet 15:01.680 --> 15:05.600 If this is the whole protein and then that's the n-terminal domain 15:06.160 --> 15:08.160 Then what she did was she added 15:08.880 --> 15:10.880 the n-terminal domain to 15:12.240 --> 15:14.240 To the glucocorticoid 15:15.120 --> 15:17.120 receptor see 15:17.120 --> 15:19.760 the n-terminal domain is what they cut off of 15:20.480 --> 15:22.480 the pre-owned protein in 15:23.600 --> 15:24.320 In 15:24.320 --> 15:29.760 Stanley Prusner's lecture and then after they cut the n-terminal domain off it assembled into the rods 15:30.560 --> 15:31.680 Here 15:31.680 --> 15:39.200 She's saying that they assemble only when it has the n-terminal domain and then when the n-terminal domain actually determines whether or not 15:39.760 --> 15:42.080 They fold incorrectly, which is interesting 15:42.960 --> 15:43.760 I 15:43.760 --> 15:47.760 I'm not really following at this as parallel and and 15:48.560 --> 15:51.520 Confirmate like they're not confirming each other in a way 15:52.240 --> 15:55.760 Here we have this pre-owned protein as I mentioned it's a translation termination factor 15:56.000 --> 16:02.640 Really important factor in this other determines when so the c part is conserved in all eukaryotes in the n and m plus 16:02.800 --> 16:05.760 Minuses only in yeast I think is what she's saying 16:06.240 --> 16:08.880 Which is also very curious because again remember 16:09.440 --> 16:14.240 cutting off the n-terminal domain of the eukaryotic version of this in in 16:14.880 --> 16:20.080 Stanley Prusner's experiments is what made it form the rod like things that he called amyloids 16:20.720 --> 16:22.960 She's suggesting that actually it's this 16:23.600 --> 16:28.720 pre-owned forming conserved part over here that's not conserved in eukaryotes 16:29.280 --> 16:37.040 that causes and controls the pre-owned like inheritance of these protein folding schemes that's 16:38.000 --> 16:41.120 We better read we better make sure we're right, but i'm pretty sure we're right 16:41.120 --> 16:45.040 I think that we just did this today and yesterday so it's pretty hard for me to believe we're wrong 16:45.760 --> 16:47.760 Which is uh definitely not 16:48.400 --> 16:52.480 Jiving parallel here ribosomes will interpret stop codons properly 16:53.040 --> 16:56.880 And it's conserved in all eukaryotes and here we have this domain stuck onto the end of it 16:57.200 --> 17:03.520 Uh, and then also remind remember that in Stanley Prusner if indeed the pre-owned protein is a 17:03.600 --> 17:05.120 They 17:05.120 --> 17:08.480 Stop factor for ribosomes and not just this 17:09.280 --> 17:13.520 Unknown protein that sits in the membrane and and changes into a 17:14.160 --> 17:17.200 It's aberrant form inside of these clathrin coated 17:17.920 --> 17:18.960 um 17:18.960 --> 17:20.960 invaginations 17:23.120 --> 17:24.880 Think about this 17:24.880 --> 17:26.880 Where is all that story? 17:27.680 --> 17:32.800 Where is all the story about how pre-owned protein is expressed on the outside of the cell and then gets into clathrin 17:33.760 --> 17:37.680 Invaginations and theirs where it turns into the pre-owned they don't really understand it 17:37.680 --> 17:39.360 Yeah, but that's what we think 17:39.360 --> 17:40.400 Is that all gone now? 17:40.400 --> 17:42.080 And now we have this yeast model 17:42.080 --> 17:49.120 Or by the way for 800 million years of evolution and its regulation base just be 104 has been conserved for hsp for 800 million years of evolution 17:49.680 --> 17:54.560 Uh, and so the question becomes why why in heaven's name would cells allow this 17:55.680 --> 18:00.800 Translation termination factor to suddenly be sucked out of solution so that ribosomes aren't performing properly 18:01.760 --> 18:04.480 And it occurred to us that this had to have some meaning 18:04.960 --> 18:12.640 Uh, because otherwise yeast can very very rapidly evolve and they could have acquired mutations in it that pre-owned domain that would have still allowed the protein to have its translation termination function 18:12.720 --> 18:14.720 But not allowed it to be sucked up into these aggregates 18:15.200 --> 18:20.960 So one thing that occurred to us was that the read-through of ribosomes might be occurring not just on those 18:21.360 --> 18:23.840 That one messenger RNA that that Brian Cox had first discovered 18:23.840 --> 18:26.960 But actually it might be happening on messenger RNAs that were coming from all over the genome 18:27.280 --> 18:29.840 And in that case we might expect to see some really new and interesting traits 18:29.920 --> 18:33.440 We started growing strains in different conditions not on rich media in the laboratory 18:33.760 --> 18:37.920 So here's an example of a really wonderful trait, uh, that's caused by the appearance of this same prion 18:38.400 --> 18:41.760 And uh, you can see that the colony morphology of these cells has changed completely 18:42.080 --> 18:45.120 Uh, the cells that normally look like this will create colonies like this 18:45.440 --> 18:51.120 Uh, created very very different types of colonies that adhere to each other cells adhere to each other. They stick to each other. They have different abilities to grow in different environments 18:52.000 --> 18:56.560 And here's an example of the fact that these prions actually spontaneously appear every once in a while 18:56.960 --> 19:01.760 And we tried brewing cells under all sorts of different conditions kind of like the story I told you about it just being 90 them a while ago 19:02.160 --> 19:05.600 Uh, and we found that in different strains we got completely different traits 19:06.000 --> 19:09.120 And that makes sense when you realize that the reasons that are downstream of stop codons 19:09.280 --> 19:12.080 And not normally under much selective pressure. They're free to vary quite a bit 19:12.160 --> 19:17.280 And so changes in those downstream sequences might be expected to accumulate over the course of evolution 19:17.440 --> 19:21.120 And then when the cell switches into the prion state by perhaps sure happenstance 19:21.360 --> 19:24.160 Um, that will cause the creation of many new phenotypes 19:24.880 --> 19:31.680 So, um, get lots of different traits and lots of different strains and each the traits and different strains were completely dependent upon the genotype of that strain 19:32.240 --> 19:38.720 So what we think is that this prion allows cells to sample genetic variation. I don't know. Don't get too excited about this, right? 19:38.720 --> 19:43.120 This is something that could could be one or two proteins slightly different or 19:43.920 --> 19:48.640 One or two assemblies slightly different or an enzyme functioning or not functioning 19:49.120 --> 19:54.560 And so the morphology of the colony changing this must shouldn't be over interpreted as much 19:55.360 --> 19:56.560 wide scale 19:56.560 --> 19:58.560 Uh, it's not a change 19:58.880 --> 20:03.760 It is a change don't get me wrong, but don't think of it as like, wow, they must have changed every single protein 20:03.840 --> 20:05.840 I mean, that's not very likely 20:05.840 --> 20:10.160 Um, I don't think that all stop codons are now missed as a result of this 20:10.240 --> 20:16.800 And so it's very tricky for me to interpret exactly how much she knows about the change that occurs when it 20:17.280 --> 20:19.920 Goes into the prion state that the yeast does 20:20.160 --> 20:23.600 Plicated traits that would be very hard for them to acquire by individual mutations 20:23.600 --> 20:27.200 For example on a stop codon that would cause one individual messenger RNA to be read through rather 20:27.360 --> 20:31.200 Multiple messages being read through at the same time could create quite complicated complicated traits 20:31.600 --> 20:38.400 The other thing could be that reading through in yeast is not the same as reading through in our cells and reading through in our cells 20:38.400 --> 20:44.000 Could be regulated by other means or more specific and and varied means and so again 20:44.560 --> 20:50.000 What we're really looking at here remember is not the discovery of an infectious disease 20:50.800 --> 20:57.200 What we're discovering is an added layer of complexity to the irreducible complexity of our own biology 20:57.760 --> 21:01.840 Represented in a very simple eukaryotic model the yeast 21:03.200 --> 21:07.600 And as long as you see it that way, then I think susan's presentation is really cool 21:08.080 --> 21:13.600 But if you see it as okay, so what are these people talking about with regard to prions in the spike protein 21:14.000 --> 21:18.560 You can clearly see that whatever susan is talking about makes them liars basically 21:19.920 --> 21:21.920 And in some weird 21:23.600 --> 21:28.800 Need to have a worst-case scenario. They can't be genuine in their belief that there is 21:29.520 --> 21:36.880 Some sequence in the spike protein that they don't have to be very specific about that they can call a pre anogenic epitope 21:37.680 --> 21:40.720 And imply that this thing has the propensity to do 21:41.280 --> 21:48.080 What she's explaining these particular proteins and these particular combinations of proteins in yeast 21:48.720 --> 21:50.720 seem to do 21:50.880 --> 21:57.120 So if this mechanism has really existed this prion has existed in order to create evolutionary novelty 21:57.440 --> 22:02.320 And the ability to sell to sometimes people who exploit new environments then evolutionary biologists 22:02.720 --> 22:07.840 And we ourselves realize that if this was how but this purpose this prion was serving such a purpose in evolution 22:08.080 --> 22:11.520 Then switching should increase with stress that as it should be tuned such that 22:11.760 --> 22:15.680 Under stressful environments the cells would be more likely to switch into the prion state because they would be more likely than need 22:16.080 --> 22:20.240 Novel new phenotypes under stress. And so we asked whether or not it does and the answer to that was yes 22:20.640 --> 22:25.200 And then the question of course becomes how but for us that it's a pretty simple logical explanation for that 22:25.520 --> 22:30.480 And that is because of how stress influences the protein folding and protein homeostasis pathways of the cell 22:31.120 --> 22:33.920 So stress increases the likelihood that proteins will misfold 22:34.240 --> 22:38.800 Making it more likely that those prion domains will now suddenly acquire that that aggregated amyloid state 22:39.200 --> 22:41.040 See so are they 22:41.040 --> 22:45.360 Misfolding or is it an evolutionary thing that is genetic inheritance? 22:45.360 --> 22:47.520 You see they're flip flopping between 22:47.520 --> 22:50.800 Is it a good thing or a bad thing? Do we understand or do we don't? 22:50.800 --> 22:53.680 Is it really just a disease or is it aberrant or what? 22:54.160 --> 22:56.160 And so it it is because 22:56.720 --> 23:00.320 Their model of what's going on does not really work 23:00.320 --> 23:02.320 I 23:02.320 --> 23:06.560 Want you to believe it's really simple and that what did she say? Why did she say back there? 23:06.640 --> 23:14.560 It's a it's a definitive model of of something something where we're going to find that it is a completely coherent mechanism 23:15.760 --> 23:19.120 Completely coherent mechanism 23:20.720 --> 23:24.640 It's very insistent. That's for sure perpetuate that state to the daughter self 23:25.360 --> 23:27.200 Stress also 23:27.200 --> 23:29.040 increases the 23:29.040 --> 23:31.440 rate at which cells lose prions because 23:32.800 --> 23:39.200 the stresses cause induction of chaperone proteins and induction of first-integration mechanisms and all sorts of other things that influence protein homeostasis so 23:39.760 --> 23:43.200 These prions which normally appear only relatively rarely 23:43.680 --> 23:47.840 Would just because of the very nature of protein homeostasis and the way in which stress influences protein homeostasis 23:48.000 --> 23:51.280 Be more likely to appear and disappear under conditions when the cells might need new phenotypes 23:51.840 --> 23:53.920 So here's the way we think it's working cells 23:54.480 --> 23:56.880 Switching to the prion state just every once in a while and in fact 23:57.280 --> 24:01.600 With scythe pre and I've just been talking to you about the translation termination effect that happens about one in a million cells 24:02.080 --> 24:05.440 And generally one would expect that when the translation termination activities and working on ribosomes are 24:06.000 --> 24:10.480 See she called it scy there. So it's not the prion protein that we have in our brain 24:10.480 --> 24:14.640 She's just calling it a prion because it frills folds in two different ways 24:15.520 --> 24:23.280 And that it appears that that folding can be passed on from from protein to protein and that the aggregation occurs 24:24.080 --> 24:31.200 So there is some mechanism here some molecular mechanism that they believe they're they're looking at in yeast that has a 24:32.720 --> 24:42.240 A homologue in in these various neurodegenerative disorders and maybe even in our own bodies, but remember she's talking about yeast 24:46.240 --> 24:48.880 Everything through stock bonuses generally not necessarily create a good trade 24:48.880 --> 24:52.320 So that one in a million cells might die not a big loss to the to the population 24:52.800 --> 24:58.640 But when you can imagine that reading through stop codons might be okay in yeast and might be really bad at a mammal 25:00.480 --> 25:02.720 And so I just don't see it 25:02.720 --> 25:09.200 I don't see this as being related to the other and the implication that they are or that we're understanding 25:09.760 --> 25:15.040 How some things work by understanding this I think is a very very misleading 25:15.600 --> 25:19.040 Assumption if the environment changes and that's what we found that in different environments 25:19.760 --> 25:23.680 Prions could sometimes give cells an ability to survive conditions that otherwise could not possibly survive 25:24.240 --> 25:25.680 um 25:25.680 --> 25:26.560 then 25:26.560 --> 25:28.560 The cells would 25:28.560 --> 25:31.120 Now be able to live in an environment where they would not otherwise be able to live 25:31.200 --> 25:33.360 They would proliferate the non prion cells might disappear 25:33.360 --> 25:34.640 And this allows this genome 25:34.640 --> 25:38.480 This the prion allowed this genome to survive under conditions when those cells would otherwise not be able to survive 25:38.560 --> 25:43.120 Because the formation that prion has allowed all kinds of new genetic variation to be exploited some of which is beneficial 25:43.760 --> 25:47.280 Uh, now the cool thing is that because this is all tied to protein homeostasis in the cell 25:47.440 --> 25:52.000 Under conditions when the environments have changed and where cells are not doing so well and protein homeostasis is not going so well 25:52.320 --> 25:54.320 They are more likely to switch 25:54.880 --> 25:58.320 And of course the return is also uh going to be influenced by stress 25:58.800 --> 26:02.480 Uh, the protein homeostasis if cells change free on may be really great here 26:02.480 --> 26:05.920 But if the environment changes under these new circumstances the prion might not be so good 26:06.000 --> 26:08.160 But under those stressful conditions would be more likely to try out 26:09.120 --> 26:17.760 And so this cartoon is laughably simple environment too obviously needs whatever adaptations are at these skipped stop codon part of the 26:18.560 --> 26:20.000 Of the genes 26:20.000 --> 26:24.000 And so this assumption is just insane like okay. There's an environment 26:24.000 --> 26:25.360 There's some stress 26:25.360 --> 26:30.080 Let's turn off all the stop codons and see if any of the proteins that we make are useful 26:30.480 --> 26:33.440 And lo and behold they are until the stress goes away 26:33.760 --> 26:36.320 Or we get back into uh environment one and then 26:36.720 --> 26:39.360 I guess it goes back to normal because there are more 26:40.080 --> 26:43.440 Uh heat shock proteins produced in environment two 26:47.360 --> 26:54.160 This is how academic biology was ruined ladies and gentlemen by p values and in vitro extrapolation on 26:54.800 --> 26:59.600 Environment and experimental models that don't mean what they say they mean 27:00.240 --> 27:04.080 Loss of the prion because shepherds have been up regulated and maybe stabilized the whole system 27:04.480 --> 27:06.640 Maybe maybe so um 27:08.640 --> 27:12.320 So we decided to look for new prions. How many new prions might there be? 27:12.800 --> 27:20.880 We serve as her definition is a prion is an a protein which can fold in two different configurations and be inherited that way 27:22.560 --> 27:27.040 In cells that divide their cytoplasm, you know kind of like how mitochondria are inherited 27:27.680 --> 27:30.800 Interesting survey the yeast genome and we found that there were about a hundred 27:31.200 --> 27:34.160 uh proteins that had domains on them. What an interesting edit 27:34.720 --> 27:40.000 We found that there were about a hundred uh proteins that had domains on them that looked a lot like a sub 35 domain 27:40.400 --> 27:43.440 So the sub 35 domain is a prion domain 27:43.520 --> 27:46.880 So if there is a domain that looks a lot like that in the spike protein 27:46.880 --> 27:52.080 Then these people could make the statement that wow it's got a prion like domain in it. You see 27:53.200 --> 27:55.360 You see how this works? 27:55.360 --> 27:57.360 a yeast gene 27:58.320 --> 28:02.800 And so we decided because we know so much about sub 35 and but how it behaves and looks when it changes into prions 28:02.800 --> 28:04.800 They we borrowed that prion domain of sub 35 28:05.120 --> 28:06.400 Uh, it took it away 28:06.400 --> 28:10.240 And we added the prion domain of these other potential blue prions 28:10.720 --> 28:12.800 And sure enough when we tested them out 28:13.200 --> 28:17.600 Many many different protein domains had the ability to switch cells from red to white in a in a very terrible way 28:17.920 --> 28:20.080 Once the cells switched from red to white they could be 28:20.640 --> 28:26.240 Struck out and they maintain that characteristic for uh generation after generation and in every case that depended upon the formation 28:26.480 --> 28:32.240 Of prion amyloids and in fact, it depends on if you want to for see she calls them prion amyloids 28:32.560 --> 28:34.560 it's a pretty 28:34.640 --> 28:39.840 I would go so far as to say that this is a pretty disingenuous use of those words on purpose 28:41.040 --> 28:44.000 Prion amyloids dang that's impressive 28:45.760 --> 28:47.600 Prion amyloids she called them 28:49.200 --> 28:55.440 25 new 25 new prion amyloid domains that's interesting because 28:56.240 --> 28:58.880 Family prisoner wasn't calling them prion amyloids 28:59.360 --> 29:01.600 Not just to the prion domain that we were testing 29:01.600 --> 29:04.880 But we went back to the endogenous protein and that's where those proteins could switch states 29:05.040 --> 29:07.600 We couldn't handle all of them, but we looked at several of them 29:07.600 --> 29:13.360 And they created some really interesting beneficial in the traits and intriguingly many of those proteins are RNA binding proteins or DNA binding proteins 29:13.360 --> 29:19.040 So they sit in the middle of regulatory networks in such a way that they're really primed to change the way information is being decoded 29:19.040 --> 29:23.440 It really creates some reason so what she's talking about is if they're regulating DNA and RNA 29:23.440 --> 29:27.840 Then there are proteins that can regulate different things depending on the confirmation they're in 29:28.320 --> 29:32.080 Sounds like enzymes that change confirmation when they work 29:33.200 --> 29:37.680 It sounds like ion channels that change confirmation from the open to the closed state 29:38.880 --> 29:44.720 It's not so special ladies and gentlemen. It's not so special ladies and gentlemen 29:46.560 --> 29:50.880 And the induction of the change from one to the other 29:51.520 --> 29:53.920 Has not really been as 29:56.880 --> 30:02.000 I don't know. I don't know if I'm convinced by this data as they are convinced by this data that 30:02.720 --> 30:06.800 Somehow this this just kind of works remember there's there's substituting 30:07.840 --> 30:12.320 But complex novelin traits. This is one example. So this is uh, the prion known as mat three 30:12.880 --> 30:15.600 And that's the prion minus cell and that's the prion plus cells 30:16.080 --> 30:18.720 Growing in rich media. There's a variety of different cells that have a prion 30:19.040 --> 30:24.320 And now we've washed away that media that sorry the cells from that that media and we look to see 30:24.960 --> 30:31.200 If they need the cells remain and in fact the prion in this case has allowed many of these cell types to acquire a new 30:31.520 --> 30:33.360 Invasive growth phenotype 30:33.360 --> 30:39.520 It also has created the capacity in some of the strains to flacculate that is to group together which really changes their growth properties 30:39.520 --> 30:42.800 It really makes them in many ways function as a community of these rather than as individuals 30:43.280 --> 30:48.160 And you can see that in different strains we're getting different phenotypes of the again a variety of different phenotypes 30:48.320 --> 30:50.320 And mat three by the way, this is a transcription 30:50.640 --> 30:53.840 Repressor so when it goes into the prion state, it can alter the expression of lots of different 30:53.840 --> 30:55.840 So are you surprised that when you 30:56.400 --> 31:01.680 Induce a transcription factor or knock it out that things change? I mean, uh darn it 31:03.040 --> 31:05.680 So this had us excited. We were very very interested in it 31:06.080 --> 31:08.080 And a lot of people other people thought it was very interesting too 31:08.560 --> 31:12.720 But there were also a lot of skeptics and that's good reason because after all second my C server visa 31:12.800 --> 31:14.960 Is the best understood organism on the planet? 31:15.040 --> 31:16.560 So the question arises well 31:16.560 --> 31:21.680 If there's so many of these things why weren't they discovered before and um, I have the answer and the answer to one aspect of that 31:22.320 --> 31:24.960 Question because I picked the Colesman Harbour yeast course many years ago 31:25.040 --> 31:28.800 It was a wonderful wonderful course and I learned so much and empowered my research in so many ways 31:28.880 --> 31:30.800 But there was one thing that was very interesting about that course 31:31.200 --> 31:33.520 They told us that whenever we found a new phenotype new 31:34.160 --> 31:38.960 Trait in a yeast cell should cross it back to the original and then look for it to segregate two to two 31:38.960 --> 31:42.480 So that you had some clean system in which to investigate and things that didn't segregate to it too 31:42.560 --> 31:45.680 Were complex and would be too difficult to deconstruct and you shouldn't shouldn't look at them 31:46.320 --> 31:47.920 Since we've been talking about these these prions 31:47.920 --> 31:49.520 I've actually had an awful lot of people come up to me and say 31:49.520 --> 31:53.120 I had these weird traits in case that we're segregating that way too and my advisor made me give it up 31:53.360 --> 31:59.600 So I think this is kind of a wonderful story where um, we really get into habits of of doing science in particular types of ways 31:59.600 --> 32:02.080 And we really need to remember that it's sometimes time to break away from those 32:02.960 --> 32:07.520 Anyway, um, the other criticism was so far these things that only been found in in laboratory strains 32:07.520 --> 32:08.960 And maybe it was just an artifact of laboratory strains 32:08.960 --> 32:12.800 So we went to the same broad group of strains that we'd worked with with our hsp 90 32:13.280 --> 32:18.720 Investigations strains collected from all over the world with all sorts of different properties and lots of different ecological niches 32:19.680 --> 32:24.800 And we asked whether or not any of them had had prions and we found that a lot of them did 32:25.680 --> 32:29.440 So here's an example of a prion that exists in a wild a strain of yeast 32:29.440 --> 32:32.320 It's a wine stream and this is the cells growing in great must media 32:32.400 --> 32:36.560 And it turned out that we were able to create isogenic cells in which we caused the cells to lose the prion 32:36.880 --> 32:40.480 Didn't make any difference really to their growth on normal media rich media that they're using the laboratory 32:40.560 --> 32:42.800 But when you look at their ability to grow in great media great must media 32:43.360 --> 32:49.280 That growth was really dependent upon the prion so the prion was creating in this case a very valuable trait for that for that that strain 32:50.480 --> 32:53.600 And we have found in fact that prions very frequently create 32:54.480 --> 32:59.840 So when she identifies one of these proteins as such she calls it a prion and they do things 32:59.920 --> 33:07.520 But it's hard to differentiate these from enzymes or any other protein that changes confirmation in that confirmation has a different effect 33:08.880 --> 33:16.960 So I really feel like there's a lot of disingenuousness here in terms of how they're selling this and then also how it's related to prions as as pathogen 33:17.520 --> 33:20.560 Pathogenic mechanisms. It's really frustrating. This is 33:20.560 --> 33:25.600 That can be beneficial. In fact in the strains that we've looked at so far about 25 percent of these traits a lot of the cells 33:25.600 --> 33:30.080 You're growing their conditions where the otherwise just simply could not grow so again because they normally appear quite rarely 33:30.480 --> 33:39.680 And you only lose a small percentage of the cells and their implication is is that they're reading through a stopcode on and adding to an endogenous protein in one state with an extra 33:40.560 --> 33:42.480 Part of amino acids at the end 33:43.120 --> 33:45.680 That's the implication, right? That's how she explained it 33:46.080 --> 33:49.920 And so by reading through the stopcode ons you get new phenotypes in 33:50.480 --> 33:52.480 proteins by adding on to their 33:53.680 --> 33:59.600 End terminal, I guess I don't get it. It's weird. It's the the model is so simple 34:00.080 --> 34:05.360 And it makes so many predictions. Can we go and look at these proteins and see that they've all become longer 34:06.480 --> 34:09.520 Can you look with like antibodies and show us like 34:10.240 --> 34:17.840 Some standard proteins in one state and then the other state and show us that they've all elongated because they've read through a stopcode on 34:18.800 --> 34:20.800 How come we didn't do any of those things 34:24.960 --> 34:31.600 And again, that's where you really need to have you know, it gets frustrating when somebody says, can you just send me a few papers? 34:32.160 --> 34:34.320 You need a lot of experience and a lot of 34:35.120 --> 34:36.480 general 34:36.480 --> 34:41.680 Knowledge in order to listen to this woman and then ask her that question like hey, did you look 34:42.560 --> 34:49.040 at a few control proteins from your yeast line before and after the pre-on state 34:49.600 --> 34:55.680 And show that all of those control proteins are now reading through a stopcode on has part of your 34:56.480 --> 34:57.680 sort of 34:57.680 --> 35:03.120 Experimental exploration of the model and what it what predictions it makes the answer is no 35:06.080 --> 35:11.120 They're not beneficial, but when they do appear they allow cells to grow into conditions where they couldn't otherwise we think this is a really plausible 35:11.200 --> 35:14.640 That hedging strategy for the acquisition of a genetic 35:15.440 --> 35:17.440 Diversity in the organism 35:17.440 --> 35:22.080 Creating lots of new phenotypes which allows it to exploit fluctuating environments and grow in a variety of different situations 35:22.480 --> 35:26.560 And that just showed you one phenotype from psi. We've seen many phenotypes from many other prions as well 35:27.200 --> 35:34.240 And in fact, although we haven't identified many of the prions that exist in those strains the genetic behavior tells us that about 236 out of a 700 wild 35:34.240 --> 35:38.400 These strains we looked at do carry prions. So the final part of the story that i'm going to tell you about is 35:39.280 --> 35:40.560 concerns 35:40.560 --> 35:47.040 The ways in which a pre-on can influence the dynamics of the growth of these microbes in communities now of course 35:47.920 --> 35:52.160 Almost all experimental investigations in the laboratory work with organisms in pure culture 35:52.560 --> 35:58.880 So yeast growing or bacteria growing and that's natural that we started out doing experiments that way because otherwise we have everything all mixed up 35:58.880 --> 36:04.960 It's just impossible to do to do the kinds of experimental investigation that we needed to do over the last century to understand biological systems 36:05.600 --> 36:11.760 But organisms never virtually never grow into those circumstances in the wild in the wild they grow in mixed communities 36:12.480 --> 36:13.680 so 36:13.680 --> 36:19.200 But i'm going to tell you about is just the first case i think of a of a pre-on influencing the dynamics of of community 36:19.520 --> 36:23.840 Organization and organismal communication to create more diverse and more robust communities 36:24.640 --> 36:28.080 Uh, so the only one we've really looked at so far, my guess is that that's just again the tip of the iceberg 36:28.720 --> 36:30.720 so 36:30.720 --> 36:33.920 E cells have a very particular type of mass dev metabolism 36:33.920 --> 36:38.480 They're very fastidious and that's the reason why we love them when we give them glucose sugars like from grapes 36:38.720 --> 36:41.520 They take those sugars and they convert them into ethanol extremely efficiently 36:41.680 --> 36:44.400 They don't use the ethanol they only make the sugar into ethanol 36:44.400 --> 36:47.200 It's only when all the sugars exhausted that they start turning around in using the ethanol 36:48.000 --> 36:51.120 So, um, they will not grow um another carbon source 36:51.520 --> 36:54.480 Uh, if there's any glucose present very very fastidious 36:55.440 --> 37:00.240 But we found a pre-on that uh, it can cells can acquire that allows them to bypass the system 37:00.720 --> 37:03.840 They don't grow quite as well in pure glucose when they have that pre-on 37:03.840 --> 37:06.480 And that's probably why this was not really found and looked at before 37:06.880 --> 37:11.360 Uh, but in mixed carbon sources where there's other carbon sources around and a little bit of glucose too 37:11.600 --> 37:13.760 Those cells can now grow and the otherwise you'd not be able to 37:15.200 --> 37:17.200 So how do we investigate this story? 37:17.360 --> 37:19.760 Well, we took advantage of a glucose in the medic 37:19.760 --> 37:21.760 It's a compound called glucosamine 37:21.760 --> 37:25.040 That looks just like glucose probably the most of you and it does to the e cells 37:25.120 --> 37:26.480 It has this little amino group here 37:26.480 --> 37:28.800 But what it does is that e cells take this glucosamine up 37:28.800 --> 37:32.640 They think they're in glucose and they shut off the pathways for utilization of all other carbon sources 37:33.760 --> 37:36.320 So here you see an example of this we've got cells that are growing 37:36.720 --> 37:38.400 Without the pre-on 37:38.400 --> 37:40.400 Growing in pure glycerol and they're just fine 37:40.400 --> 37:43.600 But if you try to get them to grow in glycerol just a little bit of this glucosamine in there 37:43.680 --> 37:45.680 They think oh, no, I do not want to use any other carbon source 37:45.680 --> 37:48.800 I only want to use glucose and won't use that other carbon source until all the glucose is going 37:48.880 --> 37:50.880 They can't metabolize it so they're just stuck 37:51.520 --> 37:55.920 This allowed us to search for strains that might bypass that uh, metabolic 37:56.880 --> 38:02.080 Metabolic problem and allow the cells to grow in the presence of other carbon sources when there was a little bit glucose present 38:02.240 --> 38:04.640 As you can see we've got strains here that do that very very well 38:05.040 --> 38:08.720 This strain is genetically identical to the strain and we tested that in many many ways 38:09.120 --> 38:15.840 Or over these cells once they acquire that ability to grow on that other carbon source can be processed and passage for hundreds of generations 38:16.240 --> 38:20.640 Back on pure glucose, but they remember they retain that trait as a biological memory 38:20.640 --> 38:22.720 And they retain that trait for many many generations 38:23.440 --> 38:28.240 Uh, that allows them the next time they're put on that diverse carbon source to be able to grow on them 38:28.720 --> 38:32.160 So really changes their metabolic potential and really quite strong way 38:33.120 --> 38:34.160 Now 38:34.160 --> 38:39.760 The cool thing about this in terms of functioning in the community when you think about it carbon source utilization strategies might change quite a bit 38:39.920 --> 38:42.560 Depending upon whether you have other neighbors that are competing with you for those carbon sources 38:43.360 --> 38:45.520 And this is something we found actually by accident 38:45.920 --> 38:50.320 Jessica Brown was working on strains looking at what causes prion switching and she said we found a 38:50.960 --> 38:55.440 Colony around which there are all kinds of strains that had switched into the prion state and that colony that was in the middle was a bacteria 38:56.080 --> 39:01.520 So this experiment we tested that more rigorously do bacteria have a capacity to secrete a factor that will cause the yeast cells 39:01.680 --> 39:04.240 To switch their metabolism the heritable way by the induction of this prion 39:04.560 --> 39:10.320 So in this experiment what we've done is we've uh, plated not left these how are they secreting that do you think they're doing that by phage 39:10.960 --> 39:15.120 Wow, this is she doesn't use the word phage very soon. I'm going to be upset 39:15.360 --> 39:21.120 Well, it's empty and we've plated the gar minus he sells here the gar plus he sells gar is for glucose associated repression 39:21.120 --> 39:22.880 I should have said that's the name of that prion 39:22.880 --> 39:32.880 Uh, and then uh, we've planted gar of minus cells here. So these cells see so glucose associated repression is a gene that is considered a prion because it has 39:33.440 --> 39:35.440 apparently in her 39:35.440 --> 39:41.760 Estimation two states of functionality based on its folding that's not strange for a protein 39:42.560 --> 39:43.760 and 39:43.760 --> 39:45.200 enzymes change 39:45.200 --> 39:52.240 Confirmation ion channels change confirmation the actin in your muscles changes confirmation like 39:52.960 --> 39:56.720 The are the actin and mycin change confirmation like this is annoying 39:57.600 --> 39:59.600 Because we're implying 39:59.600 --> 40:06.400 Essentially, I feel like we're muddying the water about what prions are and how they work by talking about these 40:07.040 --> 40:11.280 As prions and then it's establishing this biological principle 40:11.760 --> 40:17.920 That there could be a prion in you that's very very bad and then only one needed and then it can go from cell to cell to cell 40:19.440 --> 40:24.880 And so if we can establish some kind of I think prions are beautiful in yeast kind of story 40:25.360 --> 40:32.080 Then we can also establish a prions can also be dangerous because look and they obviously exist because look at yeast 40:33.760 --> 40:35.120 That's what this is 40:35.120 --> 40:42.000 Remember our genetically identical the only difference between them is that these cells have switched into a prion state that is inherited in the non-mondylian fashion 40:42.960 --> 40:46.320 Because it's a it is a single-celled organism 40:47.040 --> 40:48.800 I'm protein-based inheritance 40:48.800 --> 40:52.240 And the cells are being played on glycerol with again just a little bit of glucosamine 40:52.560 --> 40:56.240 And you can see by the way that the cells do switch spontaneously every once in a while into this prion state 40:56.240 --> 40:59.200 Which allows them to grow but they really only switch that way every once in a while 40:59.280 --> 41:01.680 This is a dilution of strains across this plate 41:02.640 --> 41:05.920 Now the next experiment is done exactly the same way except we're going to plate bacteria 41:06.320 --> 41:08.400 That seem to have the capacity to induce this prion here 41:08.800 --> 41:11.840 And what you can see is that the presence of the bacteria on that plate 41:12.240 --> 41:19.920 A substance has diffused down the plate and influenced these yeast cells here to now switch their metabolic state and be able to grow into their condition when they otherwise wouldn't 41:20.240 --> 41:24.000 But it's a diffusible compound and it doesn't get far enough to get all the way down to this row here 41:24.480 --> 41:27.360 We established by lots of experiments that I won't take the time to show you 41:27.440 --> 41:31.200 But we could just condition media with no bacteria that bacteria had grown in and get rid of bacteria 41:31.200 --> 41:34.640 And we could use the condition media to induce this this new inheritable trait 41:35.680 --> 41:39.680 So what happens that cells usually are using glucose and they make lots of ethanol and only some ATP 41:39.920 --> 41:44.160 But when they're in the presence of glucose and other carbon sources this prion now allows them to 41:44.960 --> 41:46.960 Make less ethanol but lots of ATP 41:47.680 --> 41:50.640 And what does that do? It causes both organisms to flourish 41:51.600 --> 41:54.880 So it turns out that when the when the yeast cells switch as I mentioned 41:54.880 --> 41:57.440 It allows them to grow in different kinds of carbon sources even when glucose is present 41:57.680 --> 42:01.200 It also reasons we don't quite understand creates increased longevity in those cells 42:01.200 --> 42:03.840 They can they can live in in culture for much longer periods of time 42:04.160 --> 42:09.600 And the bacteria get a big advantage out of it because the yeast cells are making less ethanol and that means that the 42:10.240 --> 42:13.760 The bacteria can grow up to much higher concentrations because the bacteria don't like ethanol it poisons them 42:14.240 --> 42:15.520 now 42:15.520 --> 42:19.440 Turns out this is a very highly conserved property of yeast and bacterial strains from all over the world 42:20.080 --> 42:24.800 That's because yeast and bacterial strains are often found in the same places in the same environments 42:24.880 --> 42:26.880 And so they often have interaction 42:27.280 --> 42:33.200 That doesn't mean that these interactions have a homolog in our own bodies and in our own physiology 42:33.280 --> 42:35.280 But that's what they want you to believe 42:35.440 --> 42:37.920 They want you to believe it. They want you to think that there's 42:38.480 --> 42:43.440 This means that prions in the the human brain work exactly the same way and they may 42:44.160 --> 42:46.160 be 42:47.680 --> 42:53.200 Lots and lots of streams are capable of yeast strains are capable of this kind of a switch and many many different bacteria are capable of inducing it 42:53.680 --> 42:55.200 Does it matter in the real world? 42:55.200 --> 42:59.120 Well, I can tell you one way in which it clearly matters in the real world and that is it spoils wine fermentations 42:59.680 --> 43:00.800 so 43:00.800 --> 43:04.560 The yeast are making less ethanol and making other kinds of metabolic byproducts and it really makes lively wine 43:04.640 --> 43:08.640 In effect, it turns out that the bacteria that contaminates spoiled wine preparations 43:09.040 --> 43:11.760 That um, monologists have been studying for a long period of time 43:11.920 --> 43:16.640 Turn out to be super-inducirous and make lots of this compound that switches the yeast cells into this new prion state 43:17.440 --> 43:20.720 So I don't think I don't think calling it a prion state is 43:21.360 --> 43:25.520 Is is intellectually genuine? It's not it's not 43:26.160 --> 43:30.960 It isn't I think that that is a misnomer on purpose. They're doing it on purpose to 43:31.680 --> 43:33.680 to muddy this water to 43:34.000 --> 43:40.000 To make the idea of infectious prions a more plausible idea and create 43:40.640 --> 43:44.400 The intellectual idea space where they could exist where otherwise 43:44.960 --> 43:48.000 Stanley Prouzner doesn't have doesn't have squat 43:49.760 --> 43:52.560 A tremendous advantage is out of it. The bacteria get tremendous advantages out of it 43:52.640 --> 43:55.040 It's to the detriment of man but to the benefit of both those organisms 43:55.520 --> 44:00.240 And again in terms of the conservation of this um across uh tens of millions of years of of yeast evolution 44:00.560 --> 44:03.680 Uh, these strains have really exactly what seems to be exactly the same mechanism 44:04.240 --> 44:06.800 Uxilensis which has uh diverged from sachromisies 44:07.120 --> 44:09.440 Uh, these strains don't have it. They have a different kind of metabolic profile 44:09.520 --> 44:13.840 Vercellensis is another yeast that has that same kind of fastidious lifestyle in terms of carbon socialization 44:13.920 --> 44:17.680 That sachromisies has and it also has that car prion state even pombe 44:17.760 --> 44:19.920 Which is diverged hundreds of millions of years ago 44:20.160 --> 44:25.440 Um, although the mechanism isn't quite the same has this capacity to switch into an epigenetic state that changes its metabolism heritably 44:25.600 --> 44:29.920 So vast amounts of evolutionary distances these um, these these these ability to switch carbon source 44:29.920 --> 44:35.920 Inheritable way induced by environmental factors has been concerned. So here we have really uh, what I consider the ultimate example of a marking evolution 44:36.160 --> 44:41.520 We have chemical communication between a prokeria and eukaryum that transforms metabolism in the heritable way 44:42.080 --> 44:45.200 And the simple exposure of the yeast cells to that chemical compound 44:45.840 --> 44:53.120 Causes them to change their biology chemical compound or phage carried by a chemical chemical compound carried by a phage 44:53.200 --> 44:55.200 I'm not sure we probably have to look into it 44:55.680 --> 44:57.200 I don't know 44:57.200 --> 45:00.720 what other ways that bacteria shed things other than phages, but 45:01.280 --> 45:04.080 Anyway, it's interesting that she hasn't mentioned phage and 45:04.640 --> 45:10.800 Prouzner never mentions phage and nobody mentions phage in the way that's heritable for hundreds of generations 45:12.720 --> 45:14.720 So again when a considering 45:15.040 --> 45:18.000 Jamba of tisth Lamarck, I think it's time to give them back his dignity 45:18.240 --> 45:21.520 There are mechanisms by which changes in the environment can produce new traits 45:21.680 --> 45:26.480 If you accept what she has presented to you in yeast as evidence of inheritance 45:26.880 --> 45:31.200 I mean that can be passed on to progeny. Okay, so once again, I want to end my lecture 45:31.840 --> 45:34.560 Amazing and wonderful people in my laboratory. He's done this work over the years 45:34.880 --> 45:39.760 Um, I've spoken about each things that each of them have have done and I if you're interested in this work 45:39.760 --> 45:43.600 I would really urge you to take a look at some of the papers. I think they're pretty cool. Thank you 45:44.640 --> 45:51.360 Okay, well, that wasn't so bad. I'm glad we finished it. Um, I don't think I'm very impressed with the state of 45:51.520 --> 45:55.440 Prion uh stuff nowadays. Uh, I'm really not 45:56.160 --> 46:04.240 Um ladies and gentlemen, uh, this has been a presentation of gigo and biological dead lady from mit and prions in 2016 46:05.120 --> 46:09.360 And uh, the goal is to really make sure that we cover all of our bases 46:09.360 --> 46:12.320 We've listened to stanley prouzner explained it in 2002 46:12.880 --> 46:18.960 We've listened to four lectures from this lady already and she's given us a pretty good idea of what's been done in yeast 46:19.600 --> 46:21.600 and we've listened, um 46:22.880 --> 46:30.080 To stephanie sineph explain, um, some of the details of how the spike protein may or may not 46:30.800 --> 46:37.920 Either have antibodies produced to it which overlap with specificity to the protein 46:38.480 --> 46:40.480 um some other kind of 46:40.480 --> 46:44.480 Of way that the prion protein is being attacked by the immune system 46:44.960 --> 46:46.960 She didn't mention prionogenic 46:47.520 --> 46:49.120 sequences, but 46:49.120 --> 46:52.480 I've heard other people mentioned prionogenic epitopes 46:53.040 --> 47:00.800 Um, and so before we really tackle the spike protein story and what I think is part of the worst case scenario illusion 47:01.520 --> 47:04.480 That was laid down in 2020 to make sure that all we knew 47:04.960 --> 47:09.280 Was that we had to argue about whether it was a lab leak or a natural virus 47:09.360 --> 47:16.160 So you can find me at giggleandbiological.com if you can please share this work wherever you social media 47:16.800 --> 47:18.800 And wherever you and whoever you email 47:19.440 --> 47:23.360 And if you really can, um, please choose to subscribe 47:24.000 --> 47:25.840 Choose to support the stream 47:25.840 --> 47:29.920 My family really depends on it. Um, this is this is what we're gonna 47:30.480 --> 47:32.880 We're gonna contribute, um at this time 47:32.880 --> 47:37.520 And so my whole family is working very hard to make sure that I have the time and the energy to do this 47:38.080 --> 47:41.920 And, uh, I am trying to make sure that I'm here every day and as we move forward 47:42.560 --> 47:44.560 Um, we're gonna be putting out some more material 47:44.560 --> 47:48.560 We haven't gone to sub stack in a while and of course that's gonna kick up again too. So 47:49.440 --> 47:57.680 Um, thank you guys everything in terms of patience. Um, and uh, yeah, it's it's a long summer and there's lots of stuff to come 47:58.320 --> 48:01.120 Um, so thanks very much for joining me. I'll play this out. Thank you 48:07.520 --> 48:09.520 So 48:37.520 --> 48:39.520 So 49:07.520 --> 49:09.520 So 49:37.520 --> 49:39.520 So