 All right, I think I'm going to get started. It is a tremendous pleasure today to welcome Tim Welles, who has welcomed him to the series and has been giving him the David Green lecture this year. Jim, who did his graduate work at Berkeley. I mean, he did his graduate work with Rob Young of Washington State, and his postdoctoral work with George Stark at Stanford, where I got to know him. And when he left Stanford, he went to Genetic, and had a hand in starting their protein engineering group, I think, at least for a family member or something, and left Genetic some years later and started a company called Sinesis. And five years ago, he left Sinesis and became chair of the Department of Performing Central Sciences at UCSF. Jim has a list of awards behind him that's too long to enumerate. He is a member of the National Academy of Sciences, and today he's going to give us a talk on engineering cells to death. And Jim, talk in the back. Thank you very much. Thank you very much, and it's great to be back here. I had the pleasure of listening, at least on two occasions, each time to see new buildings that Mike has been involved in building, and new faculty that have come and joined, as well as the established group that I remember most from my early roots in entomology. And so it's a real honor to be here. I need even more brains, you didn't tell me. So it's a very special time. And I really enjoyed talking to the faculty, and especially the students at lunch. Thanks so much for coming to that. It was really rewarding for me to hear about all the exciting things you guys are doing and the teachers that you have in front of you. So I'll tell you today about maybe a little bit of a morose topic, engineering cells to death. I used to work on growth hormone for many years when I was at Genentech. And then my wife pointed out to me that after I turned 50, I started turning to more morose topics and how cells die. But to me, this is a really fascinating area, actually, that represents and sort of brings together a lot of things that I've been interested in over many years. One is this process, a very important cellular process of apoptosis, which is an altruistic process whereby menazolins shed unwanted cells in a very, very controlled way, in a way that's dictated not by transmission or translation, but by proteolysis. So doing the action of proteases, cells are brought through this process of controlled cell death. And it also brings together an interest of mine for many years, which is engineering enzymes, which I think can be tremendous tools for untying complex problems in biology and probably underutilized for that purpose. But we found them to be extremely useful for cells that are solved and in actually expanding this area significantly. So I'll tell you today about what we've been doing on a couple of different fronts. As I mentioned, engineering enzymes, this one enzyme that we had actually engineered some years ago when I was at Genentech, an enzyme we call sublitease, which is a very useful enzyme, which you'll see for ligating biotinylated esters onto the enterminide proteins. And that's turned out to be very valuable, very useful to us. And a more recent enzyme that my group is engineering we call the sniper, which is a site-specific protease, which is activated by small molecules. And that turns out to be very useful in some of the work that I'm going to be telling you about. Now, the action of apoptosis, this altruistic removal of unwanted disease to unneeded cells, is propagated by a group of proteases, the apoptotic castases in particular. These are cysteine-specific enzymes with aspartate specificity. So they invariantly clean after aspartic acid. And that turns out to be an incredibly useful handle for us in some of the work I'm going to be telling you about. Now, they're broken into two tiers. One another is the initiator castases, which are like the genitals on the field. They listen to what signals are about, what stress signals are about. And once given the word to go, they then instruct through proteolysis the executioners, 3, 6, and 7, to go about deconstructing the cell. Now, there's another group of castases, too, with immune systems. And that's the inflammatory castases, 1, 4, and 5. And they, too, are very important enzymes, involved in directing cytokinesomes to the processing of intralukins that are very important in recruiting an immune system and activating it. So these enzymes are fake to turn on. Once they're turned on, big things happen in the cell. And we're very interested in finding out what they do and how they do it. Now, well, another, they're produced as large and precursors, inactive precursors, as almost all proteases are. This is what they pro-sequence on it. And with two proteolysis events, they're activated. One here between the pro, large subunit, the one between the large, small subunit. It was unknown, actually, which of these were going to be critical, and some of the work that farmers lead to will eliminate some of the roles in these proteolysis events for activation. Once they're processed, they generate a well-known dimeric protease structure that Iganishi and Gaisaulis saw some years ago. So there's two active sites, one here and one here, that are the active protease dimer. Dimeric proteases, it's interesting. There's almost all of them are monomers in nature. So finding dimeric proteases like this are rare. And I'll comment a bit about why we think this is a dimeric and a bit. Now, in the process of, well, caspases have the final word. There are lots of things that happen during apoptosis. One, we should just work logically. If you look at an apoptotic cell, this being a T cell, when given an apoptotic stimuli like etophysite or a variety of other cancer products, which are the key mechanism for removing cells, they'll undergo this familiar bleeding phenomenon that are brought on by lots of structural changes within the cell. This process can be initiated either from outside the cell by extrinsic hormones or signaling agents like TNF or FAS or TRAIL. The bind to receptors lead to their own organization. And then that forms a platform for the first initiator, caspase A, to bind. Once it's bound and activated, dimerized, it then cleaves these execution of caspases and then the cell goes through this disruption process. The other process of apoptosis known as the intrinsic pathway derives from internal signals. Basically, our sensibles of DNA damage, microtubule damage, ER stress, ultimately leading to activation of P53, the production of these BH3 proteins back and back, which punch holes in the mitochondria and let go of cytochrome C along with some other important factors to cause the production of the apoptosome complex, the Sheldon-Lange lab case, so nicely described. The activation in a caspase 9, the other initiator caspase, and then, in turn, activates 3, 6, and 7. So my group has really been interested in are these questions. When the caspases get activated, what do they actually cut? What are the targets that they go after that lead to these very significant consequences to the cell? What's the functional logic of what they cut? Why are they cutting the proteins that they are cutting? After that, I'd like to get into how we can begin to untie this complex system. Because as you'll see in a minute, these things cover a lot of proteins. And we want to develop ways that we can tease apart this complex process into a manageable set of hypotheses. So we can understand what are the really crucial things that are happening in this process. For example, whether the order of events, whether the function of individual substrates, they get cut by the caspases during this process. Now to do this, we wanted to develop an unbiased way of looking at proteins that are cut within a cell during apoptosis. And what happens during proteolysis, of course, is that you generate a new alpha-covoxylate and a new alpha-need. Those are the new things that are generated during proteolysis. So we wanted a way of tagging those new things so that we could use mass spectrometry, extremely powerful and unbiased way of sampling lots of things all at once to sample the new proteolytic events. So we had a decision, what are we going to try to detect, the new alpha-need or the new alpha-covoxylate? And the choice was rather easy because many years ago, Brown and Hall had shown that in a million cells, something like 85% to 90% of the enterminide are blocked by acetylation. Because as proteins are born on ribosomes, there's an acetyltransferase that cap the enterminide. And so actually, enterminide is a fairly rare event in a million cells. And in fact, the proteins that I'll mention now that we've tagged are almost exclusively in the native enterminones, secreted proteins, proteins which had single sequences off that were cleaned and removed, thereby removing the acetylrubes, or transit proteins. For instance, mitochondrial organellular proteins that had transit signals on them. OK, so we wanted to go after the enterminide. And we wanted a selective method of going after the enterminide. We did not want to hit the epsilon-amide groups of lysine because those are present at 25 full x steps over the alpha amines. Turns out that unfortunately, chemistry doesn't really help us here because though the pKa's of an alpha amine and an epsilon-amide group of lysine differ by a couple of logs, that's not enough to get around this abundance of epsilon-amide groups of lysine. And we didn't want to do this just by looking at all triptychpeptides, for instance, in a sort of bottoms-up way. Because in a teratronium, there's something on the order of 1 and 1 half million triptychpeptides. Now, Josh Kuhn's group is doing a great job of mass spectrometry. But even that sort of size of that collection would be too much to analyze all at once. So we wanted a way of fishing out these newly-tronialized alpha-mino-containing peptides. And so a postdoc, a talented postdoc in the lab here, Sam Maras, otherwise known as Spock, who looks like Spock. We knew what those ears looked like. We weren't pointed on the ears of the exact way they looked like Spock. What we wanted to do was develop a technology whereby once proteases were activated and cleaved to generate new interminod, that we could tag these proteins with biotin handles and fish them out. That way, we could enrich from the 1.5 million possible triptychpeptides down to the hundreds to thousands of potential terminal that we expected. And for this, we used an enzyme my group had designed a genetic technique called cellulite gaze. So let me tell you a little bit about cellulite gaze. Here it is. It's actually born of a protease cellulicin. And with two mutations, convert it from basically being a peptidase to being an amylase, where you take a peptide ester, and instead of going through an acyl enzyme that I need, goes through a phylacyl enzyme that I need, because we're going to use this Cysteine-221-serine-221 Cysteine mutation. Hyalase cellulites are much more prone to amylase than the hyalurons. And this was done by Tom Kaiser and his group in some work that they had done on phylaciloisin, in fact. But that enzyme did not work very well. And we hypothesized the reason was because it did not create enough room for the larger Cysteine to act as a nucleic acid. And so to create more room, we added this mutation, protein-2215-aline, to this right here, the last one, and this alpha helix, which allows the alpha helix to shrink down and to a more regularized helix, and then allow more room for this acrocyte phylum. In fact, when we did that, its activity jumped about 100 times for its ability to amylase and catalyze this reaction. And then for the proteomics work that I'm going to tell you about, Sammy Maros built this tag that we used for tagging the entomalome. It contains the ester, the cellulite-based lykes. Basically, there's four amino acid-recognition sequences here starting at this tyrosine down to this amino-eternic acid side chain. And then tag with this ester, this glycline-keyed ester, that cellulite-based loves to attack for methyl acylens on their immediate end transfer to an alpha amine. It also contains some other important whistles, namely this tag-coding-based recognition sequence that I'll show you in a minute, what we used back for, as well as biasing that we used for affinity purification. And so the proteomics technology that we use, workflow goes like this. We took that estimate described, reacted with the cellulite-based and generated with the thioacyl enzyme in their immediate. We've spent in the presence of an alpha amine from our proteome now, complex mixtures will attack the thioacyl bond generating a stable amide bond. Some lykes is free to do war work. You generate a stable amide. Now, this transfer will only work for proteins or peptides that are longer than 3-amino acid. So 3-amino acids will not work in this reaction. You need to have at least a primer or greater to go. Then the next step involves simple binding to the avan-v-trypsin digest to trypsinize gnarled the thing down to the enterminal peptide. And now we wish to sequence this. To do so, we had to remove it from the bead. We found problems. Because the avan-v-trypsin interaction is so tight, we used that 10-protease recognition sequence in between the bias on the avan-v-trypin and the enterminal peptide. We chose 10 tobacco-h-bio-protease because its specificity is explicit. And there's no sequences for 10 in us. So that means that 10 is not going to leave anywhere in here, just any last recognition sequence is not found in the human proteome, which means you could actually over-express 10 in humans if you wanted. And nothing should happen. I'm not sure if you'd want to do that, but if nothing should happen, in principle, maybe tattoo artists will use this at some point. But you wind up getting that after 10 cleavings, 10 actually cleaves in such a way that it leaves behind a small remnant, a fortunate remnant, actually, of our tag. Actually, this should be an ABU residue because we simplified that more recently to that. And that ABU, on top of this peptide, those are knocked down in the genome. So we know exactly when we've tag, when we've isolated one of our tag things because they'll always have one of these little remnant peptides on them. And so when we sequence by mass spec, what's going on, the first two things that fly out of the gate, in this case for the serine tyrosine we were using that node, are represented here. And then the sequence behind represents everything that was cut or present in the internal realm of what we were sequencing. And so what we did here then was we did this technology, we used this technology before and after apoptosis. We compare the two sample sets. And it turns out that before apoptosis, we don't find any intermini that had generated after the spartate cleavage, which is good, means our cells are healthy and they're not undergoing apoptosis. After that, we find now from about 100 data sets, we found 1,400 targets that get cleaned after a spartate acid in the protein. So a lot of things going on here, something like 7% of the polio is a target for cascade proteolysis. And it also turns out that there's on average one or 1.5 peptides per target. What that means is that there's very sparse proteolysis that these enzymes are engaged in. Targets are usually cut only once. And I refer to these proteases as the Bruce Lee proteases. With one cut, you can get these dramatic, you can proteolyze the protein. And each of these actually, in their own right, I think represents an interesting structure function study as to what that cut is actually doing as a function of the protein. Something that would take many postdocs and graduate students years to figure out. One thing we notice is that these targets often appear in protein-protein complexes. Now by that I mean that there's a lot of data from the informatics community now from pull-downs and used to hyper-data and whatnot, about what proteins interact with each other in the lab itself. And so when we map our cascade targets upon that database, that interactive database, there's a tremendous enrichment for them being in complexes with other proteins. And so that's kind of curious, you know what I mean? So something like, of these targets, something like 70 or 80% are found in protein-protein complexes with other cascade substrates. What we think that could be due to, remember I told you that the castases are diners and diners are rare as proteases. And perhaps as we reflect some processes, once they get into a complex, they find a way to basically mow through it, kind of like a weed eater or a chainsaw. And the fact that there's something like, it's not cutting everything, remember? I mean there's something like 750,000 aspartate peptides in the human protein. So we find 1,400. So it's not cutting all of them by any means. So there's selectivity. I think of these guys as like bridge demolition experts. They do sparse use of explosives. You can bring down these large structures in this way. And so also we think of these things as that they're attacking perhaps the struts of life. Perhaps they're reflecting these 7% of proteins may be really critical for the life of the male and female. For the yeast people in the audience, they know for instance that something around 15% of the yeast genome is essential for the yeast growth and probably more than that depending on the stress that you put on the yeast. So we think these are very important targets and we like to understand a lot more about it. So one of the things that's really interesting to me is that we have this complex system. It's not, I wouldn't call it an impossible system, 1400 things, but it's sort of something that I think is manageable within kind of the systems biology kind of framework. And so I'd like to tell you some of our strategy we're using to untie and make sense of this large data set. So one of the things we're doing is can we make sense, I mean just based on non-cylinder pathways. In other words, how does the target's group of the pathways, do they make sense in respect to what are the pathways that they appear and what areas of cell biology are they really attacking on? Secondly is can we understand the timing of events? So what I've shown you or what I've mentioned is this long list, a dead list really of a static list of targets. But that doesn't really tell us which of these targets are really the most important. Maybe by understanding the rates at which these things are being cut, using quantitative mass spectrometry methods, we can begin to understand which targets, what are the things that castings avidly go after. And finally, can we begin to identify each target's individual? Can we cut one target at a time? And I'll tell you some of the approaches that were one of the approaches we're using for that. So in terms of some of the pathway information, I'm gonna show you one of the striking examples. There'll be a couple more that will appear throughout the talk. But and that is the example that when one of the hallmarks of mayoptosis is that a chromatin, transcriptionally active chromatin gets cut into nucleosome cores at these exposed regions of DNA that are undergoing transcription by this nuclease, villainous cat, which is the castase-activated DNAs. And this is work of Jaron Wang, who revealed this pathway. This nuclease gets activated and it gets activated by castases, which cut and inhibitor allowing cat to go on and chop this thing into nucleosome cores. Well, it turns out there's actually something working in opposition of cat and that is this multi-protein complex known as the N-core smart complex. This is kind of like a transcription factor plucker. So it goes along when, you know, transcription's over, you gotta turn it off. It goes along in plus transcription factors off of these regions allowing the DNA to become compactly active. Now that would act in opposition of cat because cat needs to have these regions exposed to it. And so the castases will figure that out too and they grab this complex. So there's like six of these nine subunits that get cleaved by castases during apoptosis. So the strategy is that it's sort of stepping on the accelerator at the same time as it's blowing up the break. And there are at least a half a dozen more clear examples of this kind of regulation going on during apoptosis. A lot of things, whereas if there's some gain of function things that are driving the process and there's some loss of function things that are basically inhibiting the process. So that's definitely a rule. Enter now this fellow, Nick Agard, who with Sandy here, wanted to ask the question, how rapidly are these events happened? And can we begin to untie some of these, the rates at which these different targets would be cleaved in situ in an extract in a complex milieu? And for that they turned to SRM analysis because what we wanted to do was we wanted to generate progress curves so that we could calculate the catalytic efficiencies to catapult on cam for these different substrates. Now this term here, welcome to everyone in the audience. At least if they were trained by this world over here, we'd know all about this. And this can be determined, catapult on cam can be determined from simple progress curve analysis. In other words, the time course of hydrolysis of substrates and complex mixtures if we're able to actually determine the rates at which the individual proteins are cleaved and we can show saturation, we can in fact calculate a relative kick out on cam and rank the substrates based on their goodness of cleavage. And so from that, we set about to use selective reaction monitoring methods in which we told the mass spec about the 1400 peptides that we had identified. So we don't let it look for everything under the sun. We just tell it, go look at these peptides that we identified in discovery runs and tell us, we found one, sit on it and analyze that rather than the random sort of scan mode. And so what we did was we took extracts, we added castases of interest. We then took time points, quenched them with protease inhibitors. We then went through our interminomics sample workout procedure, isolated the intermini and then we could determine the rates at which these peptides would be cleaved based upon their appearance in the mass stack as a function of time. And from that we could then generate these progress curves for a number of substrates in cell extracts. And in fact, this data is only about six weeks old so it's relatively new. I wanna show you this plot here which basically shows the castase three. This is one of the major apoptotic castases. We looked at and identified 200 peptides that were cleaved by this castase alone. And then after determining the progress curves calculated their K-cats on hands for the different substrates and we're surprised to see that there's something like nearly a thousand fold range in which these substrates are cleaved from a rank from the fastest substrate up here to this group of slow substrates which simply had bottomed out the acid. We haven't gone out long enough to actually get these guys analyzed but Nick actually assures me that the range is probably more like 10,000 fold difference in terms of rates. Now, I don't know about you but I was shocked to see this, that these proteases would have that big a range in the substrates that they cleaved. And some of the things that are interesting and we're dissecting this now is that these fast substrates do not distribute randomly in cell biology. There's actually four areas that they love to attack you. One are signaling enzymes, so it's very important to let the kinases get waxed very rapidly. Transfixion factors also get hit very hard. And then two other areas that surprised me, one of which is the anesthetic pathway. So, clathrin and about 10 other proteins involved in clathrin mediated uptake gets hit almost immediately as is a number of the targets of this range here are involved in microRNA processing. So why then is, I don't know why it divides like that but I think these guys are telling us that that biology means a lot to them, so much to them that they put them in this sort of premium category of kind of something that we're going to be investigating. So this sort of an addition of pathway analysis, rate analysis, we're also analyzing, they have developed another tool that we're using to triage these targets. And that was derived from this question really, can we collect one target or activate one protease at a time? And you know, the cascases are kind of like this, but I think there's been a lot of things. What we wanted to do is do something like this. So one something that we just hit one target at a time without all the collateral damage that's going on. And so this graduate student, Daniel Gray, he marked on this development of this enzyme that we call the sniker. And basically what it is, is it's an inducible derivative of split 10. There was a group in Germany, the Weir's group in Germany showed that you can take Tev, I mentioned that earlier, remember the highly selective tobacco waste viral protease. And they could split it into two halves. And these halves were enacted because they split it, separating the catalytic triad and Tev. And, but if you added those two halves back together, they would complement and come back together again. It's basically a protein complementation assay, kind of like stuff that Steve Nijnik had pioneered in a number of others at the time. Well, in fact, we didn't want the two halves of Tev to come back on themselves because we wanted it to be under our control, not just under a mixing control. So what Dan did was to gnarl the interface, which from protein engineering, one of you was pretty easy to do. He went to an area of the proteins where you can see the diuretide interface and basically subtracted some residues that would not come together on its own. But it wouldn't come together when he used the Shriver Crab Tree Dimerization Trick, where FKBP and FRAP will bind rapamycin and get diuretized. Once these guys have fused into the N-terminal and C-terminal half upon addition of rapamycin, this thing would now come together and within a cell where we had genetically-encoding Tev reporters, FREP-based reporters, we could see that when we had rapamycin, Tev would get activated because it could cleave those reporters in a cell. Okay, so now we have a system where, and by the way, nothing happened. The cell was happy under these processes. I mentioned Tev does not cleave me any dodgemously, but we could make stably transfected cells containing each of the halves here and then upon addition of rapamycin, we could then activate this protease in a cell. Okay, so now, next trick was then to go to targets that we knew, as I mentioned, you know where the cascades was cut. Our proteomics technology told us that. So we can go to those cascade sites, we can excise those sites and replace them genetically with Tev protease sites. Remember, there's no other place we can have a place except the places that we engineer. So we could put these into proteins of interest and start asking, okay, if we make an allele which has that Tev site in it with the sniper in it, now what's the phenotype of the cell? What happens to the cell under those conditions? And so the first targets that Dan decided to go after were the execution of cascades themselves. So I mentioned that there's three of them, cascades three, six, and seven. They are always activated in concert and with initiator help as well, okay? So they're never activated just alone. And so Dan wanted to ask the question, okay, what happens if we activate them individually? Okay, so to do that, what he did was he took out their processing sites and he placed them with Tev sites and the question is, if he does that, will he observe the apoptosis? Now to summarize a lot of data on this next slide and point out that we could not only introduce Tev sites here or here, but we could introduce them in both places. So now we could actually, using this tool, we could actually ask them which proteolysis that is really critical to the processing and where the phenotype is driven by the proteins. And to summarize a lot of data, this is what we found. Okay, so this is ProCascades three, six, and seven. If we introduce a Tev site here and between the pro-large junction, nothing happens, no apoptosis, the cells are happy and they're not being driven by apoptosis. If, however, we introduce a Tev site over here and Cascades three and seven dramatically apoptosis happens, if we introduce them at one and two for these two ends, I'm saying thing, okay? So basically, site two is necessary and sufficient to drive apoptosis. Site one is not. We think we understand something about this now and then we can discuss that afterwards. The other thing we notice is that this cascades, cascades six, is a what I call a Pussycat cascades. It activated but nothing seems to happen. It's not initiating the apoptosis. Expunction is in some other area we don't know yet. In fact, there's some data that suggests that they'd be very important in neural remodeling and neural degeneration in these cells or somatic cells, not in that category. Okay, the other thing Dan noted because we can activate these guys synchronously with the small molecule, found something curious, which is that as the cascades are activated, this is a time course here, this is activity, using a cascades, a genetically encoded cascades reporter in cells. We can see that the cascades are being activated and then they're being degraded. So cascades three seed goes up here at eight hours and by 16 hours the activity is falling way down. Similarly, cascades seven up and then down. Even cascades six, which isn't killing, goes through the same kind of force. So the activity goes up and then down. And to make a long story short, what Dan found was this degradation is proteasone mediated. So we found that in fact, the cascades are being equitimated as soon after they're activated and with time, that equitimation catches up to them and they get degraded by the proteasome. That was pretty interesting. The other thing that he found was that as cascades are being activated during the apoptosis process, you see this long time here, activity along here. So the activity rises. That as it's being activated, the proteasome activity measured by this sub, in this house, using a specific proteasome substrate in cells is actually being degraded. So, which is, it's curious. So the cascades are being degraded by the proteasome, but something is actually degrading the proteasome as well. It turns out, I think it's degrading the proteasome on the cascades. Because within our data set, this is a subunit diagram of the 26S proteasome. 20 of the 33 subunits in the proteasome are being whacked by the cascades. So there's this battle going on between these guys. As cascades activity is rising, cascades' proteasome appears to, cascades are being impregnated today, dragged to the proteasome is degraded, but these guys, cascades are also hitting the proteasome to keep it in their bag. And so there's this, in apoptosis there's a negative reciprocal regulation that as cascades are activated, they go over and kick the proteasome, while the proteasome is basically destroying the cascades, there's this tidal of war going on between these two proteasomes. Recently, my kids have grown up and they moved out of the house, and so we have a, five years after they moved out of the house, we have this traditional garage sale, where you find you go up to the room, you clean out their toy chests, and you put it on the garage sale, and they ran into these characters here, and they sent us toy chests. Only one can only in Darth Vader. And so that's how I think of these guys. It's like this guy is acting as Darth Vader, it's, it would go on and destroy the cell, while the proteasome is actually keeping this in check. Now, it turns out that this makes a lot of sense too, with respect to the way that cells die. So this is another experiment that Dan did, basically based on some work with Peter Sorgher, where he put a cascades reporter into cells, and he asked the question, if I induced with Stora's form, a very potent inducer of apoptosis, what happens to the cascades activity as a function of time in individual cells? So each of these traces represents an individual, doing entomology, if you will, in an individual cell. What you see as a function of time is that, as, once they decided to apoptize, once they get, it's a highly cooperative kind of process here. It just goes, it's sort of like popcorn. Once popcorn, each curl decides to pop it out very rapidly. And this sort of popcorn effect is like what you would expect from a mechanism like this. That once it's sort of like an arm wrestle, as soon as the wrestling match reaches a certain point, the whole process goes, you know, you have a thermal nuclear, if you will, and you wind up in apoptosis length. Now, another prediction of this was this experiment that Dan ran, which is these, the compounds that activate cascades work in a highly synergistic manner with compounds that inhibit the proteasome. So this is basically a synergy plot, if you will, where in this case, we're activating cascades with rap myosin using the sniper, okay? And you get this level of cascades activity shown along this Z-axis here. If you add a, and hit or a proteasome, you can also activate the cascades, and you get sort of this rise in cascades activity, modest as it may be. But if you mix these two drugs together, you see you get this volcano plot, where this tremendous activation of the cascades is when you coincidentally inhibit the proteasome. And that, you think, is relevant to a recent drug that's been developed, namely Velcade, which is an inhibitor of the proteasome by the Pthus baronic acid. So this is used in multiple myeloma. Multiple myeloma are, is a cancer where proteins are being hyper-secreted from plasma cells. So they're misbolding proteins a lot, and their proteasomes are kind of taxed. They're also generating a lot of ER stress. And in, under those circumstances, inhibiting the proteasome leads to apoptosis. They didn't understand how it leads to apoptosis. We would propose that the reason that this is, is leading to dramatic apoptosis is that it's not clearing, it's blocking the clearance of spontaneously activated cascades. So as those things accumulate then, you get cell death through apoptosis. Okay, in the final few minutes, what I'd like to tell you about are some of the things that we're sort of applying this, this, this word for. And one is in the area of identifying new cancer drug targets. And we noted that there's an interesting correlation between targets that are found in the data set shown here and a number of targets that are being, are known to be good cancer therapy drug targets in pharma. In other words, each of these, each of these targets here, don't why some are as one or two, there are drugs to that target that induce apoptosis. A homicide from Parosh, Dr. Roops, for instance, will hit at these targets. That will induce apoptosis all by itself. Topo one and two are both lead-during apoptosis. All this like the caspases will like the cancer drugs themselves. BCL two is another one. And of course, that's, there's a drug in the clinic. We all hope for the best for that because it's the first drug in the drug. PARP is another one, classic caspase substrate being developed by Kudos in five of the NEC one, also by Pfizer, and two. All of these are known to be targets of caspases and they're also known to be, and also they're cancer drug targets. So we wonder, within our data set, maybe there's many more cancer drug targets, maybe some richer cancer drug targets because they represent kind of critical nose on a single one. And one way that we hope to find these is like, okay, well maybe the fast targets are going to be good things to go after, since the caspases seem to think that. We're also looking at the conservation of these targets throughout biology. Maybe that would also give us the clue as to the critical nose. And then knowing these, we have a tool, we think, to actually go in and see if we snipe those targets and we actually precipitate the apoptotic process. And the last thing we're using this data set for is to develop apoptotic biomarkers. So, you probably know that unfortunately, the response rate to cancer drugs really kind of sucks. I think it's, you know, a cancer drug begins at a 25 to 40% response rate when we consider a good cancer drug. And that means, of course, that 70% of people are not responding to these drugs. So what happens with them is that usually you're treated with, if you have cancer, you're treated with these drugs, it's a very, very difficult therapy, as many of you know. And it typically lasts two to six months before it's over. And you often go find out by imaging methods whether or not you respond to them because it takes that long for the x-ray for two or strange to actually be observed by x-ray. But apoptosis actually happens very fast. It's in the range of 12, 24 hours it happens. And so what we're trying to do is to identify, insure them, cast-based clean products, and then use those as biomarkers to tell a clinician when they're treating with the cancer drug that the drug is actually doing what we hope it's doing, inducing the apoptosis itself. And so the general flow of this, and this is gonna take years to go, through this process, but we're making progress. First, to take a number of cancer cell lines, identify the x-cleaned in those cell lines, develop SRN approaches to go look in syrup, see if we find them. For those that we do, generate antibodies to those proteins, which we can then use for rapid immunological assay for the cleaved products themselves, and then use these to detect. Then from serum samples, the amount of apoptosis products that are produced is a function of treatment. So if someone would come in and take a blood sample before they're treating, you then block them to determine how many of these products can find, you then treat them after a chemotherapy, 24, 40 hours afterwards, when apoptosis has clearly had a way to go through and see if we can generate the products. So to sort of test this out, we've been comparing certain apoptotic products that are produced, for instance, in T cells and P cells, to ask the question, are there similar patterns that we find? So Huey Nguyen, as well as Gerald Shue and Kazum Shumbo have been working on this problem. They've been characterizing the apoptotic products that are produced in these two different cell lines, and now there's actually five different cell lines at this point. I'm just gonna show you an eye trap result here. For something like 350 substrates that were cleaved, no cleavages in black, lots of cutting is in yellow, we found something interesting. So all of these proteins are present, but they're not present in the same amounts in these cells, in that cell. So what we get there is we can see that, for instance, in jercat cells, the jercats in these first two lines, either at 12 or 24 hours, these proteins were produced, a lot of these cleaved products were produced, and some go up, some go down. But in DB cells, which is a B cell, a lymphoma, the abundance of those proteins was so low that we couldn't hardly detect them. And that's because we also know transcribed levels have been measured on these things, we know that they're just not being made in those cells. In some cases, we find proteins that are being produced at similar levels and with similar kinetics, and then we also have DB-specific proteins that produce more in DB cells than in jercat cells. So I was kind of excited to see this, not only could we potentially identify three products that are reflected in poptosis, but the abundance of the products in sort of a multiplex way that could tell us even what cells might be undergoing poptosis. Now we're going to do this in serum, we're going to do the mastectomy for you in serum. And as those who have ventured into the serum proteomics world know that it's kind of like exploring the ambaric tract because there's this dense layer of about 20 proteins that basically you detect over and over and over again and it's very hard to drill down deeper than that in the proteome. But using this in-terminalist technology which basically will normalize these proteins one to the other because the mastectomy was saying we're not detecting all the poptosis from the poptosis, but rather just our internal lobe. Pete Wiles, who did this work, basically was able to dive on several logs of concentration from albumin all the way to VEGF within serum and detect these proteins. So this to us was very exciting and this is kind of the era that right in here sort of 10 to the minus seven that we're expecting products to be found. And we've just now started to run some serum samples before and after cancer treatment. And anyway, on one patient we were able to identify our first nine proteins which were produced in at least a five fold of burns before and after poptosis. So very early days, but this is the kind of approach that we're taking and where we would take human samples, identify the serum, what's being cleaned, using quantitative mass spectrometry methods. Those have been the candidates to generate antibodies to these clean products that will then help you detect their apoptotic signatures. Okay, so I'm gonna, this is the last slide before we acknowledge, and I just wanna say, one of the things that I'm excited about in this area of apoptosis is that because it's complex, but I don't think it's too complex to begin teasing apart some of the important nodes and modules that they're using, lot of prognomics and these engineered enzymes and other tools we hope to make progress there. And I described what we are in terms of atomic technology and also the use of the cycle for all the specific proteolysis. And finally, I'd like to thank those folks involved in this. I did along the way, but this whole group here has been without them, none of the work would have been possible. I mentioned Sandy, Dan Gray, Nick Agard, Emily and Gerald, Charlie Warding is also involved in the cycle work, as well as Pete Wiles and the synoprionics. I did talk about Jack Sadalski's work today, like he's done a beautiful project when we just, I was recently, many guys didn't know Jack, and then Dennis Bowling and Julia Zohler were also been involved in the Casay's activation project. These guys are really critical for all the work, the meschatometry group are led by Albert Bowling, A. John Trinidad and A. Malti, and then my colleague, Henry Sully and Dave Barking who helped with some of the sort of biomechanics that I've briefly talked about and these folks who they're coming in. So thanks a lot for inviting me today. I'm happy to take any questions if I have. But in our paper, we certainly suggested that colleague hot products should be combined because you shouldn't get the synthetic impality effect. And I do really think that that would be a good thing to do, so thanks. Sam? So, maybe this is really not the case, but you made the point that the Casay's is an aggregate single cut. And have people gone in and looked, or have you been able to tell whether these single cuts happened to occur in places that take the whole property apart? It's not obvious that a single, like you mentioned, split, as I said, if you had the parts together, they would assemble. But you could also imagine cases where you made a cut that kinetically that things still had to go out. So do you have a sense of how down these cuts are on the aggregate? Well, because we haven't signed a lot of your targets, we don't know for sure. But from a lot of financial work of Laundry and Dave, they've looked at about 15% of these targets, their crystal structures, and there's at least another 15% for which we can make homology models. And when you look at those, what you see is a lot of the cuts happen between domains. So, especially like the signaling enzymes, AKT will cut off the plexinomology domain. So, in that strategy seems pretty clear. That means that we can get to the membrane where it's possible that it's present. So, that seems to be a very popular kind of approach. It's separating domains, some involved in binding to localization. And also, I think that one thing that would be cool is I think some of these things would be interesting to make conditional induced diversation, right? We could take, the cascades is how it's probably where we could cut, because they were stable enough for us to see. Remember that? And they weren't degraded. We can't see, some of them do get degraded at later stages of apoptosis, for example. But you've actually, life will lead to stable products. And then, from that, we will also study this apoptotic approach. He's made this point from some of his work. He's done using his total map technology. And then back. So, it's really intriguing that your proteasome and your cascades count to balance each other. I wonder, looking at your curves, where the proteasome is meant to keep the noise down in case of spuriously activated caspases, they act on those and keep the cell from dying. Once you overwhelm the proteasome, you go into this very steep. And so it's really there for noise reduction. Yeah, I think that's a very reasonable apoptosis. The other thing is that it's, yeah, because if there's any spurious activation in caspases, it will be gnarled. There's an inhibitor of caspases on XIP. There's now, is actually the E3 ligase. So, it acts as an inhibitor and ubiquitinase. And interestingly, that ligase is also a substituted caspase. So, it gets hit as well as the proteasome. So, it's clearly wanting to, there is clearly this synergy that's going on. The other thing is that, it turns out that there are caspase activation occurs not just in apoptosis. It occurs in differentiation as well. There's some recent papers showing that during differentiation, take a propone cell, add differentiation factors to push along its lineage. Folks have seen activation in caspases transiently. The question is, what's turning them off? And we suspect that it's only wanting to know. See, I don't know how it's done. So, some of your, you know, your interest in to take that, what we're talking about is the serum, the gradient treatment, but of course, keep it there for a few, and you gotta tell them, this is when that chemo cell, or this is a calcic cell. Yeah, yeah. Well, that's where the differences that we saw between the DB and the jerk cat cell lines was kind of suggested that we probably would see differences. And so, if we know, for instance, we're trying to, for instance, for multiple myeloma, you can get cell lines, and even primary tumor tissue for that. We can identify, it's hard to identify, selective markers for multiple myeloma. And so, when we go in, we're just gonna be asked, and hey, are those markers, are those markers way up, you know, and what happens as a function of chemotherapy? Because it's known that you can wipe with, for instance, Velka and Revenant that you can really handle those cells. So if we follow them, what we should see is we should see this burst of apoptotic products with such good treatment as opposed to eliminate it, we should see the number of markers go down. But you're right, we may see other somatic markers that may or may not confuse the picture. It's just too early to tell. As far as cats are, yeah. Is there a cat there? Yeah, there are. And the sort of classic cat-based substrate is actually four amino acid-recognition sequence. But we know from our data that it's not strict. It's certain, the classic substrate is a spartate, glutamate, valine spartate, cleaned there, glycine alloy or serine as the P-1-5 residue. And actually for the fastest substrates, those are enriched for the DEVD sequence. But it's not the only one, there's a lot of variants too, and there may also be exocyte functions there because some of the protein substrates appear to be cleaned faster than some of the just the peptide substrates themselves. I was gonna ask a question along those lines, which is, if you look then at sequences in the genome, are those spartate-containing sequences more likely to occur between the linker regions and signaling proteins versus linker regions and other types of proteins? Yeah, we haven't done that sort of analysis and that would be kind of interesting to look at it, but there are a lot of signaling enzymes that are spared in the process. So I suspect that we did know the sort of, there'll be some of them that show up that we already know about, and maybe we find some new ones to really look hard to see if they are clean. But still. I just thought it was interesting because you also mentioned clathrin and some of those processes. And now since signaling is linked to endocytosis, it's actually intriguing to me that you're finding endocytic pathways coupled with signaling. I know, this is, I agree. That's an intriguing connection there, really. We have a lot more cell biologism that Dr. Nick Agard is doing now in the endocytic pathway analysis to sort of probe that a lot more. That'll be a lot of fun. Just on the acetylase complex, that's more accessible, allows the new base to clean problems. So have you seen sort of the opposite happen, which is in some of your end terminal alomics, do you see histones that will transfer bases that are potentially activated through the cascases? That's a good question. In the, yeah, in the encoder-smart complex, is HDAC-3 and HDAC-7, those are two of the substrates. And there's one other HDAC-7 that does get cleaned. And we don't know if it's activated or inhibited. I should point to the big, big acrylid. One would sort of think that most of these things are going to be inhibitory, but they may in fact be gain-functioning cuts. And it might be safe to make it as activated so that it isn't a marlin that ends up. In a remedy, there's also a kinase that's involved in possibility of histones. There's been some evidence that that attracts can to it. And we're looking at that as well. That might be another thing that actually is a feed core that accentuates. I think a lot of these, I think there's, in any of these notes, I'll bet there's two or three, you know, push-told things going on that are driving this process. It's not just one. Do you make sense of the faster sites for the slower ones to make it? Yeah, well, we looked at, we did see some sequins that were more rich from this class of DDV substrates. Like, it wasn't universal. And a lot of those are put in the cleat more rapidly than the synthetic substrates by themselves, which I think is pretty intriguing. You would think that just an only pecton would be just a total sucker for the enzyme. But these are some of the cleat faster. And you mentioned that apoptosis takes about 24 hours. 12 to 24 hours, yeah. So then the proteins that get targeted first are those proteins in modern processes that take faster, you know, like... What, their connection is made faster than the true death note? Right, right. You know, that's a great question. And what I would say is they are kind of initiating events. And initiating, maybe enough to tip a thousand push it forward so it reaches a point of no return. But as I was mentioning to the question back there, there's other rules. These enzymes are not just for apoptosis, they're also for differentiation. And they can affect that. And so one of the things we wonder, maybe those fast substrates are in that category. So they may be enriched, and not in apoptosis, they may be enriched more for that. We don't know yet, but something we're actively pursuing.