Session 8: Plant Immunity and the Evolutionary Arms Race between Host and Pathogen
Transcript of Part 2: Host and Viral Evolution: Molecular Evolutionary Arms Race Between Primate and Viral Genomes
00:00:00.28 Hello. 00:00:02.01 My name is Harmit Malik, 00:00:03.11 and I'm an evolutionary geneticist 00:00:05.03 studying the evolution of viruses and host genomes 00:00:07.28 at the Fred Hutchinson Cancer Research Center. 00:00:10.15 Today, I'm actually going to tell you 00:00:11.28 about molecular arms races between primate 00:00:14.09 and viral genomes 00:00:15.25 and how we aim to understand 00:00:18.01 the evolutionary rules that take place 00:00:20.02 between these viruses and hosts 00:00:21.24 and what that will tell us about, 00:00:23.13 not just the evolution of ourselves and viruses, 00:00:26.11 but also to design therapeutics interventions 00:00:28.23 to allow us to designed better strategies 00:00:31.06 that are going to be effective against viruses. 00:00:33.22 The work in the field of molecular arms races 00:00:36.25 is really inspired by 00:00:39.10 the character the Red Queen that was introduced to us 00:00:41.25 by Lewis Carroll in his book "Through the Looking Glass", 00:00:44.20 and the Red Queen tells Alice 00:00:46.29 in this sort of nice book 00:00:50.07 that it takes all the running you can do 00:00:52.11 to keep in the same place. 00:00:54.01 Very much the same idea 00:00:55.27 was adopted by the evolutionary biologist 00:00:58.01 Leigh Van Valen, 00:00:59.23 as the Red Queen hypothesis, 00:01:01.28 and he argued that in a system 00:01:04.00 where two entities are constantly competing 00:01:05.22 with each other in this sort of battle 00:01:07.22 for evolutionary supremacy, 00:01:09.15 the only way for this battle to be resolved 00:01:12.08 is just for one party to temporarily win 00:01:14.29 before the other party catches up, 00:01:16.26 and this requires both of these parties 00:01:19.01 to be really running as fast as they can 00:01:21.04 with this really rapid evolutionary signature, 00:01:23.09 formalized as the Red Queen Hypothesis 00:01:25.15 that's been used to invoke 00:01:27.21 all kinds of very important principles 00:01:29.19 in evolutionary biology, 00:01:31.13 including the existence of sex 00:01:33.16 and why we actually evolved 00:01:35.21 to be sexual creatures in the first place. 00:01:38.22 So, if you consider a host-virus interaction, 00:01:41.06 this is an interaction 00:01:43.00 that screams out genetic conflict. 00:01:44.23 This is what we refer to 00:01:46.13 as the usual suspects. 00:01:48.00 It doesn't take a lot of imagination 00:01:49.13 to understand that what is in the best interest of the virus 00:01:52.10 will not always be in the best interest of the host. 00:01:55.03 So, in this cartoon example, 00:01:56.25 you can see that we've got two states described here, 00:01:59.24 you've got the host that is binding the virus 00:02:02.08 on one side, 00:02:03.25 and the virus that has evolved a mutation 00:02:06.03 to evolve away from that recognition 00:02:07.28 by the host immune system. 00:02:09.17 What you'll actually appreciate 00:02:11.20 is that these state transitions, 00:02:13.11 between one state and the other, 00:02:14.28 are really profound but very simple 00:02:16.26 from a mechanistic standpoint. 00:02:18.25 What it might take is just a single amino acid mutation 00:02:21.10 for the virus to gain one step ahead 00:02:23.19 in this battle for evolutionary supremacy. 00:02:26.09 So, the important take-home message 00:02:27.25 from this kind of slide is, 00:02:29.14 one party is always losing 00:02:31.22 this high-stakes evolutionary battle. 00:02:33.13 On the left-hand side 00:02:34.28 you can see that the host is winning, 00:02:36.18 because it is recognizing a viral protein. 00:02:38.14 On the right-hand side 00:02:39.23 you can see that the host is losing, 00:02:41.13 because the virus has acquired the right mutation 00:02:43.22 that allows it to evade detection by the immune system, 00:02:46.15 which basically means that there's never going to be 00:02:49.10 a perfect equilibrium between these two states. 00:02:51.10 Over the course of evolution, 00:02:53.01 and even the course of a single infection in a person, 00:02:56.02 the immune system and the virus 00:02:58.26 are basically locked in this arms race 00:03:01.03 of very rapid evolution 00:03:03.09 and, because one party is always losing, 00:03:05.03 there's always going to be an evolutionary advantage 00:03:07.06 to be gained by innovation. 00:03:09.06 Now, we're going to actually talk about 00:03:11.04 two types of innovation today. 00:03:12.18 In the first part of my talk, 00:03:14.04 which is focused exclusively on how hosts evolve 00:03:16.28 in the face of viral challenges, 00:03:18.28 we're going to specify innovation 00:03:21.10 in protein coding genes, 00:03:23.03 and so, if you consider 00:03:25.05 what a protein coding gene arbitrarily looks like, 00:03:28.11 it's this sort of sequence that I've indicated here, 00:03:31.02 where we've got three triplets, three codons, 00:03:34.00 that specify three amino acids 00:03:35.25 that will be incorporated into the protein 00:03:37.17 that is produced from this gene. 00:03:39.15 Now, you can see on this side, 00:03:41.15 you have a mutation 00:03:43.17 that does not alter the amino acid being encoded. 00:03:45.23 We refer to these as silent or synonymous changes, 00:03:48.27 because from a very sort of rough approximation 00:03:51.17 natural selection is really acting on 00:03:54.01 the protein coding sequences, 00:03:55.19 and here, because the protein coding sequence 00:03:57.10 has not altered, we refer to these as 00:03:59.22 silent or synonymous changes. 00:04:01.14 In contrast, you can see here we have, 00:04:03.21 again, a single amino acid mutation, 00:04:05.28 which has altered one of the amino acids 00:04:08.13 that's being encoded, 00:04:10.05 so-called non-synonymous or replacement changes. 00:04:12.21 Now, both of these 00:04:14.25 are sort of equal likelihood mutations. Y 00:04:16.21 ou can actually have a synonymous mutation 00:04:18.16 or a non-synonymous mutation, 00:04:20.08 but you can appreciate that, based on the genetic code, 00:04:22.18 you're much more likely 00:04:24.22 to see an amino acid-altering mutation, 00:04:26.18 just by random chance alone. 00:04:29.18 So, consider the sort of situation 00:04:32.19 where you actually had a gene, 00:04:34.23 we refer to these as pseudogenes, 00:04:36.29 that at some point in their evolutionary history 00:04:39.02 encoded for a particular protein. 00:04:41.19 Now, if you consider this gene 00:04:44.09 now in its current degenerate form, 00:04:46.22 let's say built from the chimpanzee genome 00:04:48.16 versus the human genome, 00:04:50.09 and we were to just roughly calculate 00:04:52.07 the number of synonymous changes 00:04:54.15 versus replacement changes, 00:04:56.17 we have to correct for the fact 00:04:58.10 that there are many more possible replacement changes, 00:05:01.01 so when you normalize for that correction 00:05:03.02 you will find that, because this gene no longer codes 00:05:05.18 for a protein, 00:05:07.09 the rate of synonymous changes 00:05:08.24 and the rate of replacement changes 00:05:10.15 are roughly equal, 00:05:11.25 and that's because selection has stopped worrying 00:05:14.13 about this part of the genome 00:05:16.02 in terms of its protein-coding capacity. 00:05:17.24 It has tolerated both mutations, 00:05:19.22 and they roughly go to fixation 00:05:21.20 in a fairly random fashion. 00:05:23.15 Now, for most genes in the genome, 00:05:25.08 you do care about the final product being produced, 00:05:27.16 which is the amino acid sequence 00:05:29.19 of the resulting protein. 00:05:31.07 So, here I have this hypothetical example 00:05:33.14 where you have a protein-coding gene 00:05:36.14 that is basically representing these triplets of codons, 00:05:39.12 and what you'll see is there's a lot more blue changes, 00:05:42.11 or non-amino acid altering or silent changes, 00:05:46.23 and very rarely do you see something 00:05:48.22 which looks like a replacement 00:05:51.13 or a non-synonymous change. 00:05:53.09 The net result is that, 00:05:55.00 regardless of all of this change at the nucleotide level, 00:05:57.16 the amino acid sequence remains STEVE, 00:06:00.01 because STEVE is really what is being selected for 00:06:03.00 by evolution. 00:06:04.11 Very rarely do you see a deviation 00:06:06.13 from this optimal amino acid sequence. 00:06:08.16 For instance, we can see SiEVE coming in, 00:06:10.29 in terms of this sort of grammar. 00:06:13.00 The net result is not that 00:06:16.20 we should infer that mutation has now stopped 00:06:19.06 hitting the replacement sites. 00:06:20.27 What we infer from this is, 00:06:22.28 because mutation has introduced changes 00:06:24.22 in both replacement and synonymous positions, 00:06:27.19 the fact that we don't see replacement changes 00:06:30.01 over the course of evolution 00:06:31.26 is an indication that natural selection 00:06:34.03 acted upon these changes, 00:06:35.21 deemed them deleterious, 00:06:37.18 and removed them from the population 00:06:39.10 before they had a chance to really spread 00:06:41.28 in the population, 00:06:43.21 which means mutations is not really causing 00:06:45.19 this bias between the blue and the red changes. 00:06:48.06 It's actually natural selection, 00:06:50.02 and, more specifically, purifying selection, 00:06:52.06 that is acting to purify the population 00:06:54.16 from these presumed deleterious mutations. 00:06:56.26 The net result is, 00:06:58.21 if you were to now compare the rate of 00:07:01.02 synonymous and replacement changes, 00:07:02.29 we will find that the rate of replacement changes 00:07:04.25 is actually much lower than synonymous changes, 00:07:07.26 regardless of the fact that both of these changes 00:07:10.00 were introduced in roughly the same proportion. 00:07:13.11 My lab is actually interested in the other 00:07:15.00 class of genes that emerges 00:07:16.20 from these kinds of analyses. 00:07:18.05 Here again, now, we have a triplet code of sequences 00:07:21.03 that encodes for my name in amino acid code, 00:07:24.18 and what we will see when we compare 00:07:26.26 across this sequence 00:07:28.27 is that there are a lot more red changes 00:07:30.27 than blue changes, 00:07:32.09 in fact a lot more red changes than what you'd expect, 00:07:34.20 even by chance alone. 00:07:36.19 It's in fact easier to align these sequences 00:07:38.20 at the nucleotide level 00:07:40.12 than it is to align them at the amino acid, 00:07:43.03 where my name can change to a popular car model 00:07:45.11 very quickly, 00:07:46.29 because every mutation 00:07:48.29 has a high likelihood of altering the amino acid 00:07:51.03 being encoded, 00:07:52.21 and this is exactly the signature we see 00:07:54.14 when you have an interface 00:07:56.19 that is precisely at the interface 00:07:58.08 between a host and a virus conflict, 00:07:59.29 and that's because every single one of 00:08:02.05 these amino acid mutations 00:08:04.19 is potentially beneficial 00:08:06.01 and has been acted upon by natural selection 00:08:09.07 to increase their rate of fixation in the population, 00:08:12.15 hence the term diversifying selection. 00:08:15.11 In contrast to purifying selection, 00:08:17.08 natural selection is increasing 00:08:19.10 the amino acid diversity 00:08:21.07 of these protein-coding genes. 00:08:22.27 As a result, what we have, again, 00:08:24.22 is an apparent rate of replacement changes, 00:08:26.24 kA or dN, 00:08:28.13 which is increased 00:08:30.12 over the apparent rate of synonymous changes. 00:08:32.17 Once again, this is not a bias 00:08:34.25 that is introduced by mutation. 00:08:36.02 This is simply a different selective sieve 00:08:38.08 that is acted upon by natural selection. 00:08:41.15 This term diversifying selection 00:08:43.22 is also referred to as positive selection 00:08:45.19 or adaptive evolution. 00:08:47.03 I'll use these terms interchangeably, 00:08:48.29 and they're only different in the context of the tempo 00:08:51.02 with which these changes happen. 00:08:53.14 Now, if you were to take these characteristics 00:08:55.25 of replacement rates and synonymous rates 00:08:57.29 and calculate them for all genes 00:08:59.29 that we can compare between three sets of species, 00:09:02.25 our own species genome, 00:09:04.26 the rhesus macaque, 00:09:06.18 or the chimpanzee genome, 00:09:08.16 what we have is this very nice histogram 00:09:11.02 which really reflects the selective constraints 00:09:13.20 that have acted on all the protein-coding genes 00:09:16.10 within our genome. 00:09:18.01 What you'll see is there's a large number of genes 00:09:20.24 in the left-hand side of this histogram, 00:09:23.01 which means for the bulk of the genes in the human genome, 00:09:25.13 purifying selection, 00:09:27.04 or a dearth of replacement changes, 00:09:28.28 is really what is going on. 00:09:30.19 We are very interested in this sort of 00:09:32.26 small blip of genes right here 00:09:35.04 where you actually have a very small set of genes, 00:09:37.16 which even at the whole-gene level 00:09:39.09 have undergone much faster replacement changes, 00:09:41.10 almost breaking the speed limit of evolution, if you will, 00:09:44.16 to increase because of this diversity. 00:09:46.16 And when you take a really close look at 00:09:48.25 this category of genes, 00:09:50.13 immunity genes are really overrepresented, 00:09:52.06 as you might expect, 00:09:53.19 because these genes have been acted upon 00:09:55.14 repeatedly by natural selection. 00:09:57.21 So, we're going to consider 00:09:59.08 a very specialized case of an arms race 00:10:01.08 in today's seminar, 00:10:03.07 and this arms race ensues when a viral protein 00:10:05.14 begins to antagonize an antiviral protein, 00:10:08.21 and in this example the viral protein antagonism 00:10:11.08 is going to force the antiviral protein 00:10:13.11 to evolve to a state which this viral protein 00:10:16.27 can no longer defeat, 00:10:18.14 which will now force this viral protein 00:10:20.01 to evolve rapidly in order to restore its antagonism. 00:10:22.22 And this, in a microcosm, 00:10:24.27 is one step of this arms race, 00:10:26.29 where both the host protein 00:10:28.26 as well as the viral proteins have evolved 00:10:30.27 in these subsequent arms race interactions. 00:10:33.24 Now, what we're going to consider today 00:10:35.29 is a specialized example of this antagonism, 00:10:38.00 when the viral that is being used to antagonize 00:10:42.02 the host antiviral protein 00:10:43.28 is itself a host protein. 00:10:46.05 So, we are now basically considering 00:10:48.02 how would the host be able to distinguish 00:10:50.11 between an antagonism 00:10:52.07 that is caused by a viral mimic 00:10:54.12 versus its interaction with its own host proteins, 00:10:56.25 and that's the problem we'd like to address today, 00:10:59.04 which is, how do host genomes 00:11:01.12 confront and overcome, if they can, 00:11:04.11 the challenge of pathogen mimicry? 00:11:06.21 In today's seminar, 00:11:08.07 we're going to focus on a very specific example 00:11:10.07 of viral antagonism 00:11:11.18 that is mediated by mimicry, 00:11:13.13 and this example involves the host antiviral protein, 00:11:16.15 protein kinase R (PKR). 00:11:18.08 So, protein kinase R 00:11:20.01 is actually expressed when the organism senses 00:11:22.17 it's under some sort of viral attack 00:11:24.27 by virtue of an interferon detection pathway, 00:11:27.09 but it's actually produced as an inactive monomer, 00:11:29.16 which means it can no longer activate itself 00:11:31.24 as a kinase, 00:11:33.14 which is in the process of putting phosphate moieties 00:11:36.05 onto other proteins. 00:11:37.25 However, if this particular cell 00:11:39.27 happens to be infected by a virus, 00:11:41.23 that is detected by the fact that 00:11:45.01 there will now be double-stranded RNA in the cytoplasm, 00:11:47.05 which should not be case unless the cell 00:11:49.19 was under viral attack, 00:11:51.09 and what PKR will do is 00:11:53.08 it will use the signature of double-stranded RNA 00:11:55.06 to dimerize and activate itself as a kinase 00:11:57.22 whose primary substrate 00:11:59.21 is this protein eIF2α, 00:12:01.24 which stands for elongation initiation factor 2α, 00:12:05.04 which is a very important control step 00:12:07.22 to initiate protein production through the ribosome. 00:12:10.22 However, when PKR will phosphorylate eIF2α, 00:12:13.25 this essentially blocks protein production. 00:12:16.08 So, the cell's response to detecting itself 00:12:19.17 under viral attack is, 00:12:21.07 "I'm going to stop all protein production 00:12:23.05 so that I do not become a virus production factory." 00:12:26.04 This can be a very effective 00:12:27.29 and a very potent block to viral production, 00:12:30.17 and so what viruses have had to come up with 00:12:33.12 is several clever means by which 00:12:35.25 they can actually inhibit the PKR reaction. 00:12:37.21 Some viruses, for instance, inhibit the dimerization of PKR. 00:12:40.27 Some viruses will actually hide away 00:12:43.08 all the double-stranded RNA they produce, 00:12:45.02 whereas some viruses actually 00:12:47.08 will encode a phosphatase that specifically 00:12:49.23 takes out the phosphate residue 00:12:51.28 that is put on by PRK, 00:12:53.15 and perhaps the cleverest model 00:12:55.20 comes from the hepatitis C viruses 00:12:57.25 that actually allow PKR to block protein production, 00:13:00.15 to essentially block all manner of host protein production, 00:13:03.15 but will now nonetheless 00:13:05.20 carry on their own protein production 00:13:07.15 in an eIF2α-independent fashion, 00:13:09.20 really highlighting the clever inventions 00:13:12.00 that are really forced upon 00:13:13.27 by virtue of these Darwinian arms races. 00:13:15.25 In todays' seminar, we're actually going to focus on 00:13:18.03 only one of these antagonists, 00:13:20.02 which is encoded by the poxvirus class proteins, 00:13:23.10 which include smallpox and vaccinia virus, 00:13:26.11 and this is a protein called K3L, 00:13:28.14 which acts as a competitive and non-competitive inhibitor, 00:13:31.08 essentially breaking the interaction 00:13:33.12 between PKR and eIF2α, 00:13:36.12 which basically allows the virus to restore protein production 00:13:40.02 and go on with its life cycle. 00:13:42.05 So, we actually started this by looking at what this arms race, 00:13:45.12 with the potential for multiple antagonists from viruses, 00:13:48.15 has done to PKR evolution. 00:13:50.19 And so, to do this, 00:13:52.15 we actually sequenced the PKR gene 00:13:54.10 from a panel of primates, 00:13:56.03 which includes homonoids, 00:13:57.22 including humans, great apes, as well as gibbons, 00:13:59.28 old world monkeys, 00:14:01.20 which includes things like rhesus macaques, 00:14:03.14 and new world monkeys, 00:14:05.05 which are primates that populate Central and South America. 00:14:07.24 And when we do the sequence, 00:14:09.28 we can actually reconstruct the evolutionally history 00:14:12.25 of essentially every step and every codon 00:14:15.08 across the PKR phylogeny, 00:14:17.04 and so what we see in these numbers here 00:14:19.21 are those dN/dS or kA/kS signatures 00:14:22.24 that I talked about. 00:14:24.19 When when we have very low numbers 00:14:26.22 like this number 0.2, here, 00:14:28.19 that's an indication of not very much happening 00:14:30.19 at the protein evolution level. 00:14:32.11 In contrast, we have some amazing examples 00:14:34.16 like this lineage in old world monkeys, 00:14:36.18 where we actually have 22 replacement changes 00:14:39.17 without a single synonymous change happening. 00:14:42.08 That's a really profound signal 00:14:44.06 of multiple staccato replacement changes 00:14:46.17 occurring in the course of evolution, 00:14:48.14 in a very, very short time frame, 00:14:50.09 really highlighting the very intense 00:14:52.16 and very episodic evolutionary pressures 00:14:55.11 that have acted on PKR 00:14:57.17 over the course of the last 35 million years 00:14:59.22 of primate evolution. 00:15:01.13 If you were now to sort of turn this around 00:15:03.16 and squish it codon by codon, 00:15:05.20 we essentially get a landscape 00:15:07.24 of how PKR has been influenced 00:15:09.28 by positive selection. 00:15:11.11 All of these tick marks that I've shown 00:15:13.07 above the PKR protein 00:15:15.09 are individual codons that have recurrently evolved 00:15:17.15 under positive selection, 00:15:19.06 and you can see that, in the case of PKR, 00:15:21.25 these are really spread throughout the entire protein motif of PKR, 00:15:25.01 including in the N-terminal domain, 00:15:27.19 in this linker domain or the spacer region, 00:15:29.20 as well as in the kinase domain, 00:15:32.00 which actually carries out the very important step 00:15:34.09 of eIF2α phosphorylation. 00:15:37.26 And the reason we think that there's been such 00:15:40.07 dramatic and such widespread positive selection 00:15:42.15 is because multiple viruses 00:15:44.10 actually antagonize completely different domains of PKR 00:15:47.12 in order to mediate their antagonism of PKR. 00:15:50.12 So, what we're gonna focus on today 00:15:52.19 is just one of these antagonists, 00:15:54.05 which is, again, these poxviral antagonist K3L, 00:15:56.27 that actually specifically antagonize 00:15:59.12 the kinase domain of PKR. 00:16:01.25 So, the reason I've been spending so much time 00:16:03.28 discussing K3L with you 00:16:05.27 is because K3L is a special antagonist. 00:16:08.28 It actually is an evolutionary-derived mimic 00:16:12.24 which used to be eIF2α, 00:16:14.29 which means that at some point in poxviral evolution, 00:16:18.08 poxvirus actually stole eIF2α from a mammalian host, 00:16:22.13 and have whittled it away to become this perfect mimic, 00:16:25.29 in order to break PKR's interaction with the eIF2α substrate. 00:16:30.00 Now, what is really remarkable about this interaction 00:16:32.12 is that it not just happened once 00:16:34.25 in mammals, 00:16:36.08 but it's happened on three separate occasions 00:16:38.27 with three completely independent lineages 00:16:40.24 of double-stranded DNA viruses, 00:16:42.20 each of them acquiring a K3L-like mimic 00:16:44.20 from their own version of eIF2α. 00:16:47.16 So, this really highlights the very, very successful 00:16:50.11 strategy of mimicry that is encoded by pathogens, 00:16:54.09 and really, from an evolutionary standpoint, 00:16:56.16 the strategy of mimicry 00:16:58.19 and overcoming mimicry 00:17:00.05 is a debate that's really been going on 00:17:02.10 for a very, very long time, 00:17:04.02 going back all the way to Henry Walter Bates, 00:17:06.04 who really first detected evidence for mimicry 00:17:09.13 in these butterflies in the Amazon, 00:17:11.24 where we have model butterflies 00:17:14.10 that are basically poisonous, 00:17:16.13 and so they're avoided by predators 00:17:18.22 who can use their coloration patterns 00:17:20.21 as an indication to... 00:17:22.17 as a warning signal to stay away from them, 00:17:24.16 and mimic butterflies 00:17:26.10 that actually don't encode a poison at all, 00:17:28.05 but take advantage of this coloration pattern, 00:17:30.11 and mimic the coloration pattern, 00:17:32.08 to take all the advantages of avoidance from predators, 00:17:35.09 without actually having to encode 00:17:37.07 any of the toxins that are required. 00:17:39.19 Now, this is actually quite a really great strategy 00:17:41.27 for the mimic. 00:17:43.06 It's not so good for the model, 00:17:45.03 because as the mimics start increasing in frequency 00:17:47.03 and the predators start eating more and more butterflies 00:17:48.29 that look like this, but are quite tasty, 00:17:51.12 they will lose their avoidance of the predators, 00:17:53.16 which means that the success of the mimic 00:17:56.08 is directly, inversely correlated 00:17:58.22 with the success of the model. 00:18:01.01 And very much the same thing might be going on 00:18:03.00 at a molecular level, we feel, 00:18:04.27 where eIF2α is acting as a model protein, 00:18:07.17 which is being mimicked by this poxviral mimic K3L 00:18:10.27 in order to defeat 00:18:13.22 the PKR-eIF2α immunity response. 00:18:16.29 So, if you were to sort of rephrase 00:18:18.27 the challenge of mimicry, 00:18:20.22 it is that the PKR kinase domain 00:18:22.29 needs to bind and maintain its interaction with eIF2α, 00:18:26.22 while avoiding its interaction with the mimic, 00:18:29.12 which really is evolutionarily being selected 00:18:31.19 to look like eIF2α 00:18:33.11 from the viral perspective, 00:18:34.28 and you can see in this crystal structure 00:18:37.03 that the structures of the PKR interaction domain 00:18:39.27 between K3L and eIF2α 00:18:42.01 are almost completely super-alignable, 00:18:43.23 so how is it that PKR is able to acquire 00:18:46.23 the ability to discriminate between these two? 00:18:49.12 As I've already told you, 00:18:51.08 one of the strategies that PKR is using is very rapid evolution, 00:18:54.26 it's got that at its disposal, 00:18:56.17 and this is just a sliding window plot of dN/dS 00:18:59.12 over the entire protein of PKR, 00:19:01.15 and what you see here is that 00:19:03.14 there is not even a single domain 00:19:05.20 where the dN/dS signature drops below one, 00:19:07.26 which means pretty much every domain of PKR 00:19:10.06 is evolving under positive selection 00:19:12.06 in this comparison between human and rhesus PKR. 00:19:15.08 It's really remarkable how profound the signal is, 00:19:18.14 because when we compare PKR 00:19:20.06 to its closest relative kinase, PERK, 00:19:22.14 which is not involved in antiviral immunity, 00:19:25.04 you can see that the signature is completely profound 00:19:27.23 of purifying selection, 00:19:29.17 and not of positive selection. 00:19:31.15 And this actually gets even more interesting 00:19:33.06 when you look at eIF2α, 00:19:34.22 which is the substrate for PKR, 00:19:36.25 because eIF2α is so important for translation 00:19:41.02 that it has not tolerated any amino acid changes 00:19:43.07 over the course of evolution. 00:19:44.28 You might be actually wondering where the red line went, 00:19:47.12 and actually the red line is exactly on zero, 00:19:50.05 because no amino acid changes have occurred 00:19:52.12 over the course of primate evolution. 00:19:54.05 So, in a way, you can view this 00:19:56.19 as a very high-stakes game of rock-paper-scissors, 00:19:59.28 except eIF2α is always playing rock, 00:20:03.14 and so it would seem that mimic 00:20:05.25 would have a very, very simple game, 00:20:08.04 which is to mimic an unchanging protein 00:20:10.05 and stay there. 00:20:12.06 We wondered whether that was actually the case, 00:20:13.29 because, first of all, we've actually survived poxviruses, 00:20:16.25 and secondly, this suggested that 00:20:19.23 PKR might have some adaptive routes 00:20:21.18 in order to escape mimicry. 00:20:23.06 Furthermore, if it was the case that K3L 00:20:25.05 was simply evolving to an optimal mimic status, 00:20:28.04 we might actually presume 00:20:30.09 that K3L should now be under purifying selection, 00:20:32.14 having optimized for this role in mimicry. 00:20:35.06 Instead, what we actually find 00:20:36.21 when we compare K3L 00:20:39.00 from a panel of poxviruses, 00:20:40.20 is that, very much like PKR 00:20:42.27 shown here on the host side, 00:20:44.18 which is very rapidly evolving, 00:20:46.08 in contrast to eIF2α which is not, 00:20:48.20 K3L happens to be one of the most [quickly] 00:20:52.09 evolving proteins the poxviral genome. 00:20:54.08 So, this is truly an arms race between 00:20:56.19 K3L and PKR. 00:20:58.03 What makes this arms race really interesting 00:21:00.01 is that they're both really evolving 00:21:02.03 to get the attention of eIF2α, 00:21:03.28 which is not changing at all, 00:21:05.29 and so that's what makes the problem of mimicry 00:21:07.29 really interesting from an evolutionary standpoint. 00:21:11.12 So, we wanted to actually have a system 00:21:13.15 in which we could simply assay 00:21:15.20 for the effects of mutations and evolutionary adaptations 00:21:18.17 in a very facile assay, 00:21:20.11 and we actually took advantage of an assay 00:21:22.12 developed first by Tom Dever and Alan Hinnebusch, 00:21:25.03 who recognized that eIF2α 00:21:27.22 is so slow to evolve 00:21:29.15 that if you actually put human PKR in yeast 00:21:32.06 it will actually bind and phosphorylate yeast eIF2α 00:21:34.25 to cause a growth arrest. 00:21:36.26 Now, in this context, 00:21:38.05 if we now also introduce K3L, 00:21:40.11 we have the situation where K3L 00:21:42.18 can give you a readout of whether it's able 00:21:44.20 to defeat PKR or not, 00:21:46.19 based on whether it can rescue the growth inhibition 00:21:49.08 mediated by the PKR expression. 00:21:51.23 So, Nels Elde, 00:21:53.05 who was a postdoc in the lab, 00:21:54.29 actually took this panel of PKR genes 00:21:57.09 from a panel of different primates... 00:21:59.12 homonoids, old world monkeys, 00:22:01.02 and new world monkeys... 00:22:02.20 and he actually just put it into yeast cells, 00:22:04.24 but he put it in a form which could not be turned on. 00:22:07.14 So, when these yeast grow on glucose, 00:22:09.25 because the PKR gene 00:22:11.19 is put on a galactose promoter, 00:22:13.12 it's silenced, 00:22:15.04 and what you can see is that all of these yeast 00:22:17.01 grow perfectly fine. 00:22:18.16 You can see that, even in this serial dilution across, 00:22:20.17 you basically have no growth inhibition. 00:22:22.27 However, as soon as you turn on PKR 00:22:25.17 by putting all of these yeasts onto galactose plates, 00:22:27.27 you can see no yeast growing here, 00:22:30.27 which means all of these PKR alleles 00:22:32.29 have conserved the property of binding 00:22:35.27 and phosphorylating yeast eIF2α, 00:22:37.26 which is remarkable considering the very large degree 00:22:40.20 of evolutionary divergence that we have seen here. 00:22:43.20 Now, I can tell you that this is all because of eIF2α phosphorylation, 00:22:46.17 because in this yeast, 00:22:48.17 if I engineer a mutation in the phosphorylation site 00:22:51.05 all of the growth inhibition goes away, 00:22:53.06 and that's shown in these two panels here. 00:22:55.04 So now, the really interesting question 00:22:57.00 happens when you introduce the viral antagonist. 00:22:59.28 So, what would you predict 00:23:01.24 would happen here if you now introduced 00:23:04.07 the K3L protein from a poxvirus? 00:23:06.13 In this case, we used the vaccinia virus, 00:23:08.28 and what we find is a completely binary response. 00:23:11.27 In some situations, like in the gibbon PKR case, 00:23:15.04 the introduction of the vaccinia K3L 00:23:17.18 completely reverses the growth inhibition that is going on, 00:23:20.23 whereas in the human case, 00:23:23.01 even the presence of K3L, 00:23:25.00 at roughly equal levels of expression, 00:23:27.06 did not overcome the growth inhibition. 00:23:28.26 So, this is exactly like that cartoon example 00:23:31.08 of those two states between hosts and viruses, 00:23:33.27 and what we have in an evolutionary snapshot 00:23:37.01 of both of those states, w 00:23:38.20 here either the host is winning, 00:23:40.18 in which case the growth inhibition goes on, 00:23:42.24 or the virus in winning, 00:23:44.25 in which case the growth inhibition is completely reversed. 00:23:47.10 Now, these are all assays being done in yeast, 00:23:49.17 but we've actually done exactly the same types of assays 00:23:51.29 in vaccinia cells, 00:23:53.27 where we've actually taken either human cells 00:23:56.01 or gibbon cells 00:23:57.16 or orangutan cells 00:23:59.10 and infected them with either a wild type, 00:24:01.10 fully functional vaccinia, 00:24:03.06 or something in which the K3L 00:24:05.26 specifically had been deleted, 00:24:07.21 and what you'll notice is that, 00:24:09.12 in human cells and orangutan cells, 00:24:11.04 it actually doesn't matter 00:24:13.00 whether you've deleted the K3L gene or not, 00:24:14.27 and that's because these species 00:24:17.02 actually have a PKR that's resistant 00:24:19.00 to the K3L antagonism, 00:24:20.25 whereas in the gibbon case, 00:24:22.15 when you delete K3L, you have this 10-fold drop in fitness, 00:24:25.08 which basically is an indication 00:24:27.15 that K3L from vaccinia 00:24:29.21 is acting as a species-specific antagonist 00:24:32.00 of the PKR response. 00:24:34.07 So, we wondered whether 00:24:36.04 we could actually gain better molecular insight 00:24:38.11 into how is it that K3L is able to adopt these multiple states 00:24:41.27 by looking at the co-crystal structure 00:24:44.09 of PKR's kinase domain and the eIF2α substrate, 00:24:47.25 which was first actually established 00:24:50.04 by Arvin Dar and Frank Sicheri's lab, 00:24:52.21 and in this co-crystal structure, 00:24:54.21 one of the most important motifs for this interaction 00:24:58.00 happens to be this α-helix that I've shown here 00:25:01.06 as the g-helix. 00:25:02.23 This is effectively like a bird perch 00:25:04.23 onto which PKR will sit... 00:25:06.21 the bird perch on PKR on PRK 00:25:08.15 onto which eIF2α will sit down. 00:25:10.13 If you take a closer look at the α-helix, 00:25:12.02 shown here, 00:25:13.22 there are three residues in particular 00:25:15.15 that are making direct contacts with the backbone of eIF2α. 00:25:18.29 Now, I'll remind you that eIF2α 00:25:20.18 is not changing at all, in fact, 00:25:22.17 functionally equivalent between human and yeast, 00:25:25.19 so you would predict actually 00:25:28.04 that these three residues would be completely frozen 00:25:30.05 in evolution, 00:25:31.20 by virtue of the fact that they have to interact 00:25:33.25 with something that is completely frozen itself, 00:25:36.05 but instead what we find 00:25:38.05 is that these three residues represent some 00:25:40.12 of the fastest evolving residues 00:25:42.19 in PKR's kinase domain. 00:25:44.10 So, the very... 00:25:46.00 sort of combination lock 00:25:48.02 that is responsible for binding eIF2α 00:25:50.00 is the lock that is very rapidly changing. 00:25:52.05 So, somehow all of these combinations of residues 00:25:55.04 at the αg-helix 00:25:57.13 have preserved the property of binding eIF2α, 00:25:59.25 and yet are basically under very strong evolution. 00:26:02.07 So, we wondered whether this is in fact 00:26:05.08 a signature of the fact that this is an interface 00:26:08.12 that has been constantly challenged by viral mimicry, 00:26:11.14 and so, to test that, 00:26:12.28 we again returned to our yeast assay. 00:26:14.25 We have human PKR 00:26:16.17 that is able to continue growth inhibition 00:26:19.07 even in the presence of K3L, 00:26:21.07 gibbon PKR 00:26:22.27 that is completely reversed by the presence of K3L, 00:26:25.11 and now, in the gibbon backbone, 00:26:27.07 if we add single amino acid changes 00:26:29.27 from human into gibbon, 00:26:31.26 what we find is that we can completely reverse 00:26:34.20 the susceptibility phenotype 00:26:36.11 into the resistant phenotype. 00:26:38.06 So, this really highlights two things. 00:26:40.09 First of all, 00:26:42.10 the interface between PKR and eIF2α 00:26:44.20 is really a hotspot for positive selection, 00:26:47.07 and individual residue changes, 00:26:49.13 these single steps in the arms race 00:26:52.15 between PKR and K3L, 00:26:54.06 result in a complete reversal 00:26:56.10 from susceptibility to resistance. 00:26:58.11 Now, this also actually revealed to us 00:27:00.17 something else that we had missed earlier, 00:27:02.16 which is, even though the orangutan PKR 00:27:05.17 is completely resistant to K3L mimicry, 00:27:07.27 the orangutan g-helix 00:27:10.20 is not resistant to mimicry, 00:27:13.04 which means some other component 00:27:15.27 of the PKR backbone in orangutan 00:27:17.25 is actually necessary for mimicry, 00:27:19.25 immediately suggesting that there was another solution 00:27:22.09 to overcoming mimicry 00:27:24.11 that was evident in orangutan, 00:27:26.05 and we actually mapped that residue again 00:27:28.09 to a single residue in this helix αE, 00:27:30.20 very far away from this helix αG 00:27:33.09 which I've been telling you about today. 00:27:35.09 And so, very much like we saw 00:27:38.16 in the human/gibbon αG case, 00:27:41.09 individual residue changes between gibbon and orangutan 00:27:44.21 have the ability to switch from susceptible to resistant 00:27:48.09 and resistant to susceptible. 00:27:51.15 So, again, really highlighting 00:27:53.12 the very significant power of even individual mutations 00:27:56.04 in individual residues. 00:27:57.27 In the human case, 00:27:59.25 what we also sort of observed was... 00:28:02.09 this particular residue is very interesting, 00:28:04.14 because it's actually toggled 00:28:06.11 between leucine and phenylalanine 00:28:08.12 throughout mammalian evolution, 00:28:10.09 really reflecting the fact that there's probably 00:28:12.05 a high degree of evolutionary constraint 00:28:14.09 acting on this protein, 00:28:15.29 and yet it's toggling so as to keep one step ahead 00:28:18.16 of this mimic interface. 00:28:20.12 The human PKR actually has a very good helix αE residue, 00:28:23.17 as well as a helix αG residue, 00:28:25.28 especially against vaccinia, 00:28:27.25 and we actually have to mutate all three of these residues 00:28:29.27 in order to convert the resistant human PKR 00:28:32.10 into a susceptible version. 00:28:34.20 So, what have we learned from our examples 00:28:37.24 of PKR overcoming the mimicry of K3L? 00:28:40.17 The first really important lesson we learned 00:28:43.06 is that multiple domains of PKR 00:28:45.08 need to be under rapid evolution 00:28:47.09 in order to overcome mimicry. 00:28:48.27 Again, as I pointed out, 00:28:50.18 this is a rock-paper-scissors game, 00:28:52.11 and if only one particular domain 00:28:54.06 was under rapid evolution, 00:28:55.26 K3L would have a much easier task 00:28:57.17 antagonizing and mimicking this interface. 00:28:59.19 The fact that multiple residues 00:29:01.11 in multiple domains 00:29:03.06 are actually rapidly evolving 00:29:04.26 allows these domains to really take turns 00:29:06.28 in antagonizing... 00:29:08.22 overcoming the antagonism of K3L. 00:29:10.14 And, what appears to be the first evolutionary step 00:29:12.26 when PKR encounters this mimicry 00:29:15.10 is actually a negative affinity, 00:29:17.16 where PKR loses affinity, 00:29:19.11 not just to eIF2α, 00:29:21.12 but also to K3L, 00:29:23.11 and then it restores its affinity 00:29:25.08 by interactions in another domain. 00:29:28.08 So, this also implies that there 00:29:30.24 must be extraordinary flexibility for PKR 00:29:32.26 to basically recognize a substrate 00:29:34.23 that really has undergone no changes 00:29:36.24 over the course of evolution. 00:29:38.18 So, just as an example of this flexibility, 00:29:41.03 here again we've the orangutan G-helix 00:29:43.15 in a gibbon backbone, 00:29:45.24 and you can see this is actually susceptible to mimicry, 00:29:49.00 but you can see here, now, 00:29:50.27 because of the growth of this yeast colony, 00:29:52.25 this is telling us that this particular chimeric version of PKR 00:29:56.03 is also not doing a good job of recognizing its substrate, 00:29:59.24 and yet the orangutan backbone 00:30:02.01 has completely restored the binding to eIF2α 00:30:04.23 as well as overcome mimicry, 00:30:06.15 which means something else 00:30:08.15 in the orangutan backbone was sufficient 00:30:10.15 to restore the weakness of this G-helix 00:30:12.28 over the course of these evolutionary arms races. 00:30:15.16 So, this is great, 00:30:17.06 we've learned rules by which PKR 00:30:19.04 might actually overcome mimicry, 00:30:20.24 but this is also sort of a sobering reminder 00:30:22.21 that this overcoming of mimicry 00:30:24.29 00:30:26.24 comes at a cost. So, if you were to look at the αG helix from PKR 00:30:29.05 and three other kinases, 00:30:31.08 whose primary substrate is eIF2α, 00:30:33.11 we'll notice that PKR 00:30:35.18 is the only kinase where we see this dramatic rapid evolution. 00:30:37.25 We don't see if for these three other kinases, 00:30:40.13 which means these kinases 00:30:42.11 have had the evolutionary luxury 00:30:44.20 to optimize to an optimal binding of eIF2α 00:30:48.15 and essentially stay there, 00:30:50.20 preserve their optimal binding 00:30:52.22 by virtue of purifying selection. 00:30:54.15 PKR no longer has that luxury, 00:30:56.18 because as it gets more and more optimal 00:30:58.20 for eIF2α recognition, 00:31:00.23 it gets more and more susceptible 00:31:02.16 for K3L antagonizing it as a mimic. 00:31:05.28 So instead, PKR's evolutionary solution 00:31:08.17 has been to back away from this optimal mimicry 00:31:11.02 in order to gain more of this adaptive landscape 00:31:13.14 that keeps it one step ahead 00:31:15.28 of the virus in terms of these arms races. 00:31:18.02 This a very important sort of consideration 00:31:20.29 because it's not just antiviral genes that face mimicry. 00:31:24.18 This is a slide in which we show that 00:31:28.16 absolutely essential processes in the cell, 00:31:30.24 the cytoskeleton, 00:31:32.14 membrane trafficking, 00:31:34.01 even the cell cycle and apoptosis, 00:31:36.06 all absolutely fundamental housekeeping processes in the cell, 00:31:38.22 are all hijacked by some form of pathogen mimicry. 00:31:42.04 It's worth considering that... 00:31:44.13 what are the evolutionary pressures that have been placed on all of these processes, 00:31:47.09 as they basically tried to survive the mimicry imposed by the pathogen? 00:31:50.20 And, even though they're acquired 00:31:53.22 really great adaptations to overcome this mimicry, 00:31:56.05 some of these alleles might actually be compromised 00:31:59.05 in terms of their housekeeping function 00:32:01.28 - for the function that they were originally intended for. 00:32:03.29 And so, it's not only the fact that the mimic 00:32:06.15 is actually imposing evolutionary adaptation, 00:32:08.28 it might be pushing some of these genes away 00:32:11.20 from their optimal state for cellular function. 00:32:15.00 So, with that I'm going to end this part of the talk. 00:32:17.21 I'd like to really acknowledge Nels Elde, 00:32:20.03 who was a former postdoc in the lab 00:32:22.04 who has his own lab at the University of Utah now, 00:32:24.11 and two very talented technicians, 00:32:25.27 Emily Baker and Michael Eickbush. 00:32:28.03 And this work was done in collaboration 00:32:30.06 with my colleague Adam Geballe, 00:32:32.07 and Stephanie Child in his lab really did all of the viral work 00:32:35.08 that I've discussed. 00:32:36.27 I'd really like to thank our funding sources, 00:32:38.22 and I thank you for your attention.