Session 10: Viral Evolution

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.

00:00:01.18 Hello. My name is Harmit Malik,
00:00:03.29 and I'm an evolutionary geneticist
00:00:06.07 studying the molecular arms races
00:00:08.05 between primates and viral genomes.
00:00:10.11 I work at the Basic Sciences Division
00:00:12.10 at the Fred Hutchinson Cancer Research Center,
00:00:14.29 and what we hope to under, simultaneously,
00:00:17.15 is not just the evolutionary rules
00:00:19.23 that govern these interactions between primates
00:00:21.28 and viral genomes,
00:00:23.23 which tells us a lot about how we evolved
00:00:25.21 as well as how the viral pathogens
00:00:27.22 that we interact with evolve,
00:00:29.13 but we also would like to use these rules
00:00:31.13 to design better therapeutic strategies
00:00:33.16 to come up with sort of a better
00:00:35.26 antiviral intervention strategy.
00:00:38.10 So, in the second part of my talk today,
00:00:40.04 I'm going to talk about viral evolution
00:00:42.10 and how viruses might actually adopt
00:00:45.14 completely unexpected pathways
00:00:47.17 in order to evolve in these Darwinian arms races
00:00:49.26 between themselves as well as primate genomes.
00:00:54.12 So, a lot of the work on molecular arms races
00:00:56.20 is actually inspired by the fictional character
00:00:59.04 the Red Queen
00:01:00.22 that was introduced to us by Lewis Carroll in his book
00:01:03.06 "Through the Looking Glass",
00:01:04.26 and he pointed out that it takes all the running you can do
00:01:07.03 to keep in the same place,
00:01:08.28 which is what the Red Queen said to Alice.
00:01:11.04 This was actually recognized as
00:01:13.00 a really powerful evolutionary theorem
00:01:14.23 by the evolutionary geneticist Leigh Van Valen,
00:01:17.27 who formalized this into the Red Queen Hypothesis,
00:01:20.27 and he pointed out that
00:01:23.06 when two systems are basically antagonists of each other,
00:01:25.19 where they're both either competing for the same resources
00:01:28.11 or really taking advantage of each other
00:01:31.00 in order to gain an evolutionary dominant strategy,
00:01:33.22 they're going to be basically be always trying to evolve
00:01:36.21 to get the upper hand
00:01:38.15 in this evolutionary arms race,
00:01:40.11 forcing the other component to evolve.
00:01:42.08 And, essentially, they're just always climbing
00:01:44.12 this staircase over evolutionary adaptation,
00:01:47.00 effectively always staying in the same place.
00:01:49.09 So, there's going to be a temporary winner or a loser,
00:01:51.17 which in the next cycle of adaptation
00:01:53.20 gets switched around.
00:01:55.12 And so, this is a very typical situation
00:01:57.17 as it's seen in host-virus conflicts,
00:02:00.04 because this is exactly the type of situation
00:02:02.04 where we'd expect both the viral genome
00:02:04.04 and the host genome,
00:02:05.22 in order to be in conflict with each other,
00:02:07.22 which is why we actually refer to these
00:02:09.28 as the usual suspects.
00:02:11.06 And so, in this slide cartoon here,
00:02:13.06 we have a situation where the host protein
00:02:15.17 is either recognizing a viral protein
00:02:17.17 such that it can actually degrade it
00:02:19.15 and cleanse the organism of this infection,
00:02:22.00 or a viral protein actually acquiring
00:02:24.16 a single amino acid mutation
00:02:26.29 that allows it to evade detection by the immune system,
00:02:29.01 and thereby basically gains an advantage
00:02:31.15 by virtue of no longer being recognized.
00:02:33.23 And so, this is a situation in which
00:02:36.13 either the host or the virus is always losing this arms race,
00:02:39.26 and therefore there's always going to be
00:02:42.01 an evolutionary advantage to be gained by innovation.
00:02:44.13 Now, in a classical Darwinian sense,
00:02:46.14 this arms race can actually by typified
00:02:48.25 in a cartoon example
00:02:50.22 in a population sense.
00:02:52.03 So, here we have again, a very similar situation,
00:02:53.28 an immune surveillance protein,
00:02:55.24 let's say it's an innate immune defense gene,
00:02:58.06 which is actually surveying a population
00:03:00.17 of viruses represented by these coat proteins.
00:03:03.10 Now, very much like Darwinian selection,
00:03:05.19 a random mutation will arise,
00:03:07.24 which might actually affect
00:03:09.22 one of these coat proteins
00:03:11.11 such that it happens to have a mutation
00:03:13.13 that no longer allows it to be recognized
00:03:15.12 with the immune surveillance system.
00:03:17.12 This mutation could be extremely rare,
00:03:19.12 happening in one in a billion viruses.
00:03:21.28 Nonetheless, because all the other viruses
00:03:24.03 are being recognized
00:03:26.15 and cleansed out by the immune system,
00:03:28.11 very quickly this virus is going to take over the population,
00:03:31.01 and now the host is confronted
00:03:33.17 with a virus that is actually a variant,
00:03:35.23 and specifically a variant in an interaction interface,
00:03:39.19 forcing the host genome
00:03:41.22 to now come up with a counter-evolution strategy
00:03:44.09 which involves changes in the host protein.
00:03:46.29 So, most of the positive selection,
00:03:49.20 or most of the evolutionary adaptation,
00:03:52.02 that we've been considering
00:03:54.14 between hosts and viruses
00:03:56.16 has really been focused on these
00:03:58.19 very rapid amino acid replacements
00:04:00.21 in the host-virus interaction interface,
00:04:03.02 but today I'm actually going to tell you about
00:04:05.02 a completely novel strategy that viruses might use,
00:04:08.17 which actually has been hidden from view
00:04:10.26 from a lot of evolutionary biologists,
00:04:12.11 and we could actually capture that
00:04:14.07 by virtue of laboratory experiments
00:04:16.16 that could actually capture all stages
00:04:18.20 of this adaptation process
00:04:20.11 as it happened in a virus.
00:04:22.11 Before I tell you about that, I need to introduce
00:04:24.20 the particular system I'm going to describe,
00:04:26.08 and that's an antiviral gene
00:04:27.28 called protein kinase R, or PKR,
00:04:30.02 which is a very important innate defense system
00:04:32.06 against viruses.
00:04:33.24 So, PKR is actually expressed as an inactive monomer,
00:04:37.29 which means it's no longer active as a kinase.
00:04:40.15 A kinase is a protein that actually
00:04:42.21 puts phosphate moieties onto other proteins.
00:04:45.00 On interferon production,
00:04:47.00 you make PKR but you don't really mount a response.
00:04:49.09 It actually takes an actual viral infection
00:04:51.19 in that cell
00:04:53.23 in order for PKR to dimerize,
00:04:55.29 using the double-stranded RNA,
00:04:57.25 activate itself as a kinase,
00:04:59.21 and now phosphorylate its substrate eIF2α,
00:05:03.09 or elongation initiation factor 2α,
00:05:06.23 whose phosphorylation
00:05:08.23 will basically block protein production
00:05:10.26 through the ribosome.
00:05:12.18 So, this is a very potent block against viruses.
00:05:14.08 They can no longer go through their life cycle
00:05:16.10 if no protein production is allowed to proceed,
00:05:18.24 and this is basically...
00:05:21.03 they have invented all kinds of strategies
00:05:22.27 in order to block this PKR pathway,
00:05:25.11 including preventing its dimerization,
00:05:27.18 hiding away all the double-stranded RNA,
00:05:29.25 actually reversing this phosphorylation step,
00:05:32.04 as well as a completely eIF2α-independent form
00:05:35.14 of translation initiation.
00:05:37.19 We are very focused in the lab
00:05:40.06 on one particular type of antagonist,
00:05:42.06 which is this K3L antagonist
00:05:44.10 encoded by poxviral genomes,
00:05:46.06 which can essentially break the interaction interface
00:05:49.04 between PKR and eIF2α,
00:05:51.12 and by virtue of that essentially block the PKR pathway.
00:05:55.00 Now, in part one of the seminar,
00:05:56.29 I told you how PKR is actually undergoing very rapid evolution
00:06:00.08 in order to gain one step ahead of K3L.
00:06:03.15 What we also see is this arms race
00:06:05.28 is being played out both on the host side
00:06:08.06 as well as on the virus side,
00:06:10.00 so if you look at K3L among different poxvirus genomes,
00:06:12.26 in this case we compared
00:06:14.28 all the vaccinia proteins to all the smallpox proteins,
00:06:17.21 we have this nice histogram
00:06:19.29 of the rates of protein evolution
00:06:21.24 as they happen along the landscape
00:06:24.03 of the genome of poxviruses.
00:06:26.12 So, a very simple way to look at this histogram,
00:06:29.00 or this bar graph,
00:06:30.21 is that genes on the left-hand side
00:06:32.15 are very slow to evolve at the protein level
00:06:34.13 and genes on the right-hand side
00:06:36.17 are very fast-evolving at the protein level,
00:06:38.14 and K3L happens to be one of the fastest-evolving genes,
00:06:41.14 at the protein level, in poxviral genomes,
00:06:44.05 which means this very intense arms race
00:06:46.11 that has played out between PKR and K3L
00:06:48.20 has not only rapidly changed PKR in primate genomes,
00:06:52.19 but has also changed K3L
00:06:54.18 in [viral] genomes.
00:06:57.12 So, we wanted to actually capture the stages of adaptation,
00:06:59.21 and so to do that we actually turned to an
00:07:01.24 experimental evolution strategy,
00:07:03.12 really a very successful strategy
00:07:05.10 in terms of capturing evolutionary states
00:07:07.24 that might be very transient
00:07:09.21 and very difficult to capture in the wild.
00:07:11.25 This is a very important strategy
00:07:13.21 that's been very successfully used, for instance,
00:07:15.18 in bacterial evolution.
00:07:17.15 So, we took the vaccinia virus
00:07:19.14 and we actually made one change in that virus,
00:07:21.29 which is we knocked out this E3L gene,
00:07:24.20 which I've not introduced to you yet so far.
00:07:27.16 E3L is one of those proteins
00:07:29.13 that actually helps hide away the double-stranded RNA
00:07:32.17 to prevent the PKR activation,
00:07:35.12 so the reason we actually E3L
00:07:37.13 was we wanted to put all the selective pressure
00:07:39.21 to overcome the PKR response
00:07:41.28 onto the K3L gene,
00:07:44.03 and we knew, before we started the study,
00:07:47.07 that the vaccinia K3L gene
00:07:49.07 is actually ineffective at defeating the human PKR,
00:07:52.15 which is why, when you delete the E3L protein,
00:07:55.07 we have this dramatic drop in fitness
00:07:58.10 where the wild type [virus],
00:08:00.08 which contains E3L,
00:08:02.00 is almost 1000-fold better at infecting HeLa,
00:08:04.15 or human cells
00:08:06.04 than is this ΔE3L virus,
00:08:08.19 which has been deleted for the E3L gene.
00:08:10.27 So, what we decided to do
00:08:12.22 was simply take this virus
00:08:14.21 and passage it on a plate of HeLa cells,
00:08:17.16 and what happens when these viruses propagate
00:08:20.25 is that you basically make these small plaques,
00:08:23.28 which is where the virus has actually infected
00:08:25.25 and burst through and made more progeny viruses,
00:08:28.11 and we simply take all of these viruses
00:08:30.16 as they emerge from a plate
00:08:32.10 and transfer them to a new plate...
00:08:34.04 except, in the experimental evolution strategy,
00:08:36.20 we always take a historical record of this adaptation
00:08:39.26 by measuring the replication rate
00:08:42.00 at every step of this evolution,
00:08:43.27 as well as saving a fossil record of these viruses
00:08:46.15 at every stage of their adaptation.
00:08:48.18 So, when we now move these to new plates,
00:08:50.27 what we would hope to see is that the virus is getting better,
00:08:53.29 so we're going to see more and more plaques
00:08:55.19 as this virus learns to adapt to HeLa cells.
00:08:58.18 As a very important aside,
00:09:00.09 vaccinia virus
00:09:02.20 being passaged in chicken cells
00:09:04.13 was the basis for the smallpox vaccine,
00:09:06.17 which was responsible for perhaps saving
00:09:09.02 more lives than any other medical intervention
00:09:11.07 that we know of.
00:09:13.00 And so, what we did was simply passage these
00:09:15.18 in HeLa cells for about 10 passages,
00:09:17.17 and in just 10 passages
00:09:19.29 we observed something quite dramatic.
00:09:22.00 So, remember, the wild type fitness is about here,
00:09:26.20 the ΔE3L virus is about here,
00:09:28.20 and what we see is that, although all these viruses
00:09:30.26 started off really poor at infecting HeLa cells,
00:09:33.19 almost all of them, by 10 passages,
00:09:35.26 have really gained most of the fitness that they had lost
00:09:38.27 in terms of their HeLa cell infectivity.
00:09:42.03 So, we actually have multiple ways to test this.
00:09:44.12 This is actually a virus titer assay,
00:09:46.05 in which we see what the progeny virus count looks like,
00:09:49.28 but we've also replaced the E3L gene
00:09:52.06 with a β-galactosidase reporter gene,
00:09:54.09 and we actually measure levels of that reporter gene
00:09:56.13 as another means of actually assaying
00:09:59.00 how successful the virus is,
00:10:00.27 and both of these assays are very, very consistent
00:10:02.26 with each other,
00:10:04.28 suggesting that you started off with a very poor virus
00:10:06.24 and you've actually gained most of the infectivity back
00:10:09.18 in just 10 passages.
00:10:11.09 And so, what kind of rapid evolution
00:10:13.08 might have actually happened,
00:10:14.27 in the course of just 10 passages,
00:10:16.20 for vaccinia to have regained
00:10:18.12 most of the infectivity that it lost?
00:10:20.15 And so, to actually address the genetic basis
00:10:22.28 of how this happened,
00:10:24.16 we decided to actually take the parental strain
00:10:27.12 and sequence it to completion,
00:10:29.11 which means get very high, in-depth sequence coverage
00:10:33.15 to understand, okay,
00:10:35.12 what is the role that perhaps rare mutations are playing
00:10:37.18 in this adaptation?
00:10:39.09 Then we took these three replicates at passage 10
00:10:41.17 and sequenced them
00:10:43.15 such that we could compare.
00:10:45.00 Why are these three replicates
00:10:46.19 so much better than the parental strain
00:10:48.14 in terms of coming up with the solution
00:10:50.25 to HeLa infectivity?
00:10:52.26 So, when we first actually did this...
00:10:55.20 so, we could actually do this with very high coverage
00:10:57.17 because the vaccinia genome is about 200 kB,
00:11:00.12 and with advances in genome sequencing technology
00:11:03.09 we could essentially get about 1000-fold coverage
00:11:06.00 for every nucleotide of the vaccinia genome,
00:11:09.04 which means for any mutation at the level of 1%,
00:11:12.10 we can be very confident
00:11:14.09 that we are not going to miss it,
00:11:15.22 which is really what we wanted
00:11:17.07 to understand the basis for this evolutionary adaptation,
00:11:19.12 but, actually, the first returns
00:11:21.17 were very disappointing.
00:11:23.09 So, although we did see some really nice mutations in K3L,
00:11:26.20 which I'll return to,
00:11:28.23 we actually saw very low mutation
00:11:31.17 across the entire genome,
00:11:32.29 and that's actually consistent with the idea that vaccinia,
00:11:35.06 unlike other RNA viruses like influenza or polio,
00:11:38.03 is a very slowly-evolving virus.
00:11:41.08 So, we wondered, how is it that the virus,
00:11:43.16 which actually didn't acquire a lot of mutations,
00:11:45.24 and very few of the mutations that actually shared...
00:11:48.08 none, in fact, are shared across all three replicates...
00:11:51.10 how did it acquire this dramatic fitness gain
00:11:54.05 despite actually not having been able to
00:11:57.20 explore a lot of the mutation space,
00:11:59.10 for instance, that a rapidly-evolving RNA virus
00:12:01.10 might be able to do?
00:12:03.03 And so, this is the sort of conundrum
00:12:05.03 that really we were stuck at for a little while,
00:12:07.03 until a couple of people in the lab
00:12:10.13 really recognized
00:12:12.12 that we're actually only looking at some of the data
00:12:14.06 by looking at each individual mutation.
00:12:16.07 We have another readout
00:12:17.18 when we do these kinds of genome sequences,
00:12:19.25 which is we can look at how well is one part of the genome
00:12:23.06 represented across the entire sequence read.
00:12:25.25 So, for instance,
00:12:27.19 what we have here is an average genome coverage,
00:12:30.05 normalized to 1,
00:12:31.16 across the entire vaccinia genome.
00:12:34.00 You will see this very interesting blip right here,
00:12:36.13 and this is where we've actually deleted the E3L gene,
00:12:40.02 and so that's exactly what you'd expect
00:12:42.07 if the E3L gene is now missing
00:12:44.08 from what we are comparing to,
00:12:45.27 which is the reference sequence.
00:12:47.13 What really caught our eye, though,
00:12:49.02 was this dramatic blip upwards,
00:12:51.04 and when we took a closer look at these,
00:12:53.08 what these are are independent expansions
00:12:56.03 of the K3L gene
00:12:57.22 in every single replicate,
00:12:59.27 but not in the parental strain.
00:13:02.07 So, you can see the parental strain, shown here in blue,
00:13:05.11 is completely on the genomic average of 1,
00:13:07.12 exactly like its neighboring regions,
00:13:09.06 whereas every single one of the replicates
00:13:11.05 has an average K3L copy number between 3 or 4,
00:13:15.08 which is sort of a really dramatic example
00:13:18.00 of how each of these three replicates
00:13:20.08 has independently converged on the same evolutionary strategy,
00:13:23.14 which is to amplify K3L.
00:13:25.19 We were then wondering whether, basically,
00:13:28.05 we had viruses in here
00:13:30.10 that each have about 3 copies of K3L,
00:13:32.12 and it's a pretty homogenous population,
00:13:34.16 but now that we had the fossil record,
00:13:37.05 we could ask not only what the basis of this expansion was,
00:13:40.06 but when it occurred over the course of evolution.
00:13:43.18 And so, what we discovered when we did this fossil record
00:13:46.08 was we started with a parental virus that had no K3L expansions,
00:13:50.01 and for about 4 passages,
00:13:52.01 really we didn't see very much,
00:13:54.11 but as we went from passage 4 to 10,
00:13:56.20 we have this very heterogeneous virus population
00:14:00.09 with this accordion-like expansion of the K3L gene,
00:14:03.21 where you started with one gene
00:14:05.17 and now you've been ratcheting it upwards
00:14:07.19 with every passage,
00:14:09.16 increasing the average copy number,
00:14:11.13 but the average copy number is actually hiding the fact
00:14:14.01 that there are some viruses in here
00:14:16.04 who have undergone a 10% genome expansion,
00:14:19.15 which is a dramatic expansion for a virus,
00:14:21.28 where real estate is a really important criteria,
00:14:24.25 and they're doing so only focused on the K3L gene,
00:14:28.08 because that is the evolutionary strategy
00:14:30.12 they have come up with
00:14:32.09 to overcome this PKR response.
00:14:34.21 So, another way to actually describe what we see
00:14:37.05 is that we've been able to molecularly map the breakpoints,
00:14:40.11 they flank this K3L gene shown here,
00:14:42.24 and what we basically have is an accordion-like amplification
00:14:45.21 of this original duplication,
00:14:47.18 now to sometimes 15 copies
00:14:49.27 in these heterogeneous viruses.
00:14:51.26 So, this is a very dramatic
00:14:53.22 and very recurrent expansion.
00:14:55.22 I'm only showing you one of the three replicates we did,
00:14:57.21 but the other two replicates look almost exactly identical.
00:15:01.16 So, the fact that this expansion is so recurrent and so dramatic
00:15:04.29 led us to ask, what are the consequences
00:15:07.13 of this expansion?
00:15:08.27 So, we had multiple genes...
00:15:11.00 are they actually making a lot more protein
00:15:12.26 than what you'd expect?
00:15:14.11 And, indeed, to test that,
00:15:15.29 we actually took these passage 10 viruses
00:15:18.00 and transfected them again back...
00:15:20.04 infected them into HeLa cells,
00:15:22.03 and indeed what we see
00:15:24.01 is they are making a lot more K3L
00:15:26.05 than even wild type virus,
00:15:28.23 and if you actually blow up this picture
00:15:30.28 you can see that the parental E3L gene
00:15:33.09 is making very little K3L
00:15:35.02 compared to what is now being made
00:15:36.29 by virtue of this genomic accordion expansion
00:15:39.14 in these replicate (passage) 10 viruses.
00:15:42.13 We can now ask, okay,
00:15:44.00 we now have this K3L expansion,
00:15:46.01 is this the reason
00:15:47.19 why we're seeing this massive increase in fitness?
00:15:49.16 And, to do that,
00:15:51.05 what we did was a strategy in which
00:15:53.01 we can take small interfering RNAs
00:15:55.05 and essentially get rid of most of the K3L RNA
00:15:58.06 that is being produced in these infected cells.
00:16:00.23 And so, when we do that,
00:16:02.20 we can design RNAs and then infect vaccinia,
00:16:05.17 and when we see that what we can see is
00:16:08.02 that these siRNAs are quite effective.
00:16:10.10 So, here's the non-siRNA-inhibited replicate C,
00:16:15.12 at passage 10,
00:16:16.24 and here are a multitude of different siRNAs
00:16:18.28 that basically, to a different degree,
00:16:21.09 knock down the total levels of proteins,
00:16:23.16 and when we compare the fitness
00:16:25.09 of these knockdowns,
00:16:27.25 versus no knockdown or a scrambled siRNA,
00:16:30.02 we can see that when you knock down the K3L protein production
00:16:34.04 you essentially knock down all the gains of fitness
00:16:36.24 that you've gained over the passage 10.
00:16:38.29 So, this K3L accordion-like expansion
00:16:41.22 is both necessary and sufficient,
00:16:45.17 really, to explain this massive increase in fitness
00:16:47.21 that we saw in our laboratory.
00:16:49.29 So, there is of course the trade-off...
00:16:51.28 I mean, these are viruses that actually
00:16:54.09 usually prefer really compact genomes,
00:16:56.15 and the tradeoff is really apparent
00:16:58.10 when we make a comparison of
00:17:01.00 these passage 10 viruses in HeLa cells
00:17:03.01 versus hamster cells.
00:17:04.24 You can see, in HeLa cells,
00:17:06.15 each of the replicate viruses
00:17:08.08 is doing a lot better
00:17:10.04 than the parental virus,
00:17:11.19 whereas in hamster cells,
00:17:13.10 these viruses are actually doing worse
00:17:15.08 than the parental virus.
00:17:16.18 So, what's going on?
00:17:18.12 It turns out that vaccinia K3L,
00:17:20.05 at the starting point,
00:17:22.00 is ineffective to defeat human PKR,
00:17:24.01 and it needs this massive gene expansion
00:17:26.09 in order to overcome, biochemically,
00:17:28.22 the inhibition encoded by the PKR protein,
00:17:31.17 whereas even a single-copy vaccinia
00:17:33.22 is able to overcome
00:17:35.29 the PKR inhibition encoded by hamsters.
00:17:39.03 And so, what we have now begun to see is,
00:17:41.16 if you take this accordion-expanded virus
00:17:44.06 and now infect BHK, or hamster cells,
00:17:47.12 the accordion has now begun to collapse,
00:17:49.11 by virtue of the fact that
00:17:52.03 the fitter virus is actually the smaller virus.
00:17:54.00 And so, this is an example
00:17:55.26 where we've got this transient expansion in HeLa cells,
00:17:58.01 which is now going the opposite way
00:18:00.01 in hamster cells.
00:18:01.23 So, I'll return to those mutations
00:18:03.18 that we actually first detected,
00:18:05.06 which were so disappointing because they were not recurrent,
00:18:08.03 but one of those mutations was especially interesting
00:18:10.17 to us because it occurred in the K3L gene.
00:18:13.02 It's present at about 3% frequency in replicate A
00:18:16.00 and at 12% frequency in replicate C.
00:18:19.18 This is a mutation in the 47th amino acid,
00:18:22.07 changing a histidine to an arginine.
00:18:24.25 The reason this is really interesting
00:18:26.25 is because a completely independent assay
00:18:29.05 many, many years earlier,
00:18:31.05 from Tom Dever's group, had done a yeast selection experiment
00:18:34.16 in which they wanted to ask,
00:18:36.18 can we do a mutational experiment
00:18:39.10 asking, what mutation in K3L can actually overcome
00:18:42.04 the inhibition encoded by PKR
00:18:44.14 and allow this yeast growth to recover?
00:18:47.10 If you want to learn more about this yeast growth assay,
00:18:49.21 I suggest you watch part 1 of the seminar.
00:18:52.14 What is really interesting is they come up
00:18:54.16 with one mutation, H47R.
00:18:57.05 We have done a completely independent experiment,
00:18:59.22 in vaccinia infections,
00:19:01.25 and vaccinia is basically also telling us
00:19:03.28 that this is the evolutionary solution
00:19:06.05 that vaccinia has come up with
00:19:08.04 in completely different assays,
00:19:10.02 both in yeast and human.
00:19:11.27 So, what that means is, now,
00:19:13.12 you started off with a K3L
00:19:15.06 that was not able to defeat human PKR,
00:19:17.03 and now you've acquired a single amino acid mutation
00:19:19.20 that in a Darwinian sense
00:19:21.26 is able to defeat human PKR.
00:19:23.26 But, because we actually now have
00:19:26.05 two forms of adaptation,
00:19:27.26 this gene accordion model that we've discovered,
00:19:30.04 and this classical Darwinian adaptation model,
00:19:32.06 we could now ask, going back to the fossil record,
00:19:34.23 which occurred first,
00:19:36.10 and did one depend on the other?
00:19:38.08 And, when we basically do that,
00:19:40.08 by looking at when one type of mutation
00:19:42.18 occurred relative to the other,
00:19:44.14 what we find is that the expansion
00:19:47.07 actually already happened by passage 4,
00:19:50.00 whereas this mutation actually
00:19:52.08 only began to occur around passage 5 and 6.
00:19:55.01 Moreover, many of these H47R mutations
00:19:57.28 actually occur in these already expanded accordions,
00:20:01.18 which strongly suggests that after the accordion expansion
00:20:05.11 you actually increase the mutational probability
00:20:08.25 of acquiring an H47R mutation,
00:20:11.06 which now, by virtue of even a single copy,
00:20:14.06 is able to overcome the PKR response.
00:20:18.05 So, that actually leads to a very interesting suggestion
00:20:20.22 in terms of how these Red Queen conflicts
00:20:23.02 might actually play out in evolution.
00:20:25.13 We think of the K3L-PKR interaction
00:20:28.24 as a classical Darwinian arms race,
00:20:31.05 so starting off with a step
00:20:34.05 in which the vaccinia K3L is not able
00:20:36.02 to defeat the human PKR,
00:20:38.02 we basically acquired sort of a transient amplification
00:20:41.09 of the K3L gene,
00:20:43.15 which then allowed for the selection
00:20:45.19 of the H47R mutation,
00:20:47.15 which was able to defeat human PKR.
00:20:50.00 Now, what's really interesting
00:20:51.25 is now we have a single-copy gene
00:20:54.11 that is able to defeat human PKR,
00:20:56.08 and we are very interested in asking,
00:20:58.19 now that you've acquired the right mutation,
00:21:00.12 will the accordion collapse to mitigate
00:21:02.10 all the fitness costs of this gene expansion?
00:21:04.21 So, the reason I put this cartoon up
00:21:06.27 is because this cartoon should remind you
00:21:09.02 a lot about this cartoon of the classical arms race,
00:21:11.09 the way we think about in terms of
00:21:14.07 virus antagonizing humans,
00:21:16.00 but we might actually be missing this very, very important
00:21:18.10 transient step which involves gene amplification,
00:21:21.18 especially in viruses and pathogens
00:21:23.18 that actually don't have the high mutation rates
00:21:26.10 necessary to sample the adaptive landscape.
00:21:29.16 And so, very much like
00:21:32.05 the ability for influenza to undergo chromosome reassortment
00:21:36.04 in order to infect new hosts,
00:21:38.10 we think that gene amplification
00:21:40.09 is one of the critical strategies
00:21:42.06 that large double-stranded DNA viruses
00:21:44.06 actually might be using in order to stay...
00:21:47.05 keep pace with this sort of rapid evolution
00:21:49.12 of the host genes that they're actually antagonizing.
00:21:52.22 So, with that,
00:21:54.12 I'm going to acknowledge the people who did the work.
00:21:56.05 All of this work was actually done by a former postdoc in the lab,
00:21:58.20 Nels Elde, who now heads his own lab at the University of Utah,
00:22:02.22 in collaboration with Emily Baker and Michael Eickbush.
00:22:06.09 We do all of our poxviral work
00:22:08.14 in collaboration with my senior colleague Adam Geballe,
00:22:10.20 and I'd especially like to acknowledge
00:22:12.13 Stephanie Child, from his lab,
00:22:14.20 who really did all of the poxviral infection experiments
00:22:16.20 in collaboration with Nels.
00:22:18.15 I'd like to thank Tom Dever,
00:22:20.08 Welkin Johnson,
00:22:21.20 and Michael Emerman,
00:22:23.10 for a lot of reagents and help,
00:22:24.24 and I'd especially like to thank Jay Shendure
00:22:26.24 and Jacob Kitzman, from his lab,
00:22:28.20 for helping us with the analysis of these poxviral sequences.
00:22:31.25 And thank you, I hope you had a good time listening to this.