Although we tend to think of evolution as something that happens slowly and over long periods of time, it is actually happening around us all the time. In this session, Drs. Turner and Malik focus on the ability of viruses to rapidly evolve and adapt to changing environments. Turner explains how some viruses have evolved to infect many hosts while others infect a single cell type. He also uses experimental evolution to study the ability of a virus to survive under different environmental conditions versus its ability to effectively reproduce. Malik studies the ongoing battle between viruses and their hosts and describes some of the tricks each use to survive attacks by the other.
All Course Materials for this Session (Educators only)
- Duration: 33:03
- Duration: 38:50
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
00:30:26.24 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.
- Paul E. Turner iBioSeminar: Virus Ecology and Evolution: from virus adaptation to phage therapy
- Harmit Malik iBioSeminar: Molecular arms races between primate and viral genomes
Dr. Paul Turner is Professor of Ecology and Evolutionary Biology at Yale University, and holds an appointment in the Microbiology Program at Yale School of Medicine. His laboratory studies how viruses evolutionarily adapt to overcome environmental challenges, such as temperature changes or infection of novel host species. Turner received his bachelor’s degree in Biology from… Continue Reading
Harmit Malik received his undergraduate degree in chemical engineering from the Indian Institute of Technology, Bombay and his PhD from the University of Rochester. He moved to the Fred Hutchinson Cancer Research Center in Seattle for post-doctoral work and decided to stay, starting his own lab in the Division of Basic Sciences in 2003. Malik… Continue Reading