Session 10: Viral Evolution

Transcript of Part 4: Viral Evolution

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.