Session 10: Viral Evolution

Transcript of Part 2: Virus Adaptation to Environmental Change

00:00:14.24	Hi.
00:00:15.24	I'm Paul Turner from the Department of Ecology and Evolutionary Biology at Yale University,
00:00:19.20	and the Microbiology faculty at Yale School of Medicine.
00:00:23.06	Today, I'd like to present on virus adaptation (or not) to environmental change.
00:00:29.08	This talk describes how viruses have an amazing capacity to adapt to environmental challenges
00:00:35.03	and, yet, we'll find that these champions of adaptation sometimes encounter environments
00:00:40.17	that demonstrate that environmental change can constrain evolution and adaptation, and
00:00:46.13	even these so called champions can face constraint.
00:00:50.11	So, very many challenges exist to viruses in the natural world and you could think of
00:00:56.09	this at all different levels of biological organization.
00:00:59.24	So, if you start at the base level of molecules and cells, the primary challenge for viruses
00:01:06.02	is that they cannot control where they exist in the environment, so they might encounter
00:01:11.04	some cell type and successfully enter if they have the right protein binding to recognize
00:01:16.19	a protein on the cell surface, or they might bump into the wrong type of cell and that
00:01:22.15	protein recognition doesn't occur.
00:01:24.14	So, therefore, it's an immediate and proximate challenge to a virus to infect a cell, depending
00:01:30.02	on where it is in the environment and whether the proper cells exist to infect.
00:01:34.13	In macroorganisms like us, we have tissues composed of different cell types, so if a
00:01:41.00	virus is in your body and it's replicating in one tissue type, it might be challenged
00:01:46.01	to infect a different tissue that's nearby and it's incapable of doing so.
00:01:51.05	Hosts, such as humans, have elaborate and beautiful immune systems.
00:01:56.21	Some of them are adaptive, meaning that they change through time and this is a way of our
00:02:01.08	immune system, in a way, keeping pace with microbial invaders and changing at the same
00:02:07.03	pace that they might evolve through evolution.
00:02:10.00	But viruses and other microbes... when they encounter these immune systems, this poses
00:02:14.23	a challenge for them to continue to infect that host, or that host's progeny, or other
00:02:19.19	susceptible hosts in their env... in their environment, depending on whether those immune
00:02:24.00	systems provide an immediate and successful barrier to virus replication.
00:02:30.10	It's amazing thing that some viruses infect humans and also successfully infect very different
00:02:37.12	organisms that are not at all closely related to us.
00:02:41.11	A great example of this are the arthropods, where many pathogens are vector-transmitted
00:02:47.11	by arthropods such as mosquitoes, including viruses.
00:02:51.14	And this is pretty fascinating, because a virus has to grow within an invertebrate and,
00:02:58.05	for example, in a mosquito, it has to grow in the midgut and eventually get to the salivary
00:03:01.21	glands in order to be present in a bite that puts the virus in the bloodstream of another
00:03:07.04	host to be picked up by another mosquito.
00:03:09.21	That's got to be an incredible challenge for a virus to grow both successfully in an invertebrate,
00:03:14.23	like an arthropod, as well as a vertebrate like you, a human.
00:03:18.18	And, last, we have to remember that global-level ecosystem changes affect all biological entities
00:03:25.04	on this planet, including the very smallest ones such as viruses.
00:03:29.02	So, when you think about challenges like climate change and global warming, you have to remember
00:03:35.11	that this is something that is felt by all biological entities, and therefore viruses
00:03:40.06	can also feel the challenge of an ever-warming world.
00:03:43.23	There are many different virus study systems that my group examines.
00:03:48.11	So, these examples are shown in the very many beautiful forms behind me.
00:03:54.09	On the far left, we have vesicular stomatitis virus, which is an example of a single-stranded
00:03:59.15	RNA virus with a negative-sense genome, and in the middle we have a variety of other viruses,
00:04:04.23	also, that will infect eukaryotes, but they happen to have positive single-stranded RNA
00:04:10.05	genomes.
00:04:11.05	Closer to where I'm standing, we have single-stranded DNA filamentous phage, and also double-stranded
00:04:16.17	RNA and double-stranded DNA viruses, in this case, both phages: phage phi-6 and phage T2.
00:04:23.09	So, these are examples within my laboratory of the wide variety of viruses that exist
00:04:28.14	in the natural world, and how a single laboratory can choose to examine this great variety of
00:04:34.11	virus types.
00:04:35.16	Depending on the challenge and the question, we would like to focus on a different study
00:04:39.17	system to examine whether viruses can successfully, or not, adapt to their environments.
00:04:45.21	A big tool that we use that's very popular with others, and a very powerful tool, would
00:04:50.22	be experimental evolution.
00:04:52.24	A way to summarize this method is it's the ability to study evolution in action.
00:04:58.01	So, if you have the right study system in a controlled place, like a laboratory, you
00:05:03.17	can take that population, put it in an environment that you control explicitly, and then examine,
00:05:09.09	how does that population deal with that challenge, both in terms of the traits that it evolves
00:05:14.07	as well as the genotypic changes that it undergoes?
00:05:16.24	So, both phenotype and genotype can be the focus of these studies.
00:05:21.09	An important thing to remember is, even if a researcher is manipulating the environment
00:05:25.20	in the laboratory, it still can be a challenge to a population, and that population can evolve
00:05:32.07	through natural selection.
00:05:33.12	So, you're talking about an artificial environment and yet natural selection can occur.
00:05:38.22	That's because the researcher is not determining which variants in that population will better
00:05:43.17	match the environment; instead, that's due entirely to the mutations and the genetics
00:05:48.15	of that system to meet that challenge or not.
00:05:52.18	And that can happen through the process of evolution by natural selection.
00:05:57.11	A typical design is shown here, where we would begin with some ancestral type.
00:06:03.00	We might be interested, in the case of this hypothetical diagram, in three different treatments
00:06:07.12	that differ in some way in their environmental challenge, and we can track, over the course
00:06:13.16	of generations, how do these independent lineages evolve to meet these challenges?
00:06:20.05	And the nice thing is to include replication in these experiments, such as you can have
00:06:24.22	lineages that are experiencing the same environment, and you can look at how consistently do lineages
00:06:30.23	undergo random mutation, and yet the same mutations might be the ones that rise to fixation,
00:06:36.12	and lead to adaptation.
00:06:38.05	In other cases, there might be different solutions to the environmental challenge, and you'll
00:06:42.20	see divergence between your lineages in the sense that different mutations are meeting
00:06:47.20	the same challenge.
00:06:50.10	Another way to think about these experimental evolution studies is to create a hypothetical
00:06:55.09	diagram of some phenotypic trait that you would want measure -- this might be growth
00:07:00.06	or some other capacity of the system to meet the challenge.
00:07:03.03	So, in this example, I'm illustrating how this trait has some variation at the beginning,
00:07:09.21	and then we could create some sort of an ecological circumstance, or an environmental challenge,
00:07:14.22	in these studies, and, through time, we can keep track of how phenotypes change.
00:07:19.18	So, you'll notice that the average phenotype, along the x axis in this hypothetical example,
00:07:25.18	is shifting to the right, meaning that the mean of the distribution is changing according
00:07:29.23	to which variants are in that population and the ones that are best meeting that challenge.
00:07:34.15	Now, we can go further than a lot of systems, because it's very easy for us in virus studies
00:07:40.04	to take the entire genome from these evolving populations and explicitly look everywhere
00:07:46.08	in the genome for where a mutation might occur.
00:07:48.24	In this case, we can track through generational time, how is the genetics changing in relation
00:07:54.11	to the ecological challenge as well?
00:07:56.15	And this allows a lot of immediate power in making something called a phenotype-genotype
00:08:01.14	association -- you can infer how the changing phenotype is being controlled by underlying
00:08:10.10	genetics, and make some base inferences about what the relationship is.
00:08:15.15	And I don't want to trivialize that because one has to do a lot more work to convince
00:08:19.14	oneself that, perhaps, one mutation is responsible, maybe two or three, or even more complex things
00:08:26.00	can occur like these mutations acting with one another through properties like epistasis.
00:08:31.06	So, this provides an amazing amount of power to examine how evolution occurs according
00:08:37.13	to the environment that you create in these types of studies.
00:08:41.12	The outline for what I want to talk about today is pretty much centered on these two
00:08:45.23	questions.
00:08:47.14	We can consider environmental changes as fostering versus constraining virus adaptation, depending
00:08:54.12	on how the environment is constructed, and these types of experiments are in the natural
00:08:58.18	world.
00:08:59.18	And, especially, now, that takes us to this next question of, are there particular traits
00:09:04.15	that can evolve in viruses that match something intriguing that you see in cellular systems?
00:09:11.06	The investment in survival versus reproduction is often something that's seen as at odds
00:09:16.01	to one another in cellular systems, that you can either invest a lot in survival as an
00:09:21.07	evolutionary trait, but this minimizes your reproductive capacity, or vice versa.
00:09:27.06	So, an intriguing set of studies show that this same constraint, or the same trade-off,
00:09:34.08	can happen even in non-metabolizing organisms, such as the viruses, especially in the viruses.
00:09:42.22	How does environmental change foster versus constrain virus adaptation?
00:09:47.12	Let's look at this question first.
00:09:50.21	Virus emergence is an amazing bio... biomedical challenge that we face today.
00:09:56.01	So, even though RNA viruses, especially, are not that prevalent among the highly prevalent
00:10:01.18	viruses that exist on this planet, they seem to be especially able to jump into new host
00:10:07.20	species and cause harm through disease.
00:10:10.10	So, humans see this through recently emerging pathogens, such as Zika virus, which is sweeping
00:10:17.04	around the globe, and is problematic and creating a challenge to biomedicine, to protect people
00:10:22.16	against Zika virus infection that can disrupt normal development at an early age.
00:10:27.22	A very different example, but still called emergence, is when a virus comes from another
00:10:33.06	species, enters into the human population, and gets locked in and becomes very specific
00:10:39.16	to humans.
00:10:40.16	So, I began with the case of Zika virus, which is not specific to humans, but a great example
00:10:45.17	of a specificity evolution would be HIV, which came into the human population several times,
00:10:51.18	independently, from our primate relatives, especially chimpanzees and certain species
00:10:56.09	of monkeys, and this has led to the evolution of HIV-1 and HIV-2, independently, several
00:11:03.05	times.
00:11:04.06	A third example of emergence would be something that exists both within humans, as well as
00:11:09.21	in other species, and a great example of that would be influenza virus.
00:11:14.09	So, ordinarily, in any year, you can have plenty of the human population seeing flu
00:11:19.03	virus infection and suffering influenza, but what we fear is that there are certain forms,
00:11:24.12	or genotypes, of influenza virus that will be especially virulent and cause a high degree
00:11:29.05	of mortality, and sweep around the globe to infect a lot of humans, and adversely affect
00:11:34.06	human populations, more than a standard flu season.
00:11:37.03	And, especially, we fear that the large reservoir of influenza viruses, that mostly exist in
00:11:43.05	this planet in waterfowl, might lead to a variant that can jump immediately into humans
00:11:48.16	and then be passed from human to human.
00:11:50.23	This would be an example of a flu virus coming from a bird, coming into a very different
00:11:56.00	host species, a mammal, and causing a lot of destruction and mortality because of the
00:12:00.18	inability of the human immune system to deal with the challenge.
00:12:03.20	So, these are three different examples of the same catalogued thing; that thing is what's
00:12:09.20	called emergence, and this is a huge biomedical challenge.
00:12:14.03	We can think of how emergence can or cannot occur for a virus, and that's what I want
00:12:18.09	to focus on next.
00:12:19.11	And there are some certain fundamental expectations, if you have any lineage, whether it's a virus
00:12:25.03	or not, and whether it's encountering an environment that is constant through time, versus changing
00:12:30.21	seasonally, or in a temporal way through time.
00:12:34.22	So, I'm giving two hypothetical examples of this.
00:12:38.11	At the top, we have a hypothetical evolving lineage that sees niche A and niche B in a
00:12:44.22	flip-flopping fashion, and each one of these little circles indicates a generation.
00:12:49.23	So, necessarily, this lineage has to grow in environment A in order to make it long
00:12:55.04	enough in its environment to reach a new environment, B, and so on.
00:12:59.18	Necessarily, we would expect that this... this will select for generalization -- the
00:13:03.18	ability to thrive in both of these environments -- because there is no other option.
00:13:08.06	Now, that's very different than if that lineage has the luxury of seeing only a single environment.
00:13:13.11	In this case, environment A is the only thing it encounters, but I'm underlining the word
00:13:19.18	*tends* to select for specialization, because that's only one possibility.
00:13:24.07	This luxury affords this possibility of being highly specific to your environment and being
00:13:28.23	very good in that environment, but it also is an opportunity for generalization to occur,
00:13:35.15	if you have a correlated response to growing well in other environments.
00:13:39.04	And, essentially, that must be happening in emerging virus pathogens.
00:13:43.16	They happen to have the right genetic capacity that when they jump into a new host species
00:13:48.04	like human, they can just really hit the ground running and grow very well, cause a lot of
00:13:53.15	damage, and ultimately they might be specific to that environment... ultimately, but initially
00:13:59.17	they're highly generalized.
00:14:02.12	We've covered this topic in a variety of papers that I'm listing here that I won't have time
00:14:06.05	to go into much detail, but one can think of this challenge of virus specialism versus
00:14:11.22	generalism happening a lot in the natural world, and it's very easy and powerful to
00:14:17.05	study this in the laboratory, through the experimental evolution method that I mentioned
00:14:21.15	earlier.
00:14:23.14	One system that we've focused on a lot to study how virus specialization versus generalization,
00:14:29.01	and just simply adaptation can happen, is a model known as vesicular stomatitis virus.
00:14:34.17	So, this is a single-stranded RNA virus with a negative-sense genome that's pretty much
00:14:39.21	a workhorse in molecular virology.
00:14:42.06	It's been used for very many decades to understand fundamentals of how RNA viruses infect and
00:14:47.17	replicate in a cell.
00:14:49.05	So, some pictures, here, that I'm showing are just to reflect that we have a lot of
00:14:53.16	prior knowledge for the molecular details of this system, and that's great when you
00:14:58.14	enter into experimental evolution studies, because you don't have to go about measuring
00:15:03.06	that stuff all over again; you can think of the outcome of your experiments in the context
00:15:07.17	of the prior knowledge.
00:15:08.19	So, VSV has a very small genome in size.
00:15:11.21	It has only 11 kilobases in length.
00:15:15.08	And this comprises only 5 genes.
00:15:17.04	So, one can think of this as a pretty simple system.
00:15:20.04	And yet it has a pretty amazing capacity to do things like both reproduce in an arthropod
00:15:26.15	-- it's an arthropod-borne virus or an arbovirus -- and it also can replicate in a mammal.
00:15:31.13	So, in the case of VSV, it's a safe system to use in the laboratory because it might
00:15:36.08	get in a human by accident, but it really doesn't cause much harm.
00:15:40.07	It's agriculturally important in large mammals, domesticated horses, etc, so we do care about
00:15:45.14	it from a disease standpoint, but it's a great, powerful system to use in the laboratory,
00:15:51.04	safely.
00:15:52.04	It comes from the family rhabdoviridae, which also features rabies virus.
00:15:58.04	Here's a summary of some of the data from one of our experiments, where we harnessed
00:16:03.06	experimental evolution to examine, how does this virus deal with a constant environment
00:16:09.11	versus one that is changing through time in that temporal heterogeneous way that I outlined?
00:16:14.10	So, this is a pretty busy diagram, but I'll walk you through it.
00:16:18.02	At the top, this is merely a depiction of the VSV genome and the 5 genes N, P, M, G,
00:16:24.14	L. And what you can see is, for each one of the lineages ,the 4 lineages that saw only,
00:16:30.10	in this case, HeLa cells... those are cancer-derived cells that originally came from Henrietta
00:16:35.22	Lacks a long time ago, and these were harnessed as an immortalized cell line that people use
00:16:41.08	and a lot of studies beyond simply virus studies... but these cancer-derived cells provided a
00:16:47.07	new challenge for VSV in this experiment, and each one of the points, here, on the diagram,
00:16:52.19	are showing where these lineages changed in their genetic material relative to the ancestor
00:16:59.12	after the experiment took place.
00:17:01.18	In this way, we can catalogue, what are the mutations that arose, and which ones fixed
00:17:06.10	through natural selection, to let these lineages improve in their environment?
00:17:12.00	We also did an exp... in this experiment a challenge where the viruses had to not only
00:17:15.23	evolve on HeLa cells, but, in an alternating fashion, they had to enter into a non-cancer-derived
00:17:22.12	cell type, abbreviated as MDCK, and in this way they had to become adapted to both HeLa
00:17:28.12	cells as well as these non-cancer cells.
00:17:31.18	And you'll see that we also catalogued their genetic changes through time.
00:17:35.24	And this has a great deal of variety, even within each treatment, for the mutations that
00:17:41.15	fixed according to each lineage.
00:17:44.02	One can also catalogue the exact position where each one of these mutations took place.
00:17:48.12	Let me highlight one more thing before I move on, and that is, really, these virus populations,
00:17:55.08	after this experiment, are not carbon copies of one another.
00:17:58.19	So, there are many places where we do see that they underwent the same mutational change
00:18:04.08	at exactly the same place, and that must be evidence of some beneficial mutation coming
00:18:10.01	in and fixing in these lineages.
00:18:12.15	And yet, in some genes, they underwent different mutations from even the same populations in
00:18:17.21	the same treatment, so this indicates that there can be other genetic solutions to the
00:18:22.22	same environmental problem in a study like this.
00:18:26.06	Keep this in mind as we go on and look at a subsequent experiment that challenged the
00:18:31.02	ability of these viruses to evolve and infect yet new hosts to test, what is their emergence
00:18:37.15	capacity?
00:18:39.01	Simply remember that we lumped them together as specialists, having seen only one constant
00:18:44.05	host hype, or generalists, that were selected to see two types, and yet the lineages are
00:18:49.10	not carbon copies of one another when they're drawn from each treatment.
00:18:53.16	Here, we wanted to ask a very fundamental question that's really at the root of what
00:18:58.15	lets emergence occur.
00:19:00.11	So, a popular idea is that, if some pathogen has seen multiple hosts in the past, it's
00:19:07.02	somehow groomed through adaptation to be generalized enough that it will successfully enter and
00:19:13.16	infect a new host when it sees it just randomly through encountering it in nature.
00:19:19.08	That's because adaptation has primed that pathogen to be good at growing in multiple
00:19:23.24	hosts and, through correlated response, it just might grow very well in a new host such
00:19:28.13	as humans.
00:19:29.13	So, here, I'm depicting a picture of Henrietta Lacks, as the... ultimately, the person who
00:19:34.16	gave rise to these HeLa cells that we used in this experiment, and we asked, whether
00:19:39.13	viruses that evolved strictly on HeLa cells, are they going to be good at growing on a
00:19:45.06	variety of challenge hosts that we purchased?
00:19:48.20	Or are we going to fit with this prediction that selected generalists were pre-adapted
00:19:55.06	in some way to perform well on these new hosts and they should be the ones that we would
00:20:00.00	fear as typical of a successful emerging pathogen, something that's groomed to grow on multiple
00:20:06.01	hosts and will grow well on a challenge host when it encounters it?
00:20:10.24	To go to the data from an earlier paper, this is pretty well supported by our study, that,
00:20:17.15	yes, selected generalists emerge or they shift hosts easier.
00:20:21.16	So, this diagram is showing, what is the sheer reproductive capacity of each of these virus
00:20:28.04	lineages, indicated by each point, relative to its ability to grow in the environment
00:20:33.19	that it was previously evolved on?
00:20:35.22	So, this gives an indication of... relative to its ordinary reproductive capacity, is
00:20:41.16	it any better or equally good at growing on a new challenge host, relative to the host
00:20:46.18	that it saw prior to adaptation?
00:20:49.13	And you'll see that all the blue points are well below the zero line.
00:20:53.19	That means that these specialist viruses from our study, they can grow on this first challenge
00:20:59.16	host I'm indicating, that came from monkey cells, but they grow pretty poorly compared
00:21:04.02	to their capacity to grow on the HeLa cells that came from Henrietta Lacks, whereas the
00:21:09.06	selected generalists, they saw both host types in our prior experiment -- one happened to
00:21:15.05	be cancer-derived, one happened to be non-cancer-derived -- but those were different enough cell types
00:21:20.09	that have provided a challenge to adapt to two things simultaneously.
00:21:25.03	And you'll see that, on this challenge host, those selected generalists actually did a
00:21:29.07	better job at growing on a challenge host that was just randomly chosen and presented
00:21:34.05	to them.
00:21:35.16	All four of those triangles are very close to the zero line.
00:21:38.22	So, in summary, one could say that, in this first line of evidence, on the monkey cells,
00:21:45.00	there's both a higher mean, on average, and lesser variance across the populations drawn
00:21:50.15	from each treatment for the selected generalist to do better.
00:21:53.24	Now, if you look at all four challenge hosts, there's an amazing ability for the data to
00:22:00.21	look highly similar, no matter what the challenge host was that we randomly entered into this
00:22:05.18	experiment using.
00:22:07.02	And, to me, that's fascinating, because it indicates that there's hardly any of what
00:22:11.22	one would call genotype-by-environment interaction.
00:22:14.22	This must be due to the capacity of these viruses to just simply grow on something new,
00:22:20.17	and it's not really the interaction with that new thing, it's just that they can grow better
00:22:25.02	on something that they've been challenged to infect.
00:22:27.07	So, this provides nice evidence in four randomly chosen challenges that selected generalists
00:22:33.21	can grow much better on a new host that you present them with, and this gives us a little
00:22:38.13	more insight at what could be the root of the emergence problem.
00:22:42.16	But I haven't really told you why -- why is this happening?
00:22:48.10	Why is it that these selected generalists actually emerge easier at a mechanistic level?
00:22:53.20	Here, we've looked at the ability, the innate immune ability, of cell types, and whether
00:22:59.24	selected generalists were keying in on this line of immunity and navigating their way
00:23:05.24	through it, and if they have a generalized ability to do that, and that should carry
00:23:10.22	over to other challenge types, even though that challenge type would be drawn from a
00:23:15.08	different species.
00:23:16.16	So, this is a very detailed diagram, but it's showing some of the inner workings at the
00:23:21.11	cellular level of something that you're born with.
00:23:24.22	This is the innate immune capacity of your cells that, when they see an invader, like
00:23:29.22	a virus, that they will be able to undergo a cascade of events at the cellular level
00:23:35.13	that gives them protection against that virus infection.
00:23:39.00	And, interestingly, the signals can go out to cells that are nearby in the tissue neighborhood
00:23:45.14	to prime them to be better protected against that virus, before the virus even is able
00:23:51.08	to replicate enough to get to those cell types.
00:23:53.24	Now, this is a wonderful ability, to be immune to a pathogen, that you should remember this
00:24:00.00	is your innate immunity.
00:24:02.13	Adaptive immunity, which people are much more familiar with, is something that is occurring
00:24:06.08	much longer-term, and it takes weeks or even longer that you see a pathogen and you mount
00:24:11.00	an immune response to the its uniqueness.
00:24:13.07	Here, this is just a generalized thing that controls pathogen infections.
00:24:18.06	So, before I move on, I'll say that the VSV M protein, or the matrix protein, is known
00:24:25.09	to be the thing that interacts with the capacity of a cell to produce its anti-immune response
00:24:31.11	to virus infection, especially interferon.
00:24:34.06	So, ordinarily, this cell is going to be producing interferon as one of these key chemicals that
00:24:39.09	protects it, and signals go out and interferon production occurs in other cells in the tissue
00:24:44.18	to protect them, but VSV, as a virus, can infect a cell and down-regulate that response.
00:24:53.05	And that helps us even explain how we even did the prior experiment.
00:24:56.19	VSV has a great capacity, just as a virus, to grow in a variety of cell types, because
00:25:03.18	it can regulate this response.
00:25:06.08	However, it could be that viruses like VSV are highly generalized in moving between hosts
00:25:14.18	because they properly regulate that immunity cascade.
00:25:19.15	So, without very many details, this is a hypothetical idea of how this can occur, and what one should
00:25:25.24	expect.
00:25:26.24	So, let's imagine that the prior selection history of some virus or other pathogen, this
00:25:32.19	is relating its fitness, due to that prior evolution, in terms of whether it saw host
00:25:38.22	types that are of low or high innate immunity.
00:25:41.23	So, in our prior experiment, I highlighted in blue how these specialist viruses perform
00:25:48.12	very well on HeLa cells, but I didn't tell you one key bit of information about a lot
00:25:54.15	of cancer cells, including HeLa cells.
00:25:57.03	They have very low or completely absent innate immunity.
00:26:01.00	So, what happened in that experiment, probably, is that the lineages of viruses evolved to
00:26:06.03	infect a cell type where they didn't really have to worry at all about innate immunity
00:26:10.17	as a challenge in infecting and growing in the cell type.
00:26:14.00	So, this probably led to de-evolution, or the removal of the capacity for those viruses
00:26:20.09	to control innate immunity.
00:26:22.00	They just simply didn't need it.
00:26:24.05	And then, when you challenged them to grow on a new host type, they are very handicapped
00:26:28.19	in doing so because they don't have the capacity to track the innate immunity functions within
00:26:33.08	a cell.
00:26:34.09	Whereas, viruses could see, necessarily, in our experiment, both high and low innate immunity,
00:26:40.24	because we used cancer-derived as well as non-cancer-derived cells, so they remained
00:26:45.13	capable of navigating through both cell types, and when they see a new cell type they can
00:26:50.15	hit the ground running.
00:26:52.06	So, importantly, one can think of both alternating hosts as, necessarily, in our experiment,
00:26:59.17	keeping this capacity, but it also could have been, and we've done work like this... if
00:27:04.08	you take virus lineages and you grow them only on high innate immunity hosts, you get
00:27:09.16	a very similar capacity for them to maintain strong growth regardless of cell type.
00:27:15.15	So, we have both good news and bad news in predicting emergence.
00:27:19.21	We have the ability for selected generalists to key in on innate cell function and navigate
00:27:25.09	multiple cell types, and you'd expect them to emerge, but they don't have to do that.
00:27:30.03	They could still successfully emerge through a correlated response.
00:27:34.00	So, the next question I want to cover is whether the environmental change that is presented
00:27:40.17	to viruses either fosters or constrains their adaptation.
00:27:44.05	So, now, this is a similar diagram that I showed you before, but note that now I'm including
00:27:48.17	a different kind of a challenge.
00:27:50.19	Here's where the lineage sees pretty much a stochastic set of environments through time.
00:27:56.03	In other words, it's moving from environment to environment, but there doesn't seem to
00:28:00.13	be any pattern to what that... what those environments present, right?
00:28:04.10	So, these are shown as separate colors in this diagram to illustrate how some virus
00:28:09.10	lineages might have to navigate through very different environments, and one can create
00:28:14.02	an experiment that says, well, will these champions of adaptations still be able to
00:28:19.12	successfully navigate through such a complex set of environments and become generalized?
00:28:24.20	Or is this just simply too much and, even in champions of adaptation like RNA viruses,
00:28:30.08	they'll be constrained and unable to do this?
00:28:34.04	This actually relates in some way to certain models that come from climate change, where
00:28:40.02	the prediction is... really, the fundamental problem for evolving lineages in climate change
00:28:46.05	is that stochasticity of the environment becomes more important.
00:28:50.07	The environment simply becomes more variable through time and it will be harder for lineages
00:28:54.21	to track those changes.
00:28:56.23	Well, it should be interesting to see whether viruses can successfully do this, because,
00:29:02.10	if they cannot, then this bodes pretty bad news for other organisms that have a much
00:29:07.12	slower and reduced capacity to evolve in the face of environmental challenges.
00:29:12.11	So, let's see what happened.
00:29:14.20	one can easily construct an experiment like this, but, rather than creating host challenges
00:29:20.04	through time, let's think a little bit more about those climate change models and the
00:29:24.01	thing that we'll manipulate is temperature through time.
00:29:26.14	So, in this diagram I'm showing four different treatment groups that were created in an experiment,
00:29:31.24	where 37 degrees Celsius is the upper limit, or pretty much the ordinary temperature for
00:29:37.01	replication that we use in the laboratory for VSV; 29 degrees Celsius is a lower temperature,
00:29:44.00	where they can still grow but it's much lower than 37 C, 8 degrees lower; and then we have
00:29:49.17	alternating lineages that will see these two environments in a flip-flopping fashion; and
00:29:55.12	then we include this fourth treatment, this is really the intriguing one.
00:29:58.21	If you take that 8-degree window and you go into the laboratory and you challenge the
00:30:03.06	viruses to grow at any temperature in the 8-degree window that you randomly choose on
00:30:07.10	that day, it will create a very stochastic environment through time.
00:30:12.00	And here we want to know, across generations, especially 100 generations, is there any differing
00:30:17.09	capacity of these viruses to evolve well in the face of this challenge?
00:30:22.06	So, we can go immediately to the data that came from this experiment.
00:30:26.04	And the way this graph works is it shows you, what is the fitness after 100 generations
00:30:30.24	for each one of these lineages, at each edge of the niche space?
00:30:35.02	So, it's plotted, what is their fitness at 37 C versus their fitness at 29 C?
00:30:40.24	And the intersection of those points leads to each point on the graph.
00:30:44.11	So, you'll see that the lineages that evolved in a constant high-temperature environment
00:30:49.00	improved in that environment -- in other words, all their data are to the right and above
00:30:55.05	the dashed lines on this figure.
00:30:57.03	They've improved both in terms of the environmental challenge they saw -- 37 C -- as well as 29
00:31:03.21	C, which was the other environment that was constant in this experiment.
00:31:07.02	That's evidence of correlated selection -- you improve in one environment and it also allows
00:31:12.05	you to improve in another environment that you haven't seen.
00:31:15.01	The same thing occurred for the lineages that saw only 29 C as the challenge.
00:31:20.08	Interestingly, in green, we have an alternating environment, where populations improved, in
00:31:27.09	some cases more than populations that saw only a constant environment.
00:31:32.10	And that's intriguing because it shows that populations can improve even though they see
00:31:36.11	the challenge only half the time as their counterparts.
00:31:40.08	It must be that the genetics that underlies this, which we've shown in papers that I won't
00:31:44.00	present today, is that different mutations are responsible for this improvement in an
00:31:48.09	alternating environment versus a constant one, but, in both cases, you can have improvement
00:31:53.03	relative to the ancestor.
00:31:55.08	Most intriguing in this data set is shown in purple, where all of those purple points
00:31:59.23	and lines are nestled right near the intersection of the dashed lines, which show the ancestral
00:32:05.10	values.
00:32:06.10	This means that the random treatment in the pop... in the experimental evolution study...
00:32:12.09	these lineages did not improve any more than the ancestral performance.
00:32:17.13	In other words, the stochasticity of the environment was too much for them to deal with, and that's
00:32:22.11	bad news in terms of these champions of adaptation.
00:32:26.08	If they can't handle stochastic environments, then that bodes ill for more complex organisms
00:32:32.20	that have much slower adaptive and evolutionary capacity -- we wouldn't expect them to thrive
00:32:38.07	either, or to improve in fitness, when seeing stochastic change.
00:32:43.16	This is pointed to in the diagram in terms of the intersection of the points, and all
00:32:47.20	those purple points nestled near the dashed lines and their intercept, as indicative of
00:32:53.22	adaptive constraint.
00:32:56.20	The next question will be, do viruses evolve survival reproduction trade-offs that we observe
00:33:01.16	in cellular life?
00:33:03.00	Here, we want to examine whether the capacity to adapt in one means, and that is to produce
00:33:09.13	progeny, is something that detracts from the capacity to merely survive in the environment
00:33:15.02	when it poses a challenge.
00:33:16.02	We've seen in cellular systems that you can't have your cake and eat it too, in terms of
00:33:22.02	these two challenges improving through time, that you can either invest in survival or
00:33:26.21	reproduction, but often you have an inability to improve in both simultaneously.
00:33:31.18	Does this carry over to the virus world is an intriguing question.
00:33:35.18	We addressed it first in a phage called phi-6 that infects a bacterium known as Pseudomonas...
00:33:42.01	Pseudomonas syringae.
00:33:43.10	So, this bacterium is important in plant pathology -- it causes plant disease -- but in the laboratory
00:33:50.10	we merely use it to grow the phage as a resource, to examine how well does the phage evolve
00:33:56.12	in environments in the laboratory.
00:33:58.06	So, this is an RNA virus, so it has the capacity to undergo error rate at a high rate, and
00:34:05.04	this allows a lot of mutation and rapid change through time, and it has a very typical infection
00:34:10.11	cycle, where it infects a cell of the bacterium, bursts the cell for the progeny to be released,
00:34:16.11	and then they go on and infect more cells.
00:34:18.05	That's a lytic phase replication cycle.
00:34:21.23	This picture is showing how the virus is able to first infect cells, because the cells have
00:34:28.24	the structures that allow them to adhere to leaf surfaces and, in normal wild conditions
00:34:34.22	they would move across the leaf and enter into the plant, in order to do infection of
00:34:39.10	the plant.
00:34:40.10	So, through time, these viruses have evolved the ability to use those structures as the
00:34:45.08	thing that they attach to, through protein binding, to get into the cell.
00:34:49.13	And that's what's shown in the diagram.
00:34:51.24	So, one can first begin by examining a reaction norm, or just simply the capacity for phi-6
00:34:59.03	to grow under environmental challenges in the laboratory, and, even though those challenges
00:35:03.14	can be amazingly brief, only five minutes long in terms of heat shock, this diagram
00:35:09.14	is showing how the survival of a virus population of phi-6, relative to different heat shock
00:35:17.02	temperatures, a high degree of mortality starts to kick in well above the normal incubation
00:35:22.14	temperature in the laboratory of 25 C. When you get out to values greater than 40 C, you
00:35:29.07	find that this is highly impactful and deleterious to the viruses and their ability to thrive.
00:35:34.20	So, this is indicating how, in the absence of anything else, you can take this virus,
00:35:40.13	expose it to high heat, and, if the heat is high enough, it leads to a high degree of
00:35:45.04	mortality in the virus population.
00:35:47.17	Key in on both 45 and 50 C, where these are environments that we've manip... manipulated
00:35:53.22	in the laboratory to examine, how do viruses deal with heat shocks through time if they
00:35:59.18	see them, and can they key in on this high heat that leads to high mortality and become
00:36:05.16	better adapted to thriving in the face of heat shock?
00:36:10.01	This is a diagram from a recent paper, where it's simply showing you a typical experimental
00:36:13.22	design for a study like this.
00:36:16.03	If you just take the virus, such as in a test tube, in the absence of any cells, and you
00:36:21.03	put it in a heating block so that it'll be challenged with five minutes of high heat,
00:36:26.04	you can then take the viruses and grow them under normal low-heat conditions, where they
00:36:30.13	can replicate in the presence of bacteria, gather all that up, remove the cells, and
00:36:36.24	keep churning them through the experiment.
00:36:39.10	In this way, we're not worried about whether they can, say, co-evolve with the host bacteria;
00:36:45.14	we're mostly keying in on the thing that causes high mortality -- the heat shock.
00:36:50.05	Can they key in on that and become better at thriving and improve this value, which
00:36:56.19	shows their very strong mortality that they suffer under high-heat environments?
00:37:02.01	Going quickly to the data from a paper where we did such an experiment, we find that thermal
00:37:07.16	tolerance, or heat shock selection, can readily occur in these viruses, and this is indicative
00:37:13.22	of something that we would call environmental robustness.
00:37:16.13	So, what is the ability of some population to thrive across different environments, and
00:37:22.14	maintain high fitness?
00:37:24.02	You'll see that the lineages shown in red, those that came out of an experiment where
00:37:28.09	this virus saw intermittent heat shocks at 50 C, these lineages improved way out at this
00:37:36.07	temperature, and you'll see this through a statistical result, that they do grow better
00:37:41.20	than their ancestral virus at that very high temperature.
00:37:45.05	Now, it's not like they have absolute capacity to shrug off that heat shock, but they do
00:37:50.07	have greater capacity to do so.
00:37:52.16	And, interestingly, you can see how there's a huge effect at the lower temperatures, which
00:37:57.15	ordinarily are degrading the wild type or unevolved virus, and now these lineages that
00:38:03.04	saw only 50 C have a great capacity to thrive at very, very warm temperatures and including
00:38:10.01	the highest temperature that they saw in the experiment.
00:38:13.17	How does this occur?
00:38:15.10	We've done several experiments of this type and always, for this virus, the same key mutation
00:38:21.06	is the first one and the most important one that leads to thermal tolerance evolving.
00:38:28.01	phi-6 has a genome that's split up into three different segments called large, medium, and
00:38:33.05	small.
00:38:34.05	In this diagram, it below shows that we know what all the genes are and we know basically
00:38:37.12	what their functions.
00:38:39.04	And here we have a diagram, a cut-through, of the virus body plan that shows you that
00:38:43.20	that... all that nucleic acid is at the center of the virus and it's surrounded by a protein
00:38:48.23	shell.
00:38:49.23	But, uhh... cystoviruses -- this is the family that phi-6 belongs to -- they're are a little
00:38:54.20	different than other bacteriophages in that they have a lipid coat around the entire shell.
00:39:00.12	So, it's a pretty elaborate body plan for a phage, but I really only want you to understand
00:39:06.01	that the key mutation that provides thermal tolerance always seems to arise first on the
00:39:11.06	small segment, and it always seems to arrive in this lysin gene, which is we... going to
00:39:16.24	be responsible for virus particles both getting in and out of the cell, and it is always the
00:39:23.00	same mutation, V207F.
00:39:26.04	It's an amino acid substitution that I'll talk about further.
00:39:29.24	V207F seems to be the key mutation that always allows the viruses to evolve thermal tolerance.
00:39:36.13	And, mechanistically, this makes sense, because when you look at the structure of this lysin
00:39:42.03	protein, a very important enzyme, phenylalanine as an amino acid substitution fills a hydrophobic
00:39:49.06	pocket, and this makes the protein more stable under high heat.
00:39:53.22	So, this is only one mutation coming in but it has profound significance for the thing
00:39:59.10	that is causing high mortality in the virus populations.
00:40:03.03	It's a very simple explanation of how a single amino acid substitution can lead to a profound
00:40:09.15	ability to thrive under a key environmental challenge.
00:40:12.19	So, now, I will talk about how the reproduction is affected for this virus -- even though
00:40:20.15	the key mutation allowed better survival, it's detracting from reproduction.
00:40:25.00	So, if we look at, how does this V207F mutant thrive in an ordinary environment, 25 C, and
00:40:33.03	its ability to grow on bacteria, this diagram is first showing how the plaques... in other
00:40:38.22	words, when you take a virus and you grow it on a bacterial lawn, which is the background
00:40:44.15	in this diagram in white, each one of the particles, if they hit the lawn independently,
00:40:49.18	they'll infect a cell and the progeny will exit that cell, infect neighboring cells,
00:40:54.23	and eventually you'll get this hole in the bacterial lawn called a plaque.
00:40:58.17	Well, something interesting happens when you look at the morphology of the plaques of the
00:41:03.03	wild type virus, which is heat-sensitive, versus this V207F mutant that is heat-tolerant.
00:41:09.19	In all cases, the V207F mutant makes this weird-looking plaque that has a bull's-eye...
00:41:16.11	bull's-eye morphology to it.
00:41:18.09	So, that must be that cells missed being infected and killed as that plaque was produced on
00:41:25.10	the lawn, otherwise it wouldn't have that grayish appearance.
00:41:28.02	In other words, it is not as effective at killing cells even though it is thermal tolerant.
00:41:34.07	That's shown in the bar graph, here, where the selection coefficient or "little s" that
00:41:39.13	is associated with this one mutation has a huge deleterious value under normal growth
00:41:45.20	conditions.
00:41:46.21	The wild-type relative to itself, of course, grows equally well so we give it a value of
00:41:52.03	1, whereas the value for the thermal-tolerant mutant is a value much, much lower than 1,
00:41:58.12	and has a negative selection coefficient of 0.25.
00:42:03.00	In comparison to the data I showed you earlier, it's very evident that a life history trade-off
00:42:08.04	is occurring in this virus.
00:42:09.17	In other words, it can either invest in better survival, but the problem is this leads to
00:42:16.17	lower reproduction.
00:42:18.11	This is echoing something that we see in cellular systems, with the investment in either survival
00:42:23.05	or reproduction, but not both at the same time occurring simultaneously.
00:42:29.07	It also relates to an earlier study, where the researchers found that if you just randomly
00:42:35.02	take viruses that can infect a different bacterium -- E. coli -- and you look at what is their
00:42:40.13	mortality rate versus their multiplication rate, and you plot that on the same graph,
00:42:45.17	there's a pretty amazing relationship for these viruses that they produced in their
00:42:49.20	study or used in their study to show that these are highly correlated traits to one
00:42:55.13	another.
00:42:56.13	So, either these viruses that they studied grew well and survived poorly, or had a high
00:43:01.09	mortality rate, or they grew poorly and survived better.
00:43:05.10	So, it shows that if you just look at viruses from the natural environment and you look
00:43:11.13	at their relationship for survival versus reproduction, they show a big difference,
00:43:16.22	and they fall along this line.
00:43:18.24	You can think of our experiment as having taken any one virus on this line, and can
00:43:23.17	you move it up or down the line through an experimental evolution study, and that's exactly
00:43:27.24	what we did.
00:43:28.24	So, the survival reproduction trade-off holds, and you can move them up and down the line
00:43:33.24	if you vary the environment in the right way.
00:43:36.16	So, the bull's-eye plaque that I showed you before is a pretty strange morphology.
00:43:43.19	And there's actually... even though people have used old microbiology methods to the
00:43:47.16	current day, the ability to visualize plaques is something that has dated back to at least
00:43:52.23	the 1940s, so it's an old method, and yet we actually don't know very much mathematically.
00:43:58.15	If you construct a model about, how does a plaque form, this is still a pretty big challenge
00:44:03.04	to mathematical biologists.
00:44:05.05	The three-dimensionality that this plaque is growing in, on an agar surface, is something
00:44:10.07	that... it's very hard to describe mathematically.
00:44:12.13	And, especially if one looks through a time-lapse film of these types of bull's-eye plaques
00:44:19.02	forming, this is a very strange morphology that it's hard to understand how some cells
00:44:24.19	are killed initially, and then there's a lot of cells that do not get killed, and then...
00:44:29.10	and then more cells get killed, and so on, to lead to such a complex morphology.
00:44:33.13	I would say that this is still an ongoing challenge to simply describe how do plaques
00:44:38.07	form, mathematically and mechanistically.
00:44:40.22	In this example from our experiment, even one mutational change can lead to very different
00:44:46.00	morphology that provides an even bigger challenge to describe.
00:44:50.08	So, now, we can think of the viruses and the way that they encounter challenges in the
00:44:57.12	natural world as, yes, they have an amazing capacity to see challenges and overcome them.
00:45:05.01	So, we do fear emergence of viruses as something that will continue to be a challenge for humans,
00:45:11.23	domesticated species, conserving endangered species... all of these realms are threatened
00:45:17.24	by the emergence of viruses coming in and doing destruction.
00:45:21.14	However, there are certain environments where these champions of adaptation simply cannot
00:45:26.11	make it.
00:45:27.12	So, consistent with certain climate change...
00:45:29.14	climate change models, we have environmental change through time as something that can
00:45:33.16	constrain virus evolution, and some of the fundamental trade-offs that we see in cellular
00:45:38.13	systems, especially survival versus reproduction, carries over even into organisms that don't
00:45:45.07	undergo metabolism -- the viruses -- so it's not just they're shunting energy into one
00:45:49.16	thing or another.
00:45:51.01	It shows you more of a fundamental divide in the biological world of, can you invest
00:45:55.20	in survival versus reproduction, and get away with a co-investment in both of them?
00:46:00.07	And it seems like that's not the case.
00:46:02.10	So, I'd like to end by acknowledging the people who did this work.
00:46:06.11	I keyed in on a lot of the work done by my current lab group, as well as past lab members
00:46:10.24	who I've had the pleasure of working with.
00:46:12.20	I've had fantastic mentors and collaborators all over the world.
00:46:17.16	And I have to thank them deeply for their dedication to the experiments to present the
00:46:21.13	data that wound up in our papers.
00:46:23.22	I can also thank the funders for the work, NSF and its programs such as the BEACON Center
00:46:31.09	for experimental evolution, as well as NIH, Yale University, and nonprofits such as the
00:46:38.02	Project High Hopes Foundation have provided key funds for all the work that I showed you
00:46:43.15	today.