Virus Ecology and Evolution: from Virus Adaptation to Phage Therapy
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.