Session 10: Viral Evolution

00:00:14.17 Hi.
00:00:15.17 I'm Paul Turner.
00:00:16.17 And today I'd like to talk about the fundamentals of virus ecology and evolution.
00:00:20.10 This is not designed to be a comprehensive overview, but rather an introduction to the
00:00:24.20 very many ways that viruses interact with other organisms, and how they evolve on the
00:00:29.01 planet.
00:00:30.03 I'm currently the Chair and full professor of Ecology and Evolutionary Biology at Yale
00:00:35.20 University, and I also have an appointment in the Microbiology program at Yale School
00:00:40.20 of Medicine.
00:00:42.13 So, we can begin with a very fundamental question of, exactly what is a virus?
00:00:48.15 There are many ways of depicting the relationships between organisms on the planet as a way to
00:00:54.16 catalog biodiversity and evolutionary trees are generally the way that we use, within
00:01:00.07 evolutionary biology, a depiction of relatedness among biological species and groups.
00:01:04.21 So, for example, this is a depiction of the Tree of Life, but it's actually a pretty misleading
00:01:11.04 one.
00:01:12.04 Although many of the organisms are recognizable, here, they're really mostly drawn from only
00:01:16.00 one domain of life, the eukaryotes.
00:01:18.03 These are the most recognizable animals, plants, etc that most people are familiar with.
00:01:24.14 Instead, a more accurate depiction might be something like this wraparound tree, where
00:01:29.12 depicted in purple are the very many bacterial species, in this particular tree, that have
00:01:34.10 a name.
00:01:35.22 And another domain, shown in green, are the archaea which are, like the bacteria, single-celled
00:01:42.01 species, and they are more often found in extreme environments.
00:01:46.06 But you'll note in this tree that the archaea are close relatives to the groups with which
00:01:50.13 we belong, the eukaryotes.
00:01:52.23 So, for your reference, here are you on the tree.
00:01:57.00 And this Tree of Life also is a bit misleading in the sense that it cannot catalog the amazing
00:02:03.01 biodiversity of another very important group of biological entities on this planet, and
00:02:08.06 those are the viruses.
00:02:10.05 So, these three domains of life comprise only cellular life, and viruses are different because
00:02:16.07 they are not cellular.
00:02:18.00 At the top of this diagram, we see very familiar body plans of eukaryotes, including something
00:02:22.23 like a blue whale, a sequoia tree, and even single-celled eukaryotes are very common on
00:02:28.06 Earth.
00:02:29.10 The bacteria and the archaea, as stated earlier, these are single-celled organisms, so even
00:02:35.12 though many of them are close relatives -- closer than the bacteria than within the archaea
00:02:39.14 -- the body plans of bacteria and archaea are very simple in that they have only a single-celled
00:02:44.16 body plan.
00:02:45.16 Now, if we take a closer look at what the body plan is of a typical virus, it has a
00:02:50.18 protein shell as well as nucleic acid, either RNA or DNA, that's protected from environmental
00:02:56.05 degradation by this protein shell that has a very special name, called a capsid.
00:03:01.04 So, at the top we see, here, the influenza virus, which is a very typical virus that's
00:03:06.20 recognizable to most people by name, and it's shown that it's relatively a circular virus,
00:03:12.19 with the RNA as the nucleic acid and that's protected from the environment by a capsid,
00:03:18.04 whereas something like a phage, phage T4, this is a short form of the name bacteriophage,
00:03:23.14 which translates to eater-of-bacteria.
00:03:25.18 These are viruses that are specific to prokaryotes, and this phage, you notice, has a much more
00:03:31.23 elaborate body plan than an influenza virus.
00:03:36.05 But what they both have in common is nucleic acid that is protected from the environment
00:03:40.23 by a protein shell called a capsid.
00:03:43.08 A typical virus life cycle looks like this, in its most simple form.
00:03:48.03 As long as a virus attaches to some cell, that it has the proper protein binding recognition,
00:03:54.09 there's a possibility that it will get in, or that at least its nucleic acid will enter
00:03:59.07 the cell.
00:04:00.07 And, at this point, the primary mission of a virus is to hijack the metabolism of that
00:04:05.11 cell and divert those energy sources towards reproduction of viruses rather than normal
00:04:12.00 cellular function.
00:04:13.18 At this point, the particles can get to maturity within the cell and they can be released,
00:04:18.15 and go on and infect other cells.
00:04:21.10 So, this is very much a generalization, but it's a good way to think about the fundamentals
00:04:26.22 of how viruses replicate and reproduce.
00:04:29.19 So, now, you can really think of biological entities, perhaps life if you want to define
00:04:36.09 viruses as living, as being split into two basic body forms: the capsid-encoding organisms...
00:04:44.00 you can think of these as all viruses because they share the properties of nucleic acid
00:04:49.08 surrounded by a capsid, and the viruses themselves would be specific to either the archaea, the
00:04:54.24 bacteria or the eukaryotes; and ribosome-encoding organisms, the cellular ones, are quite different.
00:05:01.18 They are related to each other and they are the three major domains of bacteria, archaea,
00:05:07.09 and eukaryota.
00:05:08.20 So, the major mystery in all this is whether they all trace back to the same route.
00:05:15.08 This is really an open question in virology.
00:05:18.04 It deals with the evolution of virus origins and it's really unclear how viruses first
00:05:23.01 occurred on this planet, when did they first evolve, and, especially, whether they evolved
00:05:27.14 on the planet preceding cellular life.
00:05:30.24 One major idea that is popular, but is by no means the only idea of how viruses first
00:05:35.18 arose on the planet, is that they might have actually predated the evolution of all cellular
00:05:40.11 life.
00:05:41.19 And there's a key bit of data that supports this idea, in that, if you look at virus genes
00:05:48.03 and compare them in their genetic sequence to cellular life, that is available in genetic
00:05:53.10 databases, you often find a mismatch.
00:05:56.01 So, that suggests that viruses are not simply some more fundamentally simple version of
00:06:03.05 cellular life; instead, they might be something really fundamentally different that might
00:06:07.19 have predated cellular life altogether.
00:06:09.22 So, LUCA, here in this diagram, stands for last unique common ancestor, which is the
00:06:15.01 shorthand version of everything that cellular life perhaps traced back to.
00:06:19.22 And this diagram is indicating that there might have been an ancient virosphere, for
00:06:24.00 which all the viruses that are infecting the archaea, the bacteria, and the eukaryotes
00:06:30.14 existed alongside in this time period or even predating LUCA, and that, today, we see this
00:06:35.21 amazing biodiversity of virus types, but they might have been here billions of years ago
00:06:41.11 and even before cellular life.
00:06:44.02 So, the rest of the talk will feature several lines of evidence that suggest that viruses
00:06:51.08 just might be the most successful of Earth's inhabitants.
00:06:55.09 So, I'll go through five lines of evidence that really support what I would assert as
00:07:02.07 the extreme biological success of viruses relative to other entities on the planet.
00:07:07.12 The first will be growth potential; next, we'll talk about abundance, biodiversity;
00:07:12.13 adaptability; and, perhaps most important, it's very evident that viruses have huge impact
00:07:18.20 on other organisms on this planet.
00:07:22.09 First of all, growth potential... because there are so many different ways of cataloging
00:07:26.13 and determining what biological... biological success even means, let's begin with just
00:07:31.10 the sheer ability to grow, to reproduce, and produce copies of oneself.
00:07:37.01 So, growth potential can be pretty amazingly impressive for a lot of organisms.
00:07:44.09 Some eukaryotes, our close relatives, are able to reproduce very quickly, such as the
00:07:49.00 fruit fly has a very short developmental cycle, such that progeny are produced in a very short
00:07:54.13 period of time.
00:07:55.13 But, really, the abundance of the number of organisms that are... that can be produced
00:08:02.06 through reproduction... amphibians, they are also pretty impressive.
00:08:06.12 They can sometimes lay hundreds or thousands of eggs that will successfully develop eventually
00:08:11.12 into adults.
00:08:12.12 Now, the bacteria and the archaea, I would say, are even more impressive.
00:08:16.00 They undergo a form of reproduction called binary fission where each cell separates into
00:08:21.07 two daughter cells.
00:08:22.11 So, in this way they can grow very, very quickly, as long as there are abundant resources available.
00:08:27.11 So, I would say the bacteria and the archaea, on average, grow much faster than most eukaryotes
00:08:33.14 that we would consider.
00:08:36.14 All eukaryotes, bacteria, and archaea have genetic inheritance across generations that's
00:08:42.08 through DNA as the nucleic acid.
00:08:44.18 So, they sometimes do have this amazing growth potential, but let's keep in mind that the
00:08:49.09 only way that traits pass across generations is through double-stranded DNA inheritance.
00:08:56.06 Considering the viruses, they grow much, much faster than most bacteria, archaea, and eukaryotes.
00:09:02.18 So, the ability for viruses to enter a cell, hijack the metabolism, and make progeny can
00:09:09.06 sometimes lead to hundreds or thousands of particles exiting that cell.
00:09:14.08 So, this would be the case for bacteriophages that are infecting the bacteria and the archaea,
00:09:20.11 as well as viruses of eukaryotes.
00:09:22.17 If they infect a tissue, for example, in the human body, there can be an amazing capacity
00:09:27.04 for each one of those infected cells to produce upwards of 10,000 or more virus particles
00:09:33.13 per cell.
00:09:35.02 Also, in relation to inheritance across generations, I would say that the viruses have obviously
00:09:41.16 more options for how this occurs in the natural world, whereas the bacteria, archaea, and
00:09:48.00 eukaryotes are confined to largely DNA inheritance through double-stranded nucleic acid, both
00:09:53.23 double-stranded DNA and double-stranded RNA are possibilities for the nucleic acid inheritance
00:09:59.03 in viruses, as well as single-stranded forms of those nucleic acids.
00:10:03.17 So, essentially, viruses are covering the entire gamut of what is possible on this planet
00:10:08.17 for modes of inheritance through genetic... through genetic means.
00:10:12.15 So, now let's move on to talking about virus abundance.
00:10:16.11 And this is very amazing and, when you look at the numbers, you'll see that viruses easily
00:10:22.04 outnumber all other biological forms on this planet.
00:10:25.23 We'll begin with saying that viruses are very abundant and we know this because if you look
00:10:31.14 in natural habitats on Earth, sometimes these support cellular life and there's actually
00:10:36.23 an amazing capacity for cellular life to thrive in deep-sea vents and complete darkness, where
00:10:43.03 there are other forms of resources than the sun to produce progeny, ultimately, when you
00:10:48.20 look at very dry and hot regions, cold regions, atmosphere, soil... everywhere you find on
00:10:55.24 this planet the possibility of cellular life existing, you see alongside it viruses.
00:11:02.15 And the key thing here is that viruses generally outnumber cellular entities ten to one in
00:11:08.09 each of these environments.
00:11:10.09 Therefore, you can make some rough calculations and estimate, across this world, at any one
00:11:16.12 period of time, an estimate of some 10^31 particles of viruses in any instant is a very
00:11:23.02 reasonable one for the viruses on this planet.
00:11:26.23 And we all realize, hopefully you can remember, that nucleic acid exists in a compacted form
00:11:33.14 within cells and also within capsids of viruses, but if you just take these virus genes and
00:11:39.22 genomes, and you unravel them so that they're at their full length, and you lay them end
00:11:44.12 to end, off of the Earth's surface, this would extend to 250 million light years away, which
00:11:49.19 is an incredible distance.
00:11:51.15 That would allow you to reach the Perseus cluster, which is pretty far from Earth.
00:11:56.18 So, now we've talked about the amazing growth potential and the sheer abundance of viruses
00:12:03.01 on this planet, already these are two impressive ways of indicating that biological success
00:12:08.15 of viruses is just amazingly impressive, but now let's look at something that's largely
00:12:13.09 hidden to view.
00:12:15.21 Biodiversity is something that's fascinated humans for a long period of time, probably
00:12:19.11 ever since humans have observed other organisms around them.
00:12:24.02 But there's an invisible kind of biodiversity on this planet, especially in the virus world,
00:12:29.02 that has only been seen relatively recently with the kind of tools and machinery such
00:12:32.23 as electron microscopes that let us glimpse these larger number of entities that surround
00:12:38.10 us on the planet.
00:12:40.16 The first thing to state is that viruses by no means are identical to one another in their
00:12:45.13 morphologies.
00:12:46.13 Some of them are amazingly complex, with beautiful body plans that almost look like a lunar lander
00:12:51.18 -- something incredibly beautiful, I would say, in nature, but often escapes our attention
00:12:56.23 because they are so small.
00:12:59.01 Other forms of viruses are more like tubes and beautiful icosahedrons, so there's an
00:13:05.12 amazing variety, but you'll notice that the line here at the bottom...
00:13:10.08 0.5 micron, which is 1/2000 of a millimeter... all of these body plans that I'm showing you
00:13:16.07 thus far suggest that viruses are submicroscopic.
00:13:20.14 And I would say that's an amazing amount of beautiful morphology, complexity, and biodiversity
00:13:26.08 that is also amazingly small in size, so I think that that's pretty impressive.
00:13:31.15 But there's a little quirk to viruses and the way that they had been isolated from natural
00:13:36.08 sources through time, that researchers actually missed the fact that some viruses are not
00:13:42.02 submicroscopic.
00:13:43.02 In fact, some of them are actually larger than the smallest-size cells.
00:13:47.19 So, newly discovered viruses, I would say, are macroscopic -- certainly when you consider
00:13:52.24 a virus like mimivirus relative to the size of an E. coli cell.
00:13:58.04 Here's something more typical of what someone would continue... would... would think of
00:14:01.16 as a typical particle size, and that would be HIV.
00:14:04.18 So, the point here is that by no means are all viruses submicroscopic.
00:14:09.16 Instead, some of them rival or exceed the size of cellular life.
00:14:14.08 Now, these mimiviruses did escape our attention through a little quirk of how people isolated
00:14:20.00 viruses from nature, putting them through filtration and assuming that anything that
00:14:24.04 passed through a very small pore size must be a virus, whereas everything that was held
00:14:28.15 back must be cellular or cellular debris.
00:14:31.23 But all one has to do is increase the pore size in these filtration experiments and you'll
00:14:37.08 find that some amazingly large-sized viruses are evident and still thriving on this planet.
00:14:43.14 So, you can go off the coast of Chile, for example, and within sea water you could find
00:14:48.04 these large-sized viruses.
00:14:50.07 It's also available to see them in ice cores in places like Siberia, where researchers
00:14:56.23 very carefully can take old material and bring it up to the Earth's surface, some 30,000-year-old
00:15:03.10 ice cores have been brought up to the Earth's surface.
00:15:05.12 And, within these, we see these large-sized viruses.
00:15:08.22 That alone indicates that large-sized viruses are certainly not anything new; they've been
00:15:13.18 around on this planet for some period of time and they merely escaped our attention through
00:15:17.21 quirks of methodology.
00:15:21.02 And the natural history study of viruses is something that I would say is certainly thriving.
00:15:26.19 There are entire research programs that can go into places like Yellowstone Park in the
00:15:31.09 USA, where there are certain extreme environments, especially hot springs, and one can look for
00:15:37.07 viruses that are infecting some of these extremophiles.
00:15:41.14 And it's very easy to find that these viruses are completely new to virus family classification.
00:15:49.08 We see body forms and, ultimately, when one characterizes these viruses through genetics,
00:15:54.12 they are not at all closely related to other virus families that have been described in
00:15:58.16 the past.
00:15:59.16 So, I would say that natural history is alive and well in virology, and it is possible to
00:16:04.16 go out and very easily, in nature, isolate material that shows you very new viruses that
00:16:10.09 are entirely new to the biological classification.
00:16:14.00 Adaptability... so, this is the ability for organisms to interact with their environment,
00:16:21.04 to undergo selection to become better adapted through time, so that they create a better
00:16:26.00 match to their environment.
00:16:28.06 Darwin was the one who best articulated this through the process of natural selection,
00:16:32.15 so we know... we will now talk about how viruses have an amazing capacity, on average, to adapt,
00:16:38.05 and this certainly contributes to their biological success on the planet.
00:16:41.18 So, Darwin had a way of explaining natural selection that was beautifully elegant.
00:16:47.07 Essentially, if you have these four components, you could think of natural selection occurring
00:16:51.15 in any biological system with inheritance, no matter whether it's on Earth or when we
00:16:56.12 ultimately, probably, will discover life elsewhere.
00:16:59.01 So, if the individuals in a population vary from one another, and if that variation is
00:17:04.12 heritable -- if it could be moved across generations -- one can expect that some of those variants
00:17:09.21 are going to be better advantaged than others in surviving or reproducing, according to
00:17:14.01 whatever the details are of the environment or the ecology that they... that they experience.
00:17:19.05 So, ultimately the population should change genetically, or evolve, to reflect the ones
00:17:26.21 that are the most successful variants.
00:17:29.03 And if all of this is in place, then evolution by natural selection can occur.
00:17:33.24 Darwin best articulated this idea in his most famous book, On the Origin of Species.
00:17:39.23 A great example of this comes from the virus world, where HIV is of course a very important
00:17:46.02 biomedical problem that humans face, HIV that can ultimately progress to AIDS through infection.
00:17:53.00 But not too long after the AIDS epidemic occurred there was a wonder drug called AZT that people
00:17:59.22 thought would be the thing that would just eliminate this problem.
00:18:04.19 Without going into the details, the ability of AZT is to disrupt HIV's capacity as an
00:18:10.23 RNA virus to enter a cell, replicate to turn into a DNA form that inserts into the genetic
00:18:17.18 material, and then exits the cell again as RNA.
00:18:21.00 And this lifestyle in viruses we call a retrovirus.
00:18:25.24 So, here's the problem with treating HIV as an RNA virus that has an amazing mutational
00:18:33.00 capacity with only a single drug.
00:18:35.18 Ultimately, we would expect that drug to fail and unfortunately we only observed this when
00:18:40.15 AZT became popular and used as a drug, and it was seen to widely fail through time.
00:18:46.00 So, through mutation, virus populations can change, especially RNA viruses, which have
00:18:52.13 no ability to correct the errors that just simply occur in the replication process.
00:18:58.16 In this diagram, you see how an infection could begin with a HIV population that is
00:19:04.06 identical to one another, and would be entirely susceptible to AZT.
00:19:09.20 And through time, because of this error ability in the virus population, you are going to
00:19:15.18 have partially resistant or fully resistant forms of the virus simply show up, and these
00:19:21.23 would be resistant to AZT, either partially or completely, as shown in the diagram.
00:19:27.05 And importantly, keep note of this: all of that variation occurs before any AZT is brought
00:19:35.12 into the human patient.
00:19:37.02 So, ultimately, what happens is very similar to what we see with evolution by natural selection
00:19:43.12 -- in fact, it's identical and it's a great description of it.
00:19:46.02 The individuals vary, the variation is heritable, some of those variants are advantaged when
00:19:52.06 AZT eventually is the ecological challenge that the population experiences, and then
00:19:58.07 ultimately those variants take over the population, or it evolves, so that the entire population
00:20:04.23 has escaped AZT as the drug that would ordinarily, we would hope, control it.
00:20:09.21 So, unfortunately, this was observed in every single patient who was administered AZT.
00:20:14.13 It either happened very early on if those mutations occurred early, or later on, if
00:20:19.11 they just happened to occur later.
00:20:21.06 But ultimately, the unfortunate outcome is that AZT always failed.
00:20:27.02 HIV and other disease scourges of humans that are caused by virus... virus infections are
00:20:34.17 not new.
00:20:36.02 Many of them we can find evidence of having occurred in human populations long ago.
00:20:40.18 When one looks at hieroglyphics, mummified remains, artwork from older populations of
00:20:47.03 human from ancient times, this reveals the antiquity of virus diseases.
00:20:52.01 For example, this man, shown in a heiro... in a hieroglyphic from ancient Egypt around
00:20:57.00 3700 BC, has a very characteristic clubfoot that's associated with poliovirus infection
00:21:05.08 that entered in the gut, like all polio viruses ultimately do, but then sometimes poliovirus
00:21:10.18 moves to your neural system, and it can disrupt your normal development and lead to limb deformities.
00:21:16.13 So, this man is definitely showing a characteristic deformed limb of a poliovirus infection.
00:21:24.21 Smallpox is another old disease of the human population, and this artwork is showing an
00:21:30.18 unfortunate person suffering from a smallpox infection, which would be ultimately lethal.
00:21:36.15 And, fortunately, we don't worry about smallpox anymore because there was a global vaccine
00:21:41.04 campaign that eliminated it as a natural thing that would infect humans, but the point is
00:21:46.14 that we can find, through this ancient material that humans and older populations have left
00:21:51.09 behind, that humans have been suffering virus diseases for a very long time.
00:21:56.03 I would assert that, probably, this has happened as soon as humans evolved as a species on
00:22:01.11 the planet, there would be viruses that would challenge their health.
00:22:05.07 So, I'd like to end with talking about the impact, in general, that viruses have on other
00:22:10.13 organisms on this planet, and this is really quite profound.
00:22:15.21 Similar to the examples I just covered for smallpox and HIV, etc, there are many diseases
00:22:23.03 that humans face that are a challenge to our health and our mortality, and a lot of these
00:22:28.08 are endemic problems, for example, the parasite that is transmitted by a mosquito and causes
00:22:34.20 malaria in sub-Saharan Africa and other tropical regions.
00:22:38.07 This has been around for a very long time -- that happens to be a eukaryote -- and you
00:22:41.24 could see the response, even in our genetics, of ways that we would be naturally better
00:22:47.06 able of fighting off malaria, and it has contributed to ways that the human population has changed
00:22:52.10 genetically through time.
00:22:54.03 But that's different than an epidemic that is highly deadly and rises in frequency very
00:23:00.24 quickly, and impacts a lot of, in this case humans, through mortality, something that
00:23:06.08 happens very quickly.
00:23:07.13 So, here are four examples, and I'm highlighting, in red, that three of them were due to viruses,
00:23:13.23 virus illnesses.
00:23:14.23 So, the Spanish influenza of 1918, amazingly, before humans were traveling around the world
00:23:21.11 through flight, through commercial flight, still 50 to 100 million people around the
00:23:26.13 world contracted influenza and died from this highly virulent form of the virus.
00:23:33.24 More recently, the HIV/AIDS epidemic that started around 1981 is something that has
00:23:38.15 unfortunately left roughly 35 million people dead and many, many more than that are infected.
00:23:44.01 Fortunately, we have new ways, things other than AZT, that keep people alive for a long
00:23:48.22 period of time when they're HIV-infected, but certainly during the early part of that
00:23:53.05 pandemic we saw a high degree of human mortality and we still see it today.
00:23:57.20 Now, it's hard to put exact numbers on the New World smallpox epidemic, around 1520,
00:24:03.11 when this started.
00:24:04.18 This is when Europeans came to the New World, interacted with Native American populations,
00:24:09.21 in some places displacing them, and they introduced into those populations smallpox, something
00:24:16.08 that Native Americans had not seen.
00:24:18.09 So, this was experienced as a highly virulent illness in Native American populations.
00:24:24.02 It's unclear how many people died because we just don't have accurate records of how
00:24:27.12 many people were in these populations at the time.
00:24:30.00 Certainly, this could have been on the order of the two pandemics that I listed above.
00:24:36.07 And last, I will remind you that some bacterial illnesses also have the capacity to have huge
00:24:42.22 impact on human populations.
00:24:44.20 The "Great Plague" in Europe in the middle of the 1300s was something that left in its
00:24:49.06 wake some 28 million people, or roughly 40% of the European population succumbed during
00:24:55.09 the Great Plague.
00:24:56.21 It's a little controversial how this bacterial illness could account for that amount of mortality.
00:25:03.09 Some people believe that the sheer poor living conditions that people experienced at that
00:25:07.10 time left them highly vulnerable to something like virus illnesses, as well, that might
00:25:12.16 have contributed to those millions of people who died.
00:25:15.00 So, again, we're a little uncertain there, but for the most part these examples are grim
00:25:20.05 ones of how impactful viruses can be in terms of mortality.
00:25:24.10 So, I don't want to leave you with all bad news about viruses, because, certainly, there
00:25:29.15 are a lot of viruses that positively impact other organisms, including humans.
00:25:35.18 So, let's begin long, long ago, some billions of years ago when the cyanobacteria appeared
00:25:41.14 on this planet and they started to thrive through evolving photosynthesis, that, 3 billion
00:25:47.21 years ago or more, this changed the Earth's atmosphere so that 50%, today even, of the
00:25:54.16 Earth's oxygen is due to photosynthesis that comes from cyanobacteria living in the ocean.
00:25:59.23 And if they hadn't evolved long ago and changed the average amount of oxygen in the atmosphere,
00:26:05.18 it would have been impossible for large-bodied organisms like us to even evolve in their
00:26:09.21 wake.
00:26:10.21 So, we can thank the cyanobacteria for that.
00:26:13.22 But there's an interesting thing that most people don't know and that is cyanobacteria
00:26:18.23 undergo a lot of mortality from cyanophages.
00:26:22.12 These are viruses that live alongside them in the ocean and they outnumber the cyanobacteria
00:26:28.18 and they thrive on the cyanobacteria, essentially as resources.
00:26:32.24 But there was an interesting finding not long ago that these cyanophages have genes that
00:26:38.18 allow for photosynthesis to occur.
00:26:40.24 And that was a very strange discovery, because we know that viruses don't undergo metabolism.
00:26:46.18 What would they be doing with photosynthesis genes?
00:26:49.10 It starts to make more sense when we think about the virus life cycle and the necessity
00:26:53.19 of keeping the cell going and churning through its metabolism, so that viruses can undergo
00:26:59.15 proper replication.
00:27:00.23 Therefore, it's not surprising that they bring these genes into the cell with them, so that
00:27:06.01 those processes can occur.
00:27:08.08 But a factoid I'll leave you with is that perhaps 5% of the breaths that you'll take
00:27:13.03 today ultimately come from those genes that are on cyanophages, rather than any other
00:27:18.12 source of oxygen.
00:27:21.20 We are more and more familiar with microbiomes -- you hear more and more about this in the
00:27:26.04 news.
00:27:27.04 So, these are the fungi, the bacteria, and the viruses that live on and in macroorganisms
00:27:31.23 such as humans.
00:27:33.14 And it's really interesting how these communities vary, even through locations in our body.
00:27:38.24 Inside and outside, you have different communities of these microbes thriving on and in your
00:27:43.08 body.
00:27:44.08 Well, the same thing goes, but different composition of those communities exists, for animals,
00:27:50.01 plants, and other macroorganisms on the planet.
00:27:53.13 Some of these we keep in our homes or we rely on for food, so it's important to think about
00:27:58.11 how these interactions are occurring with organisms that are essential to us, or that
00:28:04.15 simply give us pleasure, like as pets in our home, they have their own characteristic microbiomes
00:28:09.07 that we're learning more and more about.
00:28:11.19 And I should also emphasize that, certainly, all wild animals and also wild animals that
00:28:17.13 we rely on for food, they have their own characteristic microbiomes too.
00:28:22.13 So, viruses in your microbiome... you hear in the news about how the microbiome is probably
00:28:30.12 the thing that is causing certain traits in humans, making... making us more disease-prone
00:28:35.24 or disease-averse, so you'll hear a lot more about this in the coming decades, but you
00:28:41.13 actually don't hear that much about your virome, the component of your microbiome that is the
00:28:46.11 viruses.
00:28:47.14 There was a neat discovery recently that, perhaps, these viruses that exist on and in
00:28:53.07 your body are interacting with your body to make you healthier.
00:28:56.19 This diagram is showing how phages, which cannot infect our cells, can interact with
00:29:03.06 the mucus layers that we naturally produce that help protect us as a barrier from bacterial
00:29:08.16 infection.
00:29:09.22 Interestingly, these phages will interact with the mucus layer and be oriented in a
00:29:15.11 way such that their tail fibers are able to interact with any bacteria getting to the
00:29:20.05 mucus layer and trying to transit through to get to your cells.
00:29:23.21 This provides an extra barrier that protects you against those bacteria, because the phage
00:29:29.03 will attach, replicate, kill the bacteria, exit the cell, and more of those infection
00:29:35.10 events can occur.
00:29:36.19 This is certainly to our benefit, but we have a lot more to learn about whether this is
00:29:40.20 something that has been an... an adaptation that evolved for these phages to interact
00:29:46.03 with macroorganisms to do this, or whether they're simply taking advantage of the fact
00:29:50.11 that phages are everywhere, sometimes they are in the correct place for this to happen.
00:29:57.19 So, a final thing that I'll leave you with is that the human genome project gave many
00:30:04.13 surprising results, but there was a very surprising one in terms of, if you look at our DNA com...
00:30:09.24 our... our... our DNA chromosomes, you'll find a nice match between roughly 8% of your
00:30:16.15 human DNA seems to be viral-derived.
00:30:19.19 And one might ask, well, how is that possible?
00:30:22.17 You can think of this as the ghost of infections past.
00:30:25.08 In our long ago species ancestors that led to humans, ultimately, there were some virus
00:30:31.17 infections that those organisms suffered and interestingly there can be viruses that avoid
00:30:39.10 detection by their hosts because they have special genes that make them hide, so, essentially,
00:30:45.17 what has occurred in the evolution of placental mammals is that we could not have existed
00:30:50.06 on this planet unless, long ago, our ancient ancestors had undergone these virus infections,
00:30:57.21 and those genes, instead of becoming deleterious and helping viruses avoid detection by our
00:31:03.22 long ago ancestors, instead they were genes that were co-opted and taken into our genome,
00:31:09.15 and ultimately helped placental mammal mothers not reject the baby in the body.
00:31:16.05 And this is an amazing thing due to what are called syncytian proteins and genes that come
00:31:20.01 from viruses.
00:31:21.14 This is clearly a way that virus genes can be highly useful to other organisms and, in
00:31:27.18 fact, there's no way that you would be sitting here listening to me give this talk unless
00:31:32.02 these ancient infection events had occurred, and had led, ultimately, to the evolution
00:31:36.15 of placental mammals.
00:31:37.24 So, I've covered a lot of evidence for what we would say indicates that viruses are the
00:31:45.16 most biologically successful entities on this planet.
00:31:49.12 If you look at their growth potential, their abundance, and their biodiversity, they simply
00:31:54.15 outnumber, and can grow faster, and produce a larger number of forms than any other organisms
00:32:02.02 that we know of.
00:32:03.19 In addition to that, they can interact with other organisms in their environment, and
00:32:08.01 have an amazing capacity to adapt to environmental change, which I find completely fascinating
00:32:13.12 because, essentially, viruses cannot control where they go in the environment, and yet
00:32:18.13 they can encounter environments that are challenging to them and improve through time through adaptation
00:32:24.12 by natural selection.
00:32:26.00 Ultimately, their impact on the rest of the biological world is profound and immense,
00:32:32.12 and literally we thank viruses for some genes that they have devoted and given to us in
00:32:38.03 our genome, and this is something that's clearly impacted -- positively -- the human species
00:32:44.16 through time.

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.

00:00:00.28 Hello.
00:00:02.01 My name is Harmit Malik,
00:00:03.11 and I'm an evolutionary geneticist
00:00:05.03 studying the evolution of viruses and host genomes
00:00:07.28 at the Fred Hutchinson Cancer Research Center.
00:00:10.15 Today, I'm actually going to tell you
00:00:11.28 about molecular arms races between primate
00:00:14.09 and viral genomes
00:00:15.25 and how we aim to understand
00:00:18.01 the evolutionary rules that take place
00:00:20.02 between these viruses and hosts
00:00:21.24 and what that will tell us about,
00:00:23.13 not just the evolution of ourselves and viruses,
00:00:26.11 but also to design therapeutics interventions
00:00:28.23 to allow us to designed better strategies
00:00:31.06 that are going to be effective against viruses.
00:00:33.22 The work in the field of molecular arms races
00:00:36.25 is really inspired by
00:00:39.10 the character the Red Queen that was introduced to us
00:00:41.25 by Lewis Carroll in his book "Through the Looking Glass",
00:00:44.20 and the Red Queen tells Alice
00:00:46.29 in this sort of nice book
00:00:50.07 that it takes all the running you can do
00:00:52.11 to keep in the same place.
00:00:54.01 Very much the same idea
00:00:55.27 was adopted by the evolutionary biologist
00:00:58.01 Leigh Van Valen,
00:00:59.23 as the Red Queen hypothesis,
00:01:01.28 and he argued that in a system
00:01:04.00 where two entities are constantly competing
00:01:05.22 with each other in this sort of battle
00:01:07.22 for evolutionary supremacy,
00:01:09.15 the only way for this battle to be resolved
00:01:12.08 is just for one party to temporarily win
00:01:14.29 before the other party catches up,
00:01:16.26 and this requires both of these parties
00:01:19.01 to be really running as fast as they can
00:01:21.04 with this really rapid evolutionary signature,
00:01:23.09 formalized as the Red Queen Hypothesis
00:01:25.15 that's been used to invoke
00:01:27.21 all kinds of very important principles
00:01:29.19 in evolutionary biology,
00:01:31.13 including the existence of sex
00:01:33.16 and why we actually evolved
00:01:35.21 to be sexual creatures in the first place.
00:01:38.22 So, if you consider a host-virus interaction,
00:01:41.06 this is an interaction
00:01:43.00 that screams out genetic conflict.
00:01:44.23 This is what we refer to
00:01:46.13 as the usual suspects.
00:01:48.00 It doesn't take a lot of imagination
00:01:49.13 to understand that what is in the best interest of the virus
00:01:52.10 will not always be in the best interest of the host.
00:01:55.03 So, in this cartoon example,
00:01:56.25 you can see that we've got two states described here,
00:01:59.24 you've got the host that is binding the virus
00:02:02.08 on one side,
00:02:03.25 and the virus that has evolved a mutation
00:02:06.03 to evolve away from that recognition
00:02:07.28 by the host immune system.
00:02:09.17 What you'll actually appreciate
00:02:11.20 is that these state transitions,
00:02:13.11 between one state and the other,
00:02:14.28 are really profound but very simple
00:02:16.26 from a mechanistic standpoint.
00:02:18.25 What it might take is just a single amino acid mutation
00:02:21.10 for the virus to gain one step ahead
00:02:23.19 in this battle for evolutionary supremacy.
00:02:26.09 So, the important take-home message
00:02:27.25 from this kind of slide is,
00:02:29.14 one party is always losing
00:02:31.22 this high-stakes evolutionary battle.
00:02:33.13 On the left-hand side
00:02:34.28 you can see that the host is winning,
00:02:36.18 because it is recognizing a viral protein.
00:02:38.14 On the right-hand side
00:02:39.23 you can see that the host is losing,
00:02:41.13 because the virus has acquired the right mutation
00:02:43.22 that allows it to evade detection by the immune system,
00:02:46.15 which basically means that there's never going to be
00:02:49.10 a perfect equilibrium between these two states.
00:02:51.10 Over the course of evolution,
00:02:53.01 and even the course of a single infection in a person,
00:02:56.02 the immune system and the virus
00:02:58.26 are basically locked in this arms race
00:03:01.03 of very rapid evolution
00:03:03.09 and, because one party is always losing,
00:03:05.03 there's always going to be an evolutionary advantage
00:03:07.06 to be gained by innovation.
00:03:09.06 Now, we're going to actually talk about
00:03:11.04 two types of innovation today.
00:03:12.18 In the first part of my talk,
00:03:14.04 which is focused exclusively on how hosts evolve
00:03:16.28 in the face of viral challenges,
00:03:18.28 we're going to specify innovation
00:03:21.10 in protein coding genes,
00:03:23.03 and so, if you consider
00:03:25.05 what a protein coding gene arbitrarily looks like,
00:03:28.11 it's this sort of sequence that I've indicated here,
00:03:31.02 where we've got three triplets, three codons,
00:03:34.00 that specify three amino acids
00:03:35.25 that will be incorporated into the protein
00:03:37.17 that is produced from this gene.
00:03:39.15 Now, you can see on this side,
00:03:41.15 you have a mutation
00:03:43.17 that does not alter the amino acid being encoded.
00:03:45.23 We refer to these as silent or synonymous changes,
00:03:48.27 because from a very sort of rough approximation
00:03:51.17 natural selection is really acting on
00:03:54.01 the protein coding sequences,
00:03:55.19 and here, because the protein coding sequence
00:03:57.10 has not altered, we refer to these as
00:03:59.22 silent or synonymous changes.
00:04:01.14 In contrast, you can see here we have,
00:04:03.21 again, a single amino acid mutation,
00:04:05.28 which has altered one of the amino acids
00:04:08.13 that's being encoded,
00:04:10.05 so-called non-synonymous or replacement changes.
00:04:12.21 Now, both of these
00:04:14.25 are sort of equal likelihood mutations. Y
00:04:16.21 ou can actually have a synonymous mutation
00:04:18.16 or a non-synonymous mutation,
00:04:20.08 but you can appreciate that, based on the genetic code,
00:04:22.18 you're much more likely
00:04:24.22 to see an amino acid-altering mutation,
00:04:26.18 just by random chance alone.
00:04:29.18 So, consider the sort of situation
00:04:32.19 where you actually had a gene,
00:04:34.23 we refer to these as pseudogenes,
00:04:36.29 that at some point in their evolutionary history
00:04:39.02 encoded for a particular protein.
00:04:41.19 Now, if you consider this gene
00:04:44.09 now in its current degenerate form,
00:04:46.22 let's say built from the chimpanzee genome
00:04:48.16 versus the human genome,
00:04:50.09 and we were to just roughly calculate
00:04:52.07 the number of synonymous changes
00:04:54.15 versus replacement changes,
00:04:56.17 we have to correct for the fact
00:04:58.10 that there are many more possible replacement changes,
00:05:01.01 so when you normalize for that correction
00:05:03.02 you will find that, because this gene no longer codes
00:05:05.18 for a protein,
00:05:07.09 the rate of synonymous changes
00:05:08.24 and the rate of replacement changes
00:05:10.15 are roughly equal,
00:05:11.25 and that's because selection has stopped worrying
00:05:14.13 about this part of the genome
00:05:16.02 in terms of its protein-coding capacity.
00:05:17.24 It has tolerated both mutations,
00:05:19.22 and they roughly go to fixation
00:05:21.20 in a fairly random fashion.
00:05:23.15 Now, for most genes in the genome,
00:05:25.08 you do care about the final product being produced,
00:05:27.16 which is the amino acid sequence
00:05:29.19 of the resulting protein.
00:05:31.07 So, here I have this hypothetical example
00:05:33.14 where you have a protein-coding gene
00:05:36.14 that is basically representing these triplets of codons,
00:05:39.12 and what you'll see is there's a lot more blue changes,
00:05:42.11 or non-amino acid altering or silent changes,
00:05:46.23 and very rarely do you see something
00:05:48.22 which looks like a replacement
00:05:51.13 or a non-synonymous change.
00:05:53.09 The net result is that,
00:05:55.00 regardless of all of this change at the nucleotide level,
00:05:57.16 the amino acid sequence remains STEVE,
00:06:00.01 because STEVE is really what is being selected for
00:06:03.00 by evolution.
00:06:04.11 Very rarely do you see a deviation
00:06:06.13 from this optimal amino acid sequence.
00:06:08.16 For instance, we can see SiEVE coming in,
00:06:10.29 in terms of this sort of grammar.
00:06:13.00 The net result is not that
00:06:16.20 we should infer that mutation has now stopped
00:06:19.06 hitting the replacement sites.
00:06:20.27 What we infer from this is,
00:06:22.28 because mutation has introduced changes
00:06:24.22 in both replacement and synonymous positions,
00:06:27.19 the fact that we don't see replacement changes
00:06:30.01 over the course of evolution
00:06:31.26 is an indication that natural selection
00:06:34.03 acted upon these changes,
00:06:35.21 deemed them deleterious,
00:06:37.18 and removed them from the population
00:06:39.10 before they had a chance to really spread
00:06:41.28 in the population,
00:06:43.21 which means mutations is not really causing
00:06:45.19 this bias between the blue and the red changes.
00:06:48.06 It's actually natural selection,
00:06:50.02 and, more specifically, purifying selection,
00:06:52.06 that is acting to purify the population
00:06:54.16 from these presumed deleterious mutations.
00:06:56.26 The net result is,
00:06:58.21 if you were to now compare the rate of
00:07:01.02 synonymous and replacement changes,
00:07:02.29 we will find that the rate of replacement changes
00:07:04.25 is actually much lower than synonymous changes,
00:07:07.26 regardless of the fact that both of these changes
00:07:10.00 were introduced in roughly the same proportion.
00:07:13.11 My lab is actually interested in the other
00:07:15.00 class of genes that emerges
00:07:16.20 from these kinds of analyses.
00:07:18.05 Here again, now, we have a triplet code of sequences
00:07:21.03 that encodes for my name in amino acid code,
00:07:24.18 and what we will see when we compare
00:07:26.26 across this sequence
00:07:28.27 is that there are a lot more red changes
00:07:30.27 than blue changes,
00:07:32.09 in fact a lot more red changes than what you'd expect,
00:07:34.20 even by chance alone.
00:07:36.19 It's in fact easier to align these sequences
00:07:38.20 at the nucleotide level
00:07:40.12 than it is to align them at the amino acid,
00:07:43.03 where my name can change to a popular car model
00:07:45.11 very quickly,
00:07:46.29 because every mutation
00:07:48.29 has a high likelihood of altering the amino acid
00:07:51.03 being encoded,
00:07:52.21 and this is exactly the signature we see
00:07:54.14 when you have an interface
00:07:56.19 that is precisely at the interface
00:07:58.08 between a host and a virus conflict,
00:07:59.29 and that's because every single one of
00:08:02.05 these amino acid mutations
00:08:04.19 is potentially beneficial
00:08:06.01 and has been acted upon by natural selection
00:08:09.07 to increase their rate of fixation in the population,
00:08:12.15 hence the term diversifying selection.
00:08:15.11 In contrast to purifying selection,
00:08:17.08 natural selection is increasing
00:08:19.10 the amino acid diversity
00:08:21.07 of these protein-coding genes.
00:08:22.27 As a result, what we have, again,
00:08:24.22 is an apparent rate of replacement changes,
00:08:26.24 kA or dN,
00:08:28.13 which is increased
00:08:30.12 over the apparent rate of synonymous changes.
00:08:32.17 Once again, this is not a bias
00:08:34.25 that is introduced by mutation.
00:08:36.02 This is simply a different selective sieve
00:08:38.08 that is acted upon by natural selection.
00:08:41.15 This term diversifying selection
00:08:43.22 is also referred to as positive selection
00:08:45.19 or adaptive evolution.
00:08:47.03 I'll use these terms interchangeably,
00:08:48.29 and they're only different in the context of the tempo
00:08:51.02 with which these changes happen.
00:08:53.14 Now, if you were to take these characteristics
00:08:55.25 of replacement rates and synonymous rates
00:08:57.29 and calculate them for all genes
00:08:59.29 that we can compare between three sets of species,
00:09:02.25 our own species genome,
00:09:04.26 the rhesus macaque,
00:09:06.18 or the chimpanzee genome,
00:09:08.16 what we have is this very nice histogram
00:09:11.02 which really reflects the selective constraints
00:09:13.20 that have acted on all the protein-coding genes
00:09:16.10 within our genome.
00:09:18.01 What you'll see is there's a large number of genes
00:09:20.24 in the left-hand side of this histogram,
00:09:23.01 which means for the bulk of the genes in the human genome,
00:09:25.13 purifying selection,
00:09:27.04 or a dearth of replacement changes,
00:09:28.28 is really what is going on.
00:09:30.19 We are very interested in this sort of
00:09:32.26 small blip of genes right here
00:09:35.04 where you actually have a very small set of genes,
00:09:37.16 which even at the whole-gene level
00:09:39.09 have undergone much faster replacement changes,
00:09:41.10 almost breaking the speed limit of evolution, if you will,
00:09:44.16 to increase because of this diversity.
00:09:46.16 And when you take a really close look at
00:09:48.25 this category of genes,
00:09:50.13 immunity genes are really overrepresented,
00:09:52.06 as you might expect,
00:09:53.19 because these genes have been acted upon
00:09:55.14 repeatedly by natural selection.
00:09:57.21 So, we're going to consider
00:09:59.08 a very specialized case of an arms race
00:10:01.08 in today's seminar,
00:10:03.07 and this arms race ensues when a viral protein
00:10:05.14 begins to antagonize an antiviral protein,
00:10:08.21 and in this example the viral protein antagonism
00:10:11.08 is going to force the antiviral protein
00:10:13.11 to evolve to a state which this viral protein
00:10:16.27 can no longer defeat,
00:10:18.14 which will now force this viral protein
00:10:20.01 to evolve rapidly in order to restore its antagonism.
00:10:22.22 And this, in a microcosm,
00:10:24.27 is one step of this arms race,
00:10:26.29 where both the host protein
00:10:28.26 as well as the viral proteins have evolved
00:10:30.27 in these subsequent arms race interactions.
00:10:33.24 Now, what we're going to consider today
00:10:35.29 is a specialized example of this antagonism,
00:10:38.00 when the viral that is being used to antagonize
00:10:42.02 the host antiviral protein
00:10:43.28 is itself a host protein.
00:10:46.05 So, we are now basically considering
00:10:48.02 how would the host be able to distinguish
00:10:50.11 between an antagonism
00:10:52.07 that is caused by a viral mimic
00:10:54.12 versus its interaction with its own host proteins,
00:10:56.25 and that's the problem we'd like to address today,
00:10:59.04 which is, how do host genomes
00:11:01.12 confront and overcome, if they can,
00:11:04.11 the challenge of pathogen mimicry?
00:11:06.21 In today's seminar,
00:11:08.07 we're going to focus on a very specific example
00:11:10.07 of viral antagonism
00:11:11.18 that is mediated by mimicry,
00:11:13.13 and this example involves the host antiviral protein,
00:11:16.15 protein kinase R (PKR).
00:11:18.08 So, protein kinase R
00:11:20.01 is actually expressed when the organism senses
00:11:22.17 it's under some sort of viral attack
00:11:24.27 by virtue of an interferon detection pathway,
00:11:27.09 but it's actually produced as an inactive monomer,
00:11:29.16 which means it can no longer activate itself
00:11:31.24 as a kinase,
00:11:33.14 which is in the process of putting phosphate moieties
00:11:36.05 onto other proteins.
00:11:37.25 However, if this particular cell
00:11:39.27 happens to be infected by a virus,
00:11:41.23 that is detected by the fact that
00:11:45.01 there will now be double-stranded RNA in the cytoplasm,
00:11:47.05 which should not be case unless the cell
00:11:49.19 was under viral attack,
00:11:51.09 and what PKR will do is
00:11:53.08 it will use the signature of double-stranded RNA
00:11:55.06 to dimerize and activate itself as a kinase
00:11:57.22 whose primary substrate
00:11:59.21 is this protein eIF2α,
00:12:01.24 which stands for elongation initiation factor 2α,
00:12:05.04 which is a very important control step
00:12:07.22 to initiate protein production through the ribosome.
00:12:10.22 However, when PKR will phosphorylate eIF2α,
00:12:13.25 this essentially blocks protein production.
00:12:16.08 So, the cell's response to detecting itself
00:12:19.17 under viral attack is,
00:12:21.07 "I'm going to stop all protein production
00:12:23.05 so that I do not become a virus production factory."
00:12:26.04 This can be a very effective
00:12:27.29 and a very potent block to viral production,
00:12:30.17 and so what viruses have had to come up with
00:12:33.12 is several clever means by which
00:12:35.25 they can actually inhibit the PKR reaction.
00:12:37.21 Some viruses, for instance, inhibit the dimerization of PKR.
00:12:40.27 Some viruses will actually hide away
00:12:43.08 all the double-stranded RNA they produce,
00:12:45.02 whereas some viruses actually
00:12:47.08 will encode a phosphatase that specifically
00:12:49.23 takes out the phosphate residue
00:12:51.28 that is put on by PRK,
00:12:53.15 and perhaps the cleverest model
00:12:55.20 comes from the hepatitis C viruses
00:12:57.25 that actually allow PKR to block protein production,
00:13:00.15 to essentially block all manner of host protein production,
00:13:03.15 but will now nonetheless
00:13:05.20 carry on their own protein production
00:13:07.15 in an eIF2α-independent fashion,
00:13:09.20 really highlighting the clever inventions
00:13:12.00 that are really forced upon
00:13:13.27 by virtue of these Darwinian arms races.
00:13:15.25 In todays' seminar, we're actually going to focus on
00:13:18.03 only one of these antagonists,
00:13:20.02 which is encoded by the poxvirus class proteins,
00:13:23.10 which include smallpox and vaccinia virus,
00:13:26.11 and this is a protein called K3L,
00:13:28.14 which acts as a competitive and non-competitive inhibitor,
00:13:31.08 essentially breaking the interaction
00:13:33.12 between PKR and eIF2α,
00:13:36.12 which basically allows the virus to restore protein production
00:13:40.02 and go on with its life cycle.
00:13:42.05 So, we actually started this by looking at what this arms race,
00:13:45.12 with the potential for multiple antagonists from viruses,
00:13:48.15 has done to PKR evolution.
00:13:50.19 And so, to do this,
00:13:52.15 we actually sequenced the PKR gene
00:13:54.10 from a panel of primates,
00:13:56.03 which includes homonoids,
00:13:57.22 including humans, great apes, as well as gibbons,
00:13:59.28 old world monkeys,
00:14:01.20 which includes things like rhesus macaques,
00:14:03.14 and new world monkeys,
00:14:05.05 which are primates that populate Central and South America.
00:14:07.24 And when we do the sequence,
00:14:09.28 we can actually reconstruct the evolutionally history
00:14:12.25 of essentially every step and every codon
00:14:15.08 across the PKR phylogeny,
00:14:17.04 and so what we see in these numbers here
00:14:19.21 are those dN/dS or kA/kS signatures
00:14:22.24 that I talked about.
00:14:24.19 When when we have very low numbers
00:14:26.22 like this number 0.2, here,
00:14:28.19 that's an indication of not very much happening
00:14:30.19 at the protein evolution level.
00:14:32.11 In contrast, we have some amazing examples
00:14:34.16 like this lineage in old world monkeys,
00:14:36.18 where we actually have 22 replacement changes
00:14:39.17 without a single synonymous change happening.
00:14:42.08 That's a really profound signal
00:14:44.06 of multiple staccato replacement changes
00:14:46.17 occurring in the course of evolution,
00:14:48.14 in a very, very short time frame,
00:14:50.09 really highlighting the very intense
00:14:52.16 and very episodic evolutionary pressures
00:14:55.11 that have acted on PKR
00:14:57.17 over the course of the last 35 million years
00:14:59.22 of primate evolution.
00:15:01.13 If you were now to sort of turn this around
00:15:03.16 and squish it codon by codon,
00:15:05.20 we essentially get a landscape
00:15:07.24 of how PKR has been influenced
00:15:09.28 by positive selection.
00:15:11.11 All of these tick marks that I've shown
00:15:13.07 above the PKR protein
00:15:15.09 are individual codons that have recurrently evolved
00:15:17.15 under positive selection,
00:15:19.06 and you can see that, in the case of PKR,
00:15:21.25 these are really spread throughout the entire protein motif of PKR,
00:15:25.01 including in the N-terminal domain,
00:15:27.19 in this linker domain or the spacer region,
00:15:29.20 as well as in the kinase domain,
00:15:32.00 which actually carries out the very important step
00:15:34.09 of eIF2α phosphorylation.
00:15:37.26 And the reason we think that there's been such
00:15:40.07 dramatic and such widespread positive selection
00:15:42.15 is because multiple viruses
00:15:44.10 actually antagonize completely different domains of PKR
00:15:47.12 in order to mediate their antagonism of PKR.
00:15:50.12 So, what we're gonna focus on today
00:15:52.19 is just one of these antagonists,
00:15:54.05 which is, again, these poxviral antagonist K3L,
00:15:56.27 that actually specifically antagonize
00:15:59.12 the kinase domain of PKR.
00:16:01.25 So, the reason I've been spending so much time
00:16:03.28 discussing K3L with you
00:16:05.27 is because K3L is a special antagonist.
00:16:08.28 It actually is an evolutionary-derived mimic
00:16:12.24 which used to be eIF2α,
00:16:14.29 which means that at some point in poxviral evolution,
00:16:18.08 poxvirus actually stole eIF2α from a mammalian host,
00:16:22.13 and have whittled it away to become this perfect mimic,
00:16:25.29 in order to break PKR's interaction with the eIF2α substrate.
00:16:30.00 Now, what is really remarkable about this interaction
00:16:32.12 is that it not just happened once
00:16:34.25 in mammals,
00:16:36.08 but it's happened on three separate occasions
00:16:38.27 with three completely independent lineages
00:16:40.24 of double-stranded DNA viruses,
00:16:42.20 each of them acquiring a K3L-like mimic
00:16:44.20 from their own version of eIF2α.
00:16:47.16 So, this really highlights the very, very successful
00:16:50.11 strategy of mimicry that is encoded by pathogens,
00:16:54.09 and really, from an evolutionary standpoint,
00:16:56.16 the strategy of mimicry
00:16:58.19 and overcoming mimicry
00:17:00.05 is a debate that's really been going on
00:17:02.10 for a very, very long time,
00:17:04.02 going back all the way to Henry Walter Bates,
00:17:06.04 who really first detected evidence for mimicry
00:17:09.13 in these butterflies in the Amazon,
00:17:11.24 where we have model butterflies
00:17:14.10 that are basically poisonous,
00:17:16.13 and so they're avoided by predators
00:17:18.22 who can use their coloration patterns
00:17:20.21 as an indication to...
00:17:22.17 as a warning signal to stay away from them,
00:17:24.16 and mimic butterflies
00:17:26.10 that actually don't encode a poison at all,
00:17:28.05 but take advantage of this coloration pattern,
00:17:30.11 and mimic the coloration pattern,
00:17:32.08 to take all the advantages of avoidance from predators,
00:17:35.09 without actually having to encode
00:17:37.07 any of the toxins that are required.
00:17:39.19 Now, this is actually quite a really great strategy
00:17:41.27 for the mimic.
00:17:43.06 It's not so good for the model,
00:17:45.03 because as the mimics start increasing in frequency
00:17:47.03 and the predators start eating more and more butterflies
00:17:48.29 that look like this, but are quite tasty,
00:17:51.12 they will lose their avoidance of the predators,
00:17:53.16 which means that the success of the mimic
00:17:56.08 is directly, inversely correlated
00:17:58.22 with the success of the model.
00:18:01.01 And very much the same thing might be going on
00:18:03.00 at a molecular level, we feel,
00:18:04.27 where eIF2α is acting as a model protein,
00:18:07.17 which is being mimicked by this poxviral mimic K3L
00:18:10.27 in order to defeat
00:18:13.22 the PKR-eIF2α immunity response.
00:18:16.29 So, if you were to sort of rephrase
00:18:18.27 the challenge of mimicry,
00:18:20.22 it is that the PKR kinase domain
00:18:22.29 needs to bind and maintain its interaction with eIF2α,
00:18:26.22 while avoiding its interaction with the mimic,
00:18:29.12 which really is evolutionarily being selected
00:18:31.19 to look like eIF2α
00:18:33.11 from the viral perspective,
00:18:34.28 and you can see in this crystal structure
00:18:37.03 that the structures of the PKR interaction domain
00:18:39.27 between K3L and eIF2α
00:18:42.01 are almost completely super-alignable,
00:18:43.23 so how is it that PKR is able to acquire
00:18:46.23 the ability to discriminate between these two?
00:18:49.12 As I've already told you,
00:18:51.08 one of the strategies that PKR is using is very rapid evolution,
00:18:54.26 it's got that at its disposal,
00:18:56.17 and this is just a sliding window plot of dN/dS
00:18:59.12 over the entire protein of PKR,
00:19:01.15 and what you see here is that
00:19:03.14 there is not even a single domain
00:19:05.20 where the dN/dS signature drops below one,
00:19:07.26 which means pretty much every domain of PKR
00:19:10.06 is evolving under positive selection
00:19:12.06 in this comparison between human and rhesus PKR.
00:19:15.08 It's really remarkable how profound the signal is,
00:19:18.14 because when we compare PKR
00:19:20.06 to its closest relative kinase, PERK,
00:19:22.14 which is not involved in antiviral immunity,
00:19:25.04 you can see that the signature is completely profound
00:19:27.23 of purifying selection,
00:19:29.17 and not of positive selection.
00:19:31.15 And this actually gets even more interesting
00:19:33.06 when you look at eIF2α,
00:19:34.22 which is the substrate for PKR,
00:19:36.25 because eIF2α is so important for translation
00:19:41.02 that it has not tolerated any amino acid changes
00:19:43.07 over the course of evolution.
00:19:44.28 You might be actually wondering where the red line went,
00:19:47.12 and actually the red line is exactly on zero,
00:19:50.05 because no amino acid changes have occurred
00:19:52.12 over the course of primate evolution.
00:19:54.05 So, in a way, you can view this
00:19:56.19 as a very high-stakes game of rock-paper-scissors,
00:19:59.28 except eIF2α is always playing rock,
00:20:03.14 and so it would seem that mimic
00:20:05.25 would have a very, very simple game,
00:20:08.04 which is to mimic an unchanging protein
00:20:10.05 and stay there.
00:20:12.06 We wondered whether that was actually the case,
00:20:13.29 because, first of all, we've actually survived poxviruses,
00:20:16.25 and secondly, this suggested that
00:20:19.23 PKR might have some adaptive routes
00:20:21.18 in order to escape mimicry.
00:20:23.06 Furthermore, if it was the case that K3L
00:20:25.05 was simply evolving to an optimal mimic status,
00:20:28.04 we might actually presume
00:20:30.09 that K3L should now be under purifying selection,
00:20:32.14 having optimized for this role in mimicry.
00:20:35.06 Instead, what we actually find
00:20:36.21 when we compare K3L
00:20:39.00 from a panel of poxviruses,
00:20:40.20 is that, very much like PKR
00:20:42.27 shown here on the host side,
00:20:44.18 which is very rapidly evolving,
00:20:46.08 in contrast to eIF2α which is not,
00:20:48.20 K3L happens to be one of the most [quickly]
00:20:52.09 evolving proteins the poxviral genome.
00:20:54.08 So, this is truly an arms race between
00:20:56.19 K3L and PKR.
00:20:58.03 What makes this arms race really interesting
00:21:00.01 is that they're both really evolving
00:21:02.03 to get the attention of eIF2α,
00:21:03.28 which is not changing at all,
00:21:05.29 and so that's what makes the problem of mimicry
00:21:07.29 really interesting from an evolutionary standpoint.
00:21:11.12 So, we wanted to actually have a system
00:21:13.15 in which we could simply assay
00:21:15.20 for the effects of mutations and evolutionary adaptations
00:21:18.17 in a very facile assay,
00:21:20.11 and we actually took advantage of an assay
00:21:22.12 developed first by Tom Dever and Alan Hinnebusch,
00:21:25.03 who recognized that eIF2α
00:21:27.22 is so slow to evolve
00:21:29.15 that if you actually put human PKR in yeast
00:21:32.06 it will actually bind and phosphorylate yeast eIF2α
00:21:34.25 to cause a growth arrest.
00:21:36.26 Now, in this context,
00:21:38.05 if we now also introduce K3L,
00:21:40.11 we have the situation where K3L
00:21:42.18 can give you a readout of whether it's able
00:21:44.20 to defeat PKR or not,
00:21:46.19 based on whether it can rescue the growth inhibition
00:21:49.08 mediated by the PKR expression.
00:21:51.23 So, Nels Elde,
00:21:53.05 who was a postdoc in the lab,
00:21:54.29 actually took this panel of PKR genes
00:21:57.09 from a panel of different primates...
00:21:59.12 homonoids, old world monkeys,
00:22:01.02 and new world monkeys...
00:22:02.20 and he actually just put it into yeast cells,
00:22:04.24 but he put it in a form which could not be turned on.
00:22:07.14 So, when these yeast grow on glucose,
00:22:09.25 because the PKR gene
00:22:11.19 is put on a galactose promoter,
00:22:13.12 it's silenced,
00:22:15.04 and what you can see is that all of these yeast
00:22:17.01 grow perfectly fine.
00:22:18.16 You can see that, even in this serial dilution across,
00:22:20.17 you basically have no growth inhibition.
00:22:22.27 However, as soon as you turn on PKR
00:22:25.17 by putting all of these yeasts onto galactose plates,
00:22:27.27 you can see no yeast growing here,
00:22:30.27 which means all of these PKR alleles
00:22:32.29 have conserved the property of binding
00:22:35.27 and phosphorylating yeast eIF2α,
00:22:37.26 which is remarkable considering the very large degree
00:22:40.20 of evolutionary divergence that we have seen here.
00:22:43.20 Now, I can tell you that this is all because of eIF2α phosphorylation,
00:22:46.17 because in this yeast,
00:22:48.17 if I engineer a mutation in the phosphorylation site
00:22:51.05 all of the growth inhibition goes away,
00:22:53.06 and that's shown in these two panels here.
00:22:55.04 So now, the really interesting question
00:22:57.00 happens when you introduce the viral antagonist.
00:22:59.28 So, what would you predict
00:23:01.24 would happen here if you now introduced
00:23:04.07 the K3L protein from a poxvirus?
00:23:06.13 In this case, we used the vaccinia virus,
00:23:08.28 and what we find is a completely binary response.
00:23:11.27 In some situations, like in the gibbon PKR case,
00:23:15.04 the introduction of the vaccinia K3L
00:23:17.18 completely reverses the growth inhibition that is going on,
00:23:20.23 whereas in the human case,
00:23:23.01 even the presence of K3L,
00:23:25.00 at roughly equal levels of expression,
00:23:27.06 did not overcome the growth inhibition.
00:23:28.26 So, this is exactly like that cartoon example
00:23:31.08 of those two states between hosts and viruses,
00:23:33.27 and what we have in an evolutionary snapshot
00:23:37.01 of both of those states, w
00:23:38.20 here either the host is winning,
00:23:40.18 in which case the growth inhibition goes on,
00:23:42.24 or the virus in winning,
00:23:44.25 in which case the growth inhibition is completely reversed.
00:23:47.10 Now, these are all assays being done in yeast,
00:23:49.17 but we've actually done exactly the same types of assays
00:23:51.29 in vaccinia cells,
00:23:53.27 where we've actually taken either human cells
00:23:56.01 or gibbon cells
00:23:57.16 or orangutan cells
00:23:59.10 and infected them with either a wild type,
00:24:01.10 fully functional vaccinia,
00:24:03.06 or something in which the K3L
00:24:05.26 specifically had been deleted,
00:24:07.21 and what you'll notice is that,
00:24:09.12 in human cells and orangutan cells,
00:24:11.04 it actually doesn't matter
00:24:13.00 whether you've deleted the K3L gene or not,
00:24:14.27 and that's because these species
00:24:17.02 actually have a PKR that's resistant
00:24:19.00 to the K3L antagonism,
00:24:20.25 whereas in the gibbon case,
00:24:22.15 when you delete K3L, you have this 10-fold drop in fitness,
00:24:25.08 which basically is an indication
00:24:27.15 that K3L from vaccinia
00:24:29.21 is acting as a species-specific antagonist
00:24:32.00 of the PKR response.
00:24:34.07 So, we wondered whether
00:24:36.04 we could actually gain better molecular insight
00:24:38.11 into how is it that K3L is able to adopt these multiple states
00:24:41.27 by looking at the co-crystal structure
00:24:44.09 of PKR's kinase domain and the eIF2α substrate,
00:24:47.25 which was first actually established
00:24:50.04 by Arvin Dar and Frank Sicheri's lab,
00:24:52.21 and in this co-crystal structure,
00:24:54.21 one of the most important motifs for this interaction
00:24:58.00 happens to be this α-helix that I've shown here
00:25:01.06 as the g-helix.
00:25:02.23 This is effectively like a bird perch
00:25:04.23 onto which PKR will sit...
00:25:06.21 the bird perch on PKR on PRK
00:25:08.15 onto which eIF2α will sit down.
00:25:10.13 If you take a closer look at the α-helix,
00:25:12.02 shown here,
00:25:13.22 there are three residues in particular
00:25:15.15 that are making direct contacts with the backbone of eIF2α.
00:25:18.29 Now, I'll remind you that eIF2α
00:25:20.18 is not changing at all, in fact,
00:25:22.17 functionally equivalent between human and yeast,
00:25:25.19 so you would predict actually
00:25:28.04 that these three residues would be completely frozen
00:25:30.05 in evolution,
00:25:31.20 by virtue of the fact that they have to interact
00:25:33.25 with something that is completely frozen itself,
00:25:36.05 but instead what we find
00:25:38.05 is that these three residues represent some
00:25:40.12 of the fastest evolving residues
00:25:42.19 in PKR's kinase domain.
00:25:44.10 So, the very...
00:25:46.00 sort of combination lock
00:25:48.02 that is responsible for binding eIF2α
00:25:50.00 is the lock that is very rapidly changing.
00:25:52.05 So, somehow all of these combinations of residues
00:25:55.04 at the αg-helix
00:25:57.13 have preserved the property of binding eIF2α,
00:25:59.25 and yet are basically under very strong evolution.
00:26:02.07 So, we wondered whether this is in fact
00:26:05.08 a signature of the fact that this is an interface
00:26:08.12 that has been constantly challenged by viral mimicry,
00:26:11.14 and so, to test that,
00:26:12.28 we again returned to our yeast assay.
00:26:14.25 We have human PKR
00:26:16.17 that is able to continue growth inhibition
00:26:19.07 even in the presence of K3L,
00:26:21.07 gibbon PKR
00:26:22.27 that is completely reversed by the presence of K3L,
00:26:25.11 and now, in the gibbon backbone,
00:26:27.07 if we add single amino acid changes
00:26:29.27 from human into gibbon,
00:26:31.26 what we find is that we can completely reverse
00:26:34.20 the susceptibility phenotype
00:26:36.11 into the resistant phenotype.
00:26:38.06 So, this really highlights two things.
00:26:40.09 First of all,
00:26:42.10 the interface between PKR and eIF2α
00:26:44.20 is really a hotspot for positive selection,
00:26:47.07 and individual residue changes,
00:26:49.13 these single steps in the arms race
00:26:52.15 between PKR and K3L,
00:26:54.06 result in a complete reversal
00:26:56.10 from susceptibility to resistance.
00:26:58.11 Now, this also actually revealed to us
00:27:00.17 something else that we had missed earlier,
00:27:02.16 which is, even though the orangutan PKR
00:27:05.17 is completely resistant to K3L mimicry,
00:27:07.27 the orangutan g-helix
00:27:10.20 is not resistant to mimicry,
00:27:13.04 which means some other component
00:27:15.27 of the PKR backbone in orangutan
00:27:17.25 is actually necessary for mimicry,
00:27:19.25 immediately suggesting that there was another solution
00:27:22.09 to overcoming mimicry
00:27:24.11 that was evident in orangutan,
00:27:26.05 and we actually mapped that residue again
00:27:28.09 to a single residue in this helix αE,
00:27:30.20 very far away from this helix αG
00:27:33.09 which I've been telling you about today.
00:27:35.09 And so, very much like we saw
00:27:38.16 in the human/gibbon αG case,
00:27:41.09 individual residue changes between gibbon and orangutan
00:27:44.21 have the ability to switch from susceptible to resistant
00:27:48.09 and resistant to susceptible.
00:27:51.15 So, again, really highlighting
00:27:53.12 the very significant power of even individual mutations
00:27:56.04 in individual residues.
00:27:57.27 In the human case,
00:27:59.25 what we also sort of observed was...
00:28:02.09 this particular residue is very interesting,
00:28:04.14 because it's actually toggled
00:28:06.11 between leucine and phenylalanine
00:28:08.12 throughout mammalian evolution,
00:28:10.09 really reflecting the fact that there's probably
00:28:12.05 a high degree of evolutionary constraint
00:28:14.09 acting on this protein,
00:28:15.29 and yet it's toggling so as to keep one step ahead
00:28:18.16 of this mimic interface.
00:28:20.12 The human PKR actually has a very good helix αE residue,
00:28:23.17 as well as a helix αG residue,
00:28:25.28 especially against vaccinia,
00:28:27.25 and we actually have to mutate all three of these residues
00:28:29.27 in order to convert the resistant human PKR
00:28:32.10 into a susceptible version.
00:28:34.20 So, what have we learned from our examples
00:28:37.24 of PKR overcoming the mimicry of K3L?
00:28:40.17 The first really important lesson we learned
00:28:43.06 is that multiple domains of PKR
00:28:45.08 need to be under rapid evolution
00:28:47.09 in order to overcome mimicry.
00:28:48.27 Again, as I pointed out,
00:28:50.18 this is a rock-paper-scissors game,
00:28:52.11 and if only one particular domain
00:28:54.06 was under rapid evolution,
00:28:55.26 K3L would have a much easier task
00:28:57.17 antagonizing and mimicking this interface.
00:28:59.19 The fact that multiple residues
00:29:01.11 in multiple domains
00:29:03.06 are actually rapidly evolving
00:29:04.26 allows these domains to really take turns
00:29:06.28 in antagonizing...
00:29:08.22 overcoming the antagonism of K3L.
00:29:10.14 And, what appears to be the first evolutionary step
00:29:12.26 when PKR encounters this mimicry
00:29:15.10 is actually a negative affinity,
00:29:17.16 where PKR loses affinity,
00:29:19.11 not just to eIF2α,
00:29:21.12 but also to K3L,
00:29:23.11 and then it restores its affinity
00:29:25.08 by interactions in another domain.
00:29:28.08 So, this also implies that there
00:29:30.24 must be extraordinary flexibility for PKR
00:29:32.26 to basically recognize a substrate
00:29:34.23 that really has undergone no changes
00:29:36.24 over the course of evolution.
00:29:38.18 So, just as an example of this flexibility,
00:29:41.03 here again we've the orangutan G-helix
00:29:43.15 in a gibbon backbone,
00:29:45.24 and you can see this is actually susceptible to mimicry,
00:29:49.00 but you can see here, now,
00:29:50.27 because of the growth of this yeast colony,
00:29:52.25 this is telling us that this particular chimeric version of PKR
00:29:56.03 is also not doing a good job of recognizing its substrate,
00:29:59.24 and yet the orangutan backbone
00:30:02.01 has completely restored the binding to eIF2α
00:30:04.23 as well as overcome mimicry,
00:30:06.15 which means something else
00:30:08.15 in the orangutan backbone was sufficient
00:30:10.15 to restore the weakness of this G-helix
00:30:12.28 over the course of these evolutionary arms races.
00:30:15.16 So, this is great,
00:30:17.06 we've learned rules by which PKR
00:30:19.04 might actually overcome mimicry,
00:30:20.24 but this is also sort of a sobering reminder
00:30:22.21 that this overcoming of mimicry
00:30:24.29
00:30:26.24 comes at a cost. So, if you were to look at the αG helix from PKR
00:30:29.05 and three other kinases,
00:30:31.08 whose primary substrate is eIF2α,
00:30:33.11 we'll notice that PKR
00:30:35.18 is the only kinase where we see this dramatic rapid evolution.
00:30:37.25 We don't see if for these three other kinases,
00:30:40.13 which means these kinases
00:30:42.11 have had the evolutionary luxury
00:30:44.20 to optimize to an optimal binding of eIF2α
00:30:48.15 and essentially stay there,
00:30:50.20 preserve their optimal binding
00:30:52.22 by virtue of purifying selection.
00:30:54.15 PKR no longer has that luxury,
00:30:56.18 because as it gets more and more optimal
00:30:58.20 for eIF2α recognition,
00:31:00.23 it gets more and more susceptible
00:31:02.16 for K3L antagonizing it as a mimic.
00:31:05.28 So instead, PKR's evolutionary solution
00:31:08.17 has been to back away from this optimal mimicry
00:31:11.02 in order to gain more of this adaptive landscape
00:31:13.14 that keeps it one step ahead
00:31:15.28 of the virus in terms of these arms races.
00:31:18.02 This a very important sort of consideration
00:31:20.29 because it's not just antiviral genes that face mimicry.
00:31:24.18 This is a slide in which we show that
00:31:28.16 absolutely essential processes in the cell,
00:31:30.24 the cytoskeleton,
00:31:32.14 membrane trafficking,
00:31:34.01 even the cell cycle and apoptosis,
00:31:36.06 all absolutely fundamental housekeeping processes in the cell,
00:31:38.22 are all hijacked by some form of pathogen mimicry.
00:31:42.04 It's worth considering that...
00:31:44.13 what are the evolutionary pressures that have been placed on all of these processes,
00:31:47.09 as they basically tried to survive the mimicry imposed by the pathogen?
00:31:50.20 And, even though they're acquired
00:31:53.22 really great adaptations to overcome this mimicry,
00:31:56.05 some of these alleles might actually be compromised
00:31:59.05 in terms of their housekeeping function
00:32:01.28 - for the function that they were originally intended for.
00:32:03.29 And so, it's not only the fact that the mimic
00:32:06.15 is actually imposing evolutionary adaptation,
00:32:08.28 it might be pushing some of these genes away
00:32:11.20 from their optimal state for cellular function.
00:32:15.00 So, with that I'm going to end this part of the talk.
00:32:17.21 I'd like to really acknowledge Nels Elde,
00:32:20.03 who was a former postdoc in the lab
00:32:22.04 who has his own lab at the University of Utah now,
00:32:24.11 and two very talented technicians,
00:32:25.27 Emily Baker and Michael Eickbush.
00:32:28.03 And this work was done in collaboration
00:32:30.06 with my colleague Adam Geballe,
00:32:32.07 and Stephanie Child in his lab really did all of the viral work
00:32:35.08 that I've discussed.
00:32:36.27 I'd really like to thank our funding sources,
00:32:38.22 and I thank you for your attention.

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