Session 8: Plant Immunity and the Evolutionary Arms Race between Host and Pathogen

00:00:07;13 Hi, I'm Sheng Yang He.
00:00:09;13 I'm a professor at Michigan State University.
00:00:11;09 And I'm an Investigator of the Howard Hughes Medical Institute.
00:00:14;27 Today, I'm going to tell you about the
00:00:17;09 fascinating world of plant-pathogen interactions.
00:00:20;12 Now, why do we care about plant-pathogen interactions?
00:00:23;11 Some of you may know a disease called potato late blight.
00:00:27;23 This disease devastate...
00:00:30;19 devastated the potato crop in the 1840s in Ireland.
00:00:36;04 That event basically killed about a million people,
00:00:42;18 and another million emigrated... forced emigration...
00:00:47;15 out of Ireland.
00:00:48;24 Many of them actually ended up in the United States.
00:00:51;02 So, this just illustrates how a plant disease
00:00:53;26 can have a profound effect on human survival and emigration.
00:00:59;02 There are many such diseases, not to this scale,
00:01:02;18 but they are major threats to our global food securities.
00:01:05;19 So, one of the other diseases, you know,
00:01:07;29 like rice blast, I grew up with in China.
00:01:10;04 I lived in a small village,
00:01:12;17 and it was very severe when I was growing up.
00:01:15;13 But, you know, now I go back 40 years later.
00:01:18;21 It's still a very severe disease.
00:01:20;22 In fact, this is the number one disease in rice production across the globe.
00:01:24;21 There also are new diseases like kiwi bacterial canker,
00:01:28;22 which is caused by a bacterial pathogen that I'm very familiar with
00:01:31;26 called Pseudomonas syringae,
00:01:33;05 sweeping across New Zealand and some European countries right now.
00:01:37;09 So, you see there are old and new diseases.
00:01:39;26 They really pose a great threat to agriculture.
00:01:42;26 And so the... many researchers are, you know,
00:01:45;28 involved in trying very hard to understand the molecular basis of these diseases.
00:01:51;08 And the goal is to... hopefully, to come up with
00:01:55;13 very innovative solutions to solve all these diseases.
00:01:58;22 It's been very challenging.
00:02:01;10 But I think, you know, this is an area that we have to do,
00:02:03;28 because the crop production has to increase
00:02:06;22 to meet the demand of the rising population in the next century, actually.
00:02:12;16 So these diseases are one of the obstacles
00:02:16;26 to increase the yield production.
00:02:18;20 And also quality... you know, disease affects quality as well.
00:02:22;14 And so, in today's... this part of my talk,
00:02:25;29 I'm going to introduce some of the very general concepts
00:02:28;26 dealing with host-pathogen interactions.
00:02:31;19 On the one side, I'm going to talk to you about plant immunity.
00:02:35;06 Yes, plants do have immune responses like a human.
00:02:37;15 But I also want to talk about pathogen virulence factors
00:02:40;29 and so-called effectors.
00:02:43;08 That's Part 1.
00:02:45;19 In Part 2, I'm gonna illustrate these concepts
00:02:48;22 using the model Arabidopsis-Pseudomonas syringae system
00:02:53;04 that we and many others are actually working on.
00:02:55;02 So, I hope you're able to watch both parts,
00:02:57;22 because if you only see one you may not get enough information
00:03:02;03 from this talk.
00:03:04;09 So, what is effector-triggered immunity in plants?
00:03:06;16 There's an older name for this.
00:03:08;19 This is called, actually, gene-for-gene resistance.
00:03:10;23 This is describing a phenomenon, probably noticed by farmers or by,
00:03:15;09 you know, other people, over many years, thousands of years, probably.
00:03:19;03 You know, if you go into the wheat field
00:03:21;15 where there's different cultivars that are planted,
00:03:23;24 some cultivars will be severely diseased in some years.
00:03:27;12 And at the same time, some cultivars will be green and,
00:03:29;24 you know, yielding really well.
00:03:31;15 What's the molecular basis of that?
00:03:33;04 What's the genetic basis of it?
00:03:34;19 And that's been, you know,
00:03:36;00 puzzling for many people for a long time, until this scientist named H. H. Flor.
00:03:42;13 He's a plant breeder and a plant pathologist.
00:03:45;06 He studied a disease called the flax rust disease.
00:03:49;08 It is caused by a fungus.
00:03:50;24 He was very careful.
00:03:52;15 He studied many strains of fungal pathogens,
00:03:55;07 but also many cultivars of the flax plant.
00:04:00;18 And he studied the genetics of the interaction,
00:04:04;07 and came up with this very interesting hypothesis called
00:04:07;02 the gene-for-gene hypothesis.
00:04:09;05 What he thought is that maybe the pathogen
00:04:11;22 has so-called avirulence genes, or Avr genes, some strains.
00:04:15;20 And some cultivars that are resistant
00:04:18;02 contain so-called resistant genes, or R genes. Okay?
00:04:21;10 So, this is the diagram he would use to describe these interactions.
00:04:26;12 If he'd taken a pathogen without any avirulence genes,
00:04:29;06 it's going to infect the plants
00:04:31;27 that either have the R genes or no R genes, right?
00:04:34;13 Because it's virulent, okay?
00:04:36;02 But, if when the pathogen has Avr genes within it,
00:04:39;14 it's going to only infect the plants
00:04:43;01 with no corresponding R genes, okay?, which is depicted right here.
00:04:47;18 If the plant has R genes that can recognize genetically this Avr gene,
00:04:50;26 then the plant will be resistant.
00:04:52;27 So, you needed both the R genes in the plant
00:04:55;09 but also Avr genes in the pathogen
00:04:57;17 for a plant to be resistant.
00:05:00;20 So, this has... you know,
00:05:02;27 was a hypothesis only, okay?
00:05:04;19 But about 10 or, you know, 15 years later,
00:05:07;17 there's actually molecular proof for the existence of these interactions.
00:05:11;20 So, scientists started to clone these so-called Avr genes
00:05:17;11 from different kind of pathogens.
00:05:18;21 The initial few Avr genes were actually cloned from bacteria.
00:05:22;20 And this was done by Brian Staskawicz at UC Berkeley
00:05:25;05 and the late Noel Keen at UC Riverside.
00:05:29;03 And then about ten years later,
00:05:30;25 a number of R genes have been cloned from plants,
00:05:34;21 from different plant species.
00:05:36;15 Okay?
00:05:38;09 So, there were some original predictions of how the Avr proteins and R protein
00:05:41;13 would work, actually, right?
00:05:43;00 So, the idea was really inspired
00:05:45;11 by an animal receptor signaling kind of model.
00:05:49;12 It says that, you know,
00:05:51;03 this Avr protein may be made in the pathogen
00:05:53;15 but is secreted outside of the bacteria. Okay?
00:05:56;16 And the R proteins may be receptors.
00:05:59;13 They may be in the membrane of the plant cell.
00:06:01;20 So, it indicated this classical ligand-receptor kind of interaction.
00:06:08;16 When the Avr genes and R genes are cloned,
00:06:12;23 you know, we'll see whether this model actually holds, right?
00:06:15;16 So, as I said, many R genes have been cloned from different species
00:06:20;20 against different kinds of pathogens.
00:06:21;29 So, we have N gene cloned from tobacco
00:06:24;03 against a viral pathogen.
00:06:25;25 A Cf9 gene... you know, the names... it's not important...
00:06:30;01 but this particular gene is against a fungal disease called leaf mold.
00:06:35;00 There's also, you know, genes...
00:06:37;19 R genes that are against bacterial diseases,
00:06:40;25 in this case, from Arabidopsis.
00:06:42;22 And also some R genes actually
00:06:45;16 against worms, like, nematodes.
00:06:48;20 So, it's very different kinds of pathogens.
00:06:52;06 Initially, we were thinking that maybe there's different kind of R genes,
00:06:53;26 you know, molecularly.
00:06:55;10 But it turns out many of these genes actually share the same kind of motif,
00:06:58;15 including the so-called leucine-rich repeat, or LRR.
00:07:02;25 And this is very exciting because
00:07:04;25 if you line up a sequence against a database,
00:07:07;01 some of the genes that come up are actually
00:07:10;03 involved in animal immune... immunity, so immune receptors,
00:07:13;09 for instance Nod1 is the bottom one diagrammed here.
00:07:17;04 It contains the leucine-rich repeat
00:07:19;24 like the plant receptors here.
00:07:21;06 It also contains so-called NB domains,
00:07:23;13 or nucleotide binding domains.
00:07:25;19 So, here's a very interesting parallel
00:07:27;23 between the animal immune system and the plant immune system.
00:07:31;00 They are based on the same kind of protein
00:07:35;02 to defend against different kinds of pathogens.
00:07:37;06 So, remember this model that I showed you just a few minutes earlier,
00:07:40;22 that indicated that these Avr proteins
00:07:43;16 may be secreted from the pathogen
00:07:45;29 and the R proteins are probably localized
00:07:48;19 to the plasma membrane in the host cell.
00:07:51;11 When you look at the Avr protein sequence, however,
00:07:54;14 you actually don't see this classical signal peptide
00:07:56;20 that indicates the protein will be secreted
00:07:59;02 through the conventional secretion system in the bacteria.
00:08:04;27 So, this model is probably not correct
00:08:07;02 in terms of this particular step.
00:08:09;06 Actually, it turns out most R proteins
00:08:12;09 are also not localized to the plant plasma membrane as originally predicted.
00:08:16;05 Most of them actually localize inside of the cytosol.
00:08:19;27 So, what's going on?
00:08:21;24 Now, this is really Puzzle #1 for a lot of people.
00:08:24;13 It doesn't really make sense.
00:08:26;17 Until we discovered that, actually,
00:08:29;14 most of these Avr proteins from bacteria
00:08:32;12 actually are directly injected into the plant cell
00:08:35;07 through the type III secretion system.
00:08:38;16 And this is actually a very conserved system
00:08:41;04 in bacterial pathogens of plants and animals, again.
00:08:45;02 So, you can see that type III secretion system.
00:08:49;10 You can see it under the electron microscope
00:08:51;24 like a syringe-like thing.
00:08:54;15 The injection system allows bacteria, in this case,
00:08:57;29 to penetrate through the plant cell wall.
00:09:00;22 So, the plant cell has a cell wall, unlike the animal cell.
00:09:04;03 And injecting through the plasma membrane into the cytosol.
00:09:07;07 So, that explains why Avr proteins
00:09:09;20 could be potentially recognized by R proteins
00:09:12;06 located inside the plant cell.
00:09:15;25 And this translocation system actually
00:09:18;14 is very common for other types of plant pathogen:
00:09:21;00 fungus and even, you know, nematodes.
00:09:24;14 They inject these proteins into the plant cell
00:09:28;04 as a very common mechanism during infection.
00:09:31;03 So, gene-for-gene resistance, you know,
00:09:33;24 became effector-triggered immunity, the common term today.
00:09:36;15 This is another way of depicting it.
00:09:38;04 So, you can see that bacteria
00:09:40;12 are injecting these red colored effectors
00:09:43;06 into the plant cell.
00:09:44;25 And they're being recognized by these immune receptors,
00:09:48;16 either containing the coiled-coil domain, CC domain,
00:09:51;16 or the TIR domain, and they are LRR proteins.
00:09:55;28 Okay? So, it's called effector-triggered immunity.
00:09:58;16 So, when the plant genome was sequenced in early 2000,
00:10:03;22 first from Arabidopsis,
00:10:05;08 people were interested to see how many R proteins are there in plants, right?
00:10:09;18 In humans, we know we have these antibodies.
00:10:11;26 You know, it's this endless combination of antibodies
00:10:15;13 that can recognize all kinds of microbes, right?,
00:10:18;00 10^14 specificity.
00:10:20;23 So, we wanted to know how many R proteins
00:10:23;02 are encoded from the plant genome.
00:10:25;15 There was a puzzle, actually.
00:10:27;05 When you see this, there's only hundreds of these genes.
00:10:30;05 How can hundreds of genes, immune receptors,
00:10:32;13 recognize thousands of microbes?
00:10:34;10 So, that's really a big puzzle.
00:10:35;21 And that was the puzzle based on this directed recognition,
00:10:38;20 so, saying that one Avr protein from a pathogen
00:10:42;15 can be recognized by a particular R protein in the plant.
00:10:46;20 So, it can't do this more than a hundred times, right?
00:10:49;19 This puzzle was partially solved by this realization
00:10:53;19 that there's a lot of so-called indirect recognition
00:10:57;00 by R proteins of these Avr proteins.
00:11:00;16 So, this is actually happening in many diseases.
00:11:03;19 So, this is a one example.
00:11:05;19 Imagine that this light blue colored circle
00:11:09;20 is a plant protein called RIN4 in Arabidopsis.
00:11:12;09 This protein is actually attacked
00:11:14;21 by two avirulence proteins, AvrB and AvrRpm1
00:11:18;05 from Pseudomonas syringae.
00:11:19;24 What they do is that these two Avr proteins,
00:11:22;20 well, they attack a RIN4 protein,
00:11:25;09 in this case inducing the phosphorylation of RIN4,
00:11:28;00 of the plant protein.
00:11:29;24 This phosphorylation event induced by two different Avr proteins
00:11:34;08 is recognized by the Rpm1 R protein.
00:11:37;08 Okay, so in this case one R protein recognized
00:11:41;02 two Avr proteins through this common modification
00:11:44;05 of another plant protein.
00:11:46;00 It's called indirect recognition.
00:11:47;29 There's actually another Avr protein called AvrRpt2,
00:11:50;22 which modifies RIN4 differently.
00:11:53;05 It actually cleaves the RIN4 because it's a protease.
00:11:56;02 That is being recognized by another R protein called Rps2.
00:12:00;10 So, you can see there's a lot of variations of so-called indirect recognition
00:12:03;15 that could potentially explain why a limited set of R proteins
00:12:07;20 could potentially recognize many different Avr proteins
00:12:10;29 from different pathogens, because they could induce modification
00:12:15;10 of another plant protein and that modification, then,
00:12:18;11 is sensed by the pathogen to say, this is not normal;
00:12:20;24 it's not my normal thing, okay?
00:12:23;12 So... so then there's another puzzle, okay?
00:12:25;13 I've being telling you these avirulence proteins from pathogens...
00:12:30;01 indicating... when you have these Avr proteins,
00:12:33;02 then the pathogen is avirulent. Okay?
00:12:35;17 Why would a pathogen send avirulence proteins
00:12:37;29 into the plant cell to become avirulent?
00:12:40;01 That... no... no... that makes no sense, okay?
00:12:42;25 And so that's Puzzle #3.
00:12:44;18 Why would the pathogen send avirulence proteins
00:12:46;27 into the plant to be recognized by R proteins?
00:12:49;13 What is the original function of these proteins? Okay?
00:12:52;18 So, I'll remind you of this again.
00:12:54;25 So, we have been talking about this effector-triggered immunity
00:12:57;10 because these particular cells contain R proteins.
00:13:01;16 The plants are resistant against pathogens, okay?
00:13:04;06 In this case, the effector proteins, or avirulence proteins,
00:13:08;07 are basically not good for pathogens.
00:13:11;02 They're being recognized.
00:13:13;04 Actually, in most plants without resistant proteins,
00:13:16;25 these effector proteins or avirulence proteins are doing something else.
00:13:20;12 They're actually suppressing another branch of immune response
00:13:23;28 called pattern-triggered immunity.
00:13:26;00 So, this is depicted on the left.
00:13:28;04 So, pattern-triggered immunity is distinct
00:13:30;13 from effector-triggered immunity.
00:13:32;07 They use different signaling pathways.
00:13:34;28 But they are normally suppressed
00:13:37;09 by these effector proteins to induce disease, okay?
00:13:41;00 So, that's why you want to send these Avr proteins into the plant cells,
00:13:44;13 because the R protein is rare.
00:13:48;01 So, what is pattern-triggered immunity?
00:13:51;03 This branch of immunity is not triggered by effectors
00:13:54;14 of the pathogen,
00:13:55;28 but it's triggered by common patterns from microbes.
00:13:59;13 There can be pathogens.
00:14:01;09 It could be non-pathogens, okay?
00:14:03;17 And so, they've evolved to recognize all kinds of microbes.
00:14:06;25 They are probably more ancient then effector-triggered immunity.
00:14:10;03 They are probably more related to the animal system of the immune system.
00:14:15;02 So, one example of these patterns from bacteria is called bacterial flagellin.
00:14:19;25 This is obviously very common
00:14:21;24 because most bacteria have to swim,
00:14:23;14 so they have to have these traits.
00:14:25;12 And that common trait is now recognized by pattern-triggered immunity.
00:14:29;02 So, one example you can see here...
00:14:32;04 you know, flagellin subunits make up the flagella.
00:14:35;12 It's like about 10,000 copies of this to make
00:14:38;26 a viable flagella.
00:14:40;13 Flagellin has a conserved domain at the N-terminus and the C-terminus,
00:14:43;21 a variable region in the middle of the protein,
00:14:46;28 and there's a peptide called flg22.
00:14:50;27 This is a 22- amino acid peptide,
00:14:54;02 which is now used very commonly in the study of
00:14:57;00 pattern-triggered immunity, called flg22.
00:14:59;03 People have identified the receptor in Arabidopsis for flg22
00:15:04;03 and flagellin.
00:15:06;06 This is done by Thomas Boller's group, very nice work.
00:15:09;15 This receptor looks like a traditional membrane-bound receptor.
00:15:14;14 You have a leucine-rich repeat domain,
00:15:16;27 which recognizes the flagellin or flg22 peptide,
00:15:19;27 but then you have a kinase domain inside the plant cell
00:15:22;25 that transduces the signal to do phosphorylation.
00:15:25;16 So, it's very similar to the animal signal/receptor system.
00:15:29;24 A critical question is,
00:15:32;09 is this receptor important for disease resistance, right? Okay.
00:15:36;14 So, this is done by Cyril Zipfel in Thomas Boller's group,
00:15:42;00 many years ago now.
00:15:44;05 They created this receptor mutant in Arabidopsis.
00:15:47;22 So, this mutant will fail to recognize flagellin of bacteria,
00:15:51;08 including Pseudomonas syringae.
00:15:52;29 On the left, you have a wild type plant
00:15:55;22 containing the full, functional fls2 receptor.
00:15:57;20 On the right is the receptor mutant.
00:16:01;20 And you can see... you see more disease after infection with Pseudomonas
00:16:06;02 in the receptor mutant compared to the wild type,
00:16:07;29 indicating the receptor is very important.
00:16:10;18 The importance of the receptor is actually most obvious
00:16:13;07 if the infection is done by putting bacteria
00:16:15;24 onto the leaf surface, okay?
00:16:18;03 For bacteria to infect the plants,
00:16:20;06 bacteria have to actually go into the leaves.
00:16:22;10 And one of the routes is through stomata.
00:16:25;08 So, these are microscopic pores on plant leaves
00:16:29;10 that allow plants to uptake CO2 to do photosynthesis.
00:16:32;25 But the stomata pores are big enough for bacteria to go in there,
00:16:36;29 so for a long time people thought this is a passive process.
00:16:40;24 The bacteria takes advantage of the open pores
00:16:43;00 to get into the plant tissue.
00:16:46;12 But I just told you...
00:16:48;16 so, the fls2 receptor mutant phenotype
00:16:52;21 is most obvious when you inoculate bacteria
00:16:55;16 onto the surface because they have to go through the stomata to infect.
00:16:59;17 If you inject bacteria directly into the leaf,
00:17:04;02 bypassing the stomata,
00:17:06;08 there's not much difference between the wild type plants
00:17:08;25 and the immune receptor mutant plants.
00:17:10;25 Okay, so, why?
00:17:12;20 It turns out... actually, my group figured out...
00:17:15;16 that this is because...
00:17:18;17 these are stomata cells that...
00:17:20;22 each stomata is actually made up of two guard cells.
00:17:23;02 They actually can recognize flagellin as a molecular pattern
00:17:26;28 and then they close the pore.
00:17:29;08 It's the first line of defense against bacterial infection.
00:17:32;09 So, this is a kind of interesting immune output,
00:17:35;14 very unique to plants.
00:17:37;21 They're recognizing the molecular pattern
00:17:39;24 and do this stomata closure as the first line of defense.
00:17:45;01 So, to summarize this part of the talk,
00:17:48;03 there are two branches of plant innate immune systems.
00:17:53;14 One is involving pattern-triggered immunity,
00:17:55;24 probably very ancient.
00:17:57;16 It evolved to recognize all kinds of pathogens or non-pathogens
00:18:02;05 so the plants won't be eaten by these microbes, then,
00:18:06;05 because plants are really rich in sugars and other nutrients.
00:18:10;18 But then, the pathogen has evolved effectors
00:18:14;02 to shut down the pattern-triggered immunity
00:18:16;09 as a mechanism of pathogenesis.
00:18:18;08 And this is a called effector-trigger susceptibility.
00:18:22;24 But then plants are smart.
00:18:25;11 They evolved this effector-triggered immunity to recognize individual effectors,
00:18:29;02 which used to be called avirulence proteins,
00:18:31;24 to activate the second branch of immunity
00:18:34;20 to fight against these pathogens.
00:18:37;01 So, this... if you go into the wheat field right now,
00:18:40;09 you have this continuation of evolution.
00:18:43;01 Sometimes the pathogen wins; sometimes the plants win.
00:18:45;24 What we want to do is to identify a way
00:18:48;23 to speed up the evolution so that we can fight against plant...
00:18:53;12 emergence of new diseases before they become a problem.
00:18:57;01 So, now I want to acknowledge colleagues
00:19:00;14 who actually gave me some slides for this talk,
00:19:02;06 so, including the slides I had,
00:19:04;13 Cyril Zipfel provided a few interesting slides for this part of my talk.
00:19:08;01 Thank you very much.

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.

00:00:07;22 Hello.
00:00:08;22 I'm Sheng Yang He.
00:00:09;23 I am a professor at Michigan State University and an Investigator at the Howard Hughes Medical Institute.
00:00:15;24 This is Part 2 of my iBiology talk.
00:00:19;08 In this part of my talk, I want to tell you some of our work involving Arabidopsis and
00:00:27;06 the Pseudomonas syringae interactions.
00:00:29;06 Particularly, I want to highlight one aspect of our research, illustrating how environmental conditions
00:00:34;24 could profoundly influence disease development in plants.
00:00:39;21 So, as you know, when you look at a plant growing in nature, outside, they not only are,
00:00:47;07 you know, exposed to potential pathogens, but they are also experiencing a lot of different conditions:
00:00:52;24 temperature fluctuation, you know, from morning to evenings; light, as you can see here;
00:00:59;20 and temperature; humidity; and microbiome, even.
00:01:03;23 We know that all of these factors actually influence pathogen and plant interactions.
00:01:09;09 The molecular bases of this are not well understood.
00:01:12;24 And so some famous scientist said, you know, without understanding environmental conditions,
00:01:16;26 we will never understand the immunity in plants... you know, in the plant system.
00:01:23;04 So, I'll just give you a couple of examples of how important the climate conditions could be
00:01:28;00 for plant disease outbreak in the field.
00:01:32;11 This is the bacterial fire blight disease in apple.
00:01:36;11 This is in Switzerland.
00:01:37;11 This is a 12-year span of disease incidents from 1995 to 2007.
00:01:45;18 So, apples are always growing in, you know, Switzerland, and pathogens are always in these orchards,
00:01:50;20 but you don't see the disease every year.
00:01:53;15 And the reason is for disease to occur you need a lot of humidity and the right temperature, right?
00:02:01;10 So, in 2007, in that year you have heavy rain and high humidity in the spring,
00:02:05;23 when the apple was flowering, and these bacteria tend to infect the flowering parts.
00:02:11;19 And so everything kind of came at the same time, and then have very severe disease.
00:02:16;17 So, that's one example.
00:02:19;08 Another example is called fusarium head blight of wheat.
00:02:23;07 This is actually a very huge global disease right now.
00:02:28;11 It's also favored by high humidity and warm temperatures in the spring.
00:02:31;22 So, you can see that... you know, normally you see a nice green top of the wheat.
00:02:38;03 In this image, you can see, basically, bleached grains.
00:02:43;15 And there were four very severe epidemics in China in the last five years,
00:02:47;28 so almost every year has very severe disease.
00:02:49;25 This disease, also, is very serious, because the fungus actually produces a toxin
00:02:56;11 which makes us sick.
00:02:57;11 And so... not only reducing yield, but also causing sickness in the human population.
00:03:05;15 So, I want to tell you that plant diseases are really, you know, problems in modern agriculture.
00:03:11;19 They're really major threats to food security, globally, right now.
00:03:16;19 Some of these diseases are very old.
00:03:18;28 On the left is a disease called rice blast, a disease I actually grew up, when in China
00:03:25;04 I lived in a village with 200 people or so.
00:03:29;04 So, you know, I saw this rice blast when I was a really small little child.
00:03:35;01 When I go back right now, 40 years later, and talk to my parents, and this is
00:03:41;04 still the number one disease locally, but also globally, in rice production.
00:03:45;06 So, many old diseases continue to really pose major problems.
00:03:49;10 Now, you also have new diseases coming up.
00:03:52;20 One example I'm giving to you here is a kiwi bacterial canker, which is caused by
00:03:57;14 a bacterial pathogen called Pseudomonas syringae, and I'm going to tell you a little bit about that today.
00:04:02;12 So, this is despite all the chemical input -- you know, pesticides, you have to spray them,
00:04:08;09 farms have to use them, because otherwise you won't have, you know, really high yield --
00:04:11;20 but also all the breeding efforts, that many scientists try to breed resistant cultivars.
00:04:18;21 You know, from wheat to rice, based on these so-called disease resistant genes.
00:04:25;03 But this is not enough.
00:04:26;03 So... because we have disease every year still.
00:04:28;18 One of the problems, as we've realized, is that we really don't understand
00:04:32;05 the basic process of disease, okay?
00:04:34;16 So, this is an area that we really want to push ahead.
00:04:39;02 So, in the last 15 years or so, you know, many laboratories including us are
00:04:45;11 really concentrating on trying to work out why disease occurs.
00:04:49;22 And so this is an overview of different kind of pathogens that can cause disease in plants.
00:04:54;19 So, we have fungus; we have bacteria; we have nematode, worms, you know; and viruses.
00:05:03;02 Many of these pathogens also cause problems in our human bodies also.
00:05:06;25 And so one... so, they look very different, but one of the common things they do is to
00:05:10;18 deliver these virulence factors -- collectively, we call them effectors -- into the plant cell.
00:05:16;27 And... and so, they use different ways of delivering these virulence proteins.
00:05:21;20 In the case of bacteria, they use a secretion system called the type III secretion system.
00:05:26;02 You can see on the right a syringe-like structure, here.
00:05:31;08 If you knock out this delivery system, bacteria become non-pathogenic, okay?
00:05:35;22 So, that illustrates how important these virulence factors are to causing diseases.
00:05:41;06 So, because of that, studying how effectors work really can provide great progress into
00:05:48;25 the molecular basis of disease susceptibility.
00:05:52;27 And interestingly, these molecules, microbial molecules, also can be very powerful probes
00:05:58;19 into the fundamental biology of the host -- and that can be plants or it could be humans --
00:06:03;13 because they usually find very intriguing RNAs or proteins or DNAs to manipulate the host physiology.
00:06:09;27 Okay, so in a sense, this is a really great, you know, probe into the biology of the host itself.
00:06:16;08 Obviously, discovering the target of these virulence factors could offer new leads into
00:06:21;24 innovative disease control we really desperately need right now.
00:06:25;25 So, how do we understand disease susceptibility?
00:06:28;07 Which approaches?
00:06:29;23 We and others are really following this very simple diagram, here.
00:06:32;24 We want to understand the host target of all these bacterial virulence proteins.
00:06:38;17 So, in the case of the bacteria we study, it has about, you know, 30 or so effectors.
00:06:43;04 What we want to do -- we means us and the many other laboratories -- is really to identify
00:06:48;12 these host proteins or RNAs or DNAs that are being targeted by these virulence factors,
00:06:54;12 and we want to associate these host targets to these particular pathways.
00:06:58;05 You know, I listed the five of them -- A, B, C, D, E -- but it could be 30, right?
00:07:02;28 So, we don't know how many pathways are being targeted by bacterial virulence factors.
00:07:08;00 What we hope to do is to... once we identify these pathways, we could genetically
00:07:14;00 perturb these pathways in the host, in this case, in the plant.
00:07:17;25 And if we're successful, then if we understood everything about the disease process,
00:07:23;13 we can create a poly mutant of the host in which these pathways are basically either
00:07:29;16 activated or inactivated to simulate the collective activity of these virulence factors.
00:07:36;03 And then if we really understood the process then, then the poly mutant of the host
00:07:40;05 would be susceptible to a bacteria that is not able to produce effectors.
00:07:44;22 In other words, if we manipulate the host already, genetically, to simulate the
00:07:50;17 action of the virulence factors, you don't need these virulence factors to start with, right?
00:07:53;28 Until then, we will never know we understood the disease, okay?
00:07:56;27 So, that's the goal.
00:07:58;06 It's very challenging, but by the end of these twenty minutes, I want to show you that
00:08:02;05 we have made progress towards that goal.
00:08:04;28 So, we use this very simple model system involving Arabidopsis, which is a model plant,
00:08:12;04 and a bacterial pathogen called Pseudomonas syringae.
00:08:14;21 It's a very common pathogen.
00:08:16;13 It infects virtually all crop plants in the field, okay?
00:08:20;25 Each individual string of this species, Pseudomonas syringae, infects a very narrow range of hosts.
00:08:27;02 So for instance, strain DC3000 in the field only infects tomato.
00:08:32;24 In the laboratory, you can also make it infect Arabidopsis, okay?
00:08:37;05 So... so because Arabidopsis is a very, you know, powerful model for plant research...
00:08:42;05 so we have been working on Arabidopsis-Pseudomonas model system for many years now.
00:08:48;00 Pseudomonas can actually live on the surface of the bacteria... of the plants as an epiphyte.
00:08:54;15 But in order to cause disease, it has to go into the interior of the leaf, in this case, okay?
00:09:00;20 They go into the leaves through so-called stomata.
00:09:03;16 So, these are microscopic pores on the leaf epidermis that allow plants to take up
00:09:09;15 the CO2 to make food for us, okay?
00:09:12;02 So, photosynthesis.
00:09:13;02 It's very important, okay?
00:09:15;22 And once bacteria go into the... inside the leaf, it lives in between the cells, okay?
00:09:21;09 So, these are called mesophyll cells.
00:09:23;12 So, these are extracellular pathogens, okay?
00:09:25;21 So, this space is called the apoplast.
00:09:27;06 Now, I want to tell you the apoplast is normally filled with air.
00:09:31;13 It's not filled with liquid.
00:09:32;13 This is very important, because CO2 has to go into the... goes through to the stomata
00:09:37;18 into the apoplast, but it has to diffuse into the mesophyll cell and the chloroplast.
00:09:42;04 So, it's a long distance for the CO2 to go in there.
00:09:45;03 You don't want water in there, because there will be very high resistance to CO2.
00:09:51;00 So, the plant has a way of keeping that space mostly filled with air.
00:09:55;04 I'll come back to this.
00:09:56;04 It's actually very relevant to pathogenesis.
00:09:58;22 So, what we do in the laboratory to kind of have a disease assay is really to grow plants
00:10:04;08 in a pot.
00:10:05;25 You probably do this at home.
00:10:07;14 Not this style, but in another way.
00:10:10;01 And then, when they are four or five weeks old, we would dip the plants entirely
00:10:15;19 into the bacterial suspension and wait for, basically, three days, okay?
00:10:19;11 You will see disease symptoms, as shown here.
00:10:21;15 So, I'm gonna play a movie which shows you the time-lapse video of the infection process.
00:10:28;05 On the left are the mock...
00:10:29;19 I mean, are the bacterially infected plants.
00:10:33;04 On the right is a mock infection, this is water, okay?
00:10:35;24 So, what you can see, now... eventually, you can see the yellowing on the plants that are infected.
00:10:42;00 And on the right are the ones that are moving, you know, they are alive, okay?
00:10:45;17 You can see there are some plants that are kind of dancing, of this thing you can see.
00:10:50;12 But the infected plants are basically paralyzed, okay?
00:10:53;02 So, we actually don't know why plants are motionless very early on in the disease.
00:10:58;00 This is one of the things we are trying to understand in the... in the next few years.
00:11:02;18 So, we have worked on several aspects of this disease process.
00:11:08;14 For instance, we have, a few years ago, figured out that entry process, right?, how bacteria
00:11:15;07 enter the plant tissues through the stomata.
00:11:18;25 For a long time, scientists think they are passive, because the stomata pores are
00:11:24;11 quite big and bacteria are kind of small.
00:11:25;26 They can... the pore has to be open for photosynthesis during the day, so we always thought bacteria
00:11:31;11 can just take advantage of that and go into the tissue, like, passively, right?
00:11:34;17 That doesn't turn out to be the case.
00:11:37;00 It turns out these guard cells -- there are two guard cells to form one stomata pore --
00:11:42;18 they actually can sense bacteria.
00:11:45;03 And so once they sense the bacteria, they close it as the first line of defense,
00:11:50;02 to prevent any microbes entering the tissue.
00:11:52;27 So, plants are very smart, okay?
00:11:54;12 So, that's about... a very intriguing mechanism defending against pathogen invasion.
00:12:00;23 We discovered one of the... so, that's bad for the bacterial pathogen, right?
00:12:04;00 It cannot even start the infection.
00:12:05;12 So, in the case of Pseudomonas syringae, it figured out a way to prevent that from happening
00:12:10;02 by producing a toxin called coronatine, which prevents stomata from closing.
00:12:15;07 And so the bacteria can massively infect to start an infection.
00:12:20;14 Once the bacteria get into the mesophyll space... as I mentioned before, it's an extracellular pathogen,
00:12:25;13 but it makes a type III secretion system injecting more virulence effectors
00:12:30;01 into the plant cell as a major weapon of pathogenesis.
00:12:32;28 So, we're working on this area as well.
00:12:35;28 So, we knew a little bit of these basic steps of this infection involving stomata entry,
00:12:41;13 involving a toxin that prevents the stomata from closing, and involving these effectors
00:12:46;17 that we think, now, are suppressing immune responses in plants, okay?
00:12:51;07 Work in the past few years, from us and many other groups, has deepened our understanding
00:12:56;26 of these basic steps, but also... in our case, we realized that we're missing two dimensions
00:13:02;21 in the last, you know, many years, actually.
00:13:05;00 One dimension involves the profound effect of environmental conditions on the host-pathogen interactions.
00:13:10;19 So, that's under the left circle, here.
00:13:14;13 We also started to realize the endogenous microbiome -- the plant also has a microbiome --
00:13:19;00 has tremendous effect on host-pathogen interactions.
00:13:21;21 So, these are new directions.
00:13:22;27 I'm gonna highlight one particular area, which is involving how environmental conditions
00:13:28;17 could influence the disease interactions, okay?
00:13:31;24 So, we are focusing on two areas.
00:13:34;27 One is the temperature, how elevated temperatures could influence disease.
00:13:39;09 This is actually very relevant right now with climate change.
00:13:42;16 The globe is warming.
00:13:44;11 But also, more importantly, the heat waves we're experiencing in different countries
00:13:49;06 are very severe right now... and how these short periods of heat waves could influence infection.
00:13:55;26 Okay, so this is one of my students, Bethany Huot, who recently published a paper
00:14:00;20 just showing very simply... you can see under... we grow plants the same way, okay?,
00:14:05;15 but during infection we put the plants in 23 degrees, which is the normal temperature, or you shift
00:14:11;04 5 degrees up, to 28 degrees, you can see dramatic differences already.
00:14:15;08 At the warm temperature, you see much more severe disease, okay?
00:14:19;20 She discovered this is based on two mechanisms.
00:14:22;21 One is the warm temperature actually enhances greatly the virulence expression.
00:14:26;28 So, the effector secretion into the plants is greatly enhanced.
00:14:32;05 But also, she discovered that the immune signaling in the host is completely shut down.
00:14:37;21 So, this is actually very important in the field, you know.
00:14:40;21 The immune pathway that she was working on is called salicylic acid signaling,
00:14:45;28 which is mimicking, like, the aspirin we take sometimes.
00:14:48;00 It's a similar chemical.
00:14:49;14 It boosts the immune response.
00:14:51;17 This response is shut down by warm temperature.
00:14:54;15 This could have a profound influence in the field, crop resistance, because most of the
00:14:58;28 crop resistance is based on their signaling cascades.
00:15:01;12 So, we don't know the details of this pathway.
00:15:03;24 This is something we're gonna work out in the next few years.
00:15:05;24 What I'm going to talk to you about in more detail is humidity's effect on plant disease,
00:15:11;02 okay?
00:15:12;02 This became, actually, obvious in our disease reconstitution experiment I mentioned in the
00:15:15;12 beginning of my talk.
00:15:16;21 We tried to figure out how many pathways are being manipulated by the bacterial pathogen.
00:15:21;18 And ultimately, we want to create a poly mutant of the plant to see whether we can
00:15:26;21 rescue the pathogenesis of a bacteria that does not, you know, deliver any of these effectors,
00:15:31;12 okay?
00:15:32;12 So, that's a very daunting task, but we... as scientists, we want to, you know,
00:15:37;08 face the challenge and try to work it out.
00:15:39;22 So, there are 30 of so effectors, I told you, in this particular bacterium, so we and others
00:15:45;03 are systematically going through to identify the host target of each of these effectors,
00:15:49;16 okay?
00:15:50;16 A model that we and others have developed in the last, you know, 15 years or so about
00:15:55;25 the function of these effectors is this, in a simple way.
00:15:58;27 So, you're seeing a bacteria sitting on the plant cell wall.
00:16:02;10 So, a plant cell, unlike an animal cell, has a cell wall surrounding it.
00:16:05;18 But in the plasma membrane, which you can see, there are receptors.
00:16:09;17 They're called immune receptors, that perceive these patterns from microbes, in this case,
00:16:14;16 flagella, these wavy things, very common for bacteria.
00:16:18;07 And once they sense these molecules, it then triggers a signal transduction pathway
00:16:22;20 -- this is a very simple diagram -- eventually leading to a form of immunity called pattern-triggered immunity.
00:16:29;06 So, this is bad for bacteria, so what bacteria are doing is to send these effectors
00:16:34;06 into the plant cell to attack different steps of this signaling cascade, to shut down
00:16:38;24 this form of immunity.
00:16:39;24 It's a major mechanism of disease.
00:16:43;05 And so, I'll just give you one example from a collaborative work from Cyril Zipfel's group
00:16:48;11 and my laboratory, also, involving a particular effector called HopAO1.
00:16:53;01 HopAO1 biochemically is a phosphatase, which removes phosphate from proteins.
00:16:59;22 And it turns out these immune receptors are phosphorylated, normally, during activation
00:17:04;14 at a tyrosine residue of the protein.
00:17:07;11 And this effector actually removes the phosphate from tyrosine to shut down this immune activation.
00:17:12;02 So, this is a very cute way of... you know, bacteria figured out how to kind of sabotage
00:17:17;14 the immune signaling.
00:17:18;18 And there are many studies to support this, very strong evidence that this is really true.
00:17:23;10 So, one of the major functions of these virulence factors is to shut down the plant immune response, right?
00:17:29;10 If there's no immunity response in the host then you can, you know, infect the plants.
00:17:32;16 And this is very similar to human pathogenesis.
00:17:35;03 And many of the bacterial pathogens are human pathogens that actually do the same thing.
00:17:39;02 They're shutting down the immune system in our body, then infect.
00:17:42;22 Okay, so our question is this.
00:17:44;20 Are all these 30 or so effectors involved in immune suppression?
00:17:48;06 If they're all attacking, you know, immune suppression, then we can reconstitute the disease
00:17:53;23 by using the immune compromised plants, right?
00:17:56;27 So, I'm coming back to point... that point later.
00:17:59;13 I've introduced you to two bacterial strains, now.
00:18:02;10 I'm talking about the wild type strain, DC3000.
00:18:05;17 It secretes these 30 or so effectors into the plant cell.
00:18:08;13 There's a mutant called delta-28E, which has 28 of these 30 effectors deleted.
00:18:14;27 It has involved a lot of work done by Alan Collmer's lab at Cornell University,
00:18:21;02 but they did it, so it's a very useful mutant, and we take advantage of this mutant.
00:18:25;20 Because this mutant has essentially no effectors that are delivered into the plant cell,
00:18:30;04 it's not pathogenic.
00:18:31;04 So, if you put into a wild type plant... you can see that on the left is infection by DC3000.
00:18:36;12 It causes disease-like symptoms.
00:18:38;09 But on the right is green; it's a healthy plant.
00:18:40;28 So, this mutant cannot cause disease in the wild type plants.
00:18:45;22 If, as I said, all effectors are attacking the immune signaling, then if we start with
00:18:51;22 immune defective plants, if there's no immunity in the plants, then this mutant, delta-28E,
00:18:58;27 should be able to infect the plants, right?
00:19:01;22 Okay, so that's the experiment we did.
00:19:03;23 You can see that, unfortunately, the delta-28E mutant was unable to cause disease.
00:19:09;14 You know, the plants are still kind of green after infection, okay?
00:19:13;04 The mutants we used, fec and bbc, these are defective immune responses in the plants.
00:19:18;16 So, the answer is no.
00:19:19;28 Okay?
00:19:20;28 So, you can also look at the bacterial population.
00:19:22;21 So, when the plants are infected by Pseudomonas syringae, it multiplied really high.
00:19:27;17 So, this is... the bar is in the logarithm... log-type scale, so each step is a tenfold increase.
00:19:36;04 You can see that DC3000 aggressively multiplied inside the leaf.
00:19:40;20 Versus the delta-28E in wild type and in mutant leaves, they are unable to achieve
00:19:47;10 a very high population.
00:19:48;10 So, there's no disease, so the answer is no.
00:19:50;05 So, the question is, what are we missing?, right?
00:19:52;26 So, some effectors must be attacking something other than immunity as a part of their mechanism.
00:19:59;02 So, I'm gonna pull you away from my... our own results to tell you something about a website.
00:20:05;22 So, if you're growing plants in your garden, this is actually for master gardeners,
00:20:10;09 so anything written on this website must be true because you have to follow that.
00:20:14;08 Okay, so you can see that I just took a few sentences out.
00:20:18;05 It says, bacterial diseases are most intense in warm and humid conditions like Florida,
00:20:23;26 okay?
00:20:24;26 So, Florida actually has a lot of diseases compared to California.
00:20:26;26 California is dry.
00:20:30;02 You can recapitulate... this is actually famous idea called the "disease triangle" dogma.
00:20:34;17 For a disease to occur, you not only need a planet which is susceptible genetically
00:20:38;06 and a pathogen which would is virulent genetically, but you also need a conducive environment.
00:20:42;26 One of the main factors is high humidity, okay, rains and things like that.
00:20:47;27 This was formulated by a very famous plant pathologist, RB Stevens, 50 years ago.
00:20:52;14 We actually don't know the molecular basis by which humidity is required for disease
00:20:56;03 very much.
00:20:58;08 You can recapitulate the humidity requirement in the laboratory.
00:21:01;07 Basically, you can grow plants, you know, for four weeks.
00:21:04;12 But during the infection period of three days, we either place the plants under high humidity,
00:21:09;13 like 95% percent, which simulates the disease outbreak condition in the field, or you,
00:21:15;02 you know, set up the plants at [30%], which is a low humidity.
00:21:19;20 You can see that at high... and only at high humidity you have disease.
00:21:22;16 At low humidity, plants look healthy.
00:21:25;11 And you can look at the disease bacterial population, also.
00:21:29;08 High humidity has a very high population, and under lower humidity you have very low.
00:21:34;20 Okay, so it's a dramatic difference, okay?
00:21:36;24 Now, if you go back to this website, you can also see a term called water soaking.
00:21:42;02 This is describing the symptom of the disease of many bacterial diseases.
00:21:45;24 Normally, if you look at leaves in your backyard you will see kind of, you know, green, okay?
00:21:51;15 There's no spots, right?
00:21:53;15 In this picture, you can see there's a lot of dark spots.
00:21:55;18 These dark spots are caused by liquid in the leaf.
00:22:00;17 And plants don't like that.
00:22:01;17 I just how you been beginning, for photosynthesis to occur, for CO2 to diffuse into the mesophyll cell,
00:22:06;10 you want to keep the apoplast air-filled.
00:22:09;20 And in these dark spots, there's liquid in there.
00:22:12;12 It's really bad for plants.
00:22:13;21 But bacteria seems to be able to do this for a purpose.
00:22:17;00 We are actually... so, phenomenon has been observed for many decades.
00:22:20;22 We don't know whether it's needed for pathogenesis, okay?
00:22:24;07 So, we were intrigued by this.
00:22:26;11 This only occurred under high humidity, also.
00:22:28;13 So, you can simulate this process in the laboratory.
00:22:31;24 This is our Arabidopsis, again, infected by Pseudomonas syringae.
00:22:36;10 You can see dark spots, here, on the right leaf, which is infected.
00:22:39;19 On the left, that was not infected.
00:22:41;25 This also occurred in tomato, because this bacteria also infected tomato.
00:22:45;01 So, under high humidity, you have this so-called water soaking symptom.
00:22:49;25 Now we can label bacteria to see where the bacteria are in the infected tissue by
00:22:55;18 loading it with a luc... you know, lucs emit light... allow bacteria to emit light.
00:23:00;28 So, you can catch the light emitted from bacteria in the infected tissue and then overlay this
00:23:07;25 with the water soaking symptom that you capture with regular light.
00:23:10;26 And if you see, in the bottom of the left leaf, we can see extensive overlap
00:23:16;18 between the luc -- the light indicating bacteria -- and the water soaking spots, suggesting that
00:23:24;17 the water soaked area is where bacteria multiply really highly, okay?
00:23:27;23 So, that is really spatially kind of indicating water soaking is quite important.
00:23:32;23 So, what causes the water soaking?
00:23:35;02 Okay, I told you this bacteria produces 30 or so effectors.
00:23:38;14 We actually screened each individual effector to see which one can cause water soaking.
00:23:43;14 In this experiment, we show that two of them can cause water soaking.
00:23:47;16 And the names are not very important, but I can show you that one is localized to
00:23:51;20 the plant plasma membrane, here.
00:23:54;01 One is localized to, actually, the endomembrane system in the plant cell, called the endosome,
00:23:58;26 which is involved in recycling all the proteins to and off the plasma membrane of the plant cell.
00:24:05;03 So, they're two... these two effectors are doing something to the plasma membrane
00:24:08;06 of the plant cell to cause water soaking.
00:24:11;24 We actually know a little bit more about one of these effectors.
00:24:14;01 They actually attack a protein in the plants that regulates the vesicle traffic.
00:24:19;02 So, it's a really intriguing phenomenon, also, because a lot of human pathogens also do that,
00:24:25;04 attack proteins that are involved in vesicle trafficking in our human cells as a way to
00:24:29;23 shutting down the immune system.
00:24:32;01 Okay.
00:24:33;01 So, now we have... in addition to the immune suppression process, we discovered a new process
00:24:38;14 we called aqueous apoplast, which is the inside of the leaf accumulating, basically,
00:24:43;17 water and other things.
00:24:45;11 Okay?
00:24:46;11 So, in order to cause water soaking, you need the so-called water soaking effectors
00:24:50;26 from bacteria.
00:24:51;26 But that's not sufficient.
00:24:52;26 You also need high humidity in the air.
00:24:55;23 The reason is that in the low humidity, even if the bacteria are producing water soaking symptoms,
00:25:01;05 it will be evaporated out through stomata, because stomata are open during the
00:25:04;24 day for... to take up CO2.
00:25:07;22 And because of that, if you have low humidity, the water just comes right out.
00:25:11;18 And because there's no water, then the bacteria will not benefit.
00:25:14;16 So, here's an example where we need variance factors in the bacteria and we need
00:25:18;18 the external environment to be humid, okay?
00:25:20;05 So, this is kind of interesting.
00:25:22;28 So now, the question.
00:25:23;28 The next question we want to ask is... okay, we have two processes now.
00:25:27;11 We know that immune suppression is not sufficient for pathogenesis.
00:25:31;00 Now we have two... are they sufficient, now, for pathogenesis?
00:25:35;00 So, this is a disease reconstitution experiment we always wanted to do.
00:25:39;15 So, we can simulate the suppression of the immune response in the plant by using
00:25:46;14 this mutant of Arabidopsis that is unable to mount an immune response.
00:25:50;00 We can also mimic the water accumulation in the apoplast by using this new mutant
00:25:55;06 that we have, called min7, okay?
00:25:57;08 The idea is to combine these two process by genetically manipulating the two pathways.
00:26:03;07 Using CRISPR/Cas9 technology, we created quadruple mutants, basically affecting both immunity
00:26:09;26 and water homeostasis.
00:26:11;27 So, the question is that, in these quadruple mutants, would bacteria that normally
00:26:17;25 cannot deliver any effectors... is going to multiply or not, okay?
00:26:22;02 So, this is the experiment we did.
00:26:24;09 So, the bacterial mutant we use is the bacteria that are unable to secrete any of these effectors,
00:26:28;25 that's defective in type III secretion, okay?
00:26:31;06 In the wild type plants, they don't cause disease.
00:26:34;06 It's green plants.
00:26:35;27 In these immune-defective mutants, it still does not cause disease, as I showed you before,
00:26:41;06 okay?
00:26:42;06 So, it's not sufficient.
00:26:43;13 In a min7 plant, also, it does not cause disease.
00:26:46;17 In the quadruple mutants, now, you can see disease-like symptoms.
00:26:50;11 And this is actually when we're seeing a non-pathogenic bacteria cause any disease on a plant system.
00:26:56;19 So this is pretty exciting to us.
00:26:58;24 If you look at the bacterial population in these leaves, you can see that the red bars
00:27:03;10 are indicating the quadruple mutants.
00:27:05;15 Only in these two quadruple mutants, you can start to see the multiplication of an otherwise
00:27:10;11 non-pathogenic bacteria, okay?
00:27:12;17 So, it's not to the extent of the totally wild type infection, so we have some distance to go,
00:27:16;08 but this is a quite significant step.
00:27:18;25 So, summarizing this part of my talk, we have identified a new pathogenic process involving
00:27:25;27 what we called aqueous living space.
00:27:29;04 We know bacteria loves water because, you know, human pathogens and plant pathogens
00:27:34;05 all love water, right?
00:27:35;13 So... but this is a case where bacteria actually create water conditions in an otherwise air-filled space.
00:27:43;22 And if you think about whether this is relevant to, you know, other diseases,
00:27:47;09 including human diseases like a lung infection and the respiratory system, which is normally filled with the air...
00:27:53;12 so, we will see whether this principle will go beyond plant diseases, okay?
00:27:57;17 We were able to reconstitute the basic features of a bacterial infection within
00:28:03;02 exclusively host mutants, okay?
00:28:05;03 So, that's also the first time we've done this.
00:28:08;02 Of course, we're getting some insight into why humidity could have profound influence
00:28:12;08 on the disease interactions.
00:28:15;13 In this case, because it's required for the virulence factors to function as virulence factors.
00:28:21;04 So now, I'd like to acknowledge the people that actually did the work.
00:28:24;07 Of course, my lab members at Michigan State.
00:28:28;01 And I also want to acknowledge a number of collaborators: Jeff Chang, Cyril Zipfel.
00:28:34;25 Also other investigators that I collaborated with for the other part of my talk.
00:28:40;24 Funding are from HHMI, Gordon and Betty Moore Foundation, NIH, DOE, USDA,
00:28:46;24 and the National Science Foundation.
00:28:49;02 Thank you.