Similar to animal immune systems, a plant’s immune system is in a constant battle against pathogens. This session covers the basics of plant immunology and reviews two major pathways that activate the plant immune response: Effector-triggered immunity and Pattern-triggered immunity. In addition, you’ll learn the fundamental factors that lead to host-pathogen co-evolution.
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.
00:05:38;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.
00:00:02.01 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
00:30:26.24 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.
00:00:08;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,
00:15:12;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,
00:15:32;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,
00:15:50;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.
00:19:20;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,
00:20:24;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.
00:24:33;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.
00:24:46;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,
00:26:42;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.
- Sheng-Yang He iBioSeminar: Plant-Pathogen Interactions
- Harmit Malik iBioSeminar: Host and Viral Evolution: Molecular Evolutionary Arms Race Between Primate and Viral Genomes
Dr. Sheng-Yang He is a University Distinguished Professor at Michigan State University and a Howard Hughes Medical Institute investigator. He obtained his bachelor’s degree (1982) and a master’s degree in plant protection (1991) from Zhejiang Agricultural University in China. He pursued his graduate degree in plant pathology (1991) at Cornell University and continued his post-doctoral… Continue Reading
Harmit Malik received his undergraduate degree in chemical engineering from the Indian Institute of Technology, Bombay and his PhD from the University of Rochester. He moved to the Fred Hutchinson Cancer Research Center in Seattle for post-doctoral work and decided to stay, starting his own lab in the Division of Basic Sciences in 2003. Malik… Continue Reading