 Talks will be also talking, continuing my talk. We'll be talking on quorum sensing in bacteria. And the first talk is really happy to present to you Pete Greenberg, who has been involved in this field from the beginning. And he's been a major contribution in the field. And he will talk to us about quorum sensing, cooperation, and conflict in populations of the opportunistic pathogen pseudomonas aeruginosa. OK, so as Vittorio said, I'm going to tell you some things that I hope you find interesting. Some of the things we're doing, working on quorum sensing these days. And that's a story about communication, cooperation, and conflict among individual bacteria and populations. And the sort of experiments that I want to talk about today, I call social engineering experiments. We define the conditions, put. You can't hear? I can hear. It won't go up much higher. Is this any better? It's just OK. Can you hear me in the back? Not so much. Hang on. I'm going to button my top button. I can. Does this work? Hard to hear? I can try that other microphone if that sounds pretty loud in the front. OK, Simon can hear. What? Where was I? Social engineering experiments for the next 35 minutes. We define the conditions. We put an assortment of bacteria together. And we hope, based on social economic theory, we can predict what will happen. So the story is a, oh, I see, it's sort of, I can't turn my head more than about two degrees in either direction. I want to tell you about Asel homo-serine lactones quorum. You know what? I have a better idea. If I just talk really loudly, can you hear in the back? Is this better? OK. Asel homo-serine lactones signaling. And it's supposed to form some sort of bacteria. And today, I want to tell you about work on form-sensing of pseudomonas or eugenosus. Social cheating and pyridgenosa and the tragedy of the commons. And I want to talk to you about how the operators can restrain themselves from cheating and how they can please social cheats. Finish with that story about real-world situations. Most of the work I'll talk about was done by these three individuals, a James Underkar, who just now, he was a fellow in my lab. He's now on my faculty at the University of Washington. Susan T. Dolly, research scientist in the lab, and May Wong, visiting. OK. I'm being taped. Maybe I'll put this in my pocket. So now, if I yell loud, it's going to hurt your ears, but I don't know what else to do. First, by way of introduction, a little review of some social economics and some form-sensing. First, I work on a field I call sociomicrobiology, a study of the genetic basis and environmental influences on social activity of microbes. And we work on this for several reasons. Now that we understand microbes are social, they're great models to understand the biology of sociality. If we want to understand bacteria, we have to consider their sociality. And there is an idea that we can develop new medicines to treat really difficult bacterial infections by targeting social activity. So what do I mean when I say sociobiology? I just define this a genetic basis and environmental influence on social activities. What do I mean when I say sociality? I mean the sum of conflict and cooperation between two or more individuals. So this is a biologist definition of sociality. And what do I mean when I use some of these words? Some of them came up this morning. Cooperation is a behavior that provides a benefit to another individual. Cheaters were discussed this morning. A cheater is an individual who doesn't cooperate through cooperates less than their fair share, but they can gain benefit from others cooperating. So cheaters have a fitness advantage over cooperators. This is Darwin's dilemma. If cheaters have a fitness advantage, they should survive, cooperators shouldn't. But we know cooperation exists in the biological world. Restraint is a sub. It will gain some benefit from it. No, I would consider those as cheaters. But they don't have a partial fitness advantage over the ones that produce it, have some other fitness advantage that counterbalance them. So if there's a fitness advantage that counterbalances, then it gets more complicated, Simon. But then maybe they're helping the cooperators. So there's reciprocal cheating, maybe, Bruce? Well, I mean, I didn't want to say cheating. Are we cheating on what? We're cheating. Bruce wants to know if we're cheating when we eat a cow. There's some Indians here. Well, I'm not putting anthropomorphic. I'm defining this very carefully. So us eating a cow wouldn't be cheating, right? OK. This could go downhill very quickly. Restraint is a self-imposed cost to cheating. OK, Simon. Policing involves cooperators hindering the success of social cheats at some expense to themselves. OK, Bruce? And the tragedy of the commons, I didn't make this up, is a depletion of a common resource by acting rationally in one's self-interest. So these are definitions, actually, that I've drawn from the field of population biology. I didn't make them up. So should cooperation be costly? There should be some cost associated with cooperation. OK? So now with those definitions behind us, I'll come back to them a little later. I'll review very quickly of what Vittorio talked about this morning. Form sensing is a kind of bacterial cell to cell signaling. And it's used to control specific genes. It allows coordination of group activities. And the system that's most well studied is the ASIL HSL type form sensing in proteobacteria. Vittorio said there were over 100 species that use ASIL HSLs. I count about 200. The signals are dedicated. They're made by the LuxEye family of signal synthases. And there are receptors, members of the LuxR family of signal receptors that are transcriptional regulators. And the INR proteins in any system have co-evolved. And they regulate different things and different bacterial species. And the basic mechanism was worked out in this marine bacterium in the 70s and 80s. There's a single gene, I, that codes for the synthase for the signal, the triangles here. They can diffuse in and out of cells at low population densities. They'll diffuse away at high population densities. They can influence the environmental concentration when the signal reaches nanomolar concentrations that combine to this transcription factor, LuxR, which activates the luminescence genes. So the signal is a proxy for population density. And this is the Vibrio Fisheri Horms Sensing signal. You saw it this morning. So what are typical Horms Sensing controlled functions? Well, Horms Sensing often controls common goods. I'm not sure whether they're common goods or public goods. I'll let you tell me. Light production in Vibrio Fisheri is an example. There are no biological detection systems that can see the light produced by a single Vibrio Fisheri cell. But we can see the consequences of a group producing light. We can see that light. As Simon mentioned this morning, exoenzymes are often controlled by Horms Sensing. I'll talk about extracellular proteases of pseudomonas today. So pseudomonas controls these proteases by Horms Sensing. Individual cells can't make enough of a protease to get anything from the environment in return. But the collective activity of the group will result in a rise of amino acids in the environment and small peptides. Pseudomonas can transport them in and eat them. And toxins, antibiotics, for example. So pseudomonas is an opportunistic pathogen of lots of preachers, including man. And it senses cell density through Horms Sensing. The idea is when it first invades a host, it will be at a low population density. The Horms Sensing signal will be low. And there'll be little activation of whatever genes are controlled by Horms Sensing. And to pathogenic bacteriologists, the genes are virulence genes and biofilm genes. It can grow underneath the radar. And when it achieves a high enough density, the signal concentration will rise. And there'll be a coordinated activation of whatever genes are controlled by Horms Sensing. That's about 300 genes on pseudomonas originosa. And genes coding for secreted products or the production of secreted products are overrepresented in this Horms Sensing Regulant. So these may be common goods. I think my battery is going dead. So pseudomonas uses two Horms Sensing systems. And the signals I'll call the C4HSL and the C12HSL. And here they are. The C4HSL is made by a LuxEye home log called RELL-I. And this transcription factor, RELL-R, responds to the C4 signal. Here's the C12 signal made by LAS-I. And this transcription factor, LAS-R, responds to the C12 signal. And LAS-R and LAS-I regulate, among other genes, RELL-R and RELL-I. So we say that LAS-R and I are on top of a Horms Sensing cascade. So because LAS-R is on top of this cascade, it's become a therapeutic target. But as we've learned more about pseudomonas infections, we've learned that in chronic infections, LAS-R mutants are common. So that's a problem if you want to develop a therapeutic. We need to know what those mutants are doing. Why do they exist in these chronic infections? Well, they could be social cheats. I'm waiting for it. OK. Is there another pointer? And the idea is that they may be individuals that don't share in the production costs of a common or a public good, like a secreted protease or an antibiotic, but they share the benefit. And cheaters, if they have a fitness advantage, should cause a tragedy of the commons. But we don't see that in these chronic infections. So Horms Sensing controls public or common goods. But there are exceptions to that rule. Among the 300 genes controlled by Horms Sensing, there are some that produce products that are associated with the cells. Adenosine catabolism is an example I'll talk about in a few minutes. But there are other examples. So a minority of Horms Sensing regulated genes code for what I call private goods. So why regulate a private good by Horms Sensing? Again, we focused on this gene, this nucleoside hydrolase gene, which codes for the ability of cells to grow on adenosine. So now that's it for the introduction, some data. We hypothesized that a few cellular processes, these private goods, are the consequence of them being in the Horms Sensing Regulant, is it allows a metabolic selection against Horms Sensing mutants as social cheats. And we developed some experiments based on some very nice experiments that were published at Martin Schuster's lab. Martin is coming to this meeting. I don't know if he's, are you here yet, Martin? I think he got stuck somewhere between Munich and here, so I can say anything I want about him. He developed a system where Lassar mutants emerge as social cheats in populations cooperating to grow on milk protein, on casing. And they used to do this, they used Lassar activated extracellular proteases. And so here's the design of the experiment. You make a minimal medium with casing as the only carbon and energy source and inoculate it with your favorite strain of pseudomonas, a wild type pseudomonas. And it will grow in log phase for more than 24 hours in this medium. So that means we can transfer once a day and keep it essentially growing in log phase. And then we can screen for cheats that don't make protease at five-day intervals. And this is the result of one of our first experiments. It was modeled after Martin's experiment. We reproduced them. And if you look for protease negatives among the population, starting with the wild type, at the beginning of the experiment, they're below detectable. But after about two weeks, they start to emerge in a significant proportion so that we can see them. And they reach about 40 to 50% of the population. They don't make protease. And then they equilibrate with the protease producers. And all the protease negatives are also unable to grow on adenosine. And you can sequence and show that they're protease negative because they have LASR mutations. So the LASR quorum sensing system is knocked out and they don't activate protease production. So now a story... Could you tell me what the well mixed culture... Yeah, so Simon's question is, what's the experimental protocol? Are they in some well mixed culture? And the answer is yes. So all of these experiments that I'll tell you about are in idealized homogeneous standard culture conditions. Right, so if we put some sort of constraint on them so that the population was fixed, you might get different results. This is liquid, this is liquid. Those sort of standard laboratory conditions. So again, I call these social engineering experiments and I wouldn't want you to believe that this has anything to do with how quorum sensing evolved over the millennia. I know I'm not smart enough to figure that out. Okay, I'm smart enough to figure out which button to push at this point. So restraint. This is again a self-imposed cost to cheating. And so we ask the simple question, what happens with casein and adenosine in the medium? So now you have one publicly and one privately metabolized Lassar-dependent carbon and energy source. So quorum sensing mutants can't use either carbon and energy source and they'll incur some sort, they might incur some sort of penalty because they can't use adenosine. So that, this is what I just told you. The adenosine in the medium will reduce the frequency of social cheats. It will be a metabolic constraint. That's the idea. And so you can do simple experiments where you include both casein and adenosine in the medium in this repeated subculturing experiment. And we use different concentrations in this particular experiment of adenosine. And here are the results. The black is the control. Pure casein, again, about 40% cheats. Anytime there was more than a half a percent adenosine in the medium, cheats didn't emerge. They got up to maybe one or 2% and that was that. So adenosine, the presence of adenosine was constraining. Okay, a tragedy of the commons. Again, this is the completion of a common resource by acting rationally in one's self-interest. So again, in this initial experiment, there was no tragedy of the commons. There was this equilibrium. And the glib explanation is sort of a tax cheat analogy. So we, among our human populations, at least in the US, we have a certain percentage of tax cheats. They're costing the economy. They're not paying into the common good, but they gain the benefit of all the common resources. And we put some energy into catching them, but we don't really put a lot of energy into catching them because they're not too expensive. But if there were more tax cheats, if we knew about more, then we would put more energy into that. Second best solution for any cheater, but it's not as good as for everybody cooperating. Right, so Simon says, why do I say there's no tragedy of the commons? This may be a different solution, but there is no tragedy of the commons, right? It's a different solution. Oh, is this the social optimum? Yeah, optimum. I don't know. If it's not the social optimum, not the social optimum. If it's not the social optimum, it seems there's any tragedy of the commons anyway. Oh, well, let's go on. Let's see. Okay. Just bear with me. The tax cheat analogy. Well, what I'm gonna tell you is that I'm gonna increase the cost of cooperation. And I'm gonna do it by taking away ammonium chloride. And so now the individuals have to use pacing for carbon, energy, and nitrogen. And in order to get enough nitrogen, they actually have to make more protease. So we've increased the cost of cooperation, okay? And the question is, will cheaters cause a tragedy of the commons if this is more expensive? You know, the good news is that this leaven is awake. I've got, I've, this one over here. I'm gonna pull it away. So here's the experiment. I'll sort of cut to the chase in this experiment with this is looking at the emergence of protease negative individuals. These are three separate experiments in the absence of ammonia and cheaters emerge. What I call cheaters emerge. They reach 80 to 90% of the population and then there's a tragic end to our experiments. When we transfer, nothing grows. There aren't enough cooperators to get the next cycle going. Here where they're making, where 80% are cheaters, 20% are cooperating and those 20% make about 10 times as much protease as 100% of our wild type pseudomonas when we inoculate endocasin. So this is very expensive for the ones that are remaining to make protease here. The red line just shows that if we add adenosine to the medium, it still serves to effectively constrain the emergence of cheats. So what about policing? This may answer your question, Simon. Policing again as cooperators, hindering the success of social cheats at some expense to themselves. So there's this equilibrium that we see that we've been arguing about a little bit. Why do the protease negatives reach 40% and stay there? So I know that Dr. Bruce Levin loves molecular reasons so I'm gonna see if we can present one. Cheaters should cause a tragedy of the commons. Maybe the cheats aren't pure. This was what was raised before. Maybe they evolved to provide some benefit to the cooperators. Maybe the proteases are partially privatized so that the cooperators have some little bit of advantage by holding on to some of the protease. Or maybe they're policing cheats. But if they do this, how could they do this? So for policing, we developed a hypothesis. Remember I told you that relar is subservient to lasar. Relar activates genes for toxic things. For example, hydrogen cyanide genes. Pseudomonas produces hydrogen cyanide. And if relar co-activates genes for resistance to toxics and we know it activates at least one of several cytochromoxidases and pseudomonas, then cooperators could hurt cheats. So again, lasar activates relar. So you can do a simple social engineering experiment starting with a relar mutant. So if you start with a relar mutant, it doesn't make cyanide. It doesn't make other toxic things. And this is what happens. We ran six parallel experiments. Starting with a relar mutant, cheats emerge at different times in four out of the six lineages and two cheats never emerged. And don't ask me any questions about those. But in the other four, cheats emerge and they don't balance off at 40 to 50%. They reach about 80% and there's a tragic ending. So relar is required to please cheats. And this just shows you can also do this experiment with a relar mutant that can't make the C4 signal. So with a relar mutant, you get the same result in three separate experiments. Cheats rise up above 50 or 60% and then we can no longer transfer the cultures. You can phenotypically compliment that mutation simply by adding the C4 HSL and that restores the equilibrium. And we were able to show that the production of cyanide was the key to this. So here we're mixing a cyanide mutant cooperator and sheet. So neither the cooperator or the cheat makes cyanide, starting with otherwise wild type and mixing them either 10% sheet or 1% sheet at the beginning of the experiment. And without the production of cyanide, the cheats quickly rise in abundance so they can take over. And this just shows that production of cyanide is costly to the cooperators. That's part of the definition of policing. Here is growth of a wild type in casing medium. It grows logarithmically for at least 40 hours. And here's a cyanide mutant. Right after about 18 hours, it picks up in growth rate. This is when it's really kicking on its quorum sensing system. So cyanide production is costly to the cooperators. So what about the real world? A non-homogeneous environment with lots of other bacterial species present. More like the cow situation. So I've been telling you about idealized homogeneous lab cultures. Glass-ar mutants emerge among cooperators. I mentioned that naturally occurring glass-ar mutants arise during certain chronic infections. And for various reasons, we're interested in infections of people with a genetic disease cystic fibrosis. So over time, they have lungs that are chronically infected with pseudomonas. Over time, glass-ar mutants arise. So what's the explanation? The situation is really more complicated than it would appear at first glance. So I'm just gonna summarize a lot of data here and then give you a model. So working with, again, Ajay Dandekar and Luke Hoffman who takes care of CF patients. They had a big collection of 2,600 clinical isolates from children and Luke sequenced glass-ar in all of them. And 580 had mutations that changed the coding sequence of glass-ar. He called them glass-ar mutants. Some were non-synonymous point mutations. Some were deletions, quite an assortment. So what we were able to show, we analyzed these. Most, but not all, turn out to be glass-ar in all. Some have active glass-ars, but almost all of the glass-ar null strains had a functional rel-ar and rel-i system. A rel-ar and rel-i system that escaped dominance of the glass-ar and glass-i system. And on a subset of those, rel-ar and rel-i had taken over enough to activate the protease genes required for growth on casein. So what we think is going on in the CF lung is that wild-type pseudomonas come in and then glass-ar mutants emerge. They may be cheats. At any rate, they seem to have some sort of growth advantage and knocking out glass-ar knocks out everything. So then the rel-system doesn't work and none of the genes controlled by quorum sensing are activated. So the fitness of the group maybe will wane and then selective pressure leads to the emergence of rewired quorum sensing system mutants somehow in this real-world situation. And what we end up with is a rel-ar rel-i system that's functional. So there must be something in the rel-ar regulon that facilitates chronic pseudomonas infestation of the CF lung. So we need to find out what these specific genes are that always are quorum sensing activated in these naturally evolved strains. And interestingly enough, we've done these social engineering experiments with some of the clinical isolates and you can get cheats that don't make protease but they're never rel-ar mutants. They always have a functional rel-ar. If you make a rel-ar mutant, mix it with the cooperator in the laboratory. It's wiped out. It has some sort of big disadvantage in these mixed cultures. So I think maybe that provides some sort of explanation for why one rarely sees a rel-ar mutants in isolates from cystic fibrosis lungs. So just some quick conclusions. In our idealized homogenous lab cultures, which now I've already been beat up about that, so no sense of beating me up more, social cheats emerge among cooperators. And naturally occurring Lassar mutants do arise in CF lung infections, but it's complicated. Well, I told you that quorum sensing co-regulation of a private good with a public good forwards a strong constraint on cheats. So this may have some practical consequences. If cooperation is expensive, the incentive to cheat increases and we can cause a tragedy in our lab experiments. And this cascade, the Lassar through rel-ar system allows cooperators to please cheats. So there's a mechanism for policing that involves quorum sensing control of toxins and antitoxins. So we've discussed these Lassar mutants in CF. The pie in the sky, the thing that I would like all of this research to have some impact on is I would hope that by learning about communication and control of cooperation, we can devise ways to induce tragedies of the commons to resolve infections. We have at least learned that Lassar might not be a good anti-quorum sensing target in chronic pseudomonas infections, but that may be quorum sensing is still a valid target. And just to finish, this is the lab group in its present form. I'll just mention a couple of people here. Amy Schaefer, a long-term research scientist in the lab, has been involved in all of the research. The last time I was in Trieste, we went home with Bruna Cattino, who's now a postdoc in the lab. She was working with Vittorio. And Becky Schultz, who's worked on common goods and bacteria just finished her PhD. And with that, I'll stop and maybe get some more questions.