13:04:21 Um, again I did introductions yesterday but now you can see my face, I'm Jay one at Indiana University.
13:04:29 My background is my classical training as an Ecology and Evolutionary Biology.
13:04:35 And then at some point I got really interested in microbes, we heard Rachel talk about this course of microbial diversity course Woods Hole I got I did the same thing I just came from there.
13:04:45 Last week, where it's a similar sort of boot camp sort of approach, but different types of, you know, people and different types of tools, I'm sure I haven't been participating in the classroom but um yeah so I think there's probably some concept of spam
13:04:59 both those, those fields.
13:05:01 So our lab is interested in the factors that generate and maintain biodiversity, we studied microbial systems in particular, But I'm also interested in how that diversity then translates into performance or function or stability of complex systems.
13:05:19 And we've been doing that in a variety of different systems.
13:05:22 As many of you here probably are as well.
13:05:25 And I would say for maybe decades, people have been trying, and pursued that question. Are there simple features of complex systems that we can use to predict how systems behave, how they respond to perturbations, how they influence the functioning of
13:05:40 a host or ocean ecosystem.
13:05:45 And to be somewhat argumentative I would say that the the examples that we have are few and far between, where we have really good strong predictors of variables of interest.
13:05:58 And I'm going to make the argument that a lot of that is due to the fact that there's a lot of metabolic heterogeneity within microbial communities and the way we study it.
13:06:09 And so I'm gonna start off with a simple assertion.
13:06:54 Okay, show trouble reading my, my handwriting. The assertion is is that not all individuals, either in a population or community should be equal in terms of their metabolic activity.
13:07:05 And I don't think that we should expect that either.
13:07:08 So if you're somebody who studies microbial communities and got settlements, maybe that's not something that's really striking you is very difficult to accept.
13:07:18 There's a lot of heterogeneity in the environment, a lot of patching this a lot of small environmental gradients operating on you know cellular scales of microns if we go out into the bay here and took a sample of sediments, there'd be a lot of variation
13:07:34 and the availability of electron acceptor and electron donors that would be used for carrying out certain types of metabolism.
13:07:40 There'll be steep environmental gradients, that are influencing the activity of those individuals.
13:07:49 So in nature we expect sort of this variation in metabolic ability among individuals in a population and certainly across PCs.
13:07:58 But even if we went into the laboratory where we have the ability to control things to the best of our means.
13:08:06 We can imagine a chemist staff for example, a vessel holding cells where there's a constant supply of resources fresh resources going in.
13:08:16 We got a spin bar at the bottom of that reactor that's making sure everything is very well mixed the outflow is balancing and flow.
13:08:24 And that population. After a few generations should come to equilibrium.
13:08:29 And in principle, all those individuals should be optimally synchronize in terms of their metabolic activities.
13:08:37 But if we look at those cells and we were to characterize them on an individual basis there'll be a lot of cell, cell variation.
13:08:46 And that now we're getting down into like what's going on with within a cell.
13:08:52 And a lot of that's due to the fact that there's noise that there's small quantities of molecules that are important for transcription and translation, that's leading to, you know, variation and log normal distributions and metabolic activity within an
13:09:08 Isagenix population.
13:09:10 And so that's the logic, from where I want to start that that metabolic heterogeneity among individuals within species and across other levels of organization, it should be the norm.
13:09:24 And I guess I want to just stop right now to say that I have no real strong agenda here with how, how we use this time so I noticed yesterday that people were interrupting so feel free to do that, we get through, only a portion of what I have over here
13:09:37 in the outline that's fine.
13:09:40 And in particular, I know there's some looking like 30 students who are zooming in and that's always awkward to try to interrupt somebody who's speaking in a different room and maybe in a different state or something but but feel free to jump in on that
13:09:53 as well.
13:09:56 Yeah.
13:10:11 What your last point about that we should expect risk takers off we're talking, we should expect heterogeneity even between individuals within a species.
13:10:16 Would you largely explain that or justify that expectation, based on spatial journey, or would you also expect that type of metabolic ingenuity if you did not have spatial structural spatial heterogeneity between individuals within the species that's
13:10:31 what I'm mostly about.
13:10:33 So I was using the example of like a chemist app because I'm familiar with those schema stats and that's a tool that you know cell biologists and microbiologist use for trying to minimize all, you know, all the heterogeneity so there is still spatial
13:10:49 heterogeneity and variation within that community but it's a well mixed reactor that approximate something that is the best of our ability to control microbial physiology.
13:10:59 Perhaps there's other ways of doing that.
13:11:02 microfluidic devices or something but but I just for to argue, I would say that we would expect there to be a lot of variation.
13:11:09 If we could somehow. Imagine reducing spatial complexity that exists within us test to.
13:11:16 So do we have data or mechanistic reasons to believe that even in such a woman systems you would have metabolic heterogeneity between individuals of us have the same species.
13:11:27 Yeah.
13:11:28 Okay, so that would just be that would fall into the work that's been done on stochastic gene expression.
13:11:34 Okay, thanks.
13:11:36 So we published a paper in term biology looking at baker's yeast the best studied eukaryotic organism and discovering that there to EPA genetically inheritable metabolic phenotypes in standard laboratory strains and, and while strange.
13:11:55 So that's an example of what you're talking about and given that it took 50 years and an extremely well studied organism to find this, you might expect that this sort of phenomenon and it's much more widespread, sorry degree very strongly was what you're
13:12:09 saying.
13:12:24 Just, just adding to it to Andrews common base at least one case and plant pathogenic Pseudomonas where they are to EPA genetic state so I would think that sort of falls under life history kind of stuff.
13:12:44 Okay.
13:13:02 biology.
13:13:02 And what I am going to do now though is define what I mean by dormancy.
13:13:11 And we thought a lot about this, and we're really vague about how we do it so it encompasses pretty much everything but it's the ability for an individual.
13:13:51 So its ability for individuals to enter a reversible state of reduced metabolic activity.
13:14:02 And,
13:14:08 yeah.
13:14:12 yeah. Sorry. Y'all was just asking. Jay about reduce through so yeah quantity reducing. Now, in that case you have many, many states of dormancy, or you can have like a threshold right passing which.
13:14:25 Right, it's very different now how do you think that relates to suspended animation, like observing animals right could you can also.
13:14:44 I'll jump ahead so one thing I want to do before I answer your question is this idea of seed banks is in the title.
13:14:50 Yeah, so, so again like I've set this up in a way that it's you know it encompasses everything and I, you know, there are there are examples of this.
13:14:52 And this is another definition and the seed bank can just be defined as the summer collection of all dormant individuals and a population or community.
13:15:00 So, so jumping forward, to answer your question, which is, there's lots of different flavors of dormancy, and maybe we can talk a little bit about that.
13:15:10 But it's something it's very ancient, it's, you know, some people consider it in origins of life models, but in the current inventory of the global biodiversity.
13:15:22 It manifests itself in many different ways.
13:15:24 So we can think about latency and viruses speculation and fungi and bacteria microbial protests will produce morphological rusting stages cysts worms can can engage in like dour stages Daya pausing and insects.
13:15:44 There's activation and to be in quiescence fish.
13:15:57 And so, and then you can go into non organism all systems where we also see the properties of dormancy and seed banks.
13:16:01 Non placental mammals can delay blastocyst formation bears hibernate. Right.
13:16:08 So stem cell dynamics, we can think about the way in which our immune systems function wound healing and the heterogeneity in neural activity in our brains all exhibit properties of seed banks and so what I've done is I've just, you know, created such
13:16:27 a broad definition here, that it encompasses lots of these things. And despite them, you know operating over disparate scales.
13:16:38 Lots of different origins independent origins.
13:16:42 There are some common rules, I think, and processes that govern seed bank dynamics, and I want to talk about that. And I think because of that way we're looking at this right now I think you can say this is a really good example of convergent evolution,
13:16:58 where organisms have come up with a common theme or solution to one of the big problems that most organisms encounter face, and that is that their environments are inherently variable fluctuating through time and they're unpredictable.
13:17:16 And those are the conditions. Traditionally, by which dormancy evolves.
13:17:22 But one of the things I think I would like to talk about today and emphasize is that, and that's that's well known in the literature.
13:17:29 But I think there are a lot of other biological features that may be enhanced or modify the bank dynamics.
13:17:38 You know population genetic processes species interactions and species interactions in theory, I mean we we thought a lot about how competition interacts with dormancy and gives rise to, you know, storage effect in the coexistence of species but I think
13:17:53 there are other types of species interactions including predator prey interactions which I hope to talk to a little bit about today, and maybe some other less well studied systems like mutualism and central fees, where perhaps the ability to engage in
13:18:07 this process where individuals can enter a reversible states of reduced metabolic activity could enhance or stabilize the long term coexistence of species.
13:18:21 Okay.
13:18:27 Everything I wanted to say right there was great.
13:18:37 So maybe now what I'll do is just get into, we got through motivations and I think definitions, like to talk a little bit about the fundamentals The way I see them.
13:18:47 And then right around here I might stop and show some slides PowerPoint presentation.
13:19:08 So if you didn't care about dormancy or the same bank. you took an introductory population biology course.
13:19:19 You have some state variable that corresponding to the abundance of a population size of this box would tell you how many individuals are there, there'd be some net reproductive rate, just going to add new individuals to the population that'd be balanced
13:19:34 out by some density dependent rate of mortality.
13:19:40 So again, if the argument is that we should probably be thinking about metabolic heterogeneity within the framework of dormancy been thinking about how we can expand this simple model.
13:19:51 So we're going to create another state variable.
13:19:55 Just going to correspond to the abundance of all the metabolically inactive individuals in a population or community.
13:20:07 And then we're going to have, there's no reproduction obviously because it's dormant and active is not replicating.
13:20:14 And there's going to be an arrow, where individuals are going to transition into a dormant state.
13:20:19 And then, because again, by definition, this is something that involves the ability for organisms to come out of that.
13:20:25 There needs to be reversible state so we have another arrow that corresponds to resuscitate so there's an initiation into a dormant state and there's a recitation
13:20:47 I think we can assume in many cases that this parameter describes the death rate of the active individuals is going to be much greater than the death rate of the dormant individuals.
13:20:50 is arrows in the bottom again represent sources of mortality to the population.
13:21:01 But that doesn't mean that this is you know necessarily zero or even approaching zero, there are costs, often associated with maintaining yourself in in metabolically active state.
13:21:13 You have to maintain homeostasis.
13:21:20 You need to maintain an energized memory.
13:21:23 And this way we can define perhaps what it means to be alive or dead. Once you lose the ability to transport protons across the membrane, your chances of rebounding from that are pretty close to zero.
13:21:26 You know, Ph can't be deviating too far from seven.
13:21:36 That cost some amount of energy. There's also just inherent DNA damage is occurring breaks in DNA that needed to be repaired. They can't shut down shop, all together.
13:21:45 There's some basal either survivorship or maintenance energy costs that's needed. And once that gets expanded coming back from a dormant state is probably increasingly unlikely.
13:22:09 Do we know whether bacteria traffic protest for example will discriminate between dormant, and active for career probably not so that would be a so yeah now there's a diet as well yeah you're getting to something that I had chemo chemo stats oh so you
13:22:30 the washout effect, which also doesn't discriminate between active and dormant, so too great to support your two great points, we're going to get, we're going to get to both of those. Yeah, those are those are really good excellent points. One so we want to use that question is some foreshadowing.
13:22:38 That's great. So, there's, there's a lot of variation amongst the bank size among ecosystems.
13:22:44 And the second idea about washout when when it when is dormancy adaptive given those physical constraints of being a small organism and in an environment where you're at the mercy of currents, for example.
13:22:57 And then the other issue is, I think you talked about the idea can produce discriminate against maybe an active individual and an inactive individual and the answer is yes, there are at least examples of how predators do that, whether they're able to
13:23:11 sense, or they're talking about taste testing or sensing some kind of chemical queue that would allow you to give preference to consuming one individual over another.
13:23:19 There is evidence for that.
13:23:22 So, so that would be figured maybe maybe what prompted that is this notion of mortality rates what's leading, it's not just perhaps energetics but also biotic interactions and interactions with the physical environment.
13:23:37 Okay, so, so those are the, those are the major arrows I also want to, since we don't case we don't get to it.
13:23:46 These arrows up here.
13:23:48 Time people will talk about dormancy is being dispersal in time, but not necessarily orthogonal trait. So one of my former PhD students dedicated a good chunk of his dissertation.
13:24:01 The thing was Navasky to basically thinking about how dormant when dormancy and dispersal positively co very how you could get selection on dormancy and how it could aid in the dispersal and lead to interesting spatial patterns of fibers.
13:24:18 And at the Edit at the population level could facilitate gene flow which might reduce kind of rates of speciation, perhaps.
13:24:44 So these arrows up here are meant to kind of indicate that this is not just one site that we're thinking about or one population that we can be thinking about dormancy in a meta population or meta community context.
13:24:44 So that's the, just the skeleton bust out some color now.
13:24:55 I won't spend too much time doing this but I think it may be useful for breaking up.
13:25:38 Now we're breaking up, there's composition within, within these black box.
13:25:38 There's only one individual. It's yellow here.
13:26:05 Can you get the point is that these, these populations are made up of individuals and individuals belong to different classes so we're thinking about a population these, you can think about these colored boxes is being different genotypes, or if you're
13:26:19 interested in communities made up of different species each one of those colored boxes could could could represent a different species.
13:26:27 And one of the things that motivated a lot of this research is that we had some tools that allowed us to go out and characterize what was likely to be in the active.
13:26:37 wasn't important that these the composition would be identical, and over and over and over again what we find is that the composition of these two boxes tend to be very different.
13:26:57 So that could be again either different species or different genotypes, but there could be other information contained within it's not just like classes those classes could have other properties are characteristics that could represent their evolutionary
13:27:11 history.
13:27:13 Demographic properties, such as the age of an individual or other functional traits characteristics and might describe those groups such as maybe the mutation rate.
13:27:25 Maybe it's a tolerance to antibiotics, its resistance to grazing.
13:27:33 Maybe other some other niche property like it's preference for pH or light availability.
13:27:39 Okay, so this is kind of, you know, constructed here so that we have different types of organisms or Gina types, with different types of properties that may be important for understanding the performance or ecology of that, that system.
13:28:00 How easy is it to exclude when you see that sort of compositional variability that you do better extracting DNA from the active guys than the guys in the dormant pole, and that that could differ for different species and so you could end up with things
13:28:17 looking compositionally different just because it's easier to score the Gina type of the active or the dorm and form.
13:28:26 So your question kind of about the mechanics of like how you characterize, I think there's a couple issues related to that question one might be for example, we started working increasingly with organisms like facilities that form into sports which are
13:28:40 notoriously difficult to break open and extract DNA from. So, yeah, I mean those are those are really important questions and if you're interested in these types of questions.
13:28:50 You need to be thinking about that sort of thing. I would also follow up and say that, you know, the way in which microbial biologists tend to study complex populations or communities focuses mostly on DNA its environmental genomics we go out into environment
13:29:09 and we take a liter of water, and we put it on a membrane filter and we extract everything.
13:29:16 And that's who's there.
13:29:17 But that molecule doesn't tell us anything about the metabolic metabolic activity of an individual there. You might be tempted to assume that the most abundant toxin in your sample is also the most metabolically active but we have evidence to suggest
13:29:31 that's not the case
13:29:34 of a, maybe a related sorry over here, disembodied voice.
13:29:39 Maybe you're going to get into it in the future but can you give us sort of order of magnitude numbers for a fraction of the population that's dormant this imbalance between active and dormant based on say taxonomic classification and if I'm also interested
13:29:56 in, if you have any intuition for how those sort of larger scale descriptors of this dormant active state depend on say the nutrient availability in the ecosystem or.
13:30:06 Yeah.
13:30:08 Maybe you're going that direction in which case you can just ignore me.
13:30:11 But if you're not I'd love to hear what your, what, what you've learned about that doing these measurements modular these got some building up to that maybe maybe too slowly, but hopefully that will hopefully we'll get there.
13:30:24 Yeah, there's a couple of them just, there's just like two more things I want to talk a little bit about before we get the fundamentals into and then, then the idea here is that, we'll get to this.
13:30:37 The prevalence and microbial system so I'll be able to tell you a little bit about that in just one second. Okay.
13:30:46 So it's really important to think about these arrows to
13:30:51 how, and by what mechanism individuals transition between these metabolic states.
13:30:58 And there's two general classes that people tend to think about in the theoretical literature, and it all depends on how variable and predictable your environment.
13:31:09 So when things are changing really rapidly super frequently, without any heads up, and it's a really unpredictable environment, you get the evolution of badging.
13:31:22 And there's there's different ways that organisms can bet hedge. So there's like conservative bet hedging where if you're in a variable environment, you might say well in a good year I could have five offspring, but in a bad year if I invest into five
13:31:38 offspring.
13:31:39 Four of them are going to die. So instead we're going to quote play it safe, the adage, and we're going to invest high amount into three individuals, irrespective of how the environment changes so that's conservative.
13:31:54 There's something called diversified but hedging in that case it's, don't put all your eggs in one basket right and so again if we're thinking about how a mother allocates energy towards potential offspring.
13:32:06 In that case, maybe, a lot of energy and resources would go to one individual there might be another individual gets an intermediate amount, and then there might be another fledgling for example that's only going to get a small amount of resource retention,
13:32:16 so that in that case. That's another way that an individual could increase its long term geometric fitness in a variable environment through the reproductive strategy.
13:32:26 And then finally, and I think microbial biologists are kind of keen on this idea, there's something called like an adaptive coin flipping strategy where from generation to generation, a strategy would be randomly chosen, so there's a stochastic element
13:32:41 to how an individual might engage in its reproductive efforts.
13:32:47 And, and there's some good examples and microbial systems.
13:33:03 its environment. And those in one of those individuals may encounter sub optimal conditions and die.
13:33:07 But over time, one of those individuals going to wake up going to experience good conditions. It's going to wake up might communicate with its neighbors and you would have some kind of coordinated response, some kind of density dependent fashion.
13:33:20 So So microbiologists are really interested in this idea that there could be stochastic initiation and emergence from from seed.
13:33:27 And again, all of those things should only arise when the environment is sufficiently unpredictable.
13:33:35 There are other features of an organism's environment, they'll that that tend to change more slowly.
13:33:42 Are there certain environmental cues that could be more predictable.
13:33:46 And in those cases, individual seems to evolve populations will evolve to engage in dormancy in a responsive manner.
13:33:55 So they're investing in sensors sensing molecules receptors readout molecules ways to interpret their environment their external environment and their internal environment, and change in an example of phenotypic plasticity in a way that is beneficial
13:34:14 to the populations fitness under, again, a less dynamic environment.
13:34:21 And even though that cost energy to invest in all that, it must outweigh the benefits of not paying those costs.
13:34:28 Um, so those are the two general categories of how microbes and other organisms on the planet, kind of deal with temporal variability, either through bad hedging when things are really hard to predict or through responsive transitioning where there's
13:34:42 investment and sensing of your environment.
13:35:01 Break.
13:35:07 Yeah, I can imagine that a lot of this theory does, importantly, we recently wrote some stuff on this and one of an editor thought that we were starting to invoke things like group selection.
13:35:19 And a lot of the theory that's been developed assumes that all the starting populations are genetically identical.
13:35:25 So a lot of these this emergence of of these strategy happens due to selection on reducing variants and demographic help.
13:35:37 So increasing geometric fitness over time.
13:35:41 And so if density dependence played into that right back perhaps that's something that.
13:35:46 Yeah, especially when there's genetic variation as well.
13:35:51 So the last thing I want to do before I get into thinking about this more explicitly in the context of microbial communities and populations is to think about the physical properties of seed banks.
13:36:04 So a lot of this has been, you know, ideas and these theories have been paid by people studying plant seed.
13:36:11 In fact, the word seed banks just conjures up plant seeds or you might be familiar with, you know, seed vaults in Northern Norway or something.
13:36:22 So, in that case, if you are a decent plant biologist and I asked you to go out and survey plant communities outside the building you might put a one meter by one meter square
13:36:36 in a box down on the ground and you count all the individuals with standing above ground biomass.
13:36:42 I don't think any unless you really cared about plant seed banks, you wouldn't be tempted to go into the soil, dig out seeds germinate them specie ate them and include that in your estimate of local diversity, that would be I mean some people are interested
13:36:57 in those questions and they do that but 99% of the people don't do that.
13:37:02 And so one other thing I want to point out in that case, the scenes take on very different physical properties, their their their densities, their sizes and their dispersal capabilities are vastly different from the actively growing trees that are grounded
13:37:19 and literally routed to that site, right. So, for some people who are interested these types of questions. This isn't a complication.
13:37:24 So, for some people who are interested these types of questions. This isn't a complication. But if you're in this class and you're attending these lectures, you're probably working with small single celled organisms that are difficult to look at and determine what's going on in terms of their metabolic activity we talked a little bit about how
13:37:36 little bit about how we go and we sample using meta genomic techniques where we're taking a whole bunch of soil and we're just Hulk extracting it right and that case, we're including whether we like it or not, individuals that span a whole spectrum of
13:37:56 metabolic activities.
13:37:56 And so, maybe you're not into the seed bank thing that's okay. But you should be aware that you're including them. And when the results and patterns that you're looking for in your data don't match theoretical expectations, you might want to just reflect
13:38:08 on this notion that in some environments, as I'll show you in a second, highly enriched with metabolically inactive cells, which can be obscuring the patterns that you're interested in.
13:38:25 And then I want to take this one step further.
13:38:28 In addition, just being a nuisance perhaps there are other microbial and biological biophysical entities were dormant individuals actually contribute directly to the development of us physical structure.
13:38:43 So we think about cancer tumors. Right. They're made up of both dormant individuals and active biofilms three dimensional structures where we have really well textbook described delineation of on the perimeter of that cell near the environment mostly
13:39:01 metabolically active cells and as you progressively go deeper into that matrix, you're going to be encountering or sister cells and garment cells and even dead cells.
13:39:11 And so in that case they're actually contributing directly to the, the physical construction of that biological feature.
13:39:22 So I think that wraps up all the things that I wanted to talk about regarding fundamentals, and maybe now we could talk a little bit about how prevalent dormancy is in the microbial world.
13:39:37 I think. Yeah.
13:39:46 I just wanted to confirm that when we do serial dilution experiments in the lab, we are necessarily. After doing a bunch of transfers getting rid of these documents.
13:39:56 Yes, right.
13:39:57 That happens so, so, So in principle and thinking if they Greg would access information about the woman says by taking a sample, or doing bring sort of meta genomic sequencing on the first transfer and sort of continuing to transfer it continuously and
13:40:14 energy for active cells and then to to manage genomic sequencing, again, and see in principle what's what's different.
13:40:23 Yeah. So, and the experiment that you know so maybe I could classic, which ones can experiment where you're transferring the coal I every day.
13:40:32 Let's imagine that there are, you know, during that time, the cells grow up, they probably reach some kind of stationery phase, and then 1% of that population is taken and transfer.
13:40:44 If you keep on doing that then eventually the accumulation of dormant individuals will be lost, because they're not replicating it's the same idea of a chemist out washing out that individuals.
13:40:56 Good, thank you. Yep.
13:41:04 While we're in very abstract land. Before you become concrete.
13:41:08 You keep talking about dormancy in a very adaptation manner. Yeah. Everything's adaptation.
13:41:16 Why is it the center of biofilm just they don't have enough energy and it's not an adaptation. So, yeah, I just noticed every sentence has been adaptation is done I kind of feel obligated to push back.
13:41:31 Okay, yeah, No, I think maybe that maybe that it reflects the fact that I want to tell you that there are a lot of dormant cells and maybe in this abstract land, what I'm trying to give you is some explanation for why it exists.
13:41:45 I agree that it may just simply be a byproduct. So for example there the the notion of the deep biosphere. Right.
13:42:02 To the best of my knowledge when people try to characterize environmental conditions and those habitats, they don't fluctuate.
13:42:08 Something like more microbial cells on the planet live in this hugely energy starved ecosystem deep below the surface.
13:42:08 It's been basically perhaps just a byproduct of organisms that have settled down into those environments, they're there. They're maintaining viability, but there's no chance that they're ever going to see the light of day again.
13:42:21 And so in that case, dormancy wouldn't be adaptive, and those environments.
13:42:28 given the timescale which that environment changes.
13:42:30 So I think that that's a fair point.
13:42:38 And I think the idea just like it arising from stochastic gene expression is another example where there's nothing that's necessarily adaptive it does, people have argued so what I want to try to get to now is some examples from microbes.
13:42:52 One of them would be, people tend to think that persisters cells. I'm not sure if everyone agrees with this but a lot of people will think that sister cells arise from variation stochastic gene expression.
13:43:03 There's some small fraction of cells that are metabolically inactive, because the drugs that people use to target those pathogens.
13:43:12 Target rhizomes, which are part of the molecular machinery for being an active cell that they can just tolerate those conditions and if you restart that population getting same outcome right, it's not there's no selection on those cells per se.
13:43:31 So, so yeah so so what are what are some examples some case studies, if you will, of microbial dormancy, I would say that there's maybe two categories there's model systems.
13:43:43 And then there's there's pathogens organisms concern for human health, perhaps, and so bacillus LS is one example and Australia that form into sports to point out that this is not a really widespread phenomena and the bacterial or microbial world it's
13:44:11 one phylum, and even within that phylum that that the ability to speculate seems to be lost. Many times, the really big gene, lots of targets for mutation. And if you relax selection then mutations will hit one of those gains and you just get the loss
13:44:15 of that trade, even though it's been around for 2.7 billion years.
13:44:21 And so that, so bacillus subtlest is people studying for lots of reasons. People started studying in the 1960s and 70s because it was, for whatever reason, it became a system for studying transcription and gene regulation, and then it became interesting
13:44:36 to people because it was a model for understanding development simple model Single, single, Sol system with genetic tools that allowed us to sell the circuits and the regulation of an interesting development the transition from a vegetative sell into
13:44:52 a spore.
13:44:54 And I guess there are some other reasons why we care about that organism. It's important for food security and after 911 people were interested in whether or not that organism could be weaponized by taking advantage and leveraging the ability of organisms
13:45:09 to form these inert sports.
13:45:14 So then I think there are other organisms that probably are less well understood or probably don't fall into the category of model systems but people still care about dormancy and acknowledge it, because of its relevance to human health.
13:45:27 So I grew up in northeastern United States where Lyme disease is pretty important.
13:45:35 So these tech bites you carries a spiral Keaton umbrella, you get sick you get a lesion bull's eye rash on your forearm. You're the doctor you get some antibiotics and more often than not, it clears up pretty readily.
13:45:47 But there are people patients who will come back a decade later, pretty sure they have not encountered a new tech and start to exhibit flu like symptoms and shakiness that are associated with Lyme disease, and in some cases it's been linked to the you
13:46:02 know this this organism persisting inside of our bodies for a long period of time below the radar system of our immune system. And then it re emerges.
13:46:12 Another example would be mycobacterium tuberculosis causative agent of TB kills about 2 million people per year, and one third of the world's population is infected with mycobacterium tuberculosis.
13:46:27 So again, another really important example where dormancy matters for clinically relevant populations. That's fine, there's a lot of work has been done on that but I was really starting to become curious about.
13:46:40 To what degree.
13:46:42 Suppose asking how prevalent is this in complex systems microbes in the wild.
13:46:50 And so that led us to about 10 years ago we started thinking about this question and before we start generating your own data, it's always nice to kind of go into literature and see what's available at least that's a philosophy that sometimes our group
13:47:04 subscribes to.
13:47:10 And
13:47:10 I'll just show you the pattern first.
13:47:18 So these are all mixed communities.
13:47:25 These are all in mixed communities. We're looking at the fraction of individuals in a sample that are, that are inactive by some measure and I'll talk about that a second so we've got the human gut.
13:47:47 wastewater treatment plants which are pretty important.
13:48:21 And know there's air bars on the salt literature mind data.
13:48:35 Smoke clarification human gut or human feces or species. Yeah. And how do you measure the y axis.
13:48:37 Yeah so so that was so this is a question that I wanted to kind of throw out there to the group, how do we measure this.
13:49:02 protein, make proteins, you need to have sufficient rhizomes, and for rhizomes you need to have RNA.
13:49:10 So a lot of what we've done, or the past decade is based on that series of assumptions right there and that if we can go into either a population or community, and we find that there are some individuals that have a high rises own content versus ones
13:49:23 that don't.
13:49:25 don't. It's a reasonable expectation that the cells with more RNA more rights than them will be more likely to be active. So in this case, these were you measure a cell by cell ribosome RNA content, these were to in this case what people did is we took
13:49:41 data where people had stained the entire population or community with a stain, like Dappy, which binds integrates with DNA before us is blue. And then these in this particular data set, they were using force them in situ hybridization is where they had
13:49:59 probes that bound to RNA opera.
13:50:03 that bound to RNA opera. So it's a ratio of the cells that stayed positive for arrives on count relative to the total community.
13:50:14 Similar things can be done with flow cytometry and certain stains that will flourish when you have an active electron transport chain have a student who's trying to do this right now with ATP dies.
13:50:20 There was a little confused about the first measurement. So yeah, the Dappy gives me the total DNA and then you're doing fish on ribosomal RNA. Yeah.
13:50:31 There's new techniques like so. Bond cats another one using, I forget the details of bond cap but there are ways to use some kind of amino acid analog that gets incorporated and if you see that signature which for us is that will tell you that that individuals
13:50:46 which can then sequence. so. So not only can you classify sell based on its activity but then you can also go back, find out who that order.
13:50:55 But if you make this single cell measurement of DNA versus rivals amo RNA is there just, is this a by modal distribution in the sense that there are some cells that clearly have almost no ribosomal RNA.
13:51:09 Yeah, so, I'm naive about how this No, that's okay. I mean, that's a good question and so this model that I have over here kind of would suggest that there's just two categories.
13:51:19 Right.
13:51:20 I think there are some pretty good examples where there can be intermediate states. So, um, and
13:51:35 The student of mine who's been working with flow cytometry and ATP shows that the the distribution of metabolic activity in a population follows normal distribution.
13:51:45 So there's going to be a small number of cells that are disproportionately active and a large fraction of the cells that are metabolically inactive.
13:51:53 But I think it might depend on the environment in which you're looking to it might depend on the, the way in which, you know, if you're a facilitator, there's clearly to sell states.
13:52:05 But maybe they're the word that I went to a meeting in Berlin and I like the way they were use this word notion that some cells can just fall into dormancy it's almost like this passive process right versus the investment in infrastructure that it would
13:52:20 take to make a sport, totally different.
13:52:24 So, when you talk about percentage of the community that's inactive you're only talking about cells, or is there any way to measure for example, percent of bacteriophage that Isagenix state, or is it are you measuring that in this.
13:52:39 So you're asking the question about other ways to look at this talking about cells or viruses also, this is, this is all bacterial and our killer cells.
13:52:47 Yeah.
13:52:51 When you make this measurement.
13:52:55 Yeah.
13:52:55 Is it really clearly by modal the distribution of that your proxy for activity there like DNA, RNA ratio thing. So what I what I should clarify.
13:53:05 I don't know about the distribution within an ecosystem type but what happens is you would just count all the total cells and you say there's 10 to the six legs are really good example.
13:53:15 You can filter in the 10 to the six bacteria per milliliter.
13:53:19 You'd count those number of individuals and then on the same sample and switch the filters over and you would look at the number of cells that fluoresce whichever whatever index of activity.
13:53:29 So I don't think there's any way to measure the distribute like there's just two values that would come out of a single sample.
13:53:36 Right.
13:53:40 Ah, yeah, this is all bulk estimate so people, but there are ways to measure things at the single cell level as well, and hope, hope. Yeah, we've done a little bit of both of those.
13:53:53 Hi, Jay. Just to clarify, so this I understood us from the literature right.
13:53:59 Yeah, yeah. okay.
13:54:01 And if this is just some I interpreted as a measurement, I am assuming going by the answer you going to sip it at this basically so measurement of growth, like protein versus DNA and, yeah, so I can imagine there are some environments where, or rather
13:54:17 the distribution of growth rate is a property of the environment. You know in some places microbes are very happy growing rapidly and others so much, but I don't understand what that has to do with dormancy.
13:54:34 Perhaps we have, because maybe my bias is that I'm thinking of dormancy is something where like sports that it's actually hard to revive them. But in, in, you know, some of these environments.
13:54:44 Well this is just, I'm trying to be pretty.
13:54:44 If you perturb it and give it, whatever is limiting that was what it was, it was limiting growth. Now they all may grow happily at least for a while. And so it's just it's just a property of the environment, you know.
13:54:52 Yeah, I think that question came up over here, too. I mean, I'm not with this method, you know, be a little bit cautious of like inferring what's going on mechanistic Lee but there's some kind of metabolic information that we're getting from this day.
13:55:10 And it seems to be repeatable because other people have made come up with similar numbers here. I think they made the question it's a little bit more interesting is that you know maybe we could ask ourselves why in the gut.
13:55:24 You know, 80% of the individuals seem to be in some by this measure.
13:55:29 We can we can debate whether or not it's a good measure, I'm fine with saying it's not that I didn't have to invest any energy and collecting any of these data that most, most of the microbiome in our, in our guts are in our feces seems to be made up
13:55:42 of individuals that are generally act.
13:55:46 individuals that are generally act. Whereas in soils, large fraction of that communities tend to nine cells that are in a gram of soil organisms that are recycling nutrients and consuming trace gases, things that we care about, or metabolically inactive.
13:56:01 So we can start to ask ourselves are there properties of the environment so residence time and flow through is one thing that we've done
13:56:11 is one of the things we've been thinking about and also just the nutrient blocks and the productivity of the ecosystem may also be strong determinant of these numbers.
13:56:19 Can I just ask you technically I don't understand how you go from an RNA, which I agrees all drivers oval RNA DNA ratio to a percentage of sales you need to make a choice.
13:56:28 Right, a conversion factor.
13:56:31 And I don't understand what that conversion factors.
13:56:35 So in this case, there was no conversion factor we just looked at data and we said, we have information two pieces of information about a sample. How many cells are there, and how many of them for us based on fluorescent and teach you hybridization.
13:56:49 And that should we know binds preferentially two cells that have high number of private zone.
13:56:56 So these are the numbers of just so I understand yes measured number of cells that have any fish probs yeah to the RNA, 16 s RNA, Yes, it.
13:57:11 Yeah. These are microscopic counts.
13:57:15 So you look on it, you filter cells on to a membrane and you die them count the number of individuals.
13:57:25 So I guess you could call that a single cell but there's no quantitative information. You see the cells either there or it's not there.
13:57:36 Yeah, you can do that.
13:57:39 We didn't we didn't try to extract that kind of information so you could use fish probe to look at the composition of individuals that are active and inactive.
13:57:47 So a lot of the 90% of the attribute dead, so it's definitely active right now.
13:57:54 So I hope we didn't get to derail by by this I don't want to get this is an old figure, it was a first stab at trying to get a handle on, you know, is this really important.
13:58:03 There are other ways that we could do this and we can talk about and I think the question I have is what what are the different ways what are the pros and cons for being able to get reliable measures of dormancy in communities and, you know, a lot of
13:58:16 that could come down to definitions or assumptions and caveats of methods. And so one of the things that we've tried to do is, you know, use a lot of different approaches and hope that there's general agreement and approaches that we use.
13:58:30 I would never want to like go out there and say that our method is great for doing this I think it's a challenging thing to do. Why don't one of the things that we started doing that lead us into a more productive area of research was to start using meta
13:58:56 data from these, you know, organisms like so mycobacterium tuberculosis. One of the ways in which cells wake up is they have something called a resuscitation promoting factors a protein that cleaves up to the glycans peptidoglycan beta one for linkage
13:58:57 there and so cells tend to get small when they're dormant. And when they resuscitate the need to get larger and so you can clean that bond. And it allows for remodeling of the cell wall and subsequent regrowth.
13:59:10 And we found that in doing a similar sort of thing here by just like looking into meta genomic libraries that the number of hits that came back for resuscitation promoting factor home logs were were quite common, 25% of all soil genome contains one of
13:59:26 So we've done a little bit of work where again another technique where we clone those genes into Nicole expression system and we can make recombinant protein that we can then apply to an environmental sample and directly, wake up.
13:59:43 Different bacteria similar things have been done in TB patients, then usually what you do is you screen sputum to test whether or not somebody has a certain variant.
13:59:53 And when they added these types of recess notation promoting factor recombinant proteins, you see a whole different set of genotypes that emerged in those in those in those samples and people refer to those as an adult population persisting but tends
14:00:09 not to grow and escapes a lot of clinical ways in which we diagnose.
14:00:16 So, we could talk a little you know a bit about about that it's two o'clock now I don't know how much time we tend to go I had another half hour. Okay, so why don't maybe we could switch to some stuff on the, on the screen, that's okay I can show you
14:00:35 some pictures I guess.
14:00:41 Just before you switch over here. Yeah, just to take the results that you put on the board seriously for a second.
14:00:49 So you suggested the beginning that dormancy is a response to variability of an environment, but it isn't obvious to me that the ranking of dormancy in these different environments, important the dormitory the provinces.
14:01:04 You know follows along any sort of intuitive notion of variability in the environment so would you say that we have incorrect intuitions about what environments are variable or are there other factors which Yeah, you are thinking about as being related.
14:01:15 Yeah, I think I got your question there towards the end,
14:01:20 it's hard to know how a microbe experiences its environment right if it's if we're doing something in the laboratory we can control that to some degree, the supply rate of eliminating resource for example or if we want to change temperature noise of some
14:01:34 kind of perturbation, right, we have the ability to control that but in nature. It's hard for me to know exactly what a microbe is experiencing and so what I have observed is that it seems like there's a lot of dormant bacteria and lots of different ecosystems
14:01:50 and so that made me think, well maybe there are other things besides just external environment driving.
14:01:59 So one thing is that there could be variability created and obviously inside of a population or community and I'm going to talk, hopefully a little bit about that in the context of like predator prey cycles and how that generates fluctuations and resources
14:02:11 that can feed back on the community.
14:02:32 But yes yeah i think i think maybe maybe there are other ways in which dormancy can be reinforced, not through just thinking about this as a external a biotic driver, the environment.
14:02:35 So half an hour.
14:02:39 I just want to talk about this idea that there are certain dynamics and properties that I think can emerge, or we should expect to emerge from from seeing banks.
14:02:52 And here's maybe some of the just a brief logic.
14:02:57 First of all, the seed bank is something that emerges from individuals behavior or transition between these two metabolic states as I had them depicted on the chalkboard.
14:03:07 I bet offer operates across spatial and temporal scales. So sometimes individuals are transitioning between metabolic states on the, on the scale of ours but individuals like bacillus and the spores they make can persist for millennia.
14:03:26 It creates structure is depicted on the right.
14:03:36 convey differences in genotypes or species.
14:03:40 And this can impart memory and delay so the process of resuscitation sometimes a hallmark property of dormancy is that organisms forgo the opportunity to reproduce even when conditions are good.
14:03:44 We have different member classes or little colored spheres that
14:03:55 So there's inherent delays to the system which can create feedback. And as I mentioned, you know, if we don't take in this into consideration. There could be things that are dynamics that we wouldn't necessarily expect based on other bodies in theory,
14:04:10 they don't think about metabolic heterogeneity.
14:04:15 This is just some brand new things that we've been thinking about it's a bit of a toy model.
14:04:28 And we were thinking about the game of life, which may be some people are really familiar with these tools were developed in the 1970s is a way to kind of explore emergent phenomena.
14:04:38 And I've been working a little bit with a student in my group named Pat wall he's a informatics background.
14:04:42 People started creating cellular automaton where you basically have really simple rules about individuals and you see dynamics and patterns emerge sometimes including chaos.
14:04:55 And so, the classical model, which is over here on the left.
14:05:02 is these labels should be reversed. This is the blue one is system without dormancy, and this one is with dormancy. So the Classic Game of Life has just a couple of simple rules.
14:05:14 And there's a two dimensional lattice of cells, and there's individuals that you can think of either being his presence or absence occupied or not. And there's a couple simple rule.
14:05:27 So, if you're surrounded for example by two to three, active individuals, then you live.
14:05:33 If you're in a relatively unpopulated local environment and you have no neighbors, maybe only one neighbor, then in the next state transition that individual will be removed.
14:05:45 So you're dying due to under population sort of basically an ollie effect.
14:05:51 And then of course if you're surrounded by a lot of active individuals that simulates overcrowding, and if you have too many neighbors then the next time step, you die.
14:06:00 And if you're an unoccupied cell, but you're surrounded by other active individuals, then you can reproduce, so really simple rules that people have been playing around with lots of variations and patterns that people love to just play around.
14:06:16 I'm sure it's a serious field research but then maybe that that kind of reflects where we're at right now with this. So we came across this paper up, you know, maybe two or three page paper published in a book in 2006 by job aid and he bought both course
14:06:32 that had come up with a different modification of some of the rules to create dormancy in a cellular automaton.
14:06:42 And I'm not going to go into the the rules because I'm not sure I believe in all of them but it creates a new state so it's not just live or dead but you have a dormant state, which is represented by these light brown colors.
14:06:58 And instead of dying, you can go into dormant state, and then the resuscitation rules are the ones that I'm not totally on board with but that's something that pat and I are currently working on, but given this this paper that was book chapters published
14:07:11 in 2006.
14:07:13 You can see qualitatively how the dynamics change very quickly and I think the unexpected, maybe for me now easily is that the fraction of living cells consistently greater problem because individuals aren't just dying.
14:07:28 But you can see much different patterns, these are patterns called still so they're they're just not changing from time stuff.
14:07:36 Quickly goes down to those five or six individuals.
14:07:41 We see lots of recurring patterns and the persistence of more cells and inactive state.
14:07:47 So this is like, almost devoid of all biology right, but we see the emergence of different patterns population densities and stability in this game of life.
14:08:02 I'm not going to spend a lot of time on this but it should be known that this is some work with will Shoemaker as part of his dissertation he's in Cuba course somewhere online right now and so in 2017, we started thinking about taking these ideas and
14:08:17 really thinking about them in the context of population genetic processes.
14:08:22 So, the ultimate source of all genetic diversity coming into a population is mutations of errors that are made, started digging into this a little bit more my assumption going into this was that all mutations are associated with replication errs, and
14:08:40 therefore mutation rates should be much greater in a active population versus the non dividing population. It turns out there's not a ton of really great data to get this there's a few examples in the East literature where people have compared mutation
14:08:55 rate and spectrum and dividing and non dividing cells, there are differences in the number of endows and the total number of mutations but an active population.
14:09:05 Yes, it's going to be replicating and therefore be more prone to making errors, but they're also repair mechanisms for fixing those errors, whereas an anon dividing state maybe cells aren't making as many replication errors, but they don't have the active
14:09:20 machinery for repairing cells but it turns out that somewhere about 10 to 2422 24 more mutations are entering a dividing population than a non dividing population so your genetic variation entering a population that has a lot of dormant individuals should
14:09:32 shut up.
14:09:35 should generally be lower. The effects of genetic drift should be reduced.
14:09:39 Because you have potentially a larger effect of population size there's more individuals in that population and genetic drift is something that's more powerful when acting on small populations.
14:09:50 Selection should be weakened.
14:09:53 So instead of being purged from population if you can enter a quiescent state, then perhaps you have a longer resonance time to that population.
14:10:02 And as I mentioned, gene flow the movement of individuals among population should also be facilitated if dormancy is something that can aid in dispersal a movement of individuals and heterogeneous.
14:10:16 And one of my collaborators in Berlin, your boss has done a lot of work using coalescence models which basically are aimed at kind of using stochastic elements and looking at mergers by looking into the path, and just past, and what the probability is
14:10:30 of reconstructing the least, the most common recent ancestor. And then this finger here all the when there's no seed bank this might be what your genealogy looks like you have what's called a week seed bank where individuals spend a relatively short amount
14:10:45 of time. In a dormant state and all it does is it stretches that genealogy, but at some point you enter a state that's known as a strong seed bank where individuals are spending a lot of time in their and their probability of merging with the proper answer,
14:11:00 because the The main thing is if you're dormant you can't coalesce with your your your your past commitments.
14:11:06 And so that actually ends up distorting the reconstruction of these genealogies. So in addition to affecting like core evolutionary population genetic processes, there are other larger scale evolutionary patterns that can be affected by see
14:11:26 can ask you about typical time skills, where that.
14:11:30 Sorry. Yeah, good point. Good.
14:11:33 So, it seems to me that you need to have, you know, dormancy on the order of a couple fixation lots of fixation times i is that reasonable based on the biology we know well yeah so I don't know if there's any way to tell when I've talked to yoke in about
14:11:51 can persist for much longer periods of time.
14:12:08 And so once you have that sufficient amount of time in the seed bank that's when through their simulations and math is beyond me in terms of how they drive this but you have to be in there a certain number of generation like an extended number of generations
14:12:23 before a strong see bank effect takes hold, and you start to see the coalescing approach fail.
14:12:33 There are examples where people have invoke things like, I know there's talking to some people about the interest in hand genomes and orphan jeans and things like that and you know how that could be due to lateral gene transfer broken is published in
14:12:49 in some work suggesting that you know an alternate explanation for that would be is that if you're sampling individuals that are in this deep seed bank, the strong seed bank that that could give rise.
14:13:03 Yeah.
14:13:07 Just a clarifying question so the implication of potential increased gene flow or dispersal as a result of dormancy is that simply because dormancy would facilitate surviving whatever that disperse mechanism is there's there's something more to it.
14:13:22 Yeah, I think that's the one is that maybe, maybe moving between habitats, can be stressful. I mean, you hear about bacteria that can live in, you know, in currents in the atmosphere for something like 14 days I would imagine it's a really not a good
14:13:37 place to be for that period of time, but then also I would add to your, your intuition, which I think is correct is that once you land in a new environment that's sort of a chance event.
14:13:48 Right. And you may land in a good environment or you may let it land in a bad environment.
14:13:53 And so your ability to ultimately colonize and reproduce once arriving in a new environment should be facilitated by.
14:14:04 Thank you. Yeah, that makes sense. So I heard.
14:14:09 Yeah.
14:14:10 Just to clarify, these genealogies of individuals are these species followed journeys. These are individuals. Okay. So, these are often not hard to measure especially with recombination, it's not even clear whether we can construct something.
14:14:29 So this again, this is some work that again I'll give some props to will Shoemaker who's in the course. So, we, we had that arrow coming out of the dormant box right we mentioned that we expect that there should be some mortality, but it should be greatly
14:14:47 reduced and I think a motivating question for a while is a very simple question is using a collection of bacteria. How long can long can individuals persist in a state of reduced energy input.
14:15:02 What's the longevity of an ENERGY STAR micro.
14:15:06 And so we.
14:15:08 I guess it was a while ago now we started this experiment because it took about three or four years just to generate the data, really simple experiment, we started off with enriching a bunch of different types of bacteria from soil.
14:15:22 We grow those individual populations up media rich media harvest the cells, and then wash away any residual media and recent spend those chemo organic traffic bacteria so they grow with oxygen is the term electron acceptor and use carbohydrates as their
14:15:42 energy source re suspended them in buffer and sailing.
14:15:47 So there's no energy there.
14:15:49 And we monitor. So then we call these things effectively closed we had to sample them and open them up. We had them in a dark drawer, no light.
14:16:02 And we would just open the caps taken Eloqua and calculate the number of colony forming units over time.
14:16:05 And I think when we first got into this the expectation was that across these different species, we would plot on a semi log plot that the abundance of colony forming units is the function of time and we would fit that line, we take that parameter and
14:16:18 interpret that as a death. My naive assumption was that this would be a first order process to be a constant number of individuals dying at each time step.
14:16:29 And that's not what we saw. In fact, of the 20 some odd species that we tracked.
14:16:35 Only one of those exhibited those population.
14:16:39 Instead, when we use this survival model we had to fit it with a viable function, which has a shape parameter. And when that a parameter equals one, you get this first order expectation, but we found is that k was always less than, which gives this kind
14:16:58 of characteristic shape that we have over here on the right.
14:17:03 And so we did this for and I'm just showing it to keep it in the theme of what is properly considered dormancy I just pulled out the bacillus populations.
14:17:12 These organisms as you might expect, lived the longest, we can estimate the time until death for an average individual, and the time to extinction to that population.
14:17:25 And for the spore former and the solid yellow dots it's somewhere on the order of 1000 days until an individual dies. The time to extinction is estimated at something on the order of 10 to the five years.
14:17:38 So one of the things that we did to kind of direct so will and I, we went back and forth for a long time about what the importance of actual sport formation was that relationship because we always always argument, like this is a biological outlier.
14:17:53 Simple let's do the experiment. So we knocked out a gene, suppose zero a gene which is the master regulatory gene involved in sports relation we redid the experiment so the other experience I'm talking about we went to let those run for 1000 days.
14:18:08 We as well and I were going back and forth on this we only ran this one for 80 days. So you can see that the shape parameter, what I'm telling you the shape parameter is identical, but the estimates for time till death and time to extinction.
14:18:22 They go down, but about 95% just still really large number.
14:18:26 So, a lot of the persistence of these populations in the absence of any dodginess resource inputs is not due to correlation or dormancy proper.
14:18:39 And one of the things that seems really important, and Will's major contribution to this is that what's leading to the lack of that first order decay process is it organisms are recycling dead cells.
14:18:52 And there's multiple lines of evidence it's hard to test directly but I looking at concentrations of metabolites by looking at cells individually with looking for the accumulation of dead cells that there's no dead cells and any population.
14:19:09 This is the number of colony forming units that form over time.
14:19:17 So, it's a viable function that incorporates.
14:19:23 Well, it's a survival analysis that has a parameter that account.
14:19:27 That is what causes the deviation from from first order. That's what gives rise to this curvature. But these liable functions are the fields to this data or some longer experiments.
14:19:51 This is just a shorter, I can show you the other data for 1000 days this is one population we did this for 20 populations. But, but those dashed lines that fits to this day there was some other data to those data for this data.
14:20:08 Yeah. Is it a good fit.
14:20:10 Yeah, I think it's a pretty good fit. Well it looks flat to me right we extrapolate thing.
14:20:30 We've tried other models. So we've tried. There are other demographic models so Gompertz model which is often used for modeling survivorship, but they're fit the fit is just, it's a statistical model.
14:20:41 Can I ask you a question about these experiments. So, so one observation. I think I think in my group we read the newspaper, and one of the surprises for us was that there was no reductive division, which is something we have seen in our lab when we take
14:20:55 some of our bacteria we put them in a neutral limited environment or sorry, let's say we remove the carbon source. And then they actually can increase an order of magnitude in CF us, because the cells.
14:21:08 apparently you know they eat their own right was something they shrink and then you get more CPUs.
14:21:11 Not Not Not more not burn biomass. And that's if that makes sense to me, and I was surprised, of course, you know, maybe not all bacteria do that but I was surprised that you don't see that.
14:21:20 Do you have any thoughts on that you're saying that you get some. I think this is a very common phenomenon okay yeah I'm not familiar with it.
14:21:34 No, no, you take a sale the sales out of exponential into an environment where there is no, I'm sorry. You start them. So, meaning that you take them out of exponential to environment we're now for example carbon is limited, so then now they stop growing,
14:21:49 and you still get a lot of divisions that are reactive, they start eating their own their own privacy concerns and I say something like that. Yeah.
14:22:01 But
14:22:04 fine but started zero, okay, but it's an order of magnitude increase.
14:22:09 So, yeah.
14:22:12 So it looks like maybe just got a few more minutes and so I wanted to since I've been a theme that seems to be popping up from discussions I've had with people and what I understand to be previous lectures that seems to be a lot of people who are interested
14:22:28 in Consumer Reports resource dynamics community ecology species interactions, and microbial Consortium.
14:22:36 And so we started thinking corner is asking like Are there other ways or features of of a system that could enhance or promote or modify.
14:22:48 When and where you would expect dormancy to arise.
14:22:53 And so we started thinking a little bit about this work so we've got our dormancy model and then we started thinking about competition as well studied and well understood in the context of dormancy, but we started thinking about well what would happen
14:23:06 if, if you're a dormant could there be some other benefits and in particular we start to think about interactions with predators and parasites.
14:23:14 Perhaps some of the motivation like is their preference for organism, you know, attacking and feeding and reproducing on organisms that nutritionally or metabolically are in a different state than individuals that are not.
14:23:26 So we had this idea of a, of a host page resource module combined with his dormancy module and you can imagine that 12 known that predators feed on prey and that alters resource dynamics and the environment.
14:23:42 And there were a couple other studies that were coming out.
14:23:45 One out of Joshua whites his group, who's collaborating on some of this work now were actually just the physical contact of a page particle with receptors in a, in a sofa low beside Atlantic is strain would would cause cell to enter a dormancy.
14:24:02 And the way it was done in character was sort of interesting, they just expose page particles to UV which kept the structure of the page intact but obliterated the DNA so there can be no replication.
14:24:14 And so when they expose those UV treated rage against the host and saw that they were dormant included in a lot of the substitute to the physical contact was another paper that came out a couple years ago with listeria which again is important food borne
14:24:29 borne pathogen that contains a crisper cast 13 mechanism. And so when that when a virus infected that population there's a crisper element that would not only target the face but also reduce the metabolic activity and the rub his own content of the host.
14:24:59 dx defense effectively deactivates the page, classical immune response and CRISPR systems but it also reduce the metabolic that can be the host, but in that population there were other individuals, it didn't have that CRISPR spacer and that way this got
14:25:02 written up in nature as an example of bacterial dormancy effectively being a mechanism of herd immunity.
14:25:09 So all these ideas were coming together at this point and we started thinking about the importance of dormancy and modifying host stage interactions.
14:25:20 So, for those of you, maybe talk a little bit about this already so won't belabor this picture on the left comes from a photograph of the postdoc, Daniel Schwartz took where there was a GFP reporter behind a germination gene and put these cells on an
14:25:34 auger pad with glucose and you can see them lighting up so as an example again of responsive transition.
14:25:41 There are some studies suggesting that this can be stochastic but by and large speculation and display relation in the form Mickey. Mickey D's is a well known example of responsive transitioning, and there are lots of different states and some of them
14:25:55 are really interesting so you can. The whole process of making a spore takes about eight hours.
14:26:01 Get under good conditions cells are replicating every 20 minutes so this is a major investment about 500 genes are involved.
14:26:09 And there's really kind of interesting stages here there's like a commitment state early on.
14:26:14 So you can start to go down this path of speculation, and if conditions return, you can go back but if you pass the commitment state. There's no longer a way to go back to your vegetative state.
14:26:26 Yeah. Yes. So, if I could go back to the page post dynamics.
14:26:32 When you talk about the effect of dormancy you also have to think about dormancy of the stages that is the fact that they can go into this Isagenix state.
14:26:40 And have you heard about that, or yeah so you could you could imagine these, these fades, the host we're using doesn't have any profane in it. So we removed all those the profane from this host and we're working with.
14:26:53 Okay, okay.
14:26:54 But in the licensing case for example you could have reproduction, even in the dormant state right. If you had proof age, it would reproduce along with along with the cell.
14:27:04 Yeah, so, so yeah, so I saw Jenny or profane jars example of where a virus can infect a cell and it can incorporate into the genome replicate sort of silently and people have talked about that as an example of age dormancy.
14:27:19 I'm going to show you another maybe if we have like just two more minutes I'll just kind of allude to another interesting way which page team to be able to, co op and take advantage of speculation, just on the slide you just showed us Can you just explain
14:27:33 what you mean by responsiveness. I didn't understand how I see that from the picture that the best list to be subtle as GFP is responsive to solving. So there's, I'm not sure what the time scale of this video is.
14:27:48 But we put this out. That would be the important thing I would need to know but but what we did here is we, there's a Jeffrey Porter behind the germination gene we put those sports I started off at time zero with only sports so we can get Trint the population
14:28:13 and purify the sport population with the men are auger pad with resources and anytime you see a green cell lighting up, it means that it's, Germany, but they don't all do it, I don't understand what responsiveness means is all I'd like you mean that some
14:28:20 of them respond to the fact that there's glucose present. Yes, okay, but not all of them respond, it because in this move because in this moment that they're not all turning and gets 100% over some amount of time or nearly so I don't know if that's.
14:28:32 I don't know if all individuals wake up.
14:28:39 So I'm gonna maybe I just to be a good place to wrap up right here so here's how we're thinking about we have some data, I'd be happy to talk to people more about this later.
14:28:47 So the question was about kind of what's going on under normal conditions. So, the classical host page life cycle is depicted in the upper left hand panel.
14:28:56 A. So you have vegetate themselves which are active, the encounter of age particle age bind to receptors inject this DNA makes New Age babies license the cell and there's gonna be about 100, on average, new virus particles produce per viral infection.
14:29:11 That's a really strong selective pressure.
14:29:13 And so one of the things that we were thinking is that well when you turn into a spore those receptors are no longer being expressed basically just have a ball of protein.
14:29:22 And so we did an absorption asset we're just measuring the binding affinity of age particles to spores versus vegetative cells, and you can see in the blue line that page are touching very rapidly.
14:29:33 But when you see absolutely no absorption of those page particles. So in this way there's sort of a spore wreckage.
14:29:42 But what I think is really interesting and we're getting much more deeply interested in this idea is a process known as entrapment.
14:29:50 And so, if a virus infects the vegetative sell it is undergoing this formulation process.
14:29:57 One of the things that's really interesting is that soon as sigma factors start being expressed for speculation.
14:30:05 The, there are
14:30:09 binding with page promoter sites, which basically halt, or stall, the process of viral genome replication. So it shuts down the capacity for the viruses to make new virus particles so that's step one.
14:30:24 The other thing is that during this whole process of sport relation, the host undergoes a genome replication so you need to have a copy of the genome and the mother cell and then another copy for the for spore because there's different types of gene expression
14:30:40 occurring in both cell types. And so what happens is that there's a process where the genome needs to get translocated into the new spore, and the virus actually takes advantage of trans locates which an enzyme, and RS genes, which are involved in chromosome
14:30:53 segregation and it effectively pulls the page genome intact into the spore, and a process known as entrapment, which I'm not a geneticist by training, and usually I don't really get interested in that kind of stuff but what was really cool to me is that
14:31:10 I think we thought about this from a more conceptual or theoretical perspective that maybe dormancy could aid in as a refuge. and then I think as we dive more deeply into it realize that the the genetic mechanisms underpinning this or actually.
14:31:25 Maybe it's chance but it seems like there really is something going on because in the end what we can do is that we can see these page, page, and they don't integrate into the genome.
14:31:37 So it's not, it's not an example of life.
14:31:42 And so I just want to end with, we're leveraging that information to in deep course with collaborators in Vietnam. Vietnam, this is a Mar lake is about 2 million years old, which is pretty long purchased in a landscape.
14:31:54 So there's no materials flowing in from outside the watershed everything that's being recorded in the segments comes, I'll talk briefly from within the lake.
14:32:04 And one of the things that we've been able to do is take those sentiment cores and this is an example in the middle of phase contrast, you can see that these bright shaped objects here are spores from 50,000 years.
14:32:14 We then heat treat those spores and wake them up, and we get virus particles that come out of them. So it's possible that some of those virus particles are not in trapped viruses, but they could be emergence of viruses from Pro age.
14:32:31 And so there are ways to kind of figure that out, you know medically, but the idea is that we should be able to reconstruct the evolutionary history of hosts age interactions by leveraging this phenomenon of virus as being basically entrapped in case
14:32:47 they're taking advantage from a survivorship perspective the virus their their their survivorship component to fitness is being presumably leverage by the virus or taking advantage of that and they're being stored in spore, and if they weren't those foods
14:33:02 would otherwise not persist. So we should be able to reconstruct and do challenges across geologic time scales to reconstruct
14:33:13 post parasite co evolution and bacteria and page again by leveraging the fact that spores are long lived.
14:33:22 And they entrap their parasite
14:33:26 as like an hour and a half, lots of really good questions. Thanks for your patience and hanging in there.