 So thank you Silvia for the introduction and yeah, I'm really happy to be here and although it's virtual and and also happy to open this workshop. And today I'm going to tell you something about the project that I recently wrapped up at MIT. And as Silvia said, it's trying to find the links between available resources and microbial community diversity. But before digging into the results, I want to give you some motivation. So the motivations that frame this project. So, as Jacopo said before we live in a really diverse world. And so life on Earth has evolved in a variety of mesmerizing variety of forms. And of all these forms I would say that the most ancient and the most abundant is not depicted in his drawing because he's invisible to the naked eye. Indeed it is represented by bacteria. So bacteria colonize every corner of the planet from the most extreme environments like deserts, geothermal areas and hydrothermal bands to our body. And microbes perform crucial functions both for our health and for the health of all these ecosystems. Indeed microbes are involved in the cycling of nutrients in the fixation of carbon dioxide and also in the production of oxygen. But microbes are not only super abundant and extremely ancient, but they're also very diverse. To give you some numbers, I can tell you that the total diversity of microbes, it has been estimated to be about between 0.8 to 1.6 million of prokaryotic species. And it's not only global diversity that is impressive, it is also at local diversity that is quite impressive as well. For example, communities, microbial communities that live in our gut can host between 500 to 1000 species. And even more impressive, a single grain of soil, a gram of soil more or less, can be home to between 100 and 1 million bacteria species. So I would say that one of the most important questions that we as microbial ecologists are trying to answer is actually how can we explain these microbial community diversity. And let's say that the question of how can we explain species coexistence has been around for quite some time. So in 1961, Archinson published a paper that then became famous and which is entitled The Paradox of the Plumpton. So Archinson was wondering how is it possible that an environment like the ocean that is so poor of resources could harbored so many different species of Plumpton. See, this was a puzzle because ecological theory says that equilibrium at equilibrium, the number of species cannot exceed the number of available resources. And indeed, when we think about diversity, we usually think about available resources. So going back to microbial communities, when we think about the heterotrophic bacteria, they usually compete for sources of carbon nitrogen and phosphorus that they can find in their environment. And here I'm just providing some examples of what's on their menu. So like you can find compounds like glucose, which are simple sugars but also complex polymers like cellulose and also desaccharides and amino acids. And there is a large series of a large variety of models that predict that if I provide just one of these compounds, let's say for example glucose as a source of carbon. And let's say that there are two species that compete and can use that can use this resource and compete for it. And for the principle of competitive exclusion, just one species is going to survive an equilibrium. And usually the species that has the highest growth rate. But actually when scientists started performing these experiments realize that if they provide just glucose as a source of carbon, then they could see many species coexisting. And if let's say the growth environment doesn't present spatial heterogeneity or variation in time of some parameters, then one of the possible explanation of the coexistence of so many species with one resource is a phenomenon that is known as cross feeding. So with cross feeding, we mean the process by which, well, microbes grow in their environment and start producing metabolites that can leak in the environment surrounding the cells. And these metabolites can become nutrients that other community members can use to grow. And so we learn and these experiments have tried some different carbon sources, not only glucose, but some amino acids and also some polysaccharides. And we learned a lot about cross feeding and species coexistence with experiments with just one carbon source. But in their natural environment, microbes are exposed to the pool of resources that varies in their size and also in their composition. So what we exactly try to ask in this project is whether we can link the availability of carbon sources and specifically the number of resources and the type of resources that are present in their environment with the community diversity. So, and in trying to answer this question, today I'm going to show you that in our experiments, we see that there are many, many compounds that when provided at single resources, they sustain remarkably rich community and we think that this is due to cross feeding. I will also show you that when these resources are combined, very surprisingly diversity increases only modestly. And then I will show you that we think we can understand this pattern. And I will show you that we just introducing resource utilization strategies and meaning the presence of generalists and specialists in our communities in a local data model, we are able to reproduce our experimental results. Before digging into the results, I'm going to briefly tell you something about our experimental protocols. So we started with the soil sample, and I actually collected these soil from the lawn in front of our building at MIT, I didn't go very far. Back in the lab, I mechanically detached the bacteria cells from the grain of soil to obtain a dense microbial suspension. And to give you an idea of the diversity of these microbial suspension, we had more than 700 species. So I use this diverse suspension to inoculate 75 different resource environments. And so to create these resource environments, I started with a pool of 16 carbon sources. And if you're interested, all these resources are quite abundant in the soil and they include simple sugars like glucose, but also the saccharite, organic acids like citrate and also some polymers. And all these compounds could be provided either as a single source of carbon or in combination of two, four, 15 and all the 16 resources together. And we kept the constant concentration of carbon constant across all these different conditions. So to grow the bacteria, we adopted a daily dilution protocol, which means that at the end of every 24 hour growth cycle, we took a small amount of the bacterial culture and we inoculated it into fresh media. And we followed this protocol for seven days. And at the end of the seven days, we extracted the DNA and thanks to 16S RNA ampli consequences, sequencing, we could get the composition of the communities. So maybe this is a good time to stop for a second. And maybe if you have questions regarding the experimental protocol. Let me know if there are any questions. Actually, I actually do have a question. You mentioned that these resources are all, I think you may even said abundant in the soil. Somebody just like went into the soil and did mass spec on it something or like these organic acids detection. And what is the thinking that, like, where do they, is there one resource that sort of gets in and then all the others are byproducts of microbial metabolism, or is it just being plant matter where does where does nutrient appear from in the soil. Sorry, this is a super nice question but it's actually good question. So, well, for sure, some of these, well, are generated micro by microbes themselves by degrading the organics, the organic compounds, like for example, cellulose come from the degradation of food. Or, but some compounds are actually produced by plants and released in the soil, not just by microbes. So for example, hydroxy this hydroxy proline here, which is a like it's similar to the amino acid proline. But it's actually a compound that is a common X to date of plant roots. So it's a mixture of things that maybe can be introduced in the environment. And now it's not the case. But if you think about compounds that contain nitrogen or, or phosphorus, they can be fertilizers so are, for example, introduced. But some of them are produced by microbes themselves, and some of them are produced by plants. And, and yeah, some someone actually does must pack not us and try to see, try to understand what's what's in the soil. But thank you for the question. So, if there aren't any questions other questions maybe I'll start showing you the results. So, in the next few slides, you will see a bunch of plots that look like this one, in which we have the number of supplied carbon sources on the X axis, and the richness measure that the number of bacteria species on the Y axis. And here, this green line that appears, it's the bound to richness that comes from the prediction from competitive exclusion. And I will start by showing you the results of single resource communities so those communities are supported by just one source of carbon. And so, each dot is that as a color corresponds to each color corresponds to a particular carbon source. So, the first observation that we see is that each single resource can support a multi species community with an average richness of about 22 species. The other thing that it's quite relevant is that there is a quite large variability around this mean with some resources like these organic acids and this hydroxyproline that I was mentioning, supporting about the dozen of species, while other resources like this are low supporting up to 40 species. And the fact that single resources support multi species community is not a new result, but we could show it for many resources. And since in our, in our growth media, they are quite homogeneous, we think that the main process that is going on here is course speeding. I will tell you more about this later. I'm going to go on with these plots. So, now we saw these quite diverse communities and we thought, Okay, now, as soon as we introduce another resource we will see a rapid increase in diversity. And yet, we actually started making some guess some guesses, how can we predict the richness in two resources. So, now I'm just showing you a couple of examples of these predictions that we made at the beginning. And let's say that we have. Now I'm showing you an example with glucose and proline again. And, but I will refer to them to the with the red and the blue compound, which is the thing that counts here. In glucose we have 20 in the red compound we have 24 species and in proline we have 11 species. And what happens when we mix them. Well, we might imagine that we can find all the species that we found in the two communities in the new community. And if there are some shared species then this is the union of the two communities. And with our data we predicted that in the, in the red and blue community will find about 30 species. But you might also think that maybe there is more niche overlap, and the union is quite a high number so maybe the, the, the richness that we might see corresponds to the maximum richness that we saw in the two constituent singles. Well, but the thing that was quite that we found is that when we measure the richness in the red plus blue, we found that there were there were about 16 species, which is much lower than the prediction that I just told you. And actually is much more similar to the mean of the constituent singles. And this is quite difficult to explain if you think about it. And we thought, yeah, maybe this is an exception, but it's not like this because when this is actually the rule for two resource communities. And indeed, let's say that the richness in two resource communities is approach is well approximated by the average richness of constituent singles. And this plot, maybe it's a bit cramped, but what this is just showing you the richness in single communities and, and the richness into resources with lines that connect the constituent singles to the respective pairs. And by you can see that all the lines tend to the majority of the lines tend to converge in the middle. Well, now, as a result, when you when we plot the 24 combinations of two resources in our in our plot we realize that adding a second resource does not significantly increase community richness. Now what happened when we when we started measuring the richness in all the other combinations. Well, what jumps to the eyes that the community diversity increased only modestly with the number of additional resources, but the thing that is most striking is that the increase is linear. And it also happens at the constant rate of about one species flash new resource that we add. And when we look at this pattern, we were quite puzzled because we couldn't understand what was going on. But we started to see the light at the end of the tunnel and when actually we started to dig deeper into the composition and the structure of single resources. So maybe I don't know, yeah, and see if you think that this is a good time to stop again and to see whether there are other questions. Yeah, sure. Let's see if somebody raises their hand. Yes, we have a question from Ankit, please you can unmute yourself and ask. Hi Martina. Hi, this might be a very naive question but like, do the race, like do we assume that the resources don't affect each other. And, like, there are no direct interactions between like even the bacteria, or chemically if in the solution the resources might change. Yeah. But I don't think so these are quite, these are not reactive compounds so they are in the solution and I think you mean they might, you mean they might break due to the solution I don't really think so. But yeah, maybe I don't know if I answered your question. There is another question from Tomasa, please you can unmute yourself. Yes, hi Martina thank you very much for the call. I hope my question makes sense maybe it makes more sense in the end. So, well if so please tell me, is there a measure of kind of similarity between the 16 resources you selected. I'm not a chemistry person. So, but is there a measure on, like, are some resources more similar to the other than others, are they kind of cluster ball and if so, do you see results being affected by these. So this is a good question first and I think I will come to that later. In the end I don't fully answer your question we can resume the discussion but yeah I mentioned some of these later in that has to do with the metabolize that you can get from these resources, but we can discuss more later if you want. Thank you, thank you very much. Thank you Tomasa. There is actually another question if you want to answer another from Benjamin. Hi can you hear me. Yeah, hi. Okay. So, if I understood correctly, you, across these experiments the carbon concentration in the solution is constant. This is how you assure that these, these are commensurate in some sense. Yeah, so, yes, exactly. So the carbon concern, sorry, concentration does not increase which means that when you provide, for example, two compounds, you have half of them, compared to single resources, and in 16 resources you have one 16 of that. Okay, so, but just my question would be, is this efficient to say that these that the different experiments are commensurate because one concern that I would. So what happens that some compounds even though they may provide as much carbon as the other they they may be harder to metabolize for whatever reason, right. Yeah, no, no, this is a fair concern. One thing that I can tell you that for example, I don't think, well I don't go too much into the composition in this talk but I can tell you that for example 60 resources. We have some microbes that are known for example to consume cellulose. And which doesn't mean that they necessarily, for example, which I'm taking as a sample cellulose. And this doesn't mean that they are necessarily metabolizing cellulose but you can take it as let's say as a an observation that could make sure that you're not losing too many species because some compounds are not completely usable. But yeah, this is a possible concern. And I can tell you that I did a pilot study in which I instead of keeping constant a concentration actually increased it. I what I saw and other people in my labs are usually see when we increase the concentration that is usually we increase the strength of interactions, and we tend to lose species. And I don't know if this answered all your questions but yeah these are possible concerns and we kind of address them but they say every every question that it has to do with concentration is actually very interesting and in these, you can imagine other axes, not just the number of resources but also the concentration. And for sure these are all that can be interactions between the amount of the number of resources and the concentration. We didn't address this here but it's certainly a possible follow up question. Okay, thank you. You're welcome. Okay, I think you can resume there are no more questions. Great. So, as I was telling before, we, let's say we went back to single resource communities and try to understand more of what we see. For example, we started to try to understand why we see so much variability in single resources, and we think that this has to do with the cross feeding. Well and cross feeding is linked to the microbial metabolism. And here I'm just showing you, well, this is a zoom in in a cell. And this is a simplified map of microbial metabolism in which I'm just showing showing the central metabolic pathway so some of you might recognize the glycolytic reactions and the TCA reactions here. But one and these maps can can be found in the online and there is a nice database, the cake database in which you can actually that you can scrape and obtain all these maps that are much more complex that the one that I'm presenting here. But this is just for I just want to draw your attention to the fact that some of the resources that we provide in the media are actually intermediates of these central metabolic pathway and for example, cited and fumarates are intermediates of the central TCA. And while there are the other sugars instead, before being converted into the intermediates of the central metabolic pathway has to have to go through a series of reactions that let's say are meant to chop them up into smaller metabolites that then can be fed into the central metabolic pathway. This is a very simplified pictures, but it's just to give you an idea that we can estimate for each resource that we provide in the media, the number of intermediates of metabolic needs that can be produced by a genetic ensemble of bacteria. And when we plot this estimated number of metabolites with the richness of the single resource communities, we find that there is a nice correlation between them. And for example, I can, it's evident that it's what we found is that for example in cellulose we can cellulose from cellulose bacteria can produce more metabolites, and in proportion we found more species in this community, while for example from citrate which is already an intermediate of the central metabolic pathway, microbes are supposed to produce less metabolites and so we saw a smaller regions. So what we thought about is what this is quite cool because we have an estimate, which again I repeat it's an estimate so there are errors, but still we can find a pretty nice correlation with the richness in single resources. The other thing that is quite intuitive here is that, let's say, different resources and from different resources, microbes can produce similar metabolites. And so what we did next was actually trying to understand what is the distribution of these metabolites that can be produced from all these different resources. So what we did, so we discovered that for example there are some common metabolites that are produced starting from the majority of the resources that we produce in the media, and that these red metabolites here. And then there are also rare metabolites that instead are produced starting from just a handful of resources, very few. And here this is a very simplified picture of this, but these circles represent the resources and the squares and symbols here represent the metabolites. And intriguingly enough, the distribution of metabolites mirrors quite well the distribution of species across single resources. So here what we did was measuring the occupancy of each species across single resources, which means how many resources a single species is found in. And again, circles represent the resources and now bugs will represent species. So also for species we found that there are some common species that we call generalists. So these pink bugs that are found in the majority of the communities supported by single resources. And then there are also microbes that instead are found just in few resources. So there are sort of specialists of these resources. And obviously there are also some species that are neither specialists nor generalists that are in between four and 12 resources. And we can also plot the distribution of these generalists and specialists across single resources set to give an idea of how these communities are structured. And you might notice that there is this core group of generalists that are everywhere in all the single resource communities and actually the let's say the most diverse communities like for example cellulose is the community that harbored the largest number of specialists. And so from these analysis, I think that we got two important points that I'm summarizing here. The first one is that in our experimental communities, there are always both generalists and specialists tax. And also, given that the distribution of metabolites and species are kind of parallel, we hypothesize that maybe generalist taxa might compete for these core metabolites, those that are common across that are always found in the resources. And also generalists are always found in all the resources, while specialists maybe might compete for these rare metabolites that instead are found just in few resources. And again, I can stop here if this is not clear or maybe I can go further and we leave the questions for later and maybe see if you let me know what it's best to do. Also, in terms of time. If you're near to the end, maybe you can finish and then we get all the questions. Perfect. Okay. Thank you. So, again, these were the two key points that we grasped from these analysis and we thought, okay, let's try to plug the meaning of Lotka-Volterra models to see whether we can reproduce our experimental results. So here I'm showing you the classical, let's say the classical Lotka-Volterra question in which the per capita growth is a function of the maximal growth rate, which is modulated by a self inhibition term and a term that accounts for inhibition by competitors. And I told you that we tried to fit, we tried to introduce, we include in this model, generalist and specialist, and we did it with by modulating the growth rates. In this model, the pool of species that can grow increases with the number of hypothetical resources that we are providing. And let's say that when we have just one resource, all the generalists can grow by definition and have non-zero growth rate, while there is just a group of specialists that can grow, the specialists for that resource while all the others can't grow. When there are two hypothetical resources present, then again, all generalists can grow and just two groups of specialists can grow while all the others have zero growth rates. And the other idea that I told you that we wanted to implement was this idea of two resource markets so that generalists and specialists compete for different pools of resources. And we included that by implementing in the model a modular interaction structure. And which means that what we did in our model, the specialists compete strongly against each other, and also generalists compete strongly against each other, but the interaction between generalists and specialists are weaker compared to the interaction within groups. And I can tell you that just by plugging in these two features in the simple Lotcavolterra model, we were able to recapitulate our main experimental result. That is, we could see a linear relationship between the richness and the number of available resources, which here is approximated by the number of specialist groups that could grow. And what I want to draw your attention on is the fact that also in this case, the rate at which we increase the number of species is constant, and it's about one species for each new resource that we have. Also, the intercept is quite similar to what we found in our experiments. So this simple model, this simple exercise that we did is telling us that most likely the way that the number and the type and the number of resources and the type of resources that determines the assembly of our communities by affecting what is the distribution of resource strategies and which in gives an idea of the distribution of metabolites. So with this I want to just briefly summarize what we found. And it's that in single resources, you can usually single compounds that provide a single resource it can can always sustain multi species community, and we think that cross feeding is underlying the observed richness. But when we combine these resources we found that community diversity increases more only more the street, but still, these results are telling us that both the number and the identity of the resources that we put in the, in the environment are important drivers of microbial community diversity. And the other thing is that by look by looking at the structure of these communities in terms of generalists and specialists, we started to understand the mechanism behind this pattern, and with the local with just plugging in the, the idea of a specialist and not cover the model we were able to reproduce our experimental result. Yes, again, to conclude I think that we found a possible link between the number and the identity of resources with, let's say the microbial community diversity, because these two characteristics of resources modulate, let's say, on them depend cross feeding and also the distribution of resource utilization strategies. So with that, I am concluding and I want to thank my PI Jeff core, and also my collaborators, he on and actually did a lot of the work I presented today. The goal of MIT that provided the feedbacks on this project. And if you're curious, I invite you to read more about this project that we have a preprint posted on by archive. And I want to thank you for listening for asking questions and I'm happy to take more. Okay, great. So thank you for the nice talk. I clap on behalf of everyone. There is one question from the chat. Aditya asks how many parameters were there in the lot of a lot of fit to the data, did you fit each interaction coefficient or just the distribution from which they are drawn. This is not a fit. This is just what, what we get from the lot cover the model and the parameter are the growth rate and the matrix of interactions. So the, the fit that you see is just a line that is a regression line that I do on the data. And it's the same type of regression that I can do from experimental data. Okay, so there is another question from Leonardo bit of some. Hello, really nice. Thank you. So I have a question for you about if you added more complex resources so cellulose is one of the most complex that you're using right. And so if you added something like lignan or kitten. That's even more complex and sort of requires or promotes more cross feeding. Do you think you would still see this linear increase, or do you think then you would see a jump that's nonlinear. That's a good question. I tried to get to get lignan but I was performing the experiment I couldn't get it from Sigma Aldrich so I want to be completely honest I wanted to use lignan. And yes, it might be but so to give you an idea of how, how many, how much I think the number of metabolites is important is that for example also starch is a complex molecule. And still it doesn't give the same number of species that we can get for example from cellulose. So, for sure, adding more complex compound can change the relationship. And I think that one possible. I have a follow up study studies actually changing the pool of resources because you for example you can have more organic acids, or let's say more TCA intermediates and maybe you have you might flatten the relationship. And let's say as you were saying if you add more complex compound maybe you get a steeper increase. So I think that this is just the platform to start to understand okay if I have. Let's say I know what I have in the pool of resources, can I start to predict how the slope changes how the intercept might change. And these are the questions that we are thinking what we think that we can explore them at least so thank you. It was a really interesting question.